nepi/3c_plotter_banner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np\n",
    "from scipy import stats\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations=pd.read_csv('data/stations.csv').set_index('ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup plot params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.collections import PolyCollection\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_ax='#B6B6B6'\n",
    "color_bg='#ECACAC'\n",
    "color_obs_right0='#F2B880'\n",
    "color_obs_left0='#E7CFBC'\n",
    "color_pred_right0='#BB8B8B'\n",
    "color_pred_left0='#966B9D'\n",
    "color_pred_talalt0='#59c687'\n",
    "color_pred_nem_talalt0='#c95498'\n",
    "font_size=12\n",
    "s=40\n",
    "obs_talalt_glyph0='★'\n",
    "obs_nem_talalt_glyph0='☆'\n",
    "pred_talalt_glyph0='✔️'\n",
    "pred_nem_talalt_glyph0='✖️'\n",
    "title_icon_right={'Temp':'☼','Wind':'🌀','Hail':'⭕️','Snow':'☃️','Snow Depth':'⛄️','Rain':'☔️','Visib':'☀️'}\n",
    "title_icon_left={'Temp':'✨️','Wind':'☘','Hail':'⚪️','Snow':'⚪️','Snow Depth':'⚪️','Rain':'🌂','Visib':'⛈️'}\n",
    "title_icon={'Temp':'♨️','Rain':'☂️','Hail':'✴️','Snow':'❄️','Snow Depth':'⛷️','Wind':'⛵️','Cloud':'☁️','Visib':'⛅️'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(data,th):\n",
    "    a1=pd.DataFrame(data[data<=th])\n",
    "    a1['g']='left'\n",
    "    a2=pd.DataFrame(data[data>th])\n",
    "    a2['g']='right'\n",
    "    a3=pd.concat([a1,a2])\n",
    "    a3['x']='x'\n",
    "    return a1,a2,a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "def violin_plot(data,th,ax,color_left,color_right):\n",
    "    a=0.3\n",
    "    a1,a2,a3=get_data(data,th)\n",
    "    a1_augment=True\n",
    "    a2_augment=True\n",
    "    if len(a1)==0:\n",
    "        a1=a3.loc[[a3.index[0]]]\n",
    "        a1['g']='left'\n",
    "        a1[a1.columns[0]]=5\n",
    "    if len(a2)==0:\n",
    "        a2=a3.loc[[a3.index[0]]]\n",
    "        a2['g']='right'\n",
    "        a2[a2.columns[0]]=5\n",
    "    if len(a1)>1: a1_augment=False\n",
    "    if not a1_augment:\n",
    "        if a1.nunique()[a1.columns[0]]==1: \n",
    "            a1_augment=True\n",
    "    if a1_augment:\n",
    "        a11=a1.copy().loc[[a1.index[0]]]\n",
    "        a11[a11.columns[0]]+=random.random()*0.1*th\n",
    "        a11['x']='x'\n",
    "        a12=a1.copy().loc[[a1.index[0]]]\n",
    "        a12[a12.columns[0]]-=random.random()*0.1*th\n",
    "        a12['x']='x'\n",
    "        a3=pd.concat([a3,a11,a12])\n",
    "    if len(a2)>1: a2_augment=False\n",
    "    if not a2_augment:\n",
    "        if a2.nunique()[a2.columns[0]]==1: \n",
    "            a2_augment=True\n",
    "    if a2_augment:\n",
    "        a21=a2.copy().loc[[a2.index[0]]]\n",
    "        a21[a21.columns[0]]+=random.random()*0.1*th\n",
    "        a21['x']='x'\n",
    "        a22=a2.copy().loc[[a2.index[0]]]\n",
    "        a22[a22.columns[0]]-=random.random()*0.1*th\n",
    "        a22['x']='x'\n",
    "        a3=pd.concat([a3,a21,a22])\n",
    "    a3=a3.sort_values('g')\n",
    "    ax.axvline(0,color=color_ax)\n",
    "    if a3.nunique()['g']>1:\n",
    "        sns.violinplot(y=a1.columns[0], x='x',hue='g', data=a3, split=True, ax=ax,\n",
    "                      inner=None,linewidth=1, scale=\"count\", saturation=1)\n",
    "\n",
    "        ax.get_children()[0].set_color(mpl.colors.colorConverter.to_rgba(color_left, alpha=a))\n",
    "        ax.get_children()[0].set_edgecolor(color_left)\n",
    "        ax.get_children()[1].set_color(mpl.colors.colorConverter.to_rgba(color_right, alpha=a))\n",
    "        ax.get_children()[1].set_edgecolor(color_right)\n",
    "        ax.legend().remove()\n",
    "    else:\n",
    "        if len(a1)>0: \n",
    "            w=a1\n",
    "            c=color_left\n",
    "        else: \n",
    "            w=a2\n",
    "            c=color_right\n",
    "        sns.violinplot(y=w.columns[0], data=w, ax=ax, \n",
    "                       inner=None,linewidth=1, scale=\"count\", saturation=1)\n",
    "        ax.set_xlim([-1,0])\n",
    "        ax.get_children()[0].set_color(mpl.colors.colorConverter.to_rgba(c, alpha=a))\n",
    "        ax.get_children()[0].set_edgecolor(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup_axes():\n",
    "    fig,axes=plt.subplots(1,3,figsize=(8,10),gridspec_kw={'width_ratios': [1, 3, 1]})\n",
    "    axi_top= axes[2].inset_axes([0.1, 0.65, 1, 0.3])\n",
    "    axi_top.axis('off')\n",
    "    axi_bottom= axes[2].inset_axes([0.1, 0, 1, 0.5])\n",
    "    axi_bottom.axis('off')\n",
    "    axes[0].axis('off')\n",
    "    axes[1].axis('off')\n",
    "    axes[2].axis('off')\n",
    "    axes[0]=axes[0].inset_axes([0, 0.15, 1, 0.85])\n",
    "    axes[1]=axes[1].inset_axes([0, 0.15, 1, 0.85])\n",
    "    axes[0].axis('off')\n",
    "    axes[1].axis('off')\n",
    "    return fig, axes, axi_top, axi_bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem_plot(data,ax,color,s=s):\n",
    "    data=pd.DataFrame(data)\n",
    "    x=data.index\n",
    "    y=data[data.columns[0]].values\n",
    "    for i,e in enumerate(y):\n",
    "        ax.plot([0,e],[x[i],x[i]],color=color)\n",
    "    ax.scatter(y,x,s,color=color,zorder=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem2_plot(data,th,ax,color_left,color_right,s=s,axv_color=None):\n",
    "    if axv_color==None:axv_color=color_right\n",
    "    a1,a2,a3=get_data(data,th)\n",
    "    stem_plot(a1,ax,color_left,s)\n",
    "    stem_plot(a2,ax,color_right,s)\n",
    "    ax.axvline(0,color=color_ax)\n",
    "    #if th!=0:\n",
    "    if True:\n",
    "        ax.axvline(th,color=axv_color,ls='--',zorder=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "def icons_plot(axes,kondicio,mennyiseg,observation_th,prediction_th):\n",
    "    ylim=axes[0].get_ylim()\n",
    "    xlim=axes[1].get_xlim()\n",
    "    y_max_coord=ylim[0]+(ylim[1]-ylim[0])*1.05\n",
    "    y_max_coord2=ylim[0]+(ylim[1]-ylim[0])*1.05 #1.04\n",
    "    x_icon_coord_shift=(xlim[1]-xlim[0])*0.1\n",
    "    axes[0].text(observation_th, y_max_coord, title_icon[kondicio], \n",
    "            horizontalalignment='center', color=color_obs_right0, fontproperties=prop2, fontsize=font_size*1.5)\n",
    "    axes[1].text(prediction_th, y_max_coord, title_icon[mennyiseg], \n",
    "            horizontalalignment='center', color=color_ax, fontproperties=prop2, fontsize=font_size*1.5)\n",
    "    axes[1].text(prediction_th+x_icon_coord_shift, y_max_coord2, title_icon_right[mennyiseg], \n",
    "            horizontalalignment='center', color=color_pred_right, fontproperties=prop2, fontsize=font_size*1.5)\n",
    "    axes[1].text(prediction_th-x_icon_coord_shift, y_max_coord2, title_icon_left[mennyiseg], \n",
    "            horizontalalignment='center', color=color_pred_left, fontproperties=prop2, fontsize=font_size*1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "def talalat_plot_line(axes,n_prediction_ts_good,n_prediction_ts_bad,\n",
    "                        n_prediction_ts_good_talalt,n_prediction_ts_good_nem_talalt,\n",
    "                      observation_th,prediction_th):\n",
