Disdrometer-Data-Processing/temperature_vs_particle_count.py

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates

#======================================================
# ==              SCRIPT PARAMETERS                  ==
#======================================================

# === CSV Parameters ===
CSV_FILENAME      =   r"c:\Users\ranny\Downloads\week_2025_41_data.csv"
CSV_SEPARATOR     =   ","

COL_DIAMETER      =   "Diameter1 [mm]"
COL_VELOCITY      =   "Velocity [m/s]"
COL_TEMPERATURE   =   "Temperature [C]"

FIG_TITLE         =   f"Rain activity of week 41 of 2025"

FIG_GRID_STYLE    =   ":"

# === Limits for Velocity & Diameter Filtering ===
LIM_VELOCITY_MIN  =   0.2   # m/s
LIM_DIAMETER_MIN  =   0.16   # mm
LIM_DIAMETER_MAX  =   8.0   # mm

# === Figure Parameters ===
FIG_WIDTH         =   9
FIG_HEIGHT        =   6

FIG_DATA_ALPHA    =   0.6
FIG_DATA_SIZE     =   35

# === Load CSV ===
df = pd.read_csv(CSV_FILENAME, sep=CSV_SEPARATOR)

# === Clean column names ===
df.columns = [col.strip() for col in df.columns]
print(df.columns)

# === Drop missing data & provide filtering ===
df = df.dropna(subset=[COL_DIAMETER, COL_VELOCITY, COL_TEMPERATURE])
df = df[df[COL_VELOCITY] >= LIM_VELOCITY_MIN]
df = df[(df[COL_DIAMETER] >= LIM_DIAMETER_MIN) & (df[COL_DIAMETER] <= LIM_DIAMETER_MAX)]

# Convert timestamp
df['Timestamp'] = pd.to_datetime(df['Timestamp'])
df = df.set_index('Timestamp')


# === Hourly resampling ===
hourly = (
    df
    .resample("1h")
    .agg({
        COL_TEMPERATURE: "mean",
        COL_DIAMETER: "count"
    })
    .rename(columns={COL_DIAMETER: "Particle Count"})
)

hourly['Temperature [C]'] = hourly['Temperature [C]'].interpolate(method='linear')

# =====================================
# 📈 Create the plot (temperature + particles)
# =====================================
fig, ax1 = plt.subplots(figsize=(9, 6))
ax2 = ax1.twinx()

ax1.plot(hourly.index, hourly['Temperature [C]'], color='tab:red', label='Temperature [°C]', linewidth=2)
ax1.set_ylabel('Temperature [°C]', color='tab:red')
ax1.tick_params(axis='y', labelcolor='tab:red')

ax2.bar(hourly.index, hourly['Particle Count'], width=0.03, alpha=0.4, color='tab:blue', label='Particles per hour')
ax2.set_ylabel('Particle count per hour', color='tab:blue')
ax2.tick_params(axis='y', labelcolor='tab:blue')

# === Formátovanie timestamp na dd.mm.yyyy ===
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d.%m.%Y'))
fig.autofmt_xdate()  # otočí dátumy, aby sa neprekrývali

plt.title(FIG_TITLE)
# === ZDIEĽANÁ LEGENDA ===
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left', frameon=True)
plt.grid(True, linestyle=FIG_GRID_STYLE)
plt.tight_layout()
plt.show()