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()
Related
I have a figure with 3 subplots, two of which share a colorbar and the third has has it's own colorbar.
I would like the colorbars to align with the vertical limits of their respective plots, and for the top two plots to have the same vertical limits.
Googling, I have found ways to do this with a single plot, but am stuck trying to make it work for my fig. My figure currently looks like this:
The code for which is as follows:
import cartopy.io.shapereader as shpreader
import cartopy.crs as ccrs
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
shpfilename = shpreader.natural_earth(resolution='50m',
category='cultural',
name='admin_0_countries')
reader = shpreader.Reader(shpfilename)
countries = reader.records()
projection = ccrs.PlateCarree()
fig = plt.figure()
axs = [plt.subplot(2, 2, x + 1, projection = projection) for x in range(2)]\
+ [plt.subplot(2, 2, (3, 4), projection = projection)]
def cmap_seg(cmap, value, k):
cmaplist = [cmap(i) for i in range(cmap.N)]
cmap = mpl.colors.LinearSegmentedColormap.from_list(
'Custom cmap', cmaplist, cmap.N)
bounds = np.linspace(0, k, k + 1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
color = cmap(norm(value))
return color, cmap
for country in countries:
c_name = country.attributes["SOVEREIGNT"]
country_dat = df.loc[c_name]
cmap = matplotlib.cm.get_cmap("plasma")
cmap_blues = matplotlib.cm.get_cmap("Blues")
ax_extent = [-170, 180, -65, 85]
alpha = 1.0
edgecolor = "k"
linewidth = 0.5
ax = axs[0]
value = country_dat.loc["wgi_bin"]
ax.add_geometries([country.geometry],
projection,
facecolor = cmap_seg(cmap, value, 5)[0],
alpha = alpha,
edgecolor = edgecolor,
linewidth = linewidth)
ax.set_xlabel("WGI group")
ax.set_extent(ax_extent)
ax = axs[1]
value = country_dat.loc["epi_bin"]
ax.add_geometries([country.geometry],
projection,
facecolor = cmap_seg(cmap, value, 5)[0],
alpha = alpha,
edgecolor = edgecolor,
linewidth = linewidth)
ax.set_xlabel("EPI group")
ax.set_extent(ax_extent)
ax = axs[2]
value = country_dat.loc["diff"]
ax.add_geometries([country.geometry],
projection,
facecolor = cmap_seg(cmap_blues, value, 4)[0],
alpha = alpha,
edgecolor = edgecolor,
linewidth = linewidth)
ax.set_xlabel("difference")
ax.set_extent(ax_extent)
subplot_labels = ["WGI group", "EPI group", "Metric difference"]
for i, ax in enumerate(axs):
ax.text(0.5, -0.07, subplot_labels[i], va='bottom', ha='center',
rotation='horizontal', rotation_mode='anchor',
transform=ax.transAxes)
sm = plt.cm.ScalarMappable(cmap=cmap_seg(cmap, 5, 5)[1], norm = plt.Normalize(0, 5))
sm._A = []
cb = plt.colorbar(sm, ax = axs[1], values = [1,2,3,4, 5], ticks = [1,2,3,4,5])
sm2 = plt.cm.ScalarMappable(cmap=cmap_seg(cmap_blues, 5, 4)[1], norm = plt.Normalize(0, 4))
sm2._A = []
cb2 = plt.colorbar(sm2, ax = axs[2], values = [0,1,2,3], ticks = [0,1,2,3])
Try this:
# update your code for this specific line (added shrink option)
cb = plt.colorbar(sm, ax=axs[1], values=[1,2,3,4,5], ticks=[1,2,3,4,5], shrink=0.6)
And add these lines of code towards the end:
p00 = axs[0].get_position()
p01 = axs[1].get_position()
p00_new = [p00.x0, p01.y0, p00.width, p01.height]
axs[0].set_position(p00_new)
The plot should be similar to this:
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
I am trying to animate multiple patches as efficiently as possible when reading data from a list?
The code below displays an animation of the scatter plot but not the patches. Each point in scatter plot contains various sizes of circles. This example would require 6 different circles to be animated at 2 subjects each time point. But what if there were 20 subjects that each had 3 circles around them.
What is the most efficient way to animate all 60 circles for each frame?
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib as mpl
x_data = np.random.randint(80, size=(400, 4))
y_data = np.random.randint(80, size=(400, 4))
fig, ax = plt.subplots(figsize = (8,6))
ax.set_xlim(0,80)
ax.set_ylim(0,80)
scatter = ax.scatter(x_data[0], y_data[0], zorder = 5) #Scatter plot
Player_1 = x_data[0][0], y_data[0][0]
Player_2 = x_data[0][1], y_data[0][1]
Player_1_IR = mpl.patches.Circle(Player_1, radius = 2, color = 'black', lw = 1, alpha = 0.8, zorder = 4)
Player_1_MR = mpl.patches.Circle(Player_1, radius = 4, color = 'gray', lw = 1, alpha = 0.8, zorder = 3)
Player_1_OR = mpl.patches.Circle(Player_1, radius = 6, color = 'lightgrey', lw = 1, alpha = 0.8, zorder = 2)
Player_2_IR = mpl.patches.Circle(Player_2, radius = 2, color = 'black', lw = 1, alpha = 0.8, zorder = 4)
Player_2_MR = mpl.patches.Circle(Player_2, radius = 4, color = 'gray', lw = 1, alpha = 0.8, zorder = 3)
Player_2_OR = mpl.patches.Circle(Player_2, radius = 6, color = 'lightgrey', lw = 1, alpha = 0.8, zorder = 2)
ax.add_patch(Player_1_IR)
ax.add_patch(Player_1_MR)
ax.add_patch(Player_1_OR)
ax.add_patch(Player_2_IR)
ax.add_patch(Player_2_MR)
ax.add_patch(Player_2_OR)
def animate(i) :
scatter.set_offsets(np.c_[x_data[i,:], y_data[i,:]])
ani = animation.FuncAnimation(fig, animate, frames=len(x_data),
interval = 700, blit = False)
plt.show()
You can store all patches that you want to update in a list through which you then iterate through every iteration step. Note that the size of the Circle patches is in data units/coordinates while the scatter plot points are in points (one point = 1/72 inch), which means that the relative size between scatter points and circles depends on the figure size and axes limits and will change when you re-scale the figure.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib as mpl
x_data = np.random.randint(80, size=(400, 20))
y_data = np.random.randint(80, size=(400, 20))
fig, ax = plt.subplots(figsize = (8,6))
ax.set_xlim(0,80)
ax.set_ylim(0,80)
scatter = ax.scatter(x_data[0], y_data[0], zorder = 5) #Scatter plot
##creating list of patches
players = []
for n in range(10):
##as there are always 3 circles, append all three patches as a list at once
players.append([
mpl.patches.Circle((x_data[0,n],y_data[0,n]), radius = 2, color = 'black', lw = 1, alpha = 0.8, zorder = 4),
mpl.patches.Circle((x_data[0,n],y_data[0,n]), radius = 4, color = 'gray', lw = 1, alpha = 0.8, zorder = 3),
mpl.patches.Circle((x_data[0,n],y_data[0,n]), radius = 6, color = 'lightgrey', lw = 1, alpha = 0.8, zorder = 2)
])
##adding patches to axes
for player in players:
for circle in player:
ax.add_patch(circle)
def animate(i):
scatter.set_offsets(np.c_[x_data[i,:], y_data[i,:]])
##updating players:
for n,player in enumerate(players):
for circle in player:
circle.center = (x_data[i,n],y_data[i,n])
ani = animation.FuncAnimation(fig, animate, frames=len(x_data),
interval = 700, blit = False)
plt.show()
Old Answer (slightly different visual effect, but could be tuned to look the same):
If you really just want circles around your scatter points, you can actually forget about the Circle patches and just overlay several scatter plots with different marker sizes.
