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further progress reference analysis
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adamjanovsky committed Jan 31, 2024
1 parent d01b7a2 commit 84089f4
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55 changes: 54 additions & 1 deletion notebooks/cc/paper2_plots.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -344,6 +344,59 @@
"plt.tight_layout(pad=0.1)\n",
"plt.savefig(RESULTS_DIR / \"stacked_barplot.pdf\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Archived certificate half-life"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 300x200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"\n",
"matplotlib.rcParams[\"xtick.minor.size\"] = 0\n",
"matplotlib.rcParams[\"xtick.minor.width\"] = 0\n",
"\n",
"df = pd.read_csv(RESULTS_DIR / \"archived_half_life.csv\")\n",
"sns.set_palette(\"Set2\")\n",
"g = sns.histplot(data=df.n_days, log_scale=True, bins=50, cumulative=True)\n",
"\n",
"g.set_xlabel(\"Number of days\")\n",
"g.set_ylabel(\"Number of certificates\")\n",
"\n",
"n_under_year = df.loc[df.n_days <= 365].shape[0]\n",
"horizontal_limit = 365 / df.n_days.max()\n",
"vertical_limit = n_under_year / 270\n",
"\n",
"g.set_xlim(0, df.n_days.max())\n",
"g.set_ylim(0, 270)\n",
"g.set_xticks([1, 10, 100, 1000], [1, 10, 100, 1000])\n",
"plt.axvline(x=365, ymin=0, ymax=0.37, color=\"red\", linewidth=0.75, linestyle=\"solid\")\n",
"plt.axhline(y=100, xmin=0, xmax=0.765, color=\"red\", linewidth=0.75, linestyle=\"solid\")\n",
"plt.text(100, 106, \"1 year\", color=\"red\", fontsize=8)\n",
"g.figure.set_size_inches(3, 2)\n",
"plt.tight_layout(pad=0.1)\n",
"\n",
"g.figure.savefig(RESULTS_DIR / \"histogram_half_life.pdf\")\n",
"g.figure.show()"
]
}
],
"metadata": {
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