fix #960: ruff linting in all notebooks

sbi-dev · Mar 6, 2024 · 9b07b1f · 9b07b1f
1 parent 1f1f057
commit 9b07b1f
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Showing 16 changed files with 26,836 additions and 16,943 deletions.
diff --git a/examples/00_HH_simulator.ipynb b/examples/00_HH_simulator.ipynb
diff --git a/examples/01_decision_making_model.ipynb b/examples/01_decision_making_model.ipynb
diff --git a/examples/HH_helper_functions.py b/examples/HH_helper_functions.py
@@ -9,15 +9,15 @@ def syn_current(duration=120, dt=0.01, t_on=10, curr_level=5e-4, seed=None):
 
     # external current
     A_soma = np.pi * ((70.0 * 1e-4) ** 2)  # cm2
-    I = np.zeros_like(t)
-    I[int(np.round(t_on / dt)) : int(np.round(t_off / dt))] = (
+    I_inj = np.zeros_like(t)
+    I_inj[int(np.round(t_on / dt)) : int(np.round(t_off / dt))] = (
         curr_level / A_soma
     )  # muA/cm2
 
-    return I, t_on, t_off, dt, t, A_soma
+    return I_inj, t_on, t_off, dt, t, A_soma
 
 
-def HHsimulator(V0, params, dt, t, I, seed=None):
+def HHsimulator(V0, params, dt, t, I_inj, seed=None):
     """Simulates the Hodgkin-Huxley model for a specified time duration and current
 
     Parameters
@@ -144,7 +144,7 @@ def tau_p(x):
             + (n[i - 1] ** 4) * gbar_K * E_K
             + g_leak * E_leak
             + gbar_M * p[i - 1] * E_K
-            + I[i - 1]
+            + I_inj[i - 1]
             + nois_fact * rng.randn() / (tstep**0.5)
         ) / (tau_V_inv * C)
         V[i] = V_inf + (V[i - 1] - V_inf) * np.exp(-tstep * tau_V_inv)
@@ -167,7 +167,7 @@ def calculate_summary_statistics(x):
     -------
     np.array, summary statistics
     """
-    I, t_on, t_off, dt, t, A_soma = syn_current()
+    I_inj, t_on, t_off, dt, t, A_soma = syn_current()
 
     n_mom = 4
     n_summary = 7

diff --git a/tutorials/03_multiround_inference.ipynb b/tutorials/03_multiround_inference.ipynb
diff --git a/tutorials/04_density_estimators.ipynb b/tutorials/04_density_estimators.ipynb
@@ -30,10 +30,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import torch\n",
+    "\n",
+    "from sbi.inference import SNPE, SNRE\n",
+    "from sbi.utils import BoxUniform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prior = BoxUniform(torch.zeros(2), torch.ones(2))\n",
     "inference = SNPE(prior=prior, density_estimator=\"maf\")"
    ]
   },
@@ -46,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +137,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.18"
   }
  },
  "nbformat": 4,

diff --git a/tutorials/05_embedding_net.ipynb b/tutorials/05_embedding_net.ipynb
diff --git a/tutorials/07_conditional_distributions.ipynb b/tutorials/07_conditional_distributions.ipynb
diff --git a/tutorials/08_restriction_estimator.ipynb b/tutorials/08_restriction_estimator.ipynb
diff --git a/tutorials/09_sensitivity_analysis.ipynb b/tutorials/09_sensitivity_analysis.ipynb
diff --git a/tutorials/10_crafting_summary_statistics.ipynb b/tutorials/10_crafting_summary_statistics.ipynb