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The following snippet creates a list myHLines of (y) values that is arithmetically growing.
I want to use them as minor y ticks in a matplotlib plot.
How can I do this?
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame({'a': [1, 3, 10, 30, 100]})
myMin = df.a.min()
myMax = df.a.max()
ratio = 3
myHLines = [myMin * ratio ** i for i in range(1000) if myMin * ratio ** i < myMax]
print("myHLines=", myHLines)
# myHLines= [1, 3, 9, 27, 81]
plt.plot(df, '-o', markersize=2, c='r')
plt.show()
Is the scale of the y-axis you want to achieve the y-axis shown in the graph below?
plt.plot(df, '-o', markersize=2, c='r')
locs, labels = plt.yticks()
new_y = sorted(myHLines + locs.tolist()[1:-1])
# print(new_y)
plt.yticks(new_y)
plt.show()
Here is a part of the plot that I have
I need to create TrendLine that would be extended to the 3th
quarter of this plot... I can's think of any solution.
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
x = [1, 8, 12, 20]
y = [1, 8.4, 12.5, 20]
fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot()
ax.set_xlim(-30, 30)
ax.set_ylim(-20, 20)
plt.subplot().spines['left'].set_position('center')
plt.subplot().spines['bottom'].set_position('center')
plt.plot(x,y, 'b.', ms=20)
plt.minorticks_on()
ax.grid(True, which='both')
mean_line = ax.plot()
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
plt.plot(x,p(x),"r--")
plt.show()
I don't think reverse x and y would do the job, it would be limited to the poly1d that pass (0,0)
I think the extending method should be using the fitted line itself.
so a more general method is extend the x and use the poly1d(z) to calculate an extended line. z is description of the fitted line, so feeding x value to z would draw the line.
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')
x = [1, 8, 12, 20]
y = [1, 8.4, 12.5, 20]
# make an xx that with from -20 to 20
#xx =np.array(x)
#xx = sorted(np.concatenate((-xx, xx), axis=0))
xx = [-20, 20] # also work
fig, ax = plt.subplots(figsize=(10,10))
ax.set_xlim(-30, 30)
ax.set_ylim(-20, 20)
plt.subplot().spines['left'].set_position('center')
plt.subplot().spines['bottom'].set_position('center')
plt.subplot().spines['right'].set_color('none')
plt.subplot().spines['top'].set_color('none')
plt.plot(x,y, 'b.', ms=20)
plt.minorticks_on()
#ax.grid(True, which='both')
plt.subplot().grid(True, which='both')
mean_line = ax.plot()
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
plt.plot(xx,p(xx),"r--")
plt.show()
if you zoomin near the (0,0), you should see it's not passing the origin point.
zoomed in near (0,0)
result image
I don't have any experience with trendlines, but I created a composite of existing x and y values with different signs and drew the following graph.
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
x = [1, 8, 12, 20]
y = [1, 8.4, 12.5, 20]
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot()
ax.set_xlim(-30, 30)
ax.set_ylim(-20, 20)
plt.subplot().spines['left'].set_position('center')
plt.subplot().spines['bottom'].set_position('center')
plt.plot(x,y, 'b.', ms=20)
plt.minorticks_on()
ax.grid(True, which='both')
mean_line = ax.plot()
# update
xx =np.array(x)
xx = sorted(np.concatenate((-xx, xx), axis=0))
yy =np.array(y)
yy = sorted(np.concatenate((-yy, yy), axis=0))
z = np.polyfit(xx, yy, 1)
p = np.poly1d(z)
plt.plot(xx,p(xx),"r--")
plt.show()
Using matplotlib I create a scatter plot animation that shows a new point after each second and shows all old points partly transparent. Each point is defined by x and y, but also by a category s. I want the color of the points to be tied to its category. Ideally that means that the array s contains values 1, 2 and 3, and the colors belonging to those values are defined seperately. However, I can not get this to work.
