ENH: Expansion of Encoders Implementation for Full Flights. (#679)

* ENH: expand encoders implementation to support full flights.

MNT: Add encoding feature to CHANGELOG.

BUG: add dill to the requirements file.

* ENH: provide from_dict classmethods for decoding basic classes.

ENH: extend encoding and decoding to Liquid and Hybrid.

MNT: correct decoding of liquid and hybrid motors.

STY: solve pylint remarks.

MNT: adapt encoding to new post merge attributes.

MNT: restore typo to correct values on flight test.

ENH: add option for including outputs on JSON export.

TST: add tests for motor encoding.

DOC: Improve docstrings of encoders signature.

MNT: Make no output encoding the default.

MNT: Standardize include outputs parameter.

DOC: Correct phrasing and typos of encoders docstring.

MNT: Correct json export environment naming.

* MNT: Allow for encoding customization of MonteCarlo.

DEV: fix CHANGELOG

MNT: reposition barometric height as env json output.

MNT: Allow for encoding customization of MonteCarlo.

Author: phmbressan

CHANGELOG.md

@@ -38,6 +38,8 @@ Attention: The newest changes should be on top -->
 - ENH: create a dataset of pre-registered motors. See #664 [#744](https://github.com/RocketPy-Team/RocketPy/pull/744)
 - DOC: add Defiance flight example [#742](https://github.com/RocketPy-Team/RocketPy/pull/742)
 - ENH: Allow for Alternative and Custom ODE Solvers. [#748](https://github.com/RocketPy-Team/RocketPy/pull/748)
+- ENH: Expansion of Encoders Implementation for Full Flights. [#679](https://github.com/RocketPy-Team/RocketPy/pull/679)
+
 
 
 ### Changed

docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/monte_carlo_class_example.inputs.txt

@@ -5,3 +5,4 @@ netCDF4>=1.6.4
 requests
 pytz
 simplekml
+dill

docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/monte_carlo_class_example.kml

@@ -1,15 +1,22 @@
 """Defines a custom JSON encoder for RocketPy objects."""
 
 import json
-import types
+from datetime import datetime
+from importlib import import_module
 
 import numpy as np
 
 from rocketpy.mathutils.function import Function
 
 
 class RocketPyEncoder(json.JSONEncoder):
-    """NOTE: This is still under construction, please don't use it yet."""
+    """Custom JSON encoder for RocketPy objects. It defines how to encode
+    different types of objects to a JSON supported format."""
+
+    def __init__(self, *args, **kwargs):
+        self.include_outputs = kwargs.pop("include_outputs", False)
+        self.include_function_data = kwargs.pop("include_function_data", True)
+        super().__init__(*args, **kwargs)
 
