flowio

Name: flowio
Author: K-Dense-AI

31kstars3.1kforks

How to install flowio?

k-dense-ai/scientific-agent-skills/flowio

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill flowio

Installs into the current project.

Summary

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

Files · 1

View on GitHub

SKILL.md

1	# FlowIO: Flow Cytometry Standard File Handler
2
3	## Overview
4
5	FlowIO is a lightweight Python library for reading and writing Flow Cytometry Standard (FCS) files. Parse FCS metadata, extract event data, and create new FCS files with minimal dependencies. The library supports FCS versions 2.0, 3.0, and 3.1, making it ideal for backend services, data pipelines, and basic cytometry file operations.
6
7	## When to Use This Skill
8
9	This skill should be used when:
10
11	- FCS files requiring parsing or metadata extraction
12	- Flow cytometry data needing conversion to NumPy arrays
13	- Event data requiring export to FCS format
14	- Multi-dataset FCS files needing separation
15	- Channel information extraction (scatter, fluorescence, time)
16	- Cytometry file validation or inspection
17	- Pre-processing workflows before advanced analysis
18
19	Related Tools: For advanced flow cytometry analysis including compensation, gating, and FlowJo/GatingML support, recommend FlowKit library as a companion to FlowIO.
20
21	## Installation
22
23	```bash
24	uv pip install flowio
25	```
26
27	Requires Python 3.9 or later.
28
29	## Quick Start
30
31	### Basic File Reading
32
33	```python
34	from flowio import FlowData
35
36	# Read FCS file
37	flow_data = FlowData('experiment.fcs')
38
39	# Access basic information
40	print(f"FCS Version: {flow_data.version}")
41	print(f"Events: {flow_data.event_count}")
42	print(f"Channels: {flow_data.pnn_labels}")
43
44	# Get event data as NumPy array
45	events = flow_data.as_array() # Shape: (events, channels)
46	```
47
48	### Creating FCS Files
49
50	```python
51	import numpy as np
52	from flowio import create_fcs
53
54	# Prepare data
55	data = np.array([[100, 200, 50], [150, 180, 60]]) # 2 events, 3 channels
56	channels = ['FSC-A', 'SSC-A', 'FL1-A']
57
58	# Create FCS file
59	create_fcs('output.fcs', data, channels)
60	```
61
62	## Core Workflows
63
64	### Reading and Parsing FCS Files
65
66	The FlowData class provides the primary interface for reading FCS files.
67
68	Standard Reading:
69
70	```python
71	from flowio import FlowData
72
73	# Basic reading
74	flow = FlowData('sample.fcs')
75
76	# Access attributes
77	version = flow.version # '3.0', '3.1', etc.
78	event_count = flow.event_count # Number of events
79	channel_count = flow.channel_count # Number of channels
80	pnn_labels = flow.pnn_labels # Short channel names
81	pns_labels = flow.pns_labels # Descriptive stain names
82
83	# Get event data
84	events = flow.as_array() # Preprocessed (gain, log scaling applied)
85	raw_events = flow.as_array(preprocess=False) # Raw data
86	```
87
88	Memory-Efficient Metadata Reading:
89
90	When only metadata is needed (no event data):
91
92	```python
93	# Only parse TEXT segment, skip DATA and ANALYSIS
94	flow = FlowData('sample.fcs', only_text=True)
95
96	# Access metadata
97	metadata = flow.text # Dictionary of TEXT segment keywords
98	print(metadata.get('$DATE')) # Acquisition date
99	print(metadata.get('$CYT')) # Instrument name
100	```
101
102	Handling Problematic Files:
103
104	Some FCS files have offset discrepancies or errors:
105
106	```python
107	# Ignore offset discrepancies between HEADER and TEXT sections
108	flow = FlowData('problematic.fcs', ignore_offset_discrepancy=True)
109
110	# Use HEADER offsets instead of TEXT offsets
111	flow = FlowData('problematic.fcs', use_header_offsets=True)
112
113	# Ignore offset errors entirely
114	flow = FlowData('problematic.fcs', ignore_offset_error=True)
115	```
116
117	Excluding Null Channels:
118
119	```python
120	# Exclude specific channels during parsing
121	flow = FlowData('sample.fcs', null_channel_list=['Time', 'Null'])
122	```
123
124	### Extracting Metadata and Channel Information
125
126	FCS files contain rich metadata in the TEXT segment.
127
128	Common Metadata Keywords:
129
130	```python
131	flow = FlowData('sample.fcs')
132
133	# File-level metadata
134	text_dict = flow.text
135	acquisition_date = text_dict.get('$DATE', 'Unknown')
136	instrument = text_dict.get('$CYT', 'Unknown')
137	data_type = flow.data_type # 'I', 'F', 'D', 'A'
138
139	# Channel metadata
140	for i in range(flow.channel_count):
141	pnn = flow.pnn_labels[i] # Short name (e.g., 'FSC-A')
142	pns = flow.pns_labels[i] # Descriptive name (e.g., 'Forward Scatter')
143	pnr = flow.pnr_values[i] # Range/max value
144	print(f"Channel {i}: {pnn} ({pns}), Range: {pnr}")
145	```
146
147	Channel Type Identification:
148
149	FlowIO automatically categorizes channels:
150
151	```python
152	# Get indices by channel type
153	scatter_idx = flow.scatter_indices # [0, 1] for FSC, SSC
154	fluoro_idx = flow.fluoro_indices # [2, 3, 4] for FL channels
155	time_idx = flow.time_index # Index of time channel (or None)
156
157	# Access specific channel types
158	events = flow.as_array()
159	scatter_data = events[:, scatter_idx]
160	fluorescence_data = events[:, fluoro_idx]
161	```
162
163	ANALYSIS Segment:
164
165	If present, access processed results:
166
167	```python
168	if flow.analysis:
169	analysis_keywords = flow.analysis # Dictionary of ANALYSIS keywords
170	pr

