Difference between revisions of "Plots With PyVista"

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We provide here a tutorial on how to make pretty visuals from GCM simulations. Do not hesitate to send us your best visuals so that we can enrich our GCM plot gallery.
 
We provide here a tutorial on how to make pretty visuals from GCM simulations. Do not hesitate to send us your best visuals so that we can enrich our GCM plot gallery.
−
 
  
 
We first import all the required libraries:
 
We first import all the required libraries:
Line 6: Line 5:
 
* netCDF4 (https://pypi.org/project/netCDF4/) to read NetCDF files in Python.
 
* netCDF4 (https://pypi.org/project/netCDF4/) to read NetCDF files in Python.
 
* PyVista (https://docs.pyvista.org/getting-started/installation.html)
 
* PyVista (https://docs.pyvista.org/getting-started/installation.html)
 +
 +
ps: the diagfi.nc file as well as the Jupyter notebook are provided here: https://web.lmd.jussieu.fr/~mturbet/FILES/PyVista_GCM_plot_tutorial/
 +
 +
ps2: the diagfi.nc file was produced following the quick install & run tutorial that you can follow here: https://lmdz-forge.lmd.jussieu.fr/mediawiki/Planets/index.php/Quick_Install_and_Run
  
 
<syntaxhighlight lang="python">
 
<syntaxhighlight lang="python">
Line 17: Line 20:
 
<syntaxhighlight lang="python">
 
<syntaxhighlight lang="python">
 
# 4D GRID VARIABLES
 
# 4D GRID VARIABLES
−
nc1 = Dataset('diagfi.nc')
+
nc1 = Dataset('diagfi_benchmark_early_Mars.nc')
  
 
Time_GCM=nc1.variables['Time'][:]
 
Time_GCM=nc1.variables['Time'][:]
Line 97: Line 100:
 
grid_scalar1 = pv.grid_from_sph_coords(xx_bounds, yy_bounds, surface_level)
 
grid_scalar1 = pv.grid_from_sph_coords(xx_bounds, yy_bounds, surface_level)
  
 +
scalar_surface = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
 
for i_time in range(0,len(Time_GCM),1):
 
for i_time in range(0,len(Time_GCM),1):
−
     # Scalar data for the surface
+
     for i in range(0,len(lat_GCM),1):
−
    scalar_surface = tsurf1[i_time,:,0:(len(lon_GCM)-1)]
+
        for j in range(0,len(lon_GCM)-1,1):
 +
            scalar_surface[i,j]=scalar_surface[i,j]+tsurf1[i_time,i,j]/len(Time_GCM)
  
 
grid_scalar1.cell_arrays["example"] = np.array(scalar_surface).swapaxes(-2, -1).ravel("C")
 
grid_scalar1.cell_arrays["example"] = np.array(scalar_surface).swapaxes(-2, -1).ravel("C")
Line 119: Line 124:
 
p.show()
 
p.show()
 
</syntaxhighlight>
 
</syntaxhighlight>
 +
 +
[[File:tutoriel_pyvista_plot1.png|thumb| Plot 1]]
  
 
2. We create the 3D spherical shell (3D grid lines)
 
2. We create the 3D spherical shell (3D grid lines)
Line 147: Line 154:
 
p.show()
 
p.show()
 
</syntaxhighlight>
 
</syntaxhighlight>
 +
 +
[[File:tutoriel_pyvista_plot2.png|thumb| Plot 2]]
  
 
3. We prepare for the array of wind arrows.
 
3. We prepare for the array of wind arrows.
  
 
<syntaxhighlight lang="python">
 
<syntaxhighlight lang="python">
 +
# 3. PREPARE THE WIND LAYER
 +
 
freq_call_wind=2 # used to change the frequency of wind pattern
 
freq_call_wind=2 # used to change the frequency of wind pattern
 
MULT_FACTOR_ZWIND=100. # scaling factor of vertical wind so that we can see it in the plot
 
