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Update nested classes branch from with model #2

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Update nested classes branch from with model #2

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aa2b873
add basic cursor
tariqshihadah Feb 10, 2024
faf7346
cursor-based construction
tariqshihadah Feb 10, 2024
78d25c8
apply cursor apporach to rtl model
tariqshihadah Feb 10, 2024
f09ef5d
edit examples
tariqshihadah Feb 15, 2024
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313 changes: 86 additions & 227 deletions example.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,8 @@
"model = Model()\n",
"\n",
"# SPF Calculations\n",
"with model.root.add_layer() as layer:\n",
" layer.add_schema({\n",
"with model.cursor.add_layer():\n",
" model.cursor.add_schema({\n",
" \"label\": \"schema1\",\n",
" \"params\": [\"x\"],\n",
" \"actions\": [\"get\"],\n",
Expand All @@ -26,8 +26,8 @@
" })\n",
"\n",
" # SPF 1\n",
" with layer.add_sequence() as seq:\n",
" seq.add_schema({\n",
" with model.cursor.add_sequence():\n",
" model.cursor.add_schema({\n",
" \"label\": \"schema2\",\n",
" \"params\": [\"x\"],\n",
" \"actions\": [\"get\"],\n",
Expand All @@ -37,13 +37,13 @@
" 2: {\"a\": 20, \"b\": 20, \"c\": 20, \"d\": 20}\n",
" }\n",
" }, hidden=True)\n",
" @seq.add_wrapped()\n",
" @model.cursor.add_wrapped(tags='af')\n",
" def process(a, b, c, d):\n",
" return a * b + c / d\n",
" \n",
" # SPF 2\n",
" with layer.add_sequence() as seq:\n",
" seq.add_schema({\n",
" with model.cursor.add_sequence():\n",
" model.cursor.add_schema({\n",
" \"label\": \"schema3\",\n",
" \"params\": [\"x\"],\n",
" \"actions\": [\"get\"],\n",
Expand All @@ -53,276 +53,135 @@
" 2: {\"a\": 20, \"b\": 20, \"c\": 20, \"d\": 20}\n",
" }\n",
" }, hidden=True)\n",
" @seq.add_wrapped()\n",
" @model.cursor.add_wrapped(tags='af')\n",
" def process(a, b, c, d):\n",
" return a * b + c / d\n",
"\n",
"# AF Calculations\n",
"with model.root.add_layer() as layer:\n",
"with model.cursor.add_layer() as layer:\n",
"\n",
" # AF 1\n",
" layer.add_wrapped()\n",
" @model.cursor.add_wrapped()\n",
" def af_lane_width(lane_width):\n",
" return lane_width / 12"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b5f1c041",
"execution_count": 3,
"id": "19660b10",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'x': 1, 'schema1': 10, 'process': 101.0}"
"{'af': [<modelsandbox.model.processors.FunctionProcessor at 0x7f288853b400>,\n",
" <modelsandbox.model.processors.FunctionProcessor at 0x7f288853b2b0>]}"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.analyze(x=1)"
"model.tagged"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb22780f",
"execution_count": 8,
"id": "3ad8af3b",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"['lane_width', 'x']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.root.hidden"
"model.params"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b5a3a05",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3640eff1-d205-46e9-97e2-8e10a19c1372",
"execution_count": 10,
"id": "b5f1c041",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"{'x': 1,\n",
" 'lane_width': 13,\n",
" 'schema1': 10,\n",
" 'process': 101.0,\n",
" 'af_lane_width': 1.0833333333333333}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import importlib\n",
"import modelsandbox\n",
"importlib.reload(modelsandbox)\n",
"from modelsandbox import Model"
"model.analyze(x=1, lane_width=13)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e8343d5-7941-467c-84a7-9a52b2f7a895",
"execution_count": 5,
"id": "cb22780f",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"['a']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from modelsandbox import Model\n",
"\n",
"model = Model()\n",
"def params(f):\n",
" num_args = f.__code__.co_argcount\n",
" return list(f.__code__.co_varnames[:num_args])\n",
"def f(a, *b, **c):\n",
" return\n",
"\n",
"model[0].set_label('First Layer')\n",
"model[0].set_tags('layer1')\n",
"model[0].add_function(lambda a, b: a + b, tags='tag1')\n",
"model[0].add_sequence()\n",
"model[0][1]"
]
},
{
"cell_type": "markdown",
"id": "f9b134bd-bfb8-46ca-88b1-d3584a512ca6",
"metadata": {},
"source": [
"# Cost example"
"params(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0bdd3247-fad7-411d-9806-239895299f6b",
