{ "cells": [ { "cell_type": "code", "execution_count": 74, "id": "ea414770-9f9a-42f8-a7d8-25ac6983d76c", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import dask.dataframe as dd\n", "\n", "import numpy as np\n", "import yaml\n", "import matplotlib.pyplot as plt\n", "from sp2xr.toolkit_legacy import read_xr_ini_file, bin_lims_to_ctrs, process_hist_and_dist\n", "from sp2xr.calibration import polynomial, powerlaw" ] }, { "cell_type": "markdown", "id": "c80d37dc-f526-426c-8083-a78e791821f9", "metadata": {}, "source": [ "# Old processing" ] }, { "cell_type": "code", "execution_count": 27, "id": "5dd8ee52-9032-4f3b-8327-a0561ac3576e", "metadata": {}, "outputs": [], "source": [ "new_config_run = yaml.safe_load(open('tests/run_config.yaml'))" ] }, { "cell_type": "code", "execution_count": 28, "id": "8d7cc57e-5b88-4388-9303-7777bad73e7f", "metadata": {}, "outputs": [], "source": [ "dir_path_pbp = '/data/user/bertoz_b/merlin6data/SP2XR_code/tests/data/pbp_files_test/date=2019-05-07'\n", "dir_path_hk = '/data/user/bertoz_b/merlin6data/SP2XR_code/tests/data/hk_files_test/date=2019-05-07'\n", "\n", "config_path = '/data/user/bertoz_b/merlin6data/SP2XR_code/meta_files/20190417210227 Calibration 20181005.ini'" ] }, { "cell_type": "code", "execution_count": 29, "id": "0225b191-7066-4840-8acb-ecac239d66d0", "metadata": {}, "outputs": [], "source": [ "dt=1\n", "rho_eff=1800\n", "BC_type=\"constant_effective_density\"\n", "inc_calib_curve=new_config_run['calibration']['incandescence']['curve_type']\n", "inc_calib_params=new_config_run['calibration']['incandescence']['parameters']\n", "scatt_calib_curve=new_config_run['calibration']['scattering']['curve_type']\n", "scatt_calib_params=new_config_run['calibration']['scattering']['parameters']\n", "config_file_dir=config_path\n", "minM=0.3\n", "maxM=400\n", "n_incbins=50\n", "minOptD=100\n", "maxOptD=500\n", "n_scattbins=20\n", "minTL=-10\n", "maxTL=400\n", "n_timelag=100" ] }, { "cell_type": "code", "execution_count": 38, "id": "e01462f6-3f2f-44f5-967a-6e63fd2df3c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'polynomial'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inc_calib_curve" ] }, { "cell_type": "code", "execution_count": 39, "id": "c29a6acb-581b-415f-8f41-e5a7447654e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "polynomial curve applied\n" ] } ], "source": [ "read_dir_pbp = dir_path_pbp\n", "read_dir_hk = dir_path_hk\n", "ddf_pbp = pd.read_parquet(\n", " read_dir_pbp\n", ") # , calculate_divisions=True)#.repartition(freq='1h')\n", "ddf_hk = pd.read_parquet(\n", " read_dir_hk\n", ") # , calculate_divisions=True)#.repartition(freq='1h')\n", "\n", "ini_params = read_xr_ini_file(config_path)\n", "ini_params[\"IncSatPoint\"] = (\n", " 1.7e9 # 2e9 # Guess for now, need to see where to obtain this from. 2e9 is probably better guess\n", ")\n", "ini_params[\"ScattSatPoint\"] = (\n", " 1.7e9 # Guess for now, need to see where to obtain this from. 1.85e9 is probably better\n", ")\n", "ini_params[\"DelayTimeThresh\"] = 200 # Also a guess for now, to be updated\n", "\n", "inc_mass_bin_lims = np.logspace(np.log10(minM), np.log10(maxM), n_incbins)\n", "inc_mass_bin_lims_for_pd_cut = np.logspace(\n", " np.log10(minM), np.log10(maxM + 0.01), n_incbins\n", ")\n", "inc_mass_bin_ctrs = bin_lims_to_ctrs(inc_mass_bin_lims)\n", "\n", "dt_str = f\"{dt}s\"\n", "\n", "# ***** ***** *****\n", "# Apply calibration\n", "# ***** ***** *****\n", "\n", "if inc_calib_curve:\n", " if inc_calib_curve == \"polynomial\":\n", " print('polynomial curve applied')\n", " ddf_pbp[\"BC mass\"] = ddf_pbp[\"Incand relPeak\"].apply(\n", " lambda x: polynomial(x, *inc_calib_params)\n", " ) # , meta=('BC mass', 'float64'))\n", " # minM_timelag = polynomial(5e6, *inc_calib_params)\n", " elif inc_calib_curve == \"powerlaw\":\n", " ddf_pbp[\"BC mass\"] = ddf_pbp[\"Incand relPeak\"].apply(\n", " lambda x: powerlaw(x, inc_calib_params)\n", " ) # , meta=('BC mass', 'float64'))\n", " # minM_timelag = powerlaw(5e6, inc_calib_params)\n", "else:\n", " ddf_pbp[\"BC mass\"] = ddf_pbp[\"Incand Mass (fg)\"]\n", " # minM_timelag = ddf_pbp.loc[\n", " # (ddf_pbp[\"Incand relPeak\"] <= 5.05e6)\n", " # & (ddf_pbp[\"Incand relPeak\"] >= 4.95e6),\n", " # \"Incand Mass (fg)\",\n", " # ].mean() # this could create problems if there are no incandescence signals in the selected range\n", "\n", "ddf_pbp.loc[ddf_pbp[\"Incand relPeak\"] == 0, \"BC mass\"] = np.nan\n", "\n", "ddf_pbp[\"BC