{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8017c719-7fa9-4b81-a0df-48d5872f89b3",
   "metadata": {},
   "source": [
    "# process global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b04003da-340b-4c09-b39d-48e21f705849",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# need to upgrade odc-stac to fix keyerror issue for some tiles\n",
    "#!pip install -q odc-stac -U\n",
    "!pip install -q odc-stac==0.3.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad638fb-219f-4c04-b8f4-aef6c1ea78d8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import rasterio as rio\n",
    "import pystac\n",
    "import pystac_client\n",
    "import stackstac\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "from datetime import datetime\n",
    "import matplotlib\n",
    "import ipyleaflet\n",
    "import sys\n",
    "import os\n",
    "import dask_gateway\n",
    "import planetary_computer\n",
    "from rechunker import rechunk\n",
    "sys.path.append('../sar_snowmelt_timing')\n",
    "import s1_rtc_bs_utils\n",
    "import contextily as ctx\n",
    "import rioxarray as rxr\n",
    "import skimage\n",
    "import pathlib\n",
    "import glob\n",
    "import re\n",
    "import time\n",
    "import fsspec\n",
    "import urllib.request\n",
    "from urllib.error import URLError\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7f5d4e3-a28f-47da-8a06-969767e3843f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import getpass\n",
    "import azure.storage.blob\n",
    "import io\n",
    "import adlfs\n",
    "\n",
    "\n",
    "#sas_token = getpass.getpass()  # prompts for the sas_token\n",
    "sas_token = \"se=2023-12-08T18%3A45Z&sp=racwdl&sv=2022-11-02&sr=c&skoid=598b2582-4d1d-4c4e-9bb2-3ad571b791b5&sktid=f6b6dd5b-f02f-441a-99a0-162ac5060bd2&skt=2023-12-01T18%3A46%3A01Z&ske=2023-12-08T18%3A45%3A00Z&sks=b&skv=2022-11-02&sig=up1iyCiyySNtrQnG%2BFb7yCIshOTLZ%2BFq9XphTbDeLxo%3D\"\n",
    "container_client = azure.storage.blob.ContainerClient(\n",
    "    \"https://snowmelt.blob.core.windows.net\",\n",
    "    container_name=\"snowmelt\",\n",
    "    credential=sas_token,\n",
    ")\n",
    "fs = adlfs.AzureBlobFileSystem(account_name=\"snowmelt\", credential=sas_token, anon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c339d12-8195-4873-a1ff-2886d6973044",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# cluster = dask_gateway.GatewayCluster()\n",
    "# client = cluster.get_client()\n",
    "# cluster.adapt(minimum=10, maximum=50)\n",
    "# print(client.dashboard_link)\n",
    "\n",
    "\n",
    "cluster = dask_gateway.GatewayCluster()\n",
    "client = cluster.get_client()\n",
    "cluster.scale(50)\n",
    "print(cluster.dashboard_link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0fe05ba-c7fd-44de-97a7-c4701c268476",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "with fsspec.open('https://github.com/egagli/mgrs_tiles_snow_area/raw/main/mgrs_land_tiles_snow_area.parquet') as file:\n",
    "    mgrs_gdf = gpd.read_parquet(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62465c0a-c29e-484d-9816-60725654cf12",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mgrs_snow_gdf = mgrs_gdf[mgrs_gdf['mountain_seasonal_and_ephemeral_snow_area_km2']>100].sort_values(by='mountain_seasonal_and_ephemeral_snow_area_km2',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "734cbf48-874d-46b1-b0b5-78fba7b9606c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mgrs_snow_gdf = mgrs_snow_gdf[mgrs_snow_gdf.epsg<32700] # northern hemisphere\n",
    "#mgrs_snow_gdf = mgrs_snow_gdf[mgrs_snow_gdf.intersects(gpd.read_file('../input/shapefiles/western_us_canada.geojson').geometry[0],align=True)] # western us and canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a17317b-db05-4cb5-9d02-cbb40b8288e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mgrs_snow_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f55d9c4-578b-454f-bf1b-b91a6e979fd1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mgrs_snow_gdf.explore(column='mountain_seasonal_and_ephemeral_snow_area_km2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05d56fa0-c2dc-43ff-9677-45b886ef282a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# nm = mgrs_snow_gdf.iloc[3000].tile\n",
    "# bbox_gdf = mgrs_gdf[mgrs_gdf.tile==nm]\n",
    "\n",
    "# f,ax=plt.subplots()\n",
    "# bbox_gdf.geometry.boundary.plot(ax=ax,color='red')\n",
    "# xmin, ymin, xmax, ymax = bbox_gdf.total_bounds\n",
    "# ax.set_xlim([xmin-10,xmax+10])\n",
    "# ax.set_ylim([ymin-10,ymax+10])\n",
    "\n",
    "# ctx.add_basemap(ax=ax,source=ctx.providers.OpenTopoMap, crs=bbox_gdf.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2821ed07-9a50-4d50-8652-ec5cc4275864",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# classes = [ # page 13 of https://esa-worldcover.s3.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf\n",
    "# #    10, # treecover\n",
    "#     20, # shrubland\n",
    "#     30, # grassland\n",
    "#     40, # cropland\n",
    "# #    50, # built-up\n",
    "#     60, #bare / sparse vegetation\n",
    "#     70, # snow and ice\n",
    "# #    80, # permanent water bodies\n",
    "#     90, # herbaceous wetlands\n",
    "#     95, # mangroves\n",
    "#     100 # loss and lichen\n",
    "# ]\n",
    "# worldcover = s1_rtc_bs_utils.get_worldcover(ts_ds)\n",
    "#ts_ds = ts_ds.where(worldcover.isin(classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c98bd17-af43-41fd-8826-32163128e88e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "resolution = 80\n",
    "min_acqusitions = 6\n",
    "years = ['allyears_median','allyears_std','all_years_polyfit','allyears_corr_strength','2015_median','2016_median','2017_median','2018_median','2019_median','2020_median','2021_median','2022_median','2023_median']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e1e1862-daaa-4083-988e-84b72fe3fc28",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_runoff_onset_mgrs(ts_ds,return_seperate_orbits_and_polarizations=False, num_acquisitions_during_melt_season=6):\n",
    "\n",
    "    #orbits = s1_rtc_bs_utils.get_orbits_with_melt_season_coverage(ts_ds,num_acquisitions_during_melt_season=num_acquisitions_during_melt_season)\n",
    "    #ts_ds = ts_ds[ts_ds['sat:relative_orbit'].isin(orbits).compute()]\n",
    "    \n",
    "    ts_ds = s1_rtc_bs_utils.remove_border_noise(ts_ds)\n",
    "    \n",
    "    counts = ts_ds.groupby('sat:relative_orbit').count()\n",
    "        \n",
    "    runoffs_int64 = ts_ds.groupby('sat:relative_orbit').map(lambda c: c.idxmin(dim='time')).astype(np.int64).where(counts>=num_acquisitions_during_melt_season)\n",
    "    \n",
    "    if return_seperate_orbits_and_polarizations==False: \n",
    "        runoffs_int64 = runoffs_int64.where(runoffs_int64>0).median(dim=['sat:relative_orbit','band'],skipna=True)\n",
    "\n",
    "    return runoffs_int64.astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6208a03-feb7-489c-96be-6993a5fc0277",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for tile in tqdm(mgrs_snow_gdf.tile):\n",
    "\n",
    "    if any(tile in s for s in fs.glob(f'snowmelt/eric/global_4326/')):\n",
    "        print(f'folder {tile} already exists, skipping...')\n",
    "        continue\n",
    "        \n",
    "    if any(tile in s for s in fs.open('snowmelt/eric/global/exclude.txt','r').readlines()):\n",
    "        print(f'{tile} is in exclude.txt, skipping...')\n",
    "        continue \n",
    "        \n",
    "    print(f'working on tile {tile}...')\n",
    "    \n",
    "    try:\n",
    "        tic = time.perf_counter()\n",
    "\n",
    "        bbox_gdf = mgrs_snow_gdf[mgrs_snow_gdf.tile==tile]\n",
