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')
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.
I'm trying to change a colorbar attached to a scatter plot so that the minimum and maximum of the colorbar are the minimum and maximum of the data, but I want the data to be centred at zero as I'm using a colormap with white at zero. Here is my example
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 61)
y = np.linspace(0, 1, 61)
C = np.linspace(-10, 50, 61)
M = np.abs(C).max() # used for vmin and vmax
fig, ax = plt.subplots(1, 1, figsize=(5,3), dpi=150)
sc=ax.scatter(x, y, c=C, marker='o', edgecolor='k', vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
cbar=fig.colorbar(sc, ax=ax, label='$R - R_0$ (mm)')
ax.set_xlabel('x')
ax.set_ylabel('y')
As you can see from the attached figure, the colorbar goes down to -M, where as I want the bar to just go down to -10, but if I let vmin=-10 then the colorbar won't be zerod at white. Normally, setting vmin to +/- M when using contourf the colorbar automatically sorts to how I want. This sort of behaviour is what I expect when contourf uses levels=np.linspace(-M,M,61) rather than setting it with vmin and vmax with levels=62. An example showing the default contourf colorbar behaviour I want in my scatter example is shown below
plt.figure(figsize=(6,5), dpi=150)
plt.contourf(x, x, np.reshape(np.linspace(-10, 50, 61*61), (61,61)),
levels=62, vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
plt.colorbar(label='$R - R_0$ (mm)')
Does anyone have any thoughts? I found this link which I thought might solve the problem, but when executing the cbar.outline.set_ydata line I get this error AttributeError: 'Polygon' object has no attribute 'set_ydata' .
EDIT a little annoyed that someone has closed this question without allowing me to clarify any questions they might have, as none of the proposed solutions are what I'm asking for.
As for Normalize.TwoSlopeNorm, I do not want to rescale the smaller negative side to use the entire colormap range, I just want the colorbar attached to the side of my graph to stop at -10.
This link also does not solve my issue, as it's the TwoSlopeNorm solution again.
After changing the ylim of the colorbar, the rectangle formed by the surrounding spines is too large. You can make this outline invisible. And then add a new rectangular border:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 61)
y = np.linspace(0, 1, 61)
C = np.linspace(-10, 50, 61)
M = np.abs(C).max() # used for vmin and vmax
fig, ax = plt.subplots(1, 1, figsize=(5, 3), dpi=150)
sc = ax.scatter(x, y, c=C, marker='o', edgecolor='k', vmin=-M, vmax=M, cmap=plt.cm.RdBu_r)
cbar = fig.colorbar(sc, ax=ax, label='$R - R_0$ (mm)')
cb_ymin = C.min()
cb_ymax = C.max()
cb_xmin, cb_xmax = cbar.ax.get_xlim()
cbar.ax.set_ylim(cb_ymin, cb_ymax)
cbar.outline.set_visible(False) # hide the surrounding spines, which are too large after set_ylim
cbar.ax.add_patch(plt.Rectangle((cb_xmin, cb_ymin), cb_xmax - cb_xmin, cb_ymax - cb_ymin,
fc='none', ec='black', clip_on=False))
plt.show()
Another approach until v3.5 is released is to make a custom colormap that does what you want (see also https://matplotlib.org/stable/tutorials/colors/colormap-manipulation.html#sphx-glr-tutorials-colors-colormap-manipulation-py)
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
fig, axs = plt.subplots(2, 1)
X = np.random.randn(32, 32) + 2
pc = axs[0].pcolormesh(X, vmin=-6, vmax=6, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[0])
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
fig, axs = plt.subplots(2, 1)
X = np.random.randn(32, 32) + 2
pc = axs[0].pcolormesh(X, vmin=-6, vmax=6, cmap='RdBu_r')
fig.colorbar(pc, ax=axs[0])
def keep_center_colormap(vmin, vmax, center=0):
vmin = vmin - center
vmax = vmax - center
dv = max(-vmin, vmax) * 2
N = int(256 * dv / (vmax-vmin))
RdBu_r = cm.get_cmap('RdBu_r', N)
newcolors = RdBu_r(np.linspace(0, 1, N))
beg = int((dv / 2 + vmin)*N / dv)
end = N - int((dv / 2 - vmax)*N / dv)
newmap = ListedColormap(newcolors[beg:end])
return newmap
newmap = keep_center_colormap(-2, 6, center=0)
pc = axs[1].pcolormesh(X, vmin=-2, vmax=6, cmap=newmap)
fig.colorbar(pc, ax=axs[1])
plt.show()
I am trying to apply a filter to the peak datapoints of my impulse plot and smoothen them out but it doesn't seem to work. Required file signal.csv
scipy savgol_filter
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import find_peaks, savgol_filter
df = pd.read_csv('signal.csv')
df.plot(grid = 1,
c = (0,0,255/255),
linewidth = 0.5,
figsize = (10,5),
legend = False,
xlim = [df.index[0], df.index[-1]],
ylim = 0)
plt.xlabel('Zeit / ms')
plt.ylabel('UHF-Signal / mV')
plt.title('UHF')
x = df.T.to_numpy()[1]
peaks, _ = find_peaks(x, distance = 150, height = 4)
sgf = savgol_filter(peaks, 51, 3)
plt.plot(sgf, x[peaks], c = 'orange')
plt.plot(peaks, x[peaks], 'o', c = 'red')
plt.show()
scipy butter filter
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import find_peaks, butter, filtfilt
df = pd.read_csv('signal.csv')
df.plot(grid = 1,
c = (0,0,255/255),
linewidth = 0.5,
figsize = (10,5),
legend = False,
xlim = [df.index[0], df.index[-1]],
ylim = 0)
plt.xlabel('Zeit / ms')
plt.ylabel('UHF-Signal / mV')
plt.title('UHF')
x = df['1'].values
peaks, _ = find_peaks(x, distance = 150, height = 4)
c, e = butter(10, 0.3)
z = filtfilt(c, e, peaks)
plt.plot(z, x[peaks], c = 'orange')
plt.plot(peaks, x[peaks], 'o', c = 'red')
plt.show()
As you can see the result is the same. How can I smoothen out the orange line? I want something like this:
Thanks in advance
You are smoothing the wrong variable. peaks are indices into x (which really are heights / y-values, which makes everything a bit confusing). Substituting
sgf = savgol_filter(x[peaks], 5, 3)
plt.plot(peaks, sgf, c = 'orange', linewidth=3)
for the corresponding lines in your code yields the following plot:
The fit is not great but neither of the methods you are using will deal with the sharp transition around x=2000 very well. I would try a Kalman filter next, or -- if the decay constant for all of your exponentials is the same -- try to fit the exponentials directly to the data using non-negative deconvolution as discussed here.
I am trying to create a 3D plot but I am having trouble with the z-axis label. It simply doesn't appear in the graph. How do I amend this? The code is as follows
# Gamma vs Current step 2
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
h = np.arange(0.1,5.1,0.1)
gamma = np.arange(0.1,5.1,0.1)
sigmaz_hgam = np.array([.009998,.03988,.08878,.15403
,.230769,.312854,.394358,.4708311,.539697879,.6,.6518698
,.696033486,.73345752165,.7651390123,.792,.814845635
,.8343567,.851098499,.865535727,.8780487,.8889486,.89848986
,.906881,.914295,.9208731,.9267338,.93197569,.93668129
,.9409202379,.94475138,.951383,.9542629,.956895,.959309
,.961526,.9635675,.96545144,.9671934,.968807,.97030539
,.9716983,.972995,.974206,.975337,.97639567,.977387,.978318
,.97919266,.98,.9807902])
mu = 1
sigmaz_hgam = mu*sigmaz_hgam
# creates an empty list for current values to be stored in
J1 = []
for i in range(sigmaz_hgam.size):
expec_sz = sigmaz_hgam[i]
J = 4*gamma[i]*(mu-expec_sz)
J1.append(J.real)
#print(J)
This part of the code is what is used to graph out which is where the problem lies
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
x = h
y = gamma
z = J1
ax.plot(x, y, z, label='Dephasing Model')
ax.legend()
ax.set_xlabel('h', fontsize=10)
ax.set_ylabel('$\gamma$')
ax.yaxis._axinfo['label']['space_factor'] = 3.0
for t in ax.zaxis.get_major_ticks(): t.label.set_fontsize(10)
# disable auto rotation
ax.zaxis.set_rotate_label(False)
ax.set_zlabel('J', fontsize=10, rotation = 0)
plt.show()
On my version of Matplotlib (2.0.2), on a Mac, I see the label (which is there – most of it is just being cropped out of the image in your case).
You could try to reduce the padding between the ticks and the label:
ax.zaxis.labelpad = 0
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)