[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

scrna/velovi.ipynb

@@ -30,6 +30,7 @@
    "source": [
     "!pip install --quiet scvi-colab\n",
     "from scvi_colab import install\n",
+    "\n",
     "install()"
    ]
   },
@@ -47,15 +48,14 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import scanpy as sc\n",
     "import scvelo as scv\n",
+    "import seaborn as sns\n",
     "import torch\n",
-    "from velovi import preprocess_data, VELOVI\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
+    "from velovi import VELOVI, preprocess_data"
    ]
   },
   {
@@ -221,13 +221,11 @@
     "    adata.var[\"fit_beta\"] = vae.get_rates()[\"beta\"] / scaling\n",
     "    adata.var[\"fit_gamma\"] = vae.get_rates()[\"gamma\"] / scaling\n",
     "    adata.var[\"fit_t_\"] = (\n",
-    "        torch.nn.functional.softplus(vae.module.switch_time_unconstr)\n",
-    "        .detach()\n",
-    "        .cpu()\n",
-    "        .numpy()\n",
+    "        torch.nn.functional.softplus(vae.module.switch_time_unconstr).detach().cpu().numpy()\n",
     "    ) * scaling\n",
     "    adata.layers[\"fit_t\"] = latent_time.values * scaling[np.newaxis, :]\n",
-    "    adata.var['fit_scaling'] = 1.0\n",
+    "    adata.var[\"fit_scaling\"] = 1.0\n",
+    "\n",
     "\n",
     "add_velovi_outputs_to_adata(adata, vae)"
    ]
@@ -297,7 +295,7 @@
     }
    ],
    "source": [
-    "scv.pl.velocity_embedding_stream(adata, basis='umap')"
+    "scv.pl.velocity_embedding_stream(adata, basis=\"umap\")"
    ]
   },
   {
@@ -507,7 +505,7 @@
    ],
    "source": [
     "sc.pl.umap(\n",
-    "    adata, \n",
+    "    adata,\n",
     "    color=\"directional_cosine_sim_variance\",\n",
     "    cmap=\"Greys\",\n",
     "    vmin=\"p1\",\n",
@@ -529,15 +527,16 @@
    "outputs": [],
    "source": [
     "def compute_extrinisic_uncertainty(adata, vae, n_samples=25) -> pd.DataFrame:\n",
-    "    from velovi._model import _compute_directional_statistics_tensor\n",
-    "    from scvi.utils import track\n",
-    "    from contextlib import redirect_stdout\n",
     "    import io\n",
+    "    from contextlib import redirect_stdout\n",
+    "\n",
+    "    from scvi.utils import track\n",
+    "    from velovi._model import _compute_directional_statistics_tensor\n",
     "\n",
     "    extrapolated_cells_list = []\n",
     "    for i in track(range(n_samples)):\n",
     "        with io.StringIO() as buf, redirect_stdout(buf):\n",
-    "            vkey = \"velocities_velovi_{i}\".format(i=i)\n",
+    "            vkey = f\"velocities_velovi_{i}\"\n",
     "            v = vae.get_velocity(n_samples=1, velo_statistic=\"mean\")\n",
     "            adata.layers[vkey] = v\n",
     "            scv.tl.velocity_graph(adata, vkey=vkey, sqrt_transform=False, approx=True)\n",
@@ -1134,10 +1133,10 @@
    ],
    "source": [
     "sc.pl.umap(\n",
-    "    adata, \n",
+    "    adata,\n",
     "    color=\"directional_cosine_sim_variance_extrinisic\",\n",
-    "    vmin=\"p1\", \n",
-    "    vmax=\"p99\", \n",
+    "    vmin=\"p1\",\n",
+    "    vmax=\"p99\",\n",
     ")"
    ]
   },