Repository: WISDEM/ORBIT (language: python)

Issue:
Added linear model functionality to ParametricManager



Discussion / hints:

Fix patch:
diff --git a/ORBIT/core/logic/vessel_logic.py b/ORBIT/core/logic/vessel_logic.py
index 0a45a4bc..a97d5d0b 100644
--- a/ORBIT/core/logic/vessel_logic.py
+++ b/ORBIT/core/logic/vessel_logic.py
@@ -40,7 +40,7 @@ def prep_for_site_operations(vessel, survey_required=False, **kwargs):
 
     if survey_required:
         survey_time = kwargs.get("rov_survey_time", pt["rov_survey_time"])
-        yield vessel.task(
+        yield vessel.task_wrapper(
             "RovSurvey",
             survey_time,
             constraints=vessel.transit_limits,
@@ -74,7 +74,7 @@ def stabilize(vessel, **kwargs):
         site_depth = kwargs.get("site_depth", 40)
         extension = kwargs.get("extension", site_depth + 10)
         jackup_time = jacksys.jacking_time(extension, site_depth)
-        yield vessel.task(
+        yield vessel.task_wrapper(
             "Jackup", jackup_time, constraints=vessel.transit_limits, **kwargs
         )
 
@@ -102,7 +102,7 @@ def jackdown_if_required(vessel, **kwargs):
         site_depth = kwargs.get("site_depth", 40)
         extension = kwargs.get("extension", site_depth + 10)
         jackdown_time = jacksys.jacking_time(extension, site_depth)
-        yield vessel.task(
+        yield vessel.task_wrapper(
             "Jackdown",
             jackdown_time,
             constraints=vessel.transit_limits,
@@ -126,7 +126,7 @@ def position_onsite(vessel, **kwargs):
 
     position_time = kwargs.get("site_position_time", pt["site_position_time"])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Position Onsite", position_time, constraints=vessel.transit_limits
     )
 
@@ -181,7 +181,7 @@ def shuttle_items_to_queue(vessel, port, queue, distance, items, **kwargs):
             # Transit to site
             vessel.update_trip_data()
             vessel.at_port = False
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Transit", transit_time, constraints=vessel.transit_limits
             )
             yield stabilize(vessel, **kwargs)
@@ -220,7 +220,7 @@ def shuttle_items_to_queue(vessel, port, queue, distance, items, **kwargs):
             # Transit back to port
             vessel.at_site = False
             yield jackdown_if_required(vessel, **kwargs)
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Transit", transit_time, constraints=vessel.transit_limits
             )
             vessel.at_port = True
@@ -295,7 +295,7 @@ def get_list_of_items_from_port(vessel, port, items, **kwargs):
                         vessel.storage.put_item(item)
 
                         if time > 0:
-                            yield vessel.task(
+                            yield vessel.task_wrapper(
                                 action,
                                 time,
                                 constraints=vessel.transit_limits,
diff --git a/ORBIT/core/vessel.py b/ORBIT/core/vessel.py
index d8ad423c..88c823a4 100644
--- a/ORBIT/core/vessel.py
+++ b/ORBIT/core/vessel.py
@@ -5,11 +5,16 @@
 __maintainer__ = "Jake Nunemaker"
 __email__ = "jake.nunemaker@nrel.gov"
 
+from math import ceil
 from collections import Counter, namedtuple
 
 import numpy as np
 from marmot import Agent, le, process
-from marmot._exceptions import AgentNotRegistered
+from marmot._exceptions import (
+    StateExhausted,
+    WindowNotFound,
+    AgentNotRegistered,
+)
 
 from ORBIT.core.components import (
     Crane,
@@ -27,7 +32,7 @@
 class Vessel(Agent):
     """Base Vessel Class"""
 
-    def __init__(self, name, config):
+    def __init__(self, name, config, avail=1):
         """
         Creates an instance of Vessel.
 
@@ -40,6 +45,7 @@ def __init__(self, name, config):
         super().__init__(name)
         self.config = config
         self.extract_vessel_dayrate()
+        self.avail = avail
 
     def submit_action_log(self, action, duration, **kwargs):
         """
@@ -76,6 +82,14 @@ def submit_action_log(self, action, duration, **kwargs):
 
             self.env._submit_log(payload, level="ACTION")
 
+    @process
+    def task_wrapper(
+        self, name, duration, constraints={}, suspendable=False, **kwargs
+    ):
+
+        duration /= self.avail
+        yield self.task(name, duration, constraints, suspendable, **kwargs)
+
     def extract_vessel_dayrate(self):
         """
         Extracts the day rate of the vessel. If it isn't found, resorts to
@@ -288,7 +302,7 @@ def get_item_from_storage(
 
         action, time = item.release(**kwargs)
         if time > 0:
-            yield self.task(
+            yield self.task_wrapper(
                 action,
                 time,
                 constraints=self.transit_limits,
@@ -312,7 +326,7 @@ def transit(self, distance, **kwargs):
         """
 
         time = self.transit_time(distance)
-        yield self.task(
+        yield self.task_wrapper(
             "Transit",
             time,
             constraints=self.transit_limits,
diff --git a/ORBIT/manager.py b/ORBIT/manager.py
index 203c5fb1..a054e8f6 100644
--- a/ORBIT/manager.py
+++ b/ORBIT/manager.py
@@ -15,6 +15,7 @@
 
 import numpy as np
 import pandas as pd
+from benedict import benedict
 
 import ORBIT
 from ORBIT.phases import DesignPhase, InstallPhase
@@ -106,7 +107,8 @@ def __init__(self, config, library_path=None, weather=None):
             ],
         )
         self._phases = {}
-        self.config = self.resolve_project_capacity(config)
+        self.config = benedict(config)
+        self.resolve_project_capacity()
         self.weather = self.transform_weather_input(weather)
 
         self.design_results = {}
@@ -239,30 +241,25 @@ def compile_input_dict(cls, phases):
 
         return config
 
-    @staticmethod
-    def resolve_project_capacity(config):
+    def resolve_project_capacity(self):
         """
         Resolves the relationship between 'project_capacity', 'num_turbines'
         and 'turbine_rating' and verifies that input and calculated values
-        match. Adds missing values that can be calculated to the 'config'.
-
-        Parameters
-        ----------
-        config : dict
+        match. Adds missing values that can be calculated to the 'self.config'.
         """
 
         try:
-            project_capacity = config["plant"]["capacity"]
+            project_capacity = self.config["plant"]["capacity"]
         except KeyError:
             project_capacity = None
 
         try:
-            turbine_rating = config["turbine"]["turbine_rating"]
+            turbine_rating = self.config["turbine"]["turbine_rating"]
         except KeyError:
             turbine_rating = None
 
         try:
-            num_turbines = config["plant"]["num_turbines"]
+            num_turbines = self.config["plant"]["num_turbines"]
         except KeyError:
             num_turbines = None
 
@@ -275,21 +272,19 @@ def resolve_project_capacity(config):
         else:
             if all((project_capacity, turbine_rating)):
                 num_turbines = ceil(project_capacity / turbine_rating)
-                config["plant"]["num_turbines"] = num_turbines
+                self.config["plant"]["num_turbines"] = num_turbines
 
             elif all((project_capacity, num_turbines)):
                 turbine_rating = project_capacity / num_turbines
                 try:
-                    config["turbine"]["turbine_rating"] = turbine_rating
+                    self.config["turbine"]["turbine_rating"] = turbine_rating
 
                 except KeyError:
-                    config["turbine"] = {"turbine_rating": turbine_rating}
+                    self.config["turbine"] = {"turbine_rating": turbine_rating}
 
             elif all((num_turbines, turbine_rating)):
                 project_capacity = turbine_rating * num_turbines
-                config["plant"]["capacity"] = project_capacity
-
-        return config
+                self.config["plant"]["capacity"] = project_capacity
 
     @classmethod
     def find_key_match(cls, target):
@@ -470,11 +465,12 @@ def run_install_phase(self, name, start, **kwargs):
         _class = self.get_phase_class(name)
         _config = self.create_config_for_phase(name)
         processes = _config.pop("processes", {})
+        kwargs = {**kwargs, **processes}
 
         if _catch:
             try:
                 phase = _class(
-                    _config, weather=weather, phase_name=name, **processes
+                    _config, weather=weather, phase_name=name, **kwargs
                 )
                 phase.run()
 
