diff --git a/api/particles/index.html b/api/particles/index.html
index 2df9f1f..3a3987f 100644
--- a/api/particles/index.html
+++ b/api/particles/index.html
@@ -4344,8 +4344,8 @@ <h1>Particles</h1>
         <span class="k">return</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>    
 
     <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">resample_particles</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">resample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">resample_particles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">resample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">equal_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">resample_particles</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">equal_weights</span><span class="o">=</span><span class="n">equal_weights</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">ParticleGroup</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
 
     <span class="c1"># Internal sorting</span>
diff --git a/assets/openPMD-beamphysics-examples.zip b/assets/openPMD-beamphysics-examples.zip
index 05c1e6a..39e02cf 100644
Binary files a/assets/openPMD-beamphysics-examples.zip and b/assets/openPMD-beamphysics-examples.zip differ
diff --git a/examples/bunching/bunching.ipynb b/examples/bunching/bunching.ipynb
index 3a8ef8c..860c2e3 100644
--- a/examples/bunching/bunching.ipynb
+++ b/examples/bunching/bunching.ipynb
@@ -33,10 +33,10 @@
    "id": "89d98045-89b5-4d05-a1b3-33f956ba329c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:21.972774Z",
-     "iopub.status.busy": "2023-11-15T16:37:21.972421Z",
-     "iopub.status.idle": "2023-11-15T16:37:22.573329Z",
-     "shell.execute_reply": "2023-11-15T16:37:22.572736Z"
+     "iopub.execute_input": "2023-11-15T21:43:12.583496Z",
+     "iopub.status.busy": "2023-11-15T21:43:12.583317Z",
+     "iopub.status.idle": "2023-11-15T21:43:13.196397Z",
+     "shell.execute_reply": "2023-11-15T21:43:13.195786Z"
     }
    },
    "outputs": [],
@@ -51,10 +51,10 @@
    "id": "2808d05b-41dc-44eb-ac98-ae00ec7ba97b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:22.576333Z",
-     "iopub.status.busy": "2023-11-15T16:37:22.575855Z",
-     "iopub.status.idle": "2023-11-15T16:37:22.593411Z",
-     "shell.execute_reply": "2023-11-15T16:37:22.592942Z"
+     "iopub.execute_input": "2023-11-15T21:43:13.199048Z",
+     "iopub.status.busy": "2023-11-15T21:43:13.198793Z",
+     "iopub.status.idle": "2023-11-15T21:43:13.217129Z",
+     "shell.execute_reply": "2023-11-15T21:43:13.216550Z"
     }
    },
    "outputs": [],
@@ -73,10 +73,10 @@
    "id": "f92a71d1-2804-4fba-a38b-3089351a910d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:22.595536Z",
-     "iopub.status.busy": "2023-11-15T16:37:22.595360Z",
-     "iopub.status.idle": "2023-11-15T16:37:23.746043Z",
-     "shell.execute_reply": "2023-11-15T16:37:23.745435Z"
+     "iopub.execute_input": "2023-11-15T21:43:13.219950Z",
+     "iopub.status.busy": "2023-11-15T21:43:13.219351Z",
+     "iopub.status.idle": "2023-11-15T21:43:14.390925Z",
+     "shell.execute_reply": "2023-11-15T21:43:14.390250Z"
     }
    },
    "outputs": [
@@ -121,10 +121,10 @@
    "id": "95d51518-4dad-478d-a67f-4416e5e5a989",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:23.748326Z",
-     "iopub.status.busy": "2023-11-15T16:37:23.748146Z",
-     "iopub.status.idle": "2023-11-15T16:37:23.761871Z",
-     "shell.execute_reply": "2023-11-15T16:37:23.761376Z"
+     "iopub.execute_input": "2023-11-15T21:43:14.393510Z",
+     "iopub.status.busy": "2023-11-15T21:43:14.393132Z",
+     "iopub.status.idle": "2023-11-15T21:43:14.407145Z",
+     "shell.execute_reply": "2023-11-15T21:43:14.406592Z"
     }
    },
    "outputs": [
@@ -150,10 +150,10 @@
    "id": "9a0ba850-3e57-4b70-b0d1-31376166da55",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:23.763816Z",
-     "iopub.status.busy": "2023-11-15T16:37:23.763647Z",
-     "iopub.status.idle": "2023-11-15T16:37:23.775837Z",
-     "shell.execute_reply": "2023-11-15T16:37:23.775407Z"
+     "iopub.execute_input": "2023-11-15T21:43:14.409427Z",
+     "iopub.status.busy": "2023-11-15T21:43:14.409081Z",
+     "iopub.status.idle": "2023-11-15T21:43:14.421507Z",
+     "shell.execute_reply": "2023-11-15T21:43:14.420963Z"
     }
    },
    "outputs": [
@@ -178,10 +178,10 @@
    "id": "4756a087-6e40-4000-a4ea-158fc7a54d74",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:23.778129Z",
-     "iopub.status.busy": "2023-11-15T16:37:23.777671Z",
-     "iopub.status.idle": "2023-11-15T16:37:23.790094Z",
-     "shell.execute_reply": "2023-11-15T16:37:23.789521Z"
+     "iopub.execute_input": "2023-11-15T21:43:14.424109Z",
+     "iopub.status.busy": "2023-11-15T21:43:14.423630Z",
+     "iopub.status.idle": "2023-11-15T21:43:14.436403Z",
+     "shell.execute_reply": "2023-11-15T21:43:14.435919Z"
     }
    },
    "outputs": [
@@ -206,10 +206,10 @@
    "id": "1627bba1-710e-47b6-b923-9c64a6d32903",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:23.792271Z",
-     "iopub.status.busy": "2023-11-15T16:37:23.791849Z",
-     "iopub.status.idle": "2023-11-15T16:37:23.804165Z",
-     "shell.execute_reply": "2023-11-15T16:37:23.803603Z"
+     "iopub.execute_input": "2023-11-15T21:43:14.438638Z",
+     "iopub.status.busy": "2023-11-15T21:43:14.438277Z",
+     "iopub.status.idle": "2023-11-15T21:43:14.450694Z",
+     "shell.execute_reply": "2023-11-15T21:43:14.450120Z"
     }
    },
    "outputs": [
@@ -242,10 +242,10 @@
    "id": "d24b9496-e828-45f1-a055-4e27e7aeba31",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:23.806304Z",
-     "iopub.status.busy": "2023-11-15T16:37:23.805979Z",
-     "iopub.status.idle": "2023-11-15T16:37:23.808661Z",
-     "shell.execute_reply": "2023-11-15T16:37:23.808188Z"
+     "iopub.execute_input": "2023-11-15T21:43:14.453178Z",
+     "iopub.status.busy": "2023-11-15T21:43:14.452682Z",
+     "iopub.status.idle": "2023-11-15T21:43:14.455517Z",
+     "shell.execute_reply": "2023-11-15T21:43:14.455001Z"
     }
    },
    "outputs": [],
@@ -261,10 +261,10 @@
    "id": "c6b5c501-bfdd-492d-891e-ba10e6b546ef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:23.810663Z",
-     "iopub.status.busy": "2023-11-15T16:37:23.810490Z",
-     "iopub.status.idle": "2023-11-15T16:37:23.813258Z",
-     "shell.execute_reply": "2023-11-15T16:37:23.812787Z"
+     "iopub.execute_input": "2023-11-15T21:43:14.457777Z",
+     "iopub.status.busy": "2023-11-15T21:43:14.457447Z",
+     "iopub.status.idle": "2023-11-15T21:43:14.460396Z",
+     "shell.execute_reply": "2023-11-15T21:43:14.459833Z"
     }
    },
    "outputs": [],
@@ -278,10 +278,10 @@
    "id": "50cda5af-4a97-4aa0-bf9c-c3984140d39c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:23.815201Z",
-     "iopub.status.busy": "2023-11-15T16:37:23.815033Z",
-     "iopub.status.idle": "2023-11-15T16:37:25.878544Z",
-     "shell.execute_reply": "2023-11-15T16:37:25.877985Z"
+     "iopub.execute_input": "2023-11-15T21:43:14.462630Z",
+     "iopub.status.busy": "2023-11-15T21:43:14.462326Z",
+     "iopub.status.idle": "2023-11-15T21:43:16.535868Z",
+     "shell.execute_reply": "2023-11-15T21:43:16.535238Z"
     }
    },
    "outputs": [
@@ -323,10 +323,10 @@
    "id": "42b088fa-aaef-43df-910f-07cddac21bb0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:25.880908Z",
-     "iopub.status.busy": "2023-11-15T16:37:25.880548Z",
-     "iopub.status.idle": "2023-11-15T16:37:26.207130Z",
-     "shell.execute_reply": "2023-11-15T16:37:26.206563Z"
+     "iopub.execute_input": "2023-11-15T21:43:16.538316Z",
+     "iopub.status.busy": "2023-11-15T21:43:16.537924Z",
+     "iopub.status.idle": "2023-11-15T21:43:16.865999Z",
+     "shell.execute_reply": "2023-11-15T21:43:16.865369Z"
     }
    },
    "outputs": [
@@ -356,10 +356,10 @@
    "id": "d9c0236c-cab9-463f-a2f3-010ef120b11c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:26.209645Z",
-     "iopub.status.busy": "2023-11-15T16:37:26.209155Z",
-     "iopub.status.idle": "2023-11-15T16:37:26.541539Z",
-     "shell.execute_reply": "2023-11-15T16:37:26.540922Z"
+     "iopub.execute_input": "2023-11-15T21:43:16.868608Z",
+     "iopub.status.busy": "2023-11-15T21:43:16.868236Z",
+     "iopub.status.idle": "2023-11-15T21:43:17.203084Z",
+     "shell.execute_reply": "2023-11-15T21:43:17.202462Z"
     }
    },
    "outputs": [
@@ -389,10 +389,10 @@
    "id": "bbc674b0-67d8-4a5c-abe0-583bdf0270f2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:26.544067Z",
-     "iopub.status.busy": "2023-11-15T16:37:26.543723Z",
-     "iopub.status.idle": "2023-11-15T16:37:26.553032Z",
-     "shell.execute_reply": "2023-11-15T16:37:26.552470Z"
+     "iopub.execute_input": "2023-11-15T21:43:17.205541Z",
+     "iopub.status.busy": "2023-11-15T21:43:17.205176Z",
+     "iopub.status.idle": "2023-11-15T21:43:17.214422Z",
+     "shell.execute_reply": "2023-11-15T21:43:17.213865Z"
     }
    },
    "outputs": [
@@ -427,10 +427,10 @@
    "id": "01b2f6d7-734c-4bea-add3-48ffa215dacc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:26.555352Z",
-     "iopub.status.busy": "2023-11-15T16:37:26.554905Z",
-     "iopub.status.idle": "2023-11-15T16:37:26.558649Z",
-     "shell.execute_reply": "2023-11-15T16:37:26.558089Z"
+     "iopub.execute_input": "2023-11-15T21:43:17.216676Z",
+     "iopub.status.busy": "2023-11-15T21:43:17.216338Z",
+     "iopub.status.idle": "2023-11-15T21:43:17.220159Z",
+     "shell.execute_reply": "2023-11-15T21:43:17.219676Z"
     }
    },
    "outputs": [
@@ -455,10 +455,10 @@
    "id": "f5ecde78-9f42-40be-907b-81109f511015",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:26.560942Z",
-     "iopub.status.busy": "2023-11-15T16:37:26.560400Z",
-     "iopub.status.idle": "2023-11-15T16:37:26.564401Z",
-     "shell.execute_reply": "2023-11-15T16:37:26.563843Z"
+     "iopub.execute_input": "2023-11-15T21:43:17.222196Z",
+     "iopub.status.busy": "2023-11-15T21:43:17.222012Z",
+     "iopub.status.idle": "2023-11-15T21:43:17.225654Z",
+     "shell.execute_reply": "2023-11-15T21:43:17.225129Z"
     }
    },
    "outputs": [
@@ -493,10 +493,10 @@
    "id": "ac7ee4c6-1b02-40a1-96a3-3be6629da002",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:26.566864Z",
-     "iopub.status.busy": "2023-11-15T16:37:26.566448Z",
-     "iopub.status.idle": "2023-11-15T16:37:26.569223Z",
-     "shell.execute_reply": "2023-11-15T16:37:26.568732Z"
+     "iopub.execute_input": "2023-11-15T21:43:17.227701Z",
+     "iopub.status.busy": "2023-11-15T21:43:17.227525Z",
+     "iopub.status.idle": "2023-11-15T21:43:17.230090Z",
+     "shell.execute_reply": "2023-11-15T21:43:17.229575Z"
     }
    },
    "outputs": [],
@@ -510,10 +510,10 @@
    "id": "af2982d8-a19d-481f-b9ec-940d5d02e290",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:26.571200Z",
-     "iopub.status.busy": "2023-11-15T16:37:26.571026Z",
-     "iopub.status.idle": "2023-11-15T16:37:26.604229Z",
-     "shell.execute_reply": "2023-11-15T16:37:26.603775Z"
+     "iopub.execute_input": "2023-11-15T21:43:17.232086Z",
+     "iopub.status.busy": "2023-11-15T21:43:17.231911Z",
+     "iopub.status.idle": "2023-11-15T21:43:17.265174Z",
+     "shell.execute_reply": "2023-11-15T21:43:17.264703Z"
     }
    },
    "outputs": [],
diff --git a/examples/fields/field_conversion/field_conversion.ipynb b/examples/fields/field_conversion/field_conversion.ipynb
index 6b82f7b..d6d7576 100644
--- a/examples/fields/field_conversion/field_conversion.ipynb
+++ b/examples/fields/field_conversion/field_conversion.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:57.311997Z",
-     "iopub.status.busy": "2023-11-15T16:36:57.311521Z",
-     "iopub.status.idle": "2023-11-15T16:36:57.655572Z",
-     "shell.execute_reply": "2023-11-15T16:36:57.654982Z"
+     "iopub.execute_input": "2023-11-15T21:42:47.741935Z",
+     "iopub.status.busy": "2023-11-15T21:42:47.741447Z",
+     "iopub.status.idle": "2023-11-15T21:42:48.080059Z",
+     "shell.execute_reply": "2023-11-15T21:42:48.079525Z"
     },
     "tags": []
    },
@@ -37,10 +37,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:57.658618Z",
-     "iopub.status.busy": "2023-11-15T16:36:57.658367Z",
-     "iopub.status.idle": "2023-11-15T16:36:57.953685Z",
-     "shell.execute_reply": "2023-11-15T16:36:57.953110Z"
+     "iopub.execute_input": "2023-11-15T21:42:48.082772Z",
+     "iopub.status.busy": "2023-11-15T21:42:48.082356Z",
+     "iopub.status.idle": "2023-11-15T21:42:48.381536Z",
+     "shell.execute_reply": "2023-11-15T21:42:48.380866Z"
     },
     "tags": []
    },
@@ -63,10 +63,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:57.956539Z",
-     "iopub.status.busy": "2023-11-15T16:36:57.956163Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.219993Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.219394Z"
+     "iopub.execute_input": "2023-11-15T21:42:48.384521Z",
+     "iopub.status.busy": "2023-11-15T21:42:48.384289Z",
+     "iopub.status.idle": "2023-11-15T21:42:48.654389Z",
+     "shell.execute_reply": "2023-11-15T21:42:48.653796Z"
     },
     "tags": []
    },
@@ -104,17 +104,17 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.249399Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.248962Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.273101Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.272622Z"
+     "iopub.execute_input": "2023-11-15T21:42:48.683993Z",
+     "iopub.status.busy": "2023-11-15T21:42:48.683586Z",
+     "iopub.status.idle": "2023-11-15T21:42:48.708790Z",
+     "shell.execute_reply": "2023-11-15T21:42:48.708206Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fa0c4d64ee0>"
+       "<FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fd1446beee0>"
       ]
      },
      "execution_count": 4,
@@ -144,17 +144,17 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.275459Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.275097Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.288479Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.288024Z"
+     "iopub.execute_input": "2023-11-15T21:42:48.711320Z",
+     "iopub.status.busy": "2023-11-15T21:42:48.710823Z",
+     "iopub.status.idle": "2023-11-15T21:42:48.724103Z",
+     "shell.execute_reply": "2023-11-15T21:42:48.723524Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fa07afc4a90>"
+       "<FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fd1446bed00>"
       ]
      },
      "execution_count": 5,
@@ -179,10 +179,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.290720Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.290382Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.303894Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.303343Z"
+     "iopub.execute_input": "2023-11-15T21:42:48.726404Z",
+     "iopub.status.busy": "2023-11-15T21:42:48.725976Z",
+     "iopub.status.idle": "2023-11-15T21:42:48.739102Z",
+     "shell.execute_reply": "2023-11-15T21:42:48.738610Z"
     }
    },
    "outputs": [
@@ -213,10 +213,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.306134Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.305968Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.319372Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.318831Z"
+     "iopub.execute_input": "2023-11-15T21:42:48.741272Z",
+     "iopub.status.busy": "2023-11-15T21:42:48.741094Z",
+     "iopub.status.idle": "2023-11-15T21:42:48.754958Z",
+     "shell.execute_reply": "2023-11-15T21:42:48.754464Z"
     }
    },
    "outputs": [
@@ -243,17 +243,17 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.321587Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.321268Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.558985Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.558395Z"
+     "iopub.execute_input": "2023-11-15T21:42:48.757148Z",
+     "iopub.status.busy": "2023-11-15T21:42:48.756971Z",
+     "iopub.status.idle": "2023-11-15T21:42:49.052796Z",
+     "shell.execute_reply": "2023-11-15T21:42:49.052147Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fa07af99ee0>"
+       "<matplotlib.legend.Legend at 0x7fd144695eb0>"
       ]
      },
      "execution_count": 8,
@@ -294,17 +294,17 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.561358Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.561009Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.799692Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.799066Z"
+     "iopub.execute_input": "2023-11-15T21:42:49.055341Z",
+     "iopub.status.busy": "2023-11-15T21:42:49.054882Z",
+     "iopub.status.idle": "2023-11-15T21:42:49.289241Z",
+     "shell.execute_reply": "2023-11-15T21:42:49.288640Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fa07aebb130>"
+       "<matplotlib.legend.Legend at 0x7fd14454feb0>"
       ]
      },
      "execution_count": 9,
@@ -350,10 +350,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.802038Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.801850Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.981922Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.981318Z"
+     "iopub.execute_input": "2023-11-15T21:42:49.291773Z",
+     "iopub.status.busy": "2023-11-15T21:42:49.291444Z",
+     "iopub.status.idle": "2023-11-15T21:42:49.436154Z",
+     "shell.execute_reply": "2023-11-15T21:42:49.435487Z"
     },
     "tags": []
    },
@@ -390,10 +390,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:58.984505Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.984146Z",
-     "iopub.status.idle": "2023-11-15T16:36:58.997947Z",
-     "shell.execute_reply": "2023-11-15T16:36:58.997382Z"
+     "iopub.execute_input": "2023-11-15T21:42:49.438789Z",
+     "iopub.status.busy": "2023-11-15T21:42:49.438413Z",
+     "iopub.status.idle": "2023-11-15T21:42:49.452899Z",
+     "shell.execute_reply": "2023-11-15T21:42:49.452411Z"
     },
     "tags": []
    },
@@ -429,10 +429,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:59.000193Z",
-     "iopub.status.busy": "2023-11-15T16:36:58.999853Z",
-     "iopub.status.idle": "2023-11-15T16:36:59.012119Z",
-     "shell.execute_reply": "2023-11-15T16:36:59.011686Z"
+     "iopub.execute_input": "2023-11-15T21:42:49.455348Z",
+     "iopub.status.busy": "2023-11-15T21:42:49.454957Z",
+     "iopub.status.idle": "2023-11-15T21:42:49.469453Z",
+     "shell.execute_reply": "2023-11-15T21:42:49.469036Z"
     },
     "tags": []
    },
@@ -463,10 +463,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:59.014083Z",
-     "iopub.status.busy": "2023-11-15T16:36:59.013922Z",
-     "iopub.status.idle": "2023-11-15T16:36:59.248587Z",
-     "shell.execute_reply": "2023-11-15T16:36:59.248107Z"
+     "iopub.execute_input": "2023-11-15T21:42:49.471711Z",
+     "iopub.status.busy": "2023-11-15T21:42:49.471523Z",
+     "iopub.status.idle": "2023-11-15T21:42:49.709363Z",
+     "shell.execute_reply": "2023-11-15T21:42:49.708768Z"
     }
    },
    "outputs": [
@@ -495,10 +495,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:59.251112Z",
-     "iopub.status.busy": "2023-11-15T16:36:59.250937Z",
-     "iopub.status.idle": "2023-11-15T16:36:59.483009Z",
-     "shell.execute_reply": "2023-11-15T16:36:59.482396Z"
+     "iopub.execute_input": "2023-11-15T21:42:49.711934Z",
+     "iopub.status.busy": "2023-11-15T21:42:49.711494Z",
+     "iopub.status.idle": "2023-11-15T21:42:49.985726Z",
+     "shell.execute_reply": "2023-11-15T21:42:49.985123Z"
     }
    },
    "outputs": [
diff --git a/examples/fields/field_conversion/index.html b/examples/fields/field_conversion/index.html
index 3f34e14..b612086 100644
--- a/examples/fields/field_conversion/index.html
+++ b/examples/fields/field_conversion/index.html
@@ -1525,7 +1525,7 @@ <h2 id="3d-rectangular-field-from-ansys">3D rectangular field from ANSYS<a class
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fa0c4d64ee0&gt;</pre>
+<pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fd1446beee0&gt;</pre>
 </div>
 </div>
 </div>
@@ -1577,7 +1577,7 @@ <h2 id="3d-rectangular-field-from-ansys">3D rectangular field from ANSYS<a class
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fa07afc4a90&gt;</pre>
+<pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fd1446bed00&gt;</pre>
 </div>
 </div>
 </div>
@@ -1741,7 +1741,7 @@ <h2 id="3d-rectangular-field-from-ansys">3D rectangular field from ANSYS<a class
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.legend.Legend at 0x7fa07af99ee0&gt;</pre>
+<pre>&lt;matplotlib.legend.Legend at 0x7fd144695eb0&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -1800,7 +1800,7 @@ <h2 id="3d-rectangular-field-from-ansys">3D rectangular field from ANSYS<a class
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.legend.Legend at 0x7fa07aebb130&gt;</pre>
+<pre>&lt;matplotlib.legend.Legend at 0x7fd14454feb0&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
diff --git a/examples/fields/field_examples/field_examples.ipynb b/examples/fields/field_examples/field_examples.ipynb
index 700d91e..35470b8 100644
--- a/examples/fields/field_examples/field_examples.ipynb
+++ b/examples/fields/field_examples/field_examples.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:01.664573Z",
-     "iopub.status.busy": "2023-11-15T16:37:01.664152Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.002106Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.001517Z"
+     "iopub.execute_input": "2023-11-15T21:42:52.271577Z",
+     "iopub.status.busy": "2023-11-15T21:42:52.271409Z",
+     "iopub.status.idle": "2023-11-15T21:42:52.614276Z",
+     "shell.execute_reply": "2023-11-15T21:42:52.613771Z"
     },
     "tags": []
    },
@@ -37,10 +37,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.005049Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.004634Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.292374Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.291773Z"
+     "iopub.execute_input": "2023-11-15T21:42:52.616949Z",
+     "iopub.status.busy": "2023-11-15T21:42:52.616588Z",
+     "iopub.status.idle": "2023-11-15T21:42:52.914845Z",
+     "shell.execute_reply": "2023-11-15T21:42:52.914144Z"
     },
     "tags": []
    },
@@ -54,10 +54,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.294939Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.294737Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.313959Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.313410Z"
+     "iopub.execute_input": "2023-11-15T21:42:52.917618Z",
+     "iopub.status.busy": "2023-11-15T21:42:52.917424Z",
+     "iopub.status.idle": "2023-11-15T21:42:52.939969Z",
+     "shell.execute_reply": "2023-11-15T21:42:52.939426Z"
     },
     "tags": []
    },
@@ -65,7 +65,7 @@
     {
      "data": {
       "text/plain": [
-       "<FieldMesh with cylindrical geometry and (101, 1, 201) shape at 0x7ff4c83e0df0>"
+       "<FieldMesh with cylindrical geometry and (101, 1, 201) shape at 0x7fd5dc32f760>"
       ]
      },
      "execution_count": 3,
@@ -90,10 +90,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.342997Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.342791Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.631539Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.630945Z"
+     "iopub.execute_input": "2023-11-15T21:42:52.965552Z",
+     "iopub.status.busy": "2023-11-15T21:42:52.965144Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.256097Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.255502Z"
     },
     "tags": []
    },
@@ -130,10 +130,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.633787Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.633584Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.900613Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.900025Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.258565Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.258387Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.527739Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.527190Z"
     },
     "tags": []
    },
@@ -172,10 +172,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.903306Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.902927Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.917672Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.917208Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.530563Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.530183Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.544868Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.544297Z"
     },
     "tags": []
    },
@@ -220,10 +220,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.919999Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.919654Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.932657Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.932087Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.547164Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.546985Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.560177Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.559692Z"
     },
     "tags": []
    },
@@ -248,10 +248,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.934798Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.934474Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.946982Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.946491Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.562249Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.562076Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.575572Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.575114Z"
     },
     "tags": []
    },
@@ -283,10 +283,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.949168Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.948844Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.961271Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.960785Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.577597Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.577426Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.590899Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.590419Z"
     },
     "tags": []
    },
@@ -330,10 +330,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.963528Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.963182Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.975325Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.974735Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.593007Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.592831Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.605857Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.605302Z"
     },
     "tags": []
    },
@@ -367,10 +367,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.977504Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.977193Z",
-     "iopub.status.idle": "2023-11-15T16:37:02.989460Z",
-     "shell.execute_reply": "2023-11-15T16:37:02.988980Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.608268Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.607934Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.620705Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.620216Z"
     },
     "tags": []
    },
@@ -402,10 +402,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:02.991645Z",
-     "iopub.status.busy": "2023-11-15T16:37:02.991316Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.003815Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.003337Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.622867Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.622686Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.636014Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.635477Z"
     },
     "tags": []
    },
@@ -474,10 +474,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.005975Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.005603Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.017582Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.017010Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.638194Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.637866Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.650043Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.649509Z"
     },
     "tags": []
    },
@@ -509,10 +509,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.019595Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.019420Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.031388Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.030940Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.652299Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.651975Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.664023Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.663501Z"
     },
     "tags": []
    },
@@ -545,10 +545,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.033554Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.033222Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.045354Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.044865Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.666133Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.665922Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.678368Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.677816Z"
     }
    },
    "outputs": [
@@ -597,10 +597,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.047537Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.047213Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.059504Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.059028Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.680685Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.680261Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.692909Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.692435Z"
     }
    },
    "outputs": [
@@ -642,10 +642,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.061588Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.061262Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.073552Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.073110Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.694985Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.694809Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.707200Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.706645Z"
     },
     "tags": []
    },
@@ -679,10 +679,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.075804Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.075465Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.319729Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.319123Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.709301Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.709126Z",
+     "iopub.status.idle": "2023-11-15T21:42:53.957146Z",
+     "shell.execute_reply": "2023-11-15T21:42:53.956560Z"
     },
     "tags": []
    },
@@ -720,10 +720,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.322048Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.321870Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.566285Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.565703Z"
+     "iopub.execute_input": "2023-11-15T21:42:53.959801Z",
+     "iopub.status.busy": "2023-11-15T21:42:53.959404Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.205906Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.205275Z"
     },
     "tags": []
    },
@@ -760,10 +760,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.568551Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.568380Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.582215Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.581631Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.208579Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.208178Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.223450Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.223001Z"
     },
     "tags": []
    },
@@ -799,10 +799,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.584464Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.584121Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.871852Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.871244Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.225552Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.225386Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.514863Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.514194Z"
     },
     "tags": []
    },
@@ -864,10 +864,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.874095Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.873914Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.887468Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.886906Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.517412Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.517219Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.530970Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.530388Z"
     },
     "tags": []
    },
@@ -899,10 +899,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.889817Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.889336Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.901344Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.900808Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.533367Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.532927Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.546261Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.545818Z"
     },
     "tags": []
    },
@@ -934,10 +934,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.903538Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.903203Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.918507Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.917968Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.548370Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.548191Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.564842Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.564269Z"
     },
     "tags": []
    },
@@ -958,10 +958,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.920958Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.920507Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.934710Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.934163Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.567525Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.567043Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.582638Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.582137Z"
     },
     "tags": []
    },
@@ -984,10 +984,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.936921Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.936590Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.954183Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.953639Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.585114Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.584764Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.604234Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.603762Z"
     },
     "tags": []
    },
@@ -1014,10 +1014,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.956631Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.956216Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.968765Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.968295Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.606827Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.606459Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.620122Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.619566Z"
     }
    },
    "outputs": [],
@@ -1037,10 +1037,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.971114Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.970665Z",
-     "iopub.status.idle": "2023-11-15T16:37:03.982974Z",
-     "shell.execute_reply": "2023-11-15T16:37:03.982441Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.622464Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.622182Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.635874Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.635368Z"
     }
    },
    "outputs": [
@@ -1080,10 +1080,10 @@
    "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:03.985139Z",
-     "iopub.status.busy": "2023-11-15T16:37:03.984812Z",
-     "iopub.status.idle": "2023-11-15T16:37:04.033786Z",
-     "shell.execute_reply": "2023-11-15T16:37:04.033216Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.638106Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.637773Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.687411Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.686937Z"
     },
     "tags": []
    },
@@ -1116,10 +1116,10 @@
    "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:04.035902Z",
-     "iopub.status.busy": "2023-11-15T16:37:04.035571Z",
-     "iopub.status.idle": "2023-11-15T16:37:04.047628Z",
-     "shell.execute_reply": "2023-11-15T16:37:04.047098Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.689540Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.689376Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.703123Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.702550Z"
     },
     "tags": []
    },
@@ -1166,10 +1166,10 @@
    "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:04.050031Z",
-     "iopub.status.busy": "2023-11-15T16:37:04.049597Z",
-     "iopub.status.idle": "2023-11-15T16:37:04.061495Z",
-     "shell.execute_reply": "2023-11-15T16:37:04.061033Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.705367Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.705037Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.717396Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.716946Z"
     },
     "tags": []
    },
@@ -1201,10 +1201,10 @@
    "execution_count": 32,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:04.063809Z",
-     "iopub.status.busy": "2023-11-15T16:37:04.063466Z",
-     "iopub.status.idle": "2023-11-15T16:37:04.075598Z",
-     "shell.execute_reply": "2023-11-15T16:37:04.075135Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.719581Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.719260Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.731956Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.731402Z"
     },
     "tags": []
    },
@@ -1252,10 +1252,10 @@
    "execution_count": 33,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:04.077592Z",
-     "iopub.status.busy": "2023-11-15T16:37:04.077433Z",
-     "iopub.status.idle": "2023-11-15T16:37:04.089671Z",
-     "shell.execute_reply": "2023-11-15T16:37:04.089193Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.734123Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.733947Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.746106Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.745575Z"
     },
     "tags": []
    },
@@ -1288,10 +1288,10 @@
    "execution_count": 34,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:04.091631Z",
-     "iopub.status.busy": "2023-11-15T16:37:04.091470Z",
-     "iopub.status.idle": "2023-11-15T16:37:04.103754Z",
-     "shell.execute_reply": "2023-11-15T16:37:04.103312Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.748131Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.747959Z",
+     "iopub.status.idle": "2023-11-15T21:42:54.760033Z",
+     "shell.execute_reply": "2023-11-15T21:42:54.759492Z"
     },
     "tags": []
    },
@@ -1322,10 +1322,10 @@
    "execution_count": 35,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:04.105740Z",
-     "iopub.status.busy": "2023-11-15T16:37:04.105536Z",
-     "iopub.status.idle": "2023-11-15T16:37:04.678792Z",
-     "shell.execute_reply": "2023-11-15T16:37:04.678164Z"
+     "iopub.execute_input": "2023-11-15T21:42:54.762114Z",
+     "iopub.status.busy": "2023-11-15T21:42:54.761937Z",
+     "iopub.status.idle": "2023-11-15T21:42:55.298595Z",
+     "shell.execute_reply": "2023-11-15T21:42:55.297949Z"
     },
     "tags": []
    },
@@ -1333,7 +1333,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7ff4742ea8b0>"
+       "<matplotlib.legend.Legend at 0x7fd58822c910>"
       ]
      },
      "execution_count": 35,
@@ -1387,10 +1387,10 @@
    "execution_count": 36,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:04.681681Z",
-     "iopub.status.busy": "2023-11-15T16:37:04.681244Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.371426Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.370818Z"
+     "iopub.execute_input": "2023-11-15T21:42:55.301263Z",
+     "iopub.status.busy": "2023-11-15T21:42:55.300921Z",
+     "iopub.status.idle": "2023-11-15T21:43:06.839364Z",
+     "shell.execute_reply": "2023-11-15T21:43:06.838738Z"
     },
     "tags": []
    },
@@ -1434,10 +1434,10 @@
    "execution_count": 37,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.373923Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.373557Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.513150Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.512672Z"
+     "iopub.execute_input": "2023-11-15T21:43:06.842024Z",
+     "iopub.status.busy": "2023-11-15T21:43:06.841639Z",
+     "iopub.status.idle": "2023-11-15T21:43:06.978521Z",
+     "shell.execute_reply": "2023-11-15T21:43:06.977937Z"
     },
     "tags": []
    },
@@ -1470,10 +1470,10 @@
    "execution_count": 38,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.515505Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.515150Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.602043Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.601502Z"
+     "iopub.execute_input": "2023-11-15T21:43:06.980933Z",
+     "iopub.status.busy": "2023-11-15T21:43:06.980564Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.067041Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.066395Z"
     },
     "tags": []
    },
@@ -1507,10 +1507,10 @@
    "execution_count": 39,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.604119Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.603955Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.636721Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.636190Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.069576Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.069220Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.103206Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.102678Z"
     },
     "tags": []
    },
@@ -1518,7 +1518,7 @@
     {
      "data": {
       "text/plain": [
-       "<FieldMesh with cylindrical geometry and (61, 1, 521) shape at 0x7ff475451d30>"
+       "<FieldMesh with cylindrical geometry and (61, 1, 521) shape at 0x7fd5880a9970>"
       ]
      },
      "execution_count": 39,
@@ -1536,10 +1536,10 @@
    "execution_count": 40,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.639067Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.638728Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.651340Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.650774Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.105330Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.105160Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.118512Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.118064Z"
     },
     "tags": []
    },
@@ -1607,10 +1607,10 @@
    "execution_count": 41,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.653723Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.653281Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.665595Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.665008Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.120808Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.120470Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.133435Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.132958Z"
     },
     "tags": []
    },
@@ -1642,10 +1642,10 @@
    "execution_count": 42,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.668022Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.667608Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.680564Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.680080Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.135810Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.135371Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.148657Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.148110Z"
     },
     "tags": []
    },
@@ -1681,17 +1681,17 @@
    "execution_count": 43,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.682560Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.682402Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.704085Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.703604Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.151022Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.150699Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.173004Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.172502Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7ff47415a910>"
+       "<FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fd58809c970>"
       ]
      },
      "execution_count": 43,
@@ -1714,10 +1714,10 @@
    "execution_count": 44,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.706270Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.705923Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.717999Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.717424Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.175219Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.174895Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.187751Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.187253Z"
     }
    },
    "outputs": [
@@ -1758,10 +1758,10 @@
    "execution_count": 45,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.720182Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.719853Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.735498Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.734957Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.190032Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.189716Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.205923Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.205363Z"
     }
    },
    "outputs": [],
@@ -1781,17 +1781,17 @@
    "execution_count": 46,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.737476Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.737313Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.749704Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.749226Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.208557Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.208163Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.222144Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.221572Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7ff47415ad30>"
+       "<FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fd58809c850>"
       ]
      },
      "execution_count": 46,
@@ -1816,10 +1816,10 @@
    "execution_count": 47,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:16.752017Z",
-     "iopub.status.busy": "2023-11-15T16:37:16.751567Z",
-     "iopub.status.idle": "2023-11-15T16:37:16.764286Z",
-     "shell.execute_reply": "2023-11-15T16:37:16.763843Z"
+     "iopub.execute_input": "2023-11-15T21:43:07.224601Z",
+     "iopub.status.busy": "2023-11-15T21:43:07.224407Z",
+     "iopub.status.idle": "2023-11-15T21:43:07.239118Z",
+     "shell.execute_reply": "2023-11-15T21:43:07.238615Z"
     }
    },
    "outputs": [],
diff --git a/examples/fields/field_examples/index.html b/examples/fields/field_examples/index.html
index e59b8ca..2ea3707 100644
--- a/examples/fields/field_examples/index.html
+++ b/examples/fields/field_examples/index.html
@@ -1445,7 +1445,7 @@ <h1 id="fieldmesh-examples">FieldMesh Examples<a class="anchor-link" href="#fiel
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;FieldMesh with cylindrical geometry and (101, 1, 201) shape at 0x7ff4c83e0df0&gt;</pre>
+<pre>&lt;FieldMesh with cylindrical geometry and (101, 1, 201) shape at 0x7fd5dc32f760&gt;</pre>
 </div>
 </div>
 </div>
@@ -3193,7 +3193,7 @@ <h2 id="write-impact-t">Write Impact-T<a class="anchor-link" href="#write-impact
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[35]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.legend.Legend at 0x7ff4742ea8b0&gt;</pre>
+<pre>&lt;matplotlib.legend.Legend at 0x7fd58822c910&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -3413,7 +3413,7 @@ <h1 id="read-superfish">Read Superfish<a class="anchor-link" href="#read-superfi
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[39]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;FieldMesh with cylindrical geometry and (61, 1, 521) shape at 0x7ff475451d30&gt;</pre>
+<pre>&lt;FieldMesh with cylindrical geometry and (61, 1, 521) shape at 0x7fd5880a9970&gt;</pre>
 </div>
 </div>
 </div>
@@ -3663,7 +3663,7 @@ <h2 id="read-ansys">Read ANSYS<a class="anchor-link" href="#read-ansys">¶</a></
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[43]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7ff47415a910&gt;</pre>
+<pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fd58809c970&gt;</pre>
 </div>
 </div>
 </div>
@@ -3802,7 +3802,7 @@ <h2 id="read-ansys">Read ANSYS<a class="anchor-link" href="#read-ansys">¶</a></
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[46]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7ff47415ad30&gt;</pre>
+<pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fd58809c850&gt;</pre>
 </div>
 </div>
 </div>
diff --git a/examples/fields/field_expansion/field_expansion.ipynb b/examples/fields/field_expansion/field_expansion.ipynb
index 7d79710..24735c0 100644
--- a/examples/fields/field_expansion/field_expansion.ipynb
+++ b/examples/fields/field_expansion/field_expansion.ipynb
@@ -14,10 +14,10 @@
    "id": "526938d7-f85a-4fa2-962e-245842ffacdd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:43.367076Z",
-     "iopub.status.busy": "2023-11-15T16:36:43.366605Z",
-     "iopub.status.idle": "2023-11-15T16:36:43.705549Z",
-     "shell.execute_reply": "2023-11-15T16:36:43.705021Z"
+     "iopub.execute_input": "2023-11-15T21:42:33.113162Z",
+     "iopub.status.busy": "2023-11-15T21:42:33.112982Z",
+     "iopub.status.idle": "2023-11-15T21:42:33.463185Z",
+     "shell.execute_reply": "2023-11-15T21:42:33.462560Z"
     }
    },
    "outputs": [],
@@ -39,10 +39,10 @@
    "id": "6d31292f-04a5-4b69-847f-cbe928e08b7a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:43.708496Z",
-     "iopub.status.busy": "2023-11-15T16:36:43.708002Z",
-     "iopub.status.idle": "2023-11-15T16:36:44.000079Z",
-     "shell.execute_reply": "2023-11-15T16:36:43.999474Z"
+     "iopub.execute_input": "2023-11-15T21:42:33.465923Z",
+     "iopub.status.busy": "2023-11-15T21:42:33.465682Z",
+     "iopub.status.idle": "2023-11-15T21:42:33.782350Z",
+     "shell.execute_reply": "2023-11-15T21:42:33.781710Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "id": "5e6661cf-6adc-4cd3-b05a-4f1663f151c0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:44.003000Z",
-     "iopub.status.busy": "2023-11-15T16:36:44.002612Z",
-     "iopub.status.idle": "2023-11-15T16:36:44.329771Z",
-     "shell.execute_reply": "2023-11-15T16:36:44.329139Z"
+     "iopub.execute_input": "2023-11-15T21:42:33.785935Z",
+     "iopub.status.busy": "2023-11-15T21:42:33.785435Z",
+     "iopub.status.idle": "2023-11-15T21:42:34.125770Z",
+     "shell.execute_reply": "2023-11-15T21:42:34.124346Z"
     }
    },
    "outputs": [
@@ -90,10 +90,10 @@
    "id": "44dc8f20-bdec-4ea2-8007-3af24a8e481d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:44.332162Z",
-     "iopub.status.busy": "2023-11-15T16:36:44.331801Z",
-     "iopub.status.idle": "2023-11-15T16:36:44.600158Z",
-     "shell.execute_reply": "2023-11-15T16:36:44.599515Z"
+     "iopub.execute_input": "2023-11-15T21:42:34.128340Z",
+     "iopub.status.busy": "2023-11-15T21:42:34.127961Z",
+     "iopub.status.idle": "2023-11-15T21:42:34.398785Z",
+     "shell.execute_reply": "2023-11-15T21:42:34.398189Z"
     }
    },
    "outputs": [
@@ -133,10 +133,10 @@
    "id": "cd13b912-0c16-47c0-bc42-41dc3ec70ada",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:44.602696Z",
-     "iopub.status.busy": "2023-11-15T16:36:44.602326Z",
-     "iopub.status.idle": "2023-11-15T16:36:44.614627Z",
-     "shell.execute_reply": "2023-11-15T16:36:44.614177Z"
+     "iopub.execute_input": "2023-11-15T21:42:34.401384Z",
+     "iopub.status.busy": "2023-11-15T21:42:34.401000Z",
+     "iopub.status.idle": "2023-11-15T21:42:34.414556Z",
+     "shell.execute_reply": "2023-11-15T21:42:34.413988Z"
     }
    },
    "outputs": [],
@@ -150,10 +150,10 @@
    "id": "28e99d21-acf4-411d-9cd7-01c77d4c73fa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:44.616792Z",
-     "iopub.status.busy": "2023-11-15T16:36:44.616444Z",
-     "iopub.status.idle": "2023-11-15T16:36:44.630285Z",
-     "shell.execute_reply": "2023-11-15T16:36:44.629734Z"
+     "iopub.execute_input": "2023-11-15T21:42:34.417170Z",
+     "iopub.status.busy": "2023-11-15T21:42:34.416790Z",
+     "iopub.status.idle": "2023-11-15T21:42:34.432239Z",
+     "shell.execute_reply": "2023-11-15T21:42:34.431740Z"
     }
    },
    "outputs": [],
@@ -172,10 +172,10 @@
    "id": "833a35d5-b804-4f63-b4db-be04ac459c76",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:44.632697Z",
-     "iopub.status.busy": "2023-11-15T16:36:44.632307Z",
-     "iopub.status.idle": "2023-11-15T16:36:44.836141Z",
-     "shell.execute_reply": "2023-11-15T16:36:44.835499Z"
+     "iopub.execute_input": "2023-11-15T21:42:34.434594Z",
+     "iopub.status.busy": "2023-11-15T21:42:34.434380Z",
+     "iopub.status.idle": "2023-11-15T21:42:34.642347Z",
+     "shell.execute_reply": "2023-11-15T21:42:34.641701Z"
     }
    },
    "outputs": [
@@ -218,17 +218,17 @@
    "id": "bacdc66f-068a-40e0-abb2-acd12a1343e7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:44.838600Z",
-     "iopub.status.busy": "2023-11-15T16:36:44.838229Z",
-     "iopub.status.idle": "2023-11-15T16:36:45.066157Z",
-     "shell.execute_reply": "2023-11-15T16:36:45.065543Z"
+     "iopub.execute_input": "2023-11-15T21:42:34.644601Z",
+     "iopub.status.busy": "2023-11-15T21:42:34.644408Z",
+     "iopub.status.idle": "2023-11-15T21:42:34.876409Z",
+     "shell.execute_reply": "2023-11-15T21:42:34.875768Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f32e4463ac0>"
+       "<matplotlib.legend.Legend at 0x7f26843c9b20>"
       ]
      },
      "execution_count": 8,
@@ -265,17 +265,17 @@
    "id": "7bb3b16d-9ceb-4052-bfa9-ade9ee9466fe",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:45.068496Z",
-     "iopub.status.busy": "2023-11-15T16:36:45.068317Z",
-     "iopub.status.idle": "2023-11-15T16:36:45.275476Z",
-     "shell.execute_reply": "2023-11-15T16:36:45.274842Z"
+     "iopub.execute_input": "2023-11-15T21:42:34.878800Z",
+     "iopub.status.busy": "2023-11-15T21:42:34.878432Z",
+     "iopub.status.idle": "2023-11-15T21:42:35.114204Z",
+     "shell.execute_reply": "2023-11-15T21:42:35.113611Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f32e4412430>"
+       "<matplotlib.legend.Legend at 0x7f268436a490>"
       ]
      },
      "execution_count": 9,
@@ -320,10 +320,10 @@
    "id": "4e4eb2f9-0418-4cc3-97ec-74907c356dce",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:45.278155Z",
-     "iopub.status.busy": "2023-11-15T16:36:45.277943Z",
-     "iopub.status.idle": "2023-11-15T16:36:45.484926Z",
-     "shell.execute_reply": "2023-11-15T16:36:45.484341Z"
+     "iopub.execute_input": "2023-11-15T21:42:35.116739Z",
+     "iopub.status.busy": "2023-11-15T21:42:35.116375Z",
+     "iopub.status.idle": "2023-11-15T21:42:35.325115Z",
+     "shell.execute_reply": "2023-11-15T21:42:35.324507Z"
     }
    },
    "outputs": [
@@ -362,17 +362,17 @@
    "id": "6c5451c8-c33a-4a7b-b58b-29c2360bc8c3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:45.487325Z",
-     "iopub.status.busy": "2023-11-15T16:36:45.487137Z",
-     "iopub.status.idle": "2023-11-15T16:36:45.502232Z",
-     "shell.execute_reply": "2023-11-15T16:36:45.501715Z"
+     "iopub.execute_input": "2023-11-15T21:42:35.327750Z",
+     "iopub.status.busy": "2023-11-15T21:42:35.327294Z",
+     "iopub.status.idle": "2023-11-15T21:42:35.342438Z",
+     "shell.execute_reply": "2023-11-15T21:42:35.341881Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<FieldMesh with cylindrical geometry and (10, 1, 201) shape at 0x7f32e437bbe0>"
+       "<FieldMesh with cylindrical geometry and (10, 1, 201) shape at 0x7f26842e2bb0>"
       ]
      },
      "execution_count": 11,
@@ -391,10 +391,10 @@
    "id": "910f4a38-9083-4cee-a90a-d74a58959b27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:45.504235Z",
-     "iopub.status.busy": "2023-11-15T16:36:45.504069Z",
-     "iopub.status.idle": "2023-11-15T16:36:45.773915Z",
-     "shell.execute_reply": "2023-11-15T16:36:45.773265Z"
+     "iopub.execute_input": "2023-11-15T21:42:35.344726Z",
+     "iopub.status.busy": "2023-11-15T21:42:35.344391Z",
+     "iopub.status.idle": "2023-11-15T21:42:35.669526Z",
+     "shell.execute_reply": "2023-11-15T21:42:35.668907Z"
     }
    },
    "outputs": [
@@ -424,10 +424,10 @@
    "id": "97b3964e-cdb4-4c6d-895a-449d92966464",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:45.776310Z",
-     "iopub.status.busy": "2023-11-15T16:36:45.776123Z",
-     "iopub.status.idle": "2023-11-15T16:36:46.060829Z",
-     "shell.execute_reply": "2023-11-15T16:36:46.060188Z"
+     "iopub.execute_input": "2023-11-15T21:42:35.672236Z",
+     "iopub.status.busy": "2023-11-15T21:42:35.671846Z",
+     "iopub.status.idle": "2023-11-15T21:42:36.007777Z",
+     "shell.execute_reply": "2023-11-15T21:42:36.007097Z"
     }
    },
    "outputs": [
@@ -484,10 +484,10 @@
    "id": "d2d45aa6-7bd1-4314-a1d9-41b8bddfaae2",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:46.063329Z",
-     "iopub.status.busy": "2023-11-15T16:36:46.063138Z",
-     "iopub.status.idle": "2023-11-15T16:36:46.415771Z",
-     "shell.execute_reply": "2023-11-15T16:36:46.415216Z"
+     "iopub.execute_input": "2023-11-15T21:42:36.010108Z",
+     "iopub.status.busy": "2023-11-15T21:42:36.009902Z",
+     "iopub.status.idle": "2023-11-15T21:42:36.380528Z",
+     "shell.execute_reply": "2023-11-15T21:42:36.379940Z"
     }
    },
    "outputs": [
@@ -518,10 +518,10 @@
    "id": "6351eda5-5129-44f1-a916-f87002f56d96",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:46.418484Z",
-     "iopub.status.busy": "2023-11-15T16:36:46.418119Z",
-     "iopub.status.idle": "2023-11-15T16:36:46.587220Z",
-     "shell.execute_reply": "2023-11-15T16:36:46.586626Z"
+     "iopub.execute_input": "2023-11-15T21:42:36.383235Z",
+     "iopub.status.busy": "2023-11-15T21:42:36.382853Z",
+     "iopub.status.idle": "2023-11-15T21:42:36.595183Z",
+     "shell.execute_reply": "2023-11-15T21:42:36.594561Z"
     }
    },
    "outputs": [
@@ -564,10 +564,10 @@
    "id": "3448a8f5-2d07-4906-b86c-f10b7a02922f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:46.589505Z",
-     "iopub.status.busy": "2023-11-15T16:36:46.589331Z",
-     "iopub.status.idle": "2023-11-15T16:36:46.901130Z",
-     "shell.execute_reply": "2023-11-15T16:36:46.900568Z"
+     "iopub.execute_input": "2023-11-15T21:42:36.597668Z",
+     "iopub.status.busy": "2023-11-15T21:42:36.597472Z",
+     "iopub.status.idle": "2023-11-15T21:42:36.958694Z",
+     "shell.execute_reply": "2023-11-15T21:42:36.958094Z"
     }
    },
    "outputs": [
@@ -599,10 +599,10 @@
    "id": "62c2a4df-1222-4d7e-8e9c-0e9d97714217",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:46.903981Z",
-     "iopub.status.busy": "2023-11-15T16:36:46.903613Z",
-     "iopub.status.idle": "2023-11-15T16:36:47.202657Z",
-     "shell.execute_reply": "2023-11-15T16:36:47.202026Z"
+     "iopub.execute_input": "2023-11-15T21:42:36.961269Z",
+     "iopub.status.busy": "2023-11-15T21:42:36.961093Z",
+     "iopub.status.idle": "2023-11-15T21:42:37.306870Z",
+     "shell.execute_reply": "2023-11-15T21:42:37.306215Z"
     }
    },
    "outputs": [
@@ -632,10 +632,10 @@
    "id": "3a2f9fb9-3e37-4ea4-a0c6-92a94125562f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:47.205102Z",
-     "iopub.status.busy": "2023-11-15T16:36:47.204714Z",
-     "iopub.status.idle": "2023-11-15T16:36:47.513729Z",
-     "shell.execute_reply": "2023-11-15T16:36:47.513141Z"
+     "iopub.execute_input": "2023-11-15T21:42:37.309332Z",
+     "iopub.status.busy": "2023-11-15T21:42:37.308965Z",
+     "iopub.status.idle": "2023-11-15T21:42:37.671086Z",
+     "shell.execute_reply": "2023-11-15T21:42:37.670439Z"
     }
    },
    "outputs": [
@@ -673,10 +673,10 @@
    "id": "00c58b7e-886f-40dd-bfa0-6eab49aa993b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:47.516425Z",
-     "iopub.status.busy": "2023-11-15T16:36:47.516067Z",
-     "iopub.status.idle": "2023-11-15T16:36:47.839624Z",
-     "shell.execute_reply": "2023-11-15T16:36:47.838989Z"
+     "iopub.execute_input": "2023-11-15T21:42:37.673705Z",
+     "iopub.status.busy": "2023-11-15T21:42:37.673195Z",
+     "iopub.status.idle": "2023-11-15T21:42:38.050489Z",
+     "shell.execute_reply": "2023-11-15T21:42:38.049894Z"
     }
    },
    "outputs": [
@@ -708,10 +708,10 @@
    "id": "4829b28d-6e03-404f-acf4-dc12c0c5c929",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:47.841921Z",
-     "iopub.status.busy": "2023-11-15T16:36:47.841728Z",
-     "iopub.status.idle": "2023-11-15T16:36:48.188491Z",
-     "shell.execute_reply": "2023-11-15T16:36:48.187940Z"
+     "iopub.execute_input": "2023-11-15T21:42:38.053133Z",
+     "iopub.status.busy": "2023-11-15T21:42:38.052681Z",
+     "iopub.status.idle": "2023-11-15T21:42:38.400514Z",
+     "shell.execute_reply": "2023-11-15T21:42:38.399942Z"
     }
    },
    "outputs": [
@@ -741,10 +741,10 @@
    "id": "28607df0-5099-4f7b-9359-f465e52cb206",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:48.191032Z",
-     "iopub.status.busy": "2023-11-15T16:36:48.190842Z",
-     "iopub.status.idle": "2023-11-15T16:36:48.548695Z",
-     "shell.execute_reply": "2023-11-15T16:36:48.548129Z"
+     "iopub.execute_input": "2023-11-15T21:42:38.403132Z",
+     "iopub.status.busy": "2023-11-15T21:42:38.402776Z",
+     "iopub.status.idle": "2023-11-15T21:42:38.766327Z",
+     "shell.execute_reply": "2023-11-15T21:42:38.765731Z"
     }
    },
    "outputs": [
@@ -774,10 +774,10 @@
    "id": "1ed7bfbd-3b79-426d-adeb-edaa420473c7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:48.551356Z",
-     "iopub.status.busy": "2023-11-15T16:36:48.550997Z",
-     "iopub.status.idle": "2023-11-15T16:36:48.909159Z",
-     "shell.execute_reply": "2023-11-15T16:36:48.908609Z"
+     "iopub.execute_input": "2023-11-15T21:42:38.769031Z",
+     "iopub.status.busy": "2023-11-15T21:42:38.768537Z",
+     "iopub.status.idle": "2023-11-15T21:42:39.131694Z",
+     "shell.execute_reply": "2023-11-15T21:42:39.131129Z"
     }
    },
    "outputs": [
@@ -815,10 +815,10 @@
    "id": "5571f83f-5307-45f6-bc87-bb208dac658c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:48.911651Z",
-     "iopub.status.busy": "2023-11-15T16:36:48.911471Z",
-     "iopub.status.idle": "2023-11-15T16:36:49.223611Z",
-     "shell.execute_reply": "2023-11-15T16:36:49.223022Z"
+     "iopub.execute_input": "2023-11-15T21:42:39.134310Z",
+     "iopub.status.busy": "2023-11-15T21:42:39.133936Z",
+     "iopub.status.idle": "2023-11-15T21:42:39.454459Z",
+     "shell.execute_reply": "2023-11-15T21:42:39.453829Z"
     }
    },
    "outputs": [
@@ -849,10 +849,10 @@
    "id": "e4cfef90-189c-4c49-8755-e421c6ca7bf6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:49.226329Z",
-     "iopub.status.busy": "2023-11-15T16:36:49.226122Z",
-     "iopub.status.idle": "2023-11-15T16:36:49.242452Z",
-     "shell.execute_reply": "2023-11-15T16:36:49.241981Z"
+     "iopub.execute_input": "2023-11-15T21:42:39.456781Z",
+     "iopub.status.busy": "2023-11-15T21:42:39.456590Z",
+     "iopub.status.idle": "2023-11-15T21:42:39.472585Z",
+     "shell.execute_reply": "2023-11-15T21:42:39.472084Z"
     }
    },
    "outputs": [],
@@ -897,10 +897,10 @@
    "id": "539bcb9f-7441-41f0-970f-267028f8ba49",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:49.244742Z",
-     "iopub.status.busy": "2023-11-15T16:36:49.244385Z",
-     "iopub.status.idle": "2023-11-15T16:36:49.856215Z",
-     "shell.execute_reply": "2023-11-15T16:36:49.855610Z"
+     "iopub.execute_input": "2023-11-15T21:42:39.474894Z",
+     "iopub.status.busy": "2023-11-15T21:42:39.474706Z",
+     "iopub.status.idle": "2023-11-15T21:42:40.120546Z",
+     "shell.execute_reply": "2023-11-15T21:42:40.119976Z"
     }
    },
    "outputs": [
@@ -908,9 +908,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_5526/1820762901.py:25: RuntimeWarning: divide by zero encountered in divide\n",
+      "/tmp/ipykernel_5629/1820762901.py:25: RuntimeWarning: divide by zero encountered in divide\n",
       "  ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')\n",
-      "/tmp/ipykernel_5526/1820762901.py:26: RuntimeWarning: divide by zero encountered in divide\n",
+      "/tmp/ipykernel_5629/1820762901.py:26: RuntimeWarning: divide by zero encountered in divide\n",
       "  ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')\n"
      ]
     },
@@ -940,10 +940,10 @@
    "id": "d64f302f-8542-443d-9994-049790f4285f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:49.858646Z",
-     "iopub.status.busy": "2023-11-15T16:36:49.858206Z",
-     "iopub.status.idle": "2023-11-15T16:36:50.377738Z",
-     "shell.execute_reply": "2023-11-15T16:36:50.377090Z"
+     "iopub.execute_input": "2023-11-15T21:42:40.123207Z",
+     "iopub.status.busy": "2023-11-15T21:42:40.123018Z",
+     "iopub.status.idle": "2023-11-15T21:42:40.653760Z",
+     "shell.execute_reply": "2023-11-15T21:42:40.653131Z"
     }
    },
    "outputs": [
@@ -973,10 +973,10 @@
    "id": "a04450e4-8141-4a7b-a3c4-719737932af1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:50.380097Z",
-     "iopub.status.busy": "2023-11-15T16:36:50.379912Z",
-     "iopub.status.idle": "2023-11-15T16:36:51.008010Z",
-     "shell.execute_reply": "2023-11-15T16:36:51.007458Z"
+     "iopub.execute_input": "2023-11-15T21:42:40.656238Z",
+     "iopub.status.busy": "2023-11-15T21:42:40.655872Z",
+     "iopub.status.idle": "2023-11-15T21:42:41.241032Z",
+     "shell.execute_reply": "2023-11-15T21:42:41.240425Z"
     }
    },
    "outputs": [
diff --git a/examples/fields/field_expansion/index.html b/examples/fields/field_expansion/index.html
index be929d7..1f8e39d 100644
--- a/examples/fields/field_expansion/index.html
+++ b/examples/fields/field_expansion/index.html
@@ -1657,7 +1657,7 @@ <h1 id="derivative-array">Derivative array<a class="anchor-link" href="#derivati
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.legend.Legend at 0x7f32e4463ac0&gt;</pre>
+<pre>&lt;matplotlib.legend.Legend at 0x7f26843c9b20&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -1710,7 +1710,7 @@ <h1 id="derivative-array">Derivative array<a class="anchor-link" href="#derivati
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.legend.Legend at 0x7f32e4412430&gt;</pre>
+<pre>&lt;matplotlib.legend.Legend at 0x7f268436a490&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -1820,7 +1820,7 @@ <h1 id="expansion-1d-2d">Expansion 1D -&gt; 2D<a class="anchor-link" href="#expa
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[11]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;FieldMesh with cylindrical geometry and (10, 1, 201) shape at 0x7f32e437bbe0&gt;</pre>
+<pre>&lt;FieldMesh with cylindrical geometry and (10, 1, 201) shape at 0x7f26842e2bb0&gt;</pre>
 </div>
 </div>
 </div>
@@ -2535,9 +2535,9 @@ <h1 id="compare-fourier-and-spline">Compare Fourier and Spline<a class="anchor-l
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>/tmp/ipykernel_5526/1820762901.py:25: RuntimeWarning: divide by zero encountered in divide
+<pre>/tmp/ipykernel_5629/1820762901.py:25: RuntimeWarning: divide by zero encountered in divide
   ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')
-/tmp/ipykernel_5526/1820762901.py:26: RuntimeWarning: divide by zero encountered in divide
+/tmp/ipykernel_5629/1820762901.py:26: RuntimeWarning: divide by zero encountered in divide
   ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')
 </pre>
 </div>
diff --git a/examples/labels/labels.ipynb b/examples/labels/labels.ipynb
index 97aee57..ea306cc 100644
--- a/examples/labels/labels.ipynb
+++ b/examples/labels/labels.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:37.306060Z",
-     "iopub.status.busy": "2023-11-15T16:36:37.305888Z",
-     "iopub.status.idle": "2023-11-15T16:36:37.319753Z",
-     "shell.execute_reply": "2023-11-15T16:36:37.319321Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.026723Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.026524Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.040749Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.040307Z"
     },
     "tags": []
    },
@@ -33,10 +33,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:37.321835Z",
-     "iopub.status.busy": "2023-11-15T16:36:37.321665Z",
-     "iopub.status.idle": "2023-11-15T16:36:37.659883Z",
-     "shell.execute_reply": "2023-11-15T16:36:37.659285Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.043258Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.042792Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.389016Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.388445Z"
     },
     "tags": []
    },
@@ -60,10 +60,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:37.689590Z",
-     "iopub.status.busy": "2023-11-15T16:36:37.689019Z",
-     "iopub.status.idle": "2023-11-15T16:36:37.984653Z",
-     "shell.execute_reply": "2023-11-15T16:36:37.984050Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.418875Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.418315Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.718549Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.717896Z"
     },
     "tags": []
    },
@@ -85,10 +85,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:37.987284Z",
-     "iopub.status.busy": "2023-11-15T16:36:37.987096Z",
-     "iopub.status.idle": "2023-11-15T16:36:38.029197Z",
-     "shell.execute_reply": "2023-11-15T16:36:38.028754Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.721480Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.721247Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.763608Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.763094Z"
     },
     "tags": []
    },
@@ -109,10 +109,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:38.031298Z",
-     "iopub.status.busy": "2023-11-15T16:36:38.031132Z",
-     "iopub.status.idle": "2023-11-15T16:36:38.044796Z",
-     "shell.execute_reply": "2023-11-15T16:36:38.044247Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.766250Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.766067Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.780220Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.779730Z"
     },
     "tags": []
    },
@@ -144,10 +144,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:38.046956Z",
-     "iopub.status.busy": "2023-11-15T16:36:38.046776Z",
-     "iopub.status.idle": "2023-11-15T16:36:38.060190Z",
-     "shell.execute_reply": "2023-11-15T16:36:38.059627Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.782303Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.782131Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.794458Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.793916Z"
     },
     "tags": []
    },
@@ -179,10 +179,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:38.062653Z",
-     "iopub.status.busy": "2023-11-15T16:36:38.062202Z",
-     "iopub.status.idle": "2023-11-15T16:36:38.074495Z",
-     "shell.execute_reply": "2023-11-15T16:36:38.073922Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.796915Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.796389Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.808552Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.808114Z"
     },
     "tags": []
    },
@@ -207,10 +207,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:38.076703Z",
-     "iopub.status.busy": "2023-11-15T16:36:38.076300Z",
-     "iopub.status.idle": "2023-11-15T16:36:38.087616Z",
-     "shell.execute_reply": "2023-11-15T16:36:38.087032Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.810840Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.810393Z",
+     "iopub.status.idle": "2023-11-15T21:42:26.821760Z",
+     "shell.execute_reply": "2023-11-15T21:42:26.821204Z"
     },
     "tags": []
    },
@@ -225,10 +225,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:38.089795Z",
-     "iopub.status.busy": "2023-11-15T16:36:38.089602Z",
-     "iopub.status.idle": "2023-11-15T16:36:39.488201Z",
-     "shell.execute_reply": "2023-11-15T16:36:39.487579Z"
+     "iopub.execute_input": "2023-11-15T21:42:26.823936Z",
+     "iopub.status.busy": "2023-11-15T21:42:26.823759Z",
+     "iopub.status.idle": "2023-11-15T21:42:28.541533Z",
+     "shell.execute_reply": "2023-11-15T21:42:28.540919Z"
     },
     "tags": []
    },
@@ -1536,10 +1536,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:39.490847Z",
-     "iopub.status.busy": "2023-11-15T16:36:39.490449Z",
-     "iopub.status.idle": "2023-11-15T16:36:39.504438Z",
-     "shell.execute_reply": "2023-11-15T16:36:39.503863Z"
+     "iopub.execute_input": "2023-11-15T21:42:28.544461Z",
+     "iopub.status.busy": "2023-11-15T21:42:28.544108Z",
+     "iopub.status.idle": "2023-11-15T21:42:28.561417Z",
+     "shell.execute_reply": "2023-11-15T21:42:28.560860Z"
     }
    },
    "outputs": [],
@@ -1552,10 +1552,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:39.506714Z",
-     "iopub.status.busy": "2023-11-15T16:36:39.506522Z",
-     "iopub.status.idle": "2023-11-15T16:36:39.519173Z",
-     "shell.execute_reply": "2023-11-15T16:36:39.518740Z"
+     "iopub.execute_input": "2023-11-15T21:42:28.564059Z",
+     "iopub.status.busy": "2023-11-15T21:42:28.563860Z",
+     "iopub.status.idle": "2023-11-15T21:42:28.580980Z",
+     "shell.execute_reply": "2023-11-15T21:42:28.580456Z"
     }
    },
    "outputs": [
@@ -1586,10 +1586,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:39.521638Z",
-     "iopub.status.busy": "2023-11-15T16:36:39.521130Z",
-     "iopub.status.idle": "2023-11-15T16:36:39.533400Z",
-     "shell.execute_reply": "2023-11-15T16:36:39.532913Z"
+     "iopub.execute_input": "2023-11-15T21:42:28.583563Z",
+     "iopub.status.busy": "2023-11-15T21:42:28.583363Z",
+     "iopub.status.idle": "2023-11-15T21:42:28.600699Z",
+     "shell.execute_reply": "2023-11-15T21:42:28.600176Z"
     },
     "tags": []
    },
@@ -1614,10 +1614,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:39.535714Z",
-     "iopub.status.busy": "2023-11-15T16:36:39.535343Z",
-     "iopub.status.idle": "2023-11-15T16:36:41.138056Z",
-     "shell.execute_reply": "2023-11-15T16:36:41.137557Z"
+     "iopub.execute_input": "2023-11-15T21:42:28.603672Z",
+     "iopub.status.busy": "2023-11-15T21:42:28.603198Z",
+     "iopub.status.idle": "2023-11-15T21:42:30.742152Z",
+     "shell.execute_reply": "2023-11-15T21:42:30.741602Z"
     },
     "tags": []
    },
diff --git a/examples/normalized_coordinates/index.html b/examples/normalized_coordinates/index.html
index 023b27e..d4fe131 100644
--- a/examples/normalized_coordinates/index.html
+++ b/examples/normalized_coordinates/index.html
@@ -1502,7 +1502,7 @@ <h1 id="simple-normalized-coordinates">Simple Normalized Coordinates<a class="an
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.collections.PathCollection at 0x7fe784cab430&gt;</pre>
+<pre>&lt;matplotlib.collections.PathCollection at 0x7f9690b69670&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -1562,7 +1562,7 @@ <h1 id="simple-normalized-coordinates">Simple Normalized Coordinates<a class="an
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.collections.PathCollection at 0x7fe77bc14f70&gt;</pre>
+<pre>&lt;matplotlib.collections.PathCollection at 0x7f9687adf370&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -1831,7 +1831,7 @@ <h1 id="simple-normalized-coordinates">Simple Normalized Coordinates<a class="an
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.collections.PathCollection at 0x7fe77bb97280&gt;</pre>
+<pre>&lt;matplotlib.collections.PathCollection at 0x7f9687a56370&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -2546,7 +2546,7 @@ <h1 id="x_bar-px_bar-jx-etc">x_bar, px_bar, Jx, etc.<a class="anchor-link" href=
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>[&lt;matplotlib.lines.Line2D at 0x7fe77b10d3d0&gt;]</pre>
+<pre>[&lt;matplotlib.lines.Line2D at 0x7f968742f4c0&gt;]</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
diff --git a/examples/normalized_coordinates/normalized_coordinates.ipynb b/examples/normalized_coordinates/normalized_coordinates.ipynb
index 34c7a08..ff082e4 100644
--- a/examples/normalized_coordinates/normalized_coordinates.ipynb
+++ b/examples/normalized_coordinates/normalized_coordinates.ipynb
@@ -22,10 +22,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:24.984815Z",
-     "iopub.status.busy": "2023-11-15T16:36:24.984644Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.590401Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.589798Z"
+     "iopub.execute_input": "2023-11-15T21:42:13.393091Z",
+     "iopub.status.busy": "2023-11-15T21:42:13.392689Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.006156Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.005562Z"
     },
     "tags": []
    },
@@ -48,10 +48,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.593099Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.592796Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.596409Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.595855Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.009132Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.008632Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.012470Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.011905Z"
     },
     "tags": []
    },
@@ -106,10 +106,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.625730Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.625208Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.804632Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.804063Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.041856Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.041409Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.219402Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.218846Z"
     },
     "tags": []
    },
@@ -117,7 +117,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fe784cab430>"
+       "<matplotlib.collections.PathCollection at 0x7f9690b69670>"
       ]
      },
      "execution_count": 3,
@@ -158,10 +158,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.806987Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.806802Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.962086Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.961504Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.221849Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.221677Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.377264Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.376657Z"
     },
     "tags": []
    },
@@ -169,7 +169,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fe77bc14f70>"
+       "<matplotlib.collections.PathCollection at 0x7f9687adf370>"
       ]
      },
      "execution_count": 4,
@@ -210,10 +210,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.964946Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.964644Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.968216Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.967716Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.380236Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.380012Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.383332Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.382906Z"
     },
     "tags": []
    },
@@ -255,10 +255,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.970292Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.970119Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.974127Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.973566Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.385378Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.385125Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.388556Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.388159Z"
     },
     "tags": []
    },
@@ -291,10 +291,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.976069Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.975896Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.979492Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.978960Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.390592Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.390328Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.393869Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.393423Z"
     },
     "tags": []
    },
@@ -330,10 +330,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.981540Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.981373Z",
-     "iopub.status.idle": "2023-11-15T16:36:25.984249Z",
-     "shell.execute_reply": "2023-11-15T16:36:25.983743Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.395945Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.395780Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.398620Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.398098Z"
     },
     "tags": []
    },
@@ -355,10 +355,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:25.986529Z",
-     "iopub.status.busy": "2023-11-15T16:36:25.986104Z",
-     "iopub.status.idle": "2023-11-15T16:36:26.150900Z",
-     "shell.execute_reply": "2023-11-15T16:36:26.150222Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.400929Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.400586Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.567340Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.566693Z"
     },
     "tags": []
    },
@@ -366,7 +366,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7fe77bb97280>"
+       "<matplotlib.collections.PathCollection at 0x7f9687a56370>"
       ]
      },
      "execution_count": 9,
@@ -410,10 +410,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:26.153383Z",
-     "iopub.status.busy": "2023-11-15T16:36:26.152931Z",
-     "iopub.status.idle": "2023-11-15T16:36:26.157111Z",
-     "shell.execute_reply": "2023-11-15T16:36:26.156646Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.572488Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.572252Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.579487Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.578734Z"
     },
     "tags": []
    },
@@ -441,10 +441,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:26.159158Z",
-     "iopub.status.busy": "2023-11-15T16:36:26.158982Z",
-     "iopub.status.idle": "2023-11-15T16:36:26.161867Z",
-     "shell.execute_reply": "2023-11-15T16:36:26.161308Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.582387Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.582176Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.585432Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.584826Z"
     },
     "tags": []
    },
@@ -468,10 +468,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:26.164046Z",
-     "iopub.status.busy": "2023-11-15T16:36:26.163702Z",
-     "iopub.status.idle": "2023-11-15T16:36:26.166531Z",
-     "shell.execute_reply": "2023-11-15T16:36:26.166026Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.588154Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.587775Z",
+     "iopub.status.idle": "2023-11-15T21:42:14.591367Z",
+     "shell.execute_reply": "2023-11-15T21:42:14.590678Z"
     },
     "tags": []
    },
@@ -531,10 +531,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:26.168865Z",
-     "iopub.status.busy": "2023-11-15T16:36:26.168527Z",
-     "iopub.status.idle": "2023-11-15T16:36:27.082685Z",
-     "shell.execute_reply": "2023-11-15T16:36:27.082045Z"
+     "iopub.execute_input": "2023-11-15T21:42:14.593953Z",
+     "iopub.status.busy": "2023-11-15T21:42:14.593509Z",
+     "iopub.status.idle": "2023-11-15T21:42:15.613426Z",
+     "shell.execute_reply": "2023-11-15T21:42:15.612808Z"
     },
     "tags": []
    },
@@ -572,10 +572,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:27.086723Z",
-     "iopub.status.busy": "2023-11-15T16:36:27.086347Z",
-     "iopub.status.idle": "2023-11-15T16:36:27.093493Z",
-     "shell.execute_reply": "2023-11-15T16:36:27.093044Z"
+     "iopub.execute_input": "2023-11-15T21:42:15.617119Z",
+     "iopub.status.busy": "2023-11-15T21:42:15.616936Z",
+     "iopub.status.idle": "2023-11-15T21:42:15.623697Z",
+     "shell.execute_reply": "2023-11-15T21:42:15.623123Z"
     },
     "tags": []
    },
@@ -608,10 +608,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:27.095843Z",
-     "iopub.status.busy": "2023-11-15T16:36:27.095455Z",
-     "iopub.status.idle": "2023-11-15T16:36:27.101881Z",
-     "shell.execute_reply": "2023-11-15T16:36:27.101396Z"
+     "iopub.execute_input": "2023-11-15T21:42:15.625842Z",
+     "iopub.status.busy": "2023-11-15T21:42:15.625662Z",
+     "iopub.status.idle": "2023-11-15T21:42:15.632097Z",
+     "shell.execute_reply": "2023-11-15T21:42:15.631618Z"
     },
     "tags": []
    },
@@ -644,10 +644,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:27.104006Z",
-     "iopub.status.busy": "2023-11-15T16:36:27.103839Z",
-     "iopub.status.idle": "2023-11-15T16:36:27.108725Z",
-     "shell.execute_reply": "2023-11-15T16:36:27.108287Z"
+     "iopub.execute_input": "2023-11-15T21:42:15.634311Z",
+     "iopub.status.busy": "2023-11-15T21:42:15.634143Z",
+     "iopub.status.idle": "2023-11-15T21:42:15.639273Z",
+     "shell.execute_reply": "2023-11-15T21:42:15.638815Z"
     },
     "tags": []
    },
@@ -680,10 +680,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:27.110962Z",
-     "iopub.status.busy": "2023-11-15T16:36:27.110623Z",
-     "iopub.status.idle": "2023-11-15T16:36:27.117599Z",
-     "shell.execute_reply": "2023-11-15T16:36:27.117163Z"
+     "iopub.execute_input": "2023-11-15T21:42:15.641447Z",
+     "iopub.status.busy": "2023-11-15T21:42:15.641124Z",
+     "iopub.status.idle": "2023-11-15T21:42:15.648405Z",
+     "shell.execute_reply": "2023-11-15T21:42:15.647830Z"
     },
     "tags": []
    },
@@ -716,10 +716,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:27.119632Z",
-     "iopub.status.busy": "2023-11-15T16:36:27.119471Z",
-     "iopub.status.idle": "2023-11-15T16:36:27.124607Z",
-     "shell.execute_reply": "2023-11-15T16:36:27.124163Z"
+     "iopub.execute_input": "2023-11-15T21:42:15.650708Z",
+     "iopub.status.busy": "2023-11-15T21:42:15.650341Z",
+     "iopub.status.idle": "2023-11-15T21:42:15.655895Z",
+     "shell.execute_reply": "2023-11-15T21:42:15.655340Z"
     },
     "tags": []
    },
@@ -752,10 +752,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:27.126916Z",
-     "iopub.status.busy": "2023-11-15T16:36:27.126583Z",
-     "iopub.status.idle": "2023-11-15T16:36:27.134793Z",
-     "shell.execute_reply": "2023-11-15T16:36:27.134335Z"
+     "iopub.execute_input": "2023-11-15T21:42:15.658055Z",
+     "iopub.status.busy": "2023-11-15T21:42:15.657730Z",
+     "iopub.status.idle": "2023-11-15T21:42:15.666341Z",
+     "shell.execute_reply": "2023-11-15T21:42:15.665833Z"
     },
     "tags": []
    },
@@ -787,10 +787,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:27.136791Z",
-     "iopub.status.busy": "2023-11-15T16:36:27.136628Z",
-     "iopub.status.idle": "2023-11-15T16:36:28.008065Z",
-     "shell.execute_reply": "2023-11-15T16:36:28.007383Z"
+     "iopub.execute_input": "2023-11-15T21:42:15.668624Z",
+     "iopub.status.busy": "2023-11-15T21:42:15.668288Z",
+     "iopub.status.idle": "2023-11-15T21:42:16.615367Z",
+     "shell.execute_reply": "2023-11-15T21:42:16.614687Z"
     },
     "tags": []
    },
@@ -827,10 +827,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:28.011954Z",
-     "iopub.status.busy": "2023-11-15T16:36:28.011749Z",
-     "iopub.status.idle": "2023-11-15T16:36:28.746371Z",
-     "shell.execute_reply": "2023-11-15T16:36:28.745761Z"
+     "iopub.execute_input": "2023-11-15T21:42:16.619903Z",
+     "iopub.status.busy": "2023-11-15T21:42:16.619343Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.402380Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.401749Z"
     },
     "tags": []
    },
@@ -867,10 +867,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:28.749476Z",
-     "iopub.status.busy": "2023-11-15T16:36:28.749296Z",
-     "iopub.status.idle": "2023-11-15T16:36:28.788249Z",
-     "shell.execute_reply": "2023-11-15T16:36:28.787723Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.405209Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.404829Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.441559Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.441022Z"
     },
     "tags": []
    },
@@ -891,10 +891,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:28.791016Z",
-     "iopub.status.busy": "2023-11-15T16:36:28.790655Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.044103Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.043463Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.444288Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.443975Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.696720Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.696108Z"
     },
     "tags": []
    },
@@ -902,7 +902,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fe77b10d3d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f968742f4c0>]"
       ]
      },
      "execution_count": 23,
@@ -941,10 +941,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:29.046908Z",
-     "iopub.status.busy": "2023-11-15T16:36:29.046509Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.072101Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.071519Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.698992Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.698817Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.722532Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.722071Z"
     },
     "tags": []
    },
@@ -980,10 +980,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:29.074673Z",
-     "iopub.status.busy": "2023-11-15T16:36:29.074258Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.078215Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.077680Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.724695Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.724527Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.728481Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.728009Z"
     },
     "tags": []
    },
@@ -1028,10 +1028,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:29.080556Z",
-     "iopub.status.busy": "2023-11-15T16:36:29.080204Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.085516Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.085045Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.730767Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.730374Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.735740Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.735162Z"
     },
     "tags": []
    },
@@ -1067,10 +1067,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:29.087610Z",
-     "iopub.status.busy": "2023-11-15T16:36:29.087442Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.092485Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.092036Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.737899Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.737625Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.743068Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.742473Z"
     },
     "tags": []
    },
@@ -1086,10 +1086,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:29.094598Z",
-     "iopub.status.busy": "2023-11-15T16:36:29.094421Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.099333Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.098899Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.745055Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.744890Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.750018Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.749582Z"
     },
     "tags": []
    },
@@ -1124,10 +1124,10 @@
    "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:29.101399Z",
-     "iopub.status.busy": "2023-11-15T16:36:29.101226Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.122420Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.121869Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.752305Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.751964Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.773400Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.772821Z"
     },
     "tags": []
    },
@@ -1228,10 +1228,10 @@
    "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:29.124593Z",
-     "iopub.status.busy": "2023-11-15T16:36:29.124412Z",
-     "iopub.status.idle": "2023-11-15T16:36:29.127103Z",
-     "shell.execute_reply": "2023-11-15T16:36:29.126612Z"
+     "iopub.execute_input": "2023-11-15T21:42:17.775909Z",
+     "iopub.status.busy": "2023-11-15T21:42:17.775578Z",
+     "iopub.status.idle": "2023-11-15T21:42:17.778632Z",
+     "shell.execute_reply": "2023-11-15T21:42:17.778052Z"
     },
     "tags": []
    },
diff --git a/examples/particle_examples/index.html b/examples/particle_examples/index.html
index 254f0e7..65b648c 100644
--- a/examples/particle_examples/index.html
+++ b/examples/particle_examples/index.html
@@ -1448,7 +1448,7 @@ <h1 id="basic-usage">Basic Usage<a class="anchor-link" href="#basic-usage">¶</a
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;ParticleGroup with 100000 particles at 0x7fd1710a0400&gt;</pre>
+<pre>&lt;ParticleGroup with 100000 particles at 0x7fb40c3bf100&gt;</pre>
 </div>
 </div>
 </div>
@@ -1566,7 +1566,7 @@ <h1 id="basic-usage">Basic Usage<a class="anchor-link" href="#basic-usage">¶</a
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;ParticleGroup with 50082 particles at 0x7fd1b8c11340&gt;</pre>
+<pre>&lt;ParticleGroup with 50082 particles at 0x7fb454ebfc40&gt;</pre>
 </div>
 </div>
 </div>
@@ -2192,7 +2192,7 @@ <h2 id="slice-statistics">Slice statistics<a class="anchor-link" href="#slice-st
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[18]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>dict_keys(['norm_emit_y', 'charge', 'norm_emit_x', 'mean_t', 'twiss', 'ptp_t', 'twiss_alpha_y', 'twiss_beta_y', 'twiss_gamma_y', 'twiss_emit_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_norm_emit_y', 'twiss_alpha_x', 'twiss_beta_x', 'twiss_gamma_x', 'twiss_emit_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_norm_emit_x', 'current'])</pre>
+<pre>dict_keys(['charge', 'mean_t', 'ptp_t', 'norm_emit_y', 'twiss', 'norm_emit_x', 'twiss_alpha_x', 'twiss_beta_x', 'twiss_gamma_x', 'twiss_emit_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_norm_emit_x', 'twiss_alpha_y', 'twiss_beta_y', 'twiss_gamma_y', 'twiss_emit_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_norm_emit_y', 'current'])</pre>
 </div>
 </div>
 </div>
@@ -2433,7 +2433,7 @@ <h2 id="resampling">Resampling<a class="anchor-link" href="#resampling">¶</a></
