slocum-tpw 0.1.6

TWR Slocum glider data processing utilities

slocum-tpw

Command-line tools and Python library for processing TWR Slocum glider data. Includes ARGOS message decoding, glider log harvesting, multi-source data merging with TEOS-10 oceanographic calculations, and battery-based recovery date estimation.

Installation

pip install slocum-tpw

Requires Python 3.12 or later.

Quick Start

# Decode ARGOS satellite positions
slocum-tpw decode-argos --nc argos.nc messages/*.txt

# Harvest glider log files
slocum-tpw log-harvest --nc log.nc osu684_*.log

# Combine log, flight, and science data into CF-1.13 NetCDF
slocum-tpw mk-combined --glider 684 --output osu684.nc

# Estimate recovery date from battery decay
slocum-tpw recover-by --threshold 15 flight.nc

# Simulate sealed-body vacuum observations (for leak-detection testing)
slocum-tpw simulate-leak --vacuum-drop-per-day 0.075 --days 4 -o sim.csv

# Estimate d(n/V)/dt and its uncertainty from vacuum observations
slocum-tpw analyze-leak sim.csv

All subcommands support --verbose (INFO) and --debug (DEBUG) logging flags.

---

CLI Reference

slocum-tpw decode-argos

Decode ARGOS satellite position messages into NetCDF.

slocum-tpw decode-argos [--nc OUTPUT] FILE [FILE ...]

Argument	Description
FILE	One or more ARGOS message files to decode (required)
--nc OUTPUT	Output NetCDF filename (default: tpw.nc)

Each input file is parsed line-by-line for ARGOS position records containing ident, satellite ID, location class, timestamp, latitude, longitude, altitude, and frequency. Results are concatenated and written as an xarray Dataset indexed by time.

Example:

slocum-tpw decode-argos --nc argos.nc incoming/argos_2025*.txt

---

slocum-tpw log-harvest

Parse Slocum glider log files and extract GPS positions, sensor readings, and timestamps into NetCDF.

slocum-tpw log-harvest [--t0 T0] [--nc OUTPUT] FILE [FILE ...]

Argument	Description
FILE	One or more log files to parse (required)
--t0 T0	Earliest timestamp prefix to include (filters by filename)
--nc OUTPUT	Output NetCDF filename (default: log.nc)

Log files must follow the naming convention {glider}_{timestamp}_*.log (e.g., osu684_20230612T153000_network.log). The --t0 filter compares against the timestamp portion of the filename.

The parser extracts vehicle name, GPS coordinates (converted from DDMM.MM to decimal degrees), and sensor readings. All observations are binned to 100-second time resolution.

Example:

slocum-tpw log-harvest --t0 20230601T000000 --nc log.nc osu684/logs/*.log

---

slocum-tpw mk-combined

Merge log, flight, and science NetCDF data for a single glider into a CF-1.13 compliant NetCDF file with derived oceanographic variables.

slocum-tpw mk-combined --output FILE [--glider N] [--prefix PFX]
                        [--nc-log FILE] [--nc-flight FILE] [--nc-science FILE]

Argument	Description
--output FILE	Output NetCDF filename (required)
--glider N	Glider number; used to filter log data and auto-derive input paths
--prefix PFX	Institution prefix (default: osu); combined with glider number as {prefix}{glider}
--nc-log FILE	Input log NetCDF (default: log.nc)
--nc-flight FILE	Input flight NetCDF; auto-derived as flt.{prefix}{glider}.nc if --glider is set
--nc-science FILE	Input science NetCDF; auto-derived as sci.{prefix}{glider}.nc if --glider is set

Input requirements:

• Log file must contain variables t, m_water_vx, m_water_vy, and optionally lat, lon, glider.
• Flight file must contain m_present_time, m_gps_lat, m_gps_lon (in DDMM.MM format).
• Science file must contain sci_m_present_time, sci_water_temp, sci_water_cond (S/m), sci_water_pressure (bar).

Processing pipeline:

1. Load log data, interpolate missing GPS, filter by glider ID
2. Load flight GPS fixes, build lat/lon interpolators (minimum 2 fixes required)
3. Load science CTD data, assign GPS via flight interpolation
4. Compute oceanographic variables using GSW (TEOS-10):
   • depth from pressure via gsw.z_from_p
   • practical salinity from conductivity via gsw.SP_from_C
   • absolute salinity via gsw.SA_from_SP
   • potential temperature via gsw.pt0_from_t
   • conservative temperature via gsw.CT_from_pt
   • sigma-0 (potential density anomaly) via gsw.sigma0
   • rho (in-situ density anomaly) via gsw.rho_t_exact
5. Merge log and science datasets, add CF-1.13 metadata, write with zlib compression

Example:

# With --glider, flight and science paths are auto-derived from --nc-log location:
slocum-tpw mk-combined --glider 684 --output osu684.nc --nc-log data/log.nc

# Or specify all paths explicitly:
slocum-tpw mk-combined --output combined.nc \
    --nc-log log.nc --nc-flight flt.osu684.nc --nc-science sci.osu684.nc

---

slocum-tpw recover-by

Estimate when a Slocum glider will need to be recovered based on battery percentage decay. Fits a linear regression to battery charge over time and extrapolates to a threshold.

slocum-tpw recover-by [options] FILE [FILE ...]

Argument	Description
FILE	One or more NetCDF files with time and battery sensor data (required)
--sensor NAME	Sensor variable name (default: m_lithium_battery_relative_charge)
--threshold PCT	Battery percentage at which recovery should happen (default: 15)
--time NAME	Name of time variable (auto-detected if omitted; searches for time, t, datetime64 dtypes, CF time units, units='timestamp', and names ending in _time)
--confidence LEVEL	Confidence level for intervals, 0 < x < 1 (default: 0.95)
--ndays N	Use only the last N days of data; use full for the entire dataset. Repeatable and/or comma-separated (e.g. --ndays 3,7,full). Cannot combine with --start/--stop
--tau T	Exponential decay time constant in days — full dataset weighted by exp(-age/T); repeatable and/or comma-separated. Cannot combine with --start/--stop
--start TIME	Use data after this UTC time (cannot combine with --ndays/--tau)
--stop TIME	Use data before this UTC time (cannot combine with --ndays/--tau)
--thin HOURS	Thinning interval in hours; resample bursty data to bin means, using within-bin stderr as fit weights (default: 1, 0 to disable)
--json	Output results as JSON instead of text
--plot	Display an interactive matplotlib plot
--output FILE	Save plot to file instead of displaying

Algorithm:

The tool fits a linear model sensor = intercept + slope * days to the battery data and solves for the day when the sensor reaches the threshold. Uncertainty is propagated through partial derivatives of the recovery date with respect to slope and intercept, using the covariance matrix from numpy.polyfit. Confidence intervals use the t-distribution. For unweighted fits the degrees of freedom are n-2; for --tau weighted fits the effective degrees of freedom are computed via Kish's formula (effective n minus 2) and the covariance matrix is rescaled accordingly, producing wider (more honest) intervals when old data is heavily downweighted.

Bursty data (multiple samples per surfacing) is thinned to hourly bin means by default (--thin 1). Within-bin standard errors are used as inverse-variance weights so more reliable bins get more influence without inflating degrees of freedom (Kish's correction applies only to the --tau importance weights, not precision weights from thinning).

The tool handles multiple time formats (CF datetime coordinates, float epoch seconds), removes duplicates and NaN values, and validates that at least 3 data points are available for fitting.

Text output:

Sensor:      m_lithium_battery_relative_charge, threshold: 15
Intercept:   100.0000+-0.0000, Slope: -1.0000+-0.0000/day (95%)
R-squared:   1.0000, Pvalue: 0.0000, DOF: 49.0
Recovery By: 2025-03-27T00:00+-0.00 days (95%)

JSON output (--json):

[{
  "file": "flight.nc",
  "sensor": "m_lithium_battery_relative_charge",
  "threshold": 15.0,
  "confidence": 0.95,
  "ndays": null,
  "tau": null,
  "n_points": 51,
  "dof": 49.0,
  "intercept": 100.0,
  "intercept_ci": 0.0,
  "slope": -1.0,
  "slope_ci": 0.0,
  "r_squared": 1.0,
  "pvalue": 0.0,
  "recovery_date": "2025-03-27T00",
  "recovery_ci_days": 0.0
}]

Examples:

# Basic usage
slocum-tpw recover-by --threshold 15 flight.nc

# Use only the last 14 days and save a plot
slocum-tpw recover-by --ndays 14 --output battery.png flight.nc

# Compare multiple time windows on one plot (use 'full' for entire dataset)
slocum-tpw recover-by --ndays 3,7,full --plot flight.nc

# Exponential downweighting (recent data weighted more)
slocum-tpw recover-by --tau 5,15 --output battery.png flight.nc

# Mix ndays windows and tau weighting
slocum-tpw recover-by --ndays 7 --tau 10 --plot flight.nc

# Process multiple gliders, output JSON
slocum-tpw recover-by --json flt.osu684.nc flt.osu685.nc

# Restrict time window
slocum-tpw recover-by --start 2025-01-10 --stop 2025-02-10 flight.nc

# Custom confidence level and sensor
slocum-tpw recover-by --confidence 0.99 --sensor my_battery_pct data.nc

---

slocum-tpw simulate-leak

Simulate sealed-body vacuum and vehicle-temperature observations for a Slocum Glider, using the van der Waals equation of state for air. The gas volume is assumed constant in time and temperature; the air temperature cycles sinusoidally. An optional constant-rate leak is added as a linear drift in the number of moles of gas.

slocum-tpw simulate-leak [options]

Argument	Description
-o PATH, --output PATH	Output CSV path (default: simulated.csv)
--days N	Simulation duration in days (default: 4)
--timestep SEC	Sampling interval in seconds (default: 3)
--vacuum-drop-per-day X	Signed leak rate in inHg/day of vacuum DROP. Positive = vacuum decreasing (gas leaking IN). Negative = vacuum increasing (gas leaking OUT). Default: 0 (no leak)
--sigma-pressure X	1-sigma Gaussian noise on vacuum, inHg (default: 0.001)
--sigma-temp X	1-sigma Gaussian noise on temperature, degC (default: 0.1)
--initial-vacuum X	Initial vacuum at t=0, inHg (default: 10)
--volume L	Sealed gas volume, liters (default: 50)
--temp-mean X	Mean air temperature, degC (default: 17.5)
--temp-amplitude X	Thermal amplitude, degC (default: 2.5)
--temp-period-hours X	Thermal cycle period, hours (default: 24)
--seed N	RNG seed for reproducibility (default: random)
--t0-epoch SEC	Seconds at t=0 (default: 0); set to a Unix epoch time to produce absolute timestamps

Output CSV uses Slocum native column names (m_present_time, m_vacuum, m_veh_temp) in units of seconds, inHg, and degC respectively, so the analyze-leak subcommand can be applied to both simulated and real glider CSV exports without column remapping.

Algorithm:

1. Initial conditions: internal absolute pressure is P_atm - initial_vacuum at the reference (warmest) air temperature. Solve van der Waals for the starting molar density rho0.
2. Derive the constant d(rho)/dt that, at the reference temperature, produces the requested vacuum drift per day.
3. Evaluate the noise-free (P, T) at each sample using van der Waals on a linearly drifting rho(t) and a sinusoidal T(t).
4. Add independent Gaussian noise to pressure and temperature at each sample.

