I have the following function:
def get_rgba_bitmap(fig):
fig.canvas.draw()
tab = fig.canvas.copy_from_bbox(fig.bbox).to_string_argb()
ncols, nrows = fig.canvas.get_width_height()
return numpy.fromstring(tab, dtype = numpy.uint8).reshape(nrows, ncols, 4)
With this function I am able to create a "map" of an image. See link for more details on why I need the "map" and the answer resulting in the function. Plotting the "map" gives:
import numpy
import matplotlib.pyplot as plt
random_gen = numpy.random.mtrand.RandomState(seed = 127260)
x_test = random_gen.uniform(-1., 1., size= (10,10))
fig1 = plt.figure()
ax1 = fig1.add_subplot(1, 1, 1)
ax1.imshow(x_test, cmap = "seismic")
bitmap_rgba1 = get_rgba_bitmap(fig1)
fig2 = plt.figure()
ax1 = fig2.add_subplot(1, 1, 1)
ax1.imshow(bitmap_rgba1)
plt.show()
The canvas that is created with get_rgba_bitmap(fig) is of the entire figure including the grey background. When plotting the "map", the grey background is thus also included, shrinking the size of the actual image. In the case of a single image this does not matter to me as there is a way to work around it. However, I now need several subplots and it does become a problem.
Therefore, what I now need is a function that creates a canvas of only the plotting frame. Ticks and labels are not important. I tried modifying the get_rgba_bitmap function but it does not fully work:
def test(fig, ax):
fig.canvas.draw()
tab = fig.canvas.copy_from_bbox(ax.bbox).to_string_argb()
bbox = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
width, height = bbox.width * fig.dpi, bbox.height * fig.dpi
ncols, nrows = width, height
return numpy.fromstring(tab, dtype = numpy.uint8).reshape(nrows + 1, ncols + 1, 4)
I think the problem lies in wrong values for ncols and nrows. Not sure where this comes from.
If I add 1 to both ncols and nrows in reshape, the script runs and produces an image close to what I want. However, the "map" does not fit correctly in the new plotting frame, which from the look of it is off by 1 pixel in x and y. This corresponds to the 1 I needed to add to nrows and ncols. How can I fix this?
Thank you for help
Related
plt.tight_layout() works by changing the axis size, so all elements in your figure fit within that figure frame.
See:
y=np.random.normal(size=100)
fig, ax = plt.subplots(); plt.plot(y)
plt.xlabel('time / s.', fontsize=40)
fig.set_size_inches([5, 2])
plt.tight_layout()
The axis got resized so all elements could fit in the figure. I want my axis size to remain constant. Is there an alternative that resizes the figure while leaving the axis untouched?
The sizes of the axes are specified in terms of figure coordinates. I.e. by default a single subplot is 77.5% of the figure width wide and 77% of the figure height high. So you cannot leave the axes untouched when changing the figure size, because the one depends on the other.
Now it depends on the goal of calling tight_layout and the desired output.
The problem
Running the code from the question results in the following figure,
As can be seen, the oversized xlabel is cut.
Saving the figure
If for example you are interested in saving your figure, you may do so without calling tight_layout() and instead specify the bbox_inches="tight" in the call to savefig.
plt.savefig("some.png", bbox_inches="tight")
This will produce a figure which is larger than the original one without changing any of the positions of the objects inside. It's hence different from tight_layout because it does not change the layout and spacings at all. The drawback here is that it will only affect the saved figure, not the one shown on screen.
Showing the figure
Now you can in principle do the same to your figure which is on screen. This is very hacky and will have consequences on how your axes are stored. The following is a function tight_figure, which treats the figure as if it was saved, but lets it stay within the original canvas on screen.
