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map_file_coordinates.py
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map_file_coordinates.py
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"""
Author: Hana Hourston @hhourston
Date: Mar. 1, 2023
Plot IOS files on a map:
Bottle (BOT and CHE)
CTD
Current meter (CUR)
Acoustic Doppler current profiler (ADCP)
Thermosalinograph (TOB)
Weather station (ANE)
"""
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
DTYPES = ['BOT_CHE', 'CTD', 'ADCP', 'CUR', 'TOB', 'ANE']
ARCTIC_PAC_LAT_CUTOFF = 68
def map_all(wdir, left_lon, bot_lat, right_lon, top_lat):
colours = ['r', 'g', 'b', 'chartreuse', 'm', 'c']
symbols = ['o', 'v', '.', 'P', '^', 's']
# Make the map
# area_thresh=1000 means don't plot coastline features less
# than 1000 km^2 in area.
# m = Basemap(llcrnrlon=left_lon, llcrnrlat=bot_lat,
# urcrnrlon=right_lon, urcrnrlat=top_lat,
# projection='lcc', area_thresh=1000.,
# resolution='c', lat_0=0.5 * (bot_lat + top_lat),
# lon_0=0.5 * (left_lon + right_lon)) # lat_0=53.4, lon_0=-129.0)
# Azimuthal equidistant projection
# width = 28000000; lon_0 = -105; lat_0 = 40
width = 23000000; lon_0 = -140; lat_0 = 20
m = Basemap(width=width,height=width,projection='aeqd',
lat_0=lat_0,lon_0=lon_0)
fig = plt.figure(num=None, figsize=(8, 6), dpi=100)
m.drawcoastlines(linewidth=0.2)
m.drawmapboundary(fill_color='white')
m.fillcontinents(color='0.8')
# m.drawrivers()
# 20 degree graticule.
m.drawparallels(np.arange(-80,81,20))
m.drawmeridians(np.arange(-180,181,20))
# parallels = np.arange(bot_lat, top_lat, 0.5) # parallels = np.arange(48., 54, 0.2), parallels = np.linspace(bot_lat, top_lat, 10)
# m.drawparallels(parallels, labels=[True, False, True, False]) # draw parallel lat lines
# meridians = np.arange(left_lon, right_lon, 0.5)
# m.drawmeridians(meridians, labels=[False, False, False, True])
# Iterate through the dtypes
for dtype, col, sym in zip(DTYPES, colours, symbols):
# for dtype, col, sym in zip(['ADCP'], ['g'], ['.']):
print(dtype)
infile = os.path.join(wdir, f'csv_file_download_list_{dtype}.csv')
dfin = pd.read_csv(infile)
x, y = m(dfin.LON.values, dfin.LAT.values)
if dtype == 'BOT_CHE':
dtype = 'BOTTLE'
# elif dtype == 'TOB':
# dtype = 'TSG'
m.scatter(x, y, marker=sym, color=col, s=5, label=dtype)
plt.legend(loc='lower right')
plt.tight_layout()
# Save the fig and close
plt.savefig(os.path.join(wdir, 'ios-wp-file-map.png'))
plt.close(fig)
return
def pacific_map_bounds():
# left_lon, bot_lat, right_lon, top_lat
return [-180, 20, -115, 70]
def map_pacific():
"""
file_list: contains the input file names, may only be one
"""
left_lon, bot_lat, right_lon, top_lat = pacific_map_bounds()
# Make the map
# area_thresh=1000 means don't plot coastline features less
# than 1000 km^2 in area.
# Use Equidistant Cylindrical Projection
# The simplest projection, just displays the world in
# latitude/longitude coordinates.
m = Basemap(llcrnrlon=left_lon, llcrnrlat=bot_lat,
urcrnrlon=right_lon, urcrnrlat=top_lat,
projection='cyl', # area_thresh=1000.,
resolution='h')
m.drawcoastlines(linewidth=0.2)
m.drawmapboundary(fill_color='white')
m.fillcontinents(color='0.8')
# Graticule.
# labels = [left,right,top,bottom]
m.drawparallels(np.arange(20,71,10), labels=[True,False,False,False])
m.drawmeridians(np.arange(-180,-114,15), labels=[True,False,False,True])
return m
def subset_pacific_data(df):
left_lon, bot_lat, right_lon, top_lat = pacific_map_bounds()
# Restrict the upper lat more to not include arctic
return ((df.LON.values >= left_lon) &
(df.LON.values <= right_lon) &
(df.LAT.values >= bot_lat) &
(df.LAT.values <= ARCTIC_PAC_LAT_CUTOFF))
def subset_arctic_data(df):
return df.LAT.values >= ARCTIC_PAC_LAT_CUTOFF
def map_arctic():
"""
file_list: contains the input file names, may only be one
"""
# Find a good map projection to use
# nplaea: North Polar Lambert Azimuthal Projection
m = Basemap(projection='nplaea', boundinglat=65,
lon_0=230, resolution='h') # , area_thresh=1000.)
m.drawcoastlines(linewidth=0.2)
m.drawmapboundary(fill_color='white')
m.fillcontinents(color='0.8')
# Graticule.
# labels = [left,right,top,bottom]
m.drawparallels(np.arange(60,81,10), labels=[True,False,False,False])
m.drawmeridians(np.arange(-180,181,30), labels=[False,True,False,True])
return m
def do_map(file_list: list, region: str, dtype: str, output_dir: str):
# Initialize figure
plt.clf() # Clear any active plots
fig = plt.figure(num=None, figsize=(8, 6), dpi=100)
# Load the data
dfall = pd.DataFrame()
for f in file_list:
dfin = pd.read_csv(f)
dfall = pd.concat((dfall, dfin))
if region.lower() == 'pacific':
# Make map instance
m = map_pacific()
# Subset the data to the region
subsetter = subset_pacific_data(dfall)
elif region.lower() == 'arctic':
# Make map instance
m = map_arctic()
# Subset the data to the region
subsetter = subset_arctic_data(dfall)
# Scatter the points
start_year = min(pd.to_datetime(
dfall.loc[subsetter, 'START TIME(UTC)']).dt.year)
end_year = max(pd.to_datetime(
dfall.loc[subsetter, 'END TIME(UTC)']).dt.year)
num_files = sum(subsetter)
x, y = m(dfall.LON.values[subsetter], dfall.LAT.values[subsetter])
# alpha defines the opacity
m.scatter(x, y, marker='o', color='r', alpha=0.25)
plt.title(
f'{start_year} - {end_year} {dtype} {region.capitalize()} Files {num_files}')
plt.tight_layout()
# Save the fig and close
plt.savefig(os.path.join(
output_dir, f'all_{region.lower()}_{dtype}_map_hires.png'))
plt.close(fig)
return
def map_regions(wdir):
"""
dtype: 'BOT and CHE' or 'CTD' or 'ADCP and CUR'
region: 'pacific' or 'arctic'
"""
for dtype in ['BOT_CHE', 'CTD', 'ADCP']:
if dtype == 'ADCP':
# Combine with current meter
file_list = [
os.path.join(wdir, f'csv_file_download_list_{dtype}.csv'),
os.path.join(wdir, f'csv_file_download_list_CUR.csv')
]
dtype = 'ADCP + Current Meter'
else:
file_list = [
os.path.join(wdir, f'csv_file_download_list_{dtype}.csv')
]
if dtype == 'BOT_CHE':
dtype = 'Bottle'
print(dtype)
do_map(file_list, 'pacific', dtype, wdir)
do_map(file_list, 'arctic', dtype, wdir)
return
input_dir = 'C:\\Users\\HourstonH\\Documents\\sopo2023\\lu_poster\\'
# left_lon, bot_lat, right_lon, top_lat = [-180, -80, 180, 90]
# map_all(input_dir, left_lon, bot_lat, right_lon, top_lat)
map_regions(input_dir)