Narrazione basata sui dati — Deliverable 3.3

import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import numpy as np

# --------------------------
# 1) Dati su fiducia/efficacia
# --------------------------
support_counts   = {1: 2, 2: 4, 3: 29, 4: 33, 5: 69}
prog_counts_raw  = {'No': 17, 'No, Not sure': 1, 'Not sure': 32, 'Yes': 86, 'Yes, No': 1}
effective_counts = {1: 1, 2: 4, 3: 26, 4: 51, 5: 55}

def likert_to_buckets(counts):
    total = sum(counts.values())
    neg = counts.get(1, 0) + counts.get(2, 0)
    neu = counts.get(3, 0)
    pos = counts.get(4, 0) + counts.get(5, 0)
    return neg, neu, pos, total

def yns_to_buckets(counts):
    neg = counts.get('No', 0)
    pos = counts.get('Yes', 0)
    neu = counts.get('Not sure', 0) + counts.get('Yes, No', 0) + counts.get('No, Not sure', 0)
    total = neg + neu + pos
    return neg, neu, pos, total

def to_pct(neg, neu, pos, total):
    f = 100.0 / total if total else 0
    return neg*f, neu*f, pos*f

sN, sU, sP = to_pct(*likert_to_buckets(support_counts))
pN, pU, pP = to_pct(*yns_to_buckets(prog_counts_raw))
eN, eU, eP = to_pct(*likert_to_buckets(effective_counts))

labels = [
    "Support for integration",
    "Programmes address racism",
    "Effectiveness on inclusion"
]
rows = [(sN, sU, sP), (pN, pU, pP), (eN, eU, eP)]

# Colori coerenti
NEG_COLOR = "#d73027"
NEU_COLOR = "#f7f7f7"
POS_COLOR = "#2b8cbe"

# --------------------------
# 2) Distribuzione età
# --------------------------
ages = [
    24,20,22,22,18,21,19,25,20,20,21,19,20,18,24,22,19,23,22,21,
    21,21,20,20,19,18,19,21,20,22,21,20,20,20,20,20,20,19,20,20,
    20,20,20,23,26,25,20,21,22,20,23,19,20,22,20,19,20,20,19,20,
    19,20,21,20,20,21,20,18,21,19,20,19,20,20,23,22,21,22,20,20,
    20,19,20,20,20,19,20,22,20,20,20,20,19,20,20,21,20,22,20,20,
    19,20,22,21,21,23,21,20,20,20,20,22,19,19,21,22,24,19,19,22,
    28,20,20,21,20,19,20,22,18,20,22,19,20
]

ages_arr = np.array(ages, dtype=int)
bins = np.arange(ages_arr.min()-0.5, ages_arr.max()+1.5, 1)  # bin di 1 anno
counts, bin_edges = np.histogram(ages_arr, bins=bins)
centers = (bin_edges[:-1] + bin_edges[1:]) / 2.0

# Media mobile semplice
kernel = np.array([1, 2, 1], dtype=float)
kernel = kernel / kernel.sum()
smooth = np.convolve(counts, kernel, mode='same').astype(float)
if smooth.max() > 0:
    smooth = smooth / smooth.max()  # normalizza 0–1

# Moda dell'età
unique, cts = np.unique(ages_arr, return_counts=True)
mode_age = int(unique[np.argmax(cts)])

# --------------------------
# Grafico principale + inset
# --------------------------
fig, ax = plt.subplots(figsize=(12, 6))

# Barre orizzontali divergenti
ypos = list(range(len(rows)))[::-1]
for y, (neg, neu, pos) in zip(ypos, rows):
    ax.barh(y, -neg, left=0, align='center', color=NEG_COLOR, edgecolor='none')
    ax.barh(y, neu, left=0, align='center', color=NEU_COLOR, edgecolor='none')
    ax.barh(y, pos, left=neu, align='center', color=POS_COLOR, edgecolor='none')

ax.set_yticks(ypos)
ax.set_yticklabels(labels)
ax.set_xlim(-100, 100)
ax.set_xlabel("Share of respondents (%)")
ax.axvline(0, linewidth=1, color="#999999")
ax.set_title("Institutional perceptions")

