🧐 update smarter summary

removed shadows from pie charts. Add sample by chip type for goats
cnr-ibba · Aug 31, 2024 · 0884c0c · 0884c0c
1 parent 157b222
commit 0884c0c
Showing 1 changed file with 45 additions and 6 deletions.
diff --git a/notebooks/results/0.2.0-bunop-smarter_summary.ipynb b/notebooks/results/0.2.0-bunop-smarter_summary.ipynb
@@ -5,7 +5,7 @@
    "id": "3cc84f47-db82-43eb-99fd-78768ecd2a99",
    "metadata": {},
    "source": [
-    "# SMARTER SUMMARY (2024/05/23)\n",
+    "# SMARTER SUMMARY (2024/08/31)\n",
     "* [Dataset composition](#datasets-composition)\n",
     "    * [Foreground / background datasets](#foreground-vs-background-datasets)\n",
     "    * [Datasets by chip type](#datasets-by-chip-type)\n",
@@ -16,6 +16,7 @@
     "    * [Foreground / background samples for goat](#foreground-background-samples-goat)\n",
     "        * [Greece foreground goat data](#greece-foreground-goat-data)\n",
     "        * [Sweden foreground goat data](#sweden-foreground-goat-data)\n",
+    "    * [Goat samples by chip type](#goat-sample-by-chip-type)\n",
     "* [Draw samples by country](#samples-by-country)\n",
     "  * [Sheep samples by country](#sheep-samples-by-country)\n",
     "  * [Goat samples by country](#goat-samples-by-country)\n",
@@ -106,7 +107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot = datasets.value_counts(\"type\").plot.pie(y=\"type\", figsize=(8,8), shadow=True, startangle=45, rotatelabels=45, autopct='%1.1f%%')\n",
+    "plot = datasets.value_counts(\"type\").plot.pie(y=\"type\", figsize=(8,8), startangle=45, rotatelabels=45, autopct='%1.1f%%')\n",
     "_ = plt.title(\"Foreground vs Background genotype datasets\")\n",
     "plt.ylabel(None)\n",
     "plt.savefig('smarter-fgVsbg-datasets.png', dpi=300, bbox_inches='tight')\n",
@@ -130,7 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot = datasets['chip_name'].value_counts().plot.pie(y=\"chip\", figsize=(8,8), shadow=True, startangle=45, rotatelabels=45, autopct='%1.1f%%')\n",
+    "plot = datasets['chip_name'].value_counts().plot.pie(y=\"chip\", figsize=(8,8), startangle=45, rotatelabels=45, autopct='%1.1f%%')\n",
     "_ = plt.title(\"Datasets by chip type\")\n",
     "plt.ylabel(None)\n",
     "plt.savefig('smarter-datasets-by-chips.png', dpi=300, bbox_inches='tight')\n",
@@ -159,7 +160,7 @@
     "foreground_sheep = SampleSheep.objects.filter(type_=\"foreground\").fields(country=True, breed=True, chip_name=True, locations=True)\n",
     "background_sheep = SampleSheep.objects.filter(type_=\"background\").fields(country=True, breed=True, chip_name=True, locations=True)\n",
     "samples_sheep = pd.Series({\"foreground\": foreground_sheep.count(), \"background\": background_sheep.count()}, name=\"Sheep\")\n",
-    "plot = samples_sheep.plot.pie(y=\"Sheep\", figsize=(8,8), shadow=True, startangle=90, rotatelabels=45, autopct='%1.1f%%')\n",
+    "plot = samples_sheep.plot.pie(y=\"Sheep\", figsize=(8,8), startangle=90, rotatelabels=45, autopct='%1.1f%%')\n",
     "_ = plt.title(\"Background VS Foreground sheep\")\n",
     "plt.ylabel(None)\n",
     "plt.savefig('sheep-foreground-vs-background-pie.png', dpi=300, bbox_inches='tight')\n",
@@ -205,7 +206,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sheep_by_chip.plot.pie(y=\"count\", figsize=(8,8), shadow=True, startangle=-45, rotatelabels=45, autopct='%1.1f%%', legend=False)\n",
+    "sheep_by_chip.plot.pie(y=\"count\", figsize=(8,8), startangle=-45, rotatelabels=45, autopct='%1.1f%%', legend=False)\n",
     "_ = plt.title(\"Sheep samples by chip type\")\n",
     "plt.ylabel(None)\n",
     "plt.savefig('sheep-samples-by-chip-type.png', dpi=300, bbox_inches='tight')\n",
@@ -345,7 +346,7 @@
     "foreground_goats = SampleGoat.objects.filter(dataset__in=Dataset.objects.filter(type_=\"foreground\")).fields(country=True, breed=True, chip_name=True, locations=True, metadata=True)\n",
     "background_goats = SampleGoat.objects.filter(dataset__in=Dataset.objects.filter(type_=\"background\")).fields(country=True, breed=True, chip_name=True, locations=True, metadata=True)\n",
     "samples_goat = pd.Series({\"foreground\": foreground_goats.count(), \"background\": background_goats.count()}, name=\"Goats\")\n",
-    "plot = samples_goat.plot.pie(y=\"Goat\", figsize=(8,8), shadow=True, rotatelabels=45, autopct='%1.1f%%')\n",
+    "plot = samples_goat.plot.pie(y=\"Goat\", figsize=(8,8), rotatelabels=45, autopct='%1.1f%%')\n",
     "_ = plt.title(\"Background VS Foreground goats\")\n",
     "plt.ylabel(None)\n",
     "plt.savefig('goat-foreground-vs-background-pie.png', dpi=300, bbox_inches='tight')\n",
@@ -479,6 +480,44 @@
     "foreground_goats.value_counts('region')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c59376dd",
+   "metadata": {},
+   "source": [
+    "<a id='goat-sample-by-chip-type'></a>\n",
+    "### Goat samples by chip type\n",
+    "Try to determine the sample composition by chip type:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33dd743b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "goat_by_chip = defaultdict(list)\n",
+    "for chip_name in SampleGoat.objects.distinct(\"chip_name\"):\n",
+    "    goat_by_chip['chip_name'].append(chip_name)\n",
+    "    goat_by_chip['count'].append(SampleGoat.objects.filter(chip_name=chip_name).count())\n",
+    "goat_by_chip = pd.DataFrame.from_dict(goat_by_chip).set_index(\"chip_name\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b78717e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "goat_by_chip.plot.pie(y=\"count\", figsize=(8,8), startangle=-45, rotatelabels=45, autopct='%1.1f%%', legend=False)\n",
+    "_ = plt.title(\"Goat samples by chip type\")\n",
+    "plt.ylabel(None)\n",
+    "plt.savefig('goat-samples-by-chip-type.png', dpi=300, bbox_inches='tight')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8c3c128e-f8a7-47fd-812b-89266bcec1ca",