I am trying to format the matrix better. My current code gives me o/p in the format as seen in the image:
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.matshow(final.corr(), fignum = 1)
plt.xticks(range(len(final.columns)), final.columns)
plt.yticks(range(len(final.columns)), final.columns)
Related
Want to plot normalised values in array but getting empty plot
import numpy as np
x_array = np.array([2,3,5,6,7,4,8,7,6])
normalized_arr = preprocessing.normalize([x_array])
print(normalized_arr)
plt.plot(normalized_arr)
plt.show()
Empty plot - https://i.stack.imgur.com/NnSbI.png
Is there function that can fill the empty plot with values?
You probably need to change your code into:
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
x_array = np.array([2,3,5,6,7,4,8,7,6])
normalized_arr = preprocessing.normalize([x_array])
print(normalized_arr)
plt.plot(x_array.reshape(-1,1),normalized_arr.reshape(-1,1))
plt.show()
Output
I have a netcdf file ('test.nc'). The variables of the netcdf file are the following:
variables(dimensions): float64 lon(lon), float64 lat(lat), int32 crs(), int16 Band1(lat,lon)
I am interested in the ´Band1´ variable.
Using cartopy, I could plot the data using the following code:
import numpy as np
import pandas as pd
import gzip
from netCDF4 import Dataset,num2date
import time
import matplotlib.pyplot as plt
import os
import matplotlib as mplt
#mplt.use('Agg')
import cartopy.crs as ccrs
import cartopy.feature as cfea
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
projection=ccrs.PlateCarree()
bbox=[-180,180,-60,85];creg='glob'
mplt.rc('xtick', labelsize=9)
mplt.rc('ytick', labelsize=9)
nc = Dataset('test.nc','r')
lat = nc.variables['lat'][:]
lon = nc.variables['lon'][:]
kopi= (nc.variables['Band1'][:,:])
nc.close()
fig=plt.figure(figsize=(11,5))
ax=fig.add_subplot(1,1,1,projection=projection)
ax.set_extent(bbox,projection)
ax.add_feature(cfea.COASTLINE,lw=.5)
ax.add_feature(cfea.RIVERS,lw=.5)
ax.add_feature(cfea.BORDERS, linewidth=0.6, edgecolor='dimgray')
ax.background_patch.set_facecolor('.9')
levels=[1,4,8,11,14,17,21,25,29]
cmap=plt.cm.BrBG
norm=mplt.colors.BoundaryNorm(levels,cmap.N)
ddlalo=.25
pc=ax.contourf(lon,lat,kopi,levels=levels,transform=projection,cmap=cmap,norm=norm,extend='both')
divider = make_axes_locatable(ax)
ax_cb = divider.new_horizontal(size="3%", pad=0.1, axes_class=plt.Axes)
fig.colorbar(pc,extend='both', cax=ax_cb)
fig.add_axes(ax_cb)
fig.colorbar(pc,extend='both', cax=ax_cb)
ttitle='Jony'
ax.set_title(ttitle,loc='left',fontsize=9)
plt.show()
However, I would like just to plot a selection of values inside the variable ´Band1´. I thought I could use the following code:
kopi= (nc.variables['Band1'][:,:])<=3
However it does not work and instead of plotting the area corresponding to the value selection it selected the all map.
How could I select and plot a desired range of values inside the variables ´Band1´?
Just mask the values with np.nan
kopi[kopi <=3] = np.nan
This should yield to white pixels in your plot.
Please provide test data in the future.
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()
I am attempting to run a DCT transform on an image. I have tried to make my image a grayscale image with the following code:
import numpy as np
import matplotlib.pyplot as plt
import scipy
from numpy import pi
from numpy import sin
from numpy import zeros
from numpy import r_
from scipy import signal
from scipy import misc
import matplotlib.pylab as pylab
#matplotlib inline
pylab.rcParams['figure.figsize'] = (20.0, 7.0)
im = misc.imread("indoorPictureResize.jpg")
#show the image
f = plt.figure()
plt.imshow(im,cmap='gray')
plt.show()
However I receive the image but it's color channel has not changed. Have I done something wrong or is it something I should change?
The array im is probably a 3-D array, with shape (m, n, 3) or (m, n, 4). Check im.shape.
From the imshow docstring: "cmap is ignored if X is 3-D".
To use a colormap, you'll have to pass a 2-D array to imshow. You could, for example, plot one of the color channels such as im[:,:,0], or plot the average over the three channels, im.mean(axis=2). (But if im has shape (m, n, 4), you probably don't want to include the alpha channel in the mean.)
Add the mode in scipy.misc.imread like this:
import numpy as np
import matplotlib.pyplot as plt
import scipy
from numpy import pi
from numpy import sin
from numpy import zeros
from numpy import r_
from scipy import signal
from scipy import misc
import matplotlib.pylab as pylab
#matplotlib inline
pylab.rcParams['figure.figsize'] = (20.0, 7.0)
im = misc.imread("indoorPictureResize.jpg", mode="L")
#show the image
f = plt.figure()
plt.imshow(im,cmap='gray')
plt.show()
I have a series of data which consists of values from several experiments (1-40, in the MWE it is 1-5). The overall amount of entries in my original data is ~4.000.000, which I try to smooth in order to display it:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import spline
from statsmodels.nonparametric.smoothers_lowess import lowess
df = pd.DataFrame()
df["values"] = np.random.randint(100000, 200000, 1000)
df["id"] = [1,2,3,4,5] * 200
plt.figure(1, figsize=(11.69,8.27))
# Both fail for my amount of data:
plt.plot(spline(df["values"], df["id"], range(100)), "r-")
plt.plot(lowess(df["values"], df["id"]), "r-")
Both, scipy.interplate and statsmodels.nonparametric.smoothers_lowess.lowess, throw out of memory exceptions for my data. Is there any efficient way to solve this like in, e.g., GNU R using ggplot2 and geom_smooth()?
I can't quite tell what you're getting at with all the dimensions to your data, but one very simple thing you can try is to just use the 'markevery' kwarg like so:
import numpy as np
import matplotlib.pyplot as plt
x=np.linspace(1,100,1E7)
y=x**2
plt.figure(1, figsize=(11.69,8.27))
plt.plot(x,y,markevery=100)
plt.show()
This will only plot every nth point (n=100 here).
If that doesn't help then you may want to try just a simple numpy interpolation with fewer samples like so:
x_large=np.linspace(1,100,1E7)
y_large=x**2
x_small=np.linspace(1,100,1E3)
y_small=np.interp(x_small,x_large,y_large)
plt.plot(x_small,y_small)