mass bin\"], mass_bins = pd.cut(\n", " ddf_pbp[\"BC mass\"],\n", " bins=inc_mass_bin_lims_for_pd_cut,\n", " right=False,\n", " include_lowest=True,\n", " retbins=True,\n", ")\n", "\n", "if scatt_calib_curve:\n", " if scatt_calib_curve == \"polynomial\":\n", " ddf_pbp[\"Opt diam\"] = ddf_pbp[\"Scatter relPeak\"].apply(\n", " lambda x: polynomial(x, *scatt_calib_params)\n", " ) # , meta=('Opt diam', 'float64'))\n", " elif scatt_calib_curve == \"powerlaw\":\n", " ddf_pbp[\"Opt diam\"] = ddf_pbp[\"Scatter relPeak\"].apply(\n", " lambda x: powerlaw(x, *scatt_calib_params)\n", " ) # , meta=('Opt diam', 'float64'))\n", "else:\n", " ddf_pbp[\"Opt diam\"] = ddf_pbp[\"Scatter Size (nm)\"]\n", "\n", "ddf_pbp.loc[ddf_pbp[\"Scatter relPeak\"] == 0, \"Opt diam\"] = np.nan\n", "\n", "ddf_pbp[\"time_lag_new\"] = ddf_pbp[\"Incand Peak Time\"] - ddf_pbp[\"Scatter Peak Time\"]" ] }, { "cell_type": "code", "execution_count": 48, "id": "6bb7dc88-4fa7-4f4e-a86a-1b5fd4eba1bf", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/data/user/bertoz_b/merlin6data/SP2XR_code/.venv/lib/python3.9/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: divide by zero encountered in log10\n", " result = getattr(ufunc, method)(*inputs, **kwargs)\n" ] } ], "source": [ "# ***** ***** ***** *****\n", "# Set flags\n", "# ***** ***** ***** *****\n", "flag_inc_transit_time = (\n", " ddf_pbp[\"Incand Transit Time\"] >= ini_params[\"IncTransitMin\"]\n", ") & (ddf_pbp[\"Incand Transit Time\"] <= ini_params[\"IncTransitMax\"])\n", "flag_inc_fwhm = (ddf_pbp[\"Incand FWHM\"] >= ini_params[\"IncFWHMMin\"]) & (\n", " ddf_pbp[\"Incand FWHM\"] <= ini_params[\"IncFWHMMax\"]\n", ")\n", "# flag_inc_not_sat = ddf_pbp[\"Incand relPeak\"] < ini_params[\"IncSatPoint\"]\n", "\n", "flag_scatt_transit_time = (\n", " ddf_pbp[\"Scatter Transit Time\"] >= ini_params[\"ScattTransitMin\"]\n", ") & (ddf_pbp[\"Scatter Transit Time\"] <= ini_params[\"ScattTransitMax\"])\n", "flag_scatt_fwhm = (ddf_pbp[\"Scatter FWHM\"] >= ini_params[\"ScattFWHMMin\"]) & (\n", " ddf_pbp[\"Scatter FWHM\"] <= ini_params[\"ScattFWHMMax\"]\n", ")\n", "flag_scatt_not_sat = ddf_pbp[\"Scatter relPeak\"] < ini_params[\"ScattSatPoint\"]\n", "\n", "flag_inc = flag_inc_transit_time & flag_inc_fwhm\n", "flag_inc_in_range = (\n", " flag_inc & (ddf_pbp[\"BC mass\"] >= minM) & (ddf_pbp[\"BC mass\"] <= maxM)\n", ")\n", "# flag_inc_in_range_tl_analysis = (\n", "# flag_inc & (ddf_pbp[\"BC mass\"] >= minM_timelag) & (ddf_pbp[\"BC mass\"] <= maxM)\n", "# )\n", "\n", "flag_scatt = flag_scatt_transit_time & flag_scatt_fwhm\n", "flag_scatt_in_range = (\n", " flag_scatt & (ddf_pbp[\"Opt diam\"] >= minOptD) & (ddf_pbp[\"Opt diam\"] <= maxOptD)\n", ")\n", "\n", "flag_negative_timelag = ddf_pbp[\"time_lag_new\"] < -10\n", "flag_extreme_positive_timelag = ddf_pbp[\"time_lag_new\"] >= 400\n", "flag_timelag_0_50 = (ddf_pbp[\"time_lag_new\"] < 50) & (\n", " ddf_pbp[\"time_lag_new\"] >= -10\n", ")\n", "flag_timelag_greater_50 = (ddf_pbp[\"time_lag_new\"] >= 50) & (\n", " ddf_pbp[\"time_lag_new\"] < 400\n", ")\n", "\n", "ddf_pbp[\"ratio_inc_scatt\"] = np.log10(ddf_pbp[\"Incand relPeak\"]) / np.log10(\n", " ddf_pbp[\"Scatter relPeak\"]\n", ")\n", "flag_low_ratio_inc_scatt = ddf_pbp[\"ratio_inc_scatt\"] < 1.1\n", "\n", "# ***** ***** ***** ***** ***** ***** *****\n", "# Create column for purely scattering particle analysis\n", "# ***** ***** ***** ***** ***** ***** *****\n", "ddf_pbp[\"Opt diam scatt only\"] = ddf_pbp[\"Opt diam\"].where(\n", " flag_scatt & ~flag_inc, np.nan\n", ") # ddf_pbp['Opt diam'].mask(ddf_pbp['BC mass'].notnull(), np.nan)\n", "\n", "# ***** ***** *****\n", "# Set flag for thin/thick\n", "# ***** ***** *****\n", "\n", "ddf_pbp[\"cnts_thin\"] = 0\n", "ddf_pbp[\"cnts_thin_noScatt\"] = 0\n", "ddf_pbp[\"cnts_thick\"] = 0\n", "ddf_pbp[\"cnts_thick_sat\"] = 0\n", "ddf_pbp[\"cnts_thin_sat\"] = 0\n", "ddf_pbp[\"cnts_ntl_sat\"] = 0\n", "ddf_pbp[\"cnts_ntl\"] = 0\n", "ddf_pbp[\"cnts_extreme_positive_timelag\"] = 0\n", "ddf_pbp[\"cnts_thin_low_inc_scatt_ratio\"] = 0\n", "ddf_pbp[\"cnts_particles_for_tl_dist\"] = (\n", " 0 # this flag is used for the calculation of time lag distributions below (it includes the particles classfified as \"thin\" or \"thick\")\n", ")\n", "\n", "ddf_pbp.loc[~flag_scatt & flag_inc_in_range, \"cnts_thin_noScatt\"] = 1\n", "ddf_pbp.loc[\n", " flag_scatt\n", " & flag_scatt_not_sat\n", " & flag_inc_in_range\n", " & flag_timelag_0_50\n", " & ~flag_low_ratio_inc_scatt,\n", " \"cnts_thin\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt\n", " & flag_scatt_not_sat\n", " & flag_inc_in_range\n", " & flag_timelag_0_50\n", " & flag_low_ratio_inc_scatt,\n", " \"cnts_thin_low_inc_scatt_ratio\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt\n", " & flag_scatt_not_sat\n", " & flag_inc_in_range\n", " & flag_timelag_greater_50\n", " & ~flag_extreme_positive_timelag,\n", " \"cnts_thick\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt\n", " & ~flag_scatt_not_sat\n", " & flag_inc_in_range\n", " & flag_timelag_greater_50\n", " & ~flag_extreme_positive_timelag,\n", " \"cnts_thick_sat\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt & ~flag_scatt_not_sat & flag_inc_in_range & flag_timelag_0_50,\n", " \"cnts_thin_sat\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt & ~flag_scatt_not_sat & flag_inc_in_range & flag_negative_timelag,\n", " \"cnts_ntl_sat\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt & flag_scatt_not_sat & flag_inc_in_range & flag_negative_timelag,\n", " \"cnts_ntl\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt & flag_inc_in_range & flag_extreme_positive_timelag,\n", " \"cnts_extreme_positive_timelag\",\n", "] = 1\n", "ddf_pbp.loc[\n", " flag_scatt\n", " & flag_scatt_not_sat\n", " & flag_inc_in_range\n", " & (\n", " (flag_timelag_0_50 & ~flag_low_ratio_inc_scatt)\n", " | (flag_timelag_greater_50 & ~flag_extreme_positive_timelag)\n", " ),\n", " \"cnts_particles_for_tl_dist\",\n", "] = 1\n", "\n", "ddf_pbp[\"cnts_thin_total\"] = ddf_pbp[\"cnts_thin\"] + ddf_pbp[\"cnts_thin_noScatt\"]\n", "ddf_pbp[\"cnts_thick_total\"] = (\n", " ddf_pbp[\"cnts_thick\"]\n", " + ddf_pbp[\"cnts_thick_sat\"]\n", " + ddf_pbp[\"cnts_ntl_sat\"]\n", " + ddf_pbp[\"cnts_ntl\"]\n", " + ddf_pbp[\"cnts_thin_sat\"]\n", ")\n", "ddf_pbp[\"cnts_unclassified\"] = (\n", " ddf_pbp[\"cnts_extreme_positive_timelag\"]\n", " + ddf_pbp[\"cnts_thin_low_inc_scatt_ratio\"]\n", ")\n", "\n", "ddf_pbp[\"temporary_col\"] = (\n", " 1 # this is to keep track of time stamps that are not originally in the file but are added when i do the resampling\n", ")\n", "\n", "# Create two new columns where the particles out of range are changed to np.nan (!! do not change them to 0, otherwise below where we resample and count, the 0s would be counted being a not null value!!)\n", "ddf_pbp[\"BC mass within range\"] = ddf_pbp[\"BC mass\"].where(\n", " flag_inc_in_range, np.nan\n", ") # ddf_pbp['BC mass'].mask(mask_BC_within_range)\n", "ddf_pbp[\"Opt diam scatt only within range\"] = ddf_pbp[\"Opt diam scatt only\"].where(\n", " flag_scatt_in_range & ~flag_inc, np.nan\n", ")\n", "\n", "ddf_pbp_1s = (\n", " ddf_pbp[\n", " [\n", " \"Dropped Records\",\n", " \"Incand Mass (fg)\", # This is the original column from the file, the calibration applied is the one uploaded in the software\n", " \"BC mass\", # If calibration parameters are provided, this is the mass calculated form those parameters. If calib param are not provided this is equal to 'Incand Mass (fg)'\n", " \"BC mass within range\", # In this column only the masses within the range specified by the user are considered\n", " \"cnts_thin\",\n", " \"cnts_thin_noScatt\",\n", " \"cnts_thick\",\n", " \"cnts_thick_sat\",\n", " \"cnts_thin_sat\",\n", " \"cnts_ntl_sat\",\n", " \"cnts_ntl\",\n", " \"cnts_extreme_positive_timelag\",\n", " \"cnts_thin_low_inc_scatt_ratio\",\n", " \"cnts_thin_total\",\n", " \"cnts_thick_total\",\n", " \"cnts_unclassified\",\n", " ]\n", " ]\n", " .resample(dt_str)\n", " .sum()\n", ")\n", "\n", "ddf_pbp_1s[\n", " [\n", " \"BC numb from file\", # number of BC particles counted from the column 'Incand Mass (fg)'\n", " \"BC numb\", # number of BC particles counted from the column 'BC mass'\n", " \"BC numb within range\", # number of BC particles counted from the column 'BC mass within range'\n", " \"scatter numb from file\", # number of scattering particles counted from the column 'Scatter Size (nm)'\n", " \"Scatt numb\", # number of purely scattering particles counted from the column 'Opt diam'\n", " \"Scatt numb within range\", # number of purely scattering particles counted from the column 'Opt diam within range'\n", " \"original_idx\",\n", " ]\n", "] = (\n", " ddf_pbp[\n", " [\n", " \"Incand Mass (fg)\",\n", " \"BC mass\",\n", " \"BC mass within range\",\n", " \"Scatter Size (nm)\",\n", " \"Opt diam scatt only\",\n", " \"Opt diam scatt only within