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| author | Frederick Muriuki Muriithi | 2021-11-18 11:59:53 +0300 |
|---|---|---|
| committer | Frederick Muriuki Muriithi | 2021-11-18 11:59:53 +0300 |
| commit | 3dd5fbda7e08999b6470cfe1fbbd19d767adea9b (patch) | |
| tree | fed4d0ae18d8d39a35184c7e9d80bd942c9f37a3 /gn3 | |
| parent | 21fbbfd599c841f082d88ddfc5f4cb362e1eb869 (diff) | |
| download | genenetwork3-3dd5fbda7e08999b6470cfe1fbbd19d767adea9b.tar.gz | |
Fix some linting errors
Issue:
https://github.com/genenetwork/gn-gemtext-threads/blob/main/topics/gn1-migration-to-gn2/partial-correlations.gmi
* Fix some obvious linting errors and remove obsolete code
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/computations/partial_correlations.py | 22 |
1 files changed, 13 insertions, 9 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py index 4b0cf30..ee47290 100644 --- a/gn3/computations/partial_correlations.py +++ b/gn3/computations/partial_correlations.py @@ -280,7 +280,7 @@ def build_data_frame( return interm_df def compute_partial( - primary_val, control_vals, target_vals, target_names, + primary_vals, control_vals, target_vals, target_names, method: str) -> Tuple[ Union[ Tuple[str, int, float, float, float, float], None], @@ -300,18 +300,22 @@ def compute_partial( """ # replace the R code with `pingouin.partial_corr` def __compute_trait_info__(target): - df = build_data_frame( - [prim for targ, prim in zip(target, primary_vals) - if targ is not None], + primary = [ + prim for targ, prim in zip(target, primary_vals) + if targ is not None] + datafrm = build_data_frame( + primary, [targ for targ in target if targ is not None], - [cont for i, cont in enumerate(control) if target[i] is not None]) - covariates = "z" if df.shape[1] == 3 else [ - col for col in df.columns if col not in ("x", "y")] + [cont for i, cont in enumerate(control_vals) + if target[i] is not None]) + covariates = "z" if datafrm.shape[1] == 3 else [ + col for col in datafrm.columns if col not in ("x", "y")] ppc = pingouin.partial_corr( - data=df, x="x", y="y", covar=covariates, method=method) + data=datafrm, x="x", y="y", covar=covariates, method=method) pc_coeff = ppc["r"] - zero_order_corr = pingouin.corr(df["x"], df["y"], method=method) + zero_order_corr = pingouin.corr( + datafrm["x"], datafrm["y"], method=method) if math.isnan(pc_coeff): return ( |