Refresh and optimize legacy code

README.md

@@ -50,8 +50,6 @@ This table provides a clear and concise overview of the 3D models and their corr
 
 - Intuitive Graphical User Interface: Simplifying Program Interaction. Experience seamless interaction with our PyQt5-powered UI. Standalone Python executable created using PyInstaller - no additional dependencies required.
 
-![GUIblank](https://raw.githubusercontent.com/supratimdasinfo/magnetocaloric.mcepy/main/images/GUIv3b.PNG?raw=True)
-
 ![GUI](https://raw.githubusercontent.com/supratimdasinfo/magnetocaloric.mcepy/main/images/GUIv3.PNG?raw=True)
 
 For the Python programming approach, follow the steps in the README.
@@ -61,7 +59,7 @@ For the Python programming approach, follow the steps in the README.
 You can easily install the **magnetocaloric** package using pip. Open your command-line interface and run the following command:
 
 ```shell
-pip install magnetocaloric==1.6.1
+pip install magnetocaloric==1.6.6
 ```
 
 This will install the specified version of the **magnetocaloric** package.

build/lib/magnetocaloric/MH_Interpolation/__init__.py

@@ -0,0 +1,5 @@
+from .interpol import interpol
+
+__all__ = [
+    'interpol'
+]

build/lib/magnetocaloric/__init__.py

@@ -1,29 +1,26 @@
-from .interpol import interpol
-from .Msqr_HbyM import Msqr_HbyM
-from .data_reading import data_reading
-from .color_marker import color_marker
-from .data_plotting import data_plotting
-from .delSm_T import delSm_T
-from .delSm_Pivoting import delSm_Pivoting
-from .MH_Pivoting import MH_Pivoting
-from .arrott_plotting import arrott_plotting
-from .data_writing import data_writing
-from .T_FWHM_RCP import T_FWHM_RCP
-from .data_3d_plotting import mce_3d
-from .mag_sus import mag_sus
+from .mce_2D_Operations.data_2d_operation import mce
+from .mce_3D_Distribution.data_3d_plotting import mce_3d
+from .MH_Interpolation.interpol import interpol
 
 __all__ = [
     'interpol',
-    'Msqr_HbyM',
-    'data_reading',
-    'color_marker',
-    'data_plotting',
-    'delSm_T',
-    'delSm_Pivoting',
-    'MH_Pivoting',
-    'arrott_plotting',
-    'data_writing',
-    'T_FWHM_RCP',
-    'mce_3d',
-    'mag_sus'
+    'mce',
+    'mce_3d'
 ]
+# magnetocaloric package
+# Developed by Supratim Das
+# First release: November 5, 2021
+# Email: supratim0707@gmail.com
+
+# This file can include package-level metadata or comments.
+# It's executed when the package is imported.
+
+# Example metadata:
+
+__version__ = '1.6.6'  # Package version
+__author__ = 'Supratim Das' # Package author
+__email__ = 'supratim0707@gmail.com' #Get in Touch
+__description__ = 'Effective Approach To Calculate Magnetocaloric Effect Of Any Magnetic Material Using Python'  # Package description
+__license__ = 'MIT'  # License information
+__url__ = 'https://pypi.org/project/magnetocaloric/'  # URL to the package's repository
+__keywords__ = ['magnetism', 'thermodynamics', 'magnetocaloric']  # Keywords describing the package

build/lib/magnetocaloric/mce_2D_Operations/__init__.py

@@ -0,0 +1,5 @@
+from .data_2d_operation import mce
+
+__all__ = [
+    'mce'
+]

