Original(2018.11.01)
I have 3 numpy：x、y、z,created by my laser scanner(40 degree / 1 step).
I want to used them to build a 3D model.
I think it must should be use matplotlib.tri
But I have no idea to decide triangulated data
Here is my data ：https://www.dropbox.com/s/d9p62kv9jcq9bwh/xyz.zip?dl=0
And Original model：https://i.imgur.com/XSyONff.jpg
Code：
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.tri as mtri
x_all=np.load("x.npy")
y_all=np.load("y.npy")
z_all=np.load("z.npy")
tri = #I have no idea...
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_trisurf(x_all,y_all,z_all,triangles=tri.triangles)
Thank so much.
Update(2018.11.02)
I try this way to decide triangulated data
Delaunay Triangulation of points from 2D surface in 3D with python?
code:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.tri as mtri
from stl import mesh
x_all=np.load("x.npy")
y_all=np.load("y.npy")
z_all=np.load("z.npy")
model=np.vstack((x_all,y_all,z_all))
model=np.transpose(model)
model -= model.mean(axis=0)
rad = np.linalg.norm(model, axis=1)
zen = np.arccos(model[:,-1] / rad)
azi = np.arctan2(model[:,1], model[:,0])
tris = mtri.Triangulation(zen, azi)
plt.show()
And my model looks like:
https://i.stack.imgur.com/KVPHP.png
https://i.stack.imgur.com/LLQsQ.png
https://i.stack.imgur.com/HdzFm.png
Even though it has better surface on it,but there is a big hole over my model.Any idea to fixs it?
Assuming you want to reduce the complexity, i.e find triangles in your files to reduce the complexity. You may look into fitting a convex hull to your points, see here fore more info
Based on the file you provided this produces a surf plot of the object.
from numpy import load, stack
from matplotlib.pyplot import subplots
from mpl_toolkits.mplot3d import Axes3D
from scipy import spatial
x = load("x.npy")
y = load("y.npy")
z = load("z.npy")
points = stack((x,y,z), axis = -1)
v = spatial.ConvexHull(points)
fig, ax = subplots(subplot_kw = dict(projection = '3d'))
ax.plot_trisurf(*v.points.T, triangles = v.simplices.T)
fig.show()
Related
I've been working to make a visual for a poster regarding the physical domain that I am studying. I'm working with a nested domain, so I have 1 smaller domain inside a larger outer domain. I'm trying to create a cartopy plot that shows both the outer domain and inner domain. Ideally, the result would look something like this:
I'm really struggling with trying to get my smaller domain inscribed onto my map. I've attempted to make a Shapely LinearRing to show the inner domain, but it is not working. Here's the code I have created so far:
# Imports
import numpy as np
import sys, os
import matplotlib.pyplot as plt
%matplotlib inline
import netCDF4
from netCDF4 import Dataset
from matplotlib.cm import get_cmap
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.feature import NaturalEarthFeature, COLORS
import metpy as mp
import metpy.calc as mpcalc
from metpy.calc import divergence, smooth_gaussian
from metpy.units import units
import xarray as xr
from wrf import getvar, interplevel, to_np, latlon_coords, get_cartopy, cartopy_xlim, cartopy_ylim, ALL_TIMES
from shapely.geometry.polygon import LinearRing
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from shapely import geometry
# Get 1km lats/lons
lats1km = getvar(ds1, 'lat')
lons1km = getvar(ds1, 'lon')
lat1km_max = to_np(np.max(lats1km))
lat1km_min = to_np(np.min(lats1km))
lon1km_max = to_np(np.max(lons1km))
lon1km_min = to_np(np.min(lons1km))
# Get 3km lats/lons
lats3km = getvar(ds3, 'lat')
lons3km = getvar(ds3, 'lon')
lat3km_max = to_np(np.max(lats3km))
lat3km_min = to_np(np.min(lats3km))
lon3km_max = to_np(np.max(lons3km))
lon3km_min = to_np(np.min(lons3km))
domain = [lon3km_min, lat3km_min, lon3km_max, lat3km_max]
lons = [lat1km_min, lat1km_min, lat1km_max, lat1km_max]
lats = [lon1km_min, lon1km_max, lon1km_max, lon1km_min]
ring = LinearRing(list(zip(lons, lats)))
geom = geometry.box(minx=lon1km_min, miny=lat1km_min, maxx=lon1km_max, maxy=lat1km_max)
# Grab CRS
crs = get_cartopy(wrfin=ds1)
# Create figure and axes
fig = plt.figure(figsize=(20,10))
ax0 = fig.add_subplot(1, 1, 1, projection=crs)
ax0.set_extent([lon3km_min, lon3km_max, lat3km_min, lat3km_max])
ax0.add_geometries([ring], crs=crs, facecolor='blue', edgecolor='black')
ax0.add_geometries([geom], crs=crs, alpha=0.3)
plot_background(ax0)
This yields my outer domain, but not my inner domain:
What am I doing wrong, and what can I do to get my inner domain shown on the map? Thank you for the help! I really appreciate it!
NOTE: I have already attempted the solution in this link. I am still unable to visualize my polygon.
Have a look at EOmaps ! (I'm the dev) it provides simple functions to add static (or interactive) indicators such as projected rectangles or ellipses to cartopy plots in 1 line!
from eomaps import Maps
m = Maps()
m.add_coastlines()
props = dict(xy=(10, 45), xy_crs=4326, radius_crs=4326, shape="rectangles")
m.add_marker(**props, radius=3, fc=(0,1,0,.5), ec="r", lw=2)
m.add_marker(**props, radius=5, fc="none", ec="k")
m.add_marker(**props, radius=(15, 10), fc="none", ec="m", ls="--", lw=2)
m.figure.ax.set_extent((-15., 65., -5., 75.))
I am trying to plot vehicle position (coordinates - x,y) against time(1s,2s,3s...). I tried with matplotlib but could not succeed. I am new in python. Could anyone help me please.
My code:
import matplotlib.pyplot as plt
import numpy as np
coordinate = [[524.447876,1399.091919], [525.1377563,1399.95105], [525.7932739,1400.767578], [526.4627686,1401.601563],
[527.2360229,1402.564575], [527.8989258,1403.390381], [528.5689697,1404.224854]]
timestamp =[0,0.05,0.1,0.15,0.2,0.25,0.3]
plt.plot(coordinate,timestamp)
Plot comes like: But this is wrong one. I did wrong.
Plot supposed to become, in particular, timestamp (1s) the vehicle position is (x,y). So there should be one line just like vehicle trajectory.
Thanks.
I believe this is the output you're looking for:
import matplotlib.pyplot as plt
import numpy as np
coordinate = [[524.447876,1399.091919],
[525.1377563,1399.95105],
[525.7932739,1400.767578],
[526.4627686,1401.601563],
[527.2360229,1402.564575],
[527.8989258,1403.390381],
[528.5689697,1404.224854]]
v1 = [y[1] for y in coordinate]
v2 = [y[0] for y in coordinate]
x = [0,0.05,0.1,0.15,0.2,0.25,0.3]
plt.plot(x,v1)
plt.plot(x,v2,'--')
plt.ylim(0,1500)
plt.show()
Does something simple like this meet your needs:
import matplotlib.pyplot as plt
coordinates = [
(524.447876,1399.091919),
(525.1377563,1399.95105),
(525.7932739,1400.767578),
(526.4627686,1401.601563),
(527.2360229,1402.564575),
(527.8989258,1403.390381),
(528.5689697,1404.224854),
]
timestamp = [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3]
x, y = zip(*coordinates)
ax = plt.axes(projection="3d")
ax.plot(x, y, timestamp);
plt.show()
Matplotlib will let you rotate the image with the mouse to view it from various angles.
Hi I think the problem over here is that you are using a two-dimensional list, so matplotlib plots the coordinates and not the timestamp.
