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+import pandas as pd
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+import matplotlib.pyplot as plt
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+import numpy as np
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+
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+#======================================================
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+# == SCRIPT PARAMETERS ==
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+#======================================================
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+
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+# === CSV Parameters ===
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+CSV_FILENAME = r"c:\Users\ranny\Downloads\week_2025_41_data.csv"
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+CSV_SEPARATOR = ","
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+
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+COL_DIAMETER = "Diameter1 [mm]"
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+COL_VELOCITY = "Velocity [m/s]"
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+COL_TEMPERATURE = "Temperature [C]"
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+COL_TEMP_GROUPED = "TempGrouped" # column added by script
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+
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+# === Limits for Velocity & Diameter Filtering ===
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+LIM_VELOCITY_MIN = 0.2 # m/s
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+LIM_DIAMETER_MIN = 0.7 # mm
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+LIM_DIAMETER_MAX = 5.0 # mm
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+
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+# === Data Analysis Parameters ===
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+TEMP_BIN_SIZE = 2 # split by 2 degrees
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+FIT_SAMPLE_COUNT = 100
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+
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+# === Figure Parameters ===
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+FIG_WIDTH = 9
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+FIG_HEIGHT = 6
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+
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+FIG_DATA_ALPHA = 0.6
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+FIG_DATA_SIZE = 35
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+
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+FIG_TR_COLOR = "black" # trendline color
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+FIG_TR_SIZE = 2
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+
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+FIG_LEGEND_TITLE = "Teplotné Skupiny [°C]"
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+FIG_LEGEND_SIZE = 8
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+FIG_LEGEND_COLS = 2
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+
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+FIG_TITLE = f"Závislosť Rýchlosť a Priemeru Častíc Zoskupené po {TEMP_BIN_SIZE}°C Teplotných Skupinách"
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+FIG_X_LABEL = "Priemer Častice [mm]"
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+FIG_Y_LABEL = "Rýchlosť Častice [m/s]"
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+
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+FIG_GRID_STYLE = ":"
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+
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+#======================================================
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+# == SCRIPT COMMANDS ==
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+#======================================================
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+
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+# === Load CSV ===
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+df = pd.read_csv(CSV_FILENAME, sep=CSV_SEPARATOR)
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+
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+# === Clean column names ===
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+df.columns = [col.strip() for col in df.columns]
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+
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+# === Drop missing data & provide filtering ===
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+df = df.dropna(subset=[COL_DIAMETER, COL_VELOCITY, COL_TEMPERATURE])
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+df = df[df[COL_VELOCITY] >= LIM_VELOCITY_MIN]
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+df = df[(df[COL_DIAMETER] >= LIM_DIAMETER_MIN) & (df[COL_DIAMETER] <= LIM_DIAMETER_MAX)]
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+
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+# === Group temperatures by 2°C bins ===
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+df[COL_TEMP_GROUPED] = (df[COL_TEMPERATURE] // TEMP_BIN_SIZE * TEMP_BIN_SIZE).astype(int)
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+
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+# Find most frequent temperature group
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+most_common_temp = df[COL_TEMP_GROUPED].value_counts().idxmax()
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+count = df[COL_TEMP_GROUPED].value_counts().max()
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+print(f"The most frequent temperature group is {most_common_temp}–{most_common_temp+1}°C with {count} samples.")
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+
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+# === Prepare plot ===
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+plt.figure(figsize=(FIG_WIDTH, FIG_HEIGHT))
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+unique_temps = sorted(df[COL_TEMP_GROUPED].unique())
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+colors = plt.cm.plasma(np.linspace(0, 1, len(unique_temps)))
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+
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+# === Scatter + power fit for each temperature group ===
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+trendline_handle = None
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+for color, temp in zip(colors, unique_temps):
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+ group = df[df[COL_TEMP_GROUPED] == temp]
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+ plt.scatter(group[COL_DIAMETER], group[COL_VELOCITY],
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+ color=color, label=f"{temp}-{temp+1} °C", alpha=FIG_DATA_ALPHA, s=FIG_DATA_SIZE)
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+
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+ # Power-law fit only for the most frequent group
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+ if temp == most_common_temp and len(group) >= 2:
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+ x = group[COL_DIAMETER].values
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+ y = group[COL_VELOCITY].values
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+ coeffs = np.polyfit(np.log(x), np.log(y), 1)
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+ b = coeffs[0]
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+ a = np.exp(coeffs[1])
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+ xfit = np.linspace(x.min(), x.max(), FIT_SAMPLE_COUNT)
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+ yfit = a * xfit ** b
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+ trendline_handle, = plt.plot(xfit, yfit, color=FIG_TR_COLOR, linewidth=FIG_TR_SIZE,
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+ label=f"Mocninová Aproximácia {temp}-{temp+1} °C")
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+ print(f"Power fit @ {temp}-{temp+1}°C: v = {a:.4e} * d^{b:.4f}")
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+
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+plt.title(FIG_TITLE)
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+plt.xlabel(FIG_X_LABEL)
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+plt.ylabel(FIG_Y_LABEL)
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+plt.legend(title=FIG_LEGEND_TITLE, fontsize=FIG_LEGEND_SIZE, ncol=FIG_LEGEND_COLS)
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+plt.grid(True, linestyle=FIG_GRID_STYLE)
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+plt.tight_layout()
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+plt.show()
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