metadata_version string | name string | version string | summary string | description string | description_content_type string | author string | author_email string | maintainer string | maintainer_email string | license string | keywords string | classifiers list | platform list | home_page string | download_url string | requires_python string | requires list | provides list | obsoletes list | requires_dist list | provides_dist list | obsoletes_dist list | requires_external list | project_urls list | uploaded_via string | upload_time timestamp[us] | filename string | size int64 | path string | python_version string | packagetype string | comment_text string | has_signature bool | md5_digest string | sha256_digest string | blake2_256_digest string | license_expression string | license_files list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2.1 | stache-ai-dynamodb | 0.1.1 | Dynamodb provider for Stache AI | # stache-ai-dynamodb
DynamoDB providers for [Stache AI](https://github.com/stache-ai/stache-ai) - serverless namespace registry and document index for AWS Lambda deployments.
## Installation
```bash
pip install stache-ai-dynamodb
```
## Providers
This package includes two providers:
| Provider | Type | Description |
|----------|------|-------------|
| `dynamodb` | Namespace | Hierarchical namespace registry with parent-child relationships |
| `dynamodb` | Document Index | Document metadata storage with namespace filtering |
## Configuration
```python
from stache_ai.config import Settings
settings = Settings(
namespace_provider="dynamodb",
dynamodb_namespace_table="my-namespaces", # Required
# Optional: Document index
enable_document_index=True,
document_index_provider="dynamodb",
dynamodb_document_table="my-documents",
)
```
### Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `NAMESPACE_PROVIDER` | Set to `dynamodb` | `none` |
| `DYNAMODB_NAMESPACE_TABLE` | Namespace table name | Required |
| `DOCUMENT_INDEX_PROVIDER` | Set to `dynamodb` | `none` |
| `DYNAMODB_DOCUMENT_TABLE` | Document table name | Required if using document index |
| `AWS_REGION` | AWS region | `us-east-1` |
## Table Schemas
### Namespace Table
Primary key: `id` (String)
GSI: `parent_id-index` on `parent_id` (String)
| Attribute | Type | Description |
|-----------|------|-------------|
| `id` | String | Unique namespace identifier (primary key) |
| `name` | String | Display name |
| `description` | String | Optional description |
| `parent_id` | String | Parent namespace ID (`__ROOT__` for top-level) |
| `metadata` | String | JSON-encoded metadata |
| `filter_keys` | String | JSON-encoded list of filter keys |
| `created_at` | String | ISO 8601 timestamp |
| `updated_at` | String | ISO 8601 timestamp |
### Document Table
Uses single-table design with composite keys:
**Primary Key:**
- `PK` (String): `DOC#{namespace}#{doc_id}`
- `SK` (String): `METADATA`
**Global Secondary Indexes:**
- `GSI1`: Namespace queries
- `GSI1PK`: `NAMESPACE#{namespace}`
- `GSI1SK`: `CREATED#{timestamp}`
- `GSI2`: Filename lookups
- `GSI2PK`: `FILENAME#{namespace}#{filename}`
- `GSI2SK`: `CREATED#{timestamp}`
| Attribute | Type | Description |
|-----------|------|-------------|
| `PK` | String | Primary key: `DOC#{namespace}#{doc_id}` |
| `SK` | String | Sort key: `METADATA` |
| `GSI1PK` | String | Namespace index: `NAMESPACE#{namespace}` |
| `GSI1SK` | String | Created timestamp: `CREATED#{timestamp}` |
| `GSI2PK` | String | Filename index: `FILENAME#{namespace}#{filename}` |
| `GSI2SK` | String | Created timestamp: `CREATED#{timestamp}` |
| `doc_id` | String | Document ID |
| `namespace` | String | Namespace ID |
| `filename` | String | Original filename |
| `title` | String | Document title |
| `source` | String | Source identifier |
| `content_type` | String | MIME type |
| `chunk_count` | Number | Number of chunks |
| `metadata` | Map | Document metadata |
| `created_at` | String | ISO 8601 timestamp |
| `updated_at` | String | ISO 8601 timestamp |
## Infrastructure Examples
### AWS SAM Template
```yaml
Resources:
# Namespace registry table
NamespaceTable:
Type: AWS::DynamoDB::Table
Properties:
TableName: !Sub '${AWS::StackName}-namespaces'
BillingMode: PAY_PER_REQUEST
AttributeDefinitions:
- AttributeName: id
AttributeType: S
- AttributeName: parent_id
AttributeType: S
KeySchema:
- AttributeName: id
KeyType: HASH
GlobalSecondaryIndexes:
- IndexName: parent_id-index
KeySchema:
- AttributeName: parent_id
KeyType: HASH
Projection:
ProjectionType: ALL
# Document index table (single-table design)
DocumentsTable:
Type: AWS::DynamoDB::Table
Properties:
TableName: !Sub '${AWS::StackName}-documents'
BillingMode: PAY_PER_REQUEST
AttributeDefinitions:
- AttributeName: PK
AttributeType: S
- AttributeName: SK
AttributeType: S
- AttributeName: GSI1PK
AttributeType: S
- AttributeName: GSI1SK
AttributeType: S
- AttributeName: GSI2PK
AttributeType: S
- AttributeName: GSI2SK
AttributeType: S
KeySchema:
- AttributeName: PK
KeyType: HASH
- AttributeName: SK
KeyType: RANGE
GlobalSecondaryIndexes:
- IndexName: GSI1
KeySchema:
- AttributeName: GSI1PK
KeyType: HASH
- AttributeName: GSI1SK
KeyType: RANGE
Projection:
ProjectionType: ALL
- IndexName: GSI2
KeySchema:
- AttributeName: GSI2PK
KeyType: HASH
- AttributeName: GSI2SK
KeyType: RANGE
Projection:
ProjectionType: ALL
# Lambda function with DynamoDB access
StacheFunction:
Type: AWS::Serverless::Function
Properties:
Environment:
Variables:
NAMESPACE_PROVIDER: dynamodb
DYNAMODB_NAMESPACE_TABLE: !Ref NamespaceTable
DOCUMENT_INDEX_PROVIDER: dynamodb
DYNAMODB_DOCUMENT_TABLE: !Ref DocumentsTable
Policies:
- DynamoDBCrudPolicy:
TableName: !Ref NamespaceTable
- DynamoDBCrudPolicy:
TableName: !Ref DocumentsTable
```
### Terraform
```hcl
resource "aws_dynamodb_table" "namespaces" {
name = "${var.prefix}-namespaces"
billing_mode = "PAY_PER_REQUEST"
hash_key = "id"
attribute {
name = "id"
type = "S"
}
attribute {
name = "parent_id"
type = "S"
}
global_secondary_index {
name = "parent_id-index"
hash_key = "parent_id"
projection_type = "ALL"
}
}
resource "aws_dynamodb_table" "documents" {
name = "${var.prefix}-documents"
billing_mode = "PAY_PER_REQUEST"
hash_key = "PK"
range_key = "SK"
attribute {
name = "PK"
type = "S"
}
attribute {
name = "SK"
type = "S"
}
attribute {
name = "GSI1PK"
type = "S"
}
attribute {
name = "GSI1SK"
type = "S"
}
attribute {
name = "GSI2PK"
type = "S"
}
attribute {
name = "GSI2SK"
type = "S"
}
global_secondary_index {
name = "GSI1"
hash_key = "GSI1PK"
range_key = "GSI1SK"
projection_type = "ALL"
}
global_secondary_index {
name = "GSI2"
hash_key = "GSI2PK"
range_key = "GSI2SK"
projection_type = "ALL"
}
}
```
### AWS CLI
```bash
# Create namespace table
aws dynamodb create-table \
--table-name stache-namespaces \
--attribute-definitions \
AttributeName=id,AttributeType=S \
AttributeName=parent_id,AttributeType=S \
--key-schema AttributeName=id,KeyType=HASH \
--global-secondary-indexes \
'IndexName=parent_id-index,KeySchema=[{AttributeName=parent_id,KeyType=HASH}],Projection={ProjectionType=ALL}' \
--billing-mode PAY_PER_REQUEST
# Create documents table
aws dynamodb create-table \
--table-name stache-documents \
--attribute-definitions \
AttributeName=PK,AttributeType=S \
AttributeName=SK,AttributeType=S \
AttributeName=GSI1PK,AttributeType=S \
AttributeName=GSI1SK,AttributeType=S \
AttributeName=GSI2PK,AttributeType=S \
AttributeName=GSI2SK,AttributeType=S \
--key-schema \
AttributeName=PK,KeyType=HASH \
AttributeName=SK,KeyType=RANGE \
--global-secondary-indexes \
'IndexName=GSI1,KeySchema=[{AttributeName=GSI1PK,KeyType=HASH},{AttributeName=GSI1SK,KeyType=RANGE}],Projection={ProjectionType=ALL}' \
'IndexName=GSI2,KeySchema=[{AttributeName=GSI2PK,KeyType=HASH},{AttributeName=GSI2SK,KeyType=RANGE}],Projection={ProjectionType=ALL}' \
--billing-mode PAY_PER_REQUEST
```
## IAM Permissions
The Lambda function needs these permissions on both tables:
```yaml
- dynamodb:DescribeTable
- dynamodb:GetItem
- dynamodb:PutItem
- dynamodb:UpdateItem
- dynamodb:DeleteItem
- dynamodb:Query
- dynamodb:Scan
```
Or use the SAM `DynamoDBCrudPolicy` as shown above.
## Requirements
- Python >= 3.10
- stache-ai >= 0.1.0
- boto3 >= 1.34.0
| text/markdown | Stache Contributors | null | null | null | MIT | stache, rag, ai, dynamodb | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"stache-ai<1.0.0,>=0.1.0",
"boto3>=1.34.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/stache-ai/stache-ai",
"Repository, https://github.com/stache-ai/stache-ai"
] | twine/5.0.0 CPython/3.12.3 | 2026-01-16T04:21:24.619027 | stache_ai_dynamodb-0.1.1.tar.gz | 22,441 | 0f/d0/83f96381fa85516827574b2b3d2462f87bf90a4ae99380eadbe537be5a4b/stache_ai_dynamodb-0.1.1.tar.gz | source | sdist | null | false | 9a793fa3b3b10c75eda02756548c27ad | 6a00cae09a486c5fa39a593fc5abf4d1ab85f9610efe5c1de77cb3f5ce6e40a6 | 0fd083f96381fa85516827574b2b3d2462f87bf90a4ae99380eadbe537be5a4b | null | [] |
2.1 | stache-ai-ocr | 0.1.2 | OCR support for Stache AI document loaders | # stache-ai-ocr
OCR support for Stache AI document loaders.
Provides a high-priority PDF loader that falls back to OCR for scanned documents.
## Installation
```bash
pip install stache-ai-ocr
apt install ocrmypdf # System dependency required
```
## Usage
Once installed, the OCR loader automatically registers and takes priority over the basic PDF loader for all PDF files.
The loader will:
1. First attempt normal text extraction with pdfplumber
2. If no text is found (scanned PDF), fall back to OCR using ocrmypdf
3. Gracefully handle missing ocrmypdf (logs warning and returns empty text)
## System Requirements
- **ocrmypdf** system binary must be installed
- Ubuntu/Debian: `apt install ocrmypdf`
- macOS: `brew install ocrmypdf`
- Includes Tesseract OCR engine
## Priority Override
This loader registers with priority 10, overriding the basic PDF loader (priority 0). This ensures OCR is used when available without affecting systems where it's not installed.
| text/markdown | Stache Contributors | null | null | null | MIT | stache, ocr, pdf, document-processing | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"stache-ai>=0.1.0",
"pdfplumber>=0.10.0"
] | [] | [] | [] | [
"Homepage, https://github.com/stache-ai/stache-ai",
"Repository, https://github.com/stache-ai/stache-ai"
] | twine/5.0.0 CPython/3.12.3 | 2026-01-16T04:21:28.509861 | stache_ai_ocr-0.1.2-py3-none-any.whl | 5,649 | ca/d7/179ec962bbde4e00c0afc0294706f055e12b12273e872b73d8942e2d9e22/stache_ai_ocr-0.1.2-py3-none-any.whl | py3 | bdist_wheel | null | false | ae5f86d8fd8593970b7df07193834809 | a9dcfcf482d32a170cbac2a3d81d054288e645d86e60fe8e0282a26e7c3c0220 | cad7179ec962bbde4e00c0afc0294706f055e12b12273e872b73d8942e2d9e22 | null | [] |
2.1 | stache-ai-ocr | 0.1.2 | OCR support for Stache AI document loaders | # stache-ai-ocr
OCR support for Stache AI document loaders.
Provides a high-priority PDF loader that falls back to OCR for scanned documents.
## Installation
```bash
pip install stache-ai-ocr
apt install ocrmypdf # System dependency required
```
## Usage
Once installed, the OCR loader automatically registers and takes priority over the basic PDF loader for all PDF files.
The loader will:
1. First attempt normal text extraction with pdfplumber
2. If no text is found (scanned PDF), fall back to OCR using ocrmypdf
3. Gracefully handle missing ocrmypdf (logs warning and returns empty text)
## System Requirements
- **ocrmypdf** system binary must be installed
- Ubuntu/Debian: `apt install ocrmypdf`
- macOS: `brew install ocrmypdf`
- Includes Tesseract OCR engine
## Priority Override
This loader registers with priority 10, overriding the basic PDF loader (priority 0). This ensures OCR is used when available without affecting systems where it's not installed.
| text/markdown | Stache Contributors | null | null | null | MIT | stache, ocr, pdf, document-processing | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"stache-ai>=0.1.0",
"pdfplumber>=0.10.0"
] | [] | [] | [] | [
"Homepage, https://github.com/stache-ai/stache-ai",
"Repository, https://github.com/stache-ai/stache-ai"
] | twine/5.0.0 CPython/3.12.3 | 2026-01-16T04:21:29.504850 | stache_ai_ocr-0.1.2.tar.gz | 12,177 | 2f/42/2e078355a54342403823b3a5dca189a101db7c4a3511c2379a10630a0430/stache_ai_ocr-0.1.2.tar.gz | source | sdist | null | false | 4171d85fdd60f7c41404a2dfef3e7bb4 | 9680a8574093d40ffa093838acd65de76ee8e0e01829cd0e2223cb40f55b5163 | 2f422e078355a54342403823b3a5dca189a101db7c4a3511c2379a10630a0430 | null | [] |
2.1 | stache-ai-ollama | 0.1.1 | Ollama provider for Stache AI | # stache-ai-ollama
Ollama provider for [Stache AI](https://github.com/stache-ai/stache-ai).
## Installation
```bash
pip install stache-ai-ollama
```
## Usage
Install the package and configure the provider in your settings:
```python
from stache_ai.config import Settings
settings = Settings(
llm_provider: "ollama"
)
```
The provider will be automatically discovered via entry points.
## Requirements
- Python >= 3.10
- stache-ai >= 0.1.0
- httpx>=0.25.0
| text/markdown | Stache Contributors | null | null | null | MIT | stache, rag, ai, ollama | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"stache-ai<1.0.0,>=0.1.0",
"httpx>=0.25.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/stache-ai/stache-ai",
"Repository, https://github.com/stache-ai/stache-ai"
] | twine/5.0.0 CPython/3.12.3 | 2026-01-16T04:21:33.451930 | stache_ai_ollama-0.1.1-py3-none-any.whl | 14,502 | c8/ca/286202ad4992bff240949d7b79d7072edb51fd9ddd897b33cbf1cd098502/stache_ai_ollama-0.1.1-py3-none-any.whl | py3 | bdist_wheel | null | false | ec0f0d36f7ef09baa3f91affdf5bfe24 | 78d3fa5a608cd5d439a531f88f2eaab35d1dd5a287da105fe8a1eaa9a391534c | c8ca286202ad4992bff240949d7b79d7072edb51fd9ddd897b33cbf1cd098502 | null | [] |
2.1 | stache-ai-ollama | 0.1.1 | Ollama provider for Stache AI | # stache-ai-ollama
Ollama provider for [Stache AI](https://github.com/stache-ai/stache-ai).
## Installation
```bash
pip install stache-ai-ollama
```
## Usage
Install the package and configure the provider in your settings:
```python
from stache_ai.config import Settings
settings = Settings(
llm_provider: "ollama"
)
```
The provider will be automatically discovered via entry points.
## Requirements
- Python >= 3.10
- stache-ai >= 0.1.0
- httpx>=0.25.0
| text/markdown | Stache Contributors | null | null | null | MIT | stache, rag, ai, ollama | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"stache-ai<1.0.0,>=0.1.0",
"httpx>=0.25.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/stache-ai/stache-ai",
"Repository, https://github.com/stache-ai/stache-ai"
] | twine/5.0.0 CPython/3.12.3 | 2026-01-16T04:21:34.546006 | stache_ai_ollama-0.1.1.tar.gz | 25,852 | 94/b0/ec1e0e81eabec725115664bf8e640471f1a8168274ed77b6f13cc24c1742/stache_ai_ollama-0.1.1.tar.gz | source | sdist | null | false | a32e022ad522f1de716bac66abadbae8 | 813c1748d470732c20e209654ca636f8022e770165ff4a836167ad5395f7b74e | 94b0ec1e0e81eabec725115664bf8e640471f1a8168274ed77b6f13cc24c1742 | null | [] |
2.1 | stache-ai-s3vectors | 0.1.1 | S3Vectors provider for Stache AI | # stache-ai-s3vectors
S3 Vectors provider for [Stache AI](https://github.com/stache-ai/stache-ai) - serverless vector database using Amazon S3 Vectors.
## Installation
```bash
pip install stache-ai-s3vectors
```
## Usage
Install the package and configure the provider in your settings:
```python
from stache_ai.config import Settings
settings = Settings(
vectordb_provider="s3vectors",
s3vectors_bucket_name="my-vector-bucket", # Required
s3vectors_region="us-east-1", # Optional, defaults to AWS_REGION
)
```
The provider will be automatically discovered via entry points.
### Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `VECTORDB_PROVIDER` | Set to `s3vectors` | Required |
| `S3VECTORS_BUCKET_NAME` | S3 Vectors bucket name | Required |
| `S3VECTORS_REGION` | AWS region for S3 Vectors | `us-east-1` |
| `AWS_REGION` | Fallback region | `us-east-1` |
## IAM Permissions
The S3 Vectors provider requires specific IAM permissions. Note that S3 Vectors uses its own service namespace (`s3vectors:`), not the standard S3 namespace.
### Minimum Required Permissions
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3vectors:GetVectorBucket"
],
"Resource": "arn:aws:s3vectors:REGION:ACCOUNT:vector-bucket/BUCKET_NAME"
},
{
"Effect": "Allow",
"Action": [
"s3vectors:CreateIndex",
"s3vectors:GetIndex",
"s3vectors:ListIndexes",
"s3vectors:PutVectors",
"s3vectors:QueryVectors",
"s3vectors:ListVectors",
"s3vectors:DeleteVectors",
"s3vectors:GetVectors"
],
"Resource": "arn:aws:s3vectors:REGION:ACCOUNT:vector-bucket/BUCKET_NAME/index/*"
}
]
}
```
### SAM Template Example
```yaml
Resources:
S3VectorsBucket:
Type: AWS::S3Vectors::VectorBucket
Properties:
VectorBucketName: !Sub '${AWS::StackName}-vectors'
StacheFunction:
Type: AWS::Serverless::Function
Properties:
Environment:
Variables:
VECTORDB_PROVIDER: s3vectors
S3VECTORS_BUCKET_NAME: !GetAtt S3VectorsBucket.VectorBucketName
Policies:
- Version: '2012-10-17'
Statement:
- Effect: Allow
Action:
- s3vectors:GetVectorBucket
Resource:
- !GetAtt S3VectorsBucket.VectorBucketArn
- Effect: Allow
Action:
- s3vectors:CreateIndex
- s3vectors:GetIndex
- s3vectors:ListIndexes
- s3vectors:PutVectors
- s3vectors:QueryVectors
- s3vectors:ListVectors
- s3vectors:DeleteVectors
- s3vectors:GetVectors
Resource:
- !Sub '${S3VectorsBucket.VectorBucketArn}/index/*'
```
### Terraform Example
```hcl
data "aws_iam_policy_document" "s3vectors" {
statement {
effect = "Allow"
actions = [
"s3vectors:GetVectorBucket"
]
resources = [
"arn:aws:s3vectors:${var.region}:${data.aws_caller_identity.current.account_id}:vector-bucket/${var.bucket_name}"
]
}
statement {
effect = "Allow"
actions = [
"s3vectors:CreateIndex",
"s3vectors:GetIndex",
"s3vectors:ListIndexes",
"s3vectors:PutVectors",
"s3vectors:QueryVectors",
"s3vectors:ListVectors",
"s3vectors:DeleteVectors",
"s3vectors:GetVectors"
]
resources = [
"arn:aws:s3vectors:${var.region}:${data.aws_caller_identity.current.account_id}:vector-bucket/${var.bucket_name}/index/*"
]
}
}
```
## Important Notes
### Bucket Name vs ARN
The provider uses the **bucket name** (not the ARN) for API calls, but IAM policies require the full ARN format:
- **Environment variable**: `S3VECTORS_BUCKET_NAME=my-vectors-bucket` (just the name)
- **IAM Resource**: `arn:aws:s3vectors:us-east-1:123456789012:vector-bucket/my-vectors-bucket` (full ARN)
S3 Vectors bucket names are **globally unique** across AWS, so include your account ID or a unique prefix:
```yaml
# SAM template example
VectorBucketName: !Sub '${AWS::StackName}-vectors-${AWS::AccountId}'
```
### Metadata Limits
S3 Vectors has metadata size limits:
- **Filterable metadata**: 2KB limit (used in query filters)
- **Non-filterable metadata**: Part of 40KB total (returned but can't filter)
When creating indexes, specify large fields like `text` as non-filterable:
```bash
aws s3vectors create-index \
--vector-bucket-name my-bucket \
--index-name my-index \
--dimension 1024 \
--distance-metric cosine \
--metadata-configuration 'nonFilterableMetadataKeys=["text"]'
```
### list_vectors Limitation
The `list_vectors` API does NOT support metadata filtering - only `query_vectors` supports filtering. For operations that need to filter without a query vector, the provider lists all vectors and filters client-side.
## Requirements
- Python >= 3.10
- stache-ai >= 0.1.0
- boto3 >= 1.34.0
| text/markdown | Stache Contributors | null | null | null | MIT | stache, rag, ai, s3vectors | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"stache-ai<1.0.0,>=0.1.0",
"boto3>=1.34.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/stache-ai/stache-ai",
"Repository, https://github.com/stache-ai/stache-ai"
] | twine/5.0.0 CPython/3.12.3 | 2026-01-16T04:21:38.251179 | stache_ai_s3vectors-0.1.1-py3-none-any.whl | 10,051 | 1f/68/06f5805f2538bf78127d6ef92bfca3479f2050ccfd56b312838b8a6c14ad/stache_ai_s3vectors-0.1.1-py3-none-any.whl | py3 | bdist_wheel | null | false | 1ff4e3b6bcdb51d4a475c7603eab733e | e966ef5bad937d8983029b9b0358c3f635283269f36afbe02269b193287c26e2 | 1f6806f5805f2538bf78127d6ef92bfca3479f2050ccfd56b312838b8a6c14ad | null | [] |
2.1 | stache-ai-s3vectors | 0.1.1 | S3Vectors provider for Stache AI | # stache-ai-s3vectors
S3 Vectors provider for [Stache AI](https://github.com/stache-ai/stache-ai) - serverless vector database using Amazon S3 Vectors.
## Installation
```bash
pip install stache-ai-s3vectors
```
## Usage
Install the package and configure the provider in your settings:
```python
from stache_ai.config import Settings
settings = Settings(
vectordb_provider="s3vectors",
s3vectors_bucket_name="my-vector-bucket", # Required
s3vectors_region="us-east-1", # Optional, defaults to AWS_REGION
)
```
The provider will be automatically discovered via entry points.
### Environment Variables
| Variable | Description | Default |
|----------|-------------|---------|
| `VECTORDB_PROVIDER` | Set to `s3vectors` | Required |
| `S3VECTORS_BUCKET_NAME` | S3 Vectors bucket name | Required |
| `S3VECTORS_REGION` | AWS region for S3 Vectors | `us-east-1` |
| `AWS_REGION` | Fallback region | `us-east-1` |
## IAM Permissions
The S3 Vectors provider requires specific IAM permissions. Note that S3 Vectors uses its own service namespace (`s3vectors:`), not the standard S3 namespace.
### Minimum Required Permissions
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3vectors:GetVectorBucket"
],
"Resource": "arn:aws:s3vectors:REGION:ACCOUNT:vector-bucket/BUCKET_NAME"
},
{
"Effect": "Allow",
"Action": [
"s3vectors:CreateIndex",
"s3vectors:GetIndex",
"s3vectors:ListIndexes",
"s3vectors:PutVectors",
"s3vectors:QueryVectors",
"s3vectors:ListVectors",
"s3vectors:DeleteVectors",
"s3vectors:GetVectors"
],
"Resource": "arn:aws:s3vectors:REGION:ACCOUNT:vector-bucket/BUCKET_NAME/index/*"
}
]
}
```
### SAM Template Example
```yaml
Resources:
S3VectorsBucket:
Type: AWS::S3Vectors::VectorBucket
Properties:
VectorBucketName: !Sub '${AWS::StackName}-vectors'
StacheFunction:
Type: AWS::Serverless::Function
Properties:
Environment:
Variables:
VECTORDB_PROVIDER: s3vectors
S3VECTORS_BUCKET_NAME: !GetAtt S3VectorsBucket.VectorBucketName
Policies:
- Version: '2012-10-17'
Statement:
- Effect: Allow
Action:
- s3vectors:GetVectorBucket
Resource:
- !GetAtt S3VectorsBucket.VectorBucketArn
- Effect: Allow
Action:
- s3vectors:CreateIndex
- s3vectors:GetIndex
- s3vectors:ListIndexes
- s3vectors:PutVectors
- s3vectors:QueryVectors
- s3vectors:ListVectors
- s3vectors:DeleteVectors
- s3vectors:GetVectors
Resource:
- !Sub '${S3VectorsBucket.VectorBucketArn}/index/*'
```
### Terraform Example
```hcl
data "aws_iam_policy_document" "s3vectors" {
statement {
effect = "Allow"
actions = [
"s3vectors:GetVectorBucket"
]
resources = [
"arn:aws:s3vectors:${var.region}:${data.aws_caller_identity.current.account_id}:vector-bucket/${var.bucket_name}"
]
}
statement {
effect = "Allow"
actions = [
"s3vectors:CreateIndex",
"s3vectors:GetIndex",
"s3vectors:ListIndexes",
"s3vectors:PutVectors",
"s3vectors:QueryVectors",
"s3vectors:ListVectors",
"s3vectors:DeleteVectors",
"s3vectors:GetVectors"
]
resources = [
"arn:aws:s3vectors:${var.region}:${data.aws_caller_identity.current.account_id}:vector-bucket/${var.bucket_name}/index/*"
]
}
}
```
## Important Notes
### Bucket Name vs ARN
The provider uses the **bucket name** (not the ARN) for API calls, but IAM policies require the full ARN format:
- **Environment variable**: `S3VECTORS_BUCKET_NAME=my-vectors-bucket` (just the name)
- **IAM Resource**: `arn:aws:s3vectors:us-east-1:123456789012:vector-bucket/my-vectors-bucket` (full ARN)
S3 Vectors bucket names are **globally unique** across AWS, so include your account ID or a unique prefix:
```yaml
# SAM template example
VectorBucketName: !Sub '${AWS::StackName}-vectors-${AWS::AccountId}'
```
### Metadata Limits
S3 Vectors has metadata size limits:
- **Filterable metadata**: 2KB limit (used in query filters)
- **Non-filterable metadata**: Part of 40KB total (returned but can't filter)
When creating indexes, specify large fields like `text` as non-filterable:
```bash
aws s3vectors create-index \
--vector-bucket-name my-bucket \
--index-name my-index \
--dimension 1024 \
--distance-metric cosine \
--metadata-configuration 'nonFilterableMetadataKeys=["text"]'
```
### list_vectors Limitation
The `list_vectors` API does NOT support metadata filtering - only `query_vectors` supports filtering. For operations that need to filter without a query vector, the provider lists all vectors and filters client-side.
## Requirements
- Python >= 3.10
- stache-ai >= 0.1.0
- boto3 >= 1.34.0
| text/markdown | Stache Contributors | null | null | null | MIT | stache, rag, ai, s3vectors | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"stache-ai<1.0.0,>=0.1.0",
"boto3>=1.34.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-asyncio>=0.21.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/stache-ai/stache-ai",
"Repository, https://github.com/stache-ai/stache-ai"
] | twine/5.0.0 CPython/3.12.3 | 2026-01-16T04:21:39.292007 | stache_ai_s3vectors-0.1.1.tar.gz | 20,964 | 00/3c/5eb0b8a5ac7fdb926ea1fd95e6346a51f473183d813b5e7c8738e92054db/stache_ai_s3vectors-0.1.1.tar.gz | source | sdist | null | false | 94bfee502545b8103ec992a751c861cd | ba2ddf30092f561b19edfc0f23689fcf3b5bb793a69b03ebaa4918bf08714532 | 003c5eb0b8a5ac7fdb926ea1fd95e6346a51f473183d813b5e7c8738e92054db | null | [] |
2.4 | oligopool | 2026.1.15 | Oligopool Calculator - Automated design and analysis of oligopool libraries | <h1 align="center">
<a href="https://github.com/ayaanhossain/oligopool/">
<img src="https://raw.githubusercontent.com/ayaanhossain/repfmt/main/oligopool/img/logo.svg" alt="Oligopool Calculator" width="460" class="center"/>
</a>
</h1>
<h4><p align="center">Version: 2026.01.15</p></h4>
<p align="center">
<a style="text-decoration: none" href="#Installation">Installation</a> •
<a style="text-decoration: none" href="#Getting-Started">Getting Started</a> •
<a style="text-decoration: none" href="#Command-Line-Interface-CLI">CLI</a> •
<a style="text-decoration: none" href="#Citation">Citation</a> •
<a style="text-decoration: none" href="#License">License</a>
</p>
`Oligopool Calculator` is a suite of algorithms for automated design and analysis of [oligopool libraries](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300125/).
It enables the scalable design of universal primer sets, error-correctable barcodes, the splitting of long constructs into multiple oligos, and the rapid packing and counting of barcoded reads -- all on a regular 8-core desktop computer.
We have used `Oligopool Calculator` in multiple projects to build libraries of tens of thousands of promoters (see [here](https://www.nature.com/articles/s41467-022-32829-5) and [here](https://www.nature.com/articles/s41587-020-0584-2)), ribozymes, and mRNA stability elements (see [here](https://www.nature.com/articles/s41467-024-54059-7)), illustrating the use of a flexible grammar to add multiple barcodes, cut sites, avoid excluded sequences, and optimize experimental constraints. These libraries were later characterized using highly efficient barcode counting provided by `Oligopool Calculator`. To learn more, please check out [our paper in ACS Synthetic Biology](https://pubs.acs.org/doi/10.1021/acssynbio.4c00661).
`Oligopool Calculator` facilitates the creative design and application of massively parallel reporter assays by automating and simplifying the whole process. It has been benchmarked on simulated libraries containing millions of defined variants and to analyze billions of reads.
<h1 align="center">
<a href="https://github.com/ayaanhossain/oligopool/">
<img src="https://raw.githubusercontent.com/ayaanhossain/repfmt/refs/heads/main/oligopool/img/workflow.svg" alt="Oligopool Calculator Workflow" width="3840" class="center"/>
</a>
</h1>
**Design and analysis of oligopool variants using `Oligopool Calculator`.** **(a)** In `Design Mode`, `Oligopool Calculator` can be used to generate optimized `barcode`s, `primer`s, `spacer`s, `motif`s and `split` longer oligos into shorter `pad`ded fragments for downstream synthesis and assembly. **(b)** Once the library is assembled and cloned, barcoded amplicon sequencing data can be processed via `Analysis Mode` for characterization. `Analysis Mode` proceeds by first `index`ing one or more sets of barcodes, `pack`ing the reads, and then producing count matrices either using `acount` (association counting) or `xcount` (combinatorial counting).
## Installation
`Oligopool Calculator` is a `Python3.10+`-exclusive library.
On `Linux`, `MacOS` and `Windows Subsystem for Linux` you can install `Oligopool Calculator` from [PyPI](https://pypi.org/project/oligopool/), where it is published as the `oligopool` package
```bash
$ pip install --upgrade oligopool # Installs and/or upgrades oligopool
```
This also installs the command line tools: `oligopool` and `op`.
or install it directly from GitHub.
```bash
$ pip install git+https://github.com/ayaanhossain/oligopool.git
```
Both approaches should install all dependencies automatically.
> **Note** This GitHub version will always be updated with all recent fixes. The PyPI version should be more stable.
If you are on `Windows` or simply prefer to, `Oligopool Calculator` can also be used via `docker` (please see [the notes](https://github.com/ayaanhossain/oligopool/blob/master/docker-notes.md)).
**Verifying Installation**
Successful installation will look like this.
```python
$ python
Python 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import oligopool as op
>>> op.__version__
'2026.01.15'
>>>
```
## Getting Started
`Oligopool Calculator` is carefully designed, easy to use, and stupid fast.
You can import the library and use its various functions either in a script or interactively inside a `jupyter` environment. Use `help(...)` to read the docs as necessary and follow along.
There are examples of a [design parser](https://github.com/ayaanhossain/oligopool/blob/master/examples/design-parser/design_parser.py) and an [analysis pipeline](https://github.com/ayaanhossain/oligopool/blob/master/examples/analysis-pipeline/analysis_pipeline.py) inside the [`examples`](https://github.com/ayaanhossain/oligopool/tree/master/examples) directory.
A notebook demonstrating [`Oligopool Calculator` in action](https://github.com/ayaanhossain/oligopool/blob/master/examples/OligopoolCalculatorInAction.ipynb) is provided there as well.
```python
$ python
Python 3.12.6 | packaged by conda-forge | (main, Sep 30 2024, 18:08:52) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
>>> import oligopool as op
>>> help(op)
...
oligopool v2026.01.15
by ah
Automated design and analysis of oligopool libraries.
The various modules in Oligopool Calculator can be used
interactively in a jupyter notebook, or be used to define
scripts for design and analysis pipelines on the cloud.
Oligopool Calculator offers two modes of operation
- Design Mode for designing oligopool libraries, and
- Analysis Mode for analyzing oligopool datasets.
Design Mode workflow
1. Initialize a pandas DataFrame with core library elements.
a. The DataFrame must contain a unique 'ID' column serving as primary key.
b. All other columns in the DataFrame must be DNA sequences.
2. Define any optional background sequences via the background module.
3. Add necessary oligopool elements with constraints via element modules.
4. Optionally, split long oligos and pad them via assembly modules.
5. Perform additional maneuvers and finalize library via auxiliary modules.
Background module available
- background
Element modules available
- primer
- barcode
- motif
- spacer
Assembly modules available
- split
- pad
Auxiliary modules available
- merge
- revcomp
- lenstat
- final
Design Mode example sketch
>>> import pandas as pd
>>> import oligopool as op
>>>
>>> # Read initial library
>>> init_df = pd.read_csv('initial_library.csv')
>>>
>>> # Add oligo elements one by one
>>> primer_df, stats = op.primer(input_data=init_df, ...)
>>> barcode_df, stats = op.barcode(input_data=primer_df, ...)
...
>>> # Check length statistics as needed
>>> length_stats = op.lenstat(input_data=further_along_df)
...
>>>
>>> # Split and pad longer oligos if needed
>>> split_df, stats = op.split(input_data=even_further_along_df, ...)
>>> first_pad_df, stats = op.pad(input_data=split_df, ...)
>>> second_pad_df, stats = op.pad(input_data=split_df, ...)
...
>>>
>>> # Finalize the library
>>> final_df, stats = op.final(input_data=ready_to_go_df, ...)
...
Analysis Mode workflow
1. Index one or more CSVs containing barcode (and associate) data.
2. Pack all NGS FastQ files, optionally merging them if required.
3. Use acount for association counting of variants and barcodes.
4. If multiple barcode combinations are to be counted use xcount.
5. Combine count DataFrames and perform stats and ML as necessary.
Indexing module available
- index
Packing module available
- pack
Counting modules available
- acount
- xcount
Analysis Mode example sketch
>>> import pandas as pd
>>> import oligopool as op
>>>
>>> # Read annotated library
>>> bc1_df = pd.read_csv('barcode_1.csv')
>>> bc2_df = pd.read_csv('barcode_2.csv')
>>> av1_df = pd.read_csv('associate_1.csv')
...
>>>
>>> # Index barcodes and any associates
>>> bc1_index_stats = op.index(barcode_data=bc1_df, barcode_column='BC1', ...)
>>> bc2_index_stats = op.index(barcode_data=bc2_df, barcode_column='BC2', ...)
...
>>>
>>> # Pack experiment FastQ files
>>> sam1_pack_stats = op.pack(r1_file='sample_1_R1.fq.gz', ...)
>>> sam2_pack_stats = op.pack(r1_file='sample_2_R1.fq.gz', ...)
...
>>>
>>> # Compute and write barcode combination count matrix
>>> xcount_df, stats = op.xcount(index_files=['bc1_index', 'bc2_index'],
... pack_file='sample_1_pack', ...)
...
You can learn more about each module using help.
>>> import oligopool as op
>>> help(op)
>>> help(op.primer)
>>> help(op.barcode)
...
>>> help(op.xcount)
For advanced uses, the following classes are also available.
- vectorDB
- Scry
...
```
### Command Line Interface (CLI)
The `oligopool` package installs a CLI with two equivalent entry points: `oligopool` and `op`.
```bash
$ op
$ op manual
$ op manual topics
$ oligopool manual barcode
```
At a glance, running `op` or `oligopool` with no arguments prints available commands.
```bash
$ op
oligopool v2026.01.15
by ah
usage: oligopool COMMAND --argument=<value> ...
COMMANDS Available:
manual show module documentation
background build background k-mer database
barcode design constrained barcodes
primer design constrained primers
motif design or add motifs
spacer design or insert spacers
split split oligos into fragments
pad pad split oligos with primers
merge merge elements into one column
revcomp reverse complement elements
lenstat compute length statistics
final finalize library
index index barcodes and associates
pack pack fastq reads
acount association counting
xcount combinatorial counting
Note: Run "oligopool COMMAND" to see command-specific options.
```
Most CLI subcommands write outputs to disk, so `--output-file` is required for commands that produce output DataFrames (for example: `barcode`, `primer`, `motif`, `spacer`, `split`, `pad`, `merge`, `revcomp`, `final`).
Example:
```bash
$ op barcode \
--input-data initial_library.csv \
--oligo-length-limit 200 \
--barcode-length 20 \
--minimum-hamming-distance 3 \
--maximum-repeat-length 6 \
--barcode-column Barcode \
--output-file library_with_barcodes.csv
```
## Citation
If you use `Oligopool Calculator` or libraries designed or analyzed using the tool in your research publication, please cite our paper.
```
Hossain A, Cetnar DP, LaFleur TL, McLellan JR, Salis HM.
Automated Design of Oligopools and Rapid Analysis of Massively Parallel Barcoded Measurements.
ACS Synth Biol. 2024;13(12):4218-4232. doi:10.1021/acssynbio.4c00661
```
You can read the complete article online at [ACS Synthetic Biology](https://doi.org/10.1021/acssynbio.4c00661).
## License
`Oligopool Calculator` (c) 2026 Ayaan Hossain.
`Oligopool Calculator` is an **open-source software** under [GPL-3.0](https://opensource.org/license/gpl-3-0) License.
See [LICENSE](https://github.com/ayaanhossain/oligopool/blob/master/LICENSE) file for more details.
| text/markdown | Ayaan Hossain and Howard Salis | auh57@psu.edu, salis@psu.edu | null | null | null | synthetic computational biology nucleotide oligo pool calculator design analysis barcode primer spacer motif split pad assembly index pack scry classifier count acount xcount | [
"Development Status :: 4 - Beta",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"Topic :: Scientific/Engineering :: Chemistry",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Programming Language ::... | [] | https://github.com/ayaanhossain/oligopool | null | <4,>=3.10 | [] | [] | [] | [
"biopython>=1.84",
"primer3-py>=2.0.3",
"msgpack>=1.1.0",
"pyfastx>=2.1.0",
"edlib>=1.3.9.post1",
"parasail>=1.3.4",
"nrpcalc>=1.7.0",
"sharedb>=1.1.2",
"numba>=0.60.0",
"seaborn>=0.13.2",
"multiprocess>=0.70.17"
] | [] | [] | [] | [
"Bug Reports, https://github.com/ayaanhossain/oligopool/issues",
"Source, https://github.com/ayaanhossain/oligopool/tree/master/oligopool"
] | twine/6.2.0 CPython/3.13.7 | 2026-01-16T04:22:06.227119 | oligopool-2026.1.15-py3-none-any.whl | 184,787 | 25/1f/8703f62a492c5c62f5dd600ba3c317144525134bab01127fd3fe047f4832/oligopool-2026.1.15-py3-none-any.whl | py3 | bdist_wheel | null | false | 6ad3fa80cd1e5bde5bd9459d6774670a | 84086058f6da9616327e61345aaffd625ece1c32b7b59412058d960e6560864d | 251f8703f62a492c5c62f5dd600ba3c317144525134bab01127fd3fe047f4832 | null | [
"LICENSE"
] |
2.4 | oligopool | 2026.1.15 | Oligopool Calculator - Automated design and analysis of oligopool libraries | <h1 align="center">
<a href="https://github.com/ayaanhossain/oligopool/">
<img src="https://raw.githubusercontent.com/ayaanhossain/repfmt/main/oligopool/img/logo.svg" alt="Oligopool Calculator" width="460" class="center"/>
</a>
</h1>
<h4><p align="center">Version: 2026.01.15</p></h4>
<p align="center">
<a style="text-decoration: none" href="#Installation">Installation</a> •
<a style="text-decoration: none" href="#Getting-Started">Getting Started</a> •
<a style="text-decoration: none" href="#Command-Line-Interface-CLI">CLI</a> •
<a style="text-decoration: none" href="#Citation">Citation</a> •
<a style="text-decoration: none" href="#License">License</a>
</p>
`Oligopool Calculator` is a suite of algorithms for automated design and analysis of [oligopool libraries](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300125/).
It enables the scalable design of universal primer sets, error-correctable barcodes, the splitting of long constructs into multiple oligos, and the rapid packing and counting of barcoded reads -- all on a regular 8-core desktop computer.
We have used `Oligopool Calculator` in multiple projects to build libraries of tens of thousands of promoters (see [here](https://www.nature.com/articles/s41467-022-32829-5) and [here](https://www.nature.com/articles/s41587-020-0584-2)), ribozymes, and mRNA stability elements (see [here](https://www.nature.com/articles/s41467-024-54059-7)), illustrating the use of a flexible grammar to add multiple barcodes, cut sites, avoid excluded sequences, and optimize experimental constraints. These libraries were later characterized using highly efficient barcode counting provided by `Oligopool Calculator`. To learn more, please check out [our paper in ACS Synthetic Biology](https://pubs.acs.org/doi/10.1021/acssynbio.4c00661).
`Oligopool Calculator` facilitates the creative design and application of massively parallel reporter assays by automating and simplifying the whole process. It has been benchmarked on simulated libraries containing millions of defined variants and to analyze billions of reads.
<h1 align="center">
<a href="https://github.com/ayaanhossain/oligopool/">
<img src="https://raw.githubusercontent.com/ayaanhossain/repfmt/refs/heads/main/oligopool/img/workflow.svg" alt="Oligopool Calculator Workflow" width="3840" class="center"/>
</a>
</h1>
**Design and analysis of oligopool variants using `Oligopool Calculator`.** **(a)** In `Design Mode`, `Oligopool Calculator` can be used to generate optimized `barcode`s, `primer`s, `spacer`s, `motif`s and `split` longer oligos into shorter `pad`ded fragments for downstream synthesis and assembly. **(b)** Once the library is assembled and cloned, barcoded amplicon sequencing data can be processed via `Analysis Mode` for characterization. `Analysis Mode` proceeds by first `index`ing one or more sets of barcodes, `pack`ing the reads, and then producing count matrices either using `acount` (association counting) or `xcount` (combinatorial counting).
## Installation
`Oligopool Calculator` is a `Python3.10+`-exclusive library.
On `Linux`, `MacOS` and `Windows Subsystem for Linux` you can install `Oligopool Calculator` from [PyPI](https://pypi.org/project/oligopool/), where it is published as the `oligopool` package
```bash
$ pip install --upgrade oligopool # Installs and/or upgrades oligopool
```
This also installs the command line tools: `oligopool` and `op`.
or install it directly from GitHub.
```bash
$ pip install git+https://github.com/ayaanhossain/oligopool.git
```
Both approaches should install all dependencies automatically.
> **Note** This GitHub version will always be updated with all recent fixes. The PyPI version should be more stable.
If you are on `Windows` or simply prefer to, `Oligopool Calculator` can also be used via `docker` (please see [the notes](https://github.com/ayaanhossain/oligopool/blob/master/docker-notes.md)).
**Verifying Installation**
Successful installation will look like this.
```python
$ python
Python 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import oligopool as op
>>> op.__version__
'2026.01.15'
>>>
```
## Getting Started
`Oligopool Calculator` is carefully designed, easy to use, and stupid fast.
You can import the library and use its various functions either in a script or interactively inside a `jupyter` environment. Use `help(...)` to read the docs as necessary and follow along.
There are examples of a [design parser](https://github.com/ayaanhossain/oligopool/blob/master/examples/design-parser/design_parser.py) and an [analysis pipeline](https://github.com/ayaanhossain/oligopool/blob/master/examples/analysis-pipeline/analysis_pipeline.py) inside the [`examples`](https://github.com/ayaanhossain/oligopool/tree/master/examples) directory.
A notebook demonstrating [`Oligopool Calculator` in action](https://github.com/ayaanhossain/oligopool/blob/master/examples/OligopoolCalculatorInAction.ipynb) is provided there as well.
```python
$ python
Python 3.12.6 | packaged by conda-forge | (main, Sep 30 2024, 18:08:52) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
>>> import oligopool as op
>>> help(op)
...
oligopool v2026.01.15
by ah
Automated design and analysis of oligopool libraries.
The various modules in Oligopool Calculator can be used
interactively in a jupyter notebook, or be used to define
scripts for design and analysis pipelines on the cloud.
Oligopool Calculator offers two modes of operation
- Design Mode for designing oligopool libraries, and
- Analysis Mode for analyzing oligopool datasets.
Design Mode workflow
1. Initialize a pandas DataFrame with core library elements.
a. The DataFrame must contain a unique 'ID' column serving as primary key.
b. All other columns in the DataFrame must be DNA sequences.
2. Define any optional background sequences via the background module.
3. Add necessary oligopool elements with constraints via element modules.
4. Optionally, split long oligos and pad them via assembly modules.
5. Perform additional maneuvers and finalize library via auxiliary modules.
Background module available
- background
Element modules available
- primer
- barcode
- motif
- spacer
Assembly modules available
- split
- pad
Auxiliary modules available
- merge
- revcomp
- lenstat
- final
Design Mode example sketch
>>> import pandas as pd
>>> import oligopool as op
>>>
>>> # Read initial library
>>> init_df = pd.read_csv('initial_library.csv')
>>>
>>> # Add oligo elements one by one
>>> primer_df, stats = op.primer(input_data=init_df, ...)
>>> barcode_df, stats = op.barcode(input_data=primer_df, ...)
...
>>> # Check length statistics as needed
>>> length_stats = op.lenstat(input_data=further_along_df)
...
>>>
>>> # Split and pad longer oligos if needed
>>> split_df, stats = op.split(input_data=even_further_along_df, ...)
>>> first_pad_df, stats = op.pad(input_data=split_df, ...)
>>> second_pad_df, stats = op.pad(input_data=split_df, ...)
...
>>>
>>> # Finalize the library
>>> final_df, stats = op.final(input_data=ready_to_go_df, ...)
...
Analysis Mode workflow
1. Index one or more CSVs containing barcode (and associate) data.
2. Pack all NGS FastQ files, optionally merging them if required.
3. Use acount for association counting of variants and barcodes.
4. If multiple barcode combinations are to be counted use xcount.
5. Combine count DataFrames and perform stats and ML as necessary.
Indexing module available
- index
Packing module available
- pack
Counting modules available
- acount
- xcount
Analysis Mode example sketch
>>> import pandas as pd
>>> import oligopool as op
>>>
>>> # Read annotated library
>>> bc1_df = pd.read_csv('barcode_1.csv')
>>> bc2_df = pd.read_csv('barcode_2.csv')
>>> av1_df = pd.read_csv('associate_1.csv')
...
>>>
>>> # Index barcodes and any associates
>>> bc1_index_stats = op.index(barcode_data=bc1_df, barcode_column='BC1', ...)
>>> bc2_index_stats = op.index(barcode_data=bc2_df, barcode_column='BC2', ...)
...
>>>
>>> # Pack experiment FastQ files
>>> sam1_pack_stats = op.pack(r1_file='sample_1_R1.fq.gz', ...)
>>> sam2_pack_stats = op.pack(r1_file='sample_2_R1.fq.gz', ...)
...
>>>
>>> # Compute and write barcode combination count matrix
>>> xcount_df, stats = op.xcount(index_files=['bc1_index', 'bc2_index'],
... pack_file='sample_1_pack', ...)
...
You can learn more about each module using help.
>>> import oligopool as op
>>> help(op)
>>> help(op.primer)
>>> help(op.barcode)
...
>>> help(op.xcount)
For advanced uses, the following classes are also available.
- vectorDB
- Scry
...
```
### Command Line Interface (CLI)
The `oligopool` package installs a CLI with two equivalent entry points: `oligopool` and `op`.
```bash
$ op
$ op manual
$ op manual topics
$ oligopool manual barcode
```
At a glance, running `op` or `oligopool` with no arguments prints available commands.
```bash
$ op
oligopool v2026.01.15
by ah
usage: oligopool COMMAND --argument=<value> ...
COMMANDS Available:
manual show module documentation
background build background k-mer database
barcode design constrained barcodes
primer design constrained primers
motif design or add motifs
spacer design or insert spacers
split split oligos into fragments
pad pad split oligos with primers
merge merge elements into one column
revcomp reverse complement elements
lenstat compute length statistics
final finalize library
index index barcodes and associates
pack pack fastq reads
acount association counting
xcount combinatorial counting
Note: Run "oligopool COMMAND" to see command-specific options.
```
Most CLI subcommands write outputs to disk, so `--output-file` is required for commands that produce output DataFrames (for example: `barcode`, `primer`, `motif`, `spacer`, `split`, `pad`, `merge`, `revcomp`, `final`).
Example:
```bash
$ op barcode \
--input-data initial_library.csv \
--oligo-length-limit 200 \
--barcode-length 20 \
--minimum-hamming-distance 3 \
--maximum-repeat-length 6 \
--barcode-column Barcode \
--output-file library_with_barcodes.csv
```
## Citation
If you use `Oligopool Calculator` or libraries designed or analyzed using the tool in your research publication, please cite our paper.
```
Hossain A, Cetnar DP, LaFleur TL, McLellan JR, Salis HM.
Automated Design of Oligopools and Rapid Analysis of Massively Parallel Barcoded Measurements.
ACS Synth Biol. 2024;13(12):4218-4232. doi:10.1021/acssynbio.4c00661
```
You can read the complete article online at [ACS Synthetic Biology](https://doi.org/10.1021/acssynbio.4c00661).
## License
`Oligopool Calculator` (c) 2026 Ayaan Hossain.
`Oligopool Calculator` is an **open-source software** under [GPL-3.0](https://opensource.org/license/gpl-3-0) License.
See [LICENSE](https://github.com/ayaanhossain/oligopool/blob/master/LICENSE) file for more details.
| text/markdown | Ayaan Hossain and Howard Salis | auh57@psu.edu, salis@psu.edu | null | null | null | synthetic computational biology nucleotide oligo pool calculator design analysis barcode primer spacer motif split pad assembly index pack scry classifier count acount xcount | [
"Development Status :: 4 - Beta",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"Topic :: Scientific/Engineering :: Chemistry",
"License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
"Programming Language ::... | [] | https://github.com/ayaanhossain/oligopool | null | <4,>=3.10 | [] | [] | [] | [
"biopython>=1.84",
"primer3-py>=2.0.3",
"msgpack>=1.1.0",
"pyfastx>=2.1.0",
"edlib>=1.3.9.post1",
"parasail>=1.3.4",
"nrpcalc>=1.7.0",
"sharedb>=1.1.2",
"numba>=0.60.0",
"seaborn>=0.13.2",
"multiprocess>=0.70.17"
] | [] | [] | [] | [
"Bug Reports, https://github.com/ayaanhossain/oligopool/issues",
"Source, https://github.com/ayaanhossain/oligopool/tree/master/oligopool"
] | twine/6.2.0 CPython/3.13.7 | 2026-01-16T04:22:07.330972 | oligopool-2026.1.15.tar.gz | 161,915 | 24/a2/42550959e9ac1fe35c26dc14d06e280ce5dc65380878e8d5f30121a10680/oligopool-2026.1.15.tar.gz | source | sdist | null | false | 2b11ad08520e1f5cd20d9255bfb06505 | d039148ed213e5fde0a0bad09d1c19e982c2e318caaacbcd034dd1a60fa7d5ec | 24a242550959e9ac1fe35c26dc14d06e280ce5dc65380878e8d5f30121a10680 | null | [
"LICENSE"
] |
2.1 | odoo-addon-fs-storage | 16.0.1.3.5.3 | Implement the concept of Storage with amazon S3, sftp... | .. image:: https://odoo-community.org/readme-banner-image
:target: https://odoo-community.org/get-involved?utm_source=readme
:alt: Odoo Community Association
==========================
Filesystem Storage Backend
==========================
..
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! This file is generated by oca-gen-addon-readme !!
!! changes will be overwritten. !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! source digest: sha256:b9f95387306ce78e4543bc0b90f958fa188ba244dd6df41af486078d2d358fdf
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
.. |badge1| image:: https://img.shields.io/badge/maturity-Beta-yellow.png
:target: https://odoo-community.org/page/development-status
:alt: Beta
.. |badge2| image:: https://img.shields.io/badge/license-LGPL--3-blue.png
:target: http://www.gnu.org/licenses/lgpl-3.0-standalone.html
:alt: License: LGPL-3
.. |badge3| image:: https://img.shields.io/badge/github-OCA%2Fstorage-lightgray.png?logo=github
:target: https://github.com/OCA/storage/tree/16.0/fs_storage
:alt: OCA/storage
.. |badge4| image:: https://img.shields.io/badge/weblate-Translate%20me-F47D42.png
:target: https://translation.odoo-community.org/projects/storage-16-0/storage-16-0-fs_storage
:alt: Translate me on Weblate
.. |badge5| image:: https://img.shields.io/badge/runboat-Try%20me-875A7B.png
:target: https://runboat.odoo-community.org/builds?repo=OCA/storage&target_branch=16.0
:alt: Try me on Runboat
|badge1| |badge2| |badge3| |badge4| |badge5|
This addon is a technical addon that allows you to define filesystem like
storage for your data. It's used by other addons to store their data in a
transparent way into different kind of storages.
Through the fs.storage record, you get access to an object that implements
the `fsspec.spec.AbstractFileSystem <https://filesystem-spec.readthedocs.io/en/
latest/api.html#fsspec.spec.AbstractFileSystem>`_ interface and therefore give
you an unified interface to access your data whatever the storage protocol you
decide to use.
The list of supported protocols depends on the installed fsspec implementations.
By default, the addon will install the following protocols:
* LocalFileSystem
* MemoryFileSystem
* ZipFileSystem
* TarFileSystem
* FTPFileSystem
* CachingFileSystem
* WholeFileSystem
* SimplCacheFileSystem
* ReferenceFileSystem
* GenericFileSystem
* DirFileSystem
* DatabricksFileSystem
* GitHubFileSystem
* JupiterFileSystem
* OdooFileSystem
The OdooFileSystem is the one that allows you to store your data into a directory
mounted into your Odoo's storage directory. This is the default FS Storage
when creating a new fs.storage record.
Others protocols are available through the installation of additional
python packages:
* DropboxDriveFileSystem -> `pip install fsspec[dropbox]`
* HTTPFileSystem -> `pip install fsspec[http]`
* HTTPSFileSystem -> `pip install fsspec[http]`
* GCSFileSystem -> `pip install fsspec[gcs]`
* GSFileSystem -> `pip install fsspec[gs]`
* GoogleDriveFileSystem -> `pip install gdrivefs`
* SFTPFileSystem -> `pip install fsspec[sftp]`
* HaddoopFileSystem -> `pip install fsspec[hdfs]`
* S3FileSystem -> `pip install fsspec[s3]`
* WandbFS -> `pip install wandbfs`
* OCIFileSystem -> `pip install fsspec[oci]`
* AsyncLocalFileSystem -> `pip install 'morefs[asynclocalfs]`
* AzureDatalakeFileSystem -> `pip install fsspec[adl]`
* AzureBlobFileSystem -> `pip install fsspec[abfs]`
* DaskWorkerFileSystem -> `pip install fsspec[dask]`
* GitFileSystem -> `pip install fsspec[git]`
* SMBFileSystem -> `pip install fsspec[smb]`
* LibArchiveFileSystem -> `pip install fsspec[libarchive]`
* OSSFileSystem -> `pip install ossfs`
* WebdavFileSystem -> `pip install webdav4`
* DVCFileSystem -> `pip install dvc`
* XRootDFileSystem -> `pip install fsspec-xrootd`
This list of supported protocols is not exhaustive or could change in the future
depending on the fsspec releases. You can find more information about the
supported protocols on the `fsspec documentation
<https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem>`_.
**Table of contents**
.. contents::
:local:
Usage
=====
Configuration
~~~~~~~~~~~~~
When you create a new backend, you must specify the following:
* The name of the backend. This is the name that will be used to
identify the backend into Odoo
* The code of the backend. This code will identify the backend into the store_fname
field of the ir.attachment model. This code must be unique. It will be used
as scheme. example of the store_fname field: ``odoofs://abs34Tg11``.
* The protocol used by the backend. The protocol refers to the supported
protocols of the fsspec python package.
* A directory path. This is a root directory from which the filesystem will
be mounted. This directory must exist.
* The protocol options. These are the options that will be passed to the
fsspec python package when creating the filesystem. These options depend
on the protocol used and are described in the fsspec documentation.
* Resolve env vars. This options resolves the protocol options values starting
with $ from environment variables
* Check Connection Method. If set, Odoo will always check the connection before using
a storage and it will remove the fs connection from the cache if the check fails.
* ``Create Marker file`` : create a hidden file on remote and then check it exists with
Use it if you have write access to the remote and if it is not an issue to leave
the marker file in the root directory.
* ``List file`` : list all files from the root directory. You can use it if the directory
path does not contain a big list of files (for performance reasons)
Some protocols defined in the fsspec package are wrappers around other
protocols. For example, the SimpleCacheFileSystem protocol is a wrapper
around any local filesystem protocol. In such cases, you must specify into the
protocol options the protocol to be wrapped and the options to be passed to
the wrapped protocol.
For example, if you want to create a backend that uses the SimpleCacheFileSystem
protocol, after selecting the SimpleCacheFileSystem protocol, you must specify
the protocol options as follows:
.. code-block:: python
{
"directory_path": "/tmp/my_backend",
"target_protocol": "odoofs",
"target_options": {...},
}
In this example, the SimpleCacheFileSystem protocol will be used as a wrapper
around the odoofs protocol.
Server Environment
~~~~~~~~~~~~~~~~~~
To ease the management of the filesystem storages configuration accross the different
environments, the configuration of the filesystem storages can be defined in
environment files or directly in the main configuration file. For example, the
configuration of a filesystem storage with the code `fsprod` can be provided in the
main configuration file as follows:
.. code-block:: ini
[fs_storage.fsprod]
protocol=s3
options={"endpoint_url": "https://my_s3_server/", "key": "KEY", "secret": "SECRET"}
directory_path=my_bucket
To work, a `storage.backend` record must exist with the code `fsprod` into the database.
In your configuration section, you can specify the value for the following fields:
* `protocol`
* `options`
* `directory_path`
Migration from storage_backend
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The fs_storage addon can be used to replace the storage_backend addon. (It has
been designed to be a drop-in replacement for the storage_backend addon). To
ease the migration, the `fs.storage` model defines the high-level methods
available in the storage_backend model. These methods are:
* `add`
* `get`
* `list_files`
* `find_files`
* `move_files`
* `delete`
These methods are wrappers around the methods of the `fsspec.AbstractFileSystem`
class (see https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem).
These methods are marked as deprecated and will be removed in a future version (V18)
of the addon. You should use the methods of the `fsspec.AbstractFileSystem` class
instead since they are more flexible and powerful. You can access the instance
of the `fsspec.AbstractFileSystem` class using the `fs` property of a `fs.storage`
record.
Known issues / Roadmap
======================
* Transactions: fsspec comes with a transactional mechanism that once started,
gathers all the files created during the transaction, and if the transaction
is committed, moves them to their final locations. It would be useful to
bridge this with the transactional mechanism of odoo. This would allow to
ensure that all the files created during a transaction are either all
moved to their final locations, or all deleted if the transaction is rolled
back. This mechanism is only valid for files created during the transaction
by a call to the `open` method of the file system. It is not valid for others
operations, such as `rm`, `mv_file`, ... .
Changelog
=========
16.0.1.2.0 (2024-02-06)
~~~~~~~~~~~~~~~~~~~~~~~
**Features**
- Invalidate FS filesystem object cache when the connection fails, forcing a reconnection. (`#320 <https://github.com/OCA/storage/issues/320>`_)
16.0.1.1.0 (2023-12-22)
~~~~~~~~~~~~~~~~~~~~~~~
**Features**
- Add parameter on storage backend to resolve protocol options values starting with $ from environment variables (`#303 <https://github.com/OCA/storage/issues/303>`_)
16.0.1.0.3 (2023-10-17)
~~~~~~~~~~~~~~~~~~~~~~~
**Bugfixes**
- Fix access to technical models to be able to upload attachments for users with basic access (`#289 <https://github.com/OCA/storage/issues/289>`_)
16.0.1.0.2 (2023-10-09)
~~~~~~~~~~~~~~~~~~~~~~~
**Bugfixes**
- Avoid config error when using the webdav protocol. The auth option is expected
to be a tuple not a list. Since our config is loaded from a json file, we
cannot use tuples. The fix converts the list to a tuple when the config is
related to a webdav protocol and the auth option is into the confix. (`#285 <https://github.com/OCA/storage/issues/285>`_)
Bug Tracker
===========
Bugs are tracked on `GitHub Issues <https://github.com/OCA/storage/issues>`_.
In case of trouble, please check there if your issue has already been reported.
If you spotted it first, help us to smash it by providing a detailed and welcomed
`feedback <https://github.com/OCA/storage/issues/new?body=module:%20fs_storage%0Aversion:%2016.0%0A%0A**Steps%20to%20reproduce**%0A-%20...%0A%0A**Current%20behavior**%0A%0A**Expected%20behavior**>`_.
Do not contact contributors directly about support or help with technical issues.
Credits
=======
Authors
~~~~~~~
* ACSONE SA/NV
Contributors
~~~~~~~~~~~~
* Laurent Mignon <laurent.mignon@acsone.eu>
* Sébastien BEAU <sebastien.beau@akretion.com>
Maintainers
~~~~~~~~~~~
This module is maintained by the OCA.
.. image:: https://odoo-community.org/logo.png
:alt: Odoo Community Association
:target: https://odoo-community.org
OCA, or the Odoo Community Association, is a nonprofit organization whose
mission is to support the collaborative development of Odoo features and
promote its widespread use.
This module is part of the `OCA/storage <https://github.com/OCA/storage/tree/16.0/fs_storage>`_ project on GitHub.
You are welcome to contribute. To learn how please visit https://odoo-community.org/page/Contribute.
| null | ACSONE SA/NV, Odoo Community Association (OCA) | support@odoo-community.org | null | null | LGPL-3 | null | [
"Programming Language :: Python",
"Framework :: Odoo",
"Framework :: Odoo :: 16.0",
"License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
"Development Status :: 4 - Beta"
] | [] | https://github.com/OCA/storage | null | >=3.10 | [] | [] | [] | [
"fsspec>=2024.5.0",
"odoo-addon-server-environment<16.1dev,>=16.0dev",
"odoo<16.1dev,>=16.0a"
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.12.3 | 2026-01-16T04:24:02.080575 | odoo_addon_fs_storage-16.0.1.3.5.3-py3-none-any.whl | 60,643 | 96/60/76554dfce1dd7f408fe268a838844f6fc88fea58429bed74b86502cff2de/odoo_addon_fs_storage-16.0.1.3.5.3-py3-none-any.whl | py3 | bdist_wheel | null | false | 4a5019087a616bb933d86812a7d2f67a | 54955953c7e67a30b091abfd9ee9d5147f910b734a24f3e50d5428d9694b4da5 | 966076554dfce1dd7f408fe268a838844f6fc88fea58429bed74b86502cff2de | null | [] |
2.1 | odoo-addon-fs-storage | 17.0.2.1.0.1 | Implement the concept of Storage with amazon S3, sftp... | .. image:: https://odoo-community.org/readme-banner-image
:target: https://odoo-community.org/get-involved?utm_source=readme
:alt: Odoo Community Association
==========================
Filesystem Storage Backend
==========================
..
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! This file is generated by oca-gen-addon-readme !!
!! changes will be overwritten. !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! source digest: sha256:43050d60f14fb2179088cecafc9185490a52d9ebfc179af1c4caefce457e4d19
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
.. |badge1| image:: https://img.shields.io/badge/maturity-Beta-yellow.png
:target: https://odoo-community.org/page/development-status
:alt: Beta
.. |badge2| image:: https://img.shields.io/badge/license-LGPL--3-blue.png
:target: http://www.gnu.org/licenses/lgpl-3.0-standalone.html
:alt: License: LGPL-3
.. |badge3| image:: https://img.shields.io/badge/github-OCA%2Fstorage-lightgray.png?logo=github
:target: https://github.com/OCA/storage/tree/17.0/fs_storage
:alt: OCA/storage
.. |badge4| image:: https://img.shields.io/badge/weblate-Translate%20me-F47D42.png
:target: https://translation.odoo-community.org/projects/storage-17-0/storage-17-0-fs_storage
:alt: Translate me on Weblate
.. |badge5| image:: https://img.shields.io/badge/runboat-Try%20me-875A7B.png
:target: https://runboat.odoo-community.org/builds?repo=OCA/storage&target_branch=17.0
:alt: Try me on Runboat
|badge1| |badge2| |badge3| |badge4| |badge5|
This addon is a technical addon that allows you to define filesystem
like storage for your data. It's used by other addons to store their
data in a transparent way into different kind of storages.
Through the fs.storage record, you get access to an object that
implements the
`fsspec.spec.AbstractFileSystem <https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem>`__
interface and therefore give you an unified interface to access your
data whatever the storage protocol you decide to use.
The list of supported protocols depends on the installed fsspec
implementations. By default, the addon will install the following
protocols:
- LocalFileSystem
- MemoryFileSystem
- ZipFileSystem
- TarFileSystem
- FTPFileSystem
- CachingFileSystem
- WholeFileSystem
- SimplCacheFileSystem
- ReferenceFileSystem
- GenericFileSystem
- DirFileSystem
- DatabricksFileSystem
- GitHubFileSystem
- JupiterFileSystem
- OdooFileSystem
The OdooFileSystem is the one that allows you to store your data into a
directory mounted into your Odoo's storage directory. This is the
default FS Storage when creating a new fs.storage record.
Others protocols are available through the installation of additional
python packages:
- DropboxDriveFileSystem -> pip install fsspec[dropbox]
- HTTPFileSystem -> pip install fsspec[http]
- HTTPSFileSystem -> pip install fsspec[http]
- GCSFileSystem -> pip install fsspec[gcs]
- GSFileSystem -> pip install fsspec[gs]
- GoogleDriveFileSystem -> pip install gdrivefs
- SFTPFileSystem -> pip install fsspec[sftp]
- HaddoopFileSystem -> pip install fsspec[hdfs]
- S3FileSystem -> pip install fsspec[s3]
- WandbFS -> pip install wandbfs
- OCIFileSystem -> pip install fsspec[oci]
- AsyncLocalFileSystem -> pip install 'morefs[asynclocalfs]
- AzureDatalakeFileSystem -> pip install fsspec[adl]
- AzureBlobFileSystem -> pip install fsspec[abfs]
- DaskWorkerFileSystem -> pip install fsspec[dask]
- GitFileSystem -> pip install fsspec[git]
- SMBFileSystem -> pip install fsspec[smb]
- LibArchiveFileSystem -> pip install fsspec[libarchive]
- OSSFileSystem -> pip install ossfs
- WebdavFileSystem -> pip install webdav4
- DVCFileSystem -> pip install dvc
- XRootDFileSystem -> pip install fsspec-xrootd
This list of supported protocols is not exhaustive or could change in
the future depending on the fsspec releases. You can find more
information about the supported protocols on the `fsspec
documentation <https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem>`__.
**Table of contents**
.. contents::
:local:
Usage
=====
Configuration
-------------
When you create a new backend, you must specify the following:
- The name of the backend. This is the name that will be used to
identify the backend into Odoo
- The code of the backend. This code will identify the backend into the
store_fname field of the ir.attachment model. This code must be
unique. It will be used as scheme. example of the store_fname field:
``odoofs://abs34Tg11``.
- The protocol used by the backend. The protocol refers to the supported
protocols of the fsspec python package.
- A directory path. This is a root directory from which the filesystem
will be mounted. This directory must exist.
- The protocol options. These are the options that will be passed to the
fsspec python package when creating the filesystem. These options
depend on the protocol used and are described in the fsspec
documentation.
- Resolve env vars. This options resolves the protocol options values
starting with $ from environment variables
- Check Connection Method. If set, Odoo will always check the connection
before using a storage and it will remove the fs connection from the
cache if the check fails.
- ``Create Marker file``: create a hidden file on remote and then
check it exists with Use it if you have write access to the remote
and if it is not an issue to leave the marker file in the root
directory.
- ``List file``: list all files from the root directory. You can use
it if the directory path does not contain a big list of files (for
performance reasons)
Some protocols defined in the fsspec package are wrappers around other
protocols. For example, the SimpleCacheFileSystem protocol is a wrapper
around any local filesystem protocol. In such cases, you must specify
into the protocol options the protocol to be wrapped and the options to
be passed to the wrapped protocol.
For example, if you want to create a backend that uses the
SimpleCacheFileSystem protocol, after selecting the
SimpleCacheFileSystem protocol, you must specify the protocol options as
follows:
.. code:: python
{
"directory_path": "/tmp/my_backend",
"target_protocol": "odoofs",
"target_options": {...},
}
In this example, the SimpleCacheFileSystem protocol will be used as a
wrapper around the odoofs protocol.
Server Environment
------------------
To ease the management of the filesystem storages configuration accross
the different environments, the configuration of the filesystem storages
can be defined in environment files or directly in the main
configuration file. For example, the configuration of a filesystem
storage with the code fsprod can be provided in the main configuration
file as follows:
.. code:: ini
[fs_storage.fsprod]
protocol=s3
options={"endpoint_url": "https://my_s3_server/", "key": "KEY", "secret": "SECRET"}
directory_path=my_bucket
To work, a storage.backend record must exist with the code fsprod into
the database. In your configuration section, you can specify the value
for the following fields:
- protocol
- options
- directory_path
When evaluating directory_path, ``{db_name}`` is replaced by the
database name. This is usefull in multi-tenant with a setup completly
controlled by configuration files.
Migration from storage_backend
------------------------------
The fs_storage addon can be used to replace the storage_backend addon.
(It has been designed to be a drop-in replacement for the
storage_backend addon). To ease the migration, the fs.storage model
defines the high-level methods available in the storage_backend model.
These methods are:
- add
- get
- list_files
- find_files
- move_files
- delete
These methods are wrappers around the methods of the
fsspec.AbstractFileSystem class (see
https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem).
These methods are marked as deprecated and will be removed in a future
version (V18) of the addon. You should use the methods of the
fsspec.AbstractFileSystem class instead since they are more flexible and
powerful. You can access the instance of the fsspec.AbstractFileSystem
class using the fs property of a fs.storage record.
Known issues / Roadmap
======================
- Transactions: fsspec comes with a transactional mechanism that once
started, gathers all the files created during the transaction, and if
the transaction is committed, moves them to their final locations. It
would be useful to bridge this with the transactional mechanism of
odoo. This would allow to ensure that all the files created during a
transaction are either all moved to their final locations, or all
deleted if the transaction is rolled back. This mechanism is only
valid for files created during the transaction by a call to the open
method of the file system. It is not valid for others operations, such
as rm, mv_file, ... .
Changelog
=========
17.0.2.1.0 (2025-10-22)
-----------------------
Features
~~~~~~~~
- Replace {db_name} by the database name in directory_path
(`#db_name <https://github.com/OCA/storage/issues/db_name>`__)
17.0.2.0.4 (2025-08-19)
-----------------------
Features
~~~~~~~~
- Allow setting check_connection_method in configuration file.
17.0.2.0.0 (2024-10-07)
-----------------------
Features
~~~~~~~~
- Invalidate FS filesystem object cache when the connection fails,
forcing a reconnection.
(`#320 <https://github.com/OCA/storage/issues/320>`__)
16.0.1.1.0 (2023-12-22)
-----------------------
**Features**
- Add parameter on storage backend to resolve protocol options values
starting with $ from environment variables
(`#303 <https://github.com/OCA/storage/issues/303>`__)
16.0.1.0.3 (2023-10-17)
-----------------------
**Bugfixes**
- Fix access to technical models to be able to upload attachments for
users with basic access
(`#289 <https://github.com/OCA/storage/issues/289>`__)
16.0.1.0.2 (2023-10-09)
-----------------------
**Bugfixes**
- Avoid config error when using the webdav protocol. The auth option is
expected to be a tuple not a list. Since our config is loaded from a
json file, we cannot use tuples. The fix converts the list to a tuple
when the config is related to a webdav protocol and the auth option is
into the confix.
(`#285 <https://github.com/OCA/storage/issues/285>`__)
Bug Tracker
===========
Bugs are tracked on `GitHub Issues <https://github.com/OCA/storage/issues>`_.
In case of trouble, please check there if your issue has already been reported.
If you spotted it first, help us to smash it by providing a detailed and welcomed
`feedback <https://github.com/OCA/storage/issues/new?body=module:%20fs_storage%0Aversion:%2017.0%0A%0A**Steps%20to%20reproduce**%0A-%20...%0A%0A**Current%20behavior**%0A%0A**Expected%20behavior**>`_.
Do not contact contributors directly about support or help with technical issues.
Credits
=======
Authors
-------
* ACSONE SA/NV
Contributors
------------
- Laurent Mignon <laurent.mignon@acsone.eu>
- Sébastien BEAU <sebastien.beau@akretion.com>
Maintainers
-----------
This module is maintained by the OCA.
.. image:: https://odoo-community.org/logo.png
:alt: Odoo Community Association
:target: https://odoo-community.org
OCA, or the Odoo Community Association, is a nonprofit organization whose
mission is to support the collaborative development of Odoo features and
promote its widespread use.
This module is part of the `OCA/storage <https://github.com/OCA/storage/tree/17.0/fs_storage>`_ project on GitHub.
You are welcome to contribute. To learn how please visit https://odoo-community.org/page/Contribute.
| text/x-rst | ACSONE SA/NV, Odoo Community Association (OCA) | support@odoo-community.org | null | null | LGPL-3 | null | [
"Programming Language :: Python",
"Framework :: Odoo",
"Framework :: Odoo :: 17.0",
"License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
"Development Status :: 4 - Beta"
] | [] | https://github.com/OCA/storage | null | >=3.10 | [] | [] | [] | [
"fsspec>=2024.5.0",
"odoo-addon-server_environment<17.1dev,>=17.0dev",
"odoo<17.1dev,>=17.0a"
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.12.3 | 2026-01-16T04:24:02.791615 | odoo_addon_fs_storage-17.0.2.1.0.1-py3-none-any.whl | 61,902 | 41/7c/2c10383f3a51e09be871089e537da2f793759de7c191c4666903316e5553/odoo_addon_fs_storage-17.0.2.1.0.1-py3-none-any.whl | py3 | bdist_wheel | null | false | 1da68b979199c3a29cbddec45862ebd8 | 8dbc95c8d475c6545ad2f29daabef0ccab4ee28e40901e6c2a1b6a4473d7f016 | 417c2c10383f3a51e09be871089e537da2f793759de7c191c4666903316e5553 | null | [] |
2.1 | odoo-addon-fs-storage | 19.0.1.0.0.5 | Implement the concept of Storage with amazon S3, sftp... | .. image:: https://odoo-community.org/readme-banner-image
:target: https://odoo-community.org/get-involved?utm_source=readme
:alt: Odoo Community Association
==========================
Filesystem Storage Backend
==========================
..
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! This file is generated by oca-gen-addon-readme !!
!! changes will be overwritten. !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! source digest: sha256:b29d04846136913c47213b78a13afecebd5b01937eddbae183e25ea982dab818
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
.. |badge1| image:: https://img.shields.io/badge/maturity-Beta-yellow.png
:target: https://odoo-community.org/page/development-status
:alt: Beta
.. |badge2| image:: https://img.shields.io/badge/license-LGPL--3-blue.png
:target: http://www.gnu.org/licenses/lgpl-3.0-standalone.html
:alt: License: LGPL-3
.. |badge3| image:: https://img.shields.io/badge/github-OCA%2Fstorage-lightgray.png?logo=github
:target: https://github.com/OCA/storage/tree/19.0/fs_storage
:alt: OCA/storage
.. |badge4| image:: https://img.shields.io/badge/weblate-Translate%20me-F47D42.png
:target: https://translation.odoo-community.org/projects/storage-19-0/storage-19-0-fs_storage
:alt: Translate me on Weblate
.. |badge5| image:: https://img.shields.io/badge/runboat-Try%20me-875A7B.png
:target: https://runboat.odoo-community.org/builds?repo=OCA/storage&target_branch=19.0
:alt: Try me on Runboat
|badge1| |badge2| |badge3| |badge4| |badge5|
This addon is a technical addon that allows you to define filesystem
like storage for your data. It's used by other addons to store their
data in a transparent way into different kind of storages.
Through the fs.storage record, you get access to an object that
implements the
`fsspec.spec.AbstractFileSystem <https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem>`__
interface and therefore give you an unified interface to access your
data whatever the storage protocol you decide to use.
The list of supported protocols depends on the installed fsspec
implementations. By default, the addon will install the following
protocols:
- LocalFileSystem
- MemoryFileSystem
- ZipFileSystem
- TarFileSystem
- FTPFileSystem
- CachingFileSystem
- WholeFileSystem
- SimplCacheFileSystem
- ReferenceFileSystem
- GenericFileSystem
- DirFileSystem
- DatabricksFileSystem
- GitHubFileSystem
- JupiterFileSystem
- OdooFileSystem
The OdooFileSystem is the one that allows you to store your data into a
directory mounted into your Odoo's storage directory. This is the
default FS Storage when creating a new fs.storage record.
Others protocols are available through the installation of additional
python packages:
- DropboxDriveFileSystem -> pip install fsspec[dropbox]
- HTTPFileSystem -> pip install fsspec[http]
- HTTPSFileSystem -> pip install fsspec[http]
- GCSFileSystem -> pip install fsspec[gcs]
- GSFileSystem -> pip install fsspec[gs]
- GoogleDriveFileSystem -> pip install gdrivefs
- SFTPFileSystem -> pip install fsspec[sftp]
- HaddoopFileSystem -> pip install fsspec[hdfs]
- S3FileSystem -> pip install fsspec[s3]
- WandbFS -> pip install wandbfs
- OCIFileSystem -> pip install fsspec[oci]
- AsyncLocalFileSystem -> pip install 'morefs[asynclocalfs]
- AzureDatalakeFileSystem -> pip install fsspec[adl]
- AzureBlobFileSystem -> pip install fsspec[abfs]
- DaskWorkerFileSystem -> pip install fsspec[dask]
- GitFileSystem -> pip install fsspec[git]
- SMBFileSystem -> pip install fsspec[smb]
- LibArchiveFileSystem -> pip install fsspec[libarchive]
- OSSFileSystem -> pip install ossfs
- WebdavFileSystem -> pip install webdav4
- DVCFileSystem -> pip install dvc
- XRootDFileSystem -> pip install fsspec-xrootd
This list of supported protocols is not exhaustive or could change in
the future depending on the fsspec releases. You can find more
information about the supported protocols on the `fsspec
documentation <https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem>`__.
**Table of contents**
.. contents::
:local:
Usage
=====
Configuration
-------------
When you create a new backend, you must specify the following:
- The name of the backend. This is the name that will be used to
identify the backend into Odoo
- The code of the backend. This code will identify the backend into the
store_fname field of the ir.attachment model. This code must be
unique. It will be used as scheme. example of the store_fname field:
``odoofs://abs34Tg11``.
- The protocol used by the backend. The protocol refers to the supported
protocols of the fsspec python package.
- A directory path. This is a root directory from which the filesystem
will be mounted. This directory must exist.
- The protocol options. These are the options that will be passed to the
fsspec python package when creating the filesystem. These options
depend on the protocol used and are described in the fsspec
documentation.
- Resolve env vars. This options resolves the protocol options values
starting with $ from environment variables
- Check Connection Method. If set, Odoo will always check the connection
before using a storage and it will remove the fs connection from the
cache if the check fails.
- ``Create Marker file``: create a hidden file on remote and then
check it exists with Use it if you have write access to the remote
and if it is not an issue to leave the marker file in the root
directory.
- ``List file``: list all files from the root directory. You can use
it if the directory path does not contain a big list of files (for
performance reasons)
Some protocols defined in the fsspec package are wrappers around other
protocols. For example, the SimpleCacheFileSystem protocol is a wrapper
around any local filesystem protocol. In such cases, you must specify
into the protocol options the protocol to be wrapped and the options to
be passed to the wrapped protocol.
For example, if you want to create a backend that uses the
SimpleCacheFileSystem protocol, after selecting the
SimpleCacheFileSystem protocol, you must specify the protocol options as
follows:
.. code:: python
{
"directory_path": "/tmp/my_backend",
"target_protocol": "odoofs",
"target_options": {...},
}
In this example, the SimpleCacheFileSystem protocol will be used as a
wrapper around the odoofs protocol.
Server Environment
------------------
To ease the management of the filesystem storages configuration accross
the different environments, the configuration of the filesystem storages
can be defined in environment files or directly in the main
configuration file. For example, the configuration of a filesystem
storage with the code fsprod can be provided in the main configuration
file as follows:
.. code:: ini
[fs_storage.fsprod]
protocol=s3
options={"endpoint_url": "https://my_s3_server/", "key": "KEY", "secret": "SECRET"}
directory_path=my_bucket
To work, a storage.backend record must exist with the code fsprod into
the database. In your configuration section, you can specify the value
for the following fields:
- protocol
- options
- directory_path
Migration from storage_backend
------------------------------
The fs_storage addon can be used to replace the storage_backend addon.
(It has been designed to be a drop-in replacement for the
storage_backend addon). To ease the migration, the fs.storage model
defines the high-level methods available in the storage_backend model.
These methods are:
- add
- get
- list_files
- find_files
- move_files
- delete
These methods are wrappers around the methods of the
fsspec.AbstractFileSystem class (see
https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem).
These methods are marked as deprecated and will be removed in a future
version (V18) of the addon. You should use the methods of the
fsspec.AbstractFileSystem class instead since they are more flexible and
powerful. You can access the instance of the fsspec.AbstractFileSystem
class using the fs property of a fs.storage record.
Known issues / Roadmap
======================
- Transactions: fsspec comes with a transactional mechanism that once
started, gathers all the files created during the transaction, and if
the transaction is committed, moves them to their final locations. It
would be useful to bridge this with the transactional mechanism of
odoo. This would allow to ensure that all the files created during a
transaction are either all moved to their final locations, or all
deleted if the transaction is rolled back. This mechanism is only
valid for files created during the transaction by a call to the open
method of the file system. It is not valid for others operations, such
as rm, mv_file, ... .
Changelog
=========
18.0.2.0.1 (2025-07-23)
-----------------------
Features
~~~~~~~~
- Allow setting check_connection_method in configuration file.
18.0.1.0.1 (2024-11-10)
-----------------------
Features
~~~~~~~~
- Invalidate FS filesystem object cache when the connection fails,
forcing a reconnection.
(`#320 <https://github.com/OCA/storage/issues/320>`__)
16.0.1.1.0 (2023-12-22)
-----------------------
**Features**
- Add parameter on storage backend to resolve protocol options values
starting with $ from environment variables
(`#303 <https://github.com/OCA/storage/issues/303>`__)
16.0.1.0.3 (2023-10-17)
-----------------------
**Bugfixes**
- Fix access to technical models to be able to upload attachments for
users with basic access
(`#289 <https://github.com/OCA/storage/issues/289>`__)
16.0.1.0.2 (2023-10-09)
-----------------------
**Bugfixes**
- Avoid config error when using the webdav protocol. The auth option is
expected to be a tuple not a list. Since our config is loaded from a
json file, we cannot use tuples. The fix converts the list to a tuple
when the config is related to a webdav protocol and the auth option is
into the confix.
(`#285 <https://github.com/OCA/storage/issues/285>`__)
Bug Tracker
===========
Bugs are tracked on `GitHub Issues <https://github.com/OCA/storage/issues>`_.
In case of trouble, please check there if your issue has already been reported.
If you spotted it first, help us to smash it by providing a detailed and welcomed
`feedback <https://github.com/OCA/storage/issues/new?body=module:%20fs_storage%0Aversion:%2019.0%0A%0A**Steps%20to%20reproduce**%0A-%20...%0A%0A**Current%20behavior**%0A%0A**Expected%20behavior**>`_.
Do not contact contributors directly about support or help with technical issues.
Credits
=======
Authors
-------
* ACSONE SA/NV
Contributors
------------
- Laurent Mignon <laurent.mignon@acsone.eu>
- Sébastien BEAU <sebastien.beau@akretion.com>
- Marie Lejeune <marie.lejeune@acsone.eu>
- Julien Coux <julien.coux@camptocamp.com>
Maintainers
-----------
This module is maintained by the OCA.
.. image:: https://odoo-community.org/logo.png
:alt: Odoo Community Association
:target: https://odoo-community.org
OCA, or the Odoo Community Association, is a nonprofit organization whose
mission is to support the collaborative development of Odoo features and
promote its widespread use.
This module is part of the `OCA/storage <https://github.com/OCA/storage/tree/19.0/fs_storage>`_ project on GitHub.
You are welcome to contribute. To learn how please visit https://odoo-community.org/page/Contribute.
| text/x-rst | ACSONE SA/NV, Odoo Community Association (OCA) | support@odoo-community.org | null | null | LGPL-3 | null | [
"Programming Language :: Python",
"Framework :: Odoo",
"Framework :: Odoo :: 19.0",
"License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
"Development Status :: 4 - Beta"
] | [] | https://github.com/OCA/storage | null | null | [] | [] | [] | [
"fsspec>=2024.5.0",
"odoo-addon-server_environment==19.0.*",
"odoo==19.0.*"
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.12.3 | 2026-01-16T04:24:16.973510 | odoo_addon_fs_storage-19.0.1.0.0.5-py3-none-any.whl | 66,712 | 2e/ff/15f04c3ddc2300001b0d3800f126d0cbb03b90d96502499e698fff84475c/odoo_addon_fs_storage-19.0.1.0.0.5-py3-none-any.whl | py3 | bdist_wheel | null | false | 369e1083da167ac5661eb0fbc2e93fa2 | 3361faa6c61b5299fd7cfc026fac6981546a0b8f44e32d017acdfccdcb4af82b | 2eff15f04c3ddc2300001b0d3800f126d0cbb03b90d96502499e698fff84475c | null | [] |
2.3 | dj-playlist-optimizer | 1.1.0 | DJ playlist optimizer using Google OR-Tools for harmonic mixing and BPM matching | # 🎧 DJ Playlist Optimizer
Optimize DJ playlists for harmonic mixing using Google OR-Tools constraint programming.
## Features
- ✨ **Longest Path Optimization**: Finds the maximum number of tracks that can be mixed together
- 🎵 **Harmonic Mixing**: Uses the Camelot Wheel system for key compatibility
- 🎧 **Rekordbox Integration**: Read playlists directly from your local Rekordbox 6/7 database (tested with v7.2.8)
- 🔊 **BPM Matching**: Supports direct, halftime, and doubletime BPM compatibility
- ⚙️ **Configurable Strictness**: STRICT, MODERATE, or RELAXED harmonic compatibility levels
- 📤 **Rekordbox Export**: Export results to Rekordbox XML or write directly to the Rekordbox database
- 🚀 **Fast**: Powered by Google OR-Tools CP-SAT solver (award-winning constraint solver)
- 📦 **SDK + CLI**: Use as a Python library or command-line tool
## Installation
```bash
uv add dj-playlist-optimizer
```
Or with pip:
```bash
pip install dj-playlist-optimizer
```
## Quick Start
### SDK Usage
```python
from dj_playlist_optimizer import PlaylistOptimizer, Track, HarmonicLevel
tracks = [
Track(id="track_001", key="8A", bpm=128),
Track(id="track_002", key="8B", bpm=130),
Track(id="track_003", key="9A", bpm=125),
]
optimizer = PlaylistOptimizer(
bpm_tolerance=10,
allow_halftime_bpm=True,
max_violation_pct=0.10,
harmonic_level=HarmonicLevel.STRICT,
)
result = optimizer.optimize(tracks)
for i, track in enumerate(result.playlist, 1):
print(f"{i}. {track.id} ({track.key}, {track.bpm} BPM)")
```
### CLI Usage
```bash
# Basic usage
dj-optimize tracks.json
# With custom settings
dj-optimize tracks.json --bpm-tolerance 8 --harmonic-level moderate
# Save results to JSON
dj-optimize tracks.json --output result.json
# Use with Rekordbox (v6/v7)
dj-optimize --rekordbox # List playlists
dj-optimize --rekordbox --playlist "Techno" # Optimize specific playlist
dj-optimize --rekordbox --playlist "Techno" --output r.xml # Export to Rekordbox XML
dj-optimize --rekordbox --playlist "Techno" --write-to-db # Write directly to Rekordbox DB
# Enable verbose logging
dj-optimize tracks.json -v # INFO level
dj-optimize tracks.json -vv # DEBUG level
```
## Rekordbox Integration
The tool provides two ways to save your optimized playlists back to Rekordbox:
### 1. XML Export (Recommended)
Export the results to an XML file that can be imported into Rekordbox:
```bash
dj-optimize --rekordbox --playlist "My Playlist" --output optimized.xml
```
In Rekordbox:
1. Go to **File > Import > Import Playlist**
2. Select `optimized.xml`
3. The playlist will appear in the `ROOT` folder (e.g., `My Playlist_20260115_120000`)
### 2. Direct Database Write (Advanced)
Write the optimized playlist directly to your Rekordbox 6 database:
```bash
dj-optimize --rekordbox --playlist "My Playlist" --write-to-db
```
**⚠️ WARNING:**
- **Close Rekordbox** before running this command.
- This modifies your `master.db` file directly.
- **Backup your database** before using this feature.
## Input Format
JSON file with tracks containing `id`, `key` (Camelot notation), and `bpm`:
```json
{
"tracks": [
{"id": "track_001", "key": "8A", "bpm": 128},
{"id": "track_002", "key": "8B", "bpm": 130},
{"id": "track_003", "key": "9A", "bpm": 125}
]
}
```
## How It Works
### 1. BPM Compatibility
Adjacent tracks must have compatible BPMs within tolerance:
| Track A | Track B | Tolerance | Match? | Reason |
|---------|---------|-----------|--------|--------|
| 128 BPM | 130 BPM | ±10 | ✅ | Direct (diff = 2) |
| 128 BPM | 64 BPM | ±10 | ✅ | Half-time (128 = 64×2) |
| 75 BPM | 150 BPM | ±10 | ✅ | Double-time (75×2 = 150) |
| 128 BPM | 100 BPM | ±10 | ❌ | Too far |
### 2. Harmonic Mixing (Camelot Wheel)
Harmonic compatibility levels:
**STRICT** (default):
- Same key (8A → 8A)
- ±1 hour same letter (8A → 7A, 9A)
- Same hour different letter (8A → 8B)
**MODERATE**:
- Above + ±1 hour different letter (8A → 9B, 7B)
**RELAXED**:
- Above + ±3 hours (8A → 5A, 11A)
### 3. Optimization Goal
Maximize playlist length while keeping non-harmonic transitions below the threshold (default: 10%).
## Configuration Options
| Parameter | Default | Description |
|-----------|---------|-------------|
| `bpm_tolerance` | 10.0 | Maximum BPM difference for direct match |
| `allow_halftime_bpm` | True | Enable half/double-time matching |
| `max_violation_pct` | 0.10 | Max percentage of non-harmonic transitions |
| `harmonic_level` | STRICT | Harmonic compatibility strictness |
| `time_limit_seconds` | 60.0 | Solver time limit |
## Examples
See `examples/` directory:
- `example_tracks.json` - Sample input data
- `sdk_usage.py` - SDK usage demonstration
- `logging_example.py` - Logging configuration example
## Logging
The library uses Python's standard `logging` module. Configure logging to see detailed information about the optimization process:
```python
import logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
```
Log levels:
- `WARNING` (default): Errors and warnings only
- `INFO`: Optimization progress, statistics, and results
- `DEBUG`: Detailed solver information, edge counts, and configuration
CLI verbosity:
- No flag: WARNING level
- `-v`: INFO level
- `-vv`: DEBUG level
## Development
```bash
# Clone repository
git clone https://github.com/yourusername/dj-playlist-optimizer
cd dj-playlist-optimizer
# Install with dev dependencies
uv sync --dev
# Install pre-commit hooks
uv run pre-commit install
uv run pre-commit install --hook-type commit-msg
# Run tests
uv run pytest
# Lint and format
uv run ruff check # Check for issues
uv run ruff check --fix # Auto-fix issues
uv run ruff format # Format code
# Run pre-commit on all files
uv run pre-commit run --all-files
# Run example
uv run python examples/sdk_usage.py
```
### Commit Message Format
This project enforces [Conventional Commits](https://www.conventionalcommits.org/):
```
<type>: <description>
[optional body]
[optional footer]
```
**Allowed types:**
- `feat` - New feature
- `fix` - Bug fix
- `docs` - Documentation changes
- `style` - Code style changes (formatting, etc.)
- `refactor` - Code refactoring
- `perf` - Performance improvements
- `test` - Test changes
- `build` - Build system changes
- `ci` - CI configuration changes
- `chore` - Other changes (deps, etc.)
- `revert` - Revert previous commit
**Examples:**
```bash
git commit -m "feat: add halftime BPM matching"
git commit -m "fix: correct Camelot wheel compatibility check"
git commit -m "docs: update README with logging examples"
git commit -m "refactor: simplify return statements in bpm.py"
```
## How the Solver Works
The optimizer uses Google OR-Tools CP-SAT solver with:
1. **Binary Variables**: `included[i]` = track i is in playlist
2. **Edge Variables**: `edge[i,j]` = track j follows track i
3. **Circuit Constraint**: `AddCircuit` ensures valid track ordering
4. **BPM Constraints**: Only create edges between BPM-compatible tracks
5. **Harmonic Soft Constraints**: Penalize non-harmonic transitions
6. **Objective**: Maximize `sum(included)`
## License
MIT
## Credits
Built with:
- [Google OR-Tools](https://developers.google.com/optimization) - Constraint programming solver
- [pyrekordbox](https://github.com/dylanljones/pyrekordbox) - Rekordbox database access
- Camelot Wheel system by Mark Davis (Mixed In Key)
| text/markdown | Sage Choi | Sage Choi <sage.choi@gmail.com> | null | null | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"ortools>=9.8.3296",
"pyrekordbox>=0.1.0",
"pytest>=7.4.0; extra == \"dev\"",
"pytest-cov>=4.1.0; extra == \"dev\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:25:37.351002 | dj_playlist_optimizer-1.1.0-py3-none-any.whl | 22,815 | d3/14/dec8190a5a61a5c3a464de5067964f948472d28495179600bcdfaa637e10/dj_playlist_optimizer-1.1.0-py3-none-any.whl | py3 | bdist_wheel | null | false | b1f6784e95f39e4f362a9a3fcc35f9dd | bafe7685f52a9a5e3289f0e5fce0b49be10a5bc404e9c01553ffd985b9a70343 | d314dec8190a5a61a5c3a464de5067964f948472d28495179600bcdfaa637e10 | null | [] |
2.3 | dj-playlist-optimizer | 1.1.0 | DJ playlist optimizer using Google OR-Tools for harmonic mixing and BPM matching | # 🎧 DJ Playlist Optimizer
Optimize DJ playlists for harmonic mixing using Google OR-Tools constraint programming.
## Features
- ✨ **Longest Path Optimization**: Finds the maximum number of tracks that can be mixed together
- 🎵 **Harmonic Mixing**: Uses the Camelot Wheel system for key compatibility
- 🎧 **Rekordbox Integration**: Read playlists directly from your local Rekordbox 6/7 database (tested with v7.2.8)
- 🔊 **BPM Matching**: Supports direct, halftime, and doubletime BPM compatibility
- ⚙️ **Configurable Strictness**: STRICT, MODERATE, or RELAXED harmonic compatibility levels
- 📤 **Rekordbox Export**: Export results to Rekordbox XML or write directly to the Rekordbox database
- 🚀 **Fast**: Powered by Google OR-Tools CP-SAT solver (award-winning constraint solver)
- 📦 **SDK + CLI**: Use as a Python library or command-line tool
## Installation
```bash
uv add dj-playlist-optimizer
```
Or with pip:
```bash
pip install dj-playlist-optimizer
```
## Quick Start
### SDK Usage
```python
from dj_playlist_optimizer import PlaylistOptimizer, Track, HarmonicLevel
tracks = [
Track(id="track_001", key="8A", bpm=128),
Track(id="track_002", key="8B", bpm=130),
Track(id="track_003", key="9A", bpm=125),
]
optimizer = PlaylistOptimizer(
bpm_tolerance=10,
allow_halftime_bpm=True,
max_violation_pct=0.10,
harmonic_level=HarmonicLevel.STRICT,
)
result = optimizer.optimize(tracks)
for i, track in enumerate(result.playlist, 1):
print(f"{i}. {track.id} ({track.key}, {track.bpm} BPM)")
```
### CLI Usage
```bash
# Basic usage
dj-optimize tracks.json
# With custom settings
dj-optimize tracks.json --bpm-tolerance 8 --harmonic-level moderate
# Save results to JSON
dj-optimize tracks.json --output result.json
# Use with Rekordbox (v6/v7)
dj-optimize --rekordbox # List playlists
dj-optimize --rekordbox --playlist "Techno" # Optimize specific playlist
dj-optimize --rekordbox --playlist "Techno" --output r.xml # Export to Rekordbox XML
dj-optimize --rekordbox --playlist "Techno" --write-to-db # Write directly to Rekordbox DB
# Enable verbose logging
dj-optimize tracks.json -v # INFO level
dj-optimize tracks.json -vv # DEBUG level
```
## Rekordbox Integration
The tool provides two ways to save your optimized playlists back to Rekordbox:
### 1. XML Export (Recommended)
Export the results to an XML file that can be imported into Rekordbox:
```bash
dj-optimize --rekordbox --playlist "My Playlist" --output optimized.xml
```
In Rekordbox:
1. Go to **File > Import > Import Playlist**
2. Select `optimized.xml`
3. The playlist will appear in the `ROOT` folder (e.g., `My Playlist_20260115_120000`)
### 2. Direct Database Write (Advanced)
Write the optimized playlist directly to your Rekordbox 6 database:
```bash
dj-optimize --rekordbox --playlist "My Playlist" --write-to-db
```
**⚠️ WARNING:**
- **Close Rekordbox** before running this command.
- This modifies your `master.db` file directly.
- **Backup your database** before using this feature.
## Input Format
JSON file with tracks containing `id`, `key` (Camelot notation), and `bpm`:
```json
{
"tracks": [
{"id": "track_001", "key": "8A", "bpm": 128},
{"id": "track_002", "key": "8B", "bpm": 130},
{"id": "track_003", "key": "9A", "bpm": 125}
]
}
```
## How It Works
### 1. BPM Compatibility
Adjacent tracks must have compatible BPMs within tolerance:
| Track A | Track B | Tolerance | Match? | Reason |
|---------|---------|-----------|--------|--------|
| 128 BPM | 130 BPM | ±10 | ✅ | Direct (diff = 2) |
| 128 BPM | 64 BPM | ±10 | ✅ | Half-time (128 = 64×2) |
| 75 BPM | 150 BPM | ±10 | ✅ | Double-time (75×2 = 150) |
| 128 BPM | 100 BPM | ±10 | ❌ | Too far |
### 2. Harmonic Mixing (Camelot Wheel)
Harmonic compatibility levels:
**STRICT** (default):
- Same key (8A → 8A)
- ±1 hour same letter (8A → 7A, 9A)
- Same hour different letter (8A → 8B)
**MODERATE**:
- Above + ±1 hour different letter (8A → 9B, 7B)
**RELAXED**:
- Above + ±3 hours (8A → 5A, 11A)
### 3. Optimization Goal
Maximize playlist length while keeping non-harmonic transitions below the threshold (default: 10%).
## Configuration Options
| Parameter | Default | Description |
|-----------|---------|-------------|
| `bpm_tolerance` | 10.0 | Maximum BPM difference for direct match |
| `allow_halftime_bpm` | True | Enable half/double-time matching |
| `max_violation_pct` | 0.10 | Max percentage of non-harmonic transitions |
| `harmonic_level` | STRICT | Harmonic compatibility strictness |
| `time_limit_seconds` | 60.0 | Solver time limit |
## Examples
See `examples/` directory:
- `example_tracks.json` - Sample input data
- `sdk_usage.py` - SDK usage demonstration
- `logging_example.py` - Logging configuration example
## Logging
The library uses Python's standard `logging` module. Configure logging to see detailed information about the optimization process:
```python
import logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
)
```
Log levels:
- `WARNING` (default): Errors and warnings only
- `INFO`: Optimization progress, statistics, and results
- `DEBUG`: Detailed solver information, edge counts, and configuration
CLI verbosity:
- No flag: WARNING level
- `-v`: INFO level
- `-vv`: DEBUG level
## Development
```bash
# Clone repository
git clone https://github.com/yourusername/dj-playlist-optimizer
cd dj-playlist-optimizer
# Install with dev dependencies
uv sync --dev
# Install pre-commit hooks
uv run pre-commit install
uv run pre-commit install --hook-type commit-msg
# Run tests
uv run pytest
# Lint and format
uv run ruff check # Check for issues
uv run ruff check --fix # Auto-fix issues
uv run ruff format # Format code
# Run pre-commit on all files
uv run pre-commit run --all-files
# Run example
uv run python examples/sdk_usage.py
```
### Commit Message Format
This project enforces [Conventional Commits](https://www.conventionalcommits.org/):
```
<type>: <description>
[optional body]
[optional footer]
```
**Allowed types:**
- `feat` - New feature
- `fix` - Bug fix
- `docs` - Documentation changes
- `style` - Code style changes (formatting, etc.)
- `refactor` - Code refactoring
- `perf` - Performance improvements
- `test` - Test changes
- `build` - Build system changes
- `ci` - CI configuration changes
- `chore` - Other changes (deps, etc.)
- `revert` - Revert previous commit
**Examples:**
```bash
git commit -m "feat: add halftime BPM matching"
git commit -m "fix: correct Camelot wheel compatibility check"
git commit -m "docs: update README with logging examples"
git commit -m "refactor: simplify return statements in bpm.py"
```
## How the Solver Works
The optimizer uses Google OR-Tools CP-SAT solver with:
1. **Binary Variables**: `included[i]` = track i is in playlist
2. **Edge Variables**: `edge[i,j]` = track j follows track i
3. **Circuit Constraint**: `AddCircuit` ensures valid track ordering
4. **BPM Constraints**: Only create edges between BPM-compatible tracks
5. **Harmonic Soft Constraints**: Penalize non-harmonic transitions
6. **Objective**: Maximize `sum(included)`
## License
MIT
## Credits
Built with:
- [Google OR-Tools](https://developers.google.com/optimization) - Constraint programming solver
- [pyrekordbox](https://github.com/dylanljones/pyrekordbox) - Rekordbox database access
- Camelot Wheel system by Mark Davis (Mixed In Key)
| text/markdown | Sage Choi | Sage Choi <sage.choi@gmail.com> | null | null | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"ortools>=9.8.3296",
"pyrekordbox>=0.1.0",
"pytest>=7.4.0; extra == \"dev\"",
"pytest-cov>=4.1.0; extra == \"dev\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:25:38.825878 | dj_playlist_optimizer-1.1.0.tar.gz | 18,209 | 11/68/d9ffcc563bf4cb688fa814081842e17b81ca5cb97bef082102eb5de177d8/dj_playlist_optimizer-1.1.0.tar.gz | source | sdist | null | false | 4f94b45da2587ffc906059f0686ff91e | ca796df41abe652f6e4b51d6acd01504e928ef5ba7c3c7181e1dd63575f728a8 | 1168d9ffcc563bf4cb688fa814081842e17b81ca5cb97bef082102eb5de177d8 | null | [] |
2.4 | hubify-dataset | 0.1.0 | Convert object detection datasets (COCO, YOLO, Pascal VOC, etc.) to HuggingFace format | # Hubify
Convert object detection datasets to HuggingFace format and upload to the Hub.
**Currently supported formats:**
- COCO format annotations
- YOLO format annotations
- YOLO OBB format annotations
**Coming soon:** Pascal VOC, Labelme, and more!
## Motivations for this tool
HuggingFace has become the defacto *open source* community to upload datasets and models. It's primarily about LLMs and language models, but there's nothing about HuggingFace's dataset hosting that's specific to language modeling.
This tool is meant to be a way to consolidate the different formats from the object detection domain (COCO, Pascal VOC, etc) into what HuggingFace suggests for their Image Datasets, and upload it to HuggingFace Hub.
## Installation
```bash
# Install with uv (recommended)
uv pip install -e .
# Or with pip
pip install -e .
```
## Usage
After installation, you can use the `hubify` command:
```bash
# Auto-detect annotations in train/validation/test directories
hubify --data-dir /path/to/images --format coco
# Manually specify annotation files
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--validation-annotations /path/to/instances_val2017.json
# Generate sample visualizations
hubify --data-dir /path/to/images --visualize
# Push to HuggingFace Hub
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--push-to-hub username/my-dataset
```
Or for yolo:
```
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo-obb --push-to-hub benjamintli/dota-v1.5
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo --push-to-hub benjamintli/dota-v1.5
```
Or run directly with Python (from the virtual environment):
```bash
source .venv/bin/activate
python -m src.main --data-dir /path/to/images
```
## Expected Directory Structure
* For coco:
```
data-dir/
├── train/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
├── validation/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
└── test/ (optional)
├── instances*.json
└── *.jpg
```
## Output
The tool generates `metadata.jsonl` files in each split directory:
```
data-dir/
├── train/
│ └── metadata.jsonl
└── validation/
└── metadata.jsonl
```
Each line in `metadata.jsonl` contains:
```json
{
"file_name": "image.jpg",
"objects": {
"bbox": [[x, y, width, height], ...],
"category": [0, 1, ...]
}
}
```
## Options
- `--data-dir`: Root directory containing train/validation/test subdirectories (required)
- `--train-annotations`: Path to training annotations JSON (optional)
- `--validation-annotations`: Path to validation annotations JSON (optional)
- `--test-annotations`: Path to test annotations JSON (optional)
- `--visualize`: Generate sample visualization images with bounding boxes
- `--push-to-hub`: Push dataset to HuggingFace Hub (format: `username/dataset-name`)
- `--token`: HuggingFace API token (optional, defaults to `HF_TOKEN` env var or `huggingface-cli login`)
### Authentication for Hub Push
When using `--push-to-hub`, the tool looks for your HuggingFace token in this order:
1. `--token YOUR_TOKEN` (CLI argument)
2. `HF_TOKEN` environment variable
3. Token from `huggingface-cli login`
If no token is found, you'll get a helpful error message with instructions.
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | >=3.12 | [] | [] | [] | [
"datasets>=4.4.2",
"huggingface-hub>=1.2.3",
"pillow>=12.1.0",
"pyyaml>=6.0",
"rich>=13.9.4",
"ruff>=0.14.10",
"ruff>=0.14.10; extra == \"dev\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:26:23.420857 | hubify_dataset-0.1.0-py3-none-any.whl | 17,431 | b0/77/4c4a7ddc14aadfbcc69ac9f05976c362154784165fe7013b9bb4e993e019/hubify_dataset-0.1.0-py3-none-any.whl | py3 | bdist_wheel | null | false | 47f913c0d59a3cacde6695a4852c8875 | 98b13ded23b75cd7d231c6a4b1553d622032c416720fafbbaac31a62cbd13da2 | b0774c4a7ddc14aadfbcc69ac9f05976c362154784165fe7013b9bb4e993e019 | null | [
"LICENSE"
] |
2.4 | hubify-dataset | 0.1.0 | Convert object detection datasets (COCO, YOLO, Pascal VOC, etc.) to HuggingFace format | # Hubify
Convert object detection datasets to HuggingFace format and upload to the Hub.
**Currently supported formats:**
- COCO format annotations
- YOLO format annotations
- YOLO OBB format annotations
**Coming soon:** Pascal VOC, Labelme, and more!
## Motivations for this tool
HuggingFace has become the defacto *open source* community to upload datasets and models. It's primarily about LLMs and language models, but there's nothing about HuggingFace's dataset hosting that's specific to language modeling.
This tool is meant to be a way to consolidate the different formats from the object detection domain (COCO, Pascal VOC, etc) into what HuggingFace suggests for their Image Datasets, and upload it to HuggingFace Hub.
## Installation
```bash
# Install with uv (recommended)
uv pip install -e .
# Or with pip
pip install -e .
```
## Usage
After installation, you can use the `hubify` command:
```bash
# Auto-detect annotations in train/validation/test directories
hubify --data-dir /path/to/images --format coco
# Manually specify annotation files
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--validation-annotations /path/to/instances_val2017.json
# Generate sample visualizations
hubify --data-dir /path/to/images --visualize
# Push to HuggingFace Hub
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--push-to-hub username/my-dataset
```
Or for yolo:
```
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo-obb --push-to-hub benjamintli/dota-v1.5
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo --push-to-hub benjamintli/dota-v1.5
```
Or run directly with Python (from the virtual environment):
```bash
source .venv/bin/activate
python -m src.main --data-dir /path/to/images
```
## Expected Directory Structure
* For coco:
```
data-dir/
├── train/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
├── validation/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
└── test/ (optional)
├── instances*.json
└── *.jpg
```
## Output
The tool generates `metadata.jsonl` files in each split directory:
```
data-dir/
├── train/
│ └── metadata.jsonl
└── validation/
└── metadata.jsonl
```
Each line in `metadata.jsonl` contains:
```json
{
"file_name": "image.jpg",
"objects": {
"bbox": [[x, y, width, height], ...],
"category": [0, 1, ...]
}
}
```
## Options
- `--data-dir`: Root directory containing train/validation/test subdirectories (required)
- `--train-annotations`: Path to training annotations JSON (optional)
- `--validation-annotations`: Path to validation annotations JSON (optional)
- `--test-annotations`: Path to test annotations JSON (optional)
- `--visualize`: Generate sample visualization images with bounding boxes
- `--push-to-hub`: Push dataset to HuggingFace Hub (format: `username/dataset-name`)
- `--token`: HuggingFace API token (optional, defaults to `HF_TOKEN` env var or `huggingface-cli login`)
### Authentication for Hub Push
When using `--push-to-hub`, the tool looks for your HuggingFace token in this order:
1. `--token YOUR_TOKEN` (CLI argument)
2. `HF_TOKEN` environment variable
3. Token from `huggingface-cli login`
If no token is found, you'll get a helpful error message with instructions.
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | >=3.12 | [] | [] | [] | [
"datasets>=4.4.2",
"huggingface-hub>=1.2.3",
"pillow>=12.1.0",
"pyyaml>=6.0",
"rich>=13.9.4",
"ruff>=0.14.10",
"ruff>=0.14.10; extra == \"dev\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:26:25.030625 | hubify_dataset-0.1.0.tar.gz | 15,884 | 36/6b/791412cf9fd119abdfab5bf521c7aff06570339938633c68aae71f8b74c7/hubify_dataset-0.1.0.tar.gz | source | sdist | null | false | 9968c6d0664e472e75abb3948dca45f6 | bf9afb8c2d18af30260185aa09264d9a3e11ff0b548b9dbcdc1b2a1183400120 | 366b791412cf9fd119abdfab5bf521c7aff06570339938633c68aae71f8b74c7 | null | [
"LICENSE"
] |
2.4 | plato-sdk-v2 | 2.0.52 | Python SDK for the Plato API | # Plato Python SDK
Python SDK for the Plato platform. Uses [Harbor](https://harborframework.com) for agent execution.
## Installation
```bash
pip install plato-sdk-v2
# For agent functionality (requires Python 3.12+)
pip install 'plato-sdk-v2[agents]'
```
Or with uv:
```bash
uv add plato-sdk-v2
uv add 'plato-sdk-v2[agents]' # for agent support
```
## Configuration
Create a `.env` file in your project root:
```bash
PLATO_API_KEY=your-api-key
PLATO_BASE_URL=https://plato.so # optional, defaults to https://plato.so
```
Or set environment variables directly:
```bash
export PLATO_API_KEY=your-api-key
```
## Agents
The SDK uses Harbor's agent framework. All agents are `BaseInstalledAgent` subclasses that run in containers.
### Available Agents
**Harbor built-in agents** (code agents):
| Agent | Description |
|-------|-------------|
| `claude-code` | Claude Code CLI |
| `openhands` | OpenHands/All Hands AI |
| `codex` | OpenAI Codex CLI |
| `aider` | Aider pair programming |
| `gemini-cli` | Google Gemini CLI |
| `goose` | Block Goose |
| `swe-agent` | SWE-agent |
| `mini-swe-agent` | Mini SWE-agent |
| `cline-cli` | Cline CLI |
| `cursor-cli` | Cursor CLI |
| `opencode` | OpenCode |
| `qwen-coder` | Qwen Coder |
**Plato custom agents** (browser/automation):
| Agent | Description |
|-------|-------------|
| `computer-use` | Browser automation (install: `pip install plato-agent-computer-use`) |
### Python Usage
```python
from plato.agents import ClaudeCode, OpenHands, AgentFactory, AgentName
from pathlib import Path
# Option 1: Use AgentFactory
agent = AgentFactory.create_agent_from_name(
AgentName.CLAUDE_CODE,
logs_dir=Path("./logs"),
model_name="anthropic/claude-sonnet-4",
)
# Option 2: Import agent class directly
agent = ClaudeCode(
logs_dir=Path("./logs"),
model_name="anthropic/claude-sonnet-4",
)
# Option 3: Create custom BaseInstalledAgent
from plato.agents import BaseInstalledAgent
```
### CLI Usage
```bash
# Run an agent
plato agent run -a claude-code -m anthropic/claude-sonnet-4 -d swe-bench-lite
# List available agents
plato agent list
# Get agent config schema
plato agent schema claude-code
# Publish custom agent to Plato PyPI
plato agent publish ./my-agent
```
### Agent Schemas
Get configuration schemas for any agent:
```python
from plato.agents import get_agent_schema, AGENT_SCHEMAS
# Get schema for specific agent
schema = get_agent_schema("claude-code")
print(schema)
# List all available schemas
print(list(AGENT_SCHEMAS.keys()))
```
### Custom Agents
Create a custom agent by extending `BaseInstalledAgent`:
```python
from harbor.agents.installed.base import BaseInstalledAgent, ExecInput
from pathlib import Path
class MyAgent(BaseInstalledAgent):
@staticmethod
def name() -> str:
return "my-agent"
@property
def _install_agent_template_path(self) -> Path:
return Path(__file__).parent / "install.sh.j2"
def create_run_agent_commands(self, instruction: str) -> list[ExecInput]:
return [ExecInput(command=f"my-agent --task '{instruction}'")]
```
Publish to Plato PyPI:
```bash
plato agent publish ./my-agent-package
```
---
## Sessions & Environments
### Flow 1: Create Session from Environments
Use this when you want to spin up environments for development, testing, or custom automation.
```python
import asyncio
from plato.v2 import AsyncPlato, Env
async def main():
plato = AsyncPlato()
# Create session with one or more environments
# (heartbeat starts automatically to keep session alive)
session = await plato.sessions.create(
envs=[
Env.simulator("gitea", dataset="blank", alias="gitea"),
Env.simulator("kanboard", alias="kanboard"),
],
timeout=600,
)
# Reset environments to initial state
await session.reset()
# Get public URLs for browser access
public_urls = await session.get_public_url()
for alias, url in public_urls.items():
print(f"{alias}: {url}")
# Get state mutations from all environments
state = await session.get_state()
print(state)
# Cleanup
await session.close()
await plato.close()
asyncio.run(main())
```
### Flow 2: Create Session from Task
Use this when running evaluations against predefined tasks. This flow includes task evaluation at the end.
```python
import asyncio
from plato.v2 import AsyncPlato
async def main():
plato = AsyncPlato()
# Create session from task ID
session = await plato.sessions.create(task=123, timeout=600)
# Reset environments to initial state
await session.reset()
# Get public URLs for browser access
public_urls = await session.get_public_url()
for alias, url in public_urls.items():
print(f"{alias}: {url}")
# Evaluate task completion
evaluation = await session.evaluate()
print(f"Task completed: {evaluation}")
# Cleanup
await session.close()
await plato.close()
asyncio.run(main())
```
## Environment Configuration
Two ways to specify environments:
```python
from plato.v2 import Env
# 1. From simulator (most common)
Env.simulator("gitea") # default tag
Env.simulator("gitea", tag="staging") # specific tag
Env.simulator("gitea", dataset="blank") # specific dataset
Env.simulator("gitea", alias="my-git") # custom alias
# 2. From artifact ID
Env.artifact("artifact-abc123")
Env.artifact("artifact-abc123", alias="my-env")
```
## Per-Environment Operations
Access individual environments within a session:
```python
# Get all environments
for env in session.envs:
print(f"{env.alias}: {env.job_id}")
# Get specific environment by alias
gitea = session.get_env("gitea")
if gitea:
# Execute shell command
result = await gitea.execute("whoami", timeout=30)
print(result)
# Get state for this environment only
state = await gitea.get_state()
# Reset this environment only
await gitea.reset()
```
## Sync Client
A synchronous client is also available:
```python
from plato.v2 import Plato, Env
plato = Plato()
session = plato.sessions.create(
envs=[Env.simulator("gitea", alias="gitea")],
timeout=600,
)
session.reset()
public_urls = session.get_public_url()
state = session.get_state()
session.close()
plato.close()
```
## Architecture
```
plato/
├── agents/ # Harbor agent re-exports + schemas
├── sims/ # Simulator clients (Spree, Firefly, etc.)
├── world/ # World/environment abstractions
├── v1/ # Legacy SDK + CLI
└── v2/ # New API client
```
## Documentation
- [Generating Simulator SDKs](docs/GENERATING_SIM_SDKS.md) - How to create API clients for simulators
- [Building Simulators](BUILDING_SIMS.md) - Internal docs for snapshotting simulators
## License
MIT
| text/markdown | null | Plato <support@plato.so> | null | null | null | null | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Typing :: Typed"
] | [] | null | null | <3.14,>=3.10 | [] | [] | [] | [
"aiohttp>=3.8.0",
"cryptography>=43.0.0",
"datamodel-code-generator>=0.43.0",
"email-validator>=2.0.0",
"google-genai>=1.0.0",
"httpx>=0.25.0",
"jinja2>=3.1.0",
"openapi-pydantic>=0.5.1",
"pydantic-settings>=2.12.0",
"pydantic>=2.0.0",
"python-dotenv>=1.2.1",
"pyyaml>=6.0",
"requests>=2.32.5... | [] | [] | [] | [] | uv/0.9.26 {"installer":{"name":"uv","version":"0.9.26","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-01-16T04:26:37.655712 | plato_sdk_v2-2.0.52.tar.gz | 687,499 | 14/d7/987522fea9dcac38edf75d8af10e29ce18e316ca5f3629594c187afc04a4/plato_sdk_v2-2.0.52.tar.gz | source | sdist | null | false | b436921f86555c92255eb317a3f6c2d9 | 048ad07c6aa56a053acd346af921cc81376d7f3839a209d62f48899ad19604a0 | 14d7987522fea9dcac38edf75d8af10e29ce18e316ca5f3629594c187afc04a4 | MIT | [] |
2.4 | plato-sdk-v2 | 2.0.52 | Python SDK for the Plato API | # Plato Python SDK
Python SDK for the Plato platform. Uses [Harbor](https://harborframework.com) for agent execution.
## Installation
```bash
pip install plato-sdk-v2
# For agent functionality (requires Python 3.12+)
pip install 'plato-sdk-v2[agents]'
```
Or with uv:
```bash
uv add plato-sdk-v2
uv add 'plato-sdk-v2[agents]' # for agent support
```
## Configuration
Create a `.env` file in your project root:
```bash
PLATO_API_KEY=your-api-key
PLATO_BASE_URL=https://plato.so # optional, defaults to https://plato.so
```
Or set environment variables directly:
```bash
export PLATO_API_KEY=your-api-key
```
## Agents
The SDK uses Harbor's agent framework. All agents are `BaseInstalledAgent` subclasses that run in containers.
### Available Agents
**Harbor built-in agents** (code agents):
| Agent | Description |
|-------|-------------|
| `claude-code` | Claude Code CLI |
| `openhands` | OpenHands/All Hands AI |
| `codex` | OpenAI Codex CLI |
| `aider` | Aider pair programming |
| `gemini-cli` | Google Gemini CLI |
| `goose` | Block Goose |
| `swe-agent` | SWE-agent |
| `mini-swe-agent` | Mini SWE-agent |
| `cline-cli` | Cline CLI |
| `cursor-cli` | Cursor CLI |
| `opencode` | OpenCode |
| `qwen-coder` | Qwen Coder |
**Plato custom agents** (browser/automation):
| Agent | Description |
|-------|-------------|
| `computer-use` | Browser automation (install: `pip install plato-agent-computer-use`) |
### Python Usage
```python
from plato.agents import ClaudeCode, OpenHands, AgentFactory, AgentName
from pathlib import Path
# Option 1: Use AgentFactory
agent = AgentFactory.create_agent_from_name(
AgentName.CLAUDE_CODE,
logs_dir=Path("./logs"),
model_name="anthropic/claude-sonnet-4",
)
# Option 2: Import agent class directly
agent = ClaudeCode(
logs_dir=Path("./logs"),
model_name="anthropic/claude-sonnet-4",
)
# Option 3: Create custom BaseInstalledAgent
from plato.agents import BaseInstalledAgent
```
### CLI Usage
```bash
# Run an agent
plato agent run -a claude-code -m anthropic/claude-sonnet-4 -d swe-bench-lite
# List available agents
plato agent list
# Get agent config schema
plato agent schema claude-code
# Publish custom agent to Plato PyPI
plato agent publish ./my-agent
```
### Agent Schemas
Get configuration schemas for any agent:
```python
from plato.agents import get_agent_schema, AGENT_SCHEMAS
# Get schema for specific agent
schema = get_agent_schema("claude-code")
print(schema)
# List all available schemas
print(list(AGENT_SCHEMAS.keys()))
```
### Custom Agents
Create a custom agent by extending `BaseInstalledAgent`:
```python
from harbor.agents.installed.base import BaseInstalledAgent, ExecInput
from pathlib import Path
class MyAgent(BaseInstalledAgent):
@staticmethod
def name() -> str:
return "my-agent"
@property
def _install_agent_template_path(self) -> Path:
return Path(__file__).parent / "install.sh.j2"
def create_run_agent_commands(self, instruction: str) -> list[ExecInput]:
return [ExecInput(command=f"my-agent --task '{instruction}'")]
```
Publish to Plato PyPI:
```bash
plato agent publish ./my-agent-package
```
---
## Sessions & Environments
### Flow 1: Create Session from Environments
Use this when you want to spin up environments for development, testing, or custom automation.
```python
import asyncio
from plato.v2 import AsyncPlato, Env
async def main():
plato = AsyncPlato()
# Create session with one or more environments
# (heartbeat starts automatically to keep session alive)
session = await plato.sessions.create(
envs=[
Env.simulator("gitea", dataset="blank", alias="gitea"),
Env.simulator("kanboard", alias="kanboard"),
],
timeout=600,
)
# Reset environments to initial state
await session.reset()
# Get public URLs for browser access
public_urls = await session.get_public_url()
for alias, url in public_urls.items():
print(f"{alias}: {url}")
# Get state mutations from all environments
state = await session.get_state()
print(state)
# Cleanup
await session.close()
await plato.close()
asyncio.run(main())
```
### Flow 2: Create Session from Task
Use this when running evaluations against predefined tasks. This flow includes task evaluation at the end.
```python
import asyncio
from plato.v2 import AsyncPlato
async def main():
plato = AsyncPlato()
# Create session from task ID
session = await plato.sessions.create(task=123, timeout=600)
# Reset environments to initial state
await session.reset()
# Get public URLs for browser access
public_urls = await session.get_public_url()
for alias, url in public_urls.items():
print(f"{alias}: {url}")
# Evaluate task completion
evaluation = await session.evaluate()
print(f"Task completed: {evaluation}")
# Cleanup
await session.close()
await plato.close()
asyncio.run(main())
```
## Environment Configuration
Two ways to specify environments:
```python
from plato.v2 import Env
# 1. From simulator (most common)
Env.simulator("gitea") # default tag
Env.simulator("gitea", tag="staging") # specific tag
Env.simulator("gitea", dataset="blank") # specific dataset
Env.simulator("gitea", alias="my-git") # custom alias
# 2. From artifact ID
Env.artifact("artifact-abc123")
Env.artifact("artifact-abc123", alias="my-env")
```
## Per-Environment Operations
Access individual environments within a session:
```python
# Get all environments
for env in session.envs:
print(f"{env.alias}: {env.job_id}")
# Get specific environment by alias
gitea = session.get_env("gitea")
if gitea:
# Execute shell command
result = await gitea.execute("whoami", timeout=30)
print(result)
# Get state for this environment only
state = await gitea.get_state()
# Reset this environment only
await gitea.reset()
```
## Sync Client
A synchronous client is also available:
```python
from plato.v2 import Plato, Env
plato = Plato()
session = plato.sessions.create(
envs=[Env.simulator("gitea", alias="gitea")],
timeout=600,
)
session.reset()
public_urls = session.get_public_url()
state = session.get_state()
session.close()
plato.close()
```
## Architecture
```
plato/
├── agents/ # Harbor agent re-exports + schemas
├── sims/ # Simulator clients (Spree, Firefly, etc.)
├── world/ # World/environment abstractions
├── v1/ # Legacy SDK + CLI
└── v2/ # New API client
```
## Documentation
- [Generating Simulator SDKs](docs/GENERATING_SIM_SDKS.md) - How to create API clients for simulators
- [Building Simulators](BUILDING_SIMS.md) - Internal docs for snapshotting simulators
## License
MIT
| text/markdown | null | Plato <support@plato.so> | null | null | null | null | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Typing :: Typed"
] | [] | null | null | <3.14,>=3.10 | [] | [] | [] | [
"aiohttp>=3.8.0",
"cryptography>=43.0.0",
"datamodel-code-generator>=0.43.0",
"email-validator>=2.0.0",
"google-genai>=1.0.0",
"httpx>=0.25.0",
"jinja2>=3.1.0",
"openapi-pydantic>=0.5.1",
"pydantic-settings>=2.12.0",
"pydantic>=2.0.0",
"python-dotenv>=1.2.1",
"pyyaml>=6.0",
"requests>=2.32.5... | [] | [] | [] | [] | uv/0.9.26 {"installer":{"name":"uv","version":"0.9.26","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-01-16T04:26:39.625010 | plato_sdk_v2-2.0.52-py3-none-any.whl | 632,936 | 71/98/f46d6a54e3278620bd788212d4efc6489048afb33060e10c9a327714de5d/plato_sdk_v2-2.0.52-py3-none-any.whl | py3 | bdist_wheel | null | false | 2fc0bd71dea0e3b7e145ed131d7ce647 | 8be6b3589e3455e8df0d4b7afbc84b3141ea78bbc1a268eb15f5b8ef32a7dd32 | 7198f46d6a54e3278620bd788212d4efc6489048afb33060e10c9a327714de5d | MIT | [] |
2.4 | keras-nightly | 3.14.0.dev2026011604 | Multi-backend Keras | # Keras 3: Deep Learning for Humans
Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).
Effortlessly build and train models for computer vision, natural language processing, audio processing,
timeseries forecasting, recommender systems, etc.
- **Accelerated model development**: Ship deep learning solutions faster thanks to the high-level UX of Keras
and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.
- **State-of-the-art performance**: By picking the backend that is the fastest for your model architecture (often JAX!),
leverage speedups ranging from 20% to 350% compared to other frameworks. [Benchmark here](https://keras.io/getting_started/benchmarks/).
- **Datacenter-scale training**: Scale confidently from your laptop to large clusters of GPUs or TPUs.
Join nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.
## Installation
### Install with pip
Keras 3 is available on PyPI as `keras`. Note that Keras 2 remains available as the `tf-keras` package.
1. Install `keras`:
```
pip install keras --upgrade
```
2. Install backend package(s).
To use `keras`, you should also install the backend of choice: `tensorflow`, `jax`, or `torch`. Additionally,
The `openvino` backend is available with support for model inference only.
### Local installation
#### Minimal installation
Keras 3 is compatible with Linux and macOS systems. For Windows users, we recommend using WSL2 to run Keras.
To install a local development version:
1. Install dependencies:
```
pip install -r requirements.txt
```
2. Run installation command from the root directory.
```
python pip_build.py --install
```
3. Run API generation script when creating PRs that update `keras_export` public APIs:
```
./shell/api_gen.sh
```
## Backend Compatibility Table
The following table lists the minimum supported versions of each backend for the latest stable release of Keras (v3.x):
| Backend | Minimum Supported Version |
|------------|---------------------------|
| TensorFlow | 2.16.1 |
| JAX | 0.4.20 |
| PyTorch | 2.1.0 |
| OpenVINO | 2025.3.0 |
#### Adding GPU support
The `requirements.txt` file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also
provide a separate `requirements-{backend}-cuda.txt` for TensorFlow, JAX, and PyTorch. These install all CUDA
dependencies via `pip` and expect a NVIDIA driver to be pre-installed. We recommend a clean Python environment for each
backend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment with `conda`:
```shell
conda create -y -n keras-jax python=3.10
conda activate keras-jax
pip install -r requirements-jax-cuda.txt
python pip_build.py --install
```
## Configuring your backend
You can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~/.keras/keras.json`
to configure your backend. Available backend options are: `"tensorflow"`, `"jax"`, `"torch"`, `"openvino"`. Example:
```
export KERAS_BACKEND="jax"
```
In Colab, you can do:
```python
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
```
**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after
the package has been imported.
**Note:** The OpenVINO backend is an inference-only backend, meaning it is designed only for running model
predictions using `model.predict()` method.
## Backwards compatibility
Keras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your
existing `tf.keras` code, make sure that your calls to `model.save()` are using the up-to-date `.keras` format, and you're
done.
If your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.
If it does include custom components (e.g. custom layers or a custom `train_step()`), it is usually possible to convert it
to a backend-agnostic implementation in just a few minutes.
In addition, Keras models can consume datasets in any format, regardless of the backend you're using:
you can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.
## Why use Keras 3?
- Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,
e.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.
- Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.
- You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.
- You can take a Keras model and use it as part of a PyTorch-native `Module` or as part of a JAX-native model function.
- Make your ML code future-proof by avoiding framework lock-in.
- As a PyTorch user: get access to power and usability of Keras, at last!
- As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.
Read more in the [Keras 3 release announcement](https://keras.io/keras_3/).
| text/markdown | null | Keras team <keras-users@googlegroups.com> | null | null | Apache License 2.0 | null | [
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3 :: Only",
"Operating System :: Unix",
"Operating System :: MacOS",
"Intended Audience :: Science/Research",
... | [] | null | null | >=3.11 | [] | [] | [] | [
"absl-py",
"numpy",
"rich",
"namex",
"h5py",
"optree",
"ml-dtypes",
"packaging"
] | [] | [] | [] | [
"Home, https://keras.io/",
"Repository, https://github.com/keras-team/keras"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:26:41.069999 | keras_nightly-3.14.0.dev2026011604-py3-none-any.whl | 1,528,839 | 47/d1/aa12bd5af539231caf2cbc16c61bcf03a4801c2df9c2b80578e40b312d9f/keras_nightly-3.14.0.dev2026011604-py3-none-any.whl | py3 | bdist_wheel | null | false | b27747d5846933feba5ca0bc52f397e1 | a208aa2d351e41786a433b9361ea4d7c202e174ed27d8fba67d64d34fb44c17b | 47d1aa12bd5af539231caf2cbc16c61bcf03a4801c2df9c2b80578e40b312d9f | null | [] |
2.4 | keras-nightly | 3.14.0.dev2026011604 | Multi-backend Keras | # Keras 3: Deep Learning for Humans
Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).
Effortlessly build and train models for computer vision, natural language processing, audio processing,
timeseries forecasting, recommender systems, etc.
- **Accelerated model development**: Ship deep learning solutions faster thanks to the high-level UX of Keras
and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.
- **State-of-the-art performance**: By picking the backend that is the fastest for your model architecture (often JAX!),
leverage speedups ranging from 20% to 350% compared to other frameworks. [Benchmark here](https://keras.io/getting_started/benchmarks/).
- **Datacenter-scale training**: Scale confidently from your laptop to large clusters of GPUs or TPUs.
Join nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.
## Installation
### Install with pip
Keras 3 is available on PyPI as `keras`. Note that Keras 2 remains available as the `tf-keras` package.
1. Install `keras`:
```
pip install keras --upgrade
```
2. Install backend package(s).
To use `keras`, you should also install the backend of choice: `tensorflow`, `jax`, or `torch`. Additionally,
The `openvino` backend is available with support for model inference only.
### Local installation
#### Minimal installation
Keras 3 is compatible with Linux and macOS systems. For Windows users, we recommend using WSL2 to run Keras.
To install a local development version:
1. Install dependencies:
```
pip install -r requirements.txt
```
2. Run installation command from the root directory.
```
python pip_build.py --install
```
3. Run API generation script when creating PRs that update `keras_export` public APIs:
```
./shell/api_gen.sh
```
## Backend Compatibility Table
The following table lists the minimum supported versions of each backend for the latest stable release of Keras (v3.x):
| Backend | Minimum Supported Version |
|------------|---------------------------|
| TensorFlow | 2.16.1 |
| JAX | 0.4.20 |
| PyTorch | 2.1.0 |
| OpenVINO | 2025.3.0 |
#### Adding GPU support
The `requirements.txt` file will install a CPU-only version of TensorFlow, JAX, and PyTorch. For GPU support, we also
provide a separate `requirements-{backend}-cuda.txt` for TensorFlow, JAX, and PyTorch. These install all CUDA
dependencies via `pip` and expect a NVIDIA driver to be pre-installed. We recommend a clean Python environment for each
backend to avoid CUDA version mismatches. As an example, here is how to create a JAX GPU environment with `conda`:
```shell
conda create -y -n keras-jax python=3.10
conda activate keras-jax
pip install -r requirements-jax-cuda.txt
python pip_build.py --install
```
## Configuring your backend
You can export the environment variable `KERAS_BACKEND` or you can edit your local config file at `~/.keras/keras.json`
to configure your backend. Available backend options are: `"tensorflow"`, `"jax"`, `"torch"`, `"openvino"`. Example:
```
export KERAS_BACKEND="jax"
```
In Colab, you can do:
```python
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
```
**Note:** The backend must be configured before importing `keras`, and the backend cannot be changed after
the package has been imported.
**Note:** The OpenVINO backend is an inference-only backend, meaning it is designed only for running model
predictions using `model.predict()` method.
## Backwards compatibility
Keras 3 is intended to work as a drop-in replacement for `tf.keras` (when using the TensorFlow backend). Just take your
existing `tf.keras` code, make sure that your calls to `model.save()` are using the up-to-date `.keras` format, and you're
done.
If your `tf.keras` model does not include custom components, you can start running it on top of JAX or PyTorch immediately.
If it does include custom components (e.g. custom layers or a custom `train_step()`), it is usually possible to convert it
to a backend-agnostic implementation in just a few minutes.
In addition, Keras models can consume datasets in any format, regardless of the backend you're using:
you can train your models with your existing `tf.data.Dataset` pipelines or PyTorch `DataLoaders`.
## Why use Keras 3?
- Run your high-level Keras workflows on top of any framework -- benefiting at will from the advantages of each framework,
e.g. the scalability and performance of JAX or the production ecosystem options of TensorFlow.
- Write custom components (e.g. layers, models, metrics) that you can use in low-level workflows in any framework.
- You can take a Keras model and train it in a training loop written from scratch in native TF, JAX, or PyTorch.
- You can take a Keras model and use it as part of a PyTorch-native `Module` or as part of a JAX-native model function.
- Make your ML code future-proof by avoiding framework lock-in.
- As a PyTorch user: get access to power and usability of Keras, at last!
- As a JAX user: get access to a fully-featured, battle-tested, well-documented modeling and training library.
Read more in the [Keras 3 release announcement](https://keras.io/keras_3/).
| text/markdown | null | Keras team <keras-users@googlegroups.com> | null | null | Apache License 2.0 | null | [
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3 :: Only",
"Operating System :: Unix",
"Operating System :: MacOS",
"Intended Audience :: Science/Research",
... | [] | null | null | >=3.11 | [] | [] | [] | [
"absl-py",
"numpy",
"rich",
"namex",
"h5py",
"optree",
"ml-dtypes",
"packaging"
] | [] | [] | [] | [
"Home, https://keras.io/",
"Repository, https://github.com/keras-team/keras"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:26:43.111910 | keras_nightly-3.14.0.dev2026011604.tar.gz | 1,166,754 | 0f/b7/cfcf9c3e85456d83b502ca5f0a083d15e13e317bb8ad2cd6c5649f51e05b/keras_nightly-3.14.0.dev2026011604.tar.gz | source | sdist | null | false | c76a172f76399646d5683502c8fe0fec | 13a12f052f099663d2a30246e2d8b68d8c95afe74eb51af53316d1ed86c8b719 | 0fb7cfcf9c3e85456d83b502ca5f0a083d15e13e317bb8ad2cd6c5649f51e05b | null | [] |
2.4 | evan-tools | 0.2.2 | It's a set of tools for Evan's development. | null | null | null | Evan <evanstonlaw555@gmail.com>, Evan <kaluoshilong@qq.com> | null | null | null | null | [] | [] | null | null | >=3.13 | [] | [] | [] | [
"humanize>=4.14.0",
"pydash>=8.0.5",
"pyyaml>=6.0.3",
"typer>=0.20.0"
] | [] | [] | [] | [] | uv/0.9.26 {"installer":{"name":"uv","version":"0.9.26","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-01-16T04:26:51.861124 | evan_tools-0.2.2.tar.gz | 15,732 | eb/67/e40bbafce6aa9e45e5eb8352d506bb9e5461f20aae23936103772fcfb875/evan_tools-0.2.2.tar.gz | source | sdist | null | false | 37c5b86202a56c3ec19a06d0e31f6678 | 9396832e30f212e0d795c3400c2bff5c6116f2f58c94d686483f52fe0915bd59 | eb67e40bbafce6aa9e45e5eb8352d506bb9e5461f20aae23936103772fcfb875 | null | [] |
2.4 | evan-tools | 0.2.2 | It's a set of tools for Evan's development. | null | null | null | Evan <evanstonlaw555@gmail.com>, Evan <kaluoshilong@qq.com> | null | null | null | null | [] | [] | null | null | >=3.13 | [] | [] | [] | [
"humanize>=4.14.0",
"pydash>=8.0.5",
"pyyaml>=6.0.3",
"typer>=0.20.0"
] | [] | [] | [] | [] | uv/0.9.26 {"installer":{"name":"uv","version":"0.9.26","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-01-16T04:26:52.850691 | evan_tools-0.2.2-py3-none-any.whl | 13,581 | 4d/cc/1c4ccc1a9b3d3fff5a3b12676d4314cd71350e12224fce7a3b7dc0ccad2b/evan_tools-0.2.2-py3-none-any.whl | py3 | bdist_wheel | null | false | 458dd5bc7a4d07ce7e5df3847a30d0bd | 7d6e02f9f3a3b7b4f335a44503b3e5aeeae5d70f0b02bd253e22dc629e62e202 | 4dcc1c4ccc1a9b3d3fff5a3b12676d4314cd71350e12224fce7a3b7dc0ccad2b | null | [] |
2.4 | ucdmcmc | 1.4 | MCMC fitting code for low temperature atmosphere spectra | # UCDMCMC
Markov Chain Monte Carlo (MCMC) fitting code for low-temperature stars, brown dwarfs ande extrasolar planet spectra, tuned particularly to the near-infrared.
## INSTALLATION NOTES
`ucdmcmc` can be installed from pip:
pip install ucdmcmc
or from git:
git clone
cd ucdmcmc
python -m setup.py install
It is recommended that you install in a conda environment to ensure the dependencies do not conflict with your own installation
conda create -n ucdmcmc python=3.13
conda activate ucdmcmc
pip install ucdmcmc
A check that this worked is that you can import `ucdmcmc` into python/jupyter noteobook, and that the `ucdmcmc.MODEL_FOLDER` points to the models folder that was downloaded
`ucdmcmc` uses the following extenal packages:
* `astropy`: https://www.astropy.org/
* `astroquery`: https://astroquery.readthedocs.io/en/latest/
* `corner`: https://corner.readthedocs.io/en/latest/
* `emcee`: https://emcee.readthedocs.io/en/stable/
* `matplotlib`: https://matplotlib.org/
* `numpy<2.0`: https://numpy.org/
* `pandas`: https://pandas.pydata.org/
* `(py)tables`: https://www.pytables.org/
* `requests`: https://requests.readthedocs.io/en/latest/
* `scipy`: https://scipy.org/
* `spectres`: https://spectres.readthedocs.io/en/latest/
* `statsmodels`: https://www.statsmodels.org/stable/index.html
* `tqdm`: https://tqdm.github.io/
### Optionally install SPLAT
To generate new model sets using the built-in `generateModels()` function, you will need to install `SPLAT` (note: this is not necessary for the other functionality in this code). `SPLAT` is not automatically installed on setup. The instructions are essentially the same:
git clone https://github.com/aburgasser/splat.git
cd splat
python -m pip install .
See https://github.com/aburgasser/splat for additional instructions
## Models
`ucdmcmc` comes with a starter set of models that play nicely with the code. An extended set can be downloaded from https://spexarchive.coolstarlab.ucsd.edu/ucdmcmc/. These should be placed in the folder `.ucdmcmc_models` in your home directory (i.e., `/home/adam/.ucdmcmc_models`). If it doesn't already exist, this directory will be created on the first call to `ucdmcmcm`. In addition, models that exist on this website and not present in this folder will be downloaded directly when `getModelSet()`` is called. You can also generate your own set of models using the `generateModels()` function (see note above).
## Spectra
`ucdmcmc` comes with a starter set of spectra for the following instruments:
* EUCLID: TBD
* NIR: TRAPPIST1 spectrum from Davoudi et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...970L...4D/abstract
* SPEX-PRISM: 2MASS J0559-1404 from Burgasser et al. (2006) https://ui.adsabs.harvard.edu/abs/2006ApJ...637.1067B/abstract
* JWST-NIRSPEC-PRISM: UNCOVER 33436 from Burgasser et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...962..177B/abstract
* JWST-NIRSPEC-G395H: TBD
* JWST-MIRI-LRS: TBD
* JWST-NIRSPEC-MIRI: Combined NIRSpec/PRISM and MIRI/LRS of SDSS J1624+0029 from Beiler et al. (2024) https://ui.adsabs.harvard.edu/abs/2024arXiv240708518B/abstract
User spectra can be read in using `ucdmcmc.Spectrum("filename")`. Files can be `.fits`, `.csv`, `.txt` (space-delimited), or `.tsv` (tab-delimited), and should have wavelength, flux, and uncertainty arrays. You can also read in these files separately and create a Spectrum object using the call `ucdmcmc.Spectrum(wave=[wave array,flux=[flux array],noise=[uncertainty array])`. See the docstring for `ucdmcmc.Spectrum` for further details.
## Usage
[TBD examples]
## Opacities
[TBD]
## Citing the code
If you use this code in your research, publications, or presentatinos, please include the following citation:
Adam Burgasser. (2025). aburgasser/ucdmcmc (vXXX). Zenodo. https://doi.org/10.5281/zenodo.16923762
or in bibtex:
@software{adam_burgasser_2025_16921711,
author = {Adam Burgasser},
doi = {10.5281/zenodo.16921711},
month = aug,
publisher = {Zenodo},
title = {aburgasser/ucdmcmc},
url = {https://doi.org/10.5281/zenodo.16921711},
version = {vXXX},
year = 2025,
bdsk-url-1 = {https://doi.org/10.5281/zenodo.16921711}}
where (vXXX) corresponds to the version used.
`ucdmcmc` and its antecedents has been used in the following publications:
* Burgasser et al. (2024, ApJ 962, 177): https://ui.adsabs.harvard.edu/abs/2024ApJ...962..177B/abstract
* Burgasser et al. (2025, ApJ 982, 79): https://ui.adsabs.harvard.edu/abs/2025ApJ...982...79B/abstract
* Lueber & Burgasser (2025, ApJ 988, 31): https://ui.adsabs.harvard.edu/abs/2025ApJ...988...31L/abstract
* Burgasser et al. (2025, Science, 390, 697): https://ui.adsabs.harvard.edu/abs/2025Sci...390..697B/abstract
* Morrissey et al. (2026, AJ, in press): https://ui.adsabs.harvard.edu/abs/2025arXiv251101167M/abstract
Please let me know if you make use of the code so we can include your publication in the list above!
| text/markdown | null | Adam Burgasser <aburgasser@ucsd.edu> | null | Adam Burgasser <aburgasser@ucsd.edu> | null | null | [
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"astropy",
"astroquery",
"corner",
"emcee",
"matplotlib",
"numpy<2.0",
"pandas",
"tables",
"requests",
"scipy",
"spectres",
"splat",
"statsmodels",
"tqdm",
"importlib_resources; python_version < \"3.7\"",
"pytest; extra == \"test\"",
"ruff; extra == \"test\""
] | [] | [] | [] | [
"Repository, https://github.com/aburgasser/ucdmcmc.git",
"Issues, https://github.com/aburgasser/ucdmcmc/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:27:37.914155 | ucdmcmc-1.4-py3-none-any.whl | 45,292,154 | f3/88/bf0dfd23263aa2ca71f4ab99487b71572b5d9f758389c69a55c29a82f709/ucdmcmc-1.4-py3-none-any.whl | py3 | bdist_wheel | null | false | b87abcfb1110b87a6a33b87b0f7b7841 | bcdd5211f5347273a17fcd730244a95c608d840ca415edf4fc072055e5765651 | f388bf0dfd23263aa2ca71f4ab99487b71572b5d9f758389c69a55c29a82f709 | null | [
"LICENSE"
] |
2.4 | ucdmcmc | 1.4 | MCMC fitting code for low temperature atmosphere spectra | # UCDMCMC
Markov Chain Monte Carlo (MCMC) fitting code for low-temperature stars, brown dwarfs ande extrasolar planet spectra, tuned particularly to the near-infrared.
## INSTALLATION NOTES
`ucdmcmc` can be installed from pip:
pip install ucdmcmc
or from git:
git clone
cd ucdmcmc
python -m setup.py install
It is recommended that you install in a conda environment to ensure the dependencies do not conflict with your own installation
conda create -n ucdmcmc python=3.13
conda activate ucdmcmc
pip install ucdmcmc
A check that this worked is that you can import `ucdmcmc` into python/jupyter noteobook, and that the `ucdmcmc.MODEL_FOLDER` points to the models folder that was downloaded
`ucdmcmc` uses the following extenal packages:
* `astropy`: https://www.astropy.org/
* `astroquery`: https://astroquery.readthedocs.io/en/latest/
* `corner`: https://corner.readthedocs.io/en/latest/
* `emcee`: https://emcee.readthedocs.io/en/stable/
* `matplotlib`: https://matplotlib.org/
* `numpy<2.0`: https://numpy.org/
* `pandas`: https://pandas.pydata.org/
* `(py)tables`: https://www.pytables.org/
* `requests`: https://requests.readthedocs.io/en/latest/
* `scipy`: https://scipy.org/
* `spectres`: https://spectres.readthedocs.io/en/latest/
* `statsmodels`: https://www.statsmodels.org/stable/index.html
* `tqdm`: https://tqdm.github.io/
### Optionally install SPLAT
To generate new model sets using the built-in `generateModels()` function, you will need to install `SPLAT` (note: this is not necessary for the other functionality in this code). `SPLAT` is not automatically installed on setup. The instructions are essentially the same:
git clone https://github.com/aburgasser/splat.git
cd splat
python -m pip install .
See https://github.com/aburgasser/splat for additional instructions
## Models
`ucdmcmc` comes with a starter set of models that play nicely with the code. An extended set can be downloaded from https://spexarchive.coolstarlab.ucsd.edu/ucdmcmc/. These should be placed in the folder `.ucdmcmc_models` in your home directory (i.e., `/home/adam/.ucdmcmc_models`). If it doesn't already exist, this directory will be created on the first call to `ucdmcmcm`. In addition, models that exist on this website and not present in this folder will be downloaded directly when `getModelSet()`` is called. You can also generate your own set of models using the `generateModels()` function (see note above).
## Spectra
`ucdmcmc` comes with a starter set of spectra for the following instruments:
* EUCLID: TBD
* NIR: TRAPPIST1 spectrum from Davoudi et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...970L...4D/abstract
* SPEX-PRISM: 2MASS J0559-1404 from Burgasser et al. (2006) https://ui.adsabs.harvard.edu/abs/2006ApJ...637.1067B/abstract
* JWST-NIRSPEC-PRISM: UNCOVER 33436 from Burgasser et al. (2024) https://ui.adsabs.harvard.edu/abs/2024ApJ...962..177B/abstract
* JWST-NIRSPEC-G395H: TBD
* JWST-MIRI-LRS: TBD
* JWST-NIRSPEC-MIRI: Combined NIRSpec/PRISM and MIRI/LRS of SDSS J1624+0029 from Beiler et al. (2024) https://ui.adsabs.harvard.edu/abs/2024arXiv240708518B/abstract
User spectra can be read in using `ucdmcmc.Spectrum("filename")`. Files can be `.fits`, `.csv`, `.txt` (space-delimited), or `.tsv` (tab-delimited), and should have wavelength, flux, and uncertainty arrays. You can also read in these files separately and create a Spectrum object using the call `ucdmcmc.Spectrum(wave=[wave array,flux=[flux array],noise=[uncertainty array])`. See the docstring for `ucdmcmc.Spectrum` for further details.
## Usage
[TBD examples]
## Opacities
[TBD]
## Citing the code
If you use this code in your research, publications, or presentatinos, please include the following citation:
Adam Burgasser. (2025). aburgasser/ucdmcmc (vXXX). Zenodo. https://doi.org/10.5281/zenodo.16923762
or in bibtex:
@software{adam_burgasser_2025_16921711,
author = {Adam Burgasser},
doi = {10.5281/zenodo.16921711},
month = aug,
publisher = {Zenodo},
title = {aburgasser/ucdmcmc},
url = {https://doi.org/10.5281/zenodo.16921711},
version = {vXXX},
year = 2025,
bdsk-url-1 = {https://doi.org/10.5281/zenodo.16921711}}
where (vXXX) corresponds to the version used.
`ucdmcmc` and its antecedents has been used in the following publications:
* Burgasser et al. (2024, ApJ 962, 177): https://ui.adsabs.harvard.edu/abs/2024ApJ...962..177B/abstract
* Burgasser et al. (2025, ApJ 982, 79): https://ui.adsabs.harvard.edu/abs/2025ApJ...982...79B/abstract
* Lueber & Burgasser (2025, ApJ 988, 31): https://ui.adsabs.harvard.edu/abs/2025ApJ...988...31L/abstract
* Burgasser et al. (2025, Science, 390, 697): https://ui.adsabs.harvard.edu/abs/2025Sci...390..697B/abstract
* Morrissey et al. (2026, AJ, in press): https://ui.adsabs.harvard.edu/abs/2025arXiv251101167M/abstract
Please let me know if you make use of the code so we can include your publication in the list above!
| text/markdown | null | Adam Burgasser <aburgasser@ucsd.edu> | null | Adam Burgasser <aburgasser@ucsd.edu> | null | null | [
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"astropy",
"astroquery",
"corner",
"emcee",
"matplotlib",
"numpy<2.0",
"pandas",
"tables",
"requests",
"scipy",
"spectres",
"splat",
"statsmodels",
"tqdm",
"importlib_resources; python_version < \"3.7\"",
"pytest; extra == \"test\"",
"ruff; extra == \"test\""
] | [] | [] | [] | [
"Repository, https://github.com/aburgasser/ucdmcmc.git",
"Issues, https://github.com/aburgasser/ucdmcmc/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:27:41.716024 | ucdmcmc-1.4.tar.gz | 46,024,861 | bd/51/06a5a741bde0c29cb960fc2e8ab447d2aee8d5c8a9669b5d0ed06b2a0471/ucdmcmc-1.4.tar.gz | source | sdist | null | false | 717c4c703347b5e3b521a580a8599947 | 9970c276dc32e523b181193edd8804dd68ca032b027c68d5462b40ff01eb57dd | bd5106a5a741bde0c29cb960fc2e8ab447d2aee8d5c8a9669b5d0ed06b2a0471 | null | [
"LICENSE"
] |
2.4 | scrapy-impersonate | 1.6.2 | Scrapy download handler that can impersonate browser fingerprints | # scrapy-impersonate
[](https://pypi.python.org/pypi/scrapy-impersonate)
`scrapy-impersonate` is a Scrapy download handler. This project integrates [curl_cffi](https://github.com/yifeikong/curl_cffi) to perform HTTP requests, so it can impersonate browsers' TLS signatures or JA3 fingerprints.
## Installation
```
pip install scrapy-impersonate
```
## Activation
To use this package, replace the default `http` and `https` Download Handlers by updating the [`DOWNLOAD_HANDLERS`](https://docs.scrapy.org/en/latest/topics/settings.html#download-handlers) setting:
```python
DOWNLOAD_HANDLERS = {
"http": "scrapy_impersonate.ImpersonateDownloadHandler",
"https": "scrapy_impersonate.ImpersonateDownloadHandler",
}
```
By setting `USER_AGENT = None`, `curl_cffi` will automatically choose the appropriate User-Agent based on the impersonated browser:
```python
USER_AGENT = None
```
Also, be sure to [install the asyncio-based Twisted reactor](https://docs.scrapy.org/en/latest/topics/asyncio.html#installing-the-asyncio-reactor) for proper asynchronous execution:
```python
TWISTED_REACTOR = "twisted.internet.asyncioreactor.AsyncioSelectorReactor"
```
## Usage
Set the `impersonate` [Request.meta](https://docs.scrapy.org/en/latest/topics/request-response.html#scrapy.http.Request.meta) key to download a request using `curl_cffi`:
```python
import scrapy
class ImpersonateSpider(scrapy.Spider):
name = "impersonate_spider"
custom_settings = {
"TWISTED_REACTOR": "twisted.internet.asyncioreactor.AsyncioSelectorReactor",
"USER_AGENT": "",
"DOWNLOAD_HANDLERS": {
"http": "scrapy_impersonate.ImpersonateDownloadHandler",
"https": "scrapy_impersonate.ImpersonateDownloadHandler",
},
"DOWNLOADER_MIDDLEWARES": {
"scrapy_impersonate.RandomBrowserMiddleware": 1000,
},
}
def start_requests(self):
for _ in range(5):
yield scrapy.Request(
"https://tls.browserleaks.com/json",
dont_filter=True,
)
def parse(self, response):
# ja3_hash: 98cc085d47985d3cca9ec1415bbbf0d1 (chrome133a)
# ja3_hash: 2d692a4485ca2f5f2b10ecb2d2909ad3 (firefox133)
# ja3_hash: c11ab92a9db8107e2a0b0486f35b80b9 (chrome124)
# ja3_hash: 773906b0efdefa24a7f2b8eb6985bf37 (safari15_5)
# ja3_hash: cd08e31494f9531f560d64c695473da9 (chrome99_android)
yield {"ja3_hash": response.json()["ja3_hash"]}
```
### impersonate-args
You can pass any necessary [arguments](https://github.com/lexiforest/curl_cffi/blob/38a91f2e7b23d9c9bda1d8085b7e41e33767c768/curl_cffi/requests/session.py#L1189-L1222) to `curl_cffi` through `impersonate_args`. For example:
```python
yield scrapy.Request(
"https://tls.browserleaks.com/json",
dont_filter=True,
meta={
"impersonate": browser,
"impersonate_args": {
"verify": False,
"timeout": 10,
},
},
)
```
## Supported browsers
The following browsers can be impersonated
| Browser | Version | Build | OS | Name |
| --- | --- | --- | --- | --- |
|  | 99 | 99.0.4844.51 | Windows 10 | `chrome99` |
|  | 99 | 99.0.4844.73 | Android 12 | `chrome99_android` |
|  | 100 | 100.0.4896.75 | Windows 10 | `chrome100` |
|  | 101 | 101.0.4951.67 | Windows 10 | `chrome101` |
|  | 104 | 104.0.5112.81 | Windows 10 | `chrome104` |
|  | 107 | 107.0.5304.107 | Windows 10 | `chrome107` |
|  | 110 | 110.0.5481.177 | Windows 10 | `chrome110` |
|  | 116 | 116.0.5845.180 | Windows 10 | `chrome116` |
|  | 119 | 119.0.6045.199 | macOS Sonoma | `chrome119` |
|  | 120 | 120.0.6099.109 | macOS Sonoma | `chrome120` |
|  | 123 | 123.0.6312.124 | macOS Sonoma | `chrome123` |
|  | 124 | 124.0.6367.60 | macOS Sonoma | `chrome124` |
|  | 131 | 131.0.6778.86 | macOS Sonoma | `chrome131` |
|  | 131 | 131.0.6778.81 | Android 14 | `chrome131_android` |
|  | 133 | 133.0.6943.55 | macOS Sequoia | `chrome133a` |
|  | 99 | 99.0.1150.30 | Windows 10 | `edge99` |
|  | 101 | 101.0.1210.47 | Windows 10 | `edge101` |
|  | 15.3 | 16612.4.9.1.8 | MacOS Big Sur | `safari15_3` |
|  | 15.5 | 17613.2.7.1.8 | MacOS Monterey | `safari15_5` |
|  | 17.0 | unclear | MacOS Sonoma | `safari17_0` |
|  | 17.2 | unclear | iOS 17.2 | `safari17_2_ios` |
|  | 18.0 | unclear | MacOS Sequoia | `safari18_0` |
|  | 18.0 | unclear | iOS 18.0 | `safari18_0_ios` |
|  | 133.0 | 133.0.3 | macOS Sonoma | `firefox133` |
|  | 135.0 | 135.0.1 | macOS Sonoma | `firefox135` |
## Thanks
This project is inspired by the following projects:
+ [curl_cffi](https://github.com/yifeikong/curl_cffi) - Python binding for curl-impersonate via cffi. A http client that can impersonate browser tls/ja3/http2 fingerprints.
+ [curl-impersonate](https://github.com/lwthiker/curl-impersonate) - A special build of curl that can impersonate Chrome & Firefox
+ [scrapy-playwright](https://github.com/scrapy-plugins/scrapy-playwright) - Playwright integration for Scrapy
| text/markdown | Jalil SA (jxlil) | null | null | null | MIT | null | [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"License :: OSI Approved :: MIT License"
] | [] | https://github.com/jxlil/scrapy-impersonate | null | >=3.8 | [] | [] | [] | [
"curl-cffi>=0.13.0",
"scrapy>=2.12.0"
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.14.2 | 2026-01-16T04:27:50.652516 | scrapy_impersonate-1.6.2-py3-none-any.whl | 7,333 | 38/6c/9c70917980d6b10e0560849420b7669a33cfbf85d7900f561824b0b1afc2/scrapy_impersonate-1.6.2-py3-none-any.whl | py3 | bdist_wheel | null | false | 5971d6c0664a7acf724fc153186b11de | 97caa429863c217fe3fa0dcd4ca94745cc12082d7b88676c4f9d27b66981635b | 386c9c70917980d6b10e0560849420b7669a33cfbf85d7900f561824b0b1afc2 | null | [
"LICENSE"
] |
2.4 | scrapy-impersonate | 1.6.2 | Scrapy download handler that can impersonate browser fingerprints | # scrapy-impersonate
[](https://pypi.python.org/pypi/scrapy-impersonate)
`scrapy-impersonate` is a Scrapy download handler. This project integrates [curl_cffi](https://github.com/yifeikong/curl_cffi) to perform HTTP requests, so it can impersonate browsers' TLS signatures or JA3 fingerprints.
## Installation
```
pip install scrapy-impersonate
```
## Activation
To use this package, replace the default `http` and `https` Download Handlers by updating the [`DOWNLOAD_HANDLERS`](https://docs.scrapy.org/en/latest/topics/settings.html#download-handlers) setting:
```python
DOWNLOAD_HANDLERS = {
"http": "scrapy_impersonate.ImpersonateDownloadHandler",
"https": "scrapy_impersonate.ImpersonateDownloadHandler",
}
```
By setting `USER_AGENT = None`, `curl_cffi` will automatically choose the appropriate User-Agent based on the impersonated browser:
```python
USER_AGENT = None
```
Also, be sure to [install the asyncio-based Twisted reactor](https://docs.scrapy.org/en/latest/topics/asyncio.html#installing-the-asyncio-reactor) for proper asynchronous execution:
```python
TWISTED_REACTOR = "twisted.internet.asyncioreactor.AsyncioSelectorReactor"
```
## Usage
Set the `impersonate` [Request.meta](https://docs.scrapy.org/en/latest/topics/request-response.html#scrapy.http.Request.meta) key to download a request using `curl_cffi`:
```python
import scrapy
class ImpersonateSpider(scrapy.Spider):
name = "impersonate_spider"
custom_settings = {
"TWISTED_REACTOR": "twisted.internet.asyncioreactor.AsyncioSelectorReactor",
"USER_AGENT": "",
"DOWNLOAD_HANDLERS": {
"http": "scrapy_impersonate.ImpersonateDownloadHandler",
"https": "scrapy_impersonate.ImpersonateDownloadHandler",
},
"DOWNLOADER_MIDDLEWARES": {
"scrapy_impersonate.RandomBrowserMiddleware": 1000,
},
}
def start_requests(self):
for _ in range(5):
yield scrapy.Request(
"https://tls.browserleaks.com/json",
dont_filter=True,
)
def parse(self, response):
# ja3_hash: 98cc085d47985d3cca9ec1415bbbf0d1 (chrome133a)
# ja3_hash: 2d692a4485ca2f5f2b10ecb2d2909ad3 (firefox133)
# ja3_hash: c11ab92a9db8107e2a0b0486f35b80b9 (chrome124)
# ja3_hash: 773906b0efdefa24a7f2b8eb6985bf37 (safari15_5)
# ja3_hash: cd08e31494f9531f560d64c695473da9 (chrome99_android)
yield {"ja3_hash": response.json()["ja3_hash"]}
```
### impersonate-args
You can pass any necessary [arguments](https://github.com/lexiforest/curl_cffi/blob/38a91f2e7b23d9c9bda1d8085b7e41e33767c768/curl_cffi/requests/session.py#L1189-L1222) to `curl_cffi` through `impersonate_args`. For example:
```python
yield scrapy.Request(
"https://tls.browserleaks.com/json",
dont_filter=True,
meta={
"impersonate": browser,
"impersonate_args": {
"verify": False,
"timeout": 10,
},
},
)
```
## Supported browsers
The following browsers can be impersonated
| Browser | Version | Build | OS | Name |
| --- | --- | --- | --- | --- |
|  | 99 | 99.0.4844.51 | Windows 10 | `chrome99` |
|  | 99 | 99.0.4844.73 | Android 12 | `chrome99_android` |
|  | 100 | 100.0.4896.75 | Windows 10 | `chrome100` |
|  | 101 | 101.0.4951.67 | Windows 10 | `chrome101` |
|  | 104 | 104.0.5112.81 | Windows 10 | `chrome104` |
|  | 107 | 107.0.5304.107 | Windows 10 | `chrome107` |
|  | 110 | 110.0.5481.177 | Windows 10 | `chrome110` |
|  | 116 | 116.0.5845.180 | Windows 10 | `chrome116` |
|  | 119 | 119.0.6045.199 | macOS Sonoma | `chrome119` |
|  | 120 | 120.0.6099.109 | macOS Sonoma | `chrome120` |
|  | 123 | 123.0.6312.124 | macOS Sonoma | `chrome123` |
|  | 124 | 124.0.6367.60 | macOS Sonoma | `chrome124` |
|  | 131 | 131.0.6778.86 | macOS Sonoma | `chrome131` |
|  | 131 | 131.0.6778.81 | Android 14 | `chrome131_android` |
|  | 133 | 133.0.6943.55 | macOS Sequoia | `chrome133a` |
|  | 99 | 99.0.1150.30 | Windows 10 | `edge99` |
|  | 101 | 101.0.1210.47 | Windows 10 | `edge101` |
|  | 15.3 | 16612.4.9.1.8 | MacOS Big Sur | `safari15_3` |
|  | 15.5 | 17613.2.7.1.8 | MacOS Monterey | `safari15_5` |
|  | 17.0 | unclear | MacOS Sonoma | `safari17_0` |
|  | 17.2 | unclear | iOS 17.2 | `safari17_2_ios` |
|  | 18.0 | unclear | MacOS Sequoia | `safari18_0` |
|  | 18.0 | unclear | iOS 18.0 | `safari18_0_ios` |
|  | 133.0 | 133.0.3 | macOS Sonoma | `firefox133` |
|  | 135.0 | 135.0.1 | macOS Sonoma | `firefox135` |
## Thanks
This project is inspired by the following projects:
+ [curl_cffi](https://github.com/yifeikong/curl_cffi) - Python binding for curl-impersonate via cffi. A http client that can impersonate browser tls/ja3/http2 fingerprints.
+ [curl-impersonate](https://github.com/lwthiker/curl-impersonate) - A special build of curl that can impersonate Chrome & Firefox
+ [scrapy-playwright](https://github.com/scrapy-plugins/scrapy-playwright) - Playwright integration for Scrapy
| text/markdown | Jalil SA (jxlil) | null | null | null | MIT | null | [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"License :: OSI Approved :: MIT License"
] | [] | https://github.com/jxlil/scrapy-impersonate | null | >=3.8 | [] | [] | [] | [
"curl-cffi>=0.13.0",
"scrapy>=2.12.0"
] | [] | [] | [] | [] | twine/6.2.0 CPython/3.14.2 | 2026-01-16T04:27:51.461913 | scrapy_impersonate-1.6.2.tar.gz | 6,707 | c4/e3/632135937df9aafc23572bb8dde909654a06969e2f704b8a24afc3570063/scrapy_impersonate-1.6.2.tar.gz | source | sdist | null | false | c22f22a95a249d45d1ec88c8614a8c6c | 5e057ae037e09d90b858c31da32597fe35c2324d198e57b86ead5a226fe2bba6 | c4e3632135937df9aafc23572bb8dde909654a06969e2f704b8a24afc3570063 | null | [
"LICENSE"
] |
2.4 | boto3-refresh-session | 6.2.3 | A simple Python package for refreshing the temporary security credentials in a boto3.session.Session object automatically. | <div align="center">
<img src="https://raw.githubusercontent.com/michaelthomasletts/boto3-refresh-session/refs/heads/main/doc/brs.png" />
</div>
</br>
<div align="center"><em>
A simple Python package for refreshing the temporary security credentials in a <code>boto3.session.Session</code> object automatically.
</em></div>
</br>
<div align="center">
<a href="https://pypi.org/project/boto3-refresh-session/">
<img
src="https://img.shields.io/pypi/v/boto3-refresh-session?color=%23FF0000FF&logo=python&label=Latest%20Version"
alt="pypi_version"
/>
</a>
<a href="https://pypi.org/project/boto3-refresh-session/">
<img
src="https://img.shields.io/pypi/pyversions/boto3-refresh-session?style=pypi&color=%23FF0000FF&logo=python&label=Compatible%20Python%20Versions"
alt="py_version"
/>
</a>
<a href="https://github.com/michaelthomasletts/boto3-refresh-session/actions/workflows/push.yml">
<img
src="https://img.shields.io/github/actions/workflow/status/michaelthomasletts/boto3-refresh-session/push.yml?logo=github&color=%23FF0000FF&label=Build"
alt="workflow"
/>
</a>
<a href="https://github.com/michaelthomasletts/boto3-refresh-session/commits/main">
<img
src="https://img.shields.io/github/last-commit/michaelthomasletts/boto3-refresh-session?logo=github&color=%23FF0000FF&label=Last%20Commit"
alt="last_commit"
/>
</a>
<a href="https://github.com/michaelthomasletts/boto3-refresh-session/stargazers">
<img
src="https://img.shields.io/github/stars/michaelthomasletts/boto3-refresh-session?style=flat&logo=github&labelColor=555&color=FF0000&label=Stars"
alt="stars"
/>
</a>
<a href="https://pepy.tech/projects/boto3-refresh-session">
<img
src="https://img.shields.io/endpoint?url=https%3A%2F%2Fmichaelthomasletts.github.io%2Fpepy-stats%2Fboto3-refresh-session.json&style=flat&logo=python&labelColor=555&color=FF0000"
alt="downloads"
/>
</a>
<a href="https://michaelthomasletts.github.io/boto3-refresh-session/index.html">
<img
src="https://img.shields.io/badge/Official%20Documentation-📘-FF0000?style=flat&labelColor=555&logo=readthedocs"
alt="documentation"
/>
</a>
<a href="https://github.com/michaelthomasletts/boto3-refresh-session">
<img
src="https://img.shields.io/badge/Source%20Code-💻-FF0000?style=flat&labelColor=555&logo=github"
alt="github"
/>
</a>
<a href="https://michaelthomasletts.github.io/boto3-refresh-session/qanda.html">
<img
src="https://img.shields.io/badge/Q%26A-❔-FF0000?style=flat&labelColor=555&logo=vercel&label=Q%26A"
alt="qanda"
/>
</a>
<a href="https://michaelthomasletts.github.io/blog/brs-rationale/">
<img
src="https://img.shields.io/badge/Blog%20Post-📘-FF0000?style=flat&labelColor=555&logo=readthedocs"
alt="blog"
/>
</a>
<a href="https://github.com/sponsors/michaelthomasletts">
<img
src="https://img.shields.io/badge/Sponsor%20this%20Project-💙-FF0000?style=flat&labelColor=555&logo=githubsponsors"
alt="sponsorship"
/>
</a>
</div>
## 😛 Features
- Drop-in replacement for `boto3.session.Session`
- MFA support included for STS
- SSO support via AWS profiles
- Optionally caches boto3 clients
- Supports automatic temporary credential refresh for:
- **STS**
- **IoT Core**
- X.509 certificates w/ role aliases over mTLS (PEM files and PKCS#11)
- MQTT actions are available!
- [Tested](https://github.com/michaelthomasletts/boto3-refresh-session/tree/main/tests), [documented](https://michaelthomasletts.github.io/boto3-refresh-session/index.html), and [published to PyPI](https://pypi.org/project/boto3-refresh-session/)
## 😌 Recognition and Testimonials
[Featured in TL;DR Sec.](https://tldrsec.com/p/tldr-sec-282)
[Featured in CloudSecList.](https://cloudseclist.com/issues/issue-290)
Recognized during AWS Community Day Midwest on June 5th, 2025 (the founder's birthday!).
A testimonial from a Cyber Security Engineer at a FAANG company:
> _Most of my work is on tooling related to AWS security, so I'm pretty choosy about boto3 credentials-adjacent code. I often opt to just write this sort of thing myself so I at least know that I can reason about it. But I found boto3-refresh-session to be very clean and intuitive [...] We're using the RefreshableSession class as part of a client cache construct [...] We're using AWS Lambda to perform lots of operations across several regions in hundreds of accounts, over and over again, all day every day. And it turns out that there's a surprising amount of overhead to creating boto3 clients (mostly deserializing service definition json), so we can run MUCH more efficiently if we keep a cache of clients, all equipped with automatically refreshing sessions._
## 💻 Installation
```bash
pip install boto3-refresh-session
```
## 📝 Usage
<details>
<summary><strong>Core Concepts (click to expand)</strong></summary>
### Core Concepts
1. `RefreshableSession` is the intended interface for using `boto3-refresh-session`. Whether you're using this package to refresh temporary credentials returned by STS, the IoT credential provider (which is really just STS, but I digress), or some custom authentication or credential provider, `RefreshableSession` is where you *ought to* be working when using `boto3-refresh-session`.
2. *You can use all of the same keyword parameters normally associated with `boto3.session.Session`!* For instance, suppose you want to pass `region_name` to `RefreshableSession` as a parameter, whereby it's passed to `boto3.session.Session`. That's perfectly fine! Just pass it like you normally would when initializing `boto3.session.Session`. These keyword parameters are *completely optional*, though. If you're confused, the main idea to remember is this: if initializing `boto3.session.Session` *requires* a particular keyword parameter then pass it to `RefreshableSession`; if not, don't worry about it.
3. To tell `RefreshableSession` which AWS service you're working with for authentication and credential retrieval purposes (STS vs. IoT vs. some custom credential provider), you'll need to pass a `method` parameter to `RefreshableSession`. Since the `service_name` namespace is already occupied by `boto3.sesssion.Session`, [`boto3-refresh-session` uses `method` instead of "service" so as to avoid confusion](https://github.com/michaelthomasletts/boto3-refresh-session/blob/04acb2adb34e505c4dc95711f6b2f97748a2a489/boto3_refresh_session/utils/typing.py#L40). If you're using `RefreshableSession` for STS, however, then `method` is set to `"sts"` by default. You don't need to pass the `method` keyword argument in that case.
4. Using `RefreshableSession` for STS, IoT, or custom flows requires different keyword parameters that are unique to those particular methods. For instance, `STSRefreshableSession`, which is the engine for STS in `boto3-refresh-session`, requires `assume_role_kwargs` and optionally allows `sts_client_kwargs` whereas `CustomRefreshableSession` and `IoTX509RefreshableSession` do not. To familiarize yourself with the keyword parameters for each method, check the documentation for each of those engines [in the Refresh Strategies section here](https://michaelthomasletts.com/boto3-refresh-session/modules/index.html).
5. Irrespective of whatever `method` you pass as a keyword parameter, `RefreshableSession` accepts a keyword parameter named `defer_refresh`. Basically, this boolean tells `boto3-refresh-session` either to refresh credentials *the moment they expire* or to *wait until credentials are explicitly needed*. If you are working in a low-latency environment then `defer_refresh = False` might be helpful. For most users, however, `defer_refresh = True` is most desirable. For that reason, `defer_refresh = True` is the default value. Most users, therefore, should not concern themselves too much with this feature.
6. Some developers struggle to imagine where `boto3-refresh-session` might be helpful. To figure out if `boto3-refresh-session` is for your use case, or whether `credential_process` satisfies your needs, check out [this blog post](https://michaelthomasletts.com/blog/brs-rationale/). `boto3-refresh-session` is not for every developer or use-case; it is a niche tool.
7. `boto3-refresh-session` supports client caching in order to minimize the massive memory footprint associated with duplicative clients. By default, `RefreshableSession` caches clients. To deactivate this feature, set `cache_clients=False`.
8. `boto3-refresh-session` supports MFA. Refer to the MFA section further below for more details.
9. `boto3-refresh-session` supports SSO; however, it _does not_ and _will never_ automatically handle `sso login` for you -- that is, not unless you write your own hacky custom credential getter and pass that to `RefreshableSession(method="custom", ...)`, which I do not recommend (but cannot prevent you from doing).
</details>
<details>
<summary><strong>Clients (click to expand)</strong></summary>
### Clients
Most developers who use `boto3` interact primarily with `boto3.client` instead of `boto3.session.Session`. But many developers may not realize that `boto3.session.Session` belies `boto3.client`! In fact, that's precisely what makes `boto3-refresh-session` possible!
Before we get to initializing clients via `RefreshableSession`, however, let's briefly talk about `boto3` clients and memory . . .
Clients consume a shocking amount of memory. So much so that many developers create their own bespoke client cache. To minimize the memory footprint associated with duplicative clients, as well as make the lives of developers a little easier, `boto3-refresh-session` includes a `cache_clients` parameter which, by default, caches clients according to the parameters passed to the `client` method!
With client caching out of the way, in order to use the `boto3.client` interface, but with the benefits of `boto3-refresh-session`, you have a few options! In the following examples, let's assume you want to use STS for retrieving temporary credentials for the sake of simplicity. Let's also focus specifically on `client`. Switching to `resource` follows the same exact idioms as below, except that `client` must be switched to `resource` in the pseudo-code, obviously. If you are not sure how to use `RefreshableSession` for STS (or custom auth flows) then check the usage instructions in the following sections!
##### `RefreshableSession.client` (Recommended)
So long as you reuse the same `session` object when creating `client` objects, this approach can be used everywhere in your code.
```python
from boto3_refresh_session import RefreshableSession
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"DurationSeconds": "<your-selection>",
...
}
session = RefreshableSession(assume_role_kwargs=assume_role_kwargs)
s3 = session.client("s3")
```
##### `DEFAULT_SESSION`
This technique can be helpful if you want to use the same instance of `RefreshableSession` everywhere in your code without reference to `boto3_refresh_session`!
```python
from boto3 import DEFAULT_SESSION, client
from boto3_refresh_session import RefreshableSession
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"DurationSeconds": "<your-selection>",
...
}
DEFAULT_SESSION = RefreshableSession(assume_role_kwargs=assume_role_kwargs)
s3 = client("s3")
```
##### `botocore_session`
```python
from boto3 import client
from boto3_refresh_session import RefreshableSession
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"DurationSeconds": "<your-selection>",
...
}
s3 = client(
service_name="s3",
botocore_session=RefreshableSession(assume_role_kwargs=assume_role_kwargs)
)
```
</details>
<details>
<summary><strong>STS (click to expand)</strong></summary>
### STS
Most developers use AWS STS to assume an IAM role and return a set of temporary security credentials. boto3-refresh-session can be used to ensure those temporary credentials refresh automatically. For additional information on the exact parameters that `RefreshableSession` takes for STS, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.sts.STSRefreshableSession.html).
```python
import boto3_refresh_session as brs
assume_role_kwargs = {
"RoleArn": "<your IAM role arn>", # required
"RoleSessionName": "<your role session name>", # required
...
}
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs, # required
sts_client_kwargs={...}, # optional
... # misc. params for boto3.session.Session
)
```
</details>
<details>
<summary><strong>MFA (click to expand)</strong></summary>
### MFA Support
When assuming a role that requires MFA, `boto3-refresh-session` supports automatic token provisioning through the `mfa_token_provider` parameter. This parameter accepts a callable that returns a fresh MFA token code (string) whenever credentials need to be refreshed.
The `mfa_token_provider` approach is **strongly recommended** over manually providing `TokenCode` in `assume_role_kwargs`, as MFA tokens expire after 30 seconds while AWS temporary credentials can last for hours. By using a callable, your application can automatically fetch fresh tokens on each refresh without manual intervention. There is nothing preventing you from manually providing `TokenCode` *without* `mfa_token_provider`; however, *you* will be responsible for updating `TokenCode` *before* automatic temporary credential refresh occurs, which is likely to be a fragile and complicated approach.
When using `mfa_token_provider`, you must also provide `SerialNumber` (your MFA device ARN) in `assume_role_kwargs`. For additional information on the exact parameters that `RefreshableSession` takes for MFA, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.sts.STSRefreshableSession.html).
⚠️ Most developers will probably find example number four most helpful.
#### Examples
```python
import boto3_refresh_session as brs
# Example 1: Interactive prompt for MFA token
def get_mfa_token():
return input("Enter MFA token: ")
# we'll reuse this object in each example for simplicity :)
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"SerialNumber": "arn:aws:iam::123456789012:mfa/your-user", # required with mfa_token_provider
}
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=get_mfa_token, # callable that returns MFA token
)
# Example 2: Using pyotp for TOTP-based MFA
import pyotp
def get_totp_token():
totp = pyotp.TOTP("<your-secret-key>")
return totp.now()
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=get_totp_token,
)
# Example 3: Retrieving token from environment variable or external service
import os
def get_env_token():
return os.environ.get("AWS_MFA_TOKEN", "")
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=get_env_token,
)
# Example 4: Using Yubikey (or any token provider CLI)
from typing import Sequence
import subprocess
def mfa_token_provider(cmd: Sequence[str], timeout: float):
p = subprocess.run(
list(cmd),
check=False,
capture_output=True,
text=True,
timeout=timeout,
)
return (p.stdout or "").strip()
mfa_token_provider_args = {
"cmd": ["ykman", "oath", "code", "--single", "AWS-prod"], # example token source
"timeout": 3.0,
}
session = RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=mfa_token_provider,
mfa_token_provider_args=mfa_token_provider_args,
)
```
</details>
<details>
<summary><strong>SSO (click to expand)</strong></summary>
### SSO
`boto3-refresh-session` supports SSO by virtue of AWS profiles.
The below pseudo-code illustrates how to assume an IAM role using an AWS profile with SSO. Not shown, however, is running `sso login` manually, which `boto3-refresh-session` does not perform automatically for you. Therefore, you must manually run `sso login` as necessary.
If you wish to automate `sso login` (not recommended) then you will need to write your own custom callable function and pass it to `RefreshableSession(method="custom", ...)`. In that event, please refer to the `Custom` documentation found in a separate section below.
```python
from boto3_refresh_session import RefreshableSession
session = RefreshableSession(
assume_role_kwargs={
"RoleArn": "<your IAM role arn>",
"RoleSessionName": "<your role session name>",
},
profile_name="<your AWS profile name>",
...
)
s3 = session.client("s3")
```
</details>
<details>
<summary><strong>Custom (click to expand)</strong></summary>
### Custom
If you have a highly sophisticated, novel, or idiosyncratic authentication flow not included in boto3-refresh-session then you will need to provide your own custom temporary credentials callable object. `RefreshableSession` accepts custom credentials callable objects, as shown below. For additional information on the exact parameters that `RefreshableSession` takes for custom authentication flows, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.custom.CustomRefreshableSession.html#boto3_refresh_session.methods.custom.CustomRefreshableSession).
```python
# create (or import) your custom credential method
def your_custom_credential_getter(...):
...
return {
"access_key": ...,
"secret_key": ...,
"token": ...,
"expiry_time": ...,
}
# and pass it to RefreshableSession
session = RefreshableSession(
method="custom", # required
custom_credentials_method=your_custom_credential_getter, # required
custom_credentials_method_args=..., # optional
region_name=region_name, # optional
profile_name=profile_name, # optional
... # misc. boto3.session.Session params
)
```
</details>
<details>
<summary><strong>IoT Core X.509 (click to expand)</strong></summary>
### IoT Core X.509
AWS IoT Core can vend temporary AWS credentials through the **credentials provider** when you connect with an X.509 certificate and a **role alias**. `boto3-refresh-session` makes this flow seamless by automatically refreshing credentials over **mTLS**.
For additional information on the exact parameters that `IOTX509RefreshableSession` takes, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.iot.x509.IOTX509RefreshableSession.html).
### PEM file
```python
import boto3_refresh_session as brs
# PEM certificate + private key example
session = brs.RefreshableSession(
method="iot",
endpoint="<your-credentials-endpoint>.credentials.iot.<region>.amazonaws.com",
role_alias="<your-role-alias>",
certificate="/path/to/certificate.pem",
private_key="/path/to/private-key.pem",
thing_name="<your-thing-name>", # optional, if used in policies
duration_seconds=3600, # optional, capped by role alias
region_name="us-east-1",
)
# Now you can use the session like any boto3 session
s3 = session.client("s3")
print(s3.list_buckets())
```
### PKCS#11
```python
session = brs.RefreshableSession(
method="iot",
endpoint="<your-credentials-endpoint>.credentials.iot.<region>.amazonaws.com",
role_alias="<your-role-alias>",
certificate="/path/to/certificate.pem",
pkcs11={
"pkcs11_lib": "/usr/local/lib/softhsm/libsofthsm2.so",
"user_pin": "1234",
"slot_id": 0,
"token_label": "MyToken",
"private_key_label": "MyKey",
},
thing_name="<your-thing-name>",
region_name="us-east-1",
)
```
### MQTT
After initializing a session object, you can can begin making actions with MQTT using the [mqtt method](https://github.com/michaelthomasletts/boto3-refresh-session/blob/deb68222925bf648f26e878ed4bc24b45317c7db/boto3_refresh_session/methods/iot/x509.py#L367)! You can reuse the same certificate, private key, et al as that used to initialize `RefreshableSession`. Or, alternatively, you can provide separate PKCS#11 or certificate information, whether those be file paths or bytes values. Either way, at a minimum, you will need to provide the endpoint and client identifier (i.e. thing name).
```python
from awscrt.mqtt.QoS import AT_LEAST_ONCE
conn = session.mqtt(
endpoint="<your endpoint>-ats.iot.<region>.amazonaws.com",
client_id="<your thing name or client ID>",
)
conn.connect()
conn.connect().result()
conn.publish(topic="foo/bar", payload=b"hi", qos=AT_LEAST_ONCE)
conn.disconnect().result()
```
</details>
## ⚠️ Changes
Browse through the various changes to `boto3-refresh-session` over time.
#### 😥 v3.0.0
**The changes introduced by v3.0.0 will not impact ~99% of users** who generally interact with `boto3-refresh-session` by only `RefreshableSession`, *which is the intended usage for this package after all.*
Advanced users, however, particularly those using low-level objects such as `BaseRefreshableSession | refreshable_session | BRSSession | utils.py`, may experience breaking changes.
Please review [this PR](https://github.com/michaelthomasletts/boto3-refresh-session/pull/75) for additional details.
#### ✂️ v4.0.0
The `ecs` module has been dropped. For additional details and rationale, please review [this PR](https://github.com/michaelthomasletts/boto3-refresh-session/pull/78).
#### 😛 v5.0.0
Support for IoT Core via X.509 certificate-based authentication (over HTTPS) is now available!
#### ➕ v5.1.0
MQTT support added for IoT Core via X.509 certificate-based authentication.
#### ➕ v6.0.0
MFA support for STS added!
#### 🔒😥 v6.2.0
- Client caching introduced to `RefreshableSession` in order to minimize memory footprint! Available via `cache_clients` parameter.
- Testing suite expanded to include IOT, MFA, caching, and much more!
- A subtle bug was uncovered where `RefreshableSession` created refreshable credentials but boto3's underlying session continued to resolve credentials via the default provider chain (i.e. env vars, shared config, etc) unless explicitly wired. `get_credentials()` and clients could, in certain setups, use base session credentials instead of the refreshable STS/IoT/custom credentials via assumed role. To fix this, I updated the implementation in `BRSSession.__post_init__` to set `self._session._credentials = self._credentials`, ensuring all boto3 clients created from `RefreshableSession` use the refreshable credentials source of truth provided to `RefreshableCredentials | DeferredRefreshableCredentials`. After this change, refreshable credentials are used consistently everywhere, irrespective of setup.
#### ✂️ v6.2.3
- The `RefreshableTemporaryCredentials` type hint was deprecated in favor of `TemporaryCredentials`.
- `expiry_time` was added as a parameter returned by the `refreshable_credentials` method and `credentials` attribute. | text/markdown | Mike Letts | lettsmt@gmail.com | Michael Letts | lettsmt@gmail.com | MIT | boto3, botocore, aws, sts, credentials, token, refresh, iot, x509, mqtt | [
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | https://github.com/michaelthomasletts/boto3-refresh-session | null | >=3.10 | [] | [] | [] | [
"boto3",
"botocore",
"requests",
"typing-extensions",
"awscrt",
"awsiotsdk"
] | [] | [] | [] | [
"Repository, https://github.com/michaelthomasletts/boto3-refresh-session",
"Documentation, https://michaelthomasletts.github.io/boto3-refresh-session/index.html"
] | poetry/2.2.1 CPython/3.10.19 Linux/6.11.0-1018-azure | 2026-01-16T04:28:29.880928 | boto3_refresh_session-6.2.3-py3-none-any.whl | 30,355 | 17/94/31a82fff6e9cb051e5999e9be27a0c338f587da941e98b808a5595656c0d/boto3_refresh_session-6.2.3-py3-none-any.whl | py3 | bdist_wheel | null | false | f2644232fbbb8483f4d794743e3d1b7a | a2b61542f3682993695e799de0e1a9e7b0efbb925fc0424d95fbc549886a765d | 179431a82fff6e9cb051e5999e9be27a0c338f587da941e98b808a5595656c0d | null | [] |
2.4 | boto3-refresh-session | 6.2.3 | A simple Python package for refreshing the temporary security credentials in a boto3.session.Session object automatically. | <div align="center">
<img src="https://raw.githubusercontent.com/michaelthomasletts/boto3-refresh-session/refs/heads/main/doc/brs.png" />
</div>
</br>
<div align="center"><em>
A simple Python package for refreshing the temporary security credentials in a <code>boto3.session.Session</code> object automatically.
</em></div>
</br>
<div align="center">
<a href="https://pypi.org/project/boto3-refresh-session/">
<img
src="https://img.shields.io/pypi/v/boto3-refresh-session?color=%23FF0000FF&logo=python&label=Latest%20Version"
alt="pypi_version"
/>
</a>
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<img
src="https://img.shields.io/pypi/pyversions/boto3-refresh-session?style=pypi&color=%23FF0000FF&logo=python&label=Compatible%20Python%20Versions"
alt="py_version"
/>
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</a>
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alt="downloads"
/>
</a>
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alt="documentation"
/>
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src="https://img.shields.io/badge/Source%20Code-💻-FF0000?style=flat&labelColor=555&logo=github"
alt="github"
/>
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alt="qanda"
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<img
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</a>
</div>
## 😛 Features
- Drop-in replacement for `boto3.session.Session`
- MFA support included for STS
- SSO support via AWS profiles
- Optionally caches boto3 clients
- Supports automatic temporary credential refresh for:
- **STS**
- **IoT Core**
- X.509 certificates w/ role aliases over mTLS (PEM files and PKCS#11)
- MQTT actions are available!
- [Tested](https://github.com/michaelthomasletts/boto3-refresh-session/tree/main/tests), [documented](https://michaelthomasletts.github.io/boto3-refresh-session/index.html), and [published to PyPI](https://pypi.org/project/boto3-refresh-session/)
## 😌 Recognition and Testimonials
[Featured in TL;DR Sec.](https://tldrsec.com/p/tldr-sec-282)
[Featured in CloudSecList.](https://cloudseclist.com/issues/issue-290)
Recognized during AWS Community Day Midwest on June 5th, 2025 (the founder's birthday!).
A testimonial from a Cyber Security Engineer at a FAANG company:
> _Most of my work is on tooling related to AWS security, so I'm pretty choosy about boto3 credentials-adjacent code. I often opt to just write this sort of thing myself so I at least know that I can reason about it. But I found boto3-refresh-session to be very clean and intuitive [...] We're using the RefreshableSession class as part of a client cache construct [...] We're using AWS Lambda to perform lots of operations across several regions in hundreds of accounts, over and over again, all day every day. And it turns out that there's a surprising amount of overhead to creating boto3 clients (mostly deserializing service definition json), so we can run MUCH more efficiently if we keep a cache of clients, all equipped with automatically refreshing sessions._
## 💻 Installation
```bash
pip install boto3-refresh-session
```
## 📝 Usage
<details>
<summary><strong>Core Concepts (click to expand)</strong></summary>
### Core Concepts
1. `RefreshableSession` is the intended interface for using `boto3-refresh-session`. Whether you're using this package to refresh temporary credentials returned by STS, the IoT credential provider (which is really just STS, but I digress), or some custom authentication or credential provider, `RefreshableSession` is where you *ought to* be working when using `boto3-refresh-session`.
2. *You can use all of the same keyword parameters normally associated with `boto3.session.Session`!* For instance, suppose you want to pass `region_name` to `RefreshableSession` as a parameter, whereby it's passed to `boto3.session.Session`. That's perfectly fine! Just pass it like you normally would when initializing `boto3.session.Session`. These keyword parameters are *completely optional*, though. If you're confused, the main idea to remember is this: if initializing `boto3.session.Session` *requires* a particular keyword parameter then pass it to `RefreshableSession`; if not, don't worry about it.
3. To tell `RefreshableSession` which AWS service you're working with for authentication and credential retrieval purposes (STS vs. IoT vs. some custom credential provider), you'll need to pass a `method` parameter to `RefreshableSession`. Since the `service_name` namespace is already occupied by `boto3.sesssion.Session`, [`boto3-refresh-session` uses `method` instead of "service" so as to avoid confusion](https://github.com/michaelthomasletts/boto3-refresh-session/blob/04acb2adb34e505c4dc95711f6b2f97748a2a489/boto3_refresh_session/utils/typing.py#L40). If you're using `RefreshableSession` for STS, however, then `method` is set to `"sts"` by default. You don't need to pass the `method` keyword argument in that case.
4. Using `RefreshableSession` for STS, IoT, or custom flows requires different keyword parameters that are unique to those particular methods. For instance, `STSRefreshableSession`, which is the engine for STS in `boto3-refresh-session`, requires `assume_role_kwargs` and optionally allows `sts_client_kwargs` whereas `CustomRefreshableSession` and `IoTX509RefreshableSession` do not. To familiarize yourself with the keyword parameters for each method, check the documentation for each of those engines [in the Refresh Strategies section here](https://michaelthomasletts.com/boto3-refresh-session/modules/index.html).
5. Irrespective of whatever `method` you pass as a keyword parameter, `RefreshableSession` accepts a keyword parameter named `defer_refresh`. Basically, this boolean tells `boto3-refresh-session` either to refresh credentials *the moment they expire* or to *wait until credentials are explicitly needed*. If you are working in a low-latency environment then `defer_refresh = False` might be helpful. For most users, however, `defer_refresh = True` is most desirable. For that reason, `defer_refresh = True` is the default value. Most users, therefore, should not concern themselves too much with this feature.
6. Some developers struggle to imagine where `boto3-refresh-session` might be helpful. To figure out if `boto3-refresh-session` is for your use case, or whether `credential_process` satisfies your needs, check out [this blog post](https://michaelthomasletts.com/blog/brs-rationale/). `boto3-refresh-session` is not for every developer or use-case; it is a niche tool.
7. `boto3-refresh-session` supports client caching in order to minimize the massive memory footprint associated with duplicative clients. By default, `RefreshableSession` caches clients. To deactivate this feature, set `cache_clients=False`.
8. `boto3-refresh-session` supports MFA. Refer to the MFA section further below for more details.
9. `boto3-refresh-session` supports SSO; however, it _does not_ and _will never_ automatically handle `sso login` for you -- that is, not unless you write your own hacky custom credential getter and pass that to `RefreshableSession(method="custom", ...)`, which I do not recommend (but cannot prevent you from doing).
</details>
<details>
<summary><strong>Clients (click to expand)</strong></summary>
### Clients
Most developers who use `boto3` interact primarily with `boto3.client` instead of `boto3.session.Session`. But many developers may not realize that `boto3.session.Session` belies `boto3.client`! In fact, that's precisely what makes `boto3-refresh-session` possible!
Before we get to initializing clients via `RefreshableSession`, however, let's briefly talk about `boto3` clients and memory . . .
Clients consume a shocking amount of memory. So much so that many developers create their own bespoke client cache. To minimize the memory footprint associated with duplicative clients, as well as make the lives of developers a little easier, `boto3-refresh-session` includes a `cache_clients` parameter which, by default, caches clients according to the parameters passed to the `client` method!
With client caching out of the way, in order to use the `boto3.client` interface, but with the benefits of `boto3-refresh-session`, you have a few options! In the following examples, let's assume you want to use STS for retrieving temporary credentials for the sake of simplicity. Let's also focus specifically on `client`. Switching to `resource` follows the same exact idioms as below, except that `client` must be switched to `resource` in the pseudo-code, obviously. If you are not sure how to use `RefreshableSession` for STS (or custom auth flows) then check the usage instructions in the following sections!
##### `RefreshableSession.client` (Recommended)
So long as you reuse the same `session` object when creating `client` objects, this approach can be used everywhere in your code.
```python
from boto3_refresh_session import RefreshableSession
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"DurationSeconds": "<your-selection>",
...
}
session = RefreshableSession(assume_role_kwargs=assume_role_kwargs)
s3 = session.client("s3")
```
##### `DEFAULT_SESSION`
This technique can be helpful if you want to use the same instance of `RefreshableSession` everywhere in your code without reference to `boto3_refresh_session`!
```python
from boto3 import DEFAULT_SESSION, client
from boto3_refresh_session import RefreshableSession
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"DurationSeconds": "<your-selection>",
...
}
DEFAULT_SESSION = RefreshableSession(assume_role_kwargs=assume_role_kwargs)
s3 = client("s3")
```
##### `botocore_session`
```python
from boto3 import client
from boto3_refresh_session import RefreshableSession
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"DurationSeconds": "<your-selection>",
...
}
s3 = client(
service_name="s3",
botocore_session=RefreshableSession(assume_role_kwargs=assume_role_kwargs)
)
```
</details>
<details>
<summary><strong>STS (click to expand)</strong></summary>
### STS
Most developers use AWS STS to assume an IAM role and return a set of temporary security credentials. boto3-refresh-session can be used to ensure those temporary credentials refresh automatically. For additional information on the exact parameters that `RefreshableSession` takes for STS, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.sts.STSRefreshableSession.html).
```python
import boto3_refresh_session as brs
assume_role_kwargs = {
"RoleArn": "<your IAM role arn>", # required
"RoleSessionName": "<your role session name>", # required
...
}
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs, # required
sts_client_kwargs={...}, # optional
... # misc. params for boto3.session.Session
)
```
</details>
<details>
<summary><strong>MFA (click to expand)</strong></summary>
### MFA Support
When assuming a role that requires MFA, `boto3-refresh-session` supports automatic token provisioning through the `mfa_token_provider` parameter. This parameter accepts a callable that returns a fresh MFA token code (string) whenever credentials need to be refreshed.
The `mfa_token_provider` approach is **strongly recommended** over manually providing `TokenCode` in `assume_role_kwargs`, as MFA tokens expire after 30 seconds while AWS temporary credentials can last for hours. By using a callable, your application can automatically fetch fresh tokens on each refresh without manual intervention. There is nothing preventing you from manually providing `TokenCode` *without* `mfa_token_provider`; however, *you* will be responsible for updating `TokenCode` *before* automatic temporary credential refresh occurs, which is likely to be a fragile and complicated approach.
When using `mfa_token_provider`, you must also provide `SerialNumber` (your MFA device ARN) in `assume_role_kwargs`. For additional information on the exact parameters that `RefreshableSession` takes for MFA, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.sts.STSRefreshableSession.html).
⚠️ Most developers will probably find example number four most helpful.
#### Examples
```python
import boto3_refresh_session as brs
# Example 1: Interactive prompt for MFA token
def get_mfa_token():
return input("Enter MFA token: ")
# we'll reuse this object in each example for simplicity :)
assume_role_kwargs = {
"RoleArn": "<your-role-arn>",
"RoleSessionName": "<your-role-session-name>",
"SerialNumber": "arn:aws:iam::123456789012:mfa/your-user", # required with mfa_token_provider
}
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=get_mfa_token, # callable that returns MFA token
)
# Example 2: Using pyotp for TOTP-based MFA
import pyotp
def get_totp_token():
totp = pyotp.TOTP("<your-secret-key>")
return totp.now()
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=get_totp_token,
)
# Example 3: Retrieving token from environment variable or external service
import os
def get_env_token():
return os.environ.get("AWS_MFA_TOKEN", "")
session = brs.RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=get_env_token,
)
# Example 4: Using Yubikey (or any token provider CLI)
from typing import Sequence
import subprocess
def mfa_token_provider(cmd: Sequence[str], timeout: float):
p = subprocess.run(
list(cmd),
check=False,
capture_output=True,
text=True,
timeout=timeout,
)
return (p.stdout or "").strip()
mfa_token_provider_args = {
"cmd": ["ykman", "oath", "code", "--single", "AWS-prod"], # example token source
"timeout": 3.0,
}
session = RefreshableSession(
assume_role_kwargs=assume_role_kwargs,
mfa_token_provider=mfa_token_provider,
mfa_token_provider_args=mfa_token_provider_args,
)
```
</details>
<details>
<summary><strong>SSO (click to expand)</strong></summary>
### SSO
`boto3-refresh-session` supports SSO by virtue of AWS profiles.
The below pseudo-code illustrates how to assume an IAM role using an AWS profile with SSO. Not shown, however, is running `sso login` manually, which `boto3-refresh-session` does not perform automatically for you. Therefore, you must manually run `sso login` as necessary.
If you wish to automate `sso login` (not recommended) then you will need to write your own custom callable function and pass it to `RefreshableSession(method="custom", ...)`. In that event, please refer to the `Custom` documentation found in a separate section below.
```python
from boto3_refresh_session import RefreshableSession
session = RefreshableSession(
assume_role_kwargs={
"RoleArn": "<your IAM role arn>",
"RoleSessionName": "<your role session name>",
},
profile_name="<your AWS profile name>",
...
)
s3 = session.client("s3")
```
</details>
<details>
<summary><strong>Custom (click to expand)</strong></summary>
### Custom
If you have a highly sophisticated, novel, or idiosyncratic authentication flow not included in boto3-refresh-session then you will need to provide your own custom temporary credentials callable object. `RefreshableSession` accepts custom credentials callable objects, as shown below. For additional information on the exact parameters that `RefreshableSession` takes for custom authentication flows, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.custom.CustomRefreshableSession.html#boto3_refresh_session.methods.custom.CustomRefreshableSession).
```python
# create (or import) your custom credential method
def your_custom_credential_getter(...):
...
return {
"access_key": ...,
"secret_key": ...,
"token": ...,
"expiry_time": ...,
}
# and pass it to RefreshableSession
session = RefreshableSession(
method="custom", # required
custom_credentials_method=your_custom_credential_getter, # required
custom_credentials_method_args=..., # optional
region_name=region_name, # optional
profile_name=profile_name, # optional
... # misc. boto3.session.Session params
)
```
</details>
<details>
<summary><strong>IoT Core X.509 (click to expand)</strong></summary>
### IoT Core X.509
AWS IoT Core can vend temporary AWS credentials through the **credentials provider** when you connect with an X.509 certificate and a **role alias**. `boto3-refresh-session` makes this flow seamless by automatically refreshing credentials over **mTLS**.
For additional information on the exact parameters that `IOTX509RefreshableSession` takes, [check this documentation](https://michaelthomasletts.com/boto3-refresh-session/modules/generated/boto3_refresh_session.methods.iot.x509.IOTX509RefreshableSession.html).
### PEM file
```python
import boto3_refresh_session as brs
# PEM certificate + private key example
session = brs.RefreshableSession(
method="iot",
endpoint="<your-credentials-endpoint>.credentials.iot.<region>.amazonaws.com",
role_alias="<your-role-alias>",
certificate="/path/to/certificate.pem",
private_key="/path/to/private-key.pem",
thing_name="<your-thing-name>", # optional, if used in policies
duration_seconds=3600, # optional, capped by role alias
region_name="us-east-1",
)
# Now you can use the session like any boto3 session
s3 = session.client("s3")
print(s3.list_buckets())
```
### PKCS#11
```python
session = brs.RefreshableSession(
method="iot",
endpoint="<your-credentials-endpoint>.credentials.iot.<region>.amazonaws.com",
role_alias="<your-role-alias>",
certificate="/path/to/certificate.pem",
pkcs11={
"pkcs11_lib": "/usr/local/lib/softhsm/libsofthsm2.so",
"user_pin": "1234",
"slot_id": 0,
"token_label": "MyToken",
"private_key_label": "MyKey",
},
thing_name="<your-thing-name>",
region_name="us-east-1",
)
```
### MQTT
After initializing a session object, you can can begin making actions with MQTT using the [mqtt method](https://github.com/michaelthomasletts/boto3-refresh-session/blob/deb68222925bf648f26e878ed4bc24b45317c7db/boto3_refresh_session/methods/iot/x509.py#L367)! You can reuse the same certificate, private key, et al as that used to initialize `RefreshableSession`. Or, alternatively, you can provide separate PKCS#11 or certificate information, whether those be file paths or bytes values. Either way, at a minimum, you will need to provide the endpoint and client identifier (i.e. thing name).
```python
from awscrt.mqtt.QoS import AT_LEAST_ONCE
conn = session.mqtt(
endpoint="<your endpoint>-ats.iot.<region>.amazonaws.com",
client_id="<your thing name or client ID>",
)
conn.connect()
conn.connect().result()
conn.publish(topic="foo/bar", payload=b"hi", qos=AT_LEAST_ONCE)
conn.disconnect().result()
```
</details>
## ⚠️ Changes
Browse through the various changes to `boto3-refresh-session` over time.
#### 😥 v3.0.0
**The changes introduced by v3.0.0 will not impact ~99% of users** who generally interact with `boto3-refresh-session` by only `RefreshableSession`, *which is the intended usage for this package after all.*
Advanced users, however, particularly those using low-level objects such as `BaseRefreshableSession | refreshable_session | BRSSession | utils.py`, may experience breaking changes.
Please review [this PR](https://github.com/michaelthomasletts/boto3-refresh-session/pull/75) for additional details.
#### ✂️ v4.0.0
The `ecs` module has been dropped. For additional details and rationale, please review [this PR](https://github.com/michaelthomasletts/boto3-refresh-session/pull/78).
#### 😛 v5.0.0
Support for IoT Core via X.509 certificate-based authentication (over HTTPS) is now available!
#### ➕ v5.1.0
MQTT support added for IoT Core via X.509 certificate-based authentication.
#### ➕ v6.0.0
MFA support for STS added!
#### 🔒😥 v6.2.0
- Client caching introduced to `RefreshableSession` in order to minimize memory footprint! Available via `cache_clients` parameter.
- Testing suite expanded to include IOT, MFA, caching, and much more!
- A subtle bug was uncovered where `RefreshableSession` created refreshable credentials but boto3's underlying session continued to resolve credentials via the default provider chain (i.e. env vars, shared config, etc) unless explicitly wired. `get_credentials()` and clients could, in certain setups, use base session credentials instead of the refreshable STS/IoT/custom credentials via assumed role. To fix this, I updated the implementation in `BRSSession.__post_init__` to set `self._session._credentials = self._credentials`, ensuring all boto3 clients created from `RefreshableSession` use the refreshable credentials source of truth provided to `RefreshableCredentials | DeferredRefreshableCredentials`. After this change, refreshable credentials are used consistently everywhere, irrespective of setup.
#### ✂️ v6.2.3
- The `RefreshableTemporaryCredentials` type hint was deprecated in favor of `TemporaryCredentials`.
- `expiry_time` was added as a parameter returned by the `refreshable_credentials` method and `credentials` attribute. | text/markdown | Mike Letts | lettsmt@gmail.com | Michael Letts | lettsmt@gmail.com | MIT | boto3, botocore, aws, sts, credentials, token, refresh, iot, x509, mqtt | [
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | https://github.com/michaelthomasletts/boto3-refresh-session | null | >=3.10 | [] | [] | [] | [
"boto3",
"botocore",
"requests",
"typing-extensions",
"awscrt",
"awsiotsdk"
] | [] | [] | [] | [
"Repository, https://github.com/michaelthomasletts/boto3-refresh-session",
"Documentation, https://michaelthomasletts.github.io/boto3-refresh-session/index.html"
] | poetry/2.2.1 CPython/3.10.19 Linux/6.11.0-1018-azure | 2026-01-16T04:28:30.769836 | boto3_refresh_session-6.2.3.tar.gz | 29,646 | 77/57/97ec6e9264600e145307c6526e51dc9919f117a92bf09362fead3fc5d3bb/boto3_refresh_session-6.2.3.tar.gz | source | sdist | null | false | ca21c9d050dc22f022bc06dd782728f6 | 7b275e9867b7d1817b903d52d06e83c5af23e53809ed2bfbd20ec8497fa47b9f | 775797ec6e9264600e145307c6526e51dc9919f117a92bf09362fead3fc5d3bb | null | [] |
2.4 | mtmeastmoney | 1.1.4 | A Personal Eastmoney Library | # EastMoney
| text/markdown | null | null | null | null | MIT License
Copyright (c) 2022 mondayfirst
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| null | [
"Development Status :: 3 - Alpha",
"Programming Language :: Python :: 3.9",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Operating System :: MacOS",
"Operating System :: POSIX :: Linux",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"ddddocr",
"requests",
"mtmtool",
"pandas"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:29:31.875240 | mtmeastmoney-1.1.4-py3-none-any.whl | 11,036 | 40/a7/734e7fdfe26114221242954dffc81ee9ba4fcc6dffaf80259b684d2956f6/mtmeastmoney-1.1.4-py3-none-any.whl | py3 | bdist_wheel | null | false | 134472da29d59696cae8fd46774d0af2 | 636c004629d8cb8bb85e281c517e64590f133036a1319905d1e9fb0339e07913 | 40a7734e7fdfe26114221242954dffc81ee9ba4fcc6dffaf80259b684d2956f6 | null | [
"LICENSE"
] |
2.4 | mtmeastmoney | 1.1.4 | A Personal Eastmoney Library | # EastMoney
| text/markdown | null | null | null | null | MIT License
Copyright (c) 2022 mondayfirst
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| null | [
"Development Status :: 3 - Alpha",
"Programming Language :: Python :: 3.9",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Operating System :: MacOS",
"Operating System :: POSIX :: Linux",
"Operating System :: Microsoft :: Windows"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"ddddocr",
"requests",
"mtmtool",
"pandas"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:29:32.975296 | mtmeastmoney-1.1.4.tar.gz | 15,115 | 65/6d/c2be4299a5ef0b97710389d5a9553b04159ce356ecc404ed3e1327e05141/mtmeastmoney-1.1.4.tar.gz | source | sdist | null | false | 5833c24c7827dc0e68c9800e928e8158 | 5c26318cf557584f235709c49ca722ec2c3c369c4c9c0523b6b0295571919ae5 | 656dc2be4299a5ef0b97710389d5a9553b04159ce356ecc404ed3e1327e05141 | null | [
"LICENSE"
] |
2.4 | tunacode-cli | 0.1.34 | Your agentic CLI developer. | # tunacode-cli
<img src="docs/images/logo.jpeg" alt="tunacode logo" width="200"/>
[](https://badge.fury.io/py/tunacode-cli)
[](https://pepy.tech/project/tunacode-cli)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://discord.gg/TN7Fpynv6H)
A TUI code agent.
> **Note:** Under active development - expect bugs.
## Interface
The Textual-based terminal user interface provides a clean, interactive environment for AI-assisted coding, with a design heavily inspired by the classic NeXTSTEP user interface.
*Agent response panel with formatted output*
*Tool rendering with syntax highlighting*
*Structured plan approval workflow*
## Theme Support
The interface supports multiple themes for different preferences and environments.
Customize the appearance with built-in themes or create your own color schemes.
## Model Setup
Configure your AI models and settings through the provided setup interface.
**Note:** TunaCode has full bash shell access. This tool assumes you know what you're doing. If you're concerned, run it in a sandboxed environment.
## v0.1.1 - Major Rewrite
This release is a complete rewrite with a new Textual-based TUI.
**Upgrading from v1?** The legacy v1 codebase is preserved in the `legacy-v1` branch and will only receive security updates.
## Requirements
- Python 3.11+
## Installation
```bash
uv tool install tunacode-cli
```
## Quick Start
1. Run the setup wizard to configure your API key:
```bash
tunacode --setup
```
2. Start coding:
```bash
tunacode
```
## Configuration
Set your API key as an environment variable or use the setup wizard:
```bash
export OPENAI_API_KEY="your-key"
# or
export ANTHROPIC_API_KEY="your-key"
```
Config file location: `~/.config/tunacode.json`
For advanced settings including **local mode** for small context models, see the [Configuration Guide](docs/configuration/README.md).
## Commands
| Command | Description |
| -------- | ---------------------------- |
| /help | Show available commands |
| /model | Change AI model |
| /clear | Clear conversation history |
| /yolo | Toggle auto-confirm mode |
| /branch | Create and switch git branch |
| /plan | Toggle read-only planning |
| /theme | Change UI theme |
| /resume | Load/delete saved sessions |
| !<cmd> | Run shell command |
| exit | Quit tunacode |
## LSP Integration (Beta)
TunaCode includes experimental Language Server Protocol support for real-time diagnostics. When an LSP server is detected in your PATH, it activates automatically.
**Supported languages:**
| Language | LSP Server |
| ---------- | ----------------------------- |
| Python | `ruff server` |
| TypeScript | `typescript-language-server` |
| JavaScript | `typescript-language-server` |
| Go | `gopls` |
| Rust | `rust-analyzer` |
Diagnostics appear in the UI when editing files. This feature is beta - expect rough edges.
## Discord Server
Join our official discord server to receive help, show us how you're using tunacode, and chat about anything LLM.
[<img src="https://discord.com/api/guilds/1447688577126367346/widget.png?style=banner3" alt="Discord Banner 3"/>](https://discord.gg/TN7Fpynv6H)
## License
MIT
| text/markdown | null | larock22 <noreply@github.com> | null | null | MIT | agent, automation, cli, development | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Software Development",
"Topic :: Utilities"
] | [] | null | null | <3.14,>=3.11 | [] | [] | [] | [
"click<8.2.0,>=8.1.0",
"defusedxml",
"html2text>=2024.2.26",
"pathspec>=0.12.1",
"prompt-toolkit<4.0.0,>=3.0.52",
"pydantic-ai<2.0.0,>=1.18.0",
"pydantic<3.0.0,>=2.12.4",
"pygments<3.0.0,>=2.19.2",
"python-levenshtein>=0.21.0",
"rich<15.0.0,>=14.2.0",
"ruff>=0.14.0",
"textual-autocomplete>=4.0... | [] | [] | [] | [
"Homepage, https://tunacode.xyz/",
"Repository, https://github.com/alchemiststudiosDOTai/tunacode",
"Issues, https://github.com/alchemiststudiosDOTai/tunacode/issues",
"Documentation, https://github.com/alchemiststudiosDOTai/tunacode#readme"
] | twine/6.2.0 CPython/3.12.12 | 2026-01-16T04:29:33.122474 | tunacode_cli-0.1.34-py3-none-any.whl | 360,304 | a2/a0/f2ca98edc58b70088842324fcd87185ceef105a999ee3343ccb7fc95c0f9/tunacode_cli-0.1.34-py3-none-any.whl | py3 | bdist_wheel | null | false | 9b7eea3a307d047dc37109791249dc8d | 7f8036f2a9d82cbdf68012208e486f3e7d8f91ae562a101177f076274e393c02 | a2a0f2ca98edc58b70088842324fcd87185ceef105a999ee3343ccb7fc95c0f9 | null | [
"LICENSE"
] |
2.4 | tunacode-cli | 0.1.34 | Your agentic CLI developer. | # tunacode-cli
<img src="docs/images/logo.jpeg" alt="tunacode logo" width="200"/>
[](https://badge.fury.io/py/tunacode-cli)
[](https://pepy.tech/project/tunacode-cli)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://discord.gg/TN7Fpynv6H)
A TUI code agent.
> **Note:** Under active development - expect bugs.
## Interface
The Textual-based terminal user interface provides a clean, interactive environment for AI-assisted coding, with a design heavily inspired by the classic NeXTSTEP user interface.
*Agent response panel with formatted output*
*Tool rendering with syntax highlighting*
*Structured plan approval workflow*
## Theme Support
The interface supports multiple themes for different preferences and environments.
Customize the appearance with built-in themes or create your own color schemes.
## Model Setup
Configure your AI models and settings through the provided setup interface.
**Note:** TunaCode has full bash shell access. This tool assumes you know what you're doing. If you're concerned, run it in a sandboxed environment.
## v0.1.1 - Major Rewrite
This release is a complete rewrite with a new Textual-based TUI.
**Upgrading from v1?** The legacy v1 codebase is preserved in the `legacy-v1` branch and will only receive security updates.
## Requirements
- Python 3.11+
## Installation
```bash
uv tool install tunacode-cli
```
## Quick Start
1. Run the setup wizard to configure your API key:
```bash
tunacode --setup
```
2. Start coding:
```bash
tunacode
```
## Configuration
Set your API key as an environment variable or use the setup wizard:
```bash
export OPENAI_API_KEY="your-key"
# or
export ANTHROPIC_API_KEY="your-key"
```
Config file location: `~/.config/tunacode.json`
For advanced settings including **local mode** for small context models, see the [Configuration Guide](docs/configuration/README.md).
## Commands
| Command | Description |
| -------- | ---------------------------- |
| /help | Show available commands |
| /model | Change AI model |
| /clear | Clear conversation history |
| /yolo | Toggle auto-confirm mode |
| /branch | Create and switch git branch |
| /plan | Toggle read-only planning |
| /theme | Change UI theme |
| /resume | Load/delete saved sessions |
| !<cmd> | Run shell command |
| exit | Quit tunacode |
## LSP Integration (Beta)
TunaCode includes experimental Language Server Protocol support for real-time diagnostics. When an LSP server is detected in your PATH, it activates automatically.
**Supported languages:**
| Language | LSP Server |
| ---------- | ----------------------------- |
| Python | `ruff server` |
| TypeScript | `typescript-language-server` |
| JavaScript | `typescript-language-server` |
| Go | `gopls` |
| Rust | `rust-analyzer` |
Diagnostics appear in the UI when editing files. This feature is beta - expect rough edges.
## Discord Server
Join our official discord server to receive help, show us how you're using tunacode, and chat about anything LLM.
[<img src="https://discord.com/api/guilds/1447688577126367346/widget.png?style=banner3" alt="Discord Banner 3"/>](https://discord.gg/TN7Fpynv6H)
## License
MIT
| text/markdown | null | larock22 <noreply@github.com> | null | null | MIT | agent, automation, cli, development | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Software Development",
"Topic :: Utilities"
] | [] | null | null | <3.14,>=3.11 | [] | [] | [] | [
"click<8.2.0,>=8.1.0",
"defusedxml",
"html2text>=2024.2.26",
"pathspec>=0.12.1",
"prompt-toolkit<4.0.0,>=3.0.52",
"pydantic-ai<2.0.0,>=1.18.0",
"pydantic<3.0.0,>=2.12.4",
"pygments<3.0.0,>=2.19.2",
"python-levenshtein>=0.21.0",
"rich<15.0.0,>=14.2.0",
"ruff>=0.14.0",
"textual-autocomplete>=4.0... | [] | [] | [] | [
"Homepage, https://tunacode.xyz/",
"Repository, https://github.com/alchemiststudiosDOTai/tunacode",
"Issues, https://github.com/alchemiststudiosDOTai/tunacode/issues",
"Documentation, https://github.com/alchemiststudiosDOTai/tunacode#readme"
] | twine/6.2.0 CPython/3.12.12 | 2026-01-16T04:29:35.152088 | tunacode_cli-0.1.34.tar.gz | 2,302,097 | c9/16/4322d48a05eceaa285af333477a926e9a69e5de9272e7b55dd71b7ae0806/tunacode_cli-0.1.34.tar.gz | source | sdist | null | false | 9e30b225900c3770f8d7febf4d9f47d6 | a1587eebfd6798ae4a5cbfd8f1eb160e2c1fcaeeb130fc268152f68c542147d9 | c9164322d48a05eceaa285af333477a926e9a69e5de9272e7b55dd71b7ae0806 | null | [
"LICENSE"
] |
2.4 | geek-cafe-saas-sdk | 0.79.0 | Base Reusable Services for SaaS | # Geek Cafe Services
[](https://www.python.org/downloads/)
[](https://github.com/geekcafe/geek-cafe-services)
[](https://aws.amazon.com/dynamodb/)
[](https://aws.amazon.com/lambda/)
> **⚠️ Beta Notice**: This library is under active development. Breaking changes may occur until we reach a stable 1.0 release. We recommend pinning to specific versions in production.
> **✨ New in v0.78.0**: Automatic security model, simplified handler patterns, and comprehensive pattern guides!
<!-- COVERAGE-BADGE:START -->
## Test Coverage
**Overall Coverage:** 78.1% (20284/25964 statements)
### Coverage Summary
| Metric | Value |
|--------|-------|
| Total Statements | 25,964 |
| Covered Statements | 20,284 |
| Missing Statements | 5,680 |
| Coverage Percentage | 78.1% |
| Total Tests | 1929 |
| Test Status | ✅ All Passing |
### Files Needing Attention (< 80% coverage)
| Coverage | Missing Lines | File |
|----------|---------------|------|
| 0.0% | 2 | `modules/executions/handlers/__init__.py` |
| 0.0% | 2 | `modules/executions/handlers/get_status/__init__.py` |
| 0.0% | 2 | `modules/feature_flags/models/__init__.py` |
| 0.0% | 2 | `modules/feature_flags/services/__init__.py` |
| 0.0% | 2 | `modules/file_system/handlers/unarchive/__init__.py` |
| 0.0% | 10 | `modules/executions/handlers/get_status/app.py` |
| 0.0% | 12 | `modules/file_system/handlers/unarchive/app.py` |
| 0.0% | 21 | `core/models/base_async_event_model.py` |
| 0.0% | 115 | `modules/feature_flags/models/feature_flag.py` |
| 0.0% | 135 | `modules/executions/handlers/workflow_step_handler.py` |
*... and 60 more files with < 80% coverage*
### Running Tests
```bash
# Run all tests with coverage
./run_unit_tests.sh
# View detailed coverage report
open reports/coverage/index.html
```
*Last updated: 2026-01-01 15:14:41*
---
<!-- COVERAGE-BADGE:END -->
## Description
**Geek Cafe Services** is a production-ready, enterprise-grade library that provides reusable database services specifically designed for multi-tenant SaaS applications. Built on top of AWS DynamoDB, this library offers a prescriptive approach to building scalable, maintainable backend services with consistent patterns and best practices.