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;ParticleGroup with 100000 particles at 0x7fd170b50310&gt;</pre>
+<pre>&lt;ParticleGroup with 100000 particles at 0x7fb40bcc9dc0&gt;</pre>
 </div>
 </div>
 </div>
@@ -2483,7 +2483,7 @@ <h2 id="resampling">Resampling<a class="anchor-link" href="#resampling">¶</a></
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;ParticleGroup with 1000 particles at 0x7fd1a0f42040&gt;</pre>
+<pre>&lt;ParticleGroup with 1000 particles at 0x7fb40bcde850&gt;</pre>
 </div>
 </div>
 </div>
@@ -2522,7 +2522,7 @@ <h2 id="resampling">Resampling<a class="anchor-link" href="#resampling">¶</a></
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" height="373" src="data:image/png;base64,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" width="700"/>
+<img alt="No description has been provided for this image" class="" height="373" src="data:image/png;base64,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" width="700"/>
 </div>
 </div>
 </div>
@@ -3851,7 +3851,7 @@ <h2 id="manual-plotting">Manual plotting<a class="anchor-link" href="#manual-plo
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[51]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.collections.PolyCollection at 0x7fd170da8700&gt;</pre>
+<pre>&lt;matplotlib.collections.PolyCollection at 0x7fb400a25d00&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
@@ -3952,7 +3952,7 @@ <h2 id="manual-binning-and-plotting">Manual binning and plotting<a class="anchor
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[53]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;matplotlib.image.AxesImage at 0x7fd183b8ae20&gt;</pre>
+<pre>&lt;matplotlib.image.AxesImage at 0x7fb40c09e8b0&gt;</pre>
 </div>
 </div>
 <div class="jp-OutputArea-child">
diff --git a/examples/particle_examples/particle_examples.ipynb b/examples/particle_examples/particle_examples.ipynb
index 647cbfa..f0c0fc2 100644
--- a/examples/particle_examples/particle_examples.ipynb
+++ b/examples/particle_examples/particle_examples.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:08.049438Z",
-     "iopub.status.busy": "2023-11-15T16:36:08.048962Z",
-     "iopub.status.idle": "2023-11-15T16:36:08.378916Z",
-     "shell.execute_reply": "2023-11-15T16:36:08.378261Z"
+     "iopub.execute_input": "2023-11-15T21:41:56.160624Z",
+     "iopub.status.busy": "2023-11-15T21:41:56.160206Z",
+     "iopub.status.idle": "2023-11-15T21:41:56.512052Z",
+     "shell.execute_reply": "2023-11-15T21:41:56.511477Z"
     }
    },
    "outputs": [],
@@ -39,10 +39,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:08.381929Z",
-     "iopub.status.busy": "2023-11-15T16:36:08.381686Z",
-     "iopub.status.idle": "2023-11-15T16:36:08.664544Z",
-     "shell.execute_reply": "2023-11-15T16:36:08.663888Z"
+     "iopub.execute_input": "2023-11-15T21:41:56.514738Z",
+     "iopub.status.busy": "2023-11-15T21:41:56.514466Z",
+     "iopub.status.idle": "2023-11-15T21:41:56.820836Z",
+     "shell.execute_reply": "2023-11-15T21:41:56.820204Z"
     }
    },
    "outputs": [],
@@ -55,17 +55,17 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:08.667307Z",
-     "iopub.status.busy": "2023-11-15T16:36:08.666929Z",
-     "iopub.status.idle": "2023-11-15T16:36:08.680588Z",
-     "shell.execute_reply": "2023-11-15T16:36:08.680086Z"
+     "iopub.execute_input": "2023-11-15T21:41:56.823475Z",
+     "iopub.status.busy": "2023-11-15T21:41:56.823279Z",
+     "iopub.status.idle": "2023-11-15T21:41:56.837322Z",
+     "shell.execute_reply": "2023-11-15T21:41:56.836743Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<ParticleGroup with 100000 particles at 0x7fd1710a0400>"
+       "<ParticleGroup with 100000 particles at 0x7fb40c3bf100>"
       ]
      },
      "execution_count": 3,
@@ -83,10 +83,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:08.708728Z",
-     "iopub.status.busy": "2023-11-15T16:36:08.708552Z",
-     "iopub.status.idle": "2023-11-15T16:36:08.713544Z",
-     "shell.execute_reply": "2023-11-15T16:36:08.713037Z"
+     "iopub.execute_input": "2023-11-15T21:41:56.867645Z",
+     "iopub.status.busy": "2023-11-15T21:41:56.867358Z",
+     "iopub.status.idle": "2023-11-15T21:41:56.872692Z",
+     "shell.execute_reply": "2023-11-15T21:41:56.872153Z"
     }
    },
    "outputs": [
@@ -111,10 +111,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:08.716249Z",
-     "iopub.status.busy": "2023-11-15T16:36:08.715909Z",
-     "iopub.status.idle": "2023-11-15T16:36:08.720512Z",
-     "shell.execute_reply": "2023-11-15T16:36:08.719939Z"
+     "iopub.execute_input": "2023-11-15T21:41:56.875645Z",
+     "iopub.status.busy": "2023-11-15T21:41:56.875288Z",
+     "iopub.status.idle": "2023-11-15T21:41:56.880062Z",
+     "shell.execute_reply": "2023-11-15T21:41:56.879488Z"
     }
    },
    "outputs": [
@@ -138,17 +138,17 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:08.722774Z",
-     "iopub.status.busy": "2023-11-15T16:36:08.722448Z",
-     "iopub.status.idle": "2023-11-15T16:36:08.739297Z",
-     "shell.execute_reply": "2023-11-15T16:36:08.738736Z"
+     "iopub.execute_input": "2023-11-15T21:41:56.882431Z",
+     "iopub.status.busy": "2023-11-15T21:41:56.882092Z",
+     "iopub.status.idle": "2023-11-15T21:41:56.899418Z",
+     "shell.execute_reply": "2023-11-15T21:41:56.898808Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<ParticleGroup with 50082 particles at 0x7fd1b8c11340>"
+       "<ParticleGroup with 50082 particles at 0x7fb454ebfc40>"
       ]
      },
      "execution_count": 6,
@@ -165,10 +165,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:08.741434Z",
-     "iopub.status.busy": "2023-11-15T16:36:08.741106Z",
-     "iopub.status.idle": "2023-11-15T16:36:09.650308Z",
-     "shell.execute_reply": "2023-11-15T16:36:09.649718Z"
+     "iopub.execute_input": "2023-11-15T21:41:56.901994Z",
+     "iopub.status.busy": "2023-11-15T21:41:56.901612Z",
+     "iopub.status.idle": "2023-11-15T21:41:57.847624Z",
+     "shell.execute_reply": "2023-11-15T21:41:57.847032Z"
     }
    },
    "outputs": [
@@ -197,10 +197,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:09.653461Z",
-     "iopub.status.busy": "2023-11-15T16:36:09.653075Z",
-     "iopub.status.idle": "2023-11-15T16:36:09.990673Z",
-     "shell.execute_reply": "2023-11-15T16:36:09.990041Z"
+     "iopub.execute_input": "2023-11-15T21:41:57.850863Z",
+     "iopub.status.busy": "2023-11-15T21:41:57.850474Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.181689Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.181124Z"
     }
    },
    "outputs": [
@@ -235,10 +235,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:09.993481Z",
-     "iopub.status.busy": "2023-11-15T16:36:09.993134Z",
-     "iopub.status.idle": "2023-11-15T16:36:09.997348Z",
-     "shell.execute_reply": "2023-11-15T16:36:09.996819Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.184869Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.184457Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.188793Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.188221Z"
     }
    },
    "outputs": [
@@ -270,10 +270,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:09.999392Z",
-     "iopub.status.busy": "2023-11-15T16:36:09.999211Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.003493Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.003050Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.191003Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.190821Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.195365Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.194838Z"
     }
    },
    "outputs": [
@@ -305,10 +305,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.005567Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.005359Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.009251Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.008765Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.197643Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.197460Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.201405Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.200936Z"
     }
    },
    "outputs": [
@@ -346,10 +346,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.011410Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.011237Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.016483Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.015915Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.203674Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.203335Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.208773Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.208199Z"
     }
    },
    "outputs": [
@@ -380,10 +380,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.018587Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.018412Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.025324Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.024767Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.211237Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.210874Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.218158Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.217655Z"
     }
    },
    "outputs": [
@@ -421,10 +421,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.027472Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.027153Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.036095Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.035545Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.220606Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.220230Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.230015Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.229483Z"
     }
    },
    "outputs": [
@@ -459,10 +459,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.038602Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.038155Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.043599Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.043145Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.232449Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.232088Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.237974Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.237395Z"
     }
    },
    "outputs": [
@@ -493,10 +493,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.045766Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.045406Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.054195Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.053733Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.240268Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.239936Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.248644Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.248092Z"
     }
    },
    "outputs": [
@@ -536,10 +536,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.056415Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.056087Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.114189Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.113594Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.251070Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.250746Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.312314Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.311720Z"
     }
    },
    "outputs": [
@@ -571,17 +571,17 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.116422Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.116089Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.231660Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.231047Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.314800Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.314439Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.430770Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.430139Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['norm_emit_y', 'charge', 'norm_emit_x', 'mean_t', 'twiss', 'ptp_t', 'twiss_alpha_y', 'twiss_beta_y', 'twiss_gamma_y', 'twiss_emit_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_norm_emit_y', 'twiss_alpha_x', 'twiss_beta_x', 'twiss_gamma_x', 'twiss_emit_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_norm_emit_x', 'current'])"
+       "dict_keys(['charge', 'mean_t', 'ptp_t', 'norm_emit_y', 'twiss', 'norm_emit_x', 'twiss_alpha_x', 'twiss_beta_x', 'twiss_gamma_x', 'twiss_emit_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_norm_emit_x', 'twiss_alpha_y', 'twiss_beta_y', 'twiss_gamma_y', 'twiss_emit_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_norm_emit_y', 'current'])"
       ]
      },
      "execution_count": 18,
@@ -619,10 +619,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.234142Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.233759Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.242898Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.242383Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.433306Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.432954Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.442857Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.442241Z"
     }
    },
    "outputs": [
@@ -659,10 +659,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.245265Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.244835Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.365907Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.365275Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.445241Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.444922Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.568481Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.567883Z"
     }
    },
    "outputs": [
@@ -706,10 +706,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.368492Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.368055Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.386604Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.386035Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.570815Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.570637Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.588588Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.588006Z"
     }
    },
    "outputs": [
@@ -751,17 +751,17 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.388935Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.388606Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.423676Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.423103Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.590912Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.590724Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.626013Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.625467Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<ParticleGroup with 100000 particles at 0x7fd170b50310>"
+       "<ParticleGroup with 100000 particles at 0x7fb40bcc9dc0>"
       ]
      },
      "execution_count": 22,
@@ -785,17 +785,17 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.426289Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.425846Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.436810Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.436279Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.628263Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.628082Z",
+     "iopub.status.idle": "2023-11-15T21:41:58.639302Z",
+     "shell.execute_reply": "2023-11-15T21:41:58.638724Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<ParticleGroup with 1000 particles at 0x7fd1a0f42040>"
+       "<ParticleGroup with 1000 particles at 0x7fb40bcde850>"
       ]
      },
      "execution_count": 23,
@@ -812,16 +812,16 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.439022Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.438643Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.959585Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.958574Z"
+     "iopub.execute_input": "2023-11-15T21:41:58.641512Z",
+     "iopub.status.busy": "2023-11-15T21:41:58.641323Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.187303Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.186674Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABXkAAALqCAYAAABgwFfPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzdd3iV9f3/8dcZ2QkJIQESEsgEwoYsEkDcigsVt1Xr17raUrVau4v8alurddW2WmqXVqt1CyoOVIYJGexNFmRCCASyxxm/PyKRkHUOJDk55Pm4Lq/rcO7P/bnf98kh5rz45P0x2O12uwAAAAAAAAAAbsno6gIAAAAAAAAAAKeOkBcAAAAAAAAA3BghLwAAAAAAAAC4MUJeAAAAAAAAAHBjhLwAAAAAAAAA4MYIeQEAAAAAAADAjRHyAgAAAAAAAIAbI+QFAAAAAAAAADdGyAsAAAAAAAAAboyQFwAAAAAAAADcGCEvAAAAAAAAALgxQl4AAAAAAAAAcGOEvAAAAAAAAADgxgh5AQAAAAAAAMCNEfICAAAAAAAAgBsj5AUAAAAAAAAAN2Z2dQFwH01NTdq2bZskKTQ0VGYzbx8AAAAAgGMsFosOHTokSZo6daq8vb1dXBEAnDlI6eCwbdu2KSUlxdVlAAAAAADcXHZ2tpKTk11dBgCcMWjXAAAAAAAAAABujJW8cFhoaGj74+zsbIWFhbmwGgAAAACAO6moqGj/7dATP18CAE4fIS8cdmIP3rCwMEVERLiwGgAAAACAu2KPFwDoW7RrAAAAAAAAAAA3RsgLAAAAAAAAAG6MkBcAAAAAAAAA3BghLwAAAAAAAAC4MTqdAwAADLClS5f2eHzJkiUDVAkAAACAMwEreQEAAAAAAADAjRHyAgAAAAAAAIAbI+QFAAAAAAAAADdGT14AAIAz0ED0/aW3MAAAADA4sJIXAAAAAAAAANwYIS8AAAAAAAAAuDFCXgAAAAAAAABwY/TkBQAAGILopwsAAACcOVjJCwAAAAAAAABujJAXAAAAAAAAANwYIS8AAAAAAAAAuDF68gIAAKBf0PcXAAAAGBis5AUAAAAAAAAAN0bICwAAAAAAAABujJAXAAAAAAAAANwYPXkBAAD6UG99aAEAAACgr7GSFwAAAAAAAADcGCt5u2GxWPTBBx9o7dq1KiwsVG1traxWa4/nGAwGrVq1aoAqBAAAZ6reVgMvWbJkgCoBAAAA4A4Iebvw5Zdf6vbbb1dxcXH7c3a7vdvxBoNBdrtdBoNhIMoDAAAAAAAAgHaEvCfZvHmzFixYoJaWFtntdnl7eys+Pl5BQUEyGuluAQAAAAAAAGBwIeQ9ySOPPKLm5mZ5eXnpqaee0u233y5vb29XlwUAAAAAAAAAXWJp6knWrVsng8Ggn//857r33nsHVcBrMBgc+u/ss892dakAAAAAAAAABggh70mampokSRdffLGLKwEAAAAAAACA3tGu4SRRUVHatWuXWltbXV1Kt+69915997vf7fa4n5/fAFYDAAAAAAAAwJUIeU9y5ZVXateuXVqzZo3S0tJcXU6XRo4cqSlTpri6DAAAAAAAAACDACHvSe677z7961//0h/+8Addf/31ioqKcnVJAAAAA27p0qWuLgEAAACAg+jJe5LQ0FB9+OGH8vHxUWpqql588UUdO3bM1WUBAAAAAAAAQJdYyduFadOmac2aNUpNTdXdd9+te+65RyEhIfL19e3xPIPBoIKCggGqEgAAAAAAAAAIebv01ltv6Y477lBtba3sdrvsdrsqKyt7Pc9gMAxAddIbb7yh//73vyouLpbZbNbo0aOVnp6ub3/72zrnnHMGpAYAAAAAAAAAgwMh70kyMzN1ww03yGq1SpLGjRunadOmKSgoSEbj4OhusXPnzg5/zs/PV35+vl566SVdeeWV+te//qXAwECn5y0tLe3xeEVFhdNzAgAAAAAAAOhfhLwnefTRR2W1WhUYGKhXX31VCxYscHVJ7Xx9fXXFFVfovPPO08SJE+Xv769Dhw5p9erVeuGFF3T48GG9++67WrhwoT799FN5eHg4NX9kZGQ/VQ4AAAAAAACgvxDynmTDhg0yGAxaunTpoAp4JamsrExBQUGdnr/gggu0ePFiLViwQJs2bdLq1av1/PPP6wc/+MHAFwkAAAAAAABgQBHynqS+vl6SNHfuXBdX0llXAe9xo0aN0ptvvqmEhAS1tLToueeeczrkLSkp6fF4RUWFUlJSnJoTAAAAAAAAQP8i5D1JdHS0duzYoYaGBleX4rSYmBhdcMEF+uCDD5Sfn6/y8nKFh4c7fH5EREQ/VgcAAAAAAACgPwyOncQGkauvvlp2u10ff/yxq0s5JZMmTWp/XFZW5sJKAAAAAAAAAAwEVvKe5MEHH9R///tfPfPMM1q4cKGSkpJcXZJT7Ha7q0sAAMCtLV26tMfjS5YsGaBKzny9vdYSrzcAAADgCFbyniQgIECrVq3SlClTdNZZZ+nnP/+5tm7dqqamJleX5pCdO3e2P3amVQMAAAAAAAAA98RK3pOYTKb2x3a7XY899pgee+wxh841GAyyWCz9VVqvCgsL9emnn0pq6887ZswYl9UCAMBg5MjKUQAAAABwN6zkPYndbm//7+Q/O/Jff1m+fHmPAfLBgwd1zTXXqLW1VZL0ve99r99qAQAAAAAAADB4sJL3JIO179vixYvV2tqqRYsWKS0tTVFRUfLx8VFVVZW+/PJLvfDCCzp8+LAkae7cuYS8AAAAAAAAwBBByHuSwRrySlJ5ebmee+45Pffcc92OWbRokV588UV5eXkNYGUAAAAAAAAAXIWQ1038+9//1urVq5WZmanCwkJVVVWppqZG/v7+ioyMVHp6um677TalpaW5ulQAAAAAAAAAA4iQ103Mnz9f8+fPd3UZAAAAAAAAAAYZNl7rA6+88opMJpPuvvtuV5cCAAAAAAAAYIhhJe9JiouLnRrf3Nys7du3y26367333tNf//rXfqoMAAAAAAAAADoj5D1JdHS0q0sAAADo0dKlS11dwoDp7V4H86a5AAAAwEAh5D2J3W4/pfO8vLz0+OOP93E1AAAAAAAAANAzQt6T/POf/+x1TEtLiyoqKvT5559rzZo1MplMeu2117Rw4cIBqBAAAAAAAAAAvkHIe5LbbrvN4bG/+tWv9O677+r666/XTTfdpMzMTE2bNq0fqwMAAAAAAACAjoyuLsDdXXnllfrud7+rxsbGIdUfDwAAAAAAAMDgQMjbB6644gpJ0po1a1xcCQAAAAAAAIChhpC3DwQHB0uSGhoaXFwJAAAAAAAAgKGGkLcPlJaWSpJiYmJcXAkAAAAAAACAoYaQtw+8+OKLkpzbtA0AAAAAAAAA+gIh72nIy8vTjTfeqPfff1833HCDHnzwQVeXBAAAAAAAAGCIMbu6gMHGkZYLdrtdlZWVampqkiQZDAatX79esbGxXY43GAwqKCjo0zoBAAAAAAAAQCLk7WTfvn1On2O323s8z2AwnHpBAABgUFm6dKmrSwAAAACADgh5T0JfXQAAAAAAAADuZEiHvBs2bFBiYmKH5/75z3+6qBoAAAAAAAAAcN6Q3ngtOTlZERERuvvuu7VixYr2HrsAAAAAAAAA4C6G9EpeSSovL9eLL76oF198Ud7e3jr33HN1+eWX67LLLlN4eLirywMAAEAPeuuRvGTJkgGqBAAAAHCdIb2St7S0VC+88IIuueQSeXt7q7GxUR988IHuvfdeRUZGKjExUY888og2bNjg6lIBAAAAAAAAoEtDOuQNDw/XXXfdpeXLl6uqqkrvvfee7rzzToWFhclut2vTpk369a9/rZSUFI0ZM6Z9bGNjo6tLBwAAAAAAAABJQzzkPZGPj48uv/xy/fWvf1VpaalycnL0q1/9SjNnzpTdbldFRYX+/ve/68orr1RISIguv/xyLVu2TOXl5a4uHQAAAAAAAMAQRsjbjeOtGnJzc2nrAAAAAAAAAGDQIuR1wIltHQ4fPqz333+/x7YOd999t7Zs2eLqsgEAAAAAAAAMAWZXF+BuvL29ddlll+myyy6TJG3YsEErVqzQ8uXLtWnTJlVUVOjFF1/UmDFjNH36dBdXCwAAAAAAAOBMR8h7mhITE5WYmKglS5aovLxcK1as0IoVK+Tr6+vq0gAAAAAAAAAMAYS8feh4W4e77rrL1aUAAAAAAAAAGCLoyQsAAAAAAAAAbmzIh7wfffSRZs2apVmzZunVV1916txXXnml/dzPPvusnyoEAAAAAAAAgO4N6ZDXbrfrgQce0JYtWzRixAjddNNNTp1/0003acSIEdq8ebMefPDBfqoSAAAAAAAAALo3pHvyfv7559q7d69MJpOeeeYZp883GAx69tlnNX36dG3fvl1ffvmlzj777D6vEwAAOGbp0qWuLgEAAAAABtyQXsn71ltvSZIuuOACTZ48+ZTmmDRpki666KIO8wEAAAAAAADAQBnSIW92drYMBoMuv/zy05rnsssuk91u1/r16/uoMgAAAAAAAABwzJAOeffv3y9JmjBhwmnNM378eEnSvn37TrckAAAAAAAAAHDKkO7Je+zYMUlScHDwac1z/PyamprTrgkAAACDS2+9npcsWTJAlQAAAABdG9IreYcNGyZJOnr06GnNc/z8gICA06wIAAAAAAAAAJwzpEPekSNHSpJ27tx5WvPs2rWrw3wAAAAAAAAAMFCGdMibkpIiu92u999//7Tmee+992QwGJScnNxHlQEAAAAAAACAY4Z0T94FCxbopZde0qeffqo1a9borLPOcnqONWvW6JNPPpHBYNCCBQv6oUoAAHBcb71RgVPB+woAAADubkiv5F20aJFiYmJkt9t13XXXac+ePU6dv3fvXl133XUyGAyKiorSNddc00+VAgAAAAAAAEDXhnTIazab9eSTT8pgMOjQoUNKSkrS008/rbq6uh7Pq6ur0zPPPKOkpCRVVlZKkp588kmZzUN6YTQAAAAAAAAAFxjyqeTChQv16KOP6uc//7kaGhr00EMPacmSJZo3b55mzZqlUaNGyc/PT/X19Tp48KA2btyotWvXqr6+Xna7XVLbr/hdeeWVrr0RAAAAAAAAAEPSkA95JemnP/2pIiIi9N3vflf19fWqq6vTypUrtXLlyi7HHw93fX199ac//Unf/va3B7BaAAAAAAAAAPjGkG7XcKJbbrlFe/fu1YMPPqjQ0FDZ7fZu/wsJCdFDDz2kvXv3EvACAAAAAAAAcClW8p4gLCxMTzzxhJ544gnt3LlTW7ZsUVVVlWpraxUQEKCQkBBNnz5dkyZNcnWpAACccZYuXerqEgAAAADALRHydmPSpEmEuQAAAAAAAAAGPUJeB1ksFlVXV0uShg8fLrOZlw4AAAAAAACA69GTtwc7duzQ4sWLlZCQIG9vb40ePVqjR4+Wt7e3EhIStHjxYm3fvt3VZQIAAAAAAAAYwgh5u2Cz2XT//fdrxowZ+stf/qI9e/bIZrO1b7xms9m0Z88e/eUvf9HMmTP1wAMPyGazubpsAAAAAAAAAEMQPQe6cMMNN+itt96S3W6XJE2ePFkpKSkaNWqU7Ha7KisrlZOTo+3bt8tqteqPf/yjysvL9frrr7u4cgAAAAAAAABDDSHvSV599VW9+eabMhgMmj59upYtW6bk5OQux+bm5uruu+/Wpk2b9Oabb+q1117TDTfcMMAVAwAAAAAAABjKaNdwkr/97W+SpPHjx2vdunXdBrySlJSUpDVr1mjChAmy2+3661//OlBlAgAAAAAAAIAkQt5Otm7dKoPBoB//+Mfy8/Prdbyfn59+/OMfS5K2bNnS3+UBAAAAAAAAQAeEvCdpaWmRJE2bNs3hc46PbW1t7ZeaAAAAAAAAAKA79OQ9ybhx47Rr1y4dO3bM4XNqamrazwUAAMDgsXTpUleXAAAAAPQ7VvKeZNGiRbLb7XrrrbccPuf4Rm1XXXVVP1YGAAAAAAAAAJ0Z7Ha73dVFDCbHjh1TYmKi9u/fr1deeUXXXXddj+PffPNN3XjjjRo3bpw2bNigwMDAAap04JWWlioyMlKSVFJSooiICBdXBAA4k7DiEmeyJUuWuLoEAHA5PlMCQP9hJe9JAgMD9dlnn2nWrFm68cYbdeWVV+rdd99VWVmZWltbZbFYVFZWpnfffVdXXXWVrr/+es2aNUurVq06owNeAAAAAAAAAIMTPXlPYjKZ2h/b7XYtX75cy5cv73a83W5Xbm6uYmJiuh1jMBhksVj6tE4AAAAAAAAAkAh5Ozm5e4Uj3SzoeAEAAAAAAADAVQh5T0K/NAAA+gc9dwEAAACgfxDynoSQFwAAAAAAAIA7YeM1AAAAAAAAAHBjhLwAAAAAAAAA4MZo1wAAwBDQWz9c2hUBAAAAgPtiJS8AAAAAAAAAuDFW8gIAAAD9rLfV9L1htT0AAAB6wkpeAAAAAAAAAHBjrOQFAAB94nRXKgLoniN/v1jtCwAAMHSxkhcAAAAAAAAA3BghLwAAAAAAAAC4MUJeAAAAAAAAAHBj9OQFAAC99vuk1ycw+PH3GAAAYOhiJS8AAAAAAAAAuDFW8gIA4OZ6W73nLtcAAAAAAJwaVvK6qeLiYj300ENKSEiQn5+fgoODlZKSoj/84Q9qaGhwdXkAAAAAAAAABggred3QBx98oJtvvlnHjh1rf66hoUE5OTnKycnRiy++qA8//FAxMTEurBIA4Cj6aAI4U/D9DAAAwDVYyetmtmzZouuuu07Hjh2Tv7+/fvOb3ygjI0OrVq3SnXfeKUnas2ePLr30UtXV1bm4WgAAAAAAAAD9jZW8bub+++9XQ0ODzGazPvnkE6WlpbUfO/fccxUfH6+HH35Yu3fv1lNPPaVf/epXLqwWAAAAgwWrbAEAAM5crOR1Izk5Ofryyy8lSXfccUeHgPe4Bx98UAkJCZKkZ555Rq2trQNZIgAAAAAAAIABxkpeN/Luu++2P7799tu7HGM0GnXrrbfqpz/9qaqrq/Xll1/qggsuGKAKAcC99LaqzREDsfKtL+oEAAwurKwGAAB9iZW8bmTt2rWSJD8/PyUmJnY7bv78+e2P161b1+91AQAAAAAAAHAdVvK6kV27dkmS4uLiZDZ3/6WbOHFip3McUVpa2uPxkpKS9scVFRUOz+sqTz31VI/Hf/jDHw5QJcDgc7p/P3o7f6Cc7t/jY8eOnXYNvX3vHKg6AOB0DcTPRn1xjb6YYzD8nNjb9/6++P8LvnG6P7sMls8Og+G9ezpO/BxpsVhcWAkAnHkMdrvd7uoi0Lumpib5+PhIki699FKtWLGix/H+/v6qr6/X7NmzlZmZ6dA1DAbDadcJAAAAAEBvsrOzlZyc7OoyAOCMQbsGN1FbW9v+2N/fv9fxfn5+kqS6urp+qwkAAAAAAACA69GuwU00NTW1P/b09Ox1vJeXlySpsbHR4Wuc2I6huxp2796tUaNGKTQ0tMeWERi6KioqlJKSIqntX+fDwsJcXBHgON6/cGe8f+HueA/DnfH+dYzFYtGhQ4ckSVOnTnVxNQBwZiGlcxPe3t7tj1taWnod39zcLEntLR4cERER0euYuLg4h+cDwsLCHHpfAYMR71+4M96/cHe8h+HOeP/2LCoqytUlAMAZiXYNbiIgIKD9sSMtGOrr6yU51toBAAAAAAAAgPsi5HUT3t7eCgkJkdT7TrvV1dXtIW9kZGS/1wYAAAAAAADAdQh53UhCQoIkKT8/XxaLpdtxu3fv7nQOAAAAAAAAgDMTIa8bmTt3rqS2VgwbNmzodtzq1avbH8+ZM6ff6wIAAAAAAADgOoS8buTKK69sf/zPf/6zyzE2m00vvfSSJCkoKEjnnHPOQJQGAAAAAAAAwEUIed1ISkqK5s2bJ0n6+9//rszMzE5jnnzySe3atUuSdN9998nDw2NAawQAAAAAAAAwsMyuLgDOefbZZzVnzhw1Njbqwgsv1M9+9jOdc845amxs1GuvvaZly5ZJksaPH68HH3zQxdUCAAAAAAAA6G8Gu91ud3URcM7y5cv1rW99SzU1NV0eHz9+vD744APFxcUNcGUAAAAAAAAABhohr5vav3+/nn32WX3wwQcqLS2Vp6en4uLidO211+r73/++fH19XV0iAAAAAAAAgAFAyAsAAAAAAAAAboyN1wAAAAAAAADAjRHyAgAAAAAAAIAbI+QFAAAAAAAAADdGyAsAAAAAAAAAboyQFwAAAAAAAADcGCEvAAAAAAAAALgxQl4AAAAAAAAAcGOEvAAAAAAAAADgxgh5AQAAAAAAAMCNEfICAAAAAAAAgBsj5AUAAAAAAAAAN0bICwAAAAAAAABuzOzqAuA+mpqatG3bNklSaGiozGbePgAAAAAAx1gsFh06dEiSNHXqVHl7e7u4otPH52QAzuqv74V894HDtm3bppSUFFeXAQAAAABwc9nZ2UpOTnZ1GaeNz8kATkdffi+kXQMAAAAAAAAAuDFW8sJhoaGh7Y+zs7MVFhbmwmoAAAAAAO6koqKifdXriZ8v3RmfkwE4q7++FxLywmEn9hYKCwtTRESEC6sBAAAAALirM6V3LZ+TAZyOvvxeSLsGAAAAAAAAAHBjhLwAAAAAAAAA4MYIeQEAAAAAAADAjRHyAgAAAAAAAIAbI+QFAAAAAAAAADdGyAsAAAAAAAAAboyQFwAAAAAAAADcGCEvAAAAAAAAALgxQt4BVlVVpccff1xz5szR6NGj5eXlpfDwcKWmpupHP/qRMjMze51j5cqVuvrqqxURESEvLy9FRETo6quv1sqVKwfgDgAAAAAAAAAMJmZXFzCUvPHGG7r33nt1+PDhDs9XVFSooqJC2dnZysvL07vvvtvl+Xa7Xffcc4+WLVvW4fmysjK98847euedd3TXXXfphRdekMFg6K/bAAAAAAAAADCIEPIOkJdeekm33367bDabRo4cqXvvvVdz585VcHCwDhw4oIKCAi1fvlweHh7dzvGLX/yiPeCdOXOmHn74YcXGxqqgoECPP/64Nm3apGXLlik0NFSPPvroQN0aAAAAAAAAABcy2O12u6uLONPt2rVLM2fOVHNzs+bNm6fly5crMDCwy7EtLS3y9PTs9Hx+fr4SEhJksViUlJSkNWvWyMfHp/14Q0OD5s+fr9zcXJnNZu3evVuxsbF9eh+lpaWKjIyUJJWUlCgiIqJP5wcAAAAAnLnOxM+UZ+I9Aehf/fV9g568A2Dx4sVqbm5WSEiI3n777W4DXkldBryS9PTTT8tisUiSnnvuuQ4BryT5+vrqueeekyRZLBY988wzfVM8AAAAAAAAgEGNkLef7d69W6tWrZIkff/731dISIjTc9jtdr333nuSpIkTJ2r27Nldjps9e7YmTJggSXr33XfFIm0AAAAAAADgzEdP3n72xhtvtD++9tpr2x9XV1erqqpKwcHBGjFiRI9zFBUVqaysTJI0f/78HsfOnz9fe/bsUWlpqfbt26fo6OjTqB4AAAAAMFjV1zUpN7tQ2Rn52rxhnzw8zUpKjVFqerymzRwrL6/u93wBAJxZCHn72fr16yVJgYGBSkhI0CuvvKLHH39cW7dubR8THR2t2267TQ8++KD8/f07zbFr1672xxMnTuzxeice37Vrl1Mhb2lpaY/HKyoqHJ4LAAAAANA/Ksqr9ezjH2nzhn2yWm0djpWVHNF7b+bKy8usOfMnaPGDC+Qf4O2iSt0fn5MBuAtC3n62c+dOSVJUVJQWL16sP//5z53GFBUV6ZFHHtGbb76pjz/+WOHh4R2Ol5SUtD/urRnz8cbNJ5/niBPPBQAAAAAMTm+9lqUN2YU9jmlutujzT3ZoyrSxuvzqxAGq7MzD52QA7oKevP3syJEjktp68/75z39WUFCQXnjhBVVWVqqpqUk5OTlasGCBJGn79u269tprZbN1/JfY2tra9sddrfQ9kZ+fX/vjurq6vroNAAAAAMAgUVfb5PjYOsfHAgDcFyt5+1l9fb0kqbm5WSaTSR999FGHjdOSkpK0YsUKXXbZZfroo4+UkZGht99+W9dcc037mKamb/6n7Onp2eP1vLy82h83NjY6VWtvK38rKiqUkpLi1JwAAAAAALgrZz4nP/XUUwoMDOw0ZsmSJf1SGwCciJC3n3l7e7cHvddee22HgPc4o9GoJ554Qh999JEk6b///W+HkNfb+5v+SS0tLT1er7m5uf2xj4+PU7X21goCAAAAAOB6keNCnBjb80bf6BmfkwG4C0LefhYQENAe8h5vy9CVyZMna8yYMSorK1NOTk6nOY7rrQXD8WtJvbd2AAAAAAC4nxu+la7hw3311Zq92rxhn1paLB2O+/l7KSk1VuecP1npZ413UZUAgIFEyNvPIiMjdeDAAUmObZpWVlamysrKDs+feF5vO3ue+KskNIgHAAAAgDOPyWzUJQtn6ZKFs9TU1KotG/drU26RPD3NSkqN0eSpkTKZ2YIHAIYSQt5+Nnny5PaVuVartcexx4+bzR2/LJMmTWp/vHv37h7nOPF4QkKCU7UCAAAAANyLt7eHUtPjlJoe5+pShrwf/vCHtHcA4DL8014/O+uss9ofFxQU9Di2sLBQkjRmzJgOz0dHRys8PFyStHr16h7nWLNmTfscUVFRzpYLAAAAAAAAwM0Q8vazK664Qh4eHpKkt99+u9txq1ev1uHDhyVJ8+bN63DMYDBo4cKFktpW6q5fv77LOdavX9++knfhwoUyGAynXT8AAAAAAACAwY2Qt5+NGDFC3/nOdyRJn376qV577bVOY2pra3X//fe3//nuu+/uNOb+++9vb+OwePFiNTY2djje2NioxYsXS2pr93DifAAAAAAAAADOXIS8A2Dp0qUaO3asJOmWW27R4sWL9cUXX2jDhg3617/+pZSUFG3evFmSdO+99yo5ObnTHOPHj9dDDz0kScrNzdWcOXP0+uuvKzc3V6+//rrmzJmj3NxcSdKPfvQjxcfHD8zNAQAAAAAAAHApg91ut7u6iKFg165duuKKK5Sfn9/tmP/7v//TCy+80N7e4WQ2m0133nmn/vGPf3Q7xx133KFly5bJaOz7/L60tFSRkZGSpJKSEhrKAwAAAAAcdiZ+pjwT7wlA/+qv7xus5B0gCQkJ2rx5s5544gmlpqYqODhYnp6eioiI0PXXX6/PP/9cf//737sNeCXJaDTq73//uz744AMtXLhQ4eHh8vT0VHh4uBYuXKgPP/xQL774Yr8EvAAAAAAAAAAGJ7OrCxhK/Pz89NBDD7W3XThVl1xyiS655JI+qgoAAAAAAACAO2PJJwAAAAAAAAC4MUJeAAAAAAAAAHBjtGsAAAAAAJzRrFab9uwsV1ZGnnZsK1VgkK9S0uKUkhar4cH+ri4PAIDTRsgLAAAAADgjHag4qn8t+1LZmQWqrWnscGzN57skSeMnhuniy2bosqtmyWAwuKJMAABOGyEvAAAAAOCM9OenP9b6dXk9jtm7u0J7d1cofuJoTZw0ZoAqw5noqaeeUmBgoFPnLFmypJ+qATDU0JMXAAAAg47NZteBiqNqbm51dSkA3NjhqjrHxx5yfCwAAIMNK3kBAAAwKNTVNmlDdqGyMvKUk1mgo0cb5Olp1ozEKKWmxyklPU6jw4JcXSYAAAAw6BDyAgAAwOXe/O96/e0vq2Sz2js839JiUXZmvrIz86Unpdlz47X0setkNNI3E0DvQkIClKcKx8aGBvRzNQAA9B9CXgAAALjcW69ldQp4u7J+XZ5K9ldpXHToAFQFwN19/8GLFBjko6yMfFUfqe903Gg0aOLkMbr4sukanxDmggoBAOgbhLwAAABwudZWq+NjWxwfC2BoGzkqUA/+7HLZbHbl7z2g7Mx8bd9SoqDhvkpJi1NSaoyGBfq6ukycIX74wx8qIiLC1WUAGKIIeQEAAOByTjVfoFMDACcZjQaNnxim8RNZrQsAODMZXV0AAAAAkJga49C4kaOGaWxUSD9XAwAAALgXVvICAADA5X70iyt07oVTlJ2Zr6yMfB0oP9p+zD/AW0mpMUpJi1P6vPHy9ORHWAAAAOBE/IQMAAAAlzOZjEpJi1NKWpy+94BdJfsPa8+uco0OC9KkKREymdt+Aa2lxaIN2YXKzszXxpwiGU1GJSZHKzU9TpOnRcpsNrn4ToaWlmaLtm7er+zMAm3KKZLJbFRiSoxS0uM0eWoEXw8AAIABQsgLAACAQcVgMGhsVEiHtgzVR+r0l2c+0fqv8tTU2NphfGHeQb3x6nr5+nlpzvwJuvcHFyhgmM9Alz2kHDnc9vXI+ipPTU0dvx4FeQf1v1cy5efvpbnzJ+qeH1wg/wBvF1UKAAAwNNCTFwAAAIPee2/m6svPdnYKeE/UUN+sTz/cqpUrtgxgZUPTO//L1upVOzsFvCeqr2vWxx9s0Scfbh3AygAAAIYmQl4AAAAMetVH6h0eW1PT0I+VuF71kXpt21ysmppGl9VQc8zxa5/pXw+cWex2u4oKKrVnZ7msFpurywEAwGG0awAAAMCgdKiyRtkZ+e2bsQ1VNptdebsrlJWRp+zMAu3ZVS5JMhoNSpgyRqnp8UpJi1NM3EgZDAYXVwu4n8aGFm3MKWr/O3a4qlbS15s+ft1jOnl2rIKG+7m4Ugx2Tz31lAIDAwfkWkuWLBmQ6wBwH4S8AAAAGHRWrtispx/7QDab3elzw8cM74eKXOeXP3pd2ZmdQ26bza4dW0u1Y2up/vHCF7rgkml6+BdXDEhNYU68xmfa1wNnlv37qvTgd1/SsaOdV5zX1Tbpy1U79eWqnfLwNGnJb69VanqcC6oEAKB3tGsAAADAoLNq5TanAl4PD5MSU2L0w59eqosundF/hQ2w6iP1XQa8XVm1cpus1oH59fJrbkzV9x64SNNnjZPJ1PkjhYenSUmpMXrwZ5fp/IunDUhNwKnIzsjrMuA9WWuLVV9+tmMAKgIA4NSwkhcAAACDjtWJgPfq61P17bvmy8fHsx8rcg2bzfHQ9lRWPZ8qs9mkK69N1pXXJqu+rkkbc4q0MbdIJpNRSamxmj5r3Bn59cCZx2p1/O+NM38fAQAYaIS8AAAAGHRMRsd7y05ICDtjA0Wj0fFfvHNVO14/f2/NOydB885JcE0BwGkwmRz/i+PM30fgRPTPBTAQ+L8UAAAABp1zLpjsUGgZEhqgaTPH9X9BLhI03FezkqMdGnvOBZO7bJ0AoHvJs+MUEODd6ziz2cg/ZAAABjVW8gIAAGDQuWThLM1MjlZ2ZoGyMvK0ZcN+tbRYJEmhIwOUkh6vlLQ4zUyKOmNX8UqSwWDQb5+6Ubt2lCk7I1/ZmfkqyDv49TFpQkK4UtLilJIWp/EJYS6uFnA/UTGheunN72tDdqGyMvOVk1mgo9X1kiQfX0/NSo5WanqckmfHKSQ0wMXVAgDQPUJeAAAADEph4cO1cFGSFi5KUlNTq/buKlfAMB9FxYTK4KreBC5gMhk1ZVqkpkyL1P/dc46qDtWoeP9hRceM1PBgP1eXB7g9/wBvzT9vkuafN0k2m10FeQfU0mLR+Inh8vAwubo8AAAcQsgLAACAQc/b2+OMbsvgjJDQYQoJHebqMoAzktFoUPwEVsXj1Pzwhz9URESEq8sAMETRtAsAAAAAAAAA3BgreQEAADCgrFabdu8sU1VlrRImj9HI0YFOz1Ff16Stm4tlNBo1bcZY+fieuX15T1d9XZO2bCqWycRrBQAAcKYi5AUAAEC/q69rUua6PGVn5is3q1C1NY3tx6JiQpWSFqfZc+I1ZXpkt/12S0uOKGPNHmVn5mv7lhJZrTZJkoeHSVNnjFVqepzSz5qg0WFBA3FLg1pp8WFlrN2rrIw87dha2uG1mjZznFLS4pR+1nheKwAAgDOEwW63211dBNxDaWmpIiMjJUklJSX0GgIAAA6pr2/WHTe+oMNVtb2OXXR9qu6574JOz2eu26tHfvqGbNaef3Q1m4167NmbNX0I9+/NWLNHS3/2pmy2nl8rDw+Tfv/szZo6Y+wAVQZgqDsTP1OeeE8PPPCAAgOd/+2U7ixZsqTP5gIwePTX90J68gIAAKBf5e894FDAK0mZX+3t8vmc9QW9BrySZLHYtCGr0Kn6zjTZ6wt6DXglqbXVqg05Q/u1AgAAOFPQrgEAAAD9y5lfHOtuqFNTdBzc2mrVti3Fyv4qX/v3HVJM3Cilpsdp0tQImc0mh+Y8Wl2v3KxC5a4vUGNji6bPGqeU9HhFRAY7XthpammxaPuWYmVnFKh4f5Vi4kYpJS1Wk6dGymQ+Ye1GX7zeGBDVR+qVm1Wg3KwCNTdZNH3WOKWmxyk8YuDeVwAA4MxAyAsAAIB+ZfZwLEjtaawzc3h8Hdxu31KsN1/L0sacIjU2tLQfz80q1P9eyZSfv5eSUmN13c1pGj8xrNM8VqtNy9/ZoM8/3q7dO8s6ZKcZa/fq+Wc/VXjEcM05a4Juum2u/AO8Ha7RGds2F+ut17K0MbfjfeSsL9Dr/8mQf4C3ElNidP230hQ/IaxPXm/0H6vVpvfezNUXn27Xnl3lHd5XX63Zo78884kixgYrfd4E3fTtufLz83JdsQAAwG0Q8gIAAKBfjZ8YrsnTIrRja2mP44wmgxYuSury2HkXTtHnn2zXsaMNPc4xIiRAZ507SS3NFv3swdc6hKInq69r1upVO7V103699v79Mho7bvi2fl2e/vzUxz1er7y0Wm+8ul4tLRZ9/4cX9zj2VDQ3t+pnD/5XTY2t3Y6pq23S6lU7tW1LsV577z6dd9FUffHpDtUca+z2HEkKCQ3QvHMS+rpk9GLdl7v1/LOf9DimtPiI/vdKpmw2u+5efP4AVQZgsFm6dGm3x+jXC+BkhLwAAADoVx4eJj39/G3aX1Sl7Mx8ZWfma/uWElmtNgUG+Sp5dqxS0uKUmBqjYcN8upxjwqRwvfruD9rbFWRn5quk+LAkKSo6VCnpcUpJj9Pkr1swVB+p6zHgPVH1kXpZrTYZjR1XtR6qrHH4Hg8ddHysM+pqm3oMeE90pKpONptdCZPH6L/v3adtm4vbX+/S4iOSpKiYUKWmxyslLdapdhXoO5VOvFeceQ8CAIChjZAXAAAA/c5gMCgqJlRRMaG67uY01dc1qfpIvcLGDJfJ5NhewJ6eZs1KjtGs5Bjdc98Fqjx4TEajUSGhAf1cvfvx9DQrMSVGiSkxuve+C3mtAAAAznCEvAAAABhwfv7e8vM/vR62I0cFdnvM189Lnp5mtbRYep1nWKBPl0Hz8BF+DtcyfIS/w2Od4efvLQ9Pk1pbrL2ODQry7dRy4rieXisMrGBn3lfBjo8FAABDm2PLJgAAAAA34uXloV/9ZpGmzRwro6nr4NNsNmpWcrR++eiiLsPR9HkTdOt3zlLE2OBurxMY5KuLLp2uW+84q89qP5G3t4d++egiTZ3R/X14eJg0Kzlav3j0ahkMXY/B4DHvnAR96//mKXzM8G7HBA3304LLZ+im2+YOYGUAAMCdGez2E/dzBbpXWlqqyMhISVJJSYkiIiJcXBEAAEDv6mqbtCGnUNkZ+SreV6WYuFFKTY/TjMQo+fp5OTRHWekR5WQWKHt9vpoaWjQ9MUopaXEaPzHM4XYTp+vk+4iNH6WUtDjNTIqWj6/ngNTgqKpDNcrNKlRTY6umzxqnqJhQAugulJYcUXZGvnKzCtTU1KqZX7+v4ieGdbsqG3BnZ+JnyhPv6YEHHlBgYOffnGCTNAAn6q/vhbRrAAAAwBnNP8Bb88+dpPnnTjrlOcZEBGvMtcG68trkPqzMOX1xH/2pMP+gvvxsp7Iy81WYd7DDsZGjhiklPU7p8yYoKTWGwPdrEZHBirg+RVdfn+LqUgAAgJsj5AUAAABwWnZuK9UD9/5bNlvXvyRYebBGK97ZqBXvbNQ9P7hAi25IHeAKAQAAzmz05AUAAABwWnbvLOs24D3Zjq0l/VwNAADA0MNKXgAAAGAQqiivVnZmgbIz8lVVVatJU8YoNT1e02eNk4/P4OrBCwCQfvjDH54RfYYBuCdCXhd7+OGH9cQTT7T/+YsvvtDZZ5/d4zkrV67UsmXLlJ2drUOHDik0NFQpKSm66667dPHFF/dzxQAAAOgvdrtd7/wvWyve3aiS/Yc7HCvMO6gV72yUh6dJMxKjdMc95yo2fpSLKu3Iy8vD8bHejo8FAACAY2jX4EJbtmzR008/7fB4u92uu+++WwsWLNA777yjsrIytbS0qKysTO+8844WLFigu+++W3a7Y78qBwAAgMGlqKBSzz/7aaeA90StLVblZBboT0+tHMDKepZ+1gRNmR7Z67jRYYFaeE3SAFQEAAAwtLCS10VsNpvuvPNOWSwWjRw5UpWVlb2e84tf/ELLli2TJM2cOVMPP/ywYmNjVVBQoMcff1ybNm3SsmXLFBoaqkcffbS/bwEAAGDIqTxwTI2NLYocFyKj0dDn8x892uD42Or6Lp+3Wm0q3lelgGE+CgkN6KvSejQ82E9PP3+bKg8cU/b6fGVnFGhTbpFaWiyaNDVCKWlxSk2PU3TsSBkMff+6AQAADHWEvC7yxz/+UTk5OZo4caKuuuoq/e53v+txfH5+vh5//HFJUlJSktasWSMfHx9JUnJysq644grNnz9fubm5+v3vf6/bb79dsbGx/X4fAAAAZ7LWVqt2bC1RVka+sjPzVbyvSlJbqJk8O1Yp6XFKSomRn7+3S+usOdagnPUFys7MV876QtXWNEqSYuJHKSUtVqnp8UqYNEYmc//+It/I0YG67MpEXXZloiwWq2xWuzy9+MgBYGh46qmnFBgY6NQ5S5Ys6adqAAw1/MTlAiUlJfrlL38pSXr++ef15Zdf9nrO008/LYvFIkl67rnn2gPe43x9ffXcc88pLS1NFotFzzzzjJ577rk+rx0AAGCoqDxwTA/c+29VHqzpdKz6SL0++XCrPvlwq7y8zPrlo4uUOif+tK85arTj4cDIUW1jP1u5TU/+drksFlunMYV5B1WYd1CvvZShyLEj9Mxfb9OwQN/TrtMRZrOJTxsAAAADhJ68LvDd735XdXV1uu2223rdZE1q68X73nvvSZImTpyo2bNndzlu9uzZmjBhgiTp3XffpTcvAADAacjKzO8y4D1Zc7NFH3+4tU+uOSYiWL9+4nqlzR0v7242KBsdHqQrr0nWw7+8QpK0csXmLgPek5UUH9aWjfv7pE4AAAAMLvzb+gD73//+pxUrVig4OFhPPPGEQ+cUFRWprKxMkjR//vwex86fP1979uxRaWmp9u3bp+jo6NOuGQAAYCiyOhCcfjPW2mfXnT0nXrPnxKul2aKtm/crKyNfhw/VatLUCKWmxytibHCHvrYWJ67tSBgMAAAA90PIO4COHj2q++67T5L0+9//XqGhoQ6dt2vXrvbHEydO7HHsicd37dpFyAsAAHCKDE5srObMWEd5epmVlBqrpNSe91kwOrGRWX9sFgcA6Bl9dwEMBELeAfTwww/rwIEDSk9P1x133OHweSUlJe2PIyIiehwbGRnZ5XmOKC0t7fF4RUWFU/MBAAC4sxmJUfLz91J9XXOP4wwGac5ZEwaoqs7mnj1R27b0/nNfUJCvJk/r+WdJAEBHfE4G4C4IeQfIunXr9OKLL8psNuuFF17o8Ct2vamtrW1/7O/v3+NYPz+/9sd1dXVO1XhiQAwAADDUjYsK0b9e/66yM/OVnVmg3KyC9sDXbDZq6oyxSkmL0+w58YoYO8JldV59fapmJkUr66s8ZWfma8f2UtmsbXszDAv0UfLsWKWkxSklLU7+Ad4uqxMA3BGfkwG4C0LeAdDS0qK77rpLdrtdDzzwgKZOnerU+U1NTe2PPT09exzr5eXV/rixsdG5QgEAANxMc3Ortmzcr+zMfOWsL1BTY6umzRyr1PR4JaXGKGi4X++T9CBouJ8uvGS6LrxkuiwWq3bvKFNjY6smTY2Qn59X7xMMkOjYkYqOHakbbp2j2ppG7dxepmGBPho/MUwmE3stD0ZNTa3asmGfsjLztSGrUE3NrZoxa1z7e3dYoK+rSwQAAG6EkHcA/Pa3v9WuXbs0duzYU+rF4+39zYqLlpaWHsc2N3/z64Q+Pj5OXae39g4VFRVKSUlxak4AAID+0NjQoj/+4SOt/WKXmpstHY59+dlOffnZThkM0uRpkbr/x5dqXFTIaV/TbDZpyvSxpz1PfwsY5qPU9DhXl4Fu1Nc16dknPtJXq/eopaXje/fzT3bo8092yGg0aOqMsbr/x5cqIjLYRZUCkPrmc/LSpUu7PUa/XgB9hZC3n+3evVu/+93vJEnPPfdch3YKjgoICGh/3FsLhvr6+vbHvbV2OFlv/X4BAAAGi3Wrd+uzldt6HGO3S9u3lOj1l7/Sw79cOECVAT1b/fkuffHpjh7H2Gx2bdm4X2+8kqkHfnLpAFUGoCt8TgbgLgh5+9nTTz+tlpYWxcTEqKGhQa+99lqnMdu3b29//Pnnn+vAgQOSpMsvv1x+fn4d/qfSW9P3E/+Vkd5BAABgMLLb7SoqqNShylpNnBSuwCDnfy29vr7nzdBOVFfr+Ni+duRwnfJ2V2h0eJDGRoU4tS8Dzky9beTXYawT73MAADC0EfL2s+PtEwoLC3XjjTf2Ov7Xv/51++OioiL5+flp0qRJ7c/t3r27x/NPPJ6QkOBsuQAAAP2isbFFG7OLvt7ELF9Vh9o2ljUYpImTxiglPU6p6XGKnxDm4kpPj91u164dZe2bteXt/mbX9dFhgUpOi1NqWpxmJkXL04sfxQEAANA3+MnSDURHRys8PFzl5eVavXp1j2PXrFkjSRozZoyioqIGoDoAAICeNTa06K5b/qoDFcc6HbPbpV07yrRrR5n+/bfVuuHWdN1xz7m9zulMj91x0affj9dRf/zDR1rxzsYujx2oOKblb2/Q8rc3aFx0iF74950ym00DVhsGB2fej33RSxoAAAwNbLXbz/71r3/Jbrf3+N+Jjda/+OKL9uePh7QGg0ELF7b1kdu9e7fWr1/f5bXWr1/fvpJ34cKF/DogAAAYFPL2VHQZ8HZl7Rc9/9bScTOTovXbp27U+RdP7bLdg6enWclpsbrv4QW67c75TtV7Ohytf39RlUr2H+7najAYpaTF6dEnrtd5F07RsMDOGyV7eZmVmh6nB35yqW66ba4LKgQAAO6Ilbxu4v7779ff/vY3WSwWLV68WGvWrJGPzzc/FDY2Nmrx4sWSJLPZrPvvv99FlQIAAHRkt9sdH2vrPPZgxVFlZxZo25ZiBQzzUcrsWE1PjFLy7Fglz46V1WpT3p4K5WQWqLGxRdNnRWn6rHHy9vbos3toqG/Wptx9yskqUFNDi6bNGqeUtDiFhAZ0GGfrov7uODMWZ5bUOfFKnRMvq9WmPbvKlbu+QM3NFs1IjNK0mWPl5dV3710AADA0EPK6ifHjx+uhhx7SY489ptzcXM2ZM0c//vGPFRsbq4KCAv3+97/Xpk2bJEk/+tGPFB8f7+KKAQAA2phMjv/y2PGxdbVNev2VDK1fm6d9RYc6jHn/rVx5epo1PXGcLlwwTfPPm6SJk8Zo4qQxfVq33W7XJx9u1eefbNfWTftlsdjaj636pG3j3Nj4UZpz1gRdc9Ns+fh4ntK9YugymYyaNCVCk6ZE9D4YAACgB4S8buQ3v/mNKisr9Y9//EObNm3SDTfc0GnMHXfcoUcffdQF1QEAAHRt/MRwxY0frfy9B3odu+CKmZKkvzz7iT79cGu341paLMrJLFBOZoFCRg7TlGmRfVbvcVkZ+frDb5b3OKYg76AK8g6qrq5J9953oS69cqZe+ee6XueeOj1SY+m3CgAAgD5CyOtGjEaj/v73v2vRokVatmyZcnJyVFVVpZCQECUnJ+vuu+/WggULXF0mAABuq7m5VR4eZhmN9LXvS55eZv3p7/+nvbsrlJ2Zr+yMfO3dXSFJ8vH11KzkaKWkxSklLVYhocMkSQfKjzo8/4Hyo/0S8jpbgyR9+86zteCyGcrOLFB2Zr425Rapudkig0FKmByhlLRYpabHKyZ+VPv7zGKxStJpbcLW3NwqT09zt3sy9MU1AAAAMHgR8g4CjzzyiB555BGHx19yySW65JJL+q8gAACGCIvFqp3bStsDuaKCSgWP8Ffy7FilpMUpMSVafv7eri7zjGAyGZUweYwSJo/Rbd+ZryOH63S4qlZRMSPl4XFmBY+jwoJ0+dWJuvzqRDU3t2p/UZVGjQ5s3yDObrertPhIW+Cdma9tm4tlMBg0IzFKKeltYXdY+PAer2GxWLVja0nbezcjX/uKDik4xP/rsDxOM5OiVH24vv0aWzftl9FodOoaAAAAcB+EvAAAYEj69KOt+sszn6iutqnD80cO1+njD7bo4w+2yGQy6vyLp+rBn13W7QpJnJrgEf4KHuHf7fGgrwNRRwQ6MdYZzszb3VgvLw+NnxjW/uf9+6r06M/f6tRnWFJ7ICtJ4yeG6Ve/WaRRYUGdxn20fJNe+ONnaqhv7vD8kao6rVy+WSuXb+6mSluHa0xICNevfrNII0cH9n6DAAAAGNQIeQEAwJD08t/XdAp4T2a12vTxB1t0zY2zFRUTOkCVQZLu+O65slpt2phTpKam1i7HjBodqAsvmaZZSdH9UsOcsyboupvT9Pkn21V1qLbLMb6+nkpOi9O3/m+eQ3N+9N6mLgPek+3dXaFVn2zXTbfN7XTspRfXdAp4T8WeXeVa9cl23XjrnNOeCwBwapYuXdrncy5ZsqTP5wQw+BHyAgCAIam52eL42G5CRvSfMRHBWvr769TSbNGWTfuVnZGvrVuKNSzA++t2A3EaGxXSryusPb3MuvN75+k73z1XhfmVys7MV05mvhobWzV91jilpsdpyvSxTrWbaGp2/L3U3fuuL9+PvLcBAADODIS8AAAAGLQ8vcxKnh2r5NmxLqvBYDAoNn6UYuNHseoVAAAAg5LR1QUAAAC4QsKUMQ6N8w/wVuS4Ef1cDYaKSQ6+7yQpYUqEU8+fioTJjtcDAACAwYuVvAAAYEj62dKr9MWnO5Sdma/crMIOPU6NJoMmT41Uanqczr1winz9vFxYKc4kF14yXSNHBWrd6t3KysjXgfKjHY5Hjh2hlPQ4nXVOgiZN7TrM/dVvF7W9dzPytSG7UA0NLU7VEDluhFLS4nTWuQma1IeBMQAAAFzHYLfb7a4uAu6htLRUkZGRkqSSkhJFRPChAABwZrBYrNqxrVS7d5RpdFiQElNi5B/g7eqycIaz2+0q2X9YG3OLZDIalZgSrfCIYKfmaG216ru3v6h9hb1v5iZJ373/Ql11XcqplAsAp+1M/Ex5Jt4TgP7VX983WMkLAACGPLPZpOkzx2n6zHGuLgVDiMFg0NioEI2NCjnlOTw8TBoe7OdwyDtyVOApXwsAAACDFz15AQAAADd29fWpDq08nzo9UjOSovq/IAAAAAw4VvICAAAMcceONqi5qVWho4bJYDC4upwu2e12HTxwTH5+XgoY5tPlGHe4D0c4ex+z58Trfyse0I6tJcrOzFdWRr6K91XJ09Os6YnjlJIWp9S0OIWNGd5nNdrtdh2oOCp/f+9uvx4AAAAYOIS8AAAAQ4zNZlfe7gplZ+YrOzNfe3aVy26XRocHtQWC6XGaPmucvLw8XFpnY2OLNuXua6/z0MEaGY0GTZw8RilpcUqeHSubzabsjHxlZxZoz65ySW33kZoep5S0wXEfvbHZ7Nq7u1zZGW0B7d7dFZKksPAgpaTHKTU9XtNmju3xPjw8TJqRGKUZiVG66/vn69jRBnn7ePTpvTc2tGhTbtE3X4/KWhmNBiVMGaPU9HilpMUpJm6kWwfsAHA6nnrqKQUGdm6Ls2TJEhdUA2CoYeM1OIyG8gAAuL/DVbX60eL/qGT/4R7H+fp66hePLlLy7NgBqqyjtV/u1uP/7z01NbWe1jy+fl765aNXKynVNffRm6pDNfrR4v+otPhIj+N8/by05LeLNCs5ZoAq62j15zv1xK/fV3OzpcdxceNH64nnvsXGhQC6dCZ+pjzxnh544AFCXgC96q/vhfTkBQAAGEKyMvJ7DXglqaGhRR++v2kAKurainc2nHbAK0kN9c368P3Np19QP8lcl9drwCu13cdHLryP5W9v6DXglaT8vQe0eeO+/i8IAAAAHdCuAQAAYAixtFodHnuoskbNza193u7gYHWtvtqxTwerazUtOkyJ4yPl7dnxx9JWJ+rsTVU/3UdfcObr0Wrpu9fEWc58PZy5JwAAAPQNQl4AAIAhxJl+qXt2lmvRxU9qRmKUUtPjdeGl0045KC06cEQr1u/Uuu1Fyiur6nDM28OspAmRmjclWpenTZa3p1l92dZ1146yPruPvubM18OVvW6duTQ9eQGgo6VLl/b5nLSAAHAyQl4AAIAhZOqMsfL28VBTo2OtEJqbLcr6ekOwTRv26Ve/WeT0NQ/X1OvG3/xHLd2sRG1qtWjd9iKt216k3L0l+v2dlyk1PV5bNxU7fa3unHgfWzbt1y9+fXWfzX06ps0cK29vD4daU6Skua6vcGp6vHZsLe11nH+AtxKmjBmAigAAAHAiQl4AAIAhJComVP987bvKWLNH2Zn52phT5PCv4ufvqTilaxYdONJtwHuyXSWVkqTrbk7TtJljtX5dnrIz85W350D7GG8fD81KitbkqRGy2e3atrlEG3OLHG4TkHeK99EfYuJG6Z+v36uv1uxVdka+Nm/Yp5aWtt63BoM0YdIYpabHKW3ueMXGj3JZnTfeOkczEqO0ft1eZWXkqyDvYPsxbx8PJSbHKOXrOocH+7msTgAAgKGKkBcAAMAJVotNu3aWKSsjT7nrC9VQ36xps8YpJS1OicnR8vXzcnWJkqT6uiblZhcqOyNf2zYXy3+Yj1JmxyolPU4TEsJ16cJZiokbKW8fT61etfOUrlFX26QNOd1fw2Ryfo/fqmP1+t/qLZo7JVoTJ43RxElj9O27zlbVoVrt2FqigGHemjJ9rDxP6OF7wy1STma+fvbga6d0H64WEjpMCxclaeGiJDU1tWrb5mI1NrRo2syxCho+eALThMljlDB5jG6/+xxVHarR9i0lGhbkqynTIjt8PXpTWnJE2Rn5ysrIU0nxYcXGjVJKepxS0+I0cnSgamsatSG7UNmZ+dq2pUSBgb5KTotVanqcxk8Ml9FIOwgAAICTEfICAAA4oLm5Vcv+tEpffLJdtbVNHY6Vl1Vr5fLNMpuNmpEYpe/98GJFRAa7pM79RYf056c/1tZNxbJabScUeVR5uyv0yr/WydPTLLvsam1xboMsn68D7H2FX19j837ZrPYurxEY5KvzL5qqO757rny9PB2+RlOLRY+99rkkKSYsWHdfmqYLEscrJDRA88+b1O15w4J8Hb6Gr+/gCOK74u3toeTZrmvL4KiQ0GE6+/zJTp3z0fJNeu3lDJWXVnd4/tDBGq3/Kk9S2/03t1hkt33zvjpQflR7dpXrP/9Yq6AgX11wyTT93z3nyGw2nf6NAICb6o8+vwONvsJA33J+eQUAAMAQtH5dnt5/K7dTwHsii8Wm3KxC/ecfawawso7+9bfV2pS7r2PAe5KWFovTAW/oqGG6/a6zv77Gl9q8YV/HgPckx4426K3Xs7Qxu1ATI0fq6rlT5eXhXChXWHFE/+8/n8pq6/5ejoufEKZLF87sdUXpyFHD9O075ztVB05ffX2znnn8w04B78mamlo7BLwnO3q0QW+8ul5bN+3v6xIBAADcGit5AQAAHFBT0+jw2KPVDad0DavFpvy9B2QwGhQbP+qU2h3UOlFnbwKGeeu6m9OVmh6nqJhQGQwGp69x9GiDjEaDfnHz+Xrw2vnasLdUn23cq/czHWsRUd/UIqvVJpOx42tx/LUymgyKiWt7re7/8aW6574LtWXjfmVl5CknM1+HKms1aWqEUtLiOt1Hb068Rmz86FNqE1BX26SC/IMKCwvSyNGBTp8vSeWlR1R1qFbxE8Lk4+v4qujBpLGhucd/FHBWbU33/9gCAAAwFBHyAgAA9LGNOYV6+Af/UUpanFLS4jQ2KqTbsceONihnfYGyM/OVu76gfaVwYJCvklJjlJIWp6TZsRo2zGegym83eWqkbrgl/bTm+PPTH2tTbpFmpcbIHOqjTUXl2phX5tQcl//3PzonJkbJwWFqLarXhqxCbcgq7PBaJc+ObXutUmOUmt4W6J6Ko9X1bV+PjHzlZheq7utrBAX5KumEawT08PUoKqhUdma+sjLytWNbSXu4GRUT2vaeSI/TlKmRMpm7DvFbWizatrlY2Zn5ys7MV2nxEUmSh4dJU2eMVWp62/sqYuyIU7pHAAAAnHkIeQEAABwwxokeu3a7tCl3nzbl7tNfn/tMt95xlm6546xO47Zs2q9fPPiamppaOx07drRBqz7erlUfb5ePr6d+/+zNSpg8pvc6I4K1ZWPf/Cr7mIiu73lMRLC2bip2aI7GhhZ99sl2vbWvQFZv51cm28x25R05opLdh/Tp2mYZu+gycexogz5buU2frdwmX19PPf7Hb2nCpHCnr7Upt0i//NHram62dDp29MRr+Hnp8T/erAkJna/xwrOf6q3Xs7qcf1/hIe0rPKT/vZKp+Ilheu5vt3dard3Y2KLv3v5ie7B7otZWqzbmFGljTpGef/ZT3X732brptrlO36crDAv0lX+Ad3tofrrCI4b3yTwAANfpqa8w/XoB59GTFwAAwAGzkqL1yO+u1byzJ8rXyV+Z/2zlti6f/2r1ni4D3pM1NrQoY+0eh6517/0X6o57ztGkqRFdthfw8/fS5KkRmjQlosv7MJoMmjo9Und9/3z93z3n9HyNKRFypPOBxdfodMBr9bCrebhddWMlGST/UmuXAe/JGhpalLlur1PXOm7d6j1dBrydrlHf3O01Vn3c9df6ZHm7K1S8r6rT83t3lXcZ8HZ9re0OjRsMPD3Nevr523T5VYkaOWpYl2PGRARr8tQIxY0f1eX7KiDAW2efP0m/fepGxU8I6+eKAQAA3AsreQEAABw0Z/4EzZk/Qa2tVi1/e4Oef/YTh86zdbNxWHfPdznWwX6m3t4euuHWObrh1jmqOdag3KxCbd20XwHDfJQ8O1aTT2gT0NDQog/e3aDVq3aqsbFVM5OidPX1qQof0/MqSR8fz/ZrHDvaoN8seUebcoocvpeuNI6wy2iRrF6SxV+ynZw/O9HO1ZGN2k52uKFBO1StyiRPmVrs8q2wyrvKJkM31y3Kr1RtTWN72wa73a59hYfU0Nji8DVtXWwwZu1h07GTHTvaoNLiw27TtiEqJlQ/+NEC2e0Xq3hflbIy8lW8r0qx8aOUkh7XYeX40ep65WYVatvmYg0L8lFqWpwSJkd02+ICAABgqCPkBQAAcJKHh0kTJjm+ktBo7DqY6u75LseanN/0a1igr869cIrOvXBK+3N2u11rv9ytVSu3aWNukRobvgkli/dVafnbG5QwZYzmnDVBCxcly9Or5x8XA4N8NW5cyGmHvC3DJXtPl3Li9k/epK079S0t+tfmTVpVVKAtBw60XT+2rYijCR4yNtsUtNOigP0WmZs7npuxdq+uueQpxcSPkoeHSVWHanXoYI3jRUpdrrQ2ObG527GjDbr9hucVHjFcKWlxuvr6FIWFD/42BgaDQeOiQzUuOrTbMUHD/XT+xVN1/sVTB7AyAAAA90XICwAAcArix4dpXHSI9hd1/pX7k52/oOugau78Cfro/U29tmzw9fVU+rwJp1TnyTbmFOn//ezNbo/bbHbt2FqqHVtLdfhQne6574Je55x79gStXLG52/swN9hkarL12LLB85jU3MOC1LpIkwKKLL22bPD181LavPG91ixJ/2/1F3pj545uj3sdsWv43u7bN9hsduXvOeDQtU42fmJYlxvyjU8IV+TYESopPuzwXOWl1Xr3jRyt/ypPL73xPRkc6aEBAMAAoLcuMHAIeQEAAE6Bp5dZL/z7Tm3bXKzszAJlZ+a391g1m42aNnOcUtLilJoe1+2v00+bOU6vvLNYOevbzs9ZX6jamkZJbStkk2fHKiUtTkmpMe1tAU5XWYlj/V4lqazUsbHTZ0XpP28vVm5WgbIy8pW7vkC1X2+wFRTkq+S0WM1KjZE5xFsbC8u1bsc+7TvQNreH2aTE+DGaMzlakWOHa0/NYX2xr1C55eWyfN12YfyIETonMUYp3wpT6/4G5WYWdHmNlLQ4JaY4/lrtO3q0x+Metc63fehJVEyoUtPjlZoep0lTIjptuia1tcL468t3aduWYmV/la/MdXtVXlbt0PwHyo/KZrPLdAqrvgEAAODeCHkBAABOkdls0sykaM1Mitbdi89XRVm1DlXWKG78aPn6eTk0x7BAX5130VSdd9FUWa025e89IKPRoJi4Ue0hYFNTq7K+ylNWZr5yMvN1qLJWCVPGKCUtTilpcYqKCVVh/kFlZ+QrO7NAu3eVafTooLbj6XGaPnOcamoalJ1ZoA/e29gvr0VgkK+mzxqn5maLmptbtSGrUEaTUVNnjtX0WVGalRit4BH+OntWvH54zXyVHjqqg0frlBA5Ur7ebQ14bXa7hlf6qsliUUNLq7ZXHlRYwDClR45VWmSkUsdEyCvBrAsvniar5evXymRQbPzo9tYHjY0tWv9V3tevRb6qDtVq0vHXKj1O0bEjXbbS9YV/3elQT1kPD5NmJUVrVlK0rr15tm644lnHr/Hsp0qdE6dpM8bp2LEG5WS2vSc25hbJZDIqMSVGKWmxSp4dq+HB/qdzO5KkQ5U1ysnMV1ZGvjZt2Nd+jdT0OCWlxmp4sN9pX2MgVB48ppyv/7Fm04Z9Mh9/rdzsPgAAwNBlsNvtTmxjgaGstLRUkZGRkqSSkhJFRES4uCIAAM5sNptdf3ziQ33y0Va1tnTfp8BoNHS5iddxBoN0Kj/xXXDJND38iyt6HVdeekSPLX1Pu3aU9Thu2syx+smSKxU6clinYyv27tGvV3+pQw313Z7vYzbrhinT9Iuz5ncKaq1Wm/74xEf6dGXPr1XoqGH6wUMLNHtOvO5e/p4+LSzodqz/PotGZTm+kVpP/Py99PbKh7rsw9uThvpmXX3xk7JanVtVbDAY1NuP+TNmjdOPl1ypkNAAp+aWpNKSI/r90ne1e2d5r9f4ySNXakSI89cYCKXFh/XY0ve0Z1f392EwtK1W/8mShYP2PgB3cSZ+pjzxnh544AEFBga6uKLBhXYNQGf99b2QlbwAAACDVEHeAX3w3qZex/UU8ErOB7weHibNSIrSzd+e69D4D97b1GvAK0lbNxXrkw+3djnv41+t7THglaRGi0X/3LxR35o2XdHDO24wlr/ngD58v/fX6tDBGv1r2ZeaPSdeD6bPUX1rq7LLSttbQ5yoPsKkmoMm+ZdYu+0FbDIZ5OFh7rGvcnTsSN1yx1lOB7xSW4/hB35yqV5/OcOpPr2OrOPYvHG/Plu5TTfcku50XSve3tBrwHviNa7/lvPXGAjvv72hx4BXavv7s3nDPn3+yXZde1PaAFUGAGeGpUuXurqEdgTOONMR8gIAALhI1aFabd9SLP8Ab02bMU6eXh1/NGtsOP1VpDazQa3+Rhkskke9VYYesr+YuFG67TtnaWZStHx8PR2+RmOj43U2dTHWZrOr+UCj/CstagkyqiXQ0LZ8shsNrZ0D1WPHGhyu4Xi940eE6D9XX6Oa5matK96vvyxfq70N1TK12OVbYZNvhVVeR2zqqpKomFDdfvc5mpkYJR9fT5WXHlFWRr6++GKnSouPKDp2pM4+J0GpaXEaOfqbVV1Vh2q0fUuJAob5aOr0sZ2+5l256NLpuujS6SovPaLszAK98MdPnV7Z252uvh6OcOZr7szYgebUffTB30cAAID+QsgLAAAwgHbvLFPm2r3KzixQ/t4D7c97e3toRmKUUtLjNOesCQoecer9Ult9jWodZlLLMJOsPqZvDljt8qizyrPWKs9jVhktHRPf5NmxSj9rwilf1xktzRZlZeQpKyNfOesLNOxwnY43cbD4GFQfZlJDuFENo01SNxuJHaqs0Vdr9ig7I1+bcotOuZZhXl66JH688qz5av6wwqFz9hUe0qv/Wqfdu8rkGe6n/CNHlbGjSFVqkMYatK/1kHblNGlrY7WifALUeKBeOesLVJh3sH0Ob28PzUyKVkp6rOacNaHXHrnhEcG68tpgvfTi6vZN5wAAAACJkBcAAGDAvPHqei3702ddHmtqatX6r/K0/qs8vfiXz/XCv76jsVEh8vQ0q6XF4vA1GkPNagjvZtM3k0GtgWa1BprVEGZX4N5GmVq+CXrjJ4x26n5O5by4CaNlt9v1wL3/1t7dXQeq5ka7AgstCiyUGkYZVXG2d/sxf09PjQsKUlFBpX5w5z97bJPQfb1hXT8/3rn7372rXFmWI7L6mro8fqC6Vh+8uUF+B7qusampVZnr9ipz3V69+JfP9dd/36lRYUG9Xjduwmhtyt3nVK09zdXf58WP7/r1Hgzix4/WSgfHxnXzvgEAABgMCHkBAAAGSHZmvkPjGuqbtX1riS5YME0v/PtOffrRVmVn5qvghFWgkuTlZda0mePk6+ulurpGbd1crGMBjv14ZzcZZPEzaVSgj1LS4nTWuQlKTIlx+p4k6eLLZmh4sL/Wfblb2Zn5qj7SsbduSGhAh2scra7vNuA9me9Bm2S1a2rYaJ0dFa1FkybJ39NTqzYXOxXwent7aEZSlFLS4nTRJdO7HLPgipkKDgnQ2i92KWd9Qaf7OJndrG4D3uM8a7vfBO5E9XXN2rm9zKGQ95HfXauPP9ii7Mx8bdm0v9NGc/ETRmtMZLCsVpu2bS7R0eqO9xE68uuvx3mTNCsp2qH6TnbZlbM0IiRA61bvVk5mQb9cYyBcfnWiQkIDtG7Nnu7vIz1e889N0MxBfB8AAACEvAAAwK1VlFcrO7NAm3KKJIOUmByjlLRYh8KyAefEDmibcos0feY4RY4bof+75xz93z3nqOpQjbIz8nXoUK0mTYnQtJlj5eXlIUmyWm3aunm/fvzvj1TV2uzQNb5913zdfsVsGb7uf1tf16SNOUXKzszXocpaJUwZo9T0OI2fGC6j0SCr1abdO8uU9VW+9uwqV1h4kFLS4jQzKVqz58Rr9px4FR+s1vufbdH6jHwZjQbNmROvyy+YrvAR3/SldXYjuN+de74um5QgP8+2PsEVZdXasnGfw+cHjxmmn/zmak2PG9Pj5mcGg6HDfTz95Ifavq6nNhDOb6TWE0c2S5PaNmO76roUXXVdihobW7R5w76vvx7DlZIW26Htg81mV96eCm3KKZLJbFRiSoyiY0e2f81PlcFgUPq88UqfN77TNZJSYxUVE3ra1xgIBoNB6WdNUPpZE9ruY3eFNuYWyexm9wEA7oiN0IC+RcgLAADcTlNTq157+Sut/WK3ivdVdTj21eo9kqSo6FCddW6Crv9WukObWw0Es0fPqz5P9OlH2/TpR9va7uO8BF1/c7pCQofpkoWzOowr3lelV/61VjnrC1Vb06iaGC/JwdW8Y8KDZTAYtHrVTq14Z4O2bSnpsKHXhuxC/ecfaxUQ4C0/fy/V1japvq5jgPzBe5tk8jTJb3KwGvwNOlhzwkpIm7R99Qb9dfUGxYWP0AWJ43XrBUkym40Ovw52g/TTLz7TI19+oYnFXvLc36IjFbUOny9JFXX1uuPpNzTc30dzp0TrzktSFREa1GlcY3Or/r4yW59vytO+g9XyOtyqHrvkOhLKOpEPejjx/jjOx8dTaXPHK23u+C6PG40GTUgI14SEcKfndtRAXGMgGI0GTZgUrgmT3Ps+AADA0DQ4PvEAAAA44f23cvXKP9f1OGZf0SHt+/sh+fl76+rrUwaosp5dflWitm8tUVOj420G9hUd0r4XDykgwEdXXpvc6fhjS99V3p4TNnCrsqjVt/vNyo4bHxGq1ISxKis9okd/+XaPY2trm3rc6KsuyKhKW4NU0/0c+eWHlV+eqWG+3rr+7BlacPkMfbR8c4/XlaRj8WbJaJDPzmbVbavrdfzJbEapeUTbaufqukYtX79TFUdqtOyBazuNfeXzjfrHyuz2P7cOM8niZZC5uesw12iVvA63ts/flaYRHjI3NMtg63aIJClu/GjNSIzq/YYAAACALhDyAgAAt3Oosoc08SRVhxwf29esVpuqj9QreIS/jMa2Xwt/44MfasuGfcrKyNdnH29TY0OLQ3N1d8+HKjuuavWssSp4R4Na/U1qGWZS6zCTbJ5GyW7XhPBQXZAyQXOnRCt+TIgMBoP251We/n16OL5ctfJoW1D7w59eplu/c5ayM/KVnVmgdRl7ZLBIVg+pcZRJDeEmNYw2yerTNre5oedVszajZLC1LZy1ehra7j3ApFZ/k3RSi4aD1V2HxZUnPW/zMOrYBB+ZGm3yrLHKo9Yqc4NNBkl+QT664IIpSkmLU1j0CGXnlWjd9iKt3pQvu9Egg9Uuj1qrPGqsshu6XtAbN36U5p87SSnpcU61ULBabTpaXa/hwf49tp8AAADA0EHICwAA0IdqjjUoN6tQ2Rn5yskqUM2xRgUF+So5LVYpaXGaNnOcvLw95O3j0W8BncHetuGXZ61V9jLJ5mGQwWZXiLVVQRMkH5tRxfsPKzsjX6s+2XZK17AbJIufUS0BZrUEndqPlGYfDynMR63j/XWkxlfGJrssPpLF36BWf8nm2UsNkqxeBrUOM6k5yCybp1EGm102D4PUl31UjQbZjQbphC4TRrNB3t4e8vb2UNiIYbp61FRdPXeqrrr4SR1rapSxxd5rpwZPT7O8vD3k5e3Ra8B77GiDcrMKlJWRr9ysttYcQcP9lJwWq9T0OCUmx8g/wPv07xUAgAGydOlSV5fgEvQiRn8h5AUAAG5nxIgeO6V2EOzE2NP1wrOf6p03smWzdVx1evRoQ3uP3VM1IqTr+wgO8dfR6vouj0ltK0hNrW317N5Rrt07yvX8s6dchiSpJcCkurFespudD1JDhvlJkp743xd6/cstsrX3tTXI5mWQ0SZ51rT9Z5ddzSOkplC1r+g9zmaWaqK9ZfXt2Me292hVCgn0c+h5Q6tdw4qaZG7s3GuhtqpBr72coddezlDQcD/98tGrNW3mOIWM8FddUWOvNUjSzu1l2rm9TH955hNNnhah3zx5o/z8vDqN+9NTK/X+W7mdWgAfra7Xpx9u1acfbpXRZNDNt83Vrd+Z79C1AQAAcGZxfNcLAACAQeKKRUladEOqRo4a1u2Y0WGBuu7mNF165axux/Slhvpmvf2/rE4B7+kaHR6k625O04IrZnZ5/OFfXKE5Z02Qt0/3fWH7WlOI2emAd8yIYfr2hUm6cu4U1TY06b9fbD4h4O2aQQZ5HZZkl2omeMhrZqD8g30kSS3DzJ0C3t74e3vq/Fnx+sXN53d5/MZzZ+qmc2dq9PAASZJnjaXLgPdkR6vrtfztDZKkH/9qodLnjXf667Fja6k25RZ1er6mplHvvdk54D2ZzWrXay9n9Pn7DwAAAO6BlbwAAGBQs1is2rG1RLt2lGvU6EAlpcYoYJiP7vnBBbp78fkq3lelrIx8bcgulMFgUGJKjFLT4xQ5boTDPU77qs7egjhnpKbH6a7vn9/rfcTGj9Ijj12rlhaLtm8p1h9+s7xTn96+ZneizcTFSRN016WzNW7U8Pb7KDrieH0GGfTovHN1yeTxCvL2kd1uV1FhpZ587QtlllY4NEdEaKB+9a0LND02XB6mtmC4tdWq7VuKtWdXhcLCg5SYEqOAAG89dO3ZevCa+covr9Kf/vSpdpYWOnSNlhaLJCluwmgt/f11ammxaNvmYj3x6Ps6XOXYhnEtzZZOzx2pcvy1am21ym63q+sOwAAAADiTEfIOgI0bN2rlypVau3attm/frsrKSnl4eCg8PFzp6em64447NG/ePIfnW7lypZYtW6bs7GwdOnRIoaGhSklJ0V133aWLL764H+8EAICB0dJi0eefbFd2Rr425BSpob65/ZjRaNCkqRFKTYvTuRdN0bjoUI2LDtV1N6e5sOK+l5WRr5Liw0pJi9P8cxM0ZfrYHsd7epo1KzlG4RHB/R7yOiN9SpSiRger/PAxfZi1W+t2FGlboWPh7HH/emCFcqdEakRSqI6NltYfLFNla418HQwzY8NGKGl8pJqbW/XRh1uVlZGvjTlFHTa9M5oMmjw1UhGRwbJYrNq2uVgHKo45VeeJPD3NSkyJUdiY4Q6HvMdVlFfr84+3KysjX7t2lJ1yDQAADGb0pgX6FiFvP5s/f77WrFnT6fmWlhbl5eUpLy9P//73v3XLLbfoxRdflKdn9zuM2O123XPPPVq2bFmH58vKyvTOO+/onXfe0V133aUXXnhhQFcuAQDQ13675B19tXpPl8dsNru2bynR9i0leu0/GXrlnR902cd0oAUM81Fs/CgV5B3ssznLS6v17hs5eveNHC353TWaO39ir+fMTIzSlo37+6yGrnjUWmXx771VgrenWdOiw1Rd16hrf/2yGptbnb6Wuc4qu8WujKNlqqytkr7Or00+kt1gl8He+888qRPbAvJf//wtZWXkdznGZrVr2+Zibdtc7HSNkjQzObrr5xOjtH1LSa/ne/t4KGHyGFUfqddd31qmpibnX6tpM8fKZKIbGwAAwFBEyNvPysraVl+Eh4fr2muv1bx58zR27FhZrVZlZmbqySefVFlZmV5++WVZLBa9+uqr3c71i1/8oj3gnTlzph5++GHFxsaqoKBAjz/+uDZt2qRly5YpNDRUjz766IDcHwAA/cHR1Yv1dc0q2V+liZPG9HNFvTMYDHr6hdv01eo9ysrI04asQtXWNrUfHxboo6SUGI0OD1J9fbM2ZheppPiww/Pv2l7mUMh707fnavK0SK1fl6eszDyVFh85pfvpzrBAH507I0Yj4keo2mTR+l3F2l9Z3X7cbDJqVtwYzZ0SrfNmxSsseJhy95Y4FfAaLHZ51FjlWWuR5zGrJKlpRMfw0uot1UZLHrV2edRJpsa21g7HxYaN0JwpUTp7eqxmxLa9P/p6VWxUdKhS0uOUNm+8pkyL7HLMLXecpSkzxirrqzxlZeSrrOSbr4fZbNS0meOUkhaneedM1MhRgdqYW+RUwBsY5Kvk2bFKSYvTnLMmnPY9AQAAwD0R8vaziRMn6re//a0WLVokk6njipfZs2frlltu0Zw5c7R3717997//1b333ttl64b8/Hw9/vjjkqSkpCStWbNGPj5tG48kJyfriiuu0Pz585Wbm6vf//73uv322xUbG9v/NwgAANr5+Hjq/Iun6vyLp8pqsWnXjlIVFR5SbNwoTZgU3r7Ksrm5VVvTi/WXZz52OITdlFukDdmFmjpjrDw9u/8RzmAwaEZilBKmjNGslGg9/+wnfRb0enqadNf3z1dqepyChvu1P19ceVQb80sV6OutlIlj5efd9ptJdrtde2vK9VH1BpnmVksmyX7YQ/aDnrIf9pBsBhlabfKoscqr2iKbl1GmJpvMDbb2uNZukFoCTDJYTQookmwmyeIntfpLNk+pObjtz+YaybPOLtmk+LGhevCyszQrbow8Pcw6dKxOX+3Yp4OhBjWP9pGp0SbPWqs8aiwyWk/ttbj86kT94KEFvY4zGAyalRStyVMjNCs5Rp9/sl1bN++Xv7+3zjo3QfPOTtC46JBT+i2s0FHD9PKb32f1LgAAAAh5+9uKFSt6PB4SEqInn3xSl19+uSTpzTff7DLkffrpp2WxtG3G8dxzz7UHvMf5+vrqueeeU1pamiwWi5555hk999xzfXQXAAAMLG8vD8fHenff6siVTGajpkwf26GXbnnpEf31uc+0IbtQzV1sstWTvD0H9JP7X5W3j4eSU2N19+LzNSosqNO4stIjWnaK1+hNS4tVf/jNchkM0oSEcN18+zzNnhOvsSODNHbkN7W02Cx6Ie9TfVy+RYeaayRJhhFtxwxBFim2UeZtJnmt8ZT58AnhZr2t/aHV06D6cE+1Bpgko0EGu2RqlkySPBokn0Nt7RokdWrZUFhYpe/+8W15mE3y9jCrtvHrns5+BkkGWb2NahluluyeMjfY5HOwVZ61zqW9IaEBDo0rLT6sv/7pM23MLmrfnE2Sqiprta/wkF56cY1GjQ7UBZdM07e+Pc+p935goC8BLwDAbS1dutTVJQw69CnG6eCnwkHg7LPPbn9cUFDQ6bjdbtd7770nqW1l8OzZs7ucZ/bs2Zowoe3X9N59992vd1cGAMD93PqdsxQQ4N3jGJPJqIsuna5x0SEDVNXp+8dfv1TG2r2nFb42NbZq7Ze79dLfO/f8l6R/vPDFaV+jN3a7tHtnuR5b+q6sVlun46sP7tR/ita2B7ydtEq+758U8J6kIcxTrYFmydj9GIPd0GNP3laL9ZuAt8sJDLL4mVQ31kvO/NQ0ISFc51801aGxLz7/udavy+sQ8J7s4IFj+s8/1mrLpn2akBCus8+fJKOp55W9gUG+uunbc52oGgAAAGcyVvIOAi0tJ+zsbOycuxcVFbX39p0/f36Pc82fP1979uxRaWmp9u3bp+jorjcBAQBgMDvvoqk6+7zJ2rWjVFmZ+crOyFdhfqWCR/grJa2t/+islJhBseGaM45V1/fdXEcbuny++kjfXaM39XXNslpsnVaTVrf0XIOhRTJYew4xbeaB20TWbjboosumK21OvGYmRav6SL2yM/KVlZGnbZuLZTS2tcBISYtTSnqcRnexgro73X2dunK0ukEms1E//39X6wc1jdqQXajszHzlZBbo6NEGxU8YrZT0OKWkxWlCQjireAEAANCOkHcQWL16dfvjiRM7b6iya9euHo+f6MTju3btIuQFALitE9sd3HHPuWpptsjsYZKxh5WdQ9XBiqPKyshXdma+tm8pcUkN9U0tytpdrHXbirSqeos03iVlnLL7H75EHua2/RP8/LwUcX2Krr4+RRZLWxsHs9nU0+l9LmCYj84+f7LOPn+ybDa7LK1WeXrxozsAAAC6xk+KLmaz2fTYY4+1//m6667rNKak5JsPaxERET3OFxn5zc7OJ57niNLS0h6PV1RUODUfAAB96UwIuEaHB0kb9/fdXJJeenG1Xv7H2j6Z01nBIf4ye5i0paBci//0juqa2n47yTDSop4iUbu3ZPeyy9DcfWBvarGp51n6zsggf5m7WRV7uuHu6LAgh4P37lYIG42GM+L9DwDuiM/JvaOPLDA48NOiiz399NPKzs6WJF111VVKSkrqNKa2trb9sb+/f4/z+fl9s9N1XV2dU7WcGBADAIC+d88PLlDwCH+t/ypPhfmVpzRH3PjRSpsbr2tvSpMkffj+ZqfO9/AwKXJc2y5oJfsPq7XVuQ3HpLZwNyUtTtfckCqj0aBPNuxpD3glyV7pKeuGABkim2QY0SrDyTmpSWpY0CzvTE8ZDxpkUOew1+dgq2wmQ/vGayfzNBs1blSwJGnfwWq1WjrfR9So4fL0MKvyaJ2O1jV2Oh4a6Ke5U6J183mzZDD0zwrx7z1wkUJGBmj9ujztKzzU6biXl1kzEqN04SXTlTBlTL/UAAA4dXxOBuAuCHldaPXq1frJT34iSRo5cqSef/75Lsc1NTW1P/b07HkHcS+vb3oTNjZ2/jADAABcx8/fW7fffY5uv/scVR48psd//b62OLiyN278KP3811crInJEh+e72visO/fef6EuXThTXl4ekqTGxha980a2/vnClw6d7+Vl1pLfXaOk1Nj2ULS+qUX7Dx49aaRB9lJv2Uu9JaNNnt5NMgc1y+5ll3mfSeZ8o4x1HVfONoR6yNRik9XHqJYAk6w+Rul48Gq1y9Ri0/Hd0W69OEl3XDVHvt5t91Hb0KR3V23VZ9l71NRqUWrCWF178SxFjhouSbLZ7NpVfFBrtxepqOKw4iNCNW9KtMZHhHa4j6zdxdqUX6ZAX2+lT47SxMiR7e1BrDabduw7qIyd+9TY3KrE8RFKnhApH0+PHl8z/wBv3XHPubrjnnNVeeCYstfna9vmYgUM81FKWpymzxrX/vUAAAAAThUhr4vs2LFDV111lSwWi7y8vPS///1Po0aN6nKst/c3u4ufuElbV5qbv9lB2sfHx6maemvvUFFRoZSUFKfmBAAAXRs5KlDRsSMdDnnz9x7UXbcs0/SZ45Q+b4IWXDFDZrNzPYqnTR/bIVD08fHU1GljHT6/udmin/3wNUXFj5RPXKDqvKXt+w/I0kXQ7FFjkddRizxqrDJaJannf6huCvWQ3eOEe7HZ5V3VKo8aizzqbTLYvzn0ft46rXt1s6JjQ2W3SwV5BztscPbRmjJ99s9szZgVpbR547Xg8hmaHDVak6NGd7yfVoveWrtVa7YWamN+WYf7+MvyDIUM81VceIgkaXfpoQ6rgV/+bIM8zSYljY/U2TNitTB9sjxMPbd2GDk6UJddmajLrkzscRwAYPDgczIAd0HI6wJFRUW68MILVV1dLZPJpP/+97+aP39+t+MDAgLaH/fWgqG+/pvdrHtr7XCy3vr9AgCAvpU2N14fvLvR4ZYJrS1W5WYVKjerUAcqjurO752needM1Htv5vZ6buS4ERoXE9rp+bgJozU6PEgHyo86XPf2hqNqLq/v9ri5zqphRc3dHu+K5zGLmkO+CaD9ylvkfdjS7fgjh+t05HD3Pxe1tliVs75AOesLVHngmO6499xOY/7wxpd6a+22bueoqmlQVU1xt8dbLFZl7NynjJ37VHG4RouvnNvtWACAe+Jzcu+WLl3a53PS5xdwHiHvACsvL9f555+v8vJyGQwG/eMf/9BVV13V4zkn/k+lt6bvJ/4rI72DAAAY3GYlx+ilN7+n9V/lKTujQDnr82WxONZ+YX9RW3/X7z1wkS64eJqyMvKUnVmgPbvK28eMGh2olPQ4pabFaWZStDw8Oq809fHx1N/+c7c25hQpOyNP67/aq8NV3Qe4kmT17nqTsuNMTY63kJCkSVMilDw7RiGxI7SvtkbrthepNH+fU3P0ZH9RVZfPF1Yc6bNrFB3ou7kAAAAAZxHyDqCqqipdcMEFKiwslCQ999xzuvXWW3s9b9KkSe2Pd+/e3ePYE48nJCScYqUAAOC42ppG5WYVKjszXxtzi2SQQbNSopWaHqfE5Bj5B3j3PkkPQkKHad4Fk+Uxxl/l1kbtz+z510JPZjAYNGFSuCZMCtet35mv6iN12rurQqPDgzQ2KqS952xNTaO+WrNHWRl52pS7TwaDQYkp0UpJj1NSSoyS02PlOcpHTeGeWvV81mndkzOSUmP0u6dv6vDcPZel6QdF/9SubWUDVkdfstvtKtl/WNmZ+crOzNeer78eqWlxSkmPU8LkMTKZeg7KnblGVka+9u6uUFh4kFL68BqOqDnW0Pb3I6Pt74fZbFRiSoxS0uI0KyVGfn5evU8CAACA00bIO0COHTumiy66SDt37pQkPfbYY/re977n0LnR0dEKDw9XeXm5Vq9e3ePYNWvWSJLGjBmjqKio06oZAICh7FBljZ787Qptyi2SzWbvcOzTD7fq0w+3ymgyKDElRg/+9DKNCAnoZqbuHThSq//3n0+UvbtENrtdntUWOTqLv3/X4fLwYH+lzolv/3PlwWN68rcrtHnDvk738cmHW/Xxh1vVOMZTrSGessguWe0KltRTp1+D1d7DUclucrxPcNBwv66fD+z6+VPhH9B10Bjg03cB5PG5vlq9R3997lNVnNT+ojDvoArzDuq/L32lgGE+WnhNkm77TvftunqybvVuLXvus07XKMg7qIITrnHlNUm69RSv0ZsDFUf15G9XaOum/Z3eVytXbNHKFVtkMhmVnBarH/7kMg0P7ruvJwAAADoj5B0ADQ0NuvTSS7Vx40ZJ0s9//nP9+Mc/dvh8g8GghQsX6vnnn9fu3bu1fv16zZ49u9O49evXt6/kXbhwYfvKHQAA4LwV72zQhuzCHsfYrHblZBZo5YotuvnbzvdjfXPtVq3f9U3P15ZhJjUNN8vrmEWGbjoeGAzSlGmRuvG2OQ5dY/k7G7Qxp6jb41YfoxpDPCR9HdSZDKof4ymfylaZWrsOc30PtKrebJDFr+uNxlqGmdQcZJLnMWuHDdO6uo8bbu36Pm694ywdra7Xrh3dr+Y9PnV3P/EYDNKU6WN1wy1dX+Oey9N0pLZB2/cd6OEa9q+v0fVVjAaDZsSF69sXJUuSXvjjJzpQcazb+aS21eH/+cdaXXTpdI0OC+pxbFdeePZTHTzQ+zVe/voao07hGr15781cbd6wr8cxVqtN69fl6bOV23TtTZ1/dgUAoDv90ee3r9E3GIMNIW8/a2lp0VVXXaWvvvpKknTffffp0UcfdXqe+++/X3/7299ksVi0ePFirVmzRj4+Pu3HGxsbtXjxYkmS2WzW/fff3yf1AwDgCqUlR7SvsFIxsSMVHhHskms01Lc4PFdDvXObjB1X33TSNUwG1Y/1Ur3NUx71VnnUWNuCUptd4bEh+vaNc5SUGqvAIF8nauv5Puxd/EZ/c4iHmkeYZWqyy6PGIt8DrR0iTnOTTYH5TbKZpZYAs1qHmdTqZ5TRqraaay0y19u6DHgjx47QzbfP7XAfdrtdxZVHVVBxWOPHhCgiNEhxE0brj3+7XdVH6pSzvkD/fGWdKourZTca1BJoUsswsyx+bcV71FnlUWuVZ41VBpsUGR+qW65L6/W1mhg5Ui/9+EYdrqnXVzv26YXPs1RecVR2k9TqL1n8JIuvJLtkbrDLo07yqJEMdmlcZLDuPm+20iaNU5C/T/t91NY09fh6n6i2pvGUQl5n3m/1p/je7NsaHH9NAAAAcGoIefvZjTfeqE8++USSdO655+qOO+7Q9u3bux3v6emp8ePHd3p+/Pjxeuihh/TYY48pNzdXc+bM0Y9//GPFxsaqoKBAv//977Vp0yZJ0o9+9CPFx8d3mgMAgMHKarFp04YiZWcWKCsjT+Wl1e3HIsYGKzUtXinpsZo+M0om86n1GbVYrNq8YV+/XqMvGCx2edZY5FHTFlwav17RezD/sD5avlmHq+o0e068xkaF9HMhBll9DLL6eMqnqlUGS+chRovkXW2Rd3UXB7tRWnJEHy3frEOHauQzJkB7Kg9r3Y4ilR76ZmVq1OhgzZ0cpbigINWW1Sp3fYGq9lXLaJdktcur2iKjxa4Wi1mtASa1DjOrdZhZDWPazr/yokk676KpDtc0YpifrkibrOzGCu3edrTjPTbZ5VdhlW+5VT4HrTK1tj3fsLVRn5flqCb1sIYF+qowv1JZGXlOhao/+v5/lDonTilpcUpNjz/t/s4AAAAYugh5+9nbb7/d/vjzzz/XtGnTehw/btw47du3r8tjv/nNb1RZWal//OMf2rRpk2644YZOY+64445TWikMAIAr/eSBV7v91e/S4iMqLc7SW69nKXl2rH771I2ndI2f3v+qNm/c3/s10mL12ydvVFRMqMNzOzP2RLHhIzr82dhsU2Beo4zWzmPtNru2bNyvLRv3629/XqUHf3aZLr5sxmnXZmq2S3Z7W2+Dbli9jTLWddM/wkl2u12bN+7XmmMHZfHvut3DvgNHdGDbQfmVd70K2WiVvI5a5XXUKptJOjbeRzbPb4L5uPBTC8DHj+h4nkeNTWM+a2oPdjvch63tPrp7Tzmivr5Zn3+yQ59/skNBQb56/t/fUUjosF7Pi4oJ1bYtvW/Q5+3t0S+tGo7X4KjomJH9UgMAAAC+QcjrRoxGo/7+979r0aJFWrZsmXJyclRVVaWQkBAlJyfr7rvv1oIFC1xdJgAATqmva+q1t+dxOesL1NjYIh8fT6euUVfb5HAYl5NZoKamVl2ycKb8A7y19svdys0qUH1dxxWaAQHeSkyN0VnnTtLc+ROcque4RXOnKsDHS59vylPmrmK1Hq7rMuDtyldr9jgU8l525SwFtN9HYadfsw/08VJicLhaQz1VUH20w4paSTIbjYo9L0bBdQY1VzZqz46yThttRUQGKzwiWDabTTu3l/X6q/x2k7oNeI/zPObY6mCjVTLX2+Qd5KP0SVG6IHG8zp4e69C5J7tl2nQN9/bWyvw8rS3eL+PB+i4D3v5w9GiDtm8p0dnnT+517NLfX6fl72xQdma+dm3v/PUYGxWilLQ4XXz5DPn59d3mcidaeE2yAoN8te7L3dqQXaiGho6BfMAwHyXPjtH88yYrbS6/YQYAANDfXBbyVlVVqaysTIcOHdLhw4fl4+Oj0NBQhYaGKiYmRkaj635Nsi/Z7T3vPn0qLrnkEl1yySV9Pi8AAK5wckDVKyeHtzRbtDGn5w3UTrY5t0izUmI0/7xJmn/eJFksVu3cVtq2gZlBSkqJUcLkCIfbOtjtdhUVVCo7I1+7d5Vr9OggpaTHauqMcbooaYIuSpqgQ4dq9PgfPtDm0gLH5jzpdevyGnPiNHX6WJ19/mSdff5kHTxSo7c/3KT1GXkyGA2aM2e8rrpklkIC/SRJzU2t+nT1Tn3w1Q5V1dRranSYrrlophImhMtgMMhut2v7lmK9//YG7d1VodBRw3TRpdN19vmT5eHRFtpaLFYt+9MqvfO/7O6L7+O9Yc+fEK0f/uBiBY/wP615DAaDLp8wUZdPmKjKQzX6XclybVf3m9b1tZP/LjQ3t2rrpmJlZ+SrqqpWk6aMUUp6vMaOG6Gbbpurm26bq5qaRuWuL9DeXRUaPSZIqWlxChszvN9rNRoNOvfCKTr3wilqbbVq57YSbcwpkslkVFJqrCZMCpfJdGb8PA8AcB9shoahbMBC3traWr333nv68ssvtXbtWuXn53c71s/PT7Nnz9a8efN06aWXatasWQNVJgAAGGDOBEEGg2Q0OZYQZmfma8U7G7Upt0hNTc4tx/zlw/+Tj6+nZiVF6/KrE5WYEqNpM8dp2sxxTs1zoOKoXnvpK2Vn5utQZW2HY2+9niVvHw+NGh0oq8Wm0pIjTs1tNreFqhXl1Xr95Yzur+HnqWGTR6gxwKh9VUe/OWiVtq7J1QtrcxXnPUy+1VYd2l+t5hNeq7XZB7X29c0KCQmQr7+X6mobdeRwffvx8rJqbdm4X889uVKzkqN1xdWJmpUco3G99Qt2IKi3OxEEZ326Wzd8tlsTJ43R/PMmaeE1Se2vjzNami166/Usrf1yt/J2Vzh9/uk6XnPmur368L1N2pRbpObmb1Y0r/tyt5b9aZVGhwUqdU68brhljkJCA9rDVlfx8DBp+qwoTZ8V5bIaAAAAhrp+D3k3bNigZ599Vm+99Zaamtp21u1tdWtdXZ1WrVqlVatW6ZFHHtGECRP0ve99T9/+9rfl5+fX3yUDAIAB5OvnpfMvnqrPVm7rdexFl82Ql5dHr+MqDxzTzx987bTqamxo0Vdr9ihj7R799737NCIkwOk5fvPLt7V7Z3m3x5saW7W/qMrpeT09zbrw0ulfX+Md7dnV/TWqfe0qa66RuumgYGiy6cjmCvUUMVdV1UpVtd0eb2xo0Ver9yhz7V699v79Sk6L1eiwQB2oONbleKNV8qy2qGV49z+KNgd7yKO+WQYHV27b7dKuHWXataNMXt5mXXZlomMnnuDt17P0jxe+cPq8vhA5boRmJI5TWekR/erh//U49kDFMb33Zq5K9h/W75+9eYAqBAAAwGDWbyHvhg0b9Itf/EKffPKJpG+C3bCwMCUnJysxMVEjR45UcHCwhg8frsbGRh05ckTV1dXau3evcnJytHXrVrW2tmr37t36wQ9+oEceeUQ/+tGPdN9998nLq3/6iwEAgIH3418t1M23z1N2Rr6yMvK0bXOxWlut8vA0afrMcUpNj1dyWqzGRAQ7NF/lwa7DxVNht0tVlbWnFPIePNB3dXh7e2j+eZOUkhanxJRo+fl7O3QNq2fPS2KNrX3TWspukKw2u6oO1Sh+Qpj++fp3tWNbqbIz8pWdka99RYckScOD/ZSSFqeUtFiFRgdrQ2G5vtpepKxd+9uWatvs8qizylxvlc2sU+qJ291rYrfb1dDQIl9fTxm62GjuoBPvG28fD5193iQlz47V6PDh2ra5uP29a7HY5Olp1vTEtvfupMljVFpyRFkZecpZX6CaY42SpNj4UUpNj1NKepwmThojk8mo/LyDDtfQl+9zAAAAuLd+CXlvv/12vfzyy7LZ2nZhnjVrlm6++WYtWrRIY8eOdXielpYWrVmzRq+++qreeecdHT58WD/96U/1/PPP6+WXX9bcuXP7o3wAAOACEZHBirg+RVdfn6LGhhaVlR7RmMhgpzdZ6w9/+/MqnXvRFKWkxSkk1Pmwty9cfnWi7vr++Q6NtUuy+hjVMsyklkDn2xY4eg2Lj1Gtw0xqGWaS1dckWe36w/J1WlCVoDmToxQWFqSwMUEaPSZIFeXVam62aHRYkMLCgxQ+JlixUaOUED1a3zpvlq6+9ClVNzbK1GxzePWuIxobWrQpt0jZmfntLS1GhwUqJT1eKWlxmjZzrPYXHVJWRr7WfrG7yzmsHoa2+wwwyeppkEFSSLC/AmeO1Mj4EMVHhGr8xDAtuiFVDfXNKi+rVsTYEfL2/mbVefzEMJ1zwWRZrTaV7D8s/wBvl72XAAAA8P/Zu+/wOKp7/+PvmdmqVe+SJUuyJFe525IlF8CAqab3GkJCEshNIPyS3EsSEtII9wKpEJJACC2hhBLAFGODjW3JknuRmySrWJasXldbZ+b3x9qyZe2uVkaSDZzX8/gJ7Jw558zsEo8+Ovs9XzyjEvI+99xzmEwmbr/9du6//34mTpx4Sv2YTCbOO+88zjvvPP785z/z2muv8etf/5p9+/bx8ccfi5BXEARBEL6grGEmciYmn/L50TEjW95px7ZadmyrBWDO/Cwe/NXV/Stph5pHR7t9yHahiIn1v6lYdEwYnR3Hx/CEy/SMN6MbQ9wUzjD8XdACjqFIbKqqZ/P+Q0TUODHatUHnHiup8I+/rSUxKZIf/fwqpk5PIzY6jJ6OvmHPxZ+Yo+//M099zOv/KsXjUQccP9LYxduvb+bt1zcH7UdToCfTgjd8cFDe5HDw5NvFPPl2MSmxkTx858XMmJBCmM0c9LOrKDKZExKGnHsoRvpzLgiCIAiCIHx+jcqWt9/61reorKzkL3/5yykHvCczm83ccsstlJeX8/LLL5Obmzsi/QqCIAiC8MWTNj6Ob99/IZlZgcM0RZGJiLQOa+M3gK2bqtlcejCktt/7n0uYMz8LgyHwGBGRlqCrlaNjbCy/ai4XXz47wBiXDhjDkWAMOeAFUM0STAjHFmsN+ZyhxjB1e/0GvCdrburmzVfLAN91zJ6XOcS9smKxBq7JHBNr4/Jr5nHhpbOw9zp55YXiQQHvcLijDH4D3pM1tnfz8ifbTnmcE2VOSOBb3z2f8UE2rzOaFOYXZvNf9184ImMKgiAIgiAIn3+jspL3iSeeGI1uAZAkieuuu27U+hcEQRAE4fOnuamLbZtrkCTfStv4hEguv3oel189j6bGTjas28/qD3fT3trL5GnjOP/C6cyen4XVaqLP7mL7lhr+8Oj7tLX2hjSe0zm4UKyjz822LTW0NnczJW8c2bnJTJ46jkd+fzN9dhfbNtdQVlLJvj2HSUqO7q/FmpAYiapqHNjXSFlJJVvLqpFkmJs/gfzCHHInpSDLgVfbTpk2cIxfvvkJTS5HSNdxzqwc7rxwPpPTk5BliSONnaxdvYenn/w46Hl6kPkAMHS+28/p8t3LqXlp/O8fbqHP7mJL2UE++mAXB/Y1kpgYybkX5FG0ZFL/vdq/t4E1H5WzsbgCWZYpXDSRc86fRs7E5P571dHeyxB7/Q5JH0b+39JtR9d1v7V+g2k+0sXWzdUoisyc+VnExUdw1fUFXHV9AY0NHRSvPcDqlbvoaLczNS+N8y+ezqy5WQNKQQiCIAiCIAjCqG28JgiCIAiCMJr2721g/Zp9lBZXUl3VPODYhNwkZswaj9GocLCymZ3bavtXdK5fs489u+qZvyCbgqIcihZPomjJJN58bVPIIe8x7W29rFm1h7KSygFjAMTGhfvGWJhL0eKJLDxrEgvPmuS3H0WRmTJtHFOmjeP2r501zDvhE2Yzs/CsSaRv3UnTgfqQzjlnZjZTM46XFkhOieaCS2YOGfKOBq9XZf2afZSVVLKppIrOTl/phraWHqoqmigrqSIjKx63y8uObbXUHGzpP/ff/9rI9i015Bdms/icKZ+p1Mep2nKgngv+528smpbFoulZnDVjAorsPyXeW37Yd60nbEh3TM7EZKbPSsdgUKiqaOrfhBDg00/2snvXIfILcygoyqFw0cRhr0QXBEEQhM+Dn/70p6d7CoLwuSNCXkEQBEEQPnfWfbKXn//o9YDHD1Y0cbCiKeDx9rZePlyxgw9X7OD8i2fwgx9fRt7MdLZvqRlybKNRYdKUVHp7nHztpqfo6XEOOcayi2fw/R9fNmTfI2FW9jg2hxDyGg0KUzOSBr0eGRVG+vg4DtW1BT7XrgYtY+ANk9GBUNa05s1IB+DXP32LdZ/s9dvG7fb2b5wWSOWBI1QeOMK/nt/Arx67kbn5ExiXHsvhQ+0hzMI/g10DXYcQV+e2dtl5q3g3bxXv5spFefzk5vMHtfl45W4e/tlbAfs4dh2BtLf28sE72/ngne1ccvls7v3hJSHNTRAEQRAEQfhiG9OQt6enh9/+9rcA3HXXXSQnB19l0djYyN/+9jcAvv/972O1hl4rThAEQRCEL66d2+tGrq+tvg3VbrtzCZOmpFKy7gBlJZW0tvT0t5EkmDQllfyiHM4+dxrpGXHs2l4XMOAdNMbRTdvGwreWFzItM4lPdx1kw+4amjuPr06WJJiWkcyivCyWzZ1IZnLsoPNlWeIPf7uDTz4qp6ykkm2bq3G5vP3HdRmUPg1Dt9cX9Pop3aCGKXRNsmLq9GLqVjE4BtZvSEyKpKAol8LFE5lXMAEYuXuk67Bzey3zF2Tzx7/dwZpVeygtrmD7lpoB12GxGpkzL4v4xEicfW62bKqmrXXgez5jQgoTpo/DFaWwo+4I+w41+xvSry0BgvaR/OzuGMPPlSAIgiAIgnBmG9OQ96233uJnP/sZubm5PPjgg0O2T05O5qWXXqKyspLJkyeLWryCIAiCMIoa6tspK6li08Yq7HYnM2ZlkF+Uw5Rp41AUGXuvky1l1ZQWV1BzsIWs7EQKinKYMz8LW7jldE//M5MkiQULc1mwMBdd1/vLPIRHWJhXkE1MrC3kvlQjOJIU+lIV3FESPW4vH1RWsDB9PBFmM15VY1d1I+t3V7O1op6IMAsLp2WyKC+LcfFRAByub6esuJJNpVX02V3MnJ1BQVEuk6amoigy3S4n6+vq+KT6IAfa2pgcH885WRNYmD6es2ZkszAviy0Nh3lr+x42H6gn3GrigtmTuHTqJNIifWPUdHawpqaatTU19LrdLEhL55ysLGYmJbP8qrmcc/40Nm6o4De/fw/N7kW1SrhiDLgjDaBIIOkQ7UaK9vqW7UqgeyRoMaGi4Eg2YZhkJy6pFZvbCV7ImpTKVy8+l2TrZGRJoafPScneWprjwJFgweDQMPaomHpUpGHU9vUnItI64DpWvreDQ7VtZGYncsElM8hfkEOYzQyAruv95REiIq3MK5hAdMzx97z+UDvPv1bMuxv2gKSjI+ENk1HD5JBX+n4e9HQ72FJ2kNLiSupqWsnOTaKgKJfZ8zL775UgCIIgCIJw5hnTkPeNN94Y1sZpkiRxww038Itf/ILXXntNhLyCIAiCMMJUr8bLLxaz+oNdg76eX76znn89vwFrmAmz2UhPtwNVPZ66HdjXyIcrdqAoMtNnpnPDbQuZmz9hTOZtMo3cI4zJPLgvSZLIzk0iO3dwOYNgc7Any3ROMeKMlwescG1G5+4V72BUJVJ6wvB0enCctHnb+t3VPPLyx6T2GTB1eOlp6xtwvHxnPf98bgN6thXHLCvNRhfqCTuL7Wpu4rU95RgkiWirlT6Phz7P0TEUwA07Slv439L1xFqt6LpOh3PgSuQtjQ08samUxFaZ5EpwHLKjaTrS0S6UHh1Tjxs1yoljmRd1ggrmwbubSWhMsx4hy9ZGZPjJm8BV8lrdpzTVZrB/6zTq6mVUTYdw3yhqmIIrzojsVLEddmPs1UIq+3Ais9m3KVnphgpeebGY8t31aOrxeba29LB5YxUGg8z0WeO56fZFzJqbSc7E5AH1fD0elZef38CqD3fRUN8BwMlRv6aAO9JAX4oR3Xi8Pq7Z6P8zOrKf3ZHbfK1k/QFee6lk0L06sK+R99/Z3n+vbr5jMTNnZ4zYuIIgCILgz0MPPXS6pyAIw3Im1JEe05B33759ABQVFYV8TmFhIQB79uwZlTkJgiAIwpdZ2cZK/vHXNUHbOPrcOPrcAY+rqsb2rbUc2H+E19//HgZD4FqtI2XZxTNYv3Zff/B2qixWIzfcGvpzyYlyJiWzcMkkNny6H/CVMWhaaEY3BI4kjU063d19AY+bulVcNX24AhzXZKiZBbrBCYOzVQC8uk5rX+AxANodJwevx0mqjm1NL3Y1YBNc53pQpwRukBbWyczEwHWBvV6Zj96dguqVCHQhYUc8mHqHv5R3fGY8S8+fhsvl4ec/eh232xuwrdersW1zDVUVTby24nvIJ5WeKF63n+ef+TToeLIKlg4vyGBP8610DTMb+cqy+X7bX3jpTErWH+BIQ+fwLuwk1jAT199c+Jn6OMbhcPOLH7+Oxx34PT12r6orm3l1xX1IX6DVy4IgCIIgCF8EYxry1tf7HvZTUlJCPudY3d7Dhw+PypwEQRAE4cuso6136EYh6rO7cLu8YxLyZmQl8I9X7uZgZTNlJZW8/842Gg93hnSu2WLg4svmkF+YzYxZGX5X8gbS3NSFJEkkJEaiKDI/+821tLX2sGljFZ9uPMBBQ/ANz6TAeaPvuCdAcnuUbiBoiDwSJI8vuAxGCw8+T6viCXrc4zageoN/TmRv8DFOlJ4RxwWXzCS/MIfMCQlIkkRHe2/QgPdE3V2+VeqyPHBOHW32kOdgkw1cce4cFk3LZHbOOExHV/J6vSoN9R3EJ0QQZjMzISeJ51+7h6oDTZSWVFJWXMne8nokWSJvRjoFRbnkF+YQHm5mU2kVZSVVbC07SF+fm/TxceQX5pBflE3ezPEjtirY3usKGvCeqLOzbzh70QmCIAiCIAhjZExDXln2fYWtb4jVJSc61tbrDe0hXRAEQRCEL4cTSypERFr4/f++H9J5M2ZncPe9y0Jq63Z72b2jjtLiSspKKqmvawd8oaIvjPOFbRdeOouCcyfx8l//fMrXI5y6+hydgxke0o0OPBVH2FpazYa1+06pL7fHy5aKw6zfXc0n7+0K+Typw4OnvAN7ZBxNti727q6nrKSKzaVV2Htd/eUO8gtzKCjKJXtiEjmTkrn5K4vos7uQZAmr1TSgz4uWz+ai5bPxeFQcfS4io8JO6ZoEQRAEQRCEL74xDXlTUlKoqKhg8+bNIZds2Lx5M3B8Ra8gCIIgCCMnISlqxPqKjLL210IdawmJoV9HYmJkSO0O7GvkR9/7F52dg385fai2jUO1bfz7XxuJjQvn4d/eSFpWPOEmE73uwKUt9CGevDRT8OWRshdktz5ku89CM4Jm8I0VcB7dEsHWfdq9piBHwWTyYjR58LgDf140Y+jXWK/Z+dumzaz43w1Y24Zf4iE2LhxFkdldc4Tv/OlNOu2+WsVGt5fQPi2+FburP9zN6g93BzjuK3ewbXMNf/njKuYVTOBXj92ILEtDbmhmNCoYRzHgjYi0YLEYcTqDr8AGiIuPEKt4BUEQBEE4JWdC3dovMnnoJiNn8eLF6LrOk08+iccz9EOkx+PhySefRJIkFi1aNAYzFARBEIQvl7n5E7j3BxeTNzN9UD3SY+ITIhiXHktYmP/gzhZu5qxzp/Krx25AMYzpo0W/+Quy+e4PLmLajLSA15GaFsM1Ny7gzm8tDanPTz4q9xvwnqy9rZe1q/dgUhT+ccVVXJSTS7jJ/71yxIPHpqNL/ksRaFEGYmcnkpIZ6/c6JA0SS9xYmjUCpazhJhNZ0TEk2U7eIixEikTDWWZ60xS0AKG06WMTlBvRnP7f78P2aDYfyKStIQLdT+aqGDQKL9lBTEYHGPyHsvZxZhzxBtQAgbYugSNBpnm+CWeCjKVdG3bAGx5h4ezzpvLLR69HliXeL9vbH/ACeCIVeseZ8ITJgUogn7LNpQcHbXZ4upjNRh7+7Y0sOnsy1gD/nUdEWDjn/Gn88tHrRT1eQRAEQRCEM9CYruS94447eOaZZ6ioqOCmm27iueeeIyzM/6qEvr4+brvtNg4cOIAkSdxxxx1jOVVBEARB+FKQZYlLrpjDJVfMobvbwZbSg2zaWIm918XMORnkF+aQNj4O8K1ULN9VT1lxJdVVzWRlJ1JQlMPU6WljUod3qOu49Iq5XHrFXLq6+vjgnR2sXV2Ow+Fhzvwsll81l8yshGH16fUEXquqA6pZwhOpoMsStZ3deLwqc1JSmXNJKh5VZU3VQe5/+T2cioriAmMvKA6QOBqQaTqy+3h0ePP5c/jq5QuIslkB6Oq088w/PuXtd7YhaTqecAVnvAHNomBpB3O7jmoGzaShWsCmGnj09uUsysnCIMvous7Bjg7+e9VKtjQ2BLgQHcWhI3sAyXdd3nAJV7xCU7wCqo6lVcPWqGLs1nBHy/SlKDjjZJAlqNVRDCpR3U6UGC+SQ8J4QMa0T+aIK5MjZGKweYhd2kbcgg6MskqTK4LDRGDXLDDLizKjDQ4b0UsifMmt7lvFqxsk+saZ6UvVkV06sseLqVtDM0HvOIW+cQqaWeq/DkN36AFveISFX/zvdUyZltb/iwm3x0t9S9fAhpKEK96IK96I5NWxtHgIax56oUKo6uvaGJ8Rd0aEpnkzx5M3czwej9pfoqSutpXs3CQKinKZMnXcafsljiAIgiAIgjA0Sdf1kV6YENRNN93Eyy+/jCRJjBs3jq9//essWbKElJQUJEmioaGBTz/9lKeffrp/o7ZrrrmGV155ZSynKfhRX19Peno6AIcOHSItLe00z0gQBEEQjivfdYjVH+ymrKSSpiMDw7qwMBNz8ydQuHgiS5floShDh1VP/PZD3npt04DXPGEyrhgDnkgFzTSwD5vFxOycVExGA80dvZTXHmHQU5auY+7wYupSMfaqSCflkimp0WRPTAIkKvY1DroOTQaPTQZJQvbqGPo0TowHZVli8rRxFBTlcNHy2cTE2vjZmo95fsf2Af1YWlTCa1XCGlWMfQMnqRrBkaRgH6fQO17xhbkn08HU5QuuDXaQ9IFtZJeGwaH5wvAwedC9wqAhR7qxdHowNukoDfLx8NvXPZ4wGWeiEU+E/zmoRh3Zq2LqULG2DL6OYMalx/KPV+7Go6qs2LiXT3cdpHRfHQ5X8ADX0KsSVeUM2ma4UsfFkF+Yw3kXTWfSlNQR7VsQBOFM80X8mfLEa7rvvvuIihq5UliC8EUjyjX4jNb/F47pSl6Av//977S2trJq1SoOHz7Mz372M7/tjmXP559/Ps8999wYzlAQBEEQhM+bPbvrufcbgZ8X+vrcrFuzj3Vr9nGoto2vfvOcIfucvyCbt9/YjKb6nkk8YTLdudaA7e1ON+t31wTtM6zBjbU1cLHbxoZOGhs6Ax6XNTD3BF6xqmk6e3bVs2dXPave38Uz//omSzIyeWnnDtSjz1aWFpVxH7sC9qF4ILxeJbxexdhjoGP64K/vW5vB3BF49almlnGbgwTpHonI9RqKx38bCfBEG/BEBX5UNXdoRFUFvo5gFizMBeC3//6Ul9dsD/k81SqjGXwB+0hpONzBW//exNtvbOap575OVnbiiPUtCIIgCIIggtUvjzEPeS0WCx9++CF/+MMfePTRRzl8+LDfdunp6Xz/+9/nnnvuOSO+wiYIgiAIwpnrYGVTyG0rK46E1C6/MIe//+tblKw7QGlxJWW1/p9ZhsPgHP6mYKfqUF0bbreXpVkTWHnrV1hdXcUn1dXsraoJuQ9zp8akuHjOysgkMdxGY08va2urOVLb/pnmJnlB8QQPSr3W4Kuth3svj5UXyS/MIW+mb+XE/vqWkM9XZIn5UzIouCINmwMO7DxMWXEldvupBc0n0zS9vwyKIAiCIAiCIAzXmIe8AJIk8d3vfpfvfOc7bN++nW3bttHa2gpAfHw8c+bMYebMmSLcFQRBEARhxFXuP8IH725n/oJs4uIjgrYdlxbLpdfNIencaNo/VNlW/NnCzdMlKyaGmy0zyHNE8ZKnl32EFlinuCx8PXoG86f47lVTRw9T5Fh+v2MdPYxMuDkW5szP4pHf39z/781NXWwqqaK2OvSQtzA+mVvzZzJzdgYGo0LFlDRiY8N5/ZXSEZvnR+/tJCLSyszZGZjMQz+mNzd1UVZSSVlxFQf2NZIyLpr8Ql+QPSEnUTxLC4IgCIIgfImMWsi7ZcsW5s6dG7SNJEnMnj2b2bNnj9Y0BEEQBEH4EggLM4fctqPdzmO/fheA3EnJfOM75zNzdsagdq2uHv5vz9tsaNmPW/OiOc1A5Geap+6vvu0oMZkMKIpMW2sPf3z0A8o2VuJxB95Mzp/utj4e+/W7uMNl1CwbffLIrETWZV/d3WB3Q9KCr/TVh7EHWEysDYBNG6t4+snVHKxsBqAnywyRoT0O7yitZu9HlSiKhKzIw76XodhcdpDNZQcxmw0ULp7If91/IZFRgzcp3rSxir89sZrqquYBr7e19rB7xyH+/tQnxCdEcOV1+Vx3c+GIz1MQBEEQBEE484xayDt//nxSU1O55JJLWL58Oeeddx4Wi2W0hhMEQRAE4UtswcJcliydQvGn+/F6Qw8iK/Yf4c+/W8lTz3190LE36kr5pKm8/9+lZDdSqgu90QR6kHhS1yHACkpHktG3KZnrs9V0HSogjY4O47avnYXBoPDOG1vY8On+zzSefZwZbbgB77Fd5/zdC0WiL8VEeKsX3eO/X2uTB8liwD24JDAA7igDrm4VU5ca9F7kTkruDzr/9PgHNNR3DBhDM8qowUpDaLpvo7weX6irqjqqOryAV5YltCFC6xO5XF7WrNrDxMmpXHvTgkHH//jo+0FrNwO0tvTwtydWc/Z5U0lMEpsACYIgCMKX1UMPPXS6pxASUTv4sxvVcg0NDQ08/fTTPP3001gsFpYuXcry5cu59NJLSU0VuwcLgiAIgjAywmxmfvLLq7HbXWzbXM3772ynrLgypHN7e5x+X+/otaPUyOhWHS1RRzLqKPO70T0SeosRqszoTSaQJQy9KqYeFWO3iuzR8YbJeCIV3BEKqkVGcWqYun3HlRMC3v5/knz/cnJYqSlHD2vgCVdwRyl4IhQ0o4ShT8PYrWLq9qI4deRwA7dcV0h+UQ4TJ6ciH1017OjrZvrUI/TazVTXRnPyKDo6yEcno4PkJzJVzRLeMB3FBbI6+LjsVNFMMpKqY+pWfdfaq6IDngjfffBEGdBlyIiK5Obl+SyalkVClI19e3y1bYvXHeBQbRuZExIoWjyRgqJccien0NDWxYbyGp5buZkjHT3H561I9GZakFQdY4+KsduLqVtF0iB5Qiw3Xr2A+YXZAwLOk99rY59G9AEHqknyzTFSwWNTkFXdd297fNchncICZqNJ4cZbF5JflEPupBSaGjspLank1ZdKaGnqDqmP3h5HgNf9f2YDtRUhryAIgiAIwhffqIW89fX1vPvuu7zzzjt8/PHHOBwOVqxYwXvvvce3vvUtZs2axfLly1m+fPmQZR0EQRAEQRCG0md3sbXsIGUllezdfWqbpB1p7GTjhgrKSirZsvkg4R7ft5C0CA1vjoYnV8U7QUVKdaN4vIR/4n9Fp7FPw9inEXbEM+iYxybRl6JgT1VwJsroii80Vfo0who1bIe9WJs0ZM0X7kpHk2Bjr+rLZzXdF/I6fIGqwelroPd62VhciabrGKRGJqTvBfca7ry2BMMNXgBa2sLYtGUcxTvS2GRPpC8DvDkq+rHSxB4wVMsoFQpaixFHlJG+FAV3zPH5Kw4dgx2M3aC4faGwZpZBktBlCc/RvnQJkHwhrydCQTf4rvNwby/rdh1EUzWSDVYqdh+mrKSKmoO++rhVFU3IiowOaLrO5KmpXH/2LA42tvHapzsH3MvjAa+KqUdDPvp2tNb53kdN1ykoyhky5FTcOtY2L9Y2b9B2w5GYFMWtdy7p//eUcTFccc18ag62sOKtrSM2jiAIgiAIgiDAKIa8qamp3HXXXdx11104HA5WrVrFu+++y4oVK2hoaGDbtm1s376dX/ziFyQnJw8o62C1WkdrWoIgCIIgfAE1NnTwna//g84O+7DPHZceC8AH727n8Yff7a8ycCK5R8a0Tca0zYAWrtH7NRdarI4u6UjBSjecpDtLoWW+yW8JAzVMxpEEcbt8AS8cD3iP/fOxVbKBVOxrJDvtUzKjSqDX95pBOX48Ia6PGWc18MfUSXR5/fRjBO9EjbawcDx2/3USVCsoruMBr29yx69HM8m44mRccUa/53t1nXW7qtn+n72YevxfS8W+Rir2NfLi39dx1rlT+fEvrmJ8YsyANrJLI6rC0R/sDpijR2Pjhgo2bqhAViT++8ErOOf8aaSlx7Gnq97vmCMt7ejnKtTX/bYdH+f39XHj49hXPvQvMowmRaziFQRBEARB+JIY1XINx1it1v5Vu+DblO2dd97h3XffZevWrTQ2NvLMM8/wzDPPiLIOgiAIgiAM27bNNcMOeHMnp1BQlMOV1+UD8MlH5X4D3pPJvTKGGhl5lkzOfclYthlp3NlBe9vQ4/eONwSs1wtgbdJQXCFfgl9nL6xGDlJidlNPIh3ewPskaKoUMOA9xtjtv6xDqCSvHjDgPdna1Xv44YOXc8M5swgzG/l4eyWb9h9CavX4DXhPpqk6az/ewznnT+Nnv7mWN18to6ykkqqKpkFt4xPCSRkXg6rqVOxvPKXN1SZNSR3wuTrZldflExZmYv3a/WzfWjNoDIvFyOx5WZx17hSWLsvz28fPH7mWN14to6ykioN+riMhMYL8olwuvXwO4RFiTwxBEARBEIQvgzEJeU82d+5c5s6dy89+9jMaGhpEWQdBEARB+JxqbOigrKSKfeWHSUqOIr8wh0lTU1GUICnjKNC00IumZmUn8Jvf3UxsXPhJfYS+MdZZ8lS+X3gZMRE2uAY8HpUVb23hid+uDHreUIt+pVBS5iHIcvA+gm9TxgmFggOTPus0h3n+3vLD5M1I58pF07ly0XQcbg9PPrWKD17eEtL5DYc7aD7SRWJyFF/95jl89ZvnsHNfPW++vYUD+xpJSI7kogtmct6iKf2fXafTw1O/X8mK/2wLaYyciUn8+vEbiYkND9pOUWQuvnwOF18+B6fTw/YtNWzdVI0sS8wrmMCMWRmYzMEf0WNiw7nzm0u585tLaWnuZlNJJfv3NpKSGk3BwlwyJyQgBfllgiAIgiAIwmgRG6idPqcl5D3RiWUdnE4nq1at4p133glY1uHSSy/l7rvvZubMmad76oIgCILwpdTe1str/9xIaXEFh2rbBhx76R/riYyyMr8gm0uvnEPezPFjMic52NLVkySlRA8KeH19hB6KbXqjipve/j2Z2YnIskTj4U56uv1vknWiocJRfQSCOU0L3ocyVMIawhSGUaHilMc40f13P090dBjzFmSz/Mq5TJ2exsS0RD4I8fzqymZuvuqPpGbHIWeE0yZ5qG/t8h00w94OB5++vJL/fXcdRdMyuXbJTGZMSGFCTlLIc0xJjRky4D2ZxWJkwcJcFizMHdZ5J0pIjDwaGp9yF4IgCIIgCMIXwNgusxmCxWLh0ksv5S9/+Qv19fVs2rSJn/70p8yePRuAxsZGnn76af7zn/+c5pkKgiAIwpfXL3/8Ov/+18ZBAe8x3V0OVq/czf33vEBHe++YzGle/gTi4iOGbKcoMucG+Ar8eRdOR1ZCTx+9Xo3K/Uc4sLcxpIAXIKLWC0FWDDuSZbzWz5agrlqbjaoG7qMgool4Q+D5yoqOKTx4zQh3FOjDXY57At0g4Y5Uhm54gs7OPlZ9sIvv3f083V19zFswwW9YH8xeyc6OttbjAe9JOnodrCjdy9cee5XOXgfzF2SHNIbBIHNOgM+VIAiCIAiCIIyF076SN5hjZR1++tOf9pd1ePfddwkLCzvdUxMEQRCEL62Gw50htdM0nSONXcNe3RgqTdORJJAkicTkKJ577W62b6mlrKSSsuIKjjT6grwwm5m5+RMoKMoJGtqdf9EMZs/LYtPGSspKqti4/gBeb/AyEMdizkCRqn70mA54bTKyUyamHJxx4I0Ar1VHN/jONvRq2BpU3JEgO0HWfStmj63+1WTwRCh4ImRUo4zRoWHsVjH0aQPG+M++yaz5TRYL8g6xcGIdC7IOEWbxAtDQGEHplnFM2+Fkmz0CZxZ4JqroUb5BdI+E3mwirAlMnTpek4QnHFTr8SsxWL0Y491IEz1InQZoNqE3mvrvguzSMHWrGHp9tWY9EQruaAXd4FtboMs6HhscKTQhe3TCmlXCGlXMbdqQC3x1wKtqNB/pJmdSMs+9dg87ttRQWlzJujX7hqzLrJpDW9/g1TSaOnqYlJ44YIzS4gqam7oBsIWbmVeQTX5hNvMXZA/4nGuaPqyV4YIgCIIgCILwWY1ayPud73yH2267jXnz5o1IfyeWdRAEQRAE4ctH13XqatsoK66krKSS3TvqsIVbmFcwgYKiXOYW+ILcgqIc9O9dwKHaNvr6XORMTMZgCG3VaFx8OJPmjafVqlHa10pXew86EopTxdSrYexR0RXoS1boS1XoS1bQDGBt0QhrVAlrUDHYdVSbjDvCgCtGQXHpqBa5P8wFHbOmYrG4USKdeGIlZI+G2SlhdCsYuxSko/UQvFaJ3nQLkqajWuXjm7ZpOkgSkqojeTUMblDN4A1T8NoUmjzh/Gf7VP6zfSpRjX1Mi2qmp9dMQ2MEx8JYCbDuA+VTiZ5ZJjDo0GXor8VgOPrH0gZahIrmVZCNGkqiFznMCxFeFI+KwevFdNCNphqQvTqKe+AKX3O3iqcVdKOEpEu4w4EUBU+kgitCQtZkZLeOuW1woK4DnkiJvhQFe4qCM0FGdsP/7izhMmkqi8dnULAwl4KFuSw+ZzI/+M5Lw/pMhcJiMfaPoesXUlfbhrPPTfbEpP7PlcejsnVzNWUbfJ/NQ3VtZGUnkl/o+zxOzUtDMZxRX6ATBEEQBEE4ZaLu7plp1ELeP/3pTzzxxBNMmjSJ2267jZtvvpn09PTRGk4QBEEQhDESGWWlrbUn5LYjYc+uev73l29z+FD7gNe7OvtY/eFuVn+4G1mWKFw0kR/85DLCbGbGZ8YPa4wdVQ389PkPqWvuPP6izfeopNoUXAk69hTwhuvHw9ajHMkKjmSF3vFgaaU/pAXwGk9oGONBmdODFK72v3TssDcavDNVHBoY9iuEvWVCUiU0y8Bw0NLkxtrsQT4pEzW4wNDiwdriQZPBkWzCmWDELRnYX5EQ8LplFegN/kgo9xh8Nb4cBvRuA9J2M+H1LhTX8UBXIfCqZ6MLcOmAjtEOtiYNtnv6Vzr744iXac434Y0YeP2aBVY1VLOqoRpFkjhvQjaPXXARUdFDf9NL8uonhO3BRdkGf3YlSSLjpM/VS8+u45WXSnD0uQe8Xl3VTHVVM6+8WEx4hIXbv3YWV1w7P6SxBUEQBEEQBGG4RnVJga7r7N+/nx/96EdkZWVx7rnn8vzzz2O3B/8qnSAIgiAIZ67/uv9CpualnZxzDpCcGs1d3z6PcWmxIzLmKy8WDwp4T6ZpOhs+3c+m0qpTGuMfKzcNDHhP4gn3lVgIeOEaWFoGBrwnk3P7BgS8/huBd4qKN1tFcelEtaqEKUdDWFUn7MjggHdQFxqENbpB13HGGVGSrBhM/lczK06dqLYTxgiBtdkzIOA9VcHi1s4pxkEB78lUXefDqkrW19WSlZ3IHd84m8SkyIDtIxrcRKAEHTctIYrvX3s2ybFD13i29zp57um1gwLek/X2OHn6ydVoQeoxC4IgCIIgCMJnMWoreVetWsWLL77I66+/Tk9PD7qus2bNGtasWcM999zDlVdeya233sp5552HNAI7SQuCIAiCMDamzxrP7//6Fbo6+9hcWkVZSSV7dx8mKTmK/KJcCopySM+I6//7vburj+1bapEkmDk3k8jI4a/udTiCh2gD2g4RuPmj6zpt3X3B2wzxq3FJB2moqrKG0EO++WfncMu3FzFtRjqKIlN9pJ0PS/byxu41IZ0v6fDt5QtZOjeXjKQYPB6V3TsP8enafWwsrUSWJYoKc1m8eFL/GAcb21i55QB/e680eN9jEFZqw3hKbeuxI0kSN92+iBtvW0htdSvF6w6wdnU57W29TM1L4+zzpjG3YAKRkVbau/so3lPDh5v3s6e2ieTYCC6YN4mzZmQzPjG6/7Pb1dnH9q01yLLM7LmZhEdYBozba3ehh3grXC4vuqbDCNfq1XWd2upWDuxrICU1RpSGEARBEARB+JIatZB36dKlLF26lCeffJK33nqLF154gY8++giv14vdbuell17ipZdeIiUlhZtvvplbb72VvDyxK7EgCIIgfF5ERYdx7gXTOfeC6YOO1Va3sH7tfspKKtlXfrh/BaMsS0zJG0dBUS4Ll0wadkmFkaRpOiV7ali3u5oNu6s53NZ92ubiz1nnTmXmuEw6ex2s2VHF+t3VlO6qwTL0qf3+87u1HC6oJ21OMh0JOsWNh9hmaERf6Du+SdrDp1WdnKM2c0FODtmp8dwQETZkyHumefjlj1m1ej/zJ6Ris0tUlTewdVN1f+BfvO4ANdUtlO+uxzY+giNuB+t319DY7nvPO3odHGrpory2iSmxsaitTnZsqmHfnsP9Ia6sSEzLS2P6/Ez0eDPljS2U7qhmZAqSDI+qamzaWNVfn7rpSFf/sfAIC/PyJ5BflEPR4onYwofziREEQRAEQRjaQw89dLqn0E/UBz5u1ELeYywWCzfccAM33HADzc3N/POf/+TFF19k69atADQ0NPDoo4/y6KOPMnPmTG6//XZuvPFGEhMTR3tqgiAIgiCMgo0bKvjpD1/1+9V0TdMp31lP+c56/vHXNfzy0RuYvyB7yD4nTUll2+aaIdvJskTOxOSQ5vnQiyt5p2RPSG0BFCcEKyKry6AadRRPkJWanQZI8Aw9liQzKTKFXoeLa37+PO09R1cZ6zpGkzRoc7NA+uxu3t93gCPxNXBo8LxUXWdzw2E2Nxzm8ZINPHvFVRSljSc1LpKGIKG3N0zBaB+iZsRnZG7XcCaGsGGeDpJDZ/v+emr+cwDZ679ZQ30HL5buxH3A/+Nvr8PF2tV72FLr8vsWa6rOjl2HWOtpQzcebaHrmIwSimfo92NCbtKIrbB95Of/4ZOPyv0e6+1xsmb1Htas3kNSchTPvnI3RmNoGw8KgiAIgiAIn19j+l2uxMRE7r33XjZv3kx5eTk//OEPSU9PR9d1dF1nx44dfO973yMtLY1LL72UV199FZfLNZZTFARBEAThM9q2uTqk2qOaprNtc3VIfd7xjXN44KErOef8aUREDF6ZmJEVz7U3LeCJZ+4MOeQt2VMbUrtjzJ06cdu8RFR6MXYPDjhlt44ue1HsHgy9KoO+x6/pyGUWlJVWDLsVJMfgMbLCE7klazHPFd5NbkQKB+pbjge8AJJEV44Ve4oRj00mlKi3L1kOXEf4BKquU3yoDlmWeP4HN/LdKxczb2IaBnng46Isa0Tmd5GwrIWkeS0YbYNDa69Fwhmt4IxWUM2Dx46KDmPJOVM478I8Zswej6wMbKPLYD2iYm3wgur/KmUXmNsgogYUt4Ti0AIGvMd4IoKHncZeNWjBDdUqHw94wfd+5AZ+PwwGmTnzs/jWd8/n0T/eEnxyw7C59GBI7ZqOdFFf1zZi4wqCIAiCIAhnrlFfyRvIlClTePjhh3n44YdZs2YNL7zwAq+//jrd3d14vV7ef/993n//fSIjI7nuuuu49dZbWbRo0emariAIgiAIIQq1RinA3t2HOVjZRFZ2IpIk4XJ52LG1lrKSSmoOtpCVnUh+YQ4zZ2dwzvnTOPu8qRzY18h7b29j+5YabDYzZ503jWUXTScmNhyAttYeNm2sYtPGKvrsLmbOziC/KKd/jCEnquvITg1LqxfdICGrOsZuddBqTY9Noi9FQTOAtUXD3K4hndBEk32homqRURwapl4VSQOqJMCMLumoaRreDA3JAdfOKeSWyxcNuI6yTysIr3EiaeCxyXgiDagWCWeiCWciSKpO9J4+vxux6fhCSTQZ2yFfcOoJB68N9ABPgFsbG9nX2sKkuHhuXzaP68+exbpdB/njRy/S2WEmOraHnGl1pGW2ohg0dB2aOyPZ8EEe9jobusG3yldSZWT1+L2WXRrGHhVJ1cmZksKPvnEhE9MTkCQJp9vL+u1V/Pjpd6HTiydKojddwRMhDViOYGrzbSgnqWDsBdk78ns6uCMU3FEKkhdMPSrGbi/yifvk+RlSNw58P4w9KopTIyY5gtce+xrWMFPI47e29FBWUsnmjVU4nR5mzskgvzCHzAkJoX12/dCH8x+kIAiCIAiC8Lkl6WfQk5/T6RxUv/cYWZYH/Lsw9urr60lPTwfg0KFDpKWlneYZCYIgCGeivz2xmldfKhnWOVHRVswWE50ddtyuwX/fm80GomJsuBxuurr8LIEFEhIjAWhp9l9mICExggWLJnLbnUuIjrFxyY+e6a/JCiB5dMKa3Ji6VGTv6Xs8Guo6VKOEJ1KhL9mEbpCI3tM3IIDWDBJ9SUbcUQq6cfCXtjR0nIngiQwc9iZqVqJ7jHS29OH2qoOOyxYvpLtwmA04vH5CTA0sLWDqZkDYe6KYcCsWk5G2brvfMXRJx2sDZxyoVrA0gaUjcLBrsKtEVToDHgdon2r1e08C0nUMfRrmDi/mNi/eMJnu3NCq8OakxvHqT24bsp3q1Xjj1TJWf7iLqoomv20SkyIpWjyJW+9cTGRUGNcv/x3tbb0hzePv//oW6RlxIbUVBEEYbV/EnylPvKb77ruPqKio0zwjQfji+aLV3R2t/y88o7bePVa/d8WKFWzbto1p06b1r1o4g7JoQRAEQRCCOPeCPOITIoZ1Tleng+YjXX4DXgCXy0vzka6AAS/4QtFAwajveA/vvLGFvz/1CQC3nDtnQCkCW6MbS5v3tAa8MPR1KB4dS5uXsEbfpmLOBOOAMgH2VBOueGPAMFMNB3ds4IAXDVwVDpobe/yGrwA9MQrtSpj/gBcw2n2BbKCAF3ybnTW2dwccQ9IljL0StnpAB08UaIbA743XKuMJD/5oa232DG+puSThtSnY08x4bTIGh4ahx/98T2Q0KNy0dE5IQxSv289f/7QqYMAL0NzUzVv/3sTzz3wKwDU3FgwqceHPwiWTSBsfG9I8BEEQBEEQhM+301auwR+Xy8Xbb7/NCy+8wIcffihW7gqCIAjC59CEnCRefP2/2Lf3MGXFlXz0/k5amntO97T6tbb45nLj0tlcWjiV0r21rN9dw4aabSH3kZQcyXkXzSC/MIeISCtbyg5SvHY/27bUjNKsB7Mgs2xhHgvzspg2LoH9uxooK6nkzcrKoOdpQzz9SZovYA3ah3GIPkbwEU5WJc7OyGRp9gQWp4+nva2P9bur2VBew4H6FgCmZiSxcFomi/KyiFaMbN54kLKSSnZsq8XjVolPiCC/MIf8whxypqWws/YI68ureb9sH141tA3kEjNiuWTBFPILc7DEWti4t4715dVsOVCP26uSGB3OwrxMFudNYP6kdGyW0Mo0HPs8DqfttTcVctHy2WwpO0hpSSWbSqro7LBjsRiZNS/Td60LsklKiQ65b0EQBEEQBOHz7YwIeT/99FNeeOEF/v3vf9Pd7Vu5cmzlbnR0NNdeey2333776ZziGaeuro4//OEPrFixgrq6OsxmMzk5OVx33XXcfffdhIWFne4pCoIgCF9iikFm2vR0pk1PJzEpit/973une0r9dmyt4XePrCC/MIfZ87I4b85Ezpszke9vamZ7yCGthCxJyLKEosjIkhTS5mYjydirEnHYjSVDwyDJSLKEJI/tHMbKU5cux2Q0oKoavfW9hDd5iKlwEnPQjgTEeJyEJ3gxOXTGTY4j/bp4rrwuH4fDTU+Xg4SkyP5vh9ntLizdKpH1bgx9Gl5zaHOQJPrf84ykWLJS4rhx6WwcLg+ddgfJMRED6+aOsvAIC2edO5Wzzp2Kpum0NHURExuOyXxGPN4LgiAIgiAIY+y0PQXu37+fF154gZdeeom6ujrgeLBrMBhYtmwZt99+O5dddhlmc4hP318SK1as4Oabb6arq6v/tb6+PjZt2sSmTZt4+umnee+995gwYcJpnKUgCIIg+MQNs3TDaHO7VVb8Zxsr/rMNo0nh2/ddwMWXzyEuPjzkPpqOdPHC39fxwt/XjeJMg/OccB0nUsebwRL4EW+oVba67KuHG2w1r+yBYEULApaCOAXR4VYMikLlgSP85PuvDFj5eqw4w749Dezb08Dzz3xKSmo0v378RtLGx2G1mrBaj6+ofeOVUv72xGq8Xt/qXW28GUIMRZtq2nm+/NPBY5iNWM1DLG0OIi4+9P8+ArWVZUms2hUEQRAEYcx80WrkflGMaU3e1tZW/vjHP5Kfn8/UqVN5+OGHqa2tRdd1dF1n5syZPP7449TX1/Puu+9y7bXXioD3JDt27OC6666jq6uL8PBwfvWrX1FcXMzq1av5+te/DvgC9EsuuYTe3tA25BAEQRCE0TR/QTZ3ffs8snOTTrkPo1EhITGSiAhLwDZx8RHDCmrBF5S+9q+NAHz9nnO5aPksYmJtpzzPoWgyeKygBvkmvxahoUWEVkLAH1uDG3ObB8njvw9jL4x3h5NsCXCdMtiTQTXp6PivX2ttAVNn4MDYEw7OGB1NOfX6xhajgUV5WTz2jeXIssRH7+8MqbRBY0Mnn3xU7vfYqy9t7A94AcL671XgeSp9KmGHXRjsx89rbOjkk1X+xxiuhUsmcee3lpKVnRiwTXxCBMuvmsttdy4ZkTEFQRAEQRCEL55RX8nrcrn4z3/+wwsvvMDKlSv76+weW7WbkpLCTTfdxO23305eXt5oT+dz795776Wvrw+DwcDKlSspLCzsP7Z06VJyc3P5wQ9+wL59+3j88cd58MEHT+NsBUEQBAEURebamxZw7U0LaG3p5q3XNvHKiyUhnWu1GvnRL65m1twMzGYjmqZTeeAIZSWV7NxWS3i4hfmF2cxfkNO/2VtrSzdvvlrGqy9tDGmMnm4njj43cfERfO9/LkXTdA7sa+DRX75DbU3rKV83QHeGgtGu446W6UtVcCTI6AYJdB1Tu0ZcowNzA+hmHU+eime6ih7he0aSuiVMpQYsJcNbJSp7dcLr3eiAN0zClSrjjVCQNLj+nLncdkEBCVG+MLyxp4cn127k1e270GUw9IGhFxQXSARZyeuVCDsCOjpei29TNK8NUMHU7fsTbNO1k+mSjqaABJjNRn5960UsnJKJxeR7VFVVjabGruCdnMDt8Z8+e056Xem/V25Uq4w7UsFjU5BVHWO3iqlHDbgR35GGTjRNR/6MJTIUg8wNtxZxw61FNDd1UVZSyeaNB3E43Mya66uvOyEncUxLQQiCIAiCIAifP6MW8q5du7a/zm5Pj2/VxbFg12q1cvnll3PbbbexbNkyZHlMFxR/bm3atIk1a9YAcOeddw4IeI+5//77efbZZ9m7dy+/+93v+J//+R+MxlP/CqEgCIIgjKT4hEgWLJoYcsgbEWmloCin/99lWWLi5BQmTk6BOxYHHKNw0cSQQ96uzj6uvugxps8aT3xCBC6nh+1ba+nq7Avp/GDaZ5pQrSeEc7qO7ZAX22GVsCMqiuv4M5DhiIJpp4YnV8WT50VL0vFOVGGYIa+OjneKimeSijdbRbfRH9e+wmq27C5nkjYerd5MeUUzBxvbsQUJdAON4Ynwrdj12gaWZ3BawR0Fpk7dF/Zq/vvWFB13JHjDwRvG8Uni4Z51KyioGseEDityrZPtm2ro7nIMa47DIQEGh4bBoQGekM5Z+d5ONm2sYv6CbBYumUTh4omfOYhNTIri0ivmcukVcz9TP4IgCIIgCMKXz6iFvOeccw6SJPUHu5IksXjxYm677Tauu+46IiLOrPp8nwdvvfVW/z/fcccdftvIssxtt93G//zP/9DR0cGaNWs4//zzx2iGgiAIgjC0CdmJRMfY6OywD9l2Tv6p1ZfPykkiOjqMzhCDWo9HZeum6lMaKxhrk0pv5vHHrdhdHmL2Bi6KqzTLKM0y5hIDvV93oiZpaGE6cl/o4aHrXA+uhYHHqKhrZd+nXhhmsHsiZwK44gIfl3QwdwZeDayj05sBWoCyFW5VpfzdShr2DVFA2A9ZkZg1J9PvsTnzs1izas+w+wyko93Oyvd2svK9nXz1m+dw420LR6xvQRAEQRCE00nU3f38GdVyDbquk52dzW233catt95KZmbmaA73hbdunW9zF5vNxty5gVd4nHXWWf3/vH79ehHyCoIgCGeUMJuZp1/6BhvW7qOspIotmw7idBxfPZmUHEV+UQ4FhTnML8wJ0lNgNpuZp//5zf4xNpdV4XIOPzD8LJJTorg0LQdjbgQNZief1FQjtbWEdK6kSRiOKEyelMrcRydg3W+iemszWzYdHPI6vOOC1/PVOw0MJ+BNjYukaFomseFWWrv7KC6vocLaHfQcxRm83IOuBA54j7G0hV6XWJJgSl4aBUU5LDxrMhmZ8X7b/feDV3DO+XmUlVRSVlxBS/PxGr9hYSbm5k8gMzsRt8vD9i217N/bEPIc9pUfDrmtIAiCIAiCIIy0UQt577rrLm677TaKiopGa4gvnb179wKQk5ODwRD4rZs8efKgcwRBEAThTBIVHcbFl8/h4svn4HZ72b2jjuambqbkpTE+Iy7kr717VJWtjQ18UlPNhro6JAkWpmdwTlYWc1JSueiy2Uxekkn8tlT+8+NPRvmqjps+azyPPXFr/3V0dzs4x5vEn7wr6SW0sgPzPVO5NW4RMyakosyUqF7WQdzWPbzzkzWjOPOBouPD+Nati1ickUGk2UKXu495Z8fzk/fX0WYPrazBWPiv/3chy6+cN2Q7xSBTtHgiRYsnous6NQdb2L+3geSUaPJmpmMwKAPa//O59Tz7lzWnNKfurj42lx6krLiS/ft8YxQU5ZJfmE1qWuwp9SkIgiAIgiAIgYxayPvUU0+NVtdfSk6nk9ZW3+YvaWlpQdvGxMRgs9mw2+0cOnQo5DHq6+uDHm9sbAy5L0EQBEEIlclkYM784ZVlaHc4+PW6taysqqTX7R5wbHdzM3/ZsgmzomCQFeweN5JXJ0vylREYCzGxNiRJYveOOp7+88fs3X0YTRve4CXlh1jz+KuQZECNk+jVPEieoa9Dcgc+BoAS+jyaXXb+6/0VmMM8JKR6cSl96Oh0EwkEWYo7REYv6YAevJ02jKfUmNjw0Bsfm4MkkZWdSFZ2YsA2sXGh92ux+mon79hWy9+f+oR95QPf8/q6djaXHuSJ30L6+DiuuHY+l109dDAtCIIgnF7i52RBED4vRrVcgzByjm1eBxAePvQPHMdC3t7e3pDHSE9PP6W5CYIgCMJYe2nnDt7YG7y2qktVcakqALpBoj3PSMxeD3KQagdD5I4ci+yCtUlNi+GaGxcA8Pv/fZ+a6tBKNJw4hidSwR2hoJmgJ8YLRysX6EaJjmlGovcFvg5zsRE10Y0e7T/MlZLdSIlu9GZj0CtRjTquowtOrYm9OBW1/1hYbB89bgXNo/g91xMBnh4dg91/2QZJk7C06jhjdVD8z6FzshFLtwfFHrhsgyxLLFiYy9xTrN08lKIlk1izag9byg4GbZc2Pparri8A4HePrKC+rj1o+0N1bfzxsQ8oXDyRhMTIEZuvIAiCMPLEz8nCl9VDDz10uqfwuXIm1DA+bSGvpmmsWbOGkpISjhw5Ql9fH7/85S9JSUnpb+N2u/F6vSiKgtlsPl1TPSM4nc7+fzaZhihiB/33y+EYvZ2oBUEQBOFUHa5vp8/uYkJ2EopBHnS8p9vBodo2xmfGEx5hGXS8wzn8v986pxrpnGTA2qIR1qgS1qBi6tVxh0v0pSr0pSo44iRMvfQft7RpaAboS1H6/0g6WI/4joc1qsgqxGRFcd3F88gvyh1QbqKrK/jGb6pJQnbr6Ap4IhTckQY8EQq6wXe+5mfVbcc0Ix2T/VxHhO867ClmnA02lA4NU7gbS7QD2XBsI1yQTDpKYRe6S0JvMqE2WKDFCBp4w8BrA0/40Zq5R/NXWRkYtBrDvMRkdaC6Fdy9JhwdFnT1eOCrK2BPB8kLBruOpU1HcUoggaFXxdSjEr1PRfLoOBNk37xTFbzhMtY+uGXRXM7OzGLOQ6k01LZTWuyroVu+u57wcAvzC7LJL8phXsEEIqPChv1ZCFVkpJXf/O4mOtp72bSxylffubQKl9ND3szxFBTlkF+YQ/qJ73ln6J/N7i6HCHkFQRAEQRCEEXFaQt4VK1bwne98h5qamgGv33///QNC3meeeYZvf/vbhIeH09DQgM1mG+OZnjksluM/4LrdQ30PE1wuFwBWqzXkMYYq7dDY2Eh+fn7I/QmCIAjCMW6Xl53ba31hXUklDfUdAIRHWJiXP4H5hdkkJUexd/dhSksq2bOrHk3TkRWJadPTyS/MoaAoh8wJCSHX6x1E1zH16JjbNcztGka7L/g02nXMHRqqRUJHxmjXMHdomLo0JB0UD/3HVROgg7ldw9KhoRxdTdvbaOfA3kaiY2xERVmJjgntmcWZYMQZd/Rx7Oh16ZKON0zHY/OtiB18HSB7JDSjgitWwWsFSQNdBtUKusG38ZnSIyNVG9D6wtA133nYVKRkN8S7kfSjm7B1K0iab2zF6QucNYMvqNWDPClKEhjMKgazA80r4+w86ZlD0jFEejClujFOdyNZNNBBcoFepeCtUNBajLhiZFwxMt4w3xycFtjZ1ERcWBhxYWHkTkggKzuRG24tQvVqSLKELJ/iZ+AUxcSGs+zimSy7eCaapqNrut9fTgiCIAhfPOLnZEEQPi/GPOR9+umn+cY3voGu+36wio+Pp7W11e8PbHfeeSc//vGP6ezs5M033+SWW24Z6+meMSIijv+UF0oJBrvdDoRW2uGYoWr9CoIgCMKpaG7q4t5vPkdLU/egY709Ttas3sOa1f5LL2iqzq7tdezaXsczf/6Yc5fl8d8/u4L0yKhhzyOpxE34IXXQ65IO1hYNa0vgsgCmHh1Tj5foA/6Pex3e/uswGGT+52dXsmTpFFJSY+hotwfsV3Zp/eEugGbQ6ckA3Rj4OsIawNRz0nOT7Ft0a3D4/gRkN6BXGaAqzG/pCVmTMPWAqccXNvel+oJm1aMgGwLXuZCNA++dbFCJGt+FctLrSKBbwZOn0hYThrt38De1dBlKD9dTerie36xfx9VTpvF/yy4AOCOCVVmWIEjInJIaTU/30Kt5FUUmIdFfii8IgiCcScTPyYIgfF6M6ZNyZWUl99xzDwBLly5lz549NDc3B2xvMpm4+uqr0XWdlStXjtU0z0gWi4X4+Hhg6MLvHR0d/SGvqB8kCIIgnG5bN1X7DXhPxeqVu3G5PNw6cxYPnnU2+ePGofj5RbFBlpkSH8+0hEQizWZkt+434B0NXq/Gqg92AfCTX13FldflMy491m/bBJeBmeGxTEqJR5ElPLbgAa+k+gl4R4mkSxi7fP8c15fOBMN4Ui0xftsmJcLc7CimJMahSBImm3twwHsCTZX8Brz+vLG3HLc6Nu/dSPjpw9dwxbXzSR3n/15FRlk5d1kejz5x66iWmhAEQRAEQRC+XMZ0Je/vfvc7PB4PeXl5vPfeeyHVll28eDFPP/0027dvH/0JnuGmTJnCunXrqKysxOv1YjD4f/v27ds34BxBEARBOJ1Ub+Cw71Roqo7ZLPOVWXP4yqw5tPTaeXHTNj6uOogEnJeTw83zZhIX7iuZ4NU0Nhyo4Zdv/nNE5xHM4a4u2vr6iE+I5O57l3H3vcso33WIt1/fzP49jcQnRnDhJTM5+/xpGAy+WrZtHXb+5833WdVTF7hj/3upjZrUSBuP3byc6fGp/a9tbz3Ey5Wl7O0+TJIlmsszZnNB+jQMsu86mnp6+dnGt9iiBtkYbxjXoQM1B1vIzUkKuVSHrutUVTRRVlJJa0sPU/PSmFcwIeQyGgBOt5ctBw5Rtv+Qb4O3KRnMyRmH0eB/s7ljEpOiuOe+C7jnvguoP9ROWXEF+/c2kJIaQ35RDpOmpKIop39F8jEn36tp09OYV5BNVLQIoAVBEARBGFlnwuZkX2RjGvKuXr0aSZK49957Qwp4AbKzswGoqwvyA8+XxKJFi1i3bh12u50tW7ZQUFDgt93atWv7/3nhwoVjNT1BEARB8Esa4fqpkiyhaTrvle1l1dYKyvbX4XQfLyXwjy1tvPLWVgomj+e8OblcNH8yc8elBulx5O1tayH/b08xPTKBcbUyam0fVfub+o8frm9nx9Za/v6XT0hNi0VTNQ7sa6Q5DZgX5BlpbEvR0qZ18LXNT5AbNg65N4r2To09LS39x3fRxqptq0gNLyVHikbp0tlb3YRrXA/KzJGbx913PE1yYhT5hTlcfPlsciYm+223t/ww77+9jbKSKtpae/pff+eNLUgSTJ46joKiHK64dj628MEb+mmazjsb97B6WwWb9x/C6Tn+uXpu5WbCzEYKpmSwbM5Els2bOGTonJYeS9r1/p/XTrc9u+t5/53tlJVU0t56vBTYgHu1MJcrrpnn914JgiAIgiAIZ5YxDXmPFSyfNWtWyOcc22ytry/47tRfBldccQUPP/wwAM8++6zfkFfTNJ5//nkAoqOjOeecc8Z0joIgCIJwstnzMomODqOz87P/XV64aCIWi5HX1u7g4Zc/DtjO4fKwZkcVa3ZU4faoXLEwj/kLstm0seozz2EougS96QZ0oPWtQ9iD1Pptae6hpfl4GBl2REJ26mgW/+GhroDHpmO0j0XaqyOlutDRKdvXg9fhDNiyta6HvuYT9gxoMaK7JCSz/yW7skHHGObG0zf0L/1th7xIGjQd6eKdN7fwwbvbeeWde4mIHLjRW0tzN/d+4x9omv8xdd0XAu8tP8y+PQ384v+uH9Tm5TXbePS1tX7O9ulzefhkeyWfbK/Eo6lcWjB1yPmfiZoaO7n3G/9AD7Ci+sR7dWBvAw89ct3YTlAQBEEQBEEYtjENeY+tdtADPVH60XJ0tUhkZOSozOnzJD8/n8WLF7Nu3TqeeeYZbr/9dgoLCwe0eeyxx9i7dy8A3/3udzEagxT2EwRBEIQxkJIawz9evZvNZQcpLa6kZN0BensCB4Ynm5CbRH5hNgVFuUzN821+UtPUHvL51Ud8bX/xf9ezZ1c9pcWVlJVUUl11fF+ACblJFBTmMCE3kbaWXjZtrGLntlo8Hl8t2JhYG/mFOcyYnYGOzo4tNRRvqMDe7bsOTQZnoow9VaEvRcEb7vs6vrFneKUqjHad8e85cCQr2FN8fR0LfBUkFmVkcNaSTJKxcaCmmfW7q6lsaAvap46OdHQJsKboeMLBezQfNfaBoQ9k79HQWNaR4t1ISW6kZDdSmG/+qjt4iQLFfdILdgPqqlikxKN9Jbn7A19FlylIyKFw8kQi1Bh2NrawpqaaA23Hr8PUoRHWoGJrVDG3DbyHHo9KU2PXoJD38KH2gAHvyepqW/2+XnOkI6Tzh9v2THO4vj1gwHuyQ7XBP1+CIAiCIAjCmWFMQ97U1FQqKys5cOAAc+fODemcY6UHMjMzR3Fmnx+///3vWbhwIQ6Hg2XLlvHAAw9wzjnn4HA4ePnll/nrX/8KwMSJE7n//vtP82wFQRCEsaBpOhX7Gyk7IbzMyk4kvzCH/KIccielII9wyYThsoVbOGvpVM5aOpWd2+u4/+7nQzovLj6cvzz39dAHUnWMvSqmbhVjj4qk6Wyxl/OeIZz8ohymzUjHZDJgMilIksSh2hbGZyWycMlECopyyZmYTGtLN2aLAVmR2LWtDlu4mcJFEylYmMusuZlYLEYuuHgmGw8d4o6nX0bygCtORjccvcc6KA4w9kLveAuqWcbg0DB2q5h6VBSHhjdMoi9FoS9FxpmgILt1wo6ohDWoWJs1bIdVTF0Q1gDORAXNIJFtjuCyubkszMoiPspGSkokWorC7k/a8Kq+xM7gBEMvKC76g13VCIpXR1PAawNPBHjDABk80WCweIiKtKN7JaQYD9LRp0NdB73DgNZkwlYro7hANeMLicN9/ywbVUw2N+Y4D0qUB9wSerMJvcmE3mpCb7CgN1h8NyXai2TQuXHuAu6bd1b/W3Zx9hT+e9ESLrvqcdpwYurSMThCXxDg6HOzdVM177+zLfTPiSAIgiAIgiBq5H7BjGnIu2TJEioqKvjnP//JjTfeOGT71tZW/vKXvyBJEkuXLh2DGZ75Zs+ezSuvvMItt9xCd3c3DzzwwKA2EydOZMWKFURERJyGGQqCIAhj6e3XN/PC39fR2WEf8Pq+PQ3s29PA8898SkysjVu/uoTlV4X2C9bRFnnSCsxgAm3+FG41D3xB1Qmvd2HqUpFOygebKtv47SPvAaAoMqo6cGVo1YEjVB04wvNPf+r3uNPp4d23tvLuW1sxmhSWnDOFe394CdFWC67YgStcTe1gaQNZPRqwWn3HvTYFr02hJ0vHkaSjWgeG7ppJojtHpjvLSFijLyA+FtLKqu9PrauHX7y0CleMjidRxisdnecJt8gbAUo4hDUeX11r8BxdCewFpQvMXaBLOp4I6EsGXZeQYrwDyv1qVVa0A2Hg9q1IPvbAaHD6/uD1IM/qQY5SB95so46U5YQsJ7oK+mEz2o4I0CToNKIDMVb/72mMwYyjPrSSHrYIM3a7i9/+ZgXFn+7vX3EdqvAANWYjwsx+X/fb9uTP4OfIcGrs2iJEPV5BEARBEITPgzHd2veuu+4C4L333uPZZ58N2ra+vp6LL76Y1tZWFEXpP1eA5cuXs3PnTu677z4mTpxIWFgY0dHRzJs3j0ceeYRt27aRk5NzuqcpCIIgjDK3y8tTf/hoUMB7so52O3/+/UrcJ2xOdjplZMVz8x2LiA4Q4B4zISeRb37nfL/Hrlkyg7NmTMBo8IWopm4Vc+fggPdkJwe4wz3ucaus/nA3mzZWMSkunnvmFxBnPRpaa2BtPh7w+uOMY1DAeyJjL5h6pf6A92S6pONI5HjA64elDRR38JXbki5h6pYw2kF1KbjabRh0X5Sre0Ert/UHvP7IE/sGB7wnj6GAPN6FlOhLm00GhXNn53B50TS/7e+57wIysxKC9hkbF85X7jqblNQYij/dz9rVe4Yd8GbnJvGN/zrP77HrzprJ4ulZGJTA1242Kpw3J5fLAlzH50HupBSuv6WIyKjgv3DJnZTMXfecO0azEgRBEARBED6LMV3JO3/+fL75zW/y1FNP8bWvfY333nuPa6+9tv/4zp072bVrFytXruTll1/G6XQiSRL333+/CC1PkpGRweOPP87jjz9+uqciCIIgnCYejzfkgMvjUfF4VEymkf+rv7mpi/17GkhMjgqpNIQkSXzl62dz251nUbGvkdLiCj79eC9tbb1MnprK4rMnM78wh4TE4/X4Tx4jISqc337rcvqcbjbtP8Srr2zkQF31iF9bIF09dt8zStFC7issYlfTEd7fe4BXD2wNfuIQv14Pkt0CoMsQIP89bhhlgBekpHH70nkUpqVjMijs6zrMRzXlPK/vDT5PY+jlFPLz0rnpqkLmTUrHavLtFaBpOnvqmmjq6GF6VgqJ0eG+8iKFOTQ1dlK2sYp1a/dxYG8jCcmRnHXWFAoW5pCdm9z/+ersCH0jv+gYG1/95tnMX5BNfELgfR6SYiL4/d1XYHe6KdtXx5rtVZSU1yBLEotnTuCsmdnMnZjWfx1jQVU1DuxrpLWlhynTxhGf8Nm/qSXLEl+7eyl3fONs9u9toKykkrKSKtpae5iWl0Z+UQ7zCrJHZCxBEARBEARhbIxpyAvwxz/+EbvdzgsvvMAbb7zBG2+80b8h280339zf7tjmbF/5ylf49a9/PdbTFARBEAQhAF3X2b3jEGUllZQWD9zALCbWxvwF2eQX5ZC/IAdrmMlvH44+t+/8kko2lVT1r0beXHqQ9nY7DQ0dJCRG0dLURVlJld8xCopymV+YzVkzs+mp7uTAmrELef9vzzt8lLKLeeETCD9ooWJLI2WlVZB+emsfD9eerQ280beDQ5PbsRgN7K45wvrdI3sfly+YyuJxE7A73Xy4aT/ry6spLq+ho9fR32ZSWgJF0zJJjAnnSFs363fXUOVqgwkKddip3b+HKqODvNojeJodbNtUzfYtNSHPoVl18vzOcg5qfSyansWMrJT+58+TNR7uoHjdAcpKKtm5rRbdq6ECxWXt2Oe20FaUS+GiXBKToj7jnQnMbndRVlxJaXEFm0sP0tV5PNDOmZjs24hwYS5Tpo0LeB2hUBSZqXlpTM1L4ytfP3sEZi4IgiAIwufJQw89dLqnMGq+jPWGJV0PdW/dkfX666/z8MMPs3Wr/xUvU6dO5cc//jE33HDDGM9MCKS+vp709HQADh06RFpa2mmekSAIwpebpuncfu2fONLYNWTb5NRonnv1nhHZgO2Pj33A269vDmnMp1/6BmbzwFWPTqeHO2/8M81N3Z95Lsmp0Tzz0jepONDIvd947jP3F6rerzlR4zUinrQgd/uW5+pA5xQrminwct2+RB13bOB+lT6IqAv8HunodGeDHmQhqbUJzB2jGzbL03uRJziGbCch8XzRPaSZ4rnm58/R1NF7ymMau71EVLuGXMjsT1+iEUfK8V843Hb+XO69asmgdqUbKnjwv19FU4M/HhtNCo/87mamzxp/CrMJzt7r5Gs3/4XWlp4h215/SxFfu1vsWyEIwufHF/FnyhOv6b777iMqavR+CSgIQujO5JB3tP6/cMxX8h5z9dVXc/XVV9PQ0MDmzZtpbm5GVVXi4uKYPXs22dnZp2tqgiAIgvC5IMsSv33qK7z/9jZKiyvZv7dhwHFJgklTUskvzOGiy2aPSMALUPzp/pDaHWnopLqqmclTxw14variyIgEvCeOMW16Oo89cSsfr9xNWUklLc0DAzKL1UjejHSMRoW21l4O7GsccFwHXDESqlVGUnUsrRrySZUwtCgNT46KZ4aKmqqh1Mn9AS/4qihEVjpxxRpwRyqoYcpJY+gYu0EzgteG39INkq6hKRqmLgnNJA8KjCUVYnareCLBHcOgjd/QdTRFA6+OwSnhtSqgjGzgK0kwtTeHBE3GFWdnR1ctLs0zoE2yJZqFCZO4KHU2kyJT2XKg/jMFvOCruzycK9FlcEcoeCIVXDEDH3nX7KjyH/IWVw4Z8IKvNvPm0qpRCXmPlWYIRfGn+0XIKwiCIAiCIACnMeQ9JjU1lcsuu+x0T0MQBEEQPpfiEyK49c4l3HrnEjrae9lcepDqqmayshOZV5BNTKxtxMcczneATm7b2+Nk2+aakZ0PvkFmzM5gxuwMerodvP/Odtas3oOzz83s+VlcfvU8xmfGA6B6NUqLK3jgT2+hd3jwRMl0TzDQl6LA0SBccehEt7kwN+roZvBM9aDnepBMRy/ICYbqwSmt4tEJa/JgbfLgjpTpmGJB9oJqAXcUeMMZUFPX3KKimSVkj054rUpEtRfl6P54XiP0jTPgijWgy2DsVrEd9mKy60evG1wxMj0ZBjyRMqZOjfA6L+ZOrX8Ir1miJ8OMZj0pDD5FMeFW/v3gbcREHN8072BtM29sLmVL20HCFQvLMmdw0bzZhIdb+tsce49Gk8ci4Y42IGngCVfw2uT+9/NkgT7Dw5nnaH0XbjjdjsV9FQRBEARBED4fxjTk3blzJzNmzDilcx955BF++MMfjvCMBEEQBOGLIyY2nPMvOrW/Z4fDYBhi97CT2mqazgfvbmf1h7vYvfNQSCslhzUfRekfY9UHuyjfNXCMQ3VtvP36ZhKTIzGbjbS39WLvdXGs4oHJrmFrcOOxQG+GGa9NQVZlVKz0Hdt3qhKo1LC43Jg6vBiaQdIHB4geq4wzwYgnQkE3SBidvteVXjD1gteg40wGbxgggyvhePjqilNom2XE0KsjAZ5w6aSQUqZ9thFzq4a1BSRNRlckZB3MXYCkYM8wYh+nI3t1kAlaOuJURNksxESE0d3Vxz+f20DJ+gM01Hf0H2+lh7+ymmeUT5g+M51ll8zkvAunY1A++zz83O6BFAlnkv8a0CcLNB+DEnoYPpz/DoZjOPdqOPMVBEEQBEEYKWdyKYQvs9F5Og3gggsu4ODBg8M+75e//CUPPPDAKMxIEARBEIThWn7VXELZ62n6zHQmZCexpewgv/3NCnZuqxvxgHf6rPFk5SSyubSK3/5mBbu2Bx6j+Ug3h2rbsPe6/B53JZjQLAZk1f/FGbs1bPtUjE2S34BXl6An24I7xoBu8N+HK+Hoat5AT2CyhDdSxhMZYBWqDpZ2GSQFPVAZBoOEZhlc6uGzkiWJa5fMBODPv/+I118uHRDwnkhVNbZvreV/f/E2O7fXMXV8EnmZyZ9pfHe0AS1Ipmno0zDY1cANjpIliWvPmun32DnLphERYfF77EQxsTYWnzNlyHanYvLUcUyakjpkO1mWuPSquaMyB0EQBEEQBOHzZ0xX8jY1NXH++eezfv16UlJSQjrnoYce4qGHHvpMOwcLgiAIgjByrr+liGUXz2DTxirKSqrYXFqFvdeFrEhMm55OfmEOBUU5ZE5IQJIkmo8MvTHcMYois/jsyeQX5ZAzMZmK/Y2UFleypexg/xh5M3xj5BeeMEZT6GMEohqDh6KyO3hArcsEDl6PtfmMT16SCpI2ds9EBlnm3Dm5LMzLZOHUzP4yDcO5381Hupg5O4PnfnADFYdbWb+7mvW7q9l5sBFN14kMM1M0NZOFeVlMSounvLaZDeXVbNxTS6/TjSLLzM5JZeG0LPJz0+g50sumkipKiytobOgEIH18HPlFOcwvzMYcZ6V0/yE2lPsfo2hqxoByEyeampfGv97+Lju21lJWUklpcSVHjo2REef73BXlkDcjHZMp9Dezt8eJNcyEEsIqXZPZwB+fvoOqiibKSiopK6lk7+7DaJpORISFeQuyKSjKYV5BNlHR/q9DEARBEARB+PIZ05A3KSmJmpoali1bxqeffkpMTEzQ9g8++CC/+tWvAFi6VGwqIQiCIAhnipjYcJZdPJNlF8/E61Wpr2snPiGC8BBWQQYze14mP/rFVf3/npWdGHAMXdepOdhCWUkl77297TONK/gX5oLMPhPjFCtGg8K62ho+qalmx5EjIW+C9vLzG+js6CO/KIfYGCsJUTYSosOxmo3YnW5iI8JIiA4nIcpGSnQknfXdZPQaOXJY51BLH1EWC1kJBtKxMC42ksisZPILc7j7vmUcaexEkWUSkwfuZJ43IYU7L8qny+6krdvO+MSYkMsgmM3G/l8i3HOfHnCMYNwuLzu21faHtA31HURGWZlfkE1+UQ7zCiYQGRU4oJUkiZyJyeRMTOam2xfR3e2go62XtPFxIQXFgiAIgiAIwpfPmIa8H374IWeffTZ79uzh4osvZvXq1YSF+X/AfeCBB3jkkUfQdZ1ly5bx1ltvjeVUBUEQBEEIkcGgkDkhIeDx4Wz+FhMXHtIYdTWt/Oy/X+NQXVvoEx2C7B1ipa4xeKwpaYCqQ5DVvNLQ1QSCz0EGXdL9losYDa5uF//+10b+XrqJttkmtKNlKJIMKv7fqcHqatv4y59W8dg763BHD370rGnqoKZpC6++shFbg9t3H48yAHaXg49XlvPxynJkWWLpsjx+8JPLkCSJlNTgCwaibBaibKf+i4dQxjjZu29t4S9/WIXT6RnweneXg9Urd7N65W5kWeLcC6fz/R8tD+nbapGRViIjrcOahyAIgiAIgvDlMqZLAWbMmME777yD1WqlrKyMK664Ao/HM6jdD3/4w/6A98ILL+Q///kPFstnWxkkCIIgCMLpkV+Uw213LiF1XOCwLDzCwrnL8rjjrrND6nPlezuHHfCqCnjNECjKDW/2kIgJi9H/78DdkQqOuMB1YSUdbIddSF4NPcAo1mYwdgNBwt5os4Uoc4DnHhn6kkE16gHHQNeRPJovcA7EqyMFCbUlr46p3Yv1iO85rWOqsT/gBWibaaQnXUE1BurhpOHCZL8B74msTZ4BAa8/mqaz6oNdHKoduXB/pL307PpBAe/JNE3no/d2cvhQ+xjNShAEQRAEQfiiG9OVvAALFy7ktdde44orrmD16tXceOONvPbaa/2rGO6//35+97vfoes6F198MW+88QYmU2g7JQuCIAiCEJiu69RWt7Jrex22cDNz8ycMu6an2+1l9446ag62kJmdyPSZ4zEag+yGhW8V7q13LuHWO5dQX9fG2tV7WPvxHvr63Myel8X5F05n2vR0FIM8aIys7ETyThpD13VamruDz9Mm4Q2XkD3gSJLpS1Fwxvk2M5NdOmGNKrZ6FVO3hhRn4idfuYiCwhzCIyx4VJUdlQ089teVVLV0IKk6xh4VU7eKckJd3v4xvOBIHDiGpILBrhHWrvtq6Eo6uGRkr4StAXR0VCt4wsEb5lvhe/O8mdxeMIfM6Bh0Xae6s4MXNm3jpc070GUw9IGxFxQHSCcUSzD0elFNMganhrnDi7FbRdZ8YbbHJuOKNeANV5BUHVOHF3OX7zp0QDNLuKIV3FEGdFnC2Kti6vBitGsDyjGY2jRs9SquaBlnvIzXJtNcZAZNx9KqEb/NjbkzSKg81GJVTUcaxqZ8bpc35LZjzTWMuQ2nrSAIgiAIgiAEM+YhL8BFF13Ec889xy233MKbb77J17/+dZ5++mm+853v8MQTT6DrOpdeein//ve/RcArCIIgCJ+BrutsLj1IyboDlJVU0nTCJmiSBJOnjqOgKIezzp1K2vg4v33Y7S7Wrt5DWXElWzdX4+hz9x+zhpmYMz/L18fSqYTZzH77OFTbxtqP91BaXMn+PYfRj+Z5H7yzne2bq5k1Lwur1UjTkS62ba4ZNMbseZkkJUfhdHjYUnaQ5qbgIa9qlWg8+6TVsCqYOsHYK6G4DLjijbjifYd+9v5a8g8eZLzRhrfZwdayg7Q2dROsCqu/MSRZwxThwmRzY7K5kU74zpTukdCajei1FqRWEwaHhMFx/Pjbh3dRvbOFRXlZLJs7iQmJsVyaNYn/vLkz6LV6w32Pcx6TjNemYOhRkT06yOCJUNBMxyfhsMh4ojQUu4okgTdMwRsm+z4MgMss4wlXMHZ5sbR7UVw6EpBScvz90AzQl6zQl6LQm67gTFRwxiuYO4cXWEqqjqnTi6lbxdirDrmKVxAEQRAEQRCEwE5LyAtwww030NHRwT333MOzzz7Lpk2b2L17N7quc/nll/Pqq69iNIb4HUBBEARBEPz65z/W84+/rfV7TNdhb/lh9pYf5qV/rOeJv99JVnbiSW107vvmc1RXNfvtw9HnZsPa/WxYu5///Hszf/7H1wbVGK2qaOLbdz6D1+s/xTvS2MUH72wPeA2OPjfFnx4IcpWDmbo0ZLeOZjo6Fx0iakFx+19Sane62fjhXnY2Bf+afdAx0Ika34XB7L8Wg2TUoccALf6DcF3X2XmwkZ0HG3n6vVL++cDN5KbGE2410+twhTQnXZHwBCuLIEl4bQpeW+DV15pJwtLuxeDyv7JW9kJ4vUp4vUpklczh8y0442WiKgMPqzg1JFVHP1avWNeJrHAEHCOY6Ogw0jL8/0LiTDB9ZjrF64b+vEbH2EgbHzsGMxIEQRAE4Yvgpz/96emegnCGO20hL8C3vvUt2tvb+clPftIf8F5xxRW8+uqrGAyndWqCIAiC8IWwc3tdSO08HpV9ew4PCnntva6AAe/JqiqacPS5B63m3Vd+OGDAO1pyxieyPCwD1wQT5Y42yuoOobiDn2O0D29XtJzxiVxmy8CZZWK3o43NjXUBA95+baH9AtvtVSmvOcLywmn8+8HbWLl5P+t3V7Oloh5VG34wOhySSsjhq6Vdw4TMnCXZTFsUjbHWze7NtdRWtw5ol5OVyNS8DNQ4Ewda2ti299CwAl6DUWbWnEzyC3M4+7ypWCxn7kKAH//yatZ9spey4ko2lVbR3XV8ubbRpPRfx1nnTsVsPnOvQxAEQRAEQfh8Oe1J6o9+9CPa29v57W9/yzXXXMO//vUvFCV4bT9BEARBEIJzOj1s31JDXW3r0I1H0Dsl5SydN5GE6PAxHfdEd337XK69qXDAaw0d3Vz6wDMjNsb8ggl89ZtLyZ6YhCRJNHX08O6Ocv7ifHvExli78yC5aQlMSkvglvPmcsGMXD5ctYvH15WN2Bgj4VfpRZy9aApx8RH9rx1p7GTrpmpkWWJu/gQSEiP7jzU3dbHyw508t8v/CnN/Xnn7XiKjfPWjNU1n/54Gykp85UNkWWLO/AkUFOWQnZs0aCX5qdA0nYp9jZQWV7BtS03/deQXHh+juamLspJKyoqraG3tYWreOPILc5g5J4Oly/JYuiwPVdXYv6eB/XsbSBkXw6y5mWd0QC0IgiAIgiB8fo1KyHsqIa0kSbz++usBa/BKkoTXKzanEARBEIRgKvY38uxf17B9Sw0e9/BWpvpbVWgwKsiyhBbC6lEdeOS1NTzy+lompSVw3VkzuXLRdEzmsf2dcnxC5KDXoq0WJIn+WsD+6MMIBzeVHmRT6UHk8TbUJDPtbhcYNJSL+8vb+qeEvnr14+2VfLy9kji7RHinRm9rH7oM5IUNMchnJPney1BHeOrRlTz16EpyJyVz2dXzuPDSWSSnRHPxZbP72+i6zjtvbOHdt7aGvDL8GINBxmaz0NPt4O9/+YT1a/bT2WEf0Gbntjr+8dc1xMVHsGTpFO6462ysYcPf16G728Hfn/qEDWv9j/HsX9Zgs5lRDPKAFboAFfsa+c+/N2M2G5g9L4uvfvMcsrITmTo9janT04Y9F0EQBEEQBEEYjlH5qUsP9hOUIAiCIAij5vGHV1B54Miwz5sxezzzF2QPet1iMXLDrUW8+lJJ0JILugSORCPIvmhwf30Lv3hpFfmTx1OwMJfpM9PZtePQsOc1YAyGDh5nzsnwex1hFhNfWTafF1Ztwav6vw5HghGDQ0UO8XfKqlmiLQZwH62X65XRK6yQ4xiw4dqJpGwHeqcRXAEanER2alDpoPfY+RpYmj04E47f65GmKxLOBCOWVg/SMB7pKvYf4bFfvzto5S5AXU0rf3zsg2HPxWhSuPn2RSgGmTdfLePdN7cGbd/W2sObr5aRnBLNVdfnD3u8N18tY8Vbwcew24PXR3a5vGzcUEFvr5Pf/vn2Yc9BEARBEATBn4ceeuiUzhO1fL88RiXkFR8gQRAEQRg+TdNpqG8nzGYmNu7Uyh10tPcO3eiovBnpLL0gj4LCHBKTowK2u+Mb53D9LUVs21xDWUkl732wA9waqlHCE6HgjlTwhCugDA4d23v6GJcVxeN/vp2mxk7KSqpY9eEu9uyqDzo3zeDb4GvAGDYZg0vH2KNi6lYxODR0GfJmjee886aRvyD4dfzXFYv46oX5lO2rY/3uat7+dDeqArJHw9jt61MaxuJnzTD4erW94VARhpTgQUp0IyW5kawaukNGP2JCbzaB58TzjkXX/iNs2Ts4ZbUd8WBt9uAJV/BE+u6NbpQxqHD52dNZNC2LuRPTONjYzvrd1azfXc2+Q81YTAYKJo9nUV4WC6dloWoaG8prWFG6l13VjQPG6Es14UgyYuxVMXarmNu9Ia/s7eywDwp5T14VG4ysSFx82WzyC3OYNTcTq9V0tI++kPvo6Aj9v4MTDWeeQ86hfeT6EgRBEARBEIShiJBXEARBEE4je6+TrZuqfbU9S6pob/OFU9m5SeQX5lCwMIfJU8ehKIFXfjYe7qC0pJKykkraWkMPt77+7XOZmhfa18jDbGYWnjWJhWdN4v2Oetra7egKwyobkJQSzfKr5pKVk8h933wuaFvp6Gpa2aMf/6OC16bgtSk4kgFVBwm+9v+WkZeZHNIcOrQemmOaaZ18GCmqBUX3BaxSrYxeoaBXKEhdoa2yDUSVJLxOE542E16HjmTQ0b0Shj4JoxMMZg1DjBcpyYWU6AYFXxmHbgN6kwmt0YjhsIyxR8Pc6X9ZsW6Q0EwSmlFCPxque2Vo6uilqbOX7j4nMyakMGNCCndfVoTd6cZkUDAafCW1vKrGrupGmjp66OjxH55KXh3Z7bv3w/Hwz95iydIp5BfmMGlKatDPrj+aqtPS1E1LUzddnX39Ia8gCIIgCIIgCIGd9o3XBEEQBOHLasPa/fz6p2/idg8O8qoqmqiqaOJfz29gXHosjz95m9/VvY/9+h0+eHfHKY1/8mrLUKXERdHaHdqqyqSYiEGvJYYwrnTC/5p6VEw9viW2zlgD9nSz7+DRcDMpxE3eHtr5b1Y0nPBVfCNI+AJMb64GXgnTttAfjWT34PDTnqLjGbCYWELXJJDBGw5ysouwlB7/5RyivciySuRKDaUjcDDak27CHetn8y6J/pW7ANcsnsEDN50LgM1yPCht6ujh64+/Rn1rV8AxwuucmDuGV9P5mEO1bbz07HpeenY96Rlx/PbPt5OYFHiFtT+lxZWUFlfCY7D8qrl85/9dRGJS6J/X4Y73Wc/z39ep/fclCIIgCIIgCKfisy1VEQRBEAThlH3w7na/Ae/JDh9qZ3Np1aDXHQ73sANes9nAgoW5/PL/rj/lkPfXX72IKxfmkRBl83vcYjSweHoWf7jnChL9BLCJyVH8/H+vo6AoB/MwN2Uzt3tB07GYDCyZPoE/3nMFCSGEvL1e58CA1w/TVgOSGvrKZMWjM8ljJTsmGrNRQZdPDngHs0Q7A9brBTBUykEDXk3Gf8Drxxvrd+H2DP58bSivCRrwSqp+ygHvyQ7VtrF1UzUp42J46DfXkl+Yg8k0vPd8xVtb8XpVrrq+gK98/SxyJvpftS1JMHlqKnd9+1wuWj7rlOZ71fX5fOXrZ5GdmxRwjHFpMaRnxAW8joSkSC69cg7f//FlpzQHQRAEQRAEQTgVo7KS97XXXuPaa68dja4BqK+vp66ujqKiolEbQxAEQRBGm9cbepDmb9MzLcAGYoE8+OtrKCjMwTTMYPVk4+Kj+Mkt56PrOhWHW1m/u5pd1Y2kxEayaHoWc3PTMBuDj1G4aCKFiybicnn44O1t/Om3K0MaWwJ+cu25XHzWtCHHOJGqDX2vhlOP96Lls7jjG2cTE+sLmJ1uL2+t28EDu9eG3om/OWhDhMzD2GtN03WONHYyfnz8gNcDbTzXb4T3zz32OS9aMomiJZNwOj1sWLuf3zz0Vkjna5qOpumYzAZuvmMxN9+xmNaWHjaXVrGl7CCKLDO3YALzCrKJiT3+i4e65k427K6muauXGVkp5E8eP2BFsz9ms/GEMbrZtLGKrZuq/Y7hdHrYsbWW0uIK2lp6mDo9jfzCHDInJCANo4yJIAiCIAiCIIyEUQl5r7/+en7+85/z4IMPjmjYW1dXx8MPP8yzzz7LAw88IEJeQRAE4XNtOEGQLPtpO8wgae78rM8c8A4cXmJiWgIT0xKGfW7NwRY+eHc7ZSWVHKptG9a5Tz74Du9mllBQlMMFl84iIzN+yHNCutfDuJ3TZ40nJjac6qpmPnx3B2UlldQcaYMrw0LvZAx89canyMyMJ78whwsvncX4zPjhXOaIOPmza7EYmZufNaw+Tn7/4hMiuPDSWVx46awBrx+ob+HtknI27K6htrljwDGDIjM3N41FeVlctWg6VnPwFdHxCZFctHw2Fy2f7fe4xWKkoCiHgqKcYV2LIAiCIAiCIIyGUSnXkJubS3