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Two kinds of subplots: two colorbars needed [duplicate]

I would like to have the following layout in matplotlib:
Image 1a Image 1b Image 2a Image 2b Colorbar a Colorbar b
The Colorbar a is for Image set a, and the Colobar b is for Image set b.
I have tried to use ImageGrid to create the axes for images, but no luck in making the colorbars right. For example:
fig = plt.figure()
grid = ImageGrid(fig, 111, (1,6), aspect=False, share_all=False)
# Get data1a, data1b, ...
im1a = grid[0].pcolormesh(data1a)
im1b = grid[1].pcolormesh(data1b)
im2a = grid[2].pcolormesh(data2a)
im2b = grid[3].pcolormesh(data2b)
plt.colorbar(im1a, cax=grid[4])
plt.colorbar(im1b, cax=grid[5])
The problem with this is that the calls to colorbar() messed up with the axis limits of the images even though I specified share_all=False in ImageGrid().
Is there any tip on this? Very much appreciated.

For future reference, it helps to have a full working example, so that someone could copy and paste your code and reproduce your issue directly. For example, I can see you've imported ImageGrid, but a full import statement would help with this, as would creating fake data sets for data1a, data1b, etc.
Also, it looks like you have a (1,6) where you should have (1,4) in your statement above: grid = ImageGrid(fig, 111, (1,4), aspect=False, share_all=False), though this is not the solution to your problem.
When I want two or more color bars, my approach is typically to use get_position() on an axis, which returns the coordinates for the axis corners as attributes x0,y0,x1,y1. From here, I define each colorbar's axis separately and place each precisely where I want it to go. To get this to suit your needs, you'll have to tinker with the details of fig.add_axes([1.01, bbox_ax.y0, 0.02, bbox_ax.y1-bbox_ax.y0]) in the code below. For example, the first two entries 1.01, bbox_ax.y0 mean "place the bottom corner at x=1.01 and y=bbox_ax.y0". The second two entries, 0.02, bbox_ax.y1-bbox_ax.y0 define the horizontal and vertical width of the colorbar axis, respectively. I like the colorbar axes to be flush with the plot axes, so I use bbox_ax.y1-bbox_ax.y0 for the vertical width.
Note that I'm using mp.subplots() instead of ImageGrid(), since I'm not as familiar with the latter, and I don't think it's necessary.
import matplotlib.pyplot as mp
import numpy
import mpl_toolkits.axes_grid1
data1a = numpy.random.rand(100,100)
data1b = numpy.random.rand(100,100)
data2a = numpy.random.rand(100,100)
data2b = numpy.random.rand(100,100)
fig, axes = mp.subplots(1, 4, figsize=(8,2))
im1a = axes[0].pcolormesh(data1a, cmap='magma')
im1b = axes[1].pcolormesh(data1b, cmap='magma')
im2a = axes[2].pcolormesh(data2a, cmap='viridis')
im2b = axes[3].pcolormesh(data2b, cmap='viridis')
fig.tight_layout()
# get bounding box information for the axes (since they're in a line, you only care about the top and bottom)
bbox_ax = axes[0].get_position()
# fig.add_axes() adds the colorbar axes
# they're bounded by [x0, y0, x_width, y_width]
cbar_im1a_ax = fig.add_axes([1.01, bbox_ax.y0, 0.02, bbox_ax.y1-bbox_ax.y0])
cbar_im1a = mp.colorbar(im1a, cax=cbar_im1a_ax)
cbar_im2a_ax = fig.add_axes([1.09, bbox_ax.y0, 0.02, bbox_ax.y1-bbox_ax.y0])
cbar_im1a = mp.colorbar(im2a, cax=cbar_im2a_ax)
This produces the figure below:
You can also do this as a 2x2 grid with slightly different syntax:
fig, axes = mp.subplots(2, 2, figsize=(4,4))
im1a = axes[0,0].pcolormesh(data1a, cmap='magma')
im1b = axes[0,1].pcolormesh(data1b, cmap='magma')
im2a = axes[1,0].pcolormesh(data2a, cmap='viridis')
im2b = axes[1,1].pcolormesh(data2b, cmap='viridis')
fig.tight_layout()
bbox_ax_top = axes[0,1].get_position()
bbox_ax_bottom = axes[1,1].get_position()
cbar_im1a_ax = fig.add_axes([1.01, bbox_ax_top.y0, 0.02, bbox_ax_top.y1-bbox_ax_top.y0])
cbar_im1a = mp.colorbar(im1a, cax=cbar_im1a_ax)
cbar_im2a_ax = fig.add_axes([1.01, bbox_ax_bottom.y0, 0.02, bbox_ax_bottom.y1-bbox_ax_bottom.y0])
cbar_im1a = mp.colorbar(im2a, cax=cbar_im2a_ax)
Which produces this figure:

Using pcolormesh, which by default is plotted to axes with automatic aspect, does not require any special treatment to create colorbars.
The easiest way of doing so is to use a grid with unequal column width. The rest comes automatically.
import matplotlib.pyplot as plt
import numpy as np
fig, axes = plt.subplots(ncols=6,figsize=(7,2.2),
gridspec_kw={"width_ratios":[1,1,1,1, 0.08,0.08]})
fig.subplots_adjust(wspace=0.6)
im0 = axes[0].pcolormesh(np.random.rand(11,11), vmin=0, vmax=1, cmap="RdBu")
im1 = axes[1].pcolormesh(np.random.rand(11,11), vmin=0, vmax=1, cmap="RdBu")
im2 = axes[2].pcolormesh(np.random.rand(11,11), vmin=0, vmax=1)
im3 = axes[3].pcolormesh(np.random.rand(11,11), vmin=0, vmax=1)
axes[0].set_ylabel("y label")
fig.colorbar(im0, cax=axes[4])
fig.colorbar(im2, cax=axes[5])
plt.show()

How to get rid of extra white space on subplots with shared axes?

I'm creating a plot using python 3.5.1 and matplotlib 1.5.1 that has two subplots (side by side) with a shared Y axis. A sample output image is shown below:
Notice the extra white space at the top and bottom of each set of axes. Try as I might I can't seem to get rid of it. The overall goal of the figure is to have a waterfall type plot on the left with a shared Y axes with the plot on the right.
Here's some sample code to reproduce the image above.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
# create some X values
periods = np.linspace(1/1440, 1, 1000)
# create some Y values (will be datetimes, not necessarily evenly spaced
# like they are in this example)
day_ints = np.linspace(1, 100, 100)
days = pd.to_timedelta(day_ints, 'D') + pd.to_datetime('2016-01-01')
# create some fake data for the number of points
points = np.random.random(len(day_ints))
# create some fake data for the color mesh
Sxx = np.random.random((len(days), len(periods)))
# Create the plots
fig = plt.figure(figsize=(8, 6))
# create first plot
ax1 = plt.subplot2grid((1,5), (0,0), colspan=4)
im = ax1.pcolormesh(periods, days, Sxx, cmap='viridis', vmin=0, vmax=1)
ax1.invert_yaxis()
ax1.autoscale(enable=True, axis='Y', tight=True)
# create second plot and use the same y axis as the first one
ax2 = plt.subplot2grid((1,5), (0,4), sharey=ax1)
ax2.scatter(points, days)
ax2.autoscale(enable=True, axis='Y', tight=True)
# Hide the Y axis scale on the second plot
plt.setp(ax2.get_yticklabels(), visible=False)
#ax1.set_adjustable('box-forced')
#ax2.set_adjustable('box-forced')
fig.colorbar(im, ax=ax1)
As you can see in the commented out code I've tried a number of approaches, as suggested by posts like https://github.com/matplotlib/matplotlib/issues/1789/ and Matplotlib: set axis tight only to x or y axis.
As soon as I remove the sharey=ax1 part of the second subplot2grid call the problem goes away, but then I also don't have a common Y axis.

Autoscale tends to add a buffer to the data so that all of the data points are easily visible and not part-way cut off by the axes.
Change:
ax1.autoscale(enable=True, axis='Y', tight=True)
to:
ax1.set_ylim(days.min(),days.max())
and
ax2.autoscale(enable=True, axis='Y', tight=True)
to:
ax2.set_ylim(days.min(),days.max())
To get:

imshow() subplots generate unwanted white spaces

When plotting two (or more) subplots, there is a large areas of white spaces within the plots (on all four sides) as seen here:
Following is the code which I used to plot it.
from pylab import *
from matplotlib import rc, rcParams
import matplotlib.pyplot as plt
for kk in range(57,58):
fn_i=str(kk)
image_file_1='RedshiftOutput00'+fn_i+'_Slice_z_RadioPowerDSA.png'
image_file_2='RedshiftOutput00'+fn_i+'_Slice_z_RadioPowerTRA.png'
image_file_3='RedshiftOutput00'+fn_i+'_Slice_z_RadioPowerDSA+TRA.png'
image_1 = plt.imread(image_file_1)
image_2 = plt.imread(image_file_2)
image_3 = plt.imread(image_file_3)
ax1 = subplot(131)
plt.imshow(image_1)
plt.axis('off') # clear x- and y-axes
ax2 = subplot(132)
plt.imshow(image_2)
plt.axis('off') # clear x- and y-axes
ax3 = subplot(133)
plt.imshow(image_3)
plt.axis('off') # clear x- and y-axes
plt.savefig('RedshiftOutput00'+fn_i+'_all.png')
I am also uploading the 3 images used in this code to making the code a Minimal Working Example
1) https://drive.google.com/file/d/0B6l5iRWTUbHWSTF2R3E1THBGeVk/view?usp=sharing
2) https://drive.google.com/file/d/0B6l5iRWTUbHWaFI4dHAzcWpiOEU/view?usp=sharing
3) https://drive.google.com/file/d/0B6l5iRWTUbHWaG8xclFlcGJNaUk/view?usp=sharing
How we can remove this white space ? I tried by fixing the whole plot size, still white space is comming.

Mel's comment above (use plt.tight_layout()) works in many situations, but sometimes you need a little more control. To manipulate axes more finely (useful, e.g., when you have lots of colorbars or twin-ned axes), you can use plt.subplots_adjust() or a GridSpec object.
GridSpec objects allow you to specify the horizontal and vertical extents of individual axes, as well as their proportional width and height & spacing. subplots_adjust() moves your axes around after you've already plotted stuff on them. I prefer using the first option, but both are documented well.
It also may help to fool around with the size of your figure. If you have lots of whitespace width-wise, make the width of the figure smaller.
Here's some example code that I used to set up a recent plot:
gs = gridspec.GridSpec(
nrows=1, ncols=3, left=0.1, bottom=0.25, right=0.95, top=0.95,
wspace=0.05, hspace=0., width_ratios=[1, 1, 1])
NII_ax = plt.subplot(gs[0])
SII_ax = plt.subplot(gs[1])
OI_ax = plt.subplot(gs[2])
And the result:
Then, if you need a colorbar, adjust the right argument in GridSpec to something like 0.85, and use fig.add_axes() with a list [left_lim, bottom, width, height] and use that as the axis argument for a fig.colorbar()

inset imshow within figure matplotlib

I'm trying to have an imshow plot inset into another in matplotlib.
I have a figure that has an plot on top and an imshow on the bottom:
fig = plt.figure()
ax1 = fig.add_subplot(2,1,1)
plt.plot()
ax2 = fig.add_subplot(2,1,2)
plt.imshow()
and then I have another figure, which also is comprised of a plot on top and an imshow below. I want this second figure to be inset into the top plot on the first figure.
I tried follow this example. The code ran without an error but I had an empty axis in the position I wanted it.
My problem is just that I'm not sure where to put the plt.setp() command If that's what I am supposed to use.

First, I don't think you can put a figure into a figure in matplotlib. You will have to arrange your Axes objects (subplots) to achieve the look you want.
The example you provided uses absolute positioning to do that. setp there is not related to positioning, though — it just removes axis ticks from insets. An example of code that does what you want:
import numpy
import matplotlib.pyplot as plt
x = numpy.linspace(0, 1)
xx, yy = numpy.meshgrid(x, x)
im = numpy.sin(xx) + numpy.cos(yy)**2
fig = plt.figure()
ax1 = fig.add_subplot(2,1,1)
ax1.plot(x, x**2)
ax2 = fig.add_subplot(2,1,2)
ax2.imshow(im)
inset1 = fig.add_axes([.15, .72, .15, .15])
inset1.plot(x, x**2)
plt.setp(inset1, xticks=[], yticks=[])
inset2 = fig.add_axes([0.15, 0.55, .15, .15])
inset2.imshow(im)
plt.setp(inset2, xticks=[], yticks=[])
fig.savefig('tt.png')
Here the insets use explicit positioning with coordinates given in "figure units" (the whole figure has size 1 by 1).
Now, of course, there's plenty of room for improvement. You probably want the widths of your plots to be equal, so you'll have to:
specify the positioning of all subplots explicitly; or
play with aspect ratios; or
use two GridSpec objects (this way you'll have the least amount of magic numbers and manual adjustment)

Instead of grid lines on a plot, can matplotlib print grid crosses?

