Visualize metadata

This notebook shows a few examples of how to use the poligrain functions to visualize CML meta data.

To add: plotting SML & PWS meta data

All the functions rely on the OpenSense naming convention so that we can easily pass an xarray.Dataset or DataArray to the functions.

import matplotlib.pyplot as plt
import xarray as xr

import poligrain as plg

Plotting length vs. frequency

Plot the distribution of frequency against the corresponding length for the entire CML dataset.

# Load example dataset
ds_cmls = xr.open_dataset("../../tests/test_data/openMRG_CML_180minutes.nc")

fig, ax = plt.subplots(figsize=(5, 4))
scatter = plg.plot_metadata.plot_len_vs_freq(
    ds_cmls.length, ds_cmls.frequency, marker_size=30, grid=True, ax=ax
)

# optionally customize output plot
ax.set_title("Link length vs. frequency of the OpenMRG-CML dataset")

Text(0.5, 1.0, 'Link length vs. frequency of the OpenMRG-CML dataset')

Frequency vs. length hexbin

Alternatively, plot the same as above but as a scatter density plot

fig, ax = plt.subplots(figsize=(5, 4))
hexbin = plg.plot_metadata.plot_len_vs_freq_hexbin(
    ds_cmls.length, ds_cmls.frequency, gridsize=50, ax=ax
)

plt.colorbar(hexbin, label="density")

<matplotlib.colorbar.Colorbar at 0x23349d2ac80>

[WIP] Frequency vs. length with margin plots

Plotting distributions of frequency, length, and polarization.

[WIP] Possibly add orientation too.

fig, ax = plt.subplots(figsize=(5, 4))
len_bars = plg.plot_metadata.plot_distribution(
    length=ds_cmls.length, frequency=ds_cmls.frequency, variable="length", ax=ax
)

We can also plot the distribution as a percentage.

fig, ax = plt.subplots(figsize=(5, 4))
len_bars = plg.plot_metadata.plot_distribution(
    length=ds_cmls.length,
    frequency=ds_cmls.frequency,
    variable="length",
    percentage=True,
    ax=ax,
)

And customize the plot a bit using keyword arguments.

fig, ax = plt.subplots(figsize=(5, 4))
len_bars = plg.plot_metadata.plot_distribution(
    length=ds_cmls.length,
    frequency=ds_cmls.frequency,
    variable="length",
    percentage=True,
    ax=ax,
    rwidth=0.5,
)

We can also plot the polarization of CMLs. In this dataset all CMLs have two sub-links, both with the same polarization, of which the vast majority is vertically polarized.

fig, ax = plt.subplots(figsize=(5, 4))
pol_bar = plg.plot_metadata.plot_polarization(
    ds_cmls.polarization, colors=["blue", "orange", "green"], ax=ax
)

[WIP] Plot availability during data period