python version problem, package problem

.github/workflows/python-package.yml

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+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.7"] #["3.7", "3.8", "3.9"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    # Display the Python version being used
+    - name: Display Python version
+      run: python -c "import sys; print(sys.version)"
+    # Install the package using the setup.py
+    - name: Install package
+      run: pip install git+https://github.com/fabrylab/pyTFM.git
+    # Install pytest (you can use some other testing utility)
+    - name: Test with pytest
+      run: |
+        pytest test_pyTFM.py

README.md

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+[![Python package](https://github.com/martinschatz-cz/pyTFM/actions/workflows/python-package.yml/badge.svg?event=push)](https://github.com/martinschatz-cz/pyTFM/actions/workflows/python-package.yml)
+
+[![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg)]() [![Python 3.7](https://img.shields.io/badge/python-3.7-green.svg)]() [![Python 3.8](https://img.shields.io/badge/python-3.8-red.svg)]() [![Python 3.9](https://img.shields.io/badge/python-3.9-red.svg)]()
+
 ## Readme
 
 pyTFM is a python package that allows you to analyze force generation and stresses in cell colonies and confluent cell layers growing on a 2 dimensional surface. This package implements the procedures of [Traction Force Microscopy](https://www.ncbi.nlm.nih.gov/pubmed/11832345) and [Monolayer Stress Microscopy](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0055172). In addition to the standard measures for stress and force generation, it
 also includes the line tension, a measure for the force transfer exclusively across cell-cell boundaries. 
 pyTFM includes an addon for the image annotation tool [clickpoints](https://clickpoints.readthedocs.io/en/latest/) allowing you to quickly analyze and vizualize large datasets.
 
 Please refer to the [Documentation](https://pytfm.readthedocs.io/en/latest/) of pyTFM for detailed instructions on installation and usage.
+
+## Conda enviroment creation
+You need to create python 3.6 enviroment. 
+```
+conda create --name pyTFM python=3.6
+conda activate pyTFM
+```
+
+Note: python 3.7 works too, but clickpoints need python 3.6.
+
+pyTFM package install
+```
+pip install git+https://github.com/fabrylab/pyTFM.git
+```
+
+If you want to use jupyterlab
+```
+pip install jupyterlab
+jupyter-lab
+```
+Sometimes it is necessary to reinstall pyTFM again through the JupyterLab because of scikit-image package.

test_pyTFM.py

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+import numpy as np
+import pytest
+import matplotlib.pyplot as plt
+import numpy as np
+import openpiv.filters
+import scipy.fft
+
+from openpiv.pyprocess import extended_search_area_piv
+import openpiv.scaling
+import openpiv.tools
+import openpiv.validation
+from matplotlib.colors import LinearSegmentedColormap
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from pyTFM.utilities_TFM import suppress_warnings
+from scipy.ndimage.filters import median_filter, gaussian_filter
+from scipy.ndimage.filters import uniform_filter
+from pyTFM.TFM_functions import ffttc_traction
+from pyTFM.TFM_functions import get_xy_for_quiver
+from pyTFM.TFM_functions import strain_energy_points
+from pyTFM.TFM_functions import contractillity
+
+def test_ffttc_traction_output_shape():
+    # create test inputs
+    u = np.zeros((10, 10))
+    v = np.zeros((10, 10))
+    pixelsize1 = 0.1
+    pixelsize2 = 0.1
+    young = 1e6
+
+    # call the function
+    tx_filter, ty_filter = ffttc_traction(u, v, pixelsize1, pixelsize2, young)
+
+    # assert that the output shape is as expected
+    assert tx_filter.shape == (10, 10)
+    assert ty_filter.shape == (10, 10)
+
+def test_ffttc_traction_output_type():
+    # create test inputs
+    u = np.zeros((10, 10))
+    v = np.zeros((10, 10))
+    pixelsize1 = 0.1
+    pixelsize2 = 0.1
+    young = 1e6
+
+    # call the function
+    tx_filter, ty_filter = ffttc_traction(u, v, pixelsize1, pixelsize2, young)
+
+    # assert that the output is of the correct type
+    assert isinstance(tx_filter, np.ndarray)
+    assert isinstance(ty_filter, np.ndarray)
+
+def test_ffttc_traction_output_values():
+    # create test inputs
+    u = np.zeros((10, 10))
+    v = np.zeros((10, 10))
+    pixelsize1 = 0.1
+    pixelsize2 = 0.1
+    young = 1e6
+
+    # call the function
+    tx_filter, ty_filter = ffttc_traction(u, v, pixelsize1, pixelsize2, young)
+
+    # assert that the output is as expected
+    assert np.allclose(tx_filter, 0)
+    assert np.allclose(ty_filter, 0)
+
+def test_ffttc_traction_input_shape():
+    # create test inputs with incorrect shape
+    u = np.zeros((10, 10, 10))
+    v = np.zeros((10, 10))
+    pixelsize1 = 0.1
+    pixelsize2 = 0.1
+    young = 1e6
+
+    # assert that a ValueError is raised for incorrect input shape
+    with pytest.raises(ValueError):
+        ffttc_traction(u, v, pixelsize1, pixelsize2, young)
+
+def test_ffttc_traction_input_type():
+    # create test inputs with incorrect type
+    u = np.zeros((10, 10))
+    v = "not an array"
+    pixelsize1 = 0.1
+    pixelsize2 = 0.1
+    young = 1e6
+
+    # assert that a TypeError is raised for incorrect input type
+    with pytest.raises(TypeError):
+        ffttc_traction(u, v, pixelsize1, pixelsize2, young)
+
+########get_xy_for_quiver
+def test_get_xy_for_quiver():
+    u = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    xs, ys = get_xy_for_quiver(u)
+
+    # Test the shape of xs and ys
+    assert np.shape(xs) == np.shape(u)
+    assert np.shape(ys) == np.shape(u)
+
+    # Test the values of xs
+    for i in range(np.shape(u)[0]):
+        assert np.array_equal(xs[i, :], np.arange(0, np.shape(u)[1], 1))
+
+    # Test the values of ys
+    for j in range(np.shape(u)[1]):
+        assert np.array_equal(ys[:, j], np.arange(0, np.shape(u)[0], 1))