"execution_count": 14,
"id": "5b5a3a05",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"'b'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import importlib\n",
"import modelsandbox\n",
"importlib.reload(modelsandbox)\n",
"from modelsandbox import Model\n",
"import inspect\n",
"\n",
"# Initialize the model class\n",
"model = Model()\n",
"\n",
"# Add a layer to the model to compute airline ticket cost\n",
"model[0].set_label('Compute ticket cost')\n",
"\n",
"# Add the process schema to the model\n",
"model[0].add_schema({\n",
" \"label\": \"ticket_cost\",\n",
" \"params\": [\"destination\", \"airline_class\"],\n",
" \"actions\": [\"get\", \"get\"],\n",
" \"data\": {\n",
" \"Chicago\": {\n",
" \"Economy\": 220,\n",
" \"Business\": 450,\n",
" \"First\": 785\n",
" },\n",
" \"Los Angeles\": {\n",
" \"Economy\": 365,\n",
" \"Business\": 520,\n",
" \"First\": 965\n",
" }\n",
" }\n",
"})\n",
"\n",
"# Add a layer to the model to compute additional costs\n",
"model[1].set_label('Compute expenses')\n",
"\n",
"# Add a processor to compute travel cost\n",
"@model[1].add_wrapped(tags=['__cost'])\n",
"def travel_cost(number_of_travelers, ticket_cost):\n",
" \"\"\"\n",
" Compute total travel cost for all travelers.\n",
" \"\"\"\n",
" return number_of_travelers * ticket_cost\n",
"\n",
"# Add processor to compute lodging cost\n",
"@model[1].add_wrapped(tags=['__cost'])\n",
"def lodging_cost(number_of_travelers, nightly_cost, number_of_nights):\n",
" \"\"\"\n",
" Compute total lodging cost for all travelers.\n",
" \"\"\"\n",
" return number_of_travelers * nightly_cost * number_of_nights\n",
"\n",
"# Add processor to compute per diem\n",
"@model[1].add_wrapped(tags=['__cost'])\n",
"def per_diem_cost(number_of_travelers, number_of_nights, per_diem):\n",
" \"\"\"\n",
" Compute total per diem cost for all travelers.\n",
" \"\"\"\n",
" return number_of_travelers * number_of_nights * per_diem\n",
"\n",
"# Add a layer to the model to aggregate costs\n",
"model[2].set_label('Aggregate expenses')\n",
"\n",
"# Add processor to compute total trip cost\n",
"@model[2].add_wrapped()\n",
"def total_trip_cost(travel_cost, lodging_cost, per_diem_cost, __cost):\n",
" \"\"\"\n",
" Compute total trip cost for all travelers.\n",
" \"\"\"\n",
" return travel_cost + lodging_cost + per_diem_cost\n",
"\n",
"model.structure"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1eba3535-f095-42be-8a4c-959b0ebe38bd",
"metadata": {},
"outputs": [],
"source": [
"model.tagged"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e95c00e-ca1a-4c2c-b630-d7ae32dd5fe7",
"metadata": {},
"outputs": [],
"source": [
"model.analyze(\n",
" airline_class=\"Business\",\n",
" destination=\"Chicago\",\n",
" nightly_cost=185,\n",
" number_of_nights=4,\n",
" number_of_travelers=3,\n",
" per_diem=72,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "676a253a-10db-4fe2-9634-1c531daa15c4",
"metadata": {},
"source": [
"# Scratch"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c829f1d-5419-4b1a-b42d-bf98f85129fd",
"metadata": {},
"outputs": [],
"source": [
"from example import model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1c7f912-bb76-4966-998f-ec3979efaf1f",
"metadata": {},
"outputs": [],
"source": [
"model.analyze(\n",
" airline_class=\"Business\",\n",
" destination=\"Chicago\",\n",
" nightly_cost=185,\n",
" number_of_nights=4,\n",
" number_of_travelers=3,\n",
" per_diem=72,\n",
" ticket_cost=500\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "740b1f87-61a9-4406-8698-d841f7eaa011",
"metadata": {},
"outputs": [],
"source": [
"model.parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8288bbf1-9a90-4004-8f95-d4557b1fd9bf",
"metadata": {},
"outputs": [],
"source": [
"from sample_model import model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "655db299-aacf-4933-84c5-4e82f0e3ae67",
"metadata": {},
"outputs": [],
"source": [
"model.analyze(\n",
" aadt = 580,\n",
" curve_length = 0.2,\n",
" curve_radius = 800,\n",
" lane_width = 11,\n",
" length = 0.6,\n",
" observed_kabco = 3.4,\n",
" shoulder_type = 'gravel',\n",
" shoulder_width = 4,\n",
" spiral = 'both'\n",
")"
"inspect.getfullargspec(f)[1]"
]
}
],
Expand Down
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