I have a velocity field and a density profile defined by vx, vy, and vz, and dens. All of these variable are 3D arrays, e.g.
vx.shape
(256,256,256)
same for vy, vz, and dens.
The flow command works properly e.g.
fl = flow(vx,vy,vz, seedtype='point', integration_direction='both', scalars=dens)
and I can visualise the field line. Now, to retrieve the values of vx,vy,vz and dens at the positions x,y,z along the line what I did in the past (a few years ago with a previous version of mayavi, I suppose) was
dataset = fl.outputs[0]
then
x, y, z = dataset.points.to_array().T
densstream = np.array(dataset.point_data.get_array(0))
vstream = np.array(dataset.point_data.get_array(1))
But running
x, y, z = dataset.points.to_array().T
I get the following error:
AttributeError: 'AlgorithmOutput' object has no attribute 'points'.
Typing
dataset
I get
<tvtk.tvtk_classes.algorithm_output.AlgorithmOutput at 0x1132917d0>
I am using a mac OS 10.12.6, Python 2.7.15 and IPython 5.5.0.
Could you help me, please?
Cheers
Newer versions of mayavi have modified the output ports for some tvtk objects, from the older version's tvtk.obj.output into tvtk_obj.output_port. The latter is a algorithm_output subclass of tvtk and does not contain the point data, which is the reason for the error message.
In mayavi/modules/streamline.py, the method _streamline_type_changed(self, value):
updates the output port of the streamline object via configure_outputs(self, st), a method of tvtk/common.py, but the connection is directed to self.stream_tracer.output_port instead of self.stream_tracer.output. And the latter is the correct poly_data subclass and does contain the point data along the streamline.
So the cure is simply:
dataset = fl.stream_tracer.output
By the way, I've also tried modifying the function call into configure_outputs(self, st.output), although fl.outputs[0] is connected correctly to the poly_data output, yet the streamlines are no longer shown in the mayavi scene.
Related
So I'm using FuncAnimation from matplotlib so dynamically plot some data as it arrives from a serial port (in my project is the vehicle class from dronekit, which is displayed with the green dot), what I have basically is the animation called which every loop is receiving a new vehicle class with data changed so it can be plotted, but for some reason it plots but after a couple of seconds later after the thread of the mission(which allows the "refresh" of the vehicle data it pops up and kills python (Wheel of death), here's what I get:
I've put some tracking prints inside the function that is called when the FuncAnimation starts running, looks like this:
def droneAnimation(i, vehicle, droneScatter):
time.sleep(1)
lat = [vehicle.location.global_relative_frame.lat]
lon = [vehicle.location.global_relative_frame.lon]
alt = [vehicle.location.global_relative_frame.alt]
print("Alt received: " + str(alt))
droneScatter._offsets3d = (lat,lon,alt)
print("Changed pos")
As you can see those prints are triggered the first few seconds but still crashes after a few iterations.
The FuncAnimation is called like this:
fig,droneScatter = plotLiveSimpleFacade(vehicle,w,2)
ani = FuncAnimation(fig,droneAnimation, fargs = (vehicle,droneScatter))
plt.draw()
plt.pause(0.1)
m = threading.Thread(target=MissionStart(vehicle,hmax) , name = "MISSION")
m.start()
For reference: fig is a plt.figure(),droneScatter is just a scatter point, vehicle is the vehicle class containing the data that dynamically updates and the MissionStart thread is just a thread to make the vehicle class change overtime.
I would like to mention as well that the fig is on interactive mode on, and the axes limits are set well (I saw that when you dynamically change data but don't scale the axes may have problems) also, trying different combinations of plt.draw() and plt.plot(block = False) leads me to not plotting at all or just a blank plot.
Since I have no idea of what is causing this I'll put the dronekit tag on this and the threading to see if anyone has any idea!
I've looked onto threading with matplotlib and looks like threading with this said library it's not the best as it's not thread safe, the best bet is to look at multiprocessing with python or approaching the problem in a different manner.
