I am trying to plot a graph with two separate x-axis. One being some valve openning and the other the corresponding leak rate. I managed to make it work pretty well, though the format of that secondary axis doesn't always show scientific notations as seen on the figure down bellow
Awful overlapping labels, see the upper axis
How to force scientific notation display so that the labels wont overlap?
Here is the script I am using:
#HEADERS
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
from matplotlib import rc
rc('font', **{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)
#/HEADERS
turns = np.array([11.000, 11.500, 11.750, 12.000, 12.250, 12.375])
leak = np.array([3.89e-05, 4.63e-05, 1.67e-04, 1.45000000e-03, 8.61e-03, 1.71e-02])
pressure1 = np.array([7.9e-07, 3.0e-06, 3.5e-05, 6.1e-04, 5.1e-03, 1.8e-02])
pressure2 = np.array([8.22e-07, 8.22e-07, 8.71e-07, 1.8e-06, 1.150e-05, 7.24e-05])
pressure3 = np.array([2e-06, 2e-06, 2e-06, 1.2e-05, 1.2e-04, 6e-04])
fig = plt.figure(num='valve', figsize = (6.68, 6.68*1.3))
fig, ax1 = plt.subplots()
ax1.plot(turns, pressure1, 'r.', label= '$P_1$')
ax1.plot(turns, pressure2, 'b.', label= '$P_2$')
ax1.plot(turns, pressure3,'k.', label= '$P_3$')
plt.legend()
plt.minorticks_on()
plt.grid(b = True, which = 'major', axis = 'both')
ax1.errorbar(turns, pressure1, yerr = .4*pressure1, fmt='none', ecolor = 'k', elinewidth = 1, capsize = 1, label= '$P_{1err}$')
ax1.errorbar(turns, pressure2, yerr = .15*pressure2, fmt='none', ecolor = 'k', elinewidth = 1, capsize = 1, label= '$P_{2err}$')
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
ax1.set_yscale('log', nonposy = 'mask')
ax1.set_ylabel(r'$P$')
ax1.set_xscale('linear')
ax1.set_xlabel('Opening (turns)')
plt.minorticks_on()
#plt.grid(b = True, which = 'major', axis = 'both')
#adding a secondary x-axis above
ax2 = ax1.twiny()
ax2.set_xlim(ax1.get_xlim())
new_tick_locations = turns
new_tick_label = leak #dtype here ?
ax2.set_xticks(new_tick_locations)
ax2.set_xticklabels(new_tick_label)
# I tried those commands from other threads but they all result in an error.
#ax2.xaxis.set_scientific(True)
#ax2.get_xaxis().set_major_formatter((matplotlib.ticker.Formatter(set_scientific(True)))
#ax2.get_xaxis().set_major_formatter().set_scientific(True)
ax2.set_xlabel(r'Leak rate (mbar$\times$L/s)')
plt.tight_layout()
#export png
plt.savefig(('export.png'), format = 'png', transparent=False, dpi = 300)
plt.show()
I'm using Python 3.6.
Thanks for your help.