People who liked this also viewed

6 picks

Type

find-skillsHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skSkillsJul 20262.6M27k
grill-meA relentless interview to sharpen a plan or design.SkillsJul 2026633k182k
vercel-react-best-practicesVercel's official collection of agent skillsSkillsJul 2026573k29k
improve-codebase-architectureScan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.SkillsJul 2026522k182k
setup-matt-pocock-skillsConfigure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.SkillsJul 2026454k182k
triageMove issues and external PRs through a state machine of triage roles — categorise, verify, grill if needed, and write agent-ready briefs.SkillsJul 2026424k182k

flowio

31kstars3.1kforks

View on GitHub

How to install flowio?

k-dense-ai/scientific-agent-skills/flowio

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill flowio

Installs into the current project.

Summary

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

Files · 1

View on GitHub

SKILL.md

1	# FlowIO: Flow Cytometry Standard File Handler
2
3	## Overview
4
5	FlowIO is a lightweight Python library for reading and writing Flow Cytometry Standard (FCS) files. Parse FCS metadata, extract event data, and create new FCS files with minimal dependencies. The library supports FCS versions 2.0, 3.0, and 3.1, making it ideal for backend services, data pipelines, and basic cytometry file operations.
6
7	## When to Use This Skill
8
9	This skill should be used when:
10
11	- FCS files requiring parsing or metadata extraction
12	- Flow cytometry data needing conversion to NumPy arrays
13	- Event data requiring export to FCS format
14	- Multi-dataset FCS files needing separation
15	- Channel information extraction (scatter, fluorescence, time)
16	- Cytometry file validation or inspection
17	- Pre-processing workflows before advanced analysis
18
19	Related Tools: For advanced flow cytometry analysis including compensation, gating, and FlowJo/GatingML support, recommend FlowKit library as a companion to FlowIO.
20
21	## Installation
22
23	```bash
24	uv pip install flowio
25	```
26
27	Requires Python 3.9 or later.
28
29	## Quick Start
30
31	### Basic File Reading
32
33	```python
34	from flowio import FlowData
35
36	# Read FCS file
37	flow_data = FlowData('experiment.fcs')
38
39	# Access basic information
40	print(f"FCS Version: {flow_data.version}")
41	print(f"Events: {flow_data.event_count}")
42	print(f"Channels: {flow_data.pnn_labels}")
43
44	# Get event data as NumPy array
45	events = flow_data.as_array() # Shape: (events, channels)
46	```
47
48	### Creating FCS Files
49
50	```python
51	import numpy as np
52	from flowio import create_fcs
53
54	# Prepare data
55	data = np.array([[100, 200, 50], [150, 180, 60]]) # 2 events, 3 channels
56	channels = ['FSC-A', 'SSC-A', 'FL1-A']
57
58	# Create FCS file
59	create_fcs('output.fcs', data, channels)
60	```
61
62	## Core Workflows
63
64	### Reading and Parsing FCS Files
65
66	The FlowData class provides the primary interface for reading FCS files.
67
68	Standard Reading:
69
70	```python
