eurobios-mews-labs / crep

This simple module aims at providing some function to tackle tabular data that have a continuous axis. In situations, this index can represent time, but this tool was originally developed to tackle rail way description.

Tabular data processing for Continuous REPresentation

pip install crep

This simple module aims at providing some function to tackle tabular data that have a continuous axis. In situations, this index can represent time, but this tool was originally developed to tackle rail way description.

This simple tools helps providing tools to represent a linear structure (cable, rail, beam, pipe) whose characteristics are piece-wize constant (of strongly heterogeneous length)

Basic usage

• Merge function merge together two dataframe

import pandas as pd

from crep import merge


df_left = pd.DataFrame(
    dict(id=[2, 2, 2],
         t1=[0, 100, 120],
         t2=[100, 120, 130],
         data1=[0.2, 0.1, 0.5])
)
df_right = pd.DataFrame(
    dict(id=[2, 2, 2],
         t1=[0, 80, 100],
         t2=[70, 100, 140],
         data2=[0.1, 0.3, 0.2])
)

ret = merge(data_left=df_left,
            data_right=df_right,
            id_continuous=["t1", "t2"],
            id_discrete=["id"],
            how="outer")

Yield the following result

Tools

Test if your data is admissible to the merge function, you can use the tools module

import pandas as pd

from crep import tools


df_admissible = pd.DataFrame(
    dict(id=[2, 2, 2],
         t1=[0, 100, 120],
         t2=[100, 120, 130],
         data1=[0.2, 0.1, 0.5])
)
df_not_admissible = pd.DataFrame(
    dict(id=[2, 2, 2],
         t1=[0, 90, 120],
         t2=[100, 120, 130],
         data1=[0.2, 0.1, 0.5])
)
# the second table is not admissible because for t in [90,100] two values are possible

assert tools.admissible_dataframe(
    df_admissible, id_continuous=["t1", "t2"],
    id_discrete=["id"])
assert not tools.admissible_dataframe(
    df_not_admissible, id_continuous=["t1", "t2"],
    id_discrete=["id"])
print(tools.sample_non_admissible_data(
    df_not_admissible, id_continuous=["t1", "t2"],
    id_discrete=["id"]
))
# id  t1   t2  data1
# 1   2  90  120    0.1

Acknowledgement

This implementation come from an SNCF DTIPG project and is developed and maintained by Mews Labs and SNCF DTIPG.