i plot a multiple plot using seabor lmplot and i want to add a x=y line to this plot, can you help me to solve this problem?
my code :
sns.set_theme(style="white")
sns.lmplot(data=data, x='Target',y='Predicted', hue="Type",col='Model', height=5,legend=False, palette=dict(Train="g", Test="m"))
plt.plot([data.iloc[:,0].min(), data.iloc[:,0].max()], [data.iloc[:,0].min(), data.iloc[:,0].max()], "--", label="Perfect model")
plt.legend(loc='upper left')
plt.show()
and my output:
i plot a multiple plot using seabor lmplot and i want to add a x=y line to this plot, can you help me to solve this problem?
my code :
sns.set_theme(style="white")
sns.lmplot(data=data, x='Target',y='Predicted', hue="Type",col='Model', height=5,legend=False, palette=dict(Train="g", Test="m"))
plt.plot([data.iloc[:,0].min(), data.iloc[:,0].max()], [data.iloc[:,0].min(), data.iloc[:,0].max()], "--", label="Perfect model")
plt.legend(loc='upper left')
plt.show()
and my output:
The plt.plot() that you are using will only add line to the last plot. Do add the line to each line, you will need to use the axes for the lmplot() and plot a line for each of the subplots. As I don't have your data, used the standard penguins dataset to show this. Hope this helps...
data = sns.load_dataset('penguins') ## My data
sns.set_theme(style="white")
g=sns.lmplot(data=data, x='bill_length_mm',y='bill_depth_mm', hue="species", col="sex", height=5,legend=False, palette=dict(Adelie="g", Chinstrap="m", Gentoo='orange'))
axes = g.fig.axes ## Get the axes for all the subplots
for ax in axes: ## For each subplot, draw the line
ax.plot([data.iloc[:,2].min(), data.iloc[:,2].max()], [data.iloc[:,3].min(), data.iloc[:,3].max()], "--", label="Perfect model")
plt.legend(loc='upper left')
plt.show()
Related
I have the following code of a piecewise_regression:
data = data_heatmap_2017.copy()
data = data[['tre200h0_2017','Leistung:']].dropna()
xx = data['tre200h0_2017'].values.tolist()
yy = data['Leistung:'].values.tolist()
pw_fit = piecewise_regression.Fit(xx, yy, n_breakpoints=1)
pw_fit.summary()
If I do a single plot with the code below, I get a diagram piecewise_regression:
# Plot the data, fit, breakpoints and confidence intervals
pw_fit.plot_data(s=0.1)
# Pass in standard matplotlib keywords to control any of the plots
pw_fit.plot_fit(color="red", linewidth=2)
pw_fit.plot_breakpoints()
pw_fit.plot_breakpoint_confidence_intervals()
plt.xlabel("Lufttemperatur [°C]")
plt.ylabel("Leistung [kW]")
plt.show()
plt.close()
Now I would like to integrate the diagram piecewise regression within this subplots on position ax10:
fig, axs = plt.subplots(2, 5, figsize=(60,50), dpi=(100))
ax10 = axs[1,0]
ax10.set_title('2017, Signatur, Zähler: ' + Zaehler)
ax10.pw_fit.plot_data(s=0.1)
ax10.pw_fit.plot_fit(color="red", linewidth=2)
ax10.set_xlabel('Lufttemperatur [°C]')
ax10.set_ylabel('Leistung [kW]')
ax10.axis([-15, 35, min_Power, max_Power])
plt.show()
plt.close()
unfortunately the lines
ax10.pw_fit.plot_data(s=0.1)
ax10.pw_fit.plot_fit(color="red", linewidth=2)
do not work with the prefix ax10. I get an AttributeError 'AxesSubplot' object has no attribute 'pw_fit'. Any idea how to solve this? Thank you!
I discovered Matplotlib and I have a rendering problem. The 5 subplot I create step on each other, the xaxis labels and titles are behind the other plot.
Here is a part of the code I use :
fig, axs = plt.subplots(5,figsize=(8,25))
axs[0].plot(array_system_index, array_system_value, label="System", color="red", linewidth=1)
axs[0].set(xlabel="time", ylabel="Latency (µs)", yscale="log", title="System")
axs[0].axis([0, len(array_system_value), 1, 10000])
axs[1].plot(array_core0_index, array_core0_value, label="core 0", color="green", linewidth=1)
axs[1].set(xlabel="Time", ylabel="Latency (µs)", yscale="log", title="Core1")
axs[1].axis([0, len(array_core0_value), 1, 10000])
...
fig.tight_layout()
plt.show()
# fig.set_size_inches((15, 8), forward=False) # Break the png file
fig.savefig("my_graph.png", dpi=500)
Here is the result :
Graph
Do you know how can I increase the size of the figure itself ?
I tried to it after the subplot but it doesn't work and break the saved .png.
I would like to generate a centered figure legend for subplot(s), for which there is a single label. For my actual use case, the number of subplot(s) is greater than or equal to one; it's possible to have a 2x2 grid of subplots and I would like to use the figure-legend instead of using ax.legend(...) since the same single label entry will apply to each/every subplot.
As a brief and simplified example, consider the code just below:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = np.sin(x)
fig, ax = plt.subplots()
ax.plot(x, y, color='orange', label='$f(x) = sin(x)$')
fig.subplots_adjust(bottom=0.15)
fig.legend(mode='expand', loc='lower center')
plt.show()
plt.close(fig)
This code will generate the figure seen below:
I would like to use the mode='expand' kwarg to make the legend span the entire width of the subplot(s); however, doing so prevents the label from being centered. As an example, removing this kwarg from the code outputs the following figure.
Is there a way to use both mode='expand' and also have the label be centered (since there is only one label)?
EDIT:
I've tried using the bbox_to_anchor kwargs (as suggested in the docs) as an alternative to mode='expand', but this doesn't work either. One can switch out the fig.legend(...) line for the line below to test for yourself.
fig.legend(loc='lower center', bbox_to_anchor=(0, 0, 1, 0.5))
The handles and labels are flush against the left side of the legend. There is no mechanism to allow for aligning them.
A workaround could be to use 3 columns of legend handles and fill the first and third with a transparent handle.
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(10)
y = np.sin(x)
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.15)
line, = ax.plot(x, y, color='orange', label='$f(x) = sin(x)$')
proxy = plt.Rectangle((0,0),1,1, alpha=0)
fig.legend(handles=[proxy, line, proxy], mode='expand', loc='lower center', ncol=3)
plt.show()
The code below produces the plot and legend correctly, however the legend does not show the specified label text, but "Container object of 10 artists" instead.
ax = plt.subplot(212)
plt.tight_layout()
l1= plt.bar(X+0.25, Y1, 0.45, align='center', color='r', label='A', edgecolor='black', hatch="/")
l2= plt.bar(X, Y2, 0.45, align='center', color='b', label='N',hatch='o', fill=False)
ax.autoscale(tight=True)
plt.xticks(X, X_values)
ymax1 = max(Y1) + 1
ymax2 = max(Y2) + 1
ymax = max(ymax1,ymax2)+1
plt.ylim(0, ymax)
plt.grid(True)
plt.legend([l1,l2], loc='upper right', prop={'size':20})
The output is shown below:
How can I correctly display the labels for each bar (as specified in the plt.bar() function) on the legend?
The problem stems from mixing two approaches to using plt.legend(). You have two options:
Manually specify the labels for the legend
Use ax.get_legend_handles_labels() to fill them in with the label parameters you passed to plt.bar()
To manually specify the labels, pass them as the second argument to your call to plt.legend() as follows:
plt.legend([l1,l2], ["A", "N"], loc='upper right', prop={'size':20})
If instead you want to automatically populate the legend you can use the following to find legend-able objects in the plot and their labels:
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc='upper right', prop={'size':20})
Im trying to plot a scatter matrix. I'm building on the example given in this thread Is there a function to make scatterplot matrices in matplotlib?. Here I have just modified the code slightly to make the axis visible for all the subplots. The modified code is given below
import itertools
import numpy as np
import matplotlib.pyplot as plt
def main():
np.random.seed(1977)
numvars, numdata = 4, 10
data = 10 * np.random.random((numvars, numdata))
fig = scatterplot_matrix(data, ['mpg', 'disp', 'drat', 'wt'],
linestyle='none', marker='o', color='black', mfc='none')
fig.suptitle('Simple Scatterplot Matrix')
plt.show()
def scatterplot_matrix(data, names, **kwargs):
"""Plots a scatterplot matrix of subplots. Each row of "data" is plotted
against other rows, resulting in a nrows by nrows grid of subplots with the
diagonal subplots labeled with "names". Additional keyword arguments are
passed on to matplotlib's "plot" command. Returns the matplotlib figure
object containg the subplot grid."""
numvars, numdata = data.shape
fig, axes = plt.subplots(nrows=numvars, ncols=numvars, figsize=(8,8))
fig.subplots_adjust(hspace=0.05, wspace=0.05)
for ax in axes.flat:
# Hide all ticks and labels
ax.xaxis.set_visible(True)
ax.yaxis.set_visible(True)
# # Set up ticks only on one side for the "edge" subplots...
# if ax.is_first_col():
# ax.yaxis.set_ticks_position('left')
# if ax.is_last_col():
# ax.yaxis.set_ticks_position('right')
# if ax.is_first_row():
# ax.xaxis.set_ticks_position('top')
# if ax.is_last_row():
# ax.xaxis.set_ticks_position('bottom')
# Plot the data.
for i, j in zip(*np.triu_indices_from(axes, k=1)):
for x, y in [(i,j), (j,i)]:
axes[x,y].plot(data[x], data[y], **kwargs)
# Label the diagonal subplots...
for i, label in enumerate(names):
axes[i,i].annotate(label, (0.5, 0.5), xycoords='axes fraction',
ha='center', va='center')
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
fig.tight_layout()
plt.xticks(rotation=45)
fig.show()
return fig
main()
I cant seem to be able to rotate the x-axis text of all the subplots. As it can be seen, i have tried the plt.xticks(rotation=45) trick. But this seems to perform the rotation for the last subplot alone.
Just iterate through the axes tied to the figure, set the active axes to the iterated object, and modify:
for ax in fig.axes:
matplotlib.pyplot.sca(ax)
plt.xticks(rotation=90)
plt only acts on the current active axes. You should bring it inside your last loop where you set some of the labels visibility to True:
# Turn on the proper x or y axes ticks.
for i, j in zip(range(numvars), itertools.cycle((-1, 0))):
axes[j,i].xaxis.set_visible(True)
axes[i,j].yaxis.set_visible(True)
for tick in axes[i,j].get_xticklabels():
tick.set_rotation(45)
for tick in axes[j,i].get_xticklabels():
tick.set_rotation(45)
for ax in fig.axes:
ax.tick_params(labelrotation=90)