-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathSpect_Generate_File_datetime.py
195 lines (151 loc) · 8.14 KB
/
Spect_Generate_File_datetime.py
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
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import matplotlib.pyplot as plt
import matplotlib
from scipy import signal
from scipy.io import wavfile
from scipy import interpolate
import numpy as np
import numpy.matlib as npmb
import dat_extract.get_tf as tf
import unpickle as up
from dat_extract.extract.Ship_Variable_Extraction import Ship
import time
folder = 'D:\PickledData\\'
destination_folder = 'D:\Generated Spectrograms\\'
def find_nearest(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return array[idx]
def better_seconds(t):
new_time = time.mktime(t.timetuple())
return new_time
def convert_time(ship):
start_time = find_nearest(ship.sampletimes,ship.file_time) #get the start index and cut array to fit it
start_index = ship.sampletimes.index(start_time)
times = ship.sampletimes[start_index:]
cpa_index = ship.sampletimes.index(ship.cpa_datetime)
result_array = []
for time in times:
result_array.append(better_seconds(time) - better_seconds(times[0])) #subtract start time
cpa_time = better_seconds(ship.cpa_datetime) - better_seconds(times[0]) #get new cpa_time in relation to file time
return result_array, start_index, cpa_index,cpa_time
def get_ticks(ranges,locs):
result = []
num_ticks = len(locs)
tick_step = len(ranges)//num_ticks
result.append(round(ranges[0]))
for x in range(num_ticks-1):
result.append(round((ranges[0+(tick_step*x)]),2))
return result
def range_spect(bins,spectrogram): #function to convert normal spectrogram into range baste
u_bins = np.unique(bins)
range_spectrogram = np.zeros((np.size(spectrogram,0),len(u_bins)))
for i in range(0,len(u_bins)):
searchval = u_bins[i]
ii = np.where(bins == searchval)[0]
#print(ii)
for x in range(0,np.size(spectrogram,0)):
counter = 0
for j in range(0,len(ii)):
counter+=spectrogram[x,(ii[j])]
range_spectrogram[x,i] = counter/len(ii)
return range_spectrogram
def get_ranges(app_bins,dep_bins,app_ranges,dep_ranges):
app_u_bins = np.unique(app_bins)
dep_u_bins = np.unique(dep_bins)
ranges = []
for bin in range(len(app_u_bins)):
ranges.append(app_ranges[bin])
for bin in range(len(dep_u_bins)):
ranges.append(dep_ranges[bin])
return(ranges)
def generate(folder):
ships = up.unpickle_ships(folder)
bad_apples = []
for ship in ships:
try:
wavfilepath = ship.filepath + ship.id + '.wav' #the original wav file
txtfilepath = ship.filepath + ship.id + '.txt' #the original txt file
destination = destination_folder + ship.year_month +'\\' + ship.id + '.png' #the destination for the spectrogram
print(txtfilepath)
converted_times,start,cpa_index,cpa_time = convert_time(ship) #convert all times and find the file start time and cpa time
#print(start)
#print(converted_times)
#print(cpa_time)
#print(cpa_index)
after_start = ship.distance[start:] #find all distances after file_time
# print(len(after_start))
# print(after_start)
closest_range = np.min(np.abs(after_start)) # find closest point of approach (cpa)
cpa_index = after_start.index(closest_range)
# print(cpa_index)
pre_cpa = after_start[:cpa_index]
post_cpa = after_start[cpa_index:] #find all distances after cpa time
# print(len(pre_cpa))
pre_times = converted_times[:cpa_index]
# print(len(pre_times))
post_times = converted_times[cpa_index:]
# print(post_times)
#print(pre_cpa)
#print(post_cpa)
approach_inter = interpolate.interp1d(pre_times,pre_cpa, axis=0, fill_value="extrapolate")
depart_inter = interpolate.interp1d(post_times,post_cpa, axis=0, fill_value="extrapolate")
sample_rate, samples = wavfile.read(wavfilepath) #get original wav file samples at the original sample rate
sound_length = len(samples)//sample_rate
#print(sound_length)
approach_times = np.arange(0,cpa_time)
depart_times = np.arange(cpa_time,sound_length)
frequencies, times, spectrogram = signal.spectrogram(samples,sample_rate, window = np.hanning(10e3), noverlap = 0, nfft = 10e3, mode='psd') #generate spectrogram
uppc = tf.get_tf(ship.harp,frequencies) #get the transfer function results
spectrogram = 10*np.log10(spectrogram) #convert to/from decibels ?
uppc = npmb.repmat(uppc,np.size(spectrogram,1),1) #copy tf results several times to make it same size as spect results
spectrogram = spectrogram + np.transpose(uppc) #add tf results to spect results
range_step = .01 # step size of 1m
range_approach = ((np.arange(pre_cpa[0], closest_range, -range_step))) # make a vector of distances between first range and cpa
range_depart = (np.arange(closest_range, post_cpa[len(post_cpa)-1], range_step)) # make a vector of distances between cpa and last range
range_desired = np.append(range_approach,range_depart)# stick them together
number_range_samples = len(range_desired)# total length is the number of samples we expect.
#print(spectrogram.shape)
spect_dis_approach = approach_inter(approach_times)
spect_dis_depart = depart_inter(depart_times)
approach_bins = np.digitize(spect_dis_approach,range_approach)
depart_bins = np.digitize(spect_dis_depart,range_depart)
approach_spect = range_spect(approach_bins,spectrogram)
depart_spect = range_spect(depart_bins,spectrogram)
#print(approach_spect.shape)
#print(depart_spect.shape)
#print(spectrogram)
#print(times)
#print(times.shape)
range_spectrogram = np.concatenate((approach_spect,depart_spect),axis=1)
ship.spect = range_spectrogram
normal = True
#ranges = get_ranges(approach_bins,depart_bins,range_approach,range_depart)
#print(range_spectrogram)
#print(ranges)
#plt.yscale('log') #make y scale log to match the new decibel units
#axes = plt.gca() #get axes object
#axes.set_ylim([10,1000]) #set upper limit of data on axes to be 1000
# plt.pcolormesh(ranges,frequencies,range_spectrogram,vmin=60,vmax=110 ) #plot the data and add color
# plt.set_cmap('jet')
# plt.ylabel('Frequency [Hz]')
# plt.xlabel('Distance [km]')
# locs, ticks = plt.xticks() #get current time ticks
# new_ticks = get_ticks(ranges,locs)
# plt.xticks(locs,new_ticks)
# plt.colorbar()
#plt.xticks(locs, new_ticks) # Set locations and labels to the distance
#plt.savefig(destination) #save spectrogram at destination
#plt.imshow(spectrogram)
#plt.show() #show plot
#plt.close()
except:
print("bad ship")
up.one_jar(folder,ship,True)
normal = False
pass
# print("bad ships:")
# print(bad_apples)
# print(len(bad_apples))
if normal:
up.one_jar(folder,ship,False)
generate(folder)