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I have a matplotlib chart working nicely as a python script. I need to create this chart style in flask. Can't use this method within flask as flask thread management doesn't play with matplotlib.
Oddly, the current method will run once successfully, subsequent runs will produce this error.
RuntimeError: main thread is not in main loop
So this is my desired chart format to produce in flask.
the code I'm using currently.
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.29, top=.91)
ax.set_title(title)
ax.set_ylabel("y label text")
ax.set_xlabel('x label text')
ax.tick_params(axis='x', labelrotation = -80)
l = ax.plot(df_output['column1'])
y_error = df_output['column2']
plt.errorbar(list(df_output.index), \
list(df_output['column1']), \
yerr = y_error,fmt='o',ecolor = 'blue',color='blue')
fig.legend(l, loc=8, labels=labels)
#loc=2 = top left corner, loc=8 = 'lower center'
#plt.show()
plt.savefig(output_path+"/"+title+'_errorbars.png')
I found this example that works with flask
https://gist.github.com/illume/1f19a2cf9f26425b1761b63d9506331f
it uses this matplotlib charting syntax. Need to convert my old matplotlib format to suit the flask compatible format. Is this chart format possible via FigureCanvasAgg?
fig = Figure()
axis = fig.add_subplot(1, 1, 1)
print("type(axis):", type(axis))
x_points = data.iloc[:, 0]
y_points = data['mean minus sterility control mean']
axis.plot(x_points, y_points)
output = io.BytesIO()
FigureCanvasAgg(fig).print_png(output)
return Response(output.getvalue(), mimetype="image/png")
I'll admit to not being strong in building matpotlib charts. changing between chart building methods throws me.
I'm digging around the docs at moment.
https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html
I did find this Q&A (RuntimeError: main thread is not in main loop with Matplotlib and Flask)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
while this appears to run for me. I want to move away from creating charts as files on the server, too much potential for file mismanagement, creating the chart as a io.BytesIO() output (or some format within the flask http response to user) is a much better solution.
(I'd like to keep at an image output, rather than change architecture to (say) a json output and constructing chart in client using javascript libraries)
I can't figure out how to rotate the text on the X Axis. Its a time stamp, so as the number of samples increase, they get closer and closer until they overlap. I'd like to rotate the text 90 degrees so as the samples get closer together, they aren't overlapping.
Below is what I have, it works fine with the exception that I can't figure out how to rotate the X axis text.
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import datetime
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 8}
matplotlib.rc('font', **font)
values = open('stats.csv', 'r').readlines()
time = [datetime.datetime.fromtimestamp(float(i.split(',')[0].strip())) for i in values[1:]]
delay = [float(i.split(',')[1].strip()) for i in values[1:]]
plt.plot(time, delay)
plt.grid(b='on')
plt.savefig('test.png')
This works for me:
plt.xticks(rotation=90)
Many "correct" answers here but I'll add one more since I think some details are left out of several. The OP asked for 90 degree rotation but I'll change to 45 degrees because when you use an angle that isn't zero or 90, you should change the horizontal alignment as well; otherwise your labels will be off-center and a bit misleading (and I'm guessing many people who come here want to rotate axes to something other than 90).
Easiest / Least Code
Option 1
plt.xticks(rotation=45, ha='right')
As mentioned previously, that may not be desirable if you'd rather take the Object Oriented approach.
Option 2
Another fast way (it's intended for date objects but seems to work on any label; doubt this is recommended though):
fig.autofmt_xdate(rotation=45)
fig you would usually get from:
fig = plt.gcf()
fig = plt.figure()
fig, ax = plt.subplots()
fig = ax.figure
Object-Oriented / Dealing directly with ax
Option 3a
If you have the list of labels:
labels = ['One', 'Two', 'Three']
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(labels, rotation=45, ha='right')
In later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks([1, 2, 3], labels, rotation=45, ha='right')
Option 3b
If you want to get the list of labels from the current plot:
# Unfortunately you need to draw your figure first to assign the labels,
# otherwise get_xticklabels() will return empty strings.
plt.draw()
ax.set_xticks(ax.get_xticks())
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
As above, in later versions of Matplotlib (3.5+), you can just use set_xticks alone:
ax.set_xticks(ax.get_xticks(), ax.get_xticklabels(), rotation=45, ha='right')
Option 4
Similar to above, but loop through manually instead.
for label in ax.get_xticklabels():
label.set_rotation(45)
label.set_ha('right')
Option 5
We still use pyplot (as plt) here but it's object-oriented because we're changing the property of a specific ax object.
plt.setp(ax.get_xticklabels(), rotation=45, ha='right')
Option 6
This option is simple, but AFAIK you can't set label horizontal align this way so another option might be better if your angle is not 90.
ax.tick_params(axis='x', labelrotation=45)
Edit:
There's discussion of this exact "bug" but a fix hasn't been released (as of 3.4.0):
https://github.com/matplotlib/matplotlib/issues/13774
Easy way
As described here, there is an existing method in the matplotlib.pyplot figure class that automatically rotates dates appropriately for you figure.
You can call it after you plot your data (i.e.ax.plot(dates,ydata) :
fig.autofmt_xdate()
If you need to format the labels further, checkout the above link.
Non-datetime objects
As per languitar's comment, the method I suggested for non-datetime xticks would not update correctly when zooming, etc. If it's not a datetime object used as your x-axis data, you should follow Tommy's answer:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
Try pyplot.setp. I think you could do something like this:
x = range(len(time))
plt.xticks(x, time)
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.plot(x, delay)
Appart from
plt.xticks(rotation=90)
this is also possible:
plt.xticks(rotation='vertical')
I came up with a similar example. Again, the rotation keyword is.. well, it's key.
from pylab import *
fig = figure()
ax = fig.add_subplot(111)
ax.bar( [0,1,2], [1,3,5] )
ax.set_xticks( [ 0.5, 1.5, 2.5 ] )
ax.set_xticklabels( ['tom','dick','harry'], rotation=45 ) ;
If you want to apply rotation on the axes object, the easiest way is using tick_params. For example.
ax.tick_params(axis='x', labelrotation=90)
Matplotlib documentation reference here.
This is useful when you have an array of axes as returned by plt.subplots, and it is more convenient than using set_xticks because in that case you need to also set the tick labels, and also more convenient that those that iterate over the ticks (for obvious reasons)
If using plt:
plt.xticks(rotation=90)
In case of using pandas or seaborn to plot, assuming ax as axes for the plot:
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
Another way of doing the above:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
My answer is inspired by cjohnson318's answer, but I didn't want to supply a hardcoded list of labels; I wanted to rotate the existing labels:
for tick in ax.get_xticklabels():
tick.set_rotation(45)
The simplest solution is to use:
plt.xticks(rotation=XX)
but also
# Tweak spacing to prevent clipping of tick-labels
plt.subplots_adjust(bottom=X.XX)
e.g for dates I used rotation=45 and bottom=0.20 but you can do some test for your data
import pylab as pl
pl.xticks(rotation = 90)
To rotate the x-axis label to 90 degrees
for tick in ax.get_xticklabels():
tick.set_rotation(45)
It will depend on what are you plotting.
import matplotlib.pyplot as plt
x=['long_text_for_a_label_a',
'long_text_for_a_label_b',
'long_text_for_a_label_c']
y=[1,2,3]
myplot = plt.plot(x,y)
for item in myplot.axes.get_xticklabels():
item.set_rotation(90)
For pandas and seaborn that give you an Axes object:
df = pd.DataFrame(x,y)
#pandas
myplot = df.plot.bar()
#seaborn
myplotsns =sns.barplot(y='0', x=df.index, data=df)
# you can get xticklabels without .axes cause the object are already a
# isntance of it
for item in myplot.get_xticklabels():
item.set_rotation(90)
If you need to rotate labels you may need change the font size too, you can use font_scale=1.0 to do that.
I would like to set different levels of transparency (= alpha) for the edge line and fill of a distribution plot that I created in matplotlib/seaborn. For example:
ax1 = sns.distplot(BSRDI_DF, label="BsrDI", bins=newBins, kde=False,
hist_kws={"edgecolor": (1,0,0,1), "color":(1,0,0,0.25)})
The above approach does not work, unfortunately. Does anybody have any idea how I could accomplish this?
The problem seems to be that seaborn sets an alpha parameter for the histogram. While alpha defaults to None for a usual histogram, such that something like
plt.hist(x, lw=3, edgecolor=(1,0,0,0.75), color=(1,0,0,0.25))
works as expected, seaborn sets this alpha to some given value. This overwrites the alpha that is set in the RGBA tuples.
The solution is to set alpha explicitely to None:
ax = sns.distplot(x, kde=False, hist_kws={"lw":3, "edgecolor": (1,0,0,0.75),
"color":(1,0,0,0.25),"alpha":None})
A complete example:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(60)
ax = sns.distplot(x, label="BsrDI", bins=np.linspace(-3,3,10), kde=False,
hist_kws={"lw":3, "edgecolor": (1,0,0,0.75),
"color":(1,0,0,0.25),"alpha":None})
plt.show()
EDIT Nevermind, I thought using color instead of facecolor was causing the problem but it seems the output that I got only looked right because the patches were overlapping, giving seemingly darker edges.
After investigating the issue further, it looks like seaborn is hard-setting the alpha level at 0.4, which supersedes the arguments passed to hist_kws=
sns.distplot(x, kde=False, hist_kws={"edgecolor": (1,0,0,1), "lw":5, "facecolor":(0,1,0,0.1), "rwidth":0.8})
While using the same parameters to plt.hist() gives:
plt.hist(x, edgecolor=(1,0,0,1), lw=5, facecolor=(0,1,0,0.1), rwidth=0.8)
Conclusion: if you want different alpha levels for edges and face colors, you'll have to use matplotlib directly, and not seaborn.
I have a vexing issue with a colorbar and even after vigorous research I cannot find the question even being asked. I have a plot where I overlay a contour and a pcolormesh and I would like a colorbar to indicate values. That works fine except for one thing:
The colorbar frame and color are offset
The colorbar frame and the actual bar are offset such that below you have a white bit in the frame and on top the color is poking out. While the frame is aligned with the axis as desired, the colorbar is offset.
Here is a working example that emulates the situation I was in, i.e. multiple plots with insets.
import matplotlib.gridspec as gridspec
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
figheight = 4.2 - (2.1 - 49.519 / 25.4)
matplotlib.rcParams['figure.figsize'] = (5.25, figheight)
matplotlib.rcParams['axes.linewidth'] = 0.5
fig = plt.figure()
grid = gridspec.GridSpec(2, 1, height_ratios=[49.519 / 25.4 / figheight, 2.1 / figheight])
ax0 = plt.subplot(grid[0, 0])
ax1 = plt.subplot(grid[1, 0])
plt.tight_layout()
###############################################################################################
#
# Define position of inset
#
###############################################################################################
ax1.axis('off')
pos1 = ax1.get_position()
pos2 = matplotlib.transforms.Bbox([[pos1.x0, pos1.y0],
[.8*pos1.x1,
0.8*pos1.height + pos1.y0]])
left, bottom, width, height = [pos2.x0, pos2.y0, pos2.width, pos2.height]
ax2 = fig.add_axes([left, bottom, width, height])
###############################################################################################
#
# ax2 (inset) plot
#
###############################################################################################
pos2 = ax2.get_position()
ax2.axis('on')
x = np.linspace(0,5)
z = (np.outer(np.sin(x), np.cos(x))+1)*0.5
im = ax2.pcolormesh(z)
c = ax2.contour(z, linewidths=7)
ax2pos = ax2.get_position()
cbar_axis = fig.add_axes([ax2pos.x1+0.05,ax2pos.y0, .02, ax2pos.height])
colorbar = fig.colorbar(im, ax = ax2,
cax = cbar_axis, ticks = [0.1, .5, .9])
colorbar.outline.set_visible(True)
plot = 'Minimal.pdf'
fig.savefig(plot)
plt.close()
The problem persists in both the inline display and the saved .pdf if 'Inline' graphics backend is chosen. Using tight layout or not changes how badly the offset is depending on the size of the bar - same with using PyQT5 rather than inline graphics backend. I thought it was gone when I was changing between the various combinations, but I just realized it's still there.
I would appreciate any input.
As suggested by ImportanceOfBeingErnest I have tried using np.round on the figsize and that didn't change things. While you can fiddle around with sizes to make it look okay, it always stands over on one or the other side by some amount. When I change the graphics backend on Spyder 3 from 'Inline' to 'QT5' the problem becomes less severe with or without rounding. A summary of this is in this picture Colorbar overlap cases. Note that with not rounded and PyQT5 the problem still occurs, but is not as severe.
On inspection, it is clear that the colorbar is not only bleeding out over the top of its axes, but it's also positioned slightly to the left.
So, the problem here appears to be a conflict between the position of the colorbar axis and the colorbar itself when rasterization occurs. You can find more details on this issue in matplotlib's github repository, but I'll summarize what's going on here.
Colorbars are rasterized when the output is produced, so as to avoid artifacting issues during rendering. The position of the colorbar is snapped to the nearest integer pixels during the rasterization process, while the axis is kept where it is supposed to be. Then, when the output is produced, the colorbar falls within borders of fixed pixels of the image, despite the fact that the image is, itself, vectorized. Thus, there are two strategies that can be employed to avoid this mishap.
Use a finer DPI
The conversion from vectorized coordinates to rasterized coordinates takes place assuming a given DPI on the image. By default, this is set to be 72. However, by using more DPI, the overall shift induced by the rasterization process will be smaller, as the closest pixel the colorbar will snap to will be much nearer. Here, we change the output to have fig.savefig(plot,dpi=4000), and the problem goes away:
Note, however, that on my machine, the output size changed from 62 KB to 78 KB due to this change (although the DPI adjustment was also, admittedly, extreme). If you are worried about file sizes, you should pick a lower DPI that fixes the problem.
Use a different colormap
This rasterization happens when more than 50 colors are in the colorbar. Thus, we can do a quick test, setting our colormap to Pastel1 via
im = ax2.pcolormesh(z,cmap='Pastel1'). Here, the colorbar / axis mismatch is mitigated.
As a fallback, adopting a colorbar with fewer than 50 colors should mitigate this problem.
Rasterize the Axis
For completeness, there is also a third option. If you rasterize the colorbar axis, both the axis boundaries and the colormap will be rasterized, and you'll lose the offset. This will also rasterize your labels, and the axis will shift as one, breaking alignment with the nearby axis. For this, you just need to include cbar_axis.set_rasterized(True).
First, a way to overlay a contour and a pcolormesh and create a colorbar would be the following
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
x = np.linspace(0,5)
z = (np.outer(np.sin(x), np.cos(x))+1)*0.5
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
im = ax.pcolormesh(z)
c = ax.contour(z, linewidths=7)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "5%", pad="3%")
colorbar = fig.colorbar(im, cax=cax, ticks = [0.1, .5, .9])
plt.show()
Now to the problem from the question. It is of course possible to create the axes to put the colorbar in manually. Replacing the colorbar creation with the code from the question still produces a nice image.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,5)
z = (np.outer(np.sin(x), np.cos(x))+1)*0.5
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
plt.subplots_adjust(right=0.8)
im = ax.pcolormesh(z)
c = ax.contour(z, linewidths=7)
ax2pos = ax.get_position()
cbar_axis = fig.add_axes([ax2pos.x1+0.05,ax2pos.y0, .05, ax2pos.height])
colorbar = fig.colorbar(im, ax = ax,
cax = cbar_axis, ticks = [0.1, .5, .9])
colorbar.outline.set_visible(True)
plt.show()
Conclusion so far: The issue is not reproducible, at least not without a Minimal, Complete, and Verifiable example.
I'm uncertain about the reasons for the behaviour in the example from the question. However, it seems that it can be overcome by rounding the figure size to 3 significant digits
matplotlib.rcParams['figure.figsize'] = (5.25, np.round(figheight,3))
I am trying to use different math font sets for two axes in the same figure, with no success. I have searched this issue using google and I have read the matplotlib's official guide on how to use the math font. But I can not find ways to achieve this effect. My complete code is as follows:
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, (ax1, ax2) = plt.subplots(ncols=2)
mpl.rcParams['mathtext.fontset'] = 'cm' # use font "cm" for first axes
ax1.text(0.3, 0.5, r"$xyz$", fontsize=50)
ax1.set_title('before')
ax1.axis('off')
ax1.set_aspect('equal')
mpl.rcParams['mathtext.fontset'] = 'stixsans' # use font "stixsans" for second axes
ax2.text(0.3, 0.5, r"$xyz$", fontsize=50)
ax2.set_title('after')
ax2.axis('off'）
ax2.set_aspect('equal')
plt.show()
The resulting figure shows that both the axes use the "stixsans" font, see picture here.
It seems that mpl.rcParams['mathtext.fontset'] = 'stixsans' in the later part has overruled the previous setting mpl.rcParams['mathtext.fontset'] = 'cm'. Any idea how to prevent this from happening and use "cm" and "stixsans" font for the two axes respectively?