MeaPy

Python API Wrapper for Measurement Data

Vision

MeaPy wants to be a easy-to-use and conformable API for working with measurement data in den Big Test Data environment.

Getting Started

pip install meapy

Usage

To create a meapy session, you need to connect to the MaDaM system.n

from meapy import MeaPy, MeasurementList, LoadingConfig

# "Basic " is the content if the HTTP Authorization-Header. In this example it is the Basic Authentication Header for user:password
mp = MeaPy("http://madam-docker.int.kistler.com:8081/", "Basic dXNlcjpwYXNzd29yZA==")

Search

# direct search (by default limited to 100 results)
result = mp.search("test")
# result is a list of meapy.Measurement

# search and iteration over the whole result set
ml = MeasurementList(mp)
count = 0
for mea in ml.items('Station.Id="d4f1ad55-72d5-403c-81b8-73b2942b58f4"'):
    count+=1
print(count)

Loading Signals

The measurements that were returned from the search can be used to load the signals directly from the MaDaM system.

result = mp.search("test", limit = 1)
measurement = result[0]

# load a measurement
config = LoadingConfig()
config.withSignals(['time'])
signals = mp.load(measurement, config)
# signals is a list of meapy.SignalData that contains the information for the requested channels

Update Measurement Metadata

result = mp.search("test", limit = 1)
measurement = result[0]

# update the metadata with some additional data
data = {'someField': 'someValue'}
mp.update(measurement, data)

Upload a new Measurement

# upload a pandas DataFrame to MaDaM with a given name.
df = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list('ABCD'))

# you can also add some additional metadata here
data = {'someField': 'someValue'}
mp.upload("random-data", df, data)
# after successful upload, you can find the new measurement "random-data.csv"