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MyParcels_Plotting.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 23 14:00:29 2020
@author: ccardoso
"""
import os
# os.chdir('/home/ccardoso/Cenas/Academico/OOM/CleanAtlantic/OceanParcels/My_scripts/CleanAtlantic_Madeira/')
print("Current working directory is:", os.getcwd() )
import sys
sys.path.append("..")
from datetime import datetime
import socket
from argparse import ArgumentParser
import csv
import math
from netCDF4 import Dataset
from scipy import stats
import cartopy.feature as cfeature
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
import matplotlib.pyplot as plt
import matplotlib.axes as maxes
from matplotlib.patches import Polygon
import matplotlib.ticker as ticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import colors
from datetime import timedelta
import numpy as np
from os import environ
environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
from copy import copy
import cmocean
from matplotlib.path import Path
from matplotlib.patches import PathPatch
import time
import pandas as pd
from netCDF4 import Dataset, num2date
import shapefile
from glob import glob
#%% loading data
def get_pop_dens():
pop_raster_file='../Files/gpw_v4_population_density_rev11_2020_2pt5_min.asc'
with open(pop_raster_file, 'r') as prism_f:
prism_header = prism_f.readlines()[:6]
# Read the PRISM ASCII raster header
prism_header = [item.strip().split()[-1] for item in prism_header]
prism_cols = int(prism_header[0])
prism_rows = int(prism_header[1])
prism_xll = float(prism_header[2])
prism_yll = float(prism_header[3])
prism_cs = float(prism_header[4])
prism_nodata = float(prism_header[5])
# Read in the PRISM array
prism_array = np.loadtxt(pop_raster_file, dtype=np.float, skiprows=6)
# Set the nodata values to nan
prism_array[prism_array == prism_nodata] = np.nan
# Calculate the extent of the PRISM array
# Extent is Xmin, Xmax, Ymin, Ymax
prism_extent = [prism_xll, prism_xll + prism_cols * prism_cs,
prism_yll, prism_yll + prism_rows * prism_cs]
return prism_extent, prism_array
def get_fishing_hours(res=0.1):
fish_files=sorted(glob('../Files/fishing_10_byyear_*.csv'))
for i, file in enumerate(fish_files):
if i == 0:
fish_table=pd.read_csv(file)
else:
fish_table=fish_table.append(pd.read_csv(file))
fish_table['lat_bin']*=0.1
fish_table['lon_bin']*=0.1
# bins=[np.unique(fish_table['lon_bin'].to_numpy()), np.unique(fish_table['lat_bin'].to_numpy())]
bins=[np.arange(-180,180,res), np.arange(-90,90,res)]
fish_matrix, xe, ye,_ = stats.binned_statistic_2d(fish_table['lon_bin'].to_numpy(),
fish_table['lat_bin'].to_numpy(),
fish_table['fishing_hours'].to_numpy(),
bins=bins,
statistic='sum')
xb = (xe[1:] + xe[:-1])/2
yb = (ye[1:] + ye[:-1])/2
X, Y = np.meshgrid(xb, yb)
fish_matrix[0,: ] = fish_matrix[-1, :]
return X, Y, fish_matrix
def load_particles_file(fname, varnames, start_date=None, finish_date=None, release=None,
organize_ocean=False, eliminate_beaching=False, calculate_age=True):
class ParticleData(object):
def __init__(self):
self.trajectory = []
self.loc = []
def def_organize_ocean(T):
interval=int(T.loc.shape[1]/47)
for hh in range(0, T.loc.shape[1], (47*100)):
for jjii, jj in enumerate(range(hh, hh+(47*100), interval)):
T.loc[:,hh+jj:hh+jj+400]=jjii
return T
print('Loading file: %s' %fname)
pfile = Dataset(fname, 'r')
nc_time_units=pfile.variables['time'].units
time_origin = num2date(0, nc_time_units)
if start_date is not None and finish_date is not None:
first_times=pfile.variables['time'][:,0][:]
start_date_seconds=(start_date-time_origin).total_seconds()
finish_date_seconds=(finish_date-time_origin).total_seconds()
time_index=np.where((first_times>=start_date_seconds) & (first_times <= finish_date_seconds))[0]
elif start_date is not None:
first_times=pfile.variables['time'][:,0][:]
start_date_seconds=(start_date-time_origin).total_seconds()
time_index=np.where(first_times>=start_date_seconds)[0]
elif finish_date is not None:
first_times=pfile.variables['time'][:,0][:]
