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Paired heavy-light modeling #92
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Will go along with https://github.com/matsengrp/dnsm-experiments-1/pull/66 |
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Looking nice. Just a few suggested clarifications. I wonder if the way you are using "token" is more specialized than it appears.
netam/sequences.py
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] | ||
STOP_CODONS = ["TAA", "TAG", "TGA"] | ||
# Each token in RESERVED_TOKENS will appear once in aa strings, and three times | ||
# in nt strings. | ||
TOKEN_TRANSLATIONS = {token * 3: token for token in RESERVED_TOKENS} |
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So these are actually only reserved token translations?
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Prepended RESERVED_
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Seems like as elegant as one could ask for given the setting 👍
netam/dxsm.py
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# # The following can be used when one wants a better traceback. | ||
# burrito = self.__class__(None, dataset, copy.deepcopy(self.model)) | ||
# return burrito.serial_find_optimal_branch_lengths(dataset, **optimization_kwargs) | ||
# TODO |
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Noting todo here
netam/framework.py
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if chosen_v_families is not None: | ||
chosen_v_families = set(chosen_v_families) | ||
pcp_df = pcp_df[pcp_df["v_family"].isin(chosen_v_families)] | ||
# TODO Does this seem like the right thing to do? |
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I think we want AND here, not or. We want the H and L to be restricted to the specified set.
(BTW, because I initiated this PR, I can't approve it 😁 ) |
Great! @willdumm if you approve then you should be able to squash n merge. |
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