### Why Geek Cafe Services?
🏗️ **Consistent Architecture**: All services follow the same proven patterns for CRUD operations, error handling, and access control
🔒 **Multi-Tenant by Design**: Built-in tenant isolation ensures secure data separation across customers
⚡ **DynamoDB Optimized**: Leverages DynamoDB's strengths with efficient GSI indexes and query patterns
🛡️ **Production Ready**: Comprehensive error handling, logging, pagination, and batch operations
🧪 **Fully Tested**: 100% test coverage with comprehensive test suites for reliability
📖 **Well Documented**: Extensive documentation with practical examples and best practices
### Perfect For
- **SaaS Applications** requiring multi-tenant data isolation
- **Serverless Architectures** built on AWS Lambda and DynamoDB
- **Teams** wanting consistent, proven patterns across services
- **Rapid Development** with pre-built, tested service components
## Installation
```bash
# Clone the repository
git clone https://github.com/geekcafe/geek-cafe-services.git
cd geek-cafe-services
# Setup the development environment
./pysetup.sh
# Install dependencies
pip install -r requirements.txt
```
## Quick Start
### For Lambda Handlers (Recommended)
```python
from geek_cafe_saas_sdk.lambda_handlers import create_handler
from geek_cafe_saas_sdk.modules.workflows.services import WorkflowService
# Module-level handler for connection pooling
handler_wrapper = create_handler(
service_class=WorkflowService,
convert_request_case=True, # camelCase → snake_case
convert_response_case=True # snake_case → camelCase
)
def lambda_handler(event, context, injected_service=None):
return handler_wrapper.execute(event, context, business_logic, injected_service)
def business_logic(event, service):
# Your business logic - security and case conversion are automatic!
body = event.get("parsed_body") or {}
result = service.create(**body)
return {"execution_id": result.data.id, "status": "created"}
```
### For Direct Service Usage
```python
from geek_cafe_saas_sdk.modules.messaging.services import MessageService
from geek_cafe_saas_sdk.core import AnonymousContextFactory
# Create request context
context = AnonymousContextFactory.create_test_context(
user_id='user_123',
tenant_id='tenant_456'
)
# Initialize service with context
service = MessageService(request_context=context)
# Create a message - security is automatic!
result = service.create(
type="notification",
content={"title": "Welcome!", "body": "Thanks for joining."}
)
if result.success:
print(f"Created message: {result.data.id}")
```
📖 **[Complete Quick Start Guide](./docs/help/QUICK_START.md)**
## Available Services
### 🚀 Lambda Handler Wrappers (NEW in v0.2.0)
**Purpose**: Eliminate 70-80% of boilerplate code in AWS Lambda functions
**Key Capabilities**:
- ✅ Automatic API key validation from environment
- ✅ Request body parsing and camelCase → snake_case conversion
- ✅ Service initialization with connection pooling for warm starts
- ✅ Built-in CORS and error handling
- ✅ User context extraction from authorizers
- ✅ Service injection for easy testing
- ✅ Support for public and secured endpoints
**Available Handlers**:
- `ApiKeyLambdaHandler` - API key validation (most common)
- `PublicLambdaHandler` - No authentication (config endpoints)
- `BaseLambdaHandler` - Extensible base for custom handlers
**Quick Example**:
```python
from geek_cafe_saas_sdk.lambda_handlers import ApiKeyLambdaHandler
from geek_cafe_saas_sdk.vote_service import VoteService
# All boilerplate handled in 3 lines
handler = ApiKeyLambdaHandler(
service_class=VoteService,
require_body=True,
convert_case=True
)
def lambda_handler(event, context):
return handler.execute(event, context, create_vote)
def create_vote(event, service, user_context):
# Just your business logic - everything else is handled!
payload = event["parsed_body"] # Already parsed & converted
return service.create_vote(
tenant_id=user_context.get("tenant_id", "anonymous"),
user_id=user_context.get("user_id", "anonymous"),
**payload
)
```
**Use Cases**: Any AWS Lambda function with API key auth, reducing code by 70-80% while maintaining all functionality
📖 **[Complete Lambda Handlers Documentation](./docs/lambda_handlers.md)**
### 📧 MessageService
**Purpose**: Complete message and notification management system
**Key Capabilities**:
- ✅ Full CRUD operations with tenant isolation
- ✅ Flexible JSON content storage for any message type
- ✅ Efficient querying by user, tenant, and message type
- ✅ Automatic audit trails and timestamps
- ✅ Built-in access control and validation
**Use Cases**: User notifications, system alerts, communication logs, announcement management
### 🗳️ Voting Services Suite
**Purpose**: Complete voting and rating system with real-time aggregation
**Architecture**: Three interconnected services working together:
#### VoteService
- ✅ Individual vote management with automatic upsert behavior
- ✅ One vote per user per target enforcement
- ✅ Support for up/down votes or custom vote types
- ✅ Comprehensive querying by user, target, and tenant
#### VoteSummaryService
- ✅ Pre-calculated vote totals for instant retrieval
- ✅ Target-based optimization for high-performance lookups
- ✅ Metadata tracking (last tallied timestamp, vote counts)
- ✅ Tenant-scoped summary management
#### VoteTallyService
- ✅ Intelligent vote aggregation with pagination support
- ✅ Batch processing for multiple targets
- ✅ Stale target detection and automated re-tallying
- ✅ Comprehensive error handling and resilience
**Use Cases**: Product ratings, content voting, feedback systems, community polls, recommendation engines
## Documentation
📖 **[Documentation Index](./docs/DOCUMENTATION_INDEX.md)** - Complete documentation roadmap
### Core Pattern Guides (Start Here)
- **[DatabaseService Patterns](./docs/patterns/DATABASE_SERVICE_PATTERNS.md)** ⭐ - CRUD, security, queries, models
- **[Lambda Handler Patterns](./docs/patterns/LAMBDA_HANDLER_PATTERNS.md)** ⭐ - API Gateway & SQS handlers
- **[Security Architecture](./docs/SECURITY_ARCHITECTURE.md)** - Automatic security model
- **[Case Conversion Guide](./docs/CASE_CONVERSION_GUIDE.md)** - camelCase ↔ snake_case
### Module-Specific Guides
- **[File System](./docs/file-system/README_FILE_SYSTEM_SDK_USAGE.md)** - File management
- **[Workflows](./src/geek_cafe_saas_sdk/modules/workflows/)** - Execution & step models
- **[Messaging](./docs/api/CHAT_API.md)** - Chat & contact threads
- **[Events](./docs/events/EVENTS_DOMAIN.md)** - Event management
- **[Analytics](./docs/analytics/website_analytics_readme.md)** - Analytics tracking
### Configuration & Architecture
- **[Configuration Guide](./docs/CONFIG.md)** - Environment variables
- **[Architecture Overview](./docs/ARCHITECTURE.md)** - System design
- **[Service Pool Telemetry](./docs/SERVICE_POOL_TELEMETRY.md)** - Connection pooling
## Core Features
### 🏛️ **Enterprise Architecture**
- **Multi-Tenant by Design**: Complete tenant isolation with automatic access control
- **Consistent Patterns**: All services follow identical CRUD interfaces and conventions
- **Scalable Design**: Built for high-throughput, multi-customer SaaS applications
### 🔧 **Developer Experience**
- **Type Safety**: Full Python type hints for better IDE support and fewer bugs
- **Comprehensive Testing**: 100% test coverage with realistic test scenarios
- **Rich Documentation**: Detailed API docs, examples, and best practices
- **Easy Integration**: Simple initialization and consistent error handling
### ⚡ **Performance & Reliability**
- **DynamoDB Optimized**: Efficient GSI indexes and query patterns for fast operations
- **Pagination Support**: Handle large datasets without memory issues
- **Batch Operations**: Process multiple items efficiently
- **Error Resilience**: Graceful handling of partial failures and edge cases
### 🛡️ **Production Ready**
- **Structured Logging**: AWS Lambda Powertools integration for observability
- **Comprehensive Validation**: Input validation with detailed error messages
- **Access Control**: Automatic tenant and user-based security enforcement
- **Audit Trails**: Complete tracking of who did what and when
## Environment Setup
```bash
# Required environment variables
export DYNAMODB_TABLE_NAME=your_table_name
# Optional AWS configuration (if not using IAM roles)
export AWS_REGION=us-east-1
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
```
## Testing
```bash
# Run all tests
pytest tests/ -v
# Run specific service tests
pytest tests/test_message_service.py -v
pytest tests/test_vote_*_service.py -v
# Run with coverage
pytest tests/ --cov=geek_cafe_saas_sdk --cov-report=html
```
## Project Structure
```
geek-cafe-services/
├── src/geek_cafe_saas_sdk/
│ ├── lambda_handlers/ # 🆕 Lambda handler wrappers (v0.2.0)
│ │ ├── base.py # Base handler with common functionality
│ │ ├── api_key_handler.py # API key validation handler
│ │ ├── public_handler.py # Public (no auth) handler
│ │ └── service_pool.py # Service connection pooling
│ ├── middleware/ # CORS, auth, error handling decorators
│ ├── utilities/ # Request/response helpers
│ ├── models/ # Data models with DynamoDB mapping
│ ├── *_service.py # Service implementations
│ ├── database_service.py # Base service class
│ └── service_result.py # Standardized response wrapper
├── tests/ # Comprehensive test suite
├── docs/ # Detailed documentation
│ └── lambda_handlers.md # 🆕 Lambda wrapper documentation
├── examples/ # Working code examples
│ └── lambda_handlers/ # 🆕 Handler examples
└── README.md # This file
```
## Contributing
We welcome contributions! Here's how to get started:
1. **Fork the repository** and create a feature branch
2. **Follow the existing patterns** - consistency is key
3. **Add comprehensive tests** for any new functionality
4. **Update documentation** for API changes
5. **Submit a Pull Request** with a clear description
### Development Guidelines
- Follow existing code style and patterns
- Maintain 100% test coverage for new code
- Update documentation for any API changes
- Use meaningful commit messages
- Test against multiple Python versions if possible
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Support
- 📖 **Documentation**: [Complete docs](./docs/services_overview.md)
- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/geekcafe/geek-cafe-services/issues)
- 💡 **Feature Requests**: [GitHub Discussions](https://github.com/geekcafe/geek-cafe-services/discussions)
- 📧 **Questions**: Create an issue with the "question" label
---
**Built with ❤️ for the SaaS development community**
| text/markdown | null | Eric Wilson <eric.wilson@geekcafe.com> | null | null | Geek Cafe Services Business Source License 1.0
Copyright (c) 2025 Geek Cafe, LLC. All rights reserved.
The "Geek Cafe Services" software (the "Software") is made available under this
Business Source License (the "License"). This License allows you to view,
study, and modify the source code, and to use it for personal, educational,
research, or non-commercial purposes, subject to the following terms.
1. Grant of Rights
a. You may copy and modify the Software for your own personal,
educational, or internal development use.
b. You may not use the Software, or modified versions of it,
to provide any commercial service or product, including
software-as-a-service, consulting, hosting, or resale,
without a separate commercial license from Geek Cafe LLC.
2. Change Date
Three (3) years from the date of first public release of a specific
version of the Software (the “Change Date”), that version will
automatically be made available under the Apache License 2.0.
Later versions may have different Change Dates.
3. Attribution
All copies or substantial portions of the Software must retain this
License text, the copyright notice above, and a clear reference to
the original source repository (https://github.com/geekcafe/geek-cafe-services).
4. Trademarks
The names “Geek Cafe”, “Geek Cafe Services”, and any related logos are
trademarks of Geek Cafe LLC and may not be used to endorse or promote
derivative products without prior written permission.
5. Disclaimer of Warranty
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT.
6. Limitation of Liability
IN NO EVENT SHALL GEEK Cafe LLC OR CONTRIBUTORS BE LIABLE FOR ANY CLAIM,
DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT, OR
OTHERWISE, ARISING FROM OR IN CONNECTION WITH THE SOFTWARE OR ITS USE.
---
For commercial licensing inquiries, contact:
legal@geekcafe.com | api gateway, aws, dynamodb, lambda, saas, serverless, services | [
"Development Status :: 4 - Beta",
"Framework :: AWS CDK",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"P... | [] | null | null | >=3.8 | [] | [] | [] | [
"boto3-assist",
"build; extra == \"dev\"",
"twine; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/geek-cafe-saas-sdk",
"Documentation, https://github.com/geek-cafe-saas-sdk/blob/main/README.md",
"Source Code, https://github.com/geek-cafe-saas-sdk"
] | twine/6.2.0 CPython/3.13.2 | 2026-01-16T04:29:53.155260 | geek_cafe_saas_sdk-0.79.0-py3-none-any.whl | 686,659 | f2/c1/33a9c58d53ff186680b5107079349604eb768179c000ccd76a6ee57d06ac/geek_cafe_saas_sdk-0.79.0-py3-none-any.whl | py3 | bdist_wheel | null | false | 6ef0a7a507f6a1a10eb27f038c1fb04d | b3167b519fb27b4184e39bbae6bcb72b0282272727906e06b090ea93c3356e92 | f2c133a9c58d53ff186680b5107079349604eb768179c000ccd76a6ee57d06ac | null | [
"LICENSE"
] |
2.4 | feldera | 0.223.0 | The feldera python client | # Feldera Python SDK
The `feldera` Python package is the Python client for the Feldera HTTP API.
The Python SDK documentation is available at: https://docs.feldera.com/python
## Getting started
### Installation
```bash
uv pip install feldera
```
### Example usage
The Python client interacts with the API server of the Feldera instance.
```python
# File: example.py
from feldera import FelderaClient, PipelineBuilder, Pipeline
# Instantiate client
client = FelderaClient() # Default: http://localhost:8080 without authentication
# client = FelderaClient(url="https://localhost:8080", api_key="apikey:...", requests_verify="/path/to/tls.crt")
# (Re)create pipeline
name = "example"
sql = """
CREATE TABLE t1 (i1 INT) WITH ('materialized' = 'true');
CREATE MATERIALIZED VIEW v1 AS SELECT * FROM t1;
"""
print("(Re)creating pipeline...")
pipeline = PipelineBuilder(client, name, sql).create_or_replace()
pipeline.start()
print(f"Pipeline status: {pipeline.status()}")
pipeline.pause()
print(f"Pipeline status: {pipeline.status()}")
pipeline.stop(force=True)
# Find existing pipeline
pipeline = Pipeline.get(name, client)
pipeline.start()
print(f"Pipeline status: {pipeline.status()}")
pipeline.stop(force=True)
pipeline.clear_storage()
```
Run using:
```bash
uv run python example.py
```
### Environment variables
Some default parameter values in the Python SDK can be overridden via environment variables.
**Environment variables for `FelderaClient(...)`**
```bash
export FELDERA_HOST="https://localhost:8080" # Overrides default for `url`
export FELDERA_API_KEY="apikey:..." # Overrides default for `api_key`
# The following together override default for `requests_verify`
# export FELDERA_TLS_INSECURE="false" # If set to "1", "true" or "yes" (all case-insensitive), disables TLS certificate verification
# export FELDERA_HTTPS_TLS_CERT="/path/to/tls.crt" # Custom TLS certificate
```
**Environment variables for `PipelineBuilder(...)`**
```bash
export FELDERA_RUNTIME_VERSION="..." # Overrides default for `runtime_version`
```
## Development
Development assumes you have cloned the Feldera code repository.
### Installation
```bash
cd python
# Optional: create and activate virtual environment if you don't have one
uv venv
source .venv/bin/activate
# Install in editable mode
uv pip install -e .
```
### Formatting
Formatting requires the `ruff` package: `uv pip install ruff`
```bash
cd python
ruff check
ruff format
```
### Tests
Running the test requires the `pytest` package: `uv pip install pytest`
```bash
# All tests
cd python
uv run python -m pytest tests/
# Specific tests directory
uv run python -m pytest tests/platform/
# Specific test file
uv run python -m pytest tests/platform/test_pipeline_crud.py
# Tip: add argument -x at the end for it to fail fast
```
For further information about the tests, please see `tests/README.md`.
### Documentation
Building documentation requires the `sphinx` package: `uv pip install sphinx`
```bash
cd python/docs
sphinx-apidoc -o . ../feldera
make html
make clean # Cleanup afterwards
```
### Installation from GitHub
Latest `main` branch:
```bash
uv pip install git+https://github.com/feldera/feldera#subdirectory=python
```
Different branch (replace `BRANCH_NAME`):
```bash
uv pip install git+https://github.com/feldera/feldera@BRANCH_NAME#subdirectory=python
```
| text/markdown | null | Feldera Team <dev@feldera.com> | null | null | null | feldera, python | [
"Programming Language :: Python :: 3.10",
"Operating System :: OS Independent"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"requests",
"pandas>=2.1.2",
"typing-extensions",
"numpy>=2.2.4",
"pretty-errors",
"ruff>=0.6.9",
"PyJWT>=2.8.0"
] | [] | [] | [] | [
"Homepage, https://www.feldera.com",
"Documentation, https://docs.feldera.com/python",
"Repository, https://github.com/feldera/feldera",
"Issues, https://github.com/feldera/feldera/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:29:53.258109 | feldera-0.223.0-py3-none-any.whl | 50,344 | d8/8e/f04724b97d6581dba1044d0fe81a873b11159696bd66ef95de1c0cdb9959/feldera-0.223.0-py3-none-any.whl | py3 | bdist_wheel | null | false | ed5e893a3359f011b31f83965e1b6652 | 1fc3e50cccef97e738747e4440c1325db519f051c1e56da357d2391b4c730877 | d88ef04724b97d6581dba1044d0fe81a873b11159696bd66ef95de1c0cdb9959 | MIT | [] |
2.4 | geek-cafe-saas-sdk | 0.79.0 | Base Reusable Services for SaaS | # Geek Cafe Services
[](https://www.python.org/downloads/)
[](https://github.com/geekcafe/geek-cafe-services)
[](https://aws.amazon.com/dynamodb/)
[](https://aws.amazon.com/lambda/)
> **⚠️ Beta Notice**: This library is under active development. Breaking changes may occur until we reach a stable 1.0 release. We recommend pinning to specific versions in production.
> **✨ New in v0.78.0**: Automatic security model, simplified handler patterns, and comprehensive pattern guides!
<!-- COVERAGE-BADGE:START -->
## Test Coverage
**Overall Coverage:** 78.1% (20284/25964 statements)
### Coverage Summary
| Metric | Value |
|--------|-------|
| Total Statements | 25,964 |
| Covered Statements | 20,284 |
| Missing Statements | 5,680 |
| Coverage Percentage | 78.1% |
| Total Tests | 1929 |
| Test Status | ✅ All Passing |
### Files Needing Attention (< 80% coverage)
| Coverage | Missing Lines | File |
|----------|---------------|------|
| 0.0% | 2 | `modules/executions/handlers/__init__.py` |
| 0.0% | 2 | `modules/executions/handlers/get_status/__init__.py` |
| 0.0% | 2 | `modules/feature_flags/models/__init__.py` |
| 0.0% | 2 | `modules/feature_flags/services/__init__.py` |
| 0.0% | 2 | `modules/file_system/handlers/unarchive/__init__.py` |
| 0.0% | 10 | `modules/executions/handlers/get_status/app.py` |
| 0.0% | 12 | `modules/file_system/handlers/unarchive/app.py` |
| 0.0% | 21 | `core/models/base_async_event_model.py` |
| 0.0% | 115 | `modules/feature_flags/models/feature_flag.py` |
| 0.0% | 135 | `modules/executions/handlers/workflow_step_handler.py` |
*... and 60 more files with < 80% coverage*
### Running Tests
```bash
# Run all tests with coverage
./run_unit_tests.sh
# View detailed coverage report
open reports/coverage/index.html
```
*Last updated: 2026-01-01 15:14:41*
---
<!-- COVERAGE-BADGE:END -->
## Description
**Geek Cafe Services** is a production-ready, enterprise-grade library that provides reusable database services specifically designed for multi-tenant SaaS applications. Built on top of AWS DynamoDB, this library offers a prescriptive approach to building scalable, maintainable backend services with consistent patterns and best practices.
### Why Geek Cafe Services?
🏗️ **Consistent Architecture**: All services follow the same proven patterns for CRUD operations, error handling, and access control
🔒 **Multi-Tenant by Design**: Built-in tenant isolation ensures secure data separation across customers
⚡ **DynamoDB Optimized**: Leverages DynamoDB's strengths with efficient GSI indexes and query patterns
🛡️ **Production Ready**: Comprehensive error handling, logging, pagination, and batch operations
🧪 **Fully Tested**: 100% test coverage with comprehensive test suites for reliability
📖 **Well Documented**: Extensive documentation with practical examples and best practices
### Perfect For
- **SaaS Applications** requiring multi-tenant data isolation
- **Serverless Architectures** built on AWS Lambda and DynamoDB
- **Teams** wanting consistent, proven patterns across services
- **Rapid Development** with pre-built, tested service components
## Installation
```bash
# Clone the repository
git clone https://github.com/geekcafe/geek-cafe-services.git
cd geek-cafe-services
# Setup the development environment
./pysetup.sh
# Install dependencies
pip install -r requirements.txt
```
## Quick Start
### For Lambda Handlers (Recommended)
```python
from geek_cafe_saas_sdk.lambda_handlers import create_handler
from geek_cafe_saas_sdk.modules.workflows.services import WorkflowService
# Module-level handler for connection pooling
handler_wrapper = create_handler(
service_class=WorkflowService,
convert_request_case=True, # camelCase → snake_case
convert_response_case=True # snake_case → camelCase
)
def lambda_handler(event, context, injected_service=None):
return handler_wrapper.execute(event, context, business_logic, injected_service)
def business_logic(event, service):
# Your business logic - security and case conversion are automatic!
body = event.get("parsed_body") or {}
result = service.create(**body)
return {"execution_id": result.data.id, "status": "created"}
```
### For Direct Service Usage
```python
from geek_cafe_saas_sdk.modules.messaging.services import MessageService
from geek_cafe_saas_sdk.core import AnonymousContextFactory
# Create request context
context = AnonymousContextFactory.create_test_context(
user_id='user_123',
tenant_id='tenant_456'
)
# Initialize service with context
service = MessageService(request_context=context)
# Create a message - security is automatic!
result = service.create(
type="notification",
content={"title": "Welcome!", "body": "Thanks for joining."}
)
if result.success:
print(f"Created message: {result.data.id}")
```
📖 **[Complete Quick Start Guide](./docs/help/QUICK_START.md)**
## Available Services
### 🚀 Lambda Handler Wrappers (NEW in v0.2.0)
**Purpose**: Eliminate 70-80% of boilerplate code in AWS Lambda functions
**Key Capabilities**:
- ✅ Automatic API key validation from environment
- ✅ Request body parsing and camelCase → snake_case conversion
- ✅ Service initialization with connection pooling for warm starts
- ✅ Built-in CORS and error handling
- ✅ User context extraction from authorizers
- ✅ Service injection for easy testing
- ✅ Support for public and secured endpoints
**Available Handlers**:
- `ApiKeyLambdaHandler` - API key validation (most common)
- `PublicLambdaHandler` - No authentication (config endpoints)
- `BaseLambdaHandler` - Extensible base for custom handlers
**Quick Example**:
```python
from geek_cafe_saas_sdk.lambda_handlers import ApiKeyLambdaHandler
from geek_cafe_saas_sdk.vote_service import VoteService
# All boilerplate handled in 3 lines
handler = ApiKeyLambdaHandler(
service_class=VoteService,
require_body=True,
convert_case=True
)
def lambda_handler(event, context):
return handler.execute(event, context, create_vote)
def create_vote(event, service, user_context):
# Just your business logic - everything else is handled!
payload = event["parsed_body"] # Already parsed & converted
return service.create_vote(
tenant_id=user_context.get("tenant_id", "anonymous"),
user_id=user_context.get("user_id", "anonymous"),
**payload
)
```
**Use Cases**: Any AWS Lambda function with API key auth, reducing code by 70-80% while maintaining all functionality
📖 **[Complete Lambda Handlers Documentation](./docs/lambda_handlers.md)**
### 📧 MessageService
**Purpose**: Complete message and notification management system
**Key Capabilities**:
- ✅ Full CRUD operations with tenant isolation
- ✅ Flexible JSON content storage for any message type
- ✅ Efficient querying by user, tenant, and message type
- ✅ Automatic audit trails and timestamps
- ✅ Built-in access control and validation
**Use Cases**: User notifications, system alerts, communication logs, announcement management
### 🗳️ Voting Services Suite
**Purpose**: Complete voting and rating system with real-time aggregation
**Architecture**: Three interconnected services working together:
#### VoteService
- ✅ Individual vote management with automatic upsert behavior
- ✅ One vote per user per target enforcement
- ✅ Support for up/down votes or custom vote types
- ✅ Comprehensive querying by user, target, and tenant
#### VoteSummaryService
- ✅ Pre-calculated vote totals for instant retrieval
- ✅ Target-based optimization for high-performance lookups
- ✅ Metadata tracking (last tallied timestamp, vote counts)
- ✅ Tenant-scoped summary management
#### VoteTallyService
- ✅ Intelligent vote aggregation with pagination support
- ✅ Batch processing for multiple targets
- ✅ Stale target detection and automated re-tallying
- ✅ Comprehensive error handling and resilience
**Use Cases**: Product ratings, content voting, feedback systems, community polls, recommendation engines
## Documentation
📖 **[Documentation Index](./docs/DOCUMENTATION_INDEX.md)** - Complete documentation roadmap
### Core Pattern Guides (Start Here)
- **[DatabaseService Patterns](./docs/patterns/DATABASE_SERVICE_PATTERNS.md)** ⭐ - CRUD, security, queries, models
- **[Lambda Handler Patterns](./docs/patterns/LAMBDA_HANDLER_PATTERNS.md)** ⭐ - API Gateway & SQS handlers
- **[Security Architecture](./docs/SECURITY_ARCHITECTURE.md)** - Automatic security model
- **[Case Conversion Guide](./docs/CASE_CONVERSION_GUIDE.md)** - camelCase ↔ snake_case
### Module-Specific Guides
- **[File System](./docs/file-system/README_FILE_SYSTEM_SDK_USAGE.md)** - File management
- **[Workflows](./src/geek_cafe_saas_sdk/modules/workflows/)** - Execution & step models
- **[Messaging](./docs/api/CHAT_API.md)** - Chat & contact threads
- **[Events](./docs/events/EVENTS_DOMAIN.md)** - Event management
- **[Analytics](./docs/analytics/website_analytics_readme.md)** - Analytics tracking
### Configuration & Architecture
- **[Configuration Guide](./docs/CONFIG.md)** - Environment variables
- **[Architecture Overview](./docs/ARCHITECTURE.md)** - System design
- **[Service Pool Telemetry](./docs/SERVICE_POOL_TELEMETRY.md)** - Connection pooling
## Core Features
### 🏛️ **Enterprise Architecture**
- **Multi-Tenant by Design**: Complete tenant isolation with automatic access control
- **Consistent Patterns**: All services follow identical CRUD interfaces and conventions
- **Scalable Design**: Built for high-throughput, multi-customer SaaS applications
### 🔧 **Developer Experience**
- **Type Safety**: Full Python type hints for better IDE support and fewer bugs
- **Comprehensive Testing**: 100% test coverage with realistic test scenarios
- **Rich Documentation**: Detailed API docs, examples, and best practices
- **Easy Integration**: Simple initialization and consistent error handling
### ⚡ **Performance & Reliability**
- **DynamoDB Optimized**: Efficient GSI indexes and query patterns for fast operations
- **Pagination Support**: Handle large datasets without memory issues
- **Batch Operations**: Process multiple items efficiently
- **Error Resilience**: Graceful handling of partial failures and edge cases
### 🛡️ **Production Ready**
- **Structured Logging**: AWS Lambda Powertools integration for observability
- **Comprehensive Validation**: Input validation with detailed error messages
- **Access Control**: Automatic tenant and user-based security enforcement
- **Audit Trails**: Complete tracking of who did what and when
## Environment Setup
```bash
# Required environment variables
export DYNAMODB_TABLE_NAME=your_table_name
# Optional AWS configuration (if not using IAM roles)
export AWS_REGION=us-east-1
export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
```
## Testing
```bash
# Run all tests
pytest tests/ -v
# Run specific service tests
pytest tests/test_message_service.py -v
pytest tests/test_vote_*_service.py -v
# Run with coverage
pytest tests/ --cov=geek_cafe_saas_sdk --cov-report=html
```
## Project Structure
```
geek-cafe-services/
├── src/geek_cafe_saas_sdk/
│ ├── lambda_handlers/ # 🆕 Lambda handler wrappers (v0.2.0)
│ │ ├── base.py # Base handler with common functionality
│ │ ├── api_key_handler.py # API key validation handler
│ │ ├── public_handler.py # Public (no auth) handler
│ │ └── service_pool.py # Service connection pooling
│ ├── middleware/ # CORS, auth, error handling decorators
│ ├── utilities/ # Request/response helpers
│ ├── models/ # Data models with DynamoDB mapping
│ ├── *_service.py # Service implementations
│ ├── database_service.py # Base service class
│ └── service_result.py # Standardized response wrapper
├── tests/ # Comprehensive test suite
├── docs/ # Detailed documentation
│ └── lambda_handlers.md # 🆕 Lambda wrapper documentation
├── examples/ # Working code examples
│ └── lambda_handlers/ # 🆕 Handler examples
└── README.md # This file
```
## Contributing
We welcome contributions! Here's how to get started:
1. **Fork the repository** and create a feature branch
2. **Follow the existing patterns** - consistency is key
3. **Add comprehensive tests** for any new functionality
4. **Update documentation** for API changes
5. **Submit a Pull Request** with a clear description
### Development Guidelines
- Follow existing code style and patterns
- Maintain 100% test coverage for new code
- Update documentation for any API changes
- Use meaningful commit messages
- Test against multiple Python versions if possible
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Support
- 📖 **Documentation**: [Complete docs](./docs/services_overview.md)
- 🐛 **Bug Reports**: [GitHub Issues](https://github.com/geekcafe/geek-cafe-services/issues)
- 💡 **Feature Requests**: [GitHub Discussions](https://github.com/geekcafe/geek-cafe-services/discussions)
- 📧 **Questions**: Create an issue with the "question" label
---
**Built with ❤️ for the SaaS development community**
| text/markdown | null | Eric Wilson <eric.wilson@geekcafe.com> | null | null | Geek Cafe Services Business Source License 1.0
Copyright (c) 2025 Geek Cafe, LLC. All rights reserved.
The "Geek Cafe Services" software (the "Software") is made available under this
Business Source License (the "License"). This License allows you to view,
study, and modify the source code, and to use it for personal, educational,
research, or non-commercial purposes, subject to the following terms.
1. Grant of Rights
a. You may copy and modify the Software for your own personal,
educational, or internal development use.
b. You may not use the Software, or modified versions of it,
to provide any commercial service or product, including
software-as-a-service, consulting, hosting, or resale,
without a separate commercial license from Geek Cafe LLC.
2. Change Date
Three (3) years from the date of first public release of a specific
version of the Software (the “Change Date”), that version will
automatically be made available under the Apache License 2.0.
Later versions may have different Change Dates.
3. Attribution
All copies or substantial portions of the Software must retain this
License text, the copyright notice above, and a clear reference to
the original source repository (https://github.com/geekcafe/geek-cafe-services).
4. Trademarks
The names “Geek Cafe”, “Geek Cafe Services”, and any related logos are
trademarks of Geek Cafe LLC and may not be used to endorse or promote
derivative products without prior written permission.
5. Disclaimer of Warranty
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT.
6. Limitation of Liability
IN NO EVENT SHALL GEEK Cafe LLC OR CONTRIBUTORS BE LIABLE FOR ANY CLAIM,
DAMAGES, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT, OR
OTHERWISE, ARISING FROM OR IN CONNECTION WITH THE SOFTWARE OR ITS USE.
---
For commercial licensing inquiries, contact:
legal@geekcafe.com | api gateway, aws, dynamodb, lambda, saas, serverless, services | [
"Development Status :: 4 - Beta",
"Framework :: AWS CDK",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"P... | [] | null | null | >=3.8 | [] | [] | [] | [
"boto3-assist",
"build; extra == \"dev\"",
"twine; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/geek-cafe-saas-sdk",
"Documentation, https://github.com/geek-cafe-saas-sdk/blob/main/README.md",
"Source Code, https://github.com/geek-cafe-saas-sdk"
] | twine/6.2.0 CPython/3.13.2 | 2026-01-16T04:29:54.765798 | geek_cafe_saas_sdk-0.79.0.tar.gz | 533,427 | a7/d2/8beb64d2934992dc7a469ed460f5cd55b52801cb7fdd913ba8da6fa2b8c2/geek_cafe_saas_sdk-0.79.0.tar.gz | source | sdist | null | false | bfa271a909c0191ee3249c5e3d6e7a0f | 416f8fde55a259aabfb329ca1bcb08a514240aa7cc5a5daf8adf947cbc7d6401 | a7d28beb64d2934992dc7a469ed460f5cd55b52801cb7fdd913ba8da6fa2b8c2 | null | [
"LICENSE"
] |
2.4 | feldera | 0.223.0 | The feldera python client | # Feldera Python SDK
The `feldera` Python package is the Python client for the Feldera HTTP API.
The Python SDK documentation is available at: https://docs.feldera.com/python
## Getting started
### Installation
```bash
uv pip install feldera
```
### Example usage
The Python client interacts with the API server of the Feldera instance.
```python
# File: example.py
from feldera import FelderaClient, PipelineBuilder, Pipeline
# Instantiate client
client = FelderaClient() # Default: http://localhost:8080 without authentication
# client = FelderaClient(url="https://localhost:8080", api_key="apikey:...", requests_verify="/path/to/tls.crt")
# (Re)create pipeline
name = "example"
sql = """
CREATE TABLE t1 (i1 INT) WITH ('materialized' = 'true');
CREATE MATERIALIZED VIEW v1 AS SELECT * FROM t1;
"""
print("(Re)creating pipeline...")
pipeline = PipelineBuilder(client, name, sql).create_or_replace()
pipeline.start()
print(f"Pipeline status: {pipeline.status()}")
pipeline.pause()
print(f"Pipeline status: {pipeline.status()}")
pipeline.stop(force=True)
# Find existing pipeline
pipeline = Pipeline.get(name, client)
pipeline.start()
print(f"Pipeline status: {pipeline.status()}")
pipeline.stop(force=True)
pipeline.clear_storage()
```
Run using:
```bash
uv run python example.py
```
### Environment variables
Some default parameter values in the Python SDK can be overridden via environment variables.
**Environment variables for `FelderaClient(...)`**
```bash
export FELDERA_HOST="https://localhost:8080" # Overrides default for `url`
export FELDERA_API_KEY="apikey:..." # Overrides default for `api_key`
# The following together override default for `requests_verify`
# export FELDERA_TLS_INSECURE="false" # If set to "1", "true" or "yes" (all case-insensitive), disables TLS certificate verification
# export FELDERA_HTTPS_TLS_CERT="/path/to/tls.crt" # Custom TLS certificate
```
**Environment variables for `PipelineBuilder(...)`**
```bash
export FELDERA_RUNTIME_VERSION="..." # Overrides default for `runtime_version`
```
## Development
Development assumes you have cloned the Feldera code repository.
### Installation
```bash
cd python
# Optional: create and activate virtual environment if you don't have one
uv venv
source .venv/bin/activate
# Install in editable mode
uv pip install -e .
```
### Formatting
Formatting requires the `ruff` package: `uv pip install ruff`
```bash
cd python
ruff check
ruff format
```
### Tests
Running the test requires the `pytest` package: `uv pip install pytest`
```bash
# All tests
cd python
uv run python -m pytest tests/
# Specific tests directory
uv run python -m pytest tests/platform/
# Specific test file
uv run python -m pytest tests/platform/test_pipeline_crud.py
# Tip: add argument -x at the end for it to fail fast
```
For further information about the tests, please see `tests/README.md`.
### Documentation
Building documentation requires the `sphinx` package: `uv pip install sphinx`
```bash
cd python/docs
sphinx-apidoc -o . ../feldera
make html
make clean # Cleanup afterwards
```
### Installation from GitHub
Latest `main` branch:
```bash
uv pip install git+https://github.com/feldera/feldera#subdirectory=python
```
Different branch (replace `BRANCH_NAME`):
```bash
uv pip install git+https://github.com/feldera/feldera@BRANCH_NAME#subdirectory=python
```
| text/markdown | null | Feldera Team <dev@feldera.com> | null | null | null | feldera, python | [
"Programming Language :: Python :: 3.10",
"Operating System :: OS Independent"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"requests",
"pandas>=2.1.2",
"typing-extensions",
"numpy>=2.2.4",
"pretty-errors",
"ruff>=0.6.9",
"PyJWT>=2.8.0"
] | [] | [] | [] | [
"Homepage, https://www.feldera.com",
"Documentation, https://docs.feldera.com/python",
"Repository, https://github.com/feldera/feldera",
"Issues, https://github.com/feldera/feldera/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:29:56.169026 | feldera-0.223.0.tar.gz | 45,035 | 36/50/c4076a1d47b326e2469aa89074e1790231e18a068bc59777b95df172a100/feldera-0.223.0.tar.gz | source | sdist | null | false | c603f1e4606ae8e56983bf03e1fb19ab | 9a1bcf529e4b86153c849cdf8a6c96b4747683af12c0dd8b43ab90b9b3f04162 | 3650c4076a1d47b326e2469aa89074e1790231e18a068bc59777b95df172a100 | MIT | [] |
2.4 | hh-applicant-tool | 1.5.3 | HH-Applicant-Tool: An automation utility for HeadHunter (hh.ru) designed to streamline the job search process by auto-applying to relevant vacancies and periodically refreshing resumes to stay at the top of recruiter searches. | # HH Applicant Tool
> Ищу почасовую или проектную [@feedback_s3rgeym_bot](https://t.me/feedback_s3rgeym_bot) (Python, Vue.js, Devops)
[]()
[]()
[]()
[]()
[]()
<div align="center">
<img src="https://github.com/user-attachments/assets/29d91490-2c83-4e3f-a573-c7a6182a4044" width="500">
</div>
### ☕ Поддержать проект
[](bitcoin:BC1QWQXZX6D5Q0J5QVGH2VYXTFXX9Y6EPPGCW3REHS?label=%D0%94%D0%BB%D1%8F%20%D0%BF%D0%BE%D0%B6%D0%B5%D1%80%D1%82%D0%B2%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B9)
**BTC Address:** `BC1QWQXZX6D5Q0J5QVGH2VYXTFXX9Y6EPPGCW3REHS`
---
## ✨ Ключевые преимущества
- 💸 **Полностью бесплатно.** В то время как сервисы в интернете или Telegram с аналогичным функционалом просят за свои услуги от 5.000 до 12.000 рублей в месяц.
- 🔒 **Безопасность личных данных.** Ваши email, телефон, пароль и другие личные данные никуда не передаются в отличие от сторонних сервисов. В этом можно убедиться, изучив [открытый исходный код](https://github.com/s3rgeym/hh-applicant-tool/tree/main/src/hh_applicant_tool). Владельцы сторонних сервисов никогда вам не покажут исходники. Они знают о вас все и эти данные спокойно продадут каким-нибудь жуликам, либо те утекут в результате взлома.
- 💾 **Сохранение контактов и прочей информации.** Контакты работодалей и информация о них и их вакансиях сохраняется в базе данных, что позволяет производить быстрый поиск нужной информации в отличии от сайта при минимальном опыте с SQL (язык запросов, придуманный в свое время для домохозяек).
- 🛡️ **Гарантия от блокировок.** Утилита выполняет запросы с вашего устройства, имитируя обычного пользователя. Сервисы рассылают запросы для сотен аккаунтов с одного сервера, что повышает вероятность блокировки вашего аккаунта до 100%.
- 😎 **Простота в использовании.** С утилитой разберется любой начинающий пользователь компьютера. Настолько, что херки в своем чате уже жалуются на массовые отклики от подростков 14-17 лет, которые успешно освоили данный инструмент.
- 👯 **Мультиаккаунтность и управление резюме.** Утилита благодаря профилям может работать с неограниченным количеством аккаунтов и резюме.
- 🖥️ **Полноценный CLI и работа на серверах.** Утилита имеет чистый консольный интерфейс. Несмотря на то, что для обхода защиты при авторизации используется браузер, он работает по умолчанию в фоновом (headless) режиме. Для `hh-applicant-tool` не нужна видеокарта или графическая оболочка (X-сервер), что позволяет авторизоваться даже с сервера или из докер-контейнера.
- 🚀 **Скриптинг.** Вы можете использовать утилиту из своих Python-скриптов.
- 🤖 **Борьба с ATS и HR.** Россиянские говнокомпании внедрили ATS с нейронками, которые отклоняют отклик в течение 5 секунд. Отказ может прийти даже из-за отсутствия одного ключевого слова в резюме. Это не говоря уже о тупопездом фильтре, отсеивающем по знаку зодика (они не любят козерогов!!!). Это обесценивает ваши усилия на написание сопроводительных писем и чтение бесконечных портянок бреда, сгенерированных нейронками по запросу каких-то дур. Если тупые ичары решили себя не утруждать чтением резюме (они сейчас и сами перестали писать), то и вам незачем читать высеры этих филологинь и психологинь. Утилита избавляет вас от этой рутины, превращающей поиск работы в полноценную работу. Сейчас доля отказов составляет 98-99%, включая "молчунов" и прочих долбоебов, и единственный способ увеличить шансы просто попасть на собеседование — это автоматическая рассылка откликов на все подходящие вакансии. У большинства телефоны с двумя симками, а значит каждый может разослать до 400 откликов в сутки, а если нарегать акков на родню — еще больше!
---
## Содержание
- [HH Applicant Tool](#hh-applicant-tool)
- [☕ Поддержать проект](#-поддержать-проект)
- [✨ Ключевые преимущества](#-ключевые-преимущества)
- [Содержание](#содержание)
- [Описание](#описание)
- [Предыстория](#предыстория)
- [Запуск через Docker](#запуск-через-docker)
- [Стандартная установка](#стандартная-установка)
- [Установка утилиты](#установка-утилиты)
- [Дополнительные зависимости](#дополнительные-зависимости)
- [Авторизация](#авторизация)
- [Описание команд](#описание-команд)
- [Использование AI](#использование-ai)
- [OpenAI/ChatGPT](#openaichatgpt)
- [Шаблоны сообщений](#шаблоны-сообщений)
- [Данные приложения](#данные-приложения)
- [Конфигурационный файл](#конфигурационный-файл)
- [Логи](#логи)
- [База данных](#база-данных)
- [Использование в скриптах](#использование-в-скриптах)
- [Дополнительные настройки](#дополнительные-настройки)
- [Лицензионное соглашение (Limited Non-Commercial License)](#лицензионное-соглашение-limited-non-commercial-license)
---
## Описание
> Данной утилите похуй на "запрет" доступа к API HH сторонним приложениям, так как она прикидывается официальным приложением под Android
> Утилита для генерации сопроводительного письма может использовать AI, в т. ч. ChatGPT. Подробное описание ниже
Утилита для успешных волчат и старых волков с опытом, служащая для автоматизации действий на HH.RU, таких как рассылка откликов на подходящие вакансии и обновление всех резюме (бесплатный аналог услуги на HH). Утилита локально хранит информацию об откликах, в т. ч. полученные контакты. Это удобно, так как контакт сохранится, даже если вышлют отказ в дальнейшем. Мой совет: скрывайте свой номер от работодателя, если рассылаете отклики через утилиту, а то количество жуликов на красном сайте, мягко говоря, зашкаливает. У утилиты есть канал в дуровграме: [HH Applicant Tool](https://t.me/hh_applicant_tool). Старый <s>[HH Resume Automate](https://t.me/hh_resume_automate)</s> был выпилен какими-то долбоёбами, углядевшими во флаге Японии с двумя буквами «h» нарушение авторских прав...
Работает с Python >= 3.10. Нужную версию Python можно поставить через
asdf/pyenv/conda и что-то еще. В школотронской Manjaro и даже в последних Ubuntu
версия Python новее.
Данная утилита кроссплатформенна. Она гарантированно работает на Linux, Mac и Windows, в т. ч. WSL. При наличии рутованного телефона можно вытащить `access` и `refresh` токены из официального приложения и добавить их в конфиг.
Пример работы:
> Если в веб-интерфейсе выставить фильтры, то они будут применяться в скрипте при отклике на подходящие
> Утилита автоматически подхватывает прокси из переменных окружения типа http_proxy или HTTPS_PROXY
---
## Предыстория
Долгое время я делал массовые заявки с помощью консоли браузера:
```js
$$('[data-qa="vacancy-serp__vacancy_response"]').forEach((el) => el.click());
```
Оно работает, хоть и не идеально. Я даже пробовал автоматизировать рассылки через `p[yu]ppeteer`, пока не прочитал [документацию](https://github.com/hhru/api). И не обнаружил, что **API** (интерфейс) содержит все необходимые мне методы. Headhunter позволяет создать свое приложение, но там ручная модерация, и навряд ли кто-то разрешит мне создать приложение для спама заявками. Я [декомпилировал](https://gist.github.com/s3rgeym/eee96bbf91b04f7eb46b7449f8884a00) официальное приложение для **Android** и получил **CLIENT_ID** и **CLIENT_SECRET**, необходимые для работы через **API**.
---
## Запуск через Docker
Это рекомендованный способ разработчиком. Так же если не работает стандартная установка, то используйте его. Так же это самый простой способ запуска и использования утилиты, требующий скопипастить 5 команд. Он подойдет обладателям выделенных серверов, используемых под VPN. Единственным недостатком использования `docker` является требовательность его к месту, так как для запуска хромиума, который используется при авторизации, нужно половину убунты поставить (более гига).
Для начала нужно установить `docker` и `docker-compose`:
```sh
sudo apt install docker.io docker-compose-v2
```
Выкачиваем репозиторий и переходим в каталог:
```sh
git clone https://github.com/s3rgeym/hh-applicant-tool
cd hh-applicant-tool
```
> Команды с docker-compose нужно запускать строго, находясь в данном каталоге!
Теперь авторизуемся:
```sh
docker-compose run -u docker -it hh_applicant_tool \
hh-applicant-tool -vv auth -k
```
Пример вывода:
```
👤 Введите email или телефон: your-mail@gmail.com
📨 Код был отправлен. Проверьте почту или SMS.
📩 Введите полученный код: 1234
🔓 Авторизация прошла успешно!
```
Капча отобразится только в терминале с поддержкой протокола **kitty**, например, в **Kitty** или **Konsole**.
Авторизация с заданными логином и паролем выглядит так:
```sh
docker-compose run -u docker -it hh_applicant_tool \
hh-applicant-tool -vv auth -k '<login>' -p '<password>'
```
Подробно про авторизацию можно почитать [здесь](#авторизация).
В случае успешной авторизации можно запускать рассылку откликов по крону:
```sh
docker-compose up -d
```
Что будет делать?
- Рассылать отклики со всех опубликованных резюме.
- Поднимать резюме.
Просмотр логов `cron`:
```sh
docker compose logs -f
```
В выводе должно быть что-то типа:
```sh
hh_applicant_tool | [Wed Jan 14 08:33:53 MSK 2026] Running startup tasks...
hh_applicant_tool | ℹ️ Токен не истек, обновление не требуется.
hh_applicant_tool | ✅ Обновлено Программист
```
Чтобы прекратить просмотр логов, нажмите `Ctrl-C`.
Информацию об ошибках можно посмотреть в файле `config/log.txt`, а контакты работодателей — в `config/data` с помощью `sqlite3`. В `config/config.json` хранятся токены, дающие доступ к аккаунту.
Запущенные сервисы докер стартуют автоматически после перезагрузки. Остановить их можно выполнив:
```sh
docker-compose down
```
Чтобы обновить утилиту в большинству случаев достаточно в каталоге выполнить:
```sh
git pull
```
В редких случаях нужно пересобрать все:
```sh
docker compose up -d --build
```
Чтобы рассылать отклики с нескольких аккаунтов, нужно переписать `docker-compose.yml`:
```yaml
services:
# Не меняем ничего тут
hh_applicant_tool:
# ...
# Добавляем новые строки
# Просто копипастим, меняя имя сервиса, container_name и значение HH_PROFILE_ID
hh_second:
extends: hh_applicant_tool
container_name: hh_second
environment:
- HH_PROFILE_ID=second
hh_third:
extends: hh_applicant_tool
container_name: hh_third
environment:
- HH_PROFILE_ID=third
hh_fourth:
extends: hh_applicant_tool
container_name: hh_fourth
environment:
- HH_PROFILE_ID=fourth
```
Здесь `HH_PROFILE_ID` — идентификатор профиля (сами придумываете). Далее нужно авторизоваться в каждом профиле:
```sh
# Авторизуемся со второго профиля
docker-compose exec -u docker -it hh_applicant_tool \
hh-applicant-tool --profile-id second auth -k
# Авторизуемся с третьего профиля
docker-compose exec -u docker -it hh_applicant_tool \
hh-applicant-tool --profile-id third auth -k
# И так далее
```
Ну и выполнить `docker-compose up -d` чтобы запустить новые сервисы.
[Команды](#описание-команд) можно потестировать в запущенном контейнере:
```sh
$ docker-compose exec -u docker -it hh_applicant_tool bash
docker@1897bdd7c80b:/app$ hh-applicant-tool config -p
/app/config/config.json
docker@1897bdd7c80b:/app$ hh-applicant-tool refresh-token
ℹ Токен не истек, обновление не требуется.
docker@1897bdd7c80b:/app$
```
> Обратите внимание, что `docker-compose exec`/`docker-compose run` запускаются с аргументами `-u docker`. Только для пользователя `docker` установлен `chromium`, необходимый для авторизации, а так же это избавляет от проблем с правами, когда созданные файлы для изменения требуют root-права.
Если хотите команду `apply-similar` вызывать с какими-то аргументами, то создайте в корне файл `apply-similar.sh`:
```sh
#!/bin/bash
/usr/local/bin/python -m hh_applicant_tool apply-similar # укажите аргументы
```
В файлах `startup.sh` и `crontab` замените `/usr/local/bin/python -m hh_applicant_tool apply-similar` на `/bin/sh /app/apply-similar.sh`.
---
## Стандартная установка
### Установка утилиты
Универсальный способ с использованием pipx (требует пакета `python-pipx` в Arch):
```bash
# Полная версия с поддержкой авторизации, включает Node.js и различные утилиты
# Обычный пакет без [playwright] можно использовать на сервере, если перенести туда конфиг, и весит
# он почти на 500МБ меньше. Думаем (c) s3rgeym. Подписаться
$ pipx install 'hh-applicant-tool[playwright]'
# Если хочется использовать самую последнюю версию, то можно установить ее через git
$ pipx install "git+https://github.com/s3rgeym/hh-applicant-tool[playwright]"
# Для обновления до новой версии
$ pipx upgrade hh-applicant-tool
```
pipx добавляет исполняемый файл `hh-applicant-tool` в `~/.local/bin`, делая эту команду доступной. Путь до `~/.local/bin` должен быть в `$PATH` (в большинстве дистрибутивов он добавлен).
Традиционный способ для Linux/Mac:
```sh
mkdir -p ~/.venvs
python -m venv ~/.venvs/hh-applicant-tool
# Это придется делать постоянно, чтобы команда hh-applicant-tool стала доступна
. ~/.venvs/hh-applicant-tool/bin/activate
pip install 'hh-applicant-tool[playwright]'
```
Отдельно я распишу процесс установки в **Windows** в подробностях:
- Для начала поставьте последнюю версию **Python 3** любым удобным способом.
- Запустите **Terminal** или **PowerShell** от Администратора и выполните:
```ps
Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy Unrestricted
```
Данная политика разрешает текущему пользователю (от которого зашли) запускать скрипты. Без нее не будут работать виртуальные окружения.
Далее можно поставить `pipx` и вернуться к инструкции в верху раздела:
- Все так же от администратора выполните:
```ps
python -m pip install --user pipx
```
А затем:
```ps
python -m pipx ensurepath
```
- Перезапускаем Terminal/Powershell и проверяем:
```ps
pipx -h
```
С использованием вирт. окружений:
- Создайте и активируйте виртуальное окружение:
```ps
PS> python -m venv hh-applicant-venv
PS> .\hh-applicant-venv\Scripts\activate
```
- Поставьте все пакеты в виртуальное окружение `hh-applicant-venv`:
```ps
(hh-applicant-venv) PS> pip install 'hh-applicant-tool[playwright]'
```
- Проверьте, работает ли оно:
```ps
(hh-applicant-venv) PS> hh-applicant-tool -h
```
- В случае неудачи вернитесь к первому шагу.
- Для последующих запусков сначала активируйте виртуальное окружение.
### Дополнительные зависимости
После вышеописанного нужно установить зависимости в виде Chromium и др:
```sh
$ hh-applicant-tool install
```
Этот шаг необязателен. Все это нужно только для авторизации.
---
## Авторизация
Прямая авторизация:
```bash
$ hh-applicant-tool authorize '<ваш телефон или email>' -p '<пароль>'
```
Если вы пропустили пункт про установку зависимостей, то увидите такую ошибку:
```sh
[E] BrowserType.launch: Executable doesn't exist at...
```
Если по какой-то причине не был установлен `playwright`:
```sh
[E] name 'async_playwright' is not defined
```
Если не помните пароль или др. причины, то можно авторизоваться с помощью одноразового кода:
```bash
$ hh-applicant-tool authorize '<ваш телефон или email>'
📨 Код был отправлен. Проверьте почту или SMS.
📩 Введите полученный код: 1387
🔓 Авторизация прошла успешно!
```
Если же при вводе правильных данных возникает ошибка авторизации, то, скорее всего, требуется ввод капчи.
Капчу можно ввести через терминал, если тот поддерживает kitty protocol (например, Kitty, Konsole, Ghostty и др):
```sh
hh-applicant-tool authorize --use-kitty
```
<img width="843" height="602" alt="Untitled" src="https://github.com/user-attachments/assets/8f5dec0c-c3d4-4c5c-bd8b-3aeffa623d87" />
Так же поддерживается sixel
protocol: `--use-sixel/--sixel/-s`.
Ручная авторизация с запуском встроенного браузера:
```sh
hh-applicant-tool authorize --manual
```
Проверка авторизации:
```bash
$ hh-applicant-tool whoami
🆔 27405918 Кузнецов Андрей Владимирович [ 📄 1 | 👁️ +115 | ✉️ +28 ]
```
В случае успешной авторизации токены будут сохранены в `config.json`.
При удачной авторизации логин (почта или телефон) и пароль, если последний был передан, запоминаются и будут подставляться автоматически, если не указать их явно.
Токен доступа выдается на две недели. Он обновляется автоматически. Для его ручного обновления нужно выполнить:
```bash
$ hh-applicant-tool refresh-token
```
Помните, что у `refresh_token` тоже есть время жизни, поэтому может потребоваться полная авторизация.
---
## Описание команд
Примеры команд:
```bash
# Общий вид: сначала глобальные настройки, затем команда и её аргументы
$ hh-applicant-tool [options] <operation> [args]
# Справка по глобальным флагам и список операций
$ hh-applicant-tool -h
# Справка по операции
$ hh-applicant-tool authorize -h
# Авторизуемся
$ hh-applicant-tool authorize
# Авторизация с использованием другого профиля
$ hh-applicant-tool --profile profile123 authorize
# Рассылаем заявки
$ hh-applicant-tool apply-similar
# Для тестирования поисковой строки и других параметров, используйте --dry-run.
# С ним отклики не отправляются, а лишь выводятся сообщения
$ hh-applicant-tool -vv apply-similar --search "Python программист" --per-page 3 --total-pages 1 --dry-run
# Поднимаем резюме
$ hh-applicant-tool update-resumes
# Ответить работодателям
$ hh-applicant-tool reply-employers
# Просмотр лога в реальном времени
$ hh-applicant-tool log -f
# Посмотреть содержимое конфига
$ hh-applicant-tool config
# Редактировать конфиг в стандартном редакторе
$ hh-applicant-tool config -e
# Вывести значение из конфига
$ hh-applicant-tool config -k token.access_token
# Установить значение в конфиге, например, socks-прокси
$ hh-applicant-tool config -s proxy_url socks5h://localhost:1080
# Удалить значение из конфига
$ hh-applicant-tool config -u proxy_url
# Утилита все данные об откликах хранит в SQLite
$ hh-applicant-tool query 'select count(*) from vacancy_contacts;'
+----------+
| count(*) |
+----------+
| 42 |
+----------+
# Экспорт контактов в csv
$ hh-applicant-tool query 'select * from vacancy_contacts' --csv -o
contacts.csv
# Выполнение запросов в интерактивном режиме
$ hh-applicant-tool query
# Чистим отказы
$ hh-applicant-tool clear-negotiations
# При обновлении может сломаться схема БД, для ее починки нужно выполнить
# поочерёдно все миграции, добавленные после выхода последней версии
$ hh-applicant-tool migrate
List of migrations:
[1]: 2026-01-07
Choose migration [1] (Keep empty to exit): 1
✅ Success!
# Вывести все настройки
$ hh-applicant-tool settings
+----------+-------------------------+-------------------------+
| Тип | Ключ | Значение |
+----------+-------------------------+-------------------------+
| str | user.email | dmitry.kozlov@yandex.ru |
+----------+-------------------------+-------------------------+
# Получить значение по ключу
$ hh-applicant-tool settings auth.username
# Установить email, используемый для автологина
$ hh-applicant-tool settings auth.username 'user@example.com'
```
Глобальные настройки:
- `-v` используется для вывода отладочной информации. Два таких флага, например, выводят запросы к **API**.
- `-c <path>` путь до каталога, где хранятся конфигурации.
- `--profile <profile-id>` можно указать профиль, данные которого будут храниться в подкаталоге.
| Операция | Описание |
| ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **authorize**, **auth** | Авторизация на hh.ru. Введенные логин и пароль "запоминаются" и будут использованы при следующем вызове команды. |
| **whoami**, **id** | Выводит информацию об авторизованном пользователе |
| **list-resumes**, **list**, **ls** | Список резюме |
| **update-resumes**, **update** | Обновить все резюме. Аналогично нажатию кнопки «Обновить дату». |
| **apply-similar** | Откликнуться на все подходящие вакансии СО ВСЕХ РЕЗЮМЕ. Лимит = 200 в день. На HH есть спам-фильтры, так что лучше не рассылайте отклики со ссылками, иначе рискуете попасть в теневой бан. |
| **reply-employers**, **reply** | Ответить во все чаты с работодателями, где нет ответа либо не прочитали ваш предыдущий ответ |
| **clear-negotiations** | Отмена откликов |
| **call-api**, **api** | Вызов произвольного метода API с выводом результата. |
| **refresh-token**, **refresh** | Обновляет access_token. |
| **config** | Показывает содержимое конфига. С флагом -e открывает его для редактирования. |
| **settings** | Просмотр и управление настройками в базе данных. Простое key-value хранилище. |
| **install** | Устанавливает зависимости, такие как браузер Chromium, необходимые для авторизации. |
| **uninstall** | Удаляет браузер Chromium, используемый для авторизации. |
| **check-proxy** | Проверяет используемые прокси |
| **migrate** | Починить базу |
| **query** | Выполнение SQL-запросов к базе. Схема БД находится в файле [schema.sql](./hh_hh_applicant_tool/storage/queries/schema.sql). Если скормить ее [DeepSeek](https://chat.deepseek.com), то он поможет написать любой запрос. |
| **log** | Просмотр файла-лога. С флагом -f будет следить за изменениями. В логах частично скрыты идентефикаторы в целях безопасности. |
Утилита использует систему плагинов. Все они лежат в [operations](https://github.com/s3rgeym/hh-applicant-tool/tree/main/src/hh_applicant_tool/operations). Модули, расположенные там, автоматически добавляются как доступные команды. За основу для своего плагина можно взять [whoami.py](https://github.com/s3rgeym/hh-applicant-tool/tree/main/src/hh_applicant_tool/operations/whoami.py).
Для тестирования запросов к API используйте команду `call-api` совместно с `jq` для обработки JSON.
Примеры поиска работодателей:
```bash
$ hh-applicant-tool call-api /employers text="IT" only_with_vacancies=true | jq -r '.items[].alternate_url'
https://hh.ru/employer/1966364
https://hh.ru/employer/4679771
...
```
Синтаксис `call-api` немного похож на `httpie` или `curlie`:
```sh
$ hh-applicant-tool call-api [-m {GET|POST|PUT|DELETE}] <endpoint> [<key=value> ...]
```
Если используется метод `GET` или `DELETE` (или ничего не указано), то параметры будут переданы как query string. Во всех остальных случаях парметры передаются как `application/x-www-form-urlencoded` в теле запроса.
Данная возможность полезна для написания Bash-скриптов.
Документация для работы с API соискателей была удалена с ха-ха.сру и его корпоративного репозитория. Можете не искать, они затерли даже историю репозитория. Но я через веб-архив выкачал документацию. Чтобы ее посмотреть, клонируйте этот репозиторий и откройте файл, например, с помощью [Swagger Viewer](https://marketplace.visualstudio.com/items?itemName=Arjun.swagger-viewer).
<img width="740" height="768" alt="image" src="https://github.com/user-attachments/assets/597fa31e-8bab-48c8-8601-ab9dfc9075b1" />
Так же существуют cli-утилиты:
```sh
npx @redocly/cli preview -d docs/hhapi
```
Потом нужно открыть в браузере [http://localhost:4000](http://localhost:4000).
> Отдельные замечания у меня к API HH. Оно пиздец какое кривое. Например, при создании заявки возвращается пустой ответ либо редирект, хотя по логике должен возвращаться созданный объект. Так же в ответах сервера нет `Content-Length`. Из-за этого нельзя узнать, есть ли тело у ответа сервера, нужно его пробовать прочитать. Я так понял, там какой-то прокси оборачивает все запросы и отдает всегда `Transfer-Encoding: Chunked`. А еще он возвращает 502 ошибку, когда бэкенд на Java падает либо долго отвечает (таймаут)? А вот [язык запросов](https://hh.ru/article/1175) мне понравился. Можно что-то вроде этого использовать `NOT (!ID:123 OR !ID:456 OR !ID:789)`, чтобы отсеить какие-то вакансии.
По сути, никакие дополнительные команды, кроме имеющихся, не нужны. Вы можете сделать что угодно с помощью `call-api`, но если хочется чего-то особенного, можно добавить свои команды.
---
## Использование AI
Для генерации опроводительных писем при откликах и ответа в чаты работодателей (`reply-employers`) можно использовать OpenAI (ChatGPT).
Пример рассылки откликов с генерированным письмом:
```sh
hh-applicant-tool apply-similar -f --ai
```
Генерацию сопроводительных писем в откликах я счи | text/markdown | Senior YAML Developer | yamldeveloper@proton.me | null | null | null | null | [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | https://github.com/s3rgeym/hh-applicant-tool | null | <4.0,>=3.10 | [] | [] | [] | [
"requests[socks]<3.0.0,>=2.32.3",
"prettytable<4.0.0,>=3.6.0",
"playwright<2.0.0,>=1.57.0; extra == \"playwright\"",
"pillow<13.0.0,>=12.1.0; extra == \"pillow\""
] | [] | [] | [] | [
"Homepage, https://github.com/s3rgeym/hh-applicant-tool",
"Repository, https://github.com/s3rgeym/hh-applicant-tool"
] | poetry/2.2.1 CPython/3.11.0 Linux/6.11.0-1018-azure | 2026-01-16T04:29:57.488995 | hh_applicant_tool-1.5.3-py3-none-any.whl | 86,161 | 42/4f/b865e6e5ac40805c07aa8c993546e673ae3a16e06b5e89fdb4dc99a61ebe/hh_applicant_tool-1.5.3-py3-none-any.whl | py3 | bdist_wheel | null | false | aa48696d32dffc7255871cc4d875b69e | 061997236dc599cffa40680a2b627a434cc623bf39552d69022e5cb782e96ce1 | 424fb865e6e5ac40805c07aa8c993546e673ae3a16e06b5e89fdb4dc99a61ebe | null | [] |
2.4 | hh-applicant-tool | 1.5.3 | HH-Applicant-Tool: An automation utility for HeadHunter (hh.ru) designed to streamline the job search process by auto-applying to relevant vacancies and periodically refreshing resumes to stay at the top of recruiter searches. | # HH Applicant Tool
> Ищу почасовую или проектную [@feedback_s3rgeym_bot](https://t.me/feedback_s3rgeym_bot) (Python, Vue.js, Devops)
[]()
[]()
[]()
[]()
[]()
<div align="center">
<img src="https://github.com/user-attachments/assets/29d91490-2c83-4e3f-a573-c7a6182a4044" width="500">
</div>
### ☕ Поддержать проект
[](bitcoin:BC1QWQXZX6D5Q0J5QVGH2VYXTFXX9Y6EPPGCW3REHS?label=%D0%94%D0%BB%D1%8F%20%D0%BF%D0%BE%D0%B6%D0%B5%D1%80%D1%82%D0%B2%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B9)
**BTC Address:** `BC1QWQXZX6D5Q0J5QVGH2VYXTFXX9Y6EPPGCW3REHS`
---
## ✨ Ключевые преимущества
- 💸 **Полностью бесплатно.** В то время как сервисы в интернете или Telegram с аналогичным функционалом просят за свои услуги от 5.000 до 12.000 рублей в месяц.
- 🔒 **Безопасность личных данных.** Ваши email, телефон, пароль и другие личные данные никуда не передаются в отличие от сторонних сервисов. В этом можно убедиться, изучив [открытый исходный код](https://github.com/s3rgeym/hh-applicant-tool/tree/main/src/hh_applicant_tool). Владельцы сторонних сервисов никогда вам не покажут исходники. Они знают о вас все и эти данные спокойно продадут каким-нибудь жуликам, либо те утекут в результате взлома.
- 💾 **Сохранение контактов и прочей информации.** Контакты работодалей и информация о них и их вакансиях сохраняется в базе данных, что позволяет производить быстрый поиск нужной информации в отличии от сайта при минимальном опыте с SQL (язык запросов, придуманный в свое время для домохозяек).
- 🛡️ **Гарантия от блокировок.** Утилита выполняет запросы с вашего устройства, имитируя обычного пользователя. Сервисы рассылают запросы для сотен аккаунтов с одного сервера, что повышает вероятность блокировки вашего аккаунта до 100%.
- 😎 **Простота в использовании.** С утилитой разберется любой начинающий пользователь компьютера. Настолько, что херки в своем чате уже жалуются на массовые отклики от подростков 14-17 лет, которые успешно освоили данный инструмент.
- 👯 **Мультиаккаунтность и управление резюме.** Утилита благодаря профилям может работать с неограниченным количеством аккаунтов и резюме.
- 🖥️ **Полноценный CLI и работа на серверах.** Утилита имеет чистый консольный интерфейс. Несмотря на то, что для обхода защиты при авторизации используется браузер, он работает по умолчанию в фоновом (headless) режиме. Для `hh-applicant-tool` не нужна видеокарта или графическая оболочка (X-сервер), что позволяет авторизоваться даже с сервера или из докер-контейнера.
- 🚀 **Скриптинг.** Вы можете использовать утилиту из своих Python-скриптов.
- 🤖 **Борьба с ATS и HR.** Россиянские говнокомпании внедрили ATS с нейронками, которые отклоняют отклик в течение 5 секунд. Отказ может прийти даже из-за отсутствия одного ключевого слова в резюме. Это не говоря уже о тупопездом фильтре, отсеивающем по знаку зодика (они не любят козерогов!!!). Это обесценивает ваши усилия на написание сопроводительных писем и чтение бесконечных портянок бреда, сгенерированных нейронками по запросу каких-то дур. Если тупые ичары решили себя не утруждать чтением резюме (они сейчас и сами перестали писать), то и вам незачем читать высеры этих филологинь и психологинь. Утилита избавляет вас от этой рутины, превращающей поиск работы в полноценную работу. Сейчас доля отказов составляет 98-99%, включая "молчунов" и прочих долбоебов, и единственный способ увеличить шансы просто попасть на собеседование — это автоматическая рассылка откликов на все подходящие вакансии. У большинства телефоны с двумя симками, а значит каждый может разослать до 400 откликов в сутки, а если нарегать акков на родню — еще больше!
---
## Содержание
- [HH Applicant Tool](#hh-applicant-tool)
- [☕ Поддержать проект](#-поддержать-проект)
- [✨ Ключевые преимущества](#-ключевые-преимущества)
- [Содержание](#содержание)
- [Описание](#описание)
- [Предыстория](#предыстория)
- [Запуск через Docker](#запуск-через-docker)
- [Стандартная установка](#стандартная-установка)
- [Установка утилиты](#установка-утилиты)
- [Дополнительные зависимости](#дополнительные-зависимости)
- [Авторизация](#авторизация)
- [Описание команд](#описание-команд)
- [Использование AI](#использование-ai)
- [OpenAI/ChatGPT](#openaichatgpt)
- [Шаблоны сообщений](#шаблоны-сообщений)
- [Данные приложения](#данные-приложения)
- [Конфигурационный файл](#конфигурационный-файл)
- [Логи](#логи)
- [База данных](#база-данных)
- [Использование в скриптах](#использование-в-скриптах)
- [Дополнительные настройки](#дополнительные-настройки)
- [Лицензионное соглашение (Limited Non-Commercial License)](#лицензионное-соглашение-limited-non-commercial-license)
---
## Описание
> Данной утилите похуй на "запрет" доступа к API HH сторонним приложениям, так как она прикидывается официальным приложением под Android
> Утилита для генерации сопроводительного письма может использовать AI, в т. ч. ChatGPT. Подробное описание ниже
Утилита для успешных волчат и старых волков с опытом, служащая для автоматизации действий на HH.RU, таких как рассылка откликов на подходящие вакансии и обновление всех резюме (бесплатный аналог услуги на HH). Утилита локально хранит информацию об откликах, в т. ч. полученные контакты. Это удобно, так как контакт сохранится, даже если вышлют отказ в дальнейшем. Мой совет: скрывайте свой номер от работодателя, если рассылаете отклики через утилиту, а то количество жуликов на красном сайте, мягко говоря, зашкаливает. У утилиты есть канал в дуровграме: [HH Applicant Tool](https://t.me/hh_applicant_tool). Старый <s>[HH Resume Automate](https://t.me/hh_resume_automate)</s> был выпилен какими-то долбоёбами, углядевшими во флаге Японии с двумя буквами «h» нарушение авторских прав...
Работает с Python >= 3.10. Нужную версию Python можно поставить через
asdf/pyenv/conda и что-то еще. В школотронской Manjaro и даже в последних Ubuntu
версия Python новее.
Данная утилита кроссплатформенна. Она гарантированно работает на Linux, Mac и Windows, в т. ч. WSL. При наличии рутованного телефона можно вытащить `access` и `refresh` токены из официального приложения и добавить их в конфиг.
Пример работы:
> Если в веб-интерфейсе выставить фильтры, то они будут применяться в скрипте при отклике на подходящие
> Утилита автоматически подхватывает прокси из переменных окружения типа http_proxy или HTTPS_PROXY
---
## Предыстория
Долгое время я делал массовые заявки с помощью консоли браузера:
```js
$$('[data-qa="vacancy-serp__vacancy_response"]').forEach((el) => el.click());
```
Оно работает, хоть и не идеально. Я даже пробовал автоматизировать рассылки через `p[yu]ppeteer`, пока не прочитал [документацию](https://github.com/hhru/api). И не обнаружил, что **API** (интерфейс) содержит все необходимые мне методы. Headhunter позволяет создать свое приложение, но там ручная модерация, и навряд ли кто-то разрешит мне создать приложение для спама заявками. Я [декомпилировал](https://gist.github.com/s3rgeym/eee96bbf91b04f7eb46b7449f8884a00) официальное приложение для **Android** и получил **CLIENT_ID** и **CLIENT_SECRET**, необходимые для работы через **API**.
---
## Запуск через Docker
Это рекомендованный способ разработчиком. Так же если не работает стандартная установка, то используйте его. Так же это самый простой способ запуска и использования утилиты, требующий скопипастить 5 команд. Он подойдет обладателям выделенных серверов, используемых под VPN. Единственным недостатком использования `docker` является требовательность его к месту, так как для запуска хромиума, который используется при авторизации, нужно половину убунты поставить (более гига).
Для начала нужно установить `docker` и `docker-compose`:
```sh
sudo apt install docker.io docker-compose-v2
```
Выкачиваем репозиторий и переходим в каталог:
```sh
git clone https://github.com/s3rgeym/hh-applicant-tool
cd hh-applicant-tool
```
> Команды с docker-compose нужно запускать строго, находясь в данном каталоге!
Теперь авторизуемся:
```sh
docker-compose run -u docker -it hh_applicant_tool \
hh-applicant-tool -vv auth -k
```
Пример вывода:
```
👤 Введите email или телефон: your-mail@gmail.com
📨 Код был отправлен. Проверьте почту или SMS.
📩 Введите полученный код: 1234
🔓 Авторизация прошла успешно!
```
Капча отобразится только в терминале с поддержкой протокола **kitty**, например, в **Kitty** или **Konsole**.
Авторизация с заданными логином и паролем выглядит так:
```sh
docker-compose run -u docker -it hh_applicant_tool \
hh-applicant-tool -vv auth -k '<login>' -p '<password>'
```
Подробно про авторизацию можно почитать [здесь](#авторизация).
В случае успешной авторизации можно запускать рассылку откликов по крону:
```sh
docker-compose up -d
```
Что будет делать?
- Рассылать отклики со всех опубликованных резюме.
- Поднимать резюме.
Просмотр логов `cron`:
```sh
docker compose logs -f
```
В выводе должно быть что-то типа:
```sh
hh_applicant_tool | [Wed Jan 14 08:33:53 MSK 2026] Running startup tasks...
hh_applicant_tool | ℹ️ Токен не истек, обновление не требуется.
hh_applicant_tool | ✅ Обновлено Программист
```
Чтобы прекратить просмотр логов, нажмите `Ctrl-C`.
Информацию об ошибках можно посмотреть в файле `config/log.txt`, а контакты работодателей — в `config/data` с помощью `sqlite3`. В `config/config.json` хранятся токены, дающие доступ к аккаунту.
Запущенные сервисы докер стартуют автоматически после перезагрузки. Остановить их можно выполнив:
```sh
docker-compose down
```
Чтобы обновить утилиту в большинству случаев достаточно в каталоге выполнить:
```sh
git pull
```
В редких случаях нужно пересобрать все:
```sh
docker compose up -d --build
```
Чтобы рассылать отклики с нескольких аккаунтов, нужно переписать `docker-compose.yml`:
```yaml
services:
# Не меняем ничего тут
hh_applicant_tool:
# ...
# Добавляем новые строки
# Просто копипастим, меняя имя сервиса, container_name и значение HH_PROFILE_ID
hh_second:
extends: hh_applicant_tool
container_name: hh_second
environment:
- HH_PROFILE_ID=second
hh_third:
extends: hh_applicant_tool
container_name: hh_third
environment:
- HH_PROFILE_ID=third
hh_fourth:
extends: hh_applicant_tool
container_name: hh_fourth
environment:
- HH_PROFILE_ID=fourth
```
Здесь `HH_PROFILE_ID` — идентификатор профиля (сами придумываете). Далее нужно авторизоваться в каждом профиле:
```sh
# Авторизуемся со второго профиля
docker-compose exec -u docker -it hh_applicant_tool \
hh-applicant-tool --profile-id second auth -k
# Авторизуемся с третьего профиля
docker-compose exec -u docker -it hh_applicant_tool \
hh-applicant-tool --profile-id third auth -k
# И так далее
```
Ну и выполнить `docker-compose up -d` чтобы запустить новые сервисы.
