I am trying to add a secondary axis to a plot and make the grid lines equally spaced along y, but I the code below doesn't do what it is supposed to. y2A,y2B values are not right - they refer to xlim values not ylim. Any ideas?
import numpy as np
import matplotlib.pyplot as plt
def CtoF(y):
return y * 1.8 + 32
def FtoC(y):
return (y - 32) / 1.8
def setAxis2(ax1):
ax2 = ax1.secondary_yaxis('right', functions=(CtoF, FtoC))
ax2.set_ylabel('Fahrenheit')
return ax2
x = np.arange(100)
y = np.random.rand(100)
plt.plot(x,y)
ax1 = plt.gca()
ax1.set_ylabel('Celsius')
ax1.grid()
#Add the 2nd axis for Fahrenheit
ax2 = setAxis2(ax1)
#Get the ylimits and space them equally
[y1A,y1B] = ax1.get_ylim()
[y2A,y2B] = ax2.get_ylim()
ax1.set_yticks(np.linspace(y1A,y1B, 10))
ax2.set_yticks(np.linspace(y2A,y2B, 10)) #Doesn't work
print(y1A,y1B) #
print(y2A,y2B) #Doesn't output the expected values
I tried another method that works well (with the same versions of matplotlib), but the question remains about the issue above. The method that works is below:
ticks1 = ax1.get_yticks()
ticks2 = CtoF(ticks1)
ax2.set_yticks(ticks2)
Instead of getting y2A and y2B from the y-limits of ax2, we can calculate them directly with CtoF:
# Get the y-limits and space them equally.
y1A, y1B = ax1.get_ylim()
y2A, y2B = map(CtoF, (y1A, y1B))
n = 10
ax1.set_yticks(np.linspace(y1A, y1B, n))
ax2.set_yticks(np.linspace(y2A, y2B, n))
I am trying to emulate the span selector for the data I have according to the example shown here (https://matplotlib.org/examples/widgets/span_selector.html).
However, my data is in a dataframe & not an array.
When I plot the data by itself with the using the code below
input_month='2017-06'
plt.close('all')
KPI_ue_data.loc[input_month].plot(x='Order_Type', y='#_Days_#_Post_stream')
plt.show()
the data chart is shown perfectly.
However when i am trying to put this into a subplot with the code below (only first two lines are added & ax=ax in the plot line), nothing shows up. I get no error either!!! can anyone help?
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(211, facecolor='#FFFFCC')
input_month='2017-06'
plt.close('all')
KPI_ue_data.loc[input_month].plot(x='Order_Type', y='#_Days_#_Post_stream',ax=ax)
plt.show()
I usually just set x, y from the dataframe and use ax.plot(x, y). For your code, it should look something like this:
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(211, facecolor='#FFFFCC')
input_month='2017-06'
#plt.close('all')
x = KPI_ue_data.loc[(input_month), 'Order_Type']
y = KPI_ue_data.loc[(input_month), '#_Days_#_Post_stream']
ax.plot(x, y)
plt.show()
Looking at the matplotlib documentation, it seems the standard way to add an AxesSubplot to a Figure is to use Figure.add_subplot:
from matplotlib import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.hist( some params .... )
I would like to be able to create AxesSubPlot-like objects independently of the figure, so I can use them in different figures. Something like
fig = pyplot.figure()
histoA = some_axes_subplot_maker.hist( some params ..... )
histoA = some_axes_subplot_maker.hist( some other params ..... )
# make one figure with both plots
fig.add_subaxes(histo1, 211)
fig.add_subaxes(histo1, 212)
fig2 = pyplot.figure()
# make a figure with the first plot only
fig2.add_subaxes(histo1, 111)
Is this possible in matplotlib and if so, how can I do this?
Update: I have not managed to decouple creation of Axes and Figures, but following examples in the answers below, can easily re-use previously created axes in new or olf Figure instances. This can be illustrated with a simple function:
def plot_axes(ax, fig=None, geometry=(1,1,1)):
if fig is None:
fig = plt.figure()
if ax.get_geometry() != geometry :
ax.change_geometry(*geometry)
ax = fig.axes.append(ax)
return fig
Typically, you just pass the axes instance to a function.
For example:
import matplotlib.pyplot as plt
import numpy as np
def main():
x = np.linspace(0, 6 * np.pi, 100)
fig1, (ax1, ax2) = plt.subplots(nrows=2)
plot(x, np.sin(x), ax1)
plot(x, np.random.random(100), ax2)
fig2 = plt.figure()
plot(x, np.cos(x))
plt.show()
def plot(x, y, ax=None):
if ax is None:
ax = plt.gca()
line, = ax.plot(x, y, 'go')
ax.set_ylabel('Yabba dabba do!')
return line
if __name__ == '__main__':
main()
To respond to your question, you could always do something like this:
def subplot(data, fig=None, index=111):
if fig is None:
fig = plt.figure()
ax = fig.add_subplot(index)
ax.plot(data)
Also, you can simply add an axes instance to another figure:
import matplotlib.pyplot as plt
fig1, ax = plt.subplots()
ax.plot(range(10))
fig2 = plt.figure()
fig2.axes.append(ax)
plt.show()
Resizing it to match other subplot "shapes" is also possible, but it's going to quickly become more trouble than it's worth. The approach of just passing around a figure or axes instance (or list of instances) is much simpler for complex cases, in my experience...
The following shows how to "move" an axes from one figure to another. This is the intended functionality of #JoeKington's last example, which in newer matplotlib versions is not working anymore, because axes cannot live in several figures at once.
You would first need to remove the axes from the first figure, then append it to the next figure and give it some position to live in.
import matplotlib.pyplot as plt
fig1, ax = plt.subplots()
ax.plot(range(10))
ax.remove()
fig2 = plt.figure()
ax.figure=fig2
fig2.axes.append(ax)
fig2.add_axes(ax)
dummy = fig2.add_subplot(111)
ax.set_position(dummy.get_position())
dummy.remove()
plt.close(fig1)
plt.show()
For line plots, you can deal with the Line2D objects themselves:
fig1 = pylab.figure()
ax1 = fig1.add_subplot(111)
lines = ax1.plot(scipy.randn(10))
fig2 = pylab.figure()
ax2 = fig2.add_subplot(111)
ax2.add_line(lines[0])
TL;DR based partly on Joe nice answer.
Opt.1: fig.add_subplot()
def fcn_return_plot():
return plt.plot(np.random.random((10,)))
n = 4
fig = plt.figure(figsize=(n*3,2))
#fig, ax = plt.subplots(1, n, sharey=True, figsize=(n*3,2)) # also works
for index in list(range(n)):
fig.add_subplot(1, n, index + 1)
fcn_return_plot()
plt.title(f"plot: {index}", fontsize=20)
Opt.2: pass ax[index] to a function that returns ax[index].plot()
def fcn_return_plot_input_ax(ax=None):
if ax is None:
ax = plt.gca()
return ax.plot(np.random.random((10,)))
n = 4
fig, ax = plt.subplots(1, n, sharey=True, figsize=(n*3,2))
for index in list(range(n)):
fcn_return_plot_input_ax(ax[index])
ax[index].set_title(f"plot: {index}", fontsize=20)
Outputs respect.
Note: Opt.1 plt.title() changed in opt.2 to ax[index].set_title(). Find more Matplotlib Gotchas in Van der Plas book.
To go deeper in the rabbit hole. Extending my previous answer, one could return a whole ax, and not ax.plot() only. E.g.
If dataframe had 100 tests of 20 types (here id):
dfA = pd.DataFrame(np.random.random((100,3)), columns = ['y1', 'y2', 'y3'])
dfB = pd.DataFrame(np.repeat(list(range(20)),5), columns = ['id'])
dfC = dfA.join(dfB)
And the plot function (this is the key of this whole answer):
def plot_feature_each_id(df, feature, id_range=[], ax=None, legend_bool=False):
feature = df[feature]
if not len(id_range): id_range=set(df['id'])
legend_arr = []
for k in id_range:
pass
mask = (df['id'] == k)
ax.plot(feature[mask])
legend_arr.append(f"id: {k}")
if legend_bool: ax.legend(legend_arr)
return ax
We can achieve:
feature_arr = dfC.drop('id',1).columns
id_range= np.random.randint(len(set(dfC.id)), size=(10,))
n = len(feature_arr)
fig, ax = plt.subplots(1, n, figsize=(n*6,4));
for i,k in enumerate(feature_arr):
plot_feature_each_id(dfC, k, np.sort(id_range), ax[i], legend_bool=(i+1==n))
ax[i].set_title(k, fontsize=20)
ax[i].set_xlabel("test nr. (id)", fontsize=20)
I have a grid that looks like this
fig = plt.figure()
ax = fig.gca()
ax.set_xticks(numpy.arange(-5,6,1))
ax.set_yticks(numpy.arange(-5,6,1))
plt.grid(True)
When plotting an exponential function, obviously the function's values grow larger than the grid very quickly, and my grid ticks get distorted. I want the grid to be fixed, and only that part of the function to be graphed which fits inside of the grid. Is this possible?
Thanks in advance.
You can set the limits of the axes:
fig = plt.figure()
ax = fig.gca()
# Exponential plot:
x = linspace(-5, 5, 100)
y = power(2, x)
ax.plot(x, y)
ax.set_xticks(numpy.arange(-5,6,1))
ax.set_yticks(numpy.arange(-5,6,1))
ax.set_xlim(-5, 6)
ax.set_ylim(-5, 6)
plt.grid(True)
I'm writing a function that modifies the axes size and position on a figure, but when comes twin axes it makes a problem:
import matplotlib.pyplot as plt
def fig_layout(fig, vspace = 0.3): # function to make space at the bottom for legend box and
#+ other text input
for ax in ~~~fig.axes~~~: # Here 'fig.axes' is not right, I need to find the exact syntax
#+ I need to put
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * vspace,
box.width, box.height * (1 - vspace)])
x = np.arange(10)
fig = plt.figure()
ax1 = fig.add_subplot(1, 1, 1)
n = 3
line = {}
for i in range(3):
line['lines'].append(ax1.plot(x, i*x**2))
line['labels'].append(r'$y = %i \cdot x^2$'%i)
ax1.set_title('example plot')
ax2 = ax1.twinx()
line['lines'].append(ax2.plot(x, x^-1, label = r'$y = x^-1$'))
line['labels'].append(r'$y = x^-1$')
leg = ax1.legend(line['lines'], line['labels'])
fig_layout(fig)
# I will put the legend box at the bottom of the axes with another function.
plt.show()
I think you can use fig.get_axes().
For example, to modify the title of the first sub-plot, you can do:
plt.gcf().get_axes()[0].set_title("example plot")