build/lib/magnetocaloric/mce_2D_Operations/data_2d_operation.py

@@ -0,0 +1,81 @@
+import matplotlib.pyplot as plt
+import sys
+from .Msqr_HbyM import Msqr_HbyM
+from .data_reading import data_reading
+from .color_marker import color_marker
+from .data_plotting import data_plotting
+from .delSm_T import delSm_T
+from .delSm_Pivoting import delSm_Pivoting
+from .MH_Pivoting import MH_Pivoting
+from .arrott_plotting import arrott_plotting
+from .data_writing import data_writing
+from .T_FWHM_RCP import T_FWHM_RCP
+from .mag_sus import mag_sus
+from .N_exponent import n_expo
+
+if not sys.warnoptions:
+    import warnings
+    warnings.simplefilter("ignore")
+
+plt.rcParams.update({'font.size': 7})
+
+
+def mce(samp_name, file_dir, sheet_index, T_row, H_col, g_name, M_unit, H_unit, T_unit, plot_legend, loc, field, linear_threshold, save_data):
+    """
+    Perform Magnetocaloric Effect (MCE) analysis.
+    Args:
+        n (int): Number of temperature values.
+        one_n (int): Number of data points at each temperature.
+    Returns:
+        None: The function performs the MCE analysis and generates plots, without returning any value.
+    """
+    # Data Collection
+    H, M, T, n, one_n, two_n = data_reading(file_dir, sheet_index, T_row, H_col)    
+    # Entropy Change Calculation
+    three_entropy_change_con, temperatures, Label_one = delSm_T(n, one_n, two_n, H, M, T)
+    five_entropy_change_con, six_entropy_change_con = delSm_Pivoting(n, three_entropy_change_con, Label_one, temperatures)
+    # Local Exponent Calculation
+    N_expo_con, T_arr, H_arr, N_H_label = n_expo(n, one_n, two_n, H, M, T)
+    # Color and Marker Definitions
+    colour, marker = color_marker()
+    # Magnetization and Field Reshaping
+    one_M_plot_final, two_M_plot_final = MH_Pivoting(one_n, n, M, H)
+    # Arrott Plot
+    H_plot_final, M_sqr, one_H_by_M_con = Msqr_HbyM(one_n, n, M, H, T, one_M_plot_final)
+    # Susceptibility
+    susceptibility_final = []
+    T_sus = []
+    sus_inv = [] 
+    sus = [] 
+    np_T_sus = [] 
+    regression_line = [] 
+    np_T_sus_bu = [] 
+    regression_bu = []
+    x = 0
+    if (g_name== 'sus_plot' or g_name== 'all_plots'):
+        susceptibility_final, T_sus, sus_inv, sus, np_T_sus, regression_line, np_T_sus_bu, regression_bu, x = mag_sus(g_name, n, one_n, T, H, M, field, linear_threshold)
+    else:
+        pass
+    T_FWHM_con = []
+    T_FWHM_con_final = []
+    RCP_con = []
+    RCP_final = []
+    H_for_RCP = []
+    if (g_name== 'RCP_plot' or g_name== 'all_plots'):       
+        # Calculate T_FWHM and RCP
+        T_FWHM_con, T_FWHM_con_final, RCP_con, RCP_final, H_for_RCP = T_FWHM_RCP(n, Label_one, six_entropy_change_con)
+    else:
+        pass
+    # Data Visualization
+    data_plotting(g_name, one_n, n, T, H, M_unit, H_unit, T_unit, colour, marker, Label_one, plot_legend, loc, one_M_plot_final, two_M_plot_final, H_plot_final, temperatures, five_entropy_change_con, M_sqr, one_H_by_M_con, N_expo_con, T_arr, N_H_label,  T_sus, sus_inv, sus, np_T_sus, regression_line, np_T_sus_bu, regression_bu, x, linear_threshold, T_FWHM_con, T_FWHM_con_final, RCP_con, RCP_final, H_for_RCP, samp_name)
+    # Modified Arrott Plot
+    M_pow_MFT, H_by_M_pow_MFT, M_pow_TMFT, H_by_M_pow_TMFT, M_pow_3DH, H_by_M_pow_3DH, M_pow_3DI, H_by_M_pow_3DI = arrott_plotting(g_name, n, one_M_plot_final, one_H_by_M_con)
+    # Store Data to Excel Files
+    if (save_data == 'allow'):
+        data_writing(g_name, file_dir, n, T, Label_one, six_entropy_change_con, M_sqr, one_H_by_M_con, M_pow_MFT, H_by_M_pow_MFT, M_pow_TMFT, H_by_M_pow_TMFT, M_pow_3DH, H_by_M_pow_3DH, M_pow_3DI, H_by_M_pow_3DI, susceptibility_final, T_FWHM_con_final, RCP_final, H_for_RCP, N_expo_con, T_arr, N_H_label)
+    else:
+        pass
+    return ("")
+
+
+