In the example below I only mark part of the scatter points with circles by slicing the array of random numbers. Also remember that in scatter plots the marker size is given as points square, so if you want to increase the circle radius from, say, 5 to 6, the given marker size should change from 25 to 36.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib as mpl
x_data = np.random.randint(80, size=(400, 20))
y_data = np.random.randint(80, size=(400, 20))
fig, ax = plt.subplots(figsize = (8,6))
ax.set_xlim(0,80)
ax.set_ylim(0,80)
scatter = ax.scatter(x_data[0], y_data[0], zorder = 5) #Scatter plot
scatter_IR = ax.scatter(
x_data[0,:10], y_data[0,:10], zorder = 4,
facecolor='black', edgecolor = 'black',
alpha = 0.8, s = 100
)
scatter_MR = ax.scatter(
x_data[0,:10], y_data[0,:10], zorder = 3,
facecolor='grey', edgecolor = 'grey',
alpha = 0.8, s = 225
)
scatter_OR = ax.scatter(
x_data[0,:10], y_data[0,:10], zorder = 2,
facecolor='lightgrey', edgecolor = 'lightgrey',
alpha = 0.8, s = 400
)
def animate(i) :
scatter.set_offsets(np.c_[x_data[i,:], y_data[i,:]])
scatter_IR.set_offsets(np.c_[x_data[i,:10], y_data[i,:10]])
scatter_MR.set_offsets(np.c_[x_data[i,:10], y_data[i,:10]])
scatter_OR.set_offsets(np.c_[x_data[i,:10], y_data[i,:10]])
ani = animation.FuncAnimation(fig, animate, frames=len(x_data),
interval = 700, blit = False)
plt.show()
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:
I would like to set legend and text boxes locations and styles exactly same, the latter especially to make text aligned.
import matplotlib.pyplot as plt
x = np.arange(10)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for i in range(3):
ax.plot(x, i * x ** 2, label = '$y = %i x^2$'%i)
ax.set_title('example plot')
# Shrink the axis by 20% to put legend and text at the bottom
#+ of the figure
vspace = .2
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * vspace,
box.width, box.height * (1 - vspace)])
# Put a legend to the bottom left of the current axis
x, y = 0, 0
# First solution
leg = ax.legend(loc = 'lower left', bbox_to_anchor = (x, y), \
bbox_transform = plt.gcf().transFigure)
# Second solution
#leg = ax.legend(loc = (x, y)) , bbox_transform = plt.gcf().transFigure)
# getting the legend location and size properties using a code line I found
#+ somewhere in SoF
bb = leg.legendPatch.get_bbox().inverse_transformed(ax.transAxes)
ax.text(x + bb.width, y, 'some text', transform = plt.gcf().transFigure, \
bbox = dict(boxstyle = 'square', ec = (0, 0, 0), fc = (1, 1, 1)))
plt.show()
This should place the text at the right of the legend box but that's not what it does. And the two boxes are not vertically aligned.
The second solution does not actually anchoring the legend to the figure, but to the axes instead.
You can use the frame data to get the right width in order to position the Text() object correctly.
In the example below I had to apply a 1.1 factor for the width (this value I haven't found how to get, and if you don't apply the factor the text clashes with the legend).
Note also that you must plt.draw() before getting the right width value.
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(10)
fig = plt.figure(figsize=(3, 2))
ax = fig.add_subplot(1, 1, 1)
for i in range(3):
ax.plot(x, i*x**2, label=r'$y = %i \cdot x^2$'%i)
ax.set_title('example plot')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
x, y = 0.2, 0.5
leg = ax.legend(loc='lower left', bbox_to_anchor=(x, y),
bbox_transform=fig.transFigure, fontsize=8)
plt.draw()
f = leg.get_frame()
w0, h0 = f.get_width(), f.get_height()
inv = fig.transFigure.inverted()
w, h = inv.transform((w0, h0))
ax.text(x+w*1.1, y+h/2., 'some text', transform=fig.transFigure,
bbox=dict(boxstyle='square', ec=(0, 0, 0), fc=(1, 1, 1)),
fontsize=7)
fig.savefig('test.jpg', bbox_inches='tight')
for x, y = 0.2, 0.5:
for x, y = -0.3, -0.3: