Merge pull request #23 from HideyoshiNakazone/updates-versioning-pipeline

Removes Broken Cache from Pipeline

.github/workflows/python-test-and-build.yml

@@ -0,0 +1,65 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python application
+
+on:
+    push
+
+permissions:
+    contents: read
+
+jobs:
+
+    test:
+        runs-on: ubuntu-latest
+        steps:
+            -   uses: actions/checkout@v3
+                with:
+                    fetch-depth: 0
+            -   name: Install poetry
+                run: pipx install poetry
+
+            -   name: Set up Python 3.10
+                uses: actions/setup-python@v3
+                with:
+                    python-version: "3.10"
+            -   name: Install dependencies
+                run: |
+                    poetry self add "poetry-dynamic-versioning[plugin]"
+                    if [ -f pyproject.toml ]; then poetry install; fi
+            -   name: Run unittest
+                run: |
+                    poetry run python -m unittest
+
+    pypi-upload:
+        needs: [test]
+        if: startsWith(github.ref, 'refs/tags/v')
+        runs-on: ubuntu-latest
+        steps:
+            -   uses: actions/checkout@v3
+                with:
+                    fetch-depth: 0
+            -   name: Install poetry
+                run: pipx install poetry
+
+            -   name: Set up Python 3.10
+                uses: actions/setup-python@v3
+                with:
+                    python-version: "3.10"
+            -   name: Install dependencies
+                run: |
+                    poetry self add "poetry-dynamic-versioning[plugin]"
+                    if [ -f pyproject.toml ]; then poetry install; fi
+            -   name: Build Python Package
+                run: |
+                    poetry build -f sdist
+                    poetry install
+                    echo "Builded yoshi-seals - $(poetry version)"
+            -   name: Configure PyPI
+                run: |
+                    poetry config repositories.pypi https://upload.pypi.org/legacy/
+                    poetry config pypi-token.pypi ${{secrets.PYPI_TOKEN}}
+            -   name: Upload Python Package
+                run: |
+                    poetry publish --repository pypi

.gitignore

@@ -0,0 +1,59 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+
+# C extensions
+*.c
+*.so
+
+# Environment variables
+.env
+.secret
+
+# Distribution / packaging
+bin/
+build/
+develop-eggs/
+dist/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# Rope
+.ropeproject
+
+# Django stuff:
+*.log
+*.pot
+
+# Sphinx documentation
+docs/_build/
+
+# IDE
+.idea/
+.vscode/

Older Versions/yoshi-seals1.2.1/LICENSE

@@ -1,278 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 2, June 1991
-
- Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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-FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN
-OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
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-PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGES.

Older Versions/yoshi-seals1.2.1/README.md

@@ -1,53 +0,0 @@
-# Seals - Numeric Calculus
-
-This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
-
-* Scan *csv* files to make a numpy matrix.
-
-* Write a matrix into a *csv* file
-
-* Insert user input into a matrix or a vector.
-
-* Use methods to proccess the matrices.
-  * Identity Matrix
-  * Gauss Elimination
-  * Inverse Matrix
-  * Cholesky Decomposition
-  * LU Decomposition
-  * Cramer
-
-## Syntax
-
-The function *scan* has the following syntax `scan(path)`, where `path` is the path to your directory.
-
-The function *solution* has the following syntax `write(array,path)`, where `array` is the matrix that you desire to output and `path` is the path to your directory.
-
-The python class *Insert* has a method for *matrix* and another for *vector*, and it has the following syntax `Insert.method(array)`, where `Insert` is the *Python Class* and `method` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
-
-### Processes
-
-The python class *process* has all the methods described in the first session.
-
-To call the method use a syntax like `sl = Seals.process()`, where `sl` is an instance and to use a method you have to append the method in front of the instance like: `sl.identity(array)`.
-
-* The method *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
-
-* The method *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
-
-* The method *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
-
-* The method *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-  
-* The method *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-* The method *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-## Installation
-
-To install the package from source `cd` into the directory and run:
-
-`pip install .`
-
-or run
-
-`pip install yoshi-seals`

Older Versions/yoshi-seals1.2.1/Seals/Seals.py

@@ -1,268 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import csv
-import pandas as pd
-import math
-
-class scan:
-
-    def __init__(self):
-        pass
-
-    def np(self, path):
-
-        df=pd.read_csv(path, sep=';',decimal=",",header=None)
-        array = df.to_numpy()
-
-        return array
-
-    def pd(self, path):
-        return pd.read_csv(path, sep=';',decimal=",")
-
-def write(array, path):
-
-    with open(path, mode='w') as sistema_linear:
-
-        solution_writer = csv.writer(sistema_linear, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
-        solution_writer.writerows(array)
-        
-    return array
-
-class insert:
-
-    def __init__(self):
-        pass
-
-    def matrix(self, matrix):
-
-
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[1]):
-                
-                matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def vector(self, vector):
-        
-        j=0
-        
-        while (j < vector.shape[0]):
-                
-            vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-            j += 1
-            
-        return vector
-
-class process:
-
-    def __init__(self):
-        pass  
-    
-    def identity(self, matrix):
-        
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[0]):
-                
-                if (i == j):
-                    
-                    matrix[i][j] = 1
-                    
-                elif (i != j):
-                    
-                    matrix[i][j] = 0
-                            
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def gauss(self, matrix):
-        
-        i = 0
-        k = 0
-        
-        while (i < matrix.shape[0]):
-            
-            if (matrix[i][i] == 0):
-                
-                n = i
-                
-                while (matrix[i][i] == 0) and (n < matrix.shape[0]):
-                    
-                    temp = matrix[i].copy()
-                    matrix[i] = matrix[n]
-                    matrix[n] = temp
-                    
-                    n += 1
-            
-            while (k < matrix.shape[0]):
-                
-                if (k == i) or (matrix[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    
-                    mult = matrix[k][i]/matrix[i][i]
-                    matrix[k] = matrix[k] - mult*matrix[i]
-                    k += 1
-                
-            i += 1
-            k = 0
-        
-        i = 0
-        
-        while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-            
-            matrix[i] = matrix[i]/matrix[i][i]
-            i += 1        
-        
-        return matrix[:,(matrix.shape[0]):]
-
-    def inverse(self, matrix):
-        
-        return self.gauss(np.hstack((matrix, self.identity(np.zeros(matrix.shape)))))
-
-    def cholesky(self, A, b):
-        
-        g = np.zeros((A.shape))
-        
-        i = 0
-        j = 0
-        
-        while j < A.shape[1]:
-            while i < A.shape[0]:
-
-                if i == 0 and j == 0:
-
-                    g[i][j] = math.sqrt(A[0][0])
-
-                elif j == 0:
-
-                    g[i][j] = A[i][0]/g[0][0]
-
-                elif i == j:
-
-                    k = 0
-                    theta = 0
-
-                    while k < i:
-
-                        theta += g[i][k]**2
-                        k += 1
-                    
-                    g[i][j] = math.sqrt(A[i][i] - theta) 
-
-                else:
-
-                    k = 0
-                    theta = 0
-
-                    while k < j:
-
-                        theta += g[i][k]*g[j][k]
-                        k += 1
-
-                    g[i][j] = (A[i][j] - theta)/g[j][j]
-
-                i += 1
-
-            j += 1
-            i = j
-                    
-        y = (self.inverse(g)).dot(b) 
-        
-        x = (self.inverse(g.T)).dot(y)
-        
-        return x
-
-    def decomposition(self, U, b):
-        
-        L = self.identity(np.zeros(U.shape))
-
-        i = 0
-        k = 0
-
-        while (i < U.shape[0]):
-            
-            k = 0
-
-            if (U[i][i] == 0):
-                
-                n = i
-                
-                while (U[i][i] == 0) and (n < U.shape[0]):
-                    
-                    temp = U[i].copy()
-                    U[i] = U[n]
-                    U[n] = temp
-                    
-                    n += 1
-            
-            while (k < U.shape[0]):
-                
-                if (k <= i) or (U[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    L[k][i] = U[k][i]/U[i][i]
-                    U[k] = U[k] - L[k][i]*U[i]
-                    k += 1
-                
-            i += 1
-
-        y = (self.inverse(L)).dot(b) 
-        
-        x = (self.inverse(U)).dot(y)
-        
-        return x
-        
-    def cramer(self, A, b):
-
-        x = np.vstack(np.zeros(b.shape))
-        k = 0
-
-        while (k < A.shape[0]):
-
-            temp = A.copy()
-            temp[:,k] = b
-
-            x[k] = np.linalg.det(temp)/np.linalg.det(A)
-            
-            k += 1
-        
-        return x

Older Versions/yoshi-seals1.2.1/Seals/__init__.py

@@ -1,23 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .Seals import process
-from .Seals import insert
-from .Seals import scan
-from .Seals import write

Older Versions/yoshi-seals1.2.1/build/lib/Seals/Seals.py

@@ -1,268 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import csv
-import pandas as pd
-import math
-
-class scan:
-
-    def __init__(self):
-        pass
-
-    def np(self, path):
-
-        df=pd.read_csv(path, sep=';',decimal=",",header=None)
-        array = df.to_numpy()
-
-        return array
-
-    def pd(self, path):
-        return pd.read_csv(path, sep=';',decimal=",")
-
-def write(array, path):
-
-    with open(path, mode='w') as sistema_linear:
-
-        solution_writer = csv.writer(sistema_linear, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
-        solution_writer.writerows(array)
-        
-    return array
-
-class insert:
-
-    def __init__(self):
-        pass
-
-    def matrix(self, matrix):
-
-
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[1]):
-                
-                matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def vector(self, vector):
-        
-        j=0
-        
-        while (j < vector.shape[0]):
-                
-            vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-            j += 1
-            
-        return vector
-
-class process:
-
-    def __init__(self):
-        pass  
-    
-    def identity(self, matrix):
-        
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[0]):
-                
-                if (i == j):
-                    
-                    matrix[i][j] = 1
-                    
-                elif (i != j):
-                    
-                    matrix[i][j] = 0
-                            
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def gauss(self, matrix):
-        
-        i = 0
-        k = 0
-        
-        while (i < matrix.shape[0]):
-            
-            if (matrix[i][i] == 0):
-                
-                n = i
-                
-                while (matrix[i][i] == 0) and (n < matrix.shape[0]):
-                    
-                    temp = matrix[i].copy()
-                    matrix[i] = matrix[n]
-                    matrix[n] = temp
-                    
-                    n += 1
-            
-            while (k < matrix.shape[0]):
-                
-                if (k == i) or (matrix[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    
-                    mult = matrix[k][i]/matrix[i][i]
-                    matrix[k] = matrix[k] - mult*matrix[i]
-                    k += 1
-                
-            i += 1
-            k = 0
-        
-        i = 0
-        
-        while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-            
-            matrix[i] = matrix[i]/matrix[i][i]
-            i += 1        
-        
-        return matrix[:,(matrix.shape[0]):]
-
-    def inverse(self, matrix):
-        
-        return self.gauss(np.hstack((matrix, self.identity(np.zeros(matrix.shape)))))
-
-    def cholesky(self, A, b):
-        
-        g = np.zeros((A.shape))
-        
-        i = 0
-        j = 0
-        
-        while j < A.shape[1]:
-            while i < A.shape[0]:
-
-                if i == 0 and j == 0:
-
-                    g[i][j] = math.sqrt(A[0][0])
-
-                elif j == 0:
-
-                    g[i][j] = A[i][0]/g[0][0]
-
-                elif i == j:
-
-                    k = 0
-                    theta = 0
-
-                    while k < i:
-
-                        theta += g[i][k]**2
-                        k += 1
-                    
-                    g[i][j] = math.sqrt(A[i][i] - theta) 
-
-                else:
-
-                    k = 0
-                    theta = 0
-
-                    while k < j:
-
-                        theta += g[i][k]*g[j][k]
-                        k += 1
-
-                    g[i][j] = (A[i][j] - theta)/g[j][j]
-
-                i += 1
-
-            j += 1
-            i = j
-                    
-        y = (self.inverse(g)).dot(b) 
-        
-        x = (self.inverse(g.T)).dot(y)
-        
-        return x
-
-    def decomposition(self, U, b):
-        
-        L = self.identity(np.zeros(U.shape))
-
-        i = 0
-        k = 0
-
-        while (i < U.shape[0]):
-            
-            k = 0
-
-            if (U[i][i] == 0):
-                
-                n = i
-                
-                while (U[i][i] == 0) and (n < U.shape[0]):
-                    
-                    temp = U[i].copy()
-                    U[i] = U[n]
-                    U[n] = temp
-                    
-                    n += 1
-            
-            while (k < U.shape[0]):
-                
-                if (k <= i) or (U[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    L[k][i] = U[k][i]/U[i][i]
-                    U[k] = U[k] - L[k][i]*U[i]
-                    k += 1
-                
-            i += 1
-
-        y = (self.inverse(L)).dot(b) 
-        
-        x = (self.inverse(U)).dot(y)
-        
-        return x
-        
-    def cramer(self, A, b):
-
-        x = np.vstack(np.zeros(b.shape))
-        k = 0
-
-        while (k < A.shape[0]):
-
-            temp = A.copy()
-            temp[:,k] = b
-
-            x[k] = np.linalg.det(temp)/np.linalg.det(A)
-            
-            k += 1
-        
-        return x

Older Versions/yoshi-seals1.2.1/build/lib/Seals/__init__.py

@@ -1,23 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .Seals import process
-from .Seals import insert
-from .Seals import scan
-from .Seals import write

Older Versions/yoshi-seals1.2.1/setup.py

@@ -1,27 +0,0 @@
-import setuptools
-
-with open("README.md", "r") as fh:
-    long_description = fh.read()
-
-setuptools.setup(
-    name="yoshi-seals",
-    version="1.2.1",
-    author="Vitor Hideyoshi",
-    author_email="vitor.h.n.batista@gmail.com",
-    description="Numeric Calculus python module in the topic of Linear Algebra",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git",
-    packages=setuptools.find_packages(),
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: GNU General Public License v2 (GPLv2)",
-        "Operating System :: OS Independent",
-        "Development Status :: 2 - Pre-Alpha",
-    ],
-    python_requires='>=3.6',
-    install_requires=[
-          'numpy',
-          'pandas',
-      ],
-)

Older Versions/yoshi-seals1.2.1/yoshi_seals.egg-info/PKG-INFO

@@ -1,69 +0,0 @@
-Metadata-Version: 2.1
-Name: yoshi-seals
-Version: 1.2.1
-Summary: Numeric Calculus python module in the topic of Linear Algebra
-Home-page: https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git
-Author: Vitor Hideyoshi
-Author-email: vitor.h.n.batista@gmail.com
-License: UNKNOWN
-Description: # Seals - Numeric Calculus
-        
-        This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
-        
-        * Scan *csv* files to make a numpy matrix.
-        
-        * Write a matrix into a *csv* file
-        
-        * Insert user input into a matrix or a vector.
-        
-        * Use methods to proccess the matrices.
-          * Identity Matrix
-          * Gauss Elimination
-          * Inverse Matrix
-          * Cholesky Decomposition
-          * LU Decomposition
-          * Cramer
-        
-        ## Syntax
-        
-        The function *scan* has the following syntax `scan(path)`, where `path` is the path to your directory.
-        
-        The function *solution* has the following syntax `write(array,path)`, where `array` is the matrix that you desire to output and `path` is the path to your directory.
-        
-        The python class *Insert* has a method for *matrix* and another for *vector*, and it has the following syntax `Insert.method(array)`, where `Insert` is the *Python Class* and `method` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
-        
-        ### Processes
-        
-        The python class *process* has all the methods described in the first session.
-        
-        To call the method use a syntax like `sl = Seals.process()`, where `sl` is an instance and to use a method you have to append the method in front of the instance like: `sl.identity(array)`.
-        
-        * The method *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
-        
-        * The method *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
-        
-        * The method *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
-        
-        * The method *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-          
-        * The method *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-        
-        * The method *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-        
-        ## Installation
-        
-        To install the package from source `cd` into the directory and run:
-        
-        `pip install .`
-        
-        or run
-        
-        `pip install yoshi-seals`
-        
-Platform: UNKNOWN
-Classifier: Programming Language :: Python :: 3
-Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
-Classifier: Operating System :: OS Independent
-Classifier: Development Status :: 2 - Pre-Alpha
-Requires-Python: >=3.6
-Description-Content-Type: text/markdown

Older Versions/yoshi-seals1.2.1/yoshi_seals.egg-info/SOURCES.txt

@@ -1,9 +0,0 @@
-README.md
-setup.py
-Seals/Seals.py
-Seals/__init__.py
-yoshi_seals.egg-info/PKG-INFO
-yoshi_seals.egg-info/SOURCES.txt
-yoshi_seals.egg-info/dependency_links.txt
-yoshi_seals.egg-info/requires.txt
-yoshi_seals.egg-info/top_level.txt

