Generates Charts for AQI data

chart.py

"""
AQI April 2025 — Knowledge Park III, Greater Noida
Generates 3 chart PNGs from aqi.csv (place both files in the same folder).

Requirements:
    pip install matplotlib pandas
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Patch
import warnings, os

warnings.filterwarnings("ignore")

# ── Load data ──────────────────────────────────────────────────────────────────
HERE   = os.path.dirname(os.path.abspath(__file__))
CSV    = os.path.join(HERE, "aqi.csv")

df = pd.read_csv(CSV)
hours  = [c for c in df.columns if c != "Date"]
pivot  = df.set_index("Date")[hours].copy()
pivot.columns = range(24)

daily_avg  = pivot.mean(axis=1)
daily_max  = pivot.max(axis=1)
daily_min  = pivot.min(axis=1)
hourly_avg = pivot.mean(axis=0)

days       = pivot.index.tolist()
day_labels = [f"Apr {d}" for d in days]

PALETTE = ["#4a9e5c", "#d4c84a", "#e8913a", "#c0392b"]
BOUNDS  = [0, 50, 100, 200, 300]
CMAP    = mcolors.ListedColormap(PALETTE)
NORM    = mcolors.BoundaryNorm(BOUNDS, CMAP.N)
BG      = "#f9f9f7"

# ══════════════════════════════════════════════════════════════════════════════
# CHART 1 — HEATMAP
# ══════════════════════════════════════════════════════════════════════════════
fig1, ax = plt.subplots(figsize=(18, 9), facecolor=BG)
fig1.suptitle(
    "Hourly AQI Heatmap — April 2025 | Knowledge Park III, Greater Noida",
    fontsize=13, fontweight="bold", color="#2c2c2c", y=1.01,
)

data = pivot.values.astype(float)
im   = ax.imshow(data, aspect="auto", cmap=CMAP, norm=NORM, interpolation="nearest")

for r, day in enumerate(days):
    for c in range(24):
        val = data[r, c]
        if not np.isnan(val):
            fc = "white" if val > 130 else "#333"
            ax.text(c, r, str(int(val)), ha="center", va="center",
                    fontsize=5.5, color=fc, fontweight="500")

ax.set_xticks(range(24))
ax.set_xticklabels([str(h) for h in range(24)], fontsize=8)
ax.set_yticks(range(len(days)))
ax.set_yticklabels(day_labels, fontsize=7.5)
ax.set_xlabel("Hour of day", fontsize=10, labelpad=5)
ax.set_ylabel("Date", fontsize=10, labelpad=5)
ax.set_title("Each cell = one hour reading. Blank cells = missing data.",
             fontsize=9, color="#666", pad=6)

cbar = fig1.colorbar(im, ax=ax, orientation="vertical",
                     fraction=0.018, pad=0.01, shrink=0.95)
cbar.set_ticks([25, 75, 150, 250])
cbar.set_ticklabels(["Good", "Satisfactory", "Moderate", "Poor"], fontsize=8)

legend_patches = [
    Patch(facecolor="#4a9e5c", label="Good  (0–50)"),
    Patch(facecolor="#d4c84a", label="Satisfactory  (51–100)"),
    Patch(facecolor="#e8913a", label="Moderate  (101–200)"),
]
ax.legend(handles=legend_patches, loc="lower right", fontsize=8,
          framealpha=0.9, bbox_to_anchor=(1.17, 0))

fig1.tight_layout()
out1 = os.path.join(HERE, "chart1_heatmap.png")
fig1.savefig(out1, dpi=180, bbox_inches="tight", facecolor=BG)
print(f"Saved {out1}")

# ══════════════════════════════════════════════════════════════════════════════
# CHART 2 — DAILY TREND
# ══════════════════════════════════════════════════════════════════════════════
fig2, ax = plt.subplots(figsize=(14, 6), facecolor=BG)
fig2.suptitle(
    "Daily AQI Trend — April 2025 | Knowledge Park III, Greater Noida",
    fontsize=13, fontweight="bold", color="#2c2c2c", y=1.01,
)

x = np.array(days)
ax.fill_between(x, daily_min, daily_max,
                color="#e8913a", alpha=0.15, label="Min–Max range")
ax.plot(x, daily_max, color="#c0392b", linewidth=1.4, linestyle="--",
        marker="o", markersize=3.5, label="Daily max")
ax.plot(x, daily_avg, color="#e8913a", linewidth=2,
        marker="o", markersize=4,   label="Daily avg", zorder=5)
ax.plot(x, daily_min, color="#4a9e5c", linewidth=1.4, linestyle="--",
        marker="o", markersize=3.5, label="Daily min")

for level, label, clr in [
    (50,  "Good / Satisfactory",     "#4a9e5c"),
    (100, "Satisfactory / Moderate", "#d4aa00"),
    (200, "Moderate / Poor",         "#c0392b"),
]:
    ax.axhline(level, color=clr, linewidth=0.8, linestyle=":", alpha=0.7)
    ax.text(30.6, level + 1.5, label, fontsize=7, color=clr, va="bottom")

best_day  = daily_avg.idxmin()
worst_day = daily_avg.idxmax()

ax.annotate(
    f"Best\nApr {best_day} ({daily_avg[best_day]:.0f})",
    xy=(best_day, daily_avg[best_day]),
    xytext=(best_day + 1.8, daily_avg[best_day] - 16),
    fontsize=8, color="#4a9e5c", fontweight="bold",
    arrowprops=dict(arrowstyle="->", color="#4a9e5c", lw=1),
)
ax.annotate(
    f"Worst\nApr {worst_day} ({daily_avg[worst_day]:.0f})",
    xy=(worst_day, daily_avg[worst_day]),
    xytext=(worst_day + 1.8, daily_avg[worst_day] + 4),
    fontsize=8, color="#c0392b", fontweight="bold",
    arrowprops=dict(arrowstyle="->", color="#c0392b", lw=1),
)

ax.set_xlim(0.5, 31.5)
ax.set_ylim(40, 190)
ax.set_xticks(range(1, 31))
ax.set_xticklabels(day_labels, rotation=45, ha="right", fontsize=8)
ax.set_ylabel("AQI", fontsize=10)
ax.set_title("Shaded band = daily min–max range; line = daily average",
             fontsize=9, color="#666", pad=6)
ax.legend(fontsize=8.5, loc="upper left", framealpha=0.9)
ax.grid(axis="y", alpha=0.25, linewidth=0.7)
ax.set_facecolor("#fafaf8")

fig2.tight_layout()
out2 = os.path.join(HERE, "chart2_daily_trend.png")
fig2.savefig(out2, dpi=180, bbox_inches="tight", facecolor=BG)
print(f"Saved {out2}")

# ══════════════════════════════════════════════════════════════════════════════
# CHART 3 — HOURLY PATTERN
# ══════════════════════════════════════════════════════════════════════════════
fig3, ax = plt.subplots(figsize=(9, 9), facecolor=BG)
fig3.suptitle(
    "Average AQI by Hour of Day — April 2025 | Knowledge Park III, Greater Noida",
    fontsize=12, fontweight="bold", color="#2c2c2c", y=1.01,
)

bar_colors = ["#d4c84a" if v <= 100 else "#e8913a" for v in hourly_avg]
bars = ax.barh(range(24), hourly_avg.values,
               color=bar_colors, edgecolor="none", height=0.65)

for i, (bar, val) in enumerate(zip(bars, hourly_avg.values)):
    ax.text(val + 0.1, i, f"{val:.1f}", va="center", fontsize=8, color="#555")

ax.set_yticks(range(24))
ax.set_yticklabels([f"{h}:00" for h in range(24)], fontsize=8.5)
ax.set_xlabel("Average AQI (across all 30 days)", fontsize=10)
ax.set_title(
    "The near-flat pattern indicates pollution is episode-driven\n"
    "(weather / regional transport), not tied to time of day.",
    fontsize=9, color="#666", pad=6,
)
ax.set_xlim(100, 120)
ax.invert_yaxis()
ax.grid(axis="x", alpha=0.25, linewidth=0.7)
ax.set_facecolor("#fafaf8")

fig3.tight_layout()
out3 = os.path.join(HERE, "chart3_hourly_pattern.png")
fig3.savefig(out3, dpi=180, bbox_inches="tight", facecolor=BG)
print(f"Saved {out3}")
print("\nDone — 3 charts generated.")

chart2.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Patch
import warnings, os

warnings.filterwarnings("ignore")

# ── Load data ──────────────────────────────────────────────────────────────────
HERE = os.path.dirname(os.path.abspath(__file__))
CSV  = os.path.join(HERE, "aqi_chandni.csv")

df = pd.read_csv(CSV, na_values="NA")
hours  = [c for c in df.columns if c != "Date"]
pivot  = df.set_index("Date")[hours].copy()
pivot.columns = range(24)

daily_avg  = pivot.mean(axis=1)
daily_max  = pivot.max(axis=1)
daily_min  = pivot.min(axis=1)
hourly_avg = pivot.mean(axis=0)

days       = pivot.index.tolist()
day_labels = [f"Apr {d}" for d in days]

# AQI colour scale — extended to cover Poor (201-300) since data reaches 287
PALETTE = ["#4a9e5c", "#d4c84a", "#e8913a", "#c0392b", "#7b241c"]
BOUNDS  = [0, 50, 100, 200, 300, 400]
CMAP    = mcolors.ListedColormap(PALETTE)
NORM    = mcolors.BoundaryNorm(BOUNDS, CMAP.N)
BG      = "#f9f9f7"

# ══════════════════════════════════════════════════════════════════════════════
# CHART 1 — HEATMAP
# ══════════════════════════════════════════════════════════════════════════════
fig1, ax = plt.subplots(figsize=(18, 9), facecolor=BG)
fig1.suptitle(
    "Hourly AQI Heatmap — April 2025 | Chandni Chowk, Delhi (IITM)",
    fontsize=13, fontweight="bold", color="#2c2c2c", y=1.01,
)

data = pivot.values.astype(float)
im   = ax.imshow(data, aspect="auto", cmap=CMAP, norm=NORM, interpolation="nearest")

for r, day in enumerate(days):
    for c in range(24):
        val = data[r, c]
        if not np.isnan(val):
            fc = "white" if val > 150 else "#333"
            ax.text(c, r, str(int(val)), ha="center", va="center",
                    fontsize=5.5, color=fc, fontweight="500")

ax.set_xticks(range(24))
ax.set_xticklabels([str(h) for h in range(24)], fontsize=8)
ax.set_yticks(range(len(days)))
ax.set_yticklabels(day_labels, fontsize=7.5)
ax.set_xlabel("Hour of day", fontsize=10, labelpad=5)
ax.set_ylabel("Date", fontsize=10, labelpad=5)
ax.set_title("Each cell = one hour reading. Grey = missing data.",
             fontsize=9, color="#666", pad=6)

cbar = fig1.colorbar(im, ax=ax, orientation="vertical",
                     fraction=0.018, pad=0.01, shrink=0.95)
cbar.set_ticks([25, 75, 150, 250, 350])
cbar.set_ticklabels(["Good", "Satisfactory", "Moderate", "Poor", "Very Poor"],
                    fontsize=8)

legend_patches = [
    Patch(facecolor="#4a9e5c", label="Good  (0–50)"),
    Patch(facecolor="#d4c84a", label="Satisfactory  (51–100)"),
    Patch(facecolor="#e8913a", label="Moderate  (101–200)"),
    Patch(facecolor="#c0392b", label="Poor  (201–300)"),
]
ax.legend(handles=legend_patches, loc="lower right", fontsize=8,
          framealpha=0.9, bbox_to_anchor=(1.17, 0))

fig1.tight_layout()
out1 = os.path.join(HERE, "chandni_chart1_heatmap.png")
fig1.savefig(out1, dpi=180, bbox_inches="tight", facecolor=BG)
print(f"Saved {out1}")

# ══════════════════════════════════════════════════════════════════════════════
# CHART 2 — DAILY TREND
# ══════════════════════════════════════════════════════════════════════════════
fig2, ax = plt.subplots(figsize=(14, 6), facecolor=BG)
fig2.suptitle(
    "Daily AQI Trend — April 2025 | Chandni Chowk, Delhi (IITM)",
    fontsize=13, fontweight="bold", color="#2c2c2c", y=1.01,
)

x = np.array(days)
ax.fill_between(x, daily_min, daily_max,
                color="#c0392b", alpha=0.12, label="Min–Max range")
ax.plot(x, daily_max, color="#7b241c", linewidth=1.4, linestyle="--",
        marker="o", markersize=3.5, label="Daily max")
ax.plot(x, daily_avg, color="#c0392b", linewidth=2,
        marker="o", markersize=4,   label="Daily avg", zorder=5)
ax.plot(x, daily_min, color="#e8913a", linewidth=1.4, linestyle="--",
        marker="o", markersize=3.5, label="Daily min")

