How to use the siphon.ncss.NCSS function in siphon

'''
        Retrieves the designated dataset, creates NCSS object, and
        creates a NCSS query object.
        '''

        keys = list(self.model.datasets.keys())
        labels = [item.split()[0].lower() for item in keys]
        if self.set_type == 'best':
            self.dataset = self.model.datasets[keys[labels.index('best')]]
        elif self.set_type == 'latest':
            self.dataset = self.model.datasets[keys[labels.index('latest')]]
        elif self.set_type == 'full':
            self.dataset = self.model.datasets[keys[labels.index('full')]]

        self.access_url = self.dataset.access_urls[self.access_url_key]
        self.ncss = NCSS(self.access_url)
        self.query = self.ncss.query()

# Helper function for finding proper time variable
def find_time_var(var, time_basename='time'):
    for coord_name in var.coordinates.split():
        if coord_name.startswith(time_basename):
            return coord_name
    raise ValueError('No time variable found for ' + var.name)


###############################################
# Create NCSS object to access the NetcdfSubset
# ---------------------------------------------
# Data from NCEI GFS 0.5 deg Analysis Archive

base_url = 'https://www.ncei.noaa.gov/thredds/ncss/grid/gfs-g4-anl-files/'
dt = datetime(2017, 4, 5, 12)
ncss = NCSS('{}{dt:%Y%m}/{dt:%Y%m%d}/gfsanl_4_{dt:%Y%m%d}_'
            '{dt:%H}00_000.grb2'.format(base_url, dt=dt))

# Create lat/lon box for location you want to get data for
query = ncss.query().time(dt)
query.lonlat_box(north=65, south=15, east=310, west=220)
query.accept('netcdf')

# Request data for vorticity
query.variables('Geopotential_height_isobaric', 'Temperature_isobaric',
                'u-component_of_wind_isobaric', 'v-component_of_wind_isobaric')
data = ncss.get_data(query)

# Pull out variables you want to use
hght_var = data.variables['Geopotential_height_isobaric']
temp_var = data.variables['Temperature_isobaric']
u_wind_var = data.variables['u-component_of_wind_isobaric']

for i in range(len(mxy)):
        ax.text(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]], symbol, color=color, size=24,
                clip_on=True, horizontalalignment='center', verticalalignment='center',
                transform=transform)
        ax.text(lon[mxy[i], mxx[i]], lat[mxy[i], mxx[i]],
                '\n' + str(np.int(data[mxy[i], mxx[i]])),
                color=color, size=12, clip_on=True, fontweight='bold',
                horizontalalignment='center', verticalalignment='top', transform=transform)


###############################
# Get NARR data
dattim = datetime(1999, 1, 3, 0)

ncss = NCSS('https://www.ncei.noaa.gov/thredds/ncss/grid/narr-a-files/{0:%Y%m}/{0:%Y%m%d}/'
            'narr-a_221_{0:%Y%m%d}_{0:%H}00_000.grb'.format(dattim))
query = ncss.query()
query.all_times().variables('Pressure_reduced_to_MSL_msl',
                            'Geopotential_height_isobaric').add_lonlat().accept('netcdf')
data = ncss.get_data(query)

###############################
# Extract data into variables

# Grab pressure levels
plev = list(data.variables['isobaric1'][:])

# Grab lat/lons and make all lons 0-360
lats = data.variables['lat'][:]
lons = data.variables['lon'][:]
lons[lons < 0] = 360 + lons[lons < 0]

raise ValueError('No time variable found for ' + var.name)

#####################################
# Obtain data


# Construct a TDSCatalog instance pointing to the gfs dataset
best_gfs = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'
                      'NCEP/GFS/Global_0p5deg/catalog.xml')

# Pull out the dataset you want to use and look at the access URLs
best_ds = list(best_gfs.datasets.values())[1]
print(best_ds.access_urls)

# Create NCSS object to access the NetcdfSubset
ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
print(best_ds.access_urls['NetcdfSubset'])

#####################################
# First Query for MSLP

# Create lat/lon box for location you want to get data for
query = ncss.query()
query.lonlat_box(north=50, south=30, east=-80, west=-115).time(datetime.utcnow())
query.accept('netcdf4')

# Request data for MSLP
query.variables('MSLP_Eta_model_reduction_msl')
data = ncss.get_data(query)

# Pull out the variables you want to use
mslp_var = data.variables['MSLP_Eta_model_reduction_msl']

from metpy.units import units
from netCDF4 import num2date
import numpy as np
import numpy.ma as ma
from scipy.ndimage import gaussian_filter
from siphon.ncss import NCSS

###########################
# **Get the data**
#
# This example will use data from the North American Mesoscale Model Analysis
# (https://nomads.ncdc.gov/) for 12 UTC 27 April 2011.

base_url = 'https://www.ncei.noaa.gov/thredds/ncss/grid/namanl/'
dt = datetime(2011, 4, 27)
ncss = NCSS('{}{dt:%Y%m}/{dt:%Y%m%d}/namanl_218_{dt:%Y%m%d}_'
            '1800_000.grb'.format(base_url, dt=dt))

# Query for required variables
gfsdata = ncss.query().all_times()
gfsdata.variables('Geopotential_height_isobaric',
                  'u-component_of_wind_isobaric',
                  'v-component_of_wind_isobaric',
                  'Temperature_isobaric',
                  'Relative_humidity_isobaric',
                  'Best_4_layer_lifted_index_layer_between_two_pressure_'
                  'difference_from_ground_layer',
                  'Absolute_vorticity_isobaric',
                  'Pressure_reduced_to_MSL_msl',
                  'Dew_point_temperature_height_above_ground'
                  ).add_lonlat()

# Copyright (c) 2016 Siphon Contributors.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Provide access to the CDMRemoteFeature endpoint on TDS."""

from io import BytesIO

from .ncstream import read_cdmrf_messages
from ..ncss import NCSS


class CDMRemoteFeature(NCSS):
    """Communicate to the CDMRemoteFeature HTTP endpoint."""

    @staticmethod
    def _parse_messages(resp):
        """Parse server responses as CDMRemoteFeature messages."""
        return read_cdmrf_messages(BytesIO(resp))

    def _get_metadata(self):
        """Get header information and store as metadata for the endpoint."""
        self.metadata = self.fetch_header()
        self.variables = {g.name for g in self.metadata.grids}

    def fetch_header(self):
        """Make a header request to the endpoint."""
        query = self.query().add_query_parameter(req='header')
        return self._parse_messages(self.get_query(query).content)[0]

import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.units import units
from netCDF4 import num2date
import numpy as np
import scipy.ndimage as ndimage
from siphon.ncss import NCSS

########################################
# Set up access to the data

# Create NCSS object to access the NetcdfSubset
base_url = 'https://www.ncei.noaa.gov/thredds/ncss/grid/gfs-g4-anl-files/'
dt = datetime(2016, 8, 22, 18)
ncss = NCSS('{}{dt:%Y%m}/{dt:%Y%m%d}/gfsanl_4_{dt:%Y%m%d}_'
            '{dt:%H}00_003.grb2'.format(base_url, dt=dt))

# Create lat/lon box for location you want to get data for
query = ncss.query()
query.lonlat_box(north=50, south=30, east=-80, west=-115)
query.time(datetime(2016, 8, 22, 21))

# Request data for geopotential height
query.variables('Geopotential_height_isobaric', 'u-component_of_wind_isobaric',
                'v-component_of_wind_isobaric')
query.vertical_level(100000)
data = ncss.get_data(query)

# Pull out variables you want to use
height_var = data.variables['Geopotential_height_isobaric']
u_wind_var = data.variables['u-component_of_wind_isobaric']

########################################
# Begin Data Ingest
# -----------------

# Request METAR data from TDS
metar = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/nws/'
                   'metar/ncdecoded/catalog.xml')
dataset = list(metar.datasets.values())[0]
print(list(dataset.access_urls))

########################################
# What variables are available in dataset?

# Access netcdf subset and use siphon to request data
ncss_url = dataset.access_urls['NetcdfSubset']
ncss = NCSS(ncss_url)
print(ncss.variables)

########################################
# Set query to get desired data from Thredds server

# get current date and time
now = datetime.utcnow()
now = datetime(now.year, now.month, now.day, now.hour)

# define time range you want the data for
start = now - timedelta(days=1)
end = now

# build the query
query = ncss.query()
query.lonlat_point(-90.08, 32.32)