I want to make a plot from a data file with matplotlib.pyplot and I want every marker (triangle) to have an orientation which has been given in the input file.
The input file :
x y angle
1 1 10
1.2 1.2 20
1.3 1.3 30
and this is my code:
import numpy as np
import matplotlib.pyplot as plt
infile = open ('traj_marker.txt')
#for s in xrange(8):
x, y = [], []
m = []
for i in xrange(3):
data = infile.readline()
raw = data.split()
x.append(float(raw[0]))
y.append(float(raw[1]))
m.append(float(raw[2]))
xnp = np.array(x)
ynp = np.array(y)
mnp = np.array(m)
fig, ax = plt.subplots()
ax.scatter(xnp, ynp, 100, marker = (3,0,mnp))
plt.xticks (range(1,3))
plt.yticks (range(1,3))
plt.grid()
fig.savefig ('trj.png')
infile.close()
But the presence of array mnp in marker produces error.
How can I solve this?
Matplotlib doesn't like the marker argument passed as a list, so run it in the following manner ...
N = 20
xnp = np.random.rand(N)
ynp = np.random.rand(N)
mnp = np.random.randint(0, 180, N)
fig, ax = plt.subplots()
for x, y, m in zip(xnp, ynp, mnp):
ax.scatter(x, y, 100, marker = (3,0,m))
plt.show()
Just in case you are not aware, you can use quiver to plot 2D fields:
x = [1, 1.2, 1.3]
y = [1, 1.2, 1.3]
angle = [10, 20, 30]
plt.quiver(x, y, np.cos(np.radians(angle)), np.sin(np.radians(angle)))
Related
I want to plot some equation in Matplotlib. But it has different result from Wolframalpha.
This is the equation:
y = 10yt + y^2t + 20
The plot result in wolframalpha is:
But when I want to plot it in the matplotlib with these code
# Creating vectors X and Y
x = np.linspace(-2, 2, 100)
# Assuming α is 10
y = ((10*y*x)+((y**2)*x)+20)
# Create the plot
fig = plt.figure(figsize = (10, 5))
plt.plot(x, y)
The result is:
Any suggestion to modify to code so it has similar plot result as wolframalpha? Thank you
As #Him has suggested in the comments, y = ((10*y*x)+((y**2)*x)+20) won't describe a relationship, so much as make an assignment, so the fact that y appears on both sides of the equation makes this difficult.
It's not trivial to express y cleanly in terms of x, but it's relatively easy to express x in terms of y, and then graph that relationship, like so:
import numpy as np
import matplotlib.pyplot as plt
y = np.linspace(-40, 40, 2000)
x = (y-20)*(((10*y)+(y**2))**-1)
fig, ax = plt.subplots()
ax.plot(x, y, linestyle = 'None', marker = '.')
ax.set_xlim(left = -4, right = 4)
ax.grid()
ax.set_xlabel('x')
ax.set_ylabel('y')
Which produces the following result:
If you tried to plot this with a line instead of points, you'll get a big discontinuity as the asymptotic limbs try to join up
So you'd have to define the same function and evaluate it in three different ranges and plot them all so you don't get any crossovers.
import numpy as np
import matplotlib.pyplot as plt
y1 = np.linspace(-40, -10, 2000)
y2 = np.linspace(-10, 0, 2000)
y3 = np.linspace(0, 40, 2000)
x = lambda y: (y-20)*(((10*y)+(y**2))**-1)
y = np.hstack([y1, y2, y3])
fig, ax = plt.subplots()
ax.plot(x(y), y, linestyle = '-', color = 'b')
ax.set_xlim(left = -4, right = 4)
ax.grid()
ax.set_xlabel('x')
ax.set_ylabel('y')
Which produces this result, that you were after:
I'm getting the blank plot by using the below code and data file. Could you please let me know what's wrong with data file or the code?
import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt('file1.txt', delimiter=' ')
lats = data[:,0]
lons = data[:,1] values = data[:,2]
lat_uniq, lat_idx = np.unique(lats, return_inverse=True)
lon_uniq, lon_idx = np.unique(lons, return_inverse=True)
xre, yre = np.meshgrid(lon_uniq, lat_uniq)
zre = np.full(xre.shape, np.nan)
zre[lat_idx, lon_idx] = values
print(zre)
fig, (ax1) = plt.subplots(1,1, figsize = (10, 5))
cp1 = ax1.contourf(xre, yre, zre, levels=4)
plt.colorbar(cp1, ax=ax1)
ax1.set_title("data are not interpolated") plt.show()
test.txt file --
1 2 3
4 5 6
7 8 9
10 11 12
Your program has no problem
There were none values in your matrix, in which case the graph will not be drawn.
