I created the following test code, and the code runs fine. But the plot does not appear when executed. Did I miss something? I use pyplot to create the plots. When I use plt.savefig("test.png") the chart is created and saved.
import numpy
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from studentRegression import studentReg
from class_vis import prettyPicture, output_image
from ages_net_worth import ageNetWorthData
ages_train, ages_test, net_worths_train, net_worth_test = ageNetWorthData()
plt.clf()
plt.scatter(ages_train, net_worths_train, color="b", label="train data")
plt.legend(loc=2)
plt.xlabel("ages")
plt.ylabel("net worths")
plt.show()
def ageNetWorthData():
random.seed(42)
numpy.random.seed(42)
ages = []
for ii in range(100):
ages.append( random.randint(20,65) )
net_worths = [ii * 6.25 + numpy.random.normal(scale=40.) for ii in ages]
### need massage list into a 2d numpy array to get it to work in LinearRegression
ages = numpy.reshape( numpy.array(ages), (len(ages), 1))
net_worths = numpy.reshape( numpy.array(net_worths), (len(net_worths), 1))
from sklearn.cross_validation import train_test_split
ages_train, ages_test, net_worths_train, net_worths_test = train_test_split(ages, net_worths)
return ages_train, ages_test, net_worths_train, net_worths_test
You are using a "non-interactive" backend (agg). Just remove the line:
matplotlib.use('agg')
You can check the docs here.
Related
How to set the size of the figure ploted by ScikitLearn's Confusion Matrix?
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
cm = confusion_matrix(np.arange(25), np.arange(25))
cmp = ConfusionMatrixDisplay(cm, display_labels=np.arange(25))
cmp.plot()
The code above shows this figure, which is too tight:
You can send a matplotlib.axes object to the .plot method of sklearn.metrics.ConfusionMatrixDisplay. Set the size of the figure in matplotlib.pyplot.subplots first.
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
import matplotlib.pyplot as plt
cm = confusion_matrix(np.arange(25), np.arange(25))
cmp = ConfusionMatrixDisplay(cm, display_labels=np.arange(25))
fig, ax = plt.subplots(figsize=(10,10))
cmp.plot(ax=ax)
I was looking for how to adjust the colorbar as someone pointed out in the commentaries in the answer offered by #Raphael and now want to add how to made this.
I used the properties of ConfusionMatrixDisplay and guided by this answer modified the code to:
import numpy as np
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
import matplotlib.pyplot as plt
cm = confusion_matrix(np.arange(25), np.arange(25))
cmp = ConfusionMatrixDisplay(cm, display_labels=np.arange(25))
fig, ax = plt.subplots(figsize=(10,10))
# Deactivate default colorbar
cmp.plot(ax=ax, colorbar=False)
# Adding custom colorbar
cax = fig.add_axes([ax.get_position().x1+0.01,ax.get_position().y0,0.02,ax.get_position().height])
plt.colorbar(cmp.im_, cax=cax)
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")
I am plotting 2D numpy arrays using
import numpy as np
import matplotlib.pyplot as plt
x = np.array([1,2,3])
y = np.array([[2,2.2,3],[1,5,1]])
plt.plot(x,y.T[:,:])
plt.legend()
plt.show()
I want a legend that tells which line belongs to which row. Of course, I realize I can't give it meaningful names, but I need some sort of unique label for the line without running through loop.
import numpy as np
import matplotlib.pyplot as plt
import uuid
x = np.array([1,2,3])
y = np.array([[2,2.2,3],[1,5,1]])
fig, ax = plt.subplots()
lines = ax.plot(x,y.T[:,:])
ax.legend(lines, [str(uuid.uuid4())[:6] for j in range(len(lines))])
plt.show()
(This is off of the current mpl master branch with a preview of the 2.0 default styles)
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
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import numpy as np
import matplotlib as mpl
import seaborn as sns
from scipy.stats import gaussian_kde
from numpy import linspace,hstack
LINE_WIDTH = 3
filename=('')
data=[ map(float, line.split()) for line in open(filename,'r') if line.strip()]
dataM=np.array(data)
meandata=np.mean(dataM,axis=0)
SD = np.std(dataM,axis=0)
sns.set_palette("hls")
mpl.rc("figure", figsize=(8, 4))
xs = np.linspace(meandata[0]-(4 * SD[0]) ,meandata[0]+( 4 * SD[0]), dataM[:,0].size)
ys=dataM[:,0]
n,bins,patches=plt.hist(ys,15)
I get this plot.
and I want to get a kernel gaussian distribution plotted over my histogram but I am getting an error TypeError: 'module' object is not callable
When I am trying to do this
my_pdf = gaussian_kde(ys)
x = linspace(30,100,1000)
plt(x,my_pdf(x),'r') # distribution function
plt.hist(ys,normed=1,alpha=.3) # histogram
plt.show()
What am I doing wrong?
You can do this directly using seaborn. It would be something like this:
import pandas as pd
import seaborn as sns
import scipy.stats
import matplotlib.pyplot as plt
data = pd.read_csv('input.txt')
sns.distplot(data, kde=False, fit=scipy.stats.norm)
plt.show()
For a kde plot just do:
sns.distplot(data);