small fixes

.gitignore

@@ -2,6 +2,9 @@
 .DS_Store
 
 docs/pdoc/html
+develop/
+dist/
+vtk_data
 
 vedo.egg-info
 build
@@ -19,12 +22,10 @@ examples/notebooks/.ipynb_checkpoints
 
 untitled*.py
 bug_*.py
-prove
-store
-speed_tester.py
 data
 www
 
 dev_*.py
 z?.py
+x?.py
 v?.py

docs/changes.md

@@ -42,16 +42,20 @@ Changes that will break existing code and need active thinking and work to adapt
 
 ## New/Revised Examples
 ```
-tests/issues/issue_1146.py
 examples/advanced/spline_draw2.py
 examples/volumetric/isosurfaces2.py
 examples/pyplot/fit_curve2.py
+
+tests/issues/issue_1146.py
+tests/issues/discussion_1190.py
+tests/snippets/test_interactive_plotxy.py
 ```
 
 ## To Do
 - improve 4/5 keys to show a scalarbar
 - add interpolate_scalar5 to webpage
 - fix draw_spline1,2 in webpage
+- fix trasform in image.tomesh() is not transmitted to mesh
 
 
 ### Broken Examples

docs/documentation.md

@@ -1,14 +1,14 @@
 
 [![license](https://img.shields.io/badge/license-MIT-blue.svg)](https://en.wikipedia.org/wiki/MIT_License)
 [![Anaconda-Server Badge](https://anaconda.org/conda-forge/vedo/badges/version.svg)](https://anaconda.org/conda-forge/vedo)
-[![Ubuntu 23.10 package](https://repology.org/badge/version-for-repo/ubuntu_23_10/vedo.svg)](https://repology.org/project/vedo/versions)
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5842090.svg)](https://doi.org/10.5281/zenodo.5842090)
+[![Ubuntu 24.10 package](https://repology.org/badge/version-for-repo/ubuntu_24_10/vedo.svg)](https://repology.org/project/vedo/versions)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4587871.svg)](https://doi.org/10.5281/zenodo.4587871)
 
 ![](https://vedo.embl.es/images/feats/teapot_banner.png)
 
 
 A python module for scientific analysis of 3D objects and
-point clouds based on [VTK](https://www.vtk.org/) and [numpy](http://www.numpy.org/).
+point clouds based on [VTK](https://www.vtk.org/) and [Numpy](http://www.numpy.org/).
 
 Check out the [**GitHub repository**](https://github.com/marcomusy/vedo)
 and the [**vedo main page here**](https://vedo.embl.es).

examples/other/make_video.py

@@ -1,16 +1,16 @@
 """Make a video file with or without graphic window"""
-from vedo import dataurl, Plotter, Mesh, Video
+from vedo import dataurl, Plotter, Mesh, Axes, Video
 
 
-msh = Mesh(dataurl+"spider.ply").rotate_x(-90)
-msh.texture(dataurl+"textures/leather.jpg")
+msh = Mesh(dataurl+"data/teapot.vtk").normalize()#.rotate_x(-90)
+msh.shift(-msh.center_of_mass())
 
-plt = Plotter(bg="beige", bg2="lb", axes=10, offscreen=False)
-plt += [msh, __doc__]
+plt = Plotter(bg="beige", bg2="lb", offscreen=False)
+plt += [msh, Axes(msh), __doc__]
 
 ##############################################################
 # Open a video file and force it to last 3 seconds in total
-video = Video("spider.mp4", duration=3) # or gif
+video = Video("vedo_video.mp4", duration=3) # or gif
 
 ##############################################################
 # Any rendering loop goes here, e.g.:
@@ -29,22 +29,19 @@
 # OR set a sequence of camera positions, e.g.:
 cam1 = dict(
     position=(5.805, 17.34, -0.8418),
-    focal_point=(3.133, 1.506, -3.132),
+    focal_point=(0.133, 0.506, -0.132),
     viewup=(-0.3099, 0.1871, -0.9322),
-    distance=16.22,
     clipping_range=(12.35, 21.13),
 )
 cam2 = dict(
     position=(-1.167, 3.356, -18.66),
-    focal_point=(3.133, 1.506, -3.132),
-    distance=16.22,
+    focal_point=(0.133, 0.506, -0.132),
     clipping_range=(8.820, 25.58),
 )
 cam3 = dict(
     position=(-4.119, 0.9889, -0.8867),
-    focal_point=(2.948, 1.048, -3.592),
+    focal_point=(0.948, 0.048, -0.592),
     viewup=(-0.01864, 0.9995, -0.02682),
-    distance=7.567,
     clipping_range=(0.07978, 17.04),
 )
 

tests/issues/discussion_1190.py

@@ -0,0 +1,37 @@
+from vedo import *
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+
+settings.default_font = "Antares"
+
+man = Mesh(dataurl + "man.vtk")
+h_knees = -0.5
+over_limit  =  1.5
+under_limit = -1.4
+
+# let the scalar be the z coordinate of the mesh vertices
+scals = man.vertices[:, 2]
+
+# build a complicated colour map
+c1 = plt.cm.viridis(np.linspace(0.0, 0.7, 128))
+c2 = plt.cm.terrain(np.linspace(0.5, 0.8, 128))
+c = np.vstack((c1, c2))
+cmap = colors.LinearSegmentedColormap.from_list("heights", c)
+cmap.set_over(color="red")
+cmap.set_under(color="orange")
+norm = colors.TwoSlopeNorm(h_knees, vmin=under_limit, vmax=over_limit)
+mapper = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
+
+# build look up table
+lut = build_lut(
+    [(v, mapper.to_rgba(v)[:3]) for v in np.linspace(under_limit, over_limit, 128)],
+    above_color=cmap.get_over()[:3],
+    below_color=cmap.get_under()[:3],
+    vmin=under_limit,
+    vmax=over_limit,
+)
+
+man.cmap(lut, scals)
+man.add_scalarbar3d(above_text="Above Eyes", below_text="Below Heels")
+man.scalarbar = man.scalarbar.clone2d("center-left", size=0.3)  # make it 2D
+show(man, axes=1, viewup="z")

tests/snippets/test_interactive_plotxy.py

@@ -0,0 +1,60 @@
+import numpy as np
+from scipy.optimize import fsolve
+from vedo import Plotter, settings
+from vedo.pyplot import plot
+
+
+# Initial values
+C_init = 0
+xdata = np.linspace(-3, 3, 50)
+
+
+# Function to solve for y given x and C (from your first script)
+def solve_for_y(x, C):
+    y_vals = []
+    for sign in [1, -1]:  # Solve for positive and negative y
+
+        def equation(y):
+            return 0.5 * y**2 + np.log(np.abs(y)) - 0.5 * x**2 - C
+
+        y_initial_guess = sign * np.exp(-0.5 * x**2 - C)
+
+        root = fsolve(equation, y_initial_guess)[0]
+        if equation(root) < 1e-5:  # Only accept the root if it's a valid solution
+            y_vals.append(root)
+    return y_vals
+
+
+# Generate the y values for plotting (positive and negative y)
+def generate_y_values(x_values, C):
+    y_positive = []
+    y_negative = []
+    for x in x_values:
+        y_vals = solve_for_y(x, C)
+        if len(y_vals) > 0:
+            y_positive.append(max(y_vals))  # Choose the largest root as positive
+            y_negative.append(min(y_vals))  # Choose the smallest root as negative
+        else:
+            y_positive.append(np.nan)       # Use NaN for missing values
+            y_negative.append(np.nan)
+    return y_positive, y_negative
+
+
+# Function to update the plot when the slider changes
+def update_plot(widget=None, event=""):
+    C_value = C_init if widget is None else widget.value
+    y_positive, y_negative = generate_y_values(xdata, C_value)
+    m = max(max(y_positive), abs(min(y_negative)))
+    p  = plot(xdata, y_positive, c='red5',  lw=4, ylim=(-m, m))
+    p += plot(xdata, y_negative, c='blue5', lw=4, like=p)
+    plt.remove("PlotXY").add(p)
+
+
+# Create Plotter and the slider to control the value of C
+settings.default_font = "Brachium"
+
+plt = Plotter(size=(900, 650), title="Exercise")
+slider = plt.add_slider(update_plot, -10.0, 10.0, value=C_init, title="C value", c="green3")
+update_plot()  # Initial plot
+
+plt.show(mode="2d", zoom=1.35)

vedo/pyplot.py

@@ -3151,7 +3151,6 @@ def _histogram_polar(
         ct, st = np.cos(t), np.sin(t)
 
         if show_errors:
-            show_lines = False
             err = np.sqrt(histodata[i]) / vmax * r2
             errl = shapes.Line(
                 ((r1 + r - err) * ct, (r1 + r - err) * st, 0.01),

vedo/version.py

@@ -1 +1 @@
-_version = '2024.5.2+dev12'
+_version = '2024.5.2+dev14'