# Annotazioni percentuali
def annotate(x_left, width, y, text):
    if abs(width) < 4: return
    ax.text(x_left + width/2, y, text, va='center', ha='center', fontsize=9)

for y, (neg, neu, pos) in zip(ypos, rows):
    annotate(-neg, neg, y, f"{neg:.0f}%")
    annotate(0, neu, y, f"{neu:.0f}%")
    annotate(neu, pos, y, f"{pos:.0f}%")

# Legenda
handles = [
    Patch(facecolor=NEG_COLOR, edgecolor='none', label="Negative (1–2 or No)"),
    Patch(facecolor=NEU_COLOR, edgecolor='none', label="Neutral (3 or Not sure / mixed)"),
    Patch(facecolor=POS_COLOR, edgecolor='none', label="Positive (4–5 or Yes)")
]
ax.legend(handles=handles, loc="upper center", ncol=3, bbox_to_anchor=(0.5, 1.12), frameon=False)

# Inset distribuzione età
mini = ax.inset_axes([0.15, 0.15, 0.22, 0.55])
mini.set_facecolor("white")
mini.plot(centers, smooth, color=POS_COLOR, lw=2)
mini.set_xticks([mode_age]); mini.set_xticklabels([str(mode_age)])
mini.set_yticks([]); mini.set_ylabel("age distribution", rotation=90)
mini.spines["top"].set_visible(False); mini.spines["right"].set_visible(False)
mini.spines["bottom"].set_alpha(0.4); mini.spines["left"].set_alpha(0.4)
mini.set_title("Age distribution", pad=6, fontsize=10)

plt.tight_layout()
plt.show()

Supporto all’integrazione (Likert 1–5)

Valore	Conteggio
1	2
2	4
3	29
4	33
5	69

Programmi che affrontano il razzismo (Sì/No/Non so)

Risposta	Conteggio
Yes	86
No	17
Not sure	32
Yes, No	1
No, Not sure	1

Efficacia sull’inclusione (Likert 1–5)

Valore	Conteggio
1	1
2	4
3	26
4	51
5	55

import matplotlib.pyplot as plt

# Dati sondaggio
discr_experience = {"No": 109, "Yes": 26, "Yes, No": 2}
n_total = sum(discr_experience.values())
n_yes = 26 + 2   # Yes + Yes, No

# Contesti tra i 28 "Yes"
context_info = [
    "Of those who responded Yes:",
    "57.1% experienced discrimination in a public space",
    "46.5% experienced discrimination in an educational setting",
    "39.3% experienced discrimination in Social media",
    "7.1% experienced discrimination in a football setting"
]

# Donut
fig, ax = plt.subplots(figsize=(8, 6))
sizes = list(discr_experience.values())
labels = list(discr_experience.keys())
colors = ["#1f77b4", "#d62728", "#bdbdbd"]

wedges, _ = ax.pie(sizes, startangle=90, colors=colors)

# foro centrale
centre_circle = plt.Circle((0, 0), 0.60, fc="white")
ax.add_artist(centre_circle)

# testo centrale
pct_yes = round(100.0 * n_yes / n_total, 1)
ax.text(0, 0.07, f"{pct_yes}%", ha="center", va="center", fontsize=12, weight="bold")
ax.text(0, -0.06, "reported ‘Yes’", ha="center", va="center", fontsize=11)

# riquadro contesti
legend_text = "\n".join(context_info)
ax.text(1.2, 0, legend_text,
        ha="left", va="center",
        fontsize=10,
        bbox=dict(boxstyle="round,pad=0.5", fc="white", ec="0.5"))

ax.set_title("Experienced racial discrimination?")
ax.set_aspect('equal')
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import math

# Dati
program_groups = {
    "Sport as tool for integration": 1,
    "Community forums & dialogue": 1,
    "Support for vulnerable groups": 1,
    "Awareness-raising": 1,
    "Education & training": 1
}
sport_counts = {"Yes": 126, "No": 11}
contexts_pct = {
    "Public space": 57.1,
    "Educational setting": 46.5,
    "Social media": 39.3,
    "Football setting": 7.1
}

# Palette
HIGHLIGHT = "#2b8cbe"
PALE      = "#cdd9e5"
NO_TONE   = "#c7c7c7"
PIE_PALE  = "#e6e6e6"
PIE_HI    = "#d73027"

# Layout 2x2
fig = plt.figure(figsize=(12.5, 8), constrained_layout=True)
gs  = fig.add_gridspec(2, 2, height_ratios=[1, 1.1])

# --- Top-left: tipologie di programma (sport evidenziato) ---
ax0 = fig.add_subplot(gs[0, 0])
labels_pg = list(program_groups.keys())
values_pg = list(program_groups.values())
colors_pg = [HIGHLIGHT if "Sport" in lab else PALE for lab in labels_pg]
ax0.barh(labels_pg, values_pg, color=colors_pg)
ax0.set_xlim(0, 1.2)
ax0.set_xticks([])
ax0.set_title("Anti-racism program types", pad=6)
for i, lab in enumerate(labels_pg):
    ax0.text(0.05, i, lab, va="center", ha="left", fontsize=10, color="black")

# --- Top-right: credenza Sì/No sull'efficacia dello sport ---
ax1 = fig.add_subplot(gs[0, 1])
yn_labels = list(sport_counts.keys())
yn_vals   = list(sport_counts.values())
yn_colors = [HIGHLIGHT if lab == "Yes" else NO_TONE for lab in yn_labels]
bars = ax1.bar(yn_labels, yn_vals, color=yn_colors)
ax1.set_title("Do students see sport as an effective anti-racism tool?", pad=6)
ax1.set_ylabel("Number of respondents")
for b in bars:
    ax1.text(b.get_x() + b.get_width()/2, b.get_height() + 0.8,
             f"{int(b.get_height())}", ha="center", va="bottom", fontsize=10)

# --- Bottom: contesti (evidenzia 'Football') ---
ax2 = fig.add_subplot(gs[1, :])
labels_ctx = list(contexts_pct.keys())
values_ctx = list(contexts_pct.values())
colors_ctx = [PIE_HI if "Football" in lab else PIE_PALE for lab in labels_ctx]
wedges, _ = ax2.pie(values_ctx, colors=colors_ctx, startangle=90)
ax2.set_title("Where discrimination occurs (among those who reported it)", pad=8)

legend_handles = [Patch(facecolor=PIE_HI,  label="Football setting"),
                  Patch(facecolor=PIE_PALE, label="Other contexts")]
ax2.legend(handles=legend_handles, loc="center left", bbox_to_anchor=(0.02, 0.5), frameon=False)

# Etichetta 7.1% sullo spicchio 'Football'
fb_idx = labels_ctx.index("Football setting")
fb_wedge = wedges[fb_idx]
angle = (fb_wedge.theta2 + fb_wedge.theta1) / 2
r = 1.1
x = r * math.cos(math.radians(angle))
y = r * math.sin(math.radians(angle))
ax2.text(x, y, "7.1%", ha="center", va="center", fontsize=11, weight="bold")

plt.show()

import matplotlib.pyplot as plt
from matplotlib.patches import Patch

women_share = 57.0
severe_poverty = 16.7

START = 90
OUTER_R, OUTER_W = 1.3, 0.28
INNER_R, INNER_W = 0.85, 0.28

BLUE_HI, BLUE_REST = "#1f77b4", "#dbe9f6"
RED_HI, RED_REST = "#d62728", "#f8c9c9"

fig, ax = plt.subplots(figsize=(7.2, 7.2))

ax.pie([severe_poverty, 100-severe_poverty],
       radius=OUTER_R, startangle=START,
       colors=[RED_HI, RED_REST],
       wedgeprops=dict(width=OUTER_W, edgecolor="white"))

ax.pie([women_share, 100-women_share],
       radius=INNER_R, startangle=START,
       colors=[BLUE_HI, BLUE_REST],
       wedgeprops=dict(width=INNER_W, edgecolor="white"))

ax.set(aspect="equal"); ax.axis("off")
ax.set_title("Demographic indicators", pad=10, fontsize=14, weight="bold")
handles = [Patch(facecolor=RED_HI, label=f"Migrant women in severe poverty (Spain): {severe_poverty:.1f}%"),
           Patch(facecolor=BLUE_HI, label=f"Women among new nationals (Spain): {women_share:.0f}%")]
ax.legend(handles=handles, loc="center left", bbox_to_anchor=(1.05, 0.0), frameon=False)
plt.tight_layout(); plt.show()