diff --git a/.gitignore b/.gitignore
index 014352a..2e2f8c4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,7 @@
 .ipynb_checkpoints
 OpenMRG/notebooks/data
 OpenRainER/notebooks/data
-*.pyc
\ No newline at end of file
+OpenMesh/notebooks/data
+*.pyc
+.DS_Store
+.idea/
\ No newline at end of file
diff --git a/OpenMesh/README.md b/OpenMesh/README.md
new file mode 100644
index 0000000..e915a07
--- /dev/null
+++ b/OpenMesh/README.md
@@ -0,0 +1,69 @@
+# OpenMesh
+
+OpenMesh is a dataset of  microwave link and personal weather station (PWS)
+measurements over New York City, covering January 2024. It also includes ASOS reference station
+data for validation. The raw data is sourced from two Zenodo records (jacoby_2025_OpenMesh).
+
+
+## Example files
+
+The root folder contains ready-to-use NetCDF sample files at three time windows.
+File names follow the convention: openmesh_{sensor}_{duration}.nc
+
+CML files (75 links, received signal level in dBm):
+  openmesh_cml_1d.nc       one day
+  openmesh_cml_1w.nc       one week
+  openmesh_cml_20d.nc      20 days
+
+PWS files (35 Weather Underground stations, rainfall, temperature, humidity, wind, pressure):
+  openmesh_wu_pws_1d.nc    one day
+  openmesh_wu_pws_1w.nc    one week
+  openmesh_wu_pws_20d.nc   20 days
+
+ASOS reference station files (airport weather stations, used for comparison and validation):
+  openmesh_asos_ws_20d.nc  20 days
+
+
+## Notebooks
+
+create_OpenMesh_data.ipynb
+  Downloads the raw CML and PWS data from Zenodo into notebooks/data/jacoby_2025_OpenMesh.
+  Generates sample NetCDF files for any time window using the functions in openmesh_data_io.py.
+  Also produces the root example files above. 
+
+analyze_OpenMesh_data.ipynb
+  Loads CML and PWS datasets either from the root example files or by creating them on the fly
+  from the raw data for a custom time window. Provides an overview of the dataset dimensions,
+  link/station metadata, and basic visualizations of signal level and weather variables.
+
+ASOS_opensense.ipynb
+  Downloads and processes ASOS airport station data (JFK, EWR, LGA) for the same time window
+  as the CML and PWS data. Converts the data to the opensense NetCDF format and saves it to
+  the samples folder. Used to generate the reference files for validation.
+
+
+## Helper modules
+
+openmesh_data_io.py
+  Central module for all data operations. Key functions:
+  - download_openmesh() and download_pws_wu(): download and extract Zenodo archives
+  - create_openmesh_sample() and create_pws_wu_sample(): slice a time window from raw data
+  - create_asos_sample(): fetch and convert ASOS data for a given time window
+  - create_all_datasets(): run all three in one call, with save and resample options
+  - load_cml() and load_pws_wu(): load the full raw source files into memory
+
+ws_opensense_conversion.py
+  Handles unit conversions and variable transformations for PWS data, converting raw
+  Weather Underground fields to the opensense standard format.
+
+
+## Data folder
+
+The raw downloaded data is stored in notebooks/data/jacoby_2025_OpenMesh and is not tracked
+by git. Running create_OpenMesh_data.ipynb will populate it automatically. The layout is:
+
+  raw/        full source NetCDF files downloaded from Zenodo
+  meta/       station and link metadata CSVs, and HTML map visualizations
+  archived/   original ZIP archives from Zenodo
+  examples/   example notebooks included in the Zenodo release
+  docs/       README from the Zenodo release
diff --git a/OpenMesh/notebooks/ASOS_opensense.ipynb b/OpenMesh/notebooks/ASOS_opensense.ipynb
new file mode 100644
index 0000000..ff79414
--- /dev/null
+++ b/OpenMesh/notebooks/ASOS_opensense.ipynb
@@ -0,0 +1,623 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ASOS → OpenSense Conversion\n",
+    "\n",
+    "Fetch ASOS 1-minute weather data from the IEM API and convert to\n",
+    "OpenSense-format NetCDF.\n",
+    "\n",
+    "**Source:** https://mesonet.agron.iastate.edu/request/asos/1min.phtml  \n",
+    "**Output format:** OpenSense v1.0 — dims `(id, time)`, compressed NetCDF\n",
+    "\n",
+    "| Step | Cell |\n",
+    "|------|------|\n",
+    "| 1 | Imports & config |\n",
+    "| 2 | Discover NYC stations |\n",
+    "| 3 | Fetch raw data |\n",
+    "| 4 | Inspect single station |\n",
+    "| 5 | Convert to OpenSense dataset |\n",
+    "| 6 | Save NetCDF |"
+   ],
+   "id": "daeca567da2197c2"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Imports & config"
+   ],
+   "id": "35236abe7115663d"
+  },
+  {
+   "cell_type": "code",
+   "id": "2d6b1639",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:12:50.408212Z",
+     "start_time": "2026-03-25T09:12:49.516084Z"
+    }
+   },
+   "source": [
+    "import warnings\n",
+    "from datetime import datetime\n",
+    "\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from OpenMesh.notebooks.ws_opensense_conversion import (\n",
+    "    # paths\n",
+    "    SAMPLE_DIR,  # station discovery\n",
+    "    fetch_asos_stations_nyc,\n",
+    "    load_asos_metadata,\n",
+    "    # fetch\n",
+    "    fetch_asos_raw,\n",
+    "    # convert + save\n",
+    "    to_opensense_dataset,\n",
+    "    save_opensense_dataset,\n",
+    ")\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# ── time window ───────────────────────────────────────────────\n",
+    "START_DT = datetime(2024, 1, 15)\n",
+    "END_DT   = datetime(2024, 1, 22)\n",
+    "\n",
+    "# ── variables to fetch ────────────────────────────────────────\n",
+    "# full list: print(list(ASOS_AVAILABLE_VARS.keys()))\n",
+    "VARIABLES = [\n",
+    "    'precip_amount',\n",
+    "    'precip_type',\n",
+    "    'precip_category',\n",
+    "    'temperature',\n",
+    "    'wind_speed',\n",
+    "    'wind_direction',\n",
+    "]\n",
+    "\n",
+    "print(f'Period    : {START_DT.date()}  →  {END_DT.date()}')\n",
+    "print(f'Variables : {VARIABLES}')\n",
+    "print(f'SAMPLE_DIR: {SAMPLE_DIR.resolve()}')"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Period    : 2024-01-15  →  2024-01-22\n",
+      "Variables : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "SAMPLE_DIR: /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/samples\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75286743",
+   "metadata": {},
+   "source": [
+    "## 2. Discover NYC-area ASOS stations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "33001b8d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:12:56.205963Z",
+     "start_time": "2026-03-25T09:12:50.422589Z"
+    }
+   },
+   "source": [
+    "# queries IEM network API + saves to ASOS_META_PATH\n",
+    "# re-run to refresh; loads from CSV if already saved:\n",
+    "# meta = load_asos_metadata()\n",
+    "\n",
+    "meta     = fetch_asos_stations_nyc()\n",
+    "stations = meta.index.tolist()\n",
+    "\n",
+    "print(f'\\nAvailable ({len(stations)}): {stations}')"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Querying NY_ASOS ... 7 stations found\n",
+      "  Querying NJ_ASOS ... 5 stations found\n",
+      "  Querying CT_ASOS ... 1 stations found\n",
+      "\n",
+      "  Total : 13 stations\n",
+      "  ID       Network    Name                                    Lat      Lon   Elev\n",
+      "  ────────────────────────────────────────────────────────────────────────\n",
+      "  BDR      CT_ASOS    Bridgeport/Sikorsky                  41.158  -73.129    5.0\n",
+      "  CDW      NJ_ASOS    CALDWELL/ESSEX CO.                   40.876  -74.283   53.0\n",
+      "  TEB      NJ_ASOS    TETERBORO AIRPORT                    40.859  -74.056    3.0\n",
+      "  MMU      NJ_ASOS    MORRISTOWN MUNI                      40.799  -74.415   57.0\n",
+      "  EWR      NJ_ASOS    Newark Intl                          40.683  -74.169    2.0\n",
+      "  LDJ      NJ_ASOS    Linden                               40.617  -74.245    7.0\n",
+      "  HPN      NY_ASOS    WHITE PLAINS                         41.067  -73.707  134.0\n",
+      "  ISP      NY_ASOS    ISLIP/MACARTHUR                      40.794  -73.102   30.0\n",
+      "  LGA      NY_ASOS    New York/LaGuardia                   40.779  -73.880    9.0\n",
+      "  NYC      NY_ASOS    NEW YORK CITY                        40.779  -73.969   27.0\n",
+      "  FRG      NY_ASOS    FARMINGDALE/REPUBLC                  40.729  -73.413   25.0\n",
+      "  JRB      NY_ASOS    Manhattan - Wall Street              40.701  -74.009    0.0\n",
+      "  JFK      NY_ASOS    NEW YORK/JF KENNEDY                  40.639  -73.762    7.0\n",
+      "\n",
+      "  Saved : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/meta/ASOS_stations.csv\n",
+      "\n",
+      "Available (13): ['BDR', 'CDW', 'TEB', 'MMU', 'EWR', 'LDJ', 'HPN', 'ISP', 'LGA', 'NYC', 'FRG', 'JRB', 'JFK']\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "id": "0242d95b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:12:56.284993Z",
+     "start_time": "2026-03-25T09:12:56.276426Z"
+    }
+   },
+   "source": [
+    "\n",
+    "\n",
+    "asos_meta = load_asos_metadata()\n",
+    "# IDs must match the index: CDW, TEB, MMU, EWR, LDJ, LGA, NYC, JRB, JFK\n",
+    "selected_stations = ['JFK', 'EWR', 'LGA', 'NYC']\n",
+    "\n",
+    "print(f\"Selected: {selected_stations}\")\n",
+    "print(asos_meta.loc[selected_stations])"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected: ['JFK', 'EWR', 'LGA', 'NYC']\n",
+      "                           Name  Latitude  Longitude  Elevation  Network\n",
+      "Station ID                                                              \n",
+      "JFK         NEW YORK/JF KENNEDY   40.6386   -73.7622        7.0  NY_ASOS\n",
+      "EWR                 Newark Intl   40.6827   -74.1693        2.0  NJ_ASOS\n",
+      "LGA          New York/LaGuardia   40.7794   -73.8803        9.0  NY_ASOS\n",
+      "NYC               NEW YORK CITY   40.7790   -73.9692       27.0  NY_ASOS\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e72c0dff",
+   "metadata": {},
+   "source": [
+    "## 3. Fetch raw ASOS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "01174cd8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.349012Z",
+     "start_time": "2026-03-25T09:12:56.291043Z"
+    }
+   },
+   "source": [
+    "# If you want a shorter window, define END_DT as datetime, not string:\n",
+    "END_DT = datetime(2024, 1, 17)\n",
+    "asos_raw = fetch_asos_raw(\n",
+    "    stations=selected_stations,\n",
+    "    start_dt=START_DT,\n",
+    "    end_dt=END_DT,\n",
+    "    variables=VARIABLES,\n",
+    ")\n",
+    "\n",
+    "print(f'\\nFetched: {list(asos_raw.keys())}')"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Fetching JFK ... 2,760 records\n",
+      "  Fetching EWR ... 1,392 records\n",
+      "  Fetching LGA ... 2,880 records\n",
+      "  Fetching NYC ... 1,795 records\n",
+      "\n",
+      "Fetched: ['JFK', 'EWR', 'LGA', 'NYC']\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a5f9302",
+   "metadata": {},
+   "source": [
+    "## 4. Inspect single station"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "892fc56e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.364571Z",
+     "start_time": "2026-03-25T09:13:07.356805Z"
+    }
+   },
+   "source": [
+    "station_id = 'JFK'   # change to any fetched station\n",
+    "\n",
+    "if station_id not in asos_raw:\n",
+    "    print(f'Not in asos_raw. Available: {list(asos_raw.keys())}')\n",
+    "else:\n",
+    "    df = asos_raw[station_id]\n",
+    "\n",
+    "    # metadata\n",
+    "    if station_id in meta.index:\n",
+    "        row = meta.loc[station_id]\n",
+    "        print(f'Station  : {station_id}  —  {row[\"Name\"]}')\n",
+    "        print(f'Lat/Lon  : {row[\"Latitude\"]:.4f}, {row[\"Longitude\"]:.4f}')\n",
+    "        print(f'Elev     : {row[\"Elevation\"]:.1f} m')\n",
+    "        print(f'Network  : {row[\"Network\"]}')\n",
+    "\n",
+    "    # data\n",
+    "    print(f'\\nRecords  : {len(df):,}')\n",
+    "    print(f'Time     : {str(df.index[0])[:19]}  →  {str(df.index[-1])[:19]}')\n",
+    "    print(f'Columns  : {df.columns.tolist()}')\n",
+    "    if 'precip_amount' in df.columns:\n",
+    "        print(f'Rain sum : {df[\"precip_amount\"].sum():.2f} mm')\n",
+    "    print()\n",
+    "    print(df.head(10))"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Station  : JFK  —  NEW YORK/JF KENNEDY\n",
+      "Lat/Lon  : 40.6386, -73.7622\n",
+      "Elev     : 7.0 m\n",
+      "Network  : NY_ASOS\n",
+      "\n",
+      "Records  : 2,760\n",
+      "Time     : 2024-01-15 00:00:00  →  2024-01-16 23:59:00\n",
+      "Columns  : ['station_id', 'precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "Rain sum : 10.16 mm\n",
+      "\n",
+      "                    station_id  precip_amount precip_type precip_category  \\\n",
+      "datetime                                                                    \n",
+      "2024-01-15 00:00:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:01:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:02:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:03:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:04:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:05:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:06:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:07:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:08:00        JFK            0.0          NP             dry   \n",
+      "2024-01-15 00:09:00        JFK            0.0          NP             dry   \n",
+      "\n",
+      "                     temperature  wind_speed  wind_direction  \n",
+      "datetime                                                      \n",
+      "2024-01-15 00:00:00     0.000000    10.28880             279  \n",
+      "2024-01-15 00:01:00     0.000000     9.77436             280  \n",
+      "2024-01-15 00:02:00     0.000000    10.80324             288  \n",
+      "2024-01-15 00:03:00    -0.555556    11.31768             289  \n",
+      "2024-01-15 00:04:00     0.000000    10.28880             284  \n",
+      "2024-01-15 00:05:00     0.000000     8.74548             286  \n",
+      "2024-01-15 00:06:00     0.000000     8.74548             287  \n",
+      "2024-01-15 00:07:00     0.000000    10.80324             289  \n",
+      "2024-01-15 00:08:00     0.000000    10.80324             290  \n",
+      "2024-01-15 00:09:00    -0.555556    10.28880             290  \n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5c5f98d",
+   "metadata": {},
+   "source": [
+    "## 5. Convert to OpenSense dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "616402f3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.568001Z",
+     "start_time": "2026-03-25T09:13:07.368945Z"
+    }
+   },
+   "source": [
+    "ds_asos = to_opensense_dataset(\n",
+    "    data=asos_raw,\n",
+    "    source='asos',\n",
+    "    variables=VARIABLES,\n",
+    "    meta=meta,\n",
+    "    start_dt=START_DT,\n",
+    "    end_dt=END_DT,\n",
+    ")\n",
+    "\n",
+    "print(f'Shape    : {dict(ds_asos.sizes)}')\n",
+    "print(f'Time     : {str(ds_asos.time.values[0])[:19]}  →  {str(ds_asos.time.values[-1])[:19]}')\n",
+    "print(f'Stations : {ds_asos.id.values}')\n",
+    "print(f'Vars     : {list(ds_asos.data_vars)}')\n",
+    "print(ds_asos)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape    : {'id': 4, 'time': 2880}\n",
+      "Time     : 2024-01-15T00:00:00  →  2024-01-16T23:59:00\n",
+      "Stations : ['JFK' 'EWR' 'LGA' 'NYC']\n",
+      "Vars     : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "<xarray.Dataset> Size: 576kB\n",
+      "Dimensions:          (id: 4, time: 2880)\n",
+      "Coordinates:\n",
+      "  * id               (id) <U3 48B 'JFK' 'EWR' 'LGA' 'NYC'\n",
+      "  * time             (time) datetime64[ns] 23kB 2024-01-15 ... 2024-01-16T23:...\n",
+      "    lat              (id) float64 32B 40.64 40.68 40.78 40.78\n",
+      "    lon              (id) float64 32B -73.76 -74.17 -73.88 -73.97\n",
+      "    elev             (id) float64 32B 7.0 2.0 9.0 27.0\n",
+      "Data variables:\n",
+      "    precip_amount    (id, time) float64 92kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    precip_type      (id, time) object 92kB 'NP' 'NP' 'NP' ... 'NP' 'NP' 'NP'\n",
+      "    precip_category  (id, time) object 92kB 'dry' 'dry' 'dry' ... 'dry' 'dry'\n",
+      "    temperature      (id, time) float64 92kB 0.0 0.0 0.0 ... -2.222 -2.222\n",
+      "    wind_speed       (id, time) float64 92kB 10.29 9.774 10.8 ... 6.688 6.688\n",
+      "    wind_direction   (id, time) float64 92kB 279.0 280.0 288.0 ... 314.0 312.0\n",
+      "Attributes:\n",
+      "    title:        OpenMesh ASOS sample (multi-station)\n",
+      "    source:       asos\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-17\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-17T00:00:00\n",
+      "    conventions:  OpenSense v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86d190a8",
+   "metadata": {},
+   "source": [
+    "## 6. Save NetCDF (multi-station + one file per station)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "dd98359e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.574993Z",
+     "start_time": "2026-03-25T09:13:07.571862Z"
+    }
+   },
+   "source": [
+    "# Set Global Attributes\n",
+    "\n",
+    "# ── note on naming ────────────────────────────────────────────\n",
+    "# ASOS data keeps 'precip_amount' / 'precip_rate' intentionally —\n",
+    "# OpenSense PWS convention uses 'rainfall_amount' / 'rainfall_rate'\n",
+    "# but ASOS measures all precipitation types (rain, snow, etc.)\n",
+    "# so 'precip' is more accurate here. No renaming applied.\n",
+    "\n",
+    "GLOBAL_ATTRS = {\n",
+    "    \"title\":        \"OpenMesh ASOS reference precipitation — NYC\",\n",
+    "    \"institution\":  \"NOAA / IEM\",\n",
+    "    \"source\":       \"NOAA ASOS 1-min via Iowa Environmental Mesonet\",\n",
+    "    \"references\":   \"https://mesonet.agron.iastate.edu/request/asos/1min.phtml\",\n",
+    "    \"history\":      f\"Created {pd.Timestamp.now().strftime('%Y-%m-%d')}\",\n",
+    "    \"comment\":      \"ASOS airport stations — precip_amount/rate kept (not renamed to rainfall_* since ASOS captures all precip types)\",\n",
+    "    \"license\":      \"Public domain (NOAA)\",\n",
+    "    \"conventions\":  \"OpenSense-PWS-v1.0\",\n",
+    "    \"featureType\":  \"timeSeries\",\n",
+    "    \"sensor_type\":  \"ASOS\",\n",
+    "    \"time_zone\":    \"UTC\",\n",
+    "}\n"
+   ],
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "code",
+   "id": "bfb195ab",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:13:07.782903Z",
+     "start_time": "2026-03-25T09:13:07.579606Z"
+    }
+   },
+   "source": [
+    "# ── date range for filenames ───────────────────────────────────\n",
+    "_date_start = START_DT.strftime(\"%Y-%m-%d\")\n",
+    "_date_end   = END_DT.strftime(\"%Y-%m-%d\")\n",
+    "\n",
+    "# ── save multi-station (one flat file) + one file per station ──\n",
+    "ds_asos_sample = to_opensense_dataset(\n",
+    "    data=asos_raw, source=\"asos\",\n",
+    "    variables=VARIABLES, meta=meta,\n",
+    "    start_dt=START_DT, end_dt=END_DT,\n",
+    "    extra_attrs=GLOBAL_ATTRS,\n",
+    ")\n",
+    "path_asos = save_opensense_dataset(\n",
+    "    ds_asos_sample, output_dir=SAMPLE_DIR,\n",
+    "    filename=f\"ASOS_{_date_start}_{_date_end}.nc\",\n",
+    ")\n",
+    "\n",
+    "print(f\"  Multi-station : {path_asos}\")\n",
+    "print(f\"  Shape        : {dict(ds_asos_sample.sizes)}\")\n",
+    "print(f\"  Stations     : {list(ds_asos_sample.id.values)}\")\n",
+    "print(f\"  Vars         : {list(ds_asos_sample.data_vars)}\")\n",
+    "print(ds_asos_sample.sel(id=\"JFK\"))\n",
+    "\n",
+    "# ── one file per station (station name in filename) ───────────\n",
+    "paths_single = []\n",
+    "for sid in ds_asos_sample.id.values:\n",
+    "    ds_one = to_opensense_dataset(\n",
+    "        data={sid: asos_raw[str(sid)]}, source=\"asos\",\n",
+    "        variables=VARIABLES, meta=meta,\n",
+    "        start_dt=START_DT, end_dt=END_DT,\n",
+    "        extra_attrs=GLOBAL_ATTRS,\n",
+    "    )\n",
+    "    p = save_opensense_dataset(\n",
+    "        ds_one, output_dir=SAMPLE_DIR,\n",
+    "        filename=f\"ASOS_{sid}_{_date_start}_{_date_end}.nc\",\n",
+    "    )\n",
+    "    paths_single.append(p)\n",
+    "print(f\"  Single-station files : {[p.name for p in paths_single]}\")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Saved : ASOS_2024-01-15_2024-01-17.nc  (1.34 MB)\n",
+      "  Multi-station : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/samples/ASOS_2024-01-15_2024-01-17.nc\n",
+      "  Shape        : {'id': 4, 'time': 2880}\n",
+      "  Stations     : [np.str_('JFK'), np.str_('EWR'), np.str_('LGA'), np.str_('NYC')]\n",
+      "  Vars         : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "<xarray.Dataset> Size: 161kB\n",
+      "Dimensions:          (time: 2880)\n",
+      "Coordinates:\n",
+      "    id               <U3 12B 'JFK'\n",
+      "  * time             (time) datetime64[ns] 23kB 2024-01-15 ... 2024-01-16T23:...\n",
+      "    lat              float64 8B 40.64\n",
+      "    lon              float64 8B -73.76\n",
+      "    elev             float64 8B 7.0\n",
+      "Data variables:\n",
+      "    precip_amount    (time) float64 23kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    precip_type      (time) object 23kB 'NP' 'NP' 'NP' 'NP' ... 'NP' 'NP' 'NP'\n",
+      "    precip_category  (time) object 23kB 'dry' 'dry' 'dry' ... 'dry' 'dry' 'dry'\n",
+      "    temperature      (time) float64 23kB 0.0 0.0 0.0 ... -2.222 -2.222 -2.222\n",
+      "    wind_speed       (time) float64 23kB 10.29 9.774 10.8 ... 9.774 10.29 10.8\n",
+      "    wind_direction   (time) float64 23kB 279.0 280.0 288.0 ... 322.0 319.0 315.0\n",
+      "Attributes: (12/14)\n",
+      "    title:        OpenMesh ASOS reference precipitation — NYC\n",
+      "    source:       NOAA ASOS 1-min via Iowa Environmental Mesonet\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-17\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-17T00:00:00\n",
+      "    conventions:  OpenSense-PWS-v1.0\n",
+      "    ...           ...\n",
+      "    history:      Created 2026-03-25\n",
+      "    comment:      ASOS airport stations — precip_amount/rate kept (not rename...\n",
+      "    license:      Public domain (NOAA)\n",
+      "    featureType:  timeSeries\n",
+      "    sensor_type:  ASOS\n",
+      "    time_zone:    UTC\n",
+      "  Saved : ASOS_JFK_2024-01-15_2024-01-17.nc  (0.38 MB)\n",
+      "  Saved : ASOS_EWR_2024-01-15_2024-01-17.nc  (0.22 MB)\n",
+      "  Saved : ASOS_LGA_2024-01-15_2024-01-17.nc  (0.38 MB)\n",
+      "  Saved : ASOS_NYC_2024-01-15_2024-01-17.nc  (0.25 MB)\n",
+      "  Single-station files : ['ASOS_JFK_2024-01-15_2024-01-17.nc', 'ASOS_EWR_2024-01-15_2024-01-17.nc', 'ASOS_LGA_2024-01-15_2024-01-17.nc', 'ASOS_NYC_2024-01-15_2024-01-17.nc']\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cf133cd",
+   "metadata": {},
+   "source": [
+    "## 7. Reload & verify"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b9b947f2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:17.558591Z",
+     "start_time": "2026-03-25T09:14:17.534663Z"
+    }
+   },
+   "source": [
+    "ds_check = xr.open_dataset(path_asos)\n",
+    "\n",
+    "print(f'Shape    : {dict(ds_check.sizes)}')\n",
+    "print(f'Stations : {ds_check.id.values}')\n",
+    "print(f'Vars     : {list(ds_check.data_vars)}')\n",
+    "print(ds_check)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape    : {'id': 4, 'time': 2880}\n",
+      "Stations : ['JFK' 'EWR' 'LGA' 'NYC']\n",
+      "Vars     : ['precip_amount', 'precip_type', 'precip_category', 'temperature', 'wind_speed', 'wind_direction']\n",
+      "<xarray.Dataset> Size: 807kB\n",
+      "Dimensions:          (id: 4, time: 2880)\n",
+      "Coordinates:\n",
+      "  * id               (id) <U3 48B 'JFK' 'EWR' 'LGA' 'NYC'\n",
+      "  * time             (time) datetime64[ns] 23kB 2024-01-15 ... 2024-01-16T23:...\n",
+      "    lat              (id) float64 32B ...\n",
+      "    lon              (id) float64 32B ...\n",
+      "    elev             (id) float64 32B ...\n",
+      "Data variables:\n",
+      "    precip_amount    (id, time) float64 92kB ...\n",
+      "    precip_type      (id, time) <U2 92kB ...\n",
+      "    precip_category  (id, time) <U7 323kB ...\n",
+      "    temperature      (id, time) float64 92kB ...\n",
+      "    wind_speed       (id, time) float64 92kB ...\n",
+      "    wind_direction   (id, time) float64 92kB ...\n",
+      "Attributes: (12/14)\n",
+      "    title:        OpenMesh ASOS reference precipitation — NYC\n",
+      "    source:       NOAA ASOS 1-min via Iowa Environmental Mesonet\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-17\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-17T00:00:00\n",
+      "    conventions:  OpenSense-PWS-v1.0\n",
+      "    ...           ...\n",
+      "    history:      Created 2026-03-25\n",
+      "    comment:      ASOS airport stations — precip_amount/rate kept (not rename...\n",
+      "    license:      Public domain (NOAA)\n",
+      "    featureType:  timeSeries\n",
+      "    sensor_type:  ASOS\n",
+      "    time_zone:    UTC\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openmesh",
+   "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": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/OpenMesh/notebooks/analyze_OpenMesh_data.ipynb b/OpenMesh/notebooks/analyze_OpenMesh_data.ipynb
new file mode 100644
index 0000000..f44ca92
--- /dev/null
+++ b/OpenMesh/notebooks/analyze_OpenMesh_data.ipynb
@@ -0,0 +1,1714 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "837f8fda",
+   "metadata": {},
+   "source": [
+    "# OpenMesh Example Notebook\n",
+    "\n",
+    "Download, explore and analyse the **OpenMesh** dataset with the\n",
+    "[OpenSense](https://github.com/OpenSenseAction) ecosystem.\n",
+    "\n",
+    "| Resource | Link |\n",
+    "|---|---|\n",
+    "| Data — CML (Zenodo) | https://doi.org/10.5281/zenodo.15287692 |\n",
+    "| Data — PWS (Zenodo) | https://doi.org/10.5281/zenodo.17508286 |\n",
+    "| Paper (ESSD) | https://doi.org/10.5194/essd-2025-238 |\n",
+    "| Code (GitHub) | https://github.com/drorjac/OpenMesh |\n",
+    "\n",
+    "**Three complementary sources:**\n",
+    "\n",
+    "| Dataset | Source | Description |\n",
+    "|---------|--------|-------------|\n",
+    "| **CML** | NYC Mesh | RSL time-series — 75 links, 3 sublinks each |\n",
+    "| **ASOS** | NOAA | Official 1-min reference rain gauges (JFK, LGA, NYC) |\n",
+    "| **PWS** | Weather Underground | Crowdsourced personal weather stations |\n",
+    "\n",
+    "**OpenSense packages used:** [`poligrain`](https://github.com/OpenSenseAction/poligrain) (CML plotting),\n",
+    "[`pypwsqc`](https://github.com/OpenSenseAction/pypwsqc) (PWS QC),\n",
+    "[`xarray`](https://docs.xarray.dev), `matplotlib`, `pyproj`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f37295d4",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3aa88416",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:59:53.695023Z",
+     "start_time": "2026-03-25T10:59:53.692180Z"
+    }
+   },
+   "source": [
+    "# Uncomment to install\n",
+    "# !pip install poligrain pypwsqc xarray netCDF4 requests tqdm pyproj matplotlib numpy pandas scipy"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab37ca9",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "0ece6bc7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:59:55.285568Z",
+     "start_time": "2026-03-25T10:59:53.700149Z"
+    }
+   },
+   "source": [
+    "import warnings\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "from pyproj import Geod\n",
+    "\n",
+    "import poligrain as plg\n",
+    "\n",
+    "\n",
+    "import openmesh_data_io as omio\n",
+    "import ws_opensense_conversion as wsoc\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "%matplotlib inline\n",
+    "\n",
+    "print(f\"poligrain : {plg.__version__}\")\n",
+    "print(f\"xarray    : {xr.__version__}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "poligrain : 0.3.1\n",
+      "xarray    : 2025.9.1\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfcbbff5",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Download datasets\n",
+    "\n",
+    "Download CML and PWS datasets from their respective Zenodo records (skips if already present):\n",
+    "- **CML**: https://doi.org/10.5281/zenodo.15287692\n",
+    "- **PWS**: https://doi.org/10.5281/zenodo.17508286\n",
+    "\n",
+    "Then extract a sample for the chosen time window."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f6390d939763dbfe",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:59:55.346779Z",
+     "start_time": "2026-03-25T10:59:55.342487Z"
+    }
+   },
+   "source": [
+    "SAMPLE_DIR     = omio.SAMPLE_DIR\n",
+    "ASOS_META_PATH = wsoc.ASOS_META_PATH\n",
+    "print(f\"Data dir: {omio.DATA_DIR}\")\n",
+    "\n",
+    "# Download from Zenodo — skips if files already present\n",
+    "omio.download_openmesh()   # → notebooks/data/raw/ds_openmesh.nc\n",
+    "omio.download_pws_wu()     # → notebooks/data/raw/pws_wu_os.nc\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data dir: /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh\n",
+      "  CML found locally  : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "  PWS found locally  : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:04:07.003510Z",
+     "start_time": "2026-03-25T11:04:06.973594Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Paths to pre-built example files in the main OpenMesh folder\n",
+    "ROOT_DIR  = Path(\"../\")\n",
+    "CML_FILE  = ROOT_DIR / \"openmesh_cml_20d.nc\"\n",
+    "PWS_FILE  = ROOT_DIR / \"openmesh_wu_pws_20d.nc\"\n",
+    "ASOS_FILE = ROOT_DIR / \"openmesh_asos_ws_20d.nc\"\n",
+    "# ── Mode ──────────────────────────────────────────────────────\n",
+    "# Set READ_FROM_FILES = True to load existing .nc files from the main folder,\n",
+    "READ_FROM_FILES = True\n",
+    "\n",
+    "if READ_FROM_FILES:\n",
+    "    ds_cmls = xr.open_dataset(CML_FILE)\n",
+    "    ds_pws  = xr.open_dataset(PWS_FILE)\n",
+    "    ds_asos = xr.open_dataset(ASOS_FILE)\n",
+    "\n",
+    "    print(f\"Loaded CML : {CML_FILE}\")\n",
+    "    print(f\"Loaded PWS : {PWS_FILE}\")\n",
+    "    print(f\"Loaded ASOS: {ASOS_FILE}\")"
+   ],
+   "id": "cfc28bf9f7c72528",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded CML : ../openmesh_cml_10d.nc\n",
+      "Loaded PWS : ../openmesh_wu_pws_10d.nc\n",
+      "Loaded ASOS: ../openmesh_asos_ws_10d.nc\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "code",
+   "id": "532c02be",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:09:39.369248Z",
+     "start_time": "2026-03-25T11:08:38.050775Z"
+    }
+   },
+   "source": [
+    "CREATE_DATA = False\n",
+    "if CREATE_DATA:\n",
+    "    # Create samples for a specific time window.\n",
+    "    START_TIME = \"2024-01-15\"\n",
+    "    END_TIME   = \"2024-01-30\"\n",
+    "\n",
+    "    datasets = omio.create_all_datasets(\n",
+    "        start_time=START_TIME,\n",
+    "        end_time=END_TIME,\n",
+    "        save=True,\n",
+    "    )\n",
