def main():
crs = ccrs.PlateCarree(central_longitude=0)
x
fname = ('../DTU/DTU10MDT_2min.nc')
dataset = netcdf_dataset(fname)
# print(dataset.variables)
mdt = dataset.variables['mdt'][:]
# mdt = bound_arr(mdt, -2, 2)
lats = dataset.variables['lat'][:]
lons = dataset.variables['lon'][:]
print(mdt.shape)
print(lats, lons)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=crs)
# ax = plt.axes(projection=crs)
# mdt = mdt + mask
vmin = np.min(mdt)
vmax = np.max(mdt)
print(vmin, vmax)
# plt.pcolormesh(lons, lats, mdt, transform=crs)
im = ax.pcolormesh(lons,
lats,
mdt,
transform=crs,
cmap='turbo',
vmin=vmin,
vmax=vmax)
ax.set_xticks([-180, -135, -90, -45, 0, 45, 90, 135, 180], crs=crs)
ax.set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=crs)
lon_formatter = LongitudeFormatter(zero_direction_label=True)
lat_formatter = LatitudeFormatter()
# Following axes formatters cannot be used with non-rectangular projections
ax.xaxis.set_major_formatter(lon_formatter)
ax.yaxis.set_major_formatter(lat_formatter)
ax.fontsize = 20
# ax.add_feature(cfeature.LAND, resolution='10m')
# ax.coastlines()
# ax.gridlines()
# ax.set_extent([100, 160, 0, 60])
# ax.set_aspect('auto', adjustable=None)
# cbar_arr = (np.linspace(vmin, vmax, 5, dtype=int))
# Uncomment following to produce colorbar corresponding to each individual plot:
fig.colorbar(im,
ax=ax,
fraction=0.0235,
pad=0.04,
ticks=[-2, -1.5, -1, -0.5, 0, 0.5, 1, 1., 1.5, 2],
format=ticker.FuncFormatter(cbar_fmt))
plt.title('DTU10 Global Mean Dynamic Topography: 2 min resolution',
fontsize=21)
plt.show()
def request_netcdf_dataset(request_url, nc_filename):
req = Request(url)
try:
response = urlopen(req, timeout=70)
except HTTPError as he:
# Update config file to show that the dataset is not valid
print("Could not access data from ERDDAP")
print(he.reason)
except URLError as e:
print(' * Failed to reach erddap server. Stopping.')
print(' * reason: ', e.reason)
except Exception:
print('generic exception: ' + traceback.format_exc())
print("General Error (likely timeout)")
else:
print(' * data file returned from erddap')
CHUNK = 16 *1024
with open(nc_filename,'wb') as f:
while True:
chunk = response.read(CHUNK)
if not chunk:
break
f.write(chunk)
try:
dataset = netcdf_dataset(nc_filename)
except:
print("Not valid netcdf returned")
else:
return dataset
def getPointDF(dirname, filename, in_lat, in_lon, make_csv=True):
##open netcdf
nc = netcdf_dataset(os.path.join('.', dirname, filename))
##pick a point
lats = nc.variables['lat'][:]
lons = nc.variables['lon'][:]
lat_idx = geo_idx(in_lat, lats)
lon_idx = geo_idx(in_lon, lons)
##pull out variables at that point (lev1 only) for each variable (pre-defined list)
vardf = pd.DataFrame(index=variable_names, columns=month_names)
#dict/df: key=varname, value=12x1 array retrieved
##pull out 1 layer of variable to add to df -- internal loop
for varname in variable_names:
var = nc[varname]
if (len(var.shape) == 3): ##2d var
vardf.loc[varname, :] = var[:, lat_idx, lon_idx]
elif (len(var.shape) == 4): ##3d var
vardf.loc[varname, :] = var[:, 0, lat_idx, lon_idx]
##write to csv with newfilename
if (make_csv):
newfilename = os.path.join(
'.', dirname,
filename.split(".")[0] + '_' + str(in_lat) + '_' + str(in_lon) +
".csv")
vardf.to_csv(newfilename)
return (vardf)
def getData(dataset_id, parameter, bounds, time, sub, mapImageDir):
lat1 = qbounds[0]
lat2 = qbounds[1]
lon1 = qbounds[2]
lon2 = qbounds[3]
lat_order = qbounds[4]
altf = qbounds[5]
time1 = time # using just one timestamp for this example
time2 = time
print(lat1)
print(lat2)
print(lon1)
print(lon2)
print(sub)
print(time1)
print(time2)
if altf == 1:
#write query with altitude, haven't tested this
alt1 = float(bounds[6])
alt2 = float(bounds[7])
altsub = '1'
print(alt1)
print(alt2)
print(altsub)
query = parameter + '[(%s):%s:(%s)][(%f):%s:(%f)][(%f):%s:(%f)][(%f):%s:(%f)]' % (
time1, sub, time2, alt1, altsub, alt2, lat1, sub, lat2, lon1, sub,
lon2)
else:
#write query without altitude
query = parameter + '[(%s):%s:(%s)][(%f):%s:(%f)][(%f):%s:(%f)]' % (
time1, sub, time2, lat1, sub, lat2, lon1, sub, lon2)
base_url = 'http://polarwatch.noaa.gov/erddap/griddap/' + dataset_id + '.nc?'
url = base_url + query
print(url)
file = mapImageDir + '/' + parameter + '.nc'
http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED',
ca_certs=certifi.where())
r = http.request('GET', url, preload_content=False)
with open(file, 'wb') as out:
while True:
data = r.read(1024 * 1024)
if not data:
break
out.write(data)
r.release_conn()
# add a real check here
print('Satellite Data File Retrieved')
data_netcdf_dataset = netcdf_dataset(file)
dataset = {"data": data_netcdf_dataset, "url": url}
return dataset
def load_cls(fname, path=None, var='mdt'):
if path is None:
path = ""
filepath = os.path.join(os.path.normpath(path), fname)
dataset = netcdf_dataset(filepath)
var = dataset.variables[var][0, :, :]
lats = dataset.variables['latitude'][:]
lons = dataset.variables['longitude'][:]
return var, lats, lons
def __init__(self,base:str,peturbed:str):
'''
Arguments:
base - the initial simulation path and filename
peturbed - the changes simulation path and filename
Both arguments require the full path to the netCDF file we wish to load.
'''
self.basename = base.split('/')[-1]
self.base = netcdf_dataset(base)
self.basevars = self.base.variables
self.peturbedname = peturbed.split('/')[-1]
self.peturbed = netcdf_dataset(peturbed)
self.peturbedvars = self.base.variables
self.varboth = set(self.basevars) & set(self.basevars)
def load_dtu(fname, path=None, var='mdt'):
if path is None:
path = ""
filepath = os.path.join(os.path.normpath(path), fname)
dataset = netcdf_dataset(filepath)
print(dataset.variables)
var = dataset.variables[var][:]
lats = dataset.variables['lat'][:]
lons = dataset.variables['lon'][:]
return var, lats, lons
def getData(dId, parameter, bounds, time, sub):
print(bounds)
print(parameter)
print(time)
lat1 = bounds[0]
lat2 = bounds[1]
lon1 = bounds[2]
lon2 = bounds[3]
altf = bounds[4]
time1 = time # using just one timestamp for this example
time2 = time
if altf == 1:
#write query with altitude, haven't tested this
alt1 = bounds[5]
alt2 = bounds[6]
altsub = 1
query = parameter + '[(%s):%s:(%s)][(%f):%s:(%f)][(%f):%s:(%f)][(%f):%s:(%f)]' % (
time1, sub, time2, alt1, altsub, alt2, lat1, sub, lat2, lon1, sub,
lon2)
else:
#write query without altitude
query = parameter + '[(%s):%s:(%s)][(%f):%s:(%f)][(%f):%s:(%f)]' % (
time1, sub, time2, lat1, sub, lat2, lon1, sub, lon2)
base_url = 'http://polarwatch.noaa.gov/erddap/griddap/' + dId + '.nc?'
url = base_url + query
print(url)
file = 'dataset.nc'
http = urllib3.PoolManager()
r = http.request('GET', url, preload_content=False)
with open(file, 'wb') as out:
while True:
data = r.read(1024 * 1024)
if not data:
break
out.write(data)
r.release_conn()
# add a real check here
print('Satellite Data File Retrieved')
datafile = netcdf_dataset(file)
dataset = {"data": datafile, "url": url}
return dataset
def datareader(dp=[], d=[], v=[], *args):
Dpath = []
Dread = []
for i in range(0, len(dp)):
Dpath.append(dp[i] + d[i])
if (d[i] == ''):
continue
temp = netcdf_dataset(Dpath[i])
try:
Dread.append(temp.variables[v[i]][0, :, :])
except:
Dread.append(temp.variables[v[i]])
try:
Dread.append(temp.variables['lat'][:])
Dread.append(temp.variables['lon'][:])
except:
print()
return Dread
def read_dataset(name='', granule_name='', variable=None, path='/tmp'):
d = netcdf_dataset(path, mode='r')
dataset = d.variables[variable]