Example:

# 0.3 inHg vacuum drop over 4 days (simulating a small leak), reproducible
slocum-tpw simulate-leak --vacuum-drop-per-day 0.075 --days 4 \
    --seed 42 -o sim_leak.csv

# No leak, quiet reference dataset for comparison
slocum-tpw simulate-leak --vacuum-drop-per-day 0 --days 4 \
    --seed 42 -o sim_noleak.csv

---

slocum-tpw analyze-leak

Estimate d(n/V)/dt and its 1-sigma uncertainty from a CSV of sealed-body observations. Works on the CSV written by simulate-leak as well as on any CSV containing time (seconds), vacuum (inHg), and vehicle temperature (degC) columns.

slocum-tpw analyze-leak [options] CSV_FILE

Argument	Description
CSV_FILE	Path to input CSV (required)
--time-col NAME	Time column name in seconds (default: m_present_time)
--vacuum-col NAME	Vacuum column name in inHg (default: m_vacuum)
--temp-col NAME	Temperature column name in degC (default: m_veh_temp)
--plot PATH	Save a fit diagnostic plot to PATH (default: no plot)

Algorithm:

1. For each sample, convert (m_vacuum, m_veh_temp) to absolute pressure (Pa) and temperature (K), then solve the van der Waals equation of state for the inferred molar density rho(t) = n / V.
2. Least-squares linear fit rho(t) = intercept + slope * t via scipy.stats.linregress. The slope is the estimated d(n/V)/dt; the regression standard error on the slope is its 1-sigma uncertainty.
3. The reported z-score slope / sigma is a quick significance indicator: |z| > ~3 suggests a real trend.

Non-numeric rows, non-finite values, and vdW inversion failures are dropped from the fit (with a warning). Input is sorted by time defensively.

Text output:

file                : sim_leak.csv
rows used           : 115201
time span           : 345600.0 s (4.0000 days)
rho range           : 27.6569 .. 28.1344 mol/m^3
residual sigma(rho) : 9.7539e-03 mol/m^3

Linear fit: rho(t) = intercept + slope * t
  slope              = +1.2069e-06 mol/(m^3 * s)
  slope 1-sigma      = 2.8805e-10 mol/(m^3 * s)
  slope 95% CI       = +/- 5.6457e-10 mol/(m^3 * s)

  slope (per day)    = +1.0428e-01 mol/(m^3 * day)
  slope 1-sigma (/d) = 2.4887e-05 mol/(m^3 * day)

  intercept          = 27.692575 mol/m^3
  intercept 1-sigma  = 5.7475e-05 mol/m^3
  slope / sigma      = +4189.94  (|z| > ~3 suggests a real trend)

Examples:

# Round-trip test against the simulator
slocum-tpw simulate-leak --vacuum-drop-per-day 0.075 --days 4 \
    --seed 42 -o sim_leak.csv
slocum-tpw analyze-leak sim_leak.csv --plot sim_leak_fit.png

# Real glider data with non-default column names
slocum-tpw analyze-leak glider.csv \
    --time-col timestamp --vacuum-col vacuum_inHg --temp-col temp_C

---

Python API

All subcommand functionality is available as importable functions.

slocum_tpw.decode_argos

from slocum_tpw.decode_argos import proc_file, process_files

# Parse a single ARGOS file
df = proc_file("messages.txt")          # Returns DataFrame or None
print(df.columns)                       # ident, nLines, nBytes, satellite,
                                        # locationClass, time, lat, lon,
                                        # altitude, frequency

# Process multiple files and write NetCDF
process_files("output.nc", ["file1.txt", "file2.txt"])

proc_file(fn: str) -> pd.DataFrame | None

Parse a single ARGOS message file. Returns a DataFrame with columns ident, nLines, nBytes, satellite, locationClass, time, lat, lon, altitude, frequency. Returns None if no valid records are found.

process_files(fn_nc: str, filenames: list[str]) -> None

Process multiple ARGOS files, concatenate results, and write to NetCDF. Writes an empty dataset if no records are found.

---

slocum_tpw.log_harvest

from slocum_tpw.log_harvest import parse_log_file, process_files

# Parse a single log file
df = parse_log_file("osu684_20230612T153000.log", "osu684")
print(df.columns)  # t, glider, lat, lon, sci_water_temp, m_water_vx, ...

# Process multiple files with timestamp filter
process_files(["file1.log", "file2.log"], t0="20230601T000000", nc="log.nc")

parse_log_file(fn: str, glider: str) -> pd.DataFrame

Parse a single Slocum log file. Reads in binary mode with UTF-8 decoding (skips invalid bytes). Extracts vehicle name, GPS coordinates (converted from DDMM.MM), and sensor readings. All observations are binned to 100-second time resolution using a hash-table lookup. Returns an empty DataFrame if no data is found.

process_files(filenames: list[str], t0: str | None, nc: str) -> None

Process multiple log files. Filenames are expected to have the format {glider}_{timestamp}_*.log. Files with timestamps before t0 are skipped. Results are concatenated and written to NetCDF.

---

slocum_tpw.mk_combined

from slocum_tpw.mk_combined import mk_combo

success = mk_combo(
    gld="osu684",
    fn_output="osu684.nc",
    fn_log="log.nc",
    fn_flt="flt.osu684.nc",
    fn_sci="sci.osu684.nc",
)

mk_combo(gld: str | None, fn_output: str, fn_log: str, fn_flt: str, fn_sci: str) -> bool

Merge log, flight, and science data into a single CF-1.13 compliant NetCDF. Interpolates GPS positions, computes TEOS-10 oceanographic variables (depth, salinity, potential temperature, density), adds comprehensive metadata, and writes with zlib compression. Pass gld=None to skip glider filtering. Returns True on success, False on failure.

Output variables:

Variable	Units	Description
u	m/s	Depth-averaged eastward current
v	m/s	Depth-averaged northward current
t	°C	In-situ temperature
s	1	Practical salinity (PSS-78)
depth	m	Depth (positive down)
theta	°C	Potential temperature (ref. 0 dbar)
sigma	kg/m³	Potential density anomaly (sigma-0)
rho	kg/m³	In-situ density anomaly (rho - 1000)
lat, lon	degrees	GPS position (science times)
latu, lonu	degrees	GPS position (log times)

---

slocum_tpw.recover_by

from slocum_tpw.recover_by import prepare_dataset, fit_recovery, FIT_COLORS

# Load and clean a NetCDF file (default: thin bursty data to 1-hour bins)
ds = prepare_dataset("flight.nc")
ds = prepare_dataset("flight.nc", thin=0)   # disable thinning
ds = prepare_dataset("flight.nc", thin=6)   # 6-hour bins

# Fit battery decay and estimate recovery date
result = fit_recovery(ds, threshold=15)
print(result["recovery_date"], result["slope"])

# Restrict to last 14 days
result = fit_recovery(ds, threshold=15, ndays=14)

# Exponential downweighting (recent data weighted more heavily)
result = fit_recovery(ds, threshold=15, tau=7)

# Use result arrays for custom plotting
import matplotlib.pyplot as plt
plt.plot(result["time"], result["sensor_values"], ".")
plt.plot(result["time"], result["intercept"] + result["slope"] * result["dDays"])

prepare_dataset(source, time_var=None, sensor="m_lithium_battery_relative_charge", thin=1) -> xr.Dataset

Load and clean a dataset for recovery fitting. source can be a filename, pathlib.Path, or an existing xr.Dataset. Handles float epoch seconds (auto-converted to datetime64), non-standard time variable names (renamed and swapped to a time dimension), duplicates, NaN sensor values, and sorting.

When time_var is None (the default), the time variable is auto-detected by searching for: well-known names (time, t), datetime64 dtypes, CF time units ("... since ..."), units='timestamp' (Slocum POSIX convention), and variable names ending in _time.

When thin is positive (default 1 hour), bursty data is resampled to bin means. Bins with multiple samples produce a _bin_stderr variable that fit_recovery uses as inverse-variance weights, giving more reliable bins more influence without inflating degrees of freedom. Pass thin=0 to disable.