    "        ts_ds = s1_rtc_bs_utils.get_s1_rtc_stac_odc_pc(bbox_gdf,start_time='2015-01-01',end_time='2023-12-31',resolution=resolution,epsg=f'EPSG:{bbox_gdf.epsg.values[0]}')\n",
    "\n",
    "        allyears = ts_ds.where((ts_ds['time.month']>=2) & (ts_ds['time.month']<8), drop=True).groupby('time.year').apply(\n",
    "        get_runoff_onset_mgrs,return_seperate_orbits_and_polarizations=False, num_acquisitions_during_melt_season=min_acqusitions)\n",
    "\n",
    "        runoffs_allyears = allyears.dt.dayofyear.where(allyears.dt.dayofyear>0).compute().dropna(dim='year',how='all').rio.write_nodata(np.iinfo(np.int16).min,encoded=True)\n",
    "\n",
    "        polyfits = runoffs_allyears.polyfit('year',deg=1,full=True,cov=True)\n",
    "\n",
    "\n",
    "        years = ['allyears_median','allyears_std','allyears_polyfit','allyears_corr_strength','2015_median','2016_median','2017_median','2018_median','2019_median','2020_median','2021_median','2022_median','2023_median']\n",
    "\n",
    "        for year in years:\n",
    "            #print(f'writing {year}...')\n",
    "\n",
    "\n",
    "            if year == 'allyears_std':\n",
    "                data_type = 'float32'\n",
    "                raster = runoffs_allyears.std(dim='year').rio.write_nodata(np.finfo(np.float32).min,encoded=True)\n",
    "\n",
    "            elif year == 'allyears_polyfit':\n",
    "                data_type = 'float32'\n",
    "                raster = polyfits.polyfit_coefficients[0].rio.write_nodata(np.finfo(np.float32).min,encoded=True).rio.set_crs(runoffs_allyears.rio.crs)\n",
    "\n",
    "            elif year == 'allyears_corr_strength':\n",
    "                data_type = 'float32'\n",
    "                raster = xr.corr(runoffs_allyears.year,runoffs_allyears,dim='year').rio.write_nodata(np.finfo(np.float32).min,encoded=True).rio.set_crs(runoffs_allyears.rio.crs)\n",
    "\n",
    "            elif year == 'allyears_median':\n",
    "                data_type = 'int16'\n",
    "                raster = runoffs_allyears.median(dim='year').rio.write_nodata(np.iinfo(np.int16).min,encoded=True)\n",
    "\n",
    "            else:\n",
    "                data_type = 'int16'\n",
    "                int_year = int(year[0:4])\n",
    "                if int_year in runoffs_allyears.year:\n",
    "                    raster = runoffs_allyears.sel(year=int_year)\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "\n",
    "            # actually changing the reasmpling here might be helpful\n",
    "            with io.BytesIO() as buffer:\n",
    "                raster.rio.to_raster(buffer, driver=\"COG\", dtype=data_type)\n",
    "                buffer.seek(0)\n",
    "                blob_client = container_client.get_blob_client(f\"eric/global/{tile}/runoff_onset_{tile}_{year}_{resolution}m.tif\")\n",
    "                blob_client.upload_blob(buffer, overwrite=True)\n",
    "\n",
    "            with io.BytesIO() as buffer:\n",
    "                raster.rio.reproject('epsg:4326').rio.to_raster(buffer, driver=\"COG\", dtype=data_type)\n",
    "                buffer.seek(0)\n",
    "                blob_client = container_client.get_blob_client(f\"eric/global_4326/{tile}/runoff_onset_{tile}_{year}_{resolution}m_4326.tif\")\n",
    "                blob_client.upload_blob(buffer, overwrite=True)\n",
    "\n",
    "\n",
    "        toc = time.perf_counter()\n",
    "        print(f'Processing time: {toc - tic:0.1f} seconds')\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        \n",
    "        exclude = tile\n",
    "        with io.BytesIO() as buffer:\n",
    "            with fs.open('snowmelt/eric/global/exclude.txt','r',newline='\\n') as exclude_file:\n",
    "                exclude_list = exclude_file.readlines()\n",
    "\n",
    "            exclude_list = [x.replace('\\n','') for x in exclude_list]\n",
