Merge pull request #682 from neutrinoceros/dep/ditch-munch

DEP/RFC: refactor out dependency on `munch`

Author: VChristiaens

pyproject.toml

@@ -32,7 +32,6 @@ dependencies = [
     "emcee>=3.1.0",
     "hciplot>=0.2.4",
     "matplotlib>=3.7.0",
-    "munch>=3.0.0",
     "nestle>=0.2.0",
     "numpy>=1.21.2",
     "pandas>=1.3.3",

src/vip_hci/metrics/roc.py

@@ -10,14 +10,11 @@
 from hciplot import plot_frames
 from scipy import stats
 from photutils.segmentation import detect_sources
-from munch import Munch
 from ..config import time_ini, timing, Progressbar
 from ..fm import cube_inject_companions
 from ..psfsub.svd import SVDecomposer
 from ..var import frame_center, get_annulus_segments, get_circle
 
-# TODO: remove the munch dependency
-
 
 class EvalRoc(object):
     """
@@ -68,7 +65,7 @@ def add_algo(self, name, algo, color, symbol, thresholds):
         thresholds : list of lists
 
         """
-        self.methods.append(Munch(algo=algo, name=name, color=color,
+        self.methods.append(dict(algo=algo, name=name, color=color,
                                   symbol=symbol, thresholds=thresholds))
 
     def inject_and_postprocess(self, patch_size, cevr=0.9,
@@ -97,11 +94,11 @@ def inject_and_postprocess(self, patch_size, cevr=0.9,
             print("{}% of CEVR with {} PCs".format(cevr, self.optpcs))
 
             # for m in methods:
-            #     if hasattr(m, "ncomp") and m.ncomp is None:  # PCA
-            #         m.ncomp = self.optpcs
+            #     if m.get("ncomp", object()) is None:  # PCA
+            #         m["ncomp"] = self.optpcs
             #
-            #     if hasattr(m, "rank") and m.rank is None:  # LLSG
-            #         m.rank = self.optpcs
+            #     if m.get("rank", object()) is None:  # LLSG
+            #         m["rank"] = self.optpcs
 
             #
             #   ------> this should be moved inside the HCIPostProcAlgo classes!
@@ -135,8 +132,8 @@ def inject_and_postprocess(self, patch_size, cevr=0.9,
             self.thetas.append(theta)
 
         for m in self.methods:
-            m.frames = []
-            m.probmaps = []
+            m["frames"] = []
+            m["probmaps"] = []
 
         self.list_xy = []
 
@@ -157,7 +154,7 @@ def inject_and_postprocess(self, patch_size, cevr=0.9,
                 # TODO: this is not elegant at all.
                 # shallow copy. Should not copy e.g. the cube in memory,
                 # just reference it.
-                algo = copy.copy(m.algo)
+                algo = copy.copy(m["algo"])
                 _dataset = copy.copy(self.dataset)
                 _dataset.cube = cufc
 
@@ -169,8 +166,8 @@ def inject_and_postprocess(self, patch_size, cevr=0.9,
                 algo.run(dataset=_dataset, verbose=False)
                 algo.make_snrmap(approximated=True, nproc=nproc, verbose=False)
 
-                m.frames.append(algo.frame_final)
-                m.probmaps.append(algo.snr_map)
+                m["frames"].append(algo.frame_final)
+                m["probmaps"].append(algo.snr_map)
 
         timing(starttime)
 
@@ -192,22 +189,22 @@ def compute_tpr_fps(self, **kwargs):
         starttime = time_ini()
 
         for m in self.methods:
-            m.detections = []
-            m.fps = []
-            m.bmaps = []
+            m["detections"] = []
+            m["fps"] = []
+            m["bmaps"] = []
 
         print('Evaluating injections:')
         for i in Progressbar(range(self.n_injections)):
             x, y = self.list_xy[i]
 
             for m in self.methods:
                 dets, fps, bmaps = compute_binary_map(
-                    m.probmaps[i], m.thresholds, fwhm=self.dataset.fwhm,
+                    m["probmaps"][i], m["thresholds"], fwhm=self.dataset.fwhm,
                     injections=(x, y), **kwargs
                 )
-                m.detections.append(dets)
-                m.fps.append(fps)
-                m.bmaps.append(bmaps)
+                m["detections"].append(dets)
+                m["fps"].append(fps)
+                m["bmaps"].append(bmaps)
 
         timing(starttime)
 
@@ -245,9 +242,9 @@ def plot_detmaps(self, i=None, thr=9, dpi=100,
 
         if vmax == 'max':
             # TODO: document this feature.
-            vmax = np.concatenate([m.frames[i] for m in self.methods if
-                                   hasattr(m, "frames") and
-                                   len(m.frames) >= i]).max()/2
+            vmax = np.concatenate([m["frames"][i] for m in self.methods if
+                                   "frames" in m and
+                                   len(m["frames"]) >= i]).max()/2
 
