Remove unused module

* Remove a module that is no longer in use

1 files changed, 0 insertions, 244 deletions

diff --git a/gn3/computations/partial_correlations_optimised.py b/gn3/computations/partial_correlations_optimised.py
deleted file mode 100644
index 601289c..0000000
--- a/gn3/computations/partial_correlations_optimised.py
+++ /dev/null
@@ -1,244 +0,0 @@
-"""
-This contains an optimised version of the
- `gn3.computations.partial_correlations.partial_correlations_entry`
-function.
-"""
-from functools import partial
-from typing import Any, Tuple
-
-from gn3.settings import TEXTDIR
-from gn3.function_helpers import  compose
-from gn3.db.partial_correlations import traits_info, traits_data
-from gn3.db.species import species_name, translate_to_mouse_gene_id
-from gn3.db.traits import export_informative, retrieve_trait_dataset
-from gn3.db.correlations import (
-    get_filename,
-    check_for_literature_info,
-    check_symbol_for_tissue_correlation)
-from gn3.computations.partial_correlations import (
-    fix_samples,
-    partial_corrs,
-    control_samples,
-    trait_for_output,
-    find_identical_traits,
-    tissue_correlation_by_list,
-    literature_correlation_by_list)
-
-def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911]
-        conn: Any, primary_trait_name: str,
-        control_trait_names: Tuple[str, ...], method: str,
-        criteria: int, target_db_name: str) -> dict:
-    """
-    This is the 'ochestration' function for the partial-correlation feature.
-
-    This function will dispatch the functions doing data fetches from the
-    database (and various other places) and feed that data to the functions
-    doing the conversions and computations. It will then return the results of
-    all of that work.
-
-    This function is doing way too much. Look into splitting out the
-    functionality into smaller functions that do fewer things.
-    """
-    threshold = 0
-    corr_min_informative = 4
-
-    all_traits = traits_info(
-        conn, threshold, (primary_trait_name,) + control_trait_names)
-    all_traits_data = traits_data(conn, all_traits)
-
-    # primary_trait = retrieve_trait_info(threshold, primary_trait_name, conn)
-    primary_trait = tuple(
-        trait for trait in all_traits
-        if trait["trait_fullname"] == primary_trait_name)[0]
-    group = primary_trait["db"]["group"]
-    # primary_trait_data = retrieve_trait_data(primary_trait, conn)
-    primary_trait_data = all_traits_data[primary_trait["trait_name"]]
-    primary_samples, primary_values, _primary_variances = export_informative(
-        primary_trait_data)
-
-    # cntrl_traits = tuple(
-    #     retrieve_trait_info(threshold, trait_full_name, conn)
-    #     for trait_full_name in control_trait_names)
-    # cntrl_traits_data = tuple(
-    #     retrieve_trait_data(cntrl_trait, conn)
-    #     for cntrl_trait in cntrl_traits)
-    cntrl_traits = tuple(
-        trait for trait in all_traits
-        if trait["trait_fullname"] != primary_trait_name)
-    cntrl_traits_data = tuple(
-        data for trait_name, data in all_traits_data.items()
-        if trait_name != primary_trait["trait_name"])
-    species = species_name(conn, group)
-
-    (cntrl_samples,
-     cntrl_values,
-     _cntrl_variances,
-     _cntrl_ns) = control_samples(cntrl_traits_data, primary_samples)
-
-    common_primary_control_samples = primary_samples
-    fixed_primary_vals = primary_values
-    fixed_control_vals = cntrl_values
-    if not all(cnt_smp == primary_samples for cnt_smp in cntrl_samples):
-        (common_primary_control_samples,
-         fixed_primary_vals,
-         fixed_control_vals,
-         _primary_variances,
-         _cntrl_variances) = fix_samples(primary_trait, cntrl_traits)
-
-    if len(common_primary_control_samples) < corr_min_informative:
-        return {
-            "status": "error",
-            "message": (
-                f"Fewer than {corr_min_informative} samples data entered for "
-                f"{group} dataset. No calculation of correlation has been "
-                "attempted."),
-            "error_type": "Inadequate Samples"}
-
-    identical_traits_names = find_identical_traits(
-        primary_trait_name, primary_values, control_trait_names, cntrl_values)
-    if len(identical_traits_names) > 0:
-        return {
-            "status": "error",
-            "message": (
-                f"{identical_traits_names[0]} and {identical_traits_names[1]} "
-                "have the same values for the {len(fixed_primary_vals)} "
-                "samples that will be used to compute the partial correlation "
-                "(common for all primary and control traits). In such cases, "
-                "partial correlation cannot be computed. Please re-select your "
-                "traits."),
-            "error_type": "Identical Traits"}
-
-    input_trait_geneid = primary_trait.get("geneid", 0)
-    input_trait_symbol = primary_trait.get("symbol", "")
-    input_trait_mouse_geneid = translate_to_mouse_gene_id(
-        species, input_trait_geneid, conn)
-
-    tissue_probeset_freeze_id = 1
-    db_type = primary_trait["db"]["dataset_type"]
-
-    if db_type == "ProbeSet" and method.lower() in (
-            "sgo literature correlation",
-            "tissue correlation, pearson's r",
-            "tissue correlation, spearman's rho"):
-        return {
-            "status": "error",
-            "message": (
-                "Wrong correlation type: It is not possible to compute the "
-                f"{method} between your trait and data in the {target_db_name} "
-                "database. Please try again after selecting another type of "
-                "correlation."),
-            "error_type": "Correlation Type"}
-
-    if (method.lower() == "sgo literature correlation" and (
-            bool(input_trait_geneid) is False or
-            check_for_literature_info(conn, input_trait_mouse_geneid))):
-        return {
-            "status": "error",
-            "message": (
-                "No Literature Information: This gene does not have any "
-                "associated Literature Information."),
-            "error_type": "Literature Correlation"}
-
-    if (
-            method.lower() in (
-                "tissue correlation, pearson's r",
-                "tissue correlation, spearman's rho")
-            and bool(input_trait_symbol) is False):
-        return {
-            "status": "error",
-            "message": (
-                "No Tissue Correlation Information: This gene does not have "
-                "any associated Tissue Correlation Information."),
-            "error_type": "Tissue Correlation"}
-
-    if (
-            method.lower() in (
-                "tissue correlation, pearson's r",
-                "tissue correlation, spearman's rho")
-            and check_symbol_for_tissue_correlation(
-                conn, tissue_probeset_freeze_id, input_trait_symbol)):
-        return {
-            "status": "error",
-            "message": (
-                "No Tissue Correlation Information: This gene does not have "
-                "any associated Tissue Correlation Information."),
-            "error_type": "Tissue Correlation"}
-
-    target_dataset = retrieve_trait_dataset(
-        ("Temp" if "Temp" in target_db_name else
-         ("Publish" if "Publish" in target_db_name else
-          "Geno" if "Geno" in target_db_name else "ProbeSet")),
-        {"db": {"dataset_name": target_db_name}, "trait_name": "_"},
-        threshold,
-        conn)
-
-    database_filename = get_filename(conn, target_db_name, TEXTDIR)
-    _total_traits, all_correlations = partial_corrs(
-        conn, common_primary_control_samples, fixed_primary_vals,
-        fixed_control_vals, len(fixed_primary_vals), species,
-        input_trait_geneid, input_trait_symbol, tissue_probeset_freeze_id,
-        method, {**target_dataset, "dataset_type": target_dataset["type"]}, database_filename)
-
-
-    def __make_sorter__(method):
-        def __sort_6__(row):
-            return row[6]
-
-        def __sort_3__(row):
-            return row[3]
-
-        if "literature" in method.lower():
-            return __sort_6__
-
-        if "tissue" in method.lower():
-            return __sort_6__
-
-        return __sort_3__
-
-    # sorted_correlations = sorted(
-    #     all_correlations, key=__make_sorter__(method))
-
-    add_lit_corr_and_tiss_corr = compose(
-        partial(literature_correlation_by_list, conn, species),
-        partial(
-            tissue_correlation_by_list, conn, input_trait_symbol,
-            tissue_probeset_freeze_id, method))
-
-    selected_results = sorted(
-        all_correlations,
-        key=__make_sorter__(method))[:min(criteria, len(all_correlations))]
-    traits_list_corr_info = {
-        "{target_dataset['dataset_name']}::{item[0]}": {
-            "noverlap": item[1],
-            "partial_corr": item[2],
-            "partial_corr_p_value": item[3],
-            "corr": item[4],
-            "corr_p_value": item[5],
-            "rank_order": (1 if "spearman" in method.lower() else 0),
-            **({
-                "tissue_corr": item[6],
-                "tissue_p_value": item[7]}
-               if len(item) == 8 else {}),
-            **({"l_corr": item[6]}
-               if len(item) == 7 else {})
-        } for item in selected_results}
-
-    trait_list = add_lit_corr_and_tiss_corr(tuple(
-        {**trait, **traits_list_corr_info.get(trait["trait_fullname"], {})}
-        for trait in traits_info(
-            conn, threshold,
-            tuple(
-                f"{target_dataset['dataset_name']}::{item[0]}"
-                for item in selected_results))))
-
-    return {
-        "status": "success",
-        "results": {
-            "primary_trait": trait_for_output(primary_trait),
-            "control_traits": tuple(
-                trait_for_output(trait) for trait in cntrl_traits),
-            "correlations": tuple(
-                trait_for_output(trait) for trait in trait_list),
-            "dataset_type": target_dataset["type"],
-            "method": "spearman" if "spearman" in method.lower() else "pearson"
-        }}