I have the code below which produces the output I want.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
plt.style.use('ggplot')
%matplotlib inline
data = dict({'Variable_Grouping':['Type_A', 'Type_A', 'Type_A', 'Type_C', 'Type_C', 'Type_C', 'Type_C', 'Type_D', 'Type_D', 'Type_E', 'Type_E', 'Type_E', 'Type_H', 'Type_H'], 'Variable':['a1', 'a2', 'a3', 'c1', 'c2', 'c3', 'c4', 'd1', 'd2', 'e1', 'e2', 'e3', 'h1', 'h2'], 'Count':[5, 3, 8, 4, 3, 9, 5, 3, 8, 5, 3, 8, 5, 3],'Percent':[0.0625, 0.125, 0.4375, 0.0, 0.125, 0.5, 0.02, 0.125, 0.03, 0.0625, 0.05, 0.44, 0.07, 0.023]})
to_plot = pd.DataFrame(data)
g = sns.FacetGrid(to_plot, col='Variable_Grouping', col_wrap = 2, sharex=False, sharey = False, height = 5, aspect = 1, margin_titles=True)
g=g.map(plt.bar, "Variable","Count").add_legend()
for ax, (_, subdata) in zip(g.axes, to_plot.groupby('Variable_Grouping')):
ax2=ax.twinx()
subdata.plot(x='Variable',y='Percent', ax = ax2, legend=True, color='g', label = 'Percent')
ax2.set_ylabel('Percent')
ax2.grid(False)
for ax in g.axes.flatten():
ax.tick_params(labelbottom=True, labelrotation = 90)
g.fig.suptitle('Analysis', fontsize=16, fontweight = 'demibold', y = 1.02)
g.fig.subplots_adjust(hspace=0.3, wspace=0.7, right = 0.9)
plt.show();
Now I am using matplotlib.backends.backend_pdf to plot the figures in pdf. I want 4 figures per page.
with PdfPages('Analysis.pdf') as pdf:
g = sns.FacetGrid(to_plot, col='Variable_Grouping', col_wrap = 2, sharex=False, sharey = False, height = 5, aspect = 1, margin_titles=True)
g=g.map(plt.bar, "Variable","Count").add_legend()
for ax, (_, subdata) in zip(g.axes, to_plot.groupby('Variable_Grouping')):
ax2=ax.twinx()
subdata.plot(x='Variable',y='Percent', ax = ax2, legend=True, color='g', label = 'Percent')
ax2.set_ylabel('Percent')
ax2.grid(False)
for ax in g.axes.flatten():
ax.tick_params(labelbottom=True, labelrotation = 90)
g.fig.suptitle('Analysis', fontsize=16, fontweight = 'demibold', y = 1.02)
g.fig.subplots_adjust(hspace=0.3, wspace=0.7, right = 0.9)
pdf.savefig(bbox_inches = 'tight')
plt.close();
The code above gives me all the plots in a single page as expected.
def grouper(iterable, n, fillvalue=None):
from itertools import zip_longest
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
if len(to_plot['Variable_Grouping'].unique()) < 4:
N_plots_per_page =len(to_plot['Variable_Grouping'].unique())
elif len(to_plot['Variable_Grouping'].unique()) >= 4:
N_plots_per_page = 4
with PdfPages('Analysis.pdf') as pdf:
for cols in grouper(to_plot['Variable_Grouping'].unique(), N_plots_per_page):
g = sns.FacetGrid(to_plot, col='Variable_Grouping', col_wrap = 2, sharex=False, sharey = False, height = 5, aspect = 1, margin_titles=True)
g=g.map(plt.bar, "Variable","Count").add_legend()
for ax, (_, subdata) in zip(g.axes, to_plot.groupby('Variable_Grouping')):
ax2=ax.twinx()
subdata.plot(x='Variable',y='Percent', ax = ax2, legend=True, color='g', label = 'Percent')
ax2.set_ylabel('Percent')
ax2.grid(False)
for ax in g.axes.flatten():
ax.tick_params(labelbottom=True, labelrotation = 90)
g.fig.suptitle('Analysis', fontsize=16, fontweight = 'demibold', y = 1.02)
g.fig.subplots_adjust(hspace=0.3, wspace=0.7, right = 0.9)
pdf.savefig(bbox_inches = 'tight')
plt.show()
plt.close();
In the code above I have tried using the grouper function (https://docs.python.org/3/library/itertools.html#itertools-recipes). This was also mentioned in Export huge seaborn chart into pdf with multiple pages and this repeats all the graphs in all the pages.
I wanted to enquire if there is an easy way to get 4 graphs per page or what's wrong with the above code I used using the grouper function which is repeating the graphs. Any help will be appreciated. Thanks.
The problem is, even you try to get the number of plots per page, you take the whole data inside the loop to plot with to_plot. You need to filter your to_plot with the cols you get by your grouper and your code will work.
The only changes I made is create the variable data_per_page and replace that with to_plot inside of sns.FaceGrid and in for ax, (_,subdata) in zip(...).
with PdfPages('Analysis.pdf') as pdf:
for cols in grouper(to_plot['Variable_Grouping'].unique(), N_plots_per_page):
data_per_page = to_plot.loc[to_plot['Variable_Grouping'].isin(cols)]
g = sns.FacetGrid(data_per_page, col='Variable_Grouping', col_wrap = 2, sharex=False, sharey = False, height = 5, aspect = 1, margin_titles=True)
g=g.map(plt.bar, "Variable","Count").add_legend()
for ax, (_,subdata) in zip(g.axes, data_per_page.groupby(['Variable_Grouping'])):
ax2=ax.twinx()
subdata.plot(x='Variable',y='Percent', ax = ax2, legend=True, color='g', label = 'Percent')
ax2.set_ylabel('Percent')
ax2.grid(False)
for ax in g.axes.flatten():
ax.tick_params(labelbottom=True, labelrotation = 90)
g.fig.suptitle('Analysis', fontsize=16, fontweight = 'demibold', y = 1.02)
g.fig.subplots_adjust(hspace=0.3, wspace=0.7, right = 0.9)
pdf.savefig(bbox_inches='tight')
plt.show()
plt.close()
As a result I get a pdf with 2 pages, on the first there are 4 plots, and on the second only 1.
Related
How can this vertical grouped bar chart be changed to a horizontal bar chart (grouped, and stacked)? I need help to alter the code such that the bars are displayed horizontally instead of vertically.
import matplotlib.pyplot as plt
import numpy as np
N = 9
labels = ['L', 'S', 'S', 'M', 'W', 'W', 'S', 'R', 'C']
M_means = [1, 45, 28, 11, 4, 7, 1, 0.02, 0.3]
PO_means = [3, 58, 17, 8, 3, 8, 1, 0.06, 1]
K_means = [1, 44, 30, 11, 3, 7, 1, 0.01, 0.5]
x = np.arange(len(labels)) # the label locations
width = 0.30 # the width of the bars
fig, ax = plt.subplots(figsize=(15, 9))
rects1 = ax.bar(x - width, M_means, width, label='M S and K', color=('#b02a2a'))
rects2 = ax.bar(x, PO_means, width, label='P O S and K', color=('#055cad'))
rects3 = ax.bar(x + width, K_means, width, label='M K', color=('#0b7d53'))
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('% of workday', fontsize=32)
#ax.set_title('Scores by group and gender')
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=32, rotation=15)
ax.legend(loc='upper right', frameon=False, fontsize=32, markerscale=2)
ax.bar_label(rects1, size = 32, padding=20, rotation=90)
ax.bar_label(rects2, size = 32, padding=20, rotation=90)
ax.bar_label(rects3, size = 32, padding=20, rotation=90)
plt.xticks(ha='center')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(32)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(32)
plt.ylim(0, 100)
plt.gca().spines['right'].set_color('none')
plt.gca().spines['top'].set_color('none')
#fig.tight_layout()
plt.show()
Functionally, only two changes are needed:
Change ax.bar to ax.barh
Swap set_x* methods with set_y* methods, e.g. set_xticks() -> set_yticks() and so on
Semantically, the variables x and width should also be renamed to y and height.
import matplotlib.pyplot as plt
import numpy as np
N = 9
labels = list('LSSMWWSRC')
M_means = [1, 45, 28, 11, 4, 7, 1, 0.02, 0.3]
K_means = [2, 40, 21, 18, 3, 3, 2, 0.52, 0.3]
PO_means = [3, 58, 17, 8, 3, 8, 1, 0.06, 1]
K = [1, 44, 30, 11, 3, 7, 1, 0.01, 0.5]
# rename x/width to y/height
y = np.arange(len(labels))
height = 0.30
fig, ax = plt.subplots()
# use ax.barh instead of ax.bar
rects1 = ax.barh(y - height, M_means, height, label='M S and K', color='#b02a2a')
rects2 = ax.barh(y, PO_means, height, label='P O S and K', color='#055cad')
rects3 = ax.barh(y + height, K_means, height, label='M K', color='#0b7d53')
# swap set_x* methods with set_y* methods
ax.set_xlabel('% of workday')
ax.set_yticks(y)
ax.set_yticklabels(labels)
ax.legend(loc='upper right', frameon=False, markerscale=2)
ax.bar_label(rects1, padding=10)
ax.bar_label(rects2, padding=10)
ax.bar_label(rects3, padding=10)
# ...
The easiest solution is to load the data into a pandas.DataFrame, and then use pandas.DataFrame.plot with kind='barh'. This is easier because pandas uses matplotlib as the default plotting backend, and the API groups the bars automatically.
This reduces the code to 14 lines (not including imports).
When using 'barh', xlabel= applies to the y-axis. Therefore, xlabel='' removes the y-axis label.
Adjust figsize=(12, 10) if planning to use smaller / larger font sizes.
See Adding value labels on a matplotlib bar chart for additional details about using .bar_label.
Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1
import pandas as pd
import matplotlib.pylot as plt
# data
labels = ['L', 'S', 'S', 'M', 'W', 'W', 'S', 'R', 'C']
M_means = [1, 45, 28, 11, 4, 7, 1, 0.02, 0.3]
PO_means = [3, 58, 17, 8, 3, 8, 1, 0.06, 1]
K_means = [1, 44, 30, 11, 3, 7, 1, 0.01, 0.5]
# create a dict with the keys as the desired legend labels
data = {'labels': labels, 'M S and K': M_means, 'P O S and K': PO_means, 'M K': K_means}
# create dataframe
df = pd.DataFrame(data)
# plot: specify y=[...] if only certain columns are desired
ax = df.plot(kind='barh', x='labels', width=.85, figsize=(12, 10), xlabel='', color=['#b02a2a', '#055cad', '#0b7d53'])
ax.set_xlabel('% of workday', fontsize=15)
ax.set_xlim(0, 100)
ax.legend(loc='upper right', frameon=False, fontsize=15, markerscale=2)
for c in ax.containers:
ax.bar_label(c, label_type='edge', padding=1, size=15)
ax.tick_params(axis='both', which='both', labelsize=15)
ax.spines[['top', 'right']].set_visible(False)
Stacked
To manually create the stacked bar without pandas, see Horizontal stacked bar chart in Matplotlib
Use the parameter stacked=True
Some bar patches are to small for the label, so custom labels have been passed to the labels= parameter in .bar_label
Using := requires at least python 3.8. Otherwise use labels = [f'{v.get_width():.0f}' if v.get_width() > 1 else '' for v in c]
ax = df.plot(kind='barh', x='labels', width=.85, figsize=(12, 10), xlabel='',
color=['#b02a2a', '#055cad', '#0b7d53'], stacked=True)
ax.set_xlabel('% of workday', fontsize=15)
ax.set_xlim(0, 100)
ax.legend(loc='upper right', frameon=False, fontsize=15, markerscale=2)
for c in ax.containers:
# custom labels only show label size for values greater than 1
labels = [f'{w:.0f}' if (w := v.get_width()) > 1 else '' for v in c]
ax.bar_label(c, labels=labels, label_type='center', padding=1, size=15)
ax.tick_params(axis='both', which='both', labelsize=15)
ax.spines[['top', 'right']].set_visible(False)
I want to create subplots with Matplotlib by looping over my data. However, I don't get the annotations into the correct position, apparently not even into the correct subplot. Also, the common x- and y-axis labels don't work.
My real data is more complex but here is an example that reproduces the error:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sns
# create data
distributions = []
first_values = []
second_values = []
for i in range(4):
distributions.append(np.random.normal(0, 0.5, 100))
first_values.append(np.random.uniform(0.7, 1))
second_values.append(np.random.uniform(0.7, 1))
# create subplot
fig, axes = plt.subplots(2, 2, figsize = (15, 10))
legend_elements = [Line2D([0], [0], color = '#76A29F', lw = 2, label = 'distribution'),
Line2D([0], [0], color = '#FEB302', lw = 2, label = '1st value', linestyle = '--'),
Line2D([0], [0], color = '#FF5D3E', lw = 2, label = '2nd value')]
# loop over data and create subplots
for data in range(4):
if data == 0:
position = axes[0, 0]
if data == 1:
position = axes[0, 1]
if data == 2:
position = axes[1, 0]
if data == 3:
position = axes[1, 1]
dist = distributions[data]
first = first_values[data]
second = second_values[data]
sns.histplot(dist, alpha = 0.5, kde = True, stat = 'density', bins = 20, color = '#76A29F', ax = position)
sns.rugplot(dist, alpha = 0.5, color = '#76A29F', ax = position)
position.annotate(f'{np.mean(dist):.2f}', (np.mean(dist), 0.825), xycoords = ('data', 'figure fraction'), color = '#76A29F')
position.axvline(first, 0, 0.75, linestyle = '--', alpha = 0.75, color = '#FEB302')
position.axvline(second, 0, 0.75, linestyle = '-', alpha = 0.75, color = '#FF5D3E')
position.annotate(f'{first:.2f}', (first, 0.8), xycoords = ('data', 'figure fraction'), color = '#FEB302')
position.annotate(f'{second:.2f}', (second, 0.85), xycoords = ('data', 'figure fraction'), color = '#FF5D3E')
position.set_xticks(np.arange(round(min(dist), 1) - 0.1, round(max(max(dist), max([first]), max([second])), 1) + 0.1, 0.1))
plt.xlabel("x-axis name")
plt.ylabel("y-axis name")
plt.legend(handles = legend_elements, bbox_to_anchor = (1.5, 0.5))
plt.show()
The resulting plot looks like this:
What I want is to have
the annotations in the correct subplot next to the vertical lines / the mean of the distribution
shared x- and y-labels for all subplot or at least for each row / column
Any help is highly appreciated!
If you use the function to make the subplot a single array (axes.flatten()) and modify it to draw the graph sequentially, you can draw the graph. The colors of the annotations have been partially changed for testing purposes.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sns
np.random.seed(202000104)
# create data
distributions = []
first_values = []
second_values = []
for i in range(4):
distributions.append(np.random.normal(0, 0.5, 100))
first_values.append(np.random.uniform(0.7, 1))
second_values.append(np.random.uniform(0.7, 1))
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
legend_elements = [Line2D([0], [0], color = '#76A29F', lw = 2, label = 'distribution'),
Line2D([0], [0], color = '#FEB302', lw = 2, label = '1st value', linestyle = '--'),
Line2D([0], [0], color = '#FF5D3E', lw = 2, label = '2nd value')]
for i,ax in enumerate(axes.flatten()):
sns.histplot(distributions[i], alpha=0.5, kde=True, stat='density', bins=20, color='#76A29F', ax=ax)
sns.rugplot(distributions[i], alpha=0.5, color='#76A29F', ax=ax)
ax.annotate(f'{np.mean(distributions[i]):.2f}', (np.mean(distributions[i]), 0.825), xycoords='data', color='red')
ax.axvline(first_values[i], 0, 0.75, linestyle = '--', alpha = 0.75, color = '#FEB302')
ax.axvline(second_values[i], 0, 0.75, linestyle = '-', alpha = 0.75, color = '#FF5D3E')
ax.annotate(f'{first_values[i]:.2f}', (first_values[i], 0.8), xycoords='data', color='#FEB302')
ax.annotate(f'{second_values[i]:.2f}', (second_values[i], 0.85), xycoords='data', color = '#FF5D3E')
ax.set_xticks(np.arange(round(min(distributions[i]), 1) - 0.1, round(max(max(distributions[i]), max([first_values[i]]), max([second_values[i]])), 1) + 0.1, 0.1))
plt.xlabel("x-axis name")
plt.ylabel("y-axis name")
plt.legend(handles = legend_elements, bbox_to_anchor = (1.35, 0.5))
plt.show()
I am trying to plot a depth map using Basemap in python. The contour and pcolormesh are working, but them when I add meridians, parallels and scale is returning a blank image.