Older Versions/yoshi-seals1.2.1/yoshi_seals.egg-info/dependency_links.txt

@@ -1 +0,0 @@
-

Older Versions/yoshi-seals1.2.1/yoshi_seals.egg-info/requires.txt

@@ -1,2 +0,0 @@
-numpy
-pandas

Older Versions/yoshi-seals1.2.1/yoshi_seals.egg-info/top_level.txt

@@ -1 +0,0 @@
-Seals

Older Versions/yoshi-seals1.2/LICENSE

@@ -1,278 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 2, June 1991
-
- Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The licenses for most software are designed to take away your
-freedom to share and change it.  By contrast, the GNU General Public
-License is intended to guarantee your freedom to share and change free
-software--to make sure the software is free for all its users.  This
-General Public License applies to most of the Free Software
-Foundation's software and to any other program whose authors commit to
-using it.  (Some other Free Software Foundation software is covered by
-the GNU Lesser General Public License instead.)  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-this service if you wish), that you receive source code or can get it
-if you want it, that you can change the software or use pieces of it
-in new free programs; and that you know you can do these things.
-
-  To protect your rights, we need to make restrictions that forbid
-anyone to deny you these rights or to ask you to surrender the rights.
-These restrictions translate to certain responsibilities for you if you
-distribute copies of the software, or if you modify it.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must give the recipients all the rights that
-you have.  You must make sure that they, too, receive or can get the
-source code.  And you must show them these terms so they know their
-rights.
-
-  We protect your rights with two steps: (1) copyright the software, and
-(2) offer you this license which gives you legal permission to copy,
-distribute and/or modify the software.
-
-  Also, for each author's protection and ours, we want to make certain
-that everyone understands that there is no warranty for this free
-software.  If the software is modified by someone else and passed on, we
-want its recipients to know that what they have is not the original, so
-that any problems introduced by others will not reflect on the original
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-
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-patents.  We wish to avoid the danger that redistributors of a free
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-patent must be licensed for everyone's free use or not licensed at all.
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-  The precise terms and conditions for copying, distribution and
-modification follow.
-
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-   TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
-
-  0. This License applies to any program or other work which contains
-a notice placed by the copyright holder saying it may be distributed
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-original copyright holder who places the Program under this License
-may add an explicit geographical distribution limitation excluding
-those countries, so that distribution is permitted only in or among
-countries not thus excluded.  In such case, this License incorporates
-the limitation as if written in the body of this License.
-
-  9. The Free Software Foundation may publish revised and/or new versions
-of the General Public License from time to time.  Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-Each version is given a distinguishing version number.  If the Program
-specifies a version number of this License which applies to it and "any
-later version", you have the option of following the terms and conditions
-either of that version or of any later version published by the Free
-Software Foundation.  If the Program does not specify a version number of
-this License, you may choose any version ever published by the Free Software
-Foundation.
-
-  10. If you wish to incorporate parts of the Program into other free
-programs whose distribution conditions are different, write to the author
-to ask for permission.  For software which is copyrighted by the Free
-Software Foundation, write to the Free Software Foundation; we sometimes
-make exceptions for this.  Our decision will be guided by the two goals
-of preserving the free status of all derivatives of our free software and
-of promoting the sharing and reuse of software generally.
-
-                            NO WARRANTY
-
-  11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
-FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN
-OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
-PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
-OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.  THE ENTIRE RISK AS
-TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU.  SHOULD THE
-PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
-REPAIR OR CORRECTION.
-
-  12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
-REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
-INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
-OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
-TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
-YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
-PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGES.

Older Versions/yoshi-seals1.2/README.md

@@ -1,53 +0,0 @@
-# Seals - Numeric Calculus
-
-This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
-
-* Scan *csv* files to make a numpy matrix.
-
-* Write a matrix into a *csv* file
-
-* Insert user input into a matrix or a vector.
-
-* Use methods to proccess the matrices.
-  * Identity Matrix
-  * Gauss Elimination
-  * Inverse Matrix
-  * Cholesky Decomposition
-  * LU Decomposition
-  * Cramer
-
-## Syntax
-
-The function *scan* has the following syntax `scan(path)`, where `path` is the path to your directory.
-
-The function *solution* has the following syntax `write(array,path)`, where `array` is the matrix that you desire to output and `path` is the path to your directory.
-
-The python class *Insert* has a method for *matrix* and another for *vector*, and it has the following syntax `Insert.method(array)`, where `Insert` is the *Python Class* and `method` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
-
-### Processes
-
-The python class *process* has all the methods described in the first session.
-
-To call the method use a syntax like `sl = Seals.process()`, where `sl` is an instance and to use a method you have to append the method in front of the instance like: `sl.identity(array)`.
-
-* The method *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
-
-* The method *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
-
-* The method *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
-
-* The method *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-  
-* The method *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-* The method *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-## Installation
-
-To install the package from source `cd` into the directory and run:
-
-`pip install .`
-
-or run
-
-`pip install yoshi-seals`

Older Versions/yoshi-seals1.2/Seals/Seals.py

@@ -1,260 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import csv
-import pandas as pd
-import math
-
-def scan(path):
-
-    df=pd.read_csv(path, sep=';',decimal=",",header=None)
-    array = df.to_numpy()
-
-    return array
-
-def write(array, path):
-
-    with open(path, mode='w') as sistema_linear:
-
-        solution_writer = csv.writer(sistema_linear, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
-        solution_writer.writerows(array)
-        
-    return array
-
-class Insert:
-
-    def __init__(self):
-        pass
-
-    def matrix(self, matrix):
-
-
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[1]):
-                
-                matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def vector(self, vector):
-        
-        j=0
-        
-        while (j < vector.shape[0]):
-                
-            vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-            j += 1
-            
-        return vector
-
-class process:
-
-    def __init__(self):
-        pass  
-    
-    def identity(self, matrix):
-        
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[0]):
-                
-                if (i == j):
-                    
-                    matrix[i][j] = 1
-                    
-                elif (i != j):
-                    
-                    matrix[i][j] = 0
-                            
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def gauss(self, matrix):
-        
-        i = 0
-        k = 0
-        
-        while (i < matrix.shape[0]):
-            
-            if (matrix[i][i] == 0):
-                
-                n = i
-                
-                while (matrix[i][i] == 0) and (n < matrix.shape[0]):
-                    
-                    temp = matrix[i].copy()
-                    matrix[i] = matrix[n]
-                    matrix[n] = temp
-                    
-                    n += 1
-            
-            while (k < matrix.shape[0]):
-                
-                if (k == i) or (matrix[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    
-                    mult = matrix[k][i]/matrix[i][i]
-                    matrix[k] = matrix[k] - mult*matrix[i]
-                    k += 1
-                
-            i += 1
-            k = 0
-        
-        i = 0
-        
-        while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-            
-            matrix[i] = matrix[i]/matrix[i][i]
-            i += 1        
-        
-        return matrix[:,(matrix.shape[0]):]
-
-    def inverse(self, matrix):
-        
-        return self.gauss(np.hstack((matrix, self.identity(np.zeros(matrix.shape)))))
-
-    def cholesky(self, A, b):
-        
-        g = np.zeros((A.shape))
-        
-        i = 0
-        j = 0
-        
-        while j < A.shape[1]:
-            while i < A.shape[0]:
-
-                if i == 0 and j == 0:
-
-                    g[i][j] = math.sqrt(A[0][0])
-
-                elif j == 0:
-
-                    g[i][j] = A[i][0]/g[0][0]
-
-                elif i == j:
-
-                    k = 0
-                    theta = 0
-
-                    while k < i:
-
-                        theta += g[i][k]**2
-                        k += 1
-                    
-                    g[i][j] = math.sqrt(A[i][i] - theta) 
-
-                else:
-
-                    k = 0
-                    theta = 0
-
-                    while k < j:
-
-                        theta += g[i][k]*g[j][k]
-                        k += 1
-
-                    g[i][j] = (A[i][j] - theta)/g[j][j]
-
-                i += 1
-
-            j += 1
-            i = j
-                    
-        y = (self.inverse(g)).dot(b) 
-        
-        x = (self.inverse(g.T)).dot(y)
-        
-        return x
-
-    def decomposition(self, U, b):
-        
-        L = self.identity(np.zeros(U.shape))
-
-        i = 0
-        k = 0
-
-        while (i < U.shape[0]):
-            
-            k = 0
-
-            if (U[i][i] == 0):
-                
-                n = i
-                
-                while (U[i][i] == 0) and (n < U.shape[0]):
-                    
-                    temp = U[i].copy()
-                    U[i] = U[n]
-                    U[n] = temp
-                    
-                    n += 1
-            
-            while (k < U.shape[0]):
-                
-                if (k <= i) or (U[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    L[k][i] = U[k][i]/U[i][i]
-                    U[k] = U[k] - L[k][i]*U[i]
-                    k += 1
-                
-            i += 1
-
-        y = (self.inverse(L)).dot(b) 
-        
-        x = (self.inverse(U)).dot(y)
-        
-        return x
-        
-    def cramer(self, A, b):
-
-        x = np.vstack(np.zeros(b.shape))
-        k = 0
-
-        while (k < A.shape[0]):
-
-            temp = A.copy()
-            temp[:,k] = b
-
-            x[k] = np.linalg.det(temp)/np.linalg.det(A)
-            
-            k += 1
-        
-        return x

