I based my heatmap off of: Heatmap in matplotlib with pcolor?
I checked out How to change separation between tick labels and axis labels in Matplotlib but it wasn't what I needed
How do I fix the positions of the labels so they align with the ticks?
#!/usr/bin/python
import matplotlib.pyplot as plt
import numpy as np
import random
in_path = '/path/to/data'
in_file = open(in_path,'r').read().split('\r')
wd = '/'.join(in_path.split('/')[:-1]) + '/'
column_labels = [str(random.random()) + '_dsiu' for i in in_file[0].split('\t')[2:]]
row_labels = []
#Organize data for matrix population
D_cyano_counts = {}
for line in in_file[2:]:
t = line.split('\t')
D_cyano_counts[(t[0],t[1])] = [int(x) for x in t[2:]]
#Populate matrix
matrix = []
for entry in sorted(D_cyano_counts.items(), key = lambda x: (np.mean([int(bool(y)) for y in x[-1]]), np.mean(x[-1]))):#, np.mean(x[-1]))):
(taxon_id,cyano), counts = entry
normalized_counts = []
for i,j in zip([int(bool(y)) for y in counts], counts):
if i > 0:
normalized_counts.append(i * (5 + np.log(j)))
else:
normalized_counts.append(0)
#Labels
label_type = 'species'
if label_type == 'species': label = cyano
if label_type == 'taxon_id': label = taxon_id
row_labels.append(str(random.random()))
#Fill in matrix
matrix.append(normalized_counts)
matrix = np.array(matrix)
#Fig
fig, ax = plt.subplots()
heatmap = ax.pcolor(matrix, cmap=plt.cm.Greens, alpha = 0.7)
#Format
fig = plt.gcf()
#
ax.set_frame_on(False)
#
font = {'size':3}
ax.xaxis.tick_top()
ax.set_xticks([i + 0.5 for i in range(len(column_labels))])
ax.set_yticks([i + 0.5 for i in range(len(row_labels))])
ax.set_xticklabels(column_labels, rotation = (45), fontsize = 10, va='bottom')#, fontweight = 'demi')
ax.set_yticklabels(row_labels, fontsize = 9, fontstyle='italic')
cbar = plt.colorbar(heatmap)
help(ax.set_xticklabels)
ax.margins(x=0.01,y=0.01)
fig.set_size_inches(20, 13)
plt.savefig('figure.png')
you have to set the horizontal alignment of the labels to left in your case. They are centered by default.
The link from #Jean-Sébastien contains your answer
ax.set_xticklabels(column_labels, rotation = (45), fontsize = 10, va='bottom', ha='left')
Related
I would like to adjust the bar value text position below each bar top with barwidth/5 offset.
text_y -= bar.get_width()/5 # <- not work
Full code:
#!/usr/bin/env python3
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.patheffects as PathEffects
import math
import numpy as np
def save_fig(fig,pngname):
fig.savefig(pngname, dpi=fig.dpi, bbox_inches="tight")
print("[[%s]]"%pngname)
return
def plot_bar(df):
xname = df.columns[0]
fig, ax = plt.subplots(figsize=(10, 5))
x = np.arange(len(df[xname]))
n = len(df.columns[1:])
bar_width = 0.95/n
fontsize = 20
colors = ['#5891ad','#004561','#ff6f31','#1c7685','#0f45a8','#4cdc8b','#0097a7']
dy = -bar_width/10
bars = []
# add bars
for i,colname in enumerate(df.columns[1:]):
bar = ax.bar(x+i*bar_width, df[colname], width=bar_width,color=colors[i])
bars.append(bar)
# add text on bars
for bar in ax.patches:
bar_value = bar.get_height()
text = f'{bar_value:,}'
text_x = bar.get_x() + bar.get_width() / 2
text_y = bar.get_y() + bar_value
text_y -= bar.get_width()/5 # <- not work
bar_color = bar.get_facecolor()
t = ax.text(text_x, text_y, text, ha='center', va='top', color=bar_color,
size=fontsize)
t.set_path_effects([PathEffects.withStroke(linewidth=bar_width*15, foreground='w')])
ax.set_xticks(x + 1/ 2 - bar_width/2)
ax.set_xticklabels(df[xname])
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
#ax.spines['left'].set_visible(False)
#ax.spines['bottom'].set_color('#DDDDDD')
ax.tick_params(bottom=False, left=False)
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
ax.set_xlabel('x', labelpad=15)
ax.set_ylabel('y', labelpad=15)
ax.set_title('title', pad=15)
fig.tight_layout()
plt.show()
return
data = [['a',3,2,1],
['b',2,3,1],
['c',3,1,3],
['d',5,1,3],
]
df = pd.DataFrame(data,columns=['f1','f2','f3','f4'])
plot_bar(df)
Is it possible to have a subplot taller than other subplots in order to make space for the X axis tick labels, but the height of the bar chart inside to be the same as the bar height in the shorter subplots? When I add height parameter to df.plot() I get "TypeError: () got multiple values for keyword argument 'height'". Here is my code:
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = OrderedDict()
data['Test Break'] = [0.1, 0.5, np.nan]
data['No Break'] = [0.9, 0.5, np.nan]
data['Not Tested'] = [0.0, 0.0, 1.0000]
index = ['Very very long name ' + str(x+1) for x in range(len(data))]
df = pd.DataFrame(data=data, index=index)
num_plots = 2
rows = num_plots + 1
cols = 1
layout = (rows, cols)
red, green, grey = '#FF0000', '#00FF00', '#888888'
light_grey = '#AAAAAA'
fig = plt.figure()
fig.set_size_inches(6, 3)
for z in range(num_plots):
is_last = z == num_plots - 1
rowspan = 2 if is_last else 1
ax = plt.subplot2grid(layout, (z, 0), rowspan=rowspan)
df.plot(ax=ax, kind='bar', stacked=True, yticks=[0,1], legend=False, color=[red, green, grey])
plt.subplots_adjust(left=0.05, right=0.95, bottom=0.02, top=0.98, hspace=0.5)
ax.grid(True, which='major', axis='y')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_edgecolor(light_grey)
ax.spines['left'].set_edgecolor(light_grey)
if not is_last:
for tick in ax.xaxis.get_major_ticks():
tick.set_visible(False)
I read the document of matplotlib and write the following code, it supposed to capture my mouse event and move the grey line position when i clicked. I read this code in jupiter notebook online, it stop to show the coordinate of my cursor as it usually do, What's happend? Can anyone help me?
import pandas as pd
import numpy as np
import matplotlib.colors as mcol
import matplotlib.cm as cm
from scipy import stats
from matplotlib.lines import Line2D
import matplotlib.pyplot as plt
import scipy.spatial as spatial
np.random.seed(12345)
df = pd.DataFrame([np.random.normal(33500,150000,3650),
np.random.normal(41000,90000,3650),
np.random.normal(41000,120000,3650),
np.random.normal(48000,55000,3650)],
index=[1992,1993,1994,1995])
fig, ax = plt.subplots()
year_avg = df.mean(axis = 1)
year_std = df.std(axis = 1)
yerr = year_std / np.sqrt(df.shape[1]) * stats.t.ppf(1-0.05/2, df.shape[1]-1)
bars=ax.bar(range(df.shape[0]), year_avg, yerr = yerr, color = 'lightslategrey')
threshold=42000
line=plt.axhline(y = threshold, color = 'grey', alpha = 0.5)
cm1 = mcol.LinearSegmentedColormap.from_list("CmapName",["yellow", "orange", "red"])
cpick = cm.ScalarMappable(cmap=cm1)
percentages = []
cpick.set_array([])
def setColor(bars, yerr,threshold):
for bar, yerr_ in zip(bars, yerr):
low = bar.get_height() - yerr_
high = bar.get_height() + yerr_
percentage = (high-threshold)/(high-low)
if percentage>1: percentage = 1
if percentage<0: percentage=0
percentages.append(percentage)
cpick.to_rgba(percentages)
bars = ax.bar(range(df.shape[0]), year_avg, yerr = yerr, color = cpick.to_rgba(percentages))
return bars
line=plt.axhline(threshold, color = 'grey', alpha = 0.5)
setColor(bars, yerr,threshold)
plt.colorbar(cpick, orientation='horizontal')
plt.xticks(range(df.shape[0]), df.index)
fig = plt.figure()
plt.show()
def onclick(event):
print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %
('double' if event.dblclick else 'single', event.button,
event.x, event.y, event.xdata, event.ydata))
line.set_ydata(event.ydata)
#plt.draw()
cid = fig.canvas.mpl_connect('button_press_event', onclick)
Right now there're some statistics plotted in 3d bar over (x, y). each bar height represents the density of the points in side the square grid of (x,y) plane. Right now, i can put different color on each bar. However, I want to put progressive color on the 3d bar, similar as the cmap, so the bar will be gradient filled depending on the density.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# height of the bars
z = np.ones((4, 4)) * np.arange(4)
# position of the bars
xpos, ypos = np.meshgrid(np.arange(4), np.arange(4))
xpos = xpos.flatten('F')
ypos = ypos.flatten('F')
zpos = np.zeros_like(xpos)
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = z.flatten()
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')
plt.show()
Output the above code:
Let me first say that matplotlib may not be the tool of choice when it comes to sophisticated 3D plots.
That said, there is no built-in method to produce bar plots with differing colors over the extend of the bar.
