Implement expected analysis

examples_artificial_data/03_energy_ratio/00_demo_energy_ratio_syntax.ipynb

@@ -33,7 +33,7 @@
     "\n",
     "from flasc import FlascDataFrame\n",
     "from flasc.analysis import energy_ratio as erp\n",
-    "from flasc.analysis.energy_ratio_input import EnergyRatioInput"
+    "from flasc.analysis.analysis_input import AnalysisInput"
    ]
   },
   {
@@ -220,7 +220,7 @@
    "source": [
     "# Energy Ratio Input\n",
     "\n",
-    "In the new syntax, the first step in computing an energy is building an EnergyRatioInput object.  This is analagous to the construction of an energy ratio suite object in prior versions.  \n",
+    "In the new syntax, the first step in computing an energy is building an AnalysisInput object.  This is analagous to the construction of an energy ratio suite object in prior versions.  \n",
     "\n",
     "The inputs to provide are the list of dataframes, and the names of those dataframes.  An optional 3rd input is how many blocks to divide the data into, this will be used in block bootstrapping and is done here to save time.  Setting the number of blocks equal to the number of rows in the dataframe with the smallest number of rows approximates non-block boostreapping/"
    ]
@@ -231,7 +231,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "er_in = EnergyRatioInput(\n",
+    "a_in = AnalysisInput(\n",
     "    [df_baseline_noisy_pd, df_wakesteering_noisy_pd], [\"baseline\", \"wakesteering\"], num_blocks=10\n",
     ")"
    ]
@@ -242,7 +242,7 @@
    "source": [
     "# Computing the energy ratio\n",
     "\n",
-    "Computing the energy ratio is now done by passing the above er_in into the compute_energy_ratio function.  The energy ratio expects to be instructed which sets of turbines to average to produce the wind speed, wind direction, reference power and test power.  However, there is also the option to use pre-defined values, in which case the columns 'wd', 'ws' and/or 'pow_ref' need to be already defined in the dataframes.\n",
+    "Computing the energy ratio is now done by passing the above a_in into the compute_energy_ratio function.  The energy ratio expects to be instructed which sets of turbines to average to produce the wind speed, wind direction, reference power and test power.  However, there is also the option to use pre-defined values, in which case the columns 'wd', 'ws' and/or 'pow_ref' need to be already defined in the dataframes.\n",
     "\n",
     "In this example since wd,ws,and pow_ref are defined, we'll use that option here"
    ]
@@ -270,7 +270,7 @@
    "outputs": [],
    "source": [
     "er_out = erp.compute_energy_ratio(\n",
-    "    er_in,\n",
+    "    a_in,\n",
     "    test_turbines=[2],\n",
     "    use_predefined_ref=True,\n",
     "    use_predefined_wd=True,\n",
@@ -374,7 +374,7 @@
    "outputs": [],
    "source": [
     "er_out = erp.compute_energy_ratio(\n",
-    "    er_in,\n",
+    "    a_in\n",
     "    test_turbines=[2],\n",
     "    use_predefined_ref=True,\n",
     "    use_predefined_wd=True,\n",
@@ -434,7 +434,7 @@
    "outputs": [],
    "source": [
     "er_out = erp.compute_energy_ratio(\n",
-    "    er_in,\n",
+    "    a_in\n",
     "    test_turbines=[2],\n",
     "    use_predefined_ref=True,\n",
     "    use_predefined_wd=True,\n",

