Filtering Data

Currently pyDARN has one filtering option.

Boxcar Filtering

Note

Filtering data can be computationally expensive and time consuming. Please allow around 5 minutes of processing time to filter a 2 hour FITACF file. Parallel processing options for the boxcar filter may be developled in the near future.

The boxcar filter filters data in time and 'space' (beams and gates) and is available by evoking the Boxcar instance after reading a file.

import pydarn

fitacf_file = "datafilename.fitacf"
darn_read = pydarn.SuperDARNRead(fitacf_file)
fitacf_data = darn_read.read_fitacf()

# Evoke filter on data
bx = pydarn.Boxcar(
    thresh=0.7,
    w=None
)
filtered_data = bx.run_filter(fitacf_data)

The variable filtered_data can be treated in the same way as the original data in fitacf_data. The data can be used to in any FITACF plotting methods. For example, the code below produces comparisons between summary plots and fan plots for the same time frame.

import bz2
import matplotlib.pyplot as plt
import pydarn

with bz2.open('fitacffilename.fitacf.bz2') as fp:
    fitacf_stream = fp.read()
fitacf_data = pydarn.SuperDARNRead(fitacf_stream, True).read_fitacf()

# Before filtering:
pydarn.RTP.plot_summary(fitacf_data, beam_num=7,
                        range_estimation=pydarn.RangeEstimation.RANGE_GATE)
plt.show()

pydarn.Fan.plot_fan(fitacf_data, scan_index=10, coastline=True)
plt.show()

# Evoke filter on data
bx = pydarn.Boxcar(
    thresh=0.7,
    w=None
)
filtered_data = bx.run_filter(fitacf_data)

# After filtering
pydarn.RTP.plot_summary(filtered_data, beam_num=7,
                        range_estimation=pydarn.RangeEstimation.RANGE_GATE)
plt.show()

pydarn.Fan.plot_fan(filtered_data, scan_index=10, coastline=True)
plt.show()