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functions.py
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functions.py
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from numpy import pi, sin, cos,pi
import plotly.express as px
import datetime
import plotly.graph_objects as go
from time import sleep
from tqdm import tqdm
from random import randint
from ooipy.request import hydrophone_request
from ooipy.tools import ooiplotlib as ooiplt
import os, json
import pandas as pd
import numpy as np
import datetime
import ooipy
import matplotlib.pyplot as plt
import matplotlib
from datetime import timedelta
import time
import warnings
warnings.filterwarnings("ignore")
from obspy import read
def choose_df(df,flag,verbose=True):
ship_cols=['MMSI', 'SHIPNAME', 'VESSEL TYPE','SPEED (KNOTSx10)','COURSE', 'HEADING', 'TIMESTAMP UTC',
'LENGTH', 'Year','ship_Loc','LAT','LON']
if flag==1:
cols=ship_cols + ['distance(in km) axial','axial_Loc']
df=df[cols].rename(columns={'distance(in km) axial':'distance(in km)'})
elif flag==2:
cols=ship_cols + ['distance(in km) central cald','central_caldera_Loc']
df=df[cols].rename(columns={'distance(in km) central cald':'distance(in km)'})
elif flag==3:
cols=ship_cols + ['distance(in km) eastern cald','eastern_caldera_Loc']
df=df[cols].rename(columns={'distance(in km) eastern cald':'distance(in km)'})
if verbose==True:
print(' Max distance: {} and Min distance: {}'.format(df['distance(in km)'].max(),df['distance(in km)'].min()))
return df
def choose_df_slope_hydrate(df,flag,verbose=True):
ship_cols=['MMSI', 'VESSEL TYPE', 'TIMESTAMP UTC',
'LENGTH','ship_Loc','LAT','LON','SOG','COG','Heading','VesselName']
if flag==4:
cols=ship_cols + ['distance(in km) oregon','oregon_slope_Loc']
df=df[cols].rename(columns={'distance(in km) oregon':'distance(in km)'})
elif flag==5:
cols=ship_cols + ['distance(in km) hydrate','Southern_hydrate_Loc']
df=df[cols].rename(columns={'distance(in km) hydrate':'distance(in km)'})
if verbose==True:
print(' Max distance: {} and Min distance: {}'.format(df['distance(in km)'].max(),df['distance(in km)'].min()))
return df
def break_duration(isolated_ships,hydro):
ncols=isolated_ships.shape[1]
isolated_ships_2=pd.DataFrame()
row={'MMSI':'','start_time':'','end_time':'','VESSEL TYPE':'','ud_group':''}
idx=0
for i in range(len(isolated_ships)):
MMSI=isolated_ships['MMSI'].iloc[i]
start=isolated_ships['start_time'].iloc[i]
end=isolated_ships['end_time'].iloc[i]
VESSEL_TYPE=isolated_ships['VESSEL TYPE'].iloc[i]
ud_group=isolated_ships['ud_group'].iloc[i]
duration=isolated_ships['len_of_recording'].iloc[i]
str_start=start.strftime("%Y%m%d%H%M%S")
str_end=end.strftime("%Y%m%d%H%M%S")
row['MMSI']=MMSI
row['VESSEL TYPE']=VESSEL_TYPE
row['ud_group']=ud_group
if duration <= 10:
row['start_time']=start
row['end_time']=end
row['len_of_recording']=(end-start).total_seconds()/60
print(row)
row['instance_id']=hydro+'_'+start.strftime("%Y%m%d%H%M%S")+'_'+end.strftime("%Y%m%d%H%M%S")
isolated_ships_2=isolated_ships_2.append(row,ignore_index=True)
idx=idx+1
else:
temp_end=start+timedelta(minutes=10)
temp_start=start
while temp_end <= end:
row['start_time']=temp_start
row['end_time']=temp_end
row['len_of_recording']=(temp_end-temp_start).total_seconds()/60
row['instance_id']=hydro+'_'+temp_start.strftime("%Y%m%d%H%M%S")+'_'+temp_end.strftime("%Y%m%d%H%M%S")
isolated_ships_2=isolated_ships_2.append(row,ignore_index=True)
idx=idx+1
temp_start=temp_end
temp_end=temp_start+timedelta(minutes=10)
if temp_start < end:
row['start_time']=temp_start
row['end_time']=end
row['len_of_recording']=(end-temp_start).total_seconds()/60
row['instance_id']=hydro+'_'+temp_start.strftime("%Y%m%d%H%M%S")+'_'+end.strftime("%Y%m%d%H%M%S")
isolated_ships_2=isolated_ships_2.append(row,ignore_index=True)
idx=idx+1
#remove duration less than 10 minutes
isolated_ships_2=isolated_ships_2[isolated_ships_2['len_of_recording']>=10]
return isolated_ships_2
def load_saved_df_axial(hydrophone_idx,inner_rad,outer_rad):
if hydrophone_idx==1:
hydro='Axial_Base'
elif hydrophone_idx==2:
hydro='Central_Caldera'
elif hydrophone_idx==3:
hydro='Eastern_Caldera'
suffix=str(inner_rad)+'_'+ str(outer_rad)+'.csv'
isolated_ais=pd.read_csv(path_to_write+hydro+'/isolated_ais_'+ suffix)
isolated_ais_10m=pd.read_csv(path_to_write+hydro+'/isolated_ais_10m_'+ suffix)
def load_saved_df_slope_hydrate(hydrophone_idx,inner_rad,outer_rad):
if hydrophone_idx==1:
hydro='Oregon_slope'
elif hydrophone_idx==2:
hydro='Southern_hydrate'
suffix=str(inner_rad)+'_'+ str(outer_rad)+'.csv'
isolated_ais=pd.read_csv(path_to_write+hydro+'/isolated_ais_'+ suffix)
isolated_ais_10m=pd.read_csv(path_to_write+hydro+'/isolated_ais_10m_'+ suffix)
def get_isolated_ships(df,ud_vessel,rad,out_rad,min_d,verbose=True): #optimised version
vessels=df[['MMSI','VESSEL TYPE']].drop_duplicates(subset=['MMSI'])
ud_vessel_=ud_vessel[['vessel','ud_group']]
df=df.sort_values(by=['TIMESTAMP UTC'],ascending=True)
df_temp=df[df['distance(in km)']<(out_rad+1)] #find all ships inside radius = rad +1
df_temp['rad_flag']=np.where(df_temp['distance(in km)'] <rad, 1, 0) #mark ships as 1 which are within radius=rad , otherwise 0
df_temp['prev_MMSI']= df_temp['MMSI'].shift(1) # this will track when a new ship comes in
df_temp['prev_rad_flag']= df_temp['rad_flag'].shift(1) # this will track when ship goes outside radius=rad
count=0
df_temp[['prev_MMSI','prev_rad_flag']].dropna(axis=0,inplace=True)
#df_temp['break']=np.where((df_temp['MMSI']==df_temp['prev_MMSI']) & (df_temp['prev_rad_flag']==df_temp['rad_flag']),count,count+=1)
df_temp['not_same_ship_as_prev']=~(df_temp['MMSI']==df_temp['prev_MMSI'])
df_temp['xor_rad_flag_with_prev']=[bool(x)^bool(y) for x,y in zip(df_temp['rad_flag'],df_temp['prev_rad_flag'])]
df_temp['or_ship_and_flag']=df_temp['not_same_ship_as_prev'] | df_temp['xor_rad_flag_with_prev']
df_temp['break']=df_temp['or_ship_and_flag'].cumsum() # create partitions when a ship is continuously within radius=rad
#df_temp_copy=df_temp.copy()
#df_temp=df_temp.assign(flag=(df_temp['MMSI']==df_temp['prev_MMSI']) & (df_temp['prev_rad_flag']==df_temp['rad_flag']).cumsum())
# df_temp.to_csv('ships_smoothpath.csv')
df_temp=df_temp[df_temp['rad_flag']==1]
df_temp_grp=df_temp.groupby(by=['MMSI','break']).agg({'TIMESTAMP UTC':['min','max','count']}).reset_index()
df_temp_grp.columns=['MMSI','break','start_time','end_time','count']
df_temp_grp.drop(df_temp_grp[df_temp_grp['end_time'] == df_temp_grp['start_time']].index,inplace=True)#remove ships which barely touched the radius
#df_temp_grp.drop(df_temp_grp[df_temp_grp['count']<(min_t +1)].index,inplace=True) #filter ships with less than minimum number of timestamps
df_temp_grp=df_temp_grp.merge(vessels,how='left',on='MMSI')
df_temp_grp['len_of_recording']=(df_temp_grp['end_time']-df_temp_grp['start_time'])
df_temp_grp['len_of_recording']=[x.total_seconds()/60.0 for x in df_temp_grp['len_of_recording']]
df_temp_grp.drop(df_temp_grp[df_temp_grp['len_of_recording']<min_d].index,inplace=True)
df_temp_grp.reset_index(inplace=True,drop=True)
df_temp_grp.drop('break',axis=1,inplace=True)
df_temp_grp=df_temp_grp.merge(ud_vessel_, how='left',left_on='VESSEL TYPE',right_on='vessel')
df_temp_grp.drop(columns=['vessel'],axis=1,inplace=True)
##Merging additional ais data
return df_temp_grp
#Check status of ships' isolated timeframe in the overall ais dataset
def check_status_in_ais(ais,MMSI,min_time,max_time,rad):
print('Min time : ', min_time)
print('Max time : ', max_time)
ais_test= ais[(ais['TIMESTAMP UTC']>min_time) & (ais['TIMESTAMP UTC']<max_time)]
ais_test_lt=ais_test[ais_test['distance(in km)']<=rad]
ais_test_gt=ais_test[ais_test['distance(in km)']>rad]
n=ais_test_lt['MMSI'].nunique()
min_distance_outside_rad=ais_test_gt['distance(in km)'].min()
print('The number of unique isolated ships within the radius in the timeframe : ', n)
print('The minimum distance of all other ships in the timeframe is : ',min_distance_outside_rad)
return ais_test
def simp_spectrogram(hydrophone_idx,start_time,end_time,fmin=None,fmax=None):
print('Start time : {} and End time : {}'.format(start_time,end_time))
if hydrophone_idx==1:
node='Axial_Base'
elif hydrophone_idx==2:
node='AXCC1'
elif hydrophone_idx==3:
node='AXEC2'
elif hydrophone_idx==4:
node='HYSB1'
elif hydrophone_idx==5:
node='HYS14'
time_diff=end_time-start_time
time_diff=time_diff.total_seconds()/60.0
