108 lines
3.2 KiB
Python
Executable File
108 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python3
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import sys
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from datetime import datetime
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import numpy as np
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import matplotlib.pyplot as pp
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from matplotlib.dates import MinuteLocator, HourLocator, DateFormatter, drange, date2num
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from matplotlib.ticker import MultipleLocator
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accumulation = sys.argv[1]
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resolution = int(sys.argv[2])
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logname = sys.argv[3]
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outname = sys.argv[4]
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print(f"Accumulation: {accumulation}")
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print(f"Resolution: {resolution} seconds")
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print(f"Input Log: {logname}")
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print(f"Output Image: {outname}")
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accumulate = {
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"avg": np.mean,
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"peak": np.max,
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}[accumulation]
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data = -1000 * np.ones((86400//resolution, 1024), dtype=float)
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counts = np.zeros(86400//resolution, dtype=int)
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basetime = None
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last_lineidx = 0
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accumulated_data = []
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with open(logname, 'r') as logfile:
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for line in logfile:
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parts = line.split(";", maxsplit=1)
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timestamp = float(parts[0])
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if not basetime:
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basetime = np.floor(timestamp / 86400) * 86400
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lineidx = int((timestamp - basetime) // resolution)
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if lineidx != last_lineidx:
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if len(accumulated_data) != 0:
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# apply the accumulation function
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data[last_lineidx] = accumulate(np.array(accumulated_data).astype(float), 0)
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accumulated_data = []
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last_lineidx = lineidx
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tmp_data = np.fromstring(parts[1], sep=";", dtype=int)
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if tmp_data.size == 1024:
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accumulated_data.append(tmp_data)
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else:
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print(f"Warning: line ignored due to wrong number of elements: has {tmp_data.size}, expected 1024.")
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x = np.linspace(0, 30, data.shape[1])
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y = np.linspace(basetime, basetime + resolution * data.shape[0])
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extent = [
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0, # MHz
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30, # MHz
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date2num(datetime.utcfromtimestamp(basetime + resolution * data.shape[0])),
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date2num(datetime.utcfromtimestamp(basetime))]
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fig_x_scale = 0.85
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fig_y_scale = 0.90
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fig_x_off = 0.05
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fig_y_off = 0.05
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dpi = pp.rcParams['figure.dpi'] #get the default dpi value
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fig_size = (data.shape[1]/dpi/fig_x_scale, data.shape[0]/dpi/fig_y_scale) # convert pixels to DPI
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fix, ax = pp.subplots(figsize=fig_size)
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image = ax.imshow(data, vmin=-100, vmax=0, cmap='inferno',
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extent=extent)
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ax.set_position(pos=[fig_x_off, fig_y_off, fig_x_scale, fig_y_scale])
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ax.set_title('Spektrogramm vom ' + datetime.fromtimestamp(basetime).strftime('%Y-%m-%d'))
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xrange = extent[1] - extent[0]
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yrange = extent[2] - extent[3]
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ax.set_aspect((data.shape[0] / yrange) / (data.shape[1] / xrange))
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ax.yaxis_date()
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ax.yaxis.set_major_locator(HourLocator(range(0, 25, 1)))
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ax.yaxis.set_minor_locator(MinuteLocator(range(0, 60, 20)))
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ax.yaxis.set_major_formatter(DateFormatter('%H:%M'))
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ax.yaxis.set_minor_formatter(DateFormatter('%M'))
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ax.xaxis.set_major_locator(MultipleLocator(1.0))
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ax.set_xlabel('Frequenz [MHz]')
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ax_top = ax.twiny()
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ax_top.xaxis.set_major_locator(MultipleLocator(1.0))
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ax_top.set_xlabel('Frequenz [MHz]')
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ax_top.set_xlim(ax.get_xlim())
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ax_top.set_position(pos=[fig_x_off, fig_y_off, fig_x_scale, fig_y_scale])
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cax = pp.axes([fig_x_scale+fig_x_off+0.02, fig_y_off, 0.03, fig_y_scale])
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pp.colorbar(cax=cax, mappable=image)
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cax.set_title('dBm')
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pp.savefig(outname)
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