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Sulfamerazine/Form_I_thermal_expansion.csv

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+SLFNMA02,,,,,,,
+Identifier,Property,Value (1/K),Std, N, Name, Reference,Comment
+1, Volumetric thermal expansion coefficient @160K, 0.000119, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD 
+2, Volumetric thermal expansion coefficient @170K, 0.000119, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+3, Volumetric thermal expansion coefficient @180K, 0.00012, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+4, Volumetric thermal expansion coefficient @190K, 0.00012, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+5, Volumetric thermal expansion coefficient @200K, 0.000121, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+6, Volumetric thermal expansion coefficient @210K, 0.000122, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+7, Volumetric thermal expansion coefficient @220K, 0.000122, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+8, Volumetric thermal expansion coefficient @230K, 0.000123, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+9, Volumetric thermal expansion coefficient @240K, 0.000124, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+10, Volumetric thermal expansion coefficient @250K, 0.000124, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+11, Volumetric thermal expansion coefficient @260K, 0.000125, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+12, Volumetric thermal expansion coefficient @270K, 0.000126, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+13, Volumetric thermal expansion coefficient @280K, 0.000126, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+14, Volumetric thermal expansion coefficient @290K, 0.000127, 0.000016, Pallipurath, 10.1021/acs.molpharmaceut.5b00504, SCXRD
+15, Volumetric thermal expansion coefficient @310K, 0.000103, 0.000010, , , PXRD
+16, Volumetric thermal expansion coefficient @320K, 0.000107, 0.000009, , , PXRD
+17, Volumetric thermal expansion coefficient @330K, 0.00011, 0.000008, , , PXRD
+18, Volumetric thermal expansion coefficient @340K, 0.000114, 0.000007, , , PXRD
+19, Volumetric thermal expansion coefficient @350K, 0.000118, 0.000005, , , PXRD
+20, Volumetric thermal expansion coefficient @360K, 0.000122, 0.000005, , , PXRD
+21, Volumetric thermal expansion coefficient @370K, 0.000126, 0.000004, , , PXRD
+22, Volumetric thermal expansion coefficient @380K, 0.000130, 0.000003, , , PXRD
+23, Volumetric thermal expansion coefficient @390K, 0.000134, 0.000003, , , PXRD
+24, Volumetric thermal expansion coefficient @400K, 0.000137, 0.000004, , , PXRD
+25, Volumetric thermal expansion coefficient @410K, 0.000141, 0.000004, , , PXRD
+26, Volumetric thermal expansion coefficient @420K, 0.000145, 0.000005, , , PXRD
+27, Volumetric thermal expansion coefficient @430K, 0.000149, 0.000006, , , PXRD
+28, Volumetric thermal expansion coefficient @440K, 0.000152, 0.000007, , , PXRD
+29, Volumetric thermal expansion coefficient @450K, 0.000156, 0.000008, , , PXRD
+30, Volumetric thermal expansion coefficient @460K, 0.000160, 0.000009, , , PXRD
+31, Volumetric thermal expansion coefficient @470K, 0.000164, 0.000011, , , PXRD 

