Clear clutter. No more test/data/ directory

39 files changed, 772 insertions, 15566 deletions

diff --git a/data/inf226.oficinas.csv b/data/inf226.oficinas.csv
index 364fee3..a46a3c8 100644
--- a/data/inf226.oficinas.csv
+++ b/data/inf226.oficinas.csv
@@ -1,4 +1,4 @@
-ubigeo,dept,prov,latitud,longitud,region_natural,id_depot
+ubigeo,dept,prov,latitud,longitud,region_natural,is_depot
 010201,AMAZONAS,BAGUA,-5.63906152,-78.53166353,SELVA,0
 010301,AMAZONAS,BONGARA,-5.90432416,-77.79809916,SELVA,0
 010101,AMAZONAS,CHACHAPOYAS,-6.22940827,-77.8724339,SELVA,0
diff --git a/data/inf226.tramos.csv b/data/inf226.tramos.csv
deleted file mode 100644
index b953143..0000000
--- a/data/inf226.tramos.csv
+++ /dev/null
@@ -1,14053 +0,0 @@
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-250301,150401

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-250301,190301

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-250301,220901

-220901,250301

-250301,250201

-250201,250301

-250301,250101

-250101,250301

diff --git a/data/inf226.bloqueo.01.txt b/data/raw/inf226.bloqueo.01.txt
index c08742f..c08742f 100644
--- a/data/inf226.bloqueo.01.txt
+++ b/data/raw/inf226.bloqueo.01.txt
diff --git a/data/inf226.bloqueo.02.txt b/data/raw/inf226.bloqueo.02.txt
index 230af54..230af54 100644
--- a/data/inf226.bloqueo.02.txt
+++ b/data/raw/inf226.bloqueo.02.txt
diff --git a/data/inf226.bloqueo.03.txt b/data/raw/inf226.bloqueo.03.txt
index fd02e20..fd02e20 100644
--- a/data/inf226.bloqueo.03.txt
+++ b/data/raw/inf226.bloqueo.03.txt
diff --git a/data/inf226.bloqueo.04.txt b/data/raw/inf226.bloqueo.04.txt
index 6a7329f..6a7329f 100644
--- a/data/inf226.bloqueo.04.txt
+++ b/data/raw/inf226.bloqueo.04.txt
diff --git a/data/inf226.bloqueo.05.txt b/data/raw/inf226.bloqueo.05.txt
index 11a5803..11a5803 100644
--- a/data/inf226.bloqueo.05.txt
+++ b/data/raw/inf226.bloqueo.05.txt
diff --git a/data/inf226.bloqueo.06.txt b/data/raw/inf226.bloqueo.06.txt
index 52e8b4f..52e8b4f 100644
--- a/data/inf226.bloqueo.06.txt
+++ b/data/raw/inf226.bloqueo.06.txt
diff --git a/data/inf226.bloqueo.07.txt b/data/raw/inf226.bloqueo.07.txt
index 8556697..8556697 100644
--- a/data/inf226.bloqueo.07.txt
+++ b/data/raw/inf226.bloqueo.07.txt
diff --git a/data/inf226.bloqueo.08.txt b/data/raw/inf226.bloqueo.08.txt
index d7d678b..d7d678b 100644
--- a/data/inf226.bloqueo.08.txt
+++ b/data/raw/inf226.bloqueo.08.txt
diff --git a/data/inf226.bloqueo.09.txt b/data/raw/inf226.bloqueo.09.txt
index 1dfbdcf..1dfbdcf 100644
--- a/data/inf226.bloqueo.09.txt
+++ b/data/raw/inf226.bloqueo.09.txt
diff --git a/data/inf226.bloqueo.10.txt b/data/raw/inf226.bloqueo.10.txt
index 73bc99e..73bc99e 100644
--- a/data/inf226.bloqueo.10.txt
+++ b/data/raw/inf226.bloqueo.10.txt
diff --git a/data/inf226.bloqueo.11.txt b/data/raw/inf226.bloqueo.11.txt
index d15707e..d15707e 100644
--- a/data/inf226.bloqueo.11.txt
+++ b/data/raw/inf226.bloqueo.11.txt
diff --git a/data/inf226.bloqueo.12.txt b/data/raw/inf226.bloqueo.12.txt
index 105f9d0..105f9d0 100644
--- a/data/inf226.bloqueo.12.txt
+++ b/data/raw/inf226.bloqueo.12.txt
diff --git a/data/inf226.oficinas.txt b/data/raw/inf226.oficinas.txt
index 3892652..3892652 100644
--- a/data/inf226.oficinas.txt
+++ b/data/raw/inf226.oficinas.txt
diff --git a/data/inf226.oficinas_L_E_E_R.txt b/data/raw/inf226.oficinas_L_E_E_R.txt
index fad9592..fad9592 100644
--- a/data/inf226.oficinas_L_E_E_R.txt
+++ b/data/raw/inf226.oficinas_L_E_E_R.txt
diff --git a/data/inf226.plan.mant.trim.abr.may.jun.txt b/data/raw/inf226.plan.mant.trim.abr.may.jun.txt
index 91cc4fc..91cc4fc 100644
--- a/data/inf226.plan.mant.trim.abr.may.jun.txt
+++ b/data/raw/inf226.plan.mant.trim.abr.may.jun.txt
diff --git a/data/inf226.tramos.txt b/data/raw/inf226.tramos.txt
index 5de5375..5de5375 100644
--- a/data/inf226.tramos.txt
+++ b/data/raw/inf226.tramos.txt
diff --git a/data/inf226.velocidades.txt b/data/raw/inf226.velocidades.txt
index 2f01908..2f01908 100644
--- a/data/inf226.velocidades.txt
+++ b/data/raw/inf226.velocidades.txt
diff --git a/data/inf226.ventas202203.txt b/data/raw/inf226.ventas202203.txt
index 77ca244..77ca244 100644
--- a/data/inf226.ventas202203.txt
+++ b/data/raw/inf226.ventas202203.txt
diff --git a/data/inf226.ventas202204.txt b/data/raw/inf226.ventas202204.txt
index 77cd984..77cd984 100644
--- a/data/inf226.ventas202204.txt
+++ b/data/raw/inf226.ventas202204.txt
diff --git a/data/inf226.ventas202205.txt b/data/raw/inf226.ventas202205.txt
index bcef74b..bcef74b 100644
--- a/data/inf226.ventas202205.txt
+++ b/data/raw/inf226.ventas202205.txt
diff --git a/data/inf226.ventas202206.txt b/data/raw/inf226.ventas202206.txt
index 64da831..64da831 100644
--- a/data/inf226.ventas202206.txt
+++ b/data/raw/inf226.ventas202206.txt
diff --git a/data/inf226.ventas202207.txt b/data/raw/inf226.ventas202207.txt
index c7eadbf..c7eadbf 100644
--- a/data/inf226.ventas202207.txt
+++ b/data/raw/inf226.ventas202207.txt
diff --git a/data/inf226.ventas202208.txt b/data/raw/inf226.ventas202208.txt
index 8e6de66..8e6de66 100644
--- a/data/inf226.ventas202208.txt
+++ b/data/raw/inf226.ventas202208.txt
diff --git a/data/inf226.ventas202209.txt b/data/raw/inf226.ventas202209.txt
index c058205..c058205 100644
--- a/data/inf226.ventas202209.txt
+++ b/data/raw/inf226.ventas202209.txt
diff --git a/data/inf226.ventas202210.txt b/data/raw/inf226.ventas202210.txt
index f6866b6..f6866b6 100644
--- a/data/inf226.ventas202210.txt
+++ b/data/raw/inf226.ventas202210.txt
diff --git a/data/inf226.ventas202211.txt b/data/raw/inf226.ventas202211.txt
index 0c36ea6..0c36ea6 100644
--- a/data/inf226.ventas202211.txt
+++ b/data/raw/inf226.ventas202211.txt
diff --git a/data/inf226.ventas202212.txt b/data/raw/inf226.ventas202212.txt
index fb07d47..fb07d47 100644
--- a/data/inf226.ventas202212.txt
+++ b/data/raw/inf226.ventas202212.txt
diff --git a/data/inf226.ventas202301.txt b/data/raw/inf226.ventas202301.txt
index bb0cf95..bb0cf95 100644
--- a/data/inf226.ventas202301.txt
+++ b/data/raw/inf226.ventas202301.txt
diff --git a/data/inf226.ventas202302.txt b/data/raw/inf226.ventas202302.txt
index 2ab360f..2ab360f 100644
--- a/data/inf226.ventas202302.txt
+++ b/data/raw/inf226.ventas202302.txt
diff --git a/data/inf226.ventas202303.txt b/data/raw/inf226.ventas202303.txt
index 489eede..489eede 100644
--- a/data/inf226.ventas202303.txt
+++ b/data/raw/inf226.ventas202303.txt
diff --git a/data/inf226.ventas202304.txt b/data/raw/inf226.ventas202304.txt
index ea8d9de..ea8d9de 100644
--- a/data/inf226.ventas202304.txt
+++ b/data/raw/inf226.ventas202304.txt
diff --git a/data/inf226.ventas202305.txt b/data/raw/inf226.ventas202305.txt
index c67e5eb..c67e5eb 100644
--- a/data/inf226.ventas202305.txt
+++ b/data/raw/inf226.ventas202305.txt
diff --git a/test/GA.ipynb b/test/GA.ipynb
deleted file mode 100644
index 9d73164..0000000
--- a/test/GA.ipynb
+++ /dev/null
@@ -1,361 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2fd45b3a-9a24-4782-812c-08223edb750e",
-   "metadata": {},
-   "source": [
-    "# Prueba del algoritmo genetico"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "45972b70-b2a6-48f2-aafa-9f660548079a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34eab22f-a400-4a14-9ac4-047d87dff69f",
-   "metadata": {},
-   "source": [
-    "Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "a5736dba-4c38-4b7f-9a1a-963bdb006236",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Ciudad:\n",
-    "    regiones = {\n",
-    "        'costa': {\n",
-    "            'plazoentrega': 1\n",
-    "        },\n",
-    "        'sierra': {\n",
-    "            'plazoentrega': 2\n",
-    "        },\n",
-    "        'selva': {\n",
-    "            'plazoentrega': 3\n",
-    "        }\n",
-    "    }\n",
-    "    def __init__(self, nombre, region, longitud, latitud):\n",
-    "        self.nombre = nombre\n",
-    "        self.region = region\n",
-    "        self.x = longitud\n",
-    "        self.y = latitud"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "8ee9610f-d128-4d1b-9458-ca9048073f20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Road_network:\n",
-    "    def __init__(self, cities, distances):\n",
-    "        \"\"\"Grafo completo del pais\n",
-    "        \n",
-    "        Params\n",
-    "        ------\n",
-    "        \n",
-    "        cities: list\n",
-    "            Lista de objetos Ciudad\n",
-    "            \n",
-    "        routes: dict\n",
-    "            (Aun no se como implementar esto)\n",
-    "        \"\"\"\n",
-    "        \n",
-    "        self.cities = cities\n",
-    "        self.routes = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "id": "7dd72c93-acba-46f9-ac99-8c3d1a8cfa67",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Vehiculo:\n",
-    "    tipos = {\n",
-    "        1: {\n",
-    "            'cargamax': 50\n",
-    "        },\n",
-    "        2: {\n",
-    "            'cargamax': 100\n",
-    "        },\n",
-    "        3: {\n",
-    "            'cargamax': 200\n",
-    "        }\n",
-    "    }\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "474a3596-f75d-411e-bf4c-20b8b8259434",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Pedido:\n",
-    "    def __init__(self, cliente, cantidad):\n",
-    "        self.cliente = cliente\n",
-    "        self.cantidad = cantidad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "7602722f-9026-44dc-b922-e17a7d3af45b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Cliente:\n",
-    "    def __init__(self, nombre, ciudad):\n",
-    "        self.nombre = nombre\n",
-    "        self.ciudad = ciudad"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "f6b4829a-9001-410c-b20c-01c65c777d8a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class VRP:\n",
-    "    def __init__(self):\n",
-    "        # Conjuntos\n",
-    "        self.I = range(2)\n",
-    "        self.J = range(3)\n",
-    "        self.T = range(5)       # en horas\n",
-    "        self.V = range(3)       # 3 tipos de vehiculos\n",
-    "    \n",
-    "    def init_data(self):\n",
-    "        \"\"\"\n",
-    "        Lista los parametros iniciales, definidos en \"Modelo Matematico\" en ISA v02\n",
-    "        \n",
-    "        i: almacen grande (depot)\n",
-    "        j: almacen pequeño (customer)\n",
-    "        v: tipo de vehiculo\n",
-    "        t: tiempo\n",
-    "        \"\"\"\n",
-    "        # Parametros\n",
-    "        # Nombres cortos confunden, pero matrices de varias dimensiones \n",
-    "        # sin etiquetas confunden mas\n",
-    "        \n",
-    "        # Demanda\n",
-    "        self.D_jt = [ [ random.choice([0,1,2,3]) for _ in self.I ] for _ in self.T ]\n",
-    "        # Capacidades de vehiculos\n",
-    "        self.VL_v = [ random.choice([10, 15, 20]) for _ in self.V ]\n",
-    "        # distancia entre almacen i, j\n",
-    "        self.d_ij = [ random.choice([25,50,100]) for _ in self.V ]\n",
-    "        #self.r_ijvt = [ [ 0 for _ in self.V ] for "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "ab559513-5c14-4dd2-a51d-7737114dfba1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10, 10, 15])"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p = VRP()\n",
-    "p.init_data()\n",
-    "np.array(p.VL_v)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "id": "2c3c85e0-a90c-4fda-86f7-778d7328c74d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[25, 50, 50]"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p.d_ij"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "511ff788-0d1a-4ac7-9575-de182d236574",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "078280b5-70ef-4691-8798-a686d85d188c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "611c9a0d-bb1a-48eb-af37-f033abe8ed66",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64c68216-b9f1-45f9-a0fe-3862ab106c24",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9cb6bb08-1547-4b64-993f-b2c453535264",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b83d9e98-db8f-45cd-9eff-07d4194f7e07",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b8c06031-9c55-4e13-a27b-91b0886902e6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9483c22-243f-44e5-9a6d-09da92354554",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "12952643-4a10-40bf-8af3-41319c744732",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b0306c4f-eb68-4009-9390-0f881c6a8cb4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b3ad92de-b2ed-4f21-a696-1fa2981f89dc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def genetic_algorithm(population, fitness_fn, ngen=100, pmut=0.1):\n",
-    "    \"Algoritmo Genetico \"\n",
-    "    \n",
-    "    popsize = len(population)\n",
-    "    evaluate_population(population, fitness_fn)  # evalua la poblacion inicial\n",
-    "    ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
-    "    bestfitness = [population[ibest[0]].fitness]\n",
-    "    print(\"Poblacion inicial, best_fitness = {}\".format(population[ibest[0]].fitness))\n",
-    "    \n",
-    "    for g in range(ngen):   # Por cada generacion\n",
-    "        \n",
-    "        ## Selecciona las parejas de padres para cruzamiento \n",
-    "        mating_pool = []\n",
-    "        for i in range(int(popsize/2)): mating_pool.append(select_parents_roulette(population)) \n",
-    "        \n",
-    "        ## Crea la poblacion descendencia cruzando las parejas del mating pool con Recombinación de 1 punto\n",
-    "        offspring_population = []\n",
-    "        for i in range(len(mating_pool)): \n",
-    "            #offspring_population.extend( mating_pool[i][0].crossover_onepoint(mating_pool[i][1]) )\n",
-    "            offspring_population.extend( mating_pool[i][0].crossover_uniform(mating_pool[i][1]) )\n",
-    "\n",
-    "        ## Aplica el operador de mutacion con probabilidad pmut en cada hijo generado\n",
-    "        for i in range(len(offspring_population)):\n",
-    "            if random.uniform(0, 1) < pmut: \n",
-    "                offspring_population[i] = offspring_population[i].mutate_position()\n",
-    "        \n",
-    "        ## Evalua la poblacion descendencia\n",
-    "        evaluate_population(offspring_population, fitness_fn)  # evalua la poblacion inicial\n",
-    "        \n",
-    "        ## Selecciona popsize individuos para la sgte. generación de la union de la pob. actual y  pob. descendencia\n",
-    "        population = select_survivors(population, offspring_population, popsize)\n",
-    "\n",
-    "        ## Almacena la historia del fitness del mejor individuo\n",
-    "        ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
-    "        bestfitness.append(population[ibest[0]].fitness)\n",
-    "        print(\"generacion {}, best_fitness = {}\".format(g, population[ibest[0]].fitness))\n",
-    "    \n",
-    "    return population[ibest[0]], bestfitness  "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test/TSP.ipynb b/test/TSP.ipynb
deleted file mode 100644
index 476619b..0000000
--- a/test/TSP.ipynb
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "234feb6e-4c52-443c-a3d6-6da8e903dd5c",
-   "metadata": {},
-   "source": [
-    "Travelling salesman problem\n",
-    "\n",
-    "TSP es *casi* (multi-depot) VRPTW pero:\n",
-    "- Solo 1 camion\n",
-    "- Todos los ciudades (aka. almacenes pequeños) tienen al menos 1 pedido\n",
-    "- Capacidad infinita\n",
-    "- Sin ventanas de tiempo (aka. plazos de entrega)\n",
-    "- Solo 1 deposito\n",
-    "\n",
-    "Cambios identificados, necesarios para adaptar el TSP a nuestro caso:\n",
-    "- Tramos no conectados -> distancia grande entre ellos\n",
-    "- Distancias no son euclidianas, usar \"geodistance\"\n",
-    "- [...]\n",
-    "\n",
-    "Refs:\n",
-    "\n",
-    "- [scikit-opt](https://github.com/guofei9987/scikit-opt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "934ae28c-ccc1-4e63-832c-4f53ceb13f50",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0fb6e4d9-d0cd-438c-b0a1-21a85a23de89",
-   "metadata": {},
-   "source": [
-    "Differential Evolution scikit-opt example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eda106bc-2411-4ace-acf0-d2b66dc2d127",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "'''\n",
-    "min f(x1, x2, x3) = x1^2 + x2^2 + x3^2\n",
-    "s.t.\n",
-    "    x1*x2 >= 1\n",
-    "    x1*x2 <= 5\n",
-    "    x2 + x3 = 1\n",
-    "    0 <= x1, x2, x3 <= 5\n",
-    "'''\n",
-    "\n",
-    "\n",
-    "def obj_func(p):\n",
-    "    x1, x2, x3 = p\n",
-    "    return x1 ** 2 + x2 ** 2 + x3 ** 2\n",
-    "\n",
-    "\n",
-    "constraint_eq = [\n",
-    "    lambda x: 1 - x[1] - x[2]\n",
-    "]\n",
-    "\n",
-    "# r(x1, x2, x3) >= 0\n",
-    "constraint_ueq = [\n",
-    "    lambda x: 1 - x[0] * x[1],\n",
-    "    lambda x: x[0] * x[1] - 5\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b3dcfaf-ab6b-42b7-9218-e58ea6828605",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sko.DE import DE\n",
-    "\n",
-    "de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5],\n",
-    "        constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)\n",
-    "\n",
-    "best_x, best_y = de.run()\n",
-    "print('best_x:', best_x, '\\n', 'best_y:', best_y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b49859f-c3a3-49a4-a605-5ec880556dc5",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7563715b-c74b-4010-98c5-28c1e5b1410d",
-   "metadata": {},
-   "source": [
-    "Genetic Algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "03283268-a9af-4e26-a673-4c8225bf5fcb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "def schaffer(p):\n",
-    "    '''\n",
-    "    This function has plenty of local minimum, with strong shocks\n",
-    "    global minimum at (0,0) with value 0\n",
-    "    https://en.wikipedia.org/wiki/Test_functions_for_optimization\n",
-    "    '''\n",
-    "    x1, x2 = p\n",
-    "    part1 = np.square(x1) - np.square(x2)\n",
-    "    part2 = np.square(x1) + np.square(x2)\n",
-    "    return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "24da5ddc-04fe-4536-b5b9-48dca9a6118c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sko.GA import GA\n",
-    "\n",