if time_diff >10:
end_time=start_time + timedelta(minutes=10)
else:
end_time=end_time
data_trace = ooipy.get_acoustic_data_LF(start_time, end_time, node, verbose=True, zero_mean=True)
print(data_trace)
if data_trace==None:
print('data trace is none. Continuing to next')
pass
else:
spec = data_trace.compute_spectrogram(L = 256,avg_time=10, overlap=0.9)
#spec.compute_psd_welch()
print('/************************************************************************************************/')
ooipy.plot(spec, fmin=fmin, fmax=fmax, xlabel_rot=30,vmax=110) #xlabel changed from 30 to 10
#ooipy.tools.ooiplotlib.plot_spectrogram(spec)
#plt.xlim([0, 10])
plt.show()
def get_spectrogram_data(hydrophone_idx,start_time,end_time,fmin=None,fmax=None):
if hydrophone_idx==1:
node='Axial_Base'
elif hydrophone_idx==2:
node='AXCC1'
elif hydrophone_idx==3:
node='AXEC2'
elif hydrophone_idx==4:
node='HYSB1'
elif hydrophone_idx==5:
node='HYS14'
time_diff=end_time-start_time
time_diff=time_diff.total_seconds()/60.0
if time_diff >10:
end_time=start_time + timedelta(minutes=10)
else:
end_time=end_time
print(start_time)
print(end_time)
data_trace = ooipy.get_acoustic_data_LF(start_time, end_time, node, verbose=True, zero_mean=True)
print(data_trace)
if data_trace==None:
print('data trace is none. Continuing to next')
pass
else:
try:
spec = data_trace.compute_spectrogram(L = 256,avg_time=10, overlap=0.9)
# spec = data_trace.compute_spectrogram(L = 256,overlap=0.9)
except TypeError:
pass
return spec
def save_spectrogram_acoustic_parallel(hydrophone_idx,i,data,inner_rad,outer_rad,path_to_write,fmin=None,fmax=None):
if hydrophone_idx==1:
node='Axial_Base'
hydro_file='Axial_Base'
hydro='AB'
elif hydrophone_idx==2:
node='AXCC1'
hydro_file='Central_Caldera'
hydro='CC'
elif hydrophone_idx==3:
node='AXEC2'
hydro_file='Eastern_Caldera'
hydro='EC'
elif hydrophone_idx==4:
node='HYSB1'
hydro_file='Oregon_Slope'
hydro='OS'
elif hydrophone_idx==5:
node='HYS14'
hydro_file='Southern_Hydrate'
hydro='SH'
start_time=data.start_time.iloc[i]
end_time=data.end_time.iloc[i]
time_diff=end_time-start_time
time_diff=time_diff.total_seconds()/60.0
if time_diff >10:
end_time=start_time + timedelta(minutes=10)
else:
end_time=end_time
str_starttime=start_time.strftime("%Y%m%d%H%M%S")
str_endtime=end_time.strftime("%Y%m%d%H%M%S")
spec_filename=path_to_write+hydro_file+'/Spectrograms'+'/'+hydro+'_'
audio_filename=path_to_write+hydro_file+'/Audio'+'/'+hydro+'_'
psd_filename=path_to_write+hydro_file+'/PSDs'+'/'+hydro+'_'
mseed_filename=path_to_write+hydro_file+'/Mseed'+'/'+hydro+'_'
psd_pickle_filename=path_to_write+hydro_file+'/PSD_pickles'+'/'+hydro+'_'
data_trace = ooipy.get_acoustic_data_LF(start_time, end_time, node, verbose=True, zero_mean=True)
if data_trace==None:
print('data trace is none. Continuing to next')
pass
else:
print('Saving wav files')
data_trace.write(mseed_filename+str_starttime+'_'+str_endtime+'.mseed', format='MSEED')
data_trace.wav_write(filename=audio_filename+str_starttime+'_'+str_endtime+'.wav')
psd = data_trace.compute_psd_welch(L = 256, overlap=0.3)
try:
spectrogram = data_trace.compute_spectrogram(L = 256,avg_time=10, overlap=0.5)
if (spectrogram==None) or (spectrogram.values.shape[0]<2) | (spectrogram.values.shape[1]<2) or (psd==None):
# print('data trace is none. Continuing to next')
pass
else:
ooiplt.plot_spectrogram(spectrogram,plot=False,save=True,fmin=fmin,fmax=fmax,vmax=110,filename=spec_filename+str_starttime+'_'+str_endtime+'.png')
psd.save(filename=psd_pickle_filename+str_starttime+'_'+str_endtime+'.json')
plot_psd(psd,plot=False,save=True,fmin=fmin,fmax=fmax,vmax=110,filename=psd_filename+str_starttime+'_'+str_endtime+'.png')
except TypeError:
pass
def get_acoustic(hydrophone_idx,start_time,end_time,fmin=None,fmax=None):
#print('Start time : {} and End time : {}'.format(start_time,end_time))
if hydrophone_idx==1:
node='Axial_Base'
elif hydrophone_idx==2:
node='AXCC1'
elif hydrophone_idx==3:
node='AXEC2'
elif hydrophone_idx==4:
node='HYSB1'
elif hydrophone_idx==5:
node='HYS14'
data_trace = ooipy.get_acoustic_data_LF(start_time, end_time, node, verbose=False, zero_mean=True)
return data_trace
def get_acoustic_parallel(hydrophone_idx,i,data,fmin=None,fmax=None):