Older Versions/yoshi-seals1.2/Seals/__init__.py

@@ -1,23 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .Seals import process
-from .Seals import Insert
-from .Seals import scan
-from .Seals import write

Older Versions/yoshi-seals1.2/build/lib/Seals/Seals.py

@@ -1,260 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import csv
-import pandas as pd
-import math
-
-def scan(path):
-
-    df=pd.read_csv(path, sep=';',decimal=",",header=None)
-    array = df.to_numpy()
-
-    return array
-
-def write(array, path):
-
-    with open(path, mode='w') as sistema_linear:
-
-        solution_writer = csv.writer(sistema_linear, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
-        solution_writer.writerows(array)
-        
-    return array
-
-class Insert:
-
-    def __init__(self):
-        pass
-
-    def matrix(self, matrix):
-
-
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[1]):
-                
-                matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def vector(self, vector):
-        
-        j=0
-        
-        while (j < vector.shape[0]):
-                
-            vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-            j += 1
-            
-        return vector
-
-class process:
-
-    def __init__(self):
-        pass  
-    
-    def identity(self, matrix):
-        
-        i = 0
-        
-        while (i < matrix.shape[0]):
-            
-            j = 0
-            
-            while (j < matrix.shape[0]):
-                
-                if (i == j):
-                    
-                    matrix[i][j] = 1
-                    
-                elif (i != j):
-                    
-                    matrix[i][j] = 0
-                            
-                j += 1
-            
-            i += 1
-            
-        return matrix
-
-    def gauss(self, matrix):
-        
-        i = 0
-        k = 0
-        
-        while (i < matrix.shape[0]):
-            
-            if (matrix[i][i] == 0):
-                
-                n = i
-                
-                while (matrix[i][i] == 0) and (n < matrix.shape[0]):
-                    
-                    temp = matrix[i].copy()
-                    matrix[i] = matrix[n]
-                    matrix[n] = temp
-                    
-                    n += 1
-            
-            while (k < matrix.shape[0]):
-                
-                if (k == i) or (matrix[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    
-                    mult = matrix[k][i]/matrix[i][i]
-                    matrix[k] = matrix[k] - mult*matrix[i]
-                    k += 1
-                
-            i += 1
-            k = 0
-        
-        i = 0
-        
-        while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-            
-            matrix[i] = matrix[i]/matrix[i][i]
-            i += 1        
-        
-        return matrix[:,(matrix.shape[0]):]
-
-    def inverse(self, matrix):
-        
-        return self.gauss(np.hstack((matrix, self.identity(np.zeros(matrix.shape)))))
-
-    def cholesky(self, A, b):
-        
-        g = np.zeros((A.shape))
-        
-        i = 0
-        j = 0
-        
-        while j < A.shape[1]:
-            while i < A.shape[0]:
-
-                if i == 0 and j == 0:
-
-                    g[i][j] = math.sqrt(A[0][0])
-
-                elif j == 0:
-
-                    g[i][j] = A[i][0]/g[0][0]
-
-                elif i == j:
-
-                    k = 0
-                    theta = 0
-
-                    while k < i:
-
-                        theta += g[i][k]**2
-                        k += 1
-                    
-                    g[i][j] = math.sqrt(A[i][i] - theta) 
-
-                else:
-
-                    k = 0
-                    theta = 0
-
-                    while k < j:
-
-                        theta += g[i][k]*g[j][k]
-                        k += 1
-
-                    g[i][j] = (A[i][j] - theta)/g[j][j]
-
-                i += 1
-
-            j += 1
-            i = j
-                    
-        y = (self.inverse(g)).dot(b) 
-        
-        x = (self.inverse(g.T)).dot(y)
-        
-        return x
-
-    def decomposition(self, U, b):
-        
-        L = self.identity(np.zeros(U.shape))
-
-        i = 0
-        k = 0
-
-        while (i < U.shape[0]):
-            
-            k = 0
-
-            if (U[i][i] == 0):
-                
-                n = i
-                
-                while (U[i][i] == 0) and (n < U.shape[0]):
-                    
-                    temp = U[i].copy()
-                    U[i] = U[n]
-                    U[n] = temp
-                    
-                    n += 1
-            
-            while (k < U.shape[0]):
-                
-                if (k <= i) or (U[i][i] == 0):
-                    
-                    k += 1
-                    
-                else:
-                    L[k][i] = U[k][i]/U[i][i]
-                    U[k] = U[k] - L[k][i]*U[i]
-                    k += 1
-                
-            i += 1
-
-        y = (self.inverse(L)).dot(b) 
-        
-        x = (self.inverse(U)).dot(y)
-        
-        return x
-        
-    def cramer(self, A, b):
-
-        x = np.vstack(np.zeros(b.shape))
-        k = 0
-
-        while (k < A.shape[0]):
-
-            temp = A.copy()
-            temp[:,k] = b
-
-            x[k] = np.linalg.det(temp)/np.linalg.det(A)
-            
-            k += 1
-        
-        return x

Older Versions/yoshi-seals1.2/build/lib/Seals/__init__.py

@@ -1,23 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .Seals import process
-from .Seals import Insert
-from .Seals import scan
-from .Seals import write

Older Versions/yoshi-seals1.2/setup.py

@@ -1,27 +0,0 @@
-import setuptools
-
-with open("README.md", "r") as fh:
-    long_description = fh.read()
-
-setuptools.setup(
-    name="yoshi-seals",
-    version="1.2",
-    author="Vitor Hideyoshi",
-    author_email="vitor.h.n.batista@gmail.com",
-    description="Numeric Calculus python module in the topic of Linear Algebra",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git",
-    packages=setuptools.find_packages(),
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: GNU General Public License v2 (GPLv2)",
-        "Operating System :: OS Independent",
-        "Development Status :: 2 - Pre-Alpha",
-    ],
-    python_requires='>=3.6',
-    install_requires=[
-          'numpy',
-          'pandas',
-      ],
-)

Older Versions/yoshi-seals1.2/yoshi_seals.egg-info/PKG-INFO

@@ -1,69 +0,0 @@
-Metadata-Version: 2.1
-Name: yoshi-seals
-Version: 1.2
-Summary: Numeric Calculus python module in the topic of Linear Algebra
-Home-page: https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git
-Author: Vitor Hideyoshi
-Author-email: vitor.h.n.batista@gmail.com
-License: UNKNOWN
-Description: # Seals - Numeric Calculus
-        
-        This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
-        
-        * Scan *csv* files to make a numpy matrix.
-        
-        * Write a matrix into a *csv* file
-        
-        * Insert user input into a matrix or a vector.
-        
-        * Use methods to proccess the matrices.
-          * Identity Matrix
-          * Gauss Elimination
-          * Inverse Matrix
-          * Cholesky Decomposition
-          * LU Decomposition
-          * Cramer
-        
-        ## Syntax
-        
-        The function *scan* has the following syntax `scan(path)`, where `path` is the path to your directory.
-        
-        The function *solution* has the following syntax `write(array,path)`, where `array` is the matrix that you desire to output and `path` is the path to your directory.
-        
-        The python class *Insert* has a method for *matrix* and another for *vector*, and it has the following syntax `Insert.method(array)`, where `Insert` is the *Python Class* and `method` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
-        
-        ### Processes
-        
-        The python class *process* has all the methods described in the first session.
-        
-        To call the method use a syntax like `sl = Seals.process()`, where `sl` is an instance and to use a method you have to append the method in front of the instance like: `sl.identity(array)`.
-        
-        * The method *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
-        
-        * The method *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
-        
-        * The method *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
-        
-        * The method *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-          
-        * The method *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-        
-        * The method *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-        
-        ## Installation
-        
-        To install the package from source `cd` into the directory and run:
-        
-        `pip install .`
-        
-        or run
-        
-        `pip install yoshi-seals`
-        
-Platform: UNKNOWN
-Classifier: Programming Language :: Python :: 3
-Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
-Classifier: Operating System :: OS Independent
-Classifier: Development Status :: 2 - Pre-Alpha
-Requires-Python: >=3.6
-Description-Content-Type: text/markdown

Older Versions/yoshi-seals1.2/yoshi_seals.egg-info/SOURCES.txt

@@ -1,9 +0,0 @@
-README.md
-setup.py
-Seals/Seals.py
-Seals/__init__.py
-yoshi_seals.egg-info/PKG-INFO
-yoshi_seals.egg-info/SOURCES.txt
-yoshi_seals.egg-info/dependency_links.txt
-yoshi_seals.egg-info/requires.txt
-yoshi_seals.egg-info/top_level.txt

Older Versions/yoshi-seals1.2/yoshi_seals.egg-info/dependency_links.txt

@@ -1 +0,0 @@
-

Older Versions/yoshi-seals1.2/yoshi_seals.egg-info/requires.txt

@@ -1,2 +0,0 @@
-numpy
-pandas

Older Versions/yoshi-seals1.2/yoshi_seals.egg-info/top_level.txt

@@ -1 +0,0 @@
-Seals

Older Versions/yoshi-seals1.3/LICENSE

@@ -1,278 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 2, June 1991
-
- Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- Everyone is permitted to copy and distribute verbatim copies
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-FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN
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-POSSIBILITY OF SUCH DAMAGES.

