Related
Dears, I need to make a fill between two thresholds in my chart I have tried with my code but it does not display, could you please tell me what I am doing wrong? I would like my figure like contour red like figure 2
fig = plt.figure(figsize=(15, 6))
ax = fig.add_subplot(111)
ax = lacebita['Prob'].plot(figsize=(15, 7), )
xtime = np.linspace(1990,2021,384)
ax.plot(xtime, lacebita['Prob'], 'black', alpha=1.00, linewidth=2, label = 'Deciles')
ax.fill_between(xtime, 0., lacebita['Prob'], lacebita['Prob']< 30., color='red', alpha=.75)
ax.axhline(50, linestyle='--', color='black',label='Percentile 50')
ax.axhline(33, linestyle='--', color='orange', label='Percentile 33')
ax.set_xlim(1990, 2021)
ax.set_ylim(0, 100, 10)
plt.grid(True)
plt.legend(loc = "upper left")
#ax.autoscale_view()
ax.set_title('Deciles para 12-Meses La Cebita(1990-2020)', fontsize=16)
ax.set_xlim(lacebita.index.min(), lacebita.index.max())
plt.savefig('deciles_12_lacebita.jpg')
There are a couple of ways to go about it. One approach is to fill the space in between the two horizontal threshold lines:
# Make a fake function
t = 20
fs = 10
samples = np.linspace(0, t, int(fs*t), endpoint=False)
wave_y = np.sin(samples)
time_x = np.arange(0, len(wave_y))
# Set upper and lower thresholds where horizontal lines will go and fill in between
upper_th = 0.5
lower_th = -0.5
# Plot function
fig, ax = plt.subplots()
ax.plot(time_x, wave_y)
ax.grid()
ax.set_ylim([-1.25, 1.25])
ax.set_ylabel('y label')
ax.set_xlim([0, 125])
ax.set_xlabel('x label')
# Fill in area under the curve and the horizontal lines
ax.fill_between(x=time_x, y1=upper_th, y2=lower_th, color='red', interpolate=True, alpha=.75)
# Horizontal lines
ax.axhline(upper_th, linestyle='--', color='black', label="upper_th: 0.5")
ax.axhline(lower_th, linestyle='--', color='orange', label='lower_th: - 0.5')
ax.legend()
plt.show()
Or if you change y1 or y2, for example to y1=0, you can play around with where exactly the fill is.
Another method is to fill in between the curve and the horizontal dashed lines. To do that you could modify the original data so that the values that go above the upper threshold and below the lower threshold become the threshold values. In other words, we want to make a new y curve that includes the threshold points by eliminating the points that go above/below the threshold so that matplotlib understands that the horizontal lines are part of the y curve.
# Copy original data, we are now going to modify
new_wave_y = np.copy(wave_y)
# Change values outside thresholds to threshold value for fill in
new_wave_y[new_wave_y < lower_th] = lower_th
new_wave_y[new_wave_y > upper_th] = upper_th
This way we can use where in fill between to point out where exactly under the curve, including under/above the horizontal lines, matplotlib needs to fill in the area. The full script:
# Make a fake function
t = 20
fs = 10
samples = np.linspace(0, t, int(fs*t), endpoint=False)
wave_y = np.sin(samples)
time_x = np.arange(0, len(wave_y))
# Set upper and lower thresholds where horizontal lines will go and fill in between
upper_th = 0.5
lower_th = -0.5
# Copy original data, we are now going to modify
new_wave_y = np.copy(wave_y)
# Change values outside thresholds to threshold value for fill in
new_wave_y[new_wave_y < lower_th] = lower_th
new_wave_y[new_wave_y > upper_th] = upper_th
# Plot function
fig, ax = plt.subplots()
ax.plot(time_x, wave_y)
ax.grid()
ax.set_ylim([-1.25, 1.25])
ax.set_ylabel('y label')
ax.set_xlim([0, 125])
ax.set_xlabel('x label')
# Fill in area under the curve and the horizontal lines
ax.fill_between(x=time_x, y1=new_wave_y, where=(lower_th < new_wave_y), color='red', interpolate=True, alpha=.75)
ax.fill_between(x=time_x, y1=new_wave_y, where=(new_wave_y < upper_th), color='red', interpolate=True, alpha=.75)
# Horizontal lines
ax.axhline(upper_th, linestyle='--', color='black', label="upper_th: 0.5")
ax.axhline(lower_th, linestyle='--', color='orange', label='lower_th: - 0.5')
ax.legend()
plt.show()
You can get some more information in the Matplotlib fill between demo and the fill between docs.
