The autocorrplot() function gives the autocorrelation plot for the sampled data from the trace.
If I already have a sample of data in the form of an array or list, can I use autocorrplot() to do the same?
Is there any alternative to generate autocorrelation plots given a sequence of data?
Please help.
autocorrplot is a wrapper around matplotlib's acorr. To get a similar look to pymc3's version, you can use something like
import numpy as np
import matplotlib.pyplot as plt
my_array = np.random.normal(size=1000)
plt.acorr(my_array, detrend=plt.mlab.detrend_mean, maxlags=100)
plt.xlim(0, 100)
Note the call to xlim at the end, since by default PyMC3 does not show negative correlations.
Related
I'm trying to edit the tick labels but I keep getting scientific notation, even after setting the ticks. Here is a MWE:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(9, 7))
fig.subplots_adjust(left=0.11, right=0.95, top=0.94)
ax.ticklabel_format(style='plain')
plt.plot([1,4],[3,6] )
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xticks([0.7,1,1.5,2,2.5,3,4,5])
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
which produces this plot
As you can see ax.ticklabel_format(style='plain') doesn't seem to work as I keep getting tick labels in scientific notation, and when using ax.set_xticks the old tick labels are still present. I took a look at this topic and it seems like the problem is in the choose of the ticks, if I use for example 0.3 instead of 0.7 as the first tick it works, however I need to do a plot in this specific range and using log scale.
Any work around?
Actually, your code is doing what you need, the problem is the labels from the minor ticks that remain unaffected and overlap with the major ticks
you can simply add the line:
ax.get_xaxis().set_minor_formatter(matplotlib.ticker.NullFormatter())
full code:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(9, 7))
fig.subplots_adjust(left=0.11, right=0.95, top=0.94)
ax.ticklabel_format(style='plain')
plt.plot([1,4],[3,6] )
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xticks([0.7,1,1.5,2,2.5,3,4,5])
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.get_xaxis().set_minor_formatter(matplotlib.ticker.NullFormatter())
I currently try to modify a plot in a python2.7 code. This plot come from a radiative transfert code (what I can't modify), and the output is a class type with a method :
plot_image(wavelength0, overplot=False, inclination=None, cmap=None,
ax=None, axes_units='AU', polarization=False, polfrac=False,
psf_fwhm=None, vmin=None, vmax=None, dynamic_range=1000000.0)
more informations here
So, when I put a plt.title() or plt.savefig() just after that, I call the respective method and the title of the plot changes and the plot is saved, as expected!
However, there is two things that I didn't find a way to do it :
Add a colorbar
trace the surface in log-scale
because no arguments in the method allow to do it.
When I try something like plt.colorbar(fig) with fig=res.plot_image(...) (with res the class, output of radiative transfert code) the error said that fig is not a mappable object (seems logic).
Any ideas that could help me ? hope I gave you enough informations.
Sorry for my English
Lo_du
If res.plot_image(...) only plots one image to one axes, the solution to obtain a colorbar might be pretty easy.
import numpy as np
import matplotlib.pyplot as plt
#.... some code
res.plot_image(...)
im, = plt.gca().get_images()
plt.colorbar(im)
plt.show()
Since I am just a python beginner I hope, that I bother you not too much with my question. I'd like to plot only parts of a time series from a dataset such as this:
Code:
import numpy as np
import matplotlib.pyplot as plt
a=np.arange(10)
b=np.arange(100, 105)
c=np.arange(30, 40)
d=np.arange(55, 60)
e=np.append(a,b)
f=np.append(c,d)
plt.plot(e,f)
Plot:
Then I get a plot with a long diagonal line between the data series. What to do to get rid of that, in other words I want that the x-axis only shows 0,1,2,3,4,5,6,7,8,9,100,101,102,103,104 and the corresponding y-values and nothing in between. Moreover if I have a very long time series (e.g. from an oscilloscope measurement), is it possible to show up that some patterns are repeated with e.g. big dots in the middle of the plot? I've tried to convert the x data into a string and several other things, but it does not seem to work.
Focusing on this part of your question:
Then I get a plot with a long diagonal line between the data series. What to do to get rid of that [...]
