Source code for pylife.core.pylifesignal

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__author__ = "Johannes Mueller"
__maintainer__ = __author__


import pandas as pd

from .broadcaster import Broadcaster
from .data_validator import DataValidator


[docs] class PylifeSignal(Broadcaster): """Base class for signal accessor classes. Notes ----- Derived classes need to implement the method `_validate(self, obj)` that gets `pandas_obj` as `obj` parameter. This `validate()` method must raise an Exception (e.g. AttributeError or ValueError) in case `obj` is not a valid DataFrame for the kind of signal. For these validation :func:`fail_if_key_missing()` and :func:`get_missing_keys()` might be helpful. For a derived class you can register methods without modifying the class' code itself. This can be useful if you want to make signal accessor classes extendable. See also -------- :func:`fail_if_key_missing()` :func:`get_missing_keys()` :func:`register_method()` """ _method_dict = {} def __init__(self, pandas_obj): """Instantiate a :class:`signal.PyLifeSignal`. Parameters ---------- pandas_obj : pandas.DataFrame or pandas.Series """ self._obj = pandas_obj self._validate()
[docs] @classmethod def from_parameters(cls, **kwargs): """Make a signal instance from a parameter set. This is a convenience function to instantiate a signal from individual parameters rather than pandas objects. A signal class like .. code-block:: python @pd.api.extensions.register_dataframe_accessor('foo_signal') class FooSignal(PylifeSignal): pass The following two blocks are equivalent: .. code-block:: python pd.Series({'foo': 1.0, 'bar': 2.0}).foo_signal .. code-block:: python FooSignal.from_parameters(foo=1.0, bar=1.0) """ # TODO: better error handling if len(kwargs) > 1 and hasattr(next(iter(kwargs.values())), '__iter__'): obj = pd.DataFrame(kwargs) else: obj = pd.Series(kwargs) return cls(obj)
[docs] def keys(self): """Get a list of missing keys that are needed for a signal object. Returns ------- keys : pd.Index a pandas index of keys Raises ------ AttributeError if `self._obj` is neither a `pandas.DataFrame` nor a `pandas.Series` Notes ----- If `self._obj` is a `pandas.DataFrame`, the `self._obj.columns` are returned. If `self._obj` is a `pandas.Series`, the `self._obj.index` are returned. """ if isinstance(self._obj, pd.Series): return self._obj.index elif isinstance(self._obj, pd.DataFrame): return self._obj.columns raise AttributeError("An accessor object needs to be either a pandas.Series or a pandas.DataFrame")
[docs] def get_missing_keys(self, keys_to_check): """Get a list of missing keys that are needed for a self._obj object. Parameters ---------- keys_to_check : list A list of keys that need to be available in `self._obj` Returns ------- missing_keys : list a list of missing keys Raises ------ AttributeError if `self._obj` is neither a `pandas.DataFrame` nor a `pandas.Series` Notes ----- If `self._obj` is a `pandas.DataFrame`, all keys of `keys_to_check` not found in the `self._obj.columns` are returned. If `self._obj` is a `pandas.Series`, all keys of `keys_to_check` not found in the `self._obj.index` are returned. """ return DataValidator().get_missing_keys(self._obj, keys_to_check)
[docs] def fail_if_key_missing(self, keys_to_check, msg=None): """Raise an exception if any key is missing in a self._obj object. Parameters ---------- self._obj : pandas.DataFrame or pandas.Series The object to be checked keys_to_check : list A list of keys that need to be available in `self._obj` Raises ------ AttributeError if `self._obj` is neither a `pandas.DataFrame` nor a `pandas.Series` AttributeError if any of the keys is not found in the self._obj's keys. Notes ----- If `self._obj` is a `pandas.DataFrame`, all keys of `keys_to_check` meed to be found in the `self._obj.columns`. If `self._obj` is a `pandas.Series`, all keys of `keys_to_check` meed to be found in the `self._obj.index`. See also -------- :func:`get_missing_keys` :class:`stresssignal.StressTensorVoigt` """ DataValidator().fail_if_key_missing(self._obj, keys_to_check)
class _MethodCaller: def __init__(self, method, obj): self._method = method self._obj = obj def __call__(self, *args, **kwargs): return self._method(self._obj, *args, **kwargs) def __getattr__(self, itemname): method = self._method_dict.get(itemname) if method is None: return super().__getattribute__(itemname) return self._MethodCaller(method, self._obj) @classmethod def _register_method(cls, method_name): def method_decorator(method): if method_name in cls._method_dict.keys(): raise ValueError("Method '%s' already registered in %s" % (method_name, cls.__name__)) if hasattr(cls, method_name): raise ValueError("%s already has an attribute '%s'" % (cls.__name__, method_name)) cls._method_dict[method_name] = method return method_decorator
[docs] def to_pandas(self): """Expose the pandas object of the signal. Returns ------- pandas_object : pd.DataFrame or pd.Series The pandas object representing the signal Notes ----- The default implementation just returns the object given when instantiating the signal class. Derived classes may return a modified object or augmented, if they store some extra information. By default the object is **not** copied. So make a copy yourself, if you intent to modify it. """ return self._obj
def register_method(cls, method_name): """Registers a method to a class derived from :class:`PyifeSignal` Parameters ---------- cls : class The class the method is registered to. method_name : str The name of the method Raises ------ ValueError if `method_name` is already registered for the class ValueError if `method_name` the class has already an attribute `method_name` Notes ----- The function is meant to be used as a decorator for a function that is to be installed as a method for a class. The class is assumed to contain a pandas object in `self._obj`. Examples -------- .. code-block:: python import pandas as pd import pylife as pl @pd.api.extensions.register_dataframe_accessor('foo') class Foo(pl.signal.PyifeSignal): def __init__(self, obj): # self._validate(obj) could come here self._obj = obj @pl.signal.register_method(Foo, 'bar') def bar(df): return pd.DataFrame({'baz': df['foo'] + df['bar']}) >>> df = pd.DataFrame({'foo': [1.0, 2.0], 'bar': [-1.0, -2.0]}) >>> df.foo.bar() baz 0 0.0 1 0.0 """ return cls._register_method(method_name)