Edit:
If you want to fill in below or above the threshold line, for example fill in below the lower threshold, you can modify the y curve so that the values above the threshold become the threshold value (same as before but reverse) and change the values in fill_between . The full script:
# Make a fake function
t = 20
fs = 10
samples = np.linspace(0, t, int(fs*t), endpoint=False)
wave_y = np.sin(samples)
time_x = np.arange(0, len(wave_y))
# Set upper and lower thresholds where horizontal lines will go and fill in between
upper_th = 0.5
lower_th = -0.5
# Copy original data, we are now going to modify
new_wave_y = np.copy(wave_y)
# Change values outside thresholds to threshold value for fill in
new_wave_y[new_wave_y > lower_th] = lower_th
# Plot function
fig, ax = plt.subplots()
ax.plot(time_x, wave_y)
ax.grid()
ax.set_ylim([-1.25, 1.25])
ax.set_ylabel('y label')
ax.set_xlim([0, 125])
ax.set_xlabel('x label')
# Fill in area under the curve and the horizontal lines
ax.fill_between(x=time_x, y1=new_wave_y, y2=lower_th, where=(new_wave_y < lower_th), color='red', interpolate=True, alpha=.75)
# Horizontal lines
ax.axhline(upper_th, linestyle='--', color='black', label="upper_th: 0.5")
ax.axhline(lower_th, linestyle='--', color='orange', label='lower_th: - 0.5')
ax.legend()
plt.show()
I am trying to understand the underlying concepts of matplotlib, especially Axes and Figure. Therefore I am trying to plot two scatters and then remove any superfluous space (the red one below) by accessing different APIs & objects in the hierarchy.
Yet I fail to understand where the remaining red space is coming from. This is the code:
# Random data
df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=list('AB'))
# Create a single Axes and preconfigure the figure with red facecolor.
# Then plot a scatter
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10,5), facecolor='r')
ax1 = df.plot(kind='scatter', x='A', y='B', ax=axes[0])
ax2 = df.plot(kind='scatter', x='B', y='A', ax=axes[1])
# Remove except the scatter
for a in [ax1, ax2]:
a.set_xlabel(''), a.set_ylabel('') # Remove x and y labels
for loc in ['left', 'right', 'bottom', 'top']:
a.spines[loc].set_visible(False) # Remove spines
a.set_xticks([], []), a.set_yticks([], []) # Remove ticks
a.set_xmargin(0), a.set_ymargin(0) # No margin beyond outer values
# On figure-level we can make it more tight
fig.tight_layout()
It produces the following figure:
I saw that there is something like..
a.set_axis_off()
.. but this doesn't seem to be the right solution. Somewhere there seems to be some kind of padding that remains. It doesn't look like it's from some X/Y axis as it's the same for all four edges in both subplots.
Any help appreciated.
Solution
Two things are needed:
First we need to initialize the Figure with frameon=False:
fig, axes = plt.subplots(
// ...
frameon=False)
The space between the subplots can be removed using the subplot layout:
plt.subplots_adjust(wspace=.0, hspace=.0)
For the finest level of layout control, you can position your axes manually instead of relying on matplotlib to do it for you. There are a couple of ways of doing this.
