NeoGeographyToolkit/vipersci on GitHub.

[earthatnasathebros]

{
"text": "VIPER Telemetry: Simulated",
"blocks": [
{
"type": "section",
"text": {
"type": "mrkdwn",
"text": "NSS (Neutron Spectrometer System)\nHydrogen Count: 1450 cps\nEst. Water Equivalent: 1.2%"
}
}
]
}

[rbeyer]

This isn't a very helpful issue report, as I can't quite understand what your issue is. Please elaborate.

[earthatnasathebros]

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

1. Setup Time Series

dates = pd.date_range(start="2024-11-01T00:00:00", periods=200, freq="T")
df = pd.DataFrame(index=dates)
t = np.linspace(0, 4*np.pi, 200)

2. Simulate NSS Data (Neutron Spectrometer)

Baseline "dry" regolith is roughly 1600 counts per second (cps)

We simulate driving over a "Wet Patch" (Ice Trap) around the middle of the dataset

dry_regolith_counts = 1600
ice_patch_dip = 400 * np.exp(-0.5 * (t - 6)**2) # Gaussian dip (The Ice)

Add noise (radioactive decay is random/Poissonian)

noise = np.random.normal(0, 15, 200)

df['NSS_Raw_Counts'] = dry_regolith_counts - ice_patch_dip + noise

3. Calculate "Water Equivalent" (%)

Real conversions are complex, but a simple log-linear model is often used for estimates

Lower Counts -> Higher Water %

df['Est_Water_Eq_%'] = 10 * np.log10(1650 / df['NSS_Raw_Counts'])

Clip negative values (noise artifacts) to 0

df['Est_Water_Eq_%'] = df['Est_Water_Eq_%'].clip(lower=0)

4. Create the Dashboard Plot

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)

Panel 1: Raw Neutron Counts (What the sensor sees)

ax1.plot(df.index, df['NSS_Raw_Counts'], color='purple', label='Neutron Flux')
ax1.set_ylabel('Neutron Counts (cps)')
ax1.set_title('NSS Sensor: Raw Telemetry')
ax1.grid(True, alpha=0.3)
ax1.legend()

Highlight the "Wet" area where counts drop

ax1.fill_between(df.index, df['NSS_Raw_Counts'], 1600,
where=(df['NSS_Raw_Counts'] < 1400),
color='purple', alpha=0.1, label='Neutron Suppression')

Panel 2: Derived Water Content (What the scientists want)

ax2.plot(df.index, df['Est_Water_Eq_%'], color='deepskyblue', linewidth=2, label='Water Eq')
ax2.set_ylabel('Est. Water Equivalent (%)')
ax2.set_title('NSS Sensor: Derived Science Product')
ax2.fill_between(df.index, df['Est_Water_Eq_%'], 0, color='deepskyblue', alpha=0.2)
ax2.grid(True, alpha=0.3)
ax2.legend()

plt.xlabel('Mission Time')
plt.tight_layout()
plt.show()