+import numpy as np

+import pandas as pd(파일 끝에 줄바꿈 문자 없음)

+import numpy as np

+from scipy.interpolate import CubicSpline

+from datetime import datetime, timedelta

+import pandas as pd

+import matplotlib.pyplot as plt

+

+

+def interpolate_value(df, timestamp, col_key, k=12, show_graph=False):

+    """

+    :param df: DataFrame with 'timestamp' and a specified column key.

+    :param timestamp: String of timestamp in format '%Y%m%d%H%M' to interpolate value for.

+    :param col_key: The key/column name in df for which value needs to be interpolated.

+    :param k: Number of hours before and after the target timestamp to be considered for interpolation.

+    :return: Interpolated value for the given timestamp.

+    """

+    # Convert timestamp to datetime object

+    target_time = datetime.strptime(timestamp, '%Y%m%d%H%M')

+

+    # Get nearest left 'o clock' time

+    left_o_clock = target_time.replace(minute=0)

+

+    # Prepare a list of relevant times for interpolation: k before and k after

+    relevant_times = [left_o_clock + timedelta(hours=i) for i in range(-k, k + 1)]

+

+    # Convert them to string format for matching with the DataFrame

+    relevant_times_str = [dt.strftime('%Y%m%d%H%M') for dt in relevant_times]

+    relevant_times_str = np.array(relevant_times_str, dtype=int)

+    # Extract relevant rows from DataFrame

+    relevant_df = df[df['관측시각'].isin(relevant_times_str)].sort_values(by='관측시각')

+

+    # Convert datetime to numerical format: -k to k

+    x = [i for i in range(-k, k+1)]

+    y = relevant_df[col_key].values

+

+    # Find the x value for the target timestamp

+    x_target = (target_time - left_o_clock).total_seconds() / 3600

+

+    # Create a cubic spline interpolation function

+    cs = CubicSpline(x, y)

+

+    if show_graph:

+        # For visualization

+        x_dense = np.linspace(min(x), max(x), 400)  # Densely sampled x values

+        y_dense = cs(x_dense)  # Interpolated values

+

+        fig, ax = plt.subplots(figsize=(6.5, 4))

+        ax.plot(x, y, 'o', label='Data')

+        ax.plot(x_dense, y_dense, label='Cubic Spline Interpolation')

+        ax.axvline(x_target, color='red', linestyle='--', label='Target Timestamp')  # Marking the target timestamp

+        ax.legend()

+        plt.show()

+

+    # Return the interpolated value at x_target

+    return cs(x_target)

+

+

+if __name__ == '__main__':

+    df = pd.read_csv("/home/juni/PycharmProjects/failure_analysis/data/weather/202007010000_202308010000.csv",

+                     encoding='utf-8')

+    timestamp = '202307271015'

+    print(interpolate_value(df, timestamp, '기온', show_graph=True))

+def parse_gcode(file_path):

+    movements = []

+    with open(file_path, 'r') as file:

+        for line in file:

+            # (parse the line to extract commands and parameters)

+            cmd, x, y, z = ...  # Simplified for brevity

+            movements.append((cmd, x, y, z))

+    return movements

+def plot_gcode(movements):

+    fig, ax = plt.subplots()

+    x_data, y_data = [], []

+

+    for cmd, x, y, z in movements:

+        if cmd in ['G0', 'G1']:

+            x_data.append(x)

+            y_data.append(y)

+

+    ax.plot(x_data, y_data)

+    plt.show()

+

+movements = parse_gcode('path_to_gcode_file.gcode')

+plot_gcode(movements)