diff --git a/docs/cycle/Basic Introduction to Functions and States.ipynb b/docs/cycle/Basic Introduction to Functions and States.ipynb index f85585b7..e843ca32 100644 --- a/docs/cycle/Basic Introduction to Functions and States.ipynb +++ b/docs/cycle/Basic Introduction to Functions and States.ipynb @@ -17,7 +17,32 @@ "## Theoretical Overview\n", "\n", "The fundamental idea is this:\n", - "- We define a \"state\" object $S$ which can be modified with a \"delta\" (a new result) $\\Delta S$.\n", + "- We define a \"state\" object $S$ which can be modified by components of `autora` (theorist, experimentalist, experiment_runner), $\\Delta S$.\n", + "- A new state at some point $i+1$ is $$S_{i+1} = S_i + \\Delta S_{i+1}$$\n", + "- The cycle state after $n$ steps is thus $$S_n = S_{0} + \\sum^{n}_{i=1} \\Delta S_{i}$$\n", + "\n", + "To represent $S$ in code, you can use `autora.state.State`. To operate on these, we define functions.\n", + "\n", + "- Each component in an `autora` cycle (theorist, experimentalist, experiment_runner, etc.) is implemented as a\n", + "function with $n$ arguments $s_j$ which are members of $S$ and $m$ others $a_k$ which are not.\n", + " $$ f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}$$\n", + "- There is a wrapper function $w$ (`autora.state.wrap_to_use_state`) which changes the signature of $f$ to\n", + "require $S$ and aggregates the resulting $\\Delta S_{i+1}$\n", + " $$w\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}\\right] \\rightarrow \\left[ f^\\prime(S_i, a_0, ..., a_m) \\rightarrow S_{i} + \\Delta S_{i+1} = S_{i+1}\\right]$$\n", + "\n", + "- Assuming that the other arguments $a_k$ are provided by partial evaluation of the $f^\\prime$, the full `autora` cycle can\n", + "then be represented as:\n", + " $$S_n = f_n^\\prime(...f_2^\\prime(f_1^\\prime(S_0)))$$\n", + "\n", + "There are additional helper functions to wrap common experimentalists, experiment runners and theorists so that we\n", + "can define a full `autora` cycle using python notation as shown in the following example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ".\n", "- A new state at some point $i+1$ is $$S_{i+1} = S_i + \\Delta S_{i+1}$$\n", "- The cycle state after $n$ steps is thus $$S_n = S_{0} + \\sum^{n}_{i=1} \\Delta S_{i}$$\n", "\n", @@ -29,9 +54,7 @@ " $$ f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}$$\n", "- There is a wrapper function $w$ (`autora.state.wrap_to_use_state`) which changes the signature of $f$ to\n", "require $S$ and aggregates the resulting $\\Delta S_{i+1}$\n", - " $$w\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta\n", - "S_{i+1}\\right] \\rightarrow \\left[ f^\\prime(S_i, a_0, ..., a_m) \\rightarrow S_{i} + \\Delta\n", - "S_{i+1} = S_{i+1}\\right]$$\n", + " $$w\\left[f(s_0, ..., s_n, a_0, ..., a_m) \\rightarrow \\Delta S_{i+1}\\right] \\rightarrow \\left[ f^\\prime(S_i, a_0, ..., a_m) \\rightarrow S_{i} + \\Delta S_{i+1} = S_{i+1}\\right]$$\n", "\n", "- Assuming that the other arguments $a_k$ are provided by partial evaluation of the $f^\\prime$, the full AER cycle can\n", "then be represented as:\n", @@ -47,7 +70,7 @@ "source": [ "## Example\n", "\n", - "First initialize the State. In this case, we use the pre-defined `StandardState` which implements the standard AER\n", + "First initialize the State. In this case, we use the pre-defined `StandardState` which implements the standard `autora`\n", "naming convention.\n", "There are two variables `x` with a range [-10, 10] and `y` with an unspecified range." ] @@ -76,16 +99,32 @@ "Specify the experimentalist. Use a standard function `random_pool`.\n", "This gets 5 independent random samples (by default, configurable using an argument)\n", "from the value_range of the independent variables, and returns them in a DataFrame.\n", - "To make this work as a function on the State objects, we wrap it in the `on_state` function." + "To make this work as a function on the State objects, we wrap it in the `on_state` function and determine the state field it will operate on, namely `conditions`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), 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=[]), conditions= x\n", + "0 5.479121\n", + "1 -1.222431\n", + "2 7.171958\n", + "3 3.947361\n", + "4 -8.116453, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from autora.experimentalist.random_ import random_pool\n", + "from autora.experimentalist.random import random_pool\n", "from autora.state import on_state\n", "\n", "experimentalist = on_state(function=random_pool, output=[\"conditions\"])\n", @@ -97,7 +136,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Specify the experiment runner. This calculates a linear function, adds noise, assigns the value to the `y` column\n", + "Specify the experiment runner with the state field it will operate on, namely `experiment_data`. This experiment runner calculates a linear function, adds noise, assigns the value to the `y` column\n", " in a new DataFrame." ] }, @@ -111,8 +150,6 @@ "import numpy as np\n", "import pandas as pd\n", "\n", - "\n", - "@on_state(output=[\"experiment_data\"])\n", "def experiment_runner(conditions: pd.DataFrame, c=[2, 4], random_state = None):\n", " rng = np.random.default_rng(random_state)\n", " x = conditions[\"x\"]\n", @@ -121,100 +158,56 @@ " observations = conditions.assign(y = y)\n", " return observations\n", "\n", - "# Which does the following:\n", - "experiment_runner(s_1, random_state=43)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A completely analogous definition, using the separate `@inputs_from_state` and `@outputs_to_delta(...)` decorators\n", - "rather than the combined `@on_state(...)` decorator would be:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from autora.state import inputs_from_state, outputs_to_delta\n", - "\n", - "\n", - "@inputs_from_state\n", - "@outputs_to_delta(\"experiment_data\")\n", - "def experiment_runner_alt_1(conditions: pd.DataFrame, c=[2, 4], random_state=None):\n", - " x = conditions[\"x\"]\n", - " rng = np.random.default_rng(random_state)\n", - " noise = rng.normal(0, 1, len(x))\n", - " y = c[0] + (c[1] * x) + noise\n", - " xy = conditions.assign(y = y)\n", - " return xy\n", - "\n", - "# Which does the following:\n", - "experiment_runner_alt_1(s_1, random_state=42)" + "experiment_runner = on_state(function=experiment_runner, output=[\"experiment_data\"])\n", + "s_2 = experiment_runner(s_1, random_state=43)\n", + "s_2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or alternatively:" + "Specify a theorist, using a standard LinearRegression from scikit-learn. We do not need to define the state field that the theorists will operate on - it will automatically operate on the `models` field." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [ - "def experiment_runner_alt_2_core(conditions: pd.DataFrame, c=[2, 4], random_state=None):\n", - " x = conditions[\"x\"]\n", - " rng = np.random.default_rng(random_state)\n", - " noise = rng.normal(0, 1, len(x))\n", - " y = c[0] + (c[1] * x) + noise\n", - " xy = conditions.assign(y = y)\n", - " return xy\n", - "\n", - "experiment_runner_alt_2 = on_state(experiment_runner_alt_2_core, output=[\"experiment_data\"])\n", - "experiment_runner_alt_2(s_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify a theorist, using a standard LinearRegression from scikit-learn." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x', value_range=(-10, 10), 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=[]), conditions= x\n", + "0 0.785198\n", + "1 9.834543\n", + "2 0.616326\n", + "3 -4.376617\n", + "4 -3.698967, experiment_data= x y\n", + "0 5.479121 24.160713\n", + "1 -1.222431 -2.211546\n", + "2 7.171958 30.102304\n", + "3 3.947361 16.880769\n", + "4 -8.116453 -32.457650\n", + "5 0.785198 3.193693\n", + "6 9.834543 41.207621\n", + "7 0.616326 3.879125\n", + "8 -4.376617 -14.668082\n", + "9 -3.698967 -11.416276, models=[LinearRegression()])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.linear_model import LinearRegression\n", "from autora.state import estimator_on_state\n", "\n", - "theorist = estimator_on_state(LinearRegression(fit_intercept=True))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run the theorist on the output from the experiment_runner,\n", - "which itself uses the output from the experimentalist." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "theorist(experiment_runner(experimentalist(s_0)))" + "theorist = estimator_on_state(LinearRegression(fit_intercept=True))\n", + "s_3 = theorist(experiment_runner(experimentalist(s_2)))\n", + "s_3" ] }, { @@ -248,7 +241,346 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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"outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.08476524] [[4.00471062]]\n" + ] + } + ], "source": [ - "print(s_.model.intercept_, s_.model.coef_)\n" + "print(s_.models[-1].intercept_, s_.models[-1].coef_)\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/cycle/Combining Experimentalists with State.ipynb b/docs/cycle/Combining Experimentalists with State.ipynb index aaec2877..0dc7c080 100644 --- a/docs/cycle/Combining Experimentalists with State.ipynb +++ b/docs/cycle/Combining Experimentalists with State.ipynb @@ -8,8 +8,8 @@ "\n", "## Introduction\n", "\n", - "One thing the State/Delta mechanism should support is making more complex experimentalists which combine others.\n", - "One example which have been suggested by the AER group are a \"mixture experimentalist\" which weights the outputs of\n", + "One thing the State should support is making more complex experimentalists which combine others.\n", + "One example that has been suggested by the `autora` group are a \"mixture experimentalist,\" which weights the outputs of\n", "other experimentalists.\n", "\n", "How experimentalists are typically defined has a major impact on whether this kind of mixture experimentalist is easy\n", @@ -35,23 +35,23 @@ "- A combination experimentalist which aggregates additional measures from the component experimentalists.\n", " - Where the measure is passed back in the conditions array, or\n", " - Where the measure is passed back in a separate array\n", - "- A combination experimentalist where the components need the full State as they have complex arguments\n", + "- A combination experimentalist where the components need the full State as they have complex arguments.\n", "\n", "\n", "### Toy Experimentalists\n", "\n", "We're combining experimentalists which samples conditions based on whether they are downvoted (or not)\n", "according to some criteria:\n", - "- The \"Avoid Negative\" experimentalist, which downvotes conditions which have negative values (with one downvote per\n", + "- The `Avoid Negative` experimentalist, which downvotes conditions which have negative values (with one downvote per\n", "negative value in the conditions $x_i$: if both $x_1$ and $x_2$ are negative, the condition gets 2 downvotes, and so\n", "on) and returns all the conditions in the \"preferred\" order (fewest downvotes first),\n", - "- The \"Avoid Even\" experimentalist, which downvotes conditions which are closer to even numbers more (with one downvote\n", + "- The `Avoid Even` experimentalist, which downvotes conditions which are closer to even numbers more (with one downvote\n", "per even value in the conditions and half a downvote if a condition is $1/2$ away from an even number) and returns all the conditions in the \"preferred\" order,\n", - "- The \"Avoid Repeat\" experimentalist, which downvotes conditions which have already been seen based on the number of\n", + "- The `Avoid Repeat` experimentalist, which downvotes conditions which have already been seen based on the number of\n", "times a condition has been seen and returns all the conditions in the \"preferred\" order,\n", - "- The \"Combine Downvotes\" experimentalist, which sums the downvotes of the others and returns the top $n$ \"preferred\"\n", + "- The `Combine Downvotes` experimentalist, which sums the downvotes of the others and returns the top $n$ \"preferred\"\n", "conditions\n", - "(with the fewest downvotes); in the case of a tie, it returns conditions the order of the original conditions list.\n", + "(with the fewest downvotes); in the case of a tie, it returns conditions in the order of the original conditions list.\n", "\n", "\n", "We also need to see what happens when we:\n", @@ -91,7 +91,88 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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x1x2downvotes
30.02.00
41.03.00
52.04.00
63.05.00
1-2.00.01
2-1.01.01
0-3.0-1.02
\n", + "
" + ], + "text/plain": [ + " x1 x2 downvotes\n", + "3 0.0 2.0 0\n", + "4 1.0 3.0 0\n", + "5 2.0 4.0 0\n", + "6 3.0 5.0 0\n", + "1 -2.0 0.0 1\n", + "2 -1.0 1.0 1\n", + "0 -3.0 -1.0 2" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def avoid_negative(conditions: pd.DataFrame):\n", " downvotes = (conditions_ < 0).sum(axis=1)\n", @@ -116,7 +286,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Avoid-even function')" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " x1 x2 avoid_negative.downvotes avoid_even.downvotes downvotes\n", + "4 1.0 3.0 0 0.0 0.0\n", + "6 3.0 5.0 0 0.0 0.0\n", + "2 -1.0 1.0 1 0.0 1.0\n", + "0 -3.0 -1.0 2 0.0 2.0\n", + "3 0.0 2.0 0 2.0 2.0\n", + "5 2.0 4.0 0 2.0 2.0\n", + "1 -2.0 0.0 1 2.0 3.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "downvote_order(conditions_, experimentalists=[avoid_negative, avoid_even])\n" ] @@ -211,7 +887,112 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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x1x2avoid_negative.downvotesavoid_even.downvotesdownvotes