You can find more information at this post
I am a recent migrant from Matlab to Python and have recently worked with Numpy and Matplotlib. I recoded one of my scripts from Matlab, which employs Matlab's contourf-function, into Python using matplotlib's corresponding contourf-function. I managed to replicate the output in Python, apart that the contourf-plots are not exacly the same, for a reason that is unknown to me. As I run the contourf-function in matplotlib, I get this otherwise nice figure but it has these sharp edges on the contour-levels on top and bottom, which should not be there (see Figure 1 below, matplotlib-output). Now, when I export the arrays I used in Python to Matlab (i.e. the exactly same data set that was used to generate the matplotlib-contourf-plot) and use Matlab's contourf-function, I get a slightly different output, without those sharp contour-level edges (see Figure 2 below, Matlab-output). I used the same number of levels in both figures. In figure 3 I have made a scatterplot of the same data, which shows that there are no such sharp edges in the data as shown in the contourf-plot (I added contour-lines just for reference). Example dataset can be downloaded through Dropbox-link given below. The data set contains three txt-files: X, Y, Z. Each of them are an 500x500 arrays, which can be directly used with contourf(), i.e. plt.contourf(X,Y,Z,...). The code that used was
plt.contourf(X,Y,Z,10, cmap=plt.cm.jet)
plt.contour(X,Y,Z,10,colors='black', linewidths=0.5)
plt.axis('equal')
plt.axis('off')
Does anyone have an idea why this happens? I would appreciate any insight on this!
Cheers,
Jussi
Below are the details of my setup:
Python 3.7.0
IPython 6.5.0
matplotlib 2.2.3
Matplotlib output
Matlab output
Matplotlib-scatter
Link to data set
The confusing thing about the matlab plot is that its colorbar shows much more levels than there are actually in the plot. Hence you don't see the actual intervals that are contoured.
You would achieve the same result in matplotlib by choosing 12 instead of 11 levels.
import numpy as np
import matplotlib.pyplot as plt
X, Y, Z = [np.loadtxt("data/roundcontourdata/{}.txt".format(i)) for i in list("XYZ")]
levels = np.linspace(Z.min(), Z.max(), 12)
cntr = plt.contourf(X,Y,Z,levels, cmap=plt.cm.jet)
plt.contour(X,Y,Z,levels,colors='black', linewidths=0.5)
plt.colorbar(cntr)
plt.axis('equal')
plt.axis('off')
plt.show()
So in conclusion, both plots are correct and show the same data. Just the levels being automatically chosen are different. This can be circumvented by choosing custom levels depending on the desired visual appearance.
I am having issues replicating a pymc2 code using pymc3.
I believe it is due to the fact pymc3 is using the theano type variables which are not compatible with the numpy operations I am using. So I am using the #theano.decorator:
I have this function:
with pymc3.Model() as model:
z_stars = pymc3.Uniform('z_star', self.z_min_ssp_limit, self.z_max_ssp_limit)
Av_stars = pymc3.Uniform('Av_star', 0.0, 5.00)
sigma_stars = pymc3.Uniform('sigma_star',0.0, 5.0)
#Fit observational wavelength
ssp_fit_output = self.ssp_fit_theano(z_stars, Av_stars, sigma_stars,
self.obj_data['obs_wave_resam'],
self.obj_data['obs_flux_norm_masked'],
self.obj_data['basesWave_resam'],
self.obj_data['bases_flux_norm'],
self.obj_data['int_mask'],
self.obj_data['normFlux_obs'])
#Define likelihood
like = pymc.Normal('ChiSq', mu=ssp_fit_output,
sd=self.obj_data['obs_fluxEr_norm'],
observed=self.obj_data['obs_fluxEr_norm'])
#Run the sampler
trace = pymc3.sample(iterations, step=step, start=start_conditions, trace=db)
where:
#theano.compile.ops.as_op(itypes=[t.dscalar,t.dscalar,t.dscalar,t.dvector,
t.dvector,t.dvector,t.dvector,t.dvector,t.dscalar],
otypes=[t.dvector])
def ssp_fit_theano(self, input_z, input_sigma, input_Av, obs_wave, obs_flux_masked,
rest_wave, bases_flux, int_mask, obsFlux_mean):
...
...
The first three variables are scalars (from the pymc3 uniform distribution). The
remaining variables are numpy arrays and the last one is a float. However, I am
getting this "'numpy.ndarray' object has no attribute 'type'" error:
File "/home/user/anaconda/lib/python2.7/site-packages/theano/gof/op.py", line 615, in __call__
node = self.make_node(*inputs, **kwargs)
File "/home/user/anaconda/lib/python2.7/site-packages/theano/gof/op.py", line 963, in make_node
if not all(inp.type == it for inp, it in zip(inputs, self.itypes)):
File "/home/user/anaconda/lib/python2.7/site-packages/theano/gof/op.py", line 963, in <genexpr>
if not all(inp.type == it for inp, it in zip(inputs, self.itypes)):
AttributeError: 'numpy.ndarray' object has no attribute 'type'
Please any advice in the right direction will be most welcomed.
