Added: initial packages [V2023.10.1]

README.md

@@ -1,2 +1,19 @@
-# unipy
-A unified module for doing undergrad-grad level coding in physics using python.
+# **[unipy](https://github.com/mandal-anik10/unipy)**
+___________________________________________________________
+A unified module for numerical calculations in physics using python.
+
+I have a dream of making a numerical universe where I can simulate nature and physical phenomena just by incorporating the most fundamental law/infromation `(if, we will find the one most fundamental theroy of the universe)` of physics. Thhis module is in developement for that purpose. If you want to contribute, please send a mail. you can find contact information [here](https://mandal-anik10.github.io).
+
+Checkout the [documantation](https://mandal-anik10.github.io/unipy/) for more information about this module.
+___________________________________________________________
+## Installation:
+- Clone the repository in your computer and move to the repository:
+    ```
+    $ git clone https://github.com/mandal-anik10/unipy.git
+    $ cd unipy  
+    ```
+- Now you should have a shell prompt ready in the `unipy` folder, and by running `$ ls` you should see a folder again called `unipy`. You can then install `unipy` with:
+    ```
+    $ python setup.py install
+    ```
+___________________________________________________________

setup.py

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+import setuptools
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+setuptools.setup(
+    name="unipy",
+    version="2023.10.1",
+    author="Anik Mandal",
+    author_email="mandal.anik10@gmail.com",
+    description="A unified module for physics",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/mandal-anik10/unipy",
+    project_urls={
+        "Unipy webpage": "https://mandal-anik10.github.io/unipy/",
+    },
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    install_requires=[
+        "numpy", 
+        "sklearn"
+    ],
+    package_dir={},
+    packages=setuptools.find_packages(where="src"),
+    python_requires=">=3.6"
+)
+
+
+
+
+
+
+
+

unipy/BasicOp.py

@@ -0,0 +1,169 @@
+"""
+Module for basic mathematical operations and physical quantities.
+
+Mandal, Anik | 2023-10-03
+"""
+
+
+from numpy import *
+
+# Mathematical Operations----------
+
+# Summation:
+def Sum(arr, step = 1):
+    '''
+    Returns sum of the elements in a input list
+
+    -----Inputs----------
+    arr : list : list of integers/float
+    step : int : increment in the index value
+    -----Output----------
+    s : int/float : sum of the elements of list
+
+    * Equivalant to numpy.sum()
+    '''
+    s = 0
+    for i in range(0, len(arr), step):
+        s = s + arr[i]
+    return s
+
+
+# Factorial:
+def Fact(n, step=1):
+    '''
+    Returns factorial of a number 
+
+    -----Inputs----------
+    n : int : Input number
+    step : int : difference between two successive numbers
+    -----Output----------
+    f : int : factorial of the number
+    '''
+    f = 1
+    for i in range(1, n+1, step):
+        f = f * i
+    return f
+
+# Integration:
+def Integrate(y_data, x_data, spacing='linear'):
+    '''
+    Function for performing integration using trapizoidal method.
+
+    -----Inputs----------
+    y_data : list : a list of numbers defing the y values of the function
+    x_data : list : a list of numbers defing the x values of the function
+    spacing : string : specification of increment vector/scaler
+        - default :'linear'
+    -----Output----------
+    s : float : value of the integration
+    '''
+    if len(y_data) != len(x_data) or len(x_data) <= 1 or len(y_data) <= 1:
+        raise ValueError("x and y data are not consistant for trapizoidal integration")
+    else:
+        if spacing == 'linear':
+            h = x_data[1] - x_data[0]
+            s = Sum(y_data[1:len(x_data)-1])
+            s = (y_data[0] + y_data[-1] + 2 * s) * h / 2
+        else:
+            s = 0
+            for i in range(len(x_data)-1):
+                h = x_data[i+1] - x_data[i]
+                s = s + h * (y_data[i] + y_data[i+1]) / 2
+    return s
+
+def Integrate_simp(y_data, x_data, spacing='linear'):
+    '''
+    Function for performing integration using Simpson's 1/3 method.
+
+    -----Inputs----------
+    y_data : list : a list of numbers defing the y values of the function
+    x_data : list : a list of numbers defing the x values of the function
+    spacing : string : specification of increment vector/scaler
+        - default :'linear'
+    -----Output----------
+    s : float : value of the integration
+    '''
+    if len(y_data) != len(y_data) or len(x_data) <= 2 or len(y_data) <= 2:
+        raise ValueError("x and y data are not consistant for simpson integration")
+    else:
+        if spacing == 'linear':
+            h = x_data[1] - x_data[0]
+            s = 4 * Sum(y_data[1, len(x_data)-1], 2)
+            s = s + 2 * Sum(y_data[2, len(x_data)-1], 2)
+            s = (y_data[0] + s + y_data[-1]) * h / 3
+        else: 
+            raise NotImplementedError('Simpson\'s 1/3 integration is not implemented yet'+\
+                                      'in nonlinear scale; use tapizoidal integration.')
+
+# Fourier Series:
+def FourierSeries(y_data, x_data, nc=10):
+    '''
+    Returns Fouries Series of a function
+
+    -----Inputs----------
+    y_data : list : a list of numbers defing the y values of the function
+    x_data : list : a list of numbers defing the x values of the function
+    nc : int : number of coefficents of sin and cos series
+        - default :10
+    -----Outputs----------
+    cc : list : coeff. of cosine series
+    cs : list : coeff. of sin series
+    y_new : list : fourier function
+    '''
+    cc, cs, y_new = [], [], []
+
+    for i in range(nc):
+        yc = y_data * cos(pi * i * x_data)
+        ys = y_data * sin(pi * i * x_data)
+
+        sc = Integrate(yc, x_data)
+        ss = Integrate(ys, x_data)
+
+        cc.append(sc)
+        cs.append(ss)
+
+    cc[0] = cc[0]/2
+
+    for i in range(len(x_data)):
+        s = 0
+        for j in range(nc):
+            s = s + cc[j] * cos(pi * j *x_data[i]) + cs[j] * sin(pi * j * x_data[i])
+
+        y_new.append(s)
+    return cc, cs, y_new     
+
+
+# Physical Quantities----------
+
+# Inertia Tensor:
+def Inertia_T(Mass_List, Pos_Tensor):
+    '''
+    Returns Inertia Tensor of discrete masses
+
+    -----Inputs----------
+    arr : list : list of integers/float
+    -----Output----------
+    s : int/float : sum of the elements of list
+    '''
+    I = np.zeros((3, 3))
+
+    for m in range(3):
+        for n in range(3):
+
+            if m == n:
+                for k in range(len(Mass_List)):
+                    s = 0
+                    for i in range(3):
+                        if i != m:
+                            s = s + Mass_List[k]*Pos_Tensor[k][i]**2
+
+                    I[m][n] = I[m][n] + s
+
+            else:
+                for k in range(len(Mass_List)):
+                    s = - Mass_List[k]*Pos_Tensor[k][m]*Pos_Tensor[k][n]
+
+                    I[m][n] = I[m][n] + s
+    return I
+
+

