changeset 313:376afe557f90 draft

Uploaded

--- a/marea_2/flux_to_map.py	Mon Aug 05 12:17:15 2024 +0000
+++ b/marea_2/flux_to_map.py	Mon Aug 05 12:23:58 2024 +0000
@@ -831,31 +831,6 @@
     dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list")
     return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs
 
-def jet_colormap(value):
-    """
-    Generate an RGB color based on a single numeric value using a colormap similar to MATLAB's 'jet'.
-
-    Args:
-        value (float): A numeric value to be mapped to a color.
-
-    Returns:
-        numpy.ndarray: An array of RGB color components (in the range [0, 1]).
-    """
-    value = abs(value)
-
-    if(np.round(value, 6) == 0.0):
-        return np.array([0.745, 0.745, 0.745])  #grey
-    
-    # Define the red, green, and blue color components
-    red = np.clip(1.5 - np.abs(4 * (value - 0.75)), 0, 1)
-    green = np.clip(1.5 - np.abs(4 * (value - 0.5)), 0, 1)
-    blue = np.clip(1.5 - np.abs(4 * (value - 0.25)), 0, 1)
-    
-    # Combine the color components into an RGB array
-    rgb = np.array([red, green, blue])
-    
-    return rgb
-
 def rgb_to_hex(rgb):
     """
     Convert RGB values (0-1 range) to hexadecimal color format.
@@ -870,9 +845,7 @@
     rgb = (rgb * 255).astype(int)
     return '#{:02x}{:02x}{:02x}'.format(rgb[0], rgb[1], rgb[2])
 
-
-
-def save_colormap_image(min_value:float, max_value:float, path:str):
+def save_colormap_image(min_value: float, max_value: float, path: str):
     """
     Create and save an image of the colormap showing the gradient and its range.
 
@@ -881,28 +854,27 @@
         max_value (float): The maximum value of the colormap range.
         filename (str): The filename for saving the image.
     """
-    # Define image size and create a new image
-    width = 256
-    height = 50
-    image = Image.new('RGB', (width, height), color='white')
-    draw = ImageDraw.Draw(image)
-    
-    # Create the gradient
-    for x in range(width):
-        value = (x / (width - 1)) * (max_value - min_value) + min_value
-        color_arr = jet_colormap((value - min_value) / (max_value - min_value))
-        color = tuple(int(c * 255) for c in color_arr)
-        draw.line([(x, 0), (x, height)], fill=color)
-    
-    # Add text for min and max values
-    font = ImageFont.load_default()
-    draw.text((10, height - 20), f'{min_value:.2f}', fill='white', font=font)
-    draw.text((width - 50, height - 20), f'{max_value:.2f}', fill='white', font=font)
-    
+    # Generate a range of values
+    values = np.linspace(min_value, max_value, 256)
+
+    # Create a colormap using seaborn
+    cmap = sns.color_palette("jet", as_cmap=True)
+
+    # Create a figure and axis
+    fig, ax = plt.subplots(figsize=(6, 1))
+    fig.subplots_adjust(bottom=0.5)
+
+    # Create a gradient image
+    gradient = np.linspace(0, 1, 256)
+    gradient = np.vstack((gradient, gradient))
+
+    # Display the gradient image
+    ax.imshow(gradient, aspect='auto', cmap=cmap)
+    ax.set_axis_off()
+
     # Save the image
-    image.save(path.show())
-    pass
-    
+    plt.savefig(path, bbox_inches='tight', pad_inches=0)
+    plt.close()
 
 def computeEnrichmentMeanMedian(metabMap: ET.ElementTree, class_pat: Dict[str, List[List[float]]], ids: List[str]) -> None:
     """
@@ -935,20 +907,16 @@
     medians = {key: median / max_flux_medians for key, median in medians.items()}
     means = {key: mean / max_flux_means for key, mean in means.items()}
 
-    save_colormap_image(min_flux_medians, max_flux_medians, utils.FilePath("Color map median", ext=utils.FileFormat.PNG, prefix="result"))
-    save_colormap_image(min_flux_means, max_flux_means, utils.FilePath("Color map mean", ext=utils.FileFormat.PNG, prefix="result"))
+    save_colormap_image(min_flux_medians, max_flux_medians, "colormap_median.png")
+    save_colormap_image(min_flux_means, max_flux_means, "colormap_mean.png")
 
     for key in class_pat:
         # Create color mappings for median and mean
-        colors_median = {rxn_id: rgb_to_hex(jet_colormap(medians[key][i])) for i, rxn_id in enumerate(ids)}
-        colors_mean = {rxn_id: rgb_to_hex(jet_colormap(means[key][i])) for i, rxn_id in enumerate(ids)}
+        colors_median = {rxn_id: rgb_to_hex(sns.color_palette("jet", as_cmap=True)((medians[key][i] + 1) / 2)) for i, rxn_id in enumerate(ids)}
+        colors_mean = {rxn_id: rgb_to_hex(sns.color_palette("jet", as_cmap=True)((means[key][i] + 1) / 2)) for i, rxn_id in enumerate(ids)}
 
         for i, rxn_id in enumerate(ids):
-
-            isNegative = False
-
-            if(medians[key][i] <0):
-                isNegative = True
+            isNegative = medians[key][i] < 0
 
             # Apply median arrows
             apply_arrow(metabMap_median, rxn_id, colors_median[rxn_id], isNegative)
@@ -960,9 +928,6 @@
         save_and_convert(metabMap_mean, "mean", key)
         save_and_convert(metabMap_median, "median", key)
 
-
-
-
 def apply_arrow(metabMap, rxn_id, color, isNegative):
     """
     Apply an arrow to a specific reaction in the metabolic map with a given color.