update code

wanshi/visualizations/crona/guide_line_heatmap.R

@@ -67,5 +67,11 @@ ht_map <- Heatmap(
   column_names_rot = 45,
 )
 
-# Draw the heatmap with gaps between row groups
+# Open the SVG Graphics Device
+#svg("heatmap.svg", width = 8, height = 6)  # Adjust 'width' and 'height' as needed
+
+# Draw the heatmap
 draw(ht_map)
+
+# Close the SVG Graphics Device
+#dev.off()

wanshi/visualizations/crona/special_needs.py

@@ -3,14 +3,12 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib as mpl
-import met_brewer
 from decide_my_facourite_color import *
 # Set the font globally
 mpl.rcParams['font.family'] = 'Arial'  # or 'Helvetica' if available
 
 csv_path = 'META-AI_ Guideline Items - Tabellenblatt1.csv'
 core_data = 'core data.csv'
-import csv
 
 # read csv with pandas
 df = pd.read_csv(csv_path, sep=',', header=0, index_col=0)
@@ -40,21 +38,15 @@
 df_core_data = pd.read_csv(core_data, sep=',', header=0, index_col=0, encoding='utf-8', engine='python')
 first_column_core = df_core_data.iloc[:,0]
 cata_count = df_core_data.groupby('cata')['test'].count()
-#print(cata_count)
 
-
-##########################
 sector = circos.sectors[0]
-#print(sector.name)
 track1 = sector.add_track((100, 100))
 track1.axis()
-#print(int(track1.size))
 
 pos_list = list(range(0, int(track1.size)))
 # add 0.5 to all positions
 pos_list = [x + 0.5 for x in pos_list]
-#print(len(pos_list))
-# Plot rect & text (style1)
+
 for i in range(int(track1.size)):
     start, end = i, i + 1
     track1.rect(start, end, fc='white', ec="black", lw=1)
@@ -84,40 +76,29 @@
 for i in range(int(track2.size)):
     start, end = i, i + 1
     track2.rect(start, end, ec="white", lw=1)
-# track2.yticks('Groups of Items', tick_length=0, label_margin=2)
 count = 0
 first_column_core_enu = enumerate(first_column_core)
-#print('----------')
 
 items = list(guide_line_group.items())
-#revert guide_line_group.items()
 items.reverse()
 count2 = 0
 
 for key,value in items:
 
-    #print(count2)
-    #print(value)
     end = count + value
-    # Access the color_guide_line_group list in reverse
     inverse_index = len(color_guide_line_group) - count2 - 1
     track2.rect(count, end, fc=color_guide_line_group[inverse_index])
-    #track2.text(str(key), (count + end) / 2, color="white", adjust_rotation=True, **{'size':8, 'va':"center"})
     count2 += 1
     count = end
 
 
-##################
 sector = circos.sectors[1]
-#print(sector.name)
 track1 = sector.add_track((90, 100))
 track1.axis()
-#print(int(track1.size))
 
 pos_list = list(range(0, int(track1.size)))
 # add 0.5 to all positions
 pos_list = [x + 0.5 for x in pos_list]
-#print(len(pos_list))
 # Plot rect & text (style1)
 
 track1.xticks(
@@ -142,8 +123,6 @@
 track2 = sector.add_track((80, 90))
 track2.axis()
 guideline_list = ['G','G','G','G','G','S','S','G','S',   'G','G','S','G','S','S',    'G','S','G','G','G','S','S','S','S','S','G']
-#print(len(guideline_list))
-from collections import OrderedDict
 
 for i in (range(0, int(track1.size))):
     start, end = i, i + 1
@@ -152,11 +131,9 @@
     else:
         color = color_Guideline_Type[1]
     track2.rect(start, end, fc=color, ec="black", lw=1)
-    #track2.text(str(guideline_list[i]), (end + start) / 2, size=8, color="white", adjust_rotation=False)
 track3 = sector.add_track((70, 80))
 year = [2009,2015,2016,2020,2020,2021,2022,2022,2023,  2019,2020,2020,2021,2022,2023,    2012,2020,2020,2020,2021,2021,2021,2021,2022,2022,2022]
 unique_years = sorted(set(year))
-#print(len(unique_years))
 # Distribute color_year evenly across the unique years
 color_mapping = [(yr, color_year[i % len(color_year)]) for i, yr in enumerate(unique_years)]
 
@@ -167,9 +144,6 @@
     start, end = i, i + 1
     track3.rect(start, end, fc=year_gradient_color_dict[year[i]], ec="black", lw=1)
 
-    #track3.text(str(year[i])[2:], (end + start) / 2, size=8, color="white",adjust_rotation=False)
-
-    #########
 
 critirian_link_list = enumerate(all_critirian_names)
 # transfer item_names from numpy array to list
@@ -211,27 +185,6 @@
 labels2 = list(colors2.keys())
 handles1 = [plt.Rectangle((0,0),1,1, color=colors1[label]) for label in labels1]
 handles2 = [plt.Rectangle((0,0),1,1, color=colors2[label]) for label in labels2]
-# plt.legend(handles, labels, loc=[1.,1.],title="Guideline Type", )
-# add another legend to the figure
-# import matplotlib.patches as mpatches
-# blue_patch = mpatches.Patch(color='blue', label='blue legend')
-# plt.legend(handles=[blue_patch])
-ax1 = fig.add_axes([0.85, 0.88, 0.02, 0.1])
-# fill the ax with gradient blue
-# color_spam4 = mpl.cm.get_cmap('Blues', 16).gradient
-
-norm = mpl.colors.Normalize(vmin=2008, vmax=2023)
-# print(mpl.cm.ScalarMappable.get_clim(cmap = mpl.cm.Blues))
-cmap = (mpl.colors.ListedColormap(color_year)
-        .with_extremes(over='0.25', under='0.75'))
-cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, orientation='vertical')
-#cb1.set_label('Year', rotation=0, labelpad=10, loc='top', size=10)
-# add color bar legend to the figure
-# ax2 = fig.add_axes([0.85, 0.1, 0.02, 0.2], frameon=False, facecolor='g')
-# cb1 = mpl.colorbar.ColorbarBase(ax2, cmap=mpl.cm.Greens, norm=norm, orientation='vertical')
-
-fig.legends.append(plt.legend(handles1, labels1, loc=[-12.,0],title="Guideline Type"))
-fig.legends.append(plt.legend(handles2, labels2, loc=[-20.,0],title="Level of Consensus"))
-# fig.legends.append([ ['Cold', 'Medium', 'Hot'], ['Cold', 'Medium', 'Hot']])
-
-fig.show()
+fig.savefig('circle.svg', format='svg', dpi=1800)
+
+fig.show()