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03_hindcast_dyn_map.py
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03_hindcast_dyn_map.py
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# coding: utf-8
"""
Dynamic map hindcast implementation
"""
__author__ = "Saeed Moghimi"
__copyright__ = "Copyright 2018, UCAR/NOAA"
__license__ = "GPL"
__version__ = "1.0"
__email__ = "[email protected]"
#Thu 19 Apr 2018 03:08:06 PM EDT
###############################################################
# Original development from https://github.com/ocefpaf/python_hurricane_gis_map
# # Exploring the NHC GIS Data
#
# This notebook aims to demonstrate how to create a simple interactive GIS map with the National Hurricane Center predictions [1] and CO-OPS [2] observations along the Hurricane's path.
#
#
# 1. http://www.nhc.noaa.gov/gis/
# 2. https://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/
#
#
# NHC codes storms are coded with 8 letter names:
# - 2 char for region `al` → Atlantic
# - 2 char for number `11` is Irma
# - and 4 char for year, `2017`
#
# Browse http://www.nhc.noaa.gov/gis/archive_wsurge.php?year=2017 to find other hurricanes code.
###############################################################
import pandas as pd
import numpy as np
import string
#
import os
import sys
from glob import glob
#
import arrow
#
from shapely.geometry import LineString
import netCDF4
#
import folium
from folium.plugins import Fullscreen, MarkerCluster,MousePosition,FloatImage
from ioos_tools.ioos import get_coordinates
from branca.element import *
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.tri as Tri
import matplotlib.pyplot as plt
from shapely.geometry import mapping, Polygon
import fiona
from bokeh.resources import CDN
from bokeh.plotting import figure
from bokeh.models import Title
from bokeh.embed import file_html
from bokeh.models import Range1d, LinearAxis, HoverTool
from bokeh.layouts import gridplot
from bokeh.models import ColumnDataSource
from folium import IFrame
from geopandas import GeoDataFrame
import pickle
try:
os.system('rm __pycache__/hurricane_funcs*.pyc' )
os.system('rm hurricane_funcs*.pyc' )
except:
pass
if 'hurricane_funcs' in sys.modules:
del(sys.modules["hurricane_funcs"])
from hurricane_funcs import *
try:
os.system('rm __pycache__/base_info_folium*.pyc' )
os.system('rm base_info_folium*.pyc' )
except:
pass
if 'base_info_folium' in sys.modules:
del(sys.modules["base_info_folium"])
from base_info_folium import *
############################
from matplotlib.colors import LinearSegmentedColormap
cdict = {'red': ((0. , 1 , 1),
(0.05, 1 , 1),
(0.11, 0 , 0),
(0.66, 1, 1),
(0.89, 1, 1),
(1 , 0.5, 0.5)),
'green': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 0, 0),
(0.375, 1, 1),
(0.64, 1, 1),
(0.91, 0, 0),
(1, 0, 0)),
'blue': ((0., 1, 1),
(0.05, 1, 1),
(0.11, 1, 1),
(0.34, 1, 1),
(0.65, 0, 0),
(1, 0, 0))}
jetMinWi = LinearSegmentedColormap('my_colormap',cdict,256)
