kiwi-longtime-waterfall/plot_wf_longterm.py

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#!/usr/bin/env python3
import sys
import argparse
from datetime import datetime
from lzma import LZMAFile
import numpy as np
import matplotlib.pyplot as pp
from matplotlib.dates import MinuteLocator, HourLocator, DayLocator, DateFormatter, AutoDateLocator, drange, date2num
from matplotlib.ticker import MultipleLocator
parser = argparse.ArgumentParser(description="Plots long term waterfalls from KiwiSDR spectrum recordings.")
parser.add_argument('-a', '--accumulation', choices=['avg_db', 'avg_pwr', 'peak'], default='avg_pwr', help='Accumulation method.')
parser.add_argument('-r', '--resolution', type=int, default=600, help='Spectrogram time resolution in seconds.')
parser.add_argument('-o', '--output', type=argparse.FileType('wb'), required=True, help='The image output name.')
parser.add_argument('input', nargs='+', type=str, help='KiwiSDR spectrum dumps to load (uncompressed or xz-compressed)')
args = parser.parse_args()
print(args)
print(f"Accumulation: {args.accumulation}")
print(f"Resolution: {args.resolution} seconds")
print(f"Input Logs: {args.input}")
print(f"Output Image: {args.output}")
loaded_data = {} # format: base_timestamp => {count, spectrum data as np.array}
for inputfilename in args.input:
try:
if inputfilename[-2:].lower() == "xz":
print(f"Loading '{inputfilename}' as XZ stream...")
stream = LZMAFile(filename=inputfilename, mode='r')
else:
print(f"Loading '{inputfilename}' as uncompressed stream...")
stream = open(inputfilename, 'r')
for line in stream:
# extract the timestamp
parts = line.decode('ascii').split(";", maxsplit=1)
timestamp = float(parts[0])
# calculate which accumulation block this spectrum fits into
basetime = (timestamp // args.resolution) * args.resolution
# split the spectrum data line
tmp_data = np.fromstring(parts[1], sep=";", dtype=int)
if tmp_data.size != 1024:
print(f"Warning: line ignored due to wrong number of elements: has {tmp_data.size}, expected 1024.")
continue
# get the block data loaded so far or initialize it
if basetime not in loaded_data.keys():
if args.accumulation == 'peak':
loaded_data[basetime] = {'data': -1000 * np.ones(1024)}
else:
loaded_data[basetime] = {'count': 0, 'data': np.zeros(1024)}
block_data = loaded_data[basetime]
# on the fly accumulation
if args.accumulation == 'peak':
block_data['data'] = np.max(np.stack([tmp_data, block_data['data']]), axis=0)
elif args.accumulation == 'avg_db':
block_data['data'] += tmp_data
block_data['count'] += 1
else: # avg_pwr
block_data['data'] += 10**(tmp_data/10) # convert dBm to mW
block_data['count'] += 1
loaded_data[basetime] = block_data
stream.close()
except Exception as e:
print(f"Error while processing '{inputfilename}': {e}")
print("This files (remaining) data is skipped.")
print("Post-processing accumulation...")
if args.accumulation == 'avg_db':
for v in loaded_data.values():
v['data'] /= v['count']
elif args.accumulation == 'avg_pwr':
for v in loaded_data.values():
v['count'] = max(1, v['count']) # prevent division by 0
v['data'] = 10 * np.log10(v['data'] / v['count']) # convert averaged value back to dBm
# peak accumulation does not need finalization
print(f"Loading completed. {len(loaded_data)} blocks accumulated.")
# create a contiguous array from the spectrum lines
timestamps = loaded_data.keys()
first_timestamp = min(timestamps)
last_timestamp = max(timestamps)
num_spectrums = int((last_timestamp - first_timestamp) // args.resolution) + 1
shape = (num_spectrums, loaded_data[first_timestamp]['data'].size)
print(f"Reserving an array with shape {shape}...")
spectrogram = -1000.0 * np.ones(shape, dtype=float)
print(f"Copying the data in...")
for k, v in loaded_data.items():
idx = int((k - first_timestamp) // args.resolution)
spectrogram[idx, :] = v['data']
del loaded_data # not needed anymore
print("Plotting image...")
basetime = first_timestamp
x = np.linspace(0, 30, spectrogram.shape[1])
y = np.linspace(basetime, basetime + args.resolution * spectrogram.shape[0])
extent = [
0, # MHz
30, # MHz
date2num(datetime.utcfromtimestamp(basetime + args.resolution * spectrogram.shape[0])),
date2num(datetime.utcfromtimestamp(basetime))]
pic_w_px = 1024 + 127 + 150
fig_x_scale = 1024 / pic_w_px
fig_x_off = 127 / pic_w_px # from left side
pic_h_px = spectrogram.shape[0] + 70 + 100
fig_y_scale = spectrogram.shape[0] / pic_h_px
fig_y_off = 70 / pic_h_px # from bottom
dpi = pp.rcParams['figure.dpi'] #get the default dpi value
fig_size = (spectrogram.shape[1]/dpi/fig_x_scale, spectrogram.shape[0]/dpi/fig_y_scale) # convert pixels to DPI
fix, ax = pp.subplots(figsize=fig_size)
image = ax.imshow(spectrogram, vmin=-100, vmax=0, cmap='inferno',
extent=extent)
ax.set_position(pos=[fig_x_off, fig_y_off, fig_x_scale, fig_y_scale])
ax.set_title('Spektrogramm von '
+ datetime.utcfromtimestamp(first_timestamp).strftime('%Y-%m-%d %H:%M:%S')
+ ' bis '
+ datetime.utcfromtimestamp(last_timestamp).strftime('%Y-%m-%d %H:%M:%S')
+ ' UTC')
xrange = extent[1] - extent[0]
yrange = extent[2] - extent[3]
ax.set_aspect((spectrogram.shape[0] / yrange) / (spectrogram.shape[1] / xrange))
print("Formatting...")
ax.yaxis_date()
#ax.yaxis.set_major_locator(HourLocator(range(0, 25, 1)))
#ax.yaxis.set_minor_locator(MinuteLocator(range(0, 60, 20)))
#ax.yaxis.set_major_formatter(DateFormatter('%H:%M'))
#ax.yaxis.set_minor_formatter(DateFormatter('%M'))
#loc = AutoDateLocator()
#ax.yaxis.set_major_locator(loc)
#ax.yaxis.set_major_formatter(DateFormatter('%Y-%m-%d %H:%M'))
ax.yaxis.set_major_locator(DayLocator(interval=max(1, args.resolution // 600)))
ax.yaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
if args.resolution <= 600:
ax.yaxis.set_minor_locator(HourLocator(interval=max(1, args.resolution // 100)))
ax.yaxis.set_minor_formatter(DateFormatter('%H'))
ax.xaxis.set_major_locator(MultipleLocator(1.0))
ax.set_xlabel('Frequenz [MHz]')
ax_top = ax.twiny()
ax_top.xaxis.set_major_locator(MultipleLocator(1.0))
ax_top.set_xlabel('Frequenz [MHz]')
ax_top.set_xlim(ax.get_xlim())
ax_top.set_position(pos=[fig_x_off, fig_y_off, fig_x_scale, fig_y_scale])
cax = pp.axes([fig_x_scale+fig_x_off+0.02, fig_y_off, 0.03, fig_y_scale])
pp.colorbar(cax=cax, mappable=image)
cax.set_title('dBm')
print("Saving...")
pp.savefig(args.output)
print("All done!")