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# Analysis and Results Parsing

This page covers the scripts in `analysis/` for summarizing, comparing, and auditing experiment results after evaluation runs. For the output directory structure, HDF5 layout, and episode result fields, see [Data Storage and Output](data.md).

## `analysis/read_results.py`
The primary script for reading and summarizing experiment results from `episode_results.jsonl` (or legacy `.json`). It supports multiple summarization modes, filtering, CSV export, and multi-folder aggregation.

### Basic Usage

```bash
python analysis/read_results.py <folder> [<folder> ...]
```

`<folder>` can be:
- A folder name relative to the default output directory (e.g., `2025-09-02_13-15-34`)
- An absolute path (e.g., `/data/experiments/my_run`)
- A glob pattern (e.g., `pi0_*`), which prompts for confirmation before proceeding

Multiple folders can be passed to aggregate results across runs.

### Summarization Modes

By default, the script prints a per-task summary table with success rate, score, and trajectory metrics. Additional modes provide different views of the same data:

| Flag | Description |
|------|-------------|
| *(default)* | Per-task table with success/failure counts, percentages, scores, and trajectory metrics |
| `--by-attributes` | Groups tasks by benchmark categories (visual, relational, procedural) with attribute breakdowns |
| `--by-difficulty` | Summarizes results grouped by difficulty label (simple, moderate, complex) |
| `--by-scene` | Aggregates results by scene instead of by task |
| `--by-wrong-objects` | Per-task breakdown of wrong object grasps: success count, fail count, and which objects were grabbed |
| `--by-instruction-type` | Pivot table comparing success rates across instruction types (default, vague, specific, etc.) |
| `--show-episodes` | Appends a detailed per-episode table after the summary |

### Filtering

| Flag | Description |
|------|-------------|
| `--task TASK [TASK ...]` | Show only the specified task name(s) |
| `--filter-pattern PATTERN` | Glob-style pattern to filter results (e.g., `pick_*`, `*cube*`) |
| `--filter-field FIELD` | Field to apply the filter on. Default: `env_name`. Other options: `task_name`, `scene`, `attributes` |

### Output Format

| Flag | Description |
|------|-------------|
| `--csv` | Print results in CSV format (tab-separated) for copy-pasting into spreadsheets |
| `--show-stddev-compact` | Stddev shown inline as `value (± stddev)` instead of separate columns. CSV mode only; behaves like `--show-stddev` in non-CSV mode (implies `--csv`) |
| `--output-csv FILE` | Write CSV output to a file instead of stdout. If the path is relative, it is placed inside the first data folder (implies `--csv`) |

### Display Options

| Flag | Description |
|------|-------------|
| `--verbose` | Show all metrics + stddev columns (equivalent to `--metrics all --show-stddev`) |
| `--metrics NAME [NAME ...]` | Pick which optional columns to show. Names: `score`, `time`, `wrongobj`, `sparc`, `pathlen`, `speed`, `timing`, `succ-eps`, `all`. Default: `score time sparc pathlen speed`. Note: `sparc`/`pathlen`/`speed` are grouped — selecting any one shows all three. |
| `--show-stddev` | Show stddev as separate columns next to value columns |
| `--exclude-containers` | Exclude container objects (bin, crate, box, etc.) from wrong-object-grabbed counts |

The success rate is always shown alongside its 95% Beta-posterior credible interval (`[lcb-ucb]` in human-readable mode; `LCB %` and `UCB %` columns in CSV mode). The interval comes from `Beta(k+1, n-k+1)` with a uniform prior — wide at small N (e.g. 10/10 → `[71.5-99.8]`), tight at large N. See [Statistical Significance and Adaptive Sampling](statistical_significance.md) for details and for the `--num-episodes-adaptive` stopping rule that targets a fixed CI width.