    "    ylim=axes[0].get_ylim()\n",
    "    xlim=axes[0].get_xlim()\n",
    "    y_max_coord=ylim[0]+(ylim[1]-ylim[0])*(-0.07)\n",
    "    x_icon_coord_shift=(xlim[1]-xlim[0])*0.1\n",
    "    x_icon_coord_shift2=(xlim[1]-xlim[0])*0.27\n",
    "    axes[0].text(observation_th+x_icon_coord_shift, y_max_coord, obs_talalt_glyph, \n",
    "            horizontalalignment='center', color=color_obs_right, fontproperties=prop2)\n",
    "    axes[0].text(observation_th-x_icon_coord_shift, y_max_coord, obs_nem_talalt_glyph, \n",
    "            horizontalalignment='center', color=color_obs_left, fontproperties=prop2)\n",
    "    axes[0].text(observation_th+x_icon_coord_shift2, y_max_coord, n_prediction_ts_good, \n",
    "            horizontalalignment='center', color=color_obs_right, fontproperties=prop)\n",
    "    axes[0].text(observation_th-x_icon_coord_shift2, y_max_coord, n_prediction_ts_bad, \n",
    "            horizontalalignment='center', color=color_obs_left, fontproperties=prop)\n",
    "    axes[0].text(observation_th, y_max_coord, '|', \n",
    "            horizontalalignment='center', color=color_obs_right0, fontproperties=prop,fontsize=19)\n",
    "\n",
    "    xlim=axes[1].get_xlim()\n",
    "    x_icon_coord_shift=(xlim[1]-xlim[0])*0.04\n",
    "    x_icon_coord_shift2=(xlim[1]-xlim[0])*0.1\n",
    "    axes[1].text(prediction_th+x_icon_coord_shift, y_max_coord, pred_talalt_glyph, \n",
    "            horizontalalignment='center', color=color_pred_talalt, fontproperties=prop2)\n",
    "    axes[1].text(prediction_th-x_icon_coord_shift, y_max_coord, pred_nem_talalt_glyph, \n",
    "            horizontalalignment='center', color=color_pred_nem_talalt, fontproperties=prop2)\n",
    "    axes[1].text(prediction_th+x_icon_coord_shift2, y_max_coord, n_prediction_ts_good_talalt, \n",
    "            horizontalalignment='center', color=color_pred_talalt, fontproperties=prop)\n",
    "    axes[1].text(prediction_th-x_icon_coord_shift2, y_max_coord, n_prediction_ts_good_nem_talalt, \n",
    "            horizontalalignment='center', color=color_pred_nem_talalt, fontproperties=prop)\n",
    "    axes[1].text(prediction_th, y_max_coord, '|', \n",
    "            horizontalalignment='center', color=color_pred_right, fontproperties=prop,fontsize=19)\n",
    "    \n",
    "    y_max_coord=ylim[0]+(ylim[1]-ylim[0])*(-0.14)\n",
    "    axes[0].text(observation_th, y_max_coord, 'feltétel', \n",
    "            horizontalalignment='center', color=color_obs_right0, fontproperties=prop)\n",
    "    axes[1].text(prediction_th, y_max_coord, 'jóslat', \n",
    "            horizontalalignment='center', color=color_pred_right, fontproperties=prop)\n",
    "    y_max_coord=ylim[0]+(ylim[1]-ylim[0])*(-0.13)\n",
    "    x_coord_shift=prediction_th+(prediction_th-xlim[0])*(-0.4)\n",
    "    axes[1].annotate('', xy=(x_coord_shift, y_max_coord),xycoords='data',annotation_clip=False,\n",
    "             xytext=(xlim[0], y_max_coord),arrowprops=dict(arrowstyle= '->',color=color_obs_right0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "def talalat_plot_violin(axes,n_prediction_ts_good,n_prediction_ts_bad,n_prediction_ts_good_talalt,n_prediction_ts_good_nem_talalt):\n",
    "    y_icon_obs=0.65\n",
    "    y_icon_pred=0.5\n",
    "    if color_obs_right==color_obs_right0: x=0.72\n",
    "    else: x=0.47\n",
    "    axes[2].text(0.72, y_icon_obs, obs_talalt_glyph, \n",
    "            horizontalalignment='center', color=color_obs_right, fontproperties=prop2)\n",
    "    axes[2].text(0.9, y_icon_obs,n_prediction_ts_good, \n",
