In [1]:
%matplotlib inline
%config InlineBackend.figure_format='retina'
from datacharm import *
from enerpi.api import enerpi_data_catalog
from enerpi.enerplot import plot_tile_last_24h, plot_power_consumption_hourly
# Catálogo y lectura de todos los datos.
cat = enerpi_data_catalog()
data_s = cat.get_summary(last_hours=1000)
data_s
==> Librerías, clases y métodos cargados:
+++ "dt" +++ "os"
+++ "json" v:2.0.9 +++ "pd" (pandas) v:0.18.1
+++ "locale" +++ "plt"
+++ "math" +++ "re" v:2.2.1
+++ "np" (numpy) v:1.11.1 +++ "sns" (seaborn) v:0.7.0
+++ "sys"
** "Colormap", "Line2D", "Normalize", "OrderedDict", "PathPatch", "namedtuple", "time"
==> Pretty printing funcs:
print_blue, print_bold, print_boldu, print_cyan, print_err, print_green, print_grey, print_greyb, print_info, print_infob, print_magenta, print_ok, print_red, print_redb, print_secc, print_tree_dict, print_warn, print_white, print_yellow, print_yellowb, printcolor
If you are in a jupyter notebook, insert this:
%matplotlib inline
%config InlineBackend.figure_format='retina'
If you are working with GEO data, insert this:
import geopandas as gpd
import shapely.geometry as sg
import cartopy.crs as ccrs
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect
because the backend has already been chosen;
matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.
warnings.warn(_use_error_msg)
***TIMEIT get_summary TOOK: 0.307 s
Out[1]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
ts
2016-08-12 10:00:00
0.071497
0.226328
2
0
348.0
317.0
296.0
2016-08-12 11:00:00
0.461430
1.000060
0
0
3452.0
461.0
299.0
2016-08-12 12:00:00
0.326755
0.999834
0
0
373.0
327.0
289.0
2016-08-12 13:00:00
0.363093
0.999993
0
0
871.0
363.0
296.0
2016-08-12 14:00:00
0.501344
0.999975
0
0
3304.0
501.0
208.0
2016-08-12 15:00:00
0.362595
1.000106
0
0
1475.0
363.0
248.0
2016-08-12 16:00:00
0.305348
1.000007
0
0
404.0
305.0
225.0
2016-08-12 17:00:00
0.368282
0.999868
0
0
900.0
368.0
304.0
2016-08-12 18:00:00
0.359484
1.000134
0
0
468.0
359.0
293.0
2016-08-12 19:00:00
0.357033
0.999837
0
0
908.0
357.0
294.0
2016-08-12 20:00:00
0.339244
1.000182
0
0
424.0
339.0
260.0
2016-08-12 21:00:00
0.319713
0.999861
0
0
2076.0
320.0
209.0
2016-08-12 22:00:00
0.223205
1.000062
0
0
247.0
223.0
200.0
2016-08-12 23:00:00
0.235032
1.000085
0
0
818.0
235.0
212.0
2016-08-13 00:00:00
0.273163
1.000049
0
0
814.0
273.0
201.0
2016-08-13 01:00:00
0.238602
0.999780
0
0
293.0
239.0
211.0
2016-08-13 02:00:00
0.232725
1.000175
0
0
786.0
233.0
203.0
2016-08-13 03:00:00
0.213644
0.999844
0
0
258.0
214.0
194.0
2016-08-13 04:00:00
0.213918
1.000024
0
0
816.0
214.0
196.0
2016-08-13 05:00:00
0.212647
1.000009
0
0
247.0
213.0
195.0
2016-08-13 06:00:00
0.214074
1.000019
0
0
809.0
214.0
196.0
2016-08-13 07:00:00
0.213130
0.999925
0
0
249.0
213.0
197.0
2016-08-13 08:00:00
0.370600
1.000230
0
0
3400.0
371.0
202.0
2016-08-13 09:00:00
0.272039
0.999762
0
0
413.0
272.0
232.0
2016-08-13 10:00:00
0.259136
1.000219
0
0
320.0
259.0
216.0
2016-08-13 11:00:00
0.294824
0.999963
0
0
922.0
295.0
231.0
2016-08-13 12:00:00
0.276144
1.000026
0
0
320.0
276.0
237.0
2016-08-13 13:00:00
0.289751
1.000010
0
0
858.0
290.0
245.0
2016-08-13 14:00:00
0.266304
0.999753
0
0
302.0
266.0
240.0
2016-08-13 15:00:00
0.359113
1.000039
0
0
869.0
359.0
247.0
...
...
...
...
...
...
...
...
2016-09-01 10:00:00
0.216010
1.000078
0
0
823.0
216.0
192.0
2016-09-01 11:00:00
0.205991
1.000014
0
0
231.0
206.0
190.0
2016-09-01 12:00:00
0.216427
1.000070
0
0
816.0
216.0
186.0
2016-09-01 13:00:00
0.205585
1.000004
0
0
228.0
206.0
191.0
2016-09-01 14:00:00
0.217650
1.000004
0
0
819.0
218.0
192.0
2016-09-01 15:00:00
0.207257
0.999988
0
0
236.0
207.0
192.0
2016-09-01 16:00:00
0.817654
0.999846
0
0
3184.0
818.0
192.0
2016-09-01 17:00:00
0.340359
0.999970
0
0
382.0
340.0
236.0
2016-09-01 18:00:00
0.302938
0.999998
0
0
393.0
303.0
199.0
2016-09-01 19:00:00
0.219488
0.999996
0
0
851.0
219.0
191.0
2016-09-01 20:00:00
0.288452
1.000107
0
0
508.0
288.0
199.0
2016-09-01 21:00:00
0.441770
1.000117
0
0
506.0
442.0
393.0
2016-09-01 22:00:00
0.346521
1.000028
0
0
928.0
347.0
239.0
2016-09-01 23:00:00
0.338695
0.999855
0
0
498.0
339.0
275.0
2016-09-02 00:00:00
0.318219
1.000045
0
0
863.0
318.0
259.0
2016-09-02 01:00:00
0.301778
0.999936
0
0
366.0
302.0
213.0
2016-09-02 02:00:00
0.237117
0.999950
0
0
796.0
237.0
204.0
2016-09-02 03:00:00
0.211544
0.999984
0
0
262.0
212.0
195.0
2016-09-02 04:00:00
0.211241
1.000018
0
0
792.0
211.0
193.0
2016-09-02 05:00:00
0.201899
1.000072
0
0
246.0
202.0
189.0
2016-09-02 06:00:00
0.210905
1.000000
0
0
788.0
211.0
189.0
2016-09-02 07:00:00
0.202320
0.999958
0
0
809.0
202.0
187.0
2016-09-02 08:00:00
0.319762
1.000071
0
0
2688.0
320.0
168.0
2016-09-02 09:00:00
0.305116
0.999905
0
0
828.0
305.0
171.0
2016-09-02 10:00:00
0.441348
1.000181
0
0
516.0
441.0
361.0
2016-09-02 11:00:00
0.056657
0.150569
0
0
464.0
376.0
331.0
2016-09-02 12:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
2016-09-02 13:00:00
0.496293
0.906546
3
3
1669.0
567.0
320.0
2016-09-02 14:00:00
0.543383
1.000144
1
1
1734.0
546.0
312.0
2016-09-02 15:00:00
0.015472
0.039793
0
0
413.0
389.0
356.0
510 rows × 7 columns
In [60]:
data_s['completo'] = data_s.t_ref > .95
#data_s[~data_s['completo']]
#data_s[data_s['completo']].t_ref.plot()
#debug = None
def _roll_hour(x):
completos = x['completo']
#completos = x[x.completo]
#idx = completos.index
##last = idx[-1]
#global debug
#if debug is None:
# debug = x
x['delta_pond'] = (x.index[-1] - x.index).days
x['pond'] = 1
x.loc[x['delta_pond'] % 7 == 0, 'pond'] *= 3
x.loc[x['delta_pond'] < 31, 'pond'] *= 2
x.loc[x['delta_pond'] < 7, 'pond'] *= 2
#x.loc[completos, 'kWh_c'] =
return pd.Series({'kWh_mean': np.mean(x[completos].kWh),
'kWh_median': np.median(x[completos].kWh),
'kWh_min': np.min(x[completos].kWh),
'kWh_max': np.max(x[completos].kWh),
'kWh_pond': (x.loc[completos, 'kWh'] * x.loc[completos, 'pond'] / x.loc[completos, 'pond'].sum()).sum()}).T
resumen = data_s.groupby(data_s.index.time).apply(_roll_hour)
resumen.plot()
resumen
Out[60]:
kWh_max
kWh_mean
kWh_median
kWh_min
kWh_pond
00:00:00
0.327747
0.256484
0.248539
0.191129
0.259648
01:00:00
0.304203
0.250057
0.240549
0.197164
0.255116
02:00:00
0.289150
0.235325
0.232945
0.191542
0.233976
03:00:00
0.271715
0.226451
0.219914
0.198104
0.222496
04:00:00
0.252653
0.220542
0.217848
0.191386
0.216446
05:00:00
0.265498
0.221857
0.218482
0.197867
0.215763
06:00:00
0.255174
0.220233
0.217727
0.188954
0.216067
07:00:00
0.296077
0.224046
0.218286
0.198262
0.216889
08:00:00
0.459738
0.289984
0.300320
0.205561
0.288664
09:00:00
0.836456
0.338475
0.299353
0.205598
0.319906
10:00:00
0.975249
0.392738
0.335956
0.206686
0.404455
11:00:00
0.819459
0.362924
0.348064
0.203157
0.378979
12:00:00
0.610755
0.353976
0.330699
0.207819
0.337692
13:00:00
1.206803
0.422734
0.346979
0.205585
0.379847
14:00:00
1.284630
0.496870
0.392266
0.209819
0.496756
15:00:00
1.415316
0.422399
0.319245
0.205866
0.372656
16:00:00
0.817654
0.340869
0.320243
0.194798
0.395428
17:00:00
0.469565
0.332298
0.342699
0.196362
0.321866
18:00:00
0.430459
0.335154
0.350210
0.193074
0.322620
19:00:00
0.839806
0.367844
0.366514
0.196456
0.327901
20:00:00
0.499794
0.348789
0.361523
0.191243
0.323951
21:00:00
1.165215
0.399241
0.382229
0.196098
0.379768
22:00:00
0.910010
0.403184
0.355888
0.191124
0.373843
23:00:00
0.558108
0.309598
0.320116
0.197018
0.308697
In [65]:
data_s['completo'] = data_s.t_ref > .95
data_s.loc[data_s.completo, 'pond'] = 1
data_s.loc[data_s.completo, 'kWh_c'] = data_s.loc[data_s.completo, 'kWh']
data_s['hay_datos'] = False
data_s.loc[data_s.t_ref > .1, 'hay_datos'] = True
#data_s.loc[~data_s.completo & , 'hay_datos'] = True
data_s[data_s.pond.isnull()]
Out[65]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
completo
pond
kWh_c
hay_datos
ts
2016-08-12 10:00:00
0.071497
0.226328
2
0
348.0
317.0
296.0
False
NaN
NaN
True
2016-08-13 21:00:00
0.005417
0.017615
1
0
319.0
285.0
269.0
False
NaN
NaN
False
2016-08-13 22:00:00
0.537020
0.755500
2
2
2124.0
675.0
312.0
False
NaN
NaN
True
2016-08-16 19:00:00
0.283807
0.710373
1
1
936.0
399.0
259.0
False
NaN
NaN
True
2016-08-16 22:00:00
0.060506
0.039242
0
0
2332.0
1542.0
270.0
False
NaN
NaN
False
2016-08-16 23:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-17 00:00:00
0.158328
0.679451
1
1
274.0
234.0
208.0
False
NaN
NaN
True
2016-08-17 14:00:00
0.200240
0.550776
1
1
1997.0
360.0
196.0
False
NaN
NaN
True
2016-08-17 20:00:00
0.259621
0.566813
1
1
652.0
458.0
309.0
False
NaN
NaN
True
2016-08-17 21:00:00
0.331131
0.848359
1
1
847.0
390.0
243.0
False
NaN
NaN
True
2016-08-17 22:00:00
0.326135
0.799145
2
2
1735.0
414.0
206.0
False
NaN
NaN
True
2016-08-18 12:00:00
0.229710
0.512776
1
1
2936.0
461.0
163.0
False
NaN
NaN
True
2016-08-19 00:00:00
0.174805
0.694723
2
2
281.0
252.0
233.0
False
NaN
NaN
True
2016-08-23 20:00:00
0.289441
0.837174
1
1
864.0
345.0
127.0
False
NaN
NaN
True
2016-08-31 00:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 01:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 02:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 03:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 04:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 05:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 06:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 07:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 08:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 09:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 10:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 11:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 12:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 13:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 14:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 15:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 16:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 17:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 18:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 19:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 20:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 21:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 22:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-08-31 23:00:00
0.186494
0.884112
1
1
236.0
211.0
194.0
False
NaN
NaN
True
2016-09-02 11:00:00
0.056657
0.150569
0
0
464.0
376.0
331.0
False
NaN
NaN
True
2016-09-02 12:00:00
0.000000
NaN
0
0
NaN
NaN
NaN
False
NaN
NaN
False
2016-09-02 13:00:00
0.496293
0.906546
3
3
1669.0
567.0
320.0
False
NaN
NaN
True
2016-09-02 15:00:00
0.015472
0.039793
0
0
413.0
389.0
356.0
False
NaN
NaN
False
In [57]:
debug['delta_pond'] = (debug.index[-1] - debug.index).days
debug['pond'] = 1
debug.loc[debug['delta_pond'] % 7 == 0, 'pond'] *= 3
debug.loc[debug['delta_pond'] < 31, 'pond'] *= 2
debug.loc[debug['delta_pond'] < 7, 'pond'] *= 2
debug.loc[debug.completo, 'kWh_c'] = (debug.loc[debug.completo, 'kWh'] * debug.loc[debug.completo, 'pond'] / debug.loc[debug.completo, 'pond'].sum()).sum()
debug
Out[57]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
completo
delta_pond
pond
kWh_c
ts
2016-08-13
0.273163
1.000049
0
0
814.0
273.0
201.0
True
20
2
0.259648
2016-08-14
0.251060
1.000278
0
0
808.0
251.0
212.0
True
19
2
0.259648
2016-08-15
0.257088
0.999997
0
0
851.0
257.0
177.0
True
18
2
0.259648
2016-08-16
0.235938
0.999976
0
0
279.0
236.0
211.0
True
17
2
0.259648
2016-08-17
0.158328
0.679451
1
1
274.0
234.0
208.0
False
16
2
NaN
2016-08-18
0.290345
1.000076
0
0
820.0
290.0
168.0
True
15
2
0.259648
2016-08-19
0.174805
0.694723
2
2
281.0
252.0
233.0
False
14
6
NaN
2016-08-20
0.327747
1.000016
0
0
590.0
328.0
192.0
True
13
2
0.259648
2016-08-21
0.238063
0.999913
0
0
787.0
238.0
200.0
True
12
2
0.259648
2016-08-22
0.244336
1.000040
0
0
786.0
244.0
198.0
True
11
2
0.259648
2016-08-23
0.286855
1.000054
0
0
810.0
287.0
242.0
True
10
2
0.259648
2016-08-24
0.246019
1.000034
0
0
311.0
246.0
212.0
True
9
2
0.259648
2016-08-25
0.290849
1.000080
0
0
864.0
291.0
215.0
True
8
2
0.259648
2016-08-26
0.224600
1.000077
0
0
254.0
225.0
201.0
True
7
6
0.259648
2016-08-27
0.271557
1.000124
0
0
386.0
272.0
216.0
True
6
4
0.259648
2016-08-28
0.242331
1.000025
0
0
375.0
242.0
196.0
True
5
4
0.259648
2016-08-29
0.191129
1.000113
0
0
210.0
191.0
174.0
True
4
4
0.259648
2016-08-30
0.215345
1.000088
0
0
788.0
215.0
191.0
True
3
4
0.259648
2016-08-31
0.000000
NaN
0
0
NaN
NaN
NaN
False
2
4
NaN
2016-09-01
0.212074
0.999891
3
0
807.0
212.0
192.0
True
1
4
0.259648
2016-09-02
0.318219
1.000045
0
0
863.0
318.0
259.0
True
0
12
0.259648
In [7]:
def reprocess_all_data(self, **kwargs_save):
paths_w_summary = self.tree[(self.tree.key == self.key_summary) & self.tree.is_cat]
for path in paths_w_summary.st:
df = self.load_store(path)
df_bis, df_s = self.process_data_summary(df)
assert((df == df_bis).all().all())
self._save_hdf([df_bis, df_s], path, [self.key_raw, self.key_summary], mode='w', **kwargs_save)
KWARGS_SAVE = dict(complevel=9, complib='blosc', fletcher32=True)
reprocess_all_data(cat, **KWARGS_SAVE)
DATA_YEAR_2016/DATA_2016_MONTH_08.h5
CURRENT_MONTH/DATA_2016_09_DAY_01.h5
In [8]:
data.index[0], data.index[-1], data.index.is_unique, data.index.is_monotonic_increasing
Out[8]:
(Timestamp('2016-08-29 12:00:00.875776'),
Timestamp('2016-09-01 23:59:59.463868'),
True,
True)
In [60]:
%matplotlib inline
%config InlineBackend.figure_format='retina'
from datacharm import *
from enerpi.api import enerpi_data_catalog
from enerpi.enerplot import plot_tile_last_24h, plot_power_consumption_hourly
# Catálogo y lectura de todos los datos.
cat = enerpi_data_catalog()
data, data_s = cat.get_all_data(with_summary_data=True, async_get=True)
## Cambio de plot tile para LDR --> resample a 30 s y selección de valor mediano:
dplot = data.ldr.iloc[-100000:]
_, ax = plot_tile_last_24h(dplot.resample('30s').median(), barplot=False, color=(1, 1, 1))
ax.set_axis_bgcolor('#DBDD0D')
ax.patch.set_alpha(0.53)
# Anterior:
_, ax = plot_tile_last_24h(dplot.resample('5min').mean(), barplot=False, color=(1, 1, 1))
ax.set_axis_bgcolor('#DBDD0D')
ax.patch.set_alpha(0.53)
# plot_power_consumption_hourly(potencia, consumo, ldr=None, rs_potencia=None, rm_potencia=None, savefig=None)
***TIMEIT _get_all_data TOOK: 0.597 s
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
In [3]:
data.iloc[-100000:].ldr.plot(figsize=(18, 5))
plt.show()
f, ax = plot_tile_last_24h(data.power.iloc[-100000:], rs_data_s='5min', barplot=False, color=(.4, 0, .9))
plt.show()
f, ax = plot_tile_last_24h(data.ldr.iloc[-100000:], rm_data_s=120, barplot=False, color=(.9, .9, .1))
plt.show()
f, ax = plot_tile_last_24h(data.ldr.iloc[-100000:], rs_data_s='5min', barplot=False, color=(.8, .8, .1))
plt.show()
In [ ]:
In [61]:
#f, ax = plot_tile_last_24h(dplot.rolling(120).mean(), barplot=False, color=(1, 1, 1))
_, ax = plot_tile_last_24h(dplot.rolling(120).median(), barplot=False, color=(1, 1, 1))
ax.set_axis_bgcolor('#DBDD0D')
ax.patch.set_alpha(0.53)
d_rs = dplot.resample('5min', label='left')
f, ax = plot_tile_last_24h(d_rs.min(), barplot=False, alpha=1, alpha_fill=.5)
_, ax = plot_tile_last_24h(d_rs.max(), barplot=False, alpha=1, alpha_fill=.25, ax=ax)
ax.set_axis_bgcolor('#DBDD0D')
ax.patch.set_alpha(0.83)
_, ax = plot_tile_last_24h(dplot.resample('30s').median(), barplot=False, color=(1, 1, 1))
ax.set_axis_bgcolor('#DBDD0D')
ax.patch.set_alpha(0.53)
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
In [62]:
#data_tile = cat.get(last_hours=72, with_summary=False, async_get=True)
def _median(arr):
if arr.empty:
return 0
else:
return np.nanmedian(arr)
data_tile.ldr.resample('30s').apply(_median).head()
np.nanmedian?
In [49]:
%timeit data_tile.ldr.resample('30s').apply(lambda x: np.nanmedian(x) if not x.empty else 0).head()
1 loop, best of 3: 398 ms per loop
In [50]:
%timeit data_tile.ldr.resample('30s').apply(_median).head()
1 loop, best of 3: 392 ms per loop
In [51]:
%timeit data_tile.ldr.resample('30s').mean().head()
100 loops, best of 3: 3.34 ms per loop
In [52]:
%timeit data_tile.ldr.resample('30s').median().head()
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
1 loop, best of 3: 600 ms per loop
In [53]:
%timeit data_tile.ldr.resample('30s').apply(np.median).head()
%timeit data_tile.ldr.resample('30s').apply(np.nanmedian).head()
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
1 loop, best of 3: 586 ms per loop
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
1 loop, best of 3: 370 ms per loop
In [56]:
%timeit data_tile.ldr.resample('30s').fillna('ffill').apply(np.nanmedian).head()
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/numpy/lib/nanfunctions.py:689: RuntimeWarning: All-NaN slice encountered
warnings.warn("All-NaN slice encountered", RuntimeWarning)
1 loop, best of 3: 266 ms per loop
In [ ]:
In [64]:
last_data, last_data_c = cat.get(last_hours=72, with_summary=True)
print_info(last_data.head())
print_info(last_data.tail())
print_cyan(last_data_c.head())
print_cyan(last_data_c.tail())
power noise ref ldr high_delta execution
ts
2016-08-24 15:00:00.548185 289.843964 0.007828 83 641 False False
2016-08-24 15:00:01.558416 293.640564 0.007826 80 641 False False
2016-08-24 15:00:02.568252 294.414093 0.007704 80 641 False False
2016-08-24 15:00:03.576464 290.377197 0.007561 77 641 False False
2016-08-24 15:00:04.584932 282.730530 0.007690 82 640 False False
power noise ref ldr high_delta execution
ts
2016-08-27 15:05:10.444973 331.143250 0.007606 83 634 False False
2016-08-27 15:05:11.449134 329.883728 0.007605 83 634 False False
2016-08-27 15:05:12.458348 328.158447 0.007586 83 634 False False
2016-08-27 15:05:13.458121 332.866119 0.007625 82 634 False False
2016-08-27 15:05:14.464443 334.043243 0.007594 82 634 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-24 15:00:00 0.859483 1.000081 0 0 3276.0 859.0 204.0
2016-08-24 16:00:00 0.528163 0.999923 0 0 1883.0 528.0 194.0
2016-08-24 17:00:00 0.469565 1.000082 0 0 1472.0 470.0 251.0
2016-08-24 18:00:00 0.397262 0.999982 0 0 1540.0 397.0 213.0
2016-08-24 19:00:00 0.430325 1.000040 0 0 917.0 430.0 284.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-27 11:00:00 0.266174 0.999807 0 0 848.0 266.0 213.0
2016-08-27 12:00:00 0.239588 0.999926 0 0 287.0 240.0 201.0
2016-08-27 13:00:00 0.225906 1.000267 0 0 788.0 226.0 201.0
2016-08-27 14:00:00 0.456035 0.999767 0 0 3036.0 456.0 208.0
2016-08-27 15:00:00 0.025450 0.087587 0 0 849.0 291.0 260.0
In [ ]:
In [88]:
# TESTING SUMMARY:
today = pd.Timestamp.today()
this_month = today.replace(day=1).date()
first_date = cat.min_ts.date()
print(today, this_month, first_date)
if first_date < this_month:
paths = pd.DatetimeIndex(start=first_date, freq='MS', end=this_month).tolist()
paths += pd.DatetimeIndex(start=this_month, freq='D', end=today).tolist()
else:
paths = pd.DatetimeIndex(start=first_date, freq='D', end=today).tolist()
paths = [cat._get_paths_interval(ts_ini=p, ts_fin=p)[0] for p in paths]
paths
2016-08-27 16:47:13.834464 2016-08-01 2016-08-12
Out[88]:
['CURRENT_MONTH/DATA_2016_08_DAY_12.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_13.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_14.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_15.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_16.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_17.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_18.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_19.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_20.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_21.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_22.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_23.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_24.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_25.h5',
'CURRENT_MONTH/DATA_2016_08_DAY_26.h5',
'CURRENT_MONTH/TODAY.h5']
In [98]:
for p in paths:
p_st = os.path.join(cat.base_path, p)
print_secc('PATH: {}'.format(p_st))
df = pd.read_hdf(p_st, cat.key_raw)
df_bis, df_s = cat.process_data_summary(df)
try:
df_s_saved = pd.read_hdf(p_st, cat.key_summary)
except KeyError:
print_warn('No hay summary')
df_s_saved = df_s
data_equal = (df == df_bis).all().all()
summary_equal = (df_s_saved == df_s).all().all()
if not summary_equal or not data_equal:
print_err('DF == DF_BIS? {}; DF_S == DF_S_CALC? {}'.format(data_equal, summary_equal))
print_blue('{}\n{}'.format(df.head(2), df.tail(2)))
print_cyan('{}\n{}'.format(df_bis.head(2), df_bis.tail(2)))
print_red('{}\n{}'.format(df_s_saved.head(2), df_s_saved.tail(2)))
print_magenta('{}\n{}'.format(df_s.head(2), df_s.tail(2)))
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_12.h5
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_13.h5
ERROR: DF == DF_BIS? False; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-13 00:00:00.784081 223.349731 0.009287 83 32 True True
2016-08-13 00:00:01.785146 224.638718 0.009195 83 32 True True
power noise ref ldr high_delta execution
ts
2016-08-13 23:59:57.982120 246.012482 0.007201 84 35 True True
2016-08-13 23:59:58.992496 243.784195 0.007142 84 34 True True
power noise ref ldr high_delta execution
ts
2016-08-13 00:00:00.784081 223.349731 0.009287 83 32 False False
2016-08-13 00:00:01.785146 224.638718 0.009195 83 32 False False
power noise ref ldr high_delta execution
ts
2016-08-13 23:59:57.982120 246.012482 0.007201 84 35 False False
2016-08-13 23:59:58.992496 243.784195 0.007142 84 34 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-13 00:00:00 0.273163 1.000047 0 0 814.0 273.0 201.0
2016-08-13 01:00:00 0.238602 0.999780 0 0 293.0 239.0 211.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-13 22:00:00 2.070342 1.982352 2 2 2124.0 675.0 312.0
2016-08-13 23:00:00 0.120417 0.354864 0 0 401.0 339.0 291.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-13 00:00:00 0.273163 1.000047 0 0 814.0 273.0 201.0
2016-08-13 01:00:00 0.238602 0.999780 0 0 293.0 239.0 211.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-13 22:00:00 2.070342 1.982352 2 2 2124.0 675.0 312.0
2016-08-13 23:00:00 0.320116 0.999967 0 0 402.0 320.0 236.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_14.h5
ERROR: DF == DF_BIS? False; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-14 00:00:00.002919 246.604477 0.007208 84 34 True True
2016-08-14 00:00:01.004088 252.202866 0.007271 83 34 True True
power noise ref ldr high_delta execution
ts
2016-08-14 23:59:58.464272 298.025055 0.007649 84 39 True True
2016-08-14 23:59:59.474740 293.809021 0.007555 84 39 True True
power noise ref ldr high_delta execution
ts
2016-08-14 00:00:00.002919 246.604477 0.007208 84 34 False False
2016-08-14 00:00:01.004088 252.202866 0.007271 83 34 False False
power noise ref ldr high_delta execution
ts
2016-08-14 23:59:58.464272 298.025055 0.007649 84 39 False False
2016-08-14 23:59:59.474740 293.809021 0.007555 84 39 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-14 00:00:00 0.251059 1.000275 0 0 808.0 251.0 212.0
2016-08-14 01:00:00 0.242496 0.999853 0 0 287.0 243.0 214.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-14 22:00:00 0.386972 1.000012 0 0 887.0 387.0 294.0
2016-08-14 23:00:00 0.199606 0.576791 0 0 458.0 346.0 304.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-14 00:00:00 0.251059 1.000275 0 0 808.0 251.0 212.0
2016-08-14 01:00:00 0.242496 0.999853 0 0 287.0 243.0 214.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-14 22:00:00 0.386972 1.000012 0 0 887.0 387.0 294.0
2016-08-14 23:00:00 0.328059 0.999930 0 0 458.0 328.0 279.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_15.h5
ERROR: DF == DF_BIS? False; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-15 00:00:00.485372 295.653412 0.007543 84 39 True True
2016-08-15 00:00:01.485388 302.146240 0.007404 81 39 True True
power noise ref ldr high_delta execution
ts
2016-08-15 23:59:58.300572 249.893311 0.007402 84 35 True True
2016-08-15 23:59:59.311180 248.625931 0.007340 84 35 True True
power noise ref ldr high_delta execution
ts
2016-08-15 00:00:00.485372 295.653412 0.007543 84 39 False False
2016-08-15 00:00:01.485388 302.146240 0.007404 81 39 False False
power noise ref ldr high_delta execution
ts
2016-08-15 23:59:58.300572 249.893311 0.007402 84 35 False False
2016-08-15 23:59:59.311180 248.625931 0.007340 84 35 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 00:00:00 0.257087 0.999994 0 0 851.0 257.0 177.0
2016-08-15 01:00:00 0.231033 1.000025 0 0 267.0 231.0 202.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 22:00:00 0.403624 0.999916 1 1 1896.0 407.0 206.0
2016-08-15 23:00:00 0.184828 0.548637 0 0 399.0 337.0 302.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 00:00:00 0.257087 0.999994 0 0 851.0 257.0 177.0
2016-08-15 01:00:00 0.231033 1.000025 0 0 267.0 231.0 202.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 22:00:00 0.403624 0.999916 1 1 1896.0 407.0 206.0
2016-08-15 23:00:00 0.317224 0.999969 0 0 399.0 317.0 216.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_16.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-16 00:00:00.321705 250.562973 0.007301 84 35 False False
2016-08-16 00:00:01.329831 253.243912 0.007317 82 35 False False
power noise ref ldr high_delta execution
ts
2016-08-16 22:02:19.338707 2285.397461 0.005144 84 72 False False
2016-08-16 22:02:20.349005 2273.201904 0.005190 84 72 False False
power noise ref ldr high_delta execution
ts
2016-08-16 00:00:00.321705 250.562973 0.007301 84 35 False False
2016-08-16 00:00:01.329831 253.243912 0.007317 82 35 False False
power noise ref ldr high_delta execution
ts
2016-08-16 22:02:19.338707 2285.397461 0.005144 84 72 False False
2016-08-16 22:02:20.349005 2273.201904 0.005190 84 72 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-16 00:00:00 0.235938 0.999976 0 0 279.0 236.0 211.0
2016-08-16 01:00:00 0.238055 1.000202 0 0 817.0 238.0 214.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-16 21:00:00 0.202495 0.517834 0 0 461.0 391.0 288.0
2016-08-16 22:00:00 0.060506 0.039242 0 0 2332.0 1542.0 270.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-16 00:00:00 0.235938 0.999976 0 0 279.0 236.0 211.0
2016-08-16 01:00:00 0.238055 1.000202 0 0 817.0 238.0 214.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-16 21:00:00 0.367758 0.999906 0 0 1027.0 368.0 252.0
2016-08-16 22:00:00 0.060506 0.039242 0 0 2332.0 1542.0 270.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_17.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-17 00:21:13.076529 222.561279 0.007420 84 20 False False
2016-08-17 00:21:14.083950 224.295410 0.007478 82 16 False False
power noise ref ldr high_delta execution
ts
2016-08-17 23:59:58.364683 338.584625 0.007169 81 38 False False
2016-08-17 23:59:59.372437 335.615997 0.007221 82 38 False False
power noise ref ldr high_delta execution
ts
2016-08-17 00:21:13.076529 222.561279 0.007420 84 20 False False
2016-08-17 00:21:14.083950 224.295410 0.007478 82 16 False False
power noise ref ldr high_delta execution
ts
2016-08-17 23:59:58.364683 338.584625 0.007169 81 38 False False
2016-08-17 23:59:59.372437 335.615997 0.007221 82 38 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-17 00:00:00 0.150972 0.646398 0 0 274.0 234.0 208.0
2016-08-17 01:00:00 0.246444 1.000204 0 0 831.0 246.0 211.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-17 22:00:00 0.394748 0.999808 2 2 1735.0 414.0 206.0
2016-08-17 23:00:00 0.347437 0.967882 0 0 2006.0 359.0 201.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-17 00:00:00 0.150972 0.646398 0 0 274.0 234.0 208.0
2016-08-17 01:00:00 0.246444 1.000204 0 0 831.0 246.0 211.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-17 22:00:00 0.394748 0.999808 2 2 1735.0 414.0 206.0
2016-08-17 23:00:00 0.358426 1.000040 0 0 2006.0 358.0 201.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_18.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-18 00:00:00.376259 327.75000 0.007362 79 38 False False
2016-08-18 00:00:01.377769 329.64978 0.007500 76 39 False False
power noise ref ldr high_delta execution
ts
2016-08-18 23:59:58.764487 258.835571 0.006968 84 25 False False
2016-08-18 23:59:59.775426 261.035095 0.006990 84 31 False False
power noise ref ldr high_delta execution
ts
2016-08-18 00:00:00.376259 327.75000 0.007362 79 38 False False
2016-08-18 00:00:01.377769 329.64978 0.007500 76 39 False False
power noise ref ldr high_delta execution
ts
2016-08-18 23:59:58.764487 258.835571 0.006968 84 25 False False
2016-08-18 23:59:59.775426 261.035095 0.006990 84 31 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-18 00:00:00 0.290345 1.000075 0 0 820.0 290.0 168.0
2016-08-18 01:00:00 0.232337 0.999977 0 0 349.0 232.0 112.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-18 22:00:00 0.346483 0.999886 0 0 471.0 347.0 312.0
2016-08-18 23:00:00 0.301713 0.897652 0 0 882.0 336.0 245.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-18 00:00:00 0.290345 1.000075 0 0 820.0 290.0 168.0
2016-08-18 01:00:00 0.232337 0.999977 0 0 349.0 232.0 112.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-18 22:00:00 0.346483 0.999886 0 0 471.0 347.0 312.0
2016-08-18 23:00:00 0.328388 1.000172 0 0 882.0 328.0 245.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_19.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-19 00:00:00.793563 264.122131 0.006978 76 33 False False
2016-08-19 00:00:01.799849 269.880646 0.006869 76 34 False False
power noise ref ldr high_delta execution
ts
2016-08-19 23:59:58.919944 396.028351 0.006444 65 45 False False
2016-08-19 23:59:59.925434 400.639099 0.006511 80 38 False False
power noise ref ldr high_delta execution
ts
2016-08-19 00:00:00.793563 264.122131 0.006978 76 33 False False
2016-08-19 00:00:01.799849 269.880646 0.006869 76 34 False False
power noise ref ldr high_delta execution
ts
2016-08-19 23:59:58.919944 396.028351 0.006444 65 45 False False
2016-08-19 23:59:59.925434 400.639099 0.006511 80 38 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-19 00:00:00 0.251378 0.999786 2 2 281.0 252.0 233.0
2016-08-19 01:00:00 0.266589 1.000118 1 1 412.0 266.0 207.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-19 22:00:00 0.910010 1.000092 0 0 5016.0 910.0 246.0
2016-08-19 23:00:00 0.143804 0.372659 0 0 515.0 386.0 277.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-19 00:00:00 0.251378 0.999786 2 2 281.0 252.0 233.0
2016-08-19 01:00:00 0.266589 1.000118 1 1 412.0 266.0 207.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-19 22:00:00 0.910010 1.000092 0 0 5016.0 910.0 246.0
2016-08-19 23:00:00 0.405113 1.000002 0 0 607.0 405.0 277.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_20.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-20 00:00:00.930517 414.855499 0.006647 83 42 False False
2016-08-20 00:00:01.930558 441.295135 0.006611 79 42 False False
power noise ref ldr high_delta execution
ts
2016-08-20 23:59:58.662129 231.120514 0.007764 81 30 False False
2016-08-20 23:59:59.664436 231.017303 0.007734 81 29 False False
power noise ref ldr high_delta execution
ts
2016-08-20 00:00:00.930517 414.855499 0.006647 83 42 False False
2016-08-20 00:00:01.930558 441.295135 0.006611 79 42 False False
power noise ref ldr high_delta execution
ts
2016-08-20 23:59:58.662129 231.120514 0.007764 81 30 False False
2016-08-20 23:59:59.664436 231.017303 0.007734 81 29 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-20 00:00:00 0.327746 1.000015 0 0 590.0 328.0 192.0
2016-08-20 01:00:00 0.253046 0.999829 0 0 631.0 253.0 122.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-20 22:00:00 0.240016 1.000196 0 0 752.0 240.0 173.0
2016-08-20 23:00:00 0.145678 0.522620 0 0 849.0 279.0 209.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-20 00:00:00 0.327746 1.000015 0 0 590.0 328.0 192.0
2016-08-20 01:00:00 0.253046 0.999829 0 0 631.0 253.0 122.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-20 22:00:00 0.240016 1.000196 0 0 752.0 240.0 173.0
2016-08-20 23:00:00 0.257742 0.999990 0 0 849.0 258.0 199.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_21.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-21 00:00:00.675817 235.900803 0.007757 82 30 False False
2016-08-21 00:00:01.679241 240.073227 0.007754 80 30 False False
power noise ref ldr high_delta execution
ts
2016-08-21 23:59:58.541229 231.446762 0.007806 84 31 False False
2016-08-21 23:59:59.551625 231.666275 0.007788 84 31 False False
power noise ref ldr high_delta execution
ts
2016-08-21 00:00:00.675817 235.900803 0.007757 82 30 False False
2016-08-21 00:00:01.679241 240.073227 0.007754 80 30 False False
power noise ref ldr high_delta execution
ts
2016-08-21 23:59:58.541229 231.446762 0.007806 84 31 False False
2016-08-21 23:59:59.551625 231.666275 0.007788 84 31 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-21 00:00:00 0.238063 0.999911 0 0 787.0 238.0 200.0
2016-08-21 01:00:00 0.296682 1.000154 0 0 432.0 297.0 210.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-21 22:00:00 0.355888 0.999914 0 0 892.0 356.0 320.0
2016-08-21 23:00:00 0.198115 0.689859 0 0 390.0 287.0 218.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-21 00:00:00 0.238063 0.999911 0 0 787.0 238.0 200.0
2016-08-21 01:00:00 0.296682 1.000154 0 0 432.0 297.0 210.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-21 22:00:00 0.355888 0.999914 0 0 892.0 356.0 320.0
2016-08-21 23:00:00 0.269552 1.000070 0 0 390.0 270.0 218.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_22.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-22 00:00:00.562102 228.107544 0.007777 84 32 False False
2016-08-22 00:00:01.572570 234.285889 0.007748 84 32 False False
power noise ref ldr high_delta execution
ts
2016-08-22 23:59:58.771425 338.310730 0.007478 84 36 False False
2016-08-22 23:59:59.782355 340.466675 0.007396 84 36 False False
power noise ref ldr high_delta execution
ts
2016-08-22 00:00:00.562102 228.107544 0.007777 84 32 False False
2016-08-22 00:00:01.572570 234.285889 0.007748 84 32 False False
power noise ref ldr high_delta execution
ts
2016-08-22 23:59:58.771425 338.310730 0.007478 84 36 False False
2016-08-22 23:59:59.782355 340.466675 0.007396 84 36 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-22 00:00:00 0.244336 1.000040 0 0 786.0 244.0 198.0
2016-08-22 01:00:00 0.264752 0.999899 0 0 828.0 265.0 214.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-22 22:00:00 0.443081 1.000052 0 0 2792.0 443.0 248.0
2016-08-22 23:00:00 0.032893 0.089656 0 0 425.0 367.0 321.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-22 00:00:00 0.244336 1.000040 0 0 786.0 244.0 198.0
2016-08-22 01:00:00 0.264752 0.999899 0 0 828.0 265.0 214.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-22 22:00:00 0.443081 1.000052 0 0 2792.0 443.0 248.0
2016-08-22 23:00:00 0.353989 1.000060 0 0 425.0 354.0 312.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_23.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-23 00:00:00.788150 343.017334 0.007433 83 37 False False
2016-08-23 00:00:01.797842 338.389099 0.007571 81 37 False False
power noise ref ldr high_delta execution
ts
2016-08-23 23:59:58.359326 263.169434 0.007732 84 33 False False
2016-08-23 23:59:59.360019 253.677887 0.007589 83 33 False False
power noise ref ldr high_delta execution
ts
2016-08-23 00:00:00.788150 343.017334 0.007433 83 37 False False
2016-08-23 00:00:01.797842 338.389099 0.007571 81 37 False False
power noise ref ldr high_delta execution
ts
2016-08-23 23:59:58.359326 263.169434 0.007732 84 33 False False
2016-08-23 23:59:59.360019 253.677887 0.007589 83 33 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-23 00:00:00 0.286856 1.000055 0 0 810.0 287.0 242.0
2016-08-23 01:00:00 0.261935 0.999902 0 0 300.0 262.0 245.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-23 22:00:00 0.825467 0.999887 0 0 4028.0 825.0 180.0
2016-08-23 23:00:00 0.362792 0.998046 0 0 490.0 364.0 176.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-23 00:00:00 0.286856 1.000055 0 0 810.0 287.0 242.0
2016-08-23 01:00:00 0.261935 0.999902 0 0 300.0 262.0 245.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-23 22:00:00 0.825467 0.999887 0 0 4028.0 825.0 180.0
2016-08-23 23:00:00 0.363301 1.000002 0 0 490.0 363.0 176.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_24.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-24 00:00:00.371214 249.948654 0.007549 84 32 False False
2016-08-24 00:00:01.381577 248.460434 0.007760 83 31 False False
power noise ref ldr high_delta execution
ts
2016-08-24 23:59:58.170438 278.057404 0.007622 84 39 False False
2016-08-24 23:59:59.181140 279.011688 0.007572 84 39 False False
power noise ref ldr high_delta execution
ts
2016-08-24 00:00:00.371214 249.948654 0.007549 84 32 False False
2016-08-24 00:00:01.381577 248.460434 0.007760 83 31 False False
power noise ref ldr high_delta execution
ts
2016-08-24 23:59:58.170438 278.057404 0.007622 84 39 False False
2016-08-24 23:59:59.181140 279.011688 0.007572 84 39 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-24 00:00:00 0.246019 1.000034 0 0 311.0 246.0 212.0
2016-08-24 01:00:00 0.234884 1.000115 0 0 825.0 235.0 178.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-24 22:00:00 0.463818 0.999951 0 0 1765.0 464.0 189.0
2016-08-24 23:00:00 0.055366 0.160836 0 0 441.0 344.0 216.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-24 00:00:00 0.246019 1.000034 0 0 311.0 246.0 212.0
2016-08-24 01:00:00 0.234884 1.000115 0 0 825.0 235.0 178.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-24 22:00:00 0.463818 0.999951 0 0 1765.0 464.0 189.0
2016-08-24 23:00:00 0.327397 0.999914 0 0 462.0 327.0 216.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_25.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-25 00:00:00.192150 282.542145 0.007581 84 40 False False
2016-08-25 00:00:01.210995 294.714203 0.007569 82 34 False False
power noise ref ldr high_delta execution
ts
2016-08-25 23:59:58.187365 229.744781 0.007563 84 30 False False
2016-08-25 23:59:59.198068 229.011017 0.007479 84 30 False False
power noise ref ldr high_delta execution
ts
2016-08-25 00:00:00.192150 282.542145 0.007581 84 40 False False
2016-08-25 00:00:01.210995 294.714203 0.007569 82 34 False False
power noise ref ldr high_delta execution
ts
2016-08-25 23:59:58.187365 229.744781 0.007563 84 30 False False
2016-08-25 23:59:59.198068 229.011017 0.007479 84 30 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-25 00:00:00 0.290849 1.000082 0 0 864.0 291.0 215.0
2016-08-25 01:00:00 0.298059 0.999885 0 0 449.0 298.0 153.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-25 22:00:00 0.400394 1.000128 0 0 1797.0 400.0 217.0
2016-08-25 23:00:00 0.107000 0.332672 0 0 840.0 322.0 183.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-25 00:00:00 0.290849 1.000082 0 0 864.0 291.0 215.0
2016-08-25 01:00:00 0.298059 0.999885 0 0 449.0 298.0 153.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-25 22:00:00 0.400394 1.000128 0 0 1797.0 400.0 217.0
2016-08-25 23:00:00 0.275033 0.999844 0 0 840.0 275.0 183.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_26.h5
ERROR: DF == DF_BIS? True; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-26 00:00:00.208880 231.718033 0.007364 84 30 False False
2016-08-26 00:00:01.215315 233.804504 0.007486 81 30 False False
power noise ref ldr high_delta execution
ts
2016-08-26 23:59:58.024840 330.056946 0.007354 84 40 False False
2016-08-26 23:59:59.035586 329.158813 0.007397 84 40 False False
power noise ref ldr high_delta execution
ts
2016-08-26 00:00:00.208880 231.718033 0.007364 84 30 False False
2016-08-26 00:00:01.215315 233.804504 0.007486 81 30 False False
power noise ref ldr high_delta execution
ts
2016-08-26 23:59:58.024840 330.056946 0.007354 84 40 False False
2016-08-26 23:59:59.035586 329.158813 0.007397 84 40 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-26 00:00:00 0.224600 1.000076 0 0 254.0 225.0 201.0
2016-08-26 01:00:00 0.236435 1.000070 0 0 813.0 236.0 149.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-26 22:00:00 0.670432 0.999961 0 0 2316.0 671.0 168.0
2016-08-26 23:00:00 0.548472 0.971488 0 0 3832.0 565.0 262.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-26 00:00:00 0.224600 1.000076 0 0 254.0 225.0 201.0
2016-08-26 01:00:00 0.236435 1.000070 0 0 813.0 236.0 149.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-26 22:00:00 0.670432 0.999961 0 0 2316.0 671.0 168.0
2016-08-26 23:00:00 0.558108 0.999789 0 0 3832.0 558.0 262.0
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/TODAY.h5
WARNING: No hay summary
In [100]:
p = '/Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_15.h5'
p_st = os.path.join(cat.base_path, p)
print_secc('PATH: {}'.format(p_st))
df = pd.read_hdf(p_st, cat.key_raw)
df_bis, df_s = cat.process_data_summary(df)
try:
df_s_saved = pd.read_hdf(p_st, cat.key_summary)
except KeyError:
print_warn('No hay summary')
df_s_saved = df_s
data_equal = (df == df_bis).all().all()
summary_equal = (df_s_saved == df_s).all().all()
if not summary_equal or not data_equal:
print_err('DF == DF_BIS? {}; DF_S == DF_S_CALC? {}'.format(data_equal, summary_equal))
print_blue('{}\n{}'.format(df.head(2), df.tail(2)))
print_cyan('{}\n{}'.format(df_bis.head(2), df_bis.tail(2)))
print_red('{}\n{}'.format(df_s_saved.head(2), df_s_saved.tail(2)))
print_magenta('{}\n{}'.format(df_s.head(2), df_s.tail(2)))
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_15.h5
ERROR: DF == DF_BIS? False; DF_S == DF_S_CALC? False
power noise ref ldr high_delta execution
ts
2016-08-15 00:00:00.485372 295.653412 0.007543 84 39 True True
2016-08-15 00:00:01.485388 302.146240 0.007404 81 39 True True
power noise ref ldr high_delta execution
ts
2016-08-15 23:59:58.300572 249.893311 0.007402 84 35 True True
2016-08-15 23:59:59.311180 248.625931 0.007340 84 35 True True
power noise ref ldr high_delta execution
ts
2016-08-15 00:00:00.485372 295.653412 0.007543 84 39 False False
2016-08-15 00:00:01.485388 302.146240 0.007404 81 39 False False
power noise ref ldr high_delta execution
ts
2016-08-15 23:59:58.300572 249.893311 0.007402 84 35 False False
2016-08-15 23:59:59.311180 248.625931 0.007340 84 35 False False
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 00:00:00 0.257087 0.999994 0 0 851.0 257.0 177.0
2016-08-15 01:00:00 0.231033 1.000025 0 0 267.0 231.0 202.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 22:00:00 0.403624 0.999916 1 1 1896.0 407.0 206.0
2016-08-15 23:00:00 0.184828 0.548637 0 0 399.0 337.0 302.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 00:00:00 0.257087 0.999994 0 0 851.0 257.0 177.0
2016-08-15 01:00:00 0.231033 1.000025 0 0 267.0 231.0 202.0
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-15 22:00:00 0.403624 0.999916 1 1 1896.0 407.0 206.0
2016-08-15 23:00:00 0.317224 0.999969 0 0 399.0 317.0 216.0
In [106]:
cols_fallo = []
for c in df:
if not (df[c] == df_bis[c]).all():
cols_fallo.append(c)
df[cols_fallo].plot(lw=1)
df_bis[cols_fallo].plot(lw=2)
Out[106]:
<matplotlib.axes._subplots.AxesSubplot at 0x11546cb38>
In [138]:
# Corrección de día 13, 14, 15 (raw + summary)
KWARGS_SAVE = dict(complevel=9, complib='blosc', fletcher32=True)
dias = [13, 14, 15]
for d in dias:
p_st = '/Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_{}.h5'.format(d)
print_secc('PATH: {}'.format(p_st))
df = pd.read_hdf(p_st, cat.key_raw)
df_bis, df_s = cat.process_data_summary(df)
df_s_saved = pd.read_hdf(p_st, cat.key_summary)
data_equal = (df == df_bis).all().all()
summary_equal = (df_s_saved == df_s).all().all()
if not summary_equal and not data_equal:
print_err('DF == DF_BIS? {}; DF_S == DF_S_CALC? {}'.format(data_equal, summary_equal))
print_red('{}\n{}'.format(df_s_saved.head(2), df_s_saved.tail(2)))
print_magenta('{}\n{}'.format(df_s.head(2), df_s.tail(2)))
df = pd.read_hdf(p_st, cat.key_raw)
df_bis, df_s = cat.process_data_summary(df)
df_s_saved = pd.read_hdf(p_st, cat.key_summary)
#with pd.HDFStore(p_st, mode='w', **KWARGS_SAVE) as st:
# st.append(cat.key_raw, df_bis)
# st.append(cat.key_summary, df_s)
# print_ok(st)
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_13.h5
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_14.h5
==> PATH: /Users/uge/ENERPIDATA/CURRENT_MONTH/DATA_2016_08_DAY_15.h5
In [140]:
data.index[-1].day != data.index[0].day
data.index[0].day, data.index[0].month
Out[140]:
(12, 8)
In [5]:
# Nuevos datos para test de archive day y month
raw = pd.read_hdf('/Users/uge/ENERPIDATA/enerpi_data.h5', 'rms')
new = cat.process_data(raw)
new.index += pd.Timedelta('4d 3h 47m')
print_info(new.head())
new.tail()
power noise ref ldr high_delta execution
ts
2016-08-31 23:54:33.664052 393.281219 0.007545 83 585 False False
2016-08-31 23:54:34.669293 396.561340 0.007572 82 585 False False
2016-08-31 23:54:35.671929 387.727539 0.007508 83 584 False False
2016-08-31 23:54:36.682851 380.000732 0.007520 84 584 False False
2016-08-31 23:54:37.686677 394.193909 0.007589 83 585 False False
Out[5]:
power
noise
ref
ldr
high_delta
execution
ts
2016-09-01 00:02:32.089362
376.478394
0.007678
84
584
False
False
2016-09-01 00:02:33.100947
381.355621
0.007586
84
584
False
False
2016-09-01 00:02:34.103450
386.890167
0.007579
82
584
False
False
2016-09-01 00:02:35.114879
392.543701
0.007645
84
583
False
False
2016-09-01 00:02:36.125862
386.716797
0.007588
84
583
False
False
In [1]:
from datacharm import *
from enerpi.api import enerpi_data_catalog
from enerpi.database import DATA_PATH, HDF_STORE
cat = enerpi_data_catalog(base_path=DATA_PATH, raw_file=HDF_STORE, check_integrity=False)
cat.tree.tail()
==> Librerías, clases y métodos cargados:
+++ "dt" +++ "os"
+++ "json" v:2.0.9 +++ "pd" (pandas) v:0.18.1
+++ "locale" +++ "plt"
+++ "math" +++ "re" v:2.2.1
+++ "np" (numpy) v:1.11.1 +++ "sns" (seaborn) v:0.7.0
+++ "sys"
** "Colormap", "Line2D", "Normalize", "OrderedDict", "PathPatch", "namedtuple", "time"
==> Pretty printing funcs:
print_blue, print_bold, print_boldu, print_cyan, print_err, print_green, print_grey, print_greyb, print_info, print_infob, print_magenta, print_ok, print_red, print_redb, print_secc, print_tree_dict, print_warn, print_white, print_yellow, print_yellowb, printcolor
If you are in a jupyter notebook, insert this:
%matplotlib inline
%config InlineBackend.figure_format='retina'
If you are working with GEO data, insert this:
import geopandas as gpd
import shapely.geometry as sg
import cartopy.crs as ccrs
Out[1]:
cols
is_cat
is_raw
key
n_rows
st
ts_fin
ts_ini
ts_st
26
[kWh, t_ref, n_jump, n_exec, p_max, p_mean, p_min]
True
False
/hours
24
CURRENT_MONTH/DATA_2016_08_DAY_25.h5
2016-08-25 23:00:00.000000
2016-08-25 00:00:00.000000
2016-08-26 00:20:25.574072
27
[power, noise, ref, ldr, high_delta, execution]
True
True
/rms
85786
CURRENT_MONTH/DATA_2016_08_DAY_25.h5
2016-08-25 23:59:59.198068
2016-08-25 00:00:00.192150
2016-08-26 00:20:25.574072
28
[kWh, t_ref, n_jump, n_exec, p_max, p_mean, p_min]
True
False
/hours
24
CURRENT_MONTH/DATA_2016_08_DAY_26.h5
2016-08-26 23:00:00.000000
2016-08-26 00:00:00.000000
2016-08-27 00:58:47.220250
29
[power, noise, ref, ldr, high_delta, execution]
True
True
/rms
85676
CURRENT_MONTH/DATA_2016_08_DAY_26.h5
2016-08-26 23:59:59.035586
2016-08-26 00:00:00.208880
2016-08-27 00:58:47.220250
0
[power, noise, ref, ldr, high_delta, execution]
True
True
/rms
71899
CURRENT_MONTH/TODAY.h5
2016-08-27 20:07:32.653716
2016-08-27 00:00:00.046510
2016-08-27 20:07:33.736793
In [6]:
cat.update_catalog(data=new)
In [16]:
today = pd.read_hdf(os.path.join(DATA_PATH, 'DATA_YEAR_2016', 'DATA_2016_MONTH_08.h5'), 'rms')
print(today.index.is_unique)
today
True
Out[16]:
power
noise
ref
ldr
high_delta
execution
ts
2016-08-12 10:46:25.990460
321.977661
0.006370
82
661
False
False
2016-08-12 10:46:27.001776
321.467957
0.006482
84
660
False
False
2016-08-12 10:46:28.001279
312.116974
0.006540
83
659
False
False
2016-08-12 10:46:29.003281
306.766022
0.006651
83
658
False
False
2016-08-12 10:46:30.013803
310.393005
0.006622
84
657
False
False
2016-08-12 10:46:31.016723
304.283630
0.006469
82
657
False
False
2016-08-12 10:46:32.021219
297.700317
0.006436
82
657
False
False
2016-08-12 10:46:33.031873
300.129700
0.006473
84
657
False
False
2016-08-12 10:46:34.042639
311.656708
0.006430
84
656
False
False
2016-08-12 10:46:35.053546
316.205658
0.006423
84
656
False
False
2016-08-12 10:46:36.064729
313.901764
0.006442
84
656
False
False
2016-08-12 10:46:37.067504
307.243713
0.006557
82
656
False
False
2016-08-12 10:46:38.077971
306.037811
0.006880
84
656
False
False
2016-08-12 10:46:39.088600
312.869995
0.006805
84
656
False
False
2016-08-12 10:46:40.099158
321.700439
0.006463
84
656
False
False
2016-08-12 10:46:41.099861
316.040131
0.006301
83
656
False
False
2016-08-12 10:46:42.110809
304.513336
0.006290
84
656
False
False
2016-08-12 10:46:43.121590
312.958618
0.006405
84
656
False
False
2016-08-12 10:46:44.132247
321.341064
0.006478
84
656
False
False
2016-08-12 10:46:45.143206
326.292053
0.006441
84
656
False
False
2016-08-12 10:46:46.153848
329.213257
0.006412
84
656
False
False
2016-08-12 10:46:47.164335
332.171112
0.006415
84
656
False
False
2016-08-12 10:46:48.174861
333.434723
0.006378
84
656
False
False
2016-08-12 10:46:49.185529
330.683929
0.006380
84
656
False
False
2016-08-12 10:46:50.196161
333.799042
0.006401
84
656
False
False
2016-08-12 10:46:51.196584
338.351501
0.006644
83
656
False
False
2016-08-12 10:46:52.200396
338.809479
0.006771
82
656
False
False
2016-08-12 10:46:53.211065
331.273651
0.006760
84
656
False
False
2016-08-12 10:46:54.221937
327.946960
0.006824
84
656
False
False
2016-08-12 10:46:55.233263
326.401337
0.006742
84
656
False
False
...
...
...
...
...
...
...
2016-08-31 23:59:30.622755
374.793823
0.007772
84
597
False
False
2016-08-31 23:59:31.633500
373.446472
0.007800
84
597
False
False
2016-08-31 23:59:32.644149
369.822266
0.007805
84
597
False
False
2016-08-31 23:59:33.654820
370.853577
0.007849
84
597
False
False
2016-08-31 23:59:34.666236
377.266754
0.007827
84
597
False
False
2016-08-31 23:59:35.676780
376.181976
0.007862
84
597
False
False
2016-08-31 23:59:36.687286
372.942200
0.007888
84
597
False
False
2016-08-31 23:59:37.697864
374.799683
0.007816
84
597
False
False
2016-08-31 23:59:38.708600
375.795258
0.007798
84
597
False
False
2016-08-31 23:59:39.719295
380.122925
0.007825
84
597
False
False
2016-08-31 23:59:40.730579
381.569092
0.007839
84
598
False
False
2016-08-31 23:59:41.741129
380.593048
0.007806
84
598
False
False
2016-08-31 23:59:42.751688
383.200836
0.007798
84
598
False
False
2016-08-31 23:59:43.762282
386.808960
0.007772
84
598
False
False
2016-08-31 23:59:44.772999
389.983307
0.007706
84
598
False
False
2016-08-31 23:59:45.783696
390.200165
0.007698
84
598
False
False
2016-08-31 23:59:46.794918
378.448395
0.007724
84
597
False
False
2016-08-31 23:59:47.805974
371.328857
0.007707
84
597
False
False
2016-08-31 23:59:48.811214
378.695526
0.007733
82
597
False
False
2016-08-31 23:59:49.821825
377.394257
0.007730
84
598
False
False
2016-08-31 23:59:50.833307
382.176178
0.007851
84
598
False
False
2016-08-31 23:59:51.844126
393.469757
0.007925
84
597
False
False
2016-08-31 23:59:52.845082
392.971039
0.007906
83
597
False
False
2016-08-31 23:59:53.848609
380.367920
0.007792
82
597
False
False
2016-08-31 23:59:54.848773
361.069092
0.007725
83
597
False
False
2016-08-31 23:59:55.859503
366.136841
0.007903
84
597
False
False
2016-08-31 23:59:56.860029
379.514038
0.007914
83
597
False
False
2016-08-31 23:59:57.870652
384.511139
0.007881
84
597
False
False
2016-08-31 23:59:58.871599
381.054688
0.007815
83
597
False
False
2016-08-31 23:59:59.882149
374.924103
0.007790
84
597
False
False
1293744 rows × 6 columns
In [9]:
new
Out[9]:
power
noise
ref
ldr
high_delta
execution
ts
2016-08-27 23:35:15.468270
337.033020
0.007548
72
634
False
False
2016-08-27 23:35:16.474974
355.264282
0.007300
77
634
False
False
2016-08-27 23:35:17.482896
370.732056
0.007188
82
634
False
False
2016-08-27 23:35:18.490626
360.768738
0.007420
82
633
False
False
2016-08-27 23:35:19.497099
337.699890
0.007577
83
633
False
False
2016-08-27 23:35:20.507528
330.467163
0.007592
84
634
False
False
2016-08-27 23:35:21.518893
330.257599
0.007629
84
634
False
False
2016-08-27 23:35:22.529447
329.213440
0.007606
84
634
False
False
2016-08-27 23:35:23.540428
323.496643
0.007584
84
634
False
False
2016-08-27 23:35:24.551261
322.625275
0.007582
84
634
False
False
2016-08-27 23:35:25.550842
332.130676
0.007546
83
634
False
False
2016-08-27 23:35:26.552601
327.286285
0.007484
83
634
False
False
2016-08-27 23:35:27.563570
315.116943
0.007414
84
634
False
False
2016-08-27 23:35:28.574056
321.135895
0.007528
84
634
False
False
2016-08-27 23:35:29.584971
333.193481
0.007618
84
634
False
False
2016-08-27 23:35:30.595603
329.595123
0.007683
84
634
False
False
2016-08-27 23:35:31.606949
334.027161
0.007685
84
634
False
False
2016-08-27 23:35:32.617634
341.920593
0.007508
84
634
False
False
2016-08-27 23:35:33.628239
336.472046
0.007517
84
634
False
False
2016-08-27 23:35:34.638764
333.742615
0.007587
84
634
False
False
2016-08-27 23:35:35.649990
330.713440
0.007537
84
634
False
False
2016-08-27 23:35:36.660571
327.242615
0.007620
84
634
False
False
2016-08-27 23:35:37.671846
333.102173
0.007739
84
634
False
False
2016-08-27 23:35:38.682484
332.839508
0.007719
84
634
False
False
2016-08-27 23:35:39.682330
334.445282
0.007664
83
634
False
False
2016-08-27 23:35:40.693153
345.222290
0.007585
84
634
False
False
2016-08-27 23:35:41.693806
339.392639
0.007543
83
634
False
False
2016-08-27 23:35:42.704355
334.808716
0.007494
84
634
False
False
2016-08-27 23:35:43.714119
332.394806
0.007475
83
634
False
False
2016-08-27 23:35:44.723598
332.938232
0.007514
83
634
False
False
...
...
...
...
...
...
...
2016-08-28 00:14:03.419874
238.646545
0.007671
84
647
False
False
2016-08-28 00:14:04.419902
247.200211
0.007679
83
647
False
False
2016-08-28 00:14:05.420334
249.816742
0.007657
83
647
False
False
2016-08-28 00:14:06.431332
246.592102
0.007686
84
647
False
False
2016-08-28 00:14:07.444138
235.673264
0.007710
84
646
False
False
2016-08-28 00:14:08.454035
240.547318
0.007753
84
646
False
False
2016-08-28 00:14:09.465378
240.448456
0.007720
84
646
False
False
2016-08-28 00:14:10.465589
238.590332
0.007692
83
646
False
False
2016-08-28 00:14:11.465912
234.369064
0.007669
83
646
False
False
2016-08-28 00:14:12.476825
231.152496
0.007655
84
646
False
False
2016-08-28 00:14:13.489313
239.803665
0.007715
84
646
False
False
2016-08-28 00:14:14.499172
248.437485
0.007762
84
647
False
False
2016-08-28 00:14:15.510327
244.503357
0.007719
84
647
False
False
2016-08-28 00:14:16.510612
233.461639
0.007664
83
647
False
False
2016-08-28 00:14:17.510864
228.584412
0.007658
83
647
False
False
2016-08-28 00:14:18.521585
231.856964
0.007646
84
647
False
False
2016-08-28 00:14:19.534651
238.529037
0.007650
84
646
False
False
2016-08-28 00:14:20.544066
236.182800
0.007648
84
646
False
False
2016-08-28 00:14:21.555111
238.830246
0.007683
84
646
False
False
2016-08-28 00:14:22.556162
238.661209
0.007642
83
646
False
False
2016-08-28 00:14:23.565377
233.412399
0.007612
83
646
False
False
2016-08-28 00:14:24.570553
241.319809
0.007771
82
646
False
False
2016-08-28 00:14:25.570777
248.366135
0.007795
83
647
False
False
2016-08-28 00:14:26.580684
235.197891
0.007712
84
646
False
False
2016-08-28 00:14:27.591587
234.580276
0.007675
84
646
False
False
2016-08-28 00:14:28.591622
244.284027
0.007608
83
646
False
False
2016-08-28 00:14:29.598794
233.266876
0.007647
82
646
False
False
2016-08-28 00:14:30.598384
233.095139
0.007686
83
646
False
False
2016-08-28 00:14:31.610458
240.176514
0.007654
84
646
False
False
2016-08-28 00:14:32.620609
235.768311
0.007692
84
647
False
False
2340 rows × 6 columns
In [116]:
ax = (data.execution[data.execution] * 1.5).plot(lw=0, markersize=10, marker='o', color='violet', alpha=.8, figsize=(18, 5))
ax = data.high_delta[data.high_delta & ~data.execution].plot(ax=ax, lw=0, markersize=10, marker='d', color='red')
ax.set_ylim((.5, 2));
ax.set_xlim((data.index[0], data.index[-1]));
In [120]:
data[data.high_delta | data.execution].loc['2016-08-26 13:50':'2016-08-26 14:00'].tail()
Out[120]:
power
noise
ref
ldr
high_delta
execution
ts
2016-08-26 13:53:23.926140
284.637787
0.007732
51
535
True
True
2016-08-26 13:53:33.619795
328.295929
0.007438
62
531
True
False
In [134]:
#data.ref.hist(bins=100)
#data.ref[data.ref < 81].resample('1s').mean().fillna(0).plot(figsize=(18, 8))
#len(data.ref[data.ref < 81].resample('5s').min())
data.ref[data.ref < 81].resample('5s').min().plot(figsize=(18, 8))
Out[134]:
<matplotlib.axes._subplots.AxesSubplot at 0x125618cc0>
In [21]:
diferencia = data_s_bis.index.difference(data_s.index)
print_red(diferencia)
data_s_bis.loc[diferencia]
DatetimeIndex(['2016-08-16 23:00:00'], dtype='datetime64[ns]', name='ts', freq=None)
Out[21]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
ts
2016-08-16 23:00:00
0.0
NaN
0
0
NaN
NaN
NaN
In [29]:
print_red(data_s[~(data_s_bis.drop(diferencia) == data_s).all(axis=1)])
data_s_bis.drop(diferencia)[~(data_s_bis.drop(diferencia) == data_s).all(axis=1)]
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-13 00:00:00 0.273163 1.000047 0 0 814.0 273.0 201.0
2016-08-13 23:00:00 0.120417 0.354864 0 0 401.0 339.0 291.0
2016-08-14 00:00:00 0.251059 1.000275 0 0 808.0 251.0 212.0
2016-08-14 23:00:00 0.199606 0.576791 0 0 458.0 346.0 304.0
2016-08-15 00:00:00 0.257087 0.999994 0 0 851.0 257.0 177.0
2016-08-15 23:00:00 0.184828 0.548637 0 0 399.0 337.0 302.0
2016-08-16 00:00:00 0.235938 0.999976 0 0 279.0 236.0 211.0
2016-08-16 21:00:00 0.202495 0.517834 0 0 461.0 391.0 288.0
2016-08-17 00:00:00 0.150972 0.646398 0 0 274.0 234.0 208.0
2016-08-17 23:00:00 0.347437 0.967882 0 0 2006.0 359.0 201.0
2016-08-18 00:00:00 0.290345 1.000075 0 0 820.0 290.0 168.0
2016-08-18 23:00:00 0.301713 0.897652 0 0 882.0 336.0 245.0
2016-08-19 00:00:00 0.251378 0.999786 2 2 281.0 252.0 233.0
2016-08-19 23:00:00 0.143804 0.372659 0 0 515.0 386.0 277.0
2016-08-20 00:00:00 0.327746 1.000015 0 0 590.0 328.0 192.0
2016-08-20 23:00:00 0.145678 0.522620 0 0 849.0 279.0 209.0
2016-08-21 00:00:00 0.238063 0.999911 0 0 787.0 238.0 200.0
2016-08-21 23:00:00 0.198115 0.689859 0 0 390.0 287.0 218.0
2016-08-22 23:00:00 0.032893 0.089656 0 0 425.0 367.0 321.0
2016-08-23 00:00:00 0.286856 1.000055 0 0 810.0 287.0 242.0
Out[29]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
ts
2016-08-13 00:00:00
0.273163
1.000049
0
0
814.0
273.0
201.0
2016-08-13 23:00:00
0.320116
0.999967
0
0
402.0
320.0
236.0
2016-08-14 00:00:00
0.251060
1.000278
0
0
808.0
251.0
212.0
2016-08-14 23:00:00
0.328059
0.999930
0
0
458.0
328.0
279.0
2016-08-15 00:00:00
0.257088
0.999997
0
0
851.0
257.0
177.0
2016-08-15 23:00:00
0.317224
0.999969
0
0
399.0
317.0
216.0
2016-08-16 00:00:00
0.235938
0.999976
0
0
279.0
236.0
211.0
2016-08-16 21:00:00
0.367758
0.999906
0
0
1027.0
368.0
252.0
2016-08-17 00:00:00
0.666060
2.960765
1
1
274.0
234.0
208.0
2016-08-17 23:00:00
0.358426
1.000040
0
0
2006.0
358.0
201.0
2016-08-18 00:00:00
0.290345
1.000076
0
0
820.0
290.0
168.0
2016-08-18 23:00:00
0.328388
1.000172
0
0
882.0
328.0
245.0
2016-08-19 00:00:00
0.251379
0.999790
2
2
281.0
252.0
233.0
2016-08-19 23:00:00
0.405113
1.000002
0
0
607.0
405.0
277.0
2016-08-20 00:00:00
0.327747
1.000016
0
0
590.0
328.0
192.0
2016-08-20 23:00:00
0.257742
0.999990
0
0
849.0
258.0
199.0
2016-08-21 00:00:00
0.238063
0.999913
0
0
787.0
238.0
200.0
2016-08-21 23:00:00
0.269552
1.000070
0
0
390.0
270.0
218.0
2016-08-22 23:00:00
0.353989
1.000060
0
0
425.0
354.0
312.0
2016-08-23 00:00:00
0.286855
1.000054
0
0
810.0
287.0
242.0
In [24]:
print(data_s_bis.loc['2016-08-16 14:00:00':'2016-08-17 3:00:00'])
data_s.loc['2016-08-16 14:00:00':'2016-08-17 3:00:00']
kWh t_ref n_jump n_exec p_max p_mean p_min
ts
2016-08-16 14:00:00 1.088713 0.999967 0 0 4090.0 1089.0 248.0
2016-08-16 15:00:00 0.393378 0.999948 0 0 443.0 393.0 328.0
2016-08-16 16:00:00 0.339787 1.000189 0 0 439.0 340.0 247.0
2016-08-16 17:00:00 0.369336 0.999785 0 0 449.0 369.0 323.0
2016-08-16 18:00:00 0.398480 0.999997 0 0 914.0 398.0 335.0
2016-08-16 19:00:00 0.399873 1.000199 1 1 936.0 399.0 259.0
2016-08-16 20:00:00 0.428319 0.999909 0 0 942.0 428.0 326.0
2016-08-16 21:00:00 0.367758 0.999906 0 0 1027.0 368.0 252.0
2016-08-16 22:00:00 0.060506 0.039242 0 0 2332.0 1542.0 270.0
2016-08-16 23:00:00 0.000000 NaN 0 0 NaN NaN NaN
2016-08-17 00:00:00 0.666060 2.960765 1 1 274.0 234.0 208.0
2016-08-17 01:00:00 0.246444 1.000204 0 0 831.0 246.0 211.0
2016-08-17 02:00:00 0.238876 0.999975 0 0 281.0 239.0 214.0
2016-08-17 03:00:00 0.247852 0.999804 0 0 795.0 248.0 213.0
Out[24]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
ts
2016-08-16 14:00:00
1.088713
0.999967
0
0
4090.0
1089.0
248.0
2016-08-16 15:00:00
0.393378
0.999948
0
0
443.0
393.0
328.0
2016-08-16 16:00:00
0.339787
1.000189
0
0
439.0
340.0
247.0
2016-08-16 17:00:00
0.369336
0.999785
0
0
449.0
369.0
323.0
2016-08-16 18:00:00
0.398480
0.999997
0
0
914.0
398.0
335.0
2016-08-16 19:00:00
0.399873
1.000199
1
1
936.0
399.0
259.0
2016-08-16 20:00:00
0.428319
0.999909
0
0
942.0
428.0
326.0
2016-08-16 21:00:00
0.202495
0.517834
0
0
461.0
391.0
288.0
2016-08-16 22:00:00
0.060506
0.039242
0
0
2332.0
1542.0
270.0
2016-08-17 00:00:00
0.150972
0.646398
0
0
274.0
234.0
208.0
2016-08-17 01:00:00
0.246444
1.000204
0
0
831.0
246.0
211.0
2016-08-17 02:00:00
0.238876
0.999975
0
0
281.0
239.0
214.0
2016-08-17 03:00:00
0.247852
0.999804
0
0
795.0
248.0
213.0
In [41]:
hora = df.loc['2016-08-22 23:00:00':'2016-08-23 00:00:00'].copy()
hora.ix[1:, 'delta'] = (hora.index.values[1:] - hora.index.values[:-1])
hora.delta = hora.delta.fillna(method='bfill')
hora['kWh'] = hora.power * hora.delta.dt.total_seconds() / (3600 * 1000.)
print_ok(hora.sum())
hora.tail()
power 1.26436e+06
noise 27.2499
ref 298201
ldr 129525
high_delta False
execution False
delta 0 days 01:00:01.228615
kWh 0.354085
dtype: object
Out[41]:
power
noise
ref
ldr
high_delta
execution
delta
kWh
ts
2016-08-22 23:59:56.749874
340.150360
0.007564
84
36
False
False
00:00:01.010684
0.000095
2016-08-22 23:59:57.760679
341.538544
0.007561
84
36
False
False
00:00:01.010805
0.000096
2016-08-22 23:59:58.771425
338.310730
0.007478
84
36
False
False
00:00:01.010746
0.000095
2016-08-22 23:59:59.782355
340.466675
0.007396
84
36
False
False
00:00:01.010930
0.000096
2016-08-23 00:00:00.788150
343.017334
0.007433
83
37
False
False
00:00:01.005795
0.000096
In [20]:
import glob
def explore_stores(path, ext='.h5'):
for f in sorted(filter(lambda f: f.endswith(ext), glob.glob(path + '/**', recursive=True))):
print_magenta(os.path.basename(f))
with pd.HDFStore(f, 'r') as st:
df = st['/rms'].sort_index()
print_info('* FROM {:%d-%m-%y %H:%M} TO {:%d-%m-%y %H:%M} [{}, unique={}, hay horas={}]'
.format(df.index[0], df.index[-1], len(df), df.index.is_unique, '/hours' in st.keys()))
explore_stores('/Users/uge/Dropbox/PYTHON/PYPROJECTS/respaldo_enerpi_rpi3/ENERPIDATA_bkp/', ext='.h5')
DATA_2016_09_DAY_01.h5
* FROM 01-09-16 00:00 TO 01-09-16 23:59 [85705, unique=True, hay horas=True]
TODAY.h5
* FROM 02-09-16 00:00 TO 02-09-16 10:25 [211794, unique=False, hay horas=False]
DATA_2016_MONTH_08.h5
* FROM 31-08-16 23:08 TO 31-08-16 23:59 [3041, unique=True, hay horas=True]
DATA_2016_08_DAY_12.h5
* FROM 12-08-16 10:46 TO 12-08-16 23:59 [47122, unique=True, hay horas=True]
DATA_2016_08_DAY_13.h5
* FROM 13-08-16 00:00 TO 13-08-16 23:59 [81159, unique=True, hay horas=True]
DATA_2016_08_DAY_14.h5
* FROM 14-08-16 00:00 TO 14-08-16 23:59 [85608, unique=True, hay horas=True]
DATA_2016_08_DAY_15.h5
* FROM 15-08-16 00:00 TO 15-08-16 23:59 [85541, unique=True, hay horas=True]
DATA_2016_08_DAY_16.h5
* FROM 16-08-16 00:00 TO 16-08-16 22:02 [77470, unique=True, hay horas=True]
DATA_2016_08_DAY_17.h5
* FROM 17-08-16 00:21 TO 17-08-16 23:59 [79375, unique=True, hay horas=True]
DATA_2016_08_DAY_18.h5
* FROM 18-08-16 00:00 TO 18-08-16 23:59 [83951, unique=True, hay horas=True]
DATA_2016_08_DAY_19.h5
* FROM 19-08-16 00:00 TO 19-08-16 23:59 [84318, unique=True, hay horas=True]
DATA_2016_08_DAY_20.h5
* FROM 20-08-16 00:00 TO 20-08-16 23:59 [85864, unique=True, hay horas=True]
DATA_2016_08_DAY_21.h5
* FROM 21-08-16 00:00 TO 21-08-16 23:59 [85799, unique=True, hay horas=True]
DATA_2016_08_DAY_22.h5
* FROM 22-08-16 00:00 TO 22-08-16 23:59 [85604, unique=True, hay horas=True]
DATA_2016_08_DAY_23.h5
* FROM 23-08-16 00:00 TO 23-08-16 23:59 [85016, unique=True, hay horas=True]
DATA_2016_08_DAY_24.h5
* FROM 24-08-16 00:00 TO 24-08-16 23:59 [85792, unique=True, hay horas=True]
DATA_2016_08_DAY_25.h5
* FROM 25-08-16 00:00 TO 25-08-16 23:59 [85786, unique=True, hay horas=True]
DATA_2016_08_DAY_26.h5
* FROM 26-08-16 00:00 TO 26-08-16 23:59 [85676, unique=True, hay horas=True]
DATA_2016_08_DAY_27.h5
* FROM 27-08-16 00:00 TO 27-08-16 23:59 [85728, unique=True, hay horas=True]
DATA_2016_08_DAY_28.h5
* FROM 28-08-16 00:00 TO 28-08-16 23:59 [85744, unique=True, hay horas=True]
DATA_2016_08_DAY_29.h5
* FROM 29-08-16 00:00 TO 29-08-16 23:59 [85742, unique=True, hay horas=True]
DATA_2016_08_DAY_30.h5
* FROM 30-08-16 00:00 TO 30-08-16 23:59 [85743, unique=True, hay horas=True]
DATA_2016_09_DAY_01.h5
* FROM 01-09-16 00:00 TO 01-09-16 23:59 [171410, unique=False, hay horas=True]
TODAY.h5
* FROM 26-08-16 00:00 TO 26-08-16 23:58 [85575, unique=True, hay horas=False]
TODAY.h5
* FROM 15-08-16 00:00 TO 15-08-16 23:32 [83931, unique=True, hay horas=False]
enerpi_data.h5
* FROM 16-08-16 18:43 TO 16-08-16 19:10 [1620, unique=True, hay horas=False]
temp_data.h5
* FROM 12-08-16 10:46 TO 13-08-16 21:01 [122200, unique=True, hay horas=False]
enerpi_data.h5
* FROM 31-08-16 23:08 TO 02-09-16 11:09 [128580, unique=True, hay horas=False]
temp_debug_day_28.h5
* FROM 27-08-16 00:00 TO 28-08-16 00:25 [87259, unique=True, hay horas=False]
temp_debug_day_29.h5
* FROM 28-08-16 00:00 TO 29-08-16 00:36 [87931, unique=True, hay horas=False]
temp_debug_day_30.h5
* FROM 29-08-16 00:00 TO 30-08-16 00:47 [88587, unique=True, hay horas=False]
temp_debug_day_31.h5
* FROM 30-08-16 00:00 TO 31-08-16 00:58 [89245, unique=True, hay horas=False]
temp_debug_month.h5
* FROM 31-08-16 23:08 TO 02-09-16 10:25 [300540, unique=False, hay horas=False]
In [21]:
month = pd.read_hdf('/Users/uge/bkp/ENERPIDATA/temp_debug_month.h5', 'rms')
In [22]:
month
Out[22]:
power
noise
ref
ldr
high_delta
execution
ts
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:00.977816
211.460403
0.007816
84
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
2016-09-01 00:00:01.989375
208.400986
0.007800
83
30
False
False
...
...
...
...
...
...
...
2016-09-01 16:16:50.793146
222.700821
0.007742
84
443
False
False
2016-09-01 16:16:51.792756
226.557755
0.007783
83
443
False
False
2016-09-01 16:16:52.804107
231.706558
0.007827
84
443
False
False
2016-09-01 16:16:53.814555
227.632858
0.007802
84
442
False
False
2016-09-01 16:16:54.825867
229.364136
0.007750
84
442
False
False
2016-09-01 16:16:55.836469
232.315567
0.007727
84
442
False
False
2016-09-01 16:16:56.847382
230.744278
0.007724
84
442
False
False
2016-09-01 16:16:57.849441
233.044281
0.007740
83
442
False
False
2016-09-01 16:16:58.860756
228.071198
0.007710
84
442
False
False
2016-09-01 16:16:59.871530
225.041306
0.007668
84
442
False
False
2016-09-01 16:17:00.876608
227.608383
0.007745
83
441
False
False
2016-09-01 16:17:01.887626
223.846512
0.007785
84
441
False
False
2016-09-01 16:17:02.888214
217.881134
0.007721
83
442
False
False
2016-09-01 16:17:03.892986
215.172043
0.007718
82
443
False
False
2016-09-01 16:17:04.894237
226.036423
0.007841
83
443
False
False
2016-09-01 16:17:05.904881
233.391693
0.007827
84
443
False
False
2016-09-01 16:17:06.905691
225.129013
0.007666
83
443
False
False
2016-09-01 16:17:07.916365
224.097351
0.007676
84
443
False
False
2016-09-01 16:17:08.921856
227.949295
0.007716
82
442
False
False
2016-09-01 16:17:09.924690
233.315643
0.007597
83
441
False
False
2016-09-01 16:17:10.936090
236.166275
0.007578
84
441
False
False
2016-09-01 16:17:11.946653
225.116470
0.007706
84
442
False
False
2016-09-01 16:17:12.946368
228.436111
0.007776
83
444
False
False
2016-09-01 16:17:13.957039
231.347305
0.007764
84
443
False
False
2016-09-01 16:17:14.968015
224.584564
0.007719
84
442
False
False
2016-09-01 16:17:15.979548
218.520264
0.007708
84
443
False
False
2016-09-01 16:17:16.979062
225.110718
0.007744
83
445
False
False
2016-09-01 16:17:17.989711
234.345718
0.007801
84
444
False
False
2016-09-01 16:17:18.989360
233.866516
0.007783
83
442
False
False
2016-09-01 16:17:19.999909
231.158295
0.007726
84
442
False
False
502144 rows × 6 columns
In [9]:
import pandas as pd
from prettyprinting import *
raw = pd.read_hdf('/Users/uge/Dropbox/PYTHON/PYPROJECTS/respaldo_enerpi_rpi3/ENERPIDATA/temp_debug_month.h5', 'rms')
print_red('{}, unique={}'.format(raw.shape, raw.index.is_unique))
df = raw.sort_index().copy() #.drop_duplicates()
print_info('{}, unique={}'.format(df.shape, df.index.is_unique))
df
(1695618, 6), unique=True
(1695618, 6), unique=True
Out[9]:
power
noise
ref
ldr
high_delta
execution
ts
2016-08-12 10:46:25.990460
321.977661
0.006370
82
661
False
False
2016-08-12 10:46:27.001776
321.467957
0.006482
84
660
False
False
2016-08-12 10:46:28.001279
312.116974
0.006540
83
659
False
False
2016-08-12 10:46:29.003281
306.766022
0.006651
83
658
False
False
2016-08-12 10:46:30.013803
310.393005
0.006622
84
657
False
False
2016-08-12 10:46:31.016723
304.283630
0.006469
82
657
False
False
2016-08-12 10:46:32.021219
297.700317
0.006436
82
657
False
False
2016-08-12 10:46:33.031873
300.129700
0.006473
84
657
False
False
2016-08-12 10:46:34.042639
311.656708
0.006430
84
656
False
False
2016-08-12 10:46:35.053546
316.205658
0.006423
84
656
False
False
2016-08-12 10:46:36.064729
313.901764
0.006442
84
656
False
False
2016-08-12 10:46:37.067504
307.243713
0.006557
82
656
False
False
2016-08-12 10:46:38.077971
306.037811
0.006880
84
656
False
False
2016-08-12 10:46:39.088600
312.869995
0.006805
84
656
False
False
2016-08-12 10:46:40.099158
321.700439
0.006463
84
656
False
False
2016-08-12 10:46:41.099861
316.040131
0.006301
83
656
False
False
2016-08-12 10:46:42.110809
304.513336
0.006290
84
656
False
False
2016-08-12 10:46:43.121590
312.958618
0.006405
84
656
False
False
2016-08-12 10:46:44.132247
321.341064
0.006478
84
656
False
False
2016-08-12 10:46:45.143206
326.292053
0.006441
84
656
False
False
2016-08-12 10:46:46.153848
329.213257
0.006412
84
656
False
False
2016-08-12 10:46:47.164335
332.171112
0.006415
84
656
False
False
2016-08-12 10:46:48.174861
333.434723
0.006378
84
656
False
False
2016-08-12 10:46:49.185529
330.683929
0.006380
84
656
False
False
2016-08-12 10:46:50.196161
333.799042
0.006401
84
656
False
False
2016-08-12 10:46:51.196584
338.351501
0.006644
83
656
False
False
2016-08-12 10:46:52.200396
338.809479
0.006771
82
656
False
False
2016-08-12 10:46:53.211065
331.273651
0.006760
84
656
False
False
2016-08-12 10:46:54.221937
327.946960
0.006824
84
656
False
False
2016-08-12 10:46:55.233263
326.401337
0.006742
84
656
False
False
...
...
...
...
...
...
...
2016-09-02 11:08:32.771709
411.560303
0.007601
84
703
False
False
2016-09-02 11:08:33.771541
402.537750
0.007616
83
703
False
False
2016-09-02 11:08:34.772014
406.602509
0.007556
83
703
False
False
2016-09-02 11:08:35.771647
410.026031
0.007590
83
703
False
False
2016-09-02 11:08:36.777146
410.596680
0.007672
83
703
False
False
2016-09-02 11:08:37.787681
415.209167
0.007754
84
703
False
False
2016-09-02 11:08:38.794084
407.709106
0.007698
82
703
False
False
2016-09-02 11:08:39.794150
411.985291
0.007622
83
703
False
False
2016-09-02 11:08:40.795653
406.503876
0.007655
83
703
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In [4]:
df = df.groupby(level=0).first()
df
Out[4]:
power
noise
ref
ldr
high_delta
execution
ts
2016-08-12 10:46:25.990460
321.977661
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661
False
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2016-08-12 10:46:27.001776
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1695618 rows × 6 columns
In [10]:
%config InlineBackend.figure_format='retina'
from datacharm import *
%matplotlib inline
df.resample('5min').power.mean().fillna(0).plot(figsize=(18, 8))
plt.show()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-10-642c81663c11> in <module>()
3 get_ipython().magic('matplotlib inline')
4
----> 5 df.resample('5min').power.mean().fillna(0).plot(figsize=(18, 8))
6 plt.show()
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/pandas/tools/plotting.py in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
3564 colormap=colormap, table=table, yerr=yerr,
3565 xerr=xerr, label=label, secondary_y=secondary_y,
-> 3566 **kwds)
3567 __call__.__doc__ = plot_series.__doc__
3568
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/pandas/tools/plotting.py in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
2643 yerr=yerr, xerr=xerr,
2644 label=label, secondary_y=secondary_y,
-> 2645 **kwds)
2646
2647
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/pandas/tools/plotting.py in _plot(data, x, y, subplots, ax, kind, **kwds)
2439 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
2440
-> 2441 plot_obj.generate()
2442 plot_obj.draw()
2443 return plot_obj.result
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/pandas/tools/plotting.py in generate(self)
1026 self._compute_plot_data()
1027 self._setup_subplots()
-> 1028 self._make_plot()
1029 self._add_table()
1030 self._make_legend()
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/pandas/tools/plotting.py in _make_plot(self)
1705 stacking_id=stacking_id,
1706 is_errorbar=is_errorbar,
-> 1707 **kwds)
1708 self._add_legend_handle(newlines[0], label, index=i)
1709
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/pandas/tools/plotting.py in _ts_plot(cls, ax, x, data, style, **kwds)
1745 lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
1746 # set date formatter, locators and rescale limits
-> 1747 format_dateaxis(ax, ax.freq)
1748 return lines
1749
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/pandas/tseries/plotting.py in format_dateaxis(subplot, freq)
292 "t = {0} y = {1:8f}".format(Period(ordinal=int(t), freq=freq), y))
293
--> 294 pylab.draw_if_interactive()
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/IPython/utils/decorators.py in wrapper(*args, **kw)
41 def wrapper(*args,**kw):
42 wrapper.called = False
---> 43 out = func(*args,**kw)
44 wrapper.called = True
45 return out
/Users/uge/anaconda/envs/py35/lib/python3.5/site-packages/matplotlib/backends/backend_macosx.py in draw_if_interactive()
248 figManager = Gcf.get_active()
249 if figManager is not None:
--> 250 figManager.canvas.invalidate()
251
252
AttributeError: 'FigureCanvasAgg' object has no attribute 'invalidate'
In [13]:
def _median(arr):
return 0 if arr.empty else np.nanmedian(arr)
LDR = df.ldr.resample('30s', label='left').apply(_median)
LDR.head()
Out[13]:
ts
2016-08-12 10:46:00 659.5
2016-08-12 10:46:30 656.0
2016-08-12 10:47:00 0.0
2016-08-12 10:47:30 657.0
2016-08-12 10:48:00 656.0
Freq: 30S, Name: ldr, dtype: float64
In [29]:
df_ldr = pd.DataFrame(df.ldr.resample('2min', label='left').apply(_median))
df_ldr['day'] = df_ldr.index.day
df_ldr['time'] = df_ldr.index.time
dias_ldr = df_ldr.groupby(['day', 'time']).first()
f, ax = plt.subplots(figsize=(18, 12))
days = list(sorted(set(dias_ldr.index.get_level_values(0))))
for i, day in enumerate(days):
dias_ldr.loc[day].plot(ax=ax, color=[i/len(days), i/len(days), 1, .9], lw=1)
plt.legend([])
plt.show()
In [27]:
ax.get_xlim()
Out[27]:
(0.0, 86280.0)
In [58]:
## LOG RSC_GEN
import re
rg_log = re.compile('\nDEBUG \[base\.py_wrapper\] - (?P<ts1>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): plot_tile_last_24h TOOK: (?P<took1>\d{1,3}\.\d\d\d) s\nDEBUG \[base\.py_wrapper\] - (?P<ts2>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): plot_tile_last_24h TOOK: (?P<took2>\d{1,3}\.\d\d\d) s\nDEBUG \[base\.py_wrapper\] - (?P<ts3>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): plot_tile_last_24h TOOK: (?P<took3>\d{1,3}\.\d\d\d) s\nDEBUG \[mule_rscgen\.py__rsc_generator\] - (?P<tstotal>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): \(MULE\) TILES generation ok\? (?P<ok>True|False). TOOK (?P<tooktotal>\d{1,3}\.\d\d\d) s')
text = open('/Users/uge/ENERPIDATA/log_rscgen.log', 'r').read()
rg_log.findall(text)
Out[58]:
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('31/08/2016 03:15:06',
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('31/08/2016 03:25:07',
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('31/08/2016 03:30:07',
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('31/08/2016 03:35:06',
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'31/08/2016 03:35:18',
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'14.025'),
('31/08/2016 03:40:06',
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'3.726',
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('31/08/2016 03:45:06',
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'True',
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('31/08/2016 04:10:06',
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'13.722'),
('31/08/2016 04:20:06',
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'15.186'),
('31/08/2016 04:25:06',
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'31/08/2016 04:25:17',
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'14.275'),
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'True',
'12.876'),
('31/08/2016 04:55:07',
'0.657',
'31/08/2016 04:55:13',
'4.225',
'31/08/2016 04:55:17',
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'True',
'13.713'),
('31/08/2016 05:00:06',
'0.649',
'31/08/2016 05:00:12',
'4.051',
'31/08/2016 05:00:16',
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'31/08/2016 05:00:17',
'True',
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('31/08/2016 05:05:06',
'0.654',
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'4.884',
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'14.792'),
('31/08/2016 05:10:07',
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'3.566',
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'0.684',
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('31/08/2016 05:15:06',
'0.642',
'31/08/2016 05:15:12',
'3.548',
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'12.099'),
('31/08/2016 05:20:07',
'0.654',
'31/08/2016 05:20:12',
'3.528',
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'True',
'12.139'),
('31/08/2016 05:25:06',
'0.659',
'31/08/2016 05:25:12',
'3.543',
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'True',
'13.804'),
('31/08/2016 05:30:06',
'0.647',
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('31/08/2016 05:35:06',
'0.639',
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'4.032',
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'0.720',
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'True',
'13.543'),
('31/08/2016 05:40:06',
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'4.277',
'31/08/2016 05:40:17',
'0.690',
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'True',
'14.465'),
('31/08/2016 05:45:06',
'0.652',
'31/08/2016 05:45:13',
'4.998',
'31/08/2016 05:45:17',
'1.706',
'31/08/2016 05:45:19',
'True',
'15.227'),
('31/08/2016 05:50:06',
'0.659',
'31/08/2016 05:50:13',
'4.263',
'31/08/2016 05:50:17',
'1.278',
'31/08/2016 05:50:19',
'True',
'14.861'),
('31/08/2016 05:55:07',
'0.648',
'31/08/2016 05:55:13',
'4.377',
'31/08/2016 05:55:18',
'0.876',
'31/08/2016 05:55:19',
'True',
'13.868'),
('31/08/2016 06:00:07',
'0.648',
'31/08/2016 06:00:13',
'3.664',
'31/08/2016 06:00:19',
'1.001',
'31/08/2016 06:00:20',
'True',
'14.480'),
('31/08/2016 06:05:06',
'0.648',
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'3.603',
'31/08/2016 06:05:15',
'0.681',
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'True',
'12.676'),
('31/08/2016 06:10:06',
'0.635',
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'4.798',
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'0.701',
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'True',
'14.014'),
('31/08/2016 06:15:06',
'0.654',
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'True',
'14.290'),
('31/08/2016 06:20:07',
'0.656',
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'3.596',
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'1.089',
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'True',
'14.150'),
('31/08/2016 06:25:07',
'0.667',
'31/08/2016 06:25:13',
'3.612',
'31/08/2016 06:25:17',
'1.223',
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'True',
'13.117'),
('31/08/2016 06:30:06',
'0.648',
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'3.879',
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'0.881',
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('31/08/2016 06:35:06',
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('31/08/2016 06:40:07',
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'5.728',
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...]
In [64]:
import datetime as dt
rg_log2 = re.compile('\nDEBUG \[base\.py_wrapper\] - (?P<ts1>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): plot_tile_last_24h TOOK: (?P<took1>\d{1,3}\.\d\d\d) s\nDEBUG \[base\.py_wrapper\] - (?P<ts2>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): plot_tile_last_24h TOOK: (?P<took2>\d{1,3}\.\d\d\d) s\nDEBUG \[base\.py_wrapper\] - (?P<ts3>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): plot_tile_last_24h TOOK: (?P<took3>\d{1,3}\.\d\d\d) s\nDEBUG \[mule_rscgen\.py__rsc_generator\] - (?P<tstotal>\d{1,2}/\d\d/\d\d\d\d \d\d:\d\d:\d\d): \(MULE\) TILES generation ok\? (?P<ok>True|False). TOOK (?P<tooktotal>\d{1,3}\.\d\d\d) s')
rscgen = pd.DataFrame(rg_log2.findall(text), columns=sorted(rg_log2.groupindex, key=lambda x: rg_log2.groupindex[x]))
for c in rscgen:
if c.startswith('took'):
rscgen[c] = rscgen[c].astype(float)
elif c.startswith('ts'):
rscgen[c] = [pd.Timestamp(dt.datetime.strptime(x, '%d/%m/%Y %H:%M:%S')) for x in rscgen[c]]
else: # == ok
rscgen[c] = rscgen[c].str.contains('True')
rscgen.set_index('tstotal', inplace=True)
In [134]:
def _get_pond(row, c_pond, df_pond, coef_same=.5):
ponds = df_pond.loc[row.time]
try:
same = ponds.loc[row[c_pond]]
v_pond = (coef_same * ponds.loc[row[c_pond]] + (1 - coef_same) * ponds).mean()
except KeyError:
v_pond = ponds.mean()
return v_pond
data_s = cat.get_summary()
data_s['completo'] = data_s.t_ref > .95
data_s.loc[data_s.completo, 'kWh_c'] = data_s.loc[data_s.completo, 'kWh']
data_s['hay_datos'] = False
data_s.loc[data_s.t_ref > .1, 'hay_datos'] = True
data_s['wd'] = data_s.index.weekday
data_s['time'] = data_s.index.time
data_s['month'] = data_s.index.month
data_s['week'] = data_s.index.week
data_usual = data_s[data_s.completo][['kWh', 't_ref', 'n_jump', 'n_exec', 'p_max', 'p_mean', 'p_min',
'time', 'wd', 'week', 'month']]
medians_wd = data_usual.groupby(['time', 'wd']).median()
medians_week = data_usual.groupby(['time', 'week']).median()
medians_month = data_usual.groupby(['time', 'month']).median()
for idx, row in data_s[~data_s.completo].iterrows():
try:
if data_s.loc[idx - pd.Timedelta('1D')].completo:
v_pond_yesterday = data_s.loc[idx - pd.Timedelta('1D')]
except KeyError:
v_pond_yesterday = None
v_pond_wd = _get_pond(row, 'wd', medians_wd, coef_same=.7)
v_pond_week = _get_pond(row, 'week', medians_week, coef_same=.7)
v_pond_month = _get_pond(row, 'month', medians_month, coef_same=.7)
v_pond = .4 * v_pond_wd + .45 * v_pond_wd + .15 * v_pond_month
if v_pond_yesterday is not None:
v_pond = .4 * v_pond + .6 * v_pond_yesterday.loc[v_pond.index]
t_ref = data_s.loc[idx, 't_ref']
if t_ref > 0:
kWh = data_s.loc[idx, 'kWh']
v_pond = v_pond * (1 - t_ref) + kWh
data_s.loc[idx, 'kWh_c'] = v_pond.kWh
data_s.loc[idx, 'p_max_c'] = v_pond.p_max
data_s.loc[idx, 'p_min_c'] = v_pond.p_min
data_s[['kWh', 'kWh_c']].plot(figsize=(18, 10))
plt.show()
(data_s['kWh_c'] - data_s['kWh']).plot()
data_s.head()
***TIMEIT get_summary TOOK: 0.180 s
Out[134]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
completo
kWh_c
hay_datos
wd
time
month
week
p_max_c
p_min_c
ts
2016-08-12 10:00:00
0.071497
0.226328
2
0
348.0
317.0
296.0
False
0.378566
True
4
10:00:00
8
32
474.893787
211.522353
2016-08-12 11:00:00
0.461430
1.000060
0
0
3452.0
461.0
299.0
True
0.461430
True
4
11:00:00
8
32
NaN
NaN
2016-08-12 12:00:00
0.326755
0.999834
0
0
373.0
327.0
289.0
True
0.326755
True
4
12:00:00
8
32
NaN
NaN
2016-08-12 13:00:00
0.363093
0.999993
0
0
871.0
363.0
296.0
True
0.363093
True
4
13:00:00
8
32
NaN
NaN
2016-08-12 14:00:00
0.501344
0.999975
0
0
3304.0
501.0
208.0
True
0.501344
True
4
14:00:00
8
32
NaN
NaN
In [119]:
data_s[['kWh', 'kWh_c']].plot()
plt.show()
(data_s['kWh_c'] - data_s['kWh']).plot()
Out[119]:
<matplotlib.axes._subplots.AxesSubplot at 0x113755668>
In [111]:
usuales
data_s['month'] = data_s.index.month
data_s['week'] = data_s.index.week
data_s
Out[111]:
kWh
t_ref
n_jump
n_exec
p_max
p_mean
p_min
completo
kWh_c
hay_datos
wd
time
p_max_c
p_min_c
month
ts
2016-08-12 10:00:00
0.071497
0.226328
2
0
348.0
317.0
296.0
False
0.369755
True
4
10:00:00
484.030992
207.526130
8
2016-08-12 11:00:00
0.461430
1.000060
0
0
3452.0
461.0
299.0
True
0.461430
True
4
11:00:00
NaN
NaN
8
2016-08-12 12:00:00
0.326755
0.999834
0
0
373.0
327.0
289.0
True
0.326755
True
4
12:00:00
NaN
NaN
8
2016-08-12 13:00:00
0.363093
0.999993
0
0
871.0
363.0
296.0
True
0.363093
True
4
13:00:00
NaN
NaN
8
2016-08-12 14:00:00
0.501344
0.999975
0
0
3304.0
501.0
208.0
True
0.501344
True
4
14:00:00
NaN
NaN
8
2016-08-12 15:00:00
0.362595
1.000106
0
0
1475.0
363.0
248.0
True
0.362595
True
4
15:00:00
NaN
NaN
8
2016-08-12 16:00:00
0.305348
1.000007
0
0
404.0
305.0
225.0
True
0.305348
True
4
16:00:00
NaN
NaN
8
2016-08-12 17:00:00
0.368282
0.999868
0
0
900.0
368.0
304.0
True
0.368282
True
4
17:00:00
NaN
NaN
8
2016-08-12 18:00:00
0.359484
1.000134
0
0
468.0
359.0
293.0
True
0.359484
True
4
18:00:00
NaN
NaN
8
2016-08-12 19:00:00
0.357033
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In [110]:
data_s.index.weekofyear
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In [151]:
Out[151]:
0
In [ ]:
Content source: azogue/enerpi
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