Prep for 0.2

docs/release_notes.rst

@@ -5,7 +5,7 @@ Dispatch Release Notes
 .. _release-v0-2-0:
 
 ---------------------------------------------------------------------------------------
-0.2.0 (2022-XX-XX)
+0.2.0 (2022-09-15)
 ---------------------------------------------------------------------------------------
 
 What's New?
@@ -24,14 +24,22 @@ What's New?
     ``retirement_date`` columns in ``fossil_plant_specs`` determine the period during
     dispatch that a generator may operate. This provides a straightforward method for
     having the portfolio you wish to dispatch change over time.
+*   Cleanup and rationalization of :meth:`dispatch.model.DispatchModel.to_file` and
+    :meth:`dispatch.model.DispatchModel.from_file` methods.
+*   Updates to system for storing and processing marginal cost data. This is now a
+    separate argument to :meth:`dispatch.model.DispatchModel.__init__` rather than a
+    messy confusing part of ``fossil_plant_specs``. This is now consistent with how
+    ``patio`` prepares and stores the data.
 
 Bug Fixes
 ^^^^^^^^^
-*   ...
+*   :meth:`dispatch.model.DispatchModel.to_file` and
+    :meth:`dispatch.model.DispatchModel.from_file` now properly deal with
+    internal data stored in both :class:`pd.DataFrame` and :class:`pd.Series`.
 
 Known Issues
 ^^^^^^^^^^^^
-*   ...
+*   Tests are still pretty rudimentary.
 
 .. _release-v0-1-0:
 

environment.yml

@@ -10,6 +10,7 @@ dependencies:
   - numexpr ~= 2.8
   - numpy >= 1.18.5,<2
   - pandas >= 1.4,<1.5
+  - pandera >= 0.12
   - pyarrow >= 7,<10
   - plotly
 

setup.cfg

@@ -23,6 +23,7 @@ install_requires =
     numexpr ~= 2.8
     numpy >= 1.18.5,<2
     pandas >= 1.4,<1.5
+    pandera ~= 0.12
     pyarrow>=7, <10
 
 [options.packages.find]
@@ -40,7 +41,6 @@ doc =
     sphinx-autoapi>=1.8,<1.10
     sphinx-autodoc-typehints
     sphinxcontrib-mermaid
-;    furo>=2022.4.7
     pydata-sphinx-theme>=0.10
     sphinx-issues>=1.2,<3.1
 tests =

src/dispatch/__init__.py

@@ -1,22 +1,33 @@
 """A template repository for a Python package created by Catalyst Cooperative."""
 import logging
-
-import pkg_resources
+from importlib.metadata import PackageNotFoundError, version
 
 from dispatch.engine import dispatch_engine, dispatch_engine_compiled
+from dispatch.helpers import apply_op_ret_date, copy_profile
 from dispatch.model import DispatchModel
 
-__all__ = ["DispatchModel", "dispatch_engine", "dispatch_engine_compiled"]
+__all__ = [
+    "DispatchModel",
+    "dispatch_engine",
+    "dispatch_engine_compiled",
+    "copy_profile",
+    "apply_op_ret_date",
+]
 
 __author__ = "RMI"
 __contact__ = "aengel@rmi.org"
 __maintainer__ = "Alex Engel"
 __license__ = "BSD 3-Clause License"
 __maintainer_email__ = "aengel@rmi.org"
-__version__ = pkg_resources.get_distribution("rmi.dispatch").version
 __docformat__ = "restructuredtext en"
 __description__ = "A simple and efficient dispatch model."
 
+try:
+    __version__ = version("rmi.dispatch")
+except PackageNotFoundError:
+    # package is not installed
+    pass
+
 __projecturl__ = "https://github.com/rmi-electricity/dispatch"
 __downloadurl__ = "https://github.com/rmi-electricity/dispatch"
 

src/dispatch/helpers.py

@@ -0,0 +1,77 @@
+"""Some helpers for profiles and such."""
+
+from __future__ import annotations
+
+import pandas as pd
+
+
+def copy_profile(
+    profiles: pd.DataFrame | pd.Series, years: range | tuple
+) -> pd.DataFrame | pd.Series:
+    """Create multiple 'years' of hourly profile data.
+
+    Args:
+        profiles: the profile to make copies of
+        years: the years, each of which will be a copy
+            of `profile`.
+
+    Returns: Copied profiles.
+
+    """
+    dfs = []
+    assert isinstance(profiles.index, pd.DatetimeIndex)
+    if isinstance(profiles, pd.Series):
+        profiles = profiles.to_frame()
+    if len(profiles.index.year.unique()) > 1:
+        raise AssertionError("`profile` must be for a single year")
+    for yr in years:
+        dfs.append(
+            profiles.assign(
+                datetime=lambda x: x.index.map(lambda y: y.replace(year=yr)),
+            ).set_index("datetime")
+        )
+    return pd.concat(dfs, axis=0).squeeze()
+
+
+def apply_op_ret_date(
+    profiles: pd.DataFrame,
+    operating_date: pd.Series,
+    retirement_date: pd.Series,
+    capacity_mw: pd.Series | None = None,
+) -> pd.DataFrame:
+    """Zero profile unless it is between operating and retirement date.
+
+    Args:
+        profiles: profiles of plants with a DatetimeIndex
+        operating_date: in service date for each plant, the index of operating date is
+            used throughout
+        retirement_date: retirement date for each plant
+        capacity_mw: capacity of each plant (only used when `profiles` are normalized)
+
+    Returns: Profiles reflecting operating and retirement dates.
+
+    """
+    assert isinstance(profiles.index, pd.DatetimeIndex)
+    if capacity_mw is None:
+        capacity_mw = pd.Series(1, index=operating_date.index, name="capacity_mw")
+    if profiles.shape[1] == len(operating_date) == len(retirement_date):
+        pass
+    else:
+        raise AssertionError(
+            "`profiles` must have same number of columns as lengths of `op_date` and `ret_date`"
+        )
+    # duplicate the DatetimeIndex so it is the same shape as `profiles`
+    dt_idx = pd.concat(
+        [profiles.index.to_series()] * profiles.shape[1],
+        axis=1,
+    ).to_numpy()
+    return pd.DataFrame(
+        (
+            (dt_idx <= retirement_date.fillna(profiles.index.max()).to_numpy())
+            & (dt_idx >= operating_date.fillna(profiles.index.min()).to_numpy())
+        )
+        * profiles.to_numpy()
+        * capacity_mw.to_numpy(),
+        index=profiles.index,
+        columns=operating_date.index,
+    )