Load Measurement Data (Time Series)

Load Measurement Data (Time Series)#

In this example Notebook, we show you how to load time series (channel) data from your Peak ODS Server.

The first sections are on initializing and connecting. The fun starts with “Load Measurement”.

Dependencies for this notebook#

The ASAM ODSBox contain some functionality that wraps the ODS HTTP API making using Python easier ;-)

It contains:

  • http wrapper implemented using protobuf and requests

  • utility that converts ODS DataMatrices into pandas.DataFrame

try:  
  import odsbox
except:
  !pip install odsbox 

from odsbox.con_i import ConI
from odsbox.submatrix_to_pandas import submatrix_to_pandas

Connect to ASAM ODS server#

The ODS HTTP API is a session based API. The session ID is called conI in the ODS documentation. The ASAM ODSBox uses con_i as API object representing the session. Close this session to release the connection license. Otherwise the session will be auto closed after 30 minutes of inactivity.

conI = ConI(url='http://79.140.180.128:10032/api', auth=('Demo','mdm'))

Load Measurement#

Query for available measurement data#

Measurement (or time series) data is contained in a structure called ‘Submatrix’ containing the individual channels (columns) of the measurement. In the example below the first 10 submatrices of some MDF4 files (see query pattern) are requested from the server. Let’s pick the first submatrix of that list for further data exploration.

sms = conI.query_data({
#    "SubMatrix": {"measurement.test.Name": {"$like": "Profile*.mf4"}},
    "SubMatrix": {"measurement.Name": {"$like": "Profile*"}},
    "$attributes": {
        "Name": 1,
        "Id": 1
    },
    "$options": {"$rowlimit": 10}
})

print(sms)

# just pick the first one
submatrix_id = sms["SubMatrix.Id"].iloc[0]
print("Submatrix id: " + str(submatrix_id))

  SubMatrix.Name  SubMatrix.Id
0     Profile_52           282
1     Profile_06           330
2     Profile_14           319
3     Profile_80           308
4     Profile_71           277
5     Profile_64           296
6     Profile_45           301
7     Profile_53           311
8     Profile_56           297
9     Profile_42           287
Submatrix id: 282

Load measurement data and convert to DataFrame#

From our imported library we use ‘submatrix_to_dataframe’ to get a pandas.DataFrame from that submatrix we’ve selected above…

df = submatrix_to_pandas(conI, submatrix_id)

if "Time" in df.columns:
    df.set_index("Time", inplace=True)
elif "time" in df.columns:
    df.set_index("time", inplace=True)

df.head()
U_q Coolant Stator_winding U_d Stator_tooth Motor_speed I_d I_q Pm Stator_yoke Ambient Torque
Time
0.0 -1.870446 78.804928 53.393323 0.627027 55.588612 2.761764 -2.001388 1.094317 49.695740 60.426506 25.321585 3.138413e-13
0.5 3.747699 78.859141 53.460695 -2.550798 55.659321 102.164216 -6.588812 21.266870 49.689940 60.490822 25.308411 1.465798e+01
1.0 13.771337 78.900886 53.480459 -9.704846 55.698835 314.802839 -11.969288 41.992287 49.675229 60.542482 25.295898 2.993536e+01
1.5 26.851160 78.939788 53.451930 -19.583736 55.723997 608.984106 -16.067978 57.526612 49.686365 60.578285 25.275402 4.141312e+01
2.0 42.088467 78.966302 53.467553 -31.885562 55.743452 961.552082 -19.428698 69.952405 49.692206 60.605028 25.263575 5.064337e+01

Working with the DataFrame#

Now that the data is in a DataFrame, you can use all operations supported on DataFrames. So let’s dump the content of the DataFrame:

df.describe()
U_q Coolant Stator_winding U_d Stator_tooth Motor_speed I_d I_q Pm Stator_yoke Ambient Torque
count 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000 3726.000000
mean 67.858769 44.695021 84.599968 -18.567216 72.656560 2664.812358 -82.763015 25.978936 68.360572 61.577348 26.270603 21.205737
std 38.303788 29.715035 24.038284 68.659465 20.899099 1582.960313 53.083745 126.220274 5.527616 21.848725 0.219805 104.113044
min -1.870446 21.892367 53.393323 -130.287115 49.406596 2.761764 -220.065137 -286.302859 49.456254 39.452287 25.258767 -240.357719
25% 34.314099 23.865233 62.909840 -69.432621 53.301582 1285.600032 -119.317662 -51.186357 65.366317 42.742347 26.277492 -51.862707
50% 63.728396 24.331117 77.509271 -28.315255 63.334331 2275.865870 -83.930571 30.829907 69.307767 49.421987 26.364420 26.210855
75% 104.456075 87.694295 105.292420 42.223311 94.827537 4200.568607 -37.703737 109.148789 71.763340 82.485244 26.382636 88.885470
max 131.007577 93.044237 129.505462 129.192577 109.745895 5878.078608 0.006143 295.802479 78.217617 99.151240 26.384509 245.538906

… and we can plot some curves (remember we set ‘time’ to be the index at the beginning) …

_ = df.plot(kind="line", subplots=True, layout=(6,3), sharex=True)
../_images/1576178c514aeac3c71e98fc9418252bb766cd21c5d451bd39ce55b802a80c33.png

… or only one curve …

_ = df[df.columns[0]].plot(kind="line")
../_images/dba0f535cf558b4044b338550b4cfdd911c41ce8806b833f98548133af7940ab.png

… or get some information of the index channel - time axis should be monotonic ;-)

df.index.is_monotonic_increasing

True

Close Session#

Don’t forget to close the session to release the connection license. Otherwise the session will be auto closed after 30 minutes of inactivity.

conI.logout()

License#

Copyright © 2024 Peak Solution GmbH

The training material in this repository is licensed under a Creative Commons BY-NC-SA 4.0 license. See LICENSE file for more information.

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