# HG changeset patch
# User luca_milaz
# Date 1722781380 0
# Node ID 3e16bced806254930988add6264673231a76b8f6
# Parent  2af2d2641e3e4f5dd62564fd0eb188cbf2e6a6e5
Uploaded

diff -r 2af2d2641e3e -r 3e16bced8062 marea_2/flux_to_map.py
--- a/marea_2/flux_to_map.py	Sun Aug 04 14:10:44 2024 +0000
+++ b/marea_2/flux_to_map.py	Sun Aug 04 14:23:00 2024 +0000
@@ -5,7 +5,6 @@
 import sys
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
 import itertools as it
 import scipy.stats as st
 import lxml.etree as ET
@@ -818,6 +817,14 @@
     """Convert an RGBA color to HEX format."""
     return '#{:02x}{:02x}{:02x}'.format(int(rgba[0] * 255), int(rgba[1] * 255), int(rgba[2] * 255))
 
+def reds_cmap(value):
+    """Map normalized value to RGB color using the Reds colormap."""
+    # The `Reds` colormap starts with white and transitions to red
+    r = value
+    g = 0
+    b = 0
+    return (r, g, b)
+
 def computeEnrichmentMedoids(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str]) -> None:
 
     metabMap_mean = metabMap.copy()
@@ -845,13 +852,10 @@
     for key, value in means.items():
         means[key] = means[key] / max_flux_means
 
-    cmap = plt.cm.Reds
-    norm = plt.Normalize(vmin=0, vmax=1)
-
-    colors_median_rgb = {k: cmap(norm(v)) for k, v in medians.items()}
+    colors_median_rgb = {k: reds_cmap(v) for k, v in medians.items()}
     colors_median = {k: rgba_to_hex(c) for k, c in colors_median_rgb.items()}
 
-    colors_mean_rgb = {k: cmap(norm(v)) for k, v in means.items()}
+    colors_mean_rgb = {k: reds_cmap(v) for k, v in means.items()}
     colors_mean = {k: rgba_to_hex(c) for k, c in colors_mean_rgb.items()}
 
     for rxn_id in ids: