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mean_spread_salinity.py
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#!/usr/bin/python3
###############################################
#
# global reqs
#
###############################################
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from numpy import meshgrid
from numpy import linspace
from matplotlib import cm
import configparser
import numpy as np
import matplotlib
import traceback
import datetime
import warnings
import xarray
import numpy
import math
import pdb
import sys
import os
# suppress warnings
warnings.filterwarnings('ignore')
###############################################
#
# initial config
#
###############################################
appname = "PlotSalinity"
###############################################
#
# main
#
###############################################
if __name__ == "__main__":
###############################################
#
# read input parameters
#
###############################################
# read config file name
configFile = None
try:
configFile = sys.argv[1]
except:
print("[ERROR] -- Config file not provided!")
sys.exit(1)
# read date
inputDate = None
try:
inputDate = sys.argv[2]
except:
inputDate = datetime.datetime.today().strftime("%Y%m%d")
# read day index
day_index = None
try:
day_index = int(sys.argv[3])
except:
day_index = 0
###############################################
#
# parse config file
#
###############################################
configParser = configparser.ConfigParser()
configParser.read(configFile)
# paths
basePath = configParser.get("default", "basePath")
baseOutputPath = configParser.get("default", "baseOutputPath")
meanFile = os.path.join(basePath, inputDate, configParser.get("salinity", "meanFile"))
stdFile = os.path.join(basePath, inputDate, configParser.get("salinity", "stdFile"))
outputFolder = configParser.get("salinity", "outputFolder")
outputFileTemplate = configParser.get("salinity", "outputName")
print("[%s] -- Mean file set to: %s" % (appname, meanFile))
print("[%s] -- Std file set to: %s" % (appname, stdFile))
# create output folder if needed
dst = os.path.join(baseOutputPath, outputFolder.format(DATE=inputDate))
if not os.path.exists(dst):
os.makedirs(dst, exist_ok=True)
print("[%s] -- Output folder set to: %s" % (appname, dst))
# black sea mask
blackSeaMaskLat = configParser.getfloat("default", "blackSeaMaskLat")
blackSeaMaskLon = configParser.getfloat("default", "blackSeaMaskLon")
print("[%s] -- Black sea boundaries set to: %s, %s" % (appname, blackSeaMaskLat, blackSeaMaskLon))
# chart details
meanColorMap = configParser.get("salinity", "meanColorMap")
meanMinValue = configParser.getfloat("salinity", "meanMinValue")
meanMaxValue = configParser.getfloat("salinity", "meanMaxValue")
meanLevels = configParser.getint("salinity", "meanLevels")
stdColorMap = configParser.get("salinity", "stdColorMap")
stdMinValue = configParser.getfloat("salinity", "stdMinValue")
stdMaxValue = configParser.getfloat("salinity", "stdMaxValue")
stdLevels = configParser.getint("salinity", "stdLevels")
resolution = configParser.get("salinity", "resolution")
print("[%s] -- Mean color map set to: %s" % (appname, meanColorMap))
print("[%s] -- Mean min value set to: %s" % (appname, meanMinValue))
print("[%s] -- Mean max value set to: %s" % (appname, meanMaxValue))
print("[%s] -- Std color map set to: %s" % (appname, stdColorMap))
print("[%s] -- Std min value set to: %s" % (appname, stdMinValue))
print("[%s] -- Std max value set to: %s" % (appname, stdMaxValue))
print("[%s] -- Resolution set to: %s" % (appname, resolution))
###############################################
#
# start processing
#
###############################################
# open dataset STD
ds1 = xarray.open_dataset(stdFile)
# open dataset MEAN
ds2 = xarray.open_dataset(meanFile)
# grid indices
x = ds1.lon.values
y = ds1.lat.values
# lats and lons
lats = ds1.lat.transpose().values
lons = ds1.lon.values
# central points
clat = (lats[-1] - lats[0]) / 2
clon = (lons[-1] - lons[0]) / 2
# create the grid
xxx, yyy = meshgrid(lons, lats)
# define a timestep index (to keep track of the timesteps) and an index to keep track of the day we are in
timestep_index = 0
day_current_index = 0
old_day = str(ds1.time[0].values).split("T")[0]
# iterate over the timestamps
for t in ds1.time.values:
# get the date of the current timestep, and optionally update the variable and index keeping track of the day
d1 = str(t).split("T")[0]
if d1 != old_day:
old_day = d1
day_current_index += 1
# get days string
d1 = str(t).split("T")[0]
hour = str(t).split("T")[1].split(":")[0]
minu = str(t).split("T")[1].split(":")[1]
d2 = "%s:%s" % (hour, minu)
d3 = "%s, %s" % (d1, d2)
d4 = "%s_%s30" % (d1, hour)
# check if it's the desired day, otherwise move on
if day_current_index != day_index:
timestep_index += 1
continue
# iterate over depth
depth_index = 0
for d in ds1.depth.values:
fig, axes = plt.subplots(nrows=2, ncols=1)
ax_index = 0
for ax in axes.flat:
if ax_index == 0:
############################################
#
# Mean
#
############################################
# initialise the map
bmap = Basemap(resolution=resolution,
llcrnrlon=lons[0],llcrnrlat=lats[0],
urcrnrlon=lons[-1],urcrnrlat=lats[-1], ax=ax)
# customize colormap
min_val, max_val = 0.15, 1.0
n = 10
orig_cmap = cm.gist_rainbow
colors = orig_cmap(np.linspace(min_val, max_val, n))
cmap = cm.colors.LinearSegmentedColormap.from_list("mycmap", colors)
# contour MEAN
meanLevelsContour = linspace(meanMinValue, meanMaxValue, num=meanLevels)
mean_data_0 = ds2.vosaline[timestep_index,depth_index,:,:]
mean_data_1 = mean_data_0.where(mean_data_0 > 0, other=np.nan)
mean_data_2 = mean_data_1.where((mean_data_1['lat'] <= blackSeaMaskLat) | (mean_data_1['lon'] <= blackSeaMaskLon), np.nan)
mean_data = mean_data_2.values
# set adaptive min and max
meanMinValue = numpy.nanmin(mean_data_2.values)
meanMaxValue = numpy.nanmax(mean_data_2.values)
mean_colormesh = bmap.contourf(xxx, yyy, mean_data, cmap=cmap, levels=meanLevelsContour, linewidths=0.15, extend='both') #, vmin=meanMinValue, vmax=meanMaxValue)