         # print information
         print('X,Y: {}'.format(self.list_xy[i]))
@@ -258,33 +255,32 @@ def plot_detmaps(self, i=None, thr=9, dpi=100,
         if plot_type in [1, "horiz"]:
             for m in self.methods:
                 print('detection state: {} | false postives: {}'.format(
-                    m.detections[i][thr], m.fps[i][thr]))
-                labels = ('{} frame'.format(m.name), '{} S/Nmap'.format(m.name),
-                          'Thresholded at {:.1f}'.format(m.thresholds[thr]))
-                plot_frames((m.frames[i] if len(m.frames) >= i else
-                            np.zeros((2, 2)), m.probmaps[i], m.bmaps[i][thr]),
+                    m["detections"][i][thr], m["fps"][i][thr]))
+                labels = (f"{m['name']} frame", f"{m['name']} S/Nmap",
+                          f"Thresholded at {m['thresholds'][thr]:.1f}")
+                plot_frames((m["frames"][i] if len(m["frames"]) >= i else
+                            np.zeros((2, 2)), m["probmaps"][i], m["bmaps"][i][thr]),
                             label=labels, dpi=dpi, horsp=0.2, axis=axis,
                             grid=grid, cmap=['viridis', 'viridis', 'gray'])
 
         elif plot_type in [2, "vert"]:
-            labels = tuple('{} frame'.format(m.name) for m in self.methods if
-                           hasattr(m, "frames") and len(m.frames) >= i)
-            plot_frames(tuple(m.frames[i] for m in self.methods if
-                        hasattr(m, "frames") and len(m.frames) >= i),
+            labels = tuple(f"{m['name']} frame" for m in self.methods if
+                           "frames" in m and len(m["frames"]) >= i)
+            plot_frames(tuple(m["frames"][i] for m in self.methods if
+                        "frames" in m and len(m["frames"]) >= i),
                         dpi=dpi, label=labels, vmax=vmax, vmin=vmin, axis=axis,
                         grid=grid)
 
-            plot_frames(tuple(m.probmaps[i] for m in self.methods), dpi=dpi,
-                        label=tuple(['{} S/Nmap'.format(m.name) for m in
+            plot_frames(tuple(m["probmaps"][i] for m in self.methods), dpi=dpi,
+                        label=tuple([f"{m['name']} S/Nmap" for m in
                                      self.methods]), axis=axis, grid=grid)
 
             for m in self.methods:
-                msg = '{} detection: {}, FPs: {}'
-                print(msg.format(m.name, m.detections[i][thr], m.fps[i][thr]))
+                print(f"{m['name']} detection: {m['detections'][i][thr]}, FPs: {m['fps'][i][thr]}")
 
-            labels = tuple('Thresholded at {:.1f}'.format(m.thresholds[thr])
+            labels = tuple(f"Thresholded at {m['thresholds'][thr]:.1f}"
                            for m in self.methods)
-            plot_frames(tuple(m.bmaps[i][thr] for m in self.methods),
+            plot_frames(tuple(m["bmaps"][i][thr] for m in self.methods),
                         dpi=dpi, label=labels, axis=axis, grid=grid,
                         colorbar=False, cmap='bone')
         else:
@@ -342,40 +338,40 @@ def plot_roc_curves(self, dpi=100, figsize=(5, 5), xmin=None, xmax=None,
         #              "SODIRF": dict(color="#9467bd", symbol="s"),
         #              "SODINN": dict(color="#1f77b4", symbol="p"),
         #              "SODINN-pw": dict(color="#1f77b4", symbol="p")
-        #             }  # maps m.name to plot style
+        #             }  # maps m["name"] to plot style
 
         for i, m in enumerate(self.methods):
 
             if not hasattr(m, "detections") or not hasattr(m, "fps"):
                 raise AttributeError("method #{} has no detections/fps. Run"
                                      "`compute_tpr_fps` first.".format(i))
 
-            m.tpr = np.zeros((n_thresholds))
-            m.mean_fps = np.zeros((n_thresholds))
+            m["tpr"] = np.zeros(n_thresholds)
+            m["mean_fps"] = np.zeros(n_thresholds)
 
             for j in range(n_thresholds):
-                m.tpr[j] = np.asarray(m.detections)[:, j].tolist().count(1) / \
+                m["tpr"][j] = np.asarray(m["detections"])[:, j].tolist().count(1) / \
                            self.n_injections
-                m.mean_fps[j] = np.asarray(m.fps)[:, j].mean()
+                m["mean_fps"][j] = np.asarray(m["fps"])[:, j].mean()
 
-            plt.plot(m.mean_fps, m.tpr, '--', color=m.color, **linekw)
-            plt.plot(m.mean_fps, m.tpr, m.symbol, label=m.name, color=m.color,
+            plt.plot(m["mean_fps"], m["tpr"], '--', color=m["color"], **linekw)
+            plt.plot(m["mean_fps"], m["tpr"], m["symbol"], label=m["name"], color=m["color"],
                      **markerkw)
 
             if show_data_labels:
                 if label_skip_one[i]:
-                    lab_x = m.mean_fps[1::2]
-                    lab_y = m.tpr[1::2]
-                    thr = m.thresholds[1::2]
+                    lab_x = m["mean_fps"][1::2]
+                    lab_y = m["tpr"][1::2]
+                    thr = m["thresholds"][1::2]
                 else:
-                    lab_x = m.mean_fps
-                    lab_y = m.tpr
-                    thr = m.thresholds
+                    lab_x = m["mean_fps"]
+                    lab_y = m["tpr"]
+                    thr = m["thresholds"]
 
                 for i, xy in enumerate(zip(lab_x + label_gap[0],
                                            lab_y + label_gap[1])):
                     labels.append(ax.annotate('{:.2f}'.format(thr[i]),
-                                  xy=xy, xycoords='data', color=m.color,
+                                  xy=xy, xycoords='data', color=m["color"],
                                               **labelskw))
                     # TODO: reverse order of `self.methods` for better annot.
                     # z-index?