ValueError: X contains non-finite values about pysteps HOT 12 CLOSED

from matplotlib import cm, pyplot
import numpy as np
import os
from pprint import pprint
from pysteps import io, rcparams
from pysteps.noise.fftgenerators import initialize_param_2d_fft_filter
from pysteps.noise.fftgenerators import initialize_nonparam_2d_fft_filter
from pysteps.noise.fftgenerators import generate_noise_2d_fft_filter
from pysteps.utils import conversion, rapsd, transformation
from pysteps.visualization import plot_precip_field, plot_spectrum1d

# Import the example radar composite
root_path = rcparams.data_sources["mch"]["root_path"]
filename = os.path.join(root_path, "20160711", "AQC161932100V_00005.801.gif")
R, _, metadata = io.import_mch_gif(filename, product="AQC", unit="mm", accutime=5.0)

# Convert to mm/h
R, metadata = conversion.to_rainrate(R, metadata)

# Nicely print the metadata
pprint(metadata)

# Plot the rainfall field
plot_precip_field(R, geodata=metadata)

# Log-transform the data
R, metadata = transformation.dB_transform(R, metadata, threshold=0.1, zerovalue=-15.0)

# Assign the fill value to all the Nans
R[~np.isfinite(R)] = metadata["zerovalue"]


# Fit the parametric PSD to the observation
Fp = initialize_param_2d_fft_filter(R)

# Compute the observed and fitted 1D PSD
L = np.max(Fp["input_shape"])
if L % 2 == 0:
    wn = np.arange(0, int(L / 2) + 1)
else:
    wn = np.arange(0, int(L / 2))
R_, freq = rapsd(R, fft_method=np.fft, return_freq=True)
f = np.exp(Fp["model"](np.log(wn), *Fp["pars"]))

# Extract the scaling break in km, beta1 and beta2
w0 = L / np.exp(Fp["pars"][0])
b1 = Fp["pars"][2]
b2 = Fp["pars"][3]

# Plot the observed power spectrum and the model
fig, ax = pyplot.subplots()
plot_scales = [512, 256, 128, 64, 32, 16, 8, 4]
plot_spectrum1d(
    freq,
    R_,
    x_units="km",
    y_units="dBR",
    color="k",
    ax=ax,
    label="Observed",
    wavelength_ticks=plot_scales,
)
plot_spectrum1d(
    freq,
    f,
    x_units="km",
    y_units="dBR",
    color="r",
    ax=ax,
    label="Fit",
    wavelength_ticks=plot_scales,
)
pyplot.legend()
ax.set_title(
    "Radially averaged log-power spectrum of R\n"
    r"$\omega_0=%.0f km, \beta_1=%.1f, \beta_2=%.1f$" % (w0, b1, b2)
)