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How the number of grid lines can be increased in seaborn? - python

I have such a piece of code for plotting:
sns.set_style("darkgrid")
fig, ax = plt.subplots(1, 1)
x = np.arange(10)
ax.plot(x, x)
And it gives me:
How the number of grid lines can be increased in seaborn, to make it denser?

Based on this question : add minor gridlines to matplotlib plot using seaborn, you can do it like that.
sns.set_style("darkgrid")
fig, ax = plt.subplots(1, 1)
x = np.arange(10)
ax.plot(x, x)
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
You obtain this figure :

Related

It is straightfoward to do scatter plot matrices with seaborn pairplot. Jointplot also allows combining scatter plots with marginal probability distributions for a single plot.
Although the option diag_kind='kde' let you plot the probability distributions in the diagonal (useful when x_varsand y_vars are the same) I want to combine both to have marginal probability distributions in a matrix scatter plot. Something like this:
How do I get marginal probability distributions in a matrix scatter plot in seaborn as shown in my screenshot above?

Many thanks mwaskom for the guiding.
As you suggested, I built my own matplotlib figure and plotted the seaborn plots there guided by this piece of documentation.
def basic_conf(f,a,xin,yin,x,y):
ax = f.add_subplot(a)
ax.tick_params(axis='both', which='major', labelsize=10)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
if xin !=0:
ax.set_yticklabels([])
ax.set_ylabel(" ",fontsize=0).set_visible(False)
ax.set_ylabel(y,fontsize=10)
ax.set_xticklabels([])
ax.set_xlabel(" ",fontsize=0).set_visible(False)
return ax
def xhist_conf(f,a,x):
ax = f.add_subplot(a)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.set_yticklabels([])
ax.yaxis.set_ticks_position('none')
ax.set_xlabel(x,fontsize=10)
ax.set_ylabel(" ").set_visible(False)#,fontsize='xx-small'
return ax
def yhist_conf(f,a,y):
ax = f.add_subplot(a)
ax.tick_params(axis='both', which='major', labelsize=10)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.xaxis.set_ticks_position('none')
ax.set_xlabel(" ",fontsize='xx-small').set_visible(False)
ax.set_ylabel(" ",fontsize=0).set_visible(False)
return ax
def includer(ax,x,y):
r,_=stats.pearsonr(concat_convert[x],concat_convert[y])
ax.text(0.1, 0.9, f'ρ = {r:.2f}', transform=ax.transAxes)#,fontsize='xx-small'
x_vars=["$P_{LA}$", "$R^{Ao}_P$", "$C^{Ao}_P$", "$R^{Ao}_S$", "$B_{VAD}$", "$A_{VAD}$", "HR", "EF"]
y_vars=["${Q}^{avg}_{M}$", "${Q}^{max}_{M}$","${Q}^{avg}_{Ao}$", "${Q}^{max}_{Ao}$", "${Q}^{avg}_{VAD}$", "${Q}^{max}_{VAD}$", "$Q_{RAT}$"]
sns.set(context="paper",font_scale=1.75,style="ticks")
f = plt.figure(figsize=(18, 16), dpi=600)
gs = f.add_gridspec(8, 9)
plt.rcParams['font.size'] = '10'
plt.rcParams['xtick.labelsize']='8'
with sns.axes_style("ticks"):
xin=0
for x in x_vars:
yin=0
for y in y_vars:
ax = basic_conf(f,gs[yin,xin],xin,yin,x,y)
sns.regplot(ax=ax, data=concat_convert, x=x, y=y, scatter_kws={'s':4})
includer(ax,x,y)
yin=yin+1
xin=xin+1
xin=0
for x in x_vars:
ax = xhist_conf(f,gs[yin,xin],x)
sns.histplot(ax=ax, data=concat_convert, x=x, kde=True)
xin=xin+1
yin=0
for y in y_vars:
ax = yhist_conf(f,gs[yin,xin],y)
sns.histplot(ax=ax, data=concat_convert, y=y, kde=True)
yin=yin+1
for i in range(len(y_vars)):
ax = f.add_subplot(gs[i,2])
ax.set_xlim((0.001,0.0014))
ax = f.add_subplot(gs[len(y_vars),0])
ax.ticklabel_format(style='sci',scilimits=(0,0), axis='x')
ax = f.add_subplot(gs[len(y_vars),5])
ax.ticklabel_format(style='sci',scilimits=(0,0), axis='x')
And it get me exactly what I want:
Many thanks.
EDIT: Final code snippet and obtained plot.

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 want to plot some data I have (square wave signals) in a subplot but I want to remove the axis for better visualization. This results in not having a ylabel. I thought I could add a simple text() so I could manually insert the text I want, but I can't seem to be able to use negative values for the y axis (as I could without a subplot). The code I thought would work was:
fig, (ax1, ax2, ax3, ax4, ax5, ax6)= plt.subplots(6,1)
#plot
ax1.plot(PathClockGeneration_4.q2bar_x,PathClockGeneration_4.clk_y, linewidth=2, color='black')
ax2.plot(PathClockGeneration_4.q2bar_x,PathClockGeneration_4.clkbar_y, linewidth=2, color='black')
ax3.plot(PathClockGeneration_4.q2bar_x,PathClockGeneration_4.q1_y, linewidth=2, color='C0')
ax4.plot(PathClockGeneration_4.q2bar_x,PathClockGeneration_4.q2_y, linewidth=2, color='C1')
ax5.plot(PathClockGeneration_4.q2bar_x,PathClockGeneration_4.q1bar_y, linewidth=2, color='C2')
ax6.plot(PathClockGeneration_4.q2bar_x,PathClockGeneration_4.q2bar_y, linewidth=2, color='C3')
#axis
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
ax4.axis('off')
ax5.axis('off')
ax6.axis('off')
#text
ax1.text(-1.5, 2, 'MyText')
If i try the last line as ax1.text(0, 2, 'MyText') it works fine, but the placement of the text is not the one I want. I suppose this comes from the size my plot is allowed to have and I would need to change it, how to do so?
EDIT
This is what I obtain hiding the axis manually (which can allow me to insert a ylabel). This is what I really want as plot obtained from the coded posted above by commenting ax1.text(-1.5, 2, 'MyText')

You can use fig instead of the ax1 to place your text. The arguments 0.05, 0.6 are the x and y coordinates in relative scale. You can choose them as per your taste.
Complete answer
import numpy as np
import matplotlib.pyplot as plt
fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(6,1)
x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)
ax1.plot(x, y, linewidth=2, color='black')
ax2.plot(x, y, linewidth=2, color='black')
ax3.plot(x, y, linewidth=2, color='C0')
ax4.plot(x, y, linewidth=2, color='C1')
ax5.plot(x, y, linewidth=2, color='C2')
ax6.plot(x, y, linewidth=2, color='C3')
# Hiding axis
for ax in [ax1, ax2, ax3, ax4, ax5, ax6]:
ax.axis('off')
fig.text(0.05, 0.6, 'MyText', rotation=90, fontsize=20)
plt.show()

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: