chore(tests): refine swath start test report

Author: j-haacker

tests/reports/l1b_swath_start.ipynb

@@ -36,11 +36,11 @@
     "location; which is why one usually requires a minimum coherence for any\n",
     "processed sample.\n",
     "\n",
-    "With those assumptions and choices, I search for the maximum coherence\n",
-    "in a 10 m window after the coherence first exceeds 0.85 and name this\n",
-    "sample the POCA. In another window from 5 to 50 m after the POCA, I\n",
-    "search for a rise in coherence. This is where the swath processing\n",
-    "starts.\n",
+    "With those assumptions and choices, I search for the maximum of the\n",
+    "smoothed coherence in a 10 m window after the coherence first exceeds\n",
+    "the coherence threshold and name this sample the POCA. In another window\n",
+    "from 5 to 50 m after the POCA, I search for a rise in coherence. This is\n",
+    "where the swath processing starts.\n",
     "\n",
     "In the following, \"edge cases\" are recognized by an early or late swath\n",
     "start. For the _k_ earliest and latest of those, the coherence waveform\n",
@@ -53,9 +53,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "k_smallest = 5\n",
-    "k_biggest = 5\n",
-    "k_random = 5"
+    "k_zero = 3\n",
+    "k_smallest = 3\n",
+    "k_biggest = 3\n",
+    "k_random = 3"
    ]
   },
   {
@@ -95,6 +96,15 @@
     "import numpy as np"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -110,8 +120,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for idx in np.argpartition(l1b_data.swath_start.values, kth=k_smallest)[:k_smallest]:\n",
-    "    cryoswath.test_plots.waveform.coherence(l1b_data.isel(time_20_ku=[idx]))"
+    "for idx in random.sample(np.argwhere(l1b_data.swath_start.values==0).flatten().tolist(), k=k_zero):\n",
+    "    ax = cryoswath.test_plots.waveform.coherence(l1b_data.isel(time_20_ku=[idx]))"
    ]
   },
   {
@@ -120,8 +130,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for idx in np.argpartition(l1b_data.swath_start.values, kth=-k_biggest)[-k_biggest:]:\n",
-    "    cryoswath.test_plots.waveform.coherence(l1b_data.isel(time_20_ku=[idx]))"
+    "sortkeys = np.argsort(l1b_data.swath_start.values)[sum(l1b_data.swath_start.values==0):]"
    ]
   },
   {
@@ -130,7 +139,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import random"
+    "for idx in sortkeys[:k_smallest]:\n",
+    "    ax = cryoswath.test_plots.waveform.coherence(l1b_data.isel(time_20_ku=[idx]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for idx in sortkeys[-k_biggest:]:\n",
+    "    cryoswath.test_plots.waveform.coherence(l1b_data.isel(time_20_ku=[idx]))"
    ]
   },
   {
@@ -139,7 +159,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for idx in random.sample(range(len(l1b_data.time_20_ku)), k=k_random):\n",
+    "for idx in random.sample(sortkeys.tolist(), k=k_random):\n",
     "    cryoswath.test_plots.waveform.coherence(l1b_data.isel(time_20_ku=[idx]))"
    ]
   }