One option is Axes.set_position
# Random data
df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=list('AB'))
# Create a pair of Axes and preconfigure the figure with red facecolor.
# Then plot a scatter
fig, axes = plt.subplots(1, 2, figsize=(10, 5), facecolor='r')
df.plot(kind='scatter', x='A', y='B', ax=axes[0]).set_position([0, 0, 0.5, 1])
df.plot(kind='scatter', x='B', y='A', ax=axes[1]).set_position([0, 0.5, 0.5, 1])
You could also use the old-fashioned Figure.add_axes method:
# Random data
df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=list('AB'))
# Create a pair of Axes and preconfigure the figure with red facecolor.
# Then plot a scatter
fig = plt.figure(figsize=(10, 5), facecolor='r')
df.plot(kind='scatter', x='A', y='B', ax=fig.add_axes([0, 0, 0.5, 1]))
df.plot(kind='scatter', x='B', y='A', ax=fig.add_axes([0, 0.5, 0.5, 1]))
I have the code below to plot circles add them to an ax.
I color the circles with respect to a colorbar.
However, to add the colorbar to my plot, I'm using sc=plot.scatter(...) and putting the colorbar using this dummy sc. Because plt.colorbar(sc,...) requires a mappable argument. How can I get rid of this dummy sc and still draw my colorbar?
import matplotlib
import numpy as np
import os
import matplotlib as mpl
from matplotlib.colors import Normalize
import matplotlib.cm as matplotlib_cm
from matplotlib import pyplot as plt
print(matplotlib.__version__)
row_list=['row1', 'row2', 'row3']
column_list=[2]
maxProcessiveGroupLength=2
index = column_list.index(maxProcessiveGroupLength)
plot1,panel1 = plt.subplots(figsize=(20+1.5*len(column_list), 10+1.5*len(row_list)))
plt.rc('axes', edgecolor='lightgray')
#make aspect ratio square
panel1.set_aspect(1.0)
panel1.text(0.1, 1.2, 'DEBUG', horizontalalignment='center', verticalalignment='top', fontsize=60, fontweight='bold', fontname='Arial',transform=panel1.transAxes)
if (len(column_list) > 1):
panel1.set_xlim([1, index + 1])
panel1.set_xticks(np.arange(0, index + 2, 1))
else:
panel1.set_xlim([0, len(column_list)])
panel1.set_xticks(np.arange(0, len(column_list)+1, 1))
if (len(row_list) > 1):
panel1.set_ylim([1, len(row_list)])
else:
panel1.set_ylim([0, len(row_list)])
panel1.set_yticks(np.arange(0, len(row_list) + 1, 1))
panel1.set_facecolor('white')
panel1.grid(color='black')
for edge, spine in panel1.spines.items():
spine.set_visible(True)
spine.set_color('black')
xlabels = None
if (index is not None):
xlabels = column_list[0:index + 1]
ylabels = row_list
cmap = matplotlib_cm.get_cmap('Blues') # Looks better
v_min = 2
v_max = 20
norm = Normalize(v_min, v_max)
bounds = np.arange(v_min, v_max+1, 2)
# Plot the circles with color
for row_index, row in enumerate(row_list):
for column_index, processive_group_length in enumerate(column_list):
radius=0.35
color=10+column_index*3+row_index*3
circle = plt.Circle((column_index + 0.5, row_index + 0.5), radius,color=cmap(norm(color)), fill=True)
panel1.add_patch(circle)
# Used for scatter plot
x = []
y = []
c = []
for row_index, processiveGroupLength in enumerate(row_list):
x.append(row_index)
y.append(row_index)
c.append(0.5)
# This code defines the ticks on the color bar
# plot the scatter plot
sc = plt.scatter(x, y, s=0, c=c, cmap=cmap, vmin=v_min, vmax=v_max, edgecolors='black')
# colorbar to the bottom
cb = plt.colorbar(sc ,orientation='horizontal') # this works because of the scatter
cb.ax.set_xlabel("colorbar label", fontsize=50, labelpad=25)
# common for horizontal colorbar and vertical colorbar
cbax = cb.ax
cbax.tick_params(labelsize=40)
text_x = cbax.xaxis.label
text_y = cbax.yaxis.label
font = mpl.font_manager.FontProperties(size=40)
text_x.set_font_properties(font)
text_y.set_font_properties(font)