Older Versions/yoshi-seals1.3/README.md

@@ -1,55 +0,0 @@
-# Seals - Numeric Calculus
-
-This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
-
-* Scan *csv* files to make a numpy matrix.
-
-* Write a matrix into a *csv* file
-
-* Insert user input into a matrix or a vector.
-
-* Calculate Eigen Values
-
-* Use methods to proccess the matrices.
-  * Identity Matrix
-  * Gauss Elimination
-  * Inverse Matrix
-  * Cholesky Decomposition
-  * LU Decomposition
-  * Cramer
-
-## Syntax
-
-The module *scan* has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `Seals.scan.np(path)` for *Numpy* and `Seals.scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
-
-The module *write* has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `Seals.write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `Seals.write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
-
-The module *insert* has a function for *matrix* and another for *vector*, and it has the following syntax `Seals.insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
-
-There is also a function that given a matrix it return all real eigen values
-
-### Processes
-
-To call the module `process` use the syntax: `sl = Seals.process`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
-
-* The function *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
-
-* The function *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
-
-* The function *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
-
-* The function *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-  
-* The function *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-* The function *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-## Installation
-
-To install the package from source `cd` into the directory and run:
-
-`pip install .`
-
-or run
-
-`pip install yoshi-seals`

Older Versions/yoshi-seals1.3/Seals/insert/insert.py

@@ -1,46 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-def matrix(matrix):
-
-    i = 0
-    
-    while (i < matrix.shape[0]):
-        
-        j = 0
-        
-        while (j < matrix.shape[1]):
-            
-            matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-            j += 1
-        
-        i += 1
-        
-    return matrix
-
-def vector(vector):
-    
-    j=0
-    
-    while (j < vector.shape[0]):
-            
-        vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-        j += 1
-        
-    return vector

Older Versions/yoshi-seals1.3/Seals/process/__init__.py

@@ -1,25 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .process import identity
-from .process import gauss
-from .process import inverse
-from .process import cholesky
-from .process import decomposition
-from .process import cramer

Older Versions/yoshi-seals1.3/Seals/process/process.py

@@ -1,203 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import math
-
-def identity(matrix):
-    
-    i = 0
-    
-    while (i < matrix.shape[0]):
-        
-        j = 0
-        
-        while (j < matrix.shape[0]):
-            
-            if (i == j):
-                
-                matrix[i][j] = 1
-                
-            elif (i != j):
-                
-                matrix[i][j] = 0
-                        
-            j += 1
-        
-        i += 1
-        
-    return matrix
-
-def gauss(matrix):
-    
-    i = 0
-    k = 0
-    
-    while (i < matrix.shape[0]):
-        
-        if (matrix[i][i] == 0):
-            
-            n = i
-            
-            while (matrix[i][i] == 0) and (n < matrix.shape[0]):
-                
-                temp = matrix[i].copy()
-                matrix[i] = matrix[n]
-                matrix[n] = temp
-                
-                n += 1
-        
-        while (k < matrix.shape[0]):
-            
-            if (k == i) or (matrix[i][i] == 0):
-                
-                k += 1
-                
-            else:
-                
-                mult = matrix[k][i]/matrix[i][i]
-                matrix[k] = matrix[k] - mult*matrix[i]
-                k += 1
-            
-        i += 1
-        k = 0
-    
-    i = 0
-    
-    while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-        
-        matrix[i] = matrix[i]/matrix[i][i]
-        i += 1        
-    
-    return matrix[:,(matrix.shape[0]):]
-
-def inverse(matrix):
-    
-    return gauss(np.hstack((matrix, identity(np.zeros(matrix.shape)))))
-
-def cholesky(A, b):
-    
-    g = np.zeros((A.shape))
-    
-    i = 0
-    j = 0
-    
-    while j < A.shape[1]:
-        while i < A.shape[0]:
-
-            if i == 0 and j == 0:
-
-                g[i][j] = math.sqrt(A[0][0])
-
-            elif j == 0:
-
-                g[i][j] = A[i][0]/g[0][0]
-
-            elif i == j:
-
-                k = 0
-                theta = 0
-
-                while k < i:
-
-                    theta += g[i][k]**2
-                    k += 1
-                
-                g[i][j] = math.sqrt(A[i][i] - theta) 
-
-            else:
-
-                k = 0
-                theta = 0
-
-                while k < j:
-
-                    theta += g[i][k]*g[j][k]
-                    k += 1
-
-                g[i][j] = (A[i][j] - theta)/g[j][j]
-
-            i += 1
-
-        j += 1
-        i = j
-                
-    y = (inverse(g)).dot(b) 
-    
-    x = (inverse(g.T)).dot(y)
-    
-    return x
-
-def decomposition(U, b):
-    
-    L = identity(np.zeros(U.shape))
-
-    i = 0
-    k = 0
-
-    while (i < U.shape[0]):
-        
-        k = 0
-
-        if (U[i][i] == 0):
-            
-            n = i
-            
-            while (U[i][i] == 0) and (n < U.shape[0]):
-                
-                temp = U[i].copy()
-                U[i] = U[n]
-                U[n] = temp
-                
-                n += 1
-        
-        while (k < U.shape[0]):
-            
-            if (k <= i) or (U[i][i] == 0):
-                
-                k += 1
-                
-            else:
-                L[k][i] = U[k][i]/U[i][i]
-                U[k] = U[k] - L[k][i]*U[i]
-                k += 1
-            
-        i += 1
-
-    y = (inverse(L)).dot(b) 
-    
-    x = (inverse(U)).dot(y)
-    
-    return x
-    
-def cramer(A, b):
-
-    x = np.vstack(np.zeros(b.shape))
-    k = 0
-
-    while (k < A.shape[0]):
-
-        temp = A.copy()
-        temp[:,k] = b
-
-        x[k] = np.linalg.det(temp)/np.linalg.det(A)
-        
-        k += 1
-    
-    return x

Older Versions/yoshi-seals1.3/build/lib/Seals/__init__.py

@@ -1,18 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

Older Versions/yoshi-seals1.3/build/lib/Seals/eigen/__init__.py

@@ -1,20 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .eigen import eigen

Older Versions/yoshi-seals1.3/build/lib/Seals/eigen/eigen.py

@@ -1,51 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-
-def eigen(a):
-
-    k = 0
-    l = np.ones((a.shape[0]))
-
-    while (k < a.shape[0]):
-
-        u = np.random.rand(a.shape[0],1)
-        u = u/max(u.min(), u.max(), key=abs)
-
-        ctrl = 0
-
-        while (ctrl != l[k]):
-            
-            ctrl = l[k]
-            u = a.dot(u)
-            l[k] = max(u.min(), u.max(), key=abs)
-            u = u/l[k]
-
-
-        i = 0
-
-        while (u[i] == 0):
-            i += 1
-
-        a = a - (1/u[i])*u*a[i]
-
-        k += 1
-
-    return l

Older Versions/yoshi-seals1.3/build/lib/Seals/insert/__init__.py

@@ -1,21 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .insert import matrix
-from .insert import vector

Older Versions/yoshi-seals1.3/build/lib/Seals/insert/insert.py

@@ -1,46 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-def matrix(matrix):
-
-    i = 0
-    
-    while (i < matrix.shape[0]):
-        
-        j = 0
-        
-        while (j < matrix.shape[1]):
-            
-            matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-            j += 1
-        
-        i += 1
-        
-    return matrix
-
-def vector(vector):
-    
-    j=0
-    
-    while (j < vector.shape[0]):
-            
-        vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-        j += 1
-        
-    return vector

Older Versions/yoshi-seals1.3/build/lib/Seals/process/__init__.py

@@ -1,25 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .process import identity
-from .process import gauss
-from .process import inverse
-from .process import cholesky
-from .process import decomposition
-from .process import cramer

Older Versions/yoshi-seals1.3/build/lib/Seals/process/process.py

@@ -1,203 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import math
-
-def identity(matrix):
-    
-    i = 0
-    
-    while (i < matrix.shape[0]):
-        
-        j = 0
-        
-        while (j < matrix.shape[0]):
-            
-            if (i == j):
-                
-                matrix[i][j] = 1
-                
-            elif (i != j):
-                
-                matrix[i][j] = 0
-                        
-            j += 1
-        
-        i += 1
-        
-    return matrix
-
-def gauss(matrix):
-    
-    i = 0
-    k = 0
-    
-    while (i < matrix.shape[0]):
-        
-        if (matrix[i][i] == 0):
-            
-            n = i
-            
-            while (matrix[i][i] == 0) and (n < matrix.shape[0]):
-                
-                temp = matrix[i].copy()
-                matrix[i] = matrix[n]
-                matrix[n] = temp
-                
-                n += 1
-        
-        while (k < matrix.shape[0]):
-            
-            if (k == i) or (matrix[i][i] == 0):
-                
-                k += 1
-                
-            else:
-                
-                mult = matrix[k][i]/matrix[i][i]
-                matrix[k] = matrix[k] - mult*matrix[i]
-                k += 1
-            
-        i += 1
-        k = 0
-    
-    i = 0
-    
-    while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-        
-        matrix[i] = matrix[i]/matrix[i][i]
-        i += 1        
-    
-    return matrix[:,(matrix.shape[0]):]
-
-def inverse(matrix):
-    
-    return gauss(np.hstack((matrix, identity(np.zeros(matrix.shape)))))
-
-def cholesky(A, b):
-    
-    g = np.zeros((A.shape))
-    
-    i = 0
-    j = 0
-    
-    while j < A.shape[1]:
-        while i < A.shape[0]:
-
-            if i == 0 and j == 0:
-
-                g[i][j] = math.sqrt(A[0][0])
-
-            elif j == 0:
-
-                g[i][j] = A[i][0]/g[0][0]
-
-            elif i == j:
-
-                k = 0
-                theta = 0
-
-                while k < i:
-
-                    theta += g[i][k]**2
-                    k += 1
-                
-                g[i][j] = math.sqrt(A[i][i] - theta) 
-
-            else:
-
-                k = 0
-                theta = 0
-
-                while k < j:
-
-                    theta += g[i][k]*g[j][k]
-                    k += 1
-
-                g[i][j] = (A[i][j] - theta)/g[j][j]
-
-            i += 1
-
-        j += 1
-        i = j
-                
-    y = (inverse(g)).dot(b) 
-    
-    x = (inverse(g.T)).dot(y)
-    
-    return x
-
-def decomposition(U, b):
-    
-    L = identity(np.zeros(U.shape))
-
-    i = 0
-    k = 0
-
-    while (i < U.shape[0]):
-        
-        k = 0
-
-        if (U[i][i] == 0):
-            
-            n = i
-            
-            while (U[i][i] == 0) and (n < U.shape[0]):
-                
-                temp = U[i].copy()
-                U[i] = U[n]
-                U[n] = temp
-                
-                n += 1
-        
-        while (k < U.shape[0]):
-            
-            if (k <= i) or (U[i][i] == 0):
-                
-                k += 1
-                
-            else:
-                L[k][i] = U[k][i]/U[i][i]
-                U[k] = U[k] - L[k][i]*U[i]
-                k += 1
-            
-        i += 1
-
-    y = (inverse(L)).dot(b) 
-    
-    x = (inverse(U)).dot(y)
-    
-    return x
-    
-def cramer(A, b):
-
-    x = np.vstack(np.zeros(b.shape))
-    k = 0
-
-    while (k < A.shape[0]):
-
-        temp = A.copy()
-        temp[:,k] = b
-
-        x[k] = np.linalg.det(temp)/np.linalg.det(A)
-        
-        k += 1
-    
-    return x

Older Versions/yoshi-seals1.3/build/lib/Seals/scan/__init__.py

@@ -1,21 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .scan import numpy as np
-from .scan import pandas as pd

Older Versions/yoshi-seals1.3/build/lib/Seals/scan/scan.py

@@ -1,45 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import pandas as pd
-
-def numpy(path, sep=None, decimal=None):
-
-    if sep is None:
-        sep = ","
-
-    if decimal is None:
-        decimal = "."
-
-    df=pd.read_csv(path, sep=sep, decimal=decimal, header=None)
-    array = df.to_numpy()
-
-    return array
-
-def pandas(path, sep=None, decimal=None):
-
-    if sep is None:
-        sep = ","
-
-    if decimal is None:
-        decimal = "."
-
-    return pd.read_csv(path, sep=sep, decimal=decimal)
-

Older Versions/yoshi-seals1.3/build/lib/Seals/write/__init__.py

@@ -1,21 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .write import numpy as np
-from .write import pandas as pd

Older Versions/yoshi-seals1.3/build/lib/Seals/write/write.py

@@ -1,33 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import csv
-
-def numpy(array, path):
-
-    with open(path, mode='w') as sistema_linear:
-
-        solution_writer = csv.writer(sistema_linear, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
-        solution_writer.writerows(array)
-        
-    return array
-
-def pandas(df, path):
-    
-    df.to_csv(path)

Older Versions/yoshi-seals1.3/setup.py

@@ -1,27 +0,0 @@
-import setuptools
-
-with open("README.md", "r") as fh:
-    long_description = fh.read()
-
-setuptools.setup(
-    name="yoshi-seals",
-    version="1.3",
-    author="Vitor Hideyoshi",
-    author_email="vitor.h.n.batista@gmail.com",
-    description="Numeric Calculus python module in the topic of Linear Algebra",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git",
-    packages=setuptools.find_packages(),
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: GNU General Public License v2 (GPLv2)",
-        "Operating System :: OS Independent",
-        "Development Status :: 2 - Pre-Alpha",
-    ],
-    python_requires='>=3.6',
-    install_requires=[
-          'numpy',
-          'pandas',
-      ],
-)

Older Versions/yoshi-seals1.3/yoshi_seals.egg-info/PKG-INFO

@@ -1,71 +0,0 @@
-Metadata-Version: 2.1
-Name: yoshi-seals
-Version: 1.3
-Summary: Numeric Calculus python module in the topic of Linear Algebra
-Home-page: https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git
-Author: Vitor Hideyoshi
-Author-email: vitor.h.n.batista@gmail.com
-License: UNKNOWN
-Description: # Seals - Numeric Calculus
-        
-        This python package is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
-        
-        * Scan *csv* files to make a numpy matrix.
-        
-        * Write a matrix into a *csv* file
-        
-        * Insert user input into a matrix or a vector.
-        
-        * Calculate Eigen Values
-        
-        * Use methods to proccess the matrices.
-          * Identity Matrix
-          * Gauss Elimination
-          * Inverse Matrix
-          * Cholesky Decomposition
-          * LU Decomposition
-          * Cramer
-        
-        ## Syntax
-        
-        The module *scan* has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `Seals.scan.np(path)` for *Numpy* and `Seals.scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
-        
-        The module *write* has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `Seals.write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `Seals.write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
-        
-        The module *insert* has a function for *matrix* and another for *vector*, and it has the following syntax `Seals.insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
-        
-        There is also a function that given a matrix it return all real eigen values
-        
-        ### Processes
-        
-        To call the module `process` use the syntax: `sl = Seals.process`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
-        
-        * The function *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
-        
-        * The function *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
-        
-        * The function *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
-        
-        * The function *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-          
-        * The function *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-        
-        * The function *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-        
-        ## Installation
-        
-        To install the package from source `cd` into the directory and run:
-        
-        `pip install .`
-        
-        or run
-        
-        `pip install yoshi-seals`
-        
-Platform: UNKNOWN
-Classifier: Programming Language :: Python :: 3
-Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
-Classifier: Operating System :: OS Independent
-Classifier: Development Status :: 2 - Pre-Alpha
-Requires-Python: >=3.6
-Description-Content-Type: text/markdown

Older Versions/yoshi-seals1.3/yoshi_seals.egg-info/SOURCES.txt

@@ -1,18 +0,0 @@
-README.md
-setup.py
-Seals/__init__.py
-Seals/eigen/__init__.py
-Seals/eigen/eigen.py
-Seals/insert/__init__.py
-Seals/insert/insert.py
-Seals/process/__init__.py
-Seals/process/process.py
-Seals/scan/__init__.py
-Seals/scan/scan.py
-Seals/write/__init__.py
-Seals/write/write.py
-yoshi_seals.egg-info/PKG-INFO
-yoshi_seals.egg-info/SOURCES.txt
-yoshi_seals.egg-info/dependency_links.txt
-yoshi_seals.egg-info/requires.txt
-yoshi_seals.egg-info/top_level.txt

Older Versions/yoshi-seals1.3/yoshi_seals.egg-info/dependency_links.txt

@@ -1 +0,0 @@
-

Older Versions/yoshi-seals1.3/yoshi_seals.egg-info/requires.txt

@@ -1,2 +0,0 @@
-numpy
-pandas

Older Versions/yoshi-seals1.3/yoshi_seals.egg-info/top_level.txt

@@ -1 +0,0 @@
-Seals

README.md

@@ -4,11 +4,11 @@ This python namespace is made for applied Numeric Calculus of Linear Algebra. It
 
 * Scan *csv* files to make a numpy matrix.
 
-* Write a matrix into a *csv* file
+* Write a matrix into a *csv* file.
 
 * Insert user input into a matrix or a vector.
 
-* Calculate Eigen Values
+* Calculate Eigenvalues and his Eigenvectors.
 
 * Use methods to proccess the matrices.
   * Identity Matrix
@@ -20,29 +20,27 @@ This python namespace is made for applied Numeric Calculus of Linear Algebra. It
 
 ## Syntax
 
-To call the package *scan* use the syntax: `from Seals import scan`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `scan.np(path)` for *Numpy* and `scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
+To call the package *scan* use the syntax: `from yoshi_seals import scan`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `scan.np(path)` for *Numpy* and `scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
 
-To call the package *write* use the syntax: `from Seals import write`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
+To call the package *write* use the syntax: `from yoshi_seals import write`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
 
-To call the package *insert* use the syntax: `from Seals import insert`. The package also has a function for *matrix* and another for *vector*, and it has the following syntax `insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
+To call the package *insert* use the syntax: `from yoshi_seals import insert`. The package also has a function for *matrix* and another for *vector*, and it has the following syntax `insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
 
-There is also a function that given a matrix it return all real eigen values
+There is also a function that given a matrix it return all real eigenvalues and all real eigenvectors, this function uses the power method to find the eigenvalues and inverse power method for the eigenvector.
 
 ### Processes
 
-To call the module `process` use the syntax: `from Seals import process as sl`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
+To call the module `process` use the syntax: `from yoshi_seals import process as sl`, where `sl` is an alias and will be used to call functions: `sl.inverse(array)`.
 
-* The function *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
+* The function *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-* The function *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
 
 * The function *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
 
 * The function *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
   
-* The function *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
+* The function *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.decomposition(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
 
-* The function *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
+* The function *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cramer(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
 
 ## Installation
 

build.py

@@ -0,0 +1,41 @@
+import os
+import numpy
+
+
+try:
+    from Cython.Build import cythonize
+except ImportError:
+    def build(setup_kwargs):
+        pass
+else:
+    from setuptools import Extension
+    from setuptools.dist import Distribution
+    from setuptools.command.build_ext import build_ext
+
+
+    def build_ext_modules_array(ext: list[dict]) -> list[Extension]:
+        return [
+            Extension(
+                name=e['name'],
+                sources=[e['path']],
+                extra_compile_args=['-O3', '-fopenmp'],
+                extra_link_args=['-fopenmp'],
+                language='c',
+            ) for e in ext
+        ]
+
+    def build(setup_kwargs):
+        extensions = [
+            {"name": "yoshi_seals.shared.array", "path": "yoshi_seals/shared/array.pyx"},
+            {"name": "yoshi_seals.process.process", "path": "yoshi_seals/process/process.pyx"},
+        ]
+
+        setup_kwargs.update({
+            'ext_modules': cythonize(
+                build_ext_modules_array(extensions),
+                language_level=3,
+                compiler_directives={'linetrace': True},
+            ),
+            'include_dirs': [numpy.get_include()],
+            'cmdclass': {'build_ext': build_ext}
+        })

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+
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+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "8c13f2e0cc0cd4be7860bdfc44ede036db41f77b195d33b52ac3e63b51540387"

pyproject.toml

@@ -0,0 +1,35 @@
+[tool.poetry]
+name = "yoshi-seals"
+version = "0.0.0"
+description = ""
+authors = ["Vitor Hideyoshi Nakazone Batista <vitor.h.n.batista@gmail.com>"]
+license = "GPL-2.0-only"
+packages = [
+    { include = "yoshi_seals", from = "." },
+]
+readme = "README.md"
+
+[tool.poetry.build]
+generate-setup-file = true
+script = "build.py"
+
+
+[tool.poetry.dependencies]
+python = "^3.10"
+numpy = "^2.0.0"
+pandas = "^2.2.2"
+cython = "^3.0.10"
+
+
+[tool.poetry.group.dev.dependencies]
+setuptools = "^70.1.0"
+
+
+[tool.poetry-dynamic-versioning]
+enable = true
+vcs = "git"
+
+
+[build-system]
+requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning>=1.0.0,<2.0.0", "setuptools>=70.1.0", "cython>=3.0.10", "numpy>=2.0.0"]
+build-backend = "poetry_dynamic_versioning.backend"

tests/test_process.py

@@ -0,0 +1,90 @@
+from yoshi_seals import process as ps
+
+import numpy as np
+
+from numpy import testing as npt
+import unittest
+
+
+class TestProcess(unittest.TestCase):
+
+    def test_det(self):
+        a = np.array(([1., 0.], [0., 1.]))
+
+        a_det = ps.det(a)
+
+        self.assertEqual(a_det, 1)
+
+    def test_inverse(self):
+        a = np.array(([1., 0.], [0., 1.]))
+        a_inv = ps.inverse(a)
+
+        npt.assert_array_equal(a, a_inv)
+
+    def test_hstack_returns_stacked_array(self):
+        a = np.array(([1., ], [1., ]))
+        b = np.array(([1., ], [1., ]))
+
+        stacked = ps.hstack(a, b)
+        expected_stacked_array = np.array(([1., 1.], [1., 1.]))
+
+        npt.assert_array_equal(stacked, expected_stacked_array)
+
+    def test_hstack_throws_exception(self):
+        a = np.array(([1., ], [1., ]))
+        b = np.array(([1., ]))
+
+        with self.assertRaises(ValueError):
+            ps.hstack(a, b)
+
+    def test_vstack_returns_stacked_array(self):
+        a = np.array(([[1., 1.]]))
+        b = np.array(([[1., 1.]]))
+
+        stacked = ps.vstack(a, b)
+        expected_stacked_array = np.array(([1., 1.], [1., 1.]))
+
+        npt.assert_array_equal(stacked, expected_stacked_array)
+
+    def test_vstack_throws_exception(self):
+        a = np.array(([[1., 1.]]))
+        b = np.array(([[1.]]))
+
+        with self.assertRaises(ValueError):
+            ps.vstack(a, b)
+
+    def test_gauss(self):
+        a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
+        b = np.array(([44.], [128.], [214.]))
+
+        matrix = ps.gauss(a, b)
+        expected_matrix = np.array([[-8.], [6.], [2.]])
+
+        npt.assert_almost_equal(matrix, expected_matrix)
+
+    def test_cholesky(self):
+        a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
+        b = np.array(([44.], [128.], [214.]))
+
+        matrix = ps.cholesky(a, b)
+        expected_matrix = np.array([[-8.], [6.], [2.]])
+
+        npt.assert_almost_equal(matrix, expected_matrix)
+
+    def test_decomposition(self):
+        a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
+        b = np.array(([44.], [128.], [214.]))
+
+        matrix = ps.decomposition(a, b)
+        expected_matrix = np.array([[-8.], [6.], [2.]])
+
+        npt.assert_almost_equal(matrix, expected_matrix)
+
+    def test_cramer(self):
+        a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
+        b = np.array(([44.], [128.], [214.]))
+
+        matrix = ps.cramer(a, b)
+        expected_matrix = np.array([[-8.], [6.], [2.]])
+
+        npt.assert_almost_equal(matrix, expected_matrix)

yoshi-seals1.3.1/LICENSE

@@ -1,278 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
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-
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- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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-FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN
-OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
-PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
-OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.  THE ENTIRE RISK AS
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-PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
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-POSSIBILITY OF SUCH DAMAGES.

yoshi-seals1.3.1/README.md

@@ -1,55 +0,0 @@
-# Seals - Numeric Calculus
-
-This python namespace is made for applied Numeric Calculus of Linear Algebra. It is made with the following objectives in mind:
-
-* Scan *csv* files to make a numpy matrix.
-
-* Write a matrix into a *csv* file
-
-* Insert user input into a matrix or a vector.
-
-* Calculate Eigen Values
-
-* Use methods to proccess the matrices.
-  * Identity Matrix
-  * Gauss Elimination
-  * Inverse Matrix
-  * Cholesky Decomposition
-  * LU Decomposition
-  * Cramer
-
-## Syntax
-
-To call the package *scan* use the syntax: `from Seals import scan`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and used the following syntax `scan.np(path)` for *Numpy* and `scan.pd(path)` for *Pandas*, where `path` is the path to your directory.
-
-To call the package *write* use the syntax: `from Seals import write`. The package also has a function for *Numpy* arrays and *Pandas* dataframes, and uses the following syntax `write.np(array,path)` for *Numpy*, where `array` is the matrix that you desire to output and `path` is the path to your directory, and `write.pd(df,path)` for *Pandas*, where `df` is the matrix that you desire to output and `path` is the path to your directory.
-
-To call the package *insert* use the syntax: `from Seals import insert`. The package also has a function for *matrix* and another for *vector*, and it has the following syntax `insert.function(array)`, where `insert` is the *Python Module* and `function` is either a `matrix` or a `vector` and `array` is either a *matrix* or a *vector*.
-
-There is also a function that given a matrix it return all real eigen values
-
-### Processes
-
-To call the module `process` use the syntax: `from Seals import process as sl`, where `sl` is an instance and to use a function you have to append the desired function in front of the instance like: `sl.identity(array)`.
-
-* The function *identity* returns a *numpy* identity matrix of the order of the matrix passed into to it, and it has the following syntax `sl.identity(array)`, which `array` is a square matrix.
-
-* The function *gauss* returns a *numpy* vector containing the vector of variables from the augmented matrix. `sl.gauss(matrix)`, which `matrix` is the augmented matrix.
-
-* The function *inverse* returns a *numpy* inverse matrix of the matrix passed into to it, and it has the following syntax `sl.inverse(matrix)`, which `matrix` is a square matrix.
-
-* The function *cholesky* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-  
-* The function *decomposition* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-* The function *cramer* returns a *numpy* vector containing the vector of variables from the coefficient matrix and the constants vector, and it has the following syntax `sl.cholesky(A,b)`, which `A` is the coefficient matrix and `b` is the constants vector.
-
-## Installation
-
-To install the package from source `cd` into the directory and run:
-
-`pip install .`
-
-or run
-
-`pip install yoshi-seals`

yoshi-seals1.3.1/Seals/__init__.py

@@ -1,18 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

yoshi-seals1.3.1/Seals/eigen/__init__.py

@@ -1,20 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .eigen import eigen

yoshi-seals1.3.1/Seals/eigen/eigen.py

@@ -1,51 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-
-def eigen(a):
-
-    k = 0
-    l = np.ones((a.shape[0]))
-
-    while (k < a.shape[0]):
-
-        u = np.random.rand(a.shape[0],1)
-        u = u/max(u.min(), u.max(), key=abs)
-
-        ctrl = 0
-
-        while (ctrl != l[k]):
-            
-            ctrl = l[k]
-            u = a.dot(u)
-            l[k] = max(u.min(), u.max(), key=abs)
-            u = u/l[k]
-
-
-        i = 0
-
-        while (u[i] == 0):
-            i += 1
-
-        a = a - (1/u[i])*u*a[i]
-
-        k += 1
-
-    return l

yoshi-seals1.3.1/Seals/insert/__init__.py

@@ -1,21 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .insert import matrix
-from .insert import vector

yoshi-seals1.3.1/Seals/insert/insert.py

@@ -1,46 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-def matrix(matrix):
-
-    i = 0
-    
-    while (i < matrix.shape[0]):
-        
-        j = 0
-        
-        while (j < matrix.shape[1]):
-            
-            matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
-            j += 1
-        
-        i += 1
-        
-    return matrix
-
-def vector(vector):
-    
-    j=0
-    
-    while (j < vector.shape[0]):
-            
-        vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
-        j += 1
-        
-    return vector

yoshi-seals1.3.1/Seals/process/__init__.py

@@ -1,25 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .process import identity
-from .process import gauss
-from .process import inverse
-from .process import cholesky
-from .process import decomposition
-from .process import cramer

yoshi-seals1.3.1/Seals/process/process.py

@@ -1,203 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import math
-
-def identity(matrix):
-    
-    i = 0
-    
-    while (i < matrix.shape[0]):
-        
-        j = 0
-        
-        while (j < matrix.shape[0]):
-            
-            if (i == j):
-                
-                matrix[i][j] = 1
-                
-            elif (i != j):
-                
-                matrix[i][j] = 0
-                        
-            j += 1
-        
-        i += 1
-        
-    return matrix
-
-def gauss(matrix):
-    
-    i = 0
-    k = 0
-    
-    while (i < matrix.shape[0]):
-        
-        if (matrix[i][i] == 0):
-            
-            n = i
-            
-            while (matrix[i][i] == 0) and (n < matrix.shape[0]):
-                
-                temp = matrix[i].copy()
-                matrix[i] = matrix[n]
-                matrix[n] = temp
-                
-                n += 1
-        
-        while (k < matrix.shape[0]):
-            
-            if (k == i) or (matrix[i][i] == 0):
-                
-                k += 1
-                
-            else:
-                
-                mult = matrix[k][i]/matrix[i][i]
-                matrix[k] = matrix[k] - mult*matrix[i]
-                k += 1
-            
-        i += 1
-        k = 0
-    
-    i = 0
-    
-    while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
-        
-        matrix[i] = matrix[i]/matrix[i][i]
-        i += 1        
-    
-    return matrix[:,(matrix.shape[0]):]
-
-def inverse(matrix):
-    
-    return gauss(np.hstack((matrix, identity(np.zeros(matrix.shape)))))
-
-def cholesky(A, b):
-    
-    g = np.zeros((A.shape))
-    
-    i = 0
-    j = 0
-    
-    while j < A.shape[1]:
-        while i < A.shape[0]:
-
-            if i == 0 and j == 0:
-
-                g[i][j] = math.sqrt(A[0][0])
-
-            elif j == 0:
-
-                g[i][j] = A[i][0]/g[0][0]
-
-            elif i == j:
-
-                k = 0
-                theta = 0
-
-                while k < i:
-
-                    theta += g[i][k]**2
-                    k += 1
-                
-                g[i][j] = math.sqrt(A[i][i] - theta) 
-
-            else:
-
-                k = 0
-                theta = 0
-
-                while k < j:
-
-                    theta += g[i][k]*g[j][k]
-                    k += 1
-
-                g[i][j] = (A[i][j] - theta)/g[j][j]
-
-            i += 1
-
-        j += 1
-        i = j
-                
-    y = (inverse(g)).dot(b) 
-    
-    x = (inverse(g.T)).dot(y)
-    
-    return x
-
-def decomposition(U, b):
-    
-    L = identity(np.zeros(U.shape))
-
-    i = 0
-    k = 0
-
-    while (i < U.shape[0]):
-        
-        k = 0
-
-        if (U[i][i] == 0):
-            
-            n = i
-            
-            while (U[i][i] == 0) and (n < U.shape[0]):
-                
-                temp = U[i].copy()
-                U[i] = U[n]
-                U[n] = temp
-                
-                n += 1
-        
-        while (k < U.shape[0]):
-            
-            if (k <= i) or (U[i][i] == 0):
-                
-                k += 1
-                
-            else:
-                L[k][i] = U[k][i]/U[i][i]
-                U[k] = U[k] - L[k][i]*U[i]
-                k += 1
-            
-        i += 1
-
-    y = (inverse(L)).dot(b) 
-    
-    x = (inverse(U)).dot(y)
-    
-    return x
-    
-def cramer(A, b):
-
-    x = np.vstack(np.zeros(b.shape))
-    k = 0
-
-    while (k < A.shape[0]):
-
-        temp = A.copy()
-        temp[:,k] = b
-
-        x[k] = np.linalg.det(temp)/np.linalg.det(A)
-        
-        k += 1
-    
-    return x

yoshi-seals1.3.1/Seals/scan/__init__.py

@@ -1,21 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .scan import numpy as np
-from .scan import pandas as pd

yoshi-seals1.3.1/Seals/scan/scan.py

@@ -1,45 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-import numpy as np
-import pandas as pd
-
-def numpy(path, sep=None, decimal=None):
-
-    if sep is None:
-        sep = ","
-
-    if decimal is None:
-        decimal = "."
-
-    df=pd.read_csv(path, sep=sep, decimal=decimal, header=None)
-    array = df.to_numpy()
-
-    return array
-
-def pandas(path, sep=None, decimal=None):
-
-    if sep is None:
-        sep = ","
-
-    if decimal is None:
-        decimal = "."
-
-    return pd.read_csv(path, sep=sep, decimal=decimal)
-

yoshi-seals1.3.1/Seals/write/__init__.py

@@ -1,21 +0,0 @@
-# Seals - Program made for educational intent, can be freely distributed
-# and can be used for economical intent. I will not take legal actions
-# unless my intelectual propperty, the code, is stolen or change without permission.  
-
-# Copyright (C) 2020  VItor Hideyoshi Nakazone Batista
-
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License version 2 as published by
-# the Free Software Foundation.
-
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-
-# You should have received a copy of the GNU General Public License along
-# with this program; if not, write to the Free Software Foundation, Inc.,
-# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-from .write import numpy as np
-from .write import pandas as pd