nepi/.ipynb_checkpoints/parser-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('hu.csv')\n",
    "df['time']=pd.to_datetime(df['time'])\n",
    "df=df.set_index('time')\n",
    "df=df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nepi=pd.read_excel('idojaras.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup plot params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.font_manager as font_manager\n",
    "path = 'KulimPark-Regular.ttf'\n",
    "path2 = 'Symbola.ttf'\n",
    "prop = font_manager.FontProperties(fname=path)\n",
    "prop2 = font_manager.FontProperties(fname=path2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_none='#E7CFBC'\n",
    "color_none2='#F2B880'\n",
    "color_ax=color_none\n",
    "color_plus='#C98686'\n",
    "color_minus='#966B9D'\n",
    "color_good='#4d9221'\n",
    "color_bad='#c51b7d'\n",
    "color_bg='#FFF4EC'\n",
    "font_size=12\n",
    "s=40\n",
    "m1={'Temp':'☼'}\n",
    "m2={'Temp':'❄️'}\n",
    "m3={'Temp':'♨️','Rain':'☂️','Snow':'⛷️'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spine_plot(title,mondas,kondicio,mennyiseg,prediction_ts,observation_ratio_good,observation_ratio_bad,ertek_is_plus,good_is_plus,axv1=0,axv2=0,tp=False):\n",
    "    y=prediction_ts.values\n",
    "    x=prediction_ts.index\n",
    "    fig,axes=plt.subplots(1,3,figsize=(8,5),gridspec_kw={'width_ratios': [1, 3, 2]})\n",
    "    ax=axes[1]\n",
    "    for i,e in enumerate(y):\n",
    "        ax.plot([0,e],[x[i],x[i]],color=color_none)\n",
    "    ax.scatter(y,x,s,color=color_none,zorder=10)\n",
    "    ex=max(y)-min(y)\n",
    "    text_off=abs(ex*0.04)\n",
    "    text_align='left'\n",
    "    if y[0]<0:\n",
    "        text_off=-text_off\n",
    "        text_align='right'\n",
    "    ax.text(y[0]+text_off, x[0], str(x[0]), fontsize=font_size*0.9, \n",
    "            horizontalalignment=text_align, verticalalignment='center', \n",
    "            color=color_none, fontproperties=prop)\n",
    "    text_off=abs(text_off)\n",
    "    text_align='left'\n",
    "    if y[-1]<0:\n",
    "        text_off=-text_off\n",
    "        text_align='right'\n",
    "    ax.text(y[-1]+text_off, x[-1], str(x[-1]), fontsize=font_size*0.9, \n",
    "            horizontalalignment=text_align, verticalalignment='center', \n",
    "            color=color_none, fontproperties=prop)\n",
    "    \n",
    "    y=prediction_ts.loc[observation_ratio_bad.index].values\n",
    "    x=prediction_ts.loc[observation_ratio_bad.index].index\n",
    "    for i,e in enumerate(y):\n",
    "        ax.plot([0,e],[x[i],x[i]],color=color_none)\n",
    "    ax.scatter(y,x,s,color=color_none,zorder=10)\n",
    "    \n",
    "\n",
    "    z=prediction_ts.loc[observation_ratio_good.index]\n",
    "    z1=z[z>=axv2].dropna()\n",
    "    z2=z[z<axv2].dropna()\n",
    "    y=z1.values\n",
    "    x=z1.index\n",
    "    for i,e in enumerate(y):\n",
    "        ax.plot([0,e],[x[i],x[i]],color=color_plus)\n",
    "    ax.scatter(y,x,s,color=color_plus,zorder=12)\n",
    "    y=z2.values\n",
    "    x=z2.index\n",
    "    for i,e in enumerate(y):\n",
    "        ax.plot([0,e],[x[i],x[i]],color=color_minus)\n",
    "    ax.scatter(y,x,s,color=color_minus,zorder=11)\n",
    "\n",
    "    if good_is_plus:\n",
    "        plus=color_good\n",
    "        minus=color_bad\n",
    "        plus_glyph='✔️'\n",
    "        minus_glyph='✖️'\n",
    "    else:\n",
    "        plus=color_bad\n",
    "        minus=color_good\n",
    "        plus_glyph='✖️'\n",
    "        minus_glyph='✔️'\n",
    "    \n",
    "    ax.set_ylim(1965,2020)\n",
    "    ax.axvline(axv2,color=color_ax)\n",
    "    # ax.set_xticks([])\n",
    "    # ax.set_yticks([])\n",
    "    ax.axis('off')\n",
    "    ax.text(ex*0.1, 1966, str(len(z1)), fontsize=font_size, \n",
    "            horizontalalignment='left', color=plus, fontproperties=prop)\n",
    "    ax.text(ex*0.02, 1966, plus_glyph, fontsize=font_size, \n",
    "            horizontalalignment='left', color=plus, fontproperties=prop2)\n",
    "    ax.text(-ex*0.1, 1966, str(len(z2)), fontsize=font_size, \n",
    "            horizontalalignment='right', color=minus, fontproperties=prop)\n",
    "    ax.text(-ex*0.02, 1966, minus_glyph, fontsize=font_size, \n",
    "            horizontalalignment='right', color=minus, fontproperties=prop2)\n",
    "    ax.text(ex*0.02, 2021, m1[mennyiseg], \n",
    "            horizontalalignment='left', color=color_plus, fontproperties=prop2, fontsize=font_size*1.5)\n",
    "    ax.text(-ex*0.02, 2021, m2[mennyiseg],\n",
    "            horizontalalignment='right', color=color_minus, fontproperties=prop2, fontsize=font_size*1.5)\n",
    "    \n",
    "    \n",
    "    if len(z1)>len(z2):\n",
    "        color_title=plus\n",
    "    else:\n",
    "        color_title=minus\n",
    "    \n",
    "    \n",
    "    if ertek_is_plus:\n",
    "        plus=color_none2\n",
    "        minus=color_none\n",
    "        plus_glyph='✔️'\n",
    "        minus_glyph='✖️'\n",
    "    else:\n",
    "        plus=color_none\n",
    "        minus=color_none2\n",
    "        plus_glyph='✖️'\n",
    "        minus_glyph='✔️'\n",
    "        \n",
    "    ax=axes[0]\n",
    "    z=pd.concat([observation_ratio_good,observation_ratio_bad])\n",
    "    ex=max(z.values)-min(z.values)\n",
    "    z1=z[z>=axv1].dropna()\n",
    "    z2=z[z<axv1].dropna()\n",
    "    y=z1.values\n",
    "    x=z1.index\n",
    "    for i,e in enumerate(y):\n",
    "        ax.plot([0,e],[x[i],x[i]],color=plus)\n",
    "    ax.scatter(y,x,s*0.4,color=plus,zorder=12)\n",
    "    ax.text(ex*0.25, 1966, str(len(z1)), fontsize=font_size, \n",
    "            horizontalalignment='left', color=plus, fontproperties=prop)\n",
    "    ax.text(ex*0.04, 1966, plus_glyph, fontsize=font_size, \n",
    "            horizontalalignment='left', color=plus, fontproperties=prop2)\n",
    "    y=z2.values\n",
    "    x=z2.index\n",
    "    for i,e in enumerate(y):\n",
    "        ax.plot([0,e],[x[i],x[i]],color=minus)\n",
    "    ax.scatter(y,x,s*0.4,color=minus,zorder=11)\n",
    "    ax.text(-ex*0.25, 1966, str(len(z2)), fontsize=font_size, \n",
    "            horizontalalignment='right', color=minus, fontproperties=prop)\n",
    "    ax.text(-ex*0.04, 1966, minus_glyph, fontsize=font_size, \n",
    "            horizontalalignment='right', color=minus, fontproperties=prop2)\n",
    "    ax.text(axv1, 2022, m3[kondicio], \n",
