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by Chaitanya

created 2024-10-17 21:04 UTC

asgardpy.config.operations.recursive_merge_dicts() C

↳ Parent: asgardpy.config.operations

Complexity

Conditions

Size

Total Lines	52
Code Lines	16

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	9
eloc	16
nop	2
dl	0
loc	52
rs	6.6666
c	0
b	0
f	0

How to fix Long Method

"""
Main AsgardpyConfig Operations Module
"""

import logging
from collections.abc import Mapping
from pathlib import Path

import numpy as np
from gammapy.modeling.models import Models, SkyModel

__all__ = [
    "all_model_templates",
    "compound_model_dict_converstion",
    "get_model_template",
    "recursive_merge_dicts",
    "deep_update",
]

CONFIG_PATH = Path(__file__).resolve().parent

log = logging.getLogger(__name__)


def all_model_templates():
    """
    Collect all Template Models provided in Asgardpy, and their small tag names.
    """
    template_files = sorted(list(CONFIG_PATH.glob("model_templates/model_template*yaml")))

    all_tags = []
    for file in template_files:
        all_tags.append(file.name.split("_")[-1].split(".")[0])
    all_tags = np.array(all_tags)

    return all_tags, template_files


def get_model_template(spec_model_tag):
    """
    Read a particular template model yaml filename to create an AsgardpyConfig
    object.
    """
    all_tags, template_files = all_model_templates()
    new_model_file = None

    for file, tag in zip(template_files, all_tags, strict=True):
        if spec_model_tag == tag:
            new_model_file = file
    return new_model_file


def check_gammapy_model(gammapy_model):
    """
    For a given object type, try to read it as a Gammapy Models object.
    """
    if isinstance(gammapy_model, Models | SkyModel):
        models_gpy = Models(gammapy_model)
    else:
        try:
            models_gpy = Models.read(gammapy_model)
        except KeyError:
            raise TypeError("%s File cannot be read by Gammapy Models", gammapy_model) from KeyError

    return models_gpy


def recursive_merge_dicts(base_config, extra_config):
    """
    recursively merge two dictionaries.
    Entries in extra_config override entries in base_config. The built-in
    update function cannot be used for hierarchical dicts.

    Also for the case when there is a list of dicts involved, one has to be
    more careful. The extra_config may have longer list of dicts as compared
    with the base_config, in which case, the extra items are simply added to
    the merged final list.

    Combined here are 2 options from SO.

    See:
    http://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth/3233356#3233356
    and also
    https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth/18394648#18394648

    Parameters
    ----------
    base_config : dict
        dictionary to be merged
    extra_config : dict
        dictionary to be merged
    Returns
    -------
    final_config : dict
        merged dict
    """
    final_config = base_config.copy()

    for key, value in extra_config.items():
        if key in final_config and isinstance(final_config[key], list):
            new_config = []

            for key_, value_ in zip(final_config[key], value, strict=False):
                key_ = recursive_merge_dicts(key_ or {}, value_)
                new_config.append(key_)

            # For example moving from a smaller list of model parameters to a
            # longer list.
            if len(final_config[key]) < len(extra_config[key]):
                for value_ in value[len(final_config[key]) :]:
                    new_config.append(value_)
            final_config[key] = new_config

        elif key in final_config and isinstance(final_config[key], dict):
            final_config[key] = recursive_merge_dicts(final_config.get(key) or {}, value)
        else:
            final_config[key] = value

    return final_config


def deep_update(d, u):
    """
    Recursively update a nested dictionary.

    Just like in Gammapy, taken from: https://stackoverflow.com/a/3233356/19802442
    """
    for k, v in u.items():
        if isinstance(v, Mapping):
            d[k] = deep_update(d.get(k, {}), v)
        else:
            d[k] = v
    return d


def compound_model_dict_converstion(dict):
    """
    Given a Gammapy CompoundSpectralModel as a dict object, convert it into
    an Asgardpy form.
    """
    ebl_abs = dict["model2"]
    ebl_abs["alpha_norm"] = ebl_abs["parameters"][0]["value"]
    ebl_abs["redshift"] = ebl_abs["parameters"][1]["value"]
    ebl_abs.pop("parameters", None)

    dict["type"] = dict["model1"]["type"]
    dict["parameters"] = dict["model1"]["parameters"]
    dict["ebl_abs"] = ebl_abs

    dict.pop("model1", None)
    dict.pop("model2", None)
    dict.pop("operator", None)