MULT_FACTOR_ZWIND=100. # scaling factor of vertical wind so that we can see it in the plot
Line 156: Line 167:
  
 
i_alt=13 # altitude of the wind layer (in the GCM data)
 
i_alt=13 # altitude of the wind layer (in the GCM data)
 +
 +
scalar_u_wind = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
 +
scalar_v_wind = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
 +
scalar_w_wind = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
  
 
for i_time in range(0,len(Time_GCM),1):
 
for i_time in range(0,len(Time_GCM),1):
−
     # Scalar data for the winds
+
     for i in range(0,len(lat_GCM),1):
−
    scalar_u_wind = u1[i_time,i_alt,:,0:(len(lon_GCM)-1)]
+
        for j in range(0,len(lon_GCM)-1,1):
−
    scalar_v_wind = v1[i_time,i_alt,:,0:(len(lon_GCM)-1)]
+
            scalar_u_wind[i,j]=scalar_u_wind[i,j]+u1[i_time,i_alt,i,j]/len(Time_GCM)
−
    scalar_w_wind = w1[i_time,i_alt,:,0:(len(lon_GCM)-1)]
+
            scalar_v_wind[i,j]=scalar_v_wind[i,j]+v1[i_time,i_alt,i,j]/len(Time_GCM)
 +
            scalar_w_wind[i,j]=scalar_w_wind[i,j]+w1[i_time,i_alt,i,j]/len(Time_GCM)
  
 
inv_axes = [*range(scalar_u_wind[::freq_call_wind,::freq_call_wind].ndim)[::-1]]
 
inv_axes = [*range(scalar_u_wind[::freq_call_wind,::freq_call_wind].ndim)[::-1]]
Line 202: Line 218:
 
p.show()
 
p.show()
 
</syntaxhighlight>
 
</syntaxhighlight>
 +
 +
[[File:tutoriel_pyvista_plot3.png|thumb| Plot 3]]
  
 
4.1 We create a cloud layer (first option using 2D cloud layer)
 
4.1 We create a cloud layer (first option using 2D cloud layer)
Line 215: Line 233:
 
table_level[2]=0.
 
table_level[2]=0.
  
 +
 +
scalar_cloud = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
 
for i_time in range(0,len(Time_GCM),1):
 
for i_time in range(0,len(Time_GCM),1):
−
     # Scalar data for the clouds
+
     for i in range(0,len(lat_GCM),1):
−
    scalar_cloud = h2o_ice_col1[i_time,:,0:(len(lon_GCM)-1)]
+
        for j in range(0,len(lon_GCM)-1,1):
 +
            scalar_cloud[i,j]=scalar_cloud[i,j]+h2o_ice_col1[i_time,i,j]/len(Time_GCM)
  
 
# 3D cloud layer
 
# 3D cloud layer
Line 254: Line 275:
 
p.show()
 
p.show()
 
</syntaxhighlight>
 
</syntaxhighlight>
 +
 +
[[File:tutoriel_pyvista_plot4.png|thumb| Plot 4]]
  
 
4.2 We create a cloud layer (second, more advanced option using 3D cloud layer)
 
4.2 We create a cloud layer (second, more advanced option using 3D cloud layer)
Line 264: Line 287:
 
         for j in range(0,len(lon_GCM)-1,1):
 
         for j in range(0,len(lon_GCM)-1,1):
 
             for k in range(0,len(alt_GCM),1):
 
             for k in range(0,len(alt_GCM),1):
−
                 scalar_ice[i,j,k]=ice1[i_time,k,i,j]
+
                 scalar_ice[i,j,k]=scalar_ice[i,j,k]+ice1[i_time,k,i,j]/len(Time_GCM)
 
              
 
              
 
nlev = len(alt_GCM)+1
 
nlev = len(alt_GCM)+1
Line 302: Line 325:
 
p.show()
 
p.show()
 
</syntaxhighlight>
 
</syntaxhighlight>
 +
 +
[[File:tutoriel_pyvista_plot5.png|thumb| Plot 5]]
  
 
We make a first series of .png files (rotating the azimuth angle) to be used to build a .gif animated plot.
 
We make a first series of .png files (rotating the azimuth angle) to be used to build a .gif animated plot.
Line 331: Line 356:
 
</syntaxhighlight>
 
</syntaxhighlight>
  
−
We make a second series of .png files (rotating the azimuth angle and the elevation of the camera) to be used to build a .gif animated plot.
+
To build a gif, you can use the imagemagick convert tool (https://imagemagick.org/script/download.php) using for instance the following command line:
 +
 
 +
<syntaxhighlight lang="bash">
 +
convert -delay 10 gif_planet_*.png animated_planet.gif
 +
</syntaxhighlight>
 +
 
 +
We make a second series of .png files (rotating not only the azimuth angle, but also the elevation of the camera) to be used to build a .gif animated plot.
  
 
<syntaxhighlight lang="python">
 
<syntaxhighlight lang="python">
Line 358: Line 389:
 
print('FINISHED')
 
print('FINISHED')
 
</syntaxhighlight>
 
</syntaxhighlight>
 +
 +
Another method allows us to directly generate the gif, without generating all the pictures.
 +
 +
<syntaxhighlight lang="python">
 +
#FINALLY WE MAKE THE GIF
 +
total_step_gif=100
 +
p = pv.Plotter(notebook=False, off_screen=True)
 +
p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
 +
p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
 +
p.add_mesh(grid_winds.glyph(orient="example", scale="example", tolerance=0.005),cmap="binary",show_scalar_bar=False)
 +
p.add_mesh(surfaces_cloud,cmap="Greys",opacity=0.7,show_scalar_bar=False)
 +
p.camera_position = 'yz'
 +
# Open a gif
 +
p.open_gif("nom_du_gif.gif")
 +
for i in range(0,total_step_gif,1):
 +
    p.camera_position = 'yz'
 +
    p.camera.azimuth = i*360/total_step_gif
 +
    p.camera.roll += 0.
 +
    p.camera.elevation = 30
 +
    p.set_background('white')
 +
    # Write a frame. This triggers a render.
 +
    p.write_frame()
 +
# Closes and finalizes movie
 +
p.close()
 +
</syntaxhighlight>
 +
 +
 +
Last words: Do not hesitate to send us your best visuals so that we can enrich our GCM plot gallery! Do not hesitate as well to update this page with additional features of PyVista :)
 +
 +
[[Category:Generic-Model]]

Latest revision as of 10:42, 24 March 2023

We provide here a tutorial on how to make pretty visuals from GCM simulations. Do not hesitate to send us your best visuals so that we can enrich our GCM plot gallery.

We first import all the required libraries:

ps: the diagfi.nc file as well as the Jupyter notebook are provided here: https://web.lmd.jussieu.fr/~mturbet/FILES/PyVista_GCM_plot_tutorial/

ps2: the diagfi.nc file was produced following the quick install & run tutorial that you can follow here: https://lmdz-forge.lmd.jussieu.fr/mediawiki/Planets/index.php/Quick_Install_and_Run 

import pyvista as pv
import numpy as np
from netCDF4 import Dataset

First, we get the data from the GCM and extract all fields to be plotted later. 

# 4D GRID VARIABLES
nc1 = Dataset('diagfi_benchmark_early_Mars.nc')

Time_GCM=nc1.variables['Time'][:]
lat_GCM=nc1.variables['latitude'][:]
lon_GCM=nc1.variables['longitude'][:]
alt_GCM=nc1.variables['altitude'][:]
aire_GCM=nc1.variables['aire'][:][:]

resolution_lon_GCM = abs(lon_GCM[1]-lon_GCM[0])
resolution_lat_GCM = abs(lat_GCM[1]-lat_GCM[0])

# 2D VARIABLES
tsurf1=nc1.variables['tsurf'][:][:][:]
h2o_ice_col1=nc1.variables['h2o_ice_col'][:][:][:]

# 3D VARIABLES
u1=nc1.variables['u'][:][:][:][:]
v1=nc1.variables['v'][:][:][:][:]
w1=nc1.variables['w'][:][:][:][:]
ice1=nc1.variables['h2o_ice'][:][:][:][:]

We define coordinates to be used by PyVista. 

# DEFINE REFERENCE RADIUS OF THE PLANET
RADIUS = 1.0

# Longitudes and latitudes used to plot the GCM simulation
x = lon_GCM[0:len(lon_GCM)-1]+180.
y = lat_GCM[0:len(lat_GCM)]

# Define polar coordinates
y_polar = 90.0 - y  # grid_from_sph_coords() expects polar angle


# FUNCTION TO CALCULATE COORDINATE CELL BOUNDARIES
def _cell_bounds(points, bound_position=0.5):
    """
    Calculate coordinate cell boundaries.

    Parameters
    ----------
    points: numpy.array
        One-dimensional array of uniformly spaced values of shape (M,)
    bound_position: bool, optional
        The desired position of the bounds relative to the position
        of the points.

    Returns
    -------
    bounds: numpy.array
        Array of shape (M+1,)

    Examples
    --------
    >>> a = np.arange(-1, 2.5, 0.5)
    >>> a
    array([-1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ])
    >>> cell_bounds(a)
    array([-1.25, -0.75, -0.25,  0.25,  0.75,  1.25,  1.75,  2.25])
    """
    assert points.ndim == 1, "Only 1D points are allowed"
    diffs = np.diff(points)
    delta = diffs[0] * bound_position
    bounds = np.concatenate([[points[0] - delta], points + delta])
    return bounds


# Create arrays of grid cell boundaries, which have shape of (x.shape[0] + 1)
xx_bounds = _cell_bounds(x)
yy_bounds = _cell_bounds(y_polar)

1. We prepare the surface layer (here using surface temperature) 

surface_level = [RADIUS] # surface level
grid_scalar1 = pv.grid_from_sph_coords(xx_bounds, yy_bounds, surface_level)

scalar_surface = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
for i_time in range(0,len(Time_GCM),1):
    for i in range(0,len(lat_GCM),1):
        for j in range(0,len(lon_GCM)-1,1):
            scalar_surface[i,j]=scalar_surface[i,j]+tsurf1[i_time,i,j]/len(Time_GCM)

grid_scalar1.cell_arrays["example"] = np.array(scalar_surface).swapaxes(-2, -1).ravel("C")

We make a first plot. 

# FIRST PLOT
    
p = pv.Plotter(1)
p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
p.camera_position = 'yz'
p.camera.azimuth = 130.
p.camera.roll =270
p.camera.elevation = 30
p.set_background('white')
p.screenshot("surface_temperature_only.png",window_size=[1000,1000])#, transparent_background=True)
p.show()
Plot 1

2. We create the 3D spherical shell (3D grid lines) 

radial_shell = np.linspace(0, RADIUS*1.2, 15)
xx_shell, yy_shell, zz_shell = np.meshgrid(np.radians(np.arange(90, 365, resolution_lon_GCM)), np.radians(np.arange(-84, 84, resolution_lat_GCM)), radial_shell)
# Transform to spherical coordinates
x_shell = zz_shell * np.cos(yy_shell) * np.cos(xx_shell)
y_shell = zz_shell * np.cos(yy_shell) * np.sin(xx_shell)
z_shell = zz_shell * np.sin(yy_shell)
grid_shell = pv.StructuredGrid(x_shell, y_shell, z_shell)
grid_shell['radials'] = zz_shell.ravel(order='F')

We make a second plot 

p = pv.Plotter(1)
p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
p.camera_position = 'yz'
p.camera.azimuth = 30.
p.camera.roll =270
p.camera.elevation = 30
p.set_background('white')
p.screenshot("surface_temperature_with_grid.png",window_size=[1000,1000])#, transparent_background=True)
p.show()
Plot 2

3. We prepare for the array of wind arrows. 

# 3. PREPARE THE WIND LAYER

freq_call_wind=2 # used to change the frequency of wind pattern
MULT_FACTOR_ZWIND=100. # scaling factor of vertical wind so that we can see it in the plot
wind_level = [RADIUS * 1.02] # altitude of the wind layer (on the plot)

i_alt=13 # altitude of the wind layer (in the GCM data)

scalar_u_wind = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
scalar_v_wind = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
scalar_w_wind = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')

for i_time in range(0,len(Time_GCM),1):
    for i in range(0,len(lat_GCM),1):
        for j in range(0,len(lon_GCM)-1,1):
            scalar_u_wind[i,j]=scalar_u_wind[i,j]+u1[i_time,i_alt,i,j]/len(Time_GCM)
            scalar_v_wind[i,j]=scalar_v_wind[i,j]+v1[i_time,i_alt,i,j]/len(Time_GCM)
            scalar_w_wind[i,j]=scalar_w_wind[i,j]+w1[i_time,i_alt,i,j]/len(Time_GCM)

inv_axes = [*range(scalar_u_wind[::freq_call_wind,::freq_call_wind].ndim)[::-1]]
# Transform vectors to cartesian coordinates
vectors = np.stack(
        [
            i.transpose(inv_axes).swapaxes(-2, -1).ravel("C")
            for i in pv.transform_vectors_sph_to_cart(
                x[::freq_call_wind],
                y_polar[::freq_call_wind],
                wind_level[::freq_call_wind],
                scalar_u_wind[::freq_call_wind,::freq_call_wind].transpose(inv_axes),
                -scalar_v_wind[::freq_call_wind,::freq_call_wind].transpose(inv_axes),  # Minus sign because y-vector in polar coords is required
                -MULT_FACTOR_ZWIND*scalar_w_wind[::freq_call_wind,::freq_call_wind].transpose(inv_axes),
            )
        ],
        axis=1,
    )
# Scale vectors to make them visible
vectors *= RADIUS * 0.01
# Create a grid for the vectors
grid_winds = pv.grid_from_sph_coords(x[::freq_call_wind], y_polar[::freq_call_wind], wind_level)
# Add vectors to the grid
grid_winds.point_arrays["example"] = vectors