range\",\n", " \"temporary_col\",\n", " ]\n", " ]\n", " .resample(dt_str)\n", " .count()\n", ")\n", "\n", "ddf_pbp_1s[[\"Secs_2GB_mean\"]] = ddf_pbp[[\"Secs_2GB\"]].resample(dt_str).mean()\n", "\n", "# ddf_pbp_1s = ddf_pbp_1s.map_partitions(add_date_column)\n", "ddf_pbp_1s[\"date\"] = ddf_pbp_1s.index.normalize()\n", "ddf_pbp_1s[\"hour\"] = ddf_pbp_1s.index.hour\n", "\n", "ddf_pbp_1s = ddf_pbp_1s[ddf_pbp_1s[\"original_idx\"] != 0]\n", "ddf_pbp_1s = ddf_pbp_1s.drop(\"original_idx\", axis=1) # .persist()\n", "\n", "# **** **** **** **** **** **** ****\n", "# Create a 1s ddf for the hk file\n", "# **** **** **** **** **** **** ****\n", "\n", "ddf_hk[\"temporary_col\"] = 1\n", "\n", "ddf_hk_1s = (\n", " ddf_hk[\n", " [\"Sample Flow Controller Read (sccm)\", \"Sample Flow Controller Read (vccm)\"]\n", " ]\n", " .resample(dt_str)\n", " .mean()\n", ") # do i need a fill na here?\n", "ddf_hk_1s[[\"original_idx\"]] = ddf_hk[[\"temporary_col\"]].resample(dt_str).count()\n", "\n", "ddf_hk_1s = ddf_hk_1s[ddf_hk_1s[\"original_idx\"] != 0]\n", "ddf_hk_1s = ddf_hk_1s.drop(\"original_idx\", axis=1) # .persist()\n", "\n", "# **** **** **** **** **** **** ****\n", "# Create a 1s join ddf for the pbp_1s and hk_1s data\n", "# **** **** **** **** **** **** ****\n", "\n", "ddf_pbp_hk = pd.merge(\n", " ddf_pbp_1s, ddf_hk_1s, how=\"left\", left_index=True, right_index=True\n", ")\n", "# ddf_pbp_hk = ddf_pbp_hk.map_partitions(calculate_concentrations, t=dt)\n", "ddf_pbp_hk[\"BC_massConc_std\"] = (\n", " ddf_pbp_hk[\"BC mass\"]\n", " * 1e-9\n", " / (ddf_pbp_hk[\"Sample Flow Controller Read (sccm)\"] * (dt / 60) * 1e-6)\n", ")\n", "ddf_pbp_hk[\"BC_massConc_vol\"] = (\n", " ddf_pbp_hk[\"BC mass\"]\n", " * 1e-9\n", " / (ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"] * (dt / 60) * 1e-6)\n", ")\n", "\n", "ddf_pbp_hk[\"BC_numConc_std\"] = ddf_pbp_hk[\"BC numb\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (sccm)\"] * (dt / 60)\n", ")\n", "ddf_pbp_hk[\"BC_numConc_vol\"] = ddf_pbp_hk[\"BC numb\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"] * (dt / 60)\n", ")\n", "\n", "ddf_pbp_hk[\"BC_massConc_within_range_std\"] = (\n", " ddf_pbp_hk[\"BC mass within range\"]\n", " * 1e-9\n", " / (ddf_pbp_hk[\"Sample Flow Controller Read (sccm)\"] * (dt / 60) * 1e-6)\n", ")\n", "ddf_pbp_hk[\"BC_massConc_within_range_vol\"] = (\n", " ddf_pbp_hk[\"BC mass within range\"]\n", " * 1e-9\n", " / (ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"] * (dt / 60) * 1e-6)\n", ")\n", "\n", "ddf_pbp_hk[\"BC_numConc_within_range_std\"] = ddf_pbp_hk[\"BC numb within range\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (sccm)\"] * (dt / 60)\n", ")\n", "ddf_pbp_hk[\"BC_numConc_within_range_vol\"] = ddf_pbp_hk[\"BC numb within range\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"] * (dt / 60)\n", ")\n", "\n", "ddf_pbp_hk[\"S_numConc_std\"] = ddf_pbp_hk[\"Scatt numb\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (sccm)\"] * (dt / 60)\n", ")\n", "ddf_pbp_hk[\"S_numConc_vol\"] = ddf_pbp_hk[\"Scatt numb\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"] * (dt / 60)\n", ")\n", "\n", "ddf_pbp_hk[\"S_numConc_within_range_std\"] = ddf_pbp_hk[\"Scatt numb within range\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (sccm)\"] * (dt / 60)\n", ")\n", "ddf_pbp_hk[\"S_numConc_within_range_vol\"] = ddf_pbp_hk[\"Scatt numb within range\"] / (\n", " ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"] * (dt / 60)\n", ")" ] }, { "cell_type": "code", "execution_count": 75, "id": "807231f0-9925-4f41-8dec-e17ffa62dd93", "metadata": {}, "outputs": [], "source": [ "# Calculate histograms of different classifications/flags:\n", "ddf_pbp[\"temporary_col\"] = 1\n", "\n", "dNdlogDmev, dMdlogDmev = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " None,\n", " None,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_thin, dMdlogDmev_thin = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thin\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_thin_noScatt, dMdlogDmev_thin_noScatt = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thin_noScatt\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_thick, dMdlogDmev_thick = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thick\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_thick_sat, dMdlogDmev_thick_sat = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thick_sat\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_thin_sat, dMdlogDmev_thin_sat = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thin_sat\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_ntl_sat, dMdlogDmev_ntl_sat = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_ntl_sat\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_ntl, dMdlogDmev_ntl = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_ntl\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_extreme_positive_timelag, dMdlogDmev_extreme_positive_timelag = (\n", " process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_extreme_positive_timelag\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", " )\n", ")\n", "dNdlogDmev_thin_low_inc_scatt_ratio, dMdlogDmev_thin_low_inc_scatt_ratio = (\n", " process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thin_low_inc_scatt_ratio\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", " )\n", ")\n", "dNdlogDmev_thin_total, dMdlogDmev_thin_total = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thin_total\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_thick_total, dMdlogDmev_thick_total = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_thick_total\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "dNdlogDmev_unclassified, dMdlogDmev_unclassified = process_hist_and_dist(\n", " ddf_pbp,\n", " \"BC mass within range\",\n", " \"cnts_unclassified\",\n", " 1,\n", " inc_mass_bin_lims,\n", " inc_mass_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=rho_eff,\n", " BC_type=BC_type,\n", " t=dt,\n", ")\n", "\n", "scatt_bin_lims = np.logspace(np.log10(minOptD), np.log10(maxOptD), n_scattbins)\n", "scatt_bin_ctrs = bin_lims_to_ctrs(scatt_bin_lims)\n", "\n", "dNdlogDsc, _ = process_hist_and_dist(\n", " ddf_pbp,\n", " \"Opt diam scatt only\",\n", " None,\n", " None,\n", " scatt_bin_lims,\n", " scatt_bin_ctrs,\n", " dt_str,\n", " flow=ddf_pbp_hk[\"Sample Flow Controller Read (vccm)\"],\n", " rho_eff=None,\n", " BC_type=None,\n", " t=dt,\n", ")\n", "\n", "timelag_bins_lims = np.linspace(minTL, maxTL, n_timelag)\n", "timelag_bin_ctrs = bin_lims_to_ctrs(timelag_bins_lims)" ] }, { "cell_type": "code", "execution_count": 83, "id": "c51a8764-cf93-4150-baac-ec76e77064ad", "metadata": {}, "outputs": [], "source": [ "final_df = pd.concat(\n", " [\n", " ddf_pbp_hk,\n", " dNdlogDmev,\n", " dMdlogDmev,\n", " dNdlogDmev_thin,\n", " dMdlogDmev_thin,\n", " dNdlogDmev_thin_noScatt,\n", " dMdlogDmev_thin_noScatt,\n", " dNdlogDmev_thick,\n", " dMdlogDmev_thick,\n", " dNdlogDmev_thick_sat,\n", " dMdlogDmev_thick_sat,\n", " dNdlogDmev_thin_sat,\n", " dMdlogDmev_thin_sat,\n", " dNdlogDmev_ntl_sat,\n", " dMdlogDmev_ntl_sat,\n", " dNdlogDmev_ntl,\n", " dMdlogDmev_ntl,\n", " dNdlogDmev_extreme_positive_timelag,\n", " dMdlogDmev_extreme_positive_timelag,\n", " dNdlogDmev_thin_low_inc_scatt_ratio,\n", " dMdlogDmev_thin_low_inc_scatt_ratio,\n", " dNdlogDmev_thin_total,\n", " dMdlogDmev_thin_total,\n", " dNdlogDmev_thick_total,\n", " dMdlogDmev_thick_total,\n", " dNdlogDmev_unclassified,\n", " dMdlogDmev_unclassified,\n", " dNdlogDsc,\n", " ],\n", " axis=1,\n", " )\n" ] }, { "cell_type": "code", "execution_count": 65, "id": "3fd14901-a2b5-4e81-801c-1c2593c9c627", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Dropped Records', 'Incand Mass (fg)', 'BC mass',\n", " 'BC mass within range', 'cnts_thin', 'cnts_thin_noScatt', 'cnts_thick',\n", " 'cnts_thick_sat', 'cnts_thin_sat', 'cnts_ntl_sat', 'cnts_ntl',\n", " 'cnts_extreme_positive_timelag', 'cnts_thin_low_inc_scatt_ratio',\n", " 'cnts_thin_total', 'cnts_thick_total', 'cnts_unclassified',\n", " 'BC numb from file', 'BC numb', 'BC numb within range',\n", " 'scatter numb from file', 'Scatt numb', 'Scatt numb within range',\n", " 'Secs_2GB_mean', 'date', 'hour', 'Sample Flow Controller Read (sccm)',\n", " 'Sample Flow Controller Read (vccm)', 'BC_massConc_std',\n", " 'BC_massConc_vol', 'BC_numConc_std', 'BC_numConc_vol',\n", " 'BC_massConc_within_range_std', 'BC_massConc_within_range_vol',\n", " 'BC_numConc_within_range_std', 'BC_numConc_within_range_vol',\n", " 'S_numConc_std', 'S_numConc_vol', 'S_numConc_within_range_std',\n", " 'S_numConc_within_range_vol'],\n", " dtype='object')" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ddf_pbp_hk.columns" ] }, { "cell_type": "markdown", "id": "59d7f603-6a83-402b-98c1-e8e279a19167", "metadata": {}, "source": [ "# New code" ] }, { "cell_type": "code", "execution_count": 8, "id": "ac398f9f-e0a9-4798-b6d9-99d61ab4bb62", "metadata": {}, "outputs": [], "source": [ "from sp2xr.helpers import load_yaml_cfg" ] }, { "cell_type": "code", "execution_count": 6, "id": "859a5acc-8038-4cf3-bc5d-256a2539042e", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "def apply_sets(cfg, sets):\n", " def coerce(v):\n", " if v.lower() in (\"true\", \"false\"):\n", " return v.lower() == \"true\"\n", " try:\n", " if \".\" in v:\n", " return float(v)\n", " return int(v)\n", " except (ValueError, TypeError):\n", " return v\n", "\n", " for item in sets:\n", " key, _, val = item.partition(\"=\")\n", " if not _:\n", " raise ValueError(f\"--set needs key=value, got: {item}\")\n", " parts = key.split(\".\")\n", " cur = cfg\n", " for p in parts[:-1]:\n", " cur = cur.setdefault(p, {})\n", " cur[parts[-1]] = coerce(val)\n", " return cfg\n", "\n", "\n", "def get(cfg, dotted, default=None):\n", " cur = cfg\n", " for part in dotted.split(\".\"):\n", " if not isinstance(cur, dict) or part not in cur:\n", " return default\n", " cur = cur[part]\n", " return cur\n", "\n", "\n", "def choose(cli_val, cfg, dotted, default=None):\n", " return cli_val if cli_val is not None else get(cfg, dotted, default)" ] }, { "cell_type": "code", "execution_count": 36, "id": "4d65613e-50a5-4a12-a4b3-06bf1f1852d6", "metadata": {}, "outputs": [], "source": [ "base = load_yaml_cfg(\"tests/run_config.yaml\")\n", "a = get(base, \"calibration.incandescence.curve_type\", None)" ] }, { "cell_type": "code", "execution_count": 48, "id": "7701756a-ed84-4ee6-b2e1-64a98c8d646b", "metadata": {}, "outputs": [], "source": [ "pbp_raw = dd.read_parquet('/data/user/bertoz_b/merlin6data/SP2XR_code/tests/data/pbp_files_test/date=2019-05-07')" ] }, { "cell_type": "code", "execution_count": 114, "id": "707f20e0-1f79-4d68-b632-f887afb7994a", "metadata": {}, "outputs": [], "source": [ "pbp_calib = pd.read_parquet('/data/user/bertoz_b/merlin6data/SP2XR_code/tests/data3/pbp_calibrated/date=2019-05-07 00:00:00')" ] }, { "cell_type": "code", "execution_count": 115, "id": "ed06bd1c-2dae-46b8-b08f-c0507fa00674", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Time (sec)Packet Time StampFlagDropped RecordsRecord CountRecord SizeParticle Time StampParticle FlagsScatter relPeakScatter Transit Time...cnts_particles_for_tl_distcnts_thin_totalcnts_thick_totalcnts_unclassifiedBC mass within rangeOpt diam scatt only within rangetemporary_colSample Flow Controller Read (vccm)BC mass binhour
calculated_time
2019-05-07 00:00:002.246402e+062246430.750.00.047.032.02246429.751.08814524.01157.0...0000NaN255.719130133.400002NaN0
2019-05-07 00:00:002.246402e+062246430.750.00.047.032.02246429.751.058048.0310.0...0000NaN108.520037133.400002NaN0
2019-05-07 00:00:002.246402e+062246430.750.00.047.032.02246429.751.020283240.01332.0...0000NaN294.642287133.400002NaN0
2019-05-07 00:00:002.246402e+062246430.750.00.047.032.02246429.751.017956730.01407.0...0000NaN288.604383133.400002NaN0
2019-05-07 00:00:002.246402e+062246430.750.00.047.032.02246429.751.01770382.0914.0...0000NaN194.579235133.400002NaN0
..................................................................
2019-05-07 09:59:592.282400e+062282429.750.00.048.032.02282429.501.019365740.01357.0...0000NaN292.333517133.299999NaN9
2019-05-07 09:59:592.282400e+062282429.750.00.048.032.02282429.501.01585856.0874.0...0000NaN190.963786133.299999NaN9
2019-05-07 09:59:592.282400e+062282429.750.00.048.032.02282429.501.01547770.0877.0...0000NaN190.174346133.299999NaN9
2019-05-07 09:59:592.282400e+062282429.750.00.048.032.02282429.501.0663630.0774.0...0000NaN164.603875133.299999NaN9
2019-05-07 09:59:592.282400e+062282429.750.00.048.032.02282429.753.0314938.0373.0...11002.679357NaN133.29999914.09
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3432203 rows × 65 columns

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" ], "text/plain": [ " Time (sec) Packet Time Stamp Flag Dropped Records \\\n", "calculated_time \n", "2019-05-07 00:00:00 2.246402e+06 2246430.75 0.0 0.0 \n", "2019-05-07 00:00:00 2.246402e+06 2246430.75 0.0 0.0 \n", "2019-05-07 00:00:00 2.246402e+06 2246430.75 0.0 0.0 \n", "2019-05-07 00:00:00 2.246402e+06 2246430.75 0.0 0.0 \n", "2019-05-07 00:00:00 2.246402e+06 2246430.75 0.0 0.0 \n", "... ... ... ... ... \n", "2019-05-07 09:59:59 2.282400e+06 2282429.75 0.0 0.0 \n", "2019-05-07 09:59:59 2.282400e+06 2282429.75 0.0 0.0 \n", "2019-05-07 09:59:59 2.282400e+06 2282429.75 0.0 0.0 \n", "2019-05-07 09:59:59 2.282400e+06 2282429.75 0.0 0.0 \n", "2019-05-07 09:59:59 2.282400e+06 