First steps with Frequency MOCs

import matplotlib.pyplot as plt
import numpy as np
from mocpy import FrequencyMOC

We use a fits built from a file from the Cassini/RPWS/HFR database. This radio instrument has a configurable spectral sampling.

The original observation file (and many others) is available for download here: https://lesia.obspm.fr/kronos/data/2012_091_180/n2/

fmoc = FrequencyMOC.from_fits("../resources/FMOC/P2012180.fits")

fmoc.min_freq

$3593.46 \; \mathrm{Hz}$

fmoc.max_freq

$16037500 \; \mathrm{Hz}$

We can plot it in frequency or wavelength

fig, ax = plt.subplots(figsize=(15, 1))
fmoc.plot_frequencies(ax, color="purple")
# this method plots the frequency ranges in log scale by default
# but we can change it to linear if needed
ax.set(xscale="linear")
# and any customization on the ax of fig objects will work too
ax.spines[["left", "top", "right"]].set_visible(False)

fig, ax = plt.subplots(figsize=(15, 1))
fmoc.plot_wavelengths(ax, color="g", length_unit="km")

We create a dictionnary of FMOCs with less and less precise order ranging from 50 to 10.

print(
    f"we first create an initial FMOC at order {fmoc.max_order}"
    " and then generate the dictionnary",
)
fmocs = {n: fmoc.degrade_to_order(n) for n in np.linspace(50, 10, 5, dtype=int)}

we first create an initial FMOC at order 50 and then generate the dictionnary

/tmp/ipykernel_918016/4230462187.py:5: UserWarning: The new order is more precise than the current order, nothing done.
  fmocs = {n: fmoc.degrade_to_order(n) for n in np.linspace(50, 10, 5, dtype=int)}

for order in fmocs:
    print(
        f"At order {order}, this F-MOC has {len(fmocs[order].to_hz_ranges())} "
        "non overlapping spectral intervals",
    )

At order 50, this F-MOC has 143 non overlapping spectral intervals
At order 40, this F-MOC has 143 non overlapping spectral intervals
At order 30, this F-MOC has 143 non overlapping spectral intervals
At order 20, this F-MOC has 143 non overlapping spectral intervals
At order 10, this F-MOC has 1 non overlapping spectral intervals

for order in fmocs:
    print(
        f"At order {order}, the spectrum covers {round(fmocs[order].to_hz_ranges()[0][0])} Hz"
        f" to {round(fmocs[order].to_hz_ranges()[-1][1])} Hz",
    )

At order 50, the spectrum covers 3593 Hz to 16037500 Hz
At order 40, the spectrum covers 3593 Hz to 16037500 Hz
At order 30, the spectrum covers 3593 Hz to 16037501 Hz
At order 20, the spectrum covers 3593 Hz to 16037733 Hz
At order 10, the spectrum covers 3523 Hz to 16253148 Hz

Next step is FT-MOC, in order to manage the time series of sweep.

NB: Cassini/RPWS observed continously from january 2000 to september 2017 :-)