Interface-V 1.1.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def __Version__():\n",
    "    return('1.1.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from PIL import Image\n",
    "import datetime \n",
    "import time\n",
    "from ipywidgets import Button, Layout\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import time\n",
    "from ipywidgets import Button, Layout\n",
    "import jinja2\n",
    "import json\n",
    "import asciify\n",
    "import scipy\n",
    "from matplotlib.colors import ListedColormap\n",
    "from IPython.core.display import display, HTML\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "BGS = asciify.Asciify('https://raw.githubusercontent.com/24-Tony/BUGSS_Interface/main/BUGSS.PNG',100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = mpimg.imread('https://bugssonline.org/wp-content/themes/BUGS3/assets/images/bugsslogo-square300.png')\n",
    "plt.axis('off')\n",
    "imgplot = plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nanColumns(dataset,columns):\n",
    "    for c in columns:\n",
    "        dataset[columns] = np.nan\n",
    "    print(type(dataset))\n",
    "    return dataset\n",
    "def fillRows(dataset,fillData,fillData2):\n",
    "    prgrs = 0\n",
    "    dataset2 = dataset\n",
    "    for index, row in fillData.iterrows():\n",
    "        prgrs +=1\n",
    "        #print(row,'--',index)\n",
    "        print(format(prgrs,'5.0f'),end='\\r')\n",
    "        #print(dataset.iloc[index])\n",
    "        dataset.iloc[index] = row\n",
    "        dataset2.iloc[index] = fillData2.iloc[index]\n",
    "    return dataset,dataset2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "clear_output()\n",
    "\n",
    "ES_ = pd.read_pickle('ES.bugssData')\n",
    "EB_ = pd.read_pickle('EB.bugssData')\n",
    "W_ = pd.read_pickle('W.bugssData')\n",
    "IS_ = pd.read_pickle('IS.bugssData')\n",
    "IB_ = pd.read_pickle('IB.bugssData')\n",
    "\n",
    "ES_['datetime']=ES_['DateTime']\n",
    "EB_['datetime']=EB_['DateTime']\n",
    "W_['datetime']=W_['DateTime']\n",
    "IS_['datetime']=IS_['DateTime']\n",
    "IB_['datetime']=IB_['DateTime']\n",
    "\n",
    "\n",
    "\n",
    "ES=ES_.set_index('datetime')\n",
    "EB=EB_.set_index('datetime')\n",
    "W=W_.set_index('datetime')\n",
    "IS=IS_.set_index('datetime')\n",
    "IB=IB_.set_index('datetime')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# !jupyter nbextension enable --py widgetsnbextension --sys-prefix\n",
    "# !jupyter serverextension enable voila --sys-prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CleanList(LST,removeCommas,removeMore):\n",
    "    listStr = str(LST)\n",
    "    listStr = listStr.replace(\"'\",'')\n",
    "    if removeCommas==True:\n",
    "        #listStr = listStr.replace(\",,\",',')\n",
    "        listStr = listStr.replace(\",, \",', ')\n",
    "    listStr = listStr.replace('\\\\','')\n",
    "    listStr = listStr.replace('/','')\n",
    "    \n",
    "    String = listStr[1:(len(listStr)-(1+removeMore))]\n",
    "    #print(String)\n",
    "    return(String)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TrimYR(dataset,year,months,DateColumn,Name):\n",
    "    m1 = months[0].month\n",
    "    m2 = months[1].month\n",
    "    From = str(year)+'-'+str(m1)\n",
    "    To = str(year)+'-'+str(m2)\n",
    "    DStrim = dataset.loc[From:To]\n",
    "    DStrim['Name'] = Name\n",
    "    return DStrim\n",
    "def prepDatasets(datasets,Years,Months):\n",
    "    Dsets = []\n",
    "    for ds in datasets:\n",
    "        if ds == 'East Surface':\n",
    "            df=ES\n",
    "        elif ds == 'East Bottom':\n",
    "            df=EB\n",
    "        elif ds == 'West':\n",
    "            df=W\n",
    "        elif ds == 'Inversion Surface':\n",
    "            df=IS\n",
    "        elif ds == 'Inversion Bottom':\n",
    "            df=IB\n",
    "        for yr in Years:\n",
    "            Dsets.append(TrimYR(df,yr,Months,'DateTime',ds+'-'+yr))\n",
    "    return(Dsets)\n",
    "\n",
    "def YPlot(datasets,DataPoints,Scale,Years,Months,Style,Transparency,fillb):\n",
    "    plt.style.use(Style)\n",
    "    margin = .1\n",
    "    RandYr='2020'#LEAP YEAR TO AVOID DATETIME ERRORS\n",
    "    MaxOpac = .9\n",
    "    prg1 = len(datasets)*len(Years)\n",
    "    prg2 = len(DataPoints)\n",
    "    prg3 = prg1*prg2+prg1\n",
    "    prg4= prg1*(prg2+prg1)+prg1\n",
    "    prg_part = 0\n",
    "    prg = 0\n",
    "    progress.max = prg3+1\n",
    "    plt.style.use(Style)\n",
    "#     fig, ax = plt.subplots()\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    #fig.style.use(Style)\n",
    "    \n",
    "    if Scale[0] == 2:\n",
    "        plt.yscale('log')\n",
    "    if Scale[1] == 2:\n",
    "        plt.xscale('log')\n",
    "    Dsets=prepDatasets(datasets,Years,Months)\n",
    "    Lines = []\n",
    "    desc = ''\n",
    "    for ds in Dsets:\n",
    "        DF_ref = pd.DataFrame(ds)\n",
    "        name = DF_ref['Name']\n",
    "        DF=pd.DataFrame({'DateTime':DF_ref.index})\n",
    "        DF_ref['DTime']=DF_ref['DateTime'].dt.strftime('-%m-%d %X')\n",
    "        DF_ref['DTime'] = RandYr+DF_ref['DTime']\n",
    "        DF_ref['DTime'] = pd.to_datetime(DF_ref['DTime'],format='%Y-%m-%d %X')\n",
    "#         DF_ref['DTime']=DF_ref['DateTime'].dt.strftime('%m-%d %X')\n",
    "#         DF_ref['DTime'] = pd.to_datetime(DF_ref['DTime'],format='%m-%d %X')        \n",
    "        DF_ref=DF_ref.set_index('DTime')\n",
    "\n",
    "        DF['DTime']=DF['DateTime']\n",
    "        DF['DTime']=DF['DateTime'].dt.strftime('-%m-%d %X')\n",
    "        DF['DTime'] = RandYr+DF['DTime']\n",
    "        DF['DTime'] = pd.to_datetime(DF['DTime'],format='%Y-%m-%d %X')\n",
    "#         DF['DTime']=DF['DateTime'].dt.strftime('%m-%d %X')\n",
    "#         DF['DTime'] = pd.to_datetime(DF['DTime'],format='%m-%d %X')\n",
    "        DF=DF.set_index('DTime')\n",
    "        df=DF.drop(columns = ['DateTime'])\n",
    "        DF_ref=DF_ref.drop(columns = ['DateTime'])\n",
    "        #print(DF_ref)\n",
    "        for dp in DataPoints:\n",
    "            \n",
    "            #print('\\n\\n',dp,'\\n\\n')\n",
    "            df[dp]=DF_ref[dp]\n",
    "            #print(DF_ref)\n",
    "            prg+=1\n",
    "            progress.value = prg\n",
    "        #    df.plot('DTime',dp)#,ax=ax, label = (DF['Name'][0]+'-'+dp))\n",
    "        Desc = str(name[1])+'\\n'+str(df.describe())+'\\n\\n'\n",
    "        desc= desc + str(Desc)\n",
    "        for key, val in df.iteritems():\n",
    "            prg_part+=1\n",
    "            prg+=1\n",
    "            alp = MaxOpac-(Transparency*(prg_part-1))/prg3\n",
    "            progress.value = prg\n",
    "            l, = ax.plot(val.index, val.values,LineWidth = 1, label=(str(name[0])+' - '+str(key)),alpha=(alp))\n",
    "            Lines.append(l)\n",
    "            ax.fill_between(val.index,\n",
    "                             val.values * 1-margin, val.values * 1+margin, \n",
    "                             color=l.get_color(), alpha=.4*alp*fillb)\n",
    "            #mpld3.show()\n",
    "    ax.legend()\n",
    "    \n",
    "    #return fig, ax, Lines,desc,htmlplot\n",
    "    return desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DPlot(Datasets,DataPoints,Scale,StartDate,EndDate,Style,Transparency,fillb):\n",
    "    plt.style.use(Style)\n",
    "    margin = .1\n",
    "    prg1 = len(Datasets)\n",
    "    prg2 = len(DataPoints)\n",
    "    prg3 = prg1*prg2\n",
    "    prg4= prg3*2\n",
    "    prg_part = 0\n",
    "    prg = 0\n",
    "    MaxOpac = .9\n",
    "    progress.max = prg3+1\n",
    "    plt.style.use(Style)\n",
    "#     try:\n",
    "#         fig.clear\n",
    "#     except:\n",
    "#         fig, ax = plt.subplots()\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    #fig.style.use(Style)\n",
    "    #fig.figure(figsize= (figx,figy))\n",
    "    #figsize= (figx,figy)\n",
    "    #ure(figsize=(figx,figy))\n",
    "    Lines = []\n",
    "    desc = ''\n",
    "    if Scale[0] == 2:\n",
    "        plt.yscale('log')\n",
    "    if Scale[1] == 2:\n",
    "        plt.xscale('log')\n",
    "    for ds in Datasets:\n",
    "        if ds == 'East Surface':\n",
    "            dfref=ES.loc[StartDate:EndDate]\n",
    "        elif ds == 'East Bottom':\n",
    "            dfref=EB.loc[StartDate:EndDate]\n",
    "        elif ds == 'West':\n",
    "            dfref=W.loc[StartDate:EndDate]\n",
    "        elif ds == 'Inversion Surface':\n",
    "            dfref=IS.loc[StartDate:EndDate]\n",
    "        elif ds == 'Inversion Bottom':\n",
    "            dfref=IB.loc[StartDate:EndDate]\n",
    "        df=pd.DataFrame({'DateTime':dfref.index}).set_index('DateTime')\n",
    "        for dp in DataPoints:\n",
    "            df[dp]=dfref[dp]\n",
    "            #df.plot('DateTime',dp,ax=ax, label = (ds, dp))#,alpha=(prg3-prg)/prg3)\n",
    "            prg+=1\n",
    "            progress.value = prg\n",
    "        #print(df)\n",
    "        Desc = str(ds)+'\\n'+str(df.describe())+'\\n\\n'\n",
    "        desc= desc + str(Desc)\n",
    "        for key, val in df.iteritems():\n",
    "            prg_part+=1\n",
    "            prg+=1\n",
    "            alp = MaxOpac-(Transparency*(prg_part-1))/prg3\n",
    "            progress.value = prg\n",
    "            l, = ax.plot(val.index, val.values,LineWidth = 1, label=(str(ds)+' - '+str(key)),alpha=(alp))\n",
    "            Lines.append(l)\n",
    "            ax.fill_between(val.index,\n",
    "                             val.values * 1-margin, val.values * 1+margin, \n",
    "                             color=l.get_color(), alpha=.4*alp*fillb)\n",
    "\n",
    "#     #print(interactive_legend)\n",
    "#     print(ax.get_label())\n",
    "#     fig.legend(ax.get_label)\n",
    "#     mpld3.display()\n",
    "#     print(out)\n",
    "#     plt.savefig(extension)\n",
    "#     img = Image.open(extension)\n",
    "#     #img.show()\n",
    "    #mpld3.show()\n",
    "    ax.legend()\n",
    "    #return fig, ax, Lines, desc, htmlplot\n",
    "    return desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepCompare(Dataset,datapoint):\n",
    "    if Dataset == 'East Surface':\n",
    "        Ds=ES[datapoint]\n",
    "    elif Dataset == 'East Bottom':\n",
    "        Ds=EB[datapoint]\n",
    "    elif Dataset == 'West':\n",
    "        Ds=W[datapoint]\n",
    "    elif Dataset == 'Inversion Surface':\n",
    "        Ds=IS[datapoint]\n",
    "    elif Dataset == 'Inversion Bottom':\n",
    "        Ds=IB[datapoint]\n",
    "    NewCol = (str(datapoint)+' - '+str(Dataset))\n",
    "    Ds = Ds.reset_index()\n",
    "    Ds[NewCol]=Ds[datapoint]\n",
    "    Ds=Ds.drop(columns=datapoint)\n",
    "    return(Ds,NewCol)\n",
    "def Compare(Dataset1,Dataset2,DataPoint1,DataPoint2,Colors,Scale,Style,ColorFormat,RegLine,alpha):\n",
    "    #define palette \n",
    "    #print('GO')\n",
    "    prg1 = 7\n",
    "    prg=0\n",
    "    progress.value = prg\n",
    "    sns.set_palette(sns.color_palette(Colors))\n",
    "    my_pal = sns.color_palette(Colors)\n",
    "    cmap = ListedColormap(Colors[1:])\n",
    "    Ds1, Col1 = prepCompare(Dataset1,DataPoint1)\n",
    "    prg+=1\n",
    "    progress.value = prg\n",
    "    Ds2, Col2 = prepCompare(Dataset2,DataPoint2)\n",
    "    prg+=1\n",
    "    progress.value = prg\n",
    "    Ds1 = Ds1[Ds1['datetime'].isin(Ds2['datetime'])]\n",
    "    Ds2 = Ds2[Ds2['datetime'].isin(Ds1['datetime'])]\n",
    "    Data = pd.merge(Ds1, Ds2, on='datetime', how='outer')\n",
    "    prg+=1\n",
    "    progress.value = prg\n",
    "    plt.style.use(Style)\n",
    "#     try:\n",
    "#         fig.clear\n",
    "#     except:\n",
    "#         fig, ax = plt.subplots()\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    #fig.style.use(Style)\n",
    "    isYear=ColorFormat=='Year'\n",
    "    isMonth=ColorFormat=='Month'\n",
    "    isNone =ColorFormat=='None' \n",
    "    margin = .1\n",
    "    MaxOpac = .9\n",
    "    desc = ''\n",
    "    Lines = []\n",
    "    if Scale[0] == 2:\n",
    "        plt.yscale('log')\n",
    "    if Scale[1] == 2:\n",
    "        plt.xscale('log')\n",
    "    Data['Color'] = ((Data['datetime'].dt.year-2015)*isYear)+(Data['datetime'].dt.month*isMonth)\n",
    "    Data['Size'] = ((Data['datetime'].dt.month)*isYear)+((Data['datetime'].dt.year-2015)*3*isMonth)+(1*isNone)\n",
    "    #Data['Color'] = Colors[Data['Color']]\n",
    "    #print(Data.head())\n",
    "    prg+=1\n",
    "    progress.value = prg\n",
    "    Desc = str(Data.describe())\n",
    "    xL = Col1\n",
    "    yL = Col2\n",
    "    try:\n",
    "        ax.scatter(Data[xL],Data[yL],s = Data['Size'],\n",
    "                   c=Data['Color'],cmap = cmap,alpha = alpha)#,alpha=1.0-((Transparency*Data['Color'])/5))\n",
    "    except:\n",
    "        xL = xL+'_x'\n",
    "        yL = yL+'_y'\n",
    "        ax.scatter(Data[xL],Data[yL],s = Data['Size'],\n",
    "                   c=Data['Color'],cmap = cmap,alpha = alpha)#,alpha=1.0-((Transparency*Data['Color'])/5))\n",
    "#     for key, val in Data.iteritems():\n",
    "#             alp = 1\n",
    "#             progress.value = prg\n",
    "#             l, = ax.plot(val.index, val.values,LineWidth = 1, label=key,alpha=(alp))\n",
    "#             Lines.append(l)\n",
    "#             ax.fill_between(val.index,\n",
    "#                              val.values * 1-margin, val.values * 1+margin, \n",
    "#                              color=l.get_color(), alpha=.4*alp*fillb)\n",
    "    \n",
    "    \n",
    "    r2data = Data[((~Data[xL].isna())&(~Data[yL].isna()))]\n",
    "    r2data = r2data.sort_values(by=[xL])\n",
    "    stdata = r2data\n",
    "    res = scipy.stats.linregress(r2data[xL],r2data[yL])\n",
    "    #print(res)\n",
    "    r2 = format(res.rvalue**2,'.6f')\n",
    "    std_err = res.stderr\n",
    "    #print(r2)\n",
    "    desc= 'Correlation (r²): '+ r2 + '\\n\\n'+str(Desc)\n",
    "    if RegLine == True:\n",
    "        r2y = (res.intercept + res.slope*r2data[xL])\n",
    "        s2y = (res.intercept + res.slope*stdata[xL])\n",
    "        ax.plot(r2data[xL], r2y, c=Colors[0], label='Regression Line')\n",
    "        #ax.fill_between(stdata[xL],s2y * (1-((100*std_err))),s2y * (1+((100*std_err))), \n",
    "        #                     color=Colors[0], alpha=.1)\n",
    "        ax.legend()\n",
    "    prg+=1\n",
    "    progress.value = prg\n",
    "    \n",
    "    return desc, Col1,Col2\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import ctypes  # An included library with Python install.\n",
    "def Mbox(title, text, style):\n",
    "    return ctypes.windll.user32.MessageBoxW(0, text, title, style)\n",
    "\n",
    "\n",
    "envDatapoints = [('Salinity_ppt','Salinity_ppt'),('pH','pH'),\n",
    "              ('DO_mg/L','DO_mg/L'),('Turb_NTU','Turb_NTU'),\n",
    "              ('Chl_ug/L','Chl_ug/L'),('Temp_C','Temp_C')]\n",
    "\n",
    "############# GENERAL #####################################################\n",
    "Header = widgets.HTML(value=f'''<h1>&Dscr;&ascr;&tscr;&ascr;\n",
    "                    &Pscr;&lscr;&oscr;&tscr;&tscr;&iscr;&nscr;&gscr; - &Vscr; {__Version__()}<h1>''',\n",
    "                      color='lime',font_size=75)\n",
    "figx=widgets.FloatSlider(\n",
    "    value=8.0,\n",
    "    min=2.0,\n",
    "    max=25.0,\n",
    "    step=0.1,\n",
    "    description='X axis size:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.1f'\n",
    ")\n",
    "figx.style.handle_color=\"lawngreen\"\n",
    "\n",
    "figy=widgets.FloatSlider(\n",
    "    value=5.0,\n",
    "    min=1.0,\n",
    "    max=15.0,\n",
    "    step=0.1,\n",
    "    description='Y axis Size:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='Vertical',\n",
    "    readout=True,\n",
    "    readout_format='.1f'\n",
    ")\n",
    "figy.style.handle_color=\"lawngreen\"\n",
    "\n",
    "\n",
    "btn_about = widgets.Button(\n",
    "    description='About',\n",
    "    tooltip='About',\n",
    "    style={'button_color': 'lawngreen'},\n",
    "    layout = Layout (width = '90%', height = '100%'),\n",
    "    font_size=75\n",
    "    #layout = Layout(width='98%', height='100%')\n",
    ")\n",
    "def aboutClick(event):\n",
    "    Mbox('About',\n",
    "     f'''An interface to manipulate and visualize the data\\n''', 0)\n",
    "\n",
    "    \n",
    "btn_about.on_click(aboutClick)\n",
    "\n",
    "Scale = widgets.Dropdown(\n",
    "    options=[('linear', 1), ('logarithmic', 2)],\n",
    "    value=1,\n",
    "    description='Y Scale',\n",
    ")\n",
    "\n",
    "xScale = widgets.Dropdown(\n",
    "    options=[('linear', 1), ('logarithmic', 2)],\n",
    "    value=1,\n",
    "    description='X Scale',\n",
    ")\n",
    "\n",
    "\n",
    "Style = widgets.Dropdown(\n",
    "    options=[('default', 'default'), ('classic', 'classic'),\n",
    "             ('Solarize_Light2', 'Solarize_Light2'),('_classic_test_patch','_classic_test_patch'),\n",
    "             ('bmh', 'bmh'),('dark_background', 'dark_background'),\n",
    "             ('fast', 'fast'),('fivethirtyeight', 'fivethirtyeight'),\n",
    "             ('ggplot', 'ggplot'),('grayscale', 'grayscale'),\n",
    "             ('seaborn-colorblind', 'seaborn-colorblind'),('seaborn-dark', 'seaborn-dark'),\n",
    "             ('seaborn-dark-palette', 'seaborn-dark-palette'),('seaborn-darkgrid', 'seaborn-darkgrid'),\n",
    "             ('seaborn-deep', 'seaborn-deep'),('seaborn-muted', 'seaborn-muted'),\n",
    "             ('seaborn-notebook', 'seaborn-notebook'),('seaborn-paper', 'seaborn-paper'),\n",
    "             ('seaborn-pastel', 'seaborn-pastel'),('seaborn-poster', 'seaborn-poster'),\n",
    "             ('seaborn-talk', 'seaborn-talk'),('seaborn-white', 'seaborn-white'),\n",
    "             ('seaborn-whitegrid', 'seaborn-whitegrid'),('seaborn-colorblind10', 'seaborn-colorblind10')],\n",
    "    value='default',\n",
    "    description='Style',\n",
    ")\n",
    "\n",
    "DataToInclude = widgets.SelectMultiple(\n",
    "    options=['East Surface', 'East Bottom', 'West','Inversion Surface','Inversion Bottom'],\n",
    "    value=['East Surface'],\n",
    "    description='Datasets',\n",
    "    rows = 5,\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "\n",
    "Transparency = widgets.Checkbox(\n",
    "    value=False,\n",
    "    description='Gradual Transparency',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "fillb = widgets.Checkbox(\n",
    "    value=False,\n",
    "    description='Fill Between',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "viewer = widgets.Checkbox(\n",
    "    value=False,\n",
    "    description='Viewer',\n",
    "    disabled=True\n",
    ")\n",
    "\n",
    "useSpecial = widgets.Checkbox(\n",
    "    value=False,\n",
    "    description='Use Special:',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "Special = widgets.ToggleButtons(\n",
    "    options=['Legend', 'Highlight'],\n",
    "    description='Special:',\n",
    "    disabled=True,\n",
    "    button_style = 'success',\n",
    "    tooltips=['Interactive Legend','Highlight'],\n",
    ")\n",
    "\n",
    "Special.style.button_color = 'lawngreen'\n",
    "\n",
    "def specialUpdate(change):\n",
    "    if useSpecial.value == True:\n",
    "        viewer.value = True\n",
    "        Special.disabled = False\n",
    "    else:\n",
    "        Special.disabled = True       \n",
    "def viewerUpdate(change):\n",
    "    if viewer.value == False:\n",
    "        useSpecial.value = False\n",
    "        Special.disabled = True       \n",
    "useSpecial.observe(specialUpdate, names='value')\n",
    "viewer.observe(viewerUpdate, names='value')\n",
    "\n",
    "vbox_General = widgets.VBox([viewer,Transparency,fillb])\n",
    "vbox_Special = widgets.VBox([useSpecial,Special])\n",
    "\n",
    "useSpecial.disabled = True\n",
    "Special.disabled = True\n",
    "\n",
    "Fmt = widgets.Tab()\n",
    "Fmt.children = [vbox_General,vbox_Special]\n",
    "Fmt.set_title(0,'General')\n",
    "Fmt.set_title(1,'Special')\n",
    "\n",
    "vbox_g = widgets.VBox([Style,Scale,xScale,DataToInclude,Fmt,figx])\n",
    "vbox_g1 = widgets.VBox([figy]) #,btn_about\n",
    "hbox_g = widgets.HBox([vbox_g1,vbox_g])\n",
    "vbox_general = widgets.VBox([Header,hbox_g])\n",
    "############################################################################\n",
    "\n",
    "\n",
    "#################### ENVIRONMENT ###########################################\n",
    "EnvDataPoints = widgets.SelectMultiple(\n",
    "    options=['Salinity_ppt', 'pH', 'DO_mg/L','Turb_NTU','Chl_ug/L','Temp_C'],\n",
    "    value=['pH'],\n",
    "    rows=6,\n",
    "    description='Data',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "StartDate = widgets.DatePicker(description='Start Date')\n",
    "\n",
    "EndDate = widgets.DatePicker(description='End Date')\n",
    "\n",
    "dates = [datetime.date(2018,i,1) for i in range(1,13)]\n",
    "options = [(i.strftime('%b'), i) for i in dates]\n",
    "Months = widgets.SelectionRangeSlider(\n",
    "    options=options,\n",
    "    index=(0,11),\n",
    "    description='Months',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "\n",
    "Years=widgets.SelectMultiple(\n",
    "    options=['2016','2017','2018', '2019', '2020'],\n",
    "    value=['2016','2017','2018', '2019', '2020'],\n",
    "    description='Years',\n",
    "    rows = 5,\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "vbox_dateselecter =  widgets.VBox([StartDate,EndDate])\n",
    "\n",
    "vbox_yearlimiter =  widgets.VBox([Months,Years])\n",
    "\n",
    "DRtab = widgets.Tab()\n",
    "DRtab.children = [vbox_dateselecter,vbox_yearlimiter]\n",
    "DRtab.set_title(0,'Date Range')\n",
    "DRtab.set_title(1,'Years Overlayed')\n",
    "\n",
    "vbox_EnvData = widgets.VBox([EnvDataPoints,DRtab])\n",
    "############################################################################\n",
    "\n",
    "\n",
    "############## SPECIES #####################################################\n",
    "species = ['Amphipod','Barnacle','Clam Worm','Colonial Ciliates','Copepod',\n",
    "             'Dark False Mussel','Dusky Sea Slug','Hydra','Magnification',\n",
    "             'Mud Crab','Oyster Flatworm','Stentor','Tube Bryzoan','Whip Mudworm']\n",
    "SpecDataPoints = widgets.SelectMultiple(\n",
    "    options=species,\n",
    "    value=['Hydra'],\n",
    "    rows=14,\n",
    "    description='Data',\n",
    "    disabled=True\n",
    ")\n",
    "\n",
    "specStartDate = widgets.DatePicker(description='Start Date',disabled = True)\n",
    "\n",
    "specEndDate = widgets.DatePicker(description='End Date',disabled = True)\n",
    "\n",
    "\n",
    "specMonths = widgets.SelectionRangeSlider(\n",
    "    options=options,\n",
    "    index=(0,11),\n",
    "    description='Months',\n",
    "    disabled=True\n",
    ")\n",
    "\n",
    "\n",
    "specYears=widgets.SelectMultiple(\n",
    "    options=['2016','2017','2018', '2019', '2020'],\n",
    "    value=['2016','2017','2018', '2019', '2020'],\n",
    "    description='Years',\n",
    "    rows = 5,\n",
    "    disabled=True\n",
    ")\n",
    "\n",
    "vbox_specdateselecter =  widgets.VBox([specStartDate,specEndDate])\n",
    "\n",
    "vbox_specyearlimiter =  widgets.VBox([specMonths,specYears])\n",
    "\n",
    "specDRtab = widgets.Tab()\n",
    "specDRtab.children = [vbox_specdateselecter,vbox_specyearlimiter]\n",
    "specDRtab.set_title(0,'Date Range')\n",
    "specDRtab.set_title(1,'Years Overlayed')\n",
    "\n",
    "\n",
    "vbox_SpecData =  widgets.VBox([SpecDataPoints,specDRtab])\n",
    "############################################################################\n",
    "\n",
    "\n",
    "############## CORRELATION #################################################\n",
    "cd2Iopt = {'East Surface','East Bottom','West','Inversion Surface','Inversion Bottom'}\n",
    "\n",
    "alpha=widgets.FloatSlider(\n",
    "    value=1.0,\n",
    "    min=0.01,\n",
    "    max=1.0,\n",
    "    step=0.01,\n",
    "    description='Opacity:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.2f'\n",
    ")\n",
    "\n",
    "corrDataToInclude1 = widgets.Dropdown(\n",
    "    options=cd2Iopt,\n",
    "    value='East Surface',\n",
    "    description='Dataset',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "corrDataToInclude2 = widgets.Dropdown(\n",
    "    options=cd2Iopt,\n",
    "    value='East Surface',\n",
    "    description='Dataset'\n",
    ")\n",
    "EnvC1DataPoints = widgets.Dropdown(\n",
    "    options=envDatapoints,\n",
    "    value='pH',\n",
    "    description='Data'\n",
    ")\n",
    "\n",
    "EnvC2DataPoints = widgets.Dropdown(\n",
    "    options=envDatapoints,\n",
    "    value='pH',\n",
    "    description='Data'\n",
    ")\n",
    "vbox_corr1 = widgets.VBox([EnvC1DataPoints,corrDataToInclude1])\n",
    "vbox_corr2 = widgets.VBox([EnvC2DataPoints,corrDataToInclude2])\n",
    "accordion = widgets.Accordion(children=[vbox_corr1,vbox_corr2])\n",
    "accordion.set_title(0, 'First Condition')\n",
    "accordion.set_title(1, 'Second Condition')\n",
    "\n",
    "SpecCDataPoints = widgets.Select(\n",
    "    options=species,\n",
    "    description='Data',\n",
    "    orientation='Vertical',\n",
    "    disabled = True\n",
    ")\n",
    "\n",
    "EnvCDataPoints = widgets.SelectMultiple(\n",
    "    options=['Salinity_ppt', 'pH', 'DO_mg/L','Turb_NTU','Chl_ug/L','Temp_C'],\n",
    "    value=['pH'],\n",
    "    rows=6,\n",
    "    description='Correlate to:',\n",
    "    disabled=True,\n",
    "    max = 3\n",
    ")\n",
    "method = widgets.Dropdown(\n",
    "    options={'Top': 0, 'Bottom': 1},\n",
    "    value=0,\n",
    "    description='',\n",
    "    disabled = True,\n",
    "    layout = Layout (width = '25%', height = '100%')\n",
    ")\n",
    "ResultNum=widgets.IntSlider(\n",
    "    value=3,\n",
    "    min=1,\n",
    "    max=len(EnvCDataPoints.value),\n",
    "    step=1,\n",
    "    description='',\n",
    "    disabled=True,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    ")\n",
    "ResultNum.style.handle_color=\"lawngreen\"\n",
    "\n",
    "Colorform = widgets.Dropdown(\n",
    "    options=['Year','Month','None'],\n",
    "    description='Colorcode by',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "colorconcise = False\n",
    "\n",
    "Color16 = widgets.ColorPicker(\n",
    "    concise=colorconcise,\n",
    "    description='2016 color',\n",
    "    value='#00ffff',\n",
    "    disabled=False\n",
    "    \n",
    ")\n",
    "Color17 = widgets.ColorPicker(\n",
    "    concise=colorconcise,\n",
    "    description='2017 color',\n",
    "    value='#0000ff',\n",
    "    disabled=False\n",
    "    \n",
    ")\n",
    "Color18 = widgets.ColorPicker(\n",
    "    concise=colorconcise,\n",
    "    description='2018 color',\n",
    "    value='#00ff00',\n",
    "    disabled=False\n",
    "    \n",
    ")\n",
    "Color19 = widgets.ColorPicker(\n",
    "    concise=colorconcise,\n",
    "    description='2019 color',\n",
    "    value='#00aa00',\n",
    "    disabled=False\n",
    "    \n",
    ")\n",
    "Color20 = widgets.ColorPicker(\n",
    "    concise=colorconcise,\n",
    "    description='2020 color',\n",
    "    value='#ffff00',\n",
    "    disabled=False\n",
    "    \n",
    ")\n",
    "\n",
    "ColorJun = widgets.ColorPicker(concise=colorconcise, description='June color',\n",
    "                               value='#c8c800', disabled=True)\n",
    "\n",
    "ColorJul = widgets.ColorPicker(concise=colorconcise, description='July color',\n",
    "                               value='#ff9600', disabled=True)\n",
    "\n",
    "ColorAug = widgets.ColorPicker(concise=colorconcise, description='August color',\n",
    "                               value='#966400', disabled=True)\n",
    "\n",
    "ColorSept = widgets.ColorPicker(concise=colorconcise, description='September color',\n",
    "                               value='#ff0000', disabled=True)\n",
    "\n",
    "ColorOct = widgets.ColorPicker(concise=colorconcise, description='October color',\n",
    "                               value='#960000', disabled=True)\n",
    "\n",
    "ColorNov = widgets.ColorPicker(concise=colorconcise, description='November color',\n",
    "                               value='#ff00ff', disabled=True)\n",
    "\n",
    "ColorDec = widgets.ColorPicker(concise=colorconcise, description='December color',\n",
    "                               value='#960096', disabled=True)\n",
    "\n",
    "RegColor = widgets.ColorPicker(concise=colorconcise, description='Regression line color',\n",
    "                               value='#000000', disabled=False)\n",
    "\n",
    "mColors = [ColorJun,ColorJul,ColorAug,ColorSept,ColorOct,ColorNov,ColorDec]\n",
    "\n",
    "def clrUpdate(event):\n",
    "    if Colorform.value=='Month':\n",
    "        for o in mColors:\n",
    "            o.disabled = False\n",
    "        Color17.disabled = False\n",
    "        Color18.disabled = False\n",
    "        Color19.disabled = False\n",
    "        Color20.disabled = False\n",
    "        Color16.description = 'January color'\n",
    "        Color17.description = 'February color'\n",
    "        Color18.description = 'March color'\n",
    "        Color19.description = 'April color'\n",
    "        Color20.description = 'May color'\n",
    "    elif Colorform.value=='Year':\n",
    "        for o in mColors:\n",
    "            o.disabled = True\n",
    "        Color17.disabled = False\n",
    "        Color18.disabled = False\n",
    "        Color19.disabled = False\n",
    "        Color20.disabled = False\n",
    "        Color16.description = '2016 color'\n",
    "        Color17.description = '2017 color'\n",
    "        Color18.description = '2018 color'\n",
    "        Color19.description = '2019 color'\n",
    "        Color20.description = '2020 color'\n",
    "    else:\n",
    "        for o in mColors:\n",
    "            o.disabled = True\n",
    "        Color16.description = 'Solid color'\n",
    "        Color17.disabled = True\n",
    "        Color18.disabled = True\n",
    "        Color19.disabled = True\n",
    "        Color20.disabled = True\n",
    "Colorform.observe(clrUpdate, names='value')\n",
    "\n",
    "Palette_V=widgets.VBox([Color16,Color17,Color18,Color19,Color20,ColorJun])\n",
    "Palette_V2=widgets.VBox([ColorJul,ColorAug,ColorSept,ColorOct,ColorNov,ColorDec])\n",
    "Palette=widgets.VBox([Palette_V,Palette_V2])\n",
    "\n",
    "showReg = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='Show regression line',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "\n",
    "def RegressionUpdate(event):\n",
    "    if showReg.value == False:\n",
    "        RegColor.disabled = True\n",
    "    else:\n",
    "        RegColor.disabled = False\n",
    "showReg.observe(RegressionUpdate, names='value')\n",
    "\n",
    "def AdjMax(change):\n",
    "    ResultNum.max=len(EnvCDataPoints.value)\n",
    "\n",
    "EnvCDataPoints.observe(AdjMax, names='value')\n",
    "\n",
    "vbox_env_env =  widgets.VBox([accordion,showReg,RegColor,alpha,Colorform,Palette])\n",
    "hbox_env_spec_sub = widgets.HBox([method,ResultNum])\n",
    "vbox_env_spec =  widgets.VBox([SpecCDataPoints,EnvCDataPoints,hbox_env_spec_sub])\n",
    "\n",
    "CorrTab = widgets.Tab()\n",
    "\n",
    "CorrTab.children = [vbox_env_env,vbox_env_spec]\n",
    "#CorrTab.disabled = [False,True]\n",
    "CorrTab.set_title(0,'Environmental Correlation')\n",
    "CorrTab.set_title(1,'Species Correlation')\n",
    "\n",
    "vbox_Corr =  widgets.VBox([CorrTab])\n",
    "\n",
    "def updateCorr(event):\n",
    "    if MainTab.selected_index == 2:\n",
    "        DataToInclude.disabled = True\n",
    "    else:\n",
    "        DataToInclude.disabled = False\n",
    "\n",
    "        \n",
    "############################################################################\n",
    "\n",
    "\n",
    "############## MAIN ########################################################\n",
    "\n",
    "MainTab = widgets.Tab()\n",
    "\n",
    "MainTab.children = [vbox_EnvData,vbox_SpecData,vbox_Corr]\n",
    "\n",
    "MainTab.set_title(0,'Environmental')\n",
    "MainTab.set_title(1,'Species')\n",
    "MainTab.set_title(2,'Correlation')\n",
    "MainTab.layout = Layout (width = '500px', height = '100%')\n",
    "vbox_main = widgets.VBox([MainTab])\n",
    "\n",
    "MainTab.observe(updateCorr, names='selected_index')\n",
    "\n",
    "\n",
    "###########################################################################\n",
    "\n",
    "\n",
    "############ DISPLAY ######################################################\n",
    "clear_output()\n",
    "   \n",
    "progress =  widgets.IntProgress(\n",
    "    value=0,\n",
    "    min=0,\n",
    "    max=2,\n",
    "    step=1,\n",
    "    description='plotting:',\n",
    "    style = {'bar_color':'lawngreen'},\n",
    "    orientation='horizontal'\n",
    "    )\n",
    "\n",
    "btn_plt = widgets.Button(\n",
    "    description='''Plot 📈''',\n",
    "    tooltip='Plot',\n",
    "    style={'button_color': 'lawngreen'},\n",
    "    layout = Layout (width = '360px', height = '100%'),\n",
    "    font_size=75\n",
    "    #layout = Layout(width='98%', height='100%')\n",
    ")\n",
    "\n",
    "btn_clear = widgets.Button(\n",
    "    description='''Clear Plot space''',\n",
    "    tooltip='Clear',\n",
    "    style={'button_color': 'lawngreen'},\n",
    "    layout = Layout (width = '360px', height = '100%'),\n",
    "    font_size=75\n",
    "    #layout = Layout(width='98%', height='100%')\n",
    ")\n",
    "\n",
    "\n",
    "Output = widgets.Output(layout = {\n",
    "    'border': '1px solid black',\n",
    "    # 'width': '100%' # Not needed and does not help\n",
    "})\n",
    "\n",
    "Out2 = widgets.Output(layout = {\n",
    "    'border': '1px solid lawngreen',\n",
    "    # 'width': '100%' # Not needed and does not help\n",
    "})\n",
    "\n",
    "with Output:\n",
    "    fig, ax = plt.subplots(1, constrained_layout=True)\n",
    "    fig.set_figwidth(figx.value)\n",
    "    fig.set_figheight(figy.value)\n",
    "    fig.canvas.layout.border = '1px solid lawngreen'\n",
    "    ax.axis('on')\n",
    "    grid();\n",
    "    \n",
    "\n",
    "\n",
    "def Clrplot(event):\n",
    "    plt.cla() # Clearing the ax\n",
    "    grid();\n",
    "\n",
    "def Masterplot(event):\n",
    "    with Out2:\n",
    "        clear_output()\n",
    "        print('Running...')\n",
    "    plt.style.use(Style.value)\n",
    "    Clrplot(event) # Clearing the ax\n",
    "    if MainTab.selected_index==0:\n",
    "        if DRtab.selected_index == 0:\n",
    "            desc = DPlot(DataToInclude.value,EnvDataPoints.value,\n",
    "                         [Scale.value,xScale.value],StartDate.value,EndDate.value,\n",
    "                         Style.value,Transparency.value,fillb.value)\n",
    "            #myplot=widgets.HTML(value=plot)\n",
    "            #mpld3.display()\n",
    "        else:\n",
    "            desc = YPlot(DataToInclude.value,EnvDataPoints.value,\n",
    "                         [Scale.value,xScale.value],Years.value,Months.value,\n",
    "                         Style.value,Transparency.value,fillb.value)\n",
    "    #         YPlot(DataToInclude.value,EnvDataPoints.value,\n",
    "    #               Scale.value,Years.value,Months.value,\n",
    "    #               figx.value,figy.value,Style.value)\n",
    "        ax.set_xlabel('Time')\n",
    "        ax.set_ylabel('Value')\n",
    "\n",
    "    elif MainTab.selected_index==2:\n",
    "        Colors = [RegColor.value,Color16.value]\n",
    "        if Colorform.value=='Year':\n",
    "            Colors = [RegColor.value,Color16.value,Color17.value,Color18.value,Color19.value,Color20.value]\n",
    "        elif Colorform.value=='Month':\n",
    "            Colors = [RegColor.value,Color16.value,Color17.value,Color18.value,Color19.value,Color20.value,ColorJun.value,\n",
    "                  ColorJul.value,ColorAug.value,ColorSept.value,ColorOct.value,ColorNov.value,ColorDec.value]\n",
    "        desc,xL,yL = Compare(corrDataToInclude1.value,corrDataToInclude2.value,EnvC1DataPoints.value,\n",
    "                EnvC2DataPoints.value,Colors,[Scale.value,xScale.value],Style.value,Colorform.value,showReg.value, alpha.value)\n",
    "        ax.set_xlabel(xL)\n",
    "        ax.set_ylabel(yL)\n",
    "    \n",
    "    ax.set_title('Plot', size=20)\n",
    "\n",
    "    with Out2:\n",
    "        clear_output()\n",
    "        print('Data Analysis:\\n\\n'+desc)\n",
    "    progress.value = progress.max\n",
    "    time.sleep(.1)\n",
    "    progress.value = 0\n",
    "    \n",
    "    \n",
    "def Plotter(figx,figy):\n",
    "    with Output:\n",
    "        fig.set_figwidth(figx)\n",
    "        fig.set_figheight(figy)\n",
    "        btn_plt.on_click(Masterplot)\n",
    "        btn_clear.on_click(Clrplot)\n",
    "    \n",
    "def clickEvent(event):\n",
    "    iplot.update()\n",
    "\n",
    "iplot = widgets.interactive(Plotter,figx=figx,figy=figy)\n",
    "\n",
    "btn_plt.on_click(clickEvent)\n",
    "btn_clear.on_click(clickEvent)\n",
    "\n",
    "\n",
    "\n",
    "Plot = HTML('')\n",
    "vbox_result = widgets.VBox([progress,btn_plt,btn_clear])\n",
    "vbox_Setup = widgets.VBox([vbox_general,vbox_result])\n",
    "hbox_Setup = widgets.HBox([vbox_Setup,vbox_main])\n",
    "vbox_Final = widgets.VBox([hbox_Setup,Output,Out2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02e19d90497d4ba5ab0226a627c3d543",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(VBox(children=(VBox(children=(HTML(value='<h1>&Dscr;&ascr;&tscr;&ascr;\\n        …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#page = widgets.HBox([vbox_Setup])\n",
    "page = vbox_Final\n",
    "display(page)\n",
    "#display(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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