diff --git a/notebooks/01.0-mc-datasets/index.html b/notebooks/01.0-mc-datasets/index.html new file mode 100644 index 0000000..2064fb9 --- /dev/null +++ b/notebooks/01.0-mc-datasets/index.html @@ -0,0 +1,115782 @@ + + + +
+ + + +# OPTIONAL: Load the "autoreload" extension so that code can change. But blacklist large modules
+%load_ext autoreload
+%autoreload 2
+%aimport -pandas
+%aimport -torch
+%aimport -numpy
+%aimport -matplotlib
+%aimport -dask
+%aimport -tqdm
+%matplotlib inline
+
+ import xarray as xr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pathlib import Path
+from tqdm.auto import tqdm
+from IPython.display import display, HTML
+
+ import warnings
+warnings.simplefilter('once')
+warnings.simplefilter(action='ignore', category=FutureWarning)
+warnings.simplefilter(action='ignore', category=DeprecationWarning)
+
+ import holoviews as hv
+from holoviews import opts
+from holoviews.operation.datashader import datashade, dynspread
+hv.extension('bokeh', inline=True)
+from seq2seq_time.visualization.hv_ggplot import ggplot_theme
+hv.renderer('bokeh').theme = ggplot_theme
+hv.archive.auto()
+
+# holoview datashader timeseries options
+%opts RGB [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
+%opts Curve [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
+%opts Scatter [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
+%opts Layout [width=800 height=200]
+
+ Automatic capture is now enabled. [2020-11-02 07:46:01] ++
# # device = "cuda" if torch.cuda.is_available() else "cpu"
+# print(f'using {device}')
+
+window_past = 48*2
+window_future = 48
+batch_size = 4
+datasets_root = Path('../data/processed/')
+
+ from seq2seq_time.data.data import IMOSCurrentsVel, AppliancesEnergyPrediction, BejingPM25, GasSensor, MetroInterstateTraffic
+
+datasets = [IMOSCurrentsVel, BejingPM25, GasSensor, AppliancesEnergyPrediction, MetroInterstateTraffic, ]
+datasets
+
+ [seq2seq_time.data.data.IMOSCurrentsVel, + seq2seq_time.data.data.BejingPM25, + seq2seq_time.data.data.GasSensor, + seq2seq_time.data.data.AppliancesEnergyPrediction, + seq2seq_time.data.data.MetroInterstateTraffic]+
# plot a batch
+def plot_batch_y(ds, i):
+ x_past, y_past, x_future, y_future = ds.get_rows(i)
+ y = pd.concat([y_past, y_future])
+ p = hv.Scatter(y)
+
+ now = y_past.index[-1]
+ p *= hv.VLine(now).relabel('now').opts(color='red')
+ return p
+
+def plot_batches_y(dataset, window_past=window_past, window_future=window_future):
+ ds_name = type(dataset).__name__
+ opts=dict(width=200, height=100, xaxis=None, yaxis=None)
+ ds_train, ds_val, ds_test = d.to_datasets(window_past=window_past,
+ window_future=window_future)
+ n = 4
+ max_i = min(len(ds_train), len(ds_val), len(ds_test))
+ ii = list(np.linspace(0, max_i-10, n-1).astype(int)) + [-1]
+ l = hv.Layout()
+ for i in ii:
+ l += plot_batch_y(ds_train, i).opts(title=f'train {i}', **opts)
+ l += plot_batch_y(ds_val, i).opts(title=f'val {i}', **opts)
+ l += plot_batch_y(ds_test, i).opts(title=f'test {i}', **opts)
+ return l.opts(shared_axes=False, toolbar='right', title=f"{ds_name} freq={d.df.index.freq.freqstr}").cols(3)
+
+ for dataset in datasets:
+ d = dataset(datasets_root)
+ display(HTML(f"<h3>{dataset.__name__}</h3>"))
+ print(d.__doc__)
+ print(f'{len(d)} rows at freq{d.df.index.freq.freqstr}')
+ print('columns_forecast', d.columns_forecast)
+ print('columns_past', d.columns_past)
+ print('columns_target', d.columns_target)
+ print
+ display(d.df)
+
+
+
+ Current Speed at Two Rocks, Western Australia, with a water depth of 200 m. The mooring is located at Lat -31.719 Lon 115.03. Has tidal periods as features.
+
+ see:
+ - https://catalogue-imos.aodn.org.au/geonetwork/srv/api/records/bbfc20d3-0e98-40a8-bd8a-3f7717eafb6d
+ - http://thredds.aodn.org.au/thredds/fileServer/IMOS/ANMN/WA/WATR20/Velocity/
+ and http://thredds.aodn.org.au/thredds/catalog/IMOS/ANMN/WA/WATR20/Velocity/catalog.html
+ And https://en.wikipedia.org/wiki/Theory_of_tides
+
+40708 rows at freq30T
+columns_forecast ['M2', 'S2', 'N2', 'K2', 'K1', 'O1', 'P1', 'Q1', 'M4', 'M6', 'S4', 'MK3', 'MM', 'SSA', 'SA']
+columns_past {'UCUR', 'DEPTH', 'WCUR', 'VCUR', 'TEMP'}
+columns_target ['SPD']
+
+ | + | VCUR | +UCUR | +WCUR | +TEMP | +DEPTH | +M2 | +S2 | +N2 | +K2 | +K1 | +... | +P1 | +Q1 | +M4 | +M6 | +S4 | +MK3 | +MM | +SSA | +SA | +SPD | +
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TIME | ++ | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 2011-06-09 15:30:00 | +0.204021 | +-0.158203 | +-0.001695 | +20.830000 | +201.967545 | +0.104076 | +-0.422618 | +0.283656 | +0.306084 | +0.583774 | +... | +0.344861 | +-0.656472 | +-0.986246 | +-0.310180 | +-0.642788 | +-0.508251 | +0.997041 | +-0.908848 | +0.213485 | +0.258172 | +
| 2011-06-09 16:00:00 | +0.105118 | +-0.094232 | +-0.008524 | +20.863333 | +201.973831 | +0.269541 | +-0.572121 | +0.437399 | +0.464780 | +0.510936 | +... | +0.261805 | +-0.596573 | +-0.853767 | +-0.722387 | +-0.338557 | +-0.267432 | +0.997619 | +-0.909047 | +0.213252 | +0.142034 | +
| 2011-06-09 16:30:00 | +0.121471 | +-0.002748 | +-0.008752 | +20.940001 | +201.987396 | +0.502359 | +-0.764101 | +0.645351 | +0.675963 | +0.395088 | +... | +0.134032 | +-0.500549 | +-0.496055 | +-0.990386 | +0.171889 | +0.117156 | +0.998467 | +-0.909346 | +0.212902 | +0.133557 | +
| 2011-06-09 17:00:00 | +0.143786 | +-0.118950 | +0.004436 | +21.020000 | +201.993332 | +0.703208 | +-0.904008 | +0.813757 | +0.840830 | +0.272444 | +... | +0.003978 | +-0.397689 | +-0.014085 | +-0.714982 | +0.636277 | +0.484663 | +0.999293 | +-0.909644 | +0.212551 | +0.186944 | +
| 2011-06-09 17:30:00 | +0.068450 | +-0.074994 | +-0.011574 | +21.129999 | +202.001282 | +0.859309 | +-0.982309 | +0.932296 | +0.948083 | +0.145112 | +... | +-0.126143 | +-0.289399 | +0.471412 | +-0.047283 | +0.930175 | +0.781508 | +1.000096 | +-0.909941 | +0.212201 | +0.102691 | +
| ... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +
| 2014-05-16 01:30:00 | +-0.428396 | +0.090252 | +-0.013913 | +15.676667 | +201.782959 | +0.990709 | +0.641816 | +0.485959 | +-0.987066 | +-0.992151 | +... | +-0.480382 | +-0.502142 | +0.955280 | +0.895072 | +-0.170206 | +-0.105201 | +0.616808 | +-0.299223 | +0.591936 | +0.438596 | +
| 2014-05-16 02:00:00 | +-0.492208 | +0.106152 | +-0.014862 | +15.690000 | +201.785599 | +0.922364 | +0.422326 | +0.255914 | +-0.975840 | +-0.989219 | +... | +-0.362221 | +-0.399381 | +0.695027 | +0.357815 | +-0.634967 | +-0.474076 | +0.620623 | +-0.299906 | +0.591647 | +0.503897 | +
| 2014-05-16 02:30:00 | +-0.519550 | +0.110623 | +-0.004837 | +15.713333 | +201.775696 | +0.795324 | +0.174055 | +0.010186 | +-0.897749 | +-0.969268 | +... | +-0.237896 | +-0.291166 | +0.260675 | +-0.375769 | +-0.929589 | +-0.773832 | +0.624425 | +-0.300590 | +0.591359 | +0.531268 | +
| 2014-05-16 03:00:00 | +-0.508658 | +0.097735 | +-0.005429 | +15.770000 | +201.748566 | +0.617673 | +-0.086077 | +-0.236166 | +-0.758145 | +-0.932642 | +... | +-0.109523 | +-0.178975 | +-0.238974 | +-0.903175 | +-0.975129 | +-0.960765 | +0.628213 | +-0.301274 | +0.591069 | +0.517970 | +
| 2014-05-16 03:30:00 | +-0.526861 | +0.112729 | +-0.001550 | +15.810000 | +201.721451 | +0.477724 | +-0.257985 | +-0.394165 | +-0.637094 | +-0.899834 | +... | +-0.022783 | +-0.102779 | +-0.551476 | +-1.008378 | +-0.866887 | +-1.013969 | +0.630731 | +-0.301729 | +0.590877 | +0.538786 | +
51433 rows × 21 columns
+
+ PM2.5 data of US Embassy in Beijing. This measures smoke as well as some pollen, fog, and dust particles of a certain size. Weather data from a nearby airport are included.
+
+
+ See:
+ - http://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
+ - https://en.wikipedia.org/wiki/Particulates
+
+41757 rows at freqH
+columns_forecast ['month', 'day', 'week', 'hour', 'minute', 'dayofweek']
+columns_past {'cbwd', 'Is', 'Iws', 'PRES', 'TEMP', 'DEWP', 'Ir'}
+columns_target ['log_pm2.5']
+
+ | + | DEWP | +TEMP | +PRES | +cbwd | +Iws | +Is | +Ir | +log_pm2.5 | +month | +day | +week | +hour | +minute | +dayofweek | +
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2010-01-02 00:00:00+08:00 | +-16.0 | +-4.0 | +1020.0 | +SE | +1.79 | +0.0 | +0.0 | +4.859812 | +1 | +2 | +53 | +0 | +0 | +5 | +
| 2010-01-02 01:00:00+08:00 | +-15.0 | +-4.0 | +1020.0 | +SE | +2.68 | +0.0 | +0.0 | +4.997212 | +1 | +2 | +53 | +1 | +0 | +5 | +
| 2010-01-02 02:00:00+08:00 | +-11.0 | +-5.0 | +1021.0 | +SE | +3.57 | +0.0 | +0.0 | +5.068904 | +1 | +2 | +53 | +2 | +0 | +5 | +
| 2010-01-02 03:00:00+08:00 | +-7.0 | +-5.0 | +1022.0 | +SE | +5.36 | +1.0 | +0.0 | +5.198497 | +1 | +2 | +53 | +3 | +0 | +5 | +
| 2010-01-02 04:00:00+08:00 | +-7.0 | +-5.0 | +1022.0 | +SE | +6.25 | +2.0 | +0.0 | +4.927254 | +1 | +2 | +53 | +4 | +0 | +5 | +
| ... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +
| 2014-12-31 19:00:00+08:00 | +-23.0 | +-2.0 | +1034.0 | +NW | +231.97 | +0.0 | +0.0 | +2.079443 | +12 | +31 | +1 | +19 | +0 | +2 | +
| 2014-12-31 20:00:00+08:00 | +-22.0 | +-3.0 | +1034.0 | +NW | +237.78 | +0.0 | +0.0 | +2.302586 | +12 | +31 | +1 | +20 | +0 | +2 | +
| 2014-12-31 21:00:00+08:00 | +-22.0 | +-3.0 | +1034.0 | +NW | +242.70 | +0.0 | +0.0 | +2.302586 | +12 | +31 | +1 | +21 | +0 | +2 | +
| 2014-12-31 22:00:00+08:00 | +-22.0 | +-4.0 | +1034.0 | +NW | +246.72 | +0.0 | +0.0 | +2.079443 | +12 | +31 | +1 | +22 | +0 | +2 | +
| 2014-12-31 23:00:00+08:00 | +-21.0 | +-3.0 | +1034.0 | +NW | +249.85 | +0.0 | +0.0 | +2.484907 | +12 | +31 | +1 | +23 | +0 | +2 | +
43800 rows × 14 columns
+
+ A metal oxide (MOX) gas sensor exposed during 3 weeks to mixtures of carbon monoxide and humid synthetic air in a gas chamber.
+
+ See: http://archive.ics.uci.edu/ml/datasets/Gas+sensor+array+temperature+modulation
+
+295653 rows at freq300L
+columns_forecast ['Flow rate (mL/min)', 'Heater voltage (V)']
+columns_past {'Temperature (C)', 'CO (ppm)', 'Humidity (%r.h.)'}
+columns_target ['R1 (MOhm)']
+
+ | + | CO (ppm) | +Humidity (%r.h.) | +Temperature (C) | +Flow rate (mL/min) | +Heater voltage (V) | +R1 (MOhm) | +
|---|---|---|---|---|---|---|
| Time (s) | ++ | + | + | + | + | + |
| 2016-10-04 10:41:24.000 | +0.0 | +54.6258 | +25.3178 | +242.5724 | +0.2030 | +55.1483 | +
| 2016-10-04 10:41:24.300 | +0.0 | +52.6300 | +25.3000 | +241.5326 | +0.2020 | +70.7619 | +
| 2016-10-04 10:41:24.600 | +0.0 | +52.6300 | +25.3000 | +241.2315 | +0.2020 | +68.5571 | +
| 2016-10-04 10:41:24.900 | +0.0 | +52.6300 | +25.3000 | +240.9315 | +0.2010 | +69.1448 | +
| 2016-10-04 10:41:25.200 | +0.0 | +52.6300 | +25.3000 | +240.6521 | +0.2007 | +61.4100 | +
| ... | +... | +... | +... | +... | +... | +... | +
| 2016-10-05 11:56:32.400 | +0.0 | +63.0900 | +25.3800 | +0.0000 | +0.2000 | +4.0125 | +
| 2016-10-05 11:56:32.700 | +0.0 | +63.0900 | +25.3800 | +0.0000 | +0.2000 | +3.3697 | +
| 2016-10-05 11:56:33.000 | +0.0 | +63.0900 | +25.3800 | +0.0000 | +0.2000 | +2.8750 | +
| 2016-10-05 11:56:33.300 | +0.0 | +63.0900 | +25.3800 | +0.0000 | +0.2000 | +2.4623 | +
| 2016-10-05 11:56:33.600 | +0.0 | +63.0900 | +25.3800 | +0.0000 | +0.2000 | +2.1432 | +
303033 rows × 6 columns
+
+ Appliances energy use in a low energy building.
+
+ See: https://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction
+
+19735 rows at freq10T
+columns_forecast ['month', 'day', 'week', 'hour', 'minute', 'dayofweek']
+columns_past {'RH_3', 'T9', 'T5', 'T2', 'Tdewpoint', 'T8', 'lights', 'rv2', 'RH_out', 'T1', 'Visibility', 'RH_6', 'T7', 'T_out', 'RH_8', 'Windspeed', 'T3', 'RH_9', 'T4', 'RH_4', 'RH_5', 'RH_2', 'RH_7', 'RH_1', 'rv1', 'T6', 'Press_mm_hg'}
+columns_target ['log_Appliances']
+
+ | + | lights | +T1 | +RH_1 | +T2 | +RH_2 | +T3 | +RH_3 | +T4 | +RH_4 | +T5 | +... | +Tdewpoint | +rv1 | +rv2 | +log_Appliances | +month | +day | +week | +hour | +minute | +dayofweek | +
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | ++ | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 2016-01-11 17:00:00 | +30 | +19.890000 | +47.596667 | +19.200000 | +44.790000 | +19.790000 | +44.730000 | +19.000000 | +45.566667 | +17.166667 | +... | +5.300000 | +13.275433 | +13.275433 | +4.094345 | +1 | +11 | +2 | +17 | +0 | +0 | +
| 2016-01-11 17:10:00 | +30 | +19.890000 | +46.693333 | +19.200000 | +44.722500 | +19.790000 | +44.790000 | +19.000000 | +45.992500 | +17.166667 | +... | +5.200000 | +18.606195 | +18.606195 | +4.094345 | +1 | +11 | +2 | +17 | +10 | +0 | +
| 2016-01-11 17:20:00 | +30 | +19.890000 | +46.300000 | +19.200000 | +44.626667 | +19.790000 | +44.933333 | +18.926667 | +45.890000 | +17.166667 | +... | +5.100000 | +28.642668 | +28.642668 | +3.912023 | +1 | +11 | +2 | +17 | +20 | +0 | +
| 2016-01-11 17:30:00 | +40 | +19.890000 | +46.066667 | +19.200000 | +44.590000 | +19.790000 | +45.000000 | +18.890000 | +45.723333 | +17.166667 | +... | +5.000000 | +45.410389 | +45.410389 | +3.912023 | +1 | +11 | +2 | +17 | +30 | +0 | +
| 2016-01-11 17:40:00 | +40 | +19.890000 | +46.333333 | +19.200000 | +44.530000 | +19.790000 | +45.000000 | +18.890000 | +45.530000 | +17.200000 | +... | +4.900000 | +10.084097 | +10.084097 | +4.094345 | +1 | +11 | +2 | +17 | +40 | +0 | +
| ... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +
| 2016-05-27 17:20:00 | +0 | +25.566667 | +46.560000 | +25.890000 | +42.025714 | +27.200000 | +41.163333 | +24.700000 | +45.590000 | +23.200000 | +... | +13.333333 | +43.096812 | +43.096812 | +4.605170 | +5 | +27 | +21 | +17 | +20 | +4 | +
| 2016-05-27 17:30:00 | +0 | +25.500000 | +46.500000 | +25.754000 | +42.080000 | +27.133333 | +41.223333 | +24.700000 | +45.590000 | +23.230000 | +... | +13.300000 | +49.282940 | +49.282940 | +4.499810 | +5 | +27 | +21 | +17 | +30 | +4 | +
| 2016-05-27 17:40:00 | +10 | +25.500000 | +46.596667 | +25.628571 | +42.768571 | +27.050000 | +41.690000 | +24.700000 | +45.730000 | +23.230000 | +... | +13.266667 | +29.199117 | +29.199117 | +5.598422 | +5 | +27 | +21 | +17 | +40 | +4 | +
| 2016-05-27 17:50:00 | +10 | +25.500000 | +46.990000 | +25.414000 | +43.036000 | +26.890000 | +41.290000 | +24.700000 | +45.790000 | +23.200000 | +... | +13.233333 | +6.322784 | +6.322784 | +6.040255 | +5 | +27 | +21 | +17 | +50 | +4 | +
| 2016-05-27 18:00:00 | +10 | +25.500000 | +46.600000 | +25.264286 | +42.971429 | +26.823333 | +41.156667 | +24.700000 | +45.963333 | +23.200000 | +... | +13.200000 | +34.118851 | +34.118851 | +6.063785 | +5 | +27 | +21 | +18 | +0 | +4 | +
19735 rows × 34 columns
+
+ Hourly traffic volume for Interstate 94 (I-94) in the U.S. state of Minnesota. Includes weather and holiday features from 2012-2018.
+
+ See: https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume
+
+40575 rows at freqH
+columns_forecast ['holiday', 'month', 'day', 'week', 'hour', 'minute', 'dayofweek']
+columns_past {'temp', 'snow_1h', 'weather_main', 'rain_1h', 'clouds_all', 'weather_description'}
+columns_target ['traffic_volume']
+
+ | + | holiday | +temp | +rain_1h | +snow_1h | +clouds_all | +weather_main | +weather_description | +traffic_volume | +month | +day | +week | +hour | +minute | +dayofweek | +
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date_time | ++ | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 2012-10-02 09:00:00 | +True | +288.28 | +0.0 | +0.0 | +40.0 | +Clouds | +scattered clouds | +5545.0 | +10 | +2 | +40 | +9 | +0 | +1 | +
| 2012-10-02 10:00:00 | +True | +289.36 | +0.0 | +0.0 | +75.0 | +Clouds | +broken clouds | +4516.0 | +10 | +2 | +40 | +10 | +0 | +1 | +
| 2012-10-02 11:00:00 | +True | +289.58 | +0.0 | +0.0 | +90.0 | +Clouds | +overcast clouds | +4767.0 | +10 | +2 | +40 | +11 | +0 | +1 | +
| 2012-10-02 12:00:00 | +True | +290.13 | +0.0 | +0.0 | +90.0 | +Clouds | +overcast clouds | +5026.0 | +10 | +2 | +40 | +12 | +0 | +1 | +
| 2012-10-02 13:00:00 | +True | +291.14 | +0.0 | +0.0 | +75.0 | +Clouds | +broken clouds | +4918.0 | +10 | +2 | +40 | +13 | +0 | +1 | +
| ... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +
| 2018-09-30 19:00:00 | +True | +283.45 | +0.0 | +0.0 | +75.0 | +Clouds | +broken clouds | +3543.0 | +9 | +30 | +39 | +19 | +0 | +6 | +
| 2018-09-30 20:00:00 | +True | +282.76 | +0.0 | +0.0 | +90.0 | +Clouds | +overcast clouds | +2781.0 | +9 | +30 | +39 | +20 | +0 | +6 | +
| 2018-09-30 21:00:00 | +True | +282.73 | +0.0 | +0.0 | +90.0 | +Thunderstorm | +proximity thunderstorm | +2159.0 | +9 | +30 | +39 | +21 | +0 | +6 | +
| 2018-09-30 22:00:00 | +True | +282.09 | +0.0 | +0.0 | +90.0 | +Clouds | +overcast clouds | +1450.0 | +9 | +30 | +39 | +22 | +0 | +6 | +
| 2018-09-30 23:00:00 | +True | +282.12 | +0.0 | +0.0 | +90.0 | +Clouds | +overcast clouds | +954.0 | +9 | +30 | +39 | +23 | +0 | +6 | +
52551 rows × 14 columns
+
+
+
+
+ # View train, test, val splits
+for dataset in datasets:
+ ds_name = type(dataset).__name__
+ d = dataset(datasets_root)
+ print(d)
+ display(plot_batches_y(d))
+
+ <IMOSCurrentsVel (51433, 21)> ++
<BejingPM25 (43800, 14)> ++
<GasSensor (303033, 6)> ++
<AppliancesEnergyPrediction (19735, 34)> ++
<MetroInterstateTraffic (52551, 14)> ++
# def plot_batch_x(ds, i):
+# """Plot input features"""
+# x_past, y_past, x_future, y_future = ds.get_rows(i)
+# x = pd.concat([x_past, x_future])
+# p = hv.NdOverlay({
+# col: hv.Curve(x[col]) for col in x.columns
+# }, kdims='column')
+# now = y_past.index[-1]
+# p *= hv.VLine(now).relabel('now').opts(color='red')
+# return p
+
+# def plot_batches_x(d):
+# """Plot input features for multiple batch"""
+# ds_train, ds_val, ds_test = d.to_datasets(window_past=window_past,
+# window_future=window_future)
+# l = plot_batch_x(ds_train, 10) + plot_batch_x(ds_val, 10) + plot_batch_x(ds_test, 10)
+# l = l.cols(1).opts(shared_axes=False, title=f'{type(d).__name__}')
+# return l
+
+ # ds_train, ds_val, ds_test = d.to_datasets(window_past=window_past,
+# window_future=window_future)
+
+ # # View input columns
+# for dataset in datasets:
+# d = dataset(datasets_root)
+# display(plot_batches_x(d))
+
+ hv.archive.export()
+hv.archive.last_export_status()
+
+ Export name: '01.0-mc-datasets' +Directory '/media/wassname/Storage5/projects2/3ST/seq2seq-time/notebooks' + +If no output appears, please check holoviews.archive.last_export_status() ++
+
+
+
+