[Команды](#описание-команд) можно потестировать в запущенном контейнере:
```sh
$ docker-compose exec -u docker -it hh_applicant_tool bash
docker@1897bdd7c80b:/app$ hh-applicant-tool config -p
/app/config/config.json
docker@1897bdd7c80b:/app$ hh-applicant-tool refresh-token
ℹ Токен не истек, обновление не требуется.
docker@1897bdd7c80b:/app$
```
> Обратите внимание, что `docker-compose exec`/`docker-compose run` запускаются с аргументами `-u docker`. Только для пользователя `docker` установлен `chromium`, необходимый для авторизации, а так же это избавляет от проблем с правами, когда созданные файлы для изменения требуют root-права.
Если хотите команду `apply-similar` вызывать с какими-то аргументами, то создайте в корне файл `apply-similar.sh`:
```sh
#!/bin/bash
/usr/local/bin/python -m hh_applicant_tool apply-similar # укажите аргументы
```
В файлах `startup.sh` и `crontab` замените `/usr/local/bin/python -m hh_applicant_tool apply-similar` на `/bin/sh /app/apply-similar.sh`.
---
## Стандартная установка
### Установка утилиты
Универсальный способ с использованием pipx (требует пакета `python-pipx` в Arch):
```bash
# Полная версия с поддержкой авторизации, включает Node.js и различные утилиты
# Обычный пакет без [playwright] можно использовать на сервере, если перенести туда конфиг, и весит
# он почти на 500МБ меньше. Думаем (c) s3rgeym. Подписаться
$ pipx install 'hh-applicant-tool[playwright]'
# Если хочется использовать самую последнюю версию, то можно установить ее через git
$ pipx install "git+https://github.com/s3rgeym/hh-applicant-tool[playwright]"
# Для обновления до новой версии
$ pipx upgrade hh-applicant-tool
```
pipx добавляет исполняемый файл `hh-applicant-tool` в `~/.local/bin`, делая эту команду доступной. Путь до `~/.local/bin` должен быть в `$PATH` (в большинстве дистрибутивов он добавлен).
Традиционный способ для Linux/Mac:
```sh
mkdir -p ~/.venvs
python -m venv ~/.venvs/hh-applicant-tool
# Это придется делать постоянно, чтобы команда hh-applicant-tool стала доступна
. ~/.venvs/hh-applicant-tool/bin/activate
pip install 'hh-applicant-tool[playwright]'
```
Отдельно я распишу процесс установки в **Windows** в подробностях:
- Для начала поставьте последнюю версию **Python 3** любым удобным способом.
- Запустите **Terminal** или **PowerShell** от Администратора и выполните:
```ps
Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy Unrestricted
```
Данная политика разрешает текущему пользователю (от которого зашли) запускать скрипты. Без нее не будут работать виртуальные окружения.
Далее можно поставить `pipx` и вернуться к инструкции в верху раздела:
- Все так же от администратора выполните:
```ps
python -m pip install --user pipx
```
А затем:
```ps
python -m pipx ensurepath
```
- Перезапускаем Terminal/Powershell и проверяем:
```ps
pipx -h
```
С использованием вирт. окружений:
- Создайте и активируйте виртуальное окружение:
```ps
PS> python -m venv hh-applicant-venv
PS> .\hh-applicant-venv\Scripts\activate
```
- Поставьте все пакеты в виртуальное окружение `hh-applicant-venv`:
```ps
(hh-applicant-venv) PS> pip install 'hh-applicant-tool[playwright]'
```
- Проверьте, работает ли оно:
```ps
(hh-applicant-venv) PS> hh-applicant-tool -h
```
- В случае неудачи вернитесь к первому шагу.
- Для последующих запусков сначала активируйте виртуальное окружение.
### Дополнительные зависимости
После вышеописанного нужно установить зависимости в виде Chromium и др:
```sh
$ hh-applicant-tool install
```
Этот шаг необязателен. Все это нужно только для авторизации.
---
## Авторизация
Прямая авторизация:
```bash
$ hh-applicant-tool authorize '<ваш телефон или email>' -p '<пароль>'
```
Если вы пропустили пункт про установку зависимостей, то увидите такую ошибку:
```sh
[E] BrowserType.launch: Executable doesn't exist at...
```
Если по какой-то причине не был установлен `playwright`:
```sh
[E] name 'async_playwright' is not defined
```
Если не помните пароль или др. причины, то можно авторизоваться с помощью одноразового кода:
```bash
$ hh-applicant-tool authorize '<ваш телефон или email>'
📨 Код был отправлен. Проверьте почту или SMS.
📩 Введите полученный код: 1387
🔓 Авторизация прошла успешно!
```
Если же при вводе правильных данных возникает ошибка авторизации, то, скорее всего, требуется ввод капчи.
Капчу можно ввести через терминал, если тот поддерживает kitty protocol (например, Kitty, Konsole, Ghostty и др):
```sh
hh-applicant-tool authorize --use-kitty
```
<img width="843" height="602" alt="Untitled" src="https://github.com/user-attachments/assets/8f5dec0c-c3d4-4c5c-bd8b-3aeffa623d87" />
Так же поддерживается sixel
protocol: `--use-sixel/--sixel/-s`.
Ручная авторизация с запуском встроенного браузера:
```sh
hh-applicant-tool authorize --manual
```
Проверка авторизации:
```bash
$ hh-applicant-tool whoami
🆔 27405918 Кузнецов Андрей Владимирович [ 📄 1 | 👁️ +115 | ✉️ +28 ]
```
В случае успешной авторизации токены будут сохранены в `config.json`.
При удачной авторизации логин (почта или телефон) и пароль, если последний был передан, запоминаются и будут подставляться автоматически, если не указать их явно.
Токен доступа выдается на две недели. Он обновляется автоматически. Для его ручного обновления нужно выполнить:
```bash
$ hh-applicant-tool refresh-token
```
Помните, что у `refresh_token` тоже есть время жизни, поэтому может потребоваться полная авторизация.
---
## Описание команд
Примеры команд:
```bash
# Общий вид: сначала глобальные настройки, затем команда и её аргументы
$ hh-applicant-tool [options] <operation> [args]
# Справка по глобальным флагам и список операций
$ hh-applicant-tool -h
# Справка по операции
$ hh-applicant-tool authorize -h
# Авторизуемся
$ hh-applicant-tool authorize
# Авторизация с использованием другого профиля
$ hh-applicant-tool --profile profile123 authorize
# Рассылаем заявки
$ hh-applicant-tool apply-similar
# Для тестирования поисковой строки и других параметров, используйте --dry-run.
# С ним отклики не отправляются, а лишь выводятся сообщения
$ hh-applicant-tool -vv apply-similar --search "Python программист" --per-page 3 --total-pages 1 --dry-run
# Поднимаем резюме
$ hh-applicant-tool update-resumes
# Ответить работодателям
$ hh-applicant-tool reply-employers
# Просмотр лога в реальном времени
$ hh-applicant-tool log -f
# Посмотреть содержимое конфига
$ hh-applicant-tool config
# Редактировать конфиг в стандартном редакторе
$ hh-applicant-tool config -e
# Вывести значение из конфига
$ hh-applicant-tool config -k token.access_token
# Установить значение в конфиге, например, socks-прокси
$ hh-applicant-tool config -s proxy_url socks5h://localhost:1080
# Удалить значение из конфига
$ hh-applicant-tool config -u proxy_url
# Утилита все данные об откликах хранит в SQLite
$ hh-applicant-tool query 'select count(*) from vacancy_contacts;'
+----------+
| count(*) |
+----------+
| 42 |
+----------+
# Экспорт контактов в csv
$ hh-applicant-tool query 'select * from vacancy_contacts' --csv -o
contacts.csv
# Выполнение запросов в интерактивном режиме
$ hh-applicant-tool query
# Чистим отказы
$ hh-applicant-tool clear-negotiations
# При обновлении может сломаться схема БД, для ее починки нужно выполнить
# поочерёдно все миграции, добавленные после выхода последней версии
$ hh-applicant-tool migrate
List of migrations:
[1]: 2026-01-07
Choose migration [1] (Keep empty to exit): 1
✅ Success!
# Вывести все настройки
$ hh-applicant-tool settings
+----------+-------------------------+-------------------------+
| Тип | Ключ | Значение |
+----------+-------------------------+-------------------------+
| str | user.email | dmitry.kozlov@yandex.ru |
+----------+-------------------------+-------------------------+
# Получить значение по ключу
$ hh-applicant-tool settings auth.username
# Установить email, используемый для автологина
$ hh-applicant-tool settings auth.username 'user@example.com'
```
Глобальные настройки:
- `-v` используется для вывода отладочной информации. Два таких флага, например, выводят запросы к **API**.
- `-c <path>` путь до каталога, где хранятся конфигурации.
- `--profile <profile-id>` можно указать профиль, данные которого будут храниться в подкаталоге.
| Операция | Описание |
| ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **authorize**, **auth** | Авторизация на hh.ru. Введенные логин и пароль "запоминаются" и будут использованы при следующем вызове команды. |
| **whoami**, **id** | Выводит информацию об авторизованном пользователе |
| **list-resumes**, **list**, **ls** | Список резюме |
| **update-resumes**, **update** | Обновить все резюме. Аналогично нажатию кнопки «Обновить дату». |
| **apply-similar** | Откликнуться на все подходящие вакансии СО ВСЕХ РЕЗЮМЕ. Лимит = 200 в день. На HH есть спам-фильтры, так что лучше не рассылайте отклики со ссылками, иначе рискуете попасть в теневой бан. |
| **reply-employers**, **reply** | Ответить во все чаты с работодателями, где нет ответа либо не прочитали ваш предыдущий ответ |
| **clear-negotiations** | Отмена откликов |
| **call-api**, **api** | Вызов произвольного метода API с выводом результата. |
| **refresh-token**, **refresh** | Обновляет access_token. |
| **config** | Показывает содержимое конфига. С флагом -e открывает его для редактирования. |
| **settings** | Просмотр и управление настройками в базе данных. Простое key-value хранилище. |
| **install** | Устанавливает зависимости, такие как браузер Chromium, необходимые для авторизации. |
| **uninstall** | Удаляет браузер Chromium, используемый для авторизации. |
| **check-proxy** | Проверяет используемые прокси |
| **migrate** | Починить базу |
| **query** | Выполнение SQL-запросов к базе. Схема БД находится в файле [schema.sql](./hh_hh_applicant_tool/storage/queries/schema.sql). Если скормить ее [DeepSeek](https://chat.deepseek.com), то он поможет написать любой запрос. |
| **log** | Просмотр файла-лога. С флагом -f будет следить за изменениями. В логах частично скрыты идентефикаторы в целях безопасности. |
Утилита использует систему плагинов. Все они лежат в [operations](https://github.com/s3rgeym/hh-applicant-tool/tree/main/src/hh_applicant_tool/operations). Модули, расположенные там, автоматически добавляются как доступные команды. За основу для своего плагина можно взять [whoami.py](https://github.com/s3rgeym/hh-applicant-tool/tree/main/src/hh_applicant_tool/operations/whoami.py).
Для тестирования запросов к API используйте команду `call-api` совместно с `jq` для обработки JSON.
Примеры поиска работодателей:
```bash
$ hh-applicant-tool call-api /employers text="IT" only_with_vacancies=true | jq -r '.items[].alternate_url'
https://hh.ru/employer/1966364
https://hh.ru/employer/4679771
...
```
Синтаксис `call-api` немного похож на `httpie` или `curlie`:
```sh
$ hh-applicant-tool call-api [-m {GET|POST|PUT|DELETE}] <endpoint> [<key=value> ...]
```
Если используется метод `GET` или `DELETE` (или ничего не указано), то параметры будут переданы как query string. Во всех остальных случаях парметры передаются как `application/x-www-form-urlencoded` в теле запроса.
Данная возможность полезна для написания Bash-скриптов.
Документация для работы с API соискателей была удалена с ха-ха.сру и его корпоративного репозитория. Можете не искать, они затерли даже историю репозитория. Но я через веб-архив выкачал документацию. Чтобы ее посмотреть, клонируйте этот репозиторий и откройте файл, например, с помощью [Swagger Viewer](https://marketplace.visualstudio.com/items?itemName=Arjun.swagger-viewer).
<img width="740" height="768" alt="image" src="https://github.com/user-attachments/assets/597fa31e-8bab-48c8-8601-ab9dfc9075b1" />
Так же существуют cli-утилиты:
```sh
npx @redocly/cli preview -d docs/hhapi
```
Потом нужно открыть в браузере [http://localhost:4000](http://localhost:4000).
> Отдельные замечания у меня к API HH. Оно пиздец какое кривое. Например, при создании заявки возвращается пустой ответ либо редирект, хотя по логике должен возвращаться созданный объект. Так же в ответах сервера нет `Content-Length`. Из-за этого нельзя узнать, есть ли тело у ответа сервера, нужно его пробовать прочитать. Я так понял, там какой-то прокси оборачивает все запросы и отдает всегда `Transfer-Encoding: Chunked`. А еще он возвращает 502 ошибку, когда бэкенд на Java падает либо долго отвечает (таймаут)? А вот [язык запросов](https://hh.ru/article/1175) мне понравился. Можно что-то вроде этого использовать `NOT (!ID:123 OR !ID:456 OR !ID:789)`, чтобы отсеить какие-то вакансии.
По сути, никакие дополнительные команды, кроме имеющихся, не нужны. Вы можете сделать что угодно с помощью `call-api`, но если хочется чего-то особенного, можно добавить свои команды.
---
## Использование AI
Для генерации опроводительных писем при откликах и ответа в чаты работодателей (`reply-employers`) можно использовать OpenAI (ChatGPT).
Пример рассылки откликов с генерированным письмом:
```sh
hh-applicant-tool apply-similar -f --ai
```
Генерацию сопроводительных писем в откликах я счи | text/markdown | Senior YAML Developer | yamldeveloper@proton.me | null | null | null | null | [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14"
] | [] | https://github.com/s3rgeym/hh-applicant-tool | null | <4.0,>=3.10 | [] | [] | [] | [
"requests[socks]<3.0.0,>=2.32.3",
"prettytable<4.0.0,>=3.6.0",
"playwright<2.0.0,>=1.57.0; extra == \"playwright\"",
"pillow<13.0.0,>=12.1.0; extra == \"pillow\""
] | [] | [] | [] | [
"Homepage, https://github.com/s3rgeym/hh-applicant-tool",
"Repository, https://github.com/s3rgeym/hh-applicant-tool"
] | poetry/2.2.1 CPython/3.11.0 Linux/6.11.0-1018-azure | 2026-01-16T04:29:59.323123 | hh_applicant_tool-1.5.3.tar.gz | 75,232 | 5d/64/2eca3c70f869a466a73ff2d09885b56ca0269ae8412331c690d5f55097bf/hh_applicant_tool-1.5.3.tar.gz | source | sdist | null | false | d59d4bdbcbe5c878b7f341ff7b02e02e | 618bd8b22b0be69613a32d000803508a4c68a9249554de3e591aa28067efbcfb | 5d642eca3c70f869a466a73ff2d09885b56ca0269ae8412331c690d5f55097bf | null | [] |
2.4 | kinemotion | 0.76.2 | Video-based kinematic analysis for athletic performance | # Kinemotion
[](https://pypi.org/project/kinemotion/)
[](https://pypi.org/project/kinemotion/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/feniix/kinemotion/actions)
[](https://sonarcloud.io/summary/overall?id=feniix_kinemotion)
[](https://sonarcloud.io/summary/overall?id=feniix_kinemotion)
[](https://www.bestpractices.dev/projects/11506)
[](https://github.com/astral-sh/ruff)
[](https://github.com/microsoft/pyright)
[](https://github.com/pre-commit/pre-commit)
> A video-based kinematic analysis tool for athletic performance. Analyzes vertical jump videos to estimate key performance metrics using MediaPipe pose tracking and advanced kinematics.
**Supported jump types:**
- **Drop Jump**: Ground contact time, flight time, reactive strength index
- **Counter Movement Jump (CMJ)**: Jump height, flight time, countermovement depth, triple extension biomechanics
- **Squat Jump (SJ)**: Pure concentric power, force production, requires athlete mass
## Features
### Core Features
- **Automatic pose tracking** using MediaPipe Pose landmarks
- **Derivative-based velocity** - smooth velocity calculation from position trajectory
- **Trajectory curvature analysis** - acceleration patterns for refined event detection
- **Sub-frame interpolation** - precise timing beyond frame boundaries
- **Intelligent auto-tuning** - automatic parameter optimization based on video characteristics
- **JSON output** for easy integration with other tools
- **Debug video overlays** with visual analysis
- **Batch processing** - CLI and Python API for parallel processing
- **Python library API** - use kinemotion programmatically
- **CSV export** - aggregated results for research
### Drop Jump Analysis
- **Ground contact detection** based on foot velocity and position
- **Automatic drop jump detection** - identifies box → drop → landing → jump phases
- **Metrics**: Ground contact time, flight time, jump height (calculated from flight time)
- **Reactive strength index** calculations
### Counter Movement Jump (CMJ) Analysis
- **Backward search algorithm** - robust phase detection from peak height
- **Flight time method** - force plate standard (h = g×t²/8)
- **Triple extension tracking** - ankle, knee, hip joint angles
- **Skeleton overlay** - biomechanical visualization
- **Metrics**: Jump height, flight time, countermovement depth, eccentric/concentric durations
- **Validated accuracy**: 50.6cm jump (±1 frame precision)
### Squat Jump (SJ) Analysis
- **Static squat start** - pure concentric power test (no countermovement)
- **Power/Force calculations** - Sayers regression (R² = 0.87, \<1% error vs force plates)
- **Mass required** - athlete body weight needed for kinetic calculations
- **Metrics**: Jump height, flight time, squat hold/concentric durations, peak/mean power, peak force
- **Phase detection**: Squat hold → concentric → flight → landing
## ⚠️ Validation Status
**Current Status:** Pre-validation (not validated against force plates or motion capture systems)
### What This Tool IS Suitable For
✅ **Training monitoring** - Track relative changes within the same athlete over time
✅ **Educational purposes** - Learn about jump biomechanics and video analysis
✅ **Exploratory analysis** - Initial investigation before formal testing
✅ **Proof-of-concept research** - Demonstrate feasibility of video-based methods
### What This Tool IS NOT Suitable For
❌ **Research publications** - As a validated measurement instrument
❌ **Clinical decision-making** - Injury assessment, return-to-play decisions
❌ **Talent identification** - Absolute performance comparisons between athletes
❌ **Legal/insurance assessments** - Any context requiring validated measurements
❌ **High-stakes testing** - Draft combines, professional athlete evaluation
### Known Limitations
- **No force plate validation** - Accuracy claims are theoretical, not empirical
- **MediaPipe constraints** - Accuracy affected by lighting, clothing, occlusion, camera quality
- **Lower sampling rate** - Typical video (30-60fps) vs validated apps (120-240Hz)
- **Indirect measurement** - Landmarks → CoM estimation introduces potential error
- **No correction factors** - Unlike validated tools (e.g., MyJump), no systematic bias corrections applied
### Recommended Use
If you need validated measurements for research or clinical use, consider:
- **Commercial validated apps**: MyJump 2, MyJumpLab (smartphone-based, force plate validated)
- **Laboratory equipment**: Force plates, optical motion capture systems
- **Validation testing**: Compare kinemotion against validated equipment in your specific use case
For detailed validation status and roadmap, see [`docs/validation-status.md`](docs/validation-status.md).
## Setup
### System Requirements
**All Platforms:**
- Python 3.10, 3.11, or 3.12
**Platform-Specific:**
#### Windows
**Required system dependencies:**
- [Microsoft Visual C++ 2022 Redistributable](https://visualstudio.microsoft.com/visual-cpp-build-tools/) - Runtime libraries for OpenCV/MediaPipe
- Python 3.10-3.12 (64-bit) - MediaPipe requires 64-bit Python
**Recommended for mobile video support:**
- [FFmpeg](https://ffmpeg.org/download.html) - Download and add to PATH for full video codec support
#### macOS
**Required system dependencies:**
- Xcode Command Line Tools - Provides compilers and system frameworks
```bash
xcode-select --install
```
**Recommended for mobile video support:**
```bash
brew install ffmpeg
```
#### Linux (Ubuntu/Debian)
**Recommended system libraries:**
```bash
sudo apt-get update
sudo apt-get install -y \
libgl1 \ # OpenGL library for OpenCV
libglib2.0-0 \ # GLib library for MediaPipe
libgomp1 \ # OpenMP library for multi-threading
ffmpeg # Video codec support and metadata extraction
```
**Note:** `ffmpeg` provides the `ffprobe` tool for video metadata extraction (rotation, aspect ratio). Kinemotion works without it, but mobile/rotated videos may not process correctly. A warning will be shown if `ffprobe` is not available.
### Installation Methods
#### From PyPI (Recommended)
```bash
pip install kinemotion
```
#### From Source (Development)
**Step 1:** Install asdf plugins (if not already installed):
```bash
asdf plugin add python
asdf plugin add uv
```
**Step 2:** Install versions specified in `.tool-versions`:
```bash
asdf install
```
**Step 3:** Install project dependencies using uv:
```bash
uv sync
```
This will install all dependencies and make the `kinemotion` command available.
## Usage
Kinemotion supports two jump types with intelligent auto-tuning that automatically optimizes parameters based on video characteristics.
### Analyzing Drop Jumps
Analyzes reactive strength and ground contact time:
```bash
# Automatic parameter tuning based on video characteristics
kinemotion dropjump-analyze video.mp4
```
### Analyzing CMJ
Analyzes jump height and biomechanics:
```bash
# No drop height needed (floor level)
kinemotion cmj-analyze video.mp4
# With triple extension visualization
kinemotion cmj-analyze video.mp4 --output debug.mp4
```
### Analyzing Squat Jump (SJ)
Analyzes pure concentric power production:
```bash
# Mass is required for power/force calculations
kinemotion sj-analyze video.mp4 --mass 75.0
# Complete analysis with all outputs
kinemotion sj-analyze video.mp4 --mass 75.0 \
--output debug.mp4 \
--json-output results.json \
--verbose
```
### Common Options (All Jump Types)
```bash
# Save metrics to JSON
kinemotion cmj-analyze video.mp4 --json-output results.json
# Generate debug video
kinemotion cmj-analyze video.mp4 --output debug.mp4
# Complete analysis with all outputs
kinemotion cmj-analyze video.mp4 \
--output debug.mp4 \
--json-output results.json \
--verbose
```
### Quality Presets
```bash
# Fast (50% faster, good for batch)
kinemotion cmj-analyze video.mp4 --quality fast
# Balanced (default)
kinemotion cmj-analyze video.mp4 --quality balanced
# Accurate (research-grade)
kinemotion cmj-analyze video.mp4 --quality accurate --verbose
```
### Batch Processing
Process multiple videos in parallel:
```bash
# Drop jumps
kinemotion dropjump-analyze videos/*.mp4 --batch --workers 4
# CMJ with output directories
kinemotion cmj-analyze videos/*.mp4 --batch --workers 4 \
--json-output-dir results/ \
--csv-summary summary.csv
```
### Quality Assessment
All analysis outputs include automatic quality assessment in the metadata section to help you know when to trust results:
```json
{
"data": {
"jump_height_m": 0.352,
"flight_time_ms": 534.2
},
"metadata": {
"quality": {
"confidence": "high",
"quality_score": 87.3,
"quality_indicators": {
"avg_visibility": 0.89,
"min_visibility": 0.82,
"tracking_stable": true,
"phase_detection_clear": true,
"outliers_detected": 2,
"outlier_percentage": 1.5,
"position_variance": 0.0008,
"fps": 60.0
},
"warnings": []
}
}
}
```
**Confidence Levels:**
- **High** (score ≥75): Trust these results, good tracking quality
- **Medium** (score 50-74): Use with caution, check quality indicators
- **Low** (score \<50): Results may be unreliable, review warnings
**Common Warnings:**
- Poor lighting or occlusion detected
- Unstable landmark tracking (jitter)
- High outlier rate (tracking glitches)
- Low frame rate (\<30fps)
- Unclear phase transitions
**Filtering by Quality:**
```python
# Only use high-confidence results
metrics = process_cmj_video("video.mp4")
if metrics.quality_assessment is not None and metrics.quality_assessment.confidence == "high":
print(f"Reliable jump height: {metrics.jump_height:.3f}m")
elif metrics.quality_assessment is not None:
print(f"Low quality - warnings: {metrics.quality_assessment.warnings}")
```
## Python API
Use kinemotion as a library for automated pipelines and custom analysis.
### Drop Jump API
```python
from kinemotion import process_dropjump_video
# Process a single video
metrics = process_dropjump_video(
video_path="athlete_jump.mp4",
quality="balanced",
verbose=True
)
# Access results
print(f"Jump height: {metrics.jump_height:.3f} m")
print(f"Ground contact time: {metrics.ground_contact_time * 1000:.1f} ms")
print(f"Flight time: {metrics.flight_time * 1000:.1f} ms")
```
### Bulk Video Processing
```python
# Drop jump bulk processing
from kinemotion import DropJumpVideoConfig, process_dropjump_videos_bulk
configs = [
DropJumpVideoConfig("video1.mp4", quality="balanced"),
DropJumpVideoConfig("video2.mp4", quality="accurate"),
]
results = process_dropjump_videos_bulk(configs, max_workers=4)
# CMJ bulk processing
from kinemotion import CMJVideoConfig, process_cmj_videos_bulk
cmj_configs = [
CMJVideoConfig("cmj1.mp4"),
CMJVideoConfig("cmj2.mp4", quality="accurate"),
]
cmj_results = process_cmj_videos_bulk(cmj_configs, max_workers=4)
for result in cmj_results:
if result.success:
print(f"{result.video_path}: {result.metrics.jump_height*100:.1f}cm")
```
See `examples/bulk/README.md` for comprehensive API documentation.
### CMJ-Specific Features
```python
# Triple extension angles available in metrics
metrics = process_cmj_video("video.mp4", output_video="debug.mp4")
# Debug video shows:
# - Skeleton overlay (foot→shin→femur→trunk)
# - Joint angles (ankle, knee, hip, trunk)
# - Phase-coded visualization
```
### Squat Jump (SJ) API
```python
from kinemotion import process_sj_video
# Mass is required for power/force calculations
metrics = process_sj_video(
video_path="athlete_sj.mp4",
mass_kg=75.0, # Required: athlete body mass
quality="balanced",
verbose=True
)
# Access results
print(f"Jump height: {metrics.jump_height:.3f}m")
print(f"Squat hold: {metrics.squat_hold_duration*1000:.1f}ms")
print(f"Concentric: {metrics.concentric_duration*1000:.1f}ms")
# Power/force (only available if mass provided)
if metrics.peak_power:
print(f"Peak power: {metrics.peak_power:.0f}W")
print(f"Mean power: {metrics.mean_power:.0f}W")
print(f"Peak force: {metrics.peak_force:.0f}N")
```
### CSV Export Example
```python
# See examples/bulk/ for complete CSV export examples
from kinemotion import process_cmj_video
import csv
# ... process videos ...
with open("results.csv", "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(["Video", "GCT (ms)", "Flight (ms)", "Jump (m)"])
for r in results:
if r.success and r.metrics:
writer.writerow([
Path(r.video_path).name,
f"{r.metrics.ground_contact_time * 1000:.1f}" if r.metrics.ground_contact_time else "N/A",
f"{r.metrics.flight_time * 1000:.1f}" if r.metrics.flight_time else "N/A",
f"{r.metrics.jump_height:.3f}" if r.metrics.jump_height else "N/A",
])
```
**See [examples/bulk/README.md](examples/bulk/README.md) for comprehensive API documentation and more examples.**
## Configuration Options
### Intelligent Auto-Tuning
Kinemotion automatically optimizes parameters based on your video:
- **FPS-based scaling**: 30fps, 60fps, 120fps videos use different thresholds automatically
- **Quality-based adjustments**: Adapts smoothing based on MediaPipe tracking confidence
- **Always enabled**: Outlier rejection, curvature analysis, drop start detection
### Parameters
All parameters are optional. Kinemotion uses intelligent auto-tuning to select optimal settings based on video characteristics.
- `--quality [fast|balanced|accurate]` (default: balanced)
- **fast**: Quick analysis, less precise (~50% faster)
- **balanced**: Good accuracy/speed tradeoff (recommended)
- **accurate**: Research-grade analysis, slower (maximum precision)
- `--verbose` / `-v`
- Show auto-selected parameters and analysis details
- Useful for understanding what the tool is doing
- `--output <path>` / `-o`
- Generate annotated debug video with pose tracking visualization
- `--json-output <path>` / `-j`
- Save metrics to JSON file instead of stdout
### Expert Overrides (Rarely Needed)
For advanced users who need manual control:
- `--drop-start-frame <int>`: Manually specify where drop begins (if auto-detection fails)
- `--smoothing-window <int>`: Override auto-tuned smoothing window
- `--velocity-threshold <float>`: Override auto-tuned velocity threshold
- `--min-contact-frames <int>`: Override auto-tuned minimum contact frames
- `--visibility-threshold <float>`: Override visibility threshold
- `--detection-confidence <float>`: Override MediaPipe detection confidence
- `--tracking-confidence <float>`: Override MediaPipe tracking confidence
> **📖 For detailed parameter explanations, see [docs/reference/parameters.md](docs/reference/parameters.md)**
>
> **Note:** Most users never need expert parameters - auto-tuning handles optimization automatically!
## Output Format
### Drop Jump JSON Output
```json
{
"data": {
"ground_contact_time_ms": 245.67,
"flight_time_ms": 456.78,
"jump_height_m": 0.339,
"jump_height_kinematic_m": 0.339,
"jump_height_trajectory_normalized": 0.0845,
"contact_start_frame": 45,
"contact_end_frame": 67,
"flight_start_frame": 68,
"flight_end_frame": 95,
"peak_height_frame": 82,
"contact_start_frame_precise": 45.234,
"contact_end_frame_precise": 67.891,
"flight_start_frame_precise": 68.123,
"flight_end_frame_precise": 94.567
},
"metadata": {
"quality": { },
"processing_info": { }
}
}
```
**Data Fields**:
- `ground_contact_time_ms`: Duration of ground contact phase in milliseconds
- `flight_time_ms`: Duration of flight phase in milliseconds
- `jump_height_m`: Jump height calculated from flight time: h = g × t² / 8
- `jump_height_kinematic_m`: Kinematic estimate (same as `jump_height_m`)
- `jump_height_trajectory_normalized`: Position-based measurement in normalized coordinates (0-1 range)
- `contact_start_frame`: Frame index where contact begins (integer, for visualization)
- `contact_end_frame`: Frame index where contact ends (integer, for visualization)
- `flight_start_frame`: Frame index where flight begins (integer, for visualization)
- `flight_end_frame`: Frame index where flight ends (integer, for visualization)
- `peak_height_frame`: Frame index at maximum jump height (integer, for visualization)
- `contact_start_frame_precise`: Sub-frame precise timing for contact start (fractional, for calculations)
- `contact_end_frame_precise`: Sub-frame precise timing for contact end (fractional, for calculations)
- `flight_start_frame_precise`: Sub-frame precise timing for flight start (fractional, for calculations)
- `flight_end_frame_precise`: Sub-frame precise timing for flight end (fractional, for calculations)
**Note**: Integer frame indices are provided for visualization in debug videos. Precise fractional frames are used for all timing calculations and provide sub-frame accuracy (±10ms at 30fps).
### CMJ JSON Output
```json
{
"data": {
"jump_height_m": 0.352,
"flight_time_ms": 534.2,
"countermovement_depth_m": 0.285,
"eccentric_duration_ms": 612.5,
"concentric_duration_ms": 321.8,
"total_movement_time_ms": 934.3,
"peak_eccentric_velocity_m_s": -2.145,
"peak_concentric_velocity_m_s": 3.789,
"transition_time_ms": 125.4,
"standing_start_frame": 12.5,
"lowest_point_frame": 45.2,
"takeoff_frame": 67.8,
"landing_frame": 102.3,
"tracking_method": "foot"
},
"metadata": {
"quality": { },
"processing_info": { }
}
}
```
**Data Fields**:
- `jump_height_m`: Jump height calculated from flight time: h = g × t² / 8
- `flight_time_ms`: Duration of flight phase in milliseconds
- `countermovement_depth_m`: Maximum downward displacement during eccentric (descent) phase
- `eccentric_duration_ms`: Time from start of countermovement to lowest point
- `concentric_duration_ms`: Time from lowest point to takeoff
- `total_movement_time_ms`: Total time from countermovement start to takeoff
- `peak_eccentric_velocity_m_s`: Maximum downward velocity during descent (negative value)
- `peak_concentric_velocity_m_s`: Maximum upward velocity during propulsion (positive value)
- `transition_time_ms`: Duration at lowest point (amortization phase between descent and propulsion)
- `standing_start_frame`: Frame where standing phase ends and countermovement begins
- `lowest_point_frame`: Frame at the lowest point of the countermovement
- `takeoff_frame`: Frame where athlete leaves ground
- `landing_frame`: Frame where athlete lands after jump
- `tracking_method`: Tracking method used - "foot" (foot landmarks) or "com" (center of mass estimation)
### Debug Video
The debug video includes:
- **Green circle**: Average foot position when on ground
- **Red circle**: Average foot position when in air
- **Yellow circles**: Individual foot landmarks (ankles, heels)
- **State indicator**: Current contact state (on_ground/in_air)
- **Phase labels**: "GROUND CONTACT" and "FLIGHT PHASE" during relevant periods
- **Peak marker**: "PEAK HEIGHT" at maximum jump height
- **Frame number**: Current frame index
## Troubleshooting
### Poor Tracking Quality
**Symptoms**: Erratic landmark positions, missing detections, incorrect contact states
**Solutions**:
1. **Check video quality**: Ensure the athlete is clearly visible in profile view
2. **Increase smoothing**: Use `--smoothing-window 7` or higher
3. **Adjust detection confidence**: Try `--detection-confidence 0.6` or `--tracking-confidence 0.6`
4. **Generate debug video**: Use `--output` to visualize what's being tracked
### No Pose Detected
**Symptoms**: "No frames processed" error or all null landmarks
**Solutions**:
1. **Verify video format**: OpenCV must be able to read the video
2. **Check framing**: Ensure full body is visible in side view
3. **Lower confidence thresholds**: Try `--detection-confidence 0.3 --tracking-confidence 0.3`
4. **Test video playback**: Verify video opens correctly with standard video players
### Incorrect Contact Detection
**Symptoms**: Wrong ground contact times, flight phases not detected
**Solutions**:
1. **Generate debug video**: Visualize contact states to diagnose the issue
2. **Adjust velocity threshold**:
- If missing contacts: decrease to `--velocity-threshold 0.01`
- If false contacts: increase to `--velocity-threshold 0.03`
3. **Adjust minimum frames**: `--min-contact-frames 5` for longer required contact
4. **Check visibility**: Lower `--visibility-threshold 0.3` if feet are partially obscured
### Jump Height Seems Wrong
**Symptoms**: Unrealistic jump height values
**Solutions**:
1. **Check video quality**: Ensure video frame rate is adequate (30fps or higher recommended)
2. **Verify flight time detection**: Check `flight_start_frame` and `flight_end_frame` in JSON
3. **Compare measurements**: JSON output includes both `jump_height_m` (primary) and `jump_height_kinematic_m` (kinematic-only)
4. **Check for drop jump detection**: If doing a drop jump, ensure first phase is elevated enough (>5% of frame height)
### Video Codec Issues
**Symptoms**: Cannot write debug video or corrupted output
**Solutions**:
1. **Install additional codecs**: Ensure OpenCV has proper video codec support
2. **Try different output format**: Use `.avi` extension instead of `.mp4`
3. **Check output path**: Ensure write permissions for output directory
## How It Works
1. **Pose Tracking**: MediaPipe extracts 2D pose landmarks (foot points: ankles, heels, foot indices) from each frame
2. **Position Calculation**: Averages ankle, heel, and foot index positions to determine foot location
3. **Smoothing**: Savitzky-Golay filter reduces tracking jitter while preserving motion dynamics
4. **Contact Detection**: Analyzes vertical position velocity to identify ground contact vs. flight phases
5. **Phase Identification**: Finds continuous ground contact and flight periods
- Automatically detects drop jumps vs regular jumps
- For drop jumps: identifies box → drop → ground contact → jump sequence
6. **Sub-Frame Interpolation**: Estimates exact transition times between frames
- Uses Savitzky-Golay derivative for smooth velocity calculation
- Linear interpolation of velocity to find threshold crossings
- Achieves sub-millisecond timing precision (at 30fps: ±10ms vs ±33ms)
- Reduces timing error by 60-70% for contact and flight measurements
- Smoother velocity curves eliminate false threshold crossings
7. **Trajectory Curvature Analysis**: Refines transitions using acceleration patterns
- Computes second derivative (acceleration) from position trajectory
- Detects landing impact by acceleration spike
- Identifies takeoff by acceleration change patterns
- Provides independent validation and refinement of velocity-based detection
8. **Metric Calculation**:
- Ground contact time = contact phase duration (using fractional frames)
- Flight time = flight phase duration (using fractional frames)
- Jump height = kinematic estimate from flight time: (g × t²) / 8
## Development
### Code Quality Standards
This project enforces strict code quality standards:
- **Type safety**: Full pyright strict mode compliance with complete type annotations
- **Linting**: Comprehensive ruff checks (pycodestyle, pyflakes, isort, pep8-naming, etc.)
- **Formatting**: Black code style
- **Testing**: pytest with 261 comprehensive tests (74.27% coverage)
- **PEP 561 compliant**: Includes py.typed marker for type checking support
### Development Commands
```bash
# Run the tool
uv run kinemotion dropjump-analyze <video_path>
# Run all tests
uv run pytest
# Run tests with verbose output
uv run pytest -v
# Format code
uv run black src/
# Lint code
uv run ruff check
# Auto-fix linting issues
uv run ruff check --fix
# Type check
uv run pyright
# Run all checks
uv run ruff check && uv run pyright && uv run pytest
```
### Contributing
Before committing code, ensure all checks pass:
1. Format with Black
2. Fix linting issues with ruff
3. Ensure type safety with pyright
4. Run all tests with pytest
See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines and requirements, or [CLAUDE.md](CLAUDE.md) for detailed development guidelines.
## Limitations
- **2D Analysis**: Only analyzes motion in the camera's view plane
- **Validation Status**: ⚠️ Accuracy has not been validated against gold standard measurements (force plates, 3D motion capture)
- **Side View Required**: Must film from the side to accurately track vertical motion
- **Single Athlete**: Designed for analyzing one athlete at a time
- **Timing precision**:
- 30fps videos: ±10ms with sub-frame interpolation (vs ±33ms without)
- 60fps videos: ±5ms with sub-frame interpolation (vs ±17ms without)
- Higher frame rates still beneficial for better temporal resolution
- **Drop jump detection**: Requires first ground phase to be >5% higher than second ground phase
## Future Enhancements
- Advanced camera calibration (intrinsic parameters, lens distortion)
- Multi-angle analysis support
- Automatic camera orientation detection
- Real-time analysis from webcam
- Comparison with reference values
- Force plate integration for validation
## License
MIT License - feel free to use for personal experiments and research.
| text/markdown | null | Sebastian Otaegui <feniix@gmail.com> | null | null | MIT | athletic-performance, drop-jump, kinemetry, kinemotion, mediapipe, pose-tracking, video-analysis | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Multimedi... | [] | null | null | <3.13,>=3.10 | [] | [] | [] | [
"click>=8.1.7",
"mediapipe>=0.10.30",
"numpy>=1.26.0",
"opencv-python>=4.9.0",
"platformdirs>=4.0.0",
"scipy>=1.11.0",
"tqdm>=4.67.1",
"typing-extensions>=4.15.0"
] | [] | [] | [] | [
"Homepage, https://github.com/feniix/kinemotion",
"Repository, https://github.com/feniix/kinemotion",
"Source, https://github.com/feniix/kinemotion",
"Issues, https://github.com/feniix/kinemotion/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:30:15.558309 | kinemotion-0.76.2-py3-none-any.whl | 5,107,595 | 75/dc/0c0354438c3bf9bcf88446c9320aadfffc6b6d2be86a296c6a83fac238b4/kinemotion-0.76.2-py3-none-any.whl | py3 | bdist_wheel | null | false | f650dd09a7be0485eedbcf59e1c84148 | 16ab7f6a6d172b0147cc0d619d16da6ff3f270854e1ecd2f5fbc58933bb49db8 | 75dc0c0354438c3bf9bcf88446c9320aadfffc6b6d2be86a296c6a83fac238b4 | null | [
"LICENSE"
] |
2.1 | odoo-addons-oca-web | 18.0.20260115.0 | null | null | null | null | null | null | null | null | null | [
"Programming Language :: Python",
"Framework :: Odoo",
"Framework :: Odoo :: 18.0"
] | [] | null | null | null | [] | [] | [] | [
"odoo-addon-web-calendar-slot-duration==18.0.*",
"odoo-addon-web-chatter-position==18.0.*",
"odoo-addon-web-company-color==18.0.*",
"odoo-addon-web-copy-confirm==18.0.*",
"odoo-addon-web-dark-mode==18.0.*",
"odoo-addon-web-datetime-picker-default-time==18.0.*",
"odoo-addon-web-dialog-size==18.0.*",
"o... | [] | [] | [] | [] | twine/6.2.0 CPython/3.12.3 | 2026-01-16T04:30:15.690914 | odoo_addons_oca_web-18.0.20260115.0-py3-none-any.whl | 1,807 | 0e/b1/e86b2ee865c7d5a48d0cb6e53b3bba535a6a11917795183bd595647cbaff/odoo_addons_oca_web-18.0.20260115.0-py3-none-any.whl | py3 | bdist_wheel | null | false | eed879fbaa9a4703fc67727041836457 | 2a5084ad984191fb78934980c90b79a5deb5ed39193e51f0af4243d5daea169e | 0eb1e86b2ee865c7d5a48d0cb6e53b3bba535a6a11917795183bd595647cbaff | null | [] |
2.4 | kis-wrapper | 0.2.0 | Korea Investment & Securities API wrapper for Python | # KIS Wrapper
한국투자증권 Open API Python SDK
## 특징
- 간결한 함수 기반 API
- 국내/해외주식 지원
- 실시간 WebSocket
- 모의투자/실전 환경 전환
- 자동 토큰 관리
## 설치
```bash
pip install kis
```
또는 개발 환경:
```bash
git clone https://github.com/your-repo/kis-wrapper
cd kis-wrapper
uv sync
```
## 빠른 시작
### 환경 설정
```bash
# .env
KIS_APP_KEY=your_app_key
KIS_APP_SECRET=your_app_secret
KIS_ACCOUNT=12345678-01
```
### 기본 사용
```python
import os
from kis import KIS, domestic
kis = KIS(
app_key=os.environ["KIS_APP_KEY"],
app_secret=os.environ["KIS_APP_SECRET"],
account=os.environ["KIS_ACCOUNT"],
env="paper", # 모의투자
)
# 삼성전자 현재가
p = domestic.price(kis, "005930")
print(f"현재가: {p['stck_prpr']}원")
# 호가
ob = domestic.orderbook(kis, "005930")
# 일봉 (최근 30일)
candles = domestic.daily(kis, "005930")
```
### 주문
```python
# 매수 (지정가)
order = domestic.buy(kis, "005930", qty=10, price=70000)
print(f"주문번호: {order['ODNO']}")
# 매수 (시장가)
order = domestic.buy(kis, "005930", qty=10)
# 매도
order = domestic.sell(kis, "005930", qty=5, price=72000)
# 주문 취소
domestic.cancel(kis, order_no="0001234567", qty=5)
# 주문 정정
domestic.modify(kis, order_no="0001234567", qty=10, price=71000)
```
### 계좌 조회
```python
# 잔고 (예수금 + 보유종목)
bal = domestic.balance(kis)
# 보유종목만
positions = domestic.positions(kis)
for p in positions:
print(f"{p['prdt_name']}: {p['hldg_qty']}주")
# 특정 종목 포지션
pos = domestic.position(kis, "005930")
if pos:
print(f"수익률: {pos['profit_rate']:.2f}%")
# 미체결 주문
pending = domestic.pending_orders(kis)
```
### 해외주식
```python
from kis import overseas
# 애플 현재가
p = overseas.price(kis, "AAPL", "NAS")
print(f"AAPL: ${p['last']}")
# 매수 (지정가)
order = overseas.buy(kis, "AAPL", "NAS", qty=1, price=150.00)
# 매수 (시장가 - 미국만 지원)
order = overseas.buy(kis, "AAPL", "NAS", qty=1)
# 잔고 조회
bal = overseas.balance(kis) # 전체
bal = overseas.balance(kis, "NAS") # 나스닥만
# 환율
rate = overseas.exchange_rate(kis)
```
#### 거래소 코드
| 코드 | 거래소 |
|------|--------|
| NYS | 뉴욕 (NYSE) |
| NAS | 나스닥 (NASDAQ) |
| AMS | 아멕스 (AMEX) |
| HKS | 홍콩 |
| SHS | 상해 |
| SZS | 심천 |
| TSE | 도쿄 |
| HNX | 하노이 |
| HSX | 호치민 |
### 실시간 데이터 (WebSocket)
```python
import asyncio
from kis import KIS, WSClient
async def main():
kis = KIS(app_key, app_secret, account, env="paper")
ws = WSClient(kis)
async def on_price(data):
print(f"{data['symbol']}: {data['price']:,}원 (거래량: {data['volume']})")
await ws.subscribe("H0STCNT0", ["005930", "000660"], on_price)
try:
await ws.run()
except KeyboardInterrupt:
await ws.close()
asyncio.run(main())
```
#### TR ID 목록
| TR ID | 설명 |
|-------|------|
| H0STCNT0 | 국내주식 실시간체결 |
| H0STASP0 | 국내주식 실시간호가 |
| H0STCNI0 | 체결통보 |
| HDFSCNT0 | 해외주식 실시간체결 |
### 환경 전환
```python
# 모의투자 -> 실전
kis_prod = kis.switch("prod")
# 또는 처음부터 실전
kis = KIS(app_key, app_secret, account, env="prod")
```
### 계산 유틸리티
```python
from kis import calc
# 수익률
rate = calc.profit_rate(buy_price=70000, current_price=75000)
print(f"수익률: {float(rate) * 100:.2f}%")
# 수익금
profit = calc.profit_amount(70000, 75000, qty=10)
# 평균단가
orders = [{"price": 70000, "qty": 10}, {"price": 72000, "qty": 5}]
avg = calc.avg_price(orders)
```
### 스냅샷
```python
from kis import snapshot
# 현재 상태 저장
data = snapshot.snapshot(kis, "005930")
snapshot.save(data, "snapshots/005930.json")
# 로드 및 검증
loaded = snapshot.load("snapshots/005930.json")
assert snapshot.verify(loaded)
```
## API 레퍼런스
### KIS 클래스
```python
KIS(app_key: str, app_secret: str, account: str, env: Env = "paper")
```
| 속성/메서드 | 설명 |
|-------------|------|
| `is_paper` | 모의투자 여부 |
| `switch(env)` | 환경 전환 |
| `close()` | 연결 종료 |
### domestic 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol)` | 현재가 조회 |
| `orderbook(kis, symbol)` | 호가 조회 |
| `daily(kis, symbol, period="D")` | 일/주/월봉 |
| `buy(kis, symbol, qty, price=None)` | 매수 |
| `sell(kis, symbol, qty, price=None)` | 매도 |
| `cancel(kis, order_no, qty)` | 취소 |
| `modify(kis, order_no, qty, price)` | 정정 |
| `balance(kis)` | 잔고 조회 |
| `positions(kis)` | 보유종목 |
| `orders(kis, start_date, end_date)` | 주문내역 |
| `pending_orders(kis)` | 미체결 |
| `position(kis, symbol)` | 종목별 포지션 |
| `sell_all(kis, symbol)` | 전량 매도 |
| `cancel_remaining(kis, order_no)` | 미체결 전량 취소 |
### overseas 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol, exchange)` | 현재가 조회 |
| `daily(kis, symbol, exchange, period="D")` | 기간별 시세 |
| `buy(kis, symbol, exchange, qty, price=None)` | 매수 |
| `sell(kis, symbol, exchange, qty, price=None)` | 매도 |
| `cancel(kis, exchange, order_no, qty)` | 취소 |
| `balance(kis, exchange=None)` | 잔고 조회 |
| `exchange_rate(kis)` | 환율 조회 |
### WSClient 클래스
```python
WSClient(kis: KIS, max_retries: int = 5, retry_delay: float = 1.0)
```
| 메서드 | 설명 |
|--------|------|
| `connect()` | WebSocket 연결 |
| `subscribe(tr_id, symbols, callback)` | 구독 |
| `unsubscribe(tr_id, symbols)` | 구독 해제 |
| `run()` | 메시지 수신 루프 |
| `close()` | 연결 종료 |
## 개발
```bash
# 테스트
uv run pytest
# 커버리지
uv run pytest --cov=kis
# 린트
uv run ruff check kis/
# 포맷
uv run ruff format kis/
```
## 라이선스
MIT License
| text/markdown | null | null | null | null | null | api, kis, korea-investment, stock, trading | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Office/Business :: Financial :: Investment"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"httpx",
"pycryptodome",
"websockets",
"mypy; extra == \"dev\"",
"pytest; extra == \"dev\"",
"pytest-asyncio; extra == \"dev\"",
"pytest-httpx; extra == \"dev\"",
"ruff; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/LaytonAI/kis-wrapper",
"Documentation, https://github.com/LaytonAI/kis-wrapper#readme",
"Issues, https://github.com/LaytonAI/kis-wrapper/issues"
] | uv/0.7.5 | 2026-01-16T04:30:43.219973 | kis_wrapper-0.2.0-py3-none-any.whl | 16,084 | 44/bd/1d5c48ad39290e10e640efb2a8d386e4f7de90fc2b049cfafaccc8a3584e/kis_wrapper-0.2.0-py3-none-any.whl | py3 | bdist_wheel | null | false | f6abd29e11c23bace67dcb9f8d03c90b | 1bc26b54aa839f312251ffa17f2f71f338d499897cd3e8d607b16308ab2b145f | 44bd1d5c48ad39290e10e640efb2a8d386e4f7de90fc2b049cfafaccc8a3584e | MIT | [
"LICENSE"
] |
2.4 | kis-wrapper | 0.2.0 | Korea Investment & Securities API wrapper for Python | # KIS Wrapper
한국투자증권 Open API Python SDK
## 특징
- 간결한 함수 기반 API
- 국내/해외주식 지원
- 실시간 WebSocket
- 모의투자/실전 환경 전환
- 자동 토큰 관리
## 설치
```bash
pip install kis
```
또는 개발 환경:
```bash
git clone https://github.com/your-repo/kis-wrapper
cd kis-wrapper
uv sync
```
## 빠른 시작
### 환경 설정
```bash
# .env
KIS_APP_KEY=your_app_key
KIS_APP_SECRET=your_app_secret
KIS_ACCOUNT=12345678-01
```
### 기본 사용
```python
import os
from kis import KIS, domestic
kis = KIS(
app_key=os.environ["KIS_APP_KEY"],
app_secret=os.environ["KIS_APP_SECRET"],
account=os.environ["KIS_ACCOUNT"],
env="paper", # 모의투자
)
# 삼성전자 현재가
p = domestic.price(kis, "005930")
print(f"현재가: {p['stck_prpr']}원")
# 호가
ob = domestic.orderbook(kis, "005930")
# 일봉 (최근 30일)
candles = domestic.daily(kis, "005930")
```
### 주문
```python
# 매수 (지정가)
order = domestic.buy(kis, "005930", qty=10, price=70000)
print(f"주문번호: {order['ODNO']}")
# 매수 (시장가)
order = domestic.buy(kis, "005930", qty=10)
# 매도
order = domestic.sell(kis, "005930", qty=5, price=72000)
# 주문 취소
domestic.cancel(kis, order_no="0001234567", qty=5)
# 주문 정정
domestic.modify(kis, order_no="0001234567", qty=10, price=71000)
```
### 계좌 조회
```python
# 잔고 (예수금 + 보유종목)
bal = domestic.balance(kis)
# 보유종목만
positions = domestic.positions(kis)
for p in positions:
print(f"{p['prdt_name']}: {p['hldg_qty']}주")
# 특정 종목 포지션
pos = domestic.position(kis, "005930")
if pos:
print(f"수익률: {pos['profit_rate']:.2f}%")
# 미체결 주문
pending = domestic.pending_orders(kis)
```
### 해외주식
```python
from kis import overseas
# 애플 현재가
p = overseas.price(kis, "AAPL", "NAS")
print(f"AAPL: ${p['last']}")
# 매수 (지정가)
order = overseas.buy(kis, "AAPL", "NAS", qty=1, price=150.00)
# 매수 (시장가 - 미국만 지원)
order = overseas.buy(kis, "AAPL", "NAS", qty=1)
# 잔고 조회
bal = overseas.balance(kis) # 전체
bal = overseas.balance(kis, "NAS") # 나스닥만
# 환율
rate = overseas.exchange_rate(kis)
```
#### 거래소 코드
| 코드 | 거래소 |
|------|--------|
| NYS | 뉴욕 (NYSE) |
| NAS | 나스닥 (NASDAQ) |
| AMS | 아멕스 (AMEX) |
| HKS | 홍콩 |
| SHS | 상해 |
| SZS | 심천 |
| TSE | 도쿄 |
| HNX | 하노이 |
| HSX | 호치민 |
### 실시간 데이터 (WebSocket)
```python
import asyncio
from kis import KIS, WSClient
async def main():
kis = KIS(app_key, app_secret, account, env="paper")
ws = WSClient(kis)
async def on_price(data):
print(f"{data['symbol']}: {data['price']:,}원 (거래량: {data['volume']})")
await ws.subscribe("H0STCNT0", ["005930", "000660"], on_price)
try:
await ws.run()
except KeyboardInterrupt:
await ws.close()
asyncio.run(main())
```
#### TR ID 목록
| TR ID | 설명 |
|-------|------|
| H0STCNT0 | 국내주식 실시간체결 |
| H0STASP0 | 국내주식 실시간호가 |
| H0STCNI0 | 체결통보 |
| HDFSCNT0 | 해외주식 실시간체결 |
### 환경 전환
```python
# 모의투자 -> 실전
kis_prod = kis.switch("prod")
# 또는 처음부터 실전
kis = KIS(app_key, app_secret, account, env="prod")
```
### 계산 유틸리티
```python
from kis import calc
# 수익률
rate = calc.profit_rate(buy_price=70000, current_price=75000)
print(f"수익률: {float(rate) * 100:.2f}%")
# 수익금
profit = calc.profit_amount(70000, 75000, qty=10)
# 평균단가
orders = [{"price": 70000, "qty": 10}, {"price": 72000, "qty": 5}]
avg = calc.avg_price(orders)
```
### 스냅샷
```python
from kis import snapshot
# 현재 상태 저장
data = snapshot.snapshot(kis, "005930")
snapshot.save(data, "snapshots/005930.json")
# 로드 및 검증
loaded = snapshot.load("snapshots/005930.json")
assert snapshot.verify(loaded)
```
## API 레퍼런스
### KIS 클래스
```python
KIS(app_key: str, app_secret: str, account: str, env: Env = "paper")
```
| 속성/메서드 | 설명 |
|-------------|------|
| `is_paper` | 모의투자 여부 |
| `switch(env)` | 환경 전환 |
| `close()` | 연결 종료 |
### domestic 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol)` | 현재가 조회 |
| `orderbook(kis, symbol)` | 호가 조회 |
| `daily(kis, symbol, period="D")` | 일/주/월봉 |
| `buy(kis, symbol, qty, price=None)` | 매수 |
| `sell(kis, symbol, qty, price=None)` | 매도 |
| `cancel(kis, order_no, qty)` | 취소 |
| `modify(kis, order_no, qty, price)` | 정정 |
| `balance(kis)` | 잔고 조회 |
| `positions(kis)` | 보유종목 |
| `orders(kis, start_date, end_date)` | 주문내역 |
| `pending_orders(kis)` | 미체결 |
| `position(kis, symbol)` | 종목별 포지션 |
| `sell_all(kis, symbol)` | 전량 매도 |
| `cancel_remaining(kis, order_no)` | 미체결 전량 취소 |
### overseas 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol, exchange)` | 현재가 조회 |
| `daily(kis, symbol, exchange, period="D")` | 기간별 시세 |
| `buy(kis, symbol, exchange, qty, price=None)` | 매수 |
| `sell(kis, symbol, exchange, qty, price=None)` | 매도 |
| `cancel(kis, exchange, order_no, qty)` | 취소 |
| `balance(kis, exchange=None)` | 잔고 조회 |
| `exchange_rate(kis)` | 환율 조회 |
### WSClient 클래스
```python
WSClient(kis: KIS, max_retries: int = 5, retry_delay: float = 1.0)
```
| 메서드 | 설명 |
|--------|------|
| `connect()` | WebSocket 연결 |
| `subscribe(tr_id, symbols, callback)` | 구독 |
| `unsubscribe(tr_id, symbols)` | 구독 해제 |
| `run()` | 메시지 수신 루프 |
| `close()` | 연결 종료 |
## 개발
```bash
# 테스트
uv run pytest
# 커버리지
uv run pytest --cov=kis
# 린트
uv run ruff check kis/
# 포맷
uv run ruff format kis/
```
## 라이선스
MIT License
| text/markdown | null | null | null | null | null | api, kis, korea-investment, stock, trading | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Office/Business :: Financial :: Investment"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"httpx",
"pycryptodome",
"websockets",
"mypy; extra == \"dev\"",
"pytest; extra == \"dev\"",
"pytest-asyncio; extra == \"dev\"",
"pytest-httpx; extra == \"dev\"",
"ruff; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/LaytonAI/kis-wrapper",
"Documentation, https://github.com/LaytonAI/kis-wrapper#readme",
"Issues, https://github.com/LaytonAI/kis-wrapper/issues"
] | uv/0.7.5 | 2026-01-16T04:30:44.667904 | kis_wrapper-0.2.0.tar.gz | 64,788 | 3b/fc/053faec283092fc713573df45e4cce9fcc8638160477738a1bdd0402c5ab/kis_wrapper-0.2.0.tar.gz | source | sdist | null | false | 6af58ef605149c6f565aac8e37eef4c5 | 0f0ee6e7b60fc4d9a0786f40ecd75281c5752db4e5d16d2d285e7236badbdd5c | 3bfc053faec283092fc713573df45e4cce9fcc8638160477738a1bdd0402c5ab | MIT | [
"LICENSE"
] |
2.4 | kis-wrapper | 0.3.0 | Korea Investment & Securities API wrapper for Python | # KIS Wrapper
한국투자증권 Open API Python SDK
## 특징
- 간결한 함수 기반 API
- 국내/해외주식 지원
- 실시간 WebSocket
- 모의투자/실전 환경 전환
- 자동 토큰 관리
## 설치
```bash
pip install kis
```
또는 개발 환경:
```bash
git clone https://github.com/your-repo/kis-wrapper
cd kis-wrapper
uv sync
```
## 빠른 시작
### 환경 설정
```bash
# .env
KIS_APP_KEY=your_app_key
KIS_APP_SECRET=your_app_secret
KIS_ACCOUNT=12345678-01
```
### 기본 사용
```python
import os
from kis import KIS, domestic
kis = KIS(
app_key=os.environ["KIS_APP_KEY"],
app_secret=os.environ["KIS_APP_SECRET"],
account=os.environ["KIS_ACCOUNT"],
env="paper", # 모의투자
)
# 삼성전자 현재가
p = domestic.price(kis, "005930")
print(f"현재가: {p['stck_prpr']}원")
# 호가
ob = domestic.orderbook(kis, "005930")
# 일봉 (최근 30일)
candles = domestic.daily(kis, "005930")
```
### 주문
```python
# 매수 (지정가)
order = domestic.buy(kis, "005930", qty=10, price=70000)
print(f"주문번호: {order['ODNO']}")
# 매수 (시장가)
order = domestic.buy(kis, "005930", qty=10)
# 매도
order = domestic.sell(kis, "005930", qty=5, price=72000)
# 주문 취소
domestic.cancel(kis, order_no="0001234567", qty=5)
# 주문 정정
domestic.modify(kis, order_no="0001234567", qty=10, price=71000)
```
### 계좌 조회
```python
# 잔고 (예수금 + 보유종목)
bal = domestic.balance(kis)
# 보유종목만
positions = domestic.positions(kis)
for p in positions:
print(f"{p['prdt_name']}: {p['hldg_qty']}주")
# 특정 종목 포지션
pos = domestic.position(kis, "005930")
if pos:
print(f"수익률: {pos['profit_rate']:.2f}%")
# 미체결 주문
pending = domestic.pending_orders(kis)
```
### 해외주식
```python
from kis import overseas
# 애플 현재가
p = overseas.price(kis, "AAPL", "NAS")
print(f"AAPL: ${p['last']}")
# 매수 (지정가)
order = overseas.buy(kis, "AAPL", "NAS", qty=1, price=150.00)
# 매수 (시장가 - 미국만 지원)
order = overseas.buy(kis, "AAPL", "NAS", qty=1)
# 잔고 조회
bal = overseas.balance(kis) # 전체
bal = overseas.balance(kis, "NAS") # 나스닥만
# 환율
rate = overseas.exchange_rate(kis)
```
#### 거래소 코드
| 코드 | 거래소 |
|------|--------|
| NYS | 뉴욕 (NYSE) |
| NAS | 나스닥 (NASDAQ) |
| AMS | 아멕스 (AMEX) |
| HKS | 홍콩 |
| SHS | 상해 |
| SZS | 심천 |
| TSE | 도쿄 |
| HNX | 하노이 |
| HSX | 호치민 |
### 실시간 데이터 (WebSocket)
```python
import asyncio
from kis import KIS, WSClient
async def main():
kis = KIS(app_key, app_secret, account, env="paper")
ws = WSClient(kis)
async def on_price(data):
print(f"{data['symbol']}: {data['price']:,}원 (거래량: {data['volume']})")
await ws.subscribe("H0STCNT0", ["005930", "000660"], on_price)
try:
await ws.run()
except KeyboardInterrupt:
await ws.close()
asyncio.run(main())
```
#### TR ID 목록
| TR ID | 설명 |
|-------|------|
| H0STCNT0 | 국내주식 실시간체결 |
| H0STASP0 | 국내주식 실시간호가 |
| H0STCNI0 | 체결통보 |
| HDFSCNT0 | 해외주식 실시간체결 |
### 환경 전환
```python
# 모의투자 -> 실전
kis_prod = kis.switch("prod")
# 또는 처음부터 실전
kis = KIS(app_key, app_secret, account, env="prod")
```
### 계산 유틸리티
```python
from kis import calc
# 수익률
rate = calc.profit_rate(buy_price=70000, current_price=75000)
print(f"수익률: {float(rate) * 100:.2f}%")
# 수익금
profit = calc.profit_amount(70000, 75000, qty=10)
# 평균단가
orders = [{"price": 70000, "qty": 10}, {"price": 72000, "qty": 5}]
avg = calc.avg_price(orders)
```
### 스냅샷
```python
from kis import snapshot
# 현재 상태 저장
data = snapshot.snapshot(kis, "005930")
snapshot.save(data, "snapshots/005930.json")
# 로드 및 검증
loaded = snapshot.load("snapshots/005930.json")
assert snapshot.verify(loaded)
```
## API 레퍼런스
### KIS 클래스
```python
KIS(app_key: str, app_secret: str, account: str, env: Env = "paper")
```
| 속성/메서드 | 설명 |
|-------------|------|
| `is_paper` | 모의투자 여부 |
| `switch(env)` | 환경 전환 |
| `close()` | 연결 종료 |
### domestic 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol)` | 현재가 조회 |
| `orderbook(kis, symbol)` | 호가 조회 |
| `daily(kis, symbol, period="D")` | 일/주/월봉 |
| `buy(kis, symbol, qty, price=None)` | 매수 |
| `sell(kis, symbol, qty, price=None)` | 매도 |
| `cancel(kis, order_no, qty)` | 취소 |
| `modify(kis, order_no, qty, price)` | 정정 |
| `balance(kis)` | 잔고 조회 |
| `positions(kis)` | 보유종목 |
| `orders(kis, start_date, end_date)` | 주문내역 |
| `pending_orders(kis)` | 미체결 |
| `position(kis, symbol)` | 종목별 포지션 |
| `sell_all(kis, symbol)` | 전량 매도 |
| `cancel_remaining(kis, order_no)` | 미체결 전량 취소 |
### overseas 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol, exchange)` | 현재가 조회 |
| `daily(kis, symbol, exchange, period="D")` | 기간별 시세 |
| `buy(kis, symbol, exchange, qty, price=None)` | 매수 |
| `sell(kis, symbol, exchange, qty, price=None)` | 매도 |
| `cancel(kis, exchange, order_no, qty)` | 취소 |
| `balance(kis, exchange=None)` | 잔고 조회 |
| `exchange_rate(kis)` | 환율 조회 |
### WSClient 클래스
```python
WSClient(kis: KIS, max_retries: int = 5, retry_delay: float = 1.0)
```
| 메서드 | 설명 |
|--------|------|
| `connect()` | WebSocket 연결 |
| `subscribe(tr_id, symbols, callback)` | 구독 |
| `unsubscribe(tr_id, symbols)` | 구독 해제 |
| `run()` | 메시지 수신 루프 |
| `close()` | 연결 종료 |
## 개발
```bash
# 테스트
uv run pytest
# 커버리지
uv run pytest --cov=kis
# 린트
uv run ruff check kis/
# 포맷
uv run ruff format kis/
```
## 라이선스
MIT License
| text/markdown | null | null | null | null | null | api, kis, korea-investment, stock, trading | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Office/Business :: Financial :: Investment"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"httpx",
"pycryptodome",
"websockets",
"mypy; extra == \"dev\"",
"pytest; extra == \"dev\"",
"pytest-asyncio; extra == \"dev\"",
"pytest-httpx; extra == \"dev\"",
"ruff; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/LaytonAI/kis-wrapper",
"Documentation, https://github.com/LaytonAI/kis-wrapper#readme",
"Issues, https://github.com/LaytonAI/kis-wrapper/issues"
] | uv/0.7.5 | 2026-01-16T04:30:45.665794 | kis_wrapper-0.3.0-py3-none-any.whl | 17,473 | f7/66/caccd627fe7d4913ef5ff1bf2e24718c2c726f4ffdf7f9f2d86f830b7464/kis_wrapper-0.3.0-py3-none-any.whl | py3 | bdist_wheel | null | false | 9accd3127fa31ff168a9929a40526070 | 14533b3898ed17cf330165968db014f5c51a4a0b8892b916c21fa5ae059aa1ff | f766caccd627fe7d4913ef5ff1bf2e24718c2c726f4ffdf7f9f2d86f830b7464 | MIT | [
"LICENSE"
] |
2.4 | kis-wrapper | 0.3.0 | Korea Investment & Securities API wrapper for Python | # KIS Wrapper
한국투자증권 Open API Python SDK
## 특징
- 간결한 함수 기반 API
- 국내/해외주식 지원
- 실시간 WebSocket
- 모의투자/실전 환경 전환
- 자동 토큰 관리
## 설치
```bash
pip install kis
```
또는 개발 환경:
```bash
git clone https://github.com/your-repo/kis-wrapper
cd kis-wrapper
uv sync
```
## 빠른 시작
### 환경 설정
```bash
# .env
KIS_APP_KEY=your_app_key
KIS_APP_SECRET=your_app_secret
KIS_ACCOUNT=12345678-01
```
### 기본 사용
```python
import os
from kis import KIS, domestic
kis = KIS(
app_key=os.environ["KIS_APP_KEY"],
app_secret=os.environ["KIS_APP_SECRET"],
account=os.environ["KIS_ACCOUNT"],
env="paper", # 모의투자
)
# 삼성전자 현재가
p = domestic.price(kis, "005930")
print(f"현재가: {p['stck_prpr']}원")
# 호가
ob = domestic.orderbook(kis, "005930")
# 일봉 (최근 30일)
candles = domestic.daily(kis, "005930")
```
### 주문
```python
# 매수 (지정가)
order = domestic.buy(kis, "005930", qty=10, price=70000)
print(f"주문번호: {order['ODNO']}")
# 매수 (시장가)
order = domestic.buy(kis, "005930", qty=10)
# 매도
order = domestic.sell(kis, "005930", qty=5, price=72000)
# 주문 취소
domestic.cancel(kis, order_no="0001234567", qty=5)
# 주문 정정
domestic.modify(kis, order_no="0001234567", qty=10, price=71000)
```
### 계좌 조회
```python
# 잔고 (예수금 + 보유종목)
bal = domestic.balance(kis)
# 보유종목만
positions = domestic.positions(kis)
for p in positions:
print(f"{p['prdt_name']}: {p['hldg_qty']}주")
# 특정 종목 포지션
pos = domestic.position(kis, "005930")
if pos:
print(f"수익률: {pos['profit_rate']:.2f}%")
# 미체결 주문
pending = domestic.pending_orders(kis)
```
### 해외주식
```python
from kis import overseas
# 애플 현재가
p = overseas.price(kis, "AAPL", "NAS")
print(f"AAPL: ${p['last']}")
# 매수 (지정가)
order = overseas.buy(kis, "AAPL", "NAS", qty=1, price=150.00)
# 매수 (시장가 - 미국만 지원)
order = overseas.buy(kis, "AAPL", "NAS", qty=1)
# 잔고 조회
bal = overseas.balance(kis) # 전체
bal = overseas.balance(kis, "NAS") # 나스닥만
# 환율
rate = overseas.exchange_rate(kis)
```
#### 거래소 코드
| 코드 | 거래소 |
|------|--------|
| NYS | 뉴욕 (NYSE) |
| NAS | 나스닥 (NASDAQ) |
| AMS | 아멕스 (AMEX) |
| HKS | 홍콩 |
| SHS | 상해 |
| SZS | 심천 |
| TSE | 도쿄 |
| HNX | 하노이 |
| HSX | 호치민 |
### 실시간 데이터 (WebSocket)
```python
import asyncio
from kis import KIS, WSClient
async def main():
kis = KIS(app_key, app_secret, account, env="paper")
ws = WSClient(kis)
async def on_price(data):
print(f"{data['symbol']}: {data['price']:,}원 (거래량: {data['volume']})")
await ws.subscribe("H0STCNT0", ["005930", "000660"], on_price)
try:
await ws.run()
except KeyboardInterrupt:
await ws.close()
asyncio.run(main())
```
#### TR ID 목록
| TR ID | 설명 |
|-------|------|
| H0STCNT0 | 국내주식 실시간체결 |
| H0STASP0 | 국내주식 실시간호가 |
| H0STCNI0 | 체결통보 |
| HDFSCNT0 | 해외주식 실시간체결 |
### 환경 전환
```python
# 모의투자 -> 실전
kis_prod = kis.switch("prod")
# 또는 처음부터 실전
kis = KIS(app_key, app_secret, account, env="prod")
```
### 계산 유틸리티
```python
from kis import calc
# 수익률
rate = calc.profit_rate(buy_price=70000, current_price=75000)
print(f"수익률: {float(rate) * 100:.2f}%")
# 수익금
profit = calc.profit_amount(70000, 75000, qty=10)
# 평균단가
orders = [{"price": 70000, "qty": 10}, {"price": 72000, "qty": 5}]
avg = calc.avg_price(orders)
```
### 스냅샷
```python
from kis import snapshot
# 현재 상태 저장
data = snapshot.snapshot(kis, "005930")
snapshot.save(data, "snapshots/005930.json")
# 로드 및 검증
loaded = snapshot.load("snapshots/005930.json")
assert snapshot.verify(loaded)
```
## API 레퍼런스
### KIS 클래스
```python
KIS(app_key: str, app_secret: str, account: str, env: Env = "paper")
```
| 속성/메서드 | 설명 |
|-------------|------|
| `is_paper` | 모의투자 여부 |
| `switch(env)` | 환경 전환 |
| `close()` | 연결 종료 |
### domestic 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol)` | 현재가 조회 |
| `orderbook(kis, symbol)` | 호가 조회 |
| `daily(kis, symbol, period="D")` | 일/주/월봉 |
| `buy(kis, symbol, qty, price=None)` | 매수 |
| `sell(kis, symbol, qty, price=None)` | 매도 |
| `cancel(kis, order_no, qty)` | 취소 |
| `modify(kis, order_no, qty, price)` | 정정 |
| `balance(kis)` | 잔고 조회 |
| `positions(kis)` | 보유종목 |
| `orders(kis, start_date, end_date)` | 주문내역 |
| `pending_orders(kis)` | 미체결 |
| `position(kis, symbol)` | 종목별 포지션 |
| `sell_all(kis, symbol)` | 전량 매도 |
| `cancel_remaining(kis, order_no)` | 미체결 전량 취소 |
### overseas 모듈
| 함수 | 설명 |
|------|------|
| `price(kis, symbol, exchange)` | 현재가 조회 |
| `daily(kis, symbol, exchange, period="D")` | 기간별 시세 |
| `buy(kis, symbol, exchange, qty, price=None)` | 매수 |
| `sell(kis, symbol, exchange, qty, price=None)` | 매도 |
| `cancel(kis, exchange, order_no, qty)` | 취소 |
| `balance(kis, exchange=None)` | 잔고 조회 |
| `exchange_rate(kis)` | 환율 조회 |
### WSClient 클래스
```python
WSClient(kis: KIS, max_retries: int = 5, retry_delay: float = 1.0)
```
| 메서드 | 설명 |
|--------|------|
| `connect()` | WebSocket 연결 |
| `subscribe(tr_id, symbols, callback)` | 구독 |
| `unsubscribe(tr_id, symbols)` | 구독 해제 |
| `run()` | 메시지 수신 루프 |
| `close()` | 연결 종료 |
## 개발
```bash
# 테스트
uv run pytest
# 커버리지
uv run pytest --cov=kis
# 린트
uv run ruff check kis/
# 포맷
uv run ruff format kis/
```
## 라이선스
MIT License
| text/markdown | null | null | null | null | null | api, kis, korea-investment, stock, trading | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Office/Business :: Financial :: Investment"
] | [] | null | null | >=3.11 | [] | [] | [] | [
"httpx",
"pycryptodome",
"websockets",
"mypy; extra == \"dev\"",
"pytest; extra == \"dev\"",
"pytest-asyncio; extra == \"dev\"",
"pytest-httpx; extra == \"dev\"",
"ruff; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/LaytonAI/kis-wrapper",
"Documentation, https://github.com/LaytonAI/kis-wrapper#readme",
"Issues, https://github.com/LaytonAI/kis-wrapper/issues"
] | uv/0.7.5 | 2026-01-16T04:30:47.300206 | kis_wrapper-0.3.0.tar.gz | 65,871 | 54/42/46e9464fc75886818671b2b78a27ec2bb4e216601c4f0f5eeae509b8228d/kis_wrapper-0.3.0.tar.gz | source | sdist | null | false | 7135270d42acbaae59f29983db96c1ac | 974e1ec038e4a11672ac3a267f881fca5fdf3ea2ac988e9eb902bfec5349d605 | 544246e9464fc75886818671b2b78a27ec2bb4e216601c4f0f5eeae509b8228d | MIT | [
"LICENSE"
] |
2.4 | ccburn | 0.3.0 | Terminal-based Claude Code usage limit visualizer with real-time burn-up charts | # 🔥 ccburn
[](https://github.com/JuanjoFuchs/ccburn/actions/workflows/ci.yml)
[](https://github.com/JuanjoFuchs/ccburn/actions/workflows/release.yml)
[](https://www.npmjs.com/package/ccburn)
[](https://pypi.org/project/ccburn/)
[](https://pypi.org/project/ccburn/)
[](https://github.com/JuanjoFuchs/ccburn/releases)
[](https://winstall.app/apps/JuanjoFuchs.ccburn)
[](https://www.npmjs.com/package/ccburn)
[](https://pepy.tech/project/ccburn)
[](https://github.com/JuanjoFuchs/ccburn/releases)
[](LICENSE)
<p align="center">
<img src="docs/cash1.png" alt="Burning tokens" width="140">
</p>
<p align="center">
<strong>Watch your tokens burn — before you get burned.</strong>
</p>
TUI and CLI for Claude Code usage limits — burn-up charts, compact mode for status bars, JSON for automation.