my_cmap = plt.cm.jet
###############################################################
#Functions
######################################
# Let's create a color code for the point track.
colors_hurricane_condition = {
'subtropical depression': '#ffff99',
'tropical depression': '#ffff66',
'tropical storm': '#ffcc99',
'subtropical storm': '#ffcc66',
'hurricane': 'red',
'major hurricane': 'crimson',
}
#######################################
############################################################
# plot ssh to pop up when click on obs locations
##
tools = "pan,box_zoom,reset"
width, height = 750, 250
def make_plot_2axes(ssh, wind):
p = figure(toolbar_location='above',
x_axis_type='datetime',
width=width,
height=height,
tools=tools,
title=ssh.name)
p.yaxis.axis_label = 'wind speed (m/s)'
l0 = p.line(
x=wind.index,
y=wind.values,
line_width=5,
line_cap='round',
line_join='round',
legend='wind speed (m/s)',
color='#9900cc',
alpha=0.5,
)
p.extra_y_ranges = {}
p.extra_y_ranges['y2'] = Range1d(
start=-1,
end=3.5
)
p.add_layout(
LinearAxis(
y_range_name='y2',
axis_label='ssh (m)'),
'right'
)
l1 = p.line(
x=ssh.index,
y=ssh.values,
line_width=5,
line_cap='round',
line_join='round',
legend='ssh (m)',
color='#0000ff',
alpha=0.5,
y_range_name='y2',
)
p.legend.location = 'top_left'
p.add_tools(
HoverTool(
tooltips=[
('wind speed (m/s)', '@y'),
],
renderers=[l0],
),
HoverTool(
tooltips=[
('ssh (m)', '@y'),
],
renderers=[l1],
),
)
return p
def make_plot_obs(obs,label=None):
#TOOLS="hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,"
TOOLS="crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,reset,save,"
p = figure(toolbar_location='above',
x_axis_type='datetime',
width=width,
height=height,
tools=TOOLS)
p.add_layout(Title(text='Station: '+obs._metadata['station_code'], text_font_style="italic"), 'above')
p.add_layout(Title(text=obs._metadata['station_name'], text_font_size="10pt"), 'above')
p.yaxis.axis_label = label
obs_val = obs.values.squeeze()
l1 = p.line(
x=obs.index,
y=obs_val,
line_width=5,
line_cap='round',
line_join='round',
legend='obs.',
color='#0000ff',
alpha=0.7,
)
minx = obs.index.min()
maxx = obs.index.max()
p.x_range = Range1d(
start = minx,
end = maxx
)
p.legend.location = 'top_left'
p.add_tools(
HoverTool(
tooltips=[
('obs.', '@y'),
],
renderers=[l1],
),
)
return p
#def make_plot(obs, model = None,label,remove_mean_diff=False,bbox_bias=None):
def make_plot(obs, model = None,label=None,remove_mean_diff=False,bbox_bias=0.0):
#TOOLS="hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,"
TOOLS="crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,reset,save,"
p = figure(toolbar_location='above',
x_axis_type='datetime',
width=width,
height=height,
tools=TOOLS)
p.add_layout(Title(text='Station: '+obs._metadata['station_code'], text_font_style="italic"), 'above')
p.add_layout(Title(text=obs._metadata['station_name'], text_font_size="10pt"), 'above')
p.yaxis.axis_label = label
obs_val = obs.values.squeeze()
l1 = p.line(
x=obs.index,
y=obs_val,
line_width=5,
line_cap='round',
line_join='round',
legend='obs.',
color='#0000ff',
alpha=0.7,
)
if model is not None:
mod_val = model.values.squeeze()
if ('SSH' in label) and remove_mean_diff:
mod_val = mod_val + obs_val.mean() - mod_val.mean()
if ('SSH' in label) and bbox_bias is not None:
mod_val = mod_val + bbox_bias
l0 = p.line(
x=model.index,
y=mod_val,
