Fix codecov upload

.github/workflows/pytest.yml

@@ -21,6 +21,5 @@ jobs:
       with:
         file: ./coverage.xml
         flags: unittests
-        env_vars: OS,PYTHON
-        name: codecov-umbrella
+        token: ${{ secrets.CODECOV_TOKEN }}
         fail_ci_if_error: true

lineage/HMM/E_step.py

@@ -140,9 +140,8 @@ def get_beta(
 
     cIDXs = np.arange(MSD.shape[0])
     cIDXs = np.delete(cIDXs, leaves_idx)
-    cIDXs = np.flip(cIDXs)
 
-    for pii in cIDXs:
+    for pii in reversed(cIDXs):
         ch_ii = cell_to_daughters[pii, :]
         ratt = (beta[ch_ii, :] / MSD_array[ch_ii, :]) @ T.T
         fac1 = np.prod(ratt, axis=0) * ELMSD[pii, :]

lineage/HMM/M_step.py

@@ -46,7 +46,7 @@ def get_all_zetas(
     TbetaMSD = np.clip(betaMSD @ T.T, np.finfo(float).eps, np.inf)
 
     cIDXs = np.arange(gammas.shape[0])
-    cIDXs = np.delete(cIDXs, leaves_idx, axis=0)
+    cIDXs = np.delete(cIDXs, leaves_idx)
 
     dIDXs = cell_to_daughters[cIDXs, :]
 

lineage/import_lineage.py

@@ -198,8 +198,8 @@ def assign_observs_MCF10A(cell, lineage, uniq_id: int):
     parent_id = lineage["motherID"].unique()
 
     # cell fate: die = 0, divide = 1
-    if not (
-        uniq_id in parent_id
+    if (
+        uniq_id not in parent_id
     ):  # if the cell has not divided, means either died or reached experiment end time
         if (
             np.max(lineage.loc[lineage["TID"] == uniq_id]["tmin"]) == t_end

lineage/states/StateDistributionGaPhs.py

@@ -12,12 +12,12 @@ class StateDistribution:
 
     def __init__(
         self,
-        bern_p1=0.9,
-        bern_p2=0.75,
-        gamma_a1=7.0,
-        gamma_scale1=3,
-        gamma_a2=14.0,
-        gamma_scale2=6,
+        bern_p1: float = 0.9,
+        bern_p2: float = 0.75,
+        gamma_a1: float = 7.0,
+        gamma_scale1: float = 3.0,
+        gamma_a2: float = 14.0,
+        gamma_scale2: float = 6.0,
     ):  # user has to identify what parameters to use for each state
         """Initialization function should take in just in the parameters for the observations that comprise the multivariate random variable emission they expect their data to have."""
         self.params = np.array(

lineage/states/StateDistributionGamma.py

@@ -13,7 +13,9 @@ class StateDistribution:
     StateDistribution for cells with gamma distributed times.
     """
 
-    def __init__(self, bern_p=0.9, gamma_a=7, gamma_scale=4.5):
+    def __init__(
+        self, bern_p: float = 0.9, gamma_a: float = 7.0, gamma_scale: float = 4.5
+    ):
         """Initialization function should take in just in the parameters
         for the observations that comprise the multivariate random variable emission they expect their data to have.
         In this case, we used Gamma distribution for cell lifetime, which has 2 parameters; shape and scale.

lineage/states/stateCommon.py

@@ -59,12 +59,12 @@ def gamma_LL(
 
 
 def gamma_estimator(
-    gamma_obs: list[np.ndarray],
-    time_cen: list[np.ndarray],
-    gammas: list[np.ndarray],
-    x0: np.ndarray,
+    gamma_obs: list[arr_type],
+    time_cen: list[arr_type],
+    gammas: list[arr_type],
+    x0: arr_type,
     phase: str = "all",
-) -> npt.NDArray[np.float64]:
+) -> arr_type:
     """
     This is a weighted, closed-form estimator for two parameters
     of the Gamma distribution for estimating shared shape and separate scale parameters of several drug concentrations at once.
@@ -100,7 +100,7 @@ def gamma_estimator(
     else:
         linc = ()
 
-    bnd = Bounds(-4.0, 7.0, keep_feasible=True)
+    bnd = Bounds(-4.0, 7.0, keep_feasible=False)
 
     with np.errstate(all="raise"):
         res = minimize(