# packages

pkgId: 8595873b8a8828b350eb5fd53bf05d6a06c08e7c834340bfe3953a396c390926
name: python-binapy
arch: src
version: 0.8.0
epoch: 0
release: 1
summary: Binary Data manipulation, for humans.
url: https://github.com/guillp/binapy
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binapy-0.8.0-1.src.rpm
checksum_type: sha256
description:
  [![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg)](https://www.python.org/)
  [![Downloads](https://pepy.tech/badge/binapy/month)](https://pepy.tech/project/binapy)
  [![Supported Versions](https://img.shields.io/pypi/pyversions/binapy.svg)](https://pypi.org/project/binapy)
  [![PyPi version](https://badgen.net/pypi/v/binapy)](https://badgen.net/pypi/v/binapy)
  [![PyPI status](https://img.shields.io/pypi/status/binapy.svg)](https://pypi.python.org/pypi/binapy/)
  [![PyPi license](https://badgen.net/pypi/license/binapy/)](https://pypi.com/project/binapy/)

  **BinaPy** is a module that makes Binary Data manipulation simpler and easier than what is offered in the Python standard library.

  With BinaPy, encoding or decoding data in a number of formats (base64, base64url, hex, url-encoding, etc.), compressing or decompressing (gzip), hashing (SHA1, SHA256, MD5, etc., with or without salt), is all a single method call away! And you can extend it with new formats and features.

  ```python
  from binapy import BinaPy

  bp = BinaPy("Hello, World!").to("deflate").to("b64u")
  print(bp)

  bp.decode_from("b64u").decode_from("deflate").ascii()

  isinstance(bp, bytes)

  ```

  - Free software: MIT
  - Documentation: <https://guillp.github.io/binapy/>



  - Fluent interface, based on a `bytes` subclass
  - Provides a convenient interface over `hashlib`, `base64`, `zlib`, `urllib.parse`, `json`, `pickle` and more
  - Easy to extend with new formats



  - add more parsing formats like YAML, CBOR, etc.
  - optionally use faster third-party modules when available


pkgId: b7cb4c4a0f125b34bfaf1836ecf056f941957cf566ab20bce6db05d2a9316d21
name: python-binary-gentoo
arch: src
version: 6.2.0
epoch: 0
release: 1
summary: CLI tools to build Gentoo packages on a non-Gentoo Linux host
url: https://github.com/hartwork/binary-gentoo
rpm_license: AGPLv3+
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binary-gentoo-6.2.0-1.src.rpm
checksum_type: sha256
description:
  [![Build and smoke test](https://github.com/hartwork/binary-gentoo/actions/workflows/smoke_test.yml/badge.svg)](https://github.com/hartwork/binary-gentoo/actions/workflows/smoke_test.yml)





  **binary-gentoo**
  is a collection of
  simple
  CLI tools
  to help **build Gentoo packages on a non-Gentoo Linux host**, primarily.
  A typical scenario is operation of an
  **_active_ [Gentoo binary package host](https://wiki.gentoo.org/wiki/Binary_package_guide#Setting_up_a_binary_package_host)**
   — an active "binhost".

  *Secondarily*, **binary-gentoo** can also build Gentoo packages on a Gentoo host
  with Docker isolation and a full `emerge` perspective
  (while not affecting your host system).

  There currently four CLI tools
  that follow the [Unix philosophy](https://en.wikipedia.org/wiki/Unix_philosophy)
  and are meant to be combined using a glue language like Bash:

  - `gentoo-build` – Builds a Gentoo package with Docker isolation
  - `gentoo-clean` — Clean Gentoo pkgdir/distdir files using `eclean` of `app-portage/gentoolkit` with Docker isolation
  - `gentoo-local-queue` – Manages simple file-based push/pop build task queues
  - `gentoo-packages` — Do operations on pkgdir (other than `emaint --fix binhost`)
  - `gentoo-tree-diff` – Lists packages/versions/revisions that one portdir has over another
  - `gentoo-tree-sync` – Brings a given portdir directory (and its backup) up to date

  **binary-gentoo**
  is software libre licensed under the `GNU Affero GPL version 3 or later` license.




  Install Docker, Python >=3.8, pip and rsync and then:

  ```console

  ```




  To set-up an active binhost using **binary-gentoo** you will likely need
  a virtual machine with…

  - Enough resources, i.e. probably
    - []()>=2 virtual cores
    - []()>=8 GB RAM
    - []()>=100 GB disk space
  - Some distribution of Linux
  - Auto-updates enabled to stay reasonably secure over time
  - Runtime dependencies installed:
    - Docker
    - Python >=3.8
    - rsync
  - An SSH or an HTTP daemon
    (to serve the produced binaries to your consuming Gentoo boxes,
    unless that is done from another machine)
  - An up-to-date copy of the `/etc/portage` folder to use
  - An up-to-date text file with the names of all packages to build,
    e.g. generated by `EIX_LIMIT=0 eix -I --only-names`
  - A set of compile flags (`CFLAGS`, `CXXFLAGS`, `LDFLAGS`, `CPU_FLAGS_X86`)
    that works for *both* the producing machine and the consuming machine(s)
    and ideally [resolve-march-native](https://github.com/hartwork/resolve-march-native)
    installed — more on finding the ideal flags below
  - Two portdir copies — one synced with `gentoo-tree-sync` (or some form of `git pull`),
    the other brought back in sync using `rsync` after a call to `gentoo-tree-diff`
  - A glue script that…
    - respects your inclusion list
      (e.g. `grep -q -f installed.txt -F <<<"${atom}" || continue`
      if a few false positives are okay)
    - respects your exclusion list
      (e.g. something like `*sys-kernel/*|*-bin-*|*-9999*|*acct-*/*`)
    - pushes `gentoo-tree-diff` news onto the queue
    - pops off the queue,
      extracts the atom using e.g. `jq -r .atom`,
      and then let's `gentoo-build` do a build
    - loops forever
    - sleeps every now and then
    - runs `gentoo-clean` every now and then




  Let me give a concrete example.




  On the consumer machine, we have this hardware situation:

  ```console

  Intel(R) Core(TM) i5-2520M CPU @ 2.50GHz


  -march=sandybridge -maes --param l1-cache-line-size=64 --param l1-cache-size=32 --param l2-cache-size=3072 -O2 -pipe


  CPU_FLAGS_X86: aes avx mmx mmxext pclmul popcnt sse sse2 sse3 sse4_1 sse4_2 ssse3
  ```




  On the [producer machine](https://www.netcup.de/bestellen/produkt.php?produkt=2554),
  we have this hardware situation:

  ```console

  QEMU Virtual CPU version 2.5+


  -march=k8-sse3 -maes -mcx16 -mno-3dnow -mno-3dnowa -mpclmul -mpopcnt -mrdrnd -msahf -msse4 -msse4.1 -msse4.2 -mssse3 -mtune=k8 --param=l1-cache-line-size=64 --param=l1-cache-size=64 --param=l2-cache-size=512 -O2 -pipe


  CPU_FLAGS_X86: aes mmx mmxext pclmul popcnt rdrand sse sse2 sse3 sse4_1 sse4_2 ssse3
  ```





  Simplified, we are looking for flags
  that are **as close to the consumer machines as possible**
  while still resulting in binaries,
  that **both the consumer machines and the producer machine can execute**
  (so that the producer machine is be able to install and run the binaries of *dependencies* of the package of interest).

  For the example above, I ended up with these values for the producer machine:

  - `CFLAGS`: `-march=x86-64 -mtune=sandybridge -maes --param l1-cache-line-size=64 --param l1-cache-size=32 --param l2-cache-size=3072 -mpclmul -mpopcnt -msse4.1 -msse4.2 -mssse3  -O2 -pipe` (note `-march` and `-mtune` in particular)
  - `CXXFLAGS`: same as `CFLAGS`
  - `LDFLAGS`: whatever `portageq envvar LDFLAGS` on the consumer machine returns
  - `CPU_FLAGS_X86`: `aes mmx mmxext pclmul popcnt sse sse2 sse3 sse4_1 sse4_2 ssse3` (i.e. without `avx` and without `rdrand`)




  - All code in **binary-gentoo** must work on a non-Gentoo Linux machine,
    provided that it has Docker installed and working internet access.

  - Big packages like Chromium need a pile of RAM and CPU time.
    Therefore, the build defaults to `MAKETOPTS=-j1`
    the package of interest is emerged separate from it dependencies.
    That allows to build e.g. a package of Chromium in a VM with only 8 GB of RAM.

  - With regard to dependency constraints,
    some packages can be *built* without conflicts but not be *installed* without conflicts.
    Hence the default is to only install dependencies, but not the package of interest.


pkgId: cf11d1b15d2350cce581be5b69011c707295f55ece631cb0f3c1b8f9023d630c
name: python-binary-protocol
arch: src
version: 0.1.0
epoch: 0
release: 1
summary: A lightweight binary protocol implementation
url: https://github.com/barisgit/binary-protocol
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binary-protocol-0.1.0-1.src.rpm
checksum_type: sha256
description:
  A Python implementation of a configurable binary protocol for efficient data transfer without HTTP/REST overhead.



  - Python 3.8 or higher
  - asyncio (built-in)
  - pytest (for running tests)



  This project implements a lightweight, configurable binary protocol for client-server communication in Python. Unlike traditional REST APIs, this implementation uses a custom binary protocol for data transfer, resulting in lower latency and reduced network overhead.



  - **Structured Message Format**: Define message structures with typed fields
  - **Efficient Data Transfer**: Custom binary protocol minimizes overhead compared to HTTP/REST
  - **Type-safe Field Handling**: Automatic serialization of primitive types
  - **Asynchronous Communication**: Built on asyncio for high-performance I/O operations
  - **Zero Dependencies**: Pure Python implementation with no external requirements
  - **Bi-directional Communication**: Supports full-duplex communication between client and server
  - **Fixed and Variable Length Fields**: Support for both fixed and variable-length data
  - **Optimized Performance**: Fast-path for fixed-length messages





  ```python
  from enum import IntEnum
  from binary_protocol.protocol.message_structure import FieldDefinition, MessageStructure


  class MessageType(IntEnum):
      SENSOR_DATA = 1
      STATUS = 2
      BENCHMARK = 3
      FIXED_BENCHMARK = 4  # New message type for fixed-length benchmarks


  sensor_data_structure = MessageStructure([
      FieldDefinition("sensor1", float, format_string=">f"),  # 4-byte float
      FieldDefinition("sensor2", float, format_string=">f"),
      FieldDefinition("sensor3", float, format_string=">f"),
      FieldDefinition("sensor4", float, format_string=">f")
  ])


  status_structure = MessageStructure([
      FieldDefinition("status_code", int, format_string=">I"),
      FieldDefinition("message", str)  # Variable length
  ])


  fixed_benchmark_structure = MessageStructure([
      FieldDefinition("value", int, format_string=">I")  # 4-byte integer
  ])
  ```



  ```python
  from binary_protocol import ProtocolConfig

  config = ProtocolConfig(
      message_type_enum=MessageType,
      message_structures={
          MessageType.SENSOR_DATA: sensor_data_structure,
          MessageType.STATUS: status_structure,
          MessageType.BENCHMARK: benchmark_structure,
          MessageType.FIXED_BENCHMARK: fixed_benchmark_structure
      },
      header_format=">H",  # 2-byte header for message type
      max_payload_size=10 * 1024 * 1024  # 10MB
  )
  ```



  The protocol includes comprehensive benchmarking capabilities:



  1. **Variable Size Benchmark**
     - Tests payload sizes from 1B to 9MB
     - Adaptive batch sizes for memory efficiency
     - Measures throughput and protocol overhead
     - Default: 50 iterations per size

  2. **Fixed Length Benchmark**
     - Optimized for 4-byte integer messages
     - Large batch processing (100,000 messages)
     - Minimal protocol overhead
     - Default: 1,000 iterations

  3. **Sensor Data Benchmark**
     - Simulates real-world sensor data (4x float values)
     - High-frequency transmission testing
     - Batch size: 50,000 messages
     - Default duration: 10 seconds



  Run benchmarks in example folder:

  ```bash
  python examples/benchmark_server.py
  python examples/benchmark_client.py
  ```



  | Option | Description | Default |
  |--------|-------------|---------|
  | message_type_enum | Enum class for message types | Required |
  | message_structures | Dict of structures per type | Required |
  | header_format | Struct format for message header | ">H" |
  | max_payload_size | Maximum allowed payload size | 1MB |
  | connect_timeout | Connection timeout in seconds | 30.0 |
  | read_timeout | Read operation timeout | 30.0 |



  | Field Type | Format | Performance Impact | Batch Support |
  |------------|--------|-------------------|---------------|
  | int/float (fixed) | ">f", ">i", etc. | Fastest | Up to 100k msgs/batch |
  | fixed-length str/bytes | fixed_length=N | Fast | Up to 50k msgs/batch |
  | variable-length str/bytes | None | Additional overhead | Dynamic batch size |



  1. **Encryption**: Consider adding TLS for secure communication
  2. **Authentication**: Implement a secure authentication mechanism
  3. **Input Validation**: Validate field contents
  4. **Rate Limiting**: Protect against DoS attacks
  5. **Message Size Limits**: Configure appropriate max_payload_size



  This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.


pkgId: 2c530a6aaf2a84de37ca70c87cd6d1bf7eab0dffdd69925c220f42bc5d345ce8
name: python-binary-reader
arch: src
version: 1.4.3
epoch: 0
release: 1
summary: A python module for basic binary file IO.
url: https://github.com/SutandoTsukai181/PyBinaryReader
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binary-reader-1.4.3-1.src.rpm
checksum_type: sha256
description:
  A python module for basic binary file IO.

  This is something I wrote to use in my personal projects, but feel free to use it if you like!


  `pip install binary-reader`


  ```py
  from binary_reader import BinaryReader
  ```

  Here is some example usage on creating and using a BinaryReader to read a small part of the DDS header:

  ```py
  f = open("example.dds", "rb")  # Open a file in binary mode

  reader = BinaryReader(f.read())  # Create a new BinaryReader from the file buffer (we can close the file afterwards)


  if reader.read_str(4) != 'DDS ':
      raise Exception('Incorrect magic.')

  size = reader.read_uint32()  # Read the next 4 bytes as an unsigned 32-bit integer
  ```

  Another example on using BinaryReader features to navigate through a buffer:

  ```py
  some_offset = reader.read_uint32()  # Read an offset that we want to move to

  prev_pos = reader.pos()  # Store the current position so we can return back later
  reader.seek(some_offset)  # Set the current position in the file to that offset

  vector = reader.read_float(3)  # Read 3 consecutive 32-bit floats, return them as a tuple

  reader.seek(prev_pos)  # Go back to the previous position

  reader.seek(4, whence=1)  # In addition to absolute seeking, we can also seek relative to the current position...

  reader.seek(0, whence=2)  # And relative to the end of the buffer. This will set the position to be the last index in the buffer

  ```

  In addition to reading, you can also write to a new buffer:
  ```py
  writer = BinaryReader()  # Create a new BinaryReader (bytearray buffer is initialized automatically)

  writer.set_endian(is_big_endian=True)  # Set the endianness to big endian

  writer.write_str('MGIC')
  writer.write_uint32(20)

  writer.align(0x10)  # Align the buffer to 0x10 (in this case, will append 8 bytes to the buffer)

  writer.pad(0xF0)  # Add 0xF0 bytes of padding
  print(writer.size())  # This should print 256 (0x100)


  with open('examplefile', 'wb') as f:
      f.write(writer.buffer())
  ```

  These are the types that can be used with BinaryReader. Just add `read_` or `write_` before the type to get the method name:
  ```
  uint8, int8,
  uint16, int16, half_float
  uint32, int32, float
  uint64, int64,
  bytes, str
  ```


  This project uses the MIT License, so feel free to include it in whatever you want.


pkgId: 7d3d25e00b6c0d30c9142e017ffa010e87573637f557e1b62f11c7d0d9926ba7
name: python-binary-trie
arch: src
version: 1.0.3
epoch: 0
release: 1
summary: A simple binary trie implementation
url: https://github.com/guillaumemichel/py-binary-trie
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binary-trie-1.0.3-1.src.rpm
checksum_type: sha256
description:
  Python binary trie implementation, helping with XOR distances.

  Author: [Guillaume Michel](https://github.com/guillaumemichel)



  [IPFS](https://ipfs.network) and [libp2p](https://libp2p.io/) are built upon the Kademlia DHT, which uses the XOR distance as a distance metric between keys. As explained in this [blogpost](https://metaquestions.me/2014/08/01/shortest-distance-between-two-points-is-not-always-a-straight-line/), the XOR distance is tricky to understand and represent. This distance metric is odd for it is non linear.
  For instance, $2 \oplus 3 = 1$ but $3 \oplus 4 = 7$. Ordering $N_8$ by XOR distance to 2 gives the following result: $[2,3,0,1,6,7,4,5]$

  One popular representation for XOR distance is the Binary Trie. A binary brie is a simple [Trie](https://en.wikipedia.org/wiki/Trie) with keyspace $\lbrace0,1\rbrace^n$ with $n$ being the size of the keyspace. The perfect visual representation of the XOR distance would be a N-dimensional binary trie. The example below displays a binary trie containing the keys $[2,3,4,6,7,9,11,13]$.

  ![Alt text](./resources/trie.svg)

  Each node in the Trie can be associated with some metadata for convenience.





  ```bash
  pip install binary-trie
  ```



  ```python
  from binary_trie import Trie, bytes_to_bitstring, bitstring_to_bytes, int_to_bitstring, bitstring_to_int
  ```


  ```python
  trie = Trie()
  ```

  The `add(key)` method takes a bitstring as input. The bitstring can either be provided directly, or be computer from an `int` or `bytes` using the `int_to_bitstring()` and `bytes_to_bitstring()` functions. Note that the `l` parameter represent the bit length of the binary representation. It is important that all keys share the same bit length. The bit length can be omitted in `bytes_to_bistring()` when working with bit lengths multiple of 8.
  ```python
  trie.add("0010")
  trie.add(4*"0")
  trie.add(int_to_bitstring(3, l=4))
  trie.add(bytes_to_bitstring(b'\x0e', l=4))
  ```
  The add function returns `True` on success, and `False` if the key was already in the trie (not inserted).


  The `find(key)` method returns `True` if the provided key is in the Trie, `False` otherwise.
  ```python
  trie.contains("0010") # True
  trie.contains("0100") # False
  ```


  The `depth(key)` method returns the depth of the provided key, if it is in the Trie and `-1` otherwise. The depth of a trie node is defined as the number of its direct ancestors, up to the root of the trie.
  ```python
  trie.depth("0")    # 1
  trie.depth("0010") # 3
  trie.depth("0111") # 4
  trie.depth("1101") # 2
  trie.depth("11")   # -1
  ```


  The `n_closest_keys(key, n)` method returns the `n` closest keys to the provided key in the trie, according to the XOR distance. The keys are sorted according to the distance to the target key. Note that only leaves of the trie will be returned, not intermediary nodes.
  ```python
  trie.n_closest_keys("0001", 1) # ["0000"]
  trie.n_closest_keys("0010", 3) # ["0010", "0011", "0000"]
  ```



  The `prefix_match_keys(prefix)` will return the list of keys of all leaves of the Trie matching the provided `prefix`.

  ```python
  trie.prefix_match_keys("00")   # ["0000", "0010", "0011"]
  trie.prefix_match_keys("1111") # []
  ```



  It is possible to attach an `object` as metadata to a trie leaf node.

  ```python
  class MyObj(object):
      def __init__(self, key, name):
          self.key = key
          self.name = name

  trie = Trie(MyObj)

  obj = MyObj(int_to_bitstring(10, 4), "Node 10")
  trie.add(obj.key, obj)

  trie.find(obj.key).name        # "Node 10"
  trie.n_closest(obj.key, 1)     # [obj]
  trie.prefix_match(obj.key[:2]) # [obj]
  ```
  Note that the `find(key)` method is similar to the `contains(key)` method, but returns the associated `metadata` if any, instead of returning a `bool`.
  The `n_closest(key, n)` method is similar to the `n_closest_keys(key, n)` method, but returns the list of `metadata` associated with the closest keys, instead of the list of keys.




  It is possible to assign some boolean variables to `metadata` objects, and make use of them using predicate in `n_closest()`, `n_closest_keys()`, `prefix_match()` and `prefix_match_keys()` methods to filter the results.

  ```python
  class MyObj(object):
      def __init__(self, key, name, some_bool):
          self.key = key
          self.name = name
          self.some_bool = some_bool

  trie = Trie(MyObj)

  nodeIDs = [0, 1, 2] # ["0000", "0001", "0010"]
  for i in nodeIDs:
      obj = MyObj(int_to_bitstring(i, 4), "Node "+str(i), i % 2 == 0)
      trie.add(obj.key, obj)

  trie.n_closest_keys("0001", 2, predicate=lambda n: n.some_bool) # ["0000", "0010"]
  trie.prefix_match_keys("000", predicate=lambda n: n.some_bool)  # ["0000"]



  ```




  There are 4 helpers functions to facilitate the use of this implementation with keys being not only `bitstring`, but also `bytes` or `int`. These helper functions help translate `bytes` and `int` to `bitstring` and reciprocally.
  ```python
  def bytes_to_bitstring(data: bytes, l: int=8*len(data)) -> bytes:
      ...
  bytes_to_bitstring(b'\xff\x00') # "1111111100000000"
  bytes_to_bitstring(b'\xf3',l=4) # "0011"

  def bitstring_to_bytes(bs: str) -> bytes:
      ...
  bitstring_to_bytes("1111111100000000") # b'\xff\x00'
  bitstring_to_bytes("0011")             # b'\x03'

  def int_to_bitstring(i, l: int) -> bytes:
      ...
  int_to_bitstring(6, 4)  # "0110"
  int_to_bitstring(6, 16) # "0000000000000110"

  def bitstring_to_int(bs: str) -> int:
      ...
  bitstring_to_int("0110")             # 6
  bitstring_to_int("0000000000000110") # 6
  ```



  The following functions help uniquely encode a specific binary prefix, that are bistrings of arbitrary size. The bitstrings can be encoded in `int` or in [`unsigned varint`](https://github.com/multiformats/unsigned-varint). The mapping goes as follow:
  ```
  bitstring       code        varint
  ""          ->  0       ->  00000000 (0x00)
  "0"         ->  1       ->  00000001 (0x01)
  "1"         ->  2       ->  00000010 (0x02)
  "00"        ->  3       ->  00000011 (0x03)
  ...
  "0000000"   ->  127     ->  01111111 (0x7f)
  "0000001"   ->  128     ->  10000000 00000001 (0x8001)
  ...
  ```

  ```python
  def encode_bitstring(bitstring: str) -> int:
      ...
  encode_bitstring("1")       # 2
  encode_bitstring("010")     # 9
  encode_bitstring("0000001") # 128

  def decode_bitstring(code: int) -> str:
      ...
  decode_bitstring(2)   # "1"
  decode_bitstring(9)   # "010"
  decode_bitstring(128) # "0000001"

  def bitstring_to_varint(bitstring: str) -> bytes:
      ...
  bitstring_to_varint("1")       # b'\x02'
  bitstring_to_varint("010")     # b'\x09'
  bitstring_to_varint("0000001") # b'\x80\x01'

  def varint_to_bitstring(varint_bytes: bytes) -> str:
      ...
  varint_to_bitstring(b'\x02')     # "1"
  varint_to_bitstring(b'\x09')     # "010"
  varint_to_bitstring(b'\x80\x01') # "0000001"
  ```


pkgId: 78130763c1d41007977a1723ab900ec7621a9e101bd1f4bb4bb690be59e1ec66
name: python-binary-waterfall
arch: src
version: 3.7.0
epoch: 0
release: 1
summary: A Raw Data Media Player
url: https://pypi.org/project/binary-waterfall/
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binary-waterfall-3.7.0-1.src.rpm
checksum_type: sha256
description:
  <p align="center"><img src="https://github.com/nimaid/binary-waterfall/blob/main/docs/example.png?raw=true" width="400px" alt="Running the program on mspaint.exe"/></p>

  <p align="center"><a href="https://www.youtube.com/watch?v=NFe0aGO9-TE">Inspired by this video.</a></p>


  After installing the module, run `binary-waterfall`.


  If you use this program to make a video or other project, you must provide attribution. Attribution is required regardless of whether your project is for-profit or not. Please reproduce the following attribution statement in full in your video description or otherwise include it in the references for your project:
  ```
  Made with the help of Binary Waterfall:
  https://github.com/nimaid/binary-waterfall
  ```


  - **Play / Pause:** `Spacebar`
  - **Back:** `Left Arrow`
  - **Forward:** `Right Arrow`
  - **Frame Back:** `<` (`,`)
  - **Frame Forward:** `>` (`.`)
  - **Restart:** `R`
  - **Volume Up:** `Up Arrow`
  - **Volume Down:** `Down Arrow`
  - **Mute / Unmute:** `M`


  [<img src="https://i.ytimg.com/vi/gZRWbv_aob0/maxresdefault.jpg" width="300px">](https://www.youtube.com/watch?v=gZRWbv_aob0 "Microsoft Paint Remix")

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: f9016a2f6e766ccd43438f98c7294594be8d323e9fbe880ab0205ab66ba65bae
name: python-binary_wheel_builder
arch: src
version: 3.7.1
epoch: 0
release: 1
summary: Bundle CLI applications or other binary data as wheel to use them in code or as standalone binary
url: https://github.com/timo-reymann/python-binary-wheel-builder
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binary_wheel_builder-3.7.1-1.src.rpm
checksum_type: sha256
description:
  [![PyPI version](https://badge.fury.io/py/binary_wheel_builder.svg)](https://pypi.org/project/binary_wheel_builder)
  [![PyPI - Downloads](https://img.shields.io/pypi/dm/binary-wheel-builder)](https://pypi.org/project/binary_wheel_builder)
  [![LICENSE](https://img.shields.io/github/license/timo-reymann/python-binary-wheel-builder)](https://github.com/timo-reymann/binary-wheel-builder/blob/main/LICENSE)
  [![CircleCI](https://circleci.com/gh/timo-reymann/python-binary-wheel-builder.svg?style=shield)](https://app.circleci.com/pipelines/github/timo-reymann/python-binary-wheel-builder)
  [![codecov](https://codecov.io/gh/timo-reymann/python-binary-wheel-builder/graph/badge.svg?token=LFBPaleiaO)](https://codecov.io/gh/timo-reymann/python-binary-wheel-builder)
  [![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=timo-reymann_python-binary-wheel-builder&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=timo-reymann_python-binary-wheel-builder)
  [![Code Smells](https://sonarcloud.io/api/project_badges/measure?project=timo-reymann_python-binary-wheel-builder&metric=code_smells)](https://sonarcloud.io/summary/new_code?id=timo-reymann_python-binary-wheel-builder)
  [![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=timo-reymann_python-binary-wheel-builder&metric=sqale_rating)](https://sonarcloud.io/summary/new_code?id=timo-reymann_python-binary-wheel-builder)
  [![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=timo-reymann_python-binary-wheel-builder&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=timo-reymann_python-binary-wheel-builder)
  [![Renovate](https://img.shields.io/badge/renovate-enabled-green?logo=data:image/svg+xml;base64,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)](https://renovatebot.com)
  <p align="center">
  	<img width="300" src="https://raw.githubusercontent.com/timo-reymann/python-binary-wheel-builder/main/.github/images/logo.png">
      <br />
      Build deterministic python wheels to distribute CLI tools (third party or your own with python and make them easily usable using python code.
  </p>

  - deterministic wheel file output
  - Use with Python Code or as CLI
  - [Prebuilt data sources](https://timo-reymann.github.io/python-binary-wheel-builder/api.wheel_sources.html) for your binaries
  - Ready to release and ship your binaries
  - Wrapper for cli calls inside Python
  - Exposes wheel as module entrypoint (`python -m your_cli`) and adds to path (`your-cli`)

  - Python 3.8+ for host running wheel build
  - For target host every 3.x should work


  ```sh

  pip install binary_wheel_builder[cli]

  pip install binary_wheel_builder
  ```

  ````sh
  pipx install binary_wheel_builder[cli]
  ````


  1. Create your config file e. g. `my-cli-wheel.yaml`:
     ```yaml
     package: my_cli
     executable: my-cli-bin
     name: my-cli
     version: 0.0.1
     summary: My CLI provides nice and clean functionality on the terminal
     description: !FileContent README.md
     license: MIT
     requires_python: ">=3.8"
     classifier:
        - "License :: OSI Approved :: MIT License"
        
     project_urls:
        
        "Homepage": "https://github.com/my-org/my-cli"
        "Source Code": "https://github.com/my-org/my-cli,git"
        "Bug Tracker": "https://github.com/my-org/my-cli/issues"
     source: !WheelSource
       
       implementation: binary_wheel_builder.api.wheel_sources.GithubReleaseBinarySource
       project_slug: my-org/my-cli
       version: "0.0.1"
       tag_prefix: "" # e.g. set to v when your tag is vX.Y.Z
      
       binary_path: my_cli/my-cli-bin
       
       asset_name_mapping:
         !WellknownPlatform MAC_SILICON: "mycli-darwin-arm64"
         !WellknownPlatform MAC_INTEL: "mycli-darwin-amd64"
         !WellknownPlatform LINUX_GENERIC_x86_64: "my-cli-linux-amd64"
         !WellKnownPlatform LINUX_GENERIC_aarch64: "my-cli-linux-arm"
       
       platforms:
         - !WellknownPlatform MAC_INTEL
         - !WellknownPlatform MAC_SILICON
         - !WellknownPlatform LINUX_GENERIC_x86_64
         - !WellKnownPlatform LINUX_GENERIC_aarch64
     ```
  2. Build your wheel for all platforms
     ```shell
     binary-wheel-builder --wheel-spec my-cli-wheel.yaml
     ```
  3. Upload your wheel files using [twine](https://twine.readthedocs.io/en/stable/):
     ```sh
     twine upload -r pypi dist/*
     ```
  4. Enjoy and consume your CLI as a package
  > If you want to have auto complete and validation right in the IDE, you can use the `wheel.schema.json` JSON Schema.
  >
  > [VSCode Guide](https://code.visualstudio.com/docs/languages/json#_json-schemas-and-settings) | [IntelliJ IDEA IDEs Guide](https://www.jetbrains.com/help/idea/json.html#ws_json_schema_add_custom)

  If you prefer to write Python Code you use almost the same structure:
  ````python
  from pathlib import Path
  from binary_wheel_builder.api import build_wheel, Wheel, well_known_platforms
  from binary_wheel_builder.api.wheel_sources import GithubReleaseBinarySource

  class MyCliGithubReleaseSource(GithubReleaseBinarySource):
      def __init__(self, version: str):
          super().__init__(
              "my-org/my-repo",
              version,
              {
                  well_known_platforms.MAC_SILICON: "mycli-darwin-arm64",
                  well_known_platforms.MAC_INTEL: "mycli-darwin-amd64",
                  well_known_platforms.LINUX_GENERIC_x86_64: "my-cli-linux-amd64",
                  well_known_platforms.LINUX_GENERIC_aarch64: "my-cli-linux-arm",
              },
              "my_cli/my-cli-bin"
          )
  dist_folder = Path("dist")
  print("Built wheels:")
  for result in build_wheel(
          Wheel(
              package="my_cli",
              executable="my-cli-bin",
              name="my-cli",
              version="0.0.1",
              summary='My CLI provides nice and clean functionality on the terminal',
              license='MIT',
              requires_python=">=3.9",
              classifier=[
                  'License :: OSI Approved :: MIT License',
                  
              ],
              project_urls={
                  'Homepage': 'https://github.com/my-org/my-cli',
                  'Source Code': 'https://github.com/my-org/my-cli,git',
                  'Bug Tracker': 'https://github.com/my-org/my-cli/issues',
              },
              source=MyCliGithubReleaseSource("0.0.1"),
              platforms=[
                  well_known_platforms.MAC_INTEL,
                  well_known_platforms.MAC_SILICON,
                  well_known_platforms.LINUX_GENERIC_x86_64,
                  well_known_platforms.LINUX_GENERIC_aarch64
              ]
          ),
          dist_folder
  ):
      print(f" > {result.file_path} [{result.checksum}]")
  ````
  Afterwards upload with twine:
  Upload your wheel files using [twine](https://twine.readthedocs.io/en/stable/):
  ```sh
  twine upload -r pypi dist/*
  ```


  ```sh
  python -m my_cli
  ```

  ````sh
  my-cli
  ````

  ```python
  import subprocess
  from my_cli import exec

  exec.exec_with_templated_output(["--help"])

  exec.create_subprocess(["--help"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)


  exec.exec_silently(["--version"])
  ```

  Sometimes managing CLI dependencies for tooling etc. can become quite a pain.
  Also sometimes there are just so many options to distribute binaries, but Python is preinstalled on almost any nix
  machine.
  This opens great possibilities with using pip(x) to install dependencies or even reuse standalone CLI applications as
  regular dependency in your code.

  - [Library documentation](https://timo-reymann.github.io/python-binary-wheel-builder/)

  I love your input! I want to make contributing to this project as easy and transparent as possible, whether it's:
  - Reporting a bug
  - Discussing the current state of the configuration
  - Submitting a fix
  - Proposing new features
  - Becoming a maintainer
  To get started please read the [Contribution Guidelines](./CONTRIBUTING.md).


  - Python 3.8+
  - Poetry


  ```sh
  poetry run pytest integration_tests/
  ```

  ```sh
  poetry run pytest binary_wheel_builder/
  ```

  ````sh
  poetry install
  ````

  - Using platform native package management
  - curl'ing dependencies


pkgId: 94d3b6960b5e165003b0d9cde5c879a54e347f15382424b48b4d33c80a9d110c
name: python-binaryai
arch: src
version: 0.8.2
epoch: 0
release: 1
url: https://www.binaryai.cn/
rpm_license: GPLv3
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binaryai-0.8.2-1.src.rpm
checksum_type: sha256
summary:
  BinaryAI-SDK is a library provides an abstracted client module to simplify the procedure of uploading file for analysis.

description:
  ![PUBLISH](https://github.com/binaryai/sdk/workflows/PUBLISH/badge.svg)
  [![readthedocs](https://readthedocs.org/projects/binaryai/badge/?version=stable&style=flat)](https://binaryai.readthedocs.io/)
  [![Downloads](https://pepy.tech/badge/binaryai/month)](https://pepy.tech/project/binaryai/month)
  [![Gitter](https://badges.gitter.im/binaryai/community.svg)](https://gitter.im/binaryai/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

  [BinaryAI](https://www.binaryai.cn) is a binary file security analysis platform. This SDK aims at providing
  a simple client to upload file and get analysis result. It also works as
  a demo on calling BinaryAI's GraphQL API directly.

  To use SDK, you need a valid credential. Read [BinaryAI docs](https://www.binaryai.cn/doc/) about detailed instructions.



  Python >= 3.9



  ```bash
  python3 -m pip install binaryai
  ```



  See the [SDK document](https://binaryai.readthedocs.io) for guide.





  The default endpoint is `https://api.binaryai.cn/v1/endpoint`.



  API Credentials are used for signing requests. We suggest you using our SDK or our library to sign it, but you can also
  have your own implementation. We are using the signing method `TC3-HMAC-SHA256`, same with the Tencent Cloud. You can
  read their [document](https://cloud.tencent.com/document/product/213/30654) about how to sign requests. BinaryAI would
  require following fields:

  ```toml
  Region  = "ap-shanghai"
  service = "binaryai"
  Action  = "BinaryAI"
  Version = "2023-04-15"
  ```



  Read the [Changelog](https://www.binaryai.cn/doc/zh/releasenotes/) of our product, and hope you can also have fun reading papers related to our job:

  1. Yu, Zeping, et al. "Codecmr: Cross-modal retrieval for function-level binary source code matching." Advances in Neural Information Processing Systems 33 (2020): 3872-3883.
  2. Yu, Zeping, et al. "Order matters: Semantic-aware neural networks for binary code similarity detection." Proceedings of the AAAI conference on artificial intelligence. Vol. 34. No. 01. 2020.
  3. Li, Zongjie, et al. "Unleashing the power of compiler intermediate representation to enhance neural program embeddings." Proceedings of the 44th International Conference on Software Engineering. 2022.
  4. Wong, Wai Kin, et al. "Deceiving Deep Neural Networks-Based Binary Code Matching with Adversarial Programs." 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2022.
  5. Wang, Huaijin, et al. "Enhancing DNN-Based Binary Code Function Search With Low-Cost Equivalence Checking." IEEE Transactions on Software Engineering 49.1 (2022): 226-250.
  6. Jia, Ang, et al. "1-to-1 or 1-to-n? Investigating the Effect of Function Inlining on Binary Similarity Analysis." ACM Transactions on Software Engineering and Methodology 32.4 (2023): 1-26.
  7. Wang, Huaijin, et al. "sem2vec: Semantics-aware Assembly Tracelet Embedding." ACM Transactions on Software Engineering and Methodology 32.4 (2023): 1-34.
  8. Jiang, Ling, et al. "Third-Party Library Dependency for Large-Scale SCA in the C/C++ Ecosystem: How Far Are We?." Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. 2023.


pkgId: 96a934e81392aa90fb56717117fd8fc887d43089bef4d2a4ce8f00b95d7d63c1
name: python-binarybeech
arch: src
version: 0.3.1
epoch: 0
release: 1
summary: Simplistic algorithms to train decision trees for regression and classification
url: https://github.com/arminwitte/binarybeech
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binarybeech-0.3.1-1.src.rpm
checksum_type: sha256
description:
  Simplistic algorithms to train decision trees for regression and classification



  - Create binary trees using the CART (Classification and Regression Tree) algorithm.
  - Train ensembles of trees using Gradient Boosting, Adaptive Boosting (AdaBoost) or Random Forest.
  - Process each datatype with a data handler as provided or impöememted to suit your needs. Just add your own implementation to the factory.
  - Metrics for different kinds of outcome variables are implemented analogously.
  - Features with high cardinality are treated with a simulated annealing solver to find the best combination.
  - No need for dummy encoding.
  - Train models using supervised or unsupervised learning.
  - Specify weights for unbalanced datasets.

  > **NOTE:**  These pure python (and a bit of numpy) algorithms are many times slower than, e.g., `sklearn` or `xgboost`.



  ```
  pip install binarybeech[visualize]
  ```
  The dependencies installed using the visualize option enable support for plotting and formatting trees.



  Load the Classification And Regression Tree model class

  ```
  import pandas as pd
  from binarybeech.binarybeech import CART
  from binarybeech.extra import k_fold_split
  ```
  get the data from a csv file
  ```
  df = pd.read_csv("data/titanic.csv")
  [(df_train, df_test)] = k_fold_split(df,frac=0.75,random=True,replace=False)
  ```
  grow a decision tree
  ```
  c = CART(df=df_train,y_name="Survived", method="classification")
  c.create_tree()
  ```
  predict
  ```
  c.predict(df_test)
  ```
  validation metrics
  ```
  c.validate(df=df_test)
  ```

  Please have a look at the jupyter notebooks in this repository for more examples. To try them out online, you can use [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/arminwitte/binarybeech/HEAD?labpath=notebooks%2Ftitanic.ipynb).



  **CART(df, y_name, X_names=None, min_leaf_samples=1, min_split_samples=1, max_depth=10, method="regression", handle_missings="simple", attribute_handlers=None)**

  Class for a Classification and Regression Tree (CART) model.