l5OTfccAOZmZk88MADlJeXn1JfdrudF198kYsuuoicnBz++te/oqoqOTnigVoQBEH4fCtcPDGkduERFmbMGj/odZvNzKw5GSH1MXteJmE287DmN1o6O+zcfcfTvP5y6bAD3mPqalp57Z8bufsrT9Pd7RiyfaTRyuyYzKBtPJNCW8obEWEhb2Y6He293PPVZ3j9lVIO1bUhu8HSHLwPd2/wlaTeTBXdHHgpraSCoTe0eRq7vUg61FYfvVd3PE1vj5N5k9IJD7KiVVfAEzYyj4gRkVbyZqQPej0qOoyp00Pb9C+/MAejURmyXUtnL7f85p/88+NtgwJe8K1gLt1Xx2P/XstP/vFBSGMLgiAIgiAIwufFqKzkLS8v5w9/+AO/+c1vqKur45FHHuGRRx4hNzeXBQsWMH/+fGbPnk1iYiIxMTHExMTgcDhob2+no6ODAwcOsGnTJsrKyigrK8PpdKLrvh94LrroIh555BHy8vJGY+qCIAiCMGaWXzmXvBnplKw/QFlJJXt3H0bTfH/fhdnMzM2fQH5hNgsW5hId47/+7cO/u4ltm2soLa6gtLiSIw2d/cdSUqMpKMolvyiH2fMyx+CKQlN/qB2PZ2RqvrrdXhrq24mcOm7Itk/Mv5Oytko2tOxnQ8t+GhzHg0C9V8YZZ8RdYMBYK2HsVjH0af2rXi1WI/mFOeQX5rBgYS5R0WHs2l434DokIHWti75Emb5Uhb4UBW/48bDU0KNhPmDA4LKixHnx5qioaVr/CuIwyURh3kRmP5GF4YDMrrJDbNpYhcvpGTBG5EEnnnAFd6SCJ0JBMx8fQ3ZpmLpVjN0qxpPCYJfLS8PhDiZOTuGth+7g010HWb+7muI9NTiP1obW0VHDoGmBGUOfhrVVJaxRxWgPvYaD1Woivyin/7MbGTV4dbMkSTz6p1vZuqma0uIKyooraTpyvE5walqM77NbmM3suaGt+q1p6hi6FMVRFYdbQ7sYQRAEQRAEQficGJWQ12Aw8L3vfY9vfOMbPPnkkzzxxBPU1dVx4MABKioqeOGFF0Lq51iwqygKl19+Od///vcpKCgYjSkLgiAIwmmRlZ1IVnYiN92+iO5uB7t3HMIWbmba9DQMhqFXLxoMCvMXZDN/QTb33KdTX9dOdVUzWdmJpI2P/VLUBn3rtU1ceKmHvJnp6Drs2lFHWXElm0ur6LO7mDErg/yiHOYVTKAwfiLJ3ngS61N44pNPUW0e9G4F7AogoQFqnI5qkjF1ejDYNXQFCs6exPJLZjFtRnrQ90XSwHZEw3ZEQ8eDJ0LCHSVj6tQw9R4LSiXACOuMaFYdNV1Fckr86cGvMm2qb9WrJ1clMy0RCVi3Zt/AMXQw9aiYelR0QDVLqBYZg0NDcYcWxsZGhnHx/MmMN9uwtrj5ePMBJE3CE67jCFPwRCq4EhTsGQroOplvOlA8Q/cLULhkIhcv990rTdcp3VvL+vIaistrcLg8zJ2YxqJpWSyYmkF+YTZJyVEkJUex7pN9HKptJTM7kSXnTCG/MIe08bF0dfaxaWMVZSWV7N5xiIhIK/MXZJNflBPyfyeCIAiCIAhnip/+9KenewrCF9SohLzH2Gw2vv/973P//ffz0Ucf8eqrr/LJJ59QU1Mz5LlWq5X8/HwuueQSbrrpJlJTU0dzqoIgCIJw2kVGWikKsYSDP5IkkZ4RR3pG3AjOauTZRrhsxOoPd7P6w90oigwSqCdtUrd65W5Wr9yNK86AJ82Ki2PHZeg+PhfFoWE77MJg1wbWrVVh7fu7Wfv+bsJsZs45fxp3f3fZkOUvJMDUo2PqCbxqWXZIyAd8j2NR4WE4HG6e/O2HrP14L44+95DXLgEGl47BFdrK6DCb2e8Yx9baWlshskZFl8AZL9Myz4QnUkYzSyie0ALkjz/czeqPduPKsOKKMuDVB74fK0r3sqJ0L5Y2L5HtKmqfd8DxPbvq2bOrnqf+8BFGk4LHPfDaWlt6qK5q5tWXSgizmTl3WR7f/O75Q26qdqLhtBUEQRAEQRCEz4NRDXmPkWWZCy64gAsuuACAw4cPU1xcTH19PS0tLbS3t2OxWEhISCAhIYHp06czb948jMbgG2IIgiAIgvD5kzkhgauvL+D9d7bRF0KQGSo1yFf1dQl6U01A4DbWJjdGe/Cv+/fZXax4ayv5C7IpXDyRK6/L58N3twe9DkWRg84tzGbm0ivmMC49lk8+KueDd3cEncOpjrH8yrmMS4sJaQxJB2uLRsweD80LzLTONJK8Q0UKsR6wO1LBHimDHmBOqk5YvYuhejs54D1Zn93FO29uoaAoh/mFOdxw9izeLimnzxV42XF6QjT/dcWiIUYWBEEQBEEQhM+XMQl5TzZu3Diuvfba0zG0IAiCIAinmSRJfPO753Pn3Usp33mIdWv28fbrm0d1TF0G5OClK2Q19LqzXV19SJLE3fcu4+v3nMvuHXWUlVSyYd0B2lp6mDxtHAsXTyS/MIfUtBgO1bVTVlxJ8YYD7N/TQEJyJIsWTRpUcqCnty/kOURGWbn5K4sGj7FuPwf2NpKQGMHCsyZTUJTD1BPG6O1xhjxGvMHKXUuWcHZmFlnR0dTVtFJWUsmLz66nz+4KeJ6uBL/X0vFSxCOiq6sPWZb4wfXncO9Vi9lW1cCG3dWsL6+huaOHGRNSWZSXyaK8LMYnxozgyIIgCIIgCIJwZjgtIa8gCIIgCF9ubpeXbVuObhi3oeJ0T2fY/vTYh2zaeJDZ+ZkYEqxsrz3ChqY6GuLcEGemSWuj41AN7RGQ0hVFtaOTT5Rqtk9sRZ9oYh9Oam01NHRJtBxqJz6ynnrHFtY1HwJC22hsyrRxXHV9AbquU3ngCKXFlZQVV7Jvz2F0HQ7Xd7B+zT7cLi8ul5eZczIwmYb36Dc7OYWvzp4zaIxgAe/p8If/e5/S4sr+zfEKJo+nYPJ4vnfNWad7aoIgCIIgnCFELVzhi06EvIIgCIIgjCmXy8M3b/8b9XXtYzampILk0dCNcsA2qlnG2Bu8XMMxbreXT9fs5e3GGjTz4D41XWd7VQMlPYdxBciw67q6WH1oFeEJmzG4fONak6JDGh8gPSMegKf+8BFvvFLmt03D4Q7e+vcm3vr3JjKzEvjzc18bVs3mY23//PuPePNV/2P4o7iC30fdAJoBZG/QZiFzubx8+vFePv14LxarkUd+fzNT89JGpnNBEARBEARB+BwQIa8gCIIgCGOq6kDTsAPe8RnxxMaH43Z72b+nIWj9WX8kIKrSiTPOiCdSQbUMDmbtqSZUs4yp24uhVxuynIDXKvsNeE/kjgjex4TEZgzK8WuJn9LJ/Ht3cnhjEi27YvHYB+5PYDDIzJidQeHiiVxy+RwA1qzaM8RMfWqqW6ipamH2vCx++egNrP5wF5s3VtFzUvkG/2OUhzTGMcZejYiDTlwxBjwRCrph4N00Gg1kXZBNRJdOZ12n38/D+Mx4YmJtuN1eDuxtDPk9dzo8lG6oECGvIAiCIAiC8KUiQl5BEARB+ALQdZ2qiibKSiqprmomKzuR/MIcsnOTkKSRrH4aXFtrD5s2VrFzex02m5n8whxmzB6P2Xw8rNT00GvfmswGHvzVNeQXZvdfh93u4tcPvkFZSdWw5qa4dWyNbmgE1SRhTzH6asfqoJkkNLOMM8GIM8GI5FSJqHVhdAaZa7DbqusoDo3ofSrGXg13lExfioI7WoKj16E4NPq22tj+0WSMYV4SZrQRN6mLpBntJM1oR9eg42AkLTtj8ToN5M+bxk3n3kWYzQyApulU7G8cVumE3bsOMT4znoKiHAqKclC9Gvv2HmZTSRV9fW5mzs5gzvwsrGGmgZejhf6eHWPqUTH1qOiAN0zGE6mgyxLT0hJ56IeXk5pyvDZuQ307ZRurqK1uYUJOEgWFOSQmR/Uft/c6+cWP32BL2cGQxh7OZ+yY1pZuykqq2L2jjohIK/MXZDNjVgYms3hcFgRBEARBEM584qlVEARBED7HqquaeePVMspKKmlv7e1/fc2qPTz7lzXExUeQX5jNVdcXkDkhYVTm0Gd38e9/baRkfQWVB44MOPbWa5swmw3MmpvJ+RfNYMnSKShK8NWvJ3K7vPz4/73cfx1X31BARlYC6Rnxww55T6S4dSJr3QNeUw34VpzqYHCFEBL6aaI4NCytHkzdKrL3xAYqcbs8eC2gWiQkFUw9Oh0k0HG0RfWqNGSTSvyUDtIWNpEyt4XYnG5ic7oBmBqXT5jNzMHKJt58tYyykira23oHzSGYJx7/kGee/JhZczNZdvEMFp09mWnT05k2PT3oebIh9PfsZBJg7NMw9vlW4tY01HP71U8wcXIKRYsncvWNC0hNi+WKa2ID9mELt5A+Pi7kkDfUz5i918lr/9xIyYYKDlY0DTj2xitlWCxGZs3LZNnFM1l89uSQ+hQEQRAE4cz00EMPne4pAKI2sDB6Tv2JXRAEQRCE00pVNX7wXy/ywTvbBwS8J2pr7eH9d7bz/f96cdglDkL11z+t4oW/rxsU8B7jcnkpLa7klz95g53basmZmEzOxORhjXHydSw5Z0r/itaRonjB4NRDC3gBg0NDcajHX9B1IqscWNq9JwW8J5zjBHOnjqnH/3HNrdC8I56tT06jveL4SlazHE52xCK8XpX/9+0X+eDdHcMOeI9xOj1s3FDBz3/0OuU7D4V0zoWXzDylsYI5sK+Rf/xtLX9/6pOQ2i9ZOmXQCmN/IiIsLFoSWiD7xO9W8tI/1g8KeI9xOj1sXF/Bzx/4N3t21YfUpyAIgiAIgiCcDmIlryAIgiB8Tjkdbjo7+0Jq29lhx+n0YBvhYBSgsaFzWG1nzsnkT898lf17GigtrqBkfQXVVc0hnd/Rbsft8jJ1ehr/fOs7bN1UTVlxxYBVrcdW/c5fkIMt3MSOrbXDGiMU4REWlpwzhXkF2ZgSLWyuPMynWytp3xna+xEKrS2BvKiFZIYXkB42G5MSRldnHz3djhEbo7Ghk7yZ4we9rus6Xk3DqCgA3PGNc7jw0lmUllRSVlLJ5o1VnEJFhABz6Ah4zOtVURQZSZKYPms8/3rrO2wpq6bs6Dw62u0AJCRGkF+YQ35hDrPnDS43EciRw4HH9jfPqdNFnV9BEARBEAThzCRCXkEQBEEQxsybr5bhcavkF2YzdXoaU6encc2NC7jqwseG3ZfNZmbx2ZNZfPZkNE2ntroFSZLIyIo/Xr/X66IrvY+aWa1U3zdyIW9iUiQ5k5KZNCWFsCgLvW4PR5q72EhoK2NDcdgxg07PXGyGSZiUsBHrN5Beh4vSvXWsL69mQ3kNrV12pmYksSgvi0V5WUwdn8QV18znimvmc9+3nmP3jpG71mNUVWPfnsOUbvCFuFUVTccD3KJcZs/NJHdSMu3tvbS19bB9cy2yLJGdm0zOJN+fUANeQRAEQRAEQfgiESGvIAiCIHxOmcxGLBYjTqdnyLYWqxGTaXT+2o+Msobc9uD/Z+++w6Os0jaA31PSCyE9ISFtUukhmZBCVxQEKSrqsgqugArYUHcXdbGsyrdiWStNVqxgAVFARUBKIJUQSkiATAoppBfSy8y83x8xY0ImyYRMysD9uy4uJ/Oe95znzLxmZp6ceY6iGO+/9QsAwD/QFS/8ez7s7K1gYiJFY6Oy2/PNzI1hZCzpcL9YLIKXj6PmZ7WgxuspP+CXK6ehFFSAErCWmkGk1M8mdJmKYry3/hfUuhujaagRBBEAtQBbESDS0wrXi8orOJ/aUiJg5BB3vD72PtiaWcLIWILmJlU3Z+vGeog5VGo1Xv3iAH5JuAClun1Jj9TLRUi9XITN++Jga2WONfdPw/Rxvhg61EIv4wPAkCEtCeyDv57Dx//9rcNK5ZLiauz7MRn7fkzutI+4E+mIO5EOAAgcMQwv/ntBu43bOmNto3vy3HpI3yfaiYiIyHCwti4NNqzJS0REZKCMjCR47sU7u61vK/Nzxt9fvBNGRh2To/qwZPkUTIjy7XES+WLaFfz281kYG0vx3L/uhI+vU5ftff2d8dyLd0Iq7X4e6dWF2JOf1JLgBQApUD+3CSrnrusSq8Va91PTSmUmRqPtHwleABCLUDPcBErTrt9emVuYQNLFRmZqKzUaIpuh9Pkz1pSrudiXfwomJkb4+4t3wrubx6q7MezsrXDfgxEYL/fCxdxi7IlL7ZDgvVZ5dR027YsDAPzt0akIi5BpTbi3MjVr+SNEZ0xMpIic5I+//m0iAGDb5iN6KUWRdj4fB349p1Pbhx+dBnl41/Owd7DC/Q9GYlyIZ69jIyIiIiLqK1zJS0REZMAmTQvEpGmBKC2pRmKcAgmxGcjKKIaXj6OmLq29g5WmfVFBJc6ezoGFpSnGjffUy1fbh7nZ4t9v3ouGhmacScrG1o2Hda5/21Dfsgp58rQgTJ4WhNKSKiTEZiAhVoHszBJ4+zhCHiFDSJhPu3nkVV1FQn4+rE1MEO7mDgvj9vOoVzV1GKt5hArNI1QQVYkgVYghOWEGaa0ApZkYzVYSNFlLIBiJAbUAoxoVzAuaIG3oPOUriAQYXVVCpBKgtJRAbSxGk40UTTZSiJvVkFapYFSratmgzdoIzyydAfkEH9jZW2keq437DuLy+RIIpgKUMhWafVVQOwlAa+K4EZBmSyBqBMqsawAZMOWWEZhyy4g/H6sYBbIyi+Ejc4I8QobQNmOcTsrGkYPncTrpMiwsTTBpWiCiJgfAW+aoKWlRr8MKas3z1djyfLkNt8Nrb92H+vomnDl1GQkxCpw9fRlW1mYIneCDsAhfeMscIQiA4lIhjh+5gOgjF9DQ0ITgUC9MmT4CY8Z5wNjkz7eiuqxI11VZWbVO7dw97PD62y3zOJ2UjYRYBc6dzoGVtZmmxm/bx4qIiIiIaLAadEne6OhoTJw4caDDICIiMij2DlaYOWccZs4Z1+FYypkcxB5PR3xMOi5nlWruNzKSYNTY4ZCHyzBxSoBOX2/viqmpEcIifZGUmHXdm5zZO1hj1p3jMOvOjvNIyM/DocwMHMnORnp5meZ+Y4kE8mFumOLphdt8ZBhmbd3lGIK1gOZgFeqLjYFGLSs4xSI0W0vRXK2CtKHzBKi0ToB1dqPmZ6WpCI1DpKgZLkGjixhKCykE6Z9vtdZeicXkU4WY4umFW719EBbpi2ND03A+p31tW1GlCEZpEhilSyDJEUOkbkkwHtx9DgrvQoRF+CJ8oi9GjHLX+lgJgoCzyS2bzSXEKpCT3fKcl5VW45svYpB6Lg9hETJETQmAo1PvnnMzM2NMiPTFhEjfDscKrlTg+JGLSIhJx7kzuVCpWlYK/7bvLNIvFEIeLkPERL8+2cxsz84kpJzObRljkh+CRnY9hpmZMcKj/BAe5af3WIiIiIiI+sOgS/Ledttt+PrrrzFv3ryBDoWIiMjgffd1LDZ/eEjrseZmFU4lZuFUYha2bT6Cj/73MIZ72vd6zIAgV7233XQyEf85Ea31WJNKheM5l3E85zLejjmOPX95AJ4WjjCTGGtd0dtKNFQJobDzr+krzSUAOk/yXru2U9IgoHyiBE222ssklNXXY1daKnalpWK8iyu+W3gfRgxxx3eI07QRF4lgudW009rB2ZklyM4swTdfxmDFUzMwf6G8Q5sdX8TgfxsPaz2/7XP+vDoe3wAAh2VJREFU6eYj2PjZMni72MLUWIqGpu5X9I7w7Lo0SKtMRRFWLf1fp7WDszKKkZVRjG++jMHK1bdh3t2h8A90RUKsQqf+ddF2jFXP3I65d4XorW8iIiIaOKyFS6TdoKvJ6+XlhXvuuQebN2/utE1NTQ3/pyYiItLByfhMndo1NDQjNSVPL2NOmzES6z/4K+YsGA8nLauDnV1tMPfuELz14V8xeXqQTn1G51zWqV29UolTBVdgY2yOryOfwCO+t2DkEHeIrknJWkhMMG2WG+6c5YvJY7xhbtK+dqxYJEJQyHBM/et4TL9jlNZ5XEttjE4TvNdKKriC+uZmzBo2Dh+G/g13uYfB2dQG0lyJzpvDnUrM0t53go7PeX0zUs/lYaiVOb598QE8NiccIz2dcW1lAktTY9wS7IuXH5yBVxffplPf587k6rw53KmElnmsfeMuPP2PWYic5A9Ts2ueD7EII8e447bZY3DL7aPg6NT1au2OY+j2mBARERERGapBt5L3xIkTmDNnDh577DEUFhZi7dq1mmNNTU346KOPsG7dOpSVleGVV14ZwEh1l5OTg3379uHIkSM4ffo08vLyoFKpYG9vj/Hjx+O+++7DPffcA6lUt6fj/Pnz+OCDD3Dw4EHk5+fD0tISgYGBWLRoER5++GGd+yEiIsNVVVWPpPhMJMQqUFR4FYEjhkEeIcOIUW46bUymjSDouuVY98aO98TY8Z4QBAE5l8uQFJ8JkQgYL/eGu4edzjVOm1UqnMm4gtziCp3HPhmfgQmWznAbboeHfabhYZ9pqGyqRWzpJRTVX8VIG3eMGeoBI/Gfr5fNShVOKfKRklUIVztrhAd5wMbSDEDL45KTXYqP//tbp4nV63HscBoiJvhCbidDqK0PsgrL8cGJ/UiBbgnJzIIyJKXnYYy3K6QSMZqbVTh/NhdX8nryWGUicKQb3NxtsWzWBNwbORpHjl3Az8fPo7S6DqM8XXDn9NEYPWZ4lxu59Ybwx1Z3JiZGmDU3GLfOGoPTJ7Pwy94zuHThChycrHHbzDGImhIASyvTlnMEAZ9uOoLtn5/QbQz9XdpERERERIPSoMsG2tjY4ODBg/jLX/6Cl19+GYWFhXj//ffx5Zdf4uWXX0Zubi5sbGzw2muvDXSoOlm7di1ee+01rR+c8/PzkZ+fj59++gnvvPMOdu7cieHDh3fZ39atW7Fy5Uo0Nv5ZA7ChoQHR0dGIjo7Gtm3bsHfvXtjZ2el9LkRENPB+/y0FP+06ibSUfKjVf762nDudg2+/ioWFpQnGy73x4NLJ8PC0h7Gx7i/1Rkb6f1sgEong4WkPjx6Wgci4UopN++IQl3oZNQ1NqHETAEvdzj2y/zyStpyFq9tQTJoWiEVLJsLG1AIzXTvW+W1lJJUgLGA4wgL+fB2ur2vCl59GI/pwGgquVHY7rkitW3wAALWA9a/+BLFEBLMgW9Sai1BeVw+TsmZdp4ncsqtY9s53sBIkcKwSo6GoVrORna5+/y0Fv/+WgqG2FpBIxCgvq2l3XR0/XYLju8/C0soUIX9cV+4e3b/HMDLS/Q8NrdddVkYxPt96DKcSMlFX92eZjaKCq0g5nYt339yHEaPcMffuEEyeFgRnF93rCRsZX98fPoiIiIiIDMWgS/ICgImJCb7//ns88cQT+Pjjj7Fjxw5cvXoVlpaWeOGFF/Dss8/CuptNVQaLK1euQBAEWFhYYP78+Zg+fTp8fX1hamqKtLQ0vP/++0hMTMTJkydxyy234NSpU7C01P7xbv/+/Vi+fDnUajWcnJzwwgsvICwsDOXl5diyZQt27dqFuLg4LFiwAIcPH4ZYPOiqcRARUS8UFlRi3cu7u2xTW9OIY7+nobjwKj745G+4864QpJzNRU11Q5fn+Qa4IHSCjx6j7Z1/bduPC7l/bt5mUgEozQB0k6szKVfBvLClTMCVvArs+DwGNjYWuOu+sB7HsPv7RHz7VazO7cVKYMilZlz1laJDzYO21AJsLiohUgO19lKUogGoaznUZC2F0lQJaUPXGWO1BGiwaylpIL5Ug8pu2nenory2y+M11Q04cigVpaXVeHfD4m77mxDpC29fJ2SmF3XZzsraDHPmBwMA1r28u8sN+9QqAedO5+Dc6RyM/MkdE6L84L0rqdsxrIeYYc788d3GTERERIZhsHyrm2VEabAZlEleAIiPj8eFCxcgCAIqKyvh7OyMU6dOwdlZtw0/Bgs7Ozv85z//wWOPPQYrK6t2x8aPH4/7778ff/nLX/Dtt98iPT0d7777Lv71r3916EepVGLVqlVQq9WwtrbGiRMn4OPz54fx22+/HStXrsTHH3+MY8eO4csvv8SDDz7Y5/MjIqL+U15ao3PbstJqAEDoBB98u/dpnDuTg4QYBRJiFMjNKYNEIsaoMe6QR8ggj/DF8B6UUNCH6vpGNDQ2w8FG+x82S6vaJx2NakUYohCgNAOUloDKRIDSQgSoBZiWqGFRoIL5FRWMqoUOG6K1PhbXUipVKCmugqPjEK2lCMpKtJ/XFfvkZgxNbUadswR1Li3/1MYiiBsFmBeoWv4VqiD5Y6GqYNQ+WsFIhKt+ppA0CjCqUsK4SgVprRoiAEoTEZqtpWiylkBpLgbELeeKm3uX4O2J0uIqrfcLgoCy0mqYmZvAwsIEtnaW2LhtKbIzS5AQq0B8jALnz+VCrRLg4WUPebgMYREyjBjtrikvUl6m+/VdUVYLmb+zZoz4GAUSYrsfg4iIiIjoRjXokrxJSUlYu3Ytfv31VwDAokWLYG9vj/feew9LlizBzp07YWFhMcBR6u4///lPl8clEgk+/vhj7N69G01NTfj++++1Jnl/+OEHKBQtO06vWbOmXYK31fr167F9+3ZUVFRg/fr1TPISERGAlq/OB4d4ITjEC48+cSsqymtgYmIEcwuTfotBEASk55fieEoWjqdk4WxmAdSCAHcHG0SN9ELUSC8E+w6DSRclI0SCCEZ1gFEdYJ7fBKMaJcTKlhW0uiorrUZiXAYSYjOQlJCJutpGWFqZYrzcG/JwH4wZ54Gcy2WIj0nHgV/PXddcRSoRjKpEMIcI0hoR1CaAuEEE4xoRjKtFupV1EASIBABtqj21/Nxyv6hZgHGNEkbVKoh0299ML4oKq/D6v3ZBHiHD6LEeuJzdksRNiFGg4EolxBIRRoxyR1iEDPJwGTy9HeDl44h7/xqB2tpGNDUqMdRWf+/jRCIRvHwc4eXjiPse6JsxiIiIiIgMwaBL8oaGhgIAZs6ciXXr1mH06NEAAE9PT6xevRpTpkzBzz//DAcHh4EMU6/s7OwwevRonDx5EhkZGVrb7N69W3N7yZIlWtuYm5tj4cKF2LRpE1JSUpCeng5fX98+iJiIiAaCnb2u1VoBO4fOyxoNtdW9H30or6rDY+/vRHp+aYdjuSWV2H44GdsPJ8PS1Bjrlt6ByBGecLSxROnVzksIqI1EkHZdgULD3qHlmzQfvP0rftp5ssPxmuoGHD2UiqOHUnXrsAs1w4zRaG/U7j6xEoAUaLKRoslGCsAEpsXNsChogri5fc1+UbMa1pkNkDZ0rOUvaRJgVqqEWWkPstp6JggCjhxKxZFOHqu2JRU++fh3+Po74//eWwRrazNYWLSs8u2MnYMVrlbWdRuDSAQMtdOexO1uDCIiIiKiG9WgS/JOmDAB//d//4dJkya1u//JJ5+Ek5MTlixZgsjISOzfvx9eXl4DFKX+tW6k1lkd3ejoaACAv79/lyUrJk+ejE2bNgEAjh8/ziQvEdENxMnFBi/+ewH2/JCElDO5UKk6Lgm1sjZD6ARv/PWhSVp6GBgnzmdpTfBeq6ahCTujzyJyhCf+veR2bNkXh5jUbFTVNXZoq3QwgY2NNcTlTSgv0l5WYbinPSZODcAd84JRV9uoNcHbW3b2VpBIxSgtroISQocEb2caHKQwL2yCRbkKjq7mqLMEiqvrWsozaEnw9jV7BytIJGKUFFe123itt9IvFiI5MQuTpwd123bNS/Pw1afROJmQqbWGtFQqxqixw3HnXSGws7fS0gMRERHdDFgLl0i7QZfkjYmJ6fTYfffdB3t7eyxYsAAREREoKCjox8j6TnFxMdLS0gAAAQEBHY7X1NQgLy+v0+NttT3e2icREd04Jk8PwuTpQaitacCpxCzExyhQVHgVgSOGISxShoCgYZBIWv5g2NSoxJnky7iQmg8n5yEIneDT76t4AaBJqXs9gabmlrZezrZ44+FZUKrUSL6Uh92HzyIluxBONpaYPXEkpoX4wtKsZcVmfm45ftmTjNjj6RCJgIiJ/rh9zli4Dhuq6TenoPskc0+ETpFh1crbNGPUVDcg+vhFvPDT77p1IBJhzSvzMSFcpll5mlNUgffe34+zeQq9xtqVCVG+eOzJGe3m8cueZGz+8JDexmhq0m3lsae3A1749wKolGqkpuQhIVaBC6lX4Oxqg7AIGYJDvPq1xAgRERERkSEZdEne7txyyy04cuQI7rjjjoEORW/Wr18PpbLlA9DChQs7HM/Ly4MgtKyqcXNz67Ivd3d3ze3c3NwexdGaSO7MjZJUJyK6EVhYmmLi1EBMnBrY7v7Gxmb8sicZ8TEKnD6ZjYaG5nbH/QNdIQ/3wa0zR8OlTRK0L/VkQ7fWpg0NzTjwy1kkxCiQfDILjY0tr5NZAD7cp8BvAa5wH24LpUqNlDO5KG2zSdrlrFIc3H8OI0a7QyIRIye7FOkX9Psatq8uB0d//R5TPL0wy9cPkcOHI2KSP6BrkhdA5CR/mBpLkRiXgejDaUiIzeh0k7i+Enc8HZnpRQga7QaJ+I/H6mKhXsfo6YZ+kj9W7I4aO1yvcRAREV0Pfk4mIkMxYEneqqoqJCcno7S0FObm5nByckJgYCDMzMy6PTc4OBgnTpzohyj7Xnx8PP773/8CaEngrlixokOb6uo/P/BZWna9AqvtpnQ1NbrvUg20TxATEZFhemPtD4iJvtTp8YtpV3Ax7Qq+2x6H7bufhKWVaZ/HNNbHFabGUjTosKIzIsgTAPD62l2IO56utY0g/DmPzpQUVeHIgfPXFW931FKg3l6M6ppqbE85i+0pZ/HBzDswy9cPIzyccP5yUbd9BPsOg6mxFL/9fAbrX9vTJ3HqqrioCsUHel+PWBtzc2OMGNX1H6iJiIgGM35OJiJD0e9J3sLCQjz99NPYtWuXZvVqKxMTE0yaNAlLlizBfffd12U/3t7efRlmvygqKsLdd98NpVIJkUiEzz77DObm5h3aNTT8WZfO2Ni4yz5NTP78GmN9fb3+giUiIoNwSccVqw31zci5XIqgkX2fgPN2scMPLy/B4dMKRKdkIelSXrsSDv5uDogc6YXJo70xyssFgO7z6C9qCVDvKEadiwS1wyRQmbevoX+uuAh3+Pljy+qFiD6XieMpWThxPhvl1X9uJGZvbY7IkV6IGuGFiaNa9hXQ9zxt7SwhD/eBy7ChqKluwMn4TGRlFOt3DHtLyMNlcHG1QXVVA5IS2o9hbCzF2PGekEfIEDnJX7PxHRERERER9Z1+TfIWFRUhPDwcOTk5mvIDbTU0NODAgQM4cOAA/v3vf2PDhg0dNmDrK0qlEkZGum2W0pVPP/0US5Ys6bZddXU17rjjDs1XP9544w1MmzZNa1tT0z9XWTU1NXXZb+sGbgB0WhXdVnflHQoKCiCXy3vUJxERDV6/7DkNiVgM3wAXiMU9+0p9TzkNtcLCyWMx1skRJ4xt8PvJdNQ1NiM0wB1TJgZiXIgnzMy6/kOmvqiMRGi2EkNpIoZIAAQRIG1Qw6hGDUnzn+9PykdI0WwphtpYhHpHMQRp549R9OXLCHXNQLj7cNw63g9Bvs7wHeuEfWcu4nJhObxd7TBrtD+mennDxcoKdbWNSIzJwOmkbL3Ny87eEl/vfrLdc7l8FfDmv3/CgV/O6mUMe4eWMa4twVBcdBXnTufAytoMY4I9YGLS+/dUREREgwE/JxORoejXJO8rr7yCy5cvAwCcnJywatUqjBw5EkOHDkVhYSHi4uKwd+9eKBQKpKWl4ZZbbsHrr7+O5557rj/D7HMNDQ2YO3cukpKSAACrV6/GP//5z07bW1n9uQKmuxIMtbW1mtvdlXa4Vnf1fomIaPDrSaL01z2n8eue07CxMcf020fhb49OhbGx/t8aNDUqsXXj7/h9fwoqK+vaHTusqMThvedgZCTBuBAvrHh6Boa52fZJwrfRRoJ6R2OozMQdj/3xX0m9CmZFzTC5qoIgFqHGU7fHI620BMv2/AiJSARTqRS1zW1qIZsAhWV5iDmcB6MfD8DjPCDKb4JapdbDrP5kPcRca7Le1k5/m+1ZDzHXWmPX0WkIpt82Sm/jEBERDRb8nExEhqJfk7z79u2DSCTC6NGjER0d3SEJuXDhQrzzzjv47bff8Pe//x1nz57FP//5T0gkEqxevbpPY5NKpUhLS+t1Py4uLl0eVyqVWLhwIQ4fPgwAWLp0Kd5+++0uz2n7otJd0fe2f2Vk7SAiopvPkuVT8OE7v6KivLb7xn+orKzDzh3xGDVmOCIn++s9pviYdOz6JqHLNs3NKiTEKmBlZYp/vjwPDy2fgo/e3d+jeXRFEAE17iZANyuWVWYS1LiLYXy1DtaZSjQ4S9Bg2zEp3On5gtA+wXsN23PNEPJU6Ph9pq6JRC21iDvt194Si5dO1nps1p1jceZUNi6kdl7DuLdjEBERERHRwOrXJG9hYctuzS+88EKXq0xnzJiBqVOnYvHixdixYwfWrFmD2bNnw8/Pr0/jCwgI6NP+1Wo1HnjgAezZ07LByr333otNmzZ1e56lpSXc3d2Rm5uLCxcudNm27fHAwMAuWhIR0Y1o0rRARE0JgOJSIeJj0vH913Goq+u61E+rqqq67htdh6qruteIr6hoSepOnh6EiVMDkX6xAAmxCny/PQ51tbrNQxtBjG4TvBoSEWbOG4eoSD+MGe+J8uYGHM3OwhdnT+NCael1xwAAkibd07u+/s6Ys2A8QifIYGFpgtNJ2YiPUSAhVoHS4ioEjnCDPNwHYRG+8PZ10qzirWtowqX8ErjaDYGjjSVc3WzxwSd/Q3HRVSTGZiA+RoHkk1kQi0UIlntDHu7TMoaFCZKTspEQq0Bs9CVUlNfAP9AVE6L8EBYug4+fk9ZVvNqoVGpkKYohlojg6e2odYVxUUElSkur4ePrDFNTlncgIiIiIuqNfk3yDhkyBGVlZZDJZN22NTIywrZt23Du3Dmkpqbivffew0cffdQPUfadRx55BDt27AAAzJ49G1988QXEYt1WB0VFRWH79u24ePEiCgsL4ezsrLXd0aNHNbcjIyN7HzQRERmcK/kVSDmTi5Qzuaivv/7E6EBIPpmFNU9/DXm4DPIIGfwDXeEf6Ipzp3OQfDK73+JY8eQMmP1RV9bV1Aj3jxoNtSDgX4cP9VsM6UIt4iqKIc2xwIQgD4RH+SE8yg+CIEAQ0C5xermoAtEpWTiRkoVTinw0/7GxnZ+bAyJHeCJqpBdGebngjnnBuGNesGZvhNakrVKpwvlzeTh/Nhfnz+SivKylPFSGoghW1mawsDCBuYUxXN1sO423qqoeSfGZiI9Jx8n4TFz9ozSHrZ0lQif4YLzcG+YWxjibnIOEGAWys0oAAEbGEowN9oQ8XIawCBlchg3V/4NJREREN4xXXnnlus576aWX9BwJ0eDSr0lePz8/xMbGIi8vD2PHju22vbGxMR5//HE8+uij2L9/f98H2IdWr16NTz75BAAwffp0fP/99z3a6G3evHnYvn07AGDbtm1aa/jW1dXh22+/BQAEBQX1+cpnIiIafD775Ci+/F/0dZ3bVQKvN1zddE/aCQJwMj4TJ+Mz8fF/f8ODSyfhgb9Ngqubba+SvCIVIFIKXW6e1sp+iAVMjDq+RfKwsbnu8Vs1W4pgVqxb26vqZuyJTcWe2FRYmhpjw5N3YYSnM0QiEdouqP1g93F8uj9Rax+X8kpwKa8En+5PRJCHE7Y9dx+kEnG7FbmNjc1Y9fD/kJ1Z0jHeJhUS4zKQGJeBj94FHnpkCv6yOKpDu7PJl/HCszvQUN+xVEV5WQ327zuD/fvOaI2x/Rj78bdHp+L+B/mHaiIiIiKintC9yJwezJw5E4Ig4KuvvtL5nNZk8JUrXdeRG8xefvllvPvuuwCAiIgI/PjjjzAxMelRH/Pnz4ePjw8AYN26dcjIyOjQ5rnnnkNFRYXmNhER3Xx+6ySR1hlTMyNETvLH2jfuxphxHn0S09jxnvjXa3chYqIfTM169rX8334+CwB45PFbsGTZZAQEuULHigHtiABYK+phUtoMcZP2Dc+cba1wz6TR2PzU3VrLC0S6D8eHs2bjFm8fmEk7JoFFAALs7DHGyRnOFtrLUhlPtIP7Le7w9HPUelwQA41DJKjyNIHSQqK5v6ahCb+fVmg9Z09cqtb7r5V6uQgZVzqWm0i/UKA1watN6/NxrejDF7QmeK/Hbz/37BomIiIiIqJ+Xsn7xBNP4P3338e3336Le+65BwsWLOj2nNakpY0eVs8MhA8++EDzVYJhw4bhzTffRFZWVpfn+Pv7d1jla2RkhPfffx9z5sxBVVUVIiMj8eKLL0Iul6OiogJbtmzBzp07AbSUdnjggQf6ZkJERNQnaqobkJSYibPJObCwMEHoBB8EjXSDRNqzv8eq1brXfF3x1AzcMS8YxsZ9+3ZAJBJh0rRATJoWiKYmJfbsSsLG9w/odG5NdT2u5JXD1c0Wix6aiEUPTURFeS3+75XdOJXY9evptaSNAizzmyDkAypTEZqtpFCaimEhiPHamnmYMNa7y5qzIpEIs3z9MMvXD7UNjfj+xBn8knYRdc3NiBg+HIsmjYe7Q8uqZUEQcOx8Jr6LPw1FZTn8bO1xb9gYRAR6acaoKK/By+/tw6mTWRCkQJOVFEoLcae1gzMLylBd3wgrs/Z/KO7Jc67S0lbbfZ1Rq7QnyFVq7fdfD7Wqp9vSERERERFRvyZ5d+zYgddeew3PPvss7r//fqxfvx6PP/54lx+odu/eDaBlBawhak28AkB+fj6iojp+xfFaWVlZ8PT07HD/rFmzsHHjRqxatQpFRUV4/PHHO7SRy+X44YcfIJFIOhwjIqLBpalRiR93nkTs8Us4fy63XXJr++cnYGllihC5N265fRTCIn116lPbCtTOBI106/ME77WMjaUIHDlM5/Y11Y1YvPBjuA23hTxchnl3h8Jl2FB4eTv2OMnbSgRA2iBA2vDnytO1K7fDbbgtwsJ9MffukE7rwsYev4SDv55DUkImamsaNfcfwhUc+W8CZH5OMDKWory0BlfyKzTH01CEl7ech/twO8jDZZh7TwhcXIfCbYQToot1+7bS0bOZmP7sRoyVuWLSaB8snDQaxkbSHj3nEi1ttd3XGbFE+x8dJDruMaDbGNexVJuIiIgGJdbBJeo//Vqu4ZFHHsGjjz6KmpoaNDc34+mnn8bo0aPx8ccfIzc3t13buro6/Pvf/8amTZtgamqKNWvW9Geog9ayZcuQlJSEZcuWwdvbG6amprCzs0NUVBQ2bNiAEydOwN7efqDDJCIiHWz56BA2f3gQ507naF29WFPdgCOHUvHic9/g3OkcnfqcNmOkTu08vRzgJdNeMqCvyXydMdyzZ69VeTnl2PVNAlav+BxqtYDwSX4wMdFvgjovpxw7v4nHMys/17o69nRSNtb+/Vsc+z2tXYK3lUqlxsW0AqScyW2X4G0rN6fsjzG+gFotYOpYH5hqqf/bGaVajZOX8vDO90fx4Y8nAAAzQwN0Otd3mD28Xe063C/zd4HbcN3qMU+7dYTW+yMm+entDwbTbtXtGiYiIiIioj/17/IdQLObc+vt1NRUPP7443j88cdhb28PJycn1NXVITc3F0qlEj4+Pti4cSPGjx/f36HqxZEjR/Te58iRI7F582a990tERP0rL7dM57a5OWUYNXZ4t+0efmwaps0YiYRYBeJjFO1WCLeuVJVH+GDUWA8YGQ3Mtz6MTaTYsG0pzp3OQUKsArHHL6Egv1Knc0tLqtHY0Iwx4zzwxc7HcTI+AwkxLX00Nir1El9JcTUaG5thZmbc7v7cHN2fr27HKKpCU5MSIX7u2PvawzhxPgvHz2fj2NlMNDbrNo/sopZE8tN3TcLsCUE4npKJ4ynZOJt5RVOCwdPZFlEjPBE50gvBvsNgpOWbPqamRtj0+XKcTb6M+BgFEmIUmiS1RCLGqDHukEfIIA+XwcPLQWss40K88OWux5EY13LdtV3pbGVthtAJ3ggO9YKpmTFSzuR2PkaELzx6+AcAIiIiIiLq5yRvYWEhTp8+jdOnT+PMmTM4ffo0Ll26BJVKBQAoKSlBSUkJRCIRBEGASCRCZWUl3njjDezduxejRo3CqFGjMGLECJibm/dn6ERERAbDy8cRXj6OuPevEaipbsCliwVwch6CYW66rdYEgKYmJc6dzmlJ+sUqUFx0FYEjhkEeLkNYhC88vOy7LLcEABXltZokbFJiFkQiIDjUC/JwGUIn+GC83Bvj5d7460MTseD2t3WO7f3dxzFlnA/GyYbh1pmjcevM0fhp50l88PavOveh0xhjfRDs6wYjad8kwz/56BAiJvtj1JjhmDDaE01DAEVjBbJSdNsErS3fYfbwHWaPh26To6q2AWm5xXC1s4a7g41O5xsbSxES5oOQMB+sfPo25OWUoaS4Cn4BLrCwNNWpj6G2FpgxawxmzBoDpVKFS2kFEEtE8PV3gaRNmYfJ04KuewwiIiIiItKuX5O8jo6OmDFjBmbMmKG5r7GxEefOndMkfc+cOYOzZ8+iqqoKgiCgrKwMhw8fbrciViQSwdvbG6NGjcLo0aNZ44WIiAySpZXuiS0ra7PrHiM4xEvn9oIg4OP//oZf955GQ31zu2Nnk3NwNjkHn3z8O5ych2Dl6tsQHuXXoY/iwqt487WfcDb5MoRrqh4cOZiKIwdTIRIBY8Z54O9r52LIEHMYGUvQ3KTqPj4x8M3R0/gm+gzMTYwwP2oUVt81CUNs9PfHX80Yx1rGWBA1Ck/fNQlWPXi+dPHjzpP4/pcklEaYos6uJWFuVAVYQLeatEMstMdjbWGKsIDuV313xW24HdyGdyztoCupVIKgUW59OgYREREREf2pX2vyamNiYoKQkBA8/PDD+OCDD3Ds2DFUVlYiIyMDu3btwtq1azF37lwMHz4cgiBAEASo1WooFAr88MMPePXVVwd6CkRERNflr3+bhLHBHl1uNGVpZYo5C8YjLFzWLzFlZ5Zg93eJHRK81yoqvIqtG37Xeuznn5Jx5lTHBG9bggCcPnUZv+45DWMTKZ76+yy4umnf7KyVykSEGjcT4I+Nwuoam/HVoVPIKizHhChfzJ4fDAtLk07PF4nQbR1fzRiiP8f48tApZBdVYEKUH+6Y1/UYPVXlI9UkeAGg2RJotBEgiDt/8KRiMcIChmPpzDC9xUFERERERIat32vy6srLywteXl6YN2+e5r6qqqp2pR5Onz6N1NTUgQuSiIh6rblZhZQzOaipacSoMe6wGWox0CH1Gw9Pe6z/8AFUV9XjVGIW4mMVOHvqMiwsTRA6QYawCBmCRrpBIu2/v8nW1XXcUKzztk1a768or+1xH61f88/LLUdCTDo2fHYE6hollGZiNFtJ0GQtgdpE++NQ19gMExMjPPncLKx8+jaknstDzPFLOHH0IurrmhAc4oXIKf4YH+oNSytT5OWWI/5EOjZ+ehjqOiVUJmI02UjQZC3tfIyGJpiaGuGpv8/CqtUtY+zdfQqHD5zXea7aqI2uuUMM1DsD9U6ApF6AUTVgXAWI1ICHuy1W3RaBsEAPWJm1JJoFQUB2ZgmyM0vgLXPEcM/uy2gQEREREdGNZ9AmebWxtrbGpEmTMGnSJM19rfV8iYjIcFSU1yI2+iISYjNw6mQW6v9I9IlEgH+gK+ThMkyI8oWvv8sAR9o/rKzNMHl6ECZPDxroUK6b4mIhYo9fQkKsAhdSr1x3P27utnC7Nwwbk8+g4qruyWIAKC+rQWz0JcTHKJCclKVZjXzk0HkUFlQiO7MEVlZmyLlcioRYBVCthBiAuE4NcbMASaOAJmsJmq0kmpW82kilEowe5wGRSNTrJK82knoBFvlKmBeoYVakgviPtzq1Z+rwU+YJXAkrgKWVGXKyS5AQq0BJcbXmXGeXIQgNlyEsXIbxYd6Q9lE9YSIiIiIiGlwMKsmrjUTLLtFERDR4Xckrx2MPbUVdbccVo4IAXEi9ggupV/D51mNY8dQMzF8oH4Aob15u7nYwNpaiqUnZbVsfXycAwO7vEvHRu/uva7zWPq7l7+aAUh2SvKZGUgx3sEHu5TKsenir1tXFggCknc9H2vn8TvuRNAuQlClhWqZEk6UY1T5/1kA2NZZiuKNNh3PchtvpXEu4MyYV6nY/G1Wp4XagAWJtD78O8ygsuIo9u5KwZ1cSgkO98J/3Fl13bEREREStuBcS0eBn8EleIiIyLClnc7UmeLWJO5HOJG8/G2Jjjg3bluK3n88iIVaBrIzidseNjFpWsYZFyHD7nLEAgNjjl3o0hs1QC4SG+2Dy1EDII7TXGv7P0juwOyYFx1OykJSej2Zl+0Sq7zB7RI30wp3hI2BtYYroA6mdlo/oKeMaNaAW4OvuoBnDyrzjJmdDbS2wYdsy/LbvDBJiFcjOLGl33MhYgpGj3WFhYYKamkacP5uL5ub28xgjtcNQqQOqXERILC2AUlGlPcF7HU4lZqGpUQnjbuoQExERERGR4eO7fiIi0gu1WkBGeiHiYxS4cD4fDk7WkIfLMHa8J8zMjDXtutqM61pCTxrrqKGhGaeTspEQo0BR4VUEjGgpD+Hr7wKxmLVMAWC4pz2WrpiGpSumobjwKuJjFSgpqkLgyGEdns+eWrQkCg8undztY21uaoy/TAvGX6YFo66hCYkXc3E2qwCudtaIGukFp6FW7doL0O+18sPLS+DhYqv5ubjoKhL/KC8iEokQHOoFebgMHp72WLZyOpatnN7usQoa5YYxwR7tHqv6uiYkn8xCWmo+nJ1tII+QwcHRWnNcpVZj8+dHsCspRm/zuPZxKS66ioRYBU4lZkMsFmG83AuhE2Swd7DqpAciIiIiIjIETPISEVGvFFypwNfbjiMhNgPlZTXtju394RSMjCUYM84Ds+eNR+RkfxgZ6V5mR9qDtt2JPX4Je3Yl4fSp7HZfr0+IVeDzT45hqK0FQif4YNGSKLi62XbR083F0XkI5swf32WbntR9dRtu1+NkurmpMSaP8cHkMT56iaE7IhHgYmuNpkYlvv06FtGH05CpaL+i+eihlo1ffXydMGlaIO6+f0K3j5WZuTEiJvkjYpK/1uMSsRiedkP1Ng+xWASRSITGxmZ8+2Usoo9c6LAyu3UeMj9nzTx68v8oERERERENDkzyEhFRr/zn1R9x/mxep8ebm1Q4GZ+Jk/GZ+GLnKgSHesHDyx6Xs0q77NfERIo77gzWS4wlxVVY+/dvu2xTUV6L334+i5zLZfhgy0N6GfdmccfccThzKhuNjV3XGRjuaY/xcu8+iSF0gg/ch9shN6es133Nnj8exiZSfPNlDD7bcrTLthnpRchIL4K5uQnm3RPa67FDJ8jgNtwWeTnlve5rzoLxMDaWYsfnJ/D51mNdtlVcKoTiUiHMLUww966QXo9NREREA4f1c4luTkzyEhFRr5QWV/egbRVGjhmOzV88gvQLBUiIVSA+RoGLaVcAAPYOVpCHyxAWIcO4EC+YmV9/WYB245boHmNJUZVexrxeNdUNMDKWwMTEaMBiaGxsRnOTCpZWHevQAi2lOa5W1mGIjTnEYhEiJvnju32rkXwyC/ExCiTEKjSPuX+gK8IiZC0lMQL6riSGnb0Vtnz1CC79cV0dOZiKPF0TviIgPNIPYREyhIb7wNFpCICeXdslxfq5buwdrPDJV4/i0oUCxMekIyE2A+kXCgAADk7WmsfSZdhQnEvOQXxMOk4ntSTYTc2MEBzSUkaibSmIEgO6/omIiIiI6PowyUtERP1OLBbBP8gV/kGueODhSaisqEVdbSNchg2FSHRz1cVVKdVIS83XJPQy04tgZCzB2GBPTcLbZZj+vsLfmSt55ZoE7Znky2huUsHb1wlhfyQMh3vYIfmPWsaJcRmoKK+FrZ0lQif4QB4uw3i5l6YUgSAIKMivgLmFCWyGWvR57K0kEjECRwxD4IhhGB/qhacf+1yn8xwdrfHqmwsBtDwf507nICFWgd8PpPRluJ1qO48ly6agorwGDfXNcHa1aff/h6eXA+YsGI/GxmYUXqmEy7ChMDaWtptHfIwChw+cH5B5EBERERFR/2GSl4iIemWonSWKCq/q1NbG1lL7/UMt+jQZONRW975t7fovKZl8MgtvrP0BlZV17e5vblIhMS4DiXEZ+Ojd/Qga5YZ/r78X1tZmeo+h6mod/vXct0hN6VhyIzO9CJnpRdj++Qmt55aX1WD/vjPYv+8MJBIxFi4Kx98enQqRSDTgdY07u9a0sbVv2XQsKSET617ejavXPB+66Mk11vO+u56LiYkRPLwcND8nxmXg/17Zjaqr9T0eqz+vfyIiIiIi0h/xQAdARESGbfU/70DERD+YmnZeXsAvwAWr19wBN/eBSfw5u9jg2RfmwD/QtdM2pqZGmBDli2een9Nvce3+LrFDgleb1HN5SIxV9EkM8TEKrQnenlKp1Pjmqxg0dVOXt7+4udti9Zo74Bfg0mkbUzMjREz0w+p/3gEA2PVNQo8TvA5O1rjrvjDcMVc/9aP14YdvE3qc4HVwssbd90/AzDnj+igqIiIiIiLqS1zJS0REveLl44hX/rMQTU1KzdfD087nw8GxpX5o6AQf2Nrpvqqyr9x2xxjcdscYVJTXIDEuA/ExChQXXkXAiGGQh8swZpwHjE3692WxqUn3hGh3m5pdr+YexNAdtUqAUqnq98exMzPnjMPMOeNQXvbnc15SXIXAEcMQFiHDqLHDNeUNgJ49H6ETfLBs5XR4ejsMuhIjPZnH5GmBWPTQxEE5j+7k55XjVGIWJGIRguXecHaxGeiQiIiIiIgGzOD4FEZERAbP2FiK8XJvjJd7D3QoXRpqa4kZs8ZgxqwxAx0K9RNbO0tNkl9fEuMyUFR4FfJwGSZODUDQSDe99d2fwiJ94eXjONBh6Ozc6RxEH7mAhFgF8nPL2x3z8LKHPFyGSdMCERA0bIAiJCIiIiIaGCzXQERENEDGjPPQqZ2RsaTPkoiBI91gZCTRS1++AS4wMzfWS18DYUywbs9Hq5zsUny/PQ5PLt+GE0cv9lFUPafrdWVsLDWoZOih/eewesXn+OHbhA4JXgC4nFWK776Ow+NLP0XcifQBiJCIiIiIaOBwJS8REdEAufeBCPgFuiD2eDoSYhS4kl+hOSYWixA0yk2zMnFYH21k5uXjiC1fPYJjv6chPiYdaSn5UKsFzXHXYUMRLPeCmbkxCq9UIikhC3W1jZrjFpYmGC/3RliEDBOnBBrcV/7bWrQkCoEjhiH2+CXExyhQeKVS53NTU/IQOdm/74Lrgb/+bSICRw5D3In0DvMQS0QYMcod8nAZJk8LhMuwoQMXaA+dP6t77ejz53IxIdK3D6MhIiIiIhpcmOQlIiIaICKRCMGh3ggO9caKp2YgP7ccZ05dhoWlCYLl3rC2NuuXOIa52eK+ByIQHuWHY4fTcOz3NNTXN2FssAem3z4Ko8YM16z2VSpVOH82F1kZJfCSOWLEKDdIpbqtBBYEAZezSpEQq0BirAK1dU0YPXY4wiJkGNlmjN5oHSM+Jh0n4zJQW9eEMeM8IA/36XYMkUikKTmy8mkB/3nlRxz6LaXXMV0PQRCQcaUMx89nIeb8ZdQ2NCLEzx1RI70w1scVRl085iKRCCFhPggJ88HKpwXkXi7D2dOXYWVthuAQL1j103VFREREfeell14a6BCIaJBhkpeIiGgQEIlEcBtuB7fhdv06bnOzCl99Go2Dv55DUeHVdsd++/ksfvv5LMzMjRE6wQfLV06Hk4sNxgR7Ykywp17GSL9QgJ074mFubozQcBmWr5wOR+ch1zWPL/93DIf2p2gd4/vtcT0aQyQSwd7RWufxTfS02VyzUoVN++Lwc0IaCsur2x1LyynGFweTYGFqjMgRnnhqwSQ421p12Z9IJMJwT3sM97TXS3wDycTUSOe2pia6tyUiIiIiuhGwJi8REdFNLDFWga+2He+QGG2rvq4Jx35Pw7YtR69rjISY7seoq2vC0UOp2LblyHWNER+Tjq8/O6HbGJ/oNo/b7hgDTy+Hbtt5+zrhlttH6RxrV46dzcT/fk3okOBtq7ahCb8lXcLGvbF6GdNQ3D5nrE7JapmfM6br6fkgIiIiIjIUXMlLRHQTUSpVyMkuhZ29FYbYmA90OKQHVVfrUFJcjeGe9tdV7qCiolbntpU9aNt+jJoejFF3fWOU934ezc0t/384OFnD2toM7h522PzlcuReLkNCrAIJsQqcO50DkUiEUWOHIyzCF/IIGdzc/6yXfLWyDmWlLc+HtjIW145xrfJq3effk7Y3Ag9Pe3zy1SPIuVyGhJg/nw+xWITR4zwQFiGDPELWZ/WriYiIiIgGMyZ5iYhucGWl1UiMy0BCjAJJCZmoq2uCSAQEBA2DPEKGsAgZfHydIRYb7oZZNxNBEKC4VPhHkisDaefzIAiAubkxguXekIf7QB4ug51951/jb2hoxpmkbMTHKnD0UGqfxNnfYxw5eP66+igtqUJCbAYSYhU4lZiF+romiMUiBIwY1pI0DJfBx9cJwz3tcff9E9DUpAQAGBu3vIVSqwVculDQkgSOUeBCan7L82FhgvGhXpBHtJyffrHlOTt1svMxDHnTuv4iEong4WkPD0973POXCWhqVAKiP58PIiKim8Urr7wy0CFosD4w0eDAd8RERDew776Ow5aPDkIQ2t8vCEDa+Xyknc/HZ1uOIiDIFe9sWKyXja+o7yiVKjyz4gukpuR1OFZX14TjRy7g+JELAIClK6bh3r9GdGh3Me0K1jy9HdVV9T0e30nHWrkXU69gzerrG0PXerwXU69gzdNfo7q6ocdjtM7j68+O49NNRzocV6sFpJ7LQ+q5PHy66QhGjnHHWx88AIlU3C6Z2NyswurHPsOF1Csd+qirbUT0kQuI/uP56G6MUWPcsf7DB+Bip3sdYBdb3dveqIz1VAuZiIiIiMjQsSYvEdENbN+PpzokeLW5kHoF6RcL+j4g6pWM9CKtCV5t9u4+pfX+Y7+n9Tj56unlgPsejMCyldN1an/099TrGuP+ByN1HuPIodQeJ3hbx1i6omWMzh6ja6WcyUVWZnGH+9MvFmhN8F6Pc2dykZ1ZgsgRnnjpgRkI9XeHVKz9bZrM1Q5/u12Ox+dF6mVsIiIiIiIyfFz+QER0A1MpVT1oq+7DSEgfevIcdfbcK3twTYwd74lnnp8NZxcbnc9pGUP3OMeFeOKZNbPh1GaM1hIK2Zkl8JY5InSCT4fyEz2ZR3CoF1b/8452YwA9ezwvZ5Z0KKnQ2/9nBBHQbClBs6UYYhWgKCiFt8wRcyNGYG7ECNTUNyL+Qg5izmejtqEJIX5uiBzppdcVvGp1S/mPxLgM1NU2YkywB8YEe8DExEhvYxARERERUd9jkpeI6AbWkxqfItbkHfR68hx19tz35Jrw9XfucYIXAMQ9GsMFTi42KC66ir0/nEJCrAIZ6UUd2sn8nBEWIcPs+cGwd7DuUQ1pmZ9zhwQv0LPH4v9e/RFbPv4doeE+uPX2URg9zuO6/p8RRECjrRRN1hI0W0qANn2s2f4b3vn5BKJGemFWWCDG+7ph+jhfTB/n2+NxunPqZBYO/XoOiXEZ7Tat+/arWJiYSDF2vCciJvnjtlljIJHyi19EREQ3CtbPJbpx8V07EdENLHJygE7tnF2GQObn3MfRUG95yxzh4mqjU9uoKdqfe3mETKfay0ZGEsjDZT0Jr80YPrqNYSyBPNwHarWApx7Zhu2fn9Ca4AUAxaVCfLXtOJ5+9DOo1QLCImSQ6pB8NDKWQB6hfR6Rk/27Pb+tstJq/LrnNJ5Z+QUupl6Bj68TnF10qyHcqs7FGLVuJmi2lrZL8LYquVqLH06kYNk73yH1cmGP+tbV2eTL+McTX+G3n8+2S/C2amxUIj5GgXf/bx+2bTnSJzEQEREREZF+cSUvEdENbPmq6Zg8PRDxMQokxChw6cKfdXdt7S0hD5dBHi5DSJg3TE359ezBzsTECJu/fARJ8ZmIj0lHQmwGykqrNcd9A1wgD/dBWIQvAoJctfYRHOKFbd+uQPwJBeJj0nE6KRuNjco/+pdiXIgX5OEyTIjyhYPj9ZUFCA71xrZvVyDueDoSYhXax4iQYUJkyxjVVfUoKa7uptcWhQVX0djQjOBQb3z27UrEnWgZI/lkNpqaWsYwNTXC2BDPlnlEdj6PVatvw/QZI5EQq0BCrALpF3VPqmZnlcA/yBWbv3wEJ+MzNX2Ul9Zo2vgFuCA03AdOzkNQeKUSCbEZSFJW6jxGxpUyBHno/48v2ZklfdKWiIiIiIgGDpO8REQ3MJFIhICgYQgIGobFSyejorwGaeevwNHRGj5+Tjp/XV2lUuNi2pU/ElkZKCqohH+QK8IifCEP94GL69A+nkn/KSyoREJMS8Iu7Xw+HJ2GICyiJRnuH+QKiWRgvwRjamqEyMn+iJzsD0EQkJFehOLCq/APcu1Qt7Yzjk5DMO22kbCxtYC1jTkSYxQAgNAIGSIm+iE4xAvmFia9itPRaQjuvCsEd94VgsbGZpw/mwuIRBg5yh3GJvp5++Ho3LsxRCIRgka5YaidBYbaWaLgyhHU6LiZ2/59p2FpaYpxIZ6YOCUAE6cEtDwfl4pQXFyFwBGuGGpr2e6chx6Ziofe3IEzWQOzyWFtTQNOJWbh4K/ndD7nQuoV7PkhCWHhMjg692zVMhERERER9R+RIOiy7zoRkJeXB3d3dwBAbm4u3NzcBjgiIuprgiDg863HsGdXEq5W1nXabrinPR594laETvDpx+j0KykhExvfO4DsrM5XLloPMcMdc4Px0CNTelTPdTApK63Gf//zM07GZ3S6QZpUKkbIBB88/Y87YGtnqbWNPjU2NmPeret12rDN2FiK3Qee06kcRHcS4zKw8f0DyMkuve4+pFIxQifI8PQ/Z3VI6mrzzMafcPhMhk59v/3IHEwde30lM9oqKa7Cu//Zh1MJWVCprn+zOE9vB6x4agbGhXj1OiYiIro53YifKdvO6emnn8aQIYb7R1HW6yXqH331u5A1eYmIqFP5ueX48n/RXSZ4ASAnuxQb3vutn6LqGxvf7zrBCwBVV+ux/fMTvUoKDrR9u08h7kR6lwlVpVKNuOPp+Pmn5H6JycTECCueug229l0nSe3srbDy6Rl6SfACwIb//tbr51KpVCP2+CX8sue0Tu2XzgpD4HDHLtuYGkkxJzwI4UGevYqt1Z5dSUiMzehVghdoKd2w5aNDeomJiIiIiIj0i+UaiIioU7U1jX3SdjCqrdU9/poa3b7SPxj1aJ46li7QhzkLxmP2/GBkXCrS1BvOziyGl8wR8nAZwiJ84S1z7HGJkbraRgSNdNNafqInj0V3Orv+C/IrkJ1VAm8fRzi52CBwuBO+WrMIxZU1iDmfjeiULJxKz4OVuQmiRnghcqQXQvzcYGIk1Xke3camx3n25zVBRERERES6Y5KXiIiIBgWRSASZvzNk/s5Y9NDEHp9fXVWv2QAtMS4T1VX1AFpKKowaOxxhEb4Ii/SFm7utvkPXUCnVOHv6cstmh7EK5F4u0xzz9HKAPEIGeYQMI0e7Y17kSMyLHNmhj6qqehz/PQ3xMQqcjG8/j9HjPFqS3n08DyIiIiIiMixM8hIRUadchtnAyEiC5mZVt22He9r3Q0R9x8PTHiVFVd22k0rFcB1muMk1jx48Tx5ehvOcXskrx6qln2oSom0plWokn8xG8slsbPrgAJ55fg5uu2MMhnvao7ysRi/jt17/a1Z/jeST2VrbZGeVIDurBN9+FYsJUb7495v3dmiTn1eOxx/+H6q1rJhVKtU4lZiFU4lZ2PTBATz7whzMmDWm29h68px325eXg976IiIior7B2rpENycmeYmIqFPWQ8zx4da/4ZefTiM+Jh0FVyrbHReJgMARbpCH++DOu0IGJkg9eeHVBfhp10nExyhw4Xw+1Or2+5I6uwyBPMIXM+eMxVBbiwGKsvdm3jkOpmbGiD5yAacSMlFX19TuuLm5McbLvTFpWiAmTw8aoCh77nRSttYE77UEAYg+nIbb7hiDta/fhZ92nkRCbAbSzufh2q1onV2GYJi7HdRqNS6kXkH9tY+VhQnGy70xeVogJk0LRHVVfacJ3mvFHU9HY2MzTEyM2t2ffDJLa4JX6zyOXNApyTt7/nhYWJq0POeJWVrn4R/oArFEjLzLZSgqvNruuFgsQtAoN4SFyzDHwP8/JyIiIiK6UTHJS0REXfKWOWHl6tuw4ukZyMspR3xMOooKriJghCtCwnwwxMZcb2MplSqcP5eHhBgFigqvIiDIFfJwGdw97HSuxXq9LK1M8ZfFUfjL4ihUXa3DyfhMpJ3Ph6PTEIRF9E8MbVVV1eNkXAZOJWYBAIJDvRAywQfW1mY6nS8IAnIvlyEhVoELqVfg5DwE8nAfjBjtjmkzRmLajJEoK6vBT98nIvZ4OgAgPMoXd94dCju7lg3QlEoVUs7kIiFWgeKiKgSOGAZ5uAxuw2379bHQhfraDG0XruRXIC+nDMPcbbHooYlY9NBEXK2sw8n4DFw4fwVOLkMQFuHbbp7NzSqknMlpeT5EQIjcGyNGu0Mq/XMTOKEHMbS013JfD/ZGE9S6jScWizD9tlGYftsoNDercO5MDk4lZEEsFmF8mDdGjHLTzEMQBORklyI+RoGS4ioEjXTD+DBvna87IiIiIiIaGEzyEhGRTkQiEdw97ODuYaf3vpNPZmHPD6eQlJCJujabRB09lIpNHxyEi6sN5BEy3PvXCDg4Wut9/GtZDzHXJEL7k1Kpwk87T+LY4TSkpbRfTbx/3xmIxSIEjhyGSVMDceddIe0SjK1Kiqvw7ZexWldef/tVLMzMjeHiOhQqlQq5l8vajZGVUYwdX8TA3cMOEokYhQVXOzwfG98/ANdhQ/94PsJh79D3z4cuJBKxzm1zL5fhofs2tJlHBOwdrDSJUG2MjCQYF+KFcSFeeolBJALEWhLlEonuyfOejNfKyEiC4BAvBHcyD5FIBA8vB5ZlICIiIiIyMEzyEhHRgCovq8E/nvxK66rGVgVXKvHj9yeRfqEQ721e0m+x9bc9u5Kw4b0DnR5XqwWcP5uH82fzIBKJMH+hvEOb19fuwvmzeZ32UV/XhExFUZdjXM4q7TLOK/kV2P1dIjIuFeKdDYu7bNtfQsJ84OQ8pEOpga60ziNTUYS3P3qw1zFYWJpiyvQgHDmU2m3bW2eOhrFJx7dhIRN84OhkjeJu6kMbGUtw66zR1x0rERER3bheeeWVTo+xXi/RjYtJXiIiGlDFRVe7TPC2VVhY2aexDLSiAt0TlJ217UkfvVXYg4RqX3NwtMan36zAuTM5SIhRIPpwWreJ0lZdPWZNTUpIJGKdV80+/+p8/PVvExEfo0BCrAIpZ3KhUqkhlYoxauxwyMNlmhIk2jg6DcGn36xAytlcJJxo6SM3pwwAYD3EDKETfCAPlyEkzBvWQ/RXKoWIiIiIiAwbk7xERER0Q2hbiiBqsj+efuzzHvchCAIyLhUhPlaBhBgFLqTmw+yPzehaErQ+GGpr2en5IpEIzq428PByQFHhVRTkV6C4qAr2DtaaMgjOLjZd1jQ2NpbCw9MeRQWVKCq6ipLiKjQ2NsPdww6eXg7w9HaAFWvkEhERERFRG0zyEhHRgBrSg9WINnrc5G0wGjJU9/l11naIjTlKS6r1FVKXBvPzMcTGogdtW+ZxNvky/u/VH1FyzQrg2ppGHPs9Dcd+TwMAhE7wwQv/XgALC5MOfX2x9Rh2fBGDpiZlu/sLCyqx+7tE7P4uEaamRli0JAr3PRjZ4fyqqnq89uJOJJ/M7nCstVTH1o2H4exqgzUvz0PQSDed50lERERERDcuJnmJiGhAuQwbilXP3I6fdp5ETrb2WrASiRijxrjjoUen9nN0/Wv2vGAU5Ffg+NGLqK6q19rGytoMUZP9MXtesNbjT/59Fv634Xec+6NMgDYWliZQKtVobGjWetzExAhSIzFqaxq1HpdIWkoPPPzY4H0+3D3s8NiTM7D3hyRNuYNrtZZQePixaQCA776O65Dg1SYxLgMn4zMweVpQu/vr65rw5afR7Taz06ahoRmffXIUd90/AUZG7TfPizt+SWuC91qFVyqx65sEJnmJiIgMHGvkEpG+MMlLREQDbu5dIZh7VwgKrlQgMTYD8TEKFBVWInDEMMjDZQgO9YKFpelAh9nnrKzNsHrNbDz591m4kJqPhBgFTiVmAQCCQ70gj5AhIGhYl/VhA0cMw/oPH0BtbSNOJWQiIVaBtPP5cHQegrAIX8jDfeDiOhQqlVrrGGERvvAPcoVEIkZBfoWmbEFJURUCRrZ5PrSsYr1ejY3NOJucg5KiqwgYMQxePo5dljPQ1YJ75VhwrxxX8sqREJfRbh5hETKMC2k/j4ZOkt7aNNR3bNvcrOw2wdtKqVRDpVJ3SPI2Nig7OUNbDE06tyUiIiIiohsbk7wD6JdffsGsWbM0P7/00kt4+eWXuz3v/Pnz+OCDD3Dw4EHk5+fD0tISgYGBWLRoER5++GFIpXxaicgwubgOxZ13heDOu0IGOpQBJZGIMWKUO0aMcsdDj1zfalkLCxNMnBqIiVMDr3sMl2FDMe/uUMy7O/S6YuhKZUUtog9fQEKsAskns9DY+Gdy08HRqqX+bYQvwiJkOm961hlXN1vMu9u2T+ZBREREREQ0GDAbOEBqa2vx2GOP9fi8rVu3YuXKlWhs/PMrtA0NDYiOjkZ0dDS2bduGvXv3ws5O+67dREREA622thFLF23C1co6rcdLiqux78dk7PsxGbfdMQbPvjCnX+IKGjkMp5Oyu20nkYjhF+DS4X4razO4DbdFXk55t314yxxhYtLxbZhfoAvEEhHUqu5XBAeyVAMREREREf2BSd4B8q9//QuXL1+Go6MjiouLdTpn//79WL58OdRqNZycnPDCCy8gLCwM5eXl2LJlC3bt2oW4uDgsWLAAhw8fhljcu5VPREREfSEro7jTBO+1kk9m9XE0f1q8bAp8A1wQG30JiXEZqCivbXdc5ucMebgPpt46Ep7eDh3OF4lEeG/zQ/h9fwriY9JxJvkymptUmuPGxlKMHe8JeYQMt9w2UmtJCv9AV3z8v6U4cug8EmIUyFS0f49ga28JebgM4VF+CI/y1dPMiYiIiIjI0DHJOwBOnTqF999/HyYmJnjttdewfPnybs9RKpVYtWoV1Go1rK2tceLECfj4+GiO33777Vi5ciU+/vhjHDt2DF9++SUefPDBvpwGERENInW1jUg+mY2EWAWyMorh5eOoqZ9rZm6sUx9FBZVIiM1AYlwGamsbMHqcB+ThMvgHukIs7n2N3MFOLBYhanIAoiYHQK0WkH6xAGeTc2BlbYqQMB/YO1h124e1tRnm3ROKefeEor6+CaeTspGdWQJvmRPGjveAiYlRt334+DrBx9cJDz86DSXFVUiMy0B9XRPGjPOAj5+TXuoVExERUf/h5mpE1B+Y5O1nKpUKy5Ytg0qlwtq1a+Hrq9sqnB9++AEKhQIAsGbNmnYJ3lbr16/H9u3bUVFRgfXr1zPJS0R0Ezh1MgvffBGDs8mXoVSqNfennc/Hzz8lw8hIglFjh+PeByIQHOLV4XyVUo3vtsfh0K/nkJ1V0u7Y2eQcfPm/aNjYmCMs0hcPPTIFdvbdJzq7Y2ys+9sPYy0lDfqDWCyCf6Ar/ANdr7sPMzPjP1bc+l13Hw6O1ph157jrPp+IiIiIiG4O/D5/P3v33Xdx6tQp+Pn54R//+IfO5+3evVtze8mSJVrbmJubY+HChQCAlJQUpKen9yZUIiIa5JRKFV5Z8z1OJWa1S/C21dyswqnELLz6/PdQKlUdjifGZ2Drht87JHjbqqysw/59Z/C/jYf1ErePr5NOiU8TEykWLgrXy5hEREREREQ3Mq7k7UfZ2dmar2l8/PHHMDEx0fnc6OhoAIC/vz+cnZ07bTd58mRs2rQJAHD8+HGdVwoTEVF7arWAwisVGGprqXO5g/7W2NCMutrG7hsCqK1pRGOjElKppN395aXVOo9XXlbTo/g6I5GI8eqbC1FaUoWEGAUSYjOQlJiJhvpmOLsMgTzCF/JwGcYEe8DUtPvyBtfGZ2tneV1xqVRqFBVUYqidJczMBudzTkREREREpA2TvP3oscceQ11dHRYtWoTp06frfF5NTQ3y8vIAAAEBAV22bXs8LS2tR/G1jtGZgoKCHvVHRGRoqqvqkZSQiYRYBRJjM1BZWacpdxAWIYM8XAa34XYDHeYNw97BGrPmBmPW3GA0NSlRW9MAm6EWOtecVSpVOH8uryVRHKPQrEZurUccFiFD0Eg3SKSdf3GpqqoeSfGZiI9Jx8n4TFz94zlvrUccFinDMDdbvcyXiIiIDI8+Pie/8sor+gpHg3V+iehaTPL2k6+//hq//vorbGxs8Pbbb/fo3Ly8PAiCAABwc3Prsq27u7vmdm5ubo/GaXsuEdHNZvf3idj43gGoVO3LHrSWOziVmIUN7x3AuBBPrHvnL10mDvuLiakRLK1MUVPd0G1bKytTmGrZ9Mve0Vrn8XrStqeMjaUwttV9Ba7iUiFeeGaH1tXFWRnFyMooxjdfxsDewQpvvHM/vHwcO7TbuSMemz86CLVKaHd/c7MKSQmZSErIxIb3fkNImDdee+s+SCQD/5wTERFR/+LnZCIyFPy00g/Ky8vx9NNPAwDWrVsHJyenHp1fXf3nV2ktLbv+AGxhYaG5XVOjn6/VEhHdDH74JqFDgleb5JPZUKQX9kNE3ZNKJfj3+nsROckfpmbayxqYmhkhcpI//v3WvVoT0yFhPlj1zO0IGDEMnS2gdXSyxry7Q7H0sWn6DL9Xfv8tRafyEaUl1Th84LzWY7u+ie+Q4NXmZHwmsjM7r1lMREREREQ00LiStx88++yzKC4uRlhYGJYvX97j8xsa/lyhZWzcdY3AtnV+6+vrezROdyt/CwoKIJfLe9QnEZGhaNayKVlnruSXwz/QtQ+j0d3I0e4YOdodTU1KpJzJQUJMBrIyi+Hl7Qh5hA9GjhkOY+POX+7FYhHm3hWCuXeFoKK8FifjM5AYl4HamkaMCW4pWeDhZa9zCYX+0tys+/OlbcO5lvu7T+pr2vZgPCIiIrpx8HMyERkKJnn/oFQqYWSk++Yunfn000+xZMkSzc9HjhzBp59+ColEgo0bN0Is7vniaVNTU83tpqamLts2Nv65AY+ZmVmPxumuFAQR0Y2sJynMN9buxrbNRyEPl2H6bSMREDSsz+LSlbGxFMGh3ggO9b7uPobaWuDWmaNx68zReoysb/Qk6dxp25486YMrx01ERET9pK8/J7O2LhHpC8s19KHGxkY88sgjAIAnnngCY8eOva5+rKysNLe7K8FQW1urud1daQciIvpTaLisR+2v5FVg93eJeGLZp7iYeqWPoqLOjJd7QyzpPvMqkYgRHOql9Zh8go9OYzk4WcPT26FH8REREREREfUnruT9g1QqRVpaWq/7cXFx0dzetWsXLl26BKlUiqCgIOzYsaND+9TUVM3tlJQUTZuwsDB4ebV8KG37l8PudvZs+1USFognItLdk8/NxKRpgUg4oUBCrAK5OWU6nScIQEZ6IfyDBkf5hptFWIQMn3z5KGKiLyI+RoHz53I19XUlEjFGjnFHWIQM4RP94eZuq7WPp/85G1NuGYGEWAXiYxTIzy3XHLOyMkXIBB+ERcgwIcoPJlo2rSMiIiIiIhosmORtIyAgQK/9tZZOUCqVWLZsWbftd+7ciZ07dwJoKfvQmuS1tLSEu7s7cnNzceHChS77aHs8MDDwekMnIrrpiEQiBId4ITjEC48+eStWr/gc507nDHRYNzRBEJCdWYL4E+lIiFWgpLgKAUHDII+QIXSCD2yGWnR5vruHHe71iMC9f41ATXUDzpy6DJEIGBPsAQtL0y7PBVrqEY+Xe2O83BuPPTkDebnluJiaDycXGwQGDdO6UR0REREREdFgxCSvgYiKisL27dtx8eJFFBYWwtnZWWu7o0ePam5HRkb2V3hERDec7hKMbZmZm3TfiDQEQcD2z09g7w9JKCmubnessOAqjhxKhUgE+AcNwyOrpmPkmOHd9mlpZYrIyf69isvN3bbTVb9ERERE14t1d4moP3CJSh9asmQJBEHo8t/hw4c17V966SXN/W03bwOAefPmaW5v27ZN63h1dXX49ttvAQBBQUHw8/PT95SIiG4a9y4Kh4eXfZdtxBIRIif7IyyiZ/V8b3Y5l8vw6aYjHRK8bQkCcOF8Pj58Z38/RkZERERERGSYuJLXQMyfPx8+Pj7IyMjAunXrcM8998DHp/2GMc899xwqKio0t4mI6Pr5B7nik68eRX5eORJiWur0nkm+DAsLU4SGt9RqHR/qDUur7ssC6IsgCLicVYra2gb4+rvA2NgwX8arq+r7pC0REREREdHNyjA/Hd6EjIyM8P7772POnDmoqqpCZGQkXnzxRcjlclRUVGDLli2aer5RUVF44IEHBjhiIqIbwzA3W8xfKMf8hfIBGb++vgnJJ7ORENuSaC4pqgIAmJoZYdx4L4RFyCCPkMHB0XpA4iMiIiIiIqKBxySvAZk1axY2btyIVatWoaioCI8//niHNnK5HD/88AMkEskAREhERPpUXHgVjy//FOWlNR2ONdQ3I/b4JcQevwSxRITnXrgTt9w+agCi7DlnFxuIxSKo1UK3bYexRi4REREZiNWrV8PNzW2gwyCimxRr8hqYZcuWISkpCcuWLYO3tzdMTU1hZ2eHqKgobNiwASdOnIC9fdc1JImIyDAkJ2VrTfBeS60S8PuBlH6ISD/sHazw9scP4tZZozvd4M7b1wn3PRiB51+Z38/RERERERERGR6u5B1gU6ZMgSB0v5KprZEjR2Lz5s19FBEREQ0WarVa97aqnr2W9JfWchPJiVmACBgv98aYYA+MHO2OkaPdoVYLSL9QgPiYdJQUVyFwxDCEhg++8hP1dU1ITmo/j7HjPWFqajTQoRERERERETHJS0RENFiJxbp/4UYsEfVhJD2jUqmxf98ZHPs9DWeTL6O5WaU5tvu7RBgZSzA22BMTpwbgtjvGwj/IFf5BrgMYsXYqlRq/7jmN6CMXupzHpGmBmDFrDMTiwfMcEBERERHRzYVJXiIiokFq3HhP2NpbdluyQSwRYdqtI/spqu7t3X0KH779a6fHm5tUSIzLQGJcBlQqNWbPG9+P0eluz64kfPTu/k6Pt52HWqXGrLnB/RgdERERDTbvvPMOhgwZorf+XnrpJb31RUQ3PiZ5iYiIBilH5yHY9s0KJJ/MRkKsAgkx6SgprgYAmJoaYVyIF8IiZAgN94Gjk/4+UPRWfm55D9pW9GEkvdOjeeQN3nkQEREREdGNj0leIiKiQczMzBgRE/0QMdEPgiAgO7MEtbWN8AtwgbExX8aJiIiIiIiISV4iIiKDIRKJ4OXjONBhdMvK2rRP2va3Hs3DavDOg4iIiIiIbnxM8hIREZFezZ43HjlZpYg7kY6GhmatbUzNjBAe5Yc7BnEd2zkLQpBzuQzx3cwjYqIfZt45rp+jIyIiosFm9erVcHNzG+gwiOgmxSQvERER6dVQWwu88O8FaGpU4uzpy4iPUeBUYhZEImC83BvycBlGjR0+6MtNDLW1wIt/zONM8mUkxLafR1iEDCPHDP55EBERERHRjY+fSoiIiKhPGJtIERLmg5Awn4EOpVeMTaQIneCD0AmGPQ8iIiIiIrpxiQc6ACIiIiIiIiIiIiK6fkzyEhERERERERERERkwJnmJiIiIiIiIiIiIDBiTvEREREREREREREQGjEleIiIiIiIiIiIiIgPGJC8RERERERERERGRAWOSl4iIiIiIiIiIiMiAMclLREREREREREREZMCY5CUiIiIiIiIiIiIyYEzyEhERERERERERERkwJnmJiIiIiIiIiIiIDBiTvEREREREREREREQGjEleIiIiIiIiIiIiIgPGJC8RERERERERERGRAWOSl4iIiIiIiIiIiMiAMclLREREREREREREZMCY5CUiIrqJqFRqZCqKUFRQOdChEBERERERkZ5IBzoAIiIi6ltVV+uQGJeBhFgFEuMyUV1VDwAY7mkPebgMYREyjBjtDiMjyQBHSkRERERERNeDSV4iIqIb2NHfU/GfV35Ec7Oqw7Gc7FLkZJfi++1xcHa1wX83LoadvdUARElERERk+N555x0MGTKkw/0vvfTSAERDRDcblmsgIiK6ge3fd0ZrgvdahVcqcSoxqx8iIiIiIiIiIn3jSl4iIqIbmEqp1rmtUtl9Mngwam5WIeVMDk6dzIIIIoyXe2HEaHdIpSw/QURERERENwcmeYmIiG5gYrFI57Yike5tB5ogCDh84Dyij1zAqYRM1NU1aY5t//wEzC1MMF7ujcnTAjFpWqBBzY2IiIiIiKinmOQlIiK6gU2cEoCT8ZndtrMeYoYxwR79EJF+7PvxFN5785dOj9fVNiL6cBqiD6fhmfrZuH322P4LjoiIiIiIqJ8xyUtERHQDmzU3GKPGeiA+Jh3xMQqknMmB8o8SDtZDzBA6wQfycBlCJ/jAytpsgKPV3eWsUp3b5mTr3paIiIiIiMgQMclLRER0g3P3sIO7hx3uvn8CamsbkZaSB0srU/j6u0Ai4R6sREREREREho5JXiIiopuIhYUJQsJ8BjqMXrOwNNG9rYXubYmIiIiIiAwRk7xERERkcGbPG4/0C4VISsiESqXW2kYqFSNkgg9m3jmun6MjIiKim9Hq1avh5uY20GEQ0U2KSV4iIiIyOPYOVnj97ftQW9uIU4lZSIhJR1JiFkQAxsu9IY+QITjEC+ZcxUtERERERDcBJnmJiIjIYFlYmGDilABMnBIw0KEQERERERENGO62QkRERERERERERGTAmOQlIiIiIiIiIiIiMmBM8hIREREREREREREZMCZ5iYiIiIiIiIiIiAwYk7xEREREREREREREBoxJXiIiIiIiIiIiIiIDxiRvP2tubsa2bdtwxx13YPjw4TAxMYG9vT1GjRqFpUuX4rvvvuu2j/Pnz+PRRx+FTCaDmZkZHBwcMGnSJGzatAlKpbIfZkFERERERERERESDhXSgA7iZnD17FosWLUJKSkq7+8vKylBWVoaUlBR8//33uOeeezrtY+vWrVi5ciUaGxs19zU0NCA6OhrR0dHYtm0b9u7dCzs7uz6bBxEREREREREREQ0eTPL2k7Nnz2Lq1KkoLy+HsbExHnroIcycORNubm6orKzE5cuXcejQIURHR3fax/79+7F8+XKo1Wo4OTnhhRdeQFhYGMrLy7Flyxbs2rULcXFxWLBgAQ4fPgyxmAu1iYiIiIiIiIiIbnRM8vaDhoYG3HPPPSgvL4eLiwt+++03jBw5skO7v/3tb2hqatLah1KpxKpVq6BWq2FtbY0TJ07Ax8dHc/z222/HypUr8fHHH+PYsWP48ssv8eCDD/bZnIiIiIiIiIiIiGhw4FLPfvDWW2/h0qVLAICvv/5aa4K3lbGxsdb7f/jhBygUCgDAmjVr2iV4W61fvx5Dhw7V3CYiIiIiIiIiIqIbH5O8fUylUmHjxo0AgClTpmDKlCnX1c/u3bs1t5csWaK1jbm5ORYuXAgASElJQXp6+nWNRURERERERERERIaDSd4+FhMTg/z8fABot6FaXV0dFAoFCgoKoFaru+2ntVavv78/nJ2dO203efJkze3jx49fb9hERERERERERERkIFiTt4/FxcVpboeHhyMhIQH/+te/cPDgQU1y19bWFvPnz8e//vUveHh4dOijpqYGeXl5AICAgIAux2t7PC0trUexto7RmYKCgh71R0RERERERGTI+DmZiAwFk7x9LDU1VXM7Li4OTzzxBJRKZbs25eXl2Lp1K3bu3Ikff/wRkyZNanc8Ly8PgiAAANzc3Locz93dXXM7Nze3R7G2PZeIiIiIiIjoZsfPyURkKFiuoY+Vl5drbj/99NNQqVT4+9//joyMDDQ2NkKhUODZZ5+FSCRCZWUlFixYoCnv0Kq6ulpz29LSssvxLCwsNLdramr0NAsiIiIiIiIiIiIarLiSt4/V1tZqbjc2NmL9+vV49tlnNff5+Phg/fr1sLW1xfPPP4+ysjKsW7cOH374oaZNQ0OD5raxsXGX45mYmGhu19fX9yjW7lb+FhQUQC6X96hPIiIiIiIiIkPFz8lEZCiY5P2DUqmEkZFRr/v59NNPsWTJEs3Ppqammttubm54+umntZ733HPP4YMPPkBBQQF27NiBDz74ACKRqEMfTU1NXY7f2NiouW1mZtaj2LsrBUFERERERER0M+HnZCIyFCzX0MesrKw0t2+99VZIJBKt7aRSKaZNmwYAKCsrQ1ZWltY+uivB0HblcHelHYiIiIiIiIiIiMjwcSXvH6RSKdLS0nrdj4uLS7uf2xZp78mmacXFxfD29u5wXnc7e7b9KgkLxBMREREREREREd34mORtIyAgQO99jhgxQnNbpVJ12bbtcan0z6fG0tIS7u7uyM3NxYULF7rso+3xwMDAnoZLREREREREREREBoblGvrYpEmTNLczMjK6bNv2+LBhw9odi4qKAgBcvHgRhYWFnfZx9OhRze3IyMgexUpERERERERERESGh0nePubl5YVx48YBAPbv34+6ujqt7aqrq3HgwAEAgI+PT4eyD/PmzdPc3rZtm9Y+6urq8O233wIAgoKC4Ofn18voiYiIiIiIiIiIaLBjkrcf/POf/wQAVFZW4plnntHa5umnn0Z1dTUA4NFHH+1wfP78+fDx8QEArFu3Tuuq4Oeeew4VFRWa20RERERERERERHTjY5K3HyxcuBCzZs0CAGzcuBGzZ8/GTz/9hOTkZPz444+YOXMmtm7dCgAYN24cVq1a1aEPIyMjvP/++xCLxaiqqkJkZCQ+/PBDJCQkYP/+/bj77rvx8ccfA2gp7fDAAw/03wSJiIiIiIiIiIhowHDjtX7yzTff4K677sJvv/2Gffv2Yd++fR3ahIaG4qeffoKpqanWPmbNmoWNGzdi1apVKCoqwuOPP96hjVwuxw8//ACJRKL3ORAREREREREREdHgw5W8/cTS0hL79+/H9u3bcfvtt8PZ2RlGRkZwcHDArbfeiv/973+IjY2Fs7Nzl/0sW7YMSUlJWLZsGby9vWFqago7OztERUVhw4YNOHHiBOzt7ftpVkRERERERERERDTQuJK3n91333247777etXHyJEjsXnzZj1FRERERERERERERIaMK3mJiIiIiIiIiIiIDBiTvEREREREREREREQGjEleIiIiIiIiIiIiIgPGJC8RERERERERERGRAWOSl4iIiIiIiIiIiMiAMclLREREREREREREZMCY5KUbkiAIEARhoMMgIiIiIiIiIiLqc9KBDoBIHwRBQEZ6ERJiFUiIVSAtJR+2dpaQh8sgD5chONQLZubGAx0mERERERER3aDeeecdDBkypMP9L7300gBEQ0Q3GyZ5yeDFn0jHe+t/Rklxdbv7S0uq8fNPyfj5p2RIpWJMnTESz/xzNiRSLmAnIiIiIiIiIqIbB7NdZPA+33qsQ4L3WkqlGgd+PouLaVf6KSoiIiIiIiIiIqL+wSQvGbz6+qY+aUtERERERERERGQIWK6BiIiIiIiIiKiXVq9eDTc3t4EOg4huUlzJSwbPL8BFp3ZGxhJ4ejv0cTRERERERERERET9iyt5yeCtXjMbY4I9EB+jwKnELNTXtS/JIPNzhjzcB7fcPhp29lYDFCUREREREREREVHfYJKXDJ6xsRQz54zDzDnj0NSkRMrZXKSey4OdvSVCJ8hg78DELhERERERERER3biY5KUbirGxFMEhXggO8RroUIiIiIiIiIiIiPoFa/ISERERERERERERGTAmeYmIiIiIiIiIiIgMGJO8RERERERERERERAaMSV4iIiIiIiIiIiIiA8YkLxEREREREREREZEBY5KXiIiIiIiIiIiIyIAxyUtERERERERERERkwJjkJSIiIiIiIiIiIjJgTPISERERERERERERGTAmeYmIiIiIiIiIiIgMGJO8RERERERERERERAaMSV4iIiIiIiIiIiIiA8YkLxEREREREREREZEBkw50AGQ4lEql5nZBQcEARkJERERERESGpu3nyLafLw0ZPycTUU/11e9CJnlJZyUlJZrbcrl8ACMhIiIiIiIiQ1ZSUgJPT8+BDqPX+DmZiHpDn78LWa6BiIiIiIiIiOg6FBUVDXQIREQAuJKXemDUqFFISEgAADg4OEAq5eVDHRUUFGj+gp2QkAAXF5cBjohId7x+yZDx+iVDx2uYDBmvX90olUrNytdRo0YNcDT6ERAQoLkdExMDd3f3AYyGqOf4+6v/9dXvQmbpSGempqYIDQ0d6DDIgLi4uMDNzW2gwyC6Lrx+yZDx+iVDx2uYDBmv367dCCUa2jI1NdXcdnd353NPBo2/v/pPX/wuZLkGIiIiIiIiIiIiIgPGJC8RERERERERERGRAWOSl4iIiIiIiIiIiMiAMclLREREREREREREZMCY5CUiIiIiIiIiIiIyYEzyEhERERERERERERkwJnmJiIiIiIiIiIiIDJhIEARhoIMgIiIiIiIiIiIiouvDlbxEREREREREREREBoxJXiIiIiIiIiIiIiIDxiQvERERERERERERkQFjkpeIiIiIiIiIiIjIgDHJS0RERERERERERGTAmOQlIiIiIiIiIiIiMmBM8hIREREREREREREZMCZ5iYiIiIiIiIiIiAwYk7xEREREREREREREBoxJXiIiIiIiIiIiIiIDxiQvEemkpqYGx44dw1tvvYWFCxfCy8sLIpEIIpEInp6ePe7v/PnzePTRRyGTyWBmZgYHBwdMmjQJmzZtglKp1LmfHTt24LbbboOLiwtMTU3h6emJBx54AHFxcT2OiW5uU6ZM0VzT3f3Thb6ucSJd5OTk4Nlnn0VgYCAsLCxga2sLuVyOt956C3V1dQMdHt2EdP19OmXKlG77+vXXX7FgwQK4ubnBxMQEbm5uWLBgAX799de+nwjdcIqLi7F3716sXbsWM2fOhL29veZ6XLJkSY/708f1WVdXh/Xr10Mul8PW1haWlpYIDAzEs88+i5ycnB7HRP2Lr8FkaPT5Gk2DjEBEpIMpU6YIALT+8/Dw6FFfn3zyiWBiYtJpfxMmTBBKS0u77KO+vl6YPXt2p32IxWLh1Vdf7cWM6WYzefLkTq+na/91Rx/XOJGu9u7dKwwZMqTT683f31/IyMgY6DDpJqPr79PJkyd32odarRaWL1/e5fnLly8X1Gp1/02MDF5X19PixYt17kdf16dCoRD8/f077WPIkCHCvn37ejlr6it8DSZDpI/XaBqcpDplgonopicIgub20KFDERISgtjYWNTU1PSon/3792P58uVQq9VwcnLCCy+8gLCwMJSXl2PLli3YtWsX4uLisGDBAhw+fBhisfYvHDz88MPYu3cvAGDq1Kl48skn4erqinPnzuGNN95ARkYG1q5dCxcXFyxduvT6J043nZCQEHz66afXfb6+rnEiXZw5cwYLFy5EXV0dLC0tsWbNGkydOhX19fXYsWMHtmzZgosXL+KOO+5AYmIiLC0tBzpkusk89thjWLFiRafHLSwsOj324osvYvPmzQCAcePG4e9//zt8fHyQkZGBN998E8nJydi8eTMcHBzw2muv6T12uvG5u7sjMDAQv/32W4/P1cf1WVNTg9mzZ+PixYsAgGXLluG+++6DmZkZDh8+jHXr1uHq1au45557EBsbi9GjR1//ZEnv+BpMhq43r9E0SA10lpmIDMOmTZuEr776SkhPT9fc5+Hh0aOVvM3NzYJMJhMACNbW1oJCoejQZsWKFZq/HH722Wda+zly5IimzZw5cwSlUtnueElJiTB8+HABgDB06FChoqJC53nSzat1JW9v/mKtr2ucSFet37KQSqVCTExMh+Nvvvmm5np75ZVXBiBCulm1XncvvfTSdZ2fnp4uSKVSAYAQEhIi1NXVtTteW1srhISEaK5/bb9vibRZu3atsGfPHqGwsFAQBEHIysrq8UpefV2fL730kmbsN998s8PxmJgYzThTp07t2USpz/E1mAxVb1+jafBikpeIrltPk7zffvut5gVl3bp1WtvU1tYKQ4cOFQAII0eO1Npm1qxZAgBBIpEIubm5Wtts375dM9Zbb72lU3x0c9NHkldf1ziRLhISEjTX2yOPPKK1jUqlEgIDAzV/9GpqaurnKOlm1dsPkG3/IBYbG6u1TWxsrKbNqlWrehEt3cyuJ8mrj+uzqalJsLGxEQAIgYGBgkql0trPI488ounn5MmTOs+L+hZfg8mQMcl74+J3RImo3+zevVtzu7ONLczNzbFw4UIAQEpKCtLT09sdr6mpwaFDhwAAt956K9zc3LT2s2DBAlhbWwMAdu3a1cvIiXSjj2ucSFdtr7eHHnpIaxuxWIwHH3wQAFBRUYEjR470Q2REvSMIAn788UcAQEBAACZMmKC13YQJE+Dv7w+g5f8HoU1pKaK+oq/r88iRI6isrAQALF68uNPyTW3fT/A97eDB12AiGoyY5CWifhMdHQ0A8Pf3h7Ozc6ftJk+erLl9/PjxdscSEhLQ2NjYod21jI2NNW+6ExIS0NzcfN1xE+lKH9c4ka5arzcLCwuMHz++03a83sjQZGVlIT8/H0DXr/Vtj+fl5SE7O7uvQyPS2/XZ+ju8u35CQkI0dTH5O3zw4GswEQ1GTPISUb+oqalBXl4egJZVD11pezwtLa3dsbY/69qPUqnkaknS2YULFxAaGgorKyuYmprCzc0Nc+fOxeeff97lHwv0dY0T6ar12pHJZJBKO99Ll9cbDaTvvvsO/v7+MDMzg5WVFXx9fbF48WIcPny403Ou57X+2vOI+oq+rk9d+5FKpfDx8dHaBw0cvgbTjeB6XqNpcGOSl4j6RV5enuZrap2VWGjl7u6uuZ2bm9vuWNufe9MPUWeKiopw8uRJ1NTUoLGxEfn5+fjpp5+wePFijB07ttM36Pq6xol00dDQgNLSUgDdX29Dhw7VrALj9Ub9LTU1FZcuXUJDQwNqamqgUCjw+eefY9q0aZg/fz6uXr3a4Ry+1tNgpq/rs/VnCwsL2NjY6NRPSUmJ5httNHD4Gkw3iut5jabBrfM/ORER6VF1dbXmtqWlZZdtW98IAS2rI/uiH6JricViTJ8+HbNmzcKYMWNgZ2eH6upqnDp1Cps2bUJaWhpSU1MxdepUJCQkYPjw4e3O57VJ/akn1xvQcs3V1tbyeqN+Y25ujjvvvBPTp09HQEAALC0tUVJSgqNHj2Ljxo0oKyvD7t27MXfuXBw4cABGRkaac/n7lAYzfb+n1fV3eNt+TExMdIqV+gZfg8nQ9eY1mgY3JnmJqF80NDRobhsbG3fZtu0b1/r6+j7ph+hau3bt0rqSZuLEiVixYgWWLVuGzz77DEVFRXjqqac6bH7Ca5P6U0+uN+DPa47XG/WX/Px8rb9Tb731Vjz++OOYOXMmkpOTcfToUWzYsAFPPPGEpg1/n9Jgpu/3tD35Ha6tH+p/fA0mQ9eb12ga3FiugegGolQqIRKJev1v27Zteo/N1NRUc7upqanLtm2/hmZmZtYn/ZBh6strvKuvShoZGeGTTz7R1FX74YcfNJuutOK1Sf2pJ9cb8Oc1x+uN+ktXv1OdnJzw/fffa5IjH3zwQbvj/H1Kg5m+39P25He4tn6o//E1mAxdb16jaXBjkpeI+oWVlZXmdndfVaqtrdXcvvYrUPrqh6inpFIpHn74Yc3PR48ebXec1yb1p55cb8Cf1xyvNxosvL29ceuttwIAFAoFrly5ojnG36c0mOn7PW1Pfodr64f6H1+D6UbX1Ws0DW4s10B0A5FKpXrZtdXFxUUP0bTXdlOCvLy8Ltu23ZSg7YYV2voJCQm5rn7IMA30NR4UFKS5fe1KXn1d40S6MDU1hb29PUpLS7u93ioqKjQfMHm90WASFBSEffv2AWj5nerq6gqAv09pcNPne9r4+HjU1taisrKyy5V1rf04ODiwHu8gwNdguhl09hpNgxuTvEQ3mNavkw82lpaWcHd3R25uLi5cuNBl27bHAwMD2x1rm2TTtR+pVAqZTNbTkGmQGshrXBCETo/p6xon0lVgYCCio6OhUCigVCohlWp/W8frjQarzn6nXs9rPcDrm/qHvq7PoKAg7Ny5U9NuwoQJWvtQKpXIyMjQ2gcNHL4G042uq889NHixXAMR9ZuoqCgAwMWLF1FYWNhpu7Zfg4+MjGx3LDQ0VFMf6Nqvy7fV1NSEuLi4DucQ9UZqaqrmtra/ZuvjGifSVev1Vltbi6SkpE7b8Xqjwaqz36leXl6an7t6rQeAY8eOAQCGDRsGT09P/QdJdA19XZ+tv8O76+fkyZOalaD8HT548DWYbnTdfe6hwYlJXiLqN/PmzdPc7mxzt7q6Onz77bcAWlY4+Pn5tTtuZWWF6dOnAwAOHjzY6Vekdu3ahaqqKgDA/Pnzexk5UctKmv/973+anydNmtShjT6ucSJdtb3ePv30U61t1Go1Pv/8cwAtm2xMnTq1P0Ij6lZmZiYOHDgAoKX237BhwzTHRCIR5s6dC6BlFVzrH22vFRcXp1klN3fuXIhEoj6Omkh/1+eUKVMwZMgQAMBnn33W6aq5tu8n+J528OBrMN3IunqNpsGNSV4i6jfz58+Hj48PAGDdunWar5619dxzz6GiokJzW5tnn30WQEvSbeXKlVCpVO2Ol5aW4h//+AeAljdUS5cu1dsc6MZ0+PBhVFZWdnq8ubkZS5cu1XxYmzNnjta6avq6xol0IZfLMXHiRADA1q1bERsb26HN22+/ralj/eSTT8LIyKhfY6Sb0549e6BUKjs9XlRUhLvvvhvNzc0AgJUrV3Zo89RTT2m+/vz444+jvr6+3fH6+no8/vjjAFrKMj311FN6ip6oe/q4Po2NjfHEE08AANLS0vDWW291aBMbG4utW7cCACZPnozQ0FB9ToN6ga/BZKj08RpNg5dIYKENItKBQqHA8ePH29337LPPoqysDHZ2dh3emN5+++1wdnbu0M/PP/+MOXPmQK1Ww8nJCS+++CLkcjkqKiqwZcsWTW2yqKgoHDlyBBKJRGs8999/P3bs2AEAmDp1Kp566im4urri3LlzeP311zXJtY0bN+KRRx7p9fzpxrZkyRLs3LkTd955J6ZMmQJ/f39YW1ujpqYGSUlJ2LRpk+ZNuqOjI+Li4uDl5aW1L31d40S6SE5ORmRkJOrr62FpaYnnn38eU6dORX19PXbs2IHNmzcDAPz8/HDy5Ml2O4IT9RVPT080NzfjrrvuQnh4ODw9PWFmZobS0lIcOXIEGzduRFlZGYCW34UHDx7UupnUmjVr8H//938AgHHjxuEf//gHfHx8kJGRgf/85z9ITk7WtHvjjTf6b4Jk0I4fPw6FQqH5ubS0VPNH18jIyA6LA5YsWaK1H31cn9XV1QgJCcGlS5cAAMuXL8d9990HMzMzHD58GG+88QZqampgZmaGmJgYjB07tjdTJz3jazAZIn29RtMgJRAR6eDTTz8VAOj87/Dhw532tXnzZsHY2LjTc+VyuVBSUtJlPHV1dcKsWbM67UMsFgsvvfSSfh8EumEtXrxYp+t61KhRwvnz57vtTx/XOJGufvrpJ8Ha2rrT683Pz09IT08f6DDpJuLh4aHT79S77rpLqKio6LQflUol/O1vf+uyj4cfflhQqVT9NzkyeLq+5rf+64y+rs/09HTB19e30z6sra2FPXv26PthID3hazAZGn29RtPgxJW8RKSTbdu24aGHHtK5/eHDhzFlypROj6ekpOD999/HoUOHcOXKFVhYWCAwMBCLFi3C0qVLO92h9lpff/01tm3bhjNnzqCyshJOTk6YOHEiVq1ahfDwcJ3jpZtbWloa9u/fj9jYWKSmpqKkpATl5eUwMTGBk5MTQkJCcPfdd2P+/Pk6r7zV1zVOpIvLly/jvffew759+5CXlwdjY2PIZDLcc889WLVqFczNzQc6RLqJHD16FEePHkVsbCwyMzNRWlqKqqoqWFpawt3dHREREVi8eLHOr9M///wzNm/ejMTERJSWlsLe3h6hoaF45JFHMHPmzD6eDd1olixZgs8++0zn9t19XNbH9VlbW4uPPvoI3333HRQKBZqamuDu7o5Zs2bhySefhIeHh87xUv/jazAZEn2/RtPgwiQvERERERERERERkQHjxmtEREREREREREREBoxJXiIiIiIiIiIiIiIDxiQvERERERERERERkQFjkpeIiIiIiIiIiIjIgDHJS0RERERERERERGTAmOQlIiIiIiIiIiIiMmBM8hIREREREREREREZMCZ5iYiIiIiIiIiIiAwYk7xEREREREREREREBoxJXiIiIiIiIiIiIiIDxiQvERERERERERERkQFjkpeIiIiIiIiIiIjIgDHJS0RERERERERERGTAmOQlIiIiIiIiIiIiMmBM8hIREREREREREREZMCZ5iYiIiIiIiIiIiAwYk7xEREREREREREREBoxJXiIiIiIiIiIiIiIDxiQvERERERERERERkQFjkpeIiIhIi+bmZvj7+0MkEuGbb74Z6HB6bcWKFRCJRFi8ePFAh0JERERksF5++WWIRCKIRKKBDoWoHSZ5iYiIiLT44IMPcOnSJQQGBuKee+4Z6HB6bc2aNTA2NsYXX3yBxMTEgQ6HiIiISO+am5uxY8cOLF68GIGBgbCzs4ORkRHs7e0xfvx4PPbYYzh48CDUarVO/eXn52sSuoPh/dMjjzyiiefw4cM9OvfQoUOac1etWtVpu8E2Z9Idk7xERERE16ipqcG6desAAGvXroVYbPhvmdzd3bF48WIIgoAXX3xxoMMhIiIi0qsff/wRAQEBuP/++/H555/jwoULKC8vh1KpRFlZGU6dOoWNGzfi1ltvRWBgIPbt29dtn3v37gUAODs7IyQkpK+n0K0HH3xQc/uLL77o0blffvml5vYDDzzQabvBNmfSneF/YiEiIiLSsw0bNqC0tBTu7u5YuHDhQIejN8888wwA4LfffuPKDCIiIrphrFu3DvPnz0dmZiYA4JZbbsEHH3yAQ4cOISkpCQcOHMCHH36I2267DWKxGJcuXcILL7zQbb979uwBAMyePXtQlGeIjIyEj48PAOD7779HfX29TufV19dj586dAAB/f3+EhYV12nawzZl0xyQvERERURsqlQoffvghAOD++++/IVbxtvL390dwcDAA4L333hvgaIiIiIh674svvsDzzz8PQRDg4OCA33//HQcOHMCqVaswbdo0BAcH45ZbbsHKlSvx66+/4vTp05g2bVq3/dbV1eH3338HAMyZM6evp6Gz1tW81dXV+PHHH3U6Z/fu3aiurgbQ9SrewTpn0s2N86mFiIiISA8OHDiAnJwcAMBf//rXAY5G/xYtWgQA2LlzJ65evTrA0RARERFdvytXruCxxx4DAJibm+PIkSOYOnVql+eMGjUKBw4cwLPPPttlu4MHD6K+vh6mpqa45ZZb9BZzbz3wwAOaFba6lmxobScSibp8fztY50y6YZKXiIiIBr3a2lo4OTlBJBLB29sbzc3NWts1NDQgKioKIpEIJiYmOHLkSI/H+vbbbwEAvr6+GDVqVKftlixZApFIBE9Pzy7727Ztm2bziuzs7HbHrt2duaqqCi+//DJGjRoFS0tLODk5YdasWYiJiWl3XnFxMV588UWMGDECFhYWsLOzw9y5c5GcnNzt/O666y4ALY+Vrqs/iIiIiPTt2vdBlZWVeOmllzBixAhYWlrC1tYWU6ZMwVdffdVpH++++y5qa2sBAK+88gqCgoJ0GlssFnf7x/zW2rTTpk2Dubm5Tv22Onv2LFxcXCASieDk5ITTp09rbZeQkIBly5bBz88PlpaWsLCwQEBAAFauXIn09HSt53h5eSEqKgpASwmu4uLiLmMpKirCgQMHAACTJ0+Gh4dHp211mfOJEyewdOlS+Pv7w9raGpaWlggICMC8efPw+eefo6qqqst4qO8wyUtERESDnoWFBZ5//nkAQFZWFrZt29ahjSAIeOCBB3DixAmIRCJ89tlnmDJlSo/Hat2peMKECb0Jucdyc3MRGhqKV155BSkpKaitrUVxcTF++eUXTJo0Cd999x2Alg8NwcHBeP3115Gamoq6ujqUl5fjp59+Qnh4uOYrdp3x8PCAi4sLAFxXEpyIiIhI37KyshASEoJXX30VqampqK2tRUVFBY4ePYq//vWvuOeee6BUKtudIwgCPvvsMwAt7xWXL1+ut3gEQdBszNbTsgUnTpzA5MmTUVhYCA8PDxw/fhxjx45t10apVGLFihUICwvDJ598gvT0dNTW1qKurg4XL17Exx9/jBEjRmDLli1ax2gt2aBUKrF9+/Yu49m+fbvmsWu7cdu1uptzfX09/vKXvyAqKgpbt27FpUuXUF1djdraWly8eBE//vgjFi9ejHfeeafLeKjvMMlLREREBuHRRx/F8OHDAQCvv/46mpqa2h1/5pln8P333wMA1q9fj/vuu6/HY+Tl5WlW24aGhvYu4B665557kJeXhzVr1uDo0aNITEzEu+++C2tra6hUKjz88MPIysrC7NmzUV9fj9dffx3Hjx9HfHw8XnnlFRgbG6OxsREPPfRQh8fmWq1zi46O7o+pEREREXXp3nvvRVZWFh599FEcPHgQiYmJ2Lp1K/z8/AC0bDK2evXqduekpqaipKQEADBx4kRYW1vrLZ6kpCRcuXIFQMsGZLr65ZdfMGPGDFRWViIwMBDHjx+Hr69vh3YPP/wwNmzYAACYOXMmvvzySyQkJCAxMRFbtmzBiBEj0NzcjOXLl2s2Qmtr4cKFMDMzA9B9yYbW4+bm5rj77rs7bdfVnNVqNebOnatJKPv6+uLdd99FdHQ0kpKSsHfvXjz//POQyWRdxkJ9TCAiIiIyEJ988okAQAAgbNiwQXP/f//7X839Tz311HX3/80332j6iY6O7rLt4sWLBQCCh4dHl+0+/fRTTZ9ZWVntjr300kuaYyYmJkJcXFyH8/ft26dp4+DgINjb2wsKhaJDu48++kjTbteuXV3G9Morr2jaFhUVddmWiIiIqC+0fR8EQPj66687tKmqqhLGjBkjABDEYrFw9uxZzbGvvvpKc+7zzz+v19jWrl0rABDGjh3bZdxtbd++XTAyMhIACKGhoUJpaanWvr///nvN+Vu2bNHapr6+Xpg2bZoAQPD09BSam5s7tLnvvvs0/aSmpmrt5/z585o2f/nLX657zm3fa8+fP19oaGjQ2odKpRLy8/O7HIf6DlfyEhERkcFYsmSJZkXHG2+8gaamJuzatUuzsuOee+7B22+/fd395+XlaW47Ojr2LtgeeuqppxAWFtbh/lmzZmlqp5WUlOC1116Dj49Ph3YPPfQQTE1NAXS/Qrft3PLz83sTNhEREVGvzZ49G/fff3+H+62srLB582YALatJN27cqDlWWlqque3k5KTXeFpr0+paqmHDhg1YtGgRmpubMW3aNPz++++ws7PT2nbdunUAgPnz52Pp0qVa25iamuLDDz8EAGRnZ2stsdW29EJnq3nb3t9VqQag8zmr1WqsX78eADBs2DB8/vnnMDEx0dqHWCyGq6trl+NQ32GSl4iIiAyGRCLBq6++CqClhu2KFSuwaNEiqNVqTJo0CV988QXE4ut/e9P6lT8AGDp0aK/j7YmuykuMHj0aQMuOyAsXLtTaxszMTPN1wMzMzC7HsrW11dxuO2ciIiKigfDQQw91ekwul2PEiBEAgIMHD2rur66u1ty2sLDQWyz5+fk4deoUAN2SvK+//jpWrFgBtVqNefPm4eeff4alpWWnfSclJQFAp+/pWgUGBsLe3h4AEBsb2+H4jBkzNPssfPXVVxAEod1xQRA0m9a5uLjglltu6XSsruZ8+vRpzaKAZcuWdTo3GnhM8hIREZFBWbhwoWbziq1bt6KhoQEjRozAjz/+2OmqAl2Vl5drbvd3krd1hbI2NjY2AAB7e/su42pt1/ZDjzZt+ygrK9M9SKL/b+/+Y6qq/ziOvy4XATEq2SgWmdJqGooksWZ/KI1NyeHUoB9606AZxeSPtB+uJXjVzRqbW7WYZuGQBtX4EeksJimmzdUMdq3IfuicI9OiVpt5BSZwvn/wPadLcH/oBS9Xno/tjsM5n3PO+3P559w37/v+AAAwCvythXD//fdLkk6ePGmtPRAXF2cdd7vdIxaLWdGamJiojIwMn2Off/55lZSUSBr4xll9fb3P59HW1lZre8WKFbLZbD5fZrXyb7/9NuRadrtdDodDktTR0aHDhw8POv7555/rl19+kSQ5HA7Z7farmrPL5bK258+f7/UaCD2SvAAAIKzYbDYVFhZav99yyy1qamqyEpzBMNsdSAMrCF9LsbGxXo+Z1cm+xniO6+vr8znOc27moh0AAACh4q9NltmOwTAM/f3335JkVblK0u+//z5isZgLneXk5Mhms/kc+/rrr0uSZs2apV27dvlMpEpSZ2fnVcV06dKlYffn5+db2/9t2XAlrRp8zdmzLYZZOYyxKTLUAQAAAFyJkydPyul0Wr+73e6gK3hNCQkJ1vZff/01qELkeuJZsew5ZwAAgFDwl0z9bysCSUpLS7O2zVYDwerq6lJLS4ukwFo15OXlqaGhQe3t7Xruuef01ltv+Rzv+Y/4mpoaqyWXP96+yZWamqq0tDR98803qq+vV3l5uSZOnKiuri41NDRIGniffN3nSubs7++E0KKSFwAAhI3Ozk499NBD+vPPP63FLNxut7Zu3Toi1/dMeJpVIv4M96HDk/mVwrHEc24keQEAQKj5q8Q1K2BtNpuV8ExJSbGqeb/44gtduHAh6DgOHDigrq4uxcTE+Oxha/rggw+0bNkySVJ5ebnWrVvnc7znYmw2m02zZs0K6JWUlOT1mmY174ULF7R3715J0p49e6z3w18Vr785e1ZMnzt3zue1EFokeQEAQFhwu93KycnR6dOndcMNN6i5udl6qN65c6c6OjqCvkdqaqq1/fPPPwd0Tmdnp89E7/nz54OOa6SZc5s0aZLuvPPOEEcDAADGu6+//jqg43fffbeioqIkDSRJCwoKJA08J1ZUVAQdh9m2ICsrK6DF3CZMmKDa2lotWbJEkvTGG2/opZde8jp+zpw51nZzc3OQ0Q5wOByKjBz4or7ZosH86dm31xt/c05PT7e2jxw5MiIxY3SQ5AUAAGNeb2+vHn30UbW2tioyMlK1tbVKT0/X5s2bZbPZ1NPTo82bNwd9n4yMDKtHrb8PG6bu7m6fCWHz629jiTm3uXPnWh8KAAAAQqWqqsrrsdbWVrW3t0vSkErTtWvXWmsWbNy4UT/++GNA9+vv71d1dfWgfYZh6JNPPpEUWKsG04QJE1RXV6ecnBxJ0rZt2/Tyyy8PO/auu+5SSkqKJOnDDz8ckSKFW2+9VQsXLpQk7d+/X+3t7VYCeeHChUpMTPR6biBzTktL05QpUyRJFRUVunjxYtAxY3SQ5AUAAGNeUVGRmpqaJEk7duzQokWLJEmzZ89WXl6epIEPB4FW33oTFRVlrd587NixgM/buHHjsPs//vjjQRUPPT09QcU3Enp6evTtt99KkubNmxfiaAAAAKS9e/eqtrZ2yP6LFy/qmWeekTSwwOyzzz476HhSUpLKy8slDVTzZmZm6vDhwz7vdeLECWVnZ2vbtm2D9re1tVntCBYvXnxF8UdFRamhocF6Ri0rK1NJScmwY8393d3dys3N1R9//OH1uj09Pdq+fbu6u7t93t9s2dDb26vly5ert7dXkv9WDYHMOSIiwqpOPnv2rJ588kmv7cj6+/tp6RBCJHkBAMCYtmnTJu3atUuSVFpaqqeffnrI8YiICPX19am0tDTo+5lVGMeOHdM///zjd7zNZlNtba2WLVumffv26fjx42ppadG6dev02GOPKSLi38etysrKIVUj19qRI0d0+fJlSf/OFQAAIJQyMjLkcDhUXFysQ4cOqa2tTZWVlcrIyJDL5ZIkFRcXD7uA2FNPPaUtW7ZIGmij9eCDDyo7O1vbt2/XoUOH5HK5dPDgQe3YsUOLFy/W7NmzdeDAgSHX2bdvnyTp3nvv1e23337Fc4iOjlZjY6Oys7MlSVu3bh20WLBpxYoVVlK2ra1NKSkpKikp0Weffabjx4/r6NGjeu+991RYWKjbbrtNxcXFVtLWmyVLlujmm2+WJH3//feSpBtvvFFLly71eV6gcy4uLtaCBQskSY2NjUpNTdWbb76po0ePyuVyqampSU6nUzNmzNA777zj854YRQYAAMAYVVFRYUgyJBn5+flexy1fvtyQZNhsNsPlcgV1z7Nnzxp2u92QZFRVVXkdl5+fb0gypk6dajz++ONWnJ6v6Oho46OPPjKio6MH7Tc5nc4h+/zdy5fMzExDkpGZmel1TEFBgSHJmD59us9rAQAAjCbP56DTp08bycnJwz5PSTLy8vKMy5cv+7xeQ0ODMW3aNK/X8HzNnDnT2L9//6Dz09PTDUlGaWlpwHEPp6ury1iwYIE1ZsuWLUPG9Pb2GuvXr7eeOX29Jk2aZFy6dMnPu2kYhYWFg85bvXq133MCnbNhGIbb7TYeeeQRv/E6nU6/18LooJIXAACMSZ9++qmKiookDfRfe/fdd72OdTqdstvtMgxDGzZsCOq+SUlJVtVDTU1NQOfU1NSorKxMKSkpiomJUXx8vJYuXaovv/xSDz/8sN5++20lJCQoISFBq1evDiq+YHR3d6uxsVGStGbNmpDFAQAA4Ck5OVltbW165ZVXdM899yg2NlY33XST5s+fr+rqatXX1/tdRyA3N1c//fSTampqtHLlSk2fPl2TJ09WZGSk4uPjlZ6erjVr1ujgwYP67rvvrD62kvTrr79aFcNX0o93ODExMdqzZ4+ysrIkDbT1eu211waNsdvtKisr04kTJ/TCCy9ozpw5mjx5sux2u+Li4jRz5kw98cQTqqqq0vnz5601I3wxq4NN/lo1XOmcY2NjVVdXp5aWFq1atUrJycmaOHGi4uLiNGPGDOXm5ur999/3ufAcRpfNMHwsBw0AADAOffXVV3rggQdkt9t16tQpTZs2bciYgoICVVVVaerUqTpz5sw1j/FqVFdXa9WqVYqPj9eZM2cUFxcX6pAAAMA4tWnTJmvh3FCnpnbu3KmioiIlJibq3LlzstlsIY3nWhiPc77eUckLAADwH3PnztWiRYvU19c3pPIiXPX39+vVV1+VJL344oskeAEAAP7P7E2bk5MzbpKd43HO1zvfte4AAADjVFlZmZqbm1VZWakNGzbojjvuCHVIQamrq9MPP/ygKVOmaO3ataEOBwAAYMyYN2+e7rvvPr8LlV1PxuOcr3ckeQEAAIaRmpqq3bt369SpU+ro6Aj7JG9fX5+cTqeysrIC6usGAAAwXqxfvz7UIVxz43HO1zuSvAAAAF6sXLky1CGMGIfDEeoQAAAAAIwSevICAAAAAAAAQBizGaFewhAAAAAAAAAAcNWo5AUAAAAAAACAMEaSFwAAAAAAAADCGEleAAAAAAAAAAhjJHkBAAAAAAAAIIyR5AUAAAAAAACAMEaSFwAAAAAAAADCGEleAAAAAAAAAAhjJHkBAAAAAAAAIIyR5AUAAAAAAACAMEaSFwAAAAAAAADCGEleAAAAAAAAAAhjJHkBAAAAAAAAIIyR5AUAAAAAAACAMEaSFwAAAAAAAADCGEleAAAAAAAAAAhjJHkBAAAAAAAAIIyR5AUAAAAAAACAMEaSFwAAAAAAAADCGEleAAAAAAAAAAhjJHkBAAAAAAAAIIz9D7dlNBhXjMtzAAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x400 with 3 Axes>"
       ]
@@ -854,10 +854,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.964610Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.964318Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.972524Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.971686Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.190158Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.189810Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.194032Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.193518Z"
     }
    },
    "outputs": [
@@ -885,10 +885,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.976321Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.976148Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.982644Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.981352Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.196222Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.195891Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.199357Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.198828Z"
     }
    },
    "outputs": [
@@ -912,10 +912,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.987384Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.986415Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.990949Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.990395Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.201548Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.201225Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.204720Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.204199Z"
     }
    },
    "outputs": [
@@ -948,10 +948,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.993208Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.992756Z",
-     "iopub.status.idle": "2023-11-15T16:36:10.997772Z",
-     "shell.execute_reply": "2023-11-15T16:36:10.997317Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.206954Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.206623Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.211066Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.210600Z"
     }
    },
    "outputs": [
@@ -982,10 +982,10 @@
    "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:10.999767Z",
-     "iopub.status.busy": "2023-11-15T16:36:10.999605Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.003566Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.003097Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.213520Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.213065Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.217080Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.216638Z"
     }
    },
    "outputs": [
@@ -1017,10 +1017,10 @@
    "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.005782Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.005331Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.010488Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.009913Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.219431Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.219053Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.224065Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.223606Z"
     }
    },
    "outputs": [],
@@ -1040,10 +1040,10 @@
    "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.012825Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.012500Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.022208Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.021716Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.226345Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.225965Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.236510Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.235977Z"
     }
    },
    "outputs": [
@@ -1078,10 +1078,10 @@
    "execution_count": 32,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.024380Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.024057Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.028610Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.028061Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.238652Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.238463Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.243002Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.242541Z"
     }
    },
    "outputs": [
@@ -1113,10 +1113,10 @@
    "execution_count": 33,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.030765Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.030447Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.059183Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.058619Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.245157Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.244978Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.273947Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.273375Z"
     }
    },
    "outputs": [
@@ -1149,10 +1149,10 @@
    "execution_count": 34,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.061419Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.061091Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.064501Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.063948Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.276190Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.276011Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.279320Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.278888Z"
     }
    },
    "outputs": [],
@@ -1172,10 +1172,10 @@
    "execution_count": 35,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.066717Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.066311Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.071201Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.070661Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.281476Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.281292Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.285669Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.285208Z"
     }
    },
    "outputs": [
@@ -1209,10 +1209,10 @@
    "execution_count": 36,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.073303Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.072982Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.076570Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.076038Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.287673Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.287517Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.291124Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.290618Z"
     }
    },
    "outputs": [
@@ -1243,10 +1243,10 @@
    "execution_count": 37,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.078716Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.078391Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.082235Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.081693Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.293358Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.292941Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.296859Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.296425Z"
     }
    },
    "outputs": [
@@ -1277,10 +1277,10 @@
    "execution_count": 38,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.084311Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.083993Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.086659Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.086112Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.298912Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.298736Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.301214Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.300701Z"
     }
    },
    "outputs": [],
@@ -1294,10 +1294,10 @@
    "execution_count": 39,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.088822Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.088504Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.105013Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.104428Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.303276Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.303099Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.320056Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.319555Z"
     }
    },
    "outputs": [],
@@ -1323,10 +1323,10 @@
    "execution_count": 40,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.107571Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.107240Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.111639Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.111111Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.322646Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.322436Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.327004Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.326406Z"
     }
    },
    "outputs": [
@@ -1365,10 +1365,10 @@
    "execution_count": 41,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.113861Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.113513Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.120119Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.119657Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.329414Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.329088Z",
+     "iopub.status.idle": "2023-11-15T21:41:59.336133Z",
+     "shell.execute_reply": "2023-11-15T21:41:59.335533Z"
     }
    },
    "outputs": [
@@ -1399,10 +1399,10 @@
    "execution_count": 42,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.122164Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.121990Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.798179Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.797565Z"
+     "iopub.execute_input": "2023-11-15T21:41:59.338468Z",
+     "iopub.status.busy": "2023-11-15T21:41:59.338134Z",
+     "iopub.status.idle": "2023-11-15T21:42:00.026322Z",
+     "shell.execute_reply": "2023-11-15T21:42:00.025699Z"
     }
    },
    "outputs": [],
@@ -1415,10 +1415,10 @@
    "execution_count": 43,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.800674Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.800488Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.932519Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.931942Z"
+     "iopub.execute_input": "2023-11-15T21:42:00.029342Z",
+     "iopub.status.busy": "2023-11-15T21:42:00.028861Z",
+     "iopub.status.idle": "2023-11-15T21:42:00.182545Z",
+     "shell.execute_reply": "2023-11-15T21:42:00.181785Z"
     }
    },
    "outputs": [
@@ -1455,10 +1455,10 @@
    "execution_count": 44,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.934885Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.934707Z",
-     "iopub.status.idle": "2023-11-15T16:36:11.951555Z",
-     "shell.execute_reply": "2023-11-15T16:36:11.951090Z"
+     "iopub.execute_input": "2023-11-15T21:42:00.185771Z",
+     "iopub.status.busy": "2023-11-15T21:42:00.185357Z",
+     "iopub.status.idle": "2023-11-15T21:42:00.203431Z",
+     "shell.execute_reply": "2023-11-15T21:42:00.202803Z"
     }
    },
    "outputs": [],
@@ -1487,10 +1487,10 @@
    "execution_count": 45,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:11.953783Z",
-     "iopub.status.busy": "2023-11-15T16:36:11.953595Z",
-     "iopub.status.idle": "2023-11-15T16:36:13.152630Z",
-     "shell.execute_reply": "2023-11-15T16:36:13.152032Z"
+     "iopub.execute_input": "2023-11-15T21:42:00.206735Z",
+     "iopub.status.busy": "2023-11-15T21:42:00.206316Z",
+     "iopub.status.idle": "2023-11-15T21:42:01.333799Z",
+     "shell.execute_reply": "2023-11-15T21:42:01.333149Z"
     }
    },
    "outputs": [
@@ -1526,10 +1526,10 @@
    "execution_count": 46,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:13.155014Z",
-     "iopub.status.busy": "2023-11-15T16:36:13.154832Z",
-     "iopub.status.idle": "2023-11-15T16:36:13.541794Z",
-     "shell.execute_reply": "2023-11-15T16:36:13.541096Z"
+     "iopub.execute_input": "2023-11-15T21:42:01.336203Z",
+     "iopub.status.busy": "2023-11-15T21:42:01.336000Z",
+     "iopub.status.idle": "2023-11-15T21:42:01.676112Z",
+     "shell.execute_reply": "2023-11-15T21:42:01.675497Z"
     }
    },
    "outputs": [
@@ -1558,10 +1558,10 @@
    "execution_count": 47,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:13.544556Z",
-     "iopub.status.busy": "2023-11-15T16:36:13.544366Z",
-     "iopub.status.idle": "2023-11-15T16:36:14.344983Z",
-     "shell.execute_reply": "2023-11-15T16:36:14.344367Z"
+     "iopub.execute_input": "2023-11-15T21:42:01.678692Z",
+     "iopub.status.busy": "2023-11-15T21:42:01.678310Z",
+     "iopub.status.idle": "2023-11-15T21:42:02.477245Z",
+     "shell.execute_reply": "2023-11-15T21:42:02.476648Z"
     }
    },
    "outputs": [
@@ -1597,10 +1597,10 @@
    "execution_count": 48,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:14.347512Z",
-     "iopub.status.busy": "2023-11-15T16:36:14.347325Z",
-     "iopub.status.idle": "2023-11-15T16:36:14.639908Z",
-     "shell.execute_reply": "2023-11-15T16:36:14.639325Z"
+     "iopub.execute_input": "2023-11-15T21:42:02.480224Z",
+     "iopub.status.busy": "2023-11-15T21:42:02.480044Z",
+     "iopub.status.idle": "2023-11-15T21:42:02.779961Z",
+     "shell.execute_reply": "2023-11-15T21:42:02.779372Z"
     }
    },
    "outputs": [
@@ -1636,10 +1636,10 @@
    "execution_count": 49,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:14.642483Z",
-     "iopub.status.busy": "2023-11-15T16:36:14.642129Z",
-     "iopub.status.idle": "2023-11-15T16:36:15.422977Z",
-     "shell.execute_reply": "2023-11-15T16:36:15.422362Z"
+     "iopub.execute_input": "2023-11-15T21:42:02.782383Z",
+     "iopub.status.busy": "2023-11-15T21:42:02.782196Z",
+     "iopub.status.idle": "2023-11-15T21:42:03.584587Z",
+     "shell.execute_reply": "2023-11-15T21:42:03.583968Z"
     }
    },
    "outputs": [
@@ -1675,10 +1675,10 @@
    "execution_count": 50,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:15.425427Z",
-     "iopub.status.busy": "2023-11-15T16:36:15.425091Z",
-     "iopub.status.idle": "2023-11-15T16:36:16.511527Z",
-     "shell.execute_reply": "2023-11-15T16:36:16.510881Z"
+     "iopub.execute_input": "2023-11-15T21:42:03.587239Z",
+     "iopub.status.busy": "2023-11-15T21:42:03.587060Z",
+     "iopub.status.idle": "2023-11-15T21:42:04.706960Z",
+     "shell.execute_reply": "2023-11-15T21:42:04.706318Z"
     }
    },
    "outputs": [
@@ -1727,17 +1727,17 @@
    "execution_count": 51,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:16.514054Z",
-     "iopub.status.busy": "2023-11-15T16:36:16.513820Z",
-     "iopub.status.idle": "2023-11-15T16:36:16.703720Z",
-     "shell.execute_reply": "2023-11-15T16:36:16.703095Z"
+     "iopub.execute_input": "2023-11-15T21:42:04.709405Z",
+     "iopub.status.busy": "2023-11-15T21:42:04.709047Z",
+     "iopub.status.idle": "2023-11-15T21:42:04.897253Z",
+     "shell.execute_reply": "2023-11-15T21:42:04.896661Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PolyCollection at 0x7fd170da8700>"
+       "<matplotlib.collections.PolyCollection at 0x7fb400a25d00>"
       ]
      },
      "execution_count": 51,
@@ -1782,10 +1782,10 @@
    "execution_count": 52,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:16.706055Z",
-     "iopub.status.busy": "2023-11-15T16:36:16.705875Z",
-     "iopub.status.idle": "2023-11-15T16:36:17.481464Z",
-     "shell.execute_reply": "2023-11-15T16:36:17.480914Z"
+     "iopub.execute_input": "2023-11-15T21:42:04.899648Z",
+     "iopub.status.busy": "2023-11-15T21:42:04.899300Z",
+     "iopub.status.idle": "2023-11-15T21:42:05.756337Z",
+     "shell.execute_reply": "2023-11-15T21:42:05.755759Z"
     }
    },
    "outputs": [
@@ -1821,17 +1821,17 @@
    "execution_count": 53,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:17.484459Z",
-     "iopub.status.busy": "2023-11-15T16:36:17.484274Z",
-     "iopub.status.idle": "2023-11-15T16:36:17.687228Z",
-     "shell.execute_reply": "2023-11-15T16:36:17.686664Z"
+     "iopub.execute_input": "2023-11-15T21:42:05.759475Z",
+     "iopub.status.busy": "2023-11-15T21:42:05.759078Z",
+     "iopub.status.idle": "2023-11-15T21:42:05.963571Z",
+     "shell.execute_reply": "2023-11-15T21:42:05.962925Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fd183b8ae20>"
+       "<matplotlib.image.AxesImage at 0x7fb40c09e8b0>"
       ]
      },
      "execution_count": 53,
@@ -1875,10 +1875,10 @@
    "execution_count": 54,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:17.689657Z",
-     "iopub.status.busy": "2023-11-15T16:36:17.689451Z",
-     "iopub.status.idle": "2023-11-15T16:36:17.693009Z",
-     "shell.execute_reply": "2023-11-15T16:36:17.692529Z"
+     "iopub.execute_input": "2023-11-15T21:42:05.965808Z",
+     "iopub.status.busy": "2023-11-15T21:42:05.965639Z",
+     "iopub.status.idle": "2023-11-15T21:42:05.969147Z",
+     "shell.execute_reply": "2023-11-15T21:42:05.968681Z"
     }
    },
    "outputs": [],
@@ -1902,10 +1902,10 @@
    "execution_count": 55,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:17.695171Z",
-     "iopub.status.busy": "2023-11-15T16:36:17.694996Z",
-     "iopub.status.idle": "2023-11-15T16:36:17.699536Z",
-     "shell.execute_reply": "2023-11-15T16:36:17.699081Z"
+     "iopub.execute_input": "2023-11-15T21:42:05.971389Z",
+     "iopub.status.busy": "2023-11-15T21:42:05.971073Z",
+     "iopub.status.idle": "2023-11-15T21:42:05.975547Z",
+     "shell.execute_reply": "2023-11-15T21:42:05.975062Z"
     }
    },
    "outputs": [
@@ -1937,10 +1937,10 @@
    "execution_count": 56,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:17.701502Z",
-     "iopub.status.busy": "2023-11-15T16:36:17.701330Z",
-     "iopub.status.idle": "2023-11-15T16:36:17.705673Z",
-     "shell.execute_reply": "2023-11-15T16:36:17.705178Z"
+     "iopub.execute_input": "2023-11-15T21:42:05.977905Z",
+     "iopub.status.busy": "2023-11-15T21:42:05.977390Z",
+     "iopub.status.idle": "2023-11-15T21:42:05.981875Z",
+     "shell.execute_reply": "2023-11-15T21:42:05.981327Z"
     }
    },
    "outputs": [
@@ -1983,10 +1983,10 @@
    "execution_count": 57,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:17.707639Z",
-     "iopub.status.busy": "2023-11-15T16:36:17.707468Z",
-     "iopub.status.idle": "2023-11-15T16:36:17.713974Z",
-     "shell.execute_reply": "2023-11-15T16:36:17.713486Z"
+     "iopub.execute_input": "2023-11-15T21:42:05.984153Z",
+     "iopub.status.busy": "2023-11-15T21:42:05.983691Z",
+     "iopub.status.idle": "2023-11-15T21:42:05.990103Z",
+     "shell.execute_reply": "2023-11-15T21:42:05.989528Z"
     }
    },
    "outputs": [
@@ -2027,10 +2027,10 @@
    "execution_count": 58,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:17.715918Z",
-     "iopub.status.busy": "2023-11-15T16:36:17.715743Z",
-     "iopub.status.idle": "2023-11-15T16:36:17.724115Z",
-     "shell.execute_reply": "2023-11-15T16:36:17.723693Z"
+     "iopub.execute_input": "2023-11-15T21:42:05.992464Z",
+     "iopub.status.busy": "2023-11-15T21:42:05.992055Z",
+     "iopub.status.idle": "2023-11-15T21:42:06.000995Z",
+     "shell.execute_reply": "2023-11-15T21:42:06.000469Z"
     },
     "tags": []
    },
diff --git a/examples/plot_examples/plot_examples.ipynb b/examples/plot_examples/plot_examples.ipynb
index 086300d..e728914 100644
--- a/examples/plot_examples/plot_examples.ipynb
+++ b/examples/plot_examples/plot_examples.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:31.406802Z",
-     "iopub.status.busy": "2023-11-15T16:36:31.406629Z",
-     "iopub.status.idle": "2023-11-15T16:36:32.011693Z",
-     "shell.execute_reply": "2023-11-15T16:36:32.011077Z"
+     "iopub.execute_input": "2023-11-15T21:42:20.066719Z",
+     "iopub.status.busy": "2023-11-15T21:42:20.066524Z",
+     "iopub.status.idle": "2023-11-15T21:42:20.689358Z",
+     "shell.execute_reply": "2023-11-15T21:42:20.688756Z"
     }
    },
    "outputs": [],
@@ -36,10 +36,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:32.014399Z",
-     "iopub.status.busy": "2023-11-15T16:36:32.014145Z",
-     "iopub.status.idle": "2023-11-15T16:36:32.028844Z",
-     "shell.execute_reply": "2023-11-15T16:36:32.028161Z"
+     "iopub.execute_input": "2023-11-15T21:42:20.692299Z",
+     "iopub.status.busy": "2023-11-15T21:42:20.691874Z",
+     "iopub.status.idle": "2023-11-15T21:42:20.706102Z",
+     "shell.execute_reply": "2023-11-15T21:42:20.705476Z"
     }
    },
    "outputs": [
@@ -82,10 +82,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:32.058623Z",
-     "iopub.status.busy": "2023-11-15T16:36:32.058074Z",
-     "iopub.status.idle": "2023-11-15T16:36:33.231769Z",
-     "shell.execute_reply": "2023-11-15T16:36:33.231141Z"
+     "iopub.execute_input": "2023-11-15T21:42:20.735514Z",
+     "iopub.status.busy": "2023-11-15T21:42:20.735232Z",
+     "iopub.status.idle": "2023-11-15T21:42:21.920760Z",
+     "shell.execute_reply": "2023-11-15T21:42:21.920142Z"
     }
    },
    "outputs": [
@@ -121,10 +121,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:33.234327Z",
-     "iopub.status.busy": "2023-11-15T16:36:33.234083Z",
-     "iopub.status.idle": "2023-11-15T16:36:33.645080Z",
-     "shell.execute_reply": "2023-11-15T16:36:33.644446Z"
+     "iopub.execute_input": "2023-11-15T21:42:21.923232Z",
+     "iopub.status.busy": "2023-11-15T21:42:21.923043Z",
+     "iopub.status.idle": "2023-11-15T21:42:22.332061Z",
+     "shell.execute_reply": "2023-11-15T21:42:22.331441Z"
     }
    },
    "outputs": [
@@ -161,10 +161,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:33.647883Z",
-     "iopub.status.busy": "2023-11-15T16:36:33.647673Z",
-     "iopub.status.idle": "2023-11-15T16:36:34.463341Z",
-     "shell.execute_reply": "2023-11-15T16:36:34.462736Z"
+     "iopub.execute_input": "2023-11-15T21:42:22.334674Z",
+     "iopub.status.busy": "2023-11-15T21:42:22.334454Z",
+     "iopub.status.idle": "2023-11-15T21:42:23.115759Z",
+     "shell.execute_reply": "2023-11-15T21:42:23.115254Z"
     }
    },
    "outputs": [
@@ -200,10 +200,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:34.466455Z",
-     "iopub.status.busy": "2023-11-15T16:36:34.465783Z",
-     "iopub.status.idle": "2023-11-15T16:36:34.469248Z",
-     "shell.execute_reply": "2023-11-15T16:36:34.468668Z"
+     "iopub.execute_input": "2023-11-15T21:42:23.118265Z",
+     "iopub.status.busy": "2023-11-15T21:42:23.117877Z",
+     "iopub.status.idle": "2023-11-15T21:42:23.120800Z",
+     "shell.execute_reply": "2023-11-15T21:42:23.120324Z"
     }
    },
    "outputs": [],
@@ -216,10 +216,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:34.471576Z",
-     "iopub.status.busy": "2023-11-15T16:36:34.471386Z",
-     "iopub.status.idle": "2023-11-15T16:36:34.476046Z",
-     "shell.execute_reply": "2023-11-15T16:36:34.475403Z"
+     "iopub.execute_input": "2023-11-15T21:42:23.122787Z",
+     "iopub.status.busy": "2023-11-15T21:42:23.122615Z",
+     "iopub.status.idle": "2023-11-15T21:42:23.126326Z",
+     "shell.execute_reply": "2023-11-15T21:42:23.125748Z"
     }
    },
    "outputs": [
@@ -243,10 +243,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:34.478808Z",
-     "iopub.status.busy": "2023-11-15T16:36:34.478173Z",
-     "iopub.status.idle": "2023-11-15T16:36:34.971870Z",
-     "shell.execute_reply": "2023-11-15T16:36:34.971244Z"
+     "iopub.execute_input": "2023-11-15T21:42:23.128583Z",
+     "iopub.status.busy": "2023-11-15T21:42:23.128173Z",
+     "iopub.status.idle": "2023-11-15T21:42:23.571614Z",
+     "shell.execute_reply": "2023-11-15T21:42:23.571028Z"
     },
     "tags": []
    },
diff --git a/examples/read_examples/index.html b/examples/read_examples/index.html
index 6f02264..bef9ce8 100644
--- a/examples/read_examples/index.html
+++ b/examples/read_examples/index.html
@@ -1631,7 +1631,7 @@ <h1 id="genesis4-hdf5-format">Genesis4 HDF5 format<a class="anchor-link" href="#
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;ParticleGroup with 14336 particles at 0x7fdbed8b5610&gt;</pre>
+<pre>&lt;ParticleGroup with 14336 particles at 0x7fdb41b34820&gt;</pre>
 </div>
 </div>
 </div>
@@ -1670,7 +1670,7 @@ <h1 id="genesis4-hdf5-format">Genesis4 HDF5 format<a class="anchor-link" href="#
 <div class="jp-OutputArea-child">
 <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
 <div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" src="data:image/png;base64,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"/>
+<img alt="No description has been provided for this image" class="" src="data:image/png;base64,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"/>
 </div>
 </div>
 </div>
diff --git a/examples/read_examples/read_examples.ipynb b/examples/read_examples/read_examples.ipynb
index c2fe0ba..23ad819 100644
--- a/examples/read_examples/read_examples.ipynb
+++ b/examples/read_examples/read_examples.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:03.794089Z",
-     "iopub.status.busy": "2023-11-15T16:36:03.793908Z",
-     "iopub.status.idle": "2023-11-15T16:36:03.807314Z",
-     "shell.execute_reply": "2023-11-15T16:36:03.806811Z"
+     "iopub.execute_input": "2023-11-15T21:41:51.449592Z",
+     "iopub.status.busy": "2023-11-15T21:41:51.449406Z",
+     "iopub.status.idle": "2023-11-15T21:41:51.463827Z",
+     "shell.execute_reply": "2023-11-15T21:41:51.463298Z"
     },
     "tags": []
    },
@@ -31,10 +31,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:03.809740Z",
-     "iopub.status.busy": "2023-11-15T16:36:03.809313Z",
-     "iopub.status.idle": "2023-11-15T16:36:04.667300Z",
-     "shell.execute_reply": "2023-11-15T16:36:04.666685Z"
+     "iopub.execute_input": "2023-11-15T21:41:51.466550Z",
+     "iopub.status.busy": "2023-11-15T21:41:51.466187Z",
+     "iopub.status.idle": "2023-11-15T21:41:52.474523Z",
+     "shell.execute_reply": "2023-11-15T21:41:52.473925Z"
     },
     "tags": []
    },
@@ -56,10 +56,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:04.670216Z",
-     "iopub.status.busy": "2023-11-15T16:36:04.669960Z",
-     "iopub.status.idle": "2023-11-15T16:36:04.683096Z",
-     "shell.execute_reply": "2023-11-15T16:36:04.682663Z"
+     "iopub.execute_input": "2023-11-15T21:41:52.477687Z",
+     "iopub.status.busy": "2023-11-15T21:41:52.477107Z",
+     "iopub.status.idle": "2023-11-15T21:41:52.490557Z",
+     "shell.execute_reply": "2023-11-15T21:41:52.490088Z"
     },
     "tags": []
    },
@@ -80,10 +80,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:04.685368Z",
-     "iopub.status.busy": "2023-11-15T16:36:04.685048Z",
-     "iopub.status.idle": "2023-11-15T16:36:04.706139Z",
-     "shell.execute_reply": "2023-11-15T16:36:04.705561Z"
+     "iopub.execute_input": "2023-11-15T21:41:52.492972Z",
+     "iopub.status.busy": "2023-11-15T21:41:52.492547Z",
+     "iopub.status.idle": "2023-11-15T21:41:52.515344Z",
+     "shell.execute_reply": "2023-11-15T21:41:52.514759Z"
     },
     "tags": []
    },
@@ -132,10 +132,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:04.734976Z",
-     "iopub.status.busy": "2023-11-15T16:36:04.734637Z",
-     "iopub.status.idle": "2023-11-15T16:36:05.300092Z",
-     "shell.execute_reply": "2023-11-15T16:36:05.299470Z"
+     "iopub.execute_input": "2023-11-15T21:41:52.545811Z",
+     "iopub.status.busy": "2023-11-15T21:41:52.545364Z",
+     "iopub.status.idle": "2023-11-15T21:41:53.127247Z",
+     "shell.execute_reply": "2023-11-15T21:41:53.126567Z"
     },
     "tags": []
    },
@@ -168,10 +168,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:05.302617Z",
-     "iopub.status.busy": "2023-11-15T16:36:05.302251Z",
-     "iopub.status.idle": "2023-11-15T16:36:05.315115Z",
-     "shell.execute_reply": "2023-11-15T16:36:05.314553Z"
+     "iopub.execute_input": "2023-11-15T21:41:53.129818Z",
+     "iopub.status.busy": "2023-11-15T21:41:53.129441Z",
+     "iopub.status.idle": "2023-11-15T21:41:53.142517Z",
+     "shell.execute_reply": "2023-11-15T21:41:53.141908Z"
     }
    },
    "outputs": [],
@@ -184,17 +184,17 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:05.317597Z",
-     "iopub.status.busy": "2023-11-15T16:36:05.317247Z",
-     "iopub.status.idle": "2023-11-15T16:36:05.455517Z",
-     "shell.execute_reply": "2023-11-15T16:36:05.454905Z"
+     "iopub.execute_input": "2023-11-15T21:41:53.145123Z",
+     "iopub.status.busy": "2023-11-15T21:41:53.144688Z",
+     "iopub.status.idle": "2023-11-15T21:41:53.286433Z",
+     "shell.execute_reply": "2023-11-15T21:41:53.285775Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<ParticleGroup with 14336 particles at 0x7fdbed8b5610>"
+       "<ParticleGroup with 14336 particles at 0x7fdb41b34820>"
       ]
      },
      "execution_count": 7,
@@ -212,16 +212,16 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:05.458030Z",
-     "iopub.status.busy": "2023-11-15T16:36:05.457713Z",
-     "iopub.status.idle": "2023-11-15T16:36:05.872998Z",
-     "shell.execute_reply": "2023-11-15T16:36:05.872356Z"
+     "iopub.execute_input": "2023-11-15T21:41:53.288775Z",
+     "iopub.status.busy": "2023-11-15T21:41:53.288600Z",
+     "iopub.status.idle": "2023-11-15T21:41:53.748294Z",
+     "shell.execute_reply": "2023-11-15T21:41:53.747673Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
diff --git a/examples/units/units.ipynb b/examples/units/units.ipynb
index f76b8e1..5eff3f3 100644
--- a/examples/units/units.ipynb
+++ b/examples/units/units.ipynb
@@ -14,10 +14,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:18.936119Z",
-     "iopub.status.busy": "2023-11-15T16:37:18.935708Z",
-     "iopub.status.idle": "2023-11-15T16:37:18.949303Z",
-     "shell.execute_reply": "2023-11-15T16:37:18.948869Z"
+     "iopub.execute_input": "2023-11-15T21:43:09.525999Z",
+     "iopub.status.busy": "2023-11-15T21:43:09.525826Z",
+     "iopub.status.idle": "2023-11-15T21:43:09.539534Z",
+     "shell.execute_reply": "2023-11-15T21:43:09.538969Z"
     },
     "tags": []
    },
@@ -33,10 +33,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:18.951419Z",
-     "iopub.status.busy": "2023-11-15T16:37:18.951247Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.556378Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.555776Z"
+     "iopub.execute_input": "2023-11-15T21:43:09.541936Z",
+     "iopub.status.busy": "2023-11-15T21:43:09.541582Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.151231Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.150618Z"
     },
     "tags": []
    },
@@ -60,10 +60,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.559107Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.558835Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.600498Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.599974Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.154048Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.153749Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.195387Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.194808Z"
     },
     "tags": []
    },
@@ -84,10 +84,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.602729Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.602548Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.617586Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.617068Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.197677Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.197492Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.212730Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.212179Z"
     },
     "tags": []
    },
@@ -124,10 +124,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.646745Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.646561Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.659127Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.658678Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.241434Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.241180Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.254425Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.253881Z"
     },
     "tags": []
    },
@@ -161,10 +161,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.661310Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.660960Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.674085Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.673632Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.256593Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.256421Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.269166Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.268687Z"
     },
     "tags": []
    },
@@ -200,10 +200,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.676188Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.675969Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.688751Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.688206Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.271309Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.271134Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.283493Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.282939Z"
     }
    },
    "outputs": [
@@ -235,10 +235,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.691128Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.690805Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.704407Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.703854Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.285544Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.285368Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.297953Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.297402Z"
     },
     "tags": []
    },
@@ -266,10 +266,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.706728Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.706355Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.719689Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.719144Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.300344Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.299929Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.312138Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.311598Z"
     },
     "tags": []
    },
@@ -301,10 +301,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.722164Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.721843Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.735381Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.734817Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.314399Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.313969Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.326269Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.325738Z"
     },
     "tags": []
    },
@@ -336,10 +336,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.737764Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.737318Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.749255Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.748820Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.328675Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.328349Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.339117Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.338541Z"
     },
     "tags": []
    },
@@ -360,10 +360,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.751360Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.751187Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.764402Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.763831Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.341291Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.341128Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.353293Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.352831Z"
     },
     "tags": []
    },
@@ -390,10 +390,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.766523Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.766196Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.778885Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.778347Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.355260Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.355100Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.367187Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.366629Z"
     },
     "tags": []
    },
@@ -418,10 +418,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.781160Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.780832Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.792044Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.791509Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.369159Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.368985Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.379579Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.379142Z"
     },
     "tags": []
    },
@@ -435,10 +435,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.794265Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.793940Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.806284Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.805740Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.381479Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.381306Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.393021Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.392478Z"
     },
     "tags": []
    },
@@ -474,10 +474,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:37:19.808833Z",
-     "iopub.status.busy": "2023-11-15T16:37:19.808298Z",
-     "iopub.status.idle": "2023-11-15T16:37:19.819825Z",
-     "shell.execute_reply": "2023-11-15T16:37:19.819369Z"
+     "iopub.execute_input": "2023-11-15T21:43:10.395371Z",
+     "iopub.status.busy": "2023-11-15T21:43:10.395046Z",
+     "iopub.status.idle": "2023-11-15T21:43:10.406077Z",
+     "shell.execute_reply": "2023-11-15T21:43:10.405536Z"
     },
     "tags": []
    },
diff --git a/examples/write_examples/index.html b/examples/write_examples/index.html
index ffc97f6..247ad58 100644
--- a/examples/write_examples/index.html
+++ b/examples/write_examples/index.html
@@ -3050,7 +3050,7 @@ <h1 id="lucretia">Lucretia<a class="anchor-link" href="#lucretia">¶</a></h1>
 <div class="jp-OutputArea-child jp-OutputArea-executeResult">
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[34]:</div>
 <div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;ParticleGroup with 10000 particles at 0x7f63da85cd00&gt;</pre>
+<pre>&lt;ParticleGroup with 10000 particles at 0x7fd27409a850&gt;</pre>
 </div>
 </div>
 </div>
diff --git a/examples/write_examples/write_examples.ipynb b/examples/write_examples/write_examples.ipynb
index 57034ec..45b8566 100644
--- a/examples/write_examples/write_examples.ipynb
+++ b/examples/write_examples/write_examples.ipynb
@@ -12,10 +12,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.041946Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.041765Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.055517Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.054985Z"
+     "iopub.execute_input": "2023-11-15T21:42:08.345577Z",
+     "iopub.status.busy": "2023-11-15T21:42:08.345400Z",
+     "iopub.status.idle": "2023-11-15T21:42:08.359170Z",
+     "shell.execute_reply": "2023-11-15T21:42:08.358606Z"
     },
     "tags": []
    },
@@ -31,10 +31,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.057880Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.057498Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.672559Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.671940Z"
+     "iopub.execute_input": "2023-11-15T21:42:08.361499Z",
+     "iopub.status.busy": "2023-11-15T21:42:08.361083Z",
+     "iopub.status.idle": "2023-11-15T21:42:08.984340Z",
+     "shell.execute_reply": "2023-11-15T21:42:08.983694Z"
     },
     "tags": []
    },
@@ -50,10 +50,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.675282Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.675014Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.692081Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.691648Z"
+     "iopub.execute_input": "2023-11-15T21:42:08.987322Z",
+     "iopub.status.busy": "2023-11-15T21:42:08.986885Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.004187Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.003590Z"
     },
     "tags": []
    },
@@ -87,10 +87,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.694467Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.694102Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.711556Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.711108Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.006978Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.006786Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.025222Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.024702Z"
     },
     "tags": []
    },
@@ -111,10 +111,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.713817Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.713441Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.731122Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.730594Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.027857Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.027677Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.045756Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.045266Z"
     },
     "tags": []
    },
@@ -137,10 +137,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.733647Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.733309Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.748489Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.748024Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.048336Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.048143Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.064595Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.064095Z"
     },
     "tags": []
    },
