eivindtostesen / hierarchical_peak_finding Goto Github PK
View Code? Open in Web Editor NEWData analysis of peak and valley regions
License: GNU General Public License v3.0
Data analysis of peak and valley regions
License: GNU General Public License v3.0
Valley finding has not yet been implemented
Please create tutorial(s) on how to make dataframes with peaks_in_pandas
and peaks_in_polars
The ongoing move described in issue #15 (from label-indexed, resampled data to position-indexed data) also allows the algorithms to be simplified to fewer passes through data.
The data set Y could be, for example, a time series of stock prices. The P = PeakTree(Y)
object is metadata that describes features of Y.
P._data
is just Y sorted differently. And P._data
is typically almost equal in size to Y. So there is redundancy in storing both Y and P._data
.SliceStr
that is independent of Y and NumSlice
that has a reference to Y and depends on its existence. Should PeakTree
also have a reference to Y instead of P._data
?NumSlice
for each tree node and they should not all serialize Y.P._data
in its current form may not contain sufficient information. But Y will.PeakTree.filter
is currently the method for finding or selecting peaks. It has a single parameter maxsize
, but it is intended to have more parameters. Some of these should be upper or lower bounds on some node properties.
But a conflict arises with new parameters. Some filtering (including bounds) is a function of each node independently. Other filtering (including maxsize
) depends on the whole input list, because it may involve some kind of sorting. For example, "smaller than 5" is very different from "smaller than the others".
The order of filtering steps matters if sorting is involved, unlike independent filters that logically are applied as a series of ANDs.
In my opinion, rather than specifying the order of filters as an argument to filter
, the order of doing things should be specified by the order of function/method calls.
Please create peaks_in_plotly
hierarchical_peak_finding/peaks_in_pandas.py
Lines 71 to 76 in 97b7201
Lambdas defined in for loop become equal
Towards an eval-uable repr, alternate constructors and different str formats (for Peaktree and HyperPeakTree).
The data set Y could be, for example, a time series of stock prices. The P = PeakTree(Y) object is a tree structure that describes features of Y.
Position-based vs label-based indexing is an issue in three cases:
P._index
).In previous versions
In future versions, maybe
Develop errors / exceptions.
Peaks_in_pandas.py has repetitive code and does not seem very pandorable.
Could be nice to have: Command line interface
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