Release v0.6.0

Author: markgeejw

Makefile

@@ -1,4 +1,4 @@
-VERSION ?= 0.5.5
+VERSION ?= 0.6.0
 SHELL := /bin/bash
 
 .PHONY: releasehere

anaconda_build/meta.yaml

@@ -1,6 +1,6 @@
 package:
   name: openprotein-python
-  version: "0.5.5"
+  version: "0.6.0"
 
 source:
   path: ../

openprotein/__init__.py

@@ -6,23 +6,27 @@
 isort:skip_file
 """
 
+from typing import TYPE_CHECKING
+import warnings
+
 from openprotein._version import __version__
 from openprotein.app import (
-    AssayDataAPI,
+    DataAPI,
     JobsAPI,
-    TrainingAPI,
-    DesignAPI,
     AlignAPI,
     EmbeddingsAPI,
     FoldAPI,
     SVDAPI,
     UMAPAPI,
     PredictorAPI,
-    DesignerAPI,
+    DesignAPI,
 )
 from openprotein.app.models import Future
 from openprotein.base import APISession
 
+if TYPE_CHECKING:
+    from openprotein.app.deprecated import TrainingAPI, DesignAPI
+
 
 class OpenProtein(APISession):
     """
@@ -31,15 +35,14 @@ class OpenProtein(APISession):
 
     _data = None
     _jobs = None
-    _train = None
-    _design = None
     _align = None
-    _embedding = None
+    _embeddings = None
     _svd = None
     _umap = None
     _fold = None
     _predictor = None
     _designer = None
+    _deprecated = None
 
     def wait(self, future: Future, *args, **kwargs):
         return future.wait(*args, **kwargs)
@@ -50,14 +53,20 @@ def load_job(self, job_id):
         return self.jobs.get(job_id=job_id)
 
     @property
-    def data(self) -> AssayDataAPI:
+    def data(self) -> DataAPI:
         """
         The data submodule gives access to functionality for uploading and accessing user data.
         """
         if self._data is None:
-            self._data = AssayDataAPI(self)
+            self._data = DataAPI(self)
         return self._data
 
+    @property
+    def train(self):
+        raise AttributeError(
+            "Access to deprecated train module is under the deprecated property, i.e. session.deprecated.train"
+        )
+
     @property
     def jobs(self) -> JobsAPI:
         """
@@ -67,24 +76,6 @@ def jobs(self) -> JobsAPI:
             self._jobs = JobsAPI(self)
         return self._jobs
 
-    @property
-    def train(self) -> TrainingAPI:
-        """
-        The train submodule gives access to functionality for training and validating ML models.
-        """
-        if self._train is None:
-            self._train = TrainingAPI(self)
-        return self._train
-
-    @property
-    def design(self) -> DesignAPI:
-        """
-        The design submodule gives access to functionality for designing new sequences using models from train.
-        """
-        if self._design is None:
-            self._design = DesignAPI(self)
-        return self._design
-
     @property
     def align(self) -> AlignAPI:
         """
@@ -99,17 +90,19 @@ def embedding(self) -> EmbeddingsAPI:
         """
         The embedding submodule gives access to protein embedding models and their inference endpoints.
         """
-        if self._embedding is None:
-            self._embedding = EmbeddingsAPI(self)
-        return self._embedding
+        if self._embeddings is None:
+            self._embeddings = EmbeddingsAPI(self)
+        return self._embeddings
+
+    embeddings = embedding
 
     @property
     def svd(self) -> SVDAPI:
         """
         The embedding submodule gives access to protein embedding models and their inference endpoints.
         """
         if self._svd is None:
-            self._svd = SVDAPI(self, self.embedding)
+            self._svd = SVDAPI(self, self.embeddings)
         return self._svd
 
     @property
@@ -127,16 +120,16 @@ def predictor(self) -> PredictorAPI:
         The predictor submodule gives access to training and predicting with predictors built on top of embeddings.
         """
         if self._predictor is None:
-            self._predictor = PredictorAPI(self, self.embedding, self.svd)
+            self._predictor = PredictorAPI(self, self.embeddings, self.svd)
         return self._predictor
 
     @property
-    def designer(self) -> DesignerAPI:
+    def design(self) -> DesignAPI:
         """
         The designer submodule gives access to functionality for designing new sequences using models from predictor train.
         """
         if self._designer is None:
-            self._designer = DesignerAPI(self)
+            self._designer = DesignAPI(self)
         return self._designer
 
     @property
@@ -148,5 +141,45 @@ def fold(self) -> FoldAPI:
             self._fold = FoldAPI(self)
         return self._fold
 
+    @property
+    def deprecated(self) -> "Deprecated":
+
+        if self._deprecated is None:
+            warnings.warn(
+                "Support for deprecated APIs will be dropped in the future! Read the documentation to migrate to the updated APIs."
+            )
+            self._deprecated = self.Deprecated(self)
+        return self._deprecated
+
+    class Deprecated:
+
+        _train = None
+        _design = None
+
+        def __init__(self, session: APISession):
+            self.session = session
+
+        @property
+        def train(self) -> "TrainingAPI":
+            """
+            The train submodule gives access to functionality for training and validating ML models.
+            """
+            from openprotein.app.deprecated import TrainingAPI
+
+            if self._train is None:
+                self._train = TrainingAPI(self.session)
+            return self._train
+
+        @property
+        def design(self) -> "DesignAPI":
+            """
+            The design submodule gives access to functionality for designing new sequences using models from train.
+            """
+            from openprotein.app.deprecated import DesignAPI
+
+            if self._design is None:
+                self._design = DesignAPI(self.session)
+            return self._design
+
 
 connect = OpenProtein

openprotein/align/__init__.py

@@ -0,0 +1,2 @@
+from openprotein.app import AlignAPI
+from openprotein.app.models import MSAFuture, PromptFuture

openprotein/api/__init__.py

@@ -1 +1,9 @@
-from . import align, assaydata, design, embedding, fold, predict, predictor, train
+from . import align, assaydata, embedding, fold, predictor
+
+__all__ = [
+    "align",
+    "assaydata",
+    "embedding",
+    "fold",
+    "predictor",
+]

openprotein/api/align.py

@@ -7,13 +7,13 @@
 from openprotein.base import APISession
 from openprotein.csv import csv_stream
 from openprotein.errors import APIError, InvalidParameterError, MissingParameterError
-from openprotein.schemas import Job, MSASamplingMethod, PoetInputType
+from openprotein.schemas import AlignType, Job, MSASamplingMethod
 
 
 def get_align_job_inputs(
     session: APISession,
     job_id: str,
-    input_type: PoetInputType,
+    input_type: AlignType,
     prompt_index: int | None = None,
 ) -> requests.Response:
     """
@@ -52,7 +52,7 @@ def get_align_job_inputs(
 def get_input(
     session: APISession,
     job: Job,
-    input_type: PoetInputType,
+    input_type: AlignType,
     prompt_index: int | None = None,
 ) -> Iterator[list[str]]:
     """
@@ -104,7 +104,7 @@ def get_prompt(
     return get_input(
         session=session,
         job=job,
-        input_type=PoetInputType.PROMPT,
+        input_type=AlignType.PROMPT,
         prompt_index=prompt_index,
     )
 
@@ -125,7 +125,7 @@ def get_seed(session: APISession, job: Job) -> Iterator[list[str]]:
     Iterator[list[str]]
         A CSV reader for the seed sequence.
     """
-    return get_input(session=session, job=job, input_type=PoetInputType.INPUT)
+    return get_input(session=session, job=job, input_type=AlignType.INPUT)
 
 
 def get_msa(session: APISession, job: Job) -> Iterator[list[str]]:
@@ -144,7 +144,7 @@ def get_msa(session: APISession, job: Job) -> Iterator[list[str]]:
     Iterator[list[str]]
         A CSV reader for the MSA data.
     """
-    return get_input(session=session, job=job, input_type=PoetInputType.MSA)
+    return get_input(session=session, job=job, input_type=AlignType.MSA)
 
 
 def msa_post(

openprotein/api/deprecated/__init__.py

@@ -0,0 +1,5 @@
+"""Deprecated OpenProtein APIs."""
+
+from . import design, predict, train
+
+__all__ = ["design", "predict", "train"]

openprotein/api/deprecated/design.py

@@ -0,0 +1,86 @@
+from openprotein.base import APISession
+from openprotein.schemas.deprecated import (
+    WorkflowDesign,
+    WorkflowDesignJob,
+    WorkflowDesignJobCreate,
+)
+
+
+def create_design_job(session: APISession, design_job: WorkflowDesignJobCreate):
+    """
+    Send a POST request for protein design job.
+
+    Parameters
+    ----------
+    session : APISession
+        The current API session for communication with the server.
+    design_job : DesignJobCreate
+        The details of the design job to be created, with the following parameters:
+        - assay_id: The ID for the assay.
+        - criteria: Criteria for evaluating the design.
+        - num_steps: The number of steps in the genetic algo. Default is 8.
+        - pop_size: The population size for the genetic algo. Default is None.
+        - n_offsprings: The number of offspring for the genetic algo. Default is None.
+        - crossover_prob: The crossover probability for the genetic algo. Default is None.
+        - crossover_prob_pointwise: The pointwise crossover probability for the genetic algo. Default is None.
+        - mutation_average_mutations_per_seq: The average number of mutations per sequence. Default is None.
+        - allowed_tokens: A dict of positions and allows tokens (e.g. *{1:['G','L']})* ) designating how mutations may occur. Default is None.
+
+    Returns
+    -------
+    WorkflowDesignJob
+        The created job as a Job instance.
+    """
+    params = design_job.model_dump(exclude_none=True)
+    # print(f"sending design: {params}")
+    response = session.post("v1/workflow/design/genetic-algorithm", json=params)
+    return WorkflowDesignJob.model_validate(response.json())
+
+
+def get_design_results(
+    session: APISession,
+    job_id: str,
+    step: int | None = None,
+    page_size: int | None = None,
+    page_offset: int | None = None,
+) -> WorkflowDesign:
+    """
+    Retrieves the results of a Design job.
+
+    This function retrieves the results of a Design job by making a GET request to design..
+
+    Parameters
+    ----------
+    session : APISession
+        APIsession with auth
+    job_id : str
+        The ID of the job whose results are to be retrieved.
+    step: int
+        The step to retrieve. -1 indicates the last step.
+    page_size : Optional[int], default is None
+        The number of results to be returned per page. If None, all results are returned.
+    page_offset : Optional[int], default is None
+        The number of results to skip. If None, defaults to 0.
+
+    Returns
+    -------
+    WorkflowDesignJob
+        The job object representing the Design job.
+
+    Raises
+    ------
+    HTTPError
+        If the GET request does not succeed.
+    """
+    endpoint = f"v1/workflow/design/{job_id}"
+    params = {}
+    if page_size is not None:
+        params["page_size"] = page_size
+    if page_offset is not None:
+        params["page_offset"] = page_offset
+    if step is not None:
+        params["step"] = step
+
+    response = session.get(endpoint, params=params)
+
+    return WorkflowDesign.model_validate(response.json())