### Examples

```bash
# Basic summary for a single run
python analysis/read_results.py 2025-09-02_13-15-34
# Verbose summary with all details
python analysis/read_results.py 2025-09-02_13-15-34 --verbose
# Aggregate results across multiple runs
python analysis/read_results.py pi0_run1 pi0_run2 pi0_run3
# Aggregate with glob pattern
python analysis/read_results.py "pi0_*"
# Filter to specific tasks
python analysis/read_results.py 2025-09-02_13-15-34 --task RubiksCubeTask BananaInBowlTask
# Filter by env_name pattern
python analysis/read_results.py 2025-09-02_13-15-34 --filter-pattern "*cube*"
# Group results by benchmark category
python analysis/read_results.py 2025-09-02_13-15-34 --by-attributes
# Compare instruction types
python analysis/read_results.py 2025-09-02_13-15-34 --by-instruction-type
# Export to CSV file
python analysis/read_results.py 2025-09-02_13-15-34 --output-csv summary.csv
# Compact CSV for spreadsheets (stddev inline as 'value (± stddev)')
python analysis/read_results.py 2025-09-02_13-15-34 --csv --show-stddev-compact
# Summary with only score and time columns (no trajectory metrics)
python analysis/read_results.py 2025-09-02_13-15-34 --metrics score time
# Show all columns + stddev
python analysis/read_results.py 2025-09-02_13-15-34 --metrics all --show-stddev
# Wrong object analysis, excluding containers
python analysis/read_results.py 2025-09-02_13-15-34 --by-wrong-objects --exclude-containers
```

### Sample Output

The default output includes the success rate, its 95% Beta credible interval, and trajectory metrics columns (EE SPARC, Path Length, Speed):

```
---------------------------------------------- EXPERIMENT SUMMARY ----------------------------------------------
Task Name                Success    %     95% CI         Score(total) Score(fail) Time(s) EE SPARC PathLen(m) Speed(cm/s)
----------------------------------------------------------------------------------------------------------------
TOTAL (2 tasks)          6/20      30.0%  [13.7-50.7]    0.400        0.143       65.59   -12.86   7.33       2.9
----------------------------------------------------------------------------------------------------------------
AnimalsInBinTask         0/10      0.0%   [0.2-28.5]     0.000        0.000       -       -7.49    2.02       2.2
AppleAndYogurtInBowlTask 6/10      60.0%  [30.8-83.3]    0.800        0.500       65.59   -18.23   12.63      3.5
----------------------------------------------------------------------------------------------------------------
```

Score columns:
- **`Score(total)`**: mean per-episode score across all episodes (successes contribute 1.0; failures contribute their fractional subtask progress in `[0, 1)`).
- **`Score(fail)`**: mean per-episode score over failed episodes only — "how close did the failures get."

`Score(total) = success_rate + (1 − success_rate) · Score(fail)`.

`EE SPARC` is the spectral arc length (smoothness) metric; more negative = less smooth. Stationary trajectories return NaN and are excluded from the average. Use `--metrics score time` (or any subset omitting `sparc`/`pathlen`/`speed`) to hide the trajectory metrics columns.

---

## `analysis/check_results.py`
Validates that episode results are consistent with `run_*.hdf5` files — checks that every episode entry has a matching demo in the HDF5, and reports missing or corrupt data.
**Usage:**
```bash
python analysis/check_results.py <folder> [<folder> ...] [--verbose] [--diagnose]
```
**Arguments:**
| Flag | Description | Default |
|------|-------------|---------|
| `folder` (positional) | Folder(s) or absolute path(s) containing results | *(required)* |
| `--verbose` | Print status for every episode, not only errors | `False` |
| `--diagnose` | Extra HDF5 diagnostics (available demos, numbering gaps, etc.) | `False` |

**Example:**
```bash
# Quick sanity check
python analysis/check_results.py 2025-09-02_13-15-34
# Full diagnostics
python analysis/check_results.py 2025-09-02_13-15-34 --verbose --diagnose
```

---

## `analysis/compile_results.py`
Compile and merge experiment results. Supports two modes:
### Mode 1: Compile results to a single file
Reads `episode_results.jsonl` (or legacy `.json`) from one or more folders and writes a single output file.

```bash
python analysis/compile_results.py "pi05_batch*" -o results.jsonl
python analysis/compile_results.py "pi05_batch*" -o results.json   # JSON array format
python analysis/compile_results.py "pi05_batch*" -o results        # defaults to .jsonl
```

### Mode 2: Merge folders

Moves task subdirectories and merges results into a single output folder. Aborts if any task folder appears in multiple sources (conflict). Source folders are removed after merge by default.

```bash
python analysis/compile_results.py "pi05_batch*" --merge output_folder
python analysis/compile_results.py "pi05_batch*" --merge output_folder --keep  # preserve sources
```

**Arguments:**
| Flag | Description | Default |
|------|-------------|---------|
| `folders` (positional) | Folders to compile/merge (glob patterns supported) | *(required)* |
| `-o` / `--output` | Output file path (compile mode). Extension determines format. | — |
| `--merge` | Output folder path (merge mode). Moves task folders + merges results. | — |
| `--keep` | Keep source folders after merge | `False` (remove) |
| `-y` / `--yes` | Skip confirmation when globs expand to many folders | `False` |
| `--task FILTER` | Filter episodes (e.g., `wrong object`) | `None` |

**Examples:**
```bash
# Compile batch results into one file
python analysis/compile_results.py run_1 run_2 run_3 -o combined.jsonl
# Merge batch folders into one folder
python analysis/compile_results.py "pi05_batch*" --merge pi05_merged
```

---

## `analysis/extract_initial_poses.py`

Extracts initial camera and object poses from HDF5 files and writes `episode_initial_poses.json`. Useful for analyzing pose distributions or debugging scene initialization.

**Usage:**
```bash
python analysis/extract_initial_poses.py <folder> [<folder> ...]
```

**Arguments:**
| Flag | Description | Default |
|------|-------------|---------|
| `folder` (positional) | Folder(s) or absolute path(s) containing results | *(required)* |
| `--overwrite` | Recompute even if `episode_initial_poses.json` exists | `False` |
| `--csv` | CSV-style output | `False` |
| `--summary` | Summary table (counts) instead of per-episode detail | `False` |
| `--all` | Include all pose columns (all cameras/objects) | `False` |
| `--compact` | Compact poses (xyz only, no orientation) | `False` |
| `--output-file FILE` | Write CSV to this path instead of stdout | `None` |

**Example:**
```bash
# Extract poses and print summary
python analysis/extract_initial_poses.py 2025-09-02_13-15-34 --summary
# Export all poses as CSV
python analysis/extract_initial_poses.py 2025-09-02_13-15-34 --csv --all --output-file poses.csv
```

---

## `scripts/read_subtask_status_from_hdf5.py`

Reads and displays subtask completion status directly from an HDF5 data file. Extracts timing, status codes, completion flags, and scores for each subtask step during episode execution.

**Usage:**
```bash
python scripts/read_subtask_status_from_hdf5.py <hdf5_file> [-e EPISODE]
```

**Arguments:**
| Flag | Description | Default |
|------|-------------|---------|
| `file` (positional) | Path to the HDF5 data file | *(required)* |
| `-e` / `--episode` | Episode index (e.g., `0` for `demo_0`). If omitted, shows all episodes | `None` |

**Example:**
```bash
# Display all episodes
python scripts/read_subtask_status_from_hdf5.py output/2025-09-02_13-15-34/RubiksCubeTask/run_0.hdf5
# Display specific episode
python scripts/read_subtask_status_from_hdf5.py output/2025-09-02_13-15-34/RubiksCubeTask/run_0.hdf5 -e 0
```

---

## See Also

- [Data Storage and Output](data.md) — Output directory structure, HDF5 layout, and episode result fields

	# Analysis and Results Parsing

	This page covers the scripts in `analysis/` for summarizing, comparing, and auditing experiment results after evaluation runs. For the output directory structure, HDF5 layout, and episode result fields, see [Data Storage and Output](data.md).

	## `analysis/read_results.py`

	The primary script for reading and summarizing experiment results from `episode_results.jsonl` (or legacy `.json`). It supports multiple summarization modes, filtering, CSV export, and multi-folder aggregation.