# By convention, but not by standard, if the dimensions exist, they will be in the order:
# time (t), altitude (z), latitude (y), longitude (x)
# but conventions aren't always followed and all dimensions aren't always present so
# see if we can make some educated deductions before defaulting to just pulling the first three
# columns.
temp_dimensions = list(map(lambda x: x.lower(), dataset.dimensions))
dataset_dimensions = dataset.dimensions
time = dataset_dimensions[temp_dimensions.index('time') if 'time' in
temp_dimensions else 0]
lat = dataset_dimensions[temp_dimensions.index('lat') if 'lat' in
temp_dimensions else 1]
lon = dataset_dimensions[temp_dimensions.index('lon') if 'lon' in
temp_dimensions else 2]
# Time is given to us in some units since an epoch. We need to convert
# these values to datetime objects. Note that we use the main object's
# time object and not the dataset specific reference to it. We need to
# grab the 'units' from it and it fails on the dataset specific object.
times = np.array(convert_times_to_datetime(d[time]))
lats = np.array(d.variables[lat][:])
lons = np.array(d.variables[lon][:])
values = np.array(dataset[:])
origin = {'source': 'PO.DAAC', 'url': 'podaac.jpl.nasa.gov'}
# Removing the downloaded temporary granule before creating the OCW
# dataset.
d.close()
path = os.path.join(os.path.dirname(__file__), granule_name)
os.remove(path)
return Dataset(lats,
lons,
times,
values,
variable,
name=name,
origin=origin)
def read_dataset(name='', granule_name ='', variable=None, path='/tmp'):
d = netcdf_dataset(path, mode='r')
dataset = d.variables[variable]
# By convention, but not by standard, if the dimensions exist, they will be in the order:
# time (t), altitude (z), latitude (y), longitude (x)
# but conventions aren't always followed and all dimensions aren't always present so
# see if we can make some educated deductions before defaulting to just pulling the first three
# columns.
temp_dimensions = list(map(lambda x: x.lower(), dataset.dimensions))
dataset_dimensions = dataset.dimensions
time = dataset_dimensions[temp_dimensions.index(
'time') if 'time' in temp_dimensions else 0]
lat = dataset_dimensions[temp_dimensions.index(
'lat') if 'lat' in temp_dimensions else 1]
lon = dataset_dimensions[temp_dimensions.index(
'lon') if 'lon' in temp_dimensions else 2]
# Time is given to us in some units since an epoch. We need to convert
# these values to datetime objects. Note that we use the main object's
# time object and not the dataset specific reference to it. We need to
# grab the 'units' from it and it fails on the dataset specific object.
times = np.array(convert_times_to_datetime(d[time]))
lats = np.array(d.variables[lat][:])
lons = np.array(d.variables[lon][:])
values = np.array(dataset[:])
origin = {
'source': 'NASA JPL PO.DAAC',
'url': 'https://podaac.jpl.nasa.gov'
}
# Removing the downloaded temporary granule before creating the OCW
# dataset.
d.close()
path = os.path.join(os.path.dirname(__file__), granule_name)
os.remove(path)
return Dataset(lats, lons, times, values, variable, name=name, origin=origin)
def read_nc(filepath,filename):
"""
Read satellite nc file
:param string filepath: path to file
:param string filename: name of file
:return netcdf dataset: Array of sst over time and space
:return list: Time indices
:return list: Time labels
"""
dataset = netcdf_dataset(filepath+filename,"r",format="NETCDF3_64BIT_DATA")
time = dataset.variables['analysed_sst'][:,0,0]
time=len(time)
time=np.arange(time)
time=np.asarray(time)
print(time)
#################
# Getting date labels to put in the images:
#################
time_label = dataset.variables['time'][:] #Time is in epoch time, need to convert it to human readable time
#Use datetime to convert to human readable:
time_list = []
for x in time:
lab = datetime.datetime.fromtimestamp(time_label[x]).strftime('%Y-%m-%d %H:%M:%S')
#print(lab)
time_list.append(lab)
#Convert to data frame, separate date from time into different columns, keep date column only:
time_label = DataFrame(time_list, columns=['date'])
time_label['date'] = time_label['date'].str.split(r'\ ').str.get(0)
return [dataset,time,time_label]
def get_grid(filename):
print(filename)
nc = netcdf_dataset(filename)
sat_height = nc.variables[
'goes_imager_projection'].perspective_point_height
radar_lon = -98.128
radar_lat = 36.741
_x = nc.variables['x'] * sat_height
_y = nc.variables['y'] * sat_height
_c = nc.variables['CMI_C13'][:] * -1
data = nc.variables['CMI_C13']
proj_var = nc.variables[data.grid_mapping]
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=proj_var.semi_major_axis,
semiminor_axis=proj_var.semi_minor_axis)
proj = ccrs.Geostationary(central_longitude=-75,
sweep_axis='x',
satellite_height=sat_height,
globe=globe)
trans = ccrs.PlateCarree(central_longitude=0)
transform_xy = trans.transform_points(proj, _x, _y)
lim = [
_nearest(transform_xy[:, 0], -103),
_nearest(transform_xy[:, 0], -92),
_nearest(transform_xy[:, 1], 42),
_nearest(transform_xy[:, 1], 30)
]
x = _x[lim[0]:lim[1]]
y = _y[lim[2]:lim[3]]
c = _c[lim[2]:lim[3], lim[0]:lim[1]]
dx = x.max() - x.min()
dy = y.max() - y.min()
print('X: ' + str(dx))
print('Y: ' + str(dy))
# x = _x
# y = _y
# c = _c
x_mesh, y_mesh = np.meshgrid(x, y)
print(proj)
print(x_mesh)
print(y_mesh)
lonlat = trans.transform_points(proj, x_mesh, y_mesh)
lons = lonlat[:, :, 0]
lats = lonlat[:, :, 1]
interp_c = interp_lonlat(lons, lats, c, radar_lon, radar_lat, grid_x,
grid_y)
_time = {
'calendar': 'gregorian',
'data': np.array([0.934]),
'long_name': 'Time of grid',
'standard_name': 'time',
'units': str('seconds since ' + nc.time_coverage_end)
}
# _fields = {'reflectivity': {'_FillValue': -9999.0, 'data': np.ma.masked_array(c, mask= False),
# 'long_name': 'reflectivity',
# 'standard_name': 'equivalent_reflectivity_factor',
# 'units': 'dBZ', 'valid_max': c.max(), 'valid_min': c.min()}}
_fields = {
'c13': {
'_FillValue': -9999.0,
'data': interp_c,
'long_name': 'channel 13 10.3 microns K',
'standard_name': 'c13',
'units': 'K',
'valid_max': c.max(),
'valid_min': c.min()
}
}
_metadata = {
'Conventions': '',
'comment': '',
'history': '',
'institution': '',
'instrument_name': '',
'original_container': 'NEXRAD Level II',
'references': '',
'source': '',
'title': '',
'vcp_pattern': '',
'version': ''
}
_origin_latitude = {
'data': np.array([0]),
'long_name': 'Latitude at grid origin',
'standard_name': 'latitude',
'units': 'degrees_north',
'valid_max': 90.0,
'valid_min': -90.0
}
# _origin_longitude = {'data': np.ma.array([radar_lon]),
# 'long_name': 'Longitude at grid origin',
# 'standard_name': 'longitude', 'units': 'degrees_east',
# 'valid_max': 180.0, 'valid_min': -180.0}
#
# _origin_altitude = {'data': np.ma.masked_array(np.array([0]), mask= False),
# 'long_name': 'Altitude at grid origin',
# 'standard_name': 'altitude', 'units': 'm'}
_x = {
'axis': 'X',
'data': grid_x['data'],
'long_name': 'X distance on the projection plane from the origin',
'standard_name': 'projection_x_coordinate',
'units': 'm'
}
_y = {
'axis': 'Y',
'data': grid_y['data'],
'long_name': 'Y distance on the projection plane from the origin',
'standard_name': 'projection_x_coordinate',
'units': 'm'
}
_z = {
'axis': 'Z',
'data': np.array([0, mesh_size]),
'long_name': 'Z distance on the projection plane from the origin',
'positive': 'up',
'standard_name': 'projection_z_coordinate',
'units': 'm'
}
grid = pyart.core.grid.Grid(time=_time,
fields=_fields,
metadata=_metadata,
origin_latitude=_orig_lat,
origin_longitude=_orig_lon,
origin_altitude=_orig_alt,
x=_x,
y=_y,
z=_z,
projection=_projection,
radar_longitude=_orig_lon,
radar_latitude=_orig_lat,
radar_altitude=_orig_alt)
grid_name = os.path.basename(filename[:-3] + '_grid.nc')
full_name = os.path.join(out_dir, grid_name)
from mpl_toolkits.axes_grid1 import AxesGrid import matplotlib.path as mpath import matplotlib.colors as colors # numpy import numpy as np # parameters from get_parameters import * # scipy from scipy import stats # data path data_path="./precip_Arctic/" file_name=data_path+"precip_statis_2x2.nc" # read data precp_file=netcdf_dataset(file_name,"r") cnt_rn_cert=np.array(precp_file.variables["cnt_rain_cert"]) cnt_rn_poss=np.array(precp_file.variables["cnt_rain_poss"]) cnt_sn_cert=np.array(precp_file.variables["cnt_snow_cert"]) cnt_sn_poss=np.array(precp_file.variables["cnt_snow_poss"]) cnt_no_prec=np.array(precp_file.variables["cnt_noprecp"]) pre_rn_cert=np.array(precp_file.variables["pre_rain_cert"]) pre_rn_poss=np.array(precp_file.variables["pre_rain_poss"]) pre_sn_cert=np.array(precp_file.variables["pre_snow_cert"]) pre_sn_poss=np.array(precp_file.variables["pre_snow_poss"]) lat=np.array(precp_file.variables["lat"]) lon=np.array(precp_file.variables["lon"]) nlat=len(lat) nlon=len(lon) # calculate frequency and ratio
means_yby_ctl_net_diag=np.zeros((years.size,2,nlat)) #year by year mean for each variable
means_yby_exp_net_diag=np.zeros((years.size,2,nlat)) #year by year mean for each variable
means_ctl_net_diag=np.zeros((2,nlat)) #multi-year mean for each variable
means_exp_net_diag=np.zeros((2,nlat)) #multi-year mean for each variable
diffs_net_diag=np.zeros((2,nlat)) #multi-year exp-ctl diff for each variable
gm_yby_ctl_net_diag=np.zeros((2,years.size)) #year by year mean for each variable
gm_yby_exp_net_diag=np.zeros((2,years.size)) #year by year mean for each variable
means_yby_exp_fice=np.zeros((years.size,nlat)) #year by year mean for each variable
means_exp_fice=np.zeros((nlat)) #multi-year mean for each variable
for iy in range(0,years.size):
# open data file
fctl=fpath_ctl+ctl_pref+"_ANN_"+str(years[iy])+".nc"
fexp=fpath_exp+exp_pref+"_ANN_"+str(years[iy])+".nc"
file_ctl=netcdf_dataset(fctl,"r")
file_exp=netcdf_dataset(fexp,"r")
fctl_diag=fpath_ctl_diag+ctl_pref_diag+"_climo_ANN.nc"
fexp_diag=fpath_exp_diag+exp_pref_diag+"_climo_ANN.nc"
file_ctl_diag=netcdf_dataset(fctl_diag,"r")
file_exp_diag=netcdf_dataset(fexp_diag,"r")
# read lat and lon
lat=file_ctl.variables["lat"]
lon=file_ctl.variables["lon"]
means_yby_exp_fice[iy,:]=means_yby_exp_fice[iy,:]+np.mean(file_exp.variables["ICEFRAC"][0,:,:],axis=1)
# read data and calculate mean/min/max
for vn in varnms_vis_dn:
def ocn_modelvsobs(config, field, streamMap=None, variableMap=None):
"""
Plots a comparison of ACME/MPAS output to SST or MLD observations
config is an instance of MpasAnalysisConfigParser containing configuration
options.