+    "    ds_cmls = datasets[\"cml\"]\n",
+    "    ds_pws  = datasets[\"pws\"]\n",
+    "    ds_asos = datasets[\"asos\"]"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  CML\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "  Window   : 2024-01-12 00:00:00  →  2024-02-01 00:00:00  (20d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_20d.nc\n",
+      "  Timesteps: 28,801\n",
+      "  CML count: 75\n",
+      "  Saved    : data/samples/openmesh_cml_20d.nc  (0.91 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  PWS\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-12 00:00:00  →  2024-02-01 00:00:00  (20d)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_20d.nc\n",
+      "  Loaded   : 37 stations, 5,552 timesteps\n",
+      "  Saved    : data/samples/openmesh_wu_pws_20d.nc  (2.04 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  ASOS\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Window   : 2024-01-12 00:00:00  →  2024-02-01 00:00:00  (20d)\n",
+      "  Stations : ['JFK', 'EWR', 'LGA', 'NYC']\n",
+      "  Variables: ['precip_amount', 'precip_rate', 'temperature', 'wind_speed']\n",
+      "  Output   : openmesh_asos_ws_20d.nc\n",
+      "  Fetching JFK ... 28,031 records\n",
+      "  Fetching EWR ... 12,652 records\n",
+      "  Fetching LGA ... 28,138 records\n",
+      "  Fetching NYC ... 25,823 records\n",
+      "  Loaded   : 4 stations, 28,266 timesteps\n",
+      "  Saved    : data/samples/openmesh_asos_ws_20d.nc  (0.58 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Summary:\n",
+      "    cml    → openmesh_cml_20d.nc\n",
+      "    pws    → openmesh_wu_pws_20d.nc\n",
+      "    asos   → openmesh_asos_ws_20d.nc\n",
+      "───────────────────────────────────────────────────────\n"
+     ]
+    }
+   ],
+   "execution_count": 30
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de2f2604",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## CML Data — Microwave Link Signal Levels\n",
+    "\n",
+    "### Dataset structure\n",
+    "\n",
+    "OpenSense v1.0 format — dims `(cml_id, sublink_id, time)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "4ebd307e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:04:22.819409Z",
+     "start_time": "2026-03-25T11:04:22.811168Z"
+    }
+   },
+   "source": [
+    "print(ds_cmls)\n",
+    "\n",
+    "t0  = pd.Timestamp(ds_cmls.time.values[0])\n",
+    "t1  = pd.Timestamp(ds_cmls.time.values[-1])\n",
+    "dt  = (ds_cmls.time.values[1] - ds_cmls.time.values[0]) / np.timedelta64(1, \"s\")\n",
+    "print(f\"\\n{ds_cmls.sizes}  |  {t0.date()} → {t1.date()}  |  {dt:.0f} s  |  vars: {list(ds_cmls.data_vars)}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset> Size: 13MB\n",
+      "Dimensions:       (cml_id: 75, sublink_id: 3, time: 14401)\n",
+      "Coordinates:\n",
+      "  * time          (time) datetime64[ns] 115kB 2024-01-15 ... 2024-01-25\n",
+      "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+      "    site_0_lat    (cml_id) float32 300B ...\n",
+      "    site_0_lon    (cml_id) float32 300B ...\n",
+      "    site_1_lat    (cml_id) float32 300B ...\n",
+      "    site_1_lon    (cml_id) float32 300B ...\n",
+      "    length        (cml_id) float32 300B ...\n",
+      "    frequency     (cml_id, sublink_id) float32 900B ...\n",
+      "    polarization  (cml_id, sublink_id) object 2kB ...\n",
+      "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+      "Data variables:\n",
+      "    rsl           (cml_id, sublink_id, time) float32 13MB ...\n",
+      "Attributes: (12/15)\n",
+      "    title:                 OpenMesh CML sample (10d), start 2024-01-15\n",
+      "    file_author:           Dror Jacoby\n",
+      "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+      "    date:                  2025-11-01\n",
+      "    source:                Community NYC Mesh Wireless Network\n",
+      "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+      "    ...                    ...\n",
+      "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+      "    Conventions:           OpenSense-CML-v1.0\n",
+      "    start_date:            2024-01-15\n",
+      "    end_date:              2024-01-25\n",
+      "    time_range:            2024-01-15T00:00:00 / 2024-01-25T00:00:00\n",
+      "    cml_subset:            all\n",
+      "\n",
+      "Frozen({'cml_id': 75, 'sublink_id': 3, 'time': 14401})  |  2024-01-15 → 2024-01-25  |  60 s  |  vars: ['rsl']\n"
+     ]
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "code",
+   "id": "9a9dd5c756f01f75",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:04:26.068601Z",
+     "start_time": "2026-03-25T11:04:25.786137Z"
+    }
+   },
+   "source": [
+    "ds_cmls.rsl.isel(sublink_id=0).resample(time='1h').min().plot.line(x='time', add_legend=False);"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0ec808f",
+   "metadata": {},
+   "source": [
+    "### Frequency & polarization distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "2487746e314bdbf0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:04:30.214183Z",
+     "start_time": "2026-03-25T11:04:30.035648Z"
+    }
+   },
+   "source": [
+    "freq_vals = ds_cmls.frequency.isel(sublink_id=0).values\n",
+    "freq_vals = freq_vals[~np.isnan(freq_vals)]\n",
+    "lengths_km = ds_cmls.length.values\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "\n",
+    "axes[0].hist(freq_vals,  bins=20, color=\"steelblue\", edgecolor=\"white\")\n",
+    "axes[0].set(xlabel=\"Frequency (MHz)\", ylabel=\"Count\", title=\"Frequency distribution\")\n",
+    "\n",
+    "axes[1].hist(lengths_km, bins=30, color=\"steelblue\", edgecolor=\"white\")\n",
+    "axes[1].axvline(np.median(lengths_km), color=\"red\", linestyle=\"--\",\n",
+    "                label=f\"Median: {np.median(lengths_km):.2f} km\")\n",
+    "axes[1].set(xlabel=\"Link length (km)\", ylabel=\"Count\", title=\"Path length distribution\")\n",
+    "axes[1].legend()\n",
+    "\n",
+    "for ax in axes: ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Frequency  : {freq_vals.min():.0f} – {freq_vals.max():.0f} MHz  |  unique: {np.unique(freq_vals)}\")\n",
+    "print(f\"Length     : {lengths_km.min():.2f} – {lengths_km.max():.2f} km  |  median: {np.median(lengths_km):.2f} km\")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency  : 5270 – 69120 MHz  |  unique: [ 5270.  5280.  5300.  5310.  5490.  5500.  5505.  5510.  5545.  5550.\n",
+      "  5560.  5570.  5700.  5710.  5750.  5760.  5770.  5795.  5810.  5815.\n",
+      "  5825. 24100. 24200. 58320. 60480. 62640. 64800. 65880. 68040. 69120.]\n",
+      "Length     : 24.50 – 6936.50 km  |  median: 1447.50 km\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cc73d27",
+   "metadata": {},
+   "source": [
+    "### CML network map\n",
+    "\n",
+    "`poligrain.scatter_lines()` draws CML paths colored by frequency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d92324ff",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:04:33.391128Z",
+     "start_time": "2026-03-25T11:04:33.183972Z"
+    }
+   },
+   "source": [
+    "x0, y0 = ds_cmls.site_0_lon.values, ds_cmls.site_0_lat.values\n",
+    "x1, y1 = ds_cmls.site_1_lon.values, ds_cmls.site_1_lat.values\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "lc = plg.plot_map.scatter_lines(\n",
+    "    x0=x0, y0=y0, x1=x1, y1=y1,\n",
+    "    c=ds_cmls.frequency.isel(sublink_id=0).values,\n",
+    "    s=2, cmap=plt.cm.plasma, ax=axes[0],\n",
+    ")\n",
+    "plt.colorbar(lc, ax=axes[0], label=\"Frequency (MHz)\", shrink=0.8)\n",
+    "axes[0].set(xlabel=\"Longitude\", ylabel=\"Latitude\",\n",
+    "            title=f\"CML network — {len(x0)} links, coloured by frequency\")\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "axes[1].scatter(lengths_km, freq_vals, alpha=0.6, edgecolors=\"white\", linewidths=0.3)\n",
+    "axes[1].set(xlabel=\"Link length (km)\", ylabel=\"Frequency (MHz)\",\n",
+    "            title=\"Frequency vs. path length\")\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x600 with 3 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "markdown",
+   "id": "354e6b25",
+   "metadata": {},
+   "source": [
+    "### Data availability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "40dbe0ec",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:04:36.187619Z",
+     "start_time": "2026-03-25T11:04:35.989126Z"
+    }
+   },
+   "source": [
+    "rsl = ds_cmls[\"rsl\"].isel(sublink_id=0)\n",
+    "valid = ~np.isnan(rsl)\n",
+    "avail_link = valid.mean(dim=\"time\").values\n",
+    "avail_time = valid.mean(dim=\"cml_id\")\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "axes[0].hist(avail_link * 100, bins=20, color=\"teal\", edgecolor=\"white\")\n",
+    "axes[0].axvline(avail_link.mean() * 100, color=\"red\", linestyle=\"--\",\n",
+    "                label=f\"Mean: {avail_link.mean()*100:.1f}%\")\n",
+    "axes[0].set(xlabel=\"Availability (%)\", ylabel=\"Count\", title=\"Per-link RSL availability\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "axes[1].plot(pd.DatetimeIndex(avail_time.time.values), avail_time.values * 100,\n",
+    "             color=\"steelblue\", linewidth=0.6)\n",
+    "axes[1].set(ylabel=\"Network availability (%)\", title=\"Network-wide availability over time\")\n",
+    "axes[1].xaxis.set_major_formatter(mdates.DateFormatter(\"%b %d\"))\n",
+    "\n",
+    "for ax in axes: ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Mean availability : {avail_link.mean()*100:.1f}%  |  \"\n",
+    "      f\">80%: {(avail_link > 0.8).sum()} / {ds_cmls.sizes['cml_id']} links\")"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ],
+      "image/png": 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amqpRo0YpKipKAQEB6tChg+Lj4yVJWVlZevLJJ9W8eXMFBQWpevXqGjRokI79PZdwQebNmyfDMPL8pKenl8cmAQAAwIVxIzIAAACgCPfff792796tBQsWqHr16lq4cKF69Oihn3/+WRUqVNAPP/yg8ePHq0WLFjp16pRGjRqlm266Sdu3by+035CQEO3du9dumb+/f1luikdjYC0AAPAUFG0BAAA8XFJgoLNDcGtpaWn66KOPtGLFCnXq1EmSFBcXp48//lizZ8/WCy+8oDVr1ti9Z8aMGbr66qt1+PBh1a5du8C+DcNQREREmcYPAAAA90PRFgAAwIOd8/NT2Nixzg7DrWVnZysnJyfPCNiAgABt2bIl3/ckJyfLMAxVqlSp0L7PnDmjqKgo5eTkqGXLlpo4caJiYmJKK3QAAAC4KYq2AAAAQCGCg4PVvn17TZw4UY0bN1Z4eLgWL16sb7/9VtHR0Xnap6en66mnntLAgQMVEhJSYL+NGjXSvHnz1Lx5c6WkpOj1119Xx44dtXPnznz7laSMjAxlZGTYnqekpEiSrFarrFbrJW6pY6xWq0zTLLf1FcfFMblqnM7myjmEY8ihZyCP7o8ceobyzqOj66FoCwAAAI+TnJys5cuXa/PmzTp48KDOnTun0NBQxcTEqHfv3urQoUOx+luwYIGGDh2qGjVqyNvbW61atdLAgQP1ww8/2LXLysrSHXfcIavVqlmzZhXaZ7t27dSuXTvb844dO6pVq1aaMWOG3njjjXzfM3nyZE2YMCHP8qSkpHK7gZnValVycrJM05SXl2vd19hqNe2ep2ekKzEx0UnRuC5XziEcQw49A3l0f+TQM5R3HlNTUx1qR9EWAADAg/lnZemLhQslSdfffbfSfX2dHFHZOn78uJ599lm9//77ioiI0NVXX62WLVsqICBAf/31l9avX69XXnlFUVFReu655zRgwACH+q1fv742btyos2fPKiUlRZGRkRowYIDq1q1ra5OVlaX+/fvrwIED+uqrrwodZZsfLy8vtWnTRvv27SuwTWxsrMaMGWN7npKSolq1aik0NLTY6yspq9UqwzAUGhrqch9Qcy4q2gb4+yssLMxJ0bguV84hHEMOPQN5dH/k0DOUdx4dveksRVsAAAAP5mWa6nLokO2xp2vRooUGDRqk7777Ts2aNcu3TVpamj7++GNNmzZNR44c0eOPP+5w/0FBQQoKCtKpU6e0atUqTZ06VdI/Bdt9+/Zp/fr1qlq1arFjN01TO3bsUPPmzQtsY7FYZLFY8iz38vIq1w+LhmGU+zodYeriY9xwuRhdhavmEI4jh56BPLo/cugZyjOPjq6Doi0AAAA8xp49exQaGlpom4CAAN1555268847lZSU5FC/q1atkmmaatiwofbv368nnnhCDRs21JAhQ5Sdna1+/frphx9+0GeffaacnBwlJCRIkqpUqSI/Pz9J0qBBg1SjRg1NnjxZkjRhwgS1a9dO0dHRSklJ0RtvvKEdO3bozTffvIQ9cHkzDGdHAAAAUDoo2gIAAMBjFFWwLWn75ORkxcbG6o8//lCVKlV022236cUXX5Svr68OHjyoTz75RJLUsmVLu/etX79eXbp0kSQdPnzYbmTF6dOnNWzYMCUkJKhixYqKiYnRpk2bdPXVVxdrGwAAAOB5KNoCAADAo6Wmpur555/Xhg0blJOTo44dO+q5555TtWrVHO6jf//+6t+/f76v1alTR6YDU09s2LDB7vlrr72m1157zeEYAAAAcPlgwg0AAAB4tAceeEAnTpzQhAkT9Nxzz+n333/XXXfd5eywAAAAgAIx0hYAAAAe5bXXXtOoUaNk/D3BaXx8vH777Td5e3tLkho2bKh27do5M0SUEaa0BQAAnoKiLQAAgIc76+vr7BDK1f79+9W2bVu99dZbiomJUc+ePdWnTx/dcsstysrK0oIFC9S7d29nh4lywI3JAACAu6JoCwAA4MHO+fmpwtNPOzuMcvXmm29q27ZtGjp0qLp27arJkydr4cKFWrNmjXJycnT77bfrkUcecXaYAAAAQIEo2gIAAMDjtG/fXvHx8XrppZfUvn17vfzyy/roo4+cHRbKmMHQWgAA4CG4ERkAAAA8ko+Pj5555hl9+umnmj59uvr166eEhARnhwUAAAAUiaItAACAB7NkZemz99/XZ++/L0tWlrPDKRe7du3S1VdfreDgYHXs2FFWq1Xr1q3Tv/71L3Xo0EGzZ892dogAAABAoSjaAgAAeDBv01SfffvUZ98+eZums8MpF0OGDNE111yj+Ph43X777XrwwQclSUOHDtW3336rLVu2qH379k6OEgAAACgYc9oCAADAo+zdu1dLlizRFVdcoejoaE2fPt32WmhoqN5//32tXr3aeQECAAAARXDqSNu4uDgZhmH3ExERYXvdNE3FxcWpevXqCggIUJcuXbRnzx4nRgwAAABX16VLFw0bNkxvv/227rrrLnXs2DFPm169ejkhMpQ3bksGAADcldOnR2jatKmOHz9u+9m1a5fttalTp2ratGmaOXOm4uPjFRERoZ49eyo1NdWJEQMAAMCVvffee2rVqpVWrFihevXqMYctAAAA3I7Tp0fw8fGxG12byzRNTZ8+XU8//bT69u0rSZo/f77Cw8O1aNEiDR8+vLxDBQAAgBuoXLmyXnnlFWeHAQAAAJSY00fa7tu3T9WrV1fdunV1xx136Pfff5ckHThwQAkJCXaXrlksFnXu3Flbt251VrgAAABwYYcPHy5W+6NHj5ZRJAAAAEDJOXWkbdu2bfXee++pQYMG+vPPP/XCCy+oQ4cO2rNnjxISEiRJ4eHhdu8JDw/XoUOHCuwzIyNDGRkZtucpKSmSJKvVKqvVWgZbYc9qtco0zXJZF8oOeXR/5NAzkEf3Rw7LjiHHvn33uuhxSb6xN6RyzeOlrKdNmza66aab9MADD+jqq6/Ot01ycrI++OADvf766xo+fLgeffTREq8PAAAAKAtOLdpef/31tsfNmzdX+/btVb9+fc2fP1/t2rWTJBmG/e0DTNPMs+xCkydP1oQJE/IsT0pKUnp6eilFXjCr1ark5GSZpikvL6cPZEYJkUf3Rw49A3l0f+Sw7ERbLPIOCXGo7dXTpkmSGpdwXfUtFp0+fbrc8ngp9y/45ZdfNGnSJF133XXy9fVV69atVb16dfn7++vUqVP6+eeftWfPHrVu3Vovv/yy3fkoAAAA4CqcPqfthYKCgtS8eXPt27dPt9xyiyQpISFBkZGRtjaJiYl5Rt9eKDY2VmPGjLE9T0lJUa1atRQaGqoQBz/YXAqr1SrDMBQaGsqHUzdGHt0fOfQM5NH9kcOysy8jQzv+vqKorFkDA1WpUqVyy6O/v3+J31ulShW98soreuGFF/T5559r8+bNOnjwoNLS0lStWjXddddd6t27t5o1a1aKEQMAAACly6WKthkZGfrll1907bXXqm7duoqIiNCaNWsUExMjScrMzNTGjRs1ZcqUAvuwWCyyWCx5lnt5eZXbh0XDMMp1fSgb5NH9kUPPQB7dHzksG6ak8pp0wlT55rE01uHv76++ffvabmiLy1NhV+gBAAC4MqcWbR9//HHdeOONql27thITE/XCCy8oJSVFgwcPlmEYGjVqlCZNmqTo6GhFR0dr0qRJCgwM1MCBA50ZNgAAgNuwZGVpwfLlkqR7br1VGb6+To4IAAAAQFGcWrT9448/dOedd+rEiRMKDQ1Vu3bt9M033ygqKkqSNHbsWKWlpWnEiBE6deqU2rZtq9WrVys4ONiZYQMAALgNb9PU7T//LEm69+/ppwAAAAC4NqcWbZcsWVLo64ZhKC4uTnFxceUTEAAAAAAAAAA4GZPLAQAAAAAAAIALoWgLAAAAj3T27FlnhwAAAACUCEVbAAAAeKTw8HANHTpUW7ZscXYoAAAAQLFQtAUAAIBHWrx4sZKTk9W9e3c1aNBAL730ko4dO+bssAAAAIAiUbQFAACAR7rxxhv10Ucf6dixY3rooYe0ePFiRUVF6YYbbtCyZcuUnZ3t7BABAACAfFG0BQAA8GDnfH0VNG6cgsaN0zlfX2eH4xRVq1bV6NGjtXPnTk2bNk1r165Vv379VL16dT377LM6d+6cs0MEAAAA7Pg4OwAAAACUIcPQOT8/Z0fhVAkJCXrvvfc0d+5cHT58WP369dN9992nY8eO6aWXXtI333yj1atXOztMAAAAwIaiLQAAADzSsmXLNHfuXK1atUpNmjTRww8/rLvvvluVKlWytWnZsqViYmKcFyQAAACQD4q2AAAAHswvO1tvffqpJGn4jTcq0+fyOf0bMmSI7rjjDn399ddq06ZNvm3q1aunp59+upwjAwAAAAp3+Zy1AwAAXIZ8rFbdu3OnJOnhPn2U6eR4ytPx48cVGBhYaJuAgAA999xz5RQRypthODsCAACAkuFGZAAAAPBIwcHBSkxMzLP85MmT8vb2dkJEAAAAgGMo2gIAAMAjmaaZ7/KMjAz5XeY3ZwMAAIBrY3oEAAAAeJQ33nhDkmQYhv7zn/+oQoUKttdycnK0adMmNWrUyFnhAQAAAEWiaAsAAACP8tprr0k6P9J2zpw5dlMh+Pn5qU6dOpozZ46zwkM5eGVwez0+f5uzwwAAACgxirYAAADwKAcOHJAkde3aVcuWLVPlypWdHBHKW1S1CkU3AgAAcGEUbQEAAOCR1q9f7+wQ4CyGswMAAAC4NBRtAQAAPNg5X1+FPvGE7bGnGzNmjCZOnKigoCCNGTOm0LbTpk0rp6jgLAbVWwAA4KYo2gIAAHgyw9CJoCBnR1FufvzxR2VlZdkeF8QwKOZ5Moq1AADA3VG0BQAAgMe4cEoEpkcAAACAu6JoCwAA4MH8srM1bdUqSdKY3r2V6cPpHzwfA6kBAIC746wdAADAg/lYrXo4Pl6SNLZnT2U6OZ6y1rdvX4fbLlu2rAwjAQAAAEqOoi0AAAA8RsWKFZ0dAlwAA20BAIC7o2gLAAAAjzF37lxnhwAAAABcMi9nBwAAAAC4utTUVI0aNUpRUVEKCAhQhw4dFP/3tBOSZJqm4uLiVL16dQUEBKhLly7as2dPkf1+9NFHatKkiSwWi5o0aaLly5eX5WZcPoyL/gUAAHAzFG0BAADgMVq1aqVTp05JkmJiYtSqVasCf4rj/vvv15o1a7RgwQLt2rVLvXr1Uo8ePXT06FFJ0tSpUzVt2jTNnDlT8fHxioiIUM+ePZWamlpgn9u2bdOAAQN0zz33aOfOnbrnnnvUv39/ffvttyXfAQAAAPAITI8AAAAAj3HzzTfLYrFIkm655ZZS6TMtLU0fffSRVqxYoU6dOkmS4uLi9PHHH2v27NmaOHGipk+frqefftp2I7T58+crPDxcixYt0vDhw/Ptd/r06erZs6diY2MlSbGxsdq4caOmT5+uxYsXl0rslyuDIbYAAMDNUbQFAACAx3juuefyfXwpsrOzlZOTI39/f7vlAQEB2rJliw4cOKCEhAT16tXL9prFYlHnzp21devWAou227Zt0+jRo+2W9e7dW9OnTy8wloyMDGVkZNiep6SkSJKsVqusVmtxN61ErFarTNMst/WVhC02F4/TWdwhhygcOfQM5NH9kUPPUN55dHQ9FG0BAAA8WJqPj+qMHGl7fDnavn27fvnlFxmGocaNG+uqq64q1vuDg4PVvn17TZw4UY0bN1Z4eLgWL16sb7/9VtHR0UpISJAkhYeH270vPDxchw4dKrDfhISEfN+T219+Jk+erAkTJuRZnpSUpPT09OJsVolZrVYlJyfLNE15ebnmbGsnTiRJktLS05WYmOjkaFyPO+QQhSOHnoE8uj9y6BnKO4+FTZ91ocvzzB0AAOAyYXp56VDlys4Owyn++OMP3Xnnnfr6669VqVIlSdLp06fVoUMHLV68WLVq1XK4rwULFmjo0KGqUaOGvL291apVKw0cOFA//PCDrY1h2F+Sb5pmnmUXK+57YmNjNWbMGNvzlJQU1apVS6GhoQoJCXF4ey6F1WqVYRgKDQ112Q+ooaGhkqQAf3+FhYU5ORrX4w45ROHIoWcgj+6PHHqG8s7jxVdvFYSiLQAAADzS0KFDlZWVpV9++UUNGzaUJO3du1dDhw7Vfffdp9WrVzvcV/369bVx40adPXtWKSkpioyM1IABA1S3bl1FRERIOj9yNjIy0vaexMTEPCNpLxQREZFnVG1R77FYLLY5ey/k5eVVrh8WDcMo93UWh3duXH/HibxcPYcoGjn0DOTR/ZFDz1CeeXR0HRxRAAAAHsw3O1tTV6/W1NWr5Zud7exwytXmzZs1e/ZsW8FWkho2bKgZM2Zo8+bNJeozKChIkZGROnXqlFatWqWbb77ZVrhds2aNrV1mZqY2btyoDh06FNhX+/bt7d4jSatXry70PXDQ36OVuR0ZAABwV4y0BQAA8GC+Vque2LpVkhTXpYuynBxPeapdu7aysvJucXZ2tmrUqFGsvlatWiXTNNWwYUPt379fTzzxhBo2bKghQ4bIMAyNGjVKkyZNUnR0tKKjozVp0iQFBgZq4MCBtj4GDRqkGjVqaPLkyZKkkSNHqlOnTpoyZYpuvvlmrVixQmvXrtWWLVsubcMBAADg9hhpCwAAAI80depUPfroo9q+fbtM05R0/qZkI0eO1CuvvFKsvpKTk/Xwww+rUaNGGjRokK655hqtXr1avr6+kqSxY8dq1KhRGjFihFq3bq2jR49q9erVCg4OtvVx+PBhHT9+3Pa8Q4cOWrJkiebOnasrr7xS8+bN09KlS9W2bdtS2PrLGyNsAQCAu2OkLQAAADxG5cqV7W7kdfbsWbVt21Y+PudPe7Ozs+Xj46OhQ4fqlltucbjf/v37q3///gW+bhiG4uLiFBcXV2CbDRs25FnWr18/9evXz+E4AAAAcHmgaAsAAACPMX36dGeHABdgMNQWAAC4OYq2AAAA8BiDBw92dggAAADAJaNoCwAAAI+XlpaW56ZkISEhTooG5YURtwAAwF2V6EZk9erV08mTJ/MsP336tOrVq3fJQQEAAACX6uzZs3rkkUcUFhamChUqqHLlynY/AAAAgKsqUdH24MGDysnJybM8IyNDR48eveSgAAAAUDrSfHzUdMQINR0xQmk+l9dFVmPHjtVXX32lWbNmyWKx6D//+Y8mTJig6tWr67333nN2eChDBkNsAQCAmyvWmfsnn3xie7xq1SpVrFjR9jwnJ0fr1q1TnTp1Si04AAAAXBrTy0s/h4U5Owyn+PTTT/Xee++pS5cuGjp0qK699lpdccUVioqK0vvvv6+77rrL2SECAAAA+SpW0faWW26RdP6b64tv8uDr66s6dero1VdfLbXgAAAAgJL666+/VLduXUnn56/966+/JEnXXHONHnroIWeGhjLGOFsAAODuijU9gtVqldVqVe3atZWYmGh7brValZGRob179+qGG24oq1gBAABQTL7Z2Xpu/Xo9t369fLOznR1OuapXr54OHjwoSWrSpIk++OADSedH4FaqVMl5gaFMtYsOk5cXZVsAAODeSjSn7YEDB1StWrVSDWTy5MkyDEOjRo2yLTNNU3FxcapevboCAgLUpUsX7dmzp1TXCwAA4Ml8rVbFbdyouI0b5Wu1OjuccjVkyBDt3LlTkhQbG2ub23b06NF64oknnBwdysqEO9rIizltAQCAmyvx3SjWrVundevW2UbcXujdd98tVl/x8fF6++23deWVV9otnzp1qqZNm6Z58+apQYMGeuGFF9SzZ0/t3btXwcHBJQ0dAAAAl4HRo0fbHnft2lW//vqrtm/frvr166tFixZOjAzlxWCiBAAA4KZKNNJ2woQJ6tWrl9atW6cTJ07o1KlTdj/FcebMGd11113697//rcqVK9uWm6ap6dOn6+mnn1bfvn3VrFkzzZ8/X+fOndOiRYtKEjYAAAAuY7Vr11bfvn0p2AIAAMDllWik7Zw5czRv3jzdc889lxzAww8/rD59+qhHjx564YUXbMsPHDighIQE9erVy7bMYrGoc+fO2rp1q4YPH37J6wYAAIBneeONNxxu+9hjj5VhJAAAAEDJlahom5mZqQ4dOlzyypcsWaIffvhB8fHxeV5LSEiQJIWHh9stDw8P16FDhwrsMyMjQxkZGbbnKSkpkv65iVpZs1qtMk2zXNaFskMe3R859Azk0f2Rw7JjyLFLprwuelySy6wMqVzzeCnree211xxqZxgGRVsAAAC4rBIVbe+//34tWrRI48ePL/GKjxw5opEjR2r16tXy9/cvsJ1x0U0ETNPMs+xCkydP1oQJE/IsT0pKUnp6eonjdZTValVycrJM05SXV4lmn4ALII/ujxx6BvLo/shh2Ym2WOQdElJkO/8LvsyOCQ5WusVS7HXVt1h0+vTpcstjampqid974MCBUowEAAAAcI4SFW3T09P19ttva+3atbryyivl6+tr9/q0adOK7OP7779XYmKirrrqKtuynJwcbdq0STNnztTevXslnR9xGxkZaWuTmJiYZ/TthWJjYzVmzBjb85SUFNWqVUuhoaEKceCDzaWyWq0yDEOhoaF8OHVj5NH9kUPPQB7dHzksO/syMrTj7yuKChOYmWl7/GNqqs5dUMR1lDUwUJUqVSq3PBb2hT4AAABwOShR0fann35Sy5YtJUm7d++2e62wUbAX6t69u3bt2mW3bMiQIWrUqJGefPJJ1atXTxEREVqzZo1iYmIknZ+WYePGjZoyZUqB/VosFlnyGUHi5eVVbh8WDcMo1/WhbJBH90cOPQN5dH/ksGyYkhyZROCcj4/aPPCA7XFJJh4wVb55vJR1jBkzRhMnTlRQUJDdF/n5cWSgAQAAAOAMJSrarl+//pJXHBwcrGbNmtktCwoKUtWqVW3LR40apUmTJik6OlrR0dGaNGmSAgMDNXDgwEtePwAAwOXA6uWl7TVqODuMcvPjjz8qKyvL9rggjg40gJsjzQAAwE2VqGhbXsaOHau0tDSNGDFCp06dUtu2bbV69WoFBwc7OzQAAAC4oAsHF5TGQAMAAADAGUpUtO3atWuhoxO++uqrEgWzYcMGu+eGYSguLk5xcXEl6g8AAOBy55udrZHffitJer1tW2X5uPR39gAAAABUwqJt7ny2ubKysrRjxw7t3r1bgwcPLo24AAAAUAp8rVa9vGaNJGlWmzbKcnI85S0+Pl4ffvihDh8+rMwLbsomScuWLXNSVAAAAEDhSlS0fe211/JdHhcXpzNnzlxSQAAAAEBpWLJkiQYNGqRevXppzZo16tWrl/bt26eEhATdeuutzg4PAAAAKFCp3v737rvv1rvvvluaXQIAAAAlMmnSJL322mv67LPP5Ofnp9dff12//PKL+vfvr9q1azs7PAAAAKBApVq03bZtm/z9/UuzSwAAAKBE/ve//6lPnz6SJIvForNnz8owDI0ePVpvv/22k6MDAAAAClai6RH69u1r99w0TR0/flzbt2/X+PHjSyUwAAAA4FJUqVJFqampkqQaNWpo9+7dat68uU6fPq1z5845OTqUh4JvnQwAAODaSlS0rVixot1zLy8vNWzYUM8//7x69epVKoEBAAAAl+Laa6/VmjVr1Lx5c/Xv318jR47UV199pTVr1qh79+7ODg8AAAAoUImKtnPnzi3tOAAAAIBSNXPmTKWnp0uSYmNj5evrqy1btqhv375cHQYAAACXVqKiba7vv/9ev/zyiwzDUJMmTRQTE1NacQEAAKAUpPv4qMvgwbbHl5MqVarYHnt5eWns2LEaO3asEyMCAAAAHFOiM/fExETdcccd2rBhgypVqiTTNJWcnKyuXbtqyZIlCg0NLe04AQAAUAJWLy9trFvX2WE4RdeuXXX33XerX79+eab3AgAAAFyZV0ne9OijjyolJUV79uzRX3/9pVOnTmn37t1KSUnRY489VtoxAgAAAMXWvHlzPfPMM4qIiNBtt92mjz/+WJmZmc4OCwAAAChSiYq2X375pWbPnq3GjRvbljVp0kRvvvmmvvjii1ILDgAAAJfGJydHI777TiO++04+OTnODqdcvfHGGzp69KhWrFih4OBgDR48WBERERo2bJg2btzo7PAAAACAApWoaGu1WuXr65tnua+vr6xW6yUHBQAAgNLhl5OjNz//XG9+/rn8LrOirXR+LttevXpp3rx5+vPPP/XWW2/pu+++U7du3ZwdGsqBYRjODgEAAKBESlS07datm0aOHKljx47Zlh09elSjR49W9+7dSy04AAAAoDQkJCRozpw5mjJlin766Se1bt3a2SEBAAAABSpR0XbmzJlKTU1VnTp1VL9+fV1xxRWqW7euUlNTNWPGjNKOEQAAACi2lJQUzZ07Vz179lStWrU0e/Zs3Xjjjfrtt9/07bffOjs8AAAAoEA+JXlTrVq19MMPP2jNmjX69ddfZZqmmjRpoh49epR2fAAAAECJhIeHq3Llyurfv78mTZqkNm3aODskAAAAwCHFKtp+9dVXeuSRR/TNN98oJCREPXv2VM+ePSVJycnJatq0qebMmaNrr722TIIFAAAAHLVixQr16NFDXl4lurgMAAAAcJpincFOnz5dDzzwgEJCQvK8VrFiRQ0fPlzTpk0rteAAAACAkurVqxcFWwAAALilYo203blzp6ZMmVLg67169dIrr7xyyUEBAAAApeG///2vPvjgAx0+fFiZmZl2r/3www9OigoAAAAoXLGGHvz555/y9fUt8HUfHx8lJSVdclAAAAAoHRne3uozcKD6DByoDG9vZ4dTrt544w0NGTJEYWFh+vHHH3X11VeratWq+v3333X99dc7OzwAAACgQMUq2taoUUO7du0q8PWffvpJkZGRlxwUAAAASkeOt7c+b9BAnzdooJzLrGg7a9Ysvf3225o5c6b8/Pw0duxYrVmzRo899piSk5OdHR4AAABQoGIVbf/1r3/p2WefVXp6ep7X0tLS9Nxzz+mGG24oteAAAACAkjp8+LA6dOggSQoICFBqaqok6Z577tHixYudGRoAAABQqGLNafvMM89o2bJlatCggR555BE1bNhQhmHol19+0ZtvvqmcnBw9/fTTZRUrAAAAisknJ0d3/fSTJOn9K69U9mU02jYiIkInT55UVFSUoqKi9M0336hFixY6cOCATNN0dngAAABAgYpVtA0PD9fWrVv10EMPKTY21nayaxiGevfurVmzZik8PLxMAgUAAEDx+eXkaN6KFZKkD5s2vayKtt26ddOnn36qVq1a6b777tPo0aP13//+V9u3b1ffvn2dHR4AAABQoGIVbSUpKipKn3/+uU6dOqX9+/fLNE1FR0ercuXKZREfAAAAUCJvv/22rFarJOnBBx9UlSpVtGXLFt1444168MEHHe4nOztbcXFxev/995WQkKDIyEjde++9euaZZ+TldX62McMw8n3v1KlT9cQTT+T72rx58zRkyJA8y9PS0uTv7+9wfAAAAPA8xS7a5qpcubLatGlTmrEAAAAApcbLy8tWVJWk/v37q3///sXuZ8qUKZozZ47mz5+vpk2bavv27RoyZIgqVqyokSNHSpKOHz9u954vvvhC9913n2677bZC+w4JCdHevXvtllGwLT0F1NIBAABcXomLtgAAAMDlYNu2bbr55pvVp08fSVKdOnW0ePFibd++3dYmIiLC7j0rVqxQ165dVa9evUL7Ngwjz3sBAAAAirYAAABAIa655hrNmTNHv/32mxo0aKCdO3dqy5Ytmj59er7t//zzT61cuVLz588vsu8zZ84oKipKOTk5atmypSZOnKiYmJgC22dkZCgjI8P2PCUlRZJktVptU0GUNavVKtM0y219l8Jd4ixv7pRD5I8cegby6P7IoWco7zw6uh6KtgAAAEAhnnzySSUnJ6tRo0by9vZWTk6OXnzxRd155535tp8/f76Cg4OLvNlZo0aNNG/ePDVv3lwpKSl6/fXX1bFjR+3cuVPR0dH5vmfy5MmaMGFCnuVJSUlKT08v/saVgNVqVXJyskzTtJt+whWlpaUpMTHR2WG4HHfKIfJHDj0DeXR/5NAzlHceU1NTHWpH0RYAAAAoxNKlS7Vw4UItWrRITZs21Y4dOzRq1ChVr15dgwcPztP+3Xff1V133VXk3LTt2rVTu3btbM87duyoVq1aacaMGXrjjTfyfU9sbKzGjBlje56SkqJatWopNDRUISEhJdzC4rFarTIMQ6GhoS7/ATUgIEBhYWHODsPluFMOkT9y6BnIo/sjh56hvPPo6P0LKNoCAAB4sAxvb91+++22x5eThQsX6u677873tSeeeEIvv/yyQ/088cQTeuqpp3THHXdIkpo3b65Dhw5p8uTJeYq2mzdv1t69e7V06dJix+vl5aU2bdpo3759BbaxWCyyWCz5vrc8PywahlHu6yyJ3DiRl7vkEAUjh56BPLo/cugZyjOPjq6DIwoAAMCD5Xh7679Nm+q/TZsq5zIr2j7yyCP67LPP8iwfPXq0Fi5c6HA/586dy3Ny7e3tne98ZO+8846uuuoqtWjRotjxmqapHTt2KDIystjvBQAAgGehaAsAAACPtGTJEt19993atGmTbdmjjz6qDz74QOvXr3e4nxtvvFEvvviiVq5cqYMHD2r58uWaNm2abr31Vrt2KSkp+vDDD3X//ffn28+gQYMUGxtrez5hwgStWrVKv//+u3bs2KH77rtPO3bs0IMPPljMLUVBDGcHAAAAUEJMjwAAAODBvHNydOuvv0qSljdqdFmNtr3uuus0Z84c3XLLLVq9erXeffddrVixQuvXr1eDBg0c7mfGjBkaP368RowYocTERFWvXl3Dhw/Xs88+a9duyZIlMk2zwBuUHT582G7E7unTpzVs2DAlJCSoYsWKiomJ0aZNm3T11VeXbIMBAADgMSjaAgAAeDBLTo4+/PBDSVLQuHE6dxkVbSXpjjvu0KlTp3TNNdcoNDRUGzdu1BVXXFGsPoKDgzV9+nRNnz690HbDhg3TsGHDCnx9w4YNds9fe+01vfbaa8WKBQAAAJcHirYAAADwGGPGjMl3eVhYmGJiYjRr1izbsmnTppVXWAAAAECxULQFAACAx/jxxx/zXV6/fn2lpKTYXjcMZjsFAACA66JoCwAAAI9RnBuMAQAAAK7Kq+gmAAAAgHvJzs6Wj4+Pdu/e7exQAAAAgGKjaAsAAACP4+Pjo6ioKOXk5Dg7FDgR02AAAAB3RdEWAAAAHumZZ55RbGys/vrrL2eHAgAAABQLc9oCAAB4sExvb9178822x5eTN954Q/v371f16tUVFRWloKAgu9d/+OEHJ0UGAAAAFI6iLQAAgAfL9vbW/JgYZ4fhFLfccouzQwAAAABKxKlF29mzZ2v27Nk6ePCgJKlp06Z69tlndf3110uSTNPUhAkT9Pbbb+vUqVNq27at3nzzTTVt2tSJUQMAAMAdPPfcc84OAQAAACgRp85pW7NmTb300kvavn27tm/frm7duunmm2/Wnj17JElTp07VtGnTNHPmTMXHxysiIkI9e/ZUamqqM8MGAABwG945OfrXb7/pX7/9Ju/L9KZc33//vRYuXKj3339fP/74o7PDAQAAAIrk1JG2N954o93zF198UbNnz9Y333yjJk2aaPr06Xr66afVt29fSdL8+fMVHh6uRYsWafjw4c4IGQAAwK1YcnK0ctEiSVLQuHE6dxnNa5uYmKg77rhDGzZsUKVKlWSappKTk9W1a1ctWbJEoaGhzg4RAAAAyJdTR9peKCcnR0uWLNHZs2fVvn17HThwQAkJCerVq5etjcViUefOnbV161YnRgoAAAB38OijjyolJUV79uzRX3/9pVOnTmn37t1KSUnRY4895uzwAAAAgAI5/UZku3btUvv27ZWenq4KFSpo+fLlatKkia0wGx4ebtc+PDxchw4dKrC/jIwMZWRk2J6npKRIkqxWq6xWaxlsgT2r1SrTNMtlXSg75NH9kUPPQB7dHzksO4Yc+/bd66LHJfnG3pDKNY+ltZ4vv/xSa9euVePGjW3LmjRpojfffNNuYAAAAADgapxetG3YsKF27Nih06dP66OPPtLgwYO1ceNG2+uGYdi1N00zz7ILTZ48WRMmTMizPCkpSenp6aUXeAGsVquSk5Nlmqa8vFxmIDOKiTy6P3LoGcij+yOHZSfaYpF3SEiR7fwv+DI7JjhY6RZLsddV32LR6dOnyy2PpXX/AqvVKl9f3zzLfX19+SIBAAAALs3pRVs/Pz9dccUVkqTWrVsrPj5er7/+up588klJUkJCgiIjI23tExMT84y+vVBsbKzGjBlje56SkqJatWopNDRUIQ58sLlUVqtVhmEoNDSUD6dujDy6P3LoGcij+yOHZWdfRoZ2/H1FUWECMzNtj39MTdW5C4q4jrIGBqpSpUrllkd/f/9S6adbt24aOXKkFi9erOrVq0uSjh49qtGjR6t79+6lsg4AAACgLDi9aHsx0zSVkZGhunXrKiIiQmvWrFFMTIwkKTMzUxs3btSUKVMKfL/FYpElnxEkXl5e5fZh0TCMcl0fygZ5dH/k0DOQR/dHDsuGKcmRsaLWix6XZHypqfLNY2mtY+bMmbr55ptVp04d1apVS4Zh6PDhw2revLkWLlxYKusAAAAAyoJTi7bjxo3T9ddfr1q1aik1NVVLlizRhg0b9OWXX8owDI0aNUqTJk1SdHS0oqOjNWnSJAUGBmrgwIHODBsAAABuoFatWvrhhx+0Zs0a/frrrzJNU02aNFGPHj2cHRoAAABQKKcWbf/880/dc889On78uCpWrKgrr7xSX375pXr27ClJGjt2rNLS0jRixAidOnVKbdu21erVqxUcHOzMsAEAANxGpre3Hv7Xv2yPLyfnzp1TYGCgevbsaTu/BAAAANyBU4u277zzTqGvG4ahuLg4xcXFlU9AAAAAHibb21uzrr7a2WE4RaVKldS6dWt16dJFXbp0UceOHRUUFOTssFCOCrl/MQAAgEt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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean availability : 97.2%  |  >80%: 73 / 75 links\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9a8c665",
+   "metadata": {},
+   "source": [
+    "### Excess attenuation\n",
+    "\n",
+    "$$A = \\mathrm{RSL_{baseline}} - \\mathrm{RSL}$$\n",
+    "\n",
+    "Baseline = per-link median RSL (dry-weather reference). Positive $A$ → signal drop → rain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ff81a8c101f119dc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:05:08.281091Z",
+     "start_time": "2026-03-25T11:05:08.008931Z"
+    }
+   },
+   "source": [
+    "# QC — remove implausible outliers (top 0.1% of values) from both networks\n",
+    "ds_asos[\"precip_amount\"] = ds_asos[\"precip_amount\"].where(ds_asos[\"precip_amount\"] <= ds_asos[\"precip_amount\"].quantile(0.999))\n",
+    "ds_pws[\"rainfall_amount\"] = ds_pws[\"rainfall_amount\"].where(ds_pws[\"rainfall_amount\"] <= ds_pws[\"rainfall_amount\"].quantile(0.999))\n",
+    "\n",
+    "ds_asos[\"precip_amount\"].resample(time=\"1h\").sum(skipna=True).plot.line(x=\"time\", add_legend=True)\n",
+    "plt.title(\"ASOS hourly accumulated precipitation (mm)\");\n",
+    "plt.show()\n",
+    "\n",
+    "ds_pws[\"rainfall_amount\"].resample(time=\"1h\").sum(skipna=True).plot.line(x=\"time\", add_legend=False)\n",
+    "plt.title(\"PWS hourly accumulated precipitation (mm)\");\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "code",
+   "id": "aa35752078d27d21",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:05:11.495978Z",
+     "start_time": "2026-03-25T11:05:11.381951Z"
+    }
+   },
+   "source": [
+    "# remove sentinel values (-99 is a known fill value in the dataset)\n",
+    "ds_cmls[\"rsl\"] = ds_cmls.rsl.where(ds_cmls.rsl > -99)\n",
+    "\n",
+    "dt_h   = (pd.Timestamp(ds_cmls.time.values[1]) - pd.Timestamp(ds_cmls.time.values[0])).total_seconds() / 3600\n",
+    "win_5h = round(5 / dt_h)   # timesteps in 5 h — diurnal window\n",
+    "win_2h = round(2 / dt_h)   # timesteps in 2 h — noise window\n",
+    "\n",
+    "# diurnal: signal fluctuates in a daily cycle (sun heating antenna) — not rain\n",
+    "qc_diurnal = (ds_cmls.rsl.rolling(time=win_5h, center=True).std() > 2).mean(dim=\"time\") > 0.1\n",
+    "\n",
+    "# noisy: signal is randomly unstable for long periods — hardware or interference issue\n",
+    "qc_noisy = (ds_cmls.rsl.rolling(time=win_2h, center=True).std() > 0.8).mean(dim=\"time\") > 0.2\n",
+    "\n",
+    "# null bad sublinks, keep the CML structure intact\n",
+    "qc_bad = qc_diurnal | qc_noisy\n",
+    "ds_cmls_clean = ds_cmls.copy()\n",
+    "ds_cmls_clean[\"rsl\"] = ds_cmls.rsl.where(~qc_bad)\n",
+    "print(f\"Bad sublinks nulled — CMLs fully intact: {ds_cmls_clean.sizes['cml_id']}\")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bad sublinks nulled — CMLs fully intact: 75\n"
+     ]
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e496a5c313f48f76",
+   "metadata": {},
+   "source": [
+    "### Baseline Computation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "398034409fc219cb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:05:20.462478Z",
+     "start_time": "2026-03-25T11:05:18.099127Z"
+    }
+   },
+   "source": [
+    "import pycomlink as pycml\n",
+    "\n",
+    "# ── config ────────────────────────────────────────────────────\n",
+    "# \"rolling\"      — rolling median, no wet mask needed\n",
+    "# \"std\"          — wet from fixed rolling std threshold, no reference\n",
+    "# \"std_adaptive\" — wet from per-link adaptive std threshold, no reference\n",
+    "# \"ref_asos\"     — wet from ASOS network mean precip (requires ds_asos)\n",
+    "BASELINE      = \"ref_asos\"\n",
+    "WINDOW_HOURS  = 6    # window duration for all methods\n",
+    "WET_STD       = 0.4  # std threshold for \"std\" method (dB)\n",
+    "WET_THRESHOLD = 0.1  # mm/window for \"ref_asos\" method\n",
+    "N_DRY         = 10   # dry samples averaged at wet event start\n",
+    "\n",
+    "dt_h   = (pd.Timestamp(ds_cmls.time.values[1]) - pd.Timestamp(ds_cmls.time.values[0])).total_seconds() / 3600\n",
+    "WINDOW = round(WINDOW_HOURS / dt_h)\n",
+    "\n",
+    "def _apply_baseline(rsl, wet):\n",
+    "    return xr.apply_ufunc(\n",
+    "        pycml.processing.baseline.baseline_constant,\n",
+    "        rsl, wet.broadcast_like(rsl),\n",
+    "        input_core_dims=[[\"time\"], [\"time\"]],\n",
+    "        output_core_dims=[[\"time\"]],\n",
+    "        vectorize=True,\n",
+    "        kwargs={\"n_average_last_dry\": N_DRY},\n",
+    "    )\n",
+    "\n",
+    "# ── baseline ──────────────────────────────────────────────────\n",
+    "if BASELINE == \"rolling\":\n",
+    "    ds_cmls[\"baseline\"] = ds_cmls.rsl.rolling(\n",
+    "        time=WINDOW, center=True, min_periods=WINDOW // 2\n",
+    "    ).median()\n",
+    "\n",
+    "elif BASELINE == \"std\":\n",
+    "    ds_cmls[\"wet\"]      = ds_cmls.rsl.rolling(time=WINDOW, center=True).std() > WET_STD\n",
+    "    ds_cmls[\"baseline\"] = _apply_baseline(ds_cmls.rsl, ds_cmls.wet)\n",
+    "\n",
+    "elif BASELINE == \"std_adaptive\":\n",
+    "    roll_std            = ds_cmls.rsl.rolling(time=WINDOW, center=True).std()\n",
+    "    ds_cmls[\"wet\"]      = roll_std > 1.1 * roll_std.quantile(0.85, dim=\"time\")\n",
+    "    ds_cmls[\"baseline\"] = _apply_baseline(ds_cmls.rsl, ds_cmls.wet)\n",
+    "\n",
+    "elif BASELINE == \"ref_asos\":\n",
+    "    rain_mean  = ds_asos[\"precip_amount\"].resample(time=\"15min\").sum(skipna=True).mean(dim=\"id\", skipna=True)\n",
+    "    wet_asos   = (rain_mean > WET_THRESHOLD).rolling(time=3, center=True).max()\n",
+    "    wet_on_cml = wet_asos.astype(int).interp(time=ds_cmls.time, method=\"nearest\").fillna(0).astype(bool)\n",
+    "    ds_cmls[\"wet\"]      = wet_on_cml\n",
+    "    ds_cmls[\"baseline\"] = _apply_baseline(ds_cmls.rsl, wet_on_cml)\n",
+    "\n",
+    "print(f\"Baseline: {BASELINE}  |  window: {WINDOW_HOURS}h = {WINDOW} samples\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline: ref_asos  |  window: 6h = 360 samples\n"
+     ]
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "code",
+   "id": "8dc5cb9fbb1bfb7e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:05:21.861021Z",
+     "start_time": "2026-03-25T11:05:21.455572Z"
+    }
+   },
+   "source": [
+    "# ── config ───────────────────────────────────────────────────\n",
+    "R_MIN   = 0.1    #  rain rates below this are set to zero\n",
+    "\n",
+    "# ── observed attenuation ─────────────────────────────────────\n",
+    "ds_cmls[\"A_obs\"] = ds_cmls[\"baseline\"] - ds_cmls.rsl\n",
+    "ds_cmls[\"A_obs\"] = ds_cmls.A_obs.where(ds_cmls.A_obs >= 0, 0)\n",
+    "\n",
+    "# ── wet antenna correction (optional) ────────────────────────\n",
+    "\n",
+    "ds_cmls[\"waa\"] = pycml.processing.wet_antenna.waa_pastorek_2021_from_A_obs(\n",
+    "    A_obs=ds_cmls.A_obs,\n",
+    "    f_Hz=ds_cmls.frequency * 1e6,\n",
+    "    pol=ds_cmls.polarization.values,\n",
+    "    L_km=ds_cmls.length / 1000,\n",
+    ")\n",
+    "ds_cmls[\"A\"] = ds_cmls.A_obs - ds_cmls.waa\n",
+    "\n",
+    "\n",
+    "ds_cmls[\"A\"] = ds_cmls.A.where(ds_cmls.A >= 0, 0)\n",
+    "\n",
+    "# ── rain rate from k-R power law ─────────────────────────────\n",
+    "ds_cmls[\"R\"] = pycml.processing.k_R_relation.calc_R_from_A(\n",
+    "    A=ds_cmls.A,\n",
+    "    L_km=ds_cmls.length.astype(float) / 1000,\n",
+    "    f_GHz=ds_cmls.frequency / 1000,\n",
+    "    pol=ds_cmls.polarization,\n",
+    "    R_min=R_MIN,\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 17
+  },
+  {
+   "cell_type": "code",
+   "id": "da983e0aa790856b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:07:22.154321Z",
+     "start_time": "2026-03-25T11:07:21.468591Z"
+    }
+   },
+   "source": [
+    "# ── baseline inspection — adjust link_idx and time window to evaluate params ──\n",
+    "link_idx = 0\n",
+    "t_check  = slice(\"2024-01-27\", \"2024-01-30\")\n",
+    "\n",
+    "n_panels = 3 + (\"wet\" in ds_cmls)\n",
+    "fig, axes = plt.subplots(n_panels, 1, figsize=(14, 3 * n_panels), sharex=True)\n",
+    "\n",
+    "ds_cmls.rsl.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(\n",
+    "    ax=axes[0], label=\"RSL\", alpha=0.7)\n",
+    "ds_cmls.baseline.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(\n",
+    "    ax=axes[0], label=\"baseline\", linewidth=2)\n",
+    "axes[0].set(title=f\"RSL vs baseline — link {link_idx}  |  method: {BASELINE}\", ylabel=\"dBm\")\n",
+    "axes[0].legend()\n",
+    "\n",
+    "ds_cmls.A_obs.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(ax=axes[1])\n",
+    "axes[1].set(title=\"A_obs\", ylabel=\"dB\")\n",
+    "\n",
+    "ds_cmls.R.isel(cml_id=link_idx, sublink_id=0).sel(time=t_check).plot(ax=axes[2])\n",
+    "axes[2].set(title=\"Rain rate R\", ylabel=\"mm/h\")\n",
+    "\n",
+    "if \"wet\" in ds_cmls:\n",
+    "    ds_cmls.wet.sel(time=t_check).plot(ax=axes[3])\n",
+    "    axes[3].set(title=\"Wet mask\", ylabel=\"wet (1/0)\")\n",
+    "\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1200 with 4 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "cell_type": "code",
+   "id": "6dfd9a15ddaab633",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:07:28.064661Z",
+     "start_time": "2026-03-25T11:07:27.872471Z"
+    }
+   },
+   "source": [
+    "ds_cmls.R.resample(time=\"1h\").mean().cumsum(dim=\"time\").stack(link=(\"cml_id\", \"sublink_id\")).plot.line(x=\"time\", add_legend=False)\n",
+    "plt.title(\"Cumulative rainfall (mm)\");"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 26
+  },
+  {
+   "cell_type": "code",
+   "id": "71de72adb4408c5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:07:30.994492Z",
+     "start_time": "2026-03-25T11:07:30.916732Z"
+    }
+   },
+   "source": [
+    "# ── filter options — set to None to skip ─────────────────────\n",
+    "LENGTH_KM  = (1, 10)   # min/max link length\n",
+    "FREQ_GHZ   = (65.0, 70.0)  # min/max frequency\n",
+    "\n",
+    "# ── build mask — True = keep ──────────────────────────────────\n",
+    "mask = xr.ones_like(ds_cmls.rsl.isel(time=0), dtype=bool)\n",
+    "\n",
+    "### filter by link length and frequency\n",
+    "if LENGTH_KM  is not None: mask = mask & (ds_cmls.length    >= LENGTH_KM[0] * 1000) & (ds_cmls.length    <= LENGTH_KM[1] * 1000)\n",
+    "if FREQ_GHZ   is not None: mask = mask & (ds_cmls.frequency >= FREQ_GHZ[0]  * 1000) & (ds_cmls.frequency <= FREQ_GHZ[1]  * 1000)\n",
+    "\n",
+    "### filter by QC flags\n",
+    "DROP_NOISY    = True\n",
+    "DROP_DIURNAL  = False\n",
+    "\n",
+    "if DROP_NOISY:    mask = mask & ~qc_noisy\n",
+    "if DROP_DIURNAL:  mask = mask & ~qc_diurnal\n",
+    "\n",
+    "ds_cmls_filtered = ds_cmls.where(mask)\n",
+    "\n",
+    "print(f\"Sublinks kept : {int(mask.sum().values)} / {ds_cmls.sizes['cml_id'] * ds_cmls.sizes['sublink_id']}\")\n",
+    "print(f\"CMLs with at least one valid sublink : {int(mask.any(dim='sublink_id').sum().values)} / {ds_cmls.sizes['cml_id']}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sublinks kept : 15 / 225\n",
+      "CMLs with at least one valid sublink : 11 / 75\n"
+     ]
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "cell_type": "code",
+   "id": "a14b55a9052536d0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:06:15.398861Z",
+     "start_time": "2026-03-25T11:06:15.204831Z"
+    }
+   },
+   "source": [
+    "ds_cmls_filtered.R.resample(time=\"1h\").mean().cumsum(dim=\"time\").stack(link=(\"cml_id\", \"sublink_id\")).plot.line(x=\"time\", add_legend=False);"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "cell_type": "code",
+   "id": "c916eb24fd52df61",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T11:06:18.896980Z",
+     "start_time": "2026-03-25T11:06:18.159363Z"
+    }
+   },
+   "source": [
+    "t_check = slice(\"2024-01-27\", \"2024-01-30\")\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 1, figsize=(14, 6), sharex=True)\n",
+    "\n",
+    "ds_cmls_filtered.rsl.sel(time=t_check).stack(link=(\"cml_id\", \"sublink_id\")).plot.line(\n",
+    "    x=\"time\", ax=axes[0], add_legend=False, linewidth=0.5, alpha=0.5)\n",
+    "axes[0].set(ylabel=\"dBm\", title=\"RSL — chosen sublinks\")\n",
+    "\n",
+    "ds_asos[\"precip_amount\"].resample(time=\"1h\").sum().sel(time=t_check).plot.line(\n",
+    "    x=\"time\", ax=axes[1], add_legend=True)\n",
+    "axes[1].set(ylabel=\"mm/h\", title=\"ASOS precipitation\")\n",
+    "\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "cannot reshape array of size 0 into shape (0,newaxis)",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mValueError\u001B[39m                                Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[23]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m      1\u001B[39m t_check = \u001B[38;5;28mslice\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33m2024-01-27\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33m2024-01-30\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      3\u001B[39m fig, axes = plt.subplots(\u001B[32m2\u001B[39m, \u001B[32m1\u001B[39m, figsize=(\u001B[32m14\u001B[39m, \u001B[32m6\u001B[39m), sharex=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m \u001B[43mds_cmls_filtered\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrsl\u001B[49m\u001B[43m.\u001B[49m\u001B[43msel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtime\u001B[49m\u001B[43m=\u001B[49m\u001B[43mt_check\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstack\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlink\u001B[49m\u001B[43m=\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mcml_id\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43msublink_id\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m.plot.line(\n\u001B[32m      6\u001B[39m     x=\u001B[33m\"\u001B[39m\u001B[33mtime\u001B[39m\u001B[33m\"\u001B[39m, ax=axes[\u001B[32m0\u001B[39m], add_legend=\u001B[38;5;28;01mFalse\u001B[39;00m, linewidth=\u001B[32m0.5\u001B[39m, alpha=\u001B[32m0.5\u001B[39m)\n\u001B[32m      7\u001B[39m axes[\u001B[32m0\u001B[39m].set(ylabel=\u001B[33m\"\u001B[39m\u001B[33mdBm\u001B[39m\u001B[33m\"\u001B[39m, title=\u001B[33m\"\u001B[39m\u001B[33mRSL — chosen sublinks\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m      9\u001B[39m ds_asos[\u001B[33m\"\u001B[39m\u001B[33mprecip_amount\u001B[39m\u001B[33m\"\u001B[39m].resample(time=\u001B[33m\"\u001B[39m\u001B[33m1h\u001B[39m\u001B[33m\"\u001B[39m).sum().sel(time=t_check).plot.line(\n\u001B[32m     10\u001B[39m     x=\u001B[33m\"\u001B[39m\u001B[33mtime\u001B[39m\u001B[33m\"\u001B[39m, ax=axes[\u001B[32m1\u001B[39m], add_legend=\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/util/deprecation_helpers.py:144\u001B[39m, in \u001B[36mdeprecate_dims.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m    136\u001B[39m     emit_user_level_warning(\n\u001B[32m    137\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mThe `\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mold_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m` argument has been renamed to `dim`, and will be removed \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    138\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33min the future. This renaming is taking place throughout xarray over the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m    141\u001B[39m         \u001B[38;5;167;01mPendingDeprecationWarning\u001B[39;00m,\n\u001B[32m    142\u001B[39m     )\n\u001B[32m    143\u001B[39m     kwargs[\u001B[33m\"\u001B[39m\u001B[33mdim\u001B[39m\u001B[33m\"\u001B[39m] = kwargs.pop(old_name)\n\u001B[32m--> \u001B[39m\u001B[32m144\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/core/dataarray.py:2985\u001B[39m, in \u001B[36mDataArray.stack\u001B[39m\u001B[34m(self, dim, create_index, index_cls, **dim_kwargs)\u001B[39m\n\u001B[32m   2919\u001B[39m \u001B[38;5;129m@partial\u001B[39m(deprecate_dims, old_name=\u001B[33m\"\u001B[39m\u001B[33mdimensions\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m   2920\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mstack\u001B[39m(\n\u001B[32m   2921\u001B[39m     \u001B[38;5;28mself\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m   2925\u001B[39m     **dim_kwargs: Sequence[Hashable | EllipsisType],\n\u001B[32m   2926\u001B[39m ) -> Self:\n\u001B[32m   2927\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m   2928\u001B[39m \u001B[33;03m    Stack any number of existing dimensions into a single new dimension.\u001B[39;00m\n\u001B[32m   2929\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m   2983\u001B[39m \u001B[33;03m    DataArray.unstack\u001B[39;00m\n\u001B[32m   2984\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m2985\u001B[39m     ds = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_to_temp_dataset\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstack\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m   2986\u001B[39m \u001B[43m        \u001B[49m\u001B[43mdim\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2987\u001B[39m \u001B[43m        \u001B[49m\u001B[43mcreate_index\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcreate_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2988\u001B[39m \u001B[43m        \u001B[49m\u001B[43mindex_cls\u001B[49m\u001B[43m=\u001B[49m\u001B[43mindex_cls\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2989\u001B[39m \u001B[43m        \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mdim_kwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m   2990\u001B[39m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   2991\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._from_temp_dataset(ds)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/util/deprecation_helpers.py:144\u001B[39m, in \u001B[36mdeprecate_dims.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m    136\u001B[39m     emit_user_level_warning(\n\u001B[32m    137\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mThe `\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mold_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m` argument has been renamed to `dim`, and will be removed \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    138\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33min the future. This renaming is taking place throughout xarray over the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m    141\u001B[39m         \u001B[38;5;167;01mPendingDeprecationWarning\u001B[39;00m,\n\u001B[32m    142\u001B[39m     )\n\u001B[32m    143\u001B[39m     kwargs[\u001B[33m\"\u001B[39m\u001B[33mdim\u001B[39m\u001B[33m\"\u001B[39m] = kwargs.pop(old_name)\n\u001B[32m--> \u001B[39m\u001B[32m144\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/core/dataset.py:5283\u001B[39m, in \u001B[36mDataset.stack\u001B[39m\u001B[34m(self, dim, create_index, index_cls, **dim_kwargs)\u001B[39m\n\u001B[32m   5281\u001B[39m result = \u001B[38;5;28mself\u001B[39m\n\u001B[32m   5282\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m new_dim, dims \u001B[38;5;129;01min\u001B[39;00m dim.items():\n\u001B[32m-> \u001B[39m\u001B[32m5283\u001B[39m     result = \u001B[43mresult\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_stack_once\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdims\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_dim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mindex_cls\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcreate_index\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5284\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/core/dataset.py:5198\u001B[39m, in \u001B[36mDataset._stack_once\u001B[39m\u001B[34m(self, dims, new_dim, index_cls, create_index)\u001B[39m\n\u001B[32m   5196\u001B[39m shape = [\u001B[38;5;28mself\u001B[39m.sizes[d] \u001B[38;5;28;01mfor\u001B[39;00m d \u001B[38;5;129;01min\u001B[39;00m vdims]\n\u001B[32m   5197\u001B[39m exp_var = var.set_dims(vdims, shape)\n\u001B[32m-> \u001B[39m\u001B[32m5198\u001B[39m stacked_var = \u001B[43mexp_var\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstack\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m{\u001B[49m\u001B[43mnew_dim\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdims\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   5199\u001B[39m new_variables[name] = stacked_var\n\u001B[32m   5200\u001B[39m stacked_var_names.append(name)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/util/deprecation_helpers.py:144\u001B[39m, in \u001B[36mdeprecate_dims.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m    136\u001B[39m     emit_user_level_warning(\n\u001B[32m    137\u001B[39m         \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mThe `\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mold_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m` argument has been renamed to `dim`, and will be removed \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m    138\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33min the future. This renaming is taking place throughout xarray over the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m   (...)\u001B[39m\u001B[32m    141\u001B[39m         \u001B[38;5;167;01mPendingDeprecationWarning\u001B[39;00m,\n\u001B[32m    142\u001B[39m     )\n\u001B[32m    143\u001B[39m     kwargs[\u001B[33m\"\u001B[39m\u001B[33mdim\u001B[39m\u001B[33m\"\u001B[39m] = kwargs.pop(old_name)\n\u001B[32m--> \u001B[39m\u001B[32m144\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/core/variable.py:1547\u001B[39m, in \u001B[36mVariable.stack\u001B[39m\u001B[34m(self, dim, **dim_kwargs)\u001B[39m\n\u001B[32m   1545\u001B[39m result = \u001B[38;5;28mself\u001B[39m\n\u001B[32m   1546\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m new_dim, dims \u001B[38;5;129;01min\u001B[39;00m dim.items():\n\u001B[32m-> \u001B[39m\u001B[32m1547\u001B[39m     result = \u001B[43mresult\u001B[49m\u001B[43m.\u001B[49m\u001B[43m_stack_once\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdims\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_dim\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1548\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m result\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/core/variable.py:1510\u001B[39m, in \u001B[36mVariable._stack_once\u001B[39m\u001B[34m(self, dim, new_dim)\u001B[39m\n\u001B[32m   1507\u001B[39m reordered = \u001B[38;5;28mself\u001B[39m.transpose(*dim_order)\n\u001B[32m   1509\u001B[39m new_shape = reordered.shape[: \u001B[38;5;28mlen\u001B[39m(other_dims)] + (-\u001B[32m1\u001B[39m,)\n\u001B[32m-> \u001B[39m\u001B[32m1510\u001B[39m new_data = \u001B[43mduck_array_ops\u001B[49m\u001B[43m.\u001B[49m\u001B[43mreshape\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreordered\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_shape\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   1511\u001B[39m new_dims = reordered.dims[: \u001B[38;5;28mlen\u001B[39m(other_dims)] + (new_dim,)\n\u001B[32m   1513\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mtype\u001B[39m(\u001B[38;5;28mself\u001B[39m)(\n\u001B[32m   1514\u001B[39m     new_dims, new_data, \u001B[38;5;28mself\u001B[39m._attrs, \u001B[38;5;28mself\u001B[39m._encoding, fastpath=\u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[32m   1515\u001B[39m )\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/xarray/core/duck_array_ops.py:454\u001B[39m, in \u001B[36mreshape\u001B[39m\u001B[34m(array, shape)\u001B[39m\n\u001B[32m    452\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mreshape\u001B[39m(array, shape):\n\u001B[32m    453\u001B[39m     xp = get_array_namespace(array)\n\u001B[32m--> \u001B[39m\u001B[32m454\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mxp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mreshape\u001B[49m\u001B[43m(\u001B[49m\u001B[43marray\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mshape\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:324\u001B[39m, in \u001B[36mreshape\u001B[39m\u001B[34m(a, shape, order, newshape, copy)\u001B[39m\n\u001B[32m    322\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m copy \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m    323\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _wrapfunc(a, \u001B[33m'\u001B[39m\u001B[33mreshape\u001B[39m\u001B[33m'\u001B[39m, shape, order=order, copy=copy)\n\u001B[32m--> \u001B[39m\u001B[32m324\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_wrapfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43ma\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mreshape\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mshape\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morder\u001B[49m\u001B[43m=\u001B[49m\u001B[43morder\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~/miniforge3/envs/openmesh/lib/python3.11/site-packages/numpy/_core/fromnumeric.py:57\u001B[39m, in \u001B[36m_wrapfunc\u001B[39m\u001B[34m(obj, method, *args, **kwds)\u001B[39m\n\u001B[32m     54\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _wrapit(obj, method, *args, **kwds)\n\u001B[32m     56\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m---> \u001B[39m\u001B[32m57\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mbound\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     58\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[32m     59\u001B[39m     \u001B[38;5;66;03m# A TypeError occurs if the object does have such a method in its\u001B[39;00m\n\u001B[32m     60\u001B[39m     \u001B[38;5;66;03m# class, but its signature is not identical to that of NumPy's. This\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m     64\u001B[39m     \u001B[38;5;66;03m# Call _wrapit from within the except clause to ensure a potential\u001B[39;00m\n\u001B[32m     65\u001B[39m     \u001B[38;5;66;03m# exception has a traceback chain.\u001B[39;00m\n\u001B[32m     66\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m _wrapit(obj, method, *args, **kwds)\n",
+      "\u001B[31mValueError\u001B[39m: cannot reshape array of size 0 into shape (0,newaxis)"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d9cc1ca67bbbd6e",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Personal Weather Station (PWS) Data\n",
+    "\n",
+    "PWS data from Weather Underground provides dense crowdsourced rainfall observations\n",
+    "across NYC. Consumer-grade stations require quality control before use.\n",
+    "\n",
+    "### Dataset Overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ef5f5fbf51d0064b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:15.415976Z",
+     "start_time": "2026-03-25T09:14:15.292428Z"
+    }
+   },
+   "source": [
+    "# data availability per station — fraction of non-NaN timesteps\n",
+    "avail = (~np.isnan(ds_pws[\"rainfall_amount\"])).mean(dim=\"time\")\n",
+    "\n",
+    "avail.plot.hist(bins=20, color=\"darkorange\", edgecolor=\"white\")\n",
+    "plt.axvline(float(avail.mean()), color=\"red\", linestyle=\"--\",\n",
+    "            label=f\"Mean: {float(avail.mean())*100:.1f}%\")\n",
+    "plt.title(\"PWS data availability\")\n",
+    "plt.xlabel(\"Availability fraction\")\n",
+    "plt.ylabel(\"Number of stations\")\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3);\n",
+    "\n",
+    "print(f\"Stations > 80% availability : {int((avail > 0.8).sum())} / {ds_pws.sizes['id']}\")\n",
+    "print(f\"Stations > 50% availability : {int((avail > 0.5).sum())} / {ds_pws.sizes['id']}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stations > 80% availability : 26 / 37\n",
+      "Stations > 50% availability : 30 / 37\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eadf7c17c312733c",
+   "metadata": {},
+   "source": [
+    "### Quality Control with `pypwsqc`\n",
+    "\n",
+    "[`pypwsqc`](https://github.com/OpenSenseAction/pypwsqc) implements QC methods\n",
+    "| Filter | What it catches |\n",
+    "|--------|----------------|\n",
+    "| **Faulty Zero (FZ)** | Station reports zero while neighbours report rain |\n",
+    "| **Suspect Zero (SZ)** | Suspiciously few non-zero readings overall |\n",
+    "| **High Accumulation (HA)** | Implausibly large values |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c71df9cb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:15.432418Z",
+     "start_time": "2026-03-25T09:14:15.426320Z"
+    }
+   },
+   "source": [
+    "import pypwsqc\n",
+    "print(f\"pypwsqc   : {pypwsqc.__version__}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pypwsqc   : 0.2.1\n"
+     ]
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "cell_type": "code",
+   "id": "905b1dbd1a9d6307",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:15.463150Z",
+     "start_time": "2026-03-25T09:14:15.438470Z"
+    }
+   },
+   "source": [
+    "import pypwsqc.peak_removal_filter as prf\n",
+    "\n",
+    "from pypwsqc import peak_removal_filter as prf\n",
+    "# ── 0. availability — drop stations with too little data ──────\n",
+    "MIN_AVAIL = 0.5   # keep stations with at least 50% valid data\n",
+    "avail     = (~np.isnan(ds_pws[\"rainfall_amount\"])).mean(dim=\"time\")\n",
+    "ds_pws    = ds_pws.sel(id=avail[avail >= MIN_AVAIL].id)\n",
+    "print(f\"[0] Availability filter : {ds_pws.sizes['id']} stations kept  (>= {MIN_AVAIL*100:.0f}%)\")\n",
+    "\n",
+    "# ── 1. quantile — remove implausible values ───────────────────\n",
+    "ds_pws[\"rainfall_amount\"] = ds_pws[\"rainfall_amount\"].where(\n",
+    "    ds_pws[\"rainfall_amount\"] <= ds_pws[\"rainfall_amount\"].quantile(0.999)\n",
+    ").assign_attrs(ds_pws[\"rainfall_amount\"].attrs)\n",
+    "print(f\"[1] Quantile filter     : top 0.1% values removed\")\n",
+    "\n",
+    "# ── project to UTM (needed by peak removal and IC filter) ───\n",
+    "ds_pws = prf.convert_to_utm(ds_pws, name_coord_lon=\"lon\", name_coord_lat=\"lat\", zone=18)\n",
+    "print(f\"[+] UTM projection      : x/y coords added\")\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0] Availability filter : 30 stations kept  (>= 50%)\n",
+      "[1] Quantile filter     : top 0.1% values removed\n",
+      "[+] UTM projection      : x/y coords added\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "cell_type": "code",
+   "id": "ced09c9fd9b74bc1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:16.246088Z",
+     "start_time": "2026-03-25T09:14:15.468397Z"
+    }
+   },
+   "source": [
+    "# ── 2. FZ + HI flagging ───────────────────────────────────────\n",
+    "NINT, N_STAT, HI_THRES_A, HI_THRES_B = 10, 3, 0.5, 2.0\n",
+    "rain    = ds_pws[\"rainfall_amount\"]\n",
+    "ref     = rain.median(dim=\"id\", skipna=True)\n",
+    "nbrs_nn = (~np.isnan(rain)).sum(dim=\"id\").broadcast_like(rain)\n",
+    "ds_qc   = xr.Dataset({\"rainfall\": rain, \"reference\": ref.broadcast_like(rain),\n",
+    "                       \"nbrs_not_nan\": nbrs_nn.astype(float)})\n",
+    "ds_qc   = pypwsqc.flagging.hi_filter(ds_qc, hi_thres_a=HI_THRES_A, hi_thres_b=HI_THRES_B,\n",
+    "                                      nint=NINT, n_stat=N_STAT)\n",
+    "ds_qc   = pypwsqc.flagging.fz_filter(ds_qc, nint=NINT, n_stat=N_STAT)\n",
+    "clean   = (ds_qc[\"hi_flag\"] != 1) & (ds_qc[\"fz_flag\"] != 1)\n",
+    "ds_pws[\"rainfall_amount_qc\"] = rain.where(clean)\n",
+    "print(f\"[2] FZ+HI filter        : HI={float((ds_qc['hi_flag']==1).mean())*100:.1f}%  \"\n",
+    "      f\"FZ={float((ds_qc['fz_flag']==1).mean())*100:.1f}%  flagged\")\n",
+    "\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2] FZ+HI filter        : HI=0.0%  FZ=1.3%  flagged\n"
+     ]
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "cell_type": "code",
+   "id": "37ecafb255bb9cad",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:16.606397Z",
+     "start_time": "2026-03-25T09:14:16.251033Z"
+    }
+   },
+   "source": [
+    "import contextlib\n",
+    "import io\n",
+    "\n",
+    "# ── 3. peak removal — redistribute spikes after NaN gaps ─────\n",
+    "MAX_DISTANCE, N_CLOSEST           = 5000, 5\n",
+    "QUANTILE, SEQ_LEN_THRESHOLD       = 0.99, 3\n",
+    "SEQ_NAN_THRESHOLD, MIN_STATION_THRESHOLD = 0.5, 2\n",
+    "\n",
+    "ds_pr      = ds_pws[\"rainfall_amount_qc\"].to_dataset(name=\"rainfall\").assign_coords(x=ds_pws.x, y=ds_pws.y)\n",
+    "nbrs_pr    = prf.get_closest_points_to_point(ds_pr, ds_pr, max_distance=MAX_DISTANCE, n_closest=N_CLOSEST)\n",
+    "w          = prf.inverse_distance_weighting(nbrs_pr)\n",
+    "n_peaks    = 0; n_affected = 0\n",
+    "for sid in ds_pr.id.values:\n",
+    "    with contextlib.redirect_stderr(io.StringIO()):\n",
+    "        peaks, starts, _, lens = prf.get_nan_sequences(ds_pr, sid, QUANTILE, SEQ_LEN_THRESHOLD)\n",
+    "    if not peaks: continue\n",
+    "    print(f\"    {sid} — {len(peaks)} peak(s) redistributed\")\n",
+    "    n_peaks += len(peaks); n_affected += 1\n",
+    "    seqs  = prf.interpolate_precipitation(ds_pr, sid, nbrs_pr, w, starts, peaks, lens,\n",
+    "                seq_nan_threshold=SEQ_NAN_THRESHOLD, min_station_threshold=MIN_STATION_THRESHOLD)\n",
+    "    ds_pr = prf.overwrite_seq(ds_pr, sid, seqs, starts, peaks)\n",
+    "ds_pws[\"rainfall_amount_qc\"] = ds_pr[\"rainfall\"]\n",
+    "print(f\"[3] Peak removal        : {n_affected} stations  |  {n_peaks} peaks redistributed\")\n",
+    "\n"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Calculate weights for stations: 100%|██████████| 30/30 [00:00<00:00, 5324.75 stations/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1896 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1802.64 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 14716.86 sequences/s]\n",
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 1996.34 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1918 — 2 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1127.73 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 2/2 [00:00<00:00, 24385.49 sequences/s]\n",
+      "Overwrite sequences: 100%|██████████| 2/2 [00:00<00:00, 2361.66 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1388 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1654.39 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 13706.88 sequences/s]\n",
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 1926.64 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1238 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 1405.01 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 17848.10 sequences/s]\n",
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 911.61 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1533 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2769.89 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 15592.21 sequences/s]\n",
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 2058.05 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    KNYNEWYO1867 — 1 peak(s) redistributed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Get precipitation values from neighbors: 100%|██████████| 4/4 [00:00<00:00, 2568.86 neighbors/s]\n",
+      "Interpolate precipitation values for sequences: 100%|██████████| 1/1 [00:00<00:00, 16980.99 sequences/s]\n",
+      "Overwrite sequences: 100%|██████████| 1/1 [00:00<00:00, 2304.56 sequences/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[3] Peak removal        : 6 stations  |  7 peaks redistributed\n"
+     ]
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "cell_type": "code",
+   "id": "f9361a0da80b68e0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:17.792998Z",
+     "start_time": "2026-03-25T09:14:16.613696Z"
+    }
+   },
+   "source": [
+    "import pypwsqc.indicator_correlation as ic\n",
+    "\n",
+    "# ── 4. IC filter — drop stations uncorrelated with neighbours ─\n",
+    "ds_pws_ic  = ds_pws[\"rainfall_amount_qc\"].assign_coords(x=ds_pws.x, y=ds_pws.y)\n",
+    "ds_asos_ic = prf.convert_to_utm(ds_asos.drop_vars(\"quantile\", errors=\"ignore\"),\n",
+    "                                  name_coord_lon=\"lon\", name_coord_lat=\"lat\", zone=18)\n",
+    "da_asos_ic = ds_asos_ic[\"precip_amount\"].assign_coords(x=ds_asos_ic.x, y=ds_asos_ic.y)\n",
+    "da_asos_ic = da_asos_ic.reindex(time=ds_pws_ic.time, method=\"nearest\", tolerance=\"2min\")\n",
+    "\n",
+    "dist_ref,  indcorr_ref = ic.indicator_distance_matrix(ds_pws_ic, ds_pws_ic,  prob=0.99, min_valid_overlap=10)\n",
+    "dist_pws,  indcorr_pws = ic.indicator_distance_matrix(da_asos_ic, ds_pws_ic, prob=0.99, min_valid_overlap=10)\n",
+    "result = ic.ic_filter(indicator_correlation_matrix_ref=indcorr_ref,\n",
+    "                      distance_correlation_matrix_ref=dist_ref,\n",
+    "                      indicator_correlation_matrix=indcorr_pws,\n",
+    "                      distance_matrix=dist_pws)\n",
+    "\n",
+    "good_stations = result.id_neighbor.values[result[\"indcorr_good\"].values]\n",
+    "bad_stations  = result.id_neighbor.values[~result[\"indcorr_good\"].values]\n",
+    "ds_pws = ds_pws.sel(id=good_stations)\n",
+    "print(f\"[4] IC filter           : {len(bad_stations)} stations dropped  |  {ds_pws.sizes['id']} remaining\")\n",
+    "print(f\"    Dropped: {list(bad_stations)}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 30/30 [00:00<00:00, 35.22it/s]\n",
+      "100%|██████████| 4/4 [00:00<00:00, 32.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4] IC filter           : 1 stations dropped  |  29 remaining\n",
+      "    Dropped: [np.str_('KNYNEWYO1622')]\n"
+     ]
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe524523201419ca",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Multi-Sensor Overview: CML + PWS + ASOS\n",
+    "\n",
+    "All three sources aligned over their common time window.\n",
+    "All aggregations use xarray `.resample()` — no raw pandas conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f5d21d336da49594",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:18.482498Z",
+     "start_time": "2026-03-25T09:14:17.893437Z"
+    }
+   },
+   "source": [
+    "# ── config ────────────────────────────────────────────────────\n",
+    "RESAMPLE = \"1h\"  # common aggregation for all sensors\n",
+    "\n",
+    "# common time window\n",
+    "t0 = max(pd.Timestamp(ds_cmls.time.values[0]),  pd.Timestamp(ds_pws.time.values[0]),  pd.Timestamp(ds_asos.time.values[0]))\n",
+    "t1 = min(pd.Timestamp(ds_cmls.time.values[-1]), pd.Timestamp(ds_pws.time.values[-1]), pd.Timestamp(ds_asos.time.values[-1]))\n",
+    "\n",
+    "cml_r  = ds_cmls_filtered.R.resample(time=RESAMPLE).mean().mean(dim=[\"cml_id\",\"sublink_id\"], skipna=True).sel(time=slice(t0,t1))\n",
+    "pws_r  = ds_pws[\"rainfall_amount_qc\"].resample(time=RESAMPLE).sum(skipna=True).mean(dim=\"id\", skipna=True).sel(time=slice(t0,t1))\n",
+    "asos_r = ds_asos[\"precip_amount\"].resample(time=RESAMPLE).sum(skipna=True).mean(dim=\"id\", skipna=True).sel(time=slice(t0,t1))\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# ── time series ───────────────────────────────────────────────\n",
+    "ax = axes[0, 0]\n",
+    "asos_r.plot(ax=ax, label=\"ASOS\", linewidth=1.5)\n",
+    "pws_r.plot(ax=ax,  label=\"PWS\",  linewidth=1.5)\n",
+    "cml_r.plot(ax=ax,  label=\"CML\",  linewidth=1.5, color=\"black\")\n",
+    "ax.set(title=f\"Network mean rainfall ({RESAMPLE})\", ylabel=\"mm/h\")\n",
+    "ax.legend(); ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ── cumulative ────────────────────────────────────────────────\n",
+    "ax = axes[0, 1]\n",
+    "asos_r.cumsum().plot(ax=ax, label=\"ASOS\", linewidth=1.5)\n",
+    "pws_r.cumsum().plot(ax=ax,  label=\"PWS\",  linewidth=1.5)\n",
+    "cml_r.cumsum().plot(ax=ax,  label=\"CML\",  linewidth=1.5, color=\"black\")\n",
+    "ax.set(title=\"Cumulative rainfall\", ylabel=\"mm\")\n",
+    "ax.legend(); ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ── CML vs ASOS hexbin ────────────────────────────────────────\n",
+    "ax = axes[1, 0]\n",
+    "ax.hexbin(asos_r.values, cml_r.values, gridsize=30, bins=\"log\", cmap=\"Blues\")\n",
+    "ax.set(title=\"CML vs ASOS\", xlabel=\"ASOS (mm/h)\", ylabel=\"CML (mm/h)\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ── PWS vs ASOS hexbin ───────────────────────────────────────\n",
+    "ax = axes[1, 1]\n",
+    "ax.hexbin(asos_r.values, pws_r.values, gridsize=30, bins=\"log\", cmap=\"Oranges\")\n",
+    "ax.set(title=\"PWS vs ASOS\", xlabel=\"ASOS (mm/h)\", ylabel=\"PWS (mm/h)\")\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 26
+  },
+  {
+   "cell_type": "code",
+   "id": "c1aff0185ba36b1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:20.014345Z",
+     "start_time": "2026-03-25T09:14:18.488973Z"
+    }
+   },
+   "source": [
+    "RESAMPLE = \"10min\"\n",
+    "\n",
+    "A_da      = ds_cmls[\"A\"].mean(dim=\"sublink_id\").resample(time=RESAMPLE).mean()\n",
+    "pws_plot  = ds_pws[\"rainfall_amount_qc\"].resample(time=RESAMPLE).sum(skipna=True)\n",
+    "asos_plot = ds_asos[\"precip_amount\"].resample(time=RESAMPLE).sum(skipna=True)\n",
+    "\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(16, 12), sharex=True)\n",
+    "\n",
+    "im0 = axes[0].pcolormesh(A_da.time.values, np.arange(A_da.sizes[\"cml_id\"]),\n",
+    "                          A_da.values, cmap=\"Blues\",\n",
+    "                          vmin=0, vmax=float(np.nanpercentile(A_da.values, 99)))\n",
+    "plt.colorbar(im0, ax=axes[0], label=\"A (dB)\", shrink=0.8)\n",
+    "axes[0].set(ylabel=\"CML index\", title=f\"Network-wide excess attenuation A ({RESAMPLE})\")\n",
+    "\n",
+    "im1 = axes[1].pcolormesh(pws_plot.time.values, np.arange(pws_plot.sizes[\"id\"]),\n",
+    "                          pws_plot.values, cmap=\"Greens\",\n",
+    "                          vmin=0, vmax=float(np.nanpercentile(pws_plot.values, 99)))\n",
+    "plt.colorbar(im1, ax=axes[1], label=\"mm\", shrink=0.8)\n",
+    "axes[1].set(ylabel=\"PWS index\", title=f\"PWS rainfall ({RESAMPLE})\")\n",
+    "\n",
+    "im2 = axes[2].pcolormesh(asos_plot.time.values, np.arange(asos_plot.sizes[\"id\"]),\n",
+    "                          asos_plot.values, cmap=\"Oranges\",\n",
+    "                          vmin=0, vmax=float(np.nanpercentile(asos_plot.values, 99)))\n",
+    "plt.colorbar(im2, ax=axes[2], label=\"mm\", shrink=0.8)\n",
+    "axes[2].set(ylabel=\"ASOS index\", title=f\"ASOS precipitation ({RESAMPLE})\")\n",
+    "axes[2].xaxis.set_major_formatter(mdates.DateFormatter(\"%b %d\"))\n",
+    "\n",
+    "for ax in axes: ax.grid(True, alpha=0.3)\n",
+    "plt.tight_layout();"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1600x1200 with 6 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "cell_type": "code",
+   "id": "02e96e27",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:14:20.309193Z",
+     "start_time": "2026-03-25T09:14:20.105528Z"
+    }
+   },
+   "source": [
+    "plg.plot_map.plot_plg(\n",
+    "    da_cmls=ds_cmls_filtered.isel(sublink_id=0).R.resample(time=\"1h\").mean().sum(dim=\"time\"),\n",
+    "    da_gauges=ds_pws[\"rainfall_amount_qc\"].resample(time=\"1h\").sum(skipna=True).sum(dim=\"time\"),\n",
+    "    vmin=0,\n",
+    "    vmax=30,\n",
+    "    cmap=\"turbo\",\n",
+    "    colorbar_label=\"Cumulative rainfall (mm)\",\n",
+    ")\n",
+    "plt.title(\"Cumulative rainfall — CML links + PWS stations\")\n",
+    "plt.gcf().autofmt_xdate(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 28
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1954a0c0d1817b",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Cross-Dataset Validation: CML vs. ASOS\n",
+    "\n",
+    "### Nearest ASOS Station per CML\n",
+    "\n",
+    "For each CML midpoint we find the nearest ASOS station using `pyproj`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdcc089b4e044bf7",
+   "metadata": {},
+   "source": [
+    "### CML Attenuation vs. ASOS Rainfall\n",
+    "\n",
+    "Hourly mean attenuation A for CMLs nearest to the wettest ASOS station,\n",
+    "compared against the station's hourly rainfall. Both aggregated via\n",
+    "xarray `.resample()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "a217f1395350ec47",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:21:59.117628Z",
+     "start_time": "2026-03-25T09:21:58.807310Z"
+    }
+   },
+   "source": [
+    "ds_asos_proj = prf.convert_to_utm(ds_asos.drop_vars(\"quantile\", errors=\"ignore\"),\n",
+    "                                   name_coord_lon=\"lon\", name_coord_lat=\"lat\", zone=18)\n",
+    "\n",
+    "ds_cmls.coords[\"site_0_x\"], ds_cmls.coords[\"site_0_y\"] = plg.spatial.project_point_coordinates(ds_cmls.site_0_lon, ds_cmls.site_0_lat, \"EPSG:32618\")\n",
+    "ds_cmls.coords[\"site_1_x\"], ds_cmls.coords[\"site_1_y\"] = plg.spatial.project_point_coordinates(ds_cmls.site_1_lon, ds_cmls.site_1_lat, \"EPSG:32618\")\n",
+    "\n",
+    "closest  = plg.spatial.get_closest_points_to_line(ds_cmls, ds_asos_proj, max_distance=20000, n_closest=1)\n",
+    "R_1h     = ds_cmls_filtered.R.isel(sublink_id=0).resample(time=\"1h\").mean()\n",
+    "\n",
+    "asos_1h  = ds_asos[\"precip_amount\"].resample(time=\"1h\").sum(skipna=True)\n",
+    "\n",
+    "pairs = [(R_1h.sel(cml_id=c).values, asos_1h.sel(id=closest.sel(cml_id=c).neighbor_id.values[0])\n",
+    "          .reindex(time=R_1h.time, method=\"nearest\").values)\n",
+    "         for c in closest.cml_id.values if closest.sel(cml_id=c).neighbor_id.values[0] is not None]\n",
+    "\n",
+    "R_all, asos_all = [np.concatenate([p[i] for p in pairs]) for i in [0, 1]]\n",
+    "mask = ~np.isnan(R_all) & ~np.isnan(asos_all)\n",
+    "\n",
+    "plt.hexbin(asos_all[mask], R_all[mask], gridsize=40, bins=\"log\", cmap=\"Blues\", extent=[0, 5, 0, 5])\n",
+    "plt.plot([0, 5], [0, 5], \"r--\")\n",
+    "plt.xlabel(\"ASOS (mm/h)\"); plt.ylabel(\"CML R (mm/h)\"); plt.title(\"CML vs nearest ASOS\");"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 34
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b116ea81",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Save Processed Datasets\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "9f63dee2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T09:17:32.186464Z",
+     "start_time": "2026-03-25T09:17:30.129564Z"
+    }
+   },
+   "source": [
+    "samples_dir = omio.SAMPLE_DIR\n",
+    "samples_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "t0 = pd.Timestamp(ds_cmls.time.values[0])\n",
+    "t1 = pd.Timestamp(ds_cmls.time.values[-1])\n",
+    "nd = max(1, int(np.ceil((t1 - t0).total_seconds() / 86400)))\n",
+    "tag = f\"{nd}d_{t0.strftime('%Y%m%d')}_{t1.strftime('%Y%m%d')}\"\n",
+    "\n",
+    "omio.save_compressed_netcdf(ds_cmls, samples_dir / f\"openmesh_cml_{tag}_processed.nc\")\n",
+    "omio.save_compressed_netcdf(ds_pws,  samples_dir / f\"openmesh_wu_pws_{tag}_qc.nc\")\n",
+    "omio.save_compressed_netcdf(ds_asos, samples_dir / f\"openmesh_asos_ws_{tag}_reference.nc\")\n",
+    "\n",
+    "print(\"Saved to\", samples_dir.resolve())"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved to /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/samples\n"
+     ]
+    }
+   ],
+   "execution_count": 33
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7cdf254",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "> **Usage example only.** This notebook is intended as a demonstration of how to work with the OpenMesh datasets. Parameters, thresholds, and methods should be adapted for real analysis.\n",
+    "\n",
+    "This notebook demonstrated how to:\n",
+    "\n",
+    "1. **Download** the OpenMesh CML and PWS datasets from Zenodo programmatically\n",
+    "2. **Load** all three datasets with a single unified function\n",
+    "3. **Explore** the CML network structure, RSL signal levels, frequency/polarization distribution, and data availability\n",
+    "4. **Map** the CML network and PWS stations using `poligrain` (`scatter_lines`, `plot_lines`, `plot_plg`)\n",
+    "5. **QC** CML data by removing sentinel values and implausible outliers\n",
+    "6. **Compute** baseline RSL using `pycomlink` (rolling or constant method)\n",
+    "7. **Derive** excess attenuation $A = \\mathrm{RSL_{baseline}} - \\mathrm{RSL}$ and rain rate $R$\n",
+    "8. **Filter** CML links by length and frequency band\n",
+    "9. **Check** PWS data availability per station\n",
+    "10. **Apply QC** to PWS data using `pypwsqc` (FZ, HI, peak removal, and IC filters)\n",
+    "11. **Fetch** NOAA ASOS reference data via the IEM API and convert to xarray\n",
+    "12. **Compare** all three sources (CML, PWS, ASOS) over a common time window\n",
+    "13. **Validate** CML attenuation against the nearest ASOS reference station\n",
+    "14. **Save** processed datasets as compressed NetCDF using `omio.save_compressed_netcdf`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openmesh",
+   "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": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/OpenMesh/notebooks/create_OpenMesh_data.ipynb b/OpenMesh/notebooks/create_OpenMesh_data.ipynb
new file mode 100644
index 0000000..912e023
--- /dev/null
+++ b/OpenMesh/notebooks/create_OpenMesh_data.ipynb
@@ -0,0 +1,3078 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "43c8be9c",
+   "metadata": {},
+   "source": [
+    "# Download and process OpenMesh dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11984e03",
+   "metadata": {},
+   "source": [
+    "This notebook \n",
+    "* downloads the dataset [OpenMesh (Jacoby et al. 2025)](https://doi.org/10.5281/zenodo.15287692)\n",
+    "* creates sample datasets for different time periods\n",
+    "* loads and inspects CML (Commercial Microwave Link) data\n",
+    "* loads and inspects PWS (Personal Weather Station) data\n",
+    "* fetches and converts ASOS reference data to OpenSense format\n",
+    "* plots comparisons of CML, PWS and ASOS reference data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "8f4ee475",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:02.728991Z",
+     "start_time": "2026-03-25T10:52:02.726080Z"
+    }
+   },
+   "source": [
+    "# Uncomment to install\n",
+    "# !pip install xarray netCDF4 requests tqdm numpy pandas poligrain pypwsqc pyproj matplotlib"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "id": "779e6250",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.885828Z",
+     "start_time": "2026-03-25T10:52:02.732827Z"
+    }
+   },
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "\n",
+    "import openmesh_data_io as omio\n",
+    "import ws_opensense_conversion as wsoc\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ],
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_config",
+   "metadata": {},
+   "source": [
+    "## Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "cell_config",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.939057Z",
+     "start_time": "2026-03-25T10:52:03.937174Z"
+    }
+   },
+   "source": "DATA_DIR       = \"data/jacoby_2025_OpenMesh/\"\nSAMPLE_DIR     = \"data/samples/\"\nCML_PATH       = \"data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\"\nPWS_PATH       = \"data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\"\nASOS_META_PATH = \"data/jacoby_2025_OpenMesh/meta/ASOS_stations.csv\"",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bd9bd75",
+   "metadata": {},
+   "source": [
+    "## Download and transform OpenMesh dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "14316229",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.946848Z",
+     "start_time": "2026-03-25T10:52:03.944468Z"
+    }
+   },
+   "source": [
+    "omio.download_openmesh(\n",
+    "    output_dir=DATA_DIR\n",
+    ");"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  CML found locally  : data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6280d99b",
+   "metadata": {},
+   "source": [
+    "## Load CML dataset"
+   ]
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:03.962088Z",
+     "start_time": "2026-03-25T10:52:03.959293Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "print(\"DATA_DIR:\", omio.DATA_DIR)\n",
+    "raw = omio.DATA_DIR / \"raw\"\n",
+    "if raw.exists():\n",
+    "  print(\"Files in raw/:\", list(raw.iterdir()))\n",
+    "else:\n",
+    "  print(\"raw/ folder does not exist!\")"
+   ],
+   "id": "3b49954f3ad19ef5",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DATA_DIR: /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh\n",
+      "Files in raw/: [PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc'), PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_opensense_sample_jan.nc'), PosixPath('/Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc')]\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
+   "id": "3257673a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.429506Z",
+     "start_time": "2026-03-25T10:52:03.971904Z"
+    }
+   },
+   "source": [
+    "ds_cml = omio.load_cml()\n",
+    "print(ds_cml)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Loaded CML        : {'cml_id': 75, 'sublink_id': 3, 'time': 354241}  ←  data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "<xarray.Dataset> Size: 322MB\n",
+      "Dimensions:       (cml_id: 75, sublink_id: 3, time: 354241)\n",
+      "Coordinates:\n",
+      "  * time          (time) datetime64[ns] 3MB 2023-10-29 ... 2024-07-01\n",
+      "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+      "    site_0_lat    (cml_id) float32 300B 40.7 40.66 40.73 ... 40.66 40.73 40.65\n",
+      "    site_0_lon    (cml_id) float32 300B -73.94 -74.0 -73.95 ... -73.95 -74.0\n",
+      "    site_1_lat    (cml_id) float32 300B 40.69 40.68 40.72 ... 40.66 40.73 40.64\n",
+      "    site_1_lon    (cml_id) float32 300B -73.92 -73.99 -73.98 ... -73.95 -73.95\n",
+      "    length        (cml_id) float32 300B 2.178e+03 2.955e+03 ... 122.4 4.506e+03\n",
+      "    frequency     (cml_id, sublink_id) float32 900B 6.804e+04 6.804e+04 ... nan\n",
+      "    polarization  (cml_id, sublink_id) object 2kB 'v' 'v' 'v' ... 'v' 'v' 'v'\n",
+      "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+      "Data variables:\n",
+      "    rsl           (cml_id, sublink_id, time) float32 319MB -49.0 -49.0 ... nan\n",
+      "Attributes:\n",
+      "    title:                 OpenMesh\n",
+      "    file_author:           Dror Jacoby\n",
+      "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+      "    date:                  2025-11-01\n",
+      "    source:                Community NYC Mesh Wireless Network\n",
+      "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+      "    naming_convention:     OpenSense-1.0\n",
+      "    license_restrictions:  CC BY 4.0 – https://creativecommons.org/licenses/b...\n",
+      "    reference:             https://doi.org/10.5281/zenodo.15287692\n",
+      "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+      "    Conventions:           OpenSense-CML-v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_slice_select",
+   "metadata": {},
+   "source": [
+    "### Slice & select CML data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "d397dd43",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.453539Z",
+     "start_time": "2026-03-25T10:52:04.436922Z"
+    }
+   },
+   "source": [
+    "# ── CML selection ─────────────────────────────────────────────\n",
+    "cml_ids     = [\"5\", \"7\", \"15\", \"42\"]       # None = all 75\n",
+    "sublink_ids = [\"sublink_1\"]   # None = all sublinks \n",
+    "\n",
+    "# ── Slice Time window ─────────────────────────────────────────────────────\n",
+    "ds_cml_jan = ds_cml.sel(\n",
+    "    time=slice(\"2024-01-01\", \"2024-02-01\"),\n",
+    "    cml_id=cml_ids,\n",
+    "    sublink_id=sublink_ids,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# ── Inspect ───────────────────────────────────────────────────\n",
+    "print(f\"Time     : {str(ds_cml_jan.time.values[0])[:19]}  →  {str(ds_cml_jan.time.values[-1])[:19]}\")\n",
+    "print(f\"CML IDs  : {ds_cml_jan.cml_id.values}\")\n",
+    "print(f\"Sublinks : {ds_cml_jan.sublink_id.values}\")\n",
+    "print(f\"Shape    : {dict(ds_cml_jan.sizes)}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time     : 2024-01-01T00:00:00  →  2024-02-01T23:59:00\n",
+      "CML IDs  : ['5' '7' '15' '42']\n",
+      "Sublinks : ['sublink_1']\n",
+      "Shape    : {'cml_id': 4, 'sublink_id': 1, 'time': 46080}\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad2ac0b8",
+   "metadata": {},
+   "source": [
+    "## Create CML samples\n",
+    "\n",
+    "Samples are **saved to disk by default** (`→ data/samples/`), returning the file path.\n",
+    "Use `return_type=\"sample\"` to get the `xr.Dataset` in memory instead.\n",
+    "\n",
+    "### In-memory sample (no disk write)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "be23ecef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.486878Z",
+     "start_time": "2026-03-25T10:52:04.461948Z"
+    }
+   },
+   "source": [
+    "ds_sample_custom = omio.create_openmesh_sample(\n",
+    "    ds=ds_cml,\n",
+    "    start_time= \"2024-01-15\",\n",
+    "    end_time  = \"2024-01-25\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    return_type=\"sample\",\n",
+    "    save = False\n",
+    ")\n",
+    "\n",
+    "ds_sample_custom"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_10d.nc\n",
+      "  Timesteps: 14,401\n",
+      "  CML count: 75\n",
+      "  Save     : skipped (save=False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset> Size: 13MB\n",
+       "Dimensions:       (cml_id: 75, sublink_id: 3, time: 14401)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 115kB 2024-01-15 ... 2024-01-25\n",
+       "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+       "    site_0_lat    (cml_id) float32 300B 40.7 40.66 40.73 ... 40.66 40.73 40.65\n",
+       "    site_0_lon    (cml_id) float32 300B -73.94 -74.0 -73.95 ... -73.95 -74.0\n",
+       "    site_1_lat    (cml_id) float32 300B 40.69 40.68 40.72 ... 40.66 40.73 40.64\n",
+       "    site_1_lon    (cml_id) float32 300B -73.92 -73.99 -73.98 ... -73.95 -73.95\n",
+       "    length        (cml_id) float32 300B 2.178e+03 2.955e+03 ... 122.4 4.506e+03\n",
+       "    frequency     (cml_id, sublink_id) float32 900B 6.804e+04 6.804e+04 ... nan\n",
+       "    polarization  (cml_id, sublink_id) object 2kB 'v' 'v' 'v' ... 'v' 'v' 'v'\n",
+       "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 13MB -50.0 -50.0 ... nan\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (10d), start 2024-01-15\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-15\n",
+       "    end_date:              2024-01-25\n",
+       "    time_range:            2024-01-15T00:00:00 / 2024-01-25T00:00:00\n",
+       "    cml_subset:            all"
+      ],
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+       "\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
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+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
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+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
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+       "}\n",
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+       "  color: var(--xr-font-color0);\n",
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+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 13MB\n",
+       "Dimensions:       (cml_id: 75, sublink_id: 3, time: 14401)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 115kB 2024-01-15 ... 2024-01-25\n",
+       "  * sublink_id    (sublink_id) object 24B &#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;\n",
+       "    site_0_lat    (cml_id) float32 300B 40.7 40.66 40.73 ... 40.66 40.73 40.65\n",
+       "    site_0_lon    (cml_id) float32 300B -73.94 -74.0 -73.95 ... -73.95 -74.0\n",
+       "    site_1_lat    (cml_id) float32 300B 40.69 40.68 40.72 ... 40.66 40.73 40.64\n",
+       "    site_1_lon    (cml_id) float32 300B -73.92 -73.99 -73.98 ... -73.95 -73.95\n",
+       "    length        (cml_id) float32 300B 2.178e+03 2.955e+03 ... 122.4 4.506e+03\n",
+       "    frequency     (cml_id, sublink_id) float32 900B 6.804e+04 6.804e+04 ... nan\n",
+       "    polarization  (cml_id, sublink_id) object 2kB &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; ... &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27;\n",
+       "  * cml_id        (cml_id) &lt;U2 600B &#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; ... &#x27;72&#x27; &#x27;73&#x27; &#x27;74&#x27; &#x27;75&#x27;\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 13MB -50.0 -50.0 ... nan\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (10d), start 2024-01-15\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-15\n",
+       "    end_date:              2024-01-25\n",
+       "    time_range:            2024-01-15T00:00:00 / 2024-01-25T00:00:00\n",
+       "    cml_subset:            all</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-503c4b16-1975-40c2-a09c-7d65b17460d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-503c4b16-1975-40c2-a09c-7d65b17460d4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>cml_id</span>: 75</li><li><span class='xr-has-index'>sublink_id</span>: 3</li><li><span class='xr-has-index'>time</span>: 14401</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-35194312-9b80-4810-bcac-f323fda2ac3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35194312-9b80-4810-bcac-f323fda2ac3e' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-15 ... 2024-01-25</div><input id='attrs-0b9a5a46-3ddf-4228-83db-4d460fc4064f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b9a5a46-3ddf-4228-83db-4d460fc4064f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a33ebc6-f802-43f3-ab5c-ed758f96f677' class='xr-var-data-in' type='checkbox'><label for='data-7a33ebc6-f802-43f3-ab5c-ed758f96f677' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time_utc</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-15T00:00:00.000000000&#x27;, &#x27;2024-01-15T00:01:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-15T00:02:00.000000000&#x27;, ..., &#x27;2024-01-24T23:58:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-24T23:59:00.000000000&#x27;, &#x27;2024-01-25T00:00:00.000000000&#x27;],\n",
+       "      shape=(14401,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sublink_id</span></div><div class='xr-var-dims'>(sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;</div><input id='attrs-bcdc543d-e254-460d-89b8-060d1f1298b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcdc543d-e254-460d-89b8-060d1f1298b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9443a6f0-ce37-4c1a-aff0-780076e84446' class='xr-var-data-in' type='checkbox'><label for='data-9443a6f0-ce37-4c1a-aff0-780076e84446' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sublink_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;sublink_1&#x27;, &#x27;sublink_2&#x27;, &#x27;sublink_3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.7 40.66 40.73 ... 40.73 40.65</div><input id='attrs-ae8bb065-2f3a-4ac3-844f-a9807372eb33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae8bb065-2f3a-4ac3-844f-a9807372eb33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fff192c7-aa33-44ee-9ae1-a8b30f82c1a3' class='xr-var-data-in' type='checkbox'><label for='data-fff192c7-aa33-44ee-9ae1-a8b30f82c1a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_latitude</dd></dl></div><div class='xr-var-data'><pre>array([40.696095, 40.657784, 40.725693, 40.657654, 40.727554, 40.73676 ,\n",
+       "       40.727554, 40.715588, 40.68601 , 40.660015, 40.830387, 40.658028,\n",
+       "       40.71111 , 40.713284, 40.7199  , 40.725903, 40.6726  , 40.72128 ,\n",
+       "       40.672546, 40.65801 , 40.606663, 40.815357, 40.6458  , 40.72579 ,\n",
+       "       40.725616, 40.729553, 40.68602 , 40.686214, 40.6726  , 40.7161  ,\n",
+       "       40.672554, 40.635117, 40.683   , 40.672558, 40.693817, 40.6726  ,\n",
+       "       40.657867, 40.672527, 40.686016, 40.674458, 40.686016, 40.7161  ,\n",
+       "       40.7199  , 40.815334, 40.695812, 40.7109  , 40.696053, 40.725735,\n",
+       "       40.6579  , 40.711086, 40.696037, 40.70845 , 40.715515, 40.686016,\n",
+       "       40.7085  , 40.725735, 40.711086, 40.724674, 40.672684, 40.69604 ,\n",
+       "       40.672684, 40.67543 , 40.682983, 40.672684, 40.696033, 40.71397 ,\n",
+       "       40.6579  , 40.7137  , 40.672684, 40.672684, 40.736717, 40.715614,\n",
+       "       40.6579  , 40.725616, 40.64778 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-73.94 -74.0 ... -73.95 -74.0</div><input id='attrs-ce61a5ab-0319-4fa8-8e2a-3f52d3b56483' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce61a5ab-0319-4fa8-8e2a-3f52d3b56483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce9e4ce9-2b54-48e4-8a6d-1714befcb1a6' class='xr-var-data-in' type='checkbox'><label for='data-ce9e4ce9-2b54-48e4-8a6d-1714befcb1a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-73.93975 , -74.004845, -73.95414 , -74.00518 , -73.946045,\n",
+       "       -73.95441 , -73.946045, -73.9844  , -73.9176  , -73.913605,\n",
+       "       -73.941025, -74.00477 , -74.00129 , -73.99037 , -73.98802 ,\n",
+       "       -73.983635, -73.9642  , -73.94671 , -73.96445 , -74.004745,\n",
+       "       -73.992935, -73.9503  , -74.0134  , -73.98355 , -73.95407 ,\n",
+       "       -73.945946, -73.917496, -73.91747 , -73.96419 , -73.986   ,\n",
+       "       -73.964455, -73.950554, -73.946   , -73.96443 , -73.906364,\n",
+       "       -73.964195, -74.004906, -73.964   , -73.917496, -73.9403  ,\n",
+       "       -73.917496, -73.986   , -73.98802 , -73.95034 , -73.9397  ,\n",
+       "       -74.0012  , -73.93978 , -73.98368 , -74.0049  , -74.001305,\n",
+       "       -73.939766, -73.92189 , -73.98438 , -73.917496, -73.92195 ,\n",
+       "       -73.98368 , -74.0013  , -73.98073 , -73.96446 , -73.93979 ,\n",
+       "       -73.96446 , -73.98058 , -73.99392 , -73.96386 , -73.93979 ,\n",
+       "       -73.92912 , -74.0049  , -73.93552 , -73.96386 , -73.96386 ,\n",
+       "       -73.95465 , -73.98442 , -74.0049  , -73.95407 , -74.00077 ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.69 40.68 40.72 ... 40.73 40.64</div><input id='attrs-0e4f534c-f385-422c-a43a-9cca4aed39b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e4f534c-f385-422c-a43a-9cca4aed39b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f050acc0-2700-4b95-9a9e-a76a62b84af0' class='xr-var-data-in' type='checkbox'><label for='data-f050acc0-2700-4b95-9a9e-a76a62b84af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_latitude</dd></dl></div><div class='xr-var-data'><pre>array([40.6862  , 40.682995, 40.715588, 40.646   , 40.729553, 40.733902,\n",
+       "       40.725616, 40.7274  , 40.680214, 40.68582 , 40.815594, 40.66764 ,\n",
+       "       40.71609 , 40.71281 , 40.725616, 40.72067 , 40.680122, 40.725616,\n",
+       "       40.658012, 40.696133, 40.601913, 40.830326, 40.64593 , 40.724674,\n",
+       "       40.7296  , 40.73391 , 40.69389 , 40.7136  , 40.67813 , 40.724674,\n",
+       "       40.660744, 40.672516, 40.696   , 40.655624, 40.70538 , 40.67351 ,\n",
+       "       40.67543 , 40.689587, 40.68582 , 40.696003, 40.705837, 40.725616,\n",
+       "       40.724674, 40.79355 , 40.674442, 40.711456, 40.6875  , 40.71088 ,\n",
+       "       40.664997, 40.71281 , 40.689   , 40.70538 , 40.71898 , 40.67673 ,\n",
+       "       40.71363 , 40.72847 , 40.715645, 40.724   , 40.667133, 40.693817,\n",
+       "       40.672047, 40.6577  , 40.681797, 40.683826, 40.691147, 40.71363 ,\n",
+       "       40.6566  , 40.70538 , 40.696   , 40.6828  , 40.73505 , 40.690742,\n",
+       "       40.65964 , 40.726616, 40.635002], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-73.92 -73.99 ... -73.95 -73.95</div><input id='attrs-436dff49-46a8-46a4-ab63-a85f51823e08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-436dff49-46a8-46a4-ab63-a85f51823e08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f7eb136-c74f-4a7d-8f33-494da70bca3a' class='xr-var-data-in' type='checkbox'><label for='data-3f7eb136-c74f-4a7d-8f33-494da70bca3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-73.9175  , -73.99379 , -73.9844  , -74.01368 , -73.945946,\n",
+       "       -73.95061 , -73.9471  , -73.94617 , -73.954796, -73.917274,\n",
+       "       -73.950005, -73.982414, -73.98606 , -73.99474 , -73.95407 ,\n",
+       "       -73.98805 , -73.96321 , -73.9471  , -74.004715, -73.93976 ,\n",
+       "       -74.001045, -73.9408  , -74.01363 , -73.98073 , -73.9459  ,\n",
+       "       -73.95059 , -73.90638 , -73.9355  , -73.962456, -73.98073 ,\n",
+       "       -73.96041 , -73.96444 , -73.9398  , -73.956795, -73.93057 ,\n",
+       "       -73.96408 , -73.98058 , -73.9512  , -73.9173  , -73.93987 ,\n",
+       "       -73.91528 , -73.95407 , -73.98073 , -73.96564 , -73.94032 ,\n",
+       "       -73.99449 , -73.9474  , -74.0012  , -73.99054 , -73.9948  ,\n",
+       "       -73.939   , -73.93057 , -73.9585  , -73.93087 , -73.93564 ,\n",
+       "       -73.9831  , -73.98454 , -73.9783  , -73.94681 , -73.906364,\n",
+       "       -73.95537 , -74.0051  , -73.994736, -73.95053 , -73.94496 ,\n",
+       "       -73.93564 , -73.9993  , -73.93057 , -73.9398  , -73.9459  ,\n",
+       "       -73.9582  , -73.9701  , -73.99854 , -73.95469 , -73.95021 ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.178e+03 2.955e+03 ... 4.506e+03</div><input id='attrs-515356d3-f01d-46b0-aca5-bb6fd326d20f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515356d3-f01d-46b0-aca5-bb6fd326d20f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f1c4853-0548-42c2-bfe6-7e362ef9017c' class='xr-var-data-in' type='checkbox'><label for='data-0f1c4853-0548-42c2-bfe6-7e362ef9017c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>distance_between_pair_of_antennas</dd></dl></div><div class='xr-var-data'><pre>array([2178.1     , 2954.633   , 2786.53    , 1480.4     ,  222.1     ,\n",
+       "        451.8     ,  232.6     , 3486.6     , 3201.9673  , 2882.7     ,\n",
+       "       1808.9     , 2171.1     , 1400.6     ,  373.3     , 2937.8     ,\n",
+       "        690.7     ,  839.7     ,  482.8     , 3768.2     , 6936.5     ,\n",
+       "        865.6     , 1845.1     ,   24.5     ,  268.4     ,  818.82947 ,\n",
+       "        623.      , 1283.1     , 3401.3     ,  631.4     , 1051.4     ,\n",
+       "       1355.5     , 4316.1     , 1535.8     , 1987.9     , 2412.3335  ,\n",
+       "        101.5     , 2834.5     , 2181.4     ,   26.932957, 2392.7     ,\n",
+       "       2209.      , 2891.0608  ,  813.2     , 2743.331   , 2373.7     ,\n",
+       "        570.4     , 1147.5     , 2216.4     , 1447.5     ,  582.3     ,\n",
+       "        784.2     ,  809.1     , 2220.      , 1530.5     , 1287.1569  ,\n",
+       "        307.7     , 1503.4     ,  218.6     , 1614.8     , 2836.2     ,\n",
+       "        771.8     , 2859.2     ,  149.      , 1673.2     ,  696.7     ,\n",
+       "        552.3     ,  495.1     , 1013.6     , 3292.4     , 1888.7     ,\n",
+       "        352.4     , 3015.2     ,  571.5     ,  122.4     , 4505.6     ],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.804e+04 6.804e+04 ... nan nan</div><input id='attrs-e7d62457-558e-4f0c-92e3-bdad861a90a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7d62457-558e-4f0c-92e3-bdad861a90a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4155a5db-cc9b-4aea-8bcf-4653db42011d' class='xr-var-data-in' type='checkbox'><label for='data-4155a5db-cc9b-4aea-8bcf-4653db42011d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>MHz</dd><dt><span>long_name :</span></dt><dd>sublink_frequency</dd></dl></div><div class='xr-var-data'><pre>array([[68040., 68040.,  5765.],\n",
+       "       [ 5545., 24100., 24200.],\n",
+       "       [68040.,  5760., 68040.],\n",
+       "       [24200., 24200.,  5300.],\n",
+       "       [60480., 60480.,    nan],\n",
+       "       [58320., 58320.,    nan],\n",
+       "       [58320., 58320.,    nan],\n",
+       "       [68040.,  5760.,    nan],\n",
+       "       [ 5710.,  5700.,    nan],\n",
+       "       [ 5505.,  5495.,    nan],\n",
+       "       [58320., 58320.,    nan],\n",
+       "       [65880., 65880.,    nan],\n",
+       "       [24100., 24200.,    nan],\n",
+       "       [ 5795.,  5795.,    nan],\n",
+       "       [ 5560.,  5560.,    nan],\n",
+       "       [62640., 62640.,    nan],\n",
+       "       [68040., 68040.,    nan],\n",
+       "       [64800., 64800.,    nan],\n",
+       "       [24100., 24100.,    nan],\n",
+       "       [69120., 65880.,    nan],\n",
+       "...\n",
+       "       [ 5570.,    nan,    nan],\n",
+       "       [ 5810.,    nan,    nan],\n",
+       "       [ 5710.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5310.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5510.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5560.,    nan,    nan],\n",
+       "       [ 5510.,    nan,    nan],\n",
+       "       [ 5510.,    nan,    nan],\n",
+       "       [ 5710.,    nan,    nan],\n",
+       "       [ 5490.,    nan,    nan],\n",
+       "       [ 5700.,    nan,    nan],\n",
+       "       [ 5760.,    nan,    nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polarization</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; ... &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27; &#x27;v&#x27;</div><input id='attrs-c5d04b03-1fa3-4cba-8f1c-facd473c6ac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5d04b03-1fa3-4cba-8f1c-facd473c6ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67408ce0-91f6-4f19-87cc-95096351c38b' class='xr-var-data-in' type='checkbox'><label for='data-67408ce0-91f6-4f19-87cc-95096351c38b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>no units</dd><dt><span>long_name :</span></dt><dd>sublink_polarization</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "...\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;],\n",
+       "       [&#x27;v&#x27;, &#x27;v&#x27;, &#x27;v&#x27;]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cml_id</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; ... &#x27;73&#x27; &#x27;74&#x27; &#x27;75&#x27;</div><input id='attrs-0694db14-2934-4de9-ab2e-5488dc3e8452' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0694db14-2934-4de9-ab2e-5488dc3e8452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf0d310d-aff5-4bd5-8cde-e3fb36276528' class='xr-var-data-in' type='checkbox'><label for='data-bf0d310d-aff5-4bd5-8cde-e3fb36276528' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>commercial_microwave_link_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;, &#x27;7&#x27;, &#x27;8&#x27;, &#x27;9&#x27;, &#x27;10&#x27;, &#x27;11&#x27;, &#x27;12&#x27;, &#x27;13&#x27;,\n",
+       "       &#x27;14&#x27;, &#x27;15&#x27;, &#x27;16&#x27;, &#x27;17&#x27;, &#x27;18&#x27;, &#x27;19&#x27;, &#x27;20&#x27;, &#x27;21&#x27;, &#x27;22&#x27;, &#x27;23&#x27;, &#x27;24&#x27;, &#x27;25&#x27;,\n",
+       "       &#x27;26&#x27;, &#x27;27&#x27;, &#x27;28&#x27;, &#x27;29&#x27;, &#x27;30&#x27;, &#x27;31&#x27;, &#x27;32&#x27;, &#x27;33&#x27;, &#x27;34&#x27;, &#x27;35&#x27;, &#x27;36&#x27;, &#x27;37&#x27;,\n",
+       "       &#x27;38&#x27;, &#x27;39&#x27;, &#x27;40&#x27;, &#x27;41&#x27;, &#x27;42&#x27;, &#x27;43&#x27;, &#x27;44&#x27;, &#x27;45&#x27;, &#x27;46&#x27;, &#x27;47&#x27;, &#x27;48&#x27;, &#x27;49&#x27;,\n",
+       "       &#x27;50&#x27;, &#x27;51&#x27;, &#x27;52&#x27;, &#x27;53&#x27;, &#x27;54&#x27;, &#x27;55&#x27;, &#x27;56&#x27;, &#x27;57&#x27;, &#x27;58&#x27;, &#x27;59&#x27;, &#x27;60&#x27;, &#x27;61&#x27;,\n",
+       "       &#x27;62&#x27;, &#x27;63&#x27;, &#x27;64&#x27;, &#x27;65&#x27;, &#x27;66&#x27;, &#x27;67&#x27;, &#x27;68&#x27;, &#x27;69&#x27;, &#x27;70&#x27;, &#x27;71&#x27;, &#x27;72&#x27;, &#x27;73&#x27;,\n",
+       "       &#x27;74&#x27;, &#x27;75&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c1b42bcf-a6d5-4870-a35e-0b72d6eb7e94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c1b42bcf-a6d5-4870-a35e-0b72d6eb7e94' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rsl</span></div><div class='xr-var-dims'>(cml_id, sublink_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-50.0 -50.0 -50.0 ... nan nan nan</div><input id='attrs-1d995459-95a7-4422-82e5-c980c7741fa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d995459-95a7-4422-82e5-c980c7741fa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51653508-6b4e-4d45-99cb-65ee6621d8ad' class='xr-var-data-in' type='checkbox'><label for='data-51653508-6b4e-4d45-99cb-65ee6621d8ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBm</dd><dt><span>long_name :</span></dt><dd>received_signal_level</dd><dt><span>sampling :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><pre>array([[[-50., -50., -50., ..., -52., -52., -52.],\n",
+       "        [-49., -49., -49., ..., -52., -52., -52.],\n",
+       "        [-72., -73., -72., ..., -73., -72., -72.]],\n",
+       "\n",
+       "       [[-63., -64., -64., ..., -63., -63., -64.],\n",
+       "        [-60., -60., -59., ..., -60., -60., -59.],\n",
+       "        [-58., -58., -58., ..., -65., -65., -64.]],\n",
+       "\n",
+       "       [[-53., -54., -53., ..., -57., -56., -56.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [-54., -55., -54., ..., -58., -57., -56.]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-61., -61., -61., ..., -62., -61., -61.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "\n",
+       "       [[-52., -52., -51., ..., -53., -52., -52.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]],\n",
+       "\n",
+       "       [[-66., -67., -68., ..., -68., -68., -68.],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan]]],\n",
+       "      shape=(75, 3, 14401), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1a259b2-6749-4845-a00b-78aa00e9960d' class='xr-section-summary-in' type='checkbox'  ><label for='section-e1a259b2-6749-4845-a00b-78aa00e9960d' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>OpenMesh CML sample (10d), start 2024-01-15</dd><dt><span>file_author :</span></dt><dd>Dror Jacoby</dd><dt><span>institution :</span></dt><dd>Cellular Environmental Monitoring (CellEnMon) Lab, School of Electrical Engineering, Tel-Aviv University; Wireless and Mobile Networking (WiMNet) Lab, Department of Electrical Engineering, Columbia University</dd><dt><span>date :</span></dt><dd>2025-11-01</dd><dt><span>source :</span></dt><dd>Community NYC Mesh Wireless Network</dd><dt><span>history :</span></dt><dd>2025-11-01: Updated metadata, converted netCDF → OpenSense-1.0 CML format</dd><dt><span>naming_convention :</span></dt><dd>OpenSense-1.0</dd><dt><span>license_restrictions :</span></dt><dd>CC BY 4.0 – https://creativecommons.org/licenses/by/4.0/</dd><dt><span>reference :</span></dt><dd>https://doi.org/10.5281/zenodo.15287692</dd><dt><span>comment :</span></dt><dd>OpenMesh: Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City. Data covers the period 2023-10-29 to 2024-07-01 (UTC). All timestamps are in UTC. Signal levels are in dBm. This dataset is described in the associated publication: https://essd.copernicus.org/preprints/essd-2025-238/</dd><dt><span>Conventions :</span></dt><dd>OpenSense-CML-v1.0</dd><dt><span>start_date :</span></dt><dd>2024-01-15</dd><dt><span>end_date :</span></dt><dd>2024-01-25</dd><dt><span>time_range :</span></dt><dd>2024-01-15T00:00:00 / 2024-01-25T00:00:00</dd><dt><span>cml_subset :</span></dt><dd>all</dd></dl></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76123ca2",
+   "metadata": {},
+   "source": [
+    "### Create & Save samples to disk\n",
+    "\n",
+    "Ways to create and save a CML sample file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "f9c6e75c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.568698Z",
+     "start_time": "2026-03-25T10:52:04.505213Z"
+    }
+   },
+   "source": [
+    "sep = \"═\" * 60\n",
+    "\n",
+    "# Option A: from an already-loaded dataset → returns saved file path\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [A] From loaded dataset (ds_cml) — 1 week, all links\")\n",
+    "print(f\"{sep}\")\n",
+    "path_1w = omio.create_openmesh_sample(\n",
+    "    ds=ds_cml,\n",
+    "    time_interval=\"1w\",\n",
+    "    start_time=\"2024-01-16\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option B: standalone (reads from disk) → returns saved file path\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [B] Standalone (reads from disk) — 1 day, all links\")\n",
+    "print(f\"{sep}\")\n",
+    "path_1d = omio.create_openmesh_sample(\n",
+    "    time_interval=\"1d\",\n",
+    "    start_time=\"2024-01-16\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option C: custom window + specific links → returns file (!) \n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [C] 1-hour at 12:00 PM, subset of links\")\n",
+    "print(f\"{sep}\")\n",
+    "cml_sample_1h = omio.create_openmesh_sample(\n",
+    "    ds=ds_cml,\n",
+    "    start_time=\"2024-01-16T12:00:00\",\n",
+    "    end_time=\"2024-01-16T13:00:00\",\n",
+    "    cml_ids=[\"1\", \"5\", \"7\", \"15\", \"42\"],\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    return_type=\"sample\",\n",
+    ")\n",
+    "\n",
+    "cml_sample_1h\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [A] From loaded dataset (ds_cml) — 1 week, all links\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-23 00:00:00  (1w)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1w.nc\n",
+      "  Timesteps: 10,081\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1w.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [B] Standalone (reads from disk) — 1 day, all links\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-17 00:00:00  (1d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1d.nc\n",
+      "  Timesteps: 1,441\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1d.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [C] 1-hour at 12:00 PM, subset of links\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 12:00:00  →  2024-01-16 13:00:00  (1h)\n",
+      "  CML IDs  : ['1', '5', '7', '15', '42']\n",
+      "  Output   : openmesh_cml_1h_cmls5.nc\n",
+      "  Timesteps: 61\n",
+      "  CML count: 5\n",
+      "  Exists   : openmesh_cml_1h_cmls5.nc  (skipped — pass replace=True to overwrite)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset> Size: 4kB\n",
+       "Dimensions:       (cml_id: 5, sublink_id: 3, time: 61)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 488B 2024-01-16T12:00:00 ... 2024-01-...\n",
+       "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+       "    site_0_lat    (cml_id) float32 20B ...\n",
+       "    site_0_lon    (cml_id) float32 20B ...\n",
+       "    site_1_lat    (cml_id) float32 20B ...\n",
+       "    site_1_lon    (cml_id) float32 20B ...\n",
+       "    length        (cml_id) float32 20B ...\n",
+       "    frequency     (cml_id, sublink_id) float32 60B ...\n",
+       "    polarization  (cml_id, sublink_id) object 120B ...\n",
+       "  * cml_id        (cml_id) <U2 40B '1' '5' '7' '15' '42'\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 4kB ...\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (1h), start 2024-01-16\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-16\n",
+       "    end_date:              2024-01-16\n",
+       "    time_range:            2024-01-16T12:00:00 / 2024-01-16T13:00:00\n",
+       "    cml_subset:            1,5,7,15,42"
+      ],
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+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4kB\n",
+       "Dimensions:       (cml_id: 5, sublink_id: 3, time: 61)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 488B 2024-01-16T12:00:00 ... 2024-01-...\n",
+       "  * sublink_id    (sublink_id) object 24B &#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;\n",
+       "    site_0_lat    (cml_id) float32 20B ...\n",
+       "    site_0_lon    (cml_id) float32 20B ...\n",
+       "    site_1_lat    (cml_id) float32 20B ...\n",
+       "    site_1_lon    (cml_id) float32 20B ...\n",
+       "    length        (cml_id) float32 20B ...\n",
+       "    frequency     (cml_id, sublink_id) float32 60B ...\n",
+       "    polarization  (cml_id, sublink_id) object 120B ...\n",
+       "  * cml_id        (cml_id) &lt;U2 40B &#x27;1&#x27; &#x27;5&#x27; &#x27;7&#x27; &#x27;15&#x27; &#x27;42&#x27;\n",
+       "Data variables:\n",
+       "    rsl           (cml_id, sublink_id, time) float32 4kB ...\n",
+       "Attributes: (12/15)\n",
+       "    title:                 OpenMesh CML sample (1h), start 2024-01-16\n",
+       "    file_author:           Dror Jacoby\n",
+       "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+       "    date:                  2025-11-01\n",
+       "    source:                Community NYC Mesh Wireless Network\n",
+       "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+       "    ...                    ...\n",
+       "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+       "    Conventions:           OpenSense-CML-v1.0\n",
+       "    start_date:            2024-01-16\n",
+       "    end_date:              2024-01-16\n",
+       "    time_range:            2024-01-16T12:00:00 / 2024-01-16T13:00:00\n",
+       "    cml_subset:            1,5,7,15,42</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-72cb3fd6-983b-40ae-bbde-aa21f8f7fe1a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72cb3fd6-983b-40ae-bbde-aa21f8f7fe1a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>cml_id</span>: 5</li><li><span class='xr-has-index'>sublink_id</span>: 3</li><li><span class='xr-has-index'>time</span>: 61</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d035d1b7-e2d3-42e3-9bf0-395268f90f1d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d035d1b7-e2d3-42e3-9bf0-395268f90f1d' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-16T12:00:00 ... 2024-01-...</div><input id='attrs-23ea024f-f7e8-4ed6-a9ea-0d6275a30fd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23ea024f-f7e8-4ed6-a9ea-0d6275a30fd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d23d2d-f669-438e-b91f-e3989611b5e6' class='xr-var-data-in' type='checkbox'><label for='data-36d23d2d-f669-438e-b91f-e3989611b5e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time_utc</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-16T12:00:00.000000000&#x27;, &#x27;2024-01-16T12:01:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:02:00.000000000&#x27;, &#x27;2024-01-16T12:03:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:04:00.000000000&#x27;, &#x27;2024-01-16T12:05:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:06:00.000000000&#x27;, &#x27;2024-01-16T12:07:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:08:00.000000000&#x27;, &#x27;2024-01-16T12:09:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:10:00.000000000&#x27;, &#x27;2024-01-16T12:11:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:12:00.000000000&#x27;, &#x27;2024-01-16T12:13:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:14:00.000000000&#x27;, &#x27;2024-01-16T12:15:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:16:00.000000000&#x27;, &#x27;2024-01-16T12:17:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:18:00.000000000&#x27;, &#x27;2024-01-16T12:19:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:20:00.000000000&#x27;, &#x27;2024-01-16T12:21:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:22:00.000000000&#x27;, &#x27;2024-01-16T12:23:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:24:00.000000000&#x27;, &#x27;2024-01-16T12:25:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:26:00.000000000&#x27;, &#x27;2024-01-16T12:27:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:28:00.000000000&#x27;, &#x27;2024-01-16T12:29:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:30:00.000000000&#x27;, &#x27;2024-01-16T12:31:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:32:00.000000000&#x27;, &#x27;2024-01-16T12:33:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:34:00.000000000&#x27;, &#x27;2024-01-16T12:35:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:36:00.000000000&#x27;, &#x27;2024-01-16T12:37:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:38:00.000000000&#x27;, &#x27;2024-01-16T12:39:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:40:00.000000000&#x27;, &#x27;2024-01-16T12:41:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:42:00.000000000&#x27;, &#x27;2024-01-16T12:43:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:44:00.000000000&#x27;, &#x27;2024-01-16T12:45:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:46:00.000000000&#x27;, &#x27;2024-01-16T12:47:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:48:00.000000000&#x27;, &#x27;2024-01-16T12:49:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:50:00.000000000&#x27;, &#x27;2024-01-16T12:51:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:52:00.000000000&#x27;, &#x27;2024-01-16T12:53:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:54:00.000000000&#x27;, &#x27;2024-01-16T12:55:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:56:00.000000000&#x27;, &#x27;2024-01-16T12:57:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T12:58:00.000000000&#x27;, &#x27;2024-01-16T12:59:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-16T13:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sublink_id</span></div><div class='xr-var-dims'>(sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;sublink_1&#x27; &#x27;sublink_2&#x27; &#x27;sublink_3&#x27;</div><input id='attrs-9293c0db-c016-4afd-ac81-e6b69d470d7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9293c0db-c016-4afd-ac81-e6b69d470d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0616a038-74fb-4628-89db-8805724046b0' class='xr-var-data-in' type='checkbox'><label for='data-0616a038-74fb-4628-89db-8805724046b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sublink_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;sublink_1&#x27;, &#x27;sublink_2&#x27;, &#x27;sublink_3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a997513-b52e-4e3a-bc1b-2b478c545949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a997513-b52e-4e3a-bc1b-2b478c545949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6e81138-7100-4821-8701-1ad919281a8b' class='xr-var-data-in' type='checkbox'><label for='data-c6e81138-7100-4821-8701-1ad919281a8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_latitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_0_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dcc55bae-c7c6-4b3b-a324-d8e75bd0e9ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc55bae-c7c6-4b3b-a324-d8e75bd0e9ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e97e0417-f540-4041-a181-7b17547ded87' class='xr-var-data-in' type='checkbox'><label for='data-e97e0417-f540-4041-a181-7b17547ded87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_0_longitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lat</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-08f8d15e-bfbb-4f75-9940-c4aa5ea2bf1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f8d15e-bfbb-4f75-9940-c4aa5ea2bf1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39405e4f-7d79-4dba-bd1c-c4b2451ca9bc' class='xr-var-data-in' type='checkbox'><label for='data-39405e4f-7d79-4dba-bd1c-c4b2451ca9bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_latitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>site_1_lon</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7139547-73ec-405e-be7c-9311a77530f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7139547-73ec-405e-be7c-9311a77530f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd91ee20-40d4-4618-ace4-dc1af89d33d5' class='xr-var-data-in' type='checkbox'><label for='data-fd91ee20-40d4-4618-ace4-dc1af89d33d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_in_WGS84_projection</dd><dt><span>long_name :</span></dt><dd>site_1_longitude</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03bb07c5-c1b7-48bb-a33f-e699b26c90ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03bb07c5-c1b7-48bb-a33f-e699b26c90ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65413607-86b8-48ef-b92c-e90cbc9470a5' class='xr-var-data-in' type='checkbox'><label for='data-65413607-86b8-48ef-b92c-e90cbc9470a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>distance_between_pair_of_antennas</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd22df7a-6764-4055-b66b-5b3f739b1191' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd22df7a-6764-4055-b66b-5b3f739b1191' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f044aadf-fcfa-4f01-a24a-e5fcbf0d3f33' class='xr-var-data-in' type='checkbox'><label for='data-f044aadf-fcfa-4f01-a24a-e5fcbf0d3f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>MHz</dd><dt><span>long_name :</span></dt><dd>sublink_frequency</dd></dl></div><div class='xr-var-data'><pre>[15 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polarization</span></div><div class='xr-var-dims'>(cml_id, sublink_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5286927c-18a1-446f-9e88-ffbe9f1cf2c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5286927c-18a1-446f-9e88-ffbe9f1cf2c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d090344-d60c-4318-8ab6-03f2663de4e5' class='xr-var-data-in' type='checkbox'><label for='data-1d090344-d60c-4318-8ab6-03f2663de4e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>no units</dd><dt><span>long_name :</span></dt><dd>sublink_polarization</dd></dl></div><div class='xr-var-data'><pre>[15 values with dtype=object]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cml_id</span></div><div class='xr-var-dims'>(cml_id)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;5&#x27; &#x27;7&#x27; &#x27;15&#x27; &#x27;42&#x27;</div><input id='attrs-60ec6b9c-0810-43af-a712-62dac459e74c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60ec6b9c-0810-43af-a712-62dac459e74c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25986eef-9b52-44f3-aaae-a141a8dfba8f' class='xr-var-data-in' type='checkbox'><label for='data-25986eef-9b52-44f3-aaae-a141a8dfba8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>commercial_microwave_link_identifier</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1&#x27;, &#x27;5&#x27;, &#x27;7&#x27;, &#x27;15&#x27;, &#x27;42&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98519b2c-99bb-48f3-add2-325eb7a5c5de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98519b2c-99bb-48f3-add2-325eb7a5c5de' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rsl</span></div><div class='xr-var-dims'>(cml_id, sublink_id, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18c01e09-3fdf-4f15-9ebc-96e702099733' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18c01e09-3fdf-4f15-9ebc-96e702099733' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45f2ddad-92e4-40a4-b3e6-7271727b0ead' class='xr-var-data-in' type='checkbox'><label for='data-45f2ddad-92e4-40a4-b3e6-7271727b0ead' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dBm</dd><dt><span>long_name :</span></dt><dd>received_signal_level</dd><dt><span>sampling :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><pre>[915 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-71b3e7da-4801-45d8-bfa9-e2908bdd2b8f' class='xr-section-summary-in' type='checkbox'  ><label for='section-71b3e7da-4801-45d8-bfa9-e2908bdd2b8f' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>OpenMesh CML sample (1h), start 2024-01-16</dd><dt><span>file_author :</span></dt><dd>Dror Jacoby</dd><dt><span>institution :</span></dt><dd>Cellular Environmental Monitoring (CellEnMon) Lab, School of Electrical Engineering, Tel-Aviv University; Wireless and Mobile Networking (WiMNet) Lab, Department of Electrical Engineering, Columbia University</dd><dt><span>date :</span></dt><dd>2025-11-01</dd><dt><span>source :</span></dt><dd>Community NYC Mesh Wireless Network</dd><dt><span>history :</span></dt><dd>2025-11-01: Updated metadata, converted netCDF → OpenSense-1.0 CML format</dd><dt><span>naming_convention :</span></dt><dd>OpenSense-1.0</dd><dt><span>license_restrictions :</span></dt><dd>CC BY 4.0 – https://creativecommons.org/licenses/by/4.0/</dd><dt><span>reference :</span></dt><dd>https://doi.org/10.5281/zenodo.15287692</dd><dt><span>comment :</span></dt><dd>OpenMesh: Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City. Data covers the period 2023-10-29 to 2024-07-01 (UTC). All timestamps are in UTC. Signal levels are in dBm. This dataset is described in the associated publication: https://essd.copernicus.org/preprints/essd-2025-238/</dd><dt><span>Conventions :</span></dt><dd>OpenSense-CML-v1.0</dd><dt><span>start_date :</span></dt><dd>2024-01-16</dd><dt><span>end_date :</span></dt><dd>2024-01-16</dd><dt><span>time_range :</span></dt><dd>2024-01-16T12:00:00 / 2024-01-16T13:00:00</dd><dt><span>cml_subset :</span></dt><dd>1,5,7,15,42</dd></dl></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71b030a7",
+   "metadata": {},
+   "source": [
+    "### Batch creation (shortcut)\n",
+    "\n",
+    "`create_openmesh_samples_batch` creates multiple time-interval samples in one call — a shortcut instead of calling `create_openmesh_sample` repeatedly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "3a9524cc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.595965Z",
+     "start_time": "2026-03-25T10:52:04.587966Z"
+    }
+   },
+   "source": [
+    "batch_samples = omio.create_openmesh_samples_batch(\n",
+    "    intervals=[\"1h\", \"1d\", \"1w\", \"1mo\"],\n",
+    "    start_time=\"2024-01-16\",\n",
+    "    ds=ds_cml,\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "batch_samples\n"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1h\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-16 01:00:00  (1h)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1h.nc\n",
+      "  Timesteps: 61\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1h.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1d\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-17 00:00:00  (1d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1d.nc\n",
+      "  Timesteps: 1,441\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1d.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1w\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-01-23 00:00:00  (1w)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1w.nc\n",
+      "  Timesteps: 10,081\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1w.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Interval : 1mo\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : provided ds\n",
+      "  Window   : 2024-01-16 00:00:00  →  2024-02-15 00:00:00  (1mo)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_1mo.nc\n",
+      "  Timesteps: 43,201\n",
+      "  CML count: 75\n",
+      "  Exists   : openmesh_cml_1mo.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Batch complete:\n",
+      "    1h     → openmesh_cml_1h.nc\n",
+      "    1d     → openmesh_cml_1d.nc\n",
+      "    1w     → openmesh_cml_1w.nc\n",
+      "    1mo    → openmesh_cml_1mo.nc\n",
+      "───────────────────────────────────────────────────────\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'1h': PosixPath('data/samples/openmesh_cml_1h.nc'),\n",
+       " '1d': PosixPath('data/samples/openmesh_cml_1d.nc'),\n",
+       " '1w': PosixPath('data/samples/openmesh_cml_1w.nc'),\n",
+       " '1mo': PosixPath('data/samples/openmesh_cml_1mo.nc')}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c12356e0",
+   "metadata": {},
+   "source": [
+    "## Load a saved CML sample & inspect\n",
+    "\n",
+    "Re-open the 1-week sample (`path_1w`) that was saved to disk above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "c171717a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.622918Z",
+     "start_time": "2026-03-25T10:52:04.608344Z"
+    }
+   },
+   "source": [
+    "ds_sample = omio.load_cml_sample(path_1w)\n",
+    "print(ds_sample)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Loaded CML sample : {'cml_id': 75, 'sublink_id': 3, 'time': 10081}  ←  openmesh_cml_1w.nc\n",
+      "<xarray.Dataset> Size: 9MB\n",
+      "Dimensions:       (cml_id: 75, sublink_id: 3, time: 10081)\n",
+      "Coordinates:\n",
+      "  * time          (time) datetime64[ns] 81kB 2024-01-16 ... 2024-01-23\n",
+      "  * sublink_id    (sublink_id) object 24B 'sublink_1' 'sublink_2' 'sublink_3'\n",
+      "    site_0_lat    (cml_id) float32 300B ...\n",
+      "    site_0_lon    (cml_id) float32 300B ...\n",
+      "    site_1_lat    (cml_id) float32 300B ...\n",
+      "    site_1_lon    (cml_id) float32 300B ...\n",
+      "    length        (cml_id) float32 300B ...\n",
+      "    frequency     (cml_id, sublink_id) float32 900B ...\n",
+      "    polarization  (cml_id, sublink_id) object 2kB ...\n",
+      "  * cml_id        (cml_id) <U2 600B '1' '2' '3' '4' '5' ... '72' '73' '74' '75'\n",
+      "Data variables:\n",
+      "    rsl           (cml_id, sublink_id, time) float32 9MB ...\n",
+      "Attributes: (12/15)\n",
+      "    title:                 OpenMesh CML sample (1w), start 2024-01-16\n",
+      "    file_author:           Dror Jacoby\n",
+      "    institution:           Cellular Environmental Monitoring (CellEnMon) Lab,...\n",
+      "    date:                  2025-11-01\n",
+      "    source:                Community NYC Mesh Wireless Network\n",
+      "    history:               2025-11-01: Updated metadata, converted netCDF → O...\n",
+      "    ...                    ...\n",
+      "    comment:               OpenMesh: Wireless Signal Dataset for Opportunisti...\n",
+      "    Conventions:           OpenSense-CML-v1.0\n",
+      "    start_date:            2024-01-16\n",
+      "    end_date:              2024-01-23\n",
+      "    time_range:            2024-01-16T00:00:00 / 2024-01-23T00:00:00\n",
+      "    cml_subset:            all\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_section",
+   "metadata": {},
+   "source": [
+    "## PWS (Personal Weather Stations)\n",
+    "\n",
+    "Download, load and create samples from the Weather Underground PWS network data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceb1e249",
+   "metadata": {},
+   "source": [
+    "### Download PWS data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "952b0b2a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.631854Z",
+     "start_time": "2026-03-25T10:52:04.629084Z"
+    }
+   },
+   "source": [
+    "omio.download_pws_wu(\n",
+    "    output_dir=DATA_DIR\n",
+    ");"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  PWS found locally  : data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_load",
+   "metadata": {},
+   "source": [
+    "### Load full PWS dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b56043fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:04.771498Z",
+     "start_time": "2026-03-25T10:52:04.644615Z"
+    }
+   },
+   "source": [
+    "import netCDF4 as nc\n",
+    "\n",
+    "pws_path = PWS_PATH\n",
+    "root        = nc.Dataset(pws_path)\n",
+    "group_names = list(root.groups.keys())\n",
+    "first       = root.groups[group_names[0]]\n",
+    "\n",
+    "print(f\"Total stations     : {len(group_names)}\")\n",
+    "print(f\"First 5 stations   : {group_names[:5]}\")\n",
+    "print(f\"Variables          : {list(first.variables.keys())}\")\n",
+    "print(f\"Dimensions         : { {k: v.size for k, v in first.dimensions.items()} }\")\n",
+    "print(f\"Station ID format  : KNYNEWYO#### (K=USA, NY=state, NEWYO=NYC, ####=number)\")\n",
+    "root.close()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total stations     : 37\n",
+      "First 5 stations   : ['KNYNEWYO1472', 'KNYNEWYO1659', 'KNYNEWYO1288', 'KNYNEWYO1896', 'KNYNEWYO1921']\n",
+      "Variables          : ['rainfall_rate', 'rainfall_amount', 'temperature', 'relative_humidity', 'wind_velocity', 'wind_direction', 'air_pressure', 'time', 'id', 'lat', 'lon', 'elev']\n",
+      "Dimensions         : {'id': 1, 'time': 60339}\n",
+      "Station ID format  : KNYNEWYO#### (K=USA, NY=state, NEWYO=NYC, ####=number)\n"
+     ]
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_create_pws_fixed",
+   "metadata": {},
+   "source": [
+    "### Create PWS samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "e9615296",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:13.869436Z",
+     "start_time": "2026-03-25T10:52:04.778375Z"
+    }
+   },
+   "source": [
+    "sep = \"═\" * 60\n",
+    "\n",
+    "# Option A: 1-day sample\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [A] PWS — 1 day\")\n",
+    "print(f\"{sep}\")\n",
+    "path_pws_1d = omio.create_pws_sample(\n",
+    "    time_interval=\"1d\",\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option B: 1-week sample\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [B] PWS — 1 week\")\n",
+    "print(f\"{sep}\")\n",
+    "path_pws_1w = omio.create_pws_sample(\n",
+    "    time_interval=\"1w\",\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")\n",
+    "\n",
+    "# Option C: custom window\n",
+    "print(f\"\\n{sep}\")\n",
+    "print(\"  [C] PWS — custom window\")\n",
+    "print(f\"{sep}\")\n",
+    "path_pws_custom = omio.create_pws_sample(\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    end_time=\"2024-02-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [A] PWS — 1 day\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-16 00:00:00  (1d)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_1d.nc\n",
+      "  Loaded   : 35 stations, 287 timesteps\n",
+      "  Exists   : openmesh_wu_pws_1d.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [B] PWS — 1 week\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-22 00:00:00  (1w)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_1w.nc\n",
+      "  Loaded   : 35 stations, 2,012 timesteps\n",
+      "  Exists   : openmesh_wu_pws_1w.nc  (skipped — pass replace=True to overwrite)\n",
+      "\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  [C] PWS — custom window\n",
+      "════════════════════════════════════════════════════════════\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-02-15 00:00:00  (31d)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_31d.nc\n",
+      "  Loaded   : 37 stations, 7,127 timesteps\n",
+      "  Exists   : openmesh_wu_pws_31d.nc  (skipped — pass replace=True to overwrite)\n"
+     ]
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_inmemory",
+   "metadata": {},
+   "source": [
+    "### Load PWS sample in memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "37895cc5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:16.834091Z",
+     "start_time": "2026-03-25T10:52:13.893880Z"
+    }
+   },
+   "source": [
+    "ds_pws_sample = omio.create_pws_sample(\n",
+    "    time_interval=\"2w\",\n",
+    "    start_time=\"2024-01-15\",\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    return_type=\"sample\",\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-29 00:00:00  (2w)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_2w.nc\n",
+      "  Loaded   : 35 stations, 3,840 timesteps\n",
+      "  Exists   : openmesh_wu_pws_2w.nc  (skipped — pass replace=True to overwrite)\n"
+     ]
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "cell_type": "code",
+   "id": "802d8974",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:52:16.866302Z",
+     "start_time": "2026-03-25T10:52:16.855129Z"
+    }
+   },
+   "source": [
+    "ds_pws_sample"
+   ],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset> Size: 8MB\n",
+       "Dimensions:            (id: 35, time: 3840)\n",
+       "Coordinates:\n",
+       "  * id                 (id) <U12 2kB 'KNYNEWYO1472' ... 'KNYNEWYO1053'\n",
+       "  * time               (time) datetime64[ns] 31kB 2024-01-15T00:04:00 ... 202...\n",
+       "    lat                (id) float64 280B ...\n",
+       "    lon                (id) float64 280B ...\n",
+       "    elev               (id) float64 280B ...\n",
+       "Data variables:\n",
+       "    rainfall_rate      (id, time) float64 1MB ...\n",
+       "    rainfall_amount    (id, time) float64 1MB ...\n",
+       "    temperature        (id, time) float64 1MB ...\n",
+       "    relative_humidity  (id, time) float64 1MB ...\n",
+       "    wind_velocity      (id, time) float64 1MB ...\n",
+       "    wind_direction     (id, time) float64 1MB ...\n",
+       "    air_pressure       (id, time) float64 1MB ...\n",
+       "Attributes:\n",
+       "    title:        OpenMesh PWS sample (2w), start 2024-01-15\n",
+       "    start_date:   2024-01-15\n",
+       "    end_date:     2024-01-29\n",
+       "    time_range:   2024-01-15T00:00:00 / 2024-01-29T00:00:00\n",
+       "    source:       https://zenodo.org/records/17508286\n",
+       "    conventions:  OpenSense v1.0"
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+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 8MB\n",
+       "Dimensions:            (id: 35, time: 3840)\n",
+       "Coordinates:\n",
+       "  * id                 (id) &lt;U12 2kB &#x27;KNYNEWYO1472&#x27; ... &#x27;KNYNEWYO1053&#x27;\n",
+       "  * time               (time) datetime64[ns] 31kB 2024-01-15T00:04:00 ... 202...\n",
+       "    lat                (id) float64 280B ...\n",
+       "    lon                (id) float64 280B ...\n",
+       "    elev               (id) float64 280B ...\n",
+       "Data variables:\n",
+       "    rainfall_rate      (id, time) float64 1MB ...\n",
+       "    rainfall_amount    (id, time) float64 1MB ...\n",
+       "    temperature        (id, time) float64 1MB ...\n",
+       "    relative_humidity  (id, time) float64 1MB ...\n",
+       "    wind_velocity      (id, time) float64 1MB ...\n",
+       "    wind_direction     (id, time) float64 1MB ...\n",
+       "    air_pressure       (id, time) float64 1MB ...\n",
+       "Attributes:\n",
+       "    title:        OpenMesh PWS sample (2w), start 2024-01-15\n",
+       "    start_date:   2024-01-15\n",
+       "    end_date:     2024-01-29\n",
+       "    time_range:   2024-01-15T00:00:00 / 2024-01-29T00:00:00\n",
+       "    source:       https://zenodo.org/records/17508286\n",
+       "    conventions:  OpenSense v1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd30a986-9fec-4566-b799-84197d8222aa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd30a986-9fec-4566-b799-84197d8222aa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 35</li><li><span class='xr-has-index'>time</span>: 3840</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ebb888ef-969e-472b-8d67-2a4a2c518fd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebb888ef-969e-472b-8d67-2a4a2c518fd7' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;KNYNEWYO1472&#x27; ... &#x27;KNYNEWYO1053&#x27;</div><input id='attrs-6b47abdf-bba7-4f56-8e3e-397ca869393b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b47abdf-bba7-4f56-8e3e-397ca869393b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63198e6f-8a9a-4a4e-8f20-3e26a053e60b' class='xr-var-data-in' type='checkbox'><label for='data-63198e6f-8a9a-4a4e-8f20-3e26a053e60b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;KNYNEWYO1472&#x27;, &#x27;KNYNEWYO1659&#x27;, &#x27;KNYNEWYO1288&#x27;, &#x27;KNYNEWYO1896&#x27;,\n",
+       "       &#x27;KNYNEWYO1921&#x27;, &#x27;KNYNEWYO1931&#x27;, &#x27;KNYNEWYO1313&#x27;, &#x27;KNYNEWYO1918&#x27;,\n",
+       "       &#x27;KNYNEWYO1298&#x27;, &#x27;KNYNEWYO1388&#x27;, &#x27;KNYNEWYO1606&#x27;, &#x27;KNYNEWYO1348&#x27;,\n",
+       "       &#x27;KNYNEWYO1238&#x27;, &#x27;KNYNEWYO1401&#x27;, &#x27;KNYNEWYO1824&#x27;, &#x27;KNYNEWYO343&#x27;,\n",
+       "       &#x27;KNYNEWYO1626&#x27;, &#x27;KNYNEWYO1747&#x27;, &#x27;KNYNEWYO589&#x27;, &#x27;KNYNEWYO1591&#x27;,\n",
+       "       &#x27;KNYNEWYO638&#x27;, &#x27;KNYNEWYO1796&#x27;, &#x27;KNYNEWYO1805&#x27;, &#x27;KNYNEWYO1622&#x27;,\n",
+       "       &#x27;KNYNEWYO1024&#x27;, &#x27;KNYNEWYO1533&#x27;, &#x27;KNYNEWYO1651&#x27;, &#x27;KNYNEWYO1120&#x27;,\n",
+       "       &#x27;KNYNEWYO1900&#x27;, &#x27;KNYNEWYO505&#x27;, &#x27;KNYNEWYO1326&#x27;, &#x27;KNYNEWYO1681&#x27;,\n",
+       "       &#x27;KNYNEWYO1867&#x27;, &#x27;KNYNEWYO1127&#x27;, &#x27;KNYNEWYO1053&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-15T00:04:00 ... 2024-01-...</div><input id='attrs-3907c01d-bd61-4b67-8272-9094ec33d046' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3907c01d-bd61-4b67-8272-9094ec33d046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e715b4c0-9c74-491b-a102-8b2f7f1eeb83' class='xr-var-data-in' type='checkbox'><label for='data-e715b4c0-9c74-491b-a102-8b2f7f1eeb83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-15T00:04:00.000000000&#x27;, &#x27;2024-01-15T00:09:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-15T00:14:00.000000000&#x27;, ..., &#x27;2024-01-28T23:49:00.000000000&#x27;,\n",
+       "       &#x27;2024-01-28T23:54:00.000000000&#x27;, &#x27;2024-01-28T23:59:00.000000000&#x27;],\n",
+       "      shape=(3840,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c71f0c58-fc14-41c7-b161-70ac87de6a8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c71f0c58-fc14-41c7-b161-70ac87de6a8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d570ea-3f78-4350-bdeb-00a513937d4b' class='xr-var-data-in' type='checkbox'><label for='data-86d570ea-3f78-4350-bdeb-00a513937d4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[35 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df8f002b-7cc6-4b80-84f0-26eb52d39e9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df8f002b-7cc6-4b80-84f0-26eb52d39e9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-705ccc9b-b255-4c05-b11f-94022a3798fa' class='xr-var-data-in' type='checkbox'><label for='data-705ccc9b-b255-4c05-b11f-94022a3798fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[35 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elev</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6646969b-0c4e-46ec-9c40-fa646d10065f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6646969b-0c4e-46ec-9c40-fa646d10065f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad61f472-bb9b-46ed-9ada-2d8de233fd40' class='xr-var-data-in' type='checkbox'><label for='data-ad61f472-bb9b-46ed-9ada-2d8de233fd40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[35 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3abfa068-8b50-4a84-979f-d4659b3490b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3abfa068-8b50-4a84-979f-d4659b3490b1' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rainfall_rate</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f885ac53-0395-4afd-a761-8a2bb66db8cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f885ac53-0395-4afd-a761-8a2bb66db8cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d56553e0-4161-4aa2-b837-a4cba7e14d0c' class='xr-var-data-in' type='checkbox'><label for='data-d56553e0-4161-4aa2-b837-a4cba7e14d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rainfall_amount</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b63701f5-ad92-4896-9566-94e8d809869f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b63701f5-ad92-4896-9566-94e8d809869f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a70db820-d0d8-4820-abbf-859fa7da5115' class='xr-var-data-in' type='checkbox'><label for='data-a70db820-d0d8-4820-abbf-859fa7da5115' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm</dd><dt><span>long_name :</span></dt><dd>rainfall_amount_per_time_unit</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bdbeaee4-6cd7-4484-9fcf-195ec09ea63b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdbeaee4-6cd7-4484-9fcf-195ec09ea63b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40416808-4cc8-4cfb-8c24-93de79a79d31' class='xr-var-data-in' type='checkbox'><label for='data-40416808-4cc8-4cfb-8c24-93de79a79d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_celsius</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e167d919-e840-49eb-a008-e9cefec77b98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e167d919-e840-49eb-a008-e9cefec77b98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a812137-a64d-41c6-b656-4558a86590d7' class='xr-var-data-in' type='checkbox'><label for='data-3a812137-a64d-41c6-b656-4558a86590d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_velocity</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-960e7892-3ea2-4ee7-884c-f3494448c68d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-960e7892-3ea2-4ee7-884c-f3494448c68d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d866c732-875b-4aad-af44-362413116106' class='xr-var-data-in' type='checkbox'><label for='data-d866c732-875b-4aad-af44-362413116106' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ms-1</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_direction</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2deb8745-76df-4a88-8268-f5495907d7e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2deb8745-76df-4a88-8268-f5495907d7e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ba6b068-f1ae-4440-aa4c-32d83234002a' class='xr-var-data-in' type='checkbox'><label for='data-6ba6b068-f1ae-4440-aa4c-32d83234002a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_pressure</span></div><div class='xr-var-dims'>(id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-808a2b89-a518-4c18-a248-863067626f44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-808a2b89-a518-4c18-a248-863067626f44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def1cf97-2ad1-4a18-8845-fef3faf08a9c' class='xr-var-data-in' type='checkbox'><label for='data-def1cf97-2ad1-4a18-8845-fef3faf08a9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>[134400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45cc6bef-6f2e-4f49-911a-51c716f14587' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45cc6bef-6f2e-4f49-911a-51c716f14587' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>OpenMesh PWS sample (2w), start 2024-01-15</dd><dt><span>start_date :</span></dt><dd>2024-01-15</dd><dt><span>end_date :</span></dt><dd>2024-01-29</dd><dt><span>time_range :</span></dt><dd>2024-01-15T00:00:00 / 2024-01-29T00:00:00</dd><dt><span>source :</span></dt><dd>https://zenodo.org/records/17508286</dd><dt><span>conventions :</span></dt><dd>OpenSense v1.0</dd></dl></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_pws_station",
+   "metadata": {},
+   "source": [
+    "### Inspect a single PWS station"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "ba112f92",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:54:19.103615Z",
+     "start_time": "2026-03-25T10:54:19.094652Z"
+    }
+   },
+   "source": [
+    "# ── single station ────────────────────────────────────────────\n",
+    "station_id = \"KNYNEWYO1472\"\n",
+    "\n",
+    "ds_pws_station = ds_pws_sample.sel(id=station_id)\n",
+    "\n",
+    "print(f\"Station  : {station_id}\")\n",
+    "print(f\"Lat/Lon  : {float(ds_pws_station.lat):.4f}, {float(ds_pws_station.lon):.4f}\")\n",
+    "print(f\"Elev     : {float(ds_pws_station.elev):.1f} m\")\n",
+    "print(f\"Time     : {str(ds_pws_station.time.values[0])[:19]}  →  {str(ds_pws_station.time.values[-1])[:19]}\")\n",
+    "print(f\"Shape    : {dict(ds_pws_station.sizes)}\")\n",
+    "print(ds_pws_station)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Station  : KNYNEWYO1472\n",
+      "Lat/Lon  : 40.8200, -73.9400\n",
+      "Elev     : 66.0 m\n",
+      "Time     : 2024-01-15T00:04:00  →  2024-01-28T23:59:00\n",
+      "Shape    : {'time': 3840}\n",
+      "<xarray.Dataset> Size: 246kB\n",
+      "Dimensions:            (time: 3840)\n",
+      "Coordinates:\n",
+      "    id                 <U12 48B 'KNYNEWYO1472'\n",
+      "  * time               (time) datetime64[ns] 31kB 2024-01-15T00:04:00 ... 202...\n",
+      "    lat                float64 8B 40.82\n",
+      "    lon                float64 8B -73.94\n",
+      "    elev               float64 8B 66.0\n",
+      "Data variables:\n",
+      "    rainfall_rate      (time) float64 31kB ...\n",
+      "    rainfall_amount    (time) float64 31kB ...\n",
+      "    temperature        (time) float64 31kB ...\n",
+      "    relative_humidity  (time) float64 31kB ...\n",
+      "    wind_velocity      (time) float64 31kB ...\n",
+      "    wind_direction     (time) float64 31kB ...\n",
+      "    air_pressure       (time) float64 31kB ...\n",
+      "Attributes:\n",
+      "    title:        OpenMesh PWS sample (2w), start 2024-01-15\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-29\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-29T00:00:00\n",
+      "    source:       https://zenodo.org/records/17508286\n",
+      "    conventions:  OpenSense v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f9c6bee",
+   "metadata": {},
+   "source": [
+    "## ASOS reference data\n",
+    "\n",
+    "Fetch 1-minute ASOS precipitation from the IEM API for the same period\n",
+    "as the CML samples and save a small OpenSense-style sample NetCDF."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0e928cf",
+   "metadata": {},
+   "source": [
+    "### Load ASOS station metadata\n",
+    "\n",
+    "Load or discover ASOS stations in the NYC metro area. If a local CSV exists it is used directly; otherwise stations are fetched from the IEM GeoJSON API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_asos_metadata",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "id": "58db427b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:54:23.967554Z",
+     "start_time": "2026-03-25T10:54:23.961473Z"
+    }
+   },
+   "source": [
+    "# Fetch ASOS observations & save as OpenSense NetCDF\n",
+    "\n",
+    "# Fetch 1-minute data from the IEM API, convert to an OpenSense-format `xr.Dataset`, and write a compressed NetCDF sample.\n",
+    "from pathlib import Path\n",
+    "\n",
+    "meta = wsoc.load_asos_metadata(ASOS_META_PATH) if Path(ASOS_META_PATH).exists() else wsoc.fetch_asos_stations_nyc()\n",
+    "print(f\"  Stations ({len(meta)}): {meta.index.tolist()}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Stations (3): ['KNYC', 'KJFK', 'KLGA']\n"
+     ]
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "cell_type": "code",
+   "id": "93613121",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:55:13.537670Z",
+     "start_time": "2026-03-25T10:54:26.442631Z"
+    }
+   },
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "start_dt  = datetime(2024, 1, 15)\n",
+    "end_dt    = datetime(2024, 1, 30)\n",
+    "stations  = [\"JFK\", \"EWR\", \"LGA\", \"NYC\"]\n",
+    "variables = [\"precip_amount\", \"precip_rate\", \"temperature\", \"wind_speed\"]\n",
+    "\n",
+    "asos_raw = wsoc.fetch_asos_raw(start_dt, end_dt, stations, variables=variables)\n",
+    "ds_asos  = wsoc.to_opensense_dataset(asos_raw, source=\"asos\", meta=meta, start_dt=start_dt, end_dt=end_dt)\n",
+    "path     = wsoc.save_opensense_dataset(ds_asos, output_dir=SAMPLE_DIR)\n",
+    "\n",
+    "print(f\"  Shape : {dict(ds_asos.sizes)}\")\n",
+    "print(f\"  Time  : {str(ds_asos.time.values[0])[:19]}  →  {str(ds_asos.time.values[-1])[:19]}\")\n",
+    "print(f\"  Vars  : {list(ds_asos.data_vars)}\")\n",
+    "print(ds_asos)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Fetching JFK ... 20,831 records\n",
+      "  Fetching EWR ... 9,834 records\n",
+      "  Fetching LGA ... 20,938 records\n",
+      "  Fetching NYC ... 18,674 records\n",
+      "  Exists : ASOS_2024-01-15_2024-01-30.nc  (skipped — pass replace=True to overwrite)\n",
+      "  Shape : {'id': 4, 'time': 21066}\n",
+      "  Time  : 2024-01-15T00:00:00  →  2024-01-29T23:59:00\n",
+      "  Vars  : ['precip_amount', 'precip_rate', 'temperature', 'wind_speed']\n",
+      "<xarray.Dataset> Size: 3MB\n",
+      "Dimensions:        (id: 4, time: 21066)\n",
+      "Coordinates:\n",
+      "  * id             (id) <U3 48B 'JFK' 'EWR' 'LGA' 'NYC'\n",
+      "  * time           (time) datetime64[ns] 169kB 2024-01-15 ... 2024-01-29T23:5...\n",
+      "    lat            (id) float64 32B nan nan nan nan\n",
+      "    lon            (id) float64 32B nan nan nan nan\n",
+      "    elev           (id) float64 32B nan nan nan nan\n",
+      "Data variables:\n",
+      "    precip_amount  (id, time) float64 674kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    precip_rate    (id, time) float64 674kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+      "    temperature    (id, time) float64 674kB 0.0 0.0 0.0 ... 2.778 2.778 2.778\n",
+      "    wind_speed     (id, time) float64 674kB 10.29 9.774 10.8 ... 4.116 3.601\n",
+      "Attributes:\n",
+      "    title:        OpenMesh ASOS sample (multi-station)\n",
+      "    source:       asos\n",
+      "    start_date:   2024-01-15\n",
+      "    end_date:     2024-01-30\n",
+      "    time_range:   2024-01-15T00:00:00 / 2024-01-30T00:00:00\n",
+      "    conventions:  OpenSense v1.0\n"
+     ]
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_create_all",
+   "metadata": {},
+   "source": [
+    "## Create all datasets\n",
+    "\n",
+    "Helper function that creates CML, PWS and ASOS samples for a given time window in one call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "b74941d3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:55:52.721005Z",
+     "start_time": "2026-03-25T10:55:19.742284Z"
+    }
+   },
+   "source": [
+    "START_TIME = \"2024-01-15\"\n",
+    "END_TIME   = \"2024-01-25\"\n",
+    "# choose sensor id (none for all ids)\n",
+    "CML_IDS  = None\n",
+    "PWS_IDS  = None #[\"KNYNEWYO1942\", \"KNYNEWYO1432\", \"KNYNEWYO1622\", \"KNYNEWYO1472\"]\n",
+    "ASOS_IDS = [\"LGA\", \"NYC\", \"JFK\", \"EWR\"]\n",
+    "\n",
+    "samples = omio.create_all_datasets(\n",
+    "    start_time=START_TIME,\n",
+    "    end_time=END_TIME,\n",
+    "    cml_ids=CML_IDS,\n",
+    "    pws_ids=PWS_IDS,\n",
+    "    asos_ids=ASOS_IDS,\n",
+    "    output_dir=SAMPLE_DIR,\n",
+    "    include_date=False,\n",
+    "    save=   True\n",
+    ")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  CML\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/ds_openmesh.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  CML IDs  : all\n",
+      "  Output   : openmesh_cml_10d.nc\n",
+      "  Timesteps: 14,401\n",
+      "  CML count: 75\n",
+      "  Saved    : openmesh_cml_10d.nc  (0.46 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  PWS\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Source   : /Users/drorjac/PycharmProjects/opensense_example_data_openmesh/OpenMesh/notebooks/data/jacoby_2025_OpenMesh/raw/pws_wu_os.nc\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  Stations : 37 groups\n",
+      "  Output   : openmesh_wu_pws_10d.nc\n",
+      "  Loaded   : 35 stations, 2,849 timesteps\n",
+      "  Saved    : openmesh_wu_pws_10d.nc  (1.04 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  ASOS\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Window   : 2024-01-15 00:00:00  →  2024-01-25 00:00:00  (10d)\n",
+      "  Stations : ['LGA', 'NYC', 'JFK', 'EWR']\n",
+      "  Variables: ['precip_amount', 'precip_rate', 'temperature', 'wind_speed']\n",
+      "  Output   : openmesh_asos_ws_10d.nc\n",
+      "  Fetching LGA ... 14,336 records\n",
+      "  Fetching NYC ... 12,078 records\n",
+      "  Fetching JFK ... 14,230 records\n",
+      "  Fetching EWR ... 4,449 records\n",
+      "  Loaded   : 4 stations, 14,400 timesteps\n",
+      "  Saved    : openmesh_asos_ws_10d.nc  (0.30 MB)\n",
+      "\n",
+      "───────────────────────────────────────────────────────\n",
+      "  Summary:\n",
+      "    cml    → openmesh_cml_10d.nc\n",
+      "    pws    → openmesh_wu_pws_10d.nc\n",
+      "    asos   → openmesh_asos_ws_10d.nc\n",
+      "───────────────────────────────────────────────────────\n"
+     ]
+    }
+   ],
+   "execution_count": 26
+  },
+  {
+   "cell_type": "markdown",
+   "id": "md_plot_overview",
+   "metadata": {},
+   "source": [
+    "### Overview plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "id": "59f81c8d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-03-25T10:57:33.511749Z",
+     "start_time": "2026-03-25T10:57:32.760881Z"
+    }
+   },
+   "source": [
+    "import matplotlib.dates as mdates\n",
+    "\n",
+    "# ── load samples ──────────────────────────────────────────────\n",
+    "ds_cml  = omio.load_cml_sample(samples[\"cml\"])\n",
+    "ds_pws  = omio.load_pws_sample(samples[\"pws\"])\n",
+    "ds_asos = xr.open_dataset(samples[\"asos\"])\n",
+    "\n",
+    "# ── plot ──────────────────────────────────────────────────────\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(14, 10), sharex=True)\n",
+    "\n",
+    "# CML RSL\n",
+    "ax = axes[0]\n",
+    "for sublink in ds_cml.sublink_id.values:\n",
+    "    rsl = ds_cml.rsl.sel(cml_id=\"22\", sublink_id=sublink)\n",
+    "    ax.plot(ds_cml.time.values, rsl.values, lw=0.8, label=sublink)\n",
+    "ax.set_ylabel(\"RSL (dBm)\")\n",
+    "ax.set_title(\"CML 22 — Received Signal Level\")\n",
+    "ax.legend(fontsize=8, loc=\"upper right\")\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "# PWS precipitation\n",
+    "ax = axes[1]\n",
+    "for i, sid in enumerate(ds_pws.id.values):\n",
+    "    rain = ds_pws[\"rainfall_amount\"].sel(id=sid)\n",
+    "    label = str(sid) if i < 5 else None\n",
+    "    ax.plot(ds_pws.time.values, rain.values, lw=0.5, alpha=0.6, label=label)\n",
+    "ax.set_ylabel(\"Precip (mm)\")\n",
+    "ax.set_ylim(0, 2)\n",
+    "ax.set_title(f\"PWS — Rainfall Amount ({len(ds_pws.id)} stations)\")\n",
+    "ax.legend(fontsize=7, loc=\"upper right\", ncol=2)\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "# ASOS precipitation\n",
+    "ax = axes[2]\n",
+    "for sid in ds_asos.id.values:\n",
+    "    precip = ds_asos[\"precip_amount\"].sel(id=sid)\n",
+    "    ax.plot(ds_asos.time.values, precip.values, lw=0.8, label=str(sid))\n",
+    "ax.set_ylabel(\"Precip (mm)\")\n",
+    "ax.set_ylim(0, 2)\n",
+    "ax.set_title(\"ASOS — Precipitation Amount\")\n",
+    "ax.legend(fontsize=8, loc=\"upper right\")\n",
+    "ax.grid(alpha=0.3)\n",
+    "\n",
+    "axes[-1].xaxis.set_major_formatter(mdates.DateFormatter(\"%m-%d\"))\n",
+    "axes[-1].xaxis.set_major_locator(mdates.DayLocator())\n",
+    "plt.setp(axes[-1].xaxis.get_majorticklabels(), rotation=45, ha=\"right\")\n",
+    "\n",
+    "fig.suptitle(\"OpenMesh — CML / PWS / ASOS overview\", fontsize=13)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Loaded CML sample : {'cml_id': 75, 'sublink_id': 3, 'time': 14401}  ←  openmesh_cml_10d.nc\n",
+      "  Loaded PWS sample : {'id': 35, 'time': 2849}  ←  openmesh_wu_pws_10d.nc\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1000 with 3 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24e943d5",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Summary\n",
+    "\n",
+    "All three data sources have been downloaded, sampled and plotted:\n",
+    "\n",
+    "| Source | Description | Format |\n",
+    "|--------|-------------|--------|\n",
+    "| **CML** | 75 commercial microwave links, 15-min resolution | `xr.Dataset` (cml_id, sublink_id, time) |\n",
+    "| **PWS** | ~200 Weather Underground personal weather stations, 5-min resolution | `xr.Dataset` (id, time) |\n",
+    "| **ASOS** | NOAA airport stations (JFK, EWR, LGA, NYC), 1-min resolution | `xr.Dataset` (id, time) |\n",
+    "\n",
+    "Sample files are saved under `data/samples/` and can be loaded independently with `omio.load_cml_sample()`, `omio.load_pws_sample()`, or `xr.open_dataset()`."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "openmesh",
+   "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": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/OpenMesh/notebooks/openmesh_data_io.py b/OpenMesh/notebooks/openmesh_data_io.py
new file mode 100644
index 0000000..05ffa26
--- /dev/null
+++ b/OpenMesh/notebooks/openmesh_data_io.py
@@ -0,0 +1,953 @@
+"""
+openmesh_data_io.py
+===================
+Download, load, and create samples from the OpenMesh CML and PWS datasets.
+
+Zenodo records
+--------------
+CML : https://zenodo.org/records/15287692
+PWS : https://zenodo.org/records/17508286
+"""
+
+from __future__ import annotations
+
+import shutil
+import tempfile
+import warnings
+import zipfile
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import List, Optional, Union
+
+import numpy as np
+import pandas as pd
+import requests
+import xarray as xr
+
+try:
+    import netCDF4 as nc4
+    _HAS_NC4 = True
+except ImportError:
+    _HAS_NC4 = False
+
+try:
+    from tqdm.auto import tqdm as _tqdm
+except ImportError:
+    class _tqdm:
+        def __init__(self, total=None, **kw): self.n = 0; self.total = total
+        def update(self, n=1): self.n += n
+        def __enter__(self): return self
+        def __exit__(self, *a): pass
+
+
+# ---------------------------------------------------------------------------
+# Constants  (all paths relative to the notebook / script working directory)
+# ---------------------------------------------------------------------------
+
+DATA_DIR   = Path(__file__).parent / "data" / "jacoby_2025_OpenMesh"   # absolute — stable regardless of CWD
+SAMPLE_DIR = Path(__file__).parent / "data" / "samples"
+CML_PATH   = DATA_DIR / "raw" / "ds_openmesh.nc"
+PWS_PATH   = DATA_DIR / "raw" / "pws_wu_os.nc"
+
+INTERVAL_DURATIONS: dict[str, timedelta] = {
+    "1h":  timedelta(hours=1),
+    "1d":  timedelta(days=1),
+    "1w":  timedelta(weeks=1),
+    "1mo": timedelta(days=30),
+}
+# Arbitrary intervals like "10d", "2w", "6h" are also accepted by _resolve_time_window.
+
+DEFAULT_START_TIME = "2024-01-15T00:00:00"
+
+_ZENODO = {
+    "cml": {  # Zenodo record 15287692 — CML (NYC Mesh wireless links, RSL time-series)
+        "url":      "https://zenodo.org/records/15287692/files/OpenMesh.zip?download=1",
+        "filename": "OpenMesh.zip",
+    },
+    "pws": {  # Zenodo record 17508286 — PWS (Weather Underground NYC, crowd-sourced stations)
+        "url":      "https://zenodo.org/records/17508286/files/PWS_NYC_WU.zip?download=1",
+        "filename": "PWS_NYC_WU.zip",
+    },
+}
+
+_PWS_VARS = [
+    "rainfall_rate", "rainfall_amount", "temperature",
+    "relative_humidity", "wind_velocity", "wind_direction", "air_pressure",
+]
+
+# Variables that should be summed (not averaged) when resampling.
+# Rates (mm/h) are averaged, only accumulations (mm) are summed.
+_ACCUMULATION_VARS = {
+    "rainfall_amount", "precip_amount",
+}
+
+
+# ---------------------------------------------------------------------------
+# Private helpers
+# ---------------------------------------------------------------------------
+
+def _rel(path: Path) -> Path:
+    """Return path relative to cwd; fall back to filename only to avoid leaking home dir."""
+    try:
+        return path.relative_to(Path.cwd())
+    except ValueError:
+        return Path(path.name)
+
+
+def _download_file(url: str, dest: Path, chunk_size: int = 8192) -> Path:
+    dest = Path(dest)
+    if dest.exists():
+        print(f"  Already exists : {dest.name} ({dest.stat().st_size / 1e6:.1f} MB) — skipping.")
+        return dest
+    print(f"  Downloading    : {dest.name} ...")
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    resp  = requests.get(url, stream=True, timeout=120)
+    resp.raise_for_status()
+    total = int(resp.headers.get("content-length", 0))
+    with open(dest, "wb") as fh:
+        with _tqdm(total=total, unit="B", unit_scale=True, desc=dest.name) as pbar:
+            for chunk in resp.iter_content(chunk_size=chunk_size):
+                if chunk:
+                    fh.write(chunk)
+                    pbar.update(len(chunk))
+    print(f"  Saved          : {dest.name} ({dest.stat().st_size / 1e6:.1f} MB)")
+    return dest
+
+
+def _resample_ds(ds: xr.Dataset, rule: str) -> xr.Dataset:
+    """Resample a dataset: sum for accumulation vars, mean for all others.
+    Non-time coordinates (lat, lon, elev) are restored after resampling."""
+    parts = {}
+    for var in ds.data_vars:
+        if var in _ACCUMULATION_VARS:
+            parts[var] = ds[var].resample(time=rule).sum(skipna=True)
+        else:
+            parts[var] = ds[var].resample(time=rule).mean(skipna=True)
+    ds_r = xr.Dataset(parts)
+    # Restore non-time coordinates (lat, lon, elev) dropped by resample
+    for coord in ds.coords:
+        if "time" not in ds[coord].dims and coord not in ds_r.coords:
+            ds_r = ds_r.assign_coords({coord: ds[coord]})
+    return ds_r
+
+
+def _compress_encoding(ds: xr.Dataset, level: int = 4) -> dict:
+    return {
+        name: {"zlib": True, "complevel": level}
+        for name, var in ds.data_vars.items()
+        if var.dtype.kind in {"f", "i", "u"}
+    }
+
+
+def save_compressed_netcdf(
+    ds: xr.Dataset,
+    path: Union[str, Path],
+    *,
+    compression_level: int = 4,
+    mode: str = "w",
+) -> Path:
+    """Write *ds* to NetCDF with zlib compression using the netCDF4 engine.
+
+    Matches the encoding used by :func:`create_openmesh_sample`,
+    :func:`create_pws_sample`, and :func:`create_asos_sample` (only float/int
+    data variables are compressed; requires ``netcdf4`` installed).
+    """
+    path = Path(path)
+    ds.to_netcdf(
+        path,
+        mode=mode,
+        encoding=_compress_encoding(ds, compression_level),
+        engine="netcdf4",
+    )
+    return path
+
+
+def _infer_interval_label(start: datetime, end: datetime) -> str:
+    total_seconds = int((end - start).total_seconds())
+    if total_seconds % 3600 == 0:
+        h = total_seconds // 3600
+        if h % 720 == 0: return f"{h // 720}mo"
+        if h % 24  == 0: return f"{h // 24}d"
+        return f"{h}h"
+    return f"{total_seconds // 60}min"
+
+
+def _parse_interval(time_interval: str) -> timedelta:
+    """Parse an interval string into a timedelta.
+
+    Accepts the fixed shortcuts in INTERVAL_DURATIONS *and* arbitrary
+    ``Nh`` / ``Nd`` / ``Nw`` / ``Nmo`` patterns (e.g. ``"10d"``, ``"48h"``).
+    """
+    import re
+    if time_interval in INTERVAL_DURATIONS:
+        return INTERVAL_DURATIONS[time_interval]
+    m = re.fullmatch(r"(\d+)(h|d|w|mo)", time_interval)
+    if m:
+        n, unit = int(m.group(1)), m.group(2)
+        if unit == "h":  return timedelta(hours=n)
+        if unit == "d":  return timedelta(days=n)
+        if unit == "w":  return timedelta(weeks=n)
+        if unit == "mo": return timedelta(days=30 * n)
+    raise ValueError(
+        f"Unknown time_interval '{time_interval}'. "
+        f"Use a fixed shortcut {list(INTERVAL_DURATIONS)} "
+        f"or an arbitrary pattern like '10d', '48h', '2w', '3mo'."
+    )
+
+
+def _resolve_time_window(
+    start_time: str,
+    time_interval: Optional[str],
+    end_time: Optional[str],
+) -> tuple[datetime, datetime, str]:
+    start_dt = datetime.fromisoformat(start_time)
+    if end_time is not None:
+        end_dt = datetime.fromisoformat(end_time)
+        label  = _infer_interval_label(start_dt, end_dt)
+    elif time_interval is not None:
+        end_dt = start_dt + _parse_interval(time_interval)
+        label  = time_interval
+    else:
+        raise ValueError("Provide either time_interval or end_time.")
+    return start_dt, end_dt, label
+
+
+def _resolve_source(
+    src_path: Union[str, Path, None],
+    default_path: Path,
+    mode: str,
+    download_fn,
+) -> Path:
+    path = Path(src_path) if src_path is not None else default_path
+    if mode == "download":
+        if not path.exists():
+            print("  Source not found — downloading ...")
+            download_fn()
+        else:
+            print(f"  Source   : {_rel(path)}  (exists, skipping download)")
+    elif mode == "local":
+        if not path.exists():
+            raise FileNotFoundError(
+                f"File not found: {path}\n"
+                f"Tip: use mode='download' to fetch it automatically."
+            )
+    else:
+        raise ValueError(f"Unknown mode '{mode}'. Choose 'local' or 'download'.")
+    return path
+
+
+# ---------------------------------------------------------------------------
+# Download
+# ---------------------------------------------------------------------------
+
+def download_openmesh(output_dir: Union[str, Path] = DATA_DIR) -> Path:
+    """Download and extract the OpenMesh CML dataset from Zenodo.
+    Skips if already present locally.
+
+    Returns
+    -------
+    Path  →  <output_dir>/raw/
+    """
+    output_dir = Path(output_dir)
+    raw_dir    = output_dir / "raw"
+    cml_nc     = raw_dir / "ds_openmesh.nc"
+
+    if cml_nc.exists():
+        print(f"  CML found locally  : {cml_nc}")
+        return raw_dir
+
+    zip_path = output_dir / "archived" / _ZENODO["cml"]["filename"]
+    _download_file(_ZENODO["cml"]["url"], zip_path)
+
+    ext_map  = {".nc": raw_dir, ".csv": output_dir / "meta",
+                ".html": output_dir / "meta" / "maps", ".ipynb": output_dir / "examples"}
+    docs_dir = output_dir / "docs"
+    for d in [*ext_map.values(), docs_dir]:
+        d.mkdir(parents=True, exist_ok=True)
+
+    with tempfile.TemporaryDirectory() as tmp:
+        with zipfile.ZipFile(zip_path, "r") as zf:
+            members = [m for m in zf.namelist() if not m.endswith("/")]
+            print(f"  Extracting {len(members)} files ...")
+            zf.extractall(tmp)
+        for f in Path(tmp).rglob("*"):
+            if f.is_file():
+                dest = ext_map.get(f.suffix.lower(), docs_dir) / f.name
+                if not dest.exists():
+                    shutil.copy2(f, dest)
+
+    print(f"  Extraction complete: {raw_dir}")
+    return raw_dir
+
+
+def download_pws_wu(output_dir: Union[str, Path] = DATA_DIR) -> Path:
+    """Download and extract the PWS Weather Underground dataset from Zenodo.
+    Skips if already present locally.
+
+    Returns
+    -------
+    Path  →  <output_dir>/raw/pws_wu_os.nc
+    """
+    output_dir = Path(output_dir)
+    pws_nc     = output_dir / "raw" / "pws_wu_os.nc"
+
+    if pws_nc.exists():
+        print(f"  PWS found locally  : {pws_nc}")
+        return pws_nc
+
+    zip_path = output_dir / "archived" / _ZENODO["pws"]["filename"]
+    _download_file(_ZENODO["pws"]["url"], zip_path)
+
+    pws_nc.parent.mkdir(parents=True, exist_ok=True)
+    print("  Extracting pws_wu_os.nc ...")
+    with zipfile.ZipFile(zip_path, "r") as zf:
+        for name in zf.namelist():
+            if name.endswith("pws_wu_os.nc"):
+                pws_nc.write_bytes(zf.read(name))
+                break
+
+    print(f"  Saved              : {_rel(pws_nc)} ({pws_nc.stat().st_size / 1e6:.1f} MB)")
+    return pws_nc
+
+
+# ---------------------------------------------------------------------------
+# CML — load
+# ---------------------------------------------------------------------------
+
+def load_cml(path: Union[str, Path, None] = None) -> xr.Dataset:
+    """Load the full CML NetCDF into memory. Defaults to CML_PATH."""
+    path = Path(path) if path is not None else CML_PATH
+    ds   = xr.load_dataset(path)   # load_dataset vs open_dataset — reads fully into memory
+    print(f"  Loaded CML        : {dict(ds.sizes)}  ←  {_rel(path)}")
+    return ds
+
+
+def load_cml_sample(path: Union[str, Path]) -> xr.Dataset:
+    """Load a CML sample NetCDF into an xarray Dataset."""
+    path = Path(path)
+    ds   = xr.open_dataset(path)
+    print(f"  Loaded CML sample : {dict(ds.sizes)}  ←  {path.name}")
+    return ds
+
+
+# ---------------------------------------------------------------------------
+# CML — sample creation
+# ---------------------------------------------------------------------------
+
+def create_openmesh_sample(
+    src_path: Union[str, Path, None] = None,
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    *,
+    ds: Optional[xr.Dataset] = None,
+    mode: str = "local",
+    start_time: str = DEFAULT_START_TIME,
+    time_interval: Optional[str] = "1d",
+    end_time: Optional[str] = None,
+    cml_ids: Optional[List[str]] = None,
+    compression_level: int = 4,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    verbose: bool = True,
+    return_type: str = "path",              # "path" | "sample"
+    save: bool = True,
+) -> Union[Path, xr.Dataset]:
+    """Create a time- and/or CML-filtered sample from the OpenMesh dataset.
+
+    Parameters
+    ----------
+    src_path : path-like or None
+        Source NetCDF. Defaults to CML_PATH.
+    output_dir : path-like
+        Output directory. Defaults to SAMPLE_DIR (./data/samples).
+    ds : xr.Dataset or None
+        Already-loaded dataset — skips all file I/O when provided.
+    mode : "local" | "download"
+        "local"    — load from disk (raises FileNotFoundError if missing).
+        "download" — auto-download from Zenodo if not found locally.
+    start_time : str
+        ISO-8601 window start.
+    time_interval : str or None
+        "1h", "1d", "1w", "1mo". Ignored when end_time is given.
+    end_time : str or None
+        Explicit ISO-8601 window end. Overrides time_interval.
+    cml_ids : list of str or None
+        CML IDs to keep. None = all 75 links.
+    compression_level : int
+        zlib level 1–9.
+    resample : str or None
+        Temporal resampling rule (e.g. ``"1h"``, ``"5min"``). RSL is averaged.
+        When set, the rule is included in the filename: ``openmesh_cml_1h_1d.nc``.
+    include_date : bool
+        If True, append ``_YYYYMMDD_YYYYMMDD`` start/end dates to the filename
+        (e.g. ``openmesh_cml_1d_20240115_20240116.nc``).
+        Default False → ``openmesh_cml_1d.nc`` (canonical root copy).
+    verbose : bool
+    return_type : str
+        "path" (default) — returns file path.
+        "sample" — returns xr.Dataset.
+    save : bool
+        Write the sample to disk. Default True.
+        When ``return_type='sample'`` and ``save=False``, returns the
+        dataset without writing to disk.
+
+    Returns
+    -------
+    Path or xr.Dataset
+        If ``return_type='path'`` (default), returns the written sample file path.
+        If ``return_type='sample'``, returns the in-memory xr.Dataset instead.
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt, end_dt, label = _resolve_time_window(start_time, time_interval, end_time)
+    date_suffix   = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag  = f"_{resample}" if resample else ""
+    cml_tag       = f"_cmls{len(cml_ids)}" if cml_ids is not None else ""
+    dst_path      = output_dir / f"openmesh_cml{resample_tag}_{label}{date_suffix}{cml_tag}.nc"
+
+    _owns_ds = ds is None
+    if ds is None:
+        resolved = _resolve_source(src_path, CML_PATH, mode, download_openmesh)
+        if verbose:
+            print(f"  Source   : {resolved}")
+        ds = xr.open_dataset(resolved)
+    else:
+        if verbose:
+            print("  Source   : provided ds")
+
+    if verbose:
+        print(f"  Window   : {start_dt}  →  {end_dt}  ({label})")
+        print(f"  CML IDs  : {'all' if cml_ids is None else cml_ids}")
+        print(f"  Output   : {dst_path.name}")
+
+    ds_sample = ds.sel(time=slice(start_dt, end_dt))
+    if cml_ids is not None:
+        ds_sample = ds_sample.sel(cml_id=cml_ids)
+    if resample:
+        ds_sample = _resample_ds(ds_sample, resample)
+
+    if verbose:
+        print(f"  Timesteps: {ds_sample.sizes['time']:,}")
+        print(f"  CML count: {ds_sample.sizes['cml_id']}")
+
+    ds_sample.attrs.update({
+        "title":      f"OpenMesh CML sample ({label}), start {start_dt.strftime('%Y-%m-%d')}",
+        "start_date": start_dt.strftime("%Y-%m-%d"),
+        "end_date":   end_dt.strftime("%Y-%m-%d"),
+        "time_range": f"{start_dt.isoformat()} / {end_dt.isoformat()}",
+        "cml_subset": "all" if cml_ids is None else ",".join(cml_ids),
+    })
+
+    if save:
+        if dst_path.exists() and not replace:
+            if verbose:
+                print(f"  Exists   : {_rel(dst_path)}  (skipped — pass replace=True to overwrite)")
+            return dst_path if return_type == "path" else xr.open_dataset(dst_path)
+        try:
+            if dst_path.exists():
+                try:
+                    dst_path.unlink()
+                except PermissionError:
+                    print(f"  WARNING: could not delete existing file {dst_path} (permission). "
+                          f"Attempting to overwrite in-place.")
+
+            try:
+                save_compressed_netcdf(
+                    ds_sample, dst_path, compression_level=compression_level
+                )
+            except PermissionError as e:
+                print(f"  WARNING: could not write NetCDF file {dst_path} (permission): {e}")
+                print("           Skipping write for this interval, downstream code should handle "
+                      "missing files gracefully.")
+        except Exception as e:
+            print(f"  WARNING: unexpected error while writing {dst_path}: {e}")
+
+        if verbose and dst_path.exists():
+            print(f"  Saved    : {_rel(dst_path)}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+    else:
+        if verbose:
+            print("  Save     : skipped (save=False)")
+
+    if _owns_ds:
+        ds.close()
+
+    return ds_sample if return_type == "sample" else dst_path
+
+
+def create_openmesh_samples_batch(
+    intervals: List[str] = ("1h", "1d", "1w"),
+    start_time: str = DEFAULT_START_TIME,
+    resample: Optional[str] = None,
+    **kwargs,
+) -> dict[str, Path]:
+    """Create multiple CML samples in one call.
+
+    Parameters
+    ----------
+    intervals : list of str
+    start_time : str
+    **kwargs : forwarded to create_openmesh_sample
+
+    Returns
+    -------
+    dict  interval → Path
+    """
+    results: dict[str, Path] = {}
+    sep = "─" * 55
+
+    for interval in intervals:
+        if kwargs.get("verbose", True):
+            print(f"\n{sep}")
+            print(f"  Interval : {interval}")
+            print(sep)
+        results[interval] = create_openmesh_sample(
+            start_time=start_time, time_interval=interval, resample=resample, **kwargs
+        )
+
+    if kwargs.get("verbose", True):
+        print(f"\n{sep}")
+        print("  Batch complete:")
+        for label, p in results.items():
+            print(f"    {label:<6} → {p.name}")
+        print(sep)
+
+    return results
+
+
+# ---------------------------------------------------------------------------
+# PWS — load
+# ---------------------------------------------------------------------------
+
+def load_pws_sample(path: Union[str, Path]) -> xr.Dataset:
+    """Load a flat (id, time) PWS sample NetCDF into an xarray Dataset."""
+    path = Path(path)
+    ds   = xr.open_dataset(path)
+    print(f"  Loaded PWS sample : {dict(ds.sizes)}  ←  {path.name}")
+    return ds
+
+
+# ---------------------------------------------------------------------------
+# PWS — sample creation
+# ---------------------------------------------------------------------------
+
+def create_pws_sample(
+    src_path: Union[str, Path, None] = None,
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    *,
+    mode: str = "local",
+    start_time: str = DEFAULT_START_TIME,
+    time_interval: Optional[str] = "1d",
+    end_time: Optional[str] = None,
+    compression_level: int = 4,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    verbose: bool = True,
+    return_type: str = "path",              # "path" | "sample"
+    save: bool = True,
+) -> Union[Path, xr.Dataset]:
+    """Crop PWS NetCDF (group-per-station) to a time window and save as
+    a flat (id, time) compressed sample.
+
+    Parameters
+    ----------
+    src_path : path-like or None
+        Defaults to PWS_PATH.
+    output_dir : path-like
+        Defaults to SAMPLE_DIR.
+    mode : "local" | "download"
+    start_time, time_interval, end_time : same as create_openmesh_sample
+    compression_level : int
+    verbose : bool
+
+    Returns
+    -------
+    Path or xr.Dataset
+        If ``return_type='path'`` (default), returns the written sample file path.
+        If ``return_type='sample'``, returns the in-memory xr.Dataset instead.
+    """
+    if not _HAS_NC4:
+        raise ImportError("netCDF4 is required for PWS sampling.  pip install netCDF4")
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt, end_dt, label = _resolve_time_window(start_time, time_interval, end_time)
+    date_suffix  = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag = f"_{resample}" if resample else ""
+    dst_path     = output_dir / f"openmesh_wu_pws{resample_tag}_{label}{date_suffix}.nc"
+
+    resolved = _resolve_source(src_path, PWS_PATH, mode, download_pws_wu)
+
+    with nc4.Dataset(str(resolved), "r") as root:
+        group_ids = list(root.groups.keys())
+
+    if verbose:
+        print(f"  Source   : {resolved}")
+        print(f"  Window   : {start_dt}  →  {end_dt}  ({label})")
+        print(f"  Stations : {len(group_ids)} groups")
+        print(f"  Output   : {dst_path.name}")
+
+    common_time: Optional[pd.DatetimeIndex] = None
+    for gid in group_ids:
+        try:
+            ds_g = xr.open_dataset(resolved, group=gid, engine="netcdf4")
+            t    = ds_g.sel(time=slice(start_dt, end_dt)).time
+            if t.sizes["time"] > 0:
+                common_time = pd.DatetimeIndex(t.values)
+                ds_g.close()
+                break
+            ds_g.close()
+        except Exception:
+            pass
+
+    if common_time is None:
+        raise RuntimeError(f"No PWS data found between {start_dt} and {end_dt}.")
+
+    n_times = len(common_time)
+    ids_out, lats_out, lons_out, elevs_out = [], [], [], []
+    var_rows: dict[str, list] = {v: [] for v in _PWS_VARS}
+    var_attrs: dict[str, dict] = {}
+    n_stations = 0
+
+    for gid in group_ids:
+        try:
+            ds_g = xr.open_dataset(resolved, group=gid, engine="netcdf4")
+            ds_c = ds_g.sel(time=slice(start_dt, end_dt))
+            if ds_c.sizes["time"] == 0:
+                ds_g.close()
+                continue
+
+            ids_out.append(gid)
+            lats_out.append(float(ds_c["lat"].values.flat[0])   if "lat"  in ds_c else np.nan)
+            lons_out.append(float(ds_c["lon"].values.flat[0])   if "lon"  in ds_c else np.nan)
+            elevs_out.append(float(ds_c["elev"].values.flat[0]) if "elev" in ds_c else np.nan)
+
+            for vname in _PWS_VARS:
+                if vname not in ds_c:
+                    var_rows[vname].append(np.full(n_times, np.nan))
+                    continue
+                if not var_attrs.get(vname):
+                    var_attrs[vname] = dict(ds_c[vname].attrs)
+                arr = ds_c[vname].values.squeeze()
+                s   = pd.Series(arr, index=pd.DatetimeIndex(ds_c.time.values))
+                var_rows[vname].append(s[~s.index.duplicated()].reindex(common_time).values)
+
+            n_stations += 1
+            ds_g.close()
+        except Exception as e:
+            warnings.warn(f"Skipping PWS group '{gid}': {e}")
+
+    if n_stations == 0:
+        raise RuntimeError(f"No PWS stations had data between {start_dt} and {end_dt}.")
+
+    if verbose:
+        print(f"  Loaded   : {n_stations} stations, {n_times:,} timesteps")
+
+    ds_out = xr.Dataset(
+        {v: (["id", "time"], np.vstack(var_rows[v]), var_attrs.get(v, {}))
+         for v in _PWS_VARS if var_rows[v]},
+        coords={
+            "id":   (["id"],   ids_out),
+            "time": (["time"], common_time),
+            "lat":  (["id"],   lats_out,  {"units": "degrees_north"}),
+            "lon":  (["id"],   lons_out,  {"units": "degrees_east"}),
+            "elev": (["id"],   elevs_out, {"units": "m"}),
+        },
+        attrs={
+            "title":       f"OpenMesh PWS sample ({label}), start {start_dt.strftime('%Y-%m-%d')}",
+            "start_date":  start_dt.strftime("%Y-%m-%d"),
+            "end_date":    end_dt.strftime("%Y-%m-%d"),
+            "time_range":  f"{start_dt.isoformat()} / {end_dt.isoformat()}",
+            "source":      "https://zenodo.org/records/17508286",
+            "conventions": "OpenSense v1.0",
+        },
+    )
+
+    if resample:
+        ds_out = _resample_ds(ds_out, resample)
+        if verbose:
+            print(f"  Resampled: {resample} → {ds_out.sizes['time']:,} timesteps")
+
+    if save:
+        if dst_path.exists() and not replace:
+            if verbose:
+                print(f"  Exists   : {_rel(dst_path)}  (skipped — pass replace=True to overwrite)")
+            return dst_path if return_type == "path" else xr.open_dataset(dst_path)
+        # Overwrite existing files explicitly; if this fails, warn but do not raise.
+        try:
+            if dst_path.exists():
+                try:
+                    dst_path.unlink()
+                except PermissionError as e:
+                    print(f"  WARNING: could not delete existing file {dst_path} (permission): {e}")
+
+            try:
+                save_compressed_netcdf(
+                    ds_out, dst_path, compression_level=compression_level
+                )
+            except PermissionError as e:
+                print(f"  WARNING: could not write PWS NetCDF file {dst_path} (permission): {e}")
+        except Exception as e:
+            print(f"  WARNING: unexpected error while writing PWS sample {dst_path}: {e}")
+
+        if verbose and dst_path.exists():
+            print(f"  Saved    : {_rel(dst_path)}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+    else:
+        if verbose:
+            print("  Save     : skipped (save=False)")
+
+    return ds_out if return_type == "sample" else dst_path
+
+
+
+# ---------------------------------------------------------------------------
+# ASOS — sample creation
+# ---------------------------------------------------------------------------
+
+def create_asos_sample(
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    *,
+    start_time: str = DEFAULT_START_TIME,
+    time_interval: Optional[str] = "1d",
+    end_time: Optional[str] = None,
+    stations: Optional[List[str]] = None,
+    variables: List[str] = ("precip_amount", "precip_rate", "temperature", "wind_speed"),
+    meta_path: Union[str, Path, None] = None,
+    compression_level: int = 4,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    save: bool = True,
+    verbose: bool = True,
+    return_type: str = "path",              # "path" | "sample"
+) -> Union[Path, xr.Dataset]:
+    """Fetch ASOS 1-min data from the IEM API and save as a flat (id, time)
+    compressed NetCDF sample following the OpenMesh naming convention.
+
+    Requires a live internet connection (IEM API call).
+
+    Parameters
+    ----------
+    output_dir : path-like
+        Output directory. Defaults to SAMPLE_DIR (data/samples).
+    start_time : str
+        ISO-8601 window start. Default ``2024-01-15T00:00:00``.
+    time_interval : str or None
+        ``"1h"``, ``"1d"``, ``"1w"``, ``"1mo"``. Ignored when end_time given.
+    end_time : str or None
+        Explicit ISO-8601 window end. Overrides time_interval.
+    stations : list of str or None
+        ASOS station IDs (e.g. ``["KJFK", "KEWR"]``).
+        None → ``["JFK", "EWR", "LGA", "NYC"]`` (NYC-area defaults).
+    variables : list of str
+        Variable names from ``ws_opensense_conversion.ASOS_AVAILABLE_VARS``.
+    meta_path : path-like or None
+        Path to ASOS station metadata CSV.
+        None → ``data/meta/ASOS_stations.csv`` (or fetched from IEM if missing).
+    compression_level : int
+        zlib level 1–9.
+    include_date : bool
+        If True, append ``_YYYY-MM-DD`` start date to the filename
+        (e.g. ``openmesh_asos_1d_2024-01-15.nc``).
+        Default False → ``openmesh_asos_1d.nc``.
+    verbose : bool
+    return_type : str
+        ``"path"`` (default) — returns file path.
+        ``"sample"`` — returns the in-memory xr.Dataset.
+
+    Returns
+    -------
+    Path or xr.Dataset
+    """
+    import ws_opensense_conversion as wsoc
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt, end_dt, label = _resolve_time_window(start_time, time_interval, end_time)
+    date_suffix  = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag = f"_{resample}" if resample else ""
+    dst_path     = output_dir / f"openmesh_asos_ws{resample_tag}_{label}{date_suffix}.nc"
+
+    _stations = stations or ["JFK", "EWR", "LGA", "NYC"]
+
+    if verbose:
+        print(f"  Window   : {start_dt}  →  {end_dt}  ({label})")
+        print(f"  Stations : {_stations}")
+        print(f"  Variables: {list(variables)}")
+        print(f"  Output   : {dst_path.name}")
+
+    # Load or fetch station metadata
+    _meta_path = Path(meta_path) if meta_path is not None else DATA_DIR / "meta" / "ASOS_stations.csv"
+    if _meta_path.exists():
+        meta = wsoc.load_asos_metadata(_meta_path)
+    else:
+        if verbose:
+            print("  Metadata not found locally — fetching from IEM ...")
+        meta = wsoc.fetch_asos_stations_nyc(save_path=_meta_path, verbose=verbose)
+
+    # Fetch from IEM API
+    asos_raw = wsoc.fetch_asos_raw(
+        start_dt, end_dt, _stations,
+        variables=list(variables),
+        verbose=verbose,
+    )
+    if not asos_raw:
+        raise RuntimeError(
+            f"No ASOS data returned for stations {_stations} "
+            f"between {start_dt} and {end_dt}."
+        )
+
+    ds_out = wsoc.to_opensense_dataset(
+        asos_raw,
+        source="asos",
+        meta=meta,
+        start_dt=start_dt,
+        end_dt=end_dt,
+        extra_attrs={
+            "title":     f"OpenMesh ASOS sample ({label}), start {start_dt.strftime('%Y-%m-%d')}",
+            "reference": "https://zenodo.org/records/15287692",
+        },
+    )
+
+    if verbose:
+        print(f"  Loaded   : {len(asos_raw)} stations, {ds_out.sizes['time']:,} timesteps")
+
+    if resample:
+        ds_out = _resample_ds(ds_out, resample)
+        if verbose:
+            print(f"  Resampled: {resample} → {ds_out.sizes['time']:,} timesteps")
+
+    if save:
+        if dst_path.exists() and not replace:
+            if verbose:
+                print(f"  Exists   : {_rel(dst_path)}  (skipped — pass replace=True to overwrite)")
+            return dst_path if return_type == "path" else xr.open_dataset(dst_path)
+
+        try:
+            if dst_path.exists():
+                dst_path.unlink()
+            save_compressed_netcdf(
+                ds_out, dst_path, compression_level=compression_level
+            )
+            if verbose:
+                print(f"  Saved    : {_rel(dst_path)}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+        except Exception as e:
+            print(f"  WARNING: could not write ASOS sample {_rel(dst_path)}: {e}")
+    else:
+        if verbose:
+            print("  Save     : skipped (save=False)")
+
+    return ds_out if return_type == "sample" else dst_path
+
+
+# ── create_all_datasets ───────────────────────────────────────
+def create_all_datasets(
+    start_time: str,
+    end_time:   str,
+    cml_ids:    Optional[List[str]] = None,   # None = all 75
+    pws_ids:    Optional[List[str]] = None,   # None = all stations
+    asos_ids:   Optional[List[str]] = None,   # None = all in meta
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    resample: Optional[str] = None,
+    include_date: bool = False,
+    replace: bool = False,
+    save: bool = True,
+    verbose:    bool = True,
+) -> dict[str, Optional[Union[Path, "xr.Dataset"]]]:
+    """Create CML, PWS and ASOS samples for a given time window.
+
+    Parameters
+    ----------
+    save : bool
+        If True (default), write samples to disk and return Paths.
+        If False, skip disk read/write entirely — always create from
+        the raw downloaded files and return xr.Dataset objects.
+
+    Returns
+    -------
+    dict with keys "cml", "pws", "asos" → Path (save=True) or xr.Dataset (save=False)
+    """
+    from datetime import datetime
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_dt    = datetime.fromisoformat(start_time)
+    end_dt      = datetime.fromisoformat(end_time)
+    label        = _infer_interval_label(start_dt, end_dt)
+    date_suffix  = f"_{start_dt.strftime('%Y%m%d')}_{end_dt.strftime('%Y%m%d')}" if include_date else ""
+    resample_tag = f"_{resample}" if resample else ""
+    results      = {"cml": None, "pws": None, "asos": None}
+    sep          = "─" * 55
+
+    return_type = "path" if save else "sample"
+
+    # ── CML ──────────────────────────────────────────────────
+    print(f"\n{sep}\n  CML\n{sep}")
+    cml_tag  = f"_cmls{len(cml_ids)}" if cml_ids else ""
+    cml_path = output_dir / f"openmesh_cml{resample_tag}_{label}{date_suffix}{cml_tag}.nc"
+    if save and cml_path.exists() and not replace:
+        print(f"  exists  : {cml_path.name}  (skipped — pass replace=True to overwrite)")
+        results["cml"] = cml_path
+    else:
+        try:
+            results["cml"] = create_openmesh_sample(
+                start_time=start_time, end_time=end_time,
+                cml_ids=cml_ids, output_dir=output_dir,
+                mode="local", resample=resample, include_date=include_date,
+                replace=replace, save=save, return_type=return_type, verbose=verbose,
+            )
+        except Exception as e:
+            print(f"  FAILED  : {e}")
+
+    # ── PWS ──────────────────────────────────────────────────
+    print(f"\n{sep}\n  PWS\n{sep}")
+    pws_path = output_dir / f"openmesh_wu_pws{resample_tag}_{label}{date_suffix}.nc"
+    if save and pws_path.exists() and not replace:
+        print(f"  exists  : {pws_path.name}  (skipped — pass replace=True to overwrite)")
+        results["pws"] = pws_path
+    else:
+        try:
+            results["pws"] = create_pws_sample(
+                start_time=start_time, end_time=end_time,
+                output_dir=output_dir, mode="local",
+                resample=resample, include_date=include_date,
+                replace=replace, save=save, return_type=return_type, verbose=verbose,
+            )
+        except Exception as e:
+            print(f"  FAILED  : {e}")
+
+    # ── ASOS ─────────────────────────────────────────────────
+    print(f"\n{sep}\n  ASOS\n{sep}")
+    asos_path = output_dir / f"openmesh_asos_ws_{label}{date_suffix}.nc"
+    if save and asos_path.exists() and not replace:
+        print(f"  exists  : {asos_path.name}  (skipped — pass replace=True to overwrite)")
+        results["asos"] = asos_path
+    else:
+        try:
+            results["asos"] = create_asos_sample(
+                output_dir=output_dir,
+                start_time=start_time, end_time=end_time,
+                stations=asos_ids,
+                resample=resample, include_date=include_date,
+                replace=replace, save=save, return_type=return_type, verbose=verbose,
+            )
+        except Exception as e:
+            print(f"  FAILED  : {e}")
+
+    # ── summary ──────────────────────────────────────────────
+    print(f"\n{sep}")
+    print("  Summary:")
+    for name, val in results.items():
+        if val is None:
+            status = "NOT created"
+        elif save:
+            status = val.name
+        else:
+            status = repr(val)
+        print(f"    {name:<6} → {status}")
+    print(sep)
+
+    return results
\ No newline at end of file
diff --git a/OpenMesh/notebooks/ws_opensense_conversion.py b/OpenMesh/notebooks/ws_opensense_conversion.py
new file mode 100644
index 0000000..763b09e
--- /dev/null
+++ b/OpenMesh/notebooks/ws_opensense_conversion.py
@@ -0,0 +1,592 @@
+"""
+ws_opensense_conversion.py
+==========================
+Convert weather station data (ASOS, PWS, or custom) to OpenSense-format
+xr.Dataset and save as compressed NetCDF.
+
+Supported sources
+-----------------
+  "asos"   — NOAA ASOS 1-minute data fetched via IEM API
+  "custom" — any dict of {station_id: pd.DataFrame} with a time index
+
+Pipeline
+--------
+  1. fetch_asos_stations_nyc()          discover stations
+  2. fetch_asos_raw()                   fetch + process to metric DataFrame
+  3. to_opensense_dataset()             convert to xr.Dataset (OpenSense v1.0)
+  4. save_opensense_dataset()           save as compressed NetCDF
+
+OpenSense output format
+-----------------------
+  dims    : (id, time)
+  coords  : id, time, lat, lon, elev
+  data_vars: variable names depend on source / variables requested
+  attrs   : title, source, conventions, time_range, start_date, end_date
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from io import StringIO
+from pathlib import Path
+from typing import Optional, Union
+
+import numpy as np
+import pandas as pd
+import requests
+import xarray as xr
+
+try:
+    from tqdm.auto import tqdm as _tqdm
+except ImportError:
+    class _tqdm:
+        def __init__(self, total=None, **kw): pass
+        def __enter__(self): return self
+        def __exit__(self, *a): pass
+        def update(self, n=1): pass
+
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+DATA_DIR        = Path(__file__).parent / "data"   # absolute — stable regardless of CWD
+SAMPLE_DIR      = DATA_DIR / "samples"
+ASOS_META_PATH  = DATA_DIR / "meta" / "ASOS_stations.csv"
+IEM_1MIN_URL    = "https://mesonet.agron.iastate.edu/cgi-bin/request/asos1min.py"
+IEM_NETWORK_URL = "https://mesonet.agron.iastate.edu/geojson/network/{network}.geojson"
+
+# All variables available from the IEM 1-min API
+ASOS_AVAILABLE_VARS = {
+    "precip_amount":       ("precip",    "mm",       "1-min precipitation amount"),
+    "precip_rate":         ("precip",    "mm/hr",    "Precipitation rate (calculated)"),
+    "precip_type":         ("ptype",     "-",        "Raw ASOS precipitation type code"),
+    "precip_category":     ("ptype",     "-",        "Simplified precip category"),
+    "temperature":         ("tmpf",      "°C",       "Air temperature"),
+    "dewpoint":            ("dwpf",      "°C",       "Dewpoint temperature"),
+    "wind_speed":          ("sknt",      "m/s",      "Wind speed"),
+    "wind_direction":      ("drct",      "degrees",  "Wind direction"),
+    "wind_gust":           ("gust_sknt", "m/s",      "Wind gust speed"),
+    "wind_gust_direction": ("gust_drct", "degrees",  "Wind gust direction"),
+}
+
+# Precipitation type mapping (METAR-based)
+PTYPE_MAP = {
+    "NP": "dry",
+    "R": "rain",  "R+": "rain",  "R-": "rain",
+    "S": "snow",  "S+": "snow",  "S-": "snow",
+    "P": "precip", "P?": "precip",
+    "M": "missing", "M ": "missing",
+    "?0": "missing", "?1": "missing", "?2": "missing", "?3": "missing",
+}
+
+
+# ---------------------------------------------------------------------------
+# Station discovery
+# ---------------------------------------------------------------------------
+
+def fetch_asos_stations_nyc(
+    lat_min: float = 40.4,
+    lat_max: float = 41.2,
+    lon_min: float = -74.5,
+    lon_max: float = -73.0,
+    networks: list[str] = ("NY_ASOS", "NJ_ASOS", "CT_ASOS"),
+    save_path: Union[str, Path, None] = ASOS_META_PATH,
+    verbose: bool = True,
+) -> pd.DataFrame:
+    """Fetch all ASOS stations within a bounding box from multiple state networks.
+
+    Queries the IEM GeoJSON API for each network and filters by bbox.
+    Default bbox covers NYC metro: Manhattan, Brooklyn, Queens, Bronx,
+    Staten Island, Newark (NJ/KEWR), JFK as the furthest east point.
+
+    Parameters
+    ----------
+    lat_min, lat_max : float   Latitude bounds  (default 40.4 – 41.0)
+    lon_min, lon_max : float   Longitude bounds (default -74.5 – -73.5)
+    networks : list of str     IEM network codes to query
+    save_path : path-like or None   Where to save CSV (None = skip)
+    verbose : bool
+
+    Returns
+    -------
+    pd.DataFrame  indexed by Station ID, columns: Name, Latitude, Longitude,
+                  Elevation, Network
+    """
+    records = []
+
+    for network in networks:
+        url = IEM_NETWORK_URL.format(network=network)
+        if verbose:
+            print(f"  Querying {network} ...", end=" ")
+        try:
+            resp     = requests.get(url, timeout=30)
+            resp.raise_for_status()
+            n_found  = 0
+            for f in resp.json()["features"]:
+                lon = f["geometry"]["coordinates"][0]
+                lat = f["geometry"]["coordinates"][1]
+                if lat_min <= lat <= lat_max and lon_min <= lon <= lon_max:
+                    p = f["properties"]
+                    records.append({
+                        "Station ID": p["sid"],
+                        "Name":       p["sname"],
+                        "Latitude":   lat,
+                        "Longitude":  lon,
+                        "Elevation":  p.get("elevation", np.nan),
+                        "Network":    network,
+                    })
+                    n_found += 1
+            if verbose:
+                print(f"{n_found} stations found")
+        except Exception as e:
+            if verbose:
+                print(f"ERROR: {e}")
+
+    if not records:
+        raise RuntimeError(
+            f"No ASOS stations found in bbox "
+            f"lat=[{lat_min},{lat_max}] lon=[{lon_min},{lon_max}]"
+        )
+
+    df = (
+        pd.DataFrame(records)
+        .set_index("Station ID")
+        .sort_values(["Network", "Latitude"], ascending=[True, False])
+    )
+    df = df[~df.index.duplicated(keep="first")]
+
+    if verbose:
+        print(f"\n  Total : {len(df)} stations")
+        print(f"  {'ID':<8} {'Network':<10} {'Name':<35} {'Lat':>7} {'Lon':>8} {'Elev':>6}")
+        print(f"  {'─'*72}")
+        for sid, row in df.iterrows():
+            print(
+                f"  {sid:<8} {row['Network']:<10} {row['Name']:<35} "
+                f"{row['Latitude']:>7.3f} {row['Longitude']:>8.3f} {row['Elevation']:>6.1f}"
+            )
+
+    if save_path is not None:
+        save_path = Path(save_path)
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_csv(save_path, index=True, index_label="Station ID")
+        if verbose:
+            print(f"\n  Saved : {save_path}")
+
+    return df
+
+
+def load_asos_metadata(path: Union[str, Path] = ASOS_META_PATH) -> pd.DataFrame:
+    """Load ASOS station metadata CSV indexed by Station ID."""
+    return pd.read_csv(Path(path), index_col="Station ID")
+
+
+# ---------------------------------------------------------------------------
+# ASOS fetch (IEM 1-min API)
+# ---------------------------------------------------------------------------
+
+def _map_precip_category(ptype) -> str:
+    if pd.isna(ptype) or str(ptype).strip() in ("", "nan", "None"):
+        return "missing"
+    s = str(ptype).strip()
+    if s in PTYPE_MAP:
+        return PTYPE_MAP[s]
+    u = s.upper()
+    if u == "NP":           return "dry"
+    if u.startswith("R"):   return "rain"
+    if u.startswith("S"):   return "snow"
+    if u.startswith("P"):   return "precip"
+    return "missing"
+
+
+def _raw_to_metric(df_raw: pd.DataFrame, station_id: str) -> pd.DataFrame:
+    """Convert raw IEM DataFrame to metric units with standardized columns."""
+    out = pd.DataFrame()
+    out["datetime"]   = pd.to_datetime(df_raw["valid(UTC)"])
+    out["station_id"] = station_id
+
+    def _num(col): return pd.to_numeric(df_raw[col], errors="coerce") if col in df_raw else None
+
+    if "tmpf"     in df_raw: out["temperature"]         = (_num("tmpf") - 32) * 5 / 9
+    if "dwpf"     in df_raw: out["dewpoint"]             = (_num("dwpf") - 32) * 5 / 9
+    if "sknt"     in df_raw: out["wind_speed"]           = _num("sknt") * 0.51444
+    if "drct"     in df_raw: out["wind_direction"]       = _num("drct")
+    if "gust_sknt"in df_raw: out["wind_gust"]            = _num("gust_sknt") * 0.51444
+    if "gust_drct"in df_raw: out["wind_gust_direction"]  = _num("gust_drct")
+    if "ptype"    in df_raw:
+        out["precip_type"]     = df_raw["ptype"].astype(str).replace("nan", None)
+        out["precip_category"] = out["precip_type"].apply(_map_precip_category)
+    if "precip"   in df_raw:
+        mm = _num("precip") * 25.4
+        out["precip_amount"] = mm
+        out["precip_rate"]   = mm * 60
+
+    return out.sort_values("datetime").reset_index(drop=True)
+
+
+def fetch_asos_raw(
+    start_dt: Union[datetime, str],
+    end_dt: Union[datetime, str],
+    stations: list[str],
+    variables: list[str] = ("precip_amount", "precip_type"),
+    verbose: bool = True,
+) -> dict[str, pd.DataFrame]:
+    """Fetch and process ASOS 1-minute data for multiple stations.
+
+    Parameters
+    ----------
+    stations : list of str   Station IDs, e.g. ["KJFK", "KEWR"]
+    start_dt, end_dt : datetime or ISO-8601 str (e.g. "2024-01-15")
+    variables : list of str
+        Output variable names (from ASOS_AVAILABLE_VARS). Controls which
+        API fields are requested and which columns appear in the output.
+        Default: precipitation only.
+        Full set: all keys of ASOS_AVAILABLE_VARS.
+    verbose : bool
+
+    Returns
+    -------
+    dict  station_id → pd.DataFrame  (time-indexed, metric units)
+    """
+    if isinstance(start_dt, str):
+        start_dt = datetime.fromisoformat(start_dt)
+    if isinstance(end_dt, str):
+        end_dt = datetime.fromisoformat(end_dt)
+    # Map requested output vars → required API fields
+    api_vars = set()
+    for v in variables:
+        if v in ASOS_AVAILABLE_VARS:
+            api_vars.add(ASOS_AVAILABLE_VARS[v][0])
+
+    api_var_str = ",".join(sorted(api_vars))
+    results: dict[str, pd.DataFrame] = {}
+
+    for sid in stations:
+        if verbose:
+            print(f"  Fetching {sid} ...", end=" ")
+        try:
+            params = {
+                "station": sid,
+                "tz":      "UTC",
+                "year1": start_dt.year,  "month1": start_dt.month,  "day1": start_dt.day,
+                "year2": end_dt.year,    "month2": end_dt.month,    "day2": end_dt.day,
+                "vars":   api_var_str,
+                "sample": "1min",
+                "what":   "download",
+                "delim":  "comma",
+            }
+            resp = requests.get(IEM_1MIN_URL, params=params, timeout=300)
+            resp.raise_for_status()
+
+            if len(resp.text) < 100:
+                if verbose: print("no data")
+                continue
+
+            df_raw = pd.read_csv(StringIO(resp.text))
+            df     = _raw_to_metric(df_raw, sid)
+
+            # Keep only requested output columns
+            keep = ["datetime", "station_id"] + [v for v in variables if v in df.columns]
+            df   = df[[c for c in keep if c in df.columns]]
+            df   = df.set_index("datetime").sort_index()
+            df   = df[~df.index.duplicated(keep="first")]
+
+            results[sid] = df
+            if verbose:
+                print(f"{len(df):,} records")
+
+        except Exception as e:
+            if verbose:
+                print(f"ERROR: {e}")
+
+    return results
+
+
+# ---------------------------------------------------------------------------
+# OpenSense conversion
+# ---------------------------------------------------------------------------
+
+def to_opensense_dataset(
+    data: dict[str, pd.DataFrame],
+    source: str = "asos",
+    variables: Optional[list[str]] = None,
+    meta: Optional[pd.DataFrame] = None,
+    lat_col: str = "Latitude",
+    lon_col: str = "Longitude",
+    elev_col: str = "Elevation",
+    start_dt: Optional[Union[datetime, str]] = None,
+    end_dt: Optional[Union[datetime, str]] = None,
+    extra_attrs: Optional[dict] = None,
+) -> xr.Dataset:
+    """Convert a dict of station DataFrames to an OpenSense-format xr.Dataset.
+
+    Parameters
+    ----------
+    data : dict  station_id → time-indexed pd.DataFrame
+    source : str  label for metadata, e.g. "asos", "pws", "custom"
+    variables : list of str or None
+        Which DataFrame columns to include. None = all numeric columns.
+    meta : pd.DataFrame or None
+        Station metadata indexed by station ID (lat/lon/elev).
+    lat_col, lon_col, elev_col : str  column names in meta
+    start_dt, end_dt : datetime or None  override time range in attrs
+    extra_attrs : dict or None  additional global attributes
+
+    Returns
+    -------
+    xr.Dataset  dims (id, time), coords (id, time, lat, lon, elev)
+    """
+    if isinstance(start_dt, str):
+        start_dt = datetime.fromisoformat(start_dt)
+    if isinstance(end_dt, str):
+        end_dt = datetime.fromisoformat(end_dt)
+
+    if not data:
+        raise ValueError("data dict is empty.")
+
+    all_times = sorted(set().union(*[df.index for df in data.values()]))
+    ids, lats, lons, elevs = [], [], [], []
+    var_arrays: dict[str, list] = {}
+
+    for sid, df in data.items():
+        ids.append(sid)
+
+        # coordinates from metadata
+        if meta is not None and sid in meta.index:
+            lats.append(float(meta.loc[sid, lat_col])   if lat_col   in meta.columns else np.nan)
+            lons.append(float(meta.loc[sid, lon_col])   if lon_col   in meta.columns else np.nan)
+            elevs.append(float(meta.loc[sid, elev_col]) if elev_col  in meta.columns else np.nan)
+        else:
+            lats.append(np.nan); lons.append(np.nan); elevs.append(np.nan)
+
+        # determine which variables to use
+        cols = variables if variables is not None else [
+            c for c in df.columns
+            if c not in ("station_id",) and pd.api.types.is_numeric_dtype(df[c])
+        ]
+
+        for col in cols:
+            if col not in var_arrays:
+                var_arrays[col] = []
+            row = df[col].reindex(all_times).values if col in df.columns else np.full(len(all_times), np.nan)
+            var_arrays[col].append(row)
+
+    t0 = start_dt or pd.to_datetime(all_times[0])
+    t1 = end_dt   or pd.to_datetime(all_times[-1])
+
+    # Build variable metadata from ASOS catalogue if available
+    def _var_attrs(col):
+        if col in ASOS_AVAILABLE_VARS:
+            _, unit, desc = ASOS_AVAILABLE_VARS[col]
+            return {"units": unit, "long_name": desc}
+        return {}
+
+    data_vars = {
+        col: (["id", "time"], np.vstack(rows), _var_attrs(col))
+        for col, rows in var_arrays.items()
+    }
+
+    attrs = {
+        "title":       f"OpenMesh {source.upper()} sample (multi-station)",
+        "source":      source,
+        "start_date":  t0.strftime("%Y-%m-%d"),
+        "end_date":    t1.strftime("%Y-%m-%d"),
+        "time_range":  f"{t0.isoformat()} / {t1.isoformat()}",
+        "conventions": "OpenSense v1.0",
+    }
+    if extra_attrs:
+        attrs.update(extra_attrs)
+
+    return xr.Dataset(
+        data_vars,
+        coords={
+            "id":   ("id",   ids),
+            "time": ("time", all_times),
+            "lat":  ("id",   lats,  {"units": "degrees_north"}),
+            "lon":  ("id",   lons,  {"units": "degrees_east"}),
+            "elev": ("id",   elevs, {"units": "m"}),
+        },
+        attrs=attrs,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Save
+# ---------------------------------------------------------------------------
+
+def _compress_encoding(ds: xr.Dataset, level: int = 4) -> dict:
+    return {
+        name: {"zlib": True, "complevel": level}
+        for name, var in ds.data_vars.items()
+        if var.dtype.kind in {"f", "i", "u"}
+    }
+
+
+def save_opensense_dataset(
+    ds: xr.Dataset,
+    output_dir: Union[str, Path] = SAMPLE_DIR,
+    filename: Optional[str] = None,
+    compression_level: int = 4,
+    replace: bool = False,
+    verbose: bool = True,
+) -> Path:
+    """Save an OpenSense xr.Dataset to a compressed NetCDF file.
+
+    Parameters
+    ----------
+    ds : xr.Dataset
+    output_dir : path-like
+    filename : str or None   auto-generated from attrs if None
+    compression_level : int
+    verbose : bool
+
+    Returns
+    -------
+    Path
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    if filename is None:
+        source    = ds.attrs.get("source", "ws").upper()
+        start_str = ds.attrs.get("start_date", "unknown")
+        end_str   = ds.attrs.get("end_date",   "unknown")
+        filename  = f"{source}_{start_str}_{end_str}.nc"
+
+    dst_path = output_dir / filename
+    if dst_path.exists():
+        if not replace:
+            if verbose:
+                print(f"  Exists : {dst_path.name}  (skipped — pass replace=True to overwrite)")
+            return dst_path
+        dst_path.unlink()
+
+    ds.to_netcdf(dst_path, encoding=_compress_encoding(ds, compression_level), engine="netcdf4")
+
+    if verbose:
+        print(f"  Saved : {dst_path.name}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+
+    return dst_path
+
+
+import netCDF4 as nc4
+from typing import Union
+
+
+def save_asos_grouped(
+    data: dict[str, pd.DataFrame],
+    meta: pd.DataFrame,
+    output_dir: Union[str, Path] = DATA_DIR / "raw",
+    filename: str = "asos_nyc.nc",
+    verbose: bool = True,
+) -> Path:
+    """Save ASOS data as grouped NetCDF mirroring pws_wu_os.nc structure exactly.
+    One group per station, dims (id=1, time), same variable layout as PWS.
+    Uses precip_amount/precip_rate instead of rainfall_* (ASOS = all precip types).
+    """
+    import netCDF4 as nc4
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    dst_path = output_dir / filename
+    if dst_path.exists():
+        dst_path.unlink()
+
+    # reference epoch — use earliest timestamp across all stations
+    all_times  = pd.DatetimeIndex(
+        sorted(set().union(*[df.index.tolist() for df in data.values()]))
+    )
+    epoch      = all_times[0].strftime("%Y-%m-%d %H:%M:%S")
+    time_units = f"minutes since {epoch}"
+
+    with nc4.Dataset(str(dst_path), "w", format="NETCDF4") as root:
+
+        # ── global attributes (mirrors pws_wu_os.nc) ─────────────
+        root.title              = "NOAA ASOS 1-min Precipitation Data — NYC"
+        root.file_author        = "OpenMesh"
+        root.institution        = "NOAA / Iowa Environmental Mesonet (IEM)"
+        root.date               = pd.Timestamp.now().strftime("%Y-%m-%d")
+        root.source             = "NOAA ASOS 1-min via Iowa Environmental Mesonet"
+        root.history            = f"{pd.Timestamp.now().strftime('%Y-%m-%d')}: Converted to OpenSense-PWS-1.0 netCDF4 format."
+        root.naming_convention  = "OpenSense-PWS-1.0"
+        root.license_restrictions = "Public domain (NOAA)"
+        root.reference          = "https://github.com/OpenSenseAction/OS_data_format_conventions/blob/main/netCDF_PWS.adoc"
+        root.comment            = (
+            "ASOS airport stations in NYC area. "
+            "precip_amount/precip_rate used instead of rainfall_* — "
+            "ASOS captures all precipitation types (rain, snow, etc.). "
+            "Each station stored in separate netCDF4 group. "
+            "All timestamps in UTC."
+        )
+        root.Conventions        = "OpenSense-PWS-v1.0"
+
+        for sid, df in data.items():
+            grp = root.createGroup(sid)
+            grp.createDimension("id",   1)
+            grp.createDimension("time", len(df))
+
+            # ── time ─────────────────────────────────────────────
+            t_var           = grp.createVariable("time", "i8", ("time",))
+            t_var.long_name = "time_utc"
+            t_var.calendar  = "proleptic_gregorian"
+            t_var.units     = time_units
+            t_var[:]        = nc4.date2num(
+                df.index.to_pydatetime(), units=time_units, calendar="proleptic_gregorian"
+            )
+
+            # ── id ───────────────────────────────────────────────
+            id_var           = grp.createVariable("id", str, ("id",))
+            id_var.long_name = "asos_station_identifier"
+            id_var[0]        = sid
+
+            # ── coordinates ──────────────────────────────────────
+            lat  = float(meta.loc[sid, "Latitude"])  if sid in meta.index else float("nan")
+            lon  = float(meta.loc[sid, "Longitude"]) if sid in meta.index else float("nan")
+            elev = float(meta.loc[sid, "Elevation"]) if sid in meta.index else float("nan")
+
+            lat_var           = grp.createVariable("lat",  "f8", ("id",), fill_value=float("nan"))
+            lat_var.units     = "degrees_in_WGS84_projection"
+            lat_var.long_name = "latitude"
+            lat_var[0]        = lat
+
+            lon_var           = grp.createVariable("lon",  "f8", ("id",), fill_value=float("nan"))
+            lon_var.units     = "degrees_in_WGS84_projection"
+            lon_var.long_name = "longitude"
+            lon_var[0]        = lon
+
+            elev_var           = grp.createVariable("elev", "f8", ("id",), fill_value=float("nan"))
+            elev_var.units     = "metres_above_sea"
+            elev_var.long_name = "ground_elevation_above_sea_level"
+            elev_var[0]        = elev
+
+            # ── data variables ───────────────────────────────────
+            VAR_DEFS = {
+                "precip_amount": {"units": "mm",          "long_name": "precip_amount_per_time_unit"},
+                "precip_rate":   {"units": "mm hr-1",     "long_name": "precipitation_rate"},
+                "temperature":   {"units": "degrees_celsius", "long_name": "air_temperature"},
+                "wind_speed":    {"units": "ms-1",        "long_name": "wind_speed"},
+                "wind_direction":{"units": "degrees",     "long_name": "wind_direction"},
+            }
+
+            for col, vattrs in VAR_DEFS.items():
+                if col not in df.columns:
+                    continue
+                var             = grp.createVariable(
+                    col, "f8", ("id", "time"), fill_value=float("nan")
+                )
+                var.units       = vattrs["units"]
+                var.long_name   = vattrs["long_name"]
+                var.coordinates = "elev lat lon"
+                var[0, :]       = df[col].values.astype("float64")
+
+    if verbose:
+        print(f"  Saved grouped : {dst_path}  ({dst_path.stat().st_size / 1e6:.2f} MB)")
+        with nc4.Dataset(str(dst_path), "r") as root:
+            groups = list(root.groups.keys())
+            first  = root.groups[groups[0]]
+            print(f"  Groups        : {groups}")
+            print(f"  Variables     : {list(first.variables.keys())}")
+            print(f"  Dimensions    : { {k: v.size for k, v in first.dimensions.items()} }")
+
+    return dst_path
+
diff --git a/OpenMesh/openmesh_asos_ws_20d.nc b/OpenMesh/openmesh_asos_ws_20d.nc
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diff --git a/README.md b/README.md
index 6ca0371..45b5657 100644
--- a/README.md
+++ b/README.md
@@ -17,4 +17,24 @@ The data will be downloaded by specific functions in `poligrain` (still to be ad
 
 ## Available data
 
-...to be added
+### OpenMRG
+CML and gauge data from Gothenburg, Sweden, covering a summer 2015 event. Includes received
+signal level from commercial microwave links, city rain gauges, SMHI gauges, and weather radar.
+Full dataset: Andersson et al. 2022, Zenodo.
+
+### OpenRainER
+CML and gauge data from a European network, covering an 8-day period. Includes received signal
+level from commercial microwave links and rain gauge reference measurements.
+Full dataset: Zenodo (10.5281/zenodo.10593848).
+
+### AMS PWS
+Personal weather station data from Amsterdam, Netherlands. Includes rainfall and meteorological
+measurements from citizen weather stations alongside reference gauge data.
+
+### OpenMesh
+CML and PWS data from New York City, covering January 2024. Includes received signal level from
+75 commercial microwave links (NYC Mesh network) and rainfall, temperature, humidity, wind, and
+pressure from 35 Weather Underground personal weather stations. Also includes ASOS airport
+reference station data (JFK, EWR, LGA) for validation.
+The full repository with detailed fetching functions and examples is available at
+https://github.com/drorjac/OpenMesh