Sulfamerazine/Supplemetary_data/thermal_expansion_coeffecient.py

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+# import required libraries
+import pandas as pd
+import numpy as np
+import sys
+from scipy.stats import t
+
+# User parameters
+INPUT_CSV = sys.argv[1]  # Change as needed
+TEMP_COL = 'Temperature/ K'
+VOLUME_COL = 'volume / Angstrom-3'
+NOTES_COL = 'notes'
+INCREMENT = 10  # Kelvin
+if "Form" in INPUT_CSV:
+    name = f"Form_{INPUT_CSV.split('_')[1]}_"
+else:
+    name = ""
+# Output file names
+OUTPUT_SCXRD = f'thermal_expansion_coefficient_{name}SCXRD.csv'
+OUTPUT_PXRD = f'thermal_expansion_coefficient_{name}PXRD.csv'
+OUTPUT_ALL = f'thermal_expansion_coefficient_{name}ALL.csv'
+# Read the data
+try:
+    df = pd.read_csv(INPUT_CSV)
+except Exception as e:
+    print(f"Error reading '{INPUT_CSV}': {e}")
+    exit(1)
+
+df.columns = df.columns.str.strip()
+
+required_cols = [TEMP_COL, VOLUME_COL, NOTES_COL]
+missing_cols = [col for col in required_cols if col not in df.columns]
+if missing_cols:
+    print(f"Error: The following required columns are missing from '{INPUT_CSV}': {missing_cols}")
+    print("Please ensure your CSV contains columns named exactly as follows:")
+    for col in required_cols:
+        print(f"  - {col}")
+    print("Or, modify this script to match your column names.")
+    exit(1)
+# Helper function to process a subset
+def process_and_save(sub_df, output_csv):
+    # Drop rows with missing temperature or volume
+    sub_df = sub_df.dropna(subset=[TEMP_COL, VOLUME_COL])
+    if len(sub_df) < 3:
+        print(f"Not enough data for {output_csv}")
+        return
+    temps = sub_df[TEMP_COL].astype(float).values
+    vols = sub_df[VOLUME_COL].astype(float).values
+    coeffs = np.polyfit(temps, vols, 2)
+    p = np.poly1d(coeffs)
+
+    def expansion_coefficient(T):
+        return 2*coeffs[0]*T + coeffs[1]
+
+    T_min = int(np.ceil(temps.min() / INCREMENT) * INCREMENT)
+    T_max = int(np.floor(temps.max() / INCREMENT) * INCREMENT)
+    T_points = np.arange(T_min, T_max + 1, INCREMENT)
+    exp_coeffs = expansion_coefficient(T_points)
+    volumes_at_points = np.polyval(coeffs, T_points)
+    norm_exp_coeffs = exp_coeffs / volumes_at_points
+
+    # Confidence band calculation
+    # Fit quadratic: vols = a*T^2 + b*T + c
+    # Get predicted values
+    y_pred = p(temps)
+    residuals = vols - y_pred
+    dof = len(temps) - 3  # 3 parameters for quadratic
+    s_err = np.sqrt(np.sum(residuals**2) / dof)
+
+    # For each T_point, calculate confidence interval
+    # Design matrix for quadratic
+    X = np.vstack([T_points**2, T_points, np.ones_like(T_points)]).T
+    X_data = np.vstack([temps**2, temps, np.ones_like(temps)]).T
+    # (X^T X)^-1
+    XT_X_inv = np.linalg.inv(X_data.T @ X_data)
+    # t-value for 95% confidence
+    t_val = t.ppf(0.975, dof)
+
+    conf_band = []
+    for i, T in enumerate(T_points):
+        x0 = np.array([T**2, T, 1])
+        # Standard error of prediction
+        se_pred = s_err * np.sqrt(x0 @ XT_X_inv @ x0.T)
+        conf_band.append(t_val * se_pred)
+    conf_band = np.array(conf_band)
+    norm_conf_band = conf_band / volumes_at_points
+    output_df = pd.DataFrame({
+        'Identifier': np.arange(1, len(T_points) + 1),
+        'Property': [f"Volumetric thermal expansion coefficient @{int(T)}K" for T in T_points],
+#        'Thermal Expansion Coefficient (dV/dT)': exp_coeffs,
+        'Value (1/K)': norm_exp_coeffs,
+#        'Regression Fit (Volume)': volumes_at_points,
+#        'Confidence Band (+/-)': conf_band,
+        'Std': norm_conf_band,
+        'N': "",
+        'Name': "",
+        'Reference': "",
+        'Comment': ""
+    })
+    output_df.to_csv(output_csv, index=False)
+
+# Separate SC-XRD and PXRD data
+df[NOTES_COL] = df[NOTES_COL].astype(str)
+scxrd_df = df[df[NOTES_COL].str.contains('SC-XRD', case=False, na=False)]
+pxrd_df = df[df[NOTES_COL].str.contains('PXRD', case=False, na=False)]
+
+has_scxrd = not scxrd_df.empty
+has_pxrd = not pxrd_df.empty
+
+if has_scxrd or has_pxrd:
+    if has_scxrd:
+        process_and_save(scxrd_df, OUTPUT_SCXRD)
+    else:
+        print("No SC-XRD data found. Explicitly labelled 'SC-XRD' in the Notes column")
+    if has_pxrd:
+        process_and_save(pxrd_df, OUTPUT_PXRD)
+    else:
+        print("No PXRD data found. Explicitly labelled 'PXRD' in the Notes column")
+else:
+    print("No Explicitly labelled SC-XRD or PXRD data found. Processing all data together.")
+    process_and_save(df, OUTPUT_ALL)