I have a h5 file containing regulargrid data. I have used a code by which I can easily get the interpolated value for three given value. I have used RegularGridInterpolator function for interpolation purpose here. Now I want to make a plot to check whether the interpolation is correct or not. But I don't understand how can I do that. Can anyone help me to do that please? Here is my code:
import numpy as np
import h5py
from scipy.interpolate import RegularGridInterpolator
f = h5py.File('file.h5', 'r')
list(f.keys())
dset = f[u'data']
dset.shape
dset.value.shape
dset[0:63,0:63,0:63]
x = np.linspace(-10, 320, 64)
y = np.linspace(-10, 320, 64)
z = np.linspace(-10, 320, 64)
my_interpolating_function = RegularGridInterpolator((x, y, z), dset.value)
pts = np.array([7.36970468e-09, -4.54271563e-09, 1.51802701e-09])
my_interpolating_function(pts)
The output of the interpolation is array([5.45534467e-10])
Related
I have found few radial basis functions like BasisExpansionFunction, Surrogates.jl, ScatteredInterpolation in Julia.
However, I am unable to replicate the results from python's scipy.interpolate.rbf() function.
Python Example
from scipy.interpolate import Rbf
import numpy as np
xs = np.arange(10)
ys = xs**2 + np.sin(xs) + 1
interp_func = Rbf(xs, ys) # By default RbF uses Multiquadratic function
newarr = interp_func(np.arange(2.1, 3, 0.1))
print(newarr)
What is correct approach to replicate the above example in Julia?
The first tutorial in Surrogates.jl shows how to make and interpolate a radial basis function.
using Surrogates
using LinearAlgebra
f = x -> x[1]*x[2]
lb = [1.0,2.0]
ub = [10.0,8.5]
x = sample(50,lb,ub,SobolSample())
y = f.(x)
my_radial_basis = RadialBasis(x,y,lb,ub)
#I want an approximation at (1.0,1.4)
approx = my_radial_basis((1.0,1.4))
I have a set of about 33K (x,y,z) points in a csv file and would like to convert this to a grid of density values using scipy.stats.gaussian_kde. I have not been able to find a way to convert this point cloud array into an appropriate input format for the gaussian_kde function (and then take the output of this and convert it into a density value grid). Can anyone provide sample code?
Here's an example with some comments which may be of use. gaussian_kde wants the data and points to be row stacked, ie. (# ndim, # num values), as per the docs. In your case you would row_stack([x, y, z]) such that the shape is (3, 33000).
from scipy.stats import gaussian_kde
import numpy as np
import matplotlib.pyplot as plt
# simulate some data
n = 33000
x = np.random.randn(n)
y = np.random.randn(n) * 2
# data must be stacked as (# ndim, # n values) as per docs.
data = np.row_stack((x, y))
# perform KDE
kernel = gaussian_kde(data)
# create grid over which to evaluate KDE
s = np.linspace(-8, 8, 128)
grid = np.meshgrid(s, s)
# again KDE needs points to be row_stacked
grid_points = np.row_stack([g.ravel() for g in grid])
# evaluate KDE and reshape result correctly
Z = kernel(grid_points)
Z = Z.reshape(grid[0].shape)
# plot KDE as image and overlay some data points
fig, ax = plt.subplots()
ax.matshow(Z, extent=(s.min(), s.max(), s.min(), s.max()))
ax.plot(x[::10], y[::10], 'w.', ms=1, alpha=0.3)
ax.set_xlim(s.min(), s.max())
ax.set_ylim(s.min(), s.max())
This is a follow-up to my previous question here. I'm trying to fit my data from this csv file with scipy.stats.skewnorm, but I can't get it working right:
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import skewnorm
df = pd.read_csv('astro_data.csv')
x = df['delta z']
number_bins = 50
fig, ax = plt.subplots()
h, edges, _ = ax.hist(x, alpha = 0.5,
density = False,
bins = number_bins)
a_est, loc_est, scale_est = skewnorm.fit(x)
ax.plot(x, skewnorm.pdf(x, a_est, loc_est, scale_est), 'r-', lw=5, alpha=0.6, label='skewnorm pdf')
Can anyone see how I can fix this?
EDIT: when I change to density=True, the result is this:
This h5 file contains the information of an analytical function on a regular 3D gird. For interpolation purpose, I have got very poor result using the Regulargridinterpolator here. Now, I want to test scipy.interpolate.Rbf interpolator for my data set. Can anyone help me to do that? I had a look at the documentation of this interpolator but didn't understand properly.
I have created a h5 file like this:
import numpy as np
from numpy import gradient
import h5py
from scipy.interpolate import Rbf
def f(x,y,z):
return ( -1 / np.sqrt(x**2 + y**2 + z**2))
#grid
x = np.linspace(0, 100, 32) # since the boxsize is 320 Mpc/h
y = np.linspace(0, 100, 32)
z = np.linspace(0, 100, 32)
mesh_data = phi_an(*np.meshgrid(x, y, z, indexing='ij', sparse=True))
#create h5 file
h5file = h5py.File('analytic.h5', 'w')
h5file.create_dataset('/x', data=x)
h5file.create_dataset('/y', data=y)
h5file.create_dataset('/z', data=z)
h5file.create_dataset('/mesh_data', data=mesh_data)
h5file.close()
The answer to this question appears relevant to my problem, however, it applies for ax.bar() instead of ax.vlines.
Matplotlib DateFormatter for axis label not working
The code below works with ax1.vlines(x, l, h, colors='k') and ax2.vlines(x, 0, v, colors='k') to plot vertical price and volume bars in a stock chart. But the horizontal axis is defined by a numpy array x = 0,1,2,3, ... etc. I have datetime objects in array d but if change to ax1.vlines(d, l, h, colors='k') and ax2.vlines(d,0,v,colors='k') then it throws an error. Thus d is defined but not used in the code below (it won't work using d but it works using x in the referenced code lines).
import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv(PATH+ticker+EXT, usecols=[0,2,3,4,5], header=None,
engine='python',skiprows=skr,skipfooter=skf)
d = pd.to_datetime(df[0]) # numpy array date
h = df[2].values # numpy array high
l = df[3].values # numpy array low
c = df[4].values # numpy array close
v = df[5].values # numpy array volume
x = np.arange(len(d))
# Draw Chart to White Background
ax1_y_label = ticker
fig1 = plt.figure()
fig1.set_size_inches(WIDE,TALL)
fig1.set_dpi(DTPI)
fig1.autofmt_xdate()
ax1 = plt.subplot2grid((5,4), (0,0), rowspan=4, colspan=4)
ax1.set_ylabel(ax1_y_label)
ax1.grid(True)
ax1.vlines(x, l, h, colors='k')
ax1.hlines(c, x, x+0.3, color='k')
ax2 = plt.subplot2grid((5,4), (4,0), sharex=ax1, rowspan=1, colspan=4)
ax2.set_ylabel(ax2_y_label)
ax2.grid(True)
ax2.vlines(x, 0, v, colors='k')
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax2.spines['right'].set_visible(False)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax1.get_yticklabels(), visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.subplots_adjust(hspace=.01)