    "            horizontalalignment='center', color=color_obs_right, fontproperties=prop)\n",
    "    axes[2].text(0.47, y_icon_obs, obs_nem_talalt_glyph, \n",
    "            horizontalalignment='center', color=color_obs_left, fontproperties=prop2)\n",
    "    axes[2].text(0.29, y_icon_obs, n_prediction_ts_bad, \n",
    "            horizontalalignment='center', color=color_obs_left, fontproperties=prop)\n",
    "    \n",
    "    axes[2].text(0.72, y_icon_pred, pred_talalt_glyph,\n",
    "            horizontalalignment='center', color=color_pred_talalt, fontproperties=prop2)\n",
    "    axes[2].text(0.9, y_icon_pred, n_prediction_ts_good_talalt, \n",
    "            horizontalalignment='center', color=color_pred_talalt, fontproperties=prop)\n",
    "    axes[2].text(0.47, y_icon_pred, pred_nem_talalt_glyph, \n",
    "            horizontalalignment='center', color=color_pred_nem_talalt, fontproperties=prop2)\n",
    "    axes[2].text(0.29, y_icon_pred, n_prediction_ts_good_nem_talalt,\n",
    "            horizontalalignment='center', color=color_pred_nem_talalt, fontproperties=prop)\n",
    "    \n",
    "    axes[2].annotate('', xy=(0.59, y_icon_pred*1.04),xycoords='data',\n",
    "             xytext=(x, y_icon_obs*0.98),arrowprops=dict(arrowstyle= '->',color=color_obs_right0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "def talalat_plot(axes,ns,observation_th,prediction_th):\n",
    "    n_prediction_ts_good,n_prediction_ts_bad,n_prediction_ts_good_talalt,n_prediction_ts_good_nem_talalt=ns\n",
    "    talalat_plot_line(axes,n_prediction_ts_good,n_prediction_ts_bad,\n",
    "                        n_prediction_ts_good_talalt,n_prediction_ts_good_nem_talalt,\n",
    "                      observation_th,prediction_th)\n",
    "    talalat_plot_violin(axes,n_prediction_ts_good,n_prediction_ts_bad,\n",
    "                        n_prediction_ts_good_talalt,n_prediction_ts_good_nem_talalt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_plot(data,ax,k):\n",
    "    y=data.values\n",
    "    x=data.index\n",
    "    ex=max(y)-min(y)\n",
    "    text_off=abs(ex*k)\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(int(x[0])), \n",
    "            horizontalalignment=text_align, verticalalignment='center', \n",
    "            color=color_ax, 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(int(x[-1])), \n",
    "            horizontalalignment=text_align, verticalalignment='center', \n",
    "            color=color_ax, fontproperties=prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spine_plot(datum,title,mondas,jelentes,kondicio,mennyiseg,\n",
    "               observation_ts,observation_th,prediction_ts,prediction_th,c):\n",
    "    \n",
    "    #data\n",
    "    prediction_ts_good=prediction_ts.loc[observation_ts[observation_ts>observation_th].index]\n",
    "    prediction_ts_bad=prediction_ts.loc[observation_ts[observation_ts<=observation_th].index]\n",
    "    n_prediction_ts_good=len(prediction_ts_good)\n",
    "    n_prediction_ts_bad=len(prediction_ts_bad)\n",
    "    \n",
    "    if color_obs_right0!=color_obs_right:\n",
    "        prediction_ts_good,prediction_ts_bad=prediction_ts_bad,prediction_ts_good\n",
    "    prediction_ts_good_nem_talalt,prediction_ts_good_talalt,\\\n",
    "        prediction_ts_good_joined=get_data(prediction_ts_good,prediction_th)\n",
    "    n_prediction_ts_good_talalt=len(prediction_ts_good_talalt)\n",
    "    n_prediction_ts_good_nem_talalt=len(prediction_ts_good_nem_talalt)\n",
    "    ns=[n_prediction_ts_good,n_prediction_ts_bad,n_prediction_ts_good_talalt,n_prediction_ts_good_nem_talalt]\n",
    "    \n",
    "    #plots\n",
    "    fig, axes, axi_top, axi_bottom=setup_axes()\n",
    "    \n",
    "    #stem2_plot(observation_ts,observation_th,axes[0],color_obs_left,color_obs_right,s/2,color_obs_right0)\n",
    "    stem2_plot(prediction_ts_good,prediction_th,axes[1],color_pred_left,color_pred_right)\n",
    "    stem_plot(prediction_ts_bad,axes[1],color_ax)\n",
    "    \n",
    "    #violin_plot(observation_ts,observation_th,axi_top,color_obs_left,color_obs_right)\n",
    "    #violin_plot(prediction_ts_good,prediction_th,axi_bottom,color_pred_left,color_pred_right)\n",
    "    \n",
    "    #icons\n",
    "    #icons_plot(axes,kondicio,mennyiseg,observation_th,prediction_th)\n",
    "    \n",
    "    #talalat\n",
    "    #talalat_plot(axes,ns,observation_th,prediction_th)\n",
    "    \n",
    "    #years\n",
    "    obs_year_index=observation_data[[obs_key,'pyear']].dropna().groupby('pyear').mean().index\n",
    "    pred_year_index=prediction_data[[pred_key,'pyear']].dropna().groupby('pyear').mean().index\n",
    "    pred_year_index_filt=prediction_ts.loc[pred_year_index].dropna().index\n",
    "    obs_year_index_filt=observation_ts.loc[obs_year_index].dropna().index\n",
    "    pred_year_index2=max(min(pred_year_index_filt),min(obs_year_index_filt))\n",
    "    pred_year_index=range(pred_year_index2,max(pred_year_index_filt)+1)\n",
    "    #year_plot(observation_ts.loc[obs_year_index].dropna(),axes[0],0.09)\n",
    "    year_plot(prediction_ts.loc[pred_year_index].dropna(),axes[1],0.03)\n",
    "    \n",
    "    #titles\n",
    "    len_ratio=0.15*(-1+(len(jelentes.split(',')[0])/len(jelentes.split(',')[1])))\n",
    "    #fig.text(0.5+len_ratio,0.04,jelentes.split(',')[0]+',',color=color_obs_right0,\n",
    "    #         fontproperties=prop,fontsize=font_size*0.7,horizontalalignment='right')\n",
    "    if color_pred_talalt==color_pred_talalt0: color_pred_side=color_pred_right\n",
    "    else: color_pred_side=color_pred_left\n",
    "    #fig.text(0.5+len_ratio,0.04,jelentes.split(',')[1],color=color_pred_side,\n",
    "    #         fontproperties=prop,fontsize=font_size*0.7,horizontalalignment='left')\n",
    "    if n_prediction_ts_good_nem_talalt>=n_prediction_ts_good_talalt:\n",
    "        color_title=color_pred_nem_talalt\n",
    "    else: \n",
    "        color_title=color_pred_talalt\n",
    "    verdict=int(100*n_prediction_ts_good_talalt/(n_prediction_ts_good_talalt+n_prediction_ts_good_nem_talalt))\n",
    "    if color_pred_talalt!=color_pred_talalt0: verdict=100-verdict\n",
    "    return_verdict=int(verdict)\n",
    "    verdict=str(verdict)+'%'\n",
    "    #plt.suptitle(title,y=0.11,color=color_title,fontproperties=prop,fontsize=font_size)\n",
    "    #fig.text(0.97,0.04,verdict, fontproperties=prop,\n",
    "    #        horizontalalignment='right', color=color_title, fontsize=font_size*2, )\n",
    "    #fig.text(0.03,0.04, datum, fontproperties=prop,\n",
    "    #        horizontalalignment='left', color=color_obs_right0, fontsize=font_size*2, )\n",
    "    \n",
    "    plt.savefig(c+'/'+str(mondas)+'_banner.png',dpi=1500, facecolor='#E6E6E6')\n",
    "    plt.show()\n",
    "    return return_verdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_data(dz,observation_range,prediction_range):\n",
    "    dgs=[]\n",
    "    dhs=[]\n",
    "    for year in range(int(dz.min()['year']),int(dz.max()['year'])):\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=dz.loc[from_date:]\n",
    "        dg=dg[:to_date]\n",
    "        dg['pyear']=year\n",
    "        dgs.append(dg)\n",
    "        dh=dz.loc[from_pred:]\n",
    "        dh=dh[:to_pred]\n",
    "        dh['pyear']=year\n",
    "        dhs.append(dh)\n",
    "    return pd.concat(dgs),pd.concat(dhs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_direction(kondicio, mennyiseg):\n",
    "    if kondicio:\n",
    "        color_obs_right=color_obs_right0\n",
    "        color_obs_left=color_obs_left0\n",
    "        obs_talalt_glyph='★'\n",
    "        obs_nem_talalt_glyph='☆'\n",
    "    else:\n",
    "        color_obs_right=color_obs_left0\n",
    "        color_obs_left=color_obs_right0\n",
    "        obs_talalt_glyph='☆'\n",
    "        obs_nem_talalt_glyph='★'\n",
    "    if mennyiseg:\n",
    "        color_pred_talalt=color_pred_talalt0\n",
    "        color_pred_nem_talalt=color_pred_nem_talalt0\n",
    "        pred_talalt_glyph='✔️'\n",
    "        pred_nem_talalt_glyph='✖️'\n",
    "    else:\n",
    "        color_pred_talalt=color_pred_nem_talalt0\n",
    "        color_pred_nem_talalt=color_pred_talalt0\n",
    "        pred_talalt_glyph='✖️'\n",
    "        pred_nem_talalt_glyph='✔️'\n",
    "    return color_obs_right,color_obs_left,obs_talalt_glyph,obs_nem_talalt_glyph,\\\n",
    "        color_pred_talalt,color_pred_nem_talalt,pred_talalt_glyph,pred_nem_talalt_glyph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sign(sign,key):\n",
    "    positive=True\n",
    "    if (('-' in sign) or ('+' in sign)):\n",
    "        if sign=='-':\n",
    "            positive=False\n",
    "        elif sign=='+':\n",
    "            positive=True\n",
    "    elif (('<' in sign) or ('>' in sign)):\n",
    "        if '<' in sign:\n",
    "            positive=False\n",
    "        elif '>' in sign:\n",
    "            positive=True\n",
    "    return positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "universal_normalize=['XTEMP','XVSB','XSPD']\n",
    "\n",
    "def get_ts_data(data,key,sign):\n",
    "    ts=data.groupby('year').mean()[key]\n",
    "    if (('-' in sign) or ('+' in sign)):\n",
    "        th=ts.mean()\n",
    "    else:\n",
    "        th=float(sign[1:])\n",
    "    if key in universal_normalize:\n",
    "        th-=ts.mean()        \n",
    "        ts-=ts.mean()\n",
    "    return ts,th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_comp_data(observation_data,obs_key,obs_sign,prediction_data,pred_key,pred_sign):\n",
    "    ertek_sign=True\n",
    "    irany_sign=True\n",
    "    observation_ts=observation_data.groupby('year').mean()[obs_key]\n",
    "    prediction_ts=prediction_data.groupby('year').mean()[pred_key]\n",
    "    prediction_th=observation_ts.mean()\n",
    "    observation_ts-=observation_ts.mean()\n",
    "    observation_th=observation_ts.min()*1.01\n",
    "    prediction_th-=prediction_ts.mean()\n",
    "    prediction_ts-=prediction_ts.mean() \n",
    "    if obs_sign=='A':\n",
    "        if pred_sign=='A':\n",
    "            observation_th=0\n",
    "            prediction_th=0\n",
    "    else:\n",
    "        irany_sign=False\n",
    "    return observation_ts,observation_th,prediction_ts,prediction_th,ertek_sign,irany_sign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "mennyiseg_key={'Temp':'XTEMP','Snow Depth':'XSD','Wind':'XSPD','Rain':'YPCP','Visib':'XVSB',\n",
    "              'Snow':'YSNW','Hail':'YHAL'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations_to_include={'ro':[150040,151700,151450,152600,152470,150800,152300,150100,151200,152000],\n",
    "                    'hu':[128820,128120,127720,128600,128390,128920,128430,128250,128220,128050,\n",
    "                         129150,129420,129600,129700,129820,129920,129350,129100]}\n",
    "stations_to_include['huro']=stations_to_include['hu']+stations_to_include['ro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_country(c,h='ds',plot=False):\n",
    "    if c=='huro':\n",
    "        hu=pd.read_csv('data/'+'hu'+'_'+h+'.csv') #daily data\n",
    "        ro=pd.read_csv('data/'+'ro'+'_'+h+'.csv') #daily data\n",
    "        df=pd.concat([hu,ro])\n",
    "    else:\n",
    "        df=pd.read_csv('data/'+c+'_'+h+'.csv') #daily data\n",
    "        # df=pd.read_csv('data/'+c+'_hs.csv') #high_res data\n",
    "    df=df[df['ID'].isin(stations_to_include[c])]\n",
    "    df['time']=pd.to_datetime(df['time'])\n",
    "    df['year']=df['time'].dt.year\n",
    "    df['month']=df['time'].dt.month\n",
    "    df['day']=df['time'].dt.day\n",
    "    df['hour']=df['time'].dt.hour\n",
    "    df=df.set_index('time')\n",
    "    df=df.sort_index()\n",
    "    if plot: df.groupby('year').nunique()['ID'].plot()\n",
    "    nepi=pd.read_excel(c+'/idojaras_'+c+'.xlsx')\n",
    "    return df,nepi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "c='ro'\n",
    "df,nepi=get_country(c)\n",
    "dz=df.groupby(['time']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ts 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:42: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72.0\n"
     ]
    }
   ],
   "source": [
    "color_pred_left=color_pred_left0\n",
    "color_pred_right=color_pred_right0\n",
    "mondasok=nepi['ID'].values\n",
    "shares=[]\n",
    "mondasok=[14]\n",
    "for mondas in mondasok:\n",
    "    nep=nepi.loc[mondas]\n",
    "    if str(nep['Mennyiség'])!='nan':\n",
    "        obs_key=mennyiseg_key[nep['Kondíció']]\n",
    "        pred_key=mennyiseg_key[nep['Mennyiség']]\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",
    "        observation_data,prediction_data=filter_data(dz,observation_range,prediction_range)\n",
    "\n",
    "        #comparison\n",
    "        if str(nep['Érték']) in ['A','B']:\n",
    "            print('comp',mondas)\n",
    "            observation_ts,observation_th,prediction_ts,prediction_th,ertek_sign,irany_sign=\\\n",
    "                get_comp_data(observation_data,obs_key,nep['Érték'],\\\n",
    "                          prediction_data,pred_key,nep['Irány'])\n",
    "        #time series\n",
    "        else:\n",
    "            print('ts',mondas)\n",
    "            ertek_sign=get_sign(nep['Érték'],obs_key)\n",
    "            irany_sign=get_sign(nep['Irány'],pred_key)\n",
    "            observation_ts,observation_th=get_ts_data(observation_data,obs_key,nep['Érték'])\n",
    "            prediction_ts,prediction_th=get_ts_data(prediction_data,pred_key,nep['Irány'])\n",
    "\n",
    "        color_obs_right,color_obs_left,obs_talalt_glyph,obs_nem_talalt_glyph,\\\n",
    "            color_pred_talalt,color_pred_nem_talalt,pred_talalt_glyph,pred_nem_talalt_glyph=\\\n",
    "            set_direction(ertek_sign, irany_sign)\n",
    "\n",
    "        #datum=str(nep['Dátums'])[:3]+'. '+str(nep['Dátum:mettől'].day)\n",
    "        datum=nep['DS']\n",
    "        shares.append(spine_plot(datum,nep['Mondás'].strip(),mondas,nep['Jelentés'].strip(),nep['Kondíció'],nep['Mennyiség'],\n",
    "               observation_ts,observation_th,prediction_ts,prediction_th,c))\n",
    "print(np.mean(shares))"
   ]
  },
  {
   "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": []
  }
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