examples_artificial_data/03_energy_ratio/01_demo_energy_ratio_options.ipynb

@@ -30,7 +30,7 @@
     "\n",
     "from flasc import FlascDataFrame\n",
     "from flasc.analysis import energy_ratio as erp\n",
-    "from flasc.analysis.energy_ratio_input import EnergyRatioInput\n",
+    "from flasc.analysis.analysis_input import AnalysisInput\n",
     "from flasc.data_processing import dataframe_manipulations as dfm"
    ]
   },
@@ -206,7 +206,7 @@
    "outputs": [],
    "source": [
     "# Build the energy ratio input\n",
-    "er_in = EnergyRatioInput([df], [\"baseline\"], num_blocks=10)"
+    "a_in = AnalysisInput([df], [\"baseline\"], num_blocks=10)"
    ]
   },
   {
@@ -242,7 +242,7 @@
     "# Calculate and plot the energy ratio of turbine 2 with respect to\n",
     "# turbine 0, using turbine 0's measurements of wind speed and wind direction\n",
     "er_out = erp.compute_energy_ratio(\n",
-    "    er_in, test_turbines=[2], ref_turbines=[0], ws_turbines=[0], wd_turbines=[0], N=50\n",
+    "    a_in, test_turbines=[2], ref_turbines=[0], ws_turbines=[0], wd_turbines=[0], N=50\n",
     ")\n",
     "er_out.plot_energy_ratios()"
    ]
@@ -280,7 +280,7 @@
     "# Reverse the above calculation showing the energy ratio of T0 / T2,\n",
     "# letting T1 supply wind speed and direction\n",
     "er_out = erp.compute_energy_ratio(\n",
-    "    er_in, test_turbines=[0], ref_turbines=[2], ws_turbines=[1], wd_turbines=[1], N=50\n",
+    "    a_inest_turbines=[0], ref_turbines=[2], ws_turbines=[1], wd_turbines=[1], N=50\n",
     ")\n",
     "er_out.plot_energy_ratios()"
    ]
@@ -314,11 +314,11 @@
    "source": [
     "# Overplot the energy ratios of turbine 2 and 3, with respect to the averages of turbines 0 and 1\n",
     "er_out_2 = erp.compute_energy_ratio(\n",
-    "    er_in, test_turbines=[2], ref_turbines=[0, 1], ws_turbines=[0, 1], wd_turbines=[0, 1], N=50\n",
+    "    a_in, test_turbines=[2], ref_turbines=[0, 1], ws_turbines=[0, 1], wd_turbines=[0, 1], N=50\n",
     ")\n",
     "\n",
     "er_out_3 = erp.compute_energy_ratio(\n",
-    "    er_in, test_turbines=[3], ref_turbines=[0, 1], ws_turbines=[0, 1], wd_turbines=[0, 1], N=50\n",
+    "    a_in, test_turbines=[3], ref_turbines=[0, 1], ws_turbines=[0, 1], wd_turbines=[0, 1], N=50\n",
     ")\n",
     "\n",
     "fig, axarr = plt.subplots(3, 1, sharex=True, figsize=(8, 11))\n",
@@ -536,10 +536,10 @@
    "source": [
     "# Now use the predefined values in the calculation of the average of turbines 2 and 3\n",
     "\n",
-    "er_in = EnergyRatioInput([df], [\"baseline\"], num_blocks=10)\n",
+    "a_in = AnalysisInput([df], [\"baseline\"], num_blocks=10)\n",
     "\n",
     "er_out = erp.compute_energy_ratio(\n",
-    "    er_in,\n",
+    "    a_in\n",
     "    test_turbines=[2, 3],\n",
     "    use_predefined_ref=True,\n",
     "    use_predefined_wd=True,\n",

examples_artificial_data/03_energy_ratio/02_energy_ratio_vs_wd_for_single_df.py

@@ -4,7 +4,7 @@
 import pandas as pd
 
 from flasc.analysis import energy_ratio as er
-from flasc.analysis.energy_ratio_input import EnergyRatioInput
+from flasc.analysis.analysis_input import AnalysisInput
 from flasc.data_processing import dataframe_manipulations as dfm
 from flasc.utilities import floris_tools as fsatools
 from flasc.utilities.utilities_examples import load_floris_artificial as load_floris
@@ -67,11 +67,11 @@ def load_data():
     df = dfm.set_pow_ref_by_turbines(df, turbine_numbers=[0, 6])
 
     # # Initialize energy ratio object for the dataframe
-    er_in = EnergyRatioInput([df], ["baseline"])
+    a_in = AnalysisInput([df], ["baseline"])
 
     # Get energy ratio without uncertainty quantification
     er_out = er.compute_energy_ratio(
-        er_in,
+        a_in,
         test_turbines=[1],
         use_predefined_ref=True,
         use_predefined_wd=True,
@@ -91,7 +91,7 @@ def load_data():
     # Get energy ratio with uncertainty quantification
     # using N=20 bootstrap samples and 5-95 percent conf. bounds.
     er_out = er.compute_energy_ratio(
-        er_in,
+        a_in,
         test_turbines=[1],
         use_predefined_ref=True,
         use_predefined_wd=True,
@@ -107,9 +107,9 @@ def load_data():
 
     # Get energy ratio with uncertainty quantification
     # using N=20 bootstrap samples and without block bootstrapping.
-    er_in_noblocks = EnergyRatioInput([df], ["baseline"], num_blocks=len(df))
+    a_in_noblocks = AnalysisInput([df], ["baseline"], num_blocks=len(df))
     er_out = er.compute_energy_ratio(
-        er_in_noblocks,
+        a_in_noblocks,
         test_turbines=[1],
         use_predefined_ref=True,
         use_predefined_wd=True,

examples_artificial_data/03_energy_ratio/03_energy_ratio_vs_wd_for_multiple_dfs.py

@@ -5,7 +5,7 @@
 from floris.utilities import wrap_360
 
 from flasc.analysis import energy_ratio as er
-from flasc.analysis.energy_ratio_input import EnergyRatioInput
+from flasc.analysis.analysis_input import AnalysisInput
 from flasc.data_processing import dataframe_manipulations as dfm
 from flasc.utilities import floris_tools as fsatools
 from flasc.utilities.utilities_examples import load_floris_artificial as load_floris
@@ -69,13 +69,13 @@ def load_data():
     # Initialize the energy ratio input object and add dataframes
     # separately. We will add the original data and the manipulated
     # dataset.
-    er_in = EnergyRatioInput([df, df2], ["Original data", "Data with wd bias of 7.5 degrees"])
+    a_in = AnalysisInput([df, df2], ["Original data", "Data with wd bias of 7.5 degrees"])
 
     # Calculate the energy ratios for test_turbines = [1] for a subset of
     # wind directions with uncertainty quantification using 50 bootstrap
     # samples
     er_out = er.compute_energy_ratio(
-        er_in,
+        a_in,
         test_turbines=[1],
         use_predefined_ref=True,
         use_predefined_wd=True,
@@ -91,7 +91,7 @@ def load_data():
 
     # Look at another test turbine with the same masked datasets
     er_out = er.compute_energy_ratio(
-        er_in,
+        a_in,
         test_turbines=[3],
         use_predefined_ref=True,
         use_predefined_wd=True,

examples_artificial_data/03_energy_ratio/04_energy_ratio_in_turbine_array.py

@@ -8,7 +8,7 @@
 from floris.utilities import wrap_360
 
 from flasc.analysis import energy_ratio as er
-from flasc.analysis.energy_ratio_input import EnergyRatioInput
+from flasc.analysis.analysis_input import AnalysisInput
 from flasc.data_processing import dataframe_manipulations as dfm
 from flasc.utilities.utilities_examples import load_floris_artificial as load_floris
 
@@ -69,7 +69,7 @@ def _calculate_energy_ratios(df, test_turbines, aligned_wd, N=1):
     df = dfm.filter_df_by_ws(df, [6, 10])
 
     # Finally, construct the energy ratio input with the dataframe
-    er_in = EnergyRatioInput([df], ["baseline"])
+    a_in = AnalysisInput([df], ["baseline"])
 
     # Now, we calculate the energy ratio for each turbine for the one wind
     # direction and wind speed bin. We save those values to
@@ -78,7 +78,7 @@ def _calculate_energy_ratios(df, test_turbines, aligned_wd, N=1):
     for ti in test_turbines:
         # Get energy ratios
         er_out = er.compute_energy_ratio(
-            er_in,
+            a_in,
             test_turbines=[ti],
             use_predefined_ref=True,
             use_predefined_wd=True,

examples_artificial_data/03_energy_ratio/06_wake_steering_example.py

@@ -5,7 +5,7 @@
 
 from flasc import FlascDataFrame
 from flasc.analysis import energy_ratio as er
-from flasc.analysis.energy_ratio_input import EnergyRatioInput
+from flasc.analysis.analysis_input import AnalysisInput
 from flasc.utilities.utilities_examples import load_floris_artificial as load_floris
 from flasc.visualization import plot_binned_mean_and_ci
 
@@ -136,7 +136,7 @@
     color_palette = sns.color_palette("Paired", 4)[::-1]
 
     # Initialize the energy ratio input object
-    er_in = EnergyRatioInput(
+    a_in = AnalysisInput(
         [df_baseline, df_wakesteering, df_baseline_noisy, df_wakesteering_noisy],
         ["Baseline", "WakeSteering", "Baseline (Noisy)", "WakeSteering (Noisy)"],
     )
@@ -145,7 +145,7 @@
     # With respect to reference turbine [0]
     # datasets with uncertainty quantification using 50 bootstrap samples
     er_out = er.compute_energy_ratio(
-        er_in,
+        a_in,
         test_turbines=[2],
         use_predefined_ref=True,
         use_predefined_wd=True,

examples_artificial_data/03_energy_ratio/07_wake_steering_total_uplift.py

@@ -3,8 +3,8 @@
 import numpy as np
 
 from flasc import FlascDataFrame
-from flasc.analysis import total_uplift as tup
-from flasc.analysis.energy_ratio_input import EnergyRatioInput
+from flasc.analysis import total_uplift_power_ratio as tup
+from flasc.analysis.analysis_input import AnalysisInput
 from flasc.utilities.utilities_examples import load_floris_artificial as load_floris
 from flasc.visualization import plot_binned_mean_and_ci
 
@@ -155,12 +155,12 @@
     )
 
     # Calculate the uplift on the non-noisy data
-    er_in = EnergyRatioInput(
+    a_in = AnalysisInput(
         [df_baseline, df_wakesteering], ["baseline", "wake_steering"], num_blocks=1
     )
 
-    total_uplift_result = tup.compute_total_uplift(
-        er_in,
+    total_uplift_result = tup.total_uplift_power_ratio(
+        a_in,
         ref_turbines=[0],
         test_turbines=[2],
         use_predefined_wd=True,
@@ -173,12 +173,12 @@
     uplift_non_noisy = total_uplift_result["uplift"]["energy_uplift_ctr_pc"]
 
     # Calculate the uplift on the noisy data
-    er_in = EnergyRatioInput(
+    a_in = AnalysisInput(
         [df_baseline_noisy, df_wakesteering_noisy], ["baseline", "wake_steering"], num_blocks=1
     )
 
-    total_uplift_result_noisy = tup.compute_total_uplift(
-        er_in,
+    total_uplift_result_noisy = tup.total_uplift_power_ratio(
+        a_in,
         ref_turbines=[0],
         test_turbines=[2],
         use_predefined_wd=True,
@@ -191,21 +191,21 @@
     uplift_noisy = total_uplift_result_noisy["uplift"]["energy_uplift_ctr_pc"]
     print("=======Total Uplift======")
     print(
-        f"The uplift calculated using the compute_total_uplift module "
+        f"The uplift calculated using the total_uplift_power_ratio module "
         f" is {uplift_non_noisy:.3}% in the"
         f" non-noisy data and {uplift_noisy:.3}% in the noisy data"
     )
 
     # Recompute using bootstrapping to understand uncertainty bounds
     # Calculate the uplift on the non-noisy data
-    er_in = EnergyRatioInput(
+    a_in = AnalysisInput(
         [df_baseline, df_wakesteering],
         ["baseline", "wake_steering"],
         num_blocks=df_baseline.shape[0],  # Use N blocks to do non-block boostrapping
     )
 
-    total_uplift_result = tup.compute_total_uplift(
-        er_in,
+    total_uplift_result = tup.total_uplift_power_ratio(
+        a_in,
         ref_turbines=[0],
         test_turbines=[2],
         use_predefined_wd=True,
@@ -220,14 +220,14 @@
     uplift_non_noisy_ub = total_uplift_result["uplift"]["energy_uplift_ub_pc"]
 
     # Calculate the uplift on the noisy data
-    er_in = EnergyRatioInput(
+    a_in = AnalysisInput(
         [df_baseline_noisy, df_wakesteering_noisy],
         ["baseline", "wake_steering"],
         num_blocks=df_baseline.shape[0],  # Use N blocks to do non-block boostrapping
     )
 
-    total_uplift_result_noisy = tup.compute_total_uplift(
-        er_in,
+    total_uplift_result_noisy = tup.total_uplift_power_ratio(
+        a_in,
         ref_turbines=[0],
         test_turbines=[2],
         use_predefined_wd=True,

examples_smarteole/05_baseline_energy_ratio_analysis.ipynb

@@ -31,7 +31,7 @@
     "import pandas as pd\n",
     "\n",
     "from flasc.analysis import energy_ratio as er\n",
-    "from flasc.analysis.energy_ratio_input import EnergyRatioInput\n",
+    "from flasc.analysis.analysis_input import AnalysisInput\n",
     "from flasc.data_processing import dataframe_manipulations as dfm\n",
     "from flasc.utilities import floris_tools as ftools\n",
     "from flasc.utilities.utilities_examples import load_floris_smarteole as load_floris"
@@ -684,7 +684,7 @@
    "outputs": [],
    "source": [
     "# Calculate and plot energy ratios\n",
-    "er_in = EnergyRatioInput(\n",
+    "a_in = AnalysisInput(\n",
     "    df_fm_list + [df_scada], [\"FLORIS: \" + wm for wm in wake_models] + [\"SCADA data\"]\n",
     ")"
    ]
@@ -729,7 +729,7 @@
     "print(\"This may take a couple seconds...\")\n",
     "np.random.seed(0)\n",
     "er_out = er.compute_energy_ratio(\n",
-    "    er_in,\n",
+    "    a_in,\n",
     "    test_turbines=[4],\n",
     "    use_predefined_ref=True,\n",
     "    use_predefined_wd=True,\n",
@@ -817,7 +817,7 @@
     "print(\"This may take a couple seconds...\")\n",
     "np.random.seed(0)\n",
     "er_out = er.compute_energy_ratio(\n",
-    "    er_in,\n",
+    "    a_in,\n",
     "    test_turbines=[4],\n",
     "    use_predefined_ref=True,\n",
     "    use_predefined_wd=True,\n",