#print('Start time : {} and End time : {}'.format(start_time,end_time))
if hydrophone_idx==1:
node='Axial_Base'
elif hydrophone_idx==2:
node='AXCC1'
elif hydrophone_idx==3:
node='AXEC2'
elif hydrophone_idx==4:
node='HYSB1'
elif hydrophone_idx==5:
node='HYS14'
min_time=data.start_time[i]
max_time=data.end_time[i]
data_trace = ooipy.get_acoustic_data_LF(start_time, end_time, node, verbose=False, zero_mean=True)
return data_trace
def get_spectogram(hydrophone_idx,lone_ships,num=5,ideal_dur=10): #ideal_duration -> ideal duration in minutes
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('plotting of spectrogram started')
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Total number of ships :',len(lone_ships))
if len(lone_ships)<num:
num=len(lone_ships)
print('Displaying spectrogram for {} ships'.format(len(lone_ships)))
for i in range(num):
list_dur=lone_ships.iloc[i]
min_time=list_dur.start_time
min_time=datetime.datetime(min_time.year,min_time.month,min_time.day,min_time.hour,min_time.minute,0)
#min_time=min_time.strftime('%Y-%m-%d %H:%M')
#min_time=datetime.datetime.strptime(min_time,'%Y-%m-%d %H:%M')
print(min_time)
max_time=list_dur.end_time
max_time=datetime.datetime(max_time.year,max_time.month,max_time.day,max_time.hour,max_time.minute,0)
#max_time=max_time.strftime('%Y-%m-%d %H:%M')
#max_time=datetime.datetime.strptime(max_time,'%Y-%m-%d %H:%M')
start_time = min_time
end_time=min_time+timedelta(minutes=ideal_dur)
if end_time > max_time:
end_time=max_time
print('{}. MMSI: {} , VESSEL TYPE: {}'.format(i+1,lone_ships['MMSI'].iloc[i],lone_ships['VESSEL TYPE'].iloc[i]))
print('Start time : {} and End time : {}'.format(start_time,end_time))
simp_spectrogram(hydrophone_idx,start_time,end_time)
#alter_spectrogram_func(start_time,end_time)
#time.sleep(20)
def get_circle_coordinates(rad,lat,lon):
R = rad #in meters
center_lon = lon
center_lat = lat
t = np.linspace(0, 2*pi, 100)
circle_lon =center_lon + R*cos(t)
circle_lat =center_lat + R*sin(t)
coords=[]
lat1=[]
lon1=[]
for lo, la in zip(list(circle_lon), list(circle_lat)):
lat1.append(la)
lon1.append(lo)
coords.append([lo, la])
### Another way to find coordinates of a circle
N = 100 # number of discrete sample points to be generated along the circle
# generate points
circlePoints = []
lat=[]
lon=[]
for k in range(N):
# compute
angle = pi*2*k/N
dx = R*cos(angle)
dy = R*sin(angle)
point = {}
la=center_lat + (180/pi)*(dy/6378137)
lo=center_lon + (180/pi)*(dx/6378137)/cos(center_lat*pi/180)
lat.append(la)
lon.append(lo)
# add to list
circlePoints.append([lo,la])
return lat,lon
def get_map_plot(fn,df,rad1,inner_rad2,outer_rad2,lat,lon,time=None):
df1 = df[(df['distance(in km)']<rad1)]
df2 = df[(df['distance(in km)']>inner_rad2) & (df['distance(in km)']<outer_rad2) ]
df3=pd.concat([df1,df2])
rad1=rad1 * 1000
inner_rad2=inner_rad2*1000
outer_rad2=outer_rad2*1000
if fn=='scatter':
fig = px.scatter_mapbox(df3,lat="LAT" ,lon="LON",mapbox_style='carto-positron',hover_name="TIMESTAMP UTC",
hover_data= ['SPEED (KNOTSx10)','VESSEL TYPE','distance(in km)'],color = "VESSEL TYPE")
elif fn=='density':
fig = px.density_mapbox(df3,lat="LAT" ,lon="LON",mapbox_style='stamen-terrain',hover_name="TIMESTAMP UTC",
hover_data= ['SPEED (KNOTSx10)','VESSEL TYPE'],radius=3,
color_continuous_scale= [
[0.0, "green"],
[0.5, "green"],
[0.51111111, "yellow"],
[0.71111111, "yellow"],
[0.71111112, "red"],
[1, "red"]])
fig2 = px.scatter_mapbox(lat=[lat],lon=[lon],mapbox_style='carto-positron')
# fig2.update_traces(marker_symbol='bus',selector=dict(type='scattermapbox'))
fig2.update_traces(marker = {'size': 8, 'color':'black','opacity':0.9})
lat_arr1,lon_arr1=get_circle_coordinates(rad1,lat,lon)
lat_arr2,lon_arr2=get_circle_coordinates(inner_rad2,lat,lon)
lat_arr3,lon_arr3=get_circle_coordinates(outer_rad2,lat,lon)
fig4=px.scatter_mapbox(lat=lat_arr1,lon=lon_arr1,mapbox_style='carto-positron')
fig4.update_traces(marker = {'size': 4, 'color':'black','opacity':0.9})
fig5=px.scatter_mapbox(lat=lat_arr2,lon=lon_arr2,mapbox_style='carto-positron')
fig5.update_traces(marker = {'size': 4, 'color':'black','opacity':0.9})
fig6=px.scatter_mapbox(lat=lat_arr3,lon=lon_arr3,mapbox_style='carto-positron')