I have tried to plot one by one, excluding meridians and paralles, and adding just scale, but returns a blank map and it is the same with the others. I used the same code before and it was working...
import netCDF4 as nc
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib import ticker
grid = nc.Dataset('remo_grd.nc', mode='r')
h = grid.variables['h'][:]
h = h.astype(int)
h=-h
lon= grid.variables['lon_rho'][:]
lat= grid.variables['lat_rho'][:]
latmin = np.min(lat)
latmax= np.max(lat)
lonmax= np.max(lon)
lonmin= np.min(lon)
fig = plt.figure(1, figsize = (7,5.4), dpi = 100)
ax = fig.add_subplot(111)
m = Basemap(projection='merc', llcrnrlon=lonmin-0.2, urcrnrlon=lonmax+0.2, llcrnrlat=latmin-0.2, urcrnrlat=latmax+0.2, lat_ts=0, resolution='i')
xi, yi = m(lon,lat)
m.ax = ax
cs= m.pcolormesh(xi, yi, np.squeeze(h), shading = 'flat', zorder = 2)
levels = [-1000, -200]
a = m.contour(xi, yi, np.squeeze(h), levels, colors = 'black', linestyles = 'solid', linewidth= 1.5, extend = 'both', zorder = 3 )
plt.clabel(a, inline=2, fontsize= 10, linewidth= 1.0, fmt = '%.f', zorder= 4)
ax.text(0.5, -0.07, 'Longitude', transform=ax.transAxes, ha='center', va='center', fontsize = '10')
ax.text(-0.15, 0.5, 'Latitude', transform=ax.transAxes, ha= 'center', va='center', rotation='vertical', fontsize = '10')
m.drawcoastlines(linewidth=1.5, color = '0.1',zorder=5)
m.fillcontinents(color=('gray'),zorder=5 )
m.drawstates(linewidth = 0.5, zorder = 7)
m.drawmapboundary(color = 'black', zorder = 8, linewidth =1.2)
m.drawparallels(np.arange(int(latmin),int(latmax),3),labels=[1,0,0,0], linewidth=0.0, zorder =0)
m.drawmeridians(np.arange(int(lonmin),int(lonmax),3),labels=[0,0,0,1], linewidth=0.0)
cbar = plt.colorbar(cs, shrink=0.97, extend = 'both')
cbar.set_ticks([-10, -250, -500, -750, -1000, -1250, -1500, -1750, -2000, -2250, -2500])
cbar.set_ticklabels([-10, -250, -500, -750, -1000, -1250, -1500, -1750, -2000, -2250, -2500])
cbar.set_label('Meters (m)' , size = 10, labelpad = 20, rotation = 270)
ax = cbar.ax.tick_params(labelsize = 9)
titulo='Depth'
plt.title(titulo, va='bottom', fontsize='12')
#plot scale
dref=200
# Coordinates
lat0=m.llcrnrlat+0.9
lon0=m.llcrnrlon+1.9
#Tricked distance to provide to the the function
distance=dref/np.cos(lat0*np.pi/180.)
# Due to the bug, the function will draw a bar of length dref
scale=m.drawmapscale(lon0,lat0,lon0,lat0,distance, barstyle='fancy', units='km', labelstyle='simple',fillcolor1='w', fillcolor2='#555555', fontcolor='#555555', zorder = 8)
#Modify the labels with dref instead of distance
scale[12].set_text(dref/2)
scale[13].set_text(dref)
plt.show()
I've solved the problem! I was setting a specific order to plot each one of the details using the function zorder, so I was overlapping the data
How to set a shared secondary axes using subplots in matplotlib.
Here is the minimal code to display the issue:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
def countour_every(ax, every, x_data, y_data,
color='black', linestyle='-', marker='o', **kwargs):
"""Draw a line with countour marks at each every points"""
line, = ax.plot(x_data, y_data, linestyle)
return line
def prettify_axes(ax, data):
"""Makes my plot pretty"""
if 'title' in data:
ax.set_title(data['title'])
if 'y_lim' in data:
ax.set_ylim(data['y_lim'])
if 'x_lim' in data:
ax.set_xlim(data['x_lim'])
# Draw legend only if labels were set (HOW TO DO IT?)
# if ax("has_some_label_set"):
ax.legend(loc='upper right', prop={'size': 6})
ax.title.set_fontsize(7)
ax.xaxis.set_tick_params(labelsize=6)
ax.xaxis.set_tick_params(direction='in')
ax.xaxis.label.set_size(7)
ax.yaxis.set_tick_params(labelsize=6)
ax.yaxis.set_tick_params(direction='in')
ax.yaxis.label.set_size(7)
def prettify_second_axes(ax):
ax.yaxis.set_tick_params(labelsize=7)
ax.yaxis.set_tick_params(labelcolor='red')
ax.yaxis.label.set_size(7)
def compare_plot(ax, data):
line1 = countour_every(ax, 10, **data[0])
if 'label' in data[0]:
line1.set_label(data[0]['label'])
line2 = countour_every(ax, 10, **data[1])
if 'label' in data[1]:
line2.set_label(data[1]['label'])
ax2 = ax.twinx()
line3 = ax.plot(
data[0]['x_data'],
data[0]['y_data']-data[1]['y_data'], '-',
color='red', alpha=.2, zorder=1)
prettify_axes(ax, data[0])
prettify_second_axes(ax2)
d0 = {'x_data': np.arange(0, 10), 'y_data': abs(np.random.random(10)), 'y_lim': [-1, 1], 'color': '.7', 'linestyle': '-', 'label': 'd0'}
d1 = {'x_data': np.arange(0, 10), 'y_data': -abs(np.random.random(10)), 'y_lim': [-1, 1], 'color': '.7', 'linestyle': '--', 'label': 'd1'}
d2 = {'x_data': np.arange(0, 10), 'y_data': np.random.random(10), 'y_lim': [-1, 1], 'color': '.7', 'linestyle': '-.'}
d3 = {'x_data': np.arange(0, 10), 'y_data': -np.ones(10), 'y_lim': [-1, 1], 'color': '.7', 'linestyle': '-.'}
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
fig.set_size_inches(6, 6)
compare_plot(axes[0][0], [d0, d1])
compare_plot(axes[0][1], [d0, d2])
compare_plot(axes[1][0], [d1, d0])
compare_plot(axes[1][1], [d3, d2])
fig.suptitle('A comparison chart')
fig.set_tight_layout({'rect': [0, 0.03, 1, 0.95]})
fig.text(0.5, 0.03, 'Position', ha='center')
fig.text(0.005, 0.5, 'Amplitude', va='center', rotation='vertical')
fig.text(0.975, 0.5, 'Error', color='red', va='center', rotation='vertical')
fig.savefig('demo.png', dpi=300)
That generates the following image
We can see that the X axis and the Y axis is correctly shared, but the secondary twin axis, is repeated in all subplots.
Also the secondary axis isn't scaling correctly to fit the data. (that should occurs independently of the principal y axis being limited).
You will need to share the twin axes manually and also remove the ticklabels
def compare_plot(ax, data):
# ...
ax2 = ax.twinx()
# ...
return ax2
sax1 = compare_plot(axes[0][0], [d0, d1])
sax2 = compare_plot(axes[0][1], [d0, d2])
sax3 = compare_plot(axes[1][0], [d1, d0])
sax4 = compare_plot(axes[1][1], [d3, d2])
for sax in [sax2, sax3, sax4]:
sax1.get_shared_y_axes().join(sax1, sax)
sax1.autoscale()
for sax in [sax1,sax3]:
sax.yaxis.set_tick_params(labelright=False)
Following my previous question that didn't get any answer, I tried to solve my problem of adding colorbar instead of legend to my plots. There are couple of problems that I couldn't solve yet.
Update:
I want to move the colorbar to the proper position on the right of the plot.
I generate two plots with the same instruction but the second one looks completely different and I couldn't understand what caused this problem.
Here is my code:
import numpy as np
import pylab as plt
from matplotlib import rc,rcParams
rc('text',usetex=True)
rcParams.update({'font.size':10})
import matplotlib.cm as cm
from matplotlib.ticker import NullFormatter
import matplotlib as mpl
def plot(Z_s,CWL,filter_id,spectral_type,model_mag,mag,plot_name):
f= ['U38','B','V','R','I','MB420','MB464','MB485','MB518','MB571','MB604','MB646','MB696','MB753','MB815','MB856','MB914']
wavetable=CWL/(1+Z_s)
dd=model_mag-mag
nplist=['E', 'Sbc', 'Scd', 'Irr', 'SB3', 'SB2']
minimum,maximum=(0.,16.)
Z = [[0,0],[0,0]]
levels = list(np.linspace(0, 1, len(f)))
NUM_COLORS = len(f)
cm = plt.get_cmap('gist_rainbow')
mycolor=[]
for i in range(NUM_COLORS):
mycolor.append( cm(1.*i/NUM_COLORS)) # color will now be an RGBA tuple
mymap = mpl.colors.LinearSegmentedColormap.from_list('mycolors',mycolor)
CS3 = plt.contourf(Z, levels, cmap=mymap)
plt.clf()
FILTER=filter_id
SED=spectral_type
for (j,d) in enumerate(nplist):
bf=(SED==j)
if (j<3):
k=j
i_subplot = k + 1
fig = plt.figure(1, figsize=(5,5))
ax = fig.add_subplot(3,1,i_subplot)
for i in range(len(f)):
bb=np.where(FILTER[bf]==i)[0]
r=mycolor[i][0]
g=mycolor[i][1]
b=mycolor[i][2]
ax.scatter(wavetable[bb], dd[bb], s=1, color=(r,g,b))
if (k<2):
ax.xaxis.set_major_formatter( NullFormatter() )
ax.set_ylabel(r'$\Delta$ MAG',fontsize=10)
else:
ax.set_xlabel(r'WL($\AA$)',fontsize=10)
ax.set_ylabel(r'$\Delta$ MAG',fontsize=10)
fig.subplots_adjust(wspace=0,hspace=0)
ax.axhline(y=0,color='k')
ax.set_xlim(1000,9000)
ax.set_ylim(-3,3)
ax.set_xticks(np.linspace(1000, 9000, 16, endpoint=False))
ax.set_yticks(np.linspace(-3, 3, 4, endpoint=False))
ax.text(8500,2.1,nplist[j], {'color': 'k', 'fontsize': 10})
fontsize=8
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
if (j==2):
cbar_ax = fig.add_axes([0.9, 0.15, 0.05, 0.7])
cbar=plt.colorbar(CS3, cax=cbar_ax, ticks=range(0,len(f)),orientation='vertical')
cbar.ax.get_yaxis().set_ticks([])
for s, lab in enumerate(f):
cbar.ax.text( 0.08,(0.95-0.01)/float(len(f)-1) * s, lab, fontsize=8,ha='left')
fname = plot_name+'.'+nplist[0]+'.'+nplist[1]+'.'+nplist[2]+'.pdf'
plt.savefig(fname)
plt.close()
else:
k=j-3
i_subplot = k + 1
fig = plt.figure(1, figsize=(5,5))
ax = fig.add_subplot(3,1,i_subplot)
for i in range(len(f)):
bb=np.where(FILTER[bf]==i)[0]
r=mycolor[i][0]
g=mycolor[i][1]
b=mycolor[i][2]
ax.scatter(wavetable[bb], dd[bb], s=1, color=(r,g,b))
if (k<2):
ax.xaxis.set_major_formatter( NullFormatter() )
ax.set_ylabel(r'$\Delta$ MAG',fontsize=10)
else:
ax.set_xlabel(r'WL($\AA$)',fontsize=10)
ax.set_ylabel(r'$\Delta$ MAG',fontsize=10)
fig.subplots_adjust(wspace=0,hspace=0)
ax.axhline(y=0,color='k')
ax.set_xlim(1000,9000)
ax.set_ylim(-3,3)
ax.set_xticks(np.linspace(1000, 9000, 16, endpoint=False))
ax.set_yticks(np.linspace(-3, 3, 4, endpoint=False))
ax.text(8500,2.1,nplist[j], {'color': 'k', 'fontsize': 10})
fontsize=8
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
if (j==5):
cbar_ax = fig.add_axes([0.9, 0.15, 0.05, 0.7])
cbar=plt.colorbar(CS3, cax=cbar_ax, ticks=range(0,len(f)),orientation='vertical')
cbar.ax.get_yaxis().set_ticks([])
for s, lab in enumerate(f):
cbar.ax.text( 0.08,(0.95-0.01)/float(len(f)-1) * s, lab , fontsize=8,ha='left')
fname = plot_name+'.'+nplist[3]+'.'+nplist[4]+'.'+nplist[5]+'.pdf'
plt.savefig(fname)
plt.close()
a=np.loadtxt('calibration.photometry.information.capak.cat')
Z_s=a[:,0]
CWL=a[:,1]
filter_id=a[:,2]
spectral_type=a[:,3]
model_mag=a[:,4]
mag=a[:,5]
plot_name='test'
plot(Z_s,CWL,filter_id,spectral_type,model_mag,mag,plot_name)
you can also download the data from here.
I will appreciate to get any help.
You can use plt.subplots() passing the gridspec_kw parameter to adjust the axes' aspect ratio in a very flexible way, and then select the top axes to include the colorbar.