for level, label, clr in [
    (100, "Satisfactory / Moderate", "#d4aa00"),
    (200, "Moderate / Poor",         "#c0392b"),
    (300, "Poor / Very Poor",        "#7b241c"),
]:
    ax.axhline(level, color=clr, linewidth=0.8, linestyle=":", alpha=0.7)
    ax.text(30.6, level + 2, label, fontsize=7, color=clr, va="bottom")

valid_avg = daily_avg.dropna()
best_day  = valid_avg.idxmin()
worst_day = valid_avg.idxmax()

ax.annotate(
    f"Best\nApr {best_day} ({valid_avg[best_day]:.0f})",
    xy=(best_day, valid_avg[best_day]),
    xytext=(best_day + 1.8, valid_avg[best_day] - 22),
    fontsize=8, color="#e8913a", fontweight="bold",
    arrowprops=dict(arrowstyle="->", color="#e8913a", lw=1),
)
ax.annotate(
    f"Worst\nApr {worst_day} ({valid_avg[worst_day]:.0f})",
    xy=(worst_day, valid_avg[worst_day]),
    xytext=(worst_day + 1.8, valid_avg[worst_day] + 5),
    fontsize=8, color="#7b241c", fontweight="bold",
    arrowprops=dict(arrowstyle="->", color="#7b241c", lw=1),
)

ax.set_xlim(0.5, 31.5)
ax.set_ylim(60, 310)
ax.set_xticks(range(1, 31))
ax.set_xticklabels(day_labels, rotation=45, ha="right", fontsize=8)
ax.set_ylabel("AQI", fontsize=10)
ax.set_title("Shaded band = daily min–max range; line = daily average. Gaps = fully missing days.",
             fontsize=9, color="#666", pad=6)
ax.legend(fontsize=8.5, loc="upper left", framealpha=0.9)
ax.grid(axis="y", alpha=0.25, linewidth=0.7)
ax.set_facecolor("#fafaf8")

fig2.tight_layout()
out2 = os.path.join(HERE, "chandni_chart2_daily_trend.png")
fig2.savefig(out2, dpi=180, bbox_inches="tight", facecolor=BG)
print(f"Saved {out2}")

# ══════════════════════════════════════════════════════════════════════════════
# CHART 3 — HOURLY PATTERN
# ══════════════════════════════════════════════════════════════════════════════
fig3, ax = plt.subplots(figsize=(9, 9), facecolor=BG)
fig3.suptitle(
    "Average AQI by Hour of Day — April 2025 | Chandni Chowk, Delhi (IITM)",
    fontsize=12, fontweight="bold", color="#2c2c2c", y=1.01,
)

def bar_color(v):
    if v <= 100:  return "#d4c84a"
    if v <= 200:  return "#e8913a"
    return "#c0392b"

bar_colors = [bar_color(v) for v in hourly_avg]
bars = ax.barh(range(24), hourly_avg.values,
               color=bar_colors, edgecolor="none", height=0.65)

for i, (bar, val) in enumerate(zip(bars, hourly_avg.values)):
    ax.text(val + 0.5, i, f"{val:.1f}", va="center", fontsize=8, color="#555")

ax.set_yticks(range(24))
ax.set_yticklabels([f"{h}:00" for h in range(24)], fontsize=8.5)
ax.set_xlabel("Average AQI (across available days)", fontsize=10)
ax.set_title(
    "Unlike Greater Noida, Chandni Chowk shows a diurnal rise —\n"
    "higher AQI in late evening / night hours.",
    fontsize=9, color="#666", pad=6,
)

xmin = max(0, hourly_avg.min() - 15)
xmax = hourly_avg.max() + 20
ax.set_xlim(xmin, xmax)

for level, clr in [(100, "#d4aa00"), (200, "#c0392b")]:
    if xmin < level < xmax:
        ax.axvline(level, color=clr, linewidth=0.8, linestyle=":", alpha=0.8)
        ax.text(level + 1, 23.5, str(level), fontsize=7, color=clr)

ax.invert_yaxis()
ax.grid(axis="x", alpha=0.25, linewidth=0.7)
ax.set_facecolor("#fafaf8")

fig3.tight_layout()
out3 = os.path.join(HERE, "chandni_chart3_hourly_pattern.png")
fig3.savefig(out3, dpi=180, bbox_inches="tight", facecolor=BG)
print(f"Saved {out3}")
print("\nDone — 3 charts generated.")

The data used is sourced from the Central Control Room for Air Quality Management