def global_predstorm_noaa(world_image):
fig = plt.figure(1,figsize=[15, 10])
fig.set_facecolor('black')
#axis PREDSTORM + OVATION
ax1 = plt.subplot(1, 2, 1, projection=ccrs.Orthographic(global_plot_longitude, global_plot_latitude))
#axis NOAA
ax2 = plt.subplot(1, 2, 2, projection=ccrs.Orthographic(global_plot_longitude, global_plot_latitude))
#load NOAA nowcast
noaa_img, dt = oup.aurora_now()
for ax in [ax1,ax2]:
ax.gridlines(linestyle='--',alpha=0.5,color='white')
#ax.coastlines(alpha=0.5,zorder=3)
#ax.add_feature(land_50m, color='darkgreen')
#ax.add_feature(land_50m, color='darkslategrey')
#ax.add_feature(carfeat.LAND,color='darkslategrey')
#ax.add_feature(carfeat.LAKES,color='navy')#,zorder=2,alpha=1)
#ax.add_feature(carfeat.OCEAN)#,zorder=2,alpha=1)
#ax.add_feature(ocean_50m,linewidth=0.5, color='navy')
#ax.add_feature(carfeat.BORDERS, alpha=0.5)#,zorder=2,alpha=0.5)
#ax.add_feature(carfeat.COASTLINE)#,zorder=2,alpha=0.5)
#ax.add_feature(carfeat.RIVERS)#,zorder=2,alpha=0.8)
#ax.add_feature(provinces_50m,alpha=0.5)#,zorder=2,alpha=0.8)
ax.stock_img()#alpha=0.2)
#ax.add_wmts(nightmap, layer)
#for testing with black background
#ax.background_patch.set_facecolor('k')
if ax==ax1:
ax.imshow(world_image, vmin=0, vmax=100, transform=crs, extent=mapextent, origin='lower', zorder=3, alpha=0.9, cmap=oup.aurora_cmap())
ax.add_feature(Nightshade(t0))
if ax==ax2:
ax.imshow(noaa_img, vmin=0, vmax=100, transform=crs, extent=mapextent, origin='lower', zorder=3, alpha=0.9, cmap=oup.aurora_cmap())
ax.add_feature(Nightshade(dt))
fig.text(0.01,0.92,'PREDSTORM aurora forecast '+t0.strftime('%Y-%m-%d %H:%M UT' )+ ' NOAA forecast '+dt.strftime('%Y-%m-%d %H:%M UT' ), color='white',fontsize=15)
fig.text(0.99,0.02,'C. Möstl / IWF-helio, Austria', color='white',fontsize=8,ha='right')
plt.tight_layout()
plot_Nhemi_comparison_filename='test/predstorm_aurora_real_Nhemi_'+t0.strftime("%Y_%m_%d_%H%M") +'.jpg'
fig.savefig(plot_Nhemi_comparison_filename,dpi=120,facecolor=fig.get_facecolor())
#plt.show()
print('Saved image: ',plot_Nhemi_comparison_filename)
def __init__(self, metric_name, date, decorations=True):
self.metric_name = metric_name
self.date = date
self.decorations = decorations
self.plotalpha = 0.35
self.fig = plt.figure(figsize=(16,24))
self.ax = plt.axes(
projection=ccrs.PlateCarree(),
frame_on=self.decorations,
)
self.ax.set_global()
if self.decorations:
self.ax.grid(linewidth=.5, color='black', alpha=0.25, linestyle='--')
self.ax.set_xticks([-180, -160, -140, -120,-100, -80, -60,-40,-20, 0, 20, 40, 60,80,100, 120,140, 160,180], crs=ccrs.PlateCarree())
self.ax.set_yticks([-80, -60,-40,-20, 0, 20, 40, 60,80], crs=ccrs.PlateCarree())
else:
self.ax.axis(False)
self.ax.outline_patch.set_visible(False)
self.ax.background_patch.set_visible(False)
self.ax.set_xmargin(0)
self.ax.set_ymargin(0)
self.ax.add_feature(cartopy.feature.NaturalEarthFeature('physical', 'land', '110m',
edgecolor='face',
facecolor=np.array((0xdd,0xdd,0xcc))/256.,
zorder=-1
)
)
self.ax.add_feature(Nightshade(self.date, alpha=0.08))
def test_nightshade_floating_point():
# Smoke test for clipping nightshade floating point values
date = datetime(1999, 12, 31, 12)
# This can cause an error with floating point precision if it is
# set to exactly -6 and arccos input is not clipped to [-1, 1]
Nightshade(date, refraction=-6.0, color='none')
def test_nightshade_image():
# Test the actual creation of the image
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
dt = datetime(2018, 11, 10, 0, 0)
ax.set_global()
ax.add_feature(Nightshade(dt, alpha=0.75))
def add_dn_terminator(self, **kwargs):
""" Adding day night terminator """
from cartopy.feature.nightshade import Nightshade
if self.plot_date:
ns_feature = Nightshade(self.plot_date, alpha=0.2)
super().add_feature(feature, **kwargs)
return
def _plot_orthographic_projection(latitude_sat, longitude_sat,
lat_footprint, lon_footprint, epoch):
"""This method plots the satellite track and footprint in the Orthographic projection.
INPUT:
------
latitude_sat = vector of latitudes of the satellite. It must be of the same size of longitude_sat. [deg]
longitude_sat = vector of longitudes of the satellite. It must be of the same size of latitude_sat. [deg]
lat_footprint = vector of latitudes of the boundary of the satellite footprint. [deg]
It must be of the same size of lon_footprint.
lon_footprint = vector of longitudes of the boundary of the satellite footprint. [deg]
It must be of the same size of lat_footprint.
epoch = epoch to display the nightshade on the map. It must be of type datetime.
"""
plt.figure()
ax = plt.axes(
projection=ccrs.Orthographic(longitude_sat[0], latitude_sat[0]))
points = ccrs.Orthographic(longitude_sat[0],
latitude_sat[0]).transform_points(
ccrs.Geodetic(), longitude_sat,
latitude_sat)
footprint_points = ccrs.Orthographic(longitude_sat[0],
latitude_sat[0]).transform_points(
ccrs.Geodetic(),
lon_footprint, lat_footprint)
ax.gridlines()
ax.coastlines()
ax.set_global()
ax.add_feature(Nightshade(epoch, alpha=0.4))
ax.plot(points[:, 0], points[:, 1], linewidth=2)
ax.plot(footprint_points[:, 0], footprint_points[:, 1], linewidth=2)
ax.plot(points[0, 0], points[0, 1], 'h', color='#1e1596', markersize=8)
def plot_clear(dtime, show=True):
"""
Plot an orthographic projection of clear area.
- dtime = datetime instance
- show = boolean: whether or not to display a plot
Either shows a plot and returns nothing, or returns the axis on which
the clear area is plotted, depending on the value of "show".
"""
print('plotting clear area')
# general earth setup
ax = plt.axes(projection=constants.ortho)
ax.add_feature(cartopy.feature.OCEAN, zorder=0)
ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black')
ax.add_feature(cartopy.feature.BORDERS, zorder=0, edgecolor='black')
ax.add_feature(Nightshade(dtime, alpha=0.2))
# add clear polys
clear_polys, _ = earth_data.get_clear(dtime)
print("adding", len(clear_polys), "clear polygons to plot")
ax.add_geometries(clear_polys, crs=constants.lonlat, facecolor='g')
# show the plot if desired
if show:
plt.show()
else:
# return the axis so we can plot more things overtop if we want
return ax
def main():
fig = plt.figure(figsize=[10, 5])
# We choose to plot in an Orthographic projection as it looks natural
# and the distortion is relatively small around the poles where
# the aurora is most likely.
# ax1 for Northern Hemisphere
ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.Orthographic(0, 90))
# ax2 for Southern Hemisphere
ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.Orthographic(180, -90))
img, crs, extent, origin, dt = aurora_forecast()
for ax in [ax1, ax2]:
ax.coastlines(zorder=3)
ax.stock_img()
ax.gridlines()
ax.add_feature(Nightshade(dt))
ax.imshow(img,
vmin=0,
vmax=100,
transform=crs,
extent=extent,
origin=origin,
zorder=2,
cmap=aurora_cmap())
plt.show()
def plot_poles(pos_ecf,
fig=None,
axarr=None,
show_night=True,
date=None,
show_aurora=True):
if axarr is None:
if fig is None:
fig = plt.figure(figsize=(10, 5))
# We choose to plot in an Orthographic projection as it looks natural
# and the distortion is relatively small around the poles where
# the aurora is most likely.
# ax1 for Northern Hemisphere
ax1 = fig.add_subplot(1, 2, 1, projection=ccrs.Orthographic(0, 90))
# ax2 for Southern Hemisphere
ax2 = fig.add_subplot(1, 2, 2, projection=ccrs.Orthographic(180, -90))
else:
ax1, ax2 = axarr
img, crs, extent, origin, dt = aurora_forecast()
lat, long = ecf_to_latlon(pos_ecf)
if show_aurora:
img, crs, extent, origin, dt = aurora_forecast()
for ax in [ax1, ax2]:
ax.coastlines(zorder=3)
ax.stock_img()
ax.gridlines()
ax.plot(long.deg, lat.deg, transform=ccrs.Geodetic())
if show_night:
if date is None:
date = datetime(2020, 6, 15, 12)
ax.set_title("Night time shading for {}".format(date))
ax.add_feature(Nightshade(date, alpha=0.2))
if show_aurora:
ax.imshow(
img,
vmin=0,
vmax=100,
transform=crs,
extent=extent,
origin=origin,
zorder=2,
cmap=aurora_cmap(),
)
return ax1, ax2
def _plot_plate_carree_projection(latitude_sat, longitude_sat,
lat_footprint, lon_footprint, epoch):
"""This method plots the satellite track and footprint in the Plate Carree projection.
INPUT:
------
latitude_sat = vector of latitudes of the satellite. It must be of the same size of longitude_sat. [deg]
longitude_sat = vector of longitudes of the satellite. It must be of the same size of latitude_sat. [deg]
lat_footprint = vector of latitudes of the boundary of the satellite footprint. [deg]
It must be of the same size of lon_footprint.
lon_footprint = vector of longitudes of the boundary of the satellite footprint. [deg]
It must be of the same size of lat_footprint.
epoch = epoch to display the nightshade on the map. It must be of type datetime.
"""
# The longitude array is cut in two piece according to positive values and negative.
# Positive longitude values will be plotted separately from the negative one.
longitude_positive = np.ma.masked_where(longitude_sat < 0,
longitude_sat)
longitude_negative = np.ma.masked_where(longitude_sat >= 0,
longitude_sat)
# The longitude array is cut in two piece according to positive values and negative.
# Positive longitude values will be plotted separately from the negative one.
longitude_footprint_positive = np.ma.masked_where(
lon_footprint < 0, lon_footprint)
longitude_footprint_negative = np.ma.masked_where(
lon_footprint >= 0, lon_footprint)
plt.figure()
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_global()
ax.gridlines()
ax.add_feature(Nightshade(epoch, alpha=0.4))
ax.set_xticks([-180, -120, -60, 0, 60, 120, 180],
crs=ccrs.PlateCarree())
ax.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())
lon_formatter = LongitudeFormatter(dateline_direction_label=True)
lat_formatter = LatitudeFormatter()
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.plot(longitude_positive, latitude_sat, color='#1f77b4', linewidth=2)
ax.plot(longitude_negative, latitude_sat, color='#1f77b4', linewidth=2)
ax.plot(longitude_footprint_positive,
lat_footprint,
color='#ff7f0e',
linewidth=2)
ax.plot(longitude_footprint_negative,
lat_footprint,
color='#ff7f0e',
linewidth=2)
ax.plot(longitude_sat[0],
latitude_sat[0],
'h',
color='#1e1596',
markersize=8)
def is_light(lons, lats, dtime, night=None):
"""
Tests whether or not arrays of longitudes and latitudes are dark.
- lons = longitudes, array
- lats = latitudes, array
- dtime = datetime instance
- night = Nightshade instance if you have one, otherwise one will be made
"""
if night is None:
night = Nightshade(dtime)
dark_poly = list(night.geometries())[0]
mesh_lons, mesh_lats = np.meshgrid(lons, lats)
# check if each pair of points are inside the dark polygon
result = np.empty_like(mesh_lons, dtype=int)
for index, lon in np.ndenumerate(mesh_lons):
lat = mesh_lats[index]
# convert coordinate systems to the dark_poly's coords
x, y = night.crs.transform_point(lon, lat, c.lonlat)
light = not dark_poly.contains(shapely.geometry.Point(x, y))
result[index] = light
return result
def GlobePlotter(solarlat, solarlon, lattrace, lontrace):
midlat = np.mean(lattrace)
midlon = np.mean(lontrace)
projection = ccrs.Orthographic(midlon, midlat)
ax = plt.axes(projection=projection)
file_location = path.abspath(path.dirname(__file__))
path_to_pic = file_location + '/images/2k_mars.jpg'
source_proj = ccrs.PlateCarree()
ax.imshow(imread(path_to_pic), origin='upper', transform=source_proj,
extent=[-180, 180, -90, 90])
# plot the ground feature labels
df = pd.read_csv('mars.csv', encoding='latin-1')
df.sort_values(by=["Diameter"], inplace=True, ascending=False)
minlen = 30 # plot only the largest 30 objects on the surface of mars. Maybe update to the the best known
df = df.head(min(minlen, len(df)))
for index, row in df.iterrows():
text = row['Clean_Feature_Name']
x, y, s = row['Center_Longitude'], row['Center_Latitude'], row['Diameter']
ax.text(x, y, text, transform=ccrs.PlateCarree(),
ha='left', va='center', fontsize=8, color='#ebc334')
ax.scatter(x, y, transform=ccrs.PlateCarree(),
s=10, color='#ebc334', edgecolor='None', lw=0)
# adjusting the gridlines to fit the occtrace location [10 MIGHT BE TO COARSE]
# STILL NEED BETTER SOLUTION
minlat = (np.floor(np.min(lattrace)/10))*10
maxlat = (np.ceil(np.max(lattrace)/10))*10
minlon = (np.floor(np.min(lontrace)/10))*10
maxlon = (np.ceil(np.max(lontrace)/10))*10
track = sgeom.LineString(zip(lontrace, lattrace))
ax.add_geometries([track], source_proj,
edgecolor='#C852C8', linewidth=8, alpha=0.5, facecolor='none')
gl = ax.gridlines(crs=ccrs.PlateCarree(), linewidth=2,
color='k', alpha=0.2, linestyle='--', draw_labels=True)
gl.top_labels = False
gl.left_labels = False
gl.right_labels = False
gl.xlines = True
gl.ylocator = mticker.FixedLocator(
list(np.arange(int(minlat), int(maxlat), 10)))
gl.xlocator = mticker.FixedLocator(
list(np.arange(int(minlon), int(maxlon), 10)))
gl.xlabel_style = {'color': 'k'}
# <<<< this involves a custom nightshade function, go into the cartopy libs and edit to include lat lon
ax.add_feature(Nightshade(solarlat, solarlon))
# plot occultation track
ax.add_geometries([track], source_proj,
edgecolor='#C852C8', linewidth=8, alpha=0.5, facecolor='none')
plt.show()
def __init__(self, metric_name, date, decorations=True, basemaps=True, alpha=0.35, centered=None):
self.metric_name = metric_name
self.date = date
self.decorations = decorations
self.basemaps = basemaps
self.plotalpha = alpha
if centered is None:
self.proj = ccrs.PlateCarree()
figsize=(16,24)
else:
self.proj = ccrs.AzimuthalEquidistant(centered[0], centered[1])
figsize=(12,12)
self.fig = plt.figure(figsize=figsize)
self.ax = plt.axes(
projection=self.proj,
frame_on=self.decorations,
facecolor='white',
)
self.ax.spines['geo'].set_edgecolor('#666')
self.ax.set_global()
if self.decorations:
if centered is None:
self.ax.grid(True, visible=True, linewidth=.5, color='black', alpha=0.25, linestyle='--')
self.ax.set_xticks([-180, -160, -140, -120,-100, -80, -60,-40,-20, 0, 20, 40, 60,80,100, 120,140, 160,180], crs=ccrs.PlateCarree())
self.ax.set_yticks([-80, -60,-40,-20, 0, 20, 40, 60,80], crs=ccrs.PlateCarree())
else:
self.ax.gridlines(
linewidth=.5,
color='black',
alpha=0.25,
linestyle='--',
xlocs=[-180, -160, -140, -120,-100, -80, -60,-40,-20, 0, 20, 40, 60,80,100, 120,140, 160,180],
ylocs=[-80, -60,-40,-20, 0, 20, 40, 60,80],
)
else:
self.ax.axis(False)
self.ax.outline_patch.set_visible(False)
self.ax.background_patch.set_visible(False)
self.ax.set_xmargin(0)
self.ax.set_ymargin(0)
if basemaps:
self.ax.add_feature(cartopy.feature.NaturalEarthFeature('physical', 'land', '110m',
edgecolor='face',
facecolor=np.array((0xdd,0xdd,0xcc))/256.,
zorder=-1
)
)
self.ax.add_feature(Nightshade(self.date, alpha=0.08))
def get_light_mask(dtime):
"""
Get 2D array associated with MERRA grid points, 1 = light 0 = dark,
at a given time (dtime = datetime instance)
"""
light_mask_file = dtime.strftime(c.light_mask_format)
if not exists(light_mask_file):
print("Creating light_mask, this might take 30 seconds")
# this is the polygon that is the "shady" area
night = Nightshade(dtime)
# get all points not within the dark_poly
light_mask = is_light(c.lons, c.lats, dtime, night)
# save result to file
np.save(light_mask_file, light_mask)
return np.load(light_mask_file)
def plot_planisphere(pos_ecf,
fig=None,
ax=None,
show_night=True,
date=None,
show_aurora=True):
if ax is None:
if fig is None:
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson())
# make the map global rather than have it zoom in to
# the extents of any plotted data
# ax.set_global()
ax.stock_img()
ax.coastlines()
ax.gridlines()
lat, long = ecf_to_latlon(pos_ecf)
ax.plot(long.deg, lat.deg, transform=ccrs.Geodetic())
if show_night:
if date is None:
date = datetime(2020, 6, 15, 12)
ax.set_title("Night time shading for {}".format(date))
ax.add_feature(Nightshade(date, alpha=0.2))
if show_aurora:
img, crs, extent, origin, dt = aurora_forecast()
ax.imshow(
img,
vmin=0,
vmax=100,
transform=crs,
extent=extent,
origin=origin,
zorder=2,
cmap=aurora_cmap(),
)
return ax
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 29 13:03:47 2018
@author: ahmed
"""
import datetime
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.feature.nightshade import Nightshade
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())
date = datetime.datetime(2019, 1, 13, 11)
ax.set_title('Night time shading for {}'.format(date))
ax.stock_img()
ax.add_feature(Nightshade(date, alpha=0.2))
plt.savefig("cartopy1.png"); plt.savefig("cartopy1.pdf")
plt.show()
def europe_canada_predstorm(world_image,dt,counter,colormap_input):
plt.close('all')
#Night map from VIIRS
#https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-EarthatNight4Products
#nightmap = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'
#define extent of the produced world maps - defined as: west east south north
mapextent=[-180,180,-90,90]
#not so good: layer='VIIRS_Black_Marble'
#better but takes time
#layer = 'VIIRS_CityLights_2012'
#need to set alpha linearly increasing in the colormap
cmap = plt.get_cmap(colormap_input) # Choose colormap
my_cmap = cmap(np.arange(cmap.N)) # Get the colormap colors
my_cmap[:,-1] = np.linspace(0.3, 1, cmap.N) # Set alpha
my_cmap = ListedColormap(my_cmap) # Create new colormap
land_50m = carfeat.NaturalEarthFeature('physical', 'land', '50m',
edgecolor='k',
facecolor=carfeat.COLORS['land'])
ocean_50m = carfeat.NaturalEarthFeature('physical', 'ocean', '50m',
edgecolor='k',
facecolor='steelblue')#carfeat.COLORS['water'])
provinces_50m = carfeat.NaturalEarthFeature('cultural',
'admin_1_states_provinces_lines',
'50m',
facecolor='none',edgecolor='black')
crs=ccrs.PlateCarree()
#16/9 ration for full hd output
fig = plt.figure(2,figsize=[16, 9],dpi=100)
fig.set_facecolor('black')
plt.cla()
# ax1 Europe
ax1 = plt.subplot(1, 2, 2, projection=ccrs.Orthographic(0, 60),position=[0.51,0.05,0.48,0.9])#[left, bottom, width, height]
# ax2 northern America
ax2 = plt.subplot(1, 2, 1, projection=ccrs.Orthographic(-100, 60), position=[0.01,0.05,0.48,0.9])
#define map extents
canada_east = -65
canada_west = -135
canada_north = 75
canada_south = 20
europe_east = 50
europe_west = -20
europe_north = 75
europe_south = 30
#plot one axis after another
for ax in [ax1, ax2]:
#ax.gridlines(linestyle='--',alpha=0.2,color='white')
#ax.coastlines(alpha=0.5,zorder=3)
#ax.add_feature(carfeat.LAND,zorder=2,alpha=1)
#ax.add_feature(land_50m, color='darkgreen')
#ax.add_feature(land_50m, color='darkslategrey')
ax.add_feature(land_50m, color='dimgrey')
ax.add_feature(provinces_50m,alpha=0.5)#,zorder=2,alpha=0.8)
#ax.add_feature(carfeat.COASTLINE)#,zorder=2,alpha=0.5)
#ax.add_feature(carfeat.OCEAN,color='mediumslateblue')#,zorder=2,alpha=1)
ax.add_feature(ocean_50m, color='darkblue')
#ax.add_feature(carfeat.RIVERS)#,zorder=2,alpha=0.8)
ax.add_feature(carfeat.LAKES,color='darkblue')#,zorder=2,alpha=1) color navy?
ax.add_feature(carfeat.BORDERS, alpha=0.5)#,zorder=2,alpha=0.5)
ax.add_feature(Nightshade(dt))
#ax.stock_img()
#ax.add_wmts(nightmap, layer)
ax.imshow(world_image, vmin=0.001, vmax=5, transform=crs, extent=mapextent, origin='lower', zorder=3, alpha=0.8, cmap=my_cmap)
if ax == ax1: ax.set_extent([europe_west, europe_east, europe_south, europe_north])
if ax == ax2: ax.set_extent([canada_west, canada_east, canada_south, canada_north])
fig.text(0.01,0.92,'PREDSTORM aurora forecast '+dt.strftime('%Y-%m-%d %H:%M UT' ), color='white',fontsize=15)
fig.text(0.99,0.02,'C. Möstl / IWF-helio, Austria', color='white',fontsize=8,ha='right')
#save as image with timestamp in filename
plot_europe_canada_filename='results/forecast_europe_canada/predstorm_aurora_real_'+dt.strftime("%Y_%m_%d_%H%M") +'.jpg'
fig.savefig(plot_europe_canada_filename,dpi=120,facecolor=fig.get_facecolor())
#save as movie frame
framestr = '%05i' % (counter)
fig.savefig('results/frames_europe_canada/aurora_'+framestr+'.jpg',dpi=120,facecolor=fig.get_facecolor())
#plt.show()
print('Saved image: ',plot_europe_canada_filename)
def plot_map_and_stations(fig,
gridspec,
station_locations,
station_names,
station_plot_dict,
region_corners=None,
ss_location=None,
date=None):
map_ax = fig.add_subplot(gridspec,
projection=ccrs.NearsidePerspective(
-95, 57, 15000000))
map_ax.stock_img()
# Plot the border of region
if region_corners is not None:
map_ax.plot(np.linspace(region_corners[0][0], region_corners[1][0],
10),
np.ones(10) * region_corners[0][1],
'--',
color='gray',
transform=ccrs.PlateCarree())
map_ax.plot(np.ones(10) * region_corners[1][0],
np.linspace(region_corners[0][1], region_corners[1][1],
10),
'--',
color='gray',
transform=ccrs.PlateCarree())
map_ax.plot(np.linspace(region_corners[0][0], region_corners[1][0],
10),
np.ones(10) * region_corners[1][1],
'--',
color='gray',
transform=ccrs.PlateCarree())
map_ax.plot(np.ones(10) * region_corners[0][0],
np.linspace(region_corners[0][1], region_corners[1][1],
10),
'--',
color='gray',
transform=ccrs.PlateCarree())
# plot all stations
map_ax.plot(station_locations[:, 0],
station_locations[:, 1],
'.',
color='black',
transform=ccrs.PlateCarree())
# plot special stations
for entry in station_plot_dict:
map_ax.plot(station_locations[entry['indices'], 0],
station_locations[entry['indices'], 1],
'.',
color=entry['color'],
transform=ccrs.PlateCarree(),
label=entry['name'])
if entry['station names']:
for j in entry['indices']:
map_ax.text(station_locations[j, 0] - 1,
station_locations[j, 1] - 1,
station_names[j],
horizontalalignment='right',
transform=ccrs.PlateCarree())
map_ax.legend()
if ss_location is not None:
map_ax.plot(ss_location[0],
ss_location[1],
'rx',
transform=ccrs.PlateCarree())
if date is not None:
map_ax.add_feature(Nightshade(date, alpha=0.2))
def plot_data_globe_colour(station_readings,
t,
list_of_stations=None,
ortho_trans=(0, 0)):
if np.all(list_of_stations == None):
list_of_stations = station_readings.station
if np.all(ortho_trans == (0, 0)):
ortho_trans = svg.auto_ortho(list_of_stations)
station_coords = svg.csv_to_coords()
num_stations = len(list_of_stations)
x = np.zeros(num_stations)
y = np.zeros(num_stations)
u = np.zeros(num_stations)
v = np.zeros(num_stations)
i = 0
for station in list_of_stations:
x[i] = station_coords.longitude.loc[dict(station=station)]
y[i] = station_coords.latitude.loc[dict(station=station)]
u[i] = station_readings.measurements.loc[dict(station=station,
time=t,
component="E")]
v[i] = station_readings.measurements.loc[dict(station=station,
time=t,
component="N")]
i += 1
fig = plt.figure(figsize=(20, 20))
ax = fig.add_subplot(1,
1,
1,
projection=ccrs.Orthographic(
ortho_trans[0], ortho_trans[1])) #(long, lat)
ax.add_feature(cfeature.OCEAN, zorder=0)
ax.add_feature(cfeature.LAND, zorder=0, edgecolor='grey')
ax.add_feature(cfeature.BORDERS, zorder=0, edgecolor='grey')
ax.add_feature(cfeature.LAKES, zorder=0)
ax.add_feature(Nightshade(t))
ax.set_global()
ax.gridlines()
ax.scatter(x, y, color="k", transform=ccrs.Geodetic()) #plots stations
colours = np.ones((num_stations, 3))
for i in range(num_stations):
colours[i, 0] = station_coords.longitude.loc[dict(
station=list_of_stations[i])] / 360
colours[i, 2] = (station_coords.latitude.loc[dict(
station=list_of_stations[i])] - 10) / 80
colours = plc.hsv_to_rgb(colours)
ax.quiver(
x,
y,
u,
v,
transform=ccrs.PlateCarree(), #plots vector data
width=0.002,
color=colours)
mytime = t.strftime('%Y.%m.%d %H:%M')
plt.title("%s" % mytime, fontsize=30)
return fig
def main():
SPH_ORDER = 3
SPH_WEIGHT = 0.8
RESIDUAL_WEIGHT = 0.9
plt.clf()
with urllib.request.urlopen(os.getenv("METRICS_URI")) as url:
data = json.loads(url.read().decode())
df = json_normalize(data)
#delete low confidence measurements
df = df.drop(df[pd.to_numeric(df.cs) == 0].index)
df = df.drop(df[df[metric] == 0].index)
df = df.dropna(subset=[metric])
#filter out data older than 1hr
age = (dt.datetime.now() -
dt.timedelta(minutes=60)).strftime('%Y-%m-%d %H:%M')
df = df.loc[df['time'] > age]
df['time'] = pd.to_datetime(df.time)
df['time'] = df['time'].dt.strftime('%Y-%m-%d %H:%M')
df[[metric]] = df[[metric]].apply(pd.to_numeric)
df[['station.longitude']] = df[['station.longitude']].apply(pd.to_numeric)
df[['station.latitude']] = df[['station.latitude']].apply(pd.to_numeric)
df['longitude_radians'] = df['station.longitude'] * np.pi / 180.
df['latitude_radians'] = (df['station.latitude'] + 90) * np.pi / 180.
df[['cs']] = df[['cs']].apply(pd.to_numeric)
df.loc[df['cs'] == -1, 'cs'] = 80
df[['cs']] = df[['cs']] / 100.
df['transformed'] = np.log(df[metric])
df = df.dropna(subset=[metric])
df.loc[df['station.longitude'] > 180,
'station.longitude'] = df['station.longitude'] - 360
df.sort_values(by=['station.longitude'], inplace=True)
sph = []
alpha = []
for n in range(SPH_ORDER):
for m in range(0 - n, n + 1):
sph.append(
real_sph(m, n, df['longitude_radians'].values,
df['latitude_radians'].values).reshape((-1, 1)))
alpha.append(0 if n == 0 else 0.005)
sph = np.hstack(sph)
wls_model = sm.WLS(df['transformed'].values, sph, df['cs'].values)
wls_result = wls_model.fit_regularized(alpha=np.array(alpha), L1_wt=0.6)
coeff = wls_result.params
numcols, numrows = 360, 180
loni = np.linspace(-180, 180, numcols)
lati = np.linspace(-90, 90, numrows)
theta = loni * np.pi / 180.
phi = (lati + 90) * np.pi / 180.
zi = np.zeros((len(phi), len(theta)))
theta, phi = np.meshgrid(theta, phi)
df['pred'] = np.zeros(len(df))
coeff_idx = 0
for n in range(SPH_ORDER):
for m in range(0 - n, n + 1):
sh = real_sph(m, n, theta, phi)
weight = 1 if n == 0 else SPH_WEIGHT
zi = zi + weight * coeff[coeff_idx] * sh
df['pred'] = df['pred'] + weight * np.real(
coeff[coeff_idx] *
real_sph(m, n, df['longitude_radians'].values,
df['latitude_radians'].values))
coeff_idx = coeff_idx + 1
df['residual'] = df['transformed'] - df['pred']
#plot data
loni, lati = np.meshgrid(loni, lati)
x, y, z = sph_to_xyz(df['station.longitude'].values,
df['station.latitude'].values)
t = df['residual'].values
stdev = 0.7 - 0.5 * df['cs']
gp = rbf.gauss.gpiso(rbf.basis.se, (0.0, 0.7, 0.8))
gp_cond = gp.condition(np.vstack((x, y, z)).T, t, sigma=stdev)
xxi, yyi, zzi = sph_to_xyz(loni, lati)
xyz = np.array([xxi.flatten(), yyi.flatten(), zzi.flatten()]).T
resi, sd = gp_cond.meansd(xyz)
resi = resi.reshape(xxi.shape)
sd = sd.reshape(xxi.shape)
zi = zi + RESIDUAL_WEIGHT * resi
zi = np.exp(zi)
fig = plt.figure(figsize=(16, 24))
ax = plt.axes(projection=ccrs.PlateCarree())
levels = 16
cmap = plt.cm.get_cmap('viridis')
cmap.set_under(cmap(1e-5))
cmap.set_over(cmap(1 - 1e-5))
norm = matplotlib.colors.Normalize(clip=False)
if metric == 'mufd':
levels = [
3, 3.5, 4, 4.6, 5.3, 6.1, 7, 8.2, 9.5, 11, 12.6, 14.6, 16.9, 19.5,
22.6, 26, 30
]
norm = matplotlib.colors.LogNorm(3.5, 30, clip=False)
mycontour = plt.contourf(loni,
lati,
zi,
levels,
cmap=cmap,
extend='both',
transform=ccrs.PlateCarree(),
alpha=0.3,
norm=norm)
ax.add_feature(
cartopy.feature.NaturalEarthFeature('physical',
'land',
'110m',
edgecolor='face',
facecolor=np.array(
(0xdd, 0xdd, 0xcc)) / 256.,
zorder=-1))
ax.set_global()
ax.add_feature(Nightshade(date, alpha=0.08))
ax.grid(linewidth=.5, color='black', alpha=0.25, linestyle='--')
ax.set_xticks([
-180, -160, -140, -120, -100, -80, -60, -40, -20, 0, 20, 40, 60, 80,
100, 120, 140, 160, 180
],
crs=ccrs.PlateCarree())
ax.set_yticks([-80, -60, -40, -20, 0, 20, 40, 60, 80],
crs=ccrs.PlateCarree())
for index, row in df.iterrows():
lon = float(row['station.longitude'])
lat = float(row['station.latitude'])
alpha = 0.2 + 0.6 * row['cs']
ax.text(lon,
lat,
int(row[metric] + 0.5),
fontsize=9,
ha='left',
transform=ccrs.PlateCarree(),
alpha=alpha,
bbox={
'boxstyle': 'circle',
'alpha': alpha - 0.1,
'color': cmap(norm(row[metric])),
'mutation_scale': 0.5,
})
CS2 = plt.contour(mycontour,
linewidths=.5,
alpha=0.66,
levels=mycontour.levels[1::1])
prev = None
levels = []
for lev in CS2.levels:
if prev is None or '%.0f' % (lev) != '%.0f' % (prev):
levels.append(lev)
prev = lev
plt.clabel(CS2,
levels,
inline=True,
fontsize=10,
fmt='%.0f',
use_clabeltext=True)
# Make a colorbar for the ContourSet returned by the contourf call.
cbar = plt.colorbar(mycontour,
fraction=0.03,
orientation='horizontal',
pad=0.02,
format=matplotlib.ticker.ScalarFormatter())
#cbar.set_label('MHz') #TODO add unit
cbar.add_lines(CS2)
plt.title(metric + ' ' + str(now))
# df = df[['station.name', 'time', metric, 'cs', 'altitude', 'station.longitude', 'station.latitude']]
# df = df.round(2)
# the_table = table(ax, df,
# bbox=[0,-1.25,1,1],
# cellLoc = 'left',)
# for key, cell in the_table.get_celld().items():
# cell.set_linewidth(.25)
plt.tight_layout()
plt.savefig('/output/{}.png'.format(metric), dpi=180, bbox_inches='tight')
plt.savefig('/output/{}.svg'.format(metric), dpi=180, bbox_inches='tight')
# Convert matplotlib contour to geojson
"""
place = np.array([0, np.pi / 2., np.pi, 1.5 * np.pi])
lab = ['', 'dawn', 'noon', 'dusk']
plt.xticks(place, lab) #, )
ax1.set_ylim(0, 10)
ax1.set_xlim(0, 2. * np.pi)
ax1.set_frame_on(False)
#ax1.axes.get_xaxis().set_visible(False)
#ax1.axes.get_yaxis().set_visible(False)
#This will be the geographic plot
ax = fig.add_subplot(1, 2, 2, projection=ccrs.Orthographic(180, -90))
ax.coastlines(zorder=3)
ax.stock_img()
ax.gridlines()
ax.add_feature(Nightshade(dt))
for i in range(len(payload)):
print('starting to plot payload ', payload[i])
#Here we are just defining the different colors to be used. This could also be done outside the loop in an array and then use mkrcolor[i] in the plotting portion, This was just an easy way for me to make sure I knew which payload was which color... The program would run faster if done the way mentioned above, but it's pretty quick already. Save to change when doing a larger project.
if payload[i] == '2I':
mkrcolor = cdk #'navy'
#Make sure we have the right sting. If I were to do this for a longer period of time, the same would need to be done for the month ect....
if day < 10:
strday = '0' + np.str(np.int(day))
else:
strday = np.str(day)
#Read in the data and get the relavent data bits.
payload_ephm = 'data/bar_' + payload[
i] + '_l2_ephm_201401' + strday + '_v04.cdf'
data = pycdf.CDF(payload_ephm)
def plot_ISS(trackdf, nhours_plot=1):
fig = plt.figure(figsize=(20, 10))
projection_type = ccrs.PlateCarree()
ax = fig.add_subplot(1, 1, 1, projection=projection_type)
ax.coastlines(resolution='50m', linewidth=0.3)
timestart = trackdf.index[-1] - np.timedelta64(nhours_plot, 'h')
track = trackdf.iloc[:1000]
jump_indices = list(track[abs(track['londiff']) > 330].index)
for i in range(len(jump_indices)):
if i == 0:
subtrack = track[:jump_indices[0]]
else:
subtrack = track[jump_indices[i - 1]:jump_indices[i]].iloc[1:]
track_points = sgeom.LineString(zip(subtrack['Lon'], subtrack['Lat']))
ax.add_geometries([track_points], projection_type,facecolor='none', \
edgecolor='r',alpha=0.8)
#plot the locations as points
ax.plot(subtrack['Lon'],
subtrack['Lat'],
'ko',
transform=projection_type,
markersize=0.5)
#plot the final subtrack
subtrack = track[jump_indices[i]:].iloc[1:]
track_points = sgeom.LineString(zip(subtrack['Lon'], subtrack['Lat']))
ax.add_geometries([track_points], projection_type,facecolor='none', \
edgecolor='r',alpha=0.8)
#plot the locations as points
ax.plot(subtrack['Lon'],
subtrack['Lat'],
'ko',
transform=projection_type,
markersize=0.5)
#Get current location
current_lon, current_lat = getISSloc()
ax.plot(current_lon,
current_lat,
'bo',
transform=projection_type,
markersize=20)
#Get current time
date = datetime.utcnow()
ax.set_title('ISS history and position for {}'.format(date))
ax.stock_img()
ax.add_feature(Nightshade(date, alpha=0.2))
#plt.show()
plt.savefig('Latest_ISS_plot.pdf', dpi=200)
#img, crs, extent, origin, dt = aurora_forecast()
fig.set_facecolor((0, 0, 0))
url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'
layer = 'VIIRS_CityLights_2012'
for ax in [ax1, ax2]:
ax.coastlines(zorder=3, color='grey', alpha=0.9)
ax.add_feature(carfeat.BORDERS, zorder=3, alpha=0.9)
#ax.stock_img()
#takes long!!
#ax.add_wmts(url, layer)
ax.gridlines(alpha=0.3)
ax.add_feature(Nightshade(dtovation), alpha=0.9)
#ax.imshow(img, vmin=0, vmax=100, transform=crs, extent=extent, origin=origin, zorder=2, cmap=aurora_cmap())
ax.imshow(pimg,
vmin=0,
vmax=100,
transform=crs,
extent=extent,
origin=origin,
zorder=2,
cmap=aurora_cmap())
plt.show()
#plt.tight_layout()
fig.savefig('predstorm_aurora_pred_' + dtovation.strftime("%Y_%m_%d_%H%M") +
'.png',
dpi=300)
""" import pygeode as pyg, numpy as np, pylab as pyl from cartopy import crs as ccrs import cartopy from cartopy.feature.nightshade import Nightshade from datetime import datetime as dt lat = pyg.gausslat(60) lon = pyg.regularlon(120) x = pyg.sin(2*np.pi * lon / 180.) * pyg.exp(-(lat - 30)**2 / (2*10**2)) y = pyg.sin(2*np.pi * lon / 180.) * pyg.exp(-(lat + 40)**2 / (2*10**2)) pyl.ioff() prj = dict(central_longitude = 60) #ax = pyg.plot.CartopyAxes(projection = 'NearsidePerspective', prj_args = prj) map = dict(projection = 'NearsidePerspective', prj_args = prj) #map = dict(projection = 'LambertConformal', prj_args = prj) cl = pyg.cldict(0.1, nozero=True) ax = pyg.vcontour(x, map = map, **cl) ax.add_feature(cartopy.feature.OCEAN) ax.add_feature(Nightshade(dt.utcnow())) ax.setp(title = '') pyl.ion() ax.render(2) ax.ax.tissot(facecolor='orange', alpha=0.8)
plt.plot([inv_lon, inv_lon], [inv_lat, inv_lat],
'bo',
markersize=radar_marker_size,
transform=ccrs.Geodetic(),
label="INV")
# Add RISR to the map
plt.plot([risr_lon, risr_lon], [risr_lat, risr_lat],
'ro',
markersize=radar_marker_size,
transform=ccrs.Geodetic(),
label="RISR",
zorder=3)
date_time = datetime.utcnow()
ax.add_feature(Nightshade(date_time, alpha=0.25))
# ax.set_title("Night time shading for " + str(date_time) + " UT")
ax.set_title("One-and-one-half-hop Geometry Check")
# Try to plot RKN's FoV # RKN is id 65
ranges = [0, 100]
rkn_beam_corners_lats, rkn_beam_corners_lons = radar_fov(65, coords='geo')
rkn_fan_shape = rkn_beam_corners_lons.shape
inv_beam_corners_lats, inv_beam_corners_lons = radar_fov(64, coords='geo')
inv_fan_shape = inv_beam_corners_lons.shape
# plot all the RKN beam boundary lines
for beam_line in range(rkn_fan_shape[1]):
def ovation_global_north(wic,dt,colormap_input,max_level, outputdir,longitude_bound,equatorial_bound):
'''
plots the ovation aurora on the northern hemisphere from a polar view, makes movie frames
wic is a world image cube with ovation results 512x1024
dt are the datetimes for each frame
colormap_input is the colormap
max_level is the maximum level for the colorbar
'''
#plotting parameters
#-100 for North America, +10 or 0 for Europe
global_plot_longitude=-100
#global_plot_longitude=0
global_plot_latitude=90
provinces_50m = carfeat.NaturalEarthFeature('cultural',
'admin_1_states_provinces_lines',
'50m',
facecolor='none',edgecolor='black')
#define extent of the produced world maps - defined as: west east south north
mapextent=[-180,180,-90,90]
#need to set alpha linearly increasing in the colormap so that it blends into background for
#small values
cmap = plt.get_cmap(colormap_input) # Choose colormap
my_cmap = cmap(np.arange(cmap.N)) # Get the colormap colors
my_cmap[:,-1] = np.linspace(0, 1, cmap.N) # Set alpha
my_cmap = ListedColormap(my_cmap) # Create new colormap
crs=ccrs.PlateCarree()
fig = plt.figure(2,figsize=[12, 12],dpi=80)
fig.set_facecolor('black')
fig.text(0.99,0.01,'C. Möstl / IWF-helio, Austria', color='white',fontsize=10,ha='right',va='bottom')
ax = plt.subplot(1, 1, 1, projection=ccrs.Orthographic(global_plot_longitude, global_plot_latitude))
ax.background_patch.set_facecolor('k')
gl=ax.gridlines(linestyle='--',alpha=0.5,color='white') #make grid
gl.n_steps=100 #make grid finer
ax.stock_img()
#ax.coastlines('50m',color='black',alpha=0.5)
ax.coastlines('50m',color='white',alpha=0.9)
ax.add_feature(provinces_50m,alpha=0.2) #,zorder=2,alpha=0.8)
ax.add_feature(carfeat.BORDERS, alpha=0.2) #,zorder=2,alpha=0.5)
#these are calls that create the first object to be removed from the plot with each frame
txt=fig.text(0.5,0.92,'')
border=ax.add_feature(Nightshade(dt[0])) #add day night border
img=ax.imshow(wic[:,:,0])
bound_e=ax.plot(0,color='k') #equatorial boundary
bound_v=ax.plot(0,color='k') #equatorial boundary
for i in np.arange(0,np.size(dt)):
print('global movie frame',i)
#plt.cla() #clear axes
txt.set_visible(False) #clear previous plot title
#plot title with time
txt=fig.text(0.5,0.92,'PREDSTORM aurora '+dt[i].strftime('%Y-%m-%d %H:%M UT'), color='white',fontsize=15, ha='center')
img.remove() #remove previous wic
border.remove() #remove previous nightshade
bound_e[0].remove() #remove equatorial boundary
bound_v[0].remove() #remove view line
border=ax.add_feature(Nightshade(dt[i])) #add day night border
img=ax.imshow(wic[:,:,i], vmin=0.01, vmax=max_level, transform=crs, extent=mapextent, origin='lower', zorder=3, alpha=0.8, cmap=my_cmap) #aurora
bound_e=ax.plot(longitude_bound,equatorial_bound[i,:],transform=crs,color='k',alpha=0.8) #equatorial boundary
#***cut view line at longitudes which are in daylight
bound_v=ax.plot(longitude_bound,equatorial_bound[i,:]-8,transform=crs,color='r',linestyle='--',alpha=0.8) #viewing line after Case et al. 2016
#save as image with timestamp in filename
#plot_Nhemi_filename='results/forecast_global/predstorm_aurora_real_Nhemi_'+dt.strftime("%Y_%m_%d_%H%M") +'.jpg'
#fig.savefig(plot_Nhemi_filename,dpi=150,facecolor=fig.get_facecolor())
#save as movie frame
framestr = '%05i' % (i)
fig.savefig('results/'+outputdir+'/frames_global/aurora_'+framestr+'.jpg',dpi=150,facecolor=fig.get_facecolor())
def plotCartoMap(latlim=None, lonlim=None, parallels=None, meridians=None,
pole_center_lon=0, figsize=(12, 8), terrain=False, ax=False,
projection='stereo', title='', resolution='110m',
states=True, grid_linewidth=0.5, grid_color='black',
grid_linestyle='--', background_color=None, border_color='k',
figure=False, nightshade=False, ns_dt=None, ns_alpha=0.1,
apex=False, igrf=False, date=None,
mlat_levels=None, mlon_levels=None, alt_km=0.0,
mlon_colors='blue', mlat_colors='red', mgrid_width=1,
mgrid_labels=True, mgrid_fontsize=12, mlon_cs='mlon',
incl_levels=None, decl_levels=None, igrf_param='incl',
mlon_labels=True, mlat_labels=True, mgrid_style='--',
label_colors='k',
decl_colors='k', incl_colors='k'):
if lonlim is None:
if projection in ['southpole', 'northpole']:
lonlim = [-180, 180]
else:
lonlim = [-40, 40]
if latlim is None:
if projection is'southpole':
latlim = [-90, 0]
elif projection is 'northpole':
latlim = [90, 0]
else:
latlim = [0, 75]
STATES = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
if not ax:
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
if projection == 'stereo':
ax = plt.axes(projection=ccrs.Stereographic(central_longitude=(sum(lonlim) / 2)))
elif projection == 'merc':
ax = plt.axes(projection=ccrs.Mercator())
elif projection == 'plate':
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=(sum(lonlim) / 2)))
elif projection == 'lambert':
ax = plt.axes(projection=ccrs.LambertConformal(central_longitude=(sum(lonlim) / 2),
central_latitude=(sum(latlim) / 2)))
elif projection == 'mollweide':
ax = plt.axes(projection=ccrs.Mollweide(central_longitude=(sum(lonlim) / 2)))
elif projection == 'northpole':
ax = plt.axes(projection=ccrs.NorthPolarStereo())
ax.set_boundary(circle, transform=ax.transAxes)
# polarLim(ax, projection)
elif projection == 'southpole':
ax = plt.axes(projection=ccrs.SouthPolarStereo())
ax.set_boundary(circle, transform=ax.transAxes)
# polarLim(ax, projection)
if background_color is not None:
ax.background_patch.set_facecolor(background_color)
ax.set_title(title)
ax.coastlines(color=border_color, resolution=resolution) # 110m, 50m or 10m
if states:
ax.add_feature(STATES, edgecolor=border_color)
ax.add_feature(cfeature.BORDERS, edgecolor=border_color)
if terrain:
ax.stock_img()
if nightshade:
assert ns_dt is not None
assert ns_alpha is not None
ax.add_feature(Nightshade(ns_dt, ns_alpha))
# Draw Parallels
if projection == 'merc' or projection == 'plate':
if isinstance(meridians, np.ndarray):
meridians = list(meridians)
if isinstance(parallels, np.ndarray):
parallels = list(parallels)
gl = ax.gridlines(crs=ccrs.PlateCarree(), color=grid_color, draw_labels=False,
linestyle=grid_linestyle, linewidth=grid_linewidth)
if meridians is None:
gl.xlines = False
else:
if len(meridians) > 0:
gl.xlocator = mticker.FixedLocator(meridians)
gl.xlabels_bottom = True
else:
gl.ylines = False
if parallels is None:
gl.ylines = False
else:
if len(parallels) > 0:
gl.ylocator = mticker.FixedLocator(parallels)
# ax.yaxis.set_major_formatter(LONGITUDE_FORMATTER)
gl.ylabels_left = True
else:
gl.ylines = False
else:
gl = ax.gridlines(crs=ccrs.PlateCarree(), color=grid_color, draw_labels=False,
linestyle=grid_linestyle, linewidth=grid_linewidth)
if meridians is None:
gl.xlines = False
else:
# if isinstance(meridians, np.ndarray):
# meridians = list(meridians)
# ax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)
# ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)
# if isinstance(meridians, np.ndarray):
# meridians = list(meridians)
gl.xlocator = mticker.FixedLocator(meridians)
# else:
# gl.xlines = False
if parallels is not None:
if isinstance(parallels, np.ndarray):
parallels = list(parallels)
gl.ylocator = mticker.FixedLocator(parallels)
else:
gl.ylines = False
# Geomagnetic coordinates @ Apex
if apex:
if date is None:
date = datetime(2017, 12, 31, 0, 0, 0)
assert isinstance(date, datetime)
A = ap.Apex(date=date)
# Define levels and ranges for conversion
if mlon_cs == 'mlt':
if mlon_levels is None:
mlon_levels = np.array([])
mlon_range = np.arange(0, 24.1, 0.1)
elif isinstance(mlon_levels, bool):
if mlon_levels == False:
mlon_levels = np.array([])
mlon_range = np.arange(0, 24.1, 0.1)
else:
mlon_range = np.arange(mlon_levels[0], mlon_levels[-1] + 0.1, 0.1)
else:
if mlon_levels is None:
mlon_levels = np.array([])
mlon_range = np.arange(-180, 180, 0.5)
elif isinstance(mlon_levels, bool):
if mlon_levels == False:
mlon_levels = np.array([])
mlon_range = np.arange(-180, 181, 0.1)
else:
mlon_range = np.arange(mlon_levels[0], mlon_levels[0] + 362, 0.1)
if mlat_levels is None:
mlat_levels = np.arange(-90, 90.1, 5)
mlat_range = np.arange(mlat_levels[0], mlat_levels[-1] + 0.1, 0.1)
# Do meridans
for mlon in mlon_levels:
MLON = mlon * np.ones(mlat_range.size)
if mlon_cs == 'mlt':
y, x = A.convert(mlat_range, MLON, 'mlt', 'geo', datetime=date)
else:
y, x = A.convert(mlat_range, MLON, 'apex', 'geo')
# Plot meridian
inmap = np.logical_and(x >= lonlim[0], x <= lonlim[1])
if np.sum(inmap) > 10:
ax.plot(np.unwrap(x, 180), np.unwrap(y, 90), c=mlon_colors,
lw=mgrid_width, linestyle=mgrid_style,
transform=ccrs.PlateCarree())
# Labels
if mlon_labels:
ix = np.argmin(abs(y - np.mean(latlim)))
mx = x[ix] - 1 if mlon >= 10 else x[ix] - 0.5
my = np.mean(latlim)
if np.logical_and(mx >= lonlim[0], mx <= lonlim[1]):
if mlon_cs == 'mlt' and mlon != 0:
ax.text(mx, my, str(int(mlon)), color=label_colors,
fontsize=14, backgroundcolor='white',
transform=ccrs.PlateCarree())
elif mlon_cs != 'mlt' and mlon != 360:
ax.text(mx, my, str(int(mlon)), color=label_colors,
fontsize=14, backgroundcolor='white',
transform=ccrs.PlateCarree())
# Do parallels
for mlat in mlat_levels:
MLAT = mlat * np.ones(mlon_range.size)
if mlon_cs == 'mlt':
gy, gx = A.convert(MLAT, mlon_range, 'mlt', 'geo', datetime=date)
else:
gy, gx = A.convert(MLAT, mlon_range, 'apex', 'geo', datetime=date)
inmap = np.logical_and(gy >= latlim[0], gy <= latlim[1])
if np.sum(inmap) > 2:
ax.plot(np.unwrap(gx, 180), np.unwrap(gy, 90), c=mlat_colors,
lw=mgrid_width, linestyle=mgrid_style,
transform=ccrs.PlateCarree())
# Labels
if mlat_labels:
ix = np.argmin(abs(gx - np.mean(lonlim)))
mx = np.mean(lonlim)
my = gy[ix] - 0.5
if np.logical_and(mx >= lonlim[0], mx <= lonlim[1]) and \
np.logical_and(my >= latlim[0], my <= latlim[1]):
ax.text(mx, my, str(int(mlat)), color=label_colors,
fontsize=14, backgroundcolor='white',
transform=ccrs.PlateCarree())
if igrf:
glon = np.arange(lonlim[0] - 40, lonlim[1] + 40.1, 0.5)
glat = np.arange(-90, 90 + 0.1, 0.5)
longrid, latgrid = np.meshgrid(glon, glat)
mag = igrf12.gridigrf12(t=date, glat=latgrid, glon=longrid, alt_km=alt_km)
if decl_levels is not None:
z = mag.decl.values
for declination in decl_levels:
ax.contour(longrid, latgrid, z, levels=declination,
colors=decl_colors, transform=ccrs.PlateCarree())
if incl_levels is not None:
z = mag.incl.values
for inclination in incl_levels:
ax.contour(longrid, latgrid, z, levels=declination,
colors=incl_colors, transform=ccrs.PlateCarree())
# Set Extent
ax.set_extent([lonlim[0], lonlim[1], latlim[0], latlim[1]], crs=ccrs.PlateCarree())
if 'fig' in locals():
return fig, ax
else:
return ax
def process_gcn(payload, root):
if write_event:
event_log = open(logfile, 'a+')
# Respond only to 'test' events.
# VERY IMPORTANT! Replace with the following code
# to respond to only real 'observation' events.
print("starting process")
print(datetime.utcnow())
if write_event:
event_log.write("starting process")
event_log.write('\n')
event_log.write(root.attrib['role'])
event_log.write('\n')
print(root.attrib['role'])
""" Uncomment when we only want to do real observations
if root.attrib['role'] != 'observation':
return
"""
# Read all of the VOEvent parameters from the "What" section.
params = {elem.attrib['name']:
elem.attrib['value']
for elem in root.iterfind('.//Param')}
# Respond only to 'CBC' events. Change 'CBC' to "Burst'
# to respond to only unmodeled burst events.
print(params['Group'])
if write_event:
event_log.write(params['Group'])
event_log.write('\n')
if params['Group'] != 'CBC':
print('not CBC')
return
if params['AlertType'] != 'Preliminary':
print('not Preliminary')
return
if 'Pkt_Ser_Num' in params: # Test events send same event twice, only choose first one.
check = float(params['Pkt_Ser_Num'])
if check > 1.1:
return
""" COMPLETE TO DEFINE WHICH EVENTS WE ARE LOOKING FOR
if 'BNS' in params:
check = float(params['BNS'])
if check < 50.0:
print("not BNS event, skipping")
return
else:
print("not BNS event, skipping")
return
"""
# Print all parameters.
for key, value in params.items():
print(key, '=', value)
if write_event:
event_log.writelines(str(key + '=' + value))
if 'skymap_fits' in params:
# Read the HEALPix sky map and the FITS header.i
skymap, header = hp.read_map(params['skymap_fits'], h=True, verbose=False)
header = dict(header)
# Print some values from the FITS header.
print('Distance =', header['DISTMEAN'], '+/-', header['DISTSTD'])
if write_event:
event_log.writelines('Distance =' + str(header['DISTMEAN']) + '+/-' + str(header['DISTSTD']))
event_log.write('\n')
# write event information to SQL
datadict = {'gracedb_id': [params['GraceID']], 'link': [params['EventPage']], 'dist': [header['DISTMEAN']],
'dist_err': [header['DISTSTD']]}
sql_engine = sql.create_engine(connect_string)
headers = ['gracedb_id', 'link', 'dist', 'dist_err']
dtypes = {'gracedb_id': 'str', 'link': 'str', 'dist': 'float64', 'dist_err': 'float64'}
df = pd.DataFrame.from_dict(datadict)
df.to_sql('events', sql_engine, if_exists='append', index=False)
# get event ID back
query = "select * from events ORDER BY 'ID' DESC"
df = pd.read_sql_query(query, sql_engine)
event = df['ID'].iloc[-1]
credible_levels = find_greedy_credible_levels(skymap)
if send_slack:
slackmsg = ""
for key, value in params.items():
slackmsg = slackmsg + str(key) + '=' + str(value) + '\n'
slack_client.chat_postMessage(channel=SLACK_CHANNEL, text=slackmsg)
# get df of galaxies from SQL
query = "select * from galaxies"
galaxies = pd.read_sql_query(query, sql_engine)
gal_ipix = getPixel(skymap, galaxies['ra'], galaxies['dec'])
"""
offset = ephem.Observer()
offset.lat = '0.0'
offset.long = '180'
offset.date = datetime.utcnow()
offangle = float(offset.sidereal_time() * 180 / np.pi)
"""
if plot_map:
#ax = plt.axes(projection=ccrs.Mollweide(central_longitude=-1 * offangle))
ax = plt.axes(projection=ccrs.Mollweide())
ax.stock_img()
galaxies['credible'] = credible_levels[gal_ipix]
banana_galaxies = galaxies[(galaxies.credible <= prob_check)]
banana_galaxies.sort_values(by=['credible'], inplace=True)
#get list of observatories from SQL
query = "select * from observers"
observatories = pd.read_sql_query(query, sql_engine)
#Find Sun RA/DEC
sun = ephem.Sun()
#setup pyephem observer
telescope = ephem.Observer()
telescope.date = datetime.utcnow()
#create a copy of galaxy list in case it ends up depleted
galaxy_list = copy.deepcopy(banana_galaxies)
galaxy_list = galaxy_list.values.tolist()
galaxy_depleted = False
slackmsg = ''
matches = []
slack_count = 0
for x in range(0, len(observatories)):
extra_matches = []
# Check if all galaxies have been assigned, if so start assigning duplicates
if len(galaxy_list) == 0:
galaxy_list = copy.deepcopy(banana_galaxies)
galaxy_list = galaxy_list.values.tolist()
galaxy_depeleted = True
telescope.lat = str(observatories['lat'][x]).strip()
telescope.long = str(observatories['lon'][x]).strip()
sun.compute(telescope)
# if sun is up, observatory can be skipped
if check_sun:
if sun.alt > max_sun:
# print(str(observatories['Code'][x]) + " skipped, Sun is up")
continue
for i in range(0, len(galaxy_list)):
star = ephem.FixedBody()
star._ra = ephem.degrees(str(galaxy_list[i][2]))
star._dec = ephem.degrees(str(galaxy_list[i][3]))
star.compute(telescope)
# if the first object is more than 20 degrees below elevation, unlikely any objects will be higher than 45 degrees
if check_minalt:
if star.alt < min_alt:
# print(str(observatories['Code'][x]) + " skipped " + str(star.alt) + " is less than -20 degrees")
break
# assign galaxy to observer
if star.alt > max_alt:
matches.append(
{'event_id': event, 'obs_ID': observatories['ID'][x], 'galaxy_id': galaxy_list[i][0]})
# print(galaxy_list[i])
listing = WEBSITE_URL+'/observe?key=' + str(
observatories['key'][x]) + '&event=' + str(event) + '&obs=' + str(
observatories['ID'][x]) + '&gal=' + str(galaxy_list[i][0]) + '&ra=' + str(
galaxy_list[i][2]) + '&dec=' + str(galaxy_list[i][3]) + '&fov=' + str(observatories['fov'][x])
print(listing)
if write_event:
event_log.writelines(listing)
event_log.write('\n')
#only writing the first 5 urls to slack
if send_slack:
if slack_count < 5:
slack_count += 1
slackmsg = slackmsg + listing + '\n'
#find extra galaxies within FOV
j = i+1
y = []
print(len(galaxy_list[j:]))
if galinfov:
for z in range(j,len(galaxy_list)):
extra = ephem.FixedBody()
extra._ra = ephem.degrees(str(galaxy_list[z][2]))
extra._dec = ephem.degrees(str(galaxy_list[z][3]))
extra.compute(telescope)
sep = float(ephem.separation(star,extra))*180/np.pi
if sep < 0.5/2:
#if sep < observatories['fov'][x]/2:
extra_matches.append({'event_id': event, 'obs_ID': observatories['ID'][x], 'galaxy_id': galaxy_list[z][0]})
print(z,sep,ephem.separation(star,extra))
y.append(z)
if len(y)>1:
print(y)
y.reverse()
for k in y:
del galaxy_list[k]
# print(str(observatories['ID'][x]) + " " + observatories['name'][x].strip()[15:] + " at Lat:" +str(telescope.lat) + ",Lon:" + str(telescope.lon) + " gets galaxy " + str(galaxy_list[i][1]) + ' ra='+str(star.ra) + ' dec=' + str(star.dec) + ' alt='+str(star.alt))
del galaxy_list[i]
if plot_map:
plt.scatter(telescope.lon * 180 / np.pi, telescope.lat * 180 / np.pi, color='red',
transform=ccrs.Geodetic())
break
if len(extra_matches) > 0:
extra_matches = pd.DataFrame(extra_matches)
extra_matches.to_sql('matches_extra', sql_engine, if_exists='append', index=False)
matches = pd.DataFrame(matches)
matches.to_sql('matches', sql_engine, if_exists='append', index=False)
event_log.close()
if send_slack:
if len(slackmsg) > 0:
slack_client.chat_postMessage(channel=SLACK_CHANNEL, text=slackmsg)
if galaxy_depleted:
text = "All" + str(len(banana_galaxies)) + " galaxies assigned"
query = "update events set assigned = " + str(len(banana_galaxies)) + ", possible = " + str(
len(banana_galaxies)) + " where ID = " + str(event)
else:
text = str(len(banana_galaxies) - len(galaxy_list)) + " galaxies assigned out of " + str(
len(banana_galaxies))
query = "update events set assigned = " + str(
len(banana_galaxies) - len(galaxy_list)) + ", possible = " + str(
len(banana_galaxies)) + " where ID = " + str(event)
slack_client.chat_postMessage(channel=SLACK_CHANNEL, text=text)
sql_engine.execute(query)
else:
slack_client.chat_postMessage(channel=SLACK_CHANNEL, text="No Galaxies Assigned")
if plot_map:
ax.add_feature(Nightshade(datetime.utcnow(), alpha=0.2))
plt.title("Map of Observable Sites")
plt.savefig("map.png")
if send_slack:
slack_client.files_upload(channels=SLACK_CHANNEL, file="map.png", title=params['GraceID'])
filename = download_file(params['skymap_fits'], cache=True)
skyplot([filename, '--annotate', '--geo', '--contour', '50', '90'])
plt.savefig("hp.png")
if send_slack:
slack_client.files_upload(channels=SLACK_CHANNEL, file="hp.png", title=params['GraceID'])
return
def plot_swath_grid(lon, lat, field, varname, levname, varunits, nrows, ncols,
cen_lon, date, nightshade, date_str, title_str):
"""
----------------------------------------------------------------------------
This function plots a swath of footprint-level data
FLASHFlux, SSF, or CRS
----------------------------------------------------------------------------
:param lon: FOV longitude
:param lat: FOV latitude
:param field: variable
:param varname: variable name
:param levname: level name
:param varunits: variable units
:param nrows: number of rows
:param ncols: number of columns
:param cen_lon: central longitude
:param cmap: colormap
:param cmap_lims: colormap limits
:param date: date info for nightshade
:param nightshade: whether to use nighshade feature
:param date_str: date string
:param title_str: title string
----------------------------------------------------------------------------
:return: plot of the data
"""
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature
from mpl_toolkits.axes_grid1 import AxesGrid
from cartopy.mpl.geoaxes import GeoAxes
from cartopy.feature.nightshade import Nightshade
# Map projection
projection = ccrs.PlateCarree(central_longitude=cen_lon)
# Axis class
axes_class = (GeoAxes, dict(map_projection=projection))
# Create figure
fig = plt.figure(figsize=(11, 8))
axgr = AxesGrid(fig,
111,
axes_class=axes_class,
nrows_ncols=(nrows, ncols),
axes_pad=(0.4, 0.4),
share_all=True,
cbar_location='right',
cbar_mode='single',
cbar_pad=+0.2,
cbar_size='3%',
label_mode=1)
# Loop over axes
for i, ax in enumerate(axgr):
if i == 14:
break
ax.add_feature(cartopy.feature.LAND,
zorder=1,
facecolor='none',
edgecolor='darkgrey')
ax.gridlines(color='grey', linestyle='--')
ax.set_title(title_str + ' surface flux in band ' + str(i + 1) +
' - all sky',
fontsize=8)
ax.set_extent([-180, 180, -90, 90], projection)
ax.text(0.5,
-0.1,
varname + ' - ' + levname + '\n' + varunits,
va='bottom',
ha='center',
rotation='horizontal',
rotation_mode='anchor',
transform=ax.transAxes,
fontsize=10)
if nightshade == 1:
ax.add_feature(Nightshade(date, alpha=0.1))
# To use a different colorbar range each time, use a tuple of tuples
# limits = ((0, 120), (0, 120), (0, 120), (0, 120), (0, 120), (0, 120), (0, 120),
# (0, 120), (0, 120), (0, 120), (0, 120), (0, 120), (0, 120), (0, 120))
limits = (0, 70)
for i in range((nrows * ncols)):
if i == 14:
break
im = axgr[i].scatter(lon,
lat,
c=field[:, i],
s=1,
transform=ccrs.PlateCarree(),
vmin=limits[0],
vmax=limits[1],
cmap=lw_colormap)
axgr.cbar_axes[i].colorbar(im)
plt.tight_layout()
plt.show()
return
def plotCartoMap(latlim=[0, 75],
lonlim=[-40, 40],
parallels=None,
meridians=None,
pole_center_lon=0,
figsize=(12, 8),
terrain=False,
ax=False,
projection='stereo',
title='',
resolution='110m',
lon0=None,
lat0=None,
states=True,
grid_linewidth=0.5,
grid_color='black',
grid_linestyle='--',
background_color=None,
border_color='k',
figure=False,
nightshade=False,
ns_alpha=0.1,
apex=False,
igrf=False,
date=None,
mlat_levels=None,
mlon_levels=None,
alt_km=0.0,
mlon_colors='blue',
mlat_colors='red',
mgrid_width=1,
mgrid_labels=True,
mgrid_fontsize=12,
mlon_cs='mlon',
incl_levels=None,
decl_levels=None,
igrf_param='incl',
mlon_labels=True,
mlat_labels=True,
mgrid_style='--',
label_colors='k',
apex_alt=0,
decl_colors='k',
incl_colors='k',
terminator=False,
terminator_altkm=350,
polarization_terminator=False,
pt_hemisphere='north',
ter_color='red',
ter_style='--',
ter_width=2,
midnight=False,
midnight_colors='m',
midnight_width=2,
midnight_style='--'):
if lonlim is None or lonlim == []:
lonlim = [-180, 180]
STATES = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
if not ax:
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
if projection == 'stereo':
ax = plt.axes(projection=ccrs.Stereographic(
central_longitude=(sum(lonlim) / 2)))
elif projection == 'merc':
ax = plt.axes(projection=ccrs.Mercator())
elif projection == 'plate':
ax = plt.axes(projection=ccrs.PlateCarree(
central_longitude=(sum(lonlim) / 2)))
elif projection == 'lambert':
ax = plt.axes(projection=ccrs.LambertConformal(
central_longitude=(sum(lonlim) / 2),
central_latitude=(sum(latlim) / 2)))
elif projection == 'mollweide':
ax = plt.axes(projection=ccrs.Mollweide(
central_longitude=(sum(lonlim) / 2)))
elif projection == 'north':
if lon0 is None:
lon0 = 0
ax = plt.axes(projection=ccrs.NorthPolarStereo(
central_longitude=lon0))
elif projection == 'south':
if lon0 is None:
lon0 = 0
ax = plt.axes(projection=ccrs.SouthPolarStereo(
central_longitude=lon0))
elif projection == 'ortographic':
if lon0 is None:
lon0 = 0
if lat0 is None:
lat0 = 0
ax = plt.axes(projection=ccrs.Orthographic(central_longitude=lon0,
central_latitude=lat0))
else:
print("Projection is invalid. Please enter the right one. \n \
'stereo', 'merc', 'plate', 'lambret', mollweide', 'north, 'south', 'ortographic'"
)
return 0
if background_color is not None:
ax.background_patch.set_facecolor(background_color)
ax.set_title(title)
ax.coastlines(color=border_color,
resolution=resolution) # 110m, 50m or 10m
if states:
ax.add_feature(STATES, edgecolor=border_color)
ax.add_feature(cfeature.BORDERS, edgecolor=border_color)
if terrain:
ax.stock_img()
if nightshade:
assert date is not None
assert ns_alpha is not None
ax.add_feature(Nightshade(date, ns_alpha))
# Draw Parralels
if projection == 'merc' or projection == 'plate':
if isinstance(meridians, np.ndarray):
meridians = list(meridians)
if isinstance(parallels, np.ndarray):
parallels = list(parallels)
gl = ax.gridlines(crs=ccrs.PlateCarree(),
color=grid_color,
draw_labels=False,
linestyle=grid_linestyle,
linewidth=grid_linewidth)
if meridians is None:
gl.xlines = False
else:
if len(meridians) > 0:
gl.xlocator = mticker.FixedLocator(meridians)
gl.xlabels_bottom = True
else:
gl.ylines = False
if parallels is None:
gl.ylines = False
else:
if len(parallels) > 0:
gl.ylocator = mticker.FixedLocator(parallels)
gl.ylabels_left = True
else:
gl.ylines = False
else:
gl = ax.gridlines(crs=ccrs.PlateCarree(),
color=grid_color,
draw_labels=False,
linestyle=grid_linestyle,
linewidth=grid_linewidth)
if meridians is None:
gl.xlines = False
else:
gl.xlocator = mticker.FixedLocator(meridians)
if parallels is not None:
if isinstance(parallels, np.ndarray):
parallels = list(parallels)
gl.ylocator = mticker.FixedLocator(parallels)
else:
gl.ylines = False
# Geomagnetic coordinates @ Apex
if apex:
if date is None:
date = datetime(2017, 12, 31, 0, 0, 0)
assert isinstance(date, datetime)
A = ap.Apex(date=date)
# Define levels and ranges for conversion
if mlon_cs == 'mlt':
if mlon_levels is None:
mlon_levels = np.array([])
mlon_range = np.arange(0, 24.01, 0.01)
elif isinstance(mlon_levels, bool):
if mlon_levels == False:
mlon_levels = np.array([])
mlon_range = np.arange(0, 24.01, 0.01)
else:
mlon_range = np.arange(mlon_levels[0], mlon_levels[-1] + 0.1,
0.01)
else:
if mlon_levels is None:
mlon_levels = np.array([])
mlon_range = np.arange(-180, 180, 0.5)
elif isinstance(mlon_levels, bool):
if mlon_levels == False:
mlon_levels = np.array([])
mlon_range = np.arange(-180, 181, 0.1)
else:
mlon_range = np.arange(mlon_levels[0], mlon_levels[0] + 362,
0.1)
if mlat_levels is None:
mlat_levels = np.arange(-90, 90.1, 1)
mlat_range = np.arange(mlat_levels[0], mlat_levels[-1] + 0.1, 0.1)
# Do meridans
for mlon in mlon_levels:
MLON = mlon * np.ones(mlat_range.size)
if mlon_cs == 'mlt':
y, x = A.convert(mlat_range,
MLON,
'mlt',
'geo',
datetime=date,
height=apex_alt)
else:
y, x = A.convert(mlat_range,
MLON,
'apex',
'geo',
height=apex_alt)
mlat_mask_extent = (y >= latlim[0] + 0.2) & (y <= latlim[1] - 0.2)
# Plot meridian
inmap = np.logical_and(x >= lonlim[0], x <= lonlim[1])
if np.sum(inmap) > 10:
ax.plot(np.unwrap(x[mlat_mask_extent], 180),
np.unwrap(y[mlat_mask_extent], 90),
c=mlon_colors,
lw=mgrid_width,
linestyle=mgrid_style,
zorder=90,
transform=ccrs.PlateCarree())
# Labels
if mlon_labels:
ix = abs(y - np.mean(latlim)).argmin()
mx = x[ix] - 1 if mlon >= 10 else x[ix] - 0.5
my = np.mean(latlim)
if np.logical_and(mx >= lonlim[0], mx <= lonlim[1]):
if mlon_cs == 'mlt' and mlon != 0:
ax.text(mx,
my,
str(int(mlon)),
color=label_colors,
fontsize=14,
backgroundcolor='white',
transform=ccrs.PlateCarree())
elif mlon_cs != 'mlt' and mlon != 360:
ax.text(mx,
my,
str(int(mlon)),
color=label_colors,
fontsize=14,
backgroundcolor='white',
transform=ccrs.PlateCarree())
# Do parallels
for mlat in mlat_levels:
MLAT = mlat * np.ones(mlon_range.size)
if mlon_cs == 'mlt':
gy, gx = A.convert(MLAT,
mlon_range,
'mlt',
'geo',
datetime=date,
height=apex_alt)
else:
gy, gx = A.convert(MLAT,
mlon_range,
'apex',
'geo',
datetime=date,
height=apex_alt)
inmap = np.logical_and(gy >= latlim[0], gy <= latlim[1])
if np.sum(inmap) > 20:
ax.plot(np.unwrap(gx, 180),
np.unwrap(gy, 90),
c=mlat_colors,
lw=mgrid_width,
linestyle=mgrid_style,
zorder=90,
transform=ccrs.PlateCarree())
# Labels
if mlat_labels:
ix = abs(gx - np.mean(lonlim)).argmin()
mx = np.mean(lonlim)
my = gy[ix] - 0.5
if np.logical_and(mx >= lonlim[0], mx <= lonlim[1]) and \
np.logical_and(my >= latlim[0], my <= latlim[1]):
ax.text(mx,
my,
str(int(mlat)),
color=label_colors,
fontsize=14,
backgroundcolor='white',
transform=ccrs.PlateCarree())
if igrf:
glon = np.arange(lonlim[0] - 40, lonlim[1] + 40.1, 0.5)
glat = np.arange(-90, 90 + 0.1, 0.5)
longrid, latgrid = np.meshgrid(glon, glat)
mag = igrf12.gridigrf12(t=date,
glat=latgrid,
glon=longrid,
alt_km=alt_km)
if decl_levels is not None:
z = mag.decl.values
for declination in decl_levels:
ax.contour(longrid,
latgrid,
z,
levels=declination,
zorder=90,
colors=decl_colors,
transform=ccrs.PlateCarree())
if incl_levels is not None:
z = mag.incl.values
for inclination in incl_levels:
ax.contour(longrid,
latgrid,
z,
levels=declination,
zorder=90,
colors=incl_colors,
transform=ccrs.PlateCarree())
# Terminators
if terminator:
assert date is not None
if not isinstance(terminator_altkm, list):
terminator_altkm = [terminator_altkm]
for takm in terminator_altkm:
try:
glon_ter, glat_ter = ter.get_terminator(date, alt_km=takm)
if glon_ter is not None and glat_ter is not None:
if isinstance(glon_ter, list):
for i in range(len(glon_ter)):
ax.plot(np.unwrap(glon_ter[i], 180),
np.unwrap(glat_ter[i], 90),
c=ter_color,
lw=ter_width,
ls=ter_style,
zorder=90,
transform=ccrs.PlateCarree())
else:
ax.plot(np.unwrap(glon_ter, 180),
np.unwrap(glat_ter, 90),
c=ter_color,
lw=ter_width,
ls=ter_style,
zorder=90,
transform=ccrs.PlateCarree())
except:
pass
if midnight:
mlat_range = np.arange(-89.9, 90.1, 0.1)
MLON = 0 * np.ones(mlat_range.size)
if mlon_cs == 'mlt':
y, x = A.convert(mlat_range,
MLON,
'mlt',
'geo',
datetime=date,
height=apex_alt)
else:
y, x = A.convert(mlat_range, MLON, 'apex', 'geo', height=apex_alt)
mlat_mask_extent = (y >= latlim[0] + 0.2) & (y <= latlim[1] - 0.2)
# Plot meridian
inmap = np.logical_and(x >= lonlim[0], x <= lonlim[1])
if np.sum(inmap) > 10:
ax.plot(np.unwrap(x[mlat_mask_extent], 180),
np.unwrap(y[mlat_mask_extent], 90),
c=midnight_colors,
lw=midnight_width,
linestyle=midnight_style,
zorder=90,
transform=ccrs.PlateCarree())
# Set Extent
if projection == 'north' or projection == 'south':
import matplotlib.path as mpath
ax.set_extent([-180, 181, latlim[0], latlim[1]],
crs=ccrs.PlateCarree())
theta = np.linspace(0, 2 * np.pi, 100)
center, radius = [0.5, 0.5], 0.5
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
circle = mpath.Path(verts * radius + center)
ax.set_boundary(circle, transform=ax.transAxes)
elif lonlim[0] != -180 and lonlim[1] != 180:
ax.set_extent([lonlim[0], lonlim[1], latlim[0], latlim[1]],
crs=ccrs.PlateCarree()) #ccrs.PlateCarree())
if 'fig' in locals():
return fig, ax
else:
return ax
