I'm trying to figure out how I can get the 3D matplotlib images below to plot higher on the canvas so it doesn't get clipped. Here is the code I'm using to create the plot. I couldn't find a way to attach the text file containing the Z elevations (referenced in the code below), but it is simply a 2D array containing a surface made up of values ranging between 0 and 1.
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.mplot3d import Axes3D
nrow=30
ncol=100
f = open(r'C:\temp\fracEvapCume_200.txt','r')
fracEvapTS = np.loadtxt(f)
f.close()
X, Y = np.meshgrid(ncol, nrow)
Y3d, X3d = np.mgrid[0:Y, 0:X]
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.auto_scale_xyz([0, 100], [0, 30], [0, 0.2])
Y3d, X3d = np.mgrid[0:Y, 0:X]
Z = fracEvapTS
surf = ax.plot_surface(X3d, Y3d, Z, cmap='autumn', cstride=2, rstride=2)
ax.set_xlabel("X-Label")
ax.set_ylabel("Y-Label")
ax.set_zlabel("Z-Label")
ax.pbaspect = [1., .33, 0.25]
ax.dist = 7
plt.tight_layout()
plt.savefig('clipped.png')
In order to get the ax.pbaspect=[1., .33, 0.25] line to work, changes to the get_proj function inside site-packages\mpl_toolkits\mplot3d\axes3d.py were made as suggested in this post. In order to get the figure to draw larger, I added ax.dist = 7 based on this post. Lastly, based on this post I was hoping that plt.tight_layout() would roll back the margins and prevent the red/yellow surface shown below from being clipped, but that didn't work either. I'm failing to find the command that will move the image up on the canvas, thereby avoiding all of the unnecessary white space at the top of the figure and preventing the red/yellow surface from getting clipped. Is there one line of Python that will accomplish this?
after adding the line plt.tight_layout(), it made matters worse:
The problem is that your modification to site-packages\mpl_toolkits\mplot3d\axes3d.py changes the projection matrix, without changing the center of the view, messing up the position of the scene once transfomed in camera coordinates.
So when the view is zoomed (with ax.dist) then moved, the plot sometimes gets out of the canvas.
You need to replace the following line to the get_proj function in axes3d.py :
# look into the middle of the new coordinates
R = np.array([0.5, 0.5, 0.5])
By :
# look into the middle of the new coordinates
try:
R = np.array(self.pbaspect)/2
except AttributeError:
R = np.array([0.5, 0.5, 0.5])
And this should work :
PS : Code used to make the figures :
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from mpl_toolkits.mplot3d import Axes3D
nrow=30
ncol=100
X, Y = np.meshgrid(ncol, nrow)
Y3d, X3d = np.mgrid[0:Y, 0:X]
Z = np.sin(Y3d/Y)*np.sin(X3d/X)
fig = plt.figure()
for i in range(4):
ax = fig.add_subplot(2,2,i,projection='3d')
ax.auto_scale_xyz([0, 100], [0, 30], [0, 0.2])
surf = ax.plot_surface(X3d, Y3d, Z, cmap='autumn', cstride=2, rstride=2)
ax.set_xlabel("X-Label")
ax.set_ylabel("Y-Label")
ax.set_zlabel("Z-Label")
ax.pbaspect = [1., .33, 0.25]
ax.dist = 7
Related
I am trying to plot vehicle position (coordinates - x,y) against time(1s,2s,3s...). I tried with matplotlib but could not succeed. I am new in python. Could anyone help me please.
My code:
import matplotlib.pyplot as plt
import numpy as np
coordinate = [[524.447876,1399.091919], [525.1377563,1399.95105], [525.7932739,1400.767578], [526.4627686,1401.601563],
[527.2360229,1402.564575], [527.8989258,1403.390381], [528.5689697,1404.224854]]
timestamp =[0,0.05,0.1,0.15,0.2,0.25,0.3]
plt.plot(coordinate,timestamp)
Plot comes like: But this is wrong one. I did wrong.
Plot supposed to become, in particular, timestamp (1s) the vehicle position is (x,y). So there should be one line just like vehicle trajectory.
Thanks.
I believe this is the output you're looking for:
import matplotlib.pyplot as plt
import numpy as np
coordinate = [[524.447876,1399.091919],
[525.1377563,1399.95105],
[525.7932739,1400.767578],
[526.4627686,1401.601563],
[527.2360229,1402.564575],
[527.8989258,1403.390381],
[528.5689697,1404.224854]]
v1 = [y[1] for y in coordinate]
v2 = [y[0] for y in coordinate]
x = [0,0.05,0.1,0.15,0.2,0.25,0.3]
plt.plot(x,v1)
plt.plot(x,v2,'--')
plt.ylim(0,1500)
plt.show()
Does something simple like this meet your needs:
import matplotlib.pyplot as plt
coordinates = [
(524.447876,1399.091919),
(525.1377563,1399.95105),
(525.7932739,1400.767578),
(526.4627686,1401.601563),
(527.2360229,1402.564575),
(527.8989258,1403.390381),
(528.5689697,1404.224854),
]
timestamp = [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3]
x, y = zip(*coordinates)
ax = plt.axes(projection="3d")
ax.plot(x, y, timestamp);
plt.show()
Matplotlib will let you rotate the image with the mouse to view it from various angles.
Hi I think the problem over here is that you are using a two-dimensional list, so matplotlib plots the coordinates and not the timestamp.
Code:
import matplotlib.pyplot as plt
import numpy as np
coordinate = np.array([[524.447876,1399.091919], [525.1377563,1399.95105], [525.7932739,1400.767578], [526.4627686,1401.601563], [527.2360229,1402.564575], [527.8989258,1403.390381], [528.5689697,1404.224854]])
timestamp =np.array([0,0.05,0.1,0.15,0.2,0.25,0.3])
plt.plot(coordinate)
Output:
You have to convert it into a single dimension list like this:
coordinate_new = np.array([524.447876,525.1377563,1399.95105, 525.7932739,1400.767578, 526.4627686,1401.601563])
timestamp =np.array([0,0.05,0.1,0.15,0.2,0.25,0.3])
plt.plot(coordinate_new, timestamp)
Then the output will be:
Hope I could help!!
If you want to plot it in 3-d, here is what you can do:
import matplotlib.pyplot as plt
#importing matplotlib
fig = plt.figure() #adding figure
ax_3d = plt.axes(projection="3d") #addign 3-d axes
coordinate_x = [524.447876, 525.137756, 525.7932739, 526.4627686, 527.2360229, 527.8989258, 528.5689697]
coordinate_y = [1399.091919, 1399.95105,1400.767578,1401.601563,1402.564575,1403.390381,1404.224854]
timestamp =[0,0.05,0.1,0.15,0.2,0.25,0.3]
# defining the variables
ax.plot(coordinate_x, coordinate_y, timestamp)
#plotting them
Output:
All the Best!
could you help me with this code, please? I am trying to integrate the force line in the given point. I don't know where is a mistake - there is no streamline in the plot.
Data - dipole magnetic field are here
I tried this example with the change of data and the change of number of streamlines.
import numpy as np
import matplotlib.pyplot as plt
from numpy import array
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D # 3d graph
from mpl_toolkits.mplot3d import proj3d # 3d graph
import math
from matplotlib import patches
import code
import yt
from yt import YTArray # arrays in yt module
from yt.visualization.api import Streamlines # force lines
import matplotlib.pylab as pl# Choose point in field
X_point = 0.007089085922957821
Y_point = 0.038439192046320805
Z_point = 0# Load data (dictionary)
try:
import cPickle as pickle
except ImportError: # python 3.x
import picklewith open('data.p', 'rb') as fp:
data = pickle.load(fp)Bx_d = data["Bx"]
By_d = data["By"]
Bz_d = data["Bz"]# 3d array of dipole magnetic field
print(type(data))
bbox = np.array([[-0.15, 0.15], [0, 0.2], [-0.1, 0.1]]) # box, border
ds = yt.load_uniform_grid(data, Bx_d.shape, length_unit="Mpc", bbox=bbox, nprocs=100) # data, dimensionc = YTArray([X_point, Y_point, Z_point], 'm') # Define c: the center of the box, chosen point
c1 = ds.domain_center
print('c1', c1)
print(type(c1))
print('center',c)
N = 1 # N: the number of streamlines
scale = ds.domain_width[0] # scale: the spatial scale of the streamlines relative to the boxsize,
pos = c# Create streamlines of the 3D vector velocity and integrate them through
# the box defined above
streamlines = Streamlines(ds, pos, 'Bx', 'By', 'Bz', length=None) # length of integration
streamlines.integrate_through_volume()# Create a 3D plot, trace the streamlines through the 3D volume of the plot
fig=pl.figure()
ax = Axes3D(fig)
ax.scatter(X_point, Y_point, Z_point, marker = 'o', s=40, c='green')
print('tisk', streamlines.streamlines)for stream in streamlines.streamlines:
stream = stream[np.all(stream != 0.0, axis=1)]
ax.plot3D(stream[:,0], stream[:,1], stream[:,2], alpha=0.1)# Save the plot to disk.
pl.savefig('streamlines.png')
plt.show()
Output:
Without knowing more about the data, as well as what the output of the print call is, it's not entirely clear what the error is. If the streamlines have meaningful values (i.e., the values of stream[:,0] etc are within the bounds of your Axes3D, it should produce results.
Options for debugging would start with examining the individual values, then proceeding to plotting them in 2D (using pairs of components of each stream -- (0,1), (1,2) and (0,2)), and then examining what happens if you allow Axes3D to autoscale the xyz axes. You may also experiment with the alpha value, to see if the lines are simply too light to see.
An example image that this produces would also help, so that it can be made clear a few things about the properties matplotlib assigns to the Axes3D object.
I would like to know how I can generate the marker for the black colored line shown in this picture. (Source: NCEP & NOAA)
It's the marker for a storm or hurricane in standard weather maps.
I can probably generate an image file of the marker symbol. But, I am not aware of how I can tell matplotlib to use the image as a marker.
The marker looks like a 6. If this is the case, you can use a 6 as a marker as follows:
import matplotlib.pyplot as plt
x = [1,2,3,4]
y = [2,3,1,4]
plt.scatter(x,y, s= 100,marker="$6$")
plt.show()
If this is not an option, you may define your custom marker using a path. To this end, the coordinates of the path need to be known. I have invented some values below, maybe they already suit the needs here.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.path as mpath
def get_hurricane():
u = np.array([ [2.444,7.553],
[0.513,7.046],
[-1.243,5.433],
[-2.353,2.975],
[-2.578,0.092],
[-2.075,-1.795],
[-0.336,-2.870],
[2.609,-2.016] ])
u[:,0] -= 0.098
codes = [1] + [2]*(len(u)-2) + [2]
u = np.append(u, -u[::-1], axis=0)
codes += codes
return mpath.Path(3*u, codes, closed=False)
hurricane = get_hurricane()
plt.scatter([1,1,2],[1.4,2.3,2.8], s=350, marker=hurricane,
edgecolors="crimson", facecolors='none', linewidth=2)
plt.scatter([0,1,2],[1,3,1], s=150, marker=hurricane,
edgecolors="k", facecolors='none')
plt.scatter([0,1.8,3],[0,2,4], s=150, marker="o",
edgecolors="k", facecolors='none')
plt.show()
I want to make a plot using .fits files (astronomical images) and I am experiencing two issues which I think they are related:
Using this example from astropy:
from matplotlib import pyplot as plt
from astropy.io import fits
from astropy.wcs import WCS
from astropy.utils.data import download_file
fits_file = 'http://data.astropy.org/tutorials/FITS-images/HorseHead.fits'
image_file = download_file(fits_file, cache=True)
hdu = fits.open(image_file)[0]
wcs = WCS(hdu.header)
fig = plt.figure()
fig.add_subplot(111, projection=wcs)
plt.imshow(hdu.data, origin='lower', cmap='cubehelix')
plt.xlabel('RA')
plt.ylabel('Dec')
plt.show()
I can generate this image:
Now I would like to plot some points using the same coordinates as the image:
plt.scatter(85, -2, color='red')
However, when I do this:
I am ploting at the pixel coordinantes. Furthermore, the image no longer matches the frame size (although the coordinates seem fine)
Any advice on how to deal with these issues?
It is very easy to plot given coordinates. All you have to do is apply a transform.
I copied your example and added comments where I changed something and why.
from matplotlib import pyplot as plt
from astropy.io import fits
from astropy.wcs import WCS
from astropy.utils.data import download_file
fits_file = 'http://data.astropy.org/tutorials/FITS-images/HorseHead.fits'
image_file = download_file(fits_file, cache=True)
# Note that it's better to open the file with a context manager so no
# file handle is accidentally left open.
with fits.open(image_file) as hdus:
img = hdus[0].data
wcs = WCS(hdus[0].header)
fig = plt.figure()
# You need to "catch" the axes here so you have access to the transform-function.
ax = fig.add_subplot(111, projection=wcs)
plt.imshow(img, origin='lower', cmap='cubehelix')
plt.xlabel('RA')
plt.ylabel('Dec')
# Apply a transform-function:
plt.scatter(85, -2, color='red', transform=ax.get_transform('world'))
And the result is:
Note that if you want the Canvas to only show the region of the image just apply the limits again afterwards:
# Add a scatter point which is in the extend of the image:
plt.scatter(85.3, -2.5, color='red', transform=ax.get_transform('world'))
plt.ylim(0, img.shape[0])
plt.xlim(0, img.shape[1])
which gives:
A side note as well here. AstroPy has a great units support so instead of converting arcmins and arcsecs to degrees you can just define the "unit". You still need the transform though:
from astropy import units as u
x0 = 85 * u.degree + 20 * u.arcmin
y0 = -(2 * u.degree + 25 * u.arcmin)
plt.scatter(x0, y0, color='red', transform=ax.get_transform('world'))
Short version: is there a Python method for displaying an image which shows, in real time, the pixel indices and intensities? So that as I move the cursor over the image, I have a continually updated display such as pixel[103,214] = 198 (for grayscale) or pixel[103,214] = (138,24,211) for rgb?
Long version:
Suppose I open a grayscale image saved as an ndarray im and display it with imshow from matplotlib:
im = plt.imread('image.png')
plt.imshow(im,cm.gray)
What I get is the image, and in the bottom right of the window frame, an interactive display of the pixel indices. Except that they're not quite, as the values are not integers: x=134.64 y=129.169 for example.
If I set the display with correct resolution:
plt.axis('equal')
the x and y values are still not integers.
The imshow method from the spectral package does a better job:
import spectral as spc
spc.imshow(im)
Then in the bottom right I now have pixel=[103,152] for example.
However, none of these methods also shows the pixel values. So I have two questions:
Can the imshow from matplotlib (and the imshow from scikit-image) be coerced into showing the correct (integer) pixel indices?
Can any of these methods be extended to show the pixel values as well?
There a couple of different ways to go about this.
You can monkey-patch ax.format_coord, similar to this official example. I'm going to use a slightly more "pythonic" approach here that doesn't rely on global variables. (Note that I'm assuming no extent kwarg was specified, similar to the matplotlib example. To be fully general, you need to do a touch more work.)
import numpy as np
import matplotlib.pyplot as plt
class Formatter(object):
def __init__(self, im):
self.im = im
def __call__(self, x, y):
z = self.im.get_array()[int(y), int(x)]
return 'x={:.01f}, y={:.01f}, z={:.01f}'.format(x, y, z)
data = np.random.random((10,10))
fig, ax = plt.subplots()
im = ax.imshow(data, interpolation='none')
ax.format_coord = Formatter(im)
plt.show()
Alternatively, just to plug one of my own projects, you can use mpldatacursor for this. If you specify hover=True, the box will pop up whenever you hover over an enabled artist. (By default it only pops up when clicked.) Note that mpldatacursor does handle the extent and origin kwargs to imshow correctly.
import numpy as np
import matplotlib.pyplot as plt
import mpldatacursor
data = np.random.random((10,10))
fig, ax = plt.subplots()
ax.imshow(data, interpolation='none')
mpldatacursor.datacursor(hover=True, bbox=dict(alpha=1, fc='w'))
plt.show()
Also, I forgot to mention how to show the pixel indices. In the first example, it's just assuming that i, j = int(y), int(x). You can add those in place of x and y, if you'd prefer.
With mpldatacursor, you can specify them with a custom formatter. The i and j arguments are the correct pixel indices, regardless of the extent and origin of the image plotted.
For example (note the extent of the image vs. the i,j coordinates displayed):
import numpy as np
import matplotlib.pyplot as plt
import mpldatacursor
data = np.random.random((10,10))
fig, ax = plt.subplots()
ax.imshow(data, interpolation='none', extent=[0, 1.5*np.pi, 0, np.pi])
mpldatacursor.datacursor(hover=True, bbox=dict(alpha=1, fc='w'),
formatter='i, j = {i}, {j}\nz = {z:.02g}'.format)
plt.show()
An absolute bare-bones "one-liner" to do this: (without relying on datacursor)
def val_shower(im):
return lambda x,y: '%dx%d = %d' % (x,y,im[int(y+.5),int(x+.5)])
plt.imshow(image)
plt.gca().format_coord = val_shower(ims)
It puts the image in closure so makes sure if you have multiple images each will display its own values.
All of the examples that I have seen only work if your x and y extents start from 0. Here is code that uses your image extents to find the z value.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
d = np.array([[i+j for i in range(-5, 6)] for j in range(-5, 6)])
im = ax.imshow(d)
im.set_extent((-5, 5, -5, 5))
def format_coord(x, y):
"""Format the x and y string display."""
imgs = ax.get_images()
if len(imgs) > 0:
for img in imgs:
try:
array = img.get_array()
extent = img.get_extent()
# Get the x and y index spacing
x_space = np.linspace(extent[0], extent[1], array.shape[1])
y_space = np.linspace(extent[3], extent[2], array.shape[0])
# Find the closest index
x_idx= (np.abs(x_space - x)).argmin()
y_idx= (np.abs(y_space - y)).argmin()
# Grab z
z = array[y_idx, x_idx]
return 'x={:1.4f}, y={:1.4f}, z={:1.4f}'.format(x, y, z)
except (TypeError, ValueError):
pass
return 'x={:1.4f}, y={:1.4f}, z={:1.4f}'.format(x, y, 0)
return 'x={:1.4f}, y={:1.4f}'.format(x, y)
# end format_coord
ax.format_coord = format_coord
If you are using PySide/PyQT here is an example to have a mouse hover tooltip for the data
import matplotlib
matplotlib.use("Qt4Agg")
matplotlib.rcParams["backend.qt4"] = "PySide"
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
# Mouse tooltip
from PySide import QtGui, QtCore
mouse_tooltip = QtGui.QLabel()
mouse_tooltip.setFrameShape(QtGui.QFrame.StyledPanel)
mouse_tooltip.setWindowFlags(QtCore.Qt.ToolTip)
mouse_tooltip.setAttribute(QtCore.Qt.WA_TransparentForMouseEvents)
mouse_tooltip.show()
def show_tooltip(msg):
msg = msg.replace(', ', '\n')
mouse_tooltip.setText(msg)
pos = QtGui.QCursor.pos()
mouse_tooltip.move(pos.x()+20, pos.y()+15)
mouse_tooltip.adjustSize()
fig.canvas.toolbar.message.connect(show_tooltip)
# Show the plot
plt.show()
with Jupyter you can do so either with datacursor(myax)or by ax.format_coord.
Sample code:
%matplotlib nbagg
import numpy as np
import matplotlib.pyplot as plt
X = 10*np.random.rand(5,3)
fig,ax = plt.subplots()
myax = ax.imshow(X, cmap=cm.jet,interpolation='nearest')
ax.set_title('hover over the image')
datacursor(myax)
plt.show()
the datacursor(myax) can also be replaced with ax.format_coord = lambda x,y : "x=%g y=%g" % (x, y)
In case you, like me, work on Google Colab, this solutions do not work as Colab disabled interactive feature of images for matplotlib.
Then you might simply use Plotly:
https://plotly.com/python/imshow/
import plotly.express as px
import numpy as np
img_rgb = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255]],
[[0, 255, 0], [0, 0, 255], [255, 0, 0]]
], dtype=np.uint8)
fig = px.imshow(img_rgb)
fig.show()
Matplotlib has built-in interactive plot which logs pixel values at the corner of the screen.
To setup first install pip install ipympl
Then use either %matplotlib notebook or %matplotlib widget instead of %matplotlib inline
The drawback with plotly or Bokeh is that they don't work on Pycharm.
For more information take a look at the doc
To get interactive pixel information of an image use the module imagetoolbox
To download the module open the command prompt and write
pip install imagetoolbox
Write the given code to get interactive pixel information of an image
enter image description here
Output:enter image description here