I am trying to save plots (DSR graphs) I make using matplotlib; however, the images are saving blank. The DSR graph is created from r.draw().
Here is my code:
import tellurium as te
import pygraphviz as pgv
import numpy as np
#import matplotlib.pylab as r
import matplotlib.pylab as r
# Load a model and carry out a simulation generating 100 points
r = te.loada ('S1 -> S2; k1*S1; k1 = 0.1; S1 = 10')
fig = r.draw()
import matplotlib.pylab as fig
fig.gcf()
fig.savefig('DSRgrafic.png', dpi=100)
fig.show()
And DSRgrafic.png is blank (also tried with .jpg)
I modified the code a little bit, now I use "pyplot" instead of "pylab", but what I get is the same blank image!
import tellurium as te
import pygraphviz as pgv
import numpy as np
# Load a model and carry out a simulation generating 100 points
r = te.loada ('S1 -> S2; k1*S1; k1 = 0.1; S1 = 10')
fig = r.draw()
import matplotlib.pyplot as fig
fig.gcf()
fig.savefig('DSRgrafic.png', dpi=100)
fig.show()
Related
The following code display the image and audio in the top-bottom style:
Here is the test code:
import librosa
import matplotlib.pyplot as plt
import IPython.display as ipd
def plot_it(name, audio, sample_rate):
plt.figure(figsize=(8, 1))
plt.plot(audio)
plt.gca().set_title(name)
plt.show()
ipd.display(ipd.Audio(data=audio, rate=sample_rate))
Is it possible for changing the "top-bottom" style to "left-right" style for displaying the audio at the right side of the plt figure?
You can use a GridspecLayout which is similar to matplotlib's GridSpec. In order to direct to output into the needed grid cells, you can capture it using the Output widget:
import librosa
import matplotlib.pyplot as plt
import IPython.display as ipd
from ipywidgets import Output, GridspecLayout
def plot_it(name, audio, sample_rate):
grid = GridspecLayout(1, 2, align_items='center')
out = Output()
with out:
fig, ax = plt.subplots(figsize=(8, 1))
ax.plot(audio)
ax.set_title(name)
plt.close(fig)
ipd.display(ax.figure)
grid[0, 0] = out
out = Output()
with out:
ipd.display(ipd.Audio(data=audio, rate=sample_rate))
grid[0, 1] = out
ipd.display(grid)
name = 'nutcracker'
filename = librosa.example(name)
y, sr = librosa.load(filename)
plot_it(name, y, sr)
(It is essential to close the figure, otherwise you'll have double output of the figure. This is easier to do this using the OOP than the pyplot interface, that's why I changed your matplotlib code a bit)
I am trying to animate the graph, but jupyter is giving error:
MovieWriter imagemagick unavailable.
And just animating the 1st image(which is obvious as MovieWriter is not working). How to fix it?
Python version: 3
Here is the code
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from matplotlib.animation import FuncAnimation
# number of nodes
size = 10
# generate graph
G=nx.complete_graph(size)
frame = np.random.randint(0, 5, (size, size)) # random ndarray between 0 and 5, length and number of frames = number of nodes in the graph
pos = nx.spring_layout(G)
nodes = nx.draw_networkx_nodes(G,pos)
edges = nx.draw_networkx_edges(G,pos)
plt.axis('off')
def update(i):
nc = frame[i] # np.random.randint(2, size=200)
nodes.set_array(nc)
return nodes,
# output animation; its important I save it
fig = plt.gcf()
ani = FuncAnimation(fig, update, interval=50, frames=range(size), blit=True)
ani.save('crap.gif', writer='imagemagick', savefig_kwargs={'facecolor':'white'}, fps=1)
Expectation: Animation should be working and will be able to show the updated color
It is working after installing the networkx package and adding pillowwritter as shown below.
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from matplotlib.animation import FuncAnimation, PillowWriter
networkx
How does matplotlib ensure that a dataset can be within plot with specified size.
How do i from a plot stored as numpy, How do i read the color of the pixels illustration a datapoint (0,4) - in the plot.
example:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from PIL import Image
import librosa
import librosa.display
from matplotlib import cm
fig = plt.figure(figsize=(12,4))
min = -1.828067
max = 22.70058
data = np.random.uniform(low=min, high=max, size=(474,40))
librosa.display.specshow(data.T,sr=16000,x_axis='frames',y_axis='mel',hop_length=160,cmap=cm.jet)
plt.show()
raw_input("sadas")
convert = plt.get_cmap(cm.jet)
numpy_output_static = convert(data.T)
plt.imshow(numpy_output_static, aspect = 'auto')
plt.show()
raw_input("asds")
First plot being :
Second plot being:
so the first has been resized to plot size 12,4 where the last basically plots the same data but just using the data shape as size... how do i change that?
Librosa just performs pcolormesh according to the GitHub source code
You need to define another figure with its own size for the second figure.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from PIL import Image
import librosa
import librosa.display
from matplotlib import cm
fig = plt.figure(figsize=(12,4))
min = -1.828067
max = 22.70058
data = np.random.uniform(low=min, high=max, size=(474,40))
librosa.display.specshow(data.T,sr=16000,x_axis='frames',y_axis='mel',hop_length=160,cmap=cm.jet)
plt.show()
raw_input("sadas")
convert = plt.get_cmap(cm.jet)
numpy_output_static = convert(data.T)
fig = plt.figure(figsize=(12,4))
plt.imshow(numpy_output_static, aspect = 'auto')
plt.show()
raw_input("asds")
Trying to do some plotting in SymPy -
As per this video I have written :
from sympy.plotting import plot, plot_parametric
e = sin(2*sin(x**3))
plot(e, (x, 0, 5));
But after evaling that cell I don't get any output? There isn't an error or
anything, it just doesn't display anything.
Another test :
from sympy import *
from sympy.plotting import plot, plot_parametric
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
expr = x**2 + sqrt(3)*x - Rational(1, 3)
lf = lambdify(x, expr)
fig = plt.figure()
axes = fig.add_subplot(111)
x_vals = np.linspace(-5., 5.)
y_vals = lf(x_vals)
axes.grid()
axes.plot(x_vals, y_vals)
plt.show();
So Im not sure what I'm doing wrong here, I'm not getting any errors though?
If the virtual environment content is of any interest here's a tree of that :
venv
I'm running this on Linux Ubuntu. The virtual environment that it's running in can be seen in the above paste link
You need to use the magic functions, more specifically the ones for matplotlib:
%matplotlib qt # displays a pop-up of the plot
%matplotlib inline # keeps it within the notebook
Runnable example using Python 3.4 Nov '15:
from sympy import *
from sympy.plotting import plot, plot_parametric
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
expr = x**2 + sqrt(3)*x - Rational(1, 3)
lf = lambdify(x, expr)
fig = plt.figure()
axes = fig.add_subplot(111)
x_vals = np.linspace(-5., 5.)
y_vals = lf(x_vals)
axes.grid()
axes.plot(x_vals, y_vals)
To get plots to show inline in the IPython notebook, you need to enable matplotlib's inline backend. You can do this by running
%matplotlib inline
I have a series of data that I'm reading in from a tutorial site.
I've managed to plot the distribution of the TV column in that data, however I also want to overlay a normal distribution curve with StdDev ticks on a second x-axis (so I can compare the two curves). I'm struggling to work out how to do it..
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import matplotlib.mlab as mlab
import math
# read data into a DataFrame
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
# draw distribution curve
h = sorted(data.TV)
hmean = np.mean(h)
hstd = np.std(h)
pdf = stats.norm.pdf(h, hmean, hstd)
plt.plot(h, pdf)
Here is a diagram close to what I'm after, where x is the StdDeviations. All this example needs is a second x axis to show the values of data.TV
Not sure what you really want, but you could probably use second axis like this
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import matplotlib.mlab as mlab
import math
# read data into a DataFrame
data = pd.read_csv('Advertising.csv', index_col=0)
fig, ax1 = plt.subplots()
# draw distribution curve
h = sorted(data.TV)
ax1.plot(h,'b-')
ax1.set_xlabel('TV')
ax1.set_ylabel('Count', color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
hmean = np.mean(h)
hstd = np.std(h)
pdf = stats.norm.pdf(h, hmean, hstd)
ax2 = ax1.twinx()
ax2.plot(h, pdf, 'r.')
ax2.set_ylabel('pdf', color='r')
for tl in ax2.get_yticklabels():
tl.set_color('r')
plt.show()
Ok, assuming that you want to plot the distribution of your data, the fitted normal distribution with two x-axes, one way to achieve this is as follows.
Plot the normalized data together with the standard normal distribution. Then use matplotlib's twiny() to add a second x-axis to the plot. Use the same tick positions as the original x-axis on the second axis, but scale the labels so that you get the corresponding original TV values. The result looks like this:
Code
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import matplotlib.mlab as mlab
import math
# read data into a DataFrame
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
h = sorted(data.TV)
hmean = np.mean(h)
hstd = np.std(h)
h_n = (h - hmean) / hstd
pdf = stats.norm.pdf( h_n )
# plot data
f,ax1 = plt.subplots()
ax1.hist( h_n, 20, normed=1 )
ax1.plot( h_n , pdf, lw=3, c='r')
ax1.set_xlim( [h_n.min(), h_n.max()] )
ax1.set_xlabel( r'TV $[\sigma]$' )
ax1.set_ylabel( r'Relative Frequency')
ax2 = ax1.twiny()
ax2.grid( False )
ax2.set_xlim( ax1.get_xlim() )
ax2.set_ylim( ax1.get_ylim() )
ax2.set_xlabel( r'TV' )
ticklocs = ax2.xaxis.get_ticklocs()
ticklocs = [ round( t*hstd + hmean, 2) for t in ticklocs ]
ax2.xaxis.set_ticklabels( map( str, ticklocs ) )