build/lib/magnetocaloric/data_plotting.py → build/lib/magnetocaloric/mce_2D_Operations/data_plotting.py

@@ -161,7 +161,7 @@ def data_plotting(g_name, one_n, n, T, H, M_unit, H_unit, T_unit, colour, marker
 
 
         fig, ax1 = plt.subplots()
-        ax1.set_xlabel(f"Temperature (T) {T_unit}")
+        ax1.set_xlabel(f"Temperature ({T_unit})")
         ax1.set_ylabel(f"1/Chi {H_unit}/({M_unit})")
         ax1.plot(T_sus, sus_inv, linestyle='solid', marker='o',label=label01, markerfacecolor='none', color='black',markersize=5, linewidth=0.5)
         if linear_threshold == 0.0 or linear_threshold == 'None':
@@ -207,7 +207,7 @@ def data_plotting(g_name, one_n, n, T, H, M_unit, H_unit, T_unit, colour, marker
 
         if len(RCP_final) >= 2:
             fig, ax1 = plt.subplots()
-            ax1.set_xlabel(f"Magnetic Field(H) {H_unit}")
+            ax1.set_xlabel(f"Magnetic Field ({H_unit})")
             ax1.set_ylabel(f"RCP ({M_unit}).{H_unit}")
             ax1.plot(H_for_RCP, RCP_final, linestyle='solid', marker='o', label=samp_name_plus_RCP, color='r', markersize=5, linewidth=0.5)
             ax1.legend(loc='upper left', frameon=False, ncol=2)

build/lib/magnetocaloric/mce_3D_Distribution/__init__.py

@@ -0,0 +1,5 @@
+from .data_3d_plotting import mce_3d
+
+__all__ = [
+    'mce_3d',
+]

build/lib/magnetocaloric/mcepy.py

@@ -1,91 +1,18 @@
-import numpy as np
-import xlsxwriter
-from random import randint
-import xlrd
-import matplotlib.pyplot as plt
-from num2words import num2words
+# Import necessary libraries
 import sys
-import itertools
-
+import matplotlib.pyplot as plt
 
-from .interpol import interpol
-from .Msqr_HbyM import Msqr_HbyM
-from .data_reading import data_reading
-from .color_marker import color_marker
-from .data_plotting import data_plotting
-from .delSm_T import delSm_T
-from .delSm_Pivoting import delSm_Pivoting
-from .MH_Pivoting import MH_Pivoting
-from .arrott_plotting import arrott_plotting
-from .data_writing import data_writing
-from .T_FWHM_RCP import T_FWHM_RCP
-from .data_3d_plotting import mce_3d
-from .mag_sus import mag_sus
-from .N_exponent import n_expo
+# Import modules from different packages
+from .mce_2D_Operations.data_2d_operation import mce
+from .mce_3D_Distribution.data_3d_plotting import mce_3d
+from .MH_Interpolation.interpol import interpol
 
+# Suppress warnings if not already suppressed
 if not sys.warnoptions:
     import warnings
     warnings.simplefilter("ignore")
 
+# Customize font size for matplotlib
 plt.rcParams.update({'font.size': 7})
 
 
-def mce(samp_name, file_dir, sheet_index, T_row, H_col, g_name, M_unit, H_unit, T_unit, plot_legend, loc, field, linear_threshold, save_data):
-    """
-    Perform Magnetocaloric Effect (MCE) analysis.
-    Args:
-        n (int): Number of temperature values.
-        one_n (int): Number of data points at each temperature.
-    Returns:
-        None: The function performs the MCE analysis and generates plots, without returning any value.
-    """
-    # Data Collection
-    H, M, T, n, one_n, two_n = data_reading(file_dir, sheet_index, T_row, H_col)    
-    # Entropy Change Calculation
-    three_entropy_change_con, temperatures, Label_one = delSm_T(n, one_n, two_n, H, M, T)
-    five_entropy_change_con, six_entropy_change_con = delSm_Pivoting(n, three_entropy_change_con, Label_one, temperatures)
-    # Local Exponent Calculation
-    N_expo_con, T_arr, H_arr, N_H_label = n_expo(n, one_n, two_n, H, M, T)
-    # Color and Marker Definitions
-    colour, marker = color_marker()
-    # Magnetization and Field Reshaping
-    one_M_plot_final, two_M_plot_final = MH_Pivoting(one_n, n, M, H)
-    # Arrott Plot
-    H_plot_final, M_sqr, one_H_by_M_con = Msqr_HbyM(one_n, n, M, H, T, one_M_plot_final)
-    # Susceptibility
-    susceptibility_final = []
-    T_sus = []
-    sus_inv = [] 
-    sus = [] 
-    np_T_sus = [] 
-    regression_line = [] 
-    np_T_sus_bu = [] 
-    regression_bu = []
-    x = 0
-    if (g_name== 'sus_plot' or g_name== 'all_plots'):
-        susceptibility_final, T_sus, sus_inv, sus, np_T_sus, regression_line, np_T_sus_bu, regression_bu, x = mag_sus(g_name, n, one_n, T, H, M, field, linear_threshold)
-    else:
-        pass
-    T_FWHM_con = []
-    T_FWHM_con_final = []
-    RCP_con = []
-    RCP_final = []
-    H_for_RCP = []
-    if (g_name== 'RCP_plot' or g_name== 'all_plots'):       
-        # Calculate T_FWHM and RCP
-        T_FWHM_con, T_FWHM_con_final, RCP_con, RCP_final, H_for_RCP = T_FWHM_RCP(n, Label_one, six_entropy_change_con)
-    else:
-        pass
-    # Data Visualization
-    data_plotting(g_name, one_n, n, T, H, M_unit, H_unit, T_unit, colour, marker, Label_one, plot_legend, loc, one_M_plot_final, two_M_plot_final, H_plot_final, temperatures, five_entropy_change_con, M_sqr, one_H_by_M_con, N_expo_con, T_arr, N_H_label,  T_sus, sus_inv, sus, np_T_sus, regression_line, np_T_sus_bu, regression_bu, x, linear_threshold, T_FWHM_con, T_FWHM_con_final, RCP_con, RCP_final, H_for_RCP, samp_name)
-    # Modified Arrott Plot
-    M_pow_MFT, H_by_M_pow_MFT, M_pow_TMFT, H_by_M_pow_TMFT, M_pow_3DH, H_by_M_pow_3DH, M_pow_3DI, H_by_M_pow_3DI = arrott_plotting(g_name, n, one_M_plot_final, one_H_by_M_con)
-    # Store Data to Excel Files
-    if (save_data == 'allow'):
-        data_writing(g_name, file_dir, n, T, Label_one, six_entropy_change_con, M_sqr, one_H_by_M_con, M_pow_MFT, H_by_M_pow_MFT, M_pow_TMFT, H_by_M_pow_TMFT, M_pow_3DH, H_by_M_pow_3DH, M_pow_3DI, H_by_M_pow_3DI, susceptibility_final, T_FWHM_con_final, RCP_final, H_for_RCP, N_expo_con, T_arr, N_H_label)
-    else:
-        pass
-    return ("")
-
-
-

magnetocaloric.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: magnetocaloric
-Version: 1.6.1
+Version: 1.6.6
 Summary: Effective Approach To Calculate Magnetocaloric Effect Of Any Magnetic Material Using Python
 Home-page: https://github.com/supratimdasinfo/magnetocaloric.mcepy
 Author: Supratim Das
@@ -67,8 +67,6 @@ This table provides a clear and concise overview of the 3D models and their corr
 
 - Intuitive Graphical User Interface: Simplifying Program Interaction. Experience seamless interaction with our PyQt5-powered UI. Standalone Python executable created using PyInstaller - no additional dependencies required.
 
-![GUIblank](https://raw.githubusercontent.com/supratimdasinfo/magnetocaloric.mcepy/main/images/GUIv3b.PNG?raw=True)
-
 ![GUI](https://raw.githubusercontent.com/supratimdasinfo/magnetocaloric.mcepy/main/images/GUIv3.PNG?raw=True)
 
 For the Python programming approach, follow the steps in the README.
@@ -78,7 +76,7 @@ For the Python programming approach, follow the steps in the README.
 You can easily install the **magnetocaloric** package using pip. Open your command-line interface and run the following command:
 
 ```shell
-pip install magnetocaloric==1.6.1
+pip install magnetocaloric==1.6.6
 ```
 
 This will install the specified version of the **magnetocaloric** package.

magnetocaloric.egg-info/SOURCES.txt

@@ -3,24 +3,28 @@ Licence.txt
 MANIFEST.in
 README.md
 setup.py
-magnetocaloric/MH_Pivoting.py
-magnetocaloric/Msqr_HbyM.py
-magnetocaloric/N_exponent.py
-magnetocaloric/T_FWHM_RCP.py
 magnetocaloric/__init__.py
-magnetocaloric/arrott_plotting.py
-magnetocaloric/color_marker.py
-magnetocaloric/data_3d_plotting.py
-magnetocaloric/data_plotting.py
-magnetocaloric/data_reading.py
-magnetocaloric/data_writing.py
-magnetocaloric/delSm_Pivoting.py
-magnetocaloric/delSm_T.py
-magnetocaloric/interpol.py
-magnetocaloric/mag_sus.py
 magnetocaloric/mcepy.py
 magnetocaloric.egg-info/PKG-INFO
 magnetocaloric.egg-info/SOURCES.txt
 magnetocaloric.egg-info/dependency_links.txt
 magnetocaloric.egg-info/requires.txt
-magnetocaloric.egg-info/top_level.txt
+magnetocaloric.egg-info/top_level.txt
+magnetocaloric/MH_Interpolation/__init__.py
+magnetocaloric/MH_Interpolation/interpol.py
+magnetocaloric/mce_2D_Operations/MH_Pivoting.py
+magnetocaloric/mce_2D_Operations/Msqr_HbyM.py
+magnetocaloric/mce_2D_Operations/N_exponent.py
+magnetocaloric/mce_2D_Operations/T_FWHM_RCP.py
+magnetocaloric/mce_2D_Operations/__init__.py
+magnetocaloric/mce_2D_Operations/arrott_plotting.py
+magnetocaloric/mce_2D_Operations/color_marker.py
+magnetocaloric/mce_2D_Operations/data_2d_operation.py
+magnetocaloric/mce_2D_Operations/data_plotting.py
+magnetocaloric/mce_2D_Operations/data_reading.py
+magnetocaloric/mce_2D_Operations/data_writing.py
+magnetocaloric/mce_2D_Operations/delSm_Pivoting.py
+magnetocaloric/mce_2D_Operations/delSm_T.py
+magnetocaloric/mce_2D_Operations/mag_sus.py
+magnetocaloric/mce_3D_Distribution/__init__.py
+magnetocaloric/mce_3D_Distribution/data_3d_plotting.py

magnetocaloric/MH_Interpolation/__init__.py

@@ -0,0 +1,5 @@
+from .interpol import interpol
+
+__all__ = [
+    'interpol'
+]