finish_date_seconds=(finish_date-time_origin).total_seconds()
time_index=np.where(first_times <= finish_date_seconds)[0]
else:
time_index=np.arange(0, pfile.variables['time'].shape[0])
# time_index=np.arange(0,np.nanmax(pfile.variables['time'])+1,
# abs(pfile.variables['time'][:][0,1]-pfile.variables['time'][:][0,0]))
loc=pfile.variables['loc'][:]
if release is None:
unique_loc=np.unique(loc[~np.isnan(loc)])
release = dict(enumerate(unique_loc.flatten(), 1))
Tall = {}
for s in release.keys():
start = time.time()
j_release = np.where(loc==s)[0]
j=np.intersect1d(time_index,j_release)
if len(j) >0:
print('Loading local "%s"' %release[s])
Tall[s]=ParticleData()
deleted_values=np.ma.getmask(pfile.variables['lon'][j,:])
for iv, v in enumerate(varnames):
print('Reading variable "%s" '%v)
if len(pfile.variables[v].shape) == 1:
setattr(Tall[s], v, np.reshape(np.repeat(pfile.variables[v][j],
pfile.variables['trajectory'][j,:].shape[1]),
pfile.variables['trajectory'][j,:].shape)[~deleted_values].flatten())
elif v == 'time' and calculate_age is True:
var_time=pfile.variables[v][j,:]
else:
setattr(Tall[s], v, pfile.variables[v][j,:][~deleted_values].flatten())
if eliminate_beaching:
index_beached=np.where(Tall[s].beached.data == 1)
Tall[s].lon.data[index_beached]=np.NaN
Tall[s].lat.data[index_beached]=np.NaN
Tall[s].age.data[index_beached]=np.NaN
Tall[s].time_origin = time_origin
if calculate_age is True:
Tall[s].time=var_time[~deleted_values].flatten()
Tall[s].age=np.concatenate((np.array([np.repeat(0, var_time.shape[0])]).T,
np.cumsum(abs(np.diff(var_time, axis=1)), axis=1)),
axis=1)[~deleted_values].flatten()
end = time.time()
period=end-start
print('Completed -> Took "%g" to load' %period)
if organize_ocean is True:
Tall=def_organize_ocean(Tall[0])
return Tall
def get_coastline(file_shp_buffer, file_shp_coast=None, stype='ROMS'):
buffer_sf = shapefile.Reader(file_shp_buffer)
buffer_records = buffer_sf.records()
buffer_shapes = buffer_sf.shapes()
if 'eez_v11_clipped.shp' in file_shp_buffer:
index_key=5
else:
index_key=4
coast_buffer={}
for ind, s in enumerate(buffer_shapes):
if stype!='ROMS':
coast_id = buffer_records[ind][index_key]
lon1=[]
lat1=[]
for i in range(len(s.parts)):
i_start = s.parts[i]
if i==len(s.parts)-1:
i_end = len(s.points)
else:
i_end = s.parts[i+1]
lon1.append([i[0] for i in s.points[i_start:i_end]])
lat1.append([i[1] for i in s.points[i_start:i_end]])
else:
coast_id = int(buffer_records[ind][4])
lon1=np.array([point[0]+0.005 for point in s.points])
lat1=np.array([point[1]-0.0034 for point in s.points])
coast_buffer.update({coast_id: {'lat': lat1, 'lon': lon1, 'count': 0 ,
'density': np.nan, 'len': 0}})
if file_shp_coast is not None:
coast_sf = shapefile.Reader(file_shp_coast)
coast_records = coast_sf.records()
coast_shapes = coast_sf.shapes()
lon=[]
lat=[]
coast_id=[]
for ind, s in enumerate(coast_shapes):
if stype!='ROMS':
lon.extend([point[0] for point in s.points])
lat.extend([point[1] for point in s.points])
cid=coast_records[ind][4]
else:
lon.extend([point[0]+0.005 for point in s.points])
lat.extend([point[1]-0.0034 for point in s.points])
cid=int(coast_records[ind][0])
coast_id.extend([cid] * len(s.points))
coast_buffer[cid]['len']=coast_records[ind][3]
data_pd=pd.DataFrame(np.array([coast_id, lat, lon]).T,
columns=['id','lat', 'lon'])
# data_pd['id']=data_pd['id'].astype(int)
data_pd['lat']=data_pd['lat'].astype(float)
data_pd['lon']=data_pd['lon'].astype(float)
else:
data_pd=None
return coast_buffer, data_pd
def calculate_distance(lon, lat, lon_r, lat_r):
if ~np.isnan(lon_r) and ~np.isnan(lat_r):
R = 6373.0
lon1=np.radians(lon)
lat1=np.radians(lat)
lon2=np.radians(lon_r)
lat2=np.radians(lat_r)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = np.sin(dlat / 2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2)**2
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
distance=(R*c)
else:
distance=np.nan
return distance
#%% functions to plot