It makes no sense to have null values in a matrix and try to draw those points.
By changing its values, the 'contourf' command can fit the values of X, Y, and Z, and the graph is drawn.
See this :
import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt('test.txt', delimiter=' ')
lats = data[:, 0]
lons = data[:, 1]
values = data[:, 2]
lat_uniq, lat_idx = np.unique(lats, return_inverse=True)
lon_uniq, lon_idx = np.unique(lons, return_inverse=True)
xre, yre = np.meshgrid(lon_uniq, lat_uniq)
# zre = np.full(xre.shape, np.nan)
zre = np.full(xre.shape, 0)
zre[lat_idx, lon_idx] = values
print(zre)
fig, (ax1) = plt.subplots(1, 1, figsize=(10, 5))
cp1 = ax1.contourf(xre, yre, zre, levels=4)
plt.colorbar(cp1, ax=ax1)
ax1.set_title("data are not interpolated")
plt.show()
Output:
I am trying to plot a dashed line in a 3-D Matplotlib plot. I would like to get a dashed line between each (x_pt, y_pt) to its corresponding z_pt.
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib
matplotlib.rcParams['mathtext.fontset'] = 'cm'
matplotlib.rcParams['axes.labelsize'] = 13
def z_function(x, y):
a = 1
b = 5.1/(4*np.pi**2)
c = 5/np.pi
r = 6
s = 10
t = 1/(8*np.pi)
return a*(y - b*x**2 + c*x - r)**2 + s*(1 - t)*np.cos(x) + s
x = np.linspace(-5, 10, 100)
y = np.linspace(0, 15, 100)
indexes = np.random.randint(0, 100, 5)
x_pt = x[indexes]
y_pt = y[indexes]
z_pt = z_function(x_pt, y_pt)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.scatter(x_pt, y_pt, color='k', marker='x', depthshade=False)
ax.scatter(x_pt, y_pt, z_pt, color='k', marker='^', depthshade=False)
ax.set_xticks([-5, 0, 5, 10])
ax.set_yticks([0, 5, 10, 15])
ax.set_zticks([100, 200, 300])
ax.view_init(30, -120)
ax.set_xlabel(r'$x_1$')
ax.set_ylabel(r'$x_2$')
ax.zaxis.set_rotate_label(False)
ax.set_zlabel(r'$f(x)$', rotation=0)
ax.w_xaxis.pane.fill = False
ax.w_yaxis.pane.fill = False
ax.w_zaxis.pane.fill = False
plt.show()
Can anyone help me with this?
If I understand your problem correctly, you need to connect the point (x,y,0) to (x,y,z) like so:
for x_,y_,z_ in zip(x_pt, y_pt, z_pt):
ax.plot([x_,x_],[y_,y_],[0,z_], '--', c='grey')
It should be as simple as:
ax.plot(x_pt, y_pt, zs=z_pt, color='blue', marker='--', depthshade=False)
alternatively using:
ax.plot3D(x_pt, y_pt, z_pt, marker='--')
UPDATE:
You will need to create extra dummy coordinates for each point on the x-y axis, like so:
import numpy as np
n = 10 # number of points in the line
for i in len(x_pt):
x_range = np.linspace(0, x_pt[i], n)
y_range = np.linspace(0, y_pt[i], n)
ax.plot3D(x_range, y_range, [z_pt[i]]*n, marker='--')
NOTE: Untested
I have the following (example) code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
cmap.set_array([])
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show();
I would like to find a substirute for cmap.to_rgba that makes the colour of each line come out as a differnet shade of blue. Basically I want to keep the same layout as the result of this code, but using the colour map Blues.
How can I do it?
