We Keep Coding

Want to plot graph side by side

I wan to two plots side by side instead of this vertically, right now it's showing one by one
def scatter_plot(surrogate, building, actual, pred,index):
#calculating max and min x axis range
min_range=pred.min()-10
max_range=pred.max()+10
min_domain=actual.min()-10
max_domain=actual.max()+10
#scaling and creating scatter plot
plt.axes([0, 0, 2, 2])
plt.scatter(x=actual,y=pred, marker="o") #(y = predicted)
#plt.gca().set_aspect('equal', adjustable='box')
plt.grid()
plt.xlabel('Actual Values', fontsize = 20)
plt.ylabel('Predicted Values', fontsize = 20)
plt.title(f'{building.idf}_{building.epw}_{variable} Scatter Plot of NN vs E+', fontsize= 25)
#adding regression line
plt.plot([min_domain, max_domain], [min_range, max_range], color='g', linestyle='-', linewidth=1,label='regression')
#adding line passing minimum and maximum actual points
plt.plot([min_domain, max_domain],[min_domain, max_domain],color='r',linestyle='-',linewidth=1,label='actual point line')
#adding legend
plt.legend(loc='lower right')
#calculating error metrics
location = building.metadata['building_attributes']['Location']
building_type = building.idf
df = csv.loc[(csv['id'] == surrogate.surrogate_id) &
(csv['Location'] == location) & (csv['Building Type'] == building_type)]
rmse = df[f'{variable} RMSE'].values[0]
r2 = df[f'{variable} R2'].values[0]
#Adding Error metric annotations
textstr = '\n'.join((r'Total Error Metrics', r'$RMSE=%.2f$' % (rmse, ),r'$R2=%.2f$' % (r2, )))
props = dict(boxstyle='round', facecolor='ivory', alpha=0.5)
plt.text(max_range, max_domain, textstr, fontsize=20, verticalalignment='top', bbox=props)
#calculating x and y range
axes = plt.gca()
y_min, y_max = axes.get_ylim()
x_min, x_max = axes.get_xlim()
#Coordinates of interested area
percentile = 10
nth_percentile = np.percentile(actual,percentile)
bottom, left, width, height = 0, 0, nth_percentile,nth_percentile
try:
x_hist = x_min +(x_max - x_min)/9 #may have to change value 9
#calculating lines for selected area
x1, y1 = [left, x_hist], [bottom+height, (y_max + y_min)/2]
x2, y2 = [left + width, x_hist], [bottom + height, (y_max + y_min)/2]
L_act = []
L_pred = []
for x, y in zip(actual, pred):
if left <= x <= width+left:
if bottom<= y <= height + bottom:
L_act.append(x)
L_pred.append(y)
#adding rectangle for selected area
rect=mpatches.Rectangle((left, bottom),width, height, fill = False, color = "black",linewidth = 2)
plt.gca().add_patch(rect)
#calculating error metrics for selected area
rmse = RMSE(L_act, L_pred)
r2 = R2(L_act, L_pred)
#adding lines to indicated the selected area
plt.plot(x1, y1, x2, y2, color = 'black', linewidth = 2)
#adding histogram
plt.axes([0.2, 1, .6, .6], facecolor='w')
plt.hist(L_act, 30)
plt.xticks([])
plt.yticks([])
textstr = '\n'.join((r'Selected Section Error Metrics', r'$RMSE=%.2f$' % (rmse, ),r'$R2=%.2f$' % (r2, )))
props = dict(boxstyle='round', facecolor='ivory', alpha=0.8)
#adding error metrics annotations for selected area
axes = plt.gca()
y_min, y_max = axes.get_ylim()
x_min, x_max = axes.get_xlim()
plt.text(x_min + x_min/10, y_max - y_max/30, textstr, fontsize=10, verticalalignment='top', bbox=props)
except ValueError:
print("Selected section doesn't contain any data points")
plt.show()
I tried using a subplot but that didn't work
def s_plot(surrogate,building):
figure, axis = plt.subplots(1, 2)
actual, pred = np.array(surrogate.test_samples[variable].values[:]), np.array(surrogate.training_samples[variable].values[:])
actual_train, pred_train = np.array(surrogate.train_actual[variable].values[:]), np.array(surrogate.train_pred[variable].values[:])
data =[[actual,pred],[actual_train, pred_train]
for ax,i in zip(axes.flatten(),data):
scatter_plot(surrogate,building,i[0],i[1],ax)
Here I am using axes instead of plt but there are so many parameters that axes doesn't have such as gca, scaling using axes, etc. and I am not able to plot histogram by subplot use
Is there any way to plot this side by side

here is a simple example of using histogram with subplots:
def func(ax):
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * np.random.randn(437)
num_bins = 50
# the histogram of the data
n, bins, patches = ax.hist(x, num_bins, density=True)
# add a 'best fit' line
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
ax.plot(bins, y, '--')
ax.set_xlabel('Smarts')
ax.set_ylabel('Probability density')
ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
fig, (ax1, ax2) = plt.subplots(1, 2)
func(ax1)
func(ax2)

Contourplot in matplot showing incorrect linestyle

I'm plotting a 2D matrix, which has positive and negative values, in matplotlib using contourplot. It is supposed to show solid lines for positive values and dashed lines for negative values:
loc = matplotlib.ticker.MaxNLocator(20)
Z = psi
lvls = loc.tick_values(Z.min(), Z.max())
fig, ax = plt.subplots(figsize=(7,7))
cp = plt.contour(X, Y, Z, 20, colors='k', linestyles=where(lvls >= 0, "-", "--"))
plt.xlabel('X')
plt.ylabel('Y')
plt.clabel(cp, inline=True, fontsize=10)
plt.gca().set_aspect('equal', adjustable='box')
plt.title('Stream function - Re = ' + str(Re) + ', t = {:.2f}'.format((t)*dt))
plt.savefig('SF' + '_Re' + str(Re) + '_N' + str(nx) + '_o' + str(order) + '_SF' + '.png')
plt.close()
However, this is what this code is plotting:
As you can see, there are dashed lines where it is supposed to show solid lines and solid lines where it is supposed to show dashed lines. Any ideas?
Edit: the code below works just fine:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
nx = 100
ny = 100
# Generate 2D mesh
x = 2*np.pi*np.arange(0,nx,1)/(nx)
#x = linspace(0,Lx,nx,endpoint=True)
y = 2*np.pi*np.arange(0,ny,1)/(ny)
#y = linspace(0,Ly,ny,endpoint=True)
X, Y = np.meshgrid(x, y,indexing='ij')
Z = -np.sin(X/2)*np.cos(Y**1.5)
loc = matplotlib.ticker.MaxNLocator(20)
lvls = loc.tick_values(Z.min(), Z.max())
fig, ax = plt.subplots(figsize=(7,7))
cp = plt.contour(X,Y,Z,20, colors='k', linestyles=np.where(lvls >= 0, "-", "--"))
plt.clabel(cp, inline=True, fontsize=10)
plt.gca().set_aspect('equal', adjustable='box')
plt.show()
The output:

You should switch the order of the line styles. Currently, your condition will assign - (solid line) to contours where lvls >= 0 otherwise it will assign -- (dashed line). That's how the where argument works.
In pseudo form, np.where(condition, A, B) means if condition is True assign A else assign B
Your present code (Not desired):
linestyles=np.where(lvls >= 0, "-", "--")
Right style (desired style):
linestyles=np.where(lvls >= 0, "--", "-")

How to draw vertical lines interactively in matplotlib?