def draw_screen_poly(ax, lons, lats, fc_color, edge_color=None, linewidth=0.8, zorder=1, alpha=0.7):
if edge_color is None:
edge_color=fc_color
xy = zip(lons,lats)
poly = Polygon(list(xy), facecolor=fc_color, edgecolor=edge_color,
alpha=alpha, linewidth=linewidth, zorder = zorder)
ax.add_patch(poly)
def add_release_locs(ax, Tall):
for i, s in enumerate(Tall):
ax.plot(Tall[s].lon[0], Tall[s].lat[0], 'o', color='none',
ms=4, zorder=100, markeredgecolor='k',markeredgewidth=0.6)
def add_text(ax, textstr, fsize=12, position = 'southeast', x=None, y=None):
# left, width = .25, .5
# bottom, height = .25, .5
# right = left + width
# top = bottom + height
if position == 'northwest':
# x=left
# y=top
x=0.02
y=0.98
horizontalalignment='left'
verticalalignment='top'
elif position == 'southeast':
# x=right
# y=bottom
x=0.98
y=0.15
horizontalalignment='right'
verticalalignment='bottom'
elif position == 'southwest':
# x=left
# y=bottom
x=0.02 if x is None else x
y=0.03 if y is None else y
horizontalalignment='left'
verticalalignment='bottom'
props = dict(boxstyle='round', facecolor='white', alpha=0.9)
ax.text(x, y, textstr, transform=ax.transAxes,
fontsize=fsize, horizontalalignment=horizontalalignment,
verticalalignment=verticalalignment,
bbox=props, zorder=3000)
def setmap_subplots(release, vertical = True, figsize=None, projection=ccrs.PlateCarree(), fc_color='red',
edge_color=None, extent=[-98, 30, 0, 50], plot_release=False, inline_plotting=False,
pop_density=False, new_config=False, fishing=False):
"""
function to generate subplots in a figure
"vertical" can be: True, False or "divided" (for beaching)
"""
SMALL_SIZE = 12
MEDIUM_SIZE = 13
BIGGER_SIZE = 15
if vertical is True:
figsize=(8.3,11.7) if figsize is None else figsize
nrows=len(release.keys())
ncols=1
constrained_layout=True
elif vertical is False:
if pop_density is True or new_config==True:
figsize=(14, 9)
elif figsize is None:
figsize=(14, 8)
nrows=round(len(release.keys())/2)
ncols=2
constrained_layout=False
elif vertical == 'divided':
figsize=(11, 10)
nrows=round(len(release.keys())/2)
ncols=4
constrained_layout=False
if vertical == 'divided':
alpha_grid=0
facecolor_coast='0.9' if pop_density is False else 'none'
border_color='gray' if pop_density is False else 'black'
border_witdh=0.6
elif pop_density is True or new_config is True:
alpha_grid=0 if fishing is True else 0.5
facecolor_coast=cfeature.COLORS['land'] if fishing is True else '0.85'
extent=[-100, 20, 0, 54]
# extent=[-100, 20, 0, 50]
border_color='gray'
border_witdh=0.6
else:
alpha_grid=0.5
facecolor_coast='0.85'
border_color='gray'
border_witdh=0.3
borders=cfeature.BORDERS
coast= cfeature.GSHHSFeature(scale='high')
if pop_density is True:
pop_raster_file='../Files/gpw_v4_population_density_rev11_2020_2pt5_min.asc'
with open(pop_raster_file, 'r') as prism_f:
prism_header = prism_f.readlines()[:6]
# Read the PRISM ASCII raster header
prism_header = [item.strip().split()[-1] for item in prism_header]
prism_cols = int(prism_header[0])
prism_rows = int(prism_header[1])
prism_xll = float(prism_header[2])
prism_yll = float(prism_header[3])
prism_cs = float(prism_header[4])
prism_nodata = float(prism_header[5])
# Read in the PRISM array
prism_array = np.loadtxt(pop_raster_file, dtype=np.float, skiprows=6)
# Set the nodata values to nan
prism_array[prism_array == prism_nodata] = np.nan
# Calculate the extent of the PRISM array
# Extent is Xmin, Xmax, Ymin, Ymax
prism_extent = [prism_xll, prism_xll + prism_cols * prism_cs,
prism_yll, prism_yll + prism_rows * prism_cs]
if inline_plotting is False:
plt.ioff()
else:
plt.ion()
fig, ax = plt.subplots(figsize=figsize, nrows=nrows, ncols=ncols,
subplot_kw=dict(projection=projection),
constrained_layout=constrained_layout)
for n, axx in enumerate(ax.flatten()):
if projection==ccrs.PlateCarree() or projection==ccrs.Mercator():
gl = axx.gridlines(crs=projection, draw_labels=True,
linewidth=0.5, color='gray', alpha=alpha_grid, linestyle='--',
zorder=300)
if vertical == 'divided':
# gl.right_labels = True if n == 1 or n == 5 else False
# gl.bottom_labels = True if n<4 else False
# gl.top_labels = gl.left_labels = False
gl.ylabels_right = True if n == 1 or n == 5 else False
gl.xlabels_bottom = True if n<4 else False
gl.xlabels_top = gl.ylabels_left = False
# extent=[-99, -49, 0, 68] if n % 2 == 0 else [-33, 17, 0, 68]
extent=[-99, -47, 0, 68] if n % 2 == 0 else [-36, 16, 0, 68]
gl.ylocator = ticker.FixedLocator(range(0, 70, 10))
elif vertical is True:
# gl.left_labels = True
# gl.bottom_labels = True if n == len(ax)-1 else False
# gl.top_labels = gl.right_labels = False
gl.ylabels_left = True
gl.xlabels_bottom = True if n == len(ax)-1 else False
gl.xlabels_top = gl.ylabels_right = False
elif vertical is False:
# gl.right_labels = True if n % 2 ==0 else False
# gl.bottom_labels = True if n<2 else False
# gl.top_labels = gl.left_labels = False
gl.ylabels_right = True if n % 2 ==0 else False
gl.xlabels_bottom = True if n<2 else False
gl.xlabels_top = gl.ylabels_left = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlocator = ticker.FixedLocator(range(-100, 60, 20))
else:
gl = axx.gridlines()
axx.set_extent(extent)
axx.add_feature(coast, edgecolor='black', linewidth=0.3, zorder=3, facecolor=facecolor_coast)
axx.add_feature(borders, linewidth=border_witdh, zorder=3, edgecolor=border_color)
if vertical =='divided':
ocean=cfeature.OCEAN
axx.add_feature(ocean, facecolor='0.5')
axx.add_feature(coast, edgecolor='none', zorder=1, facecolor='white')
if (vertical == 'divided' and n % 2 == 0):
add_text(axx, release[math.ceil(n/2)+1]['name'], BIGGER_SIZE,
position='southwest', x=0.04)
elif vertical != 'divided':
add_text(axx, release[n+1]['name'], BIGGER_SIZE, position='southwest')
if plot_release is True:
# for kk in release.keys():
lats=release[n+1]['lats'] + release[n+1]['lats'][::-1]
lons = [(release[n+1]['lons'] * 2)[i] for i in [0, 2, 1, 3]]
draw_screen_poly(axx, lons, lats, fc_color, edge_color)
if pop_density is True:
img_plot = axx.imshow(prism_array,
extent=prism_extent,
cmap='cmo.gray_r',
norm=colors.LogNorm(vmin=0.1, vmax=10000),
interpolation='nearest',
zorder=2)
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fon
if fishing is True or new_config is True:
for j, v in enumerate(ax.flatten()):
yoffset=+0.02 if j<2 else 0.016
xoffset=-0.08 if (j+1) % 2 else -0.024
pos1 = v.get_position() # get the original position
v.set_position([pos1.x0+xoffset , pos1.y0+yoffset, pos1.width *1.25, pos1.height *1.25])
# if pop_density is True:
# add_colorbar(ax, img_plot, 'Population density (persons.km$^{-2}$)', location='top', fig=fig,
# log=True, extend='max')
elif vertical == 'divided':
for j, v in enumerate(ax.flatten()):
yoffset=-0.022 if j<4 else 0.04
xoffset=+0.016 if (j == 2 or j == 3 or j == 6 or j == 7) else -0.08
xoffset+=-0.027 if j % 2 == 0 else -0.0
pos1 = v.get_position() # get the original position
v.set_position([pos1.x0+xoffset , pos1.y0+yoffset, pos1.width *1.35, pos1.height *1.35])
if pop_density is True:
add_colorbar(ax, img_plot, 'Population density (persons.km$^{-2}$)', location='top', fig=fig,
log=True, extend='max')
# cmap = plt.get_cmap('Spectral_r', lut=16)
# cnorm=colors.Normalize(vmin=0, vmax=40)
# co = plt.cm.ScalarMappable(cmap=cmap, norm=cnorm)
# add_colorbar(ax, co, 'test', location='bottom', fig=fig,
# log=True, extend='max')
elif vertical is False:
for j, v in enumerate(ax.flatten()):
yoffset=0.04 if j<2 else 0.01
xoffset=-0.08 if (j+1) % 2 else -0.024
pos1 = v.get_position() # get the original position
v.set_position([pos1.x0+xoffset , pos1.y0+yoffset, pos1.width *1.25, pos1.height *1.25])
return fig, ax
def add_colorbar(ax, mappable, label, location='right', divider=None, extend='neither',
log = False, fig=None, ticks=None, spacing='proportional', mappable2=None):
if type(ax) is not np.ndarray and type(ax) != list:
if location == 'bottom':
pad=0.4
size="4%"
elif location == 'left':
pad=0.8
size="3%"
elif location == 'right':
pad=0.1
size="3%"
elif location == 'top':
pad=0.1
size="4%"
else:
print('Invalid location!')
if divider is None:
divider = make_axes_locatable(ax)
cax = divider.append_axes(location, size=size, pad=pad, axes_class=maxes.Axes)
if location == 'bottom' or location == 'top':
cb=plt.colorbar(mappable, cax=cax, orientation='horizontal', extend=extend, spacing=spacing)
if log is True:
cb.ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda y,pos: ('{{:.{:1d}f}}'.format(int(np.maximum(-np.log10(y),0)))).format(y)))
cb.ax.set_xlabel(label)
cb.ax.xaxis.set_label_position(location)
elif location == 'left':
cb=plt.colorbar(mappable, cax=cax, extend=extend, spacing=spacing)
if log is True:
cb.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda y,pos: ('{{:.{:1d}f}}'.format(int(np.maximum(-np.log10(y),0)))).format(y)))
cb.ax.set_ylabel(label)
cb.ax.yaxis.set_label_position(location)
cb.ax.yaxis.tick_left()
else:
cb=plt.colorbar(mappable, cax=cax, extend=extend, spacing=spacing)
if log is True:
cb.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda y,pos: ('{{:.{:1d}f}}'.format(int(np.maximum(-np.log10(y),0)))).format(y)))
cb.ax.set_ylabel(label)
return divider
elif type(ax) == list:
cax = fig.add_axes([ax[10].get_position().x0,
ax[10].get_position().y0-0.02,
ax[15].get_position().x1-ax[10].get_position().x0,
0.015])
if ticks == 'log':
ticks = np.linspace(mappable.get_clim()[0],(mappable.get_clim()[0]*10)/2,5)
ticks = np.concatenate((ticks,np.linspace(mappable.get_clim()[0]*10,(mappable.get_clim()[0]*100)/2,5)))
ticks = np.concatenate((ticks,np.arange(mappable.get_clim()[0]*100,mappable.get_clim()[1]+1,10)))
cb=fig.colorbar(mappable, cax=cax, orientation='horizontal', spacing=spacing,
extend=extend, ticks=ticks, format='%.1f')
else:
cb=fig.colorbar(mappable, cax=cax, orientation='horizontal', extend=extend, spacing=spacing)
cb.set_label(label)
else:
if ax.shape[0]*ax.shape[1] == 4:
if location=='top':
cax = fig.add_axes([ax.flatten()[0].get_position().x0,
ax.flatten()[0].get_position().y1+0.006,
ax.flatten()[1].get_position().x1-ax.flatten()[0].get_position().x0,
0.025])
else:
if mappable2 is not None:
cax_top = fig.add_axes([ax.flatten()[2].get_position().x0,
ax.flatten()[2].get_position().y0-0.026,
ax.flatten()[3].get_position().x1-ax.flatten()[2].get_position().x0,
0.02])
cax_bottom = fig.add_axes([ax.flatten()[2].get_position().x0,
ax.flatten()[2].get_position().y0-0.046,
ax.flatten()[3].get_position().x1-ax.flatten()[2].get_position().x0,
0.02])
else:
cax = fig.add_axes([ax.flatten()[2].get_position().x0,
ax.flatten()[2].get_position().y0-0.028,
ax.flatten()[3].get_position().x1-ax.flatten()[2].get_position().x0,
0.025])
else:
if location == 'top':
cax = fig.add_axes([ax.flatten()[0].get_position().x0,
ax.flatten()[0].get_position().y1+0.005,
ax.flatten()[3].get_position().x1-ax.flatten()[0].get_position().x0,
0.02])
else:
cax = fig.add_axes([ax.flatten()[4].get_position().x0,
ax.flatten()[4].get_position().y0-0.025,
ax.flatten()[7].get_position().x1-ax.flatten()[4].get_position().x0,
0.02])
# cb=fig.colorbar(mappable, ax=ax.flat, orientation='horizontal', pad=0.008, fraction=0.001)
if log is True:
# cb=fig.colorbar(mappable, cax=cax, orientation='horizontal', ticks=mappable.levels.tolist(),
# format=ticker.LogFormatter(), spacing=spacing)
if ticks is None:
# ticks=[10e0, 10e1, 10e2, 10e3, 10e4, 10e5]
# ticks_format=ticker.LogFormatter(10, labelOnlyBase=False)
cb=fig.colorbar(mappable, cax=cax, orientation='horizontal', spacing=spacing,
extend=extend, ticks=ticks, format='%.1f')
cax.xaxis.set_ticks_position(location)
cax.xaxis.set_label_position(location)
elif ticks == 'log':
ticks = np.linspace(mappable.get_clim()[0],(mappable.get_clim()[0]*10)/2,5)
ticks = np.concatenate((ticks,np.linspace(mappable.get_clim()[0]*10,(mappable.get_clim()[0]*100)/2,5)))
ticks = np.concatenate((ticks,np.arange(mappable.get_clim()[0]*100,mappable.get_clim()[1]+1,10)))
if mappable2 is not None:
cb=fig.colorbar(mappable, cax=cax_bottom, orientation='horizontal', spacing=spacing,
extend=extend, ticks=ticks, format='%.1f')
cb.ax.tick_params(axis='x', which='both', direction='in')
cb_top=fig.colorbar(mappable2, cax=cax_top, orientation='horizontal', spacing=spacing,
extend=extend, ticks=cb.ax.get_xticks())
cb_top.ax.tick_params(axis='x', direction='in', which='both')
for ilabel in cb_top.ax.xaxis.get_ticklabels():
ilabel.set_visible(False)
else:
cb=fig.colorbar(mappable, cax=cax, orientation='horizontal', spacing=spacing,
extend=extend, ticks=ticks, format='%.1f')
# cb.locator=ticker.LogLocator()
# cb.formatter=ticker.LogFormatter()
cb.set_label(label)
else:
cb=fig.colorbar(mappable, cax=cax, orientation='horizontal', extend=extend, spacing=spacing,
label=label)
cax.xaxis.set_ticks_position(location)
cax.xaxis.set_label_position(location)
# cb.set_label(label)
if type(ticks)==np.ndarray:
cb.set_ticks(ticks)
elif ticks =='years':
t_ticks=np.arange(0, mappable.get_clim()[1], 365)
t_ticks_labels=np.arange(0, len(t_ticks))
cb.set_ticks(t_ticks)
cb.set_ticklabels(t_ticks_labels)
# cb.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda y,pos: ('{{:.{:1d}f}}'.format(int(np.maximum(-np.log10(y),0)))).format(y)))
return cb
def set_max(co, imax=None, imin=None, method='max'):
"""
Find maximum value between subplots
"""
if imax is None:
if method == 'second_largest':
imax = np.sort([n.get_clim()[1] for n in co])[-2]
if method == 'max':
imax = np.max([n.get_clim()[1] for n in co])
elif method == 'mean':
imax = np.mean([n.get_clim()[1] for n in co])
if imin is None:
imin= np.min([n.get_clim()[0] for n in co])
for n in co:
n.set_clim(vmin=imin, vmax=imax)
def plt_particles(fig, ax, Tall, plot_depth=False):
scat = {}
if plot_depth is False:
for i, s in enumerate(Tall):
b = Tall[s].time == 0
scat[s] = ax.scatter(Tall[s].lon[b], Tall[s].lat[b], c=plt.cm.hsv(float(s)/len(Tall)),
s=5, alpha=.5, transform=ccrs.PlateCarree())
else:
for i, s in enumerate(Tall):
b = Tall[s].time == 0
scat[s]= ax.scatter(Tall[s].lon[b], Tall[s].lat[b], c=Tall[s].z[b], cmap=plt.cm.jet,
s=5, alpha=.5, vmin=0, vmax=4500, transform=ccrs.PlateCarree())
return scat
def plt_hist_particles(fig, ax, Tall, stype='ROMS', unique = False, beaching=False, buffer=False,
percentage=False, density=False, plot_release=False, log=False, age=False,
hist_depth=False, hist2d_windage=False, method='mean', cmap_name='Spectral_r',
set_clip=None, imax=None):
def count_unique(values):
return np.unique(values).shape[0]
def count_mode(values):
return stats.mode(values)[0][0]
def count_mode_unique(values):
unique_p0_round=np.fix(np.unique(values)*100)+1
return stats.mode(unique_p0_round)[0][0]
def deal_with_H(H, xe, ye):
H[0,: ] = H[-1, :]
xb = (xe[1:] + xe[:-1])/2
yb = (ye[1:] + ye[:-1])/2
xbb, ybb = np.meshgrid(xb, yb)
xbb[xbb>180]=xbb[xbb>180]-360
return H, xbb, ybb
#creating colormaps
if hist2d_windage is True and (method == 'mode' or method=='mode_unique'):
cmap = copy(plt.get_cmap(cmap_name, 5))
elif age is True:
levels=round((imax/365)*4) if method!='std' else round((imax/365)*6)
cmap = copy(plt.get_cmap(cmap_name, levels))
else:
cmap = copy(plt.get_cmap(cmap_name))
cmap.set_under('white')
cmap.set_over('magenta')
#loading data from particle file
if stype == 'ROMS':
nc_grid='../Files/roms_grid_d02_Zlev_Okean.nc'
ROMS_grid=Dataset(nc_grid)
nc_lon=ROMS_grid.variables['lon_psi'][0,:].data
nc_lat=ROMS_grid.variables['lat_psi'][:,0].data
# nc_lon=ROMS_grid.variables['lon_rho'][0,:].data
# nc_lat=ROMS_grid.variables['lat_rho'][:,0].data
nc_lon_grid, nc_lat_grid = np.meshgrid(nc_lon, nc_lat)
bins=[nc_lon, nc_lat]
shape_0=Tall[0].lon.shape[0]
shape_1=0
for s in Tall:
shape_1+=Tall[s].lon.shape[1]
lon=np.empty((shape_0, shape_1))
lat=np.empty((shape_0, shape_1))
if unique is True or beaching is True or age is True or hist2d_windage is True:
weights=np.empty((shape_0, shape_1))
if beaching is True:
beached=np.empty((shape_0, shape_1))
try:
depth=np.empty((shape_0, shape_1))
except:
depth=None
count=0
for s in Tall:
shape_n=Tall[s].lon.shape[1]
lon[:,count:count+shape_n]=Tall[s].lon
lat[:,count:count+shape_n]=Tall[s].lat
if age is True:
weights[:,count:count+shape_n]=np.abs(Tall[s].age)/86400
elif unique is True or beaching is True:
weights[:,count:count+shape_n]=Tall[s].trajectory
elif hist2d_windage is True:
if method=='mean':
weights[:,count:count+shape_n]=Tall[s].p0*100
elif method=='mode':
weights[:,count:count+shape_n]=np.fix(Tall[s].p0*100)+1
elif method=='mode_unique':
weights[:,count:count+shape_n]=Tall[s].trajectory
if beaching is True:
beached[:,count:count+shape_n]=Tall[s].beached
if depth is not None:
depth[:,count:count+shape_n]=Tall[s].z
count+=shape_n
# lon = lon.flatten()
# lat = lat.flatten()
# if age is True or unique is True or beaching is True:
# weights=weights.flatten()
# if beaching is True:
# if depth is not None:
# depth=depth.flatten()
# beached=beached.flatten()
elif stype=='MERCATOR':
#----> finish this condition for MERCATOR!
nc_lon=np.arange(-100, 31, 0.5)
nc_lat=np.arange(0, 70, 0.5)
bins=[nc_lon, nc_lat]
nc_lon_grid, nc_lat_grid = np.meshgrid(nc_lon, nc_lat)
lon=Tall.lon
lat=Tall.lat
shape_1=len(np.unique(Tall.trajectory))
# shape_1=lon.shape[1]
if age is True:
weights=np.abs(Tall.age)/86400
elif unique is True or beaching is True:
weights=Tall.trajectory
elif hist_depth is True:
weights=Tall.z
elif hist2d_windage is True:
if method=='mean':
weights=Tall.p0*100
elif method=='mode':
weights=np.fix(Tall.p0*100)+1
elif method=='mode_unique':
weights=Tall.p0
if beaching is True:
beached=Tall.beached
try:
depth=Tall.z
except:
depth=None
else:
print('WRONG stype!!')
if hist2d_windage is True:
if method == 'mode':
HH, xe, ye,_ = stats.binned_statistic_2d(lon[~np.isnan(lon)], lat[~np.isnan(lat)].data,
weights[~np.isnan(lon)].data, bins=bins,
statistic=count_mode)
elif method=='mode_unique':
HH, xe, ye,_ = stats.binned_statistic_2d(lon[~np.isnan(lon)], lat[~np.isnan(lat)].data,
weights[~np.isnan(lon)].data, bins=bins,
statistic=count_mode_unique)
else:
HH, xe, ye,_ = stats.binned_statistic_2d(lon[~np.isnan(lon)], lat[~np.isnan(lat)].data,
weights[~np.isnan(lon)].data, bins=bins,
statistic=method)
H, xbb, ybb = deal_with_H(HH, xe, ye)
co = ax.pcolormesh(xbb, ybb, H.T,
cmap = cmap,
zorder = 0)
elif age is True or hist_depth is True:
HH, xe, ye,_ = stats.binned_statistic_2d(lon[~np.isnan(lon)], lat[~np.isnan(lat)].data,
weights[~np.isnan(lon)].data, bins=bins,
statistic=method)
H, xbb, ybb = deal_with_H(HH, xe, ye)
if method == 'std':
H[np.where(H==0)]=np.nan
co = ax.pcolormesh(xbb, ybb, H.T,
cmap = cmap,
zorder = 0)
# co = ax.contourf(xbb, ybb, H.T, levels=levels, zorder =0, extend='max')
elif unique is True:
HH, xe, ye,_ = stats.binned_statistic_2d(lon[~np.isnan(lon)], lat[~np.isnan(lat)].data,
weights[~np.isnan(lon)].data, bins=bins,
statistic=count_unique)
H=(HH/shape_1)*100
H = np.ma.masked_where(H < np.min(H)+1e-15, H)
H, xbb, ybb = deal_with_H(H, xe, ye)
cnorm=colors.LogNorm(vmin = 0.1, vmax = 30) if log is True else None
if set_clip is None:
co = ax.pcolormesh(xbb, ybb, H.T,
norm = cnorm,
cmap = cmap,
zorder = 0)
else:
cmap_gray = copy(plt.get_cmap('gray_r'))
cmap_gray.set_under('white')
ax.pcolormesh(xbb, ybb, H.T,
norm = cnorm,
cmap = cmap_gray,
zorder = 0)
for ipoly in set_clip:
patch = PathPatch(ipoly,
facecolor='none',
linewidth=0.8,
edgecolor='crimson')
ax.add_patch(patch)
co = ax.pcolormesh(xbb, ybb, H.T,
norm = cnorm,
cmap = cmap,
zorder = 1,
clip_path=patch)
elif beaching is True:
cc = (beached == 1)
if depth is not None and stype!='MERCATOR':
tt = (depth < 10)
cc = np.logical_and(cc, tt)
H, xe, ye,_ = stats.binned_statistic_2d(lon[cc], lat[cc].data,
weights[cc].data, bins=bins, statistic=count_unique)
H, xbb, ybb = deal_with_H(H, xe, ye)
H=H.T
if buffer is False:
cnorm=colors.LogNorm() if log is True else None
H = np.ma.masked_where(H < 10, H)
co= ax.pcolormesh(xbb, ybb, H,
norm = cnorm,
cmap = cmap,
zorder = 20)
else:
nonzero=np.where(H>0)
# testing
# ax.plot(lon[cc], lat[cc], '.', color='red', ms=1, zorder=100)
# ax.plot(xbb[nonzero], ybb[nonzero], '.', color='black', ms=5, zorder=110)
#gettign shapes
if stype=='ROMS':
file_shp_buffer='../Files/AllIslands_long_buffer_splitted_concelhos_Dserta_merged.shp'
file_shp_coast='../Files/Madeira_coastline_WGS84_split_concelhos.shp'
else:
raise Exception("Histogram (buffer) for beached particles on MERCATOR simulations \
must be done with 'plt_hist_particles_beached_buffer'")
coast_buffer, coastline=get_coastline(file_shp_buffer, file_shp_coast, stype=stype)
for i in range(nonzero[0].shape[0]):
lon_p=xbb[0,nonzero[1][i]]
lat_p=ybb[nonzero[0][i],0]
pnumber=H[nonzero[0][i],nonzero[1][i]]
dist=calculate_distance(coastline['lon'], coastline['lat'], lon_p, lat_p)
JJ=np.unravel_index(np.argmin(dist, axis=None), dist.shape)[0]
cid=coastline['id'][JJ]
coast_buffer[cid]['count']+=int(pnumber)
if density is True:
for key, value in coast_buffer.items():
value['density']=value['count']/value['len']
max_scale=np.max([value['density'] for key, value in coast_buffer.items()])
min_scale=0
else:
if percentage is True:
sum_beached=np.sum([value['count'] for key, value in coast_buffer.items()])
for key, value in coast_buffer.items():
value.update(percentage= (value['count']/sum_beached)*100)
max_scale=np.max([value['percentage'] for key, value in coast_buffer.items()])
cnorm=colors.LogNorm(vmin=0.1, vmax=max_scale) if log is True else \
colors.Normalize(vmin=0, vmax=max_scale)
else:
max_scale=np.max([value['count'] for key, value in coast_buffer.items()])
cnorm=colors.LogNorm(vmin=1, vmax=max_scale) if log is True else \
colors.Normalize(vmin=1, vmax=max_scale)
for pol in coast_buffer:
if percentage is True:
color = cmap(cnorm(coast_buffer[pol]['percentage']))
elif density is True:
color = cmap((coast_buffer[pol]['density']-min_scale)/max_scale)
else:
color= cmap(cnorm(coast_buffer[pol]['count']))
draw_screen_poly(ax, coast_buffer[pol]['lon'], coast_buffer[pol]['lat'],
color, alpha=1, zorder=0)
co = plt.cm.ScalarMappable(cmap=cmap, norm=cnorm)
else:
H, xe, ye = np.histogram2d(lon[~np.isnan(lon)], lat[~np.isnan(lat)], bins=bins)
H, xbb, ybb = deal_with_H(H, xe, ye)
if log is False:
if stype=='ROMS':
levels=[1, 5, 10, 50, 100, 500, 1000, 5000, 10000]
# levels= np.arange(1, 10000, 1000)
else:
levels=[1, 5, 10, 50, 100, 500, 1000, 5000, 10000, 20000, 50000, 100000]
# levels= np.arange(1, 100000, 10000)
co = ax.contourf(xbb, ybb, H.T, levels=levels, zorder =0, extend='max')
else:
# co = ax.pcolormesh(xbb, ybb, H.T, norm=colors.LogNorm(), zorder=0,
# shading='nearest', cmap=plt.get_cmap('viridis', 12))
lvls = np.linspace(10e0,10e1,10)
lvls = np.concatenate((lvls[:-1],np.linspace(10e1,10e2,10)))
lvls = np.concatenate((lvls[:-1],np.linspace(10e2,10e3,10)))
lvls = np.concatenate((lvls[:-1],np.linspace(10e3,10e4,10)))
co = ax.contourf(xbb, ybb, H.T, levels=lvls, norm = colors.LogNorm(),
zorder =0, extend='max')
co.cmap.set_over('orangered')
if plot_release is True:
add_release_locs(ax, Tall)
return co
def plt_hist_particles_beached_buffer(fig, ax_b, Tall, var='count', plot_release=False, log=False,
vertical='divided', fname=None, method='mean', max_scale=40):
"""
Function to calculate beached particles. ONLY WORKS FOR MERCATOR (subplots)
var='count', 'percentage' or 'age'
"""
def write_text_data(fname, Tall, var):
with open(fname +'.csv', 'w', newline='') as csvfile:
fieldnames = ['location', 'country']
if var=='percentage':
fieldnames.extend(['count', var])
else:
fieldnames.append(var)
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for s, (key_tall, Tall_i) in enumerate(Tall.items()):
for key_buffer, buffer in Tall_i.coast_buffer.items():
if var=='percentage':
writer.writerow({'location': key_tall,
'country': key_buffer,
'count': buffer['count'],
'percentage': buffer['percentage']})
else:
writer.writerow({'location': key_tall,
'country': key_buffer,
var: buffer[var]})
def count_unique(values):
return np.unique(values).shape[0]
def deal_with_H(H, xe, ye):
H[0,: ] = H[-1, :]