def PlotPolly(model, independent_variable, dependent_variabble, Name):
x_new = np.linspace(15, 55, 100)
y_new = model(x_new)
plt.plot(independent_variable, dependent_variabble, '.', x_new, y_new, '-') #4
plt.title('Polynomial Fit with Matplotlib for Price ~ Length')
ax = plt.gca()
ax.set_facecolor((0.898, 0.898, 0.898))
fig = plt.gcf()
plt.xlabel(Name)
plt.ylabel('Price of Cars')
plt.show()
plt.close()
I get this with this code:
But when from line 4 I remove x_new and y_new line becomes
plt.plot(independent_variable, dependent_variabble)
I get this graph :
Can you explain what is meaning of x_new and y_new and why absence of this results in this kind of graph
In your code x_new and y_new both of them have the continuous values but independent_variable and dependent_variabble have discontinuous values and for plot discontinues you need scatter plot. see this example:
import numpy as np
import matplotlib.pyplot as plt
x = np.array([2, 1, 5, 3, 4, 2, 6, 4])
y = np.array([3, 1, 2, 0, 1, 2, 6, 4])
plt.plot(x, y, linestyle='-', marker='o')
Output:
Related
I want to draw a contour plot using table data.
I have 2 variables and response (3 columns).
I dont understand how to build this plot using it. I tried code below. But I had a next mistake: Input z must be 2D, not 1D.
feature_x = data.factor1
feature_y = data.factor2
# Creating 2-D grid of features
[X, Y] = np.meshgrid(feature_x, feature_y)
fig, ax = plt.subplots(1, 1)
Z = data.response
# plots filled contour plot
ax.contourf(X, Y, Z)
ax.set_title('Filled Contour Plot')
ax.set_xlabel('feature_x')
ax.set_ylabel('feature_y')
plt.show()
Data
To have a contour plot, z needs to be 2d matrix with all values for the points (x,y). You can think the data needed for a contour plot, as a DataFrame where index is x, columns are y and values are z. So z needs to be a 2d array of shape (x.size, y.size).
Since your z is not a 2d matrix but a 1d array, you cannot have a contour plot.
What you can do, for example, is a relplot with hue and/or size
import numpy as np
import pandas as pd
import seaborn as sns
x = np.array([1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])
y = np.array([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4])
z = np.array([249, 523, 603, 775, 577, 763, 808, 695, 642, 525, 795, 758])
df = pd.DataFrame({'x':x, 'y':y, 'z':z})
sns.relplot(
data=df,
x='x', y='y',
size='z', sizes=(10, 100),
hue='z',
palette='coolwarm',
);
EDIT
But... if you're looking for a continuous estimate, you can use gaussian_kde, for example
import scipy.stats as sps
import matplotlib.pyplot as plt
offset = .25
xmin = x.min()-offset
xmax = x.max()+offset
ymin = y.min()-offset
ymax = y.max()+offset
X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([x, y])
kernel = sps.gaussian_kde(values, weights=z)
Z = np.reshape(kernel(positions).T, X.shape)
fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(np.rot90(Z), cmap=plt.cm.gist_earth_r,
extent=[xmin, xmax, ymin, ymax],
aspect='auto'
)
sns.scatterplot(
data=df,
x='x', y='y',
size='z', sizes=(10, 200),
color='k'
)
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
ax.legend(loc='upper left', bbox_to_anchor=(1,1))
plt.show()
I have some unstructured 2D data that I would like to interpolate on a unit offset grid (ie grid indices start at 1 not 0) using scipy and plot using matplotlib. The code is below
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
# X Y Z
data = [[ 9, 2, 2.0],
[ 3, 3, 5.0],
[ 6, 4, 1.0],
[ 2, 6, 3.0],
[10, 7, 4.5],
[ 5, 8, 2.0]]
data = np.array(data)
coords = data[:, 0:2]
zvals = data[:, 2]
# Create the grid on which to interpolate (unit offset)
nx = 10
ny = 10
x = np.arange(nx)
x += 1
y = np.arange(ny)
y += 1
grid_x, grid_y = np.meshgrid(x, y, indexing='xy')
# Interpolate
grid_z1 = scipy.interpolate.griddata(coords, zvals, (grid_x, grid_y), method='linear')
# Plot the results
fig, axs = plt.subplots()
plt.imshow(grid_z1)
plt.plot(coords[:,0], coords[:,1], 'k.', ms=10)
plt.show()
The point data seem to be in the right place but matplotlib seems to be plotting the gridded data as zero-offset not unit-offset. I am obviously missing something - just not sure what. Thanks in advance!
Update
fig, axs = plt.subplots()
plt.imshow(grid_z1, origin='lower', extent=[1, 10, 1, 10])
plt.plot(coords[:,0], coords[:,1], 'k.', ms=10)
plt.show()
IIUC, you want to define xlim and ylim equal to the plot and not the heatmap:
plt.plot(coords[:,0], coords[:,1], 'k.', ms=10)
ax = plt.gca()
xlim, ylim = ax.get_xlim(), ax.get_ylim()
plt.imshow(grid_z1)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
Output:
I want to change text in matplotlib's plot with loop. I am able to print text with loop, but unable to delete the previous text and they got printed on top of each other.
import numpy as np
import matplotlib.pyplot as plt
x = np.array([1,2,3,4,5])
y = np.array([1,2,3,4,5])
fig, ax = plt.subplots()
ax.set_xlim([0,5])
ax.set_ylim([0,5])
for i in x:
pt = ax.plot(i, i, 'o')
tx = ax.text(1, 2, str(i), fontsize = 12)
plt.pause(1)
removePt = pt.pop()
removePt.remove()
I tried to delete text by
removeTx = tx.pop()
removeTx.remove()
but it has not worked.
Kindly suggest how can I remove the previous text from plot.
Just add tx.remove() after the pause:
import numpy as np
import matplotlib.pyplot as plt
x = np.array([1, 2, 3, 4, 5])
y = np.array([1, 2, 3, 4, 5])
fig, ax = plt.subplots()
ax.set_xlim([0, 5])
ax.set_ylim([0, 5])
for i in x:
pt = ax.plot(i, i, 'o')
tx = ax.text(1, 2, str(i), fontsize = 12)
plt.pause(1)
tx.remove()
plt.show()
In Python matplotlib, how can you get the line in a line or step plot to display a gradient based on the y-value?
Example plot (made in Tableau):
Code for step plot with a line that changes gradient according to x-value, adapted from this answer:
fig, ax = plt.subplots(figsize=(10, 4))
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
y = [2, 3, 9, 10, 2, 9, 0, 1, 9, 1, -8]
T = np.linspace(0,1,np.size(x))**2
s = 1
for i in range(0, len(x)-s, s):
ax.step(x[i:i+s+1], y[i:i+s+1], marker='.', color=(0.0,0.5,T[i]))
ax.tick_params(axis='both', colors='lightgray', labelsize=8)
The following code is inspired by the multicolored-line example from the matplotlib docs. First the horizontal line segments are drawn and colored using their y-value. The vertical segments are subdivided in small chunks to colored individually.
vmin of the norm is set a bit lower to avoid the too-light range of the colormap.
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np
x = np.arange(50)
y = np.random.randint(-3, 4, x.size).cumsum()
fig, ax = plt.subplots()
norm = plt.Normalize(y.min() - y.ptp() * .2, y.max())
cmap = 'inferno_r' # 'Reds'
horizontal_lines = np.array([x[:-1], y[:-1], x[1:], y[:-1]]).T.reshape(-1, 2, 2)
hor_lc = LineCollection(horizontal_lines, cmap=cmap, norm=norm)
hor_lc.set_array(y[:-1])
ax.add_collection(hor_lc)
factor = 10
long_y0 = np.linspace(y[:-1], y[1:], factor)[:-1, :].T.ravel()
long_y1 = np.linspace(y[:-1], y[1:], factor)[1:, :].T.ravel()
long_x = np.repeat(x[1:], factor - 1)
vertical_lines = np.array([long_x, long_y0, long_x, long_y1]).T.reshape(-1, 2, 2)
ver_lc = LineCollection(vertical_lines, cmap=cmap, norm=norm)
ver_lc.set_array((long_y0 + long_y1) / 2)
ax.add_collection(ver_lc)
ax.scatter(x, y, c=y, cmap=cmap, norm=norm)
plt.autoscale() # needed in case the scatter plot would be omited
plt.show()
Here is another example, with a black background. In this case the darkest part of the colormap is avoided. The changed code parts are:
y = np.random.randint(-9, 10, x.size)
ax.patch.set_color('black')
norm = plt.Normalize(y.min(), y.max() + y.ptp() * .2)
cmap = 'plasma_r'
Here is an example with a TwoSlopeNorm and the blue-white-red colormap:
from matplotlib.colors import TwoSlopeNorm
y = np.random.uniform(-1, 1, x.size * 10).cumsum()[::10]
y = (y - y.min()) / y.ptp() * 15 - 5
norm = TwoSlopeNorm(vmin=-5, vcenter=0, vmax=10)
cmap = 'bwr'
Here is a simple plot:
1) How to disable the ticks?
2) How to reduce their number?
Here is a sample code:
from pylab import *
import numpy as np
x = [5e-05, 5e-06, 5e-07, 5e-08, 5e-09, 5e-10]
y = [-13, 14, 100, 120, 105, 93]
def myfunc(x,p):
sl,yt,yb,ec=p
y = yb + (yt-yb)/(1+np.power(10, sl*(np.log10(x)-np.log10(ec))))
return y
xp = np.power(10, np.linspace(np.log10(min(x)/10), np.log10(max(x)*10), 100))
pxp=myfunc(xp, [1,100,0,1e-6])
subplot(111,axisbg="#dfdfdf")
plt.plot(x, y, '.', xp, pxp, 'g-', linewidth=1)
plt.xscale('log')
plt.grid(True,ls="-", linewidth=0.4, color="#ffffff", alpha=0.5)
plt.draw()
plt.show()
Which produces:
plt.minorticks_off()
Turns em off!
To change the number of them/position them, you can use the subsx parameter. like this:
plt.xscale('log', subsx=[2, 3, 4, 5, 6, 7, 8, 9])
From the docs:
subsx/subsy: Where to place the subticks between each major tick.
Should be a sequence of integers. For example, in a log10 scale: [2,
3, 4, 5, 6, 7, 8, 9]
will place 8 logarithmically spaced minor ticks between each major
tick.
Calling plt.minorticks_off() will apply this to the current axis. (The function is actually a wrapper to gca().minorticks_off().)
You can also apply this to an individual axis in the same way:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.minorticks_off()
from pylab import *
import numpy as np
x = [5e-05, 5e-06, 5e-07, 5e-08, 5e-09, 5e-10]
y = [-13, 14, 100, 120, 105, 93]
def myfunc(x,p):
sl,yt,yb,ec=p
y = yb + (yt-yb)/(1+np.power(10, sl*(np.log10(x)-np.log10(ec))))
return y
xp = np.power(10, np.linspace(np.log10(min(x)/10), np.log10(max(x)*10), 100))
pxp=myfunc(xp, [1,100,0,1e-6])
ax=subplot(111,axisbg="#dfdfdf")
plt.plot(x, y, '.', xp, pxp, 'g-', linewidth=1)
plt.xscale('log')
plt.grid(True,ls="-", linewidth=0.4, color="#ffffff", alpha=0.5)
plt.minorticks_off() # turns off minor ticks
plt.draw()
plt.show()