I have the following code that runs to a graph, what I want is to extract the X,Y and Z into a list (so I can copy them later in excel, and play with the numbers), basically the other way around of having a set of data and plotting it into a graph:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
N = np.arange(0, 10, 1)
M = np.arange(0, 15, 1)
N, M = np.meshgrid(N, M)
DNM = 3992.88*N - 2585.96*M
surf = ax.plot_surface(N, M, DNM, rstride=1, cstride=1, cmap=cm.jet,
linewidth=0, antialiased=False)
ax.set_zlim(-25000, 20000)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
fig.colorbar(surf, shrink=0.5, aspect=10)
plt.show()
First you need to convert your 2d array dnm to a x-y-z format:
n, m = dnm.shape
rows, cols = np.mgrid[:n,:m]
xyz = np.column_stack((rows.ravel(), cols.ravel(), dnm.ravel()))
Then you can write the resulting array xyz into an excel file using pandas:
import pandas as pd
df = pd.DataFrame(xyz)
xyz_path = "xyz.xlsx"
df.to_excel(xyz_path, index=False)
You already have the x, y and z that you used to plot the figure. They are named N, M, DNM in your code. But these are 2D arrays, so all you have to do is convert them into 1D arrays for easy plot and manipulation inside Excel.
x, y, z = N.ravel(), M.ravel(), DNM.ravel()
Now, if you want to limit the z-range, apply logical indexing -25000 ≤ z ≤ 20000 like this:
limits = np.logical_and(z >= -25000, z <= 20000)
x, y, z = x[limits], y[limits], z[limits]
Finally, you can save as text using np.savetxt or save to an Excel file as #blunova did:
import pandas as pd
xyz = np.column_stack((x, y, z))
df = pd.DataFrame(xyz)
xyz_path = "xyz.xlsx"
df.to_excel(xyz_path, index=False)
Related
I have a spreadsheet file that I would like to input to create a 3D surface graph using Matplotlib in Python.
I used plot_trisurf and it worked, but I need the projections of the contour profiles onto the graph that I can get with the surface function, like this example.
I'm struggling to arrange my Z data in a 2D array that I can use to input in the plot_surface method. I tried a lot of things, but none seems to work.
Here it is what I have working, using plot_trisurf
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pandas as pd
df=pd.read_excel ("/Users/carolethais/Desktop/Dissertação Carol/Códigos/Resultados/res_02_0.5.xlsx")
fig = plt.figure()
ax = fig.gca(projection='3d')
# I got the graph using trisurf
graf=ax.plot_trisurf(df["Diametro"],df["Comprimento"], df["temp_out"], cmap=matplotlib.cm.coolwarm)
ax.set_xlim(0, 0.5)
ax.set_ylim(0, 100)
ax.set_zlim(25,40)
fig.colorbar(graf, shrink=0.5, aspect=15)
ax.set_xlabel('Diâmetro (m)')
ax.set_ylabel('Comprimento (m)')
ax.set_zlabel('Temperatura de Saída (ºC)')
plt.show()
This is a part of my df, dataframe:
Diametro Comprimento temp_out
0 0.334294 0.787092 34.801994
1 0.334294 8.187065 32.465551
2 0.334294 26.155976 29.206090
3 0.334294 43.648591 27.792126
4 0.334294 60.768219 27.163233
... ... ... ...
59995 0.437266 14.113660 31.947302
59996 0.437266 25.208851 30.317583
59997 0.437266 33.823035 29.405461
59998 0.437266 57.724209 27.891616
59999 0.437266 62.455890 27.709298
I tried this approach to use the imported data with plot_surface, but what I got was indeed a graph but it didn't work, here it's the way the graph looked with this approach:
Thank you so much
A different approach, based on re-gridding the data, that doesn't require that the original data is specified on a regular grid [deeply inspired by this example;-].
