I have a plot that looks as follows:
I want to put labels for both the lineplot and the markers in red. However the legend is not appearning because its the plot is taking out its space.
Update
it turns out I cannot put several strings in plt.legend()
I made the figure bigger by using the following:
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
However now I have only one label in the legend, with the marker appearing on the lineplot while I rather want two: one for the marker alone and another for the line alone:
Updated code:
plt.plot(range(len(y)), y, '-bD', c='blue', markerfacecolor='red', markeredgecolor='k', markevery=rare_cases, label='%s' % target_var_name)
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
# changed this over here
plt.legend()
plt.savefig(output_folder + fig_name)
plt.close()
What you want to do (have two labels for a single object) is not completely impossible but it's MUCH easier to plot separately the line and the rare values, e.g.
# boilerplate
import numpy as np
import matplotlib.pyplot as plt
# synthesize some data
N = 501
t = np.linspace(0, 10, N)
s = np.sin(np.pi*t)
rare = np.zeros(N, dtype=bool); rare[:20]=True; np.random.shuffle(rare)
plt.plot(t, s, label='Curve')
plt.scatter(t[rare], s[rare], label='rare')
plt.legend()
plt.show()
Update
[...] it turns out I cannot put several strings in plt.legend()
Well, you can, as long as ① the several strings are in an iterable (a tuple or a list) and ② the number of strings (i.e., labels) equals the number of artists (i.e., thingies) in the plot.
plt.legend(('a', 'b', 'c'))
Related
So, I have to make a bunch of contourf plots for different days that need to share colorbar ranges. That was easily made but sometimes it happens that the maximum value for a given date is above the colorbar range and that changes the look of the plot in a way I dont need. The way I want it to treat it when that happens is to add the extend triangle above the "original colorbar". It's clear in the attached picture.
I need the code to run things automatically, right now I only feed the data and the color bar range and it outputs the images, so the fitting of the colorbar in the code needs to be automatic, I can't add padding in numbers because the figure sizes changes depending on the area that is being asked to be plotted.
The reason why I need this behavior is because eventually I would want to make a .gif and I can't have the colorbar to move in that short video. I need for the triangle to be added, when needed, to the top (and below) without messing with the "main" colorbar.
Thanks!
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize, BoundaryNorm
from matplotlib import cm
###############
## Finds the appropriate option for variable "extend" in fig colorbar
def find_extend(vmin, vmax, datamin, datamax):
#extend{'neither', 'both', 'min', 'max'}
if datamin >= vmin:
if datamax <= vmax:
extend="neither"
else:
extend="max"
else:
if datamax <= vmax:
extend="min"
else:
extend="both"
return extend
###########
vmin=0
vmax=30
nlevels=8
colormap=cm.get_cmap("rainbow")
### Creating data
z_1=30*abs(np.random.rand(5, 5))
z_2=37*abs(np.random.rand(5, 5))
data={1:z_1, 2:z_2}
x=range(5)
y=range(5)
## Plot
for day in [1, 2]:
fig = plt.figure(figsize=(4,4))
## Normally figsize=get_figsize(bounds) and bounds is retrieved from gdf.total_bounds
## The function creates the figure size based on the x/y ratio of the bounds
ax = fig.add_subplot(1, 1, 1)
norm=BoundaryNorm(np.linspace(vmin, vmax, nlevels+1), ncolors=colormap.N)
z=data[day]
cs=ax.contourf(x, y, z, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax)
extend=find_extend(vmin, vmax, np.nanmin(z), np.nanmax(z))
fig.colorbar(cm.ScalarMappable(norm=norm, cmap=cmap), ax=ax, extend=extend)
plt.close(fig)
You can do something like this: putting a triangle on top of the colorbar manually:
fig, ax = plt.subplots()
pc = ax.pcolormesh(np.random.randn(20, 20))
cb = fig.colorbar(pc)
trixy = np.array([[0, 1], [1, 1], [0.5, 1.05]])
p = mpatches.Polygon(trixy, transform=cb.ax.transAxes,
clip_on=False, edgecolor='k', linewidth=0.7,
facecolor='m', zorder=4, snap=True)
cb.ax.add_patch(p)
plt.show()
I would like to show datetimes as ticklabels on the x-axis of a plot via ax.imshow(). I first tried putting the limits (as datetime objects) into extent, but it appears that extent only accepts arguments of type <float/int>. So instead, I would like to create the original plot via ax.imshow(...), then make the x-axis invisible, then add in the correct xticks and xlim.
