def test_heat_index_kelvin():
"""Test heat_index when given Kelvin temperatures."""
temp = 308.15 * units.degK
rh = 0.7
hi = heat_index(temp, rh)
# NB rounded up test value here vs the above two tests
assert_almost_equal(hi.to('degC'), 50.3406 * units.degC, 4)
def test_heat_index_undefined_flag():
"""Test whether masking values can be disabled for heat index."""
temp = units.Quantity(np.ma.array([80, 88, 92, 79, 30, 81]), units.degF)
rh = np.ma.array([40, 39, 2, 70, 50, 39]) * units.percent
hi = heat_index(temp, rh, mask_undefined=False)
mask = np.array([False] * 6)
assert_array_equal(hi.mask, mask)
def test_heat_index_invalid():
"""Test heat index for values that should be masked."""
temp = np.array([80, 88, 92, 79, 30, 81]) * units.degF
rh = np.array([40, 39, 2, 70, 50, 39]) * units.percent
hi = heat_index(temp, rh)
mask = np.array([False, True, True, True, True, True])
assert_array_equal(hi.mask, mask)
def test_heat_index_basic():
"""Test the basic heat index calculation."""
temp = np.array([80, 88, 92, 110]) * units.degF
rh = np.array([40, 100, 70, 40]) * units.percent
hi = heat_index(temp, rh)
values = np.array([80, 121, 112, 136]) * units.degF
assert_array_almost_equal(hi, values, 0)
def mcalc_feelslike(tmpf, dwpf, smps):
"""Compute a feels like temperature
Args:
temperature (temperature): The dry bulb temperature
dewpoint (temperature): The dew point temperature
speed (speed): the wind speed
Returns:
temperature (temperature): The feels like temperature
"""
rh = mcalc.relative_humidity_from_dewpoint(tmpf, dwpf)
hidx = mcalc.heat_index(tmpf, rh, mask_undefined=True)
wcht = mcalc.windchill(tmpf, smps, mask_undefined=True)
# Where heat index is masked, replace with temperature
hidx[hidx.mask] = tmpf[hidx.mask]
# where ever wcht is not masked, replace with wind chill
hidx[~ wcht.mask] = wcht[~ wcht.mask]
return hidx
def test_heat_index_ratio():
"""Test giving humidity as number [0, 1] to heat index."""
temp = units.Quantity([35., 20.], units.degC)
rh = 0.7
hi = heat_index(temp, rh)
assert_almost_equal(hi.to('degC'), units.Quantity([50.3405, np.nan], units.degC), 4)
def test_heat_index_units():
"""Test units coming out of heat index."""
temp = units.Quantity([35., 20.], units.degC)
rh = 70 * units.percent
hi = heat_index(temp, rh)
assert_almost_equal(hi.to('degC'), units.Quantity([50.3405, np.nan], units.degC), 4)
def test_heat_index_scalar():
"""Test heat index using scalars."""
hi = heat_index(96 * units.degF, 65 * units.percent)
assert_almost_equal(hi, 121 * units.degF, 0)
def GFS_fn(in_file_path, out_file_path, slice_hr):
os.mkdir(out_file_path)
start_time = time.time()
GFS_globe = xr.open_mfdataset(in_file_path + "*.grib",
engine='cfgrib',
compat='override',
combine='by_coords')
GFS_grib = GFS_globe.sel(longitude=slice(65, 100), latitude=slice(40, 5))
ref_date = str(GFS_grib.time.values) #'2020-02-26 00:00:00'
GFS_grib.to_netcdf(in_file_path + 'GFS_INDIA_' + ref_date[:13] + '.nc')
GFS = xr.open_dataset(in_file_path + 'GFS_INDIA_' + ref_date[:13] + '.nc')
t_bins = GFS.step[::2]
GFS.tp.groupby_bins('step', bins=t_bins, include_lowest=True,
right=False).groups
tp = GFS.tp.groupby_bins('step',
bins=t_bins,
include_lowest=True,
right=False).sum(
dim=('step')) #GFS.tp[:,1440,0].values
tmax = GFS.tmax.groupby_bins('step',
bins=t_bins,
include_lowest=True,
right=False).max(dim=('step')) #max
r2 = GFS.r2.groupby_bins('step',
bins=t_bins,
include_lowest=True,
right=False).max(dim=('step')) #max
W_SPD = np.sqrt(GFS.u10**2 + GFS.v10**2)
W_speed = W_SPD.groupby_bins('step',
bins=t_bins,
include_lowest=True,
right=False).max(dim=('step'))
W_speed_index = W_SPD.groupby_bins(
'step', bins=t_bins, include_lowest=True,
right=False).map(lambda x: np.argmax(x, axis=0))
HI_tmax = mpcalc.heat_index(GFS.tmax.values * units.degK,
GFS.r2.values / 100 * units.dimensionless,
mask_undefined=False).to(units.degC)
HI_t2 = mpcalc.heat_index(GFS.t2m.values * units.degK,
GFS.r2.values / 100 * units.dimensionless,
mask_undefined=False).to(units.degC)
Heatindex_tmax_hr = GFS.tmax.copy() * 0 + HI_tmax.magnitude
Heatindex_t2_hr = GFS.t2m.copy() * 0 + HI_t2.magnitude
Heat_Index_tmax = Heatindex_tmax_hr.groupby_bins(
'step', bins=t_bins, include_lowest=True,
right=False).max(dim=('step')) #max of Heat Index
Heat_Index_t2 = Heatindex_t2_hr.groupby_bins(
'step', bins=t_bins, include_lowest=True,
right=False).max(dim=('step')) #max of Heat Index
print(time.time() - start_time)
U10 = W_speed.values.copy() * 0
V10 = U10.copy() * 0
for tm in range(W_speed.shape[0]):
print(tm)
a = W_speed_index[tm, :, :].values + (tm * 2)
m, n = a.shape
I, J = np.ogrid[:m, :n]
U10[tm, :, :] = GFS.u10.values[a, I, J]
V10[tm, :, :] = GFS.v10.values[a, I, J]
ds = xr.Dataset(
{
'Total_precipitation': (['time', 'lat', 'lon'], tp.values),
'T_max': (['time', 'lat', 'lon'], tmax.values - 273.15),
'Relative_Humidity_2m': (['time', 'lat', 'lon'], r2.values),
'U10': (['time', 'lat', 'lon'], U10),
'V10': (['time', 'lat', 'lon'], V10),
'wind_speed': (['time', 'lat', 'lon'], W_speed.values),
'Heat_Index_tmax':
(['time', 'lat', 'lon'], Heat_Index_tmax.values),
'Heat_Index_t2': (['time', 'lat', 'lon'], Heat_Index_t2.values)
},
coords={
'lon': (['lon'], GFS.longitude.values),
'lat': (['lat'], GFS.latitude.values),
'time':
pd.date_range(ref_date, periods=W_speed.shape[0] + 1,
freq='6H')[1:],
'reference_time':
pd.Timestamp(ref_date)
})
os.chdir(out_file_path)
ds.Total_precipitation.to_netcdf("GFS_Total_precipitationto.nc")
ds.T_max.to_netcdf("GFS_T_max.nc")
ds.Relative_Humidity_2m.to_netcdf("GFS_Relative_Humidity_2m.nc")
ds.U10.to_netcdf("GFS_U10.nc")
ds.V10.to_netcdf("GFS_V10.nc")
ds.wind_speed.to_netcdf("GFS_wind_speed.nc")
ds.Heat_Index_tmax.to_netcdf("GFS_Heat_Index_tmax.nc")
ds.Heat_Index_t2.to_netcdf("GFS_Heat_Index_t2.nc")
lat_new = np.linspace(
ds['lat'].values.min(),
ds['lat'].values.max(),
num=int((ds['lat'].values.max() - ds['lat'].values.min()) / 0.25) + 1,
endpoint=True)
lon_new = np.linspace(
ds['lon'].values.min(),
ds['lon'].values.max(),
num=int((ds['lon'].values.max() - ds['lon'].values.min()) / 0.25) + 1,
endpoint=True)
ds_out = xr.Dataset({
'lat': (['lat'], lat_new),
'lon': (['lon'], lon_new),
})
regridder = xe.Regridder(ds, ds_out, 'bilinear', reuse_weights=True)
regridder # print basic regridder information.
U10p25 = regridder(ds.U10)
V10p25 = regridder(ds.V10)
U10p25.to_netcdf("GFS0p25_U10m.nc")
V10p25.to_netcdf("GFS0p25_V10m.nc")
print(time.time() - start_time)
data_dir_raw = Path("/home/vassar/Documents/forecast_vassarlabs/data")
brk_rh = pd.read_csv(data_dir_raw / "brk_rh.csv")["x"]
col_rh = pd.read_csv(data_dir_raw / "col_rh.csv")["x"]
brk_tem = pd.read_csv(data_dir_raw / "brk_tem.csv")["x"]
col_tem = pd.read_csv(data_dir_raw / "col_tem.csv")["x"]
brk_ws = pd.read_csv(data_dir_raw / "brk_ws.csv")["x"]
col_ws = pd.read_csv(data_dir_raw / "col_ws.csv")["x"]
brk_rf = [0, .1, 2.5, 15.6, 64.5, 115.6, 204.5, 8000]
col_rf = [
"#FFFFFF", "#C3FDCA", "#01FF04", "#048500", "#FDC0CB", "#FC0300",
"#610301"
]
mask_table = pd.read_csv(
"/home/vassar/Documents/Rahul/Block&&SubBasin/Block_UUID (1)/Block_uuid.csv"
)
mask_table = mask_table[[
'OBJECTID_1', "State_Name", 'district', 'block', 'UUID'
]]
mask_table.columns = ['Mask_id', "State_Name", 'district', 'block', 'UUID']
mask = xr.open_dataset(
'/home/vassar/Documents/Rahul/Block&&SubBasin/Block_UUID (1)/Block.nc')
nc2table_max(ds.T_max, mask.Band1, mask_table, data_dir_raw,
"bilinear_281x281_3032x2923.nc")
nc2table_max(ds.Relative_Humidity_2m, mask.Band1, mask_table, data_dir_raw,
"bilinear_281x281_3032x2923.nc")
nc2table(ds.Total_precipitation, mask.Band1, mask_table, data_dir_raw,
"bilinear_281x281_3032x2923.nc")
nc2table_max(ds.wind_speed, mask.Band1, mask_table, data_dir_raw,
"bilinear_281x281_3032x2923.nc")
nc2table_max(ds.Heat_Index_t2, mask.Band1, mask_table, data_dir_raw,
"bilinear_281x281_3032x2923.nc")
Process(target=heatmap, args=(ds.T_max, col_tem, brk_tem)).start()
Process(target=heatmap, args=(ds.Heat_Index_t2, col_tem, brk_tem)).start()
Process(target=heatmap,
args=(ds.Relative_Humidity_2m, col_rh, brk_rh)).start()
Process(target=heatmap, args=(ds.wind_speed, col_ws, brk_ws)).start()
Process(target=heatmap,
args=(ds.Total_precipitation, col_rf, brk_rf)).start()