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.tri as tri
from mpl_toolkits.mplot3d import Axes3D
np.random.seed(19880808)
# compute the sombrero over a cloud of random points
npts = 10000
x, y = np.random.uniform(-5, 5, npts), np.random.uniform(-5, 5, npts)
z = np.cos(1.5*np.sqrt(x*x + y*y))/(1+0.33*(x*x+y*y))
# prepare the interpolator
triang = tri.Triangulation(x, y)
interpolator = tri.LinearTriInterpolator(triang, z)
# do the interpolation
xi = yi = np.linspace(-5, 5, 101)
Xi, Yi = np.meshgrid(xi, yi)
Zi = interpolator(Xi, Yi)
# plotting
fig = plt.figure()
ax = fig.gca(projection='3d')
norm = plt.Normalize(-1,1)
ax.plot_surface(Xi, Yi, Zi,
cmap='inferno',
norm=plt.Normalize(-1,1))
plt.show()
plot_trisurf expects x, y, z as 1D arrays while plot_surface expects X, Y, Z as 2D arrays or as x, y, Z with x, y being 1D array and Z a 2D array.
Your data consists of 3 1D arrays, so plotting them with plot_trisurf is immediate but you need to use plot_surface to be able to project the isolines on the coordinate planes... You need to reshape your data.
It seems that you have 60000 data points, in the following I assume that you have a regular grid 300 points in the x direction and 200 points in y — but what is important is the idea of regular grid.
The code below shows
the use of plot_trisurf (with a coarser mesh), similar to your code;
the correct use of reshaping and its application in plot_surface;
note that the number of rows in reshaping corresponds to the number
of points in y and the number of columns to the number of points in x;
and 4. incorrect use of reshaping, the resulting subplots are somehow
similar to the plot you showed, maybe you just need to fix the number
of row and columns.
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
x, y = np.arange(30)/3.-5, np.arange(20)/2.-5
x, y = (arr.flatten() for arr in np.meshgrid(x, y))
z = np.cos(1.5*np.sqrt(x*x + y*y))/(1+0.1*(x*x+y*y))
fig, axes = plt.subplots(2, 2, subplot_kw={"projection" : "3d"})
axes = iter(axes.flatten())
ax = next(axes)
ax.plot_trisurf(x,y,z, cmap='Reds')
ax.set_title('Trisurf')
X, Y, Z = (arr.reshape(20,30) for arr in (x,y,z))
ax = next(axes)
ax.plot_surface(X,Y,Z, cmap='Reds')
ax.set_title('Surface 20×30')
X, Y, Z = (arr.reshape(30,20) for arr in (x,y,z))
ax = next(axes)
ax.plot_surface(X,Y,Z, cmap='Reds')
ax.set_title('Surface 30×20')
X, Y, Z = (arr.reshape(40,15) for arr in (x,y,z))
ax = next(axes)
ax.plot_surface(X,Y,Z, cmap='Reds')
ax.set_title('Surface 40×15')
plt.tight_layout()
plt.show()
I am trying to create a surface plot of a mountain in python, of which I have some xyz data. The end result should look something like that. The file is formatted as follows:
616000.0 90500.0 3096.712
616000.0 90525.0 3123.415
616000.0 90550.0 3158.902
616000.0 90575.0 3182.109
616000.0 90600.0 3192.991
616025.0 90500.0 3082.684
616025.0 90525.0 3116.597
616025.0 90550.0 3149.812
616025.0 90575.0 3177.607
616025.0 90600.0 3191.986
and so on. The first column represents the x coordinate, the middle one the y coordinate, and z the altitude that belongs to the xy coordinate.
I read in the data using pandas and then convert the columns to individual x, y, z NumPy 1D arrays. So far I managed to create a simple 3D scatter plot with a for loop iterating over each index of each 1D array, but that takes ages and makes the appearance of being quite inefficient.
I've tried to work with scipy.interpolate.griddata and plt.plot_surface, but for z data I always get the error that data should be in a 2D array, but I cannot figure out why or how it should be 2D data. I assume that given I have xyz data, there should be a way to simply create a surface from it. Is there a simple way?
Using functions plot_trisurf and scatter from matplotlib, given X Y Z data can be plotted similar to given plot.
import sys
import csv
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
# Read CSV
csvFileName = sys.argv[1]
csvData = []
with open(csvFileName, 'r') as csvFile:
csvReader = csv.reader(csvFile, delimiter=' ')
for csvRow in csvReader:
csvData.append(csvRow)
# Get X, Y, Z
csvData = np.array(csvData)
csvData = csvData.astype(np.float)
X, Y, Z = csvData[:,0], csvData[:,1], csvData[:,2]
# Plot X,Y,Z
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(X, Y, Z, color='white', edgecolors='grey', alpha=0.5)
ax.scatter(X, Y, Z, c='red')
plt.show()
Here,
file containing X Y Z data provided as argument to above script
in plot_trisurf, parameters used to control appearance. e.g. alpha used to control opacity of surface
in scatter, c parameter specifies color of points plotted on surface
For given data file, following plot is generated
Note: Here, the terrain is formed by triangulation of given set of 3D points. Hence, contours along surface in plot are not aligned to X- and Y- axes
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import pandas as pd
df = pd.read_csv("/content/1.csv")
X = df.iloc[:, 0]
Y = df.iloc[:, 1]
Z = df.iloc[:, 2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(X, Y, Z, color='white', edgecolors='grey', alpha=0.5)
ax.scatter(X, Y, Z, c='red')
plt.show()
My output image below - I had a lot of data points:
enter image description here
There is an easier way to achieve your goal without using pandas.
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
x, y = np.mgrid[-2 : 2 : 20j, -2 : 2 : 20j]
z = 50 * np.sin(x + y) # test data
output = plt.subplot(111, projection = '3d') # 3d projection
output.plot_surface(x, y, z, rstride = 2, cstride = 1, cmap = plt.cm.Blues_r)
output.set_xlabel('x') # axis label
output.set_xlabel('y')
output.set_xlabel('z')
plt.show()
I have a data file in NumPy array, I would like to view the 3D-image. I am sharing an example, where I can view 2D image of size (100, 100), this is a slice in xy-plane at z = 0.
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
X, Y, Z = np.mgrid[-10:10:100j, -10:10:100j, -10:10:100j]
T = np.sin(X*Y*Z)/(X*Y*Z)
T=T[:,:,0]
im = plt.imshow(T, cmap='hot')
plt.colorbar(im, orientation='vertical')
plt.show()
How can I view a 3D image of the data T of shape (100, 100, 100)?
I think the main problem is, that you do have 4 informations for each point, so you are actually interessted in a 4-dimensional object. Plotting this is always difficult (maybe even impossible). I suggest one of the following solutions:
You change the question to: I'm not interessted in all combinations of x,y,z, but only the ones, where z = f(x,y)
You change the accuracy of you plot a bit, saying that you don't need 100 levels of z, but only maybe 5, then you simply make 5 of the plots you already have.
In case you want to use the first method, then there are several submethods:
A. Plot the 2-dim surface f(x,y)=z and color it with T
B. Use any technic that is used to plot complex functions, for more info see here.
The plot given by method 1.A (which I think is the best solution) with z=x^2+y^2 yields:
I used this programm:
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib as mpl
X, Y = np.mgrid[-10:10:100j, -10:10:100j]
Z = (X**2+Y**2)/10 #definition of f
T = np.sin(X*Y*Z)
norm = mpl.colors.Normalize(vmin=np.amin(T), vmax=np.amax(T))
T = mpl.cm.hot(T) #change T to colors
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, facecolors=T, linewidth=0,
cstride = 1, rstride = 1)
plt.show()
The second method gives something like:
With the code:
norm = mpl.colors.Normalize(vmin=-1, vmax=1)
X, Y= np.mgrid[-10:10:101j, -10:10:101j]
fig = plt.figure()
ax = fig.gca(projection='3d')
for i in np.linspace(-1,1,5):
Z = np.zeros(X.shape)+i
T = np.sin(X*Y*Z)
T = mpl.cm.hot(T)
ax.plot_surface(X, Y, Z, facecolors=T, linewidth=0, alpha = 0.5, cstride
= 10, rstride = 10)
plt.show()
Note: I changed the function to T = sin(X*Y*Z) because dividing by X*Y*Zmakes the functions behavior bad, as you divide two number very close to 0.
I have got a solution to my question. If we have the NumPy data, then we can convert them into TVTK ImageData and then visualization is possible with the help of mlab form Mayavi. The code and its 3D visualization are the following
from tvtk.api import tvtk
import numpy as np
from mayavi import mlab
X, Y, Z = np.mgrid[-10:10:100j, -10:10:100j, -10:10:100j]
data = np.sin(X*Y*Z)/(X*Y*Z)
i = tvtk.ImageData(spacing=(1, 1, 1), origin=(0, 0, 0))
i.point_data.scalars = data.ravel()
i.point_data.scalars.name = 'scalars'
i.dimensions = data.shape
mlab.pipeline.surface(i)
mlab.colorbar(orientation='vertical')
mlab.show()
For another randomly generated data
from numpy import random
data = random.random((20, 20, 20))
The visualization will be
I have two vectors that store my X, Y values than are lengths 81, 105 and then a (81,105) array (actually a list of lists) that stores my Z values for those X, Y. What would be the best way to plot this in 3d? This is what i've tried:
Z = np.load('Z.npy')
X = np.load('X.npy')
Y = np.linspace(0, 5, 105)
fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, cmap= 'viridis')
plt.show()
I get the following error : ValueError: shape mismatch: objects cannot be broadcast to a single shape
OK, I got it running. There is some tricks here. I will mention them in the codes.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from random import shuffle
# produce some data.
x = np.linspace(0,1,81)
y = np.linspace(0,1,105)
z = [[i for i in range(81)] for x in range(105)]
array_z = np.array(z)
# Make them randomized.
shuffle(x)
shuffle(y)
shuffle(z)
# Match data in x and y.
data = []
for i in range(len(x)):
for j in range(len(y)):
data.append([x[i], y[j], array_z[j][i]])
# Be careful how you data is stored in your Z array.
# Stored in dataframe
results = pd.DataFrame(data, columns = ['x','y','z'])
# Plot the data.
fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(results.x, results.y, results.z, cmap= 'viridis')
The picture looks weird because I produced some data. Hope it helps.
I would like to print my 2d data into 3d with python as the photo in image bellow. Currently I am reading my data from files where I have the x and y numbers on 2 columns. Any help will be apreciated. The code that prints my data in 2d looks like this:
import numpy as np
import pylab as pl
import matplotlib as mpl
data1 = np.loadtxt('NL_extb_1.xye')
data2 = np.loadtxt('NL_extb_2.xye')
data3 = np.loadtxt('NL_extb_3.xye')
data4 = np.loadtxt('NL_extb_4.xye')
data5 = np.loadtxt('NL_extb_5.xye')
pl.plot(data1[:,0], data1[:,1] , 'black')
pl.plot(data2[:,0], data2[:,1], 'black')
pl.plot(data3[:,0], data3[:,1] ,'black')
pl.plot(data4[:,0], data4[:,1] ,'black')
pl.plot(data5[:,0], data5[:,1] ,'black')
pl.xlabel("2Theta")
pl.ylabel("Counts")
pl.show()
The plot in your image looks like made in mplot3d. There is an example how to do it in the tutorial:
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05) #<-- REPLACE THIS LINE WITH YOUR DATA
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
plt.show()
Setting rstride or cstride to 0 will get you the plot typy you want.
So now, the only thing you should do is to create the correct input variables X, Y, Z. I assume that all your datai have the same length, say N, and all x columns datai[:0] are identical. (If not, then more work is needed.)
Then
X, Y = np.meshgrid(range(1,6), data1[:,0])
# or maybe the other way:
# X, Y = np.meshgrid(data1[:,0], range(1,6))
and Z consists of all y columns from your data concatenated into an array of the same shape as X and Y (all of them should be N x 5 arrays or 5 x N arrays).