    "            exclude_list.append(exclude)\n",
    "\n",
    "            np.savetxt(buffer,exclude_list, fmt='%s',delimiter='\\n')\n",
    "            buffer.seek(0)\n",
    "            blob_client = container_client.get_blob_client('eric/global/exclude.txt')\n",
    "            blob_client.upload_blob(buffer, overwrite=True)\n",
    "        \n",
    "            print(f'Failed, added {exclude} to exclude.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3e48855-517e-47d4-b5bc-f630c169ce53",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "885771dc-b589-419f-bd66-cd3ed7c0f5b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b5580f2-a4d7-47cb-a5c0-7d1a5a8aaed4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b951418b-b52c-4a49-809e-93f44c918cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs.glob(f'snowmelt/eric/global_4326/{tile}/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7d91d1c-c46c-48a5-8d94-697ae27aedca",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "url = \"https://snowmelt.blob.core.windows.net/\"\n",
    "\n",
    "geotiff_list = fs.glob(f'snowmelt/eric/global/{tile}/*_????_median*')\n",
    "\n",
    "year_list = []\n",
    "\n",
    "for geotiff in geotiff_list:\n",
    "    year_list.append(re.findall(\"_(\\d{4})_\", geotiff)[0])\n",
    "year_list = [int(year) for year in year_list]\n",
    "time_var = xr.Variable('time', year_list)\n",
    "\n",
    "runoff_allyears_vis = xr.concat([rxr.open_rasterio(f\"{url}{i}\") for i in geotiff_list],dim=time_var).squeeze().sortby('time')\n",
    "runoff_allyears_vis = runoff_allyears_vis.where(runoff_allyears_vis>-32768)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83fd7aab-9ede-4321-bfef-476021c4049d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "runoff_allyears_vis.plot(col='time',col_wrap=3, vmin=80, vmax=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "384922a3-61f0-473d-8093-92eec2fdd0e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#runoff_allyears_vis.median(dim='time').where(runoff_allyears_vis.median(dim='time')>0).rio.write_nodata(-32768,encoded=True).plot(vmin=80,vmax=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16e96405-fb2a-409d-af23-23df3f6d25d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rxr.open_rasterio(f'{url}snowmelt/eric/global_4326/47WMQ/runoff_onset_47WMQ_allyears_polyfit_80m_4326.tif',mask_and_scale=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dbcc570-0340-46b1-bd9a-9b1b9f1fb094",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "url = \"https://snowmelt.blob.core.windows.net/\"\n",
    "\n",
    "geotiff_list = fs.glob(f'snowmelt/eric/global_4326/{tile}/*')\n",
    "\n",
    "year_list = []\n",
    "\n",
    "for geotiff in geotiff_list:\n",
    "    year_list.append(re.findall(\"_(\\d{4})_\", geotiff)[0])\n",
    "year_list = [int(year) for year in year_list]\n",
    "time_var = xr.Variable('time', year_list)\n",
    "\n",
    "runoff_allyears_vis = xr.concat([rxr.open_rasterio(f\"{url}{i}\") for i in geotiff_list],dim=time_var).squeeze().sortby('time')\n",
    "runoff_allyears_vis = runoff_allyears_vis.where(runoff_allyears_vis>-32768)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9abc50da-fe06-4f49-8e63-2adf0a1c00df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "runoff_allyears_vis.plot(col='time',col_wrap=3, vmin=80, vmax=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c9b4865-501c-486a-90fb-a8e16fb2b5a8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "runoff_allyears_vis.median(dim='time').where(runoff_allyears_vis.median(dim='time')>0).rio.write_nodata(-32768,encoded=True).plot(vmin=80,vmax=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a456ce7-6b30-4225-86c2-bc0a3c05c8d7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#fn = f\"{url}{fs.glob('snowmelt/eric/global/04WFA/')[3]}\"\n",