2282429.75 0.0 0.0 \n", "\n", " Record Count Record Size Particle Time Stamp \\\n", "calculated_time \n", "2019-05-07 00:00:00 47.0 32.0 2246429.75 \n", "2019-05-07 00:00:00 47.0 32.0 2246429.75 \n", "2019-05-07 00:00:00 47.0 32.0 2246429.75 \n", "2019-05-07 00:00:00 47.0 32.0 2246429.75 \n", "2019-05-07 00:00:00 47.0 32.0 2246429.75 \n", "... ... ... ... \n", "2019-05-07 09:59:59 48.0 32.0 2282429.50 \n", "2019-05-07 09:59:59 48.0 32.0 2282429.50 \n", "2019-05-07 09:59:59 48.0 32.0 2282429.50 \n", "2019-05-07 09:59:59 48.0 32.0 2282429.50 \n", "2019-05-07 09:59:59 48.0 32.0 2282429.75 \n", "\n", " Particle Flags Scatter relPeak Scatter Transit Time \\\n", "calculated_time \n", "2019-05-07 00:00:00 1.0 8814524.0 1157.0 \n", "2019-05-07 00:00:00 1.0 58048.0 310.0 \n", "2019-05-07 00:00:00 1.0 20283240.0 1332.0 \n", "2019-05-07 00:00:00 1.0 17956730.0 1407.0 \n", "2019-05-07 00:00:00 1.0 1770382.0 914.0 \n", "... ... ... ... \n", "2019-05-07 09:59:59 1.0 19365740.0 1357.0 \n", "2019-05-07 09:59:59 1.0 1585856.0 874.0 \n", "2019-05-07 09:59:59 1.0 1547770.0 877.0 \n", "2019-05-07 09:59:59 1.0 663630.0 774.0 \n", "2019-05-07 09:59:59 3.0 314938.0 373.0 \n", "\n", " ... cnts_particles_for_tl_dist cnts_thin_total \\\n", "calculated_time ... \n", "2019-05-07 00:00:00 ... 0 0 \n", "2019-05-07 00:00:00 ... 0 0 \n", "2019-05-07 00:00:00 ... 0 0 \n", "2019-05-07 00:00:00 ... 0 0 \n", "2019-05-07 00:00:00 ... 0 0 \n", "... ... ... ... \n", "2019-05-07 09:59:59 ... 0 0 \n", "2019-05-07 09:59:59 ... 0 0 \n", "2019-05-07 09:59:59 ... 0 0 \n", "2019-05-07 09:59:59 ... 0 0 \n", "2019-05-07 09:59:59 ... 1 1 \n", "\n", " cnts_thick_total cnts_unclassified \\\n", "calculated_time \n", "2019-05-07 00:00:00 0 0 \n", "2019-05-07 00:00:00 0 0 \n", "2019-05-07 00:00:00 0 0 \n", "2019-05-07 00:00:00 0 0 \n", "2019-05-07 00:00:00 0 0 \n", "... ... ... \n", "2019-05-07 09:59:59 0 0 \n", "2019-05-07 09:59:59 0 0 \n", "2019-05-07 09:59:59 0 0 \n", "2019-05-07 09:59:59 0 0 \n", "2019-05-07 09:59:59 0 0 \n", "\n", " BC mass within range Opt diam scatt only within range \\\n", "calculated_time \n", "2019-05-07 00:00:00 NaN 255.719130 \n", "2019-05-07 00:00:00 NaN 108.520037 \n", "2019-05-07 00:00:00 NaN 294.642287 \n", "2019-05-07 00:00:00 NaN 288.604383 \n", "2019-05-07 00:00:00 NaN 194.579235 \n", "... ... ... \n", "2019-05-07 09:59:59 NaN 292.333517 \n", "2019-05-07 09:59:59 NaN 190.963786 \n", "2019-05-07 09:59:59 NaN 190.174346 \n", "2019-05-07 09:59:59 NaN 164.603875 \n", "2019-05-07 09:59:59 2.679357 NaN \n", "\n", " temporary_col Sample Flow Controller Read (vccm) \\\n", "calculated_time \n", "2019-05-07 00:00:00 1 33.400002 \n", "2019-05-07 00:00:00 1 33.400002 \n", "2019-05-07 00:00:00 1 33.400002 \n", "2019-05-07 00:00:00 1 33.400002 \n", "2019-05-07 00:00:00 1 33.400002 \n", "... ... ... \n", "2019-05-07 09:59:59 1 33.299999 \n", "2019-05-07 09:59:59 1 33.299999 \n", "2019-05-07 09:59:59 1 33.299999 \n", "2019-05-07 09:59:59 1 33.299999 \n", "2019-05-07 09:59:59 1 33.299999 \n", "\n", " BC mass bin hour \n", "calculated_time \n", "2019-05-07 00:00:00 NaN 0 \n", "2019-05-07 00:00:00 NaN 0 \n", "2019-05-07 00:00:00 NaN 0 \n", "2019-05-07 00:00:00 NaN 0 \n", "2019-05-07 00:00:00 NaN 0 \n", "... ... ... \n", "2019-05-07 09:59:59 NaN 9 \n", "2019-05-07 09:59:59 NaN 9 \n", "2019-05-07 09:59:59 NaN 9 \n", "2019-05-07 09:59:59 NaN 9 \n", "2019-05-07 09:59:59 14.0 9 \n", "\n", "[3432203 rows x 65 columns]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pbp_calib" ] }, { "cell_type": "code", "execution_count": 53, "id": "807ed973-226a-4d43-b3bc-65623e06c3e2", "metadata": {}, "outputs": [], "source": [ "pbp_conc = pd.read_parquet('/data/user/bertoz_b/merlin6data/SP2XR_code/tests/data3/conc_1s/date=2019-05-07 00:00:00')" ] }, { "cell_type": "code", "execution_count": 110, "id": "7aa989a0-2ea7-4084-8718-7623345a17ef", "metadata": {}, "outputs": [], "source": [ "pbp_hist = pd.read_parquet('/data/user/bertoz_b/merlin6data/SP2XR_code/tests/data3/hists_1s/date=2019-05-07 00:00:00')" ] }, { "cell_type": "code", "execution_count": 112, "id": "ac9cc244-5dae-407c-8b28-21f25bd1e786", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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calculated_time
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82568 rows × 1392 columns

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"2019-05-07 00:00:05 0.000000\n", " ... \n", "2019-05-07 23:59:53 0.000000\n", "2019-05-07 23:59:54 82.932638\n", "2019-05-07 23:59:56 0.000000\n", "2019-05-07 23:59:58 0.000000\n", "2019-05-07 23:59:59 82.932638\n", "Name: dNdlogDmev_all_700.33, Length: 61682, dtype: float64" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pbp_hist.sort_index()['dNdlogDmev_all_700.33'].dropna()" ] }, { "cell_type": "code", "execution_count": 96, "id": "9770a008-a532-4cea-a6c7-7df8ada8bc30", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "calculated_time\n", "2019-05-07 00:00:00 0.000000\n", "2019-05-07 00:00:01 NaN\n", "2019-05-07 00:00:02 0.000000\n", "2019-05-07 00:00:03 0.000000\n", "2019-05-07 00:00:04 0.000000\n", " ... \n", "2019-05-07 23:59:55 NaN\n", "2019-05-07 23:59:56 0.000000\n", "2019-05-07 23:59:57 NaN\n", "2019-05-07 23:59:58 0.000000\n", "2019-05-07 23:59:59 82.932638\n", "Name: dNdlogDmev_all_70.03, Length: 82568, dtype: float64" 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... \n", "2019-05-07 00:00:00 0.00000 0.00000 ... \n", "2019-05-07 00:00:01 NaN NaN ... \n", "2019-05-07 00:00:02 84.75842 0.00000 ... \n", "2019-05-07 00:00:03 84.75842 0.00000 ... \n", "2019-05-07 00:00:04 0.00000 84.75842 ... \n", "... ... ... ... \n", "2019-05-07 23:59:55 NaN NaN ... \n", "2019-05-07 23:59:56 0.00000 0.00000 ... \n", "2019-05-07 23:59:57 NaN NaN ... \n", "2019-05-07 23:59:58 0.00000 0.00000 ... \n", "2019-05-07 23:59:59 0.00000 0.00000 ... \n", "\n", " dNdlogDmev_all_472.46 dNdlogDmev_all_496.16 \\\n", "calculated_time \n", "2019-05-07 00:00:00 0.0 0.0 \n", "2019-05-07 00:00:01 NaN NaN \n", "2019-05-07 00:00:02 0.0 0.0 \n", "2019-05-07 00:00:03 0.0 0.0 \n", "2019-05-07 00:00:04 0.0 0.0 \n", "... ... ... \n", "2019-05-07 23:59:55 NaN NaN \n", "2019-05-07 23:59:56 0.0 0.0 \n", "2019-05-07 23:59:57 NaN NaN \n", "2019-05-07 23:59:58 0.0 0.0 \n", "2019-05-07 23:59:59 0.0 0.0 \n", "\n", " dNdlogDmev_all_521.06 dNdlogDmev_all_547.19 \\\n", "calculated_time \n", "2019-05-07 00:00:00 0.0 0.0 \n", "2019-05-07 00:00:01 NaN NaN \n", "2019-05-07 00:00:02 0.0 0.0 \n", "2019-05-07 00:00:03 0.0 0.0 \n", "2019-05-07 00:00:04 0.0 0.0 \n", "... ... ... \n", "2019-05-07 23:59:55 NaN NaN \n", "2019-05-07 23:59:56 0.0 0.0 \n", "2019-05-07 23:59:57 NaN NaN \n", "2019-05-07 23:59:58 0.0 0.0 \n", "2019-05-07 23:59:59 0.0 0.0 \n", "\n", " dNdlogDmev_all_574.65 dNdlogDmev_all_603.47 \\\n", "calculated_time \n", "2019-05-07 00:00:00 0.0 0.0 \n", "2019-05-07 00:00:01 NaN NaN \n", "2019-05-07 00:00:02 0.0 0.0 \n", "2019-05-07 00:00:03 0.0 0.0 \n", "2019-05-07 00:00:04 0.0 0.0 \n", "... ... ... \n", "2019-05-07 23:59:55 NaN NaN \n", "2019-05-07 23:59:56 0.0 0.0 \n", "2019-05-07 23:59:57 NaN NaN \n", "2019-05-07 23:59:58 0.0 0.0 \n", "2019-05-07 23:59:59 0.0 0.0 \n", "\n", " dNdlogDmev_all_633.75 dNdlogDmev_all_665.54 \\\n", "calculated_time \n", "2019-05-07 00:00:00 0.0 0.0 \n", "2019-05-07 00:00:01 NaN NaN \n", "2019-05-07 00:00:02 0.0 0.0 \n", "2019-05-07 00:00:03 0.0 0.0 \n", "2019-05-07 00:00:04 0.0 0.0 \n", "... ... ... \n", "2019-05-07 23:59:55 NaN NaN \n", "2019-05-07 23:59:56 0.0 0.0 \n", "2019-05-07 23:59:57 NaN NaN \n", "2019-05-07 23:59:58 0.0 0.0 \n", "2019-05-07 23:59:59 0.0 0.0 \n", "\n", " dNdlogDmev_all_698.93 dNdlogDmev_all_733.99 \n", "calculated_time \n", "2019-05-07 00:00:00 0.0 0.0 \n", "2019-05-07 00:00:01 NaN NaN \n", "2019-05-07 00:00:02 0.0 0.0 \n", "2019-05-07 00:00:03 0.0 0.0 \n", "2019-05-07 00:00:04 0.0 0.0 \n", "... ... ... \n", "2019-05-07 23:59:55 NaN NaN \n", "2019-05-07 23:59:56 0.0 0.0 \n", "2019-05-07 23:59:57 NaN NaN \n", "2019-05-07 23:59:58 0.0 0.0 \n", "2019-05-07 23:59:59 0.0 0.0 \n", "\n", "[82568 rows x 49 columns]" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dNdlogDmev" ] }, { "cell_type": "code", "execution_count": 44, "id": "ee4d505d-9353-4aa9-9027-0cf31264f07f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(pbp_calib[\"Incand Mass (fg)\"], pbp_calib[\"BC mass\"])" ] }, { "cell_type": "code", "execution_count": 68, "id": "1548f1ad-f80b-4db2-b9cc-ddb37336ef63", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(pbp_conc.sort_index()['S_numConc_within_range_std'], ddf_pbp_hk['S_numConc_within_range_std'])" ] }, { "cell_type": "code", "execution_count": 55, "id": "0b68202c-05bb-4439-a5cd-a0611dee7132", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(pbp_calib[\"BC mass\"], ddf_pbp[\"BC mass\"])" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": ".venv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 5 }