From 8d7cebb6e257877f250b8c5fce96bd342c078172 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 09:44:27 -0400 Subject: [PATCH 01/26] feat: add delta state to autora --- src/autora/state/delta.py | 321 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 321 insertions(+) create mode 100644 src/autora/state/delta.py diff --git a/src/autora/state/delta.py b/src/autora/state/delta.py new file mode 100644 index 00000000..9b8221a1 --- /dev/null +++ b/src/autora/state/delta.py @@ -0,0 +1,321 @@ +"""Classes to represent cycle state $S$ as $S_n = S_{0} + \\sum_{i=1}^n \\Delta S_{i}""" +from __future__ import annotations + +import dataclasses +import inspect +from collections import UserDict +from dataclasses import dataclass, fields, replace +from functools import singledispatch, wraps +from typing import Generic, List, TypeVar + +import numpy as np +import pandas as pd + +S = TypeVar("S") +T = TypeVar("T") + + +@dataclass(frozen=True) +class State: + """ + Base object for dataclasses which use the Delta mechanism. + + Examples: + >>> from dataclasses import dataclass, field + + We define a dataclass where each field (which is going to be delta-ed) has additional + metadata "delta" which describes its delta behaviour. + >>> @dataclass(frozen=True) + ... class ListState(State): + ... l: List = field(default_factory=list, metadata={"delta": "extend"}) + ... m: List = field(default_factory=list, metadata={"delta": "replace"}) + + Now we instantiate the dataclass... + >>> l = ListState(l=list("abc"), m=list("xyz")) + >>> l + ListState(l=['a', 'b', 'c'], m=['x', 'y', 'z']) + + ... and can add deltas to it. `l` will be extended: + >>> l + Delta(l=list("def")) + ListState(l=['a', 'b', 'c', 'd', 'e', 'f'], m=['x', 'y', 'z']) + + ... wheras `m` will be replaced: + >>> l + Delta(m=list("uvw")) + ListState(l=['a', 'b', 'c'], m=['u', 'v', 'w']) + + ... they can be chained: + >>> l + Delta(l=list("def")) + Delta(m=list("uvw")) + ListState(l=['a', 'b', 'c', 'd', 'e', 'f'], m=['u', 'v', 'w']) + + ... and we update multiple fields with one Delta: + >>> l + Delta(l=list("ghi"), m=list("rst")) + ListState(l=['a', 'b', 'c', 'g', 'h', 'i'], m=['r', 's', 't']) + + Passing a nonexistent field will cause an error: + >>> l + Delta(o="not a field") + Traceback (most recent call last): + ... + AttributeError: key=`o` is missing on ListState(l=['a', 'b', 'c'], m=['x', 'y', 'z']) + + We can also use the `.update` method to do the same thing: + >>> l.update(l=list("ghi"), m=list("rst")) + ListState(l=['a', 'b', 'c', 'g', 'h', 'i'], m=['r', 's', 't']) + + We can also define fields which `append` the last result: + >>> @dataclass(frozen=True) + ... class AppendState(State): + ... n: List = field(default_factory=list, metadata={"delta": "append"}) + + >>> m = AppendState(n=list("ɑβɣ")) + >>> m + AppendState(n=['ɑ', 'β', 'ɣ']) + + `n` will be appended: + >>> m + Delta(n="∂") + AppendState(n=['ɑ', 'β', 'ɣ', '∂']) + + """ + + def __add__(self, other: Delta): + updates = dict() + for key, other_value in other.data.items(): + try: + self_field = next(filter(lambda f: f.name == key, fields(self))) + except StopIteration: + raise AttributeError("key=`%s` is missing on %s" % (key, self)) + delta_behavior = self_field.metadata["delta"] + self_value = getattr(self, key) + if delta_behavior == "extend": + extended_value = extend(self_value, other_value) + updates[key] = extended_value + elif delta_behavior == "append": + appended_value = append(self_value, other_value) + updates[key] = appended_value + elif delta_behavior == "replace": + updates[key] = other_value + else: + raise NotImplementedError( + "delta_behaviour=`%s` not implemented" % (delta_behavior) + ) + + new = replace(self, **updates) + return new + + def update(self, **kwargs): + return self + Delta(**kwargs) + + +class Delta(UserDict, Generic[S]): + """ + Represents a delta where the base object determines the extension behavior. + + Examples: + >>> from dataclasses import dataclass + + First we define the dataclass to act as the basis: + >>> from typing import Optional, List + >>> @dataclass(frozen=True) + ... class ListState: + ... l: Optional[List] = None + ... m: Optional[List] = None + ... + """ + + pass + + +Result = Delta +"""`Result` is an alias for `Delta`.""" + + +@singledispatch +def extend(a, b): + """ + Function to extend supported datatypes. + + """ + raise NotImplementedError("`extend` not implemented for %s, %s" % (a, b)) + + +@extend.register(list) +def extend_list(a, b): + """ + Examples: + >>> extend([], []) + [] + + >>> extend([1,2], [3]) + [1, 2, 3] + """ + return a + b + + +@extend.register(pd.DataFrame) +def extend_pd_dataframe(a, b): + """ + Examples: + >>> extend(pd.DataFrame({"a": []}), pd.DataFrame({"a": []})) + Empty DataFrame + Columns: [a] + Index: [] + + >>> extend(pd.DataFrame({"a": [1,2,3]}), pd.DataFrame({"a": [4,5,6]})) + a + 0 1 + 1 2 + 2 3 + 3 4 + 4 5 + 5 6 + """ + return pd.concat((a, b), ignore_index=True) + + +def append(a: List[T], b: T) -> List[T]: + # TODO: add DOCTESTS + return a + [b] + + +@extend.register(np.ndarray) +def extend_np_ndarray(a, b): + """ + Examples: + >>> extend(np.array([(1,2,3), (4,5,6)]), np.array([(7,8,9)])) + array([[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]) + """ + return np.row_stack([a, b]) + + +@extend.register(dict) +def extend_dict(a, b): + """ + Examples: + >>> extend({"a": "cats"}, {"b": "dogs"}) + {'a': 'cats', 'b': 'dogs'} + """ + return dict(a, **b) + + +def wrap_to_use_state(f): + """Decorator to make target `f` into a function on a `State` and `**kwargs`. + + This wrapper makes it easier to pass arguments to a function from a State. + + It was inspired by the pytest "fixtures" mechanism. + + Args: + f: + + Returns: + + Examples: + >>> from autora.state.delta import State, Delta + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> from typing import List, Optional + + The `State` it operates on needs to have the metadata described in the state module: + >>> @dataclass(frozen=True) + ... class S(State): + ... conditions: List[int] = field(metadata={"delta": "replace"}) + + We indicate the inputs required by the parameter names. + The output must be a `Delta` object. + >>> from autora.state.delta import Delta + >>> @wrap_to_use_state + ... def experimentalist(conditions): + ... new_conditions = [c + 10 for c in conditions] + ... return Delta(conditions=new_conditions) + + >>> experimentalist(S(conditions=[1,2,3,4])) + S(conditions=[11, 12, 13, 14]) + + >>> experimentalist(S(conditions=[101,102,103,104])) + S(conditions=[111, 112, 113, 114]) + + >>> from autora.variable import VariableCollection, Variable + >>> from sklearn.base import BaseEstimator + >>> from sklearn.linear_model import LinearRegression + + >>> @wrap_to_use_state + ... def theorist(experimental_data: pd.DataFrame, variables: VariableCollection, **kwargs): + ... ivs = [v.name for v in variables.independent_variables] + ... dvs = [v.name for v in variables.dependent_variables] + ... X, y = experimental_data[ivs], experimental_data[dvs] + ... new_model = LinearRegression(fit_intercept=True).set_params(**kwargs).fit(X, y) + ... return Delta(model=new_model) + + >>> @dataclass(frozen=True) + ... class T(State): + ... variables: VariableCollection # field(metadata={"delta":... }) omitted ∴ immutable + ... experimental_data: pd.DataFrame = field(metadata={"delta": "extend"}) + ... model: Optional[BaseEstimator] = field(metadata={"delta": "replace"}, default=None) + + >>> t = T( + ... variables=VariableCollection(independent_variables=[Variable("x")], + ... dependent_variables=[Variable("y")]), + ... experimental_data=pd.DataFrame({"x": [0,1,2,3,4], "y": [2,3,4,5,6]}) + ... ) + >>> t_prime = theorist(t) + >>> t_prime.model.coef_, t_prime.model.intercept_ + (array([[1.]]), array([2.])) + + Arguments from the state can be overridden by passing them in as keyword arguments (kwargs): + >>> theorist(t, experimental_data=pd.DataFrame({"x": [0,1,2,3], "y": [12,13,14,15]}))\\ + ... .model.intercept_ + array([12.]) + + ... and other arguments supported by the inner function can also be passed + (if and only if the inner function allows for and handles `**kwargs` arguments alongside + the values from the state). + >>> theorist(t, fit_intercept=False).model.intercept_ + 0.0 + + Any parameters not provided by the state must be provided by default values or by the + caller. If the default is specified: + >>> @wrap_to_use_state + ... def experimentalist(conditions, offset=25): + ... new_conditions = [c + offset for c in conditions] + ... return Delta(conditions=new_conditions) + + ... then it need not be passed. + >>> experimentalist(S(conditions=[1,2,3,4])) + S(conditions=[26, 27, 28, 29]) + + If a default isn't specified: + >>> @wrap_to_use_state + ... def experimentalist(conditions, offset): + ... new_conditions = [c + offset for c in conditions] + ... return Delta(conditions=new_conditions) + + ... then calling the experimentalist without it will throw an error: + >>> experimentalist(S(conditions=[1,2,3,4])) + Traceback (most recent call last): + ... + TypeError: experimentalist() missing 1 required positional argument: 'offset' + + ... which can be fixed by passing the argument as a keyword to the wrapped function. + >>> experimentalist(S(conditions=[1,2,3,4]), offset=2) + S(conditions=[3, 4, 5, 6]) + + """ + # Get the set of parameter names from function f's signature + parameters_ = set(inspect.signature(f).parameters.keys()) + + @wraps(f) + def _f(state_: S, /, **kwargs) -> S: + # Get the parameters needed which are available from the state_. + # All others must be provided as kwargs or default values on f. + assert dataclasses.is_dataclass(state_) + from_state = parameters_.intersection( + {i.name for i in dataclasses.fields(state_)} + ) + arguments_from_state = {k: getattr(state_, k) for k in from_state} + arguments = dict(arguments_from_state, **kwargs) + delta = f(**arguments) + new_state = state_ + delta + return new_state + + return _f From aa676c40106f7bed440e3fa735b7d684f0b8919f Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 09:46:17 -0400 Subject: [PATCH 02/26] feat: add new grid_ module with experimentalist --- src/autora/experimentalist/grid_.py | 105 ++++++++++++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 src/autora/experimentalist/grid_.py diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py new file mode 100644 index 00000000..279ec5e8 --- /dev/null +++ b/src/autora/experimentalist/grid_.py @@ -0,0 +1,105 @@ +"""""" +from itertools import product +from typing import Sequence, Type + +import pandas as pd + +from autora.state.delta import Result, wrap_to_use_state +from autora.variable import Variable, VariableCollection + + +@wrap_to_use_state +def experimentalist(variables: VariableCollection, format: Type = pd.DataFrame): + """ + + Args: + variables: + format: the output type required + + Returns: + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=[1, 2, 3]) + ... ])) + + >>> experimentalist(s).conditions # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + x + 0 1 + 1 2 + 2 3 + + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ])) + >>> experimentalist(t).conditions + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4 + + >>> u = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + >>> experimentalist(u).conditions + x y z + 0 -10.0 3 20.0 + 1 -10.0 3 21.0 + 2 -10.0 3 22.0 + 3 -10.0 3 23.0 + 4 -10.0 3 24.0 + ... ... .. ... + 2217 10.0 4 26.0 + 2218 10.0 4 27.0 + 2219 10.0 4 28.0 + 2220 10.0 4 29.0 + 2221 10.0 4 30.0 + + [2222 rows x 3 columns] + + """ + raw_conditions = grid_pool(variables.independent_variables) + if format is pd.DataFrame: + iv_names = [v.name for v in variables.independent_variables] + conditions = format(raw_conditions, columns=iv_names) + + else: + raise NotImplementedError + + return Result(conditions=conditions) + + +def grid_pool(ivs: Sequence[Variable]): + """ + Pipeline function to create an exhaustive pool from discrete values + using a Cartesian product of sets. + """ + # Get allowed values for each IV + l_iv_values = [] + for iv in ivs: + assert iv.allowed_values is not None, ( + f"grid_pool only supports independent variables with discrete allowed values, " + f"but allowed_values is None on {iv=} " + ) + l_iv_values.append(iv.allowed_values) + + # Return Cartesian product of all IV values + return product(*l_iv_values) From 2172730321af65acc089897ca448def07cc57195 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 09:46:35 -0400 Subject: [PATCH 03/26] chore: add deprecation info to old grid_pool module --- src/autora/experimentalist/pooler/grid.py | 27 +++++++++-------------- 1 file changed, 11 insertions(+), 16 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index dadc2a4a..5e1a888d 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -1,19 +1,14 @@ -from itertools import product -from typing import List +import logging -from autora.variable import IV +from autora.experimentalist.grid_ import grid_pool as grid_pool_ +from autora.utils.deprecation import deprecate +_logger = logging.getLogger(__name__) -def grid_pool(ivs: List[IV]): - """Creates exhaustive pool from discrete values using a Cartesian product of sets""" - # Get allowed values for each IV - l_iv_values = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"gridsearch_pool only supports independent variables with discrete allowed values, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - - # Return Cartesian product of all IV values - return product(*l_iv_values) +grid_pool = deprecate( + grid_pool_, + "`from autora.experimentalist.pooler.grid import grid_pool` " + "will be deprecated in future. Instead, use" + "`from autora.experimentalist.grid import grid_pool`", + callback=_logger.info, +) From 802e7f537195d2c5241eee390434cab9f17fee64 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 09:47:05 -0400 Subject: [PATCH 04/26] docs: update import location --- docs/experimentalists/pooler/grid/index.md | 4 +++- docs/experimentalists/pooler/grid/quickstart.md | 3 ++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/docs/experimentalists/pooler/grid/index.md b/docs/experimentalists/pooler/grid/index.md index 97b314aa..da34e0a6 100644 --- a/docs/experimentalists/pooler/grid/index.md +++ b/docs/experimentalists/pooler/grid/index.md @@ -22,8 +22,10 @@ This means that there are various combinations that these variables can form, th ### Example Code + ```python -from autora.experimentalist.pooler.grid import grid_pool + +from autora.experimentalist.grid_ import grid_pool from autora.variable import Variable iv_1 = Variable(allowed_values=[1, 2, 3]) diff --git a/docs/experimentalists/pooler/grid/quickstart.md b/docs/experimentalists/pooler/grid/quickstart.md index 740bb904..2108c72c 100644 --- a/docs/experimentalists/pooler/grid/quickstart.md +++ b/docs/experimentalists/pooler/grid/quickstart.md @@ -10,5 +10,6 @@ You will need: you can import the grid pooler via: ```python -from autora.experimentalist.pooler.grid import grid_pool + +from autora.experimentalist.grid_ import grid_pool ``` From cfe1da006e3a134056bf7396fb85026b0e399c36 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 10:58:50 -0400 Subject: [PATCH 05/26] docs: update format support for recarrays and np arrays --- src/autora/experimentalist/grid_.py | 64 +++++++++++++++++++++++++---- 1 file changed, 56 insertions(+), 8 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 279ec5e8..94033c2c 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -2,6 +2,7 @@ from itertools import product from typing import Sequence, Type +import numpy as np import pandas as pd from autora.state.delta import Result, wrap_to_use_state @@ -9,12 +10,12 @@ @wrap_to_use_state -def experimentalist(variables: VariableCollection, format: Type = pd.DataFrame): +def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): """ Args: variables: - format: the output type required + fmt: the output type required Returns: @@ -24,23 +25,35 @@ def experimentalist(variables: VariableCollection, format: Type = pd.DataFrame): >>> from dataclasses import dataclass, field >>> import pandas as pd >>> import numpy as np + + We define a state object with the fields we need: >>> @dataclass(frozen=True) ... class S(State): ... variables: VariableCollection = field(default_factory=VariableCollection) ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, ... metadata={"delta": "replace"}) + With one independent variable "x", and some allowed values: >>> s = S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x", allowed_values=[1, 2, 3]) ... ])) - >>> experimentalist(s).conditions # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ... we get exactly those values back when running the experimentalist: + >>> experimentalist(s).conditions x 0 1 1 2 2 3 + The allowed_values must be specified: + >>> experimentalist( + ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + With two independent variables, we get the cartesian product: >>> t = S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), @@ -53,6 +66,18 @@ def experimentalist(variables: VariableCollection, format: Type = pd.DataFrame): 2 2 3 3 2 4 + If any of the variables have unspecified allowed_values, we get an error: + >>> experimentalist(S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + + We can specify arrays of allowed values: >>> u = S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), @@ -75,14 +100,37 @@ def experimentalist(variables: VariableCollection, format: Type = pd.DataFrame): [2222 rows x 3 columns] + The output can be in several formats. The default is pd.DataFrame. + Alternative: `np.recarray`: + >>> experimentalist(s, fmt=np.recarray).conditions + rec.array([(1,), (2,), (3,)], + dtype=[('x', '>> experimentalist(t, fmt=np.recarray).conditions + rec.array([(1, 3), (1, 4), (2, 3), (2, 4)], + dtype=[('x1', '>> experimentalist(t, fmt=np.array).conditions + array([[1, 3], + [1, 4], + [2, 3], + [2, 4]]) + """ raw_conditions = grid_pool(variables.independent_variables) - if format is pd.DataFrame: - iv_names = [v.name for v in variables.independent_variables] - conditions = format(raw_conditions, columns=iv_names) + iv_names = [v.name for v in variables.independent_variables] + if fmt is pd.DataFrame: + conditions = pd.DataFrame(raw_conditions, columns=iv_names) + elif fmt is np.recarray: + conditions = np.core.records.fromrecords( + list(raw_conditions), names=iv_names + ) # type: ignore + elif fmt is np.array: + conditions = np.array(list(raw_conditions)) else: - raise NotImplementedError + raise NotImplementedError("fmt=%s is not supported" % (fmt)) return Result(conditions=conditions) @@ -96,7 +144,7 @@ def grid_pool(ivs: Sequence[Variable]): l_iv_values = [] for iv in ivs: assert iv.allowed_values is not None, ( - f"grid_pool only supports independent variables with discrete allowed values, " + f"grid_pool requires allowed_values to be set, " f"but allowed_values is None on {iv=} " ) l_iv_values.append(iv.allowed_values) From fc674c8ba41a27189db07478a01b232a2659b61c Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 11:08:13 -0400 Subject: [PATCH 06/26] docs: update path to grid_pool module --- tests/test_experimentalist_random.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_experimentalist_random.py b/tests/test_experimentalist_random.py index 0cac76e9..725f3a1c 100644 --- a/tests/test_experimentalist_random.py +++ b/tests/test_experimentalist_random.py @@ -3,8 +3,8 @@ import numpy as np import pytest +from autora.experimentalist.grid_ import grid_pool from autora.experimentalist.pipeline import make_pipeline -from autora.experimentalist.pooler.grid import grid_pool from autora.experimentalist.sampler.random_sampler import random_sample from autora.variable import DV, IV, ValueType, VariableCollection From b120ba074312a4cd8e7a87337f144e6b041f477f Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 11:54:25 -0400 Subject: [PATCH 07/26] test: update doctests to work on 32 and 64 bit platforms --- src/autora/experimentalist/grid_.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py index 94033c2c..d197e72e 100644 --- a/src/autora/experimentalist/grid_.py +++ b/src/autora/experimentalist/grid_.py @@ -104,11 +104,11 @@ def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): Alternative: `np.recarray`: >>> experimentalist(s, fmt=np.recarray).conditions rec.array([(1,), (2,), (3,)], - dtype=[('x', '>> experimentalist(t, fmt=np.recarray).conditions rec.array([(1, 3), (1, 4), (2, 3), (2, 4)], - dtype=[('x1', '>> experimentalist(t, fmt=np.array).conditions From 562102e14ffc95c8d331a41fca3b384f08779a41 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 15:16:40 -0400 Subject: [PATCH 08/26] =?UTF-8?q?revert:=20move=20grid=5F=20back=20to=20or?= =?UTF-8?q?iginal=20location=20in=20pooler=20=E2=80=93=20no=20need=20to=20?= =?UTF-8?q?make=20two=20changes=20in=20this=20one=20PR?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/experimentalists/pooler/grid/index.md | 2 +- .../pooler/grid/quickstart.md | 2 +- src/autora/experimentalist/grid_.py | 153 ----------------- src/autora/experimentalist/pooler/grid.py | 161 ++++++++++++++++-- tests/test_experimentalist_random.py | 1 - 5 files changed, 152 insertions(+), 167 deletions(-) delete mode 100644 src/autora/experimentalist/grid_.py diff --git a/docs/experimentalists/pooler/grid/index.md b/docs/experimentalists/pooler/grid/index.md index da34e0a6..faa1ea56 100644 --- a/docs/experimentalists/pooler/grid/index.md +++ b/docs/experimentalists/pooler/grid/index.md @@ -25,7 +25,7 @@ This means that there are various combinations that these variables can form, th ```python -from autora.experimentalist.grid_ import grid_pool +from autora.experimentalist.pooler.grid import grid_pool from autora.variable import Variable iv_1 = Variable(allowed_values=[1, 2, 3]) diff --git a/docs/experimentalists/pooler/grid/quickstart.md b/docs/experimentalists/pooler/grid/quickstart.md index 2108c72c..6c171a9d 100644 --- a/docs/experimentalists/pooler/grid/quickstart.md +++ b/docs/experimentalists/pooler/grid/quickstart.md @@ -11,5 +11,5 @@ you can import the grid pooler via: ```python -from autora.experimentalist.grid_ import grid_pool +from autora.experimentalist.pooler.grid import grid_pool ``` diff --git a/src/autora/experimentalist/grid_.py b/src/autora/experimentalist/grid_.py deleted file mode 100644 index d197e72e..00000000 --- a/src/autora/experimentalist/grid_.py +++ /dev/null @@ -1,153 +0,0 @@ -"""""" -from itertools import product -from typing import Sequence, Type - -import numpy as np -import pandas as pd - -from autora.state.delta import Result, wrap_to_use_state -from autora.variable import Variable, VariableCollection - - -@wrap_to_use_state -def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): - """ - - Args: - variables: - fmt: the output type required - - Returns: - - Examples: - >>> from autora.state.delta import State - >>> from autora.variable import VariableCollection, Variable - >>> from dataclasses import dataclass, field - >>> import pandas as pd - >>> import numpy as np - - We define a state object with the fields we need: - >>> @dataclass(frozen=True) - ... class S(State): - ... variables: VariableCollection = field(default_factory=VariableCollection) - ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, - ... metadata={"delta": "replace"}) - - With one independent variable "x", and some allowed values: - >>> s = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=[1, 2, 3]) - ... ])) - - ... we get exactly those values back when running the experimentalist: - >>> experimentalist(s).conditions - x - 0 1 - 1 2 - 2 3 - - The allowed_values must be specified: - >>> experimentalist( - ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) - Traceback (most recent call last): - ... - AssertionError: grid_pool requires allowed_values to be set... - - With two independent variables, we get the cartesian product: - >>> t = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2", allowed_values=[3, 4]), - ... ])) - >>> experimentalist(t).conditions - x1 x2 - 0 1 3 - 1 1 4 - 2 2 3 - 3 2 4 - - If any of the variables have unspecified allowed_values, we get an error: - >>> experimentalist(S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x1", allowed_values=[1, 2]), - ... Variable(name="x2"), - ... ]))) - Traceback (most recent call last): - ... - AssertionError: grid_pool requires allowed_values to be set... - - - We can specify arrays of allowed values: - >>> u = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), - ... Variable(name="y", allowed_values=[3, 4]), - ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ])) - >>> experimentalist(u).conditions - x y z - 0 -10.0 3 20.0 - 1 -10.0 3 21.0 - 2 -10.0 3 22.0 - 3 -10.0 3 23.0 - 4 -10.0 3 24.0 - ... ... .. ... - 2217 10.0 4 26.0 - 