  * Parameters
      - **df**: pandas _dataframe_ with training data
      - **y_name**: name of the column with the output data/labels
      - **X_names**: _list_ of names with the inputs to use for the modelling. If _None_, all columns except y_name are chosen. Default is _None_.
      - **min_leaf_samples**: If the number of training samples is lower than this, a terminal node (leaf) is created. Default is 1.
      - **min_split_samples**: If a split of the training data is proposed with at least one branch containing less samples than this, the split is rejected. Default is 1.
      - **max_depth**: Maximum number of sequential splits. This corresponds to the number of vertical layers of the tree. Default is 10, which corresponds to a maximum number of 1024 terminal nodes.
      - **method**: Metrics to use for the evaluation of split loss, etc. Can be either "classification", "logistic", "regression", or _None_. Default is "regression". If _None_ is chosen, the `method` is deduced from the training _dataframe_.
      - **handle_missings**: Specify the way how missing data is handeled. Can be eiter _None_ or "simple".
      - **attribute_handlers**: _dict_ with attribute handler instances for each variable. The data handler determins, e.g., how splits of the dataset are made.
  * Methods
      - **predict(df)**:
          + Parameters:
              * **df**: _dataframe_ with inputs for predictions.
          + Returns:
              * array with predicted values/labels.
      - **train(k=5, plot=True, slack=1.0)**:
          + Parameters:
              * **k**: number of different splits of the _dataframe_ into training and test sets for k-fold cross-validation.
              * **plot**: flag for plotting a diagram of the loss over cost complexity parameter alpha using _matplotlib_.
              * **slack**: the amount of slack granted in chosing the best cost complexity parameter alpha. It is given as multiplier for the standard deviation of the alpha at minimum loss and allows thus to chose an alpha that is probably larger to account for the uncertainty in the k-fold cross validation procedure.
          + Returns:
      - **create_tree(leaf_loss_threshold=1e-12)**
          + Returns
      - **prune(alpha_max=None, test_set=None, metrics_only=False)**
          + Parameters:
              * **alpha_max**: Stop the pruning procedure at this value of the cost complexity parameter alpha. If _None_, the tree is pruned down to its root giving the complete relationship between alpha and the loss. Default is _None_.
              * **test_set**: data set to use for the evaluation off the losses. If _None_, the training set is used. Default is _None_.
              * **metrics_only**: If _True_, pruning is performed on a copy of the tree, leaving the actual tree intact. Default is _False_
      - **validate(df=None)**
          + Parameters:
              * **df**: _dataframe_ to use for (cross-)validation. If _None_, the training set is used. Default is _None_.
          + Returns:
              * _dict_ with metrics, e.g. accuracy or RSquared.
  * Attributes
      - **tree**:



  **GradientBoostedTree(df, y_name, X_names=None, sample_frac=1, n_attributes=None, learning_rate=0.1, cart_settings={}, init_method="logistic", gamma=None, handle_missings="simple", s=None)**

  Class for a Gradient Boosted Tree model.

  * Parameters
      - **df**: pandas _dataframe_ with training data
      - **y_name**: name of the column with the output data/labels
      - **X_names**: _list_ of names with the inputs to use for the modelling. If _None_, all columns except y_name are chosen. Default is _None_.
      - **sample_frac**: fraction (0, 1] of the training data to use for the training of an individual tree of the ensemble. Default is 1.
      - **n_attributes**: number of attributes (elements of the X_names list) to use for the training of an individual tree of the ensemble. Default is _None_ which corresponds to all available attributes.
      - **learning_rate**: the shinkage parameter used to "downweight" individual trees of the ensemble. Default is 0.1.
      - **cart_settings**: _dict_ that is passed on to the constuctor of the individual tree (binarybeech.binarybeech.CART). For details cf. above.
      - **init_method**: Metrics to use for the evaluation of split loss, etc if the initial tree (stump). Can be either "classification", "logistic", "regression", or _None_. Default is "regression". If _None_ is chosen, the `method` is deduced from the training _dataframe_.
      - **gamma**: weight for individual trees of the ensemble. If _None_, the weight for each tree is chosen by line search minimizing the loss given by _init_method_.
      - **handle_missings**: Specify the way how missing data is handeled. Can be eiter _None_ or "simple".
      - **attribute_handlers**: _dict_ with data handler instances for each variable. The data handler determins, e.g., how splits of the dataset are made.
  * Methods
      - **predict(df)**
          + Parameters:
              * **df**: _dataframe_ with inputs for predictions.
          + Returns:
              * array with predicted values/labels.
      - **train(M)**
          + Parameters:
              * **M**: Number of individual trees to create for the ensemble.
          + Returns:
      - **validate(df=None)**
          + Parameters:
              * **df**: _dataframe_ to use for (cross-)validation. If _None_, the training set is used. Default is _None_.
          + Returns:
              * _dict_ with metrics, e.g. accuracy or RSquared.
  * Attributes
      - **trees**



  **AdaBoostTree(training_data=None, df=None, y_name=None, X_names=None, sample_frac=1, n_attributes=None, cart_settings={}, method="classification", handle_missings="simple", attribute_handlers=None, seed=None, algorithm_kwargs={})**

  Class for a AdaBoost model using CARTs as weak learners.

  * Parameters:
      - **training_data**: Preprocessed instance of class _TrainingData_.
      - **df**: pandas _dataframe_ with training data
      - **y_name**: name of the column with the output data/labels
      - **X_names**: _list_ of names with the inputs to use for the modelling. If _None_, all columns except y_name are chosen. Default is _None_.
      - **method**: Metrics to use for the evaluation of split loss, etc. Can be either "classification", "logistic", "regression", or _None_. Default is "regression". If _None_ is chosen, the `method` is deduced from the training _dataframe_.
      - **handle_missings**: Specify the way how missing data is handeled. Can be eiter _None_ or "simple".
      - **attribute_handlers**: _dict_ with attribute handler instances for each variable. The data handler determins, e.g., how splits of the dataset are made.
  * Methods
      - **predict(df)**
          + Parameters:
              * **df**: _dataframe_ with inputs for predictions.
          + Returns:
              * array with predicted values/labels.
      - **train(M)**
          + Parameters:
              * **M**: Number of individual trees to create for the ensemble.
          + Returns:
      - **validate(df=None)**
          + Parameters:
              * **df**: _dataframe_ to use for (cross-)validation. If _None_, the training set is used. Default is _None_.
          + Returns:
              * _dict_ with metrics, e.g. accuracy or RSquared.
      - **variable_importance()**:
          + Returns:
              * _dict_ with normalized importance values.
  * Attributes



  **RandomForest(df, y_name, X_names=None, verbose=False, sample_frac=1, n_attributes=None, cart_settings={}, method="regression", handle_missings="simple", attribute_handlers=None)**

  Class for a Random Forest model.

  * Parameters
      - **df**: pandas _dataframe_ with training data
      - **y_name**: name of the column with the output data/labels
      - **X_names**: _list_ of names with the inputs to use for the modelling. If _None_, all columns except y_name are chosen. Default is _None_.
      - **verbose**: if set to _True_, status messages are sent to stdout. Default is _False_.
      - **sample_frac**: fraction (0, 1] of the training data to use for the training of an individual tree of the ensemble. Default is 1.
      - **n_attributes**: number of attributes (elements of the X_names list) to use for the training of an individual tree of the ensemble. Default is _None_ which corresponds to all available attributes.
      - **cart_settings**: _dict_ that is passed on to the constuctor of the individual tree (binarybeech.binarybeech.CART). For details cf. above.
      - **method**: Metrics to use for the evaluation of split loss, etc. Can be either "classification", "logistic", "regression", or _None_. Default is "regression". If _None_ is chosen, the `method` is deduced from the training _dataframe_.
      - **handle_missings**: Specify the way how missing data is handeled. Can be eiter _None_ or "simple".
      - **attribute_handlers**: _dict_ with attribute handler instances for each variable. The data handler determins, e.g., how splits of the dataset are made.
  * Methods
      - **predict(df)**
          + Parameters:
              * **df**: _dataframe_ with inputs for predictions.
          + Returns:
              * array with predicted values/labels.
      - **train(M)**
          + Parameters:
              * **M**: Number of individual trees to create for the ensemble.
          + Returns:
      - **validate(df=None)**
          + Parameters:
              * **df**: _dataframe_ to use for (cross-)validation. If _None_, the training set is used. Default is _None_.
          + Returns:
              * _dict_ with metrics, e.g. accuracy or RSquared.
      - **validate_oob()**:
          + Returns:
              * _dict_ with metrics, e.g. accuracy or RSquared.
      - **variable_importance()**:
          + Returns:
              * _dict_ with normalized importance values.
  * Attributes


  Decision trees are, by design, data type agnostic. With only a few methods like _spliter_ for input variables and meaningful quantification for the _loss_, any data type can be perused. In this code, this is implemented using a factory pattern for _data handling_ and _metrics_ making decision tree learing simple and versatile.

  For more information please feel free to take a look at the code.







  [Decision tree](https://en.m.wikipedia.org/wiki/Decision_tree)

  [CART](https://de.m.wikipedia.org/wiki/CART_(Algorithmus))

  [Gradient Boosted Tree](https://en.m.wikipedia.org/wiki/Gradient_boosting)

  [Random Forest](https://de.m.wikipedia.org/wiki/Random_Forest)

  [pruning](https://online.stat.psu.edu/stat508/lesson/11/11.8/11.8.2)


  Contributions in the form of pull requests are always welcome.

rpm_license:
  MIT License  Copyright (c) 2023 arminwitte  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.


pkgId: 9dc3919d2e9b3283bc04d5c757cb0f4783d0e06c34cfa484887f79c71391b6c0
name: python-binaryconvert
arch: src
version: 2.7
epoch: 0
release: 1
summary: A super easy Python tool that converts your text into binary language.
url: https://github.com/wfxey/binaryconvert
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binaryconvert-2.7-1.src.rpm
checksum_type: sha256
description:
  ![PyPI - Downloads](https://img.shields.io/pypi/dm/binaryconvert)

  A super easy Python tool that converts your text into binary language (8-bit).



  ```bash
  pip install binaryconvert
  ```


  <hr>



  ```python
  from binaryconvert import convert

  convert.ToBinary("Hi, my name is wfxey!")
  ```

  ```bash
  01001000 01100001 01100001 01101100 01100001 01101100 01101100 01100001 01100001 01101111
  ```
  <hr>



  ```python
  from binaryconvert import convert

  convert.ToText("01001000 01100001 01100001 01101100 01100001 01101100 01101100 01100001 01100001 01101111")
  ```

  ```bash
  Hi, my name is wfxey!
  ```

  <hr>



  BinaryConvert also provides a command line interface (CLI) for easy conversion without writing a single line of Python code.



  Convert normal text to binary (8-bit) using the to_binary command.

  ```bash
  python -m binaryconvert to_binary "Hi, my name is wfxey!"
  ```

  ```bash
  01001000 01101001 00101100 00100000 01101101 01111001 00100000 01101110 01100001 01101101 01100101 00100000 01101001 01110011 00100000 01110111 01100110 01111000 01100101 01111001 00100001
  ```


  Convert binary (8-bit) to normal text using the to_text command.

  ```bash
  python -m binaryconvert to_text "01001000 01101001 00101100 00100000 01101101 01111001 00100000 01101110 01100001 01101101 01100101 00100000 01101001 01110011 00100000 01110111 01100110 01111000 01100101 01111001 00100001"
  ```

  ```bash
  Hi, my name is wfxey!
  ```


pkgId: 8d20764012cc409293c5f897b980e0974342abdee7208f7a3d60c8fe685fceb9
name: python-binaryornot-stubs
arch: src
version: 0.0.2
epoch: 0
release: 1
summary: Stubs for binaryornot.
url: https://github.com/Tatsh/binaryornot-stubs
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binaryornot-stubs-0.0.2-1.src.rpm
checksum_type: sha256
description:
  [![QA](https://github.com/Tatsh/binaryornot-stubs/actions/workflows/qa.yml/badge.svg)](https://github.com/Tatsh/binaryornot-stubs/actions/workflows/qa.yml)
  [![PyPI - Version](https://img.shields.io/pypi/v/binaryornot-stubs)](https://pypi.org/project/binaryornot-stubs/)
  ![GitHub tag (with filter)](https://img.shields.io/github/v/tag/Tatsh/binaryornot-stubs)
  ![GitHub](https://img.shields.io/github/license/Tatsh/binaryornot-stubs)
  ![GitHub commits since latest release (by SemVer including pre-releases)](https://img.shields.io/github/commits-since/Tatsh/binaryornot-stubs/v0.0.2/master)

  Stubs for `binaryornot`.



  ```shell
  pip install binaryornot-stubs
  ```


pkgId: d691394676c8d0dde56e97922c9f0823cdbc647ffc587e80039795745c3366b0
name: python-binarytree
arch: src
version: 6.5.1
epoch: 0
release: 1
summary: Python Library for Studying Binary Trees
url: https://github.com/joowani/binarytree
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binarytree-6.5.1-1.src.rpm
checksum_type: sha256
description:
  ![Build](https://github.com/joowani/binarytree/workflows/Build/badge.svg)
  ![CodeQL](https://github.com/joowani/binarytree/workflows/CodeQL/badge.svg)
  [![codecov](https://codecov.io/gh/joowani/binarytree/branch/main/graph/badge.svg?token=C2X2OMPL65)](https://codecov.io/gh/joowani/binarytree)
  [![PyPI version](https://badge.fury.io/py/binarytree.svg)](https://badge.fury.io/py/binarytree)
  [![GitHub license](https://img.shields.io/github/license/joowani/binarytree?color=brightgreen)](https://github.com/joowani/binarytree/blob/main/LICENSE)
  ![Python version](https://img.shields.io/badge/python-3.7%2B-blue)

  Are you studying binary trees for your next exam, assignment or technical interview?

  **Binarytree** is a Python library which lets you generate, visualize, inspect and
  manipulate [binary trees](https://en.wikipedia.org/wiki/Binary_tree). Skip the tedious
  work of setting up test data, and dive straight into practising your algorithms.
  [Heaps](https://en.wikipedia.org/wiki/Heap_(data_structure)) and
  [binary search trees](https://en.wikipedia.org/wiki/Binary_search_tree) are also supported.
  Self-balancing search trees like [red-black](https://en.wikipedia.org/wiki/Red%E2%80%93black_tree)
  or [AVL](https://en.wikipedia.org/wiki/AVL_tree) will be added in the future.

  Check out the [documentation](http://binarytree.readthedocs.io) for more details.

  ![IPython Demo](gifs/demo.gif)

  Binarytree can be used with [Graphviz](https://graphviz.org) and
  [Jupyter Notebooks](https://jupyter.org) as well:

  ![Jupyter Demo](gifs/jupyter.gif)



  Python 3.7+



  Install via [pip](https://pip.pypa.io):

  ```shell
  pip install binarytree --upgrade
  ```

  For [conda](https://docs.conda.io) users:

  ```shell
  conda install binarytree -c conda-forge
  ```



  Binarytree uses the following class to represent a node:

  ```python
  class Node:

      def __init__(self, value, left=None, right=None):
          self.value = value  # The node value (float/int/str)
          self.left = left    # Left child
          self.right = right  # Right child
  ```

  Generate and pretty-print various types of binary trees:

  ```python
  from binarytree import tree, bst, heap


  my_tree = tree(height=3, is_perfect=False)


  my_bst = bst(height=3, is_perfect=True)


  my_heap = heap(height=3, is_max=True, is_perfect=False)


  print(my_tree)









  print(my_bst)









  print(my_heap)









  ```
  Generate trees with letter values instead of numbers:

  ```python
  from binarytree import tree

  my_tree = tree(height=3, is_perfect=False, letters=True)

  print(my_tree)









  ```


  Build your own trees:

  ```python
  from binarytree import Node

  root = Node(1)
  root.left = Node(2)
  root.right = Node(3)
  root.left.right = Node(4)

  print(root)







  ```

  Inspect tree properties:

  ```python
  from binarytree import Node

  root = Node(1)
  root.left = Node(2)
  root.right = Node(3)
  root.left.left = Node(4)
  root.left.right = Node(5)

  print(root)







  assert root.height == 2
  assert root.is_balanced is True
  assert root.is_bst is False
  assert root.is_complete is True
  assert root.is_max_heap is False
  assert root.is_min_heap is True
  assert root.is_perfect is False
  assert root.is_strict is True
  assert root.leaf_count == 3
  assert root.max_leaf_depth == 2
  assert root.max_node_value == 5
  assert root.min_leaf_depth == 1
  assert root.min_node_value == 1
  assert root.size == 5


  assert root.properties == {
      'height': 2,
      'is_balanced': True,
      'is_bst': False,
      'is_complete': True,
      'is_max_heap': False,
      'is_min_heap': True,
      'is_perfect': False,
      'is_strict': True,
      'leaf_count': 3,
      'max_leaf_depth': 2,
      'max_node_value': 5,
      'min_leaf_depth': 1,
      'min_node_value': 1,
      'size': 5
  }

  print(root.leaves)


  print(root.levels)

  ```

  Compare and clone trees:
  ```python
  from binarytree import tree

  original = tree()


  clone = original.clone()


  original.equals(clone)
  ```


  Use [level-order (breadth-first)](https://en.wikipedia.org/wiki/Tree_traversal#Breadth-first_search)
  indexes to manipulate nodes:

  ```python
  from binarytree import Node

  root = Node(1)                  # index: 0, value: 1
  root.left = Node(2)             # index: 1, value: 2
  root.right = Node(3)            # index: 2, value: 3
  root.left.right = Node(4)       # index: 4, value: 4
  root.left.right.left = Node(5)  # index: 9, value: 5

  print(root)









  root.pprint(index=True)









  print(root[9])



  root[4] = Node(6, left=Node(7), right=Node(8))
  root.pprint(index=True)











  del root[1]
  root.pprint(index=True)




  ```

  Traverse trees using different algorithms:

  ```python
  from binarytree import Node

  root = Node(1)
  root.left = Node(2)
  root.right = Node(3)
  root.left.left = Node(4)
  root.left.right = Node(5)

  print(root)







  print(root.inorder)


  print(root.preorder)


  print(root.postorder)


  print(root.levelorder)


  print(list(root)) # Equivalent to root.levelorder

  ```

  Convert to [list representations](https://en.wikipedia.org/wiki/Binary_tree#Arrays):

  ```python
  from binarytree import build


  values = [7, 3, 2, 6, 9, None, 1, 5, 8]
  root = build(values)
  print(root)











  print(root.values)

  ```

  Binarytree supports another representation which is more compact but without
  the [indexing properties](https://en.wikipedia.org/wiki/Binary_tree#Arrays)
  (this method is often used in [Leetcode](https://leetcode.com/)):

  ```python
  from binarytree import build, build2, Node


  root = Node(1)
  root.left = Node(2)
  root.left.left = Node(3)
  root.left.left.left = Node(4)
  root.left.left.right = Node(5)
  print(root)











  print(root.values)



  print(root.values2)



  tree1 = build(root.values)
  tree2 = build2(root.values2)
  assert tree1.equals(tree2) is True
  ```

  Check out the [documentation](http://binarytree.readthedocs.io) for more details.


pkgId: b181250a43150ce65c6633a3f115e24c5b66d3806ac1da150b735611811a451a
name: python-binay-parajuli
arch: src
version: 1.1.1
epoch: 0
release: 1
summary: please add a summary manually as the author left a blank one
url: https://pypi.org/project/binay-parajuli/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binay-parajuli-1.1.1-1.src.rpm
checksum_type: sha256
description:
  Install -> setupttools, twine and wheel

  1.Create package_dir with  __init__.py and modules inside it.
  Also, add setup.py and can also add README.md and LICENSE.txt as optional

  2.Then build with python3 setup.py sdist bdist_wheel
  which returns binay_parajuli.egg-info , build and dist folders

  3.Upload using twine upload dist/*


  View at: https://pypi.org/project/binay-parajuli/0.0.1/


pkgId: 6768c97608943e0868afffeb4f381da43e4a089893f6a33d184f963a60c9a28a
name: python-bincache
arch: src
version: 0.0.16
epoch: 0
release: 1
url: https://github.com/simpx/bincache
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bincache-0.0.16-1.src.rpm
checksum_type: sha256
summary:
  Bincache is a command-line utility designed to cache the output of executable binaries and shell commands.

description:
  ![Build Status](https://img.shields.io/github/actions/workflow/status/simpx/bincache/python-package.yml)
  ![PyPI](https://img.shields.io/pypi/v/bincache)
  ![License](https://img.shields.io/github/license/simpx/bincache)
  ![Issues](https://img.shields.io/github/issues/simpx/bincache)
  ![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)

  Bincache is a command-line utility designed to cache the output of executable binaries and shell commands.



  ```sh
  pip install bincache
  ```



  Execute commands through bincache to cache their output.



  ```sh
  bincache <binary_or_command> <arg1> [arg2 ... argN]
  ```



  Cache the output of the date command:

  ```sh
  bincache date
  ```

  Cache the output of a binary executable:

  ```
  bincache ./a.out -l -a
  ```



  Bincache can be configured using a configuration file `bincache.conf`. The default configuration file is expected to be located at `$HOME/.cache/bincache/bincache.conf`.



  - `max_size`: Maximum cache size (e.g., 5G for 5 Gigabytes), default `5G`
  - `log_file`: Path to the log file, default empty
  - `log_level`: Logging level (INFO, DEBUG, WARNING, ERROR, CRITICAL), default `INFO`
  - `stats`: Enable or disable statistics, default `false`

  Example bincache.conf:

  ```
  max_size = 5G
  log_file = /var/log/bincache.log
  log_level = INFO
  stats = false
  ```

  Environment Variables
  - `BINCACHE_DIR`: Override the default cache directory.


  To contribute to Bincache, fork the repository, make your changes, and create a pull request. Please ensure that your changes are well-tested by run `pytest`.


  Bincache is licensed under the MIT License. See the `LICENSE` file for more information.


pkgId: 00fd7c9ff03464f0f7ce0673fe7b50dedcbf9c317a180b213c1b21dc253b02c1
name: python-binchicken
arch: src
version: 0.12.5
epoch: 0
release: 1
url: https://github.com/AroneyS/binchicken
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binchicken-0.12.5-1.src.rpm
checksum_type: sha256
summary:
  Bin Chicken - targeted recovery of low abundance metagenome assembled genomes through intelligent coassembly

description:
  [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/binchicken/README.html)
  [![DOI](https://zenodo.org/badge/531785064.svg)](https://zenodo.org/doi/10.5281/zenodo.10511708)
  ![](https://anaconda.org/bioconda/binchicken/badges/version.svg)
  ![](https://anaconda.org/bioconda/binchicken/badges/latest_release_relative_date.svg)
  ![](https://anaconda.org/bioconda/binchicken/badges/license.svg)
  ![](https://anaconda.org/bioconda/binchicken/badges/platforms.svg)



  [<img src="docs/_include/binchicken_logo.png" width="50%" />](binchicken_logo.png)

  Bin Chicken - targeted recovery of low abundance metagenome assembled genomes through intelligent coassembly.

  Documentation can be found at https://AroneyS.github.io/binchicken/

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: 83f162d53a96b3ee0cb532e6712f4ee9de9bab4399dcce7bdc184780ee994e81
name: python-binchicken
arch: src
version: 0.12.6
epoch: 0
release: 1
url: https://github.com/AroneyS/binchicken
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binchicken-0.12.6-1.src.rpm
checksum_type: sha256
summary:
  Bin Chicken - targeted recovery of low abundance metagenome assembled genomes through intelligent coassembly

description:
  [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/binchicken/README.html)
  [![DOI](https://zenodo.org/badge/531785064.svg)](https://zenodo.org/doi/10.5281/zenodo.10511708)
  ![](https://anaconda.org/bioconda/binchicken/badges/version.svg)
  ![](https://anaconda.org/bioconda/binchicken/badges/latest_release_relative_date.svg)
  ![](https://anaconda.org/bioconda/binchicken/badges/license.svg)
  ![](https://anaconda.org/bioconda/binchicken/badges/platforms.svg)



  [<img src="docs/_include/binchicken_logo.png" width="50%" />](binchicken_logo.png)

  Bin Chicken - recovery of low abundance and taxonomically targeted metagenome assembled genomes (MAGs) through strategic coassembly.

  Documentation can be found at https://AroneyS.github.io/binchicken/

  Logo by Georgina H. Joyce | www.georginajoyce.com



  Samuel T. N. Aroney, Rhys J. P. Newell, Gene W. Tyson and Ben J. Woodcroft.
  Bin Chicken: targeted metagenomic coassembly for the efficient recovery of novel genomes.
  bioRxiv (2024): 2024-11. https://doi.org/10.1101/2024.11.24.625082

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: 28fe9faf21a9b25dd251c631541273f50c941044bc81f2a7179c0f5ca76fda36
name: python-bincopy
arch: src
version: 20.0.0
epoch: 0
release: 1
url: https://github.com/eerimoq/bincopy
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bincopy-20.0.0-1.src.rpm
checksum_type: sha256
summary:
  Mangling of various file formats that conveys binary information (Motorola S-Record, Intel HEX and binary files).

description:
  Mangling of various file formats that conveys binary information
  (Motorola S-Record, Intel HEX, TI-TXT, Verilog VMEM, ELF and binary
  files).
  Project homepage: https://github.com/eerimoq/bincopy
  Documentation: https://bincopy.readthedocs.io


pkgId: c7b795a7453761c4b089d1dd6586b3c9b5da2a1806ec96ada6868ef7bef8bc4b
name: python-bincrafters-package-tools
arch: src
version: 0.33.0
epoch: 0
release: 1
summary: Bincrafters Packaging tools for build Conan projects
url: https://github.com/bincrafters/bincrafters-package-tools
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bincrafters-package-tools-0.33.0-1.src.rpm
checksum_type: sha256
description:
  [![Build status](https://github.com/bincrafters/bincrafters-package-tools/workflows/bincrafters-package-tools/badge.svg)](https://github.com/bincrafters/bincrafters-package-tools/actions)
  [![Codecov](https://codecov.io/gh/bincrafters/bincrafters-package-tools/branch/main/graph/badge.svg)](https://codecov.io/gh/bincrafters/bincrafters-package-tools)
  [![Pypi Download](https://img.shields.io/badge/download-pypi-blue.svg)](https://pypi.python.org/pypi/bincrafters-package-tools)





  This project contains files used by Conan Package Tools for all kinds of bincrafters builds.

  These scripts are used during build process to allow for rapid testing and prototyping at this time.



  To install by pip is just one step


  If you want to install by local copy

      pip install .


  Or if you want to download our pip package

      pip install bincrafters_package_tools



  All variables supported by Conan package tools, are treated by Bincrafters package tools as well.
  To solve the upload, some variables are customized by default:

  **CONAN_UPLOAD**: https://bincrafters.jfrog.io/artifactory/api/conan/public-conan
  **CONAN_REFERENCE**: Fields **name** and **version** from conanfile.py
  **CONAN_USERNAME**: Get from CI env vars. Otherwise, use **bincrafters**
  **CONAN_VERSION**: Get from CI env vars.
  **CONAN_VERSION**: Field **version** from conanfile.py
  **CONAN_UPLOAD_ONLY_WHEN_STABLE**: True for default template. False for Boost builds.
  **CONAN_STABLE_BRANCH_PATTERN**: stable/\*
  **CONAN_ARCHS**: Only x86_64 per default. To build 32-bit and 64-bit use e.g. [x86_64, x86]

  ___

  **BPT SPECIFIC ENVIRONMENT VARIBLES**:

  **BPT_MATRIX_SPLIT_BY_BUILD_TYPES**: Splits build jobs into `Release` and `Debug` build jobs.
  **BPT_MATRIX_DISCARD_DUPLICATE_BUILD_IDS**: `true`/`false`, default: `true`. This does NOT YET what it says. Right now, this only has an effect for installer_only and header_only recipes when set to `false`. In those cases, you get the full build matrix, instead of a shortened build matrix. In the future, the matrix generation actually compares build IDs and discards jobs based on the IDs.

  ___



  To install extra packages required to test

      pip install .[test]



  To run all unit test + code coverage, just execute:

      pip install -r bincrafters/requirements_test.txt
      cd tests
      pytest -v --cov=bincrafters



  [MIT](LICENSE.md)


pkgId: 6f5e020a028028183e1b15e7d24a44e0d6685bc715b33f53c8ffac06b455bb08
name: python-binda
arch: src
version: 1.0.22
epoch: 0
release: 1
summary: Read and write binary data using Pandas
url: https://github.com/jamiecash/binda
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binda-1.0.22-1.src.rpm
checksum_type: sha256
description:
  Read and write binary data using Pandas.

  Map the data in the binary file to variables, single data structures or
  repeating data structures. Once mapped, the binary file can be viewed and
  edited using Pandas DataFrames.



  binda can be installed using pip or conda.

  ```
  pip install binda
  ```

  or

  ```
  conda install binda
  ```



  The documentation for binda is available on GitHub Docs here:
  https://jamiecash.github.io/binda/binda.html



  Given the following binary data (represented as hex):
  ```
  42:4c:4f:43:4b:44:41:54:41:64:4a:61:6d:69:65:20
  43:61:73:68:20:20:20:20:20:01:65:42:6f:62:62:79
  20:53:6d:69:74:68:20:20:20:20:00:66:4d:72:20:42
  65:61:6e:20:20:20:20:20:20:20:20:01:4d:72:20:41
  75:74:68:6f:72:20:20:20:20:20:20:0a:00:ff:ec:00
  00:f7:42
  ```

  Which contaains:
  * A 9 bytes string containing 'BLOCKDATA'
  * 3 repeating records of 17 bytes containing a 1 byte integer ID, a 15 bytes
  string NAME and a 1 byte interger ACTIVE flag
  * 19 bytes containing a 15 bytes string AUTHOR, a 2 bytes integer ID and a
  2 bytes bigendian signed integer POINTS.

  First create the data handler with the data.

  ```python
  data = b'BLOCKDATAdJamie Cash     \x01eBobby Smith    \x00fMr Bean        '\
        +b'\x01Mr Author      \n\x00\xff\xec\x00\x00\xf7B'

  handler = bd.DataHandler(data)
  ```

  The name from the first record can be read by creating a variable, specifying a
  name for the variable, it's size, it's datatye and it's offset.

  ```python
  print("Name: " + handler.read_variable(bd.Variable('name', 15, str, 10)))
  ```

  ```
  Name: Jamie Cash
  ```

  The 19 bytes of author data starting at offset 60 can be read into a Pandas
  DataFrame by specifying the layout of the structure, including its variables.
  The DataHandler can then be used to read it.

  ```python
  author_struct = \
        bd.Structure(60, [bd.Variable('AUTHOR', 15, str),
                          bd.Variable('ID', 2, int),
                          bd.Variable('POINTS', 2, int,
                                      byteorder=bd.ByteOrder.BIG, signed=True)])
  handler.add_structure('author', author_struct)
  df = handler.read_structure('author')
  df
  ```
  ![image](https://github.com/jamiecash/binda/raw/main/example/img/single_structure.png)

  The three row repeating structure can be read in a similar way, but this time
  also specifying the number of rows.

  ```python
  people_struct = \
    bd.Structure(9, [bd.Variable('ID', 1, int),
                      bd.Variable('NAME', 15, str),
                      bd.Variable('ACTIVE', 1, bool)], rows=3)

  handler.add_structure('people', people_struct)
  df = handler.read_structure('people')
  df
  ```

  ![image](https://github.com/jamiecash/binda/raw/main/example/img/repeating_structure.png)

  The dataframe can be edited, and saved back to edit the binary data.

  ```python


  df.loc[df['ID'] == 101, 'ID'] = 200
  df.loc[df['ID'] == 200, 'NAME'] = 'Bobby Smythe   '
  df.loc[df['ID'] == 200, 'ACTIVE'] = True
  handler.write_structure('people', df)



  df = handler.read_structure('people')
  df
  ```

  ![image](https://github.com/jamiecash/binda/raw/main/example/img/repeating_structure_changed.png)

  We can also check the binary data to see the change.
  ```python
  print(handler.data[26:43])
  ```

  ```
  b'\xc8Bobby Smythe   \x01'
  ```



  2 Jupyter notebooks are provided in the examples folder, one containing the code
  from the **Usage** section above and one containing an example on reading and
  writimg Exif data to a .jpeg file.



  This project is maintained on [GitHub](https://github.com/jamiecash/binda):

  If you would like to contribute to this project by fixing issues or adding
  features, please follow the below steps:
  * If adding functionality or fixing an issue not already raised on GitHub, raise
  an issue.
  * Assign the issue(s) to yourself.
  * Fork the project.
  * Create a branch from master.
  * Make the required changes to the project in your local branch.
  * Commit your changes, adding comments referencing the issues that your changes
  fix.
  * Push this branch to your GitHub project.
  * Open a Pull Request on GitHub.
  * Email me at jlcash@gmail.com to discuss the changes made.
  * Once changes have been agreed, I will merge or close the pull request.
  * Sync the updated master back to your fork.
  * Close the issues that were fixed.

rpm_license:
  MIT License  Copyright (c) 2023 Jamie Cash  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.


pkgId: 4fc3d1a513fb9dc1800cf9a87bc3d12e2a49d8ce30343bed6d11a1a4e07f607f
name: python-bindle
arch: src
version: 0.0.3
epoch: 0
release: 1
summary: My blanket stick.
url: https://pypi.org/project/bindle/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bindle-0.0.3-1.src.rpm
checksum_type: sha256
description:
  - [Installation](#installation)

  ```console
  pip install bindle
  ```

  `bindle` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.


pkgId: ba4ff2f3ea5d7d1efc8a772365c833f8ebe82c00d155ffc645cc5ca4f285e573
name: python-bindry
arch: src
version: 0.1.0
epoch: 0
release: 1
summary: Elegant Python Dependency Injection with Profile-Aware Configuration
url: https://github.com/hcleungca/bindry
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bindry-0.1.0-1.src.rpm
checksum_type: sha256
description:
  Elegant Python Dependency Injection with Profile-Aware Configuration

  [![PyPI version](https://badge.fury.io/py/bindry.svg)](https://badge.fury.io/py/bindry)
  [![Python Support](https://img.shields.io/pypi/pyversions/bindry.svg)](https://pypi.org/project/bindry/)
  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)



  Bindry is a powerful yet intuitive dependency injection framework for Python that supports profile-based configuration, environment variable interpolation, and flexible component lifecycle management. It enables you to write more maintainable and testable applications by managing dependencies through configuration rather than hard-coding them.



  - Profile-based configuration management
  - Environment variable support with fallback values
  - YAML and JSON configuration file support
  - Constructor, method, and property injection
  - Singleton and prototype scoping
  - Type-based automatic dependency resolution
  - Support for constructor arguments via configuration
  - Flexible component registration through decorators or configuration files



  ```bash
  pip install bindry
  ```





  ```python
  from bindry import component, Scope, autowired


  class MessageService:
      def send_message(self, msg: str): pass


  @component(scope=Scope.SINGLETON, bean_type=MessageService)
  class EmailService(MessageService):
      def send_message(self, msg: str):
          print(f"Sending email: {msg}")


  @component(scope=Scope.SINGLETON)
  class NotificationManager:
      @autowired
      def __init__(self, message_service: MessageService):
          self.message_service = message_service

      def notify(self, message: str):
          self.message_service.send_message(message)
  ```



  Create a `config.yaml` file:

  ```yaml
  profiles:
    default:
      beans:
        MessageService:
          bean_type: "myapp.services.MessageService"
          implementation: "myapp.services.EmailService"
          scope: "singleton"
          constructor_args:
            timeout: 30

    development:
      beans:
        MessageService:
          bean_type: "myapp.services.MessageService"
          implementation: "myapp.services.MockMessageService"
          scope: "prototype"
  ```



  ```python
  from bindry import ApplicationContext


  context = ApplicationContext.get_instance()
  context.load_configuration("config.yaml", active_profiles=["development"])


  notification_manager = context.get_bean(NotificationManager)
  notification_manager.notify("Hello, World!")
  ```



  Bindry supports environment variable interpolation in configuration files:

  ```yaml
  profiles:
    default:
      beans:
        DatabaseService:
          constructor_args:
            url: "${DATABASE_URL:sqlite:///default.db}"
            timeout: "${DB_TIMEOUT:30}"
  ```





  ```python
  context = ApplicationContext.get_instance()
  context.set_active_profiles(["default", "development", "testing"])
  ```



  Set the `ACTIVE_PROFILES` environment variable:

  ```bash
  export ACTIVE_PROFILES=development,testing
  ```





  ```python
  @component(
      scope=Scope.SINGLETON,
      constructor_args={
          "timeout": 30,
          "retries": 3,
          "kwargs": {"debug": True}
      }
  )
  class DatabaseService:
      def __init__(self, timeout: int, retries: int, **kwargs):
          self.timeout = timeout
          self.retries = retries
          self.debug = kwargs.get("debug", False)
  ```



  ```python
  @component(Scope.SINGLETON)
  class ServiceManager:
      @autowired
      def configure(self, config_service: ConfigService):
          self.config_service = config_service
  ```



  We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.



  This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



  This project was developed with the assistance of AI language models:
  - Initial implementation assistance provided by ChatGPT
  - Additional feature development and refinements by Claude.ai

  While the code was primarily generated through AI assistance, all implementations have been carefully reviewed and tested to ensure quality and reliability.


pkgId: d05da0e930e4819eeabfce36a4db20b8206b43c51dedb5bf2350ba09a43e3e1d
name: python-bing-image-downloader
arch: src
version: 1.1.2
epoch: 0
release: 1
summary: Python library to download bulk images from Bing.com
url: https://github.com/gurugaurav/bing_image_downloader
rpm_license: MIT License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bing-image-downloader-1.1.2-1.src.rpm
checksum_type: sha256
description:
  ![GitHub top language](https://img.shields.io/github/languages/top/gurugaurav/bing_image_downloader)
  ![GitHub](https://img.shields.io/github/license/gurugaurav/bing_image_downloader)
  [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fgurugaurav%2Fbing_image_downloader&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)

  <hr>

  Python library to download bulk of images form Bing.com.
  This package uses async url, which makes it very fast while downloading.<br/>




  This program lets you download tons of images from Bing.
  Please do not download or use any image that violates its copyright terms.


  ```sh
  pip install bing-image-downloader
  ```

  or
  ```bash
  git clone https://github.com/gurugaurav/bing_image_downloader
  cd bing_image_downloader
  pip install .
  ```




  ```python
  from bing_image_downloader import downloader
  downloader.download(query_string, limit=100,  output_dir='dataset', adult_filter_off=True, force_replace=False, timeout=60, verbose=True)
  ```

  `query_string` : String to be searched.<br />
  `limit` : (optional, default is 100) Number of images to download.<br />
  `output_dir` : (optional, default is 'dataset') Name of output dir.<br />
  `adult_filter_off` : (optional, default is True) Enable of disable adult filteration.<br />
  `force_replace` : (optional, default is False) Delete folder if present and start a fresh download.<br />
  `timeout` : (optional, default is 60) timeout for connection in seconds.<br />
  `filter` : (optional, default is "") filter, choose from [line, photo, clipart, gif, transparent]<br />
  `verbose` : (optional, default is True) Enable downloaded message.<br />


  You can also test the programm by runnning `test.py keyword`



  https://pypi.org/project/bing-image-downloader/




  </br>


  You can buy me a coffee if this project was helpful to you.</br>

  [<img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt="Show your support" width="180"/>](https://www.buymeacoffee.com/gurugaurav)


pkgId: 708ab2e684c0560490fad49cf97ad685f2e07ac5410d771c06de8d2d2568bfce
name: python-bing-image-urls
arch: src
version: 0.1.5
epoch: 0
release: 1
summary: fetch bing image urls based on keywords
url: https://github.com/ffreemt/bing-image-urls
rpm_license: UNKNOWN
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bing-image-urls-0.1.5-1.src.rpm
checksum_type: sha256
description:
  Fetch Bing image urls based on keywords


  ```pip install bing-image-urls```



  ```python

  from bing_image_urls import bing_image_urls

  print(bing_image_urls("bear", limit=2))


  ```

  The helper function `get_image_size` may sometimes come handy if you need to know the size of the image. `get_image_size` takes a filename or a filelike object as input and outputs the width and height of the image. Hence the raw bytes of an image from the net can be wrapped in io.BytesIO and fed to `get_image_size`.

  ```python
  import io
  import httpx
  from logzero import logger
  from bing_image_urls import get_image_size

  url = "https://www.stgeorgeutah.com/wp-content/uploads/2017/01/blackbear.jpg"
  try:
      resp = httpx.get(url)
      resp.raise_for_status()
  except Exception as exc:
      raise SystemExit(exc)

  print(get_image_size(io.BytesIO(resp.content)))

  ```

  Most of the code in `get_image_size` is from [imagesize_py](https://github.com/shibukawa/imagesize_py). As soon as the [PR](https://github.com/shibukawa/imagesize_py/pull/46) about filelike object is merged to the main branch, the `imagesize_py` package will be included as a depdendant package.


pkgId: 96ce36b0c684ccff2655efc7d8cac9f97fe933f31d72920de9aedcbe5bcd0b59
name: python-bing-rewards
arch: src
version: 3.0.1
epoch: 0
release: 1
summary: Perform automated Bing Rewards searches
url: https://github.com/jack-mil/bing-rewards/README.md
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bing-rewards-3.0.1-1.src.rpm
checksum_type: sha256
description:
  <div align="center">
  <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/bing-rewards?style=flat-square&label=Python&logo=python&logoColor=yellow">
  <a href="https://pypi.org/p/bing-rewards/"> <img alt="PyPi" src="https://img.shields.io/pypi/v/bing-rewards?label=PyPI&style=flat-square&logo=pypi&logoColor=yellow"></a>
  <a href="https://pypi.org/p/bing-rewards/"> <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/bing-rewards?style=flat-square&label=Downloads&color=orange"></a>
  <br>
  <img alt="PyPI - License" src="https://img.shields.io/pypi/l/bing-rewards?style=flat-square&label=License&color=blueviolet">
  </div>


  Please submit an issue or pull-request if you have an idea for a feature

  - [Features](#features)
  - [Install](#installation)
  - [Usage](#usage)
  - [Config](#options)
  - [Development](#development--contribution)



  * Enter random search queries into your browser a la Auto Hotkey.
  * Use a mobile user agent to get mobile points (`--mobile`)
  * Configurable number of searches with `--count=`
  * Install as self-contained python application with minimal dependecies (`pynput`)
  * Fine tune delay and set browser executable with [config](#configuration) at `$XDG_CONFIG_HOME` or `%APPDATA%` on Windows



  ```bash
  pipx install bing-rewards
  ```
  Will make the executable `bing-rewards` available on your PATH.
  Look below or try the `--help` flag to see detailed usage.

  **Recommended**: Use a virtual environment or [`pipx`](https://pypa.github.io/pipx/) to avoid poluting your global package path with executable apps. See: [pipx](https://pypa.github.io/pipx/)


  Download the latest release wheel and install with pip locally.



  - At least Python 3.10

  - [pynput](https://github.com/moses-palmer/pynput) (installed automatically). Used to control keypresses and type Bing search URLS.
  WARNING: This script *will* take control away from the keyboard while running. **Pynput** performs key presses. i.e., it does not operate headless or in the background.

  - `chrome` must be discoverable on the system PATH. [Download Google Chrome](https://www.google.com/intl/en/chrome/).
  If your chromium based browser has a different name use the `--exe` flag with an absolute path to the browser executable to use (e.g. `--exe=$(which brave-browser)`). Also see the `"browser-path"` key in the [config](#configuration) file.

  - To earn points from searching, you *must* also have logged into [bing.com](https://www.bing.com) with your Microsoft account at least once, to save cookies.


  Complete mobile and desktop daily points

  `$ bing-rewards`

  Run 10 searches with mobile user-agent in a new window

  `$ bing-rewards -m -c10`

  `$ bing-rewards --mobile --count=10`

  Complete mobile and desktop daily points using specified chrome profile "Profile 1"

  `$ bing-rewards --profile "Profile 1"`

  Launches Chrome as a subprocess with special flags. Tested on Windows 10 and Linux (Ubuntu + Arch), however it should work on Mac OS as well.

  ⚠️Known Issue: No other instance of chrome.exe can be open when the script runs. Chrome prevents different user agents in each window. The script will run, but Chrome will not appear as Edge

  ⚠️Bing has gotten more and more complex with the introduction of the AI tools. Disable as much as you can to make pages load faster. See PR #39 for some modifications you can make to the default search query url parameters that may improve success.




  Running with no options will complete mobile and desktop daily search quota.
  The following options are available to change the default behavior.
  Options supplied at execution time override any config.
  | Flag                    | Option                                                                                |
  | ----------------------- | --------------------------------------------------------------------------------------|
  | `-h`, `--help`          | Display help and exit                                                                 |
  | `-c`, `--count=N`       | Override the number of searches to complete                                           |
  | `-d`, `--desktop`       | Only use desktop user agent                                                           |
  | `-m`, `--mobile`        | Only use a mobile user agent                                                          |
  | `-n`, `--dryrun`        | Do everything but type the search query                                               |
  | `--bing`                | Use this flag if Bing is already your default search engine. Bypasses constructing a bing.com URL |
  | `--open-rewards`        | Open the rewards page at the end of the run                                           |
  | `-X`, `--no-exit`       | Do not close the browser after completing a search                                    |
  | `--load-delay`          | Override the time given to Chrome to load in seconds                                  |
  | `--search-delay`        | Override the time between searches in seconds                                         |
  | `--exe EXE`             | The full path of the Chrome compatible browser executable (Brave and Chrome tested)   |
  | `--nowindow`            | Don't open a new Chrome window, just type the keys                                    |
  | `--profile "Profile N"` | Launches chrome using the specified profile. Otherwise use default.                   |
  | `--ime`                 | Triggers Windows IME to switch to English input by pressing "shift"                   |

  A config file is also generated in $XDG_CONFIG_HOME or %APPDATA% on Windows
  where precise delay modifications can be made. If updates make changes to the default configs, you will have to remove and regenerate the file.

  Example config `~/.config/bing-rewards/config.json`
  ```json
  {
      "desktop_count": 30,
      "mobile_count": 20,
      "load_delay": 1.5,
      "search_delay": 2,
      "search_url": "https://www.bing.com/search?FORM=CHROMN&q=",
      "desktop_agent": "Mozilla/5.0 ... <snip>",
      "mobile_agent": "Mozilla/5.0 (Windows Phone 10.0; Android 6.0.1) ... <snip>",
      "browser_path": "C:\\Program Files (x86)\\BraveSoftware\\Brave-Browser\\Application\\brave.exe"
  }
  ```
  Delay timings are in seconds.
  Note: the format has slightly changed in version >= 3.0, so delete and regenerate accordingly.



  If interested, the following user agents are passed to Chrome using the `--user-agent` argument.
  These are clearly defined at the top of `bing-rewards.py`.

  Edge Browser on Windows 10 desktop:
  > Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edge/126.0.0.0

  Mobile Edge Browser on Pixel 6 phone:
  >  Mozilla/5.0 (Linux; Android 14; Pixel 6 Build/AP2A.240605.024) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Mobile Safari/537.36 Edge/121.0.2277.138



  The [keywords](https://www.myhelpfulguides.com/keywords.txt) included in this repo where taken from this site
  https://www.myhelpfulguides.com/2018/07/19/bing-rewards-auto-searcher-with-python-3/.

  This script provided the original inspiration but has since been complelty rewritten and expanded.
  The original author was contacted for the original source of keywords, but declined to respond



  This project uses [`hatch`](https://hatch.pypa.io/latest/) as the build backend for dependency management and packaging.

  ```
  pipx install hatch
  ```

  Then, fork the repo, clone and install the project and dependencies with `hatch env create`.

  Install the defined pre-commit hooks: `hatch run pre-commit install`

  Launch bing-rewards in the editable dev environment: `hatch run bing-rewards --help`

  Feel free to open a PR with additional features or fixes.


pkgId: 50260dc39518ce0f2d7559543d6ca6b2d8b99342fad7f4994f78ed8a1ef372a8
name: python-bingo-game
arch: src
version: 1.0.0
epoch: 0
release: 1
summary: A simple Bingo game to play in the terminal.
url: https://github.com/AromalShaji/python-bingo
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bingo-game-1.0.0-1.src.rpm
checksum_type: sha256
description:
  A terminal-based Bingo game.


  ```bash
     pip install bingo-game
  ```


  Welcome to the Bingo Game! This is a simple console-based game where players can enjoy a classic game of Bingo. The game is implemented in Python and utilizes the Colorama library for colorizing the console output.


  1. **Setup**:
     - Ensure you have Python 3 installed on your system.
     - Install the Colorama library by running `pip install colorama`.
  2. **Running the Game**:
     - Clone this repository to your local machine.
     - Open a terminal or command prompt and navigate to the directory containing the cloned repository.
     - Run the command `python bingo_game.py`.
  3. **Gameplay**:
     - Enter the number of users playing the game.
     - Players take turns marking numbers on their Bingo cards until a player achieves Bingo.
     - The game will indicate when a player wins, and you can choose to play again or exit.


  ![Bingo Game Screenshot](https://github.com/AromalShaji/PythonBingo/blob/main/Image.PNG)


  - `bingo_game.py`: The main Python script containing the Bingo game implementation.
  - `README.md`: This file, providing instructions and information about the game.


  - Python: Programming language used for implementing the game logic.
  - Colorama: Python library used for adding color to the console output.


  - [AromalShaji](https://github.com/AromalShaji): Developer of the game.


  This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.


pkgId: 9f8cb283c98f31418c1c5341ed3ee6e052b4e9d1edf6d792bf9b14f90a98b8ed
name: python-binho-host-adapter
arch: src
version: 0.1.6
epoch: 0
release: 1
summary: Python Libraries for Binho Multi-Protocol USB Host Adapters
url: https://binho.io
rpm_license: MIT License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binho-host-adapter-0.1.6-1.src.rpm
checksum_type: sha256
description:
  Cross-platform Python 3.x library for automated control of Binho Multi-Protocol USB Host Adapters. More information about the device can be found at https://www.binho.io



  This library requires [pySerial](https://github.com/pyserial/pyserial) for cross-platform access of the serial port. This library will be installed automatically by pip, however for easy reference, this can be manually installed simply by running the following command:
  ```
  pip install pyserial
  ```



  This library can be installed easily with the following command:
  ```
  pip install binho-host-adapter
  ```



  ```python
  from binhoHostAdapter import binhoHostAdapter
  from binhoHostAdapter import binhoUtilities

  print("Demo Script with Binho Host Adapter Python Libraries")
  print

  devices = binhoUtilities.listAvailableDevices()

  if len(devices) == 0:
  	print("No Devices Found!")
  	exit()

  elif len(devices) == 1:
  	COMPORT = devices[0]
  	print("Found 1 attached adapter @ " + devices[0])
  	print
  else:
  	COMPORT = devices[0]
  	print("Found more than 1 attached adapter, using first device found on " + COMPORT)
  	print

  print("Opening " + COMPORT + "...")
  print


  binho = binhoHostAdapter.binhoHostAdapter(COMPORT)

  print("Connecting to host adapter...")
  print(binho.getDeviceID())
  print
  ```


  The full set of documentation for this python library can be found at https://support.binho.io/python-libraries


pkgId: 85d4d469fc12178a44b92876752b43c2ecdcd4c72fbc334b701748734ac72ec1
name: python-binomial-cis
arch: src
version: 0.0.11
epoch: 0
release: 1
summary: Confidence intervals for binomial distributions.
url: https://pypi.org/project/binomial-cis/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binomial-cis-0.0.11-1.src.rpm
checksum_type: sha256
description:
  This package computes confidence intervals for the probability of success parameter, $p$, of a binomial distribution.
  The confidence intervals computed by this package cover $p$ with exactly the user-specified probability and have minimal excess length.


  The documentation for the package can be found at: [https://tri-ml.github.io/binomial_cis](https://tri-ml.github.io/binomial_cis).



  Install the package with pip:
  ```
  pip install binomial_cis
  ```



  This package constructs optimal confidence intervals for the probability of success parameter, $p$, of a binomial distribution.


  Given user specified miscoverage rate ($\alpha$) and maximum expected shortage ($\text{MES}$), return a lower bound on $p$ that satisfies the following requirements:
  1. achieves exact desired coverage: $\mathbb{P}[\underline{p} \le p] = 1-\alpha$,
  2. $[\underline{p}, 1]$ is uniformly most accurate,
  3. achieves exact desired maximum expected shortage: $\max_p \ \mathbb{E}_p[\max (p - \underline{p}, 0)] = \text{MES}$,
  4. uses the minimum number of samples $n$ to achieve requirements 1,2,3.



  Given user specified miscoverage rate ($\alpha$) and maximum expected excess ($\text{MEE}$), return an upper bound on $p$ that satisfies the following requirements:
  1. achieves exact desired coverage: $\mathbb{P}[p \le \overline{p}] = 1-\alpha$,
  2. $[0, \overline{p}]$ is uniformly most accurate,
  3. achieves exact desired maximum expected excess: $\max_p \ \mathbb{E}_p[\max (\overline{p} - p, 0)] = \text{MEE}$,
  4. uses the minimum number of samples $n$ to achieve requirements 1,2,3.





  Given the user specified miscoverage rate ($\alpha$) and maximum expected width ($\text{MEW}$), return simultaneous lower and upper bounds on $p$ that satisfy the following requirements:
  1. achieves exact desired coverage: $\mathbb{P}[\underline{p} \le p \le \overline{p}] = 1-\alpha$,
  2. $[\underline{p}, \overline{p}]$ is uniformly most accurate unbiased,
  3. achieves exact desired maximum expected width: $\max_p \ \mathbb{E}_p[\overline{p} - \underline{p}] = \text{MEW}$,
  4. uses the minimum number of samples $n$ to achieve requirements 1,2,3.





  Find a lower bound on $p$:
  ```
  from binomial_cis import binom_ci

  k = 5 # number of successes
  n = 10 # number of trials
  alpha = 0.05 # miscoverage probability

  lb = binom_ci(k, n, alpha, 'lb')

  ```

  Find maximum expected shortage given miscoverage rate and number of samples:
  ```
  from binomial_cis import max_expected_shortage

  mes_ub, mes_lb, p_lb, num_iters = max_expected_shortage(alpha, n, tol=1e-3)
  ```




  Find an upper bound on $p$:
  ```
  from binomial_cis import binom_ci

  k = 5 # number of successes
  n = 10 # number of trials
  alpha = 0.05 # miscoverage probability

  ub = binom_ci(k, n, alpha, 'ub')

  ```

  Find maximum expected excess given miscoverage rate and number of samples:
  ```
  from binomial_cis import max_expected_excess

  mee_ub, mee_lb, p_lb, num_iters = max_expected_excess(alpha, n, tol=1e-3)
  ```




  Find simultaneous lower and upper bounds on $p$:
  ```
  from binomial_cis import binom_ci

  k = 5 # number of successes
  n = 10 # number of trials
  alpha = 0.05 # miscoverage probability

  lb, ub = binom_ci(k, n, alpha, 'lb,ub')

  ```

  Find maximum expected width given miscoverage rate and number of samples:
  ```
  from binomial_cis import max_expected_width

  mew_ub, mew_lb, p_lb, num_iters = max_expected_width(alpha, n, tol=1e-3)
  ```


pkgId: 8e4430f81d7d8a7b65d282f302d49a5c490ede8e7300dc5da483a9a2c669f3ad
name: python-binpacking
arch: src
version: 1.5.2
epoch: 0
release: 1
url: https://www.github.com/benmaier/binpacking
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binpacking-1.5.2-1.src.rpm
checksum_type: sha256
summary:
  Heuristic distribution of weighted items to bins (either a fixed number of bins or a fixed number of volume per bin). Data may be in form of list, dictionary, list of tuples or csv-file.

description:
  This package contains greedy algorithms to solve two typical bin packing
  problems, (i) sorting items into a constant number of bins, (ii) sorting
  items into a low number of bins of constant size. Here's a usage example
      >>> import binpacking
      >>>
      >>> b = { 'a': 10, 'b': 10, 'c':11, 'd':1, 'e': 2,'f':7 }
      >>> bins = binpacking.to_constant_bin_number(b,4) # 4 being the number of bins


pkgId: 6eb537e70f0a31b9c2f97053a4a296f6acb49429cb012d9b972e144698f7564b
name: python-binpan
arch: src
version: 0.8.13
epoch: 0
release: 1
summary: Binance API wrapper with backtesting tools.
url: https://github.com/nand0san/binpan_studio
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binpan-0.8.13-1.src.rpm
checksum_type: sha256
description:
  BinPan is a Python wrapper for Binance API. Useful creating objects with many capabilities in data analysis.
  BinPan can show plots easily and fetch API requests into the same object. It can also obtain some technical indicators.
  The target of this module is to have a fast tool for collecting and handling data from the Binance API easily.
  It is intended to be useful in Jupyter Notebooks or even the python console, but it can be used in
  many other ways.
  BinPan manages symbol objects that can do:
  - get candles with time zone and indexing options.
  - get trades.
  - calculate technical indicators.
  - plot candles, histograms, indicators, etc in a very simple and beautiful way.
  - check applied fees.
  - review Exchange policies.
  An example of a plot for candles and indicators:
  ![](https://raw.githubusercontent.com/nand0san/binpan_studio/main/docs/images/candles.png)
  > BinPan contains no binance **order method, withdraw method** or any dangerous command.
  >
  > If you decide to add API keys for using some account methods, BinPan will encrypt it in a file, and in memory,
  but it is better not enabling trading capability on the Binance API key configuration, just for your own peace of mind.
  >
  > You will be asked for the API key and secret the first time you need it. It will be encrypted and stored in a file.
  >
  > Be careful out there!
  Hope you find it useful breaking the market!!!


pkgId: f8f8d2741554537055412d794a6338ee0207895aca589a09469b4177bd3bb7fb
name: python-binpi
arch: src
version: 0.1.20
epoch: 0
release: 1
summary: Simple library for de/serializing binary data
url: https://github.com/romansvozil/binpi
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binpi-0.1.20-1.src.rpm
checksum_type: sha256
description:
  binpi aims to provide a simple interface for serializing and deserializing binary file formats.



  ```python
  import binpi

  class Data:
      ...

  class FileHeader:
      prop1 = binpi.Int()
      prop2 = binpi.Short()
      prop3 = binpi.Byte()
      is_compressed = binpi.Boolean()
      float_prop = binpi.Float()
      some_data = binpi.ByteArray(size="prop1")
      other_data = binpi.List(Data, size="prop3")
      sub_struct = binpi.WrapType(Data)
      children_count = binpi.Int()
      children = binpi.List(binpi.RecursiveType(), size="children_count")


  header_data = binpi.deserialize(FileHeader, binpi.FileReader("./some_path"), endianness=binpi.LITTLE_ENDIAN)


  header_data.prop2 = 200


  writer = binpi.serialize(header_data, binpi.FileWriter("./another_path"), endianness=binpi.LITTLE_ENDIAN)
  ```

  _For more complex examples, check `./examples/`_



  ```bash
  pip install binpi
  ```



  - Int, UInt, Short, UShort, Byte, UByte, Float, Double
  - IntEnumType
  - List, String, ByteArray
  - Boolean
  - RecursiveType (for cases where the structure contains list of substructures of the same type, check the `advanced_structure` example)
  - WrapType (for subtypes, check the `simple_image_archive_format` example)
  - All the types above support LE/BE


  - `pickle` - should be used for completely different use-cases than `binpi`, which is just simple deserializing of python objects, without having to care about its structure.
  - `struct` - anything `binpi` does can be implemented using `struct`, but `binpi` provides simpler interface for defining data structure, for the cost of performance.
  - `origami` - origami might be a better choice for (de)serializing fixed size data, but it doesn't provide (de)serializing of dynamically sized data, out of the box.
  - `bstruct` - same as `origami`
  - `construct` - probably the most comparable library to `binpi`, has even more feature, but instead of `binpi`, the data structures and output is represented using dictionaries




  ```python
  def serialize(
      value,           # value to be serialized
      writer: Writer,  # the output writer
      first=None,      # first field to serialize
      last=None        # last field to serialize
  ) -> None: ...

  class Writer(Protocol):
      """ writer can be anything that implements method write_bytes """
      def write_bytes(self, data: bytes) -> None: ...
  ```

  binpi contains `FileWriter` and `BufferWriter`


  ```python
  def deserialize(
      class_: type,    # type of the target object
      reader: Reader,  # the input reader
      first=None,      # first field to serialize
      last=None        # last field to serialize
  ) -> None: ...

  class Reader(Protocol):
      """ reader can be anything that implements method read_bytes """
      def read_bytes(self, n: int) -> bytes: ...
  ```

  binpi contains `FileReader` and `BufferReader`



  To create your own custom (de)serializable type, you have to just create a new child class of `SerializableType` that implements `load_from_bytes` and `write_from_value`

  ```python
  import typing, binpi, struct

  class CustomDoubledInt(binpi.SerializableType):
      def load_from_bytes(self, deserializer: binpi.Deserializer, instance, *args, **kwargs):
          return struct.unpack("<i", deserializer.reader.read_bytes(4))[0] * 2

      def write_from_value(self, serializer: binpi.Serializer, value, parent_instance, *args, **kwargs):
          serializer.writer.write_bytes(struct.pack("<i", value // 2))

  """ In case we want to have functional typechecking """
  CustomDoubleInt: typing.Callable[..., int]
  ```



  - Tests
  - Performance benchmarks


pkgId: f833d68930b8f7630f0c991c8574dbf0019aee05a23df01a6941fda2b0216d24
name: python-binsreg
arch: src
version: 2.1.5
epoch: 0
release: 1
url: https://github.com/nppackages/binsreg
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binsreg-2.1.5-1.src.rpm
checksum_type: sha256
summary:
  Implements binscatter methods, including partition selection, point estimation, pointwise and uniform inference methods, and graphical procedures.

description:
  Binscatter provides a flexible, yet parsimonious way of visualizing and summarizing large data sets and has been a popular methodology in applied microeconomics and other social sciences. The `binsreg` package provides tools for statistical analysis using the binscatter methods developed in Cattaneo, Crump, Farrell and Feng (2022). binsreg implements binscatter least squares regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band. `binsqreg` implements binscatter quantile regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform conf idence band. `binsglm` implements binscatter generalized linear regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band. `binstest` implements binscatter-based hypothesis testing procedures for parametric specifications of and shape restrictions on the unknown function of interest. `binspwc` implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. `binsregselect` implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, and clustering, among other features.




  Matias D. Cattaneo (<cattaneo@princeton.edu>)

  Richard K. Crump (<richard.crump@ny.frb.org>)

  Max H. Farrell (<maxhfarrell@ucsb.edu>)

  Yingjie Feng (<fengyingjiepku@gmail.com>)

  Ricardo Masini (<rmasini@princeton.edu>)




  https://nppackages.github.io/binsreg/



  This package was first released in Winter 2019, and had one major upgrade in Summer 2021.

  Summer 2021 new features include: (i) generalized linear models (logit, Probit, etc.) binscatter; (ii) quantile regression binscatter; (iii) new generic specification and shape restriction hypothesis testing function (now including Lp metrics); (iv) multi-group comparison of binscatter estimators; (v) generic point evaluation of covariate-adjusted binscatter; (vi) speed improvements and optimization. A complete list of upgrades can be found [here](https://nppackages.github.io/binsreg/binsreg_upgrades.txt).




  To install/update use pip
  ```
  pip install binsreg
  ```


  ```
  from binsreg import binsregselect, binsreg, binsqreg, binsglm, binstest, binspwc
  ```

  - Replication: [binsreg illustration](https://github.com/nppackages/binsreg/blob/master/Python/binsreg_illustration.py), [simulated data](https://github.com/nppackages/binsreg/blob/master/Python/binsreg_sim.csv).




  - numpy
  - pandas
  - scipy
  - statsmodel
  - plotnine



  For overviews and introductions, see [NP Packages website](https://nppackages.github.io/).



  - Cattaneo, Crump, Farrell and Feng (2023c): [Binscatter Regressions](https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2023_Stata.pdf).<br>
  Working paper, prepared for Stata Journal.



  - Cattaneo, Crump, Farrell and Feng (2023a): [On Binscatter](https://mdcattaneo.github.io/papers/Cattaneo-Crump-Farrell-Feng_2023_AER.pdf).<br>
  Working paper.

  - Cattaneo, Crump, Farrell and Feng (2023b): [Nonlinear Binscatter Methods](https://mdcattaneo.github.io/papers/Cattaneo-Crump-Farrell-Feng_2023_NonlinearBinscatter.pdf).<br>
  Working paper.


pkgId: 8a438052611b2e41ca12d487f8ac712a008a7855b1ffec004cda92145015b181
name: python-binsync
arch: src
version: 5.1.0
epoch: 0
release: 1
summary: A Collaboration framework for binary analysis tasks.
url: https://github.com/angr/binsync
rpm_license: BSD 2 Clause
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binsync-5.1.0-1.src.rpm
checksum_type: sha256
description:
  <p align="center">
     <img src="https://i.imgur.com/qdesKpg.png" style="width: 30%;" alt="BinSync Logo"/>
  </p>

  BinSync is a decompiler collaboration tool built on the Git versioning system to enable fined-grained reverse
  engineering collaboration regardless of decompiler. BinSync is built by [mahaloz](https://github.com/mahaloz),
  the [angr](https://angr.io) team, and the [SEFCOM](https://sefcom.asu.edu) research lab. It's also due
  in large part to its use by the [Shellphish](https://shellphish.net) hacking team.

  All good decompilers share common objects called Reverse Engineering Artifacts (REAs). These REAs are the
  center of BinSync's syncing ability. Here are the supported REAs:
  - Function headers (symbol, args, type)
  - Stack Variables (symbol, type)
  - Structs
  - Enums
  - Comments

  Note: all types support user-created types like structs.

  <p align="center">
     <img src="./assets/images/binja_sync.gif" alt="Binja Sync"/>
  </p>

  **Join our discord below for more online help**:

  [![Discord](https://img.shields.io/discord/900841083532087347?label=Discord&style=plastic)](https://discord.gg/wZSCeXnEvR)


  For all installs, it's best to use our two-part installer with Python:
  ```bash
  pip3 install binsync && binsync --install
  ```

  BinSync is also available in the Binary Ninja plugin manager.
  **If you plan on using BS with Ghidra**, you need to also do `pip3 install binsync[ghidra]`.
  If you plan on using extra BS features, like the ChatGPT User, install the package like this: `pip3 install binsync[extras]`, which will install extra packages for support.
  For full install information, please read our [quickstart docs](https://binsync.net/docs/home).

  If you are planning on installing the package to be editable (`-e`), like in a developer install, make sure `pip>=23.0.0`.


  - IDA Pro: **>= 8.4** (if you have an older version, use `v4.10.1`)
  - Binary Ninja: **>= 2.4**
  - angr-management: **>= 9.0**
  - Ghidra: **>= 10.1**

  All versions require **Python >= 3.10** and **Git** installed on your system.
  Ghidra support is still very much in early stage, so only expect the minimal features like artifact name syncing and comments.



  Although we support the decompilers in the earlier section, not every decompiler is supported at the same level of syncing.
  To understand the difference between artifact support, pull, push, and auto push, read our [decompiler use introduction](https://binsync.net/docs/dec-introduction/).



  | Operations&nbsp;&nbsp;&nbsp;&nbsp; | Function Headers&nbsp;&nbsp;&nbsp;&nbsp; | Stack Vars&nbsp;&nbsp;&nbsp;&nbsp; | Global Vars&nbsp;&nbsp;&nbsp;&nbsp; | Structs&nbsp;&nbsp;&nbsp;&nbsp; | Enums&nbsp;&nbsp;&nbsp;&nbsp; | Comments&nbsp;&nbsp;&nbsp;&nbsp; |
  |------------------------------------|------------------------------------------|------------------------------------|-------------------------------------|---------------------------------|-------------------------------|----------------------------------|
  | Symbols   	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 	                | :white_check_mark: 	            | :white_check_mark: 	          | :white_check_mark: 	             |
  | Types     	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 	                | :white_check_mark: 	            | :white_check_mark: 	          | :white_check_mark: 	             |
  | Pull      	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 	                | :white_check_mark: 	            | :white_check_mark: 	          | :white_check_mark: 	             |
  | Push      	                        | :white_check_mark: 	                     | :white_check_mark: 	               | :white_check_mark: 	                | :white_check_mark: 	            | :white_check_mark: 	          | :white_check_mark: 	             |
  | Auto Push                          | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 	                | :white_check_mark: 	            | :white_check_mark: 	          | :white_check_mark: 	             |

  <p align="center">
     <img src="./assets/images/ida_sync.gif" alt="IDA Sync"/>
  </p>




  | Operations&nbsp;&nbsp;&nbsp;&nbsp; | Function Headers&nbsp;&nbsp;&nbsp;&nbsp; | Stack Vars&nbsp;&nbsp;&nbsp;&nbsp; | Global Vars&nbsp;&nbsp;&nbsp;&nbsp; | Structs&nbsp;&nbsp;&nbsp;&nbsp; | Enums&nbsp;&nbsp;&nbsp;&nbsp; | Comments&nbsp;&nbsp;&nbsp;&nbsp; |
  |------------------------------------|------------------------------------------|------------------------------------|-------------------------------------|---------------------------------|-------------------------------|----------------------------------|
  | Symbols   	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 					            | :white_check_mark:   					      | :white_check_mark: 					      | :white_check_mark: 	             |
  | Types     	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 					            | :white_check_mark:   					      | :white_check_mark: 					      | :white_check_mark: 	             |
  | Pull      	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 					            | :white_check_mark:   					      | :white_check_mark: 					      | :white_check_mark: 	             |
  | Push      	                        | :white_check_mark:                       | :white_check_mark:		               | :white_check_mark:					             | :white_check_mark:			           | :white_check_mark: 					      | :white_check_mark: 					         |
  | Auto Push 	                        | :white_check_mark:                       | :white_check_mark:		               | :white_check_mark:					             | :white_check_mark:			           | :white_check_mark: 					      | :white_check_mark: 					         |

  <p align="center">
     <img src="./assets/images/binja_sync.gif" alt="Binja Sync"/>
  </p>



  | Operations&nbsp;&nbsp;&nbsp;&nbsp; | Function Headers&nbsp;&nbsp;&nbsp;&nbsp; | Stack Vars&nbsp;&nbsp;&nbsp;&nbsp; | Global Vars&nbsp;&nbsp;&nbsp;&nbsp; | Structs&nbsp;&nbsp;&nbsp;&nbsp; | Enums&nbsp;&nbsp;&nbsp;&nbsp; | Comments&nbsp;&nbsp;&nbsp;&nbsp; |
  |------------------------------------|------------------------------------------|------------------------------------|-------------------------------------|---------------------------------|-------------------------------|----------------------------------|
  | Symbols   	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 					            | :white_check_mark: 	 					      | :white_check_mark: 					      | :white_check_mark: 	             |
  | Types     	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark: 					            | :white_check_mark: 	 					      | :white_check_mark: 					      | :white_check_mark: 	             |
  | Pull      	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :white_check_mark:					             | :white_check_mark: 					        | :white_check_mark: 					      | :white_check_mark: 	             |
  | Push      	                        | :white_check_mark: 					                 | :white_check_mark:						           | :white_check_mark:				              | :white_check_mark: 						       | :white_check_mark: 					      | :x: 					                        |
  | Auto Push 	                        | :white_check_mark:                       | :white_check_mark:		               | :white_check_mark:					             | :white_check_mark:			           | :white_check_mark: 					      | :x: 					                        |

  <p align="center">
     <img src="./assets/images/ghidra_sync.gif" alt="Ghidra Sync"/>
  </p>



  | Operations&nbsp;&nbsp;&nbsp;&nbsp; | Function Headers&nbsp;&nbsp;&nbsp;&nbsp; | Stack Vars&nbsp;&nbsp;&nbsp;&nbsp; | Global Vars&nbsp;&nbsp;&nbsp;&nbsp; | Structs&nbsp;&nbsp;&nbsp;&nbsp; | Enums&nbsp;&nbsp;&nbsp;&nbsp; | Comments&nbsp;&nbsp;&nbsp;&nbsp; |
  |------------------------------------|------------------------------------------|------------------------------------|-------------------------------------|---------------------------------|-------------------------------|----------------------------------|
  | Symbols   	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :x: 					                           | :x: 					                       | :x: 					                     | :white_check_mark: 	             |
  | Types     	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :x: 					                           | :x: 					                       | :x: 					                     | :white_check_mark: 	             |
  | Pull      	                        | :white_check_mark: 	                     | :white_check_mark:    	            | :x: 					                           | :x: 					                       | :x: 					                     | :white_check_mark: 	             |
  | Push      	                        | :white_check_mark:                       | :white_check_mark:		               | :x:					                            | :x:					                        | :x: 					                     | :white_check_mark: 					         |
  | Auto Push 	                        | :white_check_mark:                       | :white_check_mark:		               | :x:					                            | :x:					                        | :x: 					                     | :white_check_mark: 					         |


  For scripting please see [Lib BinSync](https://github.com/binsync/libbs), which allows you to do all lifting and data manipulation in Python.


pkgId: 2bcdab719510a8dafeb28b8caa6748bdae6dac59907e5ba53e6f4b9ab2f99cd9
name: python-binubuo
arch: src
version: 0.2.1
epoch: 0
release: 1
summary: Client package for Binubuo synthetic data generator
url: https://binubuo.com
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binubuo-0.2.1-1.src.rpm
checksum_type: sha256
description:
  Binubuo python library is a powerful client to the Binubuo Synthetic Data API from [**https://binubuo.com**](https://binubuo.com/ords/r/binubuo_ui/binubuo). The API can be used to create dynamic an realistic looking synthetic data that can be used very easily for functional and performance testing as well as validating database design. One major strength of the Binubuo client, is that it integrates tightly with your database, and can use your existing design and data to create new synthetic data. This means that you can very quickly create test data from production data, that can be used by everyone in your organisation without worrying about data protection and other legal or regulatory requirements. The data will look like your production data, except it will be randomly created.

  **Highlighted Features**

  - 150+ Reeal life data generators
  - Pre made dataset creators for very quick prototyping
  - Postgres and Oracle integration
  - Create synthetic dataset creators directly from your table definition



  Binubuo is available and installable from PyPI:

      $ python -m pip install binubuo

  Binubuo officially supports Python 2.7 & 3.6+.



  You can register an account on the website; just click the "Sign up" link in the menu bar or click the "Create account now!" button on the front page, or you can create an account from the client itself. Just run the ```create_account``` function and supply an account name and email for the registration.:

  ```python
  >>> from binubuo import binubuo
  >>> b = binubuo()
  >>> b.create_account("my_account_name", "my_email@example.com")
  Please go to this url in your browser: https://binubuo.com/ords/r/binubuo_ui/binubuo/challenge-response?p_sha=1311FE507E555130581C2E70000
  Please input the code displayed in the above URL: 468039
  Your API key for Binubuo is (please save it.): 2C48D11E2BCE3731DD0E22E8EEA584A6F756BED5
  >>> b.key("2C48D11E2BCE3731DD0E22E8EEA584A6F756BED5")
  ```

  That is it. You are now ready to use the Binubuo client.




  Client documentation [Available here](https://binubuo.com/ords/r/binubuo_ui/binubuo/binubuo-documentation-page?p23_page_name=Python&p23_section_id=240&p23_section_name=Clients)

  Binubuo includes more than 150+ real life data generators: [Data Generators Documentation and API Reference](https://binubuo.com/ords/r/binubuo_ui/binubuo/binubuo-documentation-section?p22_section_name=Reference)



  You can go ahead and use the client without an account using a temporary access key, although your data rate and ability to store settings and datasets are limited. Simply start the client and start generating data:

  ```python
  >>> from binubuo import binubuo
  >>> b = binubuo()
  >>> print(b.generate('person', 'full_name'))
  Andrew Howard
  >>> print(b.generate('finance', 'bank_account_id'))
  GE02Pu5783332775138823
  >>> print(b.generate('consumer', 'food_item'))
  Pumpkin Spice, Atlantic Salmon, 2 Bonless Fillets
  >>> print(b.generate('time', 'date'))
  1945-02-10T17:37:09Z
  ```



  One of the strengths of **Binubuo** is that you can use your existing tables and data to very quickly create synthetic data creators, enabling you to create the perfect test data that matches your real data, both the way it looks (data domain) but equally important, also the way the data is distributed and formatted.

  Databases currently supported:

  - **Oracle** (cx_Oracle library required)
  - **Postgres** (psycopg2 library required)
  - **Yugabyte** (psycopg2 library required)

  Here is a quick postgres example, that will create a copy of the sample schema table customer, called customer_copy with 100 rows of synthetic data:

  ```python
  >>> from binubuo import binubuoPostgres
  >>> bo = binubuoPostgres('394129D632B0A4E9913E491286EB428DD80CEFF0', 'binu', 'postgres', 'mypassword')
  >>> bo.copy_table(source_table='customer', target_table='customer_copy', copy_method='dataset', drop_target_if_exist=True, alternate_dataset_name=False, use_comments=False, use_infer=True, use_sample_data=True, data_rows=100)
  ```

  That is how quickly you can create test data. For detailed documentation and reference commands, take a look at the [**docs online**](https://binubuo.com/ords/r/binubuo_ui/binubuo/binubuo-documentation)


pkgId: 7e89376b922b322d4183117d267439d09524f4a30d38a2b5ffbdd62ccd35c04e
name: python-binx-og-image-generator
arch: src
version: 1.2.6
epoch: 0
release: 1
summary: generates og image for xebia.com and binx.io blog
url: https://github.com/binxio/og-image-generator
rpm_license: restricted
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binx-og-image-generator-1.2.6-1.src.rpm
checksum_type: sha256
description:
  The binx-og-image-generator is a tool to generate images for your blog.

  ```
  Usage: binx-og-image-generator [OPTIONS] IMAGE

    generate an og image for blog

  Options:
    --title TEXT     of the blog  [required]
    --subtitle TEXT  of the blog  [required]
    --author TEXT    of the blog  [required]
    --output TEXT    filename of the image  [required]
    --help           Show this message and exit.
  ```


  To generate an images, type:

  ```console
  docker run --rm \
      -v $PWD:$PWD \
      -w $PWD \
      ghcr.io/binxio/og-image-generator \
      --title "foo" \
      --subtitle "bar" \
      --author "binx consultant" \
      ./banner.jpg
  ```

  The generated image can be found in your current directory and is named `og-banner.jpg`.


  If you which to install the binx-og-image-generator on your host machine follow these steps.


  - Python 3.9
  - Pipenv



  - pip install pipenv
  - pipenv shell
  - pipenv install
  - binx-og-image-generator --title "foo" --subtitle "bar" --author "binx consultant" banner.jpg


pkgId: b13434ba48d4ef5655f60a4ef12c1f7372df8e4bad1ce4a5a48b6abfcce9ef5c
name: python-binypt-lib
arch: src
version: 2.3.7
epoch: 0
release: 1
summary: Library to download data charts from Binance
url: https://github.com/Tejaromalius/Binypt-lib
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-binypt-lib-2.3.7-1.src.rpm
checksum_type: sha256
description:
  [![Generated by ChatGPT](https://img.shields.io/badge/Generated%20by-ChatGPT-45b39d.svg)](https://chat.openai.com/)


pkgId: bfa93112edc124f278f02af967da868d27c76ae6920ebbe15123857bf4a3a4bc
name: python-bio-arc
arch: src
version: 0.1.1
epoch: 0
release: 1
summary: Antigen Receptor Classifier
url: https://github.com/iedb/arc
rpm_license: UNKNOWN
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio-arc-0.1.1-1.src.rpm
checksum_type: sha256
description:
  - Linux OS
  - [HMMER3](http://hmmer.org/)
  - NCBI Blast+
  - Python 3+
    - Python packages: Pandas, BioPython


  We provide a Dockerfile for ease of use.

  ARC can also be downloaded through PyPI using the following pip command.
  ```shell
  pip install bio-arc
  ```


  A quick check for proper dependencies and successful installation can be performed by navigating to your pip package install directory (which can be located by executing ```pip show bio-arc```) and running the following command:
  ```shell
  python3 -m arc_test
  ```
  Passing all unit-tests means that your system is configured properly and ready to classify some protein sequences.



  -  A fasta format file with one or more protein sequences.
    ```
    >1WBZ_A_alpha I H2-Kb
  MVPCTLLLLLAAALAPTQTRAGPHSLRYFVTAVSRPGLGEPRYMEVGYVDDTEFVRFDSDAENPRYEPRARWMEQEGPEYWERETQKAKGNEQSFRVDLRTLLGYYNQSKGGSHTIQVISGCEVGSDGRLLRGYQQYAYDGCDYIALNEDLKTWTAADMAALITKHKWEQAGEAERLRAYLEGTCVEWLRRYLKNGNATLLRTDSPKAHVTHHSRPEDKVTLRCWALGFYPADITLTWQLNGEELIQDMELVETRPAGDGTFQKWASVVVPLGKEQYYTCHVYHQGLPEPLTLRWEPPPSTVSNMATVAVLVVLGAAIVTGAVVAFVMKMRRRNTGGKGGDYALAPGSQTSDLSLPDCKVMVHDPHSLA
  >1WBZ_B_b2m I H2-Kb
  MARSVTLVFLVLVSLTGLYAIQKTPQIQVYSRHPPENGKPNILNCYVTQFHPPHIEIQMLKNGKKIPKVEMSDMSFSKDWSFYILAHTEFTPTETDTYACRVKHASMAEPKTVYWDRDM
    ```




  -  Using Fasta file as an input:
  ```shell
  python -m ARC classify -i /path/to/input.fasta -o /path/to/output.csv
  ```

  -  Output file has 4 columns in CSV format.
  -  First column named 'ID' is the description provoded in the fasta for each sequence.
  -  Second column named 'class' is the assigned molecule class for each sequence.
     -  e.g. MHC-I, MHC-II, BCR or TCR.
  -  The third column named 'chain_type' is the assigned chain type for each sequence.
     -  e.g. alpha, beta, heavy, lambda, kappa, scFv, TscFv or construct. These will also be labelled as V for variable domain or C for constant domain.
  -  The fourth column named 'calc_mhc_allele' is the MHC allele identified using groove domain similarity to MRO alleles.

  | ID	                                  | class  | chain_type | calc_mhc_allele|
  |---------------------------------------- |------- |----------- |---------------|
  | 1WBY_A_alpha I H2-Db                    |	MHC-I  | alpha V     | |
  | 1WBY_B_b2m I H2-Db	                  |	       |            | |
  | 1HQR_A_alpha II HLA-DRA*01:01/DRB5*01:01|	MHC-II | alpha C     | HLA-DRA*01:01 |
  | 1HQR_B_beta II HLA-DRA*01:01/DRB5*01:01 |	MHC-II | beta C     | HLA-DRB5*01:01 |
  | 2CMR_H_heavy                            |	BCR	   | heavy V      | |
  | 2CMR_L_light                            |	BCR	   | kappa C     | |
  | 4RFO_L_light                            |	BCR	   | lambda V    | |
  | 3UZE_A_heavy                            |	BCR	   | scFv       | |
  | 1FYT_D_alpha                            |	TCR	   | alpha V     | |
  | 1FYT_E_beta                             | TCR	   | beta C      | |
  | 3TF7_C_alpha                            |	TCR    | TscFv      | |


  - BCR and TCR chains are identified using HMMs. A given protein sequence is searched against HMMs built using BCR and TCR chain sequences from IMGT. HMMER is used to align an input sequence to the HMMs.
  - MHC class I (alpha1-alpha2 domains) and MHC class I alpha and beta chain HMMs are downloaded from Pfam website. An input protein sequence is searched against these HMMs. A HMMER bit score threshold of 25 was used to identify MHC chain sequences.
  - To identify MHC alleles, groove domains (G-domains) are assigned based on the MRO repository.
  - IgNAR sequences are identified through querying against a custom blast database.


  Several methods for HMMER result parsing were sourced from ANARCI.

  [Dunbar J and Deane CM. ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics (2016)](https://academic.oup.com/bioinformatics/article/32/2/298/1743894)


pkgId: 5c4fbdc429dc2db79e48bb2c89746342ac9e4feb6e783c84ad48a532bc03f844
name: python-bio-compose
arch: src
version: 0.7.2
epoch: 0
release: 1
summary: Create, execute, and introspect reproducible composite simulations of dynamic biological systems.
url: https://pypi.org/project/bio-compose/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio-compose-0.7.2-1.src.rpm
checksum_type: sha256
description:
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bVtTmbMLnfFBv44rPwmBFsHvpX7woDWn?usp=sharing)
  ![GitHub CI](https://github.com/biosimulators/bio-compose/actions/workflows/ci.yaml/badge.svg)
  ![GitHub CD](https://github.com/biosimulators/bio-compose/actions/workflows/cd.yaml/badge.svg)
  [![Documentation](https://readthedocs.org/projects/bio-compose/badge/?version=latest)](https://bio-compose.readthedocs.io/en/latest/)






  The complete `BioCompose` documentation can be found here: https://bio-compose.readthedocs.io/en/latest/





  The primary method of user-facing interaction for this service is done through the use of a high-level "notebook" api called `bio_check`.
  A convenient notebook demonstrating the functionality of this service is hosted on Google Colab and can be accessed by clicking the above "Open In Colab" badge.

  Installation of this tooling can be performed using PyPI as such:

  ```bash
  pip install bio-compose
  ```





  A convienient template notebook for converting the outputs of Smoldyn simulations to Simularium trajectories can be
  [found here.](https://colab.research.google.com/drive/17uMMRq3L3KqRIXnezahM6TtOtJYK8Cu6#scrollTo=6n5Wf58hthFm)


pkgId: 3e859fbbdf60336f50013b1e949e9fcb9f8e35de22b156dc1c91fea4b411fa22
name: python-bio-curve-fit
arch: src
version: 0.2.2
epoch: 0
release: 1
summary: Curve fitting algorithms for bio-assays with scikit-learn api
url: https://pypi.org/project/bio-curve-fit/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio-curve-fit-0.2.2-1.src.rpm
checksum_type: sha256
description:
  A Python package for fitting common dose-response and standard curve models. Designed to follow the [scikit-learn](https://scikit-learn.org/stable/) api.





  ```shell
  pip install bio-curve-fit
  ```


  We recommend using python [virtual environments](https://docs.python.org/3/library/venv.html) to manage your python packages in an isolated environment. Example:

  ```shell
  python -m venv venvname
  source venvname/bin/activate
  ```



  ```python
  from bio_curve_fit.logistic import FourPLLogistic


  model = FourPLLogistic()


  standard_concentrations = [1, 2, 3, 4, 5]
  standard_responses = [0.5, 0.55, 0.9, 1.25, 1.55]



  model.fit(
  	standard_concentrations,
  	standard_responses,
  )


  model.predict([1.5, 2.5])


  model.predict_inverse([0.1, 1.0])

  ```

  Calculate and plot the curve and limits of detection:

  ```python
  plot_standard_curve(standard_concentrations, standard_responses, model, show_plot=True)
  ```

  ![standard curve](./examples/readme_fit.png)

  You can also customize the plot arbitrarily using matplotlib. For example, adding labels to the points:

  ```python
  from adjustText import adjust_text

  fig, ax = plot_standard_curve_figure(standard_concentrations, standard_responses, model)
  texts = []
  for x, y in zip(standard_concentrations, standard_responses):
  	texts.append(ax.text(x, y, f"x={x:.2f}, y={y:.2f})", fontsize=13, ha="right"))

  adjust_text(texts, ax=ax)
  ```

  ![standard curve with labels](./examples/readme_fit_labels.png)





  See the [example notebooks](./examples/) for more detailed usage.



  Contributions are welcome! We built this package to be useful for our own work, but we know there is more to add.
  Please see [CONTRIBUTING.md](CONTRIBUTING.md) for more information.


pkgId: 1c0a6ea8fb9c83fba06ade73f995fb90ea873683447f8dd4812e723a5e15f2c8
name: python-bio-dataset-manager
arch: src
version: 0.1.3
epoch: 0
release: 1
summary: A small package to manage biological data.
url: https://github.com/fabiobove-dr/bio_dataset_manager
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio-dataset-manager-0.1.3-1.src.rpm
checksum_type: sha256
description:
  <figure>
    <img src="icon.png" alt="description" >
  </figure>



  <hr>

  [![Coverage](https://codecov.io/github/tacclab/bio_dataset_manager/coverage.svg?branch=main)](https://codecov.io/gh/tacclab/bio_dataset_manager)
  ![Unit Tests](https://github.com/tacclab/bio_dataset_manager/actions/workflows/main.yml/badge.svg)<br>
  [![Powered by TaccLab](https://img.shields.io/badge/powered%20by-TaccLab-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://tacclab.org)<br>
  [![License](https://img.shields.io/github/license/tacclab/bio_dataset_manager.svg)](https://github.com/tacclab/bio_dataset_manager/blob/main/LICENSE)<br>


  <hr>
     - Fabio Bove | fabio.bove.dr@gmail.com<br>
     - Eugenio Bertolini |  <br>


  <hr>
  Bio Data Manager is a Python project designed for managing and processing bio-sequence data, including DNA, proteins, and SMILES strings. This tool facilitates the encoding of these sequences into tensors, which can then be used for AI computations and complex model implementations.



  <hr>

  - `bio_data_manager/`: Contains core modules for bioinformatics sequence processing and management.
  - `bio_sequences/`: Handles various operations related to biological sequences such as DNA and protein.



  <hr>

  1. Install it as a library
     - Using `CPU`:
        ```bash
        pip install git+ssh://git@github.com/tacclab/bio_data_manager.git@0.0.1
        ```
     - Using `CUDA`:
        ```bash
        pip install git+ssh://git@github.com/tacclab/bio_data_manager.git@0.0.1[cuda] -f https://download.pytorch.org/whl/torch_stable.html
        ```
  2. Examples of the code can be found in the `usage_examples.py` file.
  ```python
      from bio_dataset_manager import BioDataset, BioDataloader
      from bio_sequences import DnaSequence, ProteinSequence, SmilesSequence
  ```



  <hr>
  Feel free to submit issues or pull requests if you'd like to contribute to this project.



  <hr>

  [![License](https://img.shields.io/github/license/tacclab/bio_dataset_manager.svg)](https://github.com/tacclab/bio_dataset_manager/blob/main/LICENSE)<br>

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: db8768852810a38cd0bbf5d842cb6cf3afeba3687319c9eecdc98b6ab5de6e68
name: python-bio-pyvol
arch: src
version: 1.7.8
epoch: 0
release: 1
url: https://github.com/schlessinger-lab/pyvol
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio-pyvol-1.7.8-1.src.rpm
checksum_type: sha256
summary:
  a PyMOL plugin and python package for visualization, comparison, and volume calculation of protein drug-binding sites

description:
  ![image](https://img.shields.io/pypi/v/bio_pyvol.svg) ![image](https://img.shields.io/pypi/l/bio_pyvol.svg)
  See the [Full Project Documentation](https://schlessingerlab.github.io/pyvol/).
  ![image](docs/source/_static/overview_image_v01.png)


pkgId: 6cede59e95a65de316442b8aade91649a61b234d13df6d265a6a685274ed1131
name: python-bio-transformers
arch: src
version: 0.1.17
epoch: 0
release: 1
summary: Wrapper on top of ESM/Protbert model in order to easily work with protein embedding
url: https://pypi.org/project/bio-transformers/
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio-transformers-0.1.17-1.src.rpm
checksum_type: sha256
description:
  <p align="center">
    <img width="50%" src="https://raw.githubusercontent.com/DeepChainBio/bio-transformers/main/.source/_static/deepchain.png"
  </p>


  ![PyPI](https://img.shields.io/pypi/v/bio-transformers)
  [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-360/)
  [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
  ![Dependencies](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)
  [![Documentation Status](https://readthedocs.org/projects/bio-transformers/badge/?version=latest)](https://bio-transformers.readthedocs.io/en/latest/?badge=latest)
  [![codecov](https://codecov.io/gh/delfosseaurelien/bio-transformers/branch/develop/graph/badge.svg?token=URROG4GV2C)](https://codecov.io/gh/delfosseaurelien/bio-transformers)

  <details><summary>Table of contents</summary>

  - [Description](#bio-transformers)
  - [Installation](#Installation)
  - [Usage](#usage)
    - [Quick Start](#quickstart)
    - [Compute embeddings](#embeddings)
    - [Pseudo-Loglikelihood](#pseudo-loglikelihood)
  - [Roadmap](#roadmap)
  - [Citations](#citations)
  - [License](#license)
  </details>






  `bio-transformers` is a python wrapper on top of the **ESM/Protbert** models, which are **Transformers protein language models**, trained on millions of proteins and used to predict embeddings. We leverage these state of the art models in a simple interface with practical functionnalities.

  This package provides a unified interface to use all these models - which we call `backends`. For instance you'll be able to compute natural amino-acids probabilities, embeddings or easily finetune your model on multiple-GPUs with `Ray`.

  📕 Please refer to the documentation [here](https://bio-transformers.readthedocs.io/en/latest/).


   You can find the original repositories for the models here :
   - [ESM](https://github.com/facebookresearch/esm/)
   - [Protbert](https://github.com/agemagician/ProtTrans)


  `Transformers` are an AI language model that has had huge impact in speech recognition and automated chatbots and has now been pointed out as a revolutionising technique to understand the language of proteins, offering an innovative approach to study and design proteins. They have been introduced in [Attention is all you need](https://arxiv.org/abs/1706.03762) paper. This [blog post](https://jalammar.github.io/illustrated-transformer/) can give you an in depth view of the transformer architecture and the mathematics behind.


  When trained with a large database on a particular language, such as English, the model learns about the words and grammar and can generate complete sentences when prompted. Let’s look at the following example:

  <p align="center">
    <img width="50%" src="https://raw.githubusercontent.com/DeepChainBio/bio-transformers/main/.source/_static/transformers.png">
  </p>

   
   Proteins are molecules that perform critical functions in all living beings. It consists of one or more strings of amino acids. There are only 20 different amino acids and the different combinations of them have resulted in thousands of functional proteins in humans. If we consider amino acids as words that constitute proteins, which are the sentences, then we could use transformers to understand the language of proteins. When trained with the billions of protein sequences identified so far across multiple species, a transformer is capable of understanding what sequences of amino acids make sense from a language perspective and can propose new combinations.

  <p align="center">
    <img width="50%" src="https://raw.githubusercontent.com/DeepChainBio/bio-transformers/main/.source/_static/protein.png">
  </p>

  <p align="center">
    <img width="100%" src="https://raw.githubusercontent.com/DeepChainBio/bio-transformers/main/.source/_static/sequence.png">
  </p>

  Querying a transformer trained in the language of proteins on a particular sequence provides a wealth of information about the protein. As seen in the above example, the transformer can tell you which amino acids might be key and need to be present at the protein of interest from a language perspective. This information is of particular interest when trying to understand amino acid regions that might be essential to protein function or stability.




  It is recommended to work with conda environments in order to manage the specific dependencies of this package.
  The `bio-transformers` package can be found on [pypi](https://pypi.org/project/bio-transformers/).

  Please note that you are suppose to have a correct cuda/torch installation before installing this library.




  1. Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/products/individual)

  2. Create a virtual environment and activate it.

  ```bash
    conda create --name bio-transformers python=3.7 -y && conda activate bio-transformers
  ```

  3. Install the package in environment.

  ```bash
    pip install bio-transformers
  ```



  Conda:

  1. Clone this git repo via HTTPS or SSH:

   ```bash
   git clone https://github.com/DeepChainBio/bio-transformers
   cd bio-transformers
   ```

  2. Create developement environment based on the yaml file.

  ```bash
  conda env create -f environment_dev.yaml
  conda activate bio-transformers-dev
  ```

  3. Install package and pre-commit hooks.

  ```
  pip install -e .
  pre-commit install
  ```

  Docker:

  You can directly use a docker image for using bio-transformers or for development. The image is based on cuda11.1, be sure to use it on GPU.

  1. Build the image:

  ```
  docker build --tag instadeep/biotransformers-dev .
  ```

  2. Run interactively with GPUs:

  ```
  docker run --d -it --gpus all -v  /home/bio-transformers:/app/bio-transformers instadeep/biotransformers-dev /bin/bash
  ```





  The main class ```BioTranformers``` allows developers to use Protbert and ESM backends

  ```python
  > from biotransformers import BioTransformers
  > BioTransformers.list_backend()
  ```

  ```python
  >>
  Use backend in this list :

      *   esm1_t34_670M_UR100
      *   esm1_t6_43M_UR50S
      *   esm1b_t33_650M_UR50S
      *   esm_msa1_t12_100M_UR50S
      *   protbert
      *   protbert_bfd

  ```



  The embedding of a an object is a representation of the object in a lower dimensional space. In this lower space, it is easier to manipulate, visualize, and apply mathematical functions on proteins' projection. Embeddings model will take a sequence of amino acids in input (string) and return a vector of lower dimension.

  You can choose a backend and pass a list of sequences of Amino acids to compute the embeddings.
  By default, the ```compute_embeddings``` function returns the ```<CLS>``` token embeddings.
  You can add a ```pool_mode``` in addition, so you can compute the mean of the tokens embeddings.

  ```python
  from biotransformers import BioTransformers

  sequences = [
          "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
          "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE",
      ]

  bio_trans = BioTransformers(backend="protbert")
  embeddings = bio_trans.compute_embeddings(sequences, pool_mode=('cls','mean'),batch_size=2)

  cls_emb = embeddings['cls']
  mean_emb = embeddings['mean']
  ```



  If you have access to multiple GPUs, you can specify the ```num_gpus``` option to speed-up the inference. Please refer to this [section](https://bio-transformers.readthedocs.io/en/develop/documentation/multi_gpus.html) to have a full understanding.

  This option relies on  `Ray` since version 0.0.11 (```torch.nn.DataParallel``` and `multi_gpu` option deprecated.)

  ```python
  import ray

  ray.init()
  bio_trans = BioTransformers(backend="protbert",num_gpus=2)
  embeddings = bio_trans.compute_embeddings(sequences, pool_mode=('cls','mean'), batch_size=2)
  ```



  The protein loglikelihood is a metric that estimates the joint probability of observing a given sequence of amino acids. The idea behind such an estimator is to approximate the probability that a mutated protein will be “natural”, and can effectively be produced by a cell.

  These metrics rely on transformers language models. These models are trained to predict a “masked” amino acid in a sequence. As a consequence, they can provide us with an estimate of the probability of observing an amino acid given the “context” (the surrounding amino acids).  By multiplying individual probabilities computed for a given amino-acid given its context, we obtain a pseudo-likelihood, which can be a candidate estimator to approximate sequence stability.

  ```python
  from biotransformers import BioTransformers
  import ray

  sequences = [
          "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
          "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE",
      ]

  bio_trans = BioTransformers(backend="protbert",num_gpus=1)
  loglikelihood = bio_trans.compute_loglikelihood(sequences)
  ```



  You can use the `finetune` function to finetune your backend on your dataset. The model is automatically scaled on the available GPUs. More information on the [documentation](https://bio-transformers.readthedocs.io/en/main/getting_started/quick_start.html#display-available-backend)

  ```python
  import biodatasets
  import numpy as np
  from biotransformers import BioTransformers
  import ray

  data = biodatasets.load_dataset("swissProt")
  X, y = data.to_npy_arrays(input_names=["sequence"])
  X = X[0]


  length = np.array(list(map(len, X))) < 200
  train_seq = X[length][:15000]

  ray.init()
  bio_trans = BioTransformers("esm1_t6_43M_UR50S", num_gpus=2)

  bio_trans.finetune(
      train_seq,
      lr=1.0e-5,
      warmup_init_lr=1e-7,
      toks_per_batch=2000,
      epochs=20,
      batch_size=16,
      acc_batch_size=256,
      warmup_updates=1024,
      accelerator="ddp",
      checkpoint=None,
      save_last_checkpoint=False,
  )
  ```



  - support MSA transformers



  Here some papers on interest on the subject.

  The excellent ProtBert work can be found at [(biorxiv preprint)](https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3.full.pdf):

  ```bibtex
  @article{protTrans2021,
    author={Ahmed Elnaggar and Michael Heinzinger, Christian Dallago1,Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer,Debsindhu Bhowmik and Burkhard Rost},
    title={ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing},
    year={2019},
    doi={10.1101/2020.07.12.199554},
    url={https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3.full.pdf},
    journal={bioRxiv}
  }
  ```

  For the ESM model, see [(biorxiv preprint)](https://www.biorxiv.org/content/10.1101/622803v4):
  ```bibtex
  @article{rives2019biological,
    author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Liu, Jason and Guo, Demi and Ott, Myle and Zitnick, C. Lawrence and Ma, Jerry and Fergus, Rob},
    title={Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences},
    year={2019},
    doi={10.1101/622803},
    url={https://www.biorxiv.org/content/10.1101/622803v4},
    journal={bioRxiv}
  }
  ```

  For the self-attention contact prediction, see [the following paper (biorxiv preprint)](https://www.biorxiv.org/content/10.1101/2020.12.15.422761v1):

  ```bibtex
  @article{rao2020transformer,
    author = {Rao, Roshan M and Meier, Joshua and Sercu, Tom and Ovchinnikov, Sergey and Rives, Alexander},
    title={Transformer protein language models are unsupervised structure learners},
    year={2020},
    doi={10.1101/2020.12.15.422761},
    url={https://www.biorxiv.org/content/10.1101/2020.12.15.422761v1},
    journal={bioRxiv}
  }
  ```

  For the MSA Transformer, see [the following paper (biorxiv preprint)](https://doi.org/10.1101/2021.02.12.430858):

  ```bibtex
  @article{rao2021msa,
    author = {Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier, Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom and Rives, Alexander},
    title={MSA Transformer},
    year={2021},
    doi={10.1101/2021.02.12.430858},
    url={https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1},
    journal={bioRxiv}
  }
  ```





  This source code is licensed under the **Apache 2** license found in the `LICENSE` file in the root directory.


pkgId: 95661c9826ad4529cbca33aafdf2073cc1c79af7b7afeee0fa99f7c67950da9c
name: python-bio2bel-hgnc
arch: src
version: 0.3.0
epoch: 0
release: 1
summary: A package for converting HGNC to BEL
url: https://github.com/bio2bel/hgnc
rpm_license: MIT License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio2bel-hgnc-0.3.0-1.src.rpm
checksum_type: sha256
description:
  This package creates:
  - HGNC BEL namespace
  - HGNC gene families BEL namespace,
  - HGNC gene family membership BEL Script
  - orthology BEL script


pkgId: 85b9817e1298b865b7f89c84f69779df37a74a7ce3648b43a2cc4e21277b31b7
name: python-bio2bel-kegg
arch: src
version: 0.3.0
epoch: 0
release: 1
summary: A package for converting KEGG gene sets into BEL
url: https://github.com/bio2bel/kegg
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio2bel-kegg-0.3.0-1.src.rpm
checksum_type: sha256
description:
  This package allows the enrichment of BEL networks with KEGG information by wrapping its RESTful API.
  Furthermore, it is integrated in the `ComPath environment <https://github.com/ComPath>`_ for pathway database comparison.
  If you find this package useful, please consider citing [domingofernandez2018]_:
     and curating mappings across pathway databases <https://doi.org/10.1038/s41540-018-0078-8>`_.
     *Npj Systems Biology and Applications*, __5__(1), 3.
  **Warning** This package creates ``partOf`` relationships in BEL, but does not convert KEGG mechanistic
  relationships to BEL. That functionality is implemented in the
  `PathMe project <https://github.com/pathwaymerger/pathme>`_.


pkgId: 30cfa0d03ae05b2aa2f350e81918501e99445654d189d19db6d1aa3b4796208f
name: python-bio2bel-reactome
arch: src
version: 0.2.3
epoch: 0
release: 1
summary: A wrapper around Reactome RESTful API
url: https://github.com/bio2bel/reactome
rpm_license: MIT License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio2bel-reactome-0.2.3-1.src.rpm
checksum_type: sha256
description:
  This package allows the enrichment of BEL networks with Reactome information by wrapping its RESTful API.
  Furthermore, it is integrated in the `ComPath environment <https://github.com/ComPath>`_ for pathway database
  comparison.


pkgId: 2717e92728cc0c891d731f8825f9b3507ab58b541245e010ff684a9ff1638b66
name: python-bio2bel-wikipathways
arch: src
version: 0.2.3
epoch: 0
release: 1
summary: A package for converting Wikipathways to BEL
url: https://github.com/bio2bel/wikipathways
rpm_license: MIT License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bio2bel-wikipathways-0.2.3-1.src.rpm
checksum_type: sha256
description:
  This package allows the enrichment of BEL networks with WikiPathways information by wrapping its RESTful API.
  Furthermore, it is integrated in the `ComPath environment <https://github.com/ComPath>`_ for pathway database
  comparison.


pkgId: c07f2bca98f5b2227622f62fba99b9c773549e05ed2f2df665a78d05f5c2729d
name: python-bioarch
arch: src
version: 0.0.34
epoch: 0
release: 1
summary: Python bioarchaeology
url: https://github.com/TheBiggerGuy/bioarch
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioarch-0.0.34-1.src.rpm
checksum_type: sha256
description:
  [![CircleCI](https://circleci.com/gh/TheBiggerGuy/bioarch/tree/master.svg?style=svg)](https://circleci.com/gh/TheBiggerGuy/bioarch/tree/master)
  [![codecov](https://codecov.io/gh/TheBiggerGuy/bioarch/branch/master/graph/badge.svg)](https://codecov.io/gh/TheBiggerGuy/bioarch)
  [![Documentation Status](https://readthedocs.org/projects/bioarch/badge/?version=latest)](https://bioarch.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/github/issues-raw/TheBiggerGuy/bioarch.svg)](https://github.com/TheBiggerGuy/bioarch/issues)
  [![](https://tokei.rs/b1/github/TheBiggerGuy/bioarch)](https://github.com/TheBiggerGuy/bioarch)
  [![PyPI version](https://badge.fury.io/py/bioarch.svg)](https://badge.fury.io/py/bioarch)

  A Python bioarchaeology library to help with modelling data in the archaeology/bioarchaeology field.

  * Code at https://github.com/TheBiggerGuy/bioarch
  * PyPI at https://pypi.org/project/bioarch/
  * Docs at https://bioarch.readthedocs.io/en/latest/


pkgId: 14811914db4336f2ab4f4080b23e814c6cb84ef9afa442cb2e3de951f91c69e6
name: python-bioat
arch: src
version: 0.12.15
epoch: 0
release: 1
summary: bioat, a python package & command line toolkit for Bioinformatics and data science!
url: https://github.com/hermanzhaozzzz/bioat
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioat-0.12.15-1.src.rpm
checksum_type: sha256
description:
  the Bioinformatic Analysis Tools

  ![macos](https://github.com/hermanzhaozzzz/bioat/actions/workflows/macos.yml/badge.svg)

  ![linux](https://github.com/hermanzhaozzzz/bioat/actions/workflows/linux.yml/badge.svg)

  ![windows](https://github.com/hermanzhaozzzz/bioat/actions/workflows/windows.yml/badge.svg)

  [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)


  **bioat**, a python **package** & **command line toolkit** for Bioinformatics and data science!


  ```shell

  pip install --upgrade bioat
  ```



  ```shell

  bioat list

  bioat version

  bioat about


  bioat bam remove_clip --help
  samtools view -h test_sorted_n.bam | bioat bam remove_clip | tail
  ```
  [circos plot](docs/demo_circos-plot.ipynb)



  > author: [赵华男 | Huanan Herman Zhao](https://scholar.google.com/citations?user=ojSVoWQAAAAJ&hl=en)
  >
  > email: hermanzhaozzzz@gmail.com
  >
  > [Zhihu](https://www.zhihu.com/people/hymanzhaozzzz) | [BLOG](http://zhaohuanan.cc)


pkgId: fbdca380e7b48f16d8944f56aa7b8c69894f506a55f44ffed0f30cb1810d36c4
name: python-bioat
arch: src
version: 0.13.2
epoch: 0
release: 1
summary: bioat, a python package & command line toolkit for Bioinformatics and data science!
url: https://github.com/hermanzhaozzzz/bioat
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioat-0.13.2-1.src.rpm
checksum_type: sha256
description:
  the Bioinformatic Analysis Tools

  ![macos](https://github.com/hermanzhaozzzz/bioat/actions/workflows/macos.yml/badge.svg)

  ![linux](https://github.com/hermanzhaozzzz/bioat/actions/workflows/linux.yml/badge.svg)

  ![windows](https://github.com/hermanzhaozzzz/bioat/actions/workflows/windows.yml/badge.svg)

  [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)


  **bioat**, a python **package** & **command line toolkit** for Bioinformatics and data science!


  ```shell

  pip install --upgrade bioat


  conda install -c conda-forge bioat
  ```



  ```shell

  bioat list

  bioat version

  bioat about


  bioat bam remove_clip --help
  samtools view -h test_sorted_n.bam | bioat bam remove_clip | tail
  ```
  [circos plot](docs/demo_circos-plot.ipynb)



  > author: [赵华男 | Huanan Herman Zhao](https://scholar.google.com/citations?user=ojSVoWQAAAAJ&hl=en)
  >
  > email: hermanzhaozzzz@gmail.com
  >
  > [Zhihu](https://www.zhihu.com/people/hymanzhaozzzz) | [BLOG](http://zhaohuanan.cc)


pkgId: 9a584083263c727384759b5bfc2e75c437598b09d836d9ac7d56240ad934a4ba
name: python-biobb-chemistry
arch: src
version: 5.0.3
epoch: 0
release: 1
url: https://github.com/bioexcel/biobb_chemistry
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-chemistry-5.0.3-1.src.rpm
checksum_type: sha256
summary:
  Biobb_chemistry is the Biobb module collection to perform chemistry over molecular dynamics simulations.

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_chemistry?label=Version)](https://GitHub.com/bioexcel/biobb_chemistry/tags/)
  [![](https://img.shields.io/pypi/v/biobb-chemistry.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-chemistry/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_chemistry?label=Conda)](https://anaconda.org/bioconda/biobb_chemistry)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_chemistry?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_chemistry)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_chemistry?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_chemistry:5.0.3--pyhdfd78af_1)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_chemistry)
  [![](https://img.shields.io/pypi/pyversions/biobb-chemistry.svg?label=Python%20Versions)](https://pypi.org/project/biobb-chemistry/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_chemistry)

  [![](https://readthedocs.org/projects/biobb-chemistry/badge/?version=latest&label=Docs)](https://biobb-chemistry.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_chemistry/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_chemistry/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_chemistry/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_chemistry/coverage/)
  [![](https://docs.bioexcel.eu/biobb_chemistry/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_chemistry/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_chemistry?label=Last%20Commit)](https://github.com/bioexcel/biobb_chemistry/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_chemistry.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_chemistry/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  Biobb_chemistry is the Biobb module collection to perform chemistry over molecular dynamics simulations.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-chemistry.readthedocs.io/en/latest/).


  v5.0.3 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_chemistry>=5.0.3"


  * Usage: [Python API documentation](https://biobb-chemistry.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_chemistry>=5.0.3"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-chemistry.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-chemistry.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_chemistry:5.0.3--pyhdfd78af_1


  * Usage:


          docker run quay.io/biocontainers/biobb_chemistry:5.0.3--pyhdfd78af_1 <command>

  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_chemistry.sif https://depot.galaxyproject.org/singularity/biobb_chemistry:5.0.3--pyhdfd78af_1


  * Usage:


          singularity exec biobb_chemistry.sif <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-chemistry.readthedocs.io/en/latest/command_line.html).



  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: e76f5f4c8522e6d54e1342f23f58a0bb56798ea197d3a5f87073b1b52db1af74
name: python-biobb-common
arch: src
version: 5.0.0
epoch: 0
release: 1
summary: Biobb_common is the base package required to use the biobb packages.
url: https://github.com/bioexcel/biobb_common
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-common-5.0.0-1.src.rpm
checksum_type: sha256
description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_common?label=Version)](https://GitHub.com/bioexcel/biobb_common/tags/)
  [![](https://img.shields.io/pypi/v/biobb-common.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-common/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_common?label=Conda)](https://anaconda.org/bioconda/biobb_common)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_common?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_common)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_common?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_common:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)]()
  [![](https://img.shields.io/pypi/pyversions/biobb-common.svg?label=Python%20Versions)]()
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)]()

  [![](https://readthedocs.org/projects/biobb-common/badge/?version=latest&label=Docs)](https://biobb-common.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)]()
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://img.shields.io/badge/Tests-Passing-brightgreen)]()
  [![](https://img.shields.io/badge/Coverage-90%25-brightgreen)]()
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_common?label=Last%20Commit)]()
  [![](https://img.shields.io/github/issues/bioexcel/biobb_common.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_common/issues/)






  Biobb_common is the base package required to use the biobb
  packages.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-common.readthedocs.io/en/latest/).


  v5.0.0 2024.2


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 8d74e9c5a56be6fcdb5d79dd19a4d4f6d5c2f814fe13bbb3f2218f78cc4a4ae6
name: python-biobb-dna
arch: src
version: 5.0.1
epoch: 0
release: 1
summary: Biobb_dna is a package composed of different analyses for nucleic acid trajectories.
url: https://github.com/bioexcel/biobb_dna
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-dna-5.0.1-1.src.rpm
checksum_type: sha256
description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_dna?label=Version)](https://GitHub.com/bioexcel/biobb_dna/tags/)
  [![](https://img.shields.io/pypi/v/biobb-dna.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-dna/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_dna?label=Conda)](https://anaconda.org/bioconda/biobb_dna)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_dna?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_dna)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_dna?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_dna:5.0.1--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_dna)
  [![](https://img.shields.io/pypi/pyversions/biobb-dna.svg?label=Python%20Versions)](https://pypi.org/project/biobb-dna/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_dna)

  [![](https://readthedocs.org/projects/biobb-dna/badge/?version=latest&label=Docs)](https://biobb-dna.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_dna/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_dna/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_dna/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_dna/coverage/)
  [![](https://docs.bioexcel.eu/biobb_dna/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_dna/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_dna?label=Last%20Commit)](https://github.com/bioexcel/biobb_dna/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_dna.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_dna/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  Biobb_dna is a package composed of different analyses for
  nucleic acid trajectories and helical parameters.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-dna.readthedocs.io/en/latest/).


  v5.0.1 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_dna>=5.0.1"


  * Usage: [Python API documentation](https://biobb-dna.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_dna>=5.0.1"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-dna.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-dna.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_dna:5.0.1--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_dna:5.0.1--pyhdfd78af_0 <command>

  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_dna.sif https://depot.galaxyproject.org/singularity/biobb_dna:5.0.1--pyhdfd78af_0


  * Usage:


          singularity exec biobb_dna.sif <command>

  The command list and specification can be found at the [Command Line documentation](https://biobb-dna.readthedocs.io/en/latest/command_line.html).


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 8c5f16e9009f639fed85af66ded7d09db21c84003a174ce2dd661cf674afe099
name: python-biobb-gromacs
arch: src
version: 5.0.0
epoch: 0
release: 1
summary: biobb_gromacs is the Biobb module collection to perform molecular dynamics simulations.
url: https://github.com/bioexcel/biobb_gromacs
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-gromacs-5.0.0-1.src.rpm
checksum_type: sha256
description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_gromacs?label=Version)](https://GitHub.com/bioexcel/biobb_gromacs/tags/)
  [![](https://img.shields.io/pypi/v/biobb-gromacs.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-gromacs/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_gromacs?label=Conda)](https://anaconda.org/bioconda/biobb_gromacs)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_gromacs?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_gromacs)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_gromacs?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_gromacs:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_gromacs)
  [![](https://img.shields.io/pypi/pyversions/biobb-gromacs.svg?label=Python%20Versions)](https://pypi.org/project/biobb-gromacs/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_gromacs)

  [![](https://readthedocs.org/projects/biobb-gromacs/badge/?version=latest&label=Docs)](https://biobb-gromacs.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_gromacs/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_gromacs/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_gromacs/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_gromacs/coverage/)
  [![](https://docs.bioexcel.eu/biobb_gromacs/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_gromacs/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_gromacs?label=Last%20Commit)](https://github.com/bioexcel/biobb_gromacs/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_gromacs.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_gromacs/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)






  biobb_gromacs is the Biobb module collection to perform molecular dynamics simulations using the GROMACS MD suite.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-gromacs.readthedocs.io/en/latest/).


  v5.0.0 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_gromacs>=5.0.0"


  * Usage: [Python API documentation](https://biobb-gromacs.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_gromacs>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-gromacs.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-gromacs.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_gromacs:5.0.0--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_gromacs:5.0.0--pyhdfd78af_0 <command>


  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_gromacs.sif https://depot.galaxyproject.org/singularity/biobb_gromacs:5.0.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_gromacs.sif <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-gromacs.readthedocs.io/en/latest/command_line.html).



  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: fc8587fe4532a8a68c681aec6fbedcffdeadede21b15a0fc1233bb439ba48f52
name: python-biobb-haddock
arch: src
version: 5.0.0
epoch: 0
release: 1
url: https://github.com/bioexcel/biobb_haddock
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-haddock-5.0.0-1.src.rpm
checksum_type: sha256
summary:
  biobb_haddock is the Biobb module collection to compute information-driven flexible protein-protein docking.

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_haddock?label=Version)](https://GitHub.com/bioexcel/biobb_haddock/tags/)
  [![](https://img.shields.io/pypi/v/biobb-haddock.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-haddock/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_haddock?label=Conda)](https://anaconda.org/bioconda/biobb_haddock)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_haddock?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_haddock)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_haddock?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_haddock:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_haddock)
  [![](https://img.shields.io/pypi/pyversions/biobb-haddock.svg?label=Python%20Versions)](https://pypi.org/project/biobb-haddock/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_haddock)

  [![](https://readthedocs.org/projects/biobb-haddock/badge/?version=latest&label=Docs)](https://biobb-haddock.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_haddock/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_haddock/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_haddock/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_haddock/coverage/)
  [![](https://docs.bioexcel.eu/biobb_haddock/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_haddock/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_haddock?label=Last%20Commit)](https://github.com/bioexcel/biobb_haddock/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_haddock.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_haddock/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  biobb_haddock is the Biobb module collection to compute information-driven flexible protein-protein docking.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-haddock.readthedocs.io/en/latest/).


  v5.0.0 2024.2





  In this case: `cns_solve_1.3_all.tar.gz`

  From the [CNS site](http://cns-online.org) create a new folder in the Haddock
  folder and uncompress it.

  ```bash

  cd /__PATH__/__to__/haddock3
  mkdir haddock3/CNS
  cp cns_solve_1.3_all.tar.gz haddock3/CNS/
  cd haddock3/CNS/
  tar xvzf cns_solve_1.3_all.tar.gz
  ```


  In this case: `m_fortran-compiler-classic_p_2022.0.0.63_offline.dmg` and `m_cpp-compiler-classic_p_2022.0.0.62_offline.dmg`

  From the [Intel developers site](https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html) and double click to install them.





  ```bash

  cd /__PATH__/__to__/haddock3/CNS
  vim cns_solve_1.3/cns_solve_env
  ```
  Modify the `CNS_SOLVE` env var:

  ```bash


              setenv CNS_SOLVE '_CNSsolve_location_'



  ```

  In this case:

  ```bash


              setenv CNS_SOLVE '/__PATH__/__to__/haddock3/CNS/cns_solve_1.3/'

  ```



  ```bash

  cd /__PATH__/__to__/haddock3/CNS
  vim cns_solve_1.3/source/rtf.inc
  ```

  Modify all the MX (maximum) variables adding one extra zero to all of them:
  ```
  PARAMETER (MXRTRS=200,NICM=50) --> PARAMETER (MXRTRS=2000,NICM=50)
  PARAMETER (MXRTA=2000)         --> PARAMETER (MXRTA=20000)
  PARAMETER (MXRTX=2000)         --> PARAMETER (MXRTX=20000)
  PARAMETER (MXRTB=2000)         --> PARAMETER (MXRTB=20000)
  PARAMETER (MXRTT=3000)         --> PARAMETER (MXRTT=30000)
  PARAMETER (MXRTP=2000)         --> PARAMETER (MXRTP=20000)
  PARAMETER (MXRTI=2000)         --> PARAMETER (MXRTI=20000)
  ```



  ```bash

  cd /__PATH__/__to__/haddock3/CNS/cns_solve_1.3
  make install
  ```

  If everything ended well, one of the last output lines will be:

  ```
  created executable file cns_solve-xxxxxxxxx.exe
  ```

  The `xxxxxxxxx` will be a different number on each build.

  Finally link the CNS binary:

  ```bash

  cd /__PATH__/__to__/haddock3
  mkdir -p bin/

  ln -s CNS/__PATH__TO_BIN__/cns_solve-xxxxxxxxx.exe bin/cns
  ```


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_haddock>=5.0.0"


  * Usage: [Python API documentation](https://biobb-haddock.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_haddock>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-haddock.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-haddock.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_haddock:5.0.0--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_haddock:5.0.0--pyhdfd78af_0 <command>


  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_haddock.sif https://depot.galaxyproject.org/singularity/biobb_haddock:5.0.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_haddock.sif <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-haddock.readthedocs.io/en/latest/command_line.html).



  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)), EU Horizon Europe [101093290] (https://cordis.europa.eu/project/id/101093290).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 9c1e3f3845f22862196fc653517896d1d98ba5887375f819e28036af0c87f300
name: python-biobb-io
arch: src
version: 4.2.0
epoch: 0
release: 1
url: https://github.com/bioexcel/biobb_io
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-io-4.2.0-1.src.rpm
checksum_type: sha256
summary:
  Biobb_io is the Biobb module collection to fetch data to be consumed by the rest of the Biobb building blocks.

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_io?label=Version)](https://GitHub.com/bioexcel/biobb_io/tags/)
  [![](https://img.shields.io/pypi/v/biobb-io.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-io/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_io?label=Conda)](https://anaconda.org/bioconda/biobb_io)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_io?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_io)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_io?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_io:4.2.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_io)
  [![](https://img.shields.io/pypi/pyversions/biobb-io.svg?label=Python%20Versions)](https://pypi.org/project/biobb-io/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_io)

  [![](https://readthedocs.org/projects/biobb-io/badge/?version=latest&label=Docs)](https://biobb-io.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorial-blue?logo=youtube&logoColor=red)](https://www.youtube.com/watch?v=ou1DOGNs0xM)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_io/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_io/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_io/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_io/coverage/)
  [![](https://docs.bioexcel.eu/biobb_io/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_io/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_io?label=Last%20Commit)](https://github.com/bioexcel/biobb_io/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_io.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_io/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  Biobb_io is the Biobb module collection to fetch data to be consumed by the
  rest of the Biobb building blocks.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-io.readthedocs.io/en/latest/).


  v4.2.0 2024.1


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:

          pip install "biobb_io==4.2.0"

  * Usage: [Python API documentation](https://biobb-io.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:
  * Installation:


          conda install -c bioconda "biobb_io==4.2.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-io.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-io.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:
  * Installation:


          docker pull quay.io/biocontainers/biobb_io:4.2.0--pyhdfd78af_0

  * Usage:


          docker run quay.io/biocontainers/biobb_io:4.2.0--pyhdfd78af_0 <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-io.readthedocs.io/en/latest/command_line.html).

  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_io.sif https://depot.galaxyproject.org/singularity/biobb_io:4.2.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_io.sif <command>



  The command list and specification can be found at the [Command Line documentation](https://biobb-io.readthedocs.io/en/latest/command_line.html).


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: a43d2f4bb08ad72f2fd354a7d62b314da5522504a1a2a188722ce8a1b449a697
name: python-biobb-io
arch: src
version: 5.0.0
epoch: 0
release: 1
url: https://github.com/bioexcel/biobb_io
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-io-5.0.0-1.src.rpm
checksum_type: sha256
summary:
  Biobb_io is the Biobb module collection to fetch data to be consumed by the rest of the Biobb building blocks.

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_io?label=Version)](https://GitHub.com/bioexcel/biobb_io/tags/)
  [![](https://img.shields.io/pypi/v/biobb-io.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-io/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_io?label=Conda)](https://anaconda.org/bioconda/biobb_io)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_io?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_io)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_io?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_io:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_io)
  [![](https://img.shields.io/pypi/pyversions/biobb-io.svg?label=Python%20Versions)](https://pypi.org/project/biobb-io/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_io)

  [![](https://readthedocs.org/projects/biobb-io/badge/?version=latest&label=Docs)](https://biobb-io.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_io/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_io/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_io/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_io/coverage/)
  [![](https://docs.bioexcel.eu/biobb_io/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_io/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_io?label=Last%20Commit)](https://github.com/bioexcel/biobb_io/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_io.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_io/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  Biobb_io is the Biobb module collection to fetch data to be consumed by the
  rest of the Biobb building blocks.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-io.readthedocs.io/en/latest/).


  v5.0.0 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:

          pip install "biobb_io==5.0.0"

  * Usage: [Python API documentation](https://biobb-io.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:
  * Installation:


          conda install -c bioconda "biobb_io==5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-io.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-io.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:
  * Installation:


          docker pull quay.io/biocontainers/biobb_io:5.0.0--pyhdfd78af_0

  * Usage:


          docker run quay.io/biocontainers/biobb_io:5.0.0--pyhdfd78af_0 <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-io.readthedocs.io/en/latest/command_line.html).

  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_io.sif https://depot.galaxyproject.org/singularity/biobb_io:5.0.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_io.sif <command>



  The command list and specification can be found at the [Command Line documentation](https://biobb-io.readthedocs.io/en/latest/command_line.html).


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: b538410f1153bcb9a64c7ff449812f3b355bedbfa3d405b0a528cf2e27da9cc0
name: python-biobb-mem
arch: src
version: 5.0.2
epoch: 0
release: 1
url: https://github.com/bioexcel/biobb_mem
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-mem-5.0.2-1.src.rpm
checksum_type: sha256
summary:
  Biobb_mem is a complete code template to promote and facilitate the creation of new Biobbs by the community.

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_mem?label=Version)](https://GitHub.com/bioexcel/biobb_mem/tags/)
  [![](https://img.shields.io/pypi/v/biobb-mem.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-mem/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_mem?label=Conda)](https://anaconda.org/bioconda/biobb_mem)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_mem?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_mem)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_mem?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_mem:5.0.2--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_mem)
  [![](https://img.shields.io/pypi/pyversions/biobb-mem.svg?label=Python%20Versions)](https://pypi.org/project/biobb-mem/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_mem)

  [![](https://readthedocs.org/projects/biobb-mem/badge/?version=latest&label=Docs)](https://biobb-mem.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_mem/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_mem/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_mem/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_mem/coverage/)
  [![](https://docs.bioexcel.eu/biobb_mem/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_mem/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_mem?label=Last%20Commit)](https://github.com/bioexcel/biobb_mem/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_mem.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_mem/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  biobb_mem is a BioBB category for membrane analysis and manipulation.
  biobb_mem allows analysis of membrane properties and manipulation of membrane systems using tools like [MDAnalysis](https://github.com/MDAnalysis/mdanalysis), [FATSLiM](https://github.com/FATSLiM/fatslim) and [LiPyphilic](https://github.com/p-j-smith/lipyphilic).
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-mem.readthedocs.io/en/latest/).


  v5.0.2 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA or DOCKER.

  * Installation:


          pip install "biobb_mem>=5.0.2"


  * Usage: [Python API documentation](https://biobb-mem.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_mem>=5.0.2"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-mem.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-mem.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_mem:5.0.2--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_mem:5.0.2--pyhdfd78af_0


  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_mem.sif https://depot.galaxyproject.org/singularity/biobb_mem:5.0.2--pyhdfd78af_0


  * Usage:


          singularity exec biobb_mem.sif <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-mem.readthedocs.io/en/latest/command_line.html).


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: b0850ff8137da86da08f1eaa377af659a4cd64b7791f9299030588a9a46353e6
name: python-biobb-model
arch: src
version: 5.0.0
epoch: 0
release: 1
url: https://github.com/bioexcel/biobb_model
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-model-5.0.0-1.src.rpm
checksum_type: sha256
summary:
  Biobb_model is the Biobb module collection to check and model 3d structures, create mutations or reconstruct missing atoms.

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_model?label=Version)](https://GitHub.com/bioexcel/biobb_model/tags/)
  [![](https://img.shields.io/pypi/v/biobb-model.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-model/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_model?label=Conda)](https://anaconda.org/bioconda/biobb_model)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_model?label=Conda%20downloads)](https://anaconda.org/bioconda/biobb_model)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_model?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_model:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_model)
  [![](https://img.shields.io/pypi/pyversions/biobb-model.svg?label=Python%20Versions)](https://pypi.org/project/biobb-model/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_model)

  [![](https://readthedocs.org/projects/biobb-model/badge/?version=latest&label=Docs)](https://biobb-model.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)]()
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_model/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_model/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_model/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_model/coverage/)
  [![](https://docs.bioexcel.eu/biobb_model/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_model/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_model?label=Last%20Commit)](https://github.com/bioexcel/biobb_model/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_model.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_model/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  Biobb_model is the Biobb module collection to check and model 3d structures,
  create mutations or reconstruct missing atoms.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-model.readthedocs.io/en/latest/).


  v5.0.0 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_model>=5.0.0"


  * Usage: [Python API documentation](https://biobb-model.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_model>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-model.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-model.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_model:5.0.0--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_model:5.0.0--pyhdfd78af_0 <command>


  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_model.sif https://depot.galaxyproject.org/singularity/biobb_model:3.8.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_model.sif <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-model.readthedocs.io/en/latest/command_line.html).



  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 7d469543e3e28f993c0cc06c85c0595560c870fd0f9a0a75601f6c9ae7f81c40
name: python-biobb-pdb-tools
arch: src
version: 5.0.0
epoch: 0
release: 1
summary: Biobb_pdb_tools is a swiss army knife for manipulating and editing PDB files.
url: https://github.com/bioexcel/biobb_pdb_tools
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-pdb-tools-5.0.0-1.src.rpm
checksum_type: sha256
description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_pdb_tools?label=Version)](https://GitHub.com/bioexcel/biobb_pdb_tools/tags/)
  [![](https://img.shields.io/pypi/v/biobb-pdb-tools.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-pdb-tools/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_pdb_tools?label=Conda)](https://anaconda.org/bioconda/biobb_pdb_tools)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_pdb_tools?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_pdb_tools)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_pdb_tools?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_pdb_tools:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_pdb_tools)
  [![](https://img.shields.io/pypi/pyversions/biobb-pdb-tools.svg?label=Python%20Versions)](https://pypi.org/project/biobb-pdb-tools/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_pdb_tools)

  [![](https://readthedocs.org/projects/biobb-pdb-tools/badge/?version=latest&label=Docs)](https://biobb-pdb-tools.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_pdb_tools/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_pdb_tools/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_pdb_tools/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_pdb_tools/coverage/)
  [![](https://docs.bioexcel.eu/biobb_pdb_tools/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_pdb_tools/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_pdb_tools?label=Last%20Commit)](https://github.com/bioexcel/biobb_pdb_tools/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_pdb_tools.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_pdb_tools/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  biobb_pdb_tools is a swiss army knife for manipulating and editing PDB files.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-pdb-tools.readthedocs.io/en/latest/).


  v5.0.0 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_pdb_tools>=5.0.0"


  * Usage: [Python API documentation](https://biobb-pdb-tools.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_pdb_tools>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-pdb-tools.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-pdb-tools.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_pdb_tools:5.0.0--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_pdb_tools:5.0.0--pyhdfd78af_0 <command>

  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_pdb_tools.sif https://depot.galaxyproject.org/singularity/biobb_pdb_tools:5.0.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_pdb_tools.sif <command>

  The command list and specification can be found at the [Command Line documentation](https://biobb-pdb-tools.readthedocs.io/en/latest/command_line.html).


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 907997d8752826c477175eaf855971762221a3b57a42cffc18fa6015b13b8e11
name: python-biobb-pmx
arch: src
version: 5.0.0
epoch: 0
release: 1
summary: Biobb_pmx is the Biobb module collection to perform PMX (http://pmx.mpibpc.mpg.de) executions.
url: https://github.com/bioexcel/biobb_pmx
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-pmx-5.0.0-1.src.rpm
checksum_type: sha256
description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_pmx?label=Version)](https://GitHub.com/bioexcel/biobb_pmx/tags/)
  [![](https://img.shields.io/pypi/v/biobb-pmx.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-pmx/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_pmx?label=Conda)](https://anaconda.org/bioconda/biobb_pmx)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_pmx?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_pmx)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_pmx?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_pmx:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_pmx)
  [![](https://img.shields.io/pypi/pyversions/biobb-pmx.svg?label=Python%20Versions)](https://pypi.org/project/biobb-pmx/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_pmx)

  [![](https://readthedocs.org/projects/biobb-pmx/badge/?version=latest&label=Docs)](https://biobb-pmx.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_pmx/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_pmx/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_pmx/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_pmx/coverage/)
  [![](https://docs.bioexcel.eu/biobb_pmx/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_pmx/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_pmx?label=Last%20Commit)](https://github.com/bioexcel/biobb_pmx/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_pmx.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_pmx/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)





  Biobb_pmx is the Biobb module collection to perform PMX (http://pmx.mpibpc.mpg.de) executions.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-pmx.readthedocs.io/en/latest/).


  v5.0.0 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA or DOCKER.

  * Installation:

          pip install "biobb_pmx>=5.0.0"


  * Usage: [Python API documentation](https://biobb-pmx.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_pmx>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-pmx.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-pmx.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_pmx:5.0.0--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_pmx:5.0.0--pyhdfd78af_0 <command>


  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_pmx.sif https://depot.galaxyproject.org/singularity/biobb_pmx:5.0.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_pmx.sif <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-pmx.readthedocs.io/en/latest/command_line.html).



  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 8fd1422649e30c9471d2cf427aae90b0b06a39aa446615a63ae76cf60b735461
name: python-biobb-pytorch
arch: src
version: 5.0.0
epoch: 0
release: 1
summary: biobb_pytorch is the Biobb module collection to create and train ML & DL models.
url: https://github.com/bioexcel/biobb_pytorch
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-pytorch-5.0.0-1.src.rpm
checksum_type: sha256
description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_pytorch?label=Version)](https://GitHub.com/bioexcel/biobb_pytorch/tags/)
  [![](https://img.shields.io/pypi/v/biobb-pytorch.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-pytorch/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_pytorch?label=Conda)](https://anaconda.org/bioconda/biobb_pytorch)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_pytorch?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_pytorch)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_pytorch?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_pytorch:5.0.0--pyhad2cae4_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_pytorch)
  [![](https://img.shields.io/pypi/pyversions/biobb-pytorch.svg?label=Python%20Versions)](https://pypi.org/project/biobb-pytorch/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_pytorch)

  [![](https://readthedocs.org/projects/biobb-pytorch/badge/?version=latest&label=Docs)](https://biobb-pytorch.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_pytorch/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_pytorch/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_pytorch/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_pytorch/coverage/)
  [![](https://docs.bioexcel.eu/biobb_pytorch/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_pytorch/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_pytorch?label=Last%20Commit)](https://github.com/bioexcel/biobb_pytorch/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_pytorch.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_pytorch/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)





  biobb_pytorch is the Biobb module collection to create and train ML & DL models using the popular [PyTorch](https://pytorch.org/) Python library.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-pytorch.readthedocs.io/en/latest/).


  v5.0.0 2024.2


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_pytorch>=5.0.0"


  * Usage: [Python API documentation](https://biobb-pytorch.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_pytorch>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-pytorch.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-pytorch.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_pytorch:5.0.0--pyhad2cae4_0


  * Usage:


          docker run quay.io/biocontainers/biobb_pytorch:5.0.0--pyhad2cae4_0 <command>


  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_pytorch.sif https://depot.galaxyproject.org/singularity/biobb_pytorch:5.0.0--pyhad2cae4_0


  * Usage:


          singularity exec biobb_pytorch.sif <command>


  The command list and specification can be found at the [Command Line documentation](https://biobb-pytorch.readthedocs.io/en/latest/command_line.html).



  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 5c4659adb9b7af25e264fa77fa97afc14212aca7bb45e1b383f664a0ed891c34
name: python-biobb-radiospineomics
arch: src
version: 1.1.20
epoch: 0
release: 1
url: https://github.com/TerMcs/radiospineomics
rpm_license: Apache Software License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-radiospineomics-1.1.20-1.src.rpm
checksum_type: sha256
summary:
  Biobb_Radiospineomics is a wrapped code for the extraction of conventional IVD features and radiomics from T2-weighted MRI

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_template?label=Version)](https://GitHub.com/bioexcel/biobb_template/tags/)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_template)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_template)

  [![](https://readthedocs.org/projects/biobb-template/badge/?version=latest&label=Docs)](https://biobb-template.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorial-blue?logo=youtube&logoColor=red)](https://www.youtube.com/watch?v=ou1DOGNs0xM)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_template/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_template/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_template/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_template/coverage/)
  [![](https://docs.bioexcel.eu/biobb_template/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_template/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_template?label=Last%20Commit)](https://github.com/bioexcel/biobb_template/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_template.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_template/issues/)





  Biobb_template is a complete code template to promote and facilitate the creation of
  new Biobbs by the community.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-template.readthedocs.io/en/latest/).


  v4.1.0 2023.4



  If you have no experience with anaconda, please first take a look to the [New with anaconda?](https://biobb-documentation.readthedocs.io/en/latest/first_steps.html#new-with-anaconda) section of the [official documentation](https://biobb-documentation.readthedocs.io/en/latest/).



  Although the biobb_template repository is available at GitHub and thus you can clone it, we strongly recommend you to [**download it compressed**](https://github.com/bioexcel/biobb_template/archive/master.zip) and start your new project from scratch.



  Once you have the project unzipped in your computer, please follow the next steps to create a new conda environment:

  ```console
  cd biobb_template-master
  conda env create -f conda_env/environment.yml
  ```



  Edit **conda_env/biobb_template.pth** with the paths to your *biobb_template* folder. Example:

  ```console
  /home/user_name/projects/biobb_template/
  /home/user_name/projects/biobb_template/biobb_template/biobb_template
  ```

  Copy the edited **conda_env/biobb_template.pth** file to the site-packages folder of your environment. This folder is in */[anaconda-path]/envs/biobb_template/lib/python3.7/site-packages*, where */[anaconda-path]* is usually */anaconda3* or */opt/conda*.

  ```console
  cp conda_env/biobb_template.pth /[anaconda-path]/envs/biobb_template/lib/python3.7/site-packages
  ```



  Then, activate the recently created *biobb_template* conda environment:

  ```console
  conda activate biobb_template
  ```



  This template includes some folders not standard for a biobb, such as **biobb_template/adapters/**, **biobb_template/notebooks/** or **conda_env/**. For the sake of having a pure biobb structure, you should uncomment the three last lines of the **.gitignore** file before creating a new git repository:

  ```console
  biobb_template/adapters
  biobb_template/notebooks
  conda_env
  ```
  Then, inialitize repository:

  ```console
  git init
  ```



  Additionally, it's recommendable to configure binary paths in your environment in order to ease the command line execution. More info about this subject in the [Binary path configuration](https://biobb-documentation.readthedocs.io/en/latest/execution.html#binary-path-configuration) section of the [official documentation](https://biobb-documentation.readthedocs.io/en/latest/).



  To run tests, please execute the following instruction:

  ```console
  pytest /path/to/biobb_template/biobb_template/test/unitests/test_template/test_template.py
  ```
  Or, if you prefer to show the BioBB output during the test process:

  ```console
  pytest -s /path/to/biobb_template/biobb_template/test/unitests/test_template/test_template.py
  ```



  [Click here to find the API Documentation example](https://biobb-template.readthedocs.io/en/latest/template.html) for this template and [here for Command Line documentation](http://biobb-template.readthedocs.io/en/latest/command_line.html).

  And here you can find [the full documentation](https://biobb-documentation.readthedocs.io/en/latest/) about how to build a new **BioExcel building block** from scratch.


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2022 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2022 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 9b397dd38ea44e9a3da98cdb7252edf17d91ea33fdd05963458c4dc1b74ea887
name: python-biobb-structure-utils
arch: src
version: 5.0.0
epoch: 0
release: 1
url: https://github.com/bioexcel/biobb_structure_utils
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-structure-utils-5.0.0-1.src.rpm
checksum_type: sha256
summary:
  biobb_structure_utils is the Biobb module collection to perform basic manipulations on 3d structures.

description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_structure_utils?label=Version)](https://GitHub.com/bioexcel/biobb_structure_utils/tags/)
  [![](https://img.shields.io/pypi/v/biobb-structure-utils.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-structure-utils/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_structure_utils?label=Conda)](https://anaconda.org/bioconda/biobb_structure_utils)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_structure_utils?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_structure_utils)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_structure_utils?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_structure_utils:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_structure_utils)
  [![](https://img.shields.io/pypi/pyversions/biobb-structure-utils.svg?label=Python%20Versions)](https://pypi.org/project/biobb-structure-utils/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_structure_utils)

  [![](https://readthedocs.org/projects/biobb-structure-utils/badge/?version=latest&label=Docs)](https://biobb-structure-utils.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)]()
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_structure_utils/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_structure_utils/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_structure_utils/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_structure_utils/coverage/)
  [![](https://docs.bioexcel.eu/biobb_structure_utils/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_structure_utils/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_structure_utils?label=Last%20Commit)](https://github.com/bioexcel/biobb_structure_utils/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_structure_utils.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_structure_utils/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  Biobb_structure_utils is the Biobb module collection to modify or extract information from a PDB structure file, such as pulling out a particular model or chain, removing water molecules or ligands, or renumbering or sorting atoms or residues. Biobb (BioExcel building blocks) packages are Python building blocks that create new layer of compatibility and interoperability over popular bioinformatics tools. The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](https://biobb-structure-utils.readthedocs.io/en/latest/).


  v5.0.0 2024.2



  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA, DOCKER or SINGULARITY.

  * Installation:


          pip install "biobb_structure_utils>=5.0.0"


  * Usage: [Python API documentation](https://biobb-structure-utils.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_structure_utils>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-structure-utils.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-structure-utils.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_structure_utils:5.0.0--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_structure_utils:5.0.0--pyhdfd78af_0 <command>

  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_structure_utils.sif https://depot.galaxyproject.org/singularity/biobb_structure_utils:5.0.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_structure_utils.sif <command>



  The command list and specification can be found at the [Command Line documentation](https://biobb-structure-utils.readthedocs.io/en/latest/command_line.html).


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.


  This software uses functions to read and modify GRO files based in the [GROPY](https://github.com/caizkun/gropy) library created by Zhikun Cai (caizkun@gmail.com) under the [MIT](https://github.com/caizkun/gropy/blob/master/LICENSE). In this project [GROPY](https://github.com/caizkun/gropy) has been adapted to Python 3 and our own needs.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: b93790160c22216e97951daf99c9e14310655b76ced95b7ff7d5689ea9217624
name: python-biobb-vs
arch: src
version: 5.0.0
epoch: 0
release: 1
summary: Biobb_vs is the Biobb module collection to perform virtual screening studies.
url: https://github.com/bioexcel/biobb_vs
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobb-vs-5.0.0-1.src.rpm
checksum_type: sha256
description:
  [![](https://img.shields.io/github/v/tag/bioexcel/biobb_vs?label=Version)](https://GitHub.com/bioexcel/biobb_vs/tags/)
  [![](https://img.shields.io/pypi/v/biobb-vs.svg?label=Pypi)](https://pypi.python.org/pypi/biobb-vs/)
  [![](https://img.shields.io/conda/vn/bioconda/biobb_vs?label=Conda)](https://anaconda.org/bioconda/biobb_vs)
  [![](https://img.shields.io/conda/dn/bioconda/biobb_vs?label=Conda%20Downloads)](https://anaconda.org/bioconda/biobb_vs)
  [![](https://img.shields.io/badge/Docker-Quay.io-blue)](https://quay.io/repository/biocontainers/biobb_vs?tab=tags)
  [![](https://img.shields.io/badge/Singularity-GalaxyProject-blue)](https://depot.galaxyproject.org/singularity/biobb_vs:5.0.0--pyhdfd78af_0)

  [![](https://img.shields.io/badge/OS-Unix%20%7C%20MacOS-blue)](https://github.com/bioexcel/biobb_vs)
  [![](https://img.shields.io/pypi/pyversions/biobb-vs.svg?label=Python%20Versions)](https://pypi.org/project/biobb-vs/)
  [![](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
  [![](https://img.shields.io/badge/Open%20Source%3f-Yes!-blue)](https://github.com/bioexcel/biobb_vs)

  [![](https://readthedocs.org/projects/biobb-vs/badge/?version=latest&label=Docs)](https://biobb-vs.readthedocs.io/en/latest/?badge=latest)
  [![](https://img.shields.io/website?down_message=Offline&label=Biobb%20Website&up_message=Online&url=https%3A%2F%2Fmmb.irbbarcelona.org%2Fbiobb%2F)](https://mmb.irbbarcelona.org/biobb/)
  [![](https://img.shields.io/badge/Youtube-tutorials-blue?logo=youtube&logoColor=red)](https://www.youtube.com/@BioExcelCoE/search?query=biobb)
  [![](https://zenodo.org/badge/DOI/10.1038/s41597-019-0177-4.svg)](https://doi.org/10.1038/s41597-019-0177-4)
  [![](https://img.shields.io/endpoint?color=brightgreen&url=https%3A%2F%2Fapi.juleskreuer.eu%2Fcitation-badge.php%3Fshield%26doi%3D10.1038%2Fs41597-019-0177-4)](https://www.nature.com/articles/s41597-019-0177-4#citeas)

  [![](https://docs.bioexcel.eu/biobb_vs/junit/testsbadge.svg)](https://docs.bioexcel.eu/biobb_vs/junit/report.html)
  [![](https://docs.bioexcel.eu/biobb_vs/coverage/coveragebadge.svg)](https://docs.bioexcel.eu/biobb_vs/coverage/)
  [![](https://docs.bioexcel.eu/biobb_vs/flake8/flake8badge.svg)](https://docs.bioexcel.eu/biobb_vs/flake8/)
  [![](https://img.shields.io/github/last-commit/bioexcel/biobb_vs?label=Last%20Commit)](https://github.com/bioexcel/biobb_vs/commits/master)
  [![](https://img.shields.io/github/issues/bioexcel/biobb_vs.svg?color=brightgreen&label=Issues)](https://GitHub.com/bioexcel/biobb_vs/issues/)

  [![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
  [![](https://www.bestpractices.dev/projects/8847/badge)](https://www.bestpractices.dev/projects/8847)

  [](https://bestpractices.coreinfrastructure.org/projects/8847/badge)

  [//]: # (The previous line invisible link is for compatibility with the howfairis script https://github.com/fair-software/howfairis-github-action/tree/main wich uses the old bestpractices URL)




  Biobb_vs is the Biobb module collection to perform virtual screening studies.
  Biobb (BioExcel building blocks) packages are Python building blocks that
  create new layer of compatibility and interoperability over popular
  bioinformatics tools.
  The latest documentation of this package can be found in our readthedocs site:
  [latest API documentation](http://biobb-vs.readthedocs.io/en/latest/).

  > At the time of publishing the current version, **autodock_vina has not been compiled for the new ARM mac M1/M2 chips**. So the vina.autodock_vina_run only work in these computers through docker containers.


  v5.0.0 2024.1


  Using PIP:

  > **Important:** PIP only installs the package. All the dependencies must be installed separately. To perform a complete installation, please use ANACONDA or DOCKER.

  * Installation:


          pip install "biobb_vs>=5.0.0"


  * Usage: [Python API documentation](https://biobb-vs.readthedocs.io/en/latest/modules.html)

  Using ANACONDA:

  * Installation:


          conda install -c bioconda "biobb_vs>=5.0.0"


  * Usage: With conda installation BioBBs can be used with the [Python API documentation](https://biobb-vs.readthedocs.io/en/latest/modules.html) and the [Command Line documentation](https://biobb-vs.readthedocs.io/en/latest/command_line.html)

  Using DOCKER:

  * Installation:


          docker pull quay.io/biocontainers/biobb_vs:5.0.0--pyhdfd78af_0


  * Usage:


          docker run quay.io/biocontainers/biobb_vs:5.0.0--pyhdfd78af_0 <command>

  Using SINGULARITY:

  **MacOS users**: it's strongly recommended to avoid Singularity and use **Docker** as containerization system.

  * Installation:


          singularity pull --name biobb_vs.sif https://depot.galaxyproject.org/singularity/biobb_vs:5.0.0--pyhdfd78af_0


  * Usage:


          singularity exec biobb_vs.sif <command>

  The command list and specification can be found at the [Command Line documentation](https://biobb-vs.readthedocs.io/en/latest/command_line.html).


  This software has been developed in the [MMB group](http://mmb.irbbarcelona.org) at the [BSC](http://www.bsc.es/) & [IRB](https://www.irbbarcelona.org/) for the [European BioExcel](http://bioexcel.eu/), funded by the European Commission (EU H2020 [823830](http://cordis.europa.eu/projects/823830), EU H2020 [675728](http://cordis.europa.eu/projects/675728)).

  * (c) 2015-2024 [Barcelona Supercomputing Center](https://www.bsc.es/)
  * (c) 2015-2024 [Institute for Research in Biomedicine](https://www.irbbarcelona.org/)

  Licensed under the
  [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the file LICENSE for details.

  ![](https://bioexcel.eu/wp-content/uploads/2019/04/Bioexcell_logo_1080px_transp.png "Bioexcel")


pkgId: 423f56a080db629ea9243925f07b0eab5b24c1817b915b226085e93e8770beb6
name: python-bioblend
arch: src
version: 1.3.0
epoch: 0
release: 1
summary: Library for interacting with the Galaxy API
url: https://bioblend.readthedocs.io/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioblend-1.3.0-1.src.rpm
checksum_type: sha256
description:
  .. image:: https://img.shields.io/pypi/v/bioblend.svg
      :target: https://pypi.org/project/bioblend/
      :alt: latest version available on PyPI

  .. image:: https://readthedocs.org/projects/bioblend/badge/
      :alt: Documentation Status
      :target: https://bioblend.readthedocs.io/

  .. image:: https://badges.gitter.im/galaxyproject/bioblend.svg
     :alt: Join the chat at https://gitter.im/galaxyproject/bioblend
     :target: https://gitter.im/galaxyproject/bioblend?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge


  BioBlend is a Python library for interacting with the `Galaxy`_ API.

  BioBlend is supported and tested on:

  - Python 3.8 - 3.12
  - Galaxy release 19.05 and later.

  Full docs are available at https://bioblend.readthedocs.io/ with a quick library
  overview also available in `ABOUT.rst <./ABOUT.rst>`_.

  .. References/hyperlinks used above
  .. _Galaxy: https://galaxyproject.org/


pkgId: 6339dd27f7199423fbedf40b7d3a79219e23e8eff5fec3ba82e45e31c8e6326d
name: python-bioblend
arch: src
version: 1.4.0
epoch: 0
release: 1
summary: Library for interacting with the Galaxy API
url: https://bioblend.readthedocs.io/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioblend-1.4.0-1.src.rpm
checksum_type: sha256
description:
  .. image:: https://img.shields.io/pypi/v/bioblend.svg
      :target: https://pypi.org/project/bioblend/
      :alt: latest version available on PyPI

  .. image:: https://readthedocs.org/projects/bioblend/badge/
      :alt: Documentation Status
      :target: https://bioblend.readthedocs.io/

  .. image:: https://badges.gitter.im/galaxyproject/bioblend.svg
     :alt: Join the chat at https://gitter.im/galaxyproject/bioblend
     :target: https://gitter.im/galaxyproject/bioblend?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge


  BioBlend is a Python library for interacting with the `Galaxy`_ API.

  BioBlend is supported and tested on:

  - Python 3.8 - 3.13
  - Galaxy release 19.05 and later.

  Full docs are available at https://bioblend.readthedocs.io/ with a quick library
  overview also available in `ABOUT.rst <./ABOUT.rst>`_.

  .. References/hyperlinks used above
  .. _Galaxy: https://galaxyproject.org/


pkgId: 4c37a2630b8154ea4891eededd2300985137317221f5ac8d81ff696da558088e
name: python-biobookshelf
arch: src
version: 0.2.12
epoch: 0
release: 1
url: https://github.com/ahs2202/biobookshelf
rpm_license: GPLv3
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobookshelf-0.2.12-1.src.rpm
checksum_type: sha256
summary:
  a collection of python scripts and functions for exploratory analysis of bioinformatic data in Python

description:
  [![PyPI version](https://badge.fury.io/py/biobookshelf.svg)](https://badge.fury.io/py/biobookshelf)

  A collection of python scripts and functions for exploratory analysis of bioinformatic data in Python.


pkgId: 75bfe1d3d6e07e234d83c2104ff98c50d70509c442c9d178645d75840a019431
name: python-biobrary
arch: src
version: 0.2.3
epoch: 0
release: 1
summary: A library for biological computing works
url: https://github.com/hailingfang/biobrary
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobrary-0.2.3-1.src.rpm
checksum_type: sha256
description:
  Biobrary is a python library, which contain data and methods for biological computation.



  * ete3



  * using pip

  `pip install biobrary --user`

  * manally install

  `git clone https://github.com/benjaminfang/biobrary.git`

  `cd biobrary`

  `mv biobrary $PYTHONPATH`

  where PYTHONPATH is python library searching path.



  ```
  import biobrary
  dir(biobrary)
  ```



  * bioparse

      * FASTA

          class for fasta file.

      * GTF

          class for gtf file.


  * tree

      * CircleNode

          class for phylogenic tree traverse and operations. And divide tree to circle node according
          to phylogenic distance.

rpm_license:
  MIT License  Copyright (c) 2019 Hailing Fang  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.


pkgId: d4b01799173c2e182a3feeffee54566c593ee7b244379fc7ef7a7a699bdbd1d5
name: python-biobricks
arch: src
version: 0.3.9
epoch: 0
release: 1
summary: Biobricks automates bioinformatics data.
url: https://pypi.org/project/biobricks/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobricks-0.3.9-1.src.rpm
checksum_type: sha256
description:
  <!-- badges: start -->
  [![codecov](https://codecov.io/gh/biobricks-ai/biobricks/branch/master/graph/badge.svg?token=J041MF0JKG)](https://codecov.io/gh/biobricks-ai/biobricks)
  <!-- badges: end -->

  BioBricks makes loading data from biological datasets easy.

  ```bash
  pipx install biobricks
  ```


  To get started configure biobricks with a path for bricks and a biobricks.ai token:
  ```
  biobricks configure
  > Choose path to store bricks: <input a local path>
  > Input a token from biobricks.ai/token: <this is your access token>
  ```



  To install a brick the command line command:
  ```sh
  > biobricks install tox21
  ```

  You can then use the brick in python as below:
  ```python
  import biobricks as bb
  bb.assets('tox21') # get the paths for the 'tox21' brick
  ```

  To list the bricks currently available visit [status.biobricks.ai](https://status.biobricks.ai)


pkgId: 07373d6cec5512059068d088cf88657f6c23a6185a2397c802fb1da0510a4c03
name: python-biobroker
arch: src
version: 0.1.1
epoch: 0
release: 1
summary: A package to easily broker data and metadata to repositories.
url: https://pypi.org/project/biobroker/
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biobroker-0.1.1-1.src.rpm
checksum_type: sha256
description:
  This repository contains the source code for the `Biobroker` python package. This package contains
  multiple utilities, but the overall philosophy/goal can be described in a phrase:


  >Given an input file of any type containing metadata about entities, broker it to any archive
  > and return a human-readable result.

  I understand that this is a very ambitious project; for starters, the variety of existing archives
  is way too high. For now, this package is being developed by extending my knowledge on the european
  genomics/bio archives hosted by EMBL-EBI. It may be that some archives can't just be adapted, but I
  will be very happy if this library can be extended to just work with enough archives to ensure a biology
  laboratory can set up an automatic brokering process that works for them.



  ```shell
  pip3 install biobroker
  ```



  This library does not support CLI access at the moment (Maybe in the future I could think of a CLI; although I would
  much rather have a simple GUI). As such, you will have to install and write scripts using the objects provided.

  For usage, please see the examples/ folder in the [repository](https://github.com/ESapenaVentura/biobroker).

  For extensive documentation about the overall infrastructure and each class, please see the
  [ReadTheDocs page](https://biobroker.readthedocs.io/en/latest/)

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: 719954fdb4a55088955912435294cc216000f9ed1d721393a73f6b20711be37e
name: python-bioc
arch: src
version: 2.1
epoch: 0
release: 1
summary: bioc - Processing BioC, Brat, and PubTator with Python.
url: https://github.com/bionlplab/bioc
rpm_license: MIT License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioc-2.1-1.src.rpm
checksum_type: sha256
description:
  [![Build
  status](https://github.com/bionlplab/bioc/actions/workflows/pytest.yml/badge.svg)](https://github.com/bionlplab/bioc/)
  [![Latest version on
  PyPI](https://img.shields.io/pypi/v/bioc.svg)](https://pypi.python.org/pypi/bioc)
  [![Downloads](https://img.shields.io/pypi/dm/bioc.svg)](https://pypi.python.org/pypi/bioc)
  [![License](https://img.shields.io/pypi/l/bioc.svg)](https://opensource.org/licenses/MIT)
  [![codecov](https://codecov.io/gh/bionlplab/bioc/branch/master/graph/badge.svg?token=3kEUctqxTx)](https://codecov.io/gh/bionlplab/bioc)

  [BioC XML / JSON format](http://bioc.sourceforge.net/) can be used to
  share text documents and annotations.

  [Brat standoff format](https://brat.nlplab.org/standoff.html) is created by the brat annotation tool to store
  annotations on disk in a standoff format. annotations are stored separately from the annotated document text, which is
  never modified by the tool.

  [PubTator format](https://www.ncbi.nlm.nih.gov/research/pubtator/) is created by the PutTator Central system.

  `bioc` exposes an API familiar to users of the standard library
  `marshal` and `pickle` modules.

  Development of `bioc` happens on GitHub:
  <https://github.com/bionlplab/bioc>



  Installing `bioc`

  ```shell
  $ pip install bioc
  ```



  Encoding the BioC collection object `collection`:

  ```python
  from bioc import biocxml

  with open(filename, 'w') as fp:
      biocxml.dump(collection, fp)
  ```

  Decoding the BioC XML file:

  ```python
  from bioc import biocxml

  with open(filename, 'r') as fp:
      collection = biocxml.load(fp)
  ```



  Encoding the Brat document

  ```python
  from bioc import brat

  with open(annpath, 'w') as ann_fp, open(txtpath, 'w') as text_fp:
      brat.dump(doc, text_fp, ann_fp)
  ```

  Decoding the Brat document:

  ```python
  from bioc import brat

  with open(annpath) as ann_fp, open(txtpath) as text_fp:
      doc = brat.load(text_fp, ann_fp)
  ```



  Encoding the PubTator document object `doc`:

  ```python
  from bioc import pubtator

  with open(filename, 'w') as fp:
      pubtator.dump([doc], fp)
  ```

  Decoding the PubTator file

  ```python
  from bioc import pubtator

  with open(filename, 'r') as fp:
      docs = pubtator.load(fp)
  ```



  You will find complete documentation at our [Read the Docs
  site](https://bioc.readthedocs.io/en/latest/index.html).



  You can find information about contributing to bioc at our [Contribution
  page](https://bioc.readthedocs.io/en/latest/contribute.html).



  If you use bioc in your research, please cite the following paper:

    - Comeau DC, Doğan RI, Ciccarese P, Cohen KB, Krallinger M, Leitner F, Lu Z, Peng Y, Rinaldi F, Torii M,
      Valencia V, Verspoor K, Wiegers TC, Wu CH, Wilbur WJ. BioC: a minimalist approach to interoperability
      for biomedical text processing. Database (Oxford). 2013;2013:bat064. doi: 10.1093/database/bat064.
      Print 2013. PMID: 24048470; PMCID: PMC3889917



  This work is supported by the National Library of Medicine under Award No.
  4R00LM013001.



  Copyright BioNLP Lab at Weill Cornell Medicine, 2023.

  Distributed under the terms of the [MIT](https://github.com/bionlplab/bioc/blob/master/LICENSE) license,
  bioc is free and open source software.


pkgId: 68e2a3b825b7278e93f8c36a50fbe9401255eac2578e9d2a617919aa069840b7
name: python-biocarta
arch: src
version: 0.2.26
epoch: 0
release: 1
summary: please add a summary manually as the author left a blank one
url: https://github.com/rictjo/biocarta
rpm_license: Apache Software License
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biocarta-0.2.26-1.src.rpm
checksum_type: sha256
description:
  Creating Cartographic Representations of Biological Data
  [![DOI](https://zenodo.org/badge/578172132.svg)](https://zenodo.org/badge/latestdoi/578172132)


  ```
  pip install biocarta
  ```


  ```
  if __name__ == '__main__' :
      from biocarta.quantification import full_mapping
      
      adf = pd.read_csv('analytes.tsv',sep='\t',index_col=0)
      
      
      adf = adf.iloc[ np.inf != np.abs( 1.0/np.std(adf.values,1) ) ,
                      np.inf != np.abs( 1.0/np.std(adf.values,0) ) ].copy()
      
      
      
      jdf = pd.read_csv('journal.tsv',sep='\t',index_col=0)
      jdf = jdf.loc[:,adf.columns.values]
      
      alignment_label , sample_label = 'Disease' , None
      add_labels = ['Cell-line']
      
      cmd                = 'max'
      
      bVerbose           = True
      
      bExtreme           = True
      
      n_clusters         = [20,40,60,80,100]
      
      n_components       = None
      umap_dimension     = 2
      n_neighbors        = 20
      local_connectivity = 20.
      transform_seed     = 42
      
      print ( adf , jdf )
      
      
      distance_type = 'covariation' # BECOMES CO-EXPRESSION BASED
      
      results = full_mapping ( adf , jdf                  ,
          bVerbose = bVerbose             ,
          bExtreme = bExtreme             ,
          n_clusters = n_clusters         ,
          n_components = n_components     ,
          distance_type = distance_type   ,
          umap_dimension = umap_dimension ,
          umap_n_neighbors = n_neighbors  ,
          umap_local_connectivity = local_connectivity ,
          umap_seed = transform_seed      ,
          hierarchy_cmd = cmd             ,
          add_labels = add_labels         ,
          alignment_label = alignment_label ,
          sample_label = None     )
      
      map_analytes        = results[0]
      map_samples         = results[1]
      hierarchy_analytes  = results[2]
      hierarchy_samples   = results[3]
  ```
  or just call it using the default values:
  ```
  import pandas as pd
  import numpy  as np

  if __name__ == '__main__' :
      from biocarta.quantification import full_mapping
      
      adf = pd.read_csv('analytes.tsv',sep='\t',index_col=0)
      
      adf = adf.iloc[ np.inf != np.abs( 1.0/np.std(adf.values,1) ) ,
                      np.inf != np.abs( 1.0/np.std(adf.values,0) ) ].copy()
      jdf = pd.read_csv('journal.tsv',sep='\t',index_col=0)
      jdf = jdf.loc[:,adf.columns.values]
      
      alignment_label , sample_label = 'Disease' , None
      add_labels = ['Cell-line']
      
      results = full_mapping ( adf , jdf  ,
          bVerbose = True			,
          n_clusters = [40,80,120]        ,
          add_labels = add_labels         ,
          alignment_label = alignment_label )
      
      map_analytes        = results[0]
      map_samples         = results[1]
      hierarchy_analytes  = results[2]
      hierarchy_samples   = results[3]
  ```
  and plotting the information of the map analytes yields :
  [Cancer Disease Example](https://gist.github.com/rictjo/9cc40579914a51bffe7df442fec140f4)

  You can also run an alternative algorithm where the UMAP coordinates are employed directly for clustering by setting
  ```
      results = full_mapping ( adf , jdf  ,
          bVerbose = True			        ,
          bUseUmap = True                 ,
          n_clusters = [40,80,120]        ,
          add_labels = add_labels         ,
          alignment_label = alignment_label )
  ```
  with the following [results](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/8be5b5a9cc7f06ea7455d6c6ecc11ad8/raw/e00ea663a1218718f542744a939e0b05c604e8ab/index.html).

  Download the zip and open the html index:
  ```
  chromium index.html
  ```



  The clustering visualisations were created using the [Biocarta](https://pypi.org/project/biocarta/) and [hvplot](https://pypi.org/project/hvplot/) :

  What groupings corresponds to biomarker variance that describe them? Here are two visualisations of that:

  Diseases :
  [cancers](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/870d8cc26ede12d00b7ae60109feebdc/raw/42beb98a82477e9c809f99d3498966fc564846b8/index.html) [biocarta gfa enrichment](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/afcca63470e5c9398372276f9ab43d42/raw/6e68e1da85fdb6d1b1aeec8c351831a3aad83e9d/index.html) [biocarta treemap cluster 61](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/146ba66109c6554684dc387348d21a82/raw/a32f1e7c80cc6ebe53c33039e2adfb4512e3ce4b/index.html)

  Tissues :
  [tissues](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/5e760b8c4fd3da4842813a4a0cea422c/raw/caa18f0391dc389fb8fc56ae8ac2bc4f7046a939/index.html)

  Single Cells:
  [single cells](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/eb118f70c1d173f2e6d51f06779827d2/raw/c7fd997caf232df3d6bbbd80d607463812d461a1/index.html) [biocarta gfa enrichment](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/64ee6e4d2bacb31715ec46b65c9d441d/raw/a5d91114cc4ab784f865277264efe5f628ea018e/index.html) [biocarta treemap cluster 47](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/34b320ea503b79e29808b063a7266714/raw/eaf39e740eb8baaadf0d08faab521a152c282009/index.html)

  Blood Cells:
  [blood cells](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/200153c58767d8b5162e66688ff4d669/raw/cfb74069d5cc9fc58e3558c753caaa60d4ba5e9b/index.html)
  [biocarta gfa enrichment](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/42ec85df088a0c40de339a78322594bd/raw/0725bea467b0c153298655e3a0555670a812e80f/index.html)
  [biocarta treemap cluster 2](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/d754528cf594087e509fe44fa071c178/raw/a78a82066e3d6aa2971aba2a64543a4018241372/index.html)


pkgId: 0eaf988d6c2c7779752fdd5ebbc3855c45e2b4a326785b85472fbf5ddddc57d2
name: python-biochatter
arch: src
version: 0.7.7
epoch: 0
release: 1
summary: Backend library for conversational AI in biomedicine
url: https://github.com/biocypher/biochatter
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biochatter-0.7.7-1.src.rpm
checksum_type: sha256
description:
  |     |     |     |     |
  | --- | --- | --- | --- |
  | __License__ | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) | __Python__ | [![Python](https://img.shields.io/pypi/pyversions/biochatter)](https://www.python.org) |
  | __Package__ | [![PyPI version](https://img.shields.io/pypi/v/biochatter)](https://pypi.org/project/biochatter/) [![Downloads](https://static.pepy.tech/badge/biochatter)](https://pepy.tech/project/biochatter) [![DOI](https://zenodo.org/badge/650181006.svg)](https://zenodo.org/doi/10.5281/zenodo.10777945) | __Build status__ | [![CI](https://github.com/biocypher/biochatter/actions/workflows/ci.yaml/badge.svg)](https://github.com/biocypher/biochatter/actions/workflows/ci.yaml) [![Docs](https://github.com/biocypher/biochatter/actions/workflows/docs.yaml/badge.svg)](https://github.com/biocypher/biochatter/actions/workflows/docs.yaml) |
  | __Tests__ | [![Coverage](https://raw.githubusercontent.com/biocypher/biochatter/coverage/coverage.svg)](https://github.com/biocypher/biochatter/actions/workflows/ci.yaml) | __Docker__ | [![Latest image](https://img.shields.io/docker/v/biocypher/chatgse)](https://hub.docker.com/repository/docker/biocypher/chatgse/general) [![Image size](https://img.shields.io/docker/image-size/biocypher/chatgse/latest)](https://hub.docker.com/repository/docker/biocypher/chatgse/general) |
  | __Development__ | [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://black.readthedocs.io/en/stable/) | __Contributions__ | [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](CONTRIBUTING.md) |



  Generative AI models have shown tremendous usefulness in increasing
  accessibility and automation of a wide range of tasks. Yet, their application to
  the biomedical domain is still limited, in part due to the lack of a common
  framework for deploying, testing, and evaluating the diverse models and
  auxiliary technologies that are needed.  This repository contains the
  `biochatter` Python package, a generic backend library for the connection of
  biomedical applications to conversational AI.

  The library is described in [this preprint](https://arxiv.org/abs/2305.06488) and
  used in various demo applications for showcasing its use:

  - a simple Python-based frontend called [BioChatter
  Light](https://light.biochatter.org), which we develop at
  https://github.com/biocypher/biochatter-light;

  - an advanced Next.js-based frontend called [BioChatter
  Next](https://next.biochatter.org), which we develop at
  https://github.com/biocypher/biochatter-next;

  - a RESTful API server for use by the Next frontend (and any other REST-based
  application) at https://github.com/biocypher/biochatter-server.

  BioChatter is part of the [BioCypher](https://github.com/biocypher) ecosystem,
  connecting natively to BioCypher knowledge graphs. The BioChatter paper is
  being written [here](https://github.com/biocypher/biochatter-paper).



  To use the package, install it from PyPI, for instance using pip (`pip install
  biochatter`) or Poetry (`poetry add biochatter`).



  The package has some optional dependencies that can be installed using the
  following extras (e.g. `pip install biochatter[xinference]`):

  - `xinference`: support for querying open-source LLMs through Xorbits Inference

  - `podcast`: support for podcast text-to-speech (for the free Google TTS; the
  paid OpenAI TTS can be used without this extra)

  - `streamlit`: support for streamlit UI functions (used in BioChatter Light)



  Check out the [documentation](https://biochatter.org/) for examples, use cases,
  and more information. Many common functionalities covered by BioChatter can be
  seen in use in the [BioChatter
  Light](https://github.com/biocypher/biochatter-light) code base.  [![Built with
  Material for
  MkDocs](https://img.shields.io/badge/Material_for_MkDocs-526CFE?style=for-the-badge&logo=MaterialForMkDocs&logoColor=white)](https://squidfunk.github.io/mkdocs-material/)



  We are very happy about contributions from the community, large and small!
  If you would like to contribute to BioCypher development, please refer to
  our [contribution guidelines](CONTRIBUTING.md) and the [developer
  docs](DEVELOPER.md). :)

  If you want to ask informal questions, talk about dev things, or just chat,
  please join our community at https://biocypher.zulipchat.com!

  > **Imposter syndrome disclaimer:** We want your help. No, really. There may be a little voice inside your head that is telling you that you're not ready, that you aren't skilled enough to contribute. We assure you that the little voice in your head is wrong. Most importantly, there are many valuable ways to contribute besides writing code.
  >
  > This disclaimer was adapted from the [Pooch](https://github.com/fatiando/pooch) project.



  Check out [this repository](https://github.com/csbl-br/awesome-compbio-chatgpt)
  for more info on computational biology usage of large language models.



  If you're on Apple Silicon, you may encounter issues with the `grpcio`
  dependency (`grpc` library, which is used in `pymilvus`). If so, try to install
  the binary from source after removing the installed package from the virtual
  environment from
  [here](https://stackoverflow.com/questions/72620996/apple-m1-symbol-not-found-cfrelease-while-running-python-app):

  ```bash
  pip uninstall grpcio
  export GRPC_PYTHON_LDFLAGS=" -framework CoreFoundation"
  pip install grpcio==1.53.0 --no-binary :all:
  ```


pkgId: b2077ea526b748d1f7cbc54f22055d3100ffe2673a0a879f015f9adcb0ee75ad
name: python-biocre
arch: src
version: 1.17
epoch: 0
release: 1
summary: please add a summary manually as the author left a blank one
url: https://pypi.org/project/biocre/
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biocre-1.17-1.src.rpm
checksum_type: sha256
description:
  <img src="figure/BioCRE_readme.png" width="600" align='center'>
  BioCRE, an innovative computational framework, employs a sophisticated bi-orientation regression model to analyze multi-omics datasets. This approach deciphers the complex interactions within Gene Regulatory Networks (GRNs) at the chromosomal scale, pinpointing crucial Cis-Regulatory Elements (CREs) that play pivotal roles in gene expression and regulation. By integrating diverse genomic information, BioCRE enhances our understanding of cellular processes and disease mechanisms, offering new avenues for therapeutic intervention and personalized medicine strategies.


  Installation with virtual environment are suggested:
  ```
  conda create -n biocre python=3.8
  ```
  To install BioCRE, make sure you have [PyTorch](https://pytorch.org/) installed.
  ```
  pip install torch torchvision torchaudio -i https://mirrors.cloud.tencent.com/pypi/simple
  ```
  Then install BioCRE by pip:
  ```
  pip install biocre
  ```


  Executing the BioCRE pipeline necessitates the provision of three primary inputs: `rna_adata`, `atac_adata`, and `meta_data`:
  * `rna_adata` - This represents the snRNA-seq data encapsulated in an AnnData object.
  * `atac_adata` - This represents the snATAC-seq data encapsulated in an AnnData object.
  * `meta_data` - This data serves as a genomic annotation of genes and peaks. Typically, the cellranger output file named 'features.tsv.gz' serves as the metadata.

  To ensure accurate integration and comparison across multi-omics data, it is imperative that the cells represented in both AnnData objects (rna_adata and atac_adata) are identical. This means that each cell in one dataset should correspond directly to a cell in the other dataset, maintaining consistency in cell identity and order.Pre-processing steps should be applied to the cells prior to inputting them into the analysis pipeline. These pre-processing measures typically include quality control checks, normalization, filtering out low-quality cells or features, and batch effect correction, if necessary. By performing these steps, you enhance the reliability and interpretability of downstream multi-omics analyses, ensuring that any observed correlations or differences are biologically meaningful rather than artifacts of technical variability.

  After preparing the input data, you can run BioCRE using following code:
  ```
  result = linkage(rna_adata,
                   atac_adata,
                   meta_data, ,
                   min_cell=10,
                   lr=0.5,
                   max_iter=500,
                   lambda_l2=0.1,
                   downsample=None)
  ```
  The returned list encompasses the linkage for each chromosome. Given that BioCRE can be resource-intensive, employing significant amounts of memory, you may opt to expedite processing and reduce memory consumption through cell downsampling. Setting the desired cell count via the `downsample` parameter facilitates this. Alternatively, utilizing the metacells derived from rna_adata and atac_adata can also speed computations, offering a strategy to manage large datasets more efficiently.

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: 6bd5ad957cca05ab2c392951930b8245448db54dd09d589b0929f1b71e8e8500
name: python-biocutils
arch: src
version: 0.1.6
epoch: 0
release: 1
summary: Utilities to use across the biocpy packages.
url: https://github.com/biocpy/biocutils
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biocutils-0.1.6-1.src.rpm
checksum_type: sha256
description:
  <!-- These are examples of badges you might want to add to your README:
       please update the URLs accordingly

  [![Built Status](https://api.cirrus-ci.com/github/<USER>/biocutils.svg?branch=main)](https://cirrus-ci.com/github/<USER>/biocutils)
  [![ReadTheDocs](https://readthedocs.org/projects/biocutils/badge/?version=latest)](https://biocutils.readthedocs.io/en/stable/)
  [![Coveralls](https://img.shields.io/coveralls/github/<USER>/biocutils/main.svg)](https://coveralls.io/r/<USER>/biocutils)
  [![Conda-Forge](https://img.shields.io/conda/vn/conda-forge/biocutils.svg)](https://anaconda.org/conda-forge/biocutils)
  [![Twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Twitter)](https://twitter.com/biocutils)
  -->



  [![Project generated with PyScaffold](https://img.shields.io/badge/-PyScaffold-005CA0?logo=pyscaffold)](https://pyscaffold.org/)
  [![PyPI-Server](https://img.shields.io/pypi/v/biocutils.svg)](https://pypi.org/project/biocutils/)
  [![Monthly Downloads](https://pepy.tech/badge/biocutils/month)](https://pepy.tech/project/biocutils)
  ![Unit tests](https://github.com/BiocPy/biocutils/actions/workflows/pypi-test.yml/badge.svg)



  This repository contains a variety of simple utilities for the [BiocPy](https://github.com/BiocPy) project,
  mostly convenient aspects of R that aren't provided by base Python.
  The aim is to simplify development of higher-level packages like [**scranpy**](https://github.com/BiocPy/scranpy) and [**singler**](https://github.com/BiocPy/singler)
  that would otherwise have to implement these methods individually.





  ```python
  import biocutils
  biocutils.match(["A", "C", "E"], ["A", "B", "C", "D", "E"])

  ```



  ```python
  import biocutils
  biocutils.factor(["A", "B", "B", "A", "C", "D", "C", "D"])

  ```



  ```python
  import biocutils
  biocutils.intersect(["A", "B", "C", "D"], ["D", "A", "E"])

  ```



  ```python
  import biocutils
  biocutils.union(["A", "B", "C", "D"], ["D", "A", "E"])

  ```



  ```python
  import biocutils
  biocutils.subset(["A", "B", "C", "D", "E"], [0, 2, 4])


  import numpy as np
  y = np.array([10, 20, 30, 40, 50])
  biocutils.subset(y, [0, 2, 4])

  ```



  Checks if all elements of a list or tuple are of the same type.

  ```python
  import biocutils
  import numpy as np

  x = [np.random.rand(3), np.random.rand(3, 2)]
  biocutils.is_list_of_type(x, np.ndarray)

  ```


pkgId: 1b7938aa40c6be60f4a6aede525c2d35e771010f843c949ffeb5cfae5c9a1f54
name: python-biocypher
arch: src
version: 0.6.1
epoch: 0
release: 1
summary: A unifying framework for biomedical research knowledge graphs
url: https://github.com/biocypher/biocypher
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biocypher-0.6.1-1.src.rpm
checksum_type: sha256
description:
  |     |     |     |     |
  | --- | --- | --- | --- |
  | __License__ | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) | __Python__ | [![Python](https://img.shields.io/pypi/pyversions/biocypher)](https://www.python.org) |
  | __Package__ | [![PyPI version](https://img.shields.io/pypi/v/biocypher)](https://pypi.org/project/biocypher/) [![Downloads](https://static.pepy.tech/badge/biocypher)](https://pepy.tech/project/biocypher) [![DOI](https://zenodo.org/badge/405143648.svg)](https://zenodo.org/doi/10.5281/zenodo.10158203) | __Build status__ | [![CI](https://github.com/biocypher/biocypher/actions/workflows/tests_and_code_quality.yaml/badge.svg)](https://github.com/biocypher/biocypher/actions/workflows/tests_and_code_quality.yaml) [![Docs build](https://github.com/biocypher/biocypher/actions/workflows/docs.yaml/badge.svg)](https://github.com/biocypher/biocypher/actions/workflows/docs.yaml) |
  | __Tests__ | [![Coverage](https://raw.githubusercontent.com/biocypher/biocypher/coverage/coverage.svg)](https://github.com/biocypher/biocypher/actions/workflows/tests_and_code_quality.yaml) | __Docker__ | [![Latest image](https://img.shields.io/docker/v/biocypher/base)](https://hub.docker.com/repository/docker/biocypher/base/general) [![Image size](https://img.shields.io/docker/image-size/biocypher/base/latest)](https://hub.docker.com/repository/docker/biocypher/base/general) |
  | __Development__ | [![pyOpenSci](https://tinyurl.com/y22nb8up)](https://github.com/pyOpenSci/software-review/issues/110) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit) [![Code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://black.readthedocs.io/en/stable/) | __Contributions__ | [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](CONTRIBUTING.md) [![Powered by the Bioregistry](https://img.shields.io/static/v1?label=Powered%20by&message=Bioregistry&color=BA274A&style=flat&logo=image/png;base64,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)](https://github.com/biopragmatics/bioregistry) |






  Knowledge graphs (KGs) are an [approach to knowledge
  representation](https://en.wikipedia.org/wiki/Knowledge_graph) that uses graph
  structure to facilitate exploration and analysis of complex data, often
  leveraging semantic information. They are popular in many research areas,
  including the life sciences, due to their versatile use, for instance in data
  storage, integration, reasoning, and more recently in artificial intelligence.
  The creation of KGs is a complex task; BioCypher helps you in creating and
  maintaining your own KG. For more overview, usage notes, and a tutorial, read
  the docs [here](https://biocypher.org).

  <img
      style="display: block;
             margin-left: auto;
             margin-right: auto;
             width: 70%;"
      src="docs/graphical_abstract.png"
      alt="Graphical Abstract">
  </img>



  Tutorial and developer docs at https://biocypher.org. For a quickstart into your
  own pipeline, you can refer to our [project
  template](https://github.com/biocypher/project-template), and for an overview of
  existing and planned adapters for resources and outputs, as well as other
  features, visit our [GitHub Project
  Board](https://github.com/orgs/biocypher/projects/3/views/2).



  Install the package from PyPI using `pip install biocypher`. More comprehensive
  installation and configuration instructions can be found
  [here](https://biocypher.org/installation.html).

  Exemplary usage of BioCypher to build a graph database is shown in our tutorial
  and the various pipelines we have created. You can find these on the [Components
  Project Board](https://github.com/orgs/biocypher/projects/3/views/2).



  We are very happy about contributions from the community, large and small!
  If you would like to contribute to BioCypher development, please refer to
  our [contribution guidelines](CONTRIBUTING.md). :)

  If you want to ask informal questions, talk about dev things, or just chat,
  please join our community at https://biocypher.zulipchat.com!

  > **Imposter syndrome disclaimer:** We want your help. No, really. There may be a little voice inside your head that is telling you that you're not ready, that you aren't skilled enough to contribute. We assure you that the little voice in your head is wrong. Most importantly, there are many valuable ways to contribute besides writing code.
  >
  > This disclaimer was adapted from the [Pooch](https://github.com/fatiando/pooch) project.



  The BioCypher paper has been peer-reviewed in [Nature
  Biotechnology](https://www.nature.com/articles/s41587-023-01848-y). It is
  available as a self-archived version on
  [Zenodo](https://zenodo.org/records/10320714), online version
  [here](https://biocypher.github.io/biocypher-paper/).  Before, it was available
  as a preprint at https://arxiv.org/abs/2212.13543.



  This project has received funding from the European Union’s Horizon 2020
  research and innovation programme under grant agreement No 965193 for DECIDER
  and No 116030 for TransQST.


pkgId: 799bb89dd0172a02afee0ae6497cc78b62287ef61ecf716f72287b7d3f9fc2d9
name: python-biodata
arch: src
version: 0.1.5
epoch: 0
release: 1
summary: A python package for common biological data I/O
url: https://github.com/aldenleung/biodata/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biodata-0.1.5-1.src.rpm
checksum_type: sha256
description:
  The biodata package provides a standard API to access different kinds of biological data using similar syntax. For each data type, data could be accessed in either stream- or index-based method. To obtain a stream of entries, data is processed by the reader (XX-Reader) and writer (XX-Writer). For example, `FASTAReader` is used to process a FASTA file. A call to the method `read()` from `FASTAReader` yields a `FASTA` object. To obtain entries from an indexed file, random access is supported through the XX-IReader. For example, an indexed FASTA file (with the associated .fai file) can be accessed by  `FASTAIReader` to yield a `FASTA` object.





  ```
  pip install biodata
  ```



  For more advanced use, please see the Basic Usage section.

  ```python

  from biodata.fasta import FASTAReader
  seq_dict = FASTAReader.read_all(lambda fr: {f.name:f.seq for f in fr}, "input.fa")

  from biodata.bed import BEDReader
  from genomictools import GenomicCollection
  beds = BEDReader.read_all(GenomicCollection, "input.bed")

  from biodata.gff import GFF3Reader
  from genomictools import GenomicCollection
  gff3s = GFF3Reader.read_all(GenomicCollection, "input.gff3")

  from biodata.gff import GTFReader
  from genomictools import GenomicCollection
  gtfs = GTFReader.read_all(GenomicCollection, "input.gtf")
  ```





  We will demonstrate the use of biodata package using FASTA file.

  ```
  >seq1
  ACGT
  >seq2
  CCCGGGAAA
  ```





  ```python
  from biodata.fasta import FASTAReader
  with FASTAReader(filename) as fr:
  	f = fr.read()
  	print(f.name, f.seq) # seq1 ACGT
  ```



  ```python
  from biodata.fasta import FASTAReader
  with FASTAReader(filename) as fr:
  	for f in fr:
  		print(f.name, f.seq)



  with BEDReader(bedfile) as br:
  	for b in br:
  		print(b.name, str(b.genomic_pos))
  ```



  ```python
  from biodata.fasta import FASTAReader
  fasta_entries = FASTAReader.read_all(list, filename) # list of FASTA

  seq_dict = FASTAReader.read_all(lambda fr: {f.name:f.seq for f in fr}, filename)




  from biodata.bed import BEDReader
  from genomictools import GenomicCollection
  beds = BEDReader.read_all(GenomicCollection, filename)
  ```



  ```python
  from biodata.fasta import FASTAReader
  with FASTAReader(filename) as fr:
  	f = fr.peek() # Only peek the entry without proceeding to the next entry
  	print(f.name, f.seq) # seq1 ACGT
  	f = fr.read() # Read the entry and proceed to the next entry
  	print(f.name, f.seq) # seq1 ACGT
  	f = fr.read()
  	print(f.name, f.seq) # seq2 CCCGGGAAA
  ```



  ```python

  import io
  from biodata.fasta import FASTAReader
  FASTAReader.read_all(list, io.StringIO(">seq1\nACGT\n>seq2\nCCCGGGAAA\n"))
  ```




  ```python
  from biodata.fasta import FASTAIReader
  from genomictools import GenomicPos, StrandedGenomicPos
  from biodata.bed import BED

  fir = FASTAIReader(filename, faifilename) # fai file can be created using 'samtools faidx filename'
  f = fir[GenomicPos("seq2:1-4")] # Read from a region without strand
  print(f.name, f.seq) # seq2:1-4 CCCG
  f = fir[StrandedGenomicPos("seq2:1-4:-")] # Read from a region with strand
  print(f.name, f.seq) # seq2:1-4:- CGGG
  f = fir[BED("seq2", 0, 4, strand="-")] # Equivalent to StrandedGenomicPos but a BED entry is used
  print(f.name, f.seq) # seq2:1-4:- CGGG
  fir.close()
  ```





  ```python
  from biodata.fasta import FASTA, FASTAWriter
  with FASTAWriter(output_file) as fw:
  	fw.write(FASTA("seq1", "ACGT"))
  	fw.write(FASTA("seq2", "CCCGGGAAA"))
  ```



  ```python
  from biodata.fasta import FASTA, FASTAWriter
  fasta_entries = [FASTA("seq1", "ACGT"), FASTA("seq2", "CCCGGGAAA")]
  FASTAWriter.write_all(fasta_entries, output_file)
  ```





  1. Delimited - tsv, csv (`biodata.delimited`)
  2. FASTA, FASTQ (`biodata.fasta`)
  3. BED3, BED, BEDX, BEDGraph, BEDPE, BigBed, ENCODENarrowPeak (`biodata.bed`)
  4. GFF3, GTF (GFF2) (`biodata.gff`)
  5. BigWig (`biodata.bigwig`, require `pyBigWig` package)
  6. bwa FastMap (`biodata.bwa.fastmap`)
  7. MEME Motif Format (`biodata.meme`)



  Future supported formats.

  1. VCF (`biodata.vcf`)



  Users can extend the `BaseReader` and `BaseWriter` class easily to accommodate other formats not currently supported by `biodata`.

  ```python
  class ExampleNode(object):
  	def __init__(self, value1, value2):
  		self.value1 = value1
  		self.value2 = value2

  class ExampleNodeReader(BaseReader):
  	def __init__(self, filename):
  		super(ExampleNodeReader, self).__init__(filename)
  	def _read(self):
  		if self.line is None:
  			return None
  		words_array = self.line.split('\t')
  		value1 = words_array[0]
  		value2 = words_array[1]
  		self.proceed_next_line()
  		return ExampleNode(value1, value2)

  filename = "SomeDocument.txt"
  with ExampleNodeReader(filename) as er:
  	for node in er:
  		print(node.value1, node.value2)
  ```


pkgId: d67ce39af11618633908660205e15865a469298d8859d4b5875a6dd6a2892a88
name: python-biodome
arch: src
version: 2022.9.1
epoch: 0
release: 1
summary: biodome
url: https://github.com/cjrh/biodome
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biodome-2022.9.1-1.src.rpm
checksum_type: sha256
description:
  *Controlled environments*
  Reading environment variables with ``os.environ`` is pretty easy, but after
  a while one gets pretty tired of having to cast variables to the right types
  and handling fallback to defaults.
  This library provides a clean way read environment variables and fall back
  to defaults in a sane way.
  **How you were doing it:**
      import os
      try:
          TIMEOUT = int(os.environ.get('TIMEOUT', 10))
      except ValueError:
          TIMEOUT = 10
  Wordy, boilerplate, DRY violation, etc.
  **How you will be doing it:**
      import biodome
      TIMEOUT = biodome.environ.get('TIMEOUT', 10)
  That's right, it becomes a single line. But there's a magic trick here: how
  does ``biodome`` know that ``TIMEOUT`` should be set to an ``int``? It knows
  because it looks at the type of the default arguments. This works for a bunch
  of different things:
      
      os.environ['IGNORE_KEYS'] = '[1, 2, 3]'
      biodome.environ.get('TIMEOUT', []) == [1, 2, 3]
      
      os.environ['SETTINGS'] = '{"a": 1, "b": 2}'
      biodome.environ.get('SETTINGS', {}) == dict(a=1, b=2)
  If you look carefully at the above, you can see that we *set* the data via
  the stdlib ``os.environ`` dictionary; that's right, ``biodome.environ`` is a
  **drop-in replacement** for ``os.environ``. You don't even have to switch out
  your entire codebase, you can do it piece by piece.
  And while we're on the subject of *setting* env vars, with ``biodome`` you
  don't have to cast them first, it does string casting internally automatically,
  unlike ``os.environ``:
      
      biodome.environ['SETTINGS'] = dict(b=2, a=1)  # No cast required
      biodome.environ.get('SETTINGS', {}) == dict(a=1, b=2)


pkgId: 8599f4d112107290b67919894e4299792002541c728b5d11d3c3fd72921d75b4
name: python-biofilm
arch: src
version: 0.1.126
epoch: 0
release: 1
summary: make it easy to generate a FILtering Models for BIOlogical data
url: https://github.com/smautner/biofilm
rpm_license: GPLv3
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biofilm-0.1.126-1.src.rpm
checksum_type: sha256
description:
  ```
  pip install biofilm
  conda install -c conda-forge biofilm
  ```





  ```
  --methods str+ any  'extra_trees', 'passive_aggressive', 'random_forest', 'sgd', 'gradient_boosting', 'mlp'
  --out str jsongoeshere
  --n_jobs int 1
  --time int 3600
  --memoryMBthread int 8000
  --randinit int 1337  # should be the same as the one in data.py
  --preprocess bool False
  --tmp_folder str
  --refit bool True
  --instancegroups str   # a jsonfile containing a dictionary instance_name -> group name
  --autosk_debug bool False   # autosklearn logging
  --autosk_debugfile str autosklearn.log
  --autosk_debugout str+ file_handler  # console   is another option, chooses where to output debug
  --ensemble int 1  # ensemble size, autosklearn will combine the best models
  ```



  go to biofilm and run python biofilm-features.py -h

  ```

  --method str lasso  or svm or all or corr or variance
  --out str numpycompressdumpgoeshere
  --plot bool False
  --svmparamrange float+ 0.01 0.15 0.001


  --infile str myNumpyDump
  --randinit int -1
  --folds int 5
  --subsample int -1
  --Z bool False
  ```




  ```
  a) tools.ndumpfile([X,y, featurenames, instancenames],fname) where feature and instancenames are optional or
  b) provide --loader whose read function will be called (examples/npzloader)

  defaultformat: X,y in a npz dump, features and instances get enumerated
  a custom dataloader: X,y, features, instances
  loadfoldsreturns: (X,Y,x,y) features namesOfTestInstances
  ```



  ```
  optimize:
  	out.model: {score:score, modelparams:modelparams}
  	out.csv: instanceId, reallabel, predicted label, probability
  feature selection:
  	out: featuremask, featureproba, featureId
  ```


pkgId: 3a02b5aebd58fab1ef4831fadb67f437849a05027dcec966d5702ce6364b8107
name: python-biofit
arch: src
version: 0.0.1
epoch: 0
release: 1
summary: BioFit: Bioinformatics Machine Learning Framework
url: https://github.com/psmyth94/biofit
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biofit-0.0.1-1.src.rpm
checksum_type: sha256
description:
  <p align="center">
      $${\Huge{\textbf{\textsf{\color{#2E8B57}Bio\color{red}fit}}}}$$
      <br/>
      <br/>
  </p>
  <p align="center">
      <a href="https://github.com/psmyth94/biofit/actions/workflows/ci_cd_pipeline.yml?query=branch%3Amain"><img alt="Build" src="https://github.com/psmyth94/biofit/actions/workflows/ci_cd_pipeline.yml/badge.svg?branch=main"></a>
      <a href="https://github.com/psmyth94/biofit/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/psmyth94/biofit.svg?color=blue"></a>
      <a href="https://github.com/psmyth94/biofit/tree/main/docs"><img alt="Documentation" src="https://img.shields.io/website/http/github/psmyth94/biofit/tree/main/docs.svg?down_color=red&down_message=offline&up_message=online"></a>
      <a href="https://github.com/psmyth94/biofit/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/psmyth94/biofit.svg"></a>
      <a href="CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg"></a>
      <!-- <a href="https://zenodo.org/records/14028772"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.14028772.svg" alt="DOI"></a> -->
  </p>

  **Biofit** is a machine learning library designed for bioinformatics datasets. It
  provides tools for transforming, extracting, training, and evaluating machine learning
  models on biomedical data. It also provides automatic data preprocessing, visualization,
  and configurable processing pipelines. Here are some of the main features of Biofit:

  - **Automatic Data Preprocessing:** Automatically preprocess biomedical datasets using
    built-in preprocessing steps.
  - **Automatic Visualization:** Automatically visualize data using built-in visualization
    methods geared towards biomedical data.
  - **Configurable Processing Pipelines:** Define and customize data processing pipelines.
  - **Data Handling Flexibility:** Support for a wide range of data formats, including:
    - [Pandas](https://github.com/pandas-dev/pandas)
    - [Polars](https://github.com/pola-rs/polars)
    - [NumPy](https://github.com/numpy/numpy)
    - [CSR (SciPy)](https://github.com/scipy/scipy)
    - [Arrow](https://github.com/apache/arrow)
    - 🤗 [Datasets](https://github.com/huggingface/datasets)
    - [Biosets](https://github.com/psmyth94/biosets)
  - **Machine Learning Models:** Supports a wide range of machine learning models, including:
    - [Scikit-learn](https://github.com/scikit-learn/scikit-learn)
      - [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
      - [Support Vector Machine](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)
      - [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)
      - [Lasso Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html)
    - [LightGBM](https://github.com/microsoft/LightGBM)
    - More to come!
  - **Caching and Reuse:** Caches intermediate results using Apache Arrow for efficient reuse.
  - **Batch Processing and Multiprocessing:** Utilize batch processing and multiprocessing for efficient handling of large-scale data.



  You can install Biofit via pip:

  ```bash
  pip install biofit
  ```





  Biofit provides preprocessing capabilities tailored for omics data. You can use
  built-in classes to load preprocessing steps based on the experiment type or create
  custom preprocessing pipelines. The preprocessing pipeline in Biofit uses a syntax
  similar to sklearn and supports distributed processing.



  Biofit allows you to fit and transform your data in a few lines, similar to sklearn.
  For example, you can use the LogTransformer to apply a log transformation to your data:

  ```python
  from biofit.preprocessing import LogTransformer
  import pandas as pd

  dataset = pd.DataFrame({"feature1": [1, 2, 3, 4, 5]})
  log_transformer = LogTransformer()
  preprocessed_data = log_transformer.fit_transform(dataset)

  print(preprocessed_data)






  ```



  You can automatically apply standard preprocessing steps by specifying the experiment
  type. This allows you to load tailored preprocessing steps for the type of data you are
  working with, such as "otu", "asv", "snp", or "maldi":

  ```python
  from biofit.preprocessing import AutoPreprocessor

  preprocessor = AutoPreprocessor.for_experiment("snp", [{"min_prevalence": 0.1}, None])
  print(preprocessor)




  preprocessed_data = preprocessor.fit_transform(dataset)
  ```

  Biofit is made with [Biosets](https://github.com/psmyth94/biosets) in mind. You can
  pass the loaded dataset instead of a string to load the preprocessors:

  ```python
  from biosets import load_dataset

  dataset = load_dataset("csv", data_files="my_file.csv", experiment_type="snp")

  preprocessor = AutoPreprocessor.for_experiment(dataset)
  print(preprocessor)


  preprocessed_data = preprocessor.fit_transform(dataset)
  ```



  Biofit allows you to create custom preprocessing pipelines using the
  `PreprocessorPipeline` class. This allows chaining multiple preprocessing steps from
  `sklearn` and Biofit in a single operation:

  ```python
  from biofit import load_dataset
  from biofit.preprocessing import LogTransformer, PreprocessorPipeline
  from sklearn.preprocessing import StandardScaler


  dataset = load_dataset("csv", data_files="my_file.csv")


  pipeline = PreprocessorPipeline(
      [("scaler", StandardScaler()), ("log_transformer", LogTransformer())]
  )


  preprocessed_data = pipeline.fit_transform(dataset.to_pandas())
  ```

  For further details, check the [advance usage documentation](./docs/PREPROCESSING.md).



  Biofit is licensed under the Apache 2.0 License. See the [LICENSE](./LICENSE) file for
  more information.



  If you would like to contribute to Biofit, please read the
  [CONTRIBUTING](./CONTRIBUTING.md) guidelines.


pkgId: 7cd4625f1680c2ddbc2473ee838102c0b7990028f34a071fd52ada5e319953b4
name: python-biofkit
arch: src
version: 0.1.18
epoch: 0
release: 1
url: https://github.com/Chou-Uken/biofkit
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biofkit-0.1.18-1.src.rpm
checksum_type: sha256
summary:
  With biofkit you can deal with pdb file (extract sequence, atom information and so on) very easily. Pairwise alignment function is now online.

description:
  Biofkit is now containing only one module with one package which can deal with pdb file (extract sequence, atom information and so on) very easily.



  ```console
  conda install -c chou_uken biofkit
  ```

  ```
  mamba install -c chou_uken biofkit
  ```


  ```console
  pip install biofkit
  ```


  Documents will help you!
  [中文文档](https://chou-uken.github.io/biofkit/chinese/)
  Waiting for more languages...


pkgId: 25997438d6df0d4aa629e7c5ffd2973f4d0c798e24ddb1efba15f5189d457ad2
name: python-bioflow-insight
arch: src
version: 1.0.6
epoch: 0
release: 1
summary: A software to extract and analyze the structure and associated metadata from a Nextflow workflow.
url: https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioflow-insight-1.0.6-1.src.rpm
checksum_type: sha256
description:
  [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-green.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Version 1.0](https://img.shields.io/badge/version-v1.0-yellow)]()





  **BioFlow-Insight** is a Python-based open-source command-line tool designed to automatically analyse Nextflow workflow code, gathering useful information, particularly in the form of visual graphs that illustrate the workflow's structure and its various steps. Additionally, it is capable of detecting certain programming errors and generates a RO-Crate JSON-LD file that describes the workflow.

  **BioFlow-Insight** is easily installable as a CLI (see [here](https://pypi.org/project/bioflow-insight/)). It is also freely accessible as a free [web service](https://bioflow-insight.pasteur.cloud/). For more information and to start using BioFlow-Insight, visit [here](https://bioflow-insight.pasteur.cloud/) (https://bioflow-insight.pasteur.cloud/).




  - [BioFlow-Insight](#bioflow-insight)
    - [Description](#description)
    - [Table of Contents](#table-of-contents)
    - [Installation](#installation)
      - [Installing via pip](#installing-via-pip)
      - [Using from source](#using-from-source)
    - [Usage](#usage)
      - [Input](#input)
      - [Output](#output)
    - [Citing BioFlow-Insight](#citing-bioflow-insight)
    - [License](#license)
    - [Funding](#funding)





  **BioFlow-Insight** is easily installable as a CLI.

  To install it using *pip*, use the following command :

  ```
  pip install bioflow-insight
  ```




  To access its source code, simply clone its GitLab repository. **BioFlow-Insight** is developed using Python 3

  **BioFlow-Insight**'s dependencies are given in the `requirements.txt` file.

  > Note : To install graphviz, in linux you might need to execute this command `sudo apt install graphviz`




  **BioFlow-Insight** is a Python-based open-source command-line tool designed to automatically analyse Nextflow workflow code, gathering useful information, particularly in the form of visual graphs that illustrate the workflow's structure and its various steps. Additionally, it is capable of detecting certain programming errors and generates a RO-Crate JSON-LD file that describes the workflow.


  For an explanation of the different elements composing a Nextflow workflow, see [its documentation](https://www.nextflow.io/docs/latest/index.html).

  The 3 different graphs generated by **BioFlow-Insight** are :

  * *Specification graph:* **BioFlow-Insight** reconstructs the workflow’s specification graph from its source code without having to execute it. The specification graph is defined as a directed graph where nodes are processes and operations, and edges are channels that are directed from one vertex to another (steps of the workflow are ordered). This graph **represents all the possible interactions between processes and operations through channels** that are defined in the workflow code. Within the specification graph, we define two types of operations: operations are categorised in two groups: the *following operations* defined as operations that have at least one input, and the *starting operations* defined as operations without any inputs.

  * *Dependency graph:* From the specification graph, **BioFlow-Insight** also generates the dependency graph which **represents starting operations, along with processes (as nodes) and their dependencies (edges)**. This graph is obtained by removing the following operations and linking the remaining elements if a path exists between them in the original specification graph. In this representation, **the edges no longer represent interaction between its elements, but their dependencies**.

  * *Process dependency graph:* Finally **BioFlow-Insight** also generates the process dependency graph which represents only **processes (nodes) and their dependencies (edges)**. Similar to the dependency graph, this graph is constructed by removing all operations, leaving only processes, and linking them based on their dependencies in the original specification graph. Again in this representation, **the edges no longer represent interaction between its elements, but their dependencies**.

  > For a more in-depth explanation of BioFlow-Insight functionnalities, visit its webpage [here](https://bioflow-insight.pasteur.cloud/specification/) (https://bioflow-insight.pasteur.cloud/specification/).

  > To examplify **BioFlow-Insight** utilisation, let's use the rnaseq-nf workflow proposed by Nextflow (its source code can be found [here](https://github.com/nextflow-io/rnaseq-nf/tree/8253a586cc5a9679d37544ac54f72167cced324b)). Examples of the output are given below.



  In this example, we are going to use the **BioFlow-Insight** tool to analyse the rna-seq workflow. After installing **BioFlow-Insight** via pip, and cloning the the rnaseq-nf repository. Simply run this command line :


  ```
  bioflow-insight rnaseq-nf/main.nf
  ```



  After the workflow has been analysed and the graphs generated, the outputs are saved in the `results` folder.

  The structure of this folder is organised as such :

  ```
  .
  ├── debug
  │   ├── calls.nf
  │   ├── operations_in_call.nf
  │   └── operations.nf
  ├── graphs
  │   ├── dependency_graph.dot
  │   ├── dependency_graph.json
  │   ├── dependency_graph.mmd
  │   ├── dependency_graph.png
  │   ├── dependency_graph_wo_labels.dot
  │   ├── dependency_graph_wo_labels.mmd
  │   ├── dependency_graph_wo_labels.png
  │   ├── dependency_graph_wo_orphan_operations.dot
  │   ├── dependency_graph_wo_orphan_operations.mmd
  │   ├── dependency_graph_wo_orphan_operations.png
  │   ├── dependency_graph_wo_orphan_operations_wo_labels.dot
  │   ├── dependency_graph_wo_orphan_operations_wo_labels.mmd
  │   ├── dependency_graph_wo_orphan_operations_wo_labels.png
  │   ├── metadata_dependency_graph.json
  │   ├── metadata_process_dependency_graph.json
  │   ├── metadata_specification_graph.json
  │   ├── process_dependency_graph.dot
  │   ├── process_dependency_graph.json
  │   ├── process_dependency_graph.mmd
  │   ├── process_dependency_graph.png
  │   ├── specification_graph.dot
  │   ├── specification_graph.json
  │   ├── specification_graph.mmd
  │   ├── specification_graph.png
  │   ├── specification_graph_wo_labels.dot
  │   ├── specification_graph_wo_labels.mmd
  │   ├── specification_graph_wo_labels.png
  │   ├── specification_wo_orphan_operations.dot
  │   ├── specification_wo_orphan_operations.mmd
  │   ├── specification_wo_orphan_operations.png
  │   ├── specification_wo_orphan_operations_wo_labels.dot
  │   ├── specification_wo_orphan_operations_wo_labels.mmd
  │   └── specification_wo_orphan_operations_wo_labels.png
  └── ro-crate-metadata.json
  ```

  * The `ro-crate-metadata.json` describes the workflow following an extended Workflow [RO-Crate](https://www.researchobject.org/ro-crate/) profile. The description of this extended profile can be found [here](https://gitlab.liris.cnrs.fr/sharefair/posters/swat4hcls-2024).
  * the `debug` folder contains different intermediary files which are ussefull for debugging
  * the `graphs` folder contains the different graphs which are generated. For each of the 3 graphs described above, **BioFlow-Insight** generates :
    * A `json` file which describes the graph using **BioFlow-Insight** specific format
    * A `json` file which describes the metadata which are extracted from the graph
    * Where possible **BioFlow-Insight** also generates the graphs without labels on the operations and channels. Additionaly there is also a variant where the orphan operations (operations which don't have any inputs or outputs) are not represented.

  > For each graph **BioFlow-Insight** generates it in the `mermaid` format and the dot `dot` format. If the `render_graphs` option is set to `True`, the `png` image is also generated.

  Here are some of the graphs which are generated by **BioFlow-Insight**, they are rendered using Graphviz (png).

  | <img align="center" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/specification_graph.png" >  | <img align="center" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/dependency_graph.png">   | <img align="center" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/process_dependency_graph.png" >   |
  |:-:|:-:|---|
  | Specification Graph  |  Dependency Graph | Process Dependency Graph  |




  **Please cite BioFlow-Insight in any research that uses or extends BioFlow-Insight.**

  To cite **BioFlow-Insight**, please use the following publication:

  > George Marchment, Bryan Brancotte, Marie Schmit, Frédéric Lemoine, Sarah Cohen-Boulakia, BioFlow-Insight: facilitating reuse of Nextflow workflows with structure reconstruction and visualization, *NAR Genomics and Bioinformatics*, Volume 6, Issue 3, September 2024, lqae092, [https://doi.org/10.1093/nargab/lqae092](https://doi.org/10.1093/nargab/lqae092)




  This project is licensed under the [GNU Affero General Public License](https://www.gnu.org/licenses/agpl-3.0.en.html).




  This work received support from the National Research Agency under the France 2030 program, with reference to ANR-22-PESN-0007.

  ___
  <br>

  <img align="left" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/logo.png" width="16%">
  <img align="left" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/paris_saclay.png" width="16%">
  <img align="left" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/lisn.png" width="16%">
  <img align="left" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/pasteur.png" width="16%">
  <img align="left" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/sharefair.png" width="16%">
  <img align="left" src="https://gitlab.liris.cnrs.fr/sharefair/bioflow-insight/-/raw/main/img/france2030.png" width="16%">

  <br/><br/>
  <br/><br/>


pkgId: d486c4177daf7c49f9c1d7c575cbf6376ec18da74b94a10f2c9506046939388f
name: python-bioformats-jar
arch: src
version: 2020.5.27
epoch: 0
release: 1
summary: bioformats_package.jar installer for scyjava
url: https://github.com/tlambert03/bioformats_jar
rpm_license: GPL-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioformats-jar-2020.5.27-1.src.rpm
checksum_type: sha256
description:
  [![License](https://img.shields.io/pypi/l/bioformats_jar.svg?color=green)](https://github.com/tlambert03/bioformats_jar/raw/master/LICENSE)
  [![PyPI](https://img.shields.io/pypi/v/bioformats_jar.svg?color=green)](https://pypi.org/project/bioformats_jar)
  [![Python Version](https://img.shields.io/pypi/pyversions/bioformats_jar.svg?color=green)](https://python.org)
  [![CI](https://github.com/tlambert03/bioformats_jar/actions/workflows/ci.yml/badge.svg)](https://github.com/tlambert03/bioformats_jar/actions/workflows/ci.yml)



  This package is deprecated.  You should use [scyjava](https://github.com/scijava/scyjava) to download and import jars.  The functionality in this
  package can be accomplished using scyjava as follows:

  ```python
  import jpype
  import scyjava

  scyjava.config.endpoints.append('ome:formats-gpl:6.7.0')
  scyjava.start_jvm()
  loci = jpype.JPackage("loci")
  loci.common.DebugTools.setRootLevel("ERROR")
  ```

  This package remains only for packages that already depend on it.



  ```python
  from bioformats_jar import get_loci, set_loci_log_level


  loci = get_loci()


  set_loci_log_level("DEBUG")  # by default "ERROR"
  ```

  The following environment variables can also be used:

  - `BIOFORMATS_VERSION` - version of bioformats to use.  by default "LATEST"
  - `BIOFORMATS_LOG_LEVEL` - logging level for loci tools. by default "ERROR"
  - `BIOFORMATS_LICENSE` - license version of bioformats to use.  must be either
    `"gpl"` or `"bsd"`.  By default `"gpl"`

  *see also:*

  - [Bioformats Docs](https://docs.openmicroscopy.org/bio-formats/latest)
  - [Javadocs](https://downloads.openmicroscopy.org/bio-formats/latest/api/) with the loci API



  ```sh
  pip install bioformats-jar
  ```

  ```sh
  conda install -c conda-forge bioformats-jar
  ```


pkgId: 0c61ccb79d398980112de03eb21fa464de2dd791c52655680d432a9c89edf37b
name: python-bioframe
arch: src
version: 0.7.2
epoch: 0
release: 1
summary: Operations and utilities for Genomic Interval Dataframes.
url: https://pypi.org/project/bioframe/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioframe-0.7.2-1.src.rpm
checksum_type: sha256
description:
  <img src="https://github.com/open2c/bioframe/raw/main/docs/figs/bioframe-logo.png" width=75%>

  ![CI](https://github.com/open2c/bioframe/actions/workflows/ci.yml/badge.svg)
  [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/open2c/bioframe/main.svg)](https://results.pre-commit.ci/latest/github/open2c/bioframe/main)
  [![Docs status](https://readthedocs.org/projects/bioframe/badge/)](https://bioframe.readthedocs.io/en/latest/)
  [![Paper](https://img.shields.io/badge/DOI-10.1093%2Fbioinformatics%2Fbtae088-blue)](https://doi.org/10.1093/bioinformatics/btae088)
  [![Zenodo](https://zenodo.org/badge/69901992.svg)](https://zenodo.org/badge/latestdoi/69901992)
  [![Slack](https://img.shields.io/badge/chat-slack-%233F0F3F?logo=slack)](https://bit.ly/open2c-slack)
  [![NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://www.numfocus.org)

  Bioframe enables flexible and scalable operations on genomic interval dataframes in Python.

  Bioframe is built directly on top of [Pandas](https://pandas.pydata.org/). Bioframe provides:

  * A variety of genomic interval operations that work directly on dataframes.
  * Operations for special classes of genomic intervals, including chromosome arms and fixed-size bins.
  * Conveniences for diverse tabular genomic data formats and loading genome assembly summary information.

  Read the [documentation](https://bioframe.readthedocs.io/en/latest/), including the [guide](https://bioframe.readthedocs.io/en/latest/guide-intervalops.html), as well as the [publication](https://doi.org/10.1093/bioinformatics/btae088) for more information.

  Bioframe is an Affiliated Project of [NumFOCUS](https://www.numfocus.org).



  Bioframe is available on [PyPI](https://pypi.org/project/bioframe/) and [bioconda](https://bioconda.github.io/recipes/bioframe/README.html):

  ```sh
  pip install bioframe
  ```



  Interested in contributing to bioframe? That's great! To get started, check out the [contributing guide](https://github.com/open2c/bioframe/blob/main/CONTRIBUTING.md). Discussions about the project roadmap take place on the [Open2C Slack](https://bit.ly/open2c-slack) and regular developer meetings scheduled there. Anyone can join and participate!




  Key genomic interval operations in bioframe include:
  - `overlap`: Find pairs of overlapping genomic intervals between two dataframes.
  - `closest`: For every interval in a dataframe, find the closest intervals in a second dataframe.
  - `cluster`: Group overlapping intervals in a dataframe into clusters.
  - `complement`: Find genomic intervals that are not covered by any interval from a dataframe.

  Bioframe additionally has functions that are frequently used for genomic interval operations and can be expressed as combinations of these core operations and dataframe operations, including: `coverage`, `expand`, `merge`, `select`, and `subtract`.

  To `overlap` two dataframes, call:
  ```python
  import bioframe as bf

  bf.overlap(df1, df2)
  ```

  For these two input dataframes, with intervals all on the same chromosome:

  <img src="https://github.com/open2c/bioframe/raw/main/docs/figs/df1.png" width=60%>
  <img src="https://github.com/open2c/bioframe/raw/main/docs/figs/df2.png" width=60%>

  `overlap` will return the following interval pairs as overlaps:

  <img src="https://github.com/open2c/bioframe/raw/main/docs/figs/overlap_inner_0.png" width=60%>
  <img src="https://github.com/open2c/bioframe/raw/main/docs/figs/overlap_inner_1.png" width=60%>


  To `merge` all overlapping intervals in a dataframe, call:
  ```python
  import bioframe as bf

  bf.merge(df1)
  ```

  For this input dataframe, with intervals all on the same chromosome:

  <img src="https://github.com/open2c/bioframe/raw/main/docs/figs/df1.png" width=60%>

  `merge` will return a new dataframe with these merged intervals:

  <img src="https://github.com/open2c/bioframe/raw/main/docs/figs/merge_df1.png" width=60%>

  See the [guide](https://bioframe.readthedocs.io/en/latest/guide-intervalops.html) for visualizations of other interval operations in bioframe.



  Bioframe includes utilities for reading genomic file formats into dataframes and vice versa. One handy function is `read_table` which mirrors pandas’s read_csv/read_table but provides a [`schema`](https://github.com/open2c/bioframe/blob/main/bioframe/io/schemas.py) argument to populate column names for common tabular file formats.

  ```python
  jaspar_url = 'http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2022/hg38/MA0139.1.tsv.gz'
  ctcf_motif_calls = bioframe.read_table(jaspar_url, schema='jaspar', skiprows=1)
  ```


  See this [jupyter notebook](https://github.com/open2c/bioframe/tree/master/docs/tutorials/tutorial_assign_motifs_to_peaks.ipynb) for an example of how to assign TF motifs to ChIP-seq peaks using bioframe.




  If you use ***bioframe*** in your work, please cite:

  ```bibtex
  @article{bioframe_2024,
  author = {Open2C and Abdennur, Nezar and Fudenberg, Geoffrey and Flyamer, Ilya M and Galitsyna, Aleksandra A and Goloborodko, Anton and Imakaev, Maxim and Venev, Sergey},
  doi = {10.1093/bioinformatics/btae088},
  journal = {Bioinformatics},
  title = {{Bioframe: Operations on Genomic Intervals in Pandas Dataframes}},
  year = {2024}
  }
  ```


pkgId: fe1cbcea5cc9bf67fb96c6554f10abfb753f5b4488ead1bd64655c6a2a696d7f
name: python-biogeme
arch: src
version: 3.2.14
epoch: 0
release: 1
summary: Estimation and application of discrete choice models
url: https://biogeme.epfl.ch/sphinx/index.html
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biogeme-3.2.14-1.src.rpm
checksum_type: sha256
description:
  ![PyPI](https://img.shields.io/pypi/v/biogeme.svg)
  [![Downloads](https://static.pepy.tech/badge/Biogeme)](https://pepy.tech/project/Biogeme)
  [![Downloads](https://static.pepy.tech/badge/Biogeme/month)](https://pepy.tech/project/Biogeme)
  [![Downloads](https://static.pepy.tech/badge/Biogeme/week)](https://pepy.tech/project/Biogeme)

  Biogeme is an open source freeware designed for the maximum likelihood estimation of parametric models in general,
  with a special emphasis on discrete choice models.


  Biogeme requires Python >= 3.10 and can be installed [from PyPI](https://pypi.org/project/biogeme/).

  `pip install biogeme`

  Installing Biogeme from source can be done after cloning the repository with the following command executed in the
  root directory:

  `pip install .`


  Further documentation is available on [biogeme.epfl.ch](https://biogeme.epfl.ch)


  [Michel Bierlaire](https://people.epfl.ch/michel.bierlaire), [Ecole Polytechnique Fédérale de Lausanne](https://www.epfl.ch), Switzerland.


pkgId: 2bd747018271d7e2fdb4fbe42b5ca2b306b883c2b87ec1137880f63e3bd78ac1
name: python-biogeme-optimization
arch: src
version: 0.0.10
epoch: 0
release: 1
summary: Various optimization algorithms for teaching and research
url: https://pypi.org/project/biogeme-optimization/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biogeme-optimization-0.0.10-1.src.rpm
checksum_type: sha256
description:
  [![PyPi](https://img.shields.io/pypi/v/biogeme_optimization.svg)](https://pypi.python.org/pypi/biogeme_optimization)

  Various optimization algorithms used for teaching and research. In particular, they are used by Biogeme.

  The package contains the following modules:


  It contains functions dealing with linear algebra:

  - A modified Cholesky factorization introduced by Schnabel and Eskow (1999)
  - The calculation of a descent direction based on this factorization.


  The functions in this module calculate

  - the BFGS update of the hessian approximation (see Eq. (13.12) in Bierlaire (2015)),
  - the inverse BFGS update of the hessian approximation (see Eq. (13.13) in Bierlaire (2015)).


  This module mainly defines the class `Bounds`that manages the bound constraints.


  This module defines the diagnostic of some optimization subproblems
  (dogleg, and comjugate gradient).


  It defines the `OptimizationError` exception.


  It defines the class `FormattedColumns` that formats the information
  reported at each iteration of an algorithm.


  It defines the abstract class `FunctionToMinimize` that encapsulate
  the calculation of the objective function and its derivatives.


  It defines the class `HybridFunction` that calculates the objective
  function and its derivatives, where the second derivative can be
  either the analytical hessian, or a BFGS approximation.


  This module implements the line search algorithms (see Chapter 11 in Bierlaire, 2015).


  This module implements the minimization algorithm under bound
  constraints proposed by Conn et al. (1988).


  This module implements the trust region algorithms (see Chapter 12 in
  Bierlaire, 2015).



  - Bierlaire, M. (2015). [Optimization: Principles and
    Algortihms](https://transp-or.epfl.ch/books/optimization/html/OptimizationPrinciplesAlgorithms2018.pdf). EPFL
    Press.
  - Conn, A. R., Gould, N. I. M and Toint, Ph. L. (1988) [Testing a
    Class of Methods for Solving Minimization Problems with Simple
    Bounds on the
    Variables](https://www.ams.org/journals/mcom/1988-50-182/S0025-5718-1988-0929544-3/S0025-5718-1988-0929544-3.pdf). Mathematics
    of Computation, 50(182), 399-430.
  - Schnabel, R. B. and Eskow, E. (1999) [A Revised Modified Cholesky Factorization Algorithm](https://doi.org/10.1137/s105262349833266x). SIAM Journal on Optimization, 9(4), 1135-1148.


pkgId: 1b92b6bd9a2015e7d0936f2dd8fc1ce7c3252393700e1fe378a851db6a1ac48a
name: python-biography
arch: src
version: 0.0.0
epoch: 0
release: 1
summary: it is time to tell a git story
url: https://pypi.org/project/biography/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-biography-0.0.0-1.src.rpm
checksum_type: sha256
description:
  *GitStory helps you narrate the evolution of your codebase by enriching commit history with meaningful summaries and insights.*


pkgId: ecaa6367b0568befbd12500b81d67e6e77286ecab82eb7c709dcf8adb10602bf
name: python-bioimageio.core
arch: src
version: 0.6.10
epoch: 0
release: 1
summary: Python functionality for the bioimage model zoo
url: https://github.com/bioimage-io/core-bioimage-io-python
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioimageio.core-0.6.10-1.src.rpm
checksum_type: sha256
description:
  ![License](https://img.shields.io/github/license/bioimage-io/core-bioimage-io-python.svg)
  [![PyPI](https://img.shields.io/pypi/v/bioimageio-core.svg?style=popout)](https://pypi.org/project/bioimageio.core/)
  [![conda-version](https://anaconda.org/conda-forge/bioimageio.core/badges/version.svg)](https://anaconda.org/conda-forge/bioimageio.core/)
  [![downloads](https://static.pepy.tech/badge/bioimageio.core)](https://pepy.tech/project/bioimageio.core)
  [![conda-forge downloads](https://img.shields.io/conda/dn/conda-forge/bioimageio.core.svg?label=conda-forge)](https://anaconda.org/conda-forge/bioimageio.core/)
  ![code style](https://img.shields.io/badge/code%20style-black-000000.svg)



  Python specific core utilities for bioimage.io resources (in particular models).



  To get started we recommend installing bioimageio.core with conda together with a deep
  learning framework, e.g. pytorch, and run a few `bioimageio` commands to see what
  bioimage.core has to offer:

  1. install with conda (for more details on conda environments, [checkout the conda docs](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html))

      ```console
      conda install -c conda-forge bioimageio.core pytorch
      ```

  1. test a model

      ```console
      $ bioimageio test powerful-chipmunk
      ...
      ```

      <details>
      <summary>(Click to expand output)</summary>

      ```console


        ✔️                 bioimageio validation passed
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files/rdf.yaml
        format version    model 0.4.10
        bioimageio.spec   0.5.3post4
        bioimageio.core   0.6.8



        ❓   location                                     detail
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        ✔️                                                 initialized ModelDescr to describe model 0.4.10

        ✔️                                                 bioimageio.spec format validation model 0.4.10
        🔍   context.perform_io_checks                    True
        🔍   context.root                                 https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files
        🔍   context.known_files.weights.pt               3bd9c518c8473f1e35abb7624f82f3aa92f1015e66fb1f6a9d08444e1f2f5698
        🔍   context.known_files.weights-torchscript.pt   4e568fd81c0ffa06ce13061327c3f673e1bac808891135badd3b0fcdacee086b
        🔍   context.warning_level                        error

        ✔️                                                 Reproduce test outputs from test inputs

        ✔️                                                 Reproduce test outputs from test inputs
      ```

      </details>

      or

      ```console
      $ bioimageio test impartial-shrimp
      ...
      ```

      <details><summary>(Click to expand output)</summary>

      ```console
        ✔️                 bioimageio validation passed
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/impartial-shrimp/1.1/files/rdf.yaml
        format version    model 0.5.3
        bioimageio.spec   0.5.3.2
        bioimageio.core   0.6.9


        ❓   location                    detail
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        ✔️                                initialized ModelDescr to describe model 0.5.3


        ✔️                                bioimageio.spec format validation model 0.5.3

        🔍   context.perform_io_checks   False
        🔍   context.warning_level       error

        ✔️                                Reproduce test outputs from test inputs (pytorch_state_dict)


        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:

                                        0

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:

                                        0

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:

                                        1

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:

                                        1

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:

                                        2

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:

                                        2

        ✔️                                Reproduce test outputs from test inputs (torchscript)


        ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 0


        ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 0


        ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 1


        ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 1


        ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 2


        ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 2
      ```

      </details>
  1. run prediction on your data

  - display the `bioimageio-predict` command help to get an overview:

      ```console
      $ bioimageio predict --help
      ...
      ```

      <details>
      <summary>(Click to expand output)</summary>

      ```console
      usage: bioimageio predict [-h] [--inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLenmin_length=1]]]]
                                [--outputs {str,Tuple[str,...]}] [--overwrite bool] [--blockwise bool] [--stats Path]
                                [--preview bool]
                                [--weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}]
                                [--example bool]
                                SOURCE

      bioimageio-predict - Run inference on your data with a bioimage.io model.

      positional arguments:
        SOURCE                Url/path to a bioimageio.yaml/rdf.yaml file
                              or a bioimage.io resource identifier, e.g. 'affable-shark'

      optional arguments:
        -h, --help            show this help message and exit
        --inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLen(min_length=1)]]]
                              Model input sample paths (for each input tensor)

                              The input paths are expected to have shape...
                              - (n_samples,) or (n_samples,1) for models expecting a single input tensor
                              - (n_samples,) containing the substring '{input_id}', or
                              - (n_samples, n_model_inputs) to provide each input tensor path explicitly.

                              All substrings that are replaced by metadata from the model description:
                              - '{model_id}'
                              - '{input_id}'

                              Example inputs to process sample 'a' and 'b'
                              for a model expecting a 'raw' and a 'mask' input tensor:
                              --inputs="[["a_raw.tif","a_mask.tif"],["b_raw.tif","b_mask.tif"]]"
                              (Note that JSON double quotes need to be escaped.)

                              Alternatively a `bioimageio-cli.yaml` (or `bioimageio-cli.json`) file
                              may provide the arguments, e.g.:
                              ```yaml
                              inputs:
                              - [a_raw.tif, a_mask.tif]
                              - [b_raw.tif, b_mask.tif]
                              ```

                              `.npy` and any file extension supported by imageio are supported.
                              Aavailable formats are listed at
                              https://imageio.readthedocs.io/en/stable/formats/index.html#all-formats.
                              Some formats have additional dependencies.

                                (default: ('{input_id}/001.tif',))
        --outputs {str,Tuple[str,...]}
                              Model output path pattern (per output tensor)

                              All substrings that are replaced:
                              - '{model_id}' (from model description)
                              - '{output_id}' (from model description)
                              - '{sample_id}' (extracted from input paths)

                                (default: outputs_{model_id}/{output_id}/{sample_id}.tif)
        --overwrite bool      allow overwriting existing output files (default: False)
        --blockwise bool      process inputs blockwise (default: False)
        --stats Path          path to dataset statistics
                              (will be written if it does not exist,
                              but the model requires statistical dataset measures)
                                (default: dataset_statistics.json)
        --preview bool        preview which files would be processed
                              and what outputs would be generated. (default: False)
        --weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}
                              The weight format to use. (default: any)
        --example bool        generate and run an example

                              1. downloads example model inputs
                              2. creates a `{model_id}_example` folder
                              3. writes input arguments to `{model_id}_example/bioimageio-cli.yaml`
                              4. executes a preview dry-run
                              5. executes prediction with example input

                                (default: False)
      ```

      </details>

  - create an example and run prediction locally!

      ```console
      $ bioimageio predict impartial-shrimp --example=True
      ...
      ```

      <details>
      <summary>(Click to expand output)</summary>

      ```console
      🛈 bioimageio prediction preview structure:
      {'{sample_id}': {'inputs': {'{input_id}': '<input path>'},
                      'outputs': {'{output_id}': '<output path>'}}}
      🔎 bioimageio prediction preview output:
      {'1': {'inputs': {'input0': 'impartial-shrimp_example/input0/001.tif'},
            'outputs': {'output0': 'impartial-shrimp_example/outputs/output0/1.tif'}}}
      predict with impartial-shrimp: 100%|███████████████████████████████████████████████████| 1/1 [00:21<00:00, 21.76s/sample]
      🎉 Sucessfully ran example prediction!
      To predict the example input using the CLI example config file impartial-shrimp_example\bioimageio-cli.yaml, execute `bioimageio predict` from impartial-shrimp_example:
      $ cd impartial-shrimp_example
      $ bioimageio predict "impartial-shrimp"

      Alternatively run the following command in the current workind directory, not the example folder:
      $ bioimageio predict --preview=False --overwrite=True --stats="impartial-shrimp_example/dataset_statistics.json" --inputs="[[\"impartial-shrimp_example/input0/001.tif\"]]" --outputs="impartial-shrimp_example/outputs/{output_id}/{sample_id}.tif" "impartial-shrimp"
      (note that a local 'bioimageio-cli.json' or 'bioimageio-cli.yaml' may interfere with this)
    ```

    </details>





  The `bioimageio.core` package can be installed from conda-forge via

  ```console
  mamba install -c conda-forge bioimageio.core
  ```

  If you do not install any additional deep learning libraries, you will only be able to use general convenience
  functionality, but not any functionality for model prediction.
  To install additional deep learning libraries use:

  - Pytorch/Torchscript:

    CPU installation (if you don't have an nvidia graphics card):

    ```console
    mamba install -c pytorch -c conda-forge bioimageio.core pytorch torchvision cpuonly
    ```

    GPU installation (for cuda 11.6, please choose the appropriate cuda version for your system):

    ```console
    mamba install -c pytorch -c nvidia -c conda-forge bioimageio.core pytorch torchvision pytorch-cuda=11.8
    ```

    Note that the pytorch installation instructions may change in the future. For the latest instructions please refer to [pytorch.org](https://pytorch.org/).

  - Tensorflow

    Currently only CPU version supported

    ```console
    mamba install -c conda-forge bioimageio.core tensorflow
    ```

  - ONNXRuntime

    Currently only cpu version supported

    ```console
    mamba install -c conda-forge bioimageio.core onnxruntime
    ```



  The package is also available via pip
  (e.g. with recommended extras `onnx` and `pytorch`):

  ```console
  pip install "bioimageio.core[onnx,pytorch]"
  ```



  To set up a development conda environment run the following commands:

  ```console
  mamba env create -f dev/env.yaml
  mamba activate core
  pip install -e . --no-deps
  ```

  There are different environment files available that only install tensorflow or pytorch as dependencies.



  `bioimageio.core` installs a command line interface (CLI) for testing models and other functionality.
  You can list all the available commands via:

  ```console
  bioimageio
  ```



  For convenience the command line options (not arguments) may be given in a `bioimageio-cli.json`
  or `bioimageio-cli.yaml` file, e.g.:

  ```yaml

  inputs: inputs/*_{tensor_id}.h5
  outputs: outputs_{model_id}/{sample_id}_{tensor_id}.h5
  overwrite: true
  blockwise: true
  stats: inputs/dataset_statistics.json
  ```



  `bioimageio.core` is a python package that implements prediction with bioimageio models
  including standardized pre- and postprocessing operations.
  These models are described by---and can be loaded with---the bioimageio.spec package.

  In addition bioimageio.core provides functionality to convert model weight formats.

  To get an overview of this functionality, check out these example notebooks:

  - [model creation/loading with bioimageio.spec](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb)

  and the [developer documentation](https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/core.html).



  `bioimageio.spec` and `bioimageio.core` use [loguru](https://github.com/Delgan/loguru) for logging, hence the logging level
  may be controlled with the `LOGURU_LEVEL` environment variable.



  The model specification and its validation tools can be found at <https://github.com/bioimage-io/spec-bioimage-io>.





  - improve bioimageio command line interface (details in #157)
    - add `predict` command
    - package command input `path` is now required



  - testing model inference will now check all weight formats
    (previously only the first one for which model adapter creation succeeded had been checked)
  - fix predict with blocking (Thanks @thodkatz)



  - `predict()` argument `inputs` may be sample



  - add aliases to match previous API more closely



  - improve adapter error messages



  - add `bioimageio validate-format` command
  - improve error messages and display of command results



  - Fix [#386](https://github.com/bioimage-io/core-bioimage-io-python/issues/386)
  - (in model inference testing) stop assuming model inputs are tileable



  - Fix [#384](https://github.com/bioimage-io/core-bioimage-io-python/issues/384)



  - Fix [#378](https://github.com/bioimage-io/core-bioimage-io-python/pull/378) (with [#379](https://github.com/bioimage-io/core-bioimage-io-python/pull/379))*



  - add compatibility with new bioimageio.spec 0.5 (0.5.2post1)
  - improve interfaces



  - [Fix critical bug in predict with tiling](https://github.com/bioimage-io/core-bioimage-io-python/pull/359)


pkgId: 3ddac9532c5023e4d8ab349696748549dbb74c5986bca7c285e1fccc4affbbf9
name: python-bioimageio.core
arch: src
version: 0.7.0
epoch: 0
release: 1
summary: Python functionality for the bioimage model zoo
url: https://github.com/bioimage-io/core-bioimage-io-python
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioimageio.core-0.7.0-1.src.rpm
checksum_type: sha256
description:
  ![License](https://img.shields.io/github/license/bioimage-io/core-bioimage-io-python.svg)
  [![PyPI](https://img.shields.io/pypi/v/bioimageio-core.svg?style=popout)](https://pypi.org/project/bioimageio.core/)
  [![conda-version](https://anaconda.org/conda-forge/bioimageio.core/badges/version.svg)](https://anaconda.org/conda-forge/bioimageio.core/)
  [![downloads](https://static.pepy.tech/badge/bioimageio.core)](https://pepy.tech/project/bioimageio.core)
  [![conda-forge downloads](https://img.shields.io/conda/dn/conda-forge/bioimageio.core.svg?label=conda-forge)](https://anaconda.org/conda-forge/bioimageio.core/)
  ![code style](https://img.shields.io/badge/code%20style-black-000000.svg)
  [![coverage](https://bioimage-io.github.io/core-bioimage-io-python/coverage/coverage-badge.svg)](https://bioimage-io.github.io/core-bioimage-io-python/coverage/index.html)



  Python specific core utilities for bioimage.io resources (in particular models).



  To get started we recommend installing bioimageio.core with conda together with a deep
  learning framework, e.g. pytorch, and run a few `bioimageio` commands to see what
  bioimage.core has to offer:

  1. install with conda (for more details on conda environments, [checkout the conda docs](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html))

      ```console
      conda install -c conda-forge bioimageio.core pytorch
      ```

  1. test a model

      ```console
      $ bioimageio test powerful-chipmunk
      ...
      ```

      <details>
      <summary>(Click to expand output)</summary>

      ```console


        ✔️                 bioimageio validation passed
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files/rdf.yaml
        format version    model 0.4.10
        bioimageio.spec   0.5.3post4
        bioimageio.core   0.6.8



        ❓   location                                     detail
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        ✔️                                                 initialized ModelDescr to describe model 0.4.10

        ✔️                                                 bioimageio.spec format validation model 0.4.10
        🔍   context.perform_io_checks                    True
        🔍   context.root                                 https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/powerful-chipmunk/1/files
        🔍   context.known_files.weights.pt               3bd9c518c8473f1e35abb7624f82f3aa92f1015e66fb1f6a9d08444e1f2f5698
        🔍   context.known_files.weights-torchscript.pt   4e568fd81c0ffa06ce13061327c3f673e1bac808891135badd3b0fcdacee086b
        🔍   context.warning_level                        error

        ✔️                                                 Reproduce test outputs from test inputs

        ✔️                                                 Reproduce test outputs from test inputs
      ```

      </details>

      or

      ```console
      $ bioimageio test impartial-shrimp
      ...
      ```

      <details><summary>(Click to expand output)</summary>

      ```console
        ✔️                 bioimageio validation passed
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        source            https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/impartial-shrimp/1.1/files/rdf.yaml
        format version    model 0.5.3
        bioimageio.spec   0.5.3.2
        bioimageio.core   0.6.9


        ❓   location                    detail
      ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
        ✔️                                initialized ModelDescr to describe model 0.5.3


        ✔️                                bioimageio.spec format validation model 0.5.3

        🔍   context.perform_io_checks   False
        🔍   context.warning_level       error

        ✔️                                Reproduce test outputs from test inputs (pytorch_state_dict)


        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:

                                        0

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:

                                        0

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:

                                        1

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:

                                        1

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 1 and size parameter n:

                                        2

        ✔️                                Run pytorch_state_dict inference for inputs with batch_size: 2 and size parameter n:

                                        2

        ✔️                                Reproduce test outputs from test inputs (torchscript)


        ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 0


        ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 0


        ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 1


        ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 1


        ✔️                                Run torchscript inference for inputs with batch_size: 1 and size parameter n: 2


        ✔️                                Run torchscript inference for inputs with batch_size: 2 and size parameter n: 2
      ```

      </details>
  1. run prediction on your data

  - display the `bioimageio-predict` command help to get an overview:

      ```console
      $ bioimageio predict --help
      ...
      ```

      <details>
      <summary>(Click to expand output)</summary>

      ```console
      usage: bioimageio predict [-h] [--inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLenmin_length=1]]]]
                                [--outputs {str,Tuple[str,...]}] [--overwrite bool] [--blockwise bool] [--stats Path]
                                [--preview bool]
                                [--weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}]
                                [--example bool]
                                SOURCE

      bioimageio-predict - Run inference on your data with a bioimage.io model.

      positional arguments:
        SOURCE                Url/path to a bioimageio.yaml/rdf.yaml file
                              or a bioimage.io resource identifier, e.g. 'affable-shark'

      optional arguments:
        -h, --help            show this help message and exit
        --inputs Sequence[Union[str,Annotated[Tuple[str,...],MinLen(min_length=1)]]]
                              Model input sample paths (for each input tensor)

                              The input paths are expected to have shape...
                              - (n_samples,) or (n_samples,1) for models expecting a single input tensor
                              - (n_samples,) containing the substring '{input_id}', or
                              - (n_samples, n_model_inputs) to provide each input tensor path explicitly.

                              All substrings that are replaced by metadata from the model description:
                              - '{model_id}'
                              - '{input_id}'

                              Example inputs to process sample 'a' and 'b'
                              for a model expecting a 'raw' and a 'mask' input tensor:
                              --inputs="[["a_raw.tif","a_mask.tif"],["b_raw.tif","b_mask.tif"]]"
                              (Note that JSON double quotes need to be escaped.)

                              Alternatively a `bioimageio-cli.yaml` (or `bioimageio-cli.json`) file
                              may provide the arguments, e.g.:
                              ```yaml
                              inputs:
                              - [a_raw.tif, a_mask.tif]
                              - [b_raw.tif, b_mask.tif]
                              ```

                              `.npy` and any file extension supported by imageio are supported.
                              Aavailable formats are listed at
                              https://imageio.readthedocs.io/en/stable/formats/index.html#all-formats.
                              Some formats have additional dependencies.

                                (default: ('{input_id}/001.tif',))
        --outputs {str,Tuple[str,...]}
                              Model output path pattern (per output tensor)

                              All substrings that are replaced:
                              - '{model_id}' (from model description)
                              - '{output_id}' (from model description)
                              - '{sample_id}' (extracted from input paths)

                                (default: outputs_{model_id}/{output_id}/{sample_id}.tif)
        --overwrite bool      allow overwriting existing output files (default: False)
        --blockwise bool      process inputs blockwise (default: False)
        --stats Path          path to dataset statistics
                              (will be written if it does not exist,
                              but the model requires statistical dataset measures)
                                (default: dataset_statistics.json)
        --preview bool        preview which files would be processed
                              and what outputs would be generated. (default: False)
        --weight_format {typing.Literal['keras_hdf5','onnx','pytorch_state_dict','tensorflow_js','tensorflow_saved_model_bundle','torchscript'],any}
                              The weight format to use. (default: any)
        --example bool        generate and run an example

                              1. downloads example model inputs
                              2. creates a `{model_id}_example` folder
                              3. writes input arguments to `{model_id}_example/bioimageio-cli.yaml`
                              4. executes a preview dry-run
                              5. executes prediction with example input

                                (default: False)
      ```

      </details>

  - create an example and run prediction locally!

      ```console
      $ bioimageio predict impartial-shrimp --example=True
      ...
      ```

      <details>
      <summary>(Click to expand output)</summary>

      ```console
      🛈 bioimageio prediction preview structure:
      {'{sample_id}': {'inputs': {'{input_id}': '<input path>'},
                      'outputs': {'{output_id}': '<output path>'}}}
      🔎 bioimageio prediction preview output:
      {'1': {'inputs': {'input0': 'impartial-shrimp_example/input0/001.tif'},
            'outputs': {'output0': 'impartial-shrimp_example/outputs/output0/1.tif'}}}
      predict with impartial-shrimp: 100%|███████████████████████████████████████████████████| 1/1 [00:21<00:00, 21.76s/sample]
      🎉 Sucessfully ran example prediction!
      To predict the example input using the CLI example config file impartial-shrimp_example\bioimageio-cli.yaml, execute `bioimageio predict` from impartial-shrimp_example:
      $ cd impartial-shrimp_example
      $ bioimageio predict "impartial-shrimp"

      Alternatively run the following command in the current workind directory, not the example folder:
      $ bioimageio predict --preview=False --overwrite=True --stats="impartial-shrimp_example/dataset_statistics.json" --inputs="[[\"impartial-shrimp_example/input0/001.tif\"]]" --outputs="impartial-shrimp_example/outputs/{output_id}/{sample_id}.tif" "impartial-shrimp"
      (note that a local 'bioimageio-cli.json' or 'bioimageio-cli.yaml' may interfere with this)
    ```

    </details>





  The `bioimageio.core` package can be installed from conda-forge via

  ```console
  mamba install -c conda-forge bioimageio.core
  ```

  If you do not install any additional deep learning libraries, you will only be able to use general convenience
  functionality, but not any functionality for model prediction.
  To install additional deep learning libraries use:

  - Pytorch/Torchscript:

    CPU installation (if you don't have an nvidia graphics card):

    ```console
    mamba install -c pytorch -c conda-forge bioimageio.core pytorch torchvision cpuonly
    ```

    GPU installation (for cuda 11.6, please choose the appropriate cuda version for your system):

    ```console
    mamba install -c pytorch -c nvidia -c conda-forge bioimageio.core pytorch torchvision pytorch-cuda=11.8
    ```

    Note that the pytorch installation instructions may change in the future. For the latest instructions please refer to [pytorch.org](https://pytorch.org/).

  - Tensorflow

    Currently only CPU version supported

    ```console
    mamba install -c conda-forge bioimageio.core tensorflow
    ```

  - ONNXRuntime

    Currently only cpu version supported

    ```console
    mamba install -c conda-forge bioimageio.core onnxruntime
    ```



  The package is also available via pip
  (e.g. with recommended extras `onnx` and `pytorch`):

  ```console
  pip install "bioimageio.core[onnx,pytorch]"
  ```



  To set up a development conda environment run the following commands:

  ```console
  mamba env create -f dev/env.yaml
  mamba activate core
  pip install -e . --no-deps
  ```

  There are different environment files available that only install tensorflow or pytorch as dependencies.



  `bioimageio.core` installs a command line interface (CLI) for testing models and other functionality.
  You can list all the available commands via:

  ```console
  bioimageio
  ```



  For convenience the command line options (not arguments) may be given in a `bioimageio-cli.json`
  or `bioimageio-cli.yaml` file, e.g.:

  ```yaml

  inputs: inputs/*_{tensor_id}.h5
  outputs: outputs_{model_id}/{sample_id}_{tensor_id}.h5
  overwrite: true
  blockwise: true
  stats: inputs/dataset_statistics.json
  ```



  `bioimageio.core` is a python package that implements prediction with bioimageio models
  including standardized pre- and postprocessing operations.
  These models are described by---and can be loaded with---the bioimageio.spec package.

  In addition bioimageio.core provides functionality to convert model weight formats.

  To get an overview of this functionality, check out these example notebooks:

  - [model creation/loading with bioimageio.spec](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb)

  and the [developer documentation](https://bioimage-io.github.io/core-bioimage-io-python/bioimageio/core.html).



  `bioimageio.spec` and `bioimageio.core` use [loguru](https://github.com/Delgan/loguru) for logging, hence the logging level
  may be controlled with the `LOGURU_LEVEL` environment variable.



  The model specification and its validation tools can be found at <https://github.com/bioimage-io/spec-bioimage-io>.





  - breaking:
    - bioimageio CLI now has implicit boolean flags
  - non-breaking:
    - use new `ValidationDetail.recommended_env` in `ValidationSummary`
    - improve `get_io_sample_block_metas()`
      - now works for sufficiently large, but not exactly shaped inputs
    - update to support `zipfile.ZipFile` object with bioimageio.spec==0.5.3.5
    - add io helpers `resolve` and `resolve_and_extract`
    - added `enable_determinism` function and **determinism** input argument for testing with seeded
      random generators and optionally (determinsim=="full") instructing DL frameworks to use
      deterministic algorithms.



  - fix #423



  - improve bioimageio command line interface (details in #157)
    - add `predict` command
    - package command input `path` is now required



  - testing model inference will now check all weight formats
    (previously only the first one for which model adapter creation succeeded had been checked)
  - fix predict with blocking (Thanks @thodkatz)



  - `predict()` argument `inputs` may be sample



  - add aliases to match previous API more closely



  - improve adapter error messages



  - add `bioimageio validate-format` command
  - improve error messages and display of command results



  - Fix [#386](https://github.com/bioimage-io/core-bioimage-io-python/issues/386)
  - (in model inference testing) stop assuming model inputs are tileable



  - Fix [#384](https://github.com/bioimage-io/core-bioimage-io-python/issues/384)



  - Fix [#378](https://github.com/bioimage-io/core-bioimage-io-python/pull/378) (with [#379](https://github.com/bioimage-io/core-bioimage-io-python/pull/379))*



  - add compatibility with new bioimageio.spec 0.5 (0.5.2post1)
  - improve interfaces



  - [Fix critical bug in predict with tiling](https://github.com/bioimage-io/core-bioimage-io-python/pull/359)


pkgId: 664a8ef8eb232881a5bca63b7cfcc8c7815c629f06a4276946bfb79d434fa6c0
name: python-bioimageio.spec
arch: src
version: 0.5.3.3
epoch: 0
release: 1
summary: Parser and validator library for bioimage.io specifications
url: https://github.com/bioimage-io/spec-bioimage-io
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioimageio.spec-0.5.3.3-1.src.rpm
checksum_type: sha256
description:
  ![License](https://img.shields.io/github/license/bioimage-io/spec-bioimage-io.svg)
  [![PyPI](https://img.shields.io/pypi/v/bioimageio-spec.svg?style=popout)](https://pypi.org/project/bioimageio.spec/)
  [![conda-version](https://anaconda.org/conda-forge/bioimageio.spec/badges/version.svg)](https://anaconda.org/conda-forge/bioimageio.spec/)
  [![downloads](https://static.pepy.tech/badge/bioimageio.spec)](https://pepy.tech/project/bioimageio.spec)
  [![conda-forge downloads](https://img.shields.io/conda/dn/conda-forge/bioimageio.spec.svg?label=conda-forge)](https://anaconda.org/conda-forge/bioimageio.spec/)
  ![code style](https://img.shields.io/badge/code%20style-black-000000.svg)



  This repository contains the specifications of the standard format defined by the bioimage.io community for the content (i.e., models, datasets and applications) in the [bioimage.io website](https://bioimage.io).
  Each item in the content is always described using a YAML 1.2 file named `rdf.yaml` or `bioimageio.yaml`.
  This `rdf.yaml` \ `bioimageio.yaml`--- along with the files referenced in it --- can be downloaded from or uploaded to the [bioimage.io website](https://bioimage.io) and may be produced or consumed by bioimage.io-compatible consumers (e.g., image analysis software like ilastik).

  [These](https://github.com/bioimage-io/spec-bioimage-io?tab=readme-ov-file#format-version-overview) are the rules and format that bioimage.io-compatible resources must fulfill.

  Note that the Python package PyYAML does not support YAML 1.2 .
  We therefore use and recommend [ruyaml](https://ruyaml.readthedocs.io/en/latest/).
  For differences see <https://ruamelyaml.readthedocs.io/en/latest/pyyaml>.

  Please also note that the best way to check whether your `rdf.yaml` file is bioimage.io-compliant is to call `bioimageio.core.validate` from the [bioimageio.core](https://github.com/bioimage-io/core-bioimage-io-python) Python package.
  The [bioimageio.core](https://github.com/bioimage-io/core-bioimage-io-python) Python package also provides the bioimageio command line interface (CLI) with the `validate` command:

  ```terminal
  bioimageio validate path/to/your/rdf.yaml
  ```



  All bioimage.io description formats are defined as [Pydantic models](https://docs.pydantic.dev/latest/).

  | type | format version | documentation |
  | --- | --- | --- |
  | model | 0.5 </br> 0.4 | [model_descr_v0-5.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/model_descr_v0-5.md) </br> [model_descr_v0-4.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/model_descr_v0-4.md) |
  | dataset | 0.3 </br> 0.2 | [dataset_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/dataset_descr_v0-3.md) </br> [dataset_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/dataset_descr_v0-2.md) |
  | notebook | 0.3 </br> 0.2 | [notebook_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/notebook_descr_v0-3.md) </br> [notebook_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/notebook_descr_v0-2.md) |
  | application | 0.3 </br> 0.2 | [application_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/application_descr_v0-3.md) </br> [application_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/application_descr_v0-2.md) |
  | collection | 0.3 </br> 0.2 | [collection_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/collection_descr_v0-3.md) </br> [collection_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/collection_descr_v0-2.md) |
  | generic | 0.3 </br> 0.2 | [generic_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/generic_descr_v0-3.md) </br> [generic_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/generic_descr_v0-2.md) |



  Simplified descriptions are available as [JSON schema](https://json-schema.org/):

  | bioimageio.spec version | JSON schema |
  | --- | --- |
  | latest | [bioimageio_schema_latest.json](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/bioimageio_schema_latest.json) |
  | 0.5 | [bioimageio_schema_v0-5.json](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/bioimageio_schema_v0-5.json) |

  These are primarily intended for syntax highlighting and form generation.



  A flattened view of the types used by the spec that also shows values constraints.

  [rendered](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/interactive_docs_v0-5.html)

  You can also generate these docs locally by running `PYTHONPATH=./scripts python -m interactive_docs`



  We provide some [examples for using rdf.yaml files to describe models, applications, notebooks and datasets](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example_descriptions/examples.md),
  and an [example notebook to programmatically access the models, applications, notebooks and datasets descriptions](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb).



  * Due to the limitations of storage services such as Zenodo, which does not support subfolders, it is recommended to place other files in the same directory level of the `rdf.yaml` file and try to avoid using subdirectories.
  * Use the [bioimageio.core Python package](https://github.com/bioimage-io/core-bioimage-io-python) to validate your `rdf.yaml` file.
  * bioimageio.spec keeps evolving. Try to use and upgrade to the most current format version!



  The bioimageio CLI has moved entirely to [bioimageio.core](https://github.com/bioimage-io/core-bioimage-io-python).



  bioimageio.spec can be installed with either `conda` or `pip`.
  We recommend installing `bioimageio.core` instead to get access to the Python programmatic features available in the BioImage.IO community:

  ```console
  conda install -c conda-forge bioimageio.core
  ```

  or

  ```console
  pip install -U bioimageio.core
  ```

  Still, for a lighter package or just testing, you can install the `bioimageio.spec` package solely:

  ```console
  conda install -c conda-forge bioimageio.spec
  ```

  or

  ```console
  pip install -U bioimageio.spec
  ```



  TODO: link to settings in dev docs





  <a href="https://github.com/bioimage-io/spec-bioimage-io/graphs/contributors">
    <img alt="bioimageio.spec contributors" src="https://contrib.rocks/image?repo=bioimage-io/spec-bioimage-io" />
  </a>

  Made with [contrib.rocks](https://contrib.rocks).



  To keep the bioimageio.spec Python package version in sync with the (model) description format version, bioimageio.spec is versioned as MAJOR.MINRO.PATCH.LIB, where MAJOR.MINRO.PATCH correspond to the latest model description format version implemented and LIB may be bumpbed for library changes that do not affect the format version.
  [This change was introduced with bioimageio.spec 0.5.3.1](#bioimageiospec-0531).







  * expose `progressbar` to customize display of download progress
  * expose `get_resource_package_content`
  * prefer `rdf.yaml` over `bioimageio.yaml` (name `bioimageio.yaml` file `rdf.yaml` file when packaging, look for `rdf.yaml` first, etc.)
  * enforce: (generic 0.3/model 0.5 spec) documentation source file encoding has to be UTF-8.
  * bugfix: allow optional pre- and postprocessing to be missing in an RDF (before it required an empty dict).



  * bugfix "reset known files if root changes" (#619)



  note: the versioning scheme was changed as our previous `post` releases include changes beyond what a post release should entail (only changing docstrings, etc).
  This was motivated by the desire to keep the library version in sync with the (model) format version to avoid confusion.
  To keep this relation, but avoid overbearing post releases a library version number is now added as the 4th part MAJOR.MINOR.PATCH.LIB_VERSION.

  * add `load_model_description` and `load_dataset_description`
  * add `ensure_description_is_model` and `ensure_description_is_dataset`
  * expose `perform_io_checks` and `known_files` from `ValidationContext` to `load_description` and `load_description_and_validate_format_only`



  * fix pinning of pydantic



  * update resolving of bioimage.io resource IDs



  * fix SHA-256 value when resolving a RDF version from the bioimage.io collection that is not the latest



  * bump patch version during loading for model 0.5.x
  * improve validation error formatting
  * validate URLs first with a head request, if forbidden, follow up with a get request that is streamed and if that is also forbidden a regular get request.
  * `RelativePath.absolute()` is now a method (not a property) analog to `pathlib.Path`



  * remove collection description
  * update SPDX license list
  * update generic description to 0.3.1
  * update model description to 0.5.3
  * add timeout argument to all requests.get calls



  * added more information to validation summary
  * deprioritize `Path` objects in the `FileSource` union



  * resolve backup DOIs
  * fix resolving relative file paths given as strings
  * allow to bypass download and hashing of known files



  * avoid full download when validating urls



  * resolve version (un)specific collection IDs, e.g. `load_description('affable-shark')`, `load_description('affable-shark/1')`



  * fix model packaging with weights format priority



  * new patch version model 0.5.2



  * new patch version model 0.5.1



  * don't fail if CI env var is a string



  * fix `_internal.io_utils.identify_bioimageio_yaml_file()`



  * new description formats: [generic 0.3, application 0.3, collection 0.3, dataset 0.3, notebook 0.3](generic-030--application-030--collection-030--dataset-030--notebook-030) and [model 0.5](model-050).
  * various API changes, most important functions:
    * `bioimageio.spec.load_description` (replaces `load_raw_resource_description`, interface changed)
    * `bioimageio.spec.validate_format` (new)
    * `bioimageio.spec.dump_description` (replaces `serialize_raw_resource_description_to_dict`, interface changed)
    * `bioimageio.spec.update_format` (interface changed)
  * switch from Marshmallow to Pydantic
    * extended validation
    * one joint, more precise JSON schema



  * small bugixes
  * better type hints
  * improved tests



  * add `axes` and `eps` to `scale_mean_var`



  * add simple forward compatibility by treating future format versions as latest known (for the respective resource type)



  * Make CLI output more readable

  * find redirected URLs when checking for URL availability



  * Improve error message for non-existing RDF file path given as string

  * Improve documentation for model description's `documentation` field



  * fix enrich_partial_rdf_with_imjoy_plugin (see <https://github.com/bioimage-io/spec-bioimage-io/pull/452>)



  * fix rdf_update of entries in `resolve_collection_entries()`



  * pass root to `enrich_partial_rdf` arg of `resolve_collection_entries()`



  * keep `ResourceDescrption.root_path` as URI for remote resources. This fixes the collection description as the collection entries are resolved after the collection description has been loaded.



  * new bioimageio.spec.partner module adding validate-partner-collection command if optional 'lxml' dependency is available



  * new env var `BIOIMAGEIO_CACHE_WARNINGS_LIMIT` (default: 3) to avoid spam from cache hit warnings

  * more robust conversion of ImportableSourceFile for absolute paths to relative paths (don't fail on non-path source file)



  * resolve symlinks when transforming absolute to relative paths during serialization; see [#438](https://github.com/bioimage-io/spec-bioimage-io/pull/438)



  * fix loading of collection description with id (id used to be ignored)



  * support loading bioimageio resources by their animal nickname (currently only models have nicknames).



  * any field previously expecting a local relative path is now also accepting an absolute path

  * load_raw_resource_description returns a raw resource description which has no relative paths (any relative paths are converted to absolute paths).



  * add command `commands.update_rdf()`/`update-rdf`(cli)



  * fix unresolved ImportableSourceFile



  * fix collection description conversion for type field



  * fix to shape validation for model description 0.4: output shape now needs to be bigger than halo

  * moved objects from bioimageio.spec.shared.utils to bioimageio.spec.shared\[.node_transformer\]
  * additional keys to validation summary: bioimageio_spec_version, status



  * fixes to generic description:
    * ignore value of field `root_path` if present in yaml. This field is used internally and always present in RDF nodes.



  * fixes to collection description:
    * RDFs specified directly in collection description are validated correctly even if their source field does not point to an RDF.
    * nesting of collection description allowed



  * fixed missing field `icon` in generic description's raw node

  * fixes to collection description:
    * RDFs specified directly in collection description are validated correctly
    * no nesting of collection description allowed for now
    * `links` is no longer an explicit collection entry field ("moved" to unknown)



  * new model spec 0.3.5 and 0.4.1



  * `load_raw_resource_description` no longer accepts `update_to_current_format` kwarg (use `update_to_format` instead)



  * `load_raw_resource_description` accepts `update_to_format` kwarg





  * Non-breaking changes
    * remove `version_number` in favor of using `version`



  * Non-breaking changes
    * added `concatenable` flag to index, time and space input axes



  * Non-breaking changes
    * added `DataDependentSize` for `outputs.i.size` to specify an output shape that is not known before inference is run.
    * added optional `inputs.i.optional` field to indicate that a tensor may be `None`
    * made data type assumptions in `preprocessing` and `postprocessing` explicit by adding `'ensure_dtype'` operations per default.
    * allow to specify multiple thresholds (along an `axis`) in a 'binarize' processing step



  * Breaking canges that are fully auto-convertible
    * dropped `download_url`
    * dropped non-file attachments
    * `attachments.files` moved to `attachments.i.source`
  * Non-breaking changes
    * added optional `parent` field



  all generic 0.3.0 changes (except models already have the `parent` field) plus:

  * Breaking changes that are partially auto-convertible
    * `inputs.i.axes` are now defined in more detail (same for `outputs.i.axes`)
    * `inputs.i.shape` moved per axes to `inputs.i.axes.size` (same for `outputs.i.shape`)
    * new pre-/postprocessing 'fixed_zero_mean_unit_variance' separated from 'zero_mean_unit_variance', where `mode=fixed` is no longer valid.
      (for scalar values this is auto-convertible.)
  * Breaking changes that are fully auto-convertible
    * changes in `weights.pytorch_state_dict.architecture`
      * renamed `weights.pytorch_state_dict.architecture.source_file` to `...architecture.source`
    * changes in `weights.pytorch_state_dict.dependencies`
      * only conda environment allowed and specified by `weights.pytorch_state_dict.dependencies.source`
      * new optional field `weights.pytorch_state_dict.dependencies.sha256`
    * changes in `weights.tensorflow_model_bundle.dependencies`
      * same as changes in `weights.pytorch_state_dict.dependencies`
    * moved `test_inputs` to `inputs.i.test_tensor`
    * moved `test_outputs` to `outputs.i.test_tensor`
    * moved `sample_inputs` to `inputs.i.sample_tensor`
    * moved `sample_outputs` to `outputs.i.sample_tensor`
    * renamed `inputs.i.name` to `inputs.i.id`
    * renamed `outputs.i.name` to `outputs.i.id`
    * renamed `inputs.i.preprocessing.name` to `inputs.i.preprocessing.id`
    * renamed `outputs.i.postprocessing.name` to `outputs.i.postprocessing.id`
  * Non-breaking changes:
    * new pre-/postprocessing: `id`='ensure_dtype' with kwarg `dtype`



  * Breaking changes that are fully auto-convertible
    * `id` overwritten with value from `config.bioimageio.nickname` if available
  * Non-breaking changes
    * `version_number` is a new, optional field indicating that an RDF is the nth published version with a given `id`
    * `id_emoji` is a new, optional field (set from `config.bioimageio.nickname_icon` if available)
    * `uploader` is a new, optional field with `email` and an optional `name` subfields



  * Non-breaking changes
    * make pre-/postprocessing kwargs `mode` and `axes` always optional for model description 0.3 and 0.4



  * Non-breaking changes
    * `cite` field is now optional



  * Breaking changes that are fully auto-convertible
    * name field may not include '/' or '\' (conversion removes these)



  * Non-breaking changes
    * Implicit output shape can be expanded by inserting `null` into `shape:scale` and indicating length of new dimension D in the `offset` field. Keep in mind that `D=2*'offset'`.



  * Breaking changes that are fully auto-convertible
    * `parent` field changed to hold a string that is a bioimage.io ID, a URL or a local relative path (and not subfields `uri` and `sha256`)



  * Non-breaking changes
    * new optional field `training_data`



  * Non-breaking changes
    * explicitly define and document dataset description (for now, clone of generic description with type="dataset")



  * Non-breaking changes
    * add optional field `download_url`
    * add optional field `dependencies` to all weight formats (not only pytorch_state_dict)
    * add optional `pytorch_version` to the pytorch_state_dict and torchscript weight formats



  * Bug fixes:
    * in a `pytorch_state_dict` weight entry `architecture` is no longer optional.



  * Non-breaking changes
    * make `authors`, `cite`, `documentation` and `tags` optional

  * Breaking changes that are fully auto-convertible
    * Simplifies collection description 0.2.1 by merging resource type fields together to a `collection` field,
      holindg a list of all resources in the specified collection.



  * Non-breaking changes
    * `rdf_source` new optional field
    * `id` new optional field



  * First official release, extends generic description with fields `application`, `model`, `dataset`, `notebook` and (nested)
    `collection`, which hold lists linking to respective resources.



  * Non-breaking changes
    * add optional `email` and `github_user` fields to entries in `authors`
    * add optional `maintainers` field (entries like in `authors` but  `github_user` is required (and `name` is not))



  * Breaking changes that are fully auto-convertible
    * moved field `dependencies` to `weights:pytorch_state_dict:dependencies`

  * Non-breaking changes
    * `documentation` field accepts URLs as well



  * Non-breaking changes
    * `documentation` field accepts URLs as well



  * Breaking changes
    * model inputs and outputs may not use duplicated names.
    * model field `sha256` is required if `pytorch_state_dict` weights are defined.
      and is now moved to the `pytroch_state_dict` entry as `architecture_sha256`.

  * Breaking changes that are fully auto-convertible
    * model fields language and framework are removed.
    * model field `source` is renamed `architecture` and is moved together with `kwargs` to the `pytorch_state_dict`
      weights entry (if it exists, otherwise they are removed).
    * the weight format `pytorch_script` was renamed to `torchscript`.
  * Other changes
    * model inputs (like outputs) may be defined by `scale`ing and `offset`ing a `reference_tensor`
    * a `maintainers` field was added to the model description.
    * the entries in the `authors` field may now additionally contain `email` or `github_user`.
    * the summary returned by the `validate` command now also contains a list of warnings.
    * an `update_format` command was added to aid with updating older RDFs by applying auto-conversion.



  * Non-breaking changes
    * Add optional parameter `eps` to `scale_range` postprocessing.



  * Breaking changes that are fully auto-convertible
    * `reference_input` for implicit output tensor shape was renamed to `reference_tensor`



  * Breaking changes
    * The RDF file name in a package should be `rdf.yaml` for all the RDF (not `model.yaml`);
    * Change `authors` and `packaged_by` fields from List[str] to List[Author] with Author consisting of a dictionary `{name: '<Full name>', affiliation: '<Affiliation>', orcid: 'optional orcid id'}`;
    * Add a mandatory `type` field to comply with the generic description. Only valid value is 'model' for model description;
    * Only allow `license` identifier from the [SPDX license list](https://spdx.org/licenses/);

  * Non-breaking changes
    * Add optional `version` field (default 0.1.0) to keep track of model changes;
    * Allow the values in the `attachments` list to be any values besides URI;


pkgId: 8d838f95fc209f2e1d59a2658fa96a1543fa8b562e8153555f89340bef5bf9dc
name: python-bioimageio.spec
arch: src
version: 0.5.3.4
epoch: 0
release: 1
summary: Parser and validator library for bioimage.io specifications
url: https://github.com/bioimage-io/spec-bioimage-io
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
location_href: Packages/python-bioimageio.spec-0.5.3.4-1.src.rpm
checksum_type: sha256
description:
  ![License](https://img.shields.io/github/license/bioimage-io/spec-bioimage-io.svg)
  [![PyPI](https://img.shields.io/pypi/v/bioimageio-spec.svg?style=popout)](https://pypi.org/project/bioimageio.spec/)
  [![conda-version](https://anaconda.org/conda-forge/bioimageio.spec/badges/version.svg)](https://anaconda.org/conda-forge/bioimageio.spec/)
  [![downloads](https://static.pepy.tech/badge/bioimageio.spec)](https://pepy.tech/project/bioimageio.spec)
  [![conda-forge downloads](https://img.shields.io/conda/dn/conda-forge/bioimageio.spec.svg?label=conda-forge)](https://anaconda.org/conda-forge/bioimageio.spec/)
  ![code style](https://img.shields.io/badge/code%20style-black-000000.svg)
  [![coverage](https://bioimage-io.github.io/spec-bioimage-io/coverage/coverage-badge.svg)](https://bioimage-io.github.io/spec-bioimage-io/coverage/index.html)



  This repository contains the specifications of the standard format defined by the bioimage.io community for the content (i.e., models, datasets and applications) in the [bioimage.io website](https://bioimage.io).
  Each item in the content is always described using a YAML 1.2 file named `rdf.yaml` or `bioimageio.yaml`.
  This `rdf.yaml` \ `bioimageio.yaml`--- along with the files referenced in it --- can be downloaded from or uploaded to the [bioimage.io website](https://bioimage.io) and may be produced or consumed by bioimage.io-compatible consumers (e.g., image analysis software like ilastik).

  [These](https://github.com/bioimage-io/spec-bioimage-io?tab=readme-ov-file#format-version-overview) are the rules and format that bioimage.io-compatible resources must fulfill.

  Note that the Python package PyYAML does not support YAML 1.2 .
  We therefore use and recommend [ruyaml](https://ruyaml.readthedocs.io/en/latest/).
  For differences see <https://ruamelyaml.readthedocs.io/en/latest/pyyaml>.

  Please also note that the best way to check whether your `rdf.yaml` file is bioimage.io-compliant is to call `bioimageio.core.validate` from the [bioimageio.core](https://github.com/bioimage-io/core-bioimage-io-python) Python package.
  The [bioimageio.core](https://github.com/bioimage-io/core-bioimage-io-python) Python package also provides the bioimageio command line interface (CLI) with the `validate` command:

  ```terminal
  bioimageio validate path/to/your/rdf.yaml
  ```



  All bioimage.io description formats are defined as [Pydantic models](https://docs.pydantic.dev/latest/).

  | type | format version | documentation |
  | --- | --- | --- |
  | model | 0.5 </br> 0.4 | [model_descr_v0-5.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/model_descr_v0-5.md) </br> [model_descr_v0-4.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/model_descr_v0-4.md) |
  | dataset | 0.3 </br> 0.2 | [dataset_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/dataset_descr_v0-3.md) </br> [dataset_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/dataset_descr_v0-2.md) |
  | notebook | 0.3 </br> 0.2 | [notebook_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/notebook_descr_v0-3.md) </br> [notebook_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/notebook_descr_v0-2.md) |
  | application | 0.3 </br> 0.2 | [application_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/application_descr_v0-3.md) </br> [application_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/application_descr_v0-2.md) |
  | collection | 0.3 </br> 0.2 | [collection_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/collection_descr_v0-3.md) </br> [collection_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/collection_descr_v0-2.md) |
  | generic | 0.3 </br> 0.2 | [generic_descr_v0-3.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/generic_descr_v0-3.md) </br> [generic_descr_v0-2.md](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/user_docs/generic_descr_v0-2.md) |



  Simplified descriptions are available as [JSON schema](https://json-schema.org/):

  | bioimageio.spec version | JSON schema |
  | --- | --- |
  | latest | [bioimageio_schema_latest.json](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/bioimageio_schema_latest.json) ([documentation (WIP)](https://bioimage-io.github.io/spec-bioimage-io/bioimageio_schema_latest/index.html)) |
  | 0.5 | [bioimageio_schema_v0-5.json](https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/bioimageio_schema_v0-5.json) ([documentation (WIP)](https://bioimage-io.github.io/spec-bioimage-io/bioimageio_schema_v0-5/index.html)) |

  These are primarily intended for syntax highlighting.



  A flattened view of the types used by the spec that also shows values constraints.

  [rendered](https://bioimage-io.github.io/spec-bioimage-io/interactive_docs_v0-5.html)

  You can also generate these docs locally by running `PYTHONPATH=./scripts python -m interactive_docs`



  We provide some [examples for using rdf.yaml files to describe models, applications, notebooks and datasets](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example_descriptions/examples.md),
  and an [example notebook to programmatically access the models, applications, notebooks and datasets descriptions](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb).



  * Due to the limitations of storage services such as Zenodo, which does not support subfolders, it is recommended to place other files in the same directory level of the `rdf.yaml` file and try to avoid using subdirectories.
  * Use the [bioimageio.core Python package](https://github.com/bioimage-io/core-bioimage-io-python) to validate your `rdf.yaml` file.
  * bioimageio.spec keeps evolving. Try to use and upgrade to the most current format version!



  The bioimageio CLI has moved entirely to [bioimageio.core](https://github.com/bioimage-io/core-bioimage-io-python).



  bioimageio.spec can be installed with either `conda` or `pip`.
  We recommend installing `bioimageio.core` instead to get access to the Python programmatic features available in the BioImage.IO community:

  ```console
  conda install -c conda-forge bioimageio.core
  ```

  or

  ```console
  pip install -U bioimageio.core
  ```

  Still, for a lighter package or just testing, you can install the `bioimageio.spec` package solely:

  ```console
  conda install -c conda-forge bioimageio.spec
  ```

  or

  ```console
  pip install -U bioimageio.spec
  ```



  TODO: link to settings in dev docs





  <a href="https://github.com/bioimage-io/spec-bioimage-io/graphs/contributors">
    <img alt="bioimageio.spec contributors" src="https://contrib.rocks/image?repo=bioimage-io/spec-bioimage-io" />
  </a>

  Made with [contrib.rocks](https://contrib.rocks).



  To keep the bioimageio.spec Python package version in sync with the (model) description format version, bioimageio.spec is versioned as MAJOR.MINRO.PATCH.LIB, where MAJOR.MINRO.PATCH correspond to the latest model description format version implemented and LIB may be bumpbed for library changes that do not affect the format version.
  [This change was introduced with bioimageio.spec 0.5.3.1](#bioimageiospec-0531).







  * support loading and saving from/to zipfile.ZipFile objects
  * fix bug when packaging with weights priority order (#638)
  * add conda_env module providing helper to create recommended conda environments for model descriptions
  * fix summary formatting
  * improve logged origin for logged messages
  * make the `model.v0_5.ModelDescr.training_data` field a `left_to_right` Union to avoid warnings
  * the deprecated `version_number` is no longer appended to the `id`, but instead set as `version` if no `version` is specified.



  * expose `progressbar` to customize display of download progress
  * expose `get_resource_package_content`
  * prefer `rdf.yaml` over `bioimageio.yaml` (name `bioimageio.yaml` file `rdf.yaml` file when packaging, look for `rdf.yaml` first, etc.)
  * enforce: (generic 0.3/model 0.5 spec) documentation source file encoding has to be UTF-8.
  * bugfix: allow optional pre- and postprocessing to be missing in an RDF (before it required an empty dict).



  * bugfix "reset known files if root changes" (#619)



  note: the versioning scheme was changed as our previous `post` releases include changes beyond what a post release should entail (only changing docstrings, etc).
  This was motivated by the desire to keep the library version in sync with the (model) format version to avoid confusion.
  To keep this relation, but avoid overbearing post releases a library version number is now added as the 4th part MAJOR.MINOR.PATCH.LIB_VERSION.

  * add `load_model_description` and `load_dataset_description`
  * add `ensure_description_is_model` and `ensure_description_is_dataset`
  * expose `perform_io_checks` and `known_files` from `ValidationContext` to `load_description` and `load_description_and_validate_format_only`



  * fix pinning of pydantic



  * update resolving of bioimage.io resource IDs



  * fix SHA-256 value when resolving a RDF version from the bioimage.io collection that is not the latest



  * bump patch version during loading for model 0.5.x
  * improve validation error formatting
  * validate URLs first with a head request, if forbidden, follow up with a get request that is streamed and if that is also forbidden a regular get request.
  * `RelativePath.absolute()` is now a method (not a property) analog to `pathlib.Path`



  * remove collection description
  * update SPDX license list
  * update generic description to 0.3.1
  * update model description to 0.5.3
  * add timeout argument to all requests.get calls



  * added more information to validation summary
  * deprioritize `Path` objects in the `FileSource` union



  * resolve backup DOIs
  * fix resolving relative file paths given as strings
  * allow to bypass download and hashing of known files



  * avoid full download when validating urls



  * resolve version (un)specific collection IDs, e.g. `load_description('affable-shark')`, `load_description('affable-shark/1')`



  * fix model packaging with weights format priority



  * new patch version model 0.5.2



  * new patch version model 0.5.1



  * don't fail if CI env var is a string



  * fix `_internal.io_utils.identify_bioimageio_yaml_file()`



  * new description formats: [generic 0.3, application 0.3, collection 0.3, dataset 0.3, notebook 0.3](generic-030--application-030--collection-030--dataset-030--notebook-030) and [model 0.5](model-050).
  * various API changes, most important functions:
    * `bioimageio.spec.load_description` (replaces `load_raw_resource_description`, interface changed)
    * `bioimageio.spec.validate_format` (new)
    * `bioimageio.spec.dump_description` (replaces `serialize_raw_resource_description_to_dict`, interface changed)
    * `bioimageio.spec.update_format` (interface changed)
  * switch from Marshmallow to Pydantic
    * extended validation
    * one joint, more precise JSON schema



  * small bugixes
  * better type hints
  * improved tests



  * add `axes` and `eps` to `scale_mean_var`



  * add simple forward compatibility by treating future format versions as latest known (for the respective resource type)



  * Make CLI output more readable

  * find redirected URLs when checking for URL availability



  * Improve error message for non-existing RDF file path given as string

  * Improve documentation for model description's `documentation` field



  * fix enrich_partial_rdf_with_imjoy_plugin (see <https://github.com/bioimage-io/spec-bioimage-io/pull/452>)



  * fix rdf_update of entries in `resolve_collection_entries()`



  * pass root to `enrich_partial_rdf` arg of `resolve_collection_entries()`



  * keep `ResourceDescrption.root_path` as URI for remote resources. This fixes the collection description as the collection entries are resolved after the collection description has been loaded.



  * new bioimageio.spec.partner module adding validate-partner-collection command if optional 'lxml' dependency is available



  * new env var `BIOIMAGEIO_CACHE_WARNINGS_LIMIT` (default: 3) to avoid spam from cache hit warnings

  * more robust conversion of ImportableSourceFile for absolute paths to relative paths (don't fail on non-path source file)



  * resolve symlinks when transforming absolute to relative paths during serialization; see [#438](https://github.com/bioimage-io/spec-bioimage-io/pull/438)



  * fix loading of collection description with id (id used to be ignored)



  * support loading bioimageio resources by their animal nickname (currently only models have nicknames).



  * any field previously expecting a local relative path is now also accepting an absolute path

  * load_raw_resource_description returns a raw resource description which has no relative paths (any relative paths are converted to absolute paths).



  * add command `commands.update_rdf()`/`update-rdf`(cli)



  * fix unresolved ImportableSourceFile



  * fix collection description conversion for type field



  * fix to shape validation for model description 0.4: output shape now needs to be bigger than halo

  * moved objects from bioimageio.spec.shared.utils to bioimageio.spec.shared\[.node_transformer\]
  * additional keys to validation summary: bioimageio_spec_version, status



  * fixes to generic description:
    * ignore value of field `root_path` if present in yaml. This field is used internally and always present in RDF nodes.



  * fixes to collection description:
    * RDFs specified directly in collection description are validated correctly even if their source field does not point to an RDF.
    * nesting of collection description allowed



  * fixed missing field `icon` in generic description's raw node

  * fixes to collection description:
    * RDFs specified directly in collection description are validated correctly
    * no nesting of collection description allowed for now
    * `links` is no longer an explicit collection entry field ("moved" to unknown)



  * new model spec 0.3.5 and 0.4.1



  * `load_raw_resource_description` no longer accepts `update_to_current_format` kwarg (use `update_to_format` instead)



  * `load_raw_resource_description` accepts `update_to_format` kwarg





  * Non-breaking changes
    * remove `version_number` in favor of using `version`



  * Non-breaking changes
    * added `concatenable` flag to index, time and space input axes



  * Non-breaking changes
    * added `DataDependentSize` for `outputs.i.size` to specify an output shape that is not known before inference is run.
    * added optional `inputs.i.optional` field to indicate that a tensor may be `None`
    * made data type assumptions in `preprocessing` and `postprocessing` explicit by adding `'ensure_dtype'` operations per default.
    * allow to specify multiple thresholds (along an `axis`) in a 'binarize' processing step



  * Breaking canges that are fully auto-convertible
    * dropped `download_url`
    * dropped non-file attachments
    * `attachments.files` moved to `attachments.i.source`
  * Non-breaking changes
    * added optional `parent` field



  all generic 0.3.0 changes (except models already have the `parent` field) plus:

  * Breaking changes that are partially auto-convertible
    * `inputs.i.axes` are now defined in more detail (same for `outputs.i.axes`)
    * `inputs.i.shape` moved per axes to `inputs.i.axes.size` (same for `outputs.i.shape`)
    * new pre-/postprocessing 'fixed_zero_mean_unit_variance' separated from 'zero_mean_unit_variance', where `mode=fixed` is no longer valid.
      (for scalar values this is auto-convertible.)
  * Breaking changes that are fully auto-convertible
    * changes in `weights.pytorch_state_dict.architecture`
      * renamed `weights.pytorch_state_dict.architecture.source_file` to `...architecture.source`
    * changes in `weights.pytorch_state_dict.dependencies`
      * only conda environment allowed and specified by `weights.pytorch_state_dict.dependencies.source`
      * new optional field `weights.pytorch_state_dict.dependencies.sha256`
    * changes in `weights.tensorflow_model_bundle.dependencies`
      * same as changes in `weights.pytorch_state_dict.dependencies`
    * moved `test_inputs` to `inputs.i.test_tensor`
    * moved `test_outputs` to `outputs.i.test_tensor`
    * moved `sample_inputs` to `inputs.i.sample_tensor`
    * moved `sample_outputs` to `outputs.i.sample_tensor`
    * renamed `inputs.i.name` to `inputs.i.id`
    * renamed `outputs.i.name` to `outputs.i.id`
    * renamed `inputs.i.preprocessing.name` to `inputs.i.preprocessing.id`
    * renamed `outputs.i.postprocessing.name` to `outputs.i.postprocessing.id`
  * Non-breaking changes:
    * new pre-/postprocessing: `id`='ensure_dtype' with kwarg `dtype`



  * Breaking changes that are fully auto-convertible
    * `id` overwritten with value from `config.bioimageio.nickname` if available
  * Non-breaking changes
    * `version_number` is a new, optional field indicating that an RDF is the nth published version with a given `id`
    * `id_emoji` is a new, optional field (set from `config.bioimageio.nickname_icon` if available)
    * `uploader` is a new, optional field with `email` and an optional `name` subfields



  * Non-breaking changes
    * make pre-/postprocessing kwargs `mode` and `axes` always optional for model description 0.3 and 0.4



  * Non-breaking changes
    * `cite` field is now optional



  * Breaking changes that are fully auto-convertible
    * name field may not include '/' or '\' (conversion removes these)



  * Non-breaking changes
    * Implicit output shape can be expanded by inserting `null` into `shape:scale` and indicating length of new dimension D in the `offset` field. Keep in mind that `D=2*'offset'`.



  * Breaking changes that are fully auto-convertible
    * `parent` field changed to hold a string that is a bioimage.io ID, a URL or a local relative path (and not subfields `uri` and `sha256`)



  * Non-breaking changes
    * new optional field `training_data`



  * Non-breaking changes
    * explicitly define and document dataset description (for now, clone of generic description with type="dataset")



  * Non-breaking changes
    * add optional field `download_url`
    * add optional field `dependencies` to all weight formats (not only pytorch_state_dict)
    * add optional `pytorch_version` to the pytorch_state_dict and torchscript weight formats



  * Bug fixes:
    * in a `pytorch_state_dict` weight entry `architecture` is no longer optional.



  * Non-breaking changes
    * make `authors`, `cite`, `documentation` and `tags` optional

  * Breaking changes that are fully auto-convertible
    * Simplifies collection description 0.2.1 by merging resource type fields together to a `collection` field,
      holindg a list of all resources in the specified collection.



  * Non-breaking changes
    * `rdf_source` new optional field
    * `id` new optional field



  * First official release, extends generic description with fields `application`, `model`, `dataset`, `notebook` and (nested)
    `collection`, which hold lists linking to respective resources.



  * Non-breaking changes
    * add optional `email` and `github_user` fields to entries in `authors`
    * add optional `maintainers` field (entries like in `authors` but  `github_user` is required (and `name` is not))



  * Breaking changes that are fully auto-convertible
    * moved field `dependencies` to `weights:pytorch_state_dict:dependencies`

  * Non-breaking changes
    * `documentation` field accepts URLs as well



  * Non-breaking changes
    * `documentation` field accepts URLs as well



  * Breaking changes
    * model inputs and outputs may not use duplicated names.
    * model field `sha256` is required if `pytorch_state_dict` weights are defined.
      and is now moved to the `pytroch_state_dict` entry as `architecture_sha256`.

  * Breaking changes that are fully auto-convertible
    * model fields language and framework are removed.
    * model field `source` is renamed `architecture` and is moved together with `kwargs` to the `pytorch_state_dict`
      weights entry (if it exists, otherwise they are removed).
    * the weight format `pytorch_script` was renamed to `torchscript`.
  * Other changes
    * model inputs (like outputs) may be defined by `scale`ing and `offset`ing a `reference_tensor`
    * a `maintainers` field was added to the model description.
    * the entries in the `authors` field may now additionally contain `email` or `github_user`.
    * the summary returned by the `validate` command now also contains a list of warnings.
    * an `update_format` command was added to aid with updating older RDFs by applying auto-conversion.



  * Non-breaking changes
    * Add optional parameter `eps` to `scale_range` postprocessing.



  * Breaking changes that are fully auto-convertible
    * `reference_input` for implicit output tensor shape was renamed to `reference_tensor`



  * Breaking changes
    * The RDF file name in a package should be `rdf.yaml` for all the RDF (not `model.yaml`);
    * Change `authors` and `packaged_by` fields from List[str] to List[Author] with Author consisting of a dictionary `{name: '<Full name>', affiliation: '<Affiliation>', orcid: 'optional orcid id'}`;
    * Add a mandatory `type` field to comply with the generic description. Only valid value is 'model' for model description;
    * Only allow `license` identifier from the [SPDX license list](https://spdx.org/licenses/);

  * Non-breaking changes
    * Add optional `version` field (default 0.1.0) to keep track of model changes;
    * Allow the values in the `attachments` list to be any values besides URI;