-    "ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, prob_mut=0.001, lb=[-1, -1], ub=[1, 1], precision=1e-7)\n",
-    "best_x, best_y = ga.run()\n",
-    "print('best_x:', best_x, '\\n', 'best_y:', best_y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e4903dc-6eff-4087-a986-1f6cf3470645",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# col: individuos,  row: iterations\n",
-    "Y_history = pd.DataFrame(ga.all_history_Y)\n",
-    "fig, ax = plt.subplots(2, 1)\n",
-    "ax[0].plot(Y_history.index, Y_history.values, '.', color='red')\n",
-    "Y_history.min(axis=1).cummin().plot(kind='line')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bdfb384b-b2b5-4755-b869-3a1da68cedd9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "730c1714-7f9d-427a-9ecd-eecd416c293d",
-   "metadata": {},
-   "source": [
-    "TSP"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9fb2ef00-3c16-4986-afa1-428fc43f5d36",
-   "metadata": {},
-   "source": [
-    "\"geodistance\" (using longitude, latitude): https://stackoverflow.com/questions/31632190/measuring-geographic-distance-with-scipy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "740a307c-7f4d-4978-a561-2424309cc310",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from scipy import spatial\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "num_points = 5\n",
-    "\n",
-    "points_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points\n",
-    "distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')\n",
-    "\n",
-    "\n",
-    "def cal_total_distance(routine):\n",
-    "    '''The objective function. input routine, return total distance.\n",
-    "    cal_total_distance(np.arange(num_points))\n",
-    "    '''\n",
-    "    num_points, = routine.shape\n",
-    "    return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3b570dd4-09fa-41d8-8d80-ba0c6ae05fa5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sko.GA import GA_TSP\n",
-    "\n",
-    "ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1)\n",
-    "best_points, best_distance = ga_tsp.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4af7656-ace9-4594-ac32-3ccd027c7811",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(1, 2)\n",
-    "best_points_ = np.concatenate([best_points])\n",
-    "# \"path\"\n",
-    "best_points_coordinate = points_coordinate[best_points_, :]\n",
-    "ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')\n",
-    "ax[1].plot(ga_tsp.generation_best_Y)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f361519e-2397-4b70-be35-45dd14cc15af",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "40f6c413-89f8-4934-a165-1183fc5458eb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "089bbdae-063a-4a5c-8f7a-fb8bb26dc83c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f7266973-639c-42f9-86b6-3369ba04d03c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5127a9e8-17a8-4a07-b825-2aaf11a6270e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sko.ACA import ACA_TSP\n",
-    "\n",
-    "aca = ACA_TSP(func=cal_total_distance, n_dim=num_points,\n",
-    "              size_pop=50, max_iter=200,\n",
-    "              distance_matrix=distance_matrix)\n",
-    "\n",
-    "best_x, best_y = aca.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c9b939a8-3a5f-41d6-8263-25dacebc4602",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(1, 2)\n",
-    "best_points_ = np.concatenate([best_x, [best_x[0]]])\n",
-    "best_points_coordinate = points_coordinate[best_points_, :]\n",
-    "ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')\n",
-    "pd.DataFrame(aca.y_best_history).cummin().plot(ax=ax[1])\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/test/VRPTW_GA.ipynb b/test/VRPTW_GA.ipynb
deleted file mode 100644
index 042836e..0000000
--- a/test/VRPTW_GA.ipynb
+++ /dev/null
@@ -1,724 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# APLICACIONES EN CIENCIAS DE COMPUTACION\n",
-    "Dr. Edwin Villanueva"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Algoritmo genetico para resolver el VRPTW\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "import matplotlib.pyplot as plt\n",
-    "import csv"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Clase abstracta de un individuo de algoritmo genético</b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Individual:\n",
-    "    \"Clase abstracta para individuos de un algoritmo evolutivo.\"\n",
-    "\n",
-    "    def __init__(self, chromosome):\n",
-    "        self.chromosome = chromosome\n",
-    "\n",
-    "    def crossover(self, other):\n",
-    "        \"Retorna un nuevo individuo cruzando self y other.\"\n",
-    "        raise NotImplementedError\n",
-    "        \n",
-    "    def mutate(self):\n",
-    "        \"Cambia los valores de algunos genes.\"\n",
-    "        raise NotImplementedError        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Clase concreta de un individuo del problema de las n-reinas</b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Individual_VRPTW(Individual):\n",
-    "    \"Clase que implementa el individuo en VRPTW.\"\n",
-    "\n",
-    "    def __init__(self, chromosome):\n",
-    "        self.chromosome = chromosome[:]\n",
-    "        self.fitness = -1\n",
-    "        \n",
-    "    def crossover_order(self, other):\n",
-    "        \"\"\"\n",
-    "        Copies a part of the child chromosome from the first parent and constructs \n",
-    "        the remaining part by following the vertex ordering in the second parent\n",
-    "        \"\"\"\n",
-    "        cut_point1 = random.randrange(0, len(self.chromosome) + 1)\n",
-    "        cut_point2 = random.randrange(cut_point1, len(self.chromosome) + 1)\n",
-    "        \n",
-    "        c1 = self.chromosome[:]\n",
-    "        c2 = other.chromosome[:]\n",
-    "        p1_rem = self.chromosome[:cut_point1] + self.chromosome[cut_point2:]\n",
-    "        p2_rem = other.chromosome[:cut_point1] + other.chromosome[cut_point2:]\n",
-    "        # Change the genes in the remaining part of the child...\n",
-    "        for i in range(len(self.chromosome)):\n",
-    "            if i not in range(cut_point1, cut_point2):\n",
-    "                # ...following the vertex ordering in the second parent\n",
-    "                for gene in other.chromosome:\n",
-    "                    if gene in p1_rem:\n",
-    "                        c1.chromosome[i] = gene\n",
-    "                        \n",
-    "                # (now for the other child)\n",
-    "                for gene in self.chromosome:\n",
-    "                    if gene in p2_rem:\n",
-    "                        c2.chromosome[i] = gene\n",
-    "                        \n",
-    "        return [Individual_VRPTW(c1), Individual_VRPTW(c2)]\n",
-    "        \n",
-    "\n",
-    "    def crossover_onepoint(self, other):\n",
-    "        \"Retorna dos nuevos individuos del cruzamiento de un punto entre self y other \"\n",
-    "        c = random.randrange(len(self.chromosome))\n",
-    "        ind1 = Individual_VRPTW(self.chromosome[:c] + other.chromosome[c:])\n",
-    "        ind2 = Individual_VRPTW(other.chromosome[:c] + self.chromosome[c:])\n",
-    "        return [ind1, ind2]   \n",
-    "    \n",
-    "    def crossover_uniform(self, other):\n",
-    "        chromosome1 = []\n",
-    "        chromosome2 = []\n",
-    "        \"Retorna dos nuevos individuos del cruzamiento uniforme entre self y other \"\n",
-    "        for i in range(len(self.chromosome)):\n",
-    "            if random.uniform(0, 1) < 0.5:\n",
-    "                chromosome1.append(self.chromosome[i])\n",
-    "                chromosome2.append(other.chromosome[i])\n",
-    "            else:\n",
-    "                chromosome1.append(other.chromosome[i])\n",
-    "                chromosome2.append(self.chromosome[i])\n",
-    "        ind1 = Individual_VRPTW(chromosome1)\n",
-    "        ind2 = Individual_VRPTW(chromosome2)\n",
-    "        return [ind1, ind2]     \n",
-    "\n",
-    "    def mutate_position(self):\n",
-    "        mutated_ind = Individual_VRPTW(self.chromosome[:])\n",
-    "        indexPos = random.randint(0, len(mutated_ind.chromosome)-1)\n",
-    "        newPos = random.randint(0, len(mutated_ind.chromosome)-1)\n",
-    "        mutated_ind.chromosome[indexPos] = newPos\n",
-    "        return mutated_ind\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Funcion de fitness para evaluar un individuo del problema de las n-reinas</b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fitness_VRPTW(chromosome):\n",
-    "    \"\"\"Retorna el fitness de un cromosoma en el problema VRPTW (distancia total de todas las rutas) \"\"\"\n",
-    "    n = len(chromosome)  # No. of vertices\n",
-    "    fitness = 10**6\n",
-    "    # feasibility\n",
-    "    # TODO: considerar todas las restricciones\n",
-    "    # desirability\n",
-    "    for i in range(0, n):\n",
-    "        fitness -= distancia[i][i + 1]\n",
-    "        \n",