unipy/CurveFit.py

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+"""
+Module for curve fitting.
+
+Mandal, Anik | 2023-10-03
+"""
+
+import numpy as np
+from sklearn.linear_model import Lasso
+from sklearn.preprocessing import PolynomialFeatures
+
+
+# Linear Fit Module:
+def LinearFit(x_data, y_Data):
+    '''
+    Returns slope and y-intersect of the best linear fit using least
+    square fit
+
+    -----Inputs----------
+    x_data : list : x coordinate of the input values
+    y_data : list : y coordinate of the input values
+    -----Outputs----------
+    m : float : slope of the best fit line
+    c : float : y-intersect of the best fit line.
+    '''
+    (sx, sy, p1, p2) = (0, 0, 0, 0)
+    for i in range(len(x_data)):
+        sx = sx + x_data[i]
+        sy = sy + y_Data[i]
+    xb = sx/len(x_data)
+    yb = sy/len(y_Data)
+    for i in range(len(x_data)):
+        p1 = p1 + (x_data[i]-xb)*(y_Data[i]-yb)
+        p2 = p2 + (x_data[i]-xb)**2
+    m = p1/p2
+    c = yb - m*xb
+    return m, c
+
+
+# Nolinear Fit Module:
+def LassoRegression(x_data, y_data, max_degrees=10, alpha_value=0.2, num_points=int(1e5)):
+    '''
+    Returns data set of best fit curve using sklearn Lasso Regression
+
+    -----Inputs----------
+    x_data : list : x coordinates of the input values
+    y_data : list : y coordinates of the input values
+    max_degree : int : maximum degree for interpolation
+        - default : 10
+    alpha_value : float : defines alpha parameter for Lasso regression
+        - default : 0.2
+    num_points : int : number of point for interpolation
+        - default : 1e5 
+    -----Outputs----------
+    x_new : list : x coordinates of the fitted curve
+    y_new : list : y coordinates of the fitted curve
+    '''  
+    d = max_degrees
+    poly = PolynomialFeatures(degree=d, include_bias=False)
+    x_new = poly.fit_transform(x_data)
+
+    alp = alpha_value
+    sl = Lasso(alpha=alp).fit(x_new, y_data)
+
+    m = sl.coef_
+    c = sl.intercept_
+    s = sl.score(x_new, y_data)
+
+    x_new = np.linspace(min(x_data), max(x_data), num_points)
+    y_new = []
+    for i in range(len(x_new)):
+        a = 0
+        for j in range(len(m)):
+            a = a + m[j] * x_new[i] ** (j + 1)
+        y_new.append(a + c[0])
+    return x_new, y_new

unipy/GraphPlot.py

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+"""
+Module for graph plotting.
+
+Mandal, Anik | 2023-10-03
+"""
+
+def ExpData2GraphData(x_data, y_data, small_x, small_y):
+    '''
+    Returns sum of the elements in a input list
+
+    -----Inputs----------
+    arr : list : list of integers/float
+    step : int : increment in the index value
+    -----Output----------
+    s : int/float : sum of the elements of list
+    '''
+    x_new = []
+    y_new = []
+    for i in range(len(x_data)):
+        x_new.append(x_data[i]/small_x)
+        y_new.append(y_data[i]/small_y)
+    return x_new, y_new