def daily_pivot_table(var_code, agg_function, abbreviation):
df = get_table_for_variable_code(var_code)
try:
dfp = pivot_dv_df(df)
except ValueError:
df = remove_duplicates(df)
dfp = pivot_dv_df(df)
dfd = dfp.resample('D')
aggrd = dfd.agg(agg_function)
rnmed = rename_cols(aggrd, abbreviation)
return rnmed
def tide_when_rain_max(rn_mx_time_df):
td_df = get_table_for_variable_code('six_min_tide')
try:
td_df = pivot_dv_df(td_df)
except:
td_df = remove_duplicates(td_df)
td_df = pivot_dv_df(td_df)
td_df = td_df.resample('15T').mean()
rn_mx_time_rnd = cln_n_rnd_times(rn_mx_time_df)
l = []
for c in rn_mx_time_rnd.columns:
times = rn_mx_time_rnd.loc[:, c]
tides = td_df.loc[times].resample('D').max()
rain_var = c.split('_')[0]
rain_site = c.split('-')[-1]
new_cols = ['{}-{}_td-{}'.format(rain_var, rain_site, col) for col in tides.columns]
tides.columns = new_cols
l.append(tides)
new_df = pd.concat(l, axis=1)
new_df.sort_index(inplace=True)
return new_df
# coding: utf-8
import numpy as np
from hr_db_scripts.main_db_script import get_table_for_variable_code, get_db_table_as_df, hr_db_filename
import datetime
import sqlite3
df = get_table_for_variable_code('rainfall')
dfp = df.pivot(columns='SiteID', values='Value')
dfp_med = dfp.median(1)
dfp_max = dfp.max(1)
dfp_std = dfp.std(1)
dfp_nz = dfp[dfp_max > 0]
dfp_nzi = dfp_nz.index
dfp_nz_med = dfp_med.loc[dfp_nzi]
dfp_nz_std = dfp_std.loc[dfp_nzi]
dfp_nz_filt = dfp_nz.apply(lambda x: np.where(
(abs(x - dfp_nz_med) > dfp_nz_std * 2) & (x > 1.5), np.nan, x))
dfp_nz_filt.reset_index(inplace=True)
dfp_filt_mlt = dfp_nz_filt.melt(id_vars='Datetime')
df.reset_index(inplace=True)
dfp_filt_mlt.set_index(['Datetime', 'SiteID'], inplace=True)
df.set_index(['Datetime', 'SiteID'], inplace=True)
dfp_filt_i = dfp_filt_mlt.index
df_unfilt = df.loc[dfp_filt_i, :]
changed_ind = df_unfilt[
df_unfilt['Value'].isnull() != dfp_filt_mlt['value'].isnull()].index
changed_df = df.loc[changed_ind, :]