Bruning_Slow_Antenna_Software/file_unpack_fix_mini.py at main · LeemanGeophysicalLLC/Bruning_Slow_Antenna_Software · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
from time import sleep
import numpy as np
import matplotlib.pyplot as plt
import struct
import datetime
import sys
from glob import glob
import scipy.signal as signal
import pandas as pd
import datetime
import matplotlib.dates as mdates
from datetime import datetime, timedelta
import os
from matplotlib.ticker import FormatStrFormatter


time = pd.to_datetime("202306152033",format = "%Y%m%d%H%M")
paths = ["/Users/kelcy/Library/CloudStorage/OneDrive-TexasTechUniversity/jun 15/sensor 1",
         "/Users/kelcy/Library/CloudStorage/OneDrive-TexasTechUniversity/jun 15/sensor3",
         "/Users/kelcy/Library/CloudStorage/OneDrive-TexasTechUniversity/jun 15/sensor 8"]

fig, axs = plt.subplots(len(paths), figsize=(6.5, 2.5*len(paths)),sharex=False) #sharex=True,
# fig.suptitle(time)
fig.subplots_adjust(hspace=0.5)
# fig.tight_layout()
def convert_adc_to_decimal(value):
    modulo = 1 << 24
    max_value = (1 << 23) - 1
    if value > max_value:
        value -= modulo
    return value
SERIAL_SPEED = 2000000

def decode_data_packet(mp):
    # print(mp)
    result = dict()
    result['start_byte'] = struct.unpack('B', mp[0:1])[0]
    result['b1'] = struct.unpack('B', mp[1:2])[0]
    result['b2'] = struct.unpack('B', mp[2:3])[0]
    result['b3'] = struct.unpack('B', mp[3:4])[0]
    result['adc_pps_micros'] = struct.unpack('I', mp[4:8])[0]
    result['end_byte'] = struct.unpack('B', mp[8:9])[0]
    adc_hex = mp[1:4].hex()
    adc_ba = bytearray()
    adc_ba += mp[1:2]
    adc_ba += mp[2:3]
    adc_ba += mp[3:4]
    adc_ba += b'\x00'

    #print(mp[3:4])
    #print(adc_ba)
    adc_reading = struct.unpack('>i', adc_ba[:])[0]

    adc_reading = mp[1]
    adc_reading = (adc_reading << 8) | mp[2]
    adc_reading = (adc_reading << 8) | mp[3]
    adc_reading = convert_adc_to_decimal(adc_reading)


    result['adc_reading'] = adc_reading
    return result
def notch_sixty(s, fs):
    f0 = 60.0  # Frequency to be removed from signal (Hz)
    Q = 2.0  # Quality factor = center / 3dB bandwidth
    b, a = signal.iirnotch(f0, Q, fs)
    return signal.filtfilt(b, a, s)


for ind, path in enumerate(paths):
    files = sorted(glob(path+'/'+"*.raw"))
    dates = pd.to_datetime(sorted([os.path.splitext(os.path.basename(x))[0] for x in glob(path+'/'+"*.raw")]),format ="%Y%m%d%H%M%S_%f")
    sensor_num = files[0].split('/')
    min_time = time - timedelta(minutes = 4)
    max_time = time + timedelta(minutes = 4)
    # sensor_num = files.split('/')
    data_start_bytes = []
    data_packet_length = 8
    data_raw_packets = []
    data_packets = []
    inder = dates.get_loc(time, method = 'nearest')
    nearest_ind = [inder]
    if dates[inder] - timedelta(minutes = 3) >= min_time:
        nearest_ind.append(inder - 1)
    if dates[inder] + timedelta(minutes = 3) <= max_time:
        nearest_ind.append(inder + 1)

    time_arr = []
    print(nearest_ind)
    for _idx, fileind in enumerate(sorted(nearest_ind)):
        filename = files[fileind]
        data_start_bytes = []
        count = 0
        with open(filename, mode = 'rb') as file:
            ba = file.read()
            for i in range(len(ba) - data_packet_length):
                if (ba[i] == 190) and (ba[i+data_packet_length] == 239):
                    data_start_bytes.append(i)
                    count +=1
            this_packet_length = data_packet_length + 1

            data_raw_packets.extend([ba[sb:sb+this_packet_length] for sb in data_start_bytes[:-1]])


        time_offset = np.datetime64(datetime.strptime(filename[-25:-4], "%Y%m%d%H%M%S_%f"))
        timers = (time_offset + np.arange(count-1)*np.timedelta64(100, "us"))
        if _idx ==0:
            time_arr = timers
        else:
            time_arr = np.concatenate((time_arr ,timers))
    data_packets = [decode_data_packet(b) for b in data_raw_packets]


    starts = [dp['start_byte'] for dp in data_packets]
    adc_ready = [dp['adc_pps_micros'] for dp in data_packets]
    adc = [dp['adc_reading'] for dp in data_packets]
    end = [dp['end_byte'] for dp in data_packets]

    starts = np.array(starts)
    adc_ready = signal.medfilt(np.array(adc_ready), 7)
    adc = signal.medfilt(np.array(adc), 7)
    end = np.array(end)

    # print(adc.shape, adc.dtype)
    # delta_t_adc = (adc_ready[-1]-adc_ready[0])*1e-6
    # sample_rate = 1.0e6/np.median(np.ediff1d(adc_ready))
    # print(f"Elapsed time {delta_t_adc:6.3} s with sample rate {sample_rate:6.1f} Hz")

    # t = np.arange(adc.shape[0])/sample_rate

    bits_to_volts = (5/((2**24)-1))
    axs[ind].plot(time_arr, adc*bits_to_volts,label = sensor_num[7])#-adc_filt.mean())
# 	plt.gca().xaxis.set_major_formatter(FormatStrFormatter('%g'))
#     axs[ind].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S.%f'))
#     plt.setp(axs[ind].get_xticklabels(), rotation = 15,visible=True)
#     axs[ind].set_xlim([datetime(2023,6,15,20,33,35), datetime(2023,6,15,20,33,55)])
# plt.legend(loc='upper right')

    # axs.set_title(str(sensor_num[7]))
#axs[1,1].set_xlabel('Time (s)')
# 	axs[ind].set_ylim([1.495,1.65])
#time_arr[0] + np.timedelta64(216, 's') + np.timedelta64(750,'ms'), time_arr[0] + np.timedelta64(218, 's')])#, 15:20:40)
# axs[ind].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
# plt.setp(axs[ind].get_xticklabels(), rotation = 15)

# fig.subplots_adjust(vspace=0.5)
# plt.savefig("sensor_1.png",dpi = 300.0)
plt.show()


print('done with file')


# plt.savefig("sensor_1.png",dpi = 300.0)