You need to change your colormap that you are using from jet to Blues.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.Blues)
cmap.set_array([])
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show()
This produces:
The figure above is a great artwork showing the wind speed, wind direction and temperature simultaneously. detailedly:
The X axes represent the date
The Y axes shows the wind direction(Southern, western, etc)
The variant widths of the line were stand for the wind speed through timeseries
The variant colors of the line were stand for the atmospheric temperature
This simple figure visualized 3 different attribute without redundancy.
So, I really want to reproduce similar plot in matplotlib.
My attempt now
## Reference 1 http://stackoverflow.com/questions/19390895/matplotlib-plot-with-variable-line-width
## Reference 2 http://stackoverflow.com/questions/17240694/python-how-to-plot-one-line-in-different-colors
def plot_colourline(x,y,c):
c = plt.cm.jet((c-np.min(c))/(np.max(c)-np.min(c)))
lwidths=1+x[:-1]
ax = plt.gca()
for i in np.arange(len(x)-1):
ax.plot([x[i],x[i+1]], [y[i],y[i+1]], c=c[i],linewidth = lwidths[i])# = lwidths[i])
return
x=np.linspace(0,4*math.pi,100)
y=np.cos(x)
lwidths=1+x[:-1]
fig = plt.figure(1, figsize=(5,5))
ax = fig.add_subplot(111)
plot_colourline(x,y,prop)
ax.set_xlim(0,4*math.pi)
ax.set_ylim(-1.1,1.1)
Does someone has a more interested way to achieve this? Any advice would be appreciate!
Using as inspiration another question.
One option would be to use fill_between. But perhaps not in the way it was intended. Instead of using it to create your line, use it to mask everything that is not the line. Under it you can have a pcolormesh or contourf (for example) to map color any way you want.
Look, for instance, at this example:
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d
def windline(x,y,deviation,color):
y1 = y-deviation/2
y2 = y+deviation/2
tol = (y2.max()-y1.min())*0.05
X, Y = np.meshgrid(np.linspace(x.min(), x.max(), 100), np.linspace(y1.min()-tol, y2.max()+tol, 100))
Z = X.copy()
for i in range(Z.shape[0]):
Z[i,:] = c
#plt.pcolormesh(X, Y, Z)
plt.contourf(X, Y, Z, cmap='seismic')
plt.fill_between(x, y2, y2=np.ones(x.shape)*(y2.max()+tol), color='w')
plt.fill_between(x, np.ones(x.shape) * (y1.min() - tol), y2=y1, color='w')
plt.xlim(x.min(), x.max())
plt.ylim(y1.min()-tol, y2.max()+tol)
plt.show()
x = np.arange(100)
yo = np.random.randint(20, 60, 21)
y = interp1d(np.arange(0, 101, 5), yo, kind='cubic')(x)
dv = np.random.randint(2, 10, 21)
d = interp1d(np.arange(0, 101, 5), dv, kind='cubic')(x)
co = np.random.randint(20, 60, 21)
c = interp1d(np.arange(0, 101, 5), co, kind='cubic')(x)
windline(x, y, d, c)
, which results in this:
The function windline accepts as arguments numpy arrays with x, y , a deviation (like a thickness value per x value), and color array for color mapping. I think it can be greatly improved by messing around with other details but the principle, although not perfect, should be solid.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
x = np.linspace(0,4*np.pi,10000) # x data
y = np.cos(x) # y data
r = np.piecewise(x, [x < 2*np.pi, x >= 2*np.pi], [lambda x: 1-x/(2*np.pi), 0]) # red
g = np.piecewise(x, [x < 2*np.pi, x >= 2*np.pi], [lambda x: x/(2*np.pi), lambda x: -x/(2*np.pi)+2]) # green
b = np.piecewise(x, [x < 2*np.pi, x >= 2*np.pi], [0, lambda x: x/(2*np.pi)-1]) # blue
a = np.ones(10000) # alpha
w = x # width
fig, ax = plt.subplots(2)
ax[0].plot(x, r, color='r')
ax[0].plot(x, g, color='g')
ax[0].plot(x, b, color='b')
# mysterious parts
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
# mysterious parts
rgba = list(zip(r,g,b,a))
lc = LineCollection(segments, linewidths=w, colors=rgba)
ax[1].add_collection(lc)
ax[1].set_xlim(0,4*np.pi)
ax[1].set_ylim(-1.1,1.1)
fig.show()
I notice this is what I suffered.