Raises KeyError if required variables are missing or time cannot be auto-detected, OSError if a file path cannot be opened.

fit_recovery(ds, sensor=..., threshold=15, confidence=0.95, ndays=None, tau=None, start=None, stop=None) -> dict | None

Fit a linear model to battery data and extrapolate to the threshold. Returns None if the fit fails (fewer than 3 points, near-zero slope), otherwise a dict with:

Key	Type	Description
time	xr.DataArray	Time coordinates used in the fit
sensor_values	xr.DataArray	Sensor values used
dDays	np.ndarray	Days since first data point (float64)
slope, intercept	float	Linear fit coefficients
slope_ci, intercept_ci	float | None	Confidence interval half-widths
recovery_date	np.datetime64	Estimated recovery date (hourly resolution)
recovery_ci_days	float | None	CI half-width on recovery date (days)
r_squared, pvalue	float | None	Goodness-of-fit statistics
n_points	int	Number of data points used
dof	float	Degrees of freedom (Kish's effective n minus 2 when tau set, else n minus 2)
threshold, confidence	float	Input parameters echoed back
ndays, tau	float | None	Window parameters echoed back

When tau is set, the fit uses weighted least squares with effective weight exp(-age/tau) where age is days from the most recent observation. R-squared is computed as weighted R-squared. The covariance matrix is rescaled using Kish's effective sample size so that confidence intervals correctly reflect the reduced information content of downweighted data.

When _bin_stderr is present in the dataset (created by thinning in prepare_dataset), bin standard errors are used as inverse-variance precision weights (1/stderr) combined multiplicatively with tau weights when both are present. Precision weights improve fit quality without reducing degrees of freedom — only tau importance weights trigger Kish's correction.

FIT_COLORS

List of matplotlib color names used for multi-window plots: ["tab:green", "tab:orange", "tab:purple", "tab:red", "tab:brown", "tab:pink"].

---

slocum_tpw.simulate_leak

from slocum_tpw.simulate_leak import simulate, write_csv, vdw_density, vdw_pressure

# Simulate 4 days with a 0.3 inHg total vacuum drop, default 3 s cadence
result = simulate(
    days=4.0, timestep=3.0,
    vacuum_drop_per_day=0.075,
    sigma_pressure=0.001, sigma_temperature=0.1,
    seed=42,
)

# Write to a Slocum-native CSV (columns: m_present_time, m_vacuum, m_veh_temp)
write_csv("sim.csv", result["time"], result["vacuum_inHg"], result["temperature_c"])

simulate(days, timestep, vacuum_drop_per_day=0.0, initial_vacuum=10.0, volume_l=50.0, temp_mean_c=17.5, temp_amplitude_c=2.5, temp_period_hours=24.0, sigma_pressure=0.001, sigma_temperature=0.1, seed=None, t0_epoch=0.0) -> dict

Simulate sealed-body vacuum and temperature observations. Returns a dict with:

Key	Type	Description
time	np.ndarray	Sample times in seconds, offset by t0_epoch
vacuum_inHg	np.ndarray	Noisy vacuum observations (inHg)
temperature_c	np.ndarray	Noisy vehicle temperature observations (degC)
vacuum_true_inHg	np.ndarray	Noise-free vacuum
temperature_true_c	np.ndarray	Noise-free temperature
drho_dt_true	float	Constant leak rate, mol/(m^3 * s), implied by vacuum_drop_per_day at the reference temperature
rho0	float	Initial molar density (mol/m^3)
volume_m3	float	Sealed volume (m^3)

Positive vacuum_drop_per_day means vacuum is decreasing (gas leaking in); negative means vacuum is increasing (gas leaking out); 0 is no leak.

write_csv(path, time_s, vacuum_inHg, temperature_c) -> None

Write a three-column CSV with header m_present_time,m_vacuum,m_veh_temp and values rounded to 3, 6, and 4 decimal places respectively.

vdw_pressure(rho, T) -> float / vdw_density(P, T) -> float / vdw_density_vec(P, T) -> np.ndarray

Forward and inverse van der Waals EOS for air (constants A_VDW = 0.1358 Pa·m⁶/mol², B_VDW = 3.64e-5 m³/mol). vdw_density solves the forward relation for molar density via scipy.optimize.brentq and is accurate to double precision. vdw_density_vec is a vectorized Newton-method version used by analyze-leak for ~100× speedup on bulk arrays; entries that fail to converge are returned as NaN.

---

slocum_tpw.analyze_leak

from slocum_tpw.analyze_leak import load_csv, fit_leak_rate

t, vacuum_inHg, temp_c = load_csv("sim.csv")
fit = fit_leak_rate(t, vacuum_inHg, temp_c)
print(f"d(n/V)/dt = {fit['slope']:+.3e} +/- {fit['slope_stderr']:.1e} mol/m^3/s")

load_csv(path, time_col="m_present_time", vacuum_col="m_vacuum", temp_col="m_veh_temp") -> (time, vacuum_inHg, temperature_c)

Load three columns from a CSV file. Non-numeric rows and non-finite values are silently skipped. Returns three np.ndarray objects sorted by time. Raises KeyError if any requested column is missing and ValueError if the file is empty.

fit_leak_rate(time_s, vacuum_inHg, temperature_c) -> dict

Invert van der Waals per sample to get molar density rho(t) = n/V, then least-squares fit rho(t) = intercept + slope * t using scipy.stats.linregress. Returns a dict with:

Key	Type	Description
slope	float	d(n/V)/dt estimate, mol/(m^3 * s)
slope_stderr	float	1-sigma regression standard error on slope
slope_95ci	float	1.96 * slope_stderr (half-width of 95% CI)
slope_per_day, slope_stderr_per_day	float	Slope converted to mol/(m^3 * day)
intercept, intercept_stderr	float	Fit intercept (mol/m^3) and its 1-sigma
sigma_rho	float	Residual scatter of rho about the fit
z_score	float	slope / slope_stderr; `
n_points	int	Rows used after filtering
time_span_s	float	time[-1] - time[0]
time, rho	np.ndarray	Valid-sample times and inferred molar densities

Raises ValueError if fewer than 3 usable rows survive filtering.

Noise floor:

For the default simulation parameters (50 L volume, 10 inHg initial vacuum, T ~ 293 K, sigma_P = 0.001 inHg, sigma_T = 0.1 degC), the per-sample scatter in the inferred rho is dominated by the temperature measurement:

• contribution from pressure noise: sigma_P / (R * T) ≈ 1.4e-3 mol/m³
• contribution from temperature noise: rho * sigma_T / T ≈ 9.5e-3 mol/m³

Linear regression on N independent samples over a time span T reduces this to sigma_slope ~ sigma_rho * sqrt(12 / N) / T. For a 4-day window at 3 s cadence (N ~ 1.15e5) the 1-sigma slope uncertainty is about 3e-10 mol/(m^3 * s), corresponding to a vacuum drop of roughly 60 μinHg over 4 days. Leaks much larger than this are statistically detectable within the window.

---

slocum_tpw.slocum_utils

from slocum_tpw.slocum_utils import mk_degrees_scalar, mk_degrees
import numpy as np

# Scalar: 44 degrees 30 minutes -> 44.5 degrees
mk_degrees_scalar(4430.0)   # 44.5
mk_degrees_scalar(-12406.0) # -124.1

# Vectorized (values > 180 degrees become NaN)
arr = np.array([4430.0, -12406.0, 99900.0])
mk_degrees(arr)  # [44.5, -124.1, NaN]

mk_degrees_scalar(degmin: float) -> float

Convert a single DDMM.MM value to decimal degrees. Returns NaN if the absolute result exceeds 180.

mk_degrees(degmin: np.ndarray) -> np.ndarray

Vectorized conversion of DDMM.MM values to decimal degrees. Values whose absolute result exceeds 180 are set to NaN.

---

Development

git clone https://github.com/mousebrains/Slocum-tpw.git
cd Slocum-tpw
pip install -e ".[dev]"
pytest                                              # run tests
pytest --cov=slocum_tpw --cov-report=term-missing   # with coverage
ruff check src/ tests/                              # lint
ruff format src/ tests/                             # format

Pre-commit hooks are configured for trailing whitespace, YAML/TOML validation, and ruff linting/formatting:

pip install pre-commit
pre-commit install

License

GPL-3.0-or-later