2218 10.0 4 27.0 - 2219 10.0 4 28.0 - 2220 10.0 4 29.0 - 2221 10.0 4 30.0 - - [2222 rows x 3 columns] - - The output can be in several formats. The default is pd.DataFrame. - Alternative: `np.recarray`: - >>> experimentalist(s, fmt=np.recarray).conditions - rec.array([(1,), (2,), (3,)], - dtype=[('x', '>> experimentalist(t, fmt=np.recarray).conditions - rec.array([(1, 3), (1, 4), (2, 3), (2, 4)], - dtype=[('x1', '>> experimentalist(t, fmt=np.array).conditions - array([[1, 3], - [1, 4], - [2, 3], - [2, 4]]) - - """ - raw_conditions = grid_pool(variables.independent_variables) - - iv_names = [v.name for v in variables.independent_variables] - if fmt is pd.DataFrame: - conditions = pd.DataFrame(raw_conditions, columns=iv_names) - elif fmt is np.recarray: - conditions = np.core.records.fromrecords( - list(raw_conditions), names=iv_names - ) # type: ignore - elif fmt is np.array: - conditions = np.array(list(raw_conditions)) - else: - raise NotImplementedError("fmt=%s is not supported" % (fmt)) - - return Result(conditions=conditions) - - -def grid_pool(ivs: Sequence[Variable]): - """ - Pipeline function to create an exhaustive pool from discrete values - using a Cartesian product of sets. - """ - # Get allowed values for each IV - l_iv_values = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"grid_pool requires allowed_values to be set, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - - # Return Cartesian product of all IV values - return product(*l_iv_values) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index 5e1a888d..d197e72e 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -1,14 +1,153 @@ -import logging +"""""" +from itertools import product +from typing import Sequence, Type -from autora.experimentalist.grid_ import grid_pool as grid_pool_ -from autora.utils.deprecation import deprecate +import numpy as np +import pandas as pd -_logger = logging.getLogger(__name__) +from autora.state.delta import Result, wrap_to_use_state +from autora.variable import Variable, VariableCollection -grid_pool = deprecate( - grid_pool_, - "`from autora.experimentalist.pooler.grid import grid_pool` " - "will be deprecated in future. Instead, use" - "`from autora.experimentalist.grid import grid_pool`", - callback=_logger.info, -) + +@wrap_to_use_state +def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): + """ + + Args: + variables: + fmt: the output type required + + Returns: + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=[1, 2, 3]) + ... ])) + + ... we get exactly those values back when running the experimentalist: + >>> experimentalist(s).conditions + x + 0 1 + 1 2 + 2 3 + + The allowed_values must be specified: + >>> experimentalist( + ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + With two independent variables, we get the cartesian product: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ])) + >>> experimentalist(t).conditions + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4 + + If any of the variables have unspecified allowed_values, we get an error: + >>> experimentalist(S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + + We can specify arrays of allowed values: + >>> u = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + >>> experimentalist(u).conditions + x y z + 0 -10.0 3 20.0 + 1 -10.0 3 21.0 + 2 -10.0 3 22.0 + 3 -10.0 3 23.0 + 4 -10.0 3 24.0 + ... ... .. ... + 2217 10.0 4 26.0 + 2218 10.0 4 27.0 + 2219 10.0 4 28.0 + 2220 10.0 4 29.0 + 2221 10.0 4 30.0 + + [2222 rows x 3 columns] + + The output can be in several formats. The default is pd.DataFrame. + Alternative: `np.recarray`: + >>> experimentalist(s, fmt=np.recarray).conditions + rec.array([(1,), (2,), (3,)], + dtype=[('x', '>> experimentalist(t, fmt=np.recarray).conditions + rec.array([(1, 3), (1, 4), (2, 3), (2, 4)], + dtype=[('x1', '>> experimentalist(t, fmt=np.array).conditions + array([[1, 3], + [1, 4], + [2, 3], + [2, 4]]) + + """ + raw_conditions = grid_pool(variables.independent_variables) + + iv_names = [v.name for v in variables.independent_variables] + if fmt is pd.DataFrame: + conditions = pd.DataFrame(raw_conditions, columns=iv_names) + elif fmt is np.recarray: + conditions = np.core.records.fromrecords( + list(raw_conditions), names=iv_names + ) # type: ignore + elif fmt is np.array: + conditions = np.array(list(raw_conditions)) + else: + raise NotImplementedError("fmt=%s is not supported" % (fmt)) + + return Result(conditions=conditions) + + +def grid_pool(ivs: Sequence[Variable]): + """ + Pipeline function to create an exhaustive pool from discrete values + using a Cartesian product of sets. + """ + # Get allowed values for each IV + l_iv_values = [] + for iv in ivs: + assert iv.allowed_values is not None, ( + f"grid_pool requires allowed_values to be set, " + f"but allowed_values is None on {iv=} " + ) + l_iv_values.append(iv.allowed_values) + + # Return Cartesian product of all IV values + return product(*l_iv_values) diff --git a/tests/test_experimentalist_random.py b/tests/test_experimentalist_random.py index 9fed0d4e..a81ad483 100644 --- a/tests/test_experimentalist_random.py +++ b/tests/test_experimentalist_random.py @@ -3,7 +3,6 @@ import numpy as np import pytest -from autora.experimentalist.grid_ import grid_pool from autora.experimentalist.pipeline import make_pipeline from autora.experimentalist.pooler.grid import grid_pool from autora.experimentalist.pooler.random_pooler import random_pool From 7f596594e0af39e2bd110f6a66d7f882512a60b1 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 15:17:43 -0400 Subject: [PATCH 09/26] revert: doc changes --- docs/experimentalists/pooler/grid/index.md | 2 -- docs/experimentalists/pooler/grid/quickstart.md | 1 - 2 files changed, 3 deletions(-) diff --git a/docs/experimentalists/pooler/grid/index.md b/docs/experimentalists/pooler/grid/index.md index faa1ea56..97b314aa 100644 --- a/docs/experimentalists/pooler/grid/index.md +++ b/docs/experimentalists/pooler/grid/index.md @@ -22,9 +22,7 @@ This means that there are various combinations that these variables can form, th ### Example Code - ```python - from autora.experimentalist.pooler.grid import grid_pool from autora.variable import Variable diff --git a/docs/experimentalists/pooler/grid/quickstart.md b/docs/experimentalists/pooler/grid/quickstart.md index 6c171a9d..740bb904 100644 --- a/docs/experimentalists/pooler/grid/quickstart.md +++ b/docs/experimentalists/pooler/grid/quickstart.md @@ -10,6 +10,5 @@ You will need: you can import the grid pooler via: ```python - from autora.experimentalist.pooler.grid import grid_pool ``` From 1067933fa07e47d3fb402f8bd30da5dd467f06df Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 15:58:04 -0400 Subject: [PATCH 10/26] feat: add support for pd.dataframe and np.arrays on random_sampler --- .../experimentalist/sampler/random_sampler.py | 106 +++++++++++++++++- 1 file changed, 103 insertions(+), 3 deletions(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 7e28d2c3..dba879d7 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,20 +1,55 @@ import random -from typing import Iterable, Sequence, Union +from functools import singledispatch +from typing import Iterable, Optional + +import numpy as np +import pandas as pd from autora.utils.deprecation import deprecated_alias -def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): +@singledispatch +def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] = None): """ Uniform random sampling without replacement from a pool of conditions. Args: conditions: Pool of conditions - n: number of samples to collect + num_samples: number of samples to collect Returns: Sampled pool + Examples: + """ + raise NotImplementedError("%s is not supported" % (type(conditions))) + + +@random_sample.register(list) +@random_sample.register(range) +@random_sample.register(filter) +def random_sample_sequence( + conditions, num_samples: int = 1, random_state: Optional[int] = None +): + """ + Examples: + From a range: + >>> random.seed(1) + >>> random_sample(range(100), num_samples=5) + [53, 37, 65, 51, 4] + + >>> random.seed(1) + >>> random_sample([1,2,3,4,5,6,7,8,9,10], num_samples=5) + [7, 9, 10, 8, 6] + + >>> random.seed(1) + >>> random_sample(filter(lambda x: (x % 3 == 0) & (x % 5 == 0), range(1_000)), + ... num_samples=5) + [375, 390, 600, 285, 885] + + """ + if random_state is not None: + random.seed(random_state) if isinstance(conditions, Iterable): conditions = list(conditions) random.shuffle(conditions) @@ -23,4 +58,69 @@ def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): return samples +@random_sample.register(pd.DataFrame) +def random_sample_dataframe( + conditions, num_samples: int = 1, random_state: Optional[int] = None +): + """ + + Args: + conditions: + num_samples: + + Returns: + + Examples: + From a pd.DataFrame: + >>> import pandas as pd + >>> random.seed(1) + >>> random_sample(pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) + x + 67 167 + 71 171 + 64 164 + 63 163 + 96 196 + + """ + return pd.DataFrame.sample(conditions, random_state=random_state, n=num_samples) + + +@random_sample.register(np.ndarray) +@random_sample.register(np.recarray) +def random_sample_np_array( + conditions, num_samples: int = 1, random_state: Optional[int] = None +): + """ + + Args: + conditions: + num_samples: + + Returns: + + Examples: + From a pd.DataFrame: + >>> import numpy as np + >>> random_sample(np.linspace([-1, -100], [1, 100], 101), num_samples=5, + ... random_state=180) + array([[ -0.52, -52. ], + [ 0.16, 16. ], + [ 0.14, 14. ], + [ 0.34, 34. ], + [ -0.18, -18. ]]) + + >>> random_sample(np.core.records.fromrecords([ + ... ("a", 1, "alpha"), + ... ("b", 2, "beta"), + ... ("c", 3, "gamma"), + ... ("d", 4, "delta"), + ... ], names=["l", "n", "g"]), num_samples=2, random_state=1) + array([('b', 2, 'beta'), ('c', 3, 'gamma')], + dtype=(numpy.record, [('l', ' Date: Fri, 7 Jul 2023 16:02:37 -0400 Subject: [PATCH 11/26] docs: update docstrings --- .../experimentalist/sampler/random_sampler.py | 36 ++++++++----------- 1 file changed, 15 insertions(+), 21 deletions(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index dba879d7..a23fbf68 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -12,14 +12,14 @@ def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] = None): """ Uniform random sampling without replacement from a pool of conditions. + Args: conditions: Pool of conditions num_samples: number of samples to collect + random_state: initialization parameter for the random sampler Returns: Sampled pool - Examples: - """ raise NotImplementedError("%s is not supported" % (type(conditions))) @@ -32,6 +32,8 @@ def random_sample_sequence( conditions, num_samples: int = 1, random_state: Optional[int] = None ): """ + Single dispatch variant of random_sample for list, range and filter objects + Examples: From a range: >>> random.seed(1) @@ -63,12 +65,7 @@ def random_sample_dataframe( conditions, num_samples: int = 1, random_state: Optional[int] = None ): """ - - Args: - conditions: - num_samples: - - Returns: + Single dispatch variant of random_sample for pd.DataFrames Examples: From a pd.DataFrame: @@ -83,7 +80,9 @@ def random_sample_dataframe( 96 196 """ - return pd.DataFrame.sample(conditions, random_state=random_state, n=num_samples) + return pd.DataFrame.sample( + conditions, random_state=random_state, n=num_samples, replace=False + ) @random_sample.register(np.ndarray) @@ -92,23 +91,18 @@ def random_sample_np_array( conditions, num_samples: int = 1, random_state: Optional[int] = None ): """ - - Args: - conditions: - num_samples: - - Returns: + Single dispatch variant of random_sample for np.ndarray and np.recarray Examples: From a pd.DataFrame: >>> import numpy as np >>> random_sample(np.linspace([-1, -100], [1, 100], 101), num_samples=5, ... random_state=180) - array([[ -0.52, -52. ], - [ 0.16, 16. ], - [ 0.14, 14. ], - [ 0.34, 34. ], - [ -0.18, -18. ]]) + array([[ 0.12, 12. ], + [ -0.18, -18. ], + [ -0.54, -54. ], + [ 0.32, 32. ], + [ 0.96, 96. ]]) >>> random_sample(np.core.records.fromrecords([ ... ("a", 1, "alpha"), @@ -120,7 +114,7 @@ def random_sample_np_array( dtype=(numpy.record, [('l', ' Date: Fri, 7 Jul 2023 16:06:48 -0400 Subject: [PATCH 12/26] test: update test to work on 32 and 64 bit platforms --- src/autora/experimentalist/sampler/random_sampler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index a23fbf68..573c7076 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -111,7 +111,7 @@ def random_sample_np_array( ... ("d", 4, "delta"), ... ], names=["l", "n", "g"]), num_samples=2, random_state=1) array([('b', 2, 'beta'), ('c', 3, 'gamma')], - dtype=(numpy.record, [('l', ' Date: Fri, 7 Jul 2023 16:42:59 -0400 Subject: [PATCH 13/26] feat: add random experimentalist --- .../experimentalist/pooler/random_pooler.py | 190 +++++++++++++++++- 1 file changed, 188 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 78ad104e..8c87959d 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,10 +1,196 @@ import random -from typing import Iterable, List, Tuple +from itertools import product +from typing import Iterable, List, Sequence, Tuple, Type import numpy as np +import pandas as pd +from autora.state.delta import Result, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -from autora.variable import IV +from autora.variable import IV, ValueType, Variable, VariableCollection + + +@wrap_to_use_state +def experimentalist( + variables: VariableCollection, + num_samples=5, + random_state=None, + fmt: Type = pd.DataFrame, + duplicates: bool = True, +): + """ + + Args: + variables: + fmt: the output type required + + Returns: + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed_values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=range(10)) + ... ])) + + ... we get some of those values back when running the experimentalist: + >>> experimentalist(s, random_state=1).conditions + x + 0 4 + 1 5 + 2 7 + 3 9 + 4 0 + + With one independent variable "x", and a value_range: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", value_range=(-5, 5)) + ... ])) + + ... we get a sample of the range back when running the experimentalist: + >>> experimentalist(t, random_state=1).conditions + x + 0 0.118216 + 1 4.504637 + 2 -3.558404 + 3 4.486494 + 4 -1.881685 + + + + The allowed_values or value_range must be specified: + >>> experimentalist( + ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + With two independent variables, we get independent samples on both axes: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=range(1, 5)), + ... Variable(name="x2", allowed_values=range(1, 500)), + ... ])) + >>> experimentalist(t, num_samples=10, duplicates=True, random_state=1).conditions + x1 x2 + 0 2 434 + 1 3 212 + 2 4 137 + 3 4 414 + 4 1 129 + 5 1 205 + 6 4 322 + 7 4 275 + 8 1 43 + 9 2 14 + + If any of the variables have unspecified allowed_values, we get an error: + >>> experimentalist(S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ]))) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + + We can specify arrays of allowed values: + >>> u = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + >>> experimentalist(u, random_state=1).conditions + x y z + 0 -0.6 3 29.0 + 1 0.2 4 24.0 + 2 5.2 4 23.0 + 3 9.0 3 29.0 + 4 -9.4 3 22.0 + + The output can be in several formats. The default is pd.DataFrame. + Alternative: `np.recarray`: + >>> experimentalist(s, fmt=np.recarray, random_state=1).conditions + rec.array([(4,), (5,), (7,), (9,), (0,)], + dtype=[('x', '>> experimentalist(t, fmt=np.recarray, random_state=1).conditions + rec.array([(2, 72), (3, 411), (4, 474), (4, 125), (1, 156)], + dtype=[('x1', '>> experimentalist(t, fmt=np.array, random_state=1).conditions + array([[ 2, 72], + [ 3, 411], + [ 4, 474], + [ 4, 125], + [ 1, 156]]) + + """ + rng = np.random.default_rng(random_state) + + raw_conditions = {} + for iv in variables.independent_variables: + if iv.allowed_values is not None: + raw_conditions[iv.name] = rng.choice( + iv.allowed_values, size=num_samples, replace=duplicates + ) + elif (iv.value_range is not None) and (iv.type == ValueType.REAL): + raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) + + else: + raise ValueError( + "allowed_values or [value_range and type==REAL] needs to be set for " + "%s" % (iv) + ) + + iv_names = [v.name for v in variables.independent_variables] + if fmt is pd.DataFrame: + conditions = pd.DataFrame(raw_conditions) + elif fmt is np.recarray: + conditions = np.core.records.fromarrays( + [raw_conditions[n] for n in iv_names], names=iv_names + ) # type: ignore + elif fmt is np.array: + conditions = np.column_stack([raw_conditions[n] for n in iv_names]) + else: + raise NotImplementedError("fmt=%s is not supported" % (fmt)) + + return Result(conditions=conditions) + + +def grid_pool(ivs: Sequence[Variable]): + """ + Pipeline function to create an exhaustive pool from discrete values + using a Cartesian product of sets. + """ + # Get allowed values for each IV + l_iv_values = [] + for iv in ivs: + assert iv.allowed_values is not None, ( + f"grid_pool requires allowed_values to be set, " + f"but allowed_values is None on {iv=} " + ) + l_iv_values.append(iv.allowed_values) + + # Return Cartesian product of all IV values + return product(*l_iv_values) def random_pool( From 8518953b5dcd488396a4b6b08417ee1246bc9a11 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Jul 2023 16:48:01 -0400 Subject: [PATCH 14/26] chore: rename experimentalist to random_experimentalist --- .../experimentalist/pooler/random_pooler.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 8c87959d..b5bed2a7 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -11,7 +11,7 @@ @wrap_to_use_state -def experimentalist( +def random_experimentalist( variables: VariableCollection, num_samples=5, random_state=None, @@ -47,7 +47,7 @@ def experimentalist( ... ])) ... we get some of those values back when running the experimentalist: - >>> experimentalist(s, random_state=1).conditions + >>> random_experimentalist(s, random_state=1).conditions x 0 4 1 5 @@ -62,7 +62,7 @@ def experimentalist( ... ])) ... we get a sample of the range back when running the experimentalist: - >>> experimentalist(t, random_state=1).conditions + >>> random_experimentalist(t, random_state=1).conditions x 0 0.118216 1 4.504637 @@ -73,7 +73,7 @@ def experimentalist( The allowed_values or value_range must be specified: - >>> experimentalist( + >>> random_experimentalist( ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... @@ -85,7 +85,7 @@ def experimentalist( ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), ... ])) - >>> experimentalist(t, num_samples=10, duplicates=True, random_state=1).conditions + >>> random_experimentalist(t, num_samples=10, duplicates=True, random_state=1).conditions x1 x2 0 2 434 1 3 212 @@ -99,7 +99,7 @@ def experimentalist( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> experimentalist(S( + >>> random_experimentalist(S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -116,7 +116,7 @@ def experimentalist( ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), ... ])) - >>> experimentalist(u, random_state=1).conditions + >>> random_experimentalist(u, random_state=1).conditions x y z 0 -0.6 3 29.0 1 0.2 4 24.0 @@ -126,16 +126,16 @@ def experimentalist( The output can be in several formats. The default is pd.DataFrame. Alternative: `np.recarray`: - >>> experimentalist(s, fmt=np.recarray, random_state=1).conditions + >>> random_experimentalist(s, fmt=np.recarray, random_state=1).conditions rec.array([(4,), (5,), (7,), (9,), (0,)], dtype=[('x', '>> experimentalist(t, fmt=np.recarray, random_state=1).conditions + >>> random_experimentalist(t, fmt=np.recarray, random_state=1).conditions rec.array([(2, 72), (3, 411), (4, 474), (4, 125), (1, 156)], dtype=[('x1', '>> experimentalist(t, fmt=np.array, random_state=1).conditions + >>> random_experimentalist(t, fmt=np.array, random_state=1).conditions array([[ 2, 72], [ 3, 411], [ 4, 474], From 34c2da7551ff6aa5d35136efe0d4fcde1a431226 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 12:29:49 -0400 Subject: [PATCH 15/26] refactor: update docstrings and file ordering --- src/autora/experimentalist/pooler/grid.py | 244 +++++++++++++++------- 1 file changed, 164 insertions(+), 80 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index d197e72e..b9e5a868 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -1,23 +1,38 @@ """""" from itertools import product -from typing import Sequence, Type +from typing import Sequence -import numpy as np import pandas as pd from autora.state.delta import Result, wrap_to_use_state from autora.variable import Variable, VariableCollection -@wrap_to_use_state -def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): +def grid_pool(ivs: Sequence[Variable]) -> product: + """ + Low level function to create an exhaustive pool from discrete values + using a Cartesian product of sets. + """ + # Get allowed values for each IV + l_iv_values = [] + for iv in ivs: + assert iv.allowed_values is not None, ( + f"grid_pool requires allowed_values to be set, " + f"but allowed_values is None on {iv=} " + ) + l_iv_values.append(iv.allowed_values) + + # Return Cartesian product of all IV values + return product(*l_iv_values) + + +def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: """ Args: - variables: - fmt: the output type required + variables: the description of all the variables in the AER experiment. - Returns: + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field Examples: >>> from autora.state.delta import State @@ -26,40 +41,28 @@ def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): >>> import pandas as pd >>> import numpy as np - We define a state object with the fields we need: - >>> @dataclass(frozen=True) - ... class S(State): - ... variables: VariableCollection = field(default_factory=VariableCollection) - ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, - ... metadata={"delta": "replace"}) - - With one independent variable "x", and some allowed values: - >>> s = S( - ... variables=VariableCollection(independent_variables=[ - ... Variable(name="x", allowed_values=[1, 2, 3]) - ... ])) - - ... we get exactly those values back when running the experimentalist: - >>> experimentalist(s).conditions - x + With one independent variable "x", and some allowed values, we get exactly those values + back when running the executor: + >>> grid_pool_from_variables(variables=VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=[1, 2, 3])] + ... )) + {'conditions': x 0 1 1 2 - 2 3 + 2 3} The allowed_values must be specified: - >>> experimentalist( - ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + >>> grid_pool_from_variables( + ... variables=VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... AssertionError: grid_pool requires allowed_values to be set... With two independent variables, we get the cartesian product: - >>> t = S( - ... variables=VariableCollection(independent_variables=[ + >>> grid_pool_from_variables(variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2", allowed_values=[3, 4]), - ... ])) - >>> experimentalist(t).conditions + ... ]))["conditions"] x1 x2 0 1 3 1 1 4 @@ -67,24 +70,23 @@ def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): 3 2 4 If any of the variables have unspecified allowed_values, we get an error: - >>> experimentalist(S( + >>> grid_pool_from_variables( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), - ... ]))) + ... ])) Traceback (most recent call last): ... AssertionError: grid_pool requires allowed_values to be set... We can specify arrays of allowed values: - >>> u = S( + >>> grid_pool_from_variables( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), - ... ])) - >>> experimentalist(u).conditions + ... ]))["conditions"] x y z 0 -10.0 3 20.0 1 -10.0 3 21.0 @@ -100,54 +102,136 @@ def experimentalist(variables: VariableCollection, fmt: Type = pd.DataFrame): [2222 rows x 3 columns] - The output can be in several formats. The default is pd.DataFrame. - Alternative: `np.recarray`: - >>> experimentalist(s, fmt=np.recarray).conditions - rec.array([(1,), (2,), (3,)], - dtype=[('x', '>> experimentalist(t, fmt=np.recarray).conditions - rec.array([(1, 3), (1, 4), (2, 3), (2, 4)], - dtype=[('x1', '>> experimentalist(t, fmt=np.array).conditions - array([[1, 3], - [1, 4], - [2, 3], - [2, 4]]) - """ raw_conditions = grid_pool(variables.independent_variables) - iv_names = [v.name for v in variables.independent_variables] - if fmt is pd.DataFrame: - conditions = pd.DataFrame(raw_conditions, columns=iv_names) - elif fmt is np.recarray: - conditions = np.core.records.fromrecords( - list(raw_conditions), names=iv_names - ) # type: ignore - elif fmt is np.array: - conditions = np.array(list(raw_conditions)) - else: - raise NotImplementedError("fmt=%s is not supported" % (fmt)) - + conditions = pd.DataFrame(raw_conditions, columns=iv_names) return Result(conditions=conditions) -def grid_pool(ivs: Sequence[Variable]): - """ - Pipeline function to create an exhaustive pool from discrete values - using a Cartesian product of sets. - """ - # Get allowed values for each IV - l_iv_values = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"grid_pool requires allowed_values to be set, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - - # Return Cartesian product of all IV values - return product(*l_iv_values) +grid_pool_executor = wrap_to_use_state(grid_pool_from_variables) +grid_pool_executor.