## Features
- **Real-time burn-up charts** — Visualize session and weekly usage with live-updating terminal graphics
- **Pace indicators** — 🧊 Cool. 🔥 On pace. 🚨 Too hot.
- **Multiple output modes** — Full TUI, compact single-line for status bars, or JSON for scripting
- **Automatic data persistence** — SQLite-backed history for trend analysis
- **Dynamic window title** — Terminal tab shows current usage at a glance
- **Zoom views** — Focus on recent activity with `--since`
## Installation
Run `claude` and login first to refresh credentials.
### WinGet (Windows)
```powershell
winget install JuanjoFuchs.ccburn
```
### npx
```bash
npx ccburn
```
### npm
```bash
npm install -g ccburn
```
### pip
```bash
pip install ccburn
```
### From Source
```bash
git clone https://github.com/JuanjoFuchs/ccburn.git
cd ccburn
pip install -e ".[dev]"
```
## Quick Start
1. **Run Claude Code first** to ensure credentials are fresh:
```bash
claude
```
2. **Run ccburn:**
```bash
ccburn # Session limit (default)
ccburn weekly # Weekly limit
ccburn weekly-sonnet # Weekly Sonnet limit
```
## Usage Examples
```bash
# Full TUI with burn-up chart (default)
ccburn
# Weekly usage view
ccburn weekly
# Compact output for tmux/status bars
ccburn --compact
# Output: Session: 🔥 45% (2h14m) | Weekly: 🧊 12% | Sonnet: 🧊 3%
# JSON output for scripting/automation
ccburn --json
# Zoom to last 30 minutes
ccburn --since 30m
# Single snapshot (no live updates)
ccburn --once
# Custom refresh interval (seconds)
ccburn --interval 10
```
## Command Line Reference
```
Usage: ccburn [OPTIONS] [LIMIT]
Arguments:
[LIMIT] Which limit to display [default: session]
Options: session, weekly, weekly-sonnet
Options:
-i, --interval INTEGER Refresh interval in seconds [default: 5/30]
-s, --since TEXT Only show data since (e.g., 30m, 2h, 1d)
-j, --json Output JSON and exit
-o, --once Print once and exit (no live updates)
-c, --compact Single-line output for status bars
--debug Show debug information
--version Show version and exit
--help Show this message and exit
```
## Pace Indicators
| Emoji | Status | Meaning |
|-------|--------|---------|
| 🧊 | Behind pace | Usage below expected budget — you have headroom |
| 🔥 | On pace | Usage tracking with expected budget |
| 🚨 | Ahead of pace | Usage above expected budget — slow down! |
## Requirements
- **Python 3.10+**
- **Claude Code** installed with valid credentials
- Terminal with Unicode support (for charts and emojis)
## How It Works
ccburn reads your Claude Code credentials and fetches usage data from the Anthropic API. It calculates:
- **Budget pace** — Where you "should" be based on time elapsed in the window
- **Burn rate** — How fast you're consuming your limit
- **Time to limit** — Estimated time until you hit 100% (if current rate continues)
Data is stored locally in SQLite for historical analysis and to minimize API calls when running multiple instances.
## Troubleshooting
### "Credentials not found"
Ensure Claude Code is installed and you've logged in at least once:
```bash
claude # This will prompt for login if needed
```
### Chart not displaying correctly
Ensure your terminal supports Unicode and has a monospace font with emoji support. Recommended terminals:
- **Windows**: Windows Terminal
- **macOS**: iTerm2, Terminal.app
- **Linux**: Kitty, Alacritty, GNOME Terminal
### Stale data indicator
If you see "(stale)" in the header, ccburn couldn't reach the API. It will continue showing cached data and retry automatically.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
[MIT](LICENSE)
## Acknowledgments
- [Rich](https://github.com/Textualize/rich) — Beautiful terminal formatting
- [Plotext](https://github.com/piccolomo/plotext) — Terminal plotting
- [Typer](https://github.com/tiangolo/typer) — CLI framework
| text/markdown | JuanjoFuchs | null | null | null | null | claude, anthropic, usage, monitoring, tui, visualization, cli | [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"P... | [] | null | null | >=3.10 | [] | [] | [] | [
"typer[all]>=0.9.0",
"rich>=13.0.0",
"plotext>=5.2.0",
"httpx>=0.25.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"ruff>=0.1.0; extra == \"dev\"",
"mypy>=1.0.0; extra == \"dev\"",
"build>=1.0.0; extra == \"dev\"",
"twine>=4.0.0; extra == \"dev\"",
"pyinstaller>=6.... | [] | [] | [] | [
"Homepage, https://github.com/JuanjoFuchs/ccburn",
"Repository, https://github.com/JuanjoFuchs/ccburn.git",
"Issues, https://github.com/JuanjoFuchs/ccburn/issues",
"Documentation, https://github.com/JuanjoFuchs/ccburn#readme"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:31:07.292351 | ccburn-0.3.0-py3-none-any.whl | 35,948 | 72/0f/affb4c8df93a8b31e9bd670b30c24548c1adc9928bac355a256443a162d3/ccburn-0.3.0-py3-none-any.whl | py3 | bdist_wheel | null | false | db3f953988cf9c4aafed986404beb918 | 6f9fab12db62082575ab62fc861fba6d17ddd1a1d9b7157813185ca6cb551b73 | 720faffb4c8df93a8b31e9bd670b30c24548c1adc9928bac355a256443a162d3 | MIT | [
"LICENSE"
] |
2.4 | ccburn | 0.3.0 | Terminal-based Claude Code usage limit visualizer with real-time burn-up charts | # 🔥 ccburn
[](https://github.com/JuanjoFuchs/ccburn/actions/workflows/ci.yml)
[](https://github.com/JuanjoFuchs/ccburn/actions/workflows/release.yml)
[](https://www.npmjs.com/package/ccburn)
[](https://pypi.org/project/ccburn/)
[](https://pypi.org/project/ccburn/)
[](https://github.com/JuanjoFuchs/ccburn/releases)
[](https://winstall.app/apps/JuanjoFuchs.ccburn)
[](https://www.npmjs.com/package/ccburn)
[](https://pepy.tech/project/ccburn)
[](https://github.com/JuanjoFuchs/ccburn/releases)
[](LICENSE)
<p align="center">
<img src="docs/cash1.png" alt="Burning tokens" width="140">
</p>
<p align="center">
<strong>Watch your tokens burn — before you get burned.</strong>
</p>
TUI and CLI for Claude Code usage limits — burn-up charts, compact mode for status bars, JSON for automation.
## Features
- **Real-time burn-up charts** — Visualize session and weekly usage with live-updating terminal graphics
- **Pace indicators** — 🧊 Cool. 🔥 On pace. 🚨 Too hot.
- **Multiple output modes** — Full TUI, compact single-line for status bars, or JSON for scripting
- **Automatic data persistence** — SQLite-backed history for trend analysis
- **Dynamic window title** — Terminal tab shows current usage at a glance
- **Zoom views** — Focus on recent activity with `--since`
## Installation
Run `claude` and login first to refresh credentials.
### WinGet (Windows)
```powershell
winget install JuanjoFuchs.ccburn
```
### npx
```bash
npx ccburn
```
### npm
```bash
npm install -g ccburn
```
### pip
```bash
pip install ccburn
```
### From Source
```bash
git clone https://github.com/JuanjoFuchs/ccburn.git
cd ccburn
pip install -e ".[dev]"
```
## Quick Start
1. **Run Claude Code first** to ensure credentials are fresh:
```bash
claude
```
2. **Run ccburn:**
```bash
ccburn # Session limit (default)
ccburn weekly # Weekly limit
ccburn weekly-sonnet # Weekly Sonnet limit
```
## Usage Examples
```bash
# Full TUI with burn-up chart (default)
ccburn
# Weekly usage view
ccburn weekly
# Compact output for tmux/status bars
ccburn --compact
# Output: Session: 🔥 45% (2h14m) | Weekly: 🧊 12% | Sonnet: 🧊 3%
# JSON output for scripting/automation
ccburn --json
# Zoom to last 30 minutes
ccburn --since 30m
# Single snapshot (no live updates)
ccburn --once
# Custom refresh interval (seconds)
ccburn --interval 10
```
## Command Line Reference
```
Usage: ccburn [OPTIONS] [LIMIT]
Arguments:
[LIMIT] Which limit to display [default: session]
Options: session, weekly, weekly-sonnet
Options:
-i, --interval INTEGER Refresh interval in seconds [default: 5/30]
-s, --since TEXT Only show data since (e.g., 30m, 2h, 1d)
-j, --json Output JSON and exit
-o, --once Print once and exit (no live updates)
-c, --compact Single-line output for status bars
--debug Show debug information
--version Show version and exit
--help Show this message and exit
```
## Pace Indicators
| Emoji | Status | Meaning |
|-------|--------|---------|
| 🧊 | Behind pace | Usage below expected budget — you have headroom |
| 🔥 | On pace | Usage tracking with expected budget |
| 🚨 | Ahead of pace | Usage above expected budget — slow down! |
## Requirements
- **Python 3.10+**
- **Claude Code** installed with valid credentials
- Terminal with Unicode support (for charts and emojis)
## How It Works
ccburn reads your Claude Code credentials and fetches usage data from the Anthropic API. It calculates:
- **Budget pace** — Where you "should" be based on time elapsed in the window
- **Burn rate** — How fast you're consuming your limit
- **Time to limit** — Estimated time until you hit 100% (if current rate continues)
Data is stored locally in SQLite for historical analysis and to minimize API calls when running multiple instances.
## Troubleshooting
### "Credentials not found"
Ensure Claude Code is installed and you've logged in at least once:
```bash
claude # This will prompt for login if needed
```
### Chart not displaying correctly
Ensure your terminal supports Unicode and has a monospace font with emoji support. Recommended terminals:
- **Windows**: Windows Terminal
- **macOS**: iTerm2, Terminal.app
- **Linux**: Kitty, Alacritty, GNOME Terminal
### Stale data indicator
If you see "(stale)" in the header, ccburn couldn't reach the API. It will continue showing cached data and retry automatically.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
[MIT](LICENSE)
## Acknowledgments
- [Rich](https://github.com/Textualize/rich) — Beautiful terminal formatting
- [Plotext](https://github.com/piccolomo/plotext) — Terminal plotting
- [Typer](https://github.com/tiangolo/typer) — CLI framework
| text/markdown | JuanjoFuchs | null | null | null | null | claude, anthropic, usage, monitoring, tui, visualization, cli | [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Intended Audience :: Developers",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"P... | [] | null | null | >=3.10 | [] | [] | [] | [
"typer[all]>=0.9.0",
"rich>=13.0.0",
"plotext>=5.2.0",
"httpx>=0.25.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"ruff>=0.1.0; extra == \"dev\"",
"mypy>=1.0.0; extra == \"dev\"",
"build>=1.0.0; extra == \"dev\"",
"twine>=4.0.0; extra == \"dev\"",
"pyinstaller>=6.... | [] | [] | [] | [
"Homepage, https://github.com/JuanjoFuchs/ccburn",
"Repository, https://github.com/JuanjoFuchs/ccburn.git",
"Issues, https://github.com/JuanjoFuchs/ccburn/issues",
"Documentation, https://github.com/JuanjoFuchs/ccburn#readme"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:31:08.775219 | ccburn-0.3.0.tar.gz | 37,398 | 60/7b/f37a625a151cc6fc653375c7d6fe96477967299f81912cdbad8e6914e891/ccburn-0.3.0.tar.gz | source | sdist | null | false | 9e471d9c5aa2448fcf5857d47d80af1f | 6572c3d9e47bc751c2122bfbabca0cecf8b2bae48e7c33dbdc33ac9cc5db939e | 607bf37a625a151cc6fc653375c7d6fe96477967299f81912cdbad8e6914e891 | MIT | [
"LICENSE"
] |
2.4 | pyxecm | 3.1.9 | A Python library to interact with Opentext Content Management Rest API | # PYXECM
A python library to interact with Opentext Content Mangement REST API.
The product API documentation is available on [OpenText Developer](https://developer.opentext.com/ce/products/extendedecm)
Detailed documentation of this package is available [here](https://opentext.github.io/pyxecm/).
## Quick start - Library usage
Install the latest version from pypi:
```bash
pip install pyxecm
```
### Start using the package libraries
example usage of the OTCS class, more details can be found in the docs:
```python
from pyxecm import OTCS
otcs_object = OTCS(
protocol="https",
hostname="otcs.domain.tld",
port="443",
public_url="otcs.domain.tld",
username="admin",
password="********",
base_path="/cs/llisapi.dll",
)
otcs_object.authenticate()
nodes = otcs_object.get_subnodes(2000)
for node in nodes["results"]:
print(node["data"]["properties"]["id"], node["data"]["properties"]["name"])
```
## Quick start - Customizer usage
- Create an `.env` file as described here: [sample-environment-variables](customizerapisettings/#sample-environment-variables)
- Create an payload file to define what the customizer should do, as described here [payload-syntax](payload-syntax)
```bash
pip install pyxecm[customizer]
pyxecm-customizer PAYLOAD.tfvars/PAYLOAD.yaml
```
## Quick start - API
- Install pyxecm with api and customizer dependencies
- Launch the Rest API server
- Access the Customizer API at [http://localhost:8000/api](http://localhost:8000/api)
```bash
pip install pyxecm[api,customizer]
pyxecm-api
```
## Disclaimer
Copyright © 2025 Open Text Corporation, All Rights Reserved.
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| text/markdown | null | Kai Gatzweiler <kgatzweiler@opentext.com>, "Dr. Marc Diefenbruch" <mdiefenb@opentext.com> | null | null | null | appworks, archivecenter, contentserver, extendedecm, opentext, otds | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Topic :: Internet :: WWW/HTTP :: Dynamic Content :: Content Management System"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"lxml>=6.0.0",
"opentelemetry-api>=1.34.1",
"opentelemetry-exporter-otlp>=1.34.1",
"opentelemetry-instrumentation-requests>=0.55b1",
"opentelemetry-instrumentation-threading>=0.55b1",
"opentelemetry-sdk>=1.34.1",
"pandas>=2.3.1",
"requests-toolbelt>=1.0.0",
"requests>=2.32.4",
"suds>=1.2.0",
"we... | [] | [] | [] | [
"Homepage, https://github.com/opentext/pyxecm"
] | uv/0.9.25 {"installer":{"name":"uv","version":"0.9.25","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Debian GNU/Linux","version":"12","id":"bookworm","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-01-16T04:31:14.823785 | pyxecm-3.1.9.tar.gz | 633,198 | 7f/9e/7991c433283ce0894e9f493811f6e33b8359cabb702aedcdd20a22a799be/pyxecm-3.1.9.tar.gz | source | sdist | null | false | aae6c19558c34e08f2228408007b1d62 | 4d4b18ffd80e50c116649f524ea9bc2ba4579421969299f34f1e984aa007a6fa | 7f9e7991c433283ce0894e9f493811f6e33b8359cabb702aedcdd20a22a799be | null | [] |
2.4 | pyxecm | 3.1.9 | A Python library to interact with Opentext Content Management Rest API | # PYXECM
A python library to interact with Opentext Content Mangement REST API.
The product API documentation is available on [OpenText Developer](https://developer.opentext.com/ce/products/extendedecm)
Detailed documentation of this package is available [here](https://opentext.github.io/pyxecm/).
## Quick start - Library usage
Install the latest version from pypi:
```bash
pip install pyxecm
```
### Start using the package libraries
example usage of the OTCS class, more details can be found in the docs:
```python
from pyxecm import OTCS
otcs_object = OTCS(
protocol="https",
hostname="otcs.domain.tld",
port="443",
public_url="otcs.domain.tld",
username="admin",
password="********",
base_path="/cs/llisapi.dll",
)
otcs_object.authenticate()
nodes = otcs_object.get_subnodes(2000)
for node in nodes["results"]:
print(node["data"]["properties"]["id"], node["data"]["properties"]["name"])
```
## Quick start - Customizer usage
- Create an `.env` file as described here: [sample-environment-variables](customizerapisettings/#sample-environment-variables)
- Create an payload file to define what the customizer should do, as described here [payload-syntax](payload-syntax)
```bash
pip install pyxecm[customizer]
pyxecm-customizer PAYLOAD.tfvars/PAYLOAD.yaml
```
## Quick start - API
- Install pyxecm with api and customizer dependencies
- Launch the Rest API server
- Access the Customizer API at [http://localhost:8000/api](http://localhost:8000/api)
```bash
pip install pyxecm[api,customizer]
pyxecm-api
```
## Disclaimer
Copyright © 2025 Open Text Corporation, All Rights Reserved.
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| text/markdown | null | Kai Gatzweiler <kgatzweiler@opentext.com>, "Dr. Marc Diefenbruch" <mdiefenb@opentext.com> | null | null | null | appworks, archivecenter, contentserver, extendedecm, opentext, otds | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Topic :: Internet :: WWW/HTTP :: Dynamic Content :: Content Management System"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"lxml>=6.0.0",
"opentelemetry-api>=1.34.1",
"opentelemetry-exporter-otlp>=1.34.1",
"opentelemetry-instrumentation-requests>=0.55b1",
"opentelemetry-instrumentation-threading>=0.55b1",
"opentelemetry-sdk>=1.34.1",
"pandas>=2.3.1",
"requests-toolbelt>=1.0.0",
"requests>=2.32.4",
"suds>=1.2.0",
"we... | [] | [] | [] | [
"Homepage, https://github.com/opentext/pyxecm"
] | uv/0.9.25 {"installer":{"name":"uv","version":"0.9.25","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Debian GNU/Linux","version":"12","id":"bookworm","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true} | 2026-01-16T04:31:16.216426 | pyxecm-3.1.9-py3-none-any.whl | 672,370 | e9/4a/93349526f921bd849b9625a817bd253c189d72fb24fb8201ef1264806a8f/pyxecm-3.1.9-py3-none-any.whl | py3 | bdist_wheel | null | false | 9429bd26d1c22877cccfe28e55394ff2 | 74713b3bf2dd43afbc88e6111057b530fa7d54203019ebd562da50a9bf130718 | e94a93349526f921bd849b9625a817bd253c189d72fb24fb8201ef1264806a8f | null | [] |
2.1 | bids-validator-deno | 2.2.10 | Typescript implementation of the BIDS validator | [](https://github.com/bids-standard/bids-validator/actions/workflows/deno_tests.yml)
[](https://github.com/bids-standard/bids-validator/actions/workflows/web_build.yml)
[](https://bids-validator.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.3688707)
# The BIDS Validator
The BIDS Validator is a web application, command-line utility,
and Javascript/Typescript library for assessing compliance with the
[Brain Imaging Data Structure (BIDS)][BIDS] standard.
## Getting Started
In most cases,
the simplest way to use the validator is to browse to the [BIDS Validator][] web page:
The web validator runs in-browser, and does not transfer data to any remote server.
In some contexts, such as when working on a remote server,
it may be easier to use the command-line.
The BIDS Validator can be run with the [Deno][] runtime
(see [Deno - Installation][] for detailed installation instructions):
```shell
deno run -ERWN jsr:@bids/validator
```
Deno by default sandboxes applications like a web browser.
`-E`, `-R`, `-W`, and `-N` allow the validator to read environment variables,
read/write local files, and read network locations.
A pre-compiled binary is published to [PyPI][] and may be installed with:
```
pip install bids-validator-deno
bids-validator-deno --help
```
### Configuration file
The schema validator accepts a JSON configuration file that reclassifies issues as
warnings, errors or ignored.
```json
{
"ignore": [
{ "code": "JSON_KEY_RECOMMENDED", "location": "/T1w.json" }
],
"warning": [],
"error": [
{ "code": "NO_AUTHORS" }
]
}
```
The issues are partial matches of the `issues` that the validator accumulates.
Pass the `--json` flag to see the issues in detail.
### Development tools
From the repository root, use `./local-run` to run with all permissions enabled by default:
```shell
# Run from within the /bids-validator directory
cd bids-validator
# Run validator:
./local-run path/to/dataset
```
## Schema validator test suite
```shell
# Run tests:
deno test --allow-env --allow-read --allow-write src/
```
This test suite includes running expected output from bids-examples and may throw some expected failures for bids-examples datasets where either the schema or validator are misaligned with the example dataset while under development.
## Modifying and building a new schema
To modify the schema a clone of bids-standard/bids-specification will need to be made. README and schema itself live here https://github.com/bids-standard/bids-specification/tree/master/src/schema.
After changes to the schema have been made to a local copy the dereferenced single json file used by the validator will need to be built. The `bidsschematools` python package does this. It can be installed from pypi via pip or a local installation can be made. It lives in the specification repository here https://github.com/bids-standard/bids-specification/tree/master/tools/schemacode
The command to compile a dereferenced schema is `bst -v export --schema src/schema --output src/schema.json` (this assumes you are in the root of the bids-specification repo). Once compiled it can be passed to the validator via the `-s` flag, `./bids-validator-deno -s <path to schema> <path to dataset>`
## Documentation
The BIDS validator documentation is available on [Read the Docs](https://bids-validator.readthedocs.io/en/latest/).
[BIDS]: https://bids.neuroimaging.io
[BIDS Validator]: https://bids-standard.github.io/bids-validator/
[Deno]: https://deno.com/
[Deno - Installation]: https://docs.deno.com/runtime/getting_started/installation/
[PyPI]: https://pypi.org/project/bids-validator-deno/
| text/markdown | bids-standard developers | null | null | null | MIT | BIDS, BIDS validator | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"License :: OSI Approved :: MIT License",
"Programming Language :: JavaScript"
] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [
"Documentation, https://bids-validator.readthedocs.io/",
"Source code, https://github.com/bids-standard/bids-validator",
"Issues, https://github.com/bids-standard/bids-validator/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:31:58.787218 | bids_validator_deno-2.2.10-py2.py3-none-macosx_11_0_arm64.whl | 40,616,636 | ad/d2/2503076540f6478ce9ad0e26c969257fade264c1ae1a3be39d773c49dcf8/bids_validator_deno-2.2.10-py2.py3-none-macosx_11_0_arm64.whl | py2.py3 | bdist_wheel | null | false | a9a3a598e72c641f42f0734b0f885353 | 18ca532909452f668cbc7642e5b0b6cfc7a43a5cbd8c66d965759f8c488049d6 | add22503076540f6478ce9ad0e26c969257fade264c1ae1a3be39d773c49dcf8 | null | [] |
2.1 | bids-validator-deno | 2.2.10 | Typescript implementation of the BIDS validator | [](https://github.com/bids-standard/bids-validator/actions/workflows/deno_tests.yml)
[](https://github.com/bids-standard/bids-validator/actions/workflows/web_build.yml)
[](https://bids-validator.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.3688707)
# The BIDS Validator
The BIDS Validator is a web application, command-line utility,
and Javascript/Typescript library for assessing compliance with the
[Brain Imaging Data Structure (BIDS)][BIDS] standard.
## Getting Started
In most cases,
the simplest way to use the validator is to browse to the [BIDS Validator][] web page:
The web validator runs in-browser, and does not transfer data to any remote server.
In some contexts, such as when working on a remote server,
it may be easier to use the command-line.
The BIDS Validator can be run with the [Deno][] runtime
(see [Deno - Installation][] for detailed installation instructions):
```shell
deno run -ERWN jsr:@bids/validator
```
Deno by default sandboxes applications like a web browser.
`-E`, `-R`, `-W`, and `-N` allow the validator to read environment variables,
read/write local files, and read network locations.
A pre-compiled binary is published to [PyPI][] and may be installed with:
```
pip install bids-validator-deno
bids-validator-deno --help
```
### Configuration file
The schema validator accepts a JSON configuration file that reclassifies issues as
warnings, errors or ignored.
```json
{
"ignore": [
{ "code": "JSON_KEY_RECOMMENDED", "location": "/T1w.json" }
],
"warning": [],
"error": [
{ "code": "NO_AUTHORS" }
]
}
```
The issues are partial matches of the `issues` that the validator accumulates.
Pass the `--json` flag to see the issues in detail.
### Development tools
From the repository root, use `./local-run` to run with all permissions enabled by default:
```shell
# Run from within the /bids-validator directory
cd bids-validator
# Run validator:
./local-run path/to/dataset
```
## Schema validator test suite
```shell
# Run tests:
deno test --allow-env --allow-read --allow-write src/
```
This test suite includes running expected output from bids-examples and may throw some expected failures for bids-examples datasets where either the schema or validator are misaligned with the example dataset while under development.
## Modifying and building a new schema
To modify the schema a clone of bids-standard/bids-specification will need to be made. README and schema itself live here https://github.com/bids-standard/bids-specification/tree/master/src/schema.
After changes to the schema have been made to a local copy the dereferenced single json file used by the validator will need to be built. The `bidsschematools` python package does this. It can be installed from pypi via pip or a local installation can be made. It lives in the specification repository here https://github.com/bids-standard/bids-specification/tree/master/tools/schemacode
The command to compile a dereferenced schema is `bst -v export --schema src/schema --output src/schema.json` (this assumes you are in the root of the bids-specification repo). Once compiled it can be passed to the validator via the `-s` flag, `./bids-validator-deno -s <path to schema> <path to dataset>`
## Documentation
The BIDS validator documentation is available on [Read the Docs](https://bids-validator.readthedocs.io/en/latest/).
[BIDS]: https://bids.neuroimaging.io
[BIDS Validator]: https://bids-standard.github.io/bids-validator/
[Deno]: https://deno.com/
[Deno - Installation]: https://docs.deno.com/runtime/getting_started/installation/
[PyPI]: https://pypi.org/project/bids-validator-deno/
| text/markdown | bids-standard developers | null | null | null | MIT | BIDS, BIDS validator | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"License :: OSI Approved :: MIT License",
"Programming Language :: JavaScript"
] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [
"Documentation, https://bids-validator.readthedocs.io/",
"Source code, https://github.com/bids-standard/bids-validator",
"Issues, https://github.com/bids-standard/bids-validator/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:32:02.498566 | bids_validator_deno-2.2.10-py2.py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | 43,003,197 | a8/50/15c0a6d78ab1887a02f5ab2cc73e3de6b544973714e1728e8f86a3510029/bids_validator_deno-2.2.10-py2.py3-none-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl | py2.py3 | bdist_wheel | null | false | 4361a8fff6de90b47537657e88f96c4c | ef4baaf9a67011cb892fc8571e6dbf18a88ce0854c6ddca80718ee910aecffd7 | a85015c0a6d78ab1887a02f5ab2cc73e3de6b544973714e1728e8f86a3510029 | null | [] |
2.1 | bids-validator-deno | 2.2.10 | Typescript implementation of the BIDS validator | [](https://github.com/bids-standard/bids-validator/actions/workflows/deno_tests.yml)
[](https://github.com/bids-standard/bids-validator/actions/workflows/web_build.yml)
[](https://bids-validator.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.3688707)
# The BIDS Validator
The BIDS Validator is a web application, command-line utility,
and Javascript/Typescript library for assessing compliance with the
[Brain Imaging Data Structure (BIDS)][BIDS] standard.
## Getting Started
In most cases,
the simplest way to use the validator is to browse to the [BIDS Validator][] web page:
The web validator runs in-browser, and does not transfer data to any remote server.
In some contexts, such as when working on a remote server,
it may be easier to use the command-line.
The BIDS Validator can be run with the [Deno][] runtime
(see [Deno - Installation][] for detailed installation instructions):
```shell
deno run -ERWN jsr:@bids/validator
```
Deno by default sandboxes applications like a web browser.
`-E`, `-R`, `-W`, and `-N` allow the validator to read environment variables,
read/write local files, and read network locations.
A pre-compiled binary is published to [PyPI][] and may be installed with:
```
pip install bids-validator-deno
bids-validator-deno --help
```
### Configuration file
The schema validator accepts a JSON configuration file that reclassifies issues as
warnings, errors or ignored.
```json
{
"ignore": [
{ "code": "JSON_KEY_RECOMMENDED", "location": "/T1w.json" }
],
"warning": [],
"error": [
{ "code": "NO_AUTHORS" }
]
}
```
The issues are partial matches of the `issues` that the validator accumulates.
Pass the `--json` flag to see the issues in detail.
### Development tools
From the repository root, use `./local-run` to run with all permissions enabled by default:
```shell
# Run from within the /bids-validator directory
cd bids-validator
# Run validator:
./local-run path/to/dataset
```
## Schema validator test suite
```shell
# Run tests:
deno test --allow-env --allow-read --allow-write src/
```
This test suite includes running expected output from bids-examples and may throw some expected failures for bids-examples datasets where either the schema or validator are misaligned with the example dataset while under development.
## Modifying and building a new schema
To modify the schema a clone of bids-standard/bids-specification will need to be made. README and schema itself live here https://github.com/bids-standard/bids-specification/tree/master/src/schema.
After changes to the schema have been made to a local copy the dereferenced single json file used by the validator will need to be built. The `bidsschematools` python package does this. It can be installed from pypi via pip or a local installation can be made. It lives in the specification repository here https://github.com/bids-standard/bids-specification/tree/master/tools/schemacode
The command to compile a dereferenced schema is `bst -v export --schema src/schema --output src/schema.json` (this assumes you are in the root of the bids-specification repo). Once compiled it can be passed to the validator via the `-s` flag, `./bids-validator-deno -s <path to schema> <path to dataset>`
## Documentation
The BIDS validator documentation is available on [Read the Docs](https://bids-validator.readthedocs.io/en/latest/).
[BIDS]: https://bids.neuroimaging.io
[BIDS Validator]: https://bids-standard.github.io/bids-validator/
[Deno]: https://deno.com/
[Deno - Installation]: https://docs.deno.com/runtime/getting_started/installation/
[PyPI]: https://pypi.org/project/bids-validator-deno/
| text/markdown | bids-standard developers | null | null | null | MIT | BIDS, BIDS validator | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"License :: OSI Approved :: MIT License",
"Programming Language :: JavaScript"
] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [
"Documentation, https://bids-validator.readthedocs.io/",
"Source code, https://github.com/bids-standard/bids-validator",
"Issues, https://github.com/bids-standard/bids-validator/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:32:06.344359 | bids_validator_deno-2.2.10-py2.py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | 44,276,271 | 79/8e/e1a9aef82e0ba90b46022b7f2293ff8ffe950c1fc1df5e643e786985fc9f/bids_validator_deno-2.2.10-py2.py3-none-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | py2.py3 | bdist_wheel | null | false | 10f8c6e2e61790d9eeafaa2efe516744 | 292e482281df52b0b80bf21b30fc0186689668b89166e676a5916c313ece452e | 798ee1a9aef82e0ba90b46022b7f2293ff8ffe950c1fc1df5e643e786985fc9f | null | [] |
2.1 | bids-validator-deno | 2.2.10 | Typescript implementation of the BIDS validator | [](https://github.com/bids-standard/bids-validator/actions/workflows/deno_tests.yml)
[](https://github.com/bids-standard/bids-validator/actions/workflows/web_build.yml)
[](https://bids-validator.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.3688707)
# The BIDS Validator
The BIDS Validator is a web application, command-line utility,
and Javascript/Typescript library for assessing compliance with the
[Brain Imaging Data Structure (BIDS)][BIDS] standard.
## Getting Started
In most cases,
the simplest way to use the validator is to browse to the [BIDS Validator][] web page:
The web validator runs in-browser, and does not transfer data to any remote server.
In some contexts, such as when working on a remote server,
it may be easier to use the command-line.
The BIDS Validator can be run with the [Deno][] runtime
(see [Deno - Installation][] for detailed installation instructions):
```shell
deno run -ERWN jsr:@bids/validator
```
Deno by default sandboxes applications like a web browser.
`-E`, `-R`, `-W`, and `-N` allow the validator to read environment variables,
read/write local files, and read network locations.
A pre-compiled binary is published to [PyPI][] and may be installed with:
```
pip install bids-validator-deno
bids-validator-deno --help
```
### Configuration file
The schema validator accepts a JSON configuration file that reclassifies issues as
warnings, errors or ignored.
```json
{
"ignore": [
{ "code": "JSON_KEY_RECOMMENDED", "location": "/T1w.json" }
],
"warning": [],
"error": [
{ "code": "NO_AUTHORS" }
]
}
```
The issues are partial matches of the `issues` that the validator accumulates.
Pass the `--json` flag to see the issues in detail.
### Development tools
From the repository root, use `./local-run` to run with all permissions enabled by default:
```shell
# Run from within the /bids-validator directory
cd bids-validator
# Run validator:
./local-run path/to/dataset
```
## Schema validator test suite
```shell
# Run tests:
deno test --allow-env --allow-read --allow-write src/
```
This test suite includes running expected output from bids-examples and may throw some expected failures for bids-examples datasets where either the schema or validator are misaligned with the example dataset while under development.
## Modifying and building a new schema
To modify the schema a clone of bids-standard/bids-specification will need to be made. README and schema itself live here https://github.com/bids-standard/bids-specification/tree/master/src/schema.
After changes to the schema have been made to a local copy the dereferenced single json file used by the validator will need to be built. The `bidsschematools` python package does this. It can be installed from pypi via pip or a local installation can be made. It lives in the specification repository here https://github.com/bids-standard/bids-specification/tree/master/tools/schemacode
The command to compile a dereferenced schema is `bst -v export --schema src/schema --output src/schema.json` (this assumes you are in the root of the bids-specification repo). Once compiled it can be passed to the validator via the `-s` flag, `./bids-validator-deno -s <path to schema> <path to dataset>`
## Documentation
The BIDS validator documentation is available on [Read the Docs](https://bids-validator.readthedocs.io/en/latest/).
[BIDS]: https://bids.neuroimaging.io
[BIDS Validator]: https://bids-standard.github.io/bids-validator/
[Deno]: https://deno.com/
[Deno - Installation]: https://docs.deno.com/runtime/getting_started/installation/
[PyPI]: https://pypi.org/project/bids-validator-deno/
| text/markdown | bids-standard developers | null | null | null | MIT | BIDS, BIDS validator | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"License :: OSI Approved :: MIT License",
"Programming Language :: JavaScript"
] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [
"Documentation, https://bids-validator.readthedocs.io/",
"Source code, https://github.com/bids-standard/bids-validator",
"Issues, https://github.com/bids-standard/bids-validator/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:32:10.128680 | bids_validator_deno-2.2.10-py2.py3-none-win_amd64.whl | 43,508,910 | be/50/0a0e59f491fdc3517c328c354928a1fbd218f34eeb7194ce2c641cd5aa7c/bids_validator_deno-2.2.10-py2.py3-none-win_amd64.whl | py2.py3 | bdist_wheel | null | false | 188a7a82e675b4374ab8b157af11a83c | e927fb4ed61520739c8445e3faebef081800fe8c88c4d4ffc6d124f12efa2ae8 | be500a0e59f491fdc3517c328c354928a1fbd218f34eeb7194ce2c641cd5aa7c | null | [] |
2.1 | bids-validator-deno | 2.2.10 | Typescript implementation of the BIDS validator | [](https://github.com/bids-standard/bids-validator/actions/workflows/deno_tests.yml)
[](https://github.com/bids-standard/bids-validator/actions/workflows/web_build.yml)
[](https://bids-validator.readthedocs.io/en/latest/?badge=latest)
[](https://doi.org/10.5281/zenodo.3688707)
# The BIDS Validator
The BIDS Validator is a web application, command-line utility,
and Javascript/Typescript library for assessing compliance with the
[Brain Imaging Data Structure (BIDS)][BIDS] standard.
## Getting Started
In most cases,
the simplest way to use the validator is to browse to the [BIDS Validator][] web page:
The web validator runs in-browser, and does not transfer data to any remote server.
In some contexts, such as when working on a remote server,
it may be easier to use the command-line.
The BIDS Validator can be run with the [Deno][] runtime
(see [Deno - Installation][] for detailed installation instructions):
```shell
deno run -ERWN jsr:@bids/validator
```
Deno by default sandboxes applications like a web browser.
`-E`, `-R`, `-W`, and `-N` allow the validator to read environment variables,
read/write local files, and read network locations.
A pre-compiled binary is published to [PyPI][] and may be installed with:
```
pip install bids-validator-deno
bids-validator-deno --help
```
### Configuration file
The schema validator accepts a JSON configuration file that reclassifies issues as
warnings, errors or ignored.
```json
{
"ignore": [
{ "code": "JSON_KEY_RECOMMENDED", "location": "/T1w.json" }
],
"warning": [],
"error": [
{ "code": "NO_AUTHORS" }
]
}
```
The issues are partial matches of the `issues` that the validator accumulates.
Pass the `--json` flag to see the issues in detail.
### Development tools
From the repository root, use `./local-run` to run with all permissions enabled by default:
```shell
# Run from within the /bids-validator directory
cd bids-validator
# Run validator:
./local-run path/to/dataset
```
## Schema validator test suite
```shell
# Run tests:
deno test --allow-env --allow-read --allow-write src/
```
This test suite includes running expected output from bids-examples and may throw some expected failures for bids-examples datasets where either the schema or validator are misaligned with the example dataset while under development.
## Modifying and building a new schema
To modify the schema a clone of bids-standard/bids-specification will need to be made. README and schema itself live here https://github.com/bids-standard/bids-specification/tree/master/src/schema.
After changes to the schema have been made to a local copy the dereferenced single json file used by the validator will need to be built. The `bidsschematools` python package does this. It can be installed from pypi via pip or a local installation can be made. It lives in the specification repository here https://github.com/bids-standard/bids-specification/tree/master/tools/schemacode
The command to compile a dereferenced schema is `bst -v export --schema src/schema --output src/schema.json` (this assumes you are in the root of the bids-specification repo). Once compiled it can be passed to the validator via the `-s` flag, `./bids-validator-deno -s <path to schema> <path to dataset>`
## Documentation
The BIDS validator documentation is available on [Read the Docs](https://bids-validator.readthedocs.io/en/latest/).
[BIDS]: https://bids.neuroimaging.io
[BIDS Validator]: https://bids-standard.github.io/bids-validator/
[Deno]: https://deno.com/
[Deno - Installation]: https://docs.deno.com/runtime/getting_started/installation/
[PyPI]: https://pypi.org/project/bids-validator-deno/
| text/markdown | bids-standard developers | null | null | null | MIT | BIDS, BIDS validator | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Bio-Informatics",
"License :: OSI Approved :: MIT License",
"Programming Language :: JavaScript"
] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [
"Documentation, https://bids-validator.readthedocs.io/",
"Source code, https://github.com/bids-standard/bids-validator",
"Issues, https://github.com/bids-standard/bids-validator/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:32:12.907485 | bids_validator_deno-2.2.10.tar.gz | 82,126 | 87/73/e40cc4d1a95d719428337c0376f947ad600edf4a928cd406c0e8a8e7efa2/bids_validator_deno-2.2.10.tar.gz | source | sdist | null | false | 31ab00b6919ade05b1f84703e06bfb57 | 7a421a794900c64b4f32c2b5a9680a0e190004605d92980eeb59360292e7acfc | 8773e40cc4d1a95d719428337c0376f947ad600edf4a928cd406c0e8a8e7efa2 | null | [] |
2.4 | vllm-sr | 0.1.0b2.dev20260116043205 | vLLM Semantic Router - Intelligent routing for Mixture-of-Models | # vLLM Semantic Router
Intelligent Router for Mixture-of-Models (MoM).
GitHub: https://github.com/vllm-project/semantic-router
## Quick Start
### Installation
```bash
# Install from PyPI
pip install vllm-sr
# Or install from source (development)
cd src/vllm-sr
pip install -e .
```
### Usage
```bash
# Initialize vLLM Semantic Router Configuration
vllm-sr init
# Start the router (includes dashboard)
vllm-sr serve
# Open dashboard in browser
vllm-sr dashboard
# View logs
vllm-sr logs router
vllm-sr logs envoy
vllm-sr logs dashboard
# Check status
vllm-sr status
# Stop
vllm-sr stop
```
## Features
- **Router**: Intelligent request routing based on intent classification
- **Envoy Proxy**: High-performance proxy with ext_proc integration
- **Dashboard**: Web UI for monitoring and testing (http://localhost:8700)
- **Metrics**: Prometheus metrics endpoint (http://localhost:9190/metrics)
## Endpoints
After running `vllm-sr serve`, the following endpoints are available:
| Endpoint | Port | Description |
|----------|------|-------------|
| Dashboard | 8700 | Web UI for monitoring and Playground |
| API | 8888* | Chat completions API (configurable in config.yaml) |
| Metrics | 9190 | Prometheus metrics |
| gRPC | 50051 | Router gRPC (internal) |
*Default port, configurable via `listeners` in config.yaml
## Configuration
### File Descriptor Limits
The CLI automatically sets file descriptor limits to 65,536 for Envoy proxy. To customize:
```bash
export VLLM_SR_NOFILE_LIMIT=100000 # Optional (min: 8192)
vllm-sr serve
```
## License
Apache 2.0
| text/markdown | vLLM-SR Team | null | null | null | Apache-2.0 | vllm, semantic-router, llm, routing, caching | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"click>=8.1.7",
"pyyaml>=6.0.2",
"jinja2>=3.1.4",
"requests>=2.31.0",
"pydantic>=2.0.0",
"huggingface_hub[cli]>=0.20.0",
"pytest>=8.4.1; extra == \"dev\"",
"black>=22.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/vllm-project/vllm-semantic-router",
"Documentation, https://github.com/vllm-project/vllm-semantic-router/blob/main/README.md",
"Repository, https://github.com/vllm-project/vllm-semantic-router",
"Issues, https://github.com/vllm-project/vllm-semantic-router/issues"
] | twine/6.2.0 CPython/3.11.14 | 2026-01-16T04:32:16.194438 | vllm_sr-0.1.0b2.dev20260116043205-py3-none-any.whl | 44,105 | f3/ca/3ece201c591caaf0b63e3de4ea8cdb6cf4ffb7b46a8a4109a6e20257ec6b/vllm_sr-0.1.0b2.dev20260116043205-py3-none-any.whl | py3 | bdist_wheel | null | false | 3b8adae3bd7414bd39c67c638bd54693 | 9b476d83b3d6e7740dd8c0b492b535226f91ccd0c568a0353becedaf95070728 | f3ca3ece201c591caaf0b63e3de4ea8cdb6cf4ffb7b46a8a4109a6e20257ec6b | null | [] |
2.4 | vllm-sr | 0.1.0b2.dev20260116043205 | vLLM Semantic Router - Intelligent routing for Mixture-of-Models | # vLLM Semantic Router
Intelligent Router for Mixture-of-Models (MoM).
GitHub: https://github.com/vllm-project/semantic-router
## Quick Start
### Installation
```bash
# Install from PyPI
pip install vllm-sr
# Or install from source (development)
cd src/vllm-sr
pip install -e .
```
### Usage
```bash
# Initialize vLLM Semantic Router Configuration
vllm-sr init
# Start the router (includes dashboard)
vllm-sr serve
# Open dashboard in browser
vllm-sr dashboard
# View logs
vllm-sr logs router
vllm-sr logs envoy
vllm-sr logs dashboard
# Check status
vllm-sr status
# Stop
vllm-sr stop
```
## Features
- **Router**: Intelligent request routing based on intent classification
- **Envoy Proxy**: High-performance proxy with ext_proc integration
- **Dashboard**: Web UI for monitoring and testing (http://localhost:8700)
- **Metrics**: Prometheus metrics endpoint (http://localhost:9190/metrics)
## Endpoints
After running `vllm-sr serve`, the following endpoints are available:
| Endpoint | Port | Description |
|----------|------|-------------|
| Dashboard | 8700 | Web UI for monitoring and Playground |
| API | 8888* | Chat completions API (configurable in config.yaml) |
| Metrics | 9190 | Prometheus metrics |
| gRPC | 50051 | Router gRPC (internal) |
*Default port, configurable via `listeners` in config.yaml
## Configuration
### File Descriptor Limits
The CLI automatically sets file descriptor limits to 65,536 for Envoy proxy. To customize:
```bash
export VLLM_SR_NOFILE_LIMIT=100000 # Optional (min: 8192)
vllm-sr serve
```
## License
Apache 2.0
| text/markdown | vLLM-SR Team | null | null | null | Apache-2.0 | vllm, semantic-router, llm, routing, caching | [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
] | [] | null | null | >=3.10 | [] | [] | [] | [
"click>=8.1.7",
"pyyaml>=6.0.2",
"jinja2>=3.1.4",
"requests>=2.31.0",
"pydantic>=2.0.0",
"huggingface_hub[cli]>=0.20.0",
"pytest>=8.4.1; extra == \"dev\"",
"black>=22.0.0; extra == \"dev\""
] | [] | [] | [] | [
"Homepage, https://github.com/vllm-project/vllm-semantic-router",
"Documentation, https://github.com/vllm-project/vllm-semantic-router/blob/main/README.md",
"Repository, https://github.com/vllm-project/vllm-semantic-router",
"Issues, https://github.com/vllm-project/vllm-semantic-router/issues"
] | twine/6.2.0 CPython/3.11.14 | 2026-01-16T04:32:17.479510 | vllm_sr-0.1.0b2.dev20260116043205.tar.gz | 35,796 | 01/50/1648f230f34c0271d92d666afe3eac764f26769145ca377fc4bc2e009301/vllm_sr-0.1.0b2.dev20260116043205.tar.gz | source | sdist | null | false | dba140eca8be2a253d824118a7e17bb3 | 8a367ec14b2b61803d54eca68ea3d7b360fe2f68ca61027eb0e5f7967fdefdf0 | 01501648f230f34c0271d92d666afe3eac764f26769145ca377fc4bc2e009301 | null | [] |
2.4 | fast-scrape | 0.1.0 | High-performance HTML parsing library for Python | # scrape-rs (Python)
[](https://pypi.org/project/scrape-rs)
[](https://pypi.org/project/scrape-rs)
[](https://codecov.io/gh/bug-ops/scrape-rs)
[](../../LICENSE-MIT)
Python bindings for scrape-rs, a high-performance HTML parsing library.
## Installation
```bash
pip install scrape-rs
```
Alternative package managers:
```bash
# uv (recommended - 10-100x faster)
uv pip install scrape-rs
# Poetry
poetry add scrape-rs
# Pipenv
pipenv install scrape-rs
```
> [!IMPORTANT]
> Requires Python 3.10 or later.
## Quick start
```python
from scrape_rs import Soup
html = "<html><body><div class='content'>Hello, World!</div></body></html>"
soup = Soup(html)
div = soup.find("div")
print(div.text)
# Hello, World!
```
## Usage
### Find elements
```python
from scrape_rs import Soup
soup = Soup(html)
# Find first element by tag
div = soup.find("div")
# Find all elements
divs = soup.find_all("div")
# CSS selectors
for el in soup.select("div.content > p"):
print(el.text)
```
### Element properties
```python
element = soup.find("a")
# Get text content
text = element.text
# Get inner HTML
html = element.inner_html
# Get attribute
href = element.get("href")
```
### Batch processing
```python
from scrape_rs import Soup
# Process multiple documents in parallel
documents = [html1, html2, html3]
soups = Soup.parse_batch(documents)
for soup in soups:
print(soup.find("title").text)
```
> [!TIP]
> Use `parse_batch()` for processing multiple documents. It uses all CPU cores automatically.
## Type hints
This package includes type stubs for full IDE support:
```python
from scrape_rs import Soup, Tag
def extract_links(soup: Soup) -> list[str]:
return [a.get("href") for a in soup.select("a[href]")]
```
## Performance
Compared to BeautifulSoup on the same HTML documents:
| Operation | Speedup |
|-----------|---------|
| Parse (1 KB) | **9.7x** faster |
| Parse (219 KB) | **9.2x** faster |
| Parse (5.9 MB) | **10.6x** faster |
| `find(".class")` | **132x** faster |
| `select(".class")` | **40x** faster |
> [!TIP]
> Run `python benches/compare_python.py` from the project root to benchmark on your hardware.
## Related packages
Part of the [scrape-rs](https://github.com/bug-ops/scrape-rs) project:
- `scrape-core` — Rust core library
- `scrape-rs` (npm) — Node.js bindings
- `@scrape-rs/wasm` — Browser/WASM bindings
## License
Licensed under either of [Apache License, Version 2.0](../../LICENSE-APACHE) or [MIT License](../../LICENSE-MIT) at your option.
| text/markdown; charset=UTF-8; variant=GFM | null | null | null | null | MIT OR Apache-2.0 | html, parser, scraping, css-selectors, dom | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Lang... | [] | null | null | >=3.10 | [] | [] | [] | [] | [] | [] | [] | [
"Homepage, https://github.com/bug-ops/scrape-rs",
"Documentation, https://github.com/bug-ops/scrape-rs",
"Repository, https://github.com/bug-ops/scrape-rs",
"Issues, https://github.com/bug-ops/scrape-rs/issues"
] | maturin/1.10.2 | 2026-01-16T04:32:24.753289 | fast_scrape-0.1.0-cp314-cp314-macosx_11_0_arm64.whl | 556,023 | 02/3b/d45861d6c5fde9387e310e1a9ef3efe363ba18df72b39840b36fdff459a8/fast_scrape-0.1.0-cp314-cp314-macosx_11_0_arm64.whl | cp314 | bdist_wheel | null | false | eab3b1fa03ee53f0a45123a81281c024 | fa081df9b38f9bfb287811ad73cd88e444c8e1d6f5b4ac6dcc30b65853bfd21e | 023bd45861d6c5fde9387e310e1a9ef3efe363ba18df72b39840b36fdff459a8 | null | [] |
2.4 | fast-scrape | 0.1.0 | High-performance HTML parsing library for Python | # scrape-rs (Python)
[](https://pypi.org/project/scrape-rs)
[](https://pypi.org/project/scrape-rs)
[](https://codecov.io/gh/bug-ops/scrape-rs)
[](../../LICENSE-MIT)
Python bindings for scrape-rs, a high-performance HTML parsing library.
## Installation
```bash
pip install scrape-rs
```
Alternative package managers:
```bash
# uv (recommended - 10-100x faster)
uv pip install scrape-rs
# Poetry
poetry add scrape-rs
# Pipenv
pipenv install scrape-rs
```
> [!IMPORTANT]
> Requires Python 3.10 or later.
## Quick start
```python
from scrape_rs import Soup
html = "<html><body><div class='content'>Hello, World!</div></body></html>"
soup = Soup(html)
div = soup.find("div")
print(div.text)
# Hello, World!
```
## Usage
### Find elements
```python
from scrape_rs import Soup
soup = Soup(html)
# Find first element by tag
div = soup.find("div")
# Find all elements
divs = soup.find_all("div")
# CSS selectors
for el in soup.select("div.content > p"):
print(el.text)
```
### Element properties
```python
element = soup.find("a")
# Get text content
text = element.text
# Get inner HTML
html = element.inner_html
# Get attribute
href = element.get("href")
```
### Batch processing
```python
from scrape_rs import Soup
# Process multiple documents in parallel
documents = [html1, html2, html3]
soups = Soup.parse_batch(documents)
for soup in soups:
print(soup.find("title").text)
```
> [!TIP]
> Use `parse_batch()` for processing multiple documents. It uses all CPU cores automatically.
## Type hints
This package includes type stubs for full IDE support:
```python
from scrape_rs import Soup, Tag
def extract_links(soup: Soup) -> list[str]:
return [a.get("href") for a in soup.select("a[href]")]
```
## Performance
Compared to BeautifulSoup on the same HTML documents:
| Operation | Speedup |
|-----------|---------|
| Parse (1 KB) | **9.7x** faster |
| Parse (219 KB) | **9.2x** faster |
| Parse (5.9 MB) | **10.6x** faster |
| `find(".class")` | **132x** faster |
| `select(".class")` | **40x** faster |
> [!TIP]
> Run `python benches/compare_python.py` from the project root to benchmark on your hardware.
## Related packages
Part of the [scrape-rs](https://github.com/bug-ops/scrape-rs) project:
- `scrape-core` — Rust core library
- `scrape-rs` (npm) — Node.js bindings
- `@scrape-rs/wasm` — Browser/WASM bindings
## License
Licensed under either of [Apache License, Version 2.0](../../LICENSE-APACHE) or [MIT License](../../LICENSE-MIT) at your option.
| text/markdown; charset=UTF-8; variant=GFM | null | null | null | null | MIT OR Apache-2.0 | html, parser, scraping, css-selectors, dom | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Lang... | [] | null | null | >=3.10 | [] | [] | [] | [] | [] | [] | [] | [
"Homepage, https://github.com/bug-ops/scrape-rs",
"Documentation, https://github.com/bug-ops/scrape-rs",
"Repository, https://github.com/bug-ops/scrape-rs",
"Issues, https://github.com/bug-ops/scrape-rs/issues"
] | maturin/1.10.2 | 2026-01-16T04:32:27.317323 | fast_scrape-0.1.0.tar.gz | 73,462 | 47/5a/7c7b42c8b79060b175bd282e1c71a979b1263328404f70004a6fca5c0f5c/fast_scrape-0.1.0.tar.gz | source | sdist | null | false | b7d9afbed7b46add9e0394e5ee053d2a | f6f61b700b400f38c5e720745450eb529f5e6ccae8e919a82b7b73b4360e5773 | 475a7c7b42c8b79060b175bd282e1c71a979b1263328404f70004a6fca5c0f5c | null | [] |
2.4 | hubify-dataset | 0.1.1 | Convert object detection datasets (COCO, YOLO, Pascal VOC, etc.) to HuggingFace format | # Hubify
Convert object detection datasets to HuggingFace format and upload to the Hub.
**Currently supported formats:**
- COCO format annotations
- YOLO format annotations
- YOLO OBB format annotations
**Coming soon:** Pascal VOC, Labelme, and more!
## Motivations for this tool
HuggingFace has become the defacto *open source* community to upload datasets and models. It's primarily about LLMs and language models, but there's nothing about HuggingFace's dataset hosting that's specific to language modeling.
This tool is meant to be a way to consolidate the different formats from the object detection domain (COCO, Pascal VOC, etc) into what HuggingFace suggests for their Image Datasets, and upload it to HuggingFace Hub.
## Installation
```bash
# Install with uv (recommended)
uv pip install -e .
# Or with pip
pip install -e .
```
## Usage
After installation, you can use the `hubify` command:
```bash
# Auto-detect annotations in train/validation/test directories
hubify --data-dir /path/to/images --format coco
# Manually specify annotation files
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--validation-annotations /path/to/instances_val2017.json
# Generate sample visualizations
hubify --data-dir /path/to/images --visualize
# Push to HuggingFace Hub
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--push-to-hub username/my-dataset
```
Or for yolo:
```
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo-obb --push-to-hub benjamintli/dota-v1.5
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo --push-to-hub benjamintli/dota-v1.5
```
Or run directly with Python (from the virtual environment):
```bash
source .venv/bin/activate
python -m src.main --data-dir /path/to/images
```
## Expected Directory Structure
* For coco:
```
data-dir/
├── train/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
├── validation/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
└── test/ (optional)
├── instances*.json
└── *.jpg
```
## Output
The tool generates `metadata.jsonl` files in each split directory:
```
data-dir/
├── train/
│ └── metadata.jsonl
└── validation/
└── metadata.jsonl
```
Each line in `metadata.jsonl` contains:
```json
{
"file_name": "image.jpg",
"objects": {
"bbox": [[x, y, width, height], ...],
"category": [0, 1, ...]
}
}
```
## Options
- `--data-dir`: Root directory containing train/validation/test subdirectories (required)
- `--train-annotations`: Path to training annotations JSON (optional)
- `--validation-annotations`: Path to validation annotations JSON (optional)
- `--test-annotations`: Path to test annotations JSON (optional)
- `--visualize`: Generate sample visualization images with bounding boxes
- `--push-to-hub`: Push dataset to HuggingFace Hub (format: `username/dataset-name`)
- `--token`: HuggingFace API token (optional, defaults to `HF_TOKEN` env var or `huggingface-cli login`)
### Authentication for Hub Push
When using `--push-to-hub`, the tool looks for your HuggingFace token in this order:
1. `--token YOUR_TOKEN` (CLI argument)
2. `HF_TOKEN` environment variable
3. Token from `huggingface-cli login`
If no token is found, you'll get a helpful error message with instructions.
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | >=3.12 | [] | [] | [] | [
"datasets>=4.4.2",
"huggingface-hub>=1.2.3",
"pillow>=12.1.0",
"pyyaml>=6.0",
"rich>=13.9.4",
"ruff>=0.14.10",
"ruff>=0.14.10; extra == \"dev\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:34:03.903980 | hubify_dataset-0.1.1-py3-none-any.whl | 17,432 | 13/d2/6e5bbac9d21d7f8a1fbbe11b7115c1c4a962806a07c559a930f07f150050/hubify_dataset-0.1.1-py3-none-any.whl | py3 | bdist_wheel | null | false | c8300a435a7d67cc258bda7ba850e2f0 | 75b9f6ba70f19fed27dceb92191748c9ea682197d05cb2d733edbe2952aeda35 | 13d26e5bbac9d21d7f8a1fbbe11b7115c1c4a962806a07c559a930f07f150050 | null | [
"LICENSE"
] |
2.4 | hubify-dataset | 0.1.1 | Convert object detection datasets (COCO, YOLO, Pascal VOC, etc.) to HuggingFace format | # Hubify
Convert object detection datasets to HuggingFace format and upload to the Hub.
**Currently supported formats:**
- COCO format annotations
- YOLO format annotations
- YOLO OBB format annotations
**Coming soon:** Pascal VOC, Labelme, and more!
## Motivations for this tool
HuggingFace has become the defacto *open source* community to upload datasets and models. It's primarily about LLMs and language models, but there's nothing about HuggingFace's dataset hosting that's specific to language modeling.
This tool is meant to be a way to consolidate the different formats from the object detection domain (COCO, Pascal VOC, etc) into what HuggingFace suggests for their Image Datasets, and upload it to HuggingFace Hub.
## Installation
```bash
# Install with uv (recommended)
uv pip install -e .
# Or with pip
pip install -e .
```
## Usage
After installation, you can use the `hubify` command:
```bash
# Auto-detect annotations in train/validation/test directories
hubify --data-dir /path/to/images --format coco
# Manually specify annotation files
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--validation-annotations /path/to/instances_val2017.json
# Generate sample visualizations
hubify --data-dir /path/to/images --visualize
# Push to HuggingFace Hub
hubify --data-dir /path/to/images \
--train-annotations /path/to/instances_train2017.json \
--push-to-hub username/my-dataset
```
Or for yolo:
```
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo-obb --push-to-hub benjamintli/dota-v1.5
hubify --data-dir ~/Downloads/DOTAv1.5 --format yolo --push-to-hub benjamintli/dota-v1.5
```
Or run directly with Python (from the virtual environment):
```bash
source .venv/bin/activate
python -m src.main --data-dir /path/to/images
```
## Expected Directory Structure
* For coco:
```
data-dir/
├── train/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
├── validation/
│ ├── instances*.json (auto-detected)
│ └── *.jpg (images)
└── test/ (optional)
├── instances*.json
└── *.jpg
```
## Output
The tool generates `metadata.jsonl` files in each split directory:
```
data-dir/
├── train/
│ └── metadata.jsonl
└── validation/
└── metadata.jsonl
```
Each line in `metadata.jsonl` contains:
```json
{
"file_name": "image.jpg",
"objects": {
"bbox": [[x, y, width, height], ...],
"category": [0, 1, ...]
}
}
```
## Options
- `--data-dir`: Root directory containing train/validation/test subdirectories (required)
- `--train-annotations`: Path to training annotations JSON (optional)
- `--validation-annotations`: Path to validation annotations JSON (optional)
- `--test-annotations`: Path to test annotations JSON (optional)
- `--visualize`: Generate sample visualization images with bounding boxes
- `--push-to-hub`: Push dataset to HuggingFace Hub (format: `username/dataset-name`)
- `--token`: HuggingFace API token (optional, defaults to `HF_TOKEN` env var or `huggingface-cli login`)
### Authentication for Hub Push
When using `--push-to-hub`, the tool looks for your HuggingFace token in this order:
1. `--token YOUR_TOKEN` (CLI argument)
2. `HF_TOKEN` environment variable
3. Token from `huggingface-cli login`
If no token is found, you'll get a helpful error message with instructions.
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | >=3.12 | [] | [] | [] | [
"datasets>=4.4.2",
"huggingface-hub>=1.2.3",
"pillow>=12.1.0",
"pyyaml>=6.0",
"rich>=13.9.4",
"ruff>=0.14.10",
"ruff>=0.14.10; extra == \"dev\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:34:05.168221 | hubify_dataset-0.1.1.tar.gz | 15,886 | 94/aa/93c82a0c85bdb7c6e60ede814d9ff5b24531899d57f09c776ba6493cef87/hubify_dataset-0.1.1.tar.gz | source | sdist | null | false | 1e8a3adba8df609a2759e9505d4a93e4 | 96e6d82c954ee1b36191c7220ddc2b9c44151511ebeb01ecc8e6d22704634e3e | 94aa93c82a0c85bdb7c6e60ede814d9ff5b24531899d57f09c776ba6493cef87 | null | [
"LICENSE"
] |
2.4 | revidx | 1.3.0 | reVidx: Re-encode video to AVC/MP3 for legacy compatibility. | # reVidx
reVidx is a cross-platform CLI tool designed to re-encode video files to AVC (H.264) codec and audio to MP3 for compatibility with legacy devices and players.
Built on top of FFmpeg, it offers a simple, fast interface for batch processing video conversion, subtitle burning, and audio extraction (aac) with a minimal terminal progress bar.
## Requirements
- [Python](https://www.python.org/): 3.7 or higher.
- [FFmpeg](https://ffmpeg.org/) and [FFprobe](https://ffmpeg.org/ffprobe.html): Should available in your system PATH.
## Installation from source
- Install directly from github repo
```bash
git clone https://github.com/voidsnax/reVidx.git
cd revidx
pip install -e .
```
## Features
- **Video Conversion**: Converts `HEVC` (H.265) to `AVC` (H.264) with proper pixel format and colour range settings for older device compatibility.
- **Audio Conversion**: Encodes audio to `mp3` (192kbps) for video files.
- **Audio Extraction**: Extracts audio directly to `aac` (128kbps).
- **Subtitle Burning**: Hardcode subtitles into the video track.
- **Progress Bar**: Displays real-time stats including `Size`, `Duration`, `Percentage`, and `Elapsed Time`.
- **Fast Encoding**: Uses `crf` 20 (default) with `veryfast` preset for a balance of speed and quality.
- **Batch Processing**: Process multiple video files sequentially with a single command.
## Usage
```txt
usage: revidx INPUTFILES ... [OPTIONS]
positional arguments:
inputfiles Input video files
options:
-h, --help show this help message and exit
-o [PATH/NAME] Output directory or filename
-skip Skip video encoding (copy)
-burn [INDEX/PATH] Burn subtitles into the video (default: first subtitle stream from input)
-aindex INDEX Audio index (default: 0)
-audio Extract only audio to AAC
-crf VALUE CRF value (default: 20)
```
### Basic Conversion
Convert a video file to `avc/mp3`. The output will be saved as `input-AvcMp3.mp4`.
```bash
revidx inputvideo
```
### Skip Video Encoding
Convert the audio to `mp3` and Keep the original video stream.
```bash
revidx inputvideo -skip
```
### Specify Output Path
Convert a videofile and save it with specific name or convert multiple files and save them to specific folder.
```bash
revidx inputvideo -o ~/Covertedvideo.mp4
```
```bash
revidx inputvideo -o ./ConvertedVideos
```
### Burn Subtitles
Burn the first subtitle track into the video.
```bash
revidx inputvideo -burn
```
Burn a specific subtitle stream from input video.
```bash
revidx inputvideo -burn 2
```
Burn a specific external subtitle file.
```bash
revidx inputvideo -burn pathtosubfile
```
### Extract Audio Only
Extract the audio track to an `aac` file.
```bash
revidx inputvideo -audio
```
## Notes
- **Output Extensions**: When using `-o` to specify a filename, ensure it ends with `.mp4` or .`mkv` (video) or `.aac` (audio).
- **Automatic Naming**: If no name is provided, output is saved as `input-AvcMp3.mp4` or `input.aac`.
- **Overwriting**: The tool automatically overwrites existing files without prompting.
- **Subtitles**: Subtitle index start from `0` and ensure proper subtitle formats are passed when `-burn` is provided with external path.
- **Interrupting**: Press `Ctrl+C` to abort the current encoding.
## License
MIT License
| text/markdown | voidsnax | null | null | null | MIT | video, ffmpeg, converter, h264, mp3 | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.7 | [] | [] | [] | [
"colorama"
] | [] | [] | [] | [
"Homepage, https://github.com/voidsnax/reVidx"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:34:40.473011 | revidx-1.3.0-py3-none-any.whl | 8,940 | 00/85/feda0519e2806041ddaf5a4870f9547645b3401ebf7349be31ec08ea015b/revidx-1.3.0-py3-none-any.whl | py3 | bdist_wheel | null | false | 63845ae0bdcca51043d08e0be97e2962 | 389f485aba4ea7a4cb1b1a6a03ec34578f1b6bb2daf1cb11a620589443fcf867 | 0085feda0519e2806041ddaf5a4870f9547645b3401ebf7349be31ec08ea015b | null | [
"LICENSE"
] |
2.4 | revidx | 1.3.0 | reVidx: Re-encode video to AVC/MP3 for legacy compatibility. | # reVidx
reVidx is a cross-platform CLI tool designed to re-encode video files to AVC (H.264) codec and audio to MP3 for compatibility with legacy devices and players.
Built on top of FFmpeg, it offers a simple, fast interface for batch processing video conversion, subtitle burning, and audio extraction (aac) with a minimal terminal progress bar.
## Requirements
- [Python](https://www.python.org/): 3.7 or higher.
- [FFmpeg](https://ffmpeg.org/) and [FFprobe](https://ffmpeg.org/ffprobe.html): Should available in your system PATH.
## Installation from source
- Install directly from github repo
```bash
git clone https://github.com/voidsnax/reVidx.git
cd revidx
pip install -e .
```
## Features
- **Video Conversion**: Converts `HEVC` (H.265) to `AVC` (H.264) with proper pixel format and colour range settings for older device compatibility.
- **Audio Conversion**: Encodes audio to `mp3` (192kbps) for video files.
- **Audio Extraction**: Extracts audio directly to `aac` (128kbps).
- **Subtitle Burning**: Hardcode subtitles into the video track.
- **Progress Bar**: Displays real-time stats including `Size`, `Duration`, `Percentage`, and `Elapsed Time`.
- **Fast Encoding**: Uses `crf` 20 (default) with `veryfast` preset for a balance of speed and quality.
- **Batch Processing**: Process multiple video files sequentially with a single command.
## Usage
```txt
usage: revidx INPUTFILES ... [OPTIONS]
positional arguments:
inputfiles Input video files
options:
-h, --help show this help message and exit
-o [PATH/NAME] Output directory or filename
-skip Skip video encoding (copy)
-burn [INDEX/PATH] Burn subtitles into the video (default: first subtitle stream from input)
-aindex INDEX Audio index (default: 0)
-audio Extract only audio to AAC
-crf VALUE CRF value (default: 20)
```
### Basic Conversion
Convert a video file to `avc/mp3`. The output will be saved as `input-AvcMp3.mp4`.
```bash
revidx inputvideo
```
### Skip Video Encoding
Convert the audio to `mp3` and Keep the original video stream.
```bash
revidx inputvideo -skip
```
### Specify Output Path
Convert a videofile and save it with specific name or convert multiple files and save them to specific folder.
```bash
revidx inputvideo -o ~/Covertedvideo.mp4
```
```bash
revidx inputvideo -o ./ConvertedVideos
```
### Burn Subtitles
Burn the first subtitle track into the video.
```bash
revidx inputvideo -burn
```
Burn a specific subtitle stream from input video.
```bash
revidx inputvideo -burn 2
```
Burn a specific external subtitle file.
```bash
revidx inputvideo -burn pathtosubfile
```
### Extract Audio Only
Extract the audio track to an `aac` file.
```bash
revidx inputvideo -audio
```
## Notes
- **Output Extensions**: When using `-o` to specify a filename, ensure it ends with `.mp4` or .`mkv` (video) or `.aac` (audio).
- **Automatic Naming**: If no name is provided, output is saved as `input-AvcMp3.mp4` or `input.aac`.
- **Overwriting**: The tool automatically overwrites existing files without prompting.
- **Subtitles**: Subtitle index start from `0` and ensure proper subtitle formats are passed when `-burn` is provided with external path.
- **Interrupting**: Press `Ctrl+C` to abort the current encoding.
## License
MIT License
| text/markdown | voidsnax | null | null | null | MIT | video, ffmpeg, converter, h264, mp3 | [
"Programming Language :: Python :: 3",
"Operating System :: OS Independent"
] | [] | null | null | >=3.7 | [] | [] | [] | [
"colorama"
] | [] | [] | [] | [
"Homepage, https://github.com/voidsnax/reVidx"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:34:41.839542 | revidx-1.3.0.tar.gz | 9,570 | 80/a9/e8c934bc08352f811ac44b90244f35ed68bbc0e70a3880c703a619413947/revidx-1.3.0.tar.gz | source | sdist | null | false | c816625d0f09ff4f9a3fd2f9085bc0b5 | 33bc7dae814d00f75ee8de50d3e17ae77fe4dc98f12a9ae0670274e81a597082 | 80a9e8c934bc08352f811ac44b90244f35ed68bbc0e70a3880c703a619413947 | null | [
"LICENSE"
] |
2.2 | ai-atlasforge | 1.2.0 | Autonomous AI research and development platform powered by Claude | # AI-AtlasForge
An autonomous AI research and development platform powered by Claude. Run long-duration missions, accumulate cross-session knowledge, and build software autonomously.
## What is AI-AtlasForge?
AI-AtlasForge is not a chatbot wrapper. It's an **autonomous research engine** that:
- Runs multi-day missions without human intervention
- Maintains mission continuity across context windows
- Accumulates knowledge that persists across sessions
- Self-corrects when drifting from objectives
- Adversarially tests its own outputs
## Quick Start
### Prerequisites
- Python 3.10+
- Anthropic API key (get one at https://console.anthropic.com/)
- Linux environment (tested on Ubuntu 22.04+, Debian 12+)
> **Platform Notes:**
> - **Windows:** Use WSL2 (Windows Subsystem for Linux)
> - **macOS:** Should work but is untested. Please report issues.
### Option 1: Standard Installation
```bash
# Clone the repository
git clone https://github.com/DragonShadows1978/AI-AtlasForge.git
cd AI-AtlasForge
# Run the installer
./install.sh
# Configure your API key
export ANTHROPIC_API_KEY='your-key-here'
# Or edit config.yaml / .env
# Verify installation
./verify.sh
```
### Option 2: One-Liner Install
```bash
curl -sSL https://raw.githubusercontent.com/DragonShadows1978/AI-AtlasForge/main/quick_install.sh | bash
```
### Option 3: Docker Installation
```bash
git clone https://github.com/DragonShadows1978/AI-AtlasForge.git
cd AI-AtlasForge
docker compose up -d
# Dashboard at http://localhost:5050
```
For detailed installation options, see [INSTALL.md](INSTALL.md) or [QUICKSTART.md](QUICKSTART.md).
### Running Your First Mission
1. **Start the Dashboard** (optional, for monitoring):
```bash
make dashboard
# Or: python3 dashboard_v2.py
# Access at http://localhost:5050
```
2. **Create a Mission**:
- Via Dashboard: Click "Create Mission" and enter your objectives
- Via Sample: Run `make sample-mission` to load a hello-world mission
- Via JSON: Create `state/mission.json` manually
3. **Start the Engine**:
```bash
make run
# Or: python3 claude_autonomous.py --mode=rd
```
### Development Commands
Run `make help` to see all available commands:
```bash
make install # Full installation
make verify # Verify installation
make dashboard # Start dashboard
make run # Start autonomous agent
make docker # Start with Docker
make sample-mission # Load sample mission
```
## Architecture
```
+-------------------+
| Mission State |
| (mission.json) |
+--------+----------+
|
+--------------+--------------+
| |
+---------v---------+ +--------v--------+
| AtlasForge | | Dashboard |
| (Execution Engine)| | (Monitoring) |
+---------+---------+ +-----------------+
|
+---------v---------+
| R&D Engine |
| (State Machine) |
+---------+---------+
|
+---------v-------------------+
| Stage Pipeline |
| |
| PLANNING -> BUILDING -> |
| TESTING -> ANALYZING -> |
| CYCLE_END -> COMPLETE |
+-----------------------------+
```
## Mission Lifecycle
1. **PLANNING** - Understand objectives, research codebase, create implementation plan
2. **BUILDING** - Implement the solution
3. **TESTING** - Validate implementation
4. **ANALYZING** - Evaluate results, identify issues
5. **CYCLE_END** - Generate reports, prepare continuation
6. **COMPLETE** - Mission finished
Missions can iterate through multiple cycles until success criteria are met.
## Core Components
### atlasforge.py
Main execution loop. Spawns Claude instances, manages state, handles graceful shutdown.
### af_engine.py
State machine for mission execution. Manages stages, enforces constraints, tracks progress.
### dashboard_v2.py
Web-based monitoring interface showing mission status, knowledge base, and analytics.
### Knowledge Base
SQLite database accumulating learnings across all missions:
- Techniques discovered
- Insights gained
- Gotchas encountered
- Reusable code patterns
### Adversarial Testing
Separate Claude instances that test implementations:
- RedTeam agents with no implementation knowledge
- Mutation testing
- Property-based testing
### GlassBox
Post-mission introspection system:
- Transcript parsing
- Agent hierarchy reconstruction
- Stage timeline visualization
## Key Features
### Mission Continuity
Missions survive context window limits through:
- Persistent mission.json state
- Cycle-based iteration
- Continuation prompts that preserve context
### Knowledge Accumulation
Every mission adds to the knowledge base. The system improves over time as it learns patterns, gotchas, and techniques.
### Autonomous Operation
Designed for unattended execution:
- Graceful crash recovery
- Stage checkpointing
- Automatic cycle progression
## Directory Structure
```
AI-AtlasForge/
+-- atlasforge.py # Main entry point
+-- af_engine.py # Stage state machine
+-- dashboard_v2.py # Web dashboard
+-- adversarial_testing/ # Testing framework
+-- atlasforge_enhancements/ # Enhancement modules
+-- workspace/ # Active workspace
| +-- glassbox/ # Introspection tools
| +-- artifacts/ # Plans, reports
| +-- research/ # Notes, findings
| +-- tests/ # Test scripts
+-- state/ # Runtime state
| +-- mission.json # Current mission
| +-- claude_state.json # Execution state
+-- missions/ # Mission workspaces
+-- atlasforge_data/
| +-- knowledge_base/ # Accumulated learnings
+-- logs/ # Execution logs
```
## Configuration
AI-AtlasForge uses environment variables for configuration:
| Variable | Default | Description |
|----------|---------|-------------|
| `ATLASFORGE_PORT` | `5050` | Dashboard port |
| `ATLASFORGE_ROOT` | (script directory) | Base directory |
| `ATLASFORGE_DEBUG` | `false` | Enable debug logging |
## Dashboard Features
The web dashboard provides real-time monitoring:
- **Mission Status** - Current stage, progress, timing
- **Activity Feed** - Live log of agent actions
- **Knowledge Base** - Search and browse learnings
- **Analytics** - Token usage, cost tracking
- **Mission Queue** - Queue and schedule missions
- **GlassBox** - Post-mission analysis
## Philosophy
**First principles only.** No frameworks hiding integration failures. Every component built from scratch for full visibility.
**Speed of machine, not human.** Designed for autonomous operation. Check in when convenient, not when required.
**Knowledge accumulates.** Every mission adds to the knowledge base. The system gets better over time.
**Trust but verify.** Adversarial testing catches what regular testing misses. The same agent that writes code doesn't validate it.
## Requirements
- Python 3.10+
- Node.js 18+ (optional, for dashboard JS modifications)
- Anthropic API key
- Linux environment (Ubuntu 22.04+, Debian 12+)
### Python Dependencies
See `requirements.txt` or `pyproject.toml` for full list.
## Documentation
- [QUICKSTART.md](QUICKSTART.md) - Get started in 5 minutes
- [INSTALL.md](INSTALL.md) - Detailed installation guide
- [USAGE.md](USAGE.md) - How to use AI-AtlasForge
- [ARCHITECTURE.md](ARCHITECTURE.md) - System architecture
## License
MIT License - see [LICENSE](LICENSE) for details.
## Contributing
Contributions are welcome! Please feel free to submit issues and pull requests.
## Related Projects
- **[AI-AfterImage](https://github.com/DragonShadows1978/AI-AfterImage)** - Episodic memory for AI coding agents. Gives Claude Code persistent memory of code it has written across sessions. Works great with AtlasForge for cross-mission code recall.
## Acknowledgments
Built on Claude by Anthropic. Special thanks to the Claude Code team for making autonomous AI development possible.
| text/markdown | null | null | null | null | MIT | ai, claude, autonomous, research, development | [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Environment :: Web Environment",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programmi... | [] | null | null | >=3.10 | [] | [] | [] | [
"flask>=2.0.0",
"flask-socketio>=5.0.0",
"simple-websocket>=0.5.0",
"anthropic>=0.18.0",
"watchdog>=3.0.0",
"psutil>=5.9.0",
"numpy>=1.21.0",
"scikit-learn>=1.0.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"black>=23.0.0; extra == \"dev\"",
"flake8>=6.0.0; extr... | [] | [] | [] | [
"Homepage, https://github.com/DragonShadows1978/AI-AtlasForge",
"Documentation, https://github.com/DragonShadows1978/AI-AtlasForge#readme",
"Repository, https://github.com/DragonShadows1978/AI-AtlasForge.git",
"Issues, https://github.com/DragonShadows1978/AI-AtlasForge/issues"
] | twine/6.2.0 CPython/3.12.3 | 2026-01-16T04:34:47.169623 | ai_atlasforge-1.2.0-py3-none-any.whl | 211,995 | 2b/36/4f49ea4de9807cb07f6251d7f1d5468b963cdead5ecbb8ff9d84eba4f9a5/ai_atlasforge-1.2.0-py3-none-any.whl | py3 | bdist_wheel | null | false | 5799e601ce7db9e59f61f4be59f8986b | 053f830cc2705b3ec177067d95c93e50b72ee76db9b64d932375bdcabfdc4e25 | 2b364f49ea4de9807cb07f6251d7f1d5468b963cdead5ecbb8ff9d84eba4f9a5 | null | [] |
2.2 | ai-atlasforge | 1.2.0 | Autonomous AI research and development platform powered by Claude | # AI-AtlasForge
An autonomous AI research and development platform powered by Claude. Run long-duration missions, accumulate cross-session knowledge, and build software autonomously.
## What is AI-AtlasForge?
AI-AtlasForge is not a chatbot wrapper. It's an **autonomous research engine** that:
- Runs multi-day missions without human intervention
- Maintains mission continuity across context windows
- Accumulates knowledge that persists across sessions
- Self-corrects when drifting from objectives
- Adversarially tests its own outputs
## Quick Start
### Prerequisites
- Python 3.10+
- Anthropic API key (get one at https://console.anthropic.com/)
- Linux environment (tested on Ubuntu 22.04+, Debian 12+)
> **Platform Notes:**
> - **Windows:** Use WSL2 (Windows Subsystem for Linux)
> - **macOS:** Should work but is untested. Please report issues.
### Option 1: Standard Installation
```bash
# Clone the repository
git clone https://github.com/DragonShadows1978/AI-AtlasForge.git
cd AI-AtlasForge
# Run the installer
./install.sh
# Configure your API key
export ANTHROPIC_API_KEY='your-key-here'
# Or edit config.yaml / .env
# Verify installation
./verify.sh
```
### Option 2: One-Liner Install
```bash
curl -sSL https://raw.githubusercontent.com/DragonShadows1978/AI-AtlasForge/main/quick_install.sh | bash
```
### Option 3: Docker Installation
```bash
git clone https://github.com/DragonShadows1978/AI-AtlasForge.git
cd AI-AtlasForge
docker compose up -d
# Dashboard at http://localhost:5050
```
For detailed installation options, see [INSTALL.md](INSTALL.md) or [QUICKSTART.md](QUICKSTART.md).
### Running Your First Mission
1. **Start the Dashboard** (optional, for monitoring):
```bash
make dashboard
# Or: python3 dashboard_v2.py
# Access at http://localhost:5050
```
2. **Create a Mission**:
- Via Dashboard: Click "Create Mission" and enter your objectives
- Via Sample: Run `make sample-mission` to load a hello-world mission
- Via JSON: Create `state/mission.json` manually
3. **Start the Engine**:
```bash
make run
# Or: python3 claude_autonomous.py --mode=rd
```
### Development Commands
Run `make help` to see all available commands:
```bash
make install # Full installation
make verify # Verify installation
make dashboard # Start dashboard
make run # Start autonomous agent
make docker # Start with Docker
make sample-mission # Load sample mission
```
## Architecture
```
+-------------------+
| Mission State |
| (mission.json) |
+--------+----------+
|
+--------------+--------------+
| |
+---------v---------+ +--------v--------+
| AtlasForge | | Dashboard |
| (Execution Engine)| | (Monitoring) |
+---------+---------+ +-----------------+
|
+---------v---------+
| R&D Engine |
| (State Machine) |
+---------+---------+
|
+---------v-------------------+
| Stage Pipeline |
| |
| PLANNING -> BUILDING -> |
| TESTING -> ANALYZING -> |
| CYCLE_END -> COMPLETE |
+-----------------------------+
```
## Mission Lifecycle
1. **PLANNING** - Understand objectives, research codebase, create implementation plan
2. **BUILDING** - Implement the solution
3. **TESTING** - Validate implementation
4. **ANALYZING** - Evaluate results, identify issues
5. **CYCLE_END** - Generate reports, prepare continuation
6. **COMPLETE** - Mission finished
Missions can iterate through multiple cycles until success criteria are met.
## Core Components
### atlasforge.py
Main execution loop. Spawns Claude instances, manages state, handles graceful shutdown.
### af_engine.py
State machine for mission execution. Manages stages, enforces constraints, tracks progress.
### dashboard_v2.py
Web-based monitoring interface showing mission status, knowledge base, and analytics.
### Knowledge Base
SQLite database accumulating learnings across all missions:
- Techniques discovered
- Insights gained
- Gotchas encountered
- Reusable code patterns
### Adversarial Testing
Separate Claude instances that test implementations:
- RedTeam agents with no implementation knowledge
- Mutation testing
- Property-based testing
### GlassBox
Post-mission introspection system:
- Transcript parsing
- Agent hierarchy reconstruction
- Stage timeline visualization
## Key Features
### Mission Continuity
Missions survive context window limits through:
- Persistent mission.json state
- Cycle-based iteration
- Continuation prompts that preserve context
### Knowledge Accumulation
Every mission adds to the knowledge base. The system improves over time as it learns patterns, gotchas, and techniques.
### Autonomous Operation
Designed for unattended execution:
- Graceful crash recovery
- Stage checkpointing
- Automatic cycle progression
## Directory Structure
```
AI-AtlasForge/
+-- atlasforge.py # Main entry point
+-- af_engine.py # Stage state machine
+-- dashboard_v2.py # Web dashboard
+-- adversarial_testing/ # Testing framework
+-- atlasforge_enhancements/ # Enhancement modules
+-- workspace/ # Active workspace
| +-- glassbox/ # Introspection tools
| +-- artifacts/ # Plans, reports
| +-- research/ # Notes, findings
| +-- tests/ # Test scripts
+-- state/ # Runtime state
| +-- mission.json # Current mission
| +-- claude_state.json # Execution state
+-- missions/ # Mission workspaces
+-- atlasforge_data/
| +-- knowledge_base/ # Accumulated learnings
+-- logs/ # Execution logs
```
## Configuration
AI-AtlasForge uses environment variables for configuration:
| Variable | Default | Description |
|----------|---------|-------------|
| `ATLASFORGE_PORT` | `5050` | Dashboard port |
| `ATLASFORGE_ROOT` | (script directory) | Base directory |
| `ATLASFORGE_DEBUG` | `false` | Enable debug logging |
## Dashboard Features
The web dashboard provides real-time monitoring:
- **Mission Status** - Current stage, progress, timing
- **Activity Feed** - Live log of agent actions
- **Knowledge Base** - Search and browse learnings
- **Analytics** - Token usage, cost tracking
- **Mission Queue** - Queue and schedule missions
- **GlassBox** - Post-mission analysis
## Philosophy
**First principles only.** No frameworks hiding integration failures. Every component built from scratch for full visibility.
**Speed of machine, not human.** Designed for autonomous operation. Check in when convenient, not when required.
**Knowledge accumulates.** Every mission adds to the knowledge base. The system gets better over time.
**Trust but verify.** Adversarial testing catches what regular testing misses. The same agent that writes code doesn't validate it.
## Requirements
- Python 3.10+
- Node.js 18+ (optional, for dashboard JS modifications)
- Anthropic API key
- Linux environment (Ubuntu 22.04+, Debian 12+)
### Python Dependencies
See `requirements.txt` or `pyproject.toml` for full list.
## Documentation
- [QUICKSTART.md](QUICKSTART.md) - Get started in 5 minutes
- [INSTALL.md](INSTALL.md) - Detailed installation guide
- [USAGE.md](USAGE.md) - How to use AI-AtlasForge
- [ARCHITECTURE.md](ARCHITECTURE.md) - System architecture
## License
MIT License - see [LICENSE](LICENSE) for details.
## Contributing
Contributions are welcome! Please feel free to submit issues and pull requests.
## Related Projects
- **[AI-AfterImage](https://github.com/DragonShadows1978/AI-AfterImage)** - Episodic memory for AI coding agents. Gives Claude Code persistent memory of code it has written across sessions. Works great with AtlasForge for cross-mission code recall.
## Acknowledgments
Built on Claude by Anthropic. Special thanks to the Claude Code team for making autonomous AI development possible.
| text/markdown | null | null | null | null | MIT | ai, claude, autonomous, research, development | [
"Development Status :: 4 - Beta",
"Environment :: Console",
"Environment :: Web Environment",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programmi... | [] | null | null | >=3.10 | [] | [] | [] | [
"flask>=2.0.0",
"flask-socketio>=5.0.0",
"simple-websocket>=0.5.0",
"anthropic>=0.18.0",
"watchdog>=3.0.0",
"psutil>=5.9.0",
"numpy>=1.21.0",
"scikit-learn>=1.0.0",
"pytest>=7.0.0; extra == \"dev\"",
"pytest-cov>=4.0.0; extra == \"dev\"",
"black>=23.0.0; extra == \"dev\"",
"flake8>=6.0.0; extr... | [] | [] | [] | [
"Homepage, https://github.com/DragonShadows1978/AI-AtlasForge",
"Documentation, https://github.com/DragonShadows1978/AI-AtlasForge#readme",
"Repository, https://github.com/DragonShadows1978/AI-AtlasForge.git",
"Issues, https://github.com/DragonShadows1978/AI-AtlasForge/issues"
] | twine/6.2.0 CPython/3.12.3 | 2026-01-16T04:34:48.607559 | ai_atlasforge-1.2.0.tar.gz | 194,136 | c4/09/52d2926ab2a63ed2076d87e6eae8dca6b43772e765436532bc74fc9c2a8e/ai_atlasforge-1.2.0.tar.gz | source | sdist | null | false | 07b21f8025d438f559aeb7551a83ef8e | c3589dcbe9ab77cbc1a72da2fb6b00262a6472775275bfa097925afff3db0210 | c40952d2926ab2a63ed2076d87e6eae8dca6b43772e765436532bc74fc9c2a8e | null | [] |
2.4 | kestrel | 0.0.2 | a fast, efficient inference engine for moondream | # Kestrel
High-performance inference engine for the [Moondream](https://moondream.ai) vision-language model.
Kestrel provides async, micro-batched serving with streaming support, paged KV caching, and optimized CUDA kernels. It's designed for production deployments where throughput and latency matter.
## Features
- **Async micro-batching** — Cooperative scheduler batches heterogeneous requests without compromising per-request latency
- **Streaming** — Real-time token streaming for query and caption tasks
- **Multi-task** — Visual Q&A, captioning, point detection, object detection, and segmentation
- **Paged KV cache** — Efficient memory management for high concurrency
- **Prefix caching** — Radix tree-based caching for repeated prompts and images
- **LoRA adapters** — Parameter-efficient fine-tuning support with automatic cloud loading
## Requirements
- Python 3.10+
- NVIDIA Hopper GPU or newer (e.g. H100)
- `MOONDREAM_API_KEY` environment variable (get this from [moondream.ai](https://moondream.ai))
## Installation
```bash
pip install kestrel huggingface_hub
```
## Model Access
The model weights are hosted on Hugging Face and require access approval:
1. Request access at [vikhyatk/moondream-next](https://huggingface.co/vikhyatk/moondream-next)
2. Once approved, authenticate with either:
- `huggingface-cli login`, or
- Set the `HF_TOKEN` environment variable
## Quick Start
```python
import asyncio
import pyvips
from huggingface_hub import hf_hub_download
from kestrel.config import RuntimeConfig
from kestrel.engine import InferenceEngine
async def main():
# Download the model (cached after first run)
model_path = hf_hub_download(
"vikhyatk/moondream-next",
filename="model_fp8.pt",
revision="1fdf7871dc596a89f73491db95543870727b5dce",
)
# Configure the engine
cfg = RuntimeConfig(model_path)
# Create the engine (loads model and warms up)
engine = await InferenceEngine.create(cfg)
# Load an image
image = pyvips.Image.new_from_file("photo.jpg")
# Visual question answering
result = await engine.query(
image=image,
question="What's in this image?",
settings={"temperature": 0.2, "max_tokens": 512},
)
print(result.output["answer"])
# Clean up
await engine.shutdown()
asyncio.run(main())
```
## Tasks
Kestrel supports several vision-language tasks through dedicated methods on the engine.
### Query (Visual Q&A)
Ask questions about an image:
```python
result = await engine.query(
image=image,
question="How many people are in this photo?",
settings={
"temperature": 0.2, # Lower = more deterministic
"top_p": 0.9,
"max_tokens": 512,
},
)
print(result.output["answer"])
```
### Caption
Generate image descriptions:
```python
result = await engine.caption(
image,
length="normal", # "short", "normal", or "long"
settings={"temperature": 0.2, "max_tokens": 512},
)
print(result.output["caption"])
```
### Point
Locate objects as normalized (x, y) coordinates:
```python
result = await engine.point(image, "person")
print(result.output["points"])
# [{"x": 0.5, "y": 0.3}, {"x": 0.8, "y": 0.4}]
```
Coordinates are normalized to [0, 1] where (0, 0) is top-left.
### Detect
Detect objects as bounding boxes:
```python
result = await engine.detect(
image,
"car",
settings={"max_objects": 10},
)
print(result.output["objects"])
# [{"x_min": 0.1, "y_min": 0.2, "x_max": 0.5, "y_max": 0.6}, ...]
```
Bounding box coordinates are normalized to [0, 1].
### Segment
Generate a segmentation mask:
```python
result = await engine.segment(image, "dog")
seg = result.output["segments"][0]
print(seg["svg_path"]) # SVG path data for the mask
print(seg["bbox"]) # {"x_min": ..., "y_min": ..., "x_max": ..., "y_max": ...}
```
Note: Segmentation requires separate model weights. Contact [moondream.ai](https://moondream.ai) for access.
## Streaming
For longer responses, you can stream tokens as they're generated:
```python
image = pyvips.Image.new_from_file("photo.jpg")
stream = await engine.query(
image=image,
question="Describe this scene in detail.",
stream=True,
settings={"max_tokens": 1024},
)
# Print tokens as they arrive
async for chunk in stream:
print(chunk.text, end="", flush=True)
# Get the final result with metrics
result = await stream.result()
print(f"\n\nGenerated {result.metrics.output_tokens} tokens")
```
Streaming is supported for `query` and `caption` methods.
## Response Format
All methods return an `EngineResult` with these fields:
```python
result.output # Dict with task-specific output ("answer", "caption", "points", etc.)
result.finish_reason # "stop" (natural end) or "length" (hit max_tokens)
result.metrics # Timing and token counts
```
The `metrics` object contains:
```python
result.metrics.input_tokens # Number of input tokens (including image)
result.metrics.output_tokens # Number of generated tokens
result.metrics.prefill_time_ms # Time to process input
result.metrics.decode_time_ms # Time to generate output
result.metrics.ttft_ms # Time to first token
```
## Using Finetunes
If you've created a finetuned model through the [Moondream API](https://moondream.ai), you can use it by passing the adapter ID:
```python
result = await engine.query(
image=image,
question="What's in this image?",
settings={"adapter": "01J5Z3NDEKTSV4RRFFQ69G5FAV@1000"},
)
```
The adapter ID format is `{finetune_id}@{step}` where:
- `finetune_id` is the ID of your finetune job
- `step` is the training step/checkpoint to use
Adapters are automatically downloaded and cached on first use.
## Configuration
### RuntimeConfig
```python
RuntimeConfig(
model_path="/path/to/model.pt",
max_batch_size=8, # Max concurrent requests (default: 4)
)
```
### Environment Variables
| Variable | Description |
|----------|-------------|
| `MOONDREAM_API_KEY` | Required. Get this from [moondream.ai](https://moondream.ai). |
## License
Free for evaluation and non-commercial use. Commercial use requires a license from [Moondream](https://moondream.ai).
Copyright (c) 2024-2025 M87 Labs, Inc.
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"torch==2.9.1",
"kestrel-kernels==0.1.0",
"tokenizers>=0.15",
"safetensors>=0.4",
"transformers>=4.44",
"pyvips>=2.3",
"pyvips-binary>=2.48",
"pillow>=10",
"torch-c-dlpack-ext>=0.1.3",
"starlette>=0.37",
"httpx>=0.27",
"uvicorn>=0.30",
"flashinfer-python>=0.6.0",
"opencv-python-headless>=4... | [] | [] | [] | [] | uv/0.9.0 | 2026-01-16T04:35:03.460595 | kestrel-0.0.2-py3-none-any.whl | 154,382 | f4/42/4c0e5c1eb1d0ec456247af71aef37a41cdb93d364c233c29882db961df4a/kestrel-0.0.2-py3-none-any.whl | py3 | bdist_wheel | null | false | 1b616922ad9d5f86cfe90049a8a04a3b | cd5d13b56c0bdbacec866f5ac37a6e423417f44610e9320815279fdc78797ef6 | f4424c0e5c1eb1d0ec456247af71aef37a41cdb93d364c233c29882db961df4a | null | [
"LICENSE.md"
] |
2.4 | kestrel | 0.0.2 | a fast, efficient inference engine for moondream | # Kestrel
High-performance inference engine for the [Moondream](https://moondream.ai) vision-language model.
Kestrel provides async, micro-batched serving with streaming support, paged KV caching, and optimized CUDA kernels. It's designed for production deployments where throughput and latency matter.
## Features
- **Async micro-batching** — Cooperative scheduler batches heterogeneous requests without compromising per-request latency
- **Streaming** — Real-time token streaming for query and caption tasks
- **Multi-task** — Visual Q&A, captioning, point detection, object detection, and segmentation
- **Paged KV cache** — Efficient memory management for high concurrency
- **Prefix caching** — Radix tree-based caching for repeated prompts and images
- **LoRA adapters** — Parameter-efficient fine-tuning support with automatic cloud loading
## Requirements
- Python 3.10+
- NVIDIA Hopper GPU or newer (e.g. H100)
- `MOONDREAM_API_KEY` environment variable (get this from [moondream.ai](https://moondream.ai))
## Installation
```bash
pip install kestrel huggingface_hub
```
## Model Access
The model weights are hosted on Hugging Face and require access approval:
1. Request access at [vikhyatk/moondream-next](https://huggingface.co/vikhyatk/moondream-next)
2. Once approved, authenticate with either:
- `huggingface-cli login`, or
- Set the `HF_TOKEN` environment variable
## Quick Start
```python
import asyncio
import pyvips
from huggingface_hub import hf_hub_download
from kestrel.config import RuntimeConfig
from kestrel.engine import InferenceEngine
async def main():
# Download the model (cached after first run)
model_path = hf_hub_download(
"vikhyatk/moondream-next",
filename="model_fp8.pt",
revision="1fdf7871dc596a89f73491db95543870727b5dce",
)
# Configure the engine
cfg = RuntimeConfig(model_path)
# Create the engine (loads model and warms up)
engine = await InferenceEngine.create(cfg)
# Load an image
image = pyvips.Image.new_from_file("photo.jpg")
# Visual question answering
result = await engine.query(
image=image,
question="What's in this image?",
settings={"temperature": 0.2, "max_tokens": 512},
)
print(result.output["answer"])
# Clean up
await engine.shutdown()
asyncio.run(main())
```
## Tasks
Kestrel supports several vision-language tasks through dedicated methods on the engine.
### Query (Visual Q&A)
Ask questions about an image:
```python
result = await engine.query(
image=image,
question="How many people are in this photo?",
settings={
"temperature": 0.2, # Lower = more deterministic
"top_p": 0.9,
"max_tokens": 512,
},
)
print(result.output["answer"])
```
### Caption
Generate image descriptions:
```python
result = await engine.caption(
image,
length="normal", # "short", "normal", or "long"
settings={"temperature": 0.2, "max_tokens": 512},
)
print(result.output["caption"])
```
### Point
Locate objects as normalized (x, y) coordinates:
```python
result = await engine.point(image, "person")
print(result.output["points"])
# [{"x": 0.5, "y": 0.3}, {"x": 0.8, "y": 0.4}]
```
Coordinates are normalized to [0, 1] where (0, 0) is top-left.
### Detect
Detect objects as bounding boxes:
```python
result = await engine.detect(
image,
"car",
settings={"max_objects": 10},
)
print(result.output["objects"])
# [{"x_min": 0.1, "y_min": 0.2, "x_max": 0.5, "y_max": 0.6}, ...]
```
Bounding box coordinates are normalized to [0, 1].
### Segment
Generate a segmentation mask:
```python
result = await engine.segment(image, "dog")
seg = result.output["segments"][0]
print(seg["svg_path"]) # SVG path data for the mask
print(seg["bbox"]) # {"x_min": ..., "y_min": ..., "x_max": ..., "y_max": ...}
```
Note: Segmentation requires separate model weights. Contact [moondream.ai](https://moondream.ai) for access.
## Streaming
For longer responses, you can stream tokens as they're generated:
```python
image = pyvips.Image.new_from_file("photo.jpg")
stream = await engine.query(
image=image,
question="Describe this scene in detail.",
stream=True,
settings={"max_tokens": 1024},
)
# Print tokens as they arrive
async for chunk in stream:
print(chunk.text, end="", flush=True)
# Get the final result with metrics
result = await stream.result()
print(f"\n\nGenerated {result.metrics.output_tokens} tokens")
```
Streaming is supported for `query` and `caption` methods.
## Response Format
All methods return an `EngineResult` with these fields:
```python
result.output # Dict with task-specific output ("answer", "caption", "points", etc.)
result.finish_reason # "stop" (natural end) or "length" (hit max_tokens)
result.metrics # Timing and token counts
```
The `metrics` object contains:
```python
result.metrics.input_tokens # Number of input tokens (including image)
result.metrics.output_tokens # Number of generated tokens
result.metrics.prefill_time_ms # Time to process input
result.metrics.decode_time_ms # Time to generate output
result.metrics.ttft_ms # Time to first token
```
## Using Finetunes
If you've created a finetuned model through the [Moondream API](https://moondream.ai), you can use it by passing the adapter ID:
```python
result = await engine.query(
image=image,
question="What's in this image?",
settings={"adapter": "01J5Z3NDEKTSV4RRFFQ69G5FAV@1000"},
)
```
The adapter ID format is `{finetune_id}@{step}` where:
- `finetune_id` is the ID of your finetune job
- `step` is the training step/checkpoint to use
Adapters are automatically downloaded and cached on first use.
## Configuration
### RuntimeConfig
```python
RuntimeConfig(
model_path="/path/to/model.pt",
max_batch_size=8, # Max concurrent requests (default: 4)
)
```
### Environment Variables
| Variable | Description |
|----------|-------------|
| `MOONDREAM_API_KEY` | Required. Get this from [moondream.ai](https://moondream.ai). |
## License
Free for evaluation and non-commercial use. Commercial use requires a license from [Moondream](https://moondream.ai).
Copyright (c) 2024-2025 M87 Labs, Inc.
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | >=3.10 | [] | [] | [] | [
"torch==2.9.1",
"kestrel-kernels==0.1.0",
"tokenizers>=0.15",
"safetensors>=0.4",
"transformers>=4.44",
"pyvips>=2.3",
"pyvips-binary>=2.48",
"pillow>=10",
"torch-c-dlpack-ext>=0.1.3",
"starlette>=0.37",
"httpx>=0.27",
"uvicorn>=0.30",
"flashinfer-python>=0.6.0",
"opencv-python-headless>=4... | [] | [] | [] | [] | uv/0.9.0 | 2026-01-16T04:35:05.029261 | kestrel-0.0.2.tar.gz | 132,873 | d7/32/cfb0ae81c7d572e4e1125bbb6392e513390c55a7161f294c0cfc64a19600/kestrel-0.0.2.tar.gz | source | sdist | null | false | 53ba206f1d0302282f31da53cefce8f6 | f01842a3fcaf244f939506231ddd24cee16dcc9de49f11e7de87758bbbf773d0 | d732cfb0ae81c7d572e4e1125bbb6392e513390c55a7161f294c0cfc64a19600 | null | [
"LICENSE.md"
] |
2.4 | pyhuntress | 0.2.16 | A full-featured Python client for the Huntress APIs | # pyhuntress - An API library for Huntress SIEM and Huntress Managed SAT, written in Python
pyHuntress is a full-featured, type annotated API client written in Python for the Huntress APIs.
This library has been developed with the intention of making the Huntress APIs simple and accessible to non-coders while allowing experienced coders to utilize all features the API has to offer without the boilerplate.
pyHuntress currently supports both Huntress SIEM and Huntress Managed SAT products.
Features:
=========
- **100% API Coverage.** All endpoints and response models.
- **Non-coder friendly.** 100% annotated for full IDE auto-completion. Clients handle requests and authentication - just plug the right details in and go!
- **Fully annotated.** This library has a strong focus on type safety and type hinting. Models are declared and parsed using [Pydantic](https://github.com/pydantic/pydantic)
pyHuntress is currently in **development**.
Known Issues:
=============
- As this project is still a WIP, documentation or code commentary may not always align.
- Huntress Managed SAT post not built
Road Map:
=============
- Add support for post
- Add required parameters when calling completion_certificat endpoint
How-to:
=============
- [Install](#install)
- [Initializing the API Clients](#initializing-the-api-clients)
- [Huntress Managed SAT](#huntress-managed-sat)
- [Huntress SIEM](#huntress-siem)
- [Working with Endpoints](#working-with-endpoints)
- [Get many](#get-many)
- [Get one](#get-one)
- [Get with params](#get-with-params)
- [Pagination](#pagination)
- [Contributing](#contributing)
- [Supporting the project](#supporting-the-project)
# Install
Open a terminal and run ```pip install pyhuntress```
# Initializing the API Clients
### Huntress Managed SAT
```python
from pyhuntress import HuntressSATAPIClient
# init client
sat_api_client = HuntressSATAPIClient(
mycurricula.com,
# your api public key,
# your api private key,
)
```
### Huntress SIEM
```python
from pyhuntress import HuntressSIEMAPIClient
# init client
siem_api_client = HuntressSIEMAPIClient(
# huntress siem url
# your api public key,
# your api private key,
)
```
# Working with Endpoints
Endpoints are 1:1 to what's available for both the Huntress Managed SAT and Huntress SIEM.
For more information, check out the following resources:
- [Huntress Managed SAT REST API Docs](https://curricula.stoplight.io/docs/curricula-api/00fkcnpgk5vnn-getting-started)
- [Huntress SIEM REST API Docs](https://api.huntress.io/docs)
### Get many
```python
### Managed SAT ###
# sends GET request to /company/companies endpoint
companies = manage_api_client.company.companies.get()
### SIEM ###
# sends GET request to /agents endpoint
agents = siem_api_client.agents.get()
```
### Get one
```python
### Managed SAT ###
# sends GET request to /company/companies/{id} endpoint
accounts = sat_api_client.accounts.id("abc123").get()
### SIEM ###
# sends GET request to /agents/{id} endpoint
agent = siem_api_client.agents.id(250).get()
```
### Get with params
```python
### Managed SAT ###
# sends GET request to /company/companies with a conditions query string
conditional_company = sat_api_client.company.companies.get(params={
'conditions': 'company/id=250'
})
### SIEM ###
# sends GET request to /agents endpoint with a condition query string
conditional_agent = siem_api_client.clients.get(params={
'platform': 'windows'
})
```
# Pagination
The Huntress SIEM API paginates data for performance reasons through the ```page``` and ```limit``` query parameters. ```limit``` is limited to a maximum of 500.
To make working with paginated data easy, Endpoints that implement a GET response with an array also supply a ```paginated()``` method. Under the hood this wraps a GET request, but does a lot of neat stuff to make working with pages easier.
Working with pagination
```python
# initialize a PaginatedResponse instance for /agents, starting on page 1 with a pageSize of 100
paginated_agents = siem_api_client.agents.paginated(1,100)
# access the data from the current page using the .data field
page_one_data = paginated_agents.data
# if there's a next page, retrieve the next page worth of data
paginated_agents.get_next_page()
# if there's a previous page, retrieve the previous page worth of data
paginated_agents.get_previous_page()
# iterate over all companies on the current page
for agent in paginated_agents:
# ... do things ...
# iterate over all companies in all pages
# this works by yielding every item on the page, then fetching the next page and continuing until there's no data left
for agent in paginated_agents.all():
# ... do things ...
```
# Contributing
Contributions to the project are welcome. If you find any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request.
# Supporting the project
:heart:
# Inspiration and Stolen Code
The premise behind this came from the [pyConnectWise](https://github.com/HealthITAU/pyconnectwise) package and I stole **most** of the code and adapted it to the Huntress API endpoints.
# How to Build
> python -m build
> python -m twine upload dist/* | text/markdown | null | Peter Annabel <peter.annabel@gmail.com> | null | null | null | API, Annotated, Client, Huntress, MSP, Manages SAT, Python, SIEM, Typed | [
"Intended Audience :: Developers",
"License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"pydantic",
"requests",
"typing-extensions"
] | [] | [] | [] | [
"Homepage, https://github.com/brygphilomena/pyhuntress",
"Issues, https://github.com/brygphilomena/pyhuntress/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:35:50.475108 | pyhuntress-0.2.16-py3-none-any.whl | 89,237 | 95/9d/b091c449ad65ef20bc5b0c1ce5cb5d8d7d19d0f96695c7e8a2a4a07d056e/pyhuntress-0.2.16-py3-none-any.whl | py3 | bdist_wheel | null | false | 4737afbbc18cc0cf5f716ab19760fe43 | 2480ebf465e5e0729a3b319d1aa774354773505494dadc9385066d79f5e157dd | 959db091c449ad65ef20bc5b0c1ce5cb5d8d7d19d0f96695c7e8a2a4a07d056e | GPL-3.0-only | [
"LICENSE"
] |
2.4 | pyhuntress | 0.2.16 | A full-featured Python client for the Huntress APIs | # pyhuntress - An API library for Huntress SIEM and Huntress Managed SAT, written in Python
pyHuntress is a full-featured, type annotated API client written in Python for the Huntress APIs.
This library has been developed with the intention of making the Huntress APIs simple and accessible to non-coders while allowing experienced coders to utilize all features the API has to offer without the boilerplate.
pyHuntress currently supports both Huntress SIEM and Huntress Managed SAT products.
Features:
=========
- **100% API Coverage.** All endpoints and response models.
- **Non-coder friendly.** 100% annotated for full IDE auto-completion. Clients handle requests and authentication - just plug the right details in and go!
- **Fully annotated.** This library has a strong focus on type safety and type hinting. Models are declared and parsed using [Pydantic](https://github.com/pydantic/pydantic)
pyHuntress is currently in **development**.
Known Issues:
=============
- As this project is still a WIP, documentation or code commentary may not always align.
- Huntress Managed SAT post not built
Road Map:
=============
- Add support for post
- Add required parameters when calling completion_certificat endpoint
How-to:
=============
- [Install](#install)
- [Initializing the API Clients](#initializing-the-api-clients)
- [Huntress Managed SAT](#huntress-managed-sat)
- [Huntress SIEM](#huntress-siem)
- [Working with Endpoints](#working-with-endpoints)
- [Get many](#get-many)
- [Get one](#get-one)
- [Get with params](#get-with-params)
- [Pagination](#pagination)
- [Contributing](#contributing)
- [Supporting the project](#supporting-the-project)
# Install
Open a terminal and run ```pip install pyhuntress```
# Initializing the API Clients
### Huntress Managed SAT
```python
from pyhuntress import HuntressSATAPIClient
# init client
sat_api_client = HuntressSATAPIClient(
mycurricula.com,
# your api public key,
# your api private key,
)
```
### Huntress SIEM
```python
from pyhuntress import HuntressSIEMAPIClient
# init client
siem_api_client = HuntressSIEMAPIClient(
# huntress siem url
# your api public key,
# your api private key,
)
```
# Working with Endpoints
Endpoints are 1:1 to what's available for both the Huntress Managed SAT and Huntress SIEM.
For more information, check out the following resources:
- [Huntress Managed SAT REST API Docs](https://curricula.stoplight.io/docs/curricula-api/00fkcnpgk5vnn-getting-started)
- [Huntress SIEM REST API Docs](https://api.huntress.io/docs)
### Get many
```python
### Managed SAT ###
# sends GET request to /company/companies endpoint
companies = manage_api_client.company.companies.get()
### SIEM ###
# sends GET request to /agents endpoint
agents = siem_api_client.agents.get()
```
### Get one
```python
### Managed SAT ###
# sends GET request to /company/companies/{id} endpoint
accounts = sat_api_client.accounts.id("abc123").get()
### SIEM ###
# sends GET request to /agents/{id} endpoint
agent = siem_api_client.agents.id(250).get()
```
### Get with params
```python
### Managed SAT ###
# sends GET request to /company/companies with a conditions query string
conditional_company = sat_api_client.company.companies.get(params={
'conditions': 'company/id=250'
})
### SIEM ###
# sends GET request to /agents endpoint with a condition query string
conditional_agent = siem_api_client.clients.get(params={
'platform': 'windows'
})
```
# Pagination
The Huntress SIEM API paginates data for performance reasons through the ```page``` and ```limit``` query parameters. ```limit``` is limited to a maximum of 500.
To make working with paginated data easy, Endpoints that implement a GET response with an array also supply a ```paginated()``` method. Under the hood this wraps a GET request, but does a lot of neat stuff to make working with pages easier.
Working with pagination
```python
# initialize a PaginatedResponse instance for /agents, starting on page 1 with a pageSize of 100
paginated_agents = siem_api_client.agents.paginated(1,100)
# access the data from the current page using the .data field
page_one_data = paginated_agents.data
# if there's a next page, retrieve the next page worth of data
paginated_agents.get_next_page()
# if there's a previous page, retrieve the previous page worth of data
paginated_agents.get_previous_page()
# iterate over all companies on the current page
for agent in paginated_agents:
# ... do things ...
# iterate over all companies in all pages
# this works by yielding every item on the page, then fetching the next page and continuing until there's no data left
for agent in paginated_agents.all():
# ... do things ...
```
# Contributing
Contributions to the project are welcome. If you find any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request.
# Supporting the project
:heart:
# Inspiration and Stolen Code
The premise behind this came from the [pyConnectWise](https://github.com/HealthITAU/pyconnectwise) package and I stole **most** of the code and adapted it to the Huntress API endpoints.
# How to Build
> python -m build
> python -m twine upload dist/* | text/markdown | null | Peter Annabel <peter.annabel@gmail.com> | null | null | null | API, Annotated, Client, Huntress, MSP, Manages SAT, Python, SIEM, Typed | [
"Intended Audience :: Developers",
"License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Topic :: Software Development :: Libraries :: Python Modules"
] | [] | null | null | >=3.9 | [] | [] | [] | [
"pydantic",
"requests",
"typing-extensions"
] | [] | [] | [] | [
"Homepage, https://github.com/brygphilomena/pyhuntress",
"Issues, https://github.com/brygphilomena/pyhuntress/issues"
] | twine/6.1.0 CPython/3.13.7 | 2026-01-16T04:35:53.387406 | pyhuntress-0.2.16.tar.gz | 38,458 | 9d/ac/56a5cd974808db59e19dc3e68d3701702058af991598cd1c02623c0a8246/pyhuntress-0.2.16.tar.gz | source | sdist | null | false | ef5f9a785af41c4003451b1c128051de | 06bbe8030acfb209b3190cd3c71ee47ccb0421b4cbe33c6cb2e14cee7281c56b | 9dac56a5cd974808db59e19dc3e68d3701702058af991598cd1c02623c0a8246 | GPL-3.0-only | [
"LICENSE"
] |
2.1 | AOT-biomaps | 2.0.5 | Acousto-Optic Tomography | null | null | Lucas Duclos | lucas.duclos@universite-paris-saclay.fr | null | null | null | null | [] | [] | https://github.com/LucasDuclos/AcoustoOpticTomography | null | null | [] | [] | [] | [
"k-wave-python",
"setuptools",
"pyyaml",
"numba",
"tqdm",
"GPUtil",
"scikit-image",
"cupy; extra == \"gpu\"",
"nvidia-ml-py3; extra == \"gpu\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.7 | 2025-06-16T09:56:17.720013 | AOT_biomaps-2.0.5-py3-none-any.whl | 41,368 | e8/4a/ce12df5ea5481edc54cae22d51571f3d1b4a9f2bca4299f591d0df5ac29b/AOT_biomaps-2.0.5-py3-none-any.whl | py3 | bdist_wheel | null | false | 6bcac0c86923d10f979f944bc887d4df | a06deb623eed0e1bbdcebeab1f44aff584d8d7959176bc62883681920f8865aa | e84ace12df5ea5481edc54cae22d51571f3d1b4a9f2bca4299f591d0df5ac29b | null | [] |
2.4 | Adafruit-Blinka | 8.60.3 | CircuitPython APIs for non-CircuitPython versions of Python such as CPython on Linux and MicroPython. | Introduction
============
.. image:: https://readthedocs.org/projects/adafruit-micropython-blinka/badge/?version=latest
:target: https://circuitpython.readthedocs.io/projects/blinka/en/latest/
:alt: Documentation Status
.. image:: https://img.shields.io/discord/327254708534116352.svg
:target: https://adafru.it/discord
:alt: Discord
.. image:: https://travis-ci.com/adafruit/Adafruit_Blinka.svg?branch=master
:target: https://travis-ci.com/adafruit/Adafruit_Blinka
:alt: Build Status
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
:target: https://github.com/psf/black
:alt: Code Style: Black
This repository contains a selection of packages emulating the CircuitPython API
for devices or hosts running CPython or MicroPython. Working code exists to emulate these CircuitPython packages:
* **analogio** - analog input/output pins, using pin identities from board+microcontroller packages
* **bitbangio** - software-driven interfaces for I2C, SPI
* **board** - breakout-specific pin identities
* **busio** - hardware-driven interfaces for I2C, SPI, UART
* **digitalio** - digital input/output pins, using pin identities from board+microcontroller packages
* **keypad** - support for scanning keys and key matrices
* **microcontroller** - chip-specific pin identities
* **micropython** - MicroPython-specific module
* **neopixel_write** - low-level interface to NeoPixels
* **pulseio** - contains classes that provide access to basic pulse IO (PWM)
* **pwmio** - contains classes that provide access to basic pulse IO (PWM)
* **rainbowio** - provides the colorwheel() function
* **usb_hid** - act as a hid-device using usb_gadget kernel driver
For details, see the `Blinka API reference
<https://circuitpython.readthedocs.io/projects/blinka/en/latest/index.html>`_.
Dependencies
=============
The emulation described above is intended to provide a
CircuitPython-like API for devices which are running CPython or
Micropython. Since corresponding packages should be built-in to any
standard CircuitPython image, they have no value on a device already
running CircuitPython and would likely conflict in unhappy ways.
The test suites in the test/src folder under **testing.universal** are by design
intended to run on *either* CircuitPython *or* CPython/Micropython+compatibility layer to prove conformance.
Installing from PyPI
=====================
On supported GNU/Linux systems like the Raspberry Pi, you can install the driver locally `from
PyPI <https://pypi.org/project/Adafruit-Blinka/>`_. To install for current user:
.. code-block:: shell
pip3 install Adafruit-Blinka
To install system-wide (this may be required in some cases):
.. code-block:: shell
sudo pip3 install Adafruit-Blinka
To install in a virtual environment in your current project:
.. code-block:: shell
mkdir project-name && cd project-name
python3 -m venv .env
source .env/bin/activate
pip3 install Adafruit-Blinka
Usage Example
=============
The pin names may vary by board, so you may need to change the pin names in the code. This
example runs on the Raspberry Pi boards to blink an LED connected to GPIO 18 (Pin 12):
.. code-block:: python
import time
import board
import digitalio
PIN = board.D18
print("hello blinky!")
led = digitalio.DigitalInOut(PIN)
led.direction = digitalio.Direction.OUTPUT
while True:
led.value = True
time.sleep(0.5)
led.value = False
time.sleep(0.5)
Contributing
============
Contributions are welcome! Please read our `Code of Conduct
<https://github.com/adafruit/Adafruit_Blinka/blob/master/CODE_OF_CONDUCT.md>`_
before contributing to help this project stay welcoming.
Building locally
================
Sphinx documentation
-----------------------
Sphinx is used to build the documentation based on rST files and comments in the code. First,
install dependencies (feel free to reuse the virtual environment from above):
.. code-block:: shell
python3 -m venv .env
source .env/bin/activate
pip install Sphinx sphinx-rtd-theme Adafruit-PlatformDetect
Now, once you have the virtual environment activated:
.. code-block:: shell
cd docs
sphinx-build -E -W -b html . _build/html
This will output the documentation to ``docs/_build/html``. Open the index.html in your browser to
view them. It will also (due to -W) error out on any warning like Travis will. This is a good way to
locally verify it will pass.
| text/x-rst | null | Adafruit Industries <circuitpython@adafruit.com> | null | null | MIT | null | [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Topic :: Software Development :: Libraries",
"Topic :: System :: Hardware",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7"
] | [] | null | null | null | [] | [] | [] | [
"Adafruit-PlatformDetect>=3.70.1",
"Adafruit-PureIO>=1.1.7",
"Jetson.GPIO; platform_machine == \"aarch64\"",
"RPi.GPIO; platform_machine == \"armv7l\" or platform_machine == \"armv6l\" or platform_machine == \"aarch64\"",
"rpi_ws281x>=4.0.0; platform_machine == \"armv7l\" or platform_machine == \"armv6l\" o... | [] | [] | [] | [
"Homepage, https://github.com/adafruit/Adafruit_Blinka"
] | twine/6.1.0 CPython/3.11.12 | 2025-06-19T19:06:57.779654 | adafruit_blinka-8.60.3-py3-none-any.whl | 390,305 | 5b/41/0700a963bb808a4c0a9b916a6cd90d73c79fe6a217f07935a920619d3aaa/adafruit_blinka-8.60.3-py3-none-any.whl | py3 | bdist_wheel | null | false | 8c5157304b9f06b2b9026af4ff440c06 | 312bcc46ddba472ed76f5221affcf02dcb1a25c65d8556c580701c03c7cc94a7 | 5b410700a963bb808a4c0a9b916a6cd90d73c79fe6a217f07935a920619d3aaa | null | [
"LICENSE"
] |
2.1 | CalcTool | 0.1.3.4 | Tools for calculations | ```python
sort(arr: List[Any], key: Callable[[Any], Any] = lambda x: x, reverse: bool = False)
```
内省排序`Introsort`，结合了多种排序算法的优点，以确保在各种情况下都能获得高效的性能，不返回列表。`arr`，待排序的列表。`key`，用于比较的键函数，自定义排序规则，而不必修改原始数据。`reverse`，是否降序排列，默认为升序。
Introsort, which combines the advantages of multiple sorting algorithms to ensure efficient performance in all cases. Does not return a list. `arr` is the list to be sorted. `key` is a function to extract a comparison key from each element, allowing custom sorting without modifying the original data. `reverse` specifies whether to sort in descending order (default is ascending).
```python
log(n, m, precision=50)
```
精确计算以`m`为底`n`的对数，参照`math.log()`参数顺序。默认保留50位小数，若计算结果非常接近整数，函数会返回四舍五入后的整数结果。`m`为对数的底数（`int/float/Decimal`类型），`n`为真数（`int/float/Decimal`类型），`precision`为可选的计算精度参数（整数类型，默认50位小数）。
Accurately calculate the logarithm of `n` with base `m`. Follow the parameter order of `math.log()`. The result retains 50 decimal places by default. If the calculation result is very close to an integer, the function will return the rounded integer value. `m` is the base of the logarithm (of type `int/float/Decimal`), `n` is the argument (of type `int/float/Decimal`), and `precision` is an optional calculation precision parameter (integer type, defaulting to 50 decimal places).
```python:
LaTeXCalculate(LaTeX_string: str)
```
计算LaTeX表达式，返回float。`LaTeX_string`，LaTeX格式的数学表达式，字符串。
Evaluate LaTeX expression, return float. `LaTeX_string`(string), a mathematical expression in LaTeX format.
| text/markdown | Zhu Chongjing | zhuchongjing_pypi@163.com | null | null | null | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent"
] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.7 | 2025-06-28T02:08:06.906352 | CalcTool-0.1.3.4-py3-none-any.whl | 7,048 | 38/4a/0410600a695bbf470c78462f3e9881d5b235722fdcc7e505ddd6b3440815/CalcTool-0.1.3.4-py3-none-any.whl | py3 | bdist_wheel | null | false | 01600072198288806f1e99cfad5071d9 | 61b4544971a6495830edf2a531551c698ec5e318d5c78aa6775112e840ffdb10 | 384a0410600a695bbf470c78462f3e9881d5b235722fdcc7e505ddd6b3440815 | null | [] |
2.1 | CalcTool | 0.1.2.9 | Tools for calculations | ```python
def sort(arr: List[Any], key: Callable[[Any], Any] = lambda x: x, reverse: bool = False) -> None:
```
内省排序`Introsort`，结合了多种排序算法的优点，以确保在各种情况下都能获得高效的性能，不返回列表。`arr`，待排序的列表。`key`，用于比较的键函数，自定义排序规则，而不必修改原始数据。`reverse`，是否降序排列，默认为升序。
Introsort, which combines the advantages of multiple sorting algorithms to ensure efficient performance in all cases. Does not return a list. `arr` is the list to be sorted. `key` is a function to extract a comparison key from each element, allowing custom sorting without modifying the original data. `reverse` specifies whether to sort in descending order (default is ascending).
```python
def log(n, m, precision=50):
```
精确计算以`m`为底`n`的对数，参照`math.log()`参数顺序。默认保留50位小数，若计算结果非常接近整数，函数会返回四舍五入后的整数结果。`m`为对数的底数（`int/float/Decimal`类型），`n`为真数（`int/float/Decimal`类型），`precision`为可选的计算精度参数（整数类型，默认50位小数）。
Accurately calculate the logarithm of `n` with base `m`. Follow the parameter order of `math.log()`. The result retains 50 decimal places by default. If the calculation result is very close to an integer, the function will return the rounded integer value. `m` is the base of the logarithm (of type `int/float/Decimal`), `n` is the argument (of type `int/float/Decimal`), and `precision` is an optional calculation precision parameter (integer type, defaulting to 50 decimal places).
```python
def calculate_latex(latex_expr, precision=10):
```
计算LaTeX表达式，返回字符串。`latex_expr`，LaTeX格式的数学表达式，字符串。`precision`，计算结果的精度，默认为10位有效数字，整数。
Evaluate LaTeX expression, return string. `latex_expr`(`str`), a mathematical expression in LaTeX format. `precision`(`int`), the precision of the result, defaults to 10 significant digits.
| text/markdown | Zhu Chongjing | zhuchongjing_pypi@163.com | null | null | null | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent"
] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.7 | 2025-06-21T03:31:30.391722 | CalcTool-0.1.2.9-py3-none-any.whl | 7,427 | 1d/64/d9f661d222ddb2f805f1bf110dab0f3352a888b388bd3b051fae33f4d297/CalcTool-0.1.2.9-py3-none-any.whl | py3 | bdist_wheel | null | false | d498e9b8251c7725cfb7a786a479cac5 | 8b5de4c69d55ef824b4b68ba218f271206ab1de482670e02d4eaa7421136b8ee | 1d64d9f661d222ddb2f805f1bf110dab0f3352a888b388bd3b051fae33f4d297 | null | [] |
2.4 | Clothoids | 2.0.32 | G1 and G2 fitting with clothoids, spline of clothoids, circle arc and biarc. | # Clothoids Python Bindings
This directory contains the Python bindings for the Clothoids library.
## Installation
Simply install via pip:
```bash
pip install Clothoids
```
### Build from source
If a pip package isn't available, or you simply want to, you can build from source. Note that you need to have the following installed on your system:
- ruby >=2.6 with the following gems: rake, colorize, rubyzip;
- CMake with ninja;
- python 3.8-13 (other versions are untested).
Once those requirements are installed, simply:
```bash
git clone --branch stable --depth 1 https://github.com/SebastianoTaddei/Clothoids.git
cd Clothoids
ruby setup.rb
rake
pip install -e .
```
## Usage
The Python bindings provide a simple interface to the Clothoids C++ library. Look at the `example.py` file for a simple example.
## Authors
These binding were brought to you by:
- [Sebastiano Taddei](https://github.com/SebastianoTaddei)
- [Gabriele Masina](https://github.com/masinag)
| text/markdown | Enrico Bertolazzi | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.9 | 2025-06-05T13:40:55.882499 | clothoids-2.0.32-cp312-cp312-macosx_11_0_arm64.whl | 456,321 | 55/ec/e0d74c6d3339bf4909d9c0baf11d0528d4c53c9b101e0b5c766d728f5c96/clothoids-2.0.32-cp312-cp312-macosx_11_0_arm64.whl | cp312 | bdist_wheel | null | false | d853759c8162743700aac7fcb66e94a5 | 8dd005bc35153d643b8e07a8ad2422415ed5f9268c38dd38f698f26cf76fb51d | 55ece0d74c6d3339bf4909d9c0baf11d0528d4c53c9b101e0b5c766d728f5c96 | null | [
"LICENSE"
] |
2.4 | EXOSIMS | 3.6.0 | null | 
Exoplanet Open-Source Imaging Mission Simulator
<a href="http://ascl.net/1706.010"><img src="https://img.shields.io/badge/ascl-1706.010-blue.svg?colorB=262255" alt="ascl:1706.010" /></a>
[](https://exosims.readthedocs.io/en/latest/?badge=latest)
[](https://coveralls.io/github/dsavransky/EXOSIMS?branch=master)
[](https://badge.fury.io/py/EXOSIMS)
[](http://www.astropy.org/)
[](code_of_conduct.md)
Quick Install
--------------------------
Clone the repository, navigate to the top level directory (containing setup.py) and execute:
```
pip install -e .
```
Full installation and configuration instructions available here: https://exosims.readthedocs.io/en/latest/install.html
Documentation and Quick Start Guide
-----------------------------------------------------------
- https://exosims.readthedocs.io
- https://exosims.readthedocs.io/en/latest/quickstart.html
Additional EXOSIMS tutorials are available here: https://github.com/dsavransky/YieldModelingWorkshopTutorial
This repository is associated with the Yield Modeling Tools Workshops. For additional information, see here: https://exoplanets.nasa.gov/exep/events/456/exoplanet-yield-modeling-tools-workshop/
Contributing
-------------------------------------
All contributions are very welcome. Before starting on your first contribution to EXOSIMS, please read the [Contributing Guide](https://github.com/dsavransky/EXOSIMS/blob/master/CONTRIBUTING.md)
Credits and Acknowledgements
------------------------------
Created by Dmitry Savransky
Written by Christian Delacroix, Daniel Garrett, Dean Keithly, Gabriel Soto, Corey Spohn, Walker Dula, Sonny Rappaport, Michael Turmon, Rhonda Morgan, Grace Genszler, and Dmitry Savransky, with contributions by Patrick Lowrance, Ewan Douglas, Jackson Kulik, Jeremy Turner, Jayson Figueroa, Owen Sorber, Maxwell Zweig, Ahnika Gee, Claire Cahill, Saanika Choudhary, and Neil Zimmerman.
EXOSIMS makes use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013).
EXOSIMS optionally makes use of Forecaster (http://ascl.net/1701.007).
EXOSIMS optionally makes use of NASA's Navigation and Ancillary Information Facility's SPICE system components (https://naif.jpl.nasa.gov/naif/).
EXOSIMS optionally uses values from: Mamjek, E. "A Modern Mean Dwarf Stellar Color and Effective Temperature Sequence", http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt, Version 2017.09.06
EXOSIMS development is supported by NASA Grant Nos. NNX14AD99G (GSFC), NNX15AJ67G (WPS) and NNG16PJ24C (SIT).
For further information, please see EXOSIMS's ASCL page and the following papers:
- http://adsabs.harvard.edu/abs/2016JATIS...2a1006S
- http://adsabs.harvard.edu/abs/2016SPIE.9911E..19D
| text/x-rst | Dmitry Savransky | ds264@cornell.edu | null | null | null | null | [] | [] | https://github.com/dsavransky/EXOSIMS | null | null | [] | [] | [] | [
"numpy>=1.20.0",
"scipy>=1.7.2",
"astropy>=6.0.0",
"jplephem>=2.20.0",
"ortools>=9.0",
"h5py>=3.7.0",
"astroquery>=0.4.8",
"exo-det-box",
"tqdm>=4.59",
"pandas>=1.3",
"MeanStars>=3.4.0",
"synphot>=1.3.0",
"ipyparallel>=8.0.0",
"keplertools>=1.2.1"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.10.17 | 2025-06-17T14:38:02.272094 | exosims-3.6.0-py3-none-any.whl | 17,361,802 | fe/32/e5671237cc88d36e38e578ddc90e2f4c5a9396e218ea89cf0f1341dfc817/exosims-3.6.0-py3-none-any.whl | py3 | bdist_wheel | null | false | 9ef292d206def499b3047474bfcb5e1c | 436563c207f106e5c7ae8f051dbb6d1d4c0fd55809adfeef38f13ee30f726bb9 | fe32e5671237cc88d36e38e578ddc90e2f4c5a9396e218ea89cf0f1341dfc817 | null | [
"LICENSE"
] |
2.4 | F1-lap-time-telementary | 0.1.1 | Plot F1 telemetry data using FastF1 | # F1_lap_time_telementary
This module is created to easily visualize telementary data of the fastest lap from a session. It heavily uses the great FastF1 API below -
https://github.com/theOehrly/Fast-F1/tree/master
This document goes through all available API functions, if you just want a quick way to plot then review the `plot_comparison` function
# Installation
```console
pip install fastf1plot
```
# API Functions
## setup_plotting
Calls `fastf1.plotting.setup_mpl` from FastF1 to configure the plot
### Inputs
No inputs
### Output
Setup matplotlib for use with fastf1 - Nothing returned from the function
### Usage
```python
from F1_lap_time_telementary import setup_plotting
setup_plotting()
```
## get_session_data
Gets the session data from FastF1
### Inputs
year - Year of the race as an integer
gp - The full name of the gp as a string
session_type - Abbreviated session names, mapped as the following:
| session_type | Session |
| --------------- | ------------------|
| 'FP1' | Free Practice 1 |
| 'FP2' | Free Practice 2 |
| 'FP3' | Free Practice 3 |
| 'SQ' | Sprint Qualifying |
| 'SS' | Sprint Shootout |
| 'Q' | Qualifying |
| 'S' | Sprint |
| 'R' | Race |
**Note Sprint Shootout is for 2023 only**
### Outputs
session - Returns a session object
### Usage
```python
from F1_lap_time_telementary import get_session_data
year = 2025
grand_prix = 'Chinese Grand Prix'
session_type='Q'
session = get_session_data(year, grand_prix, session_type)
```
## get_driver_data
Returns the telementary data from the driver's fastest lap
### Inputs
session - A session object (which can be obtained by running `get_session_data`)
driver - Three letter abbreviation for a driver. E.g. 'BOT' for Bottas
### Output
lap - The fastest lap set by the driver in that session
car_data - telementary data from the fastest lap
### Usage
```python
from F1_lap_time_telementary import get_driver_data
session = get_session_data(2025, 'Chinese Grand Prix', 'Q')
driver = 'ALO'
lap, car_data = get_driver_data(session, driver)
```
## get_min_max_speed
Gets the minimum and maximum speed by the driver
### Inputs
car_data - Telementary Data from the car (which can be obtained by running `get_driver_data`)
### Outputs
min_speed - Slowest speed of the car in a lap
max_speed - Top speed of the car in a lap
### Usage
```python
from F1_lap_time_telementary import get_min_max_speed
min_speed, max_speed = get_min_max_speed(car_data)
```
## plot_corners
Plots corner lines in a speed-distance plot
### Inputs
ax - Axes
circuit_data - Circuit Data (Obtained by `session.get_circuit_info`)
car_data - Telementary Data from the car (which can be obtained by running `get_driver_data`)
### Outputs
No output - Adds vertical dashed lines on a speed-distance plot
### Usage
```python
from F1_lap_time_telementary import get_min_max_speed
plot_corners(axs[0], circuit_data, car_data)
```
## plot_telemetry
Plots telementry data 4 subplots -
1) Speed-Distance
2) Throttle-Distance
3) Brake-Distance
4) Gear-Distance
### Inputs
axs - The axes you are plotting on
car_data - Telementary Data from the car (which can be obtained by running `get_driver_data`)
label - The driver's trace that is being plotted
### Outputs
Nothing returned - plots a (4,1) subplot
### Usage
```python
from F1_lap_time_telementary import plot_telemetry
lap, car_data = get_driver_data('Q', 'ALO')
plot_telemetry(axs, car_data, lap)
| text/markdown | null | Sidhartha Kumar <siddkumar718@gmail.com> | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [
"fastf1>=3.0.0",
"matplotlib>=3.0"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.4 | 2025-06-10T14:06:14.925444 | f1_lap_time_telementary-0.1.1-py3-none-any.whl | 3,345 | 73/6b/5799cceb4d6fbd24295c3e7e39652761c040b8804f90feb36e4b44ccd4bd/f1_lap_time_telementary-0.1.1-py3-none-any.whl | py3 | bdist_wheel | null | false | 753e83b928d62c91c858fb41d42007d9 | 77451e0c98d5ec367728920dc9863ecd00686bdcafc0bf51669cdce001e76989 | 736b5799cceb4d6fbd24295c3e7e39652761c040b8804f90feb36e4b44ccd4bd | MIT | [
"LICENSE"
] |
2.4 | FELDSHAM | 0.0.4 | Реализация схемы разделения секрета Фельдмана-Шамира | My package description
| text/markdown | Alexander | lloollfox@mail.ru | null | null | MIT | null | [] | [] | null | null | null | [] | [] | [] | [
"sympy>=1.7"
] | [] | [] | [] | [
"Source Repository, https://github.com/A-Sharan1/"
] | twine/6.1.0 CPython/3.13.5 | 2025-06-16T13:02:32.394356 | feldsham-0.0.4-py3-none-any.whl | 6,771 | ee/ed/b643e81a2ee97cc1c34a2f26770bff88f6c636bbb55ad4b7b758e7f5934a/feldsham-0.0.4-py3-none-any.whl | py3 | bdist_wheel | null | false | 5cf850cd5d866e687ecabbd5b27af13b | 6a703174735b88d3ad37e3c53528f9cc3ab92cec7029eb0846e49495c9ff4f53 | eeedb643e81a2ee97cc1c34a2f26770bff88f6c636bbb55ad4b7b758e7f5934a | null | [
"LICENSE.txt"
] |
2.4 | FPC | 0.80 | Frank's Personal Conllection | Frank's Personal Conllection
| null | Frank | luoziluojun@126.com | Frank | luoziluojun@126.com | BSD License | null | [
"Development Status :: 4 - Beta",
"Operating System :: OS Independent",
"Intended Audience :: Developers",
"License :: OSI Approved :: BSD License",
"Programming Language :: Python :: 3",
"Topic :: Software Development :: Libraries"
] | [
"all"
] | http://ff2.pw | null | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.0.1 CPython/3.12.6 | 2025-06-10T09:40:43.120302 | fpc-0.80-py3-none-any.whl | 8,388 | 33/7f/7f0b3ed378c3a7f2630052eaae171e4a5f7f66eabc32039705f0ca17424d/fpc-0.80-py3-none-any.whl | py3 | bdist_wheel | null | false | c52cd9c3a0c792ff3e97645af25c0b2b | 4d8c56d20baa99ad3627df458ecd9c835999316315a7485418b2722b0e9286de | 337f7f0b3ed378c3a7f2630052eaae171e4a5f7f66eabc32039705f0ca17424d | null | [] |
2.4 | Flask-Bauto | 0.0.15 | null | # Flask-Bauto
Flask automated blueprints based on dataclasses
| text/markdown | null | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [
"Flask-FEFset",
"Flask-UXFab",
"Flask-SQLAlchemy",
"Flask-IAM",
"Bull-Stack; extra == \"fullstack\"",
"build; extra == \"dev\"",
"twine; extra == \"dev\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.0rc3 | 2025-06-01T16:12:09.043942 | flask_bauto-0.0.15.tar.gz | 10,663 | c0/ce/d7e0086af0c0be07528af5168d3b1a0ac83c6462b11f27fcebc68fa84735/flask_bauto-0.0.15.tar.gz | source | sdist | null | false | 4a2984d79831f97504e57e5bb3c4fd05 | f7021313fc709206978d946b185c328ed26bb5caef12a153afc55257fab1a4ed | c0ced7e0086af0c0be07528af5168d3b1a0ac83c6462b11f27fcebc68fa84735 | null | [
"LICENSE"
] |
2.4 | FloTorch-core | 2.9.7 | A Python project for FloTorch | # 🚀 FloTorch-core
**FloTorch-core** is a modular and extensible Python framework for building LLM-powered RAG (Retrieval-Augmented Generation) pipelines. It offers plug-and-play components for embeddings, chunking, retrieval, gateway-based LLM calls, and RAG evaluation.
---
## ✨ Features
- 🧩 Text Chunking (Fixed-size, Hierarchical)
- 🧠 Embedding Models (Titan, Cohere, Bedrock)
- 🔍 Document Retrieval (OpenSearch + Vector Storage)
- 💻 Bedrock/sagemaker/gateway inferencer
- 🔌 Unified LLM Gateway (OpenAI, Bedrock, Ollama, etc.)
- 📏 RAG Evaluation (RAGAS Metrics)
- ☁️ AWS Integration (S3, DynamoDB, Lambda)
- 🧢 Built-in Testing Support
---
## 📆 Installation
```bash
pip install FloTorch-core
```
To install development dependencies:
```bash
pip install FloTorch-core[dev]
```
---
## 📂 Project Structure
```
flotorch/
├── inferencer/ # LLM gateway/bedrock/sagemaker interface
├── embedding/ # Embedding models
├── chunking/ # Text chunking logic
├── evaluator/ # RAG evaluation (RAGAS)
├── storage/ # Vector DB, S3, DynamoDB
├── util/ # Utilities and helpers
├── rerank/ # Ranking documents
├── guardrails/ # Enabling guardrails
├── reader/ # reader for json/pdf
```
---
## 📖 Usage Example
### Reader
```
from flotorch_core.reader.json_reader import JSONReader
from flotorch_core.storage.s3_storage import S3StorageProvider
json_reader = JSONReader(S3StorageProvider(<S3 bucket>))
json_reader.read(<path>)
```
### Embedding
```
from flotorch_core.embedding.embedding_registry import embedding_registry
embedding_class = embedding_registry.get_model(<model id>)
# model id example: amazon.titan-text-express-v1, amazon.titan-embed-text-v2:0, cohere.embed-multilingual-v3
```
### Vector storage (opensearch)
```
from flotorch_core.storage.db.vector.open_search import OpenSearchClient
vector_storage_object = OpenSearchClient(
<opensearch_host>,
<opensearch_port>,
<opensearch_username>,
<opensearch_password>,
<index_id>,
<embedding object>
)
```
### Vector storage (bedrock knowledgebase)
```
from flotorch_core.storage.db.vector.bedrock_knowledgebase_storage import BedrockKnowledgeBaseStorage
vector_storage_object = BedrockKnowledgeBaseStorage(
knowledge_base_id=<knowledge_base_id>,
region=<aws_region>
)
```
### Guardrails over vector storage
```
from flotorch_core.storage.db.vector.guardrails_vector_storage import GuardRailsVectorStorage
base_guardrails = BedrockGuardrail(<guardrail_id>, <guardrail_version>, <aws_region>)
vector_storage_object = GuardRailsVectorStorage(
vector_storage_object,
base_guardrails,
<enable_prompt_guardrails(True/False)>,
<enable_context_guardrails(True/False)>
)
```
### Inferencer
```
from flotorch_core.inferencer.bedrock_inferencer import BedrockInferencer
from flotorch_core.inferencer.gateway_inferencer import GatewayInferencer
from flotorch_core.inferencer.sagemaker_inferencer import SageMakerInferencer
inferencer = BedrockInferencer(
<model_id>,
<region>,
<number of n_shot_prompts>,
<temperature>,
<n_shot_prompt_guide_obj>
)
inferencer = GatewayInferencer(
model_id=<model_id>,
api_key=<api_key>,
base_url=<base_url>,
n_shot_prompts=<n_shot_prompts>,
n_shot_prompt_guide_obj=<n_shot_prompt_guide_obj>
)
inferencer = SageMakerInferencer(
<model_id>,
<region>,
<arn_role>,
<n_shot_prompts>,
<temperature>,
<n_shot_prompt_guide_obj>
)
```
### GuardRail over inferencer
```
from flotorch_core.inferencer.guardrails.guardrails_inferencer import GuardRailsInferencer
inferencer = GuardRailsInferencer(inferencer, base_guardrails)
```
---
## 📬 Maintainer
**Shiva Krishna**
📧 Email: shiva.krishnaah@gmail.com
**Adil Raza**
📧 Email: adilraza.9752@gmail.com
---
## 📄 License
This project is licensed under the [MIT License](LICENSE).
---
## 🌐 Links
- GitHub: [https://github.com/FissionAI/flotorch-core](https://github.com/FissionAI/flotorch-core)
| text/markdown | null | Shiva Krishna <shiva.krishnaah@gmail.com> | null | null | MIT | null | [] | [] | null | null | null | [] | [] | [] | [
"langchain==0.3.14",
"boto3==1.36.2",
"ollama==0.4.6",
"PyPDF2==3.0.1",
"opensearch-py==2.8.0",
"sagemaker==2.235.2",
"openai==1.57.4",
"ragas==0.2.14",
"psycopg2-binary==2.9.9",
"requests==2.31.0",
"pytest==8.3.4; extra == \"dev\"",
"testcontainers==4.9.0; extra == \"dev\"",
"minio==7.2.15;... | [] | [] | [] | [] | twine/6.1.0 CPython/3.9.0 | 2025-06-04T07:18:26.138484 | flotorch_core-2.9.7-py3-none-any.whl | 91,819 | f2/97/23dbf814b497600fa83e7e594898d6aeb03336f4dc1712d0669aef1fc59a/flotorch_core-2.9.7-py3-none-any.whl | py3 | bdist_wheel | null | false | ecf3881ea4c773853d6f0db9a1d8a2ba | 2aac299bd031b2c977b502b5851b18aeb5f428efede4359dce3951d7eb7719dc | f29723dbf814b497600fa83e7e594898d6aeb03336f4dc1712d0669aef1fc59a | null | [
"LICENSE"
] |
2.4 | FloTorch-core | 2.9.11 | A Python project for FloTorch | # 🚀 FloTorch-core
**FloTorch-core** is a modular and extensible Python framework for building LLM-powered RAG (Retrieval-Augmented Generation) pipelines. It offers plug-and-play components for embeddings, chunking, retrieval, gateway-based LLM calls, and RAG evaluation.
---
## ✨ Features
- 🧩 Text Chunking (Fixed-size, Hierarchical)
- 🧠 Embedding Models (Titan, Cohere, Bedrock)
- 🔍 Document Retrieval (OpenSearch + Vector Storage)
- 💻 Bedrock/sagemaker/gateway inferencer
- 🔌 Unified LLM Gateway (OpenAI, Bedrock, Ollama, etc.)
- 📏 RAG Evaluation (RAGAS Metrics)
- ☁️ AWS Integration (S3, DynamoDB, Lambda)
- 🧢 Built-in Testing Support
---
## 📆 Installation
```bash
pip install FloTorch-core
```
To install development dependencies:
```bash
pip install FloTorch-core[dev]
```
---
## 📂 Project Structure
```
flotorch/
├── inferencer/ # LLM gateway/bedrock/sagemaker interface
├── embedding/ # Embedding models
├── chunking/ # Text chunking logic
├── evaluator/ # RAG evaluation (RAGAS)
├── storage/ # Vector DB, S3, DynamoDB
├── util/ # Utilities and helpers
├── rerank/ # Ranking documents
├── guardrails/ # Enabling guardrails
├── reader/ # reader for json/pdf
```
---
## 📖 Usage Example
### Reader
```
from flotorch_core.reader.json_reader import JSONReader
from flotorch_core.storage.s3_storage import S3StorageProvider
json_reader = JSONReader(S3StorageProvider(<S3 bucket>))
json_reader.read(<path>)
```
### Embedding
```
from flotorch_core.embedding.embedding_registry import embedding_registry
embedding_class = embedding_registry.get_model(<model id>)
# model id example: amazon.titan-text-express-v1, amazon.titan-embed-text-v2:0, cohere.embed-multilingual-v3
```
### Vector storage (opensearch)
```
from flotorch_core.storage.db.vector.open_search import OpenSearchClient
vector_storage_object = OpenSearchClient(
<opensearch_host>,
<opensearch_port>,
<opensearch_username>,
<opensearch_password>,
<index_id>,
<embedding object>
)
```
### Vector storage (bedrock knowledgebase)
```
from flotorch_core.storage.db.vector.bedrock_knowledgebase_storage import BedrockKnowledgeBaseStorage
vector_storage_object = BedrockKnowledgeBaseStorage(
knowledge_base_id=<knowledge_base_id>,
region=<aws_region>
)
```
### Guardrails over vector storage
```
from flotorch_core.storage.db.vector.guardrails_vector_storage import GuardRailsVectorStorage
base_guardrails = BedrockGuardrail(<guardrail_id>, <guardrail_version>, <aws_region>)
vector_storage_object = GuardRailsVectorStorage(
vector_storage_object,
base_guardrails,
<enable_prompt_guardrails(True/False)>,
<enable_context_guardrails(True/False)>
)
```
### Inferencer
```
from flotorch_core.inferencer.bedrock_inferencer import BedrockInferencer
from flotorch_core.inferencer.gateway_inferencer import GatewayInferencer
from flotorch_core.inferencer.sagemaker_inferencer import SageMakerInferencer
inferencer = BedrockInferencer(
<model_id>,
<region>,
<number of n_shot_prompts>,
<temperature>,
<n_shot_prompt_guide_obj>
)
inferencer = GatewayInferencer(
model_id=<model_id>,
api_key=<api_key>,
base_url=<base_url>,
n_shot_prompts=<n_shot_prompts>,
n_shot_prompt_guide_obj=<n_shot_prompt_guide_obj>
)
inferencer = SageMakerInferencer(
<model_id>,
<region>,
<arn_role>,
<n_shot_prompts>,
<temperature>,
<n_shot_prompt_guide_obj>
)
```
### GuardRail over inferencer
```
from flotorch_core.inferencer.guardrails.guardrails_inferencer import GuardRailsInferencer
inferencer = GuardRailsInferencer(inferencer, base_guardrails)
```
---
## 📬 Maintainer
**Shiva Krishna**
📧 Email: shiva.krishnaah@gmail.com
**Adil Raza**
📧 Email: adilraza.9752@gmail.com
---
## 📄 License
This project is licensed under the [MIT License](LICENSE).
---
## 🌐 Links
- GitHub: [https://github.com/FissionAI/flotorch-core](https://github.com/FissionAI/flotorch-core)
| text/markdown | null | Shiva Krishna <shiva.krishnaah@gmail.com> | null | null | MIT | null | [] | [] | null | null | null | [] | [] | [] | [
"langchain==0.3.14",
"boto3==1.36.2",
"ollama==0.4.6",
"PyPDF2==3.0.1",
"opensearch-py==2.8.0",
"sagemaker==2.235.2",
"openai==1.57.4",
"ragas==0.2.14",
"psycopg2-binary==2.9.9",
"requests<3.0.0,>=2.31.0",
"pytest==8.3.4; extra == \"dev\"",
"testcontainers==4.9.0; extra == \"dev\"",
"minio==... | [] | [] | [] | [] | twine/6.1.0 CPython/3.9.0 | 2025-06-20T06:28:05.008089 | flotorch_core-2.9.11.tar.gz | 59,989 | af/c8/34d3f4d3ec113063f65dc159bdcd20986ede979e449bb23372a647508a87/flotorch_core-2.9.11.tar.gz | source | sdist | null | false | 6ee583d6915e5bef449a766c7b6a1240 | 7f713b54e5396b0d7b32197b85b1a090d2b10435e7f108356f9919e05f8b12c8 | afc834d3f4d3ec113063f65dc159bdcd20986ede979e449bb23372a647508a87 | null | [
"LICENSE"
] |
2.4 | FloTorch-core | 2.9.8 | A Python project for FloTorch | # 🚀 FloTorch-core
**FloTorch-core** is a modular and extensible Python framework for building LLM-powered RAG (Retrieval-Augmented Generation) pipelines. It offers plug-and-play components for embeddings, chunking, retrieval, gateway-based LLM calls, and RAG evaluation.
---
## ✨ Features
- 🧩 Text Chunking (Fixed-size, Hierarchical)
- 🧠 Embedding Models (Titan, Cohere, Bedrock)
- 🔍 Document Retrieval (OpenSearch + Vector Storage)
- 💻 Bedrock/sagemaker/gateway inferencer
- 🔌 Unified LLM Gateway (OpenAI, Bedrock, Ollama, etc.)
- 📏 RAG Evaluation (RAGAS Metrics)
- ☁️ AWS Integration (S3, DynamoDB, Lambda)
- 🧢 Built-in Testing Support
---
## 📆 Installation
```bash
pip install FloTorch-core
```
To install development dependencies:
```bash
pip install FloTorch-core[dev]
```
---
## 📂 Project Structure
```
flotorch/
├── inferencer/ # LLM gateway/bedrock/sagemaker interface
├── embedding/ # Embedding models
├── chunking/ # Text chunking logic
├── evaluator/ # RAG evaluation (RAGAS)
├── storage/ # Vector DB, S3, DynamoDB
├── util/ # Utilities and helpers
├── rerank/ # Ranking documents
├── guardrails/ # Enabling guardrails
├── reader/ # reader for json/pdf
```
---
## 📖 Usage Example
### Reader
```
from flotorch_core.reader.json_reader import JSONReader
from flotorch_core.storage.s3_storage import S3StorageProvider
json_reader = JSONReader(S3StorageProvider(<S3 bucket>))
json_reader.read(<path>)
```
### Embedding
```
from flotorch_core.embedding.embedding_registry import embedding_registry
embedding_class = embedding_registry.get_model(<model id>)
# model id example: amazon.titan-text-express-v1, amazon.titan-embed-text-v2:0, cohere.embed-multilingual-v3
```
### Vector storage (opensearch)
```
from flotorch_core.storage.db.vector.open_search import OpenSearchClient
vector_storage_object = OpenSearchClient(
<opensearch_host>,
<opensearch_port>,
<opensearch_username>,
<opensearch_password>,
<index_id>,
<embedding object>
)
```
### Vector storage (bedrock knowledgebase)
```
from flotorch_core.storage.db.vector.bedrock_knowledgebase_storage import BedrockKnowledgeBaseStorage
vector_storage_object = BedrockKnowledgeBaseStorage(
knowledge_base_id=<knowledge_base_id>,
region=<aws_region>
)
```
### Guardrails over vector storage
```
from flotorch_core.storage.db.vector.guardrails_vector_storage import GuardRailsVectorStorage
base_guardrails = BedrockGuardrail(<guardrail_id>, <guardrail_version>, <aws_region>)
vector_storage_object = GuardRailsVectorStorage(
vector_storage_object,
base_guardrails,
<enable_prompt_guardrails(True/False)>,
<enable_context_guardrails(True/False)>
)
```
### Inferencer
```
from flotorch_core.inferencer.bedrock_inferencer import BedrockInferencer
from flotorch_core.inferencer.gateway_inferencer import GatewayInferencer
from flotorch_core.inferencer.sagemaker_inferencer import SageMakerInferencer
inferencer = BedrockInferencer(
<model_id>,
<region>,
<number of n_shot_prompts>,
<temperature>,
<n_shot_prompt_guide_obj>
)
inferencer = GatewayInferencer(
model_id=<model_id>,
api_key=<api_key>,
base_url=<base_url>,
n_shot_prompts=<n_shot_prompts>,
n_shot_prompt_guide_obj=<n_shot_prompt_guide_obj>
)
inferencer = SageMakerInferencer(
<model_id>,
<region>,
<arn_role>,
<n_shot_prompts>,
<temperature>,
<n_shot_prompt_guide_obj>
)
```
### GuardRail over inferencer
```
from flotorch_core.inferencer.guardrails.guardrails_inferencer import GuardRailsInferencer
inferencer = GuardRailsInferencer(inferencer, base_guardrails)
```
---
## 📬 Maintainer
**Shiva Krishna**
📧 Email: shiva.krishnaah@gmail.com
**Adil Raza**
📧 Email: adilraza.9752@gmail.com
---
## 📄 License
This project is licensed under the [MIT License](LICENSE).
---
## 🌐 Links
- GitHub: [https://github.com/FissionAI/flotorch-core](https://github.com/FissionAI/flotorch-core)
| text/markdown | null | Shiva Krishna <shiva.krishnaah@gmail.com> | null | null | MIT | null | [] | [] | null | null | null | [] | [] | [] | [
"langchain==0.3.14",
"boto3==1.36.2",
"ollama==0.4.6",
"PyPDF2==3.0.1",
"opensearch-py==2.8.0",
"sagemaker==2.235.2",
"openai==1.57.4",
"ragas==0.2.14",
"psycopg2-binary==2.9.9",
"requests<3.0.0,>=2.31.0",
"pytest==8.3.4; extra == \"dev\"",
"testcontainers==4.9.0; extra == \"dev\"",
"minio==... | [] | [] | [] | [] | twine/6.1.0 CPython/3.9.0 | 2025-06-05T06:31:33.900117 | flotorch_core-2.9.8-py3-none-any.whl | 91,826 | c4/2a/3bec58e8cdab71b4577a500c19a747ac47a418ca173a81cfd08cc10d8d7d/flotorch_core-2.9.8-py3-none-any.whl | py3 | bdist_wheel | null | false | 680baa0cfe46e9f0298b951b528aa31c | 8899133152318e1d62115199e450040ae87207e45880a1016883da5aea39edfd | c42a3bec58e8cdab71b4577a500c19a747ac47a418ca173a81cfd08cc10d8d7d | null | [
"LICENSE"
] |
2.4 | GPopt | 0.9.2 | Bayesian Optimization using Gaussian Process Regression | Bayesian Optimization using Gaussian Process Regression
| null | Thierry Moudiki | thierry.moudiki@gmail.com | null | null | BSD | null | [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3"
] | [] | https://github.com/thierrymoudiki/GPopt | https://github.com/thierrymoudiki/GPopt/tarball/0.9.2 | null | [] | [] | [] | [
"joblib",
"matplotlib",
"nnetsauce",
"numpy",
"pandas",
"scipy",
"scikit-learn",
"threadpoolctl",
"tqdm"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.9 | 2025-06-15T02:05:24.322268 | gpopt-0.9.2.tar.gz | 74,654 | d5/c2/878eca3481810bef5c30c11228d78a47d8187b578a226eb97894dd707d51/gpopt-0.9.2.tar.gz | source | sdist | null | false | 20b6870ea663300feeb5527e5067e4ae | 086d0b0d0e355b33ce238fd0a40012401142f43a0d996a3f4c346cbaa5877a1d | d5c2878eca3481810bef5c30c11228d78a47d8187b578a226eb97894dd707d51 | null | [
"LICENSE"
] |
2.1 | HardView | 0.1.0 | A Python library for collecting hardware information (BIOS, System, CPU, RAM, Disk, Network). | A comprehensive Python library for querying low-level hardware information on Windows and Linux, including BIOS, system, CPU, RAM, disk drives, and network adapters. Data is returned in JSON format.
| null | gafoo | omarwaled3374@gmail.com | null | null | null | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: Microsoft :: Windows",
"Operating System :: POSIX :: Linux",
"Topic :: System :: Hardware",
"Development Status :: 3 - Alpha"
] | [] | https://github.com/gafoo173/HardView | null | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.2 | 2025-06-17T08:52:04.680984 | hardview-0.1.0-cp38-cp38-win_amd64.whl | 15,065 | 81/2d/72a6487a66436e6108a14822dd47c00f8ff3e5324f84ebf2230912a6ea4d/hardview-0.1.0-cp38-cp38-win_amd64.whl | cp38 | bdist_wheel | null | false | 918f79b1fc19c054fe4ee25c339ff7a6 | 269607fc643acffd1a3f76e3957ade23d7c842b2a7446110f9dadedeb130cfb0 | 812d72a6487a66436e6108a14822dd47c00f8ff3e5324f84ebf2230912a6ea4d | null | [] |
2.4 | HelperFunctionsLiam167 | 0.1.4 | Reusable Plotly/Colab helpers for analytics | null | null | Liam Crowley | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [
"plotly",
"pandas"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.11.9 | 2025-06-17T14:43:06.222038 | helperfunctionsliam167-0.1.4-py3-none-any.whl | 6,376 | e9/d1/3a15cdaefa81176c77c609237376a56b4ea74c6391c2bab7e2374b7ffd86/helperfunctionsliam167-0.1.4-py3-none-any.whl | py3 | bdist_wheel | null | false | 8cf55087a5801f707615122094c21450 | 0948f10aa94bf14b4776494f755f3445ad6f11a3b39d9acd7e5ed41c5bd20944 | e9d13a15cdaefa81176c77c609237376a56b4ea74c6391c2bab7e2374b7ffd86 | null | [] |
2.4 | HyTechMaster-STT | 0.1 | this is speech to text packages created by arman rahtore | null | null | Arman Rathore | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.5 | 2025-06-28T18:53:47.743630 | hytechmaster_stt-0.1.tar.gz | 1,765 | 6c/60/20dcb53ad103fbb496470eff28a6521cc7e5ffc09600b7a476ac04d3864e/hytechmaster_stt-0.1.tar.gz | source | sdist | null | false | f6ba1baf9c1408ef717c6bf7293bd85c | 79491c3f4b5bb16880e853e91bcc3f09b31462b80cb7a1ab8570380ac6183f4e | 6c6020dcb53ad103fbb496470eff28a6521cc7e5ffc09600b7a476ac04d3864e | null | [] |
2.2 | IsoSpecPy | 2.3.0.dev1 | IsoSpecPy is a Python library for computing isotopic distributions of molecules. | null | null | null | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [
"cffi"
] | [] | [] | [] | [] | twine/5.1.1 CPython/3.12.10 | 2025-06-05T12:19:31.126746 | isospecpy-2.3.0.dev1-cp311-cp311-musllinux_1_2_x86_64.whl | 1,168,747 | 00/9f/30aa56e88fdd952ef2c7d4789761245193fe7915cd5e26f1e26320c7a789/isospecpy-2.3.0.dev1-cp311-cp311-musllinux_1_2_x86_64.whl | cp311 | bdist_wheel | null | false | e029c7917d2220464ca21e147b3b5231 | 7c8cb984711c4e35a6391e08bbe3097ed29f856fd37be25d6e38ff44e7bf3556 | 009f30aa56e88fdd952ef2c7d4789761245193fe7915cd5e26f1e26320c7a789 | null | [] |
2.2 | IsoSpecPy | 2.3.0.dev1 | IsoSpecPy is a Python library for computing isotopic distributions of molecules. | null | null | null | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [
"cffi"
] | [] | [] | [] | [] | twine/5.1.1 CPython/3.12.10 | 2025-06-05T12:20:49.173919 | isospecpy-2.3.0.dev1-cp38-cp38-win_amd64.whl | 73,579 | 81/6e/8d46db3591d52ac57f133cbddd413f9a0367b73769df64c981a141c36846/isospecpy-2.3.0.dev1-cp38-cp38-win_amd64.whl | cp38 | bdist_wheel | null | false | 98fbf821d7e3fde53ffb4feea2beb67f | 46bf95ecbb3215d5601a90eefb54eac3b34dfada3bf878db47cd98b3338376db | 816e8d46db3591d52ac57f133cbddd413f9a0367b73769df64c981a141c36846 | null | [] |
2.2 | IsoSpecPy | 2.3.0.dev1 | IsoSpecPy is a Python library for computing isotopic distributions of molecules. | null | null | null | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [
"cffi"
] | [] | [] | [] | [] | twine/5.1.1 CPython/3.12.10 | 2025-06-05T12:19:08.746830 | isospecpy-2.3.0.dev1-cp310-cp310-win32.whl | 72,839 | 54/af/f2db9a80516ad622ce7dfe93daefb49971a265692c6db416576d3221ff0f/isospecpy-2.3.0.dev1-cp310-cp310-win32.whl | cp310 | bdist_wheel | null | false | 2ce6f547e487f3be976be6e683925e96 | b4c181daf4120349e2dbd588b361802b83f744f0cd55f8f009e5066c852e56c4 | 54aff2db9a80516ad622ce7dfe93daefb49971a265692c6db416576d3221ff0f | null | [] |
2.4 | LaplaPy | 0.2.1 | Symbolic derivative & Laplace transform with step-by-step output | # LaplaPy: Advanced Symbolic Laplace Transform Analysis
**Scientific Computing Package for Differential Equations, System Analysis, and Control Theory**
A comprehensive Python library for symbolic Laplace transforms with rigorous mathematical foundations, designed for engineers, scientists, and researchers.
---
## Overview
`LaplaPy` provides a powerful symbolic computation environment for:
1. **Time-domain analysis**: Derivatives, integrals, and function manipulation
2. **Laplace transforms**: With rigorous Region of Convergence (ROC) determination
3. **System analysis**: Pole-zero identification, stability analysis, and frequency response
4. **ODE solving**: Complete solution of linear differential equations with initial conditions
5. **Control system tools**: Bode plots, time-domain responses, and transfer function analysis
---
## Key Features
- **Mathematical Rigor**: Implements Laplace transform theory with proper ROC analysis
- **Causal System Modeling**: Automatic handling of Heaviside functions for physical systems
- **Step-by-Step Solutions**: Educational mode for learning complex concepts
- **Comprehensive System Analysis**: Pole-zero identification, stability criteria, frequency response
- **ODE Solver**: Complete solution workflow for linear differential equations
- **Visualization Tools**: Bode plot generation and time-domain simulations
---
## Installation
```bash
pip install LaplaPy
```
For development:
```bash
git clone https://github.com/4211421036/LaplaPy.git
cd LaplaPy
pip install -e .[dev]
```
---
## Quickstart
### Basic Operations
```python
from LaplaPy import LaplaceOperator, t, s
# Initialize with expression (causal system by default)
op = LaplaceOperator("exp(-3*t) + sin(2*t)", show_steps=True)
# Compute derivative
d1 = op.derivative(order=1)
# Laplace transform with ROC analysis
F_s, roc, poles, zeros = op.laplace()
# Inverse Laplace transform
f_t = op.inverse_laplace()
```
### ODE Solving
```python
from sympy import Eq, Function, Derivative, exp
# Define a differential equation
f = Function('f')(t)
ode = Eq(Derivative(f, t, t) + 3*Derivative(f, t) + 2*f, exp(-t))
# Solve with initial conditions
solution = op.solve_ode(ode, {0: 0, 1: 1}) # f(0)=0, f'(0)=1
```
### System Analysis
```python
# Frequency response
magnitude, phase = op.frequency_response()
# Time-domain response to input
response = op.time_domain_response("sin(4*t)")
# Generate Bode plot data
omega, mag_db, phase_deg = op.bode_plot(ω_range=(0.1, 100), points=100)
```
---
## CLI Usage
```bash
LaplaPy "exp(-2*t)*sin(3*t)" --laplace --deriv 2
LaplaPy "s/(s**2 + 4)" --inverse
LaplaPy "Derivative(f(t), t, t) + 4*f(t) = exp(-t)" --ode --ic "f(0)=0" "f'(0)=1"
```
**Options**:
- `--deriv N`: Compute Nth derivative
- `--laplace`: Compute Laplace transform
- `--inverse`: Compute inverse Laplace transform
- `--ode`: Solve ODE (provide equation)
- `--ic`: Initial conditions (e.g., "f(0)=0", "f'(0)=1")
- `--causal/--noncausal`: System causality assumption
- `--quiet`: Suppress step-by-step output
---
## Mathematical Foundations
### Laplace Transform
$$\mathcal{L}\{f(t)\}(s) = \int_{0^-}^{\infty} e^{-st} f(t) dt$$
### Derivative Property
$$\mathcal{L}\{f^{(n)}(t)\} = s^n F(s) - \sum_{k=1}^{n} s^{n-k} f^{(k-1)}(0^+)$$
### Region of Convergence
- For causal systems: Re(s) > σ_max (right-half plane)
- Proper ROC determination for stability analysis
### Pole-Zero Analysis
- Transfer function: $H(s) = \frac{N(s)}{D(s)}$
- Poles: Roots of denominator polynomial
- Zeros: Roots of numerator polynomial
### Frequency Response
$$H(j\omega) = H(s)\big|_{s=j\omega} = |H(j\omega)| e^{j\angle H(j\omega)}$$
---
## Examples
### Second-Order System Analysis
```python
op = LaplaceOperator("1/(s**2 + 0.6*s + 1)", show_steps=True)
# Get poles and zeros
F_s, roc, poles, zeros = op.laplace()
# Frequency response
magnitude, phase = op.frequency_response()
# Bode plot data
omega, mag_db, phase_deg = op.bode_plot(ω_range=(0.1, 10), points=200)
```
### Circuit Analysis (RLC Network)
```python
# Define circuit equation: L*di/dt + R*i + 1/C*∫i dt = V_in
L, R, C = 0.5, 4, 0.25
op = LaplaceOperator("V_in(s)", show_steps=True)
# Impedance representation
Z = L*s + R + 1/(C*s)
current = op.time_domain_response("V_in(s)/" + str(Z))
# Response to step input
step_response = current.subs("V_in(s)", "1/s")
```
---
## Development & Testing
```bash
# Run tests
pytest tests/
# Generate documentation
cd docs
make html
# Contribution guidelines
CONTRIBUTING.md
```
---
## Scientific Applications
1. **Control Systems**: Stability analysis, controller design
2. **Circuit Analysis**: RLC networks, filter design
3. **Vibration Engineering**: Damped oscillator analysis
4. **Signal Processing**: System response characterization
5. **Communication Systems**: Filter design, modulation analysis
6. **Mechanical Systems**: Spring-mass-damper modeling
---
## Documentation Wiki
Full documentation available at:
[LaplaPy Documentation WiKi](https://github.com/4211421036/LaplaPy/wiki)
Includes:
- Mathematical background
- API reference
- Tutorial notebooks
- Application examples
---
## License
MIT License
---
## Cite This Work
```bibtex
@software{LaplaPy,
author = {GALIH RIDHO UTOMO},
title = {LaplaPy: Advanced Symbolic Laplace Transform Analysis},
year = {2025},
publisher = {GitHub},
howpublished = {\url{https://github.com/4211421036/LaplaPy}}
}
```
| text/markdown | null | GALIH RIDHO UTOMO <g4lihru@students.unnes.ac.id> | null | null | MIT | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License"
] | [] | null | null | null | [] | [] | [] | [
"sympy>=1.10"
] | [] | [] | [] | [
"Homepage, https://github.com/4211421036/LaplaPy",
"Repository, https://github.com/4211421036/LaplaPy"
] | twine/6.1.0 CPython/3.10.17 | 2025-06-16T18:52:11.032259 | laplapy-0.2.1-py3-none-any.whl | 12,346 | 5d/f2/235188128b170e4e2e0aa4c742577c216f4cd2a7d3b191c4998eb8c9fbfe/laplapy-0.2.1-py3-none-any.whl | py3 | bdist_wheel | null | false | d568d5f7827aafeae0f3b5f33d826e49 | dd6019e30cdb7e8a1388145fa35dc69a6a86ab15c06a8493b6ad4a89e5d1c704 | 5df2235188128b170e4e2e0aa4c742577c216f4cd2a7d3b191c4998eb8c9fbfe | null | [
"LICENSE"
] |
2.1 | LinguAligner | 1.0.3 | LinguAligner is a Python library for aligning annotations in parallel corpora. It is designed to be used in the context of parallel corpora annotation alignment, where the goal is to align annotations in the source language with annotations in the target language. | <p align="center">
<img src="img/lingualigner.png" alt="LinguAligner Logo" width="300"/>
</p>
**LinguAligner** is a Python package for automatically translating annotated corpora while preserving their annotations. It supports multiple translation APIs and alignment strategies, making it a valuable tool for NLP researchers building multilingual datasets, particularly for low-resource languages.
Natural Language Processing (NLP) research remains heavily centered on English, creating a language imbalance in AI. One way to improve linguistic diversity is by adapting annotated corpora from high-resource languages to others. However, preserving span-based annotation quality after translation requires precise alignment of annotations between the source and translated texts, a challenging task due to lexical, syntactic and semantic divergences between languages. **LinguAligner** provides an automated pipeline to align annotations within translated texts using a several annotation alignment strategies.
## 🚀 Features
- 🌐 **Translation Module**:
Supports external translation services:
- Google Translate
- Microsoft Translator
- DeepL
- 🧠 **Annotation Alignment Module**:
Implements multiple techniques:
- **Exact / Fuzzy Matching**: Levenshtein, Gestalt
- **Lemmatization-based Matching** using [spaCy](https://spacy.io/)
- **Pre-compiled Translation Dictionaries** via Microsoft Lookup API
- **Multilingual Contextual Embeddings** using [BERT-multilingual](https://huggingface.co/bert-base-multilingual-uncased)
The pipeline operates sequentially, meaning that annotations aligned by earlier methods are not addressed by subsequent pipeline elements. According to our experiments, the list above corresponds to the best order sequence.
## 📦 Installation
Install via [PyPI](https://pypi.org/project/LinguAligner/):
```bash
pip install LinguAligner
```
## 🧪 Example Usage
### 1. Translate Corpora
You can use the Translation APIs or can translate your corpus with an external tool (an API key is needed).
```python
from LinguAligner import translation
# Google Translate
translator = translation.GoogleTranslator(source_lang="en", target_lang="pt", key="Google_KEY")
translated_text = translator.translate("The soldiers were ordered to fire their weapons")
# DeepL
translator = translation.DeepLTranslator(source_lang="en", target_lang="pt", key="DEEPL_KEY")
translated_text = translator.translate("The soldiers were ordered to fire their weapons")
# Microsoft
translator = translation.MicrosoftTranslator(source_lang="en", target_lang="pt", key="MICROSOFT_KEY")
translated_text = translator.translate("The soldiers were ordered to fire their weapons")
print(translated_text)
```
### 2. Align Annotations
Users can select the aligner strategies they intend to use and specify the order in which they should be utilized. According to our findings, the best sequence order is the ones presented in the example below, however, we encourage you to experiment with different orders for your specific use case.
```python
from LinguAligner import AlignmentPipeline
# Define pipeline and model configuration
config = {
"pipeline": ["lemma", "M_trans", "word_aligner", "gestalt", "levenshtein"],
"spacy_model": "pt_core_news_lg",
"WAligner_model": "bert-base-multilingual-uncased"
}
aligner = AlignmentPipeline(config)
# Source and translated data
src_sent = "The soldiers land on the shore..."
src_ann = "land"
trans_sent = "Os soldados aterraram na costa."
trans_ann = "terra" # Expected direct translation
# Perform annotation alignment
target_annotation = aligner.align_annotation(
src_sent, src_ann, trans_sent, trans_ann
)
print(target_annotation)
# Output: ('aterraram', (12, 21))
```
In this example, the word `land` is translated to `terra` (land as a noun) when considered in isolation, but as `aterraram` (land as a verb) when translated in context. Although `terra` is a valid translation of the annotation, it does not occur in the translated sentence and therefore cannot be aligned. Such misalignments highlight the need for additional processing to determine the correct annotation offsets in the translated text, in this case, mapping the word `terra` to `aterraram` .
## 🔧 Configuration
You can customize the alignment behavior in the `config` variable:
```python
config = {
"pipeline": ["lemma", "word_aligner", "levenshtein"], # change pipeline elements and order
"spacy_model": "fr_core_news_md", # change spacy model
"WAligner_model": "bert-base-multilingual-uncased" # change multilingual model
}
```
## 🔧 Advanced Options
### Specify source annotation index to resolve ambiguity (Multiple Source Matches)
```python
src_sent = "he was a good man because he had a kind heart"
src_ann = "he"
trans_sent = "ele era um bom homem porque ele tinha um bom coração"
trans_ann = "ele"
target_annotation = aligner.align_annotation(
src_sent, src_ann, trans_sent, trans_ann, src_ann_start=29
)
print(target_annotation)
# Output: ('ele', (28, 30))
```
### Using the M_trans Method
The `M_trans` method relies on having multiple possible translations for each annotation. These must be prepared in advance and stored in a Python dictionary, where each key is a source annotation and the value is a list of alternative translations.
You can generate this translation dictionary using the Microsoft Translator API (requires a MICROSOFT_TRANSLATOR_KEY):
```python
from LinguAligner import translation
translator = translation.MicrosoftTranslator(
source_lang="en", target_lang="pt", auth_key="MICROSOFT_TRANSLATOR_KEY"
)
annotations_list = ["war", "land", "fire"]
lookup_table = {}
for word in annotations_list:
lookup_table[word] = translator.getMultipleTranslations(word)
# Use the lookup table in align_annotation
aligner.align_annotation(
"The soldiers were ordered to fire their weapons",
"fire",
"Os soldados receberam ordens para disparar as suas armas",
"incêndio",
M_trans_dict=lookup_table
)
```
#### 🔎 Example output of a lookup table:
```python
{
"fire": [
"fogo",
"incêndio",
"demitir",
"despedir",
"fogueira",
"disparar",
"chamas",
"dispare",
"lareira",
"atirar",
"atire"
]
}
```
## 📚 Use Cases
LinguAligner was used to create translated versions of the following annotated corpora:
- **ACE-2005** (EN → PT): Event extraction benchmark, now available in Portuguese via the [LDC](https://catalog.ldc.upenn.edu/LDC2024T05)
- **T2S LUSA** (PT → EN): Portuguese news event corpus adapted to English [10.25747/ESFS-1P16](https://doi.org/10.25747/ESFS-1P16)
- **MAVEN**: (EN → PT) High-coverage event trigger corpus from Wikipedia translated to Portuguese (available in this repository)
- **WikiEvents**: (EN → PT) Document-level event extraction dataset translated to Portuguese (available in this repository)
## 🧩 Citation
### Coming soon...
| text/markdown | null | lfc <lfc@di.uminho.pt> | null | null | null | null | [
"License :: OSI Approved :: MIT License"
] | [] | null | null | null | [] | [
"LinguAligner"
] | [] | [
"transformers[torch]",
"spacy",
"deep_translator",
"deepl",
"requests",
"uuid",
"fuzzywuzzy"
] | [] | [] | [] | [
"Homepage, https://github.com/lfcc1/LinguAligner",
"Issues, https://github.com/lfcc1/LinguAligner/issues"
] | python-requests/2.32.3 | 2025-06-18T00:04:36.930098 | LinguAligner-1.0.3.tar.gz | 5,104,277 | bb/09/1e74e7b9bd0ec2ce9bce79cc2abe536a24f9f0fd4f4ed2fab9627e8edc91/LinguAligner-1.0.3.tar.gz | source | sdist | null | false | bb38db49855f43686020c2fe8a1cb331 | 00eb280982d889cefe458023b65cc714b3d8b2d79acbfb9e877da94b679ba31c | bb091e74e7b9bd0ec2ce9bce79cc2abe536a24f9f0fd4f4ed2fab9627e8edc91 | null | [] |
2.4 | MBbankchecker | 0.2.5 | bankchecker base on MBBank of The DT | null | null | JussKynn | null | null | null | null | null | [] | [] | null | null | null | [] | [] | [] | [
"Pillow",
"requests",
"aiohttp",
"setuptools",
"wheel",
"wasmtime",
"mb-capcha-ocr"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.3 | 2025-06-10T12:29:42.309825 | mbbankchecker-0.2.5-py3-none-any.whl | 26,170 | d9/3a/ac08855419e3bf603d7f647fc422d1d1f104bf01edab73f4d84a38d2e5c8/mbbankchecker-0.2.5-py3-none-any.whl | py3 | bdist_wheel | null | false | e2b128ae89b3fcf4f64583c95676a5ba | ba9dae1c00fc72a53d73eb3832420cb0359d1beee7dea125b9844da19382dcdf | d93aac08855419e3bf603d7f647fc422d1d1f104bf01edab73f4d84a38d2e5c8 | null | [
"LICENSE.txt"
] |
2.1 | ManimTool | 0.1.0.2 | A package for tools in Manim | # 导入 Import
```python
from ManimTool import *
```
依赖库(Requires)：`manim`
***发现任何bug或问题，请反馈到tommy1008@dingtalk.com，谢谢！**
***If you find any bugs or issues, please report them to tommy1008@dingtalk.com, thank you!**
了解更多详情，请前往[Manim Community](https://www.manim.community)。
For more details, visit [Manim Community](https://www.manim.community).
## 公式与图形 Formulas and Graphics
```python
def ChineseMathTex(*texts, font="SimSun", tex_to_color_map={}, **kwargs):
```
**创建中文数学公式。** 在此函数的公式部分和`tex_to_color_map`中直接写入中文即可，无需包裹`\text{}`，返回`MathTex`。`font`，设置公式中的中文字体。所有原版参数都可使用。
**Creates Chinese mathematical formulas.** You can directly write Chinese characters in the formula part of this function and in `tex_to_color_map` without wrapping them in `\text{}`. Returns a `MathTex` object. The `font` parameter sets the Chinese font for the formula. All original parameters can be used.
```python
def YellowLine(**kwargs):
```
**创建黄色的`Line`。** 所有原版参数都可使用。
****Creates a yellow ``Line``.**** All original parameters can be used.
```python
def LabelDot(dot_label, dot_pos, label_pos=DOWN, buff=0.1):
```
**创建一个带有名字的点，返回带有点和名字的`VGroup`。** `dot_label`，点的名字，字符串。`dot_pos`，点的位置，位置列表`[x,y,z]`。`label_pos`，点的名字相对于点的位置，Manim中的八个方向。`buff`，点的名字与点的间距，数值。
****Creates a point with a name. Returns a ``VGroup`` containing the point and its name.**** `dot_label` is the name of the point (a string). `dot_pos` is the position of the point (a list `[x, y, z]`). `label_pos` is the position of the label relative to the point (one of the eight directions in Manim). `buff` is the spacing between the label and the point (a numerical value).
```python
def MathTexLine(mathtex: MathTex, direction=UP, buff=0.5, **kwargs):
def MathTexBrace(mathtex: MathTex, direction=UP, buff=0.5, **kwargs):
def MathTexDoublearrow(mathtex: MathTex, direction=UP, buff=0.5, **kwargs):
```
**创建可以标注内容的图形，返回带有图形和标注内容的`VGroup`。** `mathtex`，标注的公式，`MathTex`类型。`direction`，标注内容相对于线的位置，Manim中的八个方向。`buff`，标注内容与图形的间距，数值。图形的所有原版参数都可使用。
**Creates a graphical annotation for a MathTex object. Returns a `VGroup` containing the graphic and the annotation.** `mathtex` is the MathTex object to annotate. `direction` is the position of the annotation relative to the graphic (using Manim's direction constants). `buff` is the spacing between the graphic and the annotation. All original parameters of the underlying graphic can be used.
```python
def ExtendedLine(line: Line, extend_distance: float) -> Line:
```
**将一条线延长`extend_distance`的距离，返回延长后的`Line`。** `line`，`Line`类型。`extend_distance`，要延长的距离，数值。
****Extends a line by ``extend_distance``. Returns the extended ``Line``.**** `line` must be of type `Line`. `extend_distance` is the distance to extend (a numerical value).
## 交点 Intersection Points
```python
def CircleInt(circle1, circle2):
def LineCircleInt(line, circle):
def LineInt(line1: Line, line2: Line) -> Optional[Tuple[float, float]]:
def LineArcInt(line: Line, arc: Arc) -> list:
```
**函数名代表了寻找具体图形交点的功能**，例如`LineCircleInt`代表寻找`Line`和`Circle`的交点，返回点位置`[x,y,z]`，如果没有交点会返回`None`。
**The function names represent the function of finding the intersection points of specific shapes.** For example, `LineCircleInt` represents finding the intersection points of `Line` and `Circle`. Returns point position `[x,y,z]`. If there are no intersection points, it will return `None`.
## 动画 Animations
```python
def VisDrawArc(scene: Scene, arc: Arc, axis=OUT, run_time=1):
```
**创建可视化（显示半径）的绘弧动画。** 直接使用即可，无需写入`self.play()`内。 `scene`，动画场景。`arc`， 已经定义好的`Arc`。`axis`，只有2个值`IN`和`OUT`，分别表示正方向还是反方向作弧。`run_time`，这是绘弧动画的时长。
**Creates a visualized arc drawing animation (with radius display).** Can be used directly without wrapping in `self.play()`. `scene` refers to the animation scene. `arc` is the predefined `Arc` object. `axis` accepts two values: `IN` (positive direction) and `OUT` (negative direction), indicating the drawing direction of the arc. `run_time` denotes the duration of the arc drawing animation.
| text/markdown | Zhu Chongjing | tommy1008@dingtalk.com | null | null | null | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent"
] | [] | null | null | null | [] | [] | [] | [
"manim"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.7 | 2025-06-01T14:28:24.370743 | manimtool-0.1.0.2.tar.gz | 6,799 | bf/1b/63692b33283a0a7ce5643a43891f965974cfc01f0f218140893f20dc7549/manimtool-0.1.0.2.tar.gz | source | sdist | null | false | b3de3fa67034986dd474f2b34b5a4388 | 79a8e1ff8ec03ea89f958e6446345b2014aafe1f5b436b203b1bb5a226ac90c5 | bf1b63692b33283a0a7ce5643a43891f965974cfc01f0f218140893f20dc7549 | null | [] |
2.4 | Mensajes-albiery | 6.0 | Un paquete para saludar y despedir | # Mensajes
El paquete de mensajeria para pruebas de Albiery
| text/markdown | Albiery de Leon | albiery@gmail.com | null | null | null | null | [
"Environment :: Console",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language :: Python :: 3.9",
"Topic :: Utilities"
] | [] | https://www.albiery.dev | null | null | [] | [] | [] | [
"numpy>=1.23.0"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.1 | 2025-06-14T20:23:28.577531 | mensajes_albiery-6.0.tar.gz | 3,465 | b0/2e/241839f3bca035ae1600f099c4ce8e3812b2007dd46f7bb34b451ccd2079/mensajes_albiery-6.0.tar.gz | source | sdist | null | false | ff304d9dd5f19abc6abfe2ed2926ff7d | 9ee4fba78ba965fa9f824b3438a37c38be2b6da047994fa6688be4ef09a95d99 | b02e241839f3bca035ae1600f099c4ce8e3812b2007dd46f7bb34b451ccd2079 | null | [
"LICENSE"
] |
2.3 | MicroPie | 0.11 | An ultra micro ASGI web framework | [](https://patx.github.io/micropie)
## **Introduction**
**MicroPie** is a fast, lightweight, modern Python web framework that supports asynchronous web applications. Designed with **flexibility** and **simplicity** in mind, MicroPie enables you to handle high-concurrency applications with ease while allowing natural integration with external tools like Socket.IO for real-time communication.
### **Key Features**
- 🔄 **Routing:** Automatic mapping of URLs to functions with support for dynamic and query parameters.
- 🔒 **Sessions:** Simple, plugable, session management using cookies.
- 🎨 **Templates:** Jinja2, if installed, for rendering dynamic HTML pages.
- ⚙️ **Middleware:** Support for custom request middleware enabling functions like rate limiting, authentication, logging, and more.
- ✨ **ASGI-Powered:** Built w/ asynchronous support for modern web servers like Uvicorn, Hypercorn, and Daphne, enabling high concurrency.
- 🛠️ **Lightweight Design:** Only optional dependencies for flexibility and faster development/deployment.
- ⚡ **Blazing Fast:** Check out how MicroPie compares to other popular ASGI frameworks below!
### **Useful Links**
- **Homepage**: [patx.github.io/micropie](https://patx.github.io/micropie)
- **API Reference**: [README.md#api-documentation](https://github.com/patx/micropie/blob/main/README.md#api-documentation)
- **PyPI Page**: [pypi.org/project/MicroPie](https://pypi.org/project/MicroPie/)
- **GitHub Project**: [github.com/patx/micropie](https://github.com/patx/micropie)
- **File Issue/Request**: [github.com/patx/micropie/issues](https://github.com/patx/micropie/issues)
- **Example Applications**: [github.com/patx/micropie/tree/main/examples](https://github.com/patx/micropie/tree/main/examples)
- **Introduction Lightning Talk**: [Introduction to MicroPie on YouTube](https://www.youtube.com/watch?v=BzkscTLy1So)
## **Installing MicroPie**
### **Installation**
Install MicroPie with all optional dependencies via pip:
```bash
pip install micropie[standard]
```
This will install MicroPie along with `jinja2` for template rendering, and `multipart` for parsing multipart form data.
If you would like to install **all** optional dependencies (everything from `standard` plus `orjson` and `uvicorn`) you can run:
```bash
pip install micropie[all]
```
### **Minimal Setup**
You can also install MicroPie without ANY dependencies via pip:
```bash
pip install micropie
```
For an ultra-minimalistic approach, download the standalone script:
[MicroPie.py](https://raw.githubusercontent.com/patx/micropie/refs/heads/main/MicroPie.py)
Place it in your project directory, and you are good to go. Note that `jinja2` must be installed separately to use the `_render_template` method and/or `multipart` for handling file data (the `_parse_multipart` method), but this *is* optional and you can use MicroPie without them. To install the optional dependencies use:
```bash
pip install jinja2 multipart
```
By default MicroPie will use the `json` library from Python's standard library. If you need faster performance you can use `orjson`. MicroPie *will* use `orjson` *if installed* by default. If it is not installed, MicroPie will fallback to `json`. This means with or without `orjson` installed MicroPie will still handle JSON requests/responses the same. To install `orjson` and take advantage of it's performance, use:
```bash
pip install orjson
```
### **Install an ASGI Web Server**
In order to test and deploy your apps you will need a ASGI web server like Uvicorn, Hypercorn or Daphne. Install `uvicorn` with:
```bash
pip install uvicorn
```
You can also install MicroPie with `uvicorn` included using:
```bash
pip install micropie[all]
```
## **Getting Started**
### **Create Your First ASGI App**
Save the following as `app.py`:
```python
from MicroPie import App
class MyApp(App):
async def index(self):
return "Welcome to MicroPie ASGI."
app = MyApp()
```
Run the server with:
```bash
uvicorn app:app
```
Access your app at [http://127.0.0.1:8000](http://127.0.0.1:8000).
## **Core Features**
### **1. Flexible HTTP Routing for GET Requests**
MicroPie automatically maps URLs to methods within your `App` class. Routes can be defined as either synchronous or asynchronous functions, offering good flexibility.
For GET requests, pass data through query strings or URL path segments, automatically mapped to method arguments.
```python
class MyApp(App):
async def greet(self, name="Guest"):
return f"Hello, {name}!"
async def hello(self):
name = self.request.query_params.get("name", [None])[0]
return f"Hello {name}!"
```
**Access:**
- [http://127.0.0.1:8000/greet?name=Alice](http://127.0.0.1:8000/greet?name=Alice) returns `Hello, Alice!`, same as [http://127.0.0.1:8000/greet/Alice](http://127.0.0.1:8000/greet/Alice) returns `Hello, Alice!`.
- [http://127.0.0.1:8000/hello/Alice](http://127.0.0.1:8000/hello/Alice) returns a `500 Internal Server Error` because it is expecting [http://127.0.0.1:8000/hello?name=Alice](http://127.0.0.1:8000/hello?name=Alice), which returns `Hello Alice!`
### **2. Flexible HTTP POST Request Handling**
MicroPie also supports handling form data submitted via HTTP POST requests. Form data is automatically mapped to method arguments. It is able to handle default values and raw/JSON POST data:
```python
class MyApp(App):
async def submit_default_values(self, username="Anonymous"):
return f"Form submitted by: {username}"
async def submit_catch_all(self):
username = self.request.body_params.get("username", ["Anonymous"])[0]
return f"Submitted by: {username}"
```
By default, MicroPie's route handlers can accept any request method, it's up to you how to handle any incoming requests! You can check the request method (and an number of other things specific to the current request state) in the handler with`self.request.method`. You can see how to handle POST JSON data at [examples/api](https://github.com/patx/micropie/tree/main/examples/api).
You can use [middlware](https://github.com/patx/micropie#8-middleware) to add explicit routing when needed. See the [middleware router](https://github.com/patx/micropie/blob/main/examples/middleware/router.py) example.
### **3. Real-Time Communication with Socket.IO**
Because of its designed simplicity, MicroPie does not handle WebSockets out of the box. While the underlying ASGI interface can theoretically handle WebSocket connections, MicroPie’s routing and request-handling logic is designed primarily for HTTP. While MicroPie does not natively support WebSockets (*yet!*), you can easily integrate dedicated Websockets libraries like **Socket.IO** alongside Uvicorn to handle real-time, bidirectional communication. Check out [examples/socketio](https://github.com/patx/micropie/tree/main/examples/socketio) to see this in action.
### **4. Jinja2 Template Rendering**
Dynamic HTML generation is supported via Jinja2. This happens asynchronously using Pythons `asyncio` library, so make sure to use the `async` and `await` with this method.
#### **`app.py`**
```python
class MyApp(App):
async def index(self):
return await self._render_template("index.html", title="Welcome", message="Hello from MicroPie!")
```
#### **`templates/index.html`**
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>{{ title }}</title>
</head>
<body>
<h1>{{ message }}</h1>
</body>
</html>
```
### **5. Static File Serving**
Here again, like Websockets, MicroPie does not have a built in static file method. While MicroPie does not natively support static files, if you need them, you can easily integrate dedicated libraries like **ServeStatic** or **Starlette’s StaticFiles** alongside Uvicorn to handle async static file serving. Check out [examples/static_content](https://github.com/patx/micropie/tree/main/examples/static_content) to see this in action.
### **6. Streaming Responses**
Support for streaming responses makes it easy to send data in chunks.
```python
class MyApp(App):
async def stream(self):
async def generator():
for i in range(1, 6):
yield f"Chunk {i}\n"
return generator()
```
### **7. Sessions and Cookies**
Built-in session handling simplifies state management:
```python
class MyApp(App):
async def index(self):
if "visits" not in self.session:
self.request.session["visits"] = 1
else:
self.request.session["visits"] += 1
return f"You have visited {self.request.session['visits']} times."
```
You also can use the `SessionBackend` class to create your own session backend. You can see an example of this in [examples/sessions](https://github.com/patx/micropie/tree/main/examples/sessions).
### **8. Middleware**
MicroPie allows you to create pluggable middleware to hook into the request lifecycle. Take a look a trivial example using `HttpMiddleware` to send the console messages before and after the request is processed. Check out [examples/middleware](https://github.com/patx/micropie/tree/main/examples/middleware) to see more.
```python
from MicroPie import App, HttpMiddleware
class MiddlewareExample(HttpMiddleware):
async def before_request(self, request):
print("Hook before request")
async def after_request(self, request, status_code, response_body, extra_headers):
print("Hook after request")
class Root(App):
async def index(self):
return "Hello, World!"
app = Root()
app.middlewares.append(MiddlewareExample())
```
### **9. Deployment**
MicroPie apps can be deployed using any ASGI server. For example, using Uvicorn if our application is saved as `app.py` and our `App` subclass is assigned to the `app` variable we can run it with:
```bash
uvicorn app:app --workers 4 --port 8000
```
## **Learn by Examples**
The best way to get an idea of how MicroPie works is to see it in action! Check out the [examples folder](https://github.com/patx/micropie/tree/main/examples) for more advanced usage, including:
- Template rendering
- Custom HTTP request handling
- File uploads
- Serving static content
- Session usage
- JSON Requests and Responses
- Socket.io Integration
- Async Streaming
- Middleware, including rate limiting and explicit routing
- Form handling and POST requests
- And more
## **Comparisons**
### **Features vs Other Popular Frameworks**
| Feature | MicroPie | Flask | CherryPy | Bottle | Django | FastAPI |
|---------------------|---------------|--------------|------------|--------------|--------------|-----------------|
| **Routing** | Automatic | Manual | Automatic | Manual | Views | Manual |
| **Template Engine** | Jinja2 (Opt.) | Jinja2 | Plugin | SimpleTpl | Django | Jinja2 |
| **Middleware** | Yes | Yes | Yes | Yes | Yes | Yes |
| **Session Handling**| Plugin | Extension | Built-in | Plugin | Built-in | Extension |
| **Async Support** | Yes | No (Quart) | No | No | Yes | Yes |
| **Built-in Server** | No | No | Yes | Yes | Yes | No |
## Benchmark Results
The table below summarizes the performance of various ASGI frameworks based on a 15-second `wrk` test with 4 threads and 64 connections, measuring a simple "hello world" JSON response. [Learn More](https://gist.github.com/patx/26ad4babd662105007a6e728f182e1db).
| Framework | Total Requests | Req/Sec | Transfer/Sec (MB/s) | Avg Latency (ms) | Stdev Latency (ms) | Max Latency (ms) |
|-------------|----------------|-----------|---------------------|------------------|--------------------|------------------|
| Blacksheep | 831,432 | 55,060.05 | 7.98 | 1.15 | 0.39 | 15.11 |
| MicroPie | 791,721 | 52,685.82 | 8.09 | 1.35 | 1.09 | 21.59 |
| Starlette | 779,092 | 51,930.45 | 7.03 | 1.22 | 0.39 | 17.42 |
| Litestar | 610,059 | 40,401.18 | 5.47 | 1.57 | 0.63 | 33.66 |
| FastAPI | 281,493 | 18,756.73 | 2.54 | 3.52 | 1.82 | 56.73 |
## **Suggestions or Feedback?**
We welcome suggestions, bug reports, and pull requests!
- File issues or feature requests [here](https://github.com/patx/micropie/issues).
- Security issues that should not be public, email `harrisonerd [at] gmail.com`.
# **API Documentation**
## Session Backend Abstraction
MicroPie provides an abstraction for session backends, allowing you to define custom session storage mechanisms.
### `SessionBackend` Class
#### Methods
- `load(session_id: str) -> Dict[str, Any]`
- Abstract method to load session data given a session ID.
- `save(session_id: str, data: Dict[str, Any], timeout: int) -> None`
- Abstract method to save session data.
### `InMemorySessionBackend` Class
An in-memory implementation of the `SessionBackend`.
#### Methods
- `__init__()`
- Initializes the in-memory session backend.
- `load(session_id: str) -> Dict[str, Any]`
- Loads session data for the given session ID.
- `save(session_id: str, data: Dict[str, Any], timeout: int) -> None`
- Saves session data for the given session ID.
## Middleware Abstraction
MicroPie allows you to create pluggable middleware to hook into the request lifecycle.
### `HttpMiddleware` Class
#### Methods
- `before_request(request: Request) -> None`
- Abstract method called before the request is processed.
- `after_request(request: Request, status_code: int, response_body: Any, extra_headers: List[Tuple[str, str]]) -> None`
- Abstract method called after the request is processed but before the final response is sent to the client.
## Request Object
### `Request` Class
Represents an HTTP request in the MicroPie framework.
#### Attributes
- `scope`: The ASGI scope dictionary for the request.
- `method`: The HTTP method of the request.
- `path_params`: List of path parameters.
- `query_params`: Dictionary of query parameters.
- `body_params`: Dictionary of body parameters.
- `get_json`: JSON request body object.
- `session`: Dictionary of session data.
- `files`: Dictionary of multipart data/streamed content.
- `headers`: Dictionary of headers.
## Application Base
### `App` Class
The main ASGI application class for handling HTTP requests in MicroPie.
#### Methods
- `__init__(session_backend: Optional[SessionBackend] = None) -> None`
- Initializes the application with an optional session backend.
- `request -> Request`
- Retrieves the current request from the context variable.
- `__call__(scope: Dict[str, Any], receive: Callable[[], Awaitable[Dict[str, Any]]], send: Callable[[Dict[str, Any]], Awaitable[None]]) -> None`
- ASGI callable interface for the server. Checks `scope` type.
- `_asgi_app_http(scope: Dict[str, Any], receive: Callable[[], Awaitable[Dict[str, Any]]], send: Callable[[Dict[str, Any]], Awaitable[None]]) -> None`
- ASGI application entry point for handling HTTP requests.
- `request(self) -> Request`
- Accessor for the current request object. - Returns the current request from the context variable.
- `_parse_cookies(cookie_header: str) -> Dict[str, str]`
- Parses the Cookie header and returns a dictionary of cookie names and values.
- `_parse_multipart(reader: asyncio.StreamReader, boundary: bytes) -> Tuple[Dict[str, List[str]], Dict[str, Dict[str, Any]]]`
- Asynchronously parses multipart/form-data from the given reader using the specified boundary. Returns a tuple of two dictionaries: `form_data` (text fields as key-value pairs) and `files` (file fields with metadata). Each file entry in `files` contains:
- `filename`: The original filename of the uploaded file.
- `content_type`: The MIME type of the file (defaults to `application/octet-stream`).
- `content`: An `asyncio.Queue` containing chunks of file data as bytes, with a `None` sentinel signaling the end of the stream.
- Handlers can consume the file data by iterating over the queue (e.g., using `await queue.get()`).
- *Requires:* `multipart`
- `_send_response(send: Callable[[Dict[str, Any]], Awaitable[None]], status_code: int, body: Any, extra_headers: Optional[List[Tuple[str, str]]] = None) -> None`
- Sends an HTTP response using the ASGI send callable.
- `_redirect(location: str) -> Tuple[int, str]`
- Generates an HTTP redirect response.
- `_render_template(name: str, **kwargs: Any) -> str`
- Renders a template asynchronously using Jinja2.
- *Requires*: `jinja2`
The `App` class is the main entry point for creating MicroPie applications. It implements the ASGI interface and handles HTTP requests.
## Response Formats
Handlers can return responses in the following formats:
1. String or bytes or JSON
2. Tuple of (status_code, body)
3. Tuple of (status_code, body, headers)
4. Async or sync generator for streaming responses
## Error Handling
MicroPie provides built-in error handling for common HTTP status codes:
- `404 Not Found`: Automatically returned for non-existent routes
- `400 Bad Request`: Returned for missing required parameters
- `500 Internal Server Error`: Returned for unhandled exceptions
Custom error handling can be implemented through middleware.
----
© 2025 Harrison Erd
| text/markdown | null | Harrison Erd <harrisonerd@gmail.com> | null | null | null | micropie, asgi, microframework, http | [
"Framework :: AsyncIO",
"Environment :: Web Environment",
"Topic :: Internet :: WWW/HTTP",
"Topic :: Software Development :: Libraries :: Application Frameworks"
] | [] | null | null | null | [] | [
"MicroPie"
] | [] | [
"jinja2; extra == \"all\"",
"multipart; extra == \"all\"",
"orjson; extra == \"all\"",
"uvicorn; extra == \"all\"",
"jinja2; extra == \"standard\"",
"multipart; extra == \"standard\""
] | [] | [] | [] | [
"Homepage, https://patx.github.io/micropie",
"Repository, https://github.com/patx/micropie"
] | python-requests/2.32.3 | 2025-06-10T09:04:59.074819 | micropie-0.11-py2.py3-none-any.whl | 14,284 | a1/96/bf19f1d1efbd0953f7239b788c5d673555db0716676ae487fa84f2d4d658/micropie-0.11-py2.py3-none-any.whl | py2.py3 | bdist_wheel | null | false | c445ded2fbce0d321cfa805b55742e7c | 4ce97c009c63429603b749ef62c810e623b3ca03805a8fe703b462950ed44616 | a196bf19f1d1efbd0953f7239b788c5d673555db0716676ae487fa84f2d4d658 | null | [] |
2.4 | MontagePy | 2.3.0 | Montage toolkit for reprojecting, mosaicking, and displaying astronomical images. |
Montage: Astronomical Image Mosaics, Examination, and Visualization
===================================================================
Montage 7.0 adds two major capabilities. The first is a set of tools
for building HiPS map. HiPS (Hierarchical Progressive Surveys) is a
hierarchical tiling mechanism which allows one to access, visualize
and browse seamlessly image data and in particular large-scale,
high-resolution surveys.
HiPS construction through Montage consists of building large-scale
mosaics using pre-existing Montage modules (reprojection, background
matching, and coaddition) with a HiPS-specific projection (HPX).
The image hierarchy just requires repetative shrinking of higher
resolution images by factors of two. Finally, the tiles to be
served are simply 512x512 cutouts from these mosaics.
For high-resolution data, this process can benefit greatly from
massive parallelization and this can be achieved in a number of
ways. In particular, tools have been developed to streamline this
on cloud platforms like AWS.
So at arcminute scale all-sky processing can adequately be done on
a single desktop machine and at arcsecond scale the same tools can
create a set of jobs that can be submitted to run on a cloud in
a few days (or a few tens of processors if one has that in-house).
The second addition to Montage is a complete set of modern procedures
for building Python binary extension Montage wheels for Linux and Mac
systems (and extensible to some others). This is very much a moving
target and we are building as many of these as we can and pushing them
to PyPI but if someone want to extend Montage for their own use they
can use the same infrastructure to build custom wheels as well.
--------------
Montage (http://montage.ipac.caltech.edu) is an Open Source toolkit,
distributed with a BSD 3-clause license, for assembling Flexible
Image Transport System (FITS) images into mosaics according to
the user's custom specifications of coordinates, projection,
spatial sampling, rotation and background matching.
The toolkit contains utilities for reprojecting and background
matching images, assembling them into mosaics, visualizing the
results, and discovering, analyzing and understanding image metadata
from archives or the user's images.
Montage is written in ANSI-C and is portable across all common
Unix-like platforms, including Linux, Solaris, Mac OSX and Cygwin on
Windows. The package provides both stand-alone executables and
the same functionality in library form. It has been cross-compiled
to provide native Windows executables and packaged as a binary Python
extension (available via "pip install MontagePy").
The distribution contains all libraries needed to build the toolkit
from a single simple "make" command, including CFITSIO and the WCS
library (which has been extended to support HEALPix and World-Wide
Telescope TOAST projections. The toolkit is in wide use in astronomy
to support research projects, and to support pipeline development,
product generation and image visualization for major projects and
missions; e.g. Spitzer Space Telescope, Herschel, Kepler, AKARI and
others. Montage is used as an exemplar application by the computer
science community in developing next-generation cyberinfrastructure,
especially workflow frameworks on distributed platforms, including
multiple clouds.
Montage provides multiple reprojection algorithms optimized for
different needs, maximizing alternately flux conservation, range of
projections, and speed.
The visualization module supports full (three-color) display of FITS
images and publication quality overlays of catalogs (scaled symbols),
image metadata, and coordinate grids. It fits in equally well in
pipelines or as the basis for interactive image exploration and there
is Python support for the latter (It has also been used in web/Javascript
applications).
We are in the process of adding automated regression testing using Jenkins.
At the moment, this only includes a couple of dummy tests on a Jenkins server
that we maintain specifically for the Montage project).
Montage was funded from 2002 to 2005 by the National Aeronautics and
Space Administration's Earth Science Technology Office, Computation
Technologies Project, under Cooperative Agreement Number NCC5-626
between NASA and the California Institute of Technology. The Montage
distribution includes an adaptation of the MOPEX algorithm developed
at the Spitzer Science Center. Montage has also been funded by the National
Science Foundation under Award Number NSF ACI-1440620.
| text/markdown | null | John Good <jcg@ipac.caltech.edu> | null | null |
Copyright (c) 2017 California Institute of Technology, Pasadena, California.
Based on Cooperative Agreement Number NCC5-626 between NASA and the California
Institute of Technology. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions of this
BSD 3-clause license are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
| astronomy, astronomical, image, reprojection, mosaic, visualization | [] | [] | null | null | null | [] | [] | [] | [
"requests"
] | [] | [] | [] | [
"Homepage, https://github.com/Caltech-IPAC/Montage"
] | twine/6.1.0 CPython/3.12.7 | 2025-06-05T02:34:26.306335 | montagepy-2.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | 6,429,356 | 68/0d/4bf0c97151c3c5dde45627973c67ce55573714e7fcb0e0a64f1f8fcbf4ee/montagepy-2.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | cp313 | bdist_wheel | null | false | 524caf5ae8c2bb547bce9dc1030d0342 | a39d5742cfc0f7fdd468f409135c223d00d1ab62431e02f568601bd28c2c6469 | 680d4bf0c97151c3c5dde45627973c67ce55573714e7fcb0e0a64f1f8fcbf4ee | null | [
"LICENSE"
] |
2.4 | MontagePy | 2.3.0 | Montage toolkit for reprojecting, mosaicking, and displaying astronomical images. |
Montage: Astronomical Image Mosaics, Examination, and Visualization
===================================================================
Montage 7.0 adds two major capabilities. The first is a set of tools
for building HiPS map. HiPS (Hierarchical Progressive Surveys) is a
hierarchical tiling mechanism which allows one to access, visualize
and browse seamlessly image data and in particular large-scale,
high-resolution surveys.
HiPS construction through Montage consists of building large-scale
mosaics using pre-existing Montage modules (reprojection, background
matching, and coaddition) with a HiPS-specific projection (HPX).
The image hierarchy just requires repetative shrinking of higher
resolution images by factors of two. Finally, the tiles to be
served are simply 512x512 cutouts from these mosaics.
For high-resolution data, this process can benefit greatly from
massive parallelization and this can be achieved in a number of
ways. In particular, tools have been developed to streamline this
on cloud platforms like AWS.
So at arcminute scale all-sky processing can adequately be done on
a single desktop machine and at arcsecond scale the same tools can
create a set of jobs that can be submitted to run on a cloud in
a few days (or a few tens of processors if one has that in-house).
The second addition to Montage is a complete set of modern procedures
for building Python binary extension Montage wheels for Linux and Mac
systems (and extensible to some others). This is very much a moving
target and we are building as many of these as we can and pushing them
to PyPI but if someone want to extend Montage for their own use they
can use the same infrastructure to build custom wheels as well.
--------------
Montage (http://montage.ipac.caltech.edu) is an Open Source toolkit,
distributed with a BSD 3-clause license, for assembling Flexible
Image Transport System (FITS) images into mosaics according to
the user's custom specifications of coordinates, projection,
spatial sampling, rotation and background matching.
The toolkit contains utilities for reprojecting and background
matching images, assembling them into mosaics, visualizing the
results, and discovering, analyzing and understanding image metadata
from archives or the user's images.
Montage is written in ANSI-C and is portable across all common
Unix-like platforms, including Linux, Solaris, Mac OSX and Cygwin on
Windows. The package provides both stand-alone executables and
the same functionality in library form. It has been cross-compiled
to provide native Windows executables and packaged as a binary Python
extension (available via "pip install MontagePy").
The distribution contains all libraries needed to build the toolkit
from a single simple "make" command, including CFITSIO and the WCS
library (which has been extended to support HEALPix and World-Wide
Telescope TOAST projections. The toolkit is in wide use in astronomy
to support research projects, and to support pipeline development,
product generation and image visualization for major projects and
missions; e.g. Spitzer Space Telescope, Herschel, Kepler, AKARI and
others. Montage is used as an exemplar application by the computer
science community in developing next-generation cyberinfrastructure,
especially workflow frameworks on distributed platforms, including
multiple clouds.
Montage provides multiple reprojection algorithms optimized for
different needs, maximizing alternately flux conservation, range of
projections, and speed.
The visualization module supports full (three-color) display of FITS
images and publication quality overlays of catalogs (scaled symbols),
image metadata, and coordinate grids. It fits in equally well in
pipelines or as the basis for interactive image exploration and there
is Python support for the latter (It has also been used in web/Javascript
applications).
We are in the process of adding automated regression testing using Jenkins.
At the moment, this only includes a couple of dummy tests on a Jenkins server
that we maintain specifically for the Montage project).
Montage was funded from 2002 to 2005 by the National Aeronautics and
Space Administration's Earth Science Technology Office, Computation
Technologies Project, under Cooperative Agreement Number NCC5-626
between NASA and the California Institute of Technology. The Montage
distribution includes an adaptation of the MOPEX algorithm developed
at the Spitzer Science Center. Montage has also been funded by the National
Science Foundation under Award Number NSF ACI-1440620.
| text/markdown | null | John Good <jcg@ipac.caltech.edu> | null | null |
Copyright (c) 2017 California Institute of Technology, Pasadena, California.
Based on Cooperative Agreement Number NCC5-626 between NASA and the California
Institute of Technology. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions of this
BSD 3-clause license are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
| astronomy, astronomical, image, reprojection, mosaic, visualization | [] | [] | null | null | null | [] | [] | [] | [
"requests"
] | [] | [] | [] | [
"Homepage, https://github.com/Caltech-IPAC/Montage"
] | twine/6.1.0 CPython/3.12.7 | 2025-06-05T01:16:09.346846 | montagepy-2.3.0-cp312-cp312-musllinux_1_2_x86_64.whl | 670,840 | 88/87/ec80ced75683a6eed41cc32c1319c8bd49ee9794e7fe4df9eae425bea9e8/montagepy-2.3.0-cp312-cp312-musllinux_1_2_x86_64.whl | cp312 | bdist_wheel | null | false | 3f0e7693eaafc560dd1f4a03f49fe7c7 | df503e84e0c32aa23184b5114dfed4541b2feebcf9a3a4a394abf72bf06423ba | 8887ec80ced75683a6eed41cc32c1319c8bd49ee9794e7fe4df9eae425bea9e8 | null | [
"LICENSE"
] |
2.1 | Mzhtools | 5.1.1 | Python helper tools | python for mytools
| text/markdown | Author's name | 191891173@qq.com | null | null | null | null | [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent"
] | [] | https://pypi.org/project/Mzhtools/ | null | null | [] | [] | [] | [] | [] | [] | [] | [] | twine/6.1.0 CPython/3.11.9 | 2025-06-05T01:07:17.378754 | Mzhtools-5.1.1-py3-none-any.whl | 9,254 | e0/f8/c713c98509790d2334e5ba9f82e83b0cbcc7717dbd358fe5b8cdcf913024/Mzhtools-5.1.1-py3-none-any.whl | py3 | bdist_wheel | null | false | 4097677916d4ae5625d278edb8e55a78 | 51bfc34cf198ebdf9f7e27e64e0f30d607532aa17cf6842e2bd2f2e4b1735dbf | e0f8c713c98509790d2334e5ba9f82e83b0cbcc7717dbd358fe5b8cdcf913024 | null | [] |
2.4 | OpenMM | 8.3.0rc2 | Python wrapper for OpenMM (a C++ MD package) | OpenMM is a toolkit for molecular simulation. It can be used either as a
stand-alone application for running simulations, or as a library you call
from your own code. It provides a combination of extreme flexibility
(through custom forces and integrators), openness, and high performance
(especially on recent GPUs) that make it truly unique among simulation codes.
| null | Peter Eastman | null | null | null | Python Software Foundation License (BSD-like) | null | [] | [
"Linux"
] | https://openmm.org | https://openmm.org | null | [] | [] | [] | [
"numpy"
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.3 | 2025-06-07T21:23:06.911444 | openmm-8.3.0rc2-cp312-cp312-macosx_10_12_x86_64.whl | 12,913,286 | a6/44/8b9c88b572c8ec904d5f4b43000901fecd091bdd8be294775f40d435a547/openmm-8.3.0rc2-cp312-cp312-macosx_10_12_x86_64.whl | cp312 | bdist_wheel | null | false | 7640a41963cf560d8ac4237137bf1283 | 39db549b588327c31cf441b938a2671de17b8f6b4f937e35cdece1132dcf90d8 | a6448b9c88b572c8ec904d5f4b43000901fecd091bdd8be294775f40d435a547 | null | [] |
2.4 | OpenMM | 8.3.0rc2 | Python wrapper for OpenMM (a C++ MD package) | OpenMM is a toolkit for molecular simulation. It can be used either as a
stand-alone application for running simulations, or as a library you call
from your own code. It provides a combination of extreme flexibility
(through custom forces and integrators), openness, and high performance
(especially on recent GPUs) that make it truly unique among simulation codes.
| null | Peter Eastman | null | null | null | Python Software Foundation License (BSD-like) | null | [] | [
"Linux"
] | https://openmm.org | https://openmm.org | null | [] | [] | [] | [
"numpy",
"OpenMM-CUDA-12; extra == \"cuda12\"",
"OpenMM-HIP-6; extra == \"hip6\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.12.3 | 2025-06-07T21:23:02.333375 | openmm-8.3.0rc2-cp311-cp311-win_amd64.whl | 12,815,210 | 50/f8/3ae4e9d7d9d92b053a4d4c3e532e422e6089013c0a768c84d9830b3b8b79/openmm-8.3.0rc2-cp311-cp311-win_amd64.whl | cp311 | bdist_wheel | null | false | 60f73936e28fc87d161224675afb4be6 | 7a69178b9f9b0d5b643039c8a39c65b1bfaea3b193a2ba5115584bbe6f946287 | 50f83ae4e9d7d9d92b053a4d4c3e532e422e6089013c0a768c84d9830b3b8b79 | null | [] |
2.4 | PPIFold | 0.5.3 | Automatic pipeline using AlphaPulldown to predict PPI and homo-oligomer | null | null | Quentin Rouger | quentin.rouger@univ-rennes.fr | null | null | GPL-3.0 license | null | [] | [] | https://github.com/Qrouger/PPIFold | null | null | [] | [] | [] | [
"alphapulldown",
"seaborn",
"matplotlib",
"scipy"
] | [] | [] | [] | [] | twine/6.0.1 CPython/3.12.0 | 2025-06-19T09:44:14.628902 | ppifold-0.5.3-py3-none-any.whl | 33,612 | bd/6d/c43e695f6dbbe0f09fd0a55ce52871179749d2bc6217dc0f96df3cfa304c/ppifold-0.5.3-py3-none-any.whl | py3 | bdist_wheel | null | false | 8b81b50676110c67c21105df985a16f7 | 1bb83d7bf95668b74d176326c41a8543bd8ea9688a5f2026899a0056cc7811ac | bd6dc43e695f6dbbe0f09fd0a55ce52871179749d2bc6217dc0f96df3cfa304c | null | [] |
2.4 | Products.PDBDebugMode | 2.1 | Post-mortem debugging on Zope exceptions | ===============================================
Products.PDBDebugMode
===============================================
Enable various PDB debugging when debug-mode=on
-----------------------------------------------
When Zope is running in debug mode this product hooks PDB debugging
into various parts of a Zope instance. Some additional Plone specific
hooks are also included.
Requirements
------------
This version of PDBDebugMode has been tested with Zope4 and Plone 5.2 in
Python 2.7, 3.6 and 3.7
For Zope 2 (until Plone 5.1) please use `Products.PDBDebugMode = 1.3`.
If ipdb (http://pypi.python.org/pypi/ipdb) is available, it will use that
instead of standard pdb.
Its recommended that you use an editor or IDE that can cooperate with
pdb. Emacs for example, will display the corresponding lines of the
source file alongside the pdb prompt.
Remember that this product does nothing unless zope is being run with
debug-mode=on such as with "./bin/instance fg"
Post-Mortem Debugging
---------------------
To provide for better investigation of errors, any error or exception
logged with the python logging module will invoke pdb.post_mortem() if
a traceback can be retrieved and set_trace will be invoked otherwise.
Since the Zope error_log exception handler uses the logging module
when logging errors, this provides for post mortem debugging of Zope
errors. It is often useful, for example, to remove NotFound or
Unauthorized from the ignored exception in error_log and then
investigate such errors with PDB.
Runcall Requests
----------------
Any request that has the key 'pdb_runcall' will call the result of the
request traversal in the debugger thus allowing for stepping through
the resulting execution. To debug a POST or any other request which
might be tricky to insert the 'pdb_runcall' key into, use
'?toggle_runcall=1' at the end of a URL immediately preceding the
POST to set a 'pdb_runcall' cookie which will then invoke the
pdb.runcall when the POST is submitted. Use '?toggle_runcall=1' at
the end of a URL to clear the cookie. Remember that the cookie will
be set at the level in the hierarchy that it was set.
Debug View
----------
Additionaly, a view named 'pdb' is registered for all objects that
will simply raise an exception leaving you with the current context to
inspect. Use it for example by calling http://localhost:8080/Plone/foo/@@pdb.
Allow Import of pdb
-------------------
Import of the pdb module is also allowed in unprotected code such as
python scripts.
Changelog
=========
2.1 (2025-06-19)
----------------
Bug fixes:
- Include dependencies in zcml to fix use in a pip-based install.
[pbauer]
2.0 (2019-04-01)
----------------
New features:
- Add log-meesage on startup.
[pbauer]
Bug fixes:
- Remove post_mortem in tests since that feature is now a part of zope.testrunner and is unneeded here.
Fixes https://github.com/plone/Products.CMFPlone/issues/2803
[pbauer]
- Remove traces of support for Zope 2.
[pbauer]
1.4 (2019-03-02)
----------------
Breaking changes:
* Make compatible with Zope4 and drop support for Zope 2.
[pbauer]
New features:
* Add compatibility for Python 3 and 2.
[frapell]
* Improve debug mode detection, provide a ZCML feature, and enable when running
tests with '-D'.
[rpatterson]
* Add zope.testrunner support.
[rpatterson]
* Add some missing iPython support for runcall and broken_delete.
[rpatterson]
Bug fixes:
* Apparently the ipdb support only works with ipdb 0.3 or greater.
Added an "ipdb" extra for this requirement.
[rossp]
* Fix ipdb import in zcatalog.py.
[pabo]
1.3 - 2011-01-14
----------------
* Ignore invalid GenericSetup handlers.
[rossp]
* Use ipdb when available.
[neaj]
1.2 - 2011-01-07
----------------
* Add some zopectl scripts I use when evaluating upgrades.
[rossp]
* Better handling of exceptions while checking error matching.
[rossp]
* Fix a problem with doing post_mortem debugging of error_log ignored
exceptions.
[rossp]
* Fix handling of socket errors
* Fix handling of SiteErrorLog tracebacks
* Fix handling of exc_info logging arg
1.1 - 2009-04-18
----------------
* Fix a bug due to a change in monkeypatcher
1.0 - 2009-04-10
----------------
* Add collective.monkeypatcher as a requirement
[kdeldycke] 2009-04-09
* Fix some recursion errors
0.3 - 2009-04-08
----------------
* Use collective.monkeypatcher to move all patches into ZCML
* Fully deprecate the Zope exception handler in favor of the logging
hook since the Zope exception handler uses the logging module
anyways and more can be done by hooking at that level.
* Handle failed matches in Products.PDBDebugMode.pdblogging more
gracefully
* More flexible log matching. Change
Products.PDBDebugMode.pdblogging.ignore_regexes to ignore_matchers
and accept any callable.
0.2 - 2008-05-15
----------------
* Eggified
0.1 - 2006-03-11
----------------
* Initial release
| null | Ross Patterson | me@rpatterson.net | null | null | GPL | null | [
"Environment :: Web Environment",
"Topic :: Software Development :: Libraries :: Python Modules",
"Framework :: Plone",
"Framework :: Plone :: 5.2",
"Framework :: Plone :: 6.0",
"Framework :: Zope :: 4",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language ::... | [] | https://github.com/collective/Products.PDBDebugMode | null | null | [] | [] | [] | [
"setuptools",
"collective.monkeypatcher",
"six",
"ipdb>=0.3; extra == \"ipdb\"",
"zope.testrunner; extra == \"zodb\"",
"zope.testing; extra == \"zodb-testing\""
] | [] | [] | [] | [] | twine/6.1.0 CPython/3.13.3 | 2025-06-18T22:42:32.258019 | products_pdbdebugmode-2.1.tar.gz | 18,652 | af/f7/37090d4565c4e6b85b6b013cc8773ccd198a361e65bd6f62c780b8b334e5/products_pdbdebugmode-2.1.tar.gz | source | sdist | null | false | 17346fe9ebe7addcc380ec894a8bd042 | b833f190f3bed66664c5f765f9c551e8b3d942687b6a92f03873133096227b3b | aff737090d4565c4e6b85b6b013cc8773ccd198a361e65bd6f62c780b8b334e5 | null | [] |