0.1 Create or load a 'batch' file

This is what I planned to call a journal. An example is infotable.

from_db: create from database
from_file: load from file
create: make manually
to_file: save
paginate: create a suitable folder structure

class BaseJournal:
    """A journal keeps track of the details of the experiment.

    The journal should at a mimnimum contain information about the name and
    project the experiment has."""

    def __init__(self):
        self.pages = None  # pandas.DataFrame
        self.name = None
        self.project = None
        self.file_name = None
        self.parameters = None # could be its own class or a dict

    def from_db(self):
        pass

    def from_file(self, file_name):
        pass

    def to_file(self, file_name=None):
        pass

    def paginate(self):
        pass

-example session

experiment = CyclingExperiment()
#----setting some prms------
experiment.prms.Reader.some_parameter = True  # This will be valid globally
# or
cellpy.prms.Reader.some_parameter = True  # This will be valid globally
# or
experiment.dynamic_linking = True  # This is only valid for this instance

#----generating the journal--
experiment.journal.from_db(project="Nano", name="exp001", batch_col=5)

...

print(experiment.journal)
experiment.journal.pages.head(10)  # show first 10 rows of the pages DataFrame
experiment.journal.to_file()



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0.2 Create appropriate cellpy-files

This applies if they are not made already or if they need updating (i.e. could have a method / function for this, for example .update(options) or cellpy.cellreader.update(journal))

-example session continued

# checking if the cellpy-files exist and loading step tables
experiment.link()
# or just run an update anyway
experiment.update()
experiment.status()



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1. Examining the experiment

1.1 Looking at the summaries

-example session continued

(batch analysing)

# forgotten what kind of analysers and engines this contains
experiment.info()
# journal:
#   standard
# ...
ocv_analyser = OCVAnalyser(experiment)
ocv_analyser.do()

(exploring)

plotter = Plotter(experiment)
plotter.do()

from cellpy.batch.tools import get_cycles, get_ica, get_ocv_rlx, combine
voltage_cycles = get_cycles(experiment.data, "2018_very_good_cell_01", (1,2,3), tidy=True)
voltage_cycles.head()

# or work directly on the cellpydata
voltage_cycles = experiment.data["2018_very_good_cell_01"].get_cap(cycles = (1,2,3))
# etc.

charge_cap_frame = combine(experiment, "charge_capacity")
discharge_cap_frame = combine(experiment, "discharge_capacity")



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2.0 Exporting

csv_exporter = CSVExporter(experiment)
csv_exporter.do()

3.0 Reporting

html_reporter = HTMLReporter(experiment)
html_reporter.do()