diff --git a/EDA_on_COVID.ipynb b/EDA_on_COVID.ipynb new file mode 100644 index 0000000..d3f463e --- /dev/null +++ b/EDA_on_COVID.ipynb @@ -0,0 +1,27776 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "EDA on COVID.ipynb", + "provenance": [], + "collapsed_sections": [ + "pk6HgA36tLmD" + ], + "toc_visible": true, + "authorship_tag": "ABX9TyOz5HVrXfgnGWaIwb+MFXcs", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ls6e6qftk16_", + "colab_type": "code", + "outputId": "830b4484-d35a-4836-e1da-80fabecfcbbd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 71 + } + }, + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "import plotly.graph_objects as go\n", + "import folium \n", + "from folium import plugins\n", + "\n", + "plt.rcParams['figure.figsize'] = 10, 12" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", + " import pandas.util.testing as tm\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mRHnQrsnsmjs", + "colab_type": "text" + }, + "source": [ + "# **Part 1**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qoQugoi5ZnGl", + "colab_type": "text" + }, + "source": [ + "## **Indian Data Analysis**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zUDawjbonpSu", + "colab_type": "code", + "colab": {} + }, + "source": [ + "!unzip /content/covid_india.zip" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ueV_60swmhZe", + "colab_type": "code", + "outputId": "59b4b227-17c1-481f-ac72-cfe97031e4aa", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "source": [ + "indian_data = pd.read_csv(\"/content/covid_19_india.csv\")\n", + "indian_data.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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SnoDateTimeState/UnionTerritoryConfirmedIndianNationalConfirmedForeignNationalCuredDeathsConfirmed
0130/01/206:00 PMKerala10001
1231/01/206:00 PMKerala10001
2301/02/206:00 PMKerala20002
3402/02/206:00 PMKerala30003
4503/02/206:00 PMKerala30003
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" + ], + "text/plain": [ + " Sno Date Time ... Cured Deaths Confirmed\n", + "0 1 30/01/20 6:00 PM ... 0 0 1\n", + "1 2 31/01/20 6:00 PM ... 0 0 1\n", + "2 3 01/02/20 6:00 PM ... 0 0 2\n", + "3 4 02/02/20 6:00 PM ... 0 0 3\n", + "4 5 03/02/20 6:00 PM ... 0 0 3\n", + "\n", + "[5 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rOxdOoYvzJtE", + "colab_type": "code", + "outputId": "cad0ade3-d627-45c8-c615-72f365d187ad", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "source": [ + "indian_coord = pd.read_csv(\"/content/Indian_coord.csv\")\n", + "indian_coord.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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State/UnionTerritoryLatitudeLongitude
0Andaman And Nicobar11.66702692.735983
1Andhra Pradesh14.75042978.570026
2Arunachal Pradesh27.10039993.616601
3Assam26.74998194.216667
4Bihar25.78541487.479973
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" + ], + "text/plain": [ + " State/UnionTerritory Latitude Longitude\n", + "0 Andaman And Nicobar 11.667026 92.735983\n", + "1 Andhra Pradesh 14.750429 78.570026\n", + "2 Arunachal Pradesh 27.100399 93.616601\n", + "3 Assam 26.749981 94.216667\n", + "4 Bihar 25.785414 87.479973" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 44 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pxGVhtmVoPee", + "colab_type": "code", + "colab": {} + }, + "source": [ + "indian_data.drop(['Sno'], axis=1, inplace=True)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "xzHCaOpDo_CE", + "colab_type": "code", + "outputId": "7cc63bff-a4d2-486c-8e92-f22e22d7fd69", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "source": [ + "indian_data.tail()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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DateTimeState/UnionTerritoryConfirmedIndianNationalConfirmedForeignNationalCuredDeathsConfirmed
111919/04/205:00 PMTelengana--18618844
112019/04/205:00 PMTripura--102
112119/04/205:00 PMUttarakhand--9042
112219/04/205:00 PMUttar Pradesh--108171084
112319/04/205:00 PMWest Bengal--6212310
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" + ], + "text/plain": [ + " Date Time State/UnionTerritory ... Cured Deaths Confirmed\n", + "1119 19/04/20 5:00 PM Telengana ... 186 18 844\n", + "1120 19/04/20 5:00 PM Tripura ... 1 0 2\n", + "1121 19/04/20 5:00 PM Uttarakhand ... 9 0 42\n", + "1122 19/04/20 5:00 PM Uttar Pradesh ... 108 17 1084\n", + "1123 19/04/20 5:00 PM West Bengal ... 62 12 310\n", + "\n", + "[5 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YxTh419svMmm", + "colab_type": "code", + "outputId": "2d8c9654-d744-498f-b619-277d5d0bc32b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "source": [ + "indian_data.columns" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Date', 'Time', 'State/UnionTerritory', 'ConfirmedIndianNational',\n", + " 'ConfirmedForeignNational', 'Cured', 'Deaths', 'Confirmed'],\n", + " dtype='object')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 25 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2WXQuygavPmu", + "colab_type": "code", + "outputId": "4a8c13b2-3832-4bb1-9510-3cd31527e3bc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + } + }, + "source": [ + "indian_data.info()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1124 entries, 0 to 1123\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 1124 non-null object\n", + " 1 Time 1124 non-null object\n", + " 2 State/UnionTerritory 1124 non-null object\n", + " 3 ConfirmedIndianNational 1124 non-null object\n", + " 4 ConfirmedForeignNational 1124 non-null object\n", + " 5 Cured 1124 non-null int64 \n", + " 6 Deaths 1124 non-null int64 \n", + " 7 Confirmed 1124 non-null int64 \n", + "dtypes: int64(3), object(5)\n", + "memory usage: 70.4+ KB\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2LsIB6MZt4mI", + "colab_type": "code", + "outputId": "963edd0b-6bd7-4636-e501-91c674d33d10", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "total_cases = indian_data['Confirmed'].sum()\n", + "print('Total number of confirmed COVID 2019 cases across India till date (19th April, 2020):', total_cases)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Total number of confirmed COVID 2019 cases across India till date (19th April, 2020): 155422\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CqbSw1v8pQIf", + "colab_type": "code", + "outputId": "c94dd2c4-d7e0-4dce-fa98-84a4e302a758", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "indian_data.style.background_gradient(cmap='Reds')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": 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Date Time State/UnionTerritory ConfirmedIndianNational ConfirmedForeignNational Cured Deaths Confirmed
030/01/206:00 PMKerala10001
131/01/206:00 PMKerala10001
201/02/206:00 PMKerala20002
302/02/206:00 PMKerala30003
403/02/206:00 PMKerala30003
504/02/206:00 PMKerala30003
605/02/206:00 PMKerala30003
706/02/206:00 PMKerala30003
807/02/206:00 PMKerala30003
908/02/206:00 PMKerala30003
1009/02/206:00 PMKerala30003
1110/02/206:00 PMKerala30003
1211/02/206:00 PMKerala30003
1312/02/206:00 PMKerala30003
1413/02/206:00 PMKerala30003
1514/02/206:00 PMKerala30003
1615/02/206:00 PMKerala30003
1716/02/206:00 PMKerala30003
1817/02/206:00 PMKerala30003
1918/02/206:00 PMKerala30003
2019/02/206:00 PMKerala30003
2120/02/206:00 PMKerala30003
2221/02/206:00 PMKerala30003
2322/02/206:00 PMKerala30003
2423/02/206:00 PMKerala30003
2524/02/206:00 PMKerala30003
2625/02/206:00 PMKerala30003
2726/02/206:00 PMKerala30003
2827/02/206:00 PMKerala30003
2928/02/206:00 PMKerala30003
3029/02/206:00 PMKerala30003
3101/03/206:00 PMKerala30003
3202/03/206:00 PMTelengana10001
3302/03/206:00 PMKerala30003
3402/03/206:00 PMDelhi10001
3503/03/206:00 PMTelengana10001
3603/03/206:00 PMRajasthan01001
3703/03/206:00 PMKerala30303
3803/03/206:00 PMDelhi10001
3904/03/206:00 PMUttar Pradesh60006
4004/03/206:00 PMKerala30303
4104/03/206:00 PMHaryana02002
4204/03/206:00 PMDelhi10001
4304/03/206:00 PMTelengana10001
4404/03/206:00 PMRajasthan1140015
4505/03/206:00 PMDelhi20002
4605/03/206:00 PMHaryana02002
4705/03/206:00 PMKerala30303
4805/03/206:00 PMRajasthan1140015
4905/03/206:00 PMTelengana10001
5005/03/206:00 PMUttar Pradesh70007
5106/03/206:00 PMDelhi30003
5206/03/206:00 PMHaryana02002
5306/03/206:00 PMKerala30303
5406/03/206:00 PMRajasthan1140015
5506/03/206:00 PMUttar Pradesh70007
5606/03/206:00 PMTelengana10001
5707/03/206:00 PMKerala30303
5807/03/206:00 PMUttar Pradesh70007
5907/03/206:00 PMLadakh20002
6007/03/206:00 PMTelengana10001
6107/03/206:00 PMTamil Nadu10001
6207/03/206:00 PMDelhi30003
6307/03/206:00 PMHaryana02002
6407/03/206:00 PMRajasthan1140015
6508/03/206:00 PMLadakh20002
6608/03/206:00 PMTelengana10001
6708/03/206:00 PMTamil Nadu10001
6808/03/206:00 PMRajasthan1140015
6908/03/206:00 PMKerala80308
7008/03/206:00 PMHaryana02002
7108/03/206:00 PMDelhi30003
7208/03/206:00 PMUttar Pradesh70007
7309/03/206:00 PMLadakh20002
7409/03/206:00 PMKarnataka10001
7509/03/206:00 PMKerala90309
7609/03/206:00 PMMaharashtra20002
7709/03/206:00 PMPunjab10001
7809/03/206:00 PMRajasthan1140015
7909/03/206:00 PMTamil Nadu10001
8009/03/206:00 PMTelengana10001
8109/03/206:00 PMJammu and Kashmir10001
8209/03/206:00 PMUttar Pradesh70007
8309/03/206:00 PMHaryana02002
8409/03/206:00 PMDelhi40004
8510/03/206:00 PMUttar Pradesh70007
8610/03/206:00 PMLadakh20002
8710/03/206:00 PMDelhi40004
8810/03/206:00 PMHaryana02002
8910/03/206:00 PMKarnataka40004
9010/03/206:00 PMKerala1503015
9110/03/206:00 PMMaharashtra50005
9210/03/206:00 PMPunjab10001
9310/03/206:00 PMRajasthan1140015
9410/03/206:00 PMTamil Nadu10001
9510/03/206:00 PMTelengana10001
9610/03/206:00 PMJammu and Kashmir10001
9711/03/206:00 PMMaharashtra20002
9811/03/206:00 PMDelhi50005
9911/03/206:00 PMHaryana0140014
10011/03/206:00 PMKerala1703017
10111/03/206:00 PMRajasthan12003
10211/03/206:00 PMTelengana10001
10311/03/206:00 PMUttar Pradesh90009
10411/03/206:00 PMLadakh20002
10511/03/206:00 PMTamil Nadu10001
10611/03/206:00 PMJammu and Kashmir10001
10711/03/206:00 PMPunjab10001
10811/03/206:00 PMKarnataka40004
10912/03/206:00 PMDelhi60006
11012/03/206:00 PMHaryana0140014
11112/03/206:00 PMKerala1703017
11212/03/206:00 PMRajasthan12003
11312/03/206:00 PMTelengana10001
11412/03/206:00 PMUttar Pradesh1010011
11512/03/206:00 PMLadakh30003
11612/03/206:00 PMTamil Nadu10001
11712/03/206:00 PMJammu and Kashmir10001
11812/03/206:00 PMPunjab10001
11912/03/206:00 PMKarnataka40004
12012/03/206:00 PMMaharashtra1100011
12112/03/206:00 PMAndhra Pradesh10001
12213/03/206:00 PMDelhi60006
12313/03/206:00 PMHaryana0140014
12413/03/206:00 PMKerala1903019
12513/03/206:00 PMRajasthan12003
12613/03/206:00 PMTelengana10001
12713/03/206:00 PMUttar Pradesh1010011
12813/03/206:00 PMLadakh30003
12913/03/206:00 PMTamil Nadu10001
13013/03/206:00 PMJammu and Kashmir10001
13113/03/206:00 PMPunjab10001
13213/03/206:00 PMKarnataka60016
13313/03/206:00 PMMaharashtra1400014
13413/03/206:00 PMAndhra Pradesh10001
13514/03/206:00 PMDelhi70117
13614/03/206:00 PMHaryana0140014
13714/03/206:00 PMKerala1903019
13814/03/206:00 PMRajasthan12103
13914/03/206:00 PMTelengana10001
14014/03/206:00 PMUttar Pradesh1115012
14114/03/206:00 PMLadakh30003
14214/03/206:00 PMTamil Nadu10001
14314/03/206:00 PMJammu and Kashmir20002
14414/03/206:00 PMPunjab10001
14514/03/206:00 PMKarnataka60016
14614/03/206:00 PMMaharashtra1400014
14714/03/206:00 PMAndhra Pradesh10001
14815/03/206:00 PMAndhra Pradesh10001
14915/03/206:00 PMDelhi70217
15015/03/206:00 PMHaryana0140014
15115/03/206:00 PMKarnataka60016
15215/03/206:00 PMKerala2203022
15315/03/206:00 PMMaharashtra3200032
15415/03/206:00 PMPunjab10001
15515/03/206:00 PMRajasthan22304
15615/03/206:00 PMTamil Nadu10001
15715/03/206:00 PMTelengana30103
15815/03/206:00 PMJammu and Kashmir20002
15915/03/206:00 PMLadakh30003
16015/03/206:00 PMUttar Pradesh1214013
16115/03/206:00 PMUttarakhand10001
16216/03/206:00 PMAndhra Pradesh10001
16316/03/206:00 PMDelhi70217
16416/03/206:00 PMHaryana0140014
16516/03/206:00 PMKarnataka60016
16616/03/206:00 PMKerala2303023
16716/03/206:00 PMMaharashtra3200032
16816/03/206:00 PMOdisha10001
16916/03/206:00 PMPunjab10001
17016/03/206:00 PMRajasthan22304
17116/03/206:00 PMTamil Nadu10001
17216/03/206:00 PMTelengana30103
17316/03/206:00 PMJammu and Kashmir30003
17416/03/206:00 PMLadakh40004
17516/03/206:00 PMUttar Pradesh1214013
17616/03/206:00 PMUttarakhand10001
17717/03/206:00 PMAndhra Pradesh10001
17817/03/206:00 PMDelhi80218
17917/03/206:00 PMHaryana1140015
18017/03/206:00 PMKarnataka1100111
18117/03/206:00 PMKerala2423026
18217/03/206:00 PMMaharashtra3630139
18317/03/206:00 PMOdisha10001
18417/03/206:00 PMPunjab10001
18517/03/206:00 PMRajasthan22304
18617/03/206:00 PMTamil Nadu10001
18717/03/206:00 PMTelengana32105
18817/03/206:00 PMJammu and Kashmir30003
18917/03/206:00 PMLadakh60006
19017/03/206:00 PMUttar Pradesh1415015
19117/03/206:00 PMUttarakhand10001
19218/03/206:00 PMAndhra Pradesh10001
19318/03/206:00 PMDelhi912110
19418/03/206:00 PMHaryana3140017
19518/03/206:00 PMKarnataka1100111
19618/03/206:00 PMKerala2523027
19718/03/206:00 PMMaharashtra3930142
19818/03/206:00 PMOdisha10001
19918/03/206:00 PMPuducherry10001
20018/03/206:00 PMPunjab10001
20118/03/206:00 PMRajasthan22304
20218/03/206:00 PMTamil Nadu10001
20318/03/206:00 PMTelengana42106
20418/03/206:00 PMJammu and Kashmir30003
20518/03/206:00 PMLadakh80008
20618/03/206:00 PMUttar Pradesh1515016
20718/03/206:00 PMUttarakhand10001
20818/03/206:00 PMWest Bengal10001
20919/03/206:00 PMAndhra Pradesh20002
21019/03/206:00 PMChhattisgarh10001
21119/03/206:00 PMDelhi1113112
21219/03/206:00 PMHaryana3140017
21319/03/206:00 PMKarnataka1400114
21419/03/206:00 PMKerala2523027
21519/03/206:00 PMMaharashtra4430147
21619/03/206:00 PMOdisha10001
21719/03/206:00 PMPuducherry10001
21819/03/206:00 PMPunjab20012
21919/03/206:00 PMRajasthan52307
22019/03/206:00 PMTamil Nadu20102
22119/03/206:00 PMTelengana42106
22219/03/206:00 PMChandigarh10001
22319/03/206:00 PMJammu and Kashmir40004
22419/03/206:00 PMLadakh80008
22519/03/206:00 PMUttar Pradesh1819019
22619/03/206:00 PMUttarakhand10001
22719/03/206:00 PMWest Bengal10001
22820/03/206:00 PMAndhra Pradesh30003
22920/03/206:00 PMChhattisgarh10001
23020/03/206:00 PMDelhi1615117
23120/03/206:00 PMGujarat50005
23220/03/206:00 PMHaryana3140017
23320/03/206:00 PMKarnataka1501115
23420/03/206:00 PMKerala2623028
23520/03/206:00 PMMaharashtra4930152
23620/03/206:00 PMOdisha20002
23720/03/206:00 PMPuducherry10001
23820/03/206:00 PMPunjab20012
23920/03/206:00 PMRajasthan1523017
24020/03/206:00 PMTamil Nadu30103
24120/03/206:00 PMTelengana891017
24220/03/206:00 PMChandigarh10001
24320/03/206:00 PMJammu and Kashmir40004
24420/03/206:00 PMLadakh1000010
24520/03/206:00 PMUttar Pradesh2219023
24620/03/206:00 PMUttarakhand30003
24720/03/206:00 PMWest Bengal20002
24821/03/206:00 PMAndhra Pradesh30003
24921/03/206:00 PMChhattisgarh10001
25021/03/206:00 PMDelhi2515126
25121/03/206:00 PMGujarat70007
25221/03/206:00 PMHaryana3140017
25321/03/206:00 PMHimachal Pradesh20002
25421/03/206:00 PMKarnataka1501115
25521/03/206:00 PMKerala3373040
25621/03/206:00 PMMadhya Pradesh40004
25721/03/206:00 PMMaharashtra6030163
25821/03/206:00 PMOdisha20002
25921/03/206:00 PMPuducherry10001
26021/03/206:00 PMPunjab1300113
26121/03/206:00 PMRajasthan1523017
26221/03/206:00 PMTamil Nadu30103
26321/03/206:00 PMTelengana10111021
26421/03/206:00 PMChandigarh10001
26521/03/206:00 PMJammu and Kashmir40004
26621/03/206:00 PMLadakh1300013
26721/03/206:00 PMUttar Pradesh2319024
26821/03/206:00 PMUttarakhand30003
26921/03/206:00 PMWest Bengal30003
27022/03/206:00 PMAndhra Pradesh50005
27122/03/206:00 PMBihar20012
27222/03/206:00 PMChhattisgarh10001
27322/03/206:00 PMDelhi2815129
27422/03/206:00 PMGujarat1800118
27522/03/206:00 PMHaryana7140021
27622/03/206:00 PMHimachal Pradesh20002
27722/03/206:00 PMKarnataka2602126
27822/03/206:00 PMKerala4573052
27922/03/206:00 PMMadhya Pradesh40004
28022/03/206:00 PMMaharashtra6430267
28122/03/206:00 PMOdisha20002
28222/03/206:00 PMPuducherry10001
28322/03/206:00 PMPunjab2100121
28422/03/206:00 PMRajasthan2223024
28522/03/206:00 PMTamil Nadu52107
28622/03/206:00 PMTelengana11111022
28722/03/206:00 PMChandigarh50005
28822/03/206:00 PMJammu and Kashmir40004
28922/03/206:00 PMLadakh1300013
29022/03/206:00 PMUttar Pradesh2619027
29122/03/206:00 PMUttarakhand30003
29222/03/206:00 PMWest Bengal40004
29323/03/206:00 PMAndhra Pradesh70007
29423/03/206:00 PMBihar20012
29523/03/206:00 PMChhattisgarh10001
29623/03/206:00 PMDelhi2815129
29723/03/206:00 PMGujarat2900129
29823/03/206:00 PMHaryana12140026
29923/03/206:00 PMHimachal Pradesh20002
30023/03/206:00 PMKarnataka3302133
30123/03/206:00 PMKerala6073067
30223/03/206:00 PMMadhya Pradesh60006
30323/03/206:00 PMMaharashtra7130274
30423/03/206:00 PMOdisha20002
30523/03/206:00 PMPuducherry10001
30623/03/206:00 PMPunjab2100121
30723/03/206:00 PMRajasthan2623028
30823/03/206:00 PMTamil Nadu72109
30923/03/206:00 PMTelengana22101032
31023/03/206:00 PMChandigarh60006
31123/03/206:00 PMJammu and Kashmir40004
31223/03/206:00 PMLadakh1300013
31323/03/206:00 PMUttar Pradesh3019031
31423/03/206:00 PMUttarakhand30003
31523/03/206:00 PMWest Bengal70007
31624/03/206:00 PMAndhra Pradesh80008
31724/03/206:00 PMBihar30013
31824/03/206:00 PMChhattisgarh10001
31924/03/206:00 PMDelhi2916130
32024/03/206:00 PMGujarat3210133
32124/03/206:00 PMHaryana141411028
32224/03/206:00 PMHimachal Pradesh30013
32324/03/206:00 PMKarnataka3703137
32424/03/206:00 PMKerala8784095
32524/03/206:00 PMMadhya Pradesh70007
32624/03/206:00 PMMaharashtra8630289
32724/03/206:00 PMManipur10001
32824/03/206:00 PMOdisha20002
32924/03/206:00 PMPuducherry10001
33024/03/206:00 PMPunjab2900129
33124/03/206:00 PMRajasthan3023032
33224/03/206:00 PMTamil Nadu1321015
33324/03/206:00 PMTelengana25101035
33424/03/206:00 PMChandigarh70007
33524/03/206:00 PMJammu and Kashmir40004
33624/03/206:00 PMLadakh1300013
33724/03/206:00 PMUttar Pradesh32111033
33824/03/206:00 PMUttarakhand31004
33924/03/206:00 PMWest Bengal90019
34025/03/206:00 PMAndhra Pradesh90109
34125/03/206:00 PMBihar40014
34225/03/206:00 PMChhattisgarh10001
34325/03/206:00 PMDelhi3016131
34425/03/206:00 PMGujarat3710138
34525/03/206:00 PMHaryana141411028
34625/03/206:00 PMHimachal Pradesh30013
34725/03/206:00 PMKarnataka4103141
34825/03/206:00 PMKerala101840109
34925/03/206:00 PMMadhya Pradesh1400014
35025/03/206:00 PMMaharashtra125313128
35125/03/206:00 PMManipur10001
35225/03/206:00 PMMizoram10001
35325/03/206:00 PMOdisha20002
35425/03/206:00 PMPuducherry10001
35525/03/206:00 PMPunjab2900129
35625/03/206:00 PMRajasthan3423036
35725/03/206:00 PMTamil Nadu1621018
35825/03/206:00 PMTelengana25101035
35925/03/206:00 PMChandigarh70007
36025/03/206:00 PMJammu and Kashmir70107
36125/03/206:00 PMLadakh1300013
36225/03/206:00 PMUttar Pradesh36111037
36325/03/206:00 PMUttarakhand31004
36425/03/206:00 PMWest Bengal90019
36526/03/206:00 PMAndaman and Nicobar Islands10001
36626/03/206:00 PMAndhra Pradesh1101011
36726/03/206:00 PMBihar60016
36826/03/206:00 PMChandigarh70007
36926/03/206:00 PMChhattisgarh60006
37026/03/206:00 PMDelhi3516136
37126/03/206:00 PMGoa30003
37226/03/206:00 PMGujarat4210343
37326/03/206:00 PMHaryana161411030
37426/03/206:00 PMHimachal Pradesh30013
37526/03/206:00 PMJammu and Kashmir1301013
37626/03/206:00 PMKarnataka5503255
37726/03/206:00 PMKerala110860118
37826/03/206:00 PMLadakh1300013
37926/03/206:00 PMMadhya Pradesh2000120
38026/03/206:00 PMMaharashtra121313124
38126/03/206:00 PMManipur10001
38226/03/206:00 PMMizoram10001
38326/03/206:00 PMOdisha20002
38426/03/206:00 PMPuducherry10001
38526/03/206:00 PMPunjab3300133