fig6.update_traces(marker = {'size': 4, 'color':'black','opacity':0.9})
fig.add_trace(fig2.data[0])
fig.add_trace(fig4.data[0])
fig.add_trace(fig5.data[0])
fig.add_trace(fig6.data[0])
#fig.add_trace(fig4.data[0])
fig.update_layout(coloraxis_showscale=False,mapbox=dict(
bearing=10,
center=dict(
lat=lat,
lon=lon,
)))
fig.show()
return df2
def get_isolated_map_plot(fn,df,rad1,inner_rad2,lat,lon,min_d):
ais_test=pd.DataFrame()
print('Finding isolated ships')
iso_ships=get_isolated_ships(df,rad1,inner_rad2,min_d)
print('Found isolated ships')
print('Creating the dataframe with all ships and their locations in the isolated timeframes')
for i in tqdm(range(len(iso_ships))):
min_time=iso_ships.start_time[i]
max_time=iso_ships.end_time[i]
ais_test=pd.concat([ais_test,df[(df['TIMESTAMP UTC']>=min_time) & (df['TIMESTAMP UTC']<=max_time)]])
# df1 = df[(df['distance(in km) axial']<rad1)]
# df2 = df[(df['distance(in km) axial']>inner_rad2) & (df['distance(in km) axial']<outer_rad2)]
#ais_test= df[(df['TIMESTAMP UTC']>=min_time) & (df['TIMESTAMP UTC']<=max_time)]
rad1=rad1 * 1000
inner_rad2=inner_rad2*1000
print('Starting to plot now')
if fn=='scatter':
fig = px.scatter_mapbox(ais_test,lat="LAT" ,lon="LON",mapbox_style='carto-positron',hover_name="TIMESTAMP UTC",
hover_data= ['SPEED (KNOTSx10)','VESSEL TYPE','distance(in km)'],color = "VESSEL TYPE")
elif fn=='density':
fig = px.density_mapbox(ais_test,lat="LAT" ,lon="LON",mapbox_style='stamen-terrain',hover_name="TIMESTAMP UTC",
hover_data= ['SPEED (KNOTSx10)','VESSEL TYPE','distance(in km)'],radius=3,
color_continuous_scale= [
[0.0, "green"],
[0.5, "green"],
[0.51111111, "yellow"],
[0.71111111, "yellow"],
[0.71111112, "red"],
[1, "red"]])
fig2 = px.scatter_mapbox(lat=[lat],lon=[lon],mapbox_style='carto-positron')
# fig2.update_traces(marker_symbol='bus',selector=dict(type='scattermapbox'))
fig2.update_traces(marker = {'size': 8, 'color':'black','opacity':0.9})
lat_arr1,lon_arr1=get_circle_coordinates(rad1,lat,lon)
lat_arr2,lon_arr2=get_circle_coordinates(inner_rad2,lat,lon)
fig4=px.scatter_mapbox(lat=lat_arr1,lon=lon_arr1,mapbox_style='carto-positron')
fig4.update_traces(marker = {'size': 4, 'color':'black','opacity':0.9})
fig5=px.scatter_mapbox(lat=lat_arr2,lon=lon_arr2,mapbox_style='carto-positron')
fig5.update_traces(marker = {'size': 4, 'color':'black','opacity':0.9})
fig6 = px.line_mapbox( lat=[lat,lat_arr1[0]], lon=[lon,lon_arr1[0]],hover_name=[rad1,rad1])
fig7 = px.line_mapbox( lat=[lat,lat_arr2[10]], lon=[lon,lon_arr2[10]],hover_name=[inner_rad2,inner_rad2])
fig6.update_traces(marker = {'size': 8, 'color':'black','opacity':0.9})
fig7.update_traces(marker = {'size': 8, 'color':'black','opacity':0.9})
fig8 = go.Figure(go.Scattermapbox(
mode = "markers+lines",
lat = [lat,lat_arr1[0]],
lon = [lon,lon_arr1[0]],
marker = {'size': 4,'color':'black'}))
fig9 = go.Figure(go.Scattermapbox(
mode = "markers+lines",
lat = [lat,lat_arr2[10]],
lon = [lon,lon_arr2[10]],
marker = {'size': 4,'color':'black'}))
fig.add_trace(fig2.data[0])
fig.add_trace(fig4.data[0])
fig.add_trace(fig5.data[0])
# fig.add_trace(fig6.data[0])
# fig.add_trace(fig7.data[0])
fig.add_trace(fig8.data[0])
fig.add_trace(fig9.data[0])
#fig.add_trace(fig4.data[0])
fig.update_layout(coloraxis_showscale=False,mapbox=dict(
bearing=0,
center=dict(
lat=lat,
lon=lon,
)))
fig.show()
def get_single_isolated_map_plot(hydrophone_idx,fn,df,rad1,inner_rad2,lat,lon,min_d,fmin,fmax):
print('Finding isolated ships')
iso_ships=get_isolated_ships(df,rad1,inner_rad2,min_d)
print('Found isolated ships')
i=randint(0,len(iso_ships))
print('showing for MMSI: ',iso_ships.MMSI[i])
min_time=iso_ships.start_time[i]
max_time=iso_ships.end_time[i]
ais_test=df[(df['TIMESTAMP UTC']>=min_time) & (df['TIMESTAMP UTC']<=max_time)]
#check_status_in_ais(ziggly2.MMSI[i],min_time,max_time,rad1)
simp_spectrogram(hydrophone_idx,min_time,max_time,fmin,fmax)
# df1 = df[(df['distance(in km) axial']<rad1)]
# df2 = df[(df['distance(in km) axial']>inner_rad2) & (df['distance(in km) axial']<outer_rad2)]
#ais_test= df[(df['TIMESTAMP UTC']>=min_time) & (df['TIMESTAMP UTC']<=max_time)]
rad1=rad1 * 1000
inner_rad2=inner_rad2*1000
print('Starting to plot now')
if fn=='scatter':
fig = px.scatter_mapbox(ais_test,lat="LAT" ,lon="LON",mapbox_style='carto-positron',hover_name="TIMESTAMP UTC",
hover_data= ['SPEED (KNOTSx10)','VESSEL TYPE','distance(in km)'],color = "VESSEL TYPE")
elif fn=='density':
fig = px.density_mapbox(ais_test,lat="LAT" ,lon="LON",mapbox_style='stamen-terrain',hover_name="TIMESTAMP UTC",
hover_data= ['SPEED (KNOTSx10)','VESSEL TYPE','distance(in km)'],radius=3,
color_continuous_scale= [
[0.0, "green"],
[0.5, "green"],
[0.51111111, "yellow"],
[0.71111111, "yellow"],
[0.71111112, "red"],
[1, "red"]])
fig2 = px.scatter_mapbox(lat=[lat],lon=[lon],mapbox_style='carto-positron')
# fig2.update_traces(marker_symbol='bus',selector=dict(type='scattermapbox'))
fig2.update_traces(marker = {'size': 8, 'color':'black','opacity':0.9})
lat_arr1,lon_arr1=get_circle_coordinates(rad1,lat,lon)
lat_arr2,lon_arr2=get_circle_coordinates(inner_rad2,lat,lon)
fig4=px.scatter_mapbox(lat=lat_arr1,lon=lon_arr1,mapbox_style='carto-positron')
fig4.update_traces(marker = {'size': 4, 'color':'black','opacity':0.9})
fig5=px.scatter_mapbox(lat=lat_arr2,lon=lon_arr2,mapbox_style='carto-positron')
fig5.update_traces(marker = {'size': 4, 'color':'black','opacity':0.9})
fig6 = px.line_mapbox( lat=[lat,lat_arr1[0]], lon=[lon,lon_arr1[0]],hover_name=[rad1,rad1])
fig7 = px.line_mapbox( lat=[lat,lat_arr2[10]], lon=[lon,lon_arr2[10]],hover_name=[inner_rad2,inner_rad2])
fig6.update_traces(marker = {'size': 8, 'color':'black','opacity':0.9})
fig7.update_traces(marker = {'size': 8, 'color':'black','opacity':0.9})
fig8 = go.Figure(go.Scattermapbox(
mode = "markers+lines",
lat = [lat,lat_arr1[0]],
lon = [lon,lon_arr1[0]],
marker = {'size': 4,'color':'black'}))
fig9 = go.Figure(go.Scattermapbox(
mode = "markers+lines",
lat = [lat,lat_arr2[10]],
lon = [lon,lon_arr2[10]],
marker = {'size': 4,'color':'black'}))
fig.add_trace(fig2.data[0])
fig.add_trace(fig4.data[0])
fig.add_trace(fig5.data[0])
# fig.add_trace(fig6.data[0])
# fig.add_trace(fig7.data[0])
fig.add_trace(fig8.data[0])
fig.add_trace(fig9.data[0])
#fig.add_trace(fig4.data[0])
fig.update_layout(coloraxis_showscale=False,mapbox=dict(
bearing=0,
center=dict(
lat=lat,
lon=lon,
)))
fig.show()
def isolated_ais(ais,iso_ships,ud_vessel,inner_rad):
data=pd.DataFrame()
ud_vessel_=ud_vessel[['vessel','ud_group']]
for i in tqdm(range(len(iso_ships))):
min_time=iso_ships.start_time.iloc[i]
max_time=iso_ships.end_time.iloc[i]
idx=iso_ships.instance_id.iloc[i]
temp=ais[(ais['TIMESTAMP UTC']>=min_time) & (ais['TIMESTAMP UTC']<=max_time) & (ais['distance(in km)']<inner_rad)]
if len(temp)==0:
temp=data.iloc[[i-1]]
temp['instance_id']=idx
data=pd.concat([data,temp])
data=data.merge(ud_vessel_, how='left',left_on='VESSEL TYPE',right_on='vessel')
data.drop(columns=['vessel'],inplace=True)
return data
def ais_ping_distribution(ais,n=10,hist_show=False,bar_show=False):
ais = ais.sort_values(by=['MMSI','TIMESTAMP UTC'],ascending=True)
ais['prev TIMESTAMP UTC'] = ais.groupby(by='MMSI')['TIMESTAMP UTC'].shift(1)
ais_temp=ais.copy()
ais_temp.dropna(axis=0,how='any',subset=['prev TIMESTAMP UTC'],inplace=True)
ais_temp['ping_time']=(ais_temp['TIMESTAMP UTC'] - ais['prev TIMESTAMP UTC']).dt.total_seconds()/60
ships_pings=ais_temp.groupby(by=['VESSEL TYPE', 'MMSI']).agg({'ping_time': ['mean','min','max','median']}).reset_index()
ships_pings.columns=['VESSEL TYPE','MMSI','mean_ping_time','min_ping_time','max_ping_time','median_ping_time']
vessels_pings=ais_temp.groupby(by=['VESSEL TYPE']).agg({'MMSI': pd.Series.nunique,'ping_time': ['mean','min','max','median']}).reset_index()
vessels_pings.columns=['VESSEL TYPE','distinct count ships','mean_ping_time','min_ping_time','max_ping_time','median_ping_time']
vessels_pings=vessels_pings.sort_values(by='distinct count ships',ascending=False)
if(hist_show):
for vessel in vessels_pings['VESSEL TYPE'].unique()[:n]:
fig,ax= plt.subplots(2,2,figsize=(10,10))
fig.suptitle(' Distribution for {} vessel'.format(vessel), fontsize=20)
ax[0,0].hist(ships_pings['mean_ping_time'][ships_pings['VESSEL TYPE']==vessel],bins=10)
ax[0,1].hist(ships_pings['min_ping_time'][ships_pings['VESSEL TYPE']==vessel],bins=10)
ax[1,0].hist(ships_pings['max_ping_time'][ships_pings['VESSEL TYPE']==vessel],bins=10)
ax[1,1].hist(ships_pings['median_ping_time'][ships_pings['VESSEL TYPE']==vessel],bins=10)
if(bar_show):
fig,ax= plt.subplots(2,2,figsize=(20,20))
fig.suptitle(' Successive ping gaps by vessel type ', fontsize=20)
ax[0,0].bar(vessels_pings['VESSEL TYPE'].iloc[:n],vessels_pings['mean_ping_time'].iloc[:n])
ax[0,0].set_title('Mean')
ax[0,0].set_xticklabels(vessels_pings['VESSEL TYPE'].unique()[:n],rotation = 45)
ax[0,1].bar(vessels_pings['VESSEL TYPE'].iloc[:n],vessels_pings['min_ping_time'].iloc[:n])
ax[0,1].set_title('Min')
ax[0,1].set_xticklabels(vessels_pings['VESSEL TYPE'].unique()[:n],rotation = 45)
ax[1,0].bar(vessels_pings['VESSEL TYPE'].iloc[:n],vessels_pings['max_ping_time'].iloc[:n])
ax[1,0].set_title('Max')
ax[1,0].set_xticklabels(vessels_pings['VESSEL TYPE'].unique()[:n],rotation = 45)
ax[1,1].bar(vessels_pings['VESSEL TYPE'].iloc[:n],vessels_pings['median_ping_time'].iloc[:n])
ax[1,1].set_title('Median')
ax[1,1].set_xticklabels(vessels_pings['VESSEL TYPE'].unique()[:n],rotation = 45)
return ships_pings,vessels_pings
def extend_ais(hydrophone_idx,shared_link,local_link):
if hydrophone_idx==1:
hydro_file='Axial_Base'
elif hydrophone_idx==2:
hydro_file='Central_Caldera'
elif hydrophone_idx==3:
hydro_file='Eastern_Caldera'
elif hydrophone_idx==4:
hydro_file='Oregon_Slope'
elif hydrophone_idx==5:
hydro_file='Southern_Hydrate'
df_spec=pd.DataFrame()
df_audio=pd.DataFrame()
df_mseed=pd.DataFrame()
df_psds=pd.DataFrame()
df_psdpickle=pd.DataFrame()
dict_spec={'instance_id':'','spectrogram':''}
dict_audio={'instance_id':'','audio':''}
dict_mseed={'instance_id':'','mseed':''}
dict_psds={'instance_id':'','psd':''}
dict_psdpickle={'instance_id':'','psd_pickle':''}
#spectrogram
arr_spec=os.listdir(local_link+hydro_file+'/Spectrograms/')
for name in arr_spec:
file=name.split('.')[0]
new_file=file.split('_')[0]+'_'+file.split('_')[1]+'_'+file.split('_')[2]
# dst = local_link+hydro_file+'/Spectrograms/'+new_file+ '.jpg'
# src =local_link+hydro_file+'/Spectrograms/'+name # foldername/filename, if .py file is outside folder
# os.rename(src, dst)
dict_spec['instance_id']=file
dict_spec['spectrogram']=shared_link+'/Ships/'+hydro_file+'/Spectrograms/'+new_file+ '.png'
temp=pd.DataFrame([dict_spec])
df_spec=pd.concat([df_spec,temp])
#Audio
arr_audio=os.listdir(local_link+hydro_file+'/Audio/')
for name in arr_audio:
file=name.split('.')[0]
new_file=file.split('_')[0]+'_'+file.split('_')[1]+'_'+file.split('_')[2]
# dst = local_link+hydro_file+'/Audio/'+new_file+ '.wav'
# src =local_link+hydro_file+'/Audio/'+name # foldername/filename, if .py file is outside folder
# os.rename(src, dst)
dict_audio['instance_id']=file
dict_audio['audio']=shared_link+'/Ships/'+hydro_file+'/Audio/'+new_file+ '.wav'
temp=pd.DataFrame([dict_audio])
df_audio=pd.concat([df_audio,temp])
#Mseed
arr_mseed=os.listdir(local_link+hydro_file+'/Mseed/')
for name in arr_mseed:
file=name.split('.')[0]
new_file=file.split('_')[0]+'_'+file.split('_')[1]+'_'+file.split('_')[2]
# dst = local_link+hydro_file+'/Mseed/'+new_file+ '.mseed'
# src =local_link+hydro_file+'/Mseed/'+name # foldername/filename, if .py file is outside folder
# os.rename(src, dst)
dict_mseed['instance_id']=file
dict_mseed['mseed']=shared_link+'/Ships/'+hydro_file+'/Mseed/'+new_file+ '.mseed'
temp=pd.DataFrame([dict_mseed])
df_mseed=pd.concat([df_mseed,temp])
#PSDs
arr_psds=os.listdir(local_link+hydro_file+'/PSDs/')
for name in arr_psds:
file=name.split('.')[0]
new_file=file.split('_')[0]+'_'+file.split('_')[1]+'_'+file.split('_')[2]
# dst = local_link+hydro_file+'/PSDs/'+new_file+ '.jpg'
# src =local_link+hydro_file+'/PSDs/'+name # foldername/filename, if .py file is outside folder
# os.rename(src, dst)
dict_psds['instance_id']=file
dict_psds['psd']=shared_link+'/Ships/'+hydro_file+'/PSDs/'+new_file+ '.png'
temp=pd.DataFrame([dict_psds])
df_psds=pd.concat([df_psds,temp])
#PSD pickles
arr_psdpickle=os.listdir(local_link+hydro_file+'/PSD_pickles/')
for name in arr_psdpickle:
file=name.split('.')[0]
new_file=file.split('_')[0]+'_'+file.split('_')[1]+'_'+file.split('_')[2]
# dst = local_link+hydro_file+'/PSD_pickles/'+new_file+ '.pkl'
# src =local_link+hydro_file+'/PSD_pickles/'+name # foldername/filename, if .py file is outside folder
# os.rename(src, dst)
dict_psdpickle['instance_id']=file
dict_psdpickle['psd_pickle']=shared_link+'/Ships/'+hydro_file+'/PSD_pickles/'+new_file+ '.pkl'
temp=pd.DataFrame([dict_psdpickle])
df_psdpickle=pd.concat([df_psdpickle,temp])
return df_spec,df_audio,df_mseed,df_psds,df_psdpickle
def save_spectrogram_noship(i,hydrophone_idx,fmin,fmax,rad,hydro_file,hydro,df_time_grp):
min_time=df_time_grp.start_time.iloc[i]
max_time=df_time_grp.end_time.iloc[i]
str_starttime=min_time.strftime("%Y%m%d%H%M%S")
str_endtime=max_time.strftime("%Y%m%d%H%M%S")
df_time_grp['instance_id'].iloc[i]=hydro+'_'+str_starttime+'_'+str_endtime+'_'+str(i)
spectrogram=get_spectrogram_data(hydrophone_idx,min_time,max_time,fmin=fmin,fmax=fmax)
if (spectrogram==None):
print('data trace is none. Continuing to next')
pass
else:
if (spectrogram.values.shape[0]<2) | (spectrogram.values.shape[1]<2):
pass
else:
print(path_to_write)
#spectrogram.visualize(save_spec=True,plot_spec=False,filename=path_to_write+hydro+'/Spectrogram'+'/isolated_ships_'+str(inner_rad)+'_'+ str(outer_rad)+'_'+str(i)+'.png')
ooiplt.plot_spectrogram(spectrogram,plot=False,save=True,vmax=110,fmin=fmin,fmax=fmax,filename=path_to_write+hydro_file+'/Spectrograms'+'/'+hydro+'_'+str_starttime+'_'+str_endtime+'_'+str(i)+'.png')
def plot_psd(psd_obj, **kwargs):
"""
Plot a :class:`ooipy.hydrophone.basic.Psd` object using the
matplotlib package.
Parameters
----------
spec_obj : :class:`ooipy.hydrophone.basic.Psd`
Psd object to be plotted
kwargs :
See matplotlib doccumentation for list of arguments. Additional
arguments are
* plot : bool
If False, figure will be closed. Can save time if only
saving but not plotting is desired. Default is True
* save : bool
If True, figure will be saved under **filename**. Default is
False
* new_fig : bool
If True, matplotlib will create a new fugure. Default is
True
* filename : str
filename of figure if saved. Default is "spectrogram.png"
* xlabel_rot : int or float
rotation angle (deg) of x-labels. Default is 70
* fmin : int or float
minimum frequency. Default is 0
* fmax : int or float
maximum frequency. Default is 32000
* vmin : int or float
lower limit of level axis (colormap). Default is 20
* vmax : int or float
upper limit of level axis (colormap). Default is 80
* figsize : (int, int)
width and height of figure. Default is (16, 9)
* dpi : int
dots per inch, passed to matplotlib figure.savefig()
* fontsize : int
fontsize of saved plot, passed to matplotlib figure
"""
# check for keys
if "plot" not in kwargs:
kwargs["plot"] = True
if "save" not in kwargs:
kwargs["save"] = False
if "new_fig" not in kwargs:
kwargs["new_fig"] = True
if "filename" not in kwargs:
kwargs["filename"] = "psd.png"
if "title" not in kwargs:
kwargs["title"] = "PSD"
if "xlabel" not in kwargs:
kwargs["xlabel"] = "frequency"
if "xlabel_rot" not in kwargs:
kwargs["xlabel_rot"] = 0
if "ylabel" not in kwargs:
kwargs["ylabel"] = "spectral level"
if "fmin" not in kwargs:
kwargs["fmin"] = 0.0
if "fmax" not in kwargs:
kwargs["fmax"] = 32000.0
if "vmin" not in kwargs:
kwargs["vmin"] = 20.0
if "vmax" not in kwargs:
kwargs["vmax"] = 80.0
if "figsize" not in kwargs:
kwargs["figsize"] = (16, 9)
if "dpi" not in kwargs:
kwargs["dpi"] = 100
if "fontsize" not in kwargs:
kwargs["fontsize"] = 22
# set backend for plotting/saving:
if not kwargs["plot"]:
matplotlib.use("Agg")
font = {"size": kwargs["fontsize"]}
matplotlib.rc("font", **font)
if len(psd_obj.freq) != len(psd_obj.values):
f = np.linspace(0, len(psd_obj.values) - 1, len(psd_obj.values))
else:
f = psd_obj.freq
# plot PSD object
if kwargs["new_fig"]:
fig, ax = plt.subplots(figsize=kwargs["figsize"])
ax.set_xscale('log') # new addition
plt.plot(f, psd_obj.values)
plt.ylabel(kwargs["ylabel"])
plt.xlabel(kwargs["xlabel"])
plt.xlim([kwargs["fmin"], kwargs["fmax"]])
plt.ylim([kwargs["vmin"], kwargs["vmax"]])
plt.xticks(rotation=kwargs["xlabel_rot"])
plt.title(kwargs["title"])
plt.grid(True)
if kwargs["save"]:
plt.savefig(kwargs["filename"], bbox_inches="tight", dpi=kwargs["dpi"])
if not kwargs["plot"]:
plt.close(fig)