field is the name of a field to be analyize (currently one of 'mld' or
'sst')
If present, streamMap is a dictionary of MPAS-O stream names that map to
their mpas_analysis counterparts.
If present, variableMap is a dictionary of MPAS-O variable names that map
to their mpas_analysis counterparts.
Authors: Luke Van Roekel, Milena Veneziani, Xylar Asay-Davis
Modified: 12/08/2016
"""
# read parameters from config file
indir = config.get('paths', 'archive_dir_ocn')
streams_filename = config.get('input', 'ocean_streams_filename')
streams = StreamsFile(streams_filename, streamsdir=indir)
# get a list of timeSeriesStats output files from the streams file,
# reading only those that are between the start and end dates
startDate = config.get('time', 'climo_start_date')
endDate = config.get('time', 'climo_end_date')
streamName = streams.find_stream(streamMap['timeSeriesStats'])
infiles = streams.readpath(streamName, startDate=startDate,
endDate=endDate)
print 'Reading files {} through {}'.format(infiles[0], infiles[-1])
plots_dir = config.get('paths', 'plots_dir')
obsdir = config.get('paths', 'obs_' + field + 'dir')
casename = config.get('case', 'casename')
meshfile = config.get('data', 'mpas_meshfile')
climo_yr1 = config.getint('time', 'climo_yr1')
climo_yr2 = config.getint('time', 'climo_yr2')
yr_offset = config.getint('time', 'yr_offset')
outputTimes = config.getExpression(field + '_modelvsobs',
'comparisonTimes')
f = netcdf_dataset(meshfile, mode='r')
lonCell = f.variables["lonCell"][:]
latCell = f.variables["latCell"][:]
varList = [field]
if field == 'mld':
selvals = None
# Load MLD observational data
obs_filename = "{}/holtetalley_mld_climatology.nc".format(obsdir)
dsData = xr.open_mfdataset(obs_filename)
# Increment month value to be consistent with the model output
dsData.iMONTH.values += 1
# Rename the time dimension to be consistent with the SST dataset
dsData.rename({'month': 'calmonth'}, inplace=True)
dsData.rename({'iMONTH': 'month'}, inplace=True)
obsFieldName = 'mld_dt_mean'
# Reorder dataset for consistence
dsData = dsData.transpose('month', 'iLON', 'iLAT')
# Set appropriate MLD figure labels
obsTitleLabel = "Observations (HolteTalley density threshold MLD)"
fileOutLabel = "mldHolteTalleyARGO"
unitsLabel = 'm'
elif field == 'sst':
selvals = {'nVertLevels': 0}
obs_filename = \
"{}/MODEL.SST.HAD187001-198110.OI198111-201203.nc".format(obsdir)
dsData = xr.open_mfdataset(obs_filename)
# Select years for averaging (pre-industrial or present-day)
# This seems fragile as definitions can change
if yr_offset < 1900:
time_start = datetime.datetime(1870, 1, 1)
time_end = datetime.datetime(1900, 12, 31)
preIndustrial_txt = "pre-industrial 1870-1900"
else:
time_start = datetime.datetime(1990, 1, 1)
time_end = datetime.datetime(2011, 12, 31)
preIndustrial_txt = "present-day 1990-2011"
ds_tslice = dsData.sel(time=slice(time_start, time_end))
monthly_clim_data = ds_tslice.groupby('time.month').mean('time')
# Rename the observation data for code compactness
dsData = monthly_clim_data.transpose('month', 'lon', 'lat')
obsFieldName = 'SST'
# Set appropriate figure labels for SST
obsTitleLabel = \
"Observations (Hadley/OI, {})".format(preIndustrial_txt)
fileOutLabel = "sstHADOI"
unitsLabel = r'$^o$C'
elif field == 'sss':
selvals = {'nVertLevels': 0}
obs_filename = "{}/Aquarius_V3_SSS_Monthly.nc".format(obsdir)
dsData = xr.open_mfdataset(obs_filename)
time_start = datetime.datetime(2011, 8, 1)
time_end = datetime.datetime(2014, 12, 31)
ds_tslice = dsData.sel(time=slice(time_start, time_end))
# The following line converts from DASK to numpy to supress an odd
# warning that doesn't influence the figure output
ds_tslice.SSS.values
monthly_clim_data = ds_tslice.groupby('time.month').mean('time')
# Rename the observation data for code compactness
dsData = monthly_clim_data.transpose('month', 'lon', 'lat')
obsFieldName = 'SSS'
# Set appropriate figure labels for SSS
preIndustrial_txt = "2011-2014"
obsTitleLabel = "Observations (Aquarius, {})".format(preIndustrial_txt)
fileOutLabel = 'sssAquarius'
unitsLabel = 'PSU'
ds = xr.open_mfdataset(
infiles,
preprocess=lambda x: preprocess_mpas(x, yearoffset=yr_offset,
timestr='Time',
onlyvars=varList,
selvals=selvals,
varmap=variableMap))
ds = remove_repeated_time_index(ds)
time_start = datetime.datetime(yr_offset+climo_yr1, 1, 1)
time_end = datetime.datetime(yr_offset+climo_yr2, 12, 31)
ds_tslice = ds.sel(Time=slice(time_start, time_end))
monthly_clim = ds_tslice.groupby('Time.month').mean('Time')
latData, lonData = np.meshgrid(dsData.lat.values, dsData.lon.values)
latData = latData.flatten()
lonData = lonData.flatten()
daysarray = np.ones((12, dsData[obsFieldName].values.shape[1],
dsData[obsFieldName].values.shape[2]))
for i, dval in enumerate(constants.dinmonth):
daysarray[i, :, :] = dval
inds = np.where(np.isnan(dsData[obsFieldName][i, :, :].values))
daysarray[i, inds[0], inds[1]] = np.NaN
# initialize interpolation variables
d2, inds2, lonTarg, latTarg = init_tree(np.rad2deg(lonCell),
np.rad2deg(latCell),
constants.lonmin,
constants.lonmax,
constants.latmin,
constants.latmax,
constants.dLongitude,
constants.dLatitude)
d, inds, lonTargD, latTargD = init_tree(lonData, latData,
constants.lonmin,
constants.lonmax,
constants.latmin,
constants.latmax,
constants.dLongitude,
constants.dLatitude)
nLon = lonTarg.shape[0]
nLat = lonTarg.shape[1]
modelOutput = np.zeros((len(outputTimes), nLon, nLat))
observations = np.zeros((len(outputTimes), nLon, nLat))
bias = np.zeros((len(outputTimes), nLon, nLat))
# Interpolate and compute biases
for i, timestring in enumerate(outputTimes):
monthsvalue = constants.monthdictionary[timestring]
if isinstance(monthsvalue, (int, long)):
modeldata = monthly_clim.sel(month=monthsvalue)[field].values
obsdata = dsData.sel(month=monthsvalue)[obsFieldName].values
else:
modeldata = (np.sum(
constants.dinmonth[monthsvalue-1] *
monthly_clim.sel(month=monthsvalue)[field].values.T, axis=1) /
np.sum(constants.dinmonth[monthsvalue-1]))
obsdata = (np.nansum(
daysarray[monthsvalue-1, :, :] *
dsData.sel(month=monthsvalue)[obsFieldName].values, axis=0) /
np.nansum(daysarray[monthsvalue-1, :, :], axis=0))
modelOutput[i, :, :] = interp_fields(modeldata, d2, inds2, lonTarg)
observations[i, :, :] = interp_fields(obsdata.flatten(), d, inds,
lonTargD)
for i in range(len(outputTimes)):
bias[i, :, :] = modelOutput[i, :, :] - observations[i, :, :]
clevsModelObs = config.getExpression(field + '_modelvsobs',
'clevsModelObs')
cmap = plt.get_cmap(config.get(field + '_modelvsobs',
'cmapModelObs'))
cmapIndices = config.getExpression(field + '_modelvsobs',
'cmapIndicesModelObs')
cmapModelObs = cols.ListedColormap(cmap(cmapIndices), "cmapModelObs")
clevsDiff = config.getExpression(field + '_modelvsobs',
'clevsDiff')
cmap = plt.get_cmap(config.get(field + '_modelvsobs', 'cmapDiff'))
cmapIndices = config.getExpression(field + '_modelvsobs',
'cmapIndicesDiff')
cmapDiff = cols.ListedColormap(cmap(cmapIndices), "cmapDiff")
for i in range(len(outputTimes)):
fileout = "{}/{}_{}_{}_years{:04d}-{:04d}.png".format(
plots_dir, fileOutLabel, casename, outputTimes[i], climo_yr1,
climo_yr2)
title = "{} ({}, years {:04d}-{:04d})".format(
field.upper(), outputTimes[i], climo_yr1, climo_yr2)
plot_global_comparison(config,
lonTarg,
latTarg,
modelOutput[i, :, :],
observations[i, :, :],
bias[i, :, :],
cmapModelObs,
clevsModelObs,
cmapDiff,
clevsDiff,
fileout=fileout,
title=title,
modelTitle="{}".format(casename),
obsTitle=obsTitleLabel,
diffTitle="Model-Observations",
cbarlabel=unitsLabel)
def load_level4_granule(variable, datasetId='', name=''):
'''Loads a Level4 gridded Dataset from PODAAC
:param variable: The name of the variable to read from the dataset.