38626/03/206:00 PMRajasthan3923041
38726/03/206:00 PMTamil Nadu2061126
38826/03/206:00 PMTelengana34101044
38926/03/206:00 PMUttarakhand41005
39026/03/206:00 PMUttar Pradesh40111041
39126/03/206:00 PMWest Bengal1000110
39227/03/2010:00 AMAndaman and Nicobar Islands10001
39327/03/2010:00 AMAndhra Pradesh1201012
39427/03/2010:00 AMBihar60016
39527/03/2010:00 AMChandigarh70007
39627/03/2010:00 AMChhattisgarh60006
39727/03/2010:00 AMDelhi3516136
39827/03/2010:00 AMGoa30003
39927/03/2010:00 AMGujarat4210343
40027/03/2010:00 AMHaryana161411030
40127/03/2010:00 AMHimachal Pradesh30013
40227/03/2010:00 AMJammu and Kashmir1301113
40327/03/2010:00 AMKarnataka5503255
40427/03/2010:00 AMKerala1298110137
40527/03/2010:00 AMLadakh1303013
40627/03/2010:00 AMMadhya Pradesh2000120
40727/03/2010:00 AMMaharashtra1273154130
40827/03/2010:00 AMManipur10001
40927/03/2010:00 AMMizoram10001
41027/03/2010:00 AMOdisha20002
41127/03/2010:00 AMPuducherry10001
41227/03/2010:00 AMPunjab3300133
41327/03/2010:00 AMRajasthan3923041
41427/03/2010:00 AMTamil Nadu2361129
41527/03/2010:00 AMTelengana35101045
41627/03/2010:00 AMUttarakhand41005
41727/03/2010:00 AMUttar Pradesh40111041
41827/03/2010:00 AMWest Bengal1000110
41928/03/206:00 PMAndhra Pradesh1401014
42028/03/206:00 PMAndaman and Nicobar Islands60006
42128/03/206:00 PMBihar90019
42228/03/206:00 PMChandigarh80008
42328/03/206:00 PMChhattisgarh60006
42428/03/206:00 PMDelhi3816139
42528/03/206:00 PMGoa30003
42628/03/206:00 PMGujarat4410345
42728/03/206:00 PMHaryana191412033
42828/03/206:00 PMHimachal Pradesh30013
42928/03/206:00 PMJammu and Kashmir2001120
43028/03/206:00 PMKarnataka5503255
43128/03/206:00 PMKerala1688110176
43228/03/206:00 PMLadakh1303013
43328/03/206:00 PMMadhya Pradesh3000230
43428/03/206:00 PMMaharashtra1773255180
43528/03/206:00 PMManipur10001
43628/03/206:00 PMMizoram10001
43728/03/206:00 PMOdisha30003
43828/03/206:00 PMPuducherry10001
43928/03/206:00 PMPunjab3801138
44028/03/206:00 PMRajasthan5223054
44128/03/206:00 PMTamil Nadu3462140
44228/03/206:00 PMTelengana46101056
44328/03/206:00 PMUttarakhand41005
44428/03/206:00 PMUttar Pradesh54111055
44528/03/206:00 PMWest Bengal1500115
44629/03/207:30 PMAndhra Pradesh--1019
44729/03/207:30 PMAndaman and Nicobar Islands--009
44829/03/207:30 PMBihar--0111
44929/03/207:30 PMChandigarh--008
45029/03/207:30 PMChhattisgarh--007
45129/03/207:30 PMDelhi--6249
45229/03/207:30 PMGoa--005
45329/03/207:30 PMGujarat--1558
45429/03/207:30 PMHaryana--17033
45529/03/207:30 PMHimachal Pradesh--013
45629/03/207:30 PMJammu and Kashmir--1231
45729/03/207:30 PMKarnataka--5376
45829/03/207:30 PMKerala--151182
45929/03/207:30 PMLadakh--3013
46029/03/207:30 PMMadhya Pradesh--0230
46129/03/207:30 PMMaharashtra--256186
46229/03/207:30 PMManipur--001
46329/03/207:30 PMMizoram--001
46429/03/207:30 PMOdisha--003
46529/03/207:30 PMPuducherry--001
46629/03/207:30 PMPunjab--1138
46729/03/207:30 PMRajasthan--3055
46829/03/207:30 PMTamil Nadu--4149
46929/03/207:30 PMTelengana--1166
47029/03/207:30 PMUttarakhand--207
47129/03/207:30 PMUttar Pradesh--11065
47229/03/207:30 PMWest Bengal--0118
47330/03/209:30 PMAndhra Pradesh--1023
47430/03/209:30 PMAndaman and Nicobar Islands--009
47530/03/209:30 PMBihar--0115
47630/03/209:30 PMChandigarh--008
47730/03/209:30 PMChhattisgarh--007
47830/03/209:30 PMDelhi--6287
47930/03/209:30 PMGoa--005
48030/03/209:30 PMGujarat--1669
48130/03/209:30 PMHaryana--18036
48230/03/209:30 PMHimachal Pradesh--013
48330/03/209:30 PMJammu and Kashmir--2248
48430/03/209:30 PMKarnataka--5383
48530/03/209:30 PMKerala--191202
48630/03/209:30 PMLadakh--3013
48730/03/209:30 PMMadhya Pradesh--0347
48830/03/209:30 PMMaharashtra--258198
48930/03/209:30 PMManipur--001
49030/03/209:30 PMMizoram--001
49130/03/209:30 PMOdisha--003
49230/03/209:30 PMPuducherry--001
49330/03/209:30 PMPunjab--1138
49430/03/209:30 PMRajasthan--3059
49530/03/209:30 PMTamil Nadu--4167
49630/03/209:30 PMTelengana--1171
49730/03/209:30 PMUttarakhand--207
49830/03/209:30 PMUttar Pradesh--11082
49930/03/209:30 PMWest Bengal--0122
50030/03/209:30 PMUnassigned--0046
50131/03/208:30 PMAndhra Pradesh--1040
50231/03/208:30 PMAndaman and Nicobar Islands--0010
50331/03/208:30 PMBihar--0115
50431/03/208:30 PMChandigarh--0013
50531/03/208:30 PMChhattisgarh--008
50631/03/208:30 PMDelhi--6297
50731/03/208:30 PMGoa--005
50831/03/208:30 PMGujarat--3673
50931/03/208:30 PMHaryana--21040
51031/03/208:30 PMHimachal Pradesh--013
51131/03/208:30 PMJammu and Kashmir--2254
51231/03/208:30 PMKarnataka--5383
51331/03/208:30 PMKerala--191234
51431/03/208:30 PMLadakh--3013
51531/03/208:30 PMMadhya Pradesh--0347
51631/03/208:30 PMMaharashtra--399216
51731/03/208:30 PMManipur--001
51831/03/208:30 PMMizoram--001
51931/03/208:30 PMOdisha--003
52031/03/208:30 PMPuducherry--001
52131/03/208:30 PMPunjab--1341
52231/03/208:30 PMRajasthan--3074
52331/03/208:30 PMTamil Nadu--4174
52431/03/208:30 PMTelengana--1179
52531/03/208:30 PMUttarakhand--207
52631/03/208:30 PMUttar Pradesh--140101
52731/03/208:30 PMWest Bengal--0226
52831/03/208:30 PMUnassigned--0038
52901/04/207:30 PMAndhra Pradesh--1083
53001/04/207:30 PMAndaman and Nicobar Islands--0010
53101/04/207:30 PMAssam--001
53201/04/207:30 PMBihar--0123
53301/04/207:30 PMChandigarh--0016
53401/04/207:30 PMChhattisgarh--209
53501/04/207:30 PMDelhi--62152
53601/04/207:30 PMGoa--005
53701/04/207:30 PMGujarat--5682
53801/04/207:30 PMHaryana--21043
53901/04/207:30 PMHimachal Pradesh--013
54001/04/207:30 PMJammu and Kashmir--2262
54101/04/207:30 PMJharkhand--001
54201/04/207:30 PMKarnataka--83101
54301/04/207:30 PMKerala--232241
54401/04/207:30 PMLadakh--3013
54501/04/207:30 PMMadhya Pradesh--0366
54601/04/207:30 PMMaharashtra--399302
54701/04/207:30 PMManipur--001
54801/04/207:30 PMMizoram--001
54901/04/207:30 PMOdisha--004
55001/04/207:30 PMPuducherry--103
55101/04/207:30 PMPunjab--1342
55201/04/207:30 PMRajasthan--3093
55301/04/207:30 PMTamil Nadu--61234
55401/04/207:30 PMTelengana--1396
55501/04/207:30 PMUttarakhand--207
55601/04/207:30 PMUttar Pradesh--142103
55701/04/207:30 PMWest Bengal--6337
55802/04/206:00 PMAndhra Pradesh--1186
55902/04/206:00 PMAndaman and Nicobar Islands--0010
56002/04/206:00 PMAssam--005
56102/04/206:00 PMBihar--0124
56202/04/206:00 PMChandigarh--0016
56302/04/206:00 PMChhattisgarh--209
56402/04/206:00 PMDelhi--84219
56502/04/206:00 PMGoa--005
56602/04/206:00 PMGujarat--8787
56702/04/206:00 PMHaryana--21043
56802/04/206:00 PMHimachal Pradesh--113
56902/04/206:00 PMJammu and Kashmir--2262
57002/04/206:00 PMJharkhand--001
57102/04/206:00 PMKarnataka--93110
57202/04/206:00 PMKerala--252265
57302/04/206:00 PMLadakh--3013
57402/04/206:00 PMMadhya Pradesh--0699
57502/04/206:00 PMMaharashtra--4213335
57602/04/206:00 PMManipur--001
57702/04/206:00 PMMizoram--001
57802/04/206:00 PMOdisha--004
57902/04/206:00 PMPuducherry--103
58002/04/206:00 PMPunjab--1446
58102/04/206:00 PMRajasthan--30108
58202/04/206:00 PMTamil Nadu--61234
58302/04/206:00 PMTelengana--13107
58402/04/206:00 PMUttarakhand--207
58502/04/206:00 PMUttar Pradesh--142113
58602/04/206:00 PMWest Bengal--6353
58703/04/206:00 PMAndhra Pradesh--11132
58803/04/206:00 PMAndaman and Nicobar Islands--0010
58903/04/206:00 PMArunachal Pradesh--001
59003/04/206:00 PMAssam--0016
59103/04/206:00 PMBihar--0129
59203/04/206:00 PMChandigarh--0018
59303/04/206:00 PMChhattisgarh--309
59403/04/206:00 PMDelhi--84219
59503/04/206:00 PMGoa--006
59603/04/206:00 PMGujarat--10895
59703/04/206:00 PMHaryana--24049
59803/04/206:00 PMHimachal Pradesh--116
59903/04/206:00 PMJammu and Kashmir--3275
60003/04/206:00 PMJharkhand--002
60103/04/206:00 PMKarnataka--103124
60203/04/206:00 PMKerala--272286
60303/04/206:00 PMLadakh--3014
60403/04/206:00 PMMadhya Pradesh--06104
60503/04/206:00 PMMaharashtra--4216335
60603/04/206:00 PMManipur--002
60703/04/206:00 PMMizoram--001
60803/04/206:00 PMOdisha--005
60903/04/206:00 PMPuducherry--105
61003/04/206:00 PMPunjab--1548
61103/04/206:00 PMRajasthan--30167
61203/04/206:00 PMTamil Nadu--61309
61303/04/206:00 PMTelengana--17158
61403/04/206:00 PMUttarakhand--2010
61503/04/206:00 PMUttar Pradesh--142172
61603/04/206:00 PMWest Bengal--3363
61703/04/206:00 PMUnassigned--0077
61804/04/206:00 PMAndhra Pradesh--11161
61904/04/206:00 PMAndaman and Nicobar Islands--0010
62004/04/206:00 PMArunachal Pradesh--001
62104/04/206:00 PMAssam--0024
62204/04/206:00 PMBihar--0130
62304/04/206:00 PMChandigarh--0018
62404/04/206:00 PMChhattisgarh--309
62504/04/206:00 PMDelhi--156445
62604/04/206:00 PMGoa--007
62704/04/206:00 PMGujarat--1410105
62804/04/206:00 PMHaryana--24049
62904/04/206:00 PMHimachal Pradesh--116
63004/04/206:00 PMJammu and Kashmir--3275
63104/04/206:00 PMJharkhand--002
63204/04/206:00 PMKarnataka--123128
63304/04/206:00 PMKerala--412295
63404/04/206:00 PMLadakh--3014
63504/04/206:00 PMMadhya Pradesh--06104
63604/04/206:00 PMMaharashtra--4224490
63704/04/206:00 PMManipur--002
63804/04/206:00 PMMizoram--001
63904/04/206:00 PMOdisha--005
64004/04/206:00 PMPuducherry--105
64104/04/206:00 PMPunjab--1557
64204/04/206:00 PMRajasthan--210200
64304/04/206:00 PMTamil Nadu--62411
64404/04/206:00 PMTelengana--17159
64504/04/206:00 PMUttarakhand--2016
64604/04/206:00 PMUttar Pradesh--192174
64704/04/206:00 PMWest Bengal--3369
64805/04/206:00 PMAndhra Pradesh--11190
64905/04/206:00 PMAndaman and Nicobar Islands--0010
65005/04/206:00 PMArunachal Pradesh--001
65105/04/206:00 PMAssam--0026
65205/04/206:00 PMBihar--0130
65305/04/206:00 PMChandigarh--0018
65405/04/206:00 PMChhattisgarh--309
65505/04/206:00 PMDelhi--187503
65605/04/206:00 PMGoa--007
65705/04/206:00 PMGujarat--1811122
65805/04/206:00 PMHaryana--25159
65905/04/206:00 PMHimachal Pradesh--116
66005/04/206:00 PMJammu and Kashmir--42106
66105/04/206:00 PMJharkhand--003
66205/04/206:00 PMKarnataka--124144
66305/04/206:00 PMKerala--492306
66405/04/206:00 PMLadakh--10014
66505/04/206:00 PMMadhya Pradesh--09165
66605/04/206:00 PMMaharashtra--4224490
66705/04/206:00 PMManipur--002
66805/04/206:00 PMMizoram--001
66905/04/206:00 PMOdisha--0020
67005/04/206:00 PMPuducherry--105
67105/04/206:00 PMPunjab--1557
67205/04/206:00 PMRajasthan--210200
67305/04/206:00 PMTamil Nadu--63485
67405/04/206:00 PMTelengana--327269
67505/04/206:00 PMUttarakhand--2022
67605/04/206:00 PMUttar Pradesh--192227
67705/04/206:00 PMWest Bengal--10380
67806/04/206:00 PMAndhra Pradesh--13226
67906/04/206:00 PMAndaman and Nicobar Islands--0010
68006/04/206:00 PMArunachal Pradesh--001
68106/04/206:00 PMAssam--0026
68206/04/206:00 PMBihar--0132
68306/04/206:00 PMChandigarh--0018
68406/04/206:00 PMChhattisgarh--8010
68506/04/206:00 PMDelhi--197523
68606/04/206:00 PMGoa--007
68706/04/206:00 PMGujarat--2212144
68806/04/206:00 PMHaryana--25184
68906/04/206:00 PMHimachal Pradesh--2113
69006/04/206:00 PMJammu and Kashmir--42109
69106/04/206:00 PMJharkhand--004
69206/04/206:00 PMKarnataka--124151
69306/04/206:00 PMKerala--552314
69406/04/206:00 PMLadakh--10014
69506/04/206:00 PMMadhya Pradesh--09165
69606/04/206:00 PMMaharashtra--5645748
69706/04/206:00 PMManipur--002
69806/04/206:00 PMMizoram--001
69906/04/206:00 PMOdisha--2021
70006/04/206:00 PMPuducherry--105
70106/04/206:00 PMPunjab--4676
70206/04/206:00 PMRajasthan--210274
70306/04/206:00 PMTamil Nadu--85571
70406/04/206:00 PMTelengana--347321
70506/04/206:00 PMUttarakhand--4026
70606/04/206:00 PMUttar Pradesh--213305
70706/04/206:00 PMWest Bengal--10380
70807/04/206:00 PMAndhra Pradesh--13266
70907/04/206:00 PMAndaman and Nicobar Islands--0010
71007/04/206:00 PMArunachal Pradesh--001
71107/04/206:00 PMAssam--0026
71207/04/206:00 PMBihar--0132
71307/04/206:00 PMChandigarh--7018
71407/04/206:00 PMChhattisgarh--9010
71507/04/206:00 PMDelhi--217576
71607/04/206:00 PMGoa--007
71707/04/206:00 PMGujarat--2513165
71807/04/206:00 PMHaryana--25190
71907/04/206:00 PMHimachal Pradesh--2113
72007/04/206:00 PMJammu and Kashmir--42116
72107/04/206:00 PMJharkhand--004
72207/04/206:00 PMKarnataka--254175
72307/04/206:00 PMKerala--582327
72407/04/206:00 PMLadakh--10014
72507/04/206:00 PMMadhya Pradesh--013229
72607/04/206:00 PMMaharashtra--5648868
72707/04/206:00 PMManipur--002
72807/04/206:00 PMMizoram--001
72907/04/206:00 PMOdisha--2142
73007/04/206:00 PMPuducherry--105
73107/04/206:00 PMPunjab--4791
73207/04/206:00 PMRajasthan--213288
73307/04/206:00 PMTamil Nadu--85621
73407/04/206:00 PMTelengana--357364
73507/04/206:00 PMTripura--001
73607/04/206:00 PMUttarakhand--5031
73707/04/206:00 PMUttar Pradesh--213305
73807/04/206:00 PMWest Bengal--13391
73908/04/205:00 PMAndhra Pradesh--54305
74008/04/205:00 PMAndaman and Nicobar Islands--0010
74108/04/205:00 PMArunachal Pradesh--001
74208/04/205:00 PMAssam--0027
74308/04/205:00 PMBihar--0138
74408/04/205:00 PMChandigarh--7018
74508/04/205:00 PMChhattisgarh--9010
74608/04/205:00 PMDelhi--219576
74708/04/205:00 PMGoa--007
74808/04/205:00 PMGujarat--2513165
74908/04/205:00 PMHaryana--283147
75008/04/205:00 PMHimachal Pradesh--2118
75108/04/205:00 PMJammu and Kashmir--42116
75208/04/205:00 PMJharkhand--004
75308/04/205:00 PMKarnataka--254175
75408/04/205:00 PMKerala--702336
75508/04/205:00 PMLadakh--10014
75608/04/205:00 PMMadhya Pradesh--013229
75708/04/205:00 PMMaharashtra--79641018
75808/04/205:00 PMManipur--002
75908/04/205:00 PMMizoram--001
76008/04/205:00 PMOdisha--2142
76108/04/205:00 PMPuducherry--105
76208/04/205:00 PMPunjab--4791
76308/04/205:00 PMRajasthan--213328
76408/04/205:00 PMTamil Nadu--197690
76508/04/205:00 PMTelengana--357427
76608/04/205:00 PMTripura--001
76708/04/205:00 PMUttarakhand--5031
76808/04/205:00 PMUttar Pradesh--263343
76908/04/205:00 PMWest Bengal--13599
77009/04/205:00 PMAndhra Pradesh--64348
77109/04/205:00 PMAndaman and Nicobar Islands--0011
77209/04/205:00 PMArunachal Pradesh--001
77309/04/205:00 PMAssam--0028
77409/04/205:00 PMBihar--0139
77509/04/205:00 PMChandigarh--7018
77609/04/205:00 PMChhattisgarh--9010
77709/04/205:00 PMDelhi--219669
77809/04/205:00 PMGoa--007
77909/04/205:00 PMGujarat--2516179
78009/04/205:00 PMHaryana--293169
78109/04/205:00 PMHimachal Pradesh--2118
78209/04/205:00 PMJammu and Kashmir--44158
78309/04/205:00 PMJharkhand--0013
78409/04/205:00 PMKarnataka--285181
78509/04/205:00 PMKerala--832345
78609/04/205:00 PMLadakh--10014
78709/04/205:00 PMMadhya Pradesh--016259
78809/04/205:00 PMMaharashtra--117721135
78909/04/205:00 PMManipur--102
79009/04/205:00 PMMizoram--001
79109/04/205:00 PMOdisha--2142
79209/04/205:00 PMPuducherry--105
79309/04/205:00 PMPunjab--48101
79409/04/205:00 PMRajasthan--213383
79509/04/205:00 PMTamil Nadu--218738
79609/04/205:00 PMTelengana--357442
79709/04/205:00 PMTripura--001
79809/04/205:00 PMUttarakhand--5035
79909/04/205:00 PMUttar Pradesh--314410
80009/04/205:00 PMWest Bengal--165103
80110/04/205:00 PMAndhra Pradesh--76363
80210/04/205:00 PMAndaman and Nicobar Islands--0011
80310/04/205:00 PMArunachal Pradesh--001
80410/04/205:00 PMAssam--0029
80510/04/205:00 PMBihar--0160
80610/04/205:00 PMChandigarh--7018
80710/04/205:00 PMChhattisgarh--9010
80810/04/205:00 PMDelhi--2513898
80910/04/205:00 PMGoa--107
81010/04/205:00 PMGujarat--2617241
81110/04/205:00 PMHaryana--293169
81210/04/205:00 PMHimachal Pradesh--6128
81310/04/205:00 PMJammu and Kashmir--64184
81410/04/205:00 PMJharkhand--0113
81510/04/205:00 PMKarnataka--306197
81610/04/205:00 PMKerala--962357
81710/04/205:00 PMLadakh--10015
81810/04/205:00 PMMadhya Pradesh--016259
81910/04/205:00 PMMaharashtra--125971364
82010/04/205:00 PMManipur--102
82110/04/205:00 PMMizoram--001
82210/04/205:00 PMOdisha--2144
82310/04/205:00 PMPuducherry--105
82410/04/205:00 PMPunjab--511132
82510/04/205:00 PMRajasthan--213463
82610/04/205:00 PMTamil Nadu--218834
82710/04/205:00 PMTelengana--357473
82810/04/205:00 PMTripura--001
82910/04/205:00 PMUttarakhand--5035
83010/04/205:00 PMUttar Pradesh--324431
83110/04/205:00 PMWest Bengal--165116
83211/04/205:00 PMAndhra Pradesh--116381
83311/04/205:00 PMAndaman and Nicobar Islands--0011
83411/04/205:00 PMArunachal Pradesh--001
83511/04/205:00 PMAssam--0129
83611/04/205:00 PMBihar--0160
83711/04/205:00 PMChandigarh--7018
83811/04/205:00 PMChhattisgarh--9018
83911/04/205:00 PMDelhi--2514903
84011/04/205:00 PMGoa--107
84111/04/205:00 PMGujarat--3119308
84211/04/205:00 PMHaryana--293177
84311/04/205:00 PMHimachal Pradesh--6128
84411/04/205:00 PMJammu and Kashmir--64207
84511/04/205:00 PMJharkhand--0117
84611/04/205:00 PMKarnataka--376214
84711/04/205:00 PMKerala--1232364
84811/04/205:00 PMLadakh--10015
84911/04/205:00 PMMadhya Pradesh--033443
85011/04/205:00 PMMaharashtra--1881101574
85111/04/205:00 PMManipur--102
85211/04/205:00 PMMizoram--001
85311/04/205:00 PMOdisha--2148
85411/04/205:00 PMPuducherry--107
85511/04/205:00 PMPunjab--511132
85611/04/205:00 PMRajasthan--213553
85711/04/205:00 PMTamil Nadu--448911
85811/04/205:00 PMTelengana--439504
85911/04/205:00 PMTripura--002
86011/04/205:00 PMUttarakhand--5035
86111/04/205:00 PMUttar Pradesh--324433
86211/04/205:00 PMWest Bengal--165126
86312/04/205:00 PMAndhra Pradesh--116381
86412/04/205:00 PMAndaman and Nicobar Islands--10011
86512/04/205:00 PMArunachal Pradesh--001
86612/04/205:00 PMAssam--0129
86712/04/205:00 PMBihar--19164
86812/04/205:00 PMChandigarh--7019
86912/04/205:00 PMChhattisgarh--10025
87012/04/205:00 PMDelhi--25191069
87112/04/205:00 PMGoa--507
87212/04/205:00 PMGujarat--4422432
87312/04/205:00 PMHaryana--293185
87412/04/205:00 PMHimachal Pradesh--6132
87512/04/205:00 PMJammu and Kashmir--64224
87612/04/205:00 PMJharkhand--0117
87712/04/205:00 PMKarnataka--376226
87812/04/205:00 PMKerala--1422374
87912/04/205:00 PMLadakh--10015
88012/04/205:00 PMMadhya Pradesh--036564
88112/04/205:00 PMMaharashtra--2081271761
88212/04/205:00 PMManipur--102
88312/04/205:00 PMMizoram--001
88412/04/205:00 PMOdisha--12154
88512/04/205:00 PMPuducherry--107
88612/04/205:00 PMPunjab--511151
88712/04/205:00 PMRajasthan--213700
88812/04/205:00 PMTamil Nadu--4410969
88912/04/205:00 PMTelengana--439504
89012/04/205:00 PMTripura--002
89112/04/205:00 PMUttarakhand--5035
89212/04/205:00 PMUttar Pradesh--455452
89312/04/205:00 PMWest Bengal--195134
89413/04/205:00 PMAndhra Pradesh--117432
89513/04/205:00 PMAndaman and Nicobar Islands--10011
89613/04/205:00 PMArunachal Pradesh--001
89713/04/205:00 PMAssam--0131
89813/04/205:00 PMBihar--26164
89913/04/205:00 PMChandigarh--7021
90013/04/205:00 PMChhattisgarh--10031
90113/04/205:00 PMDelhi--27241154
90213/04/205:00 PMGoa--507
90313/04/205:00 PMGujarat--4726539
90413/04/205:00 PMHaryana--293185
90513/04/205:00 PMHimachal Pradesh--13132
90613/04/205:00 PMJammu and Kashmir--64245
90713/04/205:00 PMJharkhand--0219
90813/04/205:00 PMKarnataka--596247
90913/04/205:00 PMKerala--1793376
91013/04/205:00 PMLadakh--10015
91113/04/205:00 PMMadhya Pradesh--4443604
91213/04/205:00 PMMaharashtra--2171491985
91313/04/205:00 PMManipur--102
91413/04/205:00 PMMizoram--001
91513/04/205:00 PMNagaland--001
91613/04/205:00 PMOdisha--12154
91713/04/205:00 PMPuducherry--107
91813/04/205:00 PMPunjab--1411167
91913/04/205:00 PMRajasthan--213812
92013/04/205:00 PMTamil Nadu--50111075
92113/04/205:00 PMTelengana--10016562
92213/04/205:00 PMTripura--002
92313/04/205:00 PMUttarakhand--5035
92413/04/205:00 PMUttar Pradesh--475483
92513/04/205:00 PMWest Bengal--297152
92614/04/205:00 PMAndhra Pradesh--149473
92714/04/205:00 PMAndaman and Nicobar Islands--10011
92814/04/205:00 PMArunachal Pradesh--001
92914/04/205:00 PMAssam--0131
93014/04/205:00 PMBihar--26166
93114/04/205:00 PMChandigarh--7021
93214/04/205:00 PMChhattisgarh--10031
93314/04/205:00 PMDelhi--30281510
93414/04/205:00 PMGoa--507
93514/04/205:00 PMGujarat--5526617
93614/04/205:00 PMHaryana--343199
93714/04/205:00 PMHimachal Pradesh--13132
93814/04/205:00 PMJammu and Kashmir--164270
93914/04/205:00 PMJharkhand--0224
94014/04/205:00 PMKarnataka--659258
94114/04/205:00 PMKerala--1983379
94214/04/205:00 PMLadakh--10015
94314/04/205:00 PMMadhya Pradesh--5150730
94414/04/205:00 PMMaharashtra--2291602337
94514/04/205:00 PMManipur--102
94614/04/205:00 PMMeghalaya--001
94714/04/205:00 PMMizoram--001
94814/04/205:00 PMNagaland--001
94914/04/205:00 PMOdisha--18155
95014/04/205:00 PMPuducherry--107
95114/04/205:00 PMPunjab--1412176
95214/04/205:00 PMRajasthan--1333879
95314/04/205:00 PMTamil Nadu--58111173
95414/04/205:00 PMTelengana--10017624
95514/04/205:00 PMTripura--002
95614/04/205:00 PMUttarakhand--7035
95714/04/205:00 PMUttar Pradesh--495657
95814/04/205:00 PMWest Bengal--367190
95915/04/205:00 PMAndaman and Nicobar Islands--10011
96015/04/205:00 PMAndhra Pradesh--169503
96115/04/205:00 PMArunachal Pradesh--001
96215/04/205:00 PMAssam--0133
96315/04/205:00 PMBihar--29170
96415/04/205:00 PMChandigarh--7021
96515/04/205:00 PMChhattisgarh--13033
96615/04/205:00 PMDelhi--30301561
96715/04/205:00 PMGoa--507
96815/04/205:00 PMGujarat--5930695
96915/04/205:00 PMHaryana--343199
97015/04/205:00 PMHimachal Pradesh--13133
97115/04/205:00 PMJammu and Kashmir--304278
97215/04/205:00 PMJharkhand--0227
97315/04/205:00 PMKarnataka--7511277
97415/04/205:00 PMKerala--2113387
97515/04/205:00 PMLadakh--10017
97615/04/205:00 PMMadhya Pradesh--6453987
97715/04/205:00 PMMaharashtra--2591782687
97815/04/205:00 PMManipur--102
97915/04/205:00 PMMeghalaya--017
98015/04/205:00 PMMizoram--001
98115/04/205:00 PMNagaland#--000
98215/04/205:00 PMOdisha--18160
98315/04/205:00 PMPuducherry--107
98415/04/205:00 PMPunjab--1413186
98515/04/205:00 PMRajasthan--14731005
98615/04/205:00 PMTamil Nadu--81121204
98715/04/205:00 PMTelengana--12018647
98815/04/205:00 PMTripura--002
98915/04/205:00 PMUttarakhand--9037
99015/04/205:00 PMUttar Pradesh--5111735
99115/04/205:00 PMWest Bengal--377213
99216/04/205:00 PMAndaman and Nicobar Islands--10011
99316/04/205:00 PMAndhra Pradesh--2014534
99416/04/205:00 PMArunachal Pradesh--001
99516/04/205:00 PMAssam--5133
99616/04/205:00 PMBihar--29174
99716/04/205:00 PMChandigarh--7021
99816/04/205:00 PMChhattisgarh--17033
99916/04/205:00 PMDelhi--42321578
100016/04/205:00 PMGoa--507
100116/04/205:00 PMGujarat--6436871
100216/04/205:00 PMHaryana--433205
100316/04/205:00 PMHimachal Pradesh--16135
100416/04/205:00 PMJammu and Kashmir--364300
100516/04/205:00 PMJharkhand--0228
100616/04/205:00 PMKarnataka--8213315
100716/04/205:00 PMKerala--2183388
100816/04/205:00 PMLadakh--10017
100916/04/205:00 PMMadhya Pradesh--64531120
101016/04/205:00 PMMaharashtra--2951872919
101116/04/205:00 PMManipur--102
101216/04/205:00 PMMeghalaya--017
101316/04/205:00 PMMizoram--001
101416/04/205:00 PMNagaland#--000
101516/04/205:00 PMOdisha--18160
101616/04/205:00 PMPuducherry--107
101716/04/205:00 PMPunjab--2713186
101816/04/205:00 PMRajasthan--14731023
101916/04/205:00 PMTamil Nadu--118141242
102016/04/205:00 PMTelengana--12018698
102116/04/205:00 PMTripura--102
102216/04/205:00 PMUttarakhand--9037
102316/04/205:00 PMUttar Pradesh--6813773
102416/04/205:00 PMWest Bengal--427231
102517/04/205:00 PMAndaman and Nicobar Islands--10011
102617/04/205:00 PMAndhra Pradesh--3614572
102717/04/205:00 PMArunachal Pradesh--001
102817/04/205:00 PMAssam--5135
102917/04/205:00 PMBihar--37183
103017/04/205:00 PMChandigarh--9021
103117/04/205:00 PMChhattisgarh--23036
103217/04/205:00 PMDelhi--51381640
103317/04/205:00 PMGoa--607
103417/04/205:00 PMGujarat--74381021
103517/04/205:00 PMHaryana--433205
103617/04/205:00 PMHimachal Pradesh--16135
103717/04/205:00 PMJammu and Kashmir--384314
103817/04/205:00 PMJharkhand--0229
103917/04/205:00 PMKarnataka--8213353
104017/04/205:00 PMKerala--2453395
104117/04/205:00 PMLadakh--14018
104217/04/205:00 PMMadhya Pradesh--65571308
104317/04/205:00 PMMaharashtra--3001943205
104417/04/205:00 PMManipur--102
104517/04/205:00 PMMeghalaya--019
104617/04/205:00 PMMizoram--001
104717/04/205:00 PMNagaland#--000
104817/04/205:00 PMOdisha--19160
104917/04/205:00 PMPuducherry--107
105017/04/205:00 PMPunjab--2713186
105117/04/205:00 PMRajasthan--164111131
105217/04/205:00 PMTamil Nadu--180151267
105317/04/205:00 PMTelengana--18618743
105417/04/205:00 PMTripura--102
105517/04/205:00 PMUttarakhand--9037
105617/04/205:00 PMUttar Pradesh--7414846
105717/04/205:00 PMWest Bengal--5110255
105818/04/205:00 PMAndaman and Nicobar Islands--11012
105918/04/205:00 PMAndhra Pradesh--4215603
106018/04/205:00 PMArunachal Pradesh--001
106118/04/205:00 PMAssam--9135
106218/04/205:00 PMBihar--37285
106318/04/205:00 PMChandigarh--9021
106418/04/205:00 PMChhattisgarh--24036
106518/04/205:00 PMDelhi--72421707
106618/04/205:00 PMGoa--607
106718/04/205:00 PMGujarat--88481272
106818/04/205:00 PMHaryana--433225
106918/04/205:00 PMHimachal Pradesh--16138
107018/04/205:00 PMJammu and Kashmir--425328
107118/04/205:00 PMJharkhand--0233
107218/04/205:00 PMKarnataka--9213371
107318/04/205:00 PMKerala--2553396
107418/04/205:00 PMLadakh--14018
107518/04/205:00 PMMadhya Pradesh--69691355
107618/04/205:00 PMMaharashtra--3312013323
107718/04/205:00 PMManipur--102
107818/04/205:00 PMMeghalaya--0111
107918/04/205:00 PMMizoram--001
108018/04/205:00 PMNagaland#--000
108118/04/205:00 PMOdisha--21160
108218/04/205:00 PMPuducherry--307
108318/04/205:00 PMPunjab--2713202
108418/04/205:00 PMRajasthan--183111229
108518/04/205:00 PMTamil Nadu--283151323
108618/04/205:00 PMTelengana--18618791
108718/04/205:00 PMTripura--102
108818/04/205:00 PMUttarakhand--9042
108918/04/205:00 PMUttar Pradesh--8614969
109018/04/205:00 PMWest Bengal--5510287
109119/04/205:00 PMAndaman and Nicobar Islands--11014
109219/04/205:00 PMAndhra Pradesh--4215603
109319/04/205:00 PMArunachal Pradesh--001
109419/04/205:00 PMAssam--12135
109519/04/205:00 PMBihar--37286
109619/04/205:00 PMChandigarh--10023
109719/04/205:00 PMChhattisgarh--24036
109819/04/205:00 PMDelhi--72431893
109919/04/205:00 PMGoa--607
110019/04/205:00 PMGujarat--94581604
110119/04/205:00 PMHaryana--873233
110219/04/205:00 PMHimachal Pradesh--16139
110319/04/205:00 PMJammu and Kashmir--515341
110419/04/205:00 PMJharkhand--0235
110519/04/205:00 PMKarnataka--10414384
110619/04/205:00 PMKerala--2573400
110719/04/205:00 PMLadakh--14018
110819/04/205:00 PMMadhya Pradesh--127701407
110919/04/205:00 PMMaharashtra--3652113651
111019/04/205:00 PMManipur--102
111119/04/205:00 PMMeghalaya--0111
111219/04/205:00 PMMizoram--001
111319/04/205:00 PMNagaland#--000
111419/04/205:00 PMOdisha--24161
111519/04/205:00 PMPuducherry--307
111619/04/205:00 PMPunjab--3116219
111719/04/205:00 PMRajasthan--183111351
111819/04/205:00 PMTamil Nadu--365151372
111919/04/205:00 PMTelengana--18618844
112019/04/205:00 PMTripura--102
112119/04/205:00 PMUttarakhand--9042
112219/04/205:00 PMUttar Pradesh--108171084
112319/04/205:00 PMWest Bengal--6212310
" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uM014fXNqixd", + "colab_type": "text" + }, + "source": [ + "**Visulization inference:**\n", + "* First case in Kerla dated 30/01/2020 6.00pm\n", + "* Highly affected state - Maharashtra\n", + "* First Death reported in Karnataka dated 13/03/20 6:00 PM\n", + "* Total 155422 Confirmed cases in India till date 19th April 2020 \n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "80T0S0YDuZ_1", + "colab_type": "code", + "outputId": "49460b26-3e43-4d42-f477-1ee1f68b75bf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 813 + } + }, + "source": [ + "#Total active cases\n", + "indian_data['Total Active'] = indian_data['Confirmed'] - (indian_data['Deaths'] + indian_data['Cured'])\n", + "total_active = indian_data['Total Active'].sum()\n", + "print('Total number of active COVID 2019 cases across India:', total_active)\n", + "Tot_Cases = indian_data.groupby('State/UnionTerritory')['Total Active'].sum().sort_values(ascending=False).to_frame()\n", + "Tot_Cases.style.background_gradient(cmap='Reds')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Total number of active COVID 2019 cases across India: 134317\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Total Active
State/UnionTerritory
Maharashtra27133
Delhi17416
Tamil Nadu14506
Rajasthan10515
Madhya Pradesh9369
Uttar Pradesh8697
Gujarat8020
Telengana7775
Andhra Pradesh6451
Kerala5754
Karnataka3780
Jammu and Kashmir3452
Haryana2428
Punjab2311
West Bengal2268
Bihar791
Odisha606
Uttarakhand509
Assam459
Chandigarh343
Ladakh299
Himachal Pradesh288
Jharkhand259
Chhattisgarh224
Unassigned161
Andaman and Nicobar Islands159
Goa107
Puducherry100
Meghalaya41
Manipur33
Mizoram26
Tripura18
Arunachal Pradesh17
Nagaland2
Nagaland#0
" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 30 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kyb5Ovj8vu0u", + "colab_type": "text" + }, + "source": [ + " **Visualization inference:**\n", + "* Maharashtra, Delhi, & Tamil nadu are currently TOP 3 states with maximum number of Active Cases\n", + "* Nagaland has minimum number of cases\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VwPrffdv0faB", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df_full = pd.merge(indian_coord, indian_data, on='State/UnionTerritory')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "_jDi7a2owihb", + "colab_type": "code", + "outputId": "8395e165-c24c-41ee-cb9d-7d6ca7d60631", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 636 + } + }, + "source": [ + "#Confirmed VS Recovered Figures\n", + "f, ax = plt.subplots(figsize=(12, 8))\n", + "data = df_full[['State/UnionTerritory', 'Confirmed', 'Cured', 'Deaths']]\n", + "data.sort_values('Confirmed', ascending=False, inplace=True)\n", + "sns.set_color_codes('pastel')\n", + "sns.barplot(x='Confirmed', y='State/UnionTerritory', data=data, label='Total', color='r')\n", + "\n", + "sns.set_color_codes('muted')\n", + "sns.barplot(x='Cured', y='State/UnionTerritory', data=data, label='Cured', color='g' )\n", + "\n", + "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n", + "ax.set(xlim=(0, 650), ylabel=\"\",xlabel=\"Cases\")\n", + "sns.despine(left=True, bottom=True)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "\n", + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "keXZYqevdCLu", + "colab_type": "code", + "outputId": "5ea61cb9-7e78-429c-c6df-fafae6ae7c8d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + } + }, + "source": [ + "confirmed_cases = indian_data['Confirmed']\n", + "death_cases = indian_data['Deaths']\n", + "cured_cases = indian_data['Cured']\n", + "\n", + "plt.figure(figsize= (20,10))\n", + "plt.xticks(rotation = 90 ,fontsize = 11)\n", + "plt.yticks(fontsize = 10)\n", + "plt.xlabel(\"Dates\",fontsize = 20)\n", + "plt.ylabel('Total cases',fontsize = 20)\n", + "plt.title(\"Total Confirmed, Active, Death in India\" , fontsize = 20)\n", + "\n", + "ax1 = plt.plot_date(y=confirmed_cases, x=indian_data['Date'], label = 'Confirmed',linestyle ='solid',color = 'b')\n", + "ax2 = plt.plot_date(y=cured_cases, x= indian_data['Date'], label = 'Recovered',linestyle ='solid',color = 'g')\n", + "ax3 = plt.plot_date(y=death_cases, x= indian_data['Date'], label = 'Death',linestyle ='solid',color = 'r')\n", + "plt.legend()\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n", + "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n" + ], + "name": "stderr" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 194 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ], + "text/plain": [ + " Cured Deaths Confirmed Total Active\n", + "Cured 1.000000 0.687962 0.798914 0.748080\n", + "Deaths 0.687962 1.000000 0.859747 0.841638\n", + "Confirmed 0.798914 0.859747 1.000000 0.996392\n", + "Total Active 0.748080 0.841638 0.996392 1.000000" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 135 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 137 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tGOWuTRhU89s", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XvJoSmz3ZfKp", + "colab_type": "text" + }, + "source": [ + "## Age Analysis" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "amltUBz3HbGf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "age_df = pd.read_csv('/content/AgeGroupDetails.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Qz401fLiKFQq", + "colab_type": "code", + "outputId": "17c39f90-fb3f-47e4-e24b-427b0f5c97e0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "age_df.columns" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Sno', 'AgeGroup', 'TotalCases', 'Percentage'], dtype='object')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 78 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nQN3XPyJKFG7", + "colab_type": "code", + "colab": {} + }, + "source": [ + "age_df.drop(['Sno'], axis=1, inplace=True)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "xXmXO_MpKFKN", + "colab_type": "code", + "outputId": "a9f2e1df-ea23-4cd0-c90b-e463f9cfc579", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 359 + } + }, + "source": [ + "age_df" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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AgeGroupTotalCasesPercentage
00-9223.18%
110-19273.90%
220-2917224.86%
330-3914621.10%
440-4911216.18%
550-597711.13%
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9Missing91.30%
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" + ], + "text/plain": [ + " AgeGroup TotalCases Percentage\n", + "0 0-9 22 3.18%\n", + "1 10-19 27 3.90%\n", + "2 20-29 172 24.86%\n", + "3 30-39 146 21.10%\n", + "4 40-49 112 16.18%\n", + "5 50-59 77 11.13%\n", + "6 60-69 89 12.86%\n", + "7 70-79 28 4.05%\n", + "8 >=80 10 1.45%\n", + "9 Missing 9 1.30%" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 82 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fvWqq22MKFDl", + "colab_type": "code", + "outputId": "0fcfb63c-6c80-451b-db56-3212bdc4fc27", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + } + }, + "source": [ + "import plotly.express as px\n", + "\n", + "fig = px.pie(age_df, values='TotalCases', names='AgeGroup',title='Confirmed cases of India')\n", + "fig.show()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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State/UTNumPrimaryHealthCenters_HMISNumCommunityHealthCenters_HMISNumSubDistrictHospitals_HMISNumDistrictHospitals_HMISTotalPublicHealthFacilities_HMISNumPublicBeds_HMISNumRuralHospitals_NHP18NumRuralBeds_NHP18NumUrbanHospitals_NHP18NumUrbanBeds_NHP18
32Tripura1142212.091574895991140563277
33Uttar Pradesh3277671NaN17441225831044423910419337156
34Uttarakhand2756919.02038366604103284505228
35West Bengal137440670.05519055116312721968429458882
36All India29,89955681255.0100337725739024198102795883772431173
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" + ], + "text/plain": [ + " State/UT ... NumUrbanBeds_NHP18\n", + "32 Tripura ... 3277\n", + "33 Uttar Pradesh ... 37156\n", + "34 Uttarakhand ... 5228\n", + "35 West Bengal ... 58882\n", + "36 All India ... 431173\n", + "\n", + "[5 rows x 11 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 148 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TIFxdBC8Z0XL", + "colab_type": "text" + }, + "source": [ + "## Inidividual Details Analysis" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LIuZ78GFZ505", + "colab_type": "code", + "colab": {} + }, + "source": [ + "individual_det_df = pd.read_csv(\"/content/IndividualDetails.csv\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "TIwesdUtZ7dq", + "colab_type": "code", + "outputId": "177f4df1-d948-448f-f945-360dc0b1a78b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 677 + } + }, + "source": [ + "individual_det_df" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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idgovernment_iddiagnosed_dateagegenderdetected_citydetected_districtdetected_statenationalitystatus_change_datenotes
00KL-TS-P130/01/202020FThrissurThrissurKeralaIndia14/02/2020Travelled from Wuhan
11KL-AL-P102/02/2020NaNNaNAlappuzhaAlappuzhaKeralaIndia14/02/2020Travelled from Wuhan
22KL-KS-P103/02/2020NaNNaNKasaragodKasaragodKeralaIndia14/02/2020Travelled from Wuhan
33DL-P102/03/202045MEast Delhi (Mayur Vihar)East DelhiDelhiIndia15/03/2020Travelled from Austria, Italy
44TS-P102/03/202024MHyderabadHyderabadTelanganaIndia02/03/2020Travelled from Dubai to Bangalore on 20th Feb,...
....................................
1365913660KA-P34917/04/2020NaNNaNNaNBengaluruKarnatakaNaN17/04/2020NaN
1366013661KA-P35017/04/2020NaNNaNNaNBengaluruKarnatakaNaN17/04/2020NaN
1366113662KA-P35117/04/2020NaNNaNNaNBengaluruKarnatakaNaN17/04/2020NaN
1366213663KA-P35217/04/2020NaNNaNNaNBengaluruKarnatakaNaN17/04/2020NaN
1366313664KA-P35317/04/2020NaNNaNNaNBengaluruKarnatakaNaN17/04/2020NaN
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13664 rows × 11 columns

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" + ], + "text/plain": [ + " id ... notes\n", + "0 0 ... Travelled from Wuhan\n", + "1 1 ... Travelled from Wuhan\n", + "2 2 ... Travelled from Wuhan\n", + "3 3 ... Travelled from Austria, Italy\n", + "4 4 ... Travelled from Dubai to Bangalore on 20th Feb,...\n", + "... ... ... ...\n", + "13659 13660 ... NaN\n", + "13660 13661 ... NaN\n", + "13661 13662 ... NaN\n", + "13662 13663 ... NaN\n", + "13663 13664 ... NaN\n", + "\n", + "[13664 rows x 11 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 156 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ec9z4QHPZ7a8", + "colab_type": "code", + "outputId": "00675205-c2b5-42d3-80da-ff74b4010a62", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "source": [ + "individual_det_df.columns" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['id', 'government_id', 'diagnosed_date', 'age', 'gender',\n", + " 'detected_city', 'detected_district', 'detected_state', 'nationality',\n", + " 'status_change_date', 'notes'],\n", + " dtype='object')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 157 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SbGFnUZKaKbq", + "colab_type": "code", + "outputId": "9cf9f5e8-2546-485d-ff93-494e7bb6ef35", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + } + }, + "source": [ + "individual_det_df.info()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 13664 entries, 0 to 13663\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 13664 non-null int64 \n", + " 1 government_id 2208 non-null object\n", + " 2 diagnosed_date 13664 non-null object\n", + " 3 age 1563 non-null object\n", + " 4 gender 2611 non-null object\n", + " 5 detected_city 1667 non-null object\n", + " 6 detected_district 11557 non-null object\n", + " 7 detected_state 13664 non-null object\n", + " 8 nationality 1297 non-null object\n", + " 9 status_change_date 13553 non-null object\n", + " 10 notes 12299 non-null object\n", + "dtypes: int64(1), object(10)\n", + "memory usage: 1.1+ MB\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9QVn45F2aM5-", + "colab_type": "code", + "outputId": "ee71e740-4430-49d4-d404-ebf5279af33a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 462 + } + }, + "source": [ + "individual_det_df.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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government_iddiagnosed_dateagegenderdetected_citydetected_districtdetected_statenationalitystatus_change_datenotes
0KL-TS-P130/01/202020FThrissurThrissurKeralaIndia14/02/2020Travelled from Wuhan
1KL-AL-P102/02/2020NaNNaNAlappuzhaAlappuzhaKeralaIndia14/02/2020Travelled from Wuhan
2KL-KS-P103/02/2020NaNNaNKasaragodKasaragodKeralaIndia14/02/2020Travelled from Wuhan
3DL-P102/03/202045MEast Delhi (Mayur Vihar)East DelhiDelhiIndia15/03/2020Travelled from Austria, Italy
4TS-P102/03/202024MHyderabadHyderabadTelanganaIndia02/03/2020Travelled from Dubai to Bangalore on 20th Feb,...
