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chansigit committed Jul 2, 2024
2 parents 35b22e6 + a5a1998 commit bb7885c
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Showing 3 changed files with 6 additions and 5 deletions.
3 changes: 2 additions & 1 deletion .github/workflows/python-plain-run-test.yml
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Expand Up @@ -46,9 +46,10 @@ jobs:
- name: Upload coverage to Codecov
uses: Wandalen/wretry.action@v1
with:
action: codecov/codecov-action@v3
action: codecov/codecov-action@v4
with: |
fail_ci_if_error: true
token: ${{ secrets.CODECOV_TOKEN }}
verbose: true
attempt_limit: 5
attempt_delay: 10000
6 changes: 3 additions & 3 deletions dynamo/vectorfield/utils.py
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Expand Up @@ -916,7 +916,7 @@ def compute_curl(f_jac, X):

# ---------------------------------------------------------------------------------------------------
# ranking related utilies
def get_metric_gene_in_rank(mat: np.matrix, genes: list, neg: bool = False) -> Tuple[np.ndarray, np.ndarray]:
def get_metric_gene_in_rank(mat: np.asmatrix, genes: list, neg: bool = False) -> Tuple[np.ndarray, np.ndarray]:
"""Calculate ranking of genes based on mean value of matrix.
Args:
Expand All @@ -936,7 +936,7 @@ def get_metric_gene_in_rank(mat: np.matrix, genes: list, neg: bool = False) -> T


def get_metric_gene_in_rank_by_group(
mat: np.matrix, genes: list, groups: np.array, selected_group, neg: bool = False
mat: np.asmatrix, genes: list, groups: np.array, selected_group, neg: bool = False
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Calculate ranking of genes based on mean value of matrix, grouped by selected group.
Expand Down Expand Up @@ -993,7 +993,7 @@ def get_sorted_metric_genes_df(df: pd.DataFrame, genes: list, neg: bool = False)
return sorted_metric, sorted_genes


def rank_vector_calculus_metrics(mat: np.matrix, genes: list, group, groups: list, uniq_group: list) -> Tuple:
def rank_vector_calculus_metrics(mat: np.asmatrix, genes: list, group, groups: list, uniq_group: list) -> Tuple:
"""Calculate ranking of genes based on vector calculus metric.
Args:
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2 changes: 1 addition & 1 deletion requirements.txt
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@@ -1,4 +1,4 @@
numpy>=1.20.0
numpy>=1.20.0,<2.0.0
pandas>=1.3.5
scipy>=1.4.1
scikit-learn>=0.19.1,<1.5.0
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