I am pulling PNG images from Jupyter Notebooks and manage to display with IPython.display.Image but not with matplotib.pyplot.plt. What am I missing? I use python 2.7.
I am using the following algorithm:
To open the notebook JSON content I do:
import nbformat
notebook_ = nbformat.read(file_notebook, 4)
After retrieving the relevant cell information I pull the png information from it using:
def cell_to_image(cell, out_value_item_number=1):
if "execution_count" in cell.keys(): # i.e version >=4
return cell["outputs"][out_value_item_number]['data']['image/png']
elif "prompt_number" in cell.keys(): # i.e version < 4
return cell["outputs"][out_value_item_number]['png']
return None
cell_image = cell_to_image(cell)
The first few characters of cell_image (which is unicode) looks like:
iVBORw0KGgoAAAANSUhEUgAAA64AAAFMCAYAAADLFeHSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n
AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8jef/x/HXyTjZiYQkCGrU3ruR0tr9oq2qGtGo0dbe
\nm5pVlJpFUSMoVb6UoEZ/lCpatWuPUiNEEiMDmef3R75OexonJKUO3s/HI4/mXPd1X/d1f+LRR965
\n7/u6DSaTyYSIiIiIiIiIjbJ70hMQERERERERyYiCq4iIiIiIiNg0BVcRERERERGxaQquIiIiIiIi
\nYtMUXEVERERERMSmKbiKiIiIiIiITVNwFRGRxyIkJIRixYqxfv36+24/e/YsxYoVo3jx4v/yzGxb
\naGgoderUIS4uDoBdu3bRsmVLKlasyCuvvMKgQYOIjo622CcsLIyGDRtSunRp6tSpw8KFC62OW7p0
\naRo2bJju53Lnzh1GjRrFyy+/TNmyZWnRogW//fbbQ835q6++olGjRpQvX5769eszc+ZMkpOTzdtT
\nU1OZNGkSNWrUoHTp0jRp0oTdu3enGyc2NpZOn
I can easily plot in my Jupityer notebook using
from IPython.display import Image
Image(cell_image)
And now to my question:
How can I manipulate cell_image to be plt.subplot friendly?
(Assuming import matplotlib.pyplot as plt).
I realise that plt.imshow wouldn't work because this would require an array, which is not my case (which is a string, as far as I understand).
If you have your image string representation in a variable string_rep, the following code should work.
from io import BytesIO
import matplotlib.image as mpimage
import matplotlib.pyplot as plt
with BytesIO(string_rep.decode('base64')) as byte_rep:
image = mpimage.imread(byte_rep)
plt.imshow(image)
Related
Below is the code for a simple Bayesian Linear regression. After I obtain the trace and the plots for the parameters, is there any way in which I can save the data that created the plots in a file so that if I need to plot it again I can simply plot it from the data in the file rather than running the whole simulation again?
import pymc3 as pm
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,9,5)
y = 2*x + 5
yerr=np.random.rand(len(x))
def soln(x, p1, p2):
return p1+p2*x
with pm.Model() as model:
# Define priors
intercept = pm.Normal('Intercept', 15, sd=5)
slope = pm.Normal('Slope', 20, sd=5)
# Model solution
sol = soln(x, intercept, slope)
# Define likelihood
likelihood = pm.Normal('Y', mu=sol,
sd=yerr, observed=y)
# Sampling
trace = pm.sample(1000, nchains = 1)
pm.traceplot(trace)
print pm.summary(trace, ['Slope'])
print pm.summary(trace, ['Intercept'])
plt.show()
There are two easy ways of doing this:
Use a version after 3.4.1 (currently this means installing from master, with pip install git+https://github.com/pymc-devs/pymc3). There is a new feature that allows saving and loading traces efficiently. Note that you need access to the model that created the trace:
...
pm.save_trace(trace, 'linreg.trace')
# later
with model:
trace = pm.load_trace('linreg.trace')
Use cPickle (or pickle in python 3). Note that pickle is at least a little insecure, don't unpickle data from untrusted sources:
import cPickle as pickle # just `import pickle` on python 3
...
with open('trace.pkl', 'wb') as buff:
pickle.dump(trace, buff)
#later
with open('trace.pkl', 'rb') as buff:
trace = pickle.load(buff)
Update for someone like me who is still coming over to this question:
load_trace and save_trace functions were removed. Since version 4.0 even the deprecation waring for these functions were removed.
The way to do it is now to use arviz:
with model:
trace = pymc.sample(return_inferencedata=True)
trace.to_netcdf("filename.nc")
And it can be loaded with:
trace = arviz.from_netcdf("filename.nc")
This way works for me :
# saving trace
pm.save_trace(trace=trace_nb, directory=r"c:\Users\xxx\Documents\xxx\traces\trace_nb")
# loading saved traces
with model_nb:
t_nb = pm.load_trace(directory=r"c:\Users\xxx\Documents\xxx\traces\trace_nb")
Following Pickle figures from matplotlib, I am trying to load a figure from a pickle. I am using the same code with the modifications that are suggested in the responses.
Saving script:
import numpy as np
import matplotlib.pyplot as plt
import pickle as pl
# Plot simple sinus function
fig_handle = plt.figure()
x = np.linspace(0,2*np.pi)
y = np.sin(x)
plt.plot(x,y)
# plt.show()
# Save figure handle to disk
pl.dump(fig_handle,file('sinus.pickle','wb'))
Loading script:
import matplotlib.pyplot as plt
import pickle as pl
import numpy as np
# Load figure from disk and display
fig_handle = pl.load(open('sinus.pickle', 'rb'))
fig_handle.show()
The saving script produces a file named "sinus.pickle" but the loading file does not show the anticipated figure. Any suggestions?
Python 2.7.13
matplotlib 2.0.0
numpy 1.12.1
p.s. following a suggestion replaced fig_handle.show() with pat.show() which produced an error:
Traceback (most recent call last):
File "/usr/local/lib/python2.7/
site-packages/matplotlib/backends/backend_macosx.py", line 109,
in_set_device_scale
self.figure.dpi = self.figure.dpi / self._device_scale * value
File "/usr/local/lib/python2.7/site-packages/matplotlib/figure.py",
line 416, in _set_dpi
self.callbacks.process('dpi_changed', self)
File "/usr/local/lib/python2.7/site-packages/matplotlib/cbook.py",
line 546, in process
if s in self.callbacks:
AttributeError: 'CallbackRegistry' object has no attribute 'callbacks'
What you call your "loading script" doesn't make any sense.
From the very link that you provided in your question, loading the picked figure is as simple as:
# Load figure from disk and display
fig_handle2 = pl.load(open('sinus.pickle','rb'))
fig_handle2.show()
Final solution included modification of
fig_handle.show()
to
plt.show()
and modification of the backend to "TkAgg", based to an advice given by ImportanceOfBeingErnest
I am using imageio to write png images to file.
import numpy as np
import matplotlib.cm as cm
import imageio # for saving the image
import matplotlib as mpl
hm_colors = ['blue', 'white','red']
cmap = mpl.colors.LinearSegmentedColormap.from_list('bwr', hm_colors)
data = np.array([[1,2,3],[5,6,7]])
norm = mpl.colors.Normalize(vmin=-3, vmax=3)
colormap = cm.ScalarMappable(norm=norm, cmap=cmap)
im = colormap.to_rgba(data)
# scale the data to a width of w pixels
im = np.repeat(im, w, axis=1)
im = np.repeat(im, h, axis=0)
# save the picture
imageio.imwrite("my_img.png", im)
This process is performed automatically and I noticed some Error messages saying:
Error closing: 'Image' object has no attribute 'fp'.
Before this message I get warning:
/usr/local/lib/python2.7/dist-packages/imageio/core/util.py:78: UserWarning: Lossy conversion from float64 to uint8, range [0, 1] dtype_str, out_type.__name__))
However, the images seem to be generated and saved just fine.
I can't find data to recreate this message.
Any idea why I get this error and why it doesn't noticeably affect the results? I don't use PIL.
One possible reason could come from using this in Celery.
Thanks!
L.
I encountered the same issue using imageio.imwrite in Python 3.5. It's a fairly harmless except for the fact that that it's stopping garbage collection and leading to excessive memory usage when writing thousands of images. The solution was to use the PIL module, which is a dependency of imageio. The last line of your code should read:
from PIL import Image
image = Image.fromarray(im)
image.save('my_img.png')
I am struggling to have construct an interactive tool in jupyter notebook. Based on the discussion here : IPython Notebook widgets for Matplotlib interactivity, I've build the following example:
%matplotlib notebook
import matplotlib.pyplot as plt
from ipywidgets import Select,interactive,Dropdown
from IPython.display import display
fig,ax = plt.subplots()
ax.plot(range(5))
vline = ax.axvline(1, color='k')
hline = ax.axhline(0.5, color='k')
def set_cursor(x, y):
vline.set_xdata((x, x))
hline.set_ydata((y, y))
ax.figure.canvas.draw_idle()
interactive(set_cursor, x=ax.get_xlim(), y=ax.get_ylim())
It works pretty well except that that the last line (interactive...) has to be run in a different cell.
If I want to launch everything in the same cell (or from the same object if use a Class approach, which is what I want at the end), I have to use the following code:
%matplotlib notebook
import matplotlib.pyplot as plt
from ipywidgets import Select,interactive,Dropdown
from IPython.display import display
fig,ax = plt.subplots()
ax.plot(range(5))
vline = ax.axvline(1, color='k')
hline = ax.axhline(0.5, color='k')
def set_cursor(x, y):
vline.set_xdata((x, x))
hline.set_ydata((y, y))
ax.figure.canvas.draw_idle()
display(fig)
tool = interactive(set_cursor, x=ax.get_xlim(), y=ax.get_ylim())
display(tool)
But in this case, the complete figure is redraw each time a new value is selected in a widget
Is there any possibility to get everything launch smoothly from the same cell ?
Any idea is more than welcome !
I'm trying to extract features from a text document. Here is my code:
import sklearn
from sklearn.datasets import load_files
from sklearn.feature_extraction.text import CountVectorizer
files = sklearn.datasets.load_files('/home/niyas/Documents/project/container', shuffle = False)
vectorizer = CountVectorizer(min_df=1)
X = vectorizer.fit_transform(files.data[1])
Y=vectorizer.get_feature_names()
I'm getting an error "ValueError: empty vocabulary; perhaps the documents only contain stop words". The code works fine when I pass a string with the exact same content of the text doc.
Help me. Thanks in advance.