I've worked on your code simplifying it quite a bit. Furthermore, I've changed many things in your code such as: PEP8, removed repeated calls to plt.savefig()and ax methods. The result is:
import numpy as np
import pylab as plt
from matplotlib import rc, rcParams, colors
rc('text', usetex=True)
rcParams['font.size'] = 10
rcParams['axes.labelsize'] = 8
def plot(Z_s, CWL, filter_id, spectral_type, model_mag, mag, plot_name):
f= ['U38', 'B', 'V', 'R', 'I', 'MB420', 'MB464', 'MB485', 'MB518',
'MB571', 'MB604', 'MB646', 'MB696', 'B753', 'MB815', 'MB856',
'MB914']
wavetable = CWL/(1+Z_s)
dd = model_mag-mag
nplist = ['E', 'Sbc', 'Scd', 'Irr', 'SB3', 'SB2']
minimum, maximum = (0., 16.)
Z = [[0, 0],[0, 0]]
levels = list(np.linspace(0, 1, len(f)+1))
NUM_COLORS = len(f)
cmap = plt.get_cmap('gist_rainbow')
mycolor = []
for i in range(NUM_COLORS):
mycolor.append(cmap(1.*i/NUM_COLORS))
mymap = colors.LinearSegmentedColormap.from_list('mycolors', mycolor)
CS3 = plt.contourf(Z, levels, cmap=mymap)
coords = CS3.get_array()
coords = coords[:-1] + np.diff(coords)/2.
FILTER = filter_id
SED = spectral_type
dummy = 2
xmin = 1000
xmax = 9000
ymin = -3
ymax = 3
fig, axes = plt.subplots(nrows=5, figsize=(5, 6),
gridspec_kw=dict(height_ratios=[0.35, 0.05, 1, 1, 1]))
fig2, axes2 = plt.subplots(nrows=5, figsize=(5, 6),
gridspec_kw=dict(height_ratios=[0.35, 0.05, 1, 1, 1]))
fig.subplots_adjust(wspace=0, hspace=0)
fig2.subplots_adjust(wspace=0, hspace=0)
axes_all = np.concatenate((axes[dummy:], axes2[dummy:]))
dummy_axes = np.concatenate((axes[:dummy], axes2[:dummy]))
for ax in axes_all:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.axhline(y=0, color='k')
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_xticks([])
ax.set_yticks(np.linspace(ymin, ymax, 4, endpoint=False))
ax.set_ylabel(r'$\Delta$ MAG', fontsize=10)
axes[-1].set_xticks(np.linspace(xmin, xmax, 16, endpoint=False))
axes2[-1].set_xticks(np.linspace(xmin, xmax, 16, endpoint=False))
plt.setp(axes[-1].xaxis.get_majorticklabels(), rotation=30)
plt.setp(axes2[-1].xaxis.get_majorticklabels(), rotation=30)
axes[-1].set_xlabel(r'WL($\AA$)', fontsize=10)
axes2[-1].set_xlabel(r'WL($\AA$)', fontsize=10)
for ax in dummy_axes:
for s in ax.spines.values():
s.set_visible(False)
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.set_xticks([])
ax.set_yticks([])
for axes_i in [axes, axes2]:
cbar = plt.colorbar(CS3, ticks=[], orientation='horizontal',
cax=axes_i[0])
for s, lab in enumerate(f):
cbar.ax.text(coords[s], 0.5, lab, fontsize=8, va='center',
ha='center', rotation=90,
transform=cbar.ax.transAxes)
for (j, d) in enumerate(nplist):
bf = (SED==j)
if (j<3):
k = j
ax = axes[k+dummy]
ax.text(8500, 2.1, nplist[j], {'color': 'k', 'fontsize': 10})
for i in range(len(f)):
bb = np.where(FILTER[bf]==i)[0]
ax.scatter(wavetable[bb], dd[bb], s=1, color=mycolor[i])
else:
k = j-3
ax = axes2[k+dummy]
ax.text(8500, 2.1, nplist[j], {'color': 'k', 'fontsize': 10})
for i in range(len(f)):
bb = np.where(FILTER[bf]==i)[0]
ax.scatter(wavetable[bb], dd[bb], s=1, color=mycolor[i])
fname = '.'.join([plot_name, nplist[0], nplist[1], nplist[2], 'png'])
fig.savefig(fname)
fname = '.'.join([plot_name, nplist[3], nplist[4], nplist[5], 'png'])
fig2.savefig(fname)
if __name__=='__main__':
a = np.loadtxt('calibration.photometry.information.capak.cat')
Z_s = a[:, 0]
CWL = a[:, 1]
filter_id = a[:, 2]
spectral_type = a[:, 3]
model_mag = a[:, 4]
mag = a[:, 5]
plot_name = 'test'
plot(Z_s, CWL, filter_id, spectral_type, model_mag, mag, plot_name)
which gives: