# Tile-activity classification
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### ClassifyConfig
``` python
def ClassifyConfig(
delta_bic:float=10.0, min_tiles:int=20, frac_zero_high:float=0.85, frac_zero_low:float=0.15, random_state:int=42,
global_threshold:float | None=None
)->None:
```
*Decision thresholds for the per-obsid classifier.*
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source
### Pattern
``` python
def Pattern(
args:VAR_POSITIONAL, kwds:VAR_KEYWORD
):
```
*Classification of an obsid’s tile-count distribution.*
## Data loading
Defaults to the public v3.1 fan/blotch parquets via `p4tools.io`. The
per-tile coverage CSV is external and supplied as an explicit path.
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### load_v3p1_data
``` python
def load_v3p1_data(
coverage_csv:str | pathlib.Path, # Path to a CSV with columns ``[obsid, tile_id, Coverage]``
(e.g. ``FnotchCoverage_Full_v3.1.csv``).
version:str='v3.1', # Catalog version forwarded to ``p4tools.io.get_*_catalog``.
fan:pandas.DataFrame | None=None, blotch:pandas.DataFrame | None=None
)->P4Data:
```
*Load the v3.1 fan, blotch, and per-tile coverage frames.*
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### P4Data
``` python
def P4Data(
fan:DataFrame, blotch:DataFrame, coverage:DataFrame
)->None:
```
*Container for the three v3.1 dataframes used by this module.*
## Per-tile marking counts
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### count_markings_per_tile
``` python
def count_markings_per_tile(
data:P4Data
)->DataFrame:
```
*One row per (obsid, tile_id) with total marking counts.*
Tiles present in `data.coverage` but absent from the catalogs appear
with `n_markings = 0`. Returned columns:
`[obsid, tile_id, n_fans, n_blotches, n_markings, Coverage]`.
## Per-obsid classification
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### classify_obsid
``` python
def classify_obsid(
counts:ndarray, obsid:str='',
config:ClassifyConfig=ClassifyConfig(delta_bic=10.0, min_tiles=20, frac_zero_high=0.85, frac_zero_low=0.15, random_state=42, global_threshold=None),
global_threshold:float | None=None
)->ObsidClassification:
```
*Classify one obsid’s tile-count distribution.*
`global_threshold` overrides `config.global_threshold` for the
forced-unimodal small-obsid path.
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### ObsidClassification
``` python
def ObsidClassification(
obsid:str, pattern:Pattern, n_tiles:int, frac_zero:float, mean_count:float, bic1:float, bic2:float,
cluster_means:tuple[float, float] | None, busy_mask:ndarray
)->None:
```
*Result of classifying a single HiRISE observation’s tile distribution.*
## All-obsid classification
First pass: classify every obsid; gather bimodal split-points to learn
the global threshold T. Second pass: only the small-obsid
forced-unimodal entries are re-classified with T.
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### classify_all
``` python
def classify_all(
tile_counts:DataFrame,
config:ClassifyConfig=ClassifyConfig(delta_bic=10.0, min_tiles=20, frac_zero_high=0.85, frac_zero_low=0.15, random_state=42, global_threshold=None)
)->tuple: # ``[obsid, tile_id, n_fans, n_blotches, n_markings, Coverage,
pattern, busy]``.
```
*Classify every obsid and label every tile.*
## Coverage on busy tiles
Vectorised aggregation across the cap (no Python-level loop over
obsids).
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### summary
``` python
def summary(
labeled:DataFrame, params:dict
)->dict:
```
*Cap-wide one-line summary suitable for printing or logging.*
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### coverage_on_busy
``` python
def coverage_on_busy(
labeled:DataFrame
)->DataFrame:
```
*Per-obsid coverage statistics, split by busy/quiet.*
## CLI
End-to-end pipeline writing per-tile labels and per-obsid summary CSVs.
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### main
``` python
def main(
)->None: # pragma: no cover
```
*CLI entry; prints the cap-wide summary.*
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### run_pipeline
``` python
def run_pipeline(
coverage_csv:str | pathlib.Path, out_dir:str | pathlib.Path='outputs',
config:ClassifyConfig=ClassifyConfig(delta_bic=10.0, min_tiles=20, frac_zero_high=0.85, frac_zero_low=0.15, random_state=42, global_threshold=None),
version:str='v3.1'
)->dict:
```
*Run the full classification pipeline and write CSVs to `out_dir`.*
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Manual entry point so
`python -m p4tools.classify_by_activity --coverage-csv ...` works: