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    "# Basic parameter space tutorial\n",
    "\n",
    "In this tutorial you will learn the basics of constructing a parameter space that can be used in the romtools workflows. Here, we will create a simple 2D parameter space that will be used in downstream examples.\n",
    "\n",
    "The API for the parameter space is documented here: https://pressio.github.io/rom-tools-and-workflows/romtools/workflows/parameter_spaces.html\n"
   ]
  },
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   "execution_count": 6,
   "id": "358fb709-9c81-45a3-a871-2549de121580",
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   "source": [
    "#First, let's import the relavant modules:\n",
    "import romtools\n",
    "import numpy as np\n",
    "from romtools.workflows import ParameterSpace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1ffa4be-246c-44a9-b486-f517b9f7acb9",
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    "'''\n",
    "Now, we will design our parameter space class\n",
    "This parameter space is designed to work with the 1D advection diffusion reaction equation:\n",
    "\n",
    "c u_x - nu * u_xx = 1\n",
    "\n",
    "where u is the state, c, is the advection speed, and nu is the viscosity.\n",
    "'''\n",
    "\n",
    "#We inherit the class from ParameterSpace, which gives the abstract base template \n",
    "class BasicParameterSpace(ParameterSpace):\n",
    "\n",
    "    def __init__(self):\n",
    "        #We will have two variables, x and y\n",
    "        self.var_names = ['c','nu']\n",
    "\n",
    "        # The dimension of the parameter space is 2 (c and nu)\n",
    "        self.dim_ = 2\n",
    "\n",
    "        # In this example we will consider the two variables to be uncorrelated, and we will assign them uniform distributions\n",
    "        self.lower_bounds_ = np.array([0.5,1.e-3])\n",
    "        self.upper_bounds_ = np.array([1e-3,1e-1])\n",
    "        \n",
    "    def get_names(self):\n",
    "        return self.var_names\n",
    "\n",
    "    def get_dimensionality(self):\n",
    "        return self.dim_\n",
    "\n",
    "    def generate_samples(self, number_of_samples, seed=None):\n",
    "        samples = np.random.uniform(self.lower_bounds_,self.upper_bounds_,size=(number_of_samples,self.dim_))\n",
    "        return samples\n",
    "\n",
    "\n",
    "#Now, let's instatiate the parameter space\n",
    "if __name__ == \"__main__\":\n",
    "    myBasicParameterSpace = BasicParameterSpace()\n",
    "\n",
    "#That's it! We now have a parameter space that can be used for, e.g., basic sampling"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "217759fb-6ec9-4488-897b-e5b4db9f4b2c",
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   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Can't instantiate abstract class BadBasicParameterSpace without an implementation for abstract method 'get_names'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 25\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m#Python will throw an error when we try to instatiate\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m---> 25\u001b[0m     myBadBasicParameterSpace \u001b[38;5;241m=\u001b[39m BadBasicParameterSpace()\n",
      "\u001b[0;31mTypeError\u001b[0m: Can't instantiate abstract class BadBasicParameterSpace without an implementation for abstract method 'get_names'"
     ]
    }
   ],
   "source": [
    "#What if we tried to create a parameter space that didn't meet the interface of the ParameterSpace\n",
    "# As an example, let's say we didn't include the \"get_names\" method\n",
    "class BadBasicParameterSpace(ParameterSpace):\n",
    "\n",
    "    def __init__(self):\n",
    "        #We will have two variables, x and y\n",
    "        self.var_names = ['x','y']\n",
    "\n",
    "        # The dimension of the parameter space is 2 (x and y)\n",
    "        self.dim_ = 2\n",
    "\n",
    "        # In this example we will consider uncorrelated variables w/ mean [1,2] and standard deviation [0.1,0.2]\n",
    "        self.means_ = np.array([1,2])\n",
    "        self.stds_ = np.array([0.1,0.2]) \n",
    "\n",
    "    def get_dimensionality(self):\n",
    "        return self.dim_\n",
    "\n",
    "    def generate_samples(self, number_of_samples: int, seed=None):\n",
    "        samples = np.random.normal(self.means_,self.stds_,size=(number_of_samples,self.dim_))\n",
    "        return samples\n",
    "\n",
    "#Python will throw an error when we try to instatiate\n",
    "if __name__ == \"__main__\":\n",
    "    myBadBasicParameterSpace = BadBasicParameterSpace()"
   ]
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