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Merged
VChristiaens merged 1 commit into from
Aug 18, 2025
Merged

DEP/RFC: refactor out dependency on munch #682

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1 change: 0 additions & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -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",
Expand Down
100 changes: 48 additions & 52 deletions src/vip_hci/metrics/roc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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):
"""
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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!
Expand Down Expand Up @@ -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 = []

Expand All @@ -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

Expand All @@ -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)

Expand All @@ -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)

Expand Down Expand Up @@ -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]))
Expand All @@ -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:
Expand Down Expand Up @@ -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?
Expand Down