	### Basic Usage

	```bash
	python analysis/read_results.py <folder> [<folder> ...]
	```

	`<folder>` can be:
	- A folder name relative to the default output directory (e.g., `2025-09-02_13-15-34`)
	- An absolute path (e.g., `/data/experiments/my_run`)
	- A glob pattern (e.g., `pi0_*`), which prompts for confirmation before proceeding

	Multiple folders can be passed to aggregate results across runs.

	### Summarization Modes

	By default, the script prints a per-task summary table with success rate, score, and trajectory metrics. Additional modes provide different views of the same data:

	\| Flag \| Description \|
	\|------\|-------------\|
	\| (default) \| Per-task table with success/failure counts, percentages, scores, and trajectory metrics \|
	\| `--by-attributes` \| Groups tasks by benchmark categories (visual, relational, procedural) with attribute breakdowns \|
	\| `--by-difficulty` \| Summarizes results grouped by difficulty label (simple, moderate, complex) \|
	\| `--by-scene` \| Aggregates results by scene instead of by task \|
	\| `--by-wrong-objects` \| Per-task breakdown of wrong object grasps: success count, fail count, and which objects were grabbed \|
	\| `--by-instruction-type` \| Pivot table comparing success rates across instruction types (default, vague, specific, etc.) \|
	\| `--show-episodes` \| Appends a detailed per-episode table after the summary \|

	### Filtering

	\| Flag \| Description \|
	\|------\|-------------\|
	\| `--task TASK [TASK ...]` \| Show only the specified task name(s) \|
	\| `--filter-pattern PATTERN` \| Glob-style pattern to filter results (e.g., `pick_`, `cube*`) \|
	\| `--filter-field FIELD` \| Field to apply the filter on. Default: `env_name`. Other options: `task_name`, `scene`, `attributes` \|

	### Output Format

	\| Flag \| Description \|
	\|------\|-------------\|
	\| `--csv` \| Print results in CSV format (tab-separated) for copy-pasting into spreadsheets \|
	\| `--show-stddev-compact` \| Stddev shown inline as `value (± stddev)` instead of separate columns. CSV mode only; behaves like `--show-stddev` in non-CSV mode (implies `--csv`) \|
	\| `--output-csv FILE` \| Write CSV output to a file instead of stdout. If the path is relative, it is placed inside the first data folder (implies `--csv`) \|

	### Display Options

	\| Flag \| Description \|
	\|------\|-------------\|
	\| `--verbose` \| Show all metrics + stddev columns (equivalent to `--metrics all --show-stddev`) \|
	\| `--metrics NAME [NAME ...]` \| Pick which optional columns to show. Names: `score`, `time`, `wrongobj`, `sparc`, `pathlen`, `speed`, `timing`, `succ-eps`, `all`. Default: `score time sparc pathlen speed`. Note: `sparc`/`pathlen`/`speed` are grouped — selecting any one shows all three. \|
	\| `--show-stddev` \| Show stddev as separate columns next to value columns \|
	\| `--exclude-containers` \| Exclude container objects (bin, crate, box, etc.) from wrong-object-grabbed counts \|

	The success rate is always shown alongside its 95% Beta-posterior credible interval (`[lcb-ucb]` in human-readable mode; `LCB %` and `UCB %` columns in CSV mode). The interval comes from `Beta(k+1, n-k+1)` with a uniform prior — wide at small N (e.g. 10/10 → `[71.5-99.8]`), tight at large N. See [Statistical Significance and Adaptive Sampling](statistical_significance.md) for details and for the `--num-episodes-adaptive` stopping rule that targets a fixed CI width.

	### Examples

	```bash
	# Basic summary for a single run
	python analysis/read_results.py 2025-09-02_13-15-34

	# Verbose summary with all details
	python analysis/read_results.py 2025-09-02_13-15-34 --verbose

	# Aggregate results across multiple runs
	python analysis/read_results.py pi0_run1 pi0_run2 pi0_run3

	# Aggregate with glob pattern
	python analysis/read_results.py "pi0_*"

	# Filter to specific tasks
	python analysis/read_results.py 2025-09-02_13-15-34 --task RubiksCubeTask BananaInBowlTask

	# Filter by env_name pattern
	python analysis/read_results.py 2025-09-02_13-15-34 --filter-pattern "cube"

	# Group results by benchmark category
	python analysis/read_results.py 2025-09-02_13-15-34 --by-attributes

	# Compare instruction types
	python analysis/read_results.py 2025-09-02_13-15-34 --by-instruction-type

	# Export to CSV file
	python analysis/read_results.py 2025-09-02_13-15-34 --output-csv summary.csv

	# Compact CSV for spreadsheets (stddev inline as 'value (± stddev)')
	python analysis/read_results.py 2025-09-02_13-15-34 --csv --show-stddev-compact

	# Summary with only score and time columns (no trajectory metrics)
	python analysis/read_results.py 2025-09-02_13-15-34 --metrics score time

	# Show all columns + stddev
	python analysis/read_results.py 2025-09-02_13-15-34 --metrics all --show-stddev

	# Wrong object analysis, excluding containers
	python analysis/read_results.py 2025-09-02_13-15-34 --by-wrong-objects --exclude-containers
	```

	### Sample Output

	The default output includes the success rate, its 95% Beta credible interval, and trajectory metrics columns (EE SPARC, Path Length, Speed):

	```
	---------------------------------------------- EXPERIMENT SUMMARY ----------------------------------------------
	Task Name Success % 95% CI Score(total) Score(fail) Time(s) EE SPARC PathLen(m) Speed(cm/s)
	----------------------------------------------------------------------------------------------------------------
	TOTAL (2 tasks) 6/20 30.0% [13.7-50.7] 0.400 0.143 65.59 -12.86 7.33 2.9
	----------------------------------------------------------------------------------------------------------------
	AnimalsInBinTask 0/10 0.0% [0.2-28.5] 0.000 0.000 - -7.49 2.02 2.2
	AppleAndYogurtInBowlTask 6/10 60.0% [30.8-83.3] 0.800 0.500 65.59 -18.23 12.63 3.5
	----------------------------------------------------------------------------------------------------------------
	```

	Score columns:
	- `Score(total)`: mean per-episode score across all episodes (successes contribute 1.0; failures contribute their fractional subtask progress in `[0, 1)`).
	- `Score(fail)`: mean per-episode score over failed episodes only — "how close did the failures get."

	`Score(total) = success_rate + (1 − success_rate) · Score(fail)`.

	`EE SPARC` is the spectral arc length (smoothness) metric; more negative = less smooth. Stationary trajectories return NaN and are excluded from the average. Use `--metrics score time` (or any subset omitting `sparc`/`pathlen`/`speed`) to hide the trajectory metrics columns.

	---

	## `analysis/check_results.py`

	Validates that episode results are consistent with `run_*.hdf5` files — checks that every episode entry has a matching demo in the HDF5, and reports missing or corrupt data.

	Usage:
	```bash
	python analysis/check_results.py <folder> [<folder> ...] [--verbose] [--diagnose]
	```

	Arguments:
	\| Flag \| Description \| Default \|
	\|------\|-------------\|---------\|
	\| `folder` (positional) \| Folder(s) or absolute path(s) containing results \| (required) \|
	\| `--verbose` \| Print status for every episode, not only errors \| `False` \|
	\| `--diagnose` \| Extra HDF5 diagnostics (available demos, numbering gaps, etc.) \| `False` \|

	Example:
	```bash
	# Quick sanity check
	python analysis/check_results.py 2025-09-02_13-15-34

	# Full diagnostics
	python analysis/check_results.py 2025-09-02_13-15-34 --verbose --diagnose
	```

	---

	## `analysis/compile_results.py`

	Compile and merge experiment results. Supports two modes:

