From 71d2426f75a8884a7b262cf367db0abec2b5fb69 Mon Sep 17 00:00:00 2001
From: Kyle Johnsen <johnsenkyle13@gmail.com>
Date: Wed, 13 Mar 2024 16:25:21 -0400
Subject: [PATCH] clarify tau units in spikes_to_biexp

---
 pyproject.toml    |  2 +-
 wslfp/spk2curr.py | 16 +++++++++-------
 2 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index e45d61f..86f61b7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "wslfp"
-version = "0.1.1"
+version = "0.1.2"
 description = "Weighted Sum Local Field Potentials - Implementation of the proxy method for point neurons from Mazzoni, Lindén et al., 2015"
 authors = [
     "Kyle Johnsen <kyle@kjohnsen.org>",
diff --git a/wslfp/spk2curr.py b/wslfp/spk2curr.py
index 7d798c6..6c33e74 100644
--- a/wslfp/spk2curr.py
+++ b/wslfp/spk2curr.py
@@ -21,8 +21,8 @@ def spikes_to_biexp_currents(
     t_spk_ms: np.ndarray,
     i_spk: np.ndarray,
     J: Union[np.ndarray, sparse.sparray],
-    tau1: float,
-    tau2: float,
+    tau1_ms: float,
+    tau2_ms: float,
     syn_delay_ms: float = 1,
     normalize: bool = False,
     threshold: float = 0.001,
@@ -67,15 +67,15 @@ def spikes_to_biexp_currents(
     assert t_spk_conv.shape == (T, n_spk)
     assert np.all(np.diff(t_spk_ms) >= 0), "assuming t_spk_ms is sorted"
 
-    assert tau1 > tau2, "tau1 must be greater than tau2"
+    assert tau1_ms > tau2_ms, "tau1 must be greater than tau2"
 
     # Define a function for the difference between the biexp_kernel and the threshold
     def biexp(t):
-        return biexp_kernel(t, tau1, tau2, normalize=True) - threshold
+        return biexp_kernel(t, tau1_ms, tau2_ms, normalize=True) - threshold
 
     # Use fsolve to find the time when the biexp_kernel drops to the threshold
-    t_end = fsolve(biexp, 6 * tau1)[0]
-    assert t_end > tau1
+    t_end = fsolve(biexp, 6 * tau1_ms)[0]
+    assert t_end > tau1_ms
 
     I_syn = np.zeros((T, n_targets))
 
@@ -89,7 +89,9 @@ def biexp(t):
             continue
         window_sizes[t] = spk_right - spk_left
 
-        I_syn_t = biexp_kernel(t_spk_conv[t, spk_left:spk_right], tau1, tau2, normalize)
+        I_syn_t = biexp_kernel(
+            t_spk_conv[t, spk_left:spk_right], tau1_ms, tau2_ms, normalize
+        )
 
         J_t = J[i_spk[spk_left:spk_right], :]
         # numpy doesn't handle multiplication with sparse matrices