def precip_dataframe(m: Meso, start: str, end: str,
**kwargs) -> pandas.DataFrame:
'''
Returns a pandas dataframe with STID as the index and the precip observation as the first column.
:param m: Meso instance for retrieving data.
:param start: start of observation window.
:param end: end of observation window.
:param kwargs: other key word arguments for the API.
:return: the stations with a observation window within 0.05% of the expected window.
The observation window is defined as (end - start). When creating the dataframe we filter out any observations that
do not have that large of a window as they are un representative of the situation. The filter uses last_report and
first_report from the API dictionary to determine if it is valid. (Returning this invalid data may be implemented)
'''
kwargs['timeformat'] = '%s' # force timeformat
df = json_normalize(m.precip(start=start, end=end, **kwargs)['STATION'])
precip_col = np.full(df.shape[0], np.nan, dtype='float64')
epoch_delta_col = np.full(df.shape[0], 0, dtype='int')
start = datetime.strptime(start, '%Y%m%d%H%M')
end = datetime.strptime(end, '%Y%m%d%H%M')
delta = end.timestamp() - start.timestamp()
if 'units' in kwargs:
_accum_label = 'ACCUM_' + str(int(
delta / 86400)).strip() + '_DAYS[' + kwargs['units'] + ']'
else:
_accum_label = 'ACCUM_' + str(int(delta / 86400)).strip() + '_DAYS[mm]'
for i, row in df.iterrows(
): # extract precip observation into it's own column
if len(row['OBSERVATIONS.precipitation']) > 0:
_dict = row['OBSERVATIONS.precipitation'][0]
epoch_delta_col[i] = (int(_dict['last_report']) -
int(_dict['first_report']))
precip_col[i] = _dict['total']
df[_accum_label] = precip_col # precip data column
df['EPOCH_TIMEDELTA'] = epoch_delta_col # used to filter out erroneous time periods
df = df[abs(df['EPOCH_TIMEDELTA'] - delta) < .0005 * delta]
df = df[['STID', _accum_label]].set_index('STID', drop=True)
return df
startclim='04260000',
endclim='04270000',
units='precip|in')
# Fetches the latest obs for Fort Collins airport within 30 min of Apr 26 18z
attime = m.attime(stid='kfnl', attime='201504261800', within='30')
# Or just get the latest observation within the last 15 minutes
latest = m.latest(stid='kfnl', within='15')
# Returns a time series from Fort Collins airport from Apr 26 18z to Apr 26 23z
time = m.timeseries(stid='kfnl', start='201504261800', end='201504262300')
# Returns the precip obs from Fort Collins airport from Apr 26 18z to Apr 27 12z
precip = m.precip(stid='kfnl',
start='201504261800',
end='201504271200',
units='precip|in')
# Learn more about all of the networks in MesoWest with the networks() func
networks = m.networks()
# Or explore the categories MesoWest networks belong to
nettypes = m.networktypes()
# You can obtain time series statistics for any station
stats = m.time_stats(stid='mtmet',
start='201403240000',
end='201403280000',
type='all')
# Or get climatology stats for a station (remember to change the date!)
variables = m.variables() # This returns a climatology for Denver from Apr 26 OOz to Apr 27 OOz climate = m.climatology(stid='kden', startclim='04260000', endclim='04270000', units='precip|in') # Fetches the latest obs for Fort Collins airport within 30 min of Apr 26 18z attime = m.attime(stid='kfnl', attime='201504261800', within='30') # Or just get the latest observation within the last 15 minutes latest = m.latest(stid='kfnl', within='15') # Returns a time series from Fort Collins airport from Apr 26 18z to Apr 26 23z time = m.timeseries(stid='kfnl', start='201504261800', end='201504262300') # Returns the precip obs from Fort Collins airport from Apr 26 18z to Apr 27 12z precip = m.precip(stid='kfnl', start='201504261800', end='201504271200', units='precip|in') # Learn more about all of the networks in MesoWest with the networks() func networks = m.networks() # Or explore the categories MesoWest networks belong to nettypes = m.networktypes() # You can obtain time series statistics for any station stats = m.time_stats(stid='mtmet', start='201403240000', end='201403280000', type='all') # Or get climatology stats for a station (remember to change the date!) clim_stats = m.climate_stats(stid='mtmet', startclim='03240000', endclim='03280000', type='all') # Lastly, see the latency of the API by using the latency() function latency = m.latency(stid='mtmet', start='201403240000', end='201403280000')
#print len(kbdistations)
# Set default values for dictionary entries. These will later be overwritten if
for station in stationsDict:
stationsDict[station]['RECENT_OBS'] = -99
stationsDict[station]['MX_TEMP'] = -99
stationsDict[station]['MW_PCP'] = 0
stationsDict[station]['LONGITUDE'] = 0
stationsDict[station]['LATITUDE'] = 0
#stationsDict[station]['NoRainDays'] = 0
# precipitation query will return total precipitation over the last 24 hours
#start, end = '201712111800','201712121200'
print('start:', start, 'end:', end)
mwprecipdata = m.precip(stid=kbdistations,
start=start,
end=end,
units='precip|in')
# Add vars for air_temp_high_6_hour
if (mwprecipdata):
for ob in mwprecipdata['STATION']:
#print(ob)
if 'total_precip_value_1' in ob['OBSERVATIONS']:
obid = ob['STID']
if "E" in str(ob['OBSERVATIONS']['total_precip_value_1']):
stationsDict[obid]['MW_PCP'] = ob['OBSERVATIONS'][
'total_precip_value_1'][2:]
elif ob['OBSERVATIONS']['total_precip_value_1'] == "T 0":
stationsDict[obid]['MW_PCP'] = 0.0
# Try to catch any unexpected tags in data and add to log file
elif re.search(