@@ -483,9 +479,7 @@ def run_install_phase(self, name, start, **kwargs):
                 return None, None
 
         else:
-            phase = _class(
-                _config, weather=weather, phase_name=name, **processes
-            )
+            phase = _class(_config, weather=weather, phase_name=name, **kwargs)
             phase.run()
 
         self._phases[name] = phase
@@ -639,7 +633,7 @@ def run_multiple_phases_overlapping(self, phases, **kwargs):
                 self._output_logs.extend(logs)
 
         # Run remaining phases
-        self.run_dependent_phases(variable, zero)
+        self.run_dependent_phases(variable, zero, **kwargs)
 
     def run_dependent_phases(self, _phases, zero, **kwargs):
         """
diff --git a/ORBIT/parametric.py b/ORBIT/parametric.py
index 08c71b07..446facf2 100644
--- a/ORBIT/parametric.py
+++ b/ORBIT/parametric.py
@@ -4,13 +4,17 @@
 __email__ = ["jake.nunemaker@nrel.gov"]
 
 
+import re
 import time
 from copy import deepcopy
 from random import sample
 from itertools import product
 
+import yaml
 import numpy as np
 import pandas as pd
+import statsmodels.api as sm
+from yaml import Loader
 from benedict import benedict
 
 from ORBIT import ProjectManager
@@ -20,7 +24,14 @@ class ParametricManager:
     """Class for configuring parametric ORBIT runs."""
 
     def __init__(
-        self, base, params, funcs, weather=None, module=None, product=False
+        self,
+        base,
+        params,
+        funcs,
+        weather=None,
+        module=None,
+        product=False,
+        keep_inputs=[],
     ):
         """
         Creates an instance of `ParametricRun`.
@@ -46,6 +57,7 @@ def __init__(
         self.results = None
         self.module = module
         self.product = product
+        self.keep = keep_inputs
 
     def run(self, **kwargs):
         """Run the configured parametric runs and save any requested results to
@@ -73,10 +85,30 @@ def _run_config(self, run, **kwargs):
             project.run()
 
         results = self.map_funcs(project, self.funcs)
-        data = {**run, **results}
+        kept = self._get_kept_inputs(project.config)
+        data = {**run, **kept, **results}
 
         return data
 
+    def _get_kept_inputs(self, config):
+        """
+        Extract inputs in `self.keep` from `config`.
+
+        Parameters
+        ----------
+        config : dict
+        """
+
+        kept = {}
+        for k in self.keep:
+            try:
+                kept[k] = config[k]
+
+            except KeyError:
+                pass
+
+        return kept
+
     @property
     def run_list(self):
         """Returns list of configured parametric runs."""
@@ -153,3 +185,172 @@ def preview(self, num=10, **kwargs):
         print(f"{self.num_runs} runs estimated time: {estimate:.2f}s")
 
         return pd.DataFrame(outputs)
+
+    def create_model(self, x, y):
+        """"""
+
+        if self.results is None:
+            print("`ParametricManager hasn't been ran yet.")
+
+        return LinearModel(self.results, x, y)
+
+    @classmethod
+    def from_config(cls, data):
+        """"""
+
+        outputs = data.pop("outputs", {})
+
+        funcs = {}
+        for k, v in outputs.items():
+
+            split = v.split("[")
+            attr = split[0]
+
+            try:
+                key = re.sub("[^A-Za-z ]", "", split[1])
+
+            except IndexError:
+                key = None
+
+            if key is None:
+                funcs[k] = lambda run: getattr(run, attr)
+
+            else:
+                funcs[k] = lambda run: getattr(run, attr)[key]
+
+        data["funcs"] = funcs
+
+        module_name = data.pop("module", None)
+        if module_name is not None:
+            module = ProjectManager.phase_dict()[module_name]
+            data["module"] = module
+
+        weather_file = data.pop("weather", None)
+        if weather_file is not None:
+            weather = pd.read_csv(weather_file, parse_dates=["datetime"])
+            data["weather"] = weather
+
+        return cls(**data, product=True)
+
+
+class LinearModel:
+    """Simple linear regression model."""
+
+    def __init__(self, data, x, y):
+        """
+        Creates an instance of `LinearModel`.
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            Input dataset.
+        x : list
+            List of regression variables.
+        y : str
+            Output variable.
+        """
+
+        self.data = data
+        self.x = x
+        self.y = y
+
+        self.X = data[x]
+        self.Y = data[y]
+
+        self.X2 = sm.add_constant(self.X)
+        self.sm = sm.OLS(self.Y, self.X2).fit()
+
+    def predict(self, inputs):
+        """
+        Predicts the output value of `inputs` given the underlying linear model
+        developed using `self.data`, `self.x` and `self.y`.
+
+        Parameters
+        ----------
+        inputs : dict
+            Inputs with form:
+            'key': list | np.array | int | float
+        """
+
+        missing = [i for i in self.x if i not in inputs.keys()]
+        if len(missing) > 0:
+            raise ValueError(f"Missing input(s) '{missing}'")
+
+        inputs = {k: np.array(v) for k, v in inputs.items()}
+        params = deepcopy(self.sm.params)
+        const = params.pop("const")
+
+        return sum([v * inputs[k] for k, v in params.items()]) + const
+
+    @property
+    def as_string(self):
+        """
+        Returns the linear model represented as a string. To be used for
+        interfacing with NRWAL.
+
+        Returns
+        -------
+        str
+        """
+
+        params = deepcopy(self.sm.params)
+        const = params.pop("const")
+
+        out = ""
+        for i, (k, v) in enumerate(params.items()):
+
+            if i == 0:
+                pre = ""
+
+            else:
+                if v >= 0:
+                    pre = " + "
+
+                else:
+                    pre = " - "
+
+            out += pre
+            out += f"{abs(v)} * {k}"
+
+        if const < 0:
+            out += f" - {abs(const)}"
+
+        else:
+            out += f" + {const}"
+
+        return out
+
+    @property
+    def vif(self):
+        """
+        Returns the variance inflation factor of the input data series.
+
+        Returns
+        -------
+        list
+        """
+
+        data = self.X2.copy()
+
+        vif = []
+        for name, d in data.iteritems():
+            r_sq_i = sm.OLS(d, data.drop(name, axis=1)).fit().rsquared
+            vif.append(1.0 / (1.0 - r_sq_i))
+
+        return vif
+
+    @property
+    def perc_diff(self):
+        """
+        Returns the percent difference between the predicted values and the
+        output value.
+
+        Returns
+        -------
+        pd.Series
+        """
+
+        inputs = dict(zip(self.X.T.index, self.X.T.values))
+        predicted = self.predict(inputs)
+
+        return (self.Y - predicted) / self.Y
diff --git a/ORBIT/phases/base.py b/ORBIT/phases/base.py
index 5b3c35be..2e9b539d 100644
--- a/ORBIT/phases/base.py
+++ b/ORBIT/phases/base.py
@@ -9,6 +9,8 @@
 from abc import ABC, abstractmethod
 from copy import deepcopy
 
+from benedict import benedict
+
 from ORBIT.core.library import initialize_library, extract_library_data
 from ORBIT.core.exceptions import MissingInputs
 
@@ -115,7 +117,7 @@ def validate_config(self, config):
             raise MissingInputs(missing)
 
         else:
-            return config
+            return benedict(config)
 
     @abstractmethod
     def run(self):
diff --git a/ORBIT/phases/design/array_system_design.py b/ORBIT/phases/design/array_system_design.py
index 8ec5850a..43443854 100644
--- a/ORBIT/phases/design/array_system_design.py
+++ b/ORBIT/phases/design/array_system_design.py
@@ -360,6 +360,95 @@ def run(self):
         self._create_wind_farm_layout()
         self._create_cable_section_lengths()
 
+    def save_layout(self, save_name, return_df=False):
+        """Outputs a csv of the substation and turbine positional and cable
+        related components.
+
+        Parameters
+        ----------
+        save_name : str
+            The name of the file without an extension to be saved to
+            <library_path>/cables/<save_name>.csv.
+        return_df : bool, optional
+            If true, returns layout_df, a pandas.DataFrame of the cabling
+            layout, by default False.
+
+        Returns
+        -------
+        pd.DataFrame
+            The DataFrame with the layout data.
+        """
+        num_turbines = self.system.num_turbines
+        columns = [
+            "id",
+            "substation_id",
+            "name",
+            "latitude",
+            "longitude",
+            "string",
+            "order",
+            "cable_type",
+            "euclidean_distance",
+            "cable_length",
+        ]
+        layout_df = pd.DataFrame(
+            np.zeros((self.system.num_turbines + 1, len(columns))),
+            columns=columns,
+        )
+        layout_df.string = layout_df.string.astype(int)
+        layout_df.order = layout_df.order.astype(int)
+
+        strings = [("", "")]
+        strings.extend(
+            [
+                (i, j)
+                for i in range(self.num_full_strings)
+                for j in range(self.num_turbines_full_string)
+            ]
+        )
+        try:
+            strings.extend(
+                [
+                    (i, j)
+                    for i in range(self.num_full_strings, self.num_strings)
+                    for j in range(self.num_turbines_partial_string)
+                ]
+            )
+        except AttributeError:
+            pass
+        layout_df[["string", "order"]] = strings
+
+        coords = np.array(
+            [[0, 0]]
+            + list(zip(self.turbines_x.flatten(), self.turbines_y.flatten()))
+        )
+        coords = coords[: self.system.num_turbines + 1]
+        layout_df[["longitude", "latitude"]] = coords
+
+        layout_df["substation_id"] = "oss1"
+        layout_df["id"] = ["oss1"] + [
+            f"t{i}" for i in range(layout_df.shape[0] - 1)
+        ]
+        layout_df["name"] = ["substation-1"] + [
+            f"turbine-{i}" for i in range(num_turbines)
+        ]
+        layout_df.cable_type = [""] + self.sections_cables.flatten()[
+            :num_turbines
+        ].tolist()
+        layout_df.euclidean_distance = [""] + self.sections_distance.flatten()[
+            :num_turbines
+        ].tolist()
+        layout_df.cable_length = [""] + self.sections_cable_lengths.flatten()[
+            :num_turbines
+        ].tolist()
+        data = [columns] + layout_df.values.tolist()
+        print(
+            f"Saving custom array CSV to: <library_path>/cables/{save_name}.csv"
+        )
+        export_library_specs("cables", save_name, data, file_ext="csv")
+        if return_df:
+            return layout_df
+
     def _plot_oss(self, ax):
         """
         Adds the offshore substation(s) to the plot.
@@ -406,7 +495,9 @@ def _plot_oss(self, ax):
 
         return labels_set + ["Turbine"], ax
 
-    def plot_array_system(self, show=True, save_path_name=None):
+    def plot_array_system(
+        self, show=True, save_path_name=None, return_fig=False
+    ):
         """
         Plot the array cabling system.
 
@@ -417,6 +508,20 @@ def plot_array_system(self, show=True, save_path_name=None):
         save_path_name : str, default: None
             The <path_to_file>/<file_name> of where to save the created plot.
             If None then the plot will not be saved.
+        return_fig : bool, default: False
+            If true, the figure (`fig`) and axes (`ax`) objects will be
+            returned.
+
+        Returns
+        -------
+        fig : matplotlib.pyplot.figure
+            The matplotlib figure object. This will contain the overarching
+            figure settings, and can be manipulated to change dimensions,
+            resolotion, and etc.
+        ax : matplotlib.pyplot.axes
+            The matplotlib axes object. This will contain the actual plot
+            settings, and can be manipulated to add annotations, or other
+            elements.
         """
 
         fig, ax = plt.subplots(figsize=(10, 10))
@@ -502,6 +607,8 @@ def plot_array_system(self, show=True, save_path_name=None):
             plt.savefig(save_path_name, bbox_inches="tight", dpi=360)
         if show:
             plt.show()
+        if return_fig:
+            return fig, ax
 
 
 class CustomArraySystemDesign(ArraySystemDesign):
@@ -741,7 +848,8 @@ def _initialize_custom_data(self):
         missing = set(self.COLUMNS).difference(self.location_data.columns)
         if missing:
             raise ValueError(
-                f"The following columns must be included in the location data: {missing}"
+                "The following columns must be included in the location ",
+                f"data: {missing}",
             )
 
         self._format_windfarm_data()
@@ -774,8 +882,9 @@ def _initialize_custom_data(self):
         # Ensure the number of turbines matches what's expected
         if self.location_data.shape[0] != self.system.num_turbines:
             raise ValueError(
-                f"The provided number of turbines ({self.location_data.shape[0]}) ",
-                f"does not match the plant data ({self.system.num_turbines}).",
+                "The provided number of turbines ",
+                f"({self.location_data.shape[0]}) does not match the plant ",
+                f"data ({self.system.num_turbines}).",
             )
 
         n_coords = self.location_data.groupby(
@@ -818,8 +927,8 @@ def _check_optional_input(self):
             )
 
     def _compute_haversine_distance(self):
-        """Computes the haversine distance between two subsequent pairs in a string for
-        all strings.
+        """Computes the haversine distance between two subsequent pairs in a
+        string for all strings.
 
         Returns
         -------
@@ -852,9 +961,11 @@ def _create_windfarm_layout(self):
             `windfarm_y`: y-coordinates with a corresponding OSS in the first
                 column. Shape: `n_strings` x (`num_turbines_full_string` + 1).
             `sections_cables_lenghts`: custom cable lengths provided as an
-                optional column in the `location_data`. Shape: `n_strings` x `num_turbines_full_string`.
+                optional column in the `location_data`.
+                Shape: `n_strings` x `num_turbines_full_string`.
             `sections_bury_speeds`: custom cable bury speeds provided as an
-                optional column in the `location_data`. Shape: `n_strings` x `num_turbines_full_string`.
+                optional column in the `location_data`.
+                Shape: `n_strings` x `num_turbines_full_string`.
         """
 
         self.location_data_x = np.zeros(
diff --git a/ORBIT/phases/install/cable_install/array.py b/ORBIT/phases/install/cable_install/array.py
index 87ac9434..d4d8a181 100644
--- a/ORBIT/phases/install/cable_install/array.py
+++ b/ORBIT/phases/install/cable_install/array.py
@@ -123,7 +123,7 @@ def initialize_installation_vessel(self):
         vessel_specs = self.config.get("array_cable_install_vessel", None)
         name = vessel_specs.get("name", "Array Cable Installation Vessel")
 
-        vessel = Vessel(name, vessel_specs)
+        vessel = self.initialize_vessel(name, vessel_specs)
         self.env.register(vessel)
 
         vessel.initialize()
@@ -141,7 +141,7 @@ def initialize_burial_vessel(self):
             return
         name = vessel_specs.get("name", "Array Cable Burial Vessel")
 
-        vessel = Vessel(name, vessel_specs)
+        vessel = self.initialize_vessel(name, vessel_specs)
         self.env.register(vessel)
 
         vessel.initialize()
@@ -159,7 +159,7 @@ def initialize_trench_vessel(self):
             return
         name = vessel_specs.get("name", "Array Cable Trench Vessel")
 
-        vessel = Vessel(name, vessel_specs)
+        vessel = self.initialize_vessel(name, vessel_specs)
         self.env.register(vessel)
 
         vessel.initialize()
diff --git a/ORBIT/phases/install/cable_install/common.py b/ORBIT/phases/install/cable_install/common.py
index f22cc8e3..f2481138 100644
--- a/ORBIT/phases/install/cable_install/common.py
+++ b/ORBIT/phases/install/cable_install/common.py
@@ -46,7 +46,7 @@ def load_cable_on_vessel(vessel, cable, constraints={}, **kwargs):
     load_time = kwargs.get(key, pt[key])
 
     vessel.cable_storage.load_cable(cable)
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Load Cable", load_time, constraints=constraints, **kwargs
     )
 
@@ -87,7 +87,7 @@ def prep_cable(vessel, **kwargs):
     key = "cable_prep_time"
     prep_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Prepare Cable", prep_time, constraints=vessel.transit_limits, **kwargs
     )
 
@@ -106,7 +106,7 @@ def lower_cable(vessel, **kwargs):
     key = "cable_lower_time"
     lower_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lower Cable",
         lower_time,
         constraints=vessel.operational_limits,
@@ -129,7 +129,7 @@ def pull_in_cable(vessel, **kwargs):
     key = "cable_pull_in_time"
     pull_in_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Pull In Cable",
         pull_in_time,
         constraints=vessel.operational_limits,
@@ -151,7 +151,7 @@ def terminate_cable(vessel, **kwargs):
     key = "cable_termination_time"
     termination_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Terminate Cable",
         termination_time,
         constraints=vessel.operational_limits,
@@ -180,7 +180,7 @@ def lay_bury_cable(vessel, distance, **kwargs):
     lay_bury_speed = kwargs.get(key, pt[key])
     lay_bury_time = distance / lay_bury_speed
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lay/Bury Cable",
         lay_bury_time,
         constraints=vessel.operational_limits,
@@ -210,7 +210,7 @@ def lay_cable(vessel, distance, **kwargs):
     lay_speed = kwargs.get(key, pt[key])
     lay_time = distance / lay_speed
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lay Cable",
         lay_time,
         constraints=vessel.operational_limits,
@@ -240,7 +240,7 @@ def bury_cable(vessel, distance, **kwargs):
     bury_speed = kwargs.get(key, pt[key])
     bury_time = distance / bury_speed
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Bury Cable",
         bury_time,
         constraints=vessel.operational_limits,
@@ -265,7 +265,7 @@ def splice_cable(vessel, **kwargs):
     key = "cable_splice_time"
     splice_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Splice Cable",
         splice_time,
         constraints=vessel.operational_limits,
@@ -290,7 +290,7 @@ def raise_cable(vessel, **kwargs):
     key = "cable_raise_time"
     raise_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Raise Cable",
         raise_time,
         constraints=vessel.operational_limits,
@@ -334,7 +334,7 @@ def tow_plow(vessel, distance, **kwargs):
     plow_speed = kwargs.get(key, pt[key])
     plow_time = distance / plow_speed
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Tow Plow", plow_time, constraints=vessel.operational_limits, **kwargs
     )
 
@@ -359,7 +359,7 @@ def pull_winch(vessel, distance, **kwargs):
     winch_speed = kwargs.get(key, pt[key])
     winch_time = distance / winch_speed
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Pull Winch",
         winch_time,
         constraints=vessel.operational_limits,
@@ -388,7 +388,7 @@ def dig_trench(vessel, distance, **kwargs):
     trench_dig_speed = kwargs.get(key, pt[key])
     trench_dig_time = distance / trench_dig_speed
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Dig Trench",
         trench_dig_time,
         constraints=vessel.operational_limits,
diff --git a/ORBIT/phases/install/cable_install/export.py b/ORBIT/phases/install/cable_install/export.py
index 04133a70..897ac62e 100644
--- a/ORBIT/phases/install/cable_install/export.py
+++ b/ORBIT/phases/install/cable_install/export.py
@@ -208,7 +208,7 @@ def initialize_installation_vessel(self):
         vessel_specs = self.config.get("export_cable_install_vessel", None)
         name = vessel_specs.get("name", "Export Cable Installation Vessel")
 
-        vessel = Vessel(name, vessel_specs)
+        vessel = self.initialize_vessel(name, vessel_specs)
         self.env.register(vessel)
 
         vessel.initialize()
@@ -225,7 +225,7 @@ def initialize_burial_vessel(self):
 
         name = vessel_specs.get("name", "Export Cable Burial Vessel")
 
-        vessel = Vessel(name, vessel_specs)
+        vessel = self.initialize_vessel(name, vessel_specs)
         self.env.register(vessel)
 
         vessel.initialize()
@@ -241,7 +241,7 @@ def initialize_trench_vessel(self):
             return
         name = vessel_specs.get("name", "Export Cable Trench Vessel")
 
-        vessel = Vessel(name, vessel_specs)
+        vessel = self.initialize_vessel(name, vessel_specs)
         self.env.register(vessel)
 
         vessel.initialize()
diff --git a/ORBIT/phases/install/install_phase.py b/ORBIT/phases/install/install_phase.py
index 06257b14..97b93c3b 100644
--- a/ORBIT/phases/install/install_phase.py
+++ b/ORBIT/phases/install/install_phase.py
@@ -13,7 +13,7 @@
 import simpy
 import pandas as pd
 
-from ORBIT.core import Port, Environment
+from ORBIT.core import Port, Vessel, Environment
 from ORBIT.phases import BasePhase
 from ORBIT.core.defaults import common_costs
 
@@ -33,6 +33,7 @@ def __init__(self, weather, **kwargs):
 
         self.extract_phase_kwargs(**kwargs)
         self.initialize_environment(weather, **kwargs)
+        self.availability = kwargs.get("availability", None)
 
     def initialize_environment(self, weather, **kwargs):
         """
@@ -50,6 +51,20 @@ def initialize_environment(self, weather, **kwargs):
         env_name = kwargs.get("env_name", "Environment")
         self.env = Environment(name=env_name, state=weather, **kwargs)
 
+    def initialize_vessel(self, name, specs):
+        """"""
+
+        avail = getattr(self, "availability")
+        if avail is None:
+            return Vessel(name, specs)
+
+        elif isinstance(avail, dict):
+            default = avail.get("default", 1)
+            return Vessel(name, specs, avail.get(name, default))
+
+        else:
+            return Vessel(name, specs, avail)
+
     @abstractmethod
     def setup_simulation(self):
         """
diff --git a/ORBIT/phases/install/monopile_install/common.py b/ORBIT/phases/install/monopile_install/common.py
index 65d7df72..60bec171 100644
--- a/ORBIT/phases/install/monopile_install/common.py
+++ b/ORBIT/phases/install/monopile_install/common.py
@@ -97,7 +97,7 @@ def upend_monopile(vessel, length, **kwargs):
     crane_rate = vessel.crane.crane_rate(**kwargs)
     upend_time = length / crane_rate
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Upend Monopile",
         upend_time,
         constraints=vessel.operational_limits,
@@ -128,7 +128,7 @@ def lower_monopile(vessel, **kwargs):
     height = (depth + 10) / rate  # Assumed 10m deck height added to site depth
     lower_time = height / rate
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lower Monopile",
         lower_time,
         constraints=vessel.operational_limits,
@@ -162,7 +162,7 @@ def drive_monopile(vessel, **kwargs):
 
     drive_time = mono_embed_len / mono_drive_rate
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Drive Monopile",
         drive_time,
         constraints=vessel.operational_limits,
@@ -185,7 +185,7 @@ def lower_transition_piece(vessel, **kwargs):
     vessel.task representing time to "Lower Transition Piece".
     """
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lower TP", 1, constraints=vessel.operational_limits, **kwargs
     )
 
@@ -210,7 +210,7 @@ def bolt_transition_piece(vessel, **kwargs):
     key = "tp_bolt_time"
     bolt_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Bolt TP", bolt_time, constraints=vessel.operational_limits, **kwargs
     )
 
@@ -233,7 +233,7 @@ def pump_transition_piece_grout(vessel, **kwargs):
     key = "grout_pump_time"
     pump_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Pump TP Grout",
         pump_time,
         constraints=vessel.operational_limits,
@@ -259,7 +259,7 @@ def cure_transition_piece_grout(vessel, **kwargs):
     key = "grout_cure_time"
     cure_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Cure TP Grout", cure_time, constraints=vessel.transit_limits, **kwargs
     )
 
@@ -285,7 +285,7 @@ def install_monopile(vessel, monopile, **kwargs):
     reequip_time = vessel.crane.reequip(**kwargs)
 
     yield lower_monopile(vessel, **kwargs)
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Crane Reequip",
         reequip_time,
         constraints=vessel.transit_limits,
@@ -329,7 +329,7 @@ def install_transition_piece(vessel, tp, **kwargs):
     connection = kwargs.get("tp_connection_type", "bolted")
     reequip_time = vessel.crane.reequip(**kwargs)
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Crane Reequip",
         reequip_time,
         constraints=vessel.transit_limits,
diff --git a/ORBIT/phases/install/monopile_install/standard.py b/ORBIT/phases/install/monopile_install/standard.py
index 27b53b8b..3ed5712e 100644
--- a/ORBIT/phases/install/monopile_install/standard.py
+++ b/ORBIT/phases/install/monopile_install/standard.py
@@ -168,7 +168,7 @@ def initialize_wtiv(self):
         wtiv_specs = self.config.get("wtiv", None)
         name = wtiv_specs.get("name", "WTIV")
 
-        wtiv = Vessel(name, wtiv_specs)
+        wtiv = self.initialize_vessel(name, wtiv_specs)
         self.env.register(wtiv)
 
         wtiv.initialize()
@@ -189,7 +189,7 @@ def initialize_feeders(self):
             # TODO: Add in option for named feeders.
             name = "Feeder {}".format(n)
 
-            feeder = Vessel(name, feeder_specs)
+            feeder = self.initialize_vessel(name, feeder_specs)
             self.env.register(feeder)
 
             feeder.initialize()
diff --git a/ORBIT/phases/install/mooring_install/mooring.py b/ORBIT/phases/install/mooring_install/mooring.py
index 8cec663f..3b3573b9 100644
--- a/ORBIT/phases/install/mooring_install/mooring.py
+++ b/ORBIT/phases/install/mooring_install/mooring.py
@@ -94,7 +94,7 @@ def initialize_installation_vessel(self):
         vessel_specs = self.config.get("mooring_install_vessel", None)
         name = vessel_specs.get("name", "Mooring System Installation Vessel")
 
-        vessel = Vessel(name, vessel_specs)
+        vessel = self.initialize_vessel(name, vessel_specs)
         self.env.register(vessel)
 
         vessel.initialize()
@@ -204,7 +204,7 @@ def perform_mooring_site_survey(vessel, **kwargs):
     key = "mooring_site_survey_time"
     survey_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Perform Mooring Site Survey",
         survey_time,
         constraints=vessel.transit_limits,
@@ -247,7 +247,7 @@ def install_mooring_anchor(vessel, depth, _type, **kwargs):
         )
 
     install_time = fixed + 0.005 * depth
-    yield vessel.task(
+    yield vessel.task_wrapper(
         task, install_time, constraints=vessel.transit_limits, **kwargs
     )
 
@@ -271,7 +271,7 @@ def install_mooring_line(vessel, depth, **kwargs):
 
     install_time = 0.005 * depth
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Install Mooring Line",
         install_time,
         constraints=vessel.transit_limits,
diff --git a/ORBIT/phases/install/oss_install/common.py b/ORBIT/phases/install/oss_install/common.py
index 9b0c5212..f90128ac 100644
--- a/ORBIT/phases/install/oss_install/common.py
+++ b/ORBIT/phases/install/oss_install/common.py
@@ -73,7 +73,7 @@ def lift_topside(vessel, **kwargs):
     crane_rate = vessel.crane.crane_rate(**kwargs)
     lift_time = lift_height / crane_rate
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lift Topside", lift_time, constraints=vessel.operational_limits
     )
 
@@ -100,7 +100,7 @@ def attach_topside(vessel, **kwargs):
     key = "topside_attach_time"
     attach_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Attach Topside", attach_time, constraints=vessel.operational_limits
     )
 
@@ -126,7 +126,7 @@ def install_topside(vessel, topside, **kwargs):
     connection = kwargs.get("topside_connection_type", "bolted")
     reequip_time = vessel.crane.reequip(**kwargs)
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Crane Reequip",
         reequip_time,
         constraints=vessel.transit_limits,
diff --git a/ORBIT/phases/install/oss_install/standard.py b/ORBIT/phases/install/oss_install/standard.py
index 67612529..04038af9 100644
--- a/ORBIT/phases/install/oss_install/standard.py
+++ b/ORBIT/phases/install/oss_install/standard.py
@@ -144,7 +144,7 @@ def initialize_oss_install_vessel(self):
         oss_vessel_specs = self.config.get("oss_install_vessel", None)
         name = oss_vessel_specs.get("name", "Heavy Lift Vessel")
 
-        oss_vessel = Vessel(name, oss_vessel_specs)
+        oss_vessel = self.initialize_vessel(name, oss_vessel_specs)
         self.env.register(oss_vessel)
 
         oss_vessel.initialize()
@@ -165,7 +165,7 @@ def initialize_feeders(self):
             # TODO: Add in option for named feeders.
             name = "Feeder {}".format(n)
 
-            feeder = Vessel(name, feeder_specs)
+            feeder = self.initialize_vessel(name, feeder_specs)
             self.env.register(feeder)
 
             feeder.initialize()
diff --git a/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py b/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py
index 8a79d79f..00c1728c 100644
--- a/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py
+++ b/ORBIT/phases/install/quayside_assembly_tow/gravity_base.py
@@ -216,7 +216,7 @@ def initialize_support_vessel(self, **kwargs):
         """
 
         specs = self.config["support_vessel"]
-        vessel = Vessel("Multi-Purpose Support Vessel", specs)
+        vessel = self.initialize_vessel("Multi-Purpose Support Vessel", specs)
 
         self.env.register(vessel)
         vessel.initialize(mobilize=False)
@@ -363,19 +363,19 @@ def install_gravity_base_foundations(
                 vessel.mobilize()
                 yield vessel.transit(distance)
 
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Position Substructure",
                 5,
                 constraints={"windspeed": le(15), "waveheight": le(2)},
             )
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "ROV Survey",
                 1,
                 constraints={"windspeed": le(25), "waveheight": le(3)},
             )
 
             # TODO: Model for ballast pump time
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Pump Ballast",
                 12,
                 # suspendable=True,
@@ -383,7 +383,7 @@ def install_gravity_base_foundations(
             )
 
             # TODO: Model for GBF grout time
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Grout GBF",
                 6,
                 suspendable=True,
diff --git a/ORBIT/phases/install/quayside_assembly_tow/moored.py b/ORBIT/phases/install/quayside_assembly_tow/moored.py
index 0ffbee18..30d4255f 100644
--- a/ORBIT/phases/install/quayside_assembly_tow/moored.py
+++ b/ORBIT/phases/install/quayside_assembly_tow/moored.py
@@ -215,7 +215,7 @@ def initialize_support_vessel(self, **kwargs):
         """
 
         specs = self.config["support_vessel"]
-        vessel = Vessel("Multi-Purpose Support Vessel", specs)
+        vessel = self.initialize_vessel("Multi-Purpose Support Vessel", specs)
 
         self.env.register(vessel)
         vessel.initialize(mobilize=False)
@@ -372,23 +372,23 @@ def install_moored_substructures(
                 vessel.mobilize()
                 yield vessel.transit(distance)
 
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Position Substructure",
                 2,
                 constraints={"windspeed": le(15), "waveheight": le(2.5)},
             )
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Ballast to Operational Draft",
                 6,
                 constraints={"windspeed": le(15), "waveheight": le(2.5)},
             )
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Connect Mooring Lines",
                 22,
                 suspendable=True,
                 constraints={"windspeed": le(15), "waveheight": le(2.5)},
             )
-            yield vessel.task(
+            yield vessel.task_wrapper(
                 "Check Mooring Lines",
                 12,
                 suspendable=True,
diff --git a/ORBIT/phases/install/scour_protection_install/standard.py b/ORBIT/phases/install/scour_protection_install/standard.py
index c5396790..9dd3ee9a 100644
--- a/ORBIT/phases/install/scour_protection_install/standard.py
+++ b/ORBIT/phases/install/scour_protection_install/standard.py
@@ -127,7 +127,7 @@ def initialize_spi_vessel(self):
         spi_specs = self.config["spi_vessel"]
         name = spi_specs.get("name", "SPI Vessel")
 
-        spi_vessel = Vessel(name, spi_specs)
+        spi_vessel = self.initialize_vessel(name, spi_specs)
         self.env.register(spi_vessel)
 
         spi_vessel.initialize()
@@ -246,7 +246,7 @@ def load_material(vessel, mass, **kwargs):
     load_time = kwargs.get(key, pt[key])
 
     vessel.rock_storage.put(mass)
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Load SP Material",
         load_time,
         constraints=vessel.transit_limits,
@@ -276,7 +276,7 @@ def drop_material(vessel, mass, **kwargs):
     drop_time = kwargs.get(key, pt[key])
 
     _ = vessel.rock_storage.get(mass)
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Drop SP Material",
         drop_time,
         constraints=vessel.transit_limits,
diff --git a/ORBIT/phases/install/turbine_install/common.py b/ORBIT/phases/install/turbine_install/common.py
index 7a9d2a93..057ff1bd 100644
--- a/ORBIT/phases/install/turbine_install/common.py
+++ b/ORBIT/phases/install/turbine_install/common.py
@@ -125,7 +125,7 @@ def lift_nacelle(vessel, **kwargs):
     crane_rate = vessel.crane.crane_rate(**kwargs)
     lift_time = hub_height / crane_rate
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lift Nacelle",
         lift_time,
         constraints=vessel.operational_limits,
@@ -154,7 +154,7 @@ def attach_nacelle(vessel, **kwargs):
     key = "nacelle_attach_time"
     attach_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Attach Nacelle",
         attach_time,
         constraints=vessel.operational_limits,
@@ -183,7 +183,7 @@ def lift_turbine_blade(vessel, **kwargs):
     crane_rate = vessel.crane.crane_rate(**kwargs)
     lift_time = hub_height / crane_rate
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lift Blade",
         lift_time,
         constraints=vessel.operational_limits,
@@ -212,7 +212,7 @@ def attach_turbine_blade(vessel, **kwargs):
     key = "blade_attach_time"
     attach_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Attach Blade",
         attach_time,
         constraints=vessel.operational_limits,
@@ -240,7 +240,7 @@ def lift_tower_section(vessel, height, **kwargs):
     crane_rate = vessel.crane.crane_rate(**kwargs)
     lift_time = height / crane_rate
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Lift Tower Section",
         lift_time,
         constraints=vessel.operational_limits,
@@ -269,7 +269,7 @@ def attach_tower_section(vessel, **kwargs):
     key = "tower_section_attach_time"
     attach_time = kwargs.get(key, pt[key])
 
-    yield vessel.task(
+    yield vessel.task_wrapper(
         "Attach Tower Section",
         attach_time,
         constraints=vessel.operational_limits,
diff --git a/ORBIT/phases/install/turbine_install/standard.py b/ORBIT/phases/install/turbine_install/standard.py
index 217fa483..d23515a1 100644
--- a/ORBIT/phases/install/turbine_install/standard.py
+++ b/ORBIT/phases/install/turbine_install/standard.py
@@ -173,7 +173,7 @@ def initialize_wtiv(self):
         wtiv_specs = self.config.get("wtiv", None)
         name = wtiv_specs.get("name", "WTIV")
 
-        wtiv = Vessel(name, wtiv_specs)
+        wtiv = self.initialize_vessel(name, wtiv_specs)
         self.env.register(wtiv)
 
         wtiv.initialize()
@@ -194,7 +194,7 @@ def initialize_feeders(self):
             # TODO: Add in option for named feeders.
             name = "Feeder {}".format(n)
 
-            feeder = Vessel(name, feeder_specs)
+            feeder = self.initialize_vessel(name, feeder_specs)
             self.env.register(feeder)
 
             feeder.initialize()
@@ -343,13 +343,13 @@ def solo_install_turbines(
                 )
 
                 # Install nacelle
-                yield vessel.task(
+                yield vessel.task_wrapper(
                     "Reequip", reequip_time, constraints=vessel.transit_limits
                 )
                 yield install_nacelle(vessel, nacelle, **kwargs)
 
                 # Install turbine blades
-                yield vessel.task(
+                yield vessel.task_wrapper(
                     "Reequip", reequip_time, constraints=vessel.transit_limits
                 )
                 for _ in range(num_blades):
@@ -431,13 +431,13 @@ def install_turbine_components_from_queue(
                 )
 
                 # Install nacelle
-                yield wtiv.task(
+                yield wtiv.task_wrapper(
                     "Reequip", reequip_time, constraints=wtiv.transit_limits
                 )
                 yield install_nacelle(wtiv, nacelle, **kwargs)
 
                 # Install turbine blades
-                yield wtiv.task(
+                yield wtiv.task_wrapper(
                     "Reequip", reequip_time, constraints=wtiv.transit_limits
                 )
 
diff --git a/docs/source/changelog.rst b/docs/source/changelog.rst
index 9bd8ba6c..7ff8d4f7 100644
--- a/docs/source/changelog.rst
+++ b/docs/source/changelog.rst
@@ -3,6 +3,13 @@
 ORBIT Changelog
 ===============
 
+1.0.4
+-----
+
+- Added ability to directly prescribe weather downtime through the
+  ``availability`` keyword
+- Added support for generating linear models using ``ParametricManager``
+
 1.0.2
 -----
 
diff --git a/examples/custom_array_layout.ipynb b/examples/custom_array_layout.ipynb
index ec486593..7247d376 100644
--- a/examples/custom_array_layout.ipynb
+++ b/examples/custom_array_layout.ipynb
@@ -10,6 +10,7 @@
     "\n",
     "#### Author: Rob Hammond\n",
     "#### Date: 4 May 2020\n",
+    "#### Update: 30 March 2021\n",
     "\n",
     "\n",
     "\n",
@@ -18,7 +19,9 @@
     "\n",
     "This notebook will guide you through four of the main use cases on using the custom array cable layout functionality of `ORBIT` for when custom turbine locations, cable lengths or burial speeds are needed.\n",
     "\n",
-    "**Note:** All array cable layout files are CSVs, which can be edited in Microsoft Excel."
+    "**Note:** All array cable layout files are CSVs, which can be edited in Microsoft Excel.\n",
+    "\n",
+    "**Update:** This example was updated to work with ORBIT >= 1.0.0"
    ]
   },
   {
@@ -43,7 +46,8 @@
     "import pandas as pd\n",
     "\n",
     "import ORBIT\n",
-    "from ORBIT import library, ProjectManager\n",
+    "from ORBIT import ProjectManager\n",
+    "from ORBIT.core import library\n",
     "from ORBIT.phases.design import CustomArraySystemDesign\n",
     "from ORBIT.phases.install import ArrayCableInstallation\n",
     "\n",
@@ -184,7 +188,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:771\n",
+      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:846\n",
       "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated."
      ]
     }
@@ -261,10 +265,10 @@
        "      <td>DAE_A1</td>\n",
        "      <td>1.358783</td>\n",
        "      <td>53.243950</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -273,10 +277,10 @@
        "      <td>DAD_A2</td>\n",
        "      <td>1.349033</td>\n",
        "      <td>53.248467</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -285,10 +289,10 @@
        "      <td>DAC_A3</td>\n",
        "      <td>1.339283</td>\n",
        "      <td>53.252983</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -297,10 +301,10 @@
        "      <td>DAB_A4</td>\n",
        "      <td>1.329550</td>\n",
        "      <td>53.257500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -309,10 +313,10 @@
        "      <td>DAA_A5</td>\n",
        "      <td>1.319800</td>\n",
        "      <td>53.262017</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -333,10 +337,10 @@
        "      <td>DAF_L2</td>\n",
        "      <td>1.368533</td>\n",
        "      <td>53.239433</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>60</th>\n",
@@ -345,10 +349,10 @@
        "      <td>DAG_L3</td>\n",
        "      <td>1.378250</td>\n",
        "      <td>53.234917</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>61</th>\n",
@@ -357,10 +361,10 @@
        "      <td>DAH_L4</td>\n",
        "      <td>1.388000</td>\n",
        "      <td>53.230400</td>\n",
-       "      <td>11</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>62</th>\n",
@@ -369,10 +373,10 @@
        "      <td>DAJ_L5</td>\n",
        "      <td>1.397750</td>\n",
        "      <td>53.225883</td>\n",
-       "      <td>11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
@@ -393,29 +397,29 @@
       ],
       "text/plain": [
        "         id substation_id     name  Longitude   Latitude String Order  \\\n",
-       "1    DAE_A1       DOW_OSS   DAE_A1   1.358783  53.243950      0     0   \n",
-       "2    DAD_A2       DOW_OSS   DAD_A2   1.349033  53.248467      0     1   \n",
-       "3    DAC_A3       DOW_OSS   DAC_A3   1.339283  53.252983      0     2   \n",
-       "4    DAB_A4       DOW_OSS   DAB_A4   1.329550  53.257500      0     3   \n",
-       "5    DAA_A5       DOW_OSS   DAA_A5   1.319800  53.262017      0     4   \n",
+       "1    DAE_A1       DOW_OSS   DAE_A1   1.358783  53.243950    0.0   0.0   \n",
+       "2    DAD_A2       DOW_OSS   DAD_A2   1.349033  53.248467    0.0   1.0   \n",
+       "3    DAC_A3       DOW_OSS   DAC_A3   1.339283  53.252983    0.0   2.0   \n",
+       "4    DAB_A4       DOW_OSS   DAB_A4   1.329550  53.257500    0.0   3.0   \n",
+       "5    DAA_A5       DOW_OSS   DAA_A5   1.319800  53.262017    0.0   4.0   \n",
        "..      ...           ...      ...        ...        ...    ...   ...   \n",
-       "59   DAF_L2       DOW_OSS   DAF_L2   1.368533  53.239433     11     1   \n",
-       "60   DAG_L3       DOW_OSS   DAG_L3   1.378250  53.234917     11     2   \n",
-       "61   DAH_L4       DOW_OSS   DAH_L4   1.388000  53.230400     11     3   \n",
-       "62   DAJ_L5       DOW_OSS   DAJ_L5   1.397750  53.225883     11     4   \n",
+       "59   DAF_L2       DOW_OSS   DAF_L2   1.368533  53.239433   11.0   1.0   \n",
+       "60   DAG_L3       DOW_OSS   DAG_L3   1.378250  53.234917   11.0   2.0   \n",
+       "61   DAH_L4       DOW_OSS   DAH_L4   1.388000  53.230400   11.0   3.0   \n",
+       "62   DAJ_L5       DOW_OSS   DAJ_L5   1.397750  53.225883   11.0   4.0   \n",
        "0   DOW_OSS       DOW_OSS  DOW_OSS   1.378767  53.264800                \n",
        "\n",
        "   cable_length bury_speed  \n",
-       "1             0          0  \n",
-       "2             0          0  \n",
-       "3             0          0  \n",
-       "4             0          0  \n",
-       "5             0          0  \n",
+       "1           0.0        0.0  \n",
+       "2           0.0        0.0  \n",
+       "3           0.0        0.0  \n",
+       "4           0.0        0.0  \n",
+       "5           0.0        0.0  \n",
        "..          ...        ...  \n",
-       "59            0          0  \n",
-       "60            0          0  \n",
-       "61            0          0  \n",
-       "62            0          0  \n",
+       "59          0.0        0.0  \n",
+       "60          0.0        0.0  \n",
+       "61          0.0        0.0  \n",
+       "62          0.0        0.0  \n",
        "0                           \n",
        "\n",
        "[68 rows x 9 columns]"
@@ -481,14 +485,14 @@
      "name": "stdin",
      "output_type": "stream",
      "text": [
-      "../library/cables/dudgeon_array_no_data.csv already exists, overwrite [y/n]? y\n"
+      "../library/cables/dudgeon_array_no_data.csv already exists, overwrite [y/n]? n\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Save complete!\n"
+      "Cancelling save!\n"
      ]
     }
    ],
@@ -786,7 +790,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:771\n",
+      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:846\n",
       "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated."
      ]
     }
@@ -810,9 +814,17 @@
    "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "VisibleDeprecationWarning: /opt/miniconda3/envs/orbit-development/lib/python3.8/site-packages/numpy/core/_asarray.py:171\n",
+      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray."
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 1 Axes>"
       ]
@@ -1197,10 +1209,10 @@
        "      <td>DAE_A1</td>\n",
        "      <td>14.890845</td>\n",
        "      <td>33.450759</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1209,10 +1221,10 @@
        "      <td>DAD_A2</td>\n",
        "      <td>14.237528</td>\n",
        "      <td>33.953026</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1221,10 +1233,10 @@
        "      <td>DAC_A3</td>\n",
        "      <td>13.584211</td>\n",
        "      <td>34.455182</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1233,10 +1245,10 @@
        "      <td>DAB_A4</td>\n",
        "      <td>12.932034</td>\n",
        "      <td>34.957450</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1257,10 +1269,10 @@
        "      <td>DCE_L1</td>\n",
        "      <td>15.564263</td>\n",
        "      <td>34.321749</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>64</th>\n",
@@ -1269,10 +1281,10 @@
        "      <td>DAF_L2</td>\n",
        "      <td>15.544161</td>\n",
        "      <td>32.948491</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>65</th>\n",
@@ -1281,10 +1293,10 @@
        "      <td>DAG_L3</td>\n",
        "      <td>16.195266</td>\n",
        "      <td>32.446335</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>66</th>\n",
@@ -1293,10 +1305,10 @@
        "      <td>DAH_L4</td>\n",
        "      <td>16.848583</td>\n",
        "      <td>31.944067</td>\n",
-       "      <td>11</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>67</th>\n",
@@ -1305,10 +1317,10 @@
        "      <td>DAJ_L5</td>\n",
        "      <td>17.501899</td>\n",
        "      <td>31.441800</td>\n",
-       "      <td>11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1318,29 +1330,29 @@
       "text/plain": [
        "         id substation_id     name  longitude   latitude string order  \\\n",
        "0   DOW_OSS       DOW_OSS  DOW_OSS  16.229909  35.769173                \n",
-       "1    DAE_A1       DOW_OSS   DAE_A1  14.890845  33.450759      0     0   \n",
-       "2    DAD_A2       DOW_OSS   DAD_A2  14.237528  33.953026      0     1   \n",
-       "3    DAC_A3       DOW_OSS   DAC_A3  13.584211  34.455182      0     2   \n",
-       "4    DAB_A4       DOW_OSS   DAB_A4  12.932034  34.957450      0     3   \n",
+       "1    DAE_A1       DOW_OSS   DAE_A1  14.890845  33.450759    0.0   0.0   \n",
+       "2    DAD_A2       DOW_OSS   DAD_A2  14.237528  33.953026    0.0   1.0   \n",
+       "3    DAC_A3       DOW_OSS   DAC_A3  13.584211  34.455182    0.0   2.0   \n",
+       "4    DAB_A4       DOW_OSS   DAB_A4  12.932034  34.957450    0.0   3.0   \n",
        "..      ...           ...      ...        ...        ...    ...   ...   \n",
-       "63   DCE_L1       DOW_OSS   DCE_L1  15.564263  34.321749     11     0   \n",
-       "64   DAF_L2       DOW_OSS   DAF_L2  15.544161  32.948491     11     1   \n",
-       "65   DAG_L3       DOW_OSS   DAG_L3  16.195266  32.446335     11     2   \n",
-       "66   DAH_L4       DOW_OSS   DAH_L4  16.848583  31.944067     11     3   \n",
-       "67   DAJ_L5       DOW_OSS   DAJ_L5  17.501899  31.441800     11     4   \n",
+       "63   DCE_L1       DOW_OSS   DCE_L1  15.564263  34.321749   11.0   0.0   \n",
+       "64   DAF_L2       DOW_OSS   DAF_L2  15.544161  32.948491   11.0   1.0   \n",
+       "65   DAG_L3       DOW_OSS   DAG_L3  16.195266  32.446335   11.0   2.0   \n",
+       "66   DAH_L4       DOW_OSS   DAH_L4  16.848583  31.944067   11.0   3.0   \n",
+       "67   DAJ_L5       DOW_OSS   DAJ_L5  17.501899  31.441800   11.0   4.0   \n",
        "\n",
        "   cable_length bury_speed  \n",
        "0                           \n",
-       "1             0          0  \n",
-       "2             0          0  \n",
-       "3             0          0  \n",
-       "4             0          0  \n",
+       "1           0.0        0.0  \n",
+       "2           0.0        0.0  \n",
+       "3           0.0        0.0  \n",
+       "4           0.0        0.0  \n",
        "..          ...        ...  \n",
-       "63            0          0  \n",
-       "64            0          0  \n",
-       "65            0          0  \n",
-       "66            0          0  \n",
-       "67            0          0  \n",
+       "63          0.0        0.0  \n",
+       "64          0.0        0.0  \n",
+       "65          0.0        0.0  \n",
+       "66          0.0        0.0  \n",
+       "67          0.0        0.0  \n",
        "\n",
        "[68 rows x 9 columns]"
       ]
@@ -1403,7 +1415,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:771\n",
+      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:846\n",
       "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated."
      ]
     }
@@ -1427,9 +1439,17 @@
    "execution_count": 16,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "VisibleDeprecationWarning: /opt/miniconda3/envs/orbit-development/lib/python3.8/site-packages/numpy/core/_asarray.py:171\n",
+      "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray."
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 1 Axes>"
       ]
@@ -1533,7 +1553,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:771\n",
+      "UserWarning: /Users/rhammond/Documents/GitHub/ORBIT/ORBIT/phases/design/array_system_design.py:846\n",
       "Missing data in columns ['cable_length', 'bury_speed']; all values will be calculated."
      ]
     }
@@ -2012,10 +2032,10 @@
      "output_type": "stream",
      "text": [
       "Simulation                 |  Cost (in USD) |  Time (in hours)\n",
-      "straight-line distance     | $11,471,328.94 |            2,216\n",
-      "distance-based coordinates | $11,474,750.57 |            2,217\n",
-      "with exclusions            | $11,528,251.23 |            2,228\n",
-      "custom                     | $15,627,410.34 |            3,045\n"
+      "straight-line distance     | $11,444,455.51 |            2,211\n",
+      "distance-based coordinates | $11,447,877.14 |            2,212\n",
+      "with exclusions            | $11,501,377.80 |            2,222\n",
+      "custom                     | $15,600,938.01 |            3,040\n"
      ]
     }
    ],
@@ -2026,7 +2046,7 @@
     "print(f\"{'Simulation':<26} | {'Cost (in USD)':>14} | {'Time (in hours)':>16}\")\n",
     "for name, simulation in zip(names, simulations):\n",
     "    simulation.run()\n",
-    "    cost = simulation.total_phase_cost\n",
+    "    cost = simulation.installation_capex\n",
     "    time = simulation.total_phase_time\n",
     "    print(f\"{name:<26} | ${cost:>13,.2f} | {time:>16,.0f}\")"
    ]
@@ -2061,7 +2081,7 @@
        "{'design_phases': ['CustomArraySystemDesign'],\n",
        " 'install_phases': ['ArrayCableInstallation'],\n",
        " 'plant': {'layout': 'custom', 'num_turbines': 67},\n",
-       " 'port': {'monthly_rate': 10000, 'num_cranes': 1},\n",
+       " 'port': {'monthly_rate': 10000},\n",
        " 'site': {'depth': 20, 'distance': 50},\n",
        " 'turbine': 'SWT_6MW_154m_110m',\n",
        " 'array_system_design': {'cables': ['XLPE_400mm_33kV', 'XLPE_630mm_33kV'],\n",
@@ -2101,17 +2121,17 @@
      "output_type": "stream",
      "text": [
       "Custom Design        | $27,625,399.10\n",
-      "Custom Installation  | $15,627,410.34\n",
-      "Total Cost.          | $43,252,809.44\n",
-      "Project Manager Cost | $43,252,809.44\n"
+      "Custom Installation  | $15,600,938.01\n",
+      "Total Cost.          | $43,226,337.11\n",
+      "Project Manager Cost | $43,226,337.11\n"
      ]
     }
    ],
    "source": [
-    "total = array_custom.total_cable_cost + sim5.total_phase_cost\n",
+    "total = array_custom.total_cable_cost + sim5.installation_capex\n",
     "\n",
     "print(f\"Custom Design        | ${array_custom.total_cable_cost:>13,.2f}\")\n",
-    "print(f\"Custom Installation  | ${sim5.total_phase_cost:>13,.2f}\")\n",
+    "print(f\"Custom Installation  | ${sim5.installation_capex:>13,.2f}\")\n",
     "print(f\"Total Cost.          | ${total:>13,.2f}\")\n",
     "print(f\"Project Manager Cost | ${project.bos_capex:>13,.2f}\")"
    ]
@@ -2140,7 +2160,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/setup.py b/setup.py
index 5bc75c6f..6ce002c7 100644
--- a/setup.py
+++ b/setup.py
@@ -36,6 +36,7 @@
         "pyyaml",
         "openmdao>=3.2",
         "python-benedict",
+        "statsmodels",
     ],
     extras_require={
         "dev": [