# draw coastlines, country boundaries, fill continents.
bmap.drawcoastlines(linewidth=0.25)
bmap.fillcontinents(color="white")
bmap.drawparallels(range(0, 90, 5), linewidth=0.1, labels=[1,0,0,1], fontsize=2)
bmap.drawmeridians(range(-90, 90, 5), linewidth=0.1, labels=[1,0,0,1], fontsize=2)
# colorbar MEAN
meanTicks = range(int(meanMinValue), int(meanMaxValue)+1)
mean_cb = bmap.colorbar(mean_colormesh, ticks=meanTicks, location="right", ax=ax)
mean_cb.set_label("Mean salinity (psu)", fontsize=5)
for t in mean_cb.ax.get_yticklabels():
t.set_fontsize(3)
# title
# finalDate = "%s:30" % (d3.split(":")[0])
finalDate = d1
ax.set_title("Ensemble mean for salinity at %s m\nDaily mean: %s" % (int(d), finalDate), fontsize = 5)
else:
############################################
#
# Std
#
############################################
# initialise the map
bmap = Basemap(resolution=resolution,
llcrnrlon=lons[0],llcrnrlat=lats[0],
urcrnrlon=lons[-1],urcrnrlat=lats[-1], ax=ax)
# define the new colormap
max_percentage = 100
white_percentage = 10
white = np.array([256/256, 256/256, 256/256, 1])
reds = cm.get_cmap(stdColorMap, 256)
fv = reds(np.linspace(0, 1, max_percentage))
fv[:white_percentage, :] = white
fv[white_percentage:, :] = reds(np.linspace(0, 1, max_percentage-white_percentage))
newcmp = ListedColormap(fv)
# contourf STD
stdLevelsContourf = linspace(stdMinValue, stdMaxValue, num=stdLevels+1)
std_data_0 = ds1.vosaline[timestep_index,depth_index,:,:]
std_data_1 = std_data_0.where(((std_data_0.lat <= blackSeaMaskLat) | (std_data_0.lon <= blackSeaMaskLon)))
std_data = std_data_1.values
std_colormesh = bmap.contourf(xxx, yyy, std_data, cmap=newcmp, levels=stdLevelsContourf, extend='both')
# colorbar STD
stdTicks = numpy.arange(stdMinValue, stdMaxValue+0.1, 0.1)
std_cb = bmap.colorbar(std_colormesh, ticks = stdTicks, ax=ax, shrink=0.6, location="right")
std_cb.set_label("Spread (psu)", fontsize=5)
for t in std_cb.ax.get_yticklabels():
t.set_fontsize(3)
# draw coastlines, country boundaries, fill continents.
bmap.drawcoastlines(linewidth=0.25)
bmap.fillcontinents(color="white")
bmap.drawparallels(range(0, 90, 5), linewidth=0.1, labels=[1,0,0,1], fontsize=2)
bmap.drawmeridians(range(-90, 90, 5), linewidth=0.1, labels=[1,0,0,1], fontsize=2)
# title
# finalDate = "%s:30" % (d3.split(":")[0])
finalDate = d1
ax.set_title("Ensemble spread for salinity at %s m\nDaily mean: %s" % (int(d), finalDate), fontsize = 5)
ax_index += 1
# Adjust spacing between subplots
plt.tight_layout()
# save file
di = ds1.depth.values.tolist().index(d)
filename = os.path.join(dst, outputFileTemplate.format(DATE=d4, DEPTH=di))
plt.savefig(filename, dpi=300, bbox_inches="tight")
print("File %s generated" % filename)
# clear memory
fig.clear()
plt.close(fig)
# increment depth_index
depth_index += 1
# increment timestep index
timestep_index += 1