line_width=5,
line_cap='round',
line_join='round',
legend='model',
color='#9900cc',
alpha=0.7,
)
minx = max (model.index.min(),obs.index.min())
maxx = min (model.index.max(),obs.index.max())
minx = model.index.min()
maxx = model.index.max()
else:
minx = obs.index.min()
maxx = obs.index.max()
p.x_range = Range1d(
start = minx,
end = maxx
)
p.legend.location = 'top_left'
p.add_tools(
HoverTool(
tooltips=[
('model', '@y'),
],
renderers=[l0],
),
HoverTool(
tooltips=[
('obs.', '@y'),
],
renderers=[l1],
),
)
return p
#def make_plot(obs, model = None,label,remove_mean_diff=False,bbox_bias=None):
def make_dual_plot(obs, model = None,label=None,remove_mean_diff=False,bbox_bias=0.0):
#TOOLS="hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,"
if model is None:
sys.exit('Model can not be none')
df = obs.copy()
df ['obs'] = obs.values
df ['mod'] = model
df = df.dropna()
if True:
if ('SSH' in label) and remove_mean_diff:
df ['mod'] = df ['mod'] + df ['obs'].mean() - df ['mod'].mean()
if ('SSH' in label) and bbox_bias is not None:
df ['mod'] = df ['mod'] + bbox_bias
# https://bokeh.pydata.org/en/latest/docs/user_guide/interaction/linking.html#linked-brushing
# create a column data source for the plots to share
src = ColumnDataSource(data=dict(x = df.index.to_pydatetime(),
yo = df['obs'].values,
ym = df['mod'].values))
TOOLS="box_select,lasso_select,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,reset,save,help,"
left = figure(toolbar_location='above',
x_axis_type='datetime',
width=width,
height=height,
tools=TOOLS)
left.add_layout(Title(text='Station: '+obs._metadata['station_code'], text_font_style="italic"), 'above')
left.add_layout(Title(text=obs._metadata['station_name'], text_font_size="10pt"), 'above')
left.yaxis.axis_label = label
left.xaxis.axis_label = 'DateTime'
l1 = left.line(
x='x',
y='yo',
line_width=5,
line_cap='round',
line_join='round',
legend='obs.',
color='#0000ff',
alpha=0.7,
source = src,
)
l0 = left.line(
x='x',
y='ym',
line_width=5,
line_cap='round',
line_join='round',
legend='model',
color='#9900cc',
alpha=0.7,
source =src
)
left.x_range = Range1d(
start = df.index.min(),
end = df.index.max()
)
left.legend.location = 'top_left'
left.add_tools(
HoverTool(
tooltips=[
('Model', '@ym'),
],
renderers=[l0],
# display a tooltip whenever the cursor is vertically in line with a glyph
mode='vline',
),
HoverTool(
tooltips=[
('Obs.', '@yo'),
],
renderers=[l1],
# display a tooltip whenever the cursor is vertically in line with a glyph
mode='vline',
),
)
# left.add_tools(
# HoverTool(
# tooltips=[
# ('Model', '@ym'),
# ('Obs.', '@yo'),
# ],
# # display a tooltip whenever the cursor is vertically in line with a glyph
# mode='vline'
# ),
#
# )
## display a tooltip whenever the cursor is vertically in line with a glyph
#mode='vline'
#### https://bokeh.pydata.org/en/latest/docs/user_guide/tools.html
# https://bokeh.pydata.org/en/1.0.0/docs/user_guide/examples/tools_hover_tooltip_formatting.html
#rTOOLTIPS = [
#("Date" , "$x"),
#("Obs." , "$yo"),
#("Model", "$ym"),
#]
#hover_tool.formatters = { "x": "datetime"}
#right = figure(tools=TOOLS, plot_width=height, plot_height=height, title='Select',tooltips=rTOOLTIPS)
right = figure(tools=TOOLS, plot_width=height, plot_height=height, title='Select from ...')
right.circle('yo', 'ym', source=src)
right.yaxis.axis_label = 'Model'
right.xaxis.axis_label = 'Obs.'
right.add_tools(
HoverTool(
tooltips=[
#('Date' , '@x'),
('Model', '@ym'),
('Obs.' , '@yo'),
],
formatters={
'x' : 'datetime', # use 'datetime' formatter for 'date' field
# use default 'numeral' formatter for other fields
},
),
)
p = gridplot([[right,left]])
return p
#################
def make_marker(p, location, fname , color = 'green',icon= 'stats'):
html = file_html(p, CDN, fname)
#iframe = IFrame(html , width=width+45+height, height=height+80)
iframe = IFrame(html , width=width * 1.1, height=height * 1.2)
#popup = folium.Popup(iframe, max_width=2650+height)
popup = folium.Popup(iframe)
iconm = folium.Icon(color = color, icon=icon)
marker = folium.Marker(location=location,
popup=popup,
icon=iconm)
return marker
###############################
###try to add countor to map
def Read_maxele_return_plot_obj(fgrd='depth_hsofs_inp.nc',felev='maxele.63.nc'):
"""
"""
ncg = netCDF4.Dataset(fgrd)
ncgv = ncg.variables
#read maxelev
nc0 = netCDF4.Dataset(felev)
ncv0 = nc0.variables
data = ncv0['zeta_max'][:]
#data = ncv0['surge'][:]
#dep0 = ncv0['depth'][:]
lon0 = ncv0['x'][:]
lat0 = ncv0['y'][:]
elems = ncgv['element'][:,:]-1 # Move to 0-indexing by subtracting 1
data[data.mask] = 0.0
MinVal = np.min(data)
MaxVal = np.max(data)
NumLevels = 21
if True:
MinVal = max(MinVal,1)
MaxVal = min(MaxVal,4)
NumLevels = 11
levels = np.linspace(MinVal, MaxVal, num=NumLevels)
tri = Tri.Triangulation(lon0,lat0, triangles=elems)
contour = plt.tricontourf(tri, data,levels=levels,cmap = my_cmap ,extend='max')
return contour,MinVal,MaxVal,levels
#############################################################
def collec_to_gdf(collec_poly):
"""Transform a `matplotlib.contour.QuadContourSet` to a GeoDataFrame"""
polygons, colors = [], []
for i, polygon in enumerate(collec_poly.collections):
mpoly = []
for path in polygon.get_paths():
try:
path.should_simplify = False
poly = path.to_polygons()
# Each polygon should contain an exterior ring + maybe hole(s):
exterior, holes = [], []
if len(poly) > 0 and len(poly[0]) > 3:
# The first of the list is the exterior ring :
exterior = poly[0]
# Other(s) are hole(s):
if len(poly) > 1:
holes = [h for h in poly[1:] if len(h) > 3]
mpoly.append(Polygon(exterior, holes))
except:
print('Warning: Geometry error when making polygon #{}'
.format(i))
if len(mpoly) > 1:
mpoly = MultiPolygon(mpoly)
polygons.append(mpoly)
colors.append(polygon.get_facecolor().tolist()[0])
elif len(mpoly) == 1:
polygons.append(mpoly[0])
colors.append(polygon.get_facecolor().tolist()[0])
return GeoDataFrame(
geometry=polygons,
data={'RGBA': colors},
crs={'init': 'epsg:4326'})
#################
def convert_to_hex(rgba_color) :
red = str(hex(int(rgba_color[0]*255)))[2:].capitalize()
green = str(hex(int(rgba_color[1]*255)))[2:].capitalize()
blue = str(hex(int(rgba_color[2]*255)))[2:].capitalize()
if blue=='0':
blue = '00'
if red=='0':
red = '00'
if green=='0':
green='00'
return '#'+ red + green + blue
#################
def get_station_ssh(fort61):
"""
Read model ssh
"""
nc0 = netCDF4.Dataset(fort61)
ncv0 = nc0.variables
sta_lon = ncv0['x'][:]
sta_lat = ncv0['y'][:]
sta_nam = ncv0['station_name'][:].squeeze()
sta_zeta = ncv0['zeta'] [:].squeeze()
sta_date = netCDF4.num2date(ncv0['time'][:], ncv0['time'].units)
stationIDs = []
mod = []
ind = np.arange(len(sta_lat))
for ista in ind:
stationID = sta_nam[ista].tostring().decode().rstrip()
stationIDs.append(stationID)
mod_tmp = pd.DataFrame(data = np.c_[sta_date, sta_zeta[:,ista]], columns=['date_time', 'ssh']).set_index('date_time')
mod_tmp._metadata = stationID
mod.append(mod_tmp)
stationIDs = np.array(stationIDs)
mod_table = pd.DataFrame(data = np.c_[ind, stationIDs], columns=['ind', 'station_code'])
return mod,mod_table
#################
def get_station_wnd_all(fort61):
"""
Read model wind
"""
nc0 = netCDF4.Dataset(fort61)
ncv0 = nc0.variables
try:
sta_lon = ncv0['x'][:]
except:
sta_lon = ncv0['lon'][:]
try:
sta_lat = ncv0['y'][:]
except:
sta_lon = ncv0['lat'][:]
sta_nam = ncv0['station_name'][:].squeeze()
sta_uwnd = ncv0['uwnd'] [:].squeeze()
sta_vwnd = ncv0['vwnd'] [:].squeeze()
sta_pres = ncv0['pres'] [:].squeeze()
sta_date = netCDF4.num2date(ncv0['time'][:], ncv0['time'].units)
stationIDs = []
mod = []
ind = np.arange(len(sta_lat))
for ista in ind:
stationID = sta_nam[ista].tostring().decode().rstrip()
stationIDs.append(stationID)
mod_tmp = pd.DataFrame(data = np.c_[sta_date,sta_uwnd[:,ista],sta_vwnd[:,ista],sta_pres[:,ista]],
columns = ['date_time', 'uwnd' , 'vwnd', 'pres']).set_index('date_time')
mod_tmp._metadata = stationID
mod.append(mod_tmp)
stationIDs = np.array(stationIDs)
mod_table = pd.DataFrame(data = np.c_[ind, stationIDs], columns=['ind', 'station_code'])
return mod,mod_table
#################
def get_station_wnd(fort61):
"""
Read model wind
"""
nc0 = netCDF4.Dataset(fort61)
ncv0 = nc0.variables
try:
sta_lon = ncv0['x'][:]
except:
sta_lon = ncv0['lon'][:]
try:
sta_lat = ncv0['y'][:]
except:
sta_lat = ncv0['lat'][:]
sta_nam = ncv0['station_name'][:].squeeze()
sta_wnd = np.sqrt ( ncv0['uwnd'] [:].squeeze() ** 2 + ncv0['vwnd'] [:].squeeze() ** 2 )
sta_date = netCDF4.num2date(ncv0['time'][:], ncv0['time'].units)
stationIDs = []
mod = []
ind = np.arange(len(sta_lat))
for ista in ind:
stationID = sta_nam[ista].tostring().decode().rstrip()
stationIDs.append(stationID)
mod_tmp = pd.DataFrame(data = np.c_[sta_date,sta_wnd[:,ista]],
columns = ['date_time', 'wnd' ]).set_index('date_time')
mod_tmp._metadata = stationID
mod.append(mod_tmp)
stationIDs = np.array(stationIDs)
mod_table1 = pd.DataFrame(data = np.c_[ind, stationIDs], columns=['ind', 'station_code'])
nc0.close()
return mod,mod_table1
def get_model_at_station_wave(wav_at_nbdc):
"""
Read model wave
"""
nc0 = netCDF4.Dataset(wav_at_nbdc)
ncv0 = nc0.variables
sta_hsig = ncv0['hsig'] [:].squeeze()
sta_hsig [sta_hsig > 1e3 ] = 0.0
sta_date = netCDF4.num2date(ncv0['time'][:], ncv0['time'].units)
sta_nam = ncv0['station_name'][:].squeeze()
sta_lon = ncv0['lon'][:]
sta_lat = ncv0['lat'][:]
nc0.close()
stationIDs = []
mod = []
ind = np.arange(len(sta_lat))
for ista in ind:
stationID = sta_nam[ista][~sta_nam.mask[ista]].tostring().decode().split('.')[0]
stationIDs.append(stationID)
mod_tmp = pd.DataFrame(data = np.c_[sta_date,sta_hsig[:,ista]],
columns = ['date_time', 'hsig' ]).set_index('date_time')
mod_tmp._metadata = stationID
mod.append(mod_tmp)
stationIDs = np.array(stationIDs)
mod_table = pd.DataFrame(data = np.c_[ind, stationIDs], columns=['ind', 'station_code'])
return mod,mod_table
def get_model_at_station_wind(wnd_at_nbdc):
"""
Read model wind
"""
nc0 = netCDF4.Dataset(wnd_at_nbdc)
ncv0 = nc0.variables
sta_wnd = np.sqrt ( ncv0['uwnd'] [:].squeeze() ** 2 + ncv0['vwnd'] [:].squeeze() ** 2 )
sta_wnd [sta_wnd > 1e3 ] = 0.0
sta_date = netCDF4.num2date(ncv0['time'][:], ncv0['time'].units)
sta_nam = ncv0['station_name'][:].squeeze()
sta_lon = ncv0['lon'][:]
sta_lat = ncv0['lat'][:]
nc0.close()
stationIDs = []
mod = []
ind = np.arange(len(sta_lat))
for ista in ind:
stationID = sta_nam[ista][~sta_nam.mask[ista]].tostring().decode()
stationIDs.append(stationID)
mod_tmp = pd.DataFrame(data = np.c_[sta_date,sta_wnd[:,ista]],
columns = ['date_time', 'wnd' ]).set_index('date_time')
mod_tmp._metadata = stationID
mod.append(mod_tmp)
stationIDs = np.array(stationIDs)
mod_table = pd.DataFrame(data = np.c_[ind, stationIDs], columns=['ind', 'station_code'])
return mod,mod_table
#############################################################
def make_map(bbox, **kw):
"""
Creates a folium map instance.
Examples
--------
>>> from folium import Map
>>> bbox = [-87.40, 24.25, -74.70, 36.70]
>>> m = make_map(bbox)
>>> isinstance(m, Map)
True
"""
import folium
line = kw.pop('line', True)
layers = kw.pop('layers', True)
zoom_start = kw.pop('zoom_start', 5)
lon, lat = np.array(bbox).reshape(2, 2).mean(axis=0)
#
m = folium.Map(width='100%', height='100%',
location=[lat, lon], zoom_start=zoom_start)
if layers:
add = 'MapServer/tile/{z}/{y}/{x}'
base = 'http://services.arcgisonline.com/arcgis/rest/services'
ESRI = dict(Imagery='World_Imagery/MapServer',
#Ocean_Base='Ocean/World_Ocean_Base',
#Topo_Map='World_Topo_Map/MapServer',
#Physical_Map='World_Physical_Map/MapServer',
#Terrain_Base='World_Terrain_Base/MapServer',
#NatGeo_World_Map='NatGeo_World_Map/MapServer',
#Shaded_Relief='World_Shaded_Relief/MapServer',
#Ocean_Reference='Ocean/World_Ocean_Reference',
#Navigation_Charts='Specialty/World_Navigation_Charts',
#Street_Map='World_Street_Map/MapServer'
)
for name, url in ESRI.items():
url = '{}/{}/{}'.format(base, url, add)
w = folium.TileLayer(tiles=url,
name=name,
attr='ESRI',
overlay=False)
w.add_to(m)
if line: # Create the map and add the bounding box line.
p = folium.PolyLine(get_coordinates(bbox),
color='#FF0000',
weight=2,
opacity=0.5,
latlon=True)
p.add_to(m)
folium.LayerControl().add_to(m)
return m
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import (LONGITUDE_FORMATTER,
LATITUDE_FORMATTER)
import cartopy.feature as cfeature
def make_map_cartopy(projection=ccrs.PlateCarree()):
"""
Generate fig and ax using cartopy
input: projection
output: fig and ax
"""
alpha = 0.5
subplot_kw = dict(projection=projection)
fig, ax = plt.subplots(figsize=(9, 13),
subplot_kw=subplot_kw)
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
# Put a background image on for nice sea rendering.
ax.stock_img()
# Create a feature for States/Admin 1 regions at 1:50m from Natural Earth
states_provinces = cfeature.NaturalEarthFeature(
category='cultural',
name='admin_1_states_provinces_lines',
scale='50m',
facecolor='none')
SOURCE = 'Natural Earth'
LICENSE = 'public domain'
ax.add_feature(cfeature.LAND,zorder=0,alpha=alpha)
ax.add_feature(cfeature.COASTLINE,zorder=1,alpha=alpha)
ax.add_feature(cfeature.BORDERS,zorder=1,alpha=2*alpha)
ax.add_feature(states_provinces, edgecolor='gray',zorder=1)
# Add a text annotation for the license information to the
# the bottom right corner.
text = AnchoredText(r'$\mathcircled{{c}}$ {}; license: {}'
''.format(SOURCE, LICENSE),
loc=4, prop={'size': 9}, frameon=True)
ax.add_artist(text)
ax.set_xlim(-132,-65) #lon limits
ax.set_ylim( 20 , 55) #lat limits
return fig, ax
#calculate bias for bbox region
def get_bias(bias_bbox,ssh,ssh_table,fort61 ):
ind = get_ind (bias_bbox,ssh_table.lon,ssh_table.lat)
mask = get_mask(bias_bbox,ssh_table.lon,ssh_table.lat)
#
ssh_bias_table = ssh_table[~mask]
#
ssh_bias = []
ssh_biad_tab = []
for in0 in ind:
ssh_bias.append(ssh[in0])
########### Read SSH data
mod , mod_table = get_station_ssh(fort61)
############# Sea Surface height analysis ########################
# For simplicity we will use only the stations that have both wind speed and sea surface height and reject those that have only one or the other.
common = set(ssh_bias_table['station_code']).intersection(mod_table ['station_code'].values)
ssh_obs, ssh_mod = [], []
for station in common:
ssh_obs.extend([obs for obs in ssh if obs._metadata['station_code'] == station])
ssh_mod.extend([obm for obm in mod if obm._metadata == station])
index = pd.date_range(
start = bias_calc_start.replace(tzinfo=None),
end = bias_calc_end.replace (tzinfo=None),
freq=freq
)
#############################################################
#organize and re-index both observations
# Re-index and rename series.
ssh_observations = []
ssh_all = []
for series in ssh_obs:
_metadata = series._metadata
obs = series.tz_localize(None).reindex(index=index, limit=1, method='nearest')
obs._metadata = _metadata
obs.name = _metadata['station_name']
ssh_observations.append(obs)
ssh_all.append( obs['water_surface_height_above_reference_datum (m)'].values)
##############################################################
#model
ssh_from_model = []
mod_all = []
for series in ssh_mod:
_metadata = series._metadata
obs = series.tz_localize(None).reindex(index=index, limit=1, method='nearest')
obs._metadata = _metadata
obs.name = _metadata
ssh_from_model.append(obs)
mod_all.append( obs['ssh'].values)
ssh_all = np.array(ssh_all).flatten()
mask = np.isnan(ssh_all)
mod_all = np.array(mod_all).flatten()
bias = (ssh_all[~mask] - mod_all [~mask]).mean()
return bias
########################################
#### MAIN CODE from HERE #####
########################################
for key in storms.keys():
name = storms[key]['name']
year = storms[key]['year']
print('\n\n\n\n\n\n********************************************************')
print( '***** Storm name ',name, ' Year ', year, ' *********')
print( '******************************************************** \n\n\n\n\n\n')
wnd_ocn_observs = wnd_ocn_models = wnd_ocn = None
wav_ocn = wav_observs = wav_models = None
wnd_obs = wnd_observs = wnd_models = None
ssh = ssh_observations = ssh_from_model = None
name = name #[:3]
obs_dir = os.path.join(base_dirf,'work_dir','obs')
mod_dir = os.path.join(base_dirf,'work_dir','mod')
print ( ' > Read NHC information ... ')
al_code , hurricane_gis_files, df = get_nhc_storm_info (year,name)
#donload gis zip files
base = download_nhc_gis_files(hurricane_gis_files)
# get advisory cones and track points
cones , points , pts = read_advisory_cones_info(hurricane_gis_files,base,year,al_code)
# Find the bounding box to search the data.
bbox_from_track = True
if bbox_from_track:
last_cone = cones[-1]['geometry'].iloc[0]
track = LineString([point['geometry'] for point in points])
track_lons = track.coords.xy[0]
track_lats = track.coords.xy[1]
bbox = min(track_lons)-2, min(track_lats)-2, max(track_lons)+2, max(track_lats)+2
else:
bounds = np.array([last_cone.buffer(2).bounds, track.buffer(2).bounds]).reshape(4, 2)
lons, lats = bounds[:, 0], bounds[:, 1]
bbox = lons.min(), lats.min(), lons.max(), lats.max()
if storms[key]['bbox'] is not None: