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create initial version of notebook
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‎sitcomtn-179.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d7d70254-1e00-47f6-843b-e93d03ebb845",
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"metadata": {},
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"source": [
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"# Characterizing the Influence of Wind Speed and Direction on Portable and Fixed DIMM seeing measurements\n",
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"\n",
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"This is the companion notebook associated to TechNote sitcomtn-179"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4dc15ebc-b48f-4e3c-a290-665fb7ae2340",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import os \n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"from astropy.time import Time\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"from lsst_efd_client import EfdClient\n",
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"from lsst.summit.utils.efdUtils import getEfdData"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bbcbfc14-803d-4c52-8823-27979a847aee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Some initialization\n",
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"resample = '3min' # DIMMs are providing measurements every ~1 minutes, we resample this to decrease the noise \n",
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"EFD_client = EfdClient(\"usdf_efd\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fb64dd84-9913-4cfb-8b15-2fda871d00fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create directory for plots if it doesn't already exist\n",
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"if not os.path.isdir(\"plots\"):\n",
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" os.mkdir(\"plots\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3f29a4d3-dfe2-4407-be62-f51a31418d08",
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"metadata": {},
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"source": [
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"## Some interesting DIMM data taking periods corresponding to different wind speeds and directions\n",
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"\n",
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" - 2025-11-27 -28: Wind speed > 4 m/s - Wind from NE\n",
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" - 2025-12-02 -03: Wind speed < 4 m/s = Wind direction mostly from NE - Somme occurrences of very low wind speed\n",
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" - 2025-12-03 -04: Decreasing wind speed - Wind direction from S\n",
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" - 2025-12-28 -29: Low wind speed - Changing direction\n",
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" - 2026-01-01 -02: Both DIMM disagree - Wind speed between 1 and 5 m/s - Wind direction from SSE\n",
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" - 2026-01-03 -04: Wind stops and change of direction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "019cca4d-cd8e-4bd6-946a-423517ef14eb",
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"metadata": {},
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"outputs": [],
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"source": [
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"t_start = Time(\"2026-01-03 22:00:00\")\n",
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"t_end = Time(\"2026-01-04 12:00:00\")\n",
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"\n",
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"df_dimm_meas = getEfdData(\n",
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" EFD_client, \"lsst.sal.DIMM.logevent_dimmMeasurement\", begin=t_start, end=t_end)\n",
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"df_dimm_meas.dropna(inplace=True)\n",
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"df_dimm_meas = df_dimm_meas[df_dimm_meas[\"fwhm\"]<5] # Drop abnormally high fwhm\n",
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"\n",
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"df_ess_airflow = getEfdData(\n",
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" EFD_client, \"lsst.sal.ESS.airFlow\", begin=t_start, end=t_end\n",
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")\n",
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"\n",
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"# Determine the time window where we actually get DIMM measurements\n",
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"start_dimm = df_dimm_meas.index[0]\n",
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"end_dimm = df_dimm_meas.index[-1]\n",
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"\n",
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"# Get wind measurements - salIndex 301 corresponds to the weather tower located on the calibration hill\n",
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"airflow = df_ess_airflow[start_dimm:end_dimm][df_ess_airflow[\"salIndex\"] == 301]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8e649bf8-c008-4716-af77-f5fb012c3052",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define cut_1 and cut_2 to select DIMM-1 and DIMM-2 according to the salIndex\n",
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"cut_1 = df_dimm_meas[\"salIndex\"] == 1\n",
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"cut_2 = df_dimm_meas[\"salIndex\"] == 2\n",
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"\n",
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"# Determine the time window where we actually get DIMM measurements\n",
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"start_dimm = df_dimm_meas.index[0]\n",
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"end_dimm = df_dimm_meas.index[-1]\n",
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"\n",
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"# Compute resampled wind direction correctly (handling 0-360 boundary)\n",
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"sin_mean = airflow['direction'].apply(lambda x: np.sin(np.radians(x))).resample(resample).mean()\n",
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"cos_mean = airflow['direction'].apply(lambda x: np.cos(np.radians(x))).resample(resample).mean()\n",
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"wind_direction = (np.degrees(np.arctan2(sin_mean, cos_mean)) + 360) % 360\n",
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"\n",
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"# Build the combined dataframe\n",
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"df = pd.concat([\n",
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" airflow[\"speed\"].resample(resample).mean() .rename(\"wind_speed\"),\n",
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" wind_direction .rename(\"wind_direction\"),\n",
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" df_dimm_meas[\"fwhm\"][cut_1].resample(resample).mean() .rename(\"dimm_1_fwhm\"),\n",
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" df_dimm_meas[\"fwhm\"][cut_2].resample(resample).mean() .rename(\"dimm_2_fwhm\"),\n",
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" df_dimm_meas[\"secz\"][cut_1].resample(resample).mean() .rename(\"dimm_1_airmass\"),\n",
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" df_dimm_meas[\"secz\"][cut_2].resample(resample).mean() .rename(\"dimm_2_airmass\"),\n",
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"], axis=1)\n",
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"\n",
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"# Drop rows where dimm_1 or dimm_2 have no values\n",
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"df = df.dropna(subset=[\"dimm_1_fwhm\", \"dimm_2_fwhm\"], how=\"any\")\n",
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"\n",
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"t_min = df.index[0]\n",
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"t_max = df.index[-1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "139c8f0f-fa3e-4f10-acf4-91242b12908f",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(2, 1, dpi=128, figsize=(8, 6))\n",
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"\n",
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"ax[0].scatter(df.index, df[\"dimm_1_fwhm\"], marker=\"o\", s=2, color=\"red\", label=\"DIMM 1\")\n",
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"ax[0].scatter(df.index, df[\"dimm_2_fwhm\"], marker=\"o\", s=2, color=\"blue\", label=\"DIMM 2\")\n",
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"ax[0].set_xlabel(\"Time\")\n",
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"ax[0].set_ylabel(\"FWHM (arcsec)\")\n",
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"ax[0].legend()\n",
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"\n",
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"Q0 = ax[1].quiver(df.index, df[\"wind_speed\"], df[\"wind_speed\"] , df[\"wind_speed\"], \n",
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" angles=270.0-df[\"wind_direction\"], scale=15, scale_units=\"y\", width=0.0005, color=\"green\") \n",
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"qk0 = ax[1].quiverkey(Q0, 0.1, 0.9, 5, '5 m/s', labelpos='E', coordinates='axes', color=\"green\")\n",
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"ax[1].set_xlabel(\"Time\")\n",
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"ax[1].set_ylabel(\"Wind Speed (m/s)\")\n",
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"\n",
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"# Add date\n",
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"date_start = t_min.strftime(\"%Y-%m-%d\")\n",
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"date_end = t_max.strftime(\"%Y-%m-%d\")\n",
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"\n",
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"fig.suptitle(f\"FWHM(DIMM_1) -- FWHM(DIMM_2) (arcsec) - Wind speed (m/s) - {date_start} to {date_end}\")\n",
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"\n",
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"fig.tight_layout()\n",
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"plt.savefig(f\"plots/dimm-1_dimm2-wind_{date_start}-{date_end}.png\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4cebf87e-55db-4053-8321-36ab95f114c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1, 1, dpi=128, figsize=(8, 5))\n",
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"\n",
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"ax.scatter(df[\"wind_speed\"], df[\"dimm_1_fwhm\"] - df[\"dimm_2_fwhm\"], marker=\"o\", s=10, color=\"green\")\n",
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"ax.set_ylabel(\"Dimm_1 - Dimm_2 (arcsec)\")\n",
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"ax.set_xlabel(\"Wind speed (m/s)\")\n",
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"#ax.set_xlim([0., 12.])\n",
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"ax.set_ylim([-0.5, 1])\n",
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"\n",
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"# Add date\n",
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"date_start = t_min.strftime(\"%Y-%m-%d\")\n",
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"date_end = t_max.strftime(\"%Y-%m-%d\")\n",
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"\n",
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"fig.suptitle(f\"{date_start} to {date_end}\")\n",
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"\n",
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"fig.tight_layout()\n",
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"plt.savefig(f\"plots/dimm_1-dimm_2-Vs-wind_res_{resample}_{date_start}-{date_end}.png\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3e40d470-efe2-444b-b810-2dbdc8e0a5aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot DIMM-1 - DIMM-2 in polar coordinates as a function of wind_direction\n",
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"# color scale is wind_speed\n",
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"\n",
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"wind_direction_rad = np.deg2rad(df[\"wind_direction\"])\n",
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"\n",
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"fig = plt.figure(figsize=(10,7))\n",
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"ax = fig.add_subplot(111, polar=True)\n",
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"\n",
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"# Compass-like orientation\n",
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"ax.set_theta_zero_location(\"N\") # 0° at North\n",
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"ax.set_theta_direction(-1) # Clockwise\n",
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"\n",
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"# Set grid labels in degrees instead of radians\n",
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"ax.set_thetagrids(range(0, 360, 30)) # every 30°\n",
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"\n",
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"\n",
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"# Plot histogram bars on polar axis\n",
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"sc = ax.scatter(wind_direction_rad, df[\"dimm_1_fwhm\"]-df[\"dimm_2_fwhm\"],\n",
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" c=df[\"wind_speed\"], cmap='viridis', s=20)\n",
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"ax.set_rlabel_position(90)\n",
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"\n",
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"# Add a colorbar\n",
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"cbar = plt.colorbar(sc, ax=ax, pad=0.1)\n",
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"cbar.set_label(\"Wind speed (m/s)\")\n",
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"\n",
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"# Add date\n",
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"date_start = t_min.strftime(\"%Y-%m-%d\")\n",
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"date_end = t_max.strftime(\"%Y-%m-%d\")\n",
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"\n",
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"fig.suptitle(f\"FWHM(DIMM_1)- FWHM(DIMM_2) (arcsec) / {date_start} to {date_end}\")\n",
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"fig.tight_layout()\n",
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"plt.savefig(f\"plots/dimm_diff_polar_{date_start}-{date_end}.png\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d7610af4-50b2-4352-800d-75ae76e39a52",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot DIMM-1 and DIMM-2 FWHM in polar coordinates as a function of wind direction\n",
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"# color scale is wind_speed\n",
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"\n",
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"wind_direction_rad = np.deg2rad(df[\"wind_direction\"])\n",
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"\n",
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"fig = plt.figure(figsize=(15,12))\n",
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"\n",
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"ax = []\n",
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"pos = [221, 222]\n",
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"for i in [0,1]:\n",
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" ax.append(fig.add_subplot(int(pos[i]), polar=True))\n",
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" \n",
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" # Compass-like orientation\n",
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" ax[i].set_theta_zero_location(\"N\") # 0° at North\n",
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" ax[i].set_theta_direction(-1) # Clockwise\n",
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" \n",
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" # Set grid labels in degrees instead of radians\n",
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" ax[i].set_thetagrids(range(0, 360, 30)) # every 30°\n",
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" \n",
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" \n",
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" # Plot histogram bars on polar axis\n",
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" sc = ax[i].scatter(wind_direction_rad, df[f\"dimm_{i+1}_fwhm\"],\n",
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" c=df[\"wind_speed\"], cmap='viridis', s=20)\n",
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" ax[i].set_rlim(0.4, 1.5) \n",
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" ax[i].set_rlabel_position(90)\n",
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" ax[i].set_title(f\"FWHM DIMM-{i+1} (arcsec)\")\n",
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" \n",
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" # Add a colorbar\n",
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" cbar = plt.colorbar(sc, ax=ax[i], pad=0.1)\n",
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" cbar.set_label(\"Wind speed (m/s)\")\n",
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" \n",
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" # Add date\n",
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" date_start = t_min.strftime(\"%Y-%m-%d\")\n",
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" date_end = t_max.strftime(\"%Y-%m-%d\")\n",
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" \n",
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" fig.suptitle(f\"{date_start} to {date_end}\")\n",
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"fig.tight_layout(rect=[0, 0, 1, 0.97])\n",
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"plt.savefig(f\"plots/DIMM-1_DIMM-2_polar_{date_start}-{date_end}.png\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a7fa860-2852-4df9-ad95-fa48dc7c7a90",
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"metadata": {},
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"source": [
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"## Check boresight of bot DIMM\n",
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"\n",
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"Both DIMM are operating independently so there is no reason for them to point to the same target. \n",
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"\n",
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"Unfortunately we cannot get the reference star coordinates in the EFD (values are wrong). \n",
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"\n",
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"In order to compare both DIMM pointings we use the airmass as a proxy and we assume that if the airmass is the same for both DIMM then they\n",
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"are very likely pointing to the same reference star."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "89839252-7f37-4a4c-8b72-90bc4ccc3496",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1, 1, dpi=128, figsize=(12, 5))\n",
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"ax.scatter(df_dimm_meas.index[cut_1], df_dimm_meas[\"secz\"][cut_1], marker=\"o\", s=2, color=\"red\", label=\"DIMM 1\", alpha=0.5)\n",
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"ax.scatter(df_dimm_meas.index[cut_2], df_dimm_meas[\"secz\"][cut_2]+0.004, marker=\"o\", s=2, color=\"blue\", label=\"DIMM 2\", alpha=0.5)\n",
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"ax.set_xlabel(\"Time\")\n",
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"ax.set_ylabel(\"Airmass\")\n",
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"ax.legend()\n",
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"fig.tight_layout(rect=[0, 0, 1, 0.97])\n",
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"plt.savefig(f\"plots/airmass_{t_start.strftime(\"%Y-%m-%d\")}-{t_end.strftime(\"%Y-%m-%d\")}.png\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "LSST",
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"language": "python",
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"name": "lsst"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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