     def default(self, o):
         if isinstance(
@@ -33,11 +40,126 @@ def default(self, o):
             return float(o)
         elif isinstance(o, np.ndarray):
             return o.tolist()
+        elif isinstance(o, datetime):
+            return [o.year, o.month, o.day, o.hour]
+        elif hasattr(o, "__iter__") and not isinstance(o, str):
+            return list(o)
+        elif isinstance(o, Function):
+            if not self.include_function_data:
+                return str(o)
+            else:
+                encoding = o.to_dict(self.include_outputs)
+                encoding["signature"] = get_class_signature(o)
+                return encoding
         elif hasattr(o, "to_dict"):
-            return o.to_dict()
-        # elif isinstance(o, Function):
-        #     return o.__dict__()
-        elif isinstance(o, (Function, types.FunctionType)):
-            return repr(o)
+            encoding = o.to_dict(self.include_outputs)
+            encoding = remove_circular_references(encoding)
+
+            encoding["signature"] = get_class_signature(o)
+
+            return encoding
+
+        elif hasattr(o, "__dict__"):
+            encoding = remove_circular_references(o.__dict__)
+
+            if "rocketpy" in o.__class__.__module__:
+                encoding["signature"] = get_class_signature(o)
+
+            return encoding
+        else:
+            return super().default(o)
+
+
+class RocketPyDecoder(json.JSONDecoder):
+    """Custom JSON decoder for RocketPy objects. It defines how to decode
+    different types of objects from a JSON supported format."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(object_hook=self.object_hook, *args, **kwargs)
+
+    def object_hook(self, obj):
+        if "signature" in obj:
+            signature = obj.pop("signature")
+
+            try:
+                class_ = get_class_from_signature(signature)
+
+                if hasattr(class_, "from_dict"):
+                    return class_.from_dict(obj)
+                else:
+                    # Filter keyword arguments
+                    kwargs = {
+                        key: value
+                        for key, value in obj.items()
+                        if key in class_.__init__.__code__.co_varnames
+                    }
+
+                    return class_(**kwargs)
+            except (ImportError, AttributeError):
+                return obj
         else:
-            return json.JSONEncoder.default(self, o)
+            return obj
+
+
+def get_class_signature(obj):
+    """Returns the signature of a class so it can be identified on
+    decoding. The signature is a dictionary with the module and
+    name of the object's class as strings.
+
+
+    Parameters
+    ----------
+    obj : object
+        Object to get the signature from.
+
+    Returns
+    -------
+    dict
+        Signature of the class.
+    """
+    class_ = obj.__class__
+    name = getattr(class_, '__qualname__', class_.__name__)
+
+    return {"module": class_.__module__, "name": name}
+
+
+def get_class_from_signature(signature):
+    """Returns the class from its signature dictionary by
+    importing the module and loading the class.
+
+    Parameters
+    ----------
+    signature : dict
+        Signature of the class.
+
+    Returns
+    -------
+    type
+        Class defined by the signature.
+    """
+    module = import_module(signature["module"])
+    inner_class = None
+
+    for class_ in signature["name"].split("."):
+        inner_class = getattr(module, class_)
+
+    return inner_class
+
+
+def remove_circular_references(obj_dict):
+    """Removes circular references from a dictionary.
+
+    Parameters
+    ----------
+    obj_dict : dict
+        Dictionary to remove circular references from.
+
+    Returns
+    -------
+    dict
+        Dictionary without circular references.
+    """
+    obj_dict.pop("prints", None)
+    obj_dict.pop("plots", None)
+
+    return obj_dict

docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/monte_carlo_class_example.outputs.txt

@@ -366,12 +366,15 @@ def __initialize_constants(self):
         self.standard_g = 9.80665
         self.__weather_model_map = WeatherModelMapping()
         self.__atm_type_file_to_function_map = {
-            ("forecast", "GFS"): fetch_gfs_file_return_dataset,
-            ("forecast", "NAM"): fetch_nam_file_return_dataset,
-            ("forecast", "RAP"): fetch_rap_file_return_dataset,
-            ("forecast", "HIRESW"): fetch_hiresw_file_return_dataset,
-            ("ensemble", "GEFS"): fetch_gefs_ensemble,
-            # ("ensemble", "CMC"): fetch_cmc_ensemble,
+            "forecast": {
+                "GFS": fetch_gfs_file_return_dataset,
+                "NAM": fetch_nam_file_return_dataset,
+                "RAP": fetch_rap_file_return_dataset,
+                "HIRESW": fetch_hiresw_file_return_dataset,
+            },
+            "ensemble": {
+                "GEFS": fetch_gefs_ensemble,
+            },
         }
         self.__standard_atmosphere_layers = {
             "geopotential_height": [  # in geopotential m
@@ -1270,7 +1273,10 @@ def set_atmospheric_model(  # pylint: disable=too-many-statements
             self.process_windy_atmosphere(file)
         elif type in ["forecast", "reanalysis", "ensemble"]:
             dictionary = self.__validate_dictionary(file, dictionary)
-            fetch_function = self.__atm_type_file_to_function_map.get((type, file))
+            try:
+                fetch_function = self.__atm_type_file_to_function_map[type][file]
+            except KeyError:
+                fetch_function = None
 
