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NSRF_bottomT_habitat.py
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'''
Bottom temperature maps for Shrimp RAP
To generate bottom climato:
import numpy as np
import azmp_utils as azu
dc = .10
lonLims = [-66, -56] # Lab Sea
latLims = [57, 67]
lon_reg = np.arange(lonLims[0]+dc/2, lonLims[1]-dc/2, dc)
lat_reg = np.arange(latLims[0]+dc/2, latLims[1]-dc/2, dc)
Tbot_dict = azu.get_bottomT_climato('/home/cyrf0006/data/dev_database/*.nc', lon_reg, lat_reg, year_lims=[2006, 2018], season='summer', h5_outputfile='Tbot_climato_NSRF_summer_2006-2018.h5')
* see: /home/cyrf0006/AZMP/state_reports/bottomT
'''
import os
import netCDF4
import h5py
import xarray as xr
os.environ['PROJ_LIB'] = '/home/cyrf0006/anaconda3/share/proj'
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
import openpyxl, pprint
import pandas as pd
from scipy.interpolate import griddata
from scipy.interpolate import interp1d # to remove NaNs in profiles
from scipy.interpolate import RegularGridInterpolator as rgi
import azmp_utils as azu
from scipy.ndimage.filters import gaussian_filter
import scipy.ndimage
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
from matplotlib.patches import Rectangle
from matplotlib.patches import Polygon as PP
# For shapefiles
import shapefile
def draw_screen_poly( lats, lons, m):
x, y = m( lons, lats )
xy = zip(x,y)
poly = PP( xy, facecolor=[.8, .8, .8])
plt.gca().add_patch(poly)
## ---- Region parameters ---- ## <-------------------------------Would be nice to pass this in a config file '2017.report'
dataFile = '/home/cyrf0006/data/GEBCO/GRIDONE_1D.nc'
lon_0 = -50
lat_0 = 50
proj = 'merc'
zmax = 1000 # do try to compute bottom temp below that depth
zmin = 10
dz = 5 # vertical bins
season = 'summer'
year = '2021'
climato_file = 'Tbot_climato_NSRFx_summer_2006-2021.h5'
year_file = '/home/cyrf0006/data/dev_database/netCDF/' + year + '.nc'
## ---- Load Climato data ---- ##
print('Load ' + climato_file)
h5f = h5py.File(climato_file, 'r')
Tbot_climato = h5f['Tbot'][:]
lon_reg = h5f['lon_reg'][:]
lat_reg = h5f['lat_reg'][:]
Zitp = h5f['Zitp'][:]
h5f.close()
## ---- Derive some parameters ---- ##
lon_0 = np.round(np.mean(lon_reg))
lat_0 = np.round(np.mean(lat_reg))
lonLims = [lon_reg[0], lon_reg[-1]]
latLims = [lat_reg[0], lat_reg[-1]]
lon_grid, lat_grid = np.meshgrid(lon_reg,lat_reg)
dc = np.diff(lon_reg[0:2])
## ---- SFAs ---- ##
## Get SFA shapes
myshp = open('/home/cyrf0006/github/AZMP-NL/utils/SFAs/SFAs_PANOMICS_Fall2020_shp/SFAs_PANOMICS_Fall2020.shp', 'rb')
mydbf = open('/home/cyrf0006/github/AZMP-NL/utils/SFAs/SFAs_PANOMICS_Fall2020_shp/SFAs_PANOMICS_Fall2020.dbf', 'rb')
r = shapefile.Reader(shp=myshp, dbf=mydbf, encoding = "ISO8859-1")
records = r.records()
shapes = r.shapes()
# Fill dictionary with SFAs
shrimp_area = {}
for idx, rec in enumerate(records):
if rec[1] == 'Eastern Assessment Zone':
shrimp_area['2'] = np.array(shapes[idx].points)
elif rec[1] == 'Western Assessment Zone':
shrimp_area['3'] = np.array(shapes[idx].points)
else:
shrimp_area[rec[0]] = np.array(shapes[idx].points)
## sfa0 = Polygon(shrimp_area['0'])
## sfa1 = Polygon(shrimp_area['1'])
sfa2 = Polygon(shrimp_area['2'])
sfa3 = Polygon(shrimp_area['3'])
sfa4 = Polygon(shrimp_area['4'])
sfa5 = Polygon(shrimp_area['5'])
sfa6 = Polygon(shrimp_area['6'])
sfa7 = Polygon(shrimp_area['7'])
## ## ## ---- Contour of data to mask ---- ##
## contour_mask = np.load('100m_contour_labrador.npy')
## polygon_mask = Polygon(contour_mask)
## ## ---- NAFO divisions ---- ##
## nafo_div = azu.get_nafo_divisions()
## ---- Get CTD data --- ##
print('Get ' + year_file)
ds = xr.open_mfdataset(year_file)
# Selection of a subset region
ds = ds.where((ds.longitude>lonLims[0]) & (ds.longitude<lonLims[1]), drop=True)
ds = ds.where((ds.latitude>latLims[0]) & (ds.latitude<latLims[1]), drop=True)
# Select time (save several options here)
if season == 'summer':
#ds = ds.sel(time=ds['time.season']=='JJA')
ds = ds.sel(time=((ds['time.month']>=7)) & ((ds['time.month']<=9)))
elif season == 'spring':
#ds = ds.sel(time=ds['time.season']=='MAM')
ds = ds.sel(time=((ds['time.month']>=4)) & ((ds['time.month']<=6)))
elif season == 'fall':
#ds = ds.sel(time=ds['time.season']=='SON')
ds = ds.sel(time=((ds['time.month']>=10)) & ((ds['time.month']<=12)))
else:
print('!! no season specified, used them all! !!')
# Remome problematic datasets
print('!!Remove MEDBA data!!')
print(' ---> I Should be improme because I remove good data!!!!')
ds = ds.where(ds.instrument_ID!='MEDBA', drop=True)
# Vertical binning (on dataArray; more appropriate here
da_temp = ds['temperature']
lons = np.array(ds.longitude)
lats = np.array(ds.latitude)
#bins = np.arange(dz/2.0, ds.level.max(), dz)
bins = np.arange(dz/2.0, 1000, dz)
da_temp = da_temp.groupby_bins('level', bins).mean(dim='level')
#To Pandas Dataframe
df_temp = da_temp.to_pandas()
df_temp.columns = bins[0:-1] #rename columns with 'bins'
# Remove empty columns
idx_empty_rows = df_temp.isnull().all(1).nonzero()[0]
df_temp = df_temp.dropna(axis=0,how='all')
lons = np.delete(lons,idx_empty_rows)
lats = np.delete(lats,idx_empty_rows)
#df_temp.to_pickle('T_2000-2017.pkl')
print(' -> Done!')
## --- fill 3D cube --- ##
print('Fill regular cube')
z = df_temp.columns.values
V = np.full((lat_reg.size, lon_reg.size, z.size), np.nan)
# Aggregate on regular grid
for i, xx in enumerate(lon_reg):
for j, yy in enumerate(lat_reg):
idx = np.where((lons>=xx-dc/2) & (lons<xx+dc/2) & (lats>=yy-dc/2) & (lats<yy+dc/2))
tmp = np.array(df_temp.iloc[idx].mean(axis=0))
idx_good = np.argwhere((~np.isnan(tmp)) & (tmp<30))
if np.size(idx_good)==1:
V[j,i,:] = np.array(df_temp.iloc[idx].mean(axis=0))
elif np.size(idx_good)>3: # vertical interpolation between pts
#V[j,i,:] = np.interp((z), np.squeeze(z[idx_good]), np.squeeze(tmp[idx_good])) <--- this method propagate nans below max depth (extrapolation)
interp = interp1d(np.squeeze(z[idx_good]), np.squeeze(tmp[idx_good])) # <---------- Pay attention here, this is a bit unusual, but seems to work!
idx_interp = np.arange(np.int(idx_good[0]),np.int(idx_good[-1]+1))
V[j,i,idx_interp] = interp(z[idx_interp]) # interpolate only where possible (1st to last good idx)
# horizontal interpolation at each depth
lon_grid, lat_grid = np.meshgrid(lon_reg,lat_reg)
lon_vec = np.reshape(lon_grid, lon_grid.size)
lat_vec = np.reshape(lat_grid, lat_grid.size)
for k, zz in enumerate(z):
# Meshgrid 1D data (after removing NaNs)
tmp_grid = V[:,:,k]
tmp_vec = np.reshape(tmp_grid, tmp_grid.size)
#print 'interpolate depth layer ' + np.str(k) + ' / ' + np.str(z.size)
# griddata (after removing nans)
idx_good = np.argwhere(~np.isnan(tmp_vec))
if idx_good.size>4: # will ignore depth where no data exist
LN = np.squeeze(lon_vec[idx_good])
LT = np.squeeze(lat_vec[idx_good])
TT = np.squeeze(tmp_vec[idx_good])
zi = griddata((LN, LT), TT, (lon_grid, lat_grid), method='linear')
V[:,:,k] = zi
else:
continue
print(' -> Done!')
# mask using bathymetry (I don't think it is necessary, but make nice figures)
for i, xx in enumerate(lon_reg):
for j,yy in enumerate(lat_reg):
if Zitp[j,i] > -10: # remove shallower than 10m
V[j,i,:] = np.nan
# getting bottom temperature
print('Getting bottom Temp.')
Tbot = np.full([lat_reg.size,lon_reg.size], np.nan)
for i, xx in enumerate(lon_reg):
for j,yy in enumerate(lat_reg):
bottom_depth = -Zitp[j,i] # minus to turn positive
temp_vec = V[j,i,:]
idx_good = np.squeeze(np.where(~np.isnan(temp_vec)))
if idx_good.size:
idx_closest = np.argmin(np.abs(bottom_depth-z[idx_good]))
else:
continue
if np.abs([idx_closest] - bottom_depth) <= 20:
Tbot[j,i] = temp_vec[idx_good[idx_closest]]
elif np.abs(z[idx_closest] - bottom_depth) <= 50:
#print('used data located [30,50]m from bottom')
Tbot[j,i] = temp_vec[idx_good[idx_closest]]
print(' -> Done!')
## # Mask data outside Nafo div.
## print('Mask according to NAFO division for ' + season)
## # Polygons
## polygon3K = Polygon(zip(nafo_div['3K']['lon'], nafo_div['3K']['lat']))
## polygon3L = Polygon(zip(nafo_div['3L']['lon'], nafo_div['3L']['lat']))
## polygon3N = Polygon(zip(nafo_div['3N']['lon'], nafo_div['3N']['lat']))
## polygon3O = Polygon(zip(nafo_div['3O']['lon'], nafo_div['3O']['lat']))
## polygon3Ps = Polygon(zip(nafo_div['3Ps']['lon'], nafo_div['3Ps']['lat']))
## polygon2J = Polygon(zip(nafo_div['2J']['lon'], nafo_div['2J']['lat']))
## if season == 'spring':
## for i, xx in enumerate(lon_reg):
## for j,yy in enumerate(lat_reg):
## point = Point(lon_reg[i], lat_reg[j])
## #if (~polygon3L.contains(point)) & (~polygon3N.contains(point)) & (~polygon3O.contains(point)) & (~polygon3Ps.contains(point)):
## if polygon3L.contains(point) | polygon3N.contains(point) | polygon3O.contains(point) | polygon3Ps.contains(point):
## pass #nothing to do but cannot implement negative statement "if not" above
## else:
## Tbot[j,i] = np.nan
## elif season == 'fall':
## for i, xx in enumerate(lon_reg):
## for j,yy in enumerate(lat_reg):
## point = Point(lon_reg[i], lat_reg[j])
## if polygon2J.contains(point) | polygon3K.contains(point) | polygon3L.contains(point) | polygon3N.contains(point) | polygon3O.contains(point):
## pass #nothing to do but cannot implement negative statement "if not" above
## else:
## Tbot[j,i] = np.nan ### <--------------------- Do mask the fall / OR /
## #Tbot[j,i] = np.nan ### <--------------------- Do not mask the fall!!!!!
## if polygon_mask.contains(point): # mask data near Labrador in fall
## Tbot[j,i] = np.nan
## else:
## print('no division mask, all data taken')
## print(' -> Done!')
# Temperature anomaly:
anom = Tbot-Tbot_climato
## ---- Plot Anomaly ---- ##
fig, ax = plt.subplots(nrows=1, ncols=1)
m = Basemap(ax=ax, projection='merc',lon_0=lon_0,lat_0=lat_0, llcrnrlon=lonLims[0],llcrnrlat=latLims[0],urcrnrlon=lonLims[1],urcrnrlat=latLims[1], resolution= 'i')
levels = np.linspace(-3.5, 3.5, 8)
#levels = np.linspace(-3.5, 3.5, 16)
xi, yi = m(*np.meshgrid(lon_reg, lat_reg))
c = m.contourf(xi, yi, anom, levels, cmap=plt.cm.RdBu_r, extend='both')
cc = m.contour(xi, yi, -Zitp, [100, 500, 1000, 4000], colors='grey');
plt.clabel(cc, inline=1, fontsize=10, fmt='%d')
if season=='fall':
plt.title('Fall Bottom Temperature ' + year + ' Anomaly')
elif season=='spring':
plt.title('Spring Bottom Temperature ' + year + ' Anomaly')
else:
plt.title('Bottom Temperature ' + year + ' Anomaly')
m.fillcontinents(color='tan');
m.drawparallels(np.arange(58, 68, 2), labels=[1,0,0,0], fontsize=12, fontweight='normal');
m.drawmeridians(np.arange(-66, -54, 2), labels=[0,0,0,1], fontsize=12, fontweight='normal');
#m.drawparallels([40, 45, 50, 55, 60], labels=[0,0,0,0], fontsize=12, fontweight='normal');
#m.drawmeridians([-60, -55, -50, -45], labels=[0,0,0,1], fontsize=12, fontweight='normal');
cax = fig.add_axes([0.16, 0.05, 0.7, 0.025])
cb = plt.colorbar(c, cax=cax, orientation='horizontal')
cb.set_label(r'$\rm T(^{\circ}C)$', fontsize=12, fontweight='normal')
div_toplot = [ '2', '3', '4']
for div in div_toplot:
div_lon, div_lat = m(shrimp_area[div][:,0], shrimp_area[div][:,1])
m.plot(div_lon, div_lat, 'k', linewidth=2)
ax.text(np.mean(div_lon), np.mean(div_lat), 'SFA'+div, fontsize=12, color='black', fontweight='bold')
## div_toplot = ['2J', '3K', '3L', '3N', '3O', '3Ps']
## for div in div_toplot:
## div_lon, div_lat = m(nafo_div[div]['lon'], nafo_div[div]['lat'])
## m.plot(div_lon, div_lat, 'k', linewidth=2)
## ax.text(np.mean(div_lon), np.mean(div_lat), div, fontsize=12, color='black', fontweight='bold')
# Save Figure
fig.set_size_inches(w=6, h=9)
fig.set_dpi(300)
outfile = 'NSRF_bottom_temp_anomaly_' + season + '_' + year + '_habitat.png'
fig.savefig(outfile)
os.system('convert -trim ' + outfile + ' ' + outfile)
## ---- Plot Temperature ---- ##
fig, ax = plt.subplots(nrows=1, ncols=1)
m = Basemap(ax=ax, projection='merc',lon_0=lon_0,lat_0=lat_0, llcrnrlon=lonLims[0],llcrnrlat=latLims[0],urcrnrlon=lonLims[1],urcrnrlat=latLims[1], resolution= 'i')
#levels = np.linspace(-2, 6, 9)
levels = np.array([0, 2, 4])
xi, yi = m(*np.meshgrid(lon_reg, lat_reg))
c = m.contourf(xi, yi, Tbot, levels, cmap=plt.cm.RdBu_r, extend='both')
cc = m.contour(xi, yi, -Zitp, [100, 500, 1000, 4000], colors='grey');
plt.clabel(cc, inline=1, fontsize=10, fmt='%d')
if season=='fall':
plt.title('Fall Bottom Temperature ' + year)
elif season=='spring':
plt.title('Spring Bottom Temperature ' + year)
else:
plt.title('Bottom Temperature ' + year)
m.fillcontinents(color='tan');
m.drawparallels(np.arange(58, 68, 2), labels=[1,0,0,0], fontsize=12, fontweight='normal');
m.drawmeridians(np.arange(-66, -54, 2), labels=[0,0,0,1], fontsize=12, fontweight='normal');
#m.drawparallels([40, 45, 50, 55, 60], labels=[0,0,0,0], fontsize=12, fontweight='normal');
#m.drawmeridians([-60, -55, -50, -45], labels=[0,0,0,1], fontsize=12, fontweight='normal');
x, y = m(lons, lats)
m.scatter(x,y, s=50, marker='.',color='k')
cax = fig.add_axes([0.16, 0.05, 0.7, 0.025])
#cax = plt.axes([0.85,0.15,0.04,0.7], facecolor='grey')
cb = plt.colorbar(c, cax=cax, orientation='horizontal')
cb.set_label(r'$\rm T(^{\circ}C)$', fontsize=12, fontweight='normal')
div_toplot = [ '2', '3', '4']
for div in div_toplot:
div_lon, div_lat = m(shrimp_area[div][:,0], shrimp_area[div][:,1])
m.plot(div_lon, div_lat, 'k', linewidth=2)
ax.text(np.mean(div_lon), np.mean(div_lat), 'SFA'+div, fontsize=12, color='black', fontweight='bold')
## div_toplot = ['2J', '3K', '3L', '3N', '3O', '3Ps']
## for div in div_toplot:
## div_lon, div_lat = m(nafo_div[div]['lon'], nafo_div[div]['lat'])
## m.plot(div_lon, div_lat, 'k', linewidth=2)
## ax.text(np.mean(div_lon), np.mean(div_lat), div, fontsize=12, color='black', fontweight='bold')
# Save Figure
fig.set_size_inches(w=6, h=9)
fig.set_dpi(200)
outfile = 'NSRF_bottom_temp_' + season + '_' + year + '_habitat.png'
fig.savefig(outfile)
os.system('convert -trim ' + outfile + ' ' + outfile)
## ---- Plot Climato ---- ##
fig, ax = plt.subplots(nrows=1, ncols=1)
m = Basemap(ax=ax, projection='merc',lon_0=lon_0,lat_0=lat_0, llcrnrlon=lonLims[0],llcrnrlat=latLims[0],urcrnrlon=lonLims[1],urcrnrlat=latLims[1], resolution= 'i')
#levels = np.linspace(-2, 6, 9)
#levels = np.linspace(-2, 6, 17)
xi, yi = m(*np.meshgrid(lon_reg, lat_reg))
c = m.contourf(xi, yi, Tbot_climato, levels, cmap=plt.cm.RdBu_r, extend='both')
cc = m.contour(xi, yi, -Zitp, [100, 500, 1000, 4000], colors='grey');
plt.clabel(cc, inline=1, fontsize=10, fmt='%d')
if season=='fall':
plt.title('Fall Bottom Temperature Climatology')
elif season=='spring':
plt.title('Spring Bottom Temperature Climatology'
else:
plt.title('Bottom Temperature Climatology (2006-2021)')
m.fillcontinents(color='tan');
m.drawparallels(np.arange(58, 68, 2), labels=[1,0,0,0], fontsize=12, fontweight='normal');
m.drawmeridians(np.arange(-66, -54, 2), labels=[0,0,0,1], fontsize=12, fontweight='normal');
cax = fig.add_axes([0.16, 0.05, 0.7, 0.025])
cb = plt.colorbar(c, cax=cax, orientation='horizontal')
cb.set_label(r'$\rm T(^{\circ}C)$', fontsize=12, fontweight='normal')
for div in div_toplot:
div_lon, div_lat = m(shrimp_area[div][:,0], shrimp_area[div][:,1])
m.plot(div_lon, div_lat, 'k', linewidth=2)
ax.text(np.mean(div_lon), np.mean(div_lat), 'SFA'+div, fontsize=12, color='black', fontweight='bold')
## div_toplot = ['2J', '3K', '3L', '3N', '3O', '3Ps']
## for div in div_toplot:
## div_lon, div_lat = m(nafo_div[div]['lon'], nafo_div[div]['lat'])
## m.plot(div_lon, div_lat, 'k', linewidth=2)
## ax.text(np.mean(div_lon), np.mean(div_lat), div, fontsize=12, color='black', fontweight='bold')
# Save Figure
fig.set_size_inches(w=6, h=9)
fig.set_dpi(300)
outfile = 'NSRF_bottom_temp_climato_' + season + '_' + year + '_habitat.png'
fig.savefig(outfile)
os.system('convert -trim ' + outfile + ' ' + outfile)
# Convert to a subplot
os.system('montage NSRF_bottom_temp_climato_' + season + '_' + year + '_habitat.png NSRF_bottom_temp_' + season + '_' + year + '_habitat.png NSRF_bottom_temp_anomaly_' + season + '_' + year + '_habitat.png -tile 3x1 -geometry +10+10 -background white NSRF_bottomT_' + season + year + '_habitat.png')
os.system('rm NSRF_bottom_temp_climato_' + season + '_' + year + '_habitat.png NSRF_bottom_temp_' + season + '_' + year + '_habitat.png NSRF_bottom_temp_anomaly_' + season + '_' + year + '_habitat.png')