Comments (7)
Imagine:
model = Model()
model.run()
model.results # an xarray
circuit = Circuit(*models)
circuit.run()
circuit.results # an xarray
All the same syntax for different levels!
from neurolib.
Definitely tabulated values, for now, I'd use xarray
as you suggest. the integration could look like
circuit = Circuit(...)
circuit.run(...)
print(circuit.results)
# will print results in a xarray.DataField
circuit.save_results("results.hdf") # or .nc, or .pkl or .csv if only 2D
from neurolib.
actually, the model results are always 2D (number of state vector variables
x time
) so pandas
is enough there, only exploration results would need multi-d representation, so xarray
from neurolib.
I was actually tryig to avoid using pandas
because I'm not sure if it is fit for timeseries data (I might be wrong). I'm rather used to process results with it. xarr
however seems to be perfect for this job, especially if you can extend it many more dimensions in (state variables
x time
x parameters
) space. It seems kinda straightforward to save explorations in it or to dump data into an xarr
from a pypet
trajectory.
from neurolib.
yup, agreed, we need not use pandas
and use xarray
instead for greater compatibility of model results vs. exploration results
from neurolib.
in 6 months we are there :)
from neurolib.
Single simulations save their results into a .results
object, can be accessed via keys a la model['x']
or attributes, a la model.x.
now and also converted to an xr DataArray
via model.xr()
.
Exploration results are all saved in hdf
files and are also accessible via exploration.results
and a summary accessible as a pandas DataFrame
via exploration.dfResults
.
from neurolib.
Related Issues (20)
- Citation HOT 1
- Better error catching in exploration
- There are 0 valid individuals Mean score across population: nan HOT 7
- If set explicitly, local connectivity weights for ALNNode change after running the model HOT 3
- Try to make MultiModel faster HOT 1
- Neurolib satellite tutorial session at CNS*2022 HOT 20
- Testing: test_models with dynamic loading HOT 1
- TypeError: <class 'numpy.typing._dtype_like._SupportsDType'> is not a generic class HOT 3
- Help Using Neurolib: Finding an easy way to load new data for the model implementation HOT 1
- Question about the animation HOT 1
- Parameter exploration bug: PicklingError HOT 5
- Cmat is the same in two model instances, but models themselves are not HOT 1
- WW model is missing in example and test
- Documentation pipeline fails HOT 1
- model parameters and stimulation
- Parameters provided in examples (and default parameters) does not perform BOLD simulation correctly HOT 1
- Possible deprecation issues in the example file of multimodel_custom_model HOT 1
- Example files appear to be broken
- how to use EEG data with neurolib?
- Wong Wang working example to produce FCD matrix
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