We therefore need to mimic the bar somehow. A possible solution can be found below. Here, we use a plot_surface plot to create a bar that contains a gradient.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection= Axes3D.name)
def make_bar(ax, x0=0, y0=0, width = 0.5, height=1 , cmap="viridis",
norm=matplotlib.colors.Normalize(vmin=0, vmax=1), **kwargs ):
# Make data
u = np.linspace(0, 2*np.pi, 4+1)+np.pi/4.
v_ = np.linspace(np.pi/4., 3./4*np.pi, 100)
v = np.linspace(0, np.pi, len(v_)+2 )
v[0] = 0 ; v[-1] = np.pi; v[1:-1] = v_
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))
xthr = np.sin(np.pi/4.)**2 ; zthr = np.sin(np.pi/4.)
x[x > xthr] = xthr; x[x < -xthr] = -xthr
y[y > xthr] = xthr; y[y < -xthr] = -xthr
z[z > zthr] = zthr ; z[z < -zthr] = -zthr
x *= 1./xthr*width; y *= 1./xthr*width
z += zthr
z *= height/(2.*zthr)
#translate
x += x0; y += y0
#plot
ax.plot_surface(x, y, z, cmap=cmap, norm=norm, **kwargs)
def make_bars(ax, x, y, height, width=1):
widths = np.array(width)*np.ones_like(x)
x = np.array(x).flatten()
y = np.array(y).flatten()
h = np.array(height).flatten()
w = np.array(widths).flatten()
norm = matplotlib.colors.Normalize(vmin=0, vmax=h.max())
for i in range(len(x.flatten())):
make_bar(ax, x0=x[i], y0=y[i], width = w[i] , height=h[i], norm=norm)
X, Y = np.meshgrid([1,2,3], [2,3,4])
Z = np.sin(X*Y)+1.5
make_bars(ax, X,Y,Z, width=0.2, )
plt.show()
I have a problem with PolyCollection matplotlib when I work with python 2.5. In random mode, it shows me following error: array dimensions must agree except for d_0 (file:collection.py - xy = np.concatenate([xy, np.zeros((1,2))])). This is my code:
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.collections import PolyCollection
from matplotlib.colors import colorConverter
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.font_manager as fm
from matplotlib.patches import Rectangle
import matplotlib.cm as cm
colors = ['#be1e2d',
'#666699',
'#92d5ea',
'#ee8310',
'#8d10ee',
'#5a3b16',
'#26a4ed',
'#f45a90',
'#e9e744']
row_names = ['2005','2006','2007']
data = [[1,1,1,1,1,1],[2,2,2,2,2,2],[4,4,4,4,4,4],[5,5,5,5,5,5],[7,7,7,7,7,7],[8,8,8,8,8,8]]
column_names = ['Ri','Pe']
#0 to start and end list
i=0
for i in range(len(data)):
data[i].append(0)
for i in range(len(data)):
data[i].insert(0,0)
dpi = 50.0
width = 460
height = 440
fig = plt.figure(1, figsize=(width/dpi,height/dpi),facecolor='w')
ax = fig.gca(projection='3d')#,azim=40, elev=0)
#Build axes
size = len(row_names) * len(data[0])
zs = np.arange(len(data))
# Setto le properties dei font
fp = fm.FontProperties()
fp.set_size('xx-small')
#Build Graph
verts = []
step = 1.0/len(data[0])
vertsColor = []
#Verify Single series or not
if len(column_names) > 1:
idx = 0
xs = np.arange(0, size, step)
change_color = len(column_names) - 1
for z in zs:
verts.append(zip(xs, data[z]))
vertsColor.append(colors[idx])
if idx == change_color:
idx = 0
else:
idx = idx + 1
################################################
# I THINK THE PROBLEM IS HERE
poly = PolyCollection(verts,facecolors=vertsColor)
ax.add_collection3d(poly, zs=zs, zdir='y')
################################################
ax.set_ylim3d(0, len(row_names)*len(column_names))
zs = np.arange(0,len(row_names) * len(column_names), len(column_names))
ax.set_yticks(zs)
lim = ((size*step)-step) - (len(row_names) - 1)
ax.set_xlim3d(0, lim)
rect = []
serie = []
#Build legend
for i in range(len(column_names)):
rect.insert(i,Rectangle((0,0), 1,1, facecolor=colors[i]))
serie.insert(i,column_names[i])
ax.legend((rect), (serie), loc=3, ncol=3, prop=fp)
else:
xs = np.arange(0, size, step)
for z in zs:
verts.append(zip(xs, data[z]))
poly = PolyCollection(verts,facecolors=colors) #[:len(data)])
poly.set_alpha(0.6)
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_xlabel('Rec')
lim = ((size*step)-step) - (len(row_names) - 1)
ax.set_xlim3d(0, lim)
ax.set_yticks(zs)
ax.set_ylim3d(0, len(row_names))
#Find Max Value
max_value = 0
i=0
for i in data:
mass = max(i)
if mass > max_value:
max_value = mass
#Font Label X,Y,Z
for label in ax.get_xticklabels():
label.set_fontproperties(fp)
for label in ax.get_yticklabels():
label.set_fontproperties(fp)
for label in ax.get_zticklabels():
label.set_fontproperties(fp)
ax.set_xticklabels('')
ax.set_ylabel('Years')
ax.set_yticklabels(row_names, fontproperties = fp)
ax.set_zlabel('Values')
ax.set_zlim3d(0, max_value)
ax.set_title('Test',x=0.5, y=1)
plt.show()
THANKS.