I had a bunch of time-wasting-stops when I went from pymc2 to pymc3. The problem, I think, is that the doc is quite bad. I suspect they neglect the doc as far as the code is still evolving. 3 comments/advises:
I wish you could find some help using '#theano.compile.ops.as_op' here: failure to adapt pymc2 into pymc3 or here how to fit a method belonging to an instance with pymc3?
The drawback of '#theano.compile.ops.as_op' is that you implicitly exclude any analysis related to the gradient of your function. To have access to the gradient, I think you need to define your function in a more complex way presented here how to fit a method belonging to an instance with pymc3?
warning: for the moment, using theano seems to be a source of problem if you want to distribute your code under Windows. See build a .exe for Windows from a python 3 script importing theano with pyinstaller, but I am not sure whether it is just a personal clumsiness or really a problem. Personally I had to give up theano to be able to distribute my code...
I'm trying to make a grid in ggplot to plot 4 graphs, as if it were a basic pair (mfrow = c (2,2)). However, I can not execute the code. I have already tried with gridExtra and cowplot with the functions plot_grid, grid.arrange, ggplot2.multiplot and also tried with the multiplot function. The error that appears is as follows:
Error: Aesthetics must be either length 1 or the same as the data (8598): alpha, x, y, group
gridExtra::grid.arrange(ggplot(),ggplot(),ggplot(),ggplot(), nrow=2)
produces
you may want to debug your code for each individual plot first.
I've developed an gui with python pyqt. There I have a matplotlib figure with x,y-Data and vlines that needs to change dynamically with a QSlider.
Right now I change the data just with deleting everything and plot again but this is not effective
This is how I do it:
def update_verticalLines(self, Data, xData, valueSlider1, valueSlider2, PlotNr, width_wg):
if PlotNr == 2:
self.axes.cla()
self.axes.plot(xData, Data, color='b', linewidth=2)
self.axes.vlines(valueSlider1,min(Data),max(Data),color='r',linewidth=1.5, zorder = 4)
self.axes.vlines(valueSlider2,min(Data),max(Data),color='r',linewidth=1.5, zorder = 4)
self.axes.text(1,0.8*max(Data),str(np.round(width_wg,2))+u"µm", fontsize=16, bbox=dict(facecolor='m', alpha=0.5))
self.axes.text(1,0.6*max(Data),"Pos1: "+str(round(valueSlider1,2))+u"µm", fontsize=16, bbox=dict(facecolor='m', alpha=0.5))
self.axes.text(1,0.4*max(Data),"Pos2: "+str(round(valueSlider2,2))+u"µm", fontsize=16, bbox=dict(facecolor='m', alpha=0.5))
self.axes.grid(True)
self.draw()
"vlines" are LineCollections in matplotlib. I searched in the documentation but could not find any hint to a function like 'set_xdata' How can I change the x value of vertical lines when they are already drawn and embedded into FigureCanvas?
I have the same problem with changing the x and y data. When trying the known functions of matplotlib like 'set_data', I get an error that AxisSubPlot does not have this attribute.
In the following is my code for the FigureCanvas Class. The def update_verticalLines should only contain commands for changing the x coord of the vlines and not complete redraw.
Edit: solution
Thanks #Craigular Joe
This was not exactly how it worked for me. I needed to change something:
def update_verticalLines(self, Data, xData, valueSlider1, valueSlider2, PlotNr, width_wg):
self.vLine1.remove()
self.vLine1 = self.axes.vlines(valueSlider1,min(Data), max(Data), color='g', linewidth=1.5, zorder = 4)
self.vLine2.remove()
self.vLine2 = self.axes.vlines(valueSlider2,min(Data), max(Data), color='g', linewidth=1.5, zorder = 4)
self.axes.draw_artist(self.vLine1)
self.axes.draw_artist(self.vLine2)
#self.update()
#self.flush_events()
self.draw()
update() did not work without draw(). (The old vlines stayed)
flush_events() did some crazy stuff. I have two instances of FigureCanvas. flush_events() caused that within the second instance call the vlines moved with the slider but moved then back to the start position.
When you create the vlines, save a reference to them, e.g.
self.my_vlines = self.axes.vlines(...)
so that when you want to change them, you can just remove and replace them, e.g.
self.my_vlines.remove()
self.my_vlines = self.axes.vlines(...)
# Redraw vline
self.axes.draw_artist(self.my_vlines)
# Add newly-rendered lines to drawing backend
self.update()
# Flush GUI events for figure
self.flush_events()
By the way, in the future you should try your best to pare down your code sample to just the essential parts. Having a lot of unnecessary sample code makes it hard to understand your question. :)