I have a time series plot and I need to draw a moving vertical line to show the point of interest.
I am using the following toy example to accomplish the same. However, it prints all the lines at the same time while I wanted to show these vertical line plotting one at a time.
import time
ion() # turn interactive mode on
# initial data
x = arange(-8, 8, 0.1);
y1 = sin(x)
y2 = cos(x)
line1, = plt.plot(x, y1, 'r')
xvals = range(-6, 6, 2);
for i in xvals:
time.sleep(1)
# update data
plt.vlines(i, -1, 1, linestyles = 'solid', color= 'red')
plt.draw()

If I understood well, you want to use the animation tools of matplotlib. An example (adapted from the doc):
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
X_MIN = -6
X_MAX = 6
Y_MIN = -1
Y_MAX = 1
X_VALS = range(X_MIN, X_MAX+1) # possible x values for the line
def update_line(num, line):
i = X_VALS[num]
line.set_data( [i, i], [Y_MIN, Y_MAX])
return line,
fig = plt.figure()
x = np.arange(X_MIN, X_MAX, 0.1);
y = np.sin(x)
plt.scatter(x, y)
l , v = plt.plot(-6, -1, 6, 1, linewidth=2, color= 'red')
plt.xlim(X_MIN, X_MAX)
plt.ylim(Y_MIN, Y_MAX)
plt.xlabel('x')
plt.ylabel('y = sin(x)')
plt.title('Line animation')
line_anim = animation.FuncAnimation(fig, update_line, len(X_VALS), fargs=(l, ))
#line_anim.save('line_animation.gif', writer='imagemagick', fps=4);
plt.show()
Resulting gif looks like this:

Could you try calling plt.draw after plt.vlines? plt.draw is used to interactively redraw the figure after its been modified.

How to put circles on top of a polygon?

I use matplotlib to generate an image in the following way:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.fill(border[0],border[1], color='g', linewidth=1, fill=True, alpha = 0.5)
patches = []
for x1,y1,r in zip(x, y, radii):
circle = Circle((x1,y1), r)
patches.append(circle)
p = PatchCollection(patches, cmap='cool', alpha=1.0)
p.set_array(c)
ax.add_collection(p)
plt.colorbar(p)
plt.savefig(fig_name)
What I want to have is a polygon (given by its border) and colored circles on the top of this polygon. However, I get the polygon on the top of the circles.
This is strange because I plot the polygon first and then I add circles to the plot.
Does anybody know why it happens and how this problem can be resolved?
ADDED
As requested, here is fully working example:
import pandas
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Circle, Polygon
import numpy as np
def plot_xyc(df, x_col, y_col, c_col, radius, fig_name, title, zrange):
resolution = 50
x = df[x_col]
y = df[y_col]
c = df[c_col]
x0 = (max(x) + min(x))/2.0
y0 = (max(y) + min(y))/2.0
dx = (max(x) - min(x))
dy = (max(y) - min(y))
delta = max(dx, dy)
radii = [delta*radius for i in range(len(x))]
fig = plt.figure()
plt.title(title)
ax = fig.add_subplot(111)
border = ([-3, 3, 3, -3], [-3, -3, 3, 3])
ax.fill(border[0],border[1], color='g', linewidth=1, fill=True, alpha = 1.0)
patches = []
for x1,y1,r in zip(x, y, radii):
circle = Circle((x1,y1), r)
patches.append(circle)
patches.append(Circle((-100,-100), r))
patches.append(Circle((-100,-100), r))
p = PatchCollection(patches, cmap='cool', alpha=1.0)
p.set_array(c)
max_ind = max(c.index)
c.set_value(max_ind + 1, min(zrange))
c.set_value(max_ind + 2, max(zrange))
plt.xlim([x0 - delta/2.0 - 0.05*delta, x0 + delta/2.0 + 0.05*delta])
plt.ylim([y0 - delta/2.0 - 0.05*delta, y0 + delta/2.0 + 0.05*delta])
ax.add_collection(p)
plt.colorbar(p)
plt.savefig(fig_name)
if __name__ == '__main__':
df = pandas.DataFrame({'x':[1,2,3,4], 'y':[4,3,2,1], 'z':[1,1,2,2]})
plot_xyc(df, 'x', 'y', 'z', 0.1, 'test2.png', 'My Titlle', (0.0, 3.0))

You're looking for zorder.
In matplotlib, all additional arguments are just passed up the class heirarchy. zorder is a kwarg of the Artist class, so you just need to make sure that at some point it gets zorder.
You can do it two ways in your example;
either add it in here:
ax.fill(border[0],border[1], color='g', linewidth=1, fill=True, alpha = 1.0, zorder=1)
or here:
p = PatchCollection(patches, cmap='cool', alpha=1.0, zorder=2)
or if you want, both. Objects with a higher zorder sit on top of those with lower values.

Matplotlib - 2 problems. Common colorbar / labels not showing up

I finally forced the 3 plots I want into one plot with 3 subplots...now I need to add a common colorbar, preferably horizontally oriented. Also, now that I have them as subplots, I have lost the labels that were there in a previous iteration.
It seems that the examples suggest I add an axes, but I don't quite get what the numbers in the arguments are.
def plot_that_2(x_vals, y_vals, z_1_vals, z_2_vals, z_3_vals, figname, units, efficiency_or_not):
global letter_pic_width
plt.close() #I moved this up from the end of the file because it solved my QTagg problem
UI = [uniformity_calc(z_1_vals), uniformity_calc(z_2_vals), uniformity_calc(z_3_vals)]
ranges = [ str(int(np.max(z_1_vals) - np.min(z_1_vals))), str(int(np.max(z_2_vals) - np.min(z_2_vals))), str(int(np.max(z_3_vals) - np.min(z_3_vals)))]
z_vals = [z_1_vals, z_2_vals, z_3_vals]
fig = plt.figure(figsize = (letter_pic_width, letter_pic_width/3 ))
ax0 = fig.add_subplot(1,3,1, aspect = 1)
ax1 = fig.add_subplot(1,3,2, aspect = 1)
ax2 = fig.add_subplot(1,3,3, aspect = 1)
axenames = [ax0, ax1, ax2]
for z_val, unif, rangenum, ax in zip(z_vals, UI, ranges, axenames):
ax.scatter(x_vals, y_vals, c = z_val, s = 100, cmap = 'rainbow')
if efficiency_or_not:
ax.vmin = 0
ax.vmax = 1
ax.xlabel = 'Uniformity: ' + unif
else:
ax.xlabel = 'Uniformity: ' + unif + ' ' + rangenum + ' ppm'
plt.savefig('./'+ figname + '.jpg', dpi = 100)

To set the xlabel, use ax.set_xlabel('Uniformity: ' + unif) See more information here in the documentation for axes.
The example you linked to uses the add_axes method of a figure as an alternative to add_subplot. The documentation for figures explains what the numbers in add_axes are: "Add an axes at position rect [left, bottom, width, height] where all quantities are in fractions of figure width and height."
rect = l,b,w,h
fig.add_axes(rect)

To answer your question about the colorbar axis, the numbers represent
[bottom_left_x_coord, bottom_left_y_coord, width, height]
An appropriate colorbar might be
# x y w h
[0.2, 0.1, 0.6, 0.05]
Here's your code, somewhat reworked which adds a colorbar:
import numpy as np
import matplotlib.pyplot as plt
WIDTH = 9
def uniformity_calc(x):
return x.mean()
def plotter(x, y, zs, name, units, efficiency=True):
fig, axarr = plt.subplots(1, 3, figsize=(WIDTH, WIDTH/3),
subplot_kw={'aspect':1})
fig.suptitle(name)
UI = map(uniformity_calc, zs)
ranges = map(lambda x: int(np.max(x)-np.min(x)), zs)
for ax, z, unif, rangenum in zip(axarr, zs, UI, ranges):
scat = ax.scatter(x, y, c=z, s=100, cmap='rainbow')
label = 'Uniformity: %i'%unif
if not efficiency:
label += ' %i ppm'%rangenum
ax.set_xlabel(label)
# Colorbar [left, bottom, width, height
cax = fig.add_axes([0.2, 0.1, 0.6, 0.05])
cbar = fig.colorbar(scat, cax, orientation='horizontal')
cbar.set_label('This is a colorbar')
plt.show()
def main():
x, y = np.meshgrid(np.arange(10), np.arange(10))
zs = [np.random.rand(*y.shape) for _ in range(3)]
plotter(x.flatten(), y.flatten(), zs, 'name', None)
if __name__ == "__main__":
main()