linting + docs (#102)

* initial contrib docs

* isort

* flake8

* isort

* fixes

docs/README.md

@@ -31,3 +31,5 @@ where to store the model code,
 how to make it visible to users, 
 how to provide hyperparameters for model construction, 
 and how to connect it to data loaders and losses
+
+* [General contributing guidelines](contributing.md): pull requests, testing, linting, etc.

docs/contributing.md

@@ -0,0 +1,43 @@
+# Contributing
+
+We welcome community contributions to the Lightning Pose repo! 
+If you have found a bug or would like to request a minor change, please 
+[open an issue](https://github.com/danbider/lightning-pose/issues).
+
+In order to contribute code to the repo, please follow the steps below.
+
+You will also need to install the following dev packages:
+```bash
+pip install flake8 isort
+```
+
+### Create a pull request
+Please fork the Lightning Pose repo, make your changes, and then 
+[open a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork) 
+from your fork. Please read through the rest of this document before submitting the request.
+
+### Linting
+Linters automatically find (and sometimes fix) formatting issues in the code. We use two, which
+are run from the command line in the Lightning Pose repo:
+
+* `flake8`: warns of syntax errors, possible bugs, stylistic errors, etc. Please fix these!
+```bash
+flake8 .
+```
+
+* `isort`: automatically sorts import statements
+```bash
+isort .
+```
+
+### Testing
+We currently do not have a continuous integration (CI) setup for the Lightning Pose repo due to its
+reliance on GPUs (and the relative expense of CI services that provide GPU machines for testing).
+Therefore, it is imperative that you run the unit tests yourself and verify that all tests have
+passed before submitting your request (and upon each new push to that request).
+
+To run the tests locally, you must have access to a GPU. Navigate to the Lightning Pose directory
+and simply run
+```bash
+pytest
+```

lightning_pose/apps/labeled_frame_diagnostics.py

@@ -2,26 +2,30 @@
 
 Refer to apps.md for information on how to use this file.
 
-streamlit run labeled_frame_diagnostics.py -- --model_dir "/home/zeus/content/Pose-app/data/demo/models"
+streamlit run labeled_frame_diagnostics.py -- --model_dir ".../Pose-app/data/demo/models"
 
 """
 
 import argparse
 import copy
-import numpy as np
-import streamlit as st
-import seaborn as sns
-import pandas as pd
+import os
+from collections import defaultdict
 from pathlib import Path
+
 import numpy as np
-from collections import defaultdict
-import os
+import pandas as pd
+import seaborn as sns
+import streamlit as st
 
-from lightning_pose.apps.utils import build_precomputed_metrics_df, get_df_box, get_df_scatter
-from lightning_pose.apps.utils import update_labeled_file_list
-from lightning_pose.apps.utils import get_model_folders, get_model_folders_vis 
-from lightning_pose.apps.plots import make_seaborn_catplot, make_plotly_scatterplot, get_y_label
-from lightning_pose.apps.plots import make_plotly_catplot
+from lightning_pose.apps.plots import get_y_label, make_plotly_catplot, make_plotly_scatterplot
+from lightning_pose.apps.utils import (
+    build_precomputed_metrics_df,
+    get_df_box,
+    get_df_scatter,
+    get_model_folders,
+    get_model_folders_vis,
+    update_labeled_file_list,
+)
 
 # catplot_options = ["boxen", "box", "bar", "violin", "strip"]  # for seaborn
 catplot_options = ["box", "violin", "strip"]  # for plotly
@@ -31,7 +35,6 @@
 
 
 def run():
-
     args = parser.parse_args()
 
     st.title("Labeled Frame Diagnostics")
@@ -48,15 +51,15 @@ def run():
     # add a multiselect that shows existing model folders, and allows the user to de-select models
     # assume we have args.model_dir and we search two levels down for model folders
     model_folders = get_model_folders(args.model_dir)
-    
+
     # get the last two levels of each path to be presented to user
     model_folders_vis = get_model_folders_vis(model_folders)
 
     selected_models_vis = st.sidebar.multiselect("Select models", model_folders_vis, default=None)
 
     # append this to full path
     selected_models = ["/" + os.path.join(args.model_dir, f) for f in selected_models_vis]
-    
+
     # search for prediction files in the selected model folders
     prediction_files = update_labeled_file_list(selected_models, use_ood=args.use_ood)
 
@@ -65,7 +68,6 @@ def run():
     model_names = copy.copy(selected_models_vis)
 
     if len(prediction_files) > 0:  # otherwise don't try to proceed
-
         # ---------------------------------------------------
         # load data
         # ---------------------------------------------------
@@ -107,9 +109,11 @@ def run():
 
         # concat dataframes, collapsing hierarchy and making df fatter.
         keypoint_names = list(
-            [c[0] for c in dframes_metrics[new_names[0]]["confidence"].columns[1::3]])
+            [c[0] for c in dframes_metrics[new_names[0]]["confidence"].columns[1::3]]
+        )
         df_metrics = build_precomputed_metrics_df(
-            dframes=dframes_metrics, keypoint_names=keypoint_names)
+            dframes=dframes_metrics, keypoint_names=keypoint_names
+        )
         metric_options = list(df_metrics.keys())
 
         # ---------------------------------------------------'
@@ -122,7 +126,8 @@ def run():
         with col0:
             # choose from individual keypoints, their mean, or all at once
             keypoint_to_plot = st.selectbox(
-                "Keypoint:", ["mean", "ALL", *keypoint_names], key="keypoint")
+                "Keypoint:", ["mean", "ALL", *keypoint_names], key="keypoint"
+            )
 
         with col1:
             # choose which metric to plot
@@ -142,7 +147,6 @@ def run():
         # ---------------------------------------------------
 
         with sup_col00:
-
             st.header("Compare multiple models")
 
             # enumerate plotting options
@@ -158,7 +162,9 @@ def run():
                 plot_scale = st.radio("Y-axis scale", scale_options, horizontal=True)
 
             # filter data
-            df_metrics_filt = df_metrics[metric_to_plot][df_metrics[metric_to_plot].set == data_type]
+            df_metrics_filt = df_metrics[metric_to_plot][
+                df_metrics[metric_to_plot].set == data_type
+            ]
             n_frames_per_dtype = df_metrics_filt.shape[0] // len(selected_models)
 
             # plot data
@@ -180,15 +186,19 @@ def run():
                 else:
                     top = 0.9
                 fig_box.fig.subplots_adjust(top=top)
-                fig_box.fig.suptitle("All keypoints (%i %s frames)" % (n_frames_per_dtype, data_type))
+                fig_box.fig.suptitle(
+                    f"All keypoints ({n_frames_per_dtype} {data_type} frames)"
+                )
                 st.pyplot(fig_box)
 
             else:
-
                 st.markdown("###")
                 fig_box = make_plotly_catplot(
-                    x="model_name", y=keypoint_to_plot, 
-                    data=df_metrics_filt[df_metrics_filt[keypoint_to_plot] > int(plot_epsilon)], 
+                    x="model_name",
+                    y=keypoint_to_plot,
+                    data=df_metrics_filt[
+                        df_metrics_filt[keypoint_to_plot] > int(plot_epsilon)
+                    ],
                     x_label="Model name",
                     y_label=y_label,
                     title=title,
@@ -208,7 +218,6 @@ def run():
         # scatterplots
         # ---------------------------------------------------
         with sup_col01:
-
             st.header("Compare two models")
 
             col6, col7, col8 = st.columns(3)