Code:
import matplotlib.pyplot as plt
import numpy as np
coordinate = np.array([[524.447876,1399.091919], [525.1377563,1399.95105], [525.7932739,1400.767578], [526.4627686,1401.601563], [527.2360229,1402.564575], [527.8989258,1403.390381], [528.5689697,1404.224854]])
timestamp =np.array([0,0.05,0.1,0.15,0.2,0.25,0.3])
plt.plot(coordinate)
Output:
You have to convert it into a single dimension list like this:
coordinate_new = np.array([524.447876,525.1377563,1399.95105, 525.7932739,1400.767578, 526.4627686,1401.601563])
timestamp =np.array([0,0.05,0.1,0.15,0.2,0.25,0.3])
plt.plot(coordinate_new, timestamp)
Then the output will be:
Hope I could help!!
If you want to plot it in 3-d, here is what you can do:
import matplotlib.pyplot as plt
#importing matplotlib
fig = plt.figure() #adding figure
ax_3d = plt.axes(projection="3d") #addign 3-d axes
coordinate_x = [524.447876, 525.137756, 525.7932739, 526.4627686, 527.2360229, 527.8989258, 528.5689697]
coordinate_y = [1399.091919, 1399.95105,1400.767578,1401.601563,1402.564575,1403.390381,1404.224854]
timestamp =[0,0.05,0.1,0.15,0.2,0.25,0.3]
# defining the variables
ax.plot(coordinate_x, coordinate_y, timestamp)
#plotting them
Output:
All the Best!
I am trying to write a code to generate an RBG image of the Pillars of Creation. For that I am using fits file corresponding to red, blue and green, and trying to use make_lupton_rbg to generate the RBG image. However I am getting full green image. I believe, I have to make adjustments to Q and stretch values, but I can't find anything to give it a good color (as seen in the pictures).
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.visualization import make_lupton_rgb
forc=np.float_()
r=fits.open("./673nmos.fits")[0].data
g=fits.open("./656nmos.fits")[0].data
b=fits.open("./502nmos.fits")[0].data
r = np.array(r,forc)
g = np.array(g,forc)
b = np.array(b,forc)
rgb_default = make_lupton_rgb(r,g,b,Q=1,stretch=0.1,filename="pillar.png")
plt.imshow(rgb_default, origin='lower')
plt.show()
The fits file were download from here
This is the output I am getting
And this is the output I should get (or at least something like it)
Scaling the r, g, and b arrays based on their relative brightnesses and using a high linear stretch gets much closer:
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.visualization import make_lupton_rgb
forc=np.float_()
r=fits.open("/path/to/673nmos/673nmos.fits")[0].data
g=fits.open("/path/to/656nmos/656nmos.fits")[0].data
b=fits.open("/path/to/502nmos/502nmos.fits")[0].data
r = np.array(r,forc)
g = np.array(g,forc)
b = np.array(b,forc)
rgb_default = make_lupton_rgb(r*5,g*0.75,b*8,Q=0.001,stretch=300,filename="pillar.png")
plt.imshow(rgb_default, origin='lower')
plt.show()
But clearly the linear stretching handles the spikes poorly, this can be compensated for by simply threshold filtering before applying make_lupton_rgb:
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.visualization import make_lupton_rgb
forc=np.float_()
r=fits.open("/path/to/673nmos/673nmos.fits")[0].data
g=fits.open("/path/to/656nmos/656nmos.fits")[0].data
b=fits.open("/path/to/502nmos/502nmos.fits")[0].data
r = np.array(r,forc)*5
g = np.array(g,forc)*0.75
b = np.array(b,forc)*8
t = 250
r[r > t] = t
g[g > t] = t
b[b > t] = t
rgb_default = make_lupton_rgb(r,g,b,Q=0.001,stretch=300,filename="pillar.png")
plt.figure(figsize=(8,8))
plt.imshow(rgb_default, origin='lower')
plt.show()
Python (and matplotlib) newbie here coming over from R, so I hope this question is not too idiotic. I'm trying to make a loglog plot on a natural log scale. But after some googling I cannot somehow figure out how to force pyplot to use a base e scale on the axes. The code I have currently:
import matplotlib.pyplot as pyplot
import math
e = math.exp(1)
pyplot.loglog(range(1,len(degrees)+1),degrees,'o',basex=e,basey=e)
Where degrees is a vector of counts at each value of range(1,len(degrees)+1). For some reason when I run this code, pyplot keeps giving me a plot with powers of 2 on the axes. I feel like this ought to be easy, but I'm stumped...
