# Import packages
import numpy as np
import netCDF4
import matplotlib.pyplot as plt
import xarray as xr
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import seaborn as sns

# Plotting options
sns.set(font_scale=1.3)
sns.set_style("white")

# This is the base url required to download data from the thredds server.
dir_path = 'http://thredds.northwestknowledge.net:8080/thredds/dodsC/'

# These are the variable options for the met data
variable_name = ('tasmax',
                 'tasmin',
                 'rhsmax',
                 'rhsmin',
                 'pr',
                 'rsds',
                 'uas',
                 'vas',
                 'huss')

# These are var options in long form
var_long_name = ('air_temperature',
                 'air_temperature',
                 'relative_humidity',
                 'relative_humidity',
                 'precipitation',
                 'surface_downwelling_shortwave_flux_in_air',
                 'eastward_wind',
                 'northward_wind',
                 'specific_humidity')

# Models to chose from
model_name = ('bcc-csm1-1',
              'bcc-csm1-1-m',
              'BNU-ESM',
              'CanESM2',
              'CCSM4',
              'CNRM-CM5',
              'CSIRO-Mk3-6-0',
              'GFDL-ESM2G',
              'GFDL-ESM2M',
              'HadGEM2-CC365',
              'HadGEM2-ES365',
              'inmcm4',
              'IPSL-CM5A-MR',
              'IPSL-CM5A-LR',
              'IPSL-CM5B-LR',
              'MIROC5',
              'MIROC-ESM',
              'MIROC-ESM-CHEM',
              'MRI-CGCM3',
              'NorESM1-M')

# Scenarios
scenario_type = ('historical', 'rcp45', 'rcp85')

# Year start and ends (historical vs projected)
year_start = ('1950', '2006', '2006')
year_end = ('2005', '2099', '2099')
run_num = [1] * 20
run_num[4] = 6  # setting CCSM4 with run 6
domain = 'CONUS'

# Model options between 0-19
model = 2
# Options 0-8 will work for var. Var maps to the variable name below
var = 0
# Options range from 0-2
scenario = 2

try: 
    print("Great! You have selected: \n \u2705 Variable: {} \n \u2705 Model: {}, "
      "\n \u2705 Scenario: {}".format(variable_name[var], 
                                      model_name[model],
                                      scenario_type[scenario]))
except IndexError as e:
    raise IndexError("Oops, it looks like you selected value that is "
                     "not within the range of values which is 0-2. please look"
                     "closely at your selected values.")

try:
    time = year_start[scenario]+'_' + year_end[scenario]
    print("\u2705 Your selected time period is:", time)
except IndexError as e:
    raise IndexError("Oops, it looks like you selected a scenario value that is \
                     not within the range of values which is 0-2")
    

# This code creates a path to the monthly MACA v2 data
file_name = ('agg_macav2metdata_' +
             str(variable_name[var]) +
             '_' +
             str(model_name[model]) +
             '_r' +
             str(run_num[model])+'i1p1_' +
             str(scenario_type[scenario]) +
             '_' +
             time + '_' +
             domain + '_monthly.nc')

print("\u2705 You are accessing:\n", file_name, "\n data in netcdf format")

full_file_path = dir_path + file_name
print("The full path to your data is: \n", full_file_path)

# Open the data from the thredds server
try:
    max_temp_xr = xr.open_dataset(full_file_path)
except OSError as oe:
    print("Oops, it looks like the file that you are trying to connect to, "
          "{}, doesn't exist. Try to revisit your model options to ensure "
          "the data exist on the server.  ".format(full_file_path))

# View your temperature data
max_temp_xr

# Select the latitude, longitude, and timeframe to subset the data to

# Ensure your latitude value is between 25 and 50, and your longitude value is between 235 and 292
# latitude = 35
# longitude = 270
start_date = '2008-01'
end_date = '2012-09'

# Select a lat / lon location that you wish to use to extract the data
latitude = max_temp_xr.lat.values[300]
longitude = max_temp_xr.lon.values[150]
print("You selected the following x,y location:", longitude, latitude)

# Slice one lat/lon data point
temp_single_point = max_temp_xr["air_temperature"].sel(
    lat=latitude,
    lon=longitude)

temp_single_point

# Quick plot of the data
temp_single_point.plot.line()
plt.show()