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" + ], + "text/plain": [ + " government_id ... notes\n", + "0 KL-TS-P1 ... Travelled from Wuhan\n", + "1 KL-AL-P1 ... Travelled from Wuhan\n", + "2 KL-KS-P1 ... Travelled from Wuhan\n", + "3 DL-P1 ... Travelled from Austria, Italy\n", + "4 TS-P1 ... Travelled from Dubai to Bangalore on 20th Feb,...\n", + "\n", + "[5 rows x 10 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 164 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2RyVkU8Ma0te", + "colab_type": "code", + "outputId": "c9b435b8-3678-4e54-b2dc-c53c42501cd6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 692 + } + }, + "source": [ + "labels = [\"Missing\", \"Male\", \"Female\"]\n", + "sizes = []\n", + "sizes.append(individual_det_df['gender'].isnull().sum())\n", + "sizes.append(list(individual_det_df['gender'].value_counts())[0])\n", + "sizes.append(list(individual_det_df['gender'].value_counts())[1])\n", + "\n", + "explode = (0, 0.1, 0)\n", + "colors = ['#ffcc99','#66b3ff','#ff9999']\n", + "\n", + "plt.figure(figsize= (15,10))\n", + "plt.title('Percentage of Gender',fontsize = 20)\n", + "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%',shadow=True, startangle=90)\n", + "plt.axis('equal')\n", + "plt.tight_layout()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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SnoState / Union TerritoryPopulationRural populationUrban populationAreaDensityGender Ratio
01Uttar Pradesh19981234115531727844495063240,928 km2 (93,023 sq mi)828/km2 (2,140/sq mi)912
12Maharashtra1123743336155607450818259307,713 km2 (118,809 sq mi)365/km2 (950/sq mi)929
23Bihar104099452923414361175801694,163 km2 (36,357 sq mi)1,102/km2 (2,850/sq mi)918
34West Bengal91276115621831132909300288,752 km2 (34,267 sq mi)1,029/km2 (2,670/sq mi)953
45Madhya Pradesh726268095255740420069405308,245 km2 (119,014 sq mi)236/km2 (610/sq mi)931
56Tamil Nadu721470303722959034917440130,058 km2 (50,216 sq mi)555/km2 (1,440/sq mi)996
67Rajasthan685484375150035217048085342,239 km2 (132,139 sq mi)201/km2 (520/sq mi)928
78Karnataka610952973746933523625962191,791 km2 (74,051 sq mi)319/km2 (830/sq mi)973
89Gujarat604396923469460925745083196,024 km2 (75,685 sq mi)308/km2 (800/sq mi)919
910Andhra Pradesh495771033496669314610410162,968 km2 (62,922 sq mi)303/km2 (780/sq mi)993
1011Odisha41974218349705627003656155,707 km2 (60,119 sq mi)269/km2 (700/sq mi)979
1112Telengana350036742139500913608665112,077 km2 (43,273 sq mi)312/km2 (810/sq mi)988
1213Kerala33406061174711351593492638,863 km2 (15,005 sq mi)859/km2 (2,220/sq mi)1084
1314Jharkhand3298813425055073793306179,714 km2 (30,778 sq mi)414/km2 (1,070/sq mi)948
1415Assam3120557626807034439854278,438 km2 (30,285 sq mi)397/km2 (1,030/sq mi)954
1516Punjab27743338173441921039914650,362 km2 (19,445 sq mi)550/km2 (1,400/sq mi)895
1617Chhattisgarh25545198196079615937237135,191 km2 (52,198 sq mi)189/km2 (490/sq mi)991
1718Haryana2535146216509359884210344,212 km2 (17,070 sq mi)573/km2 (1,480/sq mi)879
1819Uttarakhand100862927036954304933853,483 km2 (20,650 sq mi)189/km2 (490/sq mi)963
1920Himachal Pradesh6864602617605068855255,673 km2 (21,495 sq mi)123/km2 (320/sq mi)972
2021Tripura3673917271246496145310,486 km2 (4,049 sq mi)350/km2 (910/sq mi)960
2122Meghalaya2966889237143959545022,429 km2 (8,660 sq mi)132/km2 (340/sq mi)989
2223Manipur2570390179387577651522,327 km2 (8,621 sq mi)122/km2 (320/sq mi)992
2324Nagaland1978502140753657096616,579 km2 (6,401 sq mi)119/km2 (310/sq mi)931
2425Goa14585455517319068143,702 km2 (1,429 sq mi)394/km2 (1,020/sq mi)973
2526Arunachal Pradesh1383727106635831736983,743 km2 (32,333 sq mi)17/km2 (44/sq mi)938
2627Mizoram109720652543557177121,081 km2 (8,139 sq mi)52/km2 (130/sq mi)976
2728Sikkim6105774569991535787,096 km2 (2,740 sq mi)86/km2 (220/sq mi)890
2829Delhi16787941419042163688991,484 km2 (573 sq mi)11,297/km2 (29,260/sq mi)868
2930Jammu and Kashmir1226703290642203202812125,535 km2 (48,469 sq mi)98/km2 (250/sq mi)890
3031Puducherry1247953395200852753479 km2 (185 sq mi)2,598/km2 (6,730/sq mi)1037
3132Chandigarh1055450289911026459114 km2 (44 sq mi)9,252/km2 (23,960/sq mi)818
3233Dadra and Nagar Haveli and Daman and Diu585764243510342254603 km2 (233 sq mi)970/km2 (2,500/sq mi)711
3334Andaman and Nicobar Islands3805812370931434888,249 km2 (3,185 sq mi)46/km2 (120/sq mi)876
3435Ladakh2740004384023016096,701 km2 (37,336 sq mi)2.8/km2 (7.3/sq mi)853
3536Lakshadweep64473141415033232 km2 (12 sq mi)2,013/km2 (5,210/sq mi)946
\n", + "
" + ], + "text/plain": [ + " Sno ... Gender Ratio\n", + "0 1 ... 912\n", + "1 2 ... 929\n", + "2 3 ... 918\n", + "3 4 ... 953\n", + "4 5 ... 931\n", + "5 6 ... 996\n", + "6 7 ... 928\n", + "7 8 ... 973\n", + "8 9 ... 919\n", + "9 10 ... 993\n", + "10 11 ... 979\n", + "11 12 ... 988\n", + "12 13 ... 1084\n", + "13 14 ... 948\n", + "14 15 ... 954\n", + "15 16 ... 895\n", + "16 17 ... 991\n", + "17 18 ... 879\n", + "18 19 ... 963\n", + "19 20 ... 972\n", + "20 21 ... 960\n", + "21 22 ... 989\n", + "22 23 ... 992\n", + "23 24 ... 931\n", + "24 25 ... 973\n", + "25 26 ... 938\n", + "26 27 ... 976\n", + "27 28 ... 890\n", + "28 29 ... 868\n", + "29 30 ... 890\n", + "30 31 ... 1037\n", + "31 32 ... 818\n", + "32 33 ... 711\n", + "33 34 ... 876\n", + "34 35 ... 853\n", + "35 36 ... 946\n", + "\n", + "[36 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 196 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aa7EONs7jbnx", + "colab_type": "text" + }, + "source": [ + "## Testing Details Analysis" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jQp4y_UTjbHU", + "colab_type": "code", + "colab": {} + }, + "source": [ + "test_det_df = pd.read_csv(\"/content/ICMRTestingDetails.csv\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Iapg2o6wjhVR", + "colab_type": "code", + "outputId": "7512d1c8-8b3e-426a-fc08-11fa9bff6e6d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "test_det_df" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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SNoDateTimeTotalSamplesTestedTotalIndividualsTestedTotalPositiveCasesSource
0113/03/20 18:006500.05900.078.0Press_Release_ICMR_13March2020.pdf
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2319/03/20 18:0014175.013285.0182.0ICMR_website_update_19March_6PM_IST.pdf
3420/03/20 18:0015404.014514.0236.0ICMR_website_update_20March_6PM_IST.pdf
4521/03/20 18:0016911.016021.0315.0ICMR_website_update_21March_6PM_IST.pdf
5622/03/20 18:0018127.017237.0396.0ICMR_website_update_22March_6PM_IST.pdf
6723/03/20 20:0020707.019817.0471.0ICMR_website_update_23March_8PM_IST.pdf
7824/03/20 20:0022694.021804.0536.0ICMR_website_update_24March_8PM_IST.pdf
8925/03/20 20:0025144.024254.0581.0ICMR_website_update_25March_8PM_IST.pdf
91026/03/20 20:00NaNNaNNaNNaN
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111228/03/20 18:00NaNNaNNaNNaN
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SNoDateTimeTotalSamplesTestedTotalIndividualsTestedTotalPositiveCasesSource
0113/03/20 18:006500.05900.078.0Press_Release_ICMR_13March2020.pdf
1218/03/20 18:0013125.012235.0150.0ICMR_website_update_18March_6PM_IST.pdf
2319/03/20 18:0014175.013285.0182.0ICMR_website_update_19March_6PM_IST.pdf
3420/03/20 18:0015404.014514.0236.0ICMR_website_update_20March_6PM_IST.pdf
4521/03/20 18:0016911.016021.0315.0ICMR_website_update_21March_6PM_IST.pdf
5622/03/20 18:0018127.017237.0396.0ICMR_website_update_22March_6PM_IST.pdf
6723/03/20 20:0020707.019817.0471.0ICMR_website_update_23March_8PM_IST.pdf
7824/03/20 20:0022694.021804.0536.0ICMR_website_update_24March_8PM_IST.pdf
8925/03/20 20:0025144.024254.0581.0ICMR_website_update_25March_8PM_IST.pdf
91026/03/20 20:00NaNNaNNaNNaN
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313217/04/20 21:00335123.0318449.014098.0NaN
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333419/04/20 21:00401586.0383985.017615.0NaN
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DateStateTotalSamplesNegativePositive
02020-02-17Kerala423406.03
12020-02-18Kerala425420.03
22020-02-19Kerala432423.03
32020-02-20Kerala433423.03
42020-02-21Kerala437426.03
..................
1962020-04-10Odisha32493201.048
1972020-04-10Punjab34612972.0151
1982020-04-10Rajasthan2232420673.0520
1992020-04-10Tamil Nadu84106838.0911
2002020-04-11Maharashtra3184130477.01364
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201 rows × 5 columns

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" + ], + "text/plain": [ + " Date State TotalSamples Negative Positive\n", + "0 2020-02-17 Kerala 423 406.0 3\n", + "1 2020-02-18 Kerala 425 420.0 3\n", + "2 2020-02-19 Kerala 432 423.0 3\n", + "3 2020-02-20 Kerala 433 423.0 3\n", + "4 2020-02-21 Kerala 437 426.0 3\n", + ".. ... ... ... ... ...\n", + "196 2020-04-10 Odisha 3249 3201.0 48\n", + "197 2020-04-10 Punjab 3461 2972.0 151\n", + "198 2020-04-10 Rajasthan 22324 20673.0 520\n", + "199 2020-04-10 Tamil Nadu 8410 6838.0 911\n", + "200 2020-04-11 Maharashtra 31841 30477.0 1364\n", + "\n", + "[201 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 204 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "889zgsMHkUUT", + "colab_type": "code", + "outputId": "77ae8f3a-40d4-4cbe-8b8f-0b41f20462b9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 817 + } + }, + "source": [ + "testing=state_wise_test_df.groupby('State').sum().reset_index()\n", + "fig = px.bar(testing, \n", + " x=\"TotalSamples\",\n", + " y=\"State\", \n", + " orientation='h',\n", + " height=800,\n", + " title='Testing statewise insight')\n", + "fig.show()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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ConfirmedDeathsday_count
Date
01/02/20201
01/03/20302
01/04/201834413
02/02/20304
02/03/20505
............
29/03/2010242777
30/01/201078
30/03/2012513179
31/01/201080
31/03/2013973581
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81 rows × 3 columns

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" + ], + "text/plain": [ + " Confirmed Deaths day_count\n", + "Date \n", + "01/02/20 2 0 1\n", + "01/03/20 3 0 2\n", + "01/04/20 1834 41 3\n", + "02/02/20 3 0 4\n", + "02/03/20 5 0 5\n", + "... ... ... ...\n", + "29/03/20 1024 27 77\n", + "30/01/20 1 0 78\n", + "30/03/20 1251 31 79\n", + "31/01/20 1 0 80\n", + "31/03/20 1397 35 81\n", + "\n", + "[81 rows x 3 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 213 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oVmQTKmdlxlF", + "colab_type": "code", + "colab": {} + }, + "source": [ + "y = df.Confirmed\n", + "x = df.day_count" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "N3aOQg44lxht", + "colab_type": "code", + "outputId": "d03af7d1-d45d-4046-9955-8e4a4e8f3a6b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + } + }, + "source": [ + "df['rate'] = (df.Confirmed - df.Confirmed.shift(1))/df.Confirmed\n", + "df['increase'] = (df.Confirmed - df.Confirmed.shift(1))\n", + "\n", + "plt.plot(x, y, 'o')\n", + "plt.title(\"India\")\n", + "plt.ylabel(\"Population infected\")\n", + "plt.xlabel(\"Days\")\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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ConfirmedDeathsday_countrateincrease
Date
01/02/20201NaNNaN
01/03/203020.3333331.0
01/04/2018344130.9983641831.0
02/02/20304-610.333333-1831.0
02/03/205050.4000002.0
..................
29/03/20102427770.9970701021.0
30/01/201078-1023.000000-1023.0
30/03/20125131790.9992011250.0
31/01/201080-1250.000000-1250.0
31/03/20139735810.9992841396.0
\n", + "

81 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Confirmed Deaths day_count rate increase\n", + "Date \n", + "01/02/20 2 0 1 NaN NaN\n", + "01/03/20 3 0 2 0.333333 1.0\n", + "01/04/20 1834 41 3 0.998364 1831.0\n", + "02/02/20 3 0 4 -610.333333 -1831.0\n", + "02/03/20 5 0 5 0.400000 2.0\n", + "... ... ... ... ... ...\n", + "29/03/20 1024 27 77 0.997070 1021.0\n", + "30/01/20 1 0 78 -1023.000000 -1023.0\n", + "30/03/20 1251 31 79 0.999201 1250.0\n", + "31/01/20 1 0 80 -1250.000000 -1250.0\n", + "31/03/20 1397 35 81 0.999284 1396.0\n", + "\n", + "[81 rows x 5 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 218 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uqPUQLfMlxax", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from scipy.optimize import curve_fit\n", + "import pylab\n", + "from datetime import timedelta" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "L6OqXWVsr9RP", + "colab_type": "code", + "colab": {} + }, + "source": [ + "df = df[df.Confirmed >= 100]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "YyerZ-W4lxW5", + "colab_type": "code", + "outputId": "5f654c04-0a37-4ccc-95ee-10b5cf580082", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "source": [ + "def sigmoid(x,c,a,b):\n", + " y = c*1 / (1 + np.exp(-a*(x-b)))\n", + " return y\n", + "\n", + "xdata = np.array(list(df.day_count)[::2])\n", + "ydata = np.array(list(df.Confirmed)[::2])\n", + "\n", + "population=1.332*10**9\n", + "popt, pcov = curve_fit(sigmoid, xdata, ydata, method='dogbox',bounds=([0.,0., 0.],[population,6, 100.]))\n", + "print(popt)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[4459.33331444 0. 50.34512601]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tnRkL143lxQp", + "colab_type": "code", + "outputId": "06c6aeba-d768-4d5a-fbc7-3b929c71d37d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 629 + } + }, + "source": [ + "est_a = 22500\n", + "est_b = 0.18\n", + "est_c = 32\n", + "x = np.linspace(-1, df.day_count.max()+50, 50)\n", + "y = sigmoid(x,est_a,est_b,est_c)\n", + "pylab.plot(xdata, ydata, 'o', label='data')\n", + "pylab.plot(x, y, label='fit',alpha = 0.6)\n", + "pylab.ylim(-0.05, est_a*1.05)\n", + "pylab.xlim(-0.05, est_c*2.05)\n", + "pylab.legend(loc='best')\n", + "plt.xlabel('days from day 1')\n", + "plt.ylabel('confirmed cases')\n", + "plt.title('India')\n", + "pylab.show()\n", + "\n", + "\n", + "print('model start date:',df[df.day_count==1].index[0])\n", + "print('model start infection:',int(df[df.day_count==1].Confirmed[0]))\n", + "print('model fitted max infection at:',int(est_a))\n", + "print('model sigmoidal coefficient is:',round(est_b,3))\n", + "print('model curve stop steepening, start flattening by day:',int(est_c))\n", + "print('model curve flattens by day:',int(est_c)*2)\n", + "display(df.head(3))\n", + "display(df.tail(3))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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SNoObservationDateProvince/StateCountry/RegionLast UpdateConfirmedDeathsRecovered
0101/22/2020AnhuiMainland China1/22/2020 17:001.00.00.0
1201/22/2020BeijingMainland China1/22/2020 17:0014.00.00.0
2301/22/2020ChongqingMainland China1/22/2020 17:006.00.00.0
3401/22/2020FujianMainland China1/22/2020 17:001.00.00.0
4501/22/2020GansuMainland China1/22/2020 17:000.00.00.0
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167241672504/19/2020WyomingUS2020-04-19 23:49:05313.02.00.0
167251672604/19/2020XinjiangMainland China2020-04-19 23:49:0576.03.073.0
167261672704/19/2020YukonCanada2020-04-19 23:49:059.00.00.0
167271672804/19/2020YunnanMainland China2020-04-19 23:49:05184.02.0178.0
167281672904/19/2020ZhejiangMainland China2020-04-19 23:49:051268.01.01247.0
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16729 rows × 8 columns

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" + ], + "text/plain": [ + " SNo ObservationDate Province/State ... Confirmed Deaths Recovered\n", + "0 1 01/22/2020 Anhui ... 1.0 0.0 0.0\n", + "1 2 01/22/2020 Beijing ... 14.0 0.0 0.0\n", + "2 3 01/22/2020 Chongqing ... 6.0 0.0 0.0\n", + "3 4 01/22/2020 Fujian ... 1.0 0.0 0.0\n", + "4 5 01/22/2020 Gansu ... 0.0 0.0 0.0\n", + "... ... ... ... ... ... ... ...\n", + "16724 16725 04/19/2020 Wyoming ... 313.0 2.0 0.0\n", + "16725 16726 04/19/2020 Xinjiang ... 76.0 3.0 73.0\n", + "16726 16727 04/19/2020 Yukon ... 9.0 0.0 0.0\n", + "16727 16728 04/19/2020 Yunnan ... 184.0 2.0 178.0\n", + "16728 16729 04/19/2020 Zhejiang ... 1268.0 1.0 1247.0\n", + "\n", + "[16729 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "S_Asx1FcpAvW", + "colab_type": "code", + "colab": {} + }, + "source": [ + "world_wide_data.drop(['SNo'], axis=1, inplace=True)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "weWBYO9PpGh2", + "colab_type": "code", + "outputId": "854971b1-e1b8-4974-b513-44e7dd3c0522", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "source": [ + "world_wide_data.head()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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ObservationDateProvince/StateCountry/RegionLast UpdateConfirmedDeathsRecovered
001/22/2020AnhuiMainland China1/22/2020 17:001.00.00.0
101/22/2020BeijingMainland China1/22/2020 17:0014.00.00.0
201/22/2020ChongqingMainland China1/22/2020 17:006.00.00.0
301/22/2020FujianMainland China1/22/2020 17:001.00.00.0
401/22/2020GansuMainland China1/22/2020 17:000.00.00.0
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" + ], + "text/plain": [ + " ObservationDate Province/State Country/Region ... Confirmed Deaths Recovered\n", + "0 01/22/2020 Anhui Mainland China ... 1.0 0.0 0.0\n", + "1 01/22/2020 Beijing Mainland China ... 14.0 0.0 0.0\n", + "2 01/22/2020 Chongqing Mainland China ... 6.0 0.0 0.0\n", + "3 01/22/2020 Fujian Mainland China ... 1.0 0.0 0.0\n", + "4 01/22/2020 Gansu Mainland China ... 0.0 0.0 0.0\n", + "\n", + "[5 rows x 7 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mLyzb0HLpaBf", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zHwjGIcfmG7P", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oKMHOz8amHt0", + "colab_type": "text" + }, + "source": [ + "# **Part 3**\n", + "Prediction" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PqcgZow7mQvd", + "colab_type": "code", + "colab": {} + }, + "source": [ + "from fbprophet import Prophet" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Kpd6PVEkmUh9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "confirmed = indian_data.groupby('Date').sum()['Confirmed'].reset_index()\n", + "deaths = indian_data.groupby('Date').sum()['Deaths'].reset_index()\n", + "recovered = indian_data.groupby('Date').sum()['Cured'].reset_index()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8ht1TBt6mZ61", + "colab_type": "code", + "colab": {} + }, + "source": [ + "confirmed.columns = ['ds','y']\n", + "#confirmed['ds'] = confirmed['ds'].dt.date\n", + "confirmed['ds'] = pd.to_datetime(confirmed['ds'])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "A1xAkaI5mjBa", + "colab_type": "code", + "outputId": "86b9c1ea-20ea-49bc-d8cf-a6c00ba43fcf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "source": [ + "confirmed.tail()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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dsy
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" + ], + "text/plain": [ + " ds yhat yhat_lower yhat_upper\n", + "77 2020-11-04 3551.677625 -3717.265214 11704.594095\n", + "78 2020-12-02 3771.442765 -3655.396458 11259.597035\n", + "79 2020-12-03 3164.348063 -4157.903281 10416.762075\n", + "80 2020-12-04 3816.482721 -3229.689338 11045.118315\n", + "81 2020-12-05 4042.809264 -3866.679107 11438.240200" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 131 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2gEp0qLAstAj", + "colab_type": "code", + "outputId": "9ba12a79-3889-43ff-b4f1-3fea9159f3b1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + } + }, + "source": [ + "confirmed_forecast_plot = model.plot(forecast)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4UngiR03s_DM", + "colab_type": "code", + "outputId": "54e2c6bc-ff8a-4f17-e53b-bf5830fb26f0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + } + }, + "source": [ + "confirmed_forecast_plot =model.plot_components(forecast)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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zN0egzb35VA9bD7goLXex5YCLltMf1bTEUuC0Uei0MdthJTV2xKCvaQm02QeTQJj9Z2Uh/a+MiIgMeYZh8Jf6E5SW95Uxv3OsFa8B1hFhzD5T0zIn00ZuSqxqWiQoKACKiMiQdLLbw/bKJl83X217FwATkqJZMX00BU4bc1XTIkFKAVBERIaMQ02nKC3v27H7+8PN9PQaRA0PYcboeFZMT6fQaSMvPR5b5HCzlypiKgVAEREZtHo8Xv5wpNl3le9g4ykAHNYRLD4v1XcHjswAr2kRGWgKgCIiMqjUtXdRdmbH7msHGznZ08vwEAv5afFcNX8UBU4rMzNsJKmmReQzKQCKiEhA6/UavPthK6UVfbdce7+2r6YlOSacheNG9l3ly0xgXFI0UeH6a03kXOhPioiIBJyW0z1s3d9I2f4GtuxvpOlUDyEWmJway3cLHBQ6bRQ4raTEjiBkkNe0iJhBAVBERExnGAZ/re/wfZfvraoWvAbEjwhltsNGocPG3Cwbk0bFEjdCNS0iX5UCoIiImOLUmZqWsv19H+1Wt/XVtIxPimb59HQKHDbmZCYw2jqCMNW0iPQrBUARERkwh5tO+a7y7TzcTLfHS9TwEKaPjudb09IpdPTVtCREqaZFxJ8UAEVExG96PF7eONLMr3ZX84cP93PgTE1LhnUE1+amUOi0Mi87kUxbJBFhqmkRGSgKgCIi0q/qT3RRVtF3y7VXD/TVtIQNs5CXFsc98zIpdNqYmWElOSZcNS0iJlEAFBGRr6TXa/Cn6jZKyxso2+9iT007AMnRw1kwtq+mZXJcL7MmZqqmRSRA6E+iiIh8Ya2ne9h6oJHSio9rWoZZIDcllttmf1zTkhrXV9NSV1en8CcSQPSnUUREPpdhGHxwvMN3le+tqlZ6vQZxEaHMdlgpcNiYl5VAbopqWkQGAwVAERH5VKe6Pew41OT7Pt9HNS1jR0axLD/tTDefalpEBiMFQBER8TnSfIrSchdl+xt4/VBfTcuIsGHMGG1lWX46hU4b+appERn0FABFRIJYj8fLH4+2UFbRQGmFi/2ukwCMjh/BNbmjKHTamJeZQFZilGpaRIYQBUARkSBz/EQXm/e7KK1wse1AIx3dHsJCLEy199W0FDhszHJYSYoOZ5jusysyJCkAiogMcd4zNS0ffZfvvTM1LUnRw7l4TCIFThvzsmyMT4ohWjt1RYKC37+1W11dzQUXXMDEiRPJycnhiSeeAGD16tXY7XamTJnClClTKCsr8z1n7dq1ZGdnM27cOLZu3eo7vmXLFsaNG0d2djbr1q3z99JFRAattk43L+6tY9kL7zNq9TZm/uiP/OC1g7h7Db4zO4Pnbzqf3XfO4X+W5XPP/Czy060KfyJB5HP/tF955ZX/sKn9lVde+cdvEBrKo48+ytSpU+no6CAvL48FCxYAcNddd3Hvvfee9fjy8nI2btzIvn37qKur4+KLL+bgwYMA3Hbbbbz66qukpaUxbdo0ioqKmDhx4ueepIjIUGcYBuUNJyktb6C0ooE3/6amZVaGlUKnjTmZCUxOjSVeNS0iQe9zA+BHAe1///d/OX78ON/4xjcAeOGFF0hOTv7cN0hJSSElJQWAmJgYJkyYQG1t7Wc+ftOmTSxZsoTw8HCcTifZ2dm8++67AGRnZ5OZmQnAkiVL2LRpkwKgiASt0z0eXj/UTGlFA2UVLo61dgIwNjGKpXlpFDptzM3sq2kZHqqaFhH52OcGwHnz5gFwzz33sHv3bt/xK6+8kvz8/C/0ZlVVVbz//vvMmDGDN998kyeffJKf//zn5Ofn8+ijj2K1WqmtrWXmzJm+56SlpfkCY3p6+lnHd+3a9Yn3KCkpoaSkBIDjx49TV1f3hdYYbBobG81eQlDS3M0z2Gf/YXs3O460s/1oO29+2EF3r0FEqIWpoyK5ZmwSM9KiyE2OIS4itO/TG3c7Ta52s5c96Oc+mGn25gnk2Z/zFz5OnTrFkSNHfFfgjh49yqlTp875jU6ePMm1117L448/TmxsLCtXrmTVqlVYLBZWrVrFPffcw09/+tMvfgZ/p7i4mOLiYgDy8/NJTU39yq851GlG5tDczTOYZu/u9fLm0RZKK1yUljdQcaamJT0+gmtyUyhw2piflUBmYhQjArymZTDNfajR7M0TqLM/5wD4n//5n8yfP5/MzEwMw+DYsWM888wz5/Rct9vNtddey0033cQ111wDcNbHx7fccgtXXHEFAHa7nerqat/PampqsNvtAJ95XERkKGno6GbzmR272w40cqLbQ+iwvpqWu+ZmUui0MsthI1k1LSLyJZ1zALzkkkuorKxk//79AIwfP57w8PDPfZ5hGKxYsYIJEyZw9913+47X19f7vhv4m9/8hkmTJgFQVFTEjTfeyN13301dXR2VlZVMnz4dwzCorKzk6NGj2O12Nm7cyPPPP/+FTlZEJBB5vQbv1bRTWtG3gWN3dd9HtiOjhnPhmEQKHFbmZyUwPlk1LSLSP77Qf0nee+89qqqq8Hg8/PnPfwZg6dKl//A5b775Jr/4xS/Izc1lypQpADz88MO88MIL7N27F4vFgsPh8F1NzMnJYfHixUycOJHQ0FCeeuopQkL6PtZ48sknWbhwIb29vSxfvpycnJwvfMIiIoGgvdPNtoONlJY3sHm/C9fJHixAbkoMt87K6Nu167Rhjx9BiK7yiUg/O+cA+M1vfpPDhw8zZcoUXyCzWCyfGwALCwsxDOMTxy+77LLPfM4DDzzAAw888KnP+UfPExEJVIZhUNFw0rdj949HW/B4DWLDQ5l5pqZlbmYC59lV0yIi/nfOAXD37t2Ul5f/w05AERH5WKe7l9cPNVFa7qKsooGqMzUtYxKj+MZUe1/oy0ogwxqpmhYRGVDnHAAnTZrE8ePHfd/bExGRTzrWcprSir7At+NQE51uLxGhw5g+Op4bzrdTmGljWno8iVHD9T/UImKacw6ATU1NTJw4kenTp5+1+ePz7gQiIjKUuXu9vFXV0nef3XIX+xo6ALDHRVCUM4pCh4152TayE6MDvqZFRILHOQfA1atX+3EZIiKDh6ujm837XZRVuNh6wEV718c1LXfOdVLosDHLYWVUTIRqWkQkIJ1zAJw3bx7Hjh2jsrKSiy++mNOnT9Pb2+vPtYmIBASv12BPbXvfVb6KBv5U3YZhQEJkGPOyEih02piXmcCE5BhiIlTTIiKB75z/S/WTn/yEkpISWlpaOHz4MLW1tdx6661s377dn+sTETHFiS43rx5spLTcxeb9Lo53dGMBckbF8O2ZfTUthU4b9rgIQkO0gUNEBpdzDoBPPfUU7777LjNmzABgzJgxuFwuvy1MRGQgGYbBftdJ31W+N4701bTEhIcyK8PKytlW5mTamJIahzVyuNnLFRH5Ss45AIaHhzN8+Mf/0fN4PNrBJiKDWpe7l52Hmyktb6C0wsXRltMAZCdGcpOvpsWGwxqlmhYRGVK+0HcAH374YTo7O3n11Vf58Y9/zJVXXunPtYmI9LvaEz28UlVFaYWL7ZWNdLq9hIcOY3p6PNdPSaXQYWV6hlU1LSIypJ1zAHzkkUf47//+b3Jzc3nmmWe47LLLuPnmm/25NhGRr8zT6+WtqlbfR7sfHO+raUmNDefKickUOm3Mz05QTYuIBJVzCoC9vb3k5OSwf/9+brnlFn+vSUTkK2k82c2W/S5Kz9S0tHV6CBlm4fzUWIqnJrJgUgazMqykxKqmRUSC0zkFwJCQEMaNG8eHH37I6NGj/b0mEZEvxOs12FvXTmmFi9LyBt79m5qWOc4ECpxW5mUlkpMcQ0eLi9TUVLOXLCJiqnP+CLi1tZWcnBymT59OVFSU77juBCIiZjjR5ea1g02UVjRQVvFxTcvE5BhumTGaQqeNOZk20uJGnFXT0mHekkVEAsY5B8Curi5+97vf+f7dMAzuu+8+vyxKROTvGYbBwcZTlFY0UFru4o2jzbh7DaKHhzArw8qtszKYm6WaFhGRc3HOAdDj8TBv3ryzjnV2dvb7gkREPtLl7uX3h5t9H+0eOVPTkpkQyQ1T7BQ4rczNTMCZEEl4qDZwiIicq88NgE8//TQ//vGPOXLkCJMnT/Yd7+jooKCgwK+LE5HgU93aSdn+vqt82yubOO3uJTxkGNNGx3PdeSkUOGzMyLAyMlo1LSIiX9bnBsAbb7yRSy+9lH/5l39h3bp1vuMxMTHYbDa/Lk5Ehj5Pr5d3jrX2XeWraOCv9R/XtFw+MYkCh435WQmMGRlF5HDdZ1dEpD987n9N4+LiiIuL44UXXhiI9YhIEGg62c2WA42Uljew9UAjrZ1uQoZZmJIay+2FTgqdVmY7bKppERHxE/3vtIj4nWEY7K094dux+86HrRgG2CLDKHBYKXTamJedyMTkaGIjwsxerojIkKcAKCJ+0dHl4bXKRkrLXZTtb6D+RDcAE5OjuXn6xzUt6fFn17SIiIj/+T0AVldXs3TpUhoaGrBYLBQXF3PHHXfQ0tLC9ddfT1VVFQ6HgxdffBGr1YphGNxxxx2UlZURGRnJs88+y9SpUwHYsGEDP/jBDwB48MEHWbZsmb+XLyJfwMHGk5SW913l+/2RvpqWqDM1LcUzMpiTaeP8tDhsqmkRETGV3wNgaGgojz76KFOnTqWjo4O8vDwWLFjAs88+y0UXXcT999/PunXrWLduHY888gibN2+msrKSyspKdu3axcqVK9m1axctLS2sWbOG3bt3Y7FYyMvLo6ioCKvV6u9TEJHP0O3pq2n56D67h5rO1LTYIlkyJZUCh415WappEREJNH4PgCkpKaSkpAB9O4cnTJhAbW0tmzZtYufOnQAsW7aM+fPn88gjj7Bp0yaWLl2KxWJh5syZtLW1UV9fz86dO1mwYIFv5/GCBQvYsmULN9xwg79PQUT+Rm17Z1/gK2/gtcomTvX01bTkp8dxTW5fTctM1bSIiAS0Af0OYFVVFe+//z4zZsygoaHBFwxHjRpFQ0MDALW1taSnp/uek5aWRm1t7Wce/3slJSWUlJQAcPz4cerq6vx5SoNeY2Oj2UsISoNp7r1egz31p9h+tJ3tR9opb+wrgE+KCuVCRzQzUqOYPTqWjPgIIsJCAC+ejmbqA/Sea4Np9kOJ5m4ezd48gTz7AQuAJ0+e5Nprr+Xxxx8nNjb2rJ9ZLJZ+u1JQXFxMcXExAPn5+brp+znQjMwRyHNvPtXD1gMuSstdbDngouW0mxALnJcayz8XJlPgsFHgtJIaO2JQ1rQE8uyHMs3dPJq9eQJ19gMSAN1uN9deey033XQT11xzDQDJycnU19eTkpJCfX09SUlJANjtdqqrq33PrampwW63Y7fbfR8Zf3R8/vz5A7F8kSHPMAz+Un+C0vK+7/K9c6wVrwHWEWHMPlPTMifTRm5KrGpaRESGAL8HQMMwWLFiBRMmTODuu+/2HS8qKmLDhg3cf//9bNiwgauuusp3/Mknn2TJkiXs2rWLuLg4UlJSWLhwId///vdpbW0FYNu2baxdu9bfyxcZsk52e9he2eTr5qtt7wJgQlI0K6aPpsBhZW5WAmnxIwhTTYuIyJDi9wD45ptv8otf/ILc3FymTJkCwMMPP8z999/P4sWLWb9+PRkZGbz44osAXHbZZZSVlZGdnU1kZCQ/+9nPALDZbKxatYpp06YB8NBDD+lWdCJf0KGmU5SWN1Ba0cDvDzfTc6amZcboeJZPT2eO00ZeerxqWkREhji/B8DCwkIMw/jUn23fvv0TxywWC0899dSnPn758uUsX768X9cnMpT1eLz84Uiz7yrfwcZTADisI7juvNS+O3BkJZCpmhYRkaCiO4GIDDF17V2UVTRQWuHitYONnOzpZXiIhfy0eIrmJ1PotDEzw0aSalpERIKWAqDIINfrNXj3w1ZfGfP7tScASI4J52vjRvZd5ctMYFxSNFHh+iMvIiIKgCKDUuvpHrYeaKS0ooEt+xtpOtXDMAtMTonluwUOCp02ZjuspMaNIGQQ1rSIiIh/KQCKDAKGYfDB8Y4zGzhcvFXVgteAuIhQZjtsFDiszMtKIDcllrgRqmkREZF/TAFQJECd6vaw41CT76Pd6ra+mpZxI6P4p2npZ7r5EhhtVUZmyRMAACAASURBVE2LiIh8MQqAIgHkSPMpSstdlO1v4PVDzXR7vIwIG8aM0VaW5X9c05IQpZoWERH58hQARUzU4/Hyx6Mtvl27+10nARgdP4Jrckf5NnBkJUaduc+uiIjIV6cAKDLAjp/o4lcfNPHmq3VsO9BIR7eHsBALU+1x3D03k0KnjVkOK8kx4appERERv1AAFPEzr9dgd02b7z6779W0AzAyajgXj0n0beAYnxxDtGpaRERkAOhvGxE/aOt08+rBRkrLG9i834XrZA8WIDclhpWzMzgv3uDS88dgj1dNi4iIDDwFQJF+YBgGFQ0nKT3zXb4/Hm2h12sQFxHKzAxr345dZwLn2WOJHxFGXV0dqbZIs5ctIiJBSgFQ5EvqdPfy+qGmvl27FQ1UtXYCMDYxim9OtVPotDE3K4EMayTDQ1XTIiIigUMBUOQLONZymtKKvsC341ATnW4vEaHDmD46nhvOt1PgtDF9dDyJUbrProiIBC4FQJF/wN3r5e2qVn5X3kBZhYt9DR0A2OMiKMoZRaHDxrxsG9mJ0YxQTYuIiAwSCoAif8fV0c2WAy5Ky11sPeCivctD6DAL59tjuXOuk0JHX03LqJgIhmkDh4iIDEIKgBL0vF6D92vbKdvvorS8gXer2zAMSIgMY25mAnMybczNTGBicgwxEfojIyIig5/+NpOgdKLro5oWF5v3uzje0Y0FyBkVQ/GM0b777NrjIgjVfXZFRGSIUQCUoGAYBgdcJymt6CtjfuNICx6vQfTwEGZlWLl1VgZzs2xMSY3DGqn77IqIyNCmAChDVpe7l52Hmymr6Pto90jLaQCyEiK58fyPalpsOG1RqmkREZGgogAoQ0p1aydl+xsoLXexvbKJ0+5ewkOHMS09nsXnpZypabEyMlo1LSIiErz8ftlj+fLlJCUlMWnSJN+x1atXY7fbmTJlClOmTKGsrMz3s7Vr15Kdnc24cePYunWr7/iWLVsYN24c2dnZrFu3zt/LlkHC0+vlj0ea+ZfSCib/cCejf/Aat770V3ZXt3H5hCQevyqHXbcXsrV4BmuvmMgVOaNIiglX+BMRkaDm9yuA3/rWt/jud7/L0qVLzzp+1113ce+99551rLy8nI0bN7Jv3z7q6uq4+OKLOXjwIAC33XYbr776KmlpaUybNo2ioiImTpzo7+VLAGo+1cOW/S5KK1xs2e+itdNNiAXOS43j9kInhU4rsx02UmJV0yIiIvJp/B4A586dS1VV1Tk9dtOmTSxZsoTw8HCcTifZ2dm8++67AGRnZ5OZmQnAkiVL2LRpkwJgkDAMgz/Xnei7z265i10ftuI1wBYZRoHD6tuxOyklhtiIMLOXKyIiEvBM+w7gk08+yc9//nPy8/N59NFHsVqt1NbWMnPmTN9j0tLSqK2tBSA9Pf2s47t27frU1y0pKaGkpASA48ePU1dX58ezGPwaGxvNXsKnOtXTyxvHOth+tJ3tR9ppOOUGYKwtnJsm2Zhhj2Z6WjSjYiIIGWYBujjZ0sVJc5d9zgJ17sFAszeH5m4ezd48gTx7UwLgypUrWbVqFRaLhVWrVnHPPffw05/+tF9eu7i4mOLiYgDy8/NJTU3tl9cdygJlRoeaTlFa3sDvyhv4w5FmenoNooaHMDPDyrcdVuZkJjA1LQ7bEKlpCZS5ByPN3hyau3k0e/ME6uxNCYDJycm+f77lllu44oorALDb7VRXV/t+VlNTg91uB/jM4zJ4dXt6+cPhFt+u3cqmUwA4rCNYfF4qhU4b87IScCZEEh6q++yKiIj0F1MCYH19PSkpKQD85je/8e0QLioq4sYbb+Tuu++mrq6OyspKpk+fjmEYVFZWcvToUex2Oxs3buT55583Y+nyFdW1d1FW0UBphYvXDjZysqeX4SEW8tPi+fqkURQ4rczMsJGkmhYRERG/8XsAvOGGG9i5cydNTU2kpaWxZs0adu7cyd69e7FYLDgcDp555hkAcnJyWLx4MRMnTiQ0NJSnnnqKkJC+Kz9PPvkkCxcupLe3l+XLl5OTk+PvpUs/6PUavPtha98dOMob2Ft3AoDkmHAWjhvZV8acmcC4pGiiwlVLKSIiMhAshmEYZi/CX/Lz89m9e7fZywhodXV1/f79hJbTPWzd30jZ/gY2V7hoPt1X05KbEkuh00ah08Zsh5XUuBFnNnAEH3/MXc6NZm8Ozd08mr15AmH2n5WFdMlFvjLDMPjgeIdvA8fbx/pqWuJHhDLbYaPAYWVeVgKTRsUSN0I1LSIiImZTAJQv5VS3hx2HmiitcFFW0UB1WxcA40ZG8U/T0n3dfKOtIwgL0X12RUREAokCoJyzI82nKC13UVrRwM7DzXR7vESGhTBjdDzL8tOZ47SRlx5PQtTQqGkREREZqhQA5TP1eLz88WgLpRUNlFW42O/qq1kebR3BtbkpFDqtzM1KICshiogw1bSIiIgMFgqAcpbjJ7rYfOY+u9sONNLR7SEsxEKePY5752VS4LQxM8NKcky4alpEREQGKQXAIOc1ztS0nPlo972adgCSoodz8ZhECpw25mXZmJAUo5oWERGRIUJ/owehtk432w40UlrRQOm+4zR3ehhmgUmjYvnO7AxfVUsw17SIiIgMZQqAQcAwDMobTvruwPHHoy30eg3iIkLJGzWCC8enMiczgcmpscSrpkVERGTIUwAcojrdvbx+qMn30e6x1k4AxiZGsTQvjQKnlXmZiYR1tZGRrvsqi4iIBBMFwCHkWMvpvluuVTSwo7KJLo+XEWHDmJ5u5aapdgqdNqadqWn5aANHXV27yasWERGRgaYAOIi5e728VdVCabmLsgoX+xo6AEiLi+Drk0ZR6LQxPyuBzMQoRqimRURERM5QABxkXB3dZ2paGth2oJH2Lg+hwyxMtcdx19xMCp1WZmZYGRUTwTBt4BAREZFPoQAY4Lxegz217ZSWN1C238WfqtswDEiIDGNeVgKFThvzMhOYkBxDTIR+OUVEROTzKTEEoBNdbl492EhpuYvN+10c7+jGAuSMiuHbMz+uabHHRRCq++yKiIjIF6QAGAAMw+CA66RvA8cbR1rweA1iwkOZmRHPytkZzMm0MSU1Dmuk7rMrIiIiX40CoEm63L3sPNzc99FuhYsjLacByEqI5KapdgocNuZl23BYoxgeqqt8IiIi0n8UAAdQdWtn3903Klxsr2yk0+0lPHQY09LjWXxeCgVOGzMyrCT+TU2LiIiISH9TAPQjT6+Xt4+1Unbmo92/1vfVtKTGhnPFxGQKHTbmZSUwZmQUkcP1SyEiIiIDQ6mjnzWd7GbLgUZKyxvYeqCR1k43IcMsTEmN5Y45TgocVmY7bKTEqqZFREREzKEA+BUZhsGf607wu/K+j3Z3fdiKYYAtMoxCp40Ch5V52YlMTI4mNkL32RURERHz+X13wfLly0lKSmLSpEm+Yy0tLSxYsIAxY8awYMECWltbgb4wdfvtt5Odnc3kyZPZs2eP7zkbNmxgzJgxjBkzhg0bNvh72ees5bSbvP/8A6u2HOBEl5ubp49mw5Ip7L5zDv/7rXzuu2gMMzOsCn8iIiISMPweAL/1rW+xZcuWs46tW7eOiy66iMrKSi666CLWrVsHwObNm6msrKSyspKSkhJWrlwJ9AXGNWvWsGvXLt59913WrFnjC41mS4gazv8szeP1lbN447sFlCw+j6XT0nEmRKmjT0RERAKS3xPK3LlzsdlsZx3btGkTy5YtA2DZsmW8/PLLvuNLly7FYrEwc+ZM2traqK+vZ+vWrSxYsACbzYbVamXBggWfCJVmumZyKvOzE7Gpo09EREQGAVO+A9jQ0EBKSgoAo0aNoqGhAYDa2lrS09N9j0tLS6O2tvYzj3+akpISSkpKADh+/Dh1dXX+Oo0hobGx0ewlBCXN3TyavTk0d/No9uYJ5NmbvgnEYrH0a+ddcXExxcXFAOTn55Oamtpvrz1UaUbm0NzNo9mbQ3M3j2ZvnkCdvSlfUktOTqa+vh6A+vp6kpKSALDb7VRXV/seV1NTg91u/8zjIiIiIvLFmRIAi4qKfDt5N2zYwFVXXeU7/vOf/xzDMHjnnXeIi4sjJSWFhQsXsm3bNlpbW2ltbWXbtm0sXLjQjKWLiIiIDHp+/wj4hhtuYOfOnTQ1NZGWlsaaNWu4//77Wbx4MevXrycjI4MXX3wRgMsuu4yysjKys7OJjIzkZz/7GQA2m41Vq1Yxbdo0AB566KFPbCwRERERkXNjMQzDMHsR/pKYmIjD4TB7GQGtsbGRkSNHmr2MoKO5m0ezN4fmbh7N3jyBMPuqqiqampo+cXxIB0D5fPn5+ezevdvsZQQdzd08mr05NHfzaPbmCeTZq6lYREREJMgoAIqIiIgEmZDVq1evNnsRYq68vDyzlxCUNHfzaPbm0NzNo9mbJ1Bnr+8AioiIiAQZfQQsIiIiEmQUAEVERESCjALgIFNdXc0FF1zAxIkTycnJ4YknngCgpaWFBQsWMGbMGBYsWEBraysAzz33HJMnTyY3N5fZs2fz5z//2fdaW7ZsYdy4cWRnZ7Nu3brPfM8NGzYwZswYxowZ47uDy+nTp7n88ssZP348OTk53H///X48a/MFytz/VlFREZMmTernMw08gTT7np4eiouLGTt2LOPHj+fXv/61n87afIE09xdeeIHc3FwmT57MJZdc8qmdZkOJGbO/5JJLiI+P54orrjjr+NGjR5kxYwbZ2dlcf/319PT0+OGMA0cgzf6mm25i3LhxTJo0ieXLl+N2u/v3ZA0ZVOrq6oz33nvPMAzDOHHihDFmzBhj3759xve+9z1j7dq1hmEYxtq1a43/83/+j2EYhvHmm28aLS0thmEYRllZmTF9+nTDMAzD4/EYmZmZxuHDh43u7m5j8uTJxr59+z7xfs3NzYbT6TSam5uNlpYWw+l0Gi0tLcapU6eMHTt2GIZhGN3d3UZhYaFRVlbm9/M3S6DM/SO//vWvjRtuuMHIycnx63kHgkCa/UMPPWQ88MADhmEYRm9vr9HY2OjfkzdRoMzd7XYbI0eO9M36e9/7nvGv//qv/j59Uw307A3DMF577TXjlVdeMS6//PKzjl933XXGCy+8YBiGYXz72982fvzjH/f/CQeQQJp9aWmp4fV6Da/XayxZsqTfZ68AOMgVFRUZ27ZtM8aOHWvU1dUZhtH3G3js2LGfeGxLS4uRmppqGIZhvPXWW8bXvvY1388efvhh4+GHH/7Ec55//nmjuLjY9+/FxcXG888//4nH3X777UZJSclXPp/Bwsy5d3R0GAUFBca+ffuCIgD+PTNnn5aWZpw8ebJfz2ewMGvuPT09RmJiolFVVWV4vV7j29/+tvHMM8/09+kFNH/P/iOvv/76WSHE6/UaCQkJhtvt/tTXCwZmzf7vPfbYY8b3v//9L3san0ofAQ9iVVVVvP/++8yYMYOGhgZSUlIAGDVqFA0NDZ94/Pr167n00ksBqK2tJT093feztLQ0amtrP/Gcc3lcW1sbv/3tb7nooov65bwCndlzX7VqFffccw+RkZH9el6DgZmzb2trA/rmP3XqVK677rpPfc+hyMy5h4WF8fTTT5Obm0tqairl5eWsWLGiv08xYA3E7D9Lc3Mz8fHxhIaGfqnnD3Zmzv5vud1ufvGLX3DJJZd8qed/FgXAQerkyZNce+21PP7448TGxp71M4vFgsViOevY66+/zvr163nkkUf6dR0ej4cbbriB22+/nczMzH597UBk9tz37t3L4cOHufrqq/vl9QYTs2fv8Xioqalh9uzZ7Nmzh1mzZnHvvff2y2sHMrPn7na7efrpp3n//fepq6tj8uTJrF27tl9eO9CZPftgFkiz/853vsPcuXOZM2dOv76uAuAg5Ha7ufbaa7npppu45pprAEhOTqa+vh6A+vp6kpKSfI//y1/+ws0338ymTZtISEgAwG63U11d7XtMTU0NdrudXbt2MWXKFKZMmcIrr7zymY/7SHFxMWPGjOHOO+/06zkHgkCY+9tvv83u3btxOBwUFhZy8OBB5s+fPwBnb65AmH1CQgKRkZG+97/uuuvYs2eP38/dTIEw97179wKQlZWFxWJh8eLFvPXWW34/d7MN5Ow/S0JCAm1tbXg8nrOeP9QFwuw/smbNGhobG3nsscf68xT79OsHyuJ3Xq/X+OY3v2nccccdZx2/9957z/qC6ve+9z3DMAzj2LFjRlZWlvHmm2+e9Xi32204nU7jyJEjvi+ofvDBB594v+bmZsPhcBgtLS1GS0uL4XA4jObmZsMwDOOBBx4wrrnmGqO3t9cfpxpQAmnuHzl69GhQfAcwkGZ//fXXG9u3bzcMwzB+9rOfGYsWLer38w0UgTL32tpaY9SoUYbL5TIMwzAefPBB4+677/bHKQeMgZ79Rz7te2iLFi06axPIU0899ZXPL5AF0ux/8pOfGLNmzTJOnz7dH6f2CQqAg8wbb7xhAEZubq5x3nnnGeedd55RWlpqNDU1GRdeeKGRnZ1tXHTRRb6/sFasWGHEx8f7HpuXl+d7rdLSUmPMmDFGZmam8YMf/OAz33P9+vVGVlaWkZWVZfz0pz81DMMwqqurDcAYP36877V/8pOf+PfkTRQoc/9bwRIAA2n2VVVVxpw5c4zc3FzjwgsvNI4dO+a/EzdZIM396aefNsaPH2/k5uYaV1xxhdHU1OS/Ew8AZsy+sLDQSExMNCIiIgy73W5s2bLFMAzDOHz4sDFt2jQjKyvLWLRokdHV1eXfkzdZIM0+JCTEyMzM9L32mjVr+vVcdSs4ERERkSCj7wCKiIiIBBkFQBEREZEgowAoIiIiEmQUAEVERESCjAKgiIiISJBRABQR8YPVq1fzwx/+0OxliIh8KgVAERERkSCjACgi0k/+/d//nbFjx1JYWMiBAwcA+NGPfsTEiROZPHkyS5YsMXmFIiJ9Qs1egIjIUPDee++xceNG9u7di8fjYerUqeTl5bFu3TqOHj1KeHg4bW1tZi9TRATQFUARkX7xxhtvcPXVVxMZGUlsbCxFRUUATJ48mZtuuolf/vKXhIbq/7lFJDAoAIqI+FFpaSm33XYbe/bsYdq0aXg8HrOXJCKiACgi0h/mzp3Lyy+/TGdnJx0dHfz2t7/F6/VSXV3NBRdcwCOPPEJ7ezsnT540e6kiIvoOoIhIf5g6dSrXX3895513HklJSUybNg2LxcI3vvEN2tvbMQyD22+/nfj4eLOXKiKCxTAMw+xFiIiIiMjA0UfAIiIiIkFGAVBEREQkyCgAioiIiAQZBUARERGRIKMAKCIiIhJkFABFREREgowCoIiIiEiQUQAUERERCTIKgCIiIiJBRgFQREREJMgoAIqIiIgEmVCzF+BPiYmJOByOAXkvt9tNWFjYgLxXsNFs/Uvz9S/N1380W//SfP1nIGdbVVVFU1PTJ44P6QDocDjYvXv3gLxXXV0dqampA/JewUaz9S/N1780X//RbP1L8/WfgZxtfn7+px7XR8AiIiIiQUYBUERERCTIKACKiIiIBBkFQBEREZEgM6Q3gQyU2vZOutxes5chIiIick50BfArqmvvYuza13ninTqzlyIiIiJyThQAv6LUuAgWjhvJT95zcbjppNnLEREREflcCoD94NGiHHoNuP3lfWYvRURERORzKQD2A2dCJLdMTaKswsX2ykazlyMiIiLyDykA9pM7ZqWQGDWcuzftwzAMs5cjIiIi8pkUAPtJ9PAQ1l0+gb/Ud/DUm1VmL0dERETkMykA9qN/mpZOTnIMP3itkpNdHrOXIyIiIvKpFAD70bBhFn58bS4NHd38S1mF2csRERER+VQKgP1sblYCRTnJ/PeuDznSfMrs5YiIiIh8ggKgHzx+1SR6DYPbf/OB2UsRERER+QQFQD9wJkRyxxwnpRUudlQ2mb0cERERkbMoAPrJqgVjSYgM4y7VwoiIiEiAUQD0k9iIMNZePoG/1J/gx6qFERERkQCiAOhHy6ePJic5hv/vtUpOdasWRkRERAKDAqAfhQyz8NS1k1QLIyIiIgFFAdDP5mUlcuXEZH7yzodUtZw2ezkiIiIiCoAD4fGv59BrGPzz//7V7KWIiIiIKAAOhMyEKG4vdPK7Chc7D6kWRkRERMxlegDs7e3l/PPP54orrgDg6NGjzJgxg+zsbK6//np6enoA6O7u5vrrryc7O5sZM2ZQVVVl4qq/uIe+NhZbZBh3qhZGRERETGZ6AHziiSeYMGGC79/vu+8+7rrrLg4dOoTVamX9+vUArF+/HqvVyqFDh7jrrru47777zFrylxIbEcbayybw57oT/NfbVWYvR0RERIKYqQGwpqaG0tJSbr75ZgAMw2DHjh0sWrQIgGXLlvHyyy8DsGnTJpYtWwbAokWL2L59+6C7krZixmgmJkfzb9sqOd2jWhgRERExR6iZb37nnXfyH//xH3R0dADQ3NxMfHw8oaF9y0pLS6O2thaA2tpa0tPTAQgNDSUuLo7m5mYSExPPes2SkhJKSkoAOH78OHV1dQNyLo2Njef0uDXzUrnuxYPc+eJuVl842s+rGhrOdbby5Wi+/qX5+o9m61+ar/8EwmxNC4C/+93vSEpKIi8vj507d/bb6xYXF1NcXAxAfn4+qamp/fban+dc3mtRKlzxQRu//KCJ7182GYctcgBWNvgN5K9jMNJ8/Uvz9R/N1r80X/8xe7amfQT85ptv8sorr+BwOFiyZAk7duzgjjvuoK2tDY+n7+PRmpoa7HY7AHa7nerqagA8Hg/t7e0kJCSYtfyv5PGrJuHxGtz+mw/MXoqIiIgEIdMC4Nq1a6mpqaGqqoqNGzdy4YUX8txzz3HBBRfw0ksvAbBhwwauuuoqAIqKitiwYQMAL730EhdeeCEWi8Ws5X8lWYlR/HOhk9+WN/B71cKIiIjIADN9F/Dfe+SRR3jsscfIzs6mubmZFStWALBixQqam5vJzs7mscceY926dSav9Kv5V9XCiIiIiElM3QTykfnz5zN//nwAMjMzeffddz/xmIiICP7nf/5ngFfmP7ERYTx82XhufemvPPP2MW6d7TB7SSIiIhIkAu4KYDC5eUYGE5Ki+bdtB1ULIyIiIgNGAdBEIcMsPHVtLvUd3Xy/bL/ZyxERERE/azrZzSsHWsxeRmB8BBzMLshO5PIJSZS8c4y75maSoVoYERGRIeV0j4dNHzTw3J4ath5oxOM1mD8xg/HJMaatSQEwADzx9UmMf+R1/vk3H/DKiulmL0dERES+Ik+vl+2VTTy3p5bf/LWekz29JEcP58bz7RSOCsGZYO4FHwXAAPBRLczjfzjC7w81MS878fOfJCIiIgHFMAx2V7fzyz01/Or9OhpOdhMTHsqCsSO5ZPxIFowdSYY1kuPH6wkPDTF1rQqAAeJfvzaWZ/9UzV2v7OO9u+YO2o5DERGRYHOo6RTPvVfDc3tqqWw6xfAQC4VOG/eOz+SS8UmMS4omLCSwtl0oAAaIuBF9tTArf/1XSt45xrdnOcxekoiIiHyGho5ufrW3luf21PLuh21YgLy0OB68eAyXjE/ifHsskcMDN2YF7sqC0C0zM3jijaOs2XqQpXlpjAjg3zgiIiLB5mS3h5c/OM5ze2p49WATvV6DsSOjuGOOk6+NHclsp434EWFmL/OcKGEEkJBhFn58bS4XPv0239+8n/+8apLZSxIREQlq7l4vrx5s5Ln3ann5g+OcdveSEhPO0rw0Lhk/knlZiSTHhJu9zC9MATDAXJCdyGUTkih5+0PunKNaGBERkYFmGAa7Pmzjl+/V8Ku9dTSd6iEuIpRLx4/k0vFJXDQ2kdHxkQwbNni/r68AGICe+PokJjzyOre//AGblqsWRkREZCAccJ3kuT01PL+nlsPNpwkPGcbcTBuXjE9i4fiRjB0ZeJs5viwFwACUnRjFbQUOfvTGUf5wuIm5WaqFERER8Yf6E11sfL9vM8d7Ne0Ms8C09Hi+mZfGwnEjOc8ex4gwcytb/EEBMECtWTiOn++u4a5N+9itWhgREZF+c6LLzW/+2reZY3tlE14DJiRFc9fcTBaOG8nMDCtxg2Qzx5elABig4kaE8e+Xjuc7//tX/nvXh9wyM8PsJYmIiAxaPR4vWw+4+OV7tbyy7zhdHi/2uAiWT0/nknFJzM1KYGT04NvM8WUpAAawW2aO5ok3jrB66wG+MdWuWhgREZEvwOs1eKuqhef21PLin+toOe0mfkQoV+Ykc+m4JC4em4g9bsSg3szxZSlRBLDQkGE8dU0uFz/zDg9u3s+jqoURERH5XOXHO3huT9+dOY61dhIROoz5WQl9mznGjSQ7MYrQIbKZ48tSAAxwF40dyaXjR/Jfb3/IHXMzGW1VLYyIiMjfq23v5IU9dTy3p4a9dScIscCM0VZWTB/NJeNHkpsSS8QQ3MzxZSkADgJPfH0SE/9jJ7f/5gNeVi2MiIgIAO2dbn79l3p+uaeGnYebMQzISY7h3nmZLByXxPSMeGIjhvZmji9LAXAQGDMymu8UOPj/3zjKG0eamZOZYPaSRERETNHt6aWswsVze2r5XXkD3R4vo+NHcMuM0VwyLok5mTYSg2gzx5elADhIfFQLc+fL+9h91xzVwoiISNDweg3eONrMc3tq+Z8/19HW6cEWGcbVk0ZxyfgkLhqTgD1uhP5u/AL+H3v3HVBl/f5//IkMZS8ZMgQRBdlTBZwpYmq4UnNk7oYrG59fW21nmbmVPrnKNDNHH0uz4QZFJfdCBUVwsAVxMN6/P9DzzUoj5XAY1+Mvzryv8+bmPi/ucb11FgDT0tIYOnQoly9fRk9PjzFjxjBx4kRycnIYMGAAqampuLu7s2rVKqytrVFKMXHiRH788UdMTExYsmQJISEhuiq/ylndbgszds1hvthznlHSFkYIIUQtd/jiVb7af4EVv6eTlncDY8N6dPRsyKNe9nTxAQjQPgAAIABJREFUboiHjVzM8aB0FgANDAyYPn06ISEhFBQUEBoaSnR0NEuWLKFTp0688sorfPjhh3z44Yd89NFHbNy4keTkZJKTk9mzZw/PPvsse/bs0VX5OjFG0xbmFIOlLYwQQoha6HxuESt+L7+Y4/DFAvTr6RHhZs3Trd3o6m2PXyNz6hvIxRwPS2cJolGjRjRq1AgAc3NzWrRoQXp6OuvXr2fr1q0APPXUU3To0IGPPvqI9evXM3ToUPT09GjdujV5eXlcvHhR8x51wZ22MNELd/PGppNMj/XVdUlCCCHEQ8stusW3By+yPOkC28/mABDQyJz/17EpXbzsCHe1xryB7PSoTNViNFNTU/n9999p1aoVly9f1oQ6R0dHLl++DEB6ejqurq6a17i4uJCenv6XABgXF0dcXBwAly5dIiMjo0o+Q2ZmZpUsx8cMOrpbsCA+lSeam+JsYVQly9WlqhrbukrGV7tkfLVHxla7tD2+N0rK+OVsPmuP5/Dr2XyKyxSNLYwYFmjLI+7mhDlb3J6OrZiCnCsUaLWaqlUd1l2dB8DCwkL69u3LZ599hoWFxV2P6enp/esTOseMGcOYMWMACAsLw8nJqdJq/SdVtayFT1jgM20r7ydksnZ4eJUsU9eq8vdYF8n4apeMr/bI2GpXZY9vaZli25lsvtp/ge8OXeTqzRIamhrRL9CJbi3s6dDUFifLBnXiYg5dr7s6DYDFxcX07duXwYMH06dPHwAcHBw0h3YvXryIvb09AM7OzqSlpWlee+HCBZydnXVSt641szPj2Ug35uxMZefZbNpIWxghhBDVlFKKA+lXWZ5UfjFHxtWbmBrp84hnQ7p629GluT1NbE3Qr4PTsemSzi6dUUoxcuRIWrRowQsvvKC5PzY2lqVLlwKwdOlSevbsqbl/2bJlKKXYvXs3lpaWder8vz97u6s3lg0MmLjuKEopXZcjhBBC3CU1p4j3f0nGd9pWQmZsZ+aOFDwbmvJhN292jo3k26dCeS6qCZ52phL+dEBnewB37drFl19+ib+/P0FBQQC8//77vPLKK/Tv358vvvgCNzc3Vq1aBUC3bt348ccf8fT0xMTEhMWLF+uq9GrBytiQdx/1ZtzaIyxOTGNEq8a6LkkIIUQdl33tFqsOZrB8/wV2peYCEOxswauPeBLjbUeoixVm9XV+9plAhwGwTZs299xz9euvv/7lPj09PebOnavtsmqUpyPcmLkjhbd+OsmgECcaGMoflRBCiKpVdKuE/x29zPKkdDaeuEJJmcLD1oSxke509bYjsokNNia1/4LFmkYSQw12py1Ml7jdvLHxJJ9IWxghhBBVoKS0jN9OZ7E8KZ01hy5SeKsUezMjBgY786i3He2b2tLIom5czFFTSQCs4aK97OjSvCELEs7xfDsPXKyMdV2SEEKIWkgpxf4L+SxPusDK3zO4VHATMyN9OjVryKPe9nRuboe7jVzMUVNIAKwFZvfxx2faViasPcKaOtIWRgghRNU4k3WN5UnpLE+6wKnMaxjq69HG3YYX2nsQ42WHt705RgYyHVtNIwGwFmhuZ8azEW7M3ZXKrrM5RHnY6LokIYQQNVhm4U0W/36FDavPsvtcLnpAiIslb3RuRoyXHcHOlpjKxRw1mvz2aom3u3rx1f4LTFx/hL3Pt5XzLoQQQjyQ3edy6bJwNwU3S2jW0JTxbdyJ8bInqokNVsaGui5PVBIJgLWEtYkR7zzqzfi1R1i8N40RLaUtjBBCiH/n9wv5dI3bjZWxAdM7OxMb7oWDeX1dlyW0QA7a1yLPRLjh2dCUyZtOcqO4RNflCCGEqEGOXy6gS9xujA31md/Hn25ethL+arEKBcDZs2eTm5ur7VrEQypvC+PHhfwbvLXplK7LEUIIUUOczb5G5wUJKKWY18efbj4OcipRLVehAHj58mXCw8Pp378/mzZtkqnHqrEuXvZEN2/IvPhULuRd13U5QtQpSimOXy5g7s4UHl+6j6Dp29iSkq/rsoS4rwt51+k0P4Frt0qZ28efnn6OEv7qgAoFwHfffZfk5GRGjhzJkiVLaNasGa+99hpnzpzRdn3iAczu7c+NkjImrD2i61KEqNWUUpzJusbnu88x6KsknKb+jM+0rYxbe4SdKTlkFd5ixPoz/HDskq5LFeJvXSm4SecFCWReu8Wc3n48HuhEPenjVydU+CIQPT09HB0dcXR0xMDAgNzcXB5//HGio6OZNm2aNmsU/5KXvRnPRLgxPz6V+JQcIptIWxghKsv53CK2nM5my+ksfjudRVreDQBsTQwJc7VipIsl4a5WhLlaYaBfj6iZ2+i7ZD/fDQulu4+jjqsX4v/kFN0ieuFuUnOvM7e3HwOCnaWJcx1SoQA4c+ZMli1bRsOGDRk1ahQff/wxhoaGlJWV0axZMwmA1dA7d9rCrDtCorSFEeKBXbp6gy2ns/ntdBZbTmdxJrsIAMsGBoS6WDIo2JkwVytaNbaikUUDDPTvPrCy7glv+nx7mr5L9rNmeBjdWjjo4mMIcZeCGyU8+vkejl8pYEasL0PCXDDUl+tC65IKBcCcnBzWrFmDm5vbXffXq1ePDRs2aKUw8XCsTYx4u6sXE9cdZcneNIZLWxghKiSr8CbbzmbzW3L5Xr7jVwoBMDXSJ8TFkp6+joS5WhLhbo2ThfE/zoBgY2JA/PgoImfvos/ifRIChc4V3SrhsUWJ7E/LY1oPH0a2akx9A31dlyWq2H0DYE5ODgATJ0686/YdNjY2tGjRQkuliYf1XKQ7s3em8tamkwwMdqKBobR9FOLP8q8Xs/3snT182RzMuAqAsWE9gp0sGd/GnXBXKyLcbHC1bvBAX5QNzeoTPz6KiNk76bN4H2tHhPGot4RAUfVulpTSd+k+tp/J5p2uXjwd4UYDQwl/ddF9E0FoaCh6enp/e9Wvnp4eZ8+e1Vph4uEZ6NdjTm8/un6+h7c2nWLaYz66LkkInSu8WcLOlBzNOXxJF/IpU1Bfvx4BTuY8E+FGuKsVke7WuNmYYFxJX44NzeqTML4NEbN30nuRhEBR9UpKyxj0VRKbTmTyRudmTGjrIdO51WH3/c2npKTc8zFpBVMzxHjb07lZeVuYie2a4GxprOuShKhS14tLSUjN1QS+xPN5lJQpDOrp4edozsiWjQl1sSSqiTVNbEy1+oVYviewDZGzd9J78T7WDQ+jq4RAUQXKyhQjvjnImsOXeLG9By92aIp5Awl/dVmFfvtvvfUWb7/9tuZ2WVkZTz75JMuXL9daYaLyzO7th98n25iw9gjfDQvXdTlCaNWtkjISz+fy2+0rdRPO5XKzpIx6etDC3pwhIeUXbUS62+DZ0LTKvwTtbofAiFk76SUhUFQBpRRj1xzmy/0XeDbSjVc7NZM5fUXFAmBaWhoffPABr776Kjdv3qR///4EBwdruzZRSbwdzHm6dWMWJJwjITWHCHdpCyNqj5LSMpLS8/ktufwcvp0pORQVl6IHNLcz5fGARrfP4bOmub1Ztfjis/vDnkAJgUKblFK8/L9jLEg4x7AwFyZHN8fW1EjXZYlqoEIBcNGiRQwePJgPPviALVu20K1bN55//nlt1yYq0TuPerM8KZ3n1x1l98Q20hZG1FhlZYqDGVfZciaL35Kz2H42h4Kb5XNfe9ia0MPHXhP4WjiaY21sWC3Xd3vz23sCb4fA9SPCifGy13VZopZ5e/Mppm87S//ARrzXzRsHiwa6LklUE/cNgElJSZqfJ06cyNNPP01UVBTt2rUjKSmJkJAQrRf4Z5s2bWLixImUlpYyatQoXnnllSqvoSayMTFiaowXz68/yrJ9aTwVLm1hRM2glOLY5ULNOXzbzmSTU1QMQGMrY6KbNyTMxYrWbtb4NzLH1tSoWga+v2Nv/n8XhvRctJfvR4TTRUKgqCTTt55hyuZTPObjwLQePjjJOeDiD+4bAF988cW7bltbW3Ps2DFefPFF9PT0+O2337Ra3J+VlpYyduxYfv75Z1xcXAgPDyc2NhYfH7m6tSKei3Jn9s4U3tx0kieCnKkvl/6Lakgpxemsa5rmy1tPZ3O58CYAjczrE+VuQ5irJS1drQl2scTO1KhGT12lCYGz7oTAlkR72em6LFHDLYhP5aX/HSO6WUM+jfXFzcZE1yWJaua+AXDLli1VVUeFJCYm4unpiYeHBwBPPPEE69evlwBYQYb69ZjTx59HP9/D5J9O8mEPGTdRPZzLKdL04fvtdBbp+eXTq9mZGhHmakmYqxVhLlaEN7bC3qx+rZuuyt68PgkTykNg7KJE/jeyJZ2bSwgUD+bLfWk8t+YwbZvYMKOXL552prouSVRDFToH8PLly7z22mtkZGSwceNGjh07RkJCAiNHjtR2fXdJT0/H1dVVc9vFxYU9e/ZUaQ01XVdvezp5NmTOrlQmtG0ihwSETmTk32DLHwJfSk759GrWxoaEuVjyZKgLYS6WtHKzwtH8r9Or1Ub25vWJn9CGyFk7eewLCYHiwaw5dJFhKw8Q5mLJrN6++Dpa6LokUU1VKAAOGzaM4cOH89577wHQvHlzBgwYUOUBsCLi4uKIi4sD4NKlS2RkZFTJcjMzM6tkOZXhzTb2PLI0i6dX7GVhrKeuy/lHNWlsa6KqGN/somLi0wrZlXaV+PMFnMktP6RrZlSPQAdjunnYEeRoQkgjMxqa1cfgzh6+ojyuFGm9PK36t+O7pr8nsStO8Nh/97C0tydt3OQL/F5k23C3LSn5DF93Bm/bBkxt44C9XhEZGQ/+ByTjqz3VYWwrFACzsrLo378/H3zwQfmLDAzQ16/688ecnZ1JS0vT3L5w4QLOzs53PWfMmDGMGTMGgLCwMJycnKqsvqpc1sNwcoKnW19j4e5zpBUb08rNWtcl/aOaMrY1VWWPb27RLbafzbl9WDeLwxcLADAx1CfY2YIefk6Eu5ZfuOFi9WDTq9Uk/2Z8nYDESY5EzNrBU+vOsGFkSzrJnsB7km1DuW1nshj1/Vk8bE2Y2zeADs0aVsr7yvhqj67HtkIB0NTUlOzsbM2Vdbt378bS0lKrhf2d8PBwkpOTSUlJwdnZmZUrV/L1119XeR21wbvdytvCTFh7RNrCiIdWcKOEHSnZbLndfDkpPR+loL5BPQKdLBgb6U6YqyUR7tY0tq686dVqKwfz/2sW/diiRDaMbMUjlfSFLmqfPedy6fHfRJwsGjC3j3+lhT9Ru1UoAH766afExsZy5swZoqKiyMzMZPXq1dqu7S8MDAyYM2cOMTExlJaWMmLECHx9fau8jtrAxsSIqV2bM2n9Mb7cd4Gh4a7//CIhbiu6VUK8Znq1bPam5VFapjDU18Pf0YLRrRoT5mJFZBMb3K2NZb7RB+Bo0UBzYUiPL/ZICBR/62BGPl0/342VsSFz+/jJ3mJRYRXaKoeEhLBt2zZOnjyJUgovLy8MDXXTTb9bt25069ZNJ8uubcZGNWH2zlTe3HSSAUFO0hZG3NPNklL2nMvT9OLbfS6XW6UKfT3wdTRnaKgL4a6WRLjb0NS26qdXq60kBIr7OXG5gOgFu6mvX495ffyJ8ZYekqLiKrSVLioq4tNPP+XcuXN8/vnnJCcnc/LkSXr06KHt+oQWGerXY05vP7r9N5Gpm0/xfvcWui5JVBMlpWXsu3BnerUsdqXmcL24DD3A296MAUHOhLlYEuluTTM7MyyrwfRqtZWjRQPi/xACfxjZio4SAuu8lOwiOi/cTZlSLHg8gO4+DnIqj/hXKhQAhw8fTmhoKAkJCUD5xRj9+vWTAFgLPNrCgUc8bZm9M4VxbdylLUwdVVqmOJiRz2/J2Ww5k8X2M9kU3ioFwLOhCb18HQl1tSLC3ZoW9mZYm8hcolWp0V17AhPZMLKlhMA6LD3/Op0XJlBwo4S4fgH09GtUo5uhC92oUAA8c+YM33zzDStWrADAxMQEpZRWCxNVZ3ZvfwI+2crEdUf59qkwXZcjqtC3BzNYFH+G3ekHybtePp+uu7UxXb3tCXO1pFVja/wbWWBjUj3n061L/hwCfxjVkg6eEgLrmszCm3ResJvLBTeZ18efvgGNal1jdFE1KhQAjYyMuH79uuYL4MyZM9SvX1+rhYmq4+NozqhWbny+5xyJ53JpWQPawoiHU3SrhHFrjrB4bxqOpga097Al3LV8po0gJ0sa1vDp1WqrRhYNiB8fRcTsXXT/byI/jm5J+6YSAuuKvOvFdFm4m5ScImb38mNgiHOdaJIutKNCa87UqVPp2rUraWlpDB48mE6dOjFt2jRt1yaq0HvdvDEzMmD8uiOyd7eWS84sJGLWLpbsTWNUK1fW9G/GmmHhvB7dnC5e9tib15fwV405WRoTPy6KhmZGdPs8ke1nsnRdkqgChTdLePTzPRy9XMAnPXwYGu6CoYQ/8RAqtPYsXbqU7t2789ZbbzFo0CD27dtHhw4dtFyaqEq2pkZMiWlO4vk8vtp/QdflCC1ZfTCD0Bk7OJdbxKxefky/PUm8BL6axdnq/0LgoxICa73rxaXELkpk7/lc3n/Um5GtG9f65ulC+yoUAEeOHMmNGzf4/vvvGT9+PE8//TQzZ87Udm2iio1r0wQPGxPe2HSSm8Wlui5HVKJbJWU8v+4I/Zbtx93amOWDQ3g2yh2LBnL1bk2lCYGmRnT7byI7zmbruiShBbdKynh86T62ns5mSowXz0a6SyN1USkqFAA7duzI66+/zjvvvMPo0aPZt28f8+fP13ZtoooZ6tdjVm9fzudeZ+rPp3Rdjqgk53OLaD8vnpk7UhgY7MSKJ0Po7uMgJ47XAs5WxuwaH4mtiRGPfr5HQmAtU1JaxuDlSfx4/AqvdvJkYlsPaaouKk2FAmCnTp2Iiorim2++wcvLi71793LixAlt1yZ0oFsLBzo2tWXOzlQuXr2h63LEQ9p04gohn27n8MWrfNjdm7l9/PF1tNB1WaISuViZsGt8JDYmhjz6+R52SgisFcrKFKNWHWT1oYtMaufByx09pcG6qFQVCoABAQEYGRlx5MgRDh06xJEjR7h+/bq2axM6oKenx+w+/hTdKmHi2iO6Lkc8oNIyxVubTtDtv3uwMTHiy4HBvNC+qfTvq6VcrEyIHx+FjYkhXSUE1nhKKcavPcLSfRd4JsKN1zs3w0qarYtKVqEAOGPGDLZv386aNWuwtbVl+PDhWFlZabs2oSO+juaMbNWY7w5fJPFcrq7LEf/SlYKbxMTt5p2fk3nMx4EVQ0LoHdBIrhis5VysTNg1Lgpr4/IQuCtFQmBNpJTilR+OMy8+lSdDXZjSpTm2pvKPm6h8FfpGmDNnDgMGDCA4OJj169czYsQINm7cqO3ahA69360FpkYGTJC2MDXKzrPZBH+6jZ1nc3gruhn/7R9IqKv8s1ZXuFqX7wm0NjYkJk5CYE303i/JTNtyhscDGvFBN28cLBrouiRRS1XohIIbN27wwgsvEBoaioGBnINQF9xpC/Pi98dYnpTOkFAXXZck7kMpxfStZ3nlx+M4WzRgyRNB9A5wlFYRddCdEBg5excxcXv4aUwroprY6rosUQEztp3hzU0n6d7Cno8fa4GzlUzNKbSnQnsAX3rpJVq1aiXhr44Z36YJTWxMeH3jCW6VSFuY6irvejG9F+/l5Q3H6NDUluWDgxkQ7CThrw5ztTZh1/gorG7vCYxPydF1SeIffL77HC98f4xHPG2Z0dMXdxtTXZckajk5KUjck6F+PWb1Km8L8/ZmaQtTHSVdyCPk0+1sOH6Fl9p7sPSJINp42Mq8vYLGt/cElofA3RICq7Gvky7w9OpDRLpbM7O3H83szHRdkqgDJACK++ru40CHprbM2pnKJWkLU20opYhLOEfk7F0UFZfy334BTO3qJYeMxF3uhEDL2yEwIVVCYHWz7vBFhq44QIizJbN7+eEnbZpEFZEAKO5LT0+POX38uXarhAnrpC1MdXDtZglDV/zO06sPEeJsyfJBwQwNc8XESE7REH/V2Lr86mDLBoZ0WSghsDrZfPIKA75MooW9GbN6+xIiF2yJKiQBUPwjX0dzRrZszJpDF9l7XtrC6NLxywW0mrWT5fvTeSbCja+HBNOpuZ3M5Svuy82m/JzAOyFwt4RAndtxNptei/fibm3MrF6+RLrLhTqiakkAFBXyfjdvTIwMmLBW2sLoyoqkdMI/20FG/g3m9PFjWg8fOVFcVNgfQ2C0hECd2ns+j+7/TcTBrD5z+vjRsZmdrksSdZAEQFEhDc3qM7lLc3afz+PrpHRdl1On3CwpZex3hxm0PIlmDU1ZPjiYpyPcZVoo8a+52Ziwc1wkFg0M6BK3hz3S6L3KHcq4SkzcbizqGzCvrz/RXva6LknUUToJgC+//DLe3t4EBATQu3dv8vLyNI998MEHeHp64uXlxU8//aS5f9OmTXh5eeHp6cmHH36oi7LrvPK2MMbSFqYKpeYU0WbOLs2sAF8PCeHRFg7oyyFf8YDcbU3ZNS4K8/r6RC/cLSGwCp3KLCR6YQKG+nrM6+tPV28Jf0J3dBIAo6OjNfMKN2/enA8++ACAY8eOsXLlSo4ePcqmTZt47rnnKC0tpbS0lLFjx7Jx40aOHTvGihUrOHbsmC5Kr9OMDOoxs5cf53Kv887Pyboup9bbcOwyIZ9u58TlQj55zIdZvf1o4WCu67JELSAhsOql5hTRaX4CxaWKeX386eHjIO2ahE7pJAB26dJF01S6devWXLhwAYD169fzxBNPUL9+fZo0aYKnpyeJiYkkJibi6emJh4cHRkZGPPHEE6xfv14Xpdd5PXwcaO9hy6wdKVyWtjBaUVJaxqs/HOexLxKxNzPiq8HBTGzbRCaDF5XK3daUneOiMDPSp8vC3TLvtxZl5N+g84IE8m+UMLePH738G8mFW0LndH4S0aJFixgwYAAA6enptG7dWvOYi4sL6enl55u5urredf+ePXv+9v3i4uKIi4sD4NKlS2RkZGir9LtkZmZWyXKqg7faOhC9LJunV+5lXo+mWl9eXRrby4XFjP3hLAkXCunRzJJJrR3xtlFcuXxJa8usS+OrC9V5fOsDa/p70mvlSToviGfl480JalRzLiyqzmN7R05RCX1XnSQj/ybTOrvQxl6Py5cu6rqsCqkJ41tTVYex1VoA7Ny5M5cu/fVL67333qNnz56anw0MDBg8eHClLXfMmDGMGTMGgLCwMJycnCrtvf9JVS5Ll5ycYETLQhYnnueNEmPCGltXwTJr/9huPZ3FE8uPkH+jmLdjvHg20o2GZvWrZNl1YXx1qTqPr5MTJEx0IGr2LgZ+d5pfnmlNeBX8TVeW6jy2edeL6bEynnN5t5jZy48RrRpjqF+zrr2szuNb0+l6bLUWAH/55Zf7Pr5kyRI2bNjAr7/+qjkPwtnZmbS0NM1zLly4gLOzM8A97xe68X43b1b+ns7EdUfZNaGNrsup0crKFB9tOc0bG0/Q2MqYWb2D6OnnKHP5iirTxNaUXeOjiJy9i84Ldte4EFgdFd4soft/93D4YgHTH/NhWEvXGhf+RO2mk7Vx06ZNTJs2je+//x4TExPN/bGxsaxcuZKbN2+SkpJCcnIyLVu2JDw8nOTkZFJSUrh16xYrV64kNjZWF6WL2+xut4WJP5fL10kXdF1OjZVTdIvHFiXy2o8niG5ux4ohofQPcpbwJ6pck9sXhpgYlV8Ysi9Nzgl8UDeKS+m5aC+7z+Xy3qPejI5wk79pUe3oJACOGzeOgoICoqOjCQoK4plnngHA19eX/v374+PjQ9euXZk7dy76+voYGBgwZ84cYmJiaNGiBf3798fX11cXpYs/mNDWA3drY17/UdrCPIi95/MI+XQ7P5/K5JWOTVk8IJDW7rLXReiOR0NTdo6LxNhQn84LJAQ+iOLSMvot289vp7OY3KU5z0W5Y2wo4U9UP3qqFk/rEBYWxr59+6pkWRkZGTo/nq8L3x+5RM/Fe3kzuhlvd/XWyjJq29gqpZi7K5UXvj9KQxMjPurRgscDnXT2JVHbxre6qYnjeyarkKg58dwoLuXXZyIIraZz1Fa3sS0tUwz6KolVBzN45RFPXu3kiUWDmnv1fnUb39qkKsf2XllITkgQD+UxXwfaedgwc3sKVwqkLcw/KbhRwsCvkhi/9gitG1vz1eBghoS6yB4CUa00bWjGrnGRNDDUp9OCBJIu5Ou6pGqvrEwxetVBVh3MYGLbJvynY9MaHf5E7ScBUDwUPT095vTxp/BWCRPWHdV1OdXakYtXCf9sO98ezGBclDtfDgrmkWZ20gxWVEtNG5qxc2x5CHxkfryEwPtQSvH8+qMs3pvG6FaNeaNzM6xNjHRdlhD3JQFQPDT/RhYMD3dl9cEM9qfl/fML6qBl+9JoOXMHWdduMb+vPx90b4Gbjck/v1AIHfK0ux0CDepJCLyP1zeeYPbOFIaEOPN2V68qa98kxMOQACgqxQfdW9DAUJ8J647oupRq5UZxKWO+PchTKw7g42DO8sEhjGrlhll9nfdgF6JCPO3M2DkuShMCf5cQeJf3f0nmg19P08ffkQ+6e+No0UDXJQlRIRIARaWwM6vPW9HNiU/NZYW0hQHgTNY1Imbt5PPd5xke7srXQ4KJ8baXKaBEjeNpZ8b2sbdD4AIJgXfM3H6W1zee4FFve6bH+uBiJXv1Rc0hAVBUmufbeeBmbczrG09QXFqm63J0at3hi4TO2M7Z7CI+6+nLjJ6+NLcz13VZQjyw5vblIdBIvzwEHkiv2yHwiz3neX79UTo2teWzXj6429ScKfSEAAmAohIZGdRjZi9fUnKu8+7PybouRyeKS8t46fuj9F6yD2fLBiwfHMzYKHcsjeVqQFHzNbc3Y8ftENhxft0NgSuS0hn97UEi3ayZ2ctP/rkTNZIEQFGpYn0dadvEhpk7zta5tjDp+dfpOC+e6dvO0j+wESuHhNLD1xEDmf5J1CLN7c0las6GAAAgAElEQVTY/lykJgQezKhbIfD7I5d4csXvBDtZMLO3H/5OFrouSYgHIt9MolLdaQtTcLOEiXWoLcwvpzIJnr6d39Ov8v6j3szrGyBfDKLW8nIwZ/tzkRjWq0eHeXUnBP5yKpN+y/bjbWfGrN5+hFXTBtlCVIQEQFHpApwsGBbmyrd1oC1MWZni7c2n6BK3G/MGBiwbGMSLHZpiayo9wETt5uVgzo6x5SGw47yEWh8Cd57NpueivbhZGzOrty9RTWx1XZIQD0UCoNCKO21hJtbitjCZhTd59PM9TP7pJI9627NicAh9A50wMpA/K1E3eDmYs31sJAb19Og4L4FDGVd1XZJW7E/Lo/sXiTQ0M2JObz8eaWan65KEeGjyTSW0wt68Pm9GN2NXai4rk9J1XU6li0/JIfjT7Ww9k8XrnTz5on8gLd2sdV2WEFXO+3YI1K+nR8f58bUuBB65eJUucbsxM9Jnfh9/unjb67okISqFBEChNZPaNcXN2pjXNh6npJa0hVFK8dn2s7SfF48esGhAEG9EN5fmr6JO8759OLieXnkIPHyxdoTA5MxCohfuRl9Pj3l9/Hm0hYQ/UXtIABRaY2RQj8963m4L80vNbwuTf72Yfsv2M2n9Udo0seGrQcEMCnGmgaG+rksTQue8b18YUk9Pjw7zan4IPJ9bRKcFCdwoKWVeX38e83WUebtFrSIBUGhVTz9H2jSx4bPtZ8ksuKnrch7YgfR8wj7bzrrDF3m+XRO+HBREe8+G8oUgxB+0cCwPgXpAx3nxHKmhIfDi1Rt0WpBAblExc3r709u/kczgI2odCYBCq/T09Jh7py3M+pp5QciiPeeJmLWT/OslLHw8gHe7esuUT0LcQwtHc3aMjQKgw7x4jtawEJh97RbRC3eTnn+DWb38GBDkhL6EP1ELSQAUWhfgZMFTYa6sOniRpBrUFqboVgnDVx5g5KqDBDhZsHxwMMNbNsa0voGuSxOiWmvhaM722yGwfQ0KgfnXi4mJ201yZiGfxvoyONRFGrmLWkvWbFElPuzeggYG9Zi4vmY0hz6VWUjrWTtZujeN0a0a8/XgEKK97OUwkBAV5POnEHjsUoGOK7q/azdL6PFFIgczrjKthw/Dwl2lpZOo1WTtFlXC3rw+b3Ruxs6UHL45UL3bwnx7MIOwGTs4n3ud2b39+CTWh6YNZaJ3If6tP4bAdnN3VdsQeKO4lN5L9hKfmsO7Xb0Y3bqxXNwlaj2dBsDp06ejp6dHVlYWUN5iY8KECXh6ehIQEEBSUpLmuUuXLqVZs2Y0a9aMpUuX6qpk8RAmtfegsZUxr/1wolq2hblVUsbEdUfov2w/TWyMWT44hGci3bFoYKjr0oSosXwczdn2XCRQPUNgcWkZA77cz8+nsngrujlj2zTBxEhO8xC1n84CYFpaGps3b6Zx48aa+zZu3EhycjLJycnExcXx7LPPApCTk8PUqVPZs2cPiYmJTJ06ldzcXF2VLh5QfQN9Puvly9mcIt6rZm1hzucW0W7uLmbtSGFQsDMrhoTQ3cdBTv4WohL4NrJg6x9C4PFqEgJLyxRDv/6d749e5j8dm/J8Ow/M5BxfUUfoLABOmjSJadOm3dVGY/369QwdOhQ9PT1at25NXl4eFy9e5KeffiI6OhobGxusra2Jjo5m06ZNuipdPIRefo5EuVszoxq1hdl4/DLBn27nyKUCPuregjl9/PBxtNB1WULUKn63Q6AC2laDEFhWpnj620OsPJDB+DbuvPKIJ5bGsrdf1B06CYDr16/H2dmZwMDAu+5PT0/H1dVVc9vFxYX09PR73i9qHj09Peb29efqjRImfa/bC0JKyxRvbjxBt/8mYmtixJcDg5nU3gNrEyOd1iVEbeXXyIJtt0Ngu3nxOguBSikmfX+ULxLPM7KlK29FN5e/e1HnaG1fd+fOnbl06dJf7n/vvfd4//332bx5s1aWGxcXR1xcHACXLl0iIyNDK8v5s8zMzCpZTm1gB/T3tWXl7+kM8zXHx/7+F1hoY2wzrxUz9scUdp0v4NGmFrwU0QjvhorMy39dZ2s7WXe1S8b3bjbAqsc9eXzVKdrO2cHaJ7xoamP8QO/1oGP70c50Zu25RB9vKyYEW3Lraja1bArjSiHrrvZUh7HVWgD85Zdf/vb+w4cPk5KSotn7d+HCBUJCQkhMTMTZ2Zm0tDTNcy9cuICzszPOzs5s3br1rvs7dOjwt+8/ZswYxowZA0BYWBhOTk6V84EqoCqXVdPN7GfLhvd/5e2dV9g+Luofn1+ZY7vjbDYDvj5CTlExk6ObM7aNO3Zm9Svt/WsiWXe1S8b3bk5OsM3Ong7z4umz6jQ7xkbi7WD+gO/178b2w1+TmbXnEr38HJnZ21eauv8DWXe1R9djW+WHgP39/bly5Qqpqamkpqbi4uJCUlISjo6OxMbGsmzZMpRS7N69G0tLSxo1akRMTAybN28mNzeX3NxcNm/eTExMTFWXLiqRg3l9Xu/cjB0pOayqorYwSimm/XaajvMTMKxXj6VPBPFqZ886H/6E0IUAp/JzAkvLFO3mxnPysvYPB8/ZmcKrP56gq5cd0x/zkfAn6rRq1QewW7dueHh44OnpyejRo5k3bx4ANjY2vPnmm4SHhxMeHs5bb72FjY2NjqsVD+uF9h64WjXg1R+13xYmt+gWvRbv5f/9cJyOTW1ZPjiY/kFO1DeQXl9C6EqAkwVbnougpEzRVsshcHHiecavPUJ7D1tm9PTFQ3p7ijpO5wEwNTWVhg0bArcvEJg7lzNnznD48GHCwsI0zxsxYgSnT5/m9OnTDB8+XFflikpU30Cfz3r6cja7iPd/1V5bmP1peYTO2MGPx6/wUnsPlg4Moo2H7V1XoAshdCPQyVITAtvN004IXHUgg1GrDtK6sRWze/s98OFmIWoTnQdAUbf19m9EpLs1n247S1Zh5baFUUqxID6VyNm7KCou5b/9Apja1Qsnywc74VwIoR13QuCt0jLazYvn1JXCSnvvDccuM3h5EgGNLJjV2w9/J2nxJARIABQ6pqenx9w+t9vCVOI8wYU3S3jy69959rvDhLlYsnxQMEPDXaXDvxDVVKCTJVuejeRWaRlt5+6qlBD466lMHl+6j+Z2pszs5Ud4Y+tKqFSI2kECoNC5IGdLhoa5sOJABgfT8x/6/Y5fLqDlzB2s+D2dZyPdWD4kmE7N7eSQrxDVXJCzJb89Ux4C2z1kCIxPyaHn4r24WDZgVi8/2jW1rcRKhaj5JACKauGjHj40MKjH+HVHHup9vk66QPhnO7hccJO5ffz5qLsP7jZysrcQNUWwS3kIvFFSHgKTHyAEJl3Io9t/92BrYsTcPv50am6nhUqFqNkkAIpqwcG8Pq91asaOszl8e/DfN+++WVLKc98dYvDy32luZ8ryQcGMbu2GeQM55CtETRPsUn44+EZJ+eHgfxMCj14qoMvC3Rgb6jOvjx9dvCT8CfF3JACKauOF9h64WDbg1R+O/6u2MCnZRUTN3sX8+HMMDXVhxZAQurZwQL+eHPIVoqYKdrHktzshcF48pzP/OQSeybpG9MIEAOb39aebj4Oc+iHEPUgAFNVGA0N9ZvT05Ux2ER/+drpCr/nf0UuEzNjGqcxrTH/Mh5m9/fCylxYPQtQGIXdCYHEpbebGcybr3iEwLfc6nRYkUHSrlHl9/Yn1dZTwJ8R9SAAU1UrfgEZEuFkzfdsZsq/duufzSkrLeGXDcWIX7cXRvAFfDQpmQtsmWBkbVmG1Qght+2MIjJrz9yHw0tUbdFqQQNa1W8zp7UffACfqyREAIe5LAqCoVu60hcm/fu+2MBdvb+w/2nKavv6OrBwSTKyfIwb6sjoLURuFuFjy6zMR5XsC/xQCc4pu0SVuN2l515nZy5cBwc5y+ocQFSDfmKLaCXaxZEioCyt+T+dQxt1tYbacziJ4+nYSz+fxTlcvFjweQKCzlY4qFUJUlVBXK359JoLrt0Pg2axrFNwspWvcHk5eKeTTWB+eDHXFUP4RFKJC5C9FVEvTerTASL8e49eWt4UpK1O8/0synRckYGJUj2UDg/hPR08amtXXcaVCiKoS6mrFz0+3puhWKVFzdjFkTTK/p+fzYfcWDG/ZGCMD+UoToqLkr0VUS44WDXitkyfbz+aw8nAmjy1K5PWNJ+jS3I6vB4fSL8hZNvZC1EHhja355ZnyELg/4xpvx3jxdIQbDQz1dV2aEDWKNEkT1daLHZoyP+EcL24+j6G+Hq884smENu40krl8hajTwhtbs3tCGzYdOM3otk1kikchHoD81Yhqq4GhPl/0D+SldYf4T2cvHg90wlj+yxdCAC0czbEMcMCsvnyNCfEg5C9HVGsx3vb4PtkCZycn6eklhBBCVBI5iUpUe/X09CT8CSGEEJVIAqAQQgghRB0jAVAIIYQQoo6RACiEEEIIUcdIABRCCCGEqGMkAAohhBBC1DF6Siml6yK0pWHDhri7u1fJsjIzM7Gzs6uSZdU1MrbaJeOrXTK+2iNjq10yvtpTlWObmppKVlbWX+6v1QGwKoWFhbFv3z5dl1Erydhql4yvdsn4ao+MrXbJ+GpPdRhbOQQshBBCCFHHSAAUQgghhKhj9KdMmTJF10XUFqGhobouodaSsdUuGV/tkvHVHhlb7ZLx1R5dj62cAyiEEEIIUcfIIWAhhBBCiDpGAqAQQgghRB0jARB477338PX1JSAggKCgIPbs2fPQ7zllyhQ++eSTSqiu5tLT02PIkCGa2yUlJdjZ2dGjR49Kef+6OsbZ2dkEBQURFBSEo6Mjzs7Omtu3bt2qtOVs3bq10n5X1cWkSZP47LPPNLdjYmIYNWqU5vaLL77Ip59++o/vk5qaip+fn1ZqvMPMzEyr719V7rW+WllZ4ePjo/XlL1myhHHjxml9OdWZvr6+5ncQFBREamrqX57TrVs38vLy/nJ/Xd3O/tG/yQhLliwhIyPjoZfp7u7+t737KpOBVt+9BkhISGDDhg0kJSVRv359srKyKvVLtC4zNTXlyJEjXL9+HWNjY37++WecnZ11XVaNZ2try4EDB4DyjbOZmRkvvfSSjquqGaKioli1ahXPP/88ZWVlZGVlcfXqVc3j8fHxzJgxQ4cV1j73Wl9TU1Mf6h+MkpISDAzq/FdYhRgbG2t+B3+mlEIpxY8//ljFVdUM/zYjLFmyBD8/P5ycnCq8DF2ty3V+D+DFixdp2LAh9evXB8pnD3Fycrorfe/bt48OHToA5RuwESNG0KFDBzw8PJg1a5bmvd577z2aN29OmzZtOHnypOb+zz//nPDwcAIDA+nbty9FRUUUFBTQpEkTiouLAbh69epdt2uLbt268cMPPwCwYsUKBg4cqHksJyeHXr16ERAQQOvWrTl06BAgY/wghg0bxurVqzW3/7j36OOPPyY8PJyAgAAmT54MwLVr1+jevTuBgYH4+fnxzTffALBp0ya8vb0JCQlhzZo1mvdITEwkIiKC4OBgIiMjNWPfrl27u75Y2rRpw8GDB7X6WR9GZGQkCQkJABw9ehQ/Pz/Mzc3Jzc3l5s2bHD9+HD09Pdq3b09oaCgxMTFcvHgRgP379xMYGEhgYCBz587VvOeSJUvo06cPXbt2pVmzZvznP//RPLZ582YiIiIICQmhX79+FBYWAvDKK6/g4+NDQECAJrynpKQQERGBv78/b7zxhuY9CgsL6dSpEyEhIfj7+7N+/XoA3nrrrbv2Zr7++uvMnDlTSyOnHaWlpYwePRpfX1+6dOnC9evXAejQoYOmSW5WVpZmRqclS5YQGxvLI488QqdOnbh48SLt2rUjKCgIPz8/duzYAcDixYtp3rw5LVu2ZNeuXZrl/e9//6NVq1YEBwfTuXNnLl++TFlZGc2aNSMzMxOAsrIyPD09Nbdro9TUVLy8vBg6dCh+fn6kpaXd9Z0n29n/c6+M8PbbbxMeHo6fnx9jxoxBKcXq1avZt28fgwcPJigoiOvXr983Szz55JNERUXx5JNPkp2dTZcuXfD19WXUqFH88frcXr16ERoaiq+vL3FxcQAsWrSI559/XvOczz//nEmTJv27D6fquIKCAhUYGKiaNWumnn32WbV161allFJubm4qMzNTKaXU3r17Vfv27ZVSSk2ePFlFRESoGzduqMzMTGVjY6Nu3bql9u3bp/z8/NS1a9dUfn6+atq0qfr444+VUkplZWVplvf666+rWbNmKaWUGjZsmFq7dq1SSqmFCxeqF154oao+dpUwNTVVBw8eVH379lXXr19XgYGBasuWLap79+5KKaXGjRunpkyZopRS6tdff1WBgYFKKRnjf2Py5Mnq448/Vk899ZT69ttvNfebmpoqpZT66aef1OjRo1VZWZkqLS1V3bt3V9u2bVOrV69Wo0aN0jw/Ly9PXb9+Xbm4uKhTp06psrIy1a9fP83vKj8/XxUXFyullPr5559Vnz59lFJKLVmyRE2cOFEppdTJkydVaGholXzuh+Hu7q7OnTunFixYoObPn6/eeOMN9cMPP6idO3eq1q1bq4iICHXlyhWllFIrV65Uw4cPV0op5e/vr7Zt26aUUuqll15Svr6+SimlFi9erJo0aaIZw8aNG6vz58+rzMxM1bZtW1VYWKiUUurDDz9UU6dOVVlZWap58+aqrKxMKaVUbm6uUkqpxx57TC1dulQppdScOXM0v8Pi4mKVn5+vlFIqMzNTNW3aVJWVlamUlBQVHByslFKqtLRUeXh43PV3UB3dWV+VUiolJUXp6+ur33//XSmlVL9+/dSXX36plFKqffv2au/evUqp8s/s5uamlCofa2dnZ5Wdna2UUuqTTz5R7777rlJKqZKSEnX16lWVkZGhXF1d1ZUrV9TNmzdVZGSkGjt2rFJKqZycHM24f/7555rtwZQpU9SMGTOUUuV/M3fW79qiXr16KjAwUAUGBqpevXqplJQUpaenpxISEjTPufOdJ9vZu90rI9xZB5VSasiQIer7779XSt297ip1/ywREhKiioqKlFJKjR8/Xk2dOlUppdSGDRsUoHndnWUVFRUpX19flZWVpQoKCpSHh4e6deuWUkqpiIgIdejQoX/12er8HkAzMzP2799PXFwcdnZ2DBgwgCVLltz3Nd27d6d+/fo0bNgQe3t7Ll++zI4dO+jduzcmJiZYWFgQGxuref6RI0do27Yt/v7+LF++nKNHjwIwatQoFi9eDJT/xzp8+HCtfU5dCQgIIDU1lRUrVtCtW7e7Htu5cydPPvkkAI888gjZ2dmaw3EyxpVj8+bNbN68meDgYEJCQjhx4gTJycn4+/vz888/8//+3/9jx44dWFpacuLECZo0aUKzZs3+cv5mfn4+/fr1w8/Pj0mTJmnGt1+/fmzYsIHi4mIWLVrEsGHDdPRJKy4yMpL4+Hji4+OJiIggIiJCc9vZ2ZkjR44QHR1NUFAQ7777LhcuXCAvL4+8vDzatWsHoFlv7+jUqROWlpY0aNAAHx8fzp07x+7duzl27BhRUVEEBQWxdOlSzp07p3neyJEjWbNmDSYmJgDs2rVLs4f8j++vlOK1114jICCAzp07k56ezuXLl3F3d8fW1pbff/9d8zu2tbWtolGsHE2aNCEoKAgo74n2d+em/Vl0dDQ2NjYAhIeHs3jxYqZMmcLhw4cxNzdnz549dOjQATs7O4yMjBgwYIDmtRcuXCAmJgZ/f38+/vhjzXo8YsQIli1bBpTvWalt24k7h4APHDjA2rVrAXBzc6N169Z/ea5sZ+92r4ywZcsWWrVqhb+/P7/99ptmLP6N2NhYjI2NAdi+fbtmm9u9e3esra01z5s1axaBgYG0bt2atLQ0kpOTMTMz45FHHmHDhg2cOHGC4uJi/P39/9Xy5QQKyk+Q7dChAx06dMDf35+lS5diYGBAWVkZADdu3Ljr+Xd2Bd95bUlJyX3ff9iwYaxbt47AwECWLFnC1q1bgfLzkVJTU9m6dSulpaVaP6lcV2JjY3nppZfYunUr2dnZFXqNjPG/88f1taysTHOOilKKV199laeffvovr0lKSuLHH3/kjTfeoFOnTndt6P/szTffpGPHjqxdu5bU1FTNYQwTExOio6NZv349q1atYv/+/ZX/4SpZVFQU8fHxHD58GD8/P1xdXZk+fToWFhZ06NCB9PR0zWHiO/7u5Pg/+rv1VSlFdHQ0K1as+MvzExMT+fXXX1m9ejVz5szht99+A8ovnPqz5cuXk5mZyf79+zE0NMTd3V2zTRo1ahRLlizh0qVLjBgx4l+Pha79edzuHAK+3/bX1NRU83O7du3Yvn07P/zwA8OGDeOFF17AwsLinssbP348L7zwArGxsWzdupU78yC4urri4ODAb7/9RmJiIsuXL6+sj1ht/XEcK6qubmf/nBEWLlzIoUOH2LdvH66urkyZMuUv6+kdFV2X72Xr1q388ssvJCQkYGJiQocOHe76+3///ffx9vZ+oNBd5/cAnjx5kuTkZM3tAwcO4Obmhru7u+bL7LvvvvvH92nXrh3r1q3j+vXrFBQU8L///U/zWEFBAY0aNaK4uPgvG5ahQ4cyaNCgWvUf05+NGDGCyZMn/+W/k7Zt22rGY+vWrTRs2PC+G28Z43v74/r6/fffa86/iYmJYdGiRZpzz9LT07ly5QoZGRmYmJgwZMgQXn75ZZKSkvD29iY1NZUzZ84A3BVc8vPzNRfw/HkP+ahRo5gwYQLh4eF3/ddaXUVGRrJhwwZsbGzQ19fHxsaGvLw8EhISGDhwIJmZmZoAWFxczNGjR7GyssLKyoqdO3cCVCggtG7dml27dnH69Gmg/LzLU6dOUVhYSH5+Pt26dWPGjBmacyajoqJYuXLlX94/Pz8fe3t7DA0N2bJlC+fOndM81rt3bzZt2sTevXuJiYmpnAGqBv64Pv/x3NY/O3fuHA4ODowePZpRo0aRlJREq1at2LZtG9nZ2RQXF/Ptt99qnv/H9Xjp0qV3vdeoUaMYMmQI/fr1Q19fXwufqmaQ7ezd/i4jeHl5AeXnAxYWFt61jpqbm1NQUKC5XdEs0a5dO77++msANm7cSG5uLlC+zlpbW2NiYsKJEyfYvXu35jWtWrUiLS2Nr7/++q7z6yuqzu8BLCwsZPz48eTl5WFgYICnpydxcXEcP36ckSNH8uabb2r2dtxPSEgIAwYMIDAwEHt7e8LDwzWPvfPOO7Rq1Qo7OztatWp118oxePBg3njjjQf65dUULi4uTJgw4S/337nYIyAgABMTk79skP9MxvjeRo8eTc+ePQkMDKRr166a/yy7dOnC8ePHiYiIAMoPZ3z11VecPn2al19+mXr16mFoaMj8+fNp0KABcXFxdO/eHRMTE9q2basZx//85z889dRTvPvuu3Tv3v2uZYeGhmJhYVFjNvz+/v5kZWUxaNCgu+4rLCzE3t6e1atXM2HCBPLz8ykpKeH555/H19eXxYsXM2LECPT09OjSpcs/LsfOzo4lS5YwcOBAbt68CcC7776Lubk5PXv25MaNGyilNG1nZs6cyaBBg/joo4/o2bOn5n0GDx7MY489hr+/P2FhYXh7e2seMzIyomPHjlhZWdWq0PLSSy/Rv39/zfp4L1u3buXjjz/G0NAQMzMzli1bRqNGjZgyZQoRERFYWVlpDjFD+TanX79+WFtb88gjj5CSkqJ5LDY2luHDh9eY9VhbZDt7t3tlBCsrK/z8/HB0dLxrjIYNG8YzzzyDsbExCQkJTJ48uUJZYvLkyQwcOBBfX18iIyNp3LgxAF27dmXBggW0aNECLy+vvxy279+/PwcOHHigf75lKjgdW716NevXr+fLL7/UdSm1loyxdmVkZNChQwdOnDhBvXp1/qBClSorKyMkJIRvv/2WZs2a6bqcGm3fvn1MmjRJcyWx+HdkO6sbPXr0YNKkSXTq1Olfv7bO7wHUpfHjx7Nx40bpv6RFMsbatWzZMl5//XU+/fRTCX9V7NixY/To0YPevXtL+HtIH374IfPnz68T5/5pg2xnq15eXh4tW7YkMDDwgcIfyB5AIYQQQog6R/5lF0IIIYSoYyQACiGEEELUMRIAhRBCCCHqGAmAQog6QV9fn6CgIHx9fQkMDGT69OmaBq3a8vLLL+Pr68vLL7+s1eWkpqbWuua7QgjtkquAhRB1wp3psACuXLnCoEGDuHr1KlOnTtXaMuPi4sjJyalVPfqEELWD7AEUQtQ59vb2xMXFMWfOHJRSpKam0rZtW0JCQggJCSE+Ph4on91g3bp1mtcNHjyY9evX3/VeSilefvll/Pz88Pf355tvvgHKGwsXFhYSGhqque8Of39/8vLyUEpha2urmYd26NCh/Pzzz5SWlvLyyy8THh5OQEAACxcu1Lz2448/1tw/efLkv3y2s2fPEhwczN69eytnsIQQtZLsARRC1EkeHh6UlpZy5coV7O3t+fnnn2nQoAHJyckMHDiQffv2MXLkSGbMmEGvXr3Iz88nPj7+LzPWrFmzhgMHDnDw4EGysrIIDw+nXbt2fP/995iZmWn2Ov5RVFQUu3btws3NDQ8PD3bs2MHQoUNJSEhg/vz5fPHFF1haWrJ3715u3rxJVFQUXbp0ITk5meTkZBITE1FKERsby/bt2zWzBpw8eZInnniCJUuWEBgYWCXjKISomSQACiHqvOLiYsaNG8eBAwfQ19fn1KlTALRv357nnnuOzMxMvvvuO/r27YuBwd2bzZ07dzJw4ED09fVxcHCgffv27N27l9jY2Hsur23btmzfvh03NzeeffZZ4uLiSE9Px9raGlNTUzZv3syhQ4c0c4zm5+eTnJzM5s2b2bx5M8HBwUD5NFXJyck0btyYzMxMevbsyZo1a/Dx8dHSSAkhagsJgEKIOuns2bPo6+tjb2/P1KlTcXBw4ODBg5SVldGgQQPN84YOHcpXX33FypUrWbx4caUsu0sTOCEAAAH5SURBVF27dsydO5fz58/z3nvvsXbtWlavXk3btm2B8sPKs2fPJiYm5q7X/fTTT7z66qs8/fTTd92fmpqKpaUljRs3ZufOnRIAhRD/SM4BFELUOZmZmTzzzDOMGzcOPT098vPzadSoEfXq1ePLL7+ktLRU89xhw4bx2WefAfxtsGrbti3ffPMNpaWlZGZmsn37dlq2bHnf5bu6upKVlUVycjIeHh60adOGTz75hHbt2gEQExPD/PnzKS4uBuDUqVNcu3aNmJgYFi1aRGFhIQDp6elcuXIFACMjI9auXcuyZcv4+uuvH36QhBC1muwBFELUCdevXycoKIji4mIMDAx48skneeGFFwB47rnn6Nu3L8uWLaNr166YmppqXufg4ECLFi3o1avX375v7969SUhIIDAwED09PaZNm4ajo+M/1tOqVStN0Gzbti2vvvoqbdq0AWDUqFGkpqYSEhKCUgo7OzvWrVtHly5dOH78OBEREQCYmZnx1Vdfaa4yNjU1ZcOGDURHR2NmZnbfw9BCiLpN5gIWQoj7KCoqwt/fn6SkJCwtLXVdjhBCVAo5BCyEEPfwyy+/0KJFC8aPHy/hTwhRq8geQCGEEEKIOkb2AAohhBBC1DESAIUQQggh/n+7dSAAAAAAIMjfepCLohkBBACYEUAAgBkBBACYCV3S2njLa8CsAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "eBujWwvCturi", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/pytorch_basics.ipynb b/pytorch_basics.ipynb new file mode 100644 index 0000000..6b7352c --- /dev/null +++ b/pytorch_basics.ipynb @@ -0,0 +1,374 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "udacity pytorch.ipynb", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yViblkPzZuoQ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import torch\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "CD0YIoKPZ7Ob", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def activation(x):\n", + " return 1/(1+torch.exp(-x))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "G3nTv5zNaKXc", + "colab_type": "code", + "colab": {} + }, + "source": [ + "##Generate some data\n", + "torch.manual_seed(7) #set the random seed so things are predictable\n", + "\n", + "#Feature are 5 random normal variables\n", + "features = torch.randn(1, 5)\n", + "#true wieghts for our data, random variable again\n", + "weights = torch.randn_like(features)\n", + "#and a true bias term\n", + "bias = torch.randn((1, 1))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0-N_vHBobGms", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "874f07ea-e0c7-46c3-a9d6-2aa270279b70" + }, + "source": [ + "#solution\n", + "y = activation(torch.sum(features * weights) + bias)\n", + "print(y)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor([[0.1595]])\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nZ2swlVsbbzx", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "5e6e6529-574b-48fe-e6e6-e1f07060fdfd" + }, + "source": [ + "weights = weights.view(5, 1)\n", + "ym = activation(torch.mm(features, weights) + bias)\n", + "print(ym)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor([[0.1595]])\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qLdTvgoaclKv", + "colab_type": "code", + "colab": {} + }, + "source": [ + "#example with hidden layers\n", + "##Generate some data\n", + "torch.manual_seed(7) #set the random seed so things are predictable\n", + "\n", + "#Feature are 3 random normal variables\n", + "features = torch.randn((1, 3))\n", + "\n", + "#define the size of each layer in our network\n", + "n_inputs = features.shape[1] #Number of input units, must watch number of input features\n", + "n_hidden = 2 #Number of hidden layers \n", + "n_output = 1 #Number of output layers\n", + "\n", + "#weights of inputs to hidden layers\n", + "w1 = torch.randn(n_inputs, n_hidden)\n", + "#Weights of inputs to output layers\n", + "w2 = torch.randn(n_hidden, n_output)\n", + "\n", + "#and bias terms for hidden and output layers\n", + "B1 = torch.randn((1, n_hidden))\n", + "B2 = torch.randn((1, n_output))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "LqSl9ykie6vq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "3df058db-43f7-45b5-ba09-5c4825a63cd3" + }, + "source": [ + "#solution\n", + "h = activation(torch.mm(features, w1) + B1)\n", + "output = activation(torch.mm(h, w2) + B2)\n", + "print(output)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "tensor([[0.3171]])\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GQzD9yseffi4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "ecd567d2-85f5-49c8-9df0-4e08befc3e92" + }, + "source": [ + "#Numpy to Torch and back\n", + "a = np.random.rand(4, 3)\n", + "a" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0.17630986, 0.26056779, 0.66111429],\n", + " [0.91580259, 0.52933085, 0.5894325 ],\n", + " [0.90691957, 0.82928285, 0.97077456],\n", + " [0.60524439, 0.92903578, 0.19563872]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FelrMtSHg54k", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "14cdeb23-d6ae-4db2-d6bd-c213f64d5050" + }, + "source": [ + "b = torch.from_numpy(a)\n", + "b" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensor([[0.1763, 0.2606, 0.6611],\n", + " [0.9158, 0.5293, 0.5894],\n", + " [0.9069, 0.8293, 0.9708],\n", + " [0.6052, 0.9290, 0.1956]], dtype=torch.float64)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "i2-i00f1hHYA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "b997bc53-b893-4f57-ffb0-5f4e298d9eaf" + }, + "source": [ + "b.numpy()" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0.17630986, 0.26056779, 0.66111429],\n", + " [0.91580259, 0.52933085, 0.5894325 ],\n", + " [0.90691957, 0.82928285, 0.97077456],\n", + " [0.60524439, 0.92903578, 0.19563872]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "A__w2Y7qhwHj", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "04453010-23c8-49b0-9a4f-b95f20b5cd8d" + }, + "source": [ + "#Multiply Pytorch Tensor by 2, in place\n", + "b.mul_(2)" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensor([[0.3526, 0.5211, 1.3222],\n", + " [1.8316, 1.0587, 1.1789],\n", + " [1.8138, 1.6586, 1.9415],\n", + " [1.2105, 1.8581, 0.3913]], dtype=torch.float64)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GqZHMS9xiDls", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "1199f352-88b9-4867-d926-6bf6ae4a7c45" + }, + "source": [ + "#numpy array matches new values from Tensor\n", + "a" + ], + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0.35261973, 0.52113558, 1.32222859],\n", + " [1.83160518, 1.0586617 , 1.17886499],\n", + " [1.81383915, 1.6585657 , 1.94154912],\n", + " [1.21048878, 1.85807156, 0.39127744]])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 24 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bgYu_tXIiLqY", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/tensorflow_model/mnist_convnet_keras_tensorflow2.py b/tensorflow_model/mnist_convnet_keras_tensorflow2.py new file mode 100644 index 0000000..8da468e --- /dev/null +++ b/tensorflow_model/mnist_convnet_keras_tensorflow2.py @@ -0,0 +1,116 @@ +# Python 3.6.0 +# tensorflow 1.1.0 +# Keras 2.0.4 + +import os +import os.path as path + +import keras +from keras.datasets import mnist +from keras.models import Sequential +from keras.layers import Input, Dense, Dropout, Flatten +from keras.layers import Conv2D, MaxPooling2D +from keras import backend as K + +import tensorflow as tf +from tensorflow.python.tools import freeze_graph +from tensorflow.python.tools import optimize_for_inference_lib + +MODEL_NAME = 'mnist_convnet' +EPOCHS = 1 +BATCH_SIZE = 128 + + +def load_data(): + (x_train, y_train), (x_test, y_test) = mnist.load_data() + x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) + x_test = x_test.reshape(x_test.shape[0], 28, 28, 1) + x_train = x_train.astype('float32') + x_test = x_test.astype('float32') + x_train /= 255 + x_test /= 255 + y_train = keras.utils.to_categorical(y_train, 10) + y_test = keras.utils.to_categorical(y_test, 10) + return x_train, y_train, x_test, y_test + + +def build_model(): + model = Sequential() + model.add(Conv2D(filters=64, kernel_size=3, strides=1, \ + padding='same', activation='relu', \ + input_shape=[28, 28, 1])) + # 28*28*64 + model.add(MaxPooling2D(pool_size=2, strides=2, padding='same')) + # 14*14*64 + + model.add(Conv2D(filters=128, kernel_size=3, strides=1, \ + padding='same', activation='relu')) + # 14*14*128 + model.add(MaxPooling2D(pool_size=2, strides=2, padding='same')) + # 7*7*128 + + model.add(Conv2D(filters=256, kernel_size=3, strides=1, \ + padding='same', activation='relu')) + # 7*7*256 + model.add(MaxPooling2D(pool_size=2, strides=2, padding='same')) + # 4*4*256 + + model.add(Flatten()) + model.add(Dense(1024, activation='relu')) + #model.add(Dropout(0.5)) + model.add(Dense(10, activation='softmax')) + return model + + +def train(model, x_train, y_train, x_test, y_test): + model.compile(loss=keras.losses.categorical_crossentropy, \ + optimizer=keras.optimizers.Adadelta(), \ + metrics=['accuracy']) + + model.fit(x_train, y_train, \ + batch_size=BATCH_SIZE, \ + epochs=EPOCHS, \ + verbose=1, \ + validation_data=(x_test, y_test)) + + +def export_model(saver, model, input_node_names, output_node_name): + tf.io.write_graph(K.get_session().graph_def, 'out', \ + MODEL_NAME + '_graph.pbtxt') + + saver.save(K.get_session(), 'out/' + MODEL_NAME + '.chkp') + + freeze_graph.freeze_graph('out/' + MODEL_NAME + '_graph.pbtxt', None, \ + False, 'out/' + MODEL_NAME + '.chkp', output_node_name, \ + "save/restore_all", "save/Const:0", \ + 'out/frozen_' + MODEL_NAME + '.pb', True, "") + + input_graph_def = tf.compat.v1.GraphDef() + with tf.io.gfile.GFile('out/frozen_' + MODEL_NAME + '.pb', "rb") as f: + input_graph_def.ParseFromString(f.read()) + + output_graph_def = optimize_for_inference_lib.optimize_for_inference( + input_graph_def, input_node_names, [output_node_name], + tf.float32.as_datatype_enum) + + with tf.compat.v1.gfile.FastGFile('out/opt_' + MODEL_NAME + '.pb', "wb") as f: + f.write(output_graph_def.SerializeToString()) + + print("graph saved!") + + +def main(): + if not path.exists('out'): + os.mkdir('out') + + x_train, y_train, x_test, y_test = load_data() + + model = build_model() + + train(model, x_train, y_train, x_test, y_test) + + export_model(tf.compat.v1.train.Saver(), model, ["conv2d_1_input"], "dense_2/Softmax") + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tensorflow_model/mnist_convnet_tensorflow2.py b/tensorflow_model/mnist_convnet_tensorflow2.py new file mode 100644 index 0000000..0fb17e0 --- /dev/null +++ b/tensorflow_model/mnist_convnet_tensorflow2.py @@ -0,0 +1,140 @@ +# Python 3.6.0 +# tensorflow 2.0 + +import os +import os.path as path + +import tensorflow as tf +from tensorflow.python.tools import freeze_graph +from tensorflow.python.tools import optimize_for_inference_lib + +from tensorflow.examples.tutorials.mnist import input_data + +MODEL_NAME = 'mnist_convnet' +NUM_STEPS = 3000 +BATCH_SIZE = 16 + +def model_input(input_node_name, keep_prob_node_name): + x = tf.compat.v1.placeholder(tf.float32, shape=[None, 28*28], name=input_node_name) + keep_prob = tf.compat.v1.placeholder(tf.float32, name=keep_prob_node_name) + y_ = tf.compat.v1.placeholder(tf.float32, shape=[None, 10]) + return x, keep_prob, y_ + +def build_model(x, keep_prob, y_, output_node_name): + x_image = tf.reshape(x, [-1, 28, 28, 1]) + # 28*28*1 + + conv1 = tf.compat.v1.layers.conv2d(x_image, 64, 3, 1, 'same', activation=tf.nn.relu) + # 28*28*64 + pool1 = tf.compat.v1.layers.max_pooling2d(conv1, 2, 2, 'same') + # 14*14*64 + + conv2 = tf.compat.v1.layers.conv2d(pool1, 128, 3, 1, 'same', activation=tf.nn.relu) + # 14*14*128 + pool2 = tf.compat.v1.layers.max_pooling2d(conv2, 2, 2, 'same') + # 7*7*128 + + conv3 = tf.compat.v1.layers.conv2d(pool2, 256, 3, 1, 'same', activation=tf.nn.relu) + # 7*7*256 + pool3 = tf.compat.v1.layers.max_pooling2d(conv3, 2, 2, 'same') + # 4*4*256 + + flatten = tf.reshape(pool3, [-1, 4*4*256]) + fc = tf.compat.v1.layers.dense(flatten, 1024, activation=tf.nn.relu) + dropout = tf.nn.dropout(fc, 1 - (keep_prob)) + logits = tf.compat.v1.layers.dense(dropout, 10) + outputs = tf.nn.softmax(logits, name=output_node_name) + + # loss + loss = tf.reduce_mean( + input_tensor=tf.nn.softmax_cross_entropy_with_logits(labels=tf.stop_gradient(y_), logits=logits)) + + # train step + train_step = tf.compat.v1.train.AdamOptimizer(1e-4).minimize(loss) + + # accuracy + correct_prediction = tf.equal(tf.argmax(input=outputs, axis=1), tf.argmax(input=y_, axis=1)) + accuracy = tf.reduce_mean(input_tensor=tf.cast(correct_prediction, tf.float32)) + + tf.compat.v1.summary.scalar("loss", loss) + tf.compat.v1.summary.scalar("accuracy", accuracy) + merged_summary_op = tf.compat.v1.summary.merge_all() + + return train_step, loss, accuracy, merged_summary_op + +def train(x, keep_prob, y_, train_step, loss, accuracy, + merged_summary_op, saver): + print("training start...") + + mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) + + init_op = tf.compat.v1.global_variables_initializer() + + with tf.compat.v1.Session() as sess: + sess.run(init_op) + + tf.io.write_graph(sess.graph_def, 'out', + MODEL_NAME + '.pbtxt', True) + + # op to write logs to Tensorboard + summary_writer = tf.compat.v1.summary.FileWriter('logs/', + graph=tf.compat.v1.get_default_graph()) + + for step in range(NUM_STEPS): + batch = mnist.train.next_batch(BATCH_SIZE) + if step % 100 == 0: + train_accuracy = accuracy.eval(feed_dict={ + x: batch[0], y_: batch[1], keep_prob: 1.0}) + print('step %d, training accuracy %f' % (step, train_accuracy)) + _, summary = sess.run([train_step, merged_summary_op], + feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + summary_writer.add_summary(summary, step) + + saver.save(sess, 'out/' + MODEL_NAME + '.chkp') + + test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images, + y_: mnist.test.labels, + keep_prob: 1.0}) + print('test accuracy %g' % test_accuracy) + + print("training finished!") + +def export_model(input_node_names, output_node_name): + freeze_graph.freeze_graph('out/' + MODEL_NAME + '.pbtxt', None, False, + 'out/' + MODEL_NAME + '.chkp', output_node_name, "save/restore_all", + "save/Const:0", 'out/frozen_' + MODEL_NAME + '.pb', True, "") + + input_graph_def = tf.compat.v1.GraphDef() + with tf.io.gfile.GFile('out/frozen_' + MODEL_NAME + '.pb', "rb") as f: + input_graph_def.ParseFromString(f.read()) + + output_graph_def = optimize_for_inference_lib.optimize_for_inference( + input_graph_def, input_node_names, [output_node_name], + tf.float32.as_datatype_enum) + + with tf.compat.v1.gfile.FastGFile('out/opt_' + MODEL_NAME + '.pb', "wb") as f: + f.write(output_graph_def.SerializeToString()) + + print("graph saved!") + +def main(): + if not path.exists('out'): + os.mkdir('out') + + input_node_name = 'input' + keep_prob_node_name = 'keep_prob' + output_node_name = 'output' + + x, keep_prob, y_ = model_input(input_node_name, keep_prob_node_name) + + train_step, loss, accuracy, merged_summary_op = build_model(x, keep_prob, + y_, output_node_name) + saver = tf.compat.v1.train.Saver() + + train(x, keep_prob, y_, train_step, loss, accuracy, + merged_summary_op, saver) + + export_model([input_node_name, keep_prob_node_name], output_node_name) + +if __name__ == '__main__': + main() \ No newline at end of file