	### Mode 1: Compile results to a single file

	Reads `episode_results.jsonl` (or legacy `.json`) from one or more folders and writes a single output file.

	```bash
	python analysis/compile_results.py "pi05_batch*" -o results.jsonl
	python analysis/compile_results.py "pi05_batch*" -o results.json # JSON array format
	python analysis/compile_results.py "pi05_batch*" -o results # defaults to .jsonl
	```

	### Mode 2: Merge folders

	Moves task subdirectories and merges results into a single output folder. Aborts if any task folder appears in multiple sources (conflict). Source folders are removed after merge by default.

	```bash
	python analysis/compile_results.py "pi05_batch*" --merge output_folder
	python analysis/compile_results.py "pi05_batch*" --merge output_folder --keep # preserve sources
	```

	Arguments:
	\| Flag \| Description \| Default \|
	\|------\|-------------\|---------\|
	\| `folders` (positional) \| Folders to compile/merge (glob patterns supported) \| (required) \|
	\| `-o` / `--output` \| Output file path (compile mode). Extension determines format. \| — \|
	\| `--merge` \| Output folder path (merge mode). Moves task folders + merges results. \| — \|
	\| `--keep` \| Keep source folders after merge \| `False` (remove) \|
	\| `-y` / `--yes` \| Skip confirmation when globs expand to many folders \| `False` \|
	\| `--task FILTER` \| Filter episodes (e.g., `wrong object`) \| `None` \|

	Examples:
	```bash
	# Compile batch results into one file
	python analysis/compile_results.py run_1 run_2 run_3 -o combined.jsonl

	# Merge batch folders into one folder
	python analysis/compile_results.py "pi05_batch*" --merge pi05_merged
	```

	---

	## `analysis/extract_initial_poses.py`

	Extracts initial camera and object poses from HDF5 files and writes `episode_initial_poses.json`. Useful for analyzing pose distributions or debugging scene initialization.

	Usage:
	```bash
	python analysis/extract_initial_poses.py <folder> [<folder> ...]
	```

	Arguments:
	\| Flag \| Description \| Default \|
	\|------\|-------------\|---------\|
	\| `folder` (positional) \| Folder(s) or absolute path(s) containing results \| (required) \|
	\| `--overwrite` \| Recompute even if `episode_initial_poses.json` exists \| `False` \|
	\| `--csv` \| CSV-style output \| `False` \|
	\| `--summary` \| Summary table (counts) instead of per-episode detail \| `False` \|
	\| `--all` \| Include all pose columns (all cameras/objects) \| `False` \|
	\| `--compact` \| Compact poses (xyz only, no orientation) \| `False` \|
	\| `--output-file FILE` \| Write CSV to this path instead of stdout \| `None` \|

	Example:
	```bash
	# Extract poses and print summary
	python analysis/extract_initial_poses.py 2025-09-02_13-15-34 --summary

	# Export all poses as CSV
	python analysis/extract_initial_poses.py 2025-09-02_13-15-34 --csv --all --output-file poses.csv
	```

	---

	## `scripts/read_subtask_status_from_hdf5.py`

	Reads and displays subtask completion status directly from an HDF5 data file. Extracts timing, status codes, completion flags, and scores for each subtask step during episode execution.

	Usage:
	```bash
	python scripts/read_subtask_status_from_hdf5.py <hdf5_file> [-e EPISODE]
	```

	Arguments:
	\| Flag \| Description \| Default \|
	\|------\|-------------\|---------\|
	\| `file` (positional) \| Path to the HDF5 data file \| (required) \|
	\| `-e` / `--episode` \| Episode index (e.g., `0` for `demo_0`). If omitted, shows all episodes \| `None` \|

	Example:
	```bash
	# Display all episodes
	python scripts/read_subtask_status_from_hdf5.py output/2025-09-02_13-15-34/RubiksCubeTask/run_0.hdf5

	# Display specific episode
	python scripts/read_subtask_status_from_hdf5.py output/2025-09-02_13-15-34/RubiksCubeTask/run_0.hdf5 -e 0
	```

	---

	## See Also

	- [Data Storage and Output](data.md) — Output directory structure, HDF5 layout, and episode result fields