area_mediandepth_relationship.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas_datareader.data as web\n",
    "import datetime\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.figure_factory as ff\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "matplotlib.rcParams['figure.figsize'] = 19,12\n",
    "import seaborn as sns\n",
    "sns.set_context('poster',font_scale=1.4)\n",
    "sns.set_palette('colorblind')\n",
    "sns.set_style('darkgrid')\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [[-2.11250929e-05]] \n"
     ]
    }
   ],
   "source": [
    "data = np.load('pontus_2002_area_depth_y1.npy')\n",
    "dataX = data[:,1]\n",
    "dataY = data[:,2]\n",
    "\n",
    "from sklearn import linear_model\n",
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(dataX.reshape(-1, 1), dataY.reshape(-1, 1))\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', regr.coef_, '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.112509286636946e-05"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.coef_[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.238106741928274"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.intercept_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/192.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = go.Scatter(x=dataX, \n",
    "                y=dataY, \n",
    "                mode='markers',\n",
    "                name='data points',\n",
    "                marker=dict(color='black', size=6)\n",
    "               )\n",
    "\n",
    "p2 = go.Scatter(x=dataX, \n",
    "                y=regr.predict(dataX.reshape(-1, 1)),\n",
    "                mode='lines',\n",
    "                name='linear fit',\n",
    "                text='y = ' +str(regr.coef_[0][0]) + 'x + ' +str(regr.intercept_[0]),\n",
    "                line=dict(color='violet', width=3)\n",
    "                )\n",
    "\n",
    "layout = go.Layout(xaxis=dict(title='area (degrees^2)',\n",
    "                              zeroline=False),\n",
    "                   yaxis=dict(title='median depth in i-band',\n",
    "                              zeroline=False),\n",
    "                   showlegend=False, \n",
    "                   title='baseline2018a Y10 linear fit')\n",
    "\n",
    "fig = go.Figure(data=[p1, p2], layout=layout)\n",
    "\n",
    "py.iplot(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/188.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree = 5\n",
    "model = make_pipeline(PolynomialFeatures(degree), Ridge())\n",
    "model.fit(dataX.reshape(-1, 1), dataY.reshape(-1, 1))\n",
    "y_predict = model.predict(dataX.reshape(-1, 1))\n",
    "\n",
    "p1 = go.Scatter(x=dataX, \n",
    "                y=np.log(dataY), \n",
    "                mode='markers',\n",
    "                name='data points',\n",
    "                marker=dict(color='black', size=6)\n",
    "               )\n",
    "\n",
    "p2 = go.Scatter(x=dataX, \n",
    "                y=np.log(model.predict(dataX.reshape(-1, 1))),\n",
    "                mode='lines',\n",
    "                name='fit',\n",
    "                #text='y = ' +str(model.coef_[0][0]) + 'x^2 + ' +str(model.coef_[0][1]) + 'x + ' + str(model.intercept_[0]),\n",
    "                line=dict(color='violet', width=3)\n",
    "                )\n",
    "\n",
    "layout = go.Layout(xaxis=dict(title='area (degrees^2)',\n",
    "                              zeroline=False),\n",
    "                   yaxis=dict(title='median depth in i-band',\n",
    "                              zeroline=False),\n",
    "                   showlegend=False, \n",
    "                   title='baseline2018a Y10 linear fit')\n",
    "\n",
    "fig = go.Figure(data=[p1, p2], layout=layout)\n",
    "\n",
    "py.iplot(fig)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2aef47cc7198>]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1368x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 699,
       "width": 1149
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dataX, dataY, 'o')\n",
    "plt.plot(dataX, 0.003*(dataX-25)*(1.0+np.exp(15*(dataX-26.3)))+26.565)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2aef478e3588>]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 699,
       "width": 1178
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dataX, np.log(dataY),'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2aef475a0160>]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1368x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 699,
       "width": 1168
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.log(np.exp(dataX)*),np.log(np.log(np.log(np.log(np.log((dataY-25)**100))))),'o')\n",
    "#plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lsst",
   "language": "python",
   "name": "lsst"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}