@@ -161,10 +161,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.750730Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.750379Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.765515Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.764947Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.067054Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.066872Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.083135Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.082543Z"
     },
     "tags": []
    },
@@ -196,10 +196,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.795330Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.794991Z",
-     "iopub.status.idle": "2023-11-15T16:36:20.875104Z",
-     "shell.execute_reply": "2023-11-15T16:36:20.874470Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.113366Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.112815Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.194512Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.193838Z"
     }
    },
    "outputs": [],
@@ -212,10 +212,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:20.877613Z",
-     "iopub.status.busy": "2023-11-15T16:36:20.877167Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.016253Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.015599Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.197471Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.197244Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.338418Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.337647Z"
     }
    },
    "outputs": [
@@ -252,10 +252,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.018948Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.018582Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.046890Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.046464Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.341959Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.341487Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.371864Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.371351Z"
     }
    },
    "outputs": [],
@@ -279,10 +279,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.049139Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.048821Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.098730Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.098169Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.374607Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.374396Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.424872Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.424206Z"
     }
    },
    "outputs": [],
@@ -295,10 +295,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.101001Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.100652Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.237127Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.236542Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.427948Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.427545Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.568275Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.567647Z"
     }
    },
    "outputs": [
@@ -335,10 +335,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.239393Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.239210Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.255237Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.254693Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.571104Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.570910Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.588344Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.587800Z"
     }
    },
    "outputs": [
@@ -385,10 +385,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.257414Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.257082Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.272227Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.271809Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.590880Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.590416Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.606699Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.606177Z"
     }
    },
    "outputs": [],
@@ -408,10 +408,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.274483Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.274154Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.318978Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.318381Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.609400Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.609056Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.655220Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.654611Z"
     }
    },
    "outputs": [
@@ -432,10 +432,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.320977Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.320802Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.456623Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.456114Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.657443Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.657255Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.797205Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.796580Z"
     }
    },
    "outputs": [
@@ -482,10 +482,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.459042Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.458667Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.508294Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.507706Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.800136Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.799821Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.852858Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.852288Z"
     }
    },
    "outputs": [
@@ -506,10 +506,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.510599Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.510252Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.648072Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.647343Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.855227Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.855025Z",
+     "iopub.status.idle": "2023-11-15T21:42:09.995101Z",
+     "shell.execute_reply": "2023-11-15T21:42:09.994331Z"
     }
    },
    "outputs": [
@@ -553,10 +553,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.650822Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.650628Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.750495Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.749892Z"
+     "iopub.execute_input": "2023-11-15T21:42:09.998092Z",
+     "iopub.status.busy": "2023-11-15T21:42:09.997888Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.102209Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.101640Z"
     },
     "tags": []
    },
@@ -587,10 +587,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.753058Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.752612Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.765648Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.765177Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.104634Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.104433Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.118280Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.117768Z"
     },
     "tags": []
    },
@@ -726,10 +726,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.767836Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.767653Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.783604Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.783133Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.120499Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.120314Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.137952Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.137374Z"
     },
     "tags": []
    },
@@ -775,10 +775,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.785722Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.785525Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.800191Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.799656Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.140567Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.140138Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.156307Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.155743Z"
     },
     "tags": []
    },
@@ -807,10 +807,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.802354Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.802184Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.814733Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.814236Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.158866Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.158392Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.172518Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.171957Z"
     },
     "tags": []
    },
@@ -846,10 +846,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.816773Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.816599Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.876338Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.875788Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.174893Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.174715Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.234810Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.234155Z"
     }
    },
    "outputs": [
@@ -871,10 +871,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.878526Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.878341Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.891169Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.890705Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.237355Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.236910Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.250478Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.249988Z"
     }
    },
    "outputs": [],
@@ -888,10 +888,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.893224Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.892912Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.903915Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.903362Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.252867Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.252684Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.264714Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.264280Z"
     }
    },
    "outputs": [],
@@ -913,10 +913,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.906119Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.905795Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.917018Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.916500Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.266826Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.266663Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.278154Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.277720Z"
     },
     "tags": []
    },
@@ -937,10 +937,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.919695Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.919181Z",
-     "iopub.status.idle": "2023-11-15T16:36:21.977098Z",
-     "shell.execute_reply": "2023-11-15T16:36:21.976551Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.280487Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.280152Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.337352Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.336705Z"
     },
     "tags": []
    },
@@ -968,10 +968,10 @@
    "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:21.979630Z",
-     "iopub.status.busy": "2023-11-15T16:36:21.979088Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.118086Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.117328Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.339914Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.339560Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.479794Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.479077Z"
     },
     "tags": []
    },
@@ -1011,10 +1011,10 @@
    "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.121325Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.120799Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.136216Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.135747Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.482968Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.482552Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.498297Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.497811Z"
     },
     "tags": []
    },
@@ -1028,10 +1028,10 @@
    "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.138623Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.138190Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.170174Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.169562Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.500739Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.500563Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.532173Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.531668Z"
     },
     "tags": []
    },
@@ -1064,10 +1064,10 @@
    "execution_count": 32,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.172487Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.172200Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.311615Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.311011Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.534501Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.534102Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.673138Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.672510Z"
     },
     "tags": []
    },
@@ -1115,10 +1115,10 @@
    "execution_count": 33,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.314359Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.314168Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.331470Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.330973Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.676245Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.676009Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.693329Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.692749Z"
     },
     "tags": []
    },
@@ -1147,10 +1147,10 @@
    "execution_count": 34,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.333995Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.333541Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.350339Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.349785Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.695759Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.695331Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.712896Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.712362Z"
     },
     "tags": []
    },
@@ -1166,7 +1166,7 @@
     {
      "data": {
       "text/plain": [
-       "<ParticleGroup with 10000 particles at 0x7f63da85cd00>"
+       "<ParticleGroup with 10000 particles at 0x7fd27409a850>"
       ]
      },
      "execution_count": 34,
@@ -1192,10 +1192,10 @@
    "execution_count": 35,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.352501Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.352314Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.365363Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.364835Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.715236Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.714943Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.729722Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.729130Z"
     },
     "tags": []
    },
@@ -1229,10 +1229,10 @@
    "execution_count": 36,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.367707Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.367385Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.412140Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.411647Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.732259Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.731891Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.778476Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.777901Z"
     },
     "tags": []
    },
@@ -1248,10 +1248,10 @@
    "execution_count": 37,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.414277Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.414095Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.552638Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.552044Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.781403Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.780933Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.920963Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.920337Z"
     },
     "tags": []
    },
@@ -1289,10 +1289,10 @@
    "execution_count": 38,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.555240Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.555054Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.602866Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.602382Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.924247Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.923749Z",
+     "iopub.status.idle": "2023-11-15T21:42:10.972175Z",
+     "shell.execute_reply": "2023-11-15T21:42:10.971565Z"
     },
     "tags": []
    },
@@ -1307,10 +1307,10 @@
    "execution_count": 39,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.605098Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.604741Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.742368Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.741811Z"
+     "iopub.execute_input": "2023-11-15T21:42:10.974751Z",
+     "iopub.status.busy": "2023-11-15T21:42:10.974378Z",
+     "iopub.status.idle": "2023-11-15T21:42:11.113083Z",
+     "shell.execute_reply": "2023-11-15T21:42:11.112455Z"
     },
     "tags": []
    },
@@ -1349,10 +1349,10 @@
    "execution_count": 40,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.744934Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.744538Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.806827Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.806356Z"
+     "iopub.execute_input": "2023-11-15T21:42:11.116185Z",
+     "iopub.status.busy": "2023-11-15T21:42:11.115811Z",
+     "iopub.status.idle": "2023-11-15T21:42:11.178870Z",
+     "shell.execute_reply": "2023-11-15T21:42:11.178242Z"
     }
    },
    "outputs": [],
@@ -1372,10 +1372,10 @@
    "execution_count": 41,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-15T16:36:22.809299Z",
-     "iopub.status.busy": "2023-11-15T16:36:22.808950Z",
-     "iopub.status.idle": "2023-11-15T16:36:22.823594Z",
-     "shell.execute_reply": "2023-11-15T16:36:22.823159Z"
+     "iopub.execute_input": "2023-11-15T21:42:11.181681Z",
+     "iopub.status.busy": "2023-11-15T21:42:11.181193Z",
+     "iopub.status.idle": "2023-11-15T21:42:11.197114Z",
+     "shell.execute_reply": "2023-11-15T21:42:11.196574Z"
     },
     "tags": []
    },
diff --git a/search/search_index.json b/search/search_index.json
index fe01472..26e7fb1 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"openPMD-beamphysics","text":"<p>Tools for analyzing and viewing particle data in the openPMD standard, extension beamphysics defined in: beamphysics extension</p>"},{"location":"#python-classes","title":"Python classes","text":"<p>This package provides two feature-rich classes for handling openPMD-beamphysics standard data:</p> <ul> <li> <p><code>ParticleGroup</code> for handling particle data</p> </li> <li> <p><code>FieldMesh</code> - for handling external field mesh data</p> </li> </ul> <p>For usage see the examples.</p>"},{"location":"#installation","title":"Installation","text":"<p>Installing <code>openpmd-beamphysics</code> from the <code>conda-forge</code> channel can be achieved by adding <code>conda-forge</code> to your channels with:</p> <pre><code>conda config --add channels conda-forge\n</code></pre> <p>Once the <code>conda-forge</code> channel has been enabled, <code>openpmd-beamphysics</code> can be installed with:</p> <pre><code>conda install openpmd-beamphysics\n</code></pre> <p>It is possible to list all of the versions of <code>openpmd-beamphysics</code> available on your platform with:</p> <pre><code>conda search openpmd-beamphysics --channel conda-forge\n</code></pre>"},{"location":"api/fields/","title":"Fields","text":"<p>Class for openPMD External Field Mesh data.</p> <p>Initialized on on openPMD beamphysics particle group:</p> <ul> <li>h5: open h5 handle, or str that is a file</li> <li>data: raw data</li> </ul> <p>The required data is stored in ._data, and consists of dicts:</p> <ul> <li><code>'attrs'</code></li> <li><code>'components'</code></li> </ul> <p>Component data is always 3D.</p> <p>Initialization from openPMD-beamphysics HDF5 file:</p> <ul> <li><code>FieldMesh('file.h5')</code></li> </ul> <p>Initialization from a data dict:</p> <ul> <li><code>FieldMesh(data=data)</code></li> </ul> <p>Derived properties:</p> <ul> <li><code>.r</code>, <code>.theta</code>, <code>.z</code></li> <li><code>.Br</code>, <code>.Btheta</code>, <code>.Bz</code></li> <li><code>.Er</code>, <code>.Etheta</code>, <code>.Ez</code></li> <li> <p><code>.E</code>, <code>.B</code></p> </li> <li> <p><code>.phase</code></p> </li> <li><code>.scale</code></li> <li> <p><code>.factor</code></p> </li> <li> <p><code>.harmonic</code></p> </li> <li> <p><code>.frequency</code></p> </li> <li> <p><code>.shape</code></p> </li> <li><code>.geometry</code></li> <li><code>.mins</code>, <code>.maxs</code>, <code>.deltas</code></li> <li><code>.meshgrid</code></li> <li><code>.dr</code>, <code>.dtheta</code>, <code>.dz</code></li> </ul> <p>Booleans:</p> <ul> <li><code>.is_pure_electric</code></li> <li><code>.is_pure_magnetic</code></li> <li><code>.is_static</code></li> </ul> <p>Units and labels</p> <ul> <li><code>.units</code></li> <li><code>.axis_labels</code></li> </ul> <p>Plotting:</p> <ul> <li><code>.plot</code></li> <li><code>.plot_onaxis</code></li> </ul> <p>Writers</p> <ul> <li><code>.write</code></li> <li><code>.write_astra_1d</code></li> <li><code>.write_astra_3d</code></li> <li><code>.to_cylindrical</code></li> <li><code>.to_astra_1d</code></li> <li><code>.to_impact_solrf</code></li> <li><code>.write_gpt</code></li> <li><code>.write_superfish</code></li> </ul> <p>Constructors (class methods):</p> <ul> <li><code>.from_ansys_ascii_3d</code></li> <li><code>.from_astra_3d</code></li> <li><code>.from_superfish</code></li> <li><code>.from_onaxis</code></li> <li><code>.expand_onaxis</code></li> </ul> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>class FieldMesh:\n    \"\"\"\n    Class for openPMD External Field Mesh data.\n\n    Initialized on on openPMD beamphysics particle group:\n\n    - **h5**: open h5 handle, or str that is a file\n    - **data**: raw data\n\n    The required data is stored in ._data, and consists of dicts:\n\n    - `'attrs'`\n    - `'components'`\n\n    Component data is always 3D.\n\n    Initialization from openPMD-beamphysics HDF5 file:\n\n    - `FieldMesh('file.h5')`\n\n    Initialization from a data dict:\n\n    - `FieldMesh(data=data)`\n\n    Derived properties:\n\n    - `.r`, `.theta`, `.z`\n    - `.Br`, `.Btheta`, `.Bz`\n    - `.Er`, `.Etheta`, `.Ez`\n    - `.E`, `.B`\n\n    - `.phase`\n    - `.scale`\n    - `.factor`\n\n    - `.harmonic`\n    - `.frequency`\n\n    - `.shape`\n    - `.geometry`\n    - `.mins`, `.maxs`, `.deltas`\n    - `.meshgrid`\n    - `.dr`, `.dtheta`, `.dz`\n\n    Booleans:\n\n    - `.is_pure_electric`\n    - `.is_pure_magnetic`\n    - `.is_static`\n\n    Units and labels\n\n    - `.units`\n    - `.axis_labels`\n\n    Plotting:\n\n    - `.plot`\n    - `.plot_onaxis`\n\n    Writers\n\n    - `.write`\n    - `.write_astra_1d`\n    - `.write_astra_3d`\n    - `.to_cylindrical`\n    - `.to_astra_1d`\n    - `.to_impact_solrf`\n    - `.write_gpt`\n    - `.write_superfish`\n\n    Constructors (class methods):\n\n    - `.from_ansys_ascii_3d`\n    - `.from_astra_3d`\n    - `.from_superfish`\n    - `.from_onaxis`\n    - `.expand_onaxis`\n\n\n\n\n    \"\"\"\n    def __init__(self, h5=None, data=None):\n\n        if h5:\n            # Allow filename\n            if isinstance(h5, str):\n                fname = os.path.expandvars(os.path.expanduser(h5))\n                assert os.path.exists(fname), f'File does not exist: {fname}'\n\n                with File(fname, 'r') as hh5:\n                    fp = field_paths(hh5)\n                    assert len(fp) == 1, f'Number of field paths in {h5}: {len(fp)}'\n                    data = load_field_data_h5(hh5[fp[0]])\n\n            else:\n                data = load_field_data_h5(h5)\n        else:\n            data = load_field_data_dict(data)\n\n        # Internal data\n        self._data = data\n\n        # Aliases (Do not set these! Set via slicing: .Bz[:] = 0\n        #for k in self.components:\n        #    alias = component_alias[k]\n        #    self.__dict__[alias] =  self.components[k]\n\n\n    # Direct access to internal data        \n    @property\n    def attrs(self):\n        return self._data['attrs']\n\n    @property\n    def components(self):\n        return self._data['components']  \n\n    @property\n    def data(self):\n        return self._data\n\n\n    # Conveniences \n    @property\n    def shape(self):\n        return tuple(self.attrs['gridSize'])\n\n    @property\n    def geometry(self):\n        return self.attrs['gridGeometry']\n\n    @property\n    def scale(self):\n        return self.attrs['fieldScale']    \n    @scale.setter\n    def scale(self, val):\n        self.attrs['fieldScale']  = val\n\n    @property\n    def phase(self):\n        \"\"\"\n        Returns the complex argument `phi = -2*pi*RFphase`\n        to multiply the oscillating field by. \n\n        Can be set. \n        \"\"\"\n        return -self.attrs['RFphase']*2*np.pi\n    @phase.setter\n    def phase(self, val):\n        \"\"\"\n        Complex argument in radians\n        \"\"\"\n        self.attrs['RFphase']  = -val/(2*np.pi)    \n\n    @property\n    def factor(self):\n        \"\"\"\n        factor to multiply fields by, possibly complex.\n\n        `factor = scale * exp(i*phase)`\n        \"\"\"\n        return np.real_if_close(self.scale * np.exp(1j*self.phase))           \n\n    @property\n    def axis_labels(self):\n        \"\"\"\n\n        \"\"\"\n        return axis_labels_from_geometry[self.geometry]\n\n    def axis_index(self, key):\n        \"\"\"\n        Returns axis index for a named axis label key.\n\n        Examples:\n\n        - `.axis_labels == ('x', 'y', 'z')`\n        - `.axis_index('z')` returns `2`\n        \"\"\"\n        for i, name in enumerate(self.axis_labels):\n            if name == key:\n                return i\n        raise ValueError(f'Axis not found: {key}')\n\n    @property\n    def coord_vecs(self):\n        \"\"\"\n        Uses gridSpacing, gridSize, and gridOriginOffset to return coordinate vectors.\n        \"\"\"\n        return [np.linspace(x0, x1, nx) for x0, x1, nx in zip(self.mins, self.maxs, self.shape)]\n\n    def coord_vec(self, key):\n        \"\"\"\n        Gets the coordinate vector from a named axis key. \n        \"\"\"\n        i = self.axis_index(key)\n        return np.linspace(self.mins[i], self.maxs[i], self.shape[i])\n\n    @property \n    def meshgrid(self):\n        \"\"\"\n        Usses coordinate vectors to produce a standard numpy meshgrids. \n        \"\"\"\n        vecs = self.coord_vecs\n        return np.meshgrid(*vecs, indexing='ij')\n\n\n\n    @property \n    def mins(self):\n        return np.array(self.attrs['gridOriginOffset'])\n    @property\n    def deltas(self):\n        return np.array(self.attrs['gridSpacing'])\n    @property\n    def maxs(self):\n        return self.deltas*(np.array(self.attrs['gridSize'])-1) + self.mins      \n\n    @property\n    def frequency(self):\n        if self.is_static:\n            return 0\n        else:\n            return self.attrs['harmonic']*self.attrs['fundamentalFrequency']\n\n\n    # Logicals\n    @property\n    def is_pure_electric(self):\n        \"\"\"\n        Returns True if there are no non-zero mageneticField components\n        \"\"\"\n        klist = [key for key in self.components if not self.component_is_zero(key)]\n        return all([key.startswith('electric') for key in klist])\n    # Logicals\n    @property\n    def is_pure_magnetic(self):\n        \"\"\"\n        Returns True if there are no non-zero electricField components\n        \"\"\"\n        klist = [key for key in self.components if not self.component_is_zero(key)]\n        return all([key.startswith('magnetic') for key in klist])\n\n\n\n    @property\n    def is_static(self):\n        return  self.attrs['harmonic'] == 0\n\n\n\n    def component_is_zero(self, key):\n        \"\"\"\n        Returns True if all elements in a component are zero.\n        \"\"\"\n        a = self[key]\n        return not np.any(a)\n\n\n    # Plotting\n    # TODO: more general plotting\n    def plot(self, component=None, time=None, axes=None, cmap=None, return_figure=False, **kwargs):\n\n        if self.geometry != 'cylindrical':\n            raise NotImplementedError(f'Geometry {self.geometry} not implemented')\n\n        return plot_fieldmesh_cylindrical_2d(self,\n                                             component=component,\n                                             time=time,\n                                             axes=axes,\n                                             return_figure=return_figure,\n                                             cmap=cmap, **kwargs)\n\n    @functools.wraps(plot_fieldmesh_cylindrical_1d) \n    def plot_onaxis(self, *args, **kwargs):\n        assert self.geometry == 'cylindrical'\n        return plot_fieldmesh_cylindrical_1d(self, *args, **kwargs)\n\n\n    def units(self, key):\n        \"\"\"Returns the units of any key\"\"\"\n\n        # Strip any operators\n        _, key = get_operator(key)\n\n        # Fill out aliases \n        if key in component_from_alias:\n            key = component_from_alias[key]         \n\n        return pg_units(key)    \n\n    # openPMD    \n    def write(self, h5, name=None):\n        \"\"\"\n        Writes openPMD-beamphysics format to an open h5 handle, or new file if h5 is a str.\n\n        \"\"\"\n        if isinstance(h5, str):\n            fname = os.path.expandvars(os.path.expanduser(h5))\n            h5 = File(fname, 'w')\n            pmd_field_init(h5, externalFieldPath='/ExternalFieldPath/%T/')\n            g = h5.create_group('/ExternalFieldPath/1/')\n        else:\n            g = h5\n\n        write_pmd_field(g, self.data, name=name)   \n\n    @functools.wraps(write_astra_1d_fieldmap)\n    def write_astra_1d(self, filePath):      \n        return  write_astra_1d_fieldmap(self, filePath)\n\n    def to_astra_1d(self):\n        z, fz = astra_1d_fieldmap_data(self)   \n        dat = np.array([z, fz]).T \n        return {'attrs': {'type': 'astra_1d'}, 'data': dat}\n\n    def write_astra_3d(self, common_filePath, verbose=False):      \n        return  write_astra_3d_fieldmaps(self, common_filePath)\n\n\n    @functools.wraps(create_impact_solrf_ele)      \n    def to_impact_solrf(self, *args, **kwargs):\n        return create_impact_solrf_ele(self, *args, **kwargs)\n\n    def to_cylindrical(self):\n        \"\"\"\n        Returns a new FieldMesh in cylindrical geometry.\n\n        If the current geometry is rectangular, this\n        will use the y=0 slice.\n\n        \"\"\"\n        if self.geometry == 'rectangular':\n            return FieldMesh(data=fieldmesh_rectangular_to_cylindrically_symmetric_data(self))\n        elif self.geometry == 'cylindrical':\n            return self\n        else:\n            raise NotImplementedError(f\"geometry not implemented: {self.geometry}\")\n\n\n    def write_gpt(self, filePath, asci2gdf_bin=None, verbose=True):\n        \"\"\"\n        Writes a GPT field file. \n        \"\"\"\n\n        return write_gpt_fieldmesh(self, filePath, asci2gdf_bin=asci2gdf_bin, verbose=verbose)\n\n    # Superfish\n    @functools.wraps(write_superfish_t7)\n    def write_superfish(self, filePath, verbose=False):\n        \"\"\"\n        Write a Superfish T7 file. \n\n        For static fields, a Poisson T7 file is written.\n\n        For dynamic (`harmonic /= 0`) fields, a Fish T7 file is written\n        \"\"\"\n        return write_superfish_t7(self, filePath, verbose=verbose)\n\n\n\n    @classmethod\n    @functools.wraps(read_superfish_t7)\n    def from_superfish(cls, filename, type=None, geometry='cylindrical'):\n        \"\"\"\n        Class method to parse a superfish T7 style file.\n        \"\"\"        \n        data = read_superfish_t7(filename, type=type, geometry=geometry)\n        c = cls(data=data)\n        return c               \n\n\n    @classmethod\n    def from_ansys_ascii_3d(cls, *, \n                   efile = None,\n                   hfile = None,\n                   frequency = None):\n        \"\"\"\n        Class method to return a FieldMesh from ANSYS ASCII files.\n\n        The format of each file is:\n        header1 (ignored)\n        header2 (ignored)\n        x y z re_fx im_fx re_fy im_fy re_fz im_fz \n        ...\n        in C order, with oscillations as exp(i*omega*t)\n\n        Parameters\n        ----------\n        efile: str\n            Filename with complex electric field data in V/m\n\n        hfile: str\n            Filename with complex magnetic H field data in A/m\n\n        frequency: float\n            Frequency in Hz\n\n        Returns\n        -------\n        FieldMesh\n\n        \"\"\"\n\n        if frequency is None:\n            raise ValueError(f\"Please provide a frequency\")\n\n        data = read_ansys_ascii_3d_fields(efile, hfile, frequency=frequency)\n        return cls(data=data)\n\n\n\n    @classmethod\n    def from_astra_3d(cls, common_filename, frequency=0):\n        \"\"\"\n        Class method to parse multiple 3D astra fieldmap files,\n        based on the common filename.\n        \"\"\"\n\n        data = read_astra_3d_fieldmaps(common_filename, frequency=frequency)\n        return cls(data=data)\n\n    @classmethod\n    def from_onaxis(cls, *,\n                    z=None,\n                    Bz=None,\n                    Ez=None,\n                    frequency=0,\n                    harmonic=None,\n                    eleAnchorPt = 'beginning'\n                   ):\n            \"\"\"\n\n\n            Parameters \n            ----------\n            z: array\n                z-coordinates. Must be regularly spaced.       \n\n            Bz: array, optional\n                magnetic field at r=0 in T\n                Default: None        \n\n            Ez: array, optional\n                Electric field at r=0 in V/m\n                Default: None\n\n            frequency: float, optional\n                fundamental frequency in Hz.\n                Default: 0\n\n            harmonic: int, optional\n                Harmonic of the fundamental the field actually oscillates at.\n                Default: 1 if frequency !=0, otherwise 0. \n\n            eleAnchorPt: str, optional\n                Element anchor point.\n                Should be one of 'beginning', 'center', 'end'\n                Default: 'beginning'\n\n\n            Returns\n            -------\n            field: FieldMesh\n                Instantiated fieldmesh\n\n            \"\"\"\n\n            # Get spacing\n            nz = len(z)\n            dz = np.diff(z)\n            if not np.allclose(dz, dz[0]):\n                raise NotImplementedError(\"Irregular spacing not implemented\")\n            dz = dz[0]    \n\n            components = {}\n            if Ez is not None:\n                Ez = np.squeeze(np.array(Ez))\n                if Ez.ndim != 1:\n                    raise ValueError(f'Ez ndim = {Ez.ndim} must be 1')\n                components['electricField/z'] = Ez.reshape(1,1,len(Ez))\n\n            if Bz is not None:\n                Bz = np.squeeze(np.array(Bz))\n                if Bz.ndim != 1:\n                    raise ValueError(f'Bz ndim = {Bz.ndim} must be 1')\n                components['magneticField/z'] = Bz.reshape(1,1,len(Bz))            \n\n            if Bz is None and Ez is None:\n                raise ValueError('Please enter Ez or Bz')\n\n            # Handle harmonic options\n            if frequency == 0:\n                harmonic = 0\n            elif harmonic is None:\n                harmonic = 1\n\n            attrs = {'eleAnchorPt': eleAnchorPt,\n             'gridGeometry': 'cylindrical',\n             'axisLabels': np.array(['r', 'theta', 'z'], dtype='&lt;U5'),\n             'gridLowerBound': np.array([0, 0, 0]),\n             'gridOriginOffset': np.array([ 0. ,  0. , z.min()]),\n             'gridSpacing': np.array([0. , 0.   , dz]),\n             'gridSize': np.array([1,  1, nz]),\n             'harmonic': harmonic,\n             'fundamentalFrequency': frequency,\n             'RFphase': 0,\n             'fieldScale': 1.0} \n\n            data = dict(attrs=attrs, components=components)\n            return cls(data=data)        \n\n\n\n    @functools.wraps(expand_fieldmesh_from_onaxis)\n    def expand_onaxis(self, *args, **kwargs):\n        return expand_fieldmesh_from_onaxis(self, *args, **kwargs)\n\n\n\n\n    def __eq__(self, other):\n        \"\"\"\n        Checks that all attributes and component internal data are the same\n        \"\"\"\n\n        if not tools.data_are_equal(self.attrs, other.attrs):\n            return False\n\n        return tools.data_are_equal(self.components, other.components)\n\n\n #  def __setattr__(self, key, value):\n #      print('a', key)\n #      if key in component_from_alias:\n #          print('here', key)\n #          comp = component_from_alias[key]\n #          if comp in self.components:\n #              self.components[comp] = value\n\n #  def __getattr__(self, key):\n #      print('a')\n #      if key in component_from_alias:\n #          print('here', key)\n #          comp = component_from_alias[key]\n #          if comp in self.components:\n #              return self.components[comp]\n\n\n\n\n\n    def scaled_component(self, key):\n        \"\"\"\n\n        Retruns a component scaled by the complex factor\n            factor = scale*exp(i*phase)\n\n\n        \"\"\"\n\n        if key in self.components:\n            dat = self.components[key] \n        # Aliases\n        elif key in component_from_alias:\n            comp = component_from_alias[key]\n            if comp in self.components:\n                dat = self.components[comp]   \n            else:\n                # Component not present, make zeros\n                return np.zeros(self.shape)\n        else:\n            raise ValueError(f'Component not available: {key}')\n\n        # Multiply by scale factor\n        factor = self.factor      \n\n        if factor != 1:\n            return factor*dat\n        else:\n            return dat\n\n    # Convenient properties\n    # TODO: Automate this?\n    @property\n    def r(self):\n        return self.coord_vec('r')\n    @property\n    def theta(self):\n        return self.coord_vec('theta')\n    @property\n    def z(self):\n        return self.coord_vec('z')    \n\n    # Deltas\n    ## cartesian\n    @property\n    def dx(self):\n        return self.deltas[self.axis_index('x')]\n    @property\n    def dy(self):\n        return self.deltas[self.axis_index('y')]    \n    ## cylindrical\n    @property\n    def dr(self):\n        return self.deltas[self.axis_index('r')]\n    @property\n    def dtheta(self):\n        return self.deltas[self.axis_index('theta')]\n    @property\n    def dz(self):\n        return self.deltas[self.axis_index('z')]  \n\n    # Scaled components\n    # TODO: Check geometry\n    ## cartesian\n    @property\n    def Bx(self):\n        return self.scaled_component('Bx') \n    @property\n    def By(self):\n        return self.scaled_component('By')  \n    @property\n    def Ex(self):\n        return self.scaled_component('Ex')  \n    @property\n    def Ey(self):\n        return self.scaled_component('Ey')         \n\n    ## cylindrical\n    @property\n    def Br(self):\n        return self.scaled_component('Br')\n    @property\n    def Btheta(self):\n        return self.scaled_component('Btheta')\n    @property\n    def Bz(self):\n        return self.scaled_component('Bz')\n    @property\n    def Er(self):\n        return self.scaled_component('Er')\n    @property\n    def Etheta(self):\n        return self.scaled_component('Etheta')\n    @property\n    def Ez(self):\n        return self.scaled_component('Ez')    \n\n\n\n\n    @property\n    def B(self):\n        if self.geometry=='cylindrical':\n            if self.is_static:\n                return np.hypot(self['Br'], self['Bz'])\n            else:\n                return np.abs(self['Btheta'])\n        else:\n            raise ValueError(f'Unknown geometry: {self.geometry}')    \n\n    @property\n    def E(self):\n        if self.geometry=='cylindrical':\n            return np.hypot(np.abs(self['Er']), np.abs(self['Ez']))\n        else:\n            raise ValueError(f'Unknown geometry: {self.geometry}')    \n\n    def __getitem__(self, key):\n        \"\"\"\n        Returns component data from a key\n\n        If the key starts with:\n\n        - `re_`\n        - `im_`\n        - `abs_`\n\n        the appropriate numpy operator is applied.\n\n\n\n        \"\"\"\n\n        # \n        if key in ['r', 'theta', 'z']:\n            return self.coord_vec(key)\n\n\n        # Raw components\n        if key in self.components:\n            return self.components[key]\n\n        # Check for operators\n        operator, key = get_operator(key)\n\n        # Scaled components\n        if key == 'E':\n            dat = self.E\n        elif key == 'B':\n            dat =  self.B\n        else:\n            dat = self.scaled_component(key)        \n\n        if operator:\n            dat = operator(dat)\n\n        return dat\n\n\n    def copy(self):\n        \"\"\"Returns a deep copy\"\"\"\n        return deepcopy(self)          \n\n    def __repr__(self):\n        memloc = hex(id(self))\n        return f'&lt;FieldMesh with {self.geometry} geometry and {self.shape} shape at {memloc}&gt;'        \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.axis_labels","title":"<code>axis_labels</code>  <code>property</code>","text":""},{"location":"api/fields/#pmd_beamphysics.FieldMesh.coord_vecs","title":"<code>coord_vecs</code>  <code>property</code>","text":"<p>Uses gridSpacing, gridSize, and gridOriginOffset to return coordinate vectors.</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.factor","title":"<code>factor</code>  <code>property</code>","text":"<p>factor to multiply fields by, possibly complex.</p> <p><code>factor = scale * exp(i*phase)</code></p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.is_pure_electric","title":"<code>is_pure_electric</code>  <code>property</code>","text":"<p>Returns True if there are no non-zero mageneticField components</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.is_pure_magnetic","title":"<code>is_pure_magnetic</code>  <code>property</code>","text":"<p>Returns True if there are no non-zero electricField components</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.meshgrid","title":"<code>meshgrid</code>  <code>property</code>","text":"<p>Usses coordinate vectors to produce a standard numpy meshgrids.</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.phase","title":"<code>phase</code>  <code>property</code> <code>writable</code>","text":"<p>Returns the complex argument <code>phi = -2*pi*RFphase</code> to multiply the oscillating field by. </p> <p>Can be set.</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.__eq__","title":"<code>__eq__(other)</code>","text":"<p>Checks that all attributes and component internal data are the same</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def __eq__(self, other):\n    \"\"\"\n    Checks that all attributes and component internal data are the same\n    \"\"\"\n\n    if not tools.data_are_equal(self.attrs, other.attrs):\n        return False\n\n    return tools.data_are_equal(self.components, other.components)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.__getitem__","title":"<code>__getitem__(key)</code>","text":"<p>Returns component data from a key</p> <p>If the key starts with:</p> <ul> <li><code>re_</code></li> <li><code>im_</code></li> <li><code>abs_</code></li> </ul> <p>the appropriate numpy operator is applied.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def __getitem__(self, key):\n    \"\"\"\n    Returns component data from a key\n\n    If the key starts with:\n\n    - `re_`\n    - `im_`\n    - `abs_`\n\n    the appropriate numpy operator is applied.\n\n\n\n    \"\"\"\n\n    # \n    if key in ['r', 'theta', 'z']:\n        return self.coord_vec(key)\n\n\n    # Raw components\n    if key in self.components:\n        return self.components[key]\n\n    # Check for operators\n    operator, key = get_operator(key)\n\n    # Scaled components\n    if key == 'E':\n        dat = self.E\n    elif key == 'B':\n        dat =  self.B\n    else:\n        dat = self.scaled_component(key)        \n\n    if operator:\n        dat = operator(dat)\n\n    return dat\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.axis_index","title":"<code>axis_index(key)</code>","text":"<p>Returns axis index for a named axis label key.</p> <p>Examples:</p> <ul> <li><code>.axis_labels == ('x', 'y', 'z')</code></li> <li><code>.axis_index('z')</code> returns <code>2</code></li> </ul> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def axis_index(self, key):\n    \"\"\"\n    Returns axis index for a named axis label key.\n\n    Examples:\n\n    - `.axis_labels == ('x', 'y', 'z')`\n    - `.axis_index('z')` returns `2`\n    \"\"\"\n    for i, name in enumerate(self.axis_labels):\n        if name == key:\n            return i\n    raise ValueError(f'Axis not found: {key}')\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.component_is_zero","title":"<code>component_is_zero(key)</code>","text":"<p>Returns True if all elements in a component are zero.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def component_is_zero(self, key):\n    \"\"\"\n    Returns True if all elements in a component are zero.\n    \"\"\"\n    a = self[key]\n    return not np.any(a)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.coord_vec","title":"<code>coord_vec(key)</code>","text":"<p>Gets the coordinate vector from a named axis key.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def coord_vec(self, key):\n    \"\"\"\n    Gets the coordinate vector from a named axis key. \n    \"\"\"\n    i = self.axis_index(key)\n    return np.linspace(self.mins[i], self.maxs[i], self.shape[i])\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.copy","title":"<code>copy()</code>","text":"<p>Returns a deep copy</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def copy(self):\n    \"\"\"Returns a deep copy\"\"\"\n    return deepcopy(self)          \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_ansys_ascii_3d","title":"<code>from_ansys_ascii_3d(*, efile=None, hfile=None, frequency=None)</code>  <code>classmethod</code>","text":"<p>Class method to return a FieldMesh from ANSYS ASCII files.</p> <p>The format of each file is: header1 (ignored) header2 (ignored) x y z re_fx im_fx re_fy im_fy re_fz im_fz  ... in C order, with oscillations as exp(iomegat)</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_ansys_ascii_3d--parameters","title":"Parameters","text":"<p>efile: str     Filename with complex electric field data in V/m</p> str <p>Filename with complex magnetic H field data in A/m</p> float <p>Frequency in Hz</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_ansys_ascii_3d--returns","title":"Returns","text":"<p>FieldMesh</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\ndef from_ansys_ascii_3d(cls, *, \n               efile = None,\n               hfile = None,\n               frequency = None):\n    \"\"\"\n    Class method to return a FieldMesh from ANSYS ASCII files.\n\n    The format of each file is:\n    header1 (ignored)\n    header2 (ignored)\n    x y z re_fx im_fx re_fy im_fy re_fz im_fz \n    ...\n    in C order, with oscillations as exp(i*omega*t)\n\n    Parameters\n    ----------\n    efile: str\n        Filename with complex electric field data in V/m\n\n    hfile: str\n        Filename with complex magnetic H field data in A/m\n\n    frequency: float\n        Frequency in Hz\n\n    Returns\n    -------\n    FieldMesh\n\n    \"\"\"\n\n    if frequency is None:\n        raise ValueError(f\"Please provide a frequency\")\n\n    data = read_ansys_ascii_3d_fields(efile, hfile, frequency=frequency)\n    return cls(data=data)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_astra_3d","title":"<code>from_astra_3d(common_filename, frequency=0)</code>  <code>classmethod</code>","text":"<p>Class method to parse multiple 3D astra fieldmap files, based on the common filename.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\ndef from_astra_3d(cls, common_filename, frequency=0):\n    \"\"\"\n    Class method to parse multiple 3D astra fieldmap files,\n    based on the common filename.\n    \"\"\"\n\n    data = read_astra_3d_fieldmaps(common_filename, frequency=frequency)\n    return cls(data=data)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_onaxis","title":"<code>from_onaxis(*, z=None, Bz=None, Ez=None, frequency=0, harmonic=None, eleAnchorPt='beginning')</code>  <code>classmethod</code>","text":""},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_onaxis--parameters","title":"Parameters","text":"<p>z: array     z-coordinates. Must be regularly spaced.       </p> array, optional <p>magnetic field at r=0 in T Default: None        </p> array, optional <p>Electric field at r=0 in V/m Default: None</p> float, optional <p>fundamental frequency in Hz. Default: 0</p> int, optional <p>Harmonic of the fundamental the field actually oscillates at. Default: 1 if frequency !=0, otherwise 0. </p> str, optional <p>Element anchor point. Should be one of 'beginning', 'center', 'end' Default: 'beginning'</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_onaxis--returns","title":"Returns","text":"<p>field: FieldMesh     Instantiated fieldmesh</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\ndef from_onaxis(cls, *,\n                z=None,\n                Bz=None,\n                Ez=None,\n                frequency=0,\n                harmonic=None,\n                eleAnchorPt = 'beginning'\n               ):\n        \"\"\"\n\n\n        Parameters \n        ----------\n        z: array\n            z-coordinates. Must be regularly spaced.       \n\n        Bz: array, optional\n            magnetic field at r=0 in T\n            Default: None        \n\n        Ez: array, optional\n            Electric field at r=0 in V/m\n            Default: None\n\n        frequency: float, optional\n            fundamental frequency in Hz.\n            Default: 0\n\n        harmonic: int, optional\n            Harmonic of the fundamental the field actually oscillates at.\n            Default: 1 if frequency !=0, otherwise 0. \n\n        eleAnchorPt: str, optional\n            Element anchor point.\n            Should be one of 'beginning', 'center', 'end'\n            Default: 'beginning'\n\n\n        Returns\n        -------\n        field: FieldMesh\n            Instantiated fieldmesh\n\n        \"\"\"\n\n        # Get spacing\n        nz = len(z)\n        dz = np.diff(z)\n        if not np.allclose(dz, dz[0]):\n            raise NotImplementedError(\"Irregular spacing not implemented\")\n        dz = dz[0]    \n\n        components = {}\n        if Ez is not None:\n            Ez = np.squeeze(np.array(Ez))\n            if Ez.ndim != 1:\n                raise ValueError(f'Ez ndim = {Ez.ndim} must be 1')\n            components['electricField/z'] = Ez.reshape(1,1,len(Ez))\n\n        if Bz is not None:\n            Bz = np.squeeze(np.array(Bz))\n            if Bz.ndim != 1:\n                raise ValueError(f'Bz ndim = {Bz.ndim} must be 1')\n            components['magneticField/z'] = Bz.reshape(1,1,len(Bz))            \n\n        if Bz is None and Ez is None:\n            raise ValueError('Please enter Ez or Bz')\n\n        # Handle harmonic options\n        if frequency == 0:\n            harmonic = 0\n        elif harmonic is None:\n            harmonic = 1\n\n        attrs = {'eleAnchorPt': eleAnchorPt,\n         'gridGeometry': 'cylindrical',\n         'axisLabels': np.array(['r', 'theta', 'z'], dtype='&lt;U5'),\n         'gridLowerBound': np.array([0, 0, 0]),\n         'gridOriginOffset': np.array([ 0. ,  0. , z.min()]),\n         'gridSpacing': np.array([0. , 0.   , dz]),\n         'gridSize': np.array([1,  1, nz]),\n         'harmonic': harmonic,\n         'fundamentalFrequency': frequency,\n         'RFphase': 0,\n         'fieldScale': 1.0} \n\n        data = dict(attrs=attrs, components=components)\n        return cls(data=data)        \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_superfish","title":"<code>from_superfish(filename, type=None, geometry='cylindrical')</code>  <code>classmethod</code>","text":"<p>Class method to parse a superfish T7 style file.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\n@functools.wraps(read_superfish_t7)\ndef from_superfish(cls, filename, type=None, geometry='cylindrical'):\n    \"\"\"\n    Class method to parse a superfish T7 style file.\n    \"\"\"        \n    data = read_superfish_t7(filename, type=type, geometry=geometry)\n    c = cls(data=data)\n    return c               \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.scaled_component","title":"<code>scaled_component(key)</code>","text":"<p>Retruns a component scaled by the complex factor     factor = scaleexp(iphase)</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def scaled_component(self, key):\n    \"\"\"\n\n    Retruns a component scaled by the complex factor\n        factor = scale*exp(i*phase)\n\n\n    \"\"\"\n\n    if key in self.components:\n        dat = self.components[key] \n    # Aliases\n    elif key in component_from_alias:\n        comp = component_from_alias[key]\n        if comp in self.components:\n            dat = self.components[comp]   \n        else:\n            # Component not present, make zeros\n            return np.zeros(self.shape)\n    else:\n        raise ValueError(f'Component not available: {key}')\n\n    # Multiply by scale factor\n    factor = self.factor      \n\n    if factor != 1:\n        return factor*dat\n    else:\n        return dat\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.to_cylindrical","title":"<code>to_cylindrical()</code>","text":"<p>Returns a new FieldMesh in cylindrical geometry.</p> <p>If the current geometry is rectangular, this will use the y=0 slice.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def to_cylindrical(self):\n    \"\"\"\n    Returns a new FieldMesh in cylindrical geometry.\n\n    If the current geometry is rectangular, this\n    will use the y=0 slice.\n\n    \"\"\"\n    if self.geometry == 'rectangular':\n        return FieldMesh(data=fieldmesh_rectangular_to_cylindrically_symmetric_data(self))\n    elif self.geometry == 'cylindrical':\n        return self\n    else:\n        raise NotImplementedError(f\"geometry not implemented: {self.geometry}\")\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.units","title":"<code>units(key)</code>","text":"<p>Returns the units of any key</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def units(self, key):\n    \"\"\"Returns the units of any key\"\"\"\n\n    # Strip any operators\n    _, key = get_operator(key)\n\n    # Fill out aliases \n    if key in component_from_alias:\n        key = component_from_alias[key]         \n\n    return pg_units(key)    \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.write","title":"<code>write(h5, name=None)</code>","text":"<p>Writes openPMD-beamphysics format to an open h5 handle, or new file if h5 is a str.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def write(self, h5, name=None):\n    \"\"\"\n    Writes openPMD-beamphysics format to an open h5 handle, or new file if h5 is a str.\n\n    \"\"\"\n    if isinstance(h5, str):\n        fname = os.path.expandvars(os.path.expanduser(h5))\n        h5 = File(fname, 'w')\n        pmd_field_init(h5, externalFieldPath='/ExternalFieldPath/%T/')\n        g = h5.create_group('/ExternalFieldPath/1/')\n    else:\n        g = h5\n\n    write_pmd_field(g, self.data, name=name)   \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.write_gpt","title":"<code>write_gpt(filePath, asci2gdf_bin=None, verbose=True)</code>","text":"<p>Writes a GPT field file.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def write_gpt(self, filePath, asci2gdf_bin=None, verbose=True):\n    \"\"\"\n    Writes a GPT field file. \n    \"\"\"\n\n    return write_gpt_fieldmesh(self, filePath, asci2gdf_bin=asci2gdf_bin, verbose=verbose)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.write_superfish","title":"<code>write_superfish(filePath, verbose=False)</code>","text":"<p>Write a Superfish T7 file. </p> <p>For static fields, a Poisson T7 file is written.</p> <p>For dynamic (<code>harmonic /= 0</code>) fields, a Fish T7 file is written</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@functools.wraps(write_superfish_t7)\ndef write_superfish(self, filePath, verbose=False):\n    \"\"\"\n    Write a Superfish T7 file. \n\n    For static fields, a Poisson T7 file is written.\n\n    For dynamic (`harmonic /= 0`) fields, a Fish T7 file is written\n    \"\"\"\n    return write_superfish_t7(self, filePath, verbose=verbose)\n</code></pre>"},{"location":"api/particles/","title":"Particles","text":"<p>Particle Group class</p> <p>Initialized on on openPMD beamphysics particle group:</p> <ul> <li>h5: open h5 handle, or <code>str</code> that is a file</li> <li>data: raw data</li> </ul> <p>The required bunch data is stored in <code>.data</code> with keys</p> <ul> <li><code>np.array</code>: <code>x</code>, <code>px</code>, <code>y</code>, <code>py</code>, <code>z</code>, <code>pz</code>, <code>t</code>, <code>status</code>, <code>weight</code></li> <li><code>str</code>: <code>species</code></li> </ul> <p>where:</p> <ul> <li><code>x</code>, <code>y</code>, <code>z</code> are positions in units of [m]</li> <li><code>px</code>, <code>py</code>, <code>pz</code> are momenta in units of [eV/c]</li> <li><code>t</code> is time in [s]</li> <li><code>weight</code> is the macro-charge weight in [C], used for all statistical calulations.</li> <li><code>species</code> is a proper species name: <code>'electron'</code>, etc. </li> </ul> <p>Optional data:</p> <ul> <li><code>np.array</code>: <code>id</code></li> </ul> <p>where <code>id</code> is a list of unique integers that identify the particles. </p> <p>Derived data can be computed as attributes:</p> <ul> <li><code>.gamma</code>, <code>.beta</code>, <code>.beta_x</code>, <code>.beta_y</code>, <code>.beta_z</code>: relativistic factors [1].</li> <li><code>.r</code>, <code>.theta</code>: cylidrical coordinates [m], [1]</li> <li><code>.pr</code>, <code>.ptheta</code>: momenta in the radial and angular coordinate directions [eV/c]</li> <li><code>.Lz</code>: angular momentum about the z axis [m*eV/c]</li> <li><code>.energy</code> : total energy [eV]</li> <li><code>.kinetic_energy</code>: total energy - mc^2 in [eV]. </li> <li><code>.higher_order_energy</code>: total energy with quadratic fit in z or t subtracted [eV]</li> <li><code>.p</code>: total momentum in [eV/c]</li> <li><code>.mass</code>: rest mass in [eV]</li> <li><code>.xp</code>, <code>.yp</code>: Slopes \\(x' = dx/dz = dp_x/dp_z\\) and \\(y' = dy/dz = dp_y/dp_z\\) [1].</li> </ul> <p>Normalized transvere coordinates can also be calculated as attributes:</p> <ul> <li><code>.x_bar</code>, <code>.px_bar</code>, <code>.y_bar</code>, <code>.py_bar</code> in [sqrt(m)]</li> </ul> <p>The normalization is automatically calculated from the covariance matrix.  See functions in <code>.statistics</code> for more advanced usage.</p> <p>Their cooresponding amplitudes are:</p> <p><code>.Jx</code>, <code>.Jy</code> [m]</p> <p>where <code>Jx = (x_bar^2 + px_bar^2 )/2</code>.</p> <p>The momenta are normalized by the mass, so that <code>&lt;Jx&gt; = norm_emit_x</code> and similar for <code>y</code>. </p> <p>Statistics of any of these are calculated with:</p> <ul> <li><code>.min(X)</code></li> <li><code>.max(X)</code></li> <li><code>.ptp(X)</code></li> <li><code>.avg(X)</code></li> <li><code>.std(X)</code></li> <li><code>.cov(X, Y, ...)</code></li> <li><code>.histogramdd(X, Y, ..., bins=10, range=None)</code></li> </ul> <p>with a string <code>X</code> as the name any of the properties above.</p> <p>Useful beam physics quantities are given as attributes:</p> <ul> <li><code>.norm_emit_x</code></li> <li><code>.norm_emit_y</code></li> <li><code>.norm_emit_4d</code></li> <li><code>.higher_order_energy_spread</code></li> <li><code>.average_current</code></li> </ul> <p>Twiss parameters, including dispersion, for the \\(x\\) or \\(y\\) plane:</p> <ul> <li><code>.twiss(plane='x', fraction=0.95, p0C=None)</code></li> </ul> <p>For convenience, <code>plane='xy'</code> will calculate twiss for both planes.</p> <p>Twiss matched particles, using a simple linear transformation:</p> <ul> <li><code>.twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False)</code></li> </ul> <p>The weight is required and must sum to &gt; 0. The sum of the weights is in <code>.charge</code>. This can also be set: <code>.charge = 1.234</code> # C, will rescale the .weight array</p> All attributes can be accessed with brackets <p><code>[key]</code></p> Additional keys are allowed for convenience <p><code>['min_prop']</code>   will return  <code>.min('prop')</code> <code>['max_prop']</code>   will return  <code>.max('prop')</code> <code>['ptp_prop']</code>   will return  <code>.ptp('prop')</code> <code>['mean_prop']</code>  will return  <code>.avg('prop')</code> <code>['sigma_prop']</code> will return  <code>.std('prop')</code> <code>['cov_prop1__prop2']</code> will return <code>.cov('prop1', 'prop2')[0,1]</code></p> <p>Units for all attributes can be accessed by:</p> <ul> <li><code>.units(key)</code></li> </ul> <p>Particles are often stored at the same time (i.e. from a t-based code),  or with the same z position (i.e. from an s-based code.) Routines: </p> <ul> <li><code>drift_to_z(z0)</code></li> <li><code>drift_to_t(t0)</code></li> </ul> <p>help to convert these. If no argument is given, particles will be drifted to the mean. Related properties are:</p> <ul> <li><code>.in_t_coordinates</code> returns <code>True</code> if all particles have the same \\(t\\) corrdinate</li> <li><code>.in_z_coordinates</code> returns <code>True</code> if all particles have the same \\(z\\) corrdinate</li> </ul> <p>Convenient plotting is provided with: </p> <ul> <li><code>.plot(...)</code></li> <li><code>.slice_plot(...)</code></li> </ul> <p>Use <code>help(ParticleGroup.plot)</code>, etc. for usage.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>class ParticleGroup:\n    \"\"\"\n    Particle Group class\n\n    Initialized on on openPMD beamphysics particle group:\n\n    - **h5**: open h5 handle, or `str` that is a file\n    - **data**: raw data\n\n    The required bunch data is stored in `.data` with keys\n\n    - `np.array`: `x`, `px`, `y`, `py`, `z`, `pz`, `t`, `status`, `weight`\n    - `str`: `species`\n\n    where:\n\n    - `x`, `y`, `z` are positions in units of [m]\n    - `px`, `py`, `pz` are momenta in units of [eV/c]\n    - `t` is time in [s]\n    - `weight` is the macro-charge weight in [C], used for all statistical calulations.\n    - `species` is a proper species name: `'electron'`, etc. \n\n    Optional data:\n\n    - `np.array`: `id`\n\n    where `id` is a list of unique integers that identify the particles. \n\n\n    Derived data can be computed as attributes:\n\n    - `.gamma`, `.beta`, `.beta_x`, `.beta_y`, `.beta_z`: relativistic factors [1].\n    - `.r`, `.theta`: cylidrical coordinates [m], [1]\n    - `.pr`, `.ptheta`: momenta in the radial and angular coordinate directions [eV/c]\n    - `.Lz`: angular momentum about the z axis [m*eV/c]\n    - `.energy` : total energy [eV]\n    - `.kinetic_energy`: total energy - mc^2 in [eV]. \n    - `.higher_order_energy`: total energy with quadratic fit in z or t subtracted [eV]\n    - `.p`: total momentum in [eV/c]\n    - `.mass`: rest mass in [eV]\n    - `.xp`, `.yp`: Slopes $x' = dx/dz = dp_x/dp_z$ and $y' = dy/dz = dp_y/dp_z$ [1].\n\n    Normalized transvere coordinates can also be calculated as attributes:\n\n    - `.x_bar`, `.px_bar`, `.y_bar`, `.py_bar` in [sqrt(m)]\n\n    The normalization is automatically calculated from the covariance matrix. \n    See functions in `.statistics` for more advanced usage.\n\n    Their cooresponding amplitudes are:\n\n    `.Jx`, `.Jy` [m]\n\n    where `Jx = (x_bar^2 + px_bar^2 )/2`.\n\n    The momenta are normalized by the mass, so that\n    `&lt;Jx&gt; = norm_emit_x`\n    and similar for `y`. \n\n    Statistics of any of these are calculated with:\n\n    - `.min(X)`\n    - `.max(X)`\n    - `.ptp(X)`\n    - `.avg(X)`\n    - `.std(X)`\n    - `.cov(X, Y, ...)`\n    - `.histogramdd(X, Y, ..., bins=10, range=None)`\n\n    with a string `X` as the name any of the properties above.\n\n    Useful beam physics quantities are given as attributes:\n\n    - `.norm_emit_x`\n    - `.norm_emit_y`\n    - `.norm_emit_4d`\n    - `.higher_order_energy_spread`\n    - `.average_current`\n\n    Twiss parameters, including dispersion, for the $x$ or $y$ plane:\n\n    - `.twiss(plane='x', fraction=0.95, p0C=None)`\n\n    For convenience, `plane='xy'` will calculate twiss for both planes.\n\n    Twiss matched particles, using a simple linear transformation:\n\n    - `.twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False)`\n\n    The weight is required and must sum to &gt; 0. The sum of the weights is in `.charge`.\n    This can also be set: `.charge = 1.234` # C, will rescale the .weight array\n\n    All attributes can be accessed with brackets:\n        `[key]`\n\n    Additional keys are allowed for convenience:\n        `['min_prop']`   will return  `.min('prop')`\n        `['max_prop']`   will return  `.max('prop')`\n        `['ptp_prop']`   will return  `.ptp('prop')`\n        `['mean_prop']`  will return  `.avg('prop')`\n        `['sigma_prop']` will return  `.std('prop')`\n        `['cov_prop1__prop2']` will return `.cov('prop1', 'prop2')[0,1]`\n\n    Units for all attributes can be accessed by:\n\n    - `.units(key)`\n\n    Particles are often stored at the same time (i.e. from a t-based code), \n    or with the same z position (i.e. from an s-based code.)\n    Routines: \n\n    - `drift_to_z(z0)`\n    - `drift_to_t(t0)`\n\n    help to convert these. If no argument is given, particles will be drifted to the mean.\n    Related properties are:\n\n    - `.in_t_coordinates` returns `True` if all particles have the same $t$ corrdinate\n    - `.in_z_coordinates` returns `True` if all particles have the same $z$ corrdinate\n\n    Convenient plotting is provided with: \n\n    - `.plot(...)`\n    - `.slice_plot(...)`\n\n    Use `help(ParticleGroup.plot)`, etc. for usage. \n\n\n    \"\"\"\n    def __init__(self, h5=None, data=None):\n\n\n        if h5 and data:\n            # TODO:\n            # Allow merging or changing some array with extra data\n            raise NotImplementedError('Cannot init on both h5 and data')\n\n        if h5:\n            # Allow filename\n            if isinstance(h5, str):\n                fname = os.path.expandvars(h5)\n                assert os.path.exists(fname), f'File does not exist: {fname}'\n\n                with File(fname, 'r') as hh5:\n                    pp = particle_paths(hh5)\n                    assert len(pp) == 1, f'Number of particle paths in {h5}: {len(pp)}'\n                    data = load_bunch_data(hh5[pp[0]])\n\n            else:\n                # Try dict\n                data = load_bunch_data(h5)\n        else:\n            # Fill out data. Exclude species.\n            data = full_data(data)\n            species = list(set(data['species']))\n\n            # Allow for empty data (len=0). Otherwise, check species.\n            if len(species) &gt;= 1:\n                assert len(species) == 1, f'mixed species are not allowed: {species}'\n                data['species'] = species[0]\n\n        self._settable_array_keys = ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight']\n        # Optional data\n        for k in ['id']:\n            if k in data:\n                self._settable_array_keys.append(k)  \n\n        self._settable_scalar_keys = ['species']\n        self._settable_keys =  self._settable_array_keys + self._settable_scalar_keys                       \n        # Internal data. Only allow settable keys\n        self._data = {k:data[k] for k in self._settable_keys}\n\n    #-------------------------------------------------\n    # Access to intrinsic coordinates\n    @property\n    def x(self):\n        \"\"\"\n        x coordinate in [m]\n        \"\"\"\n        return self._data['x']\n    @x.setter\n    def x(self, val):\n        self._data['x'] = full_array(len(self), val)    \n\n    @property\n    def y(self):\n        \"\"\"\n        y coordinate in [m]\n        \"\"\"\n        return self._data['y']\n    @y.setter\n    def y(self, val):\n        self._data['y'] = full_array(len(self), val)              \n\n    @property\n    def z(self):\n        \"\"\"\n        z coordinate in [m]\n        \"\"\"\n        return self._data['z']\n    @z.setter\n    def z(self, val):\n        self._data['z'] = full_array(len(self), val)      \n\n    @property\n    def px(self):\n        \"\"\"\n        px coordinate in [eV/c]\n        \"\"\"\n        return self._data['px']\n    @px.setter\n    def px(self, val):\n        self._data['px'] = full_array(len(self), val)      \n\n    @property\n    def py(self):\n        \"\"\"\n        py coordinate in [eV/c]\n        \"\"\"\n        return self._data['py']\n    @py.setter\n    def py(self, val):\n        self._data['py'] = full_array(len(self), val)    \n\n    @property\n    def pz(self):\n        \"\"\"\n        pz coordinate in [eV/c]\n        \"\"\"\n        return self._data['pz']\n    @pz.setter\n    def pz(self, val):\n        self._data['pz'] = full_array(len(self), val)    \n\n    @property\n    def t(self):\n        \"\"\"\n        t coordinate in [s]\n        \"\"\"\n        return self._data['t']\n    @t.setter\n    def t(self, val):\n        self._data['t'] = full_array(len(self), val)       \n\n    @property\n    def status(self):\n        \"\"\"\n        status coordinate in [1]\n        \"\"\"\n        return self._data['status']\n    @status.setter\n    def status(self, val):\n        self._data['status'] = full_array(len(self), val)  \n\n    @property\n    def weight(self):\n        \"\"\"\n        weight coordinate in [C]\n        \"\"\"\n        return self._data['weight']\n    @weight.setter\n    def weight(self, val):\n        self._data['weight'] = full_array(len(self), val)            \n\n    @property\n    def id(self):\n        \"\"\"\n        id integer \n        \"\"\"\n        if 'id' not in self._data:\n            self.assign_id()      \n\n        return self._data['id']\n    @id.setter\n    def id(self, val):\n        self._data['id'] = full_array(len(self), val)            \n\n\n    @property\n    def species(self):\n        \"\"\"\n        species string\n        \"\"\"\n        return self._data['species']\n    @species.setter\n    def species(self, val):\n        self._data['species'] = val\n\n    @property\n    def data(self):\n        \"\"\"\n        Internal data dict\n        \"\"\"\n        return self._data        \n\n    #-------------------------------------------------\n    # Derived data\n\n    def assign_id(self):\n        \"\"\"\n        Assigns unique ids, integers from 1 to n_particle\n\n        \"\"\"\n        if 'id' not in self._settable_array_keys: \n            self._settable_array_keys.append('id')\n        self.id = np.arange(1, self['n_particle']+1)             \n\n    @property\n    def n_particle(self):\n        \"\"\"Total number of particles. Same as len \"\"\"\n        return len(self)\n\n    @property\n    def n_alive(self):\n        \"\"\"Number of alive particles, defined by status == 1\"\"\"\n        return len(np.where(self.status==1)[0])\n\n    @property\n    def n_dead(self):\n        \"\"\"Number of alive particles, defined by status != 1\"\"\"\n        return self.n_particle - self.n_alive\n\n\n    def units(self, key):\n        \"\"\"Returns the units of any key\"\"\"\n        return pg_units(key)\n\n    @property\n    def mass(self):\n        \"\"\"Rest mass in eV\"\"\"\n        return mass_of(self.species)\n\n    @property\n    def species_charge(self):\n        \"\"\"Species charge in C\"\"\"\n        return charge_of(self.species)\n\n    @property\n    def charge(self):\n        \"\"\"Total charge in C\"\"\"\n        return np.sum(self.weight)\n    @charge.setter\n    def charge(self, val):\n        \"\"\"Rescale weight array so that it sum to this value\"\"\"\n        assert val &gt;0, 'charge must be &gt;0. This is used to weight the particles.'\n        self.weight *= val/self.charge\n\n\n    # Relativistic properties\n    @property\n    def p(self):\n        \"\"\"Total momemtum in eV/c\"\"\"\n        return np.sqrt(self.px**2 + self.py**2 + self.pz**2) \n    @property\n    def energy(self):\n        \"\"\"Total energy in eV\"\"\"\n        return np.sqrt(self.px**2 + self.py**2 + self.pz**2 + self.mass**2) \n    @property\n    def kinetic_energy(self):\n        \"\"\"Kinetic energy in eV\"\"\"\n        return self.energy - self.mass\n\n    # Slopes. Note that these are relative to pz\n    @property\n    def xp(self):\n        \"\"\"x slope px/pz (dimensionless)\"\"\"\n        return self.px/self.pz  \n    @property\n    def yp(self):\n        \"\"\"y slope py/pz (dimensionless)\"\"\"\n        return self.py/self.pz    \n\n    @property\n    def higher_order_energy(self):\n        \"\"\"\n        Fits a quadratic (order=2) to the Energy vs. time, and returns the energy with this subtracted. \n        \"\"\" \n        return self.higher_order_energy_calc(order=2)\n\n    @property\n    def higher_order_energy_spread(self):\n        \"\"\"\n        Legacy syntax to compute the standard deviation of higher_order_energy.\n        \"\"\"\n        return self.std('higher_order_energy')\n\n    def higher_order_energy_calc(self, order=2):\n        \"\"\"\n        Fits a polynmial with order `order` to the Energy vs. time, , and returns the energy with this subtracted. \n        \"\"\"\n        #order=2\n        if self.std('z') &lt; 1e-12:\n            # must be at a screen. Use t\n            t = self.t\n        else:\n            # All particles at the same time. Use z to calc t\n            t = self.z/c_light\n        energy = self.energy\n\n        best_fit_coeffs = np.polynomial.polynomial.polyfit(t, energy, order)\n        best_fit = np.polynomial.polynomial.polyval(t, best_fit_coeffs)\n        return energy - best_fit\n\n    # Polar coordinates. Note that these are centered at x=0, y=0, and not an averge center. \n    @property\n    def r(self):\n        \"\"\"Radius in the xy plane: r = sqrt(x^2 + y^2) in m\"\"\"\n        return np.hypot(self.x, self.y)\n    @property    \n    def theta(self):\n        \"\"\"Angle in xy plane: theta = arctan2(y, x) in radians\"\"\"\n        return np.arctan2(self.y, self.x)\n    @property\n    def pr(self):\n        \"\"\"\n        Momentum in the radial direction in eV/c\n        r_hat = cos(theta) xhat + sin(theta) yhat\n        pr = p dot r_hat\n        \"\"\"\n        theta = self.theta\n        return self.px * np.cos(theta)  + self.py * np.sin(theta)   \n    @property    \n    def ptheta(self):\n        \"\"\"     \n        Momentum in the polar theta direction. \n        theta_hat = -sin(theta) xhat + cos(theta) yhat\n        ptheta = p dot theta_hat\n        Note that Lz = r*ptheta\n        \"\"\"\n        theta = self.theta\n        return -self.px * np.sin(theta)  + self.py * np.cos(theta)  \n    @property    \n    def Lz(self):\n        \"\"\"\n        Angular momentum around the z axis in m*eV/c\n        Lz = x * py - y * px\n        \"\"\"\n        return self.x*self.py - self.y*self.px\n\n\n    # Relativistic quantities\n    @property\n    def gamma(self):\n        \"\"\"Relativistic gamma\"\"\"\n        return self.energy/self.mass\n\n    @gamma.setter\n    def gamma(self, val):\n        beta_x = self.beta_x\n        beta_y = self.beta_y\n        beta_z = self.beta_z\n        beta = self.beta\n        gamma_new = full_array(len(self), val)\n        energy_new = gamma_new * self.mass\n        beta_new = np.sqrt(gamma_new**2 - 1)/gamma_new\n        self._data['px'] = energy_new * beta_new * beta_x / beta\n        self._data['py'] = energy_new * beta_new * beta_y / beta\n        self._data['pz'] = energy_new * beta_new * beta_z / beta\n\n    @property\n    def beta(self):\n        \"\"\"Relativistic beta\"\"\"\n        return self.p/self.energy\n    @property\n    def beta_x(self):\n        \"\"\"Relativistic beta, x component\"\"\"\n        return self.px/self.energy\n\n    @beta_x.setter\n    def beta_x(self, val):\n        self._data['px'] = full_array(len(self), val)*self.energy    \n\n    @property\n    def beta_y(self):\n        \"\"\"Relativistic beta, y component\"\"\"\n        return self.py/self.energy\n\n    @beta_y.setter\n    def beta_y(self, val):\n        self._data['py'] = full_array(len(self), val)*self.energy    \n\n    @property\n    def beta_z(self):\n        \"\"\"Relativistic beta, z component\"\"\"\n        return self.pz/self.energy\n\n    @beta_z.setter\n    def beta_z(self, val):\n        self._data['pz'] = full_array(len(self), val)*self.energy\n\n    # Normalized coordinates for x and y\n    @property \n    def x_bar(self):\n        \"\"\"Normalized x in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'x')\n    @property     \n    def px_bar(self):\n        \"\"\"Normalized px in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'px')    \n    @property\n    def Jx(self):\n        \"\"\"Normalized amplitude J in the x-px plane\"\"\"\n        return particle_amplitude(self, 'x')\n\n    @property \n    def y_bar(self):\n        \"\"\"Normalized y in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'y')\n    @property     \n    def py_bar(self):\n        \"\"\"Normalized py in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'py')\n    @property\n    def Jy(self):\n        \"\"\"Normalized amplitude J in the y-py plane\"\"\"\n        return particle_amplitude(self, 'y')    \n\n    def delta(self, key):\n        \"\"\"Attribute (array) relative to its mean\"\"\"\n        return self[key] - self.avg(key)\n\n\n    # Statistical property functions\n\n    def min(self, key):\n        \"\"\"Minimum of any key\"\"\"\n        return np.min(self[key]) # was: getattr(self, key)\n    def max(self, key):\n        \"\"\"Maximum of any key\"\"\"\n        return np.max(self[key]) \n    def ptp(self, key):\n        \"\"\"Peak-to-Peak = max - min of any key\"\"\"\n        return np.ptp(self[key])     \n\n    def avg(self, key):\n        \"\"\"Statistical average\"\"\"\n        dat = self[key]  # equivalent to self.key for accessing properties above\n        if np.isscalar(dat): \n            return dat\n        return np.average(dat, weights=self.weight)\n    def std(self, key):\n        \"\"\"Standard deviation (actually sample)\"\"\"\n        dat = self[key]\n        if np.isscalar(dat):\n            return 0\n        avg_dat = self.avg(key)\n        return np.sqrt(np.average( (dat - avg_dat)**2, weights=self.weight))\n    def cov(self, *keys):\n        \"\"\"\n        Covariance matrix from any properties\n\n        Example: \n        P = ParticleGroup(h5)\n        P.cov('x', 'px', 'y', 'py')\n\n        \"\"\"\n        dats = np.array([ self[key] for key in keys ])\n        return np.cov(dats, aweights=self.weight)\n\n    def histogramdd(self, *keys, bins=10, range=None):\n        \"\"\"\n        Wrapper for numpy.histogramdd, but accepts property names as keys.\n\n        Computes the multidimensional histogram of keys. Internally uses weights. \n\n        Example:\n            P.histogramdd('x', 'y', bins=50)\n        Returns:\n            np.array with shape 50x50, edge list \n\n        \"\"\"\n        H, edges = np.histogramdd(np.array([self[k] for k in list(keys)]).T, weights=self.weight, bins=bins, range=range)\n\n        return H, edges\n\n\n    # Beam statistics\n    @property\n    def norm_emit_x(self):\n        \"\"\"Normalized emittance in the x plane\"\"\"\n        return norm_emit_calc(self, planes=['x'])\n    @property\n    def norm_emit_y(self):       \n        \"\"\"Normalized emittance in the x plane\"\"\"\n        return norm_emit_calc(self, planes=['y'])\n    @property\n    def norm_emit_4d(self):       \n        \"\"\"Normalized emittance in the xy planes (4D)\"\"\"\n        return norm_emit_calc(self, planes=['x', 'y'])    \n\n    def twiss(self, plane='x', fraction=1, p0c=None):\n        \"\"\"\n        Returns Twiss and Dispersion dict.\n\n        plane can be:\n\n        `'x'`, `'y'`, or `'xy'`\n\n        Optionally a fraction of the particles, based on amplitiude, can be specified.\n        \"\"\"\n        d = {}\n        for p in plane:\n            d.update(particle_twiss_dispersion(self, plane=p, fraction=fraction, p0c=p0c))\n        return d\n\n    def twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False):\n        \"\"\"\n        Returns a ParticleGroup with requested Twiss parameters.\n\n        See: statistics.matched_particles\n        \"\"\"\n\n        return matched_particles(self, beta=beta, alpha=alpha, plane=plane, inplace=inplace)\n\n\n    @property\n    def in_z_coordinates(self):\n        \"\"\"\n        Returns True if all particles have the same z coordinate\n        \"\"\" \n        # Check that z are all the same\n        return len(np.unique(self.z)) == 1           \n\n    @property\n    def in_t_coordinates(self):\n        \"\"\"\n        Returns True if all particles have the same t coordinate\n        \"\"\" \n        # Check that t are all the same\n        return len(np.unique(self.t)) == 1        \n\n\n\n    @property\n    def average_current(self):\n        \"\"\"\n        Simple average `current = charge / dt` in [A], with `dt =  (max_t - min_t)`\n        If particles are in $t$ coordinates, will try` dt = (max_z - min_z)*c_light*beta_z`\n        \"\"\"\n        dt = self.t.ptp()  # ptp 'peak to peak' is max - min\n        if dt == 0:\n            # must be in t coordinates. Calc with \n            dt = self.z.ptp() / (self.avg('beta_z')*c_light)\n        return self.charge / dt\n\n    def bunching(self, wavelength):\n        \"\"\"\n        Calculate the normalized bunching parameter, which is the magnitude of the \n        complex sum of weighted exponentials at a given point.\n\n        The formula for bunching is given by:\n\n        $$\n        B(z, \\lambda) = \\frac{\\left|\\sum w_i e^{i k z_i}\\right|}{\\sum w_i}\n        $$\n\n        where:\n        - \\( z \\) is the position array,\n        - \\( \\lambda \\) is the wavelength,\n        - \\( k = \\frac{2\\pi}{\\lambda} \\) is the wave number,\n        - \\( w_i \\) are the weights.\n\n        Parameters\n        ----------\n        wavelength : float\n            Wavelength of the wave.\n\n\n        Returns\n        -------\n        complex\n            The normalized bunching parameter.\n\n        Raises\n        ------\n        ValueError\n            If `wavelength` is not a positive number.\n        \"\"\"        \n\n        if self.in_z_coordinates:\n            # Approximate z\n            z = self.t * self.avg('beta_z')*c_light\n        else:\n            z = self.z\n\n        return statistics.bunching(z, wavelength, weight=self.weight)\n\n    def __getitem__(self, key):\n        \"\"\"\n        Returns a property or statistical quantity that can be computed:\n\n        - `P['x']` returns the x array\n        - `P['sigmx_x']` returns the std(x) scalar\n        - `P['norm_emit_x']` returns the norm_emit_x scalar\n\n        Parts can also be given. Example: `P[0:10]` returns a new ParticleGroup with the first 10 elements.\n        \"\"\"\n\n        # Allow for non-string operations: \n        if not isinstance(key, str):\n            return particle_parts(self, key)\n\n        if key.startswith('cov_'):\n            subkeys = key[4:].split('__')\n            assert len(subkeys) == 2, f'Too many properties in covariance request: {key}'\n            return self.cov(*subkeys)[0,1]\n        elif key.startswith('delta_'):\n            return self.delta(key[6:])\n        elif key.startswith('sigma_'):\n            return self.std(key[6:])\n        elif key.startswith('mean_'):\n            return self.avg(key[5:])\n        elif key.startswith('min_'):\n            return self.min(key[4:])\n        elif key.startswith('max_'):\n            return self.max(key[4:])     \n        elif key.startswith('ptp_'):\n            return self.ptp(key[4:])   \n        elif 'bunching' in key:\n            wavelength = parse_bunching_str(key)\n            bunching = self.bunching(wavelength) # complex\n\n            # abs or arg (angle):\n            if 'phase_' in key:\n                return np.angle(bunching)\n            else:\n                return np.abs(bunching)\n\n        else:\n            return getattr(self, key) \n\n    def where(self, x):\n        return self[np.where(x)]\n\n    # TODO: should the user be allowed to do this?\n    #def __setitem__(self, key, value):    \n    #    assert key in self._settable_keyes, 'Error: you cannot set:'+str(key)\n    #    \n    #    if key in self._settable_array_keys:\n    #        assert len(value) == self.n_particle\n    #        self.__dict__[key] = value\n    #    elif key == \n    #        print()\n\n    # Simple 'tracking'     \n    def drift(self, delta_t):\n        \"\"\"\n        Drifts particles by time delta_t\n        \"\"\"\n        self.x = self.x + self.beta_x * c_light * delta_t\n        self.y = self.y + self.beta_y * c_light * delta_t\n        self.z = self.z + self.beta_z * c_light * delta_t\n        self.t = self.t + delta_t\n\n    def drift_to_z(self, z=None):\n\n        if z is None:\n            z = self.avg('z')\n        dt = (z - self.z) / (self.beta_z * c_light)\n        self.drift(dt)\n        # Fix z to be exactly this value\n        self.z = np.full(self.n_particle, z)\n\n\n    def drift_to_t(self, t=None):\n        \"\"\"\n        Drifts all particles to the same t\n\n        If no z is given, particles will be drifted to the average t\n        \"\"\"\n        if t is None:\n            t = self.avg('t')\n        dt = t - self.t\n        self.drift(dt)\n        # Fix t to be exactly this value\n        self.t = np.full(self.n_particle, t)\n\n\n    # -------        \n    # dict methods\n\n    # Do not do this, it breaks deepcopy\n    #def __dict__(self):\n    #    return self.data\n\n\n    @functools.wraps(bmad.particlegroup_to_bmad)\n    def to_bmad(self, p0c=None, tref=None):\n        return bmad.particlegroup_to_bmad(self, p0c=p0c, tref=tref)\n\n\n    @classmethod\n    @functools.wraps(bmad.bmad_to_particlegroup_data)\n    def from_bmad(cls, bmad_dict):\n        \"\"\"\n        Convert Bmad particle data as a dict \n        to ParticleGroup data.\n\n        See: ParticleGroup.to_bmad or particlegroup_to_bmad\n\n        Parameters\n        ----------\n        bmad_data: dict\n            Dict with keys:\n            'x'\n            'px'\n            'y'\n            'py'\n            'z'\n            'pz', \n            'charge'\n            'species',\n            'tref'\n            'state'\n\n        Returns\n        -------\n        ParticleGroup\n        \"\"\"        \n        data = bmad.bmad_to_particlegroup_data(bmad_dict)\n        return cls(data=data)\n\n    # -------\n    # Writers\n\n    @functools.wraps(write_astra)    \n    def write_astra(self, filePath, verbose=False, probe=False):\n        write_astra(self, filePath, verbose=verbose, probe=probe)\n\n    def write_bmad(self, filePath, p0c=None, t_ref=0, verbose=False):\n        bmad.write_bmad(self, filePath, p0c=p0c, t_ref=t_ref, verbose=verbose)        \n\n    def write_elegant(self, filePath, verbose=False):\n        write_elegant(self, filePath, verbose=verbose)            \n\n    def write_genesis2_beam_file(self, filePath, n_slice=None, verbose=False):\n        # Get beam columns \n        beam_columns = genesis2_beam_data(self, n_slice=n_slice)\n        # Actually write the file\n        write_genesis2_beam_file(filePath, beam_columns, verbose=verbose)  \n\n    @functools.wraps(write_genesis4_beam)          \n    def write_genesis4_beam(self, filePath, n_slice=None, return_input_str=False, verbose=False):\n        return write_genesis4_beam(self, filePath, n_slice=n_slice, return_input_str=return_input_str, verbose=verbose)\n\n    def write_genesis4_distribution(self, filePath, verbose=False):\n        write_genesis4_distribution(self, filePath, verbose=verbose)\n\n    def write_gpt(self, filePath, asci2gdf_bin=None, verbose=False):\n        write_gpt(self, filePath, asci2gdf_bin=asci2gdf_bin, verbose=verbose)    \n\n    def write_impact(self, filePath, cathode_kinetic_energy_ref=None, include_header=True, verbose=False):\n        return write_impact(self, filePath, cathode_kinetic_energy_ref=cathode_kinetic_energy_ref,\n                            include_header=include_header, verbose=verbose)          \n\n    def write_litrack(self, filePath, p0c=None, verbose=False):        \n        return write_litrack(self, outfile=filePath, p0c=p0c, verbose=verbose)      \n\n    def write_lucretia(self, filePath, ele_name='BEGINNING', t_ref=0, stop_ix=None, verbose=False):       \n        return write_lucretia(self, filePath, ele_name=ele_name, t_ref=t_ref, stop_ix=stop_ix)\n\n    def write_simion(self, filePath, color=0, flip_z_to_x=True, verbose=False):\n        return write_simion(self, filePath, verbose=verbose, color=color, flip_z_to_x=flip_z_to_x)\n\n\n    def write_opal(self, filePath, verbose=False, dist_type='emitted'):\n        return write_opal(self, filePath, verbose=verbose, dist_type=dist_type)\n\n\n    # openPMD    \n    def write(self, h5, name=None):\n        \"\"\"\n        Writes to an open h5 handle, or new file if h5 is a str.\n\n        \"\"\"\n        if isinstance(h5, str):\n            fname = os.path.expandvars(h5)\n            g = File(fname, 'w')\n            pmd_init(g, basePath='/', particlesPath='.' )\n        else:\n            g = h5\n\n        write_pmd_bunch(g, self, name=name)        \n\n\n    # Plotting\n    # --------\n    def plot(self, key1='x', key2=None,\n             bins=None,\n             *,\n             xlim=None,\n             ylim=None,\n             return_figure=False, \n             tex=True, nice=True,\n             **kwargs):\n        \"\"\"\n        1d or 2d density plot. \n\n        If one key is given, this will plot the density of that key.\n        Example:\n            .plot('x')\n\n        If two keys arg given, this will plot a 2d marginal plot.\n        Example:\n            .plot('x', 'px')\n\n\n        Parameters\n        ----------\n        particle_group: ParticleGroup\n            The object to plot\n\n        key1: str, default = 't'\n            Key to bin on the x-axis\n\n        key2: str, default = None\n            Key to bin on the y-axis. \n\n        bins: int, default = None\n           Number of bins. If None, this will use a heuristic: bins = sqrt(n_particle/4)\n\n        xlim: tuple, default = None\n            Manual setting of the x-axis limits. Note that these are in raw, unscaled units. \n\n        ylim: tuple, default = None\n            Manual setting of the y-axis limits. Note that these are in raw, unscaled units. \n\n        tex: bool, default = True\n            Use TEX for labels   \n\n        nice: bool, default = True\n            Scale to nice units\n\n        return_figure: bool, default = False\n            If true, return a matplotlib.figure.Figure object\n\n        **kwargs\n            Any additional kwargs to send to the the plot in: plt.subplots(**kwargs)\n\n\n        Returns\n        -------\n        None or fig: matplotlib.figure.Figure\n            This only returns a figure object if return_figure=T, otherwise returns None\n\n        \"\"\"\n\n        if not key2:\n            fig = density_plot(self, key=key1,\n                               bins=bins,\n                               xlim=xlim,\n                               tex=tex,\n                               nice=nice,\n                               **kwargs)\n        else:\n            fig = marginal_plot(self, key1=key1, key2=key2,\n                                bins=bins,\n                                xlim=xlim,\n                                ylim=ylim,\n                                tex=tex,\n                                nice=nice,\n                                **kwargs)\n\n        if return_figure:\n            return fig\n\n\n\n\n    def slice_statistics(self, *keys,\n                   n_slice=100,\n                   slice_key=None):\n        \"\"\"\n        Slice statistics\n\n        \"\"\"      \n\n        if slice_key is None:\n            if self.in_t_coordinates:\n                slice_key = 'z'\n\n            else:\n                slice_key = 't'  \n\n        if slice_key in ('t', 'delta_t'):\n            density_name = 'current'\n        else:\n            density_name = 'density'\n\n        keys = set(keys)\n        keys.add('mean_'+slice_key)\n        keys.add('ptp_'+slice_key)\n        keys.add('charge')\n        slice_dat = slice_statistics(self, n_slice=n_slice, slice_key=slice_key,\n                                keys=keys)\n\n        slice_dat[density_name] = slice_dat['charge']/ slice_dat['ptp_'+slice_key]\n\n        return slice_dat\n\n    def slice_plot(self, *keys,\n                   n_slice=100,\n                   slice_key=None,\n                   tex=True,\n                   nice=True,\n                   return_figure=False,\n                   xlim=None,\n                   ylim=None,\n                   **kwargs):   \n\n        fig = slice_plot(self, *keys,\n                         n_slice=n_slice,\n                         slice_key=slice_key,\n                         tex=tex,\n                         nice=nice,\n                         xlim=xlim,\n                         ylim=ylim,\n                         **kwargs)\n\n        if return_figure:\n            return fig        \n\n\n    # New constructors\n    def split(self, n_chunks = 100, key='z'):\n        return split_particles(self, n_chunks=n_chunks, key=key)\n\n    def copy(self):\n        \"\"\"Returns a deep copy\"\"\"\n        return deepcopy(self)    \n\n    @functools.wraps(resample_particles)\n    def resample(self, n=0):\n        data = resample_particles(self, n)\n        return ParticleGroup(data=data)\n\n    # Internal sorting\n    def _sort(self, key):\n        \"\"\"Sorts internal arrays by key\"\"\"\n        ixlist = np.argsort(self[key])\n        for k in self._settable_array_keys:\n            self._data[k] = self[k][ixlist]    \n\n    # Operator overloading    \n    def __add__(self, other):\n        \"\"\"\n        Overloads the + operator to join particle groups.\n        Simply calls join_particle_groups\n        \"\"\"\n        return join_particle_groups(self, other)\n\n    # \n    def __contains__(self, item):\n        \"\"\"Checks internal data\"\"\"\n        return True if item in self._data else False    \n\n    def __eq__(self, other):\n        \"\"\"Check equality of internal data\"\"\"\n        if isinstance(other, ParticleGroup):\n            for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:\n                if not np.allclose(self[key], other[key]):\n                    return False\n            return True\n\n        return NotImplemented    \n\n    def __len__(self):\n        return len(self[self._settable_array_keys[0]])\n\n    def __str__(self):\n        s = f'ParticleGroup with {self.n_particle} particles with total charge {self.charge} C'\n        return s\n\n    def __repr__(self):\n        memloc = hex(id(self))\n        return f'&lt;ParticleGroup with {self.n_particle} particles at {memloc}&gt;'\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.Jx","title":"<code>Jx</code>  <code>property</code>","text":"<p>Normalized amplitude J in the x-px plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.Jy","title":"<code>Jy</code>  <code>property</code>","text":"<p>Normalized amplitude J in the y-py plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.Lz","title":"<code>Lz</code>  <code>property</code>","text":"<p>Angular momentum around the z axis in m*eV/c Lz = x * py - y * px</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.average_current","title":"<code>average_current</code>  <code>property</code>","text":"<p>Simple average <code>current = charge / dt</code> in [A], with <code>dt =  (max_t - min_t)</code> If particles are in \\(t\\) coordinates, will try<code>dt = (max_z - min_z)*c_light*beta_z</code></p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta","title":"<code>beta</code>  <code>property</code>","text":"<p>Relativistic beta</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta_x","title":"<code>beta_x</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic beta, x component</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta_y","title":"<code>beta_y</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic beta, y component</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta_z","title":"<code>beta_z</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic beta, z component</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.charge","title":"<code>charge</code>  <code>property</code> <code>writable</code>","text":"<p>Total charge in C</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.data","title":"<code>data</code>  <code>property</code>","text":"<p>Internal data dict</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.energy","title":"<code>energy</code>  <code>property</code>","text":"<p>Total energy in eV</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.gamma","title":"<code>gamma</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic gamma</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.higher_order_energy","title":"<code>higher_order_energy</code>  <code>property</code>","text":"<p>Fits a quadratic (order=2) to the Energy vs. time, and returns the energy with this subtracted.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.higher_order_energy_spread","title":"<code>higher_order_energy_spread</code>  <code>property</code>","text":"<p>Legacy syntax to compute the standard deviation of higher_order_energy.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.id","title":"<code>id</code>  <code>property</code> <code>writable</code>","text":"<p>id integer</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.in_t_coordinates","title":"<code>in_t_coordinates</code>  <code>property</code>","text":"<p>Returns True if all particles have the same t coordinate</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.in_z_coordinates","title":"<code>in_z_coordinates</code>  <code>property</code>","text":"<p>Returns True if all particles have the same z coordinate</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.kinetic_energy","title":"<code>kinetic_energy</code>  <code>property</code>","text":"<p>Kinetic energy in eV</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.mass","title":"<code>mass</code>  <code>property</code>","text":"<p>Rest mass in eV</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.n_alive","title":"<code>n_alive</code>  <code>property</code>","text":"<p>Number of alive particles, defined by status == 1</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.n_dead","title":"<code>n_dead</code>  <code>property</code>","text":"<p>Number of alive particles, defined by status != 1</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.n_particle","title":"<code>n_particle</code>  <code>property</code>","text":"<p>Total number of particles. Same as len</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.norm_emit_4d","title":"<code>norm_emit_4d</code>  <code>property</code>","text":"<p>Normalized emittance in the xy planes (4D)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.norm_emit_x","title":"<code>norm_emit_x</code>  <code>property</code>","text":"<p>Normalized emittance in the x plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.norm_emit_y","title":"<code>norm_emit_y</code>  <code>property</code>","text":"<p>Normalized emittance in the x plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.p","title":"<code>p</code>  <code>property</code>","text":"<p>Total momemtum in eV/c</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.pr","title":"<code>pr</code>  <code>property</code>","text":"<p>Momentum in the radial direction in eV/c r_hat = cos(theta) xhat + sin(theta) yhat pr = p dot r_hat</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.ptheta","title":"<code>ptheta</code>  <code>property</code>","text":"<p>Momentum in the polar theta direction.  theta_hat = -sin(theta) xhat + cos(theta) yhat ptheta = p dot theta_hat Note that Lz = r*ptheta</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.px","title":"<code>px</code>  <code>property</code> <code>writable</code>","text":"<p>px coordinate in [eV/c]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.px_bar","title":"<code>px_bar</code>  <code>property</code>","text":"<p>Normalized px in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.py","title":"<code>py</code>  <code>property</code> <code>writable</code>","text":"<p>py coordinate in [eV/c]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.py_bar","title":"<code>py_bar</code>  <code>property</code>","text":"<p>Normalized py in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.pz","title":"<code>pz</code>  <code>property</code> <code>writable</code>","text":"<p>pz coordinate in [eV/c]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.r","title":"<code>r</code>  <code>property</code>","text":"<p>Radius in the xy plane: r = sqrt(x^2 + y^2) in m</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.species","title":"<code>species</code>  <code>property</code> <code>writable</code>","text":"<p>species string</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.species_charge","title":"<code>species_charge</code>  <code>property</code>","text":"<p>Species charge in C</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.status","title":"<code>status</code>  <code>property</code> <code>writable</code>","text":"<p>status coordinate in [1]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.t","title":"<code>t</code>  <code>property</code> <code>writable</code>","text":"<p>t coordinate in [s]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.theta","title":"<code>theta</code>  <code>property</code>","text":"<p>Angle in xy plane: theta = arctan2(y, x) in radians</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.weight","title":"<code>weight</code>  <code>property</code> <code>writable</code>","text":"<p>weight coordinate in [C]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.x","title":"<code>x</code>  <code>property</code> <code>writable</code>","text":"<p>x coordinate in [m]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.x_bar","title":"<code>x_bar</code>  <code>property</code>","text":"<p>Normalized x in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.xp","title":"<code>xp</code>  <code>property</code>","text":"<p>x slope px/pz (dimensionless)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.y","title":"<code>y</code>  <code>property</code> <code>writable</code>","text":"<p>y coordinate in [m]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.y_bar","title":"<code>y_bar</code>  <code>property</code>","text":"<p>Normalized y in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.yp","title":"<code>yp</code>  <code>property</code>","text":"<p>y slope py/pz (dimensionless)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.z","title":"<code>z</code>  <code>property</code> <code>writable</code>","text":"<p>z coordinate in [m]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__add__","title":"<code>__add__(other)</code>","text":"<p>Overloads the + operator to join particle groups. Simply calls join_particle_groups</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __add__(self, other):\n    \"\"\"\n    Overloads the + operator to join particle groups.\n    Simply calls join_particle_groups\n    \"\"\"\n    return join_particle_groups(self, other)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__contains__","title":"<code>__contains__(item)</code>","text":"<p>Checks internal data</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __contains__(self, item):\n    \"\"\"Checks internal data\"\"\"\n    return True if item in self._data else False    \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__eq__","title":"<code>__eq__(other)</code>","text":"<p>Check equality of internal data</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __eq__(self, other):\n    \"\"\"Check equality of internal data\"\"\"\n    if isinstance(other, ParticleGroup):\n        for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:\n            if not np.allclose(self[key], other[key]):\n                return False\n        return True\n\n    return NotImplemented    \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__getitem__","title":"<code>__getitem__(key)</code>","text":"<p>Returns a property or statistical quantity that can be computed:</p> <ul> <li><code>P['x']</code> returns the x array</li> <li><code>P['sigmx_x']</code> returns the std(x) scalar</li> <li><code>P['norm_emit_x']</code> returns the norm_emit_x scalar</li> </ul> <p>Parts can also be given. Example: <code>P[0:10]</code> returns a new ParticleGroup with the first 10 elements.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __getitem__(self, key):\n    \"\"\"\n    Returns a property or statistical quantity that can be computed:\n\n    - `P['x']` returns the x array\n    - `P['sigmx_x']` returns the std(x) scalar\n    - `P['norm_emit_x']` returns the norm_emit_x scalar\n\n    Parts can also be given. Example: `P[0:10]` returns a new ParticleGroup with the first 10 elements.\n    \"\"\"\n\n    # Allow for non-string operations: \n    if not isinstance(key, str):\n        return particle_parts(self, key)\n\n    if key.startswith('cov_'):\n        subkeys = key[4:].split('__')\n        assert len(subkeys) == 2, f'Too many properties in covariance request: {key}'\n        return self.cov(*subkeys)[0,1]\n    elif key.startswith('delta_'):\n        return self.delta(key[6:])\n    elif key.startswith('sigma_'):\n        return self.std(key[6:])\n    elif key.startswith('mean_'):\n        return self.avg(key[5:])\n    elif key.startswith('min_'):\n        return self.min(key[4:])\n    elif key.startswith('max_'):\n        return self.max(key[4:])     \n    elif key.startswith('ptp_'):\n        return self.ptp(key[4:])   \n    elif 'bunching' in key:\n        wavelength = parse_bunching_str(key)\n        bunching = self.bunching(wavelength) # complex\n\n        # abs or arg (angle):\n        if 'phase_' in key:\n            return np.angle(bunching)\n        else:\n            return np.abs(bunching)\n\n    else:\n        return getattr(self, key) \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.assign_id","title":"<code>assign_id()</code>","text":"<p>Assigns unique ids, integers from 1 to n_particle</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def assign_id(self):\n    \"\"\"\n    Assigns unique ids, integers from 1 to n_particle\n\n    \"\"\"\n    if 'id' not in self._settable_array_keys: \n        self._settable_array_keys.append('id')\n    self.id = np.arange(1, self['n_particle']+1)             \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.avg","title":"<code>avg(key)</code>","text":"<p>Statistical average</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def avg(self, key):\n    \"\"\"Statistical average\"\"\"\n    dat = self[key]  # equivalent to self.key for accessing properties above\n    if np.isscalar(dat): \n        return dat\n    return np.average(dat, weights=self.weight)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching","title":"<code>bunching(wavelength)</code>","text":"<p>Calculate the normalized bunching parameter, which is the magnitude of the  complex sum of weighted exponentials at a given point.</p> <p>The formula for bunching is given by:</p> \\[ B(z, \\lambda) = \frac{\\left|\\sum w_i e^{i k z_i} ight|}{\\sum w_i} \\] <p>where: - \\( z \\) is the position array, - \\( \\lambda \\) is the wavelength, - \\( k = \frac{2\\pi}{\\lambda} \\) is the wave number, - \\( w_i \\) are the weights.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching--parameters","title":"Parameters","text":"<p>wavelength : float     Wavelength of the wave.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching--returns","title":"Returns","text":"<p>complex     The normalized bunching parameter.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching--raises","title":"Raises","text":"<p>ValueError     If <code>wavelength</code> is not a positive number.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def bunching(self, wavelength):\n    \"\"\"\n    Calculate the normalized bunching parameter, which is the magnitude of the \n    complex sum of weighted exponentials at a given point.\n\n    The formula for bunching is given by:\n\n    $$\n    B(z, \\lambda) = \\frac{\\left|\\sum w_i e^{i k z_i}\\right|}{\\sum w_i}\n    $$\n\n    where:\n    - \\( z \\) is the position array,\n    - \\( \\lambda \\) is the wavelength,\n    - \\( k = \\frac{2\\pi}{\\lambda} \\) is the wave number,\n    - \\( w_i \\) are the weights.\n\n    Parameters\n    ----------\n    wavelength : float\n        Wavelength of the wave.\n\n\n    Returns\n    -------\n    complex\n        The normalized bunching parameter.\n\n    Raises\n    ------\n    ValueError\n        If `wavelength` is not a positive number.\n    \"\"\"        \n\n    if self.in_z_coordinates:\n        # Approximate z\n        z = self.t * self.avg('beta_z')*c_light\n    else:\n        z = self.z\n\n    return statistics.bunching(z, wavelength, weight=self.weight)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.copy","title":"<code>copy()</code>","text":"<p>Returns a deep copy</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def copy(self):\n    \"\"\"Returns a deep copy\"\"\"\n    return deepcopy(self)    \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.cov","title":"<code>cov(*keys)</code>","text":"<p>Covariance matrix from any properties</p> <p>Example:  P = ParticleGroup(h5) P.cov('x', 'px', 'y', 'py')</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def cov(self, *keys):\n    \"\"\"\n    Covariance matrix from any properties\n\n    Example: \n    P = ParticleGroup(h5)\n    P.cov('x', 'px', 'y', 'py')\n\n    \"\"\"\n    dats = np.array([ self[key] for key in keys ])\n    return np.cov(dats, aweights=self.weight)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.delta","title":"<code>delta(key)</code>","text":"<p>Attribute (array) relative to its mean</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def delta(self, key):\n    \"\"\"Attribute (array) relative to its mean\"\"\"\n    return self[key] - self.avg(key)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.drift","title":"<code>drift(delta_t)</code>","text":"<p>Drifts particles by time delta_t</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def drift(self, delta_t):\n    \"\"\"\n    Drifts particles by time delta_t\n    \"\"\"\n    self.x = self.x + self.beta_x * c_light * delta_t\n    self.y = self.y + self.beta_y * c_light * delta_t\n    self.z = self.z + self.beta_z * c_light * delta_t\n    self.t = self.t + delta_t\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.drift_to_t","title":"<code>drift_to_t(t=None)</code>","text":"<p>Drifts all particles to the same t</p> <p>If no z is given, particles will be drifted to the average t</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def drift_to_t(self, t=None):\n    \"\"\"\n    Drifts all particles to the same t\n\n    If no z is given, particles will be drifted to the average t\n    \"\"\"\n    if t is None:\n        t = self.avg('t')\n    dt = t - self.t\n    self.drift(dt)\n    # Fix t to be exactly this value\n    self.t = np.full(self.n_particle, t)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.from_bmad","title":"<code>from_bmad(bmad_dict)</code>  <code>classmethod</code>","text":"<p>Convert Bmad particle data as a dict  to ParticleGroup data.</p> <p>See: ParticleGroup.to_bmad or particlegroup_to_bmad</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.from_bmad--parameters","title":"Parameters","text":"<p>bmad_data: dict     Dict with keys:     'x'     'px'     'y'     'py'     'z'     'pz',      'charge'     'species',     'tref'     'state'</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.from_bmad--returns","title":"Returns","text":"<p>ParticleGroup</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>@classmethod\n@functools.wraps(bmad.bmad_to_particlegroup_data)\ndef from_bmad(cls, bmad_dict):\n    \"\"\"\n    Convert Bmad particle data as a dict \n    to ParticleGroup data.\n\n    See: ParticleGroup.to_bmad or particlegroup_to_bmad\n\n    Parameters\n    ----------\n    bmad_data: dict\n        Dict with keys:\n        'x'\n        'px'\n        'y'\n        'py'\n        'z'\n        'pz', \n        'charge'\n        'species',\n        'tref'\n        'state'\n\n    Returns\n    -------\n    ParticleGroup\n    \"\"\"        \n    data = bmad.bmad_to_particlegroup_data(bmad_dict)\n    return cls(data=data)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.higher_order_energy_calc","title":"<code>higher_order_energy_calc(order=2)</code>","text":"<p>Fits a polynmial with order <code>order</code> to the Energy vs. time, , and returns the energy with this subtracted.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def higher_order_energy_calc(self, order=2):\n    \"\"\"\n    Fits a polynmial with order `order` to the Energy vs. time, , and returns the energy with this subtracted. \n    \"\"\"\n    #order=2\n    if self.std('z') &lt; 1e-12:\n        # must be at a screen. Use t\n        t = self.t\n    else:\n        # All particles at the same time. Use z to calc t\n        t = self.z/c_light\n    energy = self.energy\n\n    best_fit_coeffs = np.polynomial.polynomial.polyfit(t, energy, order)\n    best_fit = np.polynomial.polynomial.polyval(t, best_fit_coeffs)\n    return energy - best_fit\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.histogramdd","title":"<code>histogramdd(*keys, bins=10, range=None)</code>","text":"<p>Wrapper for numpy.histogramdd, but accepts property names as keys.</p> <p>Computes the multidimensional histogram of keys. Internally uses weights. </p> Example <p>P.histogramdd('x', 'y', bins=50)</p> <p>Returns:     np.array with shape 50x50, edge list</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def histogramdd(self, *keys, bins=10, range=None):\n    \"\"\"\n    Wrapper for numpy.histogramdd, but accepts property names as keys.\n\n    Computes the multidimensional histogram of keys. Internally uses weights. \n\n    Example:\n        P.histogramdd('x', 'y', bins=50)\n    Returns:\n        np.array with shape 50x50, edge list \n\n    \"\"\"\n    H, edges = np.histogramdd(np.array([self[k] for k in list(keys)]).T, weights=self.weight, bins=bins, range=range)\n\n    return H, edges\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.max","title":"<code>max(key)</code>","text":"<p>Maximum of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def max(self, key):\n    \"\"\"Maximum of any key\"\"\"\n    return np.max(self[key]) \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.min","title":"<code>min(key)</code>","text":"<p>Minimum of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def min(self, key):\n    \"\"\"Minimum of any key\"\"\"\n    return np.min(self[key]) # was: getattr(self, key)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.plot","title":"<code>plot(key1='x', key2=None, bins=None, *, xlim=None, ylim=None, return_figure=False, tex=True, nice=True, **kwargs)</code>","text":"<p>1d or 2d density plot. </p> <p>If one key is given, this will plot the density of that key. Example:     .plot('x')</p> <p>If two keys arg given, this will plot a 2d marginal plot. Example:     .plot('x', 'px')</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.plot--parameters","title":"Parameters","text":"<p>particle_group: ParticleGroup     The object to plot</p> str, default = 't' <p>Key to bin on the x-axis</p> str, default = None <p>Key to bin on the y-axis. </p> int, default = None <p>Number of bins. If None, this will use a heuristic: bins = sqrt(n_particle/4)</p> tuple, default = None <p>Manual setting of the x-axis limits. Note that these are in raw, unscaled units. </p> tuple, default = None <p>Manual setting of the y-axis limits. Note that these are in raw, unscaled units. </p> bool, default = True <p>Use TEX for labels   </p> bool, default = True <p>Scale to nice units</p> bool, default = False <p>If true, return a matplotlib.figure.Figure object</p> <p>kwargs     Any additional kwargs to send to the the plot in: plt.subplots(kwargs)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.plot--returns","title":"Returns","text":"<p>None or fig: matplotlib.figure.Figure     This only returns a figure object if return_figure=T, otherwise returns None</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def plot(self, key1='x', key2=None,\n         bins=None,\n         *,\n         xlim=None,\n         ylim=None,\n         return_figure=False, \n         tex=True, nice=True,\n         **kwargs):\n    \"\"\"\n    1d or 2d density plot. \n\n    If one key is given, this will plot the density of that key.\n    Example:\n        .plot('x')\n\n    If two keys arg given, this will plot a 2d marginal plot.\n    Example:\n        .plot('x', 'px')\n\n\n    Parameters\n    ----------\n    particle_group: ParticleGroup\n        The object to plot\n\n    key1: str, default = 't'\n        Key to bin on the x-axis\n\n    key2: str, default = None\n        Key to bin on the y-axis. \n\n    bins: int, default = None\n       Number of bins. If None, this will use a heuristic: bins = sqrt(n_particle/4)\n\n    xlim: tuple, default = None\n        Manual setting of the x-axis limits. Note that these are in raw, unscaled units. \n\n    ylim: tuple, default = None\n        Manual setting of the y-axis limits. Note that these are in raw, unscaled units. \n\n    tex: bool, default = True\n        Use TEX for labels   \n\n    nice: bool, default = True\n        Scale to nice units\n\n    return_figure: bool, default = False\n        If true, return a matplotlib.figure.Figure object\n\n    **kwargs\n        Any additional kwargs to send to the the plot in: plt.subplots(**kwargs)\n\n\n    Returns\n    -------\n    None or fig: matplotlib.figure.Figure\n        This only returns a figure object if return_figure=T, otherwise returns None\n\n    \"\"\"\n\n    if not key2:\n        fig = density_plot(self, key=key1,\n                           bins=bins,\n                           xlim=xlim,\n                           tex=tex,\n                           nice=nice,\n                           **kwargs)\n    else:\n        fig = marginal_plot(self, key1=key1, key2=key2,\n                            bins=bins,\n                            xlim=xlim,\n                            ylim=ylim,\n                            tex=tex,\n                            nice=nice,\n                            **kwargs)\n\n    if return_figure:\n        return fig\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.ptp","title":"<code>ptp(key)</code>","text":"<p>Peak-to-Peak = max - min of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def ptp(self, key):\n    \"\"\"Peak-to-Peak = max - min of any key\"\"\"\n    return np.ptp(self[key])     \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.slice_statistics","title":"<code>slice_statistics(*keys, n_slice=100, slice_key=None)</code>","text":"<p>Slice statistics</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def slice_statistics(self, *keys,\n               n_slice=100,\n               slice_key=None):\n    \"\"\"\n    Slice statistics\n\n    \"\"\"      \n\n    if slice_key is None:\n        if self.in_t_coordinates:\n            slice_key = 'z'\n\n        else:\n            slice_key = 't'  \n\n    if slice_key in ('t', 'delta_t'):\n        density_name = 'current'\n    else:\n        density_name = 'density'\n\n    keys = set(keys)\n    keys.add('mean_'+slice_key)\n    keys.add('ptp_'+slice_key)\n    keys.add('charge')\n    slice_dat = slice_statistics(self, n_slice=n_slice, slice_key=slice_key,\n                            keys=keys)\n\n    slice_dat[density_name] = slice_dat['charge']/ slice_dat['ptp_'+slice_key]\n\n    return slice_dat\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.std","title":"<code>std(key)</code>","text":"<p>Standard deviation (actually sample)</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def std(self, key):\n    \"\"\"Standard deviation (actually sample)\"\"\"\n    dat = self[key]\n    if np.isscalar(dat):\n        return 0\n    avg_dat = self.avg(key)\n    return np.sqrt(np.average( (dat - avg_dat)**2, weights=self.weight))\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.twiss","title":"<code>twiss(plane='x', fraction=1, p0c=None)</code>","text":"<p>Returns Twiss and Dispersion dict.</p> <p>plane can be:</p> <p><code>'x'</code>, <code>'y'</code>, or <code>'xy'</code></p> <p>Optionally a fraction of the particles, based on amplitiude, can be specified.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def twiss(self, plane='x', fraction=1, p0c=None):\n    \"\"\"\n    Returns Twiss and Dispersion dict.\n\n    plane can be:\n\n    `'x'`, `'y'`, or `'xy'`\n\n    Optionally a fraction of the particles, based on amplitiude, can be specified.\n    \"\"\"\n    d = {}\n    for p in plane:\n        d.update(particle_twiss_dispersion(self, plane=p, fraction=fraction, p0c=p0c))\n    return d\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.twiss_match","title":"<code>twiss_match(beta=None, alpha=None, plane='x', p0c=None, inplace=False)</code>","text":"<p>Returns a ParticleGroup with requested Twiss parameters.</p> <p>See: statistics.matched_particles</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False):\n    \"\"\"\n    Returns a ParticleGroup with requested Twiss parameters.\n\n    See: statistics.matched_particles\n    \"\"\"\n\n    return matched_particles(self, beta=beta, alpha=alpha, plane=plane, inplace=inplace)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.units","title":"<code>units(key)</code>","text":"<p>Returns the units of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def units(self, key):\n    \"\"\"Returns the units of any key\"\"\"\n    return pg_units(key)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.write","title":"<code>write(h5, name=None)</code>","text":"<p>Writes to an open h5 handle, or new file if h5 is a str.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def write(self, h5, name=None):\n    \"\"\"\n    Writes to an open h5 handle, or new file if h5 is a str.\n\n    \"\"\"\n    if isinstance(h5, str):\n        fname = os.path.expandvars(h5)\n        g = File(fname, 'w')\n        pmd_init(g, basePath='/', particlesPath='.' )\n    else:\n        g = h5\n\n    write_pmd_bunch(g, self, name=name)        \n</code></pre>"},{"location":"examples/bunching/","title":"Bunching","text":"In\u00a0[1]: Copied! <pre>from pmd_beamphysics import ParticleGroup\n%config InlineBackend.figure_format = 'retina'\n</pre> from pmd_beamphysics import ParticleGroup %config InlineBackend.figure_format = 'retina' In\u00a0[2]: Copied! <pre>P = ParticleGroup( 'data/bmad_particles2.h5')\nP.drift_to_t()\n\nwavelength = 0.1e-6\ndz = (P.z/wavelength % 1) * wavelength\nP.z -= dz\n</pre> P = ParticleGroup( 'data/bmad_particles2.h5') P.drift_to_t()  wavelength = 0.1e-6 dz = (P.z/wavelength % 1) * wavelength P.z -= dz In\u00a0[3]: Copied! <pre>P.plot('z')\n</pre> P.plot('z') In\u00a0[4]: Copied! <pre>b = P.bunching(wavelength)\nb\n</pre> b = P.bunching(wavelength) b Out[4]: <pre>(1-3.2133096564432656e-16j)</pre> In\u00a0[5]: Copied! <pre>P['bunching_0.1e-6']\n</pre> P['bunching_0.1e-6'] Out[5]: <pre>1.0</pre> In\u00a0[6]: Copied! <pre>P['bunching_phase_0.1e-6']\n</pre> P['bunching_phase_0.1e-6'] Out[6]: <pre>-3.2133096564432656e-16</pre> In\u00a0[7]: Copied! <pre>P['bunching_0.1_um']\n</pre> P['bunching_0.1_um'] Out[7]: <pre>1.0</pre> In\u00a0[8]: Copied! <pre>import numpy as np\n\nimport matplotlib.pyplot as plt\n</pre> import numpy as np  import matplotlib.pyplot as plt  In\u00a0[9]: Copied! <pre>wavelengths = wavelength * np.linspace(0.9, 1.1, 200)\n</pre> wavelengths = wavelength * np.linspace(0.9, 1.1, 200) In\u00a0[10]: Copied! <pre>plt.plot(wavelengths*1e6, np.abs(list(map(P.bunching, wavelengths))))\nplt.xlabel('wavelength (\u00b5m)')\nplt.ylabel('bunching')\n</pre> plt.plot(wavelengths*1e6, np.abs(list(map(P.bunching, wavelengths)))) plt.xlabel('wavelength (\u00b5m)') plt.ylabel('bunching') Out[10]: <pre>Text(0, 0.5, 'bunching')</pre> In\u00a0[11]: Copied! <pre>P.slice_plot('bunching_0.1_um')\n</pre> P.slice_plot('bunching_0.1_um') In\u00a0[12]: Copied! <pre>P.slice_plot('bunching_phase_0.1_um')\n</pre> P.slice_plot('bunching_phase_0.1_um') In\u00a0[13]: Copied! <pre>P.in_z_coordinates\n</pre> P.in_z_coordinates Out[13]: <pre>False</pre> In\u00a0[14]: Copied! <pre>P.units('bunching_0.1_um')\n</pre> P.units('bunching_0.1_um') Out[14]: <pre>pmd_unit('', 1, (0, 0, 0, 0, 0, 0, 0))</pre> In\u00a0[15]: Copied! <pre>P.units('bunching_phase_0.1_um')\n</pre> P.units('bunching_phase_0.1_um') Out[15]: <pre>pmd_unit('rad', 1, (0, 0, 0, 0, 0, 0, 0))</pre> In\u00a0[16]: Copied! <pre>from pmd_beamphysics.statistics import bunching\n</pre> from pmd_beamphysics.statistics import bunching In\u00a0[17]: Copied! <pre>?bunching\n</pre> ?bunching"},{"location":"examples/bunching/#bunching","title":"Bunching\u00b6","text":"<p>Bunching at some wavelength $\\lambda$ for a list of particles $z$ is given by the weighted sum of complex phasors:</p> <p>$$B(z, \\lambda) \\equiv \\frac{\\sum_j w_j e^{i k z_j}}{\\sum w_j} $$</p> <p>where $k = 2\\pi/\\lambda$ and $w_j$ are the weights of the particles.</p> <p>See for example D. Ratner's disseratation.</p>"},{"location":"examples/bunching/#add-bunching-to-particles","title":"Add bunching to particles\u00b6","text":"<p>This uses a simple method to add perfect bunching at 0.1 \u00b5m</p>"},{"location":"examples/bunching/#calculate-bunching","title":"Calculate bunching\u00b6","text":"<p>All of these methods will calculate the bunching. The first returns a complex number <code>bunching</code></p> <p>The string attributes return real numbers, magnitude and argument (phase):</p> <ul> <li><code>'bunching_</code> returns <code>np.abs(bunching)</code></li> <li><code>'bunching_phase_</code> returns <code>np.angle(bunching)</code></li> </ul>"},{"location":"examples/bunching/#simple-plot","title":"Simple plot\u00b6","text":""},{"location":"examples/bunching/#units","title":"Units\u00b6","text":"<p>Bunching is dimensionless</p>"},{"location":"examples/bunching/#bunching-function","title":"Bunching function\u00b6","text":"<p>This is the function that is used.</p>"},{"location":"examples/labels/","title":"TeX Labels","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>%pylab --no-import-all inline\n%config InlineBackend.figure_format = 'retina'\n</pre> %pylab --no-import-all inline %config InlineBackend.figure_format = 'retina' <pre>%pylab is deprecated, use %matplotlib inline and import the required libraries.\nPopulating the interactive namespace from numpy and matplotlib\n</pre> In\u00a0[3]: Copied! <pre>from pmd_beamphysics.labels import texlabel, TEXLABEL, mathlabel\nfrom pmd_beamphysics.units import pg_units\n</pre> from pmd_beamphysics.labels import texlabel, TEXLABEL, mathlabel from pmd_beamphysics.units import pg_units <p>This is basic function</p> In\u00a0[4]: Copied! <pre>?texlabel\n</pre> ?texlabel <p>Example:</p> In\u00a0[5]: Copied! <pre>texlabel('norm_emit_x')\n</pre> texlabel('norm_emit_x') Out[5]: <pre>'\\\\epsilon_{n, x}'</pre> <p>Example with two parts:</p> In\u00a0[6]: Copied! <pre>texlabel('cov_x__px')\n</pre> texlabel('cov_x__px') Out[6]: <pre>'\\\\left&lt;x, p_x\\\\right&gt;'</pre> <p>Returns None if a label cannot be formed:</p> In\u00a0[7]: Copied! <pre>texlabel('garbage') is None\n</pre> texlabel('garbage') is None Out[7]: <pre>True</pre> In\u00a0[8]: Copied! <pre>examples = ['cov_x__px', 'sigma_y', 'mean_Jx']\nexamples += list(TEXLABEL)\n</pre> examples = ['cov_x__px', 'sigma_y', 'mean_Jx'] examples += list(TEXLABEL) In\u00a0[9]: Copied! <pre>for key in examples:\n    tex = texlabel(key)\n    s = fr'${tex}$'\n    print(key ,'-&gt;', tex)\n    fig, ax = plt.subplots(figsize=(1,1))\n    plt.axis('off')\n    \n    ax.text(0.5, 0.5, s, fontsize=32)\n    plt.show()\n</pre> for key in examples:     tex = texlabel(key)     s = fr'${tex}$'     print(key ,'-&gt;', tex)     fig, ax = plt.subplots(figsize=(1,1))     plt.axis('off')          ax.text(0.5, 0.5, s, fontsize=32)     plt.show() <pre>cov_x__px -&gt; \\left&lt;x, p_x\\right&gt;\n</pre> <pre>sigma_y -&gt; \\sigma_{ y }\n</pre> <pre>mean_Jx -&gt; \\left&lt;J_x\\right&gt;\n</pre> <pre>t -&gt; t\n</pre> <pre>energy -&gt; E\n</pre> <pre>kinetic_energy -&gt; E_{kinetic}\n</pre> <pre>Ex -&gt; E_x\n</pre> <pre>Ey -&gt; E_y\n</pre> <pre>Ez -&gt; E_z\n</pre> <pre>Bx -&gt; B_x\n</pre> <pre>By -&gt; B_y\n</pre> <pre>Bz -&gt; B_z\n</pre> <pre>Etheta -&gt; E_{\\theta}\n</pre> <pre>Btheta -&gt; B_{\\theta}\n</pre> <pre>px -&gt; p_x\n</pre> <pre>py -&gt; p_y\n</pre> <pre>pz -&gt; p_z\n</pre> <pre>p -&gt; p\n</pre> <pre>pr -&gt; p_r\n</pre> <pre>ptheta -&gt; p_{\\theta}\n</pre> <pre>x -&gt; x\n</pre> <pre>y -&gt; y\n</pre> <pre>z -&gt; z\n</pre> <pre>r -&gt; r\n</pre> <pre>Jx -&gt; J_x\n</pre> <pre>Jy -&gt; J_y\n</pre> <pre>beta -&gt; \\beta\n</pre> <pre>beta_x -&gt; \\beta_x\n</pre> <pre>beta_y -&gt; \\beta_y\n</pre> <pre>beta_z -&gt; \\beta_z\n</pre> <pre>gamma -&gt; \\gamma\n</pre> <pre>theta -&gt; \\theta\n</pre> <pre>charge -&gt; Q\n</pre> <pre>twiss_alpha_x -&gt; Twiss\\ \\alpha_x\n</pre> <pre>twiss_beta_x -&gt; Twiss\\ \\beta_x\n</pre> <pre>twiss_gamma_x -&gt; Twiss\\ \\gamma_x\n</pre> <pre>twiss_eta_x -&gt; Twiss\\ \\eta_x\n</pre> <pre>twiss_etap_x -&gt; Twiss\\ \\eta'_x\n</pre> <pre>twiss_emit_x -&gt; Twiss\\ \\epsilon_{x}\n</pre> <pre>twiss_norm_emit_x -&gt; Twiss\\ \\epsilon_{n, x}\n</pre> <pre>twiss_alpha_y -&gt; Twiss\\ \\alpha_y\n</pre> <pre>twiss_beta_y -&gt; Twiss\\ \\beta_y\n</pre> <pre>twiss_gamma_y -&gt; Twiss\\ \\gamma_y\n</pre> <pre>twiss_eta_y -&gt; Twiss\\ \\eta_y\n</pre> <pre>twiss_etap_y -&gt; Twiss\\ \\eta'_y\n</pre> <pre>twiss_emit_y -&gt; Twiss\\ \\epsilon_{y}\n</pre> <pre>twiss_norm_emit_y -&gt; Twiss\\ \\epsilon_{n, y}\n</pre> <pre>average_current -&gt; I_{av}\n</pre> <pre>norm_emit_x -&gt; \\epsilon_{n, x}\n</pre> <pre>norm_emit_y -&gt; \\epsilon_{n, y}\n</pre> <pre>norm_emit_4d -&gt; \\epsilon_{4D}\n</pre> <pre>Lz -&gt; L_z\n</pre> <pre>xp -&gt; x'\n</pre> <pre>yp -&gt; y'\n</pre> <pre>x_bar -&gt; \\overline{x}\n</pre> <pre>px_bar -&gt; \\overline{p_x}\n</pre> <pre>y_bar -&gt; \\overline{y}\n</pre> <pre>py_bar -&gt; \\overline{p_y}\n</pre> In\u00a0[10]: Copied! <pre>?mathlabel\n</pre> ?mathlabel In\u00a0[11]: Copied! <pre>mathlabel('beta_x', units=pg_units('beta_x'))\n</pre> mathlabel('beta_x', units=pg_units('beta_x')) Out[11]: <pre>'$\\\\beta_x$'</pre> <p>Multiple keys. Note that units are not checked for consistency!</p> In\u00a0[12]: Copied! <pre>mathlabel('sigma_x', 'sigma_y', units='\u00b5m')\n</pre> mathlabel('sigma_x', 'sigma_y', units='\u00b5m') Out[12]: <pre>'$\\\\sigma_{ x }, \\\\sigma_{ y }~(\\\\mathrm{ \u00b5m } )$'</pre> In\u00a0[13]: Copied! <pre>for key in examples:\n    \n    label = mathlabel(key, units=pg_units(key))\n    print(key ,'-&gt;', label)\n    fig, ax = plt.subplots(figsize=(1,1))\n    plt.axis('off')\n    \n    ax.text(0.5, 0.5, label, fontsize=32)\n    plt.show()\n</pre> for key in examples:          label = mathlabel(key, units=pg_units(key))     print(key ,'-&gt;', label)     fig, ax = plt.subplots(figsize=(1,1))     plt.axis('off')          ax.text(0.5, 0.5, label, fontsize=32)     plt.show() <pre>cov_x__px -&gt; $\\left&lt;x, p_x\\right&gt;~(\\mathrm{ m*eV/c } )$\n</pre> <pre>sigma_y -&gt; $\\sigma_{ y }~(\\mathrm{ m } )$\n</pre> <pre>mean_Jx -&gt; $\\left&lt;J_x\\right&gt;~(\\mathrm{ m } )$\n</pre> <pre>t -&gt; $t~(\\mathrm{ s } )$\n</pre> <pre>energy -&gt; $E~(\\mathrm{ eV } )$\n</pre> <pre>kinetic_energy -&gt; $E_{kinetic}~(\\mathrm{ eV } )$\n</pre> <pre>Ex -&gt; $E_x~(\\mathrm{ V/m } )$\n</pre> <pre>Ey -&gt; $E_y~(\\mathrm{ V/m } )$\n</pre> <pre>Ez -&gt; $E_z~(\\mathrm{ V/m } )$\n</pre> <pre>Bx -&gt; $B_x~(\\mathrm{ T } )$\n</pre> <pre>By -&gt; $B_y~(\\mathrm{ T } )$\n</pre> <pre>Bz -&gt; $B_z~(\\mathrm{ T } )$\n</pre> <pre>Etheta -&gt; $E_{\\theta}~(\\mathrm{ V/m } )$\n</pre> <pre>Btheta -&gt; $B_{\\theta}~(\\mathrm{ T } )$\n</pre> <pre>px -&gt; $p_x~(\\mathrm{ eV/c } )$\n</pre> <pre>py -&gt; $p_y~(\\mathrm{ eV/c } )$\n</pre> <pre>pz -&gt; $p_z~(\\mathrm{ eV/c } )$\n</pre> <pre>p -&gt; $p~(\\mathrm{ eV/c } )$\n</pre> <pre>pr -&gt; $p_r~(\\mathrm{ eV/c } )$\n</pre> <pre>ptheta -&gt; $p_{\\theta}~(\\mathrm{ eV/c } )$\n</pre> <pre>x -&gt; $x~(\\mathrm{ m } )$\n</pre> <pre>y -&gt; $y~(\\mathrm{ m } )$\n</pre> <pre>z -&gt; $z~(\\mathrm{ m } )$\n</pre> <pre>r -&gt; $r~(\\mathrm{ m } )$\n</pre> <pre>Jx -&gt; $J_x~(\\mathrm{ m } )$\n</pre> <pre>Jy -&gt; $J_y~(\\mathrm{ m } )$\n</pre> <pre>beta -&gt; $\\beta$\n</pre> <pre>beta_x -&gt; $\\beta_x$\n</pre> <pre>beta_y -&gt; $\\beta_y$\n</pre> <pre>beta_z -&gt; $\\beta_z$\n</pre> <pre>gamma -&gt; $\\gamma$\n</pre> <pre>theta -&gt; $\\theta~(\\mathrm{ rad } )$\n</pre> <pre>charge -&gt; $Q~(\\mathrm{ C } )$\n</pre> <pre>twiss_alpha_x -&gt; $Twiss\\ \\alpha_x$\n</pre> <pre>twiss_beta_x -&gt; $Twiss\\ \\beta_x~(\\mathrm{ m } )$\n</pre> <pre>twiss_gamma_x -&gt; $Twiss\\ \\gamma_x~(\\mathrm{ /m } )$\n</pre> <pre>twiss_eta_x -&gt; $Twiss\\ \\eta_x~(\\mathrm{ m } )$\n</pre> <pre>twiss_etap_x -&gt; $Twiss\\ \\eta'_x$\n</pre> <pre>twiss_emit_x -&gt; $Twiss\\ \\epsilon_{x}~(\\mathrm{ m } )$\n</pre> <pre>twiss_norm_emit_x -&gt; $Twiss\\ \\epsilon_{n, x}~(\\mathrm{ m } )$\n</pre> <pre>twiss_alpha_y -&gt; $Twiss\\ \\alpha_y$\n</pre> <pre>twiss_beta_y -&gt; $Twiss\\ \\beta_y~(\\mathrm{ m } )$\n</pre> <pre>twiss_gamma_y -&gt; $Twiss\\ \\gamma_y~(\\mathrm{ /m } )$\n</pre> <pre>twiss_eta_y -&gt; $Twiss\\ \\eta_y~(\\mathrm{ m } )$\n</pre> <pre>twiss_etap_y -&gt; $Twiss\\ \\eta'_y$\n</pre> <pre>twiss_emit_y -&gt; $Twiss\\ \\epsilon_{y}~(\\mathrm{ m } )$\n</pre> <pre>twiss_norm_emit_y -&gt; $Twiss\\ \\epsilon_{n, y}~(\\mathrm{ m } )$\n</pre> <pre>average_current -&gt; $I_{av}~(\\mathrm{ A } )$\n</pre> <pre>norm_emit_x -&gt; $\\epsilon_{n, x}~(\\mathrm{ m } )$\n</pre> <pre>norm_emit_y -&gt; $\\epsilon_{n, y}~(\\mathrm{ m } )$\n</pre> <pre>norm_emit_4d -&gt; $\\epsilon_{4D}~(\\mathrm{ (m)^2 } )$\n</pre> <pre>Lz -&gt; $L_z~(\\mathrm{ m*eV/c } )$\n</pre> <pre>xp -&gt; $x'~(\\mathrm{ rad } )$\n</pre> <pre>yp -&gt; $y'~(\\mathrm{ rad } )$\n</pre> <pre>x_bar -&gt; $\\overline{x}~(\\mathrm{ \\sqrt{ m } } )$\n</pre> <pre>px_bar -&gt; $\\overline{p_x}~(\\mathrm{ \\sqrt{ m } } )$\n</pre> <pre>y_bar -&gt; $\\overline{y}~(\\mathrm{ \\sqrt{ m } } )$\n</pre> <pre>py_bar -&gt; $\\overline{p_y}~(\\mathrm{ \\sqrt{ m } } )$\n</pre>"},{"location":"examples/labels/#tex-labels","title":"TeX Labels\u00b6","text":"<p>TeX labels for most attributes can be retrieved.</p>"},{"location":"examples/labels/#math-label-with-unit","title":"Math label with unit\u00b6","text":"<p><code>mathlabel</code> provides the complete string with optional units</p>"},{"location":"examples/normalized_coordinates/","title":"Simple Normalized Coordinates","text":"In\u00a0[1]: Copied! <pre>from pmd_beamphysics import ParticleGroup\nfrom pmd_beamphysics.statistics import A_mat_calc, twiss_calc, normalized_particle_coordinate\nimport numpy as np\n\n\nimport matplotlib.pyplot as plt\nimport matplotlib\n%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\nmatplotlib.rcParams['figure.figsize'] = (4,4)\n</pre> from pmd_beamphysics import ParticleGroup from pmd_beamphysics.statistics import A_mat_calc, twiss_calc, normalized_particle_coordinate import numpy as np   import matplotlib.pyplot as plt import matplotlib %matplotlib inline %config InlineBackend.figure_format = 'retina' matplotlib.rcParams['figure.figsize'] = (4,4) In\u00a0[2]: Copied! <pre>help(A_mat_calc)\n</pre> help(A_mat_calc) <pre>Help on function A_mat_calc in module pmd_beamphysics.statistics:\n\nA_mat_calc(beta, alpha, inverse=False)\n    Returns the 1D normal form matrix from twiss parameters beta and alpha\n    \n        A =   sqrt(beta)         0 \n             -alpha/sqrt(beta)   1/sqrt(beta) \n    \n    If inverse, the inverse will be returned:\n    \n        A^-1 =  1/sqrt(beta)     0 \n                alpha/sqrt(beta) sqrt(beta)     \n    \n    This corresponds to the linear normal form decomposition:\n    \n        M = A . Rot(theta) . A^-1\n    \n    with a clockwise rotation matrix:\n    \n        Rot(theta) =  cos(theta) sin(theta)\n                     -sin(theta) cos(theta)\n    \n    In the Bmad manual, G_q (Bmad) = A (here) in the Linear Optics chapter.\n    \n    A^-1 can be used to form normalized coordinates: \n        x_bar, px_bar   = A^-1 . (x, px)\n\n</pre> <p>Make phase space circle. This will represent some normalized coordinates:</p> In\u00a0[3]: Copied! <pre>theta = np.linspace(0, np.pi*2, 100)\nzvec0 = np.array([np.cos(theta), np.sin(theta)])\nplt.scatter(*zvec0)\n</pre> theta = np.linspace(0, np.pi*2, 100) zvec0 = np.array([np.cos(theta), np.sin(theta)]) plt.scatter(*zvec0) Out[3]: <pre>&lt;matplotlib.collections.PathCollection at 0x7fe784cab430&gt;</pre> <p>Make a 'beam' in 'lab coordinates':</p> In\u00a0[4]: Copied! <pre>MYMAT = np.array([[10, 0],[-3, 5]])\nzvec = np.matmul(MYMAT , zvec0)\nplt.scatter(*zvec)\n</pre> MYMAT = np.array([[10, 0],[-3, 5]]) zvec = np.matmul(MYMAT , zvec0) plt.scatter(*zvec) Out[4]: <pre>&lt;matplotlib.collections.PathCollection at 0x7fe77bc14f70&gt;</pre> <p>With a beam, $\\alpha$ and $\\beta$ can be determined from moments of the covariance matrix.</p> In\u00a0[5]: Copied! <pre>help(twiss_calc)\n</pre> help(twiss_calc) <pre>Help on function twiss_calc in module pmd_beamphysics.statistics:\n\ntwiss_calc(sigma_mat2)\n    Calculate Twiss parameters from the 2D sigma matrix (covariance matrix):\n    sigma_mat = &lt;x,x&gt;   &lt;x, p&gt;\n                &lt;p, x&gt;  &lt;p, p&gt;\n    \n    This is a simple calculation. Makes no assumptions about units. \n    \n    alpha = -&lt;x, p&gt;/emit\n    beta  =  &lt;x, x&gt;/emit\n    gamma =  &lt;p, p&gt;/emit\n    emit = det(sigma_mat)\n\n</pre> <p>Calculate a sigma matrix, get the determinant:</p> In\u00a0[6]: Copied! <pre>sigma_mat2 = np.cov(*zvec)\nnp.linalg.det(sigma_mat2)\n</pre> sigma_mat2 = np.cov(*zvec) np.linalg.det(sigma_mat2) Out[6]: <pre>637.5000000000003</pre> <p>Get some twiss:</p> In\u00a0[7]: Copied! <pre>twiss = twiss_calc(sigma_mat2)\ntwiss\n</pre> twiss = twiss_calc(sigma_mat2) twiss Out[7]: <pre>{'alpha': 0.6059702963017245,\n 'beta': 2.0199009876724157,\n 'gamma': 0.6768648603788545,\n 'emit': 25.248762345905202}</pre> <p>Analyzing matrices:</p> In\u00a0[8]: Copied! <pre>A = A_mat_calc(twiss['beta'], twiss['alpha'])\nA_inv = A_mat_calc(twiss['beta'], twiss['alpha'], inverse=True)\n</pre> A = A_mat_calc(twiss['beta'], twiss['alpha']) A_inv = A_mat_calc(twiss['beta'], twiss['alpha'], inverse=True) <p>A_inv turns this back into a circle:</p> In\u00a0[9]: Copied! <pre>zvec2 = np.matmul(A_inv, zvec)\nplt.scatter(*zvec2)\n</pre> zvec2 = np.matmul(A_inv, zvec) plt.scatter(*zvec2) Out[9]: <pre>&lt;matplotlib.collections.PathCollection at 0x7fe77bb97280&gt;</pre> In\u00a0[10]: Copied! <pre>twiss_calc(np.cov(*zvec2))\n</pre> twiss_calc(np.cov(*zvec2)) Out[10]: <pre>{'alpha': 1.4672219335410167e-16,\n 'beta': 1.0,\n 'gamma': 1.0000000000000002,\n 'emit': 25.24876234590519}</pre> In\u00a0[11]: Copied! <pre>matplotlib.rcParams['figure.figsize'] = (13,8) # Reset plot\n</pre>  matplotlib.rcParams['figure.figsize'] = (13,8) # Reset plot In\u00a0[12]: Copied! <pre>help(normalized_particle_coordinate)\n</pre> help(normalized_particle_coordinate) <pre>Help on function normalized_particle_coordinate in module pmd_beamphysics.statistics:\n\nnormalized_particle_coordinate(particle_group, key, twiss=None, mass_normalize=True)\n    Returns a single normalized coordinate array from a ParticleGroup\n    \n    Position or momentum is determined by the key. \n    If the key starts with 'p', it is a momentum, else it is a position,\n    and the\n    \n    Intended use is for key to be one of:\n        x, px, y py\n        \n    and the corresponding normalized coordinates are named with suffix _bar, i.e.:\n        x_bar, px_bar, y_bar, py_bar\n    \n    If mass_normalize (default=True), the momentum will be divided by the mass, so that the units are sqrt(m).\n    \n    These are related to action-angle coordinates\n        J: amplitude\n        phi: phase\n        \n        x_bar =  sqrt(2 J) cos(phi)\n        px_bar = sqrt(2 J) sin(phi)\n        \n    So therefore:\n        J = (x_bar^2 + px_bar^2)/2\n        phi = arctan(px_bar/x_bar)\n    and:        \n        &lt;J&gt; = norm_emit_x\n     \n     Note that the center may need to be subtracted in this case.\n\n</pre> <p>Get some example particles, with a typical transverse phase space plot:</p> In\u00a0[13]: Copied! <pre>P = ParticleGroup('data/bmad_particles2.h5')\nP.plot('x', 'px')\n</pre> P = ParticleGroup('data/bmad_particles2.h5') P.plot('x', 'px') <p>If no twiss is given, then the analyzing matrix is computed from the beam itself:</p> In\u00a0[14]: Copied! <pre>normalized_particle_coordinate(P, 'x', twiss=None)\n</pre> normalized_particle_coordinate(P, 'x', twiss=None) Out[14]: <pre>array([-4.83384095e-04,  9.99855846e-04,  7.35820860e-05, ...,\n       -7.48265408e-05,  4.77803205e-05, -4.18053319e-04])</pre> <p>This is equivelent:</p> In\u00a0[15]: Copied! <pre>normalized_particle_coordinate(P, 'x', twiss=twiss_calc(P.cov('x', 'px')), mass_normalize=False)/np.sqrt(P.mass)\n</pre> normalized_particle_coordinate(P, 'x', twiss=twiss_calc(P.cov('x', 'px')), mass_normalize=False)/np.sqrt(P.mass) Out[15]: <pre>array([-4.83384095e-04,  9.99855846e-04,  7.35820860e-05, ...,\n       -7.48265408e-05,  4.77803205e-05, -4.18053319e-04])</pre> <p>And is given as a property:</p> In\u00a0[16]: Copied! <pre>P.x_bar\n</pre> P.x_bar Out[16]: <pre>array([-4.83384095e-04,  9.99855846e-04,  7.35820860e-05, ...,\n       -7.48265408e-05,  4.77803205e-05, -4.18053319e-04])</pre> <p>The amplitude is defined as:</p> In\u00a0[17]: Copied! <pre>(P.x_bar**2 + P.px_bar**2)/2\n</pre> (P.x_bar**2 + P.px_bar**2)/2 Out[17]: <pre>array([1.16831464e-07, 5.85751290e-07, 3.26876598e-07, ...,\n       3.25916449e-07, 2.21097254e-07, 2.73364174e-07])</pre> <p>This is also given as a property:</p> In\u00a0[18]: Copied! <pre>P.Jx\n</pre> P.Jx Out[18]: <pre>array([1.16831464e-07, 5.85751290e-07, 3.26876598e-07, ...,\n       3.25916449e-07, 2.21097254e-07, 2.73364174e-07])</pre> <p>Note the mass normalization is the same:</p> In\u00a0[19]: Copied! <pre>P.Jx.mean(), P['mean_Jx'], P['norm_emit_x']\n</pre> P.Jx.mean(), P['mean_Jx'], P['norm_emit_x'] Out[19]: <pre>(4.883790126887025e-07, 4.883790126887027e-07, 4.881047612307434e-07)</pre> <p>This is now nice and roundish:</p> In\u00a0[20]: Copied! <pre>P.plot('x_bar', 'px_bar')\n</pre> P.plot('x_bar', 'px_bar') <p>Jy also works. This gives some sense of where the emittance is larger.</p> In\u00a0[21]: Copied! <pre>P.plot('t', 'Jy')\n</pre> P.plot('t', 'Jy') <p>Sort by Jx:</p> In\u00a0[22]: Copied! <pre>P = P[np.argsort(P.Jx)]\n</pre> P = P[np.argsort(P.Jx)] <p>Now particles are ordered:</p> In\u00a0[23]: Copied! <pre>plt.plot(P.Jx)\n</pre> plt.plot(P.Jx) Out[23]: <pre>[&lt;matplotlib.lines.Line2D at 0x7fe77b10d3d0&gt;]</pre> <p>This can be used to calculate the 95% emittance:</p> In\u00a0[24]: Copied! <pre>P[0:int(0.95*len(P))]['norm_emit_x']\n</pre> P[0:int(0.95*len(P))]['norm_emit_x'] Out[24]: <pre>3.7399940158442355e-07</pre> In\u00a0[25]: Copied! <pre>def twiss_match(x, p, beta0=1, alpha0=0, beta1=1, alpha1=0):\n    \"\"\"\n    Simple Twiss matching. \n    \n    Takes positions x and momenta p, and transforms them according to \n    initial Twiss parameters:\n        beta0, alpha0 \n    into final  Twiss parameters:\n        beta1, alpha1\n        \n    This is simply the matrix ransformation:    \n        xnew  = (   sqrt(beta1/beta0)                  0                 ) . ( x )\n        pnew    (  (alpha0-alpha1)/sqrt(beta0*beta1)   sqrt(beta0/beta1) )   ( p ) \n        \n\n    Returns new x, p\n    \n    \"\"\"\n    m11 = np.sqrt(beta1/beta0)\n    m21 = (alpha0-alpha1)/np.sqrt(beta0*beta1)\n    \n    xnew = x * m11\n    pnew = x * m21 + p / m11\n    \n    return xnew, pnew\n</pre> def twiss_match(x, p, beta0=1, alpha0=0, beta1=1, alpha1=0):     \"\"\"     Simple Twiss matching.           Takes positions x and momenta p, and transforms them according to      initial Twiss parameters:         beta0, alpha0      into final  Twiss parameters:         beta1, alpha1              This is simply the matrix ransformation:             xnew  = (   sqrt(beta1/beta0)                  0                 ) . ( x )         pnew    (  (alpha0-alpha1)/sqrt(beta0*beta1)   sqrt(beta0/beta1) )   ( p )                Returns new x, p          \"\"\"     m11 = np.sqrt(beta1/beta0)     m21 = (alpha0-alpha1)/np.sqrt(beta0*beta1)          xnew = x * m11     pnew = x * m21 + p / m11          return xnew, pnew <p>Get some Twiss:</p> In\u00a0[26]: Copied! <pre>T0 = twiss_calc(P.cov('x', 'xp'))\nT0\n</pre> T0 = twiss_calc(P.cov('x', 'xp')) T0 Out[26]: <pre>{'alpha': -0.7756418199427733,\n 'beta': 9.761411505651415,\n 'gamma': 0.16407670467707158,\n 'emit': 3.127519925999214e-11}</pre> <p>Make a copy and maniplulate:</p> In\u00a0[27]: Copied! <pre>P2 = P.copy()\nP2.x, P2.px = twiss_match(P.x, P.px/P['mean_p'], beta0=T0['beta'], alpha0=T0['alpha'], beta1=9, alpha1=-2)\nP2.px *= P['mean_p']\n</pre> P2 = P.copy() P2.x, P2.px = twiss_match(P.x, P.px/P['mean_p'], beta0=T0['beta'], alpha0=T0['alpha'], beta1=9, alpha1=-2) P2.px *= P['mean_p'] In\u00a0[28]: Copied! <pre>twiss_calc(P2.cov('x', 'xp'))\n</pre> twiss_calc(P2.cov('x', 'xp')) Out[28]: <pre>{'alpha': -1.9995993293102663,\n 'beta': 9.00102737307016,\n 'gamma': 0.5553141070021174,\n 'emit': 3.12716295233219e-11}</pre> <p>This is a dedicated routine:</p> In\u00a0[29]: Copied! <pre>def matched_particles(particle_group, beta=None, alpha=None, plane='x', p0c=None, inplace=False):\n    \"\"\"\n    Perfoms simple Twiss 'matching' by applying a linear transformation to\n        x, px if plane == 'x', or x, py if plane == 'y'\n    \n    Returns a new ParticleGroup\n    \n    If inplace, a copy will not be made, and changes will be done in place. \n    \n    \"\"\"\n    \n    assert plane in ('x', 'y'), f'Invalid plane: {plane}'\n    \n    if inplace:\n        P = particle_group\n    else:\n        P = particle_group.copy()\n    \n    if not p0c:\n        p0c = P['mean_p']\n    \n\n    # Use Bmad-style coordinates.\n    # Get plane. \n    if plane == 'x':\n        x = P.x\n        p = P.px/p0c\n    else:\n        x = P.y\n        p = P.py/p0c\n        \n    # Get current Twiss\n    tx = twiss_calc(np.cov(x, p, aweights=P.weight))\n    \n    # If not specified, just fill in the current value.\n    if alpha is None:\n        alpha = tx['alpha']\n    if beta is None:\n        beta = tx['beta']\n    \n    # New coordinates\n    xnew, pnew = twiss_match(x, p, beta0=tx['beta'], alpha0=tx['alpha'], beta1=beta, alpha1=alpha)\n    \n    # Set\n    if plane == 'x':\n        P.x = xnew\n        P.px = pnew*p0c\n    else:\n        P.y = xnew\n        P.py = pnew*p0c\n        \n    return P\n    \n    \n# Check    \nP3 = matched_particles(P, beta=None, alpha=-4, plane='y')\nP.twiss(plane='y'), P3.twiss(plane='y')\n</pre> def matched_particles(particle_group, beta=None, alpha=None, plane='x', p0c=None, inplace=False):     \"\"\"     Perfoms simple Twiss 'matching' by applying a linear transformation to         x, px if plane == 'x', or x, py if plane == 'y'          Returns a new ParticleGroup          If inplace, a copy will not be made, and changes will be done in place.           \"\"\"          assert plane in ('x', 'y'), f'Invalid plane: {plane}'          if inplace:         P = particle_group     else:         P = particle_group.copy()          if not p0c:         p0c = P['mean_p']           # Use Bmad-style coordinates.     # Get plane.      if plane == 'x':         x = P.x         p = P.px/p0c     else:         x = P.y         p = P.py/p0c              # Get current Twiss     tx = twiss_calc(np.cov(x, p, aweights=P.weight))          # If not specified, just fill in the current value.     if alpha is None:         alpha = tx['alpha']     if beta is None:         beta = tx['beta']          # New coordinates     xnew, pnew = twiss_match(x, p, beta0=tx['beta'], alpha0=tx['alpha'], beta1=beta, alpha1=alpha)          # Set     if plane == 'x':         P.x = xnew         P.px = pnew*p0c     else:         P.y = xnew         P.py = pnew*p0c              return P           # Check     P3 = matched_particles(P, beta=None, alpha=-4, plane='y') P.twiss(plane='y'), P3.twiss(plane='y') Out[29]: <pre>({'alpha_y': 0.9058122605337396,\n  'beta_y': 14.683316714787708,\n  'gamma_y': 0.12398396674913406,\n  'emit_y': 3.214301173354259e-11,\n  'eta_y': -0.0001221701145704683,\n  'etap_y': -3.1414468851691344e-07,\n  'norm_emit_y': 5.016420878150227e-07},\n {'alpha_y': -3.9999679865267384,\n  'beta_y': 14.683316715010363,\n  'gamma_y': 1.1577591237176261,\n  'emit_y': 3.214301173290241e-11,\n  'eta_y': -0.00012217012301264445,\n  'etap_y': -4.113188244396527e-05,\n  'norm_emit_y': 5.016420878133589e-07})</pre> <p>These functions are in statistics:</p> In\u00a0[30]: Copied! <pre>from pmd_beamphysics.statistics import twiss_match, matched_particles\n</pre> from pmd_beamphysics.statistics import twiss_match, matched_particles"},{"location":"examples/normalized_coordinates/#simple-normalized-coordinates","title":"Simple Normalized Coordinates\u00b6","text":"<p>1D normalized coordinates originate from the normal form decomposition, where the transfer matrix that propagates phase space coordinates $(x, p)$ is decomposed as</p> <p>$M = A \\cdot R(\\theta) \\cdot A^{-1}$</p> <p>And the matrix $A$ can be parameterized as</p> <p>A = $\\begin{pmatrix}\\sqrt{\\beta} &amp; 0\\\\-\\alpha/\\sqrt{\\beta} &amp; 1/\\sqrt{\\beta}\\end{pmatrix}$</p>"},{"location":"examples/normalized_coordinates/#twiss-parameters","title":"Twiss parameters\u00b6","text":"<p>Effective Twiss parameters can be calculated from the second order moments of the particles.</p> <p>This does not change the phase space area.</p>"},{"location":"examples/normalized_coordinates/#x_bar-px_bar-jx-etc","title":"x_bar, px_bar, Jx, etc.\u00b6","text":"<p>These are essentially action-angle coordinates, calculated by using the an analyzing twiss dict</p>"},{"location":"examples/normalized_coordinates/#simple-matching","title":"Simple 'matching'\u00b6","text":"<p>Often a beam needs to be 'matched' for tracking in some program.</p> <p>This is a 'faked' tranformation that ultimately would need to be realized by a focusing system.</p>"},{"location":"examples/particle_examples/","title":"openPMD beamphysics examples","text":"In\u00a0[1]: Copied! <pre># Nicer plotting\nimport matplotlib\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\nmatplotlib.rcParams['figure.figsize'] = (8,4)\n</pre> # Nicer plotting import matplotlib import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina' matplotlib.rcParams['figure.figsize'] = (8,4) In\u00a0[2]: Copied! <pre>from pmd_beamphysics import ParticleGroup\n</pre> from pmd_beamphysics import ParticleGroup In\u00a0[3]: Copied! <pre>P = ParticleGroup( 'data/bmad_particles2.h5')\nP\n</pre> P = ParticleGroup( 'data/bmad_particles2.h5') P Out[3]: <pre>&lt;ParticleGroup with 100000 particles at 0x7fd1710a0400&gt;</pre> In\u00a0[4]: Copied! <pre>P.energy\n</pre> P.energy Out[4]: <pre>array([8.00032916e+09, 7.97408124e+09, 7.97338447e+09, ...,\n       7.97531701e+09, 7.97163591e+09, 7.97170403e+09])</pre> In\u00a0[5]: Copied! <pre>P['mean_energy'], P.units('mean_energy')\n</pre> P['mean_energy'], P.units('mean_energy') Out[5]: <pre>(7974939710.08345, pmd_unit('eV', 1.602176634e-19, (2, 1, -2, 0, 0, 0, 0)))</pre> In\u00a0[6]: Copied! <pre>P.where(P.x &lt; P['mean_x'])\n</pre> P.where(P.x &lt; P['mean_x']) Out[6]: <pre>&lt;ParticleGroup with 50082 particles at 0x7fd1b8c11340&gt;</pre> In\u00a0[7]: Copied! <pre>a = P.plot('x', 'px', figsize=(8,8))\n</pre> a = P.plot('x', 'px', figsize=(8,8)) In\u00a0[8]: Copied! <pre>P.write_elegant('elegant_particles.txt', verbose=True)\n</pre> P.write_elegant('elegant_particles.txt', verbose=True) <pre>writing 100000 particles to elegant_particles.txt\n</pre> <p>x positions, in meters</p> In\u00a0[9]: Copied! <pre>P.x\n</pre> P.x Out[9]: <pre>array([-1.20890504e-05,  2.50055966e-05,  1.84022924e-06, ...,\n       -1.87135206e-06,  1.19494768e-06, -1.04551798e-05])</pre> <p>relativistic gamma, calculated on the fly</p> In\u00a0[10]: Copied! <pre>P.gamma\n</pre> P.gamma Out[10]: <pre>array([15656.25362205, 15604.88772858, 15603.52416601, ...,\n       15607.30607107, 15600.10233296, 15600.23563914])</pre> <p>Both are allowed</p> In\u00a0[11]: Copied! <pre>len(P), P['n_particle']\n</pre> len(P), P['n_particle'] Out[11]: <pre>(100000, 100000)</pre> <p>Statistics on any of these. Note that these properly use the .weight array.</p> In\u00a0[12]: Copied! <pre>P.avg('gamma'), P.std('p')\n</pre> P.avg('gamma'), P.std('p') Out[12]: <pre>(15606.56770446094, 7440511.955100455)</pre> <p>Covariance matrix of any list of keys</p> In\u00a0[13]: Copied! <pre>P.cov('x', 'px', 'y', 'kinetic_energy')\n</pre> P.cov('x', 'px', 'y', 'kinetic_energy') Out[13]: <pre>array([[ 3.05290090e-10,  1.93582323e-01,  2.14462846e-12,\n        -3.94841065e+00],\n       [ 1.93582323e-01,  3.26525376e+08, -2.44058325e-05,\n        -5.36815277e+08],\n       [ 2.14462846e-12, -2.44058325e-05,  4.71979014e-10,\n        -8.48100957e-01],\n       [-3.94841065e+00, -5.36815277e+08, -8.48100957e-01,\n         5.53617715e+13]])</pre> <p>These can all be accessed with brackets. sigma_ and mean_ are also allowed</p> In\u00a0[14]: Copied! <pre>P['sigma_x'], P['sigma_energy'], P['min_y'], P['norm_emit_x'], P['norm_emit_4d']\n</pre> P['sigma_x'], P['sigma_energy'], P['min_y'], P['norm_emit_x'], P['norm_emit_4d'] Out[14]: <pre>(1.7472465109340715e-05,\n 7440511.939853717,\n -0.00017677380499644412,\n 4.881047612307434e-07,\n 2.4484888474798633e-13)</pre> <p>Covariance has a special syntax, items separated by __</p> In\u00a0[15]: Copied! <pre>P['cov_x__kinetic_energy']\n</pre> P['cov_x__kinetic_energy'] Out[15]: <pre>-3.9484106461190627</pre> <p>n-dimensional histogram. This is a wrapper for <code>numpy.histogramdd</code></p> In\u00a0[16]: Copied! <pre>H, edges = P.histogramdd('t', 'delta_pz', bins=(5,10))\nH.shape, edges\n</pre> H, edges = P.histogramdd('t', 'delta_pz', bins=(5,10)) H.shape, edges Out[16]: <pre>((5, 10),\n [array([5.16387938e-06, 5.16387943e-06, 5.16387948e-06, 5.16387953e-06,\n         5.16387958e-06, 5.16387963e-06]),\n  array([-24476455.61834908, -17729298.92490654, -10982142.231464  ,\n          -4234985.53802147,   2512171.15542107,   9259327.8488636 ,\n          16006484.54230614,  22753641.23574867,  29500797.92919121,\n          36247954.62263375,  42995111.31607628])])</pre> In\u00a0[17]: Copied! <pre>ss = P.slice_statistics('norm_emit_x')\nss.keys()\n</pre> ss = P.slice_statistics('norm_emit_x') ss.keys() Out[17]: <pre>dict_keys(['charge', 'mean_t', 'norm_emit_x', 'ptp_t', 'current'])</pre> <p>Multiple keys can also be accepted:</p> In\u00a0[18]: Copied! <pre>ss = P.slice_statistics('norm_emit_x', 'norm_emit_y', 'twiss')\nss.keys()\n</pre> ss = P.slice_statistics('norm_emit_x', 'norm_emit_y', 'twiss') ss.keys() Out[18]: <pre>dict_keys(['norm_emit_y', 'charge', 'norm_emit_x', 'mean_t', 'twiss', 'ptp_t', 'twiss_alpha_y', 'twiss_beta_y', 'twiss_gamma_y', 'twiss_emit_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_norm_emit_y', 'twiss_alpha_x', 'twiss_beta_x', 'twiss_gamma_x', 'twiss_emit_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_norm_emit_x', 'current'])</pre> <p>Note that for a slice key <code>X</code>, the method will also calculate <code>mean_X</code>, <code>ptp_X</code>, as <code>charge</code> so that a <code>density</code> calculated from these. In the special case of <code>X=t</code>, the density will be labeled as <code>current</code> according to common convention.</p> In\u00a0[19]: Copied! <pre>P.twiss('x')\n</pre> P.twiss('x') Out[19]: <pre>{'alpha_x': -0.7764646310859605,\n 'beta_x': 9.758458404204259,\n 'gamma_x': 0.16425722762079686,\n 'emit_x': 3.1255806600595395e-11,\n 'eta_x': -0.0005687740085942673,\n 'etap_x': -9.69649743612097e-06,\n 'norm_emit_x': 4.877958608683612e-07}</pre> <p>95% emittance calculation, x and y</p> In\u00a0[20]: Copied! <pre>P.twiss('xy', fraction=0.95)\n</pre> P.twiss('xy', fraction=0.95) Out[20]: <pre>{'alpha_x': -0.765323995145385,\n 'beta_x': 9.233496626510156,\n 'gamma_x': 0.1717356795249758,\n 'emit_x': 2.3954681527227138e-11,\n 'eta_x': -0.0004717155629444416,\n 'etap_x': -1.6006449526750024e-05,\n 'norm_emit_x': 3.738408555668476e-07,\n 'alpha_y': 0.9776071851091841,\n 'beta_y': 14.39339077533324,\n 'gamma_y': 0.13587596132863441,\n 'emit_y': 2.342927040318514e-11,\n 'eta_y': -4.3316354305934096e-05,\n 'etap_y': -6.000905618300805e-07,\n 'norm_emit_y': 3.656364435837357e-07}</pre> <p>This makes new particles:</p> In\u00a0[21]: Copied! <pre>P2 = P.twiss_match(beta=30, alpha=-3, plane = 'x')\nP2.twiss('x')\n</pre> P2 = P.twiss_match(beta=30, alpha=-3, plane = 'x') P2.twiss('x') Out[21]: <pre>{'alpha_x': -2.9996935644621994,\n 'beta_x': 29.99130803172276,\n 'gamma_x': 0.3333686370097817,\n 'emit_x': 3.1255806601163326e-11,\n 'eta_x': -0.000997118623912468,\n 'etap_x': -7.944656701598246e-05,\n 'norm_emit_x': 4.877958608785158e-07}</pre> In\u00a0[22]: Copied! <pre>P.resample()\n</pre> P.resample() Out[22]: <pre>&lt;ParticleGroup with 100000 particles at 0x7fd170b50310&gt;</pre> <p>With n &gt; 0, particles will be subsampled. Note that this also works for differently weighed particles.</p> In\u00a0[23]: Copied! <pre>P.resample(1000)\n</pre> P.resample(1000) Out[23]: <pre>&lt;ParticleGroup with 1000 particles at 0x7fd1a0f42040&gt;</pre> In\u00a0[24]: Copied! <pre>P.resample(1000).plot('x', 'px', bins=100)\n</pre> P.resample(1000).plot('x', 'px', bins=100) In\u00a0[25]: Copied! <pre>P.units('x'), P.units('energy'), P.units('norm_emit_x'), P.units('cov_x__kinetic_energy'), P.units('norm_emit_4d')\n</pre> P.units('x'), P.units('energy'), P.units('norm_emit_x'), P.units('cov_x__kinetic_energy'), P.units('norm_emit_4d') Out[25]: <pre>(pmd_unit('m', 1, (1, 0, 0, 0, 0, 0, 0)),\n pmd_unit('eV', 1.602176634e-19, (2, 1, -2, 0, 0, 0, 0)),\n pmd_unit('m', 1, (1, 0, 0, 0, 0, 0, 0)),\n pmd_unit('m*eV', 1.602176634e-19, (3, 1, -2, 0, 0, 0, 0)),\n pmd_unit('(m)^2', 1, (2, 0, 0, 0, 0, 0, 0)))</pre> In\u00a0[26]: Copied! <pre>P.units('mean_energy')\n</pre> P.units('mean_energy') Out[26]: <pre>pmd_unit('eV', 1.602176634e-19, (2, 1, -2, 0, 0, 0, 0))</pre> In\u00a0[27]: Copied! <pre>str(P.units('cov_x__kinetic_energy'))\n</pre> str(P.units('cov_x__kinetic_energy')) Out[27]: <pre>'m*eV'</pre> In\u00a0[28]: Copied! <pre>P.std('z'), P.std('t')\n</pre> P.std('z'), P.std('t') Out[28]: <pre>(0.0, 2.4466662184814374e-14)</pre> <p>Get the central time:</p> In\u00a0[29]: Copied! <pre>t0 = P.avg('t')\nt0\n</pre> t0 = P.avg('t') t0 Out[29]: <pre>5.163879459127423e-06</pre> <p>Drift all particles to this time. This operates in-place:</p> In\u00a0[30]: Copied! <pre>P.drift_to_t(t0)\n</pre> P.drift_to_t(t0) <p>Now these are at different z, and the same t:</p> In\u00a0[31]: Copied! <pre>P.std('z'), P.avg('t'), set(P.t)\n</pre> P.std('z'), P.avg('t'), set(P.t) Out[31]: <pre>(7.334920780350132e-06, 5.163879459127425e-06, {5.163879459127423e-06})</pre> In\u00a0[32]: Copied! <pre>P.status[0:10] = 0\nP.status, P.n_alive, P.n_dead\n</pre> P.status[0:10] = 0 P.status, P.n_alive, P.n_dead Out[32]: <pre>(array([0, 0, 0, ..., 1, 1, 1], dtype=int32), 99990, 10)</pre> <p>There is a <code>.where</code> convenience routine to make selections easier:</p> In\u00a0[33]: Copied! <pre>P0 = P.where(P.status==0)\nP1 = P.where(P.status==1)\nlen(P0), P0.charge, P1.charge\n</pre> P0 = P.where(P.status==0) P1 = P.where(P.status==1) len(P0), P0.charge, P1.charge Out[33]: <pre>(10, 2.4999999999999994e-14, 2.4997499999999996e-10)</pre> <p>Copy is a deep copy:</p> In\u00a0[34]: Copied! <pre>P2 = P1.copy()\n</pre> P2 = P1.copy() <p>Charge can also be set. This will re-scale the weight array:</p> In\u00a0[35]: Copied! <pre>P2.charge = 9.8765e-12\nP1.weight[0:2], P2.weight[0:2], P2.charge\n</pre> P2.charge = 9.8765e-12 P1.weight[0:2], P2.weight[0:2], P2.charge  Out[35]: <pre>(array([2.5e-15, 2.5e-15]),\n array([9.87748775e-17, 9.87748775e-17]),\n 9.876499999999997e-12)</pre> <p>Some codes provide ids for particles. If not, you can assign an id.</p> In\u00a0[36]: Copied! <pre>'id' in P2\n</pre> 'id' in P2 Out[36]: <pre>False</pre> <p>This will assign an id if none exists.</p> In\u00a0[37]: Copied! <pre>P2.id, 'id' in P2\n</pre> P2.id, 'id' in P2 Out[37]: <pre>(array([    1,     2,     3, ..., 99988, 99989, 99990]), True)</pre> In\u00a0[38]: Copied! <pre>import h5py\nimport numpy as np\n</pre> import h5py import numpy as np In\u00a0[39]: Copied! <pre>newh5file = 'particles.h5'\n\nwith h5py.File(newh5file, 'w') as h5:\n    P.write(h5)\n    \nwith h5py.File(newh5file, 'r') as h5:\n    P2 = ParticleGroup(h5)\n</pre> newh5file = 'particles.h5'  with h5py.File(newh5file, 'w') as h5:     P.write(h5)      with h5py.File(newh5file, 'r') as h5:     P2 = ParticleGroup(h5)     <p>Check if all are the same:</p> In\u00a0[40]: Copied! <pre>for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:\n    same = np.all(P[key] == P2[key])\n    print(key, same)\n</pre> for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:     same = np.all(P[key] == P2[key])     print(key, same) <pre>x True\npx True\ny True\npy True\nz True\npz True\nt True\nstatus True\nweight True\nid True\n</pre> <p>This does the same check:</p> In\u00a0[41]: Copied! <pre>P2 == P\n</pre> P2 == P Out[41]: <pre>True</pre> <p>Write Astra-style particles</p> In\u00a0[42]: Copied! <pre>P.write_astra('astra.dat')\n</pre> P.write_astra('astra.dat') In\u00a0[43]: Copied! <pre>!head astra.dat\n</pre> !head astra.dat <pre>  5.358867254236e-07  -2.266596025469e-08   2.743173452837e-13   5.432293116193e+02   1.634894200076e+01   7.974939693676e+09   5.163879459127e+03   0.000000000000e+00    1   -1\r\n -1.208904511904e-05   2.743402818288e-05  -5.473095269153e-06  -7.699432808273e+03   1.073320266862e+04   2.538945179648e+07  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  2.500557812617e-05   1.840484451196e-06  -6.071970122807e-06   2.432464253407e+04  -2.207080331882e+03  -8.584659148073e+05  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  1.840224855502e-06   2.319774542484e-05  -1.983837488548e-06   1.761897150333e+04  -4.269379756219e+03  -1.555244938371e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  1.282953228685e-05   2.807375273984e-06   7.759268653990e-06   1.898440718152e+04  -5.303751910566e+03  -2.614389107333e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  3.362374691900e-06   4.796982704111e-06  -1.006752695161e-06   1.012222041635e+04   1.266876546973e+04  -1.818436067077e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n -1.441736363766e-05   7.420644576948e-06   1.697647381418e-06  -8.598453241915e+03  -9.693787540264e+02  -4.437182519667e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n -1.715615894267e-05  -2.489925517264e-05   8.689767207313e-06  -1.817734573652e+04   1.917720905016e+04  -4.674564930124e+05  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  5.700269218746e-06   4.764024833770e-05   2.167342605243e-05   8.662840216364e+03  -7.175141639811e+03  -3.156898311773e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  9.426699454873e-07  -5.785285707147e-06   6.353982932653e-06  -1.498628620111e+04  -1.222058411806e+03  -3.802046580825e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n</pre> <p>Optionally, a string can be given:</p> In\u00a0[44]: Copied! <pre>P.write('particles.h5')\n</pre> P.write('particles.h5') In\u00a0[45]: Copied! <pre>P.plot('x')\n</pre> P.plot('x') In\u00a0[46]: Copied! <pre>P.slice_plot('norm_emit_x', 'norm_emit_y', ylim=(0, 1e-6))\n</pre> P.slice_plot('norm_emit_x', 'norm_emit_y', ylim=(0, 1e-6)) In\u00a0[47]: Copied! <pre>P.plot('z', 'x')\n</pre> P.plot('z', 'x') <p>Any other key that returbs an arrat can be sliced on</p> In\u00a0[48]: Copied! <pre>P.slice_plot('sigma_x', slice_key = 'Jx')\n</pre> P.slice_plot('sigma_x', slice_key = 'Jx') In\u00a0[49]: Copied! <pre>P.plot('x', 'px')\n</pre> P.plot('x', 'px') <p>Optionally the figure object can be returned, and the plot further modified.</p> In\u00a0[50]: Copied! <pre>fig = P.plot('x', return_figure=True)\nax = fig.axes[0]\nax.set_title('Density Plot')\nax.set_xlim(-50, 50)\n</pre> fig = P.plot('x', return_figure=True) ax = fig.axes[0] ax.set_title('Density Plot') ax.set_xlim(-50, 50) Out[50]: <pre>(-50.0, 50.0)</pre> In\u00a0[51]: Copied! <pre>import copy\n\nfig, ax = plt.subplots()\nax.set_aspect('equal')\nxkey = 'x'\nykey = 'y'\ndatx = P[xkey]\ndaty = P[ykey]\nax.set_xlabel(f'{xkey} ({P.units(xkey)})')\nax.set_ylabel(f'{ykey} ({P.units(ykey)})')\n\ncmap = copy.copy(plt.get_cmap('viridis'))\ncmap.set_under('white')\nax.hexbin(datx, daty, gridsize=40, cmap=cmap, vmin=1e-15)\n</pre> import copy  fig, ax = plt.subplots() ax.set_aspect('equal') xkey = 'x' ykey = 'y' datx = P[xkey] daty = P[ykey] ax.set_xlabel(f'{xkey} ({P.units(xkey)})') ax.set_ylabel(f'{ykey} ({P.units(ykey)})')  cmap = copy.copy(plt.get_cmap('viridis')) cmap.set_under('white') ax.hexbin(datx, daty, gridsize=40, cmap=cmap, vmin=1e-15) Out[51]: <pre>&lt;matplotlib.collections.PolyCollection at 0x7fd170da8700&gt;</pre> In\u00a0[52]: Copied! <pre>P.plot('delta_z', 'delta_p', figsize=(8,6))\n</pre> P.plot('delta_z', 'delta_p', figsize=(8,6)) In\u00a0[53]: Copied! <pre>H, edges = P.histogramdd('delta_z', 'delta_p', bins=(150, 150))\nextent = [edges[0].min(),edges[0].max(),edges[1].min(),edges[1].max() ]\n\nplt.imshow(H.T, origin='lower', extent = extent, aspect='auto', vmin=1e-15, cmap=cmap)\n</pre> H, edges = P.histogramdd('delta_z', 'delta_p', bins=(150, 150)) extent = [edges[0].min(),edges[0].max(),edges[1].min(),edges[1].max() ]  plt.imshow(H.T, origin='lower', extent = extent, aspect='auto', vmin=1e-15, cmap=cmap) Out[53]: <pre>&lt;matplotlib.image.AxesImage at 0x7fd183b8ae20&gt;</pre> In\u00a0[54]: Copied! <pre>from pmd_beamphysics import particle_paths\nfrom pmd_beamphysics.readers import all_components, component_str\n\nH5FILE = 'data/astra_particles.h5'\nh5 = h5py.File(H5FILE, 'r')\n</pre> from pmd_beamphysics import particle_paths from pmd_beamphysics.readers import all_components, component_str  H5FILE = 'data/astra_particles.h5' h5 = h5py.File(H5FILE, 'r') <p>Get the valid paths</p> In\u00a0[55]: Copied! <pre>ppaths = particle_paths(h5)\nppaths\n</pre> ppaths = particle_paths(h5) ppaths Out[55]: <pre>['/screen/0/./', '/screen/1/./']</pre> <p>Search for all valid components in a single path</p> In\u00a0[56]: Copied! <pre>ph5 = h5[ppaths[0]]\nall_components(ph5 )\n</pre> ph5 = h5[ppaths[0]] all_components(ph5 ) Out[56]: <pre>['momentum/x',\n 'momentum/y',\n 'momentum/z',\n 'momentumOffset/z',\n 'particleStatus',\n 'position/x',\n 'position/y',\n 'position/z',\n 'positionOffset/z',\n 'time',\n 'timeOffset',\n 'weight']</pre> <p>Get some info</p> In\u00a0[57]: Copied! <pre>for component in all_components(ph5):\n    info = component_str(ph5, component)\n    print(info)\n</pre> for component in all_components(ph5):     info = component_str(ph5, component)     print(info) <pre>momentum/x [998 items] is a momentum with units: kg*m/s\nmomentum/y [998 items] is a momentum with units: kg*m/s\nmomentum/z [998 items] is a momentum with units: kg*m/s\nmomentumOffset/z [constant 4.660805218675275e-22 with shape 998] is a momentum with units: kg*m/s\nparticleStatus [998 items]\nposition/x [998 items] is a length with units: m\nposition/y [998 items] is a length with units: m\nposition/z [998 items] is a length with units: m\npositionOffset/z [constant 0.50013 with shape 998] is a length with units: m\ntime [998 items] is a time with units: s\ntimeOffset [constant 2.0826e-09 with shape 998] is a time with units: s\nweight [998 items] is a charge with units: C\n</pre> In\u00a0[58]: Copied! <pre>import os\n\nos.remove('astra.dat')\nos.remove(newh5file)\nos.remove('elegant_particles.txt')\n</pre> import os  os.remove('astra.dat') os.remove(newh5file) os.remove('elegant_particles.txt')"},{"location":"examples/particle_examples/#openpmd-beamphysics-examples","title":"openPMD beamphysics examples\u00b6","text":""},{"location":"examples/particle_examples/#basic-usage","title":"Basic Usage\u00b6","text":""},{"location":"examples/particle_examples/#particlegroup-class","title":"ParticleGroup class\u00b6","text":""},{"location":"examples/particle_examples/#basic-statistics","title":"Basic Statistics\u00b6","text":""},{"location":"examples/particle_examples/#slice-statistics","title":"Slice statistics\u00b6","text":"<p>ParticleGroup can be sliced along one dimension into chunks of an equal number of particles. Here are the routines to create the raw data.</p>"},{"location":"examples/particle_examples/#advanced-statisics","title":"Advanced statisics\u00b6","text":"<p>Twiss and Dispersion can be calculated.</p> <p>These are the projected Twiss parameters.</p> <p>TODO: normal mode twiss.</p>"},{"location":"examples/particle_examples/#resampling","title":"Resampling\u00b6","text":"<p>Particles can be resampled to either scramble the ordering of the particle arrays or subsample.</p> <p>With no argument or n=0, the same number of particles will be returned:</p>"},{"location":"examples/particle_examples/#units","title":"Units\u00b6","text":"<p>Units can be retrieved from any computable quantitiy. These are returned as a pmd_unit type.</p>"},{"location":"examples/particle_examples/#z-vs-t","title":"z vs t\u00b6","text":"<p>These particles are from Bmad, at the same z and different times</p>"},{"location":"examples/particle_examples/#status-weight-id-copy","title":"status, weight, id, copy\u00b6","text":"<p><code>status == 1</code> is alive, otherwise dead. Set the first ten particles to a different status.</p> <p><code>n_alive</code>, <code>n_dead</code> count these</p>"},{"location":"examples/particle_examples/#writing","title":"Writing\u00b6","text":""},{"location":"examples/particle_examples/#plot","title":"Plot\u00b6","text":"<p>Some plotting is included for convenience. See plot_examples.ipynb for better plotting.</p>"},{"location":"examples/particle_examples/#1d-density-plot","title":"1D density plot\u00b6","text":""},{"location":"examples/particle_examples/#slice-statistic-plot","title":"Slice statistic plot\u00b6","text":""},{"location":"examples/particle_examples/#2d-density-plot","title":"2D density plot\u00b6","text":""},{"location":"examples/particle_examples/#manual-plotting","title":"Manual plotting\u00b6","text":""},{"location":"examples/particle_examples/#manual-binning-and-plotting","title":"Manual binning and plotting\u00b6","text":""},{"location":"examples/particle_examples/#multiple-particlegroup-in-an-hdf5-file","title":"Multiple ParticleGroup in an HDF5 file\u00b6","text":"<p>This example has two particlegroups. This also shows how to examine the components, without loading the full data.</p>"},{"location":"examples/particle_examples/#cleanup","title":"Cleanup\u00b6","text":""},{"location":"examples/plot_examples/","title":"Plot examples","text":"In\u00a0[1]: Copied! <pre>from pmd_beamphysics import ParticleGroup, particle_paths\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n%config InlineBackend.figure_format = 'retina'\nmatplotlib.rcParams['figure.figsize'] = (8,6)\n\nfrom h5py import File\nimport os\n</pre> from pmd_beamphysics import ParticleGroup, particle_paths import matplotlib import matplotlib.pyplot as plt  %config InlineBackend.figure_format = 'retina' matplotlib.rcParams['figure.figsize'] = (8,6)  from h5py import File import os In\u00a0[2]: Copied! <pre># Open a file, fine the particle paths from the root attributes\n# Pick one:\nH5FILE = 'data/bmad_particles2.h5'\n#H5FILE = 'data/distgen_particles.h5'\n#H5FILE = 'data/astra_particles.h5'\n\n# Load\nh5 = File(H5FILE, 'r')\nppaths = particle_paths(h5)\nph5 = h5[ppaths[0]]\n\nP = ParticleGroup(ph5)\nppaths\n</pre> # Open a file, fine the particle paths from the root attributes # Pick one: H5FILE = 'data/bmad_particles2.h5' #H5FILE = 'data/distgen_particles.h5' #H5FILE = 'data/astra_particles.h5'  # Load h5 = File(H5FILE, 'r') ppaths = particle_paths(h5) ph5 = h5[ppaths[0]]  P = ParticleGroup(ph5) ppaths Out[2]: <pre>['/data/00001/particles/']</pre> In\u00a0[3]: Copied! <pre>P.plot('t')\n</pre> P.plot('t') In\u00a0[4]: Copied! <pre>P.t = P.t - P['mean_t']\nP.slice_plot('sigma_x', 'sigma_y', xlim=(None, 80e-15), ylim=(0, 30e-6))\n</pre> P.t = P.t - P['mean_t'] P.slice_plot('sigma_x', 'sigma_y', xlim=(None, 80e-15), ylim=(0, 30e-6)) In\u00a0[5]: Copied! <pre>P.plot('t', 'energy', xlim = (-200e-15, 200e-15), ylim = (7.9e9, 8.1e9))\n</pre> P.plot('t', 'energy', xlim = (-200e-15, 200e-15), ylim = (7.9e9, 8.1e9)) In\u00a0[6]: Copied! <pre>from pmd_beamphysics.plot import density_and_slice_plot\n</pre> from pmd_beamphysics.plot import density_and_slice_plot In\u00a0[7]: Copied! <pre>P.species\n</pre> P.species Out[7]: <pre>'electron'</pre> In\u00a0[8]: Copied! <pre>density_and_slice_plot(P, 't', 'energy', stat_keys = ['sigma_x', 'sigma_y'], n_slice = 200, bins=200)\n</pre> density_and_slice_plot(P, 't', 'energy', stat_keys = ['sigma_x', 'sigma_y'], n_slice = 200, bins=200)"},{"location":"examples/plot_examples/#plot-examples","title":"Plot examples\u00b6","text":""},{"location":"examples/plot_examples/#density-plots","title":"Density plots\u00b6","text":""},{"location":"examples/plot_examples/#slice-statistics-plots","title":"Slice statistics Plots\u00b6","text":""},{"location":"examples/plot_examples/#marginal-plots","title":"Marginal plots\u00b6","text":""},{"location":"examples/plot_examples/#combined-density-and-slice-plot","title":"Combined density and slice plot\u00b6","text":""},{"location":"examples/read_examples/","title":"Read examples","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>from pmd_beamphysics import ParticleGroup\nfrom h5py import File\n</pre> from pmd_beamphysics import ParticleGroup from h5py import File In\u00a0[3]: Copied! <pre>from pmd_beamphysics.interfaces.elegant import elegant_h5_to_data\n</pre> from pmd_beamphysics.interfaces.elegant import elegant_h5_to_data <p>This will convert to a data dict</p> In\u00a0[4]: Copied! <pre>data = elegant_h5_to_data('data/elegant_raw.h5')\ndata\n</pre> data = elegant_h5_to_data('data/elegant_raw.h5') data Out[4]: <pre>{'x': array([-1.12390181e-04, -7.20268439e-05, -1.14543953e-04, ...,\n         1.05737263e-04, -7.24786314e-05,  5.78264247e-05]),\n 'y': array([-1.23229455e-04, -1.08820261e-04, -9.27723036e-05, ...,\n         8.55980575e-05,  8.58175111e-05,  9.07622257e-05]),\n 'z': array([0, 0, 0, ..., 0, 0, 0]),\n 'px': array([ -3083.62425792,   2225.50915788,   2725.71118129, ...,\n        -15364.10017428,  15821.42533783,  -4733.37386475]),\n 'py': array([ 83545.5978262 ,  83389.57148069,  68275.02698773, ...,\n        -70430.7889418 , -70600.14619666, -69547.74375481]),\n 'pz': array([8.00396787e+09, 8.00007258e+09, 8.00139350e+09, ...,\n        7.99685994e+09, 7.99660958e+09, 7.99818064e+09]),\n 't': array([5.11804566e-06, 5.11804568e-06, 5.11804567e-06, ...,\n        5.11804569e-06, 5.11804569e-06, 5.11804569e-06]),\n 'status': array([1, 1, 1, ..., 1, 1, 1]),\n 'species': 'electron',\n 'weight': array([2.e-17, 2.e-17, 2.e-17, ..., 2.e-17, 2.e-17, 2.e-17]),\n 'id': array([    1,     2,     3, ..., 49998, 49999, 50000], dtype=int32)}</pre> <p>Create <code>ParticleGroup</code> and plot:</p> In\u00a0[5]: Copied! <pre>P=ParticleGroup(data=data)\nP.plot('delta_t', 'delta_pz')\n</pre> P=ParticleGroup(data=data) P.plot('delta_t', 'delta_pz') In\u00a0[6]: Copied! <pre>from pmd_beamphysics.interfaces.genesis import genesis4_par_to_data\n</pre> from pmd_beamphysics.interfaces.genesis import genesis4_par_to_data In\u00a0[7]: Copied! <pre>P = ParticleGroup(data=genesis4_par_to_data('data/genesis4.par.h5'))\nP\n</pre> P = ParticleGroup(data=genesis4_par_to_data('data/genesis4.par.h5')) P Out[7]: <pre>&lt;ParticleGroup with 14336 particles at 0x7fdbed8b5610&gt;</pre> In\u00a0[8]: Copied! <pre>P.plot('z', 'pz')\n</pre> P.plot('z', 'pz')"},{"location":"examples/read_examples/#read-examples","title":"Read examples\u00b6","text":""},{"location":"examples/read_examples/#elegant-hdf5-format","title":"elegant hdf5 format\u00b6","text":""},{"location":"examples/read_examples/#genesis4-hdf5-format","title":"Genesis4 HDF5 format\u00b6","text":""},{"location":"examples/units/","title":"Units","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>from pmd_beamphysics import particle_paths\nfrom pmd_beamphysics.units import pmd_unit, dimension_name, sqrt_unit, known_unit, multiply_units\nfrom h5py import File\nimport numpy as np\n</pre> from pmd_beamphysics import particle_paths from pmd_beamphysics.units import pmd_unit, dimension_name, sqrt_unit, known_unit, multiply_units from h5py import File import numpy as np <p>This is the basic class:</p> In\u00a0[3]: Copied! <pre>?pmd_unit\n</pre> ?pmd_unit <p>Get a known units. These can be multiplied and divided:</p> In\u00a0[4]: Copied! <pre>u1 = known_unit['J']\nu2 = known_unit['m']\nu1, u2, u1/u2, u1*u2\n</pre> u1 = known_unit['J'] u2 = known_unit['m'] u1, u2, u1/u2, u1*u2 Out[4]: <pre>(pmd_unit('J', 1, (2, 1, -2, 0, 0, 0, 0)),\n pmd_unit('m', 1, (1, 0, 0, 0, 0, 0, 0)),\n pmd_unit('J/m', 1.0, (1, 1, -2, 0, 0, 0, 0)),\n pmd_unit('J*m', 1, (3, 1, -2, 0, 0, 0, 0)))</pre> <p>Special function for sqrt:</p> In\u00a0[5]: Copied! <pre>sqrt_unit(u1)\n</pre> sqrt_unit(u1) Out[5]: <pre>pmd_unit('\\sqrt{ J }', 1.0, (1.0, 0.5, -1.0, 0.0, 0.0, 0.0, 0.0))</pre> In\u00a0[6]: Copied! <pre># Pick one:\n#H5FILE = 'data/bmad_particles.h5'\nH5FILE = 'data/distgen_particles.h5'\n#H5FILE = 'data/astra_particles.h5'\nh5 = File(H5FILE, 'r')\n\nppaths = particle_paths(h5)\nprint(ppaths)\n</pre> # Pick one: #H5FILE = 'data/bmad_particles.h5' H5FILE = 'data/distgen_particles.h5' #H5FILE = 'data/astra_particles.h5' h5 = File(H5FILE, 'r')  ppaths = particle_paths(h5) print(ppaths)  <pre>['//']\n</pre> <p>This points to a single particle group:</p> In\u00a0[7]: Copied! <pre>ph5 = h5[ppaths[0]]\nlist(ph5)\n</pre> ph5 = h5[ppaths[0]] list(ph5) Out[7]: <pre>['momentum', 'particleStatus', 'position', 'time', 'weight']</pre> <p>Each component should have a dimension and a conversion factor to SI:</p> In\u00a0[8]: Copied! <pre>d = dict(ph5['momentum/x'].attrs)\nd\n</pre> d = dict(ph5['momentum/x'].attrs) d Out[8]: <pre>{'unitDimension': array([ 1,  1, -1,  0,  0,  0,  0]),\n 'unitSI': 5.344285992678308e-28,\n 'unitSymbol': 'eV/c'}</pre> In\u00a0[9]: Copied! <pre>tuple(d['unitDimension'])\n</pre> tuple(d['unitDimension']) Out[9]: <pre>(1, 1, -1, 0, 0, 0, 0)</pre> <p>This will extract the name of this dimension:</p> In\u00a0[10]: Copied! <pre>dimension_name(d['unitDimension'])\n</pre> dimension_name(d['unitDimension']) Out[10]: <pre>'momentum'</pre> In\u00a0[11]: Copied! <pre>from pmd_beamphysics.units import nice_array\n</pre> from pmd_beamphysics.units import nice_array <p>This will scale the array, and return the appropriate SI prefix:</p> In\u00a0[12]: Copied! <pre>x = 1e-4\nunit = 'm'\nnice_array(x)\n</pre> x = 1e-4 unit = 'm' nice_array(x) Out[12]: <pre>(100.00000000000001, 1e-06, '\u00b5')</pre> In\u00a0[13]: Copied! <pre>nice_array([-0.01, 0.01])\n</pre> nice_array([-0.01, 0.01]) Out[13]: <pre>(array([-10.,  10.]), 0.001, 'm')</pre> In\u00a0[14]: Copied! <pre>from pmd_beamphysics.units import nice_scale_prefix\n</pre> from pmd_beamphysics.units import nice_scale_prefix In\u00a0[15]: Copied! <pre>nice_scale_prefix(0.009)\n</pre> nice_scale_prefix(0.009) Out[15]: <pre>(0.001, 'm')</pre> In\u00a0[16]: Copied! <pre>try:\n    u1/1\nexcept:\n    print('you cannot do this')\n</pre> try:     u1/1 except:     print('you cannot do this')       <pre>you cannot do this\n</pre>"},{"location":"examples/units/#units","title":"Units\u00b6","text":"<p>This package provides unit conversion tools</p>"},{"location":"examples/units/#openpmd-hdf5-units","title":"openPMD HDF5 units\u00b6","text":"<p>Open a file, find the particle paths from the root attributes</p>"},{"location":"examples/units/#nice-arrays","title":"Nice arrays\u00b6","text":""},{"location":"examples/units/#limitations","title":"Limitations\u00b6","text":"<p>This is a simple class for use with this package. So even simple things like the example below will fail.</p> <p>For more advanced units, use a package like Pint: https://pint.readthedocs.io/</p>"},{"location":"examples/write_examples/","title":"Write examples","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>from pmd_beamphysics import ParticleGroup, particle_paths, pmd_init\nfrom h5py import File\nimport os\n</pre> from pmd_beamphysics import ParticleGroup, particle_paths, pmd_init from h5py import File import os In\u00a0[3]: Copied! <pre># Pick one:\n\n#H5File = 'data/bmad_particles2.h5'\nH5FILE = 'data/distgen_particles.h5'\n#H5FILE = 'data/astra_particles.h5'\n\nP = ParticleGroup(H5FILE)\n</pre> # Pick one:  #H5File = 'data/bmad_particles2.h5' H5FILE = 'data/distgen_particles.h5' #H5FILE = 'data/astra_particles.h5'  P = ParticleGroup(H5FILE) <p>The regular write routine writes in a proper openPMD format</p> In\u00a0[4]: Copied! <pre>P.write('openpmd_particles.h5')\n</pre> P.write('openpmd_particles.h5') <p>An open h5 hande can also be used, but it needs to be properly initialized</p> In\u00a0[5]: Copied! <pre>with File('openpmd_particles.h5', 'w') as h5:\n    pmd_init(h5, basePath='/', particlesPath='/' )\n    P.write(h5)\n</pre> with File('openpmd_particles.h5', 'w') as h5:     pmd_init(h5, basePath='/', particlesPath='/' )     P.write(h5) <p>This can be read in by another ParticleGroup</p> In\u00a0[6]: Copied! <pre>P2 = ParticleGroup('openpmd_particles.h5')\n</pre> P2 = ParticleGroup('openpmd_particles.h5') <p>Check that they are the same:</p> In\u00a0[7]: Copied! <pre>P2 == P\n</pre> P2 == P Out[7]: <pre>True</pre> In\u00a0[8]: Copied! <pre>P.write_astra('astra_particles.txt')\n</pre> P.write_astra('astra_particles.txt') In\u00a0[9]: Copied! <pre>!head astra_particles.txt\n</pre> !head astra_particles.txt <pre> -1.289814080985e-05   1.712192978919e-05   0.000000000000e+00  -9.245284702413e-01  -3.316650265292e+00   2.210558337183e+02   1.819664001274e-05   0.000000000000e+00    1    5\r\n -1.184861337727e-03  -2.101371437059e-03   0.000000000000e+00  -3.047044656318e+02  -3.039342419008e+02  -1.005645891013e+02  -9.745607002167e-04   1.000000000000e-06    1    5\r\n -5.181307340245e-04  -2.178353405029e-03   0.000000000000e+00   5.525456229648e+02   2.416723877028e+02  -6.554342847563e+01   1.280843753434e-03   1.000000000000e-06    1    5\r\n -1.773501610902e-03   2.864979597813e-03   0.000000000000e+00  -2.226004747820e+02   9.450238076106e+00  -1.055085411491e+02   3.835366744569e-04   1.000000000000e-06    1    5\r\n  1.686555815999e-03  -2.401048305081e-04   0.000000000000e+00  -1.891692499417e+02   4.859547751754e+01   3.339263495319e+02   1.902998338336e-03   1.000000000000e-06    1    5\r\n -7.779454935491e-04  -6.800063114796e-04   0.000000000000e+00   6.716138938638e+01  -2.064173000222e+02  -1.405963302134e+02   1.779005092730e-04   1.000000000000e-06    1    5\r\n -2.593702199590e-03  -2.301030494125e-03   0.000000000000e+00  -1.455653402031e+01   2.074634953296e+02  -1.397453142110e+02   1.368567098305e-03   1.000000000000e-06    1    5\r\n  1.997801509161e-03   2.648416193086e-03   0.000000000000e+00  -2.124047726665e+00  -6.792723569247e+01   6.931081537770e+01   2.616497721112e-04   1.000000000000e-06    1    5\r\n  1.999741847023e-03  -6.945690451493e-04   0.000000000000e+00  -9.991142908925e+01  -9.189412445573e+01   2.259539675809e+02  -8.681109991004e-04   1.000000000000e-06    1    5\r\n -7.033822974359e-04  -5.677746866954e-04   0.000000000000e+00   7.520962264129e+02   3.125940718167e+02  -1.451032665210e+02   4.315191990002e-04   1.000000000000e-06    1    5\r\n</pre> <p>Check the readback:</p> In\u00a0[10]: Copied! <pre>from pmd_beamphysics.interfaces.astra import parse_astra_phase_file\nimport numpy as np\nP1 = ParticleGroup(data=parse_astra_phase_file('astra_particles.txt'))\nfor k in ['x', 'px', 'y', 'py', 'z', 'pz']:\n    assert np.allclose(P[k], P1[k])\n</pre> from pmd_beamphysics.interfaces.astra import parse_astra_phase_file import numpy as np P1 = ParticleGroup(data=parse_astra_phase_file('astra_particles.txt')) for k in ['x', 'px', 'y', 'py', 'z', 'pz']:     assert np.allclose(P[k], P1[k]) In\u00a0[11]: Copied! <pre>P.write_bmad('bmad_particles.txt')\n</pre> P.write_bmad('bmad_particles.txt') In\u00a0[12]: Copied! <pre>!head bmad_particles.txt\n</pre> !head bmad_particles.txt <pre>!ASCII::3\r\n0 ! ix_ele, not used\r\n1 ! n_bunch\r\n10000 ! n_particle\r\nBEGIN_BUNCH\r\nelectron \r\n1.0000000000000003e-11  ! bunch_charge\r\n0 ! z_center\r\n0 ! t_center\r\n -1.184861337727e-03  -3.047044656318e+02  -2.101371437059e-03  -3.039342419008e+02  -9.563640602039e-13   1.204912446170e+02   1.000000000000e-15  1\r\n</pre> In\u00a0[13]: Copied! <pre>P.to_bmad()\n</pre> P.to_bmad() Out[13]: <pre>{'x': array([-0.00118486, -0.00051813, -0.0017735 , ..., -0.00052658,\n         0.00252813,  0.00113815]),\n 'y': array([-0.00210137, -0.00217835,  0.00286498, ..., -0.00161154,\n         0.00160049, -0.0010351 ]),\n 'px': array([-0.69011879,  1.25144906, -0.50416317, ...,  0.60249121,\n         0.505233  ,  0.74928061]),\n 'py': array([-0.68837433,  0.54735875,  0.02140365, ..., -0.07882388,\n         0.29433475, -0.25918357]),\n 'z': array([ 2.55529374e-07, -4.68009162e-07, -5.64739756e-08, ...,\n        -9.69805227e-09,  4.20165276e-07,  2.70278113e-07]),\n 'pz': array([ 0.01222356,  0.41059669, -0.4315584 , ..., -0.3093279 ,\n         0.02463904,  0.08781142]),\n 'charge': array([1.e-15, 1.e-15, 1.e-15, ..., 1.e-15, 1.e-15, 1.e-15]),\n 'species': 'electron',\n 'p0c': 441.52466167250947,\n 'tref': 1.8196640012738955e-14,\n 'state': array([1, 1, 1, ..., 1, 1, 1])}</pre> <p>Check that the conversion preserves information. Note that <code>==</code> uses <code>np.allclose</code>, because there is roundoff error in the conversion.</p> In\u00a0[14]: Copied! <pre>assert P == P.from_bmad(P.to_bmad())\n</pre> assert P == P.from_bmad(P.to_bmad())  In\u00a0[15]: Copied! <pre>P.write_elegant('elegant_particles.txt', verbose=True)\n</pre> P.write_elegant('elegant_particles.txt', verbose=True) <pre>writing 10000 particles to elegant_particles.txt\n</pre> In\u00a0[16]: Copied! <pre>!head -n 20 elegant_particles.txt\n</pre> !head -n 20 elegant_particles.txt <pre>SDDS1\r\n! \r\n! Created using the openPMD-beamphysics Python package\r\n! https://github.com/ChristopherMayes/openPMD-beamphysics\r\n! species: electron\r\n!