Accessing Data

Filtering

PyProBE is designed to have a simple interface for filtering data. The filtering methods use chained notation and natural language to be approachable for users who are less familiar with Python. Procedure and experiment names are specified as strings by the user, either when the data is imported or in the README.yaml file.

It is possible to filter data by a number of methods:

• First by procedure:

  cell.procedure['Procedure Name']
  

• Then by experiment:

  cell.procedure['Procedure Name'].experiment('Experiment Name')
  

• Then by the numerical filter methods in the pyprobe.filters module:

  # Filter by cycle
  cell.procedure['Procedure Name'].experiment('Experiment Name').cycle(1)
  
  # Filter by step
  cell.procedure['Procedure Name'].experiment('Experiment Name').step(3)
  
  # Filter by cycle then step
  cell.procedure['Procedure Name'].experiment('Experiment Name').cycle(1).step(1)
  
  # Filter by step type
  cell.procedure['Procedure Name'].experiment('Experiment Name').charge(2)
  cell.procedure['Procedure Name'].experiment('Experiment Name').discharge(0)
  cell.procedure['Procedure Name'].experiment('Experiment Name').rest(1)
  cell.procedure['Procedure Name'].experiment('Experiment Name').chargeordischarge(1)
  
  # Filter by cycle then step type
  cell.procedure['Procedure Name'].experiment('Experiment Name').cycle(4).charge(2)
  cell.procedure['Procedure Name'].experiment('Experiment Name').cycle(2).discharge(0)
  

  Indices are zero-based, so the first cycle is 0, the first step is 0, etc. The index count is reset after applying every filter, i.e. the first discharge of any cycle is accessed with discharge(0).

RawData objects

Any filter applied to a cell returns a RawData object. This is a special type of Result object that is designed to hold cell experimental data processed by PyProBE. It therefore has all the attributes of the Result class. This includes:

• data attribute

  a polars Dataframe containing the filtered data.

• info attribute

  the cell’s info dictionary.

To access the data, you can access the full polars Dataframe:

dataframe = cell.procedure['Procedure Name'].experiment('Experiment Name').cycle(1).step(1).data

Or you can access individual columns as 1D numpy arrays by calling the get() method:

voltage, current = cell.procedure['Procedure Name'].experiment('Experiment Name').cycle(1).step(1).get("Voltage [V]", "Current [A]")

Accessing columns directly with this method is useful for converting data to unit variants:

current_mA = cell.procedure['Procedure Name'].experiment('Experiment Name').get("Current [mA]")

To retrieve more than one column, simply pass multiple column names to get() or use array() to return an N-dimensional array of the selected columns.