    return dict


1			"""
2			Main AsgardpyConfig Operations Module
3			"""
4
5			import logging
6			from collections.abc import Mapping
7			from pathlib import Path
8
9			import numpy as np
10			from gammapy.modeling.models import Models, SkyModel
11
12			__all__ = [
13			"all_model_templates",
14			"compound_model_dict_converstion",
15			"get_model_template",
16			"recursive_merge_dicts",
17			"deep_update",
18			]
19
20			CONFIG_PATH = Path(__file__).resolve().parent
21
22			log = logging.getLogger(__name__)
23
24
25			def all_model_templates():
26			"""
27			Collect all Template Models provided in Asgardpy, and their small tag names.
28			"""
29			template_files = sorted(list(CONFIG_PATH.glob("model_templates/model_template*yaml")))
30
31			all_tags = []
32			for file in template_files:
33			all_tags.append(file.name.split("_")[-1].split(".")[0])
34			all_tags = np.array(all_tags)
35
36			return all_tags, template_files
37
38
39			def get_model_template(spec_model_tag):
40			"""
41			Read a particular template model yaml filename to create an AsgardpyConfig
42			object.
43			"""
44			all_tags, template_files = all_model_templates()
45			new_model_file = None
46
47			for file, tag in zip(template_files, all_tags, strict=True):
48			if spec_model_tag == tag:
49			new_model_file = file
50			return new_model_file
51
52
53			def check_gammapy_model(gammapy_model):
54			"""
55			For a given object type, try to read it as a Gammapy Models object.
56			"""
57			if isinstance(gammapy_model, Models \| SkyModel):
58			models_gpy = Models(gammapy_model)
59			else:
60			try:
61			models_gpy = Models.read(gammapy_model)
62			except KeyError:
63			raise TypeError("%s File cannot be read by Gammapy Models", gammapy_model) from KeyError
64
65			return models_gpy
66
67
68			def recursive_merge_dicts(base_config, extra_config):
69			"""
70			recursively merge two dictionaries.
71			Entries in extra_config override entries in base_config. The built-in
72			update function cannot be used for hierarchical dicts.
73
74			Also for the case when there is a list of dicts involved, one has to be
75			more careful. The extra_config may have longer list of dicts as compared
76			with the base_config, in which case, the extra items are simply added to
77			the merged final list.
78
79			Combined here are 2 options from SO.
80
81			See:
82			http://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth/3233356#3233356
83			and also
84			https://stackoverflow.com/questions/3232943/update-value-of-a-nested-dictionary-of-varying-depth/18394648#18394648
85
86			Parameters
87			----------
88			base_config : dict
89			dictionary to be merged
90			extra_config : dict
91			dictionary to be merged
92			Returns
93			-------
94			final_config : dict
95			merged dict
96			"""
97			final_config = base_config.copy()
98
99			for key, value in extra_config.items():
100			if key in final_config and isinstance(final_config[key], list):
101			new_config = []
102
103			for key_, value_ in zip(final_config[key], value, strict=False):
104			key_ = recursive_merge_dicts(key_ or {}, value_)
105			new_config.append(key_)
106
107			# For example moving from a smaller list of model parameters to a
108			# longer list.
109			if len(final_config[key]) < len(extra_config[key]):
110			for value_ in value[len(final_config[key]) :]:
111			new_config.append(value_)
112			final_config[key] = new_config
113
114			elif key in final_config and isinstance(final_config[key], dict):
115			final_config[key] = recursive_merge_dicts(final_config.get(key) or {}, value)
116			else:
117			final_config[key] = value
118
119			return final_config
120
121
122			def deep_update(d, u):
123			"""
124			Recursively update a nested dictionary.
125
126			Just like in Gammapy, taken from: https://stackoverflow.com/a/3233356/19802442
127			"""
128			for k, v in u.items():
129			if isinstance(v, Mapping):
130			d[k] = deep_update(d.get(k, {}), v)
131			else:
132			d[k] = v
133			return d
134
135
136			def compound_model_dict_converstion(dict):
137			"""
138			Given a Gammapy CompoundSpectralModel as a dict object, convert it into
139			an Asgardpy form.
140			"""
141			ebl_abs = dict["model2"]
142			ebl_abs["alpha_norm"] = ebl_abs["parameters"][0]["value"]
143			ebl_abs["redshift"] = ebl_abs["parameters"][1]["value"]
144			ebl_abs.pop("parameters", None)
145
146			dict["type"] = dict["model1"]["type"]
147			dict["parameters"] = dict["model1"]["parameters"]
148			dict["ebl_abs"] = ebl_abs
149
150			dict.pop("model1", None)
151			dict.pop("model2", None)
152			dict.pop("operator", None)
153
154			return dict
155

chaimain / asgardpy

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asgardpy.config.operations.recursive_merge_dicts() C

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