We make a third plot. 

p = pv.Plotter(1)
p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
p.add_mesh(grid_winds.glyph(orient="example", scale="example", tolerance=0.005),cmap="binary",show_scalar_bar=False)
p.camera_position = 'yz'
p.camera.azimuth = 30.
p.camera.roll =270
p.camera.elevation = 30
p.set_background('white')
p.screenshot("surface_temperature_with_grid_with_winds.png",window_size=[1000,1000])#, transparent_background=True)
p.show()
Plot 3

4.1 We create a cloud layer (first option using 2D cloud layer) 

# Number of vertical levels
nlev = 3
table_level=np.arange(nlev)

# artificial empty layers above and below the clouds
table_level[0]=0.
table_level[1]=1.
table_level[2]=0.


scalar_cloud = np.zeros((len(lat_GCM),len(lon_GCM)-1),dtype='f')
for i_time in range(0,len(Time_GCM),1):
    for i in range(0,len(lat_GCM),1):
        for j in range(0,len(lon_GCM)-1,1):
            scalar_cloud[i,j]=scalar_cloud[i,j]+h2o_ice_col1[i_time,i,j]/len(Time_GCM)

# 3D cloud layer
scalar_cloud_3d = (
scalar_cloud.repeat(nlev).reshape((*scalar_cloud.shape, nlev)) * table_level[np.newaxis, np.newaxis, :]).transpose(2, 0, 1)

# geometry of the grid
z_scale = RADIUS/20.
levels2 = z_scale * (np.arange(scalar_cloud_3d.shape[0] + 1)) + RADIUS*1.01
print (levels2)

# Create a structured grid by transforming coordinates
grid_scalar_cloud_3d = pv.grid_from_sph_coords(xx_bounds, yy_bounds, levels2)

# Add data to the grid
grid_scalar_cloud_3d.cell_arrays["example"] = np.array(scalar_cloud_3d).swapaxes(-2, -1).ravel("C")

# Create a set of isosurfaces
surfaces_cloud = grid_scalar_cloud_3d.cell_data_to_point_data().contour(isosurfaces=[0.00005])

We make a fourth plot. 

p = pv.Plotter(1)
p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
p.add_mesh(grid_winds.glyph(orient="example", scale="example", tolerance=0.005),cmap="binary",show_scalar_bar=False)
p.add_mesh(surfaces_cloud,cmap="Greys",opacity=0.7,show_scalar_bar=False)
p.camera_position = 'yz'
p.camera.azimuth = 30.
p.camera.roll =270
p.camera.elevation = 30
p.set_background('white')
p.screenshot("surface_temperature_with_grid_with_winds_with_2D_clouds.png",window_size=[1000,1000])#, transparent_background=True)
p.show()
Plot 4

4.2 We create a cloud layer (second, more advanced option using 3D cloud layer) 

# Get the 3D field of water ice clouds
scalar_ice = np.zeros((len(lat_GCM),len(lon_GCM)-1,len(alt_GCM)),dtype='f')
for i_time in range(0,len(Time_GCM),1):
    for i in range(0,len(lat_GCM),1):
        for j in range(0,len(lon_GCM)-1,1):
            for k in range(0,len(alt_GCM),1):
                scalar_ice[i,j,k]=scalar_ice[i,j,k]+ice1[i_time,k,i,j]/len(Time_GCM)
            
nlev = len(alt_GCM)+1
table_level=np.arange(nlev)

# geometry of the grid
z_scale = RADIUS/50.
levels2 = RADIUS*1.01+ z_scale * table_level

# 3D cloud layer
scalar_cloud_3d = (np.clip(scalar_ice,0,1.0e8)).transpose(2, 0, 1) # clip to a=amax

# Create a structured grid by transforming coordinates
grid_scalar_cloud_3d = pv.grid_from_sph_coords(xx_bounds, yy_bounds, levels2)

# Add data to the grid
grid_scalar_cloud_3d.cell_arrays["example"] = np.array(scalar_cloud_3d).swapaxes(-2, -1).ravel("C")

# Create a set of isosurfaces
surfaces_cloud = grid_scalar_cloud_3d.cell_data_to_point_data().contour(isosurfaces=[0.9e-8])

We make a fifth plot. 

p = pv.Plotter(1)
p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
p.add_mesh(grid_winds.glyph(orient="example", scale="example", tolerance=0.005),cmap="binary",show_scalar_bar=False)
p.add_mesh(surfaces_cloud,cmap="Greys",opacity=0.7,show_scalar_bar=False)
p.camera_position = 'yz'
p.camera.azimuth = 30.
p.camera.roll =270
p.camera.elevation = 30
p.set_background('white')
p.screenshot("surface_temperature_with_grid_with_winds_with_3D_clouds.png",window_size=[1000,1000])#, transparent_background=True)
p.show()
Plot 5

We make a first series of .png files (rotating the azimuth angle) to be used to build a .gif animated plot. 

total_step_gif=100
for i in range(0,total_step_gif,1):
    p = pv.Plotter(i)
    p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
    p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
    p.add_mesh(grid_winds.glyph(orient="example", scale="example", tolerance=0.005),cmap="binary",show_scalar_bar=False)
    p.add_mesh(surfaces_cloud,cmap="Greys",opacity=0.7,show_scalar_bar=False)
    p.camera_position = 'yz'
    p.camera.azimuth = i*360/total_step_gif
    p.camera.roll += 0.
    p.camera.elevation = 30
    p.set_background('white')
    if(i<10):
        p.screenshot("gif_planet_000"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    if((i>=10) and (i_time<100)):
        p.screenshot("gif_planet_00"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    if((i>=100) and (i_time<1000)):
        p.screenshot("gif_planet_0"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    if((i>=1000) and (i_time<10000)):
        p.screenshot("gif_planet_"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    print("we are at step = ", i, "/",total_step_gif)

print('FINISHED')

To build a gif, you can use the imagemagick convert tool (https://imagemagick.org/script/download.php) using for instance the following command line: 

convert -delay 10 gif_planet_*.png animated_planet.gif

We make a second series of .png files (rotating not only the azimuth angle, but also the elevation of the camera) to be used to build a .gif animated plot. 

total_step_gif=100
for i in range(0,total_step_gif,1):
    p = pv.Plotter(i)
    p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
    p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
    p.add_mesh(grid_winds.glyph(orient="example", scale="example", tolerance=0.005),cmap="binary",show_scalar_bar=False)
    p.add_mesh(surfaces_cloud,cmap="Greys",opacity=0.7,show_scalar_bar=False)
    p.camera_position = 'yz'
    p.camera.azimuth = i*360/total_step_gif
    p.camera.roll += 0.
    p.camera.elevation = 45+15*np.cos((i*360/total_step_gif)*np.pi/180.)
    p.set_background('white')
    if(i<10):
        p.screenshot("gif_planet_000"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    if((i>=10) and (i_time<100)):
        p.screenshot("gif_planet_00"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    if((i>=100) and (i_time<1000)):
        p.screenshot("gif_planet_0"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    if((i>=1000) and (i_time<10000)):
        p.screenshot("gif_planet_"+str(i)+".png",window_size=[1000,1000])#, transparent_background=True)
    print("we are at step = ", i, "/",total_step_gif)

print('FINISHED')

Another method allows us to directly generate the gif, without generating all the pictures. 

#FINALLY WE MAKE THE GIF
total_step_gif=100
p = pv.Plotter(notebook=False, off_screen=True)
p.add_mesh(grid_scalar1, opacity=1.0, cmap="Reds", smooth_shading="True",show_scalar_bar=False) #clim=[1,1],
p.add_mesh(grid_shell, style='wireframe',cmap='Greys',opacity=0.1,show_scalar_bar=False)
p.add_mesh(grid_winds.glyph(orient="example", scale="example", tolerance=0.005),cmap="binary",show_scalar_bar=False)
p.add_mesh(surfaces_cloud,cmap="Greys",opacity=0.7,show_scalar_bar=False)
p.camera_position = 'yz'
# Open a gif
p.open_gif("nom_du_gif.gif")
for i in range(0,total_step_gif,1):
    p.camera_position = 'yz'
    p.camera.azimuth = i*360/total_step_gif
    p.camera.roll += 0.
    p.camera.elevation = 30
    p.set_background('white')
    # Write a frame. This triggers a render.
    p.write_frame()
# Closes and finalizes movie
p.close()


Last words: Do not hesitate to send us your best visuals so that we can enrich our GCM plot gallery! Do not hesitate as well to update this page with additional features of PyVista :)

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