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" + ], + "text/plain": [ + " x1 x2 avoid_negative.downvotes avoid_even.downvotes downvotes\n", + "4 1.0 3.0 0 0.0 0.0\n", + "6 3.0 5.0 0 0.0 0.0\n", + "2 -1.0 1.0 1 0.0 1.0\n", + "0 -3.0 -1.0 2 0.0 2.0\n", + "3 0.0 2.0 0 2.0 2.0\n", + "5 2.0 4.0 0 2.0 2.0\n", + "1 -2.0 0.0 1 2.0 3.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from autora.state import Delta, on_state, State, StandardState, inputs_from_state\n", "\n", @@ -227,15 +1008,95 @@ "\n", "To ensure we don't mix up the order of return values and to facilitate updating the returned values in future without\n", " breaking dependents functions when returning multiple objects, we return a structured object –\n", - "in this case a simple dictionary of results. (We could just as well use a `UserDict` or a `Delta` object for this\n", - "purpose – they have the same interface.)" + "in this case a simple dictionary of results." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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x1x2
30.02.0
41.03.0
52.04.0
63.05.0
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2-1.01.0
0-3.0-1.0
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" + ], + "text/plain": [ + " x1 x2\n", + "3 0.0 2.0\n", + "4 1.0 3.0\n", + "5 2.0 4.0\n", + "6 3.0 5.0\n", + "1 -2.0 0.0\n", + "2 -1.0 1.0\n", + "0 -3.0 -1.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def avoid_negative_separate(conditions: pd.DataFrame):\n", " downvotes = (conditions_ < 0).sum(axis=1).sort_values(ascending=True)\n", @@ -249,7 +1110,33 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "( x1 x2\n", + " 0 -3.0 -1.0\n", + " 2 -1.0 1.0\n", + " 4 1.0 3.0\n", + " 6 3.0 5.0\n", + " 1 -2.0 0.0\n", + " 3 0.0 2.0\n", + " 5 2.0 4.0,\n", + " 0 0.0\n", + " 2 0.0\n", + " 4 0.0\n", + " 6 0.0\n", + " 1 2.0\n", + " 3 2.0\n", + " 5 2.0\n", + " dtype: float64)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def avoid_even_separate(conditions: pd.DataFrame):\n", " downvotes = avoid_even_function(conditions_).sum(axis=1).sort_values(ascending=True)\n", @@ -263,7 +1150,33 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'conditions': x1 x2\n", + " 0 -3.0 -1.0\n", + " 1 -2.0 0.0\n", + " 2 -1.0 1.0\n", + " 3 0.0 2.0\n", + " 4 1.0 3.0\n", + " 5 2.0 4.0\n", + " 6 3.0 5.0,\n", + " 'downvotes': initial total\n", + " 0 0 0\n", + " 1 0 0\n", + " 2 0 0\n", + " 3 0 0\n", + " 4 0 0\n", + " 5 0 0\n", + " 6 0 0}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def downvote_order_separate(conditions: pd.DataFrame, experimentalists: List):\n", " downvote_arrays = {\"initial\": pd.Series(0, index=conditions.index)}\n", @@ -273,10 +1186,7 @@ " combined_downvotes[\"total\"] = combined_downvotes.sum(axis=1)\n", " combined_downvotes_sorted = combined_downvotes.sort_values(by=\"total\", ascending=True)\n", " conditions_sorted = pd.DataFrame(conditions, index=combined_downvotes_sorted.index)\n", - " return {\n", - " \"conditions\": conditions_sorted,\n", - " \"downvotes\": combined_downvotes_sorted,\n", - " }\n", + " return {\"conditions\": conditions_sorted, \"downvotes\": combined_downvotes_sorted}\n", "\n", "downvote_order_separate(conditions_, experimentalists=[])" ] @@ -285,7 +1195,120 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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x1x2initialavoid_even_separateavoid_negative_separatetotal
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" + ], + "text/plain": [ + " x1 x2 initial avoid_even_separate avoid_negative_separate total\n", + "0 -3.0 -1.0 0 0.0 2 2.0\n", + "1 -2.0 0.0 0 2.0 1 3.0\n", + "2 -1.0 1.0 0 0.0 1 1.0\n", + "3 0.0 2.0 0 2.0 0 2.0\n", + "4 1.0 3.0 0 0.0 0 0.0\n", + "5 2.0 4.0 0 2.0 0 2.0\n", + "6 3.0 5.0 0 0.0 0 0.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results = downvote_order_separate(conditions_, experimentalists=[avoid_even_separate, avoid_negative_separate])\n", "\n", @@ -306,7 +1329,96 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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x1x2downvotes
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" + ], + "text/plain": [ + " x1 x2 downvotes\n", + "0 -3.0 -1.0 2.0\n", + "1 -2.0 0.0 0.0\n", + "2 -1.0 1.0 0.0\n", + "3 0.0 2.0 0.0\n", + "4 1.0 3.0 0.0\n", + "5 2.0 4.0 0.0\n", + "6 3.0 5.0 1.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def avoid_repeat(conditions, experiment_data: pd.DataFrame, variables: VariableCollection):\n", " iv_column_names = [v.name for v in variables.independent_variables]\n", @@ -337,7 +1449,36 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:249: UserWarning: These fields: ['already_seen'] could not be used to update StandardState, which has these fields & aliases: ['variables', 'conditions', 'experiment_data', 'models']\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "StandardState(variables=VariableCollection(independent_variables=[Variable(name='x1', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False), Variable(name='x2', value_range=None, allowed_values=None, units='', type=, variable_label='', rescale=1, is_covariate=False)], dependent_variables=[], covariates=[]), conditions= x1 x2 downvotes\n", + "0 -3.0 -1.0 2.0\n", + "1 -2.0 0.0 0.0\n", + "2 -1.0 1.0 0.0\n", + "3 0.0 2.0 0.0\n", + "4 1.0 3.0 0.0\n", + "5 2.0 4.0 0.0\n", + "6 3.0 5.0 1.0, experiment_data= x1 x2\n", + "0 -3 -1\n", + "1 3 5\n", + "2 -3 -1, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "avoid_repeat_state = on_state(avoid_repeat)\n", "s = StandardState(\n", @@ -360,7 +1501,31 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:249: UserWarning: These fields: ['already_seen'] could not be used to update StandardState, which has these fields & aliases: ['variables', 'conditions', 'experiment_data', 'models']\n", + " warnings.warn(\n" + ] + }, + { + "ename": "TypeError", + "evalue": "'str' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[32], line 24\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m d\n\u001b[0;32m 22\u001b[0m combine_downvotes_state \u001b[38;5;241m=\u001b[39m on_state(combine_downvotes)\n\u001b[1;32m---> 24\u001b[0m \u001b[43mcombine_downvotes_state\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 25\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mon_state\u001b[49m\u001b[43m(\u001b[49m\u001b[43mavoid_even\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mconditions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m7\u001b[39;49m\n\u001b[0;32m 33\u001b[0m \u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mconditions\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:939\u001b[0m, in \u001b[0;36mdelta_to_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 937\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m 938\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_f\u001b[39m(state_: S, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m S:\n\u001b[1;32m--> 939\u001b[0m delta \u001b[38;5;241m=\u001b[39m f(state_, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 940\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(delta, Mapping), (\n\u001b[0;32m 941\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOutput of \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m must be a `Delta`, `UserDict`, \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor `dict`.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m f\n\u001b[0;32m 942\u001b[0m )\n\u001b[0;32m 943\u001b[0m new_state \u001b[38;5;241m=\u001b[39m state_ \u001b[38;5;241m+\u001b[39m delta\n", + "File \u001b[1;32mc:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:675\u001b[0m, in \u001b[0;36minputs_from_state.._f\u001b[1;34m(state_, **kwargs)\u001b[0m\n\u001b[0;32m 673\u001b[0m arguments_from_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstate\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m state_\n\u001b[0;32m 674\u001b[0m arguments \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(arguments_from_state, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 675\u001b[0m result \u001b[38;5;241m=\u001b[39m f(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39marguments)\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "Cell \u001b[1;32mIn[32], line 14\u001b[0m, in \u001b[0;36mcombine_downvotes\u001b[1;34m(state, conditions, experimentalists, num_samples)\u001b[0m\n\u001b[0;32m 12\u001b[0m this_downvoted_conditions\u001b[38;5;241m.\u001b[39mattrs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[0;32m 13\u001b[0m downvoted_conditions\u001b[38;5;241m.\u001b[39mappend(this_downvoted_conditions)\n\u001b[1;32m---> 14\u001b[0m combined_downvotes \u001b[38;5;241m=\u001b[39m \u001b[43mcombine_downvotes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconditions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownvoted_conditions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 15\u001b[0m combined_downvotes_sorted_filtered \u001b[38;5;241m=\u001b[39m combined_downvotes\\\n\u001b[0;32m 16\u001b[0m \u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdownvotes\u001b[39m\u001b[38;5;124m\"\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\\\n\u001b[0;32m 17\u001b[0m \u001b[38;5;241m.\u001b[39miloc[:num_samples]\n\u001b[0;32m 19\u001b[0m d \u001b[38;5;241m=\u001b[39m Delta(conditions\u001b[38;5;241m=\u001b[39mcombined_downvotes_sorted_filtered)\n", + "Cell \u001b[1;32mIn[32], line 10\u001b[0m, in \u001b[0;36mcombine_downvotes\u001b[1;34m(state, conditions, experimentalists, num_samples)\u001b[0m\n\u001b[0;32m 8\u001b[0m downvoted_conditions \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m experimentalists:\n\u001b[1;32m---> 10\u001b[0m new_state \u001b[38;5;241m=\u001b[39m \u001b[43me\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconditions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconditions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 11\u001b[0m this_downvoted_conditions \u001b[38;5;241m=\u001b[39m new_state\u001b[38;5;241m.\u001b[39mconditions\n\u001b[0;32m 12\u001b[0m this_downvoted_conditions\u001b[38;5;241m.\u001b[39mattrs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n", + "\u001b[1;31mTypeError\u001b[0m: 'str' object is not callable" + ] + } + ], "source": [ "@on_state()\n", "def combine_downvotes_state(\n", @@ -410,7 +1575,33 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'conditions': x1 x2\n", + " 0 -3.0 -1.0\n", + " 1 -2.0 0.0\n", + " 2 -1.0 1.0\n", + " 3 0.0 2.0\n", + " 4 1.0 3.0\n", + " 5 2.0 4.0\n", + " 6 3.0 5.0,\n", + " 'downvotes': 0 2.0\n", + " 1 0.0\n", + " 2 0.0\n", + " 3 0.0\n", + " 4 0.0\n", + " 5 0.0\n", + " 6 1.0\n", + " Name: downvotes, dtype: float64}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" 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" + ], + "text/plain": [ + " x1 x2 y new_column\n", + "0 -10 -10 -10 NaN\n", + "1 5 5 5 15.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s_1.experiment_data" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -730,13 +2512,13 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2" + "pygments_lexer": "ipython3" } }, "nbformat": 4, diff --git a/docs/cycle/Dynamically Extending and Altering the State.ipynb b/docs/cycle/Dynamically Extending and Altering the State.ipynb index cdc6a83a..4452f73b 100644 --- a/docs/cycle/Dynamically Extending and Altering the State.ipynb +++ b/docs/cycle/Dynamically Extending and Altering the State.ipynb @@ -23,7 +23,7 @@ "source": [ "### Defining The State\n", "\n", - "We use the standard State object bundled with `autora`: `StandardState`. This state has four build in fields:\n", + "We use the standard State object bundled with `autora`: `StandardState`. This state has four built in fields:\n", "`variables`, `conditions`, `experiment_data` and `models`. We can initialize some (or all) of these fields:\n" ] }, @@ -31,7 +31,18 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(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=[]), conditions=None, experiment_data=None, models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -41,7 +52,17 @@ "s = StandardState(\n", " variables=VariableCollection(independent_variables=[Variable(\"x\", value_range=(-15,15))],\n", " dependent_variables=[Variable(\"y\")]),\n", - ")" + ")\n", + "\n", + "s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding Pool To The State\n", + "First, we add a new field, `pool` to state `s`. To do this, we must expand the StandardState class, while adding the field. We want the content of this field to be replaced each time a function writes into the field." ] }, { @@ -52,7 +73,7 @@ { "data": { "text/plain": [ - "{'_metadata': {'variables': {'default': None, 'delta': 'replace'}, 'conditions': {'default': None, 'delta': 'replace'}, 'experiment_data': {'default': None, 'delta': 'extend'}, 'models': {'default': None, 'delta': 'extend'}}, '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=[]), 'conditions': None, 'experiment_data': None, 'models': None}" + "ExtendedStandardState(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=[]), conditions=None, experiment_data=None, models=[], pool=[])" ] }, "execution_count": null, @@ -61,26 +82,23 @@ } ], "source": [ + "from dataclasses import dataclass, field\n", + "\n", + "@dataclass(frozen=True)\n", + "class ExtendedStandardState(StandardState):\n", + " pool: pd.DataFrame = field(\n", + " default_factory=list,\n", + " metadata={'delta': 'replace'}\n", + " )\n", + "\n", + "s = ExtendedStandardState(\n", + " variables=VariableCollection(independent_variables=[Variable(\"x\", value_range=(-15,15))],\n", + " dependent_variables=[Variable(\"y\")])\n", + ")\n", + "\n", "s" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding Pool To The State\n", - "First, we add a new field to `s`. We want the content of this field to be replaced each time a function writes into the field." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s.add_field(name='pool', delta='replace')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -96,17 +114,17 @@ { "data": { "text/plain": [ - "{'_metadata': {'variables': {'default': None, 'delta': 'replace'}, 'conditions': {'default': None, 'delta': 'extend'}, 'experiment_data': {'default': None, 'delta': 'extend'}, 'models': {'default': None, 'delta': 'extend'}, 'pool': {'default': None, 'delta': 'replace', 'aliases': None}}, '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=[]), 'conditions': None, 'experiment_data': None, 'models': None, 'pool': x\n", - "0 -12.659490\n", - "1 5.983803\n", - "2 -10.337078\n", - "3 9.287066\n", - "4 -3.707592\n", - "5 -14.107050\n", - "6 4.233732\n", - "7 -10.066782\n", - "8 -10.494731\n", - "9 -2.577266}" + "ExtendedStandardState(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=[]), conditions=None, experiment_data=None, models=[], pool= x\n", + "0 -3.599348\n", + "1 -14.328625\n", + "2 -13.764225\n", + "3 3.656028\n", + "4 2.723904\n", + "5 -7.214785\n", + "6 6.466772\n", + "7 7.363881\n", + "8 13.304111\n", + "9 2.923905)" ] }, "execution_count": null, @@ -164,24 +182,24 @@ " \n", " \n", " \n", - " 4\n", - " -13.405719\n", + " 0\n", + " -3.599348\n", " \n", " \n", - " 9\n", - " 12.941968\n", + " 1\n", + " -14.328625\n", " \n", " \n", " 5\n", - " 5.300515\n", + " -7.214785\n", " \n", " \n", - " 7\n", - " 3.970537\n", + " 9\n", + " 2.923905\n", " \n", " \n", - " 6\n", - " -3.867838\n", + " 8\n", + " 13.304111\n", " \n", " \n", "\n", @@ -189,11 +207,11 @@ ], "text/plain": [ " x\n", - "4 -13.405719\n", - "9 12.941968\n", - "5 5.300515\n", - "7 3.970537\n", - "6 -3.867838" + "0 -3.599348\n", + "1 -14.328625\n", + "5 -7.214785\n", + "9 2.923905\n", + "8 13.304111" ] }, "execution_count": null, @@ -251,16 +269,16 @@ " \n", " \n", " \n", - " 9\n", - " 12.941968\n", + " 2\n", + " -13.764225\n", " \n", " \n", - " 6\n", - " -3.867838\n", + " 3\n", + " 3.656028\n", " \n", " \n", - " 3\n", - " 11.739368\n", + " 9\n", + " 2.923905\n", " \n", " \n", "\n", @@ -268,9 +286,9 @@ ], "text/plain": [ " x\n", - "9 12.941968\n", - "6 -3.867838\n", - "3 11.739368" + "2 -13.764225\n", + "3 3.656028\n", + "9 2.923905" ] }, "execution_count": null, @@ -296,80 +314,19 @@ "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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We'll just\n", + "Given this state, we define a two part `autora` pipeline consisting of an experiment runner and a theorist. We'll just\n", "reuse the initial seed `conditions` in this example.\n", "\n", "First we define and test the experiment runner.\n", @@ -95,12 +121,11 @@ "metadata": {}, "outputs": [], "source": [ - "from autora.state import on_state, Delta\n", + "from autora.state import on_state\n", "\n", "def ground_truth(x: pd.Series, c=(432, -144, -3, 1)):\n", " return c[0] + c[1] * x + c[2] * x**2 + c[3] * x**3\n", "\n", - "@on_state\n", "def experiment_runner(conditions, std=100., random_state=None):\n", " \"\"\"Coefs from https://www.maa.org/sites/default/files/0025570x28304.di021116.02p0130a.pdf\"\"\"\n", " rng = np.random.default_rng(random_state)\n", @@ -108,7 +133,9 @@ " noise = rng.normal(0, std, len(x))\n", " y = (ground_truth(x) + noise)\n", " experiment_data = conditions.assign(y = y)\n", - " return Delta(experiment_data=experiment_data)" + " return experiment_data\n", + "\n", + "experiment_runner = on_state(experiment_runner, output=['experiment_data'])" ] }, { @@ -122,7 +149,123 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:417: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat((a, b), ignore_index=True)\n" + ] + }, + { + "data": { + "text/html": [ + "
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YGBEd+G9K6ZcsZzl8BypQImdXXMCBWVcQVpRKmlGffZd9SLe2znO2nSuyePnT7I6PSO4wGYCEnC9Y//w/yDh8xORkIuI0tpcevjsQ1oejx4sJ9PGgW0xdk0P9jwqUyJkYBnvfvZWo3E0cM/xZ13cOfbp3MTuVe7BYaHbVVPb0m0khnvQqXsXhmRezc5eWfhGp9ezFZdMXLCrpBEDflmGmzz7+V86TRMQJHVg4g+i9X1JiWPmu9dNc0q+P2ZHcTuOLbuTY1Z+RZalDGyMZ77mDWPfHarNjiYiZUldBYTb41eeDvfUBGOAk0xecoAIlchpZ678hPPFJAN4LvJUGDWI0i3Y1CW/XF8Ys4oAtioYcJHrBVfy+YrHZsUTELDtKpy/Ije7H9oP5pbOPt1CBEnF6xRlb8fzin1gx+NpjEFGdLyNl585qWRZFSgU1bE3du5ayy6sF9Sw5tPr+elb8+IXZsUTEDH8WqFW2rgDEx9YnyM/TzESnUIES+bv8Yxx76yr8jOOsMVrSasws2rdqRrNmzaplWRT5H5/gcBpO+JHtfp0JsBTQ9Zd/suzL2WbHEpGadHQPHNwKFhuzM5sCkNDWec6+O0EFSuSvDIP9791GaPF+9hkhrGk9hWaR9YmKiqJ3795aw60GePoF0WzCd2wOuhBvSwkXrLmXpR8/b3YsEakpf+59Korqwa/7SgAY1DbCzETlUoES+YvDv7xFg7TvKTZsfBo+mcv6xJkdqVayevnS+u4vWB96OTaLQd+tj7LwrYfNjiUiNeHP6Qs2+Jf++9u5UTDhgT5mJiqXCpTIn0rSNxPw04MAfFxnFHfdfnuV73FKS0tj+fLlGoxeARabJx3ufI9lgZcBMHjfiyyd+6jJqUSkWhUdh92/ADAvq3T28QQn3PsEKlAipYoLOPrujXhTyAo60O+Wx/GohvlGUlJSSE5OdrvB6NVWDC0Wmo14hqWBVwLQd9fzLJs9VevnibirXcugpABHYDTz95aueacCJeLE0j+bTOjxZA4ageRdOpOG9apnscrY2Fi3HIxencUwqkED+t47mzUxtwHQZ8/LLH/nAZUoEXe0fSEAu+pdQIkDWobXoUmIv8mhyqcCJbVe3rr/ErHtPQD+23gqF/foWG3P5a6D0auqGJ52T5bFQtfRM/g99k4ALtw7ixXv3H9ezyUiTsbhgG3fAfBVQSfAOc++O0EFSmq3vMM4vrwbgE88h3LdyFtMDuSaqqoYnm1PVrebpvNb83sAuGDv/7Fy7oPn9Xwi4kTS1kJuOoZXAG/vbwBAQjvnPHwHKlBSy6V9cjd17MfY5oimzQ0z8Pf2MDtSrVaRPVndRz5GYuxdAMTvmkni+4/UUDoRqVZbvwEgPawPOcU2Gtb1pU1koMmhTk8FSmqtvA1fEZX6NXbDQmKHx2jf2LmWCagN/n7IrqJ7suJu+g8rG91e+t/JL/Dbx09We1YRqWZ/FqhFjm5A6eBxi8ViZqIzUoGS2in/GCX/nQDAJx5X0DA0VFMLmOB8Bp/3vPlpVjS4GYDuW59mzWczqjqeiNSUQ8lwaBuG1ZNZaaV7oAc78eE7UIGSWirjs/sIKjlEiiOCwnbXk7pL69yZ4XwGn1ssFuLHPM+v4SMB6LrxP6z/5vWqjigiNWFb6d6nY+FxpBV4ERLgRZdGdU0OdWYqUFLrFG77kfCd83AYFha3mEZCXHu3nFrAFZzv4HOL1Uqv22eyJGgoAG1W/5vNP31YhQlFpEb8efhumbV09vGL24Rjszrv4TsAjZiV2qUwl/z54/AG5tsGc+3V11DHx9PtphWoTSxWK7aut/DDkkMMYjnNfr6LHX6BNO95mdnRRKQicjNh72oAXj/QAoBL20eamahCtAdKapXMb/5DcFE6+4wQwq+cTh0fT7MjSRVo1qw5Xn3uY5VXPF6WEhosvIVF8/5P49pEXMG27wCDnPod2Hq8DnX9PImPrW92qrNSgZJaw5G5nbrr3wTg24YT6dO+icmJpKpERUXRt19f2t8znz88O+NHIXGbHmFL4iKzo4nI2fx5+G6FZ+nhu8HtIqtlKa2q5vwJRaqCYXDw0wl4UsLPRmeuuEYTZrojf39/Yu78go3WlgRajtNx3cNkpSWbHUtETqcwB1KWAvBGRmsALuvg/IfvQAVKaon8jV8RfvBXCg0P9sc9THiQb408b7UtsiunVbduXUJv/5KdlkaEcJTct/9B/tEMs2OJSHmSF4O9kPyAxqzJD6e+vxdxTeqZnapCVKDE/RXnU/B16bppn3pdwbBBfWrsqatzkd3aqiKlNDw8AssN80kjhAb2/aS/fjkl+dk1mFJEKmTbtwCs9okHLAxuF+ESh+9ABUpqgSOLnqVuYRoHjHpE/+MhvD1sNfbcVbXIrvxPRUtpbNMWHB76MUeNOjQp2kbKq1dhlBTWUEoROSt7MY6tpYsHv3Ww9PDdEBc5fAcqUOLmjKN7CPjtZQAWhN7BRe1rtshU1SK78j+VKaXtO3Vna/+3OW540yL3N9Y+fzVp+/fVQEoROatdP2MtyibXUodfC5sSEuBNXBPnP/vuBBUocWuZ8/+Fl1FEoqM1g0eMMzuOVIHKltL4ixJY2e0Fig0bXY8vY/f8qdWcUEQqZNMCABK943Fg5dL2EU4/eeZfqUCJ2yrZk0j4voXYDQsbOz5Ik9AAsyOJSQZcPpKvGk4CoNeRz9n57csmJxKp5ezFsPVrAD4s6AnAEBeYPPOvVKDEPRkGhxdMAeBra19GXHZJ2U06M652Gjrm33xTv3Tx4ZjEaexb9fkp2+h3Q6SG7FoG+Ucp8q7H0oLmhNXxpltMPZf6O6gCJW6pcMv3hB9dQ6HhSWHv+wnw/t+qRTozrnayWi0MuP1ZFvsmYLMY1F94B0e2rzxpG/1uiNSQzQsA+N2vN3ZsXNo+EpvV4lJ/B1WgxP047OR88yAAn9ouJcyz5KRvMzozrvby8fKgy9jZJNq64Esh1o9GcDz9fxNtlve74UrfiEVcgr0YtpQevnvnaEfgf2ffudK/z1pMWNxO3u8fEpKXTJbhx+GmwyhJ2YmH1VI26DgqKkpnxdVidQP9ifznx2z5vwRaG7s48PZQvCf8gs2/brm/Gye+EQP6vRGpCrt/gfwjFHnVZUl2CyICfejaqC7gWv8+aw+UuJfiAkp+/A8An/ldw7C+3Vzm24zUnEaR4RSN+IQ0oz6RxXvZO+sqKCkqd1tX+kYs4hI2/xeARJ8LsGPjik5RWF3o7LsTVKDErWT98jpBRekcMOrR7LJJNGzQQPMwSbk6tm7J9gFvk2P4EpOzlpTZY8AwTtlOc3mJVCF7CWz5CoC3j5QevhvauYGZic6ZCpS4j/xjeCx/DoD5ASPp0yba5EDi7Pr26ceP7Z6ixLASu/9L9ix4zOxIIu5tz3I4fpgCr7r8UtKKluF1aB0ZaHaqc+JWBeqRRx7BYrGcdGnVqlXZ7QUFBYwbN4769esTEBDAsGHDyMg4eZHR1NRUhgwZgp+fH2FhYdx3332UlJTU9EuRc3D4p5fwd+Sww9GAOi37YbG43i5hqXlDrx7F5+H3ANB43fNk/Pq+yYlE3Nifk2f+6tETOzaX3fsEblagANq2bcuBAwfKLsuXLy+77d577+Wrr77i008/5eeffyYtLY2rrrqq7Ha73c6QIUMoKipixYoVzJ07lzlz5jBt2jQzXoqcxUlnRxVk4bvmDQC+9B/Oxd1am5xOXIXFYuEft07lS98rAai76B6yt/9qcioRN/SXw3dzjnUC4B+dXPfQuNsVKA8PDyIiIsouISEhAGRlZfH222/z/PPP079/f7p27crs2bNZsWIFq1atAuCHH35g8+bNvP/++3Tq1IlLLrmExx9/nFdffZWiovIHmIp5/jpfyJElr+DnyGW7owGDb5ig8SpSISdK+JGDGfQa+xrLrD3wogTj4+spPrzb7Hgi7mXPr3D8EPmewaxwtCGuST0aBPuaneqcuV2B2rFjB1FRUcTGxjJy5EhSU1MBWLNmDcXFxQwcOLBs21atWtGoUSNWriydTG/lypW0b9+e8PDwsm0SEhLIzs5m06ZNp33OwsJCsrOzT7pI9TtxdlTThuH4/DYLgKXho2jboK7JycRV/LWEhwT6EX7zu2w1GhPkOMbhN6+EwhyzI4q4j01fALDU0sPlD9+BmxWouLg45syZw8KFC3n99dfZtWsXF154ITk5OaSnp+Pl5UVwcPBJ9wkPDyc9PR2A9PT0k8rTidtP3HY606dPJygoqOwSHa3ByzXhxNlRPtvm4+fIYacjkrjL/ml2LHEhf5+ioGV0JJmXv0umEUxEQQr73roOHHaTU4q4gZIiHBtLl096P7crXjYrl7ZzrbXv/s6tCtQll1zC8OHD6dChAwkJCXz77bccO3aMefPmVevzTpkyhaysrLLL3r17q/X55C8Kc/Fc/RoAi0NvpGPj+iYHEldS3hQFfbp14peuL1NgeNLw4C/sn3efiQlF3ETyj1gLszhqCWKloy39WoUS5Odpdqrz4lYF6u+Cg4Np0aIFycnJREREUFRUxLFjx07aJiMjg4iICAAiIiJOOSvvxM8ntimPt7c3gYGBJ12kZhxbNosAexa7HOF0vew2s+OIm7jq8n8wr+G/AWiw9W0O/vyGyYlEXNyGTwH4xrgAB1aGdnLtw3fg5gUqNzeXnTt3EhkZSdeuXfH09GTx4sVlt2/bto3U1FTi4+MBiI+PZ8OGDWRmZpZts2jRIgIDA2nTpk2N55ezKDqOR+IrAPxQ/0a6Ngk1OZC4C4vFwjWj7+Ej/5EABC+ZQt72nwGtjSdSaYU5sO07AD4u7EUdHw/6tQozOdT5c6sCNXnyZH7++Wd2797NihUruPLKK7HZbFx33XUEBQUxZswYJk6cyJIlS1izZg0333wz8fHx9OzZE4BBgwbRpk0bbrzxRtatW8f333/PQw89xLhx4/D29jb51cnfZS1/g4CSY6Q6QumivU9SxXw8bQy4/Tl+tPbCkxIcH9+I/chul1otXsQpbPkaSvLJ9GrERqMJl7aLxMfTZnaq8+ZWBWrfvn1cd911tGzZkmuuuYb69euzatUqQkNL90y88MILXHbZZQwbNow+ffoQERHB559/XnZ/m83G119/jc1mIz4+nhtuuIGbbrqJxx7T7MROp6QIy8rSvU/f1b2e7k3Dz3IHkcoLC/Ql/KZ32Gg0oY4ji8NvDaNpw3CtjSdSGRtKxyF/UtATsHBVF9c/fAdgMYxyFn+S85KdnU1QUBBZWVkaD1VNclfNJWDh3aQbdUm5fgW9WmreJ6k+369cQ+eFVxFmOcaBiP5E3jYfrG71/VOkeuRkwPOtwHBwUeHzWOrFsmRyX6ddKaIyn9/6F0Bcj2FQsOxFAL7zH0p8C9c+FVacX0J8V75r9xyFhieR6T+R+d+pZkcScQ2bvgDDwQ7PVuwxIri6a0OnLU+VpQIlLqdo2w+EHE8h1/AhvN8dbvOXUZzbDcOGMTd0EgBh62aS/dvHJicScQF/Hr57/3gcFgtc1aWhyYGqjgqUuJwji54D4CuPQQzq0sLkNFJb2KwWrv3nZD7yKl0/0/ubuyne+0e52+pMPRHg8E7YvwYHNr629+TC5qFEufDSLX+nAiUuxbH/DyIOJ1JiWLHEj8XDpl9hqTmBPp70GPMivxid8aaQ4+9eA7mZp2ynM/Wktin3S8Ofcz8lWjpwmCCGd3WfvU+gAiUuJuP70r1P31t6cdmFPUxOI7VR0/AgjGFvstMRSVBxJgffvgZKCk/a5u9LxIi4u1O+NBgGrP/z7LvCeAJ9PLi4jXudLa0CJa7j2F7CUr8BILPdbQR4e5gcSGqrPh2asypuJtmGH6FH/+DgvLtKPzD+VN4SMSLu7JQvDfvXwJGdFFm8WeToytDODdxi7qe/UoESl5G56AVsOPjV0Y5LLk4wO47UctdfOoB3G0zFYVgI3f4J2cteNzuSiGlO+dLwx3sAfGvvQR6+DO8abWK66qECJa4h/xiBmz8EYFPMKCKCfEwOJLWdxWLh5lG38Y7vaAD8ljxEUfLP5oYScQZFx2Fj6STVn5T0oVVEHdo1cL85EVWgxCUc/fUdfIx8tjqi6XPJCLPjiADg7+3BoH8+wXdcgAd2ij66EePoHrNjiZhry1dQmE26NZxVjtYM7xbtltPNqECJ83M4MH57C4Bf6w+jVWSQyYFE/qdRiD+BI2ax0RFDgD2Lo+9cU/oNXKS2SnofgA8LL8RmtTG0k3uOBVSBEqdXuO0H6hXuJ8vwI7b/aLPjiJzigtaNSLrgVQ4ZgdTL2crhD289aVC5SK1xdDfsWoYDC/PtF5LQLoL6Ad5mp6oWKlDi9A7/NBOAhZ4D6NM2xtwwIqcxctAFvBf9GMWGjfq7vyZn8bNmRxKpeUkfAbDKaMd+QhkZ18jkQNVHBUqcmnE4hYiDywFwdB2Dzep+x9HFPVgsFm6/6UZm+d0GgP/yJyja9v1pt9ds5eJ2HA5IKj3Z5+PiPsSG+BMfW9/kUNVHBUqcWsZPr2HFYJmjI4P7XGB2HJEz8vPy4IoxU/mMAVgxKPnkFoxDyeVuq9nKxe3sXgZZqeRa/Pne0Z3r4xq55eDxE1SgxHkVHSdwS+nu4B0x15OfdUjf2MXpNQrxJ2zEy6xxNMfPkUvWnGugMOeU7TRbubidP0oHjy8o7onh4cPVbrZ0y9+pQInTyvn9Y/wcuaQ6Qom7+Bp9YxeX0ad1QzZeMJN0oy7BuTs5+sEtpYc3/kKzlYtbyT9WOn0B8Kn9Ii7rEEmwn5e5maqZCpQ4J8Og4NfSmZ1/CvwH7aLr6Ru7OK3yxjPdNCiOdxv9h0LDg7qpP5C7aLqJCUWq2cb5UFLAdqMh64ymjIxrbHaiaqcCJU6pZM8qQvO2U2B4EnrhGEDf2MV5lbd31GKxMO6Ga3nF704AAlY+Q/Hmr82KKFJ9DAPWzAFgXslFtIoIpEujYFMj1QQVKHFKmYtfAWCh9UIGdm1lchqRMzvd3lF/bw+uHvMAHzIYAPtnt8LB7WZEFKk++9dC+nqK8GS+/UJG9mzs1oPHT1CBEudz/Aihe38AILvdTXh7uNcK3uJ+zrR3NCbEn6gRz5PoaIWP4zjZc4ZDQZYJKUWqye9vA/C1PY5Cr7puO/P436lAidM5vPI9PClmgyOGdu06mx1H5Lz1bd2Ajb1eZr9Rn8C83WR9MPqUQeUiLun4kdLxT8D7JQO5olMD6vh4mhyqZqhAiXMxDBy/zwXgJ9uFHD+4z+RAIlXj5kE9mBP9HwoMT4L2/kTeD4+ZHUnk/K37CEoK2OJoxFqjOTf2dP/B4yeoQIlTKdn7G6H5O8k3vPBsmaAz7sRtWK0W7rnxGl7wHQ+A/6oXKN743zPeR7OVi1MzDPj9HQDetw+kZ2x92kQFmhyq5qhAiVNJX/IGAD9Ze/LPq4fojDtxKwHeHlwzZjJzjcsAcHx+O2RsPu32mvtMnNquZXA4mTx8WGC/gFsuaGJ2ohqlAiXOozCXkN2lp3kfbXkdXh769RT3cWJvkm9xNg2HP8Nye1u8Hfnkzh1eOo6kHJr7TJzan4PHvyi5gHr16jGgdbjJgWqWPqHEaWT9Pg8fI58URwTx/S83O45Ilfrr3qQB7RqwsdeL7HWEEnB8HzkfjAJ7ySn30dxn4rRy0jG2fgPA+/aLGd2rSa1b7F0FSpzG8cTZAKwIHELTsDompxGpWn/fm3RbQnfebvgExw1v6uxfxvHvppmcUKQS1r6HxVHC744W7PVswvBu7r3uXXlUoMQpONI3E5m9nmLDRlD8jWbHEalyf9+bZLVamHTTVczwvRsAv99fpWTdPDMjilSMvaRs5vH3SwYyvFs0gbVk6oK/UoESp5C+9E0AltGFAd3bm5xGpGbU8fFk5C0TeMsYCoCxYDwcWGduKJGz2fYNZO/jiBHAd0YPRvWKMTuRKVSgxHwlhQTu+AyA/bHD8fPyMDmQSM1pFhZA4+HTWWrviKdRSN67IyD3oNmxRE5v5atA6dQFvVs2pEmIv8mBzKECJabL2/AVAfZsDhj16NzvarPjiNS4i9tFsbnXC6Q4IvDPP0Du+yPBXmx2LJFT7VsDexMpNmy8V3Ixt/SuXVMX/JUKlJjuyIp3AfjZdwDtouuZnEbEHHckdGF29JPkGL4EpCeS/+V9ZkcSOdWq0r1PXzp64VOnPjG+hSYHMo8KlJgr7xCRB5cD4Nn5ulqxgrdIeaxWC/+68R887TcRh2HBd91sSla/Y3Yskf/J2oexaQEAb5dcQveAY+zatcvcTCZSgRJTHVz1ER7Y2eBowkUX9DY7joip6vh4csstdzKTEaVXfHcfpK4yN5TICavfwGLYWWFvw9E6Lbiic8MzTvLq7ksRqUCJqUr++AiADfUHExLgbXIaEfPFhgbQ7tpH+dbeAw+jhIL3r4MsLaotJivMxfi9dK6+t+2XMLZfcy7qc+EZJ3l196WIVKCkxp34VpK+aTmRuZsoMazU73m92bFEnEb/1hHsu+g5tjga4VN0pPTMvKLjZseS2izpQyyF2aQ4IkjyiWN41+iz3sXdlyJSgZIad+JbyYFlcwFYSUcu6tLW5FQizuXWAe35IPYpDht18D+8kfzP7gDDMDuW1EYOB0bi6wDMtg9m9AWx+HrZzno3d1+KSAVKalxsbCzNmjal0eGfAdgbfTk+nmf/yyhSm1gsFqZcl8D0Ov+m2LDhu/2/FP