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cmap_auto.py
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cmap_auto.py
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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime
import matplotlib.patches as mpatch
from matplotlib.patches import Rectangle
from prelude import make_base_dir, closest_encounter
sta_f = open('input/all_station_crossing_db.txt','r')
min_lon = -150.7
max_lon = -147.3
min_lat = 62.2
max_lat = 65.3
cenlat = (min_lat + max_lat)/2
cenlon = (min_lon + max_lon)/2
# Load the seismometer location data
seismo_data = pd.read_csv('input/all_sta.txt', sep="|")
seismo_latitudes = seismo_data['Latitude']
seismo_longitudes = seismo_data['Longitude']
sta = seismo_data['Station']
def f(y):
return -1.0/np.cos((y)*np.pi/180)
def rf(y):
return (-180*np.arccos(-1/y))/np.pi
# Loop through each station in text file that we already know comes within 2km of the nodes
for line in sta_f.readlines():
val = line.split(',')
date = val[0]
flight = val[1]
station = val[6]
flight_file = '/scratch/irseppi/nodal_data/flightradar24/' + date + '_positions/' + date + '_' + flight + '.csv'
flight_data = pd.read_csv(flight_file, sep=",")
flight_latitudes = flight_data['latitude']
flight_longitudes = flight_data['longitude']
time = flight_data['snapshot_id']
head = flight_data['heading']
for l in range(len(time)):
if str(time[l]) == str(val[3]):
for t in range(len(sta)):
if sta[t] == station:
try:
clat, clon, dist, ctime = closest_encounter(flight_latitudes, flight_longitudes, l, time, seismo_latitudes[t], seismo_longitudes[t])
dist = dist*1000
# Create a figure with two subplots side by side
fig, axs = plt.subplots(1, 2)
fig.subplots_adjust(wspace=0.5) # Adjust the spacing between subplots
y =[clat, seismo_latitudes[t]]
x = [clon, seismo_longitudes[t]]
yy = sum(y)/len(y)
xx = sum(x)/len(x)
if dist < 100:
minl = np.round((xx - 0.001),4)
maxl = np.round((xx + 0.001),4)
minla = np.round((yy - 0.001),4)
maxla = np.round((yy + 0.001),4)
axs[1].set_xticks(np.arange(minl, maxl, 0.0005))
axs[1].set_yticks(np.arange(minla, maxla, 0.0005))
else:
minl = np.round((xx - 0.025), 2)
maxl = np.round((xx + 0.025), 2)
minla = np.round((yy - 0.025), 2)
maxla = np.round((yy + 0.025), 2)
axs[1].set_xticks(np.arange(minl, maxl, 0.01))
axs[1].set_yticks(np.arange(minla, maxla, 0.01))
axs[0].set_xticks(np.arange(min_lon, max_lon, 1))
axs[0].set_yticks(np.arange(min_lat, max_lat, 1))
axs[0].set_xscale('function', functions=(f,rf))
axs[1].set_xscale('function', functions=(f,rf))
axs[0].grid(True,linestyle='dotted',color='gray')
axs[1].grid(True,linestyle='dotted',color='gray')
axs[0].scatter(seismo_longitudes, seismo_latitudes, c='#e41a1c', s = 3, label='seismometers')
axs[0].plot(flight_longitudes, flight_latitudes, '-', c='#377eb8', lw=1, ms = 1, label='flight path')
for i in range(1, len(flight_latitudes)-1, int(len(flight_latitudes)/5)):
direction = np.arctan2(flight_latitudes[i+1] - flight_latitudes[i], flight_longitudes[i+1] - flight_longitudes[i])
m = (flight_latitudes[i+1] - flight_latitudes[i])/(flight_longitudes[i+1] - flight_longitudes[i])
b = flight_latitudes[i] - m*flight_longitudes[i]
axs[0].quiver((flight_latitudes[i]-b)/m, flight_latitudes[i], np.cos(direction), np.sin(direction), angles='xy', color='#377eb8', headwidth = 5)
# Set labels and title
axs[0].set_xlim(min_lon, max_lon)
axs[0].set_ylim(min_lat, max_lat)
axs[0].tick_params(axis='both', which='major', labelsize=12)
heading = np.deg2rad(head[l])
rect = Rectangle((minl, minla), 0.1, 0.06, ls="-", lw = 1, ec = 'k', fc="none", zorder=2.5)
axs[0].add_patch(rect)
axs[1].plot(x,y, '--', c='#ff7f00')
# Draw the zoomed in map on the second subplot
axs[1].plot(flight_longitudes, flight_latitudes, c='#377eb8',linestyle ='dotted')
axs[1].set_xlim(minl, maxl)
axs[1].set_ylim(minla, maxla)
axs[1].tick_params(axis='both', which='major', labelsize=9)
axs[1].ticklabel_format(useOffset=False, style='plain')
if len(axs[1].get_xticklabels()) > 4:
axs[1].set_xticklabels([round(x, 4) for x in axs[1].get_xticks()], rotation=20, fontsize=9)
m = (flight_latitudes[l+1] - flight_latitudes[l])/(flight_longitudes[l+1] - flight_longitudes[l])
b = flight_latitudes[l] - m*flight_longitudes[l]
direction = np.arctan2(flight_latitudes[l+1] - flight_latitudes[l], flight_longitudes[l+1] - flight_longitudes[l])
axs[1].quiver(clon, clat, np.cos(direction), np.sin(direction), angles='xy', color='#377eb8', scale=7)
axs[1].quiver(clon, clat, np.cos(heading), np.sin(heading), angles='xy', scale = 7, color='#999999')
axs[1].scatter(clon, clat, c='#377eb8', s=50, zorder=3)
axs[1].scatter(seismo_longitudes[t], seismo_latitudes[t], c='#e41a1c', s=50, zorder=3)
axs[1].text(xx,yy, str(round(dist/1000, 3))+' km', fontsize=15, fontweight='bold')
# Draw dashed lines connecting the rectangle on the existing map to the zoomed-in map
con = mpatch.ConnectionPatch(xyA=(minl, minla), xyB=(maxl, minla), coordsA="data", coordsB="data", axesA=axs[1], axesB=axs[0], color="black", linestyle="--")
fig.add_artist(con)
con = mpatch.ConnectionPatch(xyA=(minl, maxla), xyB=(maxl, maxla), coordsA="data", coordsB="data", axesA=axs[1], axesB=axs[0], color="black", linestyle="--")
fig.add_artist(con)
BASE_DIR = '/scratch/irseppi/nodal_data/plane_info/map_all/' + date + '/'+flight+'/'+station+'/'
make_base_dir(BASE_DIR)
plt.savefig('/scratch/irseppi/nodal_data/plane_info/map_all/'+ date + '/'+flight+'/'+station+'/map_'+flight+'_' + str(time[l]) + '.png')
plt.close()
except:
print(date + '/'+flight+'/'+station+'/map_'+flight+'_' + str(time[l]))
else:
continue
else:
continue