Current Weather Quicklooks

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Current Weather Quicklooks#

Code to Create Plots#

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import sage_data_client
from bokeh.models.formatters import DatetimeTickFormatter
import hvplot.pandas
import hvplot.xarray
import holoviews as hv
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import xarray as xr
import matplotlib.pyplot as plt
from metpy.plots import USCOUNTIES
import metpy.calc as mpcalc
import act
import numpy as np
import pandas as pd
import warnings
from bokeh.models import DatetimeTickFormatter
import panel as pn

start = "-6h"

def apply_formatter(plot, element):
    plot.handles['xaxis'].formatter = DatetimeTickFormatter(hours='%m/%d/%Y \n %H:%M',
                                                            minutes='%m/%d/%Y \n %H:%M',
                                                            hourmin='%m/%d/%Y \n %H:%M',
                                                            days='%m/%d/%Y \n %H:%M',
                                                            months='%m/%d/%Y \n %H:%M')
xr.set_options(keep_attrs=True)
warnings.filterwarnings("ignore")
hv.extension("bokeh")



# Dictionary for renaming to standard names
variable_rename_dict = {'wxt.env.humidity':'relative_humidity',
                        'wxt.env.pressure':'air_pressure',
                        'wxt.env.temp':'air_temperature',
                        'wxt.heater.temp':'heater_temperature',
                        'wxt.heater.volt':'heater_voltage',
                        'wxt.rain.accumulation':'rain_accumulation',
                        'wxt.wind.direction':'wind_direction',
                        'wxt.wind.speed':'wind_speed',
                        'sys.gps.lat':'latitude',
                        'sys.gps.lon':'longitude',
                    }

# Dictionary for units that are missing
units_dict = {'wxt.env.temp': 'degC',
              'wxt.env.pressure':'hPa',
              'wxt.env.humidity':'percent',
              'wxt.wind.speed':'m/s',
              'wxt.wind.direction':'degrees'}

def generate_data_array(df, variable, rename_variable_dict=variable_rename_dict):
    new_variable_name = rename_variable_dict[variable]
    df_variable= df.loc[df.name == variable]
    ds = df_variable.to_xarray().rename({'value':new_variable_name,
                                         'timestamp':'time',
                                         'meta.vsn':'node'})
    ds[new_variable_name].attrs['units'] = df_variable['meta.units'].values[0]
    ds['time'] = pd.to_datetime(ds.time)
    ds.attrs['datastream'] = ds.node.values[0]
    return ds[[new_variable_name]]

def generate_dataset(df, variables, rename_variable_dict=variable_rename_dict):
    try:
        reindexed = df.set_index(['meta.vsn', 'timestamp'])
    except:
        reindexed = df.set_index(['timestamp'])
    return xr.merge([generate_data_array(reindexed, variable) for variable in variables])

crocus_nodes = "W08D"


# Query and load for n numbder of days
wxt_df = sage_data_client.query(
    start=start,
    filter={
        "sensor": "vaisala-wxt536",
        "name": "wxt.env.*",
        "vsn": crocus_nodes
    }
)

wxt_df1 = sage_data_client.query(
    start=start,
    filter={
        "sensor": "vaisala-wxt536",
        "name": "wxt.wind.*",
        "vsn": crocus_nodes
    }
)

wxt_df = pd.concat([wxt_df, wxt_df1])

try:

    # Discover what variables we have and what to load into xarray
    wxt_variables = wxt_df.name.unique()
    wxt_df['meta.units'] = wxt_df.name.map(units_dict)
    wxt_ds = generate_dataset(wxt_df, wxt_variables).squeeze().metpy.parse_cf()
    wxt_ds['air_dewpoint_temperature'] = mpcalc.dewpoint_from_relative_humidity(wxt_ds.air_temperature, wxt_ds.relative_humidity)

    # Resample to 1 minute freqency
    minute_ds = wxt_ds.resample(time='1T').mean()

    plots = []
    temp_plot = wxt_ds.air_temperature.hvplot(color='red',
                                              label='Air Temperature (degC)')
    dewp_plot = wxt_ds.air_dewpoint_temperature.hvplot(color='green',
                                                       label='Dewpoint Temperature (degC)')
    plots.append((temp_plot * dewp_plot).opts(hooks=[apply_formatter]))
    
    meteogram_variables = ['wind_speed', 'wind_direction']
    for variable in meteogram_variables:
        plots.append((wxt_ds[variable].hvplot.line(label='10 Hz Data') * 
                     minute_ds[variable].hvplot.line(label='1 Minute Data')).opts(hooks=[apply_formatter]))
    
    
except:
    minute_ds = None
    plots = pn.Row('No Data Available')

Meteogram with Temperature, Dewpoint, and Winds#

This is what we call a meteogram, which is a timeseries of:

Temperature and Dewpoint (same plot)
Wind speed and direction (different plots)

The data is collected at 10 Hertz (10 observations per second) and averaged to 1 Hertz (1 per second) for the wind plots to illustrate turbulence!