-    "    return fitness"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Funcion para evaluar toda una población de individuos con la funcion de fitnes especificada</b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_population(population, fitness_fn):\n",
-    "    \"\"\" Evalua una poblacion de individuos con la funcion de fitness pasada \"\"\"\n",
-    "    popsize = len(population)\n",
-    "    for i in range(popsize):\n",
-    "        if population[i].fitness == -1:    # si el individuo no esta evaluado\n",
-    "            population[i].fitness = fitness_fn(population[i].chromosome)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Funcion que selecciona con el metodo de la ruleta un par de individuos de population para cruzamiento </b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def select_parents_roulette(population):\n",
-    "    popsize = len(population)\n",
-    "    \n",
-    "    # Escoje el primer padre\n",
-    "    sumfitness = sum([indiv.fitness for indiv in population])  # suma total del fitness de la poblacion\n",
-    "    pickfitness = random.uniform(0, sumfitness)   # escoge un numero aleatorio entre 0 y sumfitness\n",
-    "    cumfitness = 0     # fitness acumulado\n",
-    "    for i in range(popsize):\n",
-    "        cumfitness += population[i].fitness\n",
-    "        if cumfitness > pickfitness: \n",
-    "            iParent1 = i\n",
-    "            break\n",
-    "     \n",
-    "    # Escoje el segundo padre, desconsiderando el padre ya escogido\n",
-    "    sumfitness = sumfitness - population[iParent1].fitness # retira el fitness del padre ya escogido\n",
-    "    pickfitness = random.uniform(0, sumfitness)   # escoge un numero aleatorio entre 0 y sumfitness\n",
-    "    cumfitness = 0     # fitness acumulado\n",
-    "    for i in range(popsize):\n",
-    "        if i == iParent1: continue   # si es el primer padre \n",
-    "        cumfitness += population[i].fitness\n",
-    "        if cumfitness > pickfitness: \n",
-    "            iParent2 = i\n",
-    "            break        \n",
-    "    return (population[iParent1], population[iParent2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Funcion que selecciona sobrevivientes para la sgte generacion, dada la poblacion actual y poblacion de hijos </b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def select_survivors(population, offspring_population, numsurvivors):\n",
-    "    next_population = []\n",
-    "    population.extend(offspring_population) # une las dos poblaciones\n",
-    "    isurvivors = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:numsurvivors]\n",
-    "    for i in range(numsurvivors): next_population.append(population[isurvivors[i]])\n",
-    "    return next_population"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Algoritmo Genetico</b>   \n",
-    "Recibe una poblacion inicial, funcion de fitness, numero de generaciones (ngen) y taza de mutación (pmut)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def genetic_algorithm(population, fitness_fn, ngen=100, pmut=0.1):\n",
-    "    \"Algoritmo Genetico \"\n",
-    "    \n",
-    "    popsize = len(population)\n",
-    "    evaluate_population(population, fitness_fn)  # evalua la poblacion inicial\n",
-    "    ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
-    "    bestfitness = [population[ibest[0]].fitness]\n",
-    "    print(\"Poblacion inicial, best_fitness = {}\".format(population[ibest[0]].fitness))\n",
-    "    \n",
-    "    for g in range(ngen):   # Por cada generacion\n",
-    "        \n",
-    "        ## Selecciona las parejas de padres para cruzamiento \n",
-    "        mating_pool = []\n",
-    "        for i in range(int(popsize/2)): mating_pool.append(select_parents_roulette(population)) \n",
-    "        \n",
-    "        ## Crea la poblacion descendencia cruzando las parejas del mating pool con Recombinación de 1 punto\n",
-    "        offspring_population = []\n",
-    "        for i in range(len(mating_pool)): \n",
-    "            #offspring_population.extend( mating_pool[i][0].crossover_onepoint(mating_pool[i][1]) )\n",
-    "            #offspring_population.extend( mating_pool[i][0].crossover_uniform(mating_pool[i][1]) )\n",
-    "            offspring_population.extend( mating_pool[i][0].crossover_order(mating_pool[i][1]) )\n",
-    "\n",
-    "        ## Aplica el operador de mutacion con probabilidad pmut en cada hijo generado\n",
-    "        for i in range(len(offspring_population)):\n",
-    "            if random.uniform(0, 1) < pmut: \n",
-    "                offspring_population[i] = offspring_population[i].mutate_position()\n",
-    "        \n",
-    "        ## Evalua la poblacion descendencia\n",
-    "        evaluate_population(offspring_population, fitness_fn)  # evalua la poblacion inicial\n",
-    "        \n",
-    "        ## Selecciona popsize individuos para la sgte. generación de la union de la pob. actual y  pob. descendencia\n",
-    "        population = select_survivors(population, offspring_population, popsize)\n",
-    "\n",
-    "        ## Almacena la historia del fitness del mejor individuo\n",
-    "        ibest = sorted(range(len(population)), key=lambda i: population[i].fitness, reverse=True)[:1]\n",
-    "        bestfitness.append(population[ibest[0]].fitness)\n",
-    "        print(\"generacion {}, best_fitness = {}\".format(g, population[ibest[0]].fitness))\n",
-    "    \n",
-    "    return population[ibest[0]], bestfitness  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " <b>Algoritmo de Busqueda Genetica para el VRPTW</b>   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def genetic_algorithm_VRPTW(fitness_fn, num_depots=1, num_vehicles=1, vehicle_capacity=200, popsize=10, ngen=1000, pmut=0):\n",
-    "    population = []\n",
-    "    \n",
-    "    # Crea la poblacion inicial con cromosomas aleatorios\n",
-    "    for i in range(popsize):\n",
-    "        chromosome = [j for j in range(1,num_vertices+1)]\n",
-    "        random.shuffle(chromosome)\n",
-    "        population.append(Individual_VRPTW(chromosome))\n",
-    "        \n",
-    "    return genetic_algorithm(population, fitness_fn, ngen, pmut)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def genetic_search_nqueens(fitness_fn, num_queens=10, popsize=10, ngen=100, pmut=0.5):\n",
-    "    import random\n",
-    "    population = []\n",
-    "\n",
-    "    ## Crea la poblacion inicial con cromosomas aleatorios\n",
-    "    for i in range(popsize):\n",
-    "        chromosome = [j for j in range(1,num_queens+1)]\n",
-    "        random.shuffle(chromosome)\n",
-    "        population.append( Individual_nqueens(chromosome) )\n",
-    "        \n",
-    "    ## Crea la poblacion inicial con los siguientes cromosomas    \n",
-    "    #chromosomes = [[1,3,1,3,1,3,1,3,1,3],\n",
-    "    #               [2,4,2,4,2,4,2,4,2,4],\n",
-    "    #               [3,5,3,5,3,5,3,5,3,5],\n",
-    "    #               [4,6,4,6,4,6,4,6,4,6],\n",
-    "    #               [5,7,5,7,5,7,5,7,5,7],\n",
-    "    #               [6,8,6,8,6,8,6,8,6,8],\n",
-    "    #               [7,9,7,9,7,9,7,9,7,9],\n",
-    "    #               [8,10,8,10,8,10,8,10,8,10],\n",
-    "    #               [9,1,9,1,9,1,9,1,9,1],\n",
-    "    #               [10,2,10,2,10,2,10,2,10,2] ]   \n",
-    "    #for i in range(popsize):\n",
-    "    #    population.append( Individual_nqueens(chromosomes[i]) )   \n",
-    "        \n",
-    "    ## llama al algoritmo genetico para encontrar una solucion al problema de las n reinas\n",
-    "    return genetic_algorithm(population, fitness_fn, ngen, pmut)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Probando el Algoritmo genetico"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'distancia' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [115]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# busca solucion para el problema de 10 reinas. Usa 100 individuos aleatorios, 100 generaciones y taza de mutación de 0.5\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m best_ind, bestfitness \u001b[38;5;241m=\u001b[39m \u001b[43mgenetic_algorithm_VRPTW\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfitness_VRPTW\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(bestfitness)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