I found a similar problem solved using a different approach in this example, but I think my use-case is slightly different; I don't need to convert any time-stamps, but I do know the xlim of the data (in terms of datetime objects). Also, I do not think the suggested use of matplotlib.dates.date2num fits my use-case since some of the data is spaced less than one day apart, but date2num uses days as a base-unit.
I am stuck trying to make this work using my alternate approach; a simple mini-example is below.
import numpy as np
import datetime
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
def f(x, y):
return np.sqrt(np.square(x) + np.square(y))
## SAMPLE DATA
x = np.arange(10) ## elapsed minutes
y = np.square(x) ## arbitrary y-values
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
## DATETIMES FOR ALTERNATE AXIS
lower_dt = datetime.datetime(1999, 1, 1, 0, 0, 0)
# upper_dt = datetime.datetime(2001, 10, 31, 0, 0, 0)
upper_dt = datetime.datetime(1999, 1, 1, x.size-1, 0, 0)
## DO PLOT
fig, ax = plt.subplots()
ax.xaxis.set_visible(False)
# ax.xaxis.tick_top()
ax.imshow(
Z,
origin='lower',
cmap='Oranges',
norm=Normalize(vmin=np.nanmin(Z), vmax=np.nanmax(X)),
extent=(x[0], x[-1], y[0], y[-1]))
## CONVERT XTICKLABELS OF X-AXIS TO DATETIME
mirror_ax = ax.twiny()
# mirror_ax = ax.figure.add_subplot(ax.get_subplotspec(), frameon=False)
mirror_ax.set_xlim([lower_dt, upper_dt])
plt.show()
plt.close(fig)
The obtained plot can be seen here:
I notice that the xticks are shown at the top instead of the bottom of the plot - this is unwanted behavior; using ax.tick_top (commented out above) does not change this. Even worse, the x-axis limits are not retained. I realize I could manually change the xticklabels via ax.get_xticks() and ax.set_xticklabels(...), but I would prefer to leave that for date-formatters and date-locators via matplotlib.
How can I use the approach outlined above to create a "mirror/alternate" x-axis of datetime units such that this x-axis is the same size/orientation of the "original/invisible" x-axis of float/integer units?
I have a list and a python array like these 2 examples:
example:
Neg = [37.972200755611425, 32.14963079785344]
Pos = array([[15.24373185, 13.66099865, 11.86959384, 9.72792045, 7.12928302, 6.04439412],[14.5235007 , 13. , 11.1792871 , 9.14974712, 6.4429435 , 5.04439412]
both Neg and Pos have 2 elements (in this example) therefore I would like to make 2 separate plots (pdf file) for every element.
in every plot there would be 2 lines:
1- comes from Pos and is a line plot basically which is made of all the elements in the sub-list.
2- comes from Neg and is a horizontal line on the y-axis.
I am trying to do that in a for loop for all elements at the same time. to do so, I made the following code in python but it does not return what I would like to get. do you know how to fix it ?
