I wrote the following code below to do the following graph:
fig, ax = plt.subplots(figsize=(8, 6))
ax.patch.set_facecolor('white')
ax.plot(df.index, df.X1.values, 'b',
label='NMA', linewidth=1.5)
ax.set_ylabel('Index')
ax2 = ax.twinx()
ax2.plot(df.index, df.Y.values, 'r--',
label='Rate', linewidth=1.5)
ax2.set_ylabel('Rate')
lines = ax.get_lines() + ax2.get_lines()
lgd = ax.legend(lines, [line.get_label() for line in lines],
loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.15),
frameon=False)
ax.set_title('Economic Rate and Index',
weight='bold')
for i in range(5):
plt.axvspan(Dates['Peak'][i], Dates['Trough'][i],
facecolor='grey', alpha=0.5)
plt.grid(False)
plt.savefig('C:\\test.pdf',
bbox_extra_artists=(lgd,), bbox_inches='tight')
I am having a hard time to reproduce this figure in a subplot (2X2). The only thing I would change in each of the subplots is the blue line (X1 in df... for X2, X3...). How can I have a 2X2 subplot of the above graph? Of Course I would only keep one legend at the bottom of the subplots. Thanks for the help.
The data is here and the "Dates" to reproduce the gray bars here.
This is how you could create a 2x2 raster with twinx each:
import matplotlib.pyplot as plt
fig, ((ax1a, ax2a), (ax3a, ax4a)) = plt.subplots(2, 2)
ax1b = ax1a.twinx()
ax2b = ax2a.twinx()
ax3b = ax3a.twinx()
ax4b = ax4a.twinx()
ax1a.set_ylabel('ax1a')
ax2a.set_ylabel('ax2a')
ax3a.set_ylabel('ax3a')
ax4a.set_ylabel('ax4a')
ax1b.set_ylabel('ax1b')
ax2b.set_ylabel('ax2b')
ax3b.set_ylabel('ax3b')
ax4b.set_ylabel('ax4b')
plt.tight_layout()
plt.show()
Result:
Related
I am creating a Python plot from a dataframe with 3 y-axes. For each y-axis, there are multiple y-values I want to plot. All data sets for the y-axes are plotted against a shared Date x-axis.
The code looks as follows:
df = pd.read_excel (r'test.xlsx', sheet_name='test', engine='openpyxl')
fig, ax = plt.subplots()
ax3 = ax.twinx()
rspine = ax3.spines['right']
rspine.set_position(('axes', 1.15))
ax3.set_frame_on(True)
ax3.patch.set_visible(False)
fig.subplots_adjust(right=0.7)
ax.plot(df['Date'], df['Gas1'], label="Gas1", color='g')
ax.plot(df['Date'], df['Gas2'], label="Gas2", color='b')
ax.plot(df['Date'], df['Gas3'], label="Gas3", marker="o", markersize=2, color='r')
ax.set_xlabel("Date")
ax.set_ylabel("Gas Rate")
ax2 = ax.twinx()
ax2.plot(df['Date'], df['Water1'], label="Water1", color='k')
ax2.plot(df['Date'], df['Water2'], label="Water2", color='y')
ax2.set_ylabel("Water")
ax3.plot(df['Date'], df['Pressure1'], label="Pressure1")
ax3.plot(df['Date'], df['Pressure2'], label="Pressure2")
ax3.set_ylabel("Pressure")
ax.legend()
ax2.legend()
ax3.legend()
plt.show()
The problem I am having is that I want the legends to be outside of the plot, preferably on the right-hand side after the 2nd y-axis. Is this possible? Right now the legends are just overlayed on the plot and not fully visible. I have tried using bbox_to_anchor and loc functions but had no luck. Thank you!
ax.get_legend_handles_labels() collects all the legend handles and their labels. Combining those for each of the axes, a new legend can be created.
bbox_to_anchor= sets an anchor point for the legend, using axes coordinates. loc= needs to be set, to tell which point of the legend's box will get fixed by the anchor.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame({'Date': pd.date_range('20210401', periods=30, freq='D'),
'Gas1': np.random.randn(30).cumsum(),
'Gas2': np.random.randn(30).cumsum(),
'Gas3': np.random.randn(30).cumsum(),
'Water1': np.random.randn(30).cumsum(),
'Water2': np.random.randn(30).cumsum(),
'Pressure1': np.random.randn(30).cumsum(),
'Pressure2': np.random.randn(30).cumsum()})
fig, ax = plt.subplots()
ax3 = ax.twinx()
rspine = ax3.spines['right']
rspine.set_position(('axes', 1.15))
ax3.set_frame_on(True)
ax3.patch.set_visible(False)
fig.subplots_adjust(right=0.7)
ax.plot(df['Date'], df['Gas1'], label="Gas1", color='g')
ax.plot(df['Date'], df['Gas2'], label="Gas2", color='b')
ax.plot(df['Date'], df['Gas3'], label="Gas3", marker="o", markersize=2, color='r')
ax.set_ylabel("Gas Rate")
plt.setp(ax.get_xticklabels(), rotation=45, ha='right')
ax2 = ax.twinx()
ax2.plot(df['Date'], df['Water1'], label="Water1", color='k')
ax2.plot(df['Date'], df['Water2'], label="Water2", color='y')
ax2.set_ylabel("Water")
ax3.plot(df['Date'], df['Pressure1'], label="Pressure1")
ax3.plot(df['Date'], df['Pressure2'], label="Pressure2")
ax3.set_ylabel("Pressure")
handles1, labels1 = ax.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
handles3, labels3 = ax3.get_legend_handles_labels()
ax.legend(handles=handles1 + handles2 + handles3,
labels=labels1 + labels2 + labels3,
bbox_to_anchor=(1.28, 1.02), loc='upper left')
plt.tight_layout()
plt.show()
I wrote a code that read an excel sheet and plots a scatter figure with the following code:
fig, ax = plt.subplots(figsize=(13, 8))
scatter = ax.scatter(df.Date, df.TopAcc, c="blue", s=df.Param / 10000, alpha=0.2)
plot = ax.plot(dfmax.Date, dfmax.TopAcc, marker="o", c="red")
handles, labels = scatter.legend_elements(num=5, prop="sizes", alpha=0.2, color="blue")
legend = ax.legend(handles, labels, loc="lower right", title="# Parameters", )
plt.grid()
plt.show()
And I got the following figure
I have the following issues: How to prevent the legend balls from overlapping?
You can set columnspacing in the legend object:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
fig, ax = plt.subplots(figsize=(13, 8))
df = pd.DataFrame(np.random.rand(20, 2), columns=['x', 'y'])
df['s'] = 5000 * np.random.rand(20)
scatter = ax.scatter(df.x, df.y, c="blue", s=df.s, alpha=0.2)
handles, labels = scatter.legend_elements(num=5, prop="sizes", alpha=0.2, color="blue")
legend = ax.legend(handles, labels, loc="lower right", title="# Parameters", ncol=6, columnspacing=3, bbox_to_anchor=(1, -0.12), frameon=False)
plt.grid()
plt.show()
My goal is to create plot with four subplots, where the bottom two are really just empty boxes where I will display some text. Unfortunately, all of my efforts to remove the y and x axis tick marks and labels have failed. I'm still new to matplotlib so I'm sure there's something simple that I'm missing. Here's what I'm trying and what I get:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 2, sharex=False, sharey=True, figsize=(6,6))
fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.title('Neuron Length')
plt.xlabel('Strain')
plt.ylabel('Neuron Length (um)')
aIP = fig.add_subplot(223, frameon=False)
aIP.annotate('Big Axes \nGridSpec[1:, -1]', (0.1, 0.5),
xycoords='axes fraction', va='center')
# First approach
aIP.axes.xaxis.set_ticks([])
aIP.axes.yaxis.set_ticks([])
# Second approach
ax = plt.gca()
ax.axes.yaxis.set_visible(False)
plt.show()
This is achieved by using plt.subplots() to draw four of them and remove the bottom left frame.
import matplotlib.pyplot as plt
import numpy as np
t = np.linspace(-np.pi, np.pi, 1000)
x1 = np.sin(2*t)
x2 = np.cos(2*t)
x3 = x1 + x2
fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(6,6), sharex=True, sharey=True)
axes[0,0].plot(t, x1, linewidth=2)
axes[0,1].plot(t, x2, linewidth=2)
axes[1,1].plot(t, x3, linewidth=2)
axes[1,0].axis('off') # off
axes[1,0].annotate('Big Axes \nGridSpec[1:, -1]', (0.1, 0.5), xycoords='axes fraction', va='center')
fig.suptitle('Neuron Length')
for ax in axes.flat:
ax.set(xlabel='Strain', ylabel='Neuron Length (um)')
plt.show()
I create two scatterplots with matplotlib in python with this code, the data for the code is here:
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
fig = plt.figure(figsize=(20,12))
ax1 = fig.add_subplot(111)
ax3 = ax1.twinx()
norm = Normalize(vmin=0.95*min(arr), vmax=1.05*max(arr))
ax1.scatter(x, y1, s=20, c=arr, cmap='Blues_r', norm=norm, marker='x', label='bla1')
ax3.scatter(x, y2, s=(20*(1.1-arr))**3.5, c=arr, cmap='Reds_r', norm=norm, marker='^', label='bla1')
The created fig. looks like this:
So, the dot size (in ax3) and the dot colour (in ax1 and ax3) are taken from arrays containing floats with all kinds of values in the range [0,1]. My question: How do I create a legend that displays the corresponding y-values for, let's say 5 different dot sizes and 5 different colour nuances?
I would like the legend to look like in the figure below (source here), but with the colour bar and size bar put into a single legend, if possible. Thanks for suggestions and code!
# using your data in dataframe df
# create s2
df['s2'] = (20*(1.1-df.arr))**3.5
fig = plt.figure(figsize=(20,12))
ax1 = fig.add_subplot(111)
ax3 = ax1.twinx()
norm = Normalize(vmin=0.95*min(df.arr), vmax=1.05*max(df.arr))
p1 = ax1.scatter(df.x, df.y1, s=20, c=df.arr, cmap='Blues_r', norm=norm, marker='x')
fig.colorbar(p1, label='arr')
p2 = ax3.scatter(df.x, df.y2, s=df.s2, c=df.arr, cmap='Reds_r', norm=norm, marker='^')
fig.colorbar(p2, label='arr')
# create the size legend for red
for x in [15, 80, 150]:
plt.scatter([], [], c='r', alpha=1, s=x, label=str(x), marker='^')
plt.legend(loc='upper center', bbox_to_anchor=(1.23, 1), ncol=1, fancybox=True, shadow=True, title='s2')
plt.show()
There's no legend for p1 because the size is static.
I think this would be better as two separate plots
I used Customizing Plot Legends: Legend for Size of Points
Separate
fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(20, 10))
norm = Normalize(vmin=0.95*min(df.arr), vmax=1.05*max(df.arr))
p1 = ax1.scatter(df.x, df.y1, s=20, c=df.arr, cmap='Blues_r', norm=norm, marker='x')
fig.colorbar(p1, ax=ax1, label='arr')
p2 = ax2.scatter(df.x, df.y2, s=df.s2, c=df.arr, cmap='Reds_r', norm=norm, marker='^')
fig.colorbar(p2, ax=ax2, label='arr')
# create the size legend for red
for x in [15, 80, 150]:
plt.scatter([], [], c='r', alpha=1, s=x, label=str(x), marker='^')
plt.legend(loc='upper center', bbox_to_anchor=(1.2, 1), ncol=1, fancybox=True, shadow=True, title='s2')
plt.show()
I am trying to make this animated so that the dot and the green line move due to the for loop. This code displays 3 different graphs one below the other. The middle graph has no animation section.
x =lag_range
count = 0
plt.ion()
fig, ax = plt.subplots()
for b in x:
plt.subplot(311)
plt.plot(x,pear_corr, color='b', linewidth=1.5, label ='Pearson')
plt.plot(x,spear_corr, color ='r', linewidth=1.5, label='Spearman')
plt.plot(x[count],pear_corr[count],'yo')
plt.legend()
axes = plt.gca()
plt.ylabel('Correlation coefficients')
plt.xlabel('Lag times /days')
axes.set_xlim([min(lag_list),last])
axes.set_ylim(-1,1)
plt.subplot(312)
plt.plot(x,pear_p_values, color='b', linewidth=1.5)
plt.plot(x,spear_p_values, color ='r', linewidth=1.5)
axes = plt.gca()
plt.ylabel('P values')
plt.xlabel('Lag times /days')
axes.set_xlim([min(lag_list),last])
plt.subplot(313)
ax1 = plt.subplot(313)
x_for_p = range(len(x_prices))
ax1.plot(x_for_p, x_prices, color ='grey', linewidth=1.5)
ax1.set_ylabel('Share price', color ='grey')
ax1.tick_params('y', colors='grey')
ax1.set_xlabel('Days')
axes = plt.gca()
axes.set_xlim([min(lag_list),(2*last)])
ax2 = ax1.twinx()
x_for_den = range(b,(b+len(x_prices)))
ax2.plot(x_for_den, y_planes, color='g', linewidth=1.5)
ax2.set_ylabel('Plane density', color='g')
ax2.tick_params('y', colors='g')
count += 1
plt.pause(2)
plt.draw()
cross_corr2_vis(prices, density_p3)
If you could share a working code or just definitions of variables pear_corr, spear_corr, etc., the following code might have not resulted in this simple animation:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
N_points = 1000
x = np.linspace(0,2.*np.pi,N_points)
y = np.sin(x)
fig, ax = plt.subplots()
ax.set_xlim([0,2.*np.pi])
ax.set_ylim([-1,1])
line, = ax.plot( [],[], lw=2, color='g')
sctr = ax.scatter([],[], s=100, color='r')
def animate(i):
line.set_ydata(y[:i+1]) # update
line.set_xdata(x[:i+1])
sctr.set_offsets((x[i],y[i]))
return line,sctr
ani = animation.FuncAnimation(fig, animate, N_points, interval=5, blit=True)
plt.show()