update deps

gnr/__init__.py

@@ -1 +1 @@
-__version__ = "0.0.1"
+__version__ = "0.0.5"

gnr/fn.py

@@ -14,7 +14,7 @@
 from statsmodels.tsa.stattools import grangercausalitytests
 
 # %% ../nbs/02_fn.ipynb 4
-from iza.types import SeriesLike, DataFrame
+from atyp import SeriesLike, DataFrame
 
 # %% ../nbs/02_fn.ipynb 5
 from gnr.static import (

gnr/mock.py

@@ -9,7 +9,7 @@
 from typing import List, Optional, Union
 
 # %% ../nbs/04_mock.ipynb 4
-from iza.types import SeriesLike, DataFrame, ndarray
+from atyp import SeriesLike, DataFrame, NPArray
 
 # %% ../nbs/04_mock.ipynb 5
 from .static import (MOCK_GENES)
@@ -43,7 +43,7 @@ def make_mock_trajectories(
     tbins: int = 100,
     cells: int = 50,
     genes: int = 10,
-) -> ndarray:
+) -> NPArray:
     df = pd.DataFrame()
     for i in range(cells):
         df_gxt = make_mock_genes_x_tbins(genes, tbins)
@@ -52,14 +52,14 @@ def make_mock_trajectories(
     return res
 
 # %% ../nbs/04_mock.ipynb 11
-def df_trj_to_cells_x_tbins(trajectories:ndarray, agg_fn=np.mean) -> ndarray:
+def df_trj_to_cells_x_tbins(trajectories:NPArray, agg_fn=np.mean) -> NPArray:
     '''
     Transpose and aggregate trajectories matrix (timebinse, cells, gene)
     to produce (cells, timebins)
     ''' 
     return agg_fn(np.transpose(trajectories, (1, 0, 2)), axis=2)
 
-def df_trj_to_genes_x_tbins(trajectories:ndarray, agg_fn=np.mean) -> ndarray:
+def df_trj_to_genes_x_tbins(trajectories:NPArray, agg_fn=np.mean) -> NPArray:
     '''
     Transpose and aggregate trajectories matrix (timebinse, cells, gene)
     to produce (genes, timebins)

gnr/op.py

@@ -9,11 +9,10 @@
 
 import seaborn as sns
 
-from dataclasses import dataclass, field
 from typing import Optional, ClassVar
 
 # %% ../nbs/03_op.ipynb 4
-from iza.types import SeriesLike, DataFrame
+from atyp import SeriesLike, DataFrame
 
 # %% ../nbs/03_op.ipynb 5
 from gnr.static import (

gnr/utils.py

@@ -6,15 +6,14 @@
 
 # %% ../nbs/01_utils.ipynb 3
 import pandas as pd, numpy as np
-import seaborn as sns
 
 from typing import List, Optional, Union
 
 from sklearn.preprocessing import StandardScaler
 from statsmodels.tsa.stattools import kpss
 
 # %% ../nbs/01_utils.ipynb 4
-from iza.types import SeriesLike, DataFrame, ndarray
+from atyp import SeriesLike, DataFrame, NPArray
 
 # %% ../nbs/01_utils.ipynb 5
 from gnr.static import (

nbs/01_utils.ipynb

@@ -37,7 +37,6 @@
    "source": [
     "#| export\n",
     "import pandas as pd, numpy as np\n",
-    "import seaborn as sns\n",
     "\n",
     "from typing import List, Optional, Union\n",
     "\n",
@@ -52,7 +51,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "from iza.types import SeriesLike, DataFrame, ndarray"
+    "from atyp import SeriesLike, DataFrame, NPArray"
    ]
   },
   {

nbs/02_fn.ipynb

@@ -52,7 +52,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "from iza.types import SeriesLike, DataFrame"
+    "from atyp import SeriesLike, DataFrame"
    ]
   },
   {

nbs/03_op.ipynb

@@ -41,7 +41,6 @@
     "\n",
     "import seaborn as sns\n",
     "\n",
-    "from dataclasses import dataclass, field\n",
     "from typing import Optional, ClassVar"
    ]
   },
@@ -52,7 +51,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "from iza.types import SeriesLike, DataFrame"
+    "from atyp import SeriesLike, DataFrame"
    ]
   },
   {

nbs/04_mock.ipynb

@@ -47,7 +47,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "from iza.types import SeriesLike, DataFrame, ndarray"
+    "from atyp import SeriesLike, DataFrame, NPArray"
    ]
   },
   {
@@ -313,7 +313,7 @@
     "    tbins: int = 100,\n",
     "    cells: int = 50,\n",
     "    genes: int = 10,\n",
-    ") -> ndarray:\n",
+    ") -> NPArray:\n",
     "    df = pd.DataFrame()\n",
     "    for i in range(cells):\n",
     "        df_gxt = make_mock_genes_x_tbins(genes, tbins)\n",
@@ -351,14 +351,14 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "def df_trj_to_cells_x_tbins(trajectories:ndarray, agg_fn=np.mean) -> ndarray:\n",
+    "def df_trj_to_cells_x_tbins(trajectories:NPArray, agg_fn=np.mean) -> NPArray:\n",
     "    '''\n",
     "    Transpose and aggregate trajectories matrix (timebinse, cells, gene)\n",
     "    to produce (cells, timebins)\n",
     "    ''' \n",
     "    return agg_fn(np.transpose(trajectories, (1, 0, 2)), axis=2)\n",
     "\n",
-    "def df_trj_to_genes_x_tbins(trajectories:ndarray, agg_fn=np.mean) -> ndarray:\n",
+    "def df_trj_to_genes_x_tbins(trajectories:NPArray, agg_fn=np.mean) -> NPArray:\n",
     "    '''\n",
     "    Transpose and aggregate trajectories matrix (timebinse, cells, gene)\n",
     "    to produce (genes, timebins)\n",