Mercurial > repos > bimib > marea_2
changeset 314:e8c20e0b4f27 draft
Uploaded
author | luca_milaz |
---|---|
date | Mon, 05 Aug 2024 12:29:48 +0000 |
parents | 376afe557f90 |
children | 48d2973faacf |
files | marea_2/flux_to_map.py |
diffstat | 1 files changed, 9 insertions(+), 10 deletions(-) [+] |
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--- a/marea_2/flux_to_map.py Mon Aug 05 12:23:58 2024 +0000 +++ b/marea_2/flux_to_map.py Mon Aug 05 12:29:48 2024 +0000 @@ -17,7 +17,6 @@ import pyvips from PIL import Image, ImageDraw, ImageFont from typing import Tuple, Union, Optional, List, Dict -import seaborn as sns import matplotlib.pyplot as plt ERRORS = [] @@ -854,18 +853,16 @@ max_value (float): The maximum value of the colormap range. filename (str): The filename for saving the image. """ - # 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 colormap using matplotlib + cmap = plt.get_cmap("jet") # 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.linspace(min_value, max_value, 256) gradient = np.vstack((gradient, gradient)) # Display the gradient image @@ -904,16 +901,18 @@ min_flux_medians = min(np.min(np.abs(arr)) for arr in medians.values()) min_flux_means = min(np.min(np.abs(arr)) for arr in means.values()) - medians = {key: median / max_flux_medians for key, median in medians.items()} - means = {key: mean / max_flux_means for key, mean in means.items()} + medians = {key: (median - min_flux_medians) / (max_flux_medians - min_flux_medians) for key, median in medians.items()} + means = {key: (mean - min_flux_means) / (max_flux_means - min_flux_means) for key, mean in means.items()} save_colormap_image(min_flux_medians, max_flux_medians, "colormap_median.png") save_colormap_image(min_flux_means, max_flux_means, "colormap_mean.png") + cmap = plt.get_cmap("jet") + for key in class_pat: # Create color mappings for median and mean - 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)} + colors_median = {rxn_id: rgb_to_hex(cmap(medians[key][i])) for i, rxn_id in enumerate(ids)} + colors_mean = {rxn_id: rgb_to_hex(cmap(means[key][i])) for i, rxn_id in enumerate(ids)} for i, rxn_id in enumerate(ids): isNegative = medians[key][i] < 0