Related
So i have a plotWidget where i added multiple view-boxes that show different signals.
Based on the multiplePlotAxes.py example i have created this example:
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui
import numpy as np
pg.mkQApp()
pw = pg.PlotWidget()
pw.show()
pw.setWindowTitle('pyqtgraph example: MultiplePlotAxes')
p1 = pw.plotItem
p1.setMouseEnabled(x=True, y=False)
p1.setLabels(left='axis 1')
p2 = pg.ViewBox(name='axis 2')
ax2 = pg.AxisItem('right')
p1.layout.addItem(ax2, 2, 2)
p1.scene().addItem(p2)
ax2.linkToView(p1.vb)
p2.setXLink(p1)
ax2.setLabel('axis 2', color='red')
## create third ViewBox.
## this time we need to create a new axis as well.
p3 = pg.ViewBox(name='axis 3')
ax3 = pg.AxisItem('right')
p1.layout.addItem(ax3, 2, 3)
p1.scene().addItem(p3)
ax3.linkToView(p3)
p3.setXLink(p1)
ax3.setLabel('axis 3', color='green')
## create third ViewBox.
## this time we need to create a new axis as well.
p4 = pg.ViewBox(name='axis 4')
ax4 = pg.AxisItem('right')
p1.layout.addItem(ax4, 2, 4)
p1.scene().addItem(p4)
ax4.linkToView(p4)
p4.setXLink(p1)
ax4.setLabel('axis 4', color='blue')
## Handle view resizing
def updateViews():
## view has resized; update auxiliary views to match
global p1, p2, p3, p4
p2.setGeometry(p1.vb.sceneBoundingRect())
p3.setGeometry(p1.vb.sceneBoundingRect())
p4.setGeometry(p1.vb.sceneBoundingRect())
p2.linkedViewChanged(p1.vb, p2.XAxis)
p3.linkedViewChanged(p1.vb, p3.XAxis)
p4.linkedViewChanged(p1.vb, p4.XAxis)
def onSigRangeChanged(vb:pg.ViewBox):
print(f"{vb.name} axis moved, i want it to be fully seen by expanding the view range")
# [[xmin, xmax], [ymin, ymax]] = vb.viewRange()
# print(ymin, ymax)
updateViews()
p1.vb.sigResized.connect(updateViews)
p2.sigYRangeChanged.connect(onSigRangeChanged)
p3.sigYRangeChanged.connect(onSigRangeChanged)
p4.sigYRangeChanged.connect(onSigRangeChanged)
p1.plot([1,2,4,8,16,32])
p2.addItem(pg.PlotCurveItem([10,20,40,80,40,20], pen='r'))
p3.addItem(pg.PlotCurveItem([123,456,789,987,654,321], pen='g'))
p4.addItem(pg.PlotCurveItem([3200,1600,800,400,200,100], pen='b'))
if __name__ == '__main__':
pg.exec()
Basically when i grab the axis at (1) i want the main view-box to expand to cover the moved signal as well on Y axis.
X axis is locked for zooming.
I want the user to be able to "split" the signals but they should still be visible fully.
I tried working with sigYRangeChanged as seen in the example, but they Y range of the emitted viewBox does not show the range outside of the p1.vb, so i cannot resize to that one.
Any tips? Is there any magic i'm missing?
I have two sets of data thats needs to be plotted against time.
And I need to display them individually and together with the help of a radio button (or a similar way). The radio button code is based on https://stackoverflow.com/a/6697555/3910296
Everything looks good till the first set of data is loaded.
I clear the axes and re-plot the data whenever the next set of data is to be plotted. But when I click the next item in radio button, the new data is displayed, but x axis is not rotated. Is there any way to fix this.