It sounds that a scatteplot should meet your needs, so try this:
Add this to your code:
fig, ax = plt.subplots()
ax.scatter(e, f)
for i, txt in enumerate(e):
ax.annotate(txt, (e[i],f[i]))
fig.show()
To get this plot:
Unfortunately, I'm not quite sure what you're trying to accomplish with this part:
Moreover if I have a very long time series (e.g. from an oscilloscope
measurement)
Anyway, I hope my suggestion at least fixes the main part of your problem.
I'm trying to wrap my head around using axes-objects in conjunction with a Pandas-dataframe (object df in the following snippet):
import matplotlib.pyplot as plt
fig=plt.figure()
axes=fig.add_subplot(111)
for key, grp in df.groupby(['bondYear', 'bondQuarter']):
axes = grp['priceAvg'].plot(label=key, ax=axes)
This runs through without any complaints, but now I'm looking for a way to actually make my plot show (and later save to *.png like with using savefig()). plt.show() works but opens two windows, one showing my plot and the other one being empty, so that's not it.
Apparently I'm missing something regarding the use of matplotlib, can you help me out?
Many thanks in advance for your help!
You shouldn't reassign your plot to axes, although that should not matter. Try this.
fig, ax = plt.subplots(figsize=(8,6))
for key, grp in df.groupby(['bondYear', 'bondQuarter']):
grp['priceAvg'].plot(label=key, ax=ax)
plt.legend()
plt.show()
plt.savefig('figname.png')
You can also plot them all at once without using iteration doing the following but you are easily able to control the legend.
fig, ax = plt.subplots(figsize=(8,6))
df.groupby(['col1', 'col2']).plot(ax=ax)
I have two different xarray datasets that have different latitude/longitude grid resolutions. I want to regrid the one xarray with lower resolution to the same resolution as the one xarray with higher resolution. I found some examples (e.g., http://earthpy.org/interpolation_between_grids_with_basemap.html), but it does not work for me. Here is one example that I made for testing:
import numpy as np
import xarray as xray
import mpl_toolkits.basemap
var1=xray.DataArray(np.random.randn(len(np.linspace(40.5,49.5,10)),len(np.linspace(-39.5,-20.5,20))),coords=[np.linspace(40.5,49.5,10), np.linspace(-39.5,-20.5,20)],dims=['lat','lon'])
(xlon, xlat)=np.meshgrid(np.linspace(-39.875,-20.125,80),np.linspace(40.125,49.875,40))
var2=xray.DataArray(-xlon**2+xlat**2,coords=[np.linspace(40.125,49.875,40),np.linspace(-39.875,-20.125,80)],dims=['lat','lon'])
mpl_toolkits.basemap.interp(var1,var1.lon,var1.lat,var2.lon,var2.lat,checkbounds=False,masked=False,order=0)
I get following error:
ValueError: xout and yout must have same shape!
Screenshot:
Does basemap.interp() require xout and yout to be the same shape? So var2 needs to be a square? This is almost never the case with any of my datasets! How can I regrid var1 to be the same resolution as var2?
Note: After regridding, I want to subsample var1 given some condition related to var2. For example:
var1_subset = var1.where(var2>1000)
So I want to minimize any loss of grid points during the interpolation.
basemap.interp will work only when xout and yout are same in number or number of output nlons and nlats are same,
why not generate same length output nlats and nlons and subset it later.
For example:
import numpy as np
import xarray as xray
import mpl_toolkits.basemap
var1=xray.DataArray(np.random.randn(len(np.linspace(40.5,49.5,10)),len(np.linspace(-39.5,-20.5,20))),coords=[np.linspace(40.5,49.5,10), np.linspace(-39.5,-20.5,20)],dims=['lat','lon'])
(xlon,xlat)=np.meshgrid(np.linspace(-39.875,20.125,80),np.linspace(40.125,49.875,80))
var2=xray.DataArray(-xlon**2+xlat**2,coords[np.linspace(40.125,49.875,80),np.linspace(-39.875,-20.125,80)],dims=['lat','lon'])
mpl_toolkits.basemap.interp(var1,var1.lon,var1.lat,var2.lon,var2.lat,checkbounds=False,masked=False,order=0)
Here is another cool trick with xarray.
lonreg=var1.groupby_bins('lon',np.linspace(-39.875,20.125,80)).mean(dim='lon')
regridded=lonreg.groupby_bins('lat',np.linspace(-39.5,20.5,20)).mean(dim='lat')
if you want weighted averaged regridding, it is easy to extend this for area averaged regridding by using weights and sum function on groupby object.