for i in range(len(Neg)):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(concentration, Pos[i], label='gg')
plt.axhline(y=Neg[i], color='b', linestyle='-')
ax.legend()
ax.set_xlabel("log2 concentration")
ax.set_ylabel("log2 raw counts")
ax.set_ylim(0, 40)
plt.savefig(f'{i}.pdf')
Not quite sure exactly what you want but this code creates two subplots of the data in the way I think you're describing it:
import numpy as np
from matplotlib import pyplot as plt
Neg = [37.972200755611425, 32.14963079785344]
Pos = np.array([[15.24373185, 13.66099865, 11.86959384, 9.72792045, 7.12928302, 6.04439412],[14.5235007 , 13. , 11.1792871 , 9.14974712, 6.4429435 , 5.04439412]])
fig = plt.figure()
for i in range(len(Neg)):
ax = fig.add_subplot(2,1,i+1)
ax.plot(Pos[i], label='gg')
plt.axhline(y=Neg[i], color='b', linestyle='-')
ax.legend()
ax.set_xlabel("log2 concentration")
ax.set_ylabel("log2 raw counts")
ax.set_ylim(0, 40)
plt.subplots_adjust(hspace=1.0)
extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
fig.savefig(f'{i}.pdf', bbox_inches=extent.expanded(1.2, 1.9))
Edited the code to save each subplot individually to file by grabbing a specific part of the plot for saving, as used in this question: Save a subplot in matplotlib.
Also included some additional spacing between each subplot by calling subplots_adjust(), so that each subplot can be saved to individual files without any detail from the other subplots being included. This might not be the best way of doing what you want, but I think it will do what you want now.
Alternatively, if you're not set on using subplots, you could always just use a plot per element:
fig = plt.figure()
for i in range(len(Neg)):
plt.plot(Pos[i], label='gg')
plt.axhline(y=Neg[i], color='b', linestyle='-')
plt.legend()
plt.xlabel("log2 concentration")
plt.ylabel("log2 raw counts")
plt.ylim(0, 40)
fig = plt.gcf()
fig.savefig(f'{i}.pdf')
plt.show()
I am using python-3.x and I would like to plot several boxplots in one figure, all the data from one numpy array where the shape of this array is (100, 301)
If I use the code below it will plot them all (I will have 301 boxplots in one figure which is too much)
fig, ax = plt.subplots()
ax.boxplot(my_data)
plt.show()
I don't want to plot all the data, I just want to plot 10, 15 or 20 (variable number) of the data by using for loop or any method that work best.
for example, I want to plot boxplots every 50 number of data that mean I will have around 6 boxplots from 301 in my figure, I tried to use for loop but no luck
Any advice would be much appreciated
You can just use indexing to plot every 50th data points using a variable step. To have separate box plots and avoid overlapping, you can specify the positions of individual box plot using the positions parameter. my_data[:, ::step] gives you the desired data to plot. Below is an example using some random data.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
my_data = np.random.randint(0, 20, (100, 301))
step = 50
posit = range(my_data[:, ::step].shape[1])
ax.boxplot(my_data[:, ::step], positions=posit)
plt.show()
I have a code that runs a rolling window (30) average over a range (i.e. 300)
So I have 10 averages but they plot against ticks 1-10 rather than spaced over every window of 30.
The only way I can get it to look right is to plot it over (len(windowlength)) but the x-axis isnt right.
Is there any way to manually space the results?
windows30 = (sliding_window(sequence, 30))
Overall_Mean = mean(sequence)
fig, (ax) = plt.subplots()
plt.subplots_adjust(left=0.07, bottom=0.08, right=0.96, top=0.92, wspace=0.20, hspace=0.23)
ax.set_ylabel('mean (%)')
ax.set_xlabel(' Length') # axis titles
ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)
ax.plot(windows30, color='r', marker='o', markersize=3)
ax.plot([0, len(sequence)], [Overall_Mean, Overall_Mean], lw=0.75)
plt.show()
From what I have understood you have a list of length 300 but only holds 10 values inside. If that is the case, you can remove the other values that are None from your windows30 list using the following solution.
Code Demonstration:
import numpy as np
import random
import matplotlib.pyplot as plt
# Generating the list of Nones and numbers
listofzeroes = [None] * 290
numbers = random.sample(range(50), 10)
numbers.extend(listofzeroes)
# Removing Nones from the list
numbers = [value for value in numbers if value is not None]
step = len(numbers)
x_values = np.linspace(0,300,step) # Generate x-values
plt.plot(x_values,numbers, color='red', marker='o')
This is a working example, the relevant code for you is after the second comment.
Output:
The above code will work independently of where the Nones are located in your list. I hope this solves your problem.