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Set mayaVI pipeline properties in Python script

I'm using mayaVI to plot a surface and a vector field in 3D, with the functions mayavi.mlab.surf and mayavi.mlab.quiver3D. These functions do not have many keyword arguments that let me modify the appearance of the surface and quivers, in comparison to the Mayavi pipeline, where I can edit things down to the most miniscule detail (for example quiver arrow head radius - see example figure below). The issue is that once I have made these changes in the mayaVI pipeline, there seems to be no way to save these settings until the next time I want to redraw the figure.
I'm in particular interested in editing Contour properties of the surface, and the Glyph Source properties of the vectors (Shaft radius, Tip radius, Tip length).
Question: Is there an easy way to save the Mayavi pipeline settings until next time, or edit them directly in my Python script (i.e. without using the UI)?
Example:
Code:
#!/usr/bin/env python
import numpy as np
from mayavi import mlab
xmax = 2.0*np.pi
x, y, z = np.mgrid[-xmax:xmax:25j, -xmax:xmax:25j, -xmax:xmax:1j]
v_x = np.sin(x)*np.cos(y)
v_y = np.cos(x)*np.sin(y)
v_z = np.zeros_like(z)
v_abs = np.sqrt(v_x**2 + v_y**2) # scalar field
surf = mlab.surf( x[:,:,0], y[:,:,0], v_abs[:,:,0], colormap='magma' )
obj_j = mlab.quiver3d( x[:,:,0], y[:,:,0], z[:,:,-1], v_x[:,:,0], v_y[:,:,0], v_z[:,:,0], mode='arrow')
mlab.show()

For example, to change the tip length of the arrows,
obj = mlab.quiver3d(..., mode='arrow')
obj.glyph.glyph_source.glyph_source.tip_length = 0.9
There doesn't seem to be any complete documentation of the mayavi pipeline, but one can guess from the GUI interface about the parameters:

Changing the order of axes in Mayavi

I am generating STL files for 3D printing and then using mlab/mayavi to display them. I would like the Z axis to remain vertical as I rotate the image. According to the mayavi documentation, this can be achieved using the following incantation:
fig = mlab.gcf()
from tvtk.api import tvtk
fig.scene.interactor.interactor_style = tvtk.InteractorStyleTerrain()
Unfortunately, as you can see from this screenshot of my app, it is not the Z axis but the Y axis that gets maintained vertical.
This is an issue because 3D printers always think of Z as the vertical axis, so I really need Z and not Y to be oriented vertically. Is there a way in which this can be achieved?

I have experienced the same problem, vertically aligned along the y-axis.
To get the scene to align vertically along the z-axis (which you want), you can simply add a scene.mlab.view() call before setting the interactor.
The scene.mlab.view() call aligns the camera correctly (with z up), before the interactor is set. I have found this solution by simply testing a bunch of stuff, I could not find this "hack" in the documentation.
New code:
fig = mlab.gcf()
from tvtk.api import tvtk
fig.scene.mlab.view(0, 90)
fig.scene.interactor.interactor_style = tvtk.InteractorStyleTerrain()

You can rename them using the Mayavi button to access the pipeline, then selecting the appropiate axes object and changing the labels.
With code, you can add the xlabel, ylabel and zlabel keyword arguments to specify them. This is specified in the axes API

set_array() in tripcolor bug?

I am new to Python and matplotlib, and I recently referenced to THIS to update my tripcolor plot. With following data preparation
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import math
r = np.zeros((100,100))
t = np.zeros((100,100))
for i in range(0,100):
for j in range(0,100):
r[i,j]=i
t[i,j]=2*math.pi*j/100
x=r*np.cos(t)
y=r*np.sin(t)
z=r*r
xf=x.flatten()
yf=y.flatten()
zf=z.flatten()
triang = tri.Triangulation(xf,yf)
If I use tripcolor as it is intended,
# Works well
p = plt.tripcolor(triang, zf)
correct figure appears. But, if I try to update after creating tripcolor,
# Not working well
p = plt.tripcolor(triang, xf)
p.set_array(zf)
then, wrong figure appears. Both xf and zf have identical dimensions.
What am I doing wrong? What is the cause of the problem, and how can I avoid it?
Many thanks in advance.
=========================================================
Update
Thank you all. I actually solved myself.
The key was that I need to assign color for each area, which is controlled by shading argument, and default value for tripcolor is 'flat', which is, color for each vertex. So, when I plot the first figure, I need to make sure shading is 'gouraud', which assigns color for each area.
So,
p = plt.tripcolor(triang, xf, shading='gouraud')
p.set_array(zf)
works as I intended.

The reason
p = plt.tripcolor(triang, xf)
p.set_array(zf)
is not working as (may be) expected, is the following. In your case plt.tripcolor() returns a PolyCollection. The PolyCollection's set_array() will essentially set the colors of that Collection. However, the underlying triangles will not be changed, such that you end up with the triangles from xf but the colors from zf.
Since the generation of the tripcolor PolyCollection is quite involved (as it calls Triangulation itself) and there probably is no helper function to set the data externally (at least I am not aware of any), the solution might be not to update the tripcolor at all and instead generate a new one.
Is there any reason for you to update? Couldn't you just directly create p = plt.tripcolor(triang, zf)?
In case there is a real reason to it, like in an animation or so, an option would be to delete the first tripcolor plot before setting up the next.
# create one plot
p = plt.tripcolor(triang, xf)
#delete this plot (you need both lines actually!!)
p.remove()
del p
#create second plot
p = plt.tripcolor(triang, zf)
This is not really efficient, though, and in case someone has a better idea, I'd like to hear about that one as well.

Basemap and Matplotlib - Improving Speed

I'm creating a tool for geospatial visualization of economic data using Matplotlib and Basemap.
However, right now, the only way I thought of that gives me enough flexibility is to create a new basemap every time I want to change the data.
Here are the relevant parts of the code I'm using:
class WorldMapCanvas(FigureCanvas):
def __init__(self,data,country_data):
self.text_objects = {}
self.figure = Figure()
self.canvas = FigureCanvas(self.figure)
self.axes = self.figure.add_subplot(111)
self.data = data
self.country_data = country_data
#this draws the graph
super(WorldMapCanvas, self).__init__(Figure())
self.map = Basemap(projection='robin',lon_0=0,resolution='c', ax=self.axes)
self.country_info = self.map.readshapefile(
'shapefiles/world_country_admin_boundary_shapefile_with_fips_codes', 'world', drawbounds=True,linewidth=.3)
self.map.drawmapboundary(fill_color = '#85A6D9')
self.map.fillcontinents(color='white',lake_color='#85A6D9')
self.map.drawcoastlines(color='#6D5F47', linewidth=.3)
self.map.drawcountries(color='#6D5F47', linewidth=.3)
self.countrynames = []
for shapedict in self.map.world_info:
self.countrynames.append(shapedict['CNTRY_NAME'])
min_key = min(data, key=data.get)
max_key = max(data, key=data.get)
minv = data[min_key]
maxv = data[max_key]
for key in self.data.keys():
self.ColorCountry(key,self.GetCountryColor(data[key],minv,maxv))
self.canvas.draw()
How can I create these plots faster?
I couldn't think of a solution to avoid creating a map every time I run my code. I tried creating the canvas/figure outside of the class but it didn't make that much of a difference. The slowest call is the one that creates the Basemap and loads the shape data. Everything else runs quite fast.
Also, I tried saving the Basemap for future use but since I need new axes I couldn't get it to work. Maybe you can point me in the right direction on how to do this.
I'd like you to know that I'm using the canvas as a PySide QWidget and that I'm plotting different kinds of maps depending on the data, this is just one of them (another would be a map of Europe, for instance, or the US).

You can pickle and unpickle Basemap instances (there is an example of doing this in the basemap source) which might save you a fair chunk of time on the plot creation.
Additionally, it is probably worth seeing how long the shapefile reading is taking (you may want to pickle that too).
Finally, I would seriously consider investigating the option of updating country colours for data, rather than making a new figure each time.
HTH,

Is there an interactive graphing library for python

I'm looking for an interactive graphing library for Python.
By "graph", I meant a set of nodes connected by a set of vertices (not a plot of values over x-y axis, nor a grid of pixels).
By "interactive", I meant I can drag-and-drop the nodes around and I need to be able to click on the nodes/vertices and have the library pass the nodes/vertices to my callbacks, which may add/remove nodes/vertices or display information (I cannot load the full graph at startup as the dataset is too large/complex; instead I'll be loading only the necessary slices of data depending on user inputs).
By Python, I meant the programming language Python, the graphing library should have CPython binding. I have Python 2.7 and Python 3.1, but can downgrade to 2.6 if necessary. This language requirement is because the dataset I'm working with only have Python binding.
The graphing library must support directed graph and be able to layout the nodes automatically. I need to put labels on the nodes.
Preferably, the layouting algorithm should place adjacent nodes near each other. It should be able to handle from 100-1000 nodes and about 300-4000 vertices reasonably in my 4 year old laptop (I typically start with around 100 nodes, but the number might expand depending on user input). Preferably it should be a library with not too many dependencies (except perhaps for Gnome). Open source is preferred.
I have already written a simple prototype of my program using Tkinter Canvas, but I need a more serious graphing library to expand the program. I've looked at graphviz and matplotlib, but apparently they're only for working with static graphs and apparently would need significant amount of work to do interactive manipulations (correct me if I'm wrong, I've only looked at them briefly). I've also tried generating the graph to an SVG file and using Inkscape to view it, but it's too slow and takes too much memory and because of the sheer number of vertices it becomes a tangled mess.

Looks like Nodebox might be what you want:
http://nodebox.net/code/index.php/Graph Mac OSX
http://www.cityinabottle.org/nodebox/ Windows (using OpenGL)
The graph object has functionality for mouse interaction as well, bundled
in the graph.events object. It has the
following properties:
graph.events.hovered: None or the node over which the mouse hovers.
graph.events.pressed: None or the node on which the mouse is
pressing down.
graph.events.dragged: None or the node being dragged.
graph.events.clicked: None or the last node clicked.
graph.events.popup: when True, will display a popup window over the
hovered node.
Also came accross Gephi, looks like that might have the functionality you want as well.
http://gephi.org/ Windows, Linux and Mac OSX
Gephi is an interactive visualization
and exploration platform for all kinds
of networks and complex systems,
dynamic and hierarchical graphs.

You should definitely look at the igraph library if you haven't.
It's a powerful library that can handle large graphs and different layout styles. It can also be used for directed graphs and for interactive and non-interactive visualitzations in 2D and 3D according to the list of features. There is also a tutorial.
Update: Another well-known library is NetworkX for which there are Python packages here. Note that the Mac/Windows software Nodebox, recommended by Acorn, uses NetworkX algorithms.

I have the same problem.
In the end, I think nodebox opengl seems to do the trick.
Don't try to use the graph library at the following link
http://nodebox.net/code/index.php/Graph
with nodebox opengl. It doesn't work, that graph library is only compatible with the mac OSX nodebox. But in anycase that is ok because you don't need it.
See for example the following question:
Adding label to an edge of a graph in nodebox opnegl
It shows example code which works for me, the code can be modified so that clicking on a node not only allows you to move the node, but also allows you to modify the graph.
Just delete
label = "Placeholder"
from the code and it works.
EDIT:
I put some more detailed example code here:
Nodebox open GL Graph, size function not recognized. (Ubuntu)

I thought and tried all the solutions given in this question and finally end up with the following solution.
I think the best scalable solution is using interactive mode of Matplotlib together with networkx. The following code segment explain how to display an annotation of a datapoint for a mouse click. Since we are using Networkx, this solution was much more scalable than anticipated.
import networkx as nx
import matplotlib.pyplot as plt
import nx_altair as nxa
from pylab import *
class AnnoteFinder: # thanks to http://www.scipy.org/Cookbook/Matplotlib/Interactive_Plotting
"""
callback for matplotlib to visit a node (display an annotation) when points are clicked on. The
point which is closest to the click and within xtol and ytol is identified.
"""
def __init__(self, xdata, ydata, annotes, axis=None, xtol=None, ytol=None):
self.data = list(zip(xdata, ydata, annotes))
if xtol is None: xtol = ((max(xdata) - min(xdata))/float(len(xdata)))/2
if ytol is None: ytol = ((max(ydata) - min(ydata))/float(len(ydata)))/2
self.xtol = xtol
self.ytol = ytol
if axis is None: axis = gca()
self.axis= axis
self.drawnAnnotations = {}
self.links = []
def __call__(self, event):
if event.inaxes:
clickX = event.xdata
clickY = event.ydata
print(dir(event),event.key)
if self.axis is None or self.axis==event.inaxes:
annotes = []
smallest_x_dist = float('inf')
smallest_y_dist = float('inf')
for x,y,a in self.data:
if abs(clickX-x)<=smallest_x_dist and abs(clickY-y)<=smallest_y_dist :
dx, dy = x - clickX, y - clickY
annotes.append((dx*dx+dy*dy,x,y, a) )
smallest_x_dist=abs(clickX-x)
smallest_y_dist=abs(clickY-y)
print(annotes,'annotate')
# if clickX-self.xtol < x < clickX+self.xtol and clickY-self.ytol < y < clickY+self.ytol :
# dx,dy=x-clickX,y-clickY
# annotes.append((dx*dx+dy*dy,x,y, a) )
print(annotes,clickX,clickY,self.xtol,self.ytol )
if annotes:
annotes.sort() # to select the nearest node
distance, x, y, annote = annotes[0]
self.drawAnnote(event.inaxes, x, y, annote)
def drawAnnote(self, axis, x, y, annote):
if (x, y) in self.drawnAnnotations:
markers = self.drawnAnnotations[(x, y)]
for m in markers:
m.set_visible(not m.get_visible())
self.axis.figure.canvas.draw()
else:
t = axis.text(x, y, "%s" % (annote), )
m = axis.scatter([x], [y], marker='d', c='r', zorder=100)
self.drawnAnnotations[(x, y)] = (t, m)
self.axis.figure.canvas.draw()
df = pd.DataFrame('LOAD YOUR DATA')
# Build your graph
G = nx.from_pandas_edgelist(df, 'from', 'to')
pos = nx.spring_layout(G,k=0.1, iterations=20) # the layout gives us the nodes position x,y,annotes=[],[],[] for key in pos:
x, y, annotes = [], [], []
for key in pos:
d = pos[key]
annotes.append(key)
x.append(d[0])
y.append(d[1])
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111)
ax.set_title('select nodes to navigate there')
nx.draw(G, pos, font_size=6,node_color='#A0CBE2', edge_color='#BB0000', width=0.1,
node_size=2,with_labels=True)
af = AnnoteFinder(x, y, annotes)
connect('button_press_event', af)
show()