Any advice is greatly appreciated!
When plotting using plt.loglog you can pass the keyword arguments basex and basey as shown below.
From numpy you can get the e constant with numpy.e (or np.e if you import numpy as np)
import numpy as np
import matplotlib.pyplot as plt
# Generate some data.
x = np.linspace(0, 2, 1000)
y = x**np.e
plt.loglog(x,y, basex=np.e, basey=np.e)
plt.show()
Edit
Additionally if you want pretty looking ticks you can use matplotlib.ticker to choose the format of your ticks, an example of which is given below.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
x = np.linspace(1, 4, 1000)
y = x**3
fig, ax = plt.subplots()
ax.loglog(x,y, basex=np.e, basey=np.e)
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
ax.xaxis.set_major_formatter(mtick.FuncFormatter(ticks))
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()
It can also works for semilogx and semilogy to show them in e and also change their name.
import matplotlib.ticker as mtick
fig, ax = plt.subplots()
def ticks(y, pos):
return r'$e^{:.0f}$'.format(np.log(y))
plt.semilogy(Time_Series, California_Pervalence ,'gray', basey=np.e )
ax.yaxis.set_major_formatter(mtick.FuncFormatter(ticks))
plt.show()
Take a look at the image.
It appears as if matplotlib.tri.Triangulation uses a buggy and possibly incorrect implementation of Delaunay triangulation that is due to be replaced by qHull.
I'm trying to plot a trisurf using mpl_toolkits.mplot3d.plot_trisurf() and running into a bunch of exceptions that are unhelpful (IndexErrors and KeyErrors mostly, with no indication of what exactly went wrong).
Since scipy.spatial.Delaunay already uses qHull, I was wondering if there was a way to build a matplotlib.tri.Triangulation object for use with mpl_toolkits.mplot3d.plot_trisurf() using scipy's implementation of Delaunay triangulation.
I've tried passing the delaunay.points directly to matplotlib.tri.Triangulate via the triangles parameter, but this results in a ValueError: triangles min element is out of bounds.
http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.spatial.Delaunay.html
http://matplotlib.org/dev/api/tri_api.html
So you need to pass both the points and the triangles from qhull to the Triangulation constructor:
import numpy as np
import scipy.spatial
import matplotlib
import math
import matplotlib.tri as mtri
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# First create the x and y coordinates of the points.
n_angles = 20
n_radii = 10
min_radius = 0.15
radii = np.linspace(min_radius, 0.95, n_radii)
angles = np.linspace(0, 2*math.pi, n_angles, endpoint=False)
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
angles[:, 1::2] += math.pi/n_angles
x = (radii*np.cos(angles)).flatten()
y = (radii*np.sin(angles)).flatten()
# Create the Delaunay tessalation using scipy.spatial
pts = np.vstack([x, y]).T
tess = scipy.spatial.Delaunay(pts)
# Create the matplotlib Triangulation object
x = tess.points[:, 0]
y = tess.points[:, 1]
tri = tess.vertices # or tess.simplices depending on scipy version
triang = mtri.Triangulation(x=pts[:, 0], y=pts[:, 1], triangles=tri)
# Plotting
z = x*x + y*y
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_trisurf(triang, z)
plt.show()
output (with matplotlib current master):
#Marco was curious to know how to run this for a 2d array. I hope this would be useful. The list of vertices according to coordinates should be made an array and can be tessellated using mtri.Triangulation.
Sample code below:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
verts = np.array([[0.6,0.8],[0.2,0.9],[0.1,-0.5],[0.2,-2]])
triang = mtri.Triangulation(verts[:,0], verts[:,1])
plt.triplot(triang, marker="o")
plt.show()`enter code here`