    "            horizontalalignment='center', color=color_none2, fontproperties=prop2, fontsize=font_size*1.5)\n",
    "    \n",
    "    \n",
    "    ax.set_ylim(1965,2020)\n",
    "    ax.axvline(0,color=color_ax)\n",
    "    ax.plot([axv1,axv1],[1971,2020],color=color_none2,ls='--')\n",
    "    # ax.set_xticks([])\n",
    "    # ax.set_yticks([])\n",
    "    ax.axis('off')\n",
    "    \n",
    "    ax= axes[2].inset_axes([0, 0.25, 1, 0.5])\n",
    "    x=observation_ratio_good.values\n",
    "    y=prediction_ts.loc[observation_ratio_good.index].values\n",
    "    if good_is_plus:\n",
    "        plus=color_good\n",
    "        minus=color_bad\n",
    "    else:\n",
    "        plus=color_bad\n",
    "        minus=color_good\n",
    "    colors=[plus if i>0 else minus for i in y]\n",
    "    if not ertek_is_plus: x=-x\n",
    "    if not good_is_plus: y=-y\n",
    "    x-=min(x)\n",
    "    x/=max(x)\n",
    "    y-=min(y)\n",
    "    y/=max(y)\n",
    "    ax.plot([-0.1,-0.1],[-0.1,1.1],color=color_none)\n",
    "    ax.plot([-0.1,1.1],[-0.1,-0.1],color=color_none)\n",
    "    ax.plot([-0.1,1.1],[-0.1,1.1],color=color_none,ls='--')\n",
    "    ax.text(1.15, -0.1, m3[kondicio], verticalalignment='center',\n",
    "            horizontalalignment='left', color=color_none, fontproperties=prop2, fontsize=font_size*1.3)\n",
    "    ax.text(-0.1, 1.15, m3[mennyiseg], verticalalignment='bottom',\n",
    "            horizontalalignment='center', color=color_none, fontproperties=prop2, fontsize=font_size*1.3)\n",
    "    \n",
    "    ax.scatter(x,y,s*0.4,color=colors,zorder=10)\n",
    "    ax.axis('off')\n",
    "    \n",
    "    ax=axes[2]\n",
    "    ax.axis('off')\n",
    "    \n",
    "    plt.suptitle(title,y=0.08,color=color_title,fontproperties=prop)\n",
    "    # ax.text(0, 1960, title, fontsize=font_size, horizontalalignment='center', color=color_ax)\n",
    "    \n",
    "#     plt.savefig(str(mondas)+'.png',dpi=300, transparent=tp)\n",
    "    plt.savefig(str(mondas)+'.png',dpi=300, facecolor=color_bg)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "rainy_cutoff=0.1\n",
    "temp_cutoff=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mondasok=[11,0,14]#,0,14,33,34,35,36]\n",
    "for mondas in mondasok:\n",
    "    nep=nepi.loc[mondas]\n",
    "    observation_range=[nep['Dátum:mettől']+pd.to_timedelta('-1D'),nep['Dátum:meddig']+pd.to_timedelta('+2D')]\n",
    "    prediction_range=[nep['Periódus:mettől'],nep['Periódus:meddig']+pd.to_timedelta('+1D')]\n",
    "    \n",
    "    dgs=[]\n",
    "    dhs=[]\n",
    "    axv1=0\n",
    "    axv2=0\n",
    "    for year in range(1973,2019):\n",
    "        k=0\n",
    "        from_date=pd.to_datetime(str(year)+'-'+str(observation_range[k].month)+'-'+str(observation_range[k].day))\n",
    "        from_pred=pd.to_datetime(str(year)+'-'+str(prediction_range[k].month)+'-'+str(prediction_range[k].day))\n",
    "        k=1\n",
    "        to_date=pd.to_datetime(str(year)+'-'+str(observation_range[k].month)+'-'+str(observation_range[k].day))\n",
    "        to_pred=pd.to_datetime(str(year)+'-'+str(prediction_range[k].month)+'-'+str(prediction_range[k].day))\n",
    "        if to_pred<to_date:\n",
    "            to_pred+=pd.to_timedelta('1Y')\n",
    "        dg=df.loc[from_date:]\n",
    "        dg=dg[:to_date]\n",
    "        dgs.append(dg)\n",
    "        dh=df.loc[from_pred:]\n",
    "        dh=dh[:to_pred]\n",
    "        dhs.append(dh)\n",
    "    observation_data=pd.concat(dgs)\n",
    "    prediction_data=pd.concat(dhs)\n",
    "    \n",
    "    if nep['Érték'] in ['+','>0']:\n",
    "        ertek_is_plus=True\n",
    "    else:\n",
    "        ertek_is_plus=False\n",
    "        \n",
    "    if nep['Irány'] in ['+','>0']:\n",
    "        good_is_plus=True\n",
    "    else:\n",
    "        good_is_plus=False\n",
    "        \n",
    "    if nep['Kondíció'] in ['Rain','Snow']:\n",
    "        if nep['Kondíció']=='Rain':\n",
    "            obs_key=6\n",
    "        if nep['Kondíció']=='Snow':\n",
    "            obs_key=7\n",
    "        axv1=rainy_cutoff\n",
    "        observation_total=observation_data.groupby(['year']).count()[['hour']]\n",
    "        observation_total.columns=['total']\n",
    "        observation_ratio=observation_data.groupby(['year','W']).count()[['hour']].join(observation_total)\n",
    "        observation_ratio['ratio']=observation_ratio['hour']/observation_ratio['total']\n",
    "        observation_ratio=observation_ratio.reset_index().set_index(['W','year']).loc[obs_key][['ratio']]\n",
    "        observation_ratio_good=observation_ratio[observation_ratio['ratio']>rainy_cutoff].dropna()\n",
    "        observation_ratio_bad=observation_ratio[observation_ratio['ratio']<=rainy_cutoff].dropna()\n",
    "\n",
    "        if nep['Mennyiség']=='Temp':\n",
    "            pred_key='TEMP'\n",
    "        pred_all=prediction_data.groupby('year').mean()[pred_key]\n",
    "        prediction_ts=pred_all-pred_all.mean()\n",
    "    elif nep['Kondíció']=='Temp':\n",
    "        obs_key='TEMP'\n",
    "        temp_cutoff=abs(temp_cutoff)\n",
    "        observation_data.groupby('year').mean()[obs_key] \n",
    "        observation_total=observation_data.mean()[obs_key]\n",
    "        observation_ratio=observation_data.groupby('year').mean()[obs_key]\n",
    "        observation_ratio=observation_ratio-observation_total\n",
    "        if ertek_is_plus:\n",
    "            observation_ratio_good=observation_ratio[observation_ratio>temp_cutoff].dropna()\n",
    "            observation_ratio_bad=observation_ratio[observation_ratio<=temp_cutoff].dropna()\n",
    "        else:\n",
    "            temp_cutoff=-temp_cutoff\n",
    "            observation_ratio_good=observation_ratio[observation_ratio<temp_cutoff].dropna()\n",
    "            observation_ratio_bad=observation_ratio[observation_ratio>=temp_cutoff].dropna()\n",
    "        axv1=temp_cutoff\n",
    "        \n",
    "        if nep['Mennyiség']=='Temp':\n",
    "            pred_key='TEMP'\n",
    "        pred_all=prediction_data.groupby('year').mean()[pred_key]\n",
    "        prediction_ts=pred_all-pred_all.mean()\n",
    "            \n",
    "    spine_plot(nep['Mondás'].strip(),mondas,nep['Kondíció'],nep['Mennyiség'],prediction_ts,observation_ratio_good,observation_ratio_bad,ertek_is_plus,good_is_plus,axv1,axv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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