Merge pull request #188 from kosack/fix/missing_import_plot_py

fixed missing import of QTable

docs/reference/benchmarks.rst

@@ -26,4 +26,3 @@ API reference
 -------------
 
 .. automodapi:: protopipe.benchmarks
-    :skip: accuracy_score, auc, binned_statistic, curve_fit, roc_curve, Column, LogNorm, Table, Path, cone_solid_angle, RectBivariateSpline, read_table

protopipe/benchmarks/operations.py

@@ -12,13 +12,29 @@
 we should really try to synchronize in some way with ctaplot/ctabenchmarks.
 """
 
+__all__ = [
+    "compute_weight_BTEL1010",
+    "add_BTEL1010_weigths_to_data",
+    "average_bias_of_charge_resolution",
+    "calculate_RMS_around_1",
+    "prepare_requirements",
+    "compute_resolution",
+    "compute_bias",
+    "get_evt_subarray_model_output",
+    "sum_of_squares",
+    "OnlineBinnedStats",
+    "create_lookup_function",
+    "compute_psf",
+    "load_tel_id",
+]
+
 from pathlib import Path
 
-from astropy.table import Table, Column
 import astropy.units as u
-from scipy.stats import binned_statistic
-from scipy.interpolate import RectBivariateSpline
 import numpy as np
+from astropy.table import Column, Table
+from scipy.interpolate import RectBivariateSpline
+from scipy.stats import binned_statistic
 
 try:
     from ctapipe.io import read_table

protopipe/benchmarks/plot.py

@@ -13,18 +13,39 @@
 The implementation of functions and classes is far from perfect and
 we should really try to synchronize in some way with ctaplot/ctabenchmarks.
 """
-import numpy as np
 import astropy.units as u
 import matplotlib.pyplot as plt
+import numpy as np
+from astropy.table import QTable
 from matplotlib.colors import LogNorm
-from scipy.stats import binned_statistic, norm
-from sklearn.metrics import auc, roc_curve, accuracy_score
-from scipy.optimize import curve_fit
 from pyirf.utils import cone_solid_angle
-
+from scipy.optimize import curve_fit
+from scipy.stats import binned_statistic, norm
+from sklearn.metrics import accuracy_score, auc, roc_curve
 
 LOWER_SIGMA_QUANTILE, UPPER_SIGMA_QUANTILE = norm().cdf([-1, 1])
 
+__all__ = [
+    "plot_profile",
+    "plot_DL1a_reco_true_correlation",
+    "plot_resolution",
+    "plot_bias",
+    "get_single_pixels_spectrum",
+    "plot_sensitivity_from_pyirf",
+    "plot_binned_mean",
+    "plot_binned_median",
+    "plot_hist",
+    "plot_distributions",
+    "plot_roc_curve",
+    "plot_evt_roc_curve_variation",
+    "plot_psf",
+    "plot_background_rate",
+    "BoostedDecisionTreeDiagnostic",
+    "ModelDiagnostic",
+    "RegressorDiagnostic",
+    "ClassifierDiagnostic",
+]
+
 
 def plot_profile(ax, data, xcol, ycol, n_xbin, x_range, logx=False, **kwargs):
     """Plot a profiled histogram.

protopipe/benchmarks/utils.py

@@ -1,5 +1,7 @@
 import numpy as np
 
+__all__ = ["raise_", "string_to_boolean", "add_stats", "get_fig_size"]
+
 
 def raise_(ex):
     """Raise an exception as a statement.