# CODE GOES HERE TO CENTER X-AXIS LABELS...
panel1.set_xticklabels([])
mticks = panel1.get_xticks()
panel1.set_xticks((mticks[:-1] + mticks[1:]) / 2, minor=True)
panel1.tick_params(axis='x', which='minor', length=0, labelsize=50)
if xlabels is not None:
panel1.set_xticklabels(xlabels,minor=True)
panel1.xaxis.set_ticks_position('top')
plt.tick_params(
axis='x', # changes apply to the x-axis
which='major', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False) # labels along the bottom edge are off
# CODE GOES HERE TO CENTER Y-AXIS LABELS...
panel1.set_yticklabels([])
mticks = panel1.get_yticks()
panel1.set_yticks((mticks[:-1] + mticks[1:]) / 2, minor=True)
panel1.tick_params(axis='y', which='minor', length=0, labelsize=50)
panel1.set_yticklabels(ylabels, minor=True) # fontsize
plt.tick_params(
axis='y', # changes apply to the x-axis
which='major', # both major and minor ticks are affected
left=False) # labels along the bottom edge are off
plt.show()
From the documentation of colorbar:
Note that one can create a ScalarMappable "on-the-fly" to generate
colorbars not attached to a previously drawn artist
In your example, the following allows for creating the same colorbar without the scatter plot:
cb = plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), orientation='horizontal')
This is my current script:
#!/usr/bin/env python3
import math
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
"""
Setup for a typical explanatory-style illustration style graph.
"""
h = 2
x = np.linspace(-np.pi, np.pi, 100)
y = 2 * np.sin(x)
rc = {
# Tick in the middle of the axis line.
'xtick.direction' : 'inout',
'ytick.direction' : 'inout',
# Bold is easier to read when we have few ticks.
'font.weight': 'bold',
'xtick.labelbottom': False,
'xtick.labeltop': True,
}
with plt.rc_context(rc):
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title(
'2 sin(x), not $\\sqrt{2\\pi}$',
# TODO make LaTeX part bold?
# https://stackoverflow.com/questions/14324477/bold-font-weight-for-latex-axes-label-in-matplotlib
fontweight='bold',
# Too close otherwise.
# https://stackoverflow.com/questions/16419670/increase-distance-between-title-and-plot-in-matplolib/56738085
pad=20
)
# Custom visible plot area.
# ax.set_xlim(-3, 3)
ax.set_ylim(-2.5, 2.5)
# Axes
# Axes on center:
# https://stackoverflow.com/questions/31556446/how-to-draw-axis-in-the-middle-of-the-figure
ax.spines['left'].set_position('zero')
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_visible(False)
# Axes with arrow:
# https://stackoverflow.com/questions/33737736/matplotlib-axis-arrow-tip
ax.plot(1, 0, ls="", marker=">", ms=10, color="k",
transform=ax.get_yaxis_transform(), clip_on=False)
ax.plot(0, 1, ls="", marker="^", ms=10, color="k",
transform=ax.get_xaxis_transform(), clip_on=False)
# Ticks
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
# Make ticks a bit longer.
ax.tick_params(width=1, length=10)
# Select tick positions
# https://stackoverflow.com/questions/12608788/changing-the-tick-frequency-on-x-or-y-axis-in-matplotlib
xticks = np.arange(math.ceil(min(x)), math.floor(max(x)) + 1, 1)
yticks = np.arange(math.ceil(min(y)) - 1, math.floor(max(y)) + 2, 1)
# Remove 0.
xticks = np.setdiff1d(xticks, [0])
yticks = np.setdiff1d(yticks, [0])
ax.xaxis.set_ticks(xticks)
ax.yaxis.set_ticks(yticks)
# Another approach. But because I want to be able to remove the 0,
# anyways, I just explicitly give all ticks instead.
# ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))
# ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))
# Annotations.
ax.plot([0, np.pi/2], [h, h], '--r')
ax.plot([np.pi/2, np.pi/2], [h, 0], '--r')
ax.plot(np.pi/2, h, marker='o', linewidth=2, markersize=10,
markerfacecolor='w', markeredgewidth=1.5, markeredgecolor='black')
plt.savefig(
'main.png',
format='png',
bbox_inches='tight'
)
plt.clf()
And this is the output:
And this is what I want (hacked with GIMP), notice how the negative tick labels are on a different side of the axes now.
I tried adding:
for tick in ax.xaxis.get_majorticklabels():
tick.set_verticalalignment("bottom")
as shown in answers to: How to move a tick's label in matplotlib? but that does not move the tick labels up enough, and makes the labels show on top of the axes instead.
Tested on matplotlib 3.2.2.
The following code will adjust the vertical alignment of the ticks depending one whether they are at a negative or positive x-value. However that's not enough because the labels are actually anchored at the bottom of the tick line. I'm therefore adjusting their y-position a little bit, but you have to play with the value to get the desired output
# adjust the xticks so that they are on top when x<0 and on the bottom when x≥0
ax.spines['top'].set_visible(True)
ax.spines['top'].set_position('zero')
ax.spines['bottom'].set_visible(True)
ax.spines['bottom'].set_position('zero')
ax.xaxis.set_tick_params(which='both', top=True, labeltop=True,
bottom=True, labelbottom=True)
fig.canvas.draw()
for tick in ax.xaxis.get_major_ticks():
print(tick.get_loc())
if tick.get_loc()<0:
tick.tick1line.set_visible(False)
tick.label1.set_visible(False)
else:
tick.tick2line.set_visible(False)
tick.label2.set_visible(False)
full code:
import math
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
"""
Setup for a typical explanatory-style illustration style graph.
"""
h = 10
x = np.linspace(-np.pi, np.pi, 100)
y = h * np.sin(x)
rc = {
# Tick in the middle of the axis line.
'xtick.direction' : 'inout',
'ytick.direction' : 'inout',
# Bold is easier to read when we have few ticks.
'font.weight': 'bold',
'xtick.labelbottom': False,
'xtick.labeltop': True,
}
with plt.rc_context(rc):
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title(
'2 sin(x), not $\\sqrt{2\\pi}$',
# TODO make LaTeX part bold?
# https://stackoverflow.com/questions/14324477/bold-font-weight-for-latex-axes-label-in-matplotlib
fontweight='bold',
# Too close otherwise.
# https://stackoverflow.com/questions/16419670/increase-distance-between-title-and-plot-in-matplolib/56738085
pad=20
)
# Custom visible plot area.
# ax.set_xlim(-3, 3)
ax.set_ylim(-2.5, 2.5)
# Axes
# Axes on center:
# https://stackoverflow.com/questions/31556446/how-to-draw-axis-in-the-middle-of-the-figure
ax.spines['left'].set_position('zero')
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_visible(False)
# Axes with arrow:
# https://stackoverflow.com/questions/33737736/matplotlib-axis-arrow-tip
ax.plot(1, 0, ls="", marker=">", ms=10, color="k",
transform=ax.get_yaxis_transform(), clip_on=False)
ax.plot(0, 1, ls="", marker="^", ms=10, color="k",
transform=ax.get_xaxis_transform(), clip_on=False)
# Ticks
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
# Make ticks a bit longer.
ax.tick_params(width=1, length=10)
# Select tick positions
# https://stackoverflow.com/questions/12608788/changing-the-tick-frequency-on-x-or-y-axis-in-matplotlib
xticks = np.arange(math.ceil(min(x)), math.floor(max(x)) + 1, 1)
yticks = np.arange(math.ceil(min(y)) - 1, math.floor(max(y)) + 2, 1)
# Remove 0.
xticks = np.setdiff1d(xticks, [0])
yticks = np.setdiff1d(yticks, [0])
ax.xaxis.set_ticks(xticks)
ax.yaxis.set_ticks(yticks)