:type variable: :mod:`string`
:param datasetId: dataset persistent ID. datasetId or \
shortName is required for a granule search. Example: \
PODAAC-ASOP2-25X01
:type datasetId: :mod:`string`
:param shortName: the shorter name for a dataset. \
Either shortName or datasetId is required for a \
granule search. Example: ASCATA-L2-25km
:type shortName: :mod:`string`
:param name: (Optional) A name for the loaded dataset.
:type name: :mod:`string`
:returns: A :class:`dataset.Dataset` containing the dataset pointed to by
the OpenDAP URL.
:raises: ServerError
'''
# Downloading the dataset using podaac toolkit
podaac = Podaac()
path = os.path.dirname(os.path.abspath(__file__))
granuleName = podaac.extract_l4_granule(
dataset_id=datasetId, path=path)
path = path + '/' + granuleName
d = netcdf_dataset(path, mode='r')
dataset = d.variables[variable]
# By convention, but not by standard, if the dimensions exist, they will be in the order:
# time (t), altitude (z), latitude (y), longitude (x)
# but conventions aren't always followed and all dimensions aren't always present so
# see if we can make some educated deductions before defaulting to just pulling the first three
# columns.
temp_dimensions = list(map(lambda x: x.lower(), dataset.dimensions))
dataset_dimensions = dataset.dimensions
time = dataset_dimensions[temp_dimensions.index(
'time') if 'time' in temp_dimensions else 0]
lat = dataset_dimensions[temp_dimensions.index(
'lat') if 'lat' in temp_dimensions else 1]
lon = dataset_dimensions[temp_dimensions.index(
'lon') if 'lon' in temp_dimensions else 2]
# Time is given to us in some units since an epoch. We need to convert
# these values to datetime objects. Note that we use the main object's
# time object and not the dataset specific reference to it. We need to
# grab the 'units' from it and it fails on the dataset specific object.
times = np.array(convert_times_to_datetime(d[time]))
lats = np.array(d.variables[lat][:])
lons = np.array(d.variables[lon][:])
values = np.array(dataset[:])
origin = {
'source': 'PO.DAAC',
'url': 'podaac.jpl.nasa.gov/ws'
}
# Removing the downloaded temporary granule before creating the OCW
# dataset.
d.close()
path = os.path.join(os.path.dirname(__file__), granuleName)
os.remove(path)
return Dataset(lats, lons, times, values, variable, name=name, origin=origin)
import numpy as np # parameters from get_parameters import get_area_mean_min_max #def lon_lat_contour_model_vs_model(varnm,season,scale_ctl,scale_exp,table): # data path ctl_name = "CTL" #os.environ["ctl_name"] exp_name = "TSIS" #os.environ["exp_name"] fpath_ctl = '/raid00/xianwen/data/cesm211_solar_exp/solar_CTL_cesm211_ETEST-f19_g17-ens_mean_2010-2019/climo/' fpath_exp = '/raid00/xianwen/data/cesm211_solar_exp/solar_TSIS_cesm211_ETEST-f19_g17-ens_mean_2010-2019/climo/' f1 = fpath_ctl + "solar_CTL_cesm211_ETEST-f19_g17-ens_mean_2010-2019_climo_ANN.nc" f2 = fpath_exp + "solar_TSIS_cesm211_ETEST-f19_g17-ens_mean_2010-2019_climo_ANN.nc" # open data file file_ctl = netcdf_dataset(f1, "r") file_exp = netcdf_dataset(f2, "r") # read lat and lon lat = file_ctl.variables["lat"] lon = file_ctl.variables["lon"] lev = file_ctl.variables["lev"] nlat = len(lat) nlon = len(lon) nlev = len(lev) print(nlat, nlon, nlev) #varnm="FSSDCLRS14" varnm = "CLDLOW" # #varnm_off="FLUTC_OFF" #offline computation #units=r"W/m$^2$"
means_yby_exp_sfc = np.zeros(
(years.size, varnms_sfc.size)) #year by year mean for each variable
means_ctl_sfc = np.zeros((varnms_sfc.size)) #multi-year mean for each variable
means_exp_sfc = np.zeros((varnms_sfc.size)) #multi-year mean for each variable
diffs_sfc = np.zeros(
(varnms_sfc.size)) #multi-year exp-ctl diff for each variable
pvals_sfc = np.zeros((varnms_sfc.size)) #pvalues of ttest
for iy in range(0, years.size):
# open data file
fctl = fpath_ctl + ctl_pref + "_ANN_" + str(years[iy]) + ".nc"
fexp = fpath_exp + exp_pref + "_ANN_" + str(years[iy]) + ".nc"
fctl_fssd = fpath_ctl_fssd + ctl_fssd_pref + "_climo_ANN.nc"
fexp_fssd = fpath_exp_fssd + exp_fssd_pref + "_climo_ANN.nc"
file_ctl = netcdf_dataset(fctl, "r")
file_exp = netcdf_dataset(fexp, "r")
file_ctl_fssd = netcdf_dataset(fctl_fssd, "r")
file_exp_fssd = netcdf_dataset(fexp_fssd, "r")
# read lat and lon
lat = file_ctl.variables["lat"]
lon = file_ctl.variables["lon"]
# read data and calculate mean/min/max
for iv in range(0, varnms_toa.size):
dtctl_toa=file_ctl_fssd.variables[varnms_toa[iv]][:,:,:] - \
file_ctl.variables[varnms_sub_toa[iv]][:,:,:]
dtexp_toa=file_exp_fssd.variables[varnms_toa[iv]][:,:,:] - \
file_exp.variables[varnms_sub_toa[iv]][:,:,:]
dtctl_sfc=file_ctl.variables[varnms_sfc[iv]][:,:,:] - \
from timeit import default_timer as timer
from datetime import date, datetime, timedelta
start = timer()
filelist = sorted(os.listdir('/home/scarani/Desktop/data/goes/001/'))
channel = 2
for i in range(1200, 1501, 1):
print(i)
file = str('/home/scarani/Desktop/data/goes/001/' + filelist[i])
filename = os.path.join(os.path.dirname(ccrs.__file__), 'data', 'netcdf',
file)
nc = netcdf_dataset(filename)
sat_height = nc.variables[
'goes_imager_projection'].perspective_point_height
x = nc.variables['x'][:].data * sat_height
y = nc.variables['y'][:].data * sat_height
c = nc.variables['Rad'][:]
data = nc.variables['Rad']
satvar = nc.variables.keys()
time = nc['t']
proj_var = nc.variables[nc.variables['Rad'].grid_mapping]
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=proj_var.semi_major_axis,
from cartopy import config
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
#################
#open and read the dataset, save an iterable range of times
#note to zinka: time iterable is fricken stupid as it stands
#################
#create filepath to save png files to
filepath = 'PNG_files/'
if os.path.isdir(filepath) == False:
os.mkdir(filepath)
dataset = netcdf_dataset('noaacwBLENDEDsstDaily_8423_ee32_0effevan.nc')
time = dataset.variables['analysed_sst'][:, 0, 0]
time = len(time)
time = np.arange(time)
time = np.asarray(time)
print(time)
#################
# Getting date labels to put in the images:
#################
time_label = dataset.variables[
'time'][:] #Time is in epoch time, need to convert it to human readable time
#Use datetime to convert to human readable:
time_list = []
for x in time:
lab = datetime.datetime.fromtimestamp(
import os
import matplotlib.pyplot as plt
from netCDF4 import Dataset as netcdf_dataset
import cartopy.crs as ccrs
DIRBIN = os.path.dirname(os.path.abspath(__file__))