Since you override the ticks, you can format them yourself and rotate them as well for more space:
new_tick_label = ['{:5.2e}'.format(x) for x in leak]
ax2.set_xticks(new_tick_locations)
ax2.set_xticklabels(new_tick_label, rotation=30)
Result:
Related
So, here is my code:
import pandas as pd
import scipy.stats as st
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
from fitter import Fitter, get_common_distributions
df = pd.read_csv("project3.csv")
bins = [282.33, 594.33, 906.33, 1281.33, 15030.33, 1842.33, 2154.33, 2466.33, 2778.33, 3090.33, 3402.33]
#declaring
facecolor = '#EAEAEA'
color_bars = '#3475D0'
txt_color1 = '#252525'
txt_color2 = '#004C74'
fig, ax = plt.subplots(1, figsize=(16, 6), facecolor=facecolor)
ax.set_facecolor(facecolor)
n, bins, patches = plt.hist(df.City1, color=color_bars, bins=10)
#grid
minor_locator = AutoMinorLocator(2)
plt.gca().xaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color=facecolor, lw = 0.5)
xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]
xticks_labels = [ "{:.0f}-{:.0f}".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]
plt.xticks(xticks, labels=xticks_labels, c=txt_color1, fontsize=13)
#beautify
ax.tick_params(axis='x', which='both',length=0)
plt.yticks([])
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for idx, value in enumerate(n):
if value > 0:
plt.text(xticks[idx], value+5, int(value), ha='center', fontsize=16, c=txt_color1)
plt.title('Histogram of rainfall in City1\n', loc = 'right', fontsize = 20, c=txt_color1)
plt.xlabel('\nCentimeters of rainfall', c=txt_color2, fontsize=14)
plt.ylabel('Frequency of occurrence', c=txt_color2, fontsize=14)
plt.tight_layout()
#plt.savefig('City1_Raw.png', facecolor=facecolor)
plt.show()
city1 = df['City1'].values
f = Fitter(city1, distributions=get_common_distributions())
f.fit()
fig = f.plot_pdf(names=None, Nbest=4, lw=1, method='sumsquare_error')
plt.show()
print(f.get_best(method = 'sumsquare_error'))
The issue is with the plots it shows. The first histogram it generates is
Next I get another graph with best fitted distributions which is
Then an output statement
{'chi2': {'df': 10.692966790090342, 'loc': 16.690849400411103, 'scale': 118.71595997157786}}
Process finished with exit code 0
I have a couple of questions. Why is chi2, the best fitted distribution not plotted on the graph?
How do I plot these distributions on top of the histograms and not separately? The hist() function in fitter library can do that but there I don't get to control the bins and so I end up getting like 100 bins with some flat looking data.
How do I solve this issue? I need to plot the best fit curve on the histogram that looks like image1. Can I use any other module/package to get the work done in similar way? This uses least squares fit but I am OK with least likelihood or log likelihood too.
Simple way of plotting things on top of each other (using some properties of the Fitter class)
import scipy.stats as st
import matplotlib.pyplot as plt
from fitter import Fitter, get_common_distributions
from scipy import stats
numberofpoints=50000
df = stats.norm.rvs( loc=1090, scale=500, size=numberofpoints)
fig, ax = plt.subplots(1, figsize=(16, 6))
n, bins, patches = ax.hist( df, bins=30, density=True)
f = Fitter(df, distributions=get_common_distributions())
f.fit()
errorlist = sorted(
[
[f._fitted_errors[dist], dist]
for dist in get_common_distributions()
]
)[:4]
for err, dist in errorlist:
ax.plot( f.x, f.fitted_pdf[dist] )
plt.show()
Using the histogram normalization, one would need to play with scaling to generalize again.
My question is based on this question:
Adjust hspace for some of the subplots
Which adjusts the top plot of a number of subplots and increases the difference in hspace. I want to increase the hspace between two plots within the subplots (in my case: between plot 3 and plot4 from the top).
Here is my example:
import numpy as np
import matplotlib.pyplot as plt
noise = np.random.rand(300)
gs_top = plt.GridSpec(9, 1, hspace=0.5)
gs_base = plt.GridSpec(9, 1, hspace=0)
fig = plt.figure()
fig.patch.set_facecolor('white')
ax0 = fig.add_subplot(gs_base[0,:])
ax1 = fig.add_subplot(gs_base[1,:])
ax2 = fig.add_subplot(gs_top[2,:])
ax3 = fig.add_subplot(gs_base[3,:])
ax4 = fig.add_subplot(gs_base[4,:])
ax5 = fig.add_subplot(gs_base[5,:])
ax0.plot(noise)
ax1.plot(noise)
ax2.plot(noise)
ax3.plot(noise)
ax4.plot(noise)
ax5.plot(noise)
In the example it is shown that the hspace increases between plot 3 and 4. However, I don't want to increase the space between plot 2 and plot 3.
How can I adjust the hspace variable only on one side?
Found the answer after manipulating google by asking with various word combinations. Found this: Stackoverflow answer
In short (dirty way):
Adding a seperate axis and make it invisible.
Example:
import numpy as np
import matplotlib.pyplot as plt
noise = np.random.rand(300)
gs_base = plt.GridSpec(7, 1, hspace=0, height_ratios=[1, 1, 1, 0.8, 1,1,1])
fig = plt.figure()
fig.patch.set_facecolor('white')
ax0 = fig.add_subplot(gs_base[0,:])
ax1 = fig.add_subplot(gs_base[1,:])
ax2 = fig.add_subplot(gs_base[2,:])
ax3 = fig.add_subplot(gs_base[3,:])
ax3.set_visible(False)
ax4 = fig.add_subplot(gs_base[4,:])
ax5 = fig.add_subplot(gs_base[5,:])
ax6 = fig.add_subplot(gs_base[6,:])
ax0.plot(noise)
ax1.plot(noise)
ax2.plot(noise)
ax4.plot(noise)
ax5.plot(noise)
ax6.plot(noise)
In long (correct way):
Couldn't figure it out for the moment.
I'm trying to extend the plot by plt.xlim(-1.5, 1.5) and plt.ylim(-1.5, 1.5). Could you please explain why the the range of the plot is not as expected?
import pandas as pd
from sklearn import preprocessing
from sklearn import decomposition
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
import numpy as np
# Change the image format to svg for better quality
%config InlineBackend.figure_format = 'svg'
decathlon = pd.read_csv("https://raw.githubusercontent.com/leanhdung1994/Deep-Learning/main/decathlon.txt", sep='\t')
decathlon_scaled = decathlon.copy()
decathlon_scaled.iloc[:, 0:10] = preprocessing.scale(decathlon.iloc[:, 0:10])
pca_scaled = decomposition.PCA(n_components = 10).fit(decathlon_scaled.iloc[:, 0:10])
decathlon_scaled_pca = pca_scaled.transform(decathlon_scaled.iloc[:, 0:10])
decathlon_scaled_pca_nor = decathlon_scaled_pca / np.sqrt((decathlon_scaled_pca ** 2).sum(axis = 0))
decathlon_scaled_nor = decathlon_scaled.iloc[:, 0:10] / np.sqrt((decathlon_scaled.iloc[:, 0:10] ** 2).sum(axis = 0))
decathlon_corr_circle = decathlon_scaled_pca_nor.T.dot(decathlon_scaled_nor)
decathlon_corr_circle
tmp = np.transpose(decathlon_corr_circle)[:, 0:2]
tmp = pd.DataFrame(tmp)
tmp.index = decathlon.columns[0:10]
fig = plt.figure(figsize = 1 * np.array(plt.rcParams['figure.figsize'])) # This is to have bigger plot
ax = sns.scatterplot(data = tmp,
x = tmp[0], y = tmp[1])
for i in range(10):
plt.arrow(0, 0, tmp[0][i], tmp[1][i],
color = 'orange', head_width = 0.025, length_includes_head = True)
circle = plt.Circle((0, 0), 1, color='g', fill=False)
ax.add_artist(circle)
plt.xlim(-1.5, 1.5)
plt.ylim(-1.5, 1.5)
plt.axis('equal')
The problem is that using plt.axis('equal') is equivalent to using ax.set_aspect('equal', adjustable='datalim'). That adjustable='datalim' is modifying the axis limits, even if you don't want it to.
Using the object-oriented approach for all of the last 3 lines of code is one way to solve this problem, since the default value of adjustable is box, not datalim. box means the shape of the axes will be changed to enforce the equal aspect ratio, compared to datalim which will keep the axes the same size, but change the axis limits. Using the state-machine version, plt.axis('equal'), doesn't allow you to set it to box, so the object-oriented approach is the best option to give you more control.
Change
plt.xlim(-1.5, 1.5)
plt.ylim(-1.5, 1.5)
plt.axis('equal')
to
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal')
I am trying to create a plot with 2 xtick labels for the x-axis.
My code is given below. It does not create the x-ticks as I expected (In fact there is no xticks label in my pdf). I followed the code snippet given in the link
https://matplotlib.org/examples/pylab_examples/multiline.html
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
filename = 'small-dg-ss.pdf'
rc('mathtext', default='regular')
rc('lines',lw=2.6)
rc('lines',mew=2.4)
rc('text', usetex=True)
x = np.array([5,10,20,50])
fig, ax1 = plt.subplots(frameon=False)
disp_dcg = np.array([0.85, 0.88, 0.93, 1.0])
ax1.plot(x,disp_dcg,'bs:')
ax1.set_ylabel('GD',color='b',size=14)
ax1.set_ylim([0.7,1.02])
ax2 = ax1.twinx()
disp_gc = np.array([1.0, 0.98, 0.95, 0.92])
ax2.plot(x,disp_gc,'rv:')
ax2.set_ylabel('LS',color='r',size=14)
ax2.set_ylim([0.7,1.02])
plt.xticks([0.2,0.4,0.6,0.8], [r"$\beta = 0.1$
$\alpha = 0.99$", r"$\beta = 0.5$
$\alpha = 0.89$", r"$\beta = 0.8$
$\alpha = 0.51$", r"$\beta = 0.9$
$\alpha = 0.33"] )
fig.savefig(filename,format='pdf',transparent=True, bbox_inches='tight')
I do not want the entries in the variable x to appear in x-axis, but the ticks I explicitly specify.
I am making some density plots like so:
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import r2_score
import matplotlib
from scipy import stats
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
from matplotlib.ticker import FormatStrFormatter
import matplotlib.cm as cm
from scipy.ndimage.filters import gaussian_filter
import random
matplotlib.rcParams.update({'font.size': 16})
matplotlib.rcParams['xtick.direction'] = 'in'
matplotlib.rcParams['ytick.direction'] = 'in'
x = random.sample(range(1, 10001), 1000)
y = random.sample(range(1, 10001), 1000)
def myplot(x, y, s, bins=1000):
heatmap, xedges, yedges = np.histogram2d(x, y, bins=bins)
heatmap = gaussian_filter(heatmap, sigma=s)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
return heatmap.T, extent
cmap = cm.YlOrRd
fig, (ax, ax1, cax) = plt.subplots(ncols = 3, figsize = (15, 5),
gridspec_kw={"width_ratios":[1,1, 0.5]})
img, extent = myplot(x, y, 20)
im = ax.imshow(img, extent = extent, origin = 'lower', cmap = cmap)
ax.text(0.05, 0.92, '$R^2$ = {}'.format(np.round(r2_score(x, y), 2)), fontsize=14, color = 'k', transform = ax.transAxes)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c=".3")
ax.set_xlabel("Black Sky")
ax.set_ylabel("Blue Sky")
img2, extent2 = myplot(x, y, 20)
ax1.imshow(img2, extent = extent2, origin = 'lower', cmap = cmap)
ax1.text(0.05, 0.92, '$R^2$ = {}'.format(np.round(r2_score(x, y), 2)), fontsize=14, color = 'k', transform = ax1.transAxes)
ax1.axes.get_yaxis().set_visible(False)
ax1.yaxis.set_ticks([])
ax1.plot(ax1.get_xlim(), ax1.get_ylim(), ls="--", c=".3")
ax1.set_xlabel("White Sky")
ip = InsetPosition(ax1, [1.05,0,0.05,1])
cax.set_axes_locator(ip)
fig.colorbar(im, cax=cax, ax=[ax,ax1], use_gridspec = True)
plt.subplots_adjust(wspace=0.1, hspace=0)
which gives me a plot like this:
No matter what I change wspace to the plot stays the same. I think this is because when I turn of the y-axis in ax1 I am just making the text blank instead of removing the y-axis all together. Is there a way to do this so that I can make the width spacing between the figures closer together?
As commented, wspace sets the minimal distance between plots. This distance may be larger in case of equal aspect axes. Then it will depend on the figure size, figure aspect and image aspect.
A. Use automatic aspect
You may set aspect = "auto" in your imshow plots,
ax.imshow(..., aspect = "auto")
B. Adjust the subplot parameters
You may set the left or right subplot parameter to something smaller. E.g.
plt.subplots_adjust(wspace=0.0, hspace=0, right=0.7)
C. Adjust the figure size
Using a smaller figure width, which is closer to the actual image aspect will also reduce whitespace around the figure.
E.g, making the figure only 11 inches wide and using 5% padding on the right,
plt.subplots(..., figsize = (11, 5))
plt.subplots_adjust(wspace=0.0, hspace=0, right=.95)