Setup CI(GH Actions) for pull requests and pushes

.github/workflows/test_main.yml

@@ -0,0 +1,46 @@
+name: Python package
+
+on: [push]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 4
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -e .
+        pip install -r requirements_dev.txt
+    - name: Lint with flake8
+      run: |
+        pip install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pip install pytest
+        pip install pytest-cov
+        pytest --cov=./ --cov-report=xml
+    - name: Upload coverage to Codecov
+      if: matrix.python-version == '3.8'
+      uses: codecov/codecov-action@v1
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: ./coverage.xml
+        flags: unittests
+        name: codecov-umbrella
+        yml: ./codecov.yml
+        fail_ci_if_error: true

.github/workflows/test_pull_requests.yml

@@ -0,0 +1,46 @@
+name: Python package
+
+on: [pull_request]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 4
+      matrix:
+        python-version: [3.6, 3.7, 3.8, 3.9]
+
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -e .
+        pip install -r requirements_dev.txt
+    - name: Lint with flake8
+      run: |
+        pip install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pip install pytest
+        pip install pytest-cov
+        pytest --cov=./ --cov-report=xml
+    - name: Upload coverage to Codecov
+      if: matrix.python-version == '3.8'
+      uses: codecov/codecov-action@v1
+      with:
+        token: ${{ secrets.CODECOV_TOKEN }}
+        file: ./coverage.xml
+        flags: unittests
+        name: codecov-umbrella
+        yml: ./codecov.yml
+        fail_ci_if_error: true

README.rst

@@ -13,6 +13,9 @@ space
         :target: https://space.readthedocs.io/en/latest/?badge=latest
         :alt: Documentation Status
 
+.. image:: https://codecov.io/gh/LaboratoryOfPlasmaPhysics/space/branch/main/graph/badge.svg?branch=main
+        :target: https://codecov.io/gh/LaboratoryOfPlasmaPhysics/space/branch/main
+        :alt: Coverage Status
 
 
 

requirements_dev.txt

@@ -8,3 +8,7 @@ coverage==4.5.4
 Sphinx==1.8.5
 twine==1.14.0
 
+ddt
+pandas
+numpy
+matplotlib

setup.py

@@ -10,7 +10,7 @@
 with open('HISTORY.rst') as history_file:
     history = history_file.read()
 
-requirements = ['pandas', 'numpy', 'matplotlib']
+requirements = ['pandas', 'numpy', 'matplotlib', 'scipy']
 
 setup_requirements = []
 

space/coordinates/coordinates.py

@@ -1,91 +1,100 @@
 import numpy as np
 import pandas as pd
-from datetime import timedelta, datetime
+
 
 def spherical_to_cartesian(R, theta, phi):
     x = R * np.cos(theta)
-    y = R * np.sin(theta)*np.cos(phi)
-    z = R * np.sin(theta)*np.sin(phi)
+    y = R * np.sin(theta) * np.cos(phi)
+    z = R * np.sin(theta) * np.sin(phi)
     return x, y, z
 
-def cartesian_to_spherical(X,Y,Z):
-    r     = np.sqrt(X**2+Y**2+Z**2)
-    theta = np.arccos(X/r)
-    phi   = np.arctan2(Z,Y)
-    return r,theta,phi
-
-def BaseChoice(base,R,theta,phi):
-    if base=='cartesian' :
-        return spherical_to_cartesian(R,theta,phi)
-    elif base=='spherical' :
-        return R,theta,phi
-    else :
+
+def cartesian_to_spherical(X, Y, Z):
+    r = np.sqrt(X ** 2 + Y ** 2 + Z ** 2)
+    theta = np.arccos(X / r)
+    phi = np.arctan2(Z, Y)
+    return r, theta, phi
+
+
+def base_choice(base, R, theta, phi):
+    if base == 'cartesian':
+        return spherical_to_cartesian(R, theta, phi)
+    elif base == 'spherical':
+        return R, theta, phi
+    else:
         print('Error : base parameter must be set to "cartesian" or "spherical" ')
 
 
-def Check_base(pos):
-    if ((hasattr(pos, 'R' )) | (hasattr(pos, 'r' ))) & (hasattr(pos, 'X' )) | (hasattr(pos, 'x' )):
+def check_base(pos):
+    if ((hasattr(pos, 'R')) | (hasattr(pos, 'r'))) & (hasattr(pos, 'X')) | (hasattr(pos, 'x')):
         base = 'spherical&cartesian'
-    elif (hasattr(pos, 'R' )) | (hasattr(pos, 'r' )) :
+    elif (hasattr(pos, 'R')) | (hasattr(pos, 'r')):
         base = 'spherical'
-    elif (hasattr(pos, 'X' )) | (hasattr(pos, 'x' )) :
+    elif (hasattr(pos, 'X')) | (hasattr(pos, 'x')):
         base = 'cartesian'
-    else :
+    else:
         print('must check the name of the variables : cartesian = (X,Y,Z) and spherical = (R,theta,phi)')
 
     return base
 
-def Add_cst_radiuus(pos,cst):
-    base = Check_base(pos)
-    if base=='cartesian':
-        r,theta,phi = CartesianToSpherical(pos.X,pos.Y,pos.Z)
-    else :
-        r,theta,phi = pos.R, pos.theta, pos.phi
-    r=r+cst
-    x,y,z = spherical_to_cartesian(r,theta,phi)
-    return pd.DataFrame({'X' : x, 'Y' : y, 'Z' :z})
 
