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Info_table.py

Classes

InfoTooltip (extends QWidget)

Custom tooltip widget for displaying sample analysis information and quality metrics.

Uses SNR for per-isotope quality assessment and natural-abundance ratios (from the periodic table widget) for isotope consistency anomaly detection.

Method Signature Description
__init__ (self, parent = None) Args:
set_trigger_widget (self, widget) Set the widget (e.g. info button) whose clicks should NOT auto-close us.
show (self) Show the tooltip and install a global click filter.
hide (self) Hide the tooltip and remove the global click filter.
eventFilter (self, obj, event) Hide on any mouse click that lands outside the tooltip.
setup_ui (self) Setup the user interface.
_create_stat_box (self) Returns:
update_stats (self, active_samples, total_elements, Suspected_percentage, analysis_ Update the statistics display.
update_sample_content (self, current_sample, selected_isotopes, detected_peaks, multi_elemen Update the sample content display with isotope information, SNR quality

Functions

_build_abundance_map

def _build_abundance_map(element_data)

Build a {nominal_mass: fractional_abundance} map from periodic-table element data.

The periodic table stores abundance as a percentage (0-100). We convert to fraction (0-1) for statistical calculations.

Args:

  • element_data (dict): Element dict from PeriodicTableWidget, containing
  • an 'isotopes' list of dicts with 'mass', 'abundance', 'label'.

Returns:

  • dict: {int(round(mass)): fractional_abundance} for isotopes with abundance > 0

detect_isotope_anomalies

def detect_isotope_anomalies(element_symbol, isotope_counts, element_data, min_detections = 3)

Detect anomalies in isotope detection consistency for a single element.

Compares observed detection counts across isotopes of the same element against expected ratios from natural abundances using a chi-squared test.

Anomaly types reported: missing_major – Major isotope (>10 %) has 0 detections while a minor isotope was detected. reverse_detection – Less-abundant isotope detected more than the more-abundant one (chi-squared p < 0.01). ratio_anomaly – Both detected, but observed ratio significantly deviates from natural abundance (chi-squared p < 0.01). missing_minor_unexpected – Minor isotope has 0 detections but Poisson probability of 0 given expected count is < 5 %. expected – Minor isotope not detected, consistent with low abundance (Poisson p(0) > 5 %).

Args:

  • element_symbol (str): Element symbol, e.g. "Ag".
  • isotope_counts (dict): {isotope_mass (float): n_detected_peaks (int)}
  • The keys are the exact masses from the periodic table.
  • element_data (dict): Element dict from PeriodicTableWidget.
  • min_detections (int): Minimum total detections to run the test.

Returns:

  • list[dict]: Anomaly records with keys:
  • type, isotope_a, isotope_b, details, severity, p_value

batch_pvalues

def batch_pvalues(heights, bg_scalar, sigma = 0.47)

Calculate p-values for a batch of peak heights given a background level.

Args:

  • heights (list or np.ndarray): Detected peak heights.
  • bg_scalar (float): Mean background level.
  • sigma (float): Sigma value for compound Poisson (if applicable).

Returns:

  • np.ndarray: Array of calculated p-values.