Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add find_outliers percentage of total #81

Merged
merged 1 commit into from
Sep 3, 2024
Merged

Add find_outliers percentage of total #81

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
218 changes: 23 additions & 195 deletions docs/src/examples/cosmicqc_in_a_nutshell.ipynb
View file
Open in desktop

Large diffs are not rendered by default.

4 changes: 2 additions & 2 deletions docs/src/examples/cosmicqc_in_a_nutshell.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,8 @@
# set a path for the parquet-based dataset
# (in this case, CellProfiler SQLite data processed by CytoTable)
data_path = (
"../../tests/data/cytotable/NF1_cellpainting_data/Plate_2_with_image_data.parquet"
"../../../tests/data/cytotable/NF1_cellpainting_data/"
"Plate_2_with_image_data.parquet"
)

# set a context directory for images associated with the dataset
Expand Down Expand Up @@ -76,7 +77,6 @@
# show histogram reports on the outliers and inliers
# for each threshold set in the new columns
labeled_scdf.show_report()

# show cropped images through CytoDataFrame from the dataset to help analyze outliers
labeled_scdf.sort_values(by="cqc.large_nuclei.is_outlier", ascending=False)[
[
Expand Down
6 changes: 5 additions & 1 deletion src/cosmicqc/analyze.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -196,7 +196,11 @@ def find_outliers(
]

# Print outliers count and range for each feature
print("Number of outliers:", outliers_df.shape[0])
print(
"Number of outliers:",
outliers_df.shape[0],
f"({'{:.2f}'.format((outliers_df.shape[0] / df.shape[0])*100)}%)",
)
print("Outliers Range:")
for feature in feature_thresholds:
print(f"{feature} Min:", outliers_df[feature].min())
Expand Down
2 changes: 1 addition & 1 deletion tests/test_cli.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -84,7 +84,7 @@ def test_cli_find_outliers(tmp_path: pathlib.Path, basic_outlier_csv: str):
assert returncode == 0
assert (
stdout.strip()
== """Number of outliers: 2
== """Number of outliers: 2 (20.00%)
Outliers Range:
example_feature Min: 9
example_feature Max: 10
Expand Down
Loading