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dhm('48:00')
dhm('48:00')
dhm('72:00')
dhm('72:00')
thresholds.insert(0, (1, qtm_00 + dhm('13:00')
thresholds.insert(0, (1, qtm + dhm('4:00')
thresholds.insert(0, (1, qtm_00 + dhm('24:00')
thresholds.insert(1, (2, qtm + dhm('20:00')
thresholds.insert(0, (1, qtm_00 + dhm('24:00')
thresholds.insert(1, (2, qtm_00 + dhm('37:00')
len(qtms)
pd.Timestamp(None)
min(qtms)
max(qtms)
_get_min_wait(summary)
summary.items()
min(wtimes_this)
pd.Timedelta(99, 'h')
min(wtimes)
pd.Timedelta(np.median(wtimes)
load_csv(csv_fname)
times (+1)
pd.read_csv(csv_fname, comment='#')
astype(int)
c.endswith('_time')
c.endswith('_date')
pd.to_datetime(df[c])
isna()
times (plus one extra at the end)
diff()
pd.Timedelta('10 min')
list(start_tms)
pd.Timedelta('1 min')
load_multi_csvs(csv_fnames)
times (+1)
load_csv(f)
dfs.append(df)
start_tms.extend(st[:-1])
pd.concat(dfs)
reset_index()
start_tms.append(df.iloc[-1]['scan_time'] + pd.Timedelta('1 min')
get_scan_scores(df, tm_range)
scan (mid-point)
PCODES.items()
isinstance(pc4, int)
isin(pc4_tup)
iterrows()
int(row['req_pc4'])
range(3)
re.match(f'{city_re}$', addr[5:])
options.append((row['scan_time'], row[f'opt{i}_time'])
_summary_to_scores(summary)
pd.isna(tstamp)
len(df1)
_get_min_wait(summary)
pd.Timedelta(None)
get_scan_scores_df(df, tm_ranges, decimal_comma=True)
timestamps (+one at the end)
len(tm_ranges)
get_bad_scan_times()
range(n-1)
print(f'Dropped scan at {tm_ra[0].strftime("%Y-%m-%d %H:%M")
get_scan_scores(df, tm_ra)
records.append(scores)
index.append(tm)
minwait_hs.append(minwait.total_seconds()
medwait_hs.append(medwait.total_seconds()
t.strftime('%Y-%m-%d')
t.strftime('%H:%M')
pd.DataFrame.from_records(records)
sdf.insert(0, 'Time', times)
sdf.insert(0, 'Date', dates)
np.around(minwait_hs, 2)
np.around(medwait_hs, 2)
isna()
join([str(x)
isinstance(c, tuple)
astype(str)
str.replace('.', ',', regex=False)
str.replace(',0$', '', regex=False)
str.replace('?', '', regex=False)
sorted(Path('data-ggd')
glob('ggd_scan-????-W??.csv')
input('(A)
lower()
load_multi_csvs(csv_fnames)
get_scan_scores_df(df, start_tms)
load_csv(csv_fnames[-1])
get_scan_scores_df(df, start_tms[-2:])
print(sdf)
len(sdf)
sdf.to_clipboard(index=False)
print('Copied to clipboard including headers')
len(sdf)
to_clipboard(header=False, index=False)
print('Copied to clipboard, scores only.')
print('No output.')
input('Press Enter to quit and clear clipboard.')
load_bank_data()
questionary.text("Enter a file path to a rate-sheet (.csv)