71	from flowio import FlowData
72
73	# Basic reading
74	flow = FlowData('sample.fcs')
75
76	# Access attributes
77	version = flow.version # '3.0', '3.1', etc.
78	event_count = flow.event_count # Number of events
79	channel_count = flow.channel_count # Number of channels
80	pnn_labels = flow.pnn_labels # Short channel names
81	pns_labels = flow.pns_labels # Descriptive stain names
82
83	# Get event data
84	events = flow.as_array() # Preprocessed (gain, log scaling applied)
85	raw_events = flow.as_array(preprocess=False) # Raw data
86	```
87
88	Memory-Efficient Metadata Reading:
89
90	When only metadata is needed (no event data):
91
92	```python
93	# Only parse TEXT segment, skip DATA and ANALYSIS
94	flow = FlowData('sample.fcs', only_text=True)
95
96	# Access metadata
97	metadata = flow.text # Dictionary of TEXT segment keywords
98	print(metadata.get('$DATE')) # Acquisition date
99	print(metadata.get('$CYT')) # Instrument name
100	```
101
102	Handling Problematic Files:
103
104	Some FCS files have offset discrepancies or errors:
105
106	```python
107	# Ignore offset discrepancies between HEADER and TEXT sections
108	flow = FlowData('problematic.fcs', ignore_offset_discrepancy=True)
109
110	# Use HEADER offsets instead of TEXT offsets
111	flow = FlowData('problematic.fcs', use_header_offsets=True)
112
113	# Ignore offset errors entirely
114	flow = FlowData('problematic.fcs', ignore_offset_error=True)
115	```
116
117	Excluding Null Channels:
118
119	```python
120	# Exclude specific channels during parsing
121	flow = FlowData('sample.fcs', null_channel_list=['Time', 'Null'])
122	```
123
124	### Extracting Metadata and Channel Information
125
126	FCS files contain rich metadata in the TEXT segment.
127
128	Common Metadata Keywords:
129
130	```python
131	flow = FlowData('sample.fcs')
132
133	# File-level metadata
134	text_dict = flow.text
135	acquisition_date = text_dict.get('$DATE', 'Unknown')
136	instrument = text_dict.get('$CYT', 'Unknown')
137	data_type = flow.data_type # 'I', 'F', 'D', 'A'
138
139	# Channel metadata
140	for i in range(flow.channel_count):
141	pnn = flow.pnn_labels[i] # Short name (e.g., 'FSC-A')
142	pns = flow.pns_labels[i] # Descriptive name (e.g., 'Forward Scatter')
143	pnr = flow.pnr_values[i] # Range/max value
144	print(f"Channel {i}: {pnn} ({pns}), Range: {pnr}")
145	```
146
147	Channel Type Identification:
148
149	FlowIO automatically categorizes channels:
150
151	```python
152	# Get indices by channel type
153	scatter_idx = flow.scatter_indices # [0, 1] for FSC, SSC
154	fluoro_idx = flow.fluoro_indices # [2, 3, 4] for FL channels
155	time_idx = flow.time_index # Index of time channel (or None)
156
157	# Access specific channel types
158	events = flow.as_array()
159	scatter_data = events[:, scatter_idx]
160	fluorescence_data = events[:, fluoro_idx]
161	```
162
163	ANALYSIS Segment:
164
165	If present, access processed results:
166
167	```python
168	if flow.analysis:
169	analysis_keywords = flow.analysis # Dictionary of ANALYSIS keywords
170	pr

People who liked this also viewed

6 picks

Type