__doc__ = """ + +Args: + state: a [autora.state.delta.State][] with a `variables` field + kwargs: ignored + +Returns: the [autora.state.delta.State][] with an updated `conditions` field. + +Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=[1, 2, 3]) + ... ])) + + ... we get exactly those values back when running the executor: + >>> grid_pool_executor(s).conditions + x + 0 1 + 1 2 + 2 3 + + The allowed_values must be specified: + >>> grid_pool_executor( + ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + With two independent variables, we get the cartesian product: + >>> t = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ])) + >>> grid_pool_executor(t).conditions + x1 x2 + 0 1 3 + 1 1 4 + 2 2 3 + 3 2 4 + + If any of the variables have unspecified allowed_values, we get an error: + >>> grid_pool_executor(S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ]))) + Traceback (most recent call last): + ... + AssertionError: grid_pool requires allowed_values to be set... + + + We can specify arrays of allowed values: + >>> u = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ])) + >>> grid_pool_executor(u).conditions + x y z + 0 -10.0 3 20.0 + 1 -10.0 3 21.0 + 2 -10.0 3 22.0 + 3 -10.0 3 23.0 + 4 -10.0 3 24.0 + ... ... .. ... + 2217 10.0 4 26.0 + 2218 10.0 4 27.0 + 2219 10.0 4 28.0 + 2220 10.0 4 29.0 + 2221 10.0 4 30.0 + + [2222 rows x 3 columns] + + If you require a different type than the pd.DataFrame, then you can instruct the State object + to convert it (if you have a constructor for the desired type which is compatible with the + DataFrame): + + We define a state object with the fields we need: + >>> from typing import Optional + >>> @dataclass(frozen=True) + ... class T(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: Optional[np.array] = field(default=None, + ... metadata={"delta": "replace", "converter": np.asarray}) + + >>> t = T( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=[1, 2, 3]) + ... ])) + + The returned DataFrame is converted into the array format: + >>> grid_pool_executor(t).conditions + array([[1], + [2], + [3]]) + + This also works for multiple variables: + >>> t = T( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2", allowed_values=[3, 4]), + ... ])) + + >>> grid_pool_executor(t).conditions + array([[1, 3], + [1, 4], + [2, 3], + [2, 4]]) +""" From 709b9a821f85d714af8eed25faf344690c81902a Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 13:17:58 -0400 Subject: [PATCH 16/26] refactor: reorder random_pooler file --- .../experimentalist/pooler/random_pooler.py | 134 ++++++++---------- 1 file changed, 58 insertions(+), 76 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index b5bed2a7..3c31ab40 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,17 +1,61 @@ import random -from itertools import product -from typing import Iterable, List, Sequence, Tuple, Type +from typing import Iterable, List, Tuple, Type import numpy as np import pandas as pd from autora.state.delta import Result, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -from autora.variable import IV, ValueType, Variable, VariableCollection +from autora.variable import IV, ValueType, VariableCollection + + +def random_pool( + ivs: List[IV], num_samples: int = 1, duplicates: bool = True +) -> Iterable: + """ + Creates combinations from lists of discrete values using random selection. + Args: + ivs: List of independent variables + n: Number of samples to sample + duplicates: Boolean if duplicate value are allowed. + + """ + l_samples: List[Tuple] = [] + # Create list of pools of values sample from + l_iv_values = [] + for iv in ivs: + assert iv.allowed_values is not None, ( + f"gridsearch_pool only supports independent variables with discrete allowed values, " + f"but allowed_values is None on {iv=} " + ) + l_iv_values.append(iv.allowed_values) + + # Check to ensure infinite search won't occur if duplicates not allowed + if not duplicates: + l_pool_len = [len(set(s)) for s in l_iv_values] + n_combinations = np.product(l_pool_len) + try: + assert num_samples <= n_combinations + except AssertionError: + raise AssertionError( + f"Number to sample n({num_samples}) is larger than the number " + f"of unique combinations({n_combinations})." + ) + + # Random sample from the pools until n is met + while len(l_samples) < num_samples: + l_samples.append(tuple(map(random.choice, l_iv_values))) + if not duplicates: + l_samples = [*set(l_samples)] + + return iter(l_samples) + + +random_pooler = deprecated_alias(random_pool, "random_pooler") @wrap_to_use_state -def random_experimentalist( +def random_pool_from_variables( variables: VariableCollection, num_samples=5, random_state=None, @@ -47,7 +91,7 @@ def random_experimentalist( ... ])) ... we get some of those values back when running the experimentalist: - >>> random_experimentalist(s, random_state=1).conditions + >>> random_pool_from_variables(s, random_state=1).conditions x 0 4 1 5 @@ -62,7 +106,7 @@ def random_experimentalist( ... ])) ... we get a sample of the range back when running the experimentalist: - >>> random_experimentalist(t, random_state=1).conditions + >>> random_pool_from_variables(t, random_state=1).conditions x 0 0.118216 1 4.504637 @@ -73,7 +117,7 @@ def random_experimentalist( The allowed_values or value_range must be specified: - >>> random_experimentalist( + >>> random_pool_from_variables( ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... @@ -85,7 +129,8 @@ def random_experimentalist( ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), ... ])) - >>> random_experimentalist(t, num_samples=10, duplicates=True, random_state=1).conditions + >>> random_pool_from_variables(t, + ... num_samples=10, duplicates=True, random_state=1).conditions x1 x2 0 2 434 1 3 212 @@ -99,7 +144,7 @@ def random_experimentalist( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> random_experimentalist(S( + >>> random_pool_from_variables(S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -116,7 +161,7 @@ def random_experimentalist( ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), ... ])) - >>> random_experimentalist(u, random_state=1).conditions + >>> random_pool_from_variables(u, random_state=1).conditions x y z 0 -0.6 3 29.0 1 0.2 4 24.0 @@ -126,16 +171,16 @@ def random_experimentalist( The output can be in several formats. The default is pd.DataFrame. Alternative: `np.recarray`: - >>> random_experimentalist(s, fmt=np.recarray, random_state=1).conditions + >>> random_pool_from_variables(s, fmt=np.recarray, random_state=1).conditions rec.array([(4,), (5,), (7,), (9,), (0,)], dtype=[('x', '>> random_experimentalist(t, fmt=np.recarray, random_state=1).conditions + >>> random_pool_from_variables(t, fmt=np.recarray, random_state=1).conditions rec.array([(2, 72), (3, 411), (4, 474), (4, 125), (1, 156)], dtype=[('x1', '>> random_experimentalist(t, fmt=np.array, random_state=1).conditions + >>> random_pool_from_variables(t, fmt=np.array, random_state=1).conditions array([[ 2, 72], [ 3, 411], [ 4, 474], @@ -173,66 +218,3 @@ def random_experimentalist( raise NotImplementedError("fmt=%s is not supported" % (fmt)) return Result(conditions=conditions) - - -def grid_pool(ivs: Sequence[Variable]): - """ - Pipeline function to create an exhaustive pool from discrete values - using a Cartesian product of sets. - """ - # Get allowed values for each IV - l_iv_values = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"grid_pool requires allowed_values to be set, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - - # Return Cartesian product of all IV values - return product(*l_iv_values) - - -def random_pool( - ivs: List[IV], num_samples: int = 1, duplicates: bool = True -) -> Iterable: - """ - Creates combinations from lists of discrete values using random selection. - Args: - ivs: List of independent variables - n: Number of samples to sample - duplicates: Boolean if duplicate value are allowed. - - """ - l_samples: List[Tuple] = [] - # Create list of pools of values sample from - l_iv_values = [] - for iv in ivs: - assert iv.allowed_values is not None, ( - f"gridsearch_pool only supports independent variables with discrete allowed values, " - f"but allowed_values is None on {iv=} " - ) - l_iv_values.append(iv.allowed_values) - - # Check to ensure infinite search won't occur if duplicates not allowed - if not duplicates: - l_pool_len = [len(set(s)) for s in l_iv_values] - n_combinations = np.product(l_pool_len) - try: - assert num_samples <= n_combinations - except AssertionError: - raise AssertionError( - f"Number to sample n({num_samples}) is larger than the number " - f"of unique combinations({n_combinations})." - ) - - # Random sample from the pools until n is met - while len(l_samples) < num_samples: - l_samples.append(tuple(map(random.choice, l_iv_values))) - if not duplicates: - l_samples = [*set(l_samples)] - - return iter(l_samples) - - -random_pooler = deprecated_alias(random_pool, "random_pooler") From 9e791da30cd46893006a97e515e85d8e2f715f6b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 13:27:56 -0400 Subject: [PATCH 17/26] refactor: reorganize random_pool to use pd.DataFrame as default and have two separate functions --- .../experimentalist/pooler/random_pooler.py | 187 ++++++++++++------ 1 file changed, 127 insertions(+), 60 deletions(-) diff --git a/src/autora/experimentalist/pooler/random_pooler.py b/src/autora/experimentalist/pooler/random_pooler.py index 3c31ab40..1a9b1217 100644 --- a/src/autora/experimentalist/pooler/random_pooler.py +++ b/src/autora/experimentalist/pooler/random_pooler.py @@ -1,5 +1,5 @@ import random -from typing import Iterable, List, Tuple, Type +from typing import Iterable, List, Tuple import numpy as np import pandas as pd @@ -54,16 +54,132 @@ def random_pool( random_pooler = deprecated_alias(random_pool, "random_pooler") -@wrap_to_use_state def random_pool_from_variables( variables: VariableCollection, num_samples=5, random_state=None, - fmt: Type = pd.DataFrame, duplicates: bool = True, -): +) -> pd.DataFrame: """ + Args: + variables: the description of all the variables in the AER experiment. + num_samples: the number of conditions to produce + random_state: the seed value for the random number generator + duplicates: if True, allow repeated values + + Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field + + Examples: + >>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + + With one independent variable "x", and some allowed_values we get some of those values + back when running the experimentalist: + >>> random_pool_from_variables( + ... variables=VariableCollection( + ... independent_variables=[Variable(name="x", allowed_values=range(10)) + ... ]), random_state=1) + {'conditions': x + 0 4 + 1 5 + 2 7 + 3 9 + 4 0} + + + ... we get a sample of the range back when running the experimentalist: + >>> random_pool_from_variables( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", value_range=(-5, 5)) + ... ]), random_state=1)["conditions"] + x + 0 0.118216 + 1 4.504637 + 2 -3.558404 + 3 4.486494 + 4 -1.881685 + + + + The allowed_values or value_range must be specified: + >>> random_pool_from_variables( + ... variables=VariableCollection(independent_variables=[Variable(name="x")])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + With two independent variables, we get independent samples on both axes: + >>> random_pool_from_variables(variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=range(1, 5)), + ... Variable(name="x2", allowed_values=range(1, 500)), + ... ]), num_samples=10, duplicates=True, random_state=1)["conditions"] + x1 x2 + 0 2 434 + 1 3 212 + 2 4 137 + 3 4 414 + 4 1 129 + 5 1 205 + 6 4 322 + 7 4 275 + 8 1 43 + 9 2 14 + + If any of the variables have unspecified allowed_values, we get an error: + >>> random_pool_from_variables( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x1", allowed_values=[1, 2]), + ... Variable(name="x2"), + ... ])) + Traceback (most recent call last): + ... + ValueError: allowed_values or [value_range and type==REAL] needs to be set... + + + We can specify arrays of allowed values: + + >>> random_pool_from_variables(variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=np.linspace(-10, 10, 101)), + ... Variable(name="y", allowed_values=[3, 4]), + ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), + ... ]), random_state=1)["conditions"] + x y z + 0 -0.6 3 29.0 + 1 0.2 4 24.0 + 2 5.2 4 23.0 + 3 9.0 3 29.0 + 4 -9.4 3 22.0 + + + """ + rng = np.random.default_rng(random_state) + + raw_conditions = {} + for iv in variables.independent_variables: + if iv.allowed_values is not None: + raw_conditions[iv.name] = rng.choice( + iv.allowed_values, size=num_samples, replace=duplicates + ) + elif (iv.value_range is not None) and (iv.type == ValueType.REAL): + raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) + + else: + raise ValueError( + "allowed_values or [value_range and type==REAL] needs to be set for " + "%s" % (iv) + ) + + conditions = pd.DataFrame(raw_conditions) + return Result(conditions=conditions) + + +random_pool_executor = wrap_to_use_state(random_pool_from_variables) +random_pool_executor.__doc__ = """ + Args: variables: fmt: the output type required @@ -91,7 +207,7 @@ def random_pool_from_variables( ... ])) ... we get some of those values back when running the experimentalist: - >>> random_pool_from_variables(s, random_state=1).conditions + >>> random_pool_executor(s, random_state=1).conditions x 0 4 1 5 @@ -106,7 +222,7 @@ def random_pool_from_variables( ... ])) ... we get a sample of the range back when running the experimentalist: - >>> random_pool_from_variables(t, random_state=1).conditions + >>> random_pool_executor(t, random_state=1).conditions x 0 0.118216 1 4.504637 @@ -117,7 +233,7 @@ def random_pool_from_variables( The allowed_values or value_range must be specified: - >>> random_pool_from_variables( + >>> random_pool_executor( ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... @@ -129,7 +245,7 @@ def random_pool_from_variables( ... Variable(name="x1", allowed_values=range(1, 5)), ... Variable(name="x2", allowed_values=range(1, 500)), ... ])) - >>> random_pool_from_variables(t, + >>> random_pool_executor(t, ... num_samples=10, duplicates=True, random_state=1).conditions x1 x2 0 2 434 @@ -144,7 +260,7 @@ def random_pool_from_variables( 9 2 14 If any of the variables have unspecified allowed_values, we get an error: - >>> random_pool_from_variables(S( + >>> random_pool_executor(S( ... variables=VariableCollection(independent_variables=[ ... Variable(name="x1", allowed_values=[1, 2]), ... Variable(name="x2"), @@ -161,60 +277,11 @@ def random_pool_from_variables( ... Variable(name="y", allowed_values=[3, 4]), ... Variable(name="z", allowed_values=np.linspace(20, 30, 11)), ... ])) - >>> random_pool_from_variables(u, random_state=1).conditions + >>> random_pool_executor(u, random_state=1).conditions x y z 0 -0.6 3 29.0 1 0.2 4 24.0 2 5.2 4 23.0 3 9.0 3 29.0 4 -9.4 3 22.0 - - The output can be in several formats. The default is pd.DataFrame. - Alternative: `np.recarray`: - >>> random_pool_from_variables(s, fmt=np.recarray, random_state=1).conditions - rec.array([(4,), (5,), (7,), (9,), (0,)], - dtype=[('x', '>> random_pool_from_variables(t, fmt=np.recarray, random_state=1).conditions - rec.array([(2, 72), (3, 411), (4, 474), (4, 125), (1, 156)], - dtype=[('x1', '>> random_pool_from_variables(t, fmt=np.array, random_state=1).conditions - array([[ 2, 72], - [ 3, 411], - [ 4, 474], - [ 4, 125], - [ 1, 156]]) - - """ - rng = np.random.default_rng(random_state) - - raw_conditions = {} - for iv in variables.independent_variables: - if iv.allowed_values is not None: - raw_conditions[iv.name] = rng.choice( - iv.allowed_values, size=num_samples, replace=duplicates - ) - elif (iv.value_range is not None) and (iv.type == ValueType.REAL): - raw_conditions[iv.name] = rng.uniform(*iv.value_range, size=num_samples) - - else: - raise ValueError( - "allowed_values or [value_range and type==REAL] needs to be set for " - "%s" % (iv) - ) - - iv_names = [v.name for v in variables.independent_variables] - if fmt is pd.DataFrame: - conditions = pd.DataFrame(raw_conditions) - elif fmt is np.recarray: - conditions = np.core.records.fromarrays( - [raw_conditions[n] for n in iv_names], names=iv_names - ) # type: ignore - elif fmt is np.array: - conditions = np.column_stack([raw_conditions[n] for n in iv_names]) - else: - raise NotImplementedError("fmt=%s is not supported" % (fmt)) - - return Result(conditions=conditions) +""" From 27445e0a76a9fdc1c8d6ee98d060ee5cb7280ef9 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 13:52:01 -0400 Subject: [PATCH 18/26] refactor: remake random sampler to use a result object to be used in a pipeline --- .../experimentalist/sampler/random_sampler.py | 129 +++++++++--------- 1 file changed, 62 insertions(+), 67 deletions(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index 573c7076..c77f5c2a 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,39 +1,16 @@ import random -from functools import singledispatch from typing import Iterable, Optional -import numpy as np import pandas as pd +from autora.state.delta import Result, wrap_to_use_state from autora.utils.deprecation import deprecated_alias -@singledispatch def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] = None): """ - Uniform random sampling without replacement from a pool of conditions. - - Args: - conditions: Pool of conditions - num_samples: number of samples to collect - random_state: initialization parameter for the random sampler - - Returns: Sampled pool - - """ - - raise NotImplementedError("%s is not supported" % (type(conditions))) -@random_sample.register(list) -@random_sample.register(range) -@random_sample.register(filter) -def random_sample_sequence( - conditions, num_samples: int = 1, random_state: Optional[int] = None -): - """ - Single dispatch variant of random_sample for list, range and filter objects - Examples: From a range: >>> random.seed(1) @@ -60,61 +37,79 @@ def random_sample_sequence( return samples -@random_sample.register(pd.DataFrame) -def random_sample_dataframe( - conditions, num_samples: int = 1, random_state: Optional[int] = None -): +random_sampler = deprecated_alias(random_sample, "random_sampler") + + +def random_sample_from_conditions( + conditions, + num_samples: int = 1, + random_state: Optional[int] = None, + replace: bool = False, +) -> Result: """ - Single dispatch variant of random_sample for pd.DataFrames + Take a random sample from some conditions. + + Args: + conditions: the conditions to sample from + num_samples: + random_state: + replace: + + Returns: a Result object with a field `conditions` with a DataFrame of the sampled conditions Examples: From a pd.DataFrame: >>> import pandas as pd >>> random.seed(1) - >>> random_sample(pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) - x + >>> random_sample_from_conditions( + ... pd.DataFrame({"x": range(100, 200)}), num_samples=5, random_state=180) + {'conditions': x 67 167 71 171 64 164 63 163 - 96 196 + 96 196} """ - return pd.DataFrame.sample( - conditions, random_state=random_state, n=num_samples, replace=False + return Result( + conditions=pd.DataFrame.sample( + conditions, random_state=random_state, n=num_samples, replace=replace + ) ) -@random_sample.register(np.ndarray) -@random_sample.register(np.recarray) -def random_sample_np_array( - conditions, num_samples: int = 1, random_state: Optional[int] = None -): - """ - Single dispatch variant of random_sample for np.ndarray and np.recarray - - Examples: - From a pd.DataFrame: - >>> import numpy as np - >>> random_sample(np.linspace([-1, -100], [1, 100], 101), num_samples=5, - ... random_state=180) - array([[ 0.12, 12. ], - [ -0.18, -18. ], - [ -0.54, -54. ], - [ 0.32, 32. ], - [ 0.96, 96. ]]) - - >>> random_sample(np.core.records.fromrecords([ - ... ("a", 1, "alpha"), - ... ("b", 2, "beta"), - ... ("c", 3, "gamma"), - ... ("d", 4, "delta"), - ... ], names=["l", "n", "g"]), num_samples=2, random_state=1) - array([('b', 2, 'beta'), ('c', 3, 'gamma')], - dtype=(numpy.record, [('l', '>> from autora.state.delta import State + >>> from autora.variable import VariableCollection, Variable + >>> from dataclasses import dataclass, field + >>> import pandas as pd + >>> import numpy as np + >>> from autora.experimentalist.pooler.grid import grid_pool_executor + + We define a state object with the fields we need: + >>> @dataclass(frozen=True) + ... class S(State): + ... variables: VariableCollection = field(default_factory=VariableCollection) + ... conditions: pd.DataFrame = field(default_factory=pd.DataFrame, + ... metadata={"delta": "replace"}) + + With one independent variable "x", and some allowed values: + >>> s = S( + ... variables=VariableCollection(independent_variables=[ + ... Variable(name="x", allowed_values=range(100)) + ... ])) + + ... we can update the state with a sample from the allowed values: + >>> s_ = grid_pool_executor(s) + >>> random_sample_executor(s_, num_samples=5, random_state=1 + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + S(variables=..., conditions= x + 80 80 + 84 84 + 33 33 + 81 81 + 93 93) + +""" From 792b6beaa19a660f031b248a8817ffaa39e44d97 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:05:08 -0400 Subject: [PATCH 19/26] test: update doctests to support windows --- src/autora/experimentalist/pooler/grid.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index b9e5a868..d9b8a37d 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -220,7 +220,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: >>> grid_pool_executor(t).conditions array([[1], [2], - [3]]) + [3]]...) This also works for multiple variables: >>> t = T( @@ -233,5 +233,5 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: array([[1, 3], [1, 4], [2, 3], - [2, 4]]) + [2, 4]]...) """ From aad27026acb37199ddefcf6fd5a5a7c6a9ea214b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:29:10 -0400 Subject: [PATCH 20/26] revert: changes to grid_pool function --- src/autora/experimentalist/pooler/grid.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index d9b8a37d..46c204ad 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -9,15 +9,12 @@ def grid_pool(ivs: Sequence[Variable]) -> product: - """ - Low level function to create an exhaustive pool from discrete values - using a Cartesian product of sets. - """ + """Creates exhaustive pool from discrete values using a Cartesian product of sets""" # Get allowed values for each IV l_iv_values = [] for iv in ivs: assert iv.allowed_values is not None, ( - f"grid_pool requires allowed_values to be set, " + f"gridsearch_pool only supports independent variables with discrete allowed values, " f"but allowed_values is None on {iv=} " ) l_iv_values.append(iv.allowed_values) From 5aae4dc17d0b3584804d553c2a3e1c67ce29d1a5 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:29:25 -0400 Subject: [PATCH 21/26] docs: update docstrings and tests to work --- src/autora/experimentalist/pooler/grid.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/autora/experimentalist/pooler/grid.py b/src/autora/experimentalist/pooler/grid.py index 46c204ad..20e49d68 100644 --- a/src/autora/experimentalist/pooler/grid.py +++ b/src/autora/experimentalist/pooler/grid.py @@ -24,10 +24,11 @@ def grid_pool(ivs: Sequence[Variable]) -> product: def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: - """ + """Creates exhaustive pool of conditions given a definition of variables with allowed_values. Args: - variables: the description of all the variables in the AER experiment. + variables: a VariableCollection with `independent_variables` – a sequence of Variable + objects, each of which has an attribute `allowed_values` containing a sequence of values. Returns: a Result / Delta object with the conditions as a pd.DataFrame in the `conditions` field @@ -53,7 +54,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... variables=VariableCollection(independent_variables=[Variable(name="x")])) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... With two independent variables, we get the cartesian product: >>> grid_pool_from_variables(variables=VariableCollection(independent_variables=[ @@ -74,7 +75,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... ])) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... We can specify arrays of allowed values: @@ -147,7 +148,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... S(variables=VariableCollection(independent_variables=[Variable(name="x")]))) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... With two independent variables, we get the cartesian product: >>> t = S( @@ -170,7 +171,7 @@ def grid_pool_from_variables(variables: VariableCollection) -> pd.DataFrame: ... ]))) Traceback (most recent call last): ... - AssertionError: grid_pool requires allowed_values to be set... + AssertionError: gridsearch_pool only supports independent variables with discrete... We can specify arrays of allowed values: From 2a0e11d9310eb965ead6ef284c99ff967b10f521 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:34:16 -0400 Subject: [PATCH 22/26] revert: changes to random_sample function --- src/autora/experimentalist/sampler/random_sampler.