-      "Input \u001b[0;32mIn [113]\u001b[0m, in \u001b[0;36mgenetic_algorithm_VRPTW\u001b[0;34m(fitness_fn, num_vertices, num_depots, num_vehicles, popsize, ngen, pmut)\u001b[0m\n\u001b[1;32m      7\u001b[0m     random\u001b[38;5;241m.\u001b[39mshuffle(chromosome)\n\u001b[1;32m      8\u001b[0m     population\u001b[38;5;241m.\u001b[39mappend(Individual_VRPTW(chromosome))\n\u001b[0;32m---> 10\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgenetic_algorithm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfitness_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mngen\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpmut\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36mgenetic_algorithm\u001b[0;34m(population, fitness_fn, ngen, pmut)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAlgoritmo Genetico \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      4\u001b[0m popsize \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(population)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mevaluate_population\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfitness_fn\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# evalua la poblacion inicial\u001b[39;00m\n\u001b[1;32m      6\u001b[0m ibest \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(population)), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m i: population[i]\u001b[38;5;241m.\u001b[39mfitness, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)[:\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m      7\u001b[0m bestfitness \u001b[38;5;241m=\u001b[39m [population[ibest[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39mfitness]\n",
-      "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36mevaluate_population\u001b[0;34m(population, fitness_fn)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(popsize):\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m population[i]\u001b[38;5;241m.\u001b[39mfitness \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:    \u001b[38;5;66;03m# si el individuo no esta evaluado\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m         population[i]\u001b[38;5;241m.\u001b[39mfitness \u001b[38;5;241m=\u001b[39m \u001b[43mfitness_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpopulation\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchromosome\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Input \u001b[0;32mIn [109]\u001b[0m, in \u001b[0;36mfitness_VRPTW\u001b[0;34m(chromosome)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# feasibility\u001b[39;00m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# TODO: considerar todas las restricciones\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# desirability\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, n):\n\u001b[0;32m----> 9\u001b[0m     fitness \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mdistancia\u001b[49m[i][i \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fitness\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'distancia' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "best_ind, bestfitness = genetic_algorithm_VRPTW(fitness_VRPTW)\n",
-    "plt.plot(bestfitness)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Poblacion inicial, best_fitness = 44\n",
-      "generacion 0, best_fitness = 44\n",
-      "generacion 1, best_fitness = 44\n",
-      "generacion 2, best_fitness = 44\n",
-      "generacion 3, best_fitness = 44\n",
-      "generacion 4, best_fitness = 44\n",
-      "generacion 5, best_fitness = 44\n",
-      "generacion 6, best_fitness = 44\n",
-      "generacion 7, best_fitness = 44\n",
-      "generacion 8, best_fitness = 44\n",
-      "generacion 9, best_fitness = 44\n",
-      "generacion 10, best_fitness = 44\n",
-      "generacion 11, best_fitness = 44\n",
-      "generacion 12, best_fitness = 44\n",
-      "generacion 13, best_fitness = 44\n",
-      "generacion 14, best_fitness = 44\n",
-      "generacion 15, best_fitness = 44\n",
-      "generacion 16, best_fitness = 44\n",
-      "generacion 17, best_fitness = 44\n",
-      "generacion 18, best_fitness = 44\n",
-      "generacion 19, best_fitness = 44\n",
-      "generacion 20, best_fitness = 44\n",
-      "generacion 21, best_fitness = 44\n",
-      "generacion 22, best_fitness = 44\n",
-      "generacion 23, best_fitness = 44\n",
-      "generacion 24, best_fitness = 44\n",
-      "generacion 25, best_fitness = 44\n",
-      "generacion 26, best_fitness = 44\n",
-      "generacion 27, best_fitness = 44\n",
-      "generacion 28, best_fitness = 44\n",
-      "generacion 29, best_fitness = 44\n",
-      "generacion 30, best_fitness = 45\n",
-      "generacion 31, best_fitness = 45\n",
-      "generacion 32, best_fitness = 45\n",
-      "generacion 33, best_fitness = 45\n",
-      "generacion 34, best_fitness = 45\n",
-      "generacion 35, best_fitness = 45\n",
-      "generacion 36, best_fitness = 45\n",
-      "generacion 37, best_fitness = 45\n",
-      "generacion 38, best_fitness = 45\n",
-      "generacion 39, best_fitness = 45\n",
-      "generacion 40, best_fitness = 45\n",
-      "generacion 41, best_fitness = 45\n",
-      "generacion 42, best_fitness = 45\n",
-      "generacion 43, best_fitness = 45\n",
-      "generacion 44, best_fitness = 45\n",
-      "generacion 45, best_fitness = 45\n",
-      "generacion 46, best_fitness = 45\n",
-      "generacion 47, best_fitness = 45\n",
-      "generacion 48, best_fitness = 45\n",
-      "generacion 49, best_fitness = 45\n",
-      "generacion 50, best_fitness = 45\n",
-      "generacion 51, best_fitness = 45\n",
-      "generacion 52, best_fitness = 45\n",
-      "generacion 53, best_fitness = 45\n",
-      "generacion 54, best_fitness = 45\n",
-      "generacion 55, best_fitness = 45\n",
-      "generacion 56, best_fitness = 45\n",
-      "generacion 57, best_fitness = 45\n",
-      "generacion 58, best_fitness = 45\n",
-      "generacion 59, best_fitness = 45\n",
-      "generacion 60, best_fitness = 45\n",
-      "generacion 61, best_fitness = 45\n",
-      "generacion 62, best_fitness = 45\n",
-      "generacion 63, best_fitness = 45\n",
-      "generacion 64, best_fitness = 45\n",
-      "generacion 65, best_fitness = 45\n",
-      "generacion 66, best_fitness = 45\n",
-      "generacion 67, best_fitness = 45\n",
-      "generacion 68, best_fitness = 45\n",
-      "generacion 69, best_fitness = 45\n",
-      "generacion 70, best_fitness = 45\n",
-      "generacion 71, best_fitness = 45\n",
-      "generacion 72, best_fitness = 45\n",
-      "generacion 73, best_fitness = 45\n",
-      "generacion 74, best_fitness = 45\n",
-      "generacion 75, best_fitness = 45\n",
-      "generacion 76, best_fitness = 45\n",
-      "generacion 77, best_fitness = 45\n",
-      "generacion 78, best_fitness = 45\n",
-      "generacion 79, best_fitness = 45\n",
-      "generacion 80, best_fitness = 45\n",
-      "generacion 81, best_fitness = 45\n",
-      "generacion 82, best_fitness = 45\n",
-      "generacion 83, best_fitness = 45\n",
-      "generacion 84, best_fitness = 45\n",
-      "generacion 85, best_fitness = 45\n",
-      "generacion 86, best_fitness = 45\n",
-      "generacion 87, best_fitness = 45\n",
-      "generacion 88, best_fitness = 45\n",
-      "generacion 89, best_fitness = 45\n",
-      "generacion 90, best_fitness = 45\n",
-      "generacion 91, best_fitness = 45\n",
-      "generacion 92, best_fitness = 45\n",
-      "generacion 93, best_fitness = 45\n",
-      "generacion 94, best_fitness = 45\n",
-      "generacion 95, best_fitness = 45\n",
-      "generacion 96, best_fitness = 45\n",
-      "generacion 97, best_fitness = 45\n",
-      "generacion 98, best_fitness = 45\n",
-      "generacion 99, best_fitness = 45\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 343 ms, sys: 108 ms, total: 450 ms\n",
-      "Wall time: 329 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# busca solucion para el problema de 10 reinas. Usa 100 individuos aleatorios, 100 generaciones y taza de mutación de 0.5\n",
-    "best_ind, bestfitness = genetic_search_nqueens(fitness_nqueens, 10, 100, 100, 0.90)\n",
-    "plt.plot(bestfitness)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Lectura de datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CUST NO.XCOORD. YCOORD. DEMAND  READY TIME  DUE DATE    SERVICE TIME\n",
-      "1       40      50      0       0           240         0           \n",
-      "2       25      85      20      145         175         10          \n",
-      "3       22      75      30      50          80          10          \n",
-      "4       22      85      10      109         139         10          \n",
-      "5       20      80      40      141         171         10          \n",
-      "6       20      85      20      41          71          10          \n",
-      "7       18      75      20      95          125         10          \n",
-      "8       15      75      20      79          109         10          \n",
-      "9       15      80      10      91          121         10          \n",
-      "10      10      35      20      91          121         10          \n",
-      "11      10      40      30      119         149         10          \n",
-      "12      8       40      40      59          89          10          \n",
-      "13      8       45      20      64          94          10          \n",
-      "14      5       35      10      142         172         10          \n",
-      "15      5       45      10      35          65          10          \n",
-      "16      2       40      20      58          88          10          \n",
-      "17      0       40      20      72          102         10          \n",
-      "18      0       45      20      149         179         10          \n",
-      "19      44      5       20      87          117         10          \n",
-      "20      42      10      40      72          102         10          \n",
-      "21      42      15      10      122         152         10          \n",
-      "22      40      5       10      67          97          10          \n",
-      "23      40      15      40      92          122         10          \n",
-      "24      38      5       30      65          95          10          \n",
-      "25      38      15      10      148         178         10          \n",
-      "26      35      5       20      154         184         10          \n",
-      "27      95      30      30      115         145         10          \n",
-      "28      95      35      20      62          92          10          \n",
-      "29      92      30      10      62          92          10          \n",
-      "30      90      35      10      67          97          10          \n",
-      "31      88      30      10      74          104         10          \n",
-      "32      88      35      20      61          91          10          \n",
-      "33      87      30      10      131         161         10          \n",
-      "34      85      25      10      51          81          10          \n",
-      "35      85      35      30      111         141         10          \n",
-      "36      67      85      20      139         169         10          \n",
-      "37      65      85      40      43          73          10          \n",
-      "38      65      82      10      124         154         10          \n",
-      "39      62      80      30      75          105         10          \n",
-      "40      60      80      10      37          67          10          \n",
-      "41      60      85      30      85          115         10          \n",
-      "42      58      75      20      92          122         10          \n",
-      "43      55      80      10      33          63          10          \n",
-      "44      55      85      20      128         158         10          \n",
-      "45      55      82      10      64          94          10          \n",
-      "46      20      82      10      37          67          10          \n",
-      "47      18      80      10      113         143         10          \n",
-      "48      2       45      10      45          75          10          \n",
-      "49      42      5       10      151         181         10          \n",
-      "50      42      12      10      104         134         10          \n",
-      "51      72      35      30      116         146         10          \n",
-      "52      55      20      19      83          113         10          \n",
-      "53      25      30      3       52          82          10          \n",
-      "54      20      50      5       91          121         10          \n",
-      "55      55      60      16      139         169         10          \n",
-      "56      30      60      16      140         170         10          \n",
-      "57      50      35      19      130         160         10          \n",
-      "58      30      25      23      96          126         10          \n",
-      "59      15      10      20      152         182         10          \n",
-      "60      10      20      19      42          72          10          \n",
-      "61      15      60      17      155         185         10          \n",
-      "62      45      65      9       66          96          10          \n",
-      "63      65      35      3       52          82          10          \n",
-      "64      65      20      6       39          69          10          \n",
-      "65      45      30      17      53          83          10          \n",
-      "66      35      40      16      11          41          10          \n",
-      "67      41      37      16      133         163         10          \n",
-      "68      64      42      9       70          100         10          \n",
-      "69      40      60      21      144         174         10          \n",
-      "70      31      52      27      41          71          10          \n",
-      "71      35      69      23      180         210         10          \n",
-      "72      65      55      14      65          95          10          \n",
-      "73      63      65      8       30          60          10          \n",
-      "74      2       60      5       77          107         10          \n",
-      "75      20      20      8       141         171         10          \n",
-      "76      5       5       16      74          104         10          \n",
-      "77      60      12      31      75          105         10          \n",
-      "78      23      3       7       150         180         10          \n",
-      "79      8       56      27      90          120         10          \n",
-      "80      6       68      30      89          119         10          \n",
-      "81      47      47      13      192         222         10          \n",
-      "82      49      58      10      86          116         10          \n",
-      "83      27      43      9       42          72          10          \n",
-      "84      37      31      14      35          65          10          \n",
-      "85      57      29      18      96          126         10          \n",
-      "86      63      23      2       87          117         10          \n",
-      "87      21      24      28      87          117         10          \n",
-      "88      12      24      13      90          120         10          \n",
-      "89      24      58      19      67          97          10          \n",
-      "90      67      5       25      144         174         10          \n",
-      "91      37      47      6       86          116         10          \n",
-      "92      49      42      13      167         197         10          \n",
-      "93      53      43      14      14          44          10          \n",
-      "94      61      52      3       178         208         10          \n",
-      "95      57      48      23      95          125         10          \n",
-      "96      56      37      6       34          64          10          \n",
-      "97      55      54      26      132         162         10          \n",
-      "98      4       18      35      120         150         10          \n",
-      "99      26      52      9       46          76          10          \n",
-      "100     26      35      15      77          107         10          \n",
-      "101     31      67      3       180         210         10          \n"
-     ]
-    }
-   ],
-   "source": [
-    "with open('data/RC101.csv', newline='') as csvfile:\n",
-    "    orders = csv.reader(csvfile)\n",
-    "    for row in orders:\n",
-    "        print(f\"{row[0]:8}{row[1]:8}{row[2]:8}{row[3]:8}{row[4]:12}{row[5]:12}{row[6]:12}\")\n",
-    "        #print(\", \".join(row))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/test/data/RC101.csv b/test/data/RC101.csv
deleted file mode 100644
index ff64970..0000000
--- a/test/data/RC101.csv
+++ /dev/null
@@ -1,102 +0,0 @@
-CUST NO.,XCOORD.,YCOORD.,DEMAND,READY TIME,DUE DATE,SERVICE TIME
-1,40,50,0,0,240,0
-2,25,85,20,145,175,10
-3,22,75,30,50,80,10
-4,22,85,10,109,139,10
-5,20,80,40,141,171,10
-6,20,85,20,41,71,10
-7,18,75,20,95,125,10
-8,15,75,20,79,109,10
-9,15,80,10,91,121,10
-10,10,35,20,91,121,10
-11,10,40,30,119,149,10
-12,8,40,40,59,89,10
-13,8,45,20,64,94,10
-14,5,35,10,142,172,10
-15,5,45,10,35,65,10
-16,2,40,20,58,88,10
-17,0,40,20,72,102,10
-18,0,45,20,149,179,10
-19,44,5,20,87,117,10
-20,42,10,40,72,102,10
-21,42,15,10,122,152,10
-22,40,5,10,67,97,10
-23,40,15,40,92,122,10
-24,38,5,30,65,95,10
-25,38,15,10,148,178,10
-26,35,5,20,154,184,10
-27,95,30,30,115,145,10
-28,95,35,20,62,92,10
-29,92,30,10,62,92,10
-30,90,35,10,67,97,10
-31,88,30,10,74,104,10
-32,88,35,20,61,91,10
-33,87,30,10,131,161,10
-34,85,25,10,51,81,10
-35,85,35,30,111,141,10
-36,67,85,20,139,169,10
-37,65,85,40,43,73,10
-38,65,82,10,124,154,10
-39,62,80,30,75,105,10
-40,60,80,10,37,67,10
-41,60,85,30,85,115,10
-42,58,75,20,92,122,10
-43,55,80,10,33,63,10
-44,55,85,20,128,158,10
-45,55,82,10,64,94,10
-46,20,82,10,37,67,10
-47,18,80,10,113,143,10
-48,2,45,10,45,75,10
-49,42,5,10,151,181,10
-50,42,12,10,104,134,10
-51,72,35,30,116,146,10
-52,55,20,19,83,113,10
-53,25,30,3,52,82,10
-54,20,50,5,91,121,10
-55,55,60,16,139,169,10
-56,30,60,16,140,170,10
-57,50,35,19,130,160,10
-58,30,25,23,96,126,10
-59,15,10,20,152,182,10
-60,10,20,19,42,72,10
-61,15,60,17,155,185,10
-62,45,65,9,66,96,10
-63,65,35,3,52,82,10
-64,65,20,6,39,69,10
-65,45,30,17,53,83,10
-66,35,40,16,11,41,10
-67,41,37,16,133,163,10
-68,64,42,9,70,100,10
-69,40,60,21,144,174,10
-70,31,52,27,41,71,10
-71,35,69,23,180,210,10
-72,65,55,14,65,95,10
-73,63,65,8,30,60,10
-74,2,60,5,77,107,10
-75,20,20,8,141,171,10
-76,5,5,16,74,104,10
-77,60,12,31,75,105,10
-78,23,3,7,150,180,10
-79,8,56,27,90,120,10
-80,6,68,30,89,119,10
-81,47,47,13,192,222,10
-82,49,58,10,86,116,10
-83,27,43,9,42,72,10
-84,37,31,14,35,65,10
-85,57,29,18,96,126,10
-86,63,23,2,87,117,10
-87,21,24,28,87,117,10
-88,12,24,13,90,120,10
-89,24,58,19,67,97,10
-90,67,5,25,144,174,10
-91,37,47,6,86,116,10
-92,49,42,13,167,197,10
-93,53,43,14,14,44,10
-94,61,52,3,178,208,10
-95,57,48,23,95,125,10
-96,56,37,6,34,64,10
-97,55,54,26,132,162,10
-98,4,18,35,120,150,10
-99,26,52,9,46,76,10
-100,26,35,15,77,107,10
-101,31,67,3,180,210,10
diff --git a/test/mitsuo.ipynb b/test/mitsuo.ipynb
new file mode 100644
index 0000000..653663c
--- /dev/null
+++ b/test/mitsuo.ipynb
@@ -0,0 +1,771 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "956275e8-e4e6-4a03-afe5-31eca1c3272b",
+   "metadata": {},
+   "source": [
+    "# Testing possible algorithms to solve MDHVRPTW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "234feb6e-4c52-443c-a3d6-6da8e903dd5c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Travelling salesman problem\n",
+    "\n",
+    "TSP es *casi* MDHVRPTW (*Multi-Depot Heterogeneous VRP with Time Windows) *del profe* pero:\n",
+    "- Solo 1 camion\n",
+    "- Todos los ciudades (aka. almacenes pequeños) tienen al menos 1 pedido\n",
+    "- Capacidad infinita (1 solo tipo de vehiculo)\n",
+    "- Sin ventanas de tiempo (aka. plazos de entrega)\n",
+    "- Solo 1 deposito\n",
+    "- No hay entregas parciales\n",
+    "  - Relajar la restriccion de que la demanda de los \"customers\" debe ser satisfecha al 100% por\n",
+    "    un solo camion.  Entonces, si un \"customer\" requiere 8 paquetes,  se le puede entregar solo 5,\n",
+    "    y quedan pendientes 3.  **Esto modifica la solucion,  ahora es una lista donde los nodos a visitar\n",
+    "    ya no son unicos**.  Se debe terminar la solucion cuando ya no hayan pedidos pendientes (paquetes por entregar > 0).\n",
+    "- No hay \"trasvaces\"\n",
+    "  - F\n",
+    "\n",
+    "Cambios identificados, necesarios para adaptar el TSP a nuestro caso:\n",
+    "- Tramos no conectados -> distancia grande entre ellos\n",
+    "- Distancias no son euclidianas, usar \"geodistance\"\n",
+    "- [...]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ce403ac-eb32-4f74-ae8d-41cb70fe4a20",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Posibilidades\n",
+    "\n",
+    "Una manera de implementar \n",
+    "\n",
+    "- [scikit-opt](https://github.com/guofei9987/scikit-opt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98fe5cbb-4fb3-478b-b22f-c14a0408e638",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## NetworkX to find all_pairs_shortest_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b854709-fe5c-4c5a-8a05-b2e438c92a1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "G = nx.Graph()\n",
+    "G.add_edge(1, 2)  # default edge data=1\n",
+    "G.add_edge(2, 3, weight=0.9)  # specify edge data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44bf2120-1e55-4c1d-b806-3e66227483a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "G.add_edge('y', 'x', function=math.cos)\n",
+    "G.add_node(math.cos)  # any hashable can be a node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ed6d6e2-921a-4376-8f8e-c6731385d561",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "elist = [(1, 2), (2, 3), (1, 4), (4, 2)]\n",
+    "G.add_edges_from(elist)\n",
+    "elist = [('a', 'b', 5.0), ('b', 'c', 3.0), ('a', 'c', 1.0), ('c', 'd', 7.3)]\n",
+    "G.add_weighted_edges_from(elist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16aad098-3702-4d81-889b-3dc8b3e46ca7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "#G = nx.cubical_graph()\n",
+    "subax1 = plt.subplot(121)\n",
+    "nx.draw(G)   # default spring_layout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cc6e56b-3dcf-45fe-9127-0cd63f01ccec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(G.adj)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bad6e2e8-cff5-4a7d-9e3f-f37bae5dff2d",
+   "metadata": {},
+   "source": [
+    "## Pre-processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22af16ef-7df6-49c8-8e55-157069c03830",
+   "metadata": {},
+   "source": [
+    "La data del profe es muy tedioso de leer.  Formato raro.  Pasar primero a csv.\n",
+    "\n",
+    "Distancia entre nodos:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "db46bd0b-46c7-4125-bfff-33205c578e27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/mitsuo/docs/courses/2022-1/INF226_DP1/project/code/DP_project/test'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b87961d0-f9b4-417e-9138-3db1b0a36d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import csv\n",
+    "import math\n",
+    "import random\n",
+    "\n",
+    "import networkx as nx\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "aeca1be3-1394-4ae1-b488-6abe8bd1e808",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def degreesToRadians(degrees):\n",
+    "    return degrees * math.pi / 180\n",
+    "\n",
+    "def distanceInKmBetweenEarthCoordinates(lat1, lon1, lat2, lon2):\n",
+    "    earthRadiusKm = 6371        # Avg. radius\n",
+    "\n",
+    "    dLat = degreesToRadians(lat2-lat1)\n",
+    "    dLon = degreesToRadians(lon2-lon1)\n",
+    "\n",
+    "    lat1 = degreesToRadians(lat1)\n",
+    "    lat2 = degreesToRadians(lat2)\n",
+    "\n",
+    "    a = math.sin(dLat/2) * math.sin(dLat/2) \\\n",
+    "      + math.sin(dLon/2) * math.sin(dLon/2) * math.cos(lat1) * math.cos(lat2)\n",
+    "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a));\n",
+    "    return earthRadiusKm * c\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "80a0fef2-ae25-40e3-94ab-e3d07b9b60c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Node:\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    lat : float\n",
+    "        Latitud (angulo de los Paralelos) en grados sexagesimales\n",
+    "    lon : float\n",
+    "        Longitud (angulo de los Meridianos) en grados sexagesimales\n",
+    "    x : float\n",
+    "        Aproximacion local, en Km respecto (\"Lima\": -12.04591952,-77.03049615  (lat, long))\n",
+    "        x = (longitud - (-77.03049615)) * (pi / 180) * earthRadiusKm\n",
+    "    y : float\n",
+    "        Aproximacion local, en Km respecto (\"Lima\")\n",
+    "        y = (latitud - (-12.04591952)) * (pi / 180) * earthRadiusKm\n",
+    "    demand : float\n",
+    "        Cantidad de paquetes  (facil cambiar a int)\n",
+    "    *_time : float\n",
+    "        Cantidades de tiempo en minutos\n",
+    "    \n",
+    "    Notes\n",
+    "    -----\n",
+    "    Web Mercator projection (BAD):\n",
+    "    \n",
+    "        x = floor(256 / (2 * math.pi) * 2**(zoom_level) * (lon + math.pi))\n",
+    "        y = floor(265 / (2 * math.pi) * 2**(zoom_level) * (math.pi - math.ln(math.tan( math.pi / 4 + lat / 2 ))))\n",
+    "        \n",
+    "        x = R * lon\n",
+    "        y = R * ln(tan(pi/4 + lat/2)\n",
+    "    \n",
+    "    Both `lon` and `lat` in radians.\n",
+    "    \n",
+    "    \"Lima\": -12.04591952,-77.03049615  (lat, long)\n",
+    "    \"\"\"\n",
+    "    def __init__(self, id:  int, ubigeo, lat, lon, is_depot,\n",
+    "                 demand, ready_time, due_time, service_time):\n",
+    "        super()\n",
+    "        self.id = id\n",
+    "        self.ubigeo = ubigeo\n",
+    "\n",
+    "        if is_depot:\n",
+    "            self.is_depot = True\n",
+    "        else:\n",
+    "            self.is_depot = False\n",
+    "    \n",
+    "        earthRadiusKm = 6371        # Avg. radius\n",
+    "        zoom_level = 1\n",
+    "        lima_lat = (-12.04591952)\n",
+    "        lima_lon = (-77.03049615)\n",
+    "        \n",
+    "        self.lat = lat\n",
+    "        self.lon = lon\n",
+    "        self.x = (lon - lima_lon) * (math.pi / 180) * earthRadiusKm\n",
+    "        self.y = (lat - lima_lat) * (math.pi / 180) * earthRadiusKm\n",
+    "        #self.lat *= (math.pi / 180)\n",
+    "        #self.lon *= (math.pi / 180)\n",
+    "        #self.x = 256 / (2 * math.pi) * 2**(zoom_level) * (self.lon + math.pi)\n",
+    "        #self.y = 256 / (2 * math.pi) * 2**(zoom_level) * (math.pi - math.log(math.tan( math.pi / 4 + self.lat / 2 )))\n",
+    "        self.x = round(self.x, 3)\n",
+    "        self.y = round(self.y, 3)\n",
+    "        self.demand = demand\n",
+    "        self.ready_time = 0 #ready_time\n",
+    "        self.due_time = due_time\n",
+    "        self.service_time = service_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fd72a504-3538-4b43-9e90-bef4299ca83d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nodes = []\n",
+    "\n",
+    "with open('data/VRPTW_python/inf226.oficinas_mod.csv', newline='') as csvfile:\n",
+    "    orders = csv.reader(csvfile)\n",
+    "    count = 1\n",
+    "    id = 0\n",
+    "    for row in orders:\n",
+    "        if count >= 2:\n",
+    "            ubigeo, dept, prov, latitud, longitud, region_natural, is_depot = row[:7]\n",
+    "            demand, ready_time, due_time, service_time = row[7:]\n",
+    "            nodes.append(Node(\n",
+    "                id, ubigeo, float(latitud), float(longitud), int(is_depot),\n",
+    "                #int(100 * random.random()), 0, int(5000 + 500 * (1.5 - random.random())), service_time=60\n",
+    "                demand=float(demand), ready_time=0, due_time=float(due_time), service_time=60\n",
+    "            ))\n",
+    "            id += 1\n",
+    "        count += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8a9cb3eb-d434-44de-a179-01464ba7bbf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': 34, 'ubigeo': '040101', 'is_depot': True, 'lat': -16.39881421, 'lon': -71.537019649, 'x': 610.847, 'y': -484.02, 'demand': 0.0, 'ready_time': 0, 'due_time': 8000.0, 'service_time': 60}\n",
+      "{'id': 122, 'ubigeo': '130101', 'is_depot': True, 'lat': -8.11176389, 'lon': -79.02868652, 'x': -222.189, 'y': 437.458, 'demand': 0.0, 'ready_time': 0, 'due_time': 8000.0, 'service_time': 60}\n",
+      "{'id': 134, 'ubigeo': '150101', 'is_depot': True, 'lat': -12.04591952, 'lon': -77.03049615, 'x': 0.0, 'y': 0.0, 'demand': 0.0, 'ready_time': 0, 'due_time': 8000.0, 'service_time': 60}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for node in nodes:\n",
+    "    if node.is_depot:\n",
+    "        print(node.__dict__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "79fcde2d-d0df-4cca-8334-3ffd7a0da997",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_num = len(nodes)\n",
+    "\n",
+    "tramos = []\n",
+    "with open('../data/inf226.tramos.v.2.0.csv', newline='') as f:\n",
+    "    rows = csv.reader(f)\n",
+    "    count = 1\n",
+    "    for row in rows:\n",
+    "        if count >= 2:\n",
+    "            tramos.append([row[0], row[1]])\n",
+    "        count += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "945de058-6fc3-44c1-8d59-227064323bae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7008\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[['010201', '010301'],\n",
+       " ['010301', '010201'],\n",
+       " ['010201', '010401'],\n",
+       " ['010401', '010201'],\n",
+       " ['010201', '010501']]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(tramos))\n",
+    "tramos[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e6895a35-ff22-48a1-88a8-ceb8344f36cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_dist_mat = np.zeros((node_num, node_num))\n",
+    "\n",
+    "nodes_d = dict(zip([n.ubigeo for n in nodes], nodes))\n",
+    "for ubigeo1, ubigeo2 in tramos:\n",
+    "    #if ubigeo1 in nodes_d.keys() and ubigeo2 in nodes_d.keys():\n",
+    "    id1 = nodes_d[ubigeo1].id\n",
+    "    id2 = nodes_d[ubigeo2].id\n",
+    "    n1 = nodes[id1]\n",
+    "    n2 = nodes[id2]\n",
+    "    node_dist_mat[id1][id2] = round(distanceInKmBetweenEarthCoordinates(n1.lat, n1.lon, n2.lat, n2.lon), 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b971e3b3-cea5-43b8-9c57-5bbb83180ee6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(196, 196)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "node_dist_mat.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "abc75da4-fed7-493e-ae9d-f5daba33c693",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  0.   ,  86.349,   0.   , 137.864],\n",
+       "       [ 86.349,   0.   ,   0.   , 146.07 ],\n",
+       "       [  0.   ,   0.   ,   0.   ,   0.   ],\n",
+       "       [137.864, 146.07 ,   0.   ,   0.   ]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "node_dist_mat[:4, :4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e818c9bb-28cb-475d-b8b5-98dfe4d11f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# depots are '040101', '130101', '150101'\n",
+    "\n",
+    "depots = []\n",
+    "customers = []\n",
+    "for n in nodes:\n",
+    "    if n.is_depot:\n",
+    "        depots.append(n)\n",
+    "    else:\n",
+    "        customers.append(n)\n",
+    "depots_customers = depots + customers\n",
+    "\n",
+    "with open('data/VRPTW_python/pedidosperu195.txt', 'w', newline='') as csvfile:\n",
+    "    spamwriter = csv.writer(csvfile, delimiter=' ', quoting=csv.QUOTE_MINIMAL)\n",
+    "    i = 0\n",
+    "    for node in depots_customers:\n",
+    "        spamwriter.writerow([\n",
+    "            #node.id,\n",
+    "            i,\n",
+    "            node.x, node.y,\n",
+    "            node.demand, node.ready_time, node.due_time, node.service_time\n",
+    "        ])\n",
+    "        i += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cf94976-9003-4b9a-a600-ad9b48d3e60e",
+   "metadata": {},
+   "source": [
+    "### Transformar a matriz conexa de nodos \"customer\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac9f4ce7-7f54-4d26-9bbf-75252f083046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for n in nodes[:5]:\n",
+    "    print(n.__dict__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3f18eaf-a962-454f-99d4-67bf71067e39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count = 0\n",
+    "for n in nodes:\n",
+    "    if n.demand:\n",
+    "        print(n.__dict__)\n",
+    "        count += 1\n",
+    "count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4308a3df-27ca-4910-bff3-1302c8fb589d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14d8d583-7766-421b-96cc-ec95ad9604e6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f85c9cab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#node_dist_mat = np.zeros((node_num, node_num))\n",
+    "elist = []\n",
+    "\n",
+    "nodes_d = dict(zip([n.ubigeo for n in nodes], nodes))\n",
+    "for ubigeo1, ubigeo2 in tramos:\n",
+    "    #if ubigeo1 in nodes_d.keys() and ubigeo2 in nodes_d.keys():\n",
+    "    id1 = nodes_d[ubigeo1].id\n",
+    "    id2 = nodes_d[ubigeo2].id\n",
+    "    n1 = nodes[id1]\n",
+    "    n2 = nodes[id2]\n",
+    "    #node_dist_mat[id1][id2] = round(distanceInKmBetweenEarthCoordinates(n1.lat, n1.lon, n2.lat, n2.lon), 3)\n",
+    "    dist = round(distanceInKmBetweenEarthCoordinates(n1.lat, n1.lon, n2.lat, n2.lon), 3)\n",
+    "    elist.append((ubigeo1, ubigeo2, dist))\n",
+    "    \n",
+    "G = nx.Graph()\n",
+    "G.add_weighted_edges_from(elist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3c522d4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print(len(list(nodes_d)))\n",
+    "print(len(list(G.nodes)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86771f42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G['010201']['010301']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "001a04b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_dist_mat[nodes_d['010201'].id, nodes_d['010301'].id]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71e54f4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G['010201']['250301']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ebe3cdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path1 = nx.shortest_path(G, source='010201', target='250301', weight='weight')\n",
+    "path1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cca62d40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path2 = nx.shortest_path(G, source='010201', target='250301')\n",
+    "path2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "756f1845",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l1, l2 = 0, 0\n",
+    "for i in range(len(path1)):\n",
+    "    if i == 0:\n",
+    "        continue\n",
+    "    l1 += G[path1[i-1]][path1[i]]['weight']\n",
+    "    l2 += G[path2[i-1]][path2[i]]['weight']\n",
+    "print(l1, l2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e124757",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# https://stackoverflow.com/a/63169428/7498073\n",
+    "H = nx.subgraph(G, path1)\n",
+    "nx.draw(H, with_labels=True, font_weight='bold', node_color='lightblue', node_size=500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5ded33b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len_path = dict(nx.all_pairs_dijkstra(G, weight='weight'))\n",
+    "#for node, (dist, path) in len_path:\n",
+    "#    print(f\"{node}: {dist} [{path}]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3361d460",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(len_path['010201'][0]['250301'])\n",
+    "print(len_path['010201'][1]['250301'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba3d4ae7-c6b5-484a-a1b4-0f6ffe1890b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(G)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "596bf42a-3600-4446-a904-a4e9080d2f2b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "132bea6b-c50c-4406-8e2f-2279a87e9cbf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf5cac1d-9ae4-4423-ab96-48728c14fa01",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00f4cd39-c093-4397-ae96-cb3ae2a433c6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff3f7473-6418-4f4a-828f-172f2996ba54",
+   "metadata": {},
+   "source": [
+    "## Pruebitas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6956f235-0401-4a67-8ed2-d2b3fd07b3bf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ab3843c-faba-46a0-9f7b-97897430ba67",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e45dd91b-fc32-4da3-a340-d3dc888b1335",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fdfcaa8-9000-48d7-b3ab-4d2f2efc2bcc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}