\r\n&amp;parameter name=Charge, type=double, units=C, description=\"total charge in Coulombs\" &amp;end\r\n&amp;column name=t,  type=double, units=s, description=\"time in seconds\" &amp;end\r\n&amp;column name=x,  type=double, units=m, description=\"x in meters\" &amp;end\r\n&amp;column name=xp, type=double, description=\"px/pz\" &amp;end\r\n&amp;column name=y,  type=double, units=m, description=\"y in meters\" &amp;end\r\n&amp;column name=yp, type=double, description=\"py/pz\" &amp;end\r\n&amp;column name=p,  type=double, units=\"m$be$nc\", description=\"relativistic gamma*beta\" &amp;end\r\n&amp;data mode=ascii &amp;end\r\n1.0000000000000003e-11\r\n10000\r\n -9.563640602039e-13  -1.184861337727e-03  -2.528851507845e+00  -2.101371437059e-03  -2.522459145201e+00   8.746038816275e-04\r\n  1.299040393447e-12  -5.181307340245e-04   3.553064606663e+00  -2.178353405029e-03   1.554039289185e+00   1.218815083118e-03\r\n  4.017333144697e-13  -1.773501610902e-03  -1.926488019169e+00   2.864979597813e-03   8.178675472159e-02   4.911575370556e-04\r\n  1.921194978349e-12   1.686555815999e-03  -3.408564376497e-01  -2.401048305081e-04   8.756222989528e-02   1.151365621035e-03\r\n</pre> In\u00a0[17]: Copied! <pre>P.write_genesis2_beam_file('genesis2.beam', n_slice=50, verbose=True)\n</pre> P.write_genesis2_beam_file('genesis2.beam', n_slice=50, verbose=True) <pre>Beam written: genesis2.beam\n</pre> In\u00a0[18]: Copied! <pre>!head genesis2.beam\n</pre> !head genesis2.beam <pre>? VERSION=1.0\r\n? SIZE=50\r\n? COLUMNS TPOS CURPEAK GAMMA0 DELGAM EMITX EMITY RXBEAM RYBEAM XBEAM YBEAM PXBEAM PYBEAM ALPHAX ALPHAY\r\n-1.96236040e-12 2.75359318e+00 1.00000042e+00 3.87022121e-07 1.19639887e-06 9.26536586e-07 2.04948080e-03 1.83621527e-03 -2.06231144e-04 5.85738124e-05 1.38209180e-05 4.17553934e-05 -1.01973407e-02 7.54079832e-04\r\n-1.88646733e-12 2.46723994e+00 1.00000043e+00 3.39783124e-07 1.04698472e-06 1.03470799e-06 2.05064183e-03 1.92139881e-03 -2.53107201e-05 -1.13678787e-04 -2.85190907e-05 -2.90105675e-05 -2.32400675e-02 -2.55583997e-03\r\n-1.80734757e-12 2.61898031e+00 1.00000043e+00 3.65257967e-07 9.78086023e-07 1.03038032e-06 1.97065976e-03 1.88550433e-03 -7.56576638e-05 1.77044304e-04 -4.48907172e-05 6.98821769e-05 5.38708456e-03 4.70844009e-03\r\n-1.72583745e-12 2.30361285e+00 1.00000043e+00 3.43343193e-07 9.42099457e-07 1.07137205e-06 1.90156644e-03 1.92054974e-03 4.82559113e-05 -6.43105052e-05 1.66595205e-05 1.33986604e-05 9.16890850e-02 8.20635086e-03\r\n-1.64047654e-12 2.48779835e+00 1.00000043e+00 3.76740497e-07 1.12901501e-06 1.01496464e-06 2.07085748e-03 1.84284581e-03 -2.63780641e-04 2.16425678e-05 9.07228824e-06 -2.65046022e-05 1.91567484e-03 3.63234933e-03\r\n-1.55820367e-12 2.33273410e+00 1.00000041e+00 3.38657590e-07 9.79796547e-07 1.04648255e-06 1.84685458e-03 1.99189484e-03 3.36520774e-05 1.02123362e-04 3.01402301e-06 6.13937003e-05 -7.84493116e-02 2.84788841e-02\r\n-1.47800120e-12 2.62363489e+00 1.00000044e+00 3.34570582e-07 1.12580877e-06 1.08628803e-06 1.97095399e-03 2.02149469e-03 -1.94415715e-04 -1.05666747e-04 -1.70675102e-05 -5.61809635e-06 -1.81522208e-01 -5.08353286e-03\r\n</pre> In\u00a0[19]: Copied! <pre>input_str = P.write_genesis4_beam('genesis4_beam.h5', n_slice=123, verbose=True, return_input_str=True)\n</pre> input_str = P.write_genesis4_beam('genesis4_beam.h5', n_slice=123, verbose=True, return_input_str=True) <pre>Genesis4 beam file written: genesis4_beam.h5\n</pre> <p>This string is optionally returned for use in the main Genesis4 input file:</p> In\u00a0[20]: Copied! <pre>print(input_str)\n</pre> print(input_str) <pre>&amp;profile_file\n  label = current\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/current\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = gamma\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/gamma\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = delgam\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/delgam\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = ex\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/ex\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = ey\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/ey\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = xcenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/xcenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = ycenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/ycenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = pxcenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/pxcenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = pycenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/pycenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = alphax\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/alphax\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = alphay\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/alphay\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = betax\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/betax\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = betay\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/betay\n  isTime = T\n  reverse = T\n&amp;end\n&amp;beam\n  current = @current\n  gamma = @gamma\n  delgam = @delgam\n  ex = @ex\n  ey = @ey\n  xcenter = @xcenter\n  ycenter = @ycenter\n  pxcenter = @pxcenter\n  pycenter = @pycenter\n  alphax = @alphax\n  alphay = @alphay\n  betax = @betax\n  betay = @betay\n&amp;end\n</pre> <p>These are the datasets written:</p> In\u00a0[21]: Copied! <pre>with File('genesis4_beam.h5', 'r') as h5:\n    for g in h5:\n        print(g, len(h5[g]), h5[g].attrs['unitSymbol'])\n</pre> with File('genesis4_beam.h5', 'r') as h5:     for g in h5:         print(g, len(h5[g]), h5[g].attrs['unitSymbol']) <pre>alphax 123 \nalphay 123 \nbetax 123 m\nbetay 123 m\ncurrent 123 A\ndelgam 123 \nex 123 m\ney 123 m\ngamma 123 \npxcenter 123 \npycenter 123 \nt 123 s\nxcenter 123 m\nycenter 123 m\n</pre> In\u00a0[22]: Copied! <pre>P.write_genesis4_distribution('genesis4_distribution.h5', verbose=True)\n</pre> P.write_genesis4_distribution('genesis4_distribution.h5', verbose=True) <pre>Datasets x, xp, y, yp, t, p written to: genesis4_distribution.h5\n</pre> <p>This is what is written:</p> In\u00a0[23]: Copied! <pre>with File('genesis4_distribution.h5', 'r') as h5:\n    for g in h5:\n        print(g, len(h5[g]))\n</pre> with File('genesis4_distribution.h5', 'r') as h5:     for g in h5:         print(g, len(h5[g])) <pre>p 10000\nt 10000\nx 10000\nxp 10000\ny 10000\nyp 10000\n</pre> In\u00a0[24]: Copied! <pre>P.write_gpt('gpt_particles.txt', verbose=True)\n</pre> P.write_gpt('gpt_particles.txt', verbose=True) <pre>writing 10000 particles to gpt_particles.txt\nASCII particles written. Convert to GDF using: asci2df -o particles.gdf gpt_particles.txt\n</pre> In\u00a0[25]: Copied! <pre>if os.path.exists(os.path.expandvars('$ASCI2GDF_BIN')):\n    P.write_gpt('gpt_particles.gdf', verbose=True, asci2gdf_bin='$ASCI2GDF_BIN')\n</pre> if os.path.exists(os.path.expandvars('$ASCI2GDF_BIN')):     P.write_gpt('gpt_particles.gdf', verbose=True, asci2gdf_bin='$ASCI2GDF_BIN') In\u00a0[26]: Copied! <pre>#!head gpt_particles.txt\n</pre> #!head gpt_particles.txt In\u00a0[27]: Copied! <pre>P.drift_to_t(P['mean_t'])\n</pre> P.drift_to_t(P['mean_t']) <p>This will return settings for Impact-T to use:</p> In\u00a0[28]: Copied! <pre>P.write_impact('impact_particles.txt')\n</pre> P.write_impact('impact_particles.txt') Out[28]: <pre>{'input_particle_file': 'impact_particles.txt',\n 'Np': 10000,\n 'Tini': 1.8196640012738955e-14,\n 'Flagimg': 0}</pre> In\u00a0[29]: Copied! <pre>!head impact_particles.txt\n</pre> !head impact_particles.txt <pre>10000\r\n-1.185035553808772143e-03 -5.962917646539026830e-04 -2.101545212762252063e-03 -5.947844744118675406e-04 6.889138464881934423e-08 2.357954837617718942e-04\r\n-5.185459410277813361e-04 1.081304810831444467e-03 -2.178535008255722601e-03 4.729410651485857963e-04 -1.168588385748075652e-07 3.043301854978374224e-04\r\n-1.773451522909312962e-03 -4.356182625855454594e-04 2.864977471386669170e-03 1.849365458795189412e-05 -2.599963881922582336e-08 2.261204109504710640e-04\r\n1.686767013783966630e-03 -3.701949875665073173e-04 -2.401590848712557098e-04 9.509897724357652376e-05 -6.196091998406064887e-07 1.086073040365844247e-03\r\n-7.779525032181127363e-04 1.314315604491483489e-04 -6.799847675988795461e-04 -4.039485795854574836e-04 -8.397600117458311948e-09 1.574553206124528761e-04\r\n-2.593690512006350136e-03 -2.848642647956871966e-05 -2.301197068594195913e-03 4.059959327304890142e-04 -6.528501143099030074e-08 1.591207173856017771e-04\r\n1.997801835211931738e-03 -4.156657712632428962e-06 2.648426620219643604e-03 -1.329302842842789139e-04 -4.457257401140207729e-08 5.682333576145901953e-04\r\n1.999690961886589624e-03 -1.955217894072916647e-04 -6.946158470527933476e-04 -1.798323156157682705e-04 2.276631907326255499e-07 8.747763597149907193e-04\r\n-7.035727003852429310e-04 1.471815600429043306e-03 -5.678538239535645561e-04 6.117313388152665482e-04 -1.922838101944327160e-08 1.486354662711140203e-04\r\n</pre> In\u00a0[30]: Copied! <pre>P.drift_to_z()\n</pre> P.drift_to_z() In\u00a0[31]: Copied! <pre>P.write_litrack('litrack.zd', verbose=True)\n</pre> P.write_litrack('litrack.zd', verbose=True) <pre>Using mean_p as the reference momentum: 441.52466167250947 eV/c\nwriting 10000 LiTrack particles to litrack.zd\n</pre> Out[31]: <pre>'litrack.zd'</pre> In\u00a0[32]: Copied! <pre>!head -n 20 litrack.zd\n</pre> !head -n 20 litrack.zd <pre>% LiTrack particles\r\n% \r\n% Created using the openPMD-beamphysics Python package\r\n% https://github.com/ChristopherMayes/openPMD-beamphysics\r\n%\r\n% species: electron\r\n% n_particle: 10000\r\n% total charge: 1.0000000000000003e-11 (C)\r\n% reference momentum p0: 441.52466167250947 (eV/c)\r\n%\r\n% Columns: ct, delta = p/p0 -1\r\n% Units: mm, percent\r\n -2.910140500388e-01   1.222356070583e+00\r\n  3.861082943987e-01   4.105966931910e+01\r\n  1.159491741202e-01  -4.315583986424e+01\r\n  5.750254785583e-01   3.325339997689e+01\r\n  5.234406211768e-02  -4.756793241308e+01\r\n  4.093643740668e-01  -4.942448528425e+01\r\n  8.211012905157e-02  -3.245820052454e+01\r\n -2.559578717432e-01   5.807578013130e+00\r\n</pre> In\u00a0[33]: Copied! <pre>P.write_lucretia('lucretia.mat', ele_name='BEGINNING', t_ref=0, stop_ix=None, verbose=True)\n</pre> P.write_lucretia('lucretia.mat', ele_name='BEGINNING', t_ref=0, stop_ix=None, verbose=True) <pre>writing 10000 particles in the Lucretia format to lucretia.mat\n</pre> <p>Read back:</p> In\u00a0[34]: Copied! <pre>from pmd_beamphysics.interfaces.lucretia import lucretia_to_data, list_element_names\n\nParticleGroup(data=lucretia_to_data('lucretia.mat', verbose=True))\n</pre> from pmd_beamphysics.interfaces.lucretia import lucretia_to_data, list_element_names  ParticleGroup(data=lucretia_to_data('lucretia.mat', verbose=True)) <pre>1 elements found in the file!\n10000 particles detected, 0 found dead!\n</pre> Out[34]: <pre>&lt;ParticleGroup with 10000 particles at 0x7f63da85cd00&gt;</pre> <p>Helper function to list the available elements:</p> In\u00a0[35]: Copied! <pre>list_element_names('lucretia.mat')\n</pre> list_element_names('lucretia.mat') Out[35]: <pre>['BEGINNING']</pre> In\u00a0[36]: Copied! <pre>P.drift_to_t()\n\nP.write_opal('opal_injected.txt', dist_type='injected')\n</pre> P.drift_to_t()  P.write_opal('opal_injected.txt', dist_type='injected') In\u00a0[37]: Copied! <pre>!head opal_injected.txt\n</pre> !head opal_injected.txt <pre>10000\r\n -1.185024568260e-03  -5.962917646539e-04  -2.101534254983e-03  -5.947844744119e-04   6.454729863911e-08   2.357954837618e-04\r\n -5.185658620175e-04   1.081304810831e-03  -2.178543721298e-03   4.729410651486e-04  -1.224655448770e-07   3.043301854978e-04\r\n -1.773443497464e-03  -4.356182625855e-04   2.864977130676e-03   1.849365458795e-05  -3.016548099423e-08   2.261204109505e-04\r\n  1.686773833925e-03  -3.701949875665e-04  -2.401608368896e-04   9.509897724358e-05  -6.396180367454e-07   1.086073040366e-03\r\n -7.779549245968e-04   1.314315604491e-04  -6.799773256079e-04  -4.039485795855e-04  -1.129841753741e-08   1.574553206125e-04\r\n -2.593689987198e-03  -2.848642647957e-05  -2.301204548304e-03   4.059959327305e-04  -6.821651066751e-08   1.591207173856e-04\r\n  1.997801911791e-03  -4.156657712632e-06   2.648429069209e-03  -1.329302842843e-04  -5.504120158406e-08   5.682333576146e-04\r\n  1.999694564006e-03  -1.955217894073e-04  -6.946125339825e-04  -1.798323156158e-04   2.115470906675e-07   8.747763597150e-04\r\n -7.035998157808e-04   1.471815600429e-03  -5.678650939369e-04   6.117313388153e-04  -2.196670602435e-08   1.486354662711e-04\r\n</pre> <p>Emitted particles must be at the same z:</p> In\u00a0[38]: Copied! <pre>P.drift_to_z(P['mean_z'])\nP.write_opal('opal_emitted.txt', dist_type='emitted')\n</pre> P.drift_to_z(P['mean_z']) P.write_opal('opal_emitted.txt', dist_type='emitted') In\u00a0[39]: Copied! <pre>!head opal_emitted.txt\n</pre> !head opal_emitted.txt <pre>10000\r\n -1.184838617375e-03  -5.962917646539e-04  -2.101348774139e-03  -5.947844744119e-04  -1.083461177889e-12   2.357954837618e-04\r\n -5.181626563723e-04   1.081304810831e-03  -2.178367367225e-03   4.729410651486e-04   1.200565320731e-12   3.043301854978e-04\r\n -1.773484302458e-03  -4.356182625855e-04   2.864978863003e-03   1.849365458795e-05   2.691980940714e-13   2.261204109505e-04\r\n  1.686558878409e-03  -3.701949875665e-04  -2.401056172069e-04   9.509897724358e-05   1.893601130019e-12   1.086073040366e-03\r\n -7.779529930790e-04   1.314315604491e-04  -6.799832620349e-04  -4.039485795855e-04   5.764311716727e-15   1.574553206125e-04\r\n -2.593700591157e-03  -2.848642647957e-05  -2.301053417928e-03   4.059959327305e-04   1.198422972634e-12   1.591207173856e-04\r\n  1.997801574883e-03  -4.156657712632e-06   2.648418294875e-03  -1.329302842843e-04   2.271058970013e-13   5.682333576146e-04\r\n  1.999743855144e-03  -1.955217894073e-04  -6.945671981683e-04  -1.798323156158e-04  -8.841733180528e-13   8.747763597150e-04\r\n -7.034712631509e-04   1.471815600429e-03  -5.678116635531e-04   6.117313388153e-04   2.480886363183e-13   1.486354662711e-04\r\n</pre> In\u00a0[40]: Copied! <pre>P.write_simion('simion_particles.ion')\n</pre> P.write_simion('simion_particles.ion') In\u00a0[41]: Copied! <pre>for file in [\n    'astra_particles.txt',\n    'bmad_particles.txt',\n    'elegant_particles.txt',\n    'gpt_particles.txt',\n    'impact_particles.txt',\n    'opal_injected.txt',\n    'opal_emitted.txt',\n    'openpmd_particles.h5',\n    'genesis4_beam.h5',\n    'genesis4_distribution.h5',\n    'genesis2.beam',\n    'litrack.zd',\n    'gpt_particles.gdf',\n    'lucretia.mat',\n    'simion_particles.ion'\n    ]:\n    if os.path.exists(file):\n        os.remove(file)\n</pre> for file in [     'astra_particles.txt',     'bmad_particles.txt',     'elegant_particles.txt',     'gpt_particles.txt',     'impact_particles.txt',     'opal_injected.txt',     'opal_emitted.txt',     'openpmd_particles.h5',     'genesis4_beam.h5',     'genesis4_distribution.h5',     'genesis2.beam',     'litrack.zd',     'gpt_particles.gdf',     'lucretia.mat',     'simion_particles.ion'     ]:     if os.path.exists(file):         os.remove(file)"},{"location":"examples/write_examples/#write-examples","title":"Write examples\u00b6","text":""},{"location":"examples/write_examples/#openpmd","title":"openPMD\u00b6","text":""},{"location":"examples/write_examples/#astra","title":"Astra\u00b6","text":""},{"location":"examples/write_examples/#bmad-ascii","title":"Bmad ASCII\u00b6","text":""},{"location":"examples/write_examples/#bmad-dict","title":"Bmad dict\u00b6","text":""},{"location":"examples/write_examples/#elegant","title":"elegant\u00b6","text":""},{"location":"examples/write_examples/#genesis-13-v2","title":"Genesis 1.3 v2\u00b6","text":""},{"location":"examples/write_examples/#genesis-13-v4","title":"Genesis 1.3 v4\u00b6","text":""},{"location":"examples/write_examples/#beam-file-slice-statistics","title":"beam file (slice statistics)\u00b6","text":""},{"location":"examples/write_examples/#distribution-file-particles","title":"Distribution file (particles)\u00b6","text":""},{"location":"examples/write_examples/#gpt-ascii","title":"GPT ASCII\u00b6","text":""},{"location":"examples/write_examples/#impact-t","title":"Impact-T\u00b6","text":"<p>Impact-T particles must all be a the same time:</p>"},{"location":"examples/write_examples/#litrack","title":"LiTrack\u00b6","text":"<p>LiTrack particles must be at the same z:</p>"},{"location":"examples/write_examples/#lucretia","title":"Lucretia\u00b6","text":""},{"location":"examples/write_examples/#opal","title":"OPAL\u00b6","text":"<p>Injected particled must be at the same time:</p>"},{"location":"examples/write_examples/#simion","title":"SIMION\u00b6","text":"<p>Write SIMION input files (*.ion)</p>"},{"location":"examples/write_examples/#cleanup","title":"Cleanup\u00b6","text":""},{"location":"examples/fields/field_conversion/","title":"FieldMesh Conversion","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh\n</pre> from pmd_beamphysics import FieldMesh In\u00a0[3]: Copied! <pre>FM1 = FieldMesh('../data/rfgun.h5')\nFM1.plot('re_E', aspect='equal', figsize=(12,4))\n</pre> FM1 = FieldMesh('../data/rfgun.h5') FM1.plot('re_E', aspect='equal', figsize=(12,4)) In\u00a0[4]: Copied! <pre>FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',\n                              hfile='../data/ansys_rfgun_2856MHz_H.dat',\n                              frequency=2856e6)\n\n\n\nFM3D\n</pre> FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',                               hfile='../data/ansys_rfgun_2856MHz_H.dat',                               frequency=2856e6)    FM3D Out[4]: <pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fa0c4d64ee0&gt;</pre> <p>This will convert to 2D cylindrical:</p> In\u00a0[5]: Copied! <pre>FM2 = FM3D.to_cylindrical()\nFM2\n</pre> FM2 = FM3D.to_cylindrical() FM2 Out[5]: <pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fa07afc4a90&gt;</pre> <p>Spacing is different:</p> In\u00a0[6]: Copied! <pre>FM1.dr, FM2.dr\n</pre> FM1.dr, FM2.dr Out[6]: <pre>(0.00025, 0.001)</pre> <p>Set a scale to rotate and normalize Ez to be mostly real</p> In\u00a0[7]: Copied! <pre>E0 = FM2.components['electricField/z'][0,0,0]\nFM2.scale = -np.exp(-1j * np.angle(E0)) / np.abs(E0) # - sign to agree with FM1\n\nFM2.Ez[0,0,0]\n</pre> E0 = FM2.components['electricField/z'][0,0,0] FM2.scale = -np.exp(-1j * np.angle(E0)) / np.abs(E0) # - sign to agree with FM1  FM2.Ez[0,0,0] Out[7]: <pre>(-1-1.890985933883628e-16j)</pre> In\u00a0[8]: Copied! <pre>z1 = FM1.coord_vec('z')\nz2 = FM2.coord_vec('z')\n\n\nfig, ax = plt.subplots()\nax.plot(z1, np.real(FM1.Ez[0,0,:]), label='Superfish')\nax.plot(z2, np.real(FM2.Ez[0,0,:]), label='ANSYS')\nax.set_xlabel(r'$z$ (m)')\nax.set_ylabel(r'$E_z$ (V/m)')\nplt.legend()\n</pre> z1 = FM1.coord_vec('z') z2 = FM2.coord_vec('z')   fig, ax = plt.subplots() ax.plot(z1, np.real(FM1.Ez[0,0,:]), label='Superfish') ax.plot(z2, np.real(FM2.Ez[0,0,:]), label='ANSYS') ax.set_xlabel(r'$z$ (m)') ax.set_ylabel(r'$E_z$ (V/m)') plt.legend()  Out[8]: <pre>&lt;matplotlib.legend.Legend at 0x7fa07af99ee0&gt;</pre> In\u00a0[9]: Copied! <pre>fig, ax = plt.subplots()\nax.plot(z1, np.imag(FM1.Btheta[4,0,:]), label='superfish')\nax.plot(z2, np.imag(FM2.Btheta[1,0,:]), label='ansys')\n\nax.set_title(fr'$r$ = {FM2.dr*1000} mm')\nax.set_xlabel(r'$z$ (m)')\nax.set_ylabel(r'$B_\\theta$ (T)')\nplt.legend()\n</pre> fig, ax = plt.subplots() ax.plot(z1, np.imag(FM1.Btheta[4,0,:]), label='superfish') ax.plot(z2, np.imag(FM2.Btheta[1,0,:]), label='ansys')  ax.set_title(fr'$r$ = {FM2.dr*1000} mm') ax.set_xlabel(r'$z$ (m)') ax.set_ylabel(r'$B_\\theta$ (T)') plt.legend() Out[9]: <pre>&lt;matplotlib.legend.Legend at 0x7fa07aebb130&gt;</pre> <p>The magnetic field is out of phase, so use the <code>im_</code> syntax:</p> In\u00a0[10]: Copied! <pre>FM2.plot('im_Btheta', aspect='equal', figsize=(12,4))\n</pre> FM2.plot('im_Btheta', aspect='equal', figsize=(12,4)) <p>Max on-axis field:</p> In\u00a0[11]: Copied! <pre>np.abs(FM2.Ez[0,0,:]).max()\n</pre> np.abs(FM2.Ez[0,0,:]).max() Out[11]: <pre>1.0153992993439902</pre> In\u00a0[12]: Copied! <pre>def check_oscillation(FM, label=''):\n\n    c_light = 299792458.\n    \n    dr = FM.dr\n    omega = FM.frequency*2*np.pi\n    \n    # Check the first off-axis grid points\n    z0 = FM.z\n    Ez0 = np.real(FM.Ez[0,0,:])\n    B1 = -np.imag(FM.Btheta[1,0,:])\n    \n    plt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$')\n    plt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$')\n    plt.ylabel('field (V/m)')\n    plt.xlabel('z (m)')\n    plt.legend()\n    plt.title(fr'Complex field oscillation{label}')\n</pre> def check_oscillation(FM, label=''):      c_light = 299792458.          dr = FM.dr     omega = FM.frequency*2*np.pi          # Check the first off-axis grid points     z0 = FM.z     Ez0 = np.real(FM.Ez[0,0,:])     B1 = -np.imag(FM.Btheta[1,0,:])          plt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$')     plt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$')     plt.ylabel('field (V/m)')     plt.xlabel('z (m)')     plt.legend()     plt.title(fr'Complex field oscillation{label}') In\u00a0[13]: Copied! <pre>check_oscillation(FM1, ', Superfish')\n</pre> check_oscillation(FM1, ', Superfish') In\u00a0[14]: Copied! <pre>check_oscillation(FM2, ', ANSYS')\n</pre> check_oscillation(FM2, ', ANSYS')"},{"location":"examples/fields/field_conversion/#fieldmesh-conversion","title":"FieldMesh Conversion\u00b6","text":""},{"location":"examples/fields/field_conversion/#2d-cylindrically-symmetric-rf-gun-fieldmesh","title":"2D cylindrically symmetric RF gun FieldMesh\u00b6","text":"<p>This data was originally generated using Superfish.</p>"},{"location":"examples/fields/field_conversion/#3d-rectangular-field-from-ansys","title":"3D rectangular field from ANSYS\u00b6","text":""},{"location":"examples/fields/field_conversion/#verify-the-oscillation","title":"Verify the oscillation\u00b6","text":"<p>Complex fields oscillate as $e^{-i\\omega t}$. For TM fields, the spatial components $E_z$ and $B_\\theta$ near the axis</p> <p>$\\Re E_{z} = -\\frac{r}{2}\\frac{\\omega}{c^2} \\Im B_\\theta$</p>"},{"location":"examples/fields/field_examples/","title":"FieldMesh Examples","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh, tools\n</pre> from pmd_beamphysics import FieldMesh, tools In\u00a0[3]: Copied! <pre>FM = FieldMesh('../data/solenoid.h5')\nFM\n</pre> FM = FieldMesh('../data/solenoid.h5') FM Out[3]: <pre>&lt;FieldMesh with cylindrical geometry and (101, 1, 201) shape at 0x7ff4c83e0df0&gt;</pre> <p>Built-in plotting:</p> In\u00a0[4]: Copied! <pre>FM.plot('B', aspect='equal')\n</pre> FM.plot('B', aspect='equal') <p>On-axis field plotting</p> In\u00a0[5]: Copied! <pre>FM.plot_onaxis()\n</pre> FM.plot_onaxis() In\u00a0[6]: Copied! <pre>FM.attrs, FM.components.keys()\n</pre> FM.attrs, FM.components.keys() Out[6]: <pre>({'eleAnchorPt': 'beginning',\n  'gridGeometry': 'cylindrical',\n  'axisLabels': array(['r', 'theta', 'z'], dtype='&lt;U5'),\n  'gridLowerBound': array([0, 1, 0]),\n  'gridOriginOffset': array([ 0. ,  0. , -0.1]),\n  'gridSpacing': array([0.001, 0.   , 0.001]),\n  'gridSize': array([101,   1, 201]),\n  'harmonic': 0,\n  'fundamentalFrequency': 0,\n  'RFphase': 0,\n  'fieldScale': 1.0},\n dict_keys(['magneticField/z', 'magneticField/r']))</pre> In\u00a0[7]: Copied! <pre>FM.shape\n</pre> FM.shape Out[7]: <pre>(101, 1, 201)</pre> In\u00a0[8]: Copied! <pre>FM.frequency\n</pre> FM.frequency Out[8]: <pre>0</pre> <p>Coordinate vectors: <code>.r</code>, <code>.theta</code>, <code>.z</code>, etc.</p> In\u00a0[9]: Copied! <pre>FM.r, FM.dr\n</pre> FM.r, FM.dr Out[9]: <pre>(array([0.   , 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008,\n        0.009, 0.01 , 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017,\n        0.018, 0.019, 0.02 , 0.021, 0.022, 0.023, 0.024, 0.025, 0.026,\n        0.027, 0.028, 0.029, 0.03 , 0.031, 0.032, 0.033, 0.034, 0.035,\n        0.036, 0.037, 0.038, 0.039, 0.04 , 0.041, 0.042, 0.043, 0.044,\n        0.045, 0.046, 0.047, 0.048, 0.049, 0.05 , 0.051, 0.052, 0.053,\n        0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06 , 0.061, 0.062,\n        0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07 , 0.071,\n        0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08 ,\n        0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089,\n        0.09 , 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098,\n        0.099, 0.1  ]),\n 0.001)</pre> <p>Grid info</p> In\u00a0[10]: Copied! <pre>FM.mins, FM.maxs, FM.deltas\n</pre> FM.mins, FM.maxs, FM.deltas Out[10]: <pre>(array([ 0. ,  0. , -0.1]),\n array([0.1, 0. , 0.1]),\n array([0.001, 0.   , 0.001]))</pre> <p>Convenient logicals</p> In\u00a0[11]: Copied! <pre>FM.is_static, FM.is_pure_magnetic, FM.is_pure_magnetic,  FM.is_pure_electric\n</pre> FM.is_static, FM.is_pure_magnetic, FM.is_pure_magnetic,  FM.is_pure_electric Out[11]: <pre>(True, True, True, False)</pre> In\u00a0[12]: Copied! <pre>FM.components\n</pre> FM.components Out[12]: <pre>{'magneticField/z': array([[[ 4.10454985e-03,  4.31040451e-03,  4.52986744e-03, ...,\n           4.67468517e-04,  3.93505841e-04,  3.31380794e-04]],\n \n        [[ 4.10132316e-03,  4.30698128e-03,  4.52613784e-03, ...,\n           4.63910019e-04,  3.90463457e-04,  3.28826095e-04]],\n \n        [[ 4.09178241e-03,  4.29666227e-03,  4.51500745e-03, ...,\n           4.53304832e-04,  3.81497195e-04,  3.21252672e-04]],\n \n        ...,\n \n        [[-8.55276742e-05, -9.25454620e-05, -9.97134392e-05, ...,\n          -1.67910069e-13, -1.66617291e-13, -1.69112101e-13]],\n \n        [[-8.66606075e-05, -9.34605759e-05, -1.00393739e-04, ...,\n          -1.63746446e-13, -1.62385457e-13, -1.63975660e-13]],\n \n        [[-8.76493773e-05, -9.42325632e-05, -1.00947206e-04, ...,\n          -1.59165583e-13, -1.57653026e-13, -1.58633209e-13]]]),\n 'magneticField/r': array([[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n \n        [[-9.96833640e-05, -1.06224487e-04, -1.13203127e-04, ...,\n           4.01781064e-05,  3.37384918e-05,  2.82880488e-05]],\n \n        [[-1.99034573e-04, -2.12052909e-04, -2.25955469e-04, ...,\n           7.94909429e-05,  6.67047656e-05,  5.59040132e-05]],\n \n        ...,\n \n        [[-3.28171418e-04, -3.29256623e-04, -3.30141629e-04, ...,\n           5.98175517e-14,  5.84734577e-14,  5.07218297e-14]],\n \n        [[-3.18048731e-04, -3.18985999e-04, -3.19728362e-04, ...,\n           5.74787071e-14,  5.71575012e-14,  5.06326785e-14]],\n \n        [[-3.08270029e-04, -3.09070567e-04, -3.09682173e-04, ...,\n           5.47143752e-14,  5.54052993e-14,  4.98595495e-14]]])}</pre> <p>Convenient access to component data</p> In\u00a0[13]: Copied! <pre>FM.Bz is FM['magneticField/z']\n</pre> FM.Bz is FM['magneticField/z'] Out[13]: <pre>True</pre> <p>Setting .scale will set the underlying attribute</p> In\u00a0[14]: Copied! <pre>FM.scale = 2\nFM.attrs['fieldScale'], FM.scale\n</pre> FM.scale = 2 FM.attrs['fieldScale'], FM.scale Out[14]: <pre>(2, 2)</pre> <p>Raw components accessed by their full key</p> In\u00a0[15]: Copied! <pre>FM['magneticField/z']\n</pre> FM['magneticField/z'] Out[15]: <pre>array([[[ 4.10454985e-03,  4.31040451e-03,  4.52986744e-03, ...,\n          4.67468517e-04,  3.93505841e-04,  3.31380794e-04]],\n\n       [[ 4.10132316e-03,  4.30698128e-03,  4.52613784e-03, ...,\n          4.63910019e-04,  3.90463457e-04,  3.28826095e-04]],\n\n       [[ 4.09178241e-03,  4.29666227e-03,  4.51500745e-03, ...,\n          4.53304832e-04,  3.81497195e-04,  3.21252672e-04]],\n\n       ...,\n\n       [[-8.55276742e-05, -9.25454620e-05, -9.97134392e-05, ...,\n         -1.67910069e-13, -1.66617291e-13, -1.69112101e-13]],\n\n       [[-8.66606075e-05, -9.34605759e-05, -1.00393739e-04, ...,\n         -1.63746446e-13, -1.62385457e-13, -1.63975660e-13]],\n\n       [[-8.76493773e-05, -9.42325632e-05, -1.00947206e-04, ...,\n         -1.59165583e-13, -1.57653026e-13, -1.58633209e-13]]])</pre> <p>Scaled component accessed by shorter keys, e.g.</p> In\u00a0[16]: Copied! <pre>FM['Bz']\n</pre> FM['Bz'] Out[16]: <pre>array([[[ 8.20909970e-03,  8.62080901e-03,  9.05973488e-03, ...,\n          9.34937033e-04,  7.87011682e-04,  6.62761588e-04]],\n\n       [[ 8.20264631e-03,  8.61396257e-03,  9.05227569e-03, ...,\n          9.27820038e-04,  7.80926913e-04,  6.57652189e-04]],\n\n       [[ 8.18356483e-03,  8.59332454e-03,  9.03001490e-03, ...,\n          9.06609663e-04,  7.62994390e-04,  6.42505344e-04]],\n\n       ...,\n\n       [[-1.71055348e-04, -1.85090924e-04, -1.99426878e-04, ...,\n         -3.35820138e-13, -3.33234581e-13, -3.38224202e-13]],\n\n       [[-1.73321215e-04, -1.86921152e-04, -2.00787478e-04, ...,\n         -3.27492892e-13, -3.24770914e-13, -3.27951319e-13]],\n\n       [[-1.75298755e-04, -1.88465126e-04, -2.01894411e-04, ...,\n         -3.18331166e-13, -3.15306051e-13, -3.17266417e-13]]])</pre> In\u00a0[17]: Copied! <pre>FM['magneticField/z'].max(), FM['Bz'].max()\n</pre> FM['magneticField/z'].max(), FM['Bz'].max() Out[17]: <pre>(2.150106838829148, 4.300213677658296)</pre> In\u00a0[18]: Copied! <pre>FM = FieldMesh('../data/rfgun.h5')\nFM.plot('re_E', aspect='equal', figsize=(12,4))\n</pre> FM = FieldMesh('../data/rfgun.h5') FM.plot('re_E', aspect='equal', figsize=(12,4)) <p>The magnetic field is out of phase, so use the <code>im_</code> syntax:</p> In\u00a0[19]: Copied! <pre>FM.plot('im_Btheta', aspect='equal', figsize=(12,4))\n</pre> FM.plot('im_Btheta', aspect='equal', figsize=(12,4)) <p>Max on-axis field:</p> In\u00a0[20]: Copied! <pre>np.abs(FM.Ez[0,0,:]).max()\n</pre> np.abs(FM.Ez[0,0,:]).max() Out[20]: <pre>1.0</pre> In\u00a0[21]: Copied! <pre>c_light = 299792458.\n\ndr = FM.dr\nomega = FM.frequency*2*np.pi\n\n# Check the first off-axis grid points\nz0 = FM.z\nEz0 = np.real(FM.Ez[0,0,:])\nB1 = -np.imag(FM.Btheta[1,0,:])\n\nplt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$')\nplt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$')\nplt.ylabel('field (V/m)')\nplt.xlabel('z (m)')\nplt.legend()\nplt.title(r'Complex field oscillation')\n</pre> c_light = 299792458.  dr = FM.dr omega = FM.frequency*2*np.pi  # Check the first off-axis grid points z0 = FM.z Ez0 = np.real(FM.Ez[0,0,:]) B1 = -np.imag(FM.Btheta[1,0,:])  plt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$') plt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$') plt.ylabel('field (V/m)') plt.xlabel('z (m)') plt.legend() plt.title(r'Complex field oscillation') Out[21]: <pre>Text(0.5, 1.0, 'Complex field oscillation')</pre> In\u00a0[22]: Copied! <pre>FM.units('Bz')\n</pre> FM.units('Bz') Out[22]: <pre>pmd_unit('T', 1, (0, 1, -2, -1, 0, 0, 0))</pre> <p>This also works:</p> In\u00a0[23]: Copied! <pre>FM.units('abs_Ez')\n</pre> FM.units('abs_Ez') Out[23]: <pre>pmd_unit('V/m', 1, (1, 1, -3, -1, 0, 0, 0))</pre> In\u00a0[24]: Copied! <pre>FM.write('rfgun2.h5')\n</pre> FM.write('rfgun2.h5') <p>Read back and make sure the data are the same.</p> In\u00a0[25]: Copied! <pre>FM2 = FieldMesh('rfgun2.h5')\n\nassert FM == FM2\n</pre> FM2 = FieldMesh('rfgun2.h5')  assert FM == FM2 <p>Write to open HDF5 file and test reload:</p> In\u00a0[26]: Copied! <pre>import h5py\nwith h5py.File('test.h5', 'w') as h5:\n    FM.write(h5, name='myfield')\n    FM2 = FieldMesh(h5=h5['myfield'])\n    assert FM == FM2\n</pre> import h5py with h5py.File('test.h5', 'w') as h5:     FM.write(h5, name='myfield')     FM2 = FieldMesh(h5=h5['myfield'])     assert FM == FM2 In\u00a0[27]: Copied! <pre>FM.write_astra_1d('astra_1d.dat')\n</pre> FM.write_astra_1d('astra_1d.dat') <p>Another method returns the array data with some annotation</p> In\u00a0[28]: Copied! <pre>FM.to_astra_1d()\n</pre> FM.to_astra_1d() Out[28]: <pre>{'attrs': {'type': 'astra_1d'},\n 'data': array([[ 0.00000000e+00, -1.00000000e+00],\n        [ 2.50000000e-04, -9.99967412e-01],\n        [ 5.00000000e-04, -9.99865106e-01],\n        ...,\n        [ 1.29500000e-01,  1.45678834e-04],\n        [ 1.29750000e-01,  1.40392476e-04],\n        [ 1.30000000e-01,  1.35399670e-04]])}</pre> In\u00a0[29]: Copied! <pre>idata = FM.to_impact_solrf()\nidata.keys()\n</pre> idata = FM.to_impact_solrf() idata.keys() Out[29]: <pre>dict_keys(['line', 'rfdata', 'ele', 'fmap'])</pre> <p>This is an element that can be used with LUME-Impact</p> In\u00a0[30]: Copied! <pre>idata['ele']\n</pre> idata['ele'] Out[30]: <pre>{'L': 0.13,\n 'type': 'solrf',\n 'zedge': 0,\n 'rf_field_scale': 1,\n 'rf_frequency': 2855998506.158,\n 'theta0_deg': 0.0,\n 'filename': 'rfdata666',\n 'radius': 0.15,\n 'x_offset': 0,\n 'y_offset': 0,\n 'x_rotation': 0.0,\n 'y_rotation': 0.0,\n 'z_rotation': 0.0,\n 'solenoid_field_scale': 0,\n 'name': 'solrf_666',\n 's': 0.13}</pre> <p>This is a line that would be used</p> In\u00a0[31]: Copied! <pre>idata['line']\n</pre>  idata['line'] Out[31]: <pre>'0.13 0 0 105 0 1 2855998506.158 0.0 666 0.15 0 0 0 0 0 0 /name:solrf_666'</pre> <p>Data that would be written to the rfdata999 file</p> In\u00a0[32]: Copied! <pre>idata['rfdata']\n</pre> idata['rfdata'] Out[32]: <pre>array([ 5.90000000e+01, -1.30000000e-01,  1.30000000e-01,  2.60000000e-01,\n        2.38794165e-01, -3.04717859e-01, -3.56926831e-17, -7.49524991e-01,\n        7.29753328e-18, -2.59757413e-01, -4.68937899e-17,  1.27535902e-01,\n       -6.30755527e-17,  4.49651550e-02, -1.49234509e-16,  4.67567146e-03,\n       -1.74265243e-16,  3.32025307e-02,  6.08199268e-17, -2.74904837e-03,\n        1.81697659e-16, -1.55710320e-02, -1.45475359e-16,  2.64475111e-03,\n       -1.20376479e-16,  9.51814907e-04,  2.52133666e-16, -2.68547441e-03,\n        4.07323339e-16,  1.50824509e-03,  3.51376926e-16,  7.16574075e-04,\n        1.71392948e-16, -9.25573616e-04, -1.42917735e-16,  2.40084183e-04,\n       -1.19272642e-16,  2.61495768e-04,  1.87867359e-16, -2.41687337e-04,\n       -9.72891282e-18,  6.22859548e-05, -3.09382521e-16,  7.85163760e-05,\n       -2.59194056e-16, -8.56926328e-05, -2.56768407e-16,  2.38418521e-06,\n       -2.89351931e-16,  2.84696849e-05, -1.77696470e-16, -2.10693774e-05,\n       -8.56616099e-18,  4.74764623e-06, -1.26336157e-16,  9.74703896e-06,\n       -1.86248124e-16, -6.17509459e-06, -2.08540055e-16, -1.04844029e-06,\n       -1.86006363e-16,  3.01586958e-06,  1.90371674e-16,  1.00000000e+00,\n       -1.30000000e-01,  1.30000000e-01,  2.60000000e-01,  0.00000000e+00])</pre> <p>This is the fieldmap that makes that data:</p> In\u00a0[33]: Copied! <pre>fmap = idata['fmap']\nfmap.keys()\n</pre> fmap = idata['fmap'] fmap.keys() Out[33]: <pre>dict_keys(['info', 'data', 'field'])</pre> <p>Additional info:</p> In\u00a0[34]: Copied! <pre>fmap['info']\n</pre> fmap['info'] Out[34]: <pre>{'format': 'solrf',\n 'Ez_scale': 1.0,\n 'Ez_err': 7.68588630296298e-08,\n 'Bz_scale': 0.0,\n 'Bz_err': 0,\n 'zmin': -0.13,\n 'zmax': 0.13}</pre> In\u00a0[35]: Copied! <pre>from pmd_beamphysics.interfaces.impact import fourier_field_reconsruction\nL = z0.ptp()\nzlist = np.linspace(0, L, len(Ez0))\nfcoefs = fmap['field']['Ez']['fourier_coefficients']\nreconstructed_Ez0 = np.array([fourier_field_reconsruction(z, fcoefs, z0=-L, zlen = 2*L) for z in zlist])\n\nfig, ax = plt.subplots()\nax2 = ax.twinx()\nax.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$', color='black')\nax.plot(zlist, reconstructed_Ez0, '--',  label='reconstructed', color='red', )\nax2.plot(zlist, abs(reconstructed_Ez0/Ez0 -1), '--',  label='reconstructed', color='black' )\nax2.set_ylabel('relative error')\nax2.set_yscale('log')\nax.set_ylabel('field (V/m)')\nax.set_xlabel('z (m)')\nplt.legend()\n</pre> from pmd_beamphysics.interfaces.impact import fourier_field_reconsruction L = z0.ptp() zlist = np.linspace(0, L, len(Ez0)) fcoefs = fmap['field']['Ez']['fourier_coefficients'] reconstructed_Ez0 = np.array([fourier_field_reconsruction(z, fcoefs, z0=-L, zlen = 2*L) for z in zlist])  fig, ax = plt.subplots() ax2 = ax.twinx() ax.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$', color='black') ax.plot(zlist, reconstructed_Ez0, '--',  label='reconstructed', color='red', ) ax2.plot(zlist, abs(reconstructed_Ez0/Ez0 -1), '--',  label='reconstructed', color='black' ) ax2.set_ylabel('relative error') ax2.set_yscale('log') ax.set_ylabel('field (V/m)') ax.set_xlabel('z (m)') plt.legend() Out[35]: <pre>&lt;matplotlib.legend.Legend at 0x7ff4742ea8b0&gt;</pre> <p>This function can also be used to study the reconstruction error as a function of the number of coefficients:</p> In\u00a0[36]: Copied! <pre>ncoefs = np.arange(10, FM2.shape[2]//2)\nerrs = np.array([FM2.to_impact_solrf(n_coef = n, zmirror=True)['fmap']['info']['Ez_err'] for n in ncoefs])\n\nfig, ax = plt.subplots()\nax.plot(ncoefs, errs, marker='.', color='black')\nax.set_xlabel('n_coef')\nax.set_ylabel('Ez reconstruction error')\nax.set_yscale('log')\n</pre> ncoefs = np.arange(10, FM2.shape[2]//2) errs = np.array([FM2.to_impact_solrf(n_coef = n, zmirror=True)['fmap']['info']['Ez_err'] for n in ncoefs])  fig, ax = plt.subplots() ax.plot(ncoefs, errs, marker='.', color='black') ax.set_xlabel('n_coef') ax.set_ylabel('Ez reconstruction error') ax.set_yscale('log') In\u00a0[37]: Copied! <pre>#FM.write_gpt('solenoid.gdf', asci2gdf_bin='$ASCI2GDF_BIN', verbose=True)\nFM.write_gpt('rfgun_for_gpt.txt', verbose=True)\n</pre> #FM.write_gpt('solenoid.gdf', asci2gdf_bin='$ASCI2GDF_BIN', verbose=True) FM.write_gpt('rfgun_for_gpt.txt', verbose=True) <pre>ASCII field data written. Convert to GDF using: asci2df -o field.gdf rfgun_for_gpt.txt\n</pre> Out[37]: <pre>'rfgun_for_gpt.txt'</pre> In\u00a0[38]: Copied! <pre>FM.write_superfish('rfgun2.t7')\n</pre> FM.write_superfish('rfgun2.t7') Out[38]: <pre>'rfgun2.t7'</pre> In\u00a0[39]: Copied! <pre>FM3 = FieldMesh.from_superfish('rfgun2.t7')\nFM3\n</pre> FM3 = FieldMesh.from_superfish('rfgun2.t7') FM3 Out[39]: <pre>&lt;FieldMesh with cylindrical geometry and (61, 1, 521) shape at 0x7ff475451d30&gt;</pre> In\u00a0[40]: Copied! <pre>help(FieldMesh.from_superfish)\n</pre> help(FieldMesh.from_superfish) <pre>Help on method read_superfish_t7 in module pmd_beamphysics.interfaces.superfish:\n\nread_superfish_t7(type=None, geometry='cylindrical') method of builtins.type instance\n    Parses a T7 file written by Posson/Superfish.\n    \n    Fish or Poisson T7 are automatically detected according to the second line.\n    \n    For Poisson problems, the type must be specified.\n    \n    Superfish fields oscillate as:\n        Er, Ez ~ cos(wt)\n        Hphi   ~ -sin(wt)\n      \n    For complex fields oscillating as e^-iwt\n    \n        Re(Ex*e^-iwt)   ~ cos\n        Re(-iB*e^-iwt) ~ -sin        \n    and therefore B = -i * mu_0 * H_phi is the complex magnetic field in Tesla\n    \n    \n    Parameters:\n    ----------\n    filename: str\n        T7 filename to read\n    type: str, optional\n        For Poisson files, required to be 'electric' or 'magnetic'. \n        Not used for Fish files\n    geometry: str, optional\n        field geometry, currently required to be the default: 'cylindrical'\n    \n    Returns:\n    -------\n    fieldmesh_data: dict of dicts:\n        attrs\n        components\n        \n        \n    A FieldMesh object is instantiated from this as:\n        FieldMesh(data=fieldmesh_data)\n\n</pre> <p>Note that writing the ASCII and conversions alter the data slightly</p> In\u00a0[41]: Copied! <pre>FM == FM3\n</pre> FM == FM3 Out[41]: <pre>False</pre> <p>But the data are all close:</p> In\u00a0[42]: Copied! <pre>for c in FM.components:\n    close = np.allclose(FM.components[c], FM3.components[c])\n    equal = np.all(FM.components[c] == FM3.components[c])\n    print(c, equal, close)\n</pre> for c in FM.components:     close = np.allclose(FM.components[c], FM3.components[c])     equal = np.all(FM.components[c] == FM3.components[c])     print(c, equal, close) <pre>electricField/z False True\nelectricField/r False True\nmagneticField/theta False True\n</pre> In\u00a0[43]: Copied! <pre>FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',\n                              hfile='../data/ansys_rfgun_2856MHz_H.dat',\n                              frequency=2856e6)\n\n\n\nFM3D\n</pre> FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',                               hfile='../data/ansys_rfgun_2856MHz_H.dat',                               frequency=2856e6)    FM3D Out[43]: <pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7ff47415a910&gt;</pre> In\u00a0[44]: Copied! <pre>FM3D.attrs\n</pre> FM3D.attrs Out[44]: <pre>{'eleAnchorPt': 'beginning',\n 'gridGeometry': 'rectangular',\n 'axisLabels': ('x', 'y', 'z'),\n 'gridLowerBound': (0, 0, 0),\n 'gridOriginOffset': (-0.001, -0.001, 0.0),\n 'gridSpacing': (0.001, 0.001, 0.00025),\n 'gridSize': (3, 3, 457),\n 'harmonic': 1,\n 'fundamentalFrequency': 2856000000.0,\n 'RFphase': 0,\n 'fieldScale': 1.0}</pre> <p>This can then be written:</p> In\u00a0[45]: Copied! <pre>FM3D.write('../data/rfgun_rectangular.h5')\n</pre> FM3D.write('../data/rfgun_rectangular.h5') <p>The y=0 plane can be extracted to be used as cylindrically symmetric data:</p> In\u00a0[46]: Copied! <pre>FM2D = FM3D.to_cylindrical()\nFM2D\n</pre>  FM2D = FM3D.to_cylindrical() FM2D Out[46]: <pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7ff47415ad30&gt;</pre> In\u00a0[47]: Copied! <pre>import os\nfor file in ('test.h5',\n             'astra_1d.dat',\n             'rfgun_for_gpt.txt',\n             'rfgun2.h5',\n             'rfgun2.t7'):\n    os.remove(file)\n</pre> import os for file in ('test.h5',              'astra_1d.dat',              'rfgun_for_gpt.txt',              'rfgun2.h5',              'rfgun2.t7'):     os.remove(file)"},{"location":"examples/fields/field_examples/#fieldmesh-examples","title":"FieldMesh Examples\u00b6","text":""},{"location":"examples/fields/field_examples/#internal-data","title":"Internal data\u00b6","text":"<p>attributes and components</p>"},{"location":"examples/fields/field_examples/#properties","title":"Properties\u00b6","text":"<p>Convenient access to these</p>"},{"location":"examples/fields/field_examples/#components","title":"Components\u00b6","text":""},{"location":"examples/fields/field_examples/#oscillating-fields","title":"Oscillating fields\u00b6","text":"<p>Oscillating fields have <code>.harmonic &gt; 0</code></p>"},{"location":"examples/fields/field_examples/#verify-the-oscillation","title":"Verify the oscillation\u00b6","text":"<p>Complex fields oscillate as $e^{-i\\omega t}$. For TM fields, the spatial components $E_z$ and $B_\\theta$ near the axis</p> <p>$\\Re E_{z} = -\\frac{r}{2}\\frac{\\omega}{c^2} \\Im B_\\theta$</p>"},{"location":"examples/fields/field_examples/#units","title":"Units\u00b6","text":""},{"location":"examples/fields/field_examples/#write","title":"Write\u00b6","text":""},{"location":"examples/fields/field_examples/#write-astra-1d","title":"Write Astra 1D\u00b6","text":"<p>Astra primarily uses simple 1D (on-axis) fieldmaps.</p>"},{"location":"examples/fields/field_examples/#write-impact-t","title":"Write Impact-T\u00b6","text":"<p>Impact-T uses a particular Fourier representation for 1D fields. These routines form this data.</p>"},{"location":"examples/fields/field_examples/#write-gpt","title":"Write GPT\u00b6","text":""},{"location":"examples/fields/field_examples/#read-superfish","title":"Read Superfish\u00b6","text":"<p>Proper Superfish T7 can also be read.</p>"},{"location":"examples/fields/field_examples/#read-ansys","title":"Read ANSYS\u00b6","text":"<p>Read ANSYS E and H ASCII files:</p>"},{"location":"examples/fields/field_examples/#cleanup","title":"Cleanup\u00b6","text":""},{"location":"examples/fields/field_expansion/","title":"Field expansion","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh\n</pre> from pmd_beamphysics import FieldMesh In\u00a0[3]: Copied! <pre>FM = FieldMesh('../data/solenoid.h5')\nFM.plot()\n</pre> FM = FieldMesh('../data/solenoid.h5') FM.plot() In\u00a0[4]: Copied! <pre>FM.plot_onaxis()\n</pre> FM.plot_onaxis() In\u00a0[5]: Copied! <pre>from pmd_beamphysics.fields.expansion import fft_derivative_array, spline_derivative_array\n</pre> from pmd_beamphysics.fields.expansion import fft_derivative_array, spline_derivative_array In\u00a0[6]: Copied! <pre>Z = FM.coord_vec('z')\nDZ = FM.dz\nFZ = FM.Bz[0,0,:]\n\ndfield1 = fft_derivative_array(FZ, DZ, ncoef=10)\ndfield2 = spline_derivative_array(Z, FZ, s=1e-9)\n</pre> Z = FM.coord_vec('z') DZ = FM.dz FZ = FM.Bz[0,0,:]  dfield1 = fft_derivative_array(FZ, DZ, ncoef=10) dfield2 = spline_derivative_array(Z, FZ, s=1e-9) In\u00a0[7]: Copied! <pre>plt.plot(Z, dfield1[:,1], label='fft')\nplt.plot(Z, dfield2[:,1], label='spline')\nplt.xlabel('z (m)')\nplt.ylabel(r'$dB_z/dz$' + r\" (T/m)\")\n</pre> plt.plot(Z, dfield1[:,1], label='fft') plt.plot(Z, dfield2[:,1], label='spline') plt.xlabel('z (m)') plt.ylabel(r'$dB_z/dz$' + r\" (T/m)\") Out[7]: <pre>Text(0, 0.5, '$dB_z/dz$ (T/m)')</pre> In\u00a0[8]: Copied! <pre>plt.plot(Z, dfield1[:,2], label='fft')\nplt.plot(Z, dfield2[:,2], label='spline')\nplt.xlabel('z (m)')\nplt.ylabel(r'$d^2B_z/dz^2$' + r\" (T/m^2)\")\nplt.legend()\n</pre> plt.plot(Z, dfield1[:,2], label='fft') plt.plot(Z, dfield2[:,2], label='spline') plt.xlabel('z (m)') plt.ylabel(r'$d^2B_z/dz^2$' + r\" (T/m^2)\") plt.legend() Out[8]: <pre>&lt;matplotlib.legend.Legend at 0x7f32e4463ac0&gt;</pre> In\u00a0[9]: Copied! <pre>plt.plot(Z, dfield1[:,3], label='fft')\nplt.plot(Z, dfield2[:,3], label='spline')\nplt.xlabel('z (m)')\nplt.ylabel(r'$d^3B_z/dz^3$' + r\" (T/m^3)\")\nplt.legend()\n</pre> plt.plot(Z, dfield1[:,3], label='fft') plt.plot(Z, dfield2[:,3], label='spline') plt.xlabel('z (m)') plt.ylabel(r'$d^3B_z/dz^3$' + r\" (T/m^3)\") plt.legend() Out[9]: <pre>&lt;matplotlib.legend.Legend at 0x7f32e4412430&gt;</pre> In\u00a0[10]: Copied! <pre>FM2 = FieldMesh.from_onaxis(z=Z, Bz=FZ)\nFM2.plot_onaxis()\n</pre> FM2 = FieldMesh.from_onaxis(z=Z, Bz=FZ) FM2.plot_onaxis() In\u00a0[11]: Copied! <pre>FM3 = FM2.expand_onaxis(dr=FM.dr, nr=10)\nFM3\n</pre> FM3 = FM2.expand_onaxis(dr=FM.dr, nr=10) FM3 Out[11]: <pre>&lt;FieldMesh with cylindrical geometry and (10, 1, 201) shape at 0x7f32e437bbe0&gt;</pre> In\u00a0[12]: Copied! <pre>FM3.plot('Br')\n</pre> FM3.plot('Br') In\u00a0[13]: Copied! <pre>def compare(fm1, fm2, component='Ez'):\n    \n    z = fm1.coord_vec('z')\n    dr = fm1.dr\n    nr = min(fm1.shape[0], fm2.shape[0])\n    \n    unit = fm1.units(component)\n    Fz1 =  np.squeeze(fm1[component])[0:nr, :]\n    Fz2 =  np.squeeze(fm2[component])[0:nr, :]\n    err = abs(Fz1-Fz2) / np.abs(Fz1).max() \n    \n    extent = np.array([z.min(), z.max(), 0, dr*(nr-1)]) * 1000\n    plt.imshow(err, origin='lower', extent = extent , aspect='auto')\n    plt.xlabel('z (mm)')\n    plt.ylabel('r (mm)')\n    \n    plt.title(f\"{component} expansion error, max err = {err.max()}\")\n    plt.colorbar(label=f'relatic expansion error')\n    \ncompare(FM, FM3, 'B')\n</pre> def compare(fm1, fm2, component='Ez'):          z = fm1.coord_vec('z')     dr = fm1.dr     nr = min(fm1.shape[0], fm2.shape[0])          unit = fm1.units(component)     Fz1 =  np.squeeze(fm1[component])[0:nr, :]     Fz2 =  np.squeeze(fm2[component])[0:nr, :]     err = abs(Fz1-Fz2) / np.abs(Fz1).max()           extent = np.array([z.min(), z.max(), 0, dr*(nr-1)]) * 1000     plt.imshow(err, origin='lower', extent = extent , aspect='auto')     plt.xlabel('z (mm)')     plt.ylabel('r (mm)')          plt.title(f\"{component} expansion error, max err = {err.max()}\")     plt.colorbar(label=f'relatic expansion error')      compare(FM, FM3, 'B')     In\u00a0[14]: Copied! <pre>FM = FieldMesh('../data/rfgun.h5')\nFM.plot()\n</pre> FM = FieldMesh('../data/rfgun.h5') FM.plot() In\u00a0[15]: Copied! <pre>Z = FM.coord_vec('z')\nDZ = FM.dz\nFZ = np.real(FM.Ez[0,0,:])\n\nFM2 = FieldMesh.from_onaxis(z=Z, Ez=FZ, frequency=FM.frequency)\nFM2.plot_onaxis()\n</pre> Z = FM.coord_vec('z') DZ = FM.dz FZ = np.real(FM.Ez[0,0,:])  FM2 = FieldMesh.from_onaxis(z=Z, Ez=FZ, frequency=FM.frequency) FM2.plot_onaxis() In\u00a0[\u00a0]: Copied! <pre>\n</pre> In\u00a0[16]: Copied! <pre>NR = 40\nFM3 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft')\ncompare(FM, FM3, 'Er')\n</pre> NR = 40 FM3 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft') compare(FM, FM3, 'Er')    In\u00a0[17]: Copied! <pre>compare(FM, FM3, 'Ez')\n</pre> compare(FM, FM3, 'Ez')   In\u00a0[18]: Copied! <pre>compare(FM, FM3, 'Btheta')\n</pre> compare(FM, FM3, 'Btheta')   In\u00a0[19]: Copied! <pre>NR = 40\nFM4 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9)\ncompare(FM, FM4, 'Er')\n</pre> NR = 40 FM4 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9) compare(FM, FM4, 'Er')    In\u00a0[20]: Copied! <pre>compare(FM, FM4, 'Ez')\n</pre> compare(FM, FM4, 'Ez')   In\u00a0[21]: Copied! <pre>compare(FM, FM4, 'Btheta')\n</pre> compare(FM, FM4, 'Btheta')   In\u00a0[22]: Copied! <pre>compare(FM, FM4, 'Btheta')\n</pre> compare(FM, FM4, 'Btheta')   In\u00a0[23]: Copied! <pre># Differences between the two methods\ncompare(FM3, FM4, 'E')\n</pre> # Differences between the two methods compare(FM3, FM4, 'E')   In\u00a0[24]: Copied! <pre>def compare2(comp='Er'):\n    NR = 10\n    dr = FM.dr\n    r = (NR-1)*FM.dr\n    FM5 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft', ncoef=15)\n    \n    if comp.startswith('E'):\n        func = np.real\n    else:\n        func = np.imag\n    \n    f0 = func(FM[comp][NR-1,0,:])\n    \n    f5 = func(FM5[comp][NR-1,0,:])\n    \n    FM6 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9)\n    f6 = func(FM6[comp][NR-1,0,:])\n    \n    fix, ax = plt.subplots()\n    ax2 = ax.twinx()\n    ax.plot(f0, label='original')\n    ax.plot(f5, '--', label='fourier')\n    ax.plot(f6, '--', label='spline')\n    ax.legend(loc='upper left')\n    ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')\n    ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')\n    ax2.set_yscale('log')\n    ax.set_ylabel(comp)\n    ax2.set_ylabel('relative error')\n    ax2.legend(loc='upper right')\n    ax.set_xlabel('index along z')\n</pre>  def compare2(comp='Er'):     NR = 10     dr = FM.dr     r = (NR-1)*FM.dr     FM5 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft', ncoef=15)          if comp.startswith('E'):         func = np.real     else:         func = np.imag          f0 = func(FM[comp][NR-1,0,:])          f5 = func(FM5[comp][NR-1,0,:])          FM6 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9)     f6 = func(FM6[comp][NR-1,0,:])          fix, ax = plt.subplots()     ax2 = ax.twinx()     ax.plot(f0, label='original')     ax.plot(f5, '--', label='fourier')     ax.plot(f6, '--', label='spline')     ax.legend(loc='upper left')     ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')     ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')     ax2.set_yscale('log')     ax.set_ylabel(comp)     ax2.set_ylabel('relative error')     ax2.legend(loc='upper right')     ax.set_xlabel('index along z') In\u00a0[25]: Copied! <pre>compare2('Er')\n</pre> compare2('Er') <pre>/tmp/ipykernel_5526/1820762901.py:25: RuntimeWarning: divide by zero encountered in divide\n  ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')\n/tmp/ipykernel_5526/1820762901.py:26: RuntimeWarning: divide by zero encountered in divide\n  ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')\n</pre> In\u00a0[26]: Copied! <pre>compare2('Ez')\n</pre> compare2('Ez') In\u00a0[27]: Copied! <pre>compare2('Btheta')\n</pre> compare2('Btheta')"},{"location":"examples/fields/field_expansion/#field-expansion","title":"Field expansion\u00b6","text":""},{"location":"examples/fields/field_expansion/#derivative-array","title":"Derivative array\u00b6","text":"<p>Field expansions depend on numerical derivatives of the on-axis field. Here are two methods.</p>"},{"location":"examples/fields/field_expansion/#fieldmesh-from-1d-data","title":"FieldMesh from 1D data\u00b6","text":""},{"location":"examples/fields/field_expansion/#expansion-1d-2d","title":"Expansion 1D -&gt; 2D\u00b6","text":""},{"location":"examples/fields/field_expansion/#rf-gun-1d-2d","title":"RF Gun 1D -&gt; 2D\u00b6","text":""},{"location":"examples/fields/field_expansion/#spline-based-expansion","title":"Spline-based expansion\u00b6","text":""},{"location":"examples/fields/field_expansion/#compare-fourier-and-spline","title":"Compare Fourier and Spline\u00b6","text":""},{"location":"examples/fields/field_tracking/","title":"Field Phasing and Scaling (Autophase)","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh\n</pre> from pmd_beamphysics import FieldMesh In\u00a0[3]: Copied! <pre>FM = FieldMesh('../data/rfgun.h5')\nFM.plot(aspect='equal', figsize=(12,8))\n</pre> FM = FieldMesh('../data/rfgun.h5') FM.plot(aspect='equal', figsize=(12,8)) In\u00a0[4]: Copied! <pre># On-axis field\nz0 = FM.coord_vec('z')\nEz0 = FM.Ez[0,0,:]  # this is complex\nplt.plot(z0, np.real(Ez0))\n</pre> # On-axis field z0 = FM.coord_vec('z') Ez0 = FM.Ez[0,0,:]  # this is complex plt.plot(z0, np.real(Ez0)) Out[4]: <pre>[&lt;matplotlib.lines.Line2D at 0x1228734c0&gt;]</pre> In\u00a0[5]: Copied! <pre>from pmd_beamphysics.fields.analysis import accelerating_voltage_and_phase\n</pre> from pmd_beamphysics.fields.analysis import accelerating_voltage_and_phase In\u00a0[6]: Copied! <pre>?accelerating_voltage_and_phase\n</pre> ?accelerating_voltage_and_phase In\u00a0[7]: Copied! <pre>V0, phase0 = accelerating_voltage_and_phase(z0, -Ez0*120e6, FM.frequency)\n\nV0, (phase0 * 180/np.pi) % 360\n</pre> V0, phase0 = accelerating_voltage_and_phase(z0, -Ez0*120e6, FM.frequency)  V0, (phase0 * 180/np.pi) % 360 Out[7]: <pre>(5795904.446882586, 322.1355626180106)</pre> <p>Equations of motion:</p> <p>$\\frac{dz}{dt} = \\frac{pc}{\\sqrt{(pc)^2 + m^2 c^4)}} c$</p> <p>$\\frac{dp}{dt} = q E_z $</p> <p>$E_z = \\Re f(z) \\exp(-i \\omega t) $</p> In\u00a0[8]: Copied! <pre>from pmd_beamphysics.fields.analysis import track_field_1d\nfrom pmd_beamphysics.units import mec2, c_light\n</pre> from pmd_beamphysics.fields.analysis import track_field_1d from pmd_beamphysics.units import mec2, c_light In\u00a0[9]: Copied! <pre>?track_field_1d\n</pre> ?track_field_1d In\u00a0[10]: Copied! <pre>Z = FM.coord_vec('z')\nE = FM.Ez[0,0,:]*np.exp(1j*2*np.pi /360 * 0)*120e6 \n\n# Final z (m) and pz (eV/c)\ntrack_field_1d(Z, E, FM.frequency, pz0=0, t0=0)\n</pre> Z = FM.coord_vec('z') E = FM.Ez[0,0,:]*np.exp(1j*2*np.pi /360 * 0)*120e6   # Final z (m) and pz (eV/c) track_field_1d(Z, E, FM.frequency, pz0=0, t0=0) Out[10]: <pre>(0.13000001229731462, 3896770.3798088795)</pre> In\u00a0[11]: Copied! <pre># Use debug mode to see the actual track\nsol = track_field_1d(Z, E, FM.frequency, pz0=0, t0=0, debug=True, max_step=1/FM.frequency/100)\n</pre> # Use debug mode to see the actual track sol = track_field_1d(Z, E, FM.frequency, pz0=0, t0=0, debug=True, max_step=1/FM.frequency/100) In\u00a0[12]: Copied! <pre># Plot the track\nfig, ax = plt.subplots()\n\nax2 = ax.twinx()\n\nax.set_xlabel('f*t')\nax.set_ylabel('z (m)')\nax2.set_ylabel('KE (MeV)')\n\nax.plot(sol.t*FM.frequency, sol.y[0])\nax2.plot(sol.t*FM.frequency, (np.hypot(sol.y[1], mec2)-mec2)/1e6, color='red')\n</pre> # Plot the track fig, ax = plt.subplots()  ax2 = ax.twinx()  ax.set_xlabel('f*t') ax.set_ylabel('z (m)') ax2.set_ylabel('KE (MeV)')  ax.plot(sol.t*FM.frequency, sol.y[0]) ax2.plot(sol.t*FM.frequency, (np.hypot(sol.y[1], mec2)-mec2)/1e6, color='red') Out[12]: <pre>[&lt;matplotlib.lines.Line2D at 0x122e69940&gt;]</pre> In\u00a0[13]: Copied! <pre>from pmd_beamphysics.fields.analysis import autophase_field\n</pre> from pmd_beamphysics.fields.analysis import autophase_field In\u00a0[14]: Copied! <pre>phase_deg1, pz1 = autophase_field(FM, pz0=0, scale=120e6, verbose=True)\nphase_deg1, pz1\n</pre> phase_deg1, pz1 = autophase_field(FM, pz0=0, scale=120e6, verbose=True) phase_deg1, pz1  <pre>v=c voltage: 5795904.446882586 V, phase: -37.86443738198939 deg\n    iterations: 18\n    function calls: 23\n</pre> Out[14]: <pre>(304.334830187332, 6234145.7957780445)</pre> In\u00a0[15]: Copied! <pre># Use debug mode to visualize. This returns the phasiing function\nphase_f = autophase_field(FM, pz0=0, scale=120e6, debug=True)\nphase_f(304.3348289439232)\n</pre> # Use debug mode to visualize. This returns the phasiing function phase_f = autophase_field(FM, pz0=0, scale=120e6, debug=True) phase_f(304.3348289439232) Out[15]: <pre>6234145.795777954</pre> In\u00a0[16]: Copied! <pre>plist = np.linspace(280, 330, 100)\npzlist = np.array([phase_f(p) for p in plist])\n\nplt.plot(plist, pzlist/1e6)\nplt.scatter(phase_deg1, pz1/1e6, color='red')\nplt.xlabel('phase (deg)')\nplt.ylabel('pz (MeV/c)')\n</pre> plist = np.linspace(280, 330, 100) pzlist = np.array([phase_f(p) for p in plist])  plt.plot(plist, pzlist/1e6) plt.scatter(phase_deg1, pz1/1e6, color='red') plt.xlabel('phase (deg)') plt.ylabel('pz (MeV/c)') Out[16]: <pre>Text(0, 0.5, 'pz (MeV/c)')</pre> In\u00a0[17]: Copied! <pre>from pmd_beamphysics.fields.analysis import autophase_and_scale_field\n?autophase_and_scale_field\n</pre> from pmd_beamphysics.fields.analysis import autophase_and_scale_field ?autophase_and_scale_field In\u00a0[18]: Copied! <pre>phase_deg2, scale2 = autophase_and_scale_field(FM, 6e6, pz0=0, verbose=True)\nphase_deg2, scale2\n</pre> phase_deg2, scale2 = autophase_and_scale_field(FM, 6e6, pz0=0, verbose=True) phase_deg2, scale2 <pre>v=c voltage: 0.048299203724021564 V, phase: -37.86443738198939 deg\n    Pass 1 delta energy: 6000000.288677918 at phase  304.9786263011349 deg\n    Pass 2 delta energy: 5999999.999982537 at phase  305.14631150985355 deg\n</pre> Out[18]: <pre>(305.14631150985355, 125273551.27124627)</pre> In\u00a0[19]: Copied! <pre># Use debug mode to visualize. This returns the phasing function\nps_f = autophase_and_scale_field(FM, 6e6, pz0=0, debug=True)\nps_f(phase_deg2, scale2)\n</pre> # Use debug mode to visualize. This returns the phasing function ps_f = autophase_and_scale_field(FM, 6e6, pz0=0, debug=True) ps_f(phase_deg2, scale2) Out[19]: <pre>5999999.999982537</pre> In\u00a0[20]: Copied! <pre>plist = np.linspace(280, 330, 100)\ndenergy = np.array([ps_f(p, scale2) for p in plist])\n\nplt.plot(plist, denergy/1e6)\nplt.scatter(phase_deg2, ps_f(phase_deg2, scale2)/1e6, color='red', label='Autophased')\nplt.xlabel('phase (deg)')\nplt.ylabel('Voltage (MV)')\nplt.legend()\n</pre> plist = np.linspace(280, 330, 100) denergy = np.array([ps_f(p, scale2) for p in plist])  plt.plot(plist, denergy/1e6) plt.scatter(phase_deg2, ps_f(phase_deg2, scale2)/1e6, color='red', label='Autophased') plt.xlabel('phase (deg)') plt.ylabel('Voltage (MV)') plt.legend() Out[20]: <pre>&lt;matplotlib.legend.Legend at 0x1232768b0&gt;</pre>"},{"location":"examples/fields/field_tracking/#field-phasing-and-scaling-autophase","title":"Field Phasing and Scaling (Autophase)\u00b6","text":""},{"location":"examples/fields/field_tracking/#get-field","title":"Get field\u00b6","text":""},{"location":"examples/fields/field_tracking/#vc-voltage-and-phase","title":"v=c voltage and phase\u00b6","text":""},{"location":"examples/fields/field_tracking/#tracking","title":"Tracking\u00b6","text":""},{"location":"examples/fields/field_tracking/#autophase","title":"Autophase\u00b6","text":""},{"location":"examples/fields/field_tracking/#autophase-and-scale","title":"Autophase and Scale\u00b6","text":""}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"openPMD-beamphysics","text":"<p>Tools for analyzing and viewing particle data in the openPMD standard, extension beamphysics defined in: beamphysics extension</p>"},{"location":"#python-classes","title":"Python classes","text":"<p>This package provides two feature-rich classes for handling openPMD-beamphysics standard data:</p> <ul> <li> <p><code>ParticleGroup</code> for handling particle data</p> </li> <li> <p><code>FieldMesh</code> - for handling external field mesh data</p> </li> </ul> <p>For usage see the examples.</p>"},{"location":"#installation","title":"Installation","text":"<p>Installing <code>openpmd-beamphysics</code> from the <code>conda-forge</code> channel can be achieved by adding <code>conda-forge</code> to your channels with:</p> <pre><code>conda config --add channels conda-forge\n</code></pre> <p>Once the <code>conda-forge</code> channel has been enabled, <code>openpmd-beamphysics</code> can be installed with:</p> <pre><code>conda install openpmd-beamphysics\n</code></pre> <p>It is possible to list all of the versions of <code>openpmd-beamphysics</code> available on your platform with:</p> <pre><code>conda search openpmd-beamphysics --channel conda-forge\n</code></pre>"},{"location":"api/fields/","title":"Fields","text":"<p>Class for openPMD External Field Mesh data.</p> <p>Initialized on on openPMD beamphysics particle group:</p> <ul> <li>h5: open h5 handle, or str that is a file</li> <li>data: raw data</li> </ul> <p>The required data is stored in ._data, and consists of dicts:</p> <ul> <li><code>'attrs'</code></li> <li><code>'components'</code></li> </ul> <p>Component data is always 3D.</p> <p>Initialization from openPMD-beamphysics HDF5 file:</p> <ul> <li><code>FieldMesh('file.h5')</code></li> </ul> <p>Initialization from a data dict:</p> <ul> <li><code>FieldMesh(data=data)</code></li> </ul> <p>Derived properties:</p> <ul> <li><code>.r</code>, <code>.theta</code>, <code>.z</code></li> <li><code>.Br</code>, <code>.Btheta</code>, <code>.Bz</code></li> <li><code>.Er</code>, <code>.Etheta</code>, <code>.Ez</code></li> <li> <p><code>.E</code>, <code>.B</code></p> </li> <li> <p><code>.phase</code></p> </li> <li><code>.scale</code></li> <li> <p><code>.factor</code></p> </li> <li> <p><code>.harmonic</code></p> </li> <li> <p><code>.frequency</code></p> </li> <li> <p><code>.shape</code></p> </li> <li><code>.geometry</code></li> <li><code>.mins</code>, <code>.maxs</code>, <code>.deltas</code></li> <li><code>.meshgrid</code></li> <li><code>.dr</code>, <code>.dtheta</code>, <code>.dz</code></li> </ul> <p>Booleans:</p> <ul> <li><code>.is_pure_electric</code></li> <li><code>.is_pure_magnetic</code></li> <li><code>.is_static</code></li> </ul> <p>Units and labels</p> <ul> <li><code>.units</code></li> <li><code>.axis_labels</code></li> </ul> <p>Plotting:</p> <ul> <li><code>.plot</code></li> <li><code>.plot_onaxis</code></li> </ul> <p>Writers</p> <ul> <li><code>.write</code></li> <li><code>.write_astra_1d</code></li> <li><code>.write_astra_3d</code></li> <li><code>.to_cylindrical</code></li> <li><code>.to_astra_1d</code></li> <li><code>.to_impact_solrf</code></li> <li><code>.write_gpt</code></li> <li><code>.write_superfish</code></li> </ul> <p>Constructors (class methods):</p> <ul> <li><code>.from_ansys_ascii_3d</code></li> <li><code>.from_astra_3d</code></li> <li><code>.from_superfish</code></li> <li><code>.from_onaxis</code></li> <li><code>.expand_onaxis</code></li> </ul> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>class FieldMesh:\n    \"\"\"\n    Class for openPMD External Field Mesh data.\n\n    Initialized on on openPMD beamphysics particle group:\n\n    - **h5**: open h5 handle, or str that is a file\n    - **data**: raw data\n\n    The required data is stored in ._data, and consists of dicts:\n\n    - `'attrs'`\n    - `'components'`\n\n    Component data is always 3D.\n\n    Initialization from openPMD-beamphysics HDF5 file:\n\n    - `FieldMesh('file.h5')`\n\n    Initialization from a data dict:\n\n    - `FieldMesh(data=data)`\n\n    Derived properties:\n\n    - `.r`, `.theta`, `.z`\n    - `.Br`, `.Btheta`, `.Bz`\n    - `.Er`, `.Etheta`, `.Ez`\n    - `.E`, `.B`\n\n    - `.phase`\n    - `.scale`\n    - `.factor`\n\n    - `.harmonic`\n    - `.frequency`\n\n    - `.shape`\n    - `.geometry`\n    - `.mins`, `.maxs`, `.deltas`\n    - `.meshgrid`\n    - `.dr`, `.dtheta`, `.dz`\n\n    Booleans:\n\n    - `.is_pure_electric`\n    - `.is_pure_magnetic`\n    - `.is_static`\n\n    Units and labels\n\n    - `.units`\n    - `.axis_labels`\n\n    Plotting:\n\n    - `.plot`\n    - `.plot_onaxis`\n\n    Writers\n\n    - `.write`\n    - `.write_astra_1d`\n    - `.write_astra_3d`\n    - `.to_cylindrical`\n    - `.to_astra_1d`\n    - `.to_impact_solrf`\n    - `.write_gpt`\n    - `.write_superfish`\n\n    Constructors (class methods):\n\n    - `.from_ansys_ascii_3d`\n    - `.from_astra_3d`\n    - `.from_superfish`\n    - `.from_onaxis`\n    - `.expand_onaxis`\n\n\n\n\n    \"\"\"\n    def __init__(self, h5=None, data=None):\n\n        if h5:\n            # Allow filename\n            if isinstance(h5, str):\n                fname = os.path.expandvars(os.path.expanduser(h5))\n                assert os.path.exists(fname), f'File does not exist: {fname}'\n\n                with File(fname, 'r') as hh5:\n                    fp = field_paths(hh5)\n                    assert len(fp) == 1, f'Number of field paths in {h5}: {len(fp)}'\n                    data = load_field_data_h5(hh5[fp[0]])\n\n            else:\n                data = load_field_data_h5(h5)\n        else:\n            data = load_field_data_dict(data)\n\n        # Internal data\n        self._data = data\n\n        # Aliases (Do not set these! Set via slicing: .Bz[:] = 0\n        #for k in self.components:\n        #    alias = component_alias[k]\n        #    self.__dict__[alias] =  self.components[k]\n\n\n    # Direct access to internal data        \n    @property\n    def attrs(self):\n        return self._data['attrs']\n\n    @property\n    def components(self):\n        return self._data['components']  \n\n    @property\n    def data(self):\n        return self._data\n\n\n    # Conveniences \n    @property\n    def shape(self):\n        return tuple(self.attrs['gridSize'])\n\n    @property\n    def geometry(self):\n        return self.attrs['gridGeometry']\n\n    @property\n    def scale(self):\n        return self.attrs['fieldScale']    \n    @scale.setter\n    def scale(self, val):\n        self.attrs['fieldScale']  = val\n\n    @property\n    def phase(self):\n        \"\"\"\n        Returns the complex argument `phi = -2*pi*RFphase`\n        to multiply the oscillating field by. \n\n        Can be set. \n        \"\"\"\n        return -self.attrs['RFphase']*2*np.pi\n    @phase.setter\n    def phase(self, val):\n        \"\"\"\n        Complex argument in radians\n        \"\"\"\n        self.attrs['RFphase']  = -val/(2*np.pi)    \n\n    @property\n    def factor(self):\n        \"\"\"\n        factor to multiply fields by, possibly complex.\n\n        `factor = scale * exp(i*phase)`\n        \"\"\"\n        return np.real_if_close(self.scale * np.exp(1j*self.phase))           \n\n    @property\n    def axis_labels(self):\n        \"\"\"\n\n        \"\"\"\n        return axis_labels_from_geometry[self.geometry]\n\n    def axis_index(self, key):\n        \"\"\"\n        Returns axis index for a named axis label key.\n\n        Examples:\n\n        - `.axis_labels == ('x', 'y', 'z')`\n        - `.axis_index('z')` returns `2`\n        \"\"\"\n        for i, name in enumerate(self.axis_labels):\n            if name == key:\n                return i\n        raise ValueError(f'Axis not found: {key}')\n\n    @property\n    def coord_vecs(self):\n        \"\"\"\n        Uses gridSpacing, gridSize, and gridOriginOffset to return coordinate vectors.\n        \"\"\"\n        return [np.linspace(x0, x1, nx) for x0, x1, nx in zip(self.mins, self.maxs, self.shape)]\n\n    def coord_vec(self, key):\n        \"\"\"\n        Gets the coordinate vector from a named axis key. \n        \"\"\"\n        i = self.axis_index(key)\n        return np.linspace(self.mins[i], self.maxs[i], self.shape[i])\n\n    @property \n    def meshgrid(self):\n        \"\"\"\n        Usses coordinate vectors to produce a standard numpy meshgrids. \n        \"\"\"\n        vecs = self.coord_vecs\n        return np.meshgrid(*vecs, indexing='ij')\n\n\n\n    @property \n    def mins(self):\n        return np.array(self.attrs['gridOriginOffset'])\n    @property\n    def deltas(self):\n        return np.array(self.attrs['gridSpacing'])\n    @property\n    def maxs(self):\n        return self.deltas*(np.array(self.attrs['gridSize'])-1) + self.mins      \n\n    @property\n    def frequency(self):\n        if self.is_static:\n            return 0\n        else:\n            return self.attrs['harmonic']*self.attrs['fundamentalFrequency']\n\n\n    # Logicals\n    @property\n    def is_pure_electric(self):\n        \"\"\"\n        Returns True if there are no non-zero mageneticField components\n        \"\"\"\n        klist = [key for key in self.components if not self.component_is_zero(key)]\n        return all([key.startswith('electric') for key in klist])\n    # Logicals\n    @property\n    def is_pure_magnetic(self):\n        \"\"\"\n        Returns True if there are no non-zero electricField components\n        \"\"\"\n        klist = [key for key in self.components if not self.component_is_zero(key)]\n        return all([key.startswith('magnetic') for key in klist])\n\n\n\n    @property\n    def is_static(self):\n        return  self.attrs['harmonic'] == 0\n\n\n\n    def component_is_zero(self, key):\n        \"\"\"\n        Returns True if all elements in a component are zero.\n        \"\"\"\n        a = self[key]\n        return not np.any(a)\n\n\n    # Plotting\n    # TODO: more general plotting\n    def plot(self, component=None, time=None, axes=None, cmap=None, return_figure=False, **kwargs):\n\n        if self.geometry != 'cylindrical':\n            raise NotImplementedError(f'Geometry {self.geometry} not implemented')\n\n        return plot_fieldmesh_cylindrical_2d(self,\n                                             component=component,\n                                             time=time,\n                                             axes=axes,\n                                             return_figure=return_figure,\n                                             cmap=cmap, **kwargs)\n\n    @functools.wraps(plot_fieldmesh_cylindrical_1d) \n    def plot_onaxis(self, *args, **kwargs):\n        assert self.geometry == 'cylindrical'\n        return plot_fieldmesh_cylindrical_1d(self, *args, **kwargs)\n\n\n    def units(self, key):\n        \"\"\"Returns the units of any key\"\"\"\n\n        # Strip any operators\n        _, key = get_operator(key)\n\n        # Fill out aliases \n        if key in component_from_alias:\n            key = component_from_alias[key]         \n\n        return pg_units(key)    \n\n    # openPMD    \n    def write(self, h5, name=None):\n        \"\"\"\n        Writes openPMD-beamphysics format to an open h5 handle, or new file if h5 is a str.\n\n        \"\"\"\n        if isinstance(h5, str):\n            fname = os.path.expandvars(os.path.expanduser(h5))\n            h5 = File(fname, 'w')\n            pmd_field_init(h5, externalFieldPath='/ExternalFieldPath/%T/')\n            g = h5.create_group('/ExternalFieldPath/1/')\n        else:\n            g = h5\n\n        write_pmd_field(g, self.data, name=name)   \n\n    @functools.wraps(write_astra_1d_fieldmap)\n    def write_astra_1d(self, filePath):      \n        return  write_astra_1d_fieldmap(self, filePath)\n\n    def to_astra_1d(self):\n        z, fz = astra_1d_fieldmap_data(self)   \n        dat = np.array([z, fz]).T \n        return {'attrs': {'type': 'astra_1d'}, 'data': dat}\n\n    def write_astra_3d(self, common_filePath, verbose=False):      \n        return  write_astra_3d_fieldmaps(self, common_filePath)\n\n\n    @functools.wraps(create_impact_solrf_ele)      \n    def to_impact_solrf(self, *args, **kwargs):\n        return create_impact_solrf_ele(self, *args, **kwargs)\n\n    def to_cylindrical(self):\n        \"\"\"\n        Returns a new FieldMesh in cylindrical geometry.\n\n        If the current geometry is rectangular, this\n        will use the y=0 slice.\n\n        \"\"\"\n        if self.geometry == 'rectangular':\n            return FieldMesh(data=fieldmesh_rectangular_to_cylindrically_symmetric_data(self))\n        elif self.geometry == 'cylindrical':\n            return self\n        else:\n            raise NotImplementedError(f\"geometry not implemented: {self.geometry}\")\n\n\n    def write_gpt(self, filePath, asci2gdf_bin=None, verbose=True):\n        \"\"\"\n        Writes a GPT field file. \n        \"\"\"\n\n        return write_gpt_fieldmesh(self, filePath, asci2gdf_bin=asci2gdf_bin, verbose=verbose)\n\n    # Superfish\n    @functools.wraps(write_superfish_t7)\n    def write_superfish(self, filePath, verbose=False):\n        \"\"\"\n        Write a Superfish T7 file. \n\n        For static fields, a Poisson T7 file is written.\n\n        For dynamic (`harmonic /= 0`) fields, a Fish T7 file is written\n        \"\"\"\n        return write_superfish_t7(self, filePath, verbose=verbose)\n\n\n\n    @classmethod\n    @functools.wraps(read_superfish_t7)\n    def from_superfish(cls, filename, type=None, geometry='cylindrical'):\n        \"\"\"\n        Class method to parse a superfish T7 style file.\n        \"\"\"        \n        data = read_superfish_t7(filename, type=type, geometry=geometry)\n        c = cls(data=data)\n        return c               \n\n\n    @classmethod\n    def from_ansys_ascii_3d(cls, *, \n                   efile = None,\n                   hfile = None,\n                   frequency = None):\n        \"\"\"\n        Class method to return a FieldMesh from ANSYS ASCII files.\n\n        The format of each file is:\n        header1 (ignored)\n        header2 (ignored)\n        x y z re_fx im_fx re_fy im_fy re_fz im_fz \n        ...\n        in C order, with oscillations as exp(i*omega*t)\n\n        Parameters\n        ----------\n        efile: str\n            Filename with complex electric field data in V/m\n\n        hfile: str\n            Filename with complex magnetic H field data in A/m\n\n        frequency: float\n            Frequency in Hz\n\n        Returns\n        -------\n        FieldMesh\n\n        \"\"\"\n\n        if frequency is None:\n            raise ValueError(f\"Please provide a frequency\")\n\n        data = read_ansys_ascii_3d_fields(efile, hfile, frequency=frequency)\n        return cls(data=data)\n\n\n\n    @classmethod\n    def from_astra_3d(cls, common_filename, frequency=0):\n        \"\"\"\n        Class method to parse multiple 3D astra fieldmap files,\n        based on the common filename.\n        \"\"\"\n\n        data = read_astra_3d_fieldmaps(common_filename, frequency=frequency)\n        return cls(data=data)\n\n    @classmethod\n    def from_onaxis(cls, *,\n                    z=None,\n                    Bz=None,\n                    Ez=None,\n                    frequency=0,\n                    harmonic=None,\n                    eleAnchorPt = 'beginning'\n                   ):\n            \"\"\"\n\n\n            Parameters \n            ----------\n            z: array\n                z-coordinates. Must be regularly spaced.       \n\n            Bz: array, optional\n                magnetic field at r=0 in T\n                Default: None        \n\n            Ez: array, optional\n                Electric field at r=0 in V/m\n                Default: None\n\n            frequency: float, optional\n                fundamental frequency in Hz.\n                Default: 0\n\n            harmonic: int, optional\n                Harmonic of the fundamental the field actually oscillates at.\n                Default: 1 if frequency !=0, otherwise 0. \n\n            eleAnchorPt: str, optional\n                Element anchor point.\n                Should be one of 'beginning', 'center', 'end'\n                Default: 'beginning'\n\n\n            Returns\n            -------\n            field: FieldMesh\n                Instantiated fieldmesh\n\n            \"\"\"\n\n            # Get spacing\n            nz = len(z)\n            dz = np.diff(z)\n            if not np.allclose(dz, dz[0]):\n                raise NotImplementedError(\"Irregular spacing not implemented\")\n            dz = dz[0]    \n\n            components = {}\n            if Ez is not None:\n                Ez = np.squeeze(np.array(Ez))\n                if Ez.ndim != 1:\n                    raise ValueError(f'Ez ndim = {Ez.ndim} must be 1')\n                components['electricField/z'] = Ez.reshape(1,1,len(Ez))\n\n            if Bz is not None:\n                Bz = np.squeeze(np.array(Bz))\n                if Bz.ndim != 1:\n                    raise ValueError(f'Bz ndim = {Bz.ndim} must be 1')\n                components['magneticField/z'] = Bz.reshape(1,1,len(Bz))            \n\n            if Bz is None and Ez is None:\n                raise ValueError('Please enter Ez or Bz')\n\n            # Handle harmonic options\n            if frequency == 0:\n                harmonic = 0\n            elif harmonic is None:\n                harmonic = 1\n\n            attrs = {'eleAnchorPt': eleAnchorPt,\n             'gridGeometry': 'cylindrical',\n             'axisLabels': np.array(['r', 'theta', 'z'], dtype='&lt;U5'),\n             'gridLowerBound': np.array([0, 0, 0]),\n             'gridOriginOffset': np.array([ 0. ,  0. , z.min()]),\n             'gridSpacing': np.array([0. , 0.   , dz]),\n             'gridSize': np.array([1,  1, nz]),\n             'harmonic': harmonic,\n             'fundamentalFrequency': frequency,\n             'RFphase': 0,\n             'fieldScale': 1.0} \n\n            data = dict(attrs=attrs, components=components)\n            return cls(data=data)        \n\n\n\n    @functools.wraps(expand_fieldmesh_from_onaxis)\n    def expand_onaxis(self, *args, **kwargs):\n        return expand_fieldmesh_from_onaxis(self, *args, **kwargs)\n\n\n\n\n    def __eq__(self, other):\n        \"\"\"\n        Checks that all attributes and component internal data are the same\n        \"\"\"\n\n        if not tools.data_are_equal(self.attrs, other.attrs):\n            return False\n\n        return tools.data_are_equal(self.components, other.components)\n\n\n #  def __setattr__(self, key, value):\n #      print('a', key)\n #      if key in component_from_alias:\n #          print('here', key)\n #          comp = component_from_alias[key]\n #          if comp in self.components:\n #              self.components[comp] = value\n\n #  def __getattr__(self, key):\n #      print('a')\n #      if key in component_from_alias:\n #          print('here', key)\n #          comp = component_from_alias[key]\n #          if comp in self.components:\n #              return self.components[comp]\n\n\n\n\n\n    def scaled_component(self, key):\n        \"\"\"\n\n        Retruns a component scaled by the complex factor\n            factor = scale*exp(i*phase)\n\n\n        \"\"\"\n\n        if key in self.components:\n            dat = self.components[key] \n        # Aliases\n        elif key in component_from_alias:\n            comp = component_from_alias[key]\n            if comp in self.components:\n                dat = self.components[comp]   \n            else:\n                # Component not present, make zeros\n                return np.zeros(self.shape)\n        else:\n            raise ValueError(f'Component not available: {key}')\n\n        # Multiply by scale factor\n        factor = self.factor      \n\n        if factor != 1:\n            return factor*dat\n        else:\n            return dat\n\n    # Convenient properties\n    # TODO: Automate this?\n    @property\n    def r(self):\n        return self.coord_vec('r')\n    @property\n    def theta(self):\n        return self.coord_vec('theta')\n    @property\n    def z(self):\n        return self.coord_vec('z')    \n\n    # Deltas\n    ## cartesian\n    @property\n    def dx(self):\n        return self.deltas[self.axis_index('x')]\n    @property\n    def dy(self):\n        return self.deltas[self.axis_index('y')]    \n    ## cylindrical\n    @property\n    def dr(self):\n        return self.deltas[self.axis_index('r')]\n    @property\n    def dtheta(self):\n        return self.deltas[self.axis_index('theta')]\n    @property\n    def dz(self):\n        return self.deltas[self.axis_index('z')]  \n\n    # Scaled components\n    # TODO: Check geometry\n    ## cartesian\n    @property\n    def Bx(self):\n        return self.scaled_component('Bx') \n    @property\n    def By(self):\n        return self.scaled_component('By')  \n    @property\n    def Ex(self):\n        return self.scaled_component('Ex')  \n    @property\n    def Ey(self):\n        return self.scaled_component('Ey')         \n\n    ## cylindrical\n    @property\n    def Br(self):\n        return self.scaled_component('Br')\n    @property\n    def Btheta(self):\n        return self.scaled_component('Btheta')\n    @property\n    def Bz(self):\n        return self.scaled_component('Bz')\n    @property\n    def Er(self):\n        return self.scaled_component('Er')\n    @property\n    def Etheta(self):\n        return self.scaled_component('Etheta')\n    @property\n    def Ez(self):\n        return self.scaled_component('Ez')    \n\n\n\n\n    @property\n    def B(self):\n        if self.geometry=='cylindrical':\n            if self.is_static:\n                return np.hypot(self['Br'], self['Bz'])\n            else:\n                return np.abs(self['Btheta'])\n        else:\n            raise ValueError(f'Unknown geometry: {self.geometry}')    \n\n    @property\n    def E(self):\n        if self.geometry=='cylindrical':\n            return np.hypot(np.abs(self['Er']), np.abs(self['Ez']))\n        else:\n            raise ValueError(f'Unknown geometry: {self.geometry}')    \n\n    def __getitem__(self, key):\n        \"\"\"\n        Returns component data from a key\n\n        If the key starts with:\n\n        - `re_`\n        - `im_`\n        - `abs_`\n\n        the appropriate numpy operator is applied.\n\n\n\n        \"\"\"\n\n        # \n        if key in ['r', 'theta', 'z']:\n            return self.coord_vec(key)\n\n\n        # Raw components\n        if key in self.components:\n            return self.components[key]\n\n        # Check for operators\n        operator, key = get_operator(key)\n\n        # Scaled components\n        if key == 'E':\n            dat = self.E\n        elif key == 'B':\n            dat =  self.B\n        else:\n            dat = self.scaled_component(key)        \n\n        if operator:\n            dat = operator(dat)\n\n        return dat\n\n\n    def copy(self):\n        \"\"\"Returns a deep copy\"\"\"\n        return deepcopy(self)          \n\n    def __repr__(self):\n        memloc = hex(id(self))\n        return f'&lt;FieldMesh with {self.geometry} geometry and {self.shape} shape at {memloc}&gt;'        \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.axis_labels","title":"<code>axis_labels</code>  <code>property</code>","text":""},{"location":"api/fields/#pmd_beamphysics.FieldMesh.coord_vecs","title":"<code>coord_vecs</code>  <code>property</code>","text":"<p>Uses gridSpacing, gridSize, and gridOriginOffset to return coordinate vectors.</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.factor","title":"<code>factor</code>  <code>property</code>","text":"<p>factor to multiply fields by, possibly complex.</p> <p><code>factor = scale * exp(i*phase)</code></p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.is_pure_electric","title":"<code>is_pure_electric</code>  <code>property</code>","text":"<p>Returns True if there are no non-zero mageneticField components</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.is_pure_magnetic","title":"<code>is_pure_magnetic</code>  <code>property</code>","text":"<p>Returns True if there are no non-zero electricField components</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.meshgrid","title":"<code>meshgrid</code>  <code>property</code>","text":"<p>Usses coordinate vectors to produce a standard numpy meshgrids.</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.phase","title":"<code>phase</code>  <code>property</code> <code>writable</code>","text":"<p>Returns the complex argument <code>phi = -2*pi*RFphase</code> to multiply the oscillating field by. </p> <p>Can be set.</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.__eq__","title":"<code>__eq__(other)</code>","text":"<p>Checks that all attributes and component internal data are the same</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def __eq__(self, other):\n    \"\"\"\n    Checks that all attributes and component internal data are the same\n    \"\"\"\n\n    if not tools.data_are_equal(self.attrs, other.attrs):\n        return False\n\n    return tools.data_are_equal(self.components, other.components)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.__getitem__","title":"<code>__getitem__(key)</code>","text":"<p>Returns component data from a key</p> <p>If the key starts with:</p> <ul> <li><code>re_</code></li> <li><code>im_</code></li> <li><code>abs_</code></li> </ul> <p>the appropriate numpy operator is applied.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def __getitem__(self, key):\n    \"\"\"\n    Returns component data from a key\n\n    If the key starts with:\n\n    - `re_`\n    - `im_`\n    - `abs_`\n\n    the appropriate numpy operator is applied.\n\n\n\n    \"\"\"\n\n    # \n    if key in ['r', 'theta', 'z']:\n        return self.coord_vec(key)\n\n\n    # Raw components\n    if key in self.components:\n        return self.components[key]\n\n    # Check for operators\n    operator, key = get_operator(key)\n\n    # Scaled components\n    if key == 'E':\n        dat = self.E\n    elif key == 'B':\n        dat =  self.B\n    else:\n        dat = self.scaled_component(key)        \n\n    if operator:\n        dat = operator(dat)\n\n    return dat\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.axis_index","title":"<code>axis_index(key)</code>","text":"<p>Returns axis index for a named axis label key.</p> <p>Examples:</p> <ul> <li><code>.axis_labels == ('x', 'y', 'z')</code></li> <li><code>.axis_index('z')</code> returns <code>2</code></li> </ul> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def axis_index(self, key):\n    \"\"\"\n    Returns axis index for a named axis label key.\n\n    Examples:\n\n    - `.axis_labels == ('x', 'y', 'z')`\n    - `.axis_index('z')` returns `2`\n    \"\"\"\n    for i, name in enumerate(self.axis_labels):\n        if name == key:\n            return i\n    raise ValueError(f'Axis not found: {key}')\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.component_is_zero","title":"<code>component_is_zero(key)</code>","text":"<p>Returns True if all elements in a component are zero.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def component_is_zero(self, key):\n    \"\"\"\n    Returns True if all elements in a component are zero.\n    \"\"\"\n    a = self[key]\n    return not np.any(a)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.coord_vec","title":"<code>coord_vec(key)</code>","text":"<p>Gets the coordinate vector from a named axis key.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def coord_vec(self, key):\n    \"\"\"\n    Gets the coordinate vector from a named axis key. \n    \"\"\"\n    i = self.axis_index(key)\n    return np.linspace(self.mins[i], self.maxs[i], self.shape[i])\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.copy","title":"<code>copy()</code>","text":"<p>Returns a deep copy</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def copy(self):\n    \"\"\"Returns a deep copy\"\"\"\n    return deepcopy(self)          \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_ansys_ascii_3d","title":"<code>from_ansys_ascii_3d(*, efile=None, hfile=None, frequency=None)</code>  <code>classmethod</code>","text":"<p>Class method to return a FieldMesh from ANSYS ASCII files.</p> <p>The format of each file is: header1 (ignored) header2 (ignored) x y z re_fx im_fx re_fy im_fy re_fz im_fz  ... in C order, with oscillations as exp(iomegat)</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_ansys_ascii_3d--parameters","title":"Parameters","text":"<p>efile: str     Filename with complex electric field data in V/m</p> str <p>Filename with complex magnetic H field data in A/m</p> float <p>Frequency in Hz</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_ansys_ascii_3d--returns","title":"Returns","text":"<p>FieldMesh</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\ndef from_ansys_ascii_3d(cls, *, \n               efile = None,\n               hfile = None,\n               frequency = None):\n    \"\"\"\n    Class method to return a FieldMesh from ANSYS ASCII files.\n\n    The format of each file is:\n    header1 (ignored)\n    header2 (ignored)\n    x y z re_fx im_fx re_fy im_fy re_fz im_fz \n    ...\n    in C order, with oscillations as exp(i*omega*t)\n\n    Parameters\n    ----------\n    efile: str\n        Filename with complex electric field data in V/m\n\n    hfile: str\n        Filename with complex magnetic H field data in A/m\n\n    frequency: float\n        Frequency in Hz\n\n    Returns\n    -------\n    FieldMesh\n\n    \"\"\"\n\n    if frequency is None:\n        raise ValueError(f\"Please provide a frequency\")\n\n    data = read_ansys_ascii_3d_fields(efile, hfile, frequency=frequency)\n    return cls(data=data)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_astra_3d","title":"<code>from_astra_3d(common_filename, frequency=0)</code>  <code>classmethod</code>","text":"<p>Class method to parse multiple 3D astra fieldmap files, based on the common filename.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\ndef from_astra_3d(cls, common_filename, frequency=0):\n    \"\"\"\n    Class method to parse multiple 3D astra fieldmap files,\n    based on the common filename.\n    \"\"\"\n\n    data = read_astra_3d_fieldmaps(common_filename, frequency=frequency)\n    return cls(data=data)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_onaxis","title":"<code>from_onaxis(*, z=None, Bz=None, Ez=None, frequency=0, harmonic=None, eleAnchorPt='beginning')</code>  <code>classmethod</code>","text":""},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_onaxis--parameters","title":"Parameters","text":"<p>z: array     z-coordinates. Must be regularly spaced.       </p> array, optional <p>magnetic field at r=0 in T Default: None        </p> array, optional <p>Electric field at r=0 in V/m Default: None</p> float, optional <p>fundamental frequency in Hz. Default: 0</p> int, optional <p>Harmonic of the fundamental the field actually oscillates at. Default: 1 if frequency !=0, otherwise 0. </p> str, optional <p>Element anchor point. Should be one of 'beginning', 'center', 'end' Default: 'beginning'</p>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_onaxis--returns","title":"Returns","text":"<p>field: FieldMesh     Instantiated fieldmesh</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\ndef from_onaxis(cls, *,\n                z=None,\n                Bz=None,\n                Ez=None,\n                frequency=0,\n                harmonic=None,\n                eleAnchorPt = 'beginning'\n               ):\n        \"\"\"\n\n\n        Parameters \n        ----------\n        z: array\n            z-coordinates. Must be regularly spaced.       \n\n        Bz: array, optional\n            magnetic field at r=0 in T\n            Default: None        \n\n        Ez: array, optional\n            Electric field at r=0 in V/m\n            Default: None\n\n        frequency: float, optional\n            fundamental frequency in Hz.\n            Default: 0\n\n        harmonic: int, optional\n            Harmonic of the fundamental the field actually oscillates at.\n            Default: 1 if frequency !=0, otherwise 0. \n\n        eleAnchorPt: str, optional\n            Element anchor point.\n            Should be one of 'beginning', 'center', 'end'\n            Default: 'beginning'\n\n\n        Returns\n        -------\n        field: FieldMesh\n            Instantiated fieldmesh\n\n        \"\"\"\n\n        # Get spacing\n        nz = len(z)\n        dz = np.diff(z)\n        if not np.allclose(dz, dz[0]):\n            raise NotImplementedError(\"Irregular spacing not implemented\")\n        dz = dz[0]    \n\n        components = {}\n        if Ez is not None:\n            Ez = np.squeeze(np.array(Ez))\n            if Ez.ndim != 1:\n                raise ValueError(f'Ez ndim = {Ez.ndim} must be 1')\n            components['electricField/z'] = Ez.reshape(1,1,len(Ez))\n\n        if Bz is not None:\n            Bz = np.squeeze(np.array(Bz))\n            if Bz.ndim != 1:\n                raise ValueError(f'Bz ndim = {Bz.ndim} must be 1')\n            components['magneticField/z'] = Bz.reshape(1,1,len(Bz))            \n\n        if Bz is None and Ez is None:\n            raise ValueError('Please enter Ez or Bz')\n\n        # Handle harmonic options\n        if frequency == 0:\n            harmonic = 0\n        elif harmonic is None:\n            harmonic = 1\n\n        attrs = {'eleAnchorPt': eleAnchorPt,\n         'gridGeometry': 'cylindrical',\n         'axisLabels': np.array(['r', 'theta', 'z'], dtype='&lt;U5'),\n         'gridLowerBound': np.array([0, 0, 0]),\n         'gridOriginOffset': np.array([ 0. ,  0. , z.min()]),\n         'gridSpacing': np.array([0. , 0.   , dz]),\n         'gridSize': np.array([1,  1, nz]),\n         'harmonic': harmonic,\n         'fundamentalFrequency': frequency,\n         'RFphase': 0,\n         'fieldScale': 1.0} \n\n        data = dict(attrs=attrs, components=components)\n        return cls(data=data)        \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.from_superfish","title":"<code>from_superfish(filename, type=None, geometry='cylindrical')</code>  <code>classmethod</code>","text":"<p>Class method to parse a superfish T7 style file.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@classmethod\n@functools.wraps(read_superfish_t7)\ndef from_superfish(cls, filename, type=None, geometry='cylindrical'):\n    \"\"\"\n    Class method to parse a superfish T7 style file.\n    \"\"\"        \n    data = read_superfish_t7(filename, type=type, geometry=geometry)\n    c = cls(data=data)\n    return c               \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.scaled_component","title":"<code>scaled_component(key)</code>","text":"<p>Retruns a component scaled by the complex factor     factor = scaleexp(iphase)</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def scaled_component(self, key):\n    \"\"\"\n\n    Retruns a component scaled by the complex factor\n        factor = scale*exp(i*phase)\n\n\n    \"\"\"\n\n    if key in self.components:\n        dat = self.components[key] \n    # Aliases\n    elif key in component_from_alias:\n        comp = component_from_alias[key]\n        if comp in self.components:\n            dat = self.components[comp]   \n        else:\n            # Component not present, make zeros\n            return np.zeros(self.shape)\n    else:\n        raise ValueError(f'Component not available: {key}')\n\n    # Multiply by scale factor\n    factor = self.factor      \n\n    if factor != 1:\n        return factor*dat\n    else:\n        return dat\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.to_cylindrical","title":"<code>to_cylindrical()</code>","text":"<p>Returns a new FieldMesh in cylindrical geometry.</p> <p>If the current geometry is rectangular, this will use the y=0 slice.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def to_cylindrical(self):\n    \"\"\"\n    Returns a new FieldMesh in cylindrical geometry.\n\n    If the current geometry is rectangular, this\n    will use the y=0 slice.\n\n    \"\"\"\n    if self.geometry == 'rectangular':\n        return FieldMesh(data=fieldmesh_rectangular_to_cylindrically_symmetric_data(self))\n    elif self.geometry == 'cylindrical':\n        return self\n    else:\n        raise NotImplementedError(f\"geometry not implemented: {self.geometry}\")\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.units","title":"<code>units(key)</code>","text":"<p>Returns the units of any key</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def units(self, key):\n    \"\"\"Returns the units of any key\"\"\"\n\n    # Strip any operators\n    _, key = get_operator(key)\n\n    # Fill out aliases \n    if key in component_from_alias:\n        key = component_from_alias[key]         \n\n    return pg_units(key)    \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.write","title":"<code>write(h5, name=None)</code>","text":"<p>Writes openPMD-beamphysics format to an open h5 handle, or new file if h5 is a str.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def write(self, h5, name=None):\n    \"\"\"\n    Writes openPMD-beamphysics format to an open h5 handle, or new file if h5 is a str.\n\n    \"\"\"\n    if isinstance(h5, str):\n        fname = os.path.expandvars(os.path.expanduser(h5))\n        h5 = File(fname, 'w')\n        pmd_field_init(h5, externalFieldPath='/ExternalFieldPath/%T/')\n        g = h5.create_group('/ExternalFieldPath/1/')\n    else:\n        g = h5\n\n    write_pmd_field(g, self.data, name=name)   \n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.write_gpt","title":"<code>write_gpt(filePath, asci2gdf_bin=None, verbose=True)</code>","text":"<p>Writes a GPT field file.</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>def write_gpt(self, filePath, asci2gdf_bin=None, verbose=True):\n    \"\"\"\n    Writes a GPT field file. \n    \"\"\"\n\n    return write_gpt_fieldmesh(self, filePath, asci2gdf_bin=asci2gdf_bin, verbose=verbose)\n</code></pre>"},{"location":"api/fields/#pmd_beamphysics.FieldMesh.write_superfish","title":"<code>write_superfish(filePath, verbose=False)</code>","text":"<p>Write a Superfish T7 file. </p> <p>For static fields, a Poisson T7 file is written.</p> <p>For dynamic (<code>harmonic /= 0</code>) fields, a Fish T7 file is written</p> Source code in <code>pmd_beamphysics/fields/fieldmesh.py</code> <pre><code>@functools.wraps(write_superfish_t7)\ndef write_superfish(self, filePath, verbose=False):\n    \"\"\"\n    Write a Superfish T7 file. \n\n    For static fields, a Poisson T7 file is written.