Skip to content

small notebook to check hrrr_availability #46

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
bbuzz31 wants to merge 1 commit into
base: main
Choose a base branch
from
Open

small notebook to check hrrr_availability #46

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
261 changes: 261 additions & 0 deletions notebooks/HRRR_Availability.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,261 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 90,
"id": "b410f20b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatic pdb calling has been turned OFF\n",
"(ARIA) 2023-09-07 09:27:21.243326\n"
]
}
],
"source": [
"import os\n",
"from pathlib import Path\n",
"from datetime import datetime\n",
"from herbie import Herbie\n",
"%pdb off\n",
"%matplotlib inline\n",
"print (os.getenv('CONDA_PROMPT_MODIFIER'), datetime.now())"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "0784f49b",
"metadata": {},
"outputs": [],
"source": [
"model = 'hrrr'\n",
"product = 'nat'\n",
"fxx = 0\n",
"wd = Path(os.getenv('dataroot', './'))\n",
"wd = wd / 'HRRR_availability'\n",
"valid_range = (datetime(2016, 7, 15, 0), datetime.today())\n",
"available_dates = dates = pd.date_range(valid_range[0], valid_range[1], freq='H')\n",
"n_dt = len(available_dates)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "73955f15",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Started at: 2023-09-06 19:36:59.289994\n",
"Date 0 of 62636\n",
"Date 5000 of 62636\n",
"Date 10000 of 62636\n",
"Date 15000 of 62636\n",
"Date 20000 of 62636\n",
"Date 25000 of 62636\n",
"Date 30000 of 62636\n",
"Date 35000 of 62636\n",
"Date 40000 of 62636\n",
"Date 45000 of 62636\n",
"Date 50000 of 62636\n",
"Date 55000 of 62636\n",
"Finished at: 2023-09-07 09:06:18.240831\n"
]
}
],
"source": [
"lst_exist = []\n",
"lst_miss = []\n",
"srcs = []\n",
"print ('Started at:', datetime.now())\n",
"for i, dt in enumerate(available_dates):\n",
" if i % 5000 == 0:\n",
" print (f'Date {i} of {n_dt}')\n",
" \n",
" H = Herbie(\n",
" dt.strftime('%Y-%m-%d %H:%M'),\n",
" model=model,\n",
" product=product,\n",
" fxx=fxx,\n",
" overwrite=False,\n",
" verbose=False,\n",
" save_dir=wd\n",
" )\n",
" \n",
" src = H.grib_source\n",
" if src is None:\n",
" lst_miss.append(dt)\n",
" else:\n",
" lst_exist.append(dt)\n",
" srcs.append(src)\n",
"\n",
"print ('Finished at:', datetime.now())"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "0ed67a9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"61739"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(lst_exist)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "432f163a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"897"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(lst_miss)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"id": "b7708ccf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percent Missing: 1.43%\n"
]
}
],
"source": [
"print (f'Percent Missing: {100*len(lst_miss)/n_dt:.2f}%')"
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "d70ad257",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Number of hours')"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(figsize=(12,8))\n",
"pd.DataFrame(lst_miss).hist(ax=axes)\n",
"axes.set_ylabel('Number of hours')"
]
},
{
"cell_type": "code",
"execution_count": 115,
"id": "08c14d63",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"aws 61651\n",
"pando 87\n",
"local 1\n",
"dtype: int64"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_srcs = pd.Series(srcs)\n",
"ser_srcs.value_counts() # local is one I downloaded"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": false,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}