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from astropy.coordinates import SkyCoord, ICRS, LSR
import astropy.units as u
import numpy as np
from numpy.linalg import norm

# convert from degrees to radian
deg = np.pi / 180.0

# coordinates of AK Sco
# 16 54 44.8497760618 -36 53 18.560813962

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import numpy as np
from matplotlib import pyplot as plt

from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, WhiteKernel

plt.style.use("seaborn-v0_8-white")

# Training data is y values representing the brightness profile
y_train = np.array(

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import numpy as np 
from astropy.io import fits


def get_extent(header):
    """Get extent (in RA and Dec, units of [arcsec]) of image"""

    # get the coordinate labels
    nx = header["NAXIS1"]
    ny = header["NAXIS2"]

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# custom dataset loader
class UVDataset(torch_ud.Dataset):
    r"""
    Container for loose interferometric visibilities.

    Args:
        uu (2d numpy array): (nchan, nvis) length array of u spatial frequency coordinates. Units of [:math:`\mathrm{k}\lambda`]
        vv (2d numpy array): (nchan, nvis) length array of v spatial frequency coordinates. Units of [:math:`\mathrm{k}\lambda`]

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import torch 
from torch import nn

import numpy as np

import pyro
import pyro.distributions as dist
from torch.distributions import constraints
from pyro.nn import PyroModule, PyroParam, PyroSample
from pyro.infer import Predictive, MCMC, NUTS

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import matplotlib.pyplot as plt
import numpy as np

n = 100000
x = np.random.normal(size=n)
y = np.random.normal(size=n)

pkw = {"marker": "o", "alpha": 0.1, "mew": 0, "ms": 2, "ls": ""}

fig, ax = plt.subplots(nrows=1)

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(finufft_install) ➜  finufft git:(master) ✗ make python3 
g++ -c -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp src/spreadinterp.cpp -o src/spreadinterp.o
g++ -c -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp src/utils.cpp -o src/utils.o
g++ -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp   -c -o src/finufft1d.o src/finufft1d.cpp
g++ -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp   -c -o src/finufft2d.o src/finufft2d.cpp
g++ -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp   -c -o src/finufft3d.o src/finufft3d.cpp
g++ -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp   -c -o src/dirft1d.o src/dirft1d.cpp
g++ -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp   -c -o src/dirft2d.o src/dirft2d.cpp
g++ -fPIC -O3 -I src -Xpreprocessor -fopenmp -DNEED_EXTERN_C -Xpreprocessor -fopenmp   -c -

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import numpy as np
from scipy.io import readsav

from matplotlib import pyplot as plt

w_idl = readsav("w_rl23.2142")
w = w_idl["w"]  # wavelengths in AA

fl_idl = readsav("rl23.2127")
f = fl_idl["sp"]  # fluxes in arbitrary units

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import numpy as np

c_kms = 2.99792458e5 # km/s

dv = 0.5 # km/s
wl_start = 5000.0
wl_end = 5100.0

CDELT1 = np.log10(dv/c_kms + 1.)

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date, PA, PA_err, rho, rho_err
1949.650 , 160.  ,   . ,  .    ,   .
1952.86  ,     . ,   . , 0.119 ,   .
1952.88  ,     . ,   . , 0.107 ,   .
1953.89  ,     . ,   . , 0.119 ,   .
1955.99  ,     . ,   . , 0.110 ,   .
1959.98  ,  341.9,   . , 0.111 ,   .
1962.01  ,  344.3,   . , 0.117 ,   .
1963.03  ,     . ,   . , 0.113 ,   .
1964.016 ,  161.2,   . , 0.280 ,   .