             # Fetches the dataset using OpenDAP protocol or uses the file path
             dataset = fetch_function() if fetch_function is not None else file
@@ -2748,6 +2754,96 @@ def decimal_degrees_to_arc_seconds(angle):
         arc_seconds = (remainder * 60 - arc_minutes) * 60
         return degrees, arc_minutes, arc_seconds
 
+    def to_dict(self, include_outputs=False):
+        env_dict = {
+            "gravity": self.gravity,
+            "date": self.date,
+            "latitude": self.latitude,
+            "longitude": self.longitude,
+            "elevation": self.elevation,
+            "datum": self.datum,
+            "timezone": self.timezone,
+            "max_expected_height": self.max_expected_height,
+            "atmospheric_model_type": self.atmospheric_model_type,
+            "pressure": self.pressure,
+            "temperature": self.temperature,
+            "wind_velocity_x": self.wind_velocity_x,
+            "wind_velocity_y": self.wind_velocity_y,
+            "wind_heading": self.wind_heading,
+            "wind_direction": self.wind_direction,
+            "wind_speed": self.wind_speed,
+        }
+
+        if include_outputs:
+            env_dict["density"] = self.density
+            env_dict["barometric_height"] = self.barometric_height
+            env_dict["speed_of_sound"] = self.speed_of_sound
+            env_dict["dynamic_viscosity"] = self.dynamic_viscosity
+
+        return env_dict
+
+    @classmethod
+    def from_dict(cls, data):  # pylint: disable=too-many-statements
+        env = cls(
+            gravity=data["gravity"],
+            date=data["date"],
+            latitude=data["latitude"],
+            longitude=data["longitude"],
+            elevation=data["elevation"],
+            datum=data["datum"],
+            timezone=data["timezone"],
+            max_expected_height=data["max_expected_height"],
+        )
+        atmospheric_model = data["atmospheric_model_type"]
+
+        if atmospheric_model == "standard_atmosphere":
+            env.set_atmospheric_model("standard_atmosphere")
+        elif atmospheric_model == "custom_atmosphere":
+            env.set_atmospheric_model(
+                type="custom_atmosphere",
+                pressure=data["pressure"],
+                temperature=data["temperature"],
+                wind_u=data["wind_velocity_x"],
+                wind_v=data["wind_velocity_y"],
+            )
+        else:
+            env.__set_pressure_function(data["pressure"])
+            env.__set_temperature_function(data["temperature"])
+            env.__set_wind_velocity_x_function(data["wind_velocity_x"])
+            env.__set_wind_velocity_y_function(data["wind_velocity_y"])
+            env.__set_wind_heading_function(data["wind_heading"])
+            env.__set_wind_direction_function(data["wind_direction"])
+            env.__set_wind_speed_function(data["wind_speed"])
+            env.elevation = data["elevation"]
+            env.max_expected_height = data["max_expected_height"]
+
+            if atmospheric_model in ("windy", "forecast", "reanalysis", "ensemble"):
+                env.atmospheric_model_init_date = data["atmospheric_model_init_date"]
+                env.atmospheric_model_end_date = data["atmospheric_model_end_date"]
+                env.atmospheric_model_interval = data["atmospheric_model_interval"]
+                env.atmospheric_model_init_lat = data["atmospheric_model_init_lat"]
+                env.atmospheric_model_end_lat = data["atmospheric_model_end_lat"]
+                env.atmospheric_model_init_lon = data["atmospheric_model_init_lon"]
+                env.atmospheric_model_end_lon = data["atmospheric_model_end_lon"]
+
+            if atmospheric_model == "ensemble":
+                env.level_ensemble = data["level_ensemble"]
+                env.height_ensemble = data["height_ensemble"]
+                env.temperature_ensemble = data["temperature_ensemble"]
+                env.wind_u_ensemble = data["wind_u_ensemble"]
+                env.wind_v_ensemble = data["wind_v_ensemble"]
+                env.wind_heading_ensemble = data["wind_heading_ensemble"]
+                env.wind_direction_ensemble = data["wind_direction_ensemble"]
+                env.wind_speed_ensemble = data["wind_speed_ensemble"]
+                env.num_ensemble_members = data["num_ensemble_members"]
+
+            env.__reset_barometric_height_function()
+            env.calculate_density_profile()
+            env.calculate_speed_of_sound_profile()
+            env.calculate_dynamic_viscosity()
+
+        return env
+
 
 if __name__ == "__main__":
     import doctest

docs/notebooks/monte_carlo_analysis/monte_carlo_class_usage.ipynb

@@ -423,7 +423,10 @@ def __check_coordinates_inside_grid(
             or lat_index > len(lat_array) - 1
         ):
             raise ValueError(
-                f"Latitude and longitude pair {(self.latitude, self.longitude)} is outside the grid available in the given file, which is defined by {(lat_array[0], lon_array[0])} and {(lat_array[-1], lon_array[-1])}."
+                f"Latitude and longitude pair {(self.latitude, self.longitude)} "
+                "is outside the grid available in the given file, which "
+                f"is defined by {(lat_array[0], lon_array[0])} and "
+                f"{(lat_array[-1], lon_array[-1])}."
             )
 
     def __localize_input_dates(self):

requirements.txt

@@ -22,6 +22,8 @@
     RBFInterpolator,
 )
 
+from rocketpy.tools import from_hex_decode, to_hex_encode
+
 from ..plots.plot_helpers import show_or_save_plot
 
 # Numpy 1.x compatibility,
@@ -711,9 +713,9 @@ def set_discrete(
         if func.__dom_dim__ == 1:
             xs = np.linspace(lower, upper, samples)
             ys = func.get_value(xs.tolist()) if one_by_one else func.get_value(xs)
-            func.set_source(np.concatenate(([xs], [ys])).transpose())
-            func.set_interpolation(interpolation)
-            func.set_extrapolation(extrapolation)
+            func.__interpolation__ = interpolation
+            func.__extrapolation__ = extrapolation
+            func.set_source(np.column_stack((xs, ys)))
         elif func.__dom_dim__ == 2:
             lower = 2 * [lower] if isinstance(lower, NUMERICAL_TYPES) else lower
             upper = 2 * [upper] if isinstance(upper, NUMERICAL_TYPES) else upper
@@ -3418,6 +3420,50 @@ def __validate_extrapolation(self, extrapolation):
                 extrapolation = "natural"
         return extrapolation
 
+    def to_dict(self, include_outputs=False):  # pylint: disable=unused-argument
+        """Serializes the Function instance to a dictionary.
+
+        Returns
+        -------
+        dict
+            A dictionary containing the Function's attributes.
+        """
+        source = self.source
+
+        if callable(source):
+            source = to_hex_encode(source)
+
+        return {
+            "source": source,
+            "title": self.title,
+            "inputs": self.__inputs__,
+            "outputs": self.__outputs__,
+            "interpolation": self.__interpolation__,
+            "extrapolation": self.__extrapolation__,
+        }
+
+    @classmethod
+    def from_dict(cls, func_dict):
+        """Creates a Function instance from a dictionary.
+
+        Parameters
+        ----------
+        func_dict
+            The JSON like Function dictionary.
+        """
+        source = func_dict["source"]
+        if func_dict["interpolation"] is None and func_dict["extrapolation"] is None:
+            source = from_hex_decode(source)
+
+        return cls(
+            source=source,
+            interpolation=func_dict["interpolation"],
+            extrapolation=func_dict["extrapolation"],
+            inputs=func_dict["inputs"],
+            outputs=func_dict["outputs"],
+            title=func_dict["title"],
+        )
+
 
 def funcify_method(*args, **kwargs):  # pylint: disable=too-many-statements
     """Decorator factory to wrap methods as Function objects and save them as