\n\n    For dynamic (`harmonic /= 0`) fields, a Fish T7 file is written\n    \"\"\"\n    return write_superfish_t7(self, filePath, verbose=verbose)\n</code></pre>"},{"location":"api/particles/","title":"Particles","text":"<p>Particle Group class</p> <p>Initialized on on openPMD beamphysics particle group:</p> <ul> <li>h5: open h5 handle, or <code>str</code> that is a file</li> <li>data: raw data</li> </ul> <p>The required bunch data is stored in <code>.data</code> with keys</p> <ul> <li><code>np.array</code>: <code>x</code>, <code>px</code>, <code>y</code>, <code>py</code>, <code>z</code>, <code>pz</code>, <code>t</code>, <code>status</code>, <code>weight</code></li> <li><code>str</code>: <code>species</code></li> </ul> <p>where:</p> <ul> <li><code>x</code>, <code>y</code>, <code>z</code> are positions in units of [m]</li> <li><code>px</code>, <code>py</code>, <code>pz</code> are momenta in units of [eV/c]</li> <li><code>t</code> is time in [s]</li> <li><code>weight</code> is the macro-charge weight in [C], used for all statistical calulations.</li> <li><code>species</code> is a proper species name: <code>'electron'</code>, etc. </li> </ul> <p>Optional data:</p> <ul> <li><code>np.array</code>: <code>id</code></li> </ul> <p>where <code>id</code> is a list of unique integers that identify the particles. </p> <p>Derived data can be computed as attributes:</p> <ul> <li><code>.gamma</code>, <code>.beta</code>, <code>.beta_x</code>, <code>.beta_y</code>, <code>.beta_z</code>: relativistic factors [1].</li> <li><code>.r</code>, <code>.theta</code>: cylidrical coordinates [m], [1]</li> <li><code>.pr</code>, <code>.ptheta</code>: momenta in the radial and angular coordinate directions [eV/c]</li> <li><code>.Lz</code>: angular momentum about the z axis [m*eV/c]</li> <li><code>.energy</code> : total energy [eV]</li> <li><code>.kinetic_energy</code>: total energy - mc^2 in [eV]. </li> <li><code>.higher_order_energy</code>: total energy with quadratic fit in z or t subtracted [eV]</li> <li><code>.p</code>: total momentum in [eV/c]</li> <li><code>.mass</code>: rest mass in [eV]</li> <li><code>.xp</code>, <code>.yp</code>: Slopes \\(x' = dx/dz = dp_x/dp_z\\) and \\(y' = dy/dz = dp_y/dp_z\\) [1].</li> </ul> <p>Normalized transvere coordinates can also be calculated as attributes:</p> <ul> <li><code>.x_bar</code>, <code>.px_bar</code>, <code>.y_bar</code>, <code>.py_bar</code> in [sqrt(m)]</li> </ul> <p>The normalization is automatically calculated from the covariance matrix.  See functions in <code>.statistics</code> for more advanced usage.</p> <p>Their cooresponding amplitudes are:</p> <p><code>.Jx</code>, <code>.Jy</code> [m]</p> <p>where <code>Jx = (x_bar^2 + px_bar^2 )/2</code>.</p> <p>The momenta are normalized by the mass, so that <code>&lt;Jx&gt; = norm_emit_x</code> and similar for <code>y</code>. </p> <p>Statistics of any of these are calculated with:</p> <ul> <li><code>.min(X)</code></li> <li><code>.max(X)</code></li> <li><code>.ptp(X)</code></li> <li><code>.avg(X)</code></li> <li><code>.std(X)</code></li> <li><code>.cov(X, Y, ...)</code></li> <li><code>.histogramdd(X, Y, ..., bins=10, range=None)</code></li> </ul> <p>with a string <code>X</code> as the name any of the properties above.</p> <p>Useful beam physics quantities are given as attributes:</p> <ul> <li><code>.norm_emit_x</code></li> <li><code>.norm_emit_y</code></li> <li><code>.norm_emit_4d</code></li> <li><code>.higher_order_energy_spread</code></li> <li><code>.average_current</code></li> </ul> <p>Twiss parameters, including dispersion, for the \\(x\\) or \\(y\\) plane:</p> <ul> <li><code>.twiss(plane='x', fraction=0.95, p0C=None)</code></li> </ul> <p>For convenience, <code>plane='xy'</code> will calculate twiss for both planes.</p> <p>Twiss matched particles, using a simple linear transformation:</p> <ul> <li><code>.twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False)</code></li> </ul> <p>The weight is required and must sum to &gt; 0. The sum of the weights is in <code>.charge</code>. This can also be set: <code>.charge = 1.234</code> # C, will rescale the .weight array</p> All attributes can be accessed with brackets <p><code>[key]</code></p> Additional keys are allowed for convenience <p><code>['min_prop']</code>   will return  <code>.min('prop')</code> <code>['max_prop']</code>   will return  <code>.max('prop')</code> <code>['ptp_prop']</code>   will return  <code>.ptp('prop')</code> <code>['mean_prop']</code>  will return  <code>.avg('prop')</code> <code>['sigma_prop']</code> will return  <code>.std('prop')</code> <code>['cov_prop1__prop2']</code> will return <code>.cov('prop1', 'prop2')[0,1]</code></p> <p>Units for all attributes can be accessed by:</p> <ul> <li><code>.units(key)</code></li> </ul> <p>Particles are often stored at the same time (i.e. from a t-based code),  or with the same z position (i.e. from an s-based code.) Routines: </p> <ul> <li><code>drift_to_z(z0)</code></li> <li><code>drift_to_t(t0)</code></li> </ul> <p>help to convert these. If no argument is given, particles will be drifted to the mean. Related properties are:</p> <ul> <li><code>.in_t_coordinates</code> returns <code>True</code> if all particles have the same \\(t\\) corrdinate</li> <li><code>.in_z_coordinates</code> returns <code>True</code> if all particles have the same \\(z\\) corrdinate</li> </ul> <p>Convenient plotting is provided with: </p> <ul> <li><code>.plot(...)</code></li> <li><code>.slice_plot(...)</code></li> </ul> <p>Use <code>help(ParticleGroup.plot)</code>, etc. for usage.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>class ParticleGroup:\n    \"\"\"\n    Particle Group class\n\n    Initialized on on openPMD beamphysics particle group:\n\n    - **h5**: open h5 handle, or `str` that is a file\n    - **data**: raw data\n\n    The required bunch data is stored in `.data` with keys\n\n    - `np.array`: `x`, `px`, `y`, `py`, `z`, `pz`, `t`, `status`, `weight`\n    - `str`: `species`\n\n    where:\n\n    - `x`, `y`, `z` are positions in units of [m]\n    - `px`, `py`, `pz` are momenta in units of [eV/c]\n    - `t` is time in [s]\n    - `weight` is the macro-charge weight in [C], used for all statistical calulations.\n    - `species` is a proper species name: `'electron'`, etc. \n\n    Optional data:\n\n    - `np.array`: `id`\n\n    where `id` is a list of unique integers that identify the particles. \n\n\n    Derived data can be computed as attributes:\n\n    - `.gamma`, `.beta`, `.beta_x`, `.beta_y`, `.beta_z`: relativistic factors [1].\n    - `.r`, `.theta`: cylidrical coordinates [m], [1]\n    - `.pr`, `.ptheta`: momenta in the radial and angular coordinate directions [eV/c]\n    - `.Lz`: angular momentum about the z axis [m*eV/c]\n    - `.energy` : total energy [eV]\n    - `.kinetic_energy`: total energy - mc^2 in [eV]. \n    - `.higher_order_energy`: total energy with quadratic fit in z or t subtracted [eV]\n    - `.p`: total momentum in [eV/c]\n    - `.mass`: rest mass in [eV]\n    - `.xp`, `.yp`: Slopes $x' = dx/dz = dp_x/dp_z$ and $y' = dy/dz = dp_y/dp_z$ [1].\n\n    Normalized transvere coordinates can also be calculated as attributes:\n\n    - `.x_bar`, `.px_bar`, `.y_bar`, `.py_bar` in [sqrt(m)]\n\n    The normalization is automatically calculated from the covariance matrix. \n    See functions in `.statistics` for more advanced usage.\n\n    Their cooresponding amplitudes are:\n\n    `.Jx`, `.Jy` [m]\n\n    where `Jx = (x_bar^2 + px_bar^2 )/2`.\n\n    The momenta are normalized by the mass, so that\n    `&lt;Jx&gt; = norm_emit_x`\n    and similar for `y`. \n\n    Statistics of any of these are calculated with:\n\n    - `.min(X)`\n    - `.max(X)`\n    - `.ptp(X)`\n    - `.avg(X)`\n    - `.std(X)`\n    - `.cov(X, Y, ...)`\n    - `.histogramdd(X, Y, ..., bins=10, range=None)`\n\n    with a string `X` as the name any of the properties above.\n\n    Useful beam physics quantities are given as attributes:\n\n    - `.norm_emit_x`\n    - `.norm_emit_y`\n    - `.norm_emit_4d`\n    - `.higher_order_energy_spread`\n    - `.average_current`\n\n    Twiss parameters, including dispersion, for the $x$ or $y$ plane:\n\n    - `.twiss(plane='x', fraction=0.95, p0C=None)`\n\n    For convenience, `plane='xy'` will calculate twiss for both planes.\n\n    Twiss matched particles, using a simple linear transformation:\n\n    - `.twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False)`\n\n    The weight is required and must sum to &gt; 0. The sum of the weights is in `.charge`.\n    This can also be set: `.charge = 1.234` # C, will rescale the .weight array\n\n    All attributes can be accessed with brackets:\n        `[key]`\n\n    Additional keys are allowed for convenience:\n        `['min_prop']`   will return  `.min('prop')`\n        `['max_prop']`   will return  `.max('prop')`\n        `['ptp_prop']`   will return  `.ptp('prop')`\n        `['mean_prop']`  will return  `.avg('prop')`\n        `['sigma_prop']` will return  `.std('prop')`\n        `['cov_prop1__prop2']` will return `.cov('prop1', 'prop2')[0,1]`\n\n    Units for all attributes can be accessed by:\n\n    - `.units(key)`\n\n    Particles are often stored at the same time (i.e. from a t-based code), \n    or with the same z position (i.e. from an s-based code.)\n    Routines: \n\n    - `drift_to_z(z0)`\n    - `drift_to_t(t0)`\n\n    help to convert these. If no argument is given, particles will be drifted to the mean.\n    Related properties are:\n\n    - `.in_t_coordinates` returns `True` if all particles have the same $t$ corrdinate\n    - `.in_z_coordinates` returns `True` if all particles have the same $z$ corrdinate\n\n    Convenient plotting is provided with: \n\n    - `.plot(...)`\n    - `.slice_plot(...)`\n\n    Use `help(ParticleGroup.plot)`, etc. for usage. \n\n\n    \"\"\"\n    def __init__(self, h5=None, data=None):\n\n\n        if h5 and data:\n            # TODO:\n            # Allow merging or changing some array with extra data\n            raise NotImplementedError('Cannot init on both h5 and data')\n\n        if h5:\n            # Allow filename\n            if isinstance(h5, str):\n                fname = os.path.expandvars(h5)\n                assert os.path.exists(fname), f'File does not exist: {fname}'\n\n                with File(fname, 'r') as hh5:\n                    pp = particle_paths(hh5)\n                    assert len(pp) == 1, f'Number of particle paths in {h5}: {len(pp)}'\n                    data = load_bunch_data(hh5[pp[0]])\n\n            else:\n                # Try dict\n                data = load_bunch_data(h5)\n        else:\n            # Fill out data. Exclude species.\n            data = full_data(data)\n            species = list(set(data['species']))\n\n            # Allow for empty data (len=0). Otherwise, check species.\n            if len(species) &gt;= 1:\n                assert len(species) == 1, f'mixed species are not allowed: {species}'\n                data['species'] = species[0]\n\n        self._settable_array_keys = ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight']\n        # Optional data\n        for k in ['id']:\n            if k in data:\n                self._settable_array_keys.append(k)  \n\n        self._settable_scalar_keys = ['species']\n        self._settable_keys =  self._settable_array_keys + self._settable_scalar_keys                       \n        # Internal data. Only allow settable keys\n        self._data = {k:data[k] for k in self._settable_keys}\n\n    #-------------------------------------------------\n    # Access to intrinsic coordinates\n    @property\n    def x(self):\n        \"\"\"\n        x coordinate in [m]\n        \"\"\"\n        return self._data['x']\n    @x.setter\n    def x(self, val):\n        self._data['x'] = full_array(len(self), val)    \n\n    @property\n    def y(self):\n        \"\"\"\n        y coordinate in [m]\n        \"\"\"\n        return self._data['y']\n    @y.setter\n    def y(self, val):\n        self._data['y'] = full_array(len(self), val)              \n\n    @property\n    def z(self):\n        \"\"\"\n        z coordinate in [m]\n        \"\"\"\n        return self._data['z']\n    @z.setter\n    def z(self, val):\n        self._data['z'] = full_array(len(self), val)      \n\n    @property\n    def px(self):\n        \"\"\"\n        px coordinate in [eV/c]\n        \"\"\"\n        return self._data['px']\n    @px.setter\n    def px(self, val):\n        self._data['px'] = full_array(len(self), val)      \n\n    @property\n    def py(self):\n        \"\"\"\n        py coordinate in [eV/c]\n        \"\"\"\n        return self._data['py']\n    @py.setter\n    def py(self, val):\n        self._data['py'] = full_array(len(self), val)    \n\n    @property\n    def pz(self):\n        \"\"\"\n        pz coordinate in [eV/c]\n        \"\"\"\n        return self._data['pz']\n    @pz.setter\n    def pz(self, val):\n        self._data['pz'] = full_array(len(self), val)    \n\n    @property\n    def t(self):\n        \"\"\"\n        t coordinate in [s]\n        \"\"\"\n        return self._data['t']\n    @t.setter\n    def t(self, val):\n        self._data['t'] = full_array(len(self), val)       \n\n    @property\n    def status(self):\n        \"\"\"\n        status coordinate in [1]\n        \"\"\"\n        return self._data['status']\n    @status.setter\n    def status(self, val):\n        self._data['status'] = full_array(len(self), val)  \n\n    @property\n    def weight(self):\n        \"\"\"\n        weight coordinate in [C]\n        \"\"\"\n        return self._data['weight']\n    @weight.setter\n    def weight(self, val):\n        self._data['weight'] = full_array(len(self), val)            \n\n    @property\n    def id(self):\n        \"\"\"\n        id integer \n        \"\"\"\n        if 'id' not in self._data:\n            self.assign_id()      \n\n        return self._data['id']\n    @id.setter\n    def id(self, val):\n        self._data['id'] = full_array(len(self), val)            \n\n\n    @property\n    def species(self):\n        \"\"\"\n        species string\n        \"\"\"\n        return self._data['species']\n    @species.setter\n    def species(self, val):\n        self._data['species'] = val\n\n    @property\n    def data(self):\n        \"\"\"\n        Internal data dict\n        \"\"\"\n        return self._data        \n\n    #-------------------------------------------------\n    # Derived data\n\n    def assign_id(self):\n        \"\"\"\n        Assigns unique ids, integers from 1 to n_particle\n\n        \"\"\"\n        if 'id' not in self._settable_array_keys: \n            self._settable_array_keys.append('id')\n        self.id = np.arange(1, self['n_particle']+1)             \n\n    @property\n    def n_particle(self):\n        \"\"\"Total number of particles. Same as len \"\"\"\n        return len(self)\n\n    @property\n    def n_alive(self):\n        \"\"\"Number of alive particles, defined by status == 1\"\"\"\n        return len(np.where(self.status==1)[0])\n\n    @property\n    def n_dead(self):\n        \"\"\"Number of alive particles, defined by status != 1\"\"\"\n        return self.n_particle - self.n_alive\n\n\n    def units(self, key):\n        \"\"\"Returns the units of any key\"\"\"\n        return pg_units(key)\n\n    @property\n    def mass(self):\n        \"\"\"Rest mass in eV\"\"\"\n        return mass_of(self.species)\n\n    @property\n    def species_charge(self):\n        \"\"\"Species charge in C\"\"\"\n        return charge_of(self.species)\n\n    @property\n    def charge(self):\n        \"\"\"Total charge in C\"\"\"\n        return np.sum(self.weight)\n    @charge.setter\n    def charge(self, val):\n        \"\"\"Rescale weight array so that it sum to this value\"\"\"\n        assert val &gt;0, 'charge must be &gt;0. This is used to weight the particles.'\n        self.weight *= val/self.charge\n\n\n    # Relativistic properties\n    @property\n    def p(self):\n        \"\"\"Total momemtum in eV/c\"\"\"\n        return np.sqrt(self.px**2 + self.py**2 + self.pz**2) \n    @property\n    def energy(self):\n        \"\"\"Total energy in eV\"\"\"\n        return np.sqrt(self.px**2 + self.py**2 + self.pz**2 + self.mass**2) \n    @property\n    def kinetic_energy(self):\n        \"\"\"Kinetic energy in eV\"\"\"\n        return self.energy - self.mass\n\n    # Slopes. Note that these are relative to pz\n    @property\n    def xp(self):\n        \"\"\"x slope px/pz (dimensionless)\"\"\"\n        return self.px/self.pz  \n    @property\n    def yp(self):\n        \"\"\"y slope py/pz (dimensionless)\"\"\"\n        return self.py/self.pz    \n\n    @property\n    def higher_order_energy(self):\n        \"\"\"\n        Fits a quadratic (order=2) to the Energy vs. time, and returns the energy with this subtracted. \n        \"\"\" \n        return self.higher_order_energy_calc(order=2)\n\n    @property\n    def higher_order_energy_spread(self):\n        \"\"\"\n        Legacy syntax to compute the standard deviation of higher_order_energy.\n        \"\"\"\n        return self.std('higher_order_energy')\n\n    def higher_order_energy_calc(self, order=2):\n        \"\"\"\n        Fits a polynmial with order `order` to the Energy vs. time, , and returns the energy with this subtracted. \n        \"\"\"\n        #order=2\n        if self.std('z') &lt; 1e-12:\n            # must be at a screen. Use t\n            t = self.t\n        else:\n            # All particles at the same time. Use z to calc t\n            t = self.z/c_light\n        energy = self.energy\n\n        best_fit_coeffs = np.polynomial.polynomial.polyfit(t, energy, order)\n        best_fit = np.polynomial.polynomial.polyval(t, best_fit_coeffs)\n        return energy - best_fit\n\n    # Polar coordinates. Note that these are centered at x=0, y=0, and not an averge center. \n    @property\n    def r(self):\n        \"\"\"Radius in the xy plane: r = sqrt(x^2 + y^2) in m\"\"\"\n        return np.hypot(self.x, self.y)\n    @property    \n    def theta(self):\n        \"\"\"Angle in xy plane: theta = arctan2(y, x) in radians\"\"\"\n        return np.arctan2(self.y, self.x)\n    @property\n    def pr(self):\n        \"\"\"\n        Momentum in the radial direction in eV/c\n        r_hat = cos(theta) xhat + sin(theta) yhat\n        pr = p dot r_hat\n        \"\"\"\n        theta = self.theta\n        return self.px * np.cos(theta)  + self.py * np.sin(theta)   \n    @property    \n    def ptheta(self):\n        \"\"\"     \n        Momentum in the polar theta direction. \n        theta_hat = -sin(theta) xhat + cos(theta) yhat\n        ptheta = p dot theta_hat\n        Note that Lz = r*ptheta\n        \"\"\"\n        theta = self.theta\n        return -self.px * np.sin(theta)  + self.py * np.cos(theta)  \n    @property    \n    def Lz(self):\n        \"\"\"\n        Angular momentum around the z axis in m*eV/c\n        Lz = x * py - y * px\n        \"\"\"\n        return self.x*self.py - self.y*self.px\n\n\n    # Relativistic quantities\n    @property\n    def gamma(self):\n        \"\"\"Relativistic gamma\"\"\"\n        return self.energy/self.mass\n\n    @gamma.setter\n    def gamma(self, val):\n        beta_x = self.beta_x\n        beta_y = self.beta_y\n        beta_z = self.beta_z\n        beta = self.beta\n        gamma_new = full_array(len(self), val)\n        energy_new = gamma_new * self.mass\n        beta_new = np.sqrt(gamma_new**2 - 1)/gamma_new\n        self._data['px'] = energy_new * beta_new * beta_x / beta\n        self._data['py'] = energy_new * beta_new * beta_y / beta\n        self._data['pz'] = energy_new * beta_new * beta_z / beta\n\n    @property\n    def beta(self):\n        \"\"\"Relativistic beta\"\"\"\n        return self.p/self.energy\n    @property\n    def beta_x(self):\n        \"\"\"Relativistic beta, x component\"\"\"\n        return self.px/self.energy\n\n    @beta_x.setter\n    def beta_x(self, val):\n        self._data['px'] = full_array(len(self), val)*self.energy    \n\n    @property\n    def beta_y(self):\n        \"\"\"Relativistic beta, y component\"\"\"\n        return self.py/self.energy\n\n    @beta_y.setter\n    def beta_y(self, val):\n        self._data['py'] = full_array(len(self), val)*self.energy    \n\n    @property\n    def beta_z(self):\n        \"\"\"Relativistic beta, z component\"\"\"\n        return self.pz/self.energy\n\n    @beta_z.setter\n    def beta_z(self, val):\n        self._data['pz'] = full_array(len(self), val)*self.energy\n\n    # Normalized coordinates for x and y\n    @property \n    def x_bar(self):\n        \"\"\"Normalized x in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'x')\n    @property     \n    def px_bar(self):\n        \"\"\"Normalized px in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'px')    \n    @property\n    def Jx(self):\n        \"\"\"Normalized amplitude J in the x-px plane\"\"\"\n        return particle_amplitude(self, 'x')\n\n    @property \n    def y_bar(self):\n        \"\"\"Normalized y in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'y')\n    @property     \n    def py_bar(self):\n        \"\"\"Normalized py in units of sqrt(m)\"\"\"\n        return normalized_particle_coordinate(self, 'py')\n    @property\n    def Jy(self):\n        \"\"\"Normalized amplitude J in the y-py plane\"\"\"\n        return particle_amplitude(self, 'y')    \n\n    def delta(self, key):\n        \"\"\"Attribute (array) relative to its mean\"\"\"\n        return self[key] - self.avg(key)\n\n\n    # Statistical property functions\n\n    def min(self, key):\n        \"\"\"Minimum of any key\"\"\"\n        return np.min(self[key]) # was: getattr(self, key)\n    def max(self, key):\n        \"\"\"Maximum of any key\"\"\"\n        return np.max(self[key]) \n    def ptp(self, key):\n        \"\"\"Peak-to-Peak = max - min of any key\"\"\"\n        return np.ptp(self[key])     \n\n    def avg(self, key):\n        \"\"\"Statistical average\"\"\"\n        dat = self[key]  # equivalent to self.key for accessing properties above\n        if np.isscalar(dat): \n            return dat\n        return np.average(dat, weights=self.weight)\n    def std(self, key):\n        \"\"\"Standard deviation (actually sample)\"\"\"\n        dat = self[key]\n        if np.isscalar(dat):\n            return 0\n        avg_dat = self.avg(key)\n        return np.sqrt(np.average( (dat - avg_dat)**2, weights=self.weight))\n    def cov(self, *keys):\n        \"\"\"\n        Covariance matrix from any properties\n\n        Example: \n        P = ParticleGroup(h5)\n        P.cov('x', 'px', 'y', 'py')\n\n        \"\"\"\n        dats = np.array([ self[key] for key in keys ])\n        return np.cov(dats, aweights=self.weight)\n\n    def histogramdd(self, *keys, bins=10, range=None):\n        \"\"\"\n        Wrapper for numpy.histogramdd, but accepts property names as keys.\n\n        Computes the multidimensional histogram of keys. Internally uses weights. \n\n        Example:\n            P.histogramdd('x', 'y', bins=50)\n        Returns:\n            np.array with shape 50x50, edge list \n\n        \"\"\"\n        H, edges = np.histogramdd(np.array([self[k] for k in list(keys)]).T, weights=self.weight, bins=bins, range=range)\n\n        return H, edges\n\n\n    # Beam statistics\n    @property\n    def norm_emit_x(self):\n        \"\"\"Normalized emittance in the x plane\"\"\"\n        return norm_emit_calc(self, planes=['x'])\n    @property\n    def norm_emit_y(self):       \n        \"\"\"Normalized emittance in the x plane\"\"\"\n        return norm_emit_calc(self, planes=['y'])\n    @property\n    def norm_emit_4d(self):       \n        \"\"\"Normalized emittance in the xy planes (4D)\"\"\"\n        return norm_emit_calc(self, planes=['x', 'y'])    \n\n    def twiss(self, plane='x', fraction=1, p0c=None):\n        \"\"\"\n        Returns Twiss and Dispersion dict.\n\n        plane can be:\n\n        `'x'`, `'y'`, or `'xy'`\n\n        Optionally a fraction of the particles, based on amplitiude, can be specified.\n        \"\"\"\n        d = {}\n        for p in plane:\n            d.update(particle_twiss_dispersion(self, plane=p, fraction=fraction, p0c=p0c))\n        return d\n\n    def twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False):\n        \"\"\"\n        Returns a ParticleGroup with requested Twiss parameters.\n\n        See: statistics.matched_particles\n        \"\"\"\n\n        return matched_particles(self, beta=beta, alpha=alpha, plane=plane, inplace=inplace)\n\n\n    @property\n    def in_z_coordinates(self):\n        \"\"\"\n        Returns True if all particles have the same z coordinate\n        \"\"\" \n        # Check that z are all the same\n        return len(np.unique(self.z)) == 1           \n\n    @property\n    def in_t_coordinates(self):\n        \"\"\"\n        Returns True if all particles have the same t coordinate\n        \"\"\" \n        # Check that t are all the same\n        return len(np.unique(self.t)) == 1        \n\n\n\n    @property\n    def average_current(self):\n        \"\"\"\n        Simple average `current = charge / dt` in [A], with `dt =  (max_t - min_t)`\n        If particles are in $t$ coordinates, will try` dt = (max_z - min_z)*c_light*beta_z`\n        \"\"\"\n        dt = self.t.ptp()  # ptp 'peak to peak' is max - min\n        if dt == 0:\n            # must be in t coordinates. Calc with \n            dt = self.z.ptp() / (self.avg('beta_z')*c_light)\n        return self.charge / dt\n\n    def bunching(self, wavelength):\n        \"\"\"\n        Calculate the normalized bunching parameter, which is the magnitude of the \n        complex sum of weighted exponentials at a given point.\n\n        The formula for bunching is given by:\n\n        $$\n        B(z, \\lambda) = \\frac{\\left|\\sum w_i e^{i k z_i}\\right|}{\\sum w_i}\n        $$\n\n        where:\n        - \\( z \\) is the position array,\n        - \\( \\lambda \\) is the wavelength,\n        - \\( k = \\frac{2\\pi}{\\lambda} \\) is the wave number,\n        - \\( w_i \\) are the weights.\n\n        Parameters\n        ----------\n        wavelength : float\n            Wavelength of the wave.\n\n\n        Returns\n        -------\n        complex\n            The normalized bunching parameter.\n\n        Raises\n        ------\n        ValueError\n            If `wavelength` is not a positive number.\n        \"\"\"        \n\n        if self.in_z_coordinates:\n            # Approximate z\n            z = self.t * self.avg('beta_z')*c_light\n        else:\n            z = self.z\n\n        return statistics.bunching(z, wavelength, weight=self.weight)\n\n    def __getitem__(self, key):\n        \"\"\"\n        Returns a property or statistical quantity that can be computed:\n\n        - `P['x']` returns the x array\n        - `P['sigmx_x']` returns the std(x) scalar\n        - `P['norm_emit_x']` returns the norm_emit_x scalar\n\n        Parts can also be given. Example: `P[0:10]` returns a new ParticleGroup with the first 10 elements.\n        \"\"\"\n\n        # Allow for non-string operations: \n        if not isinstance(key, str):\n            return particle_parts(self, key)\n\n        if key.startswith('cov_'):\n            subkeys = key[4:].split('__')\n            assert len(subkeys) == 2, f'Too many properties in covariance request: {key}'\n            return self.cov(*subkeys)[0,1]\n        elif key.startswith('delta_'):\n            return self.delta(key[6:])\n        elif key.startswith('sigma_'):\n            return self.std(key[6:])\n        elif key.startswith('mean_'):\n            return self.avg(key[5:])\n        elif key.startswith('min_'):\n            return self.min(key[4:])\n        elif key.startswith('max_'):\n            return self.max(key[4:])     \n        elif key.startswith('ptp_'):\n            return self.ptp(key[4:])   \n        elif 'bunching' in key:\n            wavelength = parse_bunching_str(key)\n            bunching = self.bunching(wavelength) # complex\n\n            # abs or arg (angle):\n            if 'phase_' in key:\n                return np.angle(bunching)\n            else:\n                return np.abs(bunching)\n\n        else:\n            return getattr(self, key) \n\n    def where(self, x):\n        return self[np.where(x)]\n\n    # TODO: should the user be allowed to do this?\n    #def __setitem__(self, key, value):    \n    #    assert key in self._settable_keyes, 'Error: you cannot set:'+str(key)\n    #    \n    #    if key in self._settable_array_keys:\n    #        assert len(value) == self.n_particle\n    #        self.__dict__[key] = value\n    #    elif key == \n    #        print()\n\n    # Simple 'tracking'     \n    def drift(self, delta_t):\n        \"\"\"\n        Drifts particles by time delta_t\n        \"\"\"\n        self.x = self.x + self.beta_x * c_light * delta_t\n        self.y = self.y + self.beta_y * c_light * delta_t\n        self.z = self.z + self.beta_z * c_light * delta_t\n        self.t = self.t + delta_t\n\n    def drift_to_z(self, z=None):\n\n        if z is None:\n            z = self.avg('z')\n        dt = (z - self.z) / (self.beta_z * c_light)\n        self.drift(dt)\n        # Fix z to be exactly this value\n        self.z = np.full(self.n_particle, z)\n\n\n    def drift_to_t(self, t=None):\n        \"\"\"\n        Drifts all particles to the same t\n\n        If no z is given, particles will be drifted to the average t\n        \"\"\"\n        if t is None:\n            t = self.avg('t')\n        dt = t - self.t\n        self.drift(dt)\n        # Fix t to be exactly this value\n        self.t = np.full(self.n_particle, t)\n\n\n    # -------        \n    # dict methods\n\n    # Do not do this, it breaks deepcopy\n    #def __dict__(self):\n    #    return self.data\n\n\n    @functools.wraps(bmad.particlegroup_to_bmad)\n    def to_bmad(self, p0c=None, tref=None):\n        return bmad.particlegroup_to_bmad(self, p0c=p0c, tref=tref)\n\n\n    @classmethod\n    @functools.wraps(bmad.bmad_to_particlegroup_data)\n    def from_bmad(cls, bmad_dict):\n        \"\"\"\n        Convert Bmad particle data as a dict \n        to ParticleGroup data.\n\n        See: ParticleGroup.to_bmad or particlegroup_to_bmad\n\n        Parameters\n        ----------\n        bmad_data: dict\n            Dict with keys:\n            'x'\n            'px'\n            'y'\n            'py'\n            'z'\n            'pz', \n            'charge'\n            'species',\n            'tref'\n            'state'\n\n        Returns\n        -------\n        ParticleGroup\n        \"\"\"        \n        data = bmad.bmad_to_particlegroup_data(bmad_dict)\n        return cls(data=data)\n\n    # -------\n    # Writers\n\n    @functools.wraps(write_astra)    \n    def write_astra(self, filePath, verbose=False, probe=False):\n        write_astra(self, filePath, verbose=verbose, probe=probe)\n\n    def write_bmad(self, filePath, p0c=None, t_ref=0, verbose=False):\n        bmad.write_bmad(self, filePath, p0c=p0c, t_ref=t_ref, verbose=verbose)        \n\n    def write_elegant(self, filePath, verbose=False):\n        write_elegant(self, filePath, verbose=verbose)            \n\n    def write_genesis2_beam_file(self, filePath, n_slice=None, verbose=False):\n        # Get beam columns \n        beam_columns = genesis2_beam_data(self, n_slice=n_slice)\n        # Actually write the file\n        write_genesis2_beam_file(filePath, beam_columns, verbose=verbose)  \n\n    @functools.wraps(write_genesis4_beam)          \n    def write_genesis4_beam(self, filePath, n_slice=None, return_input_str=False, verbose=False):\n        return write_genesis4_beam(self, filePath, n_slice=n_slice, return_input_str=return_input_str, verbose=verbose)\n\n    def write_genesis4_distribution(self, filePath, verbose=False):\n        write_genesis4_distribution(self, filePath, verbose=verbose)\n\n    def write_gpt(self, filePath, asci2gdf_bin=None, verbose=False):\n        write_gpt(self, filePath, asci2gdf_bin=asci2gdf_bin, verbose=verbose)    \n\n    def write_impact(self, filePath, cathode_kinetic_energy_ref=None, include_header=True, verbose=False):\n        return write_impact(self, filePath, cathode_kinetic_energy_ref=cathode_kinetic_energy_ref,\n                            include_header=include_header, verbose=verbose)          \n\n    def write_litrack(self, filePath, p0c=None, verbose=False):        \n        return write_litrack(self, outfile=filePath, p0c=p0c, verbose=verbose)      \n\n    def write_lucretia(self, filePath, ele_name='BEGINNING', t_ref=0, stop_ix=None, verbose=False):       \n        return write_lucretia(self, filePath, ele_name=ele_name, t_ref=t_ref, stop_ix=stop_ix)\n\n    def write_simion(self, filePath, color=0, flip_z_to_x=True, verbose=False):\n        return write_simion(self, filePath, verbose=verbose, color=color, flip_z_to_x=flip_z_to_x)\n\n\n    def write_opal(self, filePath, verbose=False, dist_type='emitted'):\n        return write_opal(self, filePath, verbose=verbose, dist_type=dist_type)\n\n\n    # openPMD    \n    def write(self, h5, name=None):\n        \"\"\"\n        Writes to an open h5 handle, or new file if h5 is a str.\n\n        \"\"\"\n        if isinstance(h5, str):\n            fname = os.path.expandvars(h5)\n            g = File(fname, 'w')\n            pmd_init(g, basePath='/', particlesPath='.' )\n        else:\n            g = h5\n\n        write_pmd_bunch(g, self, name=name)        \n\n\n    # Plotting\n    # --------\n    def plot(self, key1='x', key2=None,\n             bins=None,\n             *,\n             xlim=None,\n             ylim=None,\n             return_figure=False, \n             tex=True, nice=True,\n             **kwargs):\n        \"\"\"\n        1d or 2d density plot. \n\n        If one key is given, this will plot the density of that key.\n        Example:\n            .plot('x')\n\n        If two keys arg given, this will plot a 2d marginal plot.\n        Example:\n            .plot('x', 'px')\n\n\n        Parameters\n        ----------\n        particle_group: ParticleGroup\n            The object to plot\n\n        key1: str, default = 't'\n            Key to bin on the x-axis\n\n        key2: str, default = None\n            Key to bin on the y-axis. \n\n        bins: int, default = None\n           Number of bins. If None, this will use a heuristic: bins = sqrt(n_particle/4)\n\n        xlim: tuple, default = None\n            Manual setting of the x-axis limits. Note that these are in raw, unscaled units. \n\n        ylim: tuple, default = None\n            Manual setting of the y-axis limits. Note that these are in raw, unscaled units. \n\n        tex: bool, default = True\n            Use TEX for labels   \n\n        nice: bool, default = True\n            Scale to nice units\n\n        return_figure: bool, default = False\n            If true, return a matplotlib.figure.Figure object\n\n        **kwargs\n            Any additional kwargs to send to the the plot in: plt.subplots(**kwargs)\n\n\n        Returns\n        -------\n        None or fig: matplotlib.figure.Figure\n            This only returns a figure object if return_figure=T, otherwise returns None\n\n        \"\"\"\n\n        if not key2:\n            fig = density_plot(self, key=key1,\n                               bins=bins,\n                               xlim=xlim,\n                               tex=tex,\n                               nice=nice,\n                               **kwargs)\n        else:\n            fig = marginal_plot(self, key1=key1, key2=key2,\n                                bins=bins,\n                                xlim=xlim,\n                                ylim=ylim,\n                                tex=tex,\n                                nice=nice,\n                                **kwargs)\n\n        if return_figure:\n            return fig\n\n\n\n\n    def slice_statistics(self, *keys,\n                   n_slice=100,\n                   slice_key=None):\n        \"\"\"\n        Slice statistics\n\n        \"\"\"      \n\n        if slice_key is None:\n            if self.in_t_coordinates:\n                slice_key = 'z'\n\n            else:\n                slice_key = 't'  \n\n        if slice_key in ('t', 'delta_t'):\n            density_name = 'current'\n        else:\n            density_name = 'density'\n\n        keys = set(keys)\n        keys.add('mean_'+slice_key)\n        keys.add('ptp_'+slice_key)\n        keys.add('charge')\n        slice_dat = slice_statistics(self, n_slice=n_slice, slice_key=slice_key,\n                                keys=keys)\n\n        slice_dat[density_name] = slice_dat['charge']/ slice_dat['ptp_'+slice_key]\n\n        return slice_dat\n\n    def slice_plot(self, *keys,\n                   n_slice=100,\n                   slice_key=None,\n                   tex=True,\n                   nice=True,\n                   return_figure=False,\n                   xlim=None,\n                   ylim=None,\n                   **kwargs):   \n\n        fig = slice_plot(self, *keys,\n                         n_slice=n_slice,\n                         slice_key=slice_key,\n                         tex=tex,\n                         nice=nice,\n                         xlim=xlim,\n                         ylim=ylim,\n                         **kwargs)\n\n        if return_figure:\n            return fig        \n\n\n    # New constructors\n    def split(self, n_chunks = 100, key='z'):\n        return split_particles(self, n_chunks=n_chunks, key=key)\n\n    def copy(self):\n        \"\"\"Returns a deep copy\"\"\"\n        return deepcopy(self)    \n\n    @functools.wraps(resample_particles)\n    def resample(self, n=0, equal_weights=False):\n        data = resample_particles(self, n, equal_weights=equal_weights)\n        return ParticleGroup(data=data)\n\n    # Internal sorting\n    def _sort(self, key):\n        \"\"\"Sorts internal arrays by key\"\"\"\n        ixlist = np.argsort(self[key])\n        for k in self._settable_array_keys:\n            self._data[k] = self[k][ixlist]    \n\n    # Operator overloading    \n    def __add__(self, other):\n        \"\"\"\n        Overloads the + operator to join particle groups.\n        Simply calls join_particle_groups\n        \"\"\"\n        return join_particle_groups(self, other)\n\n    # \n    def __contains__(self, item):\n        \"\"\"Checks internal data\"\"\"\n        return True if item in self._data else False    \n\n    def __eq__(self, other):\n        \"\"\"Check equality of internal data\"\"\"\n        if isinstance(other, ParticleGroup):\n            for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:\n                if not np.allclose(self[key], other[key]):\n                    return False\n            return True\n\n        return NotImplemented    \n\n    def __len__(self):\n        return len(self[self._settable_array_keys[0]])\n\n    def __str__(self):\n        s = f'ParticleGroup with {self.n_particle} particles with total charge {self.charge} C'\n        return s\n\n    def __repr__(self):\n        memloc = hex(id(self))\n        return f'&lt;ParticleGroup with {self.n_particle} particles at {memloc}&gt;'\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.Jx","title":"<code>Jx</code>  <code>property</code>","text":"<p>Normalized amplitude J in the x-px plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.Jy","title":"<code>Jy</code>  <code>property</code>","text":"<p>Normalized amplitude J in the y-py plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.Lz","title":"<code>Lz</code>  <code>property</code>","text":"<p>Angular momentum around the z axis in m*eV/c Lz = x * py - y * px</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.average_current","title":"<code>average_current</code>  <code>property</code>","text":"<p>Simple average <code>current = charge / dt</code> in [A], with <code>dt =  (max_t - min_t)</code> If particles are in \\(t\\) coordinates, will try<code>dt = (max_z - min_z)*c_light*beta_z</code></p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta","title":"<code>beta</code>  <code>property</code>","text":"<p>Relativistic beta</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta_x","title":"<code>beta_x</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic beta, x component</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta_y","title":"<code>beta_y</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic beta, y component</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.beta_z","title":"<code>beta_z</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic beta, z component</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.charge","title":"<code>charge</code>  <code>property</code> <code>writable</code>","text":"<p>Total charge in C</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.data","title":"<code>data</code>  <code>property</code>","text":"<p>Internal data dict</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.energy","title":"<code>energy</code>  <code>property</code>","text":"<p>Total energy in eV</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.gamma","title":"<code>gamma</code>  <code>property</code> <code>writable</code>","text":"<p>Relativistic gamma</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.higher_order_energy","title":"<code>higher_order_energy</code>  <code>property</code>","text":"<p>Fits a quadratic (order=2) to the Energy vs. time, and returns the energy with this subtracted.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.higher_order_energy_spread","title":"<code>higher_order_energy_spread</code>  <code>property</code>","text":"<p>Legacy syntax to compute the standard deviation of higher_order_energy.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.id","title":"<code>id</code>  <code>property</code> <code>writable</code>","text":"<p>id integer</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.in_t_coordinates","title":"<code>in_t_coordinates</code>  <code>property</code>","text":"<p>Returns True if all particles have the same t coordinate</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.in_z_coordinates","title":"<code>in_z_coordinates</code>  <code>property</code>","text":"<p>Returns True if all particles have the same z coordinate</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.kinetic_energy","title":"<code>kinetic_energy</code>  <code>property</code>","text":"<p>Kinetic energy in eV</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.mass","title":"<code>mass</code>  <code>property</code>","text":"<p>Rest mass in eV</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.n_alive","title":"<code>n_alive</code>  <code>property</code>","text":"<p>Number of alive particles, defined by status == 1</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.n_dead","title":"<code>n_dead</code>  <code>property</code>","text":"<p>Number of alive particles, defined by status != 1</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.n_particle","title":"<code>n_particle</code>  <code>property</code>","text":"<p>Total number of particles. Same as len</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.norm_emit_4d","title":"<code>norm_emit_4d</code>  <code>property</code>","text":"<p>Normalized emittance in the xy planes (4D)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.norm_emit_x","title":"<code>norm_emit_x</code>  <code>property</code>","text":"<p>Normalized emittance in the x plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.norm_emit_y","title":"<code>norm_emit_y</code>  <code>property</code>","text":"<p>Normalized emittance in the x plane</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.p","title":"<code>p</code>  <code>property</code>","text":"<p>Total momemtum in eV/c</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.pr","title":"<code>pr</code>  <code>property</code>","text":"<p>Momentum in the radial direction in eV/c r_hat = cos(theta) xhat + sin(theta) yhat pr = p dot r_hat</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.ptheta","title":"<code>ptheta</code>  <code>property</code>","text":"<p>Momentum in the polar theta direction.  theta_hat = -sin(theta) xhat + cos(theta) yhat ptheta = p dot theta_hat Note that Lz = r*ptheta</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.px","title":"<code>px</code>  <code>property</code> <code>writable</code>","text":"<p>px coordinate in [eV/c]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.px_bar","title":"<code>px_bar</code>  <code>property</code>","text":"<p>Normalized px in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.py","title":"<code>py</code>  <code>property</code> <code>writable</code>","text":"<p>py coordinate in [eV/c]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.py_bar","title":"<code>py_bar</code>  <code>property</code>","text":"<p>Normalized py in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.pz","title":"<code>pz</code>  <code>property</code> <code>writable</code>","text":"<p>pz coordinate in [eV/c]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.r","title":"<code>r</code>  <code>property</code>","text":"<p>Radius in the xy plane: r = sqrt(x^2 + y^2) in m</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.species","title":"<code>species</code>  <code>property</code> <code>writable</code>","text":"<p>species string</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.species_charge","title":"<code>species_charge</code>  <code>property</code>","text":"<p>Species charge in C</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.status","title":"<code>status</code>  <code>property</code> <code>writable</code>","text":"<p>status coordinate in [1]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.t","title":"<code>t</code>  <code>property</code> <code>writable</code>","text":"<p>t coordinate in [s]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.theta","title":"<code>theta</code>  <code>property</code>","text":"<p>Angle in xy plane: theta = arctan2(y, x) in radians</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.weight","title":"<code>weight</code>  <code>property</code> <code>writable</code>","text":"<p>weight coordinate in [C]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.x","title":"<code>x</code>  <code>property</code> <code>writable</code>","text":"<p>x coordinate in [m]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.x_bar","title":"<code>x_bar</code>  <code>property</code>","text":"<p>Normalized x in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.xp","title":"<code>xp</code>  <code>property</code>","text":"<p>x slope px/pz (dimensionless)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.y","title":"<code>y</code>  <code>property</code> <code>writable</code>","text":"<p>y coordinate in [m]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.y_bar","title":"<code>y_bar</code>  <code>property</code>","text":"<p>Normalized y in units of sqrt(m)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.yp","title":"<code>yp</code>  <code>property</code>","text":"<p>y slope py/pz (dimensionless)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.z","title":"<code>z</code>  <code>property</code> <code>writable</code>","text":"<p>z coordinate in [m]</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__add__","title":"<code>__add__(other)</code>","text":"<p>Overloads the + operator to join particle groups. Simply calls join_particle_groups</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __add__(self, other):\n    \"\"\"\n    Overloads the + operator to join particle groups.\n    Simply calls join_particle_groups\n    \"\"\"\n    return join_particle_groups(self, other)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__contains__","title":"<code>__contains__(item)</code>","text":"<p>Checks internal data</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __contains__(self, item):\n    \"\"\"Checks internal data\"\"\"\n    return True if item in self._data else False    \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__eq__","title":"<code>__eq__(other)</code>","text":"<p>Check equality of internal data</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __eq__(self, other):\n    \"\"\"Check equality of internal data\"\"\"\n    if isinstance(other, ParticleGroup):\n        for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:\n            if not np.allclose(self[key], other[key]):\n                return False\n        return True\n\n    return NotImplemented    \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.__getitem__","title":"<code>__getitem__(key)</code>","text":"<p>Returns a property or statistical quantity that can be computed:</p> <ul> <li><code>P['x']</code> returns the x array</li> <li><code>P['sigmx_x']</code> returns the std(x) scalar</li> <li><code>P['norm_emit_x']</code> returns the norm_emit_x scalar</li> </ul> <p>Parts can also be given. Example: <code>P[0:10]</code> returns a new ParticleGroup with the first 10 elements.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def __getitem__(self, key):\n    \"\"\"\n    Returns a property or statistical quantity that can be computed:\n\n    - `P['x']` returns the x array\n    - `P['sigmx_x']` returns the std(x) scalar\n    - `P['norm_emit_x']` returns the norm_emit_x scalar\n\n    Parts can also be given. Example: `P[0:10]` returns a new ParticleGroup with the first 10 elements.\n    \"\"\"\n\n    # Allow for non-string operations: \n    if not isinstance(key, str):\n        return particle_parts(self, key)\n\n    if key.startswith('cov_'):\n        subkeys = key[4:].split('__')\n        assert len(subkeys) == 2, f'Too many properties in covariance request: {key}'\n        return self.cov(*subkeys)[0,1]\n    elif key.startswith('delta_'):\n        return self.delta(key[6:])\n    elif key.startswith('sigma_'):\n        return self.std(key[6:])\n    elif key.startswith('mean_'):\n        return self.avg(key[5:])\n    elif key.startswith('min_'):\n        return self.min(key[4:])\n    elif key.startswith('max_'):\n        return self.max(key[4:])     \n    elif key.startswith('ptp_'):\n        return self.ptp(key[4:])   \n    elif 'bunching' in key:\n        wavelength = parse_bunching_str(key)\n        bunching = self.bunching(wavelength) # complex\n\n        # abs or arg (angle):\n        if 'phase_' in key:\n            return np.angle(bunching)\n        else:\n            return np.abs(bunching)\n\n    else:\n        return getattr(self, key) \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.assign_id","title":"<code>assign_id()</code>","text":"<p>Assigns unique ids, integers from 1 to n_particle</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def assign_id(self):\n    \"\"\"\n    Assigns unique ids, integers from 1 to n_particle\n\n    \"\"\"\n    if 'id' not in self._settable_array_keys: \n        self._settable_array_keys.append('id')\n    self.id = np.arange(1, self['n_particle']+1)             \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.avg","title":"<code>avg(key)</code>","text":"<p>Statistical average</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def avg(self, key):\n    \"\"\"Statistical average\"\"\"\n    dat = self[key]  # equivalent to self.key for accessing properties above\n    if np.isscalar(dat): \n        return dat\n    return np.average(dat, weights=self.weight)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching","title":"<code>bunching(wavelength)</code>","text":"<p>Calculate the normalized bunching parameter, which is the magnitude of the  complex sum of weighted exponentials at a given point.</p> <p>The formula for bunching is given by:</p> \\[ B(z, \\lambda) = \frac{\\left|\\sum w_i e^{i k z_i} ight|}{\\sum w_i} \\] <p>where: - \\( z \\) is the position array, - \\( \\lambda \\) is the wavelength, - \\( k = \frac{2\\pi}{\\lambda} \\) is the wave number, - \\( w_i \\) are the weights.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching--parameters","title":"Parameters","text":"<p>wavelength : float     Wavelength of the wave.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching--returns","title":"Returns","text":"<p>complex     The normalized bunching parameter.</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.bunching--raises","title":"Raises","text":"<p>ValueError     If <code>wavelength</code> is not a positive number.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def bunching(self, wavelength):\n    \"\"\"\n    Calculate the normalized bunching parameter, which is the magnitude of the \n    complex sum of weighted exponentials at a given point.\n\n    The formula for bunching is given by:\n\n    $$\n    B(z, \\lambda) = \\frac{\\left|\\sum w_i e^{i k z_i}\\right|}{\\sum w_i}\n    $$\n\n    where:\n    - \\( z \\) is the position array,\n    - \\( \\lambda \\) is the wavelength,\n    - \\( k = \\frac{2\\pi}{\\lambda} \\) is the wave number,\n    - \\( w_i \\) are the weights.\n\n    Parameters\n    ----------\n    wavelength : float\n        Wavelength of the wave.\n\n\n    Returns\n    -------\n    complex\n        The normalized bunching parameter.\n\n    Raises\n    ------\n    ValueError\n        If `wavelength` is not a positive number.\n    \"\"\"        \n\n    if self.in_z_coordinates:\n        # Approximate z\n        z = self.t * self.avg('beta_z')*c_light\n    else:\n        z = self.z\n\n    return statistics.bunching(z, wavelength, weight=self.weight)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.copy","title":"<code>copy()</code>","text":"<p>Returns a deep copy</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def copy(self):\n    \"\"\"Returns a deep copy\"\"\"\n    return deepcopy(self)    \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.cov","title":"<code>cov(*keys)</code>","text":"<p>Covariance matrix from any properties</p> <p>Example:  P = ParticleGroup(h5) P.cov('x', 'px', 'y', 'py')</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def cov(self, *keys):\n    \"\"\"\n    Covariance matrix from any properties\n\n    Example: \n    P = ParticleGroup(h5)\n    P.cov('x', 'px', 'y', 'py')\n\n    \"\"\"\n    dats = np.array([ self[key] for key in keys ])\n    return np.cov(dats, aweights=self.weight)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.delta","title":"<code>delta(key)</code>","text":"<p>Attribute (array) relative to its mean</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def delta(self, key):\n    \"\"\"Attribute (array) relative to its mean\"\"\"\n    return self[key] - self.avg(key)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.drift","title":"<code>drift(delta_t)</code>","text":"<p>Drifts particles by time delta_t</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def drift(self, delta_t):\n    \"\"\"\n    Drifts particles by time delta_t\n    \"\"\"\n    self.x = self.x + self.beta_x * c_light * delta_t\n    self.y = self.y + self.beta_y * c_light * delta_t\n    self.z = self.z + self.beta_z * c_light * delta_t\n    self.t = self.t + delta_t\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.drift_to_t","title":"<code>drift_to_t(t=None)</code>","text":"<p>Drifts all particles to the same t</p> <p>If no z is given, particles will be drifted to the average t</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def drift_to_t(self, t=None):\n    \"\"\"\n    Drifts all particles to the same t\n\n    If no z is given, particles will be drifted to the average t\n    \"\"\"\n    if t is None:\n        t = self.avg('t')\n    dt = t - self.t\n    self.drift(dt)\n    # Fix t to be exactly this value\n    self.t = np.full(self.n_particle, t)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.from_bmad","title":"<code>from_bmad(bmad_dict)</code>  <code>classmethod</code>","text":"<p>Convert Bmad particle data as a dict  to ParticleGroup data.</p> <p>See: ParticleGroup.to_bmad or particlegroup_to_bmad</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.from_bmad--parameters","title":"Parameters","text":"<p>bmad_data: dict     Dict with keys:     'x'     'px'     'y'     'py'     'z'     'pz',      'charge'     'species',     'tref'     'state'</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.from_bmad--returns","title":"Returns","text":"<p>ParticleGroup</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>@classmethod\n@functools.wraps(bmad.bmad_to_particlegroup_data)\ndef from_bmad(cls, bmad_dict):\n    \"\"\"\n    Convert Bmad particle data as a dict \n    to ParticleGroup data.\n\n    See: ParticleGroup.to_bmad or particlegroup_to_bmad\n\n    Parameters\n    ----------\n    bmad_data: dict\n        Dict with keys:\n        'x'\n        'px'\n        'y'\n        'py'\n        'z'\n        'pz', \n        'charge'\n        'species',\n        'tref'\n        'state'\n\n    Returns\n    -------\n    ParticleGroup\n    \"\"\"        \n    data = bmad.bmad_to_particlegroup_data(bmad_dict)\n    return cls(data=data)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.higher_order_energy_calc","title":"<code>higher_order_energy_calc(order=2)</code>","text":"<p>Fits a polynmial with order <code>order</code> to the Energy vs. time, , and returns the energy with this subtracted.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def higher_order_energy_calc(self, order=2):\n    \"\"\"\n    Fits a polynmial with order `order` to the Energy vs. time, , and returns the energy with this subtracted. \n    \"\"\"\n    #order=2\n    if self.std('z') &lt; 1e-12:\n        # must be at a screen. Use t\n        t = self.t\n    else:\n        # All particles at the same time. Use z to calc t\n        t = self.z/c_light\n    energy = self.energy\n\n    best_fit_coeffs = np.polynomial.polynomial.polyfit(t, energy, order)\n    best_fit = np.polynomial.polynomial.polyval(t, best_fit_coeffs)\n    return energy - best_fit\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.histogramdd","title":"<code>histogramdd(*keys, bins=10, range=None)</code>","text":"<p>Wrapper for numpy.histogramdd, but accepts property names as keys.</p> <p>Computes the multidimensional histogram of keys. Internally uses weights. </p> Example <p>P.histogramdd('x', 'y', bins=50)</p> <p>Returns:     np.array with shape 50x50, edge list</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def histogramdd(self, *keys, bins=10, range=None):\n    \"\"\"\n    Wrapper for numpy.histogramdd, but accepts property names as keys.\n\n    Computes the multidimensional histogram of keys. Internally uses weights. \n\n    Example:\n        P.histogramdd('x', 'y', bins=50)\n    Returns:\n        np.array with shape 50x50, edge list \n\n    \"\"\"\n    H, edges = np.histogramdd(np.array([self[k] for k in list(keys)]).T, weights=self.weight, bins=bins, range=range)\n\n    return H, edges\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.max","title":"<code>max(key)</code>","text":"<p>Maximum of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def max(self, key):\n    \"\"\"Maximum of any key\"\"\"\n    return np.max(self[key]) \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.min","title":"<code>min(key)</code>","text":"<p>Minimum of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def min(self, key):\n    \"\"\"Minimum of any key\"\"\"\n    return np.min(self[key]) # was: getattr(self, key)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.plot","title":"<code>plot(key1='x', key2=None, bins=None, *, xlim=None, ylim=None, return_figure=False, tex=True, nice=True, **kwargs)</code>","text":"<p>1d or 2d density plot. </p> <p>If one key is given, this will plot the density of that key. Example:     .plot('x')</p> <p>If two keys arg given, this will plot a 2d marginal plot. Example:     .plot('x', 'px')</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.plot--parameters","title":"Parameters","text":"<p>particle_group: ParticleGroup     The object to plot</p> str, default = 't' <p>Key to bin on the x-axis</p> str, default = None <p>Key to bin on the y-axis. </p> int, default = None <p>Number of bins. If None, this will use a heuristic: bins = sqrt(n_particle/4)</p> tuple, default = None <p>Manual setting of the x-axis limits. Note that these are in raw, unscaled units. </p> tuple, default = None <p>Manual setting of the y-axis limits. Note that these are in raw, unscaled units. </p> bool, default = True <p>Use TEX for labels   </p> bool, default = True <p>Scale to nice units</p> bool, default = False <p>If true, return a matplotlib.figure.Figure object</p> <p>kwargs     Any additional kwargs to send to the the plot in: plt.subplots(kwargs)</p>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.plot--returns","title":"Returns","text":"<p>None or fig: matplotlib.figure.Figure     This only returns a figure object if return_figure=T, otherwise returns None</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def plot(self, key1='x', key2=None,\n         bins=None,\n         *,\n         xlim=None,\n         ylim=None,\n         return_figure=False, \n         tex=True, nice=True,\n         **kwargs):\n    \"\"\"\n    1d or 2d density plot. \n\n    If one key is given, this will plot the density of that key.\n    Example:\n        .plot('x')\n\n    If two keys arg given, this will plot a 2d marginal plot.\n    Example:\n        .plot('x', 'px')\n\n\n    Parameters\n    ----------\n    particle_group: ParticleGroup\n        The object to plot\n\n    key1: str, default = 't'\n        Key to bin on the x-axis\n\n    key2: str, default = None\n        Key to bin on the y-axis. \n\n    bins: int, default = None\n       Number of bins. If None, this will use a heuristic: bins = sqrt(n_particle/4)\n\n    xlim: tuple, default = None\n        Manual setting of the x-axis limits. Note that these are in raw, unscaled units. \n\n    ylim: tuple, default = None\n        Manual setting of the y-axis limits. Note that these are in raw, unscaled units. \n\n    tex: bool, default = True\n        Use TEX for labels   \n\n    nice: bool, default = True\n        Scale to nice units\n\n    return_figure: bool, default = False\n        If true, return a matplotlib.figure.Figure object\n\n    **kwargs\n        Any additional kwargs to send to the the plot in: plt.subplots(**kwargs)\n\n\n    Returns\n    -------\n    None or fig: matplotlib.figure.Figure\n        This only returns a figure object if return_figure=T, otherwise returns None\n\n    \"\"\"\n\n    if not key2:\n        fig = density_plot(self, key=key1,\n                           bins=bins,\n                           xlim=xlim,\n                           tex=tex,\n                           nice=nice,\n                           **kwargs)\n    else:\n        fig = marginal_plot(self, key1=key1, key2=key2,\n                            bins=bins,\n                            xlim=xlim,\n                            ylim=ylim,\n                            tex=tex,\n                            nice=nice,\n                            **kwargs)\n\n    if return_figure:\n        return fig\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.ptp","title":"<code>ptp(key)</code>","text":"<p>Peak-to-Peak = max - min of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def ptp(self, key):\n    \"\"\"Peak-to-Peak = max - min of any key\"\"\"\n    return np.ptp(self[key])     \n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.slice_statistics","title":"<code>slice_statistics(*keys, n_slice=100, slice_key=None)</code>","text":"<p>Slice statistics</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def slice_statistics(self, *keys,\n               n_slice=100,\n               slice_key=None):\n    \"\"\"\n    Slice statistics\n\n    \"\"\"      \n\n    if slice_key is None:\n        if self.in_t_coordinates:\n            slice_key = 'z'\n\n        else:\n            slice_key = 't'  \n\n    if slice_key in ('t', 'delta_t'):\n        density_name = 'current'\n    else:\n        density_name = 'density'\n\n    keys = set(keys)\n    keys.add('mean_'+slice_key)\n    keys.add('ptp_'+slice_key)\n    keys.add('charge')\n    slice_dat = slice_statistics(self, n_slice=n_slice, slice_key=slice_key,\n                            keys=keys)\n\n    slice_dat[density_name] = slice_dat['charge']/ slice_dat['ptp_'+slice_key]\n\n    return slice_dat\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.std","title":"<code>std(key)</code>","text":"<p>Standard deviation (actually sample)</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def std(self, key):\n    \"\"\"Standard deviation (actually sample)\"\"\"\n    dat = self[key]\n    if np.isscalar(dat):\n        return 0\n    avg_dat = self.avg(key)\n    return np.sqrt(np.average( (dat - avg_dat)**2, weights=self.weight))\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.twiss","title":"<code>twiss(plane='x', fraction=1, p0c=None)</code>","text":"<p>Returns Twiss and Dispersion dict.</p> <p>plane can be:</p> <p><code>'x'</code>, <code>'y'</code>, or <code>'xy'</code></p> <p>Optionally a fraction of the particles, based on amplitiude, can be specified.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def twiss(self, plane='x', fraction=1, p0c=None):\n    \"\"\"\n    Returns Twiss and Dispersion dict.\n\n    plane can be:\n\n    `'x'`, `'y'`, or `'xy'`\n\n    Optionally a fraction of the particles, based on amplitiude, can be specified.\n    \"\"\"\n    d = {}\n    for p in plane:\n        d.update(particle_twiss_dispersion(self, plane=p, fraction=fraction, p0c=p0c))\n    return d\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.twiss_match","title":"<code>twiss_match(beta=None, alpha=None, plane='x', p0c=None, inplace=False)</code>","text":"<p>Returns a ParticleGroup with requested Twiss parameters.</p> <p>See: statistics.matched_particles</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def twiss_match(self, beta=None, alpha=None, plane='x', p0c=None, inplace=False):\n    \"\"\"\n    Returns a ParticleGroup with requested Twiss parameters.\n\n    See: statistics.matched_particles\n    \"\"\"\n\n    return matched_particles(self, beta=beta, alpha=alpha, plane=plane, inplace=inplace)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.units","title":"<code>units(key)</code>","text":"<p>Returns the units of any key</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def units(self, key):\n    \"\"\"Returns the units of any key\"\"\"\n    return pg_units(key)\n</code></pre>"},{"location":"api/particles/#pmd_beamphysics.ParticleGroup.write","title":"<code>write(h5, name=None)</code>","text":"<p>Writes to an open h5 handle, or new file if h5 is a str.</p> Source code in <code>pmd_beamphysics/particles.py</code> <pre><code>def write(self, h5, name=None):\n    \"\"\"\n    Writes to an open h5 handle, or new file if h5 is a str.\n\n    \"\"\"\n    if isinstance(h5, str):\n        fname = os.path.expandvars(h5)\n        g = File(fname, 'w')\n        pmd_init(g, basePath='/', particlesPath='.' )\n    else:\n        g = h5\n\n    write_pmd_bunch(g, self, name=name)        \n</code></pre>"},{"location":"examples/bunching/","title":"Bunching","text":"In\u00a0[1]: Copied! <pre>from pmd_beamphysics import ParticleGroup\n%config InlineBackend.figure_format = 'retina'\n</pre> from pmd_beamphysics import ParticleGroup %config InlineBackend.figure_format = 'retina' In\u00a0[2]: Copied! <pre>P = ParticleGroup( 'data/bmad_particles2.h5')\nP.drift_to_t()\n\nwavelength = 0.1e-6\ndz = (P.z/wavelength % 1) * wavelength\nP.z -= dz\n</pre> P = ParticleGroup( 'data/bmad_particles2.h5') P.drift_to_t()  wavelength = 0.1e-6 dz = (P.z/wavelength % 1) * wavelength P.z -= dz In\u00a0[3]: Copied! <pre>P.plot('z')\n</pre> P.plot('z') In\u00a0[4]: Copied! <pre>b = P.bunching(wavelength)\nb\n</pre> b = P.bunching(wavelength) b Out[4]: <pre>(1-3.2133096564432656e-16j)</pre> In\u00a0[5]: Copied! <pre>P['bunching_0.1e-6']\n</pre> P['bunching_0.1e-6'] Out[5]: <pre>1.0</pre> In\u00a0[6]: Copied! <pre>P['bunching_phase_0.1e-6']\n</pre> P['bunching_phase_0.1e-6'] Out[6]: <pre>-3.2133096564432656e-16</pre> In\u00a0[7]: Copied! <pre>P['bunching_0.1_um']\n</pre> P['bunching_0.1_um'] Out[7]: <pre>1.0</pre> In\u00a0[8]: Copied! <pre>import numpy as np\n\nimport matplotlib.pyplot as plt\n</pre> import numpy as np  import matplotlib.pyplot as plt  In\u00a0[9]: Copied! <pre>wavelengths = wavelength * np.linspace(0.9, 1.1, 200)\n</pre> wavelengths = wavelength * np.linspace(0.9, 1.1, 200) In\u00a0[10]: Copied! <pre>plt.plot(wavelengths*1e6, np.abs(list(map(P.bunching, wavelengths))))\nplt.xlabel('wavelength (\u00b5m)')\nplt.ylabel('bunching')\n</pre> plt.plot(wavelengths*1e6, np.abs(list(map(P.bunching, wavelengths)))) plt.xlabel('wavelength (\u00b5m)') plt.ylabel('bunching') Out[10]: <pre>Text(0, 0.5, 'bunching')</pre> In\u00a0[11]: Copied! <pre>P.slice_plot('bunching_0.1_um')\n</pre> P.slice_plot('bunching_0.1_um') In\u00a0[12]: Copied! <pre>P.slice_plot('bunching_phase_0.1_um')\n</pre> P.slice_plot('bunching_phase_0.1_um') In\u00a0[13]: Copied! <pre>P.in_z_coordinates\n</pre> P.in_z_coordinates Out[13]: <pre>False</pre> In\u00a0[14]: Copied! <pre>P.units('bunching_0.1_um')\n</pre> P.units('bunching_0.1_um') Out[14]: <pre>pmd_unit('', 1, (0, 0, 0, 0, 0, 0, 0))</pre> In\u00a0[15]: Copied! <pre>P.units('bunching_phase_0.1_um')\n</pre> P.units('bunching_phase_0.1_um') Out[15]: <pre>pmd_unit('rad', 1, (0, 0, 0, 0, 0, 0, 0))</pre> In\u00a0[16]: Copied! <pre>from pmd_beamphysics.statistics import bunching\n</pre> from pmd_beamphysics.statistics import bunching In\u00a0[17]: Copied! <pre>?bunching\n</pre> ?bunching"},{"location":"examples/bunching/#bunching","title":"Bunching\u00b6","text":"<p>Bunching at some wavelength $\\lambda$ for a list of particles $z$ is given by the weighted sum of complex phasors:</p> <p>$$B(z, \\lambda) \\equiv \\frac{\\sum_j w_j e^{i k z_j}}{\\sum w_j} $$</p> <p>where $k = 2\\pi/\\lambda$ and $w_j$ are the weights of the particles.</p> <p>See for example D. Ratner's disseratation.</p>"},{"location":"examples/bunching/#add-bunching-to-particles","title":"Add bunching to particles\u00b6","text":"<p>This uses a simple method to add perfect bunching at 0.1 \u00b5m</p>"},{"location":"examples/bunching/#calculate-bunching","title":"Calculate bunching\u00b6","text":"<p>All of these methods will calculate the bunching. The first returns a complex number <code>bunching</code></p> <p>The string attributes return real numbers, magnitude and argument (phase):</p> <ul> <li><code>'bunching_</code> returns <code>np.abs(bunching)</code></li> <li><code>'bunching_phase_</code> returns <code>np.angle(bunching)</code></li> </ul>"},{"location":"examples/bunching/#simple-plot","title":"Simple plot\u00b6","text":""},{"location":"examples/bunching/#units","title":"Units\u00b6","text":"<p>Bunching is dimensionless</p>"},{"location":"examples/bunching/#bunching-function","title":"Bunching function\u00b6","text":"<p>This is the function that is used.</p>"},{"location":"examples/labels/","title":"TeX Labels","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>%pylab --no-import-all inline\n%config InlineBackend.figure_format = 'retina'\n</pre> %pylab --no-import-all inline %config InlineBackend.figure_format = 'retina' <pre>%pylab is deprecated, use %matplotlib inline and import the required libraries.\nPopulating the interactive namespace from numpy and matplotlib\n</pre> In\u00a0[3]: Copied! <pre>from pmd_beamphysics.labels import texlabel, TEXLABEL, mathlabel\nfrom pmd_beamphysics.units import pg_units\n</pre> from pmd_beamphysics.labels import texlabel, TEXLABEL, mathlabel from pmd_beamphysics.units import pg_units <p>This is basic function</p> In\u00a0[4]: Copied! <pre>?texlabel\n</pre> ?texlabel <p>Example:</p> In\u00a0[5]: Copied! <pre>texlabel('norm_emit_x')\n</pre> texlabel('norm_emit_x') Out[5]: <pre>'\\\\epsilon_{n, x}'</pre> <p>Example with two parts:</p> In\u00a0[6]: Copied! <pre>texlabel('cov_x__px')\n</pre> texlabel('cov_x__px') Out[6]: <pre>'\\\\left&lt;x, p_x\\\\right&gt;'</pre> <p>Returns None if a label cannot be formed:</p> In\u00a0[7]: Copied! <pre>texlabel('garbage') is None\n</pre> texlabel('garbage') is None Out[7]: <pre>True</pre> In\u00a0[8]: Copied! <pre>examples = ['cov_x__px', 'sigma_y', 'mean_Jx']\nexamples += list(TEXLABEL)\n</pre> examples = ['cov_x__px', 'sigma_y', 'mean_Jx'] examples += list(TEXLABEL) In\u00a0[9]: Copied! <pre>for key in examples:\n    tex = texlabel(key)\n    s = fr'${tex}$'\n    print(key ,'-&gt;', tex)\n    fig, ax = plt.subplots(figsize=(1,1))\n    plt.axis('off')\n    \n    ax.text(0.5, 0.5, s, fontsize=32)\n    plt.show()\n</pre> for key in examples:     tex = texlabel(key)     s = fr'${tex}$'     print(key ,'-&gt;', tex)     fig, ax = plt.subplots(figsize=(1,1))     plt.axis('off')          ax.text(0.5, 0.5, s, fontsize=32)     plt.show() <pre>cov_x__px -&gt; \\left&lt;x, p_x\\right&gt;\n</pre> <pre>sigma_y -&gt; \\sigma_{ y }\n</pre> <pre>mean_Jx -&gt; \\left&lt;J_x\\right&gt;\n</pre> <pre>t -&gt; t\n</pre> <pre>energy -&gt; E\n</pre> <pre>kinetic_energy -&gt; E_{kinetic}\n</pre> <pre>Ex -&gt; E_x\n</pre> <pre>Ey -&gt; E_y\n</pre> <pre>Ez -&gt; E_z\n</pre> <pre>Bx -&gt; B_x\n</pre> <pre>By -&gt; B_y\n</pre> <pre>Bz -&gt; B_z\n</pre> <pre>Etheta -&gt; E_{\\theta}\n</pre> <pre>Btheta -&gt; B_{\\theta}\n</pre> <pre>px -&gt; p_x\n</pre> <pre>py -&gt; p_y\n</pre> <pre>pz -&gt; p_z\n</pre> <pre>p -&gt; p\n</pre> <pre>pr -&gt; p_r\n</pre> <pre>ptheta -&gt; p_{\\theta}\n</pre> <pre>x -&gt; x\n</pre> <pre>y -&gt; y\n</pre> <pre>z -&gt; z\n</pre> <pre>r -&gt; r\n</pre> <pre>Jx -&gt; J_x\n</pre> <pre>Jy -&gt; J_y\n</pre> <pre>beta -&gt; \\beta\n</pre> <pre>beta_x -&gt; \\beta_x\n</pre> <pre>beta_y -&gt; \\beta_y\n</pre> <pre>beta_z -&gt; \\beta_z\n</pre> <pre>gamma -&gt; \\gamma\n</pre> <pre>theta -&gt; \\theta\n</pre> <pre>charge -&gt; Q\n</pre> <pre>twiss_alpha_x -&gt; Twiss\\ \\alpha_x\n</pre> <pre>twiss_beta_x -&gt; Twiss\\ \\beta_x\n</pre> <pre>twiss_gamma_x -&gt; Twiss\\ \\gamma_x\n</pre> <pre>twiss_eta_x -&gt; Twiss\\ \\eta_x\n</pre> <pre>twiss_etap_x -&gt; Twiss\\ \\eta'_x\n</pre> <pre>twiss_emit_x -&gt; Twiss\\ \\epsilon_{x}\n</pre> <pre>twiss_norm_emit_x -&gt; Twiss\\ \\epsilon_{n, x}\n</pre> <pre>twiss_alpha_y -&gt; Twiss\\ \\alpha_y\n</pre> <pre>twiss_beta_y -&gt; Twiss\\ \\beta_y\n</pre> <pre>twiss_gamma_y -&gt; Twiss\\ \\gamma_y\n</pre> <pre>twiss_eta_y -&gt; Twiss\\ \\eta_y\n</pre> <pre>twiss_etap_y -&gt; Twiss\\ \\eta'_y\n</pre> <pre>twiss_emit_y -&gt; Twiss\\ \\epsilon_{y}\n</pre> <pre>twiss_norm_emit_y -&gt; Twiss\\ \\epsilon_{n, y}\n</pre> <pre>average_current -&gt; I_{av}\n</pre> <pre>norm_emit_x -&gt; \\epsilon_{n, x}\n</pre> <pre>norm_emit_y -&gt; \\epsilon_{n, y}\n</pre> <pre>norm_emit_4d -&gt; \\epsilon_{4D}\n</pre> <pre>Lz -&gt; L_z\n</pre> <pre>xp -&gt; x'\n</pre> <pre>yp -&gt; y'\n</pre> <pre>x_bar -&gt; \\overline{x}\n</pre> <pre>px_bar -&gt; \\overline{p_x}\n</pre> <pre>y_bar -&gt; \\overline{y}\n</pre> <pre>py_bar -&gt; \\overline{p_y}\n</pre> In\u00a0[10]: Copied! <pre>?mathlabel\n</pre> ?mathlabel In\u00a0[11]: Copied! <pre>mathlabel('beta_x', units=pg_units('beta_x'))\n</pre> mathlabel('beta_x', units=pg_units('beta_x')) Out[11]: <pre>'$\\\\beta_x$'</pre> <p>Multiple keys. Note that units are not checked for consistency!</p> In\u00a0[12]: Copied! <pre>mathlabel('sigma_x', 'sigma_y', units='\u00b5m')\n</pre> mathlabel('sigma_x', 'sigma_y', units='\u00b5m') Out[12]: <pre>'$\\\\sigma_{ x }, \\\\sigma_{ y }~(\\\\mathrm{ \u00b5m } )$'</pre> In\u00a0[13]: Copied! <pre>for key in examples:\n    \n    label = mathlabel(key, units=pg_units(key))\n    print(key ,'-&gt;', label)\n    fig, ax = plt.subplots(figsize=(1,1))\n    plt.axis('off')\n    \n    ax.text(0.5, 0.5, label, fontsize=32)\n    plt.show()\n</pre> for key in examples:          label = mathlabel(key, units=pg_units(key))     print(key ,'-&gt;', label)     fig, ax = plt.subplots(figsize=(1,1))     plt.axis('off')          ax.text(0.5, 0.5, label, fontsize=32)     plt.show() <pre>cov_x__px -&gt; $\\left&lt;x, p_x\\right&gt;~(\\mathrm{ m*eV/c } )$\n</pre> <pre>sigma_y -&gt; $\\sigma_{ y }~(\\mathrm{ m } )$\n</pre> <pre>mean_Jx -&gt; $\\left&lt;J_x\\right&gt;~(\\mathrm{ m } )$\n</pre> <pre>t -&gt; $t~(\\mathrm{ s } )$\n</pre> <pre>energy -&gt; $E~(\\mathrm{ eV } )$\n</pre> <pre>kinetic_energy -&gt; $E_{kinetic}~(\\mathrm{ eV } )$\n</pre> <pre>Ex -&gt; $E_x~(\\mathrm{ V/m } )$\n</pre> <pre>Ey -&gt; $E_y~(\\mathrm{ V/m } )$\n</pre> <pre>Ez -&gt; $E_z~(\\mathrm{ V/m } )$\n</pre> <pre>Bx -&gt; $B_x~(\\mathrm{ T } )$\n</pre> <pre>By -&gt; $B_y~(\\mathrm{ T } )$\n</pre> <pre>Bz -&gt; $B_z~(\\mathrm{ T } )$\n</pre> <pre>Etheta -&gt; $E_{\\theta}~(\\mathrm{ V/m } )$\n</pre> <pre>Btheta -&gt; $B_{\\theta}~(\\mathrm{ T } )$\n</pre> <pre>px -&gt; $p_x~(\\mathrm{ eV/c } )$\n</pre> <pre>py -&gt; $p_y~(\\mathrm{ eV/c } )$\n</pre> <pre>pz -&gt; $p_z~(\\mathrm{ eV/c } )$\n</pre> <pre>p -&gt; $p~(\\mathrm{ eV/c } )$\n</pre> <pre>pr -&gt; $p_r~(\\mathrm{ eV/c } )$\n</pre> <pre>ptheta -&gt; $p_{\\theta}~(\\mathrm{ eV/c } )$\n</pre> <pre>x -&gt; $x~(\\mathrm{ m } )$\n</pre> <pre>y -&gt; $y~(\\mathrm{ m } )$\n</pre> <pre>z -&gt; $z~(\\mathrm{ m } )$\n</pre> <pre>r -&gt; $r~(\\mathrm{ m } )$\n</pre> <pre>Jx -&gt; $J_x~(\\mathrm{ m } )$\n</pre> <pre>Jy -&gt; $J_y~(\\mathrm{ m } )$\n</pre> <pre>beta -&gt; $\\beta$\n</pre> <pre>beta_x -&gt; $\\beta_x$\n</pre> <pre>beta_y -&gt; $\\beta_y$\n</pre> <pre>beta_z -&gt; $\\beta_z$\n</pre> <pre>gamma -&gt; $\\gamma$\n</pre> <pre>theta -&gt; $\\theta~(\\mathrm{ rad } )$\n</pre> <pre>charge -&gt; $Q~(\\mathrm{ C } )$\n</pre> <pre>twiss_alpha_x -&gt; $Twiss\\ \\alpha_x$\n</pre> <pre>twiss_beta_x -&gt; $Twiss\\ \\beta_x~(\\mathrm{ m } )$\n</pre> <pre>twiss_gamma_x -&gt; $Twiss\\ \\gamma_x~(\\mathrm{ /m } )$\n</pre> <pre>twiss_eta_x -&gt; $Twiss\\ \\eta_x~(\\mathrm{ m } )$\n</pre> <pre>twiss_etap_x -&gt; $Twiss\\ \\eta'_x$\n</pre> <pre>twiss_emit_x -&gt; $Twiss\\ \\epsilon_{x}~(\\mathrm{ m } )$\n</pre> <pre>twiss_norm_emit_x -&gt; $Twiss\\ \\epsilon_{n, x}~(\\mathrm{ m } )$\n</pre> <pre>twiss_alpha_y -&gt; $Twiss\\ \\alpha_y$\n</pre> <pre>twiss_beta_y -&gt; $Twiss\\ \\beta_y~(\\mathrm{ m } )$\n</pre> <pre>twiss_gamma_y -&gt; $Twiss\\ \\gamma_y~(\\mathrm{ /m } )$\n</pre> <pre>twiss_eta_y -&gt; $Twiss\\ \\eta_y~(\\mathrm{ m } )$\n</pre> <pre>twiss_etap_y -&gt; $Twiss\\ \\eta'_y$\n</pre> <pre>twiss_emit_y -&gt; $Twiss\\ \\epsilon_{y}~(\\mathrm{ m } )$\n</pre> <pre>twiss_norm_emit_y -&gt; $Twiss\\ \\epsilon_{n, y}~(\\mathrm{ m } )$\n</pre> <pre>average_current -&gt; $I_{av}~(\\mathrm{ A } )$\n</pre> <pre>norm_emit_x -&gt; $\\epsilon_{n, x}~(\\mathrm{ m } )$\n</pre> <pre>norm_emit_y -&gt; $\\epsilon_{n, y}~(\\mathrm{ m } )$\n</pre> <pre>norm_emit_4d -&gt; $\\epsilon_{4D}~(\\mathrm{ (m)^2 } )$\n</pre> <pre>Lz -&gt; $L_z~(\\mathrm{ m*eV/c } )$\n</pre> <pre>xp -&gt; $x'~(\\mathrm{ rad } )$\n</pre> <pre>yp -&gt; $y'~(\\mathrm{ rad } )$\n</pre> <pre>x_bar -&gt; $\\overline{x}~(\\mathrm{ \\sqrt{ m } } )$\n</pre> <pre>px_bar -&gt; $\\overline{p_x}~(\\mathrm{ \\sqrt{ m } } )$\n</pre> <pre>y_bar -&gt; $\\overline{y}~(\\mathrm{ \\sqrt{ m } } )$\n</pre> <pre>py_bar -&gt; $\\overline{p_y}~(\\mathrm{ \\sqrt{ m } } )$\n</pre>"},{"location":"examples/labels/#tex-labels","title":"TeX Labels\u00b6","text":"<p>TeX labels for most attributes can be retrieved.</p>"},{"location":"examples/labels/#math-label-with-unit","title":"Math label with unit\u00b6","text":"<p><code>mathlabel</code> provides the complete string with optional units</p>"},{"location":"examples/normalized_coordinates/","title":"Simple Normalized Coordinates","text":"In\u00a0[1]: Copied! <pre>from pmd_beamphysics import ParticleGroup\nfrom pmd_beamphysics.statistics import A_mat_calc, twiss_calc, normalized_particle_coordinate\nimport numpy as np\n\n\nimport matplotlib.pyplot as plt\nimport matplotlib\n%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\nmatplotlib.rcParams['figure.figsize'] = (4,4)\n</pre> from pmd_beamphysics import ParticleGroup from pmd_beamphysics.statistics import A_mat_calc, twiss_calc, normalized_particle_coordinate import numpy as np   import matplotlib.pyplot as plt import matplotlib %matplotlib inline %config InlineBackend.figure_format = 'retina' matplotlib.rcParams['figure.figsize'] = (4,4) In\u00a0[2]: Copied! <pre>help(A_mat_calc)\n</pre> help(A_mat_calc) <pre>Help on function A_mat_calc in module pmd_beamphysics.statistics:\n\nA_mat_calc(beta, alpha, inverse=False)\n    Returns the 1D normal form matrix from twiss parameters beta and alpha\n    \n        A =   sqrt(beta)         0 \n             -alpha/sqrt(beta)   1/sqrt(beta) \n    \n    If inverse, the inverse will be returned:\n    \n        A^-1 =  1/sqrt(beta)     0 \n                alpha/sqrt(beta) sqrt(beta)     \n    \n    This corresponds to the linear normal form decomposition:\n    \n        M = A . Rot(theta) . A^-1\n    \n    with a clockwise rotation matrix:\n    \n        Rot(theta) =  cos(theta) sin(theta)\n                     -sin(theta) cos(theta)\n    \n    In the Bmad manual, G_q (Bmad) = A (here) in the Linear Optics chapter.\n    \n    A^-1 can be used to form normalized coordinates: \n        x_bar, px_bar   = A^-1 . (x, px)\n\n</pre> <p>Make phase space circle. This will represent some normalized coordinates:</p> In\u00a0[3]: Copied! <pre>theta = np.linspace(0, np.pi*2, 100)\nzvec0 = np.array([np.cos(theta), np.sin(theta)])\nplt.scatter(*zvec0)\n</pre> theta = np.linspace(0, np.pi*2, 100) zvec0 = np.array([np.cos(theta), np.sin(theta)]) plt.scatter(*zvec0) Out[3]: <pre>&lt;matplotlib.collections.PathCollection at 0x7f9690b69670&gt;</pre> <p>Make a 'beam' in 'lab coordinates':</p> In\u00a0[4]: Copied! <pre>MYMAT = np.array([[10, 0],[-3, 5]])\nzvec = np.matmul(MYMAT , zvec0)\nplt.scatter(*zvec)\n</pre> MYMAT = np.array([[10, 0],[-3, 5]]) zvec = np.matmul(MYMAT , zvec0) plt.scatter(*zvec) Out[4]: <pre>&lt;matplotlib.collections.PathCollection at 0x7f9687adf370&gt;</pre> <p>With a beam, $\\alpha$ and $\\beta$ can be determined from moments of the covariance matrix.</p> In\u00a0[5]: Copied! <pre>help(twiss_calc)\n</pre> help(twiss_calc) <pre>Help on function twiss_calc in module pmd_beamphysics.statistics:\n\ntwiss_calc(sigma_mat2)\n    Calculate Twiss parameters from the 2D sigma matrix (covariance matrix):\n    sigma_mat = &lt;x,x&gt;   &lt;x, p&gt;\n                &lt;p, x&gt;  &lt;p, p&gt;\n    \n    This is a simple calculation. Makes no assumptions about units. \n    \n    alpha = -&lt;x, p&gt;/emit\n    beta  =  &lt;x, x&gt;/emit\n    gamma =  &lt;p, p&gt;/emit\n    emit = det(sigma_mat)\n\n</pre> <p>Calculate a sigma matrix, get the determinant:</p> In\u00a0[6]: Copied! <pre>sigma_mat2 = np.cov(*zvec)\nnp.linalg.det(sigma_mat2)\n</pre> sigma_mat2 = np.cov(*zvec) np.linalg.det(sigma_mat2) Out[6]: <pre>637.5000000000003</pre> <p>Get some twiss:</p> In\u00a0[7]: Copied! <pre>twiss = twiss_calc(sigma_mat2)\ntwiss\n</pre> twiss = twiss_calc(sigma_mat2) twiss Out[7]: <pre>{'alpha': 0.6059702963017245,\n 'beta': 2.0199009876724157,\n 'gamma': 0.6768648603788545,\n 'emit': 25.248762345905202}</pre> <p>Analyzing matrices:</p> In\u00a0[8]: Copied! <pre>A = A_mat_calc(twiss['beta'], twiss['alpha'])\nA_inv = A_mat_calc(twiss['beta'], twiss['alpha'], inverse=True)\n</pre> A = A_mat_calc(twiss['beta'], twiss['alpha']) A_inv = A_mat_calc(twiss['beta'], twiss['alpha'], inverse=True) <p>A_inv turns this back into a circle:</p> In\u00a0[9]: Copied! <pre>zvec2 = np.matmul(A_inv, zvec)\nplt.scatter(*zvec2)\n</pre> zvec2 = np.matmul(A_inv, zvec) plt.scatter(*zvec2) Out[9]: <pre>&lt;matplotlib.collections.PathCollection at 0x7f9687a56370&gt;</pre> In\u00a0[10]: Copied! <pre>twiss_calc(np.cov(*zvec2))\n</pre> twiss_calc(np.cov(*zvec2)) Out[10]: <pre>{'alpha': 1.4672219335410167e-16,\n 'beta': 1.0,\n 'gamma': 1.0000000000000002,\n 'emit': 25.24876234590519}</pre> In\u00a0[11]: Copied! <pre>matplotlib.rcParams['figure.figsize'] = (13,8) # Reset plot\n</pre>  matplotlib.rcParams['figure.figsize'] = (13,8) # Reset plot In\u00a0[12]: Copied! <pre>help(normalized_particle_coordinate)\n</pre> help(normalized_particle_coordinate) <pre>Help on function normalized_particle_coordinate in module pmd_beamphysics.statistics:\n\nnormalized_particle_coordinate(particle_group, key, twiss=None, mass_normalize=True)\n    Returns a single normalized coordinate array from a ParticleGroup\n    \n    Position or momentum is determined by the key. \n    If the key starts with 'p', it is a momentum, else it is a position,\n    and the\n    \n    Intended use is for key to be one of:\n        x, px, y py\n        \n    and the corresponding normalized coordinates are named with suffix _bar, i.e.:\n        x_bar, px_bar, y_bar, py_bar\n    \n    If mass_normalize (default=True), the momentum will be divided by the mass, so that the units are sqrt(m).\n    \n    These are related to action-angle coordinates\n        J: amplitude\n        phi: phase\n        \n        x_bar =  sqrt(2 J) cos(phi)\n        px_bar = sqrt(2 J) sin(phi)\n        \n    So therefore:\n        J = (x_bar^2 + px_bar^2)/2\n        phi = arctan(px_bar/x_bar)\n    and:        \n        &lt;J&gt; = norm_emit_x\n     \n     Note that the center may need to be subtracted in this case.\n\n</pre> <p>Get some example particles, with a typical transverse phase space plot:</p> In\u00a0[13]: Copied! <pre>P = ParticleGroup('data/bmad_particles2.h5')\nP.plot('x', 'px')\n</pre> P = ParticleGroup('data/bmad_particles2.h5') P.plot('x', 'px') <p>If no twiss is given, then the analyzing matrix is computed from the beam itself:</p> In\u00a0[14]: Copied! <pre>normalized_particle_coordinate(P, 'x', twiss=None)\n</pre> normalized_particle_coordinate(P, 'x', twiss=None) Out[14]: <pre>array([-4.83384095e-04,  9.99855846e-04,  7.35820860e-05, ...,\n       -7.48265408e-05,  4.77803205e-05, -4.18053319e-04])</pre> <p>This is equivelent:</p> In\u00a0[15]: Copied! <pre>normalized_particle_coordinate(P, 'x', twiss=twiss_calc(P.cov('x', 'px')), mass_normalize=False)/np.sqrt(P.mass)\n</pre> normalized_particle_coordinate(P, 'x', twiss=twiss_calc(P.cov('x', 'px')), mass_normalize=False)/np.sqrt(P.mass) Out[15]: <pre>array([-4.83384095e-04,  9.99855846e-04,  7.35820860e-05, ...,\n       -7.48265408e-05,  4.77803205e-05, -4.18053319e-04])</pre> <p>And is given as a property:</p> In\u00a0[16]: Copied! <pre>P.x_bar\n</pre> P.x_bar Out[16]: <pre>array([-4.83384095e-04,  9.99855846e-04,  7.35820860e-05, ...,\n       -7.48265408e-05,  4.77803205e-05, -4.18053319e-04])</pre> <p>The amplitude is defined as:</p> In\u00a0[17]: Copied! <pre>(P.x_bar**2 + P.px_bar**2)/2\n</pre> (P.x_bar**2 + P.px_bar**2)/2 Out[17]: <pre>array([1.16831464e-07, 5.85751290e-07, 3.26876598e-07, ...,\n       3.25916449e-07, 2.21097254e-07, 2.73364174e-07])</pre> <p>This is also given as a property:</p> In\u00a0[18]: Copied! <pre>P.Jx\n</pre> P.Jx Out[18]: <pre>array([1.16831464e-07, 5.85751290e-07, 3.26876598e-07, ...,\n       3.25916449e-07, 2.21097254e-07, 2.73364174e-07])</pre> <p>Note the mass normalization is the same:</p> In\u00a0[19]: Copied! <pre>P.Jx.mean(), P['mean_Jx'], P['norm_emit_x']\n</pre> P.Jx.mean(), P['mean_Jx'], P['norm_emit_x'] Out[19]: <pre>(4.883790126887025e-07, 4.883790126887027e-07, 4.881047612307434e-07)</pre> <p>This is now nice and roundish:</p> In\u00a0[20]: Copied! <pre>P.plot('x_bar', 'px_bar')\n</pre> P.plot('x_bar', 'px_bar') <p>Jy also works. This gives some sense of where the emittance is larger.</p> In\u00a0[21]: Copied! <pre>P.plot('t', 'Jy')\n</pre> P.plot('t', 'Jy') <p>Sort by Jx:</p> In\u00a0[22]: Copied! <pre>P = P[np.argsort(P.Jx)]\n</pre> P = P[np.argsort(P.Jx)] <p>Now particles are ordered:</p> In\u00a0[23]: Copied! <pre>plt.plot(P.Jx)\n</pre> plt.plot(P.Jx) Out[23]: <pre>[&lt;matplotlib.lines.Line2D at 0x7f968742f4c0&gt;]</pre> <p>This can be used to calculate the 95% emittance:</p> In\u00a0[24]: Copied! <pre>P[0:int(0.95*len(P))]['norm_emit_x']\n</pre> P[0:int(0.95*len(P))]['norm_emit_x'] Out[24]: <pre>3.7399940158442355e-07</pre> In\u00a0[25]: Copied! <pre>def twiss_match(x, p, beta0=1, alpha0=0, beta1=1, alpha1=0):\n    \"\"\"\n    Simple Twiss matching. \n    \n    Takes positions x and momenta p, and transforms them according to \n    initial Twiss parameters:\n        beta0, alpha0 \n    into final  Twiss parameters:\n        beta1, alpha1\n        \n    This is simply the matrix ransformation:    \n        xnew  = (   sqrt(beta1/beta0)                  0                 ) . ( x )\n        pnew    (  (alpha0-alpha1)/sqrt(beta0*beta1)   sqrt(beta0/beta1) )   ( p ) \n        \n\n    Returns new x, p\n    \n    \"\"\"\n    m11 = np.sqrt(beta1/beta0)\n    m21 = (alpha0-alpha1)/np.sqrt(beta0*beta1)\n    \n    xnew = x * m11\n    pnew = x * m21 + p / m11\n    \n    return xnew, pnew\n</pre> def twiss_match(x, p, beta0=1, alpha0=0, beta1=1, alpha1=0):     \"\"\"     Simple Twiss matching.           Takes positions x and momenta p, and transforms them according to      initial Twiss parameters:         beta0, alpha0      into final  Twiss parameters:         beta1, alpha1              This is simply the matrix ransformation:             xnew  = (   sqrt(beta1/beta0)                  0                 ) . ( x )         pnew    (  (alpha0-alpha1)/sqrt(beta0*beta1)   sqrt(beta0/beta1) )   ( p )                Returns new x, p          \"\"\"     m11 = np.sqrt(beta1/beta0)     m21 = (alpha0-alpha1)/np.sqrt(beta0*beta1)          xnew = x * m11     pnew = x * m21 + p / m11          return xnew, pnew <p>Get some Twiss:</p> In\u00a0[26]: Copied! <pre>T0 = twiss_calc(P.cov('x', 'xp'))\nT0\n</pre> T0 = twiss_calc(P.cov('x', 'xp')) T0 Out[26]: <pre>{'alpha': -0.7756418199427733,\n 'beta': 9.761411505651415,\n 'gamma': 0.16407670467707158,\n 'emit': 3.127519925999214e-11}</pre> <p>Make a copy and maniplulate:</p> In\u00a0[27]: Copied! <pre>P2 = P.copy()\nP2.x, P2.px = twiss_match(P.x, P.px/P['mean_p'], beta0=T0['beta'], alpha0=T0['alpha'], beta1=9, alpha1=-2)\nP2.px *= P['mean_p']\n</pre> P2 = P.copy() P2.x, P2.px = twiss_match(P.x, P.px/P['mean_p'], beta0=T0['beta'], alpha0=T0['alpha'], beta1=9, alpha1=-2) P2.px *= P['mean_p'] In\u00a0[28]: Copied! <pre>twiss_calc(P2.cov('x', 'xp'))\n</pre> twiss_calc(P2.cov('x', 'xp')) Out[28]: <pre>{'alpha': -1.9995993293102663,\n 'beta': 9.00102737307016,\n 'gamma': 0.5553141070021174,\n 'emit': 3.12716295233219e-11}</pre> <p>This is a dedicated routine:</p> In\u00a0[29]: Copied! <pre>def matched_particles(particle_group, beta=None, alpha=None, plane='x', p0c=None, inplace=False):\n    \"\"\"\n    Perfoms simple Twiss 'matching' by applying a linear transformation to\n        x, px if plane == 'x', or x, py if plane == 'y'\n    \n    Returns a new ParticleGroup\n    \n    If inplace, a copy will not be made, and changes will be done in place. \n    \n    \"\"\"\n    \n    assert plane in ('x', 'y'), f'Invalid plane: {plane}'\n    \n    if inplace:\n        P = particle_group\n    else:\n        P = particle_group.copy()\n    \n    if not p0c:\n        p0c = P['mean_p']\n    \n\n    # Use Bmad-style coordinates.\n    # Get plane. \n    if plane == 'x':\n        x = P.x\n        p = P.px/p0c\n    else:\n        x = P.y\n        p = P.py/p0c\n        \n    # Get current Twiss\n    tx = twiss_calc(np.cov(x, p, aweights=P.weight))\n    \n    # If not specified, just fill in the current value.\n    if alpha is None:\n        alpha = tx['alpha']\n    if beta is None:\n        beta = tx['beta']\n    \n    # New coordinates\n    xnew, pnew = twiss_match(x, p, beta0=tx['beta'], alpha0=tx['alpha'], beta1=beta, alpha1=alpha)\n    \n    # Set\n    if plane == 'x':\n        P.x = xnew\n        P.px = pnew*p0c\n    else:\n        P.y = xnew\n        P.py = pnew*p0c\n        \n    return P\n    \n    \n# Check    \nP3 = matched_particles(P, beta=None, alpha=-4, plane='y')\nP.twiss(plane='y'), P3.twiss(plane='y')\n</pre> def matched_particles(particle_group, beta=None, alpha=None, plane='x', p0c=None, inplace=False):     \"\"\"     Perfoms simple Twiss 'matching' by applying a linear transformation to         x, px if plane == 'x', or x, py if plane == 'y'          Returns a new ParticleGroup          If inplace, a copy will not be made, and changes will be done in place.           \"\"\"          assert plane in ('x', 'y'), f'Invalid plane: {plane}'          if inplace:         P = particle_group     else:         P = particle_group.copy()          if not p0c:         p0c = P['mean_p']           # Use Bmad-style coordinates.     # Get plane.      if plane == 'x':         x = P.x         p = P.px/p0c     else:         x = P.y         p = P.py/p0c              # Get current Twiss     tx = twiss_calc(np.cov(x, p, aweights=P.weight))          # If not specified, just fill in the current value.     if alpha is None:         alpha = tx['alpha']     if beta is None:         beta = tx['beta']          # New coordinates     xnew, pnew = twiss_match(x, p, beta0=tx['beta'], alpha0=tx['alpha'], beta1=beta, alpha1=alpha)          # Set     if plane == 'x':         P.x = xnew         P.px = pnew*p0c     else:         P.y = xnew         P.py = pnew*p0c              return P           # Check     P3 = matched_particles(P, beta=None, alpha=-4, plane='y') P.twiss(plane='y'), P3.twiss(plane='y') Out[29]: <pre>({'alpha_y': 0.9058122605337396,\n  'beta_y': 14.683316714787708,\n  'gamma_y': 0.12398396674913406,\n  'emit_y': 3.214301173354259e-11,\n  'eta_y': -0.0001221701145704683,\n  'etap_y': -3.1414468851691344e-07,\n  'norm_emit_y': 5.016420878150227e-07},\n {'alpha_y': -3.9999679865267384,\n  'beta_y': 14.683316715010363,\n  'gamma_y': 1.1577591237176261,\n  'emit_y': 3.214301173290241e-11,\n  'eta_y': -0.00012217012301264445,\n  'etap_y': -4.113188244396527e-05,\n  'norm_emit_y': 5.016420878133589e-07})</pre> <p>These functions are in statistics:</p> In\u00a0[30]: Copied! <pre>from pmd_beamphysics.statistics import twiss_match, matched_particles\n</pre> from pmd_beamphysics.statistics import twiss_match, matched_particles"},{"location":"examples/normalized_coordinates/#simple-normalized-coordinates","title":"Simple Normalized Coordinates\u00b6","text":"<p>1D normalized coordinates originate from the normal form decomposition, where the transfer matrix that propagates phase space coordinates $(x, p)$ is decomposed as</p> <p>$M = A \\cdot R(\\theta) \\cdot A^{-1}$</p> <p>And the matrix $A$ can be parameterized as</p> <p>A = $\\begin{pmatrix}\\sqrt{\\beta} &amp; 0\\\\-\\alpha/\\sqrt{\\beta} &amp; 1/\\sqrt{\\beta}\\end{pmatrix}$</p>"},{"location":"examples/normalized_coordinates/#twiss-parameters","title":"Twiss parameters\u00b6","text":"<p>Effective Twiss parameters can be calculated from the second order moments of the particles.</p> <p>This does not change the phase space area.</p>"},{"location":"examples/normalized_coordinates/#x_bar-px_bar-jx-etc","title":"x_bar, px_bar, Jx, etc.\u00b6","text":"<p>These are essentially action-angle coordinates, calculated by using the an analyzing twiss dict</p>"},{"location":"examples/normalized_coordinates/#simple-matching","title":"Simple 'matching'\u00b6","text":"<p>Often a beam needs to be 'matched' for tracking in some program.</p> <p>This is a 'faked' tranformation that ultimately would need to be realized by a focusing system.</p>"},{"location":"examples/particle_examples/","title":"openPMD beamphysics examples","text":"In\u00a0[1]: Copied! <pre># Nicer plotting\nimport matplotlib\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\nmatplotlib.rcParams['figure.figsize'] = (8,4)\n</pre> # Nicer plotting import matplotlib import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina' matplotlib.rcParams['figure.figsize'] = (8,4) In\u00a0[2]: Copied! <pre>from pmd_beamphysics import ParticleGroup\n</pre> from pmd_beamphysics import ParticleGroup In\u00a0[3]: Copied! <pre>P = ParticleGroup( 'data/bmad_particles2.h5')\nP\n</pre> P = ParticleGroup( 'data/bmad_particles2.h5') P Out[3]: <pre>&lt;ParticleGroup with 100000 particles at 0x7fb40c3bf100&gt;</pre> In\u00a0[4]: Copied! <pre>P.energy\n</pre> P.energy Out[4]: <pre>array([8.00032916e+09, 7.97408124e+09, 7.97338447e+09, ...,\n       7.97531701e+09, 7.97163591e+09, 7.97170403e+09])</pre> In\u00a0[5]: Copied! <pre>P['mean_energy'], P.units('mean_energy')\n</pre> P['mean_energy'], P.units('mean_energy') Out[5]: <pre>(7974939710.08345, pmd_unit('eV', 1.602176634e-19, (2, 1, -2, 0, 0, 0, 0)))</pre> In\u00a0[6]: Copied! <pre>P.where(P.x &lt; P['mean_x'])\n</pre> P.where(P.x &lt; P['mean_x']) Out[6]: <pre>&lt;ParticleGroup with 50082 particles at 0x7fb454ebfc40&gt;</pre> In\u00a0[7]: Copied! <pre>a = P.plot('x', 'px', figsize=(8,8))\n</pre> a = P.plot('x', 'px', figsize=(8,8)) In\u00a0[8]: Copied! <pre>P.write_elegant('elegant_particles.txt', verbose=True)\n</pre> P.write_elegant('elegant_particles.txt', verbose=True) <pre>writing 100000 particles to elegant_particles.txt\n</pre> <p>x positions, in meters</p> In\u00a0[9]: Copied! <pre>P.x\n</pre> P.x Out[9]: <pre>array([-1.20890504e-05,  2.50055966e-05,  1.84022924e-06, ...,\n       -1.87135206e-06,  1.19494768e-06, -1.04551798e-05])</pre> <p>relativistic gamma, calculated on the fly</p> In\u00a0[10]: Copied! <pre>P.gamma\n</pre> P.gamma Out[10]: <pre>array([15656.25362205, 15604.88772858, 15603.52416601, ...,\n       15607.30607107, 15600.10233296, 15600.23563914])</pre> <p>Both are allowed</p> In\u00a0[11]: Copied! <pre>len(P), P['n_particle']\n</pre> len(P), P['n_particle'] Out[11]: <pre>(100000, 100000)</pre> <p>Statistics on any of these. Note that these properly use the .weight array.</p> In\u00a0[12]: Copied! <pre>P.avg('gamma'), P.std('p')\n</pre> P.avg('gamma'), P.std('p') Out[12]: <pre>(15606.56770446094, 7440511.955100455)</pre> <p>Covariance matrix of any list of keys</p> In\u00a0[13]: Copied! <pre>P.cov('x', 'px', 'y', 'kinetic_energy')\n</pre> P.cov('x', 'px', 'y', 'kinetic_energy') Out[13]: <pre>array([[ 3.05290090e-10,  1.93582323e-01,  2.14462846e-12,\n        -3.94841065e+00],\n       [ 1.93582323e-01,  3.26525376e+08, -2.44058325e-05,\n        -5.36815277e+08],\n       [ 2.14462846e-12, -2.44058325e-05,  4.71979014e-10,\n        -8.48100957e-01],\n       [-3.94841065e+00, -5.36815277e+08, -8.48100957e-01,\n         5.53617715e+13]])</pre> <p>These can all be accessed with brackets. sigma_ and mean_ are also allowed</p> In\u00a0[14]: Copied! <pre>P['sigma_x'], P['sigma_energy'], P['min_y'], P['norm_emit_x'], P['norm_emit_4d']\n</pre> P['sigma_x'], P['sigma_energy'], P['min_y'], P['norm_emit_x'], P['norm_emit_4d'] Out[14]: <pre>(1.7472465109340715e-05,\n 7440511.939853717,\n -0.00017677380499644412,\n 4.881047612307434e-07,\n 2.4484888474798633e-13)</pre> <p>Covariance has a special syntax, items separated by __</p> In\u00a0[15]: Copied! <pre>P['cov_x__kinetic_energy']\n</pre> P['cov_x__kinetic_energy'] Out[15]: <pre>-3.9484106461190627</pre> <p>n-dimensional histogram. This is a wrapper for <code>numpy.histogramdd</code></p> In\u00a0[16]: Copied! <pre>H, edges = P.histogramdd('t', 'delta_pz', bins=(5,10))\nH.shape, edges\n</pre> H, edges = P.histogramdd('t', 'delta_pz', bins=(5,10)) H.shape, edges Out[16]: <pre>((5, 10),\n [array([5.16387938e-06, 5.16387943e-06, 5.16387948e-06, 5.16387953e-06,\n         5.16387958e-06, 5.16387963e-06]),\n  array([-24476455.61834908, -17729298.92490654, -10982142.231464  ,\n          -4234985.53802147,   2512171.15542107,   9259327.8488636 ,\n          16006484.54230614,  22753641.23574867,  29500797.92919121,\n          36247954.62263375,  42995111.31607628])])</pre> In\u00a0[17]: Copied! <pre>ss = P.slice_statistics('norm_emit_x')\nss.keys()\n</pre> ss = P.slice_statistics('norm_emit_x') ss.keys() Out[17]: <pre>dict_keys(['charge', 'mean_t', 'norm_emit_x', 'ptp_t', 'current'])</pre> <p>Multiple keys can also be accepted:</p> In\u00a0[18]: Copied! <pre>ss = P.slice_statistics('norm_emit_x', 'norm_emit_y', 'twiss')\nss.keys()\n</pre> ss = P.slice_statistics('norm_emit_x', 'norm_emit_y', 'twiss') ss.keys() Out[18]: <pre>dict_keys(['charge', 'mean_t', 'ptp_t', 'norm_emit_y', 'twiss', 'norm_emit_x', 'twiss_alpha_x', 'twiss_beta_x', 'twiss_gamma_x', 'twiss_emit_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_norm_emit_x', 'twiss_alpha_y', 'twiss_beta_y', 'twiss_gamma_y', 'twiss_emit_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_norm_emit_y', 'current'])</pre> <p>Note that for a slice key <code>X</code>, the method will also calculate <code>mean_X</code>, <code>ptp_X</code>, as <code>charge</code> so that a <code>density</code> calculated from these. In the special case of <code>X=t</code>, the density will be labeled as <code>current</code> according to common convention.</p> In\u00a0[19]: Copied! <pre>P.twiss('x')\n</pre> P.twiss('x') Out[19]: <pre>{'alpha_x': -0.7764646310859605,\n 'beta_x': 9.758458404204259,\n 'gamma_x': 0.16425722762079686,\n 'emit_x': 3.1255806600595395e-11,\n 'eta_x': -0.0005687740085942673,\n 'etap_x': -9.69649743612097e-06,\n 'norm_emit_x': 4.877958608683612e-07}</pre> <p>95% emittance calculation, x and y</p> In\u00a0[20]: Copied! <pre>P.twiss('xy', fraction=0.95)\n</pre> P.twiss('xy', fraction=0.95) Out[20]: <pre>{'alpha_x': -0.765323995145385,\n 'beta_x': 9.233496626510156,\n 'gamma_x': 0.1717356795249758,\n 'emit_x': 2.3954681527227138e-11,\n 'eta_x': -0.0004717155629444416,\n 'etap_x': -1.6006449526750024e-05,\n 'norm_emit_x': 3.738408555668476e-07,\n 'alpha_y': 0.9776071851091841,\n 'beta_y': 14.39339077533324,\n 'gamma_y': 0.13587596132863441,\n 'emit_y': 2.342927040318514e-11,\n 'eta_y': -4.3316354305934096e-05,\n 'etap_y': -6.000905618300805e-07,\n 'norm_emit_y': 3.656364435837357e-07}</pre> <p>This makes new particles:</p> In\u00a0[21]: Copied! <pre>P2 = P.twiss_match(beta=30, alpha=-3, plane = 'x')\nP2.twiss('x')\n</pre> P2 = P.twiss_match(beta=30, alpha=-3, plane = 'x') P2.twiss('x') Out[21]: <pre>{'alpha_x': -2.9996935644621994,\n 'beta_x': 29.99130803172276,\n 'gamma_x': 0.3333686370097817,\n 'emit_x': 3.1255806601163326e-11,\n 'eta_x': -0.000997118623912468,\n 'etap_x': -7.944656701598246e-05,\n 'norm_emit_x': 4.877958608785158e-07}</pre> In\u00a0[22]: Copied! <pre>P.resample()\n</pre> P.resample() Out[22]: <pre>&lt;ParticleGroup with 100000 particles at 0x7fb40bcc9dc0&gt;</pre> <p>With n &gt; 0, particles will be subsampled. Note that this also works for differently weighed particles.</p> In\u00a0[23]: Copied! <pre>P.resample(1000)\n</pre> P.resample(1000) Out[23]: <pre>&lt;ParticleGroup with 1000 particles at 0x7fb40bcde850&gt;</pre> In\u00a0[24]: Copied! <pre>P.resample(1000).plot('x', 'px', bins=100)\n</pre> P.resample(1000).plot('x', 'px', bins=100) In\u00a0[25]: Copied! <pre>P.units('x'), P.units('energy'), P.units('norm_emit_x'), P.units('cov_x__kinetic_energy'), P.units('norm_emit_4d')\n</pre> P.units('x'), P.units('energy'), P.units('norm_emit_x'), P.units('cov_x__kinetic_energy'), P.units('norm_emit_4d') Out[25]: <pre>(pmd_unit('m', 1, (1, 0, 0, 0, 0, 0, 0)),\n pmd_unit('eV', 1.602176634e-19, (2, 1, -2, 0, 0, 0, 0)),\n pmd_unit('m', 1, (1, 0, 0, 0, 0, 0, 0)),\n pmd_unit('m*eV', 1.602176634e-19, (3, 1, -2, 0, 0, 0, 0)),\n pmd_unit('(m)^2', 1, (2, 0, 0, 0, 0, 0, 0)))</pre> In\u00a0[26]: Copied! <pre>P.units('mean_energy')\n</pre> P.units('mean_energy') Out[26]: <pre>pmd_unit('eV', 1.602176634e-19, (2, 1, -2, 0, 0, 0, 0))</pre> In\u00a0[27]: Copied! <pre>str(P.units('cov_x__kinetic_energy'))\n</pre> str(P.units('cov_x__kinetic_energy')) Out[27]: <pre>'m*eV'</pre> In\u00a0[28]: Copied! <pre>P.std('z'), P.std('t')\n</pre> P.std('z'), P.std('t') Out[28]: <pre>(0.0, 2.4466662184814374e-14)</pre> <p>Get the central time:</p> In\u00a0[29]: Copied! <pre>t0 = P.avg('t')\nt0\n</pre> t0 = P.avg('t') t0 Out[29]: <pre>5.163879459127423e-06</pre> <p>Drift all particles to this time. This operates in-place:</p> In\u00a0[30]: Copied! <pre>P.drift_to_t(t0)\n</pre> P.drift_to_t(t0) <p>Now these are at different z, and the same t:</p> In\u00a0[31]: Copied! <pre>P.std('z'), P.avg('t'), set(P.t)\n</pre> P.std('z'), P.avg('t'), set(P.t) Out[31]: <pre>(7.334920780350132e-06, 5.163879459127425e-06, {5.163879459127423e-06})</pre> In\u00a0[32]: Copied! <pre>P.status[0:10] = 0\nP.status, P.n_alive, P.n_dead\n</pre> P.status[0:10] = 0 P.status, P.n_alive, P.n_dead Out[32]: <pre>(array([0, 0, 0, ..., 1, 1, 1], dtype=int32), 99990, 10)</pre> <p>There is a <code>.where</code> convenience routine to make selections easier:</p> In\u00a0[33]: Copied! <pre>P0 = P.where(P.status==0)\nP1 = P.where(P.status==1)\nlen(P0), P0.charge, P1.charge\n</pre> P0 = P.where(P.status==0) P1 = P.where(P.status==1) len(P0), P0.charge, P1.charge Out[33]: <pre>(10, 2.4999999999999994e-14, 2.4997499999999996e-10)</pre> <p>Copy is a deep copy:</p> In\u00a0[34]: Copied! <pre>P2 = P1.copy()\n</pre> P2 = P1.copy() <p>Charge can also be set. This will re-scale the weight array:</p> In\u00a0[35]: Copied! <pre>P2.charge = 9.8765e-12\nP1.weight[0:2], P2.weight[0:2], P2.charge\n</pre> P2.charge = 9.8765e-12 P1.weight[0:2], P2.weight[0:2], P2.charge  Out[35]: <pre>(array([2.5e-15, 2.5e-15]),\n array([9.87748775e-17, 9.87748775e-17]),\n 9.876499999999997e-12)</pre> <p>Some codes provide ids for particles. If not, you can assign an id.</p> In\u00a0[36]: Copied! <pre>'id' in P2\n</pre> 'id' in P2 Out[36]: <pre>False</pre> <p>This will assign an id if none exists.</p> In\u00a0[37]: Copied! <pre>P2.id, 'id' in P2\n</pre> P2.id, 'id' in P2 Out[37]: <pre>(array([    1,     2,     3, ..., 99988, 99989, 99990]), True)</pre> In\u00a0[38]: Copied! <pre>import h5py\nimport numpy as np\n</pre> import h5py import numpy as np In\u00a0[39]: Copied! <pre>newh5file = 'particles.h5'\n\nwith h5py.File(newh5file, 'w') as h5:\n    P.write(h5)\n    \nwith h5py.File(newh5file, 'r') as h5:\n    P2 = ParticleGroup(h5)\n</pre> newh5file = 'particles.h5'  with h5py.File(newh5file, 'w') as h5:     P.write(h5)      with h5py.File(newh5file, 'r') as h5:     P2 = ParticleGroup(h5)     <p>Check if all are the same:</p> In\u00a0[40]: Copied! <pre>for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:\n    same = np.all(P[key] == P2[key])\n    print(key, same)\n</pre> for key in ['x', 'px', 'y', 'py', 'z', 'pz', 't', 'status', 'weight', 'id']:     same = np.all(P[key] == P2[key])     print(key, same) <pre>x True\npx True\ny True\npy True\nz True\npz True\nt True\nstatus True\nweight True\nid True\n</pre> <p>This does the same check:</p> In\u00a0[41]: Copied! <pre>P2 == P\n</pre> P2 == P Out[41]: <pre>True</pre> <p>Write Astra-style particles</p> In\u00a0[42]: Copied! <pre>P.write_astra('astra.dat')\n</pre> P.write_astra('astra.dat') In\u00a0[43]: Copied! <pre>!head astra.dat\n</pre> !head astra.dat <pre>  5.358867254236e-07  -2.266596025469e-08   2.743173452837e-13   5.432293116193e+02   1.634894200076e+01   7.974939693676e+09   5.163879459127e+03   0.000000000000e+00    1   -1\r\n -1.208904511904e-05   2.743402818288e-05  -5.473095269153e-06  -7.699432808273e+03   1.073320266862e+04   2.538945179648e+07  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  2.500557812617e-05   1.840484451196e-06  -6.071970122807e-06   2.432464253407e+04  -2.207080331882e+03  -8.584659148073e+05  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  1.840224855502e-06   2.319774542484e-05  -1.983837488548e-06   1.761897150333e+04  -4.269379756219e+03  -1.555244938371e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  1.282953228685e-05   2.807375273984e-06   7.759268653990e-06   1.898440718152e+04  -5.303751910566e+03  -2.614389107333e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  3.362374691900e-06   4.796982704111e-06  -1.006752695161e-06   1.012222041635e+04   1.266876546973e+04  -1.818436067077e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n -1.441736363766e-05   7.420644576948e-06   1.697647381418e-06  -8.598453241915e+03  -9.693787540264e+02  -4.437182519667e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n -1.715615894267e-05  -2.489925517264e-05   8.689767207313e-06  -1.817734573652e+04   1.917720905016e+04  -4.674564930124e+05  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  5.700269218746e-06   4.764024833770e-05   2.167342605243e-05   8.662840216364e+03  -7.175141639811e+03  -3.156898311773e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n  9.426699454873e-07  -5.785285707147e-06   6.353982932653e-06  -1.498628620111e+04  -1.222058411806e+03  -3.802046580825e+06  -1.818989403546e-12   2.500000000000e-06    1   -1\r\n</pre> <p>Optionally, a string can be given:</p> In\u00a0[44]: Copied! <pre>P.write('particles.h5')\n</pre> P.write('particles.h5') In\u00a0[45]: Copied! <pre>P.plot('x')\n</pre> P.plot('x') In\u00a0[46]: Copied! <pre>P.slice_plot('norm_emit_x', 'norm_emit_y', ylim=(0, 1e-6))\n</pre> P.slice_plot('norm_emit_x', 'norm_emit_y', ylim=(0, 1e-6)) In\u00a0[47]: Copied! <pre>P.plot('z', 'x')\n</pre> P.plot('z', 'x') <p>Any other key that returbs an arrat can be sliced on</p> In\u00a0[48]: Copied! <pre>P.slice_plot('sigma_x', slice_key = 'Jx')\n</pre> P.slice_plot('sigma_x', slice_key = 'Jx') In\u00a0[49]: Copied! <pre>P.plot('x', 'px')\n</pre> P.plot('x', 'px') <p>Optionally the figure object can be returned, and the plot further modified.</p> In\u00a0[50]: Copied! <pre>fig = P.plot('x', return_figure=True)\nax = fig.axes[0]\nax.set_title('Density Plot')\nax.set_xlim(-50, 50)\n</pre> fig = P.plot('x', return_figure=True) ax = fig.axes[0] ax.set_title('Density Plot') ax.set_xlim(-50, 50) Out[50]: <pre>(-50.0, 50.0)</pre> In\u00a0[51]: Copied! <pre>import copy\n\nfig, ax = plt.subplots()\nax.set_aspect('equal')\nxkey = 'x'\nykey = 'y'\ndatx = P[xkey]\ndaty = P[ykey]\nax.set_xlabel(f'{xkey} ({P.units(xkey)})')\nax.set_ylabel(f'{ykey} ({P.units(ykey)})')\n\ncmap = copy.copy(plt.get_cmap('viridis'))\ncmap.set_under('white')\nax.hexbin(datx, daty, gridsize=40, cmap=cmap, vmin=1e-15)\n</pre> import copy  fig, ax = plt.subplots() ax.set_aspect('equal') xkey = 'x' ykey = 'y' datx = P[xkey] daty = P[ykey] ax.set_xlabel(f'{xkey} ({P.units(xkey)})') ax.set_ylabel(f'{ykey} ({P.units(ykey)})')  cmap = copy.copy(plt.get_cmap('viridis')) cmap.set_under('white') ax.hexbin(datx, daty, gridsize=40, cmap=cmap, vmin=1e-15) Out[51]: <pre>&lt;matplotlib.collections.PolyCollection at 0x7fb400a25d00&gt;</pre> In\u00a0[52]: Copied! <pre>P.plot('delta_z', 'delta_p', figsize=(8,6))\n</pre> P.plot('delta_z', 'delta_p', figsize=(8,6)) In\u00a0[53]: Copied! <pre>H, edges = P.histogramdd('delta_z', 'delta_p', bins=(150, 150))\nextent = [edges[0].min(),edges[0].max(),edges[1].min(),edges[1].max() ]\n\nplt.imshow(H.T, origin='lower', extent = extent, aspect='auto', vmin=1e-15, cmap=cmap)\n</pre> H, edges = P.histogramdd('delta_z', 'delta_p', bins=(150, 150)) extent = [edges[0].min(),edges[0].max(),edges[1].min(),edges[1].max() ]  plt.imshow(H.T, origin='lower', extent = extent, aspect='auto', vmin=1e-15, cmap=cmap) Out[53]: <pre>&lt;matplotlib.image.AxesImage at 0x7fb40c09e8b0&gt;</pre> In\u00a0[54]: Copied! <pre>from pmd_beamphysics import particle_paths\nfrom pmd_beamphysics.readers import all_components, component_str\n\nH5FILE = 'data/astra_particles.h5'\nh5 = h5py.File(H5FILE, 'r')\n</pre> from pmd_beamphysics import particle_paths from pmd_beamphysics.readers import all_components, component_str  H5FILE = 'data/astra_particles.h5' h5 = h5py.File(H5FILE, 'r') <p>Get the valid paths</p> In\u00a0[55]: Copied! <pre>ppaths = particle_paths(h5)\nppaths\n</pre> ppaths = particle_paths(h5) ppaths Out[55]: <pre>['/screen/0/./', '/screen/1/./']</pre> <p>Search for all valid components in a single path</p> In\u00a0[56]: Copied! <pre>ph5 = h5[ppaths[0]]\nall_components(ph5 )\n</pre> ph5 = h5[ppaths[0]] all_components(ph5 ) Out[56]: <pre>['momentum/x',\n 'momentum/y',\n 'momentum/z',\n 'momentumOffset/z',\n 'particleStatus',\n 'position/x',\n 'position/y',\n 'position/z',\n 'positionOffset/z',\n 'time',\n 'timeOffset',\n 'weight']</pre> <p>Get some info</p> In\u00a0[57]: Copied! <pre>for component in all_components(ph5):\n    info = component_str(ph5, component)\n    print(info)\n</pre> for component in all_components(ph5):     info = component_str(ph5, component)     print(info) <pre>momentum/x [998 items] is a momentum with units: kg*m/s\nmomentum/y [998 items] is a momentum with units: kg*m/s\nmomentum/z [998 items] is a momentum with units: kg*m/s\nmomentumOffset/z [constant 4.660805218675275e-22 with shape 998] is a momentum with units: kg*m/s\nparticleStatus [998 items]\nposition/x [998 items] is a length with units: m\nposition/y [998 items] is a length with units: m\nposition/z [998 items] is a length with units: m\npositionOffset/z [constant 0.50013 with shape 998] is a length with units: m\ntime [998 items] is a time with units: s\ntimeOffset [constant 2.0826e-09 with shape 998] is a time with units: s\nweight [998 items] is a charge with units: C\n</pre> In\u00a0[58]: Copied! <pre>import os\n\nos.remove('astra.dat')\nos.remove(newh5file)\nos.remove('elegant_particles.txt')\n</pre> import os  os.remove('astra.dat') os.remove(newh5file) os.remove('elegant_particles.txt')"},{"location":"examples/particle_examples/#openpmd-beamphysics-examples","title":"openPMD beamphysics examples\u00b6","text":""},{"location":"examples/particle_examples/#basic-usage","title":"Basic Usage\u00b6","text":""},{"location":"examples/particle_examples/#particlegroup-class","title":"ParticleGroup class\u00b6","text":""},{"location":"examples/particle_examples/#basic-statistics","title":"Basic Statistics\u00b6","text":""},{"location":"examples/particle_examples/#slice-statistics","title":"Slice statistics\u00b6","text":"<p>ParticleGroup can be sliced along one dimension into chunks of an equal number of particles. Here are the routines to create the raw data.</p>"},{"location":"examples/particle_examples/#advanced-statisics","title":"Advanced statisics\u00b6","text":"<p>Twiss and Dispersion can be calculated.</p> <p>These are the projected Twiss parameters.</p> <p>TODO: normal mode twiss.</p>"},{"location":"examples/particle_examples/#resampling","title":"Resampling\u00b6","text":"<p>Particles can be resampled to either scramble the ordering of the particle arrays or subsample.</p> <p>With no argument or n=0, the same number of particles will be returned:</p>"},{"location":"examples/particle_examples/#units","title":"Units\u00b6","text":"<p>Units can be retrieved from any computable quantitiy. These are returned as a pmd_unit type.</p>"},{"location":"examples/particle_examples/#z-vs-t","title":"z vs t\u00b6","text":"<p>These particles are from Bmad, at the same z and different times</p>"},{"location":"examples/particle_examples/#status-weight-id-copy","title":"status, weight, id, copy\u00b6","text":"<p><code>status == 1</code> is alive, otherwise dead. Set the first ten particles to a different status.</p> <p><code>n_alive</code>, <code>n_dead</code> count these</p>"},{"location":"examples/particle_examples/#writing","title":"Writing\u00b6","text":""},{"location":"examples/particle_examples/#plot","title":"Plot\u00b6","text":"<p>Some plotting is included for convenience. See plot_examples.ipynb for better plotting.</p>"},{"location":"examples/particle_examples/#1d-density-plot","title":"1D density plot\u00b6","text":""},{"location":"examples/particle_examples/#slice-statistic-plot","title":"Slice statistic plot\u00b6","text":""},{"location":"examples/particle_examples/#2d-density-plot","title":"2D density plot\u00b6","text":""},{"location":"examples/particle_examples/#manual-plotting","title":"Manual plotting\u00b6","text":""},{"location":"examples/particle_examples/#manual-binning-and-plotting","title":"Manual binning and plotting\u00b6","text":""},{"location":"examples/particle_examples/#multiple-particlegroup-in-an-hdf5-file","title":"Multiple ParticleGroup in an HDF5 file\u00b6","text":"<p>This example has two particlegroups. This also shows how to examine the components, without loading the full data.</p>"},{"location":"examples/particle_examples/#cleanup","title":"Cleanup\u00b6","text":""},{"location":"examples/plot_examples/","title":"Plot examples","text":"In\u00a0[1]: Copied! <pre>from pmd_beamphysics import ParticleGroup, particle_paths\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n%config InlineBackend.figure_format = 'retina'\nmatplotlib.rcParams['figure.figsize'] = (8,6)\n\nfrom h5py import File\nimport os\n</pre> from pmd_beamphysics import ParticleGroup, particle_paths import matplotlib import matplotlib.pyplot as plt  %config InlineBackend.figure_format = 'retina' matplotlib.rcParams['figure.figsize'] = (8,6)  from h5py import File import os In\u00a0[2]: Copied! <pre># Open a file, fine the particle paths from the root attributes\n# Pick one:\nH5FILE = 'data/bmad_particles2.h5'\n#H5FILE = 'data/distgen_particles.h5'\n#H5FILE = 'data/astra_particles.h5'\n\n# Load\nh5 = File(H5FILE, 'r')\nppaths = particle_paths(h5)\nph5 = h5[ppaths[0]]\n\nP = ParticleGroup(ph5)\nppaths\n</pre> # Open a file, fine the particle paths from the root attributes # Pick one: H5FILE = 'data/bmad_particles2.h5' #H5FILE = 'data/distgen_particles.h5' #H5FILE = 'data/astra_particles.h5'  # Load h5 = File(H5FILE, 'r') ppaths = particle_paths(h5) ph5 = h5[ppaths[0]]  P = ParticleGroup(ph5) ppaths Out[2]: <pre>['/data/00001/particles/']</pre> In\u00a0[3]: Copied! <pre>P.plot('t')\n</pre> P.plot('t') In\u00a0[4]: Copied! <pre>P.t = P.t - P['mean_t']\nP.slice_plot('sigma_x', 'sigma_y', xlim=(None, 80e-15), ylim=(0, 30e-6))\n</pre> P.t = P.t - P['mean_t'] P.slice_plot('sigma_x', 'sigma_y', xlim=(None, 80e-15), ylim=(0, 30e-6)) In\u00a0[5]: Copied! <pre>P.plot('t', 'energy', xlim = (-200e-15, 200e-15), ylim = (7.9e9, 8.1e9))\n</pre> P.plot('t', 'energy', xlim = (-200e-15, 200e-15), ylim = (7.9e9, 8.1e9)) In\u00a0[6]: Copied! <pre>from pmd_beamphysics.plot import density_and_slice_plot\n</pre> from pmd_beamphysics.plot import density_and_slice_plot In\u00a0[7]: Copied! <pre>P.species\n</pre> P.species Out[7]: <pre>'electron'</pre> In\u00a0[8]: Copied! <pre>density_and_slice_plot(P, 't', 'energy', stat_keys = ['sigma_x', 'sigma_y'], n_slice = 200, bins=200)\n</pre> density_and_slice_plot(P, 't', 'energy', stat_keys = ['sigma_x', 'sigma_y'], n_slice = 200, bins=200)"},{"location":"examples/plot_examples/#plot-examples","title":"Plot examples\u00b6","text":""},{"location":"examples/plot_examples/#density-plots","title":"Density plots\u00b6","text":""},{"location":"examples/plot_examples/#slice-statistics-plots","title":"Slice statistics Plots\u00b6","text":""},{"location":"examples/plot_examples/#marginal-plots","title":"Marginal plots\u00b6","text":""},{"location":"examples/plot_examples/#combined-density-and-slice-plot","title":"Combined density and slice plot\u00b6","text":""},{"location":"examples/read_examples/","title":"Read examples","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>from pmd_beamphysics import ParticleGroup\nfrom h5py import File\n</pre> from pmd_beamphysics import ParticleGroup from h5py import File In\u00a0[3]: Copied! <pre>from pmd_beamphysics.interfaces.elegant import elegant_h5_to_data\n</pre> from pmd_beamphysics.interfaces.elegant import elegant_h5_to_data <p>This will convert to a data dict</p> In\u00a0[4]: Copied! <pre>data = elegant_h5_to_data('data/elegant_raw.h5')\ndata\n</pre> data = elegant_h5_to_data('data/elegant_raw.h5') data Out[4]: <pre>{'x': array([-1.12390181e-04, -7.20268439e-05, -1.14543953e-04, ...,\n         1.05737263e-04, -7.24786314e-05,  5.78264247e-05]),\n 'y': array([-1.23229455e-04, -1.08820261e-04, -9.27723036e-05, ...,\n         8.55980575e-05,  8.58175111e-05,  9.07622257e-05]),\n 'z': array([0, 0, 0, ..., 0, 0, 0]),\n 'px': array([ -3083.62425792,   2225.50915788,   2725.71118129, ...,\n        -15364.10017428,  15821.42533783,  -4733.37386475]),\n 'py': array([ 83545.5978262 ,  83389.57148069,  68275.02698773, ...,\n        -70430.7889418 , -70600.14619666, -69547.74375481]),\n 'pz': array([8.00396787e+09, 8.00007258e+09, 8.00139350e+09, ...,\n        7.99685994e+09, 7.99660958e+09, 7.99818064e+09]),\n 't': array([5.11804566e-06, 5.11804568e-06, 5.11804567e-06, ...,\n        5.11804569e-06, 5.11804569e-06, 5.11804569e-06]),\n 'status': array([1, 1, 1, ..., 1, 1, 1]),\n 'species': 'electron',\n 'weight': array([2.e-17, 2.e-17, 2.e-17, ..., 2.e-17, 2.e-17, 2.e-17]),\n 'id': array([    1,     2,     3, ..., 49998, 49999, 50000], dtype=int32)}</pre> <p>Create <code>ParticleGroup</code> and plot:</p> In\u00a0[5]: Copied! <pre>P=ParticleGroup(data=data)\nP.plot('delta_t', 'delta_pz')\n</pre> P=ParticleGroup(data=data) P.plot('delta_t', 'delta_pz') In\u00a0[6]: Copied! <pre>from pmd_beamphysics.interfaces.genesis import genesis4_par_to_data\n</pre> from pmd_beamphysics.interfaces.genesis import genesis4_par_to_data In\u00a0[7]: Copied! <pre>P = ParticleGroup(data=genesis4_par_to_data('data/genesis4.par.h5'))\nP\n</pre> P = ParticleGroup(data=genesis4_par_to_data('data/genesis4.par.h5')) P Out[7]: <pre>&lt;ParticleGroup with 14336 particles at 0x7fdb41b34820&gt;</pre> In\u00a0[8]: Copied! <pre>P.plot('z', 'pz')\n</pre> P.plot('z', 'pz')"},{"location":"examples/read_examples/#read-examples","title":"Read examples\u00b6","text":""},{"location":"examples/read_examples/#elegant-hdf5-format","title":"elegant hdf5 format\u00b6","text":""},{"location":"examples/read_examples/#genesis4-hdf5-format","title":"Genesis4 HDF5 format\u00b6","text":""},{"location":"examples/units/","title":"Units","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>from pmd_beamphysics import particle_paths\nfrom pmd_beamphysics.units import pmd_unit, dimension_name, sqrt_unit, known_unit, multiply_units\nfrom h5py import File\nimport numpy as np\n</pre> from pmd_beamphysics import particle_paths from pmd_beamphysics.units import pmd_unit, dimension_name, sqrt_unit, known_unit, multiply_units from h5py import File import numpy as np <p>This is the basic class:</p> In\u00a0[3]: Copied! <pre>?pmd_unit\n</pre> ?pmd_unit <p>Get a known units. These can be multiplied and divided:</p> In\u00a0[4]: Copied! <pre>u1 = known_unit['J']\nu2 = known_unit['m']\nu1, u2, u1/u2, u1*u2\n</pre> u1 = known_unit['J'] u2 = known_unit['m'] u1, u2, u1/u2, u1*u2 Out[4]: <pre>(pmd_unit('J', 1, (2, 1, -2, 0, 0, 0, 0)),\n pmd_unit('m', 1, (1, 0, 0, 0, 0, 0, 0)),\n pmd_unit('J/m', 1.0, (1, 1, -2, 0, 0, 0, 0)),\n pmd_unit('J*m', 1, (3, 1, -2, 0, 0, 0, 0)))</pre> <p>Special function for sqrt:</p> In\u00a0[5]: Copied! <pre>sqrt_unit(u1)\n</pre> sqrt_unit(u1) Out[5]: <pre>pmd_unit('\\sqrt{ J }', 1.0, (1.0, 0.5, -1.0, 0.0, 0.0, 0.0, 0.0))</pre> In\u00a0[6]: Copied! <pre># Pick one:\n#H5FILE = 'data/bmad_particles.h5'\nH5FILE = 'data/distgen_particles.h5'\n#H5FILE = 'data/astra_particles.h5'\nh5 = File(H5FILE, 'r')\n\nppaths = particle_paths(h5)\nprint(ppaths)\n</pre> # Pick one: #H5FILE = 'data/bmad_particles.h5' H5FILE = 'data/distgen_particles.h5' #H5FILE = 'data/astra_particles.h5' h5 = File(H5FILE, 'r')  ppaths = particle_paths(h5) print(ppaths)  <pre>['//']\n</pre> <p>This points to a single particle group:</p> In\u00a0[7]: Copied! <pre>ph5 = h5[ppaths[0]]\nlist(ph5)\n</pre> ph5 = h5[ppaths[0]] list(ph5) Out[7]: <pre>['momentum', 'particleStatus', 'position', 'time', 'weight']</pre> <p>Each component should have a dimension and a conversion factor to SI:</p> In\u00a0[8]: Copied! <pre>d = dict(ph5['momentum/x'].attrs)\nd\n</pre> d = dict(ph5['momentum/x'].attrs) d Out[8]: <pre>{'unitDimension': array([ 1,  1, -1,  0,  0,  0,  0]),\n 'unitSI': 5.344285992678308e-28,\n 'unitSymbol': 'eV/c'}</pre> In\u00a0[9]: Copied! <pre>tuple(d['unitDimension'])\n</pre> tuple(d['unitDimension']) Out[9]: <pre>(1, 1, -1, 0, 0, 0, 0)</pre> <p>This will extract the name of this dimension:</p> In\u00a0[10]: Copied! <pre>dimension_name(d['unitDimension'])\n</pre> dimension_name(d['unitDimension']) Out[10]: <pre>'momentum'</pre> In\u00a0[11]: Copied! <pre>from pmd_beamphysics.units import nice_array\n</pre> from pmd_beamphysics.units import nice_array <p>This will scale the array, and return the appropriate SI prefix:</p> In\u00a0[12]: Copied! <pre>x = 1e-4\nunit = 'm'\nnice_array(x)\n</pre> x = 1e-4 unit = 'm' nice_array(x) Out[12]: <pre>(100.00000000000001, 1e-06, '\u00b5')</pre> In\u00a0[13]: Copied! <pre>nice_array([-0.01, 0.01])\n</pre> nice_array([-0.01, 0.01]) Out[13]: <pre>(array([-10.,  10.]), 0.001, 'm')</pre> In\u00a0[14]: Copied! <pre>from pmd_beamphysics.units import nice_scale_prefix\n</pre> from pmd_beamphysics.units import nice_scale_prefix In\u00a0[15]: Copied! <pre>nice_scale_prefix(0.009)\n</pre> nice_scale_prefix(0.009) Out[15]: <pre>(0.001, 'm')</pre> In\u00a0[16]: Copied! <pre>try:\n    u1/1\nexcept:\n    print('you cannot do this')\n</pre> try:     u1/1 except:     print('you cannot do this')       <pre>you cannot do this\n</pre>"},{"location":"examples/units/#units","title":"Units\u00b6","text":"<p>This package provides unit conversion tools</p>"},{"location":"examples/units/#openpmd-hdf5-units","title":"openPMD HDF5 units\u00b6","text":"<p>Open a file, find the particle paths from the root attributes</p>"},{"location":"examples/units/#nice-arrays","title":"Nice arrays\u00b6","text":""},{"location":"examples/units/#limitations","title":"Limitations\u00b6","text":"<p>This is a simple class for use with this package. So even simple things like the example below will fail.</p> <p>For more advanced units, use a package like Pint: https://pint.readthedocs.io/</p>"},{"location":"examples/write_examples/","title":"Write examples","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2 In\u00a0[2]: Copied! <pre>from pmd_beamphysics import ParticleGroup, particle_paths, pmd_init\nfrom h5py import File\nimport os\n</pre> from pmd_beamphysics import ParticleGroup, particle_paths, pmd_init from h5py import File import os In\u00a0[3]: Copied! <pre># Pick one:\n\n#H5File = 'data/bmad_particles2.h5'\nH5FILE = 'data/distgen_particles.h5'\n#H5FILE = 'data/astra_particles.h5'\n\nP = ParticleGroup(H5FILE)\n</pre> # Pick one:  #H5File = 'data/bmad_particles2.h5' H5FILE = 'data/distgen_particles.h5' #H5FILE = 'data/astra_particles.h5'  P = ParticleGroup(H5FILE) <p>The regular write routine writes in a proper openPMD format</p> In\u00a0[4]: Copied! <pre>P.write('openpmd_particles.h5')\n</pre> P.write('openpmd_particles.h5') <p>An open h5 hande can also be used, but it needs to be properly initialized</p> In\u00a0[5]: Copied! <pre>with File('openpmd_particles.h5', 'w') as h5:\n    pmd_init(h5, basePath='/', particlesPath='/' )\n    P.write(h5)\n</pre> with File('openpmd_particles.h5', 'w') as h5:     pmd_init(h5, basePath='/', particlesPath='/' )     P.write(h5) <p>This can be read in by another ParticleGroup</p> In\u00a0[6]: Copied! <pre>P2 = ParticleGroup('openpmd_particles.h5')\n</pre> P2 = ParticleGroup('openpmd_particles.h5') <p>Check that they are the same:</p> In\u00a0[7]: Copied! <pre>P2 == P\n</pre> P2 == P Out[7]: <pre>True</pre> In\u00a0[8]: Copied! <pre>P.write_astra('astra_particles.txt')\n</pre> P.write_astra('astra_particles.txt') In\u00a0[9]: Copied! <pre>!head astra_particles.txt\n</pre> !head astra_particles.txt <pre> -1.289814080985e-05   1.712192978919e-05   0.000000000000e+00  -9.245284702413e-01  -3.316650265292e+00   2.210558337183e+02   1.819664001274e-05   0.000000000000e+00    1    5\r\n -1.184861337727e-03  -2.101371437059e-03   0.000000000000e+00  -3.047044656318e+02  -3.039342419008e+02  -1.005645891013e+02  -9.745607002167e-04   1.000000000000e-06    1    5\r\n -5.181307340245e-04  -2.178353405029e-03   0.000000000000e+00   5.525456229648e+02   2.416723877028e+02  -6.554342847563e+01   1.280843753434e-03   1.000000000000e-06    1    5\r\n -1.773501610902e-03   2.864979597813e-03   0.000000000000e+00  -2.226004747820e+02   9.450238076106e+00  -1.055085411491e+02   3.835366744569e-04   1.000000000000e-06    1    5\r\n  1.686555815999e-03  -2.401048305081e-04   0.000000000000e+00  -1.891692499417e+02   4.859547751754e+01   3.339263495319e+02   1.902998338336e-03   1.000000000000e-06    1    5\r\n -7.779454935491e-04  -6.800063114796e-04   0.000000000000e+00   6.716138938638e+01  -2.064173000222e+02  -1.405963302134e+02   1.779005092730e-04   1.000000000000e-06    1    5\r\n -2.593702199590e-03  -2.301030494125e-03   0.000000000000e+00  -1.455653402031e+01   2.074634953296e+02  -1.397453142110e+02   1.368567098305e-03   1.000000000000e-06    1    5\r\n  1.997801509161e-03   2.648416193086e-03   0.000000000000e+00  -2.124047726665e+00  -6.792723569247e+01   6.931081537770e+01   2.616497721112e-04   1.000000000000e-06    1    5\r\n  1.999741847023e-03  -6.945690451493e-04   0.000000000000e+00  -9.991142908925e+01  -9.189412445573e+01   2.259539675809e+02  -8.681109991004e-04   1.000000000000e-06    1    5\r\n -7.033822974359e-04  -5.677746866954e-04   0.000000000000e+00   7.520962264129e+02   3.125940718167e+02  -1.451032665210e+02   4.315191990002e-04   1.000000000000e-06    1    5\r\n</pre> <p>Check the readback:</p> In\u00a0[10]: Copied! <pre>from pmd_beamphysics.interfaces.astra import parse_astra_phase_file\nimport numpy as np\nP1 = ParticleGroup(data=parse_astra_phase_file('astra_particles.txt'))\nfor k in ['x', 'px', 'y', 'py', 'z', 'pz']:\n    assert np.allclose(P[k], P1[k])\n</pre> from pmd_beamphysics.interfaces.astra import parse_astra_phase_file import numpy as np P1 = ParticleGroup(data=parse_astra_phase_file('astra_particles.txt')) for k in ['x', 'px', 'y', 'py', 'z', 'pz']:     assert np.allclose(P[k], P1[k]) In\u00a0[11]: Copied! <pre>P.write_bmad('bmad_particles.txt')\n</pre> P.write_bmad('bmad_particles.txt') In\u00a0[12]: Copied! <pre>!head bmad_particles.txt\n</pre> !head bmad_particles.txt <pre>!ASCII::3\r\n0 ! ix_ele, not used\r\n1 ! n_bunch\r\n10000 ! n_particle\r\nBEGIN_BUNCH\r\nelectron \r\n1.0000000000000003e-11  ! bunch_charge\r\n0 ! z_center\r\n0 ! t_center\r\n -1.184861337727e-03  -3.047044656318e+02  -2.101371437059e-03  -3.039342419008e+02  -9.563640602039e-13   1.204912446170e+02   1.000000000000e-15  1\r\n</pre> In\u00a0[13]: Copied! <pre>P.to_bmad()\n</pre> P.to_bmad() Out[13]: <pre>{'x': array([-0.00118486, -0.00051813, -0.0017735 , ..., -0.00052658,\n         0.00252813,  0.00113815]),\n 'y': array([-0.00210137, -0.00217835,  0.00286498, ..., -0.00161154,\n         0.00160049, -0.0010351 ]),\n 'px': array([-0.69011879,  1.25144906, -0.50416317, ...,  0.60249121,\n         0.505233  ,  0.74928061]),\n 'py': array([-0.68837433,  0.54735875,  0.02140365, ..., -0.07882388,\n         0.29433475, -0.25918357]),\n 'z': array([ 2.55529374e-07, -4.68009162e-07, -5.64739756e-08, ...,\n        -9.69805227e-09,  4.20165276e-07,  2.70278113e-07]),\n 'pz': array([ 0.01222356,  0.41059669, -0.4315584 , ..., -0.3093279 ,\n         0.02463904,  0.08781142]),\n 'charge': array([1.e-15, 1.e-15, 1.e-15, ..., 1.e-15, 1.e-15, 1.e-15]),\n 'species': 'electron',\n 'p0c': 441.52466167250947,\n 'tref': 1.8196640012738955e-14,\n 'state': array([1, 1, 1, ..., 1, 1, 1])}</pre> <p>Check that the conversion preserves information. Note that <code>==</code> uses <code>np.allclose</code>, because there is roundoff error in the conversion.</p> In\u00a0[14]: Copied! <pre>assert P == P.from_bmad(P.to_bmad())\n</pre> assert P == P.from_bmad(P.to_bmad())  In\u00a0[15]: Copied! <pre>P.write_elegant('elegant_particles.txt', verbose=True)\n</pre> P.write_elegant('elegant_particles.txt', verbose=True) <pre>writing 10000 particles to elegant_particles.txt\n</pre> In\u00a0[16]: Copied! <pre>!head -n 20 elegant_particles.txt\n</pre> !head -n 20 elegant_particles.txt <pre>SDDS1\r\n! \r\n! Created using the openPMD-beamphysics Python package\r\n! https://github.com/ChristopherMayes/openPMD-beamphysics\r\n! species: electron\r\n!\r\n&amp;parameter name=Charge, type=double, units=C, description=\"total charge in Coulombs\" &amp;end\r\n&amp;column name=t,  type=double, units=s, description=\"time in seconds\" &amp;end\r\n&amp;column name=x,  type=double, units=m, description=\"x in meters\" &amp;end\r\n&amp;column name=xp, type=double, description=\"px/pz\" &amp;end\r\n&amp;column name=y,  type=double, units=m, description=\"y in meters\" &amp;end\r\n&amp;column name=yp, type=double, description=\"py/pz\" &amp;end\r\n&amp;column name=p,  type=double, units=\"m$be$nc\", description=\"relativistic gamma*beta\" &amp;end\r\n&amp;data mode=ascii &amp;end\r\n1.0000000000000003e-11\r\n10000\r\n -9.563640602039e-13  -1.184861337727e-03  -2.528851507845e+00  -2.101371437059e-03  -2.522459145201e+00   8.746038816275e-04\r\n  1.299040393447e-12  -5.181307340245e-04   3.553064606663e+00  -2.178353405029e-03   1.554039289185e+00   1.218815083118e-03\r\n  4.017333144697e-13  -1.773501610902e-03  -1.926488019169e+00   2.864979597813e-03   8.178675472159e-02   4.911575370556e-04\r\n  1.921194978349e-12   1.686555815999e-03  -3.408564376497e-01  -2.401048305081e-04   8.756222989528e-02   1.151365621035e-03\r\n</pre> In\u00a0[17]: Copied! <pre>P.write_genesis2_beam_file('genesis2.beam', n_slice=50, verbose=True)\n</pre> P.write_genesis2_beam_file('genesis2.beam', n_slice=50, verbose=True) <pre>Beam written: genesis2.beam\n</pre> In\u00a0[18]: Copied! <pre>!head genesis2.beam\n</pre> !head genesis2.beam <pre>? VERSION=1.0\r\n? SIZE=50\r\n? COLUMNS TPOS CURPEAK GAMMA0 DELGAM EMITX EMITY RXBEAM RYBEAM XBEAM YBEAM PXBEAM PYBEAM ALPHAX ALPHAY\r\n-1.96236040e-12 2.75359318e+00 1.00000042e+00 3.87022121e-07 1.19639887e-06 9.26536586e-07 2.04948080e-03 1.83621527e-03 -2.06231144e-04 5.85738124e-05 1.38209180e-05 4.17553934e-05 -1.01973407e-02 7.54079832e-04\r\n-1.88646733e-12 2.46723994e+00 1.00000043e+00 3.39783124e-07 1.04698472e-06 1.03470799e-06 2.05064183e-03 1.92139881e-03 -2.53107201e-05 -1.13678787e-04 -2.85190907e-05 -2.90105675e-05 -2.32400675e-02 -2.55583997e-03\r\n-1.80734757e-12 2.61898031e+00 1.00000043e+00 3.65257967e-07 9.78086023e-07 1.03038032e-06 1.97065976e-03 1.88550433e-03 -7.56576638e-05 1.77044304e-04 -4.48907172e-05 6.98821769e-05 5.38708456e-03 4.70844009e-03\r\n-1.72583745e-12 2.30361285e+00 1.00000043e+00 3.43343193e-07 9.42099457e-07 1.07137205e-06 1.90156644e-03 1.92054974e-03 4.82559113e-05 -6.43105052e-05 1.66595205e-05 1.33986604e-05 9.16890850e-02 8.20635086e-03\r\n-1.64047654e-12 2.48779835e+00 1.00000043e+00 3.76740497e-07 1.12901501e-06 1.01496464e-06 2.07085748e-03 1.84284581e-03 -2.63780641e-04 2.16425678e-05 9.07228824e-06 -2.65046022e-05 1.91567484e-03 3.63234933e-03\r\n-1.55820367e-12 2.33273410e+00 1.00000041e+00 3.38657590e-07 9.79796547e-07 1.04648255e-06 1.84685458e-03 1.99189484e-03 3.36520774e-05 1.02123362e-04 3.01402301e-06 6.13937003e-05 -7.84493116e-02 2.84788841e-02\r\n-1.47800120e-12 2.62363489e+00 1.00000044e+00 3.34570582e-07 1.12580877e-06 1.08628803e-06 1.97095399e-03 2.02149469e-03 -1.94415715e-04 -1.05666747e-04 -1.70675102e-05 -5.61809635e-06 -1.81522208e-01 -5.08353286e-03\r\n</pre> In\u00a0[19]: Copied! <pre>input_str = P.write_genesis4_beam('genesis4_beam.h5', n_slice=123, verbose=True, return_input_str=True)\n</pre> input_str = P.write_genesis4_beam('genesis4_beam.h5', n_slice=123, verbose=True, return_input_str=True) <pre>Genesis4 beam file written: genesis4_beam.h5\n</pre> <p>This string is optionally returned for use in the main Genesis4 input file:</p> In\u00a0[20]: Copied! <pre>print(input_str)\n</pre> print(input_str) <pre>&amp;profile_file\n  label = current\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/current\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = gamma\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/gamma\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = delgam\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/delgam\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = ex\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/ex\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = ey\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/ey\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = xcenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/xcenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = ycenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/ycenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = pxcenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/pxcenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = pycenter\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/pycenter\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = alphax\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/alphax\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = alphay\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/alphay\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = betax\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/betax\n  isTime = T\n  reverse = T\n&amp;end\n&amp;profile_file\n  label = betay\n  xdata = genesis4_beam.h5/t\n  ydata = genesis4_beam.h5/betay\n  isTime = T\n  reverse = T\n&amp;end\n&amp;beam\n  current = @current\n  gamma = @gamma\n  delgam = @delgam\n  ex = @ex\n  ey = @ey\n  xcenter = @xcenter\n  ycenter = @ycenter\n  pxcenter = @pxcenter\n  pycenter = @pycenter\n  alphax = @alphax\n  alphay = @alphay\n  betax = @betax\n  betay = @betay\n&amp;end\n</pre> <p>These are the datasets written:</p> In\u00a0[21]: Copied! <pre>with File('genesis4_beam.h5', 'r') as h5:\n    for g in h5:\n        print(g, len(h5[g]), h5[g].attrs['unitSymbol'])\n</pre> with File('genesis4_beam.h5', 'r') as h5:     for g in h5:         print(g, len(h5[g]), h5[g].attrs['unitSymbol']) <pre>alphax 123 \nalphay 123 \nbetax 123 m\nbetay 123 m\ncurrent 123 A\ndelgam 123 \nex 123 m\ney 123 m\ngamma 123 \npxcenter 123 \npycenter 123 \nt 123 s\nxcenter 123 m\nycenter 123 m\n</pre> In\u00a0[22]: Copied! <pre>P.write_genesis4_distribution('genesis4_distribution.h5', verbose=True)\n</pre> P.write_genesis4_distribution('genesis4_distribution.h5', verbose=True) <pre>Datasets x, xp, y, yp, t, p written to: genesis4_distribution.h5\n</pre> <p>This is what is written:</p> In\u00a0[23]: Copied! <pre>with File('genesis4_distribution.h5', 'r') as h5:\n    for g in h5:\n        print(g, len(h5[g]))\n</pre> with File('genesis4_distribution.h5', 'r') as h5:     for g in h5:         print(g, len(h5[g])) <pre>p 10000\nt 10000\nx 10000\nxp 10000\ny 10000\nyp 10000\n</pre> In\u00a0[24]: Copied! <pre>P.write_gpt('gpt_particles.txt', verbose=True)\n</pre> P.write_gpt('gpt_particles.txt', verbose=True) <pre>writing 10000 particles to gpt_particles.txt\nASCII particles written. Convert to GDF using: asci2df -o particles.gdf gpt_particles.txt\n</pre> In\u00a0[25]: Copied! <pre>if os.path.exists(os.path.expandvars('$ASCI2GDF_BIN')):\n    P.write_gpt('gpt_particles.gdf', verbose=True, asci2gdf_bin='$ASCI2GDF_BIN')\n</pre> if os.path.exists(os.path.expandvars('$ASCI2GDF_BIN')):     P.write_gpt('gpt_particles.gdf', verbose=True, asci2gdf_bin='$ASCI2GDF_BIN') In\u00a0[26]: Copied! <pre>#!head gpt_particles.txt\n</pre> #!head gpt_particles.txt In\u00a0[27]: Copied! <pre>P.drift_to_t(P['mean_t'])\n</pre> P.drift_to_t(P['mean_t']) <p>This will return settings for Impact-T to use:</p> In\u00a0[28]: Copied! <pre>P.write_impact('impact_particles.txt')\n</pre> P.write_impact('impact_particles.txt') Out[28]: <pre>{'input_particle_file': 'impact_particles.txt',\n 'Np': 10000,\n 'Tini': 1.8196640012738955e-14,\n 'Flagimg': 0}</pre> In\u00a0[29]: Copied! <pre>!head impact_particles.txt\n</pre> !head impact_particles.txt <pre>10000\r\n-1.185035553808772143e-03 -5.962917646539026830e-04 -2.101545212762252063e-03 -5.947844744118675406e-04 6.889138464881934423e-08 2.357954837617718942e-04\r\n-5.185459410277813361e-04 1.081304810831444467e-03 -2.178535008255722601e-03 4.729410651485857963e-04 -1.168588385748075652e-07 3.043301854978374224e-04\r\n-1.773451522909312962e-03 -4.356182625855454594e-04 2.864977471386669170e-03 1.849365458795189412e-05 -2.599963881922582336e-08 2.261204109504710640e-04\r\n1.686767013783966630e-03 -3.701949875665073173e-04 -2.401590848712557098e-04 9.509897724357652376e-05 -6.196091998406064887e-07 1.086073040365844247e-03\r\n-7.779525032181127363e-04 1.314315604491483489e-04 -6.799847675988795461e-04 -4.039485795854574836e-04 -8.397600117458311948e-09 1.574553206124528761e-04\r\n-2.593690512006350136e-03 -2.848642647956871966e-05 -2.301197068594195913e-03 4.059959327304890142e-04 -6.528501143099030074e-08 1.591207173856017771e-04\r\n1.997801835211931738e-03 -4.156657712632428962e-06 2.648426620219643604e-03 -1.329302842842789139e-04 -4.457257401140207729e-08 5.682333576145901953e-04\r\n1.999690961886589624e-03 -1.955217894072916647e-04 -6.946158470527933476e-04 -1.798323156157682705e-04 2.276631907326255499e-07 8.747763597149907193e-04\r\n-7.035727003852429310e-04 1.471815600429043306e-03 -5.678538239535645561e-04 6.117313388152665482e-04 -1.922838101944327160e-08 1.486354662711140203e-04\r\n</pre> In\u00a0[30]: Copied! <pre>P.drift_to_z()\n</pre> P.drift_to_z() In\u00a0[31]: Copied! <pre>P.write_litrack('litrack.zd', verbose=True)\n</pre> P.write_litrack('litrack.zd', verbose=True) <pre>Using mean_p as the reference momentum: 441.52466167250947 eV/c\nwriting 10000 LiTrack particles to litrack.zd\n</pre> Out[31]: <pre>'litrack.zd'</pre> In\u00a0[32]: Copied! <pre>!head -n 20 litrack.zd\n</pre> !head -n 20 litrack.zd <pre>% LiTrack particles\r\n% \r\n% Created using the openPMD-beamphysics Python package\r\n% https://github.com/ChristopherMayes/openPMD-beamphysics\r\n%\r\n% species: electron\r\n% n_particle: 10000\r\n% total charge: 1.0000000000000003e-11 (C)\r\n% reference momentum p0: 441.52466167250947 (eV/c)\r\n%\r\n% Columns: ct, delta = p/p0 -1\r\n% Units: mm, percent\r\n -2.910140500388e-01   1.222356070583e+00\r\n  3.861082943987e-01   4.105966931910e+01\r\n  1.159491741202e-01  -4.315583986424e+01\r\n  5.750254785583e-01   3.325339997689e+01\r\n  5.234406211768e-02  -4.756793241308e+01\r\n  4.093643740668e-01  -4.942448528425e+01\r\n  8.211012905157e-02  -3.245820052454e+01\r\n -2.559578717432e-01   5.807578013130e+00\r\n</pre> In\u00a0[33]: Copied! <pre>P.write_lucretia('lucretia.mat', ele_name='BEGINNING', t_ref=0, stop_ix=None, verbose=True)\n</pre> P.write_lucretia('lucretia.mat', ele_name='BEGINNING', t_ref=0, stop_ix=None, verbose=True) <pre>writing 10000 particles in the Lucretia format to lucretia.mat\n</pre> <p>Read back:</p> In\u00a0[34]: Copied! <pre>from pmd_beamphysics.interfaces.lucretia import lucretia_to_data, list_element_names\n\nParticleGroup(data=lucretia_to_data('lucretia.mat', verbose=True))\n</pre> from pmd_beamphysics.interfaces.lucretia import lucretia_to_data, list_element_names  ParticleGroup(data=lucretia_to_data('lucretia.mat', verbose=True)) <pre>1 elements found in the file!\n10000 particles detected, 0 found dead!\n</pre> Out[34]: <pre>&lt;ParticleGroup with 10000 particles at 0x7fd27409a850&gt;</pre> <p>Helper function to list the available elements:</p> In\u00a0[35]: Copied! <pre>list_element_names('lucretia.mat')\n</pre> list_element_names('lucretia.mat') Out[35]: <pre>['BEGINNING']</pre> In\u00a0[36]: Copied! <pre>P.drift_to_t()\n\nP.write_opal('opal_injected.txt', dist_type='injected')\n</pre> P.drift_to_t()  P.write_opal('opal_injected.txt', dist_type='injected') In\u00a0[37]: Copied! <pre>!head opal_injected.txt\n</pre> !head opal_injected.txt <pre>10000\r\n -1.185024568260e-03  -5.962917646539e-04  -2.101534254983e-03  -5.947844744119e-04   6.454729863911e-08   2.357954837618e-04\r\n -5.185658620175e-04   1.081304810831e-03  -2.178543721298e-03   4.729410651486e-04  -1.224655448770e-07   3.043301854978e-04\r\n -1.773443497464e-03  -4.356182625855e-04   2.864977130676e-03   1.849365458795e-05  -3.016548099423e-08   2.261204109505e-04\r\n  1.686773833925e-03  -3.701949875665e-04  -2.401608368896e-04   9.509897724358e-05  -6.396180367454e-07   1.086073040366e-03\r\n -7.779549245968e-04   1.314315604491e-04  -6.799773256079e-04  -4.039485795855e-04  -1.129841753741e-08   1.574553206125e-04\r\n -2.593689987198e-03  -2.848642647957e-05  -2.301204548304e-03   4.059959327305e-04  -6.821651066751e-08   1.591207173856e-04\r\n  1.997801911791e-03  -4.156657712632e-06   2.648429069209e-03  -1.329302842843e-04  -5.504120158406e-08   5.682333576146e-04\r\n  1.999694564006e-03  -1.955217894073e-04  -6.946125339825e-04  -1.798323156158e-04   2.115470906675e-07   8.747763597150e-04\r\n -7.035998157808e-04   1.471815600429e-03  -5.678650939369e-04   6.117313388153e-04  -2.196670602435e-08   1.486354662711e-04\r\n</pre> <p>Emitted particles must be at the same z:</p> In\u00a0[38]: Copied! <pre>P.drift_to_z(P['mean_z'])\nP.write_opal('opal_emitted.txt', dist_type='emitted')\n</pre> P.drift_to_z(P['mean_z']) P.write_opal('opal_emitted.txt', dist_type='emitted') In\u00a0[39]: Copied! <pre>!head opal_emitted.txt\n</pre> !head opal_emitted.txt <pre>10000\r\n -1.184838617375e-03  -5.962917646539e-04  -2.101348774139e-03  -5.947844744119e-04  -1.083461177889e-12   2.357954837618e-04\r\n -5.181626563723e-04   1.081304810831e-03  -2.178367367225e-03   4.729410651486e-04   1.200565320731e-12   3.043301854978e-04\r\n -1.773484302458e-03  -4.356182625855e-04   2.864978863003e-03   1.849365458795e-05   2.691980940714e-13   2.261204109505e-04\r\n  1.686558878409e-03  -3.701949875665e-04  -2.401056172069e-04   9.509897724358e-05   1.893601130019e-12   1.086073040366e-03\r\n -7.779529930790e-04   1.314315604491e-04  -6.799832620349e-04  -4.039485795855e-04   5.764311716727e-15   1.574553206125e-04\r\n -2.593700591157e-03  -2.848642647957e-05  -2.301053417928e-03   4.059959327305e-04   1.198422972634e-12   1.591207173856e-04\r\n  1.997801574883e-03  -4.156657712632e-06   2.648418294875e-03  -1.329302842843e-04   2.271058970013e-13   5.682333576146e-04\r\n  1.999743855144e-03  -1.955217894073e-04  -6.945671981683e-04  -1.798323156158e-04  -8.841733180528e-13   8.747763597150e-04\r\n -7.034712631509e-04   1.471815600429e-03  -5.678116635531e-04   6.117313388153e-04   2.480886363183e-13   1.486354662711e-04\r\n</pre> In\u00a0[40]: Copied! <pre>P.write_simion('simion_particles.ion')\n</pre> P.write_simion('simion_particles.ion') In\u00a0[41]: Copied! <pre>for file in [\n    'astra_particles.txt',\n    'bmad_particles.txt',\n    'elegant_particles.txt',\n    'gpt_particles.txt',\n    'impact_particles.txt',\n    'opal_injected.txt',\n    'opal_emitted.txt',\n    'openpmd_particles.h5',\n    'genesis4_beam.h5',\n    'genesis4_distribution.h5',\n    'genesis2.beam',\n    'litrack.zd',\n    'gpt_particles.gdf',\n    'lucretia.mat',\n    'simion_particles.ion'\n    ]:\n    if os.path.exists(file):\n        os.remove(file)\n</pre> for file in [     'astra_particles.txt',     'bmad_particles.txt',     'elegant_particles.txt',     'gpt_particles.txt',     'impact_particles.txt',     'opal_injected.txt',     'opal_emitted.txt',     'openpmd_particles.h5',     'genesis4_beam.h5',     'genesis4_distribution.h5',     'genesis2.beam',     'litrack.zd',     'gpt_particles.gdf',     'lucretia.mat',     'simion_particles.ion'     ]:     if os.path.exists(file):         os.remove(file)"},{"location":"examples/write_examples/#write-examples","title":"Write examples\u00b6","text":""},{"location":"examples/write_examples/#openpmd","title":"openPMD\u00b6","text":""},{"location":"examples/write_examples/#astra","title":"Astra\u00b6","text":""},{"location":"examples/write_examples/#bmad-ascii","title":"Bmad ASCII\u00b6","text":""},{"location":"examples/write_examples/#bmad-dict","title":"Bmad dict\u00b6","text":""},{"location":"examples/write_examples/#elegant","title":"elegant\u00b6","text":""},{"location":"examples/write_examples/#genesis-13-v2","title":"Genesis 1.3 v2\u00b6","text":""},{"location":"examples/write_examples/#genesis-13-v4","title":"Genesis 1.3 v4\u00b6","text":""},{"location":"examples/write_examples/#beam-file-slice-statistics","title":"beam file (slice statistics)\u00b6","text":""},{"location":"examples/write_examples/#distribution-file-particles","title":"Distribution file (particles)\u00b6","text":""},{"location":"examples/write_examples/#gpt-ascii","title":"GPT ASCII\u00b6","text":""},{"location":"examples/write_examples/#impact-t","title":"Impact-T\u00b6","text":"<p>Impact-T particles must all be a the same time:</p>"},{"location":"examples/write_examples/#litrack","title":"LiTrack\u00b6","text":"<p>LiTrack particles must be at the same z:</p>"},{"location":"examples/write_examples/#lucretia","title":"Lucretia\u00b6","text":""},{"location":"examples/write_examples/#opal","title":"OPAL\u00b6","text":"<p>Injected particled must be at the same time:</p>"},{"location":"examples/write_examples/#simion","title":"SIMION\u00b6","text":"<p>Write SIMION input files (*.ion)</p>"},{"location":"examples/write_examples/#cleanup","title":"Cleanup\u00b6","text":""},{"location":"examples/fields/field_conversion/","title":"FieldMesh Conversion","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh\n</pre> from pmd_beamphysics import FieldMesh In\u00a0[3]: Copied! <pre>FM1 = FieldMesh('../data/rfgun.h5')\nFM1.plot('re_E', aspect='equal', figsize=(12,4))\n</pre> FM1 = FieldMesh('../data/rfgun.h5') FM1.plot('re_E', aspect='equal', figsize=(12,4)) In\u00a0[4]: Copied! <pre>FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',\n                              hfile='../data/ansys_rfgun_2856MHz_H.dat',\n                              frequency=2856e6)\n\n\n\nFM3D\n</pre> FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',                               hfile='../data/ansys_rfgun_2856MHz_H.dat',                               frequency=2856e6)    FM3D Out[4]: <pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fd1446beee0&gt;</pre> <p>This will convert to 2D cylindrical:</p> In\u00a0[5]: Copied! <pre>FM2 = FM3D.to_cylindrical()\nFM2\n</pre> FM2 = FM3D.to_cylindrical() FM2 Out[5]: <pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fd1446bed00&gt;</pre> <p>Spacing is different:</p> In\u00a0[6]: Copied! <pre>FM1.dr, FM2.dr\n</pre> FM1.dr, FM2.dr Out[6]: <pre>(0.00025, 0.001)</pre> <p>Set a scale to rotate and normalize Ez to be mostly real</p> In\u00a0[7]: Copied! <pre>E0 = FM2.components['electricField/z'][0,0,0]\nFM2.scale = -np.exp(-1j * np.angle(E0)) / np.abs(E0) # - sign to agree with FM1\n\nFM2.Ez[0,0,0]\n</pre> E0 = FM2.components['electricField/z'][0,0,0] FM2.scale = -np.exp(-1j * np.angle(E0)) / np.abs(E0) # - sign to agree with FM1  FM2.Ez[0,0,0] Out[7]: <pre>(-1-1.890985933883628e-16j)</pre> In\u00a0[8]: Copied! <pre>z1 = FM1.coord_vec('z')\nz2 = FM2.coord_vec('z')\n\n\nfig, ax = plt.subplots()\nax.plot(z1, np.real(FM1.Ez[0,0,:]), label='Superfish')\nax.plot(z2, np.real(FM2.Ez[0,0,:]), label='ANSYS')\nax.set_xlabel(r'$z$ (m)')\nax.set_ylabel(r'$E_z$ (V/m)')\nplt.legend()\n</pre> z1 = FM1.coord_vec('z') z2 = FM2.coord_vec('z')   fig, ax = plt.subplots() ax.plot(z1, np.real(FM1.Ez[0,0,:]), label='Superfish') ax.plot(z2, np.real(FM2.Ez[0,0,:]), label='ANSYS') ax.set_xlabel(r'$z$ (m)') ax.set_ylabel(r'$E_z$ (V/m)') plt.legend()  Out[8]: <pre>&lt;matplotlib.legend.Legend at 0x7fd144695eb0&gt;</pre> In\u00a0[9]: Copied! <pre>fig, ax = plt.subplots()\nax.plot(z1, np.imag(FM1.Btheta[4,0,:]), label='superfish')\nax.plot(z2, np.imag(FM2.Btheta[1,0,:]), label='ansys')\n\nax.set_title(fr'$r$ = {FM2.dr*1000} mm')\nax.set_xlabel(r'$z$ (m)')\nax.set_ylabel(r'$B_\\theta$ (T)')\nplt.legend()\n</pre> fig, ax = plt.subplots() ax.plot(z1, np.imag(FM1.Btheta[4,0,:]), label='superfish') ax.plot(z2, np.imag(FM2.Btheta[1,0,:]), label='ansys')  ax.set_title(fr'$r$ = {FM2.dr*1000} mm') ax.set_xlabel(r'$z$ (m)') ax.set_ylabel(r'$B_\\theta$ (T)') plt.legend() Out[9]: <pre>&lt;matplotlib.legend.Legend at 0x7fd14454feb0&gt;</pre> <p>The magnetic field is out of phase, so use the <code>im_</code> syntax:</p> In\u00a0[10]: Copied! <pre>FM2.plot('im_Btheta', aspect='equal', figsize=(12,4))\n</pre> FM2.plot('im_Btheta', aspect='equal', figsize=(12,4)) <p>Max on-axis field:</p> In\u00a0[11]: Copied! <pre>np.abs(FM2.Ez[0,0,:]).max()\n</pre> np.abs(FM2.Ez[0,0,:]).max() Out[11]: <pre>1.0153992993439902</pre> In\u00a0[12]: Copied! <pre>def check_oscillation(FM, label=''):\n\n    c_light = 299792458.\n    \n    dr = FM.dr\n    omega = FM.frequency*2*np.pi\n    \n    # Check the first off-axis grid points\n    z0 = FM.z\n    Ez0 = np.real(FM.Ez[0,0,:])\n    B1 = -np.imag(FM.Btheta[1,0,:])\n    \n    plt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$')\n    plt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$')\n    plt.ylabel('field (V/m)')\n    plt.xlabel('z (m)')\n    plt.legend()\n    plt.title(fr'Complex field oscillation{label}')\n</pre> def check_oscillation(FM, label=''):      c_light = 299792458.          dr = FM.dr     omega = FM.frequency*2*np.pi          # Check the first off-axis grid points     z0 = FM.z     Ez0 = np.real(FM.Ez[0,0,:])     B1 = -np.imag(FM.Btheta[1,0,:])          plt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$')     plt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$')     plt.ylabel('field (V/m)')     plt.xlabel('z (m)')     plt.legend()     plt.title(fr'Complex field oscillation{label}') In\u00a0[13]: Copied! <pre>check_oscillation(FM1, ', Superfish')\n</pre> check_oscillation(FM1, ', Superfish') In\u00a0[14]: Copied! <pre>check_oscillation(FM2, ', ANSYS')\n</pre> check_oscillation(FM2, ', ANSYS')"},{"location":"examples/fields/field_conversion/#fieldmesh-conversion","title":"FieldMesh Conversion\u00b6","text":""},{"location":"examples/fields/field_conversion/#2d-cylindrically-symmetric-rf-gun-fieldmesh","title":"2D cylindrically symmetric RF gun FieldMesh\u00b6","text":"<p>This data was originally generated using Superfish.</p>"},{"location":"examples/fields/field_conversion/#3d-rectangular-field-from-ansys","title":"3D rectangular field from ANSYS\u00b6","text":""},{"location":"examples/fields/field_conversion/#verify-the-oscillation","title":"Verify the oscillation\u00b6","text":"<p>Complex fields oscillate as $e^{-i\\omega t}$. For TM fields, the spatial components $E_z$ and $B_\\theta$ near the axis</p> <p>$\\Re E_{z} = -\\frac{r}{2}\\frac{\\omega}{c^2} \\Im B_\\theta$</p>"},{"location":"examples/fields/field_examples/","title":"FieldMesh Examples","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh, tools\n</pre> from pmd_beamphysics import FieldMesh, tools In\u00a0[3]: Copied! <pre>FM = FieldMesh('../data/solenoid.h5')\nFM\n</pre> FM = FieldMesh('../data/solenoid.h5') FM Out[3]: <pre>&lt;FieldMesh with cylindrical geometry and (101, 1, 201) shape at 0x7fd5dc32f760&gt;</pre> <p>Built-in plotting:</p> In\u00a0[4]: Copied! <pre>FM.plot('B', aspect='equal')\n</pre> FM.plot('B', aspect='equal') <p>On-axis field plotting</p> In\u00a0[5]: Copied! <pre>FM.plot_onaxis()\n</pre> FM.plot_onaxis() In\u00a0[6]: Copied! <pre>FM.attrs, FM.components.keys()\n</pre> FM.attrs, FM.components.keys() Out[6]: <pre>({'eleAnchorPt': 'beginning',\n  'gridGeometry': 'cylindrical',\n  'axisLabels': array(['r', 'theta', 'z'], dtype='&lt;U5'),\n  'gridLowerBound': array([0, 1, 0]),\n  'gridOriginOffset': array([ 0. ,  0. , -0.1]),\n  'gridSpacing': array([0.001, 0.   , 0.001]),\n  'gridSize': array([101,   1, 201]),\n  'harmonic': 0,\n  'fundamentalFrequency': 0,\n  'RFphase': 0,\n  'fieldScale': 1.0},\n dict_keys(['magneticField/z', 'magneticField/r']))</pre> In\u00a0[7]: Copied! <pre>FM.shape\n</pre> FM.shape Out[7]: <pre>(101, 1, 201)</pre> In\u00a0[8]: Copied! <pre>FM.frequency\n</pre> FM.frequency Out[8]: <pre>0</pre> <p>Coordinate vectors: <code>.r</code>, <code>.theta</code>, <code>.z</code>, etc.</p> In\u00a0[9]: Copied! <pre>FM.r, FM.dr\n</pre> FM.r, FM.dr Out[9]: <pre>(array([0.   , 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008,\n        0.009, 0.01 , 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017,\n        0.018, 0.019, 0.02 , 0.021, 0.022, 0.023, 0.024, 0.025, 0.026,\n        0.027, 0.028, 0.029, 0.03 , 0.031, 0.032, 0.033, 0.034, 0.035,\n        0.036, 0.037, 0.038, 0.039, 0.04 , 0.041, 0.042, 0.043, 0.044,\n        0.045, 0.046, 0.047, 0.048, 0.049, 0.05 , 0.051, 0.052, 0.053,\n        0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06 , 0.061, 0.062,\n        0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07 , 0.071,\n        0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079, 0.08 ,\n        0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088, 0.089,\n        0.09 , 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097, 0.098,\n        0.099, 0.1  ]),\n 0.001)</pre> <p>Grid info</p> In\u00a0[10]: Copied! <pre>FM.mins, FM.maxs, FM.deltas\n</pre> FM.mins, FM.maxs, FM.deltas Out[10]: <pre>(array([ 0. ,  0. , -0.1]),\n array([0.1, 0. , 0.1]),\n array([0.001, 0.   , 0.001]))</pre> <p>Convenient logicals</p> In\u00a0[11]: Copied! <pre>FM.is_static, FM.is_pure_magnetic, FM.is_pure_magnetic,  FM.is_pure_electric\n</pre> FM.is_static, FM.is_pure_magnetic, FM.is_pure_magnetic,  FM.is_pure_electric Out[11]: <pre>(True, True, True, False)</pre> In\u00a0[12]: Copied! <pre>FM.components\n</pre> FM.components Out[12]: <pre>{'magneticField/z': array([[[ 4.10454985e-03,  4.31040451e-03,  4.52986744e-03, ...,\n           4.67468517e-04,  3.93505841e-04,  3.31380794e-04]],\n \n        [[ 4.10132316e-03,  4.30698128e-03,  4.52613784e-03, ...,\n           4.63910019e-04,  3.90463457e-04,  3.28826095e-04]],\n \n        [[ 4.09178241e-03,  4.29666227e-03,  4.51500745e-03, ...,\n           4.53304832e-04,  3.81497195e-04,  3.21252672e-04]],\n \n        ...,\n \n        [[-8.55276742e-05, -9.25454620e-05, -9.97134392e-05, ...,\n          -1.67910069e-13, -1.66617291e-13, -1.69112101e-13]],\n \n        [[-8.66606075e-05, -9.34605759e-05, -1.00393739e-04, ...,\n          -1.63746446e-13, -1.62385457e-13, -1.63975660e-13]],\n \n        [[-8.76493773e-05, -9.42325632e-05, -1.00947206e-04, ...,\n          -1.59165583e-13, -1.57653026e-13, -1.58633209e-13]]]),\n 'magneticField/r': array([[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n \n        [[-9.96833640e-05, -1.06224487e-04, -1.13203127e-04, ...,\n           4.01781064e-05,  3.37384918e-05,  2.82880488e-05]],\n \n        [[-1.99034573e-04, -2.12052909e-04, -2.25955469e-04, ...,\n           7.94909429e-05,  6.67047656e-05,  5.59040132e-05]],\n \n        ...,\n \n        [[-3.28171418e-04, -3.29256623e-04, -3.30141629e-04, ...,\n           5.98175517e-14,  5.84734577e-14,  5.07218297e-14]],\n \n        [[-3.18048731e-04, -3.18985999e-04, -3.19728362e-04, ...,\n           5.74787071e-14,  5.71575012e-14,  5.06326785e-14]],\n \n        [[-3.08270029e-04, -3.09070567e-04, -3.09682173e-04, ...,\n           5.47143752e-14,  5.54052993e-14,  4.98595495e-14]]])}</pre> <p>Convenient access to component data</p> In\u00a0[13]: Copied! <pre>FM.Bz is FM['magneticField/z']\n</pre> FM.Bz is FM['magneticField/z'] Out[13]: <pre>True</pre> <p>Setting .scale will set the underlying attribute</p> In\u00a0[14]: Copied! <pre>FM.scale = 2\nFM.attrs['fieldScale'], FM.scale\n</pre> FM.scale = 2 FM.attrs['fieldScale'], FM.scale Out[14]: <pre>(2, 2)</pre> <p>Raw components accessed by their full key</p> In\u00a0[15]: Copied! <pre>FM['magneticField/z']\n</pre> FM['magneticField/z'] Out[15]: <pre>array([[[ 4.10454985e-03,  4.31040451e-03,  4.52986744e-03, ...,\n          4.67468517e-04,  3.93505841e-04,  3.31380794e-04]],\n\n       [[ 4.10132316e-03,  4.30698128e-03,  4.52613784e-03, ...,\n          4.63910019e-04,  3.90463457e-04,  3.28826095e-04]],\n\n       [[ 4.09178241e-03,  4.29666227e-03,  4.51500745e-03, ...,\n          4.53304832e-04,  3.81497195e-04,  3.21252672e-04]],\n\n       ...,\n\n       [[-8.55276742e-05, -9.25454620e-05, -9.97134392e-05, ...,\n         -1.67910069e-13, -1.66617291e-13, -1.69112101e-13]],\n\n       [[-8.66606075e-05, -9.34605759e-05, -1.00393739e-04, ...,\n         -1.63746446e-13, -1.62385457e-13, -1.63975660e-13]],\n\n       [[-8.76493773e-05, -9.42325632e-05, -1.00947206e-04, ...,\n         -1.59165583e-13, -1.57653026e-13, -1.58633209e-13]]])</pre> <p>Scaled component accessed by shorter keys, e.g.</p> In\u00a0[16]: Copied! <pre>FM['Bz']\n</pre> FM['Bz'] Out[16]: <pre>array([[[ 8.20909970e-03,  8.62080901e-03,  9.05973488e-03, ...,\n          9.34937033e-04,  7.87011682e-04,  6.62761588e-04]],\n\n       [[ 8.20264631e-03,  8.61396257e-03,  9.05227569e-03, ...,\n          9.27820038e-04,  7.80926913e-04,  6.57652189e-04]],\n\n       [[ 8.18356483e-03,  8.59332454e-03,  9.03001490e-03, ...,\n          9.06609663e-04,  7.62994390e-04,  6.42505344e-04]],\n\n       ...,\n\n       [[-1.71055348e-04, -1.85090924e-04, -1.99426878e-04, ...,\n         -3.35820138e-13, -3.33234581e-13, -3.38224202e-13]],\n\n       [[-1.73321215e-04, -1.86921152e-04, -2.00787478e-04, ...,\n         -3.27492892e-13, -3.24770914e-13, -3.27951319e-13]],\n\n       [[-1.75298755e-04, -1.88465126e-04, -2.01894411e-04, ...,\n         -3.18331166e-13, -3.15306051e-13, -3.17266417e-13]]])</pre> In\u00a0[17]: Copied! <pre>FM['magneticField/z'].max(), FM['Bz'].max()\n</pre> FM['magneticField/z'].max(), FM['Bz'].max() Out[17]: <pre>(2.150106838829148, 4.300213677658296)</pre> In\u00a0[18]: Copied! <pre>FM = FieldMesh('../data/rfgun.h5')\nFM.plot('re_E', aspect='equal', figsize=(12,4))\n</pre> FM = FieldMesh('../data/rfgun.h5') FM.plot('re_E', aspect='equal', figsize=(12,4)) <p>The magnetic field is out of phase, so use the <code>im_</code> syntax:</p> In\u00a0[19]: Copied! <pre>FM.plot('im_Btheta', aspect='equal', figsize=(12,4))\n</pre> FM.plot('im_Btheta', aspect='equal', figsize=(12,4)) <p>Max on-axis field:</p> In\u00a0[20]: Copied! <pre>np.abs(FM.Ez[0,0,:]).max()\n</pre> np.abs(FM.Ez[0,0,:]).max() Out[20]: <pre>1.0</pre> In\u00a0[21]: Copied! <pre>c_light = 299792458.\n\ndr = FM.dr\nomega = FM.frequency*2*np.pi\n\n# Check the first off-axis grid points\nz0 = FM.z\nEz0 = np.real(FM.Ez[0,0,:])\nB1 = -np.imag(FM.Btheta[1,0,:])\n\nplt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$')\nplt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$')\nplt.ylabel('field (V/m)')\nplt.xlabel('z (m)')\nplt.legend()\nplt.title(r'Complex field oscillation')\n</pre> c_light = 299792458.  dr = FM.dr omega = FM.frequency*2*np.pi  # Check the first off-axis grid points z0 = FM.z Ez0 = np.real(FM.Ez[0,0,:]) B1 = -np.imag(FM.Btheta[1,0,:])  plt.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$') plt.plot(z0, B1*2/dr *c_light**2/omega, '--', label=r'$-\\frac{r}{2}\\frac{\\omega}{c^2} \\Im\\left(B_\\theta\\right)$') plt.ylabel('field (V/m)') plt.xlabel('z (m)') plt.legend() plt.title(r'Complex field oscillation') Out[21]: <pre>Text(0.5, 1.0, 'Complex field oscillation')</pre> In\u00a0[22]: Copied! <pre>FM.units('Bz')\n</pre> FM.units('Bz') Out[22]: <pre>pmd_unit('T', 1, (0, 1, -2, -1, 0, 0, 0))</pre> <p>This also works:</p> In\u00a0[23]: Copied! <pre>FM.units('abs_Ez')\n</pre> FM.units('abs_Ez') Out[23]: <pre>pmd_unit('V/m', 1, (1, 1, -3, -1, 0, 0, 0))</pre> In\u00a0[24]: Copied! <pre>FM.write('rfgun2.h5')\n</pre> FM.write('rfgun2.h5') <p>Read back and make sure the data are the same.</p> In\u00a0[25]: Copied! <pre>FM2 = FieldMesh('rfgun2.h5')\n\nassert FM == FM2\n</pre> FM2 = FieldMesh('rfgun2.h5')  assert FM == FM2 <p>Write to open HDF5 file and test reload:</p> In\u00a0[26]: Copied! <pre>import h5py\nwith h5py.File('test.h5', 'w') as h5:\n    FM.write(h5, name='myfield')\n    FM2 = FieldMesh(h5=h5['myfield'])\n    assert FM == FM2\n</pre> import h5py with h5py.File('test.h5', 'w') as h5:     FM.write(h5, name='myfield')     FM2 = FieldMesh(h5=h5['myfield'])     assert FM == FM2 In\u00a0[27]: Copied! <pre>FM.write_astra_1d('astra_1d.dat')\n</pre> FM.write_astra_1d('astra_1d.dat') <p>Another method returns the array data with some annotation</p> In\u00a0[28]: Copied! <pre>FM.to_astra_1d()\n</pre> FM.to_astra_1d() Out[28]: <pre>{'attrs': {'type': 'astra_1d'},\n 'data': array([[ 0.00000000e+00, -1.00000000e+00],\n        [ 2.50000000e-04, -9.99967412e-01],\n        [ 5.00000000e-04, -9.99865106e-01],\n        ...,\n        [ 1.29500000e-01,  1.45678834e-04],\n        [ 1.29750000e-01,  1.40392476e-04],\n        [ 1.30000000e-01,  1.35399670e-04]])}</pre> In\u00a0[29]: Copied! <pre>idata = FM.to_impact_solrf()\nidata.keys()\n</pre> idata = FM.to_impact_solrf() idata.keys() Out[29]: <pre>dict_keys(['line', 'rfdata', 'ele', 'fmap'])</pre> <p>This is an element that can be used with LUME-Impact</p> In\u00a0[30]: Copied! <pre>idata['ele']\n</pre> idata['ele'] Out[30]: <pre>{'L': 0.13,\n 'type': 'solrf',\n 'zedge': 0,\n 'rf_field_scale': 1,\n 'rf_frequency': 2855998506.158,\n 'theta0_deg': 0.0,\n 'filename': 'rfdata666',\n 'radius': 0.15,\n 'x_offset': 0,\n 'y_offset': 0,\n 'x_rotation': 0.0,\n 'y_rotation': 0.0,\n 'z_rotation': 0.0,\n 'solenoid_field_scale': 0,\n 'name': 'solrf_666',\n 's': 0.13}</pre> <p>This is a line that would be used</p> In\u00a0[31]: Copied! <pre>idata['line']\n</pre>  idata['line'] Out[31]: <pre>'0.13 0 0 105 0 1 2855998506.158 0.0 666 0.15 0 0 0 0 0 0 /name:solrf_666'</pre> <p>Data that would be written to the rfdata999 file</p> In\u00a0[32]: Copied! <pre>idata['rfdata']\n</pre> idata['rfdata'] Out[32]: <pre>array([ 5.90000000e+01, -1.30000000e-01,  1.30000000e-01,  2.60000000e-01,\n        2.38794165e-01, -3.04717859e-01, -3.56926831e-17, -7.49524991e-01,\n        7.29753328e-18, -2.59757413e-01, -4.68937899e-17,  1.27535902e-01,\n       -6.30755527e-17,  4.49651550e-02, -1.49234509e-16,  4.67567146e-03,\n       -1.74265243e-16,  3.32025307e-02,  6.08199268e-17, -2.74904837e-03,\n        1.81697659e-16, -1.55710320e-02, -1.45475359e-16,  2.64475111e-03,\n       -1.20376479e-16,  9.51814907e-04,  2.52133666e-16, -2.68547441e-03,\n        4.07323339e-16,  1.50824509e-03,  3.51376926e-16,  7.16574075e-04,\n        1.71392948e-16, -9.25573616e-04, -1.42917735e-16,  2.40084183e-04,\n       -1.19272642e-16,  2.61495768e-04,  1.87867359e-16, -2.41687337e-04,\n       -9.72891282e-18,  6.22859548e-05, -3.09382521e-16,  7.85163760e-05,\n       -2.59194056e-16, -8.56926328e-05, -2.56768407e-16,  2.38418521e-06,\n       -2.89351931e-16,  2.84696849e-05, -1.77696470e-16, -2.10693774e-05,\n       -8.56616099e-18,  4.74764623e-06, -1.26336157e-16,  9.74703896e-06,\n       -1.86248124e-16, -6.17509459e-06, -2.08540055e-16, -1.04844029e-06,\n       -1.86006363e-16,  3.01586958e-06,  1.90371674e-16,  1.00000000e+00,\n       -1.30000000e-01,  1.30000000e-01,  2.60000000e-01,  0.00000000e+00])</pre> <p>This is the fieldmap that makes that data:</p> In\u00a0[33]: Copied! <pre>fmap = idata['fmap']\nfmap.keys()\n</pre> fmap = idata['fmap'] fmap.keys() Out[33]: <pre>dict_keys(['info', 'data', 'field'])</pre> <p>Additional info:</p> In\u00a0[34]: Copied! <pre>fmap['info']\n</pre> fmap['info'] Out[34]: <pre>{'format': 'solrf',\n 'Ez_scale': 1.0,\n 'Ez_err': 7.68588630296298e-08,\n 'Bz_scale': 0.0,\n 'Bz_err': 0,\n 'zmin': -0.13,\n 'zmax': 0.13}</pre> In\u00a0[35]: Copied! <pre>from pmd_beamphysics.interfaces.impact import fourier_field_reconsruction\nL = z0.ptp()\nzlist = np.linspace(0, L, len(Ez0))\nfcoefs = fmap['field']['Ez']['fourier_coefficients']\nreconstructed_Ez0 = np.array([fourier_field_reconsruction(z, fcoefs, z0=-L, zlen = 2*L) for z in zlist])\n\nfig, ax = plt.subplots()\nax2 = ax.twinx()\nax.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$', color='black')\nax.plot(zlist, reconstructed_Ez0, '--',  label='reconstructed', color='red', )\nax2.plot(zlist, abs(reconstructed_Ez0/Ez0 -1), '--',  label='reconstructed', color='black' )\nax2.set_ylabel('relative error')\nax2.set_yscale('log')\nax.set_ylabel('field (V/m)')\nax.set_xlabel('z (m)')\nplt.legend()\n</pre> from pmd_beamphysics.interfaces.impact import fourier_field_reconsruction L = z0.ptp() zlist = np.linspace(0, L, len(Ez0)) fcoefs = fmap['field']['Ez']['fourier_coefficients'] reconstructed_Ez0 = np.array([fourier_field_reconsruction(z, fcoefs, z0=-L, zlen = 2*L) for z in zlist])  fig, ax = plt.subplots() ax2 = ax.twinx() ax.plot(z0, Ez0, label=r'$\\Re \\left( E_z\\right)$', color='black') ax.plot(zlist, reconstructed_Ez0, '--',  label='reconstructed', color='red', ) ax2.plot(zlist, abs(reconstructed_Ez0/Ez0 -1), '--',  label='reconstructed', color='black' ) ax2.set_ylabel('relative error') ax2.set_yscale('log') ax.set_ylabel('field (V/m)') ax.set_xlabel('z (m)') plt.legend() Out[35]: <pre>&lt;matplotlib.legend.Legend at 0x7fd58822c910&gt;</pre> <p>This function can also be used to study the reconstruction error as a function of the number of coefficients:</p> In\u00a0[36]: Copied! <pre>ncoefs = np.arange(10, FM2.shape[2]//2)\nerrs = np.array([FM2.to_impact_solrf(n_coef = n, zmirror=True)['fmap']['info']['Ez_err'] for n in ncoefs])\n\nfig, ax = plt.subplots()\nax.plot(ncoefs, errs, marker='.', color='black')\nax.set_xlabel('n_coef')\nax.set_ylabel('Ez reconstruction error')\nax.set_yscale('log')\n</pre> ncoefs = np.arange(10, FM2.shape[2]//2) errs = np.array([FM2.to_impact_solrf(n_coef = n, zmirror=True)['fmap']['info']['Ez_err'] for n in ncoefs])  fig, ax = plt.subplots() ax.plot(ncoefs, errs, marker='.', color='black') ax.set_xlabel('n_coef') ax.set_ylabel('Ez reconstruction error') ax.set_yscale('log') In\u00a0[37]: Copied! <pre>#FM.write_gpt('solenoid.gdf', asci2gdf_bin='$ASCI2GDF_BIN', verbose=True)\nFM.write_gpt('rfgun_for_gpt.txt', verbose=True)\n</pre> #FM.write_gpt('solenoid.gdf', asci2gdf_bin='$ASCI2GDF_BIN', verbose=True) FM.write_gpt('rfgun_for_gpt.txt', verbose=True) <pre>ASCII field data written. Convert to GDF using: asci2df -o field.gdf rfgun_for_gpt.txt\n</pre> Out[37]: <pre>'rfgun_for_gpt.txt'</pre> In\u00a0[38]: Copied! <pre>FM.write_superfish('rfgun2.t7')\n</pre> FM.write_superfish('rfgun2.t7') Out[38]: <pre>'rfgun2.t7'</pre> In\u00a0[39]: Copied! <pre>FM3 = FieldMesh.from_superfish('rfgun2.t7')\nFM3\n</pre> FM3 = FieldMesh.from_superfish('rfgun2.t7') FM3 Out[39]: <pre>&lt;FieldMesh with cylindrical geometry and (61, 1, 521) shape at 0x7fd5880a9970&gt;</pre> In\u00a0[40]: Copied! <pre>help(FieldMesh.from_superfish)\n</pre> help(FieldMesh.from_superfish) <pre>Help on method read_superfish_t7 in module pmd_beamphysics.interfaces.superfish:\n\nread_superfish_t7(type=None, geometry='cylindrical') method of builtins.type instance\n    Parses a T7 file written by Posson/Superfish.\n    \n    Fish or Poisson T7 are automatically detected according to the second line.\n    \n    For Poisson problems, the type must be specified.\n    \n    Superfish fields oscillate as:\n        Er, Ez ~ cos(wt)\n        Hphi   ~ -sin(wt)\n      \n    For complex fields oscillating as e^-iwt\n    \n        Re(Ex*e^-iwt)   ~ cos\n        Re(-iB*e^-iwt) ~ -sin        \n    and therefore B = -i * mu_0 * H_phi is the complex magnetic field in Tesla\n    \n    \n    Parameters:\n    ----------\n    filename: str\n        T7 filename to read\n    type: str, optional\n        For Poisson files, required to be 'electric' or 'magnetic'. \n        Not used for Fish files\n    geometry: str, optional\n        field geometry, currently required to be the default: 'cylindrical'\n    \n    Returns:\n    -------\n    fieldmesh_data: dict of dicts:\n        attrs\n        components\n        \n        \n    A FieldMesh object is instantiated from this as:\n        FieldMesh(data=fieldmesh_data)\n\n</pre> <p>Note that writing the ASCII and conversions alter the data slightly</p> In\u00a0[41]: Copied! <pre>FM == FM3\n</pre> FM == FM3 Out[41]: <pre>False</pre> <p>But the data are all close:</p> In\u00a0[42]: Copied! <pre>for c in FM.components:\n    close = np.allclose(FM.components[c], FM3.components[c])\n    equal = np.all(FM.components[c] == FM3.components[c])\n    print(c, equal, close)\n</pre> for c in FM.components:     close = np.allclose(FM.components[c], FM3.components[c])     equal = np.all(FM.components[c] == FM3.components[c])     print(c, equal, close) <pre>electricField/z False True\nelectricField/r False True\nmagneticField/theta False True\n</pre> In\u00a0[43]: Copied! <pre>FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',\n                              hfile='../data/ansys_rfgun_2856MHz_H.dat',\n                              frequency=2856e6)\n\n\n\nFM3D\n</pre> FM3D = FieldMesh.from_ansys_ascii_3d(efile='../data/ansys_rfgun_2856MHz_E.dat',                               hfile='../data/ansys_rfgun_2856MHz_H.dat',                               frequency=2856e6)    FM3D Out[43]: <pre>&lt;FieldMesh with rectangular geometry and (3, 3, 457) shape at 0x7fd58809c970&gt;</pre> In\u00a0[44]: Copied! <pre>FM3D.attrs\n</pre> FM3D.attrs Out[44]: <pre>{'eleAnchorPt': 'beginning',\n 'gridGeometry': 'rectangular',\n 'axisLabels': ('x', 'y', 'z'),\n 'gridLowerBound': (0, 0, 0),\n 'gridOriginOffset': (-0.001, -0.001, 0.0),\n 'gridSpacing': (0.001, 0.001, 0.00025),\n 'gridSize': (3, 3, 457),\n 'harmonic': 1,\n 'fundamentalFrequency': 2856000000.0,\n 'RFphase': 0,\n 'fieldScale': 1.0}</pre> <p>This can then be written:</p> In\u00a0[45]: Copied! <pre>FM3D.write('../data/rfgun_rectangular.h5')\n</pre> FM3D.write('../data/rfgun_rectangular.h5') <p>The y=0 plane can be extracted to be used as cylindrically symmetric data:</p> In\u00a0[46]: Copied! <pre>FM2D = FM3D.to_cylindrical()\nFM2D\n</pre>  FM2D = FM3D.to_cylindrical() FM2D Out[46]: <pre>&lt;FieldMesh with cylindrical geometry and (2, 1, 457) shape at 0x7fd58809c850&gt;</pre> In\u00a0[47]: Copied! <pre>import os\nfor file in ('test.h5',\n             'astra_1d.dat',\n             'rfgun_for_gpt.txt',\n             'rfgun2.h5',\n             'rfgun2.t7'):\n    os.remove(file)\n</pre> import os for file in ('test.h5',              'astra_1d.dat',              'rfgun_for_gpt.txt',              'rfgun2.h5',              'rfgun2.t7'):     os.remove(file)"},{"location":"examples/fields/field_examples/#fieldmesh-examples","title":"FieldMesh Examples\u00b6","text":""},{"location":"examples/fields/field_examples/#internal-data","title":"Internal data\u00b6","text":"<p>attributes and components</p>"},{"location":"examples/fields/field_examples/#properties","title":"Properties\u00b6","text":"<p>Convenient access to these</p>"},{"location":"examples/fields/field_examples/#components","title":"Components\u00b6","text":""},{"location":"examples/fields/field_examples/#oscillating-fields","title":"Oscillating fields\u00b6","text":"<p>Oscillating fields have <code>.harmonic &gt; 0</code></p>"},{"location":"examples/fields/field_examples/#verify-the-oscillation","title":"Verify the oscillation\u00b6","text":"<p>Complex fields oscillate as $e^{-i\\omega t}$. For TM fields, the spatial components $E_z$ and $B_\\theta$ near the axis</p> <p>$\\Re E_{z} = -\\frac{r}{2}\\frac{\\omega}{c^2} \\Im B_\\theta$</p>"},{"location":"examples/fields/field_examples/#units","title":"Units\u00b6","text":""},{"location":"examples/fields/field_examples/#write","title":"Write\u00b6","text":""},{"location":"examples/fields/field_examples/#write-astra-1d","title":"Write Astra 1D\u00b6","text":"<p>Astra primarily uses simple 1D (on-axis) fieldmaps.</p>"},{"location":"examples/fields/field_examples/#write-impact-t","title":"Write Impact-T\u00b6","text":"<p>Impact-T uses a particular Fourier representation for 1D fields. These routines form this data.</p>"},{"location":"examples/fields/field_examples/#write-gpt","title":"Write GPT\u00b6","text":""},{"location":"examples/fields/field_examples/#read-superfish","title":"Read Superfish\u00b6","text":"<p>Proper Superfish T7 can also be read.</p>"},{"location":"examples/fields/field_examples/#read-ansys","title":"Read ANSYS\u00b6","text":"<p>Read ANSYS E and H ASCII files:</p>"},{"location":"examples/fields/field_examples/#cleanup","title":"Cleanup\u00b6","text":""},{"location":"examples/fields/field_expansion/","title":"Field expansion","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh\n</pre> from pmd_beamphysics import FieldMesh In\u00a0[3]: Copied! <pre>FM = FieldMesh('../data/solenoid.h5')\nFM.plot()\n</pre> FM = FieldMesh('../data/solenoid.h5') FM.plot() In\u00a0[4]: Copied! <pre>FM.plot_onaxis()\n</pre> FM.plot_onaxis() In\u00a0[5]: Copied! <pre>from pmd_beamphysics.fields.expansion import fft_derivative_array, spline_derivative_array\n</pre> from pmd_beamphysics.fields.expansion import fft_derivative_array, spline_derivative_array In\u00a0[6]: Copied! <pre>Z = FM.coord_vec('z')\nDZ = FM.dz\nFZ = FM.Bz[0,0,:]\n\ndfield1 = fft_derivative_array(FZ, DZ, ncoef=10)\ndfield2 = spline_derivative_array(Z, FZ, s=1e-9)\n</pre> Z = FM.coord_vec('z') DZ = FM.dz FZ = FM.Bz[0,0,:]  dfield1 = fft_derivative_array(FZ, DZ, ncoef=10) dfield2 = spline_derivative_array(Z, FZ, s=1e-9) In\u00a0[7]: Copied! <pre>plt.plot(Z, dfield1[:,1], label='fft')\nplt.plot(Z, dfield2[:,1], label='spline')\nplt.xlabel('z (m)')\nplt.ylabel(r'$dB_z/dz$' + r\" (T/m)\")\n</pre> plt.plot(Z, dfield1[:,1], label='fft') plt.plot(Z, dfield2[:,1], label='spline') plt.xlabel('z (m)') plt.ylabel(r'$dB_z/dz$' + r\" (T/m)\") Out[7]: <pre>Text(0, 0.5, '$dB_z/dz$ (T/m)')</pre> In\u00a0[8]: Copied! <pre>plt.plot(Z, dfield1[:,2], label='fft')\nplt.plot(Z, dfield2[:,2], label='spline')\nplt.xlabel('z (m)')\nplt.ylabel(r'$d^2B_z/dz^2$' + r\" (T/m^2)\")\nplt.legend()\n</pre> plt.plot(Z, dfield1[:,2], label='fft') plt.plot(Z, dfield2[:,2], label='spline') plt.xlabel('z (m)') plt.ylabel(r'$d^2B_z/dz^2$' + r\" (T/m^2)\") plt.legend() Out[8]: <pre>&lt;matplotlib.legend.Legend at 0x7f26843c9b20&gt;</pre> In\u00a0[9]: Copied! <pre>plt.plot(Z, dfield1[:,3], label='fft')\nplt.plot(Z, dfield2[:,3], label='spline')\nplt.xlabel('z (m)')\nplt.ylabel(r'$d^3B_z/dz^3$' + r\" (T/m^3)\")\nplt.legend()\n</pre> plt.plot(Z, dfield1[:,3], label='fft') plt.plot(Z, dfield2[:,3], label='spline') plt.xlabel('z (m)') plt.ylabel(r'$d^3B_z/dz^3$' + r\" (T/m^3)\") plt.legend() Out[9]: <pre>&lt;matplotlib.legend.Legend at 0x7f268436a490&gt;</pre> In\u00a0[10]: Copied! <pre>FM2 = FieldMesh.from_onaxis(z=Z, Bz=FZ)\nFM2.plot_onaxis()\n</pre> FM2 = FieldMesh.from_onaxis(z=Z, Bz=FZ) FM2.plot_onaxis() In\u00a0[11]: Copied! <pre>FM3 = FM2.expand_onaxis(dr=FM.dr, nr=10)\nFM3\n</pre> FM3 = FM2.expand_onaxis(dr=FM.dr, nr=10) FM3 Out[11]: <pre>&lt;FieldMesh with cylindrical geometry and (10, 1, 201) shape at 0x7f26842e2bb0&gt;</pre> In\u00a0[12]: Copied! <pre>FM3.plot('Br')\n</pre> FM3.plot('Br') In\u00a0[13]: Copied! <pre>def compare(fm1, fm2, component='Ez'):\n    \n    z = fm1.coord_vec('z')\n    dr = fm1.dr\n    nr = min(fm1.shape[0], fm2.shape[0])\n    \n    unit = fm1.units(component)\n    Fz1 =  np.squeeze(fm1[component])[0:nr, :]\n    Fz2 =  np.squeeze(fm2[component])[0:nr, :]\n    err = abs(Fz1-Fz2) / np.abs(Fz1).max() \n    \n    extent = np.array([z.min(), z.max(), 0, dr*(nr-1)]) * 1000\n    plt.imshow(err, origin='lower', extent = extent , aspect='auto')\n    plt.xlabel('z (mm)')\n    plt.ylabel('r (mm)')\n    \n    plt.title(f\"{component} expansion error, max err = {err.max()}\")\n    plt.colorbar(label=f'relatic expansion error')\n    \ncompare(FM, FM3, 'B')\n</pre> def compare(fm1, fm2, component='Ez'):          z = fm1.coord_vec('z')     dr = fm1.dr     nr = min(fm1.shape[0], fm2.shape[0])          unit = fm1.units(component)     Fz1 =  np.squeeze(fm1[component])[0:nr, :]     Fz2 =  np.squeeze(fm2[component])[0:nr, :]     err = abs(Fz1-Fz2) / np.abs(Fz1).max()           extent = np.array([z.min(), z.max(), 0, dr*(nr-1)]) * 1000     plt.imshow(err, origin='lower', extent = extent , aspect='auto')     plt.xlabel('z (mm)')     plt.ylabel('r (mm)')          plt.title(f\"{component} expansion error, max err = {err.max()}\")     plt.colorbar(label=f'relatic expansion error')      compare(FM, FM3, 'B')     In\u00a0[14]: Copied! <pre>FM = FieldMesh('../data/rfgun.h5')\nFM.plot()\n</pre> FM = FieldMesh('../data/rfgun.h5') FM.plot() In\u00a0[15]: Copied! <pre>Z = FM.coord_vec('z')\nDZ = FM.dz\nFZ = np.real(FM.Ez[0,0,:])\n\nFM2 = FieldMesh.from_onaxis(z=Z, Ez=FZ, frequency=FM.frequency)\nFM2.plot_onaxis()\n</pre> Z = FM.coord_vec('z') DZ = FM.dz FZ = np.real(FM.Ez[0,0,:])  FM2 = FieldMesh.from_onaxis(z=Z, Ez=FZ, frequency=FM.frequency) FM2.plot_onaxis() In\u00a0[\u00a0]: Copied! <pre>\n</pre> In\u00a0[16]: Copied! <pre>NR = 40\nFM3 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft')\ncompare(FM, FM3, 'Er')\n</pre> NR = 40 FM3 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft') compare(FM, FM3, 'Er')    In\u00a0[17]: Copied! <pre>compare(FM, FM3, 'Ez')\n</pre> compare(FM, FM3, 'Ez')   In\u00a0[18]: Copied! <pre>compare(FM, FM3, 'Btheta')\n</pre> compare(FM, FM3, 'Btheta')   In\u00a0[19]: Copied! <pre>NR = 40\nFM4 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9)\ncompare(FM, FM4, 'Er')\n</pre> NR = 40 FM4 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9) compare(FM, FM4, 'Er')    In\u00a0[20]: Copied! <pre>compare(FM, FM4, 'Ez')\n</pre> compare(FM, FM4, 'Ez')   In\u00a0[21]: Copied! <pre>compare(FM, FM4, 'Btheta')\n</pre> compare(FM, FM4, 'Btheta')   In\u00a0[22]: Copied! <pre>compare(FM, FM4, 'Btheta')\n</pre> compare(FM, FM4, 'Btheta')   In\u00a0[23]: Copied! <pre># Differences between the two methods\ncompare(FM3, FM4, 'E')\n</pre> # Differences between the two methods compare(FM3, FM4, 'E')   In\u00a0[24]: Copied! <pre>def compare2(comp='Er'):\n    NR = 10\n    dr = FM.dr\n    r = (NR-1)*FM.dr\n    FM5 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft', ncoef=15)\n    \n    if comp.startswith('E'):\n        func = np.real\n    else:\n        func = np.imag\n    \n    f0 = func(FM[comp][NR-1,0,:])\n    \n    f5 = func(FM5[comp][NR-1,0,:])\n    \n    FM6 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9)\n    f6 = func(FM6[comp][NR-1,0,:])\n    \n    fix, ax = plt.subplots()\n    ax2 = ax.twinx()\n    ax.plot(f0, label='original')\n    ax.plot(f5, '--', label='fourier')\n    ax.plot(f6, '--', label='spline')\n    ax.legend(loc='upper left')\n    ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')\n    ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')\n    ax2.set_yscale('log')\n    ax.set_ylabel(comp)\n    ax2.set_ylabel('relative error')\n    ax2.legend(loc='upper right')\n    ax.set_xlabel('index along z')\n</pre>  def compare2(comp='Er'):     NR = 10     dr = FM.dr     r = (NR-1)*FM.dr     FM5 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='fft', ncoef=15)          if comp.startswith('E'):         func = np.real     else:         func = np.imag          f0 = func(FM[comp][NR-1,0,:])          f5 = func(FM5[comp][NR-1,0,:])          FM6 = FM2.expand_onaxis(dr=FM.dr, nr=NR, method='spline', spline_s=1e-9)     f6 = func(FM6[comp][NR-1,0,:])          fix, ax = plt.subplots()     ax2 = ax.twinx()     ax.plot(f0, label='original')     ax.plot(f5, '--', label='fourier')     ax.plot(f6, '--', label='spline')     ax.legend(loc='upper left')     ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')     ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')     ax2.set_yscale('log')     ax.set_ylabel(comp)     ax2.set_ylabel('relative error')     ax2.legend(loc='upper right')     ax.set_xlabel('index along z') In\u00a0[25]: Copied! <pre>compare2('Er')\n</pre> compare2('Er') <pre>/tmp/ipykernel_5629/1820762901.py:25: RuntimeWarning: divide by zero encountered in divide\n  ax2.plot(abs((f5-f0)/f0), color='purple', label='relative fourier error')\n/tmp/ipykernel_5629/1820762901.py:26: RuntimeWarning: divide by zero encountered in divide\n  ax2.plot(abs((f6-f0)/f0), color='grey', label='relative spline error')\n</pre> In\u00a0[26]: Copied! <pre>compare2('Ez')\n</pre> compare2('Ez') In\u00a0[27]: Copied! <pre>compare2('Btheta')\n</pre> compare2('Btheta')"},{"location":"examples/fields/field_expansion/#field-expansion","title":"Field expansion\u00b6","text":""},{"location":"examples/fields/field_expansion/#derivative-array","title":"Derivative array\u00b6","text":"<p>Field expansions depend on numerical derivatives of the on-axis field. Here are two methods.</p>"},{"location":"examples/fields/field_expansion/#fieldmesh-from-1d-data","title":"FieldMesh from 1D data\u00b6","text":""},{"location":"examples/fields/field_expansion/#expansion-1d-2d","title":"Expansion 1D -&gt; 2D\u00b6","text":""},{"location":"examples/fields/field_expansion/#rf-gun-1d-2d","title":"RF Gun 1D -&gt; 2D\u00b6","text":""},{"location":"examples/fields/field_expansion/#spline-based-expansion","title":"Spline-based expansion\u00b6","text":""},{"location":"examples/fields/field_expansion/#compare-fourier-and-spline","title":"Compare Fourier and Spline\u00b6","text":""},{"location":"examples/fields/field_tracking/","title":"Field Phasing and Scaling (Autophase)","text":"In\u00a0[1]: Copied! <pre># Useful for debugging\n%load_ext autoreload\n%autoreload 2\n\n# Nicer plotting\nimport matplotlib.pyplot as plt\n%matplotlib inline\n%config InlineBackend.figure_format = 'retina'\n\nimport numpy as np\n</pre> # Useful for debugging %load_ext autoreload %autoreload 2  # Nicer plotting import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format = 'retina'  import numpy as np In\u00a0[2]: Copied! <pre>from pmd_beamphysics import FieldMesh\n</pre> from pmd_beamphysics import FieldMesh In\u00a0[3]: Copied! <pre>FM = FieldMesh('../data/rfgun.h5')\nFM.plot(aspect='equal', figsize=(12,8))\n</pre> FM = FieldMesh('../data/rfgun.h5') FM.plot(aspect='equal', figsize=(12,8)) In\u00a0[4]: Copied! <pre># On-axis field\nz0 = FM.coord_vec('z')\nEz0 = FM.Ez[0,0,:]  # this is complex\nplt.plot(z0, np.real(Ez0))\n</pre> # On-axis field z0 = FM.coord_vec('z') Ez0 = FM.Ez[0,0,:]  # this is complex plt.plot(z0, np.real(Ez0)) Out[4]: <pre>[&lt;matplotlib.lines.Line2D at 0x1228734c0&gt;]</pre> In\u00a0[5]: Copied! <pre>from pmd_beamphysics.fields.analysis import accelerating_voltage_and_phase\n</pre> from pmd_beamphysics.fields.analysis import accelerating_voltage_and_phase In\u00a0[6]: Copied! <pre>?accelerating_voltage_and_phase\n</pre> ?accelerating_voltage_and_phase In\u00a0[7]: Copied! <pre>V0, phase0 = accelerating_voltage_and_phase(z0, -Ez0*120e6, FM.frequency)\n\nV0, (phase0 * 180/np.pi) % 360\n</pre> V0, phase0 = accelerating_voltage_and_phase(z0, -Ez0*120e6, FM.frequency)  V0, (phase0 * 180/np.pi) % 360 Out[7]: <pre>(5795904.446882586, 322.1355626180106)</pre> <p>Equations of motion:</p> <p>$\\frac{dz}{dt} = \\frac{pc}{\\sqrt{(pc)^2 + m^2 c^4)}} c$</p> <p>$\\frac{dp}{dt} = q E_z $</p> <p>$E_z = \\Re f(z) \\exp(-i \\omega t) $</p> In\u00a0[8]: Copied! <pre>from pmd_beamphysics.fields.analysis import track_field_1d\nfrom pmd_beamphysics.units import mec2, c_light\n</pre> from pmd_beamphysics.fields.analysis import track_field_1d from pmd_beamphysics.units import mec2, c_light In\u00a0[9]: Copied! <pre>?track_field_1d\n</pre> ?track_field_1d In\u00a0[10]: Copied! <pre>Z = FM.coord_vec('z')\nE = FM.Ez[0,0,:]*np.exp(1j*2*np.pi /360 * 0)*120e6 \n\n# Final z (m) and pz (eV/c)\ntrack_field_1d(Z, E, FM.frequency, pz0=0, t0=0)\n</pre> Z = FM.coord_vec('z') E = FM.Ez[0,0,:]*np.exp(1j*2*np.pi /360 * 0)*120e6   # Final z (m) and pz (eV/c) track_field_1d(Z, E, FM.frequency, pz0=0, t0=0) Out[10]: <pre>(0.13000001229731462, 3896770.3798088795)</pre> In\u00a0[11]: Copied! <pre># Use debug mode to see the actual track\nsol = track_field_1d(Z, E, FM.frequency, pz0=0, t0=0, debug=True, max_step=1/FM.frequency/100)\n</pre> # Use debug mode to see the actual track sol = track_field_1d(Z, E, FM.frequency, pz0=0, t0=0, debug=True, max_step=1/FM.frequency/100) In\u00a0[12]: Copied! <pre># Plot the track\nfig, ax = plt.subplots()\n\nax2 = ax.twinx()\n\nax.set_xlabel('f*t')\nax.set_ylabel('z (m)')\nax2.set_ylabel('KE (MeV)')\n\nax.plot(sol.t*FM.frequency, sol.y[0])\nax2.plot(sol.t*FM.frequency, (np.hypot(sol.y[1], mec2)-mec2)/1e6, color='red')\n</pre> # Plot the track fig, ax = plt.subplots()  ax2 = ax.twinx()  ax.set_xlabel('f*t') ax.set_ylabel('z (m)') ax2.set_ylabel('KE (MeV)')  ax.plot(sol.t*FM.frequency, sol.y[0]) ax2.plot(sol.t*FM.frequency, (np.hypot(sol.y[1], mec2)-mec2)/1e6, color='red') Out[12]: <pre>[&lt;matplotlib.lines.Line2D at 0x122e69940&gt;]</pre> In\u00a0[13]: Copied! <pre>from pmd_beamphysics.fields.analysis import autophase_field\n</pre> from pmd_beamphysics.fields.analysis import autophase_field In\u00a0[14]: Copied! <pre>phase_deg1, pz1 = autophase_field(FM, pz0=0, scale=120e6, verbose=True)\nphase_deg1, pz1\n</pre> phase_deg1, pz1 = autophase_field(FM, pz0=0, scale=120e6, verbose=True) phase_deg1, pz1  <pre>v=c voltage: 5795904.446882586 V, phase: -37.86443738198939 deg\n    iterations: 18\n    function calls: 23\n</pre> Out[14]: <pre>(304.334830187332, 6234145.7957780445)</pre> In\u00a0[15]: Copied! <pre># Use debug mode to visualize. This returns the phasiing function\nphase_f = autophase_field(FM, pz0=0, scale=120e6, debug=True)\nphase_f(304.3348289439232)\n</pre> # Use debug mode to visualize. This returns the phasiing function phase_f = autophase_field(FM, pz0=0, scale=120e6, debug=True) phase_f(304.3348289439232) Out[15]: <pre>6234145.795777954</pre> In\u00a0[16]: Copied! <pre>plist = np.linspace(280, 330, 100)\npzlist = np.array([phase_f(p) for p in plist])\n\nplt.plot(plist, pzlist/1e6)\nplt.scatter(phase_deg1, pz1/1e6, color='red')\nplt.xlabel('phase (deg)')\nplt.ylabel('pz (MeV/c)')\n</pre> plist = np.linspace(280, 330, 100) pzlist = np.array([phase_f(p) for p in plist])  plt.plot(plist, pzlist/1e6) plt.scatter(phase_deg1, pz1/1e6, color='red') plt.xlabel('phase (deg)') plt.ylabel('pz (MeV/c)') Out[16]: <pre>Text(0, 0.5, 'pz (MeV/c)')</pre> In\u00a0[17]: Copied! <pre>from pmd_beamphysics.fields.analysis import autophase_and_scale_field\n?autophase_and_scale_field\n</pre> from pmd_beamphysics.fields.analysis import autophase_and_scale_field ?autophase_and_scale_field In\u00a0[18]: Copied! <pre>phase_deg2, scale2 = autophase_and_scale_field(FM, 6e6, pz0=0, verbose=True)\nphase_deg2, scale2\n</pre> phase_deg2, scale2 = autophase_and_scale_field(FM, 6e6, pz0=0, verbose=True) phase_deg2, scale2 <pre>v=c voltage: 0.048299203724021564 V, phase: -37.86443738198939 deg\n    Pass 1 delta energy: 6000000.288677918 at phase  304.9786263011349 deg\n    Pass 2 delta energy: 5999999.999982537 at phase  305.14631150985355 deg\n</pre> Out[18]: <pre>(305.14631150985355, 125273551.27124627)</pre> In\u00a0[19]: Copied! <pre># Use debug mode to visualize. This returns the phasing function\nps_f = autophase_and_scale_field(FM, 6e6, pz0=0, debug=True)\nps_f(phase_deg2, scale2)\n</pre> # Use debug mode to visualize. This returns the phasing function ps_f = autophase_and_scale_field(FM, 6e6, pz0=0, debug=True) ps_f(phase_deg2, scale2) Out[19]: <pre>5999999.999982537</pre> In\u00a0[20]: Copied! <pre>plist = np.linspace(280, 330, 100)\ndenergy = np.array([ps_f(p, scale2) for p in plist])\n\nplt.plot(plist, denergy/1e6)\nplt.scatter(phase_deg2, ps_f(phase_deg2, scale2)/1e6, color='red', label='Autophased')\nplt.xlabel('phase (deg)')\nplt.ylabel('Voltage (MV)')\nplt.legend()\n</pre> plist = np.linspace(280, 330, 100) denergy = np.array([ps_f(p, scale2) for p in plist])  plt.plot(plist, denergy/1e6) plt.scatter(phase_deg2, ps_f(phase_deg2, scale2)/1e6, color='red', label='Autophased') plt.xlabel('phase (deg)') plt.ylabel('Voltage (MV)') plt.legend() Out[20]: <pre>&lt;matplotlib.legend.Legend at 0x1232768b0&gt;</pre>"},{"location":"examples/fields/field_tracking/#field-phasing-and-scaling-autophase","title":"Field Phasing and Scaling (Autophase)\u00b6","text":""},{"location":"examples/fields/field_tracking/#get-field","title":"Get field\u00b6","text":""},{"location":"examples/fields/field_tracking/#vc-voltage-and-phase","title":"v=c voltage and phase\u00b6","text":""},{"location":"examples/fields/field_tracking/#tracking","title":"Tracking\u00b6","text":""},{"location":"examples/fields/field_tracking/#autophase","title":"Autophase\u00b6","text":""},{"location":"examples/fields/field_tracking/#autophase-and-scale","title":"Autophase and Scale\u00b6","text":""}]}
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 81352fd..f4291ff 100644
Binary files a/sitemap.xml.gz and b/sitemap.xml.gz differ