Sample code to reproduce the problem I am facing
import datetime
import matplotlib.pyplot as plt
from matplotlib.widgets import RadioButtons
import matplotlib.dates as mdates
data0_usage = [45, 76, 20, 86, 79, 95, 14, 94, 59, 84]
data1_usage = [57, 79, 25, 28, 17, 46, 29, 52, 68, 92]
data0_timestamp = []
def draw_data_plot(ax, data_no):
if data_no == 0:
data_usage = data0_usage
data_color = 'go'
elif data_no == 1:
data_usage = data1_usage
data_color = 'ro'
ax.plot_date(data0_timestamp, data_usage, data_color)
ax.plot_date(data0_timestamp, data_usage, 'k', markersize=1)
def draw_plot():
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid(True)
# Adjust the subplots region to leave some space for the sliders and buttons
fig.subplots_adjust(left=0.25, bottom=0.25)
# Beautify the dates on x axis
time_format = mdates.DateFormatter('%Y-%b-%d %H:%M:%S')
plt.gca().xaxis.set_major_formatter(time_format)
plt.gcf().autofmt_xdate()
# Draw data0 plot
draw_data_plot(ax, 0)
# Add a set of radio buttons for changing color
color_radios_ax = fig.add_axes(
[0.025, 0.5, 0.15, 0.15], axisbg='lightgoldenrodyellow')
color_radios = RadioButtons(
color_radios_ax, ('data 0', 'data 1', 'all'),
active=0)
def color_radios_on_clicked(label):
ax.cla()
ax.grid(True)
if label == 'all':
draw_data_plot(ax, 0)
draw_data_plot(ax, 1)
else:
draw_data_plot(ax, int(label[5]))
ax.xaxis.set_major_formatter(time_format)
plt.gcf().autofmt_xdate()
fig.canvas.draw_idle()
color_radios.on_clicked(color_radios_on_clicked)
plt.show()
current_date = datetime.datetime.today()
for days in range(0, 10):
data0_timestamp.append(current_date + datetime.timedelta(days))
draw_plot()
Using Windows 10, Python 2.7.32, matplotlib 2.1.0
The problem
The reason plt.gcf().autofmt_xdate() fails for subsequent updates of the axes content is that at the point when it is called, there are axes inside the figure, which are no subplots. This is the axes created by fig.add_axes, being the radiobutton axes. autofmt_xdate will not know what to do with this axes, i.e. it would not know if this is the axes for which to rotate the labels or not, hence it will decide not to do anything. In the matplotlib source code this looks like
allsubplots = all(hasattr(ax, 'is_last_row') for ax in self.axes)
if len(self.axes) == 1:
# rotate labels
else:
if allsubplots:
for ax in self.get_axes():
if ax.is_last_row():
#rotate labels
else:
#set labels invisible
Because you have an axes, which is not a subplot, allsubplots == False and no rotation takes place.
The solution
The solution would be not to use autofmt_xdate(), but to do the work of rotating the ticklabels manually - which is really only 3 lines of code. Replace the line plt.gcf().autofmt_xdate() by
for label in ax.get_xticklabels():
label.set_ha("right")
label.set_rotation(30)
I am trying to make two sets of box plots using Matplotlib. I want each set of box plot filled (and points and whiskers) in a different color. So basically there will be two colors on the plot
My code is below, would be great if you can help make these plots in color. d0 and d1 are each list of lists of data. I want the set of box plots made with data in d0 in one color, and the set of box plots with data in d1 in another color.
plt.boxplot(d0, widths = 0.1)
plt.boxplot(d1, widths = 0.1)
To colorize the boxplot, you need to first use the patch_artist=True keyword to tell it that the boxes are patches and not just paths. Then you have two main options here:
set the color via ...props keyword argument, e.g.
boxprops=dict(facecolor="red"). For all keyword arguments, refer to the documentation
Use the plt.setp(item, properties) functionality to set the properties of the boxes, whiskers, fliers, medians, caps.
obtain the individual items of the boxes from the returned dictionary and use item.set_<property>(...) on them individually. This option is detailed in an answer to the following question: python matplotlib filled boxplots, where it allows to change the color of the individual boxes separately.
The complete example, showing options 1 and 2:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.normal(0.1, size=(100,6))
data[76:79,:] = np.ones((3,6))+0.2
plt.figure(figsize=(4,3))
# option 1, specify props dictionaries
c = "red"
plt.boxplot(data[:,:3], positions=[1,2,3], notch=True, patch_artist=True,
boxprops=dict(facecolor=c, color=c),
capprops=dict(color=c),
whiskerprops=dict(color=c),
flierprops=dict(color=c, markeredgecolor=c),
medianprops=dict(color=c),
)
# option 2, set all colors individually
c2 = "purple"
box1 = plt.boxplot(data[:,::-2]+1, positions=[1.5,2.5,3.5], notch=True, patch_artist=True)
for item in ['boxes', 'whiskers', 'fliers', 'medians', 'caps']:
plt.setp(box1[item], color=c2)
plt.setp(box1["boxes"], facecolor=c2)
plt.setp(box1["fliers"], markeredgecolor=c2)
plt.xlim(0.5,4)
plt.xticks([1,2,3], [1,2,3])
plt.show()
You can change the color of a box plot using setp on the returned value from boxplot(). This example defines a box_plot() function that allows the edge and fill colors to be specified:
import matplotlib.pyplot as plt
def box_plot(data, edge_color, fill_color):
bp = ax.boxplot(data, patch_artist=True)
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
plt.setp(bp[element], color=edge_color)
for patch in bp['boxes']:
patch.set(facecolor=fill_color)
return bp
example_data1 = [[1,2,0.8], [0.5,2,2], [3,2,1]]
example_data2 = [[5,3, 4], [6,4,3,8], [6,4,9]]
fig, ax = plt.subplots()
bp1 = box_plot(example_data1, 'red', 'tan')
bp2 = box_plot(example_data2, 'blue', 'cyan')
ax.legend([bp1["boxes"][0], bp2["boxes"][0]], ['Data 1', 'Data 2'])
ax.set_ylim(0, 10)
plt.show()
This would display as follows:
This question seems to be similar to that one (Face pattern for boxes in boxplots)
I hope this code solves your problem
import matplotlib.pyplot as plt
# fake data
d0 = [[4.5, 5, 6, 4],[4.5, 5, 6, 4]]
d1 = [[1, 2, 3, 3.3],[1, 2, 3, 3.3]]
# basic plot
bp0 = plt.boxplot(d0, patch_artist=True)
bp1 = plt.boxplot(d1, patch_artist=True)
for box in bp0['boxes']:
# change outline color
box.set(color='red', linewidth=2)
# change fill color
box.set(facecolor = 'green' )
# change hatch
box.set(hatch = '/')
for box in bp1['boxes']:
box.set(color='blue', linewidth=5)
box.set(facecolor = 'red' )
plt.show()
Change the color of a boxplot
import numpy as np
import matplotlib.pyplot as plt
#generate some random data
data = np.random.randn(200)
d= [data, data]
#plot
box = plt.boxplot(d, showfliers=False)
# change the color of its elements
for _, line_list in box.items():
for line in line_list:
line.set_color('r')
plt.show()
I am trying to animate a scatter plot (it needs to be a scatter plot as I want to vary the circle sizes). I have gotten the matplotlib documentation tutorial matplotlib documentation tutorial to work in my PyQT application, but would like to introduce blitting into the equation as my application will likely run on slower machines where the animation may not be as smooth.
I have had a look at many examples of animations with blitting, but none ever use a scatter plot (they use plot or lines) and so I am really struggling to figure out how to initialise the animation (the bits that don't get re-rendered every time) and the ones that do. I have tried quite a few things, and seem to be getting nowhere (and I am sure they would cause more confusion than help!). I assume that I have missed something fairly fundamental. Has anyone done this before? Could anyone help me out splitting the figure into the parts that need to be initiated and the ones that get updates?
The code below works, but does not blit. Appending
blit=True
to the end of the animation call yields the following error:
RuntimeError: The animation function must return a sequence of Artist objects.
Any help would be great.
Regards
FP
import numpy as np
from PyQt4 import QtGui, uic
import sys
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
class MainWindow(QtGui.QMainWindow):
def __init__(self):
super(MainWindow, self).__init__()
self.setupAnim()
self.show()
def setupAnim(self):
self.fig = plt.figure(figsize=(7, 7))
self.ax = self.fig.add_axes([0, 0, 1, 1], frameon=False)
self.ax.set_xlim(0, 1), self.ax.set_xticks([])
self.ax.set_ylim(0, 1), self.ax.set_yticks([])
# Create rain data
self.n_drops = 50
self.rain_drops = np.zeros(self.n_drops, dtype=[('position', float, 2),
('size', float, 1),
('growth', float, 1),
('color', float, 4)])
# Initialize the raindrops in random positions and with
# random growth rates.
self.rain_drops['position'] = np.random.uniform(0, 1, (self.n_drops, 2))
self.rain_drops['growth'] = np.random.uniform(50, 200, self.n_drops)
# Construct the scatter which we will update during animation
# as the raindrops develop.
self.scat = self.ax.scatter(self.rain_drops['position'][:, 0], self.rain_drops['position'][:, 1],
s=self.rain_drops['size'], lw=0.5, edgecolors=self.rain_drops['color'],
facecolors='none')
self.animation = FuncAnimation(self.fig, self.update, interval=10)
plt.show()
def update(self, frame_number):
# Get an index which we can use to re-spawn the oldest raindrop.
self.current_index = frame_number % self.n_drops
# Make all colors more transparent as time progresses.
self.rain_drops['color'][:, 3] -= 1.0/len(self.rain_drops)
self.rain_drops['color'][:, 3] = np.clip(self.rain_drops['color'][:, 3], 0, 1)
# Make all circles bigger.
self.rain_drops['size'] += self.rain_drops['growth']
# Pick a new position for oldest rain drop, resetting its size,
# color and growth factor.
self.rain_drops['position'][self.current_index] = np.random.uniform(0, 1, 2)
self.rain_drops['size'][self.current_index] = 5
self.rain_drops['color'][self.current_index] = (0, 0, 0, 1)
self.rain_drops['growth'][self.current_index] = np.random.uniform(50, 200)
# Update the scatter collection, with the new colors, sizes and positions.
self.scat.set_edgecolors(self.rain_drops['color'])
self.scat.set_sizes(self.rain_drops['size'])
self.scat.set_offsets(self.rain_drops['position'])
if __name__== '__main__':
app = QtGui.QApplication(sys.argv)
window = MainWindow()
sys.exit(app.exec_())
You need to add return self.scat, at the end of the update method if you want to use FuncAnimation with blit=True. See also this nice StackOverflow post that presents an example of a scatter plot animation with matplotlib using blit.
As a side-note, if you wish to embed a mpl figure in a Qt application, it is better to avoid using the pyplot interface and to use instead the Object Oriented API of mpl as suggested in the matplotlib documentation.
This could be achieved, for example, as below, where mplWidget can be embedded as any other Qt widget in your main application. Note that I renamed the update method to update_plot to avoid conflict with the already existing method of the FigureCanvasQTAgg class.
import numpy as np
from PyQt4 import QtGui
import sys
import matplotlib as mpl
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
class mplWidget(FigureCanvasQTAgg):
def __init__(self):
super(mplWidget, self).__init__(mpl.figure.Figure(figsize=(7, 7)))
self.setupAnim()
self.show()
def setupAnim(self):
ax = self.figure.add_axes([0, 0, 1, 1], frameon=False)
ax.axis([0, 1, 0, 1])
ax.axis('off')
# Create rain data
self.n_drops = 50
self.rain_drops = np.zeros(self.n_drops, dtype=[('position', float, 2),
('size', float, 1),
('growth', float, 1),
('color', float, 4)
])
# Initialize the raindrops in random positions and with
# random growth rates.
self.rain_drops['position'] = np.random.uniform(0, 1, (self.n_drops, 2))
self.rain_drops['growth'] = np.random.uniform(50, 200, self.n_drops)
# Construct the scatter which we will update during animation
# as the raindrops develop.
self.scat = ax.scatter(self.rain_drops['position'][:, 0],
self.rain_drops['position'][:, 1],
s=self.rain_drops['size'],
lw=0.5, facecolors='none',
edgecolors=self.rain_drops['color'])
self.animation = FuncAnimation(self.figure, self.update_plot,
interval=10, blit=True)
def update_plot(self, frame_number):
# Get an index which we can use to re-spawn the oldest raindrop.
indx = frame_number % self.n_drops
# Make all colors more transparent as time progresses.
self.rain_drops['color'][:, 3] -= 1./len(self.rain_drops)
self.rain_drops['color'][:, 3] = np.clip(self.rain_drops['color'][:, 3], 0, 1)
# Make all circles bigger.
self.rain_drops['size'] += self.rain_drops['growth']
# Pick a new position for oldest rain drop, resetting its size,
# color and growth factor.
self.rain_drops['position'][indx] = np.random.uniform(0, 1, 2)
self.rain_drops['size'][indx] = 5
self.rain_drops['color'][indx] = (0, 0, 0, 1)
self.rain_drops['growth'][indx] = np.random.uniform(50, 200)
# Update the scatter collection, with the new colors,
# sizes and positions.
self.scat.set_edgecolors(self.rain_drops['color'])
self.scat.set_sizes(self.rain_drops['size'])
self.scat.set_offsets(self.rain_drops['position'])
return self.scat,
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
window = mplWidget()
sys.exit(app.exec_())
This is my first attempt using Matplotlib and I am in need of some guidance. I am trying to generate plot with 4 y-axes, two on the left and two on the right with shared x axis. Here's my dataset on shared dropbox folder
import pandas as pd
%matplotlib inline
url ='http://dropproxy.com/f/D34'
df= pd.read_csv(url, index_col=0, parse_dates=[0])
df.plot()
This is what the simple pandas plot looks like:
I would like to plot this similar to the example below, with TMAX and TMIN on primary y-axis (on same scale).
My attempt:
There's one example I found on the the matplotlib listserv..I am trying to adapt it to my data but something is not working right...Here's the script.
# multiple_yaxes_with_spines.py
# This is a template Python program for creating plots (line graphs) with 2, 3,
# or 4 y-axes. (A template program is one that you can readily modify to meet
# your needs). Almost all user-modifiable code is in Section 2. For most
# purposes, it should not be necessary to modify anything else.
# Dr. Phillip M. Feldman, 27 Oct, 2009
# Acknowledgment: This program is based on code written by Jae-Joon Lee,
# URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/
# examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup
# Section 1: Import modules, define functions, and allocate storage.
import matplotlib.pyplot as plt
from numpy import *
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for sp in ax.spines.itervalues():
sp.set_visible(False)
def make_spine_invisible(ax, direction):
if direction in ["right", "left"]:
ax.yaxis.set_ticks_position(direction)
ax.yaxis.set_label_position(direction)
elif direction in ["top", "bottom"]:
ax.xaxis.set_ticks_position(direction)
ax.xaxis.set_label_position(direction)
else:
raise ValueError("Unknown Direction : %s" % (direction,))
ax.spines[direction].set_visible(True)
# Create list to store dependent variable data:
y= [0, 0, 0, 0, 0]
# Section 2: Define names of variables and the data to be plotted.
# `labels` stores the names of the independent and dependent variables). The
# first (zeroth) item in the list is the x-axis label; remaining labels are the
# first y-axis label, second y-axis label, and so on. There must be at least
# two dependent variables and not more than four.
labels= ['Date', 'Maximum Temperature', 'Solar Radiation',
'Rainfall', 'Minimum Temperature']
# Plug in your data here, or code equations to generate the data if you wish to
# plot mathematical functions. x stores values of the independent variable;
# y[1], y[2], ... store values of the dependent variable. (y[0] is not used).
# All of these objects should be NumPy arrays.
# If you are plotting mathematical functions, you will probably want an array of
# uniformly spaced values of x; such an array can be created using the
# `linspace` function. For example, to define x as an array of 51 values
# uniformly spaced between 0 and 2, use the following command:
# x= linspace(0., 2., 51)
# Here is an example of 6 experimentally measured y1-values:
# y[1]= array( [3, 2.5, 7.3e4, 4, 8, 3] )
# Note that the above statement requires both parentheses and square brackets.
# With a bit of work, one could make this program read the data from a text file
# or Excel worksheet.
# Independent variable:
x = df.index
# First dependent variable:
y[1]= df['TMAX']
# Second dependent variable:
y[2]= df['RAD']
y[3]= df['RAIN']
y[4]= df['TMIN']
# Set line colors here; each color can be specified using a single-letter color
# identifier ('b'= blue, 'r'= red, 'g'= green, 'k'= black, 'y'= yellow,
# 'm'= magenta, 'y'= yellow), an RGB tuple, or almost any standard English color
# name written without spaces, e.g., 'darkred'. The first element of this list
# is not used.
colors= [' ', '#C82121', '#E48E3C', '#4F88BE', '#CF5ADC']
# Set the line width here. linewidth=2 is recommended.
linewidth= 2
# Section 3: Generate the plot.
N_dependents= len(labels) - 1
if N_dependents > 4: raise Exception, \
'This code currently handles a maximum of four independent variables.'
# Open a new figure window, setting the size to 10-by-7 inches and the facecolor
# to white:
fig= plt.figure(figsize=(16,9), dpi=120, facecolor=[1,1,1])
host= fig.add_subplot(111)
host.set_xlabel(labels[0])
# Use twinx() to create extra axes for all dependent variables except the first
# (we get the first as part of the host axes). The first element of y_axis is
# not used.
y_axis= (N_dependents+2) * [0]
y_axis[1]= host
for i in range(2,len(labels)+1): y_axis[i]= host.twinx()
if N_dependents >= 3:
# The following statement positions the third y-axis to the right of the
# frame, with the space between the frame and the axis controlled by the
# numerical argument to set_position; this value should be between 1.10 and
# 1.2.
y_axis[3].spines["right"].set_position(("axes", 1.15))
make_patch_spines_invisible(y_axis[3])
make_spine_invisible(y_axis[3], "right")
plt.subplots_adjust(left=0.0, right=0.8)
if N_dependents >= 4:
# The following statement positions the fourth y-axis to the left of the
# frame, with the space between the frame and the axis controlled by the
# numerical argument to set_position; this value should be between 1.10 and
# 1.2.
y_axis[4].spines["left"].set_position(("axes", -0.15))
make_patch_spines_invisible(y_axis[4])
make_spine_invisible(y_axis[4], "left")
plt.subplots_adjust(left=0.2, right=0.8)
p= (N_dependents+1) * [0]
# Plot the curves:
for i in range(1,N_dependents+1):
p[i], = y_axis[i].plot(x, y[i], colors[i],
linewidth=linewidth, label=labels[i])
# Set axis limits. Use ceil() to force upper y-axis limits to be round numbers.
host.set_xlim(x.min(), x.max())
host.set_xlabel(labels[0], size=16)
for i in range(1,N_dependents+1):
y_axis[i].set_ylim(0.0, ceil(y[i].max()))
y_axis[i].set_ylabel(labels[i], size=16)
y_axis[i].yaxis.label.set_color(colors[i])
for sp in y_axis[i].spines.itervalues():
sp.set_color(colors[i])
for obj in y_axis[i].yaxis.get_ticklines():
# `obj` is a matplotlib.lines.Line2D instance
obj.set_color(colors[i])
obj.set_markeredgewidth(3)
for obj in y_axis[i].yaxis.get_ticklabels():
obj.set_color(colors[i])
obj.set_size(12)
obj.set_weight(600)
# To enable the legend, uncomment the following two lines:
lines= p[1:]
host.legend(lines, [l.get_label() for l in lines])
plt.draw(); plt.show()
And the output
How can I put the scale on max and min temp on a same scale? Also, how can I get rid of second y-axis with black color, scaled from 0 to 10?
Is there a simpler way to achieve this?
How can I put the scale on max and min temp on a same scale?
Plot them in the same axes.
Also, how can I get rid of second y-axis with black color, scaled from 0 to 10?
Do not create that axes.
You want to plot four variables, two of them can go in the same subplot so you only need three subplots. But you are creating five of them?
Step by step
Keep in mind: different y scales <-> different subplots sharing x-axis.
Two variables with a common scale (left), two variables with independent scales (right).
Create the primary subplot, let's call it ax1. Plot everything you want in it, in this case TMIN and TMAX as stated in your question.
Create a twin subplot sharing x axis twinx(ax=ax1). Plot the third variable, say RAIN.
Create another twin subplot twinx(ax=ax1). Plot the fourth variable 'RAD'.
Adjust colors, labels, spine positions... to your heart's content.
Unsolicited advice: do not try to fix code you don't understand.
Variation of the original plot showing how you can plot variables on multiple axes
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
url ='http://dropproxy.com/f/D34'
df= pd.read_csv(url, index_col=0, parse_dates=[0])
fig = plt.figure()
ax = fig.add_subplot(111) # Primary y
ax2 = ax.twinx() # Secondary y
# Plot variables
ax.plot(df.index, df['TMAX'], color='red')
ax.plot(df.index, df['TMIN'], color='green')
ax2.plot(df.index, df['RAIN'], color='orange')
ax2.plot(df.index, df['RAD'], color='yellow')
# Custom ylimit
ax.set_ylim(0,50)
# Custom x axis date formats
import matplotlib.dates as mdates
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
I modified #bishopo's suggestions to generate what I wanted, however, the plot still needs some tweaking with font sizes for axes label.
Here's what I have done so far.
import pandas as pd
%matplotlib inline
url ='http://dropproxy.com/f/D34'
df= pd.read_csv(url, index_col=0, parse_dates=[0])
from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import matplotlib.pyplot as plt
if 1:
# Set the figure size, dpi, and background color
fig = plt.figure(1, (16,9),dpi =300, facecolor = 'W',edgecolor ='k')
# Update the tick label size to 12
plt.rcParams.update({'font.size': 12})
host = host_subplot(111, axes_class=AA.Axes)
plt.subplots_adjust(right=0.75)
par1 = host.twinx()
par2 = host.twinx()
par3 = host.twinx()
offset = 60
new_fixed_axis = par2.get_grid_helper().new_fixed_axis
new_fixed_axis1 = host.get_grid_helper().new_fixed_axis
par2.axis["right"] = new_fixed_axis(loc="right",
axes=par2,
offset=(offset, 0))
par3.axis["left"] = new_fixed_axis1(loc="left",
axes=par3,
offset=(-offset, 0))
par2.axis["right"].toggle(all=True)
par3.axis["left"].toggle(all=True)
par3.axis["right"].set_visible(False)
# Set limit on both y-axes
host.set_ylim(-30, 50)
par3.set_ylim(-30,50)
host.set_xlabel("Date")
host.set_ylabel("Minimum Temperature ($^\circ$C)")
par1.set_ylabel("Solar Radiation (W$m^{-2}$)")
par2.set_ylabel("Rainfall (mm)")
par3.set_ylabel('Maximum Temperature ($^\circ$C)')
p1, = host.plot(df.index,df['TMIN'], 'm,')
p2, = par1.plot(df.index, df.RAD, color ='#EF9600', linestyle ='--')
p3, = par2.plot(df.index, df.RAIN, '#09BEEF')
p4, = par3.plot(df.index, df['TMAX'], '#FF8284')
par1.set_ylim(0, 36)
par2.set_ylim(0, 360)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
par2.axis["right"].label.set_color(p3.get_color())
par3.axis["left"].label.set_color(p4.get_color())
tkw = dict(size=5, width=1.5)
host.tick_params(axis='y', colors=p1.get_color(), **tkw)
par1.tick_params(axis='y', colors=p2.get_color(), **tkw)
par2.tick_params(axis='y', colors=p3.get_color(), **tkw)
par3.tick_params(axis='y', colors=p4.get_color(), **tkw)
host.tick_params(axis='x', **tkw)
par1.axis["right"].label.set_fontsize(16)
par2.axis["right"].label.set_fontsize(16)
par3.axis["left"].label.set_fontsize(16)
host.axis["bottom"].label.set_fontsize(16)
host.axis["left"].label.set_fontsize(16)
plt.figtext(.5,.92,'Weather Data', fontsize=22, ha='center')
plt.draw()
plt.show()
fig.savefig("Test1.png")
The output