Test patch:
diff --git a/tests/test_project_manager.py b/tests/test_project_manager.py
index 6b9438d9..2e96979e 100644
--- a/tests/test_project_manager.py
+++ b/tests/test_project_manager.py
@@ -312,10 +312,13 @@ def test_resolve_project_capacity():
     # Missing turbine rating
     config1 = {"plant": {"capacity": 600, "num_turbines": 40}}
 
-    out1 = ProjectManager.resolve_project_capacity(config1)
-    assert out1["plant"]["capacity"] == config1["plant"]["capacity"]
-    assert out1["plant"]["num_turbines"] == config1["plant"]["num_turbines"]
-    assert out1["turbine"]["turbine_rating"] == 15
+    project1 = ProjectManager(config1)
+    assert project1.config["plant"]["capacity"] == config1["plant"]["capacity"]
+    assert (
+        project1.config["plant"]["num_turbines"]
+        == config1["plant"]["num_turbines"]
+    )
+    assert project1.config["turbine"]["turbine_rating"] == 15
 
     # Missing plant capacity
     config2 = {
@@ -323,22 +326,25 @@ def test_resolve_project_capacity():
         "turbine": {"turbine_rating": 15},
     }
 
-    out2 = ProjectManager.resolve_project_capacity(config2)
-    assert out2["plant"]["capacity"] == 600
-    assert out2["plant"]["num_turbines"] == config2["plant"]["num_turbines"]
+    project2 = ProjectManager(config2)
+    assert project2.config["plant"]["capacity"] == 600
+    assert (
+        project2.config["plant"]["num_turbines"]
+        == config2["plant"]["num_turbines"]
+    )
     assert (
-        out2["turbine"]["turbine_rating"]
+        project2.config["turbine"]["turbine_rating"]
         == config2["turbine"]["turbine_rating"]
     )
 
     # Missing number of turbines
     config3 = {"plant": {"capacity": 600}, "turbine": {"turbine_rating": 15}}
 
-    out3 = ProjectManager.resolve_project_capacity(config3)
-    assert out3["plant"]["capacity"] == config3["plant"]["capacity"]
-    assert out3["plant"]["num_turbines"] == 40
+    project3 = ProjectManager(config3)
+    assert project3.config["plant"]["capacity"] == config3["plant"]["capacity"]
+    assert project3.config["plant"]["num_turbines"] == 40
     assert (
-        out3["turbine"]["turbine_rating"]
+        project3.config["turbine"]["turbine_rating"]
         == config3["turbine"]["turbine_rating"]
     )
 
@@ -348,11 +354,14 @@ def test_resolve_project_capacity():
         "turbine": {"turbine_rating": 15.0},
     }
 
-    out4 = ProjectManager.resolve_project_capacity(config4)
-    assert out4["plant"]["capacity"] == config4["plant"]["capacity"]
-    assert out4["plant"]["num_turbines"] == config4["plant"]["num_turbines"]
+    project4 = ProjectManager(config4)
+    assert project4.config["plant"]["capacity"] == config4["plant"]["capacity"]
+    assert (
+        project4.config["plant"]["num_turbines"]
+        == config4["plant"]["num_turbines"]
+    )
     assert (
-        out4["turbine"]["turbine_rating"]
+        project4.config["turbine"]["turbine_rating"]
         == config4["turbine"]["turbine_rating"]
     )
 
@@ -363,19 +372,19 @@ def test_resolve_project_capacity():
     }
 
     with pytest.raises(AttributeError):
-        _ = ProjectManager.resolve_project_capacity(config5)
+        _ = ProjectManager(config5)
 
     # Test for not enough information
     config6 = {"plant": {"capacity": 600}}
 
-    out6 = ProjectManager.resolve_project_capacity(config6)
-    assert out6["plant"]["capacity"] == config6["plant"]["capacity"]
+    project6 = ProjectManager(config6)
+    assert project6.config["plant"]["capacity"] == config6["plant"]["capacity"]
 
     with pytest.raises(KeyError):
-        _ = out6["turbine"]["turbine_rating"]
+        _ = project6.config["turbine"]["turbine_rating"]
 
     with pytest.raises(KeyError):
-        _ = out6["plant"]["num_turbines"]
+        _ = project6.config["plant"]["num_turbines"]
 
 
 ### Exceptions