DOC: plot with SVG where needed

ComPWA · Dec 2, 2023 · 6dcdcd1 · 6dcdcd1
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commit 6dcdcd1
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Showing 7 changed files with 33 additions and 17 deletions.
diff --git a/docs/amplitude-analysis.ipynb b/docs/amplitude-analysis.ipynb
@@ -748,15 +748,15 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib import cm\n",
     "\n",
     "resonances = sorted(\n",
     "    reaction.get_intermediate_particles(),\n",
     "    key=lambda p: p.mass,\n",
     ")\n",
     "evenly_spaced_interval = np.linspace(0, 1, len(resonances))\n",
-    "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n",
+    "colors = [plt.cm.rainbow(x) for x in evenly_spaced_interval]\n",
     "fig, ax = plt.subplots(figsize=(9, 4))\n",
     "ax.hist(\n",
     "    np.real(data_frame[\"m_12\"]),\n",
@@ -1465,15 +1465,14 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from matplotlib import cm\n",
     "\n",
     "resonances = sorted(\n",
     "    reaction.get_intermediate_particles(),\n",
     "    key=lambda p: p.mass,\n",
     ")\n",
     "\n",
     "evenly_spaced_interval = np.linspace(0, 1, len(resonances))\n",
-    "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n",
+    "colors = [plt.cm.rainbow(x) for x in evenly_spaced_interval]\n",
     "\n",
     "\n",
     "def indicate_masses(ax):\n",

diff --git a/docs/amplitude-analysis/analytic-continuation.ipynb b/docs/amplitude-analysis/analytic-continuation.ipynb
@@ -256,8 +256,8 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import numpy as np\n",
-    "from matplotlib import cm\n",
     "\n",
     "phsp = helicity_transformer(phsp_momenta)\n",
     "intensities = np.array(intensity(phsp))\n",
@@ -267,7 +267,7 @@
     "    key=lambda p: p.mass,\n",
     ")\n",
     "evenly_spaced_interval = np.linspace(0, 1, len(resonances))\n",
-    "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n",
+    "colors = [plt.cm.rainbow(x) for x in evenly_spaced_interval]\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.set_xlabel(\"$m_{02}$ [GeV]\")\n",

diff --git a/docs/usage.ipynb b/docs/usage.ipynb
@@ -191,6 +191,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(5, 3))\n",
@@ -245,6 +246,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "%matplotlib widget\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.animation import PillowWriter\n",
@@ -702,19 +704,20 @@
      "source_hidden": true
     },
     "tags": [
+     "full-width",
      "hide-input"
     ]
    },
    "outputs": [],
    "source": [
-    "from matplotlib import cm\n",
+    "%config InlineBackend.figure_formats = ['png']\n",
     "\n",
     "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(7, 4.3))\n",
     "fig.canvas.toolbar_visible = False\n",
     "fig.canvas.header_visible = False\n",
     "fig.canvas.footer_visible = False\n",
-    "ax1.hist2d(*cartesian_data.values(), bins=100, cmap=cm.coolwarm)\n",
-    "ax2.hist2d(polar_data[\"phi\"], polar_data[\"r\"], bins=100, cmap=cm.coolwarm)\n",
+    "ax1.hist2d(*cartesian_data.values(), bins=100, cmap=plt.cm.coolwarm)\n",
+    "ax2.hist2d(polar_data[\"phi\"], polar_data[\"r\"], bins=100, cmap=plt.cm.coolwarm)\n",
     "fig.suptitle(\"Hit-and-miss intensity distribution\")\n",
     "ax1.set_title(\"cartesian\")\n",
     "ax2.set_title(\"polar\")\n",
@@ -770,11 +773,11 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "%matplotlib widget\n",
     "import ipywidgets\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from matplotlib import cm\n",
     "\n",
     "size = 200\n",
     "X, Y = np.meshgrid(\n",
@@ -809,7 +812,7 @@
     "    Z = polar_function(polar_domain)\n",
     "    if color_mesh is not None:\n",
     "        color_mesh.remove()\n",
-    "    color_mesh = ax_interactive.pcolormesh(X, Y, Z, cmap=cm.coolwarm)"
+    "    color_mesh = ax_interactive.pcolormesh(X, Y, Z, cmap=plt.cm.coolwarm)"
    ]
   },
   {

diff --git a/docs/usage/basics.ipynb b/docs/usage/basics.ipynb
@@ -225,6 +225,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "parameter_defaults = {\n",
     "    a: 0.15,\n",
     "    b: 0.05,\n",
@@ -367,6 +368,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "plt.scatter(x_values, y_values)\n",
     "plt.gca().set_xlabel(\"$x$\")\n",
     "plt.gca().set_ylabel(\"$f(x)$\");"
@@ -420,6 +422,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "x_domain = np.linspace(0, 5, num=200)\n",
     "y_values = function_1d({\"x\": x_domain})\n",
     "fig = plt.figure(figsize=(8, 5))\n",
@@ -564,6 +567,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "plt.hist(\n",
     "    domain[\"x\"],\n",
     "    bins=200,\n",
@@ -636,6 +640,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "plt.hist(data[\"x\"], bins=200);"
    ]
   },
@@ -694,6 +699,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "plt.hist(data[\"x\"], bins=200, density=True)\n",
     "plt.hist(\n",
     "    domain[\"x\"],\n",
@@ -803,6 +809,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "plt.hist(data[\"x\"], bins=200, density=True)\n",
     "plt.hist(\n",
     "    domain[\"x\"],\n",
@@ -874,6 +881,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "fit_traceback = pd.read_csv(\"traceback-1D.csv\")\n",
     "fig, (ax1, ax2) = plt.subplots(\n",
     "    2, figsize=(7, 9), sharex=True, gridspec_kw={\"height_ratios\": [1, 2]}\n",
@@ -962,6 +970,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "y_range = (y, -sp.pi, +sp.pi)\n",
     "substituted_expr_2d = expression_2d.subs(parameter_defaults)\n",
     "plot3d(substituted_expr_2d, x_range, y_range)"
@@ -1015,6 +1024,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
     "intensities = np.array(function_2d(domain_2d))\n",
     "kwargs = {\n",
@@ -1072,6 +1082,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "fig, axes = plt.subplots(1, 2, figsize=(9, 4), sharey=True, tight_layout=True)\n",
     "axes[0].hist2d(**data_2d, bins=50)\n",
     "axes[1].hist2d(**domain_2d, weights=function_2d(domain_2d), bins=50)\n",
@@ -1224,6 +1235,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['png']\n",
     "fig, axes = plt.subplots(1, 2, figsize=(9, 4), sharey=True, tight_layout=True)\n",
     "fig.suptitle(\"Final fit result\")\n",
     "axes[0].hist2d(**data_2d, bins=50)\n",
@@ -1257,6 +1269,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "minuit_traceback = pd.read_csv(\"traceback.csv\")\n",
     "scipy_traceback = pd.read_csv(\"traceback-scipy.csv\")\n",
     "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(\n",

diff --git a/docs/usage/binned-fit.ipynb b/docs/usage/binned-fit.ipynb
@@ -106,6 +106,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 5))\n",

diff --git a/docs/usage/chi-squared.ipynb b/docs/usage/chi-squared.ipynb
@@ -169,6 +169,7 @@
    },
    "outputs": [],
    "source": [
+    "%config InlineBackend.figure_formats = ['svg']\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "\n",

diff --git a/docs/usage/unbinned-fit.ipynb b/docs/usage/unbinned-fit.ipynb
@@ -112,7 +112,6 @@
     "%config InlineBackend.figure_formats = ['png']\n",
     "import matplotlib as mpl\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib import cm\n",
     "\n",
     "mpl.rcParams[\"figure.dpi\"] = 300\n",
     "\n",
@@ -139,7 +138,7 @@
     "\n",
     "bins_x, bins_y = 80, 50\n",
     "bin_values, bin_edges_x, bin_edges_y, _ = ax2d.hist2d(\n",
-    "    data[\"x\"], data[\"y\"], bins=(bins_x, bins_y), cmap=cm.coolwarm\n",
+    "    data[\"x\"], data[\"y\"], bins=(bins_x, bins_y), cmap=plt.cm.coolwarm\n",
     ")\n",
     "xlim = 0, 4\n",
     "ylim = -3, +3\n",
@@ -250,8 +249,8 @@
     ")\n",
     "ax1.set_title(\"Data distribution\")\n",
     "ax2.set_title(\"Initial fit model\")\n",
-    "ax1.hist2d(data[\"x\"], data[\"y\"], cmap=cm.coolwarm, bins=(bins_x, bins_y))\n",
-    "ax2.pcolormesh(X, Y, Z, cmap=cm.coolwarm)\n",
+    "ax1.hist2d(data[\"x\"], data[\"y\"], cmap=plt.cm.coolwarm, bins=(bins_x, bins_y))\n",
+    "ax2.pcolormesh(X, Y, Z, cmap=plt.cm.coolwarm)\n",
     "for ax in [ax1, ax2]:\n",
     "    ax.set_xlim(*xlim)\n",
     "    ax.set_ylim(*ylim)\n",
@@ -330,8 +329,8 @@
     ")\n",
     "ax1.set_title(\"Data distribution\")\n",
     "ax2.set_title(\"Model with optimized parameters\")\n",
-    "ax1.hist2d(data[\"x\"], data[\"y\"], cmap=cm.coolwarm, bins=(bins_x, bins_y))\n",
-    "ax2.pcolormesh(X, Y, Z, cmap=cm.coolwarm)\n",
+    "ax1.hist2d(data[\"x\"], data[\"y\"], cmap=plt.cm.coolwarm, bins=(bins_x, bins_y))\n",
+    "ax2.pcolormesh(X, Y, Z, cmap=plt.cm.coolwarm)\n",
     "for ax in [ax1, ax2]:\n",
     "    ax.set_xlim(*xlim)\n",
     "    ax.set_ylim(*ylim)\n",