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Let's say there is a 100*100 grid in the range of [0,1]. Each point in this grid with coordinates (x,y) has some value Z, where x and y can take any of the values {0.01, 0.02, 0.03, …, 1}. However, in the chart below, only points in certain areas matter (e.g. the rectangles in chart below).
I have a method F that when given a point with its coordinates tells me whether that point matters, I used a rectangle in the chart below just for demonstration - it’s not necessarily a rectangle. So I can calculate the centers of all the points for which I need the heatmap for - the rest should be white.
I was thinking maybe I can first build a 2D array of size 100*100 and populate it with the corresponding Z value for that coordinate, but then using the method F, set the Z for coordinates outside of those rectangles to some number (0, or -10000) that will result in white in the final heatmap, but couldn't quite make it happen.
Any help would be appreciated.
If all you are trying to do is blank out some of the image, just set it to NaN:
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
data = np.random.randn(100, 100)
x = np.arange(100)
y = np.arange(100)
X, Y = np.meshgrid(x,y)
data[((X**2 + (Y-30)**2) > 25**2)
& ~((X>20) & (X<83) & (Y>60) & (Y<85))] = np.NaN
ax.imshow(data)
imshow has an extent argument that you can use to arbitrarily place images on the axes.
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
ax.imshow(np.random.randn(4, 5), extent=(3, 8, 2, 6))
ax.imshow(np.random.randn(7, 3), extent=(1, 2.7, 6.3, 9.8))
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
plt.show()
You can do the same thing with pcolormesh.
How do i set xticks to 'a different interval'
For instance:
plt.plot(1/(np.arange(0.1,3,0.1)))
returns:
If I would like the x axis to be on a scale from 0 to 3, how can i do that? I've tried
plt.xticks([0,1,2])
but that returns:
You want to learn about plt.xlim and adjacent functions. This causes the X axis to have limits (minimum, maximum) that you specify. Otherwise Matplotlib decides for you based on the values you try to plot.
y = 1 / np.arange(0.1,3,0.1)
plt.plot(y)
plt.xlim(0, 3) # minimum 0, maximum 3
plt.show()
Your plot uses only Y values, so the X values are automatically chosen to be 1, 2, 3, ... to pair up with each Y value you provide.
If you desire to determine the X too, you can do that:
x = np.arange(0.1,3,0.1)
y = 1/x
plt.plot(x, y)
plt.xticks([0,1,2,3]) # ticks at those positions, if you don't like the automatic ones
plt.show()
You can use numpy.arange() to get the desired range with a specific step:
import matplotlib.pyplot as plt
import numpy as np
y = 1/(np.arange(0.1,3,0.1))
plt.tight_layout()
plt.plot(y)
plt.xticks(np.arange(0, len(y), 6), [str(round(i, 2)) for i in np.arange(0, 3, (3*6)/len(y))])
plt.show()
Also, you can see more examples of xticks() on the official matplotlib documentation.
I've been reading about multiple lines plotting and multicolored lines, but every time I read a post about it people use continuous set of data, like some trigonometrical function:
x = np.linspace(0, 3 * np.pi, 500)
y = np.sin(x)
So, here is my problem: I'm making a plotting script for 1D finite element problems like the image attached. I'm plotting the elements as individual lines with the X and Y coordinates array, and I would like to color the lines based on a third array like the axial stress or temperature, or any other
The problem is when I try to follow the examples I've found, every line has local color distribution, instead of a global distribution. I'm thinking about defining a global color scale based on the maximum and mininum values of the third array, let's say temperature, and passing the coordinates of each element + the current average temperature may do the job, but I don't know if something alike is possible
Anyone can help?
You can map values to a particular colormap, and plot each element as a single line with a particular color, like this:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import Normalize
import numpy as np
# roto-translation matrix
T = lambda t, u, v: np.array([[np.cos(t), -np.sin(t), u], [np.sin(t), np.cos(t), 0], [0, 0, 1]])
# coordinates of a triangle
triangle = np.array([[-0.5, -0.5, 1], [0.5, -0.5, 1], [-0.5, 0.5, 1], [-0.5, -0.5, 1]]).T
n_elements = 10
values = np.linspace(-5, 5, n_elements)
# create a normalizer
norm = Normalize(vmin=values.min(), vmax=values.max())
# normalize values
norm_values = norm(values)
# choose a colormap
cmap = cm.magma
# create colors
colors = cmap(norm_values)
# map values to a colorbar
mappable = cm.ScalarMappable(norm=norm, cmap=cmap)
mappable.set_array(values)
f, ax = plt.subplots(1)
ax.set_aspect("equal")
for i in range(n_elements):
tr = np.matmul(T(i * np.pi, int(i / 2), 0), triangle)
ax.plot(tr[0, :], tr[1, :], color=colors[i])
cb = f.colorbar(mappable)
cb.set_label("Value")
I am taking a course on linear algebra and I want to visualize the vectors in action, such as vector addition, normal vector, so on.
For instance:
V = np.array([[1,1],[-2,2],[4,-7]])
In this case I want to plot 3 vectors V1 = (1,1), M2 = (-2,2), M3 = (4,-7).
Then I should be able to add V1,V2 to plot a new vector V12(all together in one figure).
when I use the following code, the plot is not as intended
import numpy as np
import matplotlib.pyplot as plt
M = np.array([[1,1],[-2,2],[4,-7]])
print("vector:1")
print(M[0,:])
# print("vector:2")
# print(M[1,:])
rows,cols = M.T.shape
print(cols)
for i,l in enumerate(range(0,cols)):
print("Iteration: {}-{}".format(i,l))
print("vector:{}".format(i))
print(M[i,:])
v1 = [0,0],[M[i,0],M[i,1]]
# v1 = [M[i,0]],[M[i,1]]
print(v1)
plt.figure(i)
plt.plot(v1)
plt.show()
How about something like
import numpy as np
import matplotlib.pyplot as plt
V = np.array([[1,1], [-2,2], [4,-7]])
origin = np.array([[0, 0, 0],[0, 0, 0]]) # origin point
plt.quiver(*origin, V[:,0], V[:,1], color=['r','b','g'], scale=21)
plt.show()
Then to add up any two vectors and plot them to the same figure, do so before you call plt.show(). Something like:
plt.quiver(*origin, V[:,0], V[:,1], color=['r','b','g'], scale=21)
v12 = V[0] + V[1] # adding up the 1st (red) and 2nd (blue) vectors
plt.quiver(*origin, v12[0], v12[1])
plt.show()
NOTE: in Python2 use origin[0], origin[1] instead of *origin
This may also be achieved using matplotlib.pyplot.quiver, as noted in the linked answer;
plt.quiver([0, 0, 0], [0, 0, 0], [1, -2, 4], [1, 2, -7], angles='xy', scale_units='xy', scale=1)
plt.xlim(-10, 10)
plt.ylim(-10, 10)
plt.show()
Your main problem is you create new figures in your loop, so each vector gets drawn on a different figure. Here's what I came up with, let me know if it's still not what you expect:
CODE:
import numpy as np
import matplotlib.pyplot as plt
M = np.array([[1,1],[-2,2],[4,-7]])
rows,cols = M.T.shape
#Get absolute maxes for axis ranges to center origin
#This is optional
maxes = 1.1*np.amax(abs(M), axis = 0)
for i,l in enumerate(range(0,cols)):
xs = [0,M[i,0]]
ys = [0,M[i,1]]
plt.plot(xs,ys)
plt.plot(0,0,'ok') #<-- plot a black point at the origin
plt.axis('equal') #<-- set the axes to the same scale
plt.xlim([-maxes[0],maxes[0]]) #<-- set the x axis limits
plt.ylim([-maxes[1],maxes[1]]) #<-- set the y axis limits
plt.legend(['V'+str(i+1) for i in range(cols)]) #<-- give a legend
plt.grid(b=True, which='major') #<-- plot grid lines
plt.show()
OUTPUT:
EDIT CODE:
import numpy as np
import matplotlib.pyplot as plt
M = np.array([[1,1],[-2,2],[4,-7]])
rows,cols = M.T.shape
#Get absolute maxes for axis ranges to center origin
#This is optional
maxes = 1.1*np.amax(abs(M), axis = 0)
colors = ['b','r','k']
for i,l in enumerate(range(0,cols)):
plt.axes().arrow(0,0,M[i,0],M[i,1],head_width=0.05,head_length=0.1,color = colors[i])
plt.plot(0,0,'ok') #<-- plot a black point at the origin
plt.axis('equal') #<-- set the axes to the same scale
plt.xlim([-maxes[0],maxes[0]]) #<-- set the x axis limits
plt.ylim([-maxes[1],maxes[1]]) #<-- set the y axis limits
plt.grid(b=True, which='major') #<-- plot grid lines
plt.show()
EDIT OUTPUT:
What did you expect the following to do?
v1 = [0,0],[M[i,0],M[i,1]]
v1 = [M[i,0]],[M[i,1]]
This is making two different tuples, and you overwrite what you did the first time... Anyway, matplotlib does not understand what a "vector" is in the sense you are using. You have to be explicit, and plot "arrows":
In [5]: ax = plt.axes()
In [6]: ax.arrow(0, 0, *v1, head_width=0.05, head_length=0.1)
Out[6]: <matplotlib.patches.FancyArrow at 0x114fc8358>
In [7]: ax.arrow(0, 0, *v2, head_width=0.05, head_length=0.1)
Out[7]: <matplotlib.patches.FancyArrow at 0x115bb1470>
In [8]: plt.ylim(-5,5)
Out[8]: (-5, 5)
In [9]: plt.xlim(-5,5)
Out[9]: (-5, 5)
In [10]: plt.show()
Result:
Thanks to everyone, each of your posts helped me a lot.
rbierman code was pretty straight for my question, I have modified a bit and created a function to plot vectors from given arrays. I'd love to see any suggestions to improve it further.
import numpy as np
import matplotlib.pyplot as plt
def plotv(M):
rows,cols = M.T.shape
print(rows,cols)
#Get absolute maxes for axis ranges to center origin
#This is optional
maxes = 1.1*np.amax(abs(M), axis = 0)
colors = ['b','r','k']
fig = plt.figure()
fig.suptitle('Vectors', fontsize=10, fontweight='bold')
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
ax.set_title('Vector operations')
ax.set_xlabel('x')
ax.set_ylabel('y')
for i,l in enumerate(range(0,cols)):
# print(i)
plt.axes().arrow(0,0,M[i,0],M[i,1],head_width=0.2,head_length=0.1,zorder=3)
ax.text(M[i,0],M[i,1], str(M[i]), style='italic',
bbox={'facecolor':'red', 'alpha':0.5, 'pad':0.5})
plt.plot(0,0,'ok') #<-- plot a black point at the origin
# plt.axis('equal') #<-- set the axes to the same scale
plt.xlim([-maxes[0],maxes[0]]) #<-- set the x axis limits
plt.ylim([-maxes[1],maxes[1]]) #<-- set the y axis limits
plt.grid(b=True, which='major') #<-- plot grid lines
plt.show()
r = np.random.randint(4,size=[2,2])
print(r[0,:])
print(r[1,:])
r12 = np.add(r[0,:],r[1,:])
print(r12)
plotv(np.vstack((r,r12)))
Vector addition performed on random vectors
All nice solutions, borrowing and improvising for special case -> If you want to add a label near the arrowhead:
arr = [2,3]
txt = “Vector X”
ax.annotate(txt, arr)
ax.arrow(0, 0, *arr, head_width=0.05, head_length=0.1)
In order to match the vector lenght and angle with the x,y coordinates of the plot, you can use to following options to plt.quiver:
plt.figure(figsize=(5,2), dpi=100)
plt.quiver(0,0,250,100, angles='xy', scale_units='xy', scale=1)
plt.xlim(0,250)
plt.ylim(0,100)
Quiver is a good method once you figure out its annoying nuances, like not plotting vectors in their original scales. To do as far as I can tell you must pass these params to quiver call as many have pointed out: angles='xy', scale_units='xy', scale=1 AND you should set your plt.xlim and plt.ylim such that you get a square or near square grid. That is the only way I have gotten it to consistently plot the way I want. For instance passing a origin as *[0,0] and U, V as *[5,3] means the resulting plot should be a vector centered at 0,0 origin that goes over 5 units to the right on the x-axis and 3 units up on the y-axis.
I am trying to fix how python plots my data.
Say:
x = [0,5,9,10,15]
y = [0,1,2,3,4]
matplotlib.pyplot.plot(x,y)
matplotlib.pyplot.show()
The x axis' ticks are plotted in intervals of 5. Is there a way to make it show intervals of 1?
You could explicitly set where you want to tick marks with plt.xticks:
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
For example,
import numpy as np
import matplotlib.pyplot as plt
x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
plt.show()
(np.arange was used rather than Python's range function just in case min(x) and max(x) are floats instead of ints.)
The plt.plot (or ax.plot) function will automatically set default x and y limits. If you wish to keep those limits, and just change the stepsize of the tick marks, then you could use ax.get_xlim() to discover what limits Matplotlib has already set.
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, stepsize))
The default tick formatter should do a decent job rounding the tick values to a sensible number of significant digits. However, if you wish to have more control over the format, you can define your own formatter. For example,
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
Here's a runnable example:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.show()
Another approach is to set the axis locator:
import matplotlib.ticker as plticker
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
There are several different types of locator depending upon your needs.
Here is a full example:
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.show()
I like this solution (from the Matplotlib Plotting Cookbook):
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
x = [0,5,9,10,15]
y = [0,1,2,3,4]
tick_spacing = 1
fig, ax = plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()
This solution give you explicit control of the tick spacing via the number given to ticker.MultipleLocater(), allows automatic limit determination, and is easy to read later.
In case anyone is interested in a general one-liner, simply get the current ticks and use it to set the new ticks by sampling every other tick.
ax.set_xticks(ax.get_xticks()[::2])
if you just want to set the spacing a simple one liner with minimal boilerplate:
plt.gca().xaxis.set_major_locator(plt.MultipleLocator(1))
also works easily for minor ticks:
plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(1))
a bit of a mouthfull, but pretty compact
This is a bit hacky, but by far the cleanest/easiest to understand example that I've found to do this. It's from an answer on SO here:
Cleanest way to hide every nth tick label in matplotlib colorbar?
for label in ax.get_xticklabels()[::2]:
label.set_visible(False)
Then you can loop over the labels setting them to visible or not depending on the density you want.
edit: note that sometimes matplotlib sets labels == '', so it might look like a label is not present, when in fact it is and just isn't displaying anything. To make sure you're looping through actual visible labels, you could try:
visible_labels = [lab for lab in ax.get_xticklabels() if lab.get_visible() is True and lab.get_text() != '']
plt.setp(visible_labels[::2], visible=False)
This is an old topic, but I stumble over this every now and then and made this function. It's very convenient:
import matplotlib.pyplot as pp
import numpy as np
def resadjust(ax, xres=None, yres=None):
"""
Send in an axis and I fix the resolution as desired.
"""
if xres:
start, stop = ax.get_xlim()
ticks = np.arange(start, stop + xres, xres)
ax.set_xticks(ticks)
if yres:
start, stop = ax.get_ylim()
ticks = np.arange(start, stop + yres, yres)
ax.set_yticks(ticks)
One caveat of controlling the ticks like this is that one does no longer enjoy the interactive automagic updating of max scale after an added line. Then do
gca().set_ylim(top=new_top) # for example
and run the resadjust function again.
I developed an inelegant solution. Consider that we have the X axis and also a list of labels for each point in X.
Example:
import matplotlib.pyplot as plt
x = [0,1,2,3,4,5]
y = [10,20,15,18,7,19]
xlabels = ['jan','feb','mar','apr','may','jun']
Let's say that I want to show ticks labels only for 'feb' and 'jun'
xlabelsnew = []
for i in xlabels:
if i not in ['feb','jun']:
i = ' '
xlabelsnew.append(i)
else:
xlabelsnew.append(i)
Good, now we have a fake list of labels. First, we plotted the original version.
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabels,rotation=45)
plt.show()
Now, the modified version.
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabelsnew,rotation=45)
plt.show()
Pure Python Implementation
Below's a pure python implementation of the desired functionality that handles any numeric series (int or float) with positive, negative, or mixed values and allows for the user to specify the desired step size:
import math
def computeTicks (x, step = 5):
"""
Computes domain with given step encompassing series x
# params
x - Required - A list-like object of integers or floats
step - Optional - Tick frequency
"""
xMax, xMin = math.ceil(max(x)), math.floor(min(x))
dMax, dMin = xMax + abs((xMax % step) - step) + (step if (xMax % step != 0) else 0), xMin - abs((xMin % step))
return range(dMin, dMax, step)
Sample Output
# Negative to Positive
series = [-2, 18, 24, 29, 43]
print(list(computeTicks(series)))
[-5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]
# Negative to 0
series = [-30, -14, -10, -9, -3, 0]
print(list(computeTicks(series)))
[-30, -25, -20, -15, -10, -5, 0]
# 0 to Positive
series = [19, 23, 24, 27]
print(list(computeTicks(series)))
[15, 20, 25, 30]
# Floats
series = [1.8, 12.0, 21.2]
print(list(computeTicks(series)))
[0, 5, 10, 15, 20, 25]
# Step – 100
series = [118.3, 293.2, 768.1]
print(list(computeTicks(series, step = 100)))
[100, 200, 300, 400, 500, 600, 700, 800]
Sample Usage
import matplotlib.pyplot as plt
x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(computeTicks(x))
plt.show()
Notice the x-axis has integer values all evenly spaced by 5, whereas the y-axis has a different interval (the matplotlib default behavior, because the ticks weren't specified).
Generalisable one liner, with only Numpy imported:
ax.set_xticks(np.arange(min(x),max(x),1))
Set in the context of the question:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
x = [0,5,9,10,15]
y = [0,1,2,3,4]
ax.plot(x,y)
ax.set_xticks(np.arange(min(x),max(x),1))
plt.show()
How it works:
fig, ax = plt.subplots() gives the ax object which contains the axes.
np.arange(min(x),max(x),1) gives an array of interval 1 from the min of x to the max of x. This is the new x ticks that we want.
ax.set_xticks() changes the ticks on the ax object.
xmarks=[i for i in range(1,length+1,1)]
plt.xticks(xmarks)
This worked for me
if you want ticks between [1,5] (1 and 5 inclusive) then replace
length = 5
Since None of the above solutions worked for my usecase, here I provide a solution using None (pun!) which can be adapted to a wide variety of scenarios.
Here is a sample piece of code that produces cluttered ticks on both X and Y axes.
# Note the super cluttered ticks on both X and Y axis.
# inputs
x = np.arange(1, 101)
y = x * np.log(x)
fig = plt.figure() # create figure
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_xticks(x) # set xtick values
ax.set_yticks(y) # set ytick values
plt.show()
Now, we clean up the clutter with a new plot that shows only a sparse set of values on both x and y axes as ticks.
# inputs
x = np.arange(1, 101)
y = x * np.log(x)
fig = plt.figure() # create figure
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_xticks(x)
ax.set_yticks(y)
# which values need to be shown?
# here, we show every third value from `x` and `y`
show_every = 3
sparse_xticks = [None] * x.shape[0]
sparse_xticks[::show_every] = x[::show_every]
sparse_yticks = [None] * y.shape[0]
sparse_yticks[::show_every] = y[::show_every]
ax.set_xticklabels(sparse_xticks, fontsize=6) # set sparse xtick values
ax.set_yticklabels(sparse_yticks, fontsize=6) # set sparse ytick values
plt.show()
Depending on the usecase, one can adapt the above code simply by changing show_every and using that for sampling tick values for X or Y or both the axes.
If this stepsize based solution doesn't fit, then one can also populate the values of sparse_xticks or sparse_yticks at irregular intervals, if that is what is desired.
You can loop through labels and show or hide those you want:
for i, label in enumerate(ax.get_xticklabels()):
if i % interval != 0:
label.set_visible(False)