I want to have some grid lines on a plot, but actually full-length lines are too much/distracting, even dashed light grey lines. I went and manually did some editing of the SVG output to get the effect I was looking for. Can this be done with matplotlib? I had a look at the pyplot api for grid, and the only thing I can see that might be able to get near it are the xdata and ydata Line2D kwargs.

This cannot be done through the basic API, because the grid lines are created using only two points. The grid lines would need a 'data' point at every tick mark for there to be a marker drawn. This is shown in the following example:
import matplotlib.pyplot as plt
ax = plt.subplot(111)
ax.grid(clip_on=False, marker='o', markersize=10)
plt.savefig('crosses.png')
plt.show()
This results in:
Notice how the 'o' markers are only at the beginning and the end of the Axes edges, because the grid lines only involve two points.
You could write a method to emulate what you want, creating the cross marks using a series of Artists, but it's quicker to just leverage the basic plotting capabilities to draw the cross pattern.
This is what I do in the following example:
import matplotlib.pyplot as plt
import numpy as np
NPOINTS=100
def set_grid_cross(ax, in_back=True):
xticks = ax.get_xticks()
yticks = ax.get_yticks()
xgrid, ygrid = np.meshgrid(xticks, yticks)
kywds = dict()
if in_back:
kywds['zorder'] = 0
grid_lines = ax.plot(xgrid, ygrid, 'k+', **kywds)
xvals = np.arange(NPOINTS)
yvals = np.random.random(NPOINTS) * NPOINTS
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)
ax1.plot(xvals, yvals, linewidth=4)
ax1.plot(xvals, xvals, linewidth=7)
set_grid_cross(ax1)
ax2.plot(xvals, yvals, linewidth=4)
ax2.plot(xvals, xvals, linewidth=7)
set_grid_cross(ax2, in_back=False)
plt.savefig('gridpoints.png')
plt.show()
This results in the following figure:
As you can see, I take the tick marks in x and y to define a series of points where I want grid marks ('+'). I use meshgrid to take two 1D arrays and make 2 2D arrays corresponding to the double loop over each grid point. I plot this with the mark style as '+', and I'm done... almost. This plots the crosses on top, and I added an extra keyword to reorder the list of lines associated with the plot. I adjust the zorder of the grid marks if they are to be drawn behind everything.*****
The example shows the left subplot where by default the grid is placed in back, and the right subplot disables this option. You can notice the difference if you follow the green line in each plot.
If you are bothered by having grid crosses on the boarder, you can remove the first and last tick marks for both x and y before you define the grid in set_grid_cross, like so:
xticks = ax.get_xticks()[1:-1] #< notice the slicing
yticks = ax.get_yticks()[1:-1] #< notice the slicing
xgrid, ygrid = np.meshgrid(xticks, yticks)
I do this in the following example, using a larger, different marker to make my point:
***** Thanks to the answer by #fraxel for pointing this out.

You can draw on line segments at every intersection of the tickpoints. Its pretty easy to do, just grab the tick locations get_ticklocs() for both axis, then loop through all combinations, drawing short line segments using axhline and axvline, thus creating a cross hair at every intersection. I've set zorder=0 so the cross-hairs are drawn first, so that they are behind the plot data. Its easy to control the color/alpha and cross-hair size. Couple of slight 'gotchas'... do the plot before you get the tick locations.. and also the xmin and xmax parameters seem to require normalisation.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot((0,2,3,5,5,5,6,7,8,6,6,4,3,32,7,99), 'r-',linewidth=4)
x_ticks = ax.xaxis.get_ticklocs()
y_ticks = ax.yaxis.get_ticklocs()
for yy in y_ticks[1:-1]:
for xx in x_ticks[1:-1]:
plt.axhline(y=yy, xmin=xx / max(x_ticks) - 0.02,
xmax=xx / max(x_ticks) + 0.02, color='gray', alpha=0.5, zorder=0)
plt.axvline(x=xx, ymin=yy / max(y_ticks) - 0.02,
ymax=yy / max(y_ticks) + 0.02, color='gray', alpha=0.5, zorder=0)
plt.show()