infile = os.path.join(
DIRBIN,
"b.e11.BRCP85C5CNBDRD.f09_g16.021.cice.h.hi_nh.208101-210012.nc"
)
dataset = netcdf_dataset(infile)
sst = dataset.variables['hi'][0, :, :]
lats = dataset.variables['TLAT'][:]
lons = dataset.variables['TLON'][:]
ax = plt.axes(projection=ccrs.PlateCarree())
plt.contourf(lons, lats, sst, 60,
transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
import numpy as np # parameters from get_parameters import get_area_mean_min_max #def lon_lat_contour_model_vs_model(varnm,season,scale_ctl,scale_exp,table): # data path ctl_name = "CTL" #os.environ["ctl_name"] exp_name = "TSIS" #os.environ["exp_name"] fpath_ctl = '/Volumes/WD4T_1/cesm2_solar_exp/solar_CTL_cesm211_ETEST-f19_g17-ens_mean_2010-2019/climo/' #fpath_exp='/Volumes/WD4T_1/cesm2_solar_exp/solar_TSIS_cesm211_ETEST-f19_g17-ens_mean_2010-2019/climo/' f1 = fpath_ctl + "solar_CTL_cesm211_ETEST-f19_g17-ens_mean_2010-2019_climo_ANN.nc" #f2=fpath_exp+"solar_TSIS_cesm211_ETEST-f19_g17-ens_mean_2010-2019_climo_ANN.nc" # open data file file_ctl = netcdf_dataset(f1, "r") #file_exp=netcdf_dataset(f2,"r") # read lat and lon lat = file_ctl.variables["lat"] lon = file_ctl.variables["lon"] lev = file_ctl.variables["lev"] nlat = len(lat) nlon = len(lon) nlev = len(lev) #varnm="FSSDCLRS14" varnm = "FSSUS10" # FSSUS10, FSSUS08 varnm2 = "FSSDS10" # FSSDS10, FSSDS08 varnm3 = "FSSUS08" # FSSUS10, FSSUS08
def plotsat():
nc = netcdf_dataset(localfile.name)
sat_height = nc.variables[
'goes_imager_projection'].perspective_point_height
x = nc.variables['x'][:].data * sat_height
y = nc.variables['y'][:].data * sat_height
c = nc.variables['CMI_C02'][:]
data = nc.variables['CMI_C02']
satvar = nc.variables.keys()
time = nc['t']
proj_var = nc.variables[nc.variables['CMI_C13'].grid_mapping]
globe = ccrs.Globe(ellipse='sphere',
semimajor_axis=proj_var.semi_major_axis,
semiminor_axis=proj_var.semi_minor_axis)
proj = ccrs.Geostationary(central_longitude=-75,
sweep_axis='x',
satellite_height=sat_height,
globe=globe)
north = y.max()
south = y.min()
east = x.max()
west = x.min()
x, y = np.meshgrid(x, y)
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(1, 1, 1, projection=proj)
ax.set_xlim(west, east)
ax.set_ylim(south, north)
# vmin = 190 #13
# vmax = 305 #13
# colormap = 'Greys' #13
vmin = 0.03 #02
vmax = 1.2 #02
colormap = 'Greys_r' #02
im = ax.pcolormesh(x, y, c, cmap=colormap, vmin=vmin, vmax=vmax)
ax.add_feature(cfeature.STATES, linewidth=2, edgecolor='black')
ax.coastlines(resolution='10m', linewidth=1, edgecolor='black')
ax.add_feature(cfeature.BORDERS, linewidth=1, edgecolor='black')
cbar_ticks = np.arange(vmin, vmax,
round(((abs(vmax) - abs(vmin)) / 6), 2))
cbar = fig.colorbar(im,
ticks=cbar_ticks,
orientation='horizontal',
shrink=0.6,
pad=.02)
cbar.ax.set_yticklabels(str(cbar_ticks))
cbar.set_label(data.units, rotation=0, labelpad=5, fontsize=13)
# Figure Text
txt = open(
'/home/scarani/Desktop/precipitation-onset/GOES/channel_title.txt',
"r")
titles = txt.readlines()
channel = data.ancillary_variables[-2:]
ch = titles[int(channel)][:-1]
#times = str(str(.time.values)[:-10]+ 'UTC')
orbital_slot = nc.orbital_slot
fmt = '%Y%m%d_%H%M%S'
epoch = '20000101_120000'
sd = datetime.strptime(epoch, fmt)
times = sd + timedelta(seconds=int(time[0].data))
time_title = times.strftime('%Y-%m-%dT%H:%M:%SUTC')
savetime = times.strftime('%Y%m%d%H%M%S')
sector = '***sector error***'
if nc.dataset_name[15:-58] == 'M1':
sector = 'Mesoscale-1'
if nc.dataset_name[15:-58] == 'M2':
sector = 'Mesoscale-2'
if nc.dataset_name[15:-58] == 'F':
sector = 'Disk'
if nc.dataset_name[15:-58] == 'C':
sector = 'CONUS'
ax.set_title(orbital_slot + ': ' + sector + '\n' + ch + '\n' +
str(time_title),
fontsize=17)
plt.savefig(savelocation + str(savetime) + '_' + str(scan) + '_' +
str(channel) + '.png',
bbox_inches='tight',
dpi=300)
plt.show()
plt.close()
gc.collect()
mid = timer()
print(round((mid - start) / 60, 2))
'clevsDiff_conc_{}'.format(season))
cmap = plt.get_cmap(config.get('seaice_modelvsobs', 'cmapDiff'))
cmapIndices = config.getExpression('seaice_modelvsobs',
'cmapIndicesDiff')
cmapDiff = cols.ListedColormap(cmap(cmapIndices), "cmapDiff")
lon0 = config.getfloat('seaice_modelvsobs',
'lon0_{}'.format(hemisphere))
latmin = config.getfloat('seaice_modelvsobs',
'latmin_{}'.format(hemisphere))
# Load in sea-ice data
# Model...
# ice concentrations
fileName = "{}/{}".format(climodir_regridded, climofiles[climName])
f = netcdf_dataset(fileName, mode='r')
iceconc = f.variables["iceAreaCell"][:]
if (first):
lons = f.variables["lon"][:]
lats = f.variables["lat"][:]
print "Min lon: ", np.amin(lons), "Max lon: ", np.amax(lons)
print "Min lat: ", np.amin(lats), "Max lat: ", np.amax(lats)
Lons, Lats = np.meshgrid(lons, lats)
first = False
f.close()
# ...and observations
# ice concentrations from NASATeam (or Bootstrap) algorithm
for obsName in ['NASATeam', 'Bootstrap']:
fileName = obs_iceconc_filenames['{}_{}'.format(climName, obsName)]
def getData(dataset_id, parameter, qbounds, time):
#print(qbounds)
#print(parameter)
#print(time)
lat1 = qbounds[0]
lat2 = qbounds[1]
lon1 = qbounds[2]
lon2 = qbounds[3]
lat_order = qbounds[4]
altf = qbounds[5]
sub = qbounds[6]
time1 = time # using just one timestamp for this example
time2 = time
if altf == 1:
#write query with altitude, haven't tested this
alt1 = float(qbounds[7])
alt2 = float(qbounds[8])
altsub = 1
query = parameter+'[(%s):%s:(%s)][(%f):%s:(%f)][(%f):%s:(%f)][(%f):%s:(%f)]' % (time1,sub,time2,alt1, altsub, alt2, lat1,sub,lat2,lon1,sub,lon2)
else:
#write query without altitude
query = parameter+'[(%s):%s:(%s)][(%f):%s:(%f)][(%f):%s:(%f)]' % (time1,sub,time2,lat1,sub,lat2,lon1,sub,lon2)
base_url = 'http://polarwatch.noaa.gov/erddap/griddap/'+ dataset_id +'.nc?'
url = base_url + query
print(url)
datasetObj = {
"url" : url
}
file = 'dataset.nc'
http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED', ca_certs=certifi.where())
try:
r = http.request('GET', url, preload_content=False)
except urllib3.exceptions.HTTPError as he:
# Update config file to show that the dataset is not valid
datasetObj["inERDDAP"] = 0
datasetObj["inERDDAPReason"] = "Could not access actual data when trying to make plots"
entry["validDatasets"][subEntry] = 0
logger.error(' * The server couldnt fullfill the request. Adding a not valid flag and not updating graphics today.')
except urllib3.exceptions.URLError as e:
logger.error(' * Failed to reach erddap server. Stopping.')
logger.error(' * reason: ', e.reason)
datasetObj["inERDDAP"] = 0
datasetObj["inERDDAPReason"] = "Failed to reach ERDDAP when trying to make plots"
entry["validDatasets"][subEntry] = 0
except Exception:
logger.error('generic exception: ' + traceback.format_exc())
datasetObj["inERDDAP"] = 0
datasetObj["inERDDAPReason"] = "General Error when trying to make plots (likely timeout)"
else:
# update the file with the new data
with open(file, 'wb') as out:
while True:
data = r.read(1024*1024)
if not data:
break
out.write(data)
r.release_conn()
print ('Satellite Data File Retrieved')
#Check netcdf response
try:
data_netcdf_dataset = netcdf_dataset(file)
except Exception:
print(' an unusual error has occured')
datasetObj["inERDDAP"] = 0
datasetObj["inERDDAPReason"] = "Valid NetCDF not returned in make images"
getDataError = 'generic exception: ' + traceback.format_exc()
datasetObj["goodFile"] = 0
datasetObj["error"] = getDataError
datasetObj["data_netcdf_dataset"] = 0
else:
print('retrieved valid netcdf')
datasetObj["data_netcdf_dataset"] = data_netcdf_dataset
datasetObj["goodFile"] = 1
datasetObj["inERDDAP"] = 1
return datasetObj
def getClosestIceMask(logger, date_str):
print('Date of data: '+ date_str)
date_datetime= dateutil.parser.parse(date_str)
date_datetime.replace(tzinfo=timezone('UTC'))
searchStartDate_datetime = date_datetime - timedelta(days=33)
searchStartDate_str = searchStartDate_datetime.strftime("%Y-%m-%dT%I:%M:%SZ")
searchEndDate_datetime = date_datetime + timedelta(days=33)
searchEndDate_str = searchEndDate_datetime.strftime("%Y-%m-%dT%I:%M:%SZ")
# See if we need to modify these times based
# on the extent of the ice mask dataset time range
# because erddap throws an error if the date is too
# far into the future outside of the range of the dataset
maskInfoUrl = 'https://polarwatch.noaa.gov/erddap/info/jplMURSST41/index.csv'
http = urllib3.PoolManager(cert_reqs='CERT_REQUIRED', ca_certs=certifi.where())
try:
r = http.request('GET', maskInfoUrl, preload_content=False, timeout=20)
except:
logger.error(" * Error: Retrieving ice mask info file")
else:
reader = csv.reader(io.StringIO(r.read().decode('utf-8')), delimiter=',')
for row in reader:
if row[0] == "attribute" and row[2] == "time_coverage_end":
maxIceMaskDate_datetime = dateutil.parser.parse(row[4])
maxIceMaskDate_datetime.replace(tzinfo=timezone('UTC'))
# compare times, replace if needed
if maxIceMaskDate_datetime < searchEndDate_datetime:
print('replacing')
searchEndDate_str = maxIceMaskDate_datetime.strftime("%Y-%m-%dT%I:%M:%SZ")
# create a plus or minus two months on that date to grab a limited section of the times
# get ice mask times from erddap so we can search and find the closest one
iceMaskTimesUrl = 'https://polarwatch.noaa.gov/erddap/griddap/jplMURSST41.json?time[(' + searchStartDate_str + '):1:(' + searchEndDate_str+ ')]'
try:
response = http.request('GET', iceMaskTimesUrl, timeout=15)
except urllib3.exceptions.HTTPError as e:
logger.error('The server couldnt fullfill the request.')
logger.error('Error Code: ', e.code)
except urllib3.exceptions.URLError as e:
logger.error('Failed to reach erddap server for: '+ iceMaskTimesUrl)
logger.error('reason: ', e.reason)
else:
timesResponse = json.loads(response.data.decode('utf-8'))
try:
timesResponse = json.loads(response.data.decode('utf-8'))
except:
print('couldn''t read json')
gotTimes=0
else:
timeList_datetime = []
for val in timesResponse['table']['rows']:
val_datetime = dateutil.parser.parse(val[0])
val_datetime.replace(tzinfo=timezone('UTC'))
timeList_datetime.append(val_datetime)
gotTimes =1
# find the closest time
if gotTimes == 1 :
timeList_datetime = np.array(timeList_datetime)
timeList_datetime.sort
datematch = np.searchsorted(timeList_datetime, date_datetime )
closest_date_str = timeList_datetime[datematch-1].strftime("%Y-%m-%dT%I:%M:%SZ")
print('Date of ice mask request: '+ closest_date_str)
#now request that mask
maskUrl = 'https://polarwatch.noaa.gov/erddap/griddap/jplMURSST41.nc?mask[('+closest_date_str+'):1:('+closest_date_str+')][(-89.990000):35:(89.990000)][(-179.990000):71:(180.000000)]'
print(maskUrl)
try:
r = http.request('GET', maskUrl, preload_content=False, timeout=70)
except urllib3.exceptions.HTTPError as he:
print('HTTP ERROR: the server couldnt fulfill the request')
print(he)
except urllib3.exceptions.URLError as e:
logger.error(' * Failed to reach erddap server. Stopping.')
logger.error(' * reason: ', e.reason)
except Exception:
logger.error('generic exception: ' + traceback.format_exc())
else:
with open('icemask.nc', 'wb') as out:
while True:
icedata = r.read(1024*1024)
if not icedata:
break
out.write(icedata)
r.release_conn()
# Try opening the returned mask data file to confirm we retrieved valid data
maskNetCDF = netcdf_dataset('icemask.nc')
return maskNetCDF
import os
import matplotlib.pyplot as plt
from netCDF4 import Dataset as netcdf_dataset
import numpy as np
from cartopy import config
import cartopy.crs as ccrs
dataset = netcdf_dataset('data.nc')
dimensions = dataset.variables.keys()
sst = dataset.variables['u10'][0, :, :]
lats = dataset.variables['latitude'][:]
lons = dataset.variables['longitude'][:]
print(sst, lats, lons)
ax = plt.axes(projection=ccrs.PlateCarree())
plt.contourf(lons, lats, sst, 60, transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
def ohc_timeseries(config, streamMap=None, variableMap=None):
"""
Performs analysis of ocean heat content (OHC) from time-series output.
config is an instance of an MpasAnalysisConfigParser containing
configuration options.
config is an instance of MpasAnalysisConfigParser containing configuration
options.
If present, streamMap is a dictionary of MPAS-O stream names that map to
their mpas_analysis counterparts.
If present, variableMap is a dictionary of MPAS-O variable names that map
to their mpas_analysis counterparts.
Author: Xylar Asay-Davis, Milena Veneziani
Last Modified: 12/04/2016
"""
# read parameters from config file
indir = config.get('paths', 'archive_dir_ocn')
namelist_filename = config.get('input', 'ocean_namelist_filename')
namelist = NameList(namelist_filename, path=indir)
streams_filename = config.get('input', 'ocean_streams_filename')
streams = StreamsFile(streams_filename, streamsdir=indir)
# Note: input file, not a mesh file because we need dycore specific fields
# such as refBottomDepth and namelist fields such as config_density0 that
# are not guaranteed to be in the mesh file.
inputfile = streams.readpath('input')[0]
# get a list of timeSeriesStats output files from the streams file,
# reading only those that are between the start and end dates
startDate = config.get('time', 'timeseries_start_date')
endDate = config.get('time', 'timeseries_end_date')
streamName = streams.find_stream(streamMap['timeSeriesStats'])
infiles = streams.readpath(streamName, startDate=startDate,
endDate=endDate)
print 'Reading files {} through {}'.format(infiles[0], infiles[-1])
casename = config.get('case', 'casename')
ref_casename_v0 = config.get('case', 'ref_casename_v0')
indir_v0data = config.get('paths', 'ref_archive_v0_ocndir')
compare_with_obs = config.getboolean('ohc_timeseries', 'compare_with_obs')
plots_dir = config.get('paths', 'plots_dir')
yr_offset = config.getint('time', 'yr_offset')
N_movavg = config.getint('ohc_timeseries', 'N_movavg')
regions = config.getExpression('regions', 'regions')
plot_titles = config.getExpression('regions', 'plot_titles')
iregions = config.getExpression('ohc_timeseries', 'regionIndicesToPlot')
# Define/read in general variables
print " Read in depth and compute specific depth indexes..."
f = netcdf_dataset(inputfile, mode='r')
# reference depth [m]
depth = f.variables["refBottomDepth"][:]
# specific heat [J/(kg*degC)]
cp = namelist.getfloat('config_specific_heat_sea_water')
# [kg/m3]
rho = namelist.getfloat('config_density0')
fac = 1e-22*rho*cp
k700m = np.where(depth > 700.)[0][0] - 1
k2000m = np.where(depth > 2000.)[0][0] - 1
kbtm = len(depth)-1
# Load data
print " Load ocean data..."
varList = ['avgLayerTemperature',
'sumLayerMaskValue',
'avgLayerArea',
'avgLayerThickness']
ds = xr.open_mfdataset(
infiles,
preprocess=lambda x: preprocess_mpas(x,
yearoffset=yr_offset,
timestr='Time',
onlyvars=varList,
varmap=variableMap))
ds = remove_repeated_time_index(ds)
# convert the start and end dates to datetime objects using
# the Date class, which ensures the results are within the
# supported range
time_start = Date(startDate).to_datetime(yr_offset)
time_end = Date(endDate).to_datetime(yr_offset)
# select only the data in the specified range of years
ds = ds.sel(Time=slice(time_start, time_end))
# Select year-1 data and average it (for later computing anomalies)
time_start = datetime.datetime(time_start.year, 1, 1)
time_end = datetime.datetime(time_start.year, 12, 31)
ds_yr1 = ds.sel(Time=slice(time_start, time_end))
mean_yr1 = ds_yr1.mean('Time')
print " Compute temperature anomalies..."
avgLayerTemperature = ds.avgLayerTemperature
avgLayerTemperature_yr1 = mean_yr1.avgLayerTemperature
avgLayTemp_anomaly = avgLayerTemperature - avgLayerTemperature_yr1
year_start = (pd.to_datetime(ds.Time.min().values)).year
year_end = (pd.to_datetime(ds.Time.max().values)).year
time_start = datetime.datetime(year_start, 1, 1)
time_end = datetime.datetime(year_end, 12, 31)
if ref_casename_v0 != "None":
print " Load in OHC for ACMEv0 case..."
infiles_v0data = "{}/OHC.{}.year*.nc".format(
indir_v0data, ref_casename_v0)
ds_v0 = xr.open_mfdataset(
infiles_v0data,
preprocess=lambda x: preprocess_mpas(x, yearoffset=yr_offset))
ds_v0 = remove_repeated_time_index(ds_v0)
ds_v0_tslice = ds_v0.sel(Time=slice(time_start, time_end))
sumLayerMaskValue = ds.sumLayerMaskValue
avgLayerArea = ds.avgLayerArea
avgLayerThickness = ds.avgLayerThickness
print " Compute OHC and make plots..."
for index in range(len(iregions)):
iregion = iregions[index]
# Compute volume of each layer in the region:
layerArea = sumLayerMaskValue[:, iregion, :] * \
avgLayerArea[:, iregion, :]
layerVolume = layerArea * avgLayerThickness[:, iregion, :]
# Compute OHC:
ohc = layerVolume * avgLayTemp_anomaly[:, iregion, :]
# OHC over 0-bottom depth range:
ohc_tot = ohc.sum('nVertLevels')
ohc_tot = fac*ohc_tot
# OHC over 0-700m depth range:
ohc_700m = fac*ohc[:, 0:k700m].sum('nVertLevels')
# OHC over 700m-2000m depth range:
ohc_2000m = fac*ohc[:, k700m+1:k2000m].sum('nVertLevels')
# OHC over 2000m-bottom depth range:
ohc_btm = ohc[:, k2000m+1:kbtm].sum('nVertLevels')
ohc_btm = fac*ohc_btm
title = 'OHC, {}, 0-bottom (thick-), 0-700m (thin-), 700-2000m (--),' \
' 2000m-bottom (-.) \n {}'.format(plot_titles[iregion], casename)
xlabel = "Time [years]"
ylabel = "[x$10^{22}$ J]"
if ref_casename_v0 != "None":
figname = "{}/ohc_{}_{}_{}.png".format(plots_dir,
regions[iregion],
casename,
ref_casename_v0)
ohc_v0_tot = ds_v0_tslice.ohc_tot
ohc_v0_700m = ds_v0_tslice.ohc_700m
ohc_v0_2000m = ds_v0_tslice.ohc_2000m
ohc_v0_btm = ds_v0_tslice.ohc_btm
title = "{} (r), {} (b)".format(title, ref_casename_v0)
timeseries_analysis_plot(config, [ohc_tot, ohc_700m, ohc_2000m,
ohc_btm, ohc_v0_tot, ohc_v0_700m,
ohc_v0_2000m, ohc_v0_btm],
N_movavg, title, xlabel, ylabel, figname,
lineStyles=['r-', 'r-', 'r--', 'r-.',
'b-', 'b-', 'b--', 'b-.'],
lineWidths=[2, 1, 1.5, 1.5, 2, 1, 1.5,
1.5])
if not compare_with_obs and ref_casename_v0 == "None":
figname = "{}/ohc_{}_{}.png".format(plots_dir, regions[iregion],
casename)
timeseries_analysis_plot(config, [ohc_tot, ohc_700m, ohc_2000m,
ohc_btm],
N_movavg, title, xlabel, ylabel, figname,
lineStyles=['r-', 'r-', 'r--', 'r-.'],
lineWidths=[2, 1, 1.5, 1.5])
def ohc_timeseries(config, streamMap=None, variableMap=None):
"""
Performs analysis of ocean heat content (OHC) from time-series output.
config is an instance of an MpasAnalysisConfigParser containing
configuration options.
config is an instance of MpasAnalysisConfigParser containing configuration
options.
If present, streamMap is a dictionary of MPAS-O stream names that map to
their mpas_analysis counterparts.
If present, variableMap is a dictionary of MPAS-O variable names that map
to their mpas_analysis counterparts.
Author: Xylar Asay-Davis, Milena Veneziani
Last Modified: 01/07/2017
"""
# read parameters from config file
casename = config.get('case', 'casename')
ref_casename_v0 = config.get('case', 'ref_casename_v0')
indir_v0data = config.get('paths', 'ref_archive_v0_ocndir')
compare_with_obs = config.getboolean('ohc_timeseries', 'compare_with_obs')
plots_dir = config.get('paths', 'plots_dir')
yr_offset = config.getint('time', 'yr_offset')
N_movavg = config.getint('ohc_timeseries', 'N_movavg')
regions = config.getExpression('regions', 'regions')
plot_titles = config.getExpression('regions', 'plot_titles')
iregions = config.getExpression('ohc_timeseries', 'regionIndicesToPlot')
indir = config.get('paths', 'archive_dir_ocn')
namelist_filename = config.get('input', 'ocean_namelist_filename')
namelist = NameList(namelist_filename, path=indir)
streams_filename = config.get('input', 'ocean_streams_filename')
streams = StreamsFile(streams_filename, streamsdir=indir)
# Note: input file, not a mesh file because we need dycore specific fields
# such as refBottomDepth and namelist fields such as config_density0, as
# well as simulationStartTime, that are not guaranteed to be in the mesh file.
try:
inputfile = streams.readpath('restart')[0]
except ValueError:
raise IOError(
'No MPAS-O restart file found: need at least one restart file for OHC calculation'
)
# get a list of timeSeriesStats output files from the streams file,
# reading only those that are between the start and end dates
startDate = config.get('time', 'timeseries_start_date')
endDate = config.get('time', 'timeseries_end_date')
streamName = streams.find_stream(streamMap['timeSeriesStats'])
infiles = streams.readpath(streamName,
startDate=startDate,
endDate=endDate)
print 'Reading files {} through {}'.format(infiles[0], infiles[-1])
# Define/read in general variables
print ' Read in depth and compute specific depth indexes...'
f = netcdf_dataset(inputfile, mode='r')
# reference depth [m]
depth = f.variables['refBottomDepth'][:]
# simulation start time
simStartTime = netCDF4.chartostring(f.variables['simulationStartTime'][:])
simStartTime = str(simStartTime)
f.close()
# specific heat [J/(kg*degC)]
cp = namelist.getfloat('config_specific_heat_sea_water')
# [kg/m3]
rho = namelist.getfloat('config_density0')
fac = 1e-22 * rho * cp
k700m = np.where(depth > 700.)[0][0] - 1
k2000m = np.where(depth > 2000.)[0][0] - 1
kbtm = len(depth) - 1
# Load data
print ' Load ocean data...'
varList = [
'avgLayerTemperature', 'sumLayerMaskValue', 'avgLayerArea',
'avgLayerThickness'
]
ds = xr.open_mfdataset(
infiles,
preprocess=lambda x: preprocess_mpas(x,
yearoffset=yr_offset,
timestr='Time',
onlyvars=varList,
varmap=variableMap))
ds = remove_repeated_time_index(ds)
# convert the start and end dates to datetime objects using
# the Date class, which ensures the results are within the
# supported range
time_start = Date(startDate).to_datetime(yr_offset)
time_end = Date(endDate).to_datetime(yr_offset)
# select only the data in the specified range of years
ds = ds.sel(Time=slice(time_start, time_end))
# Select year-1 data and average it (for later computing anomalies)
time_start_yr1 = Date(simStartTime).to_datetime(yr_offset)
if time_start_yr1 < time_start:
startDate_yr1 = simStartTime
endDate_yr1 = startDate_yr1[0:5] + '12-31' + startDate_yr1[10:]
infiles_yr1 = streams.readpath(streamName,
startDate=startDate_yr1,
endDate=endDate_yr1)
ds_yr1 = xr.open_mfdataset(
infiles_yr1,
preprocess=lambda x: preprocess_mpas(x,
yearoffset=yr_offset,
timestr='Time',
onlyvars=varList,
varmap=variableMap))
ds_yr1 = remove_repeated_time_index(ds_yr1)
else:
time_start = datetime.datetime(time_start.year, 1, 1)
time_end = datetime.datetime(time_start.year, 12, 31)
ds_yr1 = ds.sel(Time=slice(time_start, time_end))
mean_yr1 = ds_yr1.mean('Time')
print ' Compute temperature anomalies...'
avgLayerTemperature = ds.avgLayerTemperature
avgLayerTemperature_yr1 = mean_yr1.avgLayerTemperature
avgLayTemp_anomaly = avgLayerTemperature - avgLayerTemperature_yr1
year_start = (pd.to_datetime(ds.Time.min().values)).year
year_end = (pd.to_datetime(ds.Time.max().values)).year
time_start = datetime.datetime(year_start, 1, 1)
time_end = datetime.datetime(year_end, 12, 31)
if ref_casename_v0 != 'None':
print ' Load in OHC for ACMEv0 case...'
infiles_v0data = '{}/OHC.{}.year*.nc'.format(indir_v0data,
ref_casename_v0)
ds_v0 = xr.open_mfdataset(
infiles_v0data,
preprocess=lambda x: preprocess_mpas(x, yearoffset=yr_offset))
ds_v0 = remove_repeated_time_index(ds_v0)
year_end_v0 = (pd.to_datetime(ds_v0.Time.max().values)).year
if year_start <= year_end_v0:
ds_v0_tslice = ds_v0.sel(Time=slice(time_start, time_end))
else:
print ' Warning: v0 time series lies outside current bounds of v1 time series. Skipping it.'
ref_casename_v0 = 'None'
sumLayerMaskValue = ds.sumLayerMaskValue
avgLayerArea = ds.avgLayerArea
avgLayerThickness = ds.avgLayerThickness
print ' Compute OHC and make plots...'
for index in range(len(iregions)):
iregion = iregions[index]
# Compute volume of each layer in the region:
layerArea = sumLayerMaskValue[:, iregion, :] * \
avgLayerArea[:, iregion, :]
layerVolume = layerArea * avgLayerThickness[:, iregion, :]
# Compute OHC:
ohc = layerVolume * avgLayTemp_anomaly[:, iregion, :]
# OHC over 0-bottom depth range:
ohc_tot = ohc.sum('nVertLevels')
ohc_tot = fac * ohc_tot
# OHC over 0-700m depth range:
ohc_700m = fac * ohc[:, 0:k700m].sum('nVertLevels')
# OHC over 700m-2000m depth range:
ohc_2000m = fac * ohc[:, k700m + 1:k2000m].sum('nVertLevels')
# OHC over 2000m-bottom depth range:
ohc_btm = ohc[:, k2000m + 1:kbtm].sum('nVertLevels')
ohc_btm = fac * ohc_btm
title = 'OHC, {}, 0-bottom (thick-), 0-700m (thin-), 700-2000m (--),' \
' 2000m-bottom (-.) \n {}'.format(plot_titles[iregion], casename)
xlabel = 'Time [years]'
ylabel = '[x$10^{22}$ J]'
if ref_casename_v0 != 'None':
figname = '{}/ohc_{}_{}_{}.png'.format(plots_dir, regions[iregion],
casename, ref_casename_v0)
ohc_v0_tot = ds_v0_tslice.ohc_tot
ohc_v0_700m = ds_v0_tslice.ohc_700m
ohc_v0_2000m = ds_v0_tslice.ohc_2000m
ohc_v0_btm = ds_v0_tslice.ohc_btm
title = '{} (r), {} (b)'.format(title, ref_casename_v0)
timeseries_analysis_plot(
config, [
ohc_tot, ohc_700m, ohc_2000m, ohc_btm, ohc_v0_tot,
ohc_v0_700m, ohc_v0_2000m, ohc_v0_btm
],
N_movavg,
title,
xlabel,
ylabel,
figname,
lineStyles=[
'r-', 'r-', 'r--', 'r-.', 'b-', 'b-', 'b--', 'b-.'
],
lineWidths=[2, 1, 1.5, 1.5, 2, 1, 1.5, 1.5])
if not compare_with_obs and ref_casename_v0 == 'None':
figname = '{}/ohc_{}_{}.png'.format(plots_dir, regions[iregion],
casename)
timeseries_analysis_plot(config,
[ohc_tot, ohc_700m, ohc_2000m, ohc_btm],
N_movavg,
title,
xlabel,
ylabel,
figname,
lineStyles=['r-', 'r-', 'r--', 'r-.'],
lineWidths=[2, 1, 1.5, 1.5])
#from get_parameters import * # scipy from scipy import stats # data path ctl_name = "CESM2" #os.environ["ctl_name"] exp_name = "TSIS-1" #os.environ["exp_name"] ctl_pref = "solar_CTL_cesm211_ETEST-f19_g17-ens_mean_2010-2019" #exp_pref="solar_TSIS_cesm211_ETEST-f19_g17-ens_mean_2010-2019" fpath_ctl = "/Volumes/WD4T_1/cesm2_solar_exp/" + ctl_pref + "/climo/" #fpath_exp="/raid00/xianwen/data/cesm211_solar_exp/"+exp_pref+"/climo/" fctl = fpath_ctl + "solar_CTL_cesm211_ETEST-f19_g17-ens_mean_2010-2019_climo_ANN.nc" file_ctl = netcdf_dataset(fctl, "r") #years=np.arange(2010,2020) #months_all=["01","02","03","04","05","06","07","08","09","10","11","12"] #varnm="ICEFRAC" varnm = "FSSUS10" # FSSUS10, FSSUS08 varnm2 = "FSSDS10" # FSSDS10, FSSDS08 varnm3 = "FSSUS08" # FSSUS10, FSSUS08 varnm4 = "FSSDS08" # FSSDS10, FSSDS08 #varnm_off="FLUTC_OFF" #offline computation #units=r"W/m$^2$" units = "" figure_name = "lat_lon_albedo_VIS" dtctl = file_ctl.variables[varnm][0, :, :] / file_ctl.variables[varnm2][
nlev = 32
means_yby_ctl = np.zeros(
(years.size, nlat, nlon)) #year by year mean for each variable
means_yby_exp = np.zeros(
(years.size, nlat, nlon)) #year by year mean for each variable
means_ctl = np.zeros((nlat, nlon)) #year by year mean for each variable
means_exp = np.zeros((nlat, nlon)) #year by year mean for each variable
diffs = np.zeros((nlat, nlon)) #multi-year exp-ctl diff for each variable
pvals = np.zeros((nlat, nlon)) #pvalues of ttest
for iy in range(0, years.size):
# open data file
fctl = fpath_ctl + ctl_pref + "_" + season + "_" + str(years[iy]) + ".nc"
fexp = fpath_exp + exp_pref + "_" + season + "_" + str(years[iy]) + ".nc"
file_ctl = netcdf_dataset(fctl, "r")
file_exp = netcdf_dataset(fexp, "r")
# read lat and lon
lat = file_ctl.variables["lat"]
lon = file_ctl.variables["lon"]
means_yby_ctl[iy, :, :] = file_ctl.variables[varnm][0, :, :]
means_yby_exp[iy, :, :] = file_exp.variables[varnm][0, :, :]
means_ctl[:, :] = np.mean(means_yby_ctl, axis=0)
means_exp[:, :] = np.mean(means_yby_exp, axis=0)
diffs = means_exp - means_ctl
if pole == "N":
latbound1 = np.min(np.where(lat[:] > 55))
# You can reduce the resolution of the data returned from the server
# This can be helpful during testing if the dataset is very large
# Set this to one for full resolution, two for half, and so on
req["sub"] = '4'
print(req["sub"])
# In[8]:
# GET DATA
# m0 = datetime.now()
#dataset = getData(dataset_id, parameter, qbounds, '', req["sub"],basemapImageDir)
file = basemapImageDir + '/' + parameter + '.nc'
datafile = netcdf_dataset(file)
dataset = {"data": datafile}
dataObj = dataset['data']
# check data file for expected altitude dimension response
# pull out parameter data and x, y
if len(np.shape(dataObj.variables[parameter])) == 3:
data = dataObj.variables[parameter][0, :, :]
elif len(np.shape(dataObj.variables[parameter])) == 4:
data = dataObj.variables[parameter][0, 0, :, :]
elif len(np.shape(dataObj.variables[parameter])) == 2:
data = dataObj.variables[parameter][:, :]
xgrid = dataObj.variables[xcoord_dimension["name"]][:]
ygrid = dataObj.variables[ycoord_dimension["name"]][:]
import os
import matplotlib.pyplot as plt
from netCDF4 import Dataset as netcdf_dataset
import numpy as np
import cartopy.crs as ccrs
# get the path of the file. It can be found in the data directory, conveniently
# at the same level as the cartopy/crs.py file.
fname = os.path.join(os.path.dirname(ccrs.__file__),
'data', 'netcdf', 'HadISST1_SST_update.nc'
)
dataset = netcdf_dataset(fname)
sst = dataset.variables['sst'][0, :, :]
lats = dataset.variables['lat'][:]
lons = dataset.variables['lon'][:]
ax = plt.axes(projection=ccrs.PlateCarree())
plt.contourf(lons, lats, sst, 60,
transform=ccrs.PlateCarree())
ax.coastlines()
plt.show()
'clevsDiff_conc_{}'.format(season))
cmap = plt.get_cmap(config.get('seaice_modelvsobs', 'cmapDiff'))
cmapIndices = config.getExpression('seaice_modelvsobs',
'cmapIndicesDiff')
cmapDiff = cols.ListedColormap(cmap(cmapIndices), "cmapDiff")
lon0 = config.getfloat('seaice_modelvsobs',
'lon0_{}'.format(hemisphere))
latmin = config.getfloat('seaice_modelvsobs',
'latmin_{}'.format(hemisphere))
# Load in sea-ice data
# Model...
# ice concentrations
fileName = "{}/{}".format(climodir_regridded, climofiles[climName])
f = netcdf_dataset(fileName, mode='r')
iceconc = f.variables["iceAreaCell"][:]
if(first):
lons = f.variables["lon"][:]
lats = f.variables["lat"][:]
print "Min lon: ", np.amin(lons), "Max lon: ", np.amax(lons)
print "Min lat: ", np.amin(lats), "Max lat: ", np.amax(lats)
Lons, Lats = np.meshgrid(lons, lats)
first = False
f.close()
# ...and observations
# ice concentrations from NASATeam (or Bootstrap) algorithm
for obsName in ['NASATeam', 'Bootstrap']:
fileName = obs_iceconc_filenames['{}_{}'.format(climName, obsName)]
