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"deeplabcut.filterpredictions" and "deeplabcut.analyze_skeleton" return mappings #1908

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Merged
jeylau merged 7 commits into from
Oct 13, 2023
Merged

"deeplabcut.filterpredictions" and "deeplabcut.analyze_skeleton" return mappings #1908

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eff4142
REF: Denest filterpredictions for loop
Jul 16, 2022
59ac8ed
REF: Reduce the code within the try block
Jul 16, 2022
8a0efd9
FEAT: Return video filename to filtered dataframe mapping
Jul 16, 2022
b598c33
REF: Reduce scope of try/except
Jul 16, 2022
61ac8f5
FIX: Actually return the mapping
Jul 16, 2022
b6b408d
FEAT: Return mapping from video filename to skeleton dataframe
Jul 16, 2022
6e7e47e
Add return_data flag
jeylau Oct 3, 2023
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FEAT: Return video filename to filtered dataframe mapping 
the filterpredictions now returns a mapping instead of returning None.

	modified:   deeplabcut/post_processing/filtering.py
  • Loading branch information
Poruri Sai Rahul committed Jul 16, 2022
commit 8a0efd9122e073d7ff9973a1e9af568a78d7a945
17 changes: 14 additions & 3 deletions deeplabcut/post_processing/filtering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,12 @@ def filterpredictions(

Returns
-------
None
video_to_filtered_df
Dictionary mapping video filepaths to filtered dataframes.

* If no videos exist, the dictionary will be empty.
* If a video is not analyzed, the corresponding value in the dictionary will be
None.

Examples
--------
Expand Down Expand Up @@ -200,9 +205,11 @@ def filterpredictions(
)
Videos = auxiliaryfunctions.get_list_of_videos(video, videotype)

video_to_filtered_df = {}

if not len(Videos):
print("No video(s) were found. Please check your paths and/or 'videotype'.")
return
return video_to_filtered_df

for video in Videos:
if destfolder is None:
Expand All @@ -212,10 +219,11 @@ def filterpredictions(
vname = Path(video).stem

try:
_ = auxiliaryfunctions.load_analyzed_data(
df, filepath, _, _ = auxiliaryfunctions.load_analyzed_data(
destfolder, vname, DLCscorer, True, track_method
)
print(f"Data from {vname} were already filtered. Skipping...")
video_to_filtered_df[video] = df
# Data has been filtered so continue to the next video
continue
except FileNotFoundError:
Expand All @@ -227,6 +235,7 @@ def filterpredictions(
destfolder, vname, DLCscorer, track_method=track_method
)
except FileNotFoundError as e:
video_to_filtered_df[video] = None
print(e)
continue

Expand Down Expand Up @@ -276,6 +285,8 @@ def filterpredictions(
else:
raise ValueError(f"Unknown filter type {filtertype}")

video_to_filtered_df[video] = data

outdataname = filepath.replace(".h5", "_filtered.h5")
data.to_hdf(outdataname, "df_with_missing", format="table", mode="w")
if save_as_csv:
Expand Down