import numpy as np
import matplotlib.pyplot as plt
import io
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
from matplotlib import tight_bbox
def tight_figure(fig,**kwargs):
canvas = fig.canvas._get_output_canvas("png")
print_method = getattr(canvas, 'print_png')
print_method(io.BytesIO(), dpi=fig.dpi,
facecolor=fig.get_facecolor(), dryrun=True)
renderer = fig._cachedRenderer
bbox_inches = fig.get_tightbbox(renderer)
bbox_artists = fig.get_default_bbox_extra_artists()
bbox_filtered = []
for a in bbox_artists:
bbox = a.get_window_extent(renderer)
if a.get_clip_on():
clip_box = a.get_clip_box()
if clip_box is not None:
bbox = Bbox.intersection(bbox, clip_box)
clip_path = a.get_clip_path()
if clip_path is not None and bbox is not None:
clip_path = \
clip_path.get_fully_transformed_path()
bbox = Bbox.intersection(
bbox, clip_path.get_extents())
if bbox is not None and (
bbox.width != 0 or bbox.height != 0):
bbox_filtered.append(bbox)
if bbox_filtered:
_bbox = Bbox.union(bbox_filtered)
trans = Affine2D().scale(1.0 / fig.dpi)
bbox_extra = TransformedBbox(_bbox, trans)
bbox_inches = Bbox.union([bbox_inches, bbox_extra])
pad = kwargs.pop("pad_inches", None)
if pad is None:
pad = plt.rcParams['savefig.pad_inches']
bbox_inches = bbox_inches.padded(pad)
tight_bbox.adjust_bbox(fig, bbox_inches, canvas.fixed_dpi)
w = bbox_inches.x1 - bbox_inches.x0
h = bbox_inches.y1 - bbox_inches.y0
fig.set_size_inches(w,h)
y=np.random.normal(size=100)
fig, ax = plt.subplots(); plt.plot(y)
plt.xlabel('time / s.', fontsize=40)
fig.set_size_inches([5, 2])
tight_figure(fig)
plt.show()
The output with tight_figure would look like
I am stuck in a rather complicated situation. I am plotting some data as an image with imshow(). Unfortunately my script is long and a little messy, so it is difficult to make a working example, but I am showing the key steps. This is how I get the data for my image from a bigger array, written in a file:
data = np.tril(np.loadtxt('IC-heatmap-20K.mtx'), 1)
#
#Here goes lot's of other stuff, where I define start and end
#
chrdata = data[start:end, start:end]
chrdata = ndimage.rotate(chrdata, 45, order=0, reshape=True,
prefilter=False, cval=0)
ax1 = host_subplot(111)
#I don't really need host_subplot() in this case, I could use something more common;
#It is just divider.append_axes("bottom", ...) is really convenient.
plt.imshow(chrdata, origin='lower', interpolation='none',
extent=[0, length*resolution, 0, length*resolution]) #resolution=20000
So the values I am interested in are all in a triangle with the top angle in the middle of the top side of a square. At the same time I plot some data (lot's of coloured lines in this case) along with the image near it's bottom.
So at first this looks OK, but is actually is not: all pixels in the image are not square, but elongated with their height being bigger, than their width. This is how they look if I zoom in:
This doesn't happen, If I don't set extent when calling imshow(), but I need it so that coordinates in the image and other plots (coloured lines at the bottom in this case), where identical (see Converting coordinates of a picture in matplotlib?).
I tried to fix it using aspect. I tried to do that and it fixed the pixels' shape, but I got a really weird picture:
The thing is, later in the code I explicitly set this:
ax1.set_ylim(0*resolution, length*resolution) #resolution=20000
But after setting aspect I get absolutely different y limits. And the worst thing: ax1 is now wider, than axes of another plot at the bottom, so that their coordinates do not match anymore! I add it in this way:
axPlotx = divider.append_axes("bottom", size=0.1, pad=0, sharex=ax1)
I would really appreciate help with getting it fixed: square pixels, identical coordinates in two (or more, in other cases) plots. As I see it, the axes of the image need to become wider (as aspect does), the ylims should apply and the width of the second axes should be identical to the image's.
Thanks for reading this probably unclear explanation, please, let me know, if I should clarify anything.
UPDATE
As suggested in the comments, I tried to use
ax1.set(adjustable='box-forced')
And it did help with the image itself, but it caused two axes to get separated by white space. Is there any way to keep them close to each other?
Re-edited my entire answer as I found the solution to your problem. I solved it using the set_adjustable("box_forced") option as suggested by the comment of tcaswell.
import numpy
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import host_subplot, make_axes_locatable
#Calculate aspect ratio
def determine_aspect(shape, extent):
dx = (extent[1] - extent[0]) / float(shape[1])
dy = (extent[3] - extent[2]) / float(shape[0])
return dx / dy
data = numpy.random.random((30,60))
shape = data.shape
extent = [-10, 10, -20, 20]
x_size, y_size = 6, 6
fig = plt.figure(figsize = (x_size, y_size))
ax = host_subplot(1, 1, 1)
ax.imshow(data, extent = extent, interpolation = "None", aspect = determine_aspect(shape, extent))
#Determine width and height of the subplot frame
bbox = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
width, height = bbox.width, bbox.height
#Calculate distance, the second plot needs to be elevated by
padding = (y_size - (height - width)) / float(1 / (2. * determine_aspect(shape, extent)))
#Create second image in subplot with shared x-axis
divider = make_axes_locatable(ax)
axPlotx = divider.append_axes("bottom", size = 0.1, pad = -padding, sharex = ax)
#Turn off yticks for axPlotx and xticks for ax
axPlotx.set_yticks([])
plt.setp(ax.get_xticklabels(), visible=False)
#Make the plot obey the frame
ax.set_adjustable("box-forced")
fig.savefig("test.png", dpi=300, bbox_inches = "tight")
plt.show()
This results in the following image where the x-axis is shared:
Hope that helps!
My question is a bit similar to this question that draws line with width given in data coordinates. What makes my question a bit more challenging is that unlike the linked question, the segment that I wish to expand is of a random orientation.
Let's say if the line segment goes from (0, 10) to (10, 10), and I wish to expand it to a width of 6. Then it is simply
x = [0, 10]
y = [10, 10]
ax.fill_between(x, y - 3, y + 3)
However, my line segment is of random orientation. That is, it is not necessarily along x-axis or y-axis. It has a certain slope.
A line segment s is defined as a list of its starting and ending points: [(x1, y1), (x2, y2)].
Now I wish to expand the line segment to a certain width w. The solution is expected to work for a line segment in any orientation. How to do this?
plt.plot(x, y, linewidth=6.0) cannot do the trick, because I want my width to be in the same unit as my data.
The following code is a generic example on how to make a line plot in matplotlib using data coordinates as linewidth. There are two solutions; one using callbacks, one using subclassing Line2D.
Using callbacks.
It is implemted as a class data_linewidth_plot that can be called with a signature pretty close the the normal plt.plot command,
l = data_linewidth_plot(x, y, ax=ax, label='some line', linewidth=1, alpha=0.4)
where ax is the axes to plot to. The ax argument can be omitted, when only one subplot exists in the figure. The linewidth argument is interpreted in (y-)data units.
Further features:
It's independend on the subplot placements, margins or figure size.
If the aspect ratio is unequal, it uses y data coordinates as the linewidth.
It also takes care that the legend handle is correctly set (we may want to have a huge line in the plot, but certainly not in the legend).
It is compatible with changes to the figure size, zoom or pan events, as it takes care of resizing the linewidth on such events.
Here is the complete code.
import matplotlib.pyplot as plt
class data_linewidth_plot():
def __init__(self, x, y, **kwargs):
self.ax = kwargs.pop("ax", plt.gca())
self.fig = self.ax.get_figure()
self.lw_data = kwargs.pop("linewidth", 1)
self.lw = 1
self.fig.canvas.draw()
self.ppd = 72./self.fig.dpi
self.trans = self.ax.transData.transform
self.linehandle, = self.ax.plot([],[],**kwargs)
if "label" in kwargs: kwargs.pop("label")
self.line, = self.ax.plot(x, y, **kwargs)
self.line.set_color(self.linehandle.get_color())
self._resize()
self.cid = self.fig.canvas.mpl_connect('draw_event', self._resize)
def _resize(self, event=None):
lw = ((self.trans((1, self.lw_data))-self.trans((0, 0)))*self.ppd)[1]
if lw != self.lw:
self.line.set_linewidth(lw)
self.lw = lw
self._redraw_later()
def _redraw_later(self):
self.timer = self.fig.canvas.new_timer(interval=10)
self.timer.single_shot = True
self.timer.add_callback(lambda : self.fig.canvas.draw_idle())
self.timer.start()
fig1, ax1 = plt.subplots()
#ax.set_aspect('equal') #<-not necessary
ax1.set_ylim(0,3)
x = [0,1,2,3]
y = [1,1,2,2]
# plot a line, with 'linewidth' in (y-)data coordinates.
l = data_linewidth_plot(x, y, ax=ax1, label='some 1 data unit wide line',
linewidth=1, alpha=0.4)
plt.legend() # <- legend possible
plt.show()
(I updated the code to use a timer to redraw the canvas, due to this issue)
Subclassing Line2D
The above solution has some drawbacks. It requires a timer and callbacks to update itself on changing axis limits or figure size. The following is a solution without such needs. It will use a dynamic property to always calculate the linewidth in points from the desired linewidth in data coordinates on the fly. It is much shorter than the above.
A drawback here is that a legend needs to be created manually via a proxyartist.
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
class LineDataUnits(Line2D):
def __init__(self, *args, **kwargs):
_lw_data = kwargs.pop("linewidth", 1)
super().__init__(*args, **kwargs)
self._lw_data = _lw_data
def _get_lw(self):
if self.axes is not None:
ppd = 72./self.axes.figure.dpi
trans = self.axes.transData.transform
return ((trans((1, self._lw_data))-trans((0, 0)))*ppd)[1]
else:
return 1
def _set_lw(self, lw):
self._lw_data = lw
_linewidth = property(_get_lw, _set_lw)
fig, ax = plt.subplots()
#ax.set_aspect('equal') # <-not necessary, if not given, y data is assumed
ax.set_xlim(0,3)
ax.set_ylim(0,3)
x = [0,1,2,3]
y = [1,1,2,2]
line = LineDataUnits(x, y, linewidth=1, alpha=0.4)
ax.add_line(line)
ax.legend([Line2D([],[], linewidth=3, alpha=0.4)],
['some 1 data unit wide line']) # <- legend possible via proxy artist
plt.show()
Just to add to the previous answer (can't comment yet), here's a function that automates this process without the need for equal axes or the heuristic value of 0.8 for labels. The data limits and size of the axis need to be fixed and not changed after this function is called.
def linewidth_from_data_units(linewidth, axis, reference='y'):
"""
Convert a linewidth in data units to linewidth in points.
Parameters
----------
linewidth: float
Linewidth in data units of the respective reference-axis
axis: matplotlib axis
The axis which is used to extract the relevant transformation
data (data limits and size must not change afterwards)
reference: string
The axis that is taken as a reference for the data width.
Possible values: 'x' and 'y'. Defaults to 'y'.
Returns
-------
linewidth: float
Linewidth in points
"""
fig = axis.get_figure()
if reference == 'x':
length = fig.bbox_inches.width * axis.get_position().width
value_range = np.diff(axis.get_xlim())
elif reference == 'y':
length = fig.bbox_inches.height * axis.get_position().height
value_range = np.diff(axis.get_ylim())
# Convert length to points
length *= 72
# Scale linewidth to value range
return linewidth * (length / value_range)
Explanation:
Set up the figure with a known height and make the scale of the two axes equal (or else the idea of "data coordinates" does not apply). Make sure the proportions of the figure match the expected proportions of the x and y axes.
Compute the height of the whole figure point_hei (including margins) in units of points by multiplying inches by 72
Manually assign the y-axis range yrange (You could do this by plotting a "dummy" series first and then querying the plot axis to get the lower and upper y limits.)
Provide the width of the line that you would like in data units linewid
Calculate what those units would be in points pointlinewid while adjusting for the margins. In a single-frame plot, the plot is 80% of the full image height.
Plot the lines, using a capstyle that does not pad the ends of the line (has a big effect at these large line sizes)
Voilà? (Note: this should generate the proper image in the saved file, but no guarantees if you resize a plot window.)
import matplotlib.pyplot as plt
rez=600
wid=8.0 # Must be proportional to x and y limits below
hei=6.0
fig = plt.figure(1, figsize=(wid, hei))
sp = fig.add_subplot(111)
# # plt.figure.tight_layout()
# fig.set_autoscaley_on(False)
sp.set_xlim([0,4000])
sp.set_ylim([0,3000])
plt.axes().set_aspect('equal')
# line is in points: 72 points per inch
point_hei=hei*72
xval=[100,1300,2200,3000,3900]
yval=[10,200,2500,1750,1750]
x1,x2,y1,y2 = plt.axis()
yrange = y2 - y1
# print yrange
linewid = 500 # in data units
# For the calculation below, you have to adjust width by 0.8
# because the top and bottom 10% of the figure are labels & axis
pointlinewid = (linewid * (point_hei/yrange)) * 0.8 # corresponding width in pts
plt.plot(xval,yval,linewidth = pointlinewid,color="blue",solid_capstyle="butt")
# just for fun, plot the half-width line on top of it
plt.plot(xval,yval,linewidth = pointlinewid/2,color="red",solid_capstyle="butt")
plt.savefig('mymatplot2.png',dpi=rez)
I would like to plot a number of curves over an image
Using this code I am reasonably close:
G=plt.matplotlib.gridspec.GridSpec(64,1)
fig = plt.figure()
plt.imshow(img.data[:,:],cmap='gray')
plt.axis('off')
plt.axis([0,128,0,64])
for i in arange(64):
fig.add_subplot(G[i,0])
plt.axis('off')
# note that vtc.data.shape = (64, 128*400=51200)
# so every trace for each image pixel is 400 points long
plt.plot(vtc.data[i,:])
plt.axis([0, 51200, 0, 5])
The result that I am getting looks like this:
The problem is that while I seem to be able to get rid of all the padding in the horizontal (x) direction, there is different amount of padding in the image and the stacked plots in the vertical direction.
