Merge pull request #155 from OpenPIV/windef_refactoring

openpiv-python refactoring

Author: alexlib

.gitignore

@@ -1,40 +1,11 @@
-openpiv/docs/sphinx-docs/downloads/tutorial-part1
 *.m
-openpiv/src/process.c
-openpiv/src/lib.c
 *.jvc
 *.pyc
 build/
 *.txt
 /setup.cfg
-openpiv/lib.so
-openpiv/process.so
-mark_data/Camera 1-120.tif
-mark_data/Camera 1-120.tif.png
-mark_data/Camera 1-121.tif
-mark_data/Camera 1-122.tif
-mark_data/Camera 1-123.tif
-mark_data/Camera 1-124.tif
-mark_data/Camera 1-125.tif
-mark_data/Camera 1-126.tif
-mark_data/Camera 1-127.tif
-mark_data/Camera 1-128.tif
-mark_data/Camera 1-129.tif
-mark_data/Camera 1-130.tif
-mark_data/neg.tif
-mark_data/tutorial-part1.py
-mark_result/Camera 1-120.tif.png
-mark_result/Camera 1-121.tif.png
-mark_result/Camera 1-122.tif.png
-mark_result/Camera 1-123.tif.png
-mark_result/Camera 1-124.tif.png
-mark_result/Camera 1-125.tif.png
-mark_result/Camera 1-126.tif.png
-mark_result/Camera 1-127.tif.png
-mark_result/Camera 1-128.tif.png
-openpiv-python-mark-data-checkpoint.ipynb
-openpiv-python-tutorial-part1-checkpoint.ipynb
-dist
+
+dist/*
 masking_tutorial/.ipynb_checkpoints
 openpiv/masking_tutorial/.ipynb_checkpoints
 .eggs
@@ -43,3 +14,4 @@ OpenPIV.egg-info
 *.ipynb_checkpoints*
 .vscode/settings.json
 *.so
+

CHANGES.txt

@@ -5,3 +5,4 @@ v0.17, July 1, 2014  -- Fixed the bug in lib.pyx of different casting of np.int3
 v0.18  Aug. 8, 2014 -- small updates to the tutorial-part1, MANIFEST.IN, readme and install files
 ...
 v0.22.3 Sep. 22, 2020 -- @erfan-mtr added two-phase extension, see phase_separation.ipynb for the demo
+v0.22.4 Nov, 2020 -- windef refactoring : no more process.pyx, everything in pyprocess.py, numpy vectorized correlation version from windef moved to pyprocess, get_field_shape has less arguments (it's a backward compatability problem, it breaks stuff), new tests, new documentation settings with Jupyter notebook and markdown inserts, tools.save requires also sig2noise column, as in windef, frame_interpolation is now deform_windows with optionaly kx,ky, 

TODO

@@ -1,6 +1,6 @@
-'''
+"""
 functions to plot 3D-deformation fields and simple 3D-structures
-'''
+"""
 
 
 import matplotlib
@@ -11,8 +11,8 @@
 
 
 def set_axes_equal(ax):
-    
-    '''
+
+    """
     Following https://stackoverflow.com/questions/13685386/matplotlib-equal-unit-length-with-equal-aspect-ratio-z-axis-is-not-equal-to
     Make axes of 3D plot have equal scale so that spheres appear as spheres,
     cubes as cubes, etc..  This is one possible solution to Matplotlib's
@@ -23,7 +23,7 @@ def set_axes_equal(ax):
      ax: matplotlib.axes object
        
      
-    '''
+    """
 
     x_limits = ax.get_xlim3d()
     y_limits = ax.get_ylim3d()
@@ -38,17 +38,17 @@ def set_axes_equal(ax):
 
     # The plot bounding box is a sphere in the sense of the infinity
     # norm, hence I call half the max range the plot radius.
-    plot_radius = 0.5*max([x_range, y_range, z_range])
+    plot_radius = 0.5 * max([x_range, y_range, z_range])
 
     ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])
     ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])
     ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])
 
 
 def scatter_3D(a, cmap="jet", sca_args=None, control="color", size=60):
-    
+
     # default arguments for the quiver plot. can be overwritten by quiv_args
-    if not isinstance(sca_args,dict):
+    if not isinstance(sca_args, dict):
         sca_args = {}
     scatter_args = {"alpha": 1}
     scatter_args.update(sca_args)
@@ -59,21 +59,30 @@ def scatter_3D(a, cmap="jet", sca_args=None, control="color", size=60):
     z = z.flatten()
 
     fig = plt.figure()
-    ax = fig.gca(projection='3d', rasterized=True)
+    ax = fig.gca(projection="3d", rasterized=True)
 
     if control == "color":
         # make cmap
         cbound = [np.nanmin(a), np.nanmax(a)]
         # create normalized color map for arrows
-        norm = matplotlib.colors.Normalize(vmin=cbound[0], vmax=cbound[1])  # 10 ) #cbound[1] ) #)
+        norm = matplotlib.colors.Normalize(
+            vmin=cbound[0], vmax=cbound[1]
+        )  # 10 ) #cbound[1] ) #)
         sm = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm)
         sm.set_array([])
         # different option
         cm = matplotlib.cm.get_cmap(cmap)
         colors = cm(norm(a)).reshape(a.shape[0] * a.shape[1] * a.shape[2], 4)  #
         # plotting
         nan_filter = ~np.isnan(a.flatten())
-        ax.scatter(x[nan_filter], y[nan_filter], z[nan_filter], c=colors[nan_filter], s=size, **scatter_args)
+        ax.scatter(
+            x[nan_filter],
+            y[nan_filter],
+            z[nan_filter],
+            c=colors[nan_filter],
+            s=size,
+            **scatter_args
+        )
         plt.colorbar(sm)
 
     if control == "alpha":
@@ -88,13 +97,25 @@ def scatter_3D(a, cmap="jet", sca_args=None, control="color", size=60):
         ax_scale = plt.axes([0.88, 0.1, 0.05, 0.7])
         # ax_scale.set_ylim((0.1,1.2))
         nm = 5
-        ax_scale.scatter([0] * nm, np.linspace(a.min(), a.max(), nm), s=sizes.max() * np.linspace(0, 1, nm))
+        ax_scale.scatter(
+            [0] * nm,
+            np.linspace(a.min(), a.max(), nm),
+            s=sizes.max() * np.linspace(0, 1, nm),
+        )
         ax_scale.spines["left"].set_visible(False)
         ax_scale.spines["right"].set_visible(True)
         ax_scale.spines["bottom"].set_visible(False)
         ax_scale.spines["top"].set_visible(False)
-        ax_scale.tick_params(axis="both", which="both", labelbottom=False, labelleft=False, labelright=True,
-                             bottom=False, left=False, right=True)
+        ax_scale.tick_params(
+            axis="both",
+            which="both",
+            labelbottom=False,
+            labelleft=False,
+            labelright=True,
+            bottom=False,
+            left=False,
+            right=True,
+        )
 
     ax.set_xlim(0, a.shape[0])
     ax.set_ylim(0, a.shape[1])
@@ -130,7 +151,9 @@ def plot_3D_alpha(data):
 
     data_fil = data.copy()
     data_fil[(data == np.inf)] = np.nanmax(data[~(data == np.inf)])
-    data_fil = (data_fil - np.nanmin(data_fil)) / (np.nanmax(data_fil) - np.nanmin(data_fil))
+    data_fil = (data_fil - np.nanmin(data_fil)) / (
+        np.nanmax(data_fil) - np.nanmin(data_fil)
+    )
     data_fil[np.isnan(data_fil)] = 0
 
     col[:, :, :, 2] = 1
@@ -149,16 +172,31 @@ def plot_3D_alpha(data):
     z[:, :, 1::2] += 0.95
 
     fig = plt.figure()
-    ax = fig.gca(projection='3d')
+    ax = fig.gca(projection="3d")
     ax.voxels(x, y, z, fill, facecolors=col_exp, edgecolors=col_exp)
     ax.set_xlabel("x")
     ax.set_ylabel("y")
     ax.set_zlabel("z")
     plt.show()
 
 
-def quiver_3D(u, v, w, x=None, y=None, z=None, mask_filtered=None, filter_def=0, filter_reg=(1, 1, 1),
-              cmap="jet", quiv_args=None, vmin=None, vmax=None, arrow_scale=0.15, equal_ax=True):
+def quiver_3D(
+    u,
+    v,
+    w,
+    x=None,
+    y=None,
+    z=None,
+    mask_filtered=None,
+    filter_def=0,
+    filter_reg=(1, 1, 1),
+    cmap="jet",
+    quiv_args=None,
+    vmin=None,
+    vmax=None,
+    arrow_scale=0.15,
+    equal_ax=True,
+):
     """ 
     Displaying 3D deformation fields vector arrows
 
@@ -212,15 +250,21 @@ def quiver_3D(u, v, w, x=None, y=None, z=None, mask_filtered=None, filter_def=0,
     """
 
     # default arguments for the quiver plot. can be overwritten by quiv_args
-    quiver_args = {"normalize":False, "alpha":0.8, "pivot":'tail', "linewidth":1, "length":1}
+    quiver_args = {
+        "normalize": False,
+        "alpha": 0.8,
+        "pivot": "tail",
+        "linewidth": 1,
+        "length": 1,
+    }
     if isinstance(quiv_args, dict):
         quiver_args.update(quiv_args)
-    # overwriting length if an arrow scale and a "length" argument in quiv_args 
+    # overwriting length if an arrow scale and a "length" argument in quiv_args
     # is provided at the same
     if arrow_scale is not None:
         quiver_args["length"] = 1
-        
-    # convert filter ot list if proveided as int    
+
+    # convert filter ot list if proveided as int
     if not isinstance(filter_reg, (tuple, list)):
         filter_reg = [filter_reg] * 3
 
@@ -234,8 +278,9 @@ def quiver_3D(u, v, w, x=None, y=None, z=None, mask_filtered=None, filter_def=0,
             x, y, z = np.indices(u.shape)
         else:
             raise ValueError(
-                "displacement data has wrong number of dimensions (%s). Use 1d array, list, or 3d array." % str(
-                    len(u.shape)))
+                "displacement data has wrong number of dimensions (%s). Use 1d array, list, or 3d array."
+                % str(len(u.shape))
+            )
 
     # conversion to array
     x, y, z = np.array([x, y, z])
@@ -246,7 +291,7 @@ def quiver_3D(u, v, w, x=None, y=None, z=None, mask_filtered=None, filter_def=0,
         if isinstance(filter_reg, list):
             show_only = np.zeros(u.shape).astype(bool)
             # filtering out every x-th
-            show_only[::filter_reg[0], ::filter_reg[1], ::filter_reg[2]] = True
+            show_only[:: filter_reg[0], :: filter_reg[1], :: filter_reg[2]] = True
             mask_filtered = np.logical_and(mask_filtered, show_only)
 
     xf = x[mask_filtered]
@@ -265,28 +310,32 @@ def quiver_3D(u, v, w, x=None, y=None, z=None, mask_filtered=None, filter_def=0,
     # different option
     colors = matplotlib.cm.jet(norm(df))  #
 
-    colors = [c for c, d in zip(colors, df) if d > 0] + list(chain(*[[c, c] for c, d in zip(colors, df) if d > 0]))
+    colors = [c for c, d in zip(colors, df) if d > 0] + list(
+        chain(*[[c, c] for c, d in zip(colors, df) if d > 0])
+    )
     # colors in ax.quiver 3d is really fucked up/ will probably change with updates:
     # requires list with: first len(u) entries define the colors of the shaft, then the next len(u)*2 entries define
     # the color ofleft and right arrow head side in alternating order. Try for example:
     # colors = ["red" for i in range(len(cf))] + list(chain(*[["blue", "yellow"] for i in range(len(cf))]))
     # to see this effect.
-    # BUT WAIT THERS MORE: zeor length arrows are apparently filtered out in the matplolib with out filtering 
+    # BUT WAIT THERS MORE: zeor length arrows are apparently filtered out in the matplolib with out filtering
     # the color list appropriately so we have to do this our selfs as well
 
     # scale arrows to axis dimensions:
     ax_dims = [(x.min(), x.max()), (y.min(), y.max()), (z.min(), z.max())]
     if arrow_scale is not None:
         max_length = df.max()
-        max_dim_length= np.max([(d[1] - d[0] + 1) for d in ax_dims] )
+        max_dim_length = np.max([(d[1] - d[0] + 1) for d in ax_dims])
         scale = max_dim_length * arrow_scale / max_length
     else:
         scale = 1
 
     # plotting
     fig = plt.figure()
-    ax = fig.gca(projection='3d', rasterized=True)
-    ax.quiver(xf, yf, zf, vf*scale, uf*scale, wf*scale, colors=colors, **quiver_args)
+    ax = fig.gca(projection="3d", rasterized=True)
+    ax.quiver(
+        xf, yf, zf, vf * scale, uf * scale, wf * scale, colors=colors, **quiver_args
+    )
     plt.colorbar(sm)
 
     ax.set_xlim(ax_dims[0])
@@ -302,6 +351,5 @@ def quiver_3D(u, v, w, x=None, y=None, z=None, mask_filtered=None, filter_def=0,
     ax.w_xaxis.set_pane_color((0.2, 0.2, 0.2, 1.0))
     ax.w_yaxis.set_pane_color((0.2, 0.2, 0.2, 1.0))
     ax.w_zaxis.set_pane_color((0.2, 0.2, 0.2, 1.0))
-    
-    return fig
 
+    return fig

openpiv/PIV_3D_plotting.py

@@ -1,3 +1,4 @@
 def test():
     import pytest
+
     pytest.main()

openpiv/__init__.py

@@ -39,17 +39,6 @@ Or use `pip` ::
     pip install numpy cython
     pip install openpiv --pre
 
-
-In Python 3 the project changed name to `progressbar2` package. Install it separately using `pip`
-
-    pip install progressbar2
-    
-Or using Conda:   
-
-    conda install progressbar2
-    
-We will remove this requirement in the future, so don't be surprised it if just works without progressbar. 
-
 Get OpenPIV source code!
 ========================