Merge pull request #172 from OpenPIV/sig2noise_validation

version 0.23.2

Author: alexlib

CHANGES.txt

@@ -9,3 +9,8 @@ v0.22.4 Nov, 2020 -- windef refactoring : no more process.pyx, everything in pyp
 v0.23.0 - refactored windef.py, with the main functions moved to pyprocess.py
 v0.23.1 - fixed bugs in 0.23.0, new normalized_correlation, normalize_intensity, find_subpixel_position, new tests, new jupyter notebooks, see also 
             test_robustness
+v0.23.2 - added mask_coordinats to preprocess, allows to use dynamic_masking to create
+    image mask as well as a polygon that propagates into multi-process and validation
+    created new Jupyter notebook to test von Karman vortex street case and compare with PIVLab
+    breakes backward compatibility of windef with removing validation and filtering steps, to be compatible
+    with the first_pass. Both first_pass and multi_pass now apply filtering externally

openpiv/examples/notebooks/PIV_3D_example.ipynb

@@ -0,0 +1,84 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# compare the OpenPIV Python with PIVLab\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openpiv import tools, scaling, validation, filters, preprocess\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "from skimage import exposure\n",
+    "from skimage import img_as_float, img_as_ubyte\n",
+    "from scipy.ndimage import gaussian_filter, median_filter\n",
+    "from skimage.filters import threshold_otsu\n",
+    "from skimage.color import rgb2gray, rgba2rgb\n",
+    "from skimage import io\n",
+    "import os\n",
+    "\n",
+    "import matplotlib\n",
+    "matplotlib.rcParams['figure.figsize'] = (8.0, 6.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# test_directory = os.path.split(os.path.abspath(__file__))[0]\n",
+    "test_directory = '../../test/'\n",
+    "img = rgb2gray(rgba2rgb(io.imread(os.path.join(test_directory, \"moon.png\"))))\n",
+    "img1, mask = preprocess.dynamic_masking(img_as_float(img), method=\"intensity\")\n",
+    "mask_coords = preprocess.mask_coordinates(mask,1.5,3, plot=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,py:percent"
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:openpiv] *",
+   "language": "python",
+   "name": "conda-env-openpiv-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

openpiv/examples/notebooks/all_test_cases_sample.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +122,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f3c1b7a7520>"
+       "<matplotlib.image.AxesImage at 0x7fcdde832af0>"
       ]
      },
      "execution_count": 10,
@@ -211,7 +211,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x7f3c1b6ea7f0>"
+       "<matplotlib.contour.QuadContourSet at 0x7fcdde776dc0>"
       ]
      },
      "execution_count": 12,
@@ -262,7 +262,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.quiver.Quiver at 0x7f3c1b4a8a60>"
+       "<matplotlib.quiver.Quiver at 0x7fcdde518160>"
       ]
      },
      "execution_count": 15,