# import python modules
import fabmodules as fm
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
from itertools import combinations
from collections import Counter
from matplotlib.patches import Patch

# set parameters
fm.output_path = output_path = "outputs/" 

# set constants
sdg_colors = {
    1: "#E5243B",
    2: "#DDA63A",
    3: "#4C9F38",
    4: "#C5192D",
    5: "#FF3A21",
    6: "#26BDE2",
    7: "#FCC30B",
    8: "#A21942",
    9: "#FD6925",
    10: "#DD1367",
    11: "#FD9D24",
    12: "#BF8B2E",
    13: "#3F7E44",
    14: "#0A97D9",
    15: "#56C02B",
    16: "#00689D",
    17: "#19486A"
}

sdg_info = {
    "SDG-01": ("No poverty",                        "#E5243B"),
    "SDG-02": ("Zero hunger",                       "#DDA63A"),
    "SDG-03": ("Good health and well-being",        "#4C9F38"),
    "SDG-04": ("Quality education",                 "#C5192D"),
    "SDG-05": ("Gender equality",                   "#FF3A21"),
    "SDG-06": ("Clean water and sanitation",        "#26BDE2"),
    "SDG-07": ("Affordable and clean energy",       "#FCC30B"),
    "SDG-08": ("Decent work and economic growth",   "#A21942"),
    "SDG-09": ("Industry, innovation & infrastructure", "#FD6925"),
    "SDG-10": ("Reduced inequalities",              "#DD1367"),
    "SDG-11": ("Sustainable cities & communities",  "#FD9D24"),
    "SDG-12": ("Responsible consumption & production", "#BF8B2E"),
    "SDG-13": ("Climate action",                    "#3F7E44"),
    "SDG-14": ("Life below water",                  "#0A97D9"),
    "SDG-15": ("Life on land",                      "#56C02B"),
    "SDG-16": ("Peace, justice & strong institutions", "#00689D"),
    "SDG-17": ("Partnerships for the goals",        "#19486A"),
}

# read_fabnetdata from local (/output) directory or from gitlab.fabcloud.org repository (/public)
# use fd.read_fabnetdata
# parameter 1: filename
# parameter 2: "local" directory
# parameter 2: default: "public"directory
fablabs, fabcities, countries, continents, retrieved = fm.read_fabnetdata("fabnetdata.xlsx")

url https://gitlab.fabcloud.org/fl-management/fablab-network-data/-/raw/main/public/fabnetdata.xlsx
fablabs  (2597, 29)
fabcities  (56, 7)
countries  (250, 44)
continents  (7, 35)
data retrieved 2025-12-01

# ✅ 1. Extract SDGs from "SDG-04-08-09-17"

import re

def extract_sdgs(text):
    if not isinstance(text, str) or not text.startswith("SDG-"):
        return []

    # Extract exactly two-digit SDG codes between 01–17
    codes = re.findall(r"SDG-(\d{2}(?:-\d{2})*)", text)

    if not codes:
        return []

    # Take the FIRST matched block (e.g., "04-08-09-17")
    numbers = codes[0].split("-")

    # Convert to integers
    return [int(n) for n in numbers if 1 <= int(n) <= 17]

fablabs["sdgs"] = fablabs["profile"].apply(extract_sdgs)

# ✅ 2. Build SDG co-occurrence edges (unchanged)

edge_counter = Counter()

for sdg_list in fablabs["sdgs"]:
    if len(sdg_list) > 1:
        for a, b in combinations(sorted(sdg_list), 2):
            edge_counter[(a, b)] += 1

edge_df = pd.DataFrame(
    [(a, b, w) for (a,b), w in edge_counter.items()],
    columns=["sdg1", "sdg2", "weight"]
)

# ✅ 3. Build co-occurrence graph (unchanged)
G = nx.Graph()

for sdg in range(1, 18):
    G.add_node(sdg)

for _, row in edge_df.iterrows():
    G.add_edge(row["sdg1"], row["sdg2"], weight=row["weight"])

# ✅ 4. Compute network metrics (unchanged)

degree_centrality = nx.degree_centrality(G)
between_centrality = nx.betweenness_centrality(G, weight="weight")
strength = {n: sum(w["weight"] for _, _, w in G.edges(n, data=True)) for n in G.nodes()}

metrics = pd.DataFrame({
    "degree": degree_centrality,
    "betweenness": between_centrality,
    "strength": strength
}).sort_values("strength", ascending=False)

metrics

# Add color
for sdg, color in sdg_colors.items():
    if sdg in G.nodes():
        G.nodes[sdg]["viz"] = {"color": {
            "r": int(color[1:3], 16),
            "g": int(color[3:5], 16),
            "b": int(color[5:7], 16)
        }}

# Export
nx.write_gexf(G, output_path+"sdg_network.gexf")

# (Assumes you've already built G as before)
pos = nx.spring_layout(G, weight="weight", seed=42)

plt.figure(figsize=(10,10))
plt.title(f"World: Fablab SDG Profiles:\nSDG Co-occurrence Network\n(data retrieved: {retrieved})")

# Draw edges
weights = [G[u][v]["weight"] for u,v in G.edges()]
nx.draw_networkx_edges(G, pos,
                       width=[w*0.2 for w in weights],
                       alpha=0.5)

# Draw nodes, using SDG colours
node_colors = [sdg_colors.get(node, "#888888") for node in G.nodes()]
nx.draw_networkx_nodes(G, pos, node_size=800,
                       node_color=node_colors)

# Labels
nx.draw_networkx_labels(G, pos, font_size=12)

#plt.axis("off")

from matplotlib.patches import Patch

legend_handles = [
    Patch(
        facecolor=color,
        edgecolor="black",
        label=f"{sdg}: {label}"
    )
    for sdg, (label, color) in sdg_info.items()
]

#plt.legend(handles=legend_elements, title="SDGs", bbox_to_anchor=(1.05, 1), loc="upper left")
plt.savefig(output_path+"sdg_network.png")
plt.show()

	degree	betweenness	strength
4	1.0000	0.000000	1274
9	1.0000	0.000000	1033
12	1.0000	0.000000	628
11	1.0000	0.000000	618
5	1.0000	0.000000	517
8	0.9375	0.011885	442
7	0.9375	0.000000	261
17	1.0000	0.000000	260
3	1.0000	0.005833	237
13	1.0000	0.000000	201
10	0.8125	0.012500	151
6	0.8750	0.101091	83
15	0.9375	0.108651	80
2	0.8750	0.150556	57
1	0.8125	0.348631	54
14	0.8125	0.167103	47
16	0.7500	0.335992	41

DataScience Session 6: Density Estimation¶

Synopsis¶

Resources¶

Assignment¶

A. Research ideas¶

Conclusion¶

B. Research planning and design¶

C. Data collection¶

D. Data processing¶

1. SDG Co-occurrence Network¶

E. Data Study and Analysis¶

Conclusion¶

F. Data Publishing and Access¶

G. Data Preservation¶

H. Data Re-use¶

Evaluation and Follow-up¶

Follow-up¶

Review¶