    "fn = f\"{url}snowmelt/eric/global_4326/47WMQ/runoff_onset_47WMQ_allyears_std_80m_4326.tif\"\n",
    "fn = f\"{url}snowmelt/eric/global_4326/47WMQ/runoff_onset_47WMQ_2021_median_80m_4326.tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dc2bf22-9f7a-4c67-b808-6dff56ebeff6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f35512de-6660-49a1-8067-28a241a4334b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!gdalinfo $fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61f37009-8448-4c1f-a67d-a8c91db4e259",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ts_ds = s1_rtc_bs_utils.get_s1_rtc_stac_odc_pc(bbox_gdf,start_time='2015-01-01',end_time='2023-12-31',resolution=resolution,epsg=f'EPSG:{bbox_gdf.epsg.values[0]}')\n",
    "# allyears = ts_ds.where((ts_ds['time.month']>=2) & (ts_ds['time.month']<8), drop=True).groupby('time.year').apply(\n",
    "#     get_runoff_onset_2,return_seperate_orbits_and_polarizations=False, num_acquisitions_during_melt_season=min_acqusitions)\n",
    "# %%time\n",
    "# runoffs_allyears = allyears.dt.dayofyear.where(allyears.dt.dayofyear>0).compute().dropna(dim='year',how='all')\n",
    "# runoffs_allyears.plot(col='year',col_wrap=4,vmin=80,vmax=240)\n",
    "# runoffs_allyears#.rio.write_nodata(-32768,encoded=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3877cd3-053e-4e2b-8a35-c98b866dfc56",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# min_acqusitions = 8\n",
    "# counts = ts_ds_oneyear.groupby('sat:relative_orbit').count()\n",
    "# counts\n",
    "# runoffs_median = s1_rtc_bs_utils.get_runoff_onset(ts_ds_oneyear,return_seperate_orbits_and_polarizations=True,num_acquisitions_during_melt_season=8)\n",
    "# runoffs_median\n",
    "# runoffs_median_masked = runoffs_median.where(counts>min_acqusitions)\n",
    "# runoffs_median_masked\n",
    "# runoffs_median_masked_compute = runoffs_median_masked.dt.dayofyear.where(runoffs_median_masked.dt.dayofyear>0).compute()\n",
    "# runoffs_median_masked_compute\n",
    "# runoffs_median_masked_compute.plot(col='band',row='sat:relative_orbit',vmin=80,vmax=240)\n",
    "# runoffs_median_masked_compute.median(dim=['sat:relative_orbit','band'],skipna=True).plot(vmin=80,vmax=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41f77f33-4bdc-4236-a6fc-ac8938e135d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# year = 2020\n",
    "# melt_year = slice(f'{year}-02-01',f'{year}-08-01')\n",
    "# ts_ds_oneyear = ts_ds.sel(time=melt_year)\n",
    "# ts_ds_oneyear\n",
    "#runoffs = get_runoff_onset_mgrs(ts_ds_oneyear, return_seperate_orbits_and_polarizations=True, num_acquisitions_during_melt_season=min_acqusitions).compute()\n",
    "#runoffs.dt.dayofyear.where(runoffs.dt.dayofyear>0).plot(col='band',row='sat:relative_orbit',vmin=80,vmax=240)\n",
    "# runoffs_med = get_runoff_onset_2(ts_ds_oneyear, return_seperate_orbits_and_polarizations=False, num_acquisitions_during_melt_season=min_acqusitions).compute()\n",
    "# runoffs_med.dt.dayofyear.where(runoffs_med.dt.dayofyear>0).plot(vmin=80,vmax=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9accaa5-2bf2-432a-93d0-d4439c45e611",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the exclude file\n",
    "# with fs.open('snowmelt/eric/global/exclude.txt','r',newline='\\n') as exclude_file:\n",
    "#     exclude_list = exclude_file.readlines()\n",
    "\n",
    "# exclude_list = [x.replace('\\n','') for x in exclude_list]\n",
    "# exclude_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e99ee3-5e70-4782-a671-12b43dbc9ad4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#WARNING THIS LINE RESETS THE EXCLUDE FILE\n",
    "##############fs.touch('snowmelt/eric/global/exclude.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeef8249-8e26-4441-9d24-7be26b52a6b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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