/8nNmxpDbavhDLkRSOGf58Z+vHTfExZidyCipQUuOioqLo3sBK/ZIMcgxfml44wuxIIk7J39uDe24ZzdPWMQDYlv6nbAFXkRphGPDrSwB8ZO/PlXEtCPKrfcu2lEcFSkyRvmwOAMs84unevIHbn60hcq6i6/kx4IYHeM9+MVYMij/9J2RuMTuW1BZ7foW9qyg0PHnXcQljervneKZzoQIlNa+4gJDUbwHIaTEMq9Xi9mdriJyP+Kb1YfB0Vtrb4GU/TtZbV5CessnsWFIbLHsWgHn2i2jRIJSIIB+TAzkPFSipcTkbvsbfkUuaUY9ufS8H3P9sDZHzdUOvZvzY/hn2OMIIKsrA+sVtUFJkdixxZ/vXQMoSSgwrb9gv585+zcxO5FRUoKTGHV35HgAr/PrTLDwIcP+zNUQq4kyHsi0WC/dfdQEzgqeSY/gSlrORwi8n6sw8qT7LSk9a+K/jAuK6dCaujb7g/pUKlNSs/GNlS7fYOl1rchgR53K2Q9leHlYevW0Ej3pNwmFY8F7/HvZVs2o4pdQKGZth2zc4DAuz7Fdwd//mZidyOipQUqMOrfkcT0rY5mjIhRf0MTuOiFOpyKHs+gHejLnldp41RgJg+f7fkPxjTUUUN1PeXs+0tDQyv5gCwHeO7nTpEkej+n5mRXRaKlBSo/LWfgbAhuD+WrpF5G8qeii7dWQgHa95iHklF2HFQdHHoyFza82EFLdS3l7PtI3LCUkvnafv/xxDGd9fY5/KowIlNef4ERocKV0Y1a/z1SaHEXFtCe0iOdzvKVY7WuJVkkPBu1dD3iGzY4mLKW+vZ+sj32PF4Cd7J9p160N0Pe19Ko8KlNSYzNWf4YGdzY7G9IqLNzuOiMu7o39rFrR4mlRHKD65e8l//zooKTQ7lriQU/Z6HtuL7/b/AvB/xlDG6cy701KBkhqTn/QpAJvrDSDYz8vkNCKuz2KxMG1EH54NeZxswxffA6sp+mK8zsyTc/fzU1gdxaywt6FFt4tpEOxrdiKnpQIlNcLIzaThsd8BCOh6jclpRNyHj6eNqTdfxcNe/6LEsOK1aR4lP88wO5a4okM7MJI+AuBF41ru7NfU5EDOTQVKakT6qnnYcLDBiOWC7t3MjiPiVkLreHP7mH/ypHEzAB5Ln8DY+LnJqcTVOJY8icWws8jehQ49LyYySHufzkQFSmpE8fr5AGytfzF1fLQQpUhVaxURSO/r72d2yWAA7J/fDntXm5xKXMaB9Vg3lZbuWbbrdOZdBahASbUzsg/QMPsPAOp21+E7kerSv1U4RsITLLJ3wcNRRNF718ARrS8pZ2df/DgA/7X3IqG/xqlWhArUabz66qvExMTg4+NDXFwcq1frm9y5Slv5MVYM/jBa0KtrJ7PjiLi1m3s3ZVWnp9ngiMGr6CgFc4bB8SNmxxJnlroKW/IPlBhWPvQdyU3xMWYncgkqUOX45JNPmDhxIg8//DBr166lY8eOJCQkkJmZaXY0l+TYULpbeGfYxfh5eZicRsS9WSwWRsU14LnAKew36uOTnULB+9dqegMpn2FQ/MMjAMyzX8R1l/TDx9NmbiYXoQJVjueff55bb72Vm2++mTZt2jBr1iz8/Px45513yt2+sLCQ7Ozsky5SynF0L9G563EYFjxaDDI7jkitkLp7F/H1CnjQ8wGyDV980hJJ+7/hpO3fb3Y0cTY7f8Jz30oKDU9+CLmJf3TUgu4VpQL1N0VFRaxZs4aBAweWXWe1Whk4cCArV64s9z7Tp08nKCio7BIdHV1TcZ3e/pXzAFhjtCDQS99qRGpCbGwsbVs0495rBvGQ530UGzaiDv5M8Q/TzI4mzsRhp3DhQwC8bx/IbZf1wWq1mBzKdahA/c2hQ4ew2+2Eh4efdH14eDjp6enl3mfKlClkZWWVXfbu3VsTUV2CY/OXAGwJiKdVc80pIlITTswu3bFFDHfccisPG7cC0HjPZzgS3wTKX0RW3N9J/9//eA/vQ5vJMvz4I2YMvZqFmB3PpWhAShXw9vbG21sL4/6dkZtJw9z1ADTuN+qsC6SKSNVrExXI4Bsm88K7h7nX41Ms390HgRGkHK5LcnIygP5u1iInFg+2leQRsvJRvICX7Vdz9+U9zY7mcrQH6m9CQkKw2WxkZGScdH1GRgYREREmpXJNaYmfY8PBRiOWHh07mh1HpNbq0yKU6KEP82FJPywYlHw6hpZ+WacsIivu78Tiwa0PfYdX4RF2OiKx9biVFuF1zI7mclSg/sbLy4uuXbuyePHisuscDgeLFy8mPl4L4FZG4YbSw3c76/fFV+OfREx1dbdojvabzo/2zng4CglaeCe9W4Zp71MtExUVRe/WkdTZ9B4Ar3jezF2DWpucyjWpQJVj4sSJvPnmm8ydO5ctW7YwduxY8vLyuPnmm82O5joKsml4LBGAgM5XmhxGxH1VZizTnf1b8UvHZ0hyNMWr6BiFc66ALJ2ZV9sc/2YKNqOEpfaO9Lv8Bq0OcY5UoMoxYsQInn32WaZNm0anTp1ISkpi4cKFpwwsl9NLX/MVXpSw04iie3ftuROpLifGtKSknH3GcYvFwtQruzEn5ml2OiLxzkujcM5QTbRZm6QsxS/le0oMK1+G36lpC86DCtRpjB8/nj179lBYWEhiYiJxcXFmR3IpOUlfALAlqA+B+nYjUm1OjGmp6FgmD5uV6Tf0Z0bYdNKNungf3U7hu1dDUV41JxXT2UvI/e99AHzguJjbrx6CxaJpC86VCpRUveICGhxcDoBXuytMDiPi3k5MWVCZsUy+XjaevuUyptV5nGOGP97payj68AawF1djUjFb8YpXCcjazjHDn4Nd76VlhAaOnw8VKKlyh9Z/jx/5pBn16BLf3+w4IlKOID9PHr9tOP/yfoh8wwuv3T9RPP92cDjMjiZV5KTxcUf3YCx5EoCZHjdx++BuJqdzfSpQUqXS0tLYs3QuAOv9exNSx8fkRCJyOuGBPky5bRT/sk2m2LDhuXk+JV9PBMMwO5pUgbLxcTt3kj3/LrwcBaxytKb7lfdo4HgVUIGSKpWSvIOmOaVn39H6MnPDiMhZNQnx5/Yxd/BvxpWuWbl2NvbvH1KJcgMnxse1tG8hcN/PFBoeLGo6hYR2kWZHcwsqUFKlGlgPEkwuR40AOlxwqdlxRKQC2jUIYtioCUx1lC75Yls1E/vSp01OJecrKiqK3l3a4PfrdADesQ7jzmGDTU7lPlSgpEoV7SidgHStT0+i6mmAooir6Blbn4tvuI8n7DcCYPt5Oo5fXzE5lZyvo19Owb/4CNsdDWh0xb+pH6Blx6qKCpRUHcOg/r4fAShsrr1PIq6mb8swuo54iOdLhgNgXfQQxm9vm5xKzlVJyi/U3foxAAsa3selHRubnMi9qEBJlclLTSLEnkm+4UWrC/5hdhwROQeD20UQe9UjzCq5HADLNxMxfp9jaiY5B4U55M27HYBPuZjR112nOZ+qWKUL1KhRo1i2bFl1ZBEXt2/1AgD+8OhIbGSouWFE5JwN7dKQoMuf4J2S0vEylq/vwVgzt1LLxoi5Dn02kaCC/ewzQvC59HHCdEZ0lat0gcrKymLgwIE0b96cJ598kv37tY6SlPLZ+QMAxxpq7icRV3ddXGM8L32K2SUJpVd8dQ+5v7xe4WVjxDx5SV8QsmMeDsPC/MbTuKx7K7MjuaVKF6gFCxawf/9+xo4dyyeffEJMTAyXXHIJn332GcXFmsW2trJnZxBdsAWA8O6afVzEHdzYqwkelz7NnJJBWDBotuUV4n13VXjZGKl5RnYaxpd3A/CB51Xccv31OnRXTc5pDFRoaCgTJ05k3bp1JCYm0qxZM2688UaioqK499572bFjR1XnFCeXmrgAKwabaULH1q3NjiMiVeTGXk2wXvoM75ZcjBWDFlteIvLAIrNjSXkcDtLfHUOAI5uNjiZ0uOEpTZhZjc5rEPmBAwdYtGgRixYtwmazcemll7JhwwbatGnDCy+8UFUZxQUUbv4WgNT6ffCw6dwEEXdyU68mcOkM5pZcjAUDy1d341j5utmx5G8yf3qFyEMryDe82NjzWTrGhJkdya1V+pOuuLiY+fPnc9lll9G4cWM+/fRTJkyYQFpaGnPnzuXHH39k3rx5PPbYY9WRV5xRSSGNjq4CwK+9Zh8XcUc39WqCdciz/F/JEACs3z+AY9nzFbqvBp9Xv4K9SQQt/w8AH9S5hWsGDzA5kfvzqOwdIiMjcTgcXHfddaxevZpOnTqdsk2/fv0IDg6ugnjiCtLX/0gEBWQYwXSKu8jsOCJyDtLS0khJSSE2NpaoqKhyt7kxPobPPJ/m5QXe3O3xOdafHsVefBxb/wfhDONsTqzJBpz2seXcGcePkPvutYRQxFJHJyI6XorVqnFP1a3SBeqFF15g+PDh+Pic/pTI4OBgdu3adV7BxHUcXvslEcAm/57099UstyKuqKIl5+pu0Xzr/SQz5nlzn+0jbL/MoKQwB4/B08Fa/kGNE4PONfj83J224Drs7H3rehoVH2CPEcaejvdxcdvm5gWtRSpdoG688cbqyCEuKm3/furvL12+xd5cayyJuKrKlJxL20fi6/kYj3/gzVTbHDxWz6I49yCeV80iLfPQKR/0UVFR2vN0nk5XcHd/9hAxR1aSb3ixoddMRiUkmBWx1ql0gRL5q5S1S+htHKTQ8KRV/OVmxxGRc1TZktOvVRjeo6fywLsBPM7reG6eT2HeYfY0GUfy7rSyxzxXFTmk6K7Ke+3lFdwDifOJ2fwaAF82eoBrBg2q+bC1mAqUnBffIxsA+MPWnp4RISanEZGa1KtpCIG3/YuJbwfzlH0G/nuW0j47E2v7B4k+z8N1tXncVHmv/e8FN2f/Fup8Nx6Ab3yv4MpR92q+pxqm883lvASnLwcgq9FAk5OIiBnaNQjivnHjmOz3Hw4bdQg4upmOSQ8S5Z1/Xo8bGxtLs2bNauW4qbO99sKsDPJmX0UAx/nD0oYet7+Gl4c+zmuaxTAMw+wQ7iY7O5ugoCCysrIIDAw0O061Kc45iPW55tgwWDf8Vzq2bWd2JBGpQpU5jHY4t5CH3l7Ag0f+TUPLIYq8gvC6/iOIuaCG0tYO9oJc9r4wgJjCrew3Qske+R2tW2jQeFWpzOe3Kqucs92rFmDDYCsxtGvd1uw4IlLFThxKOtPadyfmeCrMPsxzY4fxbPRrJDma4lWUhX3uPzD++KAGE7s3w17MjleHE1O4laNGAAf+8YHKk4lUoOScFW4tXTx4X/0LsGnOERG3U5HDaH8tWX5eHjx3yyC+7foWX9vjsBklWP57JyU/PAIOR80Fd0eGwcb/u4VWOSsoMDzZ1PcNunWNMztVraYCJefGYSf6yEoAfNpo+gIRdxQVFUXv3r3PePju7yXLZrXw7yu6kD3kDV61DwXAY8ULFH54PRRk10Rst/THe/fTPvNL7IaFlV2fpXe/IWZHqvVUoOScHNyeSJCRQ47hS7seWjJApLY6Xcm6vmcMnUc/x4OMp9DwwDv5O/Jfuwgyt5qU1HWt/WAqnVP+D4Cfmz9Av3+MNjeQACpQco4OrPkagI0+nQmu429yGhFxRr2ahvDP8f9mUsBTpBn18M1Oofj/+mJs/MLsaK7BMPhtzv102fEyAEuibqPfyPtNDiUnqEDJOfFLXQrA8eh+5gYREafWJMSfp+++mZnN3uFXe1s87flYPhtN0XcPgr3Y7HhOy3A4WPX2RLrvngXAz43upO+tz2iuJyeiAiWVVpRzhCYFmwGI6qbj8CJyZv7eHjxxQ192JrzLG/bSFQu8EmdyfNZAOLwT+N/ZfGlpaWZGdQqGw8Gq/xtHz33vALA89l4uumX6SeVJ75f5VKCk0nb99g02i8FOGtKyRRuz44iIC7BYLNzUuxndbn2FBzz+xTHDH7+DSRS91hvHHx+QsnPnWadMqA0KCwtY9coo4jM+BGBVywfofdMjp2xXkSkmpHppKRepsBOT6vmsLx3/tLdeL5pq+gIRqYQujerS5N77eHpeF67Y9Sg92QL/vZM2jQZja3IzjWvhzOMnHDqYQdqb1xBflITDsLC2/UP0vHpyudtWZvFnqR7aAyUVlpKSQvKOHTQ+lgiAVystXCkilVfX34snRw9m7+Uf86LjWkoMK/VSF9Ip8S4is/84r8euyKGt6jr8dT6Pu33TH+S91pcORUkcx5vNF71Ot9OUJ6jYFBNSvVSgpMJiY2NpVNdGKEfJN7xoHZdgdiQRcVEWi4Xh3WO48p7n+Xe9Z9nlCMe3IBPLx9eT98GNkHvwnB63Ioe2quvw17k+7qofPyd83hAaG2lkWEI5MuJr2vW/rkqzSdVTgZIKi4qKol7BLgA2eXekbpD7rvMnIjWjcX1/nhx/Mz9c9DlvOi6nxLDiv+NLCl7sSsnaD6GSy7VWZPb06lqouLKPm3c8n8WvTaDbL2MIsuSxw6sNvuN+pmHrHlWaS6qHFhOuBu68mPDWp/vSKv8PlsROpt9NU82OIyJuZM/hPN785HOuT59BG+seALJCuhA09Flo2NXkdFVr4/rfsS24g9aOHQCsD7mUtre9g83L1+RktZsWE5ZqUZyfTdPj6wEI73KZyWlExN00ru/P42NvYPsV/+Vly0iOG94EHVoLb/Un68NbINv1T9kvKrbzw9wniZ1/Ka0dO8jGn20XvkSH8R+pPLkY7YGqBu66B2rbz/NoueRW9hJOg6lbsdrUv0WkehzNK2LOwhU0XvcsV1l/AaDI4kNR9zsI6HsP+NUzOWHFnDh7uUmTJuxLTcbzx4foYi/9IrrdrwuRo+dQJ6yxySnlBO2BkmqRt3khALuD41WeRKRa1fX34t5hfely9yc8Hf0avzta4GUUELD6RQqfbcOxL6ec80DzmpSSksIf69axYfZddFt4BV3s6ynEky0dHqDF5MUqTy5Mn4JSYZEHfwXAs+XFJicRkdoiJsSf+8eMxHLLQp4NfohNjsZ4O/IJXvsaRc+15dBn95bNZl4eM2fs3p2WzpG18xmd+TiDSxZjsxhsrjeAojsSaX3VFLDqI9iV6RBeNXDHQ3iH926j/ts9KDZsZN+zg/r16psdSURqod92HebXbz+gb8ZsOln/N11Aev04gi68Hd/2/wCbZ9n1y5cvJzk5mWbNmtG7d+9qz2cYBuvWr+Pg4pfpmfUtdSz5AOzxboHPkKcJ79C/2jPIuavM57dmIpcKSf39O+oD2zxb0k7lSURMkJaWRuH+FK658mqOGaN49buPaZP6IRdZ1hFxOBEWJJL9VT2ym/2DsB5X49WkV43N2H3wWA4bln+F1/r3iS9cgc1igAXSPBtREHc3sf3HaI+Tm1GBkorZ9TMAR8N7mRxERGqrExNVAvTu3Zs2/7ydgzmj+XjFahxr5jKocBFh9iMEbpsD2+aQYw2iIKo/7br8g+CgVlWe58Chw2z9ZQG27V/T6fgq+luOl95ggR0B3fHvew9RXYaoOLkpFSg5K8NhJyb7dwAC22j8k4iYo7y9SaF1vLk+4UKMQb1ZuzuThUvnUz/1ey5w/EawI4s6+76AfV8AkOkRxdF6nbA16k5gdHuCo5riVbcheHid8XntdgdHjhwmfc9WjuxIxHIgidCczTR17KafxV66kQWOWOqSFjWQ6IvvonlMx+p5E8RpaAxUNXC3MVB7Nq2i8acJ5Bo+eEzZg4+Pj9mRREROy+Ew2LTvMNtWf4938re0Ov4Hza37y98WC0csdcn2CKHE4oHDYsOOBw6LFe+SHAJKjlLPOIaPpbjc+2daw0hvMIjwuOGEt+mjvU0uTmOgpEplJC2kMbDdtyNdVJ5ExMlZrRbaNwqhfaORwEhyCor5bfc+Dm5ZAftWU+/YBsJK0ojiED6WYkKMI4QUHzn9A1pK/8jGn33ezSgM64Rfk+40bNuLsLBYwiyWGnld4lxUoOSsfPeWTmKX3/BCk5OIiFReHR9PurdqAq2aACOB0rPljuYVsSdjPznpKRRlpWNxlIBhx+IoxuIowTsgmIB6kQSHNmDLzr3s3LO/xs7mE+enAiVnVFSQT7P89WCBsI4JZscREakSFouFegHe1AuIhaZnP0OvqUcgFk/faj+bT1yHCpScUfLan2hjKeIQwTRt083sOCIipoiKiiIqKsrsGOJENNpNzih78yIAUgK7a/kWEXFL5zpbeVXNcm7mbOly7rQHSs6obvqK0v+IucjcICLiNk4ssBsbG3tee3Wq6nH+Or9UZR7nXO9XXY8jNUsFSk4r6+ghmhVvBws06n6J2XFExE04W/E419nKq2qW85qaLV2qlgqUnNbO1QvpYjFItTagUXQzs+OIiJtwtuJxruObqmpclMZXuSYVKDmtoh2LAThQL45GJmcREfeh4iHuwK1GBcfExGCxWE66PPXUUydts379ei688EJ8fHyIjo7mmWeeOeVxPv30U1q1aoWPjw/t27fn22+/ramX4FSiDicC4N1ygMlJREREnItbFSiAxx57jAMHDpRd7rrrrrLbsrOzGTRoEI0bN2bNmjXMmDGDRx55hDfeeKNsmxUrVnDdddcxZswY/vjjD4YOHcrQoUPZuHGjGS/HNGl7kmlk7MduWGjafbDZcURERJyK2x3Cq1OnDhEREeXe9sEHH1BUVMQ777yDl5cXbdu2JSkpieeff57bbrsNgJdeeonBgwdz3333AfD444+zaNEiZs6cyaxZs2rsdZgtdc13RAE7vVrQIjjE7DgiIiJOxe32QD311FPUr1+fzp07M2PGDEpKSspuW7lyJX369MHL638rbyckJLBt2zaOHj1ats3AgQNPesyEhARWrlx52ucsLCwkOzv7pIurs+wuXb7laHi8yUlEREScj1vtgbr77rvp0qUL9erVY8WKFUyZMoUDBw7w/PPPA5Cenk6TJk1Ouk94eHjZbXXr1iU9Pb3sur9uk56eftrnnT59Oo8++mgVvxrzGIZBdPZaAOq07GdyGhER91FVc1eJ+Zx+D9QDDzxwysDwv1+2bt0KwMSJE+nbty8dOnTgjjvu4LnnnuOVV16hsLCwWjNOmTKFrKysssvevXur9fmq256dm4niIMWGjdgu/c2OIyLiNk7MXZWSkmJ2FDlPTr8HatKkSYwePfqM25xuDpC4uDhKSkrYvXs3LVu2JCIigoyMjJO2OfHziXFTp9vmdOOqALy9vfH29j7bS3EZaUmLiAFSvFvS0j/Q7DgiIm5Dk2a6D6cvUKGhoYSGhp7TfZOSkrBarYSFhQEQHx/Pgw8+SHFxMZ6engAsWrSIli1bUrdu3bJtFi9ezIQJE8oeZ9GiRcTH156xQNY9vwKQEx5nchIREfeiuavch9MfwquolStX8uKLL7Ju3TpSUlL44IMPuPfee7nhhhvKytH111+Pl5cXY8aMYdOmTXzyySe89NJLTJw4sexx7rnnHhYuXMhzzz3H1q1beeSRR/j9998ZP368WS+tRjnsDhrnlI5/Cmyjw3ciIiLlcZsC5e3tzccff8xFF11E27ZteeKJJ7j33ntPmuMpKCiIH374gV27dtG1a1cmTZrEtGnTyqYwAOjVqxcffvghb7zxBh07duSzzz5jwYIFtGvXzoyXVeNSkjcRySGKDRu+4a21QriI1EppaWn690/OyOkP4VVUly5dWLVq1Vm369ChA7/88ssZtxk+fDjDhw+vqmguJS3pR5oBu3xakZmWoRXCRaRWqqqFisV9uU2BkqrhsffP8U8RPTXYUURqLf37J2ejAiVl7HYHsbml45/qtumvwY4iUmvp3z85G7cZAyXnb8e2DURwmGJsNO6kCTRFRERORwVKgNIBkxt/+hiAXd6tsXn7m5xIRETEealACVA6YDL48B8A5EX1MjmNiIiIc9MYKAGgUaMYvBzbwFI6/klEREROT3ugBIDsrINEWI5QhAeNOvQxO46IiIhTU4ESADLXLwJgj09rrBr/JCIickYqUAKA974VABxvoPFPIiIiZ6MCJRQV22l2PAmAkHYa/yQiInI2KlDCtm0bCLccpRgPotpq/JOIiMjZqEAJmRuXAJDq0wKLl5/JaURERJyfCpTgsa90EebjET1MTiIiIuIaVKBqObvDoFHOOgACW+rwnYiISEWoQNVy25N30sRyAAcWGnbQAHIREZGKUIGq5fZv+Kn0T88YbP51TU4jIiLiGrSUSy2VlpZGSkoK9pTlAGSHdjM5kYiIiOvQHqhaKiUlhe07kmmYux4Av+a9TU4kIiLiOlSgaqnY2FgCQ8NpxW4AGnQcYG4gERERF6ICVUtFRUURzDFsFoNMWzhe9aLNjiQiIuIyVKBqsZJdvwJwuF5Xk5OIiIi4FhWoWsowDEKPrAXAq6nGP4mIiFSGClQtlXrwKG0d2wFo2FHzP4mIiFSGClQtlZy0HB9LMdmWQLwjWpkdR0RExKWoQNVSBcml8z9lBHcGi8XkNCIiIq5FBaqWCj60BgBrTC+Tk4iIiLgeFahaKO1oHm3tWwCI1Pp3IiIilaYCVQttXb+aYEse+fjg16iL2XFERERcjgpULZS7YxkAB+q0B5uWQxQREaksFahaKCCjdP4ne8MeJicRERFxTSpQtUx2QTFNizYDENqmj8lpREREXJMKVC2zcdsOGlsycWAhuHm82XFERERckgpULXNoc+n4p3TvJuATZHIaERER16QCVctY9/8GQF6Yzr4TERE5VypQtUix3UFUznoAAppdYHIaERER16UCVYts3XeQtuwCIFwDyEVERM6ZClQtsnvjKrwtxWRbg7CGNDU7joiIiMtSgapFinatBOBw3U5aQFhEROQ8qEDVEoZhUPfwHwDYGmkCTRERkfOhAlVL7DtynDaObQCEt73I5DQiIiKuTQWqlti8dRMRlqOUYMO7UVez44iIiLg0FahaImv7cgAy/FqAl5/JaURERFybClQt4X1gDQDFUd1NTiIiIuL6VKBqgeyCYmILNgJQt6Um0BQRETlfKlC1wLqUNFpbUgEIatHb5DQiIiKuTwWqFjiw6Vc8LA6OeoRCUEOz44iIiLg8FahawNibCEB2iBYQFhERqQoqUG6uxO4gPKt0AWGf2HiT04iIiLgHFSg3ty09m45sByC09YUmpxEREXEPKlBuLnnreupacinCE2tkB7PjiIiIuAUVKDeXu3MVAIfqtAIPL5PTiIiIuAcVKDfndzAJAHukBpCLiIhUFRUoN3Y0r4gmhVsAqNeil8lpRERE3IfLFKgnnniCXr164efnR3BwcLnbpKamMmTIEPz8/AgLC+O+++6jpKTkpG2WLl1Kly5d8Pb2plmzZsyZM+eUx3n11VeJiYnBx8eHuLg4Vq9eXQ2vqPqt351BG8seAPxj40xOIyIi4j5cpkAVFRUxfPhwxo4dW+7tdrudIUOGUFRUxIoVK5g7dy5z5sxh2rRpZdvs2rWLIUOG0K9fP5KSkpgwYQL//Oc/+f7778u2+eSTT5g4cSIPP/wwa9eupWPHjiQkJJCZmVntr7GqpW1NxMtiJ9cWBHVjzI4jIiLiNiyGYRhmh6iMOXPmMGHCBI4dO3bS9d999x2XXXYZaWlphIeHAzBr1izuv/9+Dh48iJeXF/fffz/ffPMNGzduLLvftddey7Fjx1i4cCEAcXFxdO/enZkzZwLgcDiIjo7mrrvu4oEHHqhQxuzsbIKCgsjKyiIwMLAKXvW5ee/FB7jx2OvsC+1Dw3FfmZZDRETEFVTm89tl9kCdzcqVK2nfvn1ZeQJISEggOzubTZs2lW0zcODAk+6XkJDAypUrgdK9XGvWrDlpG6vVysCBA8u2KU9hYSHZ2dknXczmcBjUO7oBAK/G3U1OIyIi4l7cpkClp6efVJ6Asp/T09PPuE12djb5+fkcOnQIu91e7jYnHqM806dPJygoqOwSHR1dFS/pvCQfzKWtsQOA+hpALiIiUqVMLVAPPPAAFovljJetW7eaGbFCpkyZQlZWVtll7969ZkdiU3IKMdYMAGzR3UxOIyIi4l48zHzySZMmMXr06DNuExsbW6HHioiIOOVsuYyMjLLbTvx54rq/bhMYGIivry82mw2bzVbuNiceozze3t54e3tXKGdNObq9dALNwz6Nqe8bbG4YERERN2NqgQoNDSU0NLRKHis+Pp4nnniCzMxMwsLCAFi0aBGBgYG0adOmbJtvv/32pPstWrSI+PjSRXa9vLzo2rUrixcvZujQoUDpIPLFixczfvz4KslZUzzT1wJQGN7Z5CQiIiLux2XGQKWmppKUlERqaip2u52kpCSSkpLIzc0FYNCgQbRp04Ybb7yRdevW8f333/PQQw8xbty4sr1Dd9xxBykpKfzrX/9i69atvPbaa8ybN49777237HkmTpzIm2++ydy5c9myZQtjx44lLy+Pm2++2ZTXfS6yC4pplL8ZgMBmPU1OIyIi4n5M3QNVGdOmTWPu3LllP3fuXLpnZcmSJfTt2xebzcbXX3/N2LFjiY+Px9/fn1GjRvHYY4+V3adJkyZ888033Hvvvbz00ks0bNiQt956i4SEhLJtRowYwcGDB5k2bRrp6el06tSJhQsXnjKw3JmtSz1KR8tOAAI0gaaIiEiVc7l5oFyB2fNAvfvVj9y0ZhjFFk88H0zTIsIiIiIVUCvngZL/KdidCMDRwNbg4UVaWhrLly8nLS3N5GQiIiLuwWUO4UnFGIZBncPrACio3w6AlJQUkpOTAYiKijItm4iIiLtQgXIzuw7l0caxA6yQ69sA+N9UEBWdEkJERETOTAXKzazblcEQyx4A6rcfBJTuddKeJxERkaqjMVBu5uCO3/Cy2MnzqEt4S62BJyIiUh1UoNyMJW0NALkhHcFiMTmNiIiIe1KBciOFJXbCcjYB4BOjvU8iIiLVRQXKjWw5kEM7UgAIjO1hchoRERH3pQLlRjbv2kdT6wEALA26mJxGRETEfalAuZGjyb8BkO0dCf4hJqcRERFxXypQbsQj4w8ACsI6mJxERETEvalAuYnsgmIaHN8GQEATDSAXERGpTipQbmLDvizaW0oHkPs17mZyGhEREfemAuUmtqTsobE1s/SHqE6mZhEREXF3WsrFTeTuKh1AnuUbTZBvXZPTiIiYy263U1xcbHYMcTKenp7YbLYqeSwVKDfhnbkOgJKITuYGERExkWEYpKenc+zYMbOjiJMKDg4mIiICy3mu1qEC5eLS0tL4fVMyTYp2gA3qxGoAuYjUXifKU1hYGH5+fuf9ISnuwzAMjh8/TmZm6XCXyMjI83o8FSgXl5KSwq9b93G3tXQAuVe0BpCLSO1kt9vLylP9+vXNjiNOyNfXF4DMzEzCwsLO63CeBpG7uNjYWGxeXjSwHMaBBSI1B5SI1E4nxjz5+fmZnESc2Ynfj/MdI6cC5eKioqKoV5QGQE5AE/CuY3IiERFz6bCdnElV/X6oQLk4h8PA9/B6AIzIzianERERqR1UoFzcrsN5tLQnA1AntofJaURE5Fz07duXCRMmmB0DgAULFtCsWTNsNhsTJkxgzpw5BAcHmx3L6ahAubh1qUfp+OcAclvDLianERERZ7R06VIsFkuFpne4/fbbufrqq9m7dy+PP/44I0aMYPv27WW3P/LII3Tq1Kn6wroInYXn4nbvSuYqSxYObFjD25kdR0REXFhubi6ZmZkkJCQQFRVVdv2Js9fkf7QHysUVpv4OQE5Qc/DSmSciIn9lGAbHi0pMuRiGUamsJSUljB8/nqCgIEJCQpg6depJj1FYWMjkyZNp0KAB/v7+xMXFsXTp0rLb9+zZw+WXX07dunXx9/enbdu2fPvtt+zevZt+/foBULduXSwWC6NHjz7l+ZcuXUqdOqUnIvXv3x+LxcLSpUtPOoQ3Z84cHn30UdatW4fFYsFisTBnzpxKvU53oT1QLqyoxEHQsU1gBWsDDSAXEfm7/GI7baZ9b8pzb34sAT+vin/Mzp07lzFjxrB69Wp+//13brvtNho1asStt94KwPjx49m8eTMff/wxUVFRfPHFFwwePJgNGzbQvHlzxo0bR1FREcuWLcPf35/NmzcTEBBAdHQ08+fPZ9iwYWzbto3AwMBy9yj16tWLbdu20bJlS+bPn0+vXr2oV68eu3fvLttmxIgRbNy4kYULF/Ljjz8CEBQUdH5vlItSgXJh2zNyaGeUDiAPaKIZyEVEXFl0dDQvvPACFouFli1bsmHDBl544QVuvfVWUlNTmT17NqmpqWWH1iZPnszChQuZPXs2Tz75JKmpqQwbNoz27dsDpfMEnlCvXj0AwsLCTjsg3MvLi7CwsLLtIyIiTtnG19eXgIAAPDw8yr29NlGBcmEb9x0jwboLAEuU9kCJiPydr6eNzY8lmPbcldGzZ8+T5iiKj4/nueeew263s2HDBux2Oy1atDjpPoWFhWWzrt99992MHTuWH374gYEDBzJs2DA6dNDkytVFBcqFpe7eTl1LLnaLB7bwtmbHERFxOhaLpVKH0ZxVbm4uNpuNNWvWnLL8SEBAAAD//Oc/SUhI4JtvvuGHH35g+vTpPPfcc9x1111mRHZ7GkTuwkr2/QFAbmBz8PA2OY2IiJyPxMTEk35etWoVzZs3x2az0blzZ+x2O5mZmTRr1uyky18PpUVHR3PHHXfw+eefM2nSJN58802g9PAclK4XeL68vLyq5HFcnQqUiyodQL4ZAFuDTuaGERGR85aamsrEiRPZtm0bH330Ea+88gr33HMPAC1atGDkyJHcdNNNfP755+zatYvVq1czffp0vvnmGwAmTJjA999/z65du1i7di1LliyhdevWADRu3BiLxcLXX3/NwYMHyc3NPeecMTEx7Nq1i6SkJA4dOkRhYeH5v3gXpALlorZn5NCa0vFP/o01gaaIiKu76aabyM/Pp0ePHowbN4577rmH2267rez22bNnc9NNNzFp0iRatmzJ0KFD+e2332jUqBFQundp3LhxtG7dmsGDB9OiRQtee+01ABo0aMCjjz7KAw88QHh4OOPHjz/nnMOGDWPw4MH069eP0NBQPvroo/N74S7KYlR2ogo5q+zsbIKCgsjKyiIwMLBanuOj1an0/+ZCwi3HYMwiiNYyLiJSuxUUFLBr1y6aNGmCj4+P2XHESZ3p96Qyn9/aA+Widu3aSbjlGA6soAHkIiIiNUoFykUV7UsCIK9OLHj5mxtGRESkllGBckGFJfbSGcgBa1RHk9OIiIjUPipQLmh7ei6t2Q2AX2NNoCkiIlLTVKBc0Pr9x2h3YgbyyE7mhhEREamFVKBcUMqeVBpaDpX+ENHe3DAiIiK1kAqUCyrYmwRAnn8j8A02NYuIiEhtpALlYv46gNwSqQHkIiIiZlCBcjHb0nNo9ecAcl/NQC4iImIKFSgXs35fFm0tuwHtgRIREfPMmTOH4OBgs2MwevRohg4dWuPPqwLlYnakptHUeqD0BxUoERFxUrt378ZisZCUlOSUj3e+VKBcTP6fA8jzfSPAP8TcMCIiYpqioiKzI1QJV30dKlAupKDYTp2jm0t/0N4nEZGzMwwoyjPnYhgVjpmTk8PIkSPx9/cnMjKSF154gb59+zJhwoSybWJiYnj88ce56aabCAwM5LbbbgNg/vz5tG3bFm9vb2JiYnjuuedOemyLxcKCBQtOui44OJg5c+YA/9uz8/nnn9OvXz/8/Pzo2LEjK1euPOk+c+bMoVGjRvj5+XHllVdy+PDhM76mJk2aANC5c2csFgt9+/YF/nfI7YknniAqKoqWLVtWKOfpHu+EZ599lsjISOrXr8+4ceMoLi4+Y77z5VGtjy5Valt6Dm0spRNoFgc3w9fkPCIiTq/4ODwZZc5z/zutwmuVTpw4kV9//ZUvv/yS8PBwpk2bxtq1a+nUqdNJ2z377LNMmzaNhx9+GIA1a9ZwzTXX8MgjjzBixAhWrFjBnXfeSf369Rk9enSl4j744IM8++yzNG/enAcffJDrrruO5ORkPDw8SExMZMyYMUyfPp2hQ4eycOHCsgyns3r1anr06MGPP/5I27Zt8fLyKrtt8eLFBAYGsmjRogrnO9PjLVmyhMjISJYsWUJycjIjRoygU6dO3HrrrZV6DypDBcqFrN+fRfc/B5Dvs9enjblxRESkCuTk5DB37lw+/PBDBgwYAMDs2bOJijq1+PXv359JkyaV/Txy5EgGDBjA1KlTAWjRogWbN29mxowZlS5QkydPZsiQIQA8+uijtG3bluTkZFq1asVLL73E4MGD+de//lX2PCtWrGDhwoWnfbzQ0FAA6tevT0RExEm3+fv789Zbb51Ugs7mTI9Xt25dZs6cic1mo1WrVgwZMoTFixerQEmp/Lxsmlv2AVCvXX+T04iIuABPv9I9QWY9dwWkpKRQXFxMjx49yq4LCgoqO7T1V926dTvp5y1btnDFFVecdN0FF1zAiy++iN1ux2azVThuhw4dyv47MjISgMzMTFq1asWWLVu48sorT9o+Pj7+jAXqTNq3b1+p8nQ2bdu2Pem1RkZGsmHDhip7/PKoQLmQ21oWwi8Ghl8oEc06mR1HRMT5WSwVPozmCvz9K/9aLBYLxt/GY5U3PsjT0/Ok+wA4HI5KP19FlPc6KpqzPH/NfuKxqiv7CRpE7koOrAPAEtmh9B8FERFxebGxsXh6evLbb7+VXZeVlcX27dvPet/WrVvz66+/nnTdr7/+SosWLcr2yISGhnLgwIGy23fs2MHx48crlbF169YkJiaedN2qVavOeJ8Te5jsdnuFnuNsOSv7eNVNe6BcScEx8PDVGXgiIm6kTp06jBo1ivvuu4969eoRFhbGww8/jNVqLdsTdDqTJk2ie/fuPP7444wYMYKVK1cyc+ZMXnvttbJt+vfvz8yZM4mPj8dut3P//fefssfmbO6++24uuOACnn32Wa644gq+//77sx6+CwsLw9fXl4ULF9KwYUN8fHwICgo67fZny1nZx6tu2gPlSi6cBP/eX/qniIi4jeeff574+Hguu+wyBg4cyAUXXEDr1q3x8fE54/26dOnCvHnz+Pjjj2nXrh3Tpk3jscceO2kA+XPPPUd0dDQXXngh119/PZMnT8bPr2Ljs07o2bMnb775Ji+99BIdO3bkhx9+4KGHHjrjfTw8PHj55Zf5v//7P6Kiok4Zq/V3Z8tZ2cerdoaL+M9//mPEx8cbvr6+RlBQULnbAKdcPvroo5O2WbJkidG5c2fDy8vLaNq0qTF79uxTHmfmzJlG48aNDW9vb6NHjx5GYmJipbJmZWUZgJGVlVWp+4mIyLnLz883Nm/ebOTn55sd5bzl5uYaQUFBxltvvWV2FLdzpt+Tynx+u8weqKKiIoYPH87YsWPPuN3s2bM5cOBA2eWv6+Ps2rWLIUOG0K9fP5KSkpgwYQL//Oc/+f7778u2+eSTT5g4cSIPP/wwa9eupWPHjiQkJJCZmVldL01ERGq5P/74g48++oidO3eydu1aRo4cCWD+XhY5LZcZA/Xoo48ClM1IejrBwcGnzA9xwqxZs2jSpEnZLK2tW7dm+fLlvPDCCyQkJAClu1FvvfVWbr755rL7fPPNN7zzzjs88MAD5T5uYWEhhYWFZT9nZ2dX6rWJiIg8++yzbNu2DS8vL7p27covv/xCSIiW7HJWLrMHqqLGjRtHSEgIPXr04J133jnplMiVK1cycODAk7ZPSEgom66+qKiINWvWnLSN1Wpl4MCBp0xp/1fTp08nKCio7BIdHV3Fr0pERNxZ586dWbNmDbm5uRw5coRFixbRvn17s2PJGbhVgXrssceYN28eixYtYtiwYdx555288sorZbenp6cTHh5+0n3Cw8PJzs4mPz+fQ4cOYbfby90mPT39tM87ZcoUsrKyyi579+6t2hcmIiIiTsXUQ3gPPPAATz/99Bm32bJlC61atarQ452Yyh5K23xeXh4zZszg7rvvPq+cZ+Pt7Y23t3e1PoeIiFSMUYlFfKX2qarfD1ML1KRJk866Vk9sbOw5P35cXByPP/44hYWFeHt7ExERQUZGxknbZGRkEBgYiK+vLzabDZvNVu42pxtXJSIizuHEnEHHjx/H11fLrUv5TkzOWdm5sP7O1AIVGhpatjhgdUhKSqJu3bple4fi4+P59ttvT9pm0aJFxMfHA5QN3Fu8eHHZ2XsOh4PFixczfvz4asspIiLnz2azERwcXHbWtJ+f31knopTawzAMjh8/TmZmJsHBwZVaJ7A8LnMWXmpqKkeOHCE1NRW73U5SUhIAzZo1IyAggK+++oqMjAx69uyJj48PixYt4sknn2Ty5Mllj3HHHXcwc+ZM/vWvf3HLLbfw008/MW/ePL755puybSZOnMioUaPo1q0bPXr04MUXXyQvL6/srDwREXFeJ44WaOoZOZ0zna1fGRbDRQ4Wjx49mrlz555y/ZIlS+jbty8LFy5kypQpJCcnYxgGzZo1Y+zYsdx6661Yrf8bK7906VLuvfdeNm/eTMOGDZk6deophxFnzpzJjBkzSE9Pp1OnTrz88svExcVVOGt2djZBQUFkZWURGBh4zq9ZRETOjd1ur/BCtFJ7eHp6nnHPU2U+v12mQLkSFSgRERHXU5nPb7eaxkBERESkJqhAiYiIiFSSCpSIiIhIJbnMWXiu5MSwMq2JJyIi4jpOfG5XZHi4ClQ1yMnJAdCaeCIiIi4oJyeHoKCgM26js/CqgcPhIC0tjTp16lT5JG7Z2dlER0ezd+9eneF3FnqvKk7vVcXpvao4vVcVp/eq4qrzvTIMg5ycHKKiok6aAqk82gNVDaxWKw0bNqzW5wgMDNRfsgrSe1Vxeq8qTu9Vxem9qji9VxVXXe/V2fY8naBB5CIiIiKVpAIlIiIiUkkqUC7G29ubhx9+uGyBZDk9vVcVp/eq4vReVZzeq4rTe1VxzvJeaRC5iIiISCVpD5SIiIhIJalAiYiIiFSSCpSIiIhIJalAiYiIiFSSCpSLeOKJJ+jVqxd+fn4EBweXu43FYjnl8vHHH9dsUCdRkfcrNTWVIUOG4OfnR1hYGPfddx8lJSU1G9QJxcTEnPJ79NRTT5kdy2m8+uqrxMTE4OPjQ1xcHKtXrzY7ktN55JFHTvkdatWqldmxnMKyZcu4/PLLiYqKwmKxsGDBgpNuNwyDadOmERkZia+vLwMHDmTHjh3mhDXZ2d6r0aNHn/J7Nnjw4BrLpwLlIoqKihg+fDhjx44943azZ8/mwIEDZZehQ4fWTEAnc7b3y263M2TIEIqKilixYgVz585lzpw5TJs2rYaTOqfHHnvspN+ju+66y+xITuGTTz5h4sSJPPzww6xdu5aOHTuSkJBAZmam2dGcTtu2bU/6HVq+fLnZkZxCXl4eHTt25NVXXy339meeeYaXX36ZWbNmkZiYiL+/PwkJCRQUFNRwUvOd7b0CGDx48Em/Zx999FHNBTTEpcyePdsICgoq9zbA+OKLL2o0j7M73fv17bffGlar1UhPTy+77vXXXzcCAwONwsLCGkzofBo3bmy88MILZsdwSj169DDGjRtX9rPdbjeioqKM6dOnm5jK+Tz88MNGx44dzY7h9P7+b7bD4TAiIiKMGTNmlF137Ngxw9vb2/joo49MSOg8yvt8GzVqlHHFFVeYkscwDEN7oNzMuHHjCAkJoUePHrzzzjsYmuarXCtXrqR9+/aEh4eXXZeQkEB2djabNm0yMZlzeOqpp6hfvz6dO3dmxowZOrRJ6V7NNWvWMHDgwLLrrFYrAwcOZOXKlSYmc047duwgKiqK2NhYRo4cSWpqqtmRnN6uXbtIT08/6XcsKCiIuLg4/Y6dxtKlSwkLC6Nly5aMHTuWw4cP19hzazFhN/LYY4/Rv39//Pz8+OGHH7jzzjvJzc3l7rvvNjua00lPTz+pPAFlP6enp5sRyWncfffddOnShXr16rFixQqmTJnCgQMHeP75582OZqpDhw5ht9vL/b3ZunWrSamcU1xcHHPmzKFly5YcOHCARx99lAsvvJCNGzdSp04ds+M5rRP/9pT3O1bb/10qz+DBg7nqqqto0qQJO3fu5N///jeXXHIJK1euxGazVfvzq0CZ6IEHHuDpp58+4zZbtmyp8ODLqVOnlv13586dycvLY8aMGW5ToKr6/apNKvPeTZw4sey6Dh064OXlxe2338706dNNXzpBXMMll1xS9t8dOnQgLi6Oxo0bM2/ePMaMGWNiMnEn1157bdl/t2/fng4dOtC0aVOWLl3KgAEDqv35VaBMNGnSJEaPHn3GbWJjY8/58ePi4nj88ccpLCx0iw++qny/IiIiTjl7KiMjo+w2d3M+711cXBwlJSXs3r2bli1bVkM61xASEoLNZiv7PTkhIyPDLX9nqlJwcDAtWrQgOTnZ7ChO7cTvUUZGBpGRkWXXZ2Rk0KlTJ5NSuY7Y2FhCQkJITk5WgXJ3oaGhhIaGVtvjJyUlUbduXbcoT1C171d8fDxPPPEEmZmZhIWFAbBo0SICAwNp06ZNlTyHMzmf9y4pKQmr1Vr2PtVWXl5edO3alcWLF5ed3epwOFi8eDHjx483N5yTy83NZefOndx4441mR3FqTZo0ISIigsWLF5cVpuzsbBITE896BrbAvn37OHz48EnlszqpQLmI1NRUjhw5QmpqKna7naSkJACaNWtGQEAAX331FRkZGfTs2RMfHx8WLVrEk08+yeTJk80NbpKzvV+DBg2iTZs23HjjjTzzzDOkp6fz0EMPMW7cOLcpnOdi5cqVJCYm0q9fP+rUqcPKlSu59957ueGGG6hbt67Z8Uw3ceJERo0aRbdu3ejRowcvvvgieXl53HzzzWZHcyqTJ0/m8ssvp3HjxqSlpfHwww9js9m47rrrzI5mutzc3JP2xO3atYukpCTq1atHo0aNmDBhAv/5z39o3rw5TZo0YerUqURFRdXKKWnO9F7Vq1ePRx99lGHDhhEREcHOnTv517/+RbNmzUhISKiZgKad/yeVMmrUKAM45bJkyRLDMAzju+++Mzp16mQEBAQY/v7+RseOHY1Zs2YZdrvd3OAmOdv7ZRiGsXv3buOSSy4xfH19jZCQEGPSpElGcXGxeaGdwJo1a4y4uDgjKCjI8PHxMVq3bm08+eSTRkFBgdnRnMYrr7xiNGrUyPDy8jJ69OhhrFq1yuxITmfEiBFGZGSk4eXlZTRo0MAYMWKEkZycbHYsp7BkyZJy/20aNWqUYRilUxlMnTrVCA8PN7y9vY0BAwYY27ZtMze0Sc70Xh0/ftwYNGiQERoaanh6ehqNGzc2br311pOmpqluFsPQee4iIiIilaF5oEREREQqSQVKREREpJJUoEREREQqSQVKREREpJJUoEREREQqSQVKREREpJJUoEREREQqSQVKREREpJJUoEREREQqSQVKREREpJJUoEREREQqSQVKROQsDh48SEREBE8++WTZdStWrMDLy4vFixebmExEzKLFhEVEKuDbb79l6NChrFixgpYtW9KpUyeuuOIKnn/+ebOjiYgJVKBERCpo3Lhx/Pjjj3Tr1o0NGzbw22+/4e3tbXYsETGBCpSISAXl5+fTrl079u7dy5o1a2jfvr3ZkUTEJBoDJSJSQTt37iQtLQ2Hw8Hu3bvNjiMiJtIeKBGRCigqKqJHjx506tSJli1b8uKLL7JhwwbCwsLMjiYiJlCBEhGpgPvuu4/PPvuMdevWERAQwEUXXURQUBBff/212dFExAQ6hCcichZLly7lxRdf5L333iMwMBCr1cp7773HL7/8wuuvv252PBExgfZAiYiIiFSS9kCJiIiIVJIKlIiIiEglqUCJiIiIVJIKlIiIiEglqUCJiIiIVJIKlIiIiEglqUCJiIiIVJIKlIiIiEglqUCJiIiIVJIKlIiIiEglqUCJiIiIVNL/A7HAAmZTKmL3AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib import pyplot as plt\n", "\n", @@ -271,7 +749,7 @@ " 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", + " plt.plot(observed_x, state.models[-1].predict(observed_x), label=\"best fit\")\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", @@ -279,7 +757,7 @@ " plt.legend()\n", "\n", "def show_coefficients(state):\n", - " return get_equation(state.model)\n", + " return get_equation(state.models[-1])\n", "\n", "show_best_fit(u)\n", "show_coefficients(u)" @@ -297,7 +775,26 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:417: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat((a, b), ignore_index=True)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "v = s\n", "while len(v.experiment_data) < 1_000: # any condition on the state can be used here.\n", @@ -318,7 +815,26 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:417: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat((a, b), ignore_index=True)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def cycle(state: StandardState) -> StandardState:\n", " s_ = state\n", @@ -361,6 +877,41 @@ "cycle_generator = cycle(v0)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(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=[]), conditions= x\n", + "0 -15.0\n", + "1 -14.7\n", + "2 -14.4\n", + "3 -14.1\n", + "4 -13.8\n", + ".. ...\n", + "96 13.8\n", + "97 14.1\n", + "98 14.4\n", + "99 14.7\n", + "100 15.0\n", + "\n", + "[101 rows x 1 columns], experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -372,9 +923,20 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "v0.models=[], \n", + "v0.experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: []\n" + ] + } + ], "source": [ - "print(f\"{v0.model=}, \\n{v0.experiment_data=}\")" + "print(f\"{v0.models=}, \\n{v0.experiment_data=}\")" ] }, { @@ -388,10 +950,42 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#-- running experiment_runner --#\n", + "\n", + "v1.models=[], \n", + "v1.experiment_data= x y\n", + "0 -15.0 -1504.798665\n", + "1 -14.7 -1447.778278\n", + "2 -14.4 -1079.358506\n", + "3 -14.1 -1075.973379\n", + "4 -13.8 -601.183784\n", + ".. ... ...\n", + "96 13.8 610.172788\n", + "97 14.1 566.573162\n", + "98 14.4 595.721089\n", + "99 14.7 788.030909\n", + "100 15.0 1009.839502\n", + "\n", + "[101 rows x 2 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:417: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat((a, b), ignore_index=True)\n" + ] + } + ], "source": [ "v1 = next(cycle_generator)\n", - "print(f\"{v1.model=}, \\n{v1.experiment_data=}\")" + "print(f\"{v1.models=}, \\n{v1.experiment_data=}\")" ] }, { @@ -405,10 +999,22 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#-- running theorist --#\n", + "\n", + "v2.models=[Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", + " ('linearregression', LinearRegression())])], \n", + "v2.experiment_data.shape=(101, 2)\n" + ] + } + ], "source": [ "v2 = next(cycle_generator)\n", - "print(f\"{v2.model=}, \\n{v2.experiment_data.shape=}\")" + "print(f\"{v2.models=}, \\n{v2.experiment_data.shape=}\")" ] }, { @@ -422,10 +1028,23 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#-- running theorist --#\n", + "\n", + "v3.models=[Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", + " ('linearregression', LinearRegression())]), Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=5)),\n", + " ('linearregression', LinearRegression())])], \n", + "v3.experiment_data.shape=(202, 2)\n" + ] + } + ], "source": [ "v3 = next(cycle_generator)\n", - "print(f\"{v3.model=}, \\n{v3.experiment_data.shape=}\")\n" + "print(f\"{v3.models=}, \\n{v3.experiment_data.shape=}\")\n" ] }, { @@ -442,7 +1061,25 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "StandardState(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=[]), conditions= x\n", + "0 13.318426\n", + "1 3.322472\n", + "2 -10.317879\n", + "3 6.496320\n", + "4 -2.501831, experiment_data=Empty DataFrame\n", + "Columns: [x, y]\n", + "Index: [], models=[])" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from autora.experimentalist.random import random_pool\n", "experimentalist = on_state(random_pool, output=[\"conditions\"])\n", @@ -453,7 +1090,66 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwill\\GitHub\\virtualEnvs\\autoraEnv\\lib\\site-packages\\autora\\state.py:417: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " return pd.concat((a, b), ignore_index=True)\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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HHijX2nTu3DlTy1piYiKTJ09m8ODBvPjii3h5efH444/z9ddfl5veIDk5mR9++MF0lVdWVhZff/01kZGRpharoUOHMmPGDNauXUtMTEyZ+2dkZODi4nLVyRRrUk3kcnZ2Nt2/Ku+R9u3b4+Pjw7x58xg1apRpHNTChQvLFdelLWFXFq7btm0jNja2TDdj6b+Ta93/Sv+96vBKu3fvLjMeDG5sDNT8+fPJy8srs/+PP/7go48+4u233za9X8H4fl+5ciWrVq1iyJAhgLG1asWKFdxxxx0Vjo8S1kEKKCGu4UbGQMXFxdGnTx+GDRtGs2bN0Gq17Ny5k2+//ZaQkBCeeeaZMseXtoBUdf6p7t27M3bsWGbPns3evXvp27cvtra2xMXFsWLFCj744APTf+iV5eDgQN++ffn999/LTQXw6KOP8sYbb/Doo4/Svn17Nm3aZGrFuVJ0dDT33nsvU6dOJT09nbCwMBYtWsTJkyf58ssvyxz78MMP89dff5k+JLVaLV988QX9+/enRYsWjBo1ivr163P27Fk2btyIm5sbP//8M4qi8Mgjj+Do6Minn34KGKd0+P7773nmmWfo06dPmQ/Epk2bMnr0aHbs2IGfnx9fffUVaWlpLFiwwHTM5MmT+b//+z8GDhxomt4hNzeXAwcOsHLlSk6ePIm3t3eVXs/qUBO5SscFPf3008TExKDT6bjvvvuuez9bW1v+97//MXbsWHr16sWwYcNITExkwYIF5cZADRw4kFWrVnH33XczYMAAEhMTmTdvHhEREWUupnB0dCQiIoJly5bRtGlTPD09admyJS1btjRN+1BcXEz9+vVZt24diYmJFWbbtWsXFy9e5K677qrwuVZF3759y20rLfC6d+9e5o+gIUOG0LFjR0aNGsXhw4fx9vbmk08+Qa/Xm81s76KGqHX5nxDmpqJpDG7EuXPnlDFjxijNmjVTnJ2dFTs7O6VJkybKhAkTlHPnzpU73tvbW+nYseN1z3u1mcjnz5+vREVFKY6Ojoqrq6vSqlUr5fnnn1eSk5NNxwQHBysDBgwod9+KZo9etWqVotFoTJfHl8rLy1NGjx6tuLu7K66ursrQoUOV9PT0Ci9Bz8/PVyZNmqT4+/sr9vb2yi233FLh5dyll5j/1549e5R77rlH8fLyUuzt7ZXg4GBl6NChplmmP/jgg3JTEyiKoiQlJSlubm7K7bffXu65r127VmndurVib2+vNGvWrMzM0aWys7OVqVOnKmFhYYqdnZ3i7e2tdO7cWXn77beVoqIiRVH+nS7gypnWS11tGgNnZ+cKn3uLFi3Kba/od3Wzuf77OyopKVGeeuopxcfHR9FoNFWe0uCTTz5RGjVqpNjb2yvt27dXNm3aVO69ZDAYlNdff10JDg5W7O3tlbZt2yq//PJLhe/jLVu2KFFRUYqdnV2ZrGfOnFHuvvtuxcPDQ3F3d1fuvfdeJTk5ucL33AsvvKA0bNiwzPQX1elq0xgoiqJcvHhRGT16tOLl5aU4OTkp3bt3r/A4YV00ilLLIwuFECaHDx+mRYsW/PLLLwwYMEDtOIBxYHhERARDhw5l1qxZase5aSEhIbRs2ZJffvlF7SiihhQWFhISEsKUKVPKtfAKUVNkKRchVLRx40Y6depkNsUTGMeezJw5k7lz51brvFVC1JQFCxZga2vL448/rnYUUYdIC5QQwqpJC1TlXO9yf51OV6WJQ4WwdjKIXAghxHUv9w8ODq6xhbaFsETSAiWEEIJdu3Zd83J/R0dHunTpUouJhDBvUkAJIYQQQlSRDCIXQgghhKgiGQNVAwwGA8nJybi6ut7wkglCCCGEqF2KopCdnU1gYOB1F5KWAqoGJCcnl1muQgghhBCW4/Tp02UWVK+IFFA1wNXVFTD+Aq5cTFUIIYQQ5isrK4ugoCDT5/i1SAFVA0q77dzc3KSAEkIIISxMZYbfyCByIYQQQogqkgJKCCGEEKKKpIASQgghhKgiKaCEEEIIIapICighhBBCiCqSAkoIIYQQooqkgBJCCCGEqCIpoIQQQgghqkgKKCGEEEKIKpICSgghhBCiiqSAEkIIIYSoIimghBBCCCGqyKIKqE2bNnHHHXcQGBiIRqPhxx9/LLNfURSmTZtGQEAAjo6O9OnTh7i4uDLHXLx4keHDh+Pm5oaHhwejR48mJyenzDH79++nW7duODg4EBQUxFtvvVXTT00IIYQQFsSiCqjc3FzatGnD3LlzK9z/1ltv8eGHHzJv3jy2bduGs7MzMTExFBQUmI4ZPnw4hw4dYv369fzyyy9s2rSJMWPGmPZnZWXRt29fgoOD2bVrF3PmzGHGjBnMnz+/xp+fEGpLTk5m8+bNJCcnqx1FCCHMmkZRFEXtEDdCo9Hwww8/MGjQIMDY+hQYGMhzzz3HpEmTAMjMzMTPz4+FCxdy3333ceTIESIiItixYwft27cHYM2aNdx+++2cOXOGwMBAPv30U1566SVSU1Oxs7MDYMqUKfz4448cPXq0UtmysrJwd3cnMzMTNze36n/yQtSQzZs3Ex8fT1hYGF27dlU7jhBC1KqqfH5bVAvUtSQmJpKamkqfPn1M29zd3YmOjiY2NhaA2NhYPDw8TMUTQJ8+fdBqtWzbts10zK233moqngBiYmI4duwYly5dqvCxCwsLycrKKnMTwhKFhoYSFhZGaGio2lGEEMKsWU0BlZqaCoCfn1+Z7X5+fqZ9qamp+Pr6ltlvY2ODp6dnmWMqOseVj/Ffs2fPxt3d3XQLCgq6+SckhAoCAwPp2rUrgYGBakcRQgizZqN2AGswdepUJk6caPo5KytLiihRqxRFIaewhPTsQtKzCjmXU0iJ3oBOq8FGq8VGp8HZzoZADwcCPRxxsNWpHVkIISya1RRQ/v7+AKSlpREQEGDanpaWRmRkpOmY9PT0MvcrKSnh4sWLpvv7+/uTlpZW5pjSn0uP+S97e3vs7e2r5XkIcT2KonA2I599SZc4cvIsJ86kkJKWhq44Bz06snEkW3EiB0fysAc05c7h62pPkKcTrRu40z7Yk6jgevi7O9T+kxFCiKo6Hw+XTkJwJ7BzVi2G1RRQjRo1wt/fnw0bNpgKpqysLLZt28a4ceMA6NSpExkZGezatYuoqCgA/vjjDwwGA9HR0aZjXnrpJYqLi7G1tQVg/fr1hIeHU69evdp/YkIAysVEzhyO5fSRHehTDxFUfJL+mnQGaC5fA6IFKqjhi7El2aY+p3UNOUEDduf7s6MohORsb9KzC9l16hIL/jkJQH0PR25t6k3/lgF0auyFrc5qeviFENZk32L4+x1ocz/cPU+1GBZ1FV5OTg7x8fEAtG3blnfffZeePXvi6elJw4YNefPNN3njjTdYtGgRjRo14pVXXmH//v0cPnwYBwfjX9f9+/cnLS2NefPmUVxczKhRo2jfvj2LFy8GjFfuhYeH07dvX1544QUOHjzII488wnvvvVdmuoNrkavwxE3Tl8DpbeQe/IXCQ7/imX/q6odqbVHs3NA6uqFV9FCQBYXZoOivep9Ct0ac9ezIVk1rVl0KZXeaHsMV/xN4ONnSN8KPO9oE0qWxN1pt+VYsIYRQxRd94MwOuOsTaDu8Wk9dlc9viyqg/vzzT3r27Flu+4gRI1i4cCGKojB9+nTmz59PRkYGXbt25ZNPPqFp06amYy9evMiTTz7Jzz//jFarZfDgwXz44Ye4uLiYjtm/fz/jx49nx44deHt789RTT/HCCy9UOqcUUOKGnTsG2+ej378SXWGGaXOxouMowWS5heMe0obGLaNxDGgOTp5gU0HTk6JAcR7kpMP543DuqPHcaYcg9UDZ4kprQ0njPhzzG8jSzAhWH77Ihdwi0+5Qb2ce7hTM4KgGuDrY1uCTF0KI6yjIgjdDjP+HTTgIHtU73thqCyhLIQWUqBKDAeLWwfbP4MQfps2XFBc2GiJJqNeV5t3upkfrMJztq6HXvSATEv+GhI2Q8CdciP93n4MHhpaDOeh7F8uTPflpTzLZhSUAONvpGBLVgDHdG1Pfw/HmcwghRFUdWwNLhoFnKDy9p9pPLwWUyqSAEpWiKHDk/+D3V+HiCQAMiobfDe34Wt8X5/CejOnRhKhgz5rNkX4U9i2B/csh+4oZyBt1p6DDU6y4FMairUnEpxuXPLLTaXkguiFP9GyMr6sMPBdC1KI1L8LWuRA1Eu74oNpPLwWUyqSAEtd1dhesfQmSjJO8ZuHMkpIefKO/jVYtWjMpJpzGPi7XOUk1M+ghcRPs+RYO/fBvN59/K5TOz/CP/a3M/SuR2IQLADjYahnROYRx3Rvj4WR3jRMLIUQ1+bQrpB2AIV9By8HVfnopoFQmBZS4qqxkWD8dDiwHoBB75pUM4LOSgQT5+TD9jgg6h3mrHBLISILYT2D3IuNYKgC/lnDbTLbQhjnrjrEnKQOAek62TOnfjHujgmSwuRCi5uRegDmXV0mYFAcuvtc+/gZIAaUyKaBEhQ6shF8nGscgAT8ptzK78F5yHfyY1Dec4dENsTG3qQPyLsKOLyD2Y1NuGvdCuW0mf1zy5c01RzmeZuzaa9fQg1mDWtIi0F3FwEIIq3XoR1gxAnwj4InYGnkIKaBUJgVU3ZKcnExCQgKhoaEVL4GSnwGrJ5tanU7aN+PJrAc5qITSIcST9+6LNP9B2XkXYdPbsH0+GIoBDbR7iOJer7Jwdwbv/36c3CI9Wg2M7NyIyTHhONrJbOdCiGr0y0TY+SVEPw7936yRh6iTiwkLoZaEhATi4+NJSEgovzPxb/i0CxxYjqLR8ZXNUPpkvsgRTWMmx4SzZExH8y+ewDhdQr/X4cnt0OJuQIHdX2P7aTSPee5lw8TuDGgdgEGBr/5JZMBHf7P3dIbaqYUQ1iRxk/Fro1vVzXGZtEDVAGmBqlsqbIFSFIidC+teBhTyXRoyOnsMWwpDaejpxIf3tyUyyEPN2Dfn1Bb4+RnjHFMATfvBgHf4M9WOF77fT1pWITqthvE9w3iqV5jMai6EuDlZyfBuc9Bo4flEcPSomYeRLjx1SQFVx+mL4dfnjAOwgbjAu7g78S5yFAeiG3ky78Eo6jlbwVVrJYXw97vGJRUMxWDnAv3eICN8KK/832F+3mecEqFVfXc+ur8tId7qrVklhLBw+5bCD2MhsC2M+bPGHka68IRQS95F+PYe2L0IRaPlt/rPcFvCUHIUB+6NasA3o6Oto3gC4wzoPafC45shKBqKcuD/nsTj18f4aFAIH97fFndHWw6czeSOjzaz9lCq2omFEJbKzLrvQAooIarPhRPGNZoSN6HYufBZ4GuMOxGNRqNhSv9mvDWkNXY2Zf/JJScns3nzZpKTk69yUgvg2wxG/QZ9ZoDWBg7/BJ924U73BNZOuJX2wfXILixh7De7eH31EUr0BrUTCyEsiaJIASWE1Uo/Cl/1g4snUNwb8Lr/B7xxIhgbrYaP72/H490bo9GUnyPpmgPQLYlWB12fhdHrwLMxZJ2FhQPx3/U2Sx69hUe7NgJg/qYEHvh8G+lZBSoHFkJYjEuJkHna+Adaw05qpzGRAkqIm5V+BBYNhNx0DH4tmezxHp8fd8RWp+HTB6MY0DrgqncNDQ0lLCyM0NDQWgxcg+pHwdhN0PZBQIFNc7BdOoyXe/nzyfB2uNjbsP3kRe78+B8Ons1UO60QwhKUtj41uAXszGcspRRQQtyMtMOwcCDknsPg35pn7Wey8lgxdjotnz0UxW0Rfte8e2BgIF27dq14/ihLZe8Cd82FwV+CjSOc2ADze3C79zn+78kuNPZxJjWrgHvnxbJOxkUJIa7H1H3XXd0c/yEFlBA3KvWgseUp7zxKQBsmOczkp+MF2Nlo+XxEe3o1u3bxdCWrGAv1X62GwKO/Q70QyDgFX/YlNGU1q57oQtcwb/KL9Yz9dhfzN51ALgYWQlTITMc/gRRQQtyYc8dg0R2QdwElIJLZ3m+w6mgedjotX45oT/emPlU6ndWMhfov/5bw2EYI6wMl+bDqMdw3z2LByCiGRzdEUeD11UeZuuqADC4XQpSXfhhyzxlbsxu0VztNGVJACVFVOenw3RDIvwiBbfky9H3m77iERgPvDmtDtyZVK57ACsdCXcnJEx5YDt0mGX/+5wNsVz3C/wY0ZvodEWg1sHTHaR7/djcFxXp1swohzEv8BuPXkK7GqVPMiBRQQlRFUS4sHgoZSVCvET+3/JD/bTB2u70yIIKBrW9sLJNVjoW6klYHvV+Bu+eD1hYO/4Tm6zsZ1caFeQ9GYWej5fcjaTz85XYy84vVTiuEMBcn/jB+bdxL3RwVkAJKiMrSl8DKRyB5Dzh6sr3L5zz7yxkAxtwayiOXL9UX19BmGDz8Izh4wJkd8EVv+vpm8c0jHXC9fIXesM9iZZoDIQQU5xuXjQIpoISwWIoCa16A42vAxoEzty/gkZ8vUmJQuLNNIFP6NVM7oeUI6fqfweW3EW2bwMf3NMbdXsPR1GwGz9tC0oU8tZMKIdR0agvoC8E1EHzC1U5TjhRQQlRG7FzY8QWgIf+OTxm5XkNOYQkdQjyZc29rtNryk2SKa/BuAo9uMM7rUpABX9+F18nfGFH/PL5OWk5fzGfY/FhOns9VO6kQQi1Xdt9VMBGx2qSAEuJ6Ev+G9a8AoPSdxcQDwcSn5+DnZs/Hw9tib6NTOaCFcvaGh38y/udYnEuLfa/Sw+kEz7YyEFzPnpTMAobNj+XEuRy1kwoh1HBio/FrmPl134EUUEJcW3aqcdyTYoA29zOvsD+/HUzFVqfhk+FR+Lo6qJ3Qstk5w/1LIWIQGkMxbeM/pOGZn3i+vR1N/VxIyyrk3k//Ydlvf1nXHFlCiGvLToX0Q4AGGvVQOUzFpIAS4mpKB43npoNvC/5p9iJz1h0DYMadLYgKrqdyQCthYw9DvoKokWgw0OXCMroWb2bJYx1p5u/KxbwSXt2cxV9749ROKoSoLaWtT4GR4OylapSrkQJKiKv5Yyac+gfsXEnrP58nVxzFoMDQ9g14oENDtdNZF60OBr5vXJAYcN/6Jl775rHksY409XEkT6/lrR0F0p0nhJU6ciKJX37f9G9LsxlPX1BKCighKnL0V/jnAwD0d37M0+uyuZRXTKv67sy8qyUaMxzQaPE0Gug9HbpPMf68fhr1dn/MinHdiAhw41J+CcM/38bpi3J1nhDWZtHmOJ78PZvpPx0Cg0EKKCEs0qVT8MM44/cdn2D++VZsS7yIk52Oj+5vi4OtDBqvMRoN9JwKPV8y/rzhVdx3vM83ozsQ5utCalYB93++lZTM/OueyirXFxTCSsVl2wDQItgH0g5A3nmwc4EGHVROdnVSQAlxJYMBfnwCCjOhQQcORDzHu+svj3u6owUh3s4qB6wjuj8PvacZv9/4P7x2vs/iR6MJ8XLizKV8hn++jfRs42SbVyuUrHZ9QSGsTE5hCftTjFOW3NWhyb+tTyHdwMZOxWTXJgWUEFfaNg9ObQZbZwrumMczKw5SrFfo39Kfe9s3UDtd3dLtOejzqvH7P1/Hd/+nfPdYR+p7OJJwPpeHvthOZl7xVQslq15fUAgrEnviAsV6hYaeToR4OVlE9x2AjdoBhDAb547Dhssf2DH/Y9aWPBLO5eLnZs/rd7eScU9q6DrB+PX36fD7DOrbOrH4sQe5d14sx9KyGb1oB2/dHgJQrlAKDAy03rUFhbAifx5LB6B7Ux80xXmQtNW4w8wLKGmBEgKMUxb8MBZKCqBxbzY43c5325IAeHdoJPWczbcZ2ep1nQDdXzB+/9vzBJ/6nkWPdMDVwYadpy7x2sYUOnbqLMWSEBZIURT+On4OgB7hPpeXbykC94bg1VjldNcmBZQQAJvfg+Td4OBOdr/3efHHgwA81q0RXcK8VQ4n6DEVOj9l/P7/nqb5ubV8OeIW7G20bDiazpRVB1AURd2MQogqSzify5lL+djptHRq7AXxG4w7Gvc0y+VbriQFlBAp++CvN4zf3/42b2zOJC2rkEbezjzX1/wWsKyTNBq4bRbc8iigwA9j6VCwhY8faIdOq2HlrjO8seao2imFEFX05zFj61OHRp442dlA3DrjjrA+KqaqHCmgRN2mL4Efx4OhBJrfwXaX3qauu9n3tJIpC8yJRgP950DkcFD0sPIRbnM8zux7WgHw2V8JLPwnUeWQQoiqKO2+697UBy6cgIsnQGtrbIEyc1JAibpt+2fGOUccPCiIeZspqw4AcH+HhnQMNc/lA+o0rRbu+BCaDQR9ISy5n6H1LzI5xthS+Oovh1l3KFXlkEKIysgv0rM14QJwefzT8bXGHcGdwd5VxWSVIwWUqLsyz8LG143f3/YqH2/LJOF8Lr6u9kzp30zdbOLqdDYw+EsI7gpF2fDtYJ5oreH+DkEoCjy9dA/7TmeonVIIcR1bEy9QVGIg0N2BMF+Xf7vvmvRVN1glSQEl6q61U6EoBxp04EjAIOb9dQKAmXe1xN3RVuVw4ppsHeD+xeDfCnLPofnmbmb18qZ7Ux8Kig2MXrRDlnwRwsz9dXn8U7i7ntSkeOPaowBNY1RMVXlSQIm6Ke53OPwTaHQYbn+HqT8cosRgnDCzX0t/tdOJynBwhwdXQb1GkHEKmyX3MndwGBEBbpzPKWLkgu1k5BWpnVIIcRWl45989ee5tOtH4/QF9ULAK0zVXJUlBZSoe4rzYfVzxu+jH+f75HrsPZ2Bi70Nr97ZQt1sompcfOGhH8DFD9IO4vLTKL56qA0B7g6cOJfLE9/tplhvUDulEOI/Tl3IJfF8Ljot9G4ZRHCRccksmsSY/fQFpaSAEnXP5vfg0klwDSCr0yTeXGP8h/tM7yb4ujmom01UnWcjeGA52DpDwp/4//U8X41oj7Odji0nLjD9/w7JHFFCmJnS1qf2wZ707dEV57OXu+8sZPwTSAEl6poLJ4wFFEC/2Xy0OZXzOYWE+jgzonOIqtHETQiMhKFfg0YH+5bQ/OjHfHBfWzQaWLwtiUVbTqqdUAhxhdLxT93DfSDtIGQng40jhHRVOVnlSQEl6pbfpxv72Rv3It67Dwv+OQnAtIER2NnIPweL1qQP3PG+8ftNb9Enfw1TL19NOfOXw6b1toQQ6ioo1vPPifMA9Gjq++/0BaHdjReIWAj5xBB1x6lYOPIzaLQofV/j1V8OU2JQ6NPclx7hvmqnE9Wh3cNw6/PG7395lscCEhjavgEGBZ5avIf49Gx18wkh2HLiPAXFxukLmge4Qtx64w4L6r4DKaBEXaEosO5l4/dtH+L3C178HXceO52WlwdEqJtNVK+eL0Kb+0HRo1kxitc66+jQyJPswhJGL9opV+YJobINR4ytwb2a+6LJvwRntht3SAElhBk69AOc3Qm2zhR2e4FZvxwGYHS3RoR4O6scTlQrjcY4W/nliTZtl93PZ3cH0aCeI6cu5PHUkj2UyJV5QqhCURT+OGosoHo384MTf4BiAN8I8AhSOV3VSAElrF9JIfw+w/h9l2f45mAhSRfz8HW1Z3xPy5hvRFSRjR0M+wY8G0NmEvV+GsEXD7TA0VbH33HneWvtMbUTClEnHU7JIiWzAEdbHZ0ae/07/snCWp9ACihRF2z/HDJOgYs/We3GMndjPAATb2uKi72NyuFEjXHyhOErwMEDzu6k2dYpvD3EuPDw/E0J/LjnrLr5hKiDSrvvujbxxkEHxP9u3CEFlBDqS05OZvPmzSQnJ0PeRdg0x7ij18vMj03jUl4xjX2cGRLVQN2gouZ5NYZh3xpXdz+0igEXFvJEj8YAvPD9fg6cyVQ5oBB1y4YjaQD0ae4Lp7dD/kXjqgJB0SonqzopoITVSUhIID4+noSEBPj7HSjIAN8WpIfew5ebEwGYHNMMG528/euERt3KTG8wKfAgPcN9KCwxMPabnZzPKVQ1nhB1RXp2Afsu/9HSM9wXjv5i3NEkxrhIuIWRTxBhdUJDQwkLCyPMz9nYfQdw20w+2JhAfrGetg09iGnhp25IUbvaPgidnwZA+3/j+agHhHo7k5xZwFOLZVC5ELVh4+XB420auOPrag9HfzXuaDZAxVQ3TgooYXUCAwPp2rUr/nGLQV8IDTuR4B7N0h2nAXihXzM0FrLWkrhxZbpyAfrMMI6zKCnAZdUIvrinAU52OmITLjBnnQwqF6Km/X55/FPv5n5w7ihcSgSdHYT1VjnZjZECSlinzLOwa6Hx+x5TeWd9HHqDQs9wHzqGeqkaTdSOMl25AFodDP4CvMMhO5nQDWN5527jTOWf/ZXAbwdSVEwrhHUrKNazOc44+3ivZr7/tj6F9gB7V/WC3QQpoIR12vyuccmW4C7st23NrwdS0Gjg+X7N1E4maklpV25oaOi/Gx3c4f4lpivz+ifO5rGuIQBMWrFPZioXoobEJlwgv1hPgLsDLQLdLL77DqSAEtYo4zTs/tr4fc8Xeff3OADujqxP8wA3FYOJ2lTalRsYGFh2h1djGLrIuPDw/qVM8fidjqGe5BbpGfvNLnIKS9QJLIQVK736rlczXzTZKZC8G9BA0/7qBrsJUkAJ6/P3O8bWp5Bu7NW15M9j59BpNTzdu4nayYS5CO0B/WYDoNswg886Z+Hv5sCJc7m88P1+FEVRN58QVkRRFP4wjX/yhWOrjTsa3AKulntBjxRQwrpkJMGeb43f93yRDzcYW5/uigyUJVtEWR3GQOSDoBhw/2UMX9zpjY1Ww6/7U1i05aTa6YSwGodTskjOLMDBVkvnxt5XdN/drm6wmyQFlLAum94GQzE06s4BXQv+OJqOVgNP9ZLWJ/EfGg0MeAfqR0FBBi03jWN6vxAAXlt9hN1Jl9TNJ4SVWHvI2H3XvakPDvocSPzbuKPZQBVT3TwpoIT1uHQK9n4HwD6vgbz56wEA7oqsTyNpfRIVsXUwzlTu7Avph3gw7S0GtPSnWK8w/rvdXMwtUjuhEBZv7cFUAGJa+EPceuMfuV5NwNuy/7C1qgJqxowZaDSaMrdmzf696qqgoIDx48fj5eWFi4sLgwcPJi0trcw5kpKSGDBgAE5OTvj6+jJ58mRKSmRQqUXY8hEYSrjkGcnqM05sTsxEq4Ene8mCweIa3AKNCw9rbdEc+oF3628k1NuZlMwCnlm6B71BxkMJcaMSz+dyLC0bG62G3s38/h3/ZMFX35WyqgIKoEWLFqSkpJhumzdvNu179tln+fnnn1mxYgV//fUXycnJ3HPPPab9er2eAQMGUFRUxJYtW1i0aBELFy5k2rRpajwVURW5501jn/SdnmFnvg8Ad7QJpLGPi5rJhCVo2BFufwsA+7/+x8Lu2TjYavk77jwf/xGvcjghLNfaQ8bWp06NvXC3U4wtUCAFlDmysbHB39/fdPP29gYgMzOTL7/8knfffZdevXoRFRXFggUL2LJlC1u3bgVg3bp1HD58mG+//ZbIyEj69+/PrFmzmDt3LkVF0pRv1rZ/DiX5ENiW9MDe7EorQaOBp6T1SVRW+0eg3cOAQsM/nuK9vp4AvL/hOFviz6ubTQgLVVpA9W3hDyf/hsIsY5d5/fYqJ7t5VldAxcXFERgYSGhoKMOHDycpKQmAXbt2UVxcTJ8+fUzHNmvWjIYNGxIbGwtAbGwsrVq1ws/v38sqY2JiyMrK4tChQ1d9zMLCQrKyssrcRC0qyoXt843fd3mGjzYaWwwGtg4kzNcyZ7gVKuk/BwLbQv4l+h+ezAPtfFAUeHrpXtKzC9ROJ4RFSc0sYE9SBhoNxET4/bt4cHh/0Fp++WH5z+AK0dHRLFy4kDVr1vDpp5+SmJhIt27dyM7OJjU1FTs7Ozw8PMrcx8/Pj9RUY4Wcmppapngq3V+672pmz56Nu7u76RYUFFS9T0xc255vIf8i1GtEgncv1lz+i0dan0SV2TrA0G/AyQtS9jHT5iua+jpzPqeQZ5ftlfFQQlTB+sPG/4vbBnng62ILR3427mh+p4qpqo9VFVD9+/fn3nvvpXXr1sTExLB69WoyMjJYvnx5jT7u1KlTyczMNN1Onz5do48nrqAvgS0fG7/v/BSf/5OEokDvZr409ZPWJ3EDPIJgyALQaLHZv4RvIw/jaKvjn/gLMh5KiCoo/WO2X0t/OPUP5J4zLqMU2l3dYNXEqgqo//Lw8KBp06bEx8fj7+9PUVERGRkZZY5JS0vD398fAH9//3JX5ZX+XHpMRezt7XFzcytzE7Xk8I+QmQRO3pxrPJjvd58BYGz3xurmEpYttDv0ng6A7+ZpfNJdD8AHG46z5YSMhxLiejLyitiacBG4PH3BoR+NO5oNBJ2tesGqkVUXUDk5OZw4cYKAgACioqKwtbVlw4YNpv3Hjh0jKSmJTp06AdCpUycOHDhAenq66Zj169fj5uZGRERErecX16Eo8M/7xu+jH2fRjjSKSgy0bejBLSH1VI0mrECXZyDiLjAU03P/ZEa2ccGgwDNL93Iuu1DtdEKYtd+PpKM3KDTzdyW4nsO/3XctBqmaqzpZVQE1adIk/vrrL06ePMmWLVu4++670el03H///bi7uzN69GgmTpzIxo0b2bVrF6NGjaJTp0507NgRgL59+xIREcFDDz3Evn37WLt2LS+//DLjx4/H3t5e5WcnyjnxB6QeAFtnctuM5JutpwAYe2soGo1G5XDC4mk0cNdc44R/WWd5pfAdmvk6ci67kInL92KQ8VBCXNWaKyfPTIqF3HRwcIdG1tF9B1ZWQJ05c4b777+f8PBwhg4dipeXF1u3bsXHxzgn0HvvvcfAgQMZPHgwt956K/7+/qxatcp0f51Oxy+//IJOp6NTp048+OCDPPzww8ycOVOtpySuZcuHxq9RI1h2MIfM/GIaeTtzW8TVu1uFqBJ7V+NM5bbO6E7+xXdhG03zQ322KUHtdEKYpdzCEv6OOwdcHv90ZfedjZ16waqZRpFlx6tdVlYW7u7uZGZmyniompJ+FD6JBo2W4if30OPzBM5m5PPa3S0ZHh2sdjphbQ6shO9HA7Ap6mMe/scTnVbD8rGdiAqW7mIhrrT6QApPfLebhp5O/PVcNzTvRUBOGjywApr2VTveNVXl89uqWqBEHVI671P47aw+bcvZjHy8XewY3K6BurmEdWo1BDqMBaDboZcY0UxBb1B4eskeMvOKVQ4nhHn59UAKYGx90pzeZiyeHNwhtIe6waqZFFDC8hRkwr6lACgdxjDvL2NXyohOITjY6tRMJqxZ3/9Bg1vQFGQyLe8NwurpOJuRzwvf70ca8oUwyisq4Y8jxguxBrQKMF4pDVbXfQdSQAlLtOc7KM4F3whiDREcScnC0VbHQ52k607UIBs7uHcROHmjSz/AsoY/YqvTsOZQKt9evoDhapKTk9m8eTPJycm1FFYIdWw4kk5+sZ6Gnk60ru8Kh//PuCNikKq5aoIUUMKyGAyw43Pj9x0eY+EW4wfX4Kj6eDhZ1183wgy514fBXwAavI4t4Ys2xwGY9esRjqZefQmnhIQE4uPjSUiQgefCuv2639h9N6B1wOXuu1Sr7L4DKaCEpYn/HS4mgL07Z4Lu5PcjxolOR3QKUTeXqDsa94QeUwG49fgbPNgoh6ISA08t3kN+kb7Cu4SGhhIWFkZoaGhtJhWiVuUUlrDxmLH7bmDrgH+vvgsfYHXddyAFlLA02z8zfm37IN/sOodBgW5NvGkiy7aI2nTrZGjcC01JPjMK3iTYxUBceg6zfj1c4eGBgYF07dqVwMDAWg4qRO35/XAahSUGQr2difB3gSOXu++saPLMK0kBJSzH+XhjCxQa8tuOYsn2JEBan4QKtFq453Nwq4/NpROsDFyMRqOweFsSv12+AkmIuuaX/cYxfgNbB6BJioXsFLC3zu47kAJKWJLSsU9N+vLDSXuyCkpo6OlEz2a+6uYSdZOzN9y7ELQ2+CStZn74HgBe+H4/Zy7lqZtNiFqWmV/MX8eNk2cObBMI+5cZd0TcCTbWuZKHFFDCMhRmw97FgHHqgkVbTgLwcKdgdFpZtkWoJKgD3GZcqaBP0gcM8U8nq6CECUv3UqI3qBxOiNqz/nAaxXqFJr4uNPWyg8M/GXe0HqpusBokBZSwDAdWQGEWeIURq2nNsbRsHG113Ns+SO1koq7r+AQ0G4jGUMwb+rcJtC9g56lLfPRHvNrJhKg1/3bfBULcOuN8fW71IbiryslqjhRQwjLsWmT8GjWKhVuMY58GR9XH3dFWxVBC8O+iw/VCsMk+w/cB3wAKH/0Rx/bEi2qnE6LGXcotYnPceQAGtgn4t/uu5WDjeEErZb3PTFiPlH2Qshe0tpwNvkumLhDmx9HDOMmmzo6A1I182HAzBgUmLJWlXoT1W3solRKDQvMANxq7lMDxtcYdVtx9B1JACUuw+2vj1+YD+fZALgYFuobJ1AXCzARGQr/ZANxxbj4DPU6RnFnA1B9kqRdh3X65PHnmwNYBxqkL9EXg0xz8WqqcrGZJASXMW1Ee7F8BQHGbh1mx8zQAD3ZsqGYqISrWfjS0HIxG0fOu9gN8tdmsPpDKsh2n1U4mRI1IyyrgnxPG7rs72wTC/uXGHa3vNXZvWzEpoIR5O/wTFGaCRzDrC8I5n1OEj6s9vZv7qZ1MiPI0GrjjA/Bqgl1eKiv9F6HBwIyfDxGfnqN2OiGq3U97z6IocEtIPYJ0l+DkZuOOVveqG6wWSAElzNvuy4PH2z3Ekh1nABjavgG2OnnrCjNl72qcH8rGgYYXt/CG7wYKig08s3QPhSUVL/UihKVatfssAIPa1oeDKwEFGnYGD+vvJZBPIWG+zh2HpFjQ6DgTfA9/x51Ho4H7brH+f5jCwvm3hNvnADA0exG9HeM4lJzF22uPqRxMiOpzJCWLo6nZ2Om0DGwVaBpuQWvrb30CKaCEOSttfWoaw3dHjFcydWviQ5Cnk4qhhKiktg9B6/vQKAY+sZ+LF5l8/ncimy7P1iyEpfthj7H1qVczX9yz4yDtAGhtIWKQusFqiRRQwjyVFMG+JQAUt3nQNHj8gQ7S+iQshEYDA98F73DsC9JZ5vMVWgw8t2IfF3IK1U4nxE3RGxR+2mssoO5uVx8OXB483uQ2cPJUMVntkQJKmKdjv0LeBXANYH1Ra87nFOHrak/v5rLunbAgds4wdBHYOhGWvYNp7qs5l13I8ytlagNh2WJPXCAtqxAPJ1t6NvGEfUuNO1oPUzdYLZICSpin0rmf2j7I4p3GJQKGtg+SwePC8vg2hwHvADCiaCndbI+w4Wg6X8eeUjmYEDdu1R7jRT0DWwdgd/JPyE4BR08I769usFokn0bC/GQlw4mNAJwJvofN8cbB48NukXXvhIWKfAAiH0SjGPjM8VO8yeS11Uc4lpqtdjIhqiyvqIQ1B1MBuLttfdjzjXFH62FgY69istolBZQwPwdWUHop7LfHjW/RW2XwuLB0t88Bn+Y4FZ3na4/PKCkp4ekleygolqkNhGVZdyiNvCI9wV5OtPPSw7HfjDvaPqhusFomBZQwL4pi6kvXtxrK97uNzcT3y+BxYcaSk5PZvHkzycnJVz/IzunyeChnIgr28oLTzxxLy+aN347WXlAhqsGqy1ffDYqsj+bACjAUQ2Bb4/QddYgUUMK8pB6A9MOgs+cf+66cyy7Ey9lOBo8Ls5aQkEB8fDwJCQnXPtAnHAa+B8AYw3I6aw+ycMtJ/jiaVgsphbh5aVkFbI4zTsVxd2Tgv913daz1CaSAEuam9EqO8P4sPZAFwF2R9WXwuDBroaGhhIWFERoaev2D2wyDtg+hQWG+06f4cIlJK/aTnlVQ80GFuEkrd53BoED74HqEFB0z/sFr4wAth6gdrdbJp5IwH/qSy+OfICd8CL8fTgdgSFQDNVMJcV2BgYF07dqVwMDAyt3h9jng2wKXkkt84TKPzNx8nluxD4NBpjYQ5stgUFh+eU6+YbcEwZ5vjTua3wGOHuoFU4kUUMJ8JGyE3HRw8uKHnGYU6Q1EBLgREeimdjIhqpeto3E8lJ0LbUoO8JzdKv6OO89X/ySqnUyIq9qWeJFTF/JwsbdhQHN3OLDSuKMOdt+BFFDCnJR237UcwvLdxjEh97aX1idhpbybwB0fADBO+yPdtPt5c81RDp7NVDmYEBVbtiMJgDsjA3E6sQYKs8C9IYTcqnIydUgBJcxDQRYc/QWAkw0GcuBsJrY6DXdF1lc5mBA1qNUQaP8IGhQ+cfgUT/0Fnl66h7yiErWTCVFGZl4xv12e+2lY+6ArBo8PB23dLCXq5rMW5ufI/0FJAXg35bskL8C4QKWns53KwYSoYTGzwb81roZM5jnO5dS5LGb9cljtVEKU8dO+sxSWGGjm70prp4uQuAnQGCeJraOkgBLm4Yq5n37YmwLAkCiZeVzUAbYOcO9CsHOlrXKESTbLWbL9NL8dSFE7mRAmS7cbB4/fd0sQml1fGTc27gUedXeOPimghPoykuDk3wBscerD+ZxCvF3s6BHuo3IwIWqJV2O462MAxtn8TC/tbqasOkByRr7KwYSAg2czOZyShZ2NlkEtPf+9+q7DY+oGU5kUUEJ9B783fg3uyndHDQDc4gvn0lJVDCVELWsxCKIfB+AD+3m45CczYdle9DK1gVDZ0suDx/u18McjcTXkXwL3IGjSV+Vk6pICSqjv4CoA8sIHseHyjMzBhtTrz+oshLW5bRbUj8JVyeFT+4/Yk5jO3I3xaqcSdVh+kZ6f9hiXKBp2SxDs+MK4o/0o0OpUTKY+KaCEui6cgNT9oNGx9GI4xXqFRh62dG3ZqHKzOgthTWzsjOOhHDxorYnnRZvveP/34+w8eVHtZKKOWn0ghezCEoI8HenkcBrO7gStLbR9WO1oqpMCSqjrcusTod1ZfjgPgOgAXdVmdRbCmng0hLs/A2CUzVr6abbyzNK9ZOYXqxxM1EVfbz0FwH23NES760vjxhaDwEXGqEoBJdR1yFhAXQodyNGLejTAfR0bq5tJCLWF94MuEwCYY/c5dpkJvPjDARRFxkOJ2rPvdAb7Tmdgp9NyfyvXf2cev+VRdYOZCSmghHrSjxoXotTasiqvHQAdGnkSGR6ibi4hzEGvV6BhZ5zJ51Pb99mw/6RpHTIhasPXscbWpwGtA/CM+x5K8sGvJQRFq5zMPEgBJdRzufWJxr1YcSgbgEFtZeZxIQDQ2cC9C8DZl2ba0/zPdgHT/+8g8enZaicTdcDF3CJ+3m8cPP5wxysGj98yGjQaFZOZDymghDoUxTT+KSWoP0dTs7HVaejf0l/lYEKYEVd/GPIlikbLEN0m7jT8wZOL91BQrFc7mbByy3eepqjEQKv67kSW7IeLJ8DOFVoNVTua2ZACSqgj7SBciAOdPUuzWgHQI9wXDydZukWIMhrdiqbXywDMsl2ILu0Ar68+UuGhycnJbN68meTk5NpMKKyM3qDwzeXuu4c6BaPZPt+4I/J+sHdRMZl5kQJKqONy65MS1oeVB7MAGCQLBwtRsS7PQtN+2FPMJ7bv82PsYdYeKj/RbEJCAvHx8TKHmrgpG4+mczYjHw8nW+5qkAfHfjPu6DBG3WBmRgooUfsUxTT+KcEvhrMZ+bjY29C7ua/KwYQwU1otDPoUPBoSrE3nHdt5vLBib7mlXkJDQwkLC5M51MRNKZ26YFj7IOx3zgMUaNofvJuoG8zMSAElal/yHrh0Emwc+fZiMwBiWvjjYFu3Z7UV4pqcPGHo1yg6O27T7eKB4lU8s3QPJXqD6ZDAwECZQ03clMTzuWw6fg6NBh5u4wJ7Fxt3dH5S3WBmSAooUfsutz7pm8Tww+FMAO6KlP/whbiuwLZobp8DwHM2y7FN+pv3f49TOZSwJqVjn3qG+1I/fgmUFEBAJAR3UTeYGZICStQuRYFDPwFw2LM3GXnFeLvY07mxl8rBhLAQ7UZA5IPoNAof2X7Eqj+3sTnuvNqphBXIzC9m2eWFg0d28IfSweOdn5KpCyogBZSoXSn7IDPJ2H13oSkAA1r5Y6OTt6IQlaLRwIC3wb81Xpps5tp+wOSlO0jPLlA7mbBwS7cnkVukJ9zPlW75f0DuOXBrABF3qR3NLMmnlqhdR38BQN+4N6uPGLvvBraR7jshqsTWEYZ9g+LgQVttPE8UfsGzy/aiN8hSL+LGFJUYWPDPSQAe7RqCZusnxh0dHwedrXrBzJgUUKJ2HTEWUEc9upNdWIKfmz1RDeupHEoIC1QvBM09n6Og4SGb3/FP+IFP/4xXO5WwUL/sTyY1qwBfV3sGuR6Gc0eNE2e2e1jtaGZLCihRe87Hw7kjoLVh8aXmAPRvGYBWK33rQtyQpn3RdH8BgNdtv2Tt72vZmnBB5VDC0iiKwvxNxrnDRnYJwXbb5danqBHg4K5iMvMmBZSoPUd/BsAQ3JX/O5YHGBepFEJUTZkZx7u/YJxkU1PMp7bv8crivziXXah2RGFBNsef52hqNk52Oh5qeAkS/wKNDqIfVzuaWZMCStSey913x+r1lO47IW5CmRnHtVq4+zMM9UJpoDnP9MK3eW7pLhkPJSqttPVp2C1BuG57z7ix1RDwCFIxlfmTAkrUjqxkOLsT0JjWvru9lXTfCXEjys047uiB9r7vMNg40lV3iM6nPuGjP2R+KHF9R1Ky+DvuPFoNjA3Ph2O/AhroNkntaGZPCihRO47+CoChfntWxZUAMKCVdN8JcSMqnHHcLwLtIOPYlcdtfiZu4zf8Ey/zQ4lr+/xvY+vT7a0C8N/7kXFji7vBp6mKqSyDFFCidhwxjn+K9+pFdmEJ/m4OtJPuOyGqV8t7jJMeAnNsPuOjJT+QliXzQ4mKnbmUx//tTQbgqVYlcNg4yTG3SutTZUgBJWpe3kU4uRmA5bltAOjfyl+674SoCb1noG/UAydNIXOK32TqtxspvmK9PCFKffrnCUoMCl3CvAg/Ph9QoNlA8GuhdjSLIAXUVcydO5eQkBAcHByIjo5m+/btakeyXMfXgKLH4BvB0ngbAAbK1XdC1AydDbp7F1DsFkyQ9hyjU2Yx57eDaqcSZiY5I5/lO08D8PwttnDwe+OOWyermMqySAFVgWXLljFx4kSmT5/O7t27adOmDTExMaSnp6sdzTJdvvou0bsnOYUlBLg70DZIuu+EqDFOntgOX0qJzokuukP4b32NNQdT1E4lzMhnf52gWK/QMdSTNolfgmKApv0gMFLtaBZDCqgKvPvuuzz22GOMGjWKiIgI5s2bh5OTE1999ZXa0SxPUS6c2ADAqvx2gEyeKUSt8IvAZvBnADxis4ZNKz4k4VyOyqGEOUjLKmDJDmPr0+QO9rBvqXHHrc+rmMrySAH1H0VFRezatYs+ffqYtmm1Wvr06UNsbGyF9yksLCQrK6vMTVwWvwFKClA8gvk60QUwjn8SQtSCiDsxdDN2yUxnPh8tWkx+kV7lUEJt8/46QVGJgVtC6tHu1Feg6KFxL2gQpXY0iyIF1H+cP38evV6Pn59fme1+fn6kpqZWeJ/Zs2fj7u5uugUFyeRjJsfXAHDWryfZBXq8Xezl6jshapG254sUNu6HvaaEF7P/x5vL1qMoMslmXZWeXcDibUkAvNBeg2bvd8YdPaaqmMoySQFVDaZOnUpmZqbpdvr0abUjmQeDAY6vBWBtcVsAbovwQyfdd0LUHq0W+6FfkOcRjo8mk3vjnuebTUfKHFJmaRhh1T7flEBhiYF2DT2Iiv/YOPYpfAAEdVA7msWRAuo/vL290el0pKWlldmelpaGv3/FXU/29va4ubmVuQkgeTfknUexd+PLJGOLXkwLv+vcSQhRXUyF0YVsnEauJN+2Hi20p/Dd8AxbT5wzHVdmaRhhtc7nFPLtVmPr08ttctAc/QU0Wug9TeVklkkKqP+ws7MjKiqKDRs2mLYZDAY2bNhAp06dVExmgS53310K6EZyjgFXexs6N/ZWOZQQdUeZwsijIQ4PLqFEY0s/7XYOfDuFlMx8oIKlYYRVmrsxnvxiPW3qu9H2+PvGjZEPgG8zVXNZKimgKjBx4kQ+//xzFi1axJEjRxg3bhy5ubmMGjVK7WiW5ZixgPpH2x6Ans18sbORt5wQteW/hZEmuBOGAe8D8JiyksVfvEthib7ipWGEVTl9MY9vt54C4PVWqWhObQEbBxn7dBNs1A5gjoYNG8a5c+eYNm0aqampREZGsmbNmnIDy8U1ZJ6BtAMoaPgitTEAMS3k6jshalNgYGC5osiu/YNkJR/CbfcnPJn1Hl8tDubxh4aj0dz42MTk5GQSEhIIDQ2VIsxMvbPuGMV6hVvD6tHi8GvGjR3GgHsDdYNZMGkOuIonn3ySU6dOUVhYyLZt24iOjlY7kmW5PHi8wC+KfRdtsLPR0iPcR+VQQggAt4H/43yD27DXFDP0xBR++GPzTZ1PxlCZt4NnM/nx8pp3r4cdhfRD4OAOXZ9VOZllkwJK1IzLBdQeR2Ph2S3MG2d7afAUwixodXg/vIh01+Z4abJps2kM2w/F3/DpZAyVeXtr7TEA7mnlTYM97xk3dn0WnDxVTGX5pIAS1a8oDxL/AuDbi80B6b4TwuzYOePz2A9csvGlsSYZ7YqHSErPuKFTyRgq87Ul/jybjp/DVqdhmtcfkJkEroHQYaza0SyeFFCi+iX+BSUFlLg2YHV6PbQa6N3cV+1UQoj/0LgF4DRqFXkaR9pzmGOfjyKnoFjtWKKaKIrCG2uOAjCurQMeuz407rhtJtg5qZjMOkgBJarf5ekLjrt3ATTcEuKJl4u9upmEEBWyr9+KgkELKEHLbcV/8Oe8CRgMMlO5Nfj1QAr7z2TibKdjfPFCKM6Dhp2h1RC1o1kFKaBE9VIU0/inH3JbAdJ9J4S582zTn7NdjFdmDcz4lnXfzFY5kbhZ+UV6Zq82tj692voC9sd+Mk6aeftbcBNXXIp/SQElqlfqfshOQbF14ptU4+WxfWX2cSHMXvBtT3AkfBwAtyW8xeZfvlY5kbgZn/51grMZ+QS52XBP6gfGjbc8Cv6t1A1mRaSAEtXrcutTildHChQ7mge40aCe9LULYQma3zebA753oNMoRO14jgNb16sdSdyApAt5zPvrBACfNduD9vwxcPKCni+qnMy6SAElqtfl8U9/GNoB0EcGjwthOTQaWjz2FQedo3HUFNFgzShOH9+rdipRRbN+PUxRiYHbG2lofmyucWPv6eBYT91gVkYKKFF9cs/D2d0AfJkWBkDv5tJ9J4Ql0draEfbESo7bNKUe2dguGUJG6km1Y4lK2ngsnfWH07DRanjDZTmawiwIbAttH1I7mtWRAkpUnxMbAYVcj2YkFrrh42pP6/ruaqcSQlSRg7Mbno/9QJImEH/lHNmfD6QgM13tWOI6Ckv0zPz5MACvtTiDW9wPlweOvwNa+bivbvKKiupzYgMAe+yjAOgV7otWK1d7CGGJvP0aUDJ8Fal4EaQ/TeonA9HnZ6kdS1zDl5sTSTyfSyOXEu5Nece4seMT0CBK3WBWSgooUT0MBog3FlArMpoCMnmmEJYuNKw5qXct5aLiSkjhMZLm3oVSnK92LFGBUxdy+WiDcTmeBYE/oc1JBc/G0OtllZNZLymgRPVIOwi56RhsnPgtMwQ7Gy1dm3irnUoIcZMi23bgYM8FZCuONMrZzanP7gO9zFZuTgwGhSnfHyC/WM9jgScJSfoe0MBdH4Oto9rxrJYUUKJ6xP8OQJJ7FEXY0rmxF052sniwENbg1h638Ve7DylUbAk5/ydnvnoY9CVqxxKXLd6eRGzCBbxsi3ih+BPjxg5jILizusGsnBRQonqc+AOA9UXGSdrk6jshrMuAO+/l+7DXKVJ0NDi7mtRvRoNBr3asOu9sRj6zVx8B4Nvg1dhknwGPhtB7msrJrJ8UUOLmFWZDUiwA355vAkDvZjL+SQhrotFouG/4Y3xdfwYlihb/kz9ybsnjxvGPQhWKojB11QFyi/SM9k+g+Znlxh13fgT2LuqGqwOkgBI3L3ETGErIcQrilOJHRIAbgR7S7y6EtdFqNYwY/STzfV5Er2jwiVvOxRVPGdfAFNUmOTmZzZs3k5ycfM3jVu46w6bj56hvk8mLhe8ZN97yKIT2qPmQQgooUQ0uX32301ZmHxfC2tnqtIwaM5G5HpMxKBo8j3xL1qoJ0hJVjRISEoiPjychIeGqx6Rk5jPrl8NoMbDM+yt0+RfAryX0fa0Wk9ZtUkCJm6MopgHkKy6FAzL+SQhr52inY+S453nf+RkMiga3AwvJ+X68jImqJqGhoYSFhREaGlrh/hK9gWeW7CWroISZnmtpkLEDbJ1gyAKwdajltHWXFFDi5lxMgIxTGLS2bCxqho+rPa1k9nEhrJ6bgy0PP/Eibzo8g17R4HJoMXnLx1R4dV5lu6SEUWBgIF27diUwMLDC/a//tIftJy/S1fY4w/O/M268/W3waVqLKUWVC6gRI0awadOmmsgiLNHl1qdTzq3Jw4Ge4T4y+7gQdYS3iz2jnpjKLIfnKFZ0OB1dSf7SkeXmiapMl5SonNgTF1iwPRUPsvnE/iM0igFaD4PIB9SOVudUuYDKzMykT58+NGnShNdff52zZ8/WRC5hKS6Pf1pX2BKAHuEy/kmIusTf3YEx4yYxzX4yRYoOx7ifKVg8HIoLTMdcr0tKVM6FnEImLNuDFj2LXD/FreSCcbbxAe+ARv5wrW1VLqB+/PFHzp49y7hx41i2bBkhISH079+flStXUlwss9PWKSWFcPJvAH7MboZOq6FLmMw+LkRdE+jhyPhxzzDVbioFii0OJ9ZStOhuyM8w7r9Ol5S4PkVRmLRiH2lZhbzpupw2xXvBxhHuXQj2rmrHq5NuaAyUj48PEydOZN++fWzbto2wsDAeeughAgMDefbZZ4mLi6vunMIcJcVCcR55dt4cURoSFVwPd0dbtVMJIVTQoJ4TE8aN5zm7V8hSHLE7s4WiL/tDVora0azCvL8S2HjsHA/abmRI8c/GjXfPg4DW6garw25qEHlKSgrr169n/fr16HQ6br/9dg4cOEBERATvvfdedWUU5urERgD22LYFNPQI91E3jxBCVUGeTkwZ9xjPOL5GuuKB3fnDFH9+G5yPVzuaRVt/OI231h6lo/YwM20WGDf2fBlaDFI1V11X5QKquLiY77//noEDBxIcHMyKFSuYMGECycnJLFq0iN9//53ly5czc+bMmsgrzEnCnwD8lG2cfbxHUxn/JERdF+TpxOxxw5no8iaJBj9ss09T8sVtcGaX2tEs0tHULCYs3UNDUvnK8UO0Sgm0HAK3TlI7Wp1X5dVeAwICMBgM3H///Wzfvp3IyMhyx/Ts2RMPD49qiCfMVt5FSNkHwJ9FLfBzs6d5gPTDCyGMA8s/GHc3T37uzNRLr9C6IBHDgv5o7/kMWtytdjyLcT6nkNELd2JblMFil/dwKsmC+u3hro9l0LgZqHIL1HvvvUdycjJz586tsHgC8PDwIDEx8WazCXN2cjOgcM4hhHTq0b2pDxr5By2EuMzLxZ55j/fjDd85/KGPRKsvhBUj4a+3ZOmXSigs0fP4N7vIyrjAMqe3qF9yGtzqw32LwVaWyjIHVS6gHnroIRwcZKbTOu9y993f+hZA+ekLZOI8IYS7oy2fP9aTbxq9wRcl/Y0bN74G3z8KxfnqhjNjBoPClO8PcPhUCl87zCHccAKcvOGhH8BVVnowFzITubgxiX8B8FtuODqthq5Nyk5fIBPnCSEAnO1t+HxENPFtX2RK8aMUKzo4uBJl4UDIlHkE/0tRFKb/3yFW70nkS7u3acsxcHCHh38En3C144krSAElqi7zDFyIx4CWrYYIooLr4eZQdvoCmThPCFHKRqdl9j2tqN/rcR4unkKG4ozm7E6Uz241Xc0rjMXTG78dZdnWeObZvkcn7WGwc4UHfwD/VmWOlVZ+9UkBJaouwdj6lGjXlGycKpy+QCbOE0JcSaPR8FTvJgwZ/AD3FL/GIUMwmrzzKN/cbRwXZTCoHbHGVLbY+XBDPF9vOsw82/foqdtnXCB4+ApoEFXuWGnlV1+Vr8ITorT7bl1Bc0CmLxBCVN7gqAbUr3cnj33rw1NFX3C/zUbjuKjT2+Ge+eDkqXbEalda7ABX/aPy800JLPp9J0vs3iJSm2CcZfy+xRDcqcLjS1v3pZVfPVJAiapRFNMA8k0lETJ9gRCiyjqGerHi6d6M+dqdXWlN+Z/NVzjEr0f5pBOau+ZCkz5qR6xW1yp2FEXhoz/iWfn733xv9waNtGng6AkPLIOgDlc9Z2BgoLTwq0y68ETVnDsGOWkUa+zYbWgi0xcIIW5IfQ9HVj7emaKW9zOoaBYnDAFoclLhu8Hwy0QoylU7YrW52pAGvUHh5R8Psv73NXxvN51G2jQUj4Ywet01iydhHqSAElVzuftuvzaCQuzoLt13Qogb5Gin44P7Ihlyewx3lcxmQUmMccfOL2FeVzi9Q92ANaigWM+4b3eRs2MxS+1m4aPJAv9WaEavB+8mascTlSAFlKiay9136wuaodFAlzAvdfMIISyaRqPh0W6hfDeuJwvdxzG8aCrJiidcTED5qi+sfh4KMtWOWa0u5RbxyOd/0ev4LD6w+wRnTSE07gUjV4Orv9rxRCVJASUqT19yeQZy2GxoSesGHng42akcSghhDdoEefDr093wa9OPfoVvsErfFY1igO2fwUftYf8Kq5jBfNepSzzx/hKmpT7NfTZ/oqCB7lNg+EpwcFM7nqgCGUQuKi95DxRmkat15bASwvj/TJ4phBA3w8XehneHRfJDU2+m/eTByqJbmWW7kMa5ybDqUdi9CPq/BX4RaketMkVR+GLTCU6t/4QvdN/irC2kxNEHm3u/hNDuascTN0BaoETlJf4JQKyhBQa0dA2TAkoIUf3ubtuADRO74x7Rh/6Fs3mreCiF2MHJv+HTzrBqLFy0nPVWM/KKmPX5EtpvGMb/bL7EWVNIScit2DzxjxRPFkxaoETlXZ5A88/i5jjb6WjbsJ7KgYQQ1srXzYFPH4xizcH6TPvJhf/L6cJUm+8YoNsO+5fCwZXQ7mG4dTK4mefl/Iqi8OuOI+T+NpOXDGvQaRWKdM7Y9nkJm+jHQatTO6K4CVJAicopLjBOdAf8Y2hJp6Ze2NlIA6YQomqSk5NJSEggNDS0UvMY9WvpT6fGXry77hjPbPPl05ITTLJdQQ/2wc6vYM930GoIRD8OAa1r4RlUTsLps2xdPoe+Wd/jrckCDWQ0vguPu94EtwC144lqIAWUqJyzO0FfyCWtJ4mKPyOblF++RQghrqcys3L/l7ujLa/e1ZIRnUN4c40vIw+F0kFzhOftltNefwz2fme8BXeB6LEQPgB0Nffxdq0i8GLaaY7++BatklfwgCYfNHDJMRiXez7Ao0nPGsskap8UUKJySq++K2kGaOgmA8iFEDfgZpYgCfVx4bOH2rPj5EVeX+3BkKRmtNPEMdJmLQN029Gd+gdO/QNOXtD8Tmh5j7GouomusoqKpXJFYEkR5w+s5ezf3xJ+YQOdNcWggbO2wTj0eA6vjg+AzvZaDyMskBRQonIuF1Cx+ubU93CkkbezyoGEEJaoOpYguSXEk1XjOhObcIH5m3x5+lhTXiu+yIM2v/OQ7UY88i7ArgXGm7MPNBsIIV2hYSdwr1+lx6qoxSw0NBRdSR5hNmdJ/24szgmr8dZn4Q2ggeM2TcnvOIE2ve4HrQx1sFYaRbGCiTXMTFZWFu7u7mRmZuLmZgXzehQXwJvBUFJAr8K36dA+mjcGm89YAyFE3XYsNZsv/k7gp73JGPRFdNQeYYB2KwNtd+KqZJc92L0hNOxonArBIxjqBYNHiHER4/8uS6UopJw6TvKx3QR7OeKtyUA5s5OCxG04ZMSh4d+Pz3OKO39rbkET1odBD4xFI4WTRarK57cUUDXA6gqok//Awtu5qPGgXf5c5j4QxYDWMghSCGFeMvOK+e1gCj/uPcu2xIvolBK6aA/RXbuPDjZxNCcRHYYK76vo7NCjQ2tjh9bGDrQ2KPkZaEryr/p4pw0+bNO25lzIHYS16oRj/vlKD44X5qkqn9/ShSeu79Q/APxT0gyNRiPLtwghalRVr9Qr5e5ky30dGnJfh4YkZ+Sz+kAKW04E8m5iFDkFJThRQFttHFGaOEK0qQRp0gnWnseXi2j0RcYPRH0+FBrPV9oelaU4ck7x4KzizX4llKPapng07UT3di25o6k39jYyHUFdJAWUuL6TfwOw1RBB6/rusnyLEKJG3ciVev8V6OHIo91CebRbKCV6A4eSs9iacIEjKaH8daEbX1/IJSOvGAB7ivAmExuNHhv+vWXhRJbOE1/PekQGeRDZ0IOeQR487ueKjU666Oo6KaDEtZUUmuZ/2mpoTn+ZvkAIUcNu5kq9itjotLQJ8qBNkEeZ7Rl5RSRnFFBYoqeoxEBhiYFivQE3R1u8XezxdrHDxd4GzX/HRgmBFFDies7uhpICLuDOCSWQrjJ9gRCihlXHlXqV4eFkV6ZFvbTrsH5oKIHenjX++MKySRukuDbT9AXNcLS1oZ0s3yKEsALJycls3ryZ5ORk07bSrsOEhAQVkwlLIS1Q4tpOGQuorYYIbmnsKcu3CCGswtXmd7ryqxDXIgWUuLqSIkjaBhjHP93bWK6+E0JYh4qKpdrqOhTWQQoocXXJu6EknwuKG/FKfbqEyfgnIYR1kGJJ3CzpjxFXd7K0+64Z7o52NA+wgklBhRBCiGpgVQVUSEgIGo2mzO2NN94oc8z+/fvp1q0bDg4OBAUF8dZbb5U7z4oVK2jWrBkODg60atWK1atX19ZTMC+XC6hthuZ0CvVCp5VLeYUQQgiwsgIKYObMmaSkpJhuTz31lGlfVlYWffv2JTg4mF27djFnzhxmzJjB/PnzTcds2bKF+++/n9GjR7Nnzx4GDRrEoEGDOHjwoBpPRz36YjhdOv4pQmYfF0IIIa5gdWOgXF1d8ff3r3Dfd999R1FREV999RV2dna0aNGCvXv38u677zJmzBgAPvjgA/r168fkyZMBmDVrFuvXr+fjjz9m3rx5tfY8VJe8B4rzuKi4EqfUp1NjGf8khBBClLK6Fqg33ngDLy8v2rZty5w5cygpKTHti42N5dZbb8XO7t+J02JiYjh27BiXLl0yHdOnT58y54yJiSE2NrZ2noC5uLz+3TZDM3zdHGns46xyICGEEMJ8WFUL1NNPP027du3w9PRky5YtTJ06lZSUFN59910AUlNTadSoUZn7+Pn5mfbVq1eP1NRU07Yrj0lNTb3q4xYWFlJYWGj6OSsrq7qeknpOGQvGHYZmdGnsLUsZCCGEEFcw+xaoKVOmlBsY/t/b0aNHAZg4cSI9evSgdevWPP7447zzzjt89NFHZYqbmjB79mzc3d1Nt6CgoBp9vBpnMMDprQDsMITTWaYvEEIIIcow+xao5557jpEjR17zmKvNGhsdHU1JSQknT54kPDwcf39/0tLSyhxT+nPpuKmrHXO1cVUAU6dOZeLEiaafs7KyLLuIOncUCjLJVew5rATTWSbQFEIIIcow+wLKx8cHHx+fG7rv3r170Wq1+Pr6AtCpUydeeukliouLsbW1BWD9+vWEh4dTr1490zEbNmxgwoQJpvOsX7+eTp06XfVx7O3tsbe3v6GMZinJ2H23xxBGQ283Aj0cVQ4khBBCmBez78KrrNjYWN5//3327dtHQkIC3333Hc8++ywPPvigqTh64IEHsLOzY/To0Rw6dIhly5bxwQcflGk9euaZZ1izZg3vvPMOR48eZcaMGezcuZMnn3xSradW+5JKu++aSeuTEEIIUQGzb4GqLHt7e5YuXcqMGTMoLCykUaNGPPvss2WKI3d3d9atW8f48eOJiorC29ubadOmmaYwAOjcuTOLFy/m5Zdf5sUXX6RJkyb8+OOPtGzZUo2npY7LLVA7lHCGy/QFQghBcnIyCQkJhIaGyhIwAgCNoiiK2iGsTVZWFu7u7mRmZuLmZmHLn2SchvdbUqJoaV34BZtfuQNPZ7vr308IIazY5s2biY+PJywsjK5du6odR9SQqnx+W00LlKgml2cfP6SEEBzgK8WTEELw78VKV7toSdQ9UkCJsi533+00hNMx1FPlMEIIYR4CAwOl606UYTWDyEU1uTyAfLshnI6hMoBcCCGEqIgUUOJf+RkoaYcA2K2EE91IWqCEEEKIikgBJf51ejsaFBIM/nj7B+HhJOOfhBBCiIpIASX+JeOfhBBCiEqRAkr8q3QCTSWc6EYy/kkIIYS4GimghFFJIcrZXYCxBUrGPwkhhBBXJwWUMEreg0ZfyDnFDXvfJtST+Z+EEEKIq5ICShhdOf5Jlm8RQgghrkkKKGF0efzTTpn/SQghhLguKaAEGAwYTAVUUzrI+CchhBDimqSAEnAhHm1BBvmKHSW+rWT9OyGEEOI6pIAScGY7APuVUG5p7KdyGCGEEML8SQEl4LSxgNpjaCITaAohhBCVIAWUoCTJWEDtNoTRQSbQFEIIIa5LCqi6riAT3fmjAGR6tZXxT0IIIUQlSAFV153dhQaFJIMP4WGN1U4jhBBCWAQpoOq60zsA2K00kekLhBBCiEqSAqqOKz61DYDdhiZ0CJECSgghhKgMKaDqMoMBzhhboFJdW+Hr5qByICGEEMIySAFVl12Iw7Y4i3zFjnqh7dROI4QQQlgMKaDqsEsH1gHGCTSjQn1VTiOEEEJYDimg6rD8uE2AcQJNGf8khBBCVJ4UUHWYa3Y8AHG24QR7OamcRgghhLAcUkDVVQWZuOQkAmAb0gmNRqNyICGEEMJySAFVV53ZaZpAs3mTMLXTCCGEEBZFCqg6Sl+6/p3ShFtk/JMQQghRJVJA1VF5CbEAHNY1I9zfVeU0QgghhGWRAqouMhiwS90FQJF/FDqtjH8SQgghqkIKqLro/HHsS3LIV+zwbxqldhohhBDC4kgBVQcpp43r3+1XQmkf6qdyGiGEEMLySAFVB2WdMBZQB2hCqwbuKqcRQgghLI8UUHWQ/rRx/FOmZxvsbXQqpxFCCCEsjxRQdU1RHu7ZcQC4NI5WOYwQQghhmaSAqmtS96NDT5riQfOmzdROI4QQQlgkKaDqmOwTWwHYZ2hM2+B6KqcRQgghLJMUUHVM9uUB5MnOLXB1sFU5jRBCCGGZpICqYxzT9wKg1G+nbhAhhBDCgkkBVZfknqdeUTIAvuEdVQ4jhBBCWC4poOqQwlM7AThhCKB1WLDKaYQQQgjLJQVUHZJ+9B8Ajtk0pUE9R5XTCCGEEJZLCqg6xHDGOIFmjlcbNBpZQFgIIYS4UVJA1RWKgmfGAQAcQjqoHEYIIYSwbFJA1RGGiydxNWRRqNgQ0kIKKCGEEOJmSAFVR6QdMY5/OkoIzYN8VE4jhBBCWDYpoOqIzHjjDOQpLhHY6uTXLoQQQtwM+SStI+zT9gJgCJAJNIUQQoibJQVUXaAvJiD/GACe4Z1UDiOEEEJYPimg6oBLp/bhQBFZihPNI9qqHUcIIYSweFJA1QHJh4wDyONtm+DubK9yGiGEEMLySQFVBxQn7QAg07O1ykmEEEII6yAFVB3gcdE4gaZ9sMz/JIQQQlQHKaCsXEFuFkElpwBo2LKLymmEEEII6yAFlJU7eWgrOo3COepRv2Go2nGEEEIIqyAFlJW7FL8dgLNOzWQBYSGEEKKaSAFl5bQpewEo8pUB5EIIIUR1sZgC6rXXXqNz5844OTnh4eFR4TFJSUkMGDAAJycnfH19mTx5MiUlJWWO+fPPP2nXrh329vaEhYWxcOHCcueZO3cuISEhODg4EB0dzfbt22vgGdUOn5yjALiF3qJyEiGEEMJ6WEwBVVRUxL333su4ceMq3K/X6xkwYABFRUVs2bKFRYsWsXDhQqZNm2Y6JjExkQEDBtCzZ0/27t3LhAkTePTRR1m7dq3pmGXLljFx4kSmT5/O7t27adOmDTExMaSnp9f4c6xuqefOE2w4A0DDlp1VTiOEEEJYD42iKIraIapi4cKFTJgwgYyMjDLbf/vtNwYOHEhycjJ+fn4AzJs3jxdeeIFz585hZ2fHCy+8wK+//srBgwdN97vvvvvIyMhgzZo1AERHR3PLLbfw8ccfA2AwGAgKCuKpp55iypQplcqYlZWFu7s7mZmZuLm5VcOzvjGxG3+h01/DOa/xwnt6gmo5hBBCCEtQlc9vi2mBup7Y2FhatWplKp4AYmJiyMrK4tChQ6Zj+vTpU+Z+MTExxMbGAsZWrl27dpU5RqvV0qdPH9MxliQrwTiB5jnX5ionEUIIIayLjdoBqktqamqZ4gkw/ZyamnrNY7KyssjPz+fSpUvo9foKjzl69OhVH7uwsJDCwkLTz1lZWTf1XKqLffo+AJSASHWDCCGEEFZG1RaoKVOmoNFornm7VuFiLmbPno27u7vpFhQUpHYkCkv0NCg4DoBnk2iV0wghhBDWRdUWqOeee46RI0de85jQ0MpN/ujv71/uarm0tDTTvtKvpduuPMbNzQ1HR0d0Oh06na7CY0rPUZGpU6cyceJE089ZWVmqF1FHTyXTimQA/MKlgBJCCCGqk6oFlI+PDz4+PtVyrk6dOvHaa6+Rnp6Or68vAOvXr8fNzY2IiAjTMatXry5zv/Xr19OpUycA7OzsiIqKYsOGDQwaNAgwDiLfsGEDTz755FUf297eHnt7+2p5HtXlzOHttNEoXNT54Onqd/07CCGEEKLSLGYQeVJSEnv37iUpKQm9Xs/evXvZu3cvOTk5APTt25eIiAgeeugh9u3bx9q1a3n55ZcZP368qbh5/PHHSUhI4Pnnn+fo0aN88sknLF++nGeffdb0OBMnTuTzzz9n0aJFHDlyhHHjxpGbm8uoUaNUed43Kj9pFwAZHhEqJxFCCCGsj8UMIp82bRqLFi0y/dy2bVsANm7cSI8ePdDpdPzyyy+MGzeOTp064ezszIgRI5g5c6bpPo0aNeLXX3/l2Wef5YMPPqBBgwZ88cUXxMTEmI4ZNmwY586dY9q0aaSmphIZGcmaNWvKDSw3d64XDgBg06CdykmEEEII62Nx80BZArXngUrPKiDr7baEaZPJH7oMx4h+tZ5BCCGEsDR1ch4o8a99J84QqkkBwLFhlMpphBBCCOsjBZQVSju2Ha1GIcPWF1yqZ5C+EEIIIf4lBZQVKjxlnIH8klszlZMIIYQQ1kkKKCtTrDfgk2OcfDTHqaHKaYQQQgjrJAWUlTmSkkULTSIA/i26qZxGCCGEsE5SQFmZgwlnaKw1DiD3adXnOkcLIYQQ4kZIAWVlQktOAJBt7w/O3iqnEUIIIayTFFBWpqPDaQBcG7VXOYkQQghhvaSAsjYp+4xfAyJVjSGEEEJYMymgrE3qfuPXgNbq5hBCCCGsmBRQ1qQoD84fN37vLwWUEEIIUVOkgLIm6YdBMYCzD7j6q51GCCGEsFpSQFmT0vFP/q1Bo1E3ixBCCGHFpICyJjL+SQghhKgVUkBZk5TLBZSMfxJCCCFqlBRQ1kJfYhwDBRDQRt0sQgghhJWTAspanD8OJQVg5wr1GqmdRgghhLBqUkBZi9LxT/4tQSu/ViGEEKImySettZDxT0IIIUStsVE7gKgmcgWeEEKY6PV6iouL1Y4hzIytrS06na5aziUFlDVQlCu68KSAEkLUXYqikJqaSkZGhtpRhJny8PDA398fzU3OlygFlDXIOAUFmaC1BZ9maqcRQgjVlBZPvr6+ODk53fSHpLAeiqKQl5dHeno6AAEBATd1PimgrEHqAeNX3+ZgY6duFiGEUIlerzcVT15eXmrHEWbI0dERgPT0dHx9fW+qO08GkVuDFBn/JIQQpWOenJycVE4izFnp++Nmx8hJAWUNTOOfZAJNIYSQbjtxLdX1/pACyhpIC5QQQli0Hj16MGHCBLVjAPDjjz8SFhaGTqdjwoQJLFy4EA8PD7VjmR0poCxd7nnITgY04NdS7TRCCCHM0J9//olGo6nU1Yljx45lyJAhnD59mlmzZjFs2DCOHz9u2j9jxgwiIyNrLqyFkEHkli5ln/GrV2Owd1E3ixBCCIuWk5NDeno6MTExBAYGmraXDr4W/5IWKEsn8z8JIYRVKCkp4cknn8Td3R1vb29eeeUVFEUx7S8sLGTSpEnUr18fZ2dnoqOj+fPPP037T506xR133EG9evVwdnamRYsWrF69mpMnT9KzZ08A6tWrh0ajYeTIkeUe/88//8TV1RWAXr16odFo+PPPP8t04S1cuJBXX32Vffv2odFo0Gg0LFy4sKZeErMmLVCWTsY/CSHEVSmKQn6xXpXHdrTVVWnA8qJFixg9ejTbt29n586djBkzhoYNG/LYY48B8OSTT3L48GGWLl1KYGAgP/zwA/369ePAgQM0adKE8ePHU1RUxKZNm3B2dubw4cO4uLgQFBTE999/z+DBgzl27Bhubm4Vtih17tyZY8eOER4ezvfff0/nzp3x9PTk5MmTpmOGDRvGwYMHWbNmDb///jsA7u7uN/dCWSgpoCydtEAJIcRV5RfriZi2VpXHPjwzBie7yn/MBgUF8d5776HRaAgPD+fAgQO89957PPbYYyQlJbFgwQKSkpJMXWuTJk1izZo1LFiwgNdff52kpCQGDx5Mq1atAAgNDTWd29PTEwBfX9+rDgi3s7PD19fXdLy/v3+5YxwdHXFxccHGxqbC/XWJFFCWrCgXLpwwfi8FlBBCWLSOHTuWabHq1KkT77zzDnq9ngMHDqDX62natGmZ+xQWFpomDX366acZN24c69ato0+fPgwePJjWreWzoaZIAWXJ0g4DCrj4gYuP2mmEEMLsONrqODwzRrXHri45OTnodDp27dpVbvZsFxfjBUSPPvooMTEx/Prrr6xbt47Zs2fzzjvv8NRTT1VbDvEvKaAsWdpB41eZvkAIISqk0Wiq1I2mpm3btpX5eevWrTRp0gSdTkfbtm3R6/Wkp6fTrVu3q54jKCiIxx9/nMcff5ypU6fy+eef89RTT2FnZ1zmS6+/+fFgdnZ21XIeSydX4Vmy0gLKXwooIYSwdElJSUycOJFjx46xZMkSPvroI5555hkAmjZtyvDhw3n44YdZtWoViYmJbN++ndmzZ/Prr78CMGHCBNauXUtiYiK7d+9m48aNNG/eHIDg4GA0Gg2//PIL586dIycn54ZzhoSEkJiYyN69ezl//jyFhYU3/+QtkBRQlixVWqCEEMJaPPzww+Tn59OhQwfGjx/PM888w5gxY0z7FyxYwMMPP8xzzz1HeHg4gwYNYseOHTRs2BAwti6NHz+e5s2b069fP5o2bconn3wCQP369Xn11VeZMmUKfn5+PPnkkzecc/DgwfTr14+ePXvi4+PDkiVLbu6JWyiNcuUkE6JaZGVl4e7uTmZmJm5ubjXzIIoCs4OgKBvGxYJfRM08jhBCWIiCggISExNp1KgRDg4OascRZupa75OqfH5LC5SlyjhlLJ50duDdRO00QgghRJ0iBZSlKu2+8wkHna26WYQQQog6RgooS5V2yPjVr5W6OYQQQog6SAooS5V2wPhVrsATQgghap0UUJbKdAVeC3VzCCGEEHWQFFCWqDAHLiUav5cuPCGEEKLWSQFlidIPG7+6BoCzl7pZhBBCiDpICihLlHp5/JN03wkhhBCqkALKEpmuwJMB5EIIIYQapICyRKY18GT8kxBCCHUsXLgQDw8PtWMwcuRIBg0aVOuPKwWUpTEYrmiBki48IYQQ5unkyZNoNBr27t1rlue7WVJAWZqMU1CUAzp78JIlXIQQoq4qKipSO0K1sNTnIQWUpSntvvNtBjobdbMIIYSoFtnZ2QwfPhxnZ2cCAgJ477336NGjBxMmTDAdExISwqxZs3j44Ydxc3NjzJgxAHz//fe0aNECe3t7QkJCeOedd8qcW6PR8OOPP5bZ5uHhwcKFC4F/W3ZWrVpFz549cXJyok2bNsTGxpa5z8KFC2nYsCFOTk7cfffdXLhw4ZrPqVGjRgC0bdsWjUZDjx49gH+73F577TUCAwMJDw+vVM6rna/U22+/TUBAAF5eXowfP57i4uJr5rtZ8glsaUwTaMoAciGEuC5FgeI8dR7b1gk0mkodOnHiRP755x/+7//+Dz8/P6ZNm8bu3buJjIwsc9zbb7/NtGnTmD59OgC7du1i6NChzJgxg2HDhrFlyxaeeOIJvLy8GDlyZJXivvTSS7z99ts0adKEl156ifvvv5/4+HhsbGzYtm0bo0ePZvbs2QwaNIg1a9aYMlzN9u3b6dChA7///jstWrTAzs7OtG/Dhg24ubmxfv36Sue71vk2btxIQEAAGzduJD4+nmHDhhEZGcljjz1WpdegKqSAsjRpUkAJIUSlFefB64HqPPaLyWDnfN3DsrOzWbRoEYsXL6Z3794ALFiwgMDA8rl79erFc889Z/p5+PDh9O7dm1deeQWApk2bcvjwYebMmVPlAmrSpEkMGDAAgFdffZUWLVoQHx9Ps2bN+OCDD+jXrx/PP/+86XG2bNnCmjVrrno+Hx8fALy8vPD39y+zz9nZmS+++KJMEXQ91zpfvXr1+Pjjj9HpdDRr1owBAwawYcOGGi2gpAvP0piuwJMCSgghrEFCQgLFxcV06NDBtM3d3d3UtXWl9u3bl/n5yJEjdOnSpcy2Ll26EBcXh16vr1KO1q1bm74PCAgAID093fQ40dHRZY7v1KlTlc5/pVatWlWpeLqeFi1aoNPpTD8HBASYstcUaYGyJAVZcOmk8XtpgRJCiOuzdTK2BKn12NXM2fn6LVr/pdFoUBSlzLaKxgfZ2tqWuQ+AwWCo8uNVRkXPo7I5K3Jl9tJz1VT2UlJAWZL0I8avroHg5KluFiGEsAQaTaW60dQUGhqKra0tO3bsoGHDhgBkZmZy/Phxbr311mvet3nz5vzzzz9ltv3zzz80bdrU1CLj4+NDSkqKaX9cXBx5eVUbF9a8eXO2bdtWZtvWrVuveZ/SFqbKtoRdL2dVz1fTpICyJGmXl3CR7jshhLAarq6ujBgxgsmTJ+Pp6Ymvry/Tp09Hq9WaWoKu5rnnnuOWW25h1qxZDBs2jNjYWD7++GM++eQT0zG9evXi448/plOnTuj1el544YVyLTbX8/TTT9OlSxfefvtt7rrrLtauXXvN8U8Avr6+ODo6smbNGho0aICDgwPu7u5XPf56Oat6vpomY6AsSUEm2DhK950QQliZd999l06dOjFw4ED69OlDly5daN68OQ4ODte8X7t27Vi+fDlLly6lZcuWTJs2jZkzZ5YZQP7OO+8QFBREt27deOCBB5g0aRJOTlXrXuzYsSOff/45H3zwAW3atGHdunW8/PLL17yPjY0NH374IZ999hmBgYHcdddd1zz+ejmrer4ap1iI//3vf0qnTp0UR0dHxd3dvcJjgHK3JUuWlDlm48aNStu2bRU7OzulcePGyoIFC8qd5+OPP1aCg4MVe3t7pUOHDsq2bduqlDUzM1MBlMzMzCrdr1L0JYpSmFP95xVCCAuXn5+vHD58WMnPz1c7yk3LyclR3N3dlS+++ELtKFbnWu+Tqnx+W0wLVFFREffeey/jxo275nELFiwgJSXFdLtyfZzExEQGDBhAz5492bt3LxMmTODRRx9l7dq1pmOWLVvGxIkTmT59Ort376ZNmzbExMTU+Gj+StPqzL4/XwghRNXs2bOHJUuWcOLECXbv3s3w4cMB1G9lEVdlMWOgXn31VQDTjKRX4+HhUW5+iFLz5s2jUaNGpllamzdvzubNm3nvvfeIiYkBjM2ojz32GKNGjTLd59dff+Wrr75iypQp1fRshBBCiLLefvttjh07hp2dHVFRUfz99994e3urHUtchcW0QFXW+PHj8fb2pkOHDnz11VdlLomMjY2lT58+ZY6PiYkxTVdfVFTErl27yhyj1Wrp06dPuSnthRBCiOrStm1bdu3aRU5ODhcvXmT9+vW0atVK7VjiGiymBaoyZs6cSa9evXBycmLdunU88cQT5OTk8PTTTwOQmpqKn59fmfv4+fmRlZVFfn4+ly5dQq/XV3jM0aNHr/q4hYWFFBYWmn7OysqqxmclhBBCCHOjagvUlClT0Gg017xdq3D5r1deeYUuXbrQtm1bXnjhBZ5//nnmzJlTg8/AaPbs2bi7u5tuQUFBNf6YQgghhFCPqi1Qzz333HXX6gkNDb3h80dHRzNr1iwKCwuxt7fH39+ftLS0MsekpaXh5uaGo6MjOp0OnU5X4TFXG1cFMHXqVCZOnGj6OSsrS4ooIYRQifKf2ayFuFJ1vT9ULaB8fHxMiwPWhL1791KvXj3s7e0B47o9q1evLnPM+vXrTev5lA7c27Bhg+nqPYPBwIYNG3jyySev+jj29vamxxBCCKGO0kkX8/LycHR0VDmNMFels5tXdTLR/7KYMVBJSUlcvHiRpKQk9Ho9e/fuBSAsLAwXFxd+/vln0tLS6NixIw4ODqxfv57XX3+dSZMmmc7x+OOP8/HHH/P888/zyCOP8Mcff7B8+XJ+/fVX0zETJ05kxIgRtG/fng4dOvD++++Tm5truipPCCGEedLpdHh4eJimnXFycrruTN6i7lAUhby8PNLT0/Hw8Ciz+PCNsJgCatq0aSxatMj0c9u2bQHYuHEjPXr0wNbWlrlz5/Lss8+iKAphYWGmKQlKNWrUiF9//ZVnn32WDz74gAYNGvDFF1+YpjAAGDZsGOfOnWPatGmkpqYSGRnJmjVryg0sF0IIYX5Kh1uYzdx9wuxca7qjqtAo0llc7bKysnB3dyczMxM3Nze14wghRJ2j1+spLi5WO4YwM7a2ttdsearK57fFtEAJIYQQlVV6UZAQNcXqJtIUQgghhKhpUkAJIYQQQlSRFFBCCCGEEFUkY6BqQOm4fFnSRQghhLAcpZ/blbm+TgqoGpCdnQ0gs5ELIYQQFig7Oxt3d/drHiPTGNQAg8FAcnIyrq6u1T6JW+kyMadPn5YpEq5DXqvKk9eq8uS1qjx5rSpPXqvKq8nXSlEUsrOzCQwMRKu99ignaYGqAVqtlgYNGtToY7i5uck/skqS16ry5LWqPHmtKk9eq8qT16ryauq1ul7LUykZRC6EEEIIUUVSQAkhhBBCVJEUUBbG3t6e6dOnY29vr3YUsyevVeXJa1V58lpVnrxWlSevVeWZy2slg8iFEEIIIapIWqCEEEIIIapICighhBBCiCqSAkoIIYQQooqkgBJCCCGEqCIpoCzEa6+9RufOnXFycsLDw6PCYzQaTbnb0qVLazeomajM65WUlMSAAQNwcnLC19eXyZMnU1JSUrtBzVBISEi599Ebb7yhdiyzMXfuXEJCQnBwcCA6Oprt27erHcnszJgxo9x7qFmzZmrHMgubNm3ijjvuIDAwEI1Gw48//lhmv6IoTJs2jYCAABwdHenTpw9xcXHqhFXZ9V6rkSNHlnuf9evXr9bySQFlIYqKirj33nsZN27cNY9bsGABKSkpptugQYNqJ6CZud7rpdfrGTBgAEVFRWzZsoVFixaxcOFCpk2bVstJzdPMmTPLvI+eeuoptSOZhWXLljFx4kSmT5/O7t27adOmDTExMaSnp6sdzey0aNGizHto8+bNakcyC7m5ubRp04a5c+dWuP+tt97iww8/ZN68eWzbtg1nZ2diYmIoKCio5aTqu95rBdCvX78y77MlS5bUXkBFWJQFCxYo7u7uFe4DlB9++KFW85i7q71eq1evVrRarZKammra9umnnypubm5KYWFhLSY0P8HBwcp7772ndgyz1KFDB2X8+PGmn/V6vRIYGKjMnj1bxVTmZ/r06UqbNm3UjmH2/vt/tsFgUPz9/ZU5c+aYtmVkZCj29vbKkiVLVEhoPir6fBsxYoRy1113qZJHURRFWqCszPjx4/H29qZDhw589dVXKDLNV4ViY2Np1aoVfn5+pm0xMTFkZWVx6NAhFZOZhzfeeAMvLy/atm3LnDlzpGsTY6vmrl276NOnj2mbVqulT58+xMbGqpjMPMXFxREYGEhoaCjDhw8nKSlJ7UhmLzExkdTU1DLvMXd3d6Kjo+U9dhV//vknvr6+hIeHM27cOC5cuFBrjy2LCVuRmTNn0qtXL5ycnFi3bh1PPPEEOTk5PP3002pHMzupqalliifA9HNqaqoakczG008/Tbt27fD09GTLli1MnTqVlJQU3n33XbWjqer8+fPo9foK3zdHjx5VKZV5io6OZuHChYSHh5OSksKrr75Kt27dOHjwIK6urmrHM1ul//dU9B6r6/8vVaRfv37cc889NGrUiBMnTvDiiy/Sv39/YmNj0el0Nf74UkCpaMqUKbz55pvXPObIkSOVHnz5yiuvmL5v27Ytubm5zJkzx2oKqOp+veqSqrx2EydONG1r3bo1dnZ2jB07ltmzZ6u+dIKwDP379zd937p1a6KjowkODmb58uWMHj1axWTCmtx3332m71u1akXr1q1p3Lgxf/75J717967xx5cCSkXPPfccI0eOvOYxoaGhN3z+6OhoZs2aRWFhoVV88FXn6+Xv71/u6qm0tDTTPmtzM69ddHQ0JSUlnDx5kvDw8BpIZxm8vb3R6XSm90mptLQ0q3zPVCcPDw+aNm1KfHy82lHMWun7KC0tjYCAANP2tLQ0IiMjVUplOUJDQ/H29iY+Pl4KKGvn4+ODj49PjZ1/79691KtXzyqKJ6je16tTp0689tprpKen4+vrC8D69etxc3MjIiKiWh7DnNzMa7d37160Wq3pdaqr7OzsiIqKYsOGDaarWw0GAxs2bODJJ59UN5yZy8nJ4cSJEzz00ENqRzFrjRo1wt/fnw0bNpgKpqysLLZt23bdK7AFnDlzhgsXLpQpPmuSFFAWIikpiYsXL5KUlIRer2fv3r0AhIWF4eLiws8//0xaWhodO3bEwcGB9evX8/rrrzNp0iR1g6vkeq9X3759iYiI4KGHHuKtt94iNTWVl19+mfHjx1tNwXkjYmNj2bZtGz179sTV1ZXY2FieffZZHnzwQerVq6d2PNVNnDiRESNG0L59ezp06MD7779Pbm4uo0aNUjuaWZk0aRJ33HEHwcHBJCcnM336dHQ6Hffff7/a0VSXk5NTpiUuMTGRvXv34unpScOGDZkwYQL/+9//aNKkCY0aNeKVV14hMDCwTk5Jc63XytPTk1dffZXBgwfj7+/PiRMneP755wkLCyMmJqZ2Aqp2/Z+okhEjRihAudvGjRsVRVGU3377TYmMjFRcXFwUZ2dnpU2bNsq8efMUvV6vbnCVXO/1UhRFOXnypNK/f3/F0dFR8fb2Vp577jmluLhYvdBmYNeuXUp0dLTi7u6uODg4KM2bN1def/11paCgQO1oZuOjjz5SGjZsqNjZ2SkdOnRQtm7dqnYkszNs2DAlICBAsbOzU+rXr68MGzZMiY+PVzuWWdi4cWOF/zeNGDFCURTjVAavvPKK4ufnp9jb2yu9e/dWjh07pm5olVzrtcrLy1P69u2r+Pj4KLa2tkpwcLDy2GOPlZmapqZpFEWucxdCCCGEqAqZB0oIIYQQooqkgBJCCCGEqCIpoIQQQgghqkgKKCGEEEKIKpICSgghhBCiiqSAEkIIIYSoIimghBBCCCGqSAooIYQQQogqkgJKCCGEEKKKpIASQgjx/+3dIcoyYRiG0QeDFtEm7mCKgkGsLkH3YXYLRpnmAqxGMSkIgkUMYhYEweICBIP87a+fb3rLOXHSHS+eGRggkYAC+MPr9Yp2ux2z2ez/s+PxGNVqNXa7XcZlQC7+hQfwg81mE+PxOI7HYxRFEb1eL0ajUczn89zTgAwEFMCPJpNJbLfb6Pf7cb1e43Q6Ra1Wyz0LyEBAAfzo/X5Hp9OJx+MR5/M5ut1u7klAJr6BAvjR7XaL5/MZ3+837vd77jlARi5QAD/4fD4xGAyi1+tFURRRlmVcr9dotVq5pwEZCCiAH0yn01itVnG5XKJer8dwOIxmsxnr9Tr3NCADr/AA/rDf76Msy1gul9FoNKJSqcRyuYzD4RCLxSL3PCADFygAgEQuUAAAiQQUAEAiAQUAkEhAAQAkElAAAIkEFABAIgEFAJBIQAEAJBJQAACJBBQAQCIBBQCQSEABACT6B5FSCP5WhrYUAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "u0 = s\n", "for i in range(5):\n", @@ -463,13 +1159,6 @@ " show_best_fit(u0)\n", " plt.title(f\"{i=}, {len(u0.experiment_data)=}\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {