commit 7db66b0e3d30157894510e870ee7976442dc7492
Author: max/sooulix <maxime@freepoteries.fr>
Date:   Mon Feb 26 01:17:10 2024 +0100

    initial commit

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..afe2c08
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+*.swo
+.~*
+*.swp
+**/__pycache__
diff --git a/main.py b/main.py
new file mode 100755
index 0000000..19720f8
--- /dev/null
+++ b/main.py
@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+from pprint import pprint
+import csv
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+filename="synthesis_artworks.csv"
+def scan_csv_list(content):
+
+    column_labels = {
+        "field": {
+        },
+        "theme": {
+        },
+        "size": {
+        },
+        "anthropomorphic": {
+            "name": "shape",
+            "values": {
+                True: "anthropormorphic",
+                False: "non-anthropormorphic",
+            },
+        },
+        "design": {
+            "values": {
+                "indus_brut" : "industrial",
+                "indus_custom" : "custom"
+            },
+            },
+        "mob_fix": {
+            "name": "mobility",
+            "values":{
+                True: "yes",
+                False: "no"},
+            },
+        "mob_area": {
+            "name": "proxemics",
+            "values":{
+                "5": "personal",
+                "100": "social",
+                "500": "public",}},
+        "mob_openspace": {
+            "name": "open space",
+            "values":{
+                True: "yes",
+                False: "no"}},
+        # "mob_technology": {},
+        "physical_interaction": {
+            "name": "physical contact",
+            "values": {
+                "touch": "haptics",
+                "close": "close-contact"}},
+        "contact": {},
+        "role": {},
+        "importance": {
+            "name": "focus",
+            "values": {
+                "2nd": "secondary"}},
+        "safety": {
+            "values":{
+                True: "yes",
+                False: "no"}},
+    }
+
+
+    csv_reader = csv.reader(content, dialect=csv.excel_tab)
+    keys = next(csv_reader)
+    values = {}
+    for key in keys:
+        if key not in values:
+            values[key] = {}
+
+    for row in csv_reader:
+        for i in range(len(keys)):
+            key = keys[i]
+            full_cell = row[i]
+            if full_cell == '0':
+                cells = [False]
+            elif full_cell == '1':
+                cells = [True]
+            else:
+                if ", " in full_cell:
+                    cells = full_cell.split(', ')
+                else:
+                    cells = [full_cell]
+
+            for cell in cells:
+                if cell not in values[key]:
+                    values[key][cell] = 1
+                else: 
+                    values[key][cell] += 1
+
+    
+
+    ax = init_graph()
+    for i, (column_name, column_label) in enumerate(reversed(list(column_labels.items()))):
+        add_graph_field(
+            ax,
+            i % 2,
+            column_name,
+            column_label,
+            *percentage_keys(values[column_name])
+        )
+
+    ax.set_xlabel("Percentage")
+    ax.set_title("Criteria")
+    plt.savefig("trululu.pdf")
+
+
+    for column_name in column_labels.keys():
+        print(column_labels[column_name].get("name", column_name))
+        for key, percentage in zip(*percentage_keys(values[column_name])):
+            key_label = column_labels[column_name].get("values", {}).get(key, key)
+            print(f"\t{key_label}\t{percentage}")
+        print()
+
+
+def percentage_keys(values):
+    total = 0
+    sorted_keys = sorted(
+        values.keys(),
+        key=lambda key: -values[key]
+    )
+    for key, n in values.items():
+        total += n
+
+    return sorted_keys, [
+        round((values[key] * 100) / total)
+        for key in sorted_keys
+    ]
+
+def init_graph():
+    fig, ax = plt.subplots(figsize=(10,6))
+    return ax
+
+def add_graph_field(ax, color_index, field, labels, values, percentages):
+    start = 0
+
+    category_colors = [
+        plt.colormaps['coolwarm'](np.linspace(0.15, 0.85, len(values))),
+        plt.colormaps['Spectral'](np.linspace(0.15, 0.85, len(values)))
+    ][color_index]
+
+    for i, (value, percentage) in enumerate(zip(values, percentages)):
+        rects = ax.barh([labels.get("name", field)], [percentage], left=[start], label=value, height=0.8, color=category_colors[i])
+
+        start += percentage
+
+        ax.bar_label(rects, labels=[
+            labels.get("values", {}).get(value, value)
+        ], label_type="center")
+
+
+def main():
+    with open(filename) as fh:
+        scan_csv_list(fh.readlines())
+        
+if __name__ == '__main__':
+    main()