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/autora/experimentalist/sampler/random_sampler.py b/src/autora/experimentalist/sampler/random_sampler.py index c77f5c2a..7f8a3a3f 100644 --- a/src/autora/experimentalist/sampler/random_sampler.py +++ b/src/autora/experimentalist/sampler/random_sampler.py @@ -1,5 +1,5 @@ import random -from typing import Iterable, Optional +from typing import Iterable, Optional, Sequence, Union import pandas as pd @@ -7,9 +7,14 @@ from autora.utils.deprecation import deprecated_alias -def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] = None): +def random_sample(conditions: Union[Iterable, Sequence], num_samples: int = 1): """ + Uniform random sampling without replacement from a pool of conditions. + Args: + conditions: Pool of conditions + num_samples: number of samples to collect + Returns: Sampled pool Examples: From a range: @@ -27,8 +32,6 @@ def random_sample(conditions, num_samples: int = 1, random_state: Optional[int] [375, 390, 600, 285, 885] """ - if random_state is not None: - random.seed(random_state) if isinstance(conditions, Iterable): conditions = list(conditions) random.shuffle(conditions) From b0ea2cda5629da30ab7fb72c57109fcdab624ad9 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 16:43:21 -0400 Subject: [PATCH 23/26] docs: add explanation on wrapper. --- src/autora/state/wrapper.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/autora/state/wrapper.py b/src/autora/state/wrapper.py index 13bf5528..74ecbade 100644 --- a/src/autora/state/wrapper.py +++ b/src/autora/state/wrapper.py @@ -1,6 +1,8 @@ -"""Utilities to wrap common theorist, experimentalist and experiment runners as `f(State)`. +"""Utilities to wrap common theorist, experimentalist and experiment runners as `f(State)` so that $n$ processes $f_i$ on states $S$ can be represented as $$f_n(...(f_1(f_0(S))))$$ + +These are special cases of the [autora.state.delta.wrap_to_use_state][] function. """ from __future__ import annotations From b1d57f232a9e00f616b76249e045fddb000bffa5 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 17:07:32 -0400 Subject: [PATCH 24/26] docs: update docs to use the new experimentalist functions --- ...Workflows using Functions and States.ipynb | 208 ++++++++++-------- 1 file changed, 118 insertions(+), 90 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index d9473ab7..41249fc6 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -149,8 +149,8 @@ " y = ground_truth(x) + rng.normal(0, std, len(x))\n", " return y\n", "\n", - "def noisy_observation_df(df: pd.DataFrame, std=1000, rng=None) -> pd.DataFrame:\n", - " y = pd.DataFrame({\"y\": noisy_observation(df[\"x\"], std=std, rng=rng)}) \n", + "def noisy_observation_df(df: pd.DataFrame, std=100, rng=None) -> pd.DataFrame:\n", + " y = pd.DataFrame({\"y\": noisy_observation(df[\"x\"], std=std, rng=rng)})\n", " return y" ] }, @@ -216,27 +216,27 @@ " \n", " 0\n", " -15.0\n", - " -1457.368847\n", + " -1456.807800\n", " \n", " \n", " 1\n", " -14.7\n", - " -1276.238863\n", + " -1276.368140\n", " \n", " \n", " 2\n", " -14.4\n", - " -1101.204891\n", + " -1101.905609\n", " \n", " \n", " 3\n", " -14.1\n", - " -938.114081\n", + " -937.150175\n", " \n", " \n", " 4\n", " -13.8\n", - " -780.385208\n", + " -779.888407\n", " \n", " \n", " ...\n", @@ -246,27 +246,27 @@ " \n", " 96\n", " 13.8\n", - " 501.515831\n", + " 503.013830\n", " \n", " \n", " 97\n", " 14.1\n", - " 608.036899\n", + " 607.702964\n", " \n", " \n", " 98\n", " 14.4\n", - " 722.340558\n", + " 723.076065\n", " \n", " \n", " 99\n", " 14.7\n", - " 844.702516\n", + " 843.761041\n", " \n", " \n", " 100\n", " 15.0\n", - " 972.762139\n", + " 971.355949\n", " \n", " \n", "\n", @@ -275,17 +275,17 @@ ], "text/plain": [ " x y\n", - "0 -15.0 -1457.368847\n", - "1 -14.7 -1276.238863\n", - "2 -14.4 -1101.204891\n", - "3 -14.1 -938.114081\n", - "4 -13.8 -780.385208\n", + "0 -15.0 -1456.807800\n", + "1 -14.7 -1276.368140\n", + "2 -14.4 -1101.905609\n", + "3 -14.1 -937.150175\n", + "4 -13.8 -779.888407\n", ".. ... ...\n", - "96 13.8 501.515831\n", - "97 14.1 608.036899\n", - "98 14.4 722.340558\n", - "99 14.7 844.702516\n", - "100 15.0 972.762139\n", + "96 13.8 503.013830\n", + "97 14.1 607.702964\n", + "98 14.4 723.076065\n", + "99 14.7 843.761041\n", + "100 15.0 971.355949\n", "\n", "[101 rows x 2 columns]" ] @@ -389,27 +389,27 @@ " \n", " 1\n", " x\n", - " -161.235264\n", + " -145.723526\n", " \n", " \n", " 2\n", " x^2\n", - " -2.092934\n", + " -2.909293\n", " \n", " \n", " 3\n", " x^3\n", - " 1.487881\n", + " 1.048788\n", " \n", " \n", " 4\n", " x^4\n", - " -0.002423\n", + " -0.000242\n", " \n", " \n", " 5\n", " x^5\n", - " -0.002523\n", + " -0.000252\n", " \n", " \n", "\n", @@ -418,11 +418,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -161.235264\n", - "2 x^2 -2.092934\n", - "3 x^3 1.487881\n", - "4 x^4 -0.002423\n", - "5 x^5 -0.002523" + "1 x -145.723526\n", + "2 x^2 -2.909293\n", + "3 x^3 1.048788\n", + "4 x^4 -0.000242\n", + "5 x^5 -0.000252" ] }, "execution_count": null, @@ -503,27 +503,27 @@ " \n", " 1\n", " x\n", - " -161.235264\n", + " -145.723526\n", " \n", " \n", " 2\n", " x^2\n", - " -2.092934\n", + " -2.909293\n", " \n", " \n", " 3\n", " x^3\n", - " 1.487881\n", + " 1.048788\n", " \n", " \n", " 4\n", " x^4\n", - " -0.002423\n", + " -0.000242\n", " \n", " \n", " 5\n", " x^5\n", - " -0.002523\n", + " -0.000252\n", " \n", " \n", "\n", @@ -532,11 +532,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -161.235264\n", - "2 x^2 -2.092934\n", - "3 x^3 1.487881\n", - "4 x^4 -0.002423\n", - "5 x^5 -0.002523" + "1 x -145.723526\n", + "2 x^2 -2.909293\n", + "3 x^3 1.048788\n", + "4 x^4 -0.000242\n", + "5 x^5 -0.000252" ] }, "execution_count": null, @@ -595,27 +595,27 @@ " \n", " 1\n", " x\n", - " -169.149674\n", + " -140.575016\n", " \n", " \n", " 2\n", " x^2\n", - " 2.803475\n", + " -3.162851\n", " \n", " \n", " 3\n", " x^3\n", - " 1.617091\n", + " 0.961146\n", " \n", " \n", " 4\n", " x^4\n", - " -0.022233\n", + " 0.000429\n", " \n", " \n", " 5\n", " x^5\n", - " -0.002868\n", + " 0.000109\n", " \n", " \n", "\n", @@ -624,11 +624,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -169.149674\n", - "2 x^2 2.803475\n", - "3 x^3 1.617091\n", - "4 x^4 -0.022233\n", - "5 x^5 -0.002868" + "1 x -140.575016\n", + "2 x^2 -3.162851\n", + "3 x^3 0.961146\n", + "4 x^4 0.000429\n", + "5 x^5 0.000109" ] }, "execution_count": null, @@ -687,27 +687,27 @@ " \n", " 1\n", " x\n", - " -169.149674\n", + " -140.575016\n", " \n", " \n", " 2\n", " x^2\n", - " 2.803475\n", + " -3.162851\n", " \n", " \n", " 3\n", " x^3\n", - " 1.617091\n", + " 0.961146\n", " \n", " \n", " 4\n", " x^4\n", - " -0.022233\n", + " 0.000429\n", " \n", " \n", " 5\n", " x^5\n", - " -0.002868\n", + " 0.000109\n", " \n", " \n", "\n", @@ -716,11 +716,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -169.149674\n", - "2 x^2 2.803475\n", - "3 x^3 1.617091\n", - "4 x^4 -0.022233\n", - "5 x^5 -0.002868" + "1 x -140.575016\n", + "2 x^2 -3.162851\n", + "3 x^3 0.961146\n", + "4 x^4 0.000429\n", + "5 x^5 0.000109" ] }, "execution_count": null, @@ -729,7 +729,7 @@ }, { "data": { - "image/png": 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DDfz+++8XHP8EEBQUhLe3N/Pnz6dBgwZ4eXnh7+9/3vYXq9Pe7ZU3jYESERFxsHfeeYdu3bpx7bXX0q9fP3r06EHLli3x8vK64N916tSJ2bNn8/XXX9OmTRvGjx/Pyy+/XGwA+dtvv01YWBi9evXi9ttv5/HHH8fHx77Di5dffjmffPIJ7733Hu3bt2fBggU8//zzF/wbNzc3pkyZwkcffURoaCg33HDDBdtfrE57t1feLMaZBxyl1DIyMvD39yc9PR0/Pz9HlyMiUi3k5OQQHx9PeHj4RYNHZZeVlUX9+vV5++23GTZsmKPLqVIu9Dqx5/Nbh/BEREQcbOPGjezcuZOuXbuSnp7Oyy+/DODwXhY5PwUoERGRSuCtt95i165deHh40LlzZ5YvX07dunUdXZachwKUiIiIg3Xs2JH169c7ugyxgwaRi4iIiNhJAUpERKoUnRslF1JWrw8FKBERqRIK5wyyd5JIqV4KXx/2zoV1Jo2BEhGRKsHV1ZWAgICiNdB8fHwuOhGlVB+GYZCdnU1ycjIBAQHF1s67FApQIiJSZYSEhACU+0Ky4rwCAgKKXieloQAlIiJVhsVioV69egQFBZV4IVqpPtzd3Uvd81RIAUpERKocV1fXMvugFDkXDSIXERERsZMClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlIiIiYicFKBERERE7KUCJiIiI2EkBSkRERMROClAiIiIidlKAEhEREbGTApSIiIiInRSgREREROykACUiIiJiJwUokUuUkJDAihUrSEhIcHQpIiJSwdwcXYCIs4qLiyM2NhaA0NBQB1cjIiIVSQFK5BJFREQU+ykiItWHApTIJQoNDVXPk4hINaUxUCIiIiJ2UoASERERsZPTBKiJEydy2WWXUbNmTYKCghg8eDC7du0q1iYnJ4cRI0ZQp04datSowU033URSUlKxNgcOHGDQoEH4+PgQFBTEE088QUFBQbE2S5cupVOnTnh6ehIZGcnMmTPL++GJiIiIE3GaAPXnn38yYsQIVq9ezcKFC8nPz6d///5kZWUVtRkzZgy//PIL3377LX/++ScJCQnceOONRfdbrVYGDRpEXl4eq1atYtasWcycOZPx48cXtYmPj2fQoEH06dOHTZs2MXr0aO6//35+//33Cn28IiIiUnlZDMMwHF3EpUhJSSEoKIg///yT3r17k56eTmBgIF9++SU333wzADt37qRly5bExMRw+eWX89tvv3HttdeSkJBAcHAwANOnT+epp54iJSUFDw8PnnrqKX799Ve2bt1atK9bb72VtLQ05s+fX6LaMjIy8Pf3Jz09HT8/v7J/8CIiIlLm7Pn8dpoeqDOlp6cDULt2bQDWr19Pfn4+/fr1K2rTokULGjZsSExMDAAxMTG0bdu2KDwBREdHk5GRwbZt24ranL6NwjaF2ziX3NxcMjIyil1ERESk6nLKAGWz2Rg9ejQ9evSgTZs2ACQmJuLh4UFAQECxtsHBwSQmJha1OT08Fd5feN+F2mRkZHDy5Mlz1jNx4kT8/f2LLmFhYaV+jCIiIlJ5OWWAGjFiBFu3buXrr792dCkAPPPMM6SnpxddDh486OiSREREpBw53USaI0eOZO7cuSxbtowGDRoU3R4SEkJeXh5paWnFeqGSkpIICQkparN27dpi2ys8S+/0NmeeuZeUlISfnx/e3t7nrMnT0xNPT89SPzYRERFxDk7TA2UYBiNHjuTHH3/kjz/+IDw8vNj9nTt3xt3dncWLFxfdtmvXLg4cOEC3bt0A6NatG1u2bCE5ObmozcKFC/Hz86NVq1ZFbU7fRmGbwm2IiIiIOM1ZeI888ghffvklP/30E82bNy+63d/fv6hnaPjw4cybN4+ZM2fi5+fHqFGjAFi1ahVgTmPQoUMHQkNDmTRpEomJidx1113cf//9vP7664A5jUGbNm0YMWIE9913H3/88QePPvoov/76K9HR0SWqVWfhiYiIOB97Pr+dJkBZLJZz3j5jxgzuuecewJxIc9y4cXz11Vfk5uYSHR3Nhx9+WHR4DmD//v0MHz6cpUuX4uvry9ChQ3njjTdwc/vnaObSpUsZM2YM27dvp0GDBrzwwgtF+ygJBSgRERHnUyUDlDNRgKqeEhISiIuLIyIiQosMi4g4IXs+v51uELlIZRUXF0dsbCyAApSISBWnACVSRiIiIor9FBGRqksBSqSMhIaGqudJRKSacJppDEREREQqCwUoERERETspQImIiIjYSQFKRERExE4KUCIiIiJ2UoASERERsZMClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiBSTkJDAihUrSEhIOOd1EREBN0cXICKVS1xcHLGxsQCEhoaedV1ERBSgROQMERERF/wpIiIKUCJyhtDQ0GI9TWdeFxERjYESERERsZsClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlInIBCQkJrFixgoSEBEeXIlJpVcf/J1pMWETkAuLi4oiNjQXQosoi51Ed/58oQImIXEBERESxnyJytur4/0QBSkTkAkJDQ6vNN2qRS1Ud/59oDJSIiIiInRSgREREROykACUiIiJiJwUoERERETspQImIiIjYSQFKRERExE4KUCIiIiJ2UoAScQJnLpNQHZdNEBGpTDSRpogTOHOZhOq4bIKIVE8JCQnExcURERFRqd7vFKBEnMCZyyRUx2UTRKR6qqxfGBWgRJzAmcskVMdlE6qayvqtWqSyqaxfGBWgREQcoLJ+qxapbCrrF0YFKBGRUyqyV6iyfqsWkZJRgBIROaUie4VK8q1ah/lEKi8FKBGRU8qqV6isgo8O84lUXgpQ4rT07VzKWlmNtSir4KPDfCKVl1NNpLls2TKuu+46QkNDsVgszJkzp9j9hmEwfvx46tWrh7e3N/369WPPnj3F2qSmpnLHHXfg5+dHQEAAw4YNIzMzs1ibv//+m169euHl5UVYWBiTJk0q74cml6DwQyouLs7RpYgUExERQWRkZKmDT2hoKD179tQXBJFKyKkCVFZWFu3bt+eDDz445/2TJk1iypQpTJ8+nTVr1uDr60t0dDQ5OTlFbe644w62bdvGwoULmTt3LsuWLePBBx8suj8jI4P+/fvTqFEj1q9fz5tvvsmLL77Ixx9/XO6PT+xTVh9SUj6q82zpCj4iVZ/FMAzD0UVcCovFwo8//sjgwYMBs/cpNDSUcePG8fjjjwOQnp5OcHAwM2fO5NZbb2XHjh20atWKdevW0aVLFwDmz5/PNddcw6FDhwgNDWXatGk899xzJCYm4uHhAcDTTz/NnDlz2LlzZ4lqy8jIwN/fn/T0dPz8/Mr+wYs4gRUrVhAbG0tkZCQ9e/Z0dDkiIhdlz+e3U/VAXUh8fDyJiYn069ev6DZ/f3+ioqKIiYkBICYmhoCAgKLwBNCvXz9cXFxYs2ZNUZvevXsXhSeA6Ohodu3axfHjx8+579zcXDIyMopdRKo79RCKSFVWZQaRJyYmAhAcHFzs9uDg4KL7EhMTCQoKKna/m5sbtWvXLtYmPDz8rG0U3lerVq2z9j1x4kReeumlsnkgImcyDMhKgROJ5s/sY5B1FHLSoCAXrHn//LS4gKsHuHmCqzu4eYNPbfCuBT51zN9rhoJvXbBYyrXsyjr5nYg4ucPrYcELcPUr0KCzw8qoMgHKkZ555hnGjh1bdD0jI4OwsDAHViROyWaFY7GQsAmSt0FqHKTGm5f8rLLdl5s3BDQ0L7UaQWALCGoJQa3MkCUiUhkZhhme9q+EtR9Dg48cVkqVCVAhISEAJCUlUa9evaLbk5KS6NChQ1Gb5OTkYn9XUFBAampq0d+HhISQlJRUrE3h9cI2Z/L09MTT07NMHodUIzkZ5ptA/HKsh/7CkrgFl4Lscza14UKGawCZrgFkufqT7RZArrsfrh4+uHt64enljZeXF36ebvh72HAz8sGaD3lZcPI4ZKfCyVSz5yorBQpOwtFd5uVMNYIhpC3U7wINukD9zmUSqjTthIiU2s5fzfdNNy/o+4JDS6kyASo8PJyQkBAWL15cFJgyMjJYs2YNw4cPB6Bbt26kpaWxfv16Onc2u/3++OMPbDYbUVFRRW2ee+458vPzcXd3B2DhwoU0b978nIfvRErMZsN2cC3pf8+FuD/xP74NF6wAuJ5qkm14ss1oxHZbI+KNeuwzgtlvhHDICCS/hP9dXSxQv5Y3jev40iSwBm0i/WnfwJ+IwBq4uligIA8yDkHaAfNybC+k7ITkHZC2HzKTIDYJYhf9s9HaTaBxDwi/Ahr3gprB5y/gPDQppIiUijUfFo43f+82EvwbOLQcpzoLLzMzs+gNuGPHjrzzzjv06dOH2rVr07BhQ/7zn//wxhtvMGvWLMLDw3nhhRf4+++/2b59O15eXgAMHDiQpKQkpk+fTn5+Pvfeey9dunThyy+/BMwz95o3b07//v156qmn2Lp1K/fddx+TJ08uNt3BhegsPClkWAtI2rqE9A3fE3xoIQHWo8Xuj7cFs8rWhr9szdjr3pR8/whCavkS7OdFTS83fD3d8PVww8fTFReLhQKrjQKbQYHV4GS+ldSsPFKz8jiWlcuxzDwOpmaTlWc9Zy2+Hq60qe9Pl8a16BFZl04Na+Hl7lq8UW6mGaYSNsKhv+DwX+ZhxTPVbQ5N+kCzaGjUwxxzdRHqgRKRUlnzEfz2JPgGwqMbwbNmme/Cns9vpwpQS5cupU+fPmfdPnToUGbOnIlhGEyYMIGPP/6YtLQ0evbsyYcffkizZs2K2qampjJy5Eh++eUXXFxcuOmmm5gyZQo1atQoavP3338zYsQI1q1bR926dRk1ahRPPfVUietUgKreDMNg9/ZNpK74lGZH5lKHtKL7Thje/Gl0JM4/ipwGPajfuBmt6vkREVgDf2/3Mtl3SmYu8SlZ7DuWxa7ETLYcTmPr4QxO5hcPVp5uLnQNr02PyLr0axlMZFCNc280OxUOrYP4ZeYlcQtw2tuGRw2IuBKaDYDm14BvnVI/DhEFbinmZBpM6WgORRj0Dlw2rFx2U2UDlLNQgKqe9iaksGPxFzSI/5YOtm1Ft6cZvmzw6U5G+DU06DyQdo2C8XCr2BlECqw2YlMy2XwwjdVxqayMPUryidxibSKDahDdOpgBrevRpr4flvOdpZedCvtWQOxC2L0AMhP/uc/iCuG9ofVgaHGdwpRcMs0jVjGcJqguHA8r3zN7v4evAtfyGYGkAOVgClDVR77VxpKNuzj+54f0zZhDXYs5B5jVsLDdN4q8dnfSovfN+Pp4O7jS4gzDIDY5k5WxR1myK4VVe4+Sb/3nrSCstjc3dmzATZ0a0LCOz/k3ZLNB4mYzSO385VTv1CkWV7Nnqv1t0GIQeFxgOyJncJoPdifnFEH1+H6Y2sWcquX22ebQgXKiAOVgClBVX2pWHt//EUONjR9xvXURvhazN+eYayApzW6l4VUP4BPYyMFVllxGTj5Ldibz+7ZElu5KIfu0cVSXNa7FTZ0acF37UHw9L/Kt79he2D4Hts2BxL//ud2jBrS6AdrfCo16gkuVmcNXxKk5RVD9bhhs/c48ieXun8p1DjsFKAdTgKq6kk/k8NWitQRtnMLNlj9wt5hBI9m3GW69RlP7sn+bE1g6sZN5VhZsT+S79YdYGXsU26l3iJqebtzUuQF3Xt7o/OOlTndsL/w9GzZ/ZZ7dV6hWOHQeCh3uhBqB5fMgRKRqOLAaPosGLPDQMqjXrlx3pwDlYApQVU9SRg4zFm2k1qZp3G35DW9LHgApgZcTcPWTuDe9qtxn9naExPQcftp0mK/XHST+6D+TefaIrMPQbo3p1zIYF5eLPG7DMN8EN38F236E3FNLHbm4m4f2utxnjpuqgs+fiJSCzQofX2EODeh4F9wwtdx3qQDlYApQVcfJPCufLt1N1ooPGG75Hn+LOdFlep2O+F37CpbwXg6usGLYbAYr9x7l/2L2s3hHUlGvVESgLw/1jmBwx/p4urleeCNgTuy59QdYP9OcIqFQYEuIegjaDdFYKRExrfsv/DoWPP1h1PoK6bFWgHIwBSjnZ7MZ/LT5MAvm/cBjuR/TwuUgAFn+TfEZ+BKW5tdU2x6TQ8ez+WL1Af63Zj8ncgoACKrpyb09wrmrWyNqXGycVKHELfDXDNj89T9L1XgFQKe7zTDl4EnyRMSBslPh/U7mSgoD/gOXP1whu1WAcjAFKOe29XA6b/6wjBuSp3Oj6woA8jwCcO//EpZOd4FLCXpa7OAUgzjP4UROPl+vPch/V8STmJEDQICPOw/0imBo98YlD1In02DT/8x1rY7vM29zcYM2N0P3URDSplzqF5FKbO5Y+Ou/5vqcDy0vt2kLzqQA5WAKUM4pJ9/Kuwt3k7pqJs+7/h9+lmwMLFg73o3b1S+W2yK7TnEa8QXkFdj4adNhpi3dS9ypcVK1fNx5sHcT7u7W6OJn7hWyWWHPAoj5APYt/+f2Jn2hx2OVZpyUswZeEadx5G9z7JNhg6FzoQKHSihAOZgClPOJ2XuMN7/7k+GZU7nadT0Aye5hJHQYQ4dB5TPjbaGq8oFcYLXx8+YEpizew75j5lixujU8eKxvU27t2hB3VzumLji8AVZNge0/mW+iAA26Qu8noOnVFw1S5fmcOnvgFanUDAM+GwAHV0PrG+GWGRW6ewUoB1OAch4n86y8Pm8Hx9Z+w6vun1HbkonN4k7mZaP4u8aVREQ2depQ4wgFVhs/bUpgyh972H8qSDWu48MT0S24pm3I+Wc4P5fUeIiZChs+B+upmdND2plBqsW1551PqjxDTlUJvCKV0uZv4McHwd0HRv4F/vUrdPcKUA6mAOUcdhzJ4MkvV3Fv2vtFY52sQW1xvXG6xt2UgXyrja/WHuC9RXs4lmVO+9A+LIAXBrWkS2M7D4eeSDSD1LrP/hlwHtQa+jxjBqkzQplCjogTyk6FqZdB9lG46gXo/XiFl6AA5WAKUJWbYRjMWrWP2b8tZIrLZCJdEjAsrlh6jTN7Ntw8HF1ilZKZW8Any+L4ZHlc0Qzn17UP5emBLagfYOcSN1nHYM00c1X2wvmkQtrClc9C84GVYoyUiFyin0bCxs8hsIU5cNwB78UKUA6mAFV5Hc/KY9y3m6m1+ztedf8Mb0sethohuNwyExp1c3R5VVryiRzeWbCbb/46iGGAl7sLD/VuwsNXNMHb4/xnNp6zNyk7FVZ/CKunQV6meVtoR/Nba5OqOampSJW2byXMvMb8/d75Dns/VoByMAWoyml7QgajPl/FAyemcavbUgCMiD5YbvxES4pUoK2H03l57nbWxqcCEOrvxfjrWhHd+tzjoy44ninrGMS8D2s+/ufQXuNe0HcChF1W3g9FRMpCQS5M7wlHd0OnoXD9FIeVogDlYApQF+aI8Sk/bTrMpO+X8b7lLTq5xGJgwdLnWeg1rszndZKLMwyD37Ym8tqvOzicdhKAK5sH8tL1rWlUx7dY2xK9XjJTYPnb5rwxVnO8Fc0HQd/xENSiPB+KiJRCQkIC+YtepVHc/8A3CEauBe9aDqtHAcrBFKAurCJPAy+w2vjP/J0sX/En//V4k/qWY9g8/XH590zzUI841Mk8Kx8sieXjZXHkWW14uLkw/IomDL+yCV7ulxBs0w7A0v/A5i/N6Q8sLuYaWn2eJeGETQPLRSqZ9b9/TYeYR3DFCjf9F9re7NB6FKAcTAHqwiqqB+pETj6P/G8Dnnt/5z33qfhacjFqN8Fy+2yoG1lu+xX7xaVkMuHnbSzfcxSAiLq+vPavtnRrUufSNpiyCxa/DDvnmtfdfTjQ4HqWWTvSuFkbzd8kUhkYBrkf98fzyFpywnrhdd8vDh+/qADlYApQjnck/ST3zlhHj5RveM7tf7hYDAi/Av49y6Hdw3J+hmEwb0siL/2yjeQT5pxPQ7qE8cw1LQjwucSzcfbHwMIX4NA6wFySJztqDAF9RunQrVRa1WYajsLFgt28YcRqqNXY0RXZ9fltx9TAIs5hx5EM/jV1JYOPfswL7l+Y4anLfXDn99U+PCUkJLBixQoSEhIcXcpZLBYLg9rVY9G4K7jz8oYAfPPXQfq98yc/b07gQt/1zvu4GnWDYQvh3/8HtSPwyEsjYPkEmN4L9i4pz4cjcsni4uKIjY0lLi7O0aWUn+P7YMEL5u/9JlSK8GQvBSipUuas3snNHyxl9Mn3edjtF/PGfi/BoHfA1d2xxVUCzvDG7OflzquD2/Ldw92IDKrB0cw8Hv1qIw9+vp6kU4sWn+mCj8tigVY3wCNrYMAb4BUAydvg88Hwv3/D0T3l+nhE7BUREUFkZCQRERGOLqV82GzmnE/5WdCwO3R9yNEVXRIdwisHOoTnGD9tOszT36xlstsHDHBdh2FxwXLde9DpbkeXVmk426GB3AIr05fGMXXJHvKtBn5ebrxwbStu7tyg2JQHdj2u7FT4cxKs+wRsBeDiZr6BX/EkeAeU7wMSEVj7Ccx73FyuZfhKqF15gqLGQDmYAlTF+3rtAV79cS0fu71Nd9ftGK4eWG7+DFpe5+jS7OZsIaci7EzM4Mnv/ubvQ+kA9G4WyMQb29o/k/npjsbCgudh92/mdZ865kScne7W+CiR8pIaD9O6Q342DHwToh50dEXFaAyUVCv/XRHPKz+s5TP3SWZ48qiJ5c7vyzQ8VeTYIWc4zFbRWoT48cPw7jw9sAUebi4s253CgMnLmL3u4AXHRl1Q3Ui4/WtzbFzd5pB9DOaOho+ugP2ryrT+6q4yj72TClR06C7bnPD2svsdXVGpKECJ0zIMg/cX7+HtuRuY4TGJri67MDz9sAz9CcJ7l+m+KjLUVPnxD5fIzdWFh69owm+P9aJjwwBO5Bbw5Pd/M2zWX+cdG1Uikf3MwwgDJ4GXPyRtgRkD4fv7IUMf+GWhsn0pUKBzkLUfw/4V4O4L178PLs4dQZy7eqm2DMPgzd93MW3h38XD091zoH7nMt9fRYaa0NBQevbsqcN359EksAbfPXyqN8rVhT92JtN/8jJ+2nT40nujXN0h6iEYtRE63wNYYMu38H4XWDEZCvLK8iFUO5XtS0FlC3TVQtI2WDje/P3ql6B2uGPrKQMaA1UONAaq/L2zYBef/rGVGR6TiHLZCZ5+cNccaFD24Ukqr91JJxg3ezNbDptjowa1rcerg9tQy7eUq7gnbIR5T8Khteb1OpFmD1Vk31JWLJWBxhlWsPyT8MlVkLwdmkbD7d84fMLM89EgcgdTgCpfUxbv4YOFW5lxasyTGZ5+hAZdLml7ejN1bvlWGx8u2cv7f+yhwGYQVNOTSTe348rmQaXbsM0Gf39jfmvOSjZva3k9RL8OAWGlL1ykupj3JKz9yFzrbviqSr14e7kOIh86dCjLli275OJESmPa0r28u3An77l/YIYnjxpw5w+XHJ5A3fnOzt3Vhcf6NeWHR7rTJNCX5BO53DNjHc/P2UJ2XsGlb9jFBTrcBqP+gqjh5rp6O36GD7qaCxfrsJ7Ixe3+3QxPAIOnXVJ4stoMcgus2GyVq7/H7h6owYMHM2/ePBo1asS9997L0KFDqV+/fnnV55TUA1U+Pl0ex6u/bmei26fc5rYEXD3MM6hKOWBcPVBVR06+lTd+28nMVfsAc029yUM60D4soPQbT9xqzl1zIMa8XrcZXPMWRFxR+m2LXIRTvk+dSDKnLMg+an4JGfjGeZtm5haw7XA6WxMy2JWYQfKJXJIzcknJzOVYZi6F2cnFYp5Q4uHqwv29whndr1mZllzuh/BSUlL4/PPPmTVrFtu3b6dfv34MGzaMG264AXd3zfasAFX2vl57gKd/2MLjbt8w0u0nszfgllnQ6npHlyaV0Io9R3n8280kZuTg5mLhsb5NGX5lE9xcS3nejGGYh/UWPA9ZKeZtbW6G6NegZkjpCxc5jxUrVhAbG0tkZGSpFsOusCBms8GXt0DsIghuA/cvBnevorvTsvNYtucof+5KYeOB48Qfy8LeNDKyTySPRzcv07IrdAzUhg0bmDFjBp9++ik1atTgzjvv5JFHHqFp06al2axTU4AqW/O3JvLI/9Zzj8tvjHf/3LzxuvdOnS0ljuAM34bTsvN4bs5Wfv37CACdG9Vi8r870LCOT+k3fjINlrwG6z4FwwYeNeGq5+CyB8DVrfTbF6dUnv8vymrbZRXELmr527D4ZXDzggeXQlBL4lIymbflCEtOhaYzj8iF+nvRur4/rer5UT/Am8CangTW9CSopiee7q4UWG0U2AzyrTYKrAZ+3u7ULu0JI2ew5/O7VP/Tjxw5wsKFC1m4cCGurq5cc801bNmyhVatWjFp0iTGjBlTms2LsDruGI9+vZFrLDH/hKe+4xWeHKxw3BhQaQNUgI8HU2/rSL+WQYyfs431+48z8L1lvHxDG27sVL/YUjB28w6Aa96EDneYq8kfXg/zn4ZNX8K1k0s1Jk9Kx5Hhvjz/X4SGhpbJNgunkijXKSXil8EfrwKQ1fd1ftjrzfffrmTTwbRizZoF16BP8yC6NalDm/r+1K3hWX41lQO7e6Dy8/P5+eefmTFjBgsWLKBdu3bcf//93H777UVp7ccff+S+++7j+PHj5VJ0ZaceqLKxLSGdWz9aTWTeDmZ7vYq7kW8eRx8wsdKeAltdOEMP1OkOpmYzdvYm1u0z35OubVeP1wa3xd+nDIYc2GywYRYsehFy0gCLGfD7TQDvWqXfvtilwnpYzsHZ/l+Ui4wjGB/1wpKVwqqa0QxNHUq+1bzL1QKt67oxoE09bohqWrqlmMpJuR7Cq1u3Ljabjdtuu40HHniADh06nNUmLS2Njh07Eh8fb1fhVYUCVOkdOJbNjdNW4Zl5iF99JhBgS4Pm18CQL7ROmVwSq81g2tJYJi/ag9VmEOrvxTtDOnB5RJ2y2UFmijnlweYvzes+dc2xUe2GKPBXIIUYx8nJyeHERwMJPL6BHbaG/CvvJXLwpHWoH//qWJ+Q3EMcPRTnkHBbUuUaoD7//HNuueUWvLy8Lt64mlKAKp3jWXncOG0VKUdT+MXnFcJt+yG4Ldw3HzxrOLo8cXKbDqbx2Ncb2X8sG4sFHrmyCaP7NcO9tAPMC+1baR7WS9lpXm/cCwa9DYFlO9hV5GIuNUza+3fpJ/OZtWofASte5m7jZzIMb26xvU7nTpdx1+WNaFnPr1T1lNXjKglNpOlgClCXLrfAyl2frmX9vhS+8JlMN9sGqBECDywG/waOLk+qiKzcAl78eRvfrj8EQPuwAN4b0oHGdX3LZgcFeRAzFf6cBAUnwcUdejwKvR4HjzIYxC5SApd6OLOkf3c8K4/PVsYzc+U+uuev4iOPdwFY0OZNul5zDwE+ZTvA2976LoUClIMpQF0awzAYO3szP248zCueX3CXZR64ecO986B+p0vaprrz5UJ+/fsIz/zwNxk5Bfh6uPLi9a25uXOD0g0wP93x/fDbk7B7vnk9oJE5d1Sz/mWzfZELKK8eqLTsPKb/GcfnMfvIyrPSwnKAHz1fxJscbJePxGXAa2X5MOyurzQUoBxMAerSvLdoD5MX7eYmtxW87faheeO//w9a3XDJ23TkgFJxDglpJxnzzSbWxKcCMKhdPV4vqwHmYM4dtfNXM0hlHDZva3UDDHgD/BTqxXmczLMyY1U805fuJSPHnOW/W7CNz/KfxDs7ASKuhDu+d+qpPBSgHEwByn4/bTrMY19vopVlHz97v4SbLRd6P2nOrVMK6oGSkrDaDKb/uZfJC3dTcGqA+eQhHYgqqwHmALmZ8OcbEPMhGFZzGaI+z0HXB536A0eqvgKrjdl/HeK9xbtJysgFoEVITR7vG07fdQ9gORADtZuYQy2c/MxTBSgHU4Cyz/r9x7nt49V4WzNY6v8itXITILIf3D5bZ9xJhdp8aoD5vmPZuFhgRJ9IHu3btOwGmIO5JMzcMXBorXk9pC1c+67mjpJK6c/dKbw6dzt7kjMBqB/gzbj+zbihfSiucx+FjZ+Dpz/cvwgCy3ZZFUdQgHIwBaiSS0zP4bqpKzh24iQ/13qPNifXmeNEHlwKPrUdXZ5UQ5mnBph/d2qAeYewAN67tQON6pTRAHMw547a+H+wcILD5o5S76xcyN6UTF6du50lu8wli2r5uDPqqqbccXlDPN1cYfU0c/JYiwvc/i007efgisuGPZ/fZfi1Sqq6hIQEVqxYQUJCQplsLyffykNfrCflRC6v+P9ihic3L3OuJ4UncZAanm68dUt7pt7ekZpebmw6mMY17y3n+/WHKLPvmy4uZmAatR7a3w4YsH4GvN8FNn2F3YuCXYLCWbPj4uLKfV/iPDJy8nn5l+1ET17Gkl0puLlYuL9nOEuf6MN9PcPN8LTzV/j9WfMP+r9aZcKTvXTgXUqsLJcpMAyD5+dsZfPBNK7x2sodud+Yd1z3HtRrV9pSRUrt2nahdAgLYOw3m1m7L5Vx325mya7kspvBHMC3LvxrGnS8A+aOhaO7YM7D5mGRQW9DUMuy2c85VMiSHuI0DMPgx42HeX3eTo5mmuOc+rUM4tlrWhIReNr8ewfXwnf3mWtAdrobLn/EQRU7ng7hlYOqegivJF3+JT0sMGNlPC/9sp0Qy3GW+72Ae24qXHa/+aEhUokUDjB/Z+Hu8pnBvNBZc0e5QbeRcMWT4FGGhw9FzrDjSAbjf9patNRRRKAvL17Xmt7NAos3TNkNn/WHk8ehaTTc+mWVOwFCY6AcrKoGqJIoybQBq2KPctdnazFsVpbXm0L942vNmcbvXwTumuFeKqczZzB/+IomjOnXDA+3Mh4JcXw/zH8Gdv1qXvcPM9d/bHGtloSRMpWZW8A7C3YzK2YfVpuBt7srj/ZtyrCe4We/rk8kwqdXQ/oBqN8Zhv5SJYO9ApSDVecAdbEeqCPpJxk0ZQWpWXl8EPYHg1I+BXdfeOhPqNvUARWLlFxmbgEvnTaDeZv6frw7pCORQeWwxNCu32Dek+YHFkDk1XDNJKitQ25SOoZh8Pu2RF78eTuJGTkAXNM2hOcHtSL0XAv85mTAjGsgaYs5XcGwBebh5ypIAcrBqlOAsudMnrwCG7d+HMOGA2ncHHiINzOfwWJYYfA06HD7JW9XpKL9tuUIz/y4hbTsfLzcXXhuUCvujGpYdjOYF8rLhuVvwcopYMsHV0/oOQZ6jgb3yreSvVR+B1OzefHnbSzemQxAw9o+vDK4DVecebiuUF42/O9m2L8SfIPM8FQ7vAIrrlg6C08uqqzOqLPnTJ43ftvJhgNp1PfK4Q3eM8NT239D+9tKtV2RijawbT3mP9abXk3rkpNv44U5W7lv5jqST+SU7Y48fKDveHhkNUT0AWuuORnnB1FmD5VICRVYbXy8bC/9Jy9j8c5k3F0tjLoqkgVjep8/POXnwNe3meHJ0w/u+LZKhyd7KUBVU2UVUCIiIoiMjLzomTzzthzhs5XxgMEPDWbjduKweSji2nfOOa6jpNsVcZQQfy9m3duVF65thYebC0t2pRA9eRnztx4p+53VjYS7foRbZmL1DYa0/fDVrfC/f0OqvmTIhf19KI0bPljJ6/N2cjLfSlR4bX57rDfj+jfHy/08kxUX5MHsuyFuqTnM4o7vILRDRZZd6ekQXjlwhkN4FXmILC4lk+unriQzt4AprXdx/d6XzNXp718IoR3Ldd/iXJz10O3upBOM/noT249kAHBTpwZMuL4Vfl5lNN3BaVYtXUjNDdNofeJPXIwC87Bej8fMQ3sePmW+P3FeWbkFvLNwNzNWxmMzwN/bneeuacktXS6yYLY1H769B3bONRd0v/M7aFw91hLVGCgHc4YAVVFO5lkZ/MFKdiWdYGBDKx+mj8CSmwFXPQ+9n3B0eVLJOPPiz3kFNt5dtJvpf+7FZphLXrx5czu6R5btYNvCkNm0toXgv96EuCXmHX4NoP8r0PpfOltPWLIzmefnbOVw2kkAbugQygvXtqJuDc8L/6G1AH58ELZ+b4bz27+GJldVQMWVgwKUgylA/ePp7//m63UHqevrwaoG7+Ox/0+o3wXu+73KzR8ipVfRPVDlsb+/9qUydvZmDqRmA3BP98Y8NaAF3h7lsK6jYcCOX+D35/45W69RTxj4HwhpU9TMWXv2xH4pJ3J56ZdtzP3bPJTcoJY3r/2r7fnHOZ2uIBe+H2a+plzczVUhmg8o54orFw0iLwMffPABjRs3xsvLi6ioKNauXevokpzOL5sT+HrdQSwWmN15mxme3LzhX9MVnuScQkND6dmzZ4V9yJfHyQpdGtfmt8d6cUdUQwBmrtrHNVOWs37/8TLbRxGLBVpdDyPWwJXPmEsh7V8BH/UyZzbPOgaU3eMs6+WcpOwYhsE36w7Q9+2lzP37CC4WeLB3xIUHiZ8uLxu+us0MT64e8O//KwpP+nc/NwWoc/jmm28YO3YsEyZMYMOGDbRv357o6GiSk5MdXZrTOJiazbM/bAFgTNsCGv31unnH1S9pviepNMrrZAVfTzde+1dbZt3XlRA/L+KPZnHL9FVM/G0HOfnWMt0XYI59uvJpGLkOWt1gLrPx13/h/Y4Q8yERjcPK5HHq7NjKKS4lk1s/Xs1T328hI6eA1qF+/DyyJ89e0xIfjxJ8Wc3JgC9ugr2Lwd0Hbp8NLa75Z/v6dz8nHcI7h6ioKC677DKmTp0KgM1mIywsjFGjRvH0009f9O+r+yG8fKuNm6fHsPlgGl0b+vFJ7hP4p+8grVZ7AkYtNRdSFakm0rPzefGXbfy48TAATQJ9efOW9nRqWKv8dhq/DOY/a058CFCnKUS/Dk2vLtX4qLJczklKL6/Axkd/7uX9JbHkFdjwcndh7NXNuK9HOG6uJXyfzU6FL26EhI3g6W9OVdAwqliT6vRvqjFQpZCXl4ePjw/fffcdgwcPLrp96NChpKWl8dNPP531N7m5ueTm5hZdz8jIICwsrNoGqIm/7eCjP+Pw83Jjea9t+C9/iQJXH44N+YXgZl0cXZ6IQyzYlshzc7aSciIXFwsM6xl+4dPIS8tmNRclXvwKZB81b4u4Evq/Vmx8VFlz5hMBnMn6/ak8/f0W9iRnAtC7WSCvDW5DWG07zsRMjYP/3QLHYsGnDtz5Q7WfqkBjoErh6NGjWK1WgoODi90eHBxMYmLiOf9m4sSJ+Pv7F13CwsIqotRKadnuFD760+zmfX9AbfxjJgHgNug/Ck9SrfVvHcLCMb25sVN9bAZ8sjyege8tZ3XcsfLZoYsrdL4HHt0A3UeZ41rilsL0nvDTSMgoh/mq0Bxu5S09O59nftjCTdNi2JOcSR1fD967tQOz7r3MvvB0cC182s8MT34N4J551T482UsBqgw888wzpKenF10OHjzo6JIc4lhmLmNnbwbgzqgwrtj9qrmqfHhv6HiXg6sTcbwAHw/e+XcHPrunC8F+nsQfzeLWj1fzzA9byMjJL5+devlD/1dhxFpzigMMs2fq/U6wZCLknijT3VX0iQDVhWEY/LTpMH3f+ZOv1ppnXN7SuQGLxl7BDR3q27eM0LYfYea1kH0M6rWHBxZDUAu76tHAcgWos9StWxdXV1eSkpKK3Z6UlERISMg5/8bT0xM/P79il+rGMAye/mELRzNzaRZcgwlhm81vu25ecO27mpdG5DRXtQhm4dgruP3UmXpfrT1Av7f/ZP7Wc/dyl4na4XDLTBi2EBpcBvnZ5rIwUzrC2k/MmacdSB/I57fvaBZ3f7aWx77exNHMXJoE+vL1g5fz5i3tqeXrUfINGQaseNecJNOaC80Gmj1PNc/92XYhGliuAHUWDw8POnfuzOLFi4tus9lsLF68mG7dujmwsspt9l8HWbg9CXdXC+9f1wD3Rc+bd/R5Fuo0cWxxIpWQn5c7r/+rLd88eDkRdX1JPpHLw1+s58H/+4uEU5MflouwrmaIumUW1G4CWSkw73H4oKs5eaLNVn77vgB9IJ8tJ9/K5IW76f/uMpbvOYqHmwvjrm7GvMd6cXlEHfs2lpcF398PiyaY17s+BLf+DzxrXFJtOlSrQeTn9M033zB06FA++ugjunbtyrvvvsvs2bPZuXPnWWOjzqW6nYW372gW10xZTnaelWcGtuCh5Fdh2w8Q0g4eWKI5n0QuIiffytQ/Ypn+514KbAY+Hq6M6deMe3o0xr2kZ1NdCms+bJgFS/8DWaemaQluC31fgKb9K7TnuDqd6VUSS3YlM+GnbUUTsvZqWpeXb2hDeF1f+zd2bC98cyckbwcXN/OMzKiHyrjiqkFn4ZWBqVOn8uabb5KYmEiHDh2YMmUKUVFRF/9DqleAKrDauOWjGDYeSCMqvDZf9j6O6ze3gcUVHvijTAcl6g1WqrpdiSd4fs4W1u0zJ91sEVKT1/7Vls6NynHKA4DcTIj5AFa9D3mnxkQ16Ap9x0N4r/LdtxRzMDWbV3/dzu/bzGEkIX5evHBtK65pG2LfOKdCO+fBjw9BbgbUCDZ7HhvpaMr5KEA5WHUKUO8t2sPkRbup6eXG7490JvSL3pBx2Fzc9OqXy3RfOj1aqgObzeC79Yd4/bcdpGWbA8tv7tyApwa0ILDmRdYxK63sVFj5Lqz52DwBBMyTQK58Bhp1L999V3M5+VamLd3L9D/3kltgw9XFwn09GvNYv2bU8LyEXvyCPFjyKqx8z7wedjn8e9YljXeqThSgHKy6BKjNB9O4cdoqrDaDd4d0YPDRj80334CG8MiaMl8Zvqx6oNSTJc4gNSuPifN28O36QwDU9HTjsX5NGdq9nA/rAZxIhGVvwfqZYDt1dmB4b7jiaWjco3z3XcYq+/93wzD4fVsir8zdUbTwb7eIOrx4fWuah9S8tI2m7DLXtEs8NZFq1MPmmZiu7mVUddWlAOVg1SFA5eRbufb9FcQmZ3Jtu3q8388Hy/QeYCuA276G5gMdXeJ5VZeerMr+wSEls+HAcV78eRt/H0oHoGlQDV64thW9S7K+WWmlHYDl78DGL/4JUo17Qa9x5qScTnB2bWX+/771cDqvzN3OmvhUAEL9vXhuUCkO1xkGrPsUFjwPBTngXRuunwItryvjyqsuez6/NbpXLsk7C3cTm5xJYE1PXr2hNZbvbjHDU7MBlTo8AUVnjVT1s0cKz2oCFKCcWKeGtZjzSA9m/3WQSb/vYk9yJnd/tpYrmgXy3KCWNAu+xF6KkghoCNe9C73G/hOk9i03L6GdzCDV/JpKvTyTo/+/n+uLTFJGDm/+vovvNxzCMMDTzYUHe0cw/MomJVu77lzSDpLz/XC8Di43rzfpC4M/1CG7cqQeqHLgjD1Q9vRW/LUvlVs+isEw4L9Du9DXugK+u8+c8+mR1eZ8M+Jw6oGqetKz85nyxx7+L2Yf+VYDFwsMuawhY69uVv7jowDSDpoDzTfMMns4AAJbQPdHoe3N4FYBNTiZ03vA2neJ4pPl8XyyLI6TpxaVHtwhlCcGtKB+gPel7cBmhTUfwR+vQn4WVtzY12wYTW59o1IH28pKh/AczBkDVEm7uU/mWRn43jL2Hcvm5s4NeOv6CJh6GZw4Alc+C1c+VYFViziPsgy0+45m8cZvO5m/zZx408fDlft7hnN/7wj8vCpgnEtmCqyZZk7AmZth3lYjBLo+AF3uA5/a5V+Dk0hISGDXnr1syqrJ5+tTSM0yJyzt3KgWzw9qScfSLCqdsAl+eQyObAIgN6QzW8Pvp17bK/Wl6RIpQDmYMwaokr65v/jzNmau2keInxe/j+mN//KXzG+ktRqbA8fdvSquaBEnUh5jcdbGp/Lar9vZfGp8VICPO8OvaMLd3Rrj7VFOixSfLicd/pph9oCcODWDuLsPdLgduj4Igc3Lv4ZKzGoz+HnzYd5esJtDx80B4hF1fXk8ujkD21ziOCeArGPmLPLrPgXDBp7+0P9l6Hi3ep1KSQHKwZwxQJVEzN5j3PbJagBm3deVKwKOQeHA8dtnQ7NoB1co1YGzHposr7oLz+J6a4E5LhEgqKYnI/pEMuSyMLzcKyBIFeSZk+eumgpJW/65PfwKs1eq2cBqNaGu1WYw9+8E3lu8h7iULMD8Nxndrxn/7tIAt0s9izI/B9ZMh+Vv/9Pz1/pGGPAG1Lz4JM9ycQpQDlYVA1R2XgHR7y7jYOpJbuvakIn/agNf3Ah7/zAHkd72laNLlGqiMp9V5UhWm8EPGw7x7qI9RafDB9b05KHeEdwR1ahieqQMA+KXmT1Su38ze0cA/BpA56HQ4Q7wr1/+dThIYXCasngPe08FpwAfdx7sHcG93cMv/d/AZjWX2Vn8MqSfWqw+pJ05NUHEFWVUvYAClMNVxQD10i/bmLFyH/UDvPl9TG9q7P8DvrwFXNxhxBqtdycVxll7oCpKboGV2esOMm3pXhLSzYHedXw9GNYrnDuiGuHvXUFzAaUdgL8+g/Wz4KR5mj4WF4jsBx3vMs/WrSLzEuXkW/lhw2E+WR5H/FEzOPl7u/NAr3CGdm9MzUsdl2bNh79nw4p34Jh5Ri1+9c0Z4tv+W4fryoEClINVtQC1fn8qN083z7r7v/u60rtJAEzrDkd3Q/dR5rcgEak0EhIS2B27l505/vxv01EOppo9Ur4ergy5rCH39mhMWO2ynej2vPJzYPsc2PA57F/xz+0+daHNjWYQaNDFKeaUOlNGTj5frN7PjJX7SDmRC5jB6f6e4dzToxTBKT8HNn0BK96D9APmbd61zPfbyx8B90s8Y08uSgHKwapSgMrJtzJoynL2pmSZZ93d0t5c5uG3J8CnDjy6Ebz8HV2miJzm9MOcUd2689OmBD5etpfdSeYYKVcXCwPbhHBvj8Z0aljr0gcz2+vYXtj4OWz6EjKT/rm9Vji0vQXa3GQOPK/kYWpvSib/t2of3284TGZuAQD1/L0Y1jOc27o2xPdSll4BSI0zB+Vv/OKfXjvfIDM4dbkPPGuU0SOQ81GAcrCqFKDe/H0nHyzZS2BNTxaNuQJ/SyZM6Qgnj8Ogt+Gy+x1dooic4VyHOQ3DYNmeo3yyLI4VsUeL2ras58cdUQ0Z3LH+pa25dimsBRC3FLbMhh1zIT/rn/vqNDVnzm55HYR2rDRhymozWLIzmVkx+1i+55/nr2lQDR66ognXtw/Fw+0SDqkV5EHsIvNwZ+wi4NRHsn+YOb9Wp7vU41SBFKAcrKoEqK2H07nhg5VYbQbT7+zMgDYhMP8ZWP0hBLaEh1dUqzNrRKqKbQnpzFi5j182J5BbYA709vVw5YaO9bm5cwM6hgVUXK9UXhbsnAdbvoW4JWDN++c+v/rmmKnIfubSMV4V/3564Fg2364/yPfrDxWNKbNYoG+LIIZ2b0yPJnVxcbHzubJZYf9K2PId7PjZ/EJaKLKf+cW0aX9wqYCB/1KMApSDVYUAlW+1ccPUlWw/ksGgtvX44I5OcDQWPowypy2483vzP7qIOK207Dy+W3+IL9ccIO7oP71A4XV9ubFjfQZ3rF9xY6UAcjJgzwLY8QvsWVi8Z8rFDcKizKkRGnU3x02VU8/MiZx8FmxL4tv1B1kdl1p0u7+3O0MuC+OuyxvZ/7zknjDPUIxdBLt+MycfLuQbBO3+bR6m0wk5DqUA5WBVIUB9uDSWSfN3EeDjzsIxV5jLRHx1G+yaZ34zuuNbR5coImXEMAxW7T3Gd+sPMX9rYtEyIwCdGgYwoE0IA1rXo2GdCgxT+Sdh3wozcOxZCKl7i9/v6mGux9eom3moL7SjedjrEnvOTuTks3hHMnP/PsKy3SnkWc2eOYsFekbW5d9dwri6VXDJ59XKy4KEjXBwDexdAgdizC+fhbz8oeX15hI4jXupt6mSUIByMGcPUPuOZhH97jJyC2y8fUt7burcAPbHwIwBYHGFR2Kq/QzDIlVVZm4Bv29N5IeNh1i19xinf0K0rOdH/1bBXNE8kPYNAnC199BVaaTGmfPO7V8F+1ZCZuLZbbxrm0EquBXUbWaOp6rbDHzrnHOTB1OzWbIrmaW7UlgRe5S8U4czAZoE+jK4Q31u7NzgwuvUGQZkpUDKLvPM5KStcGgdJG0Hw1q8be2I4ocktXZgpaMA5WDOHKAMw+DO/65hZewxekbW5fNhXbEAfBZtfpPqNBSun+LoMkWkAiRl5LBgWyLztyWyOi4Vq+2fjws/Lzd6Nq1Lr6aBRIXXJryub8WNmzIMOB5vhqmDa8w14ZK3F+/hOZ1XAPjVJ883hCTqsDfHj79TXdh7wo0T+JBh+JCNF/UCfOndPIjezevRuK4vFmue2ZOUlwn52XAyzQxuJ5LMQ3AnjphnFuaknXu/fg3MQ42NekBkXx2ecwIKUA7mzAHq+/WHGPftZjzdXFgwpjeN6viaAzy/vg3cvMxpC/w0eaFIdZOalcei7Uks3Z3Mij1HycgpHlZq+bjTuVEtOjWqRYewAFqE+FHb16PiCszPgeRtcGQzpOzGlrKLguTdeGQeqoCdW6BWI6jb3Oydb9AFGlym90onpADlYM4aoI5l5tLvnT85np3PUwNaMPzKJubZItN6QMoO6DkG+r3o6DKlCtGs4s6pwGrj78PpLN99lBWxKWw+lF7s8FehwJqetAipSfPgmjSu60uDWt40qOVDg1reZbJGn2EYHM3M40BqFvuPZZ+6ZLEz8QSxyZkU2Ay8ySHMkkKIJZUQSyqta2TSyjeTBl45BLrn4pZ/wlwUOS/bXHrGsJ76aQNXT/DwNS/uPuBZE2rWg5oh/1xqhZs9S5pqoEqw5/Nb56BLkVd/3cHx7HxahNTk/l7h5o2bvzbDk1cA9BjtyPKkCoqLiyM21lyiwpkDVHULgm6uLnRqWItODWvxWL+m5BXY2JaQzvr9x9lw4DhbDqdzMPUkKSdySTmRW2zepEK1fT0I8HEnwNudWj4e+Pu44+XuioerC24uFtxcXXCxQG6BjZx8a9HPtOx8jmXlcSwzl9SsPAps5+8DCPBxp3VoHVqHtqJLo1pc1rg2tSqyV0yqNAUoJ1dWb9zLdqfw48bDWCzwxk3tcHd1MbvEl7xuNug1FrwDyqZokVMiIiKK/XRWVSUIXioPNxc6NqxFx4a1im7LzC1gd9IJdiWal0PHszl0/CSHjp8kM7eA1Kw8UrPyLrDVkrFYINTfm4a1fWhUx4eGdXxoFlSTVqF+1PP3qrhxWVLtKEA5ubJ4487Jt/L8nK0ADO3WmA5hAeYd6z6BjEPmZHZdHyyLckWKCQ0NrRKBo6oEwbJUw9OtqJfqdIZhkH4yn8SMHNKy80nLzjN/nswnN99GvtVGvs1GgdXAZhh4ubvi6eaCp5v5M8DHnTo1PKnj60GdGh7U9vXA001TAEjFU4BycmXxxj31j1gOpGZTz9+Lx6NPTU+Qkw7L3zZ/v/JpHd8XuYCqEgQrgsViIcDHgwAfHUoT56YA5eRK+8Ydm3yCj5aZE9RNuK71P2thxXxgLi9Qtxm0v70sShUREakyLmHlQ6kqDMPguR+3km816NsiiOjWweYd2akQ86H5e5/ntN6diFR7CQkJrFixgoSEBEeXIpWEAlQ19sOGw6yJT8XL3YUXr2/9z2DLmKmQdwKC25hLDVQAvTmJSGVWON40Li7O0aVIJaGuhWoqLTuP1+btAODRvk3/WRgz6xis+cj8/cpnwKViMnZ1P4tJRCo3nSggZ1KAqqb+M38nqVl5NA2qwf09T3tDWDXFXLYgpB20GFRh9ejNSUQqM50oIGdSgKqG1u9P5au1BwF4dXAbPNxO9TJlpsDaT8zf+zx7yauaXwq9OYmIiDPRGKhqpsBq4/k52wC4uXMDoiJOW6V81XuQn2WuZt5sgIMqFBERqfwUoKqZL1bvZ8eRDPy93XlmYIt/7jiRBGs/NX+/smJ7n0RERJyNAlQ1knwih7cX7Abgiejm1Knh+c+dK9+DgpNQvws0vdpBFYqIiDgHBahq5I15OzmRW0C7Bv7c1rXhP3dkJsNf/zV/7/OMep9EpFLSdCdSmShAVRNr4o7xw6nFgl+5oQ2uLqeFpJgPoCAH6neGJn0dV6SIOJ2KDDWai0kqE52FVw3kW22M/8kcOH7rZQ1pX7hYMJjLtaw71fvU63H1PomIXSpyDjdNdyKViQJUNTBr1T52JZ2glo87TxYuFlxozcfmrONBrXXmnYjYrSJDjaY7kcpEAaqKS87I4d1FewB4akALavmetgJ6biasmWb+3mtshc06LiJVh0KNlKWEhATi4uKIiIio9K8rBagq7o3fdpKZW0D7sAD+3SWs+J1/fWYewqsdAa3/5ZgCRURETnGmZb0UoKqwdftSiwaOv3x9a1xOHzien2MuGgzQcwy4uDqmSBERkVOcaZybAlQVZbUZpw0cDys+cBxg0xeQmQR+DaDdrRVfoIhIJeFMh42qOmc6JKwAVQUlJCTw0R872HEkB39vd56IblG8gTUfVrxn/t7jUXDzOHsjInbQB5A4M2c6bCSVhwJUFbRpRyzfbM8GXHi8fzNq+54RkLZ8B+kHwDcQOt3tkBqlatEHkDgzZzpsJJWHAlQVND/BgxybC03renN7VKPidxoGrJpi/n75cHD3rvgCpcrRB5A4M2c6bCSVhwJUFfP3oTR+2X4MgIk3dyg+4zhA7GJI3g7uvtDlPgdUKFWRPoBEpLrRxD9ViM1mMOHnbRgG/Ktjfbo0rn12o8Lep85DwbtWxRYoIlKFaG2+6k09UFXIjxsPs/FAGr4erjw9sMXZDRI2QfyfYHE1D9+JiMgl09i/6k0BqorIzC3gjfk7ARjVtynBfl5nNyqc96n1vyCgYQVWJyIlobMZnYsjx/7pteJ4ClBVxPt/7CHlRC6N6/hwb4/GZzdIOwBbfzB/7/FohdYmIiWjHg3n4sixf3qtOJ4CVBUQl5LJZyviARh/XSs83c4xq/jq6WBYIfwKqNe+gisUkZLQ2YxSUnqtOJ4CVBXwytzt5FsN+jQP5KoWwWc3OJkGG2aZv3dX75NIZaWzGaWk9FpxPJ2F5+T+2JnEkl0puLtaeOHaVudu9NdnkJcJQa0hsm/FFigiIlIFKUA5sbwCG6/M3QHAfT3DiQiscXajgjxY85H5e/dRYLGc3UZERETs4jQB6rXXXqN79+74+PgQEBBwzjYHDhxg0KBB+Pj4EBQUxBNPPEFBQUGxNkuXLqVTp054enoSGRnJzJkzz9rOBx98QOPGjfHy8iIqKoq1a9eWwyMqvRkr44k/mkVgTU9GXdX03I22/wSZiVAjBNrcVLEFioiIVFFOE6Dy8vK45ZZbGD783PMXWa1WBg0aRF5eHqtWrWLWrFnMnDmT8ePHF7WJj49n0KBB9OnTh02bNjF69Gjuv/9+fv/996I233zzDWPHjmXChAls2LCB9u3bEx0dTXJycrk/Rnskn8jh/T/MMzCeGtCCGp7nGc62Zpr587JhWjS4EtDEeyIiVYPFMAzD0UXYY+bMmYwePZq0tLRit//2229ce+21JCQkEBxsDqSePn06Tz31FCkpKXh4ePDUU0/x66+/snXr1qK/u/XWW0lLS2P+/PkAREVFcdlllzF1qjlnks1mIywsjFGjRvH000+XqMaMjAz8/f1JT0/Hz8+vDB712Z74djPfrj9E+wb+/PhID1zOXLIF4OA6+G8/cPWAMduhRmC51CIlt2LFCmJjY4mMjKRnz56OLkdERE5jz+e30/RAXUxMTAxt27YtCk8A0dHRZGRksG3btqI2/fr1K/Z30dHRxMTEAGYv1/r164u1cXFxoV+/fkVtziU3N5eMjIxil/K0+WAa364/BMCE61ufOzzBP71PbW9ReKokIiIiiIyM1KnHIiJOrsoEqMTExGLhCSi6npiYeME2GRkZnDx5kqNHj2K1Ws/ZpnAb5zJx4kT8/f2LLmFhYWXxkM7JMAxe+sUMhDd2rE+nhudZzy4jwRz/BBD18CXtS4ebyl5oaCg9e/bU6cciIk7OoQHq6aefxmKxXPCyc+dOR5ZYIs888wzp6elFl4MHD5bbvn7alMCGA2n4eLjy1LnWuyu07r9gK4BGPaBeu0vaV+FMt3FxcZdYrYiISNXk0Ik0x40bxz333HPBNiU91BESEnLW2XJJSUlF9xX+LLzt9DZ+fn54e3vj6uqKq6vrOdsUbuNcPD098fT0LFGdpZGVW8DE38xpC0b0iTz3encA+Tmwfob5e9RDl7w/zXQrIiJybg4NUIGBgQQGls3YnG7duvHaa6+RnJxMUFAQAAsXLsTPz49WrVoVtZk3b16xv1u4cCHdunUDwMPDg86dO7N48WIGDx4MmIPIFy9ezMiRI8ukztKYtnQvSRm5NKztw7Ce4edvuOVbyD4G/mHQfNAl708z3YpUH1qcVsQ+TrOUy4EDB0hNTeXAgQNYrVY2bdoEQGRkJDVq1KB///60atWKu+66i0mTJpGYmMjzzz/PiBEjinqHHn74YaZOncqTTz7Jfffdxx9//MHs2bP59ddfi/YzduxYhg4dSpcuXejatSvvvvsuWVlZ3HvvvY542MV0aVyLiEBfnhrQAi/3c6x3B2AYsGa6+XvXB8DVaf6JRcSBtDitiJ0MJzF06FADOOuyZMmSojb79u0zBg4caHh7ext169Y1xo0bZ+Tn5xfbzpIlS4wOHToYHh4eRkREhDFjxoyz9vX+++8bDRs2NDw8PIyuXbsaq1evtqvW9PR0AzDS09Mv5aFeUF6B1bDZbOdvEL/cMCb4GcarIYaRnVrm+xeRqunw4cPG8uXLjcOHDzu6FBGHsefz2+nmgXIG5TkP1EW72WcPhe1zoPO9cN27ZbpvERGRqsyez28d33EyF+xmP5EIO+eav192fwVXJiIiUn0oQDmZC54Zt+H/zKkLwi6HkDYVXJmIiEj1oQDlZM57Zpy1AP46NXWBep9Eqg2dPSfiGFVmJvJqb/d8OJEAPnWh1fWOrkZEKkh1nvBWqyWII6kHqqpY96n5s9Nd4Fb+k3qKSOVQnSe81dQL4kgKUFXBsb0QtwSwmGffiUi1UZ0nvK3O4VEcTwGqKvjrM/Nn0/5Qq5FjaxGRKqOyj6+qzuFRHE8Bytnln4SNX5i/a/C4iJQhHSITOT8FKGe39QfISYOAhhDZ19HViEgVokNkIuenAOXsCgePd7kPXM6zPp6IyCXQITKR89M0Bs7syN+QsAFc3KHjXY6uRkREpNpQgHJmG/7P/NnyWvCt69haREREqhEFKGeVlw1/zzZ/73S3Y2sRERGpZhSgnNWOnyE3HQIaQfiVjq5GRESkWlGAclbrZ5k/O90FLvpnFBERqUj65HVGKbvhwCqwuECHOxxdjYiISLWjAOWMNp4aPN40Gvx0irGIiEhFU4ByNgV5sOkr8/fOQx1bi4iISDWlAOVsds2D7KNQsx5EXu3oakRERKolBShns+HU4PEOd4CrJpIXERFxBAUoZ3J8P+xdYv7e8U7H1iIiIlKNKUA5k41fAAZEXAm1wx1djYiISLWlY0DOpHEPSLoG2t/q6EpERESqNQUoZxJxpXkRERERh9IhPBEREQdKSEhgxYoVJCQkOLoUsYN6oERERBwoLi6O2NhYAEJDNTmys1CAEhERcaCIiIhiP8U5KECJiIg4UGhoqHqenJDGQImIiIjYSQFKRERExE4KUCIiIiJ2UoASERERsZMClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlIiIiYicFKJFylJCQwIoVK0hISHB0KSIiUobcHF2ASFUWFxdHbGwsAKGhoQ6uRkREyooClEg5ioiIKPZTRKS6SEhIIC4ujoiIiCr5BVIBSqQchYaGVsk3DhGRi6nqPfAKUCIiImeo6r0nFaGq98ArQImIiJyhqveeVISq3gOvACUiInKGqt57IqWnACUiIhXKGQ6PVfXeEyk9BSgREalQOjwmVYFTTKS5b98+hg0bRnh4ON7e3jRp0oQJEyaQl5dXrN3ff/9Nr1698PLyIiwsjEmTJp21rW+//ZYWLVrg5eVF27ZtmTdvXrH7DcNg/Pjx1KtXD29vb/r168eePXvK9fGJiFQnERERREZG6vCYODWnCFA7d+7EZrPx0UcfsW3bNiZPnsz06dN59tlni9pkZGTQv39/GjVqxPr163nzzTd58cUX+fjjj4varFq1ittuu41hw4axceNGBg8ezODBg9m6dWtRm0mTJjFlyhSmT5/OmjVr8PX1JTo6mpycnAp9zCIiVVVoaCg9e/aslr1PWp2g6rAYhmE4uohL8eabbzJt2jTi4uIAmDZtGs899xyJiYl4eHgA8PTTTzNnzhx27twJwJAhQ8jKymLu3LlF27n88svp0KED06dPxzAMQkNDGTduHI8//jgA6enpBAcHM3PmTG699dYS1ZaRkYG/vz/p6en4+fmV5cMWEREntmLFCmJjY4mMjKRnz56OLkfOYM/nt1P0QJ1Leno6tWvXLroeExND7969i8ITQHR0NLt27eL48eNFbfr161dsO9HR0cTExAAQHx9PYmJisTb+/v5ERUUVtTmX3NxcMjIyil1ERETOpMOXVYdTBqjY2Fjef/99HnrooaLbEhMTCQ4OLtau8HpiYuIF25x+/+l/d6425zJx4kT8/f2LLmFhYZf4yEREpCqrzocvqxqHBqinn34ai8VywUvh4bdChw8fZsCAAdxyyy088MADDqq8uGeeeYb09PSiy8GDBx1dkoiIiJQjh05jMG7cOO65554Ltjm9mzMhIYE+ffrQvXv3YoPDAUJCQkhKSip2W+H1kJCQC7Y5/f7C2+rVq1esTYcOHc5bo6enJ56enhd8HCIiIlJ1ODRABQYGEhgYWKK2hw8fpk+fPnTu3JkZM2bg4lK886xbt24899xz5Ofn4+7uDsDChQtp3rw5tWrVKmqzePFiRo8eXfR3CxcupFu3bgCEh4cTEhLC4sWLiwJTRkYGa9asYfjw4aV8tCIiIlJVOMUYqMOHD3PllVfSsGFD3nrrLVJSUkhMTCw2Lun222/Hw8ODYcOGsW3bNr755hvee+89xo4dW9TmscceY/78+bz99tvs3LmTF198kb/++ouRI0cCYLFYGD16NK+++io///wzW7Zs4e677yY0NJTBgwdX9MMWERGRSsopZiJfuHAhsbGxxMbG0qBBg2L3Fc7C4O/vz4IFCxgxYgSdO3embt26jB8/ngcffLCobffu3fnyyy95/vnnefbZZ2natClz5syhTZs2RW2efPJJsrKyePDBB0lLS6Nnz57Mnz8fLy+vinmwIiIiUuk57TxQlZnmgRIREXE+1WIeKBERERFHUYASERERsZMClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAaoaSEhIYMWKFSQkJDi6FBERkSrBKWYil9KJi4sjNjYWgNDQUAdXIyIi4vwUoKqBiIiIYj9FRESkdBSgqoHQ0FD1PImIiJQhjYESERERsZMClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlIiIiYicFKBERERE7uTm6gKrIMAwAMjIyHFyJiIiIlFTh53bh5/iFKECVgxMnTgAQFhbm4EpERETEXidOnMDf3/+CbSxGSWKW2MVms5GQkEDNmjWxWCxluu2MjAzCwsI4ePAgfn5+ZbrtqkbPVcnpuSo5PVclp+eq5PRclVx5PleGYXDixAlCQ0NxcbnwKCf1QJUDFxcXGjRoUK778PPz03+yEtJzVXJ6rkpOz1XJ6bkqOT1XJVdez9XFep4KaRC5iIiIiJ0UoERERETspADlZDw9PZkwYQKenp6OLqXS03NVcnquSk7PVcnpuSo5PVclV1meKw0iFxEREbGTeqBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAcpJvPbaa3Tv3h0fHx8CAgLO2cZisZx1+frrryu20EqiJM/XgQMHGDRoED4+PgQFBfHEE09QUFBQsYVWQo0bNz7rdfTGG284uqxK44MPPqBx48Z4eXkRFRXF2rVrHV1SpfPiiy+e9Rpq0aKFo8uqFJYtW8Z1111HaGgoFouFOXPmFLvfMAzGjx9PvXr18Pb2pl+/fuzZs8cxxTrYxZ6re+6556zX2YABAyqsPgUoJ5GXl8ctt9zC8OHDL9huxowZHDlypOgyePDgiimwkrnY82W1Whk0aBB5eXmsWrWKWbNmMXPmTMaPH1/BlVZOL7/8crHX0ahRoxxdUqXwzTffMHbsWCZMmMCGDRto37490dHRJCcnO7q0Sqd169bFXkMrVqxwdEmVQlZWFu3bt+eDDz445/2TJk1iypQpTJ8+nTVr1uDr60t0dDQ5OTkVXKnjXey5AhgwYECx19lXX31VcQUa4lRmzJhh+Pv7n/M+wPjxxx8rtJ7K7nzP17x58wwXFxcjMTGx6LZp06YZfn5+Rm5ubgVWWPk0atTImDx5sqPLqJS6du1qjBgxoui61Wo1QkNDjYkTJzqwqspnwoQJRvv27R1dRqV35nu2zWYzQkJCjDfffLPotrS0NMPT09P46quvHFBh5XGuz7ehQ4caN9xwg0PqMQzDUA9UFTNixAjq1q1L165d+eyzzzA0zdc5xcTE0LZtW4KDg4tui46OJiMjg23btjmwssrhjTfeoE6dOnTs2JE333xThzYxezXXr19Pv379im5zcXGhX79+xMTEOLCyymnPnj2EhoYSERHBHXfcwYEDBxxdUqUXHx9PYmJisdeYv78/UVFReo2dx9KlSwkKCqJ58+YMHz6cY8eOVdi+tZhwFfLyyy9z1VVX4ePjw4IFC3jkkUfIzMzk0UcfdXRplU5iYmKx8AQUXU9MTHRESZXGo48+SqdOnahduzarVq3imWee4ciRI7zzzjuOLs2hjh49itVqPefrZufOnQ6qqnKKiopi5syZNG/enCNHjvDSSy/Rq1cvtm7dSs2aNR1dXqVV+N5zrtdYdX9fOpcBAwZw4403Eh4ezt69e3n22WcZOHAgMTExuLq6lvv+FaAc6Omnn+Y///nPBdvs2LGjxIMvX3jhhaLfO3bsSFZWFm+++WaVCVBl/XxVJ/Y8d2PHji26rV27dnh4ePDQQw8xceJEhy+dIM5h4MCBRb+3a9eOqKgoGjVqxOzZsxk2bJgDK5Oq5NZbby36vW3btrRr144mTZqwdOlS+vbtW+77V4ByoHHjxnHPPfdcsE1ERMQlbz8qKopXXnmF3NzcKvHBV5bPV0hIyFlnTyUlJRXdV9WU5rmLioqioKCAffv20bx583KozjnUrVsXV1fXotdJoaSkpCr5milLAQEBNGvWjNjYWEeXUqkVvo6SkpKoV69e0e1JSUl06NDBQVU5j4iICOrWrUtsbKwCVFUXGBhIYGBguW1/06ZN1KpVq0qEJyjb56tbt2689tprJCcnExQUBMDChQvx8/OjVatWZbKPyqQ0z92mTZtwcXEpep6qKw8PDzp37szixYuLzm612WwsXryYkSNHOra4Si4zM5O9e/dy1113ObqUSi08PJyQkBAWL15cFJgyMjJYs2bNRc/AFjh06BDHjh0rFj7LkwKUkzhw4ACpqakcOHAAq9XKpk2bAIiMjKRGjRr88ssvJCUlcfnll+Pl5cXChQt5/fXXefzxxx1buINc7Pnq378/rVq14q677mLSpEkkJiby/PPPM2LEiCoTOC9FTEwMa9asoU+fPtSsWZOYmBjGjBnDnXfeSa1atRxdnsONHTuWoUOH0qVLF7p27cq7775LVlYW9957r6NLq1Qef/xxrrvuOho1akRCQgITJkzA1dWV2267zdGlOVxmZmaxnrj4+Hg2bdpE7dq1adiwIaNHj+bVV1+ladOmhIeH88ILLxAaGlotp6S50HNVu3ZtXnrpJW666SZCQkLYu3cvTz75JJGRkURHR1dMgQ47/0/sMnToUAM467JkyRLDMAzjt99+Mzp06GDUqFHD8PX1Ndq3b29Mnz7dsFqtji3cQS72fBmGYezbt88YOHCg4e3tbdStW9cYN26ckZ+f77iiK4H169cbUVFRhr+/v+Hl5WW0bNnSeP31142cnBxHl1ZpvP/++0bDhg0NDw8Po2vXrsbq1asdXVKlM2TIEKNevXqGh4eHUb9+fWPIkCFGbGyso8uqFJYsWXLO96ahQ4cahmFOZfDCCy8YwcHBhqenp9G3b19j165dji3aQS70XGVnZxv9+/c3AgMDDXd3d6NRo0bGAw88UGxqmvJmMQyd5y4iIiJiD80DJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInZSgBIRERGxkwKUiIiIiJ0UoERERETspAAlIiIiYicFKBERERE7KUCJiIiI2EkBSkTkIlJSUggJCeH1118vum3VqlV4eHiwePFiB1YmIo6ixYRFREpg3rx5DB48mFWrVtG8eXM6dOjADTfcwDvvvOPo0kTEARSgRERKaMSIESxatIguXbqwZcsW1q1bh6enp6PLEhEHUIASESmhkydP0qZNGw4ePMj69etp27ato0sSEQfRGCgRkRLau3cvCQkJ2Gw29u3b5+hyRMSB1AMlIlICeXl5dO3alQ4dOtC8eXPeffddtmzZQlBQkKNLExEHUIASESmBJ554gu+++47NmzdTo0YNrrjiCvz9/Zk7d66jSxMRB9AhPBGRi1i6dCnvvvsun3/+OX5+fri4uPD555+zfPlypk2b5ujyRMQB1AMlIiIiYif1QImIiIjYSQFKRERExE4KUCIiIiJ2UoASERERsZMClIiIiIidFKBERERE7KQAJSIiImInBSgREREROylAiYiIiNhJAUpERETETgpQIiIiInb6fwa12cmhtM2UAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
" ] @@ -740,16 +740,18 @@ ], "source": [ "def show_best_fit(state):\n", - " state.experiment_data.plot.scatter(\"x\", \"y\", s=1, alpha=0.5, c=\"gray\")\n", + " fig, ax = plt.subplots(1,1)\n", "\n", " observed_x = state.experiment_data[[\"x\"]].sort_values(by=\"x\")\n", " observed_x = pd.DataFrame({\"x\": np.linspace(observed_x[\"x\"].min(), observed_x[\"x\"].max(), 101)})\n", "\n", " plt.plot(observed_x, state.model.predict(observed_x), label=\"best fit\")\n", - " \n", + "\n", " allowed_x = pd.Series(np.linspace(*state.variables.independent_variables[0].value_range, 101), name=\"x\")\n", " plt.plot(allowed_x, ground_truth(allowed_x), label=\"ground truth\")\n", - " \n", + "\n", + " state.experiment_data.plot.scatter(\"x\", \"y\", s=1, alpha=0.75, c=\"black\", ax=ax, zorder=2)\n", + "\n", " plt.legend()\n", "\n", "def show_coefficients(state):\n", @@ -774,7 +776,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -806,7 +808,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -974,7 +976,26 @@ "source": [ "## Adding The Experimentalist\n", "Modifying the code to use a custom experimentalist is simple.\n", - "We define an experimentalist which adds four observations each cycle:" + "We define an experimentalist which adds some random observations each cycle:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.experimentalist.pooler.random_pooler import random_pool_executor\n", + "\n", + "experimentalist = random_pool_executor" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "If we call the experimentalist with `num_samples=4`, the state is returned with new\n", + "conditions:" ] }, { @@ -987,8 +1008,11 @@ "text/plain": [ "Snapshot(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), params={}, experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", - "Index: [], conditions= x\n", - "0 2.281691, model=None)" + "Index: [], conditions= x\n", + "0 0.354649\n", + "1 13.513911\n", + "2 -10.675212\n", + "3 13.459483, model=None)" ] }, "execution_count": null, @@ -997,18 +1021,14 @@ } ], "source": [ - "experimentalist_rng = np.random.default_rng(180)\n", - "@wrap_to_use_state\n", - "\n", - "def experimentalist(variables: VariableCollection, n_samples=1):\n", - " names = [v.name for v in variables.independent_variables]\n", - " low = [v.value_range[0] for v in variables.independent_variables]\n", - " high = [v.value_range[1] for v in variables.independent_variables]\n", - " x_range = experimentalist_rng.uniform(low, high, size=n_samples)\n", - " conditions = pd.DataFrame({\"x\": x_range})\n", - " return Delta(conditions=conditions)\n", - "\n", - "experimentalist(s)" + "experimentalist(s, num_samples=4, random_state=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can call the experimentalist as part of the cycle:" ] }, { @@ -1018,7 +1038,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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", 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", + "image/png": 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FFKAqp7V7Mrn500Xsy86lSqAP7w9tS8e6J7QkbZwJP46ErD3m49g2cOlYqNujfASnfzMMSE40g9S6HwEDsJljpLo/CtXqn9Np0zNzeHzqamavNacHaV49hLeGtKFuRNBZjhSRSm3DLzBpiHn18P0rSuUl3Pn+1p99IiVg6Y6DDPkwkX3ZuTSODuaH4V3/CU9HDsCUO+HL68zwFF4Hrvsc7pgL9S4pn+EJzLpqd4HBn8M9C6DpAMCA1d/CuxfAtHvNq/zcFBnix4c3teOt69sQFuDN6t2Z9HnrLyYvTkZ/z4nIaW2eY/6sd4m1dRyjFqhSoBaoyuWvTfu48/MlHMlz0r52OJ/EX0Co/7EJJNf+ANNHw+G9YLND5+HQ/THw8dAr0VJWwNznzXlYwLxy75InoN0t53TVXmpGDqO+TiocG3Vli2jGXd1SE3CKyMneagsHtsDgidCkb6m8hLrwLKYAVXnMXJPKvV8uJ8/p4qIG1fjfTe0I8PGCgjyY+ag5SBwgogn0fxdqtLO24JKyIxF+eQhSV5mPo1pAn1egVie3T+VyGXz451ZembmBApdB9TB/3hvallY1w0q2ZhHxXAe3w5utwOaAR7aDX+l8t6oLT6QM/LIqhXsmLiPP6aJ3s2g+HtbeDE+ZKZDQ51h4ssFFo+E/v1ec8ARQuzPc+bt5JYxfKKStgk97mWO8crPcOpXdbuOubvWYck8X4qoGsPvQUa79IJEvFuxQl56ImI5339XsUGrhyV0KUCLn4PeNe7nvq+U4XQYD21bnnRva4OvlMKcC+N/FsGuRGSxu+NocKO5VAddKtDvMy4jvXQZtbza3LR0P73WBrb+7fbqWNcKYdu+FXN40ijyni/9OXc3ob1ZwNM9ZwoWLiMfZ8pv5s96l1tZxAgUoETct2X6A/3y+hHynQZ8WMbx8TStzUsjFn8CEq+BwOkQ2MweJN7zc6nJLX2A16Pc2DPsRwmpBRjJ81s8c+5Wb7dapQvy8+d9N7Xj0isbYbTBl2W6ufu9vdh44UkrFi0i558yHbX+Y9+uXjwHkoAAl4pY1ezK4JWExOfkuujWM4PXBrXHYgN9fgumjzKVRmg+C22eXyhwl7po2bRoDBw5k2rRppf9idS6GuxOh/W3m48UfwwcXwp7lbp3GZrPxn271mHh7J6oF+bI+NYv+7/7Nwq37S6FoESn3di2B3EzwrwIxra2uppAClEgxbdt3mGGfLiIrp4AL4sL54MZ2+NiBGY/C3OfMnbo9AoM+AZ/TLBRcxhISEpg7dy4JCQll84K+QdD3Nbj5B3Oh4oPb4OPLIPE9c14pN3SuV5Uf7+1K8+ohHDicx42fLOSrRcmlVLiIlFtbjo1/qtvdHDpQTihAiRTDoSN53JqwmH3ZeTSLDeGT+Avwdxjwwz2w8H1zp94vmjN1l6N5neLj4+nRowfx8fFl+8J1u8Ndf5qzl7vyzSsSv7rBnBPLDTGh/nzzny70aRlDvtNgzJRVPDVtDQVOV+nULSLlz/HxT/XLz/gn0DQGpULTGFQs+U4Xwz5dxPwt+6ke5s/U4V2J8Ae+uQU2TDcvqx3wHrQaYnWp5Y9hmF15Mx8z1/wLqQ7XToCaF7h5GoO3f9vMa7M3AnBp40jevqGNedWjiFRch/fDy/UAA0atg5DYUn05TWMgUkIMw+CpY4vgBvg4+HhYeyICHPDdbWZ4cvjCkIkKT6djs5lX6t3+K1SpB5m7IeFKWPa5m6excd+lDXh/aFt8vezMWZ/O9R8uYG9WbikVLiLlwuZfAQOimpd6eHKXApTIGXyWuIOJC5Ox2eDNIW1oEhUEP95nrg3n8IEbJkOjK6wus/yLaWXOhdW4r9kSNW0E/PyQeXWNG65oEcOXd3QiPMCbFbsyGPj+32zZ696VfiLiQTbNNH82KH9XNCtAiZzGn5v28sxPawF4pHdjLmsSCbMeh6SJZrfdNeOhXg+Lq/QgvsHmGoA9HjcfL/oQPusP2XvdOk272uFMuacrtasGsPPAUQa9P5+lO9wbWyUiHsBZcKwFCmjYy9paTkEBSuQU9hw6yr2TzIkyB7WtwX8urmtOVbDgPXOH/u+W2lpMFZrdDt0ehiGTwCcYdvwNH/WA9HVunaZOtUC+u7sLrWuGcehIPkM/Xsi8DemlVLSIWGLXIsjJAP9wqOHeuMmyoAAl8i/5Thf3TlrOoSP5tKgeyvMDm2Nb/DHMe97c4YqXoPX11hbp6RpfCXf8Zo6LytgJn/SCrfPcOkW1IF8m3dGJ7o0iyMl3cfuEJUxbsad06hWRsrfxWPdd/Z7lavqC4xSgRP7l1VkbWbrjIMG+Xrx7Q1t8t8+FXx42n+z+GHT8j7UFVhQRDc3B5bU6Q24GfDEIlk906xT+Pg4+vKk9/VrFUuAyuP+r5Xy+YEcpFSwiZWrTLPNng/LXfQcKUCJFzN2Qzge/bwHgpWtaUsvYA9/cCoYLWt9odj9JyQmoAjdNNWdvdxWY82r99n9uTbrp42XnjcGtualTbQwDnpi6mnfnbi69mkWk9B3aCelrwWYvd/M/HacAJXJMSsZRRk1OAmBY59pcUd8fJg0xW0dqdjRn2C5Hk2RWGN5+MPBjuGi0+fiPl+GH4eYA0mKy2208078Z911SH4CXZ27gtVkb0DR3Ih7q+NV3NTqYf2iVQwpQIoDTZXD/pCQOHsmnefUQHruioTnX0/5NEFIDBn8BXr5Wl1lx2e1w6Vi46i3zL86kifD1zZCfU+xT2Gw2Rl3eiEevaAzAW79t5oUZ6xWiRDzRxmPdd+V4QXYFKBHgk7+2smj7AYJ8vXjn+rb4zn3avHzWyx+u/xKCIq0usXJoN8yc6sDha05U+sUgyMl06xT/6VaPJ69qCsD/ft/K0z+uVYgS8ST5R2HbH+b9cjr+CRSgRNiUlsUrs8wlQp7o24S41JmQ+I755NXvm5NAStlp0hdu/O7YNAd/wYS+bs8VdUvXOjx3dXMAEuZv5/Gpq3G5FKJEPMK2P6HgqLn0U1Qzq6s5LQUoqdQKnC4e/GYFeQUuejSK4Lp6TvjxfvPJC0dBs6utLbCyqnMRxP8EAdUgZQWM7w0Zu906xdCOtXnpmpbYbPDlwmTGTlutligRT3Di7OPleNypApRUah/8voUVuzII8fPihQFNsH13O+RmmoPGj8+YLdaIbQ23zoTQmrB/s7mG3qFkt05xXfuavHJNK2w2+GJBMk9NW6MQJVKeGcYJ45/Kb/cdKEBJJbZ2TyZvztkEwNP9mxG19FXYvQT8QmHQx+DwsrhCoVp9uOVnCKsNB7fD+D5wYJtbpxjUrgYvDjJboiYk7uCZnzQmSqTc2rseMpLNcZB1Lra6mjNSgJJKKf9Y112+0+DyplEMCNkEf71hPtnvbQirZWl9coKwWnDLL8dmLU+GhD6wf4tbp7iufU3GXd0CgPF/b+e56esUokTKo+Ozj9e5GHwCra3lLBSgpFL6+M9trE3JJDzAm+d7RWP7/j+AAe1ugab9rS5P/i20utkSVa0RZO6G8VfC3o1unWJIh1qFA8s//msbr89273gRKQMbfjF/lvPuO1CAkkpo54EjvDnH/PL875VNqDZnNGSnQUQT6PW8xdXJaQVHQ/x0iGwG2akw4Sq3W6KGdqzN0/3Mq3re+m0z//vdveNFpBRlp8POheb9RldaW0sxKEBJpfP0j2vIyXfRsU4VBnr/DRtngMMHrvkEfAKsLk/OJCgChv1YNES5OSZqWJc4Hu7dCIBxv6znC62dJ1I+bPgFMCC2jdnqXM4pQEmlMmtNKr+uS8fLbmPc5ZHYfnnEfKLbI+V6vhE5QWBVuPkHiGhsdudNuAoOuheC7ulen3u61wPgiR9W8/3yXaVRqYi4Y/1082fjPtbWUUwKUFJpHMkr4Okf1wJw58V1qbvoKcg5BNEtoev9ltYmbgqKgJunQdX6kLHTDFEZ7oWgh3o1Ir5LHIYBD36zkllrUkupWBE5q9xs2DrPvN+4r6WlFJcClFQab87ZxO5DR6kR7s/9MWth3TSwe0H/d8HhbXV54q7gKLM7L7wOHNphhqis4ocgm83G2L5NuaZdDZwugxGTlrNg6/5SLFhETmvLHHDmQpW6ZuuyB1CAkkphY1oWn/xpjpV5rlcsvrMeNp+48AGIaWlhZXJeQmLNGcvDasGBrfD51XDkQLEPt9ttvDCwBZc1jSKvwMUdE5awZk9GKRYsIqd0YvddOZ59/EQKUFIpPPvTWgpcBpc1jaLb1tfg8F7zr5yLH7K6NDlfoTXM7rygaEhfCxOvgdysYh/u5bDz9vVt6FCnClm5BQz7dDE79h8uxYJFpAhnvnkxD0Ajzxj/BApQUgnM25DOn5v24eOw83/NU2HlZLDZza47L1+ry5OSUKUO3DwV/MNh91L46gbIzyn24X7eDj4e1p4mMSHsy87lxk8Wkp5Z/ONF5Dzs+BtyMsy1L2t2sLqaYlOAkgqtwOniuenrALi1UwxRf401n+h4F9Rob2FlUuIim8CN34FPMGz7A76JN/+yLaYQP28m3HoBtasGsPPAUeLHLyYrp/jHi8g5Wv+z+bPRFWB3WFuLGxSgpEL7avFONqVnEx7gzf1Bc+DAFgiMhO6PWl2alIbq7eCGr8DLDzb+Aj+MAJer2IdHBvvx+a0dqRbkw9qUTO6ZuIy8guIfLyJuMowTxj95xtV3xylASYWVlZNfuFzHo11D8J//qvnEZc+AX4iFlUmpirsQrvsMbA5Y+RX8Otatw2tVDeDT+AsI8HHw56Z9jJmyUuvmiZSWlBWQuQu8A6FuN6urcYsClFRY783bwv7DedStFsg1B/4H+YehZkdoOdjq0qS0NexljnEDmP82/P2WW4e3rBHGu0Pb4rDbmLJsN6/M2lAKRYpIYetT/UvB29/aWtykACUV0q6DR/jkL3PagpfaZ2FfMwWwwZUvg12/9pVC6+vN1kaA2U9A0iS3Du/RKJJxV7cA4N25W/hcS76IlLwNx8Y/ecjs4yfSN4lUSK/O2khegYuudcJot/bYAsHtb4WYVtYWJmWr6/3QeYR5/4fhsHGWW4dfd0FNRvZsAMCTP6zmt/VpJV2hSOV1YCukrTa72xtcbnU1blOAkgpnU1oWU5N2A/By3GJs6WvBvwpc8l+LKxNLXPas2W1rOOGbYeY0B264/9IGXNuuBi4DRny5XBNtipSUNVPNn3UuhoAqlpZyLhSgpMJ5/deNGAYMaBxI7PLXzY2XPuGR/4NKCbAfm/Or3qWQfwQmXmf+5VtMNpuN5we2oGv9qhzJc3JrwmJSMo6WYsEilcTaqebPZgOsrOKcKUBJhbJmTwY/r0rFZoMnwmeZiwVHNIG2w6wuTazk8IbrJpgLRx/ZB19cA4eLv+6dt8POe0Pb0SAyiLTMXG5NWEJ2bkEpFixSwR3Yal6BZ3NA46usruacKEBJhXJ82oKbmnhRddUn5saeT3nU5GxSSnyDYeg3EFrTnA9s0hDIL35LUqi/N5/GX0C1IF/WpWQyfOIyCpyaI0rknBR2310EgVUtLeVcKUBJhbE8+SC/rkvHboMHfb+Hghyo1cW8pF0EIDjanK3cLxR2LYLvbgeXs9iH16wSwCfD2uPnbef3jXv5v2Oz3IuImwq77662tIzzoQAlFcZrx1qf7m5aQMj6yebGy572mJW9pYxENIIhk8DhA+t/gpmPu3V4q5phvDG4NQAJ87fzeeL2kq9RpCI7sM3ju+9AAUoqiIVb9/Pnpn142W0MNyaC4TKXBfCghSmlDMV1hav/Z95f+D4s/NCtw3s3j+GhXo0AeOrHtfy5aW9JVyhScR1vffLg7jtQgJIK4njr00NNDxKwdSbY7HDpkxZXJeVa84H//I7MeAQ2znTr8Hu612Ng2+o4XQb3TFzG5vSsUihSpAI6Pv6p6QArqzhvClDi8ZZsP8DCbQfwdkD8kQRzY5ubIKKhpXWJB7jwAfN3xXDBN7dAyspiH2qz2Rg3sAUXxIWTlVPArQlLOHg4rxSLFakADmyDlCSz+66J53bfQQULUE899RQ2m63IrXHjxoXP5+TkMHz4cKpWrUpQUBCDBg0iLa3ozMLJycn06dOHgIAAIiMjeeihhygo0OXK5dl787YA8Fi9ZHz3LAIvf+g+xuKqxCPYbND3dajTzVwr8cvrIGN3sQ/39XLwwY3tqFnFn+QDR7h74lLydWWeyOmt/cH8GXchBFaztpbzVKECFECzZs1ISUkpvP3111+Fzz3wwAP8+OOPfPPNN/z+++/s2bOHgQMHFj7vdDrp06cPeXl5zJ8/nwkTJpCQkMDYse6t5i5lZ11KJr+tT8duM7gh59haZx3ugJBYawsTz+Hwhus+g2qNICsFvhwMudnFPrxqkC+fDLuAQB8HC7Ye4Okf15RisSIezsMnzzxRhQtQXl5eREdHF96qVTMTbkZGBp988gmvvfYal1xyCe3atWP8+PHMnz+fBQsWADBr1izWrl3LF198QevWrbniiit49tlneffdd8nLU9N8efT+sdanB+sk45u+ArwDoMt9FlclHsc/DIZ+DYERkLYKptzh1vQGDaOCeXNIG2w2+GJBshYeFjmVA9tgz3JzjKoHX313XIULUJs2bSI2Npa6desydOhQkpOTAVi6dCn5+fn07NmzcN/GjRtTq1YtEhMTAUhMTKRFixZERUUV7tOrVy8yMzNZs+b0f1Xm5uaSmZlZ5Calb8f+w/y0cg9gEF/wtbmx/a0QFGFpXeKhwuNgyJfg8DVXiP/1KbcO79k06p8r86atYf6WfSVfo4gnO7H7rgL8O12hAlTHjh1JSEhgxowZvP/++2zbto2LLrqIrKwsUlNT8fHxISwsrMgxUVFRpKamApCamlokPB1//vhzpzNu3DhCQ0MLbzVr1izZNyan9L8/tuIyYHjNHQSkLwcvP+h6v9VliSer2cFcNw9g/luw7PNiHTZt2jQGDhxI9Yw1DGgdW3hl3o79h0uxWBEPs/pb86cHT555Ii+rCyhJV1xxReH9li1b0rFjR2rXrs3XX3+Nv79/qb3uo48+yqhRowofZ2ZmKkSVsvTMHL5dsgswuItj/1O2vxWCIi2tSyqAltfCvo3wx0vw00ioUsf8i/kMEhISmDt3LgBfTv6GbfsOs2JXBnd+tpQp93Qh0LdC/VMr4r709ZC6CuzeHj99wXEVqgXq38LCwmjYsCGbN28mOjqavLw8Dh06VGSftLQ0oqOjAYiOjj7pqrzjj4/vcyq+vr6EhIQUuUnp+vivbeQ5XdwSvYPgvcvU+iQlq/uj5l/JrgKYfCPs33LG3ePj4+nRowfx8fH4eTv48Ob2RAT7siEtiwe/WYFhGGVUuEg5terYMIsGl0FAFWtrKSEVOkBlZ2ezZcsWYmJiaNeuHd7e3syZM6fw+Q0bNpCcnEznzp0B6Ny5M6tWrSI9Pb1wn9mzZxMSEkLTpk3LvH45tYyj+UxcsAMwuN/rO3Nju3hznTORkmC3w4D3IbYtHD1oLjyck3Ha3fv168eUKVPo168fAFEhfnxwYzt8HHZ+WZ3KO79tLqvKRcofw4BV35j3W1xrbS0lqEIFqAcffJDff/+d7du3M3/+fK6++mocDgfXX389oaGh3HbbbYwaNYq5c+eydOlSbrnlFjp37kynTp0AuPzyy2natCk33XQTK1asYObMmfz3v/9l+PDh+Pr6Wvzu5LhvluzkcJ6T66puJ2zfUnNNM7U+SUnz9ofrJ0FwrNml9+2tbl2Z1652OM/0bwbAq7M38uvatLMcIVKxJO08RErGUdi5EA4lg08wNLri7Ad6iAoVoHbt2sX1119Po0aNuO6666hatSoLFiwgIsIc7f/666/Tt29fBg0axMUXX0x0dDRTpkwpPN7hcPDTTz/hcDjo3LkzN954IzfffDPPPPOMVW9J/qXA6WL839sBGO137IqOtsM075OUjuBouP5Lc3LWzb/CbPfmhBvSoRY3daoNwMjJSVruRSqNadOmcXmffrS6dRy7/5hgbmxylfmHSQVhM9Q5X+IyMzMJDQ0lIyND46FK2IzVKdz1xTK6BiQz0TUG7F5wXxKEadC+lKLVU+DbW8z7/d+FNjcW+9B8p4uhHy9k0bYD1I0IZOrwroT4eZdSoSLlQ59+A/hl1q8E1G5B5q2p2I8egBunQP1LrS7tjNz5/q5QLVBS8X3613YAngg/Npat+SCFJyl9zQdCt0fM+z+OhOQFxT7U22HnvaFtiQn1Y+vew4yavAKXS3+3SsXWskc//Gq1pH/XJmZ4Cow0l0yqQBSgxGOs2pXBou0HiHPso9GBYwGqy73WFiWVR7cx0KQfuPLhq6HmmI5iqhbkaw4q97Lz67o03pmrQeVSseVWb0tQi56kL53JtA355h+7joo1nYcClHiM8X9vA+CZyN+xGS6o2wOiW1hclVQadjtc/YH5O3dkH3x1A+QVf6LMVjXD+L8BzQF4/deNzFmnQeVSMRmGwd+b95GzaiaLN6aQkJRvzq9WwShAiUdIz8zhx5V7CCWbrlm/mBu7as07KWM+gTBkEgRUMycFnHqPeYl2MV3XviY3daqNYZiDyrft00zlUvFs2ZtNWmYuPdvU5JI4B/EX1jKnBKlgFKDEI3yxYAf5ToNHqv2No+AIRLUwW6BEylpYTRj8hTmj8tqp8Ocrbh3+RN+mtK8dTlZOAXd+toTDuQWlU6eIRf7aZK4D+WTrg0wZHEC/oXeAzWZxVSVPAUrKvZx8J18sTMaHfAblTzc3drm3Qv4PKR6idmfocyw4/fZ/sP7nYh/q42UOKo8M9mVTejaPfLdSM5VLhfLX5v1UI4MWucvNDRVo8swTKUBJuTd9ZQoHDudxS9AifHP3mRMbNh9odVlS2bWLhwvuMO9PuQPS1xX70MgQP94b2hYvu42fVqbwyV/bSqdGkTJW4HSxYOt+rnb8iR0n1LgAqtazuqxSoQAl5d7EhTuw4eI/Psf+yu90Nzg0j46UA73HQdxFkJcNk66HIweKfWj7uCr8t08TAMb9sp4FW/eXVpUiZWbFrgyyc/MZ4v27ucGNOdM8jQKUlGtr92SyLPkQlzhWUuXINvANMf/yFykPHN5w3WcQVgsObjOXe3EWf0zTsC5xDGgdi9NlMOLLZaRm5JRisSKl7+/N+2ht20I9dpsz+DeruL0FClBSrn25aAcAo0LnmRva3AR+mt1dypGAKuaVed4BsHUu/PpksQ+12WyMG9iSxtHB7MvO456JS8krcJVisSKl6+/N+7jOMc980LR/hf73WgFKyq3DuQVMXb6H2rZUmh1ZBNjggtusLkvkZNHNYcD75v3Ed2DF5GIf6u/j4IMb2xHs58Wy5EM8/3Pxx1KJlCdH8gpYm5zKVY5Ec0MF7r4DBSgpx6at2EN2bgEjguaZGxpcVmEHI0oF0GwAXPSgeX/avbB7WbEPjasWyOvXtQYgYf52fkjaXfL1iZSyRdsOcKmxiGDbUYyw2lC7q9UllSoFKCmXDMPgiwU78CeHfsZcc+PxK55Eyqsej0PD3uDMNZd7yU4v9qE9m0YxvIf5B8KY71axITWrtKoUKRUndt/Z2txozt5fgVXsdycea+WuDNbsyWSQdyK+BVkQHgf1e1pdlsiZ2e0w8EOo1hCy9sDkm6Agr9iHj7qsERfWr8bRfCd3f7GUrJz8UixWpGRt2rCGLo61GNig1fVWl1PqFKCkXJq4cAdgcHfAb+aGC+6o8H/NSAXhFwpDvjSvGN25AGY8UuxDHXYbbw5pTUyoH1v3HeahbzTJpniGvVm5tDlgTjWTX/tic8b+Ck7fSFLuZBzN58cVKbS3baB67hbzUtg2Q60uS6T4qjWAQR8DNljyKSwZX+xDqwb58t7Qtng7bMxYk8rHf2qSTSn/ft+QxiDHHwD4tL/Z4mrKhgKUlDs/JO3maL6TEUHHxj61vBb8w60tSsRdDXvBJf817//8ECQvKPahbWqF80TfpgC8MGM9i7YVf4JOESvsWT6TGrZ95DiCoXEfq8spEwpQUu58s2QXERzk4oJjl8Jq8Lh4qotGm3PhuPLN8VAZxb+67qZOtenX6p9JNtOzNMmmlE/5ThcNdk8BIKtBf/D2t7iisqEAJeXK+tRMVu3O4Ebv37AbBVCrM8S0tLoskXNjs0H/9yCyGRxOh8k3Qn7xgpA5yWYL6kcGkZ6Vy32TllPg1CSbUv6sXL+RS42FAFS5qPL8wasAJeXKt0t24cDJMJ955oYLbre0HpHz5hsEQyaa3dB7lsFPD0AxB4YH+nrxwY1tCfBxsGDrAV6bvbGUixVxX1bieHxsTrb7NcVRvbXV5ZQZBSgpN/KdLqYm7aabfQVhzv0QUBWa9LO6LJHzV6UOXDMebHZY8SUs+rDYh9aPDObFQWYr7HvztvDr2rTSqlLEfS4nTXZ/B8CBphV75vF/U4CScuP3DXvZl53Hzb7HVvFudT14+VhblEhJqdcDLnvWvD/jUdj2Z7EPvapVLPFd4gAY9XUSOw8cKYUCRdy3b/lPRBl7OWQEUrebApSIJb5ZutMcPG4cWwKjbeW4FFYqkc7DoeVgMJzwzTA4lFzsQx+7sgmta4aRmVPA3ROXkpPvLMVCRYonZ8FHAPwZ2Iuw0FCLqylbClBSLuzPzmXOunSucfyJHSfU7AQRjawuS6Rk2Wxw1ZsQ0wqO7IevboC84rUm+XjZeXdoW8IDvFm9O5Nnf1pbysWKnMXB7cTu/QuAjGY3WVxM2VOAknJh2oo9FLhc3HS8++4MrU/Tpk1j4MCBTJs2rYyqEylB3v4weCIEVIPUVTBtRLEHlVcP8+f1wa2x2WDiwmS+X76rlIsVOb2CxeOxY/Cnsznt2ra3upwypwAl5cI3S3bRyb6OWFcK+ASbK9ufRkJCAnPnziUhIaHM6hMpUWE14brPwO4Fq7+D+W8V+9DujSK595IGADw2ZTUb07TosFigIBfX0s8AmO57JY2jgy0uqOwpQInl1uzJYG1KJtd7zTM3tLgGfAJPu398fDw9evQgPj6+TOoTKRVxXaH3C+b9X5+Czb8W+9D7L21QuOjwXV8s5XBuQenUKHI6637EJ/cAqUY4jiZXYrPZrK6ozClAieW+W7qbELK50mFOxEbbM/el9+vXjylTptCvn6Y4EA93we1md7Xhgm9vhf1binXY8UWHo0P82Lr3MGOmrNKiw1KmjMUfA/CVswfdGsdYXI01FKDEUk6XwbQVexjg+BtvIx+imkNsW6vLEikbNhtc+QrU6AA5Geag8tzidclVDfLlnRva4LDb+HHFHr5YsKOUixU5JnU1tuRECgw73xk96Vq/mtUVWUIBSiyVuGU/+7JzuNF7nrmh7c3ml4pIZeHlC4M/h+AY2LsepvwHXMVbsqV9XBXG9G4MwLM/rWPlrkOlWKjIMQveA2CG6wLi6tYn0NfL4oKsoQAllvohaTctbNtoyA5w+EKLa60uSaTsBUfD4C/A4QMbpsPvLxb70NsvqsPlTaPIc7q4+4tlHDqSV4qFSqWXlQarvgHgk4Ir6dUs2uKCrKMAJZbJyXcyY00qgxx/mBuaXAUBVawtSsQqNdqbc0QB/P4CrC3eNB02m42Xr21FrSoB7D50lNFfr8Dl0ngoKSVLPgFnHstc9UmiAZc3i7K6IssoQIll5m3Yy9GcHPp7LTA3tLre2oJErNb6Buh0j3n/+7sgbU2xDvt99i/4zHuNvK2LmLM+nf/9sbUUi5RKK/8oHBs8/nHBlbSvHU5ksJ/FRVlHAUosM22FuXBwOJkQGAl1u1tdkoj1LnsW6nSD/MMw6Xo4cuCshyQkJLA08S9q7DWvZH155noWbN1f2pVKZbPyaziyn72OSGa6LqjU3XegACUWycrJZ866dK52mMsA0OJacFTOgYgiRTi84NoECI+DQzvMNfOcZ57n6fjcaI+PvJuBbarjMuDeSctJz8opk5KlEjCMwsHjH+ZehhOHApTVBUjlNGtNGr4FWVzuOLZwcKvB1hYkUp4EVIEhk8AnCLb9ATMfO+Pux+dG69+/P/93dXMaRgWxNyuX+yclUeB0afkjOX9b5sDe9eQ7AviqoAfNq4dQs0qA1VVZSgFKLPHDij1c4ViED/kQ0QSiW1pdkkj5EtUUrv6feX/R/2BpQrEOC/Dx4r2h7QjwcZC4dT+v/7pRyx/J+Us0W5/mBvQiiwB6V/LWJ1CAEgvsy87l7837GOj409zQarDmfhI5lSZ9ocd/zfvTH4Qd84t1WP3IIF4YZP5R8u7cLbTtOUDLH8m5S18HW+Zg2Oy8cKAbAL2bK0ApQEmZ+3lVCjFGOh3t6wEbtLjO6pJEyq+LH4SmA8CVD5NvgkPJxTqsX6tYbu5cG4Bv9kbz1idfaPkjOTeJ7wCQEn0pW52R1I8Mon5k5Vs8+N8UoKTM/ZC0h/72v80HdS6C0OrWFiRSntlsMOA9s5v7yD6YdAPkHS7WoY/3aUKrGqFkHM1n+MRl5BY4S7lYqXAO7YQVXwHwhd0M4Oq+MylASZlKyTjK0h0H/um+aznE2oJEPIFPIAz5EgIjIG2VOUdUMZZ78fVy8O7QtoT6e7NiVwbPTV9XBsVKhTL/LXAV4Kx9EeOTIwF13x2nACVlasbqVFratlLPngJe/tBUXQoixRJW01zuxe4N66bBvHHFOqxGeABvDG4NwGeJO/ghaXcpFikVSnY6LPsMgGW1b+NovpPqYf40iw2xuLDyQQFKytQvq1P/aX1q3Ad81Y8uUmy1Ov2z3MsfL8Gqb4t1WI/GkYzoUR+AR6esYnN6VmlVKBVJ4jtQkAPV2/NZqjmernfzaGy66AdQgJIylJ6Vw7Lte+nrOL50i7rvRNzWZih0ude8/8Nw2LW0WIc9cFlDutSrypE8J3d9sYzDuWeenFMquSMHYPEnABzt/ACz16UB5sUJYlKAkjIzc00aHW1rqWbLBP8qWrpF5Fz1fBoa9jZbB766ATL3nPUQh93Gm0PaEBXiy+b0bMZMWYVhaNFhOY1FH0JeNkQ155fcVuTku6hbLZCWNUKtrqzcUICSMvPLqhT62o+1PjXtBw5vawsS8VR2Bwz8yJyENjsVJg0p1pV5EcG+vHtDW7zsNn5csYfPEneUQbHicXKzYMH75v2LRvF9khnQ+7euru67EyhASZnYn53L0m3p9HYsNjc0G2htQSKezi8EbvgKAqpCygr4/j/FujKvfVwVxlzRGID/m76WZckHS7tS8TRLPoWcQ1C1Puk1e/P35n0A9G+t7rsTKUBJmZi9No3OrCLclg2BkRB3odUliXi+8DgYPBEcPrDuR/jtmWIddtuFdbiyRTT5ToPhE5exPzu3dOsUz5F/FOabE2dy4QP8tCodlwGta4YRVy3Q2trKGQUoKRM/r079Z/B40/5mF4SInL/anaHfsS+8v16H5V+c9RCbzcaLg1pSt1ogKRk53P9VEk6XxkMJsPhjOJwOobWg5eDCaS8GqPXpJApQUuoyjuSzZHMKl9uXmBuaq/tOpES1GgwXP2Te/3EkbP/rrIcE+3nz/o3t8Pd28Nfmfbw2e0Pp1ijlX04m/Pmaeb/7I2w7mMeKXRk47Db66uq7kyhASambvS6NLqwgxHYEgmOhZierSxKpeLo/Bs2uPrZm3o2wf8tZD2kUHcwLg1oA5qLDs9emlXaVUp4lvgtHD0DVBtByCFOXm61PF9avRrUgX4uLK38UoKTETZs2jYEDBzJt2jTg2NV3jkTzyWYDwK5fO5ESZ7fDgPehejs4ehAmXgOH95/1sP6tqxPfJQ6AUV8nsX1f8dbZkwrm8P7CRYO55HEMu+Of7rs2an06FX2Tnca7775LXFwcfn5+dOzYkUWLFlldksdISEhg7ty5JCQkkJWTz6JNe+hpX2Y+qavvREqPtz8MmWSOXzmw1ZwjKj/nrIc9dmUT2tYKIyungLu+WMrRPC06XOn89Zo571N0S2jSn6Sdh9i+/wj+3g4ub6q1705FAeoUJk+ezKhRo3jyySdZtmwZrVq1olevXqSnp1tdmkeIj4+nR48exMfH8/vGvXQ1lhFkyzH/Ua/R3uryRCq24CgY+g34hsLOBfDDPWed3sDHy857Q9tRLciH9alZPP69JtmsVDJ2w6KPzPuXjgW7nR+Ozf10WdMoAn29LCyu/FKAOoXXXnuNO+64g1tuuYWmTZvywQcfEBAQwKeffmp1aR6hX79+TJkyhX79+vHr2rSi3XeahE2k9EU2hsGfg90LVn8Hvz171kOiQ/14+/q2OOw2pizfzecLNMlmpfHHy+DMhVpdoH5PcvKdTD3WfXd1m+oWF1d+KUD9S15eHkuXLqVnz56F2+x2Oz179iQxMdHCyjxPvtNF4vpkLrUvNzfo6juRslO3G/R727z/12uwNOGsh3SuV5Uxvc1JNp/5cS1LdxwoxQKlXNi/BZZ/bt6/9Amw2Zi5JpVDR/KJCfXj4oYR1tZXjilA/cu+fftwOp1ERUUV2R4VFUVqauopj8nNzSUzM7PITWDJ9oNckLcYf1seRngdiGltdUkilUvrG6DbI+b9n0bBptlnPeT2i+rQp2UMBS6Du79YRnqmOYbq3xeHSAXx2/+BqwDqXwa1uwAwaVEyANe1r4nDrl6D01GAKgHjxo0jNDS08FazZk2rSyoXZq9NK1y6xabuOxFrdH8UWl0PhhO+vhl2Lzvj7jabjZcGtaRhVBDpWbncM3EZeQWuIheHSAWxIxHWTAFs5tgnYOvebBZsPYDdBtddoO+yM1GA+pdq1arhcDhISys6H0paWhrR0ae+EuHRRx8lIyOj8LZz586yKLVcMwyD39cm092eZG5ocpWl9YhUWjYbXPUW1O0B+Ufgy+vMK/TOINDXiw9ubEewrxdLdhzkuelri1wcIhWAywUzxpj3294EMS0BmLzY/P7q3iiS6mH+VlXnERSg/sXHx4d27doxZ86cwm0ul4s5c+bQuXPnUx7j6+tLSEhIkVtltyk9m9oZiwmy5eAKjoXYtlaXJFJ5efmYg8qjW8LhvfDFIDi874yH1I0I4rXBrQGYkLiD3Ng2hReHSAWw4ktISQKfYLjkCQDyClx8u3QXAEPU+nRWClCnMGrUKD766CMmTJjAunXruPvuuzl8+DC33HKL1aV5jNlr0+htN7vv7E2uUvediNV8g83pDY7PEfXldZB35kkzL2saxX2XNgDg8amrWbHzUBkUKqUuNwvmHFt4uttDEBQJmP9u7z+cR2SwL5c0jrSwQM+gAHUKgwcP5pVXXmHs2LG0bt2apKQkZsyYcdLAcjm9OWv20NOx1HzQpK+1xYiIKTgabvwO/MNh91L4Jh6c+Wc8ZOSlDejZJJK8Ahd3fbGUvVm5ZVOrlJ4/X4XsNAivAx3vKtx84uBxL4fiwdnoEzqNESNGsGPHDnJzc1m4cCEdO3a0uiSPkZ6Vg++eBVSxZePyq2LOLSIi5UNEQ7h+Mnj5w6ZZ8MPwM060abfbeG1wa+pGBJKSkcPwicvId555Yk4pxw5sM9e8A+j1HHiZa9wl7z/CX5v3YbPBYHXfFYsClJS439alc7l9CQD2xleCQ7PYipQrtTrCdRPA5oCVk2HW43CGmcdD/Lz58Kb2BPl6sWj7AZ79aW0ZFislavYT4MyDOt2g0ZWFm79abLY+XVi/GjWrBFhVnUdRgJISN3tNKr2OTV+g7juRcqphL3PxYYAF75ndOmdQPzKIN44NKv8scQdfHevuEQ+yeQ6s+xFsdug9rnBsam6Bk6+XmIPHb+hQy8oKPYoClJSoI3kFZGxZRKztAC6vAPPSaREpn1oNhl7jzPu/PQtLxp9x955Noxh1WUMAnvhhNUu2a6Zyj5F3BH56wLzf4U6Ialb41I8rUtiXnUtUiC+XNtFY3+JSgJIS9ffm/VzCQgBsDS8Hbz+LKxKRM+p8D1w02rz/0wPm2nlncO8l9enTIoZ8p8FdXyxl96GjZVCknLffX4BDOyCkBlzy38LNhmHw8Z/mvGDxXerg46VYUFz6pKREzV2fRq9j0xfYNHmmiGe45AloFw8YMOVO2PDLaXe12Wy8fG1LmsaEsC87jzs/W8LRPGeZlSrnIGUlzH/HvN/nFXNKi2Pmb9nP+tQs/L0d6r5zkwKUlBjDMNi+bhn17ClM3Wgw8InPtG6WiCew2aDPa9DiOnNdtK+HwZa5p909wMeLD29uR9VAH9bsyeTBb1dgnGEQuljI5YQf7zOX8mk6ABpdUeTp461P17WvQWiAtwUFei4FKCkxm9KzaXPkbwAmbAxm7u9/at0sEU9hd5iDyhv3BWcufHUDJC847e41wgN4/8Z2eDtsTF+Zwtu/bS7DYqXYFn0Ie5aDbyhc8WKRpzanZzF3w15sNrilax2LCvRcClBSIqZNm8bga6/Bd7O52vstN1yrdbNEPI3DC675FOpdaq6bN/Fa88v3NDrUqcKz/ZsD8Nrsjfy0ck9ZVSrFcWgnzHnWvH/Z0+ZEqif45K/t5lNNooirFljGxXk+twPUsGHD+OOPP0qjFvFgCQkJrF3yN/NW7sbARr87H9O6WSKeyMsXBn9hToCbmwmfDYCUFafdfUiHWtx2odl6MfrrFSRpuZfyweWCH++H/MPmf8u2w4o8vT87lynLzKkLbr+orhUVejy3A1RGRgY9e/akQYMGPP/88+zevbs06hIPM3joTdSpFUN8a29yo9oWrq0kIh7IJwBumAw1LoCcQ/BZf3Mg8mk8dmUTLmkcSW6Bizs+W8IeXZlnvcUfwZY54OUHV70J9qJf9xMXJpNb4KJljVAuiAu3qEjP5naAmjp1Krt37+buu+9m8uTJxMXFccUVV/Dtt9+Sn3/mNZWk4gpr3Jl3rq1Ov0be+DW78uwHiEj55hdirptXvT0cPQif9YPUVafc1WG38eaQ1jSKCmZvVi63T1jC4dyCMi5YCqWvg1lPmPcv/z9z+Z4T5OQ7+SxxBwC3XVgHmxZ7PyfnNAYqIiKCUaNGsWLFChYuXEj9+vW56aabiI2N5YEHHmDTpk0lXaeUc3+v28mF9tXmg4a9rS1GREqGXyjcNAWqtzND1IR+kLr6lLsG+3nz8bD2VAvyYW1KJvd/tRynS1fmlbmCXPjuDvNCgPqXwQW3n7TL5MU72ZedS2yoH1e2iLGgyIrhvAaRp6SkMHv2bGbPno3D4eDKK69k1apVNG3alNdff72kapRyzjAMjq6fg58tn5yA2CIz3IqIh/MLhRunQGxbOHrAbIk6zZiomlUC+N9N7fHxsvPrunStmWeF3/4P0lZBQFXo/27hci3H5eQ7eW+eecXk3T3q4+3QtWTnyu1PLj8/n++++46+fftSu3ZtvvnmG0aOHMmePXuYMGECv/76K19//TXPPPNMadQr5dDGtGza5Jizj3s1ufKk/2FFxMP5h8FN35sh6sh+mHAV7Fpyyl3b1Q7n9etaA5Awfzuf/rWt7Oqs7Lb9AfPfNu/3exuCT16WZdKiZNIyzdan69rXKOMCKxYvdw+IiYnB5XJx/fXXs2jRIlq3bn3SPj169CAsLKwEyhNPMG99Gv0d5qXOXo2vOMveIuKR/MPg5qkw8TrYucAcWH7DZIi78KRd+7SMYdfBxoz7ZT3PTl9L9XB/ejWLPmk/KUFHDsD3dwOGecVd4z4n7WK2Pm0BYPgl9fH1cpRxkRWL2y1Qr7/+Onv27OHdd989ZXgCCAsLY9s2/dVRWWxfnUi07SD5Dv9T/mMqIhXE8TFRdbpBXjZ8MQg2/XrKXe+8uC5DO9bCMOD+r5ZreoPS5HLCt7dC5i6oUhd6PX/K3SYuTGZvVi7Vw/y5tl3NMi6y4nE7QN100034+WmBWDFl5eQTnWYu+ZBXu5sWDxap6HwC4YavoUEvKMiBSUNg7clLNtlsNp7u14zujSLIyXdxW8Jitu87bEHBlcDc52DrXPAOgOs+B9+gk3Y5mufk/WOtTyMuqa9Fg0uAPkE5L39v3k8P2zIAApv3tbgaESkT3n7mZJtN+4MrH74ZBks+PWk3L4edd25oS/PqIew/nMfNny4iPSvHgoIrsHU/wp+vmvf7vQ3RzU+528SFO9iXnUuNcH+uaaexTyVBAUrOy/K162hpP9Zd27CXtcWISNnx8oFBn0Lbm8FwwU8PwLwX4V+LCgf5ejE+vgO1qgSQfOAIt4xfTLbmiCoZezceG/cEdBoOLa455W5H8gr44Hez9eneS3TlXUnRpyjnxbF5FgAZVVpq9nGRysbhBVe9BRc/ZD6e9zxMH2WOyTlBRLAvn93agaqBPqzZk8ldny8lr8BlQcEVSG4WTB4KeVlQ+0JzrbvT+OTPbezLzqNWlQAGtlXrU0lRgJJzlrz/CG2OmtMX+DU/+YoPEakEbDa45L9w5SuAzezK+2YY5BddziWuWiDjb7mAAB8Hf23ex4PfrMCliTbPjbPAnCxz30YIjoVrx4PD+5S7pmbkFF55N/ryhmp9KkH6JOWcJW7YxYV2c2kH36YKUCKVWoc7jn2R+5jjchL6QFZakV1a1gjj/Rvb4WW3MW3FHp6ctgbDUIhyi2GYrXwbfzHXuRv8+Rlb/1+csZ6j+U7a1Q6nX6vYMiy04lOAknO2b/Vs/G15ZPlGQdSpBy6KSCXS7Gpzwk3/cNi9FD6+9KSlX7o1jODV61phs8HnC3bw8swNFhXroX5/EZZNAJsdBn0CNdqfdtdlyQf5fvluAJ68qqnWvCthClByTpwugyp7/gAgJ+5SzT4uIqa4C+H2OVClHmTshE97wcZZRXbp37o6/zfA/KPrvXlbCi+vl7NYmgDzxpn3r3wFmpz+ymeXy+DpH82ldK5pV4OWNcJKv75KRgFKzsmaPRl0cpmzj1dppe47ETlB1Xpw+68Qd5E54eakwTD/nSJX6A3tWJsxVzQGzG6miQt3WFWtZ9jwi3mlI5iD9i+47Yy7T03azYqdhwj0cfBwr0ZlUGDlowAl52TlyuXUsadRgBeOet2sLkdEypuAKuYixG1uMqc5mPW4OVt2bnbhLnd1q8fwHvUA+O/U1Xy7dJdV1ZZvW+fBN7eYn2ObG6HH42fc/XBuAS/8sh4wl2yJDNEEx6VBAUrOSf4Gs0l+b5U24BtscTUiUi55+ZiTO17xEti9YM0U+Lgn7NtcuMuDlzdiWOfaGAY89O0Kvl+uEFXEpl/hy8FQcBQa9oa+b5x1yMRbczaRnpVLrSoB3Nq1TtnUWQkpQInbjuY5iTuYCIBPo8strkZEyjWbDTr+B+KnQ1A07F0HH/WA9dOPPW3jyauaccOxdfNGf72CH5J2W1x0ObHhF/jqenPJnEZ94LrPTjtdwXHLkw/y0Z9bAXiib1P8vLVgcGlRgBK3Ld6SQkebOTixSqsrLK5GRDxCrU7wnz+gVhfIzYSvboBfHoH8HOx2G//XvznXd6iJy4AHJifx44o9VldsrbXTYPKN4Mwzl8y5bgJ4+Z7xkJx8Jw99uxKXAf1bx3JZ06gyKrZyUoASt+1c/isBtlwyvapi0/QFIlJcwVEwbBp0HmE+XvgBfHQJpK/Dbrfx3IAWDG5vhqiRk5OYVllD1IrJ8E08uAqg+TXmkjlnaXkCeOPXTWxOz6ZakC9PXdWs9Ous5BSgxG2+O+YCcCi2m6YvEBH3OLyh13Mw9FsIjID0NfBhd1j0EXYbjBvYgmva1cDpMrj/q+VMXpxsdcVlx+WCOc/C93eC4YRWN8DAD80lc85iefJBPvzDnA7iuaubEx7oU9rVVnoKUOKWvVm5tDi6BIDwluq+E5Fz1OAyuHs+1L/MHOPz84Mw8RrsmTt5aVBLhh4bE/XId6v49K9tVldb+vIOwzc3w5+vmI+7joT+74L97GOY/t1116tZdOnWKoAClLhp2apVNLLvwomd4KY9rS5HRDxZUCQM/QZ6vwgOX9j8K7zbCfviD/m//k254yLzCrJnflrLu3M3n+VkHixjF3za21wCx+EDAz4wFwe2F+8r+vXZG9V1ZwEFKHFLxuqZAKQENTPneREROR82G3S6C+7+G2p1hvzD8MvD2MZfwWMX2BnZswEAL8/cwPM/r/PIBYinTZvGwIEDmTZt2slPbv4VPuwBqSshoBoM+wlaX1/sc89ak8r//jCvulPXXdlSgJJiMwyDiNQ/ASioc4nF1YhIhVKtAcT/bC5R4hMEOxdi++BCRro+46nLawDw4R9beeDrJHILnBYX656EhATmzp1LQkLCPxvzjsDPD8EXg+BwOkQ2gzvnQq2OxT7vtn2HGf31CgDiu8Sp666MKUBJse3al0k7ZxIA0e20fIuIlDC7HTrcAcMXmpNGuvJh/tvEL7maKResx9fu4oekPdwyfjGZOflWV1ts8fHx9OjRg/j4eHPDnuXwYTdY9KH5uMN/zKVvwmoV+5xH8gq46/OlZOUW0L52OI9d2aTkC5czshmG4XntoeVcZmYmoaGhZGRkEBISYnU5Jea3mT9wSeLNZNhCCH1iR7H750VEzsmm2TDzMdi3EYDDoQ148OBAfslrSePoEBJu6UB0qActU5KbDX+9Dn+/YU5REBQNA96F+u6NJzUMg5GTk/ghaQ8Rwb5Mv/dCLddSQtz5/tY3oBRbwcbZAOyu2lnhSURK3/Er9a54CfzCCMzYxPv2F/nF7wlqpf/GgHf+YMXOQ1ZXeXYuFyyfCG+3M6+ycxWYk2Pek+h2eAJImL+dH5L24LDbePeGtgpPFtG3oBSLYRjUPDAfAK+Gl1lcjYhUGg5vcymY+5ZDl3vBO5AmbOVDn9cZnzuK8R++ytSl5Xiag+1/wUfd4Yd7IDsVwuPgus/h2gnndCHOjNWpPPuTuRLEY1c2oUMdXcxjFXXhlYKK2IWXvDOZWp+0AODofevwrxJrcUUiUikd3g8L3sVY+D9sedkApBthbKo+gE7XjMJRpbbFBQLOAlg3DRa8B7sWm9t8Q+Dih8wweJYlWU7nr037uDVhMXlOF9e2q8FL17TEpsmMS5Q7399nn95UBNi5bAa1gB1ecdRWeBIRqwRWhUvHYutyL64FH3B0/odE5h8gck8CrrcmkFfnEnza3gANLge/Mv4D9vB+WPElLPwfZOw0tzl8oM1N0P1RCIo451Mv3XGQOz9fQp7TxRXNoxk3sIXCk8UUoKRYbFt/B2BfRGfKwd93IlLZ+Ydj7/EogReNZunsieQt+JjOttX4bJsD2+aA3RvqdoPGfaHRFRBcSpf4Z+yG9dPNFqcdf4PhMrcHVIMLboMLbjcnDD0P61IyuWX8Io7kObmoQTXeGNIaL4dG4FhNAUrOyjAMamcsAsC/yaUWVyMicgIvH9pdcQvr2wxi2OfT6ZjxM73ti6lLijlJ5eZf4aeR5tijGhdAjQ5Qoz1ENAKfQPdey1kA+zaY0xDsWW52z6WsKLpPTCszNLW4DrzPf3D3mj0ZDPt0MZk5BbSrHc7/bmqHr9fZl3eR0qcxUKWgoo2BSt68hlpfdCHfcOB8eBt+gaFWlyQicpIjeQU8PW0tk5fspJ5tN7dWXcM1AUn4pied+oCAqhBaA0JrQnCM2d1md5gD120OyMkwJ7nM3mv+PLQTCo7+6yQ2qNkRmlwFTfqaQa2EzN2QzoiJyzic56RJTAhf3dmJUH/vEju/nExjoKREpSw3xz9t9m1CE4UnESmnAny8ePGalnSpX5XHv/fi8X3Veda7N4/1iGVozf04di82W412L4GjB+HIfvP271akM/EJgpjWENsaYttA3IWl0j345cJknvhhNU6XQZd6VXn/xnYKT+WMApSclXfyHwAciu5icSUiImfXv3V12tQM55HvVpK4dT9jZ+3iu5phvDToHhp1DzZ3OnrIHOidsctsWTqcbs7P5MwHl9OcBd03xBy/FBhh/gyOhSp1S3UePJfL4KWZG/jg9y0ADGxbnRcGtsTHS2Oeyht14ZWCitSFZ7icZDxTizCyWd37G5p3utzqkkREisUwDCYv3slz09eRlVuAt8NGfJc4RvRoQGhA+WvN2XXwCA99Y4Y+gJE9G3D/pQ10tV0Z0kzkUmJ2rVtEGNlkG/7Ub32x1eWIiBSbzWZjSIdazB7VjcuaRpHvNPjoz210e2Uun/61jbwCl9UlAseDXjK93/iTxK378fd28Oq1rRjZs6HCUzmmACVnlL5iBgAb/Frh56flAkTE80SH+vHhTe0Yf8sFNIwK4tCRfJ75aS2Xvf47U5btIt9pXZDac+got09YwiPfrSI717zS7pf7L2JQuxqW1STFozFQckYBu/4CILv6hRZXIiJy7mw2Gz0aRXJR/Wp8u3QXr87eyI79Rxj19QpenrmBW7rGMaRDLUL8yqZrLzUjh/fmbearRTvJc7rwcdgZfXlDbr+oLg67Wp08gcZAlYKKMgbKyD9K7nO18COPlf1n07JNB6tLEhEpEYdzC0iYv53xf29nX3YuAEG+XgxqW51+rWNpUzMceykEmd2HjvLRH1v5clFyYRdixzpVeKZ/cxpFB5f464l73Pn+VoAqBRUlQO1ZPoPYHwaTZoQT9t/N+HqrwVJEKpbcAic/LN/Dh39uZXN6duH26mH+9GkZQ69m0bSoHnpeV8GlZ+YwfVUKP61MYemOg4XbL4gL54HLGtKlXrXzeg9ScjQPlJSIQ6tnEwusD2hHN4UnEamAfL0cXHdBTa5pV4M/N+/jh+W7mbU2jd2HjvLhH1v58I+t+HrZaVkjlLa1wmlVM4yYUD8iQ/yICPItDFaGYZDvNDiSV8CWvdmsTcliXUoma/ZksnLXIY43Vdhs0KlOVYb3qE/X+lU1SNyDVahvxbi4OHbs2FFk27hx4xgzZkzh45UrVzJ8+HAWL15MREQE9957Lw8//HCRY7755hueeOIJtm/fToMGDXjxxRe58sory+Q9lCdBu83xT4c1/klEKji73Ua3hhF0axhBTr6TeRvS+XFFCn9v2cehI/ks3n6QxdsPnnRciJ8XTpdBToELp+v0HTpta4XRt2UsV7aIITpUF+RUBBUqQAE888wz3HHHHYWPg4P/6VPOzMzk8ssvp2fPnnzwwQesWrWKW2+9lbCwMO68804A5s+fz/XXX8+4cePo27cvX375JQMGDGDZsmU0b968zN+PZY4epEbOBgDCmvW0uBgRkbLj5+2gd/MYejePwTAMtu47zLIdB1mWfJC1KVnszcxhb3Yu+U6DzJyCk46PCvGlSUwITWJCaBoTQtva4VQP87fgnUhpqlBjoOLi4hg5ciQjR4485fPvv/8+jz/+OKmpqfj4+AAwZswYpk6dyvr16wEYPHgwhw8f5qeffio8rlOnTrRu3ZoPPvigWHVUhDFQh5Z+R9iPt7LRVZ2Yx1YQXEZXpoiIeAKXy+DQ0XwOHM7Fy27H38eBn7cDf2+HZg33YJV6Is0XXniBqlWr0qZNG15++WUKCv756yAxMZGLL764MDwB9OrViw0bNnDw4MHCfXr2LNri0qtXLxITE0/7mrm5uWRmZha5ebqDa34FYIN/G4UnEZF/sdttVAn0oX5kMHHVAokK8SPU31vhqRKpUF149913H23btqVKlSrMnz+fRx99lJSUFF577TUAUlNTqVOnTpFjoqKiCp8LDw8nNTW1cNuJ+6Smpp72dceNG8fTTz9dwu/GWgF7zMB4JFbr34mIiPxbuY/KY8aMwWaznfF2vPtt1KhRdO/enZYtW3LXXXfx6quv8vbbb5Obm1uqNT766KNkZGQU3nbu3Fmqr1fqsvcSlbMNgPCmPSwuRkREpPwp9y1Qo0ePJj4+/oz71K1b95TbO3bsSEFBAdu3b6dRo0ZER0eTlpZWZJ/jj6Ojowt/nmqf48+fiq+vL76+vmd7Kx7j8KbfCQTWuWrRuvGpP1sREZHKrNwHqIiICCIiIs7p2KSkJOx2O5GRkQB07tyZxx9/nPz8fLy9zXE9s2fPplGjRoSHhxfuM2fOnCID0WfPnk3nzp3P7414kINrfiMQWOPTkmuCdbmtiIjIv5X7LrziSkxM5I033mDFihVs3bqViRMn8sADD3DjjTcWhqMbbrgBHx8fbrvtNtasWcPkyZN58803GTVqVOF57r//fmbMmMGrr77K+vXreeqpp1iyZAkjRoyw6q2VOb/dfwOQFVN5QqOIiIg7KkyA8vX15auvvqJbt240a9aM5557jgceeIAPP/ywcJ/Q0FBmzZrFtm3baNeuHaNHj2bs2LGFc0ABdOnShS+//JIPP/yQVq1a8e233zJ16tTKMwdUdjrVjm7HZdgIb9zd6mpERCqEadOmMXDgQKZNm2Z1KVJCKtQ8UOWFJ88DlbfiW3y+v421rtr435dInWqBVpckIuIxpk2bRkJCAvHx8fTr169w+8CBA5k7dy49evRgypQpFlYoZ6K18OScHVzzG1FAkqMF11cNsLocERGPkpCQwNy5cwGKBKjjF0Od7aIo8RwKUFKE9y5z/FNGVEctciki4qbTBaV+/foVCVTi+RSg5B9ZqVQ5Yo5/Cm7UzepqREQ8joJS5VFhBpHL+XNt+wuAtUZtWjeMs7YYERGRckwBSgodWmf22y+1NaNxdLDF1YiIiJRfClBSyGvHnwDsq9YBL4d+NURERE5H35Jiykwh5MgOnIYNv3oXWl2NiIhIuaYAJabt5vinNUYcTevVsrgYERGR8k0BSgDI2fw7AAtcTWlbM9ziakRERMo3BSgBwLnVHP+0I7gtoQHeFlcjIiJSvilACWSlEZhtzv/kiOtkdTUiIiLlngKUQPJ8ANYbtWhaR+OfREREzkYBSnBtNwPUIlcj2tbW+CcREZGzUYAScrea69+t9mpK/Yggi6sREREp/xSgKrucDPz2rwUgt3on7HYtICwiInI2ClCV3c5F2DDY7oqiXp16VlcjIiLiERSgKrsd5vinxa5GtK2l8U8iIiLFoQBVyeVvM8c/LTYa07pWmLXFiIiIeAgFqMosPwdHyjIA9oa3I8RPE2iKiIgUhwJUZbZnGXZXPnuNUKLrNLG6GhEREY+hAFWZ7fhn/qc2tatYXIyIiIjnUICqxFyFA8gbawC5iIiIGxSgKqlpU6cy6IVfmLYhn3U+zalbLdDqkkRERDyGl9UFiDUSPnybeVtzcLp8qXpZS02gKSIi4gYFqEoqvkdD2PYH7VrWI6hWNavLERER8Sjqwquk+tU+zJTBAfjU70KrmqFWlyMiIuJRFKAqI8PAtSMRMGcgb1kjzNp6REREPIwCVGV0YCv2w+nkGl4cCGtBlUAfqysSERHxKApQlVGy2fq00qhLk1qRFhcjIiLieRSgKqPkBQAscTWiVQ2NfxIREXGXAlQlZOxcBMBSV0Na1wyzthgREREPpABV2Rw9iG3fBgBW0JBmsWqBEhERcZcCVGWzawkAW13RRERVx9/HYXFBIiIinkcBqrLZuRCAZUZDWqn7TkRE5JwoQFU2xwLUUlcDWmsCTRERkXOiAFWZOAswdi0FzAHkaoESERE5NwpQlUn6Gmz5h8k0/NntXYsGkcFWVyQiIuKRFKAqk2PTFyx3NaBZ9XAcdpvFBYmIiHgmBajK5PgAclcDzf8kIiJyHhSgKpPjA8iNhrTUDOQiIiLnTAGqsshKhUPJuAwbSa56tKoRZnVFIiIiHksBqrI4Nv5pg1ET38AwaoT7W1yQiIiI51KAqixOmP+pVc0wbDYNIBcRETlXClCVxQkLCGv8k4iIyPlRgKoM8nMgJQnQAHIREZGSoABVGaSsAGce+4wQko1ImldXgBIRETkfClCVQeH4p4ZEhfgRGexncUEiIiKeTQGqMjhhAs0Wan0SERE5bwpQFZ1hwK7FgHkFnrrvREREzp8CVEWXsROy0yjAwSqjrlqgRERESoACVEW3awkA61w1ycVHAUpERKQEKEBVdLuXArDc1YDIYF8iQzSAXERE5Hx5TIB67rnn6NKlCwEBAYSFhZ1yn+TkZPr06UNAQACRkZE89NBDFBQUFNln3rx5tG3bFl9fX+rXr09CQsJJ53n33XeJi4vDz8+Pjh07smjRolJ4R2XkWAtUkqueWp9ERERKiMcEqLy8PK699lruvvvuUz7vdDrp06cPeXl5zJ8/nwkTJpCQkMDYsWML99m2bRt9+vShR48eJCUlMXLkSG6//XZmzpxZuM/kyZMZNWoUTz75JMuWLaNVq1b06tWL9PT0Un+PJc6ZXziBZpJRXwPIRURESojNMAzD6iLckZCQwMiRIzl06FCR7b/88gt9+/Zlz549REVFAfDBBx/wyCOPsHfvXnx8fHjkkUeYPn06q1evLjxuyJAhHDp0iBkzZgDQsWNHLrjgAt555x0AXC4XNWvW5N5772XMmDHFqjEzM5PQ0FAyMjIICQkpgXd9jvYkwYfdyCKQljn/48ObO3BZ0yjr6hERESnH3Pn+9pgWqLNJTEykRYsWheEJoFevXmRmZrJmzZrCfXr27FnkuF69epGYmAiYrVxLly4tso/dbqdnz56F+3iU3Wb33XJXXQzs6sITEREpIV5WF1BSUlNTi4QnoPBxamrqGffJzMzk6NGjHDx4EKfTecp91q9ff9rXzs3NJTc3t/BxZmbmeb2XErPr+ADy+lQL8iUqxNfigkRERCoGS1ugxowZg81mO+PtTMGlvBg3bhyhoaGFt5o1a1pdkmn38QHk9WlRPQSbzWZxQSIiIhWDpS1Qo0ePJj4+/oz71K1bt1jnio6OPulqubS0tMLnjv88vu3EfUJCQvD398fhcOBwOE65z/FznMqjjz7KqFGjCh9nZmZaH6KOHoJ9GwFY4arHjeq+ExERKTGWBqiIiAgiIiJK5FydO3fmueeeIz09ncjISABmz55NSEgITZs2Ldzn559/LnLc7Nmz6dy5MwA+Pj60a9eOOXPmMGDAAMAcRD5nzhxGjBhx2tf29fXF17ecdY/tWWb+sEdzgBBdgSciIlKCPGYQeXJyMklJSSQnJ+N0OklKSiIpKYns7GwALr/8cpo2bcpNN93EihUrmDlzJv/9738ZPnx4Ybi566672Lp1Kw8//DDr16/nvffe4+uvv+aBBx4ofJ1Ro0bx0UcfMWHCBNatW8fdd9/N4cOHueWWWyx53+fs2PinJflmC16LGgpQIiIiJcVjBpGPHTuWCRMmFD5u06YNAHPnzqV79+44HA5++ukn7r77bjp37kxgYCDDhg3jmWeeKTymTp06TJ8+nQceeIA333yTGjVq8PHHH9OrV6/CfQYPHszevXsZO3YsqamptG7dmhkzZpw0sLzcK7wCrx7VgnyI1gzkIiIiJcbj5oHyBJbPA2UY8HJ9OLKPq3OfJrRhFxJu6VD2dYiIiHiQSjkPlJzg0A44so8Cmxdrjdqa/0lERKSEKUBVRMfWv9tir0MuPhpALiIiUsIUoCqi3eYA8oV5dQBoFmvhcjIiIiIVkAJURXSsBWqZsz4hfl5UD/O3uCAREZGKRQGqoinIg5QVACQZ9WgaqxnIRURESpoCVEWTtgqcuRxxhLDdiKZZrMY/iYiIlDQFqIpmtzkD+QavhoCNpjEa/yQiIlLSFKAqmmMBakFuHABNNYBcRESkxClAVTR7lgOwJK82Pg479SKCLC5IRESk4lGAqkhys2HfBgBWuurSICoIHy/9JxYRESlp+natSFJXguEiyyeSvYRr/JOIiEgpUYCqSI513232qg9o/JOIiEhpUYCqSI4NIF+UFwegFigREZFSogBVkRxrgfr7SE0AmqgFSkREpFQoQFUURw/BgS2AOYC8VpUAQvy8ra1JRESkgvKyugApISlJAGT6VedQTjCd1H0nIpWY0+kkPz/f6jKknPH29sbhcJTIuRSgKopj3XdbfRoAGkAuIpWTYRikpqZy6NAhq0uRciosLIzo6OjzXidWAaqiKJxAsw6gAeQiUjkdD0+RkZEEBARoMXUpZBgGR44cIT09HYCYmJjzOp8CVEWx2wxQ87KqA2qBEpHKx+l0FoanqlWrWl2OlEP+/v4ApKenExkZeV7deRpEXhEc3gcZyQCscMYRFuBNTKifxUWJiJSt42OeAgICLK5EyrPjvx/nO0ZOAaoi2JMEQGZgHbIIoGlMiJqtRaTS0r9/ciYl9fuhAFUR7DEn0Nx+fAC5xj+JiHiU7t27M3LkSKvLAGDq1KnUr18fh8PByJEjSUhIICwszOqyyh0FqIrg2ADyZQXHBpBr/JOIiJxg3rx52Gy2Yl2d+J///IdrrrmGnTt38uyzzzJ48GA2btxY+PxTTz1F69atS69YD6FB5BXBsQA1J9McQN4sNtTKakRExENlZ2eTnp5Or169iI2NLdx+fPC1/EMtUJ4uMwWyUjBsdtYTh4+XnboRgVZXJSIibiooKGDEiBGEhoZSrVo1nnjiCQzDKHw+NzeXBx98kOrVqxMYGEjHjh2ZN29e4fM7duzgqquuIjw8nMDAQJo1a8bPP//M9u3b6dGjBwDh4eHYbDbi4+NPev158+YRHBwMwCWXXILNZmPevHlFuvASEhJ4+umnWbFiBTabDZvNRkJCQml9JOWaWqA83bHWJ1tEYxbe1Y+0rBy8HcrFIiJgzv1zNN9pyWv7ezvcGrA8YcIEbrvtNhYtWsSSJUu48847qVWrFnfccQcAI0aMYO3atXz11VfExsby/fff07t3b1atWkWDBg0YPnw4eXl5/PHHHwQGBrJ27VqCgoKoWbMm3333HYMGDWLDhg2EhIScskWpS5cubNiwgUaNGvHdd9/RpUsXqlSpwvbt2wv3GTx4MKtXr2bGjBn8+uuvAISGVs5eDwUoT3csQBHbFrvdRkyomllFRI47mu+k6diZlrz22md6EeBT/K/ZmjVr8vrrr2Oz2WjUqBGrVq3i9ddf54477iA5OZnx48eTnJxc2LX24IMPMmPGDMaPH8/zzz9PcnIygwYNokWLFgDUrVu38NxVqlQBIDIy8rQDwn18fIiMjCzcPzo6+qR9/P39CQoKwsvL65TPVyYKUJ7u2BV4xLa2tAwRETk/nTp1KtJi1blzZ1599VWcTierVq3C6XTSsGHDIsfk5uYWThp63333cffddzNr1ix69uzJoEGDaNmyZZm+h8pEAcqTGUaRFigRESnK39vB2md6WfbaJSU7OxuHw8HSpUtPmj07KCgIgNtvv51evXoxffp0Zs2axbhx43j11Ve59957S6wO+YcClCfL3A1H9oPdC6KaWV2NiEi5Y7PZ3OpGs9LChQuLPF6wYAENGjTA4XDQpk0bnE4n6enpXHTRRac9R82aNbnrrru46667ePTRR/noo4+499578fHxAczlbs6Xj49PiZzH02m0sSdLWWH+jGgC3n5MmzaNgQMHMm3aNGvrEhERtyUnJzNq1Cg2bNjApEmTePvtt7n//vsBaNiwIUOHDuXmm29mypQpbNu2jUWLFjFu3DimT58OwMiRI5k5cybbtm1j2bJlzJ07lyZNmgBQu3ZtbDYbP/30E3v37iU7O/uc64yLi2Pbtm0kJSWxb98+cnNzz//NeyAFKE92PEDFtALMy0vnzp1baS8pFRHxZDfffDNHjx6lQ4cODB8+nPvvv58777yz8Pnx48dz8803M3r0aBo1asSAAQNYvHgxtWrVAszWpeHDh9OkSRN69+5Nw4YNee+99wCoXr06Tz/9NGPGjCEqKooRI0acc52DBg2id+/e9OjRg4iICCZNmnR+b9xD2YwTJ5mQEpGZmUloaCgZGRmEhJTirOBfDoaNM+CKl6HjnUybNo2EhATi4+Pp169f6b2uiEg5lJOTw7Zt26hTpw5+flpQXU7tTL8n7nx/e0bHsJzav1qg+vXrp+AkIiJSBtSF56my0iArBbBBdHOrqxEREalUFKA8VepK82e1huCjpVtERETKkgKUp0pJMn8e674TERGRsqMA5an+Nf5JREREyo4ClKdSgBIREbGMApQnOnIADiWb96NbWFuLiIhIJaQA5YmODyAPjwP/MCsrERERqZQUoDyRuu9EREQspQDliRSgRETEYgkJCYSFhVldBvHx8QwYMKDMX1cByhMpQImISDm3fft2bDYbSUlJ5fJ850sBytPkZML+zeb9aAUoEZHKKi8vz+oSSoSnvg8FKE+Tttr8GVIdgiKsrUVEREpEVlYWQ4cOJTAwkJiYGF5//XW6d+/OyJEjC/eJi4vj2Wef5eabbyYkJIQ777wTgO+++45mzZrh6+tLXFwcr776apFz22w2pk6dWmRbWFgYCQkJwD8tO1OmTKFHjx4EBATQqlUrEhMTixyTkJBArVq1CAgI4Oqrr2b//v1nfE916tQBoE2bNthsNrp37w780+X23HPPERsbS6NGjYpV5+nOd9wrr7xCTEwMVatWZfjw4eTn55+xvvOlxYQ9TcqxK/DUfScicnaGAflHrHlt7wCw2Yq166hRo/j777+ZNm0aUVFRjB07lmXLltG6desi+73yyiuMHTuWJ598EoClS5dy3XXX8dRTTzF48GDmz5/PPffcQ9WqVYmPj3er3Mcff5xXXnmFBg0a8Pjjj3P99dezefNmvLy8WLhwIbfddhvjxo1jwIABzJgxo7CG01m0aBEdOnTg119/pVmzZvj4+BQ+N2fOHEJCQpg9e3ax6zvT+ebOnUtMTAxz585l8+bNDB48mNatW3PHHXe49Rm4QwHK02j8k4hI8eUfgedjrXntx/YUa63SrKwsJkyYwJdffsmll14KwPjx44mNPbnuSy65hNGjRxc+Hjp0KJdeeilPPPEEAA0bNmTt2rW8/PLLbgeoBx98kD59+gDw9NNP06xZMzZv3kzjxo1588036d27Nw8//HDh68yfP58ZM2ac9nwREWYvSdWqVYmOji7yXGBgIB9//HGREHQ2ZzpfeHg477zzDg6Hg8aNG9OnTx/mzJlTqgFKXXieRgFKRKRC2bp1K/n5+XTo0KFwW2hoaGHX1onat29f5PG6devo2rVrkW1du3Zl06ZNOJ1Ot+po2bJl4f2YmBgA0tPTC1+nY8eORfbv3LmzW+c/UYsWLdwKT2fTrFkzHA5H4eOYmJjC2kuLWqA8Sf5R2LvevK8AJSJydt4BZkuQVa9dwgIDz96i9W82mw3DMIpsO9X4IG9v7yLHALhcLrdfrzhO9T6KW+epnFj78XOVVu3HKUB5krS1YDghMAKCY6yuRkSk/LPZitWNZqW6devi7e3N4sWLqVWrFgAZGRls3LiRiy+++IzHNmnShL///rvItr///puGDRsWtshERESQkpJS+PymTZs4csS9cWFNmjRh4cKFRbYtWLDgjMccb2EqbkvY2ep093ylTQHKk6QkmT9jWhV7YKKIiJRvwcHBDBs2jIceeogqVaoQGRnJk08+id1uL2wJOp3Ro0dzwQUX8OyzzzJ48GASExN55513eO+99wr3ueSSS3jnnXfo3LkzTqeTRx555KQWm7O577776Nq1K6+88gr9+/dn5syZZxz/BBAZGYm/vz8zZsygRo0a+Pn5ERoaetr9z1anu+crbRoD5UlyDoGXv7rvREQqmNdee43OnTvTt29fevbsSdeuXWnSpAl+fn5nPK5t27Z8/fXXfPXVVzRv3pyxY8fyzDPPFBlA/uqrr1KzZk0uuugibrjhBh588EECAtzrXuzUqRMfffQRb775Jq1atWLWrFn897//PeMxXl5evPXWW/zvf/8jNjaW/v37n3H/s9Xp7vlKneEh/u///s/o3Lmz4e/vb4SGhp5yH+Ck26RJk4rsM3fuXKNNmzaGj4+PUa9ePWP8+PEnneedd94xateubfj6+hodOnQwFi5c6FatGRkZBmBkZGS4dVyxOAsMIyer5M8rIuLhjh49aqxdu9Y4evSo1aWct+zsbCM0NNT4+OOPrS6lwjnT74k7398e0wKVl5fHtddey913333G/caPH09KSkrh7cT1cbZt20afPn3o0aMHSUlJjBw5kttvv52ZM2cW7jN58mRGjRrFk08+ybJly2jVqhW9evUq9dH8xWZ3gG+Q1VWIiEgJWr58OZMmTWLLli0sW7aMoUOHAljfyiKn5TFjoJ5++mmAwhlJTycsLOyk+SGO++CDD6hTp07hLK1NmjThr7/+4vXXX6dXr16A2Yx6xx13cMsttxQeM336dD799FPGjBlTQu9GRESkqFdeeYUNGzbg4+NDu3bt+PPPP6lWrZrVZclpeEwLVHENHz6catWq0aFDBz799NMil0QmJibSs2fPIvv36tWrcLr6vLw8li5dWmQfu91Oz549T5rS/kS5ublkZmYWuYmIiBRXmzZtWLp0KdnZ2Rw4cIDZs2fTokULq8uSM/CYFqjieOaZZ7jkkksICAhg1qxZ3HPPPWRnZ3PfffcBkJqaSlRUVJFjoqKiyMzM5OjRoxw8eBCn03nKfdavX3/a1x03blxhC5mIiIhUfJa2QI0ZMwabzXbG25mCy7898cQTdO3alTZt2vDII4/w8MMP8/LLL5fiOzA9+uijZGRkFN527txZ6q8pIiIi1rG0BWr06NFnXaunbt2653z+jh078uyzz5Kbm4uvry/R0dGkpaUV2SctLY2QkBD8/f1xOBw4HI5T7nO6cVUAvr6++Pr6nnOdIiJScox/zWYtcqKS+v2wNEBFREQULg5YGpKSkggPDy8MN507d+bnn38uss/s2bML1/M5PnBvzpw5hVfvuVwu5syZw4gRI0qtThEROX/HJ108cuQI/v7+Flcj5dXx2c3dnUz03zxmDFRycjIHDhwgOTkZp9NJUlISAPXr1ycoKIgff/yRtLQ0OnXqhJ+fH7Nnz+b555/nwQcfLDzHXXfdxTvvvMPDDz/Mrbfeym+//cbXX3/N9OnTC/cZNWoUw4YNo3379nTo0IE33niDw4cPF16VJyIi5ZPD4SAsLKxw2pmAgICzzuQtlYdhGBw5coT09HTCwsKKLD58LjwmQI0dO5YJEyYUPm7Tpg0Ac+fOpXv37nh7e/Puu+/ywAMPYBgG9evXL5yS4Lg6deowffp0HnjgAd58801q1KjBxx9/XDiFAcDgwYPZu3cvY8eOJTU1ldatWzNjxoyTBpaLiEj5c3y4RbmZu0/KnTNNd+QOm6HO4hKXmZlJaGgoGRkZhISEWF2OiEil43Q6yc/Pt7oMKWe8vb3P2PLkzve3x7RAiYiIFNfxi4JESkuFm0hTREREpLQpQImIiIi4SQFKRERExE0aA1UKjo/L15p4IiIinuP493Zxrq9TgCoFWVlZANSsWdPiSkRERMRdWVlZhIaGnnEfTWNQClwuF3v27CE4OLjEJ3HLzMykZs2a7Ny5U1MknIU+q+LTZ1V8+qyKT59V8emzKr7S/KwMwyArK4vY2Fjs9jOPclILVCmw2+3UqFGjVF8jJCRE/5MVkz6r4tNnVXz6rIpPn1Xx6bMqvtL6rM7W8nScBpGLiIiIuEkBSkRERMRNClAextfXlyeffBJfX1+rSyn39FkVnz6r4tNnVXz6rIpPn1XxlZfPSoPIRURERNykFigRERERNylAiYiIiLhJAUpERETETQpQIiIiIm5SgPIQzz33HF26dCEgIICwsLBT7mOz2U66ffXVV2VbaDlRnM8rOTmZPn36EBAQQGRkJA899BAFBQVlW2g5FBcXd9Lv0QsvvGB1WeXGu+++S1xcHH5+fnTs2JFFixZZXVK589RTT530O9S4cWOryyoX/vjjD6666ipiY2Ox2WxMnTq1yPOGYTB27FhiYmLw9/enZ8+ebNq0yZpiLXa2zyo+Pv6k37PevXuXWX0KUB4iLy+Pa6+9lrvvvvuM+40fP56UlJTC24ABA8qmwHLmbJ+X0+mkT58+5OXlMX/+fCZMmEBCQgJjx44t40rLp2eeeabI79G9995rdUnlwuTJkxk1ahRPPvkky5Yto1WrVvTq1Yv09HSrSyt3mjVrVuR36K+//rK6pHLh8OHDtGrVinffffeUz7/00ku89dZbfPDBByxcuJDAwEB69epFTk5OGVdqvbN9VgC9e/cu8ns2adKksivQEI8yfvx4IzQ09JTPAcb3339fpvWUd6f7vH7++WfDbrcbqamphdvef/99IyQkxMjNzS3DCsuf2rVrG6+//rrVZZRLHTp0MIYPH1742Ol0GrGxsca4ceMsrKr8efLJJ41WrVpZXUa59+9/s10ulxEdHW28/PLLhdsOHTpk+Pr6GpMmTbKgwvLjVN9vw4YNM/r3729JPYZhGGqBqmCGDx9OtWrV6NChA59++imGpvk6pcTERFq0aEFUVFThtl69epGZmcmaNWssrKx8eOGFF6hatSpt2rTh5ZdfVtcmZqvm0qVL6dmzZ+E2u91Oz549SUxMtLCy8mnTpk3ExsZSt25dhg4dSnJystUllXvbtm0jNTW1yO9YaGgoHTt21O/YacybN4/IyEgaNWrE3Xffzf79+8vstbWYcAXyzDPPcMkllxAQEMCsWbO45557yM7O5r777rO6tHInNTW1SHgCCh+npqZaUVK5cd9999G2bVuqVKnC/PnzefTRR0lJSeG1116zujRL7du3D6fTecrfm/Xr11tUVfnUsWNHEhISaNSoESkpKTz99NNcdNFFrF69muDgYKvLK7eO/9tzqt+xyv7v0qn07t2bgQMHUqdOHbZs2cJjjz3GFVdcQWJiIg6Ho9RfXwHKQmPGjOHFF1884z7r1q0r9uDLJ554ovB+mzZtOHz4MC+//HKFCVAl/XlVJu58dqNGjSrc1rJlS3x8fPjPf/7DuHHjLF86QTzDFVdcUXi/ZcuWdOzYkdq1a/P1119z2223WViZVCRDhgwpvN+iRQtatmxJvXr1mDdvHpdeemmpv74ClIVGjx5NfHz8GfepW7fuOZ+/Y8eOPPvss+Tm5laIL76S/Lyio6NPunoqLS2t8LmK5nw+u44dO1JQUMD27dtp1KhRKVTnGapVq4bD4Sj8PTkuLS2tQv7OlKSwsDAaNmzI5s2brS6lXDv+e5SWlkZMTEzh9rS0NFq3bm1RVZ6jbt26VKtWjc2bNytAVXQRERFERESU2vmTkpIIDw+vEOEJSvbz6ty5M8899xzp6elERkYCMHv2bEJCQmjatGmJvEZ5cj6fXVJSEna7vfBzqqx8fHxo164dc+bMKby61eVyMWfOHEaMGGFtceVcdnY2W7Zs4aabbrK6lHKtTp06REdHM2fOnMLAlJmZycKFC896BbbArl272L9/f5HwWZoUoDxEcnIyBw4cIDk5GafTSVJSEgD169cnKCiIH3/8kbS0NDp16oSfnx+zZ8/m+eef58EHH7S2cIuc7fO6/PLLadq0KTfddBMvvfQSqamp/Pe//2X48OEVJnCei8TERBYuXEiPHj0IDg4mMTGRBx54gBtvvJHw8HCry7PcqFGjGDZsGO3bt6dDhw688cYbHD58mFtuucXq0sqVBx98kKuuuoratWuzZ88ennzySRwOB9dff73VpVkuOzu7SEvctm3bSEpKokqVKtSqVYuRI0fyf//3fzRo0IA6derwxBNPEBsbWymnpDnTZ1WlShWefvppBg0aRHR0NFu2bOHhhx+mfv369OrVq2wKtOz6P3HLsGHDDOCk29y5cw3DMIxffvnFaN26tREUFGQEBgYarVq1Mj744APD6XRaW7hFzvZ5GYZhbN++3bjiiisMf39/o1q1asbo0aON/Px864ouB5YuXWp07NjRCA0NNfz8/IwmTZoYzz//vJGTk2N1aeXG22+/bdSqVcvw8fExOnToYCxYsMDqksqdwYMHGzExMYaPj49RvXp1Y/DgwcbmzZutLqtcmDt37in/bRo2bJhhGOZUBk888YQRFRVl+Pr6GpdeeqmxYcMGa4u2yJk+qyNHjhiXX365ERERYXh7exu1a9c27rjjjiJT05Q2m2HoOncRERERd2geKBERERE3KUCJiIiIuEkBSkRERMRNClAiIiIiblKAEhEREXGTApSIiIiImxSgRERERNykACUiIiLiJgUoERERETcpQImIiIi4SQFKROQs9u7dS3R0NM8//3zhtvnz5+Pj48OcOXMsrExErKK18EREiuHnn39mwIABzJ8/n0aNGtG6dWv69+/Pa6+9ZnVpImIBBSgRkWIaPnw4v/76K+3bt2fVqlUsXrwYX19fq8sSEQsoQImIFNPRo0dp3rw5O3fuZOnSpbRo0cLqkkTEIhoDJSJSTFu2bGHPnj24XC62b99udTkiYiG1QImIFENeXh4dOnSgdevWNGrUiDfeeINVq1YRGRlpdWkiYgEFKBGRYnjooYf49ttvWbFiBUFBQXTr1o3Q0FB++uknq0sTEQuoC09E5CzmzZvHG2+8weeff05ISAh2u53PP/+cP//8k/fff9/q8kTEAmqBEhEREXGTWqBERERE3KQAJSIiIuImBSgRERERNylAiYiIiLhJAUpERETETQpQIiIiIm5SgBIRERFxkwKUiIiIiJsUoERERETcpAAlIiIi4iYFKBERERE3KUCJiIiIuOn/AVUnMer5Q/B5AAAAAElFTkSuQmCC", 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" ] @@ -1058,7 +1078,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1070,12 +1090,20 @@ "source": [ "u0 = s\n", "for i in range(5):\n", - " u0 = experimentalist(u0, n_samples=10)\n", + " u0 = experimentalist(u0, num_samples=5, random_state=i)\n", " u0 = experiment_runner(u0)\n", " u0 = theorist(u0)\n", " show_best_fit(u0)\n", - " plt.title(f\"{i=}\")\n" + " plt.title(f\"{i=}\")\n", + " plt.show()\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From e028638255035fa719012a39a4278f1901373537 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 17:29:37 -0400 Subject: [PATCH 25/26] docs: update docs to use two chained experimentalist functions --- ...Workflows using Functions and States.ipynb | 181 +++++++++++++----- 1 file changed, 133 insertions(+), 48 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 41249fc6..1dc04f20 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -72,15 +72,13 @@ "@dataclass(frozen=True)\n", "class Snapshot(State):\n", " variables: VariableCollection = field(metadata={\"delta\": \"replace\"})\n", - " params: dict = field(metadata={\"delta\": \"replace\"})\n", - " experiment_data: pd.DataFrame = field(metadata={\"delta\": \"extend\"})\n", - " conditions: pd.Series = field(default=None, metadata={\"delta\": \"replace\"})\n", + " experiment_data: pd.DataFrame = field(default_factory=pd.DataFrame, metadata={\"delta\": \"extend\"})\n", + " conditions: pd.Series = field(default_factory=pd.Series, metadata={\"delta\": \"replace\"})\n", " model: Optional[BaseEstimator] = field(default=None, metadata={\"delta\": \"replace\"})\n", "\n", "s = Snapshot(\n", " variables=VariableCollection(independent_variables=[Variable(\"x\", value_range=(-15,15))],\n", " dependent_variables=[Variable(\"y\")]),\n", - " params={},\n", " conditions=pd.DataFrame({\"x\": np.linspace(-15,15,101)}),\n", " experiment_data = pd.DataFrame(columns=[\"x\",\"y\"]),\n", ")" @@ -94,7 +92,7 @@ { "data": { "text/plain": [ - "Snapshot(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), params={}, experiment_data=Empty DataFrame\n", + "Snapshot(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-15, 15), allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[Variable(name='y', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], covariates=[]), experiment_data=Empty DataFrame\n", "Columns: [x, y]\n", "Index: [], conditions= x\n", "0 -15.0\n", @@ -216,27 +214,27 @@ " \n", " 0\n", " -15.0\n", - " -1456.807800\n", + " -1456.979354\n", " \n", " \n", " 1\n", " -14.7\n", - " -1276.368140\n", + " -1275.903805\n", " \n", " \n", " 2\n", " -14.4\n", - " -1101.905609\n", + " -1101.590466\n", " \n", " \n", " 3\n", " -14.1\n", - " -937.150175\n", + " -936.923388\n", " \n", " \n", " 4\n", " -13.8\n", - " -779.888407\n", + " -780.252340\n", " \n", " \n", " ...\n", @@ -246,27 +244,27 @@ " \n", " 96\n", " 13.8\n", - " 503.013830\n", + " 502.578979\n", " \n", " \n", " 97\n", " 14.1\n", - " 607.702964\n", + " 609.939995\n", " \n", " \n", " 98\n", " 14.4\n", - " 723.076065\n", + " 723.255149\n", " \n", " \n", " 99\n", " 14.7\n", - " 843.761041\n", + " 843.905227\n", " \n", " \n", " 100\n", " 15.0\n", - " 971.355949\n", + " 972.154947\n", " \n", " \n", "\n", @@ -275,17 +273,17 @@ ], "text/plain": [ " x y\n", - "0 -15.0 -1456.807800\n", - "1 -14.7 -1276.368140\n", - "2 -14.4 -1101.905609\n", - "3 -14.1 -937.150175\n", - "4 -13.8 -779.888407\n", + "0 -15.0 -1456.979354\n", + "1 -14.7 -1275.903805\n", + "2 -14.4 -1101.590466\n", + "3 -14.1 -936.923388\n", + "4 -13.8 -780.252340\n", ".. ... ...\n", - "96 13.8 503.013830\n", - "97 14.1 607.702964\n", - "98 14.4 723.076065\n", - "99 14.7 843.761041\n", - "100 15.0 971.355949\n", + "96 13.8 502.578979\n", + "97 14.1 609.939995\n", + "98 14.4 723.255149\n", + "99 14.7 843.905227\n", + "100 15.0 972.154947\n", "\n", "[101 rows x 2 columns]" ] @@ -595,27 +593,27 @@ " \n", " 1\n", " x\n", - " -140.575016\n", + " -143.576922\n", " \n", " \n", " 2\n", " x^2\n", - " -3.162851\n", + " -2.506925\n", " \n", " \n", " 3\n", " x^3\n", - " 0.961146\n", + " 0.998978\n", " \n", " \n", " 4\n", " x^4\n", - " 0.000429\n", + " -0.002146\n", " \n", " \n", " 5\n", " x^5\n", - " 0.000109\n", + " -0.000055\n", " \n", " \n", "\n", @@ -624,11 +622,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -140.575016\n", - "2 x^2 -3.162851\n", - "3 x^3 0.961146\n", - "4 x^4 0.000429\n", - "5 x^5 0.000109" + "1 x -143.576922\n", + "2 x^2 -2.506925\n", + "3 x^3 0.998978\n", + "4 x^4 -0.002146\n", + "5 x^5 -0.000055" ] }, "execution_count": null, @@ -687,27 +685,27 @@ " \n", " 1\n", " x\n", - " -140.575016\n", + " -143.576922\n", " \n", " \n", " 2\n", " x^2\n", - " -3.162851\n", + " -2.506925\n", " \n", " \n", " 3\n", " x^3\n", - " 0.961146\n", + " 0.998978\n", " \n", " \n", " 4\n", " x^4\n", - " 0.000429\n", + " -0.002146\n", " \n", " \n", " 5\n", " x^5\n", - " 0.000109\n", + " -0.000055\n", " \n", " \n", "\n", @@ -716,11 +714,11 @@ "text/plain": [ " t coefficient\n", "0 1 0.000000\n", - "1 x -140.575016\n", - "2 x^2 -3.162851\n", - "3 x^3 0.961146\n", - "4 x^4 0.000429\n", - "5 x^5 0.000109" + "1 x -143.576922\n", + "2 x^2 -2.506925\n", + "3 x^3 0.998978\n", + "4 x^4 -0.002146\n", + "5 x^5 -0.000055" ] }, "execution_count": null, @@ -729,7 +727,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -776,7 +774,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -808,7 +806,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1098,6 +1096,93 @@ " plt.show()\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A more complicated experimentalist can be constructed using a pooling function and sampler(s), which are chained\n", + "together.\n", + "In this example, the `grid_pool_executor` requires explicit specification of the allowed states, so we add those to\n", + "the `variables` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After pooler: r.conditions= x\n", + "0 -15.00\n", + "1 -14.95\n", + "2 -14.90\n", + "3 -14.85\n", + "4 -14.80\n", + ".. ...\n", + "596 14.80\n", + "597 14.85\n", + "598 14.90\n", + "599 14.95\n", + "600 15.00\n", + "\n", + "[601 rows x 1 columns]\n", + "After sampler: r.conditions= x\n", + "453 7.65\n", + "567 13.35\n", + "428 6.40\n", + "103 -9.85\n", + "86 -10.70\n", + ".. ...\n", + "576 13.80\n", + "90 -10.50\n", + "215 -4.25\n", + "584 14.20\n", + "96 -10.20\n", + "\n", + "[100 rows x 1 columns]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from autora.experimentalist.sampler.random_sampler import random_sample_executor\n", + "from autora.experimentalist.pooler.grid import grid_pool_executor\n", + "\n", + "variables = VariableCollection(independent_variables=[Variable(name=\"x\",\n", + " allowed_values=np.linspace(-15, 15, 601),\n", + " value_range=(-15, 15))],\n", + " dependent_variables=[Variable(\"y\")])\n", + "\n", + "r = Snapshot(variables=variables)\n", + "\n", + "# The experimentalist is built of two functions acting in sequence.\n", + "# The first makes a full list of all allowable conditions:\n", + "r = grid_pool_executor(r)\n", + "print(f\"After pooler: {r.conditions=}\")\n", + "\n", + "# The second samples ten of those allowable conditions.\n", + "r = random_sample_executor(r, num_samples=100, random_state=i)\n", + "print(f\"After sampler: {r.conditions=}\")\n", + "\n", + "# ... then we continue with the experiment_runner and the theorist.\n", + "r = experiment_runner(r)\n", + "r = theorist(r)\n", + "\n", + "show_best_fit(r)\n" + ] + }, { "cell_type": "code", "execution_count": null, From 1fc8d0268a005f1db7978bb62cc9ee11e92dd68b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 10 Jul 2023 17:46:22 -0400 Subject: [PATCH 26/26] docs: update docs to use chained experimentalist functions --- ...Workflows using Functions and States.ipynb | 149 ++++++++++++++++-- 1 file changed, 133 insertions(+), 16 deletions(-) diff --git a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb index 1dc04f20..c44e78b6 100644 --- a/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb +++ b/docs/cycle/Linear and Cyclical Workflows using Functions and States.ipynb @@ -973,6 +973,8 @@ "metadata": {}, "source": [ "## Adding The Experimentalist\n", + "\n", + "### Single Function Experimentalists\n", "Modifying the code to use a custom experimentalist is simple.\n", "We define an experimentalist which adds some random observations each cycle:" ] @@ -1100,6 +1102,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### Chained Experimentalists\n", + "\n", "A more complicated experimentalist can be constructed using a pooling function and sampler(s), which are chained\n", "together.\n", "In this example, the `grid_pool_executor` requires explicit specification of the allowed states, so we add those to\n", @@ -1130,24 +1134,24 @@ "\n", "[601 rows x 1 columns]\n", "After sampler: r.conditions= x\n", - "453 7.65\n", - "567 13.35\n", - "428 6.40\n", - "103 -9.85\n", - "86 -10.70\n", + "446 7.30\n", + "404 5.20\n", + "509 10.45\n", + "455 7.75\n", + "201 -4.95\n", ".. ...\n", - "576 13.80\n", - "90 -10.50\n", - "215 -4.25\n", - "584 14.20\n", - "96 -10.20\n", + "439 6.95\n", + "9 -14.55\n", + "189 -5.55\n", + "373 3.65\n", + "517 10.85\n", "\n", "[100 rows x 1 columns]\n" ] }, { 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", 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", "text/plain": [ "
" ] @@ -1160,10 +1164,12 @@ "from autora.experimentalist.sampler.random_sampler import random_sample_executor\n", "from autora.experimentalist.pooler.grid import grid_pool_executor\n", "\n", - "variables = VariableCollection(independent_variables=[Variable(name=\"x\",\n", - " allowed_values=np.linspace(-15, 15, 601),\n", - " value_range=(-15, 15))],\n", - " dependent_variables=[Variable(\"y\")])\n", + "variables = VariableCollection(\n", + " independent_variables=[Variable(name=\"x\",\n", + " allowed_values=np.linspace(-15, 15, 601),\n", + " value_range=(-15, 15))],\n", + " dependent_variables=[Variable(\"y\")]\n", + ")\n", "\n", "r = Snapshot(variables=variables)\n", "\n", @@ -1173,7 +1179,7 @@ "print(f\"After pooler: {r.conditions=}\")\n", "\n", "# The second samples ten of those allowable conditions.\n", - "r = random_sample_executor(r, num_samples=100, random_state=i)\n", + "r = random_sample_executor(r, num_samples=100, random_state=1)\n", "print(f\"After sampler: {r.conditions=}\")\n", "\n", "# ... then we continue with the experiment_runner and the theorist.\n", @@ -1183,6 +1189,117 @@ "show_best_fit(r)\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Experimentalists could be chained together as a single line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r = Snapshot(variables=variables)\n", + "\n", + "r = random_sample_executor(grid_pool_executor(r), num_samples=50, random_state=1) # experimentalist\n", + "r = experiment_runner(r)\n", + "r = theorist(r)\n", + "\n", + "show_best_fit(r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The experimentalists could also be chained together using a `def`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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hmllg8zA7oksKD/LlyzvO54ouYcwuHsj4ovsotnjDzh/g45HOO25ERKBWD9+BCiiRE5TYHUz8dB3bU7NpXM+LL8PnYDuy07kK7r++qJX/EqoOsbGx1bL4pa+XjTdv6Mk9A9qw1NGLMQUPkWfxh8QVMGuotn8RESgpcu59B9C69k0gBxVQIid4aclOft11GF9PG9+euxm/Xd+C1QOune22e9udTExMDHFxccTExFT5ta1WC5Mva8vrN/Qg3taJawoeI8MSDGmb4MPBcGx/lb+miLiQA6uhKAf8GkFoF7PTnJQKKJG/WbwllbeX7QHg3f52Ilb9x3lg0LPQrI+JyWpedHQ0/fv3Jzo6utpeY1i3CD67rQ8HfdpwdcEUUiwhcCwBZl3hnEAqInXT34fvrLWzVKmdqURMkHA4l/u/2ADAxN6BXLT+AXCUQKcR0OcOk9PVvGHDhjFv3jyGDRtWra9zTosGfD2hLyVBUVydP5V9REDWAedw3uHd1fraIlJLlU4gr6XDd6ACSgSAvKISJnyyluzCEno3C+T+rBcgOxkatXXucac77qpV65AAvvn3+TQKb861BY+z22jq/PxjroBDO8yOJyI1KfcwpDj/MUvL/uZmOQUVUFLnGYbB/83bxPbUbBrV8+bDtiuw7lsOnv5w3cfgHWB2xDohJNCHuXecR7vWrRld+BjbjWaQkwYxQyFtq9nxRKSm7IkDDOfcp1o871QFlNR5X687yPz4ZGxWC7OGeFNvxXTngSumQ0h7c8PVMQE+nnwQfQ7ndmrHDYX/xxZHc8g9BLOvhPTtZscTkZpQtn1L7e19AhVQUsclHc3jydgtADwwoAVdVj8EjmJofyV0/5fJ6eombw8bb/6rBwN6deRfRY+xydHCuX/eR8PgyB6z44lIdTIMl5j/BCqgpA5zOAwe+HIDOYUl9GpenzvscyF9K/g1ItYyhJGjRlX5GkhSMR42Kxd67qNo+fsM23IZ2xyRzuG82cO0xIGIO0vbAjmp4OELzfqaneaUVEBJnfXBbwmsSjiKn5eNGRcWYF1xfJPgq14jZu431bYGklTMRx/NJnXbnwSkbWRs0f+x23H87rzZV0HmQbPjiUh1KO19anEheHibm+U0tB+F1Ek7UrOZ/qPz7q6nhrQg7OfRgAHd/gUdriQ62gFQrWsgyamVfvbR0dEkBXTkXz8+xhdeT9MiYz/GR8OwRC+s1RNMReQM7Frs/LOWD98BWAzDMMwO4W6ysrIICgoiMzOTwMBAs+PI/ygssTN8xgq2pWQxoH0I7zeag+XPDyCwKfx7BfgEmR3RJcXGxhITE0N0dHS1rB317vI9zF74K3O9n6Gp5TBGaGcs0d+Db3CVv5aImKAgE15o6Vx/7+510LBVjUeozO9vDeFJnTMjbg/bUrKo7+fJ9L4FzuIJYPgMFU9noTq3fgG4/eJW3HLlJfyr6DEOGUFY0jZjzLkeivKq5fVEpIbtiXMWTw1bm1I8VZaG8KRO2Z2ew9vLnKtb/2dYexr8fIPzQPcx0LKfecHcwN+H3KrLrRdG4WG1cNOCAuZ6PUNg4kr46mYY/QnYPKvtdUWkBpQO37UZbG6OClIBJXWGYRg89s0miu0G/ds15oq8byFtM/jWh8ueNjueyxs2bFi1b/sCMO78FhjG5dz6XT4fe03DZ+ci+PYuGP52rd0zS0ROw+H4q4BqO8jcLBWknzZSZ3y97iCrEo7i42nl2UvrY4mb5jxw2dPg38jccFIp0RdEMejyEfy7+F5KDCts/BwWP+5cQ0ZEXE/KeueiuV4B0Ox8s9NUiAooqROO5Rbx3MJtANw7oC0RK58kdnMGI2N9iE3URH9XEhsby8iRIwnN2Eyvy27gweLjGz3/MQNWzig7rjW8RFzIzuO9T636gYeXqVEqSgWU1AnTftjG0dwi2oUGMD50B2z/jpj4EuL25BPz0Udmx5NK+Ptk9Yn9W9P80lt4tvj4qvGLHyPm9WfPajK7CjARE+z60fmni8x/As2BkjpgdcJRvvjzAADThrXCY8EQAKKvHwEbirXWk4v538nq9w5owwvF9zDr9yPc7PEjN4Vsgz49zvi/a2mBBtTInC6ROi87DZLXO//exjXmP4EKKHFzJXYHT8zfDMANvSPpmfQJZCRCYFOG3fUhw7z8TU4olfW/k9UtFgsPDWnP1ILH+GHdUYa3XcNQz714ntfmjK5fE3cTisjf7F7i/DO8u0stjqshPHFrX/x5gB1p2QT7efLIhcHw+/HtWgY9Ayqe3IbFYuHJq7sS1/FZ1jja4lmcRWHMCMhKrvS1hg0bxrx589T7JFJTdh4fvmvrOsN3oAJK3FhOYQkvL3Fu13LvgDYErXwBinOh6bnQaYTJ6aSqWa0WnrvuXD6Nep49jnC881LIm30NFOaYHU1E/klJkXMBTVABJVJbvL1sN4dzimjZyJ8bo7Jh/SfOA4OeBYvF3HBSLTxsVp6/sR9vRjzPYSMQvyNbyJ0TDQ672dFE5GQSV0BRNviHQHgPs9NUigoocUsHM/J5/9cEAB65vD2eS6cCBnQcDs36mJpNqpePp41nbr6S/wZPodDwxH/fEnK+e9TsWCJyMqXLF7S5zOUWwnWttCIVNH3RdgpLHJzXsgGXeW2CPT+D1RMGTjU7mtSAet4ePDL+Jv7rc6/z63XvkPf7OyanEpETlC1f4Dp335VSASVuJz4pg/nxyVgs8PjlbbEsfsJ5oM8d0KClueGkxjSs583Nd9zP2zbnfofeSx6hcPtik1OJSJkje+DIbrB6QKv+ZqepNBVQ4lYMw+A/320FYGSPpnROXwCHtjn3u7v4AZPTSU2LbODHpbe9wLdcgg0HjrnjKEndanYsEQHY/p3zzxYXgU+QuVnOgAoocSs/b0/nz/3H8PG08uCAZlC6390lDzuLKKlz2oUH0nTsu6wyOuBr5JH54SiM3CNmxxKR7d87/2w/1NwcZ0gFlLgNwzB4eclOAKLPjyJs5xzISYWgSDjnFpPTiZl6tQojZ9iHJBqNaViUzMF3rwV7sdmxROqu7DRIWu38e7srzM1yhlRAidv4cUsaW5Kz8Peycft5ofDby84DFz8AHt7mhhPTDejVkfUXzCTH8KFp5lr2fPRvMAyzY4nUCdFPvsX5A6/gq3nznQ07fwAMiOgJQU3MjHbGVECJW3A4DF453vt0y4VRNNj6MeQeguDm0H2Myemktrh60EB+aPcfHIaFVvu/YNf3r5gdScTtHczI54vPPmHVb8uZPXu2s9HFh+9ABZS4iYWbU9iRlk2Ajwe39Q6B3191HrjkIbB5mppNapdR19/GgpA7AIha8wz7V39vciIR97Z85yHqdRlIWPtzGH/rzVCYDXuXOQ+2v9LUbGdDBZS4PLvD4NWfdgFw24UtCdo0C/KOQP0o6Hq9yemktrFaLVx++3Ms9x2Ih8VB8MLbObR/u9mxRNzWLzsO4demD4+89J5zj8ndP4G9CBq0gsbtzI53xlRAictbsCGZ3ek5BPl6cvO5DWHF684D/R4Bm4e54aRW8vK00e3fMWyztiWIHHJmX0dOdobZsUTcTrHdwe+7DwNwSbvGzsa/D9+58LZaKqDEpZXYHby21Nn7dPvFLQmM/wDyj0HDNtD5GpPTSW0WFBBAUPTnHCaYKMd+tr19IyUl2jNPpCqtT8wgu7CEBv5edI4Icm4eXLp9iwsP34EKKHFx8+OTSTicSwN/L6J71oeVbzgPqPdJKiCiWSuOXvkBRYaNc/N+Je79hzB0Z55IlfllZzoAF7VphNVqgf2/QWGmc/PgpueYnO7suFUB9eSTT2KxWMo92rdvX3a8oKCAiRMn0rBhQ+rVq8eoUaNIS0srd43ExESGDh2Kn58fISEhPPjgg5SUlNT0W5EKcDgMZv6yB4DxF7XEf8MsKMiExu2h0wiT04mraHvOQHae8xQAA1I+YPE3MeYGEnEjv+w8BMAlbf9n+K7d5WC1mZSqarhVAQXQqVMnUlJSyh6//fZb2bH77ruPBQsW8OWXX/LLL7+QnJzMyJEjy47b7XaGDh1KUVERK1asYPbs2cTExDBlyhQz3oqcxtLt6exOzyHA24MxvRrDqpnOAxdOdvn/MaVmdb7qbrY2uQ6rxeD8DY/y2x8rzI4k4vIOZRey+WAWABe1aQwOB2xf6Dzo4sN34IYFlIeHB2FhYWWPRo0aAZCZmckHH3zAyy+/zKWXXkqvXr2YNWsWK1as4I8//gBg8eLFbN26lU8++YTu3btz+eWX88wzzzBjxgyKiorMfFtyEqW9Tzf2bU7g9i+d6z4FRULnkad5psiJOtz8Jgn+3Qiw5BP2w21sTThodiQRl/brLmfvU+cmgTQO8IaU9ZCdDF71IOpik9OdPbcroHbt2kVERAQtW7ZkzJgxJCYmArB27VqKi4sZOHBg2bnt27enWbNmrFy5EoCVK1fSpUsXQkNDy84ZPHgwWVlZbNmy5R9fs7CwkKysrHIPqV5r9h1l7f5jeHlYublvJKw4Pvep711a90nOiMXDm6a3z+WorSGtLQdJ/egWUjPyzY4l4rJOGL7bdnzz4NYDwdPHpFRVx60KqD59+hATE8OiRYt4++23SUhI4KKLLiI7O5vU1FS8vLwIDg4u95zQ0FBSU1MBSE1NLVc8lR4vPfZPpk2bRlBQUNkjMjKyat+YnODtZc7ep1E9mxKS9CMcSwDfBtBzrMnJxJV5BoXjdcOnFOPBpcYfLHr3YfKKNAdSpLLsDoPlZQVUiHPbpK3znQc7XGVesCrkVgXU5ZdfzrXXXkvXrl0ZPHgwCxcuJCMjgy+++KJaX/fRRx8lMzOz7JGUlFStr1fX7UjN5uft6VgscPtFUX+tOt77dvDyNzWbuL56rfuS3f9ZAMbmfsR7s97H4dCdeSKVsflgJsfyignw9qBHs2BI3QRH94KHD7QdbHa8KuFWBdT/Cg4Opm3btuzevZuwsDCKiorIyMgod05aWhphYWEAhIWFnXBXXunXpeecjLe3N4GBgeUeUn3eOT736fLOYURlrYGUDeDh6yygRKpAg4vv4FCb6/h+ZxE/vXofdz/xrNmRRFxK6fDdBa0b4WmzwpZvnAfaXAbeASYmqzpuXUDl5OSwZ88ewsPD6dWrF56enixdurTs+I4dO0hMTKRv374A9O3bl02bNpGenl52zpIlSwgMDKRjx441nl9OdDAjn9gNyQDceUmrv3qfet4E/g3NCybuxWKh8XVv8O4WX37bV8COedNZsHaP2alEXEbZ/Kd2jcsP33UcblqmquZWBdQDDzzAL7/8wr59+1ixYgUjRozAZrNxww03EBQUxK233srkyZOJi4tj7dq13HzzzfTt25fzzjsPgEGDBtGxY0fGjh3Lhg0b+PHHH3n88ceZOHEi3t7eJr87AXj/172UOAzOb9WQrtYE54aUFhv0nWh2NHE3nj7c/uh0Lo7y5Z7uxRR8O5n4pIxqe7nY2FhGjhxJbGxstb2GSE3IzCtmfeIxAC5u2xhSN/5t+G6Iyemqjlst1XzgwAFuuOEGjhw5QuPGjbnwwgv5448/aNzYeQfAK6+8gtVqZdSoURQWFjJ48GDeeuutsufbbDa+++47JkyYQN++ffH392fcuHE8/fTTZr0l+ZvsgmK+WOOcX3bHJa3g94ecBzqPgvrNTUwm7mrYDbcw9NwoLJ+MxMoy/hPzPKH3TCU8yLfKXysmJoa4uDjn6w4bVuXXF6kpv+4+hMOANiH1aBLsC3/Odx5ocxl41zM1W1VyqwLq888/P+VxHx8fZsyYwYwZM/7xnObNm7Nw4cKqjiZV4Ou1B8gtstM6pB4XhxTA1m+dBy64x9xg4tZsrftTeMmjeP/yLA+WvM/DH7Zl2sRx+HqVX6w1NjaWmJgYoqOjz6gAio6OLveniKv6eZtzGky/0uG70vlPbrZDhFsN4Yn7cjgMPlq5H4BxfZtj+fNDMOzQ4iII62JyOnF33pc8QH7UYLwtxTyQ8SxPzl1+wp55pT1IMTExZ/Qaw4YNY968eep9EpdWYnfw8w5nAXVZxzDn8N2xBOfwXRv3uPuulAoocQnLdx1i7+FcArw9GNmlIayNcR7QnXdSE6xWfK97l4KA5jS1HOaKnVN46+cd5U6Jjo6mf//+6kGSOm3t/mNk5BVT38+Tns2C/3b33SC3Gr4DFVDiImav2AfANec0xX/Xt5B/1LltS7srzA0mdYdvMD5jPqPE6sMlto0U/fwCi7f8tcCuepBE4KdtzqV/+rcPwcNqgS3znQfcbPgOVECJC0g4nEvcjkNYLDDuvOaw6h3ngXNvA5tbTeOT2i6sMx7DXgXgXo95fDk3hu2p2rpJBMAwDJZsdRZQl3UIda7RdyzBuU6fmyye+XcqoKTW+2jlPgD6tW1Mi7xNzjF1Dx/n2k8iNa37Ddh7jsNqMfiv5Q0em/UDR3O12bjInkO57DuSh5fNykVtG/+19lPbQW65S4QKKKnVcgtL+OrPAwCMO78FrD7e+9T1OvBrYF4wqdNsl79ASWhXGlhyeDz/Be75ZBXFdofZsURMVTp817dVQ+p52f6a/+RGi2f+nQooqdXmrTtAdmEJLRv5c3FoEWw9vshg7zvMDSZ1m6cPHtd/jN07iB7W3QxIeoNnv99mdioRU/10fPhuYMdQSF4Hx/a57fAdqICSWswwDGKOTx6/qW9zrGtnOZcuaH4hhHU2N5xI/RbYRr0HwM0eP3L4jznMXZNocigRcxzJKWTt8dXHB3YIgY1fOA+0H+qWw3egAkpqsT/2HmXPoVz8vWyM6tYI1s5yHuijpQuklmg7GC66H4DnPd/jg/mLWbv/mMmhRGrez9vTMQzo3CSQ8HqesPlr54Guo80NVo1UQEmtVfqv+WHdIwjY8z3kHYHAptBuqMnJRP6m3/9hNL+QepYCXre9yr0fryA1s8DsVCI1qnT+08AOoc49SnMPgV8jaNXf3GDVSAWU1EoZeUUs3OxcY+f6c5vB2tnOA72itXSB1C42DyzXfIjDP5T21iQmFc7kjo//pKDYbnYykRpRUGxn+c7DwPECauNc54HOo8DmaWKy6qUCSmqlb9YfpKjEQYfwQLp6p0LiCrDYoMcYs6OJnCggFOs1H2BYrFxjW067lPk8Pn/zCdu9iLijlXuOkF9sJzzIh06NrLD9O+cBNx6+AxVQUgsZhsHnq5MAuP7cSCzrP3YeaDsYAiNMTCZyClEXYbn0cQCe8Yhh67rfylbQF3FnS/42fGfZvhCK86BBS2jS0+Rk1UsFlNQ68UkZ7EjLxtvDyvDOjSD+M+eBnuPMDSZyOhfcB20G4W0p5i3P13jl+7Ws3HPE7FQi1cbuMFi85W/LF2w6fvdd19FgsZiYrPqpgJJap7T3aWiXcIISf3TuexfYBFoPNDmZyGlYrTDiHYygprSwpjHN9g4TP13LgWN5ZicTqRarE45yOKeQIF9Pzg+1w56fnQe6XGtusBqgAkpqlZzCEhZsTAZg9LmRsDbGeaDHjZo8Lq7BrwGWa2djWD25wraaYYULuOPjteQXaVK5uJ+Fm1IAGNQxFM9t34DhgKbnQsNWJierfiqgpFZZsCGZvCI7LRv50zsoExKWAxZnASXiKpqeg2XQfwB4zPMzPFPW8ei8jZpULm7F7jD44fjd0ld0Df/r7js3nzxeSgWU1Cqfr3EO340+NxLL+o+cja0HQHAzE1OJnIE+d0CHYXhSwgyv11kWv4MPfkswO5VIlVmzzzl8F+jjwQXBxyB5vfNu6U4jzI5WI1RASa2xLSWLDUkZeNosjOoeCus/dR7oFW1qLpEzYrHA1W9Cg5Y0sRzmZc+3ef6HrazYfdjsZCJV4ofS4btOYXht+dLZ2Hog+DcyMVXNUQEltcZXaw8AzlthGx38GXLTwT8E2g4xOZnIGfIJgmtnY9i8udQWz3jLAiZ+to6ko5pULq7N8bfhu6GdQiB+jvNAt7oxfAcqoKSWKLE7+DbeOXl8ZM+msO74yuM9xrj1SrZSB4R3xXLFdAAe8PyCNvkbNalcXN6f+4+Rnl1IgI8HF9o2Q9YB8K0P7a80O1qNUQEltcJvuw9zOKeQ+n6eXBJe8tetsD3GmhtMpCr0vAm6Xo8NBzO83yQtJUmTysWlld59d1nHUDw3fOJs7HIdeHibmKpmqYCSWuGb9QcBuKpbBF5bv3beCht5Xp24FVbqAIsFrnwZGrenMcd4zWsGsfEHNKlcXJJz+M5ZQA1v6w3bv3ce6Fm3/sGrAkpMl1NYwo9bnGPpI7pHwIbSsfTrTUwlcvZiY2MZOXIksbGx4OUP184GTz8utG7mLtt8pv2wXZPKxeWsTTxGWlYhAd4enJ/3MziKIbw7hHUxO1qNUgElplu0OZWCYgdRjfzp7pUE6VvB5g2dhpsdTeSsxMTEEBcXR0xMjLMhpD1c+QoAkzy/pg+bmPjZOq1ULi7l+43Hh+86hOARf/xu6Tq4Vp8KKDHdN+udd9+N6NEEy4bjC7G1u9w5IVHEhUVHR9O/f3+io6P/aux2PfS8CSsGM7zfwjMvXZPKxWU4HAaLjt99d33Tw5C+BTx86sTWLf9LBZSYKjWzgBXHN1sd0e1vG1F2u8HEVCJVY9iwYcybN49hw4aVP3D5CxDamfpGBm/7zGB78jFNKheXsCrhKKlZBQT4eNDr6HfOxg7DwDfY1FxmUAEl1arcHJCT+Db+IIYB57aoT+TRPyD3EPg1cq4+LuKuPH3huo/AK4BebOUBzy+ZH5+sSeVS680/fsPP8E71sW2Z52ysg8N3oAJKqtkJc0D+R+nddyN6NP1r8niXa7X2k7i/hq3g6jcAmGCLpb91vSaVS61WUGwvW75gXPBGKMyC4ObQ4iKTk5lDBZRUq5POATlua3IW21Oz8bJZGdrG769bYXX3ndQVnUZA7zsAmOEzkzBHuiaVS621dFs62YUlNAn2pdWB0t6nsWCtm6VE3XzXUmP+cQ4If00eH9AhhKCE78BeCI07QHi3mo4pYp5Bz0BET/wc2XxYbwY5efmaVC61UunP7Oh2JVj2/w5YoHvdna+qAkpM4XAYfHf8VtjhPZrAhs+dB7pd71x0UKSu8PCGa2PAJ5h2JTt52vdztiRnaVK51CpHc4tYtuMQANcYPzob2wyCoKYmpjKXCigxxfqkY6RkFhDg7UG/xrmQuBKwQNfrzI4mUvPqN4cR7wBwg7GQK22rNKlcapXvNiZT4jDoFeFF/R1fOht7jzc3lMlUQIkpSnufLusYive242PpLS+BwAgTU4mYqN0QuPA+AF7xeY/A3Qu597YbeX7mJyYHE/nrhp9JoRuhMBPqR0Grun23tAooqXEOh1F2J8fQruFQeitsHVyITaSc/o9D8wvxtOfRdNsnFOzfwLTXZ5J0VJPKxTwJh3NZn5iB1WJw3uHjP6/PvbXOTh4vVbffvZjiz/3H91Hy8eCi4EPOrVusntB+qNnRRMxl84BrPgD/EO7sWsz5rYLw7tCf2z9eS15RidnppI4qXfsputkhPA9tdq483n2MyanMpwJKatzL739K+rxnaZW3Ha9t3zobWw/U1i0iAAFhcM2HDGvvza/X5nJ3t2K2pWTx0FeaVC41zzAM5sc7C6ibPZc4GztfA34NTExVO6iAkhpldxj8OH8uBYkbObJ+MWz+2nmg80hzg4nUJlEXwaVPAPAYH9DVto/vNqbwzvK9JgeTumZd4jH2H8mjqVcOTZMXOxt732ZuqFpCBZTUqNUJR/FsfykBUd2574bL4OgeZ3dwu8vNjiZSu1wwCdoOweoo4tPAtwgkl/8u2s6yHelmJ5M6ZO6aJAAeDV2NxVEMTc6BiB4mp6odVEBJjfp+UzJ+bfpwxzMzGBl51NnYZhB4B5gbTKS2sVphxEwIbkZA/gE+bxwDhoO756wn4XCu2emkDsguKGbBhhRs2BmYt9DZeK56n0qpgJIaU2J3sGhzKgBDu4TD5m+cBzqPMjGVSO0UGxvLyDG3Eht8K9i86Zj9O083/InsghLGf/Qn2QXFZkcUN/dtfDL5xXbG1N+Gd24y+DV0bj8kgAooqUGrE45yOKeIYD9PLvBJgMxE8PR39kCJSDllG3HHLocrpgNwY95HXFFvJ7vTc7hv7gYcDk0ql+rz+ZpEAO70OT55vMdY8PQxMVHtogJKasyC44tnDukUhse2+c7G9leAl595oURqqXIbcfe8CbqPwWI4eM3jTSJtGfy0LY1Xf9ppdkxxU5sOZLL5YBbdbfuJOLYGrB51fuXx/6UCSmpEid3Bj1tKh+9CYcvx4btOuvtO5GTKbcRtscAVL0JoFzwLDvNNyLt4UsLrP+/mh+OL0opUpTnHe58eb7DU2dBpZJ3e9+5kVEBJjfhz/zGO5jqH7/p67ITsFPAOgtZ1eysAkQrz8oPRH4F3EI2OxfNJ5AIAJn+xga3JWSaHE3eSW1jCt+sPEsFheuXEORvPv8vcULWQCiipEYu3pAEwoH0oHluP9z51uNK5E72IVEyDljDSuelwn0Nf8nDEBvKL7Yz/6E+O5BSaHE7cxXcbk8ktsnNvvZ+wGHaIuhjCu5kdq9ZRASXVzjAMFm91Dt8N6tgItsU6D2j4TqTy2l0OFz8IwJ1Zr9O/fjoHM/L596frKLY7TA4n7mDO6iQCyGOEcXz47vx7zA1US6mAkmq3LSWbA8fy8fG00s8nAXIPgU8QtLzE7Ggirqnfo9BqAJaSfN7xfIVw70JWJRzlqQVbzE4mLm5bShbxSRmM8YzDy54Ljds7t9qSE6iAkmpX2vt0UZvGeO/+3tnY7gqweZqYSsSFWW0w6n0IaoZX1n5im3yM1eLgkz8S+XjlPrPTiQubszoRD0q4w/v4ti1973LexCAnUAEl1a50/tOgDiGwzTnxlfZXmphIxA34NXBOKrd50zj5Zz5v9xsATy7Yyordh00OJ64oM7+Yr9YeYKj1D37dkszIr+3E7tcyM/9EBZRUq6SjeWxNycJqgUEN0iAzCTz9oNWlZkcTcX0RPeDKVwDovW8mj7Xej91hMOHTdezTdi9SSV/+mUReUQl3+ywiJr6YuH12Yj7+1OxYtZYKKKlWS7Y6e596RzUgaN8PzsbWA7V4pkhV6TEGznUucHjboee4PDyHzPxibp29hixt9yIVVGJ3MOv3fVxi3Uhrx16ie9Wj/6UDnAu5ykmpgJJq9dfdd2Gw7TtnY4dhJiYScUODn4PI87AUZvOG5UVaBhjsOZTL3Z+tp0R35kkF/LQtjYMZedzvNQ+AYWP/zbxvv3Mu5ConpQJKqs2x3CJWJxwF4PKwTDi8A6ye0FZ734lUKQ8vuO4jCAjH4+hOvon4GF9P+GXnIZ5duM3sdOICPvx9HxdYN9OVXeDhA+ffbXakWk8FlFSbpdvTcRjQMTyQ8OSfnI0tL3EuYSAiVSsgFK77GKyeBO1fxPyuqwGY9fs+Pl213+RwUpttPpjJ6oQjTPJw9j7RKxoCwkzN5ApUQEm1WXx877tBnULL7r6LTW/KyJEjiY2NNTOaiHuKPBeGvghAuy2v8UZP5/+DU77dwu+6M0/+wYe/J3CedRvnWneAzQsuuNfsSC5BBZRUi/wiO8t3HQJgaGQJpMQDFmJ+2U1cXBwxMTFmxhNxX72i4ZxbAYMrd0/ljg5FzjvzPlnLnkM5ZqeTWiY9u4AFG5K5x3a896nnTRAYYW4oF6EC6h/MmDGDFi1a4OPjQ58+fVi9erXZkVzKr7sOUVDsoGl9X1ofXeZsbH4+0bfeQf/+/XVnh0h1uvy/0PxCLEXZPJzxNBdHepBVUMKtMWvIyCsyO53UIp/8kUh3xzbOt211zlG98D6zI7kMFVAnMXfuXCZPnszUqVNZt24d3bp1Y/DgwaSnp5sdzWXE7XB+VgM7hGLZXnr33VUMGzaMefPm6c4Okepk84TrZkNQM6zH9vK+3wyaBXmx70get3+8lsISu9kJpRYoKLbz6R/7uad07lOPGyGoqbmhXIgKqJN4+eWXGT9+PDfffDMdO3Zk5syZ+Pn58eGHH5odzSUYhkHcdufw3aDmVti/wnmg/VATU4nUMf6N4IbPwNMPr/2/ML/dYgK8PVidcJRHv96EYRhmJxSTfb46keZ5m7nIthnD6qHep0pSAfU/ioqKWLt2LQMH/rV5otVqZeDAgaxcufKkzyksLCQrK6vcoy7blpJNalYBvp42zi38AzAgvBsENzM7mkjdEtYFhr8NQION7/J1373YrBbmrT/I60t3mxxOzFRYYued5Xu5z+MrACzdboD6zU1O5VpUQP2Pw4cPY7fbCQ0NLdceGhpKamrqSZ8zbdo0goKCyh6RkZE1EbXWKh2+u6B1Qzz3HN+QUnvfiZij03C45GEA2q5+gncuLgTglZ92Mn/9QRODiZnmrTtIq+w1zt4nmxdc/IDZkVyOCqgq8Oijj5KZmVn2SEpKMjuSqeK2H5//1CYQ9i5zNrYdYl4gkbrukkeg43BwFDNww2Qe7uMNwENfbSxb7FbqjhK7g5lxu3jEYw4AlnNvg/otzA3lglRA/Y9GjRphs9lIS0sr156WlkZY2MkXFvP29iYwMLDco646llvEusRjAFzmuxOK8yCwiXMoQUTMYbU6h/LCu0P+Ue5MfowRHQMosjsY/9Gf7E7X8gZ1yYKNyXTLXEpn6z4M7wC4SL1PZ0IF1P/w8vKiV69eLF26tKzN4XCwdOlS+vbta2Iy17B81yEcBrQPC6DhwZ+djW0Hg8VibjCRus7LD26YAwHhWA5t50XLa/SKDCAzv5joWatJzy4wO6HUAIfD4N2ft/OgxxcAWC6YBP4NzQ3lolRAncTkyZN57733mD17Ntu2bWPChAnk5uZy8803mx2t1isdvuvfrjHs/NHZ2PZyExOJSJnACLj+M/DwxbbnJz6JjKVFQz8OHMvnlpg15BaWmJ1QqtmPW1Lpc/RbIq2HcNQLhfMmmB3JZamAOonRo0fz4osvMmXKFLp37058fDyLFi06YWK5lGd3GPyy8/jq4yFHIOsgePhC1EUmJxORMk16woiZAPiue4+ve26igb8Xmw9mcddn6yixO0wOKNXFMAw++Hkjd3t8A4C136Pg5W9yKtelAuof3HXXXezfv5/CwkJWrVpFnz59zI5U68UnHeNYXjFBvp50yPrd2diqP3j6mhtMRMrrNBwGPglAw9+m8mX/DHw8rcTtOMQT327WGlFu6qdt6Vx0aA4NLdnY67eCHmPNjuTSVEBJlSldPPPito2x7S4dvtPddyK10gWToOc4MBy0+uUeZg32xGqBOauTePWnXWankypmdxh8sPB3xtsWAmC77EmweZgbysWpgJIq8/Px+U9DmgMH1zob2w42L5CI/DOLBYa+BK0uheI8+v7xb14c5JxM/NrSXXzyx36TA0pV+nrdAW7IfA8/SyElEedCh6vMjuTyVEBJlUjNLGBrShYWC1xiiXc2RvSAgJMv/SAitYDNE66dDSGdICeNkdvu48FLwgF44tvNLNqcYnJAqQoFxXaW/fgNV9tWYGDB48oXdWd0FVABJVVi2fHVx7tHBlNv/0/ORt19J1L7+QTCmC+gXhikb+XfaU9w4zlhGAbc83k8f+w9YnZCOUsf/7aLewrfBcDe82aI6G5uIDehAkqqxLIdzvlPA9sEwZ6/rf8kIrVfUFMY8yV4BWDZ9xtPO95gSMfGFJU4GD/7T7YkZ5qdUM5QZl4xR395m/bWJAo9g/EY+ITZkdyGCig5ayV2B7/vOQzA5fV2O1cfD4hwbiAsIqaKjY1l5MiRxMbGnvrE8K5w/adg9cS69RvebPgVvVvUJ7uwhHEfrmbvIa1W7opiflrFBGMuAJ6DnwK/BiYnch8qoOSsbTiQSXZBCUG+nrQ48quzUauPi9QKMTExxMXFERMTc/qTW15StkaUx5p3+KjDH3SKCORwThE3vr+K5Iz86g0rVSo5I5/IP/9LoCWfrPqdsfbUsgVVSQWUnLXfdjl7ny5s1RDrLi1fIFKbREdH079/f6Kjoyv2hC7XwKBnAfCJe4rP++yjZWN/kjMLuPGDVRzOKay+sFKlvvrma0ZalwMQMOo1sNpMTuReVEDJWft11/HVxyOyITMJbN4QdbHJqUQEYNiwYcybN49hw4ZV/Enn3wV97wIgYNG9fNkvgybBvuw9lMtNH6wmM7+4mtJKVVmzO4VBCc8DcKTtaCxNzzE5kftRASVnJaugmPVJGQCcb6x3Nra4wLlxqYi4rsuegW7/AsNOw4W38+XgYhrV82JrShY3z1pNjvbNqzEVnsd2XLHdwc4vp9LemkSOLZiGV0+r5oR1kwooOSsvv/8ZKV/9h8C0eIKTnV3FtB5obigROXtWKwx7A9pfCfZCIn64hS+u9CLI15N1iRncErOGvCIVUTWhUvPYgO8W/cDogi+dXwx9GfwbVl+4OkwFlJyVzz/9mILEjZRs+wn2Hd//TgWUiHuwecCoDyDqEijKoeWP4/hiZDAB3h6sTjjK+I/+pKDYbnbKCqlsL05tUpl5bGnHsui4+lE8LA4SwwZRr+eo6g9YR6mAkrPi0a4fPs26Mn7ouWAvhKBIaNTW7FgiUlU8feD6z6DJOZB/jHY/jmXONSH4e9n4ffcR7vh4LYUltb+IqmwvTm1SmXlsaz95gnaW/WRaAmk6ZkYNpKu7VEDJGUs6mkdOWA8irnmcid2P797eeoCWLxBxN971nAtthnSEnFQ6LxnDp6NC8fW08cvOQ0z8dF2tL6IqfTeiC1q/5lcuO/wxAJn9n8MaEFJ2zJV74GorFVByxn49vnxBz2b18dp3fPVxDd+JuCe/BnDTt84e5qyDdP95LJ+MCsPbw8pP29K58+O1tXo474zuRnQhBQX51PvhHjwtdrYGXUyzi24sd9yVe+BqKxVQcsZKly+4omkBHNkNVg8tXyDizuqFwLgF0LA1ZCbRa9lYPrmmCT6eVuJ2HOL2Wl5EubN1sybTxrGXDOrR9Ma3TxgJqAs9cDVNBZScEbvD4Pfdzh6oAZ6bnI2RfcAnyMRUIlLtAsKcRVT9KMhI5NxfxvLptZH4etpYvvMQt83+k/wiFVE1aevyLzk/7TMAki78L4GNm55wjrv3wJlBBZSckY0HMsgqKCHQx4OmR1Y4G1sPMDeUiNSMwAiI/g6Cm8OxffSKG8Oc6yLw87Lx2+7D3ByzmlytE1UjctL3E/HzfQCsaDiKLgNvPM0zpKqogJIzUjr/6eJWQVgStP6TSJ0T1LRcEdV9yQ18cU1j6nl78Mfeo4x5fxUZeUVmp3Rv9hLSY24kmGx2WFrS9ZY3zE5Up6iAkjNSOv9peMMDUJwL/iEQ2sXkVCJSo4KbwS2LoGEbyDpA58U38NWo+gT7eRKflMHod/4gPavA7JRua+/XU2iZt5Fsw5eC4R9Qz9/f7Eh1SqULqHHjxrF8+fLqyCIuIrewhPWJGQD0tq9zNrYe4Fy5WETqlsAIuHkhhHSCnDTaL7qeb0cFEhrozY60bK6ZuZLEI3mnvYxus6+czC2LabH1LQB+avUo3br1NDlR3VPp33iZmZkMHDiQNm3a8Nxzz3Hw4MHqyCW12Jp9RylxGDSt70vggV+cjRq+E6m76oU4h/PCu0PeEZovuI7YYR40a+BH4tE8rpm5gu2pWae8hG6zr7iSQ7uxfnULVgy+8xzM5TfcZXakOqnSBdT8+fM5ePAgEyZMYO7cubRo0YLLL7+cr776iuJi7dBdF6zcewSAwZEOSNsMWKBlf3NDiYi5/BrAuFiIPA8KMgmdP5rYgRm0DwsgPbuQa99eyYrjd+6ejG6zP7WyHrovPyPzg5EEGNlsNFrT4eY38fG0mR2vTjqjMZfGjRszefJkNmzYwKpVq2jdujVjx44lIiKC++67j127dlV1TqlFVu5xFlBX+G1zNkT00GaVIuJcxmTsN9B2CJQUELzgFub12UXvqAZkF5YwbtZq5q07cNKn6jb7U4uJiSHu559599n7aFiwn4NGQ9KvnEWriJDTP1mqxVlNWklJSWHJkiUsWbIEm83GFVdcwaZNm+jYsSOvvPJKVWWUWiSroJjNBzMB2PvbfEbOzSM2PcLkVCJSa3j5wehPocdYMBz4/TiZT9ss48ouYRTbDSZ/sYE3f96FYRhmJ3Up0ePGcWG7htzeIZtcw5tFXV5l4LldzY5Vp1W6gCouLubrr7/myiuvpHnz5nz55ZdMmjSJ5ORkZs+ezU8//cQXX3zB008/XR15xWSr9x7FYUBUQz/m/PgHcftKiPnt5P+iFJE6yuYBw96Aix8EwHP587xRL4YJF0UC8OLinTzy9SaKShxmpjRdZSbOD2qQxILLD3NlWy/ebvgo40ZcWQMJ5VQ8KvuE8PBwHA4HN9xwA6tXr6Z79+4nnNO/f3+Cg4OrIJ7UNqXzn66OyKRlZwPs3kTfcY/JqUSk1rFY4NLHoV4o/PAQlvUf8XDzvbS4/FkeXXSQuX8msfdwDm/f2ItG9bwrdenY2FhiYmKIjo526SG/0onzwCnfR8mGr/Ba+gQAMzzGEn3Lv/Gw6a5ns1W6gHrllVe49tpr8fHx+cdzgoODSUhIOKtgUjuVzn8a6LOVzu08GXbFABgxyuRUIlJr9R7vXGzzq1tg/2+MzhpHixFvcdv3OazZd4xhb/zGuzedQ+cmFd8GqqKFR21XOmH+VBPnHdu+w/LNeKw4mOsYwMW3PF3pglOqR6VL2LFjx56yeBL3dSy3iK0pzluR2+SudTa27GdeIBFxDW0HwW1LylYt77P0en68spCWjfxJzizgmpkrWLAhucKXc5c79k43cd7YtQTHF9HYcPCt/QJCb5hBt2b1azil/BP1AUqFrUpw9j61a+yDd9JKZ6MKKBGpiJAOMD4Omp0PhVlELBzHwh5/0K9NQwqKHdw9Zz1PLdhSoXlRdeKOvb2/YJ8zBg+jmO/tvWH42/TrEG52KvkbFVBSYaXDd9eGpTq3b/FrCKGdTU4lIi7DvyHc9C30HAeGA59fpzHLazqTzm8AwKzf93HtOytJOnr6lcvd2v6VFH86Gg9HIUvsPUm7bAZX92pudir5HyqgpMJKJ5Bf4rHF2RB1ibZvEZHK8fCCYa/D1W+Bhy+WPT8xafetfHmFjSBfTzYkZTD09V9ZvCXV7KTm2LaAktlX42nPZ7m9Cxv6vsotF7c1O5WchH77SYUczilkZ1oOAC2y1jgbNXwnImeqxxgYvxQatoasg5y7bAy/XLCJHk0DyCoo4faP1/LE/M3kFZWYnbTGGKvexZg7Fg9HIT/Ze/D7Oa9z/+Va66m2UgElFfLH8d6nHqE2PFOObyDc8hITE4mIywvt5JwX1WkEOEoI/u1pvvZ9jvvPdd5l9vEf++l9+/NcOuRK995k2OHAWDwVyw8PYsHg05IB7Oj3No8M64HFYjE7nfwDFVBSIStK5z81TgRHCdRv4XyIiFTAPy4a6RMI18yCq14Dr3pYk1Zy945xLL44gbAAb/auWMgvy5bxf/99g8ISuznhq1NxPvZ5t2NZ8SoA04uvw37FS0wc0EHFUy2nAkoq5I/jBdQFls3OBg3fiUgllK7dFBMTc+JBiwV6RcOdvznv0ivKoe3qx/g18m1GXDkI72ZdSQ/ry9DXfyu7mcUtHNpJ0cz+2DZ/SbFh48GSO2l77ZPcdH6U2cmkAiq9kKbUPWlZBew9nIvVAk2PrXY2qoASkUqoyKKRNIiC6O9g5Qz4+Rk89/7Epw1+Z+dzExi79Vx2p+dww3t/MLx7BD0te5g/9zPXXY08fg72BffhZc/nkBHIY9ZJ3DjmJi5u29jsZFJBFkM7Ola5rKwsgoKCyMzMJDAw0Ow4Z+3b+IPc+3k8F4bb+eTYWMACD+5x3pIsIlId0rfB9/fD/t8BsDdozez6d/HM1hAMA45++xz2A5u5bOAA5n8zz+SwlVCUi+O7+7FunAPA7/ZOvBvyKM+NHUiTYF+Tw0llfn9rCE9Oa3XCUQBGBu9xNoR3VfEkItUrpANEfw8j3gH/xtiO7uaWPZNY33Y2Q8My8Ok4ACO8E7uCevLN+gPYHbW8L8AwYNt3FL12LtaNc7AbFl4qvobFvd7m3QlXqHhyQSqg5LTW7HMWUL2Njc4GDd+JSE2wWKDb9XDXn9D7DrBYCd7/I29mTGTV4N0MGjuB3LAe3Dd3A1e89itLtqZRWwZVyk2aP7qX4o+vgblj8Mo9yAGjEbdZptL62qd5ang3vD1sZseVM6A5UHJKx3KLjq//ZBB+ZJWzMUrLF4hIDfINhitegHNugbhnsWyLJSr5e76xLGJri6v4v9SL2ZAG4z/6k7ah9bjlgiiG92iCj6d5hUlMTAxxP/+Mkb6dofGH8HQUUWTYeNd+JQc6TWD6VT21KbCLUw+UnNKf+48BcGHDHGzZB8HqCc3OMzmViNRJIe1h9Mdw+zJoPRCLYadT6ny+ZTJxoa9yhVc8u9KyeGTeJs5//mdeXryDlMz8ms+ZncqYrh5cElHAzZH7sDmK+NXemfH1XqfnzS/z/A19VTy5AU0irwbuNIn8uYXbeHf5Xl5svZFrDjwPkefBrT+aHUtEBPavhBVvwI6FgPNXWaZvJJ8XX8yc3J7sM8KxWKB3iwYM6x7BFZ3Dqe/vVT1ZDAPStpCx/C3qbfsCD6MYgB2OpszyvJ5WF/+Lmy5ooeG6Wq4yv781hCenVDqB/Fy2OhtaXGhiGhGRv2ne1/k4tg/WvA/rPiIoP4k7+JQ7vD8lwaMVX+b3YtG+3jyWcISp326hb6uGzkfLhnRpEoSH7cwGYmJjY4mJieGm4QO5sH46lq3f0DB/H8HHj//paMvXvtfS7dLreKpXpAonN6QeqGrgLj1QeUUldH1yMSUOB7sbPYhHTjKMnQ+t+psdTUTkREW5sHkebJkHe38B46+Vy49ZgllZ0pY/He1Y42jHTqMpHl6+9GxenzYhAUQ18iOqUT1aNPIjyNcTX09bWXFlGAZ5RXaOZueTd3ArhftWcd/UF/lzx0EGtLAyb7QfAIWGJ8uMHqxvcgNd+l7OkM5h2KxaTdyVqAdKqkR8YgYlDoNegZnO4snqCZG9zY4lInJyXv7Qc6zzkXsEtn8HW+dDwq/Ud2RwhW01V9hWl52ebgSTtL8xSfsak2o0YB0erDFsFGPDjo361lzCrFk0MI7RmGNEWtKJtBQAcG+HYmIKrYzp5k2cowd7QgYRcu5I+nVrxWAfT5M+AKlJKqDkH60+vnzBqAb7IBVo0sv5A0pEpLbzbwi9xjkfxfmQvB4SV0LiH5C0GgoyCLE4H73Ydepr/a0TKQ8f9ni2IfCibkz+1zk06nQprZpH0v8MhwLFdamAkn9UOv/pPKvmP4mIC/P0hebnOx/gnPCdf8w5dypjPxzbDznpxK7YRsxPmxh7SVuu6NmEEs9ACn0aYdQLw79hBN4Nm+HXqC1drJrPJCqg5B8U2x2sT8wADCKz1jkbVUCJiDuwWMCvgfPRpGdZc8y7I4nbdgTCfBgx7T28AfW5yz9Rn6Oc1OaDmeQX2+nkewxPzX8SkRpUbhXvGhQdHU3//v1PveGxyHHqgZKTKt2+5ZqG++Awmv8kIjUmJiaGuLg4AIYNG1Zjrzts2LAafT1xbSqg5KRWJzhXIL/AY5uzQcN3IlJDSnuA1BMktZmG8OQEDofBn/uPAgYtstc7G1VAiUgNGTZsGPPmzauW3qDqGB40a8hRzKUeKDnB7kM5ZOQV08bzCF65mv8kIu6jOoYHzRpyFHOpgJITlC5fcG2jfXAMzX8SEbdRHcODGnKsm1RAyQn+PD6B/EKP7c4GDd+JiJuojonimnxeN7nVHKgWLVpgsVjKPZ5//vly52zcuJGLLroIHx8fIiMjeeGFF064zpdffkn79u3x8fGhS5cuLFy4sKbeQq2w7vj6Ty1zNf9JRNyf5jDJmXCrAgrg6aefJiUlpexx9913lx3Lyspi0KBBNG/enLVr1zJ9+nSefPJJ3n333bJzVqxYwQ033MCtt97K+vXrGT58OMOHD2fz5s1mvJ0adyi7kMSjeTSzpOOTl6L5TyLi9krnMMXExJgdRVyI2w3hBQQEEBYWdtJjn376KUVFRXz44Yd4eXnRqVMn4uPjefnll7n99tsBeO211xgyZAgPPvggAM888wxLlizhzTffZObMmTX2PsyyLtG5fMHw4ATIR/OfRMTtaQ6TnAm364F6/vnnadiwIT169GD69OmUlJSUHVu5ciUXX3wxXl5eZW2DBw9mx44dHDt2rOycgQMHlrvm4MGDWbly5T++ZmFhIVlZWeUermrdfufncLHPbmdD6d5RIiJuqjqXTRD35VY9UPfccw89e/akQYMGrFixgkcffZSUlBRefvllAFJTU4mKiir3nNDQ0LJj9evXJzU1tazt7+ekpqb+4+tOmzaNp556qorfjTlKe6DaFR4fslQBJSIicoJa3wP1yCOPnDAx/H8f27c77xabPHky/fr1o2vXrtx555289NJLvPHGGxQWFlZrxkcffZTMzMyyR1JSUrW+XnUpKnGw8UAmjckgIC8RsEDTc82OJSIiUuvU+h6o+++//7Tj0i1btjxpe58+fSgpKWHfvn20a9eOsLAw0tLSyp1T+nXpvKl/Ouef5lUBeHt74+3tfbq3UuttTcmisMRBP9/dYAChncE32OxYIiIitU6tL6AaN25M48aNz+i58fHxWK1WQkJCAOjbty+PPfYYxcXFeHp6ArBkyRLatWtH/fr1y85ZunQpkyZNKrvOkiVL6Nu379m9ERdQOv9pUL0EyAaau/97FhERORO1fgivolauXMmrr77Khg0b2Lt3L59++in33XcfN954Y1lx9K9//QsvLy9uvfVWtmzZwty5c3nttdeYPHly2XXuvfdeFi1axEsvvcT27dt58skn+fPPP7nrrrvMems1pnT+Uzfj+AbCzc4zMY2IiEjtVet7oCrK29ubzz//nCeffJLCwkKioqK47777yhVHQUFBLF68mIkTJ9KrVy8aNWrElClTypYwADj//PP57LPPePzxx/m///s/2rRpw/z58+ncubMZb6tGrdt/jHrk0Th3p7OhmXqgRERETsZiGIZhdgh3k5WVRVBQEJmZmQQGBpodp0JSMvPpO+1nLrFtYrbnNAhuDpM2mh1LRESkxlTm97fbDOHJ2Vm3PwOAIQF7nQ1avkBEROQfqYAS4K/5T308SofvNP9JRETkn6iAEsBZQHlSQvO8rc6GZuqBEhER+ScqoISCYjubD2bSxbIXm6MQ/BpCozZmxxIRqTaxsbGMHDmS2NhYs6OIi3Kbu/DkzG1JzqTYbnCJ3x5w4Lz7zmIxO5aISLWJiYkhLi4OQHvgyRlRASWsPb6AZj+f3ZCHli8QEbdXusPF6Xa6EPknKqCEdfszsOCgXVHp/CcVUCLi3oYNG6aeJzkrmgNVxxmGwbrEY7S2JONTkgmefhDe1exYIiIitZoKqDouNauA9OxC+th2OBuangM2T3NDiYiI1HIqoOq4DUkZAFzqt8fZoOULRERETksFVB0Xn5QJQA9ju7NBC2iKiIiclgqoOm5DUgYhHKN+cSpYrM4hPBERETklFVB1mN1hsOlgJj2tu5wNoZ3AO8DcUCIiIi5ABVQdtvdQDjmFJZznebyAatrb3EAiIiIuQgVUHRZ/fAJ5X6+9zoZIFVAiIiIVoQKqDttwIAMvimlVstvZoAJKRESkQlRA1WEbkjLpbEnAwygGv0ZQP8rsSCIiIi5BBVQdVVBsZ1tKFj1KJ5BH9tYGwiIiIhWkAqqO2pqSRYnD4Hyv4wtoavhORESkwlRA1VHOFciNv5Yw0B14IiIiFaYCqo7akJRBBEeobz8CVg+I6GF2JBEREZehAqqO2nAgk17Wnc4vwrqAl5+5gURERFyICqg66LMvv2bVu/+HZfcvzgYN34mIiFSKh9kBpObNfO9DChI3ss3DEzqiCeQiIiKVpB6oOqj9RVfh36wzE7sVOxtUQImIiFSKeqDqIEezXgwZlctw721QLwyCIs2OJCIi4lLUA1XHGIZBfFLmX8sXRJ6rBTRFREQqSQVUHZOcWcDhnELOsZUWUH3MDSQiIuKCVEDVMZsOZAAG59iObyCsO/BEREQqTQVUHbPpYCaRlnTqGxlg9YTwbmZHEhERcTkqoOqYTQez6GU5PnwX0R08fUzNIyIi4opUQNUhhmGw+WAm3a2lw3fnmhtIRETERamAqkOSMws4mltEd+seZ0OTXuYGEhERcVEqoOqQTQcy8aKYTtb9zgYVUCIiImdEBVQdsiU5kw6W/XhSAn4NoX4LsyOJiIi4JBVQdcimg5nlh++0gKaIiMgZUQFVR5ROIO+m+U8iIiJnTQVUHZGaVcDhnL9PID/H3EAiIiIuTAVUHbHpQCaB5NDSkuJsaNLT3EAiIiIuTAVUHeEcvtvr/KJBS/BrYG4gERERF6YCqo7YdDCT7pbjC2hq/pOIiMhZUQFVBxiGwaaDWX+bQK75TyIiImdDBVQdkJ5dyOGcAq1ALiIiUkVUQNUBmw5k0tRymEaWLLB6QlgXsyOJiIi4NBVQdUC5+U9hncHTx9xAIiIiLk4FVB1QfgFNzX8SERE5Wyqg6oBNWoFcRESkSqmAcnPpWQUczc6liyXB2aACSkRE5KypgHJzmw5m0tZyAF9LEXgHQcPWZkcSERFxeSqg3Nzmg1l/W76gB1j1n1xERORs6bepm9uakkk3iyaQi4iIVCUVUG5ua0oW3a3awkVERKQqqYByY5n5xRw5eow2loPOBhVQIiIiVUIFlBvbnpJFR8s+rBYDAiIgINTsSCIiIm5BBZQb25qSRVfr8eULInqYG0ZERMSNqIByY1uTs+hi3ev8QgWUiIhIlVEB5ca2pmTR1aICSkREpKq5TAH17LPPcv755+Pn50dwcPBJz0lMTGTo0KH4+fkREhLCgw8+SElJSblzli1bRs+ePfH29qZ169bExMSccJ0ZM2bQokULfHx86NOnD6tXr66Gd1S9ikocJKelE2VJdTZEdDc1j4iIiDtxmQKqqKiIa6+9lgkTJpz0uN1uZ+jQoRQVFbFixQpmz55NTEwMU6ZMKTsnISGBoUOH0r9/f+Lj45k0aRK33XYbP/74Y9k5c+fOZfLkyUydOpV169bRrVs3Bg8eTHp6erW/x6q051AObR0JWC0GRlBT8G9kdiQRERG3YTEMwzA7RGXExMQwadIkMjIyyrX/8MMPXHnllSQnJxMa6rzbbObMmTz88MMcOnQILy8vHn74Yb7//ns2b95c9rzrr7+ejIwMFi1aBECfPn0499xzefPNNwFwOBxERkZy991388gjj1QoY1ZWFkFBQWRmZhIYGFgF77ryvl57gG3znuNxz0+hw1Uw+hNTcoiIiLiKyvz+dpkeqNNZuXIlXbp0KSueAAYPHkxWVhZbtmwpO2fgwIHlnjd48GBWrlwJOHu51q5dW+4cq9XKwIEDy845mcLCQrKysso9zOa8A0/zn0RERKqD2xRQqamp5YonoOzr1NTUU56TlZVFfn4+hw8fxm63n/Sc0muczLRp0wgKCip7REZGVsVbOitbk7PobDm+hEF4d1OziIiIuBtTC6hHHnkEi8Vyysf27dvNjFghjz76KJmZmWWPpKQkU/MYhkFScgotraUTyNUDJSIiUpU8zHzx+++/n+jo6FOe07JlywpdKyws7IS75dLS0sqOlf5Z2vb3cwIDA/H19cVms2Gz2U56Tuk1Tsbb2xtvb+8K5awJyZkFRBbtAi8wgptj8WtgdiQRERG3YmoB1bhxYxo3blwl1+rbty/PPvss6enphISEALBkyRICAwPp2LFj2TkLFy4s97wlS5bQt29fALy8vOjVqxdLly5l+PDhgHMS+dKlS7nrrruqJGdN2Jr81/pPFvU+iYiIVDmXmQOVmJhIfHw8iYmJ2O124uPjiY+PJycnB4BBgwbRsWNHxo4dy4YNG/jxxx95/PHHmThxYlnv0J133snevXt56KGH2L59O2+99RZffPEF9913X9nrTJ48mffee4/Zs2ezbds2JkyYQG5uLjfffLMp7/tMOFcgL93CpbupWURERNyRqT1QlTFlyhRmz55d9nWPHs6elbi4OPr164fNZuO7775jwoQJ9O3bF39/f8aNG8fTTz9d9pyoqCi+//577rvvPl577TWaNm3K+++/z+DBg8vOGT16NIcOHWLKlCmkpqbSvXt3Fi1adMLE8tpsa0omw7UCuYiISLVxuXWgXIHZ60Bd8d9vWZh/k/OLh/eBb/0azyAiIuJq6uQ6UOKUmV9Mg8xtANiDo1Q8iYiIVAMVUG5m+982ELY10fCdiIhIdVAB5Wa2pmTRuWwCuQooERGR6qACys1sTdYWLiIiItVNBZSbOXAwiaaWw84vwruZG0ZERMRNqYByIyV2B35HNhG7o5gR8yzELl5mdiQRERG3pALKjew7kkt7x15i4otZtiefmJgYsyOJiIi4JZdZSFNOb1tKNp2s+2jX3RNC25x2n0ERERE5Myqg3MiO1GyusyTQrJ0nw559HVpeYnYkERERt6QhPDey/2AKzayHnF+EdTE3jIiIiBtTAeVOUjcCUODfFPwamBxGRETEfamAchM5hSWE5O4AwBqh5QtERESqkwooN7Ej1TmBHMCraXdTs4iIiLg7FVBuYntqFp0s+5xfhHU1NYuIiIi7UwHlJvYmH6K15aDzC61ALiIiUq1UQLmJ/AObsFkMCrwaQkCY2XFERETcmgooN2AYBr5HNgNQHNIZLBaTE4mIiLg3FVBuIDWrgFYlewHwbdbT5DQiIiLuTyuRu4Htqdl0PH4HnkcTzX8SEbHb7RQXF5sdQ2oZT09PbDZblVxLBZQb2HnwKNGWJOcXugNPROowwzBITU0lIyPD7ChSSwUHBxMWFoblLKe7qIByAxmJW/C2FFNk88erfpTZcURETFNaPIWEhODn53fWvyTFfRiGQV5eHunp6QCEh4ef1fVUQLkBa/omAHIbdMTLqmltIlI32e32suKpYcOGZseRWsjX1xeA9PR0QkJCzmo4T79tXVyx3UGj7O2A5j+JSN1WOufJz8/P5CRSm5V+f5ztHDkVUC5u76FcOloSAKjXXHfgiYho2E5Opaq+P1RAubjtKRl0sOwHwKIVyEVERGqECigXl7p/B4GWfIotXtC4ndlxRETkDPTr149JkyaZHQOA+fPn07p1a2w2G5MmTSImJobg4GCzY9U6KqBcnP1gPACZAW3A5mluGBERqZWWLVuGxWKp0PIOd9xxB9dccw1JSUk888wzjB49mp07d5Ydf/LJJ+nevXv1hXURugvPxfkf3QKAI7SLyUlERMTV5eTkkJ6ezuDBg4mIiChrL717Tf6iHigXlplfTPOiPQAEtOhlchoRkdrHMAzyikpMeRiGUamsJSUl3HXXXQQFBdGoUSOeeOKJctcoLCzkgQceoEmTJvj7+9OnTx+WLVtWdnz//v1cddVV1K9fH39/fzp16sTChQvZt28f/fv3B6B+/fpYLBaio6NPeP1ly5YREBAAwKWXXorFYmHZsmXlhvBiYmJ46qmn2LBhAxaLBYvFQkxMTKXep7tQD5QL25WWTafjW7j46g48EZET5Bfb6TjlR1Nee+vTg/Hzqviv2dmzZ3PrrbeyevVq/vzzT26//XaaNWvG+PHjAbjrrrvYunUrn3/+OREREXzzzTcMGTKETZs20aZNGyZOnEhRURHLly/H39+frVu3Uq9ePSIjI/n6668ZNWoUO3bsIDAw8KQ9Sueffz47duygXbt2fP3115x//vk0aNCAffv2lZ0zevRoNm/ezKJFi/jpp58ACAoKOrsPykWpgHJhiYn7OMeSiQMr1pCOZscREZGzEBkZySuvvILFYqFdu3Zs2rSJV155hfHjx5OYmMisWbNITEwsG1p74IEHWLRoEbNmzeK5554jMTGRUaNG0aWLc0pHy5Yty67doEEDAEJCQv5xQriXlxchISFl54eFhZ1wjq+vL/Xq1cPDw+Okx+sSFVAuLDdxPQBHfZrSyEsLx4mI/C9fTxtbnx5s2mtXxnnnnVdujaK+ffvy0ksvYbfb2bRpE3a7nbZt25Z7TmFhYdmq6/fccw8TJkxg8eLFDBw4kFGjRtG1q/ZHrS4qoFyYxyHnBPK8+h1MTiIiUjtZLJZKDaPVVjk5OdhsNtauXXvC9iP16tUD4LbbbmPw4MF8//33LF68mGnTpvHSSy9x9913mxHZ7WkSuQurn+W8rdQWoX9hiIi4ulWrVpX7+o8//qBNmzbYbDZ69OiB3W4nPT2d1q1bl3v8fSgtMjKSO++8k3nz5nH//ffz3nvvAc7hOXDuF3i2vLy8quQ6rk4FlIvKzCsmyu7cwqV+VA+T04iIyNlKTExk8uTJ7Nixgzlz5vDGG29w7733AtC2bVvGjBnDTTfdxLx580hISGD16tVMmzaN77//HoBJkybx448/kpCQwLp164iLi6NDB+cIRfPmzbFYLHz33XccOnSInJycM87ZokULEhISiI+P5/DhwxQWFp79m3dBKqBc1K7kw7SyJAPgF9nd3DAiInLWbrrpJvLz8+nduzcTJ07k3nvv5fbbby87PmvWLG666Sbuv/9+2rVrx/Dhw1mzZg3NmjUDnL1LEydOpEOHDgwZMoS2bdvy1ltvAdCkSROeeuopHnnkEUJDQ7nrrrvOOOeoUaMYMmQI/fv3p3HjxsyZM+fs3riLshiVXahCTisrK4ugoCAyMzMJDAysltdYuPgHrlhxPTnWAOo9kQTaPFNE6riCggISEhKIiorCx8fH7DhSS53q+6Qyv7/VA+Wiig5uBOCwf1sVTyIiIjVMBZSL8j68FYCiRlr/SUREpKapgHJRjfN2AeDTtJvJSUREROoeFVAuKCO3kNaOfQA0aq098ERERGqaCigXtC9hF8GWXEqw4dekk9lxRERE6hwVUC7o2N51AKR4RoKHt8lpRERE6h4VUC7IkbIJgMzAdiYnERERqZtUQLkg/2PbALCHaPhORETEDCqgXFB4wR4AApp1NzeIiIhIHaUCysUcPXaMSCMFgLC255icRkRE6qqYmBiCg4PNjkF0dDTDhw+v8ddVAeVikneuw2oxOEoQfg2bmB1HRETkpPbt24fFYiE+Pr5WXu9sqYByMTmJ8QAk+7QyN4iIiJiqqKjI7AhVwlXfhwooF2NJ2wxATnAHk5OIiLgAw4CiXHMehlHhmNnZ2YwZMwZ/f3/Cw8N55ZVX6NevH5MmTSo7p0WLFjzzzDPcdNNNBAYGcvvttwPw9ddf06lTJ7y9vWnRogUvvfRSuWtbLBbmz59fri04OJiYmBjgr56defPm0b9/f/z8/OjWrRsrV64s95yYmBiaNWuGn58fI0aM4MiRI6d8T1FRUQD06NEDi8VCv379gL+G3J599lkiIiJo165dhXL+0/VKvfjii4SHh9OwYUMmTpxIcXHxKfOdLY9qvbpUuaCs7QBYw7uYnERExAUU58FzEea89v8lg5d/hU6dPHkyv//+O7GxsYSGhjJlyhTWrVtH9+7dy5334osvMmXKFKZOnQrA2rVrue6663jyyScZPXo0K1as4N///jcNGzYkOjq6UnEfe+wxXnzxRdq0acNjjz3GDTfcwO7du/Hw8GDVqlXceuutTJs2jeHDh7No0aKyDP9k9erV9O7dm59++olOnTrh5eVVdmzp0qUEBgayZMmSCuc71fXi4uIIDw8nLi6O3bt3M3r0aLp378748eMr9RlUhgooV+Jw0LQoAYDgqB4mhxERkaqQnZ3N7Nmz+eyzzxgwYAAAs2bNIiLixMLv0ksv5f777y/7esyYMQwYMIAnnngCgLZt27J161amT59e6QLqgQceYOjQoQA89dRTdOrUid27d9O+fXtee+01hgwZwkMPPVT2OitWrGDRokX/eL3GjRsD0LBhQ8LCwsod8/f35/333y9XBJ3Oqa5Xv3593nzzTWw2G+3bt2fo0KEsXbpUBZQ4HUveRX3yKTQ8aNpGmwiLiJyWp5+zJ8is166AvXv3UlxcTO/evcvagoKCyoa2/u6cc8rffb1t2zauvvrqcm0XXHABr776Kna7HZvNVuG4Xbt2Lft7eHg4AOnp6bRv355t27YxYsSIcuf37dv3lAXUqXTp0qVSxdPpdOrUqdx7DQ8PZ9OmTVV2/ZNRAeVC0nev5dcdxbwZD3f1WsKwYcPMjiQiUrtZLBUeRnMF/v6Vfy8WiwXjf+ZjnWx+kKenZ7nnADgcjkq/XkWc7H1UNOfJ/D176bWqK3spTSJ3IQVJG4iJL2blvvyySXUiIuLaWrZsiaenJ2vWrClry8zMZOfOnad9bocOHfj999/Ltf3++++0bdu2rEemcePGpKSklB3ftWsXeXl5lcrYoUMHVq1aVa7tjz/+OOVzSnuY7HZ7hV7jdDkre73qph4oF7I78Dwad72EjvWOVHpsW0REaqeAgADGjRvHgw8+SIMGDQgJCWHq1KlYrdaynqB/cv/993PuuefyzDPPMHr0aFauXMmbb77JW2+9VXbOpZdeyptvvknfvn2x2+08/PDDJ/TYnM4999zDBRdcwIsvvsjVV1/Njz/+eNrhu5CQEHx9fVm0aBFNmzbFx8eHoKCgfzz/dDkre73qph4oFzLq6hG8PWcJy37/U8N3IiJu5OWXX6Zv375ceeWVDBw4kAsuuIAOHTrg4+Nzyuf17NmTL774gs8//5zOnTszZcoUnn766XL/yH7ppZeIjIzkoosu4l//+hcPPPAAfn4Vm59V6rzzzuO9997jtddeo1u3bixevJjHH3/8lM/x8PDg9ddf55133iEiIuKEuVr/63Q5K3u9ame4iP/85z9G3759DV9fXyMoKOik5wAnPObMmVPunLi4OKNHjx6Gl5eX0apVK2PWrFknXOfNN980mjdvbnh7exu9e/c2Vq1aVamsmZmZBmBkZmZW6nkiInLm8vPzja1btxr5+flmRzlrOTk5RlBQkPH++++bHcXtnOr7pDK/v12mB6qoqIhrr72WCRMmnPK8WbNmkZKSUvb4+/44CQkJDB06lP79+xMfH8+kSZO47bbb+PHHH8vOmTt3LpMnT2bq1KmsW7eObt26MXjwYNLT06vrrYmISB23fv165syZw549e1i3bh1jxowBML+XRf6Ry8yBeuqppwBOO3k6ODj4hPUhSs2cOZOoqKiyVVo7dOjAb7/9xiuvvMLgwYMBZzfq+PHjufnmm8ue8/333/Phhx/yyCOPnPS6hYWFFBYWln2dlZVVqfcmIiLy4osvsmPHDry8vOjVqxe//vorjRo1MjuW/AOX6YGqqIkTJ9KoUSN69+7Nhx9+WO6WyJUrVzJw4MBy5w8ePLhsufqioiLWrl1b7hyr1crAgQNPWNL+76ZNm0ZQUFDZIzIysorflYiIuLMePXqwdu1acnJyOHr0KEuWLKFLF+04UZu5VQH19NNP88UXX7BkyRJGjRrFv//9b954442y46mpqYSGhpZ7TmhoKFlZWeTn53P48GHsdvtJz0lNTf3H13300UfJzMwseyQlJVXtGxMREZFaxdQhvEceeYT//ve/pzxn27ZttG/fvkLXK13KHpzVfG5uLtOnT+eee+45q5yn4+3tjbe3d7W+hoiIVIxRiU18pe6pqu8PUwuo+++//7TrGbVs2fKMr9+nTx+eeeYZCgsL8fb2JiwsjLS0tHLnpKWlERgYiK+vLzabDZvNdtJz/mlelYiI1A6lawbl5eXh6+trchqprUoX56zsWlj/y9QCqnHjxmWbA1aH+Ph46tevX9Y71LdvXxYuXFjunCVLltC3b1+Asol7S5cuLbt7z+FwsHTpUu66665qyykiImfPZrMRHBxcdte0n5/faReilLrDMAzy8vJIT08nODi4UvsEnozL3IWXmJjI0aNHSUxMxG63Ex8fD0Dr1q2pV68eCxYsIC0tjfPOOw8fHx+WLFnCc889xwMPPFB2jTvvvJM333yThx56iFtuuYWff/6ZL774gu+//77snMmTJzNu3DjOOeccevfuzauvvkpubm7ZXXkiIlJ7lY4WaOkZ+Senulu/MiyGiwwWR0dHM3v27BPa4+Li6NevH4sWLeLRRx9l9+7dGIZB69atmTBhAuPHj8dq/Wuu/LJly7jvvvvYunUrTZs25YknnjhhGPHNN99k+vTppKam0r17d15//XX69OlT4axZWVkEBQWRmZlJYGDgGb9nERE5M3a7vcIb0Urd4enpecqep8r8/naZAsqVqIASERFxPZX5/e1WyxiIiIiI1AQVUCIiIiKVpAJKREREpJJc5i48V1I6rUx74omIiLiO0t/bFZkergKqGmRnZwNoTzwREREXlJ2dTVBQ0CnP0V141cDhcJCcnExAQECVL+KWlZVFZGQkSUlJusPvNPRZVZw+q4rTZ1Vx+qwqTp9VxVXnZ2UYBtnZ2URERJRbAulk1ANVDaxWK02bNq3W1wgMDNT/ZBWkz6ri9FlVnD6ritNnVXH6rCquuj6r0/U8ldIkchEREZFKUgElIiIiUkkqoFyMt7c3U6dOLdsgWf6ZPquK02dVcfqsKk6fVcXps6q42vJZaRK5iIiISCWpB0pERESkklRAiYiIiFSSCigRERGRSlIBJSIiIlJJKqBcxLPPPsv555+Pn58fwcHBJz3HYrGc8Pj8889rNmgtUZHPKzExkaFDh+Ln50dISAgPPvggJSUlNRu0FmrRosUJ30fPP/+82bFqjRkzZtCiRQt8fHzo06cPq1evNjtSrfPkk0+e8D3Uvn17s2PVCsuXL+eqq64iIiICi8XC/Pnzyx03DIMpU6YQHh6Or68vAwcOZNeuXeaENdnpPqvo6OgTvs+GDBlSY/lUQLmIoqIirr32WiZMmHDK82bNmkVKSkrZY/jw4TUTsJY53edlt9sZOnQoRUVFrFixgtmzZxMTE8OUKVNqOGnt9PTTT5f7Prr77rvNjlQrzJ07l8mTJzN16lTWrVtHt27dGDx4MOnp6WZHq3U6depU7nvot99+MztSrZCbm0u3bt2YMWPGSY+/8MILvP7668ycOZNVq1bh7+/P4MGDKSgoqOGk5jvdZwUwZMiQct9nc+bMqbmAhriUWbNmGUFBQSc9BhjffPNNjeap7f7p81q4cKFhtVqN1NTUsra3337bCAwMNAoLC2swYe3TvHlz45VXXjE7Rq3Uu3dvY+LEiWVf2+12IyIiwpg2bZqJqWqfqVOnGt26dTM7Rq33vz+zHQ6HERYWZkyfPr2sLSMjw/D29jbmzJljQsLa42S/38aNG2dcffXVpuQxDMNQD5SbmThxIo0aNaJ37958+OGHGFrm66RWrlxJly5dCA0NLWsbPHgwWVlZbNmyxcRktcPzzz9Pw4YN6dGjB9OnT9fQJs5ezbVr1zJw4MCyNqvVysCBA1m5cqWJyWqnXbt2ERERQcuWLRkzZgyJiYlmR6r1EhISSE1NLfc9FhQURJ8+ffQ99g+WLVtGSEgI7dq1Y8KECRw5cqTGXlubCbuRp59+mksvvRQ/Pz8WL17Mv//9b3JycrjnnnvMjlbrpKamliuegLKvU1NTzYhUa9xzzz307NmTBg0asGLFCh599FFSUlJ4+eWXzY5mqsOHD2O320/6fbN9+3aTUtVOffr0ISYmhnbt2pGSksJTTz3FRRddxObNmwkICDA7Xq1V+rPnZN9jdf3n0skMGTKEkSNHEhUVxZ49e/i///s/Lr/8clauXInNZqv211cBZaJHHnmE//73v6c8Z9u2bRWefPnEE0+U/b1Hjx7k5uYyffp0tymgqvrzqksq89lNnjy5rK1r1654eXlxxx13MG3aNNO3ThDXcPnll5f9vWvXrvTp04fmzZvzxRdfcOutt5qYTNzJ9ddfX/b3Ll260LVrV1q1asWyZcsYMGBAtb++CigT3X///URHR5/ynJYtW57x9fv06cMzzzxDYWGhW/ziq8rPKyws7IS7p9LS0sqOuZuz+ez69OlDSUkJ+/bto127dtWQzjU0atQIm81W9n1SKi0tzS2/Z6pScHAwbdu2Zffu3WZHqdVKv4/S0tIIDw8va09LS6N79+4mpXIdLVu2pFGjRuzevVsFlLtr3LgxjRs3rrbrx8fHU79+fbconqBqP6++ffvy7LPPkp6eTkhICABLliwhMDCQjh07Vslr1CZn89nFx8djtVrLPqe6ysvLi169erF06dKyu1sdDgdLly7lrrvuMjdcLZeTk8OePXsYO3as2VFqtaioKMLCwli6dGlZwZSVlcWqVatOewe2wIEDBzhy5Ei54rM6qYByEYmJiRw9epTExETsdjvx8fEAtG7dmnr16rFgwQLS0tI477zz8PHxYcmSJTz33HM88MAD5gY3yek+r0GDBtGxY0fGjh3LCy+8QGpqKo8//jgTJ050m4LzTKxcuZJVq1bRv39/AgICWLlyJffddx833ngj9evXNzue6SZPnsy4ceM455xz6N27N6+++iq5ubncfPPNZkerVR544AGuuuoqmjdvTnJyMlOnTsVms3HDDTeYHc10OTk55XriEhISiI+Pp0GDBjRr1oxJkybxn//8hzZt2hAVFcUTTzxBREREnVyS5lSfVYMGDXjqqacYNWoUYWFh7Nmzh4ceeojWrVszePDgmglo2v1/Uinjxo0zgBMecXFxhmEYxg8//GB0797dqFevnuHv729069bNmDlzpmG3280NbpLTfV6GYRj79u0zLr/8csPX19do1KiRcf/99xvFxcXmha4F1q5da/Tp08cICgoyfHx8jA4dOhjPPfecUVBQYHa0WuONN94wmjVrZnh5eRm9e/c2/vjjD7Mj1TqjR482wsPDDS8vL6NJkybG6NGjjd27d5sdq1aIi4s76c+mcePGGYbhXMrgiSeeMEJDQw1vb29jwIABxo4dO8wNbZJTfVZ5eXnGoEGDjMaNGxuenp5G8+bNjfHjx5dbmqa6WQxD97mLiIiIVIbWgRIRERGpJBVQIiIiIpWkAkpERESkklRAiYiIiFSSCigRERGRSlIBJSIiIlJJKqBEREREKkkFlIiIiEglqYASERERqSQVUCIiIiKVpAJKREREpJJUQImInMahQ4cICwvjueeeK2tbsWIFXl5eLF261MRkImIWbSYsIlIBCxcuZPjw4axYsYJ27drRvXt3rr76al5++WWzo4mICVRAiYhU0MSJE/npp58455xz2LRpE2vWrMHb29vsWCJiAhVQIiIVlJ+fT+fOnUlKSmLt2rV06dLF7EgiYhLNgRIRqaA9e/aQnJyMw+Fg3759ZscREROpB0pEpAKKioro3bs33bt3p127drz66qts2rSJkJAQs6OJiAlUQImIVMCDDz7IV199xYYNG6hXrx6XXHIJQUFBfPfdd2ZHExETaAhPROQ0li1bxquvvsrHH39MYGAgVquVjz/+mF9//ZW3337b7HgiYgL1QImIiIhUknqgRERERCpJBZSIiIhIJamAEhEREakkFVAiIiIilaQCSkRERKSSVECJiIiIVJIKKBEREZFKUgElIiIiUkkqoEREREQqSQWUiIiISCWpgBIRERGppP8H2MdCLflem/UAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def experimentalist(state):\n", + " return random_sample_executor(grid_pool_executor(state), num_samples=50, random_state=1)\n", + "\n", + "r = Snapshot(variables=variables)\n", + "\n", + "r = experimentalist(r)\n", + "r = experiment_runner(r)\n", + "r = theorist(r)\n", + "\n", + "show_best_fit(r)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The experimentalists can be chained together using the `autora.experimentalist.pipeline.Pipeline`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The experimentalist is built of two functions acting in sequence.\n", + "from autora.experimentalist.pipeline import Pipeline as ExperimentalistPipeline\n", + "\n", + "experimentalist = ExperimentalistPipeline(\n", + " [(\"pool\", grid_pool_executor),\n", + " (\"sample\", random_sample_executor)],\n", + " params={\"sample\": {\"num_samples\": 50, \"random_state\": 1}}\n", + ")\n", + "\n", + "r = Snapshot(variables=variables)\n", + "\n", + "r = experimentalist(r)\n", + "r = experiment_runner(r)\n", + "r = theorist(r)\n", + "\n", + "show_best_fit(r)\n" + ] + }, { "cell_type": "code", "execution_count": null,