Adding Plotly Configurations

protzilla/constants/colors.py

@@ -1,8 +1,14 @@
-PROTZILLA_DISCRETE_COLOR_SEQUENCE = [
+PLOT_COLOR_SEQUENCE = [
     "#4A536A",
-    "#87A8B9",
     "#CE5A5A",
+    "#87A8B9",
     "#8E3325",
     "#E2A46D",
 ]
-PROTZILLA_DISCRETE_COLOR_OUTLIER_SEQUENCE = ["#4A536A", "#CE5A5A"]
+"""List of colors to use in plots."""
+
+PLOT_PRIMARY_COLOR = PLOT_COLOR_SEQUENCE[0]
+"""First color in list. Conventionally used for visualizing outliers."""
+
+PLOT_SECONDARY_COLOR = PLOT_COLOR_SEQUENCE[1]
+"""Second color in list."""

protzilla/constants/paths.py

@@ -1,8 +1,9 @@
 from pathlib import Path
 
 PROJECT_PATH = Path(__file__).resolve().parent.parent.parent
-RUNS_PATH = Path(PROJECT_PATH, "user_data/runs")
-WORKFLOWS_PATH = Path(PROJECT_PATH, "user_data/workflows")
+USER_DATA_PATH = Path(PROJECT_PATH, "user_data")
+RUNS_PATH = USER_DATA_PATH / "runs"
+WORKFLOWS_PATH = USER_DATA_PATH / "workflows"
 EXTERNAL_DATA_PATH = Path(PROJECT_PATH, "user_data/external_data")
 WORKFLOW_META_PATH = Path(PROJECT_PATH, "protzilla/constants/workflow_meta.json")
 UI_PATH = Path(PROJECT_PATH, "ui")

protzilla/data_analysis/model_evaluation_plots.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plot
 from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay
 
-from protzilla.constants.colors import PROTZILLA_DISCRETE_COLOR_SEQUENCE
+from protzilla.constants.colors import PLOT_PRIMARY_COLOR
 from protzilla.data_analysis.classification_helper import encode_labels
 from protzilla.utilities.utilities import fig_to_base64
 
@@ -28,7 +28,7 @@ def precision_recall_curve_plot(model, input_test_df, labels_test_df, plot_title
     display = PrecisionRecallDisplay.from_estimator(
         model, input_test_df, labels_test_df["Encoded Label"]
     )
-    display.plot(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[0])
+    display.plot(color=PLOT_PRIMARY_COLOR)
     plot.title(plot_title)
     return [fig_to_base64(display.figure_)]
 
@@ -55,6 +55,6 @@ def roc_curve_plot(model, input_test_df, labels_test_df, plot_title=None):
     display = RocCurveDisplay.from_estimator(
         model, input_test_df, labels_test_df["Encoded Label"]
     )
-    display.plot(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[0])
+    display.plot(color=PLOT_PRIMARY_COLOR)
     plot.title(plot_title)
     return [fig_to_base64(display.figure_)]

protzilla/data_analysis/plots.py

@@ -8,7 +8,7 @@
 from scipy import stats
 from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances
 
-from protzilla.constants.colors import PROTZILLA_DISCRETE_COLOR_SEQUENCE
+from protzilla.constants.colors import PLOT_COLOR_SEQUENCE, PLOT_PRIMARY_COLOR, PLOT_SECONDARY_COLOR
 from protzilla.utilities.clustergram import Clustergram
 from protzilla.utilities.transform_dfs import is_long_format, long_to_wide
 
@@ -171,11 +171,11 @@ def create_volcano_plot(
         )
     )
     fig.update_traces(
-        marker=dict(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[2]),
+        marker=dict(color=PLOT_SECONDARY_COLOR),
         selector=dict(name=f"Significant {item_type}s"),
     )
     fig.update_traces(
-        marker=dict(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[0]),
+        marker=dict(color=PLOT_PRIMARY_COLOR),
         selector=dict(name=f"Not Significant {item_type}s"),
     )
 
@@ -326,8 +326,8 @@ def prot_quant_plot(
     fig = go.Figure()
 
     color_mapping = {
-        "A": PROTZILLA_DISCRETE_COLOR_SEQUENCE[0],
-        "C": PROTZILLA_DISCRETE_COLOR_SEQUENCE[1],
+        "A": PLOT_PRIMARY_COLOR,
+        "C": PLOT_COLOR_SEQUENCE[2],
     }
 
     lower_upper_x = []
@@ -381,7 +381,7 @@ def prot_quant_plot(
                 y=wide_df[group],
                 mode="lines",
                 name=group[:15] + "..." if len(group) > 15 else group,
-                line=dict(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[1]),
+                line=dict(color=PLOT_COLOR_SEQUENCE[2]),
                 showlegend=len(similar_groups) <= 7,
             )
         )
@@ -392,7 +392,7 @@ def prot_quant_plot(
                 x=[None],
                 y=[None],
                 mode="lines",
-                line=dict(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[1]),
+                line=dict(color=PLOT_COLOR_SEQUENCE[2]),
                 name="Similar Protein Groups",
             )
         )
@@ -406,7 +406,7 @@ def prot_quant_plot(
             y=wide_df[protein_group],
             mode="lines",
             name=formatted_protein_name,
-            line=dict(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[2]),
+            line=dict(color=PLOT_SECONDARY_COLOR),
         )
     )
 

protzilla/data_integration/di_plots.py

@@ -7,7 +7,7 @@
 from protzilla.constants.protzilla_logging import logger
 from protzilla.utilities.utilities import fig_to_base64
 
-from ..constants.colors import PROTZILLA_DISCRETE_COLOR_SEQUENCE
+from protzilla.constants.colors import PLOT_COLOR_SEQUENCE
 
 
 def GO_enrichment_bar_plot(
@@ -17,7 +17,7 @@ def GO_enrichment_bar_plot(
     value,
     gene_sets=[],
     title="",
-    colors=PROTZILLA_DISCRETE_COLOR_SEQUENCE,
+    colors=PLOT_COLOR_SEQUENCE,
     figsize=None,
 ):
     """
@@ -39,7 +39,7 @@ def GO_enrichment_bar_plot(
     :type value: str
     :param title: Title of the plot, defaults to ""
     :type title: str, optional
-    :param colors: Colors to use for the bars, defaults to PROTZILLA_DISCRETE_COLOR_SEQUENCE
+    :param colors: Colors to use for the bars, defaults to PROTZILLA_COLOR_SEQUENCE
     :type colors: list, optional
     :param figsize: Size of the plot, defaults to None and is calculated dynamically if not provided.
     :type figsize: tuple, optional
@@ -110,7 +110,7 @@ def GO_enrichment_bar_plot(
 
 
     if colors == "" or colors is None or len(colors) == 0:
-        colors = PROTZILLA_DISCRETE_COLOR_SEQUENCE
+        colors = PLOT_COLOR_SEQUENCE
     size_y = top_terms * 0.5 * len(gene_sets)
     try:
         ax = gseapy.barplot(