# Another approach. But because I want to be able to remove the 0,
# anyways, I just explicitly give all ticks instead.
# ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))
# ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(1.0))
for g,t in zip(ax.get_xticks(),ax.get_xticklabels()):
if g<0:
t.set_va('bottom')
else:
t.set_va('top')
t.set_transform(ax.transData)
t.set_position((g,0.15*-(g/abs(g))))
# Annotations.
ax.plot([0, np.pi/2], [h, h], '--r')
ax.plot([np.pi/2, np.pi/2], [h, 0], '--r')
ax.plot(np.pi/2, h, marker='o', linewidth=2, markersize=10,
markerfacecolor='w', markeredgewidth=1.5, markeredgecolor='black')
# adjust the xticks so that they are on top when x<0 and on the bottom when x≥0
ax.spines['top'].set_visible(True)
ax.spines['top'].set_position('zero')
ax.spines['bottom'].set_visible(True)
ax.spines['bottom'].set_position('zero')
ax.xaxis.set_tick_params(which='both', top=True, labeltop=True,
bottom=True, labelbottom=True)
fig.canvas.draw()
for tick in ax.xaxis.get_major_ticks():
print(tick.get_loc())
if tick.get_loc()<0:
tick.tick1line.set_visible(False)
tick.label1.set_visible(False)
else:
tick.tick2line.set_visible(False)
tick.label2.set_visible(False)
Im trying to plot a scatter matrix. I'm building on the example given in this thread Is there a function to make scatterplot matrices in matplotlib?. Here I have just modified the code slightly to make the axis visible for all the subplots. The modified code is given below
import itertools
import numpy as np
import matplotlib.pyplot as plt
def main():
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'],
linestyle='none', marker='o', color='black', mfc='none')
fig.suptitle('Simple Scatterplot Matrix')
plt.show()
def scatterplot_matrix(data, names, **kwargs):
"""Plots a scatterplot matrix of subplots. Each row of "data" is plotted
against other rows, resulting in a nrows by nrows grid of subplots with the
diagonal subplots labeled with "names". Additional keyword arguments are
passed on to matplotlib's "plot" command. Returns the matplotlib figure
object containg the subplot grid."""
numvars, numdata = data.shape
fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ax in axes.flat:
# Hide all ticks and labels
ax.xaxis.set_visible(True)
ax.yaxis.set_visible(True)
# # Set up ticks only on one side for the "edge" subplots...
# if ax.is_first_col():
# ax.yaxis.set_ticks_position('left')
# if ax.is_last_col():
# ax.yaxis.set_ticks_position('right')
# if ax.is_first_row():
# ax.xaxis.set_ticks_position('top')
# if ax.is_last_row():
# ax.xaxis.set_ticks_position('bottom')
# Plot the data.
for i, j in zip(*np.triu_indices_from(axes, k=1)):
for x, y in [(i,j), (j,i)]:
axes[x,y].plot(data[x], data[y], **kwargs)
# Label the diagonal subplots...
for i, label in enumerate(names):
axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
ha='center', va='center')
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
fig.tight_layout()
plt.xticks(rotation=45)
fig.show()
return fig
main()
I cant seem to be able to rotate the x-axis text of all the subplots. As it can be seen, i have tried the plt.xticks(rotation=45) trick. But this seems to perform the rotation for the last subplot alone.
Just iterate through the axes tied to the figure, set the active axes to the iterated object, and modify:
for ax in fig.axes:
matplotlib.pyplot.sca(ax)
plt.xticks(rotation=90)
plt only acts on the current active axes. You should bring it inside your last loop where you set some of the labels visibility to True:
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
for tick in axes[i,j].get_xticklabels():
tick.set_rotation(45)
for tick in axes[j,i].get_xticklabels():
tick.set_rotation(45)
for ax in fig.axes:
ax.tick_params(labelrotation=90)