What I do get to work is to specify the edgecolors of each point individually in s, the code for this is shown below.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as plti
import matplotlib.animation
s = [[1,0,0],[0,1,0],[0,0,1]];
x = [525,480,260];
y = [215,180,180];
img = plti.imread('myimage.png')
fig, ax = plt.subplots()
plt.imshow(img)
plt.axis('off')
x_vals = []
y_vals = []
intensity = []
iterations = len(x)
colors = []
t_vals = np.linspace(0,iterations-1,iterations,dtype=int)
scatter = ax.scatter(x_vals, y_vals, s=100, c=colors, vmin=0, vmax=1)
def init():
pass
def update(t):
global x, y, x_vals, y_vals, intensity
x_vals.extend([x[t]])
y_vals.extend([y[t]])
scatter.set_offsets(np.c_[x_vals,y_vals])
intensity = np.concatenate((np.array(intensity), np.ones(1)))
if len(intensity) > 1:
intensity[-2] = 0.5
scatter.set_array(intensity)
colors.extend([s[t]])
scatter.set_color(colors)
return ani
ani = matplotlib.animation.FuncAnimation(fig, update, frames=t_vals, interval=1000, repeat=False, init_func=init)
plt.show()
Simply changing c=colors to facecolor=colors does not work. Also I have tried to use colormaps but I cannot get it to work using that either.
The resulting animation from the code above looks as below.
However, the animation should look like this..
So my question is; does someone know how to tie the facecolor of each point to the category that that point belongs to?
The normal way to plot plots with points in different colors in matplotlib is to pass a list of colors as a parameter.
E.g.:
import matplotlib.pyplot
matplotlib.pyplot.scatter([1,2,3],[4,5,6],color=['red','green','blue'])
But if for some reason you wanted to do it with just one call, you can make a big list of colors, with a list comprehension and a bit of flooring division:
import matplotlib
import numpy as np
X = [1,2,3,4]
Ys = np.array([[4,8,12,16],
[1,4,9,16],
[17, 10, 13, 18],
[9, 10, 18, 11],
[4, 15, 17, 6],
[7, 10, 8, 7],
[9, 0, 10, 11],
[14, 1, 15, 5],
[8, 15, 9, 14],
[20, 7, 1, 5]])
nCols = len(X)
nRows = Ys.shape[0]
colors = matplotlib.cm.rainbow(np.linspace(0, 1, len(Ys)))
cs = [colors[i//len(X)] for i in range(len(Ys)*len(X))] #could be done with numpy's repmat
Xs=X*nRows #use list multiplication for repetition
matplotlib.pyplot.scatter(Xs,Ys.flatten(),color=cs)
The problem occurred because the line scatter.set_array(intensity) was called before scatter.set_color(colors). So instead of defining the intensity by a seperate variable, it is instead integrated into the colors directly. The following code produces the desired result.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as plti
import matplotlib.animation
s = [1,2,3];
x = [525,480,260];
y = [215,180,180];
img = plti.imread('myimage.png')
fig, ax = plt.subplots()
plt.imshow(img)
plt.axis('off')
x_vals = []
y_vals = []
iterations = len(x)
colors = []
t_vals = np.linspace(0,iterations-1,iterations,dtype=int)
scatter = ax.scatter(x_vals, y_vals, s=100, color=colors, vmin=0, vmax=1)
def init():
pass
def update(t):
global x, y, x_vals, y_vals
x_vals.extend([x[t]])
y_vals.extend([y[t]])
scatter.set_offsets(np.c_[x_vals,y_vals])
if t > 0:
if s[t-1] == 1:
colors[t-1] = [1,0,0,0.5];
elif s[t-1] == 2:
colors[t-1] = [0,1,0,0.5];
else:
colors[t-1] = [0,0,1,0.5];
if s[t] == 1:
colors.extend([[1,0,0,1]])
elif s[t] == 2:
colors.extend([[0,1,0,1]])
else:
colors.extend([[0,0,1,1]])
scatter.set_color(colors);
return ani
ani = matplotlib.animation.FuncAnimation(fig, update, frames=t_vals, init_func=init, interval=1000, repeat=False)
plt.show()
I am trying to create several plots all with the same colorbar limits in a loop.
I set the limits of the contour plot with map.contourf(x, y, U_10m, vmin=0, vmax=25) and this seems to give consistent colour scales for each plot. However, when I use cbar = plt.colorbar(boundaries=np.linspace(0,1,25), ticks=[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]) # sets all cbar to same limits each plot does not have the same colorbar limits (examples of two plots with different colorbars and code below).
from netCDF4 import Dataset as NetCDFFile
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
def wrf_tseries_contour_plotter (
ncfile, time_ind, lowerllat, upperrlat, lowerllon, upperrlon, output_dir):
'''
EDITED FROM http://www.atmos.washington.edu/~ovens/wrfwinds.html
'''
print 'timestep:', + time_ind
#which figure is being generated 0 = 00:00, 144 = 23:50
nc = NetCDFFile(ncfile, 'r')
#
# get the actual longitudes, latitudes, and corners
lons = nc.variables['XLONG'][time_ind]
lats = nc.variables['XLAT'][time_ind]
#get the u10 to plot as a contour instead of t2m
U10_raw = nc.variables['U10'][time_ind] #61 is the index for 10:00am
V10_raw = nc.variables['V10'][time_ind]
#bodge to calculate U from U and V (u10 = sqrt(u^2+v^2))
v2 = np.square(V10_raw)
u2 = np.square(U10_raw)
U_10m = np.sqrt(u2 + v2)
# Make map
map = Basemap(projection='cyl',llcrnrlat=lowerllat,urcrnrlat=upperrlat,
llcrnrlon=lowerllon,urcrnrlon=upperrlon,
resolution='h')
# lllat, urlat,lllon, urlon set outside of f(x) lower left and
# upper right lat/lon for basemap axis limits
x, y = map(lons[:,:], lats[:,:])
map.contourf(x, y, U_10m, vmin=0, vmax=25)
map.drawcoastlines(linewidth = 0.5, color = '0.15')
#thinner lines for larger scale map
#plt.clim(0, 25) #added
cbar = plt.colorbar(boundaries=np.linspace(0,1,25), ticks=[0, 2, 4, 6,
8, 10, 12, 14, 16, 18, 20, 22, 24]) # sets all cbar to same limits
cbar.set_label('10m U (m/s)', size=12)
cbar.ax.set_yticklabels([0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24])
#cbar.set_clim(0, 25)
time_str = str(time_ind)
plt.title('gust 20070724' + '_' + time_str)
fig_name = '\gust20070724_'+ time_str + '.png'
plt.savefig(output_dir + fig_name)
plt.close()
#set inputs for wrf_tseries_contour_plotter(ncfile, time_ind, lllat, urlat,
lllon, urlon, output_dir)
ncfile = 'E:\WRFout_UK2Fino\wrfout_d03_2007-07-24_00%3A00%3A00'
tlist = np.arange(0,145)
#set the lower left/upper right lat/lon for axis limits on the maps
lowerllat=48
upperrlat=63
lowerllon=-10
upperrlon=25
#set output directory for figures
output_dir = '''C:\cbar_test'''
for time_ind in tlist:
wrf_tseries_contour_plotter(ncfile, time_ind, lowerllat, upperrlat,
lowerllon, upperrlon, output_dir)
You have to use vmin and vmax values to set boundaries of a colorbar like in this example:
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
# test data
x = np.linspace(0,15,100)
X,Y = np.meshgrid(x,x)
SPD1 = np.sqrt(X*X + Y*Y)
SPD2 = SPD1 * 1.3
fig = plt.figure()
# implement boundaries of colorbar and it ticks
vmin, vmax = 0, 26
levels = np.linspace(vmin,vmax,14)
# 1st subplot
ax1 = fig.add_subplot(221)
# Set contour levels and limits
CF1 = ax1.contourf(X, Y, SPD1, levels=levels, vmax=vmax, vmin=vmin)
cbar = plt.colorbar(CF1)
cbar.set_label('10m U (m/s)', size=12)
#2nd subplot
ax1 = fig.add_subplot(222)
CF1 = ax1.contourf(X, Y, SPD2, levels=levels, vmax=vmax, vmin=vmin)
cbar = plt.colorbar(CF1)
cbar.set_label('10m U (m/s)', size=12)
plt.tight_layout()
plt.show()
However you have to select vmin, vmax correctly because of if your values are outside boundaries of colorbar they will not shown (right upper corner of 2nd subplot).
import matplotlib.pyplot as plt
gridnumber = range(1,4)
b1 = plt.bar(gridnumber, [0.2, 0.3, 0.1], width=0.4,
label="Bar 1", align="center")
b2 = plt.bar(gridnumber, [0.3, 0.2, 0.2], color="red", width=0.4,
label="Bar 2", align="center")
plt.ylim([0,0.5])
plt.xlim([0,4])
plt.xticks(gridnumber)
plt.legend()
plt.show()
Currently b1 and b2 overlap each other. How do I plot them separately like so:
There is an example in the matplotlib site. Basically, you just shift the x values by width. Here is the relevant bit:
import numpy as np
import matplotlib.pyplot as plt
N = 5
menMeans = (20, 35, 30, 35, 27)
menStd = (2, 3, 4, 1, 2)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
fig = plt.figure()
ax = fig.add_subplot(111)
rects1 = ax.bar(ind, menMeans, width, color='royalblue', yerr=menStd)
womenMeans = (25, 32, 34, 20, 25)
womenStd = (3, 5, 2, 3, 3)
rects2 = ax.bar(ind+width, womenMeans, width, color='seagreen', yerr=womenStd)
# add some
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind + width / 2)
ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )
ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )
plt.show()
Below answer will explain each and every line of code in the simplest manner possible:
# Numbers of pairs of bars you want
N = 3
# Data on X-axis
# Specify the values of blue bars (height)
blue_bar = (23, 25, 17)
# Specify the values of orange bars (height)
orange_bar = (19, 18, 14)
# Position of bars on x-axis
ind = np.arange(N)
# Figure size
plt.figure(figsize=(10,5))
# Width of a bar
width = 0.3
# Plotting
plt.bar(ind, blue_bar , width, label='Blue bar label')
plt.bar(ind + width, orange_bar, width, label='Orange bar label')
plt.xlabel('Here goes x-axis label')
plt.ylabel('Here goes y-axis label')
plt.title('Here goes title of the plot')
# xticks()
# First argument - A list of positions at which ticks should be placed
# Second argument - A list of labels to place at the given locations
plt.xticks(ind + width / 2, ('Xtick1', 'Xtick3', 'Xtick3'))
# Finding the best position for legends and putting it
plt.legend(loc='best')
plt.show()
Sometimes could be tricky to find the right bar width. I usually use this np.diff to find the right dimension.
import numpy as np
import matplotlib.pyplot as plt
#The data
womenMeans = (25, 32, 34, 20, 25)
menMeans = (20, 35, 30, 35, 27)
indices = [5.5,6,7,8.5,8.9]
#Calculate optimal width
width = np.min(np.diff(indices))/3
fig = plt.figure()
ax = fig.add_subplot(111)
# matplotlib 3.0 you have to use align
ax.bar(indices-width,womenMeans,width,color='b',label='-Ymin',align='edge')
ax.bar(indices,menMeans,width,color='r',label='Ymax',align='edge')
ax.set_xlabel('Test histogram')
plt.show()
# matplotlib 2.0 (you could avoid using align)
# ax.bar(indices-width,womenMeans,width,color='b',label='-Ymin')
# ax.bar(indices,menMeans,width,color='r',label='Ymax')
This is the result:
What if my indices on my x axis are nominal values like names:
#
import numpy as np
import matplotlib.pyplot as plt
# The data
womenMeans = (25, 32, 34, 20, 25)
menMeans = (20, 35, 30, 35, 27)
indices = range(len(womenMeans))
names = ['Asian','European','North Amercian','African','Austrailian','Martian']
# Calculate optimal width
width = np.min(np.diff(indices))/3.
fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(indices-width/2.,womenMeans,width,color='b',label='-Ymin')
ax.bar(indices+width/2.,menMeans,width,color='r',label='Ymax')
#tiks = ax.get_xticks().tolist()
ax.axes.set_xticklabels(names)
ax.set_xlabel('Test histogram')
plt.show()
Here are two examples of creating a side-by-side bar chart when you have more than two "categories" in a group.
Manual Method
Manually set the position and width of each bar.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
coins = ['penny', 'nickle', 'dime', 'quarter']
worth = np.array([.01, .05, .10, .25])
# Coin values times *n* coins
# This controls how many bars we get in each group
values = [worth*i for i in range(1,6)]
n = len(values) # Number of bars to plot
w = .15 # With of each column
x = np.arange(0, len(coins)) # Center position of group on x axis
for i, value in enumerate(values):
position = x + (w*(1-n)/2) + i*w
plt.bar(position, value, width=w, label=f'{i+1}x')
plt.xticks(x, coins);
plt.ylabel('Monetary Value')
plt.gca().yaxis.set_major_formatter(ticker.FormatStrFormatter('$%.2f'))
plt.legend()
Pandas Method
If you put the data into a pandas DataFrame, pandas will do the hard stuff for you.
import pandas as pd
coins = ['penny', 'nickle', 'dime', 'quarter']
worth = [0.01, 0.05, 0.10, 0.25]
df = pd.DataFrame(worth, columns=['1x'], index=coins)
df['2x'] = df['1x'] * 2
df['3x'] = df['1x'] * 3
df['4x'] = df['1x'] * 4
df['5x'] = df['1x'] * 5
from matplotlib import ticker
import matplotlib.pyplot as plt
df.plot(kind='bar')
plt.ylabel('Monetary Value')
plt.gca().yaxis.set_major_formatter(ticker.FormatStrFormatter('$%.2f'))
plt.gca().xaxis.set_tick_params(rotation=0)
Pandas creates a similar figure...