Make detection algorithm prototype slightly easier to implement

1 files changed, 5 insertions, 5 deletions

diff --git a/viz.ipynb b/viz.ipynb
index 7166bef..04a185d 100644
--- a/viz.ipynb
+++ b/viz.ipynb
@@ -1699,7 +1699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 103,
    "metadata": {
     "scrolled": false
    },
@@ -2503,7 +2503,7 @@
     "for ax, capture in zip(axs.flatten(), [data_downstairs[0] + data_downstairs[1], data_upstairs]):\n",
     "    cap = np.array(capture)\n",
     "    cap -= np.mean(cap).astype(int)\n",
-    "    lens = np.array([ len(g) for g in (list(g) for g_key, g in itertools.groupby(cap, lambda x: 1 if x >= 0 else -1)) for _x in g ])\n",
+    "    lens = np.array([ x for g in (list(g) for g_key, g in itertools.groupby(cap, lambda x: 1 if x >= 0 else -1)) for x in range(len(g)) ])\n",
     "    w = 32\n",
     "    k = 8\n",
     "    lens = np.array(list(sum(xs) for xs in zip(*(lens[i::k] for i in range(w))))) / w\n",
@@ -2518,10 +2518,10 @@
     "    for idx, data in enumerate(zip(*(both_data[i*p:] for i in range(q)))):\n",
     "        h_len, h_rms = zip(*data)\n",
     "        if all(x > 1500 for x in h_rms):\n",
-    "            if all(1.0 < x < 1.5 for x in h_len[::2]) and \\\n",
-    "                all(1.3 < x < 1.8 for x in h_len[1::2]):\n",
+    "            if all(0.1 < x < 0.25 for x in h_len[::2]) and \\\n",
+    "                all(0.25 < x < 0.4 for x in h_len[1::2]):\n",
     "                ax.axvspan(idx, idx + p*q, color='lightgreen')\n",
-    "            elif all(1.4 < x < 1.8 for x in h_len):\n",
+    "            elif all(0.25 < x < 0.4 for x in h_len):\n",
     "                ax.axvspan(idx, idx + p*q, color='pink')"
    ]
   }