-def SWI_base(omni_data):
-    norm_V =np.sqrt(omni_data.Vx**2+(omni_data.Vy+29.8)**2+omni_data.Vz**2)
-    X = np.array([-np.array([vx,vy+29.8,vz])/V  for vx,vy,vz,V in zip( omni_data.Vx.values, omni_data.Vy.values, omni_data.Vz.values,norm_V)])
-    B = np.array([np.array([bx,by,bz]) for bx,by,bz in zip( np.sign(omni_data.COA.values)*omni_data.Bx.values, np.sign(omni_data.COA.values)*omni_data.By.values, np.sign(omni_data.COA.values)*omni_data.Bz.values)])
+def add_cst_radiuus(pos, cst):
+    base = check_base(pos)
+    if base == 'cartesian':
+        r, theta, phi = cartesian_to_spherical(pos.X, pos.Y, pos.Z)
+    else:
+        r, theta, phi = pos.R, pos.theta, pos.phi
+    r = r + cst
+    x, y, z = spherical_to_cartesian(r, theta, phi)
+    return pd.DataFrame({'X': x, 'Y': y, 'Z': z})
 
 
-    Z = np.cross(X,B)
+def swi_base(omni_data):
+    norm_V = np.sqrt(omni_data.Vx ** 2 + (omni_data.Vy + 29.8) ** 2 + omni_data.Vz ** 2)
+    X = np.array([-np.array([vx, vy + 29.8, vz]) / V for vx, vy, vz, V in
+                  zip(omni_data.Vx.values, omni_data.Vy.values, omni_data.Vz.values, norm_V)])
+    B = np.array([np.array([bx, by, bz]) for bx, by, bz in zip(np.sign(omni_data.COA.values) * omni_data.Bx.values,
+                                                               np.sign(omni_data.COA.values) * omni_data.By.values,
+                                                               np.sign(omni_data.COA.values) * omni_data.Bz.values)])
 
-    norm_cross = np.array(np.sqrt(Z[:,0]**2+Z[:,1]**2+Z[:,2]**2))
-    Z = np.array([z/n for z,n in zip(Z,norm_cross)])
-    Y = np.cross(Z,X)
-    return X,Y,Z
+    Z = np.cross(X, B)
+
+    norm_cross = np.array(np.sqrt(Z[:, 0] ** 2 + Z[:, 1] ** 2 + Z[:, 2] ** 2))
+    Z = np.array([z / n for z, n in zip(Z, norm_cross)])
+    Y = np.cross(Z, X)
+    return X, Y, Z
 
 
 def to_swi(omni_data, msh_data, pos_msh):
-    X_swi, Y_swi, Z_swi = SWI_base(omni_data)
+    X_swi, Y_swi, Z_swi = swi_base(omni_data)
     data = msh_data.copy()
     o_data = omni_data.copy()
     pos = pos_msh.copy()
 
-
-    o_data['Vx'] = X_swi[:,0]*omni_data['Vx']+X_swi[:,1]*omni_data['Vy']+X_swi[:,2]*omni_data['Vz']
-    o_data['Vy'] = Y_swi[:,0]*omni_data['Vx']+Y_swi[:,1]*omni_data['Vy']+Y_swi[:,2]*omni_data['Vz']
-    o_data['Vz'] = Z_swi[:,0]*omni_data['Vx']+Z_swi[:,1]*omni_data['Vy']+Z_swi[:,2]*omni_data['Vz']
-
-    o_data['Bx'] = np.sign(omni_data['COA'])*(X_swi[:,0]*omni_data['Bx']+X_swi[:,1]*omni_data['By']+X_swi[:,2]*omni_data['Bz'])
-    o_data['By'] = np.sign(omni_data['COA'])*(Y_swi[:,0]*omni_data['Bx']+Y_swi[:,1]*omni_data['By']+Y_swi[:,2]*omni_data['Bz'])
-    o_data['Bz'] = np.sign(omni_data['COA'])*(Z_swi[:,0]*omni_data['Bx']+Z_swi[:,1]*omni_data['By']+Z_swi[:,2]*omni_data['Bz'])
-
-    data['Vx'] = X_swi[:,0]*msh_data['Vx']+X_swi[:,1]*msh_data['Vy']+X_swi[:,2]*msh_data['Vz']
-    data['Vy'] = Y_swi[:,0]*msh_data['Vx']+Y_swi[:,1]*msh_data['Vy']+Y_swi[:,2]*msh_data['Vz']
-    data['Vz'] = Z_swi[:,0]*msh_data['Vx']+Z_swi[:,1]*msh_data['Vy']+Z_swi[:,2]*msh_data['Vz']
-
-    data['Bx'] = np.sign(omni_data['COA'])*(X_swi[:,0]*msh_data['Bx']+X_swi[:,1]*msh_data['By']+X_swi[:,2]*msh_data['Bz'])
-    data['By'] = np.sign(omni_data['COA'])*(Y_swi[:,0]*msh_data['Bx']+Y_swi[:,1]*msh_data['By']+Y_swi[:,2]*msh_data['Bz'])
-    data['Bz'] = np.sign(omni_data['COA'])*(Z_swi[:,0]*msh_data['Bx']+Z_swi[:,1]*msh_data['By']+Z_swi[:,2]*msh_data['Bz'])
-
-
-    pos['X'] = X_swi[:,0]*pos_msh['X']+X_swi[:,1]*pos_msh['Y']+X_swi[:,2]*pos_msh['Z']
-    pos['Y'] = Y_swi[:,0]*pos_msh['X']+Y_swi[:,1]*pos_msh['Y']+Y_swi[:,2]*pos_msh['Z']
-    pos['Z'] = Z_swi[:,0]*pos_msh['X']+Z_swi[:,1]*pos_msh['Y']+Z_swi[:,2]*pos_msh['Z']
-
-
-    return data,pos,o_data
+    o_data['Vx'] = X_swi[:, 0] * omni_data['Vx'] + X_swi[:, 1] * omni_data['Vy'] + X_swi[:, 2] * omni_data['Vz']
+    o_data['Vy'] = Y_swi[:, 0] * omni_data['Vx'] + Y_swi[:, 1] * omni_data['Vy'] + Y_swi[:, 2] * omni_data['Vz']
+    o_data['Vz'] = Z_swi[:, 0] * omni_data['Vx'] + Z_swi[:, 1] * omni_data['Vy'] + Z_swi[:, 2] * omni_data['Vz']
+
+    o_data['Bx'] = np.sign(omni_data['COA']) * (
+            X_swi[:, 0] * omni_data['Bx'] + X_swi[:, 1] * omni_data['By'] + X_swi[:, 2] * omni_data['Bz'])
+    o_data['By'] = np.sign(omni_data['COA']) * (
+            Y_swi[:, 0] * omni_data['Bx'] + Y_swi[:, 1] * omni_data['By'] + Y_swi[:, 2] * omni_data['Bz'])
+    o_data['Bz'] = np.sign(omni_data['COA']) * (
+            Z_swi[:, 0] * omni_data['Bx'] + Z_swi[:, 1] * omni_data['By'] + Z_swi[:, 2] * omni_data['Bz'])
+
+    data['Vx'] = X_swi[:, 0] * msh_data['Vx'] + X_swi[:, 1] * msh_data['Vy'] + X_swi[:, 2] * msh_data['Vz']
+    data['Vy'] = Y_swi[:, 0] * msh_data['Vx'] + Y_swi[:, 1] * msh_data['Vy'] + Y_swi[:, 2] * msh_data['Vz']
+    data['Vz'] = Z_swi[:, 0] * msh_data['Vx'] + Z_swi[:, 1] * msh_data['Vy'] + Z_swi[:, 2] * msh_data['Vz']
+
+    data['Bx'] = np.sign(omni_data['COA']) * (
+            X_swi[:, 0] * msh_data['Bx'] + X_swi[:, 1] * msh_data['By'] + X_swi[:, 2] * msh_data['Bz'])
+    data['By'] = np.sign(omni_data['COA']) * (
+            Y_swi[:, 0] * msh_data['Bx'] + Y_swi[:, 1] * msh_data['By'] + Y_swi[:, 2] * msh_data['Bz'])
+    data['Bz'] = np.sign(omni_data['COA']) * (
+            Z_swi[:, 0] * msh_data['Bx'] + Z_swi[:, 1] * msh_data['By'] + Z_swi[:, 2] * msh_data['Bz'])
+
+    pos['X'] = X_swi[:, 0] * pos_msh['X'] + X_swi[:, 1] * pos_msh['Y'] + X_swi[:, 2] * pos_msh['Z']
+    pos['Y'] = Y_swi[:, 0] * pos_msh['X'] + Y_swi[:, 1] * pos_msh['Y'] + Y_swi[:, 2] * pos_msh['Z']
+    pos['Z'] = Z_swi[:, 0] * pos_msh['X'] + Z_swi[:, 1] * pos_msh['Y'] + Z_swi[:, 2] * pos_msh['Z']
+
+    return data, pos, o_data