I tried using
ax = plt.gca()
ax.autoscale_view('tight')
but that didn't reduce the margin either.
How can I get a grid of m-by-n line plots to line up precisely with a blown up (by factor f) version of an image with dimensions (fm)-by-(fn)?
UPDATE and Solution:
The answer by #RutgerKassies works quite well. I achieved it using his code like so:
fig, axs = plt.subplots(1,1,figsize=(8,4))
axs.imshow(img.data[:,:],cmap='gray', interpolation='none')
nplots = 64
fig.canvas.draw()
box = axs._position.bounds
height = box[3] / nplots
for i in arange(nplots):
tmpax = fig.add_axes([box[0], box[1] + i * height, box[2], height])
tmpax.set_axis_off()
# make sure to get image orientation right and
tmpax.plot(vtc.data[nplots-i-1,:],alpha=.3)
tmpax.set_ylim(0,5)
tmpax.set_xlim(0, 51200)
I think the easiest way is to use the boundaries from your 'imshow axes' to manually calculate the boundaries of all your 'lineplot axes':
import matplotlib.pyplot as plt
import numpy as np
fig, axs = plt.subplots(1,1,figsize=(15,10))
axs.imshow(np.random.rand(50,100) ,cmap='gray', interpolation='none', alpha=0.3)
nplots = 50
fig.canvas.draw()
box = axs._position.bounds
height = box[3] / nplots
for i in arange(nplots):
tmpax = fig.add_axes([box[0], box[1] + i * height, box[2], height])
tmpax.set_axis_off()
tmpax.plot(np.sin(np.linspace(0,np.random.randint(20,1000),1000))*0.4)
tmpax.set_ylim(-1,1)
The above code seems nice, but i do have some issues with the autoscale chopping off part of the plot. Try removing the last line to see the effect, im not sure why thats happening.
i create a figure with 4 subplots (2 x 2), where 3 of them are of the type imshow and the other is errorbar. Each imshow plots have in addition a colorbar at the right side of them. I would like to resize my 3rd plot, that the area of the graph would be exactly under the one above it (with out colorbar)
as example (this is what i now have):
How could i resize the 3rd plot?
Regards
To adjust the dimensions of an axes instance, you need to use the set_position() method. This applies to subplotAxes as well. To get the current position/dimensions of the axis, use the get_position() method, which returns a Bbox instance. For me, it's conceptually easier to just interact with the position, ie [left,bottom,right,top] limits. To access this information from a Bbox, the bounds property.
Here I apply these methods to something similar to your example above:
import matplotlib.pyplot as plt
import numpy as np
x,y = np.random.rand(2,10)
img = np.random.rand(10,10)
fig = plt.figure()
ax1 = fig.add_subplot(221)
im = ax1.imshow(img,extent=[0,1,0,1])
plt.colorbar(im)
ax2 = fig.add_subplot(222)
im = ax2.imshow(img,extent=[0,1,0,1])
plt.colorbar(im)
ax3 = fig.add_subplot(223)
ax3.plot(x,y)
ax3.axis([0,1,0,1])
ax4 = fig.add_subplot(224)
im = ax4.imshow(img,extent=[0,1,0,1])
plt.colorbar(im)
pos4 = ax4.get_position().bounds
pos1 = ax1.get_position().bounds
# set the x limits (left and right) to first axes limits
# set the y limits (bottom and top) to the last axes limits
newpos = [pos1[0],pos4[1],pos1[2],pos4[3]]
ax3.set_position(newpos)
plt.show()
You may feel that the two plots do not exactly look the same (in my rendering, the left or xmin position is not quite right), so feel free to adjust the position until you get the desired effect.