diff --git a/JediMasters/DWave Challenge/CDL_Hackathon_2020_FoodBankDeliveryScheduling.ipynb b/JediMasters/DWave Challenge/CDL_Hackathon_2020_FoodBankDeliveryScheduling.ipynb
new file mode 100644
index 0000000..65a7780
--- /dev/null
+++ b/JediMasters/DWave Challenge/CDL_Hackathon_2020_FoodBankDeliveryScheduling.ipynb	
@@ -0,0 +1,377 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "CDL_Hackathon_2020-JediMasters.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "authorship_tag": "ABX9TyOVkbtDsT8ddCrTD9WjVH0E",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/tina-seb/Hackathon2020/blob/master/JediMasters/CDL_Hackathon_2020_FoodBankDeliveryScheduling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7-W1uufjf2Yp",
+        "colab_type": "text"
+      },
+      "source": [
+        "# **Food Bank Delivery Route Scheduler**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ye5iy8A7Z-Pa",
+        "colab_type": "text"
+      },
+      "source": [
+        "**Problem** : Food banks use volunteers on a daily basis to deliver food and groceries to the homebound. Currently they depend on manual scheduling and/or classical software to schedule routes for a variable number of volunteers, which may change during the day.\n",
+        "\n",
+        "**Challenges** : The route maps need to be generated quickly if a volunteer drops off, and also need to be the most cost-efficient and fastest route possible.\n",
+        "\n",
+        "**Proposed Solution** : We implement a variation of the Multiple Travelling Salesman algorithm using DWave Leap Hybrid Solver and QBSolve to generate the most efficient routes for a variable number of volunteers, by minimizing the cost as energy and total mileage.\n",
+        "\n",
+        "**Constraints** : \n",
+        "1) All volunteeers must start and end at the same food bank location.\n",
+        "2) Each volunteer should only visit one home for delivery.\n",
+        "3) If a volunteer does not show up or cancels, another volunteer should be able to pick up his or her route as an extension, without having to reschedule every other route.\n",
+        "4) The cost of each individual route as well as the cost of the entire schedule should be minimized."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LWSG8rcob1ug",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from dwave_qbsolv import QBSolv\n",
+        "from dwave.system import LeapHybridSampler\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import re"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cRJL6Pohc8_r",
+        "colab_type": "text"
+      },
+      "source": [
+        "The number of houses with homebound clients served by a food bank changes every day. This is currently defined as a variable and can be changed into an input variable. The home addresses and the distance information of each address needs to be provided as input files to the algorithm."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nGR7Wx-jcm-6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Number of houses in our food delivery problem\n",
+        "Total_Number_Houses = 48\n",
+        "Number_Volunteers = 8\n",
+        "Number_Routes = (int)(Total_Number_Houses/Number_Volunteers)\n",
+        "\n",
+        "# Tunable parameters. \n",
+        "A = 8500\n",
+        "B = 1\n",
+        "chainstrength = 4500\n",
+        "numruns = 100"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rAiho6obdpHA",
+        "colab_type": "text"
+      },
+      "source": [
+        "We initialize and construct a distance matrix where the row and column represent a house address and each entry is a distance in metres."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Iol81rlvd2Wf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "## Constructing the distance matrix\n",
+        "D = [[0 for z in range(Total_Number_Houses)] for y in range(Total_Number_Houses)]\n",
+        "\n",
+        "# Input file containing inter-house distances for all counties\n",
+        "fn = \"FoodBankClientDeliveryData.txt\"\n",
+        "\n",
+        "# check that the user has provided input file\n",
+        "try:\n",
+        "  with open(fn, \"r\") as myfile:\n",
+        "    distance_text = myfile.readlines()\n",
+        "    myfile.close()\n",
+        "except IOError:\n",
+        "  print(\"Input inter-house distance file missing\")\n",
+        "  exit(1)\n",
+        "\n",
+        "# Extract the distances from the input file. \n",
+        "for i in distance_text:\n",
+        "  if re.search(\"^between\", i):\n",
+        "    m = re.search(\"^between_(\\d+)_(\\d+) = (\\d+)\", i)\n",
+        "    housea = int(m.group(1))\n",
+        "    houseb = int(m.group(2))\n",
+        "    D[housea][houseb] = D[houseb][housea] = int(m.group(3))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VKVAwVx3d8X9",
+        "colab_type": "text"
+      },
+      "source": [
+        "We then construct a QUBO (Quadratic Unconstrained Binary Optimization) to feed into the various DWave samplers. We define the objective and constraints based on our preidentified requirements here."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3etPBbyEeoi-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "\n",
+        "# Function to compute index in QUBO for variable \n",
+        "def return_QUBO_Index(a, b):\n",
+        "    return (a)*Total_Number_Houses+(b)\n",
+        "\n",
+        "## Creating the QUBO\n",
+        "# Start with an empty QUBO\n",
+        "Q = {}\n",
+        "for i in range(Total_Number_Houses*Total_Number_Houses):\n",
+        "    for j in range(Total_Number_Houses*Total_Number_Houses):\n",
+        "        Q.update({(i,j): 0})\n",
+        "\n",
+        "# Constraint that each row has exactly one 1, constant = N*A\n",
+        "for v in range(Total_Number_Houses):\n",
+        "    for j in range(Total_Number_Houses):\n",
+        "        Q[(return_QUBO_Index(v,j), return_QUBO_Index(v,j))] += -1*A\n",
+        "        for k in range(j+1, Total_Number_Houses):\n",
+        "            Q[(return_QUBO_Index(v,j), return_QUBO_Index(v,k))] += 2*A\n",
+        "            Q[(return_QUBO_Index(v,k), return_QUBO_Index(v,j))] += 2*A\n",
+        "\n",
+        "# Constraint that each col has exactly one 1\n",
+        "for j in range(Total_Number_Houses):\n",
+        "    for v in range(Total_Number_Houses):\n",
+        "        Q[(return_QUBO_Index(v,j), return_QUBO_Index(v,j))] += -1*A\n",
+        "        for w in range(v+1,Total_Number_Houses):\n",
+        "            Q[(return_QUBO_Index(v,j), return_QUBO_Index(w,j))] += 2*A\n",
+        "            Q[(return_QUBO_Index(w,j), return_QUBO_Index(v,j))] += 2*A\n",
+        "\n",
+        "# Objective that minimizes distance\n",
+        "for u in range(Total_Number_Houses):\n",
+        "    for v in range(Total_Number_Houses):\n",
+        "        if u!=v:\n",
+        "            for j in range(Total_Number_Houses):\n",
+        "                Q[(return_QUBO_Index(u,j), return_QUBO_Index(v,(j+1)%Total_Number_Houses))] += B*D[u][v]\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O2t7kZISevtf",
+        "colab_type": "text"
+      },
+      "source": [
+        "We have two approaches below : A classical solver approach using QBSolv and a Quantum solver approach using Leap Hybrid Sampler. No significant timing differences were found between the two solutions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WWSWT04AfRZO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Run the QUBO using qbsolv (classically solving)\n",
+        "#resp = QBSolv().sample_qubo(Q)\n",
+        "\n",
+        "# Use LeapHybridSampler() for faster QPU access\n",
+        "sampler = LeapHybridSampler()\n",
+        "resp = sampler.sample_qubo(Q)\n",
+        "\n",
+        "# First solution is the lowest energy solution found\n",
+        "sample = next(iter(resp))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SGFuyufYfTrS",
+        "colab_type": "text"
+      },
+      "source": [
+        "Display the results obtained as a schedule of route maps for the required number of routes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Q0CaOlx0Z2D7",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Display energy for best solution found\n",
+        "print('Energy: ', next(iter(resp.data())).energy)\n",
+        "\n",
+        "# Print route for solution found\n",
+        "route = [-1]*Total_Number_Houses\n",
+        "for node in sample:\n",
+        "    if sample[node]>0:\n",
+        "        j = node%Total_Number_Houses\n",
+        "        v = (node-j)/Total_Number_Houses\n",
+        "        route[j] = int(v)\n",
+        "\n",
+        "# Compute and display total mileage\n",
+        "mileage = 0\n",
+        "for i in range(Total_Number_Houses):\n",
+        "    mileage+=D[route[i]][route[(i+1)%Total_Number_Houses]]\n",
+        "\n",
+        "print('Mileage: ', mileage)\n",
+        "\n",
+        "# Print route:\n",
+        "\n",
+        "houses = [',']*Total_Number_Houses\n",
+        "cities = [',']*Total_Number_Houses\n",
+        "\n",
+        "with open('FoodBankHouseLookup.txt', \"r\") as myfile:\n",
+        "    address_text = myfile.readlines()\n",
+        "    myfile.close()\n",
+        "\n",
+        "for i in address_text:\n",
+        "    index, city, house = i.split(',')\n",
+        "    cities[int(index)] = city.rstrip()\n",
+        "    houses[int(index)] = house.rstrip()\n",
+        "\n",
+        "output = open('Food_Bank_Delivery.route.offline', 'w')\n",
+        "\n",
+        "r_no = 1\n",
+        "for i in range(Total_Number_Houses):\n",
+        "    if(i % Number_Routes == 0):\n",
+        "        output.write('\\n' + 'Route ' + str(r_no) + '\\n')\n",
+        "        r_no = r_no + 1\n",
+        "    output.write(houses[route[i]] + ',' + cities[route[i]] + '\\n')\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gB9pntwGfqFD",
+        "colab_type": "text"
+      },
+      "source": [
+        "The route schedule output is as follows :\n",
+        "\n",
+        "\n",
+        "Route 1\n",
+        "9980 Brookside Dr.,Columbus OH\n",
+        "417 Theatre Lane,Columbus OH\n",
+        "26 Walnut Drive,Columbus OH\n",
+        "7085 Myers St.,Columbus OH\n",
+        "821 Garfield Rd.,Columbus OH\n",
+        "7297 North Park Road,Columbus OH\n",
+        "\n",
+        "Route 2\n",
+        "16 Park Ave.,Columbus OH\n",
+        "51 Lexington Dr.,Columbus OH\n",
+        "31 York Drive,Columbus OH\n",
+        "349 W. Rockaway Drive,Columbus OH\n",
+        "19 Center Street,Columbus OH\n",
+        "7750 John Drive,Columbus OH\n",
+        "\n",
+        "Route 3\n",
+        "11 High Noon St.,Columbus OH\n",
+        "69 Buckingham St.,Columbus OH\n",
+        "8891 Vine Street,Columbus OH\n",
+        "977 Elizabeth Lane,Columbus OH\n",
+        "78 High Point St.,Columbus OH\n",
+        "44 NW. San Pablo St.,Columbus OH\n",
+        "\n",
+        "Route 4\n",
+        "82 Paris Hill St.,Columbus OH\n",
+        "23 S. Woodside Road,Columbus OH\n",
+        "7655 Catherine Road,Columbus OH\n",
+        "70 Coffee Ave.,Columbus OH\n",
+        "7152 Augusta St.,Columbus OH\n",
+        "5 South Greystone Street,Columbus OH\n",
+        "\n",
+        "Route 5\n",
+        "192 West Albany St.,Columbus OH\n",
+        "7 Marvon Road,Columbus OH\n",
+        "92 South Country Club Rd.,Columbus OH\n",
+        "98 Galvin Street,Columbus OH\n",
+        "3 Hillcrest Court,Columbus OH\n",
+        "9070 Ramblewood Ave.,Columbus OH\n",
+        "\n",
+        "Route 6\n",
+        "8043 N. Glen Ridge Dr.,Columbus OH\n",
+        "7678 Clinton Road,Columbus OH\n",
+        "685 Oakland Court,Columbus OH\n",
+        "737 Pacific Street,Columbus OH\n",
+        "32 Southampton Street,Columbus OH\n",
+        "9697 Bowman Drive,Columbus OH\n",
+        "\n",
+        "Route 7\n",
+        "9304 Brickyard St.,Columbus OH\n",
+        "8858 Canal Lane,Columbus OH\n",
+        "8 Old Jackson Avenue,Columbus OH\n",
+        "51 Columbia Street,Columbus OH\n",
+        "1961 Cleveland Ave,Columbus OH\n",
+        "7798 Brown Drive,Columbus OH\n",
+        "\n",
+        "Route 8\n",
+        "3432 Main St,Columbus OH\n",
+        "23 Oak Meadow Drive,Columbus OH\n",
+        "287 Military Dr.,Columbus OH\n",
+        "1944 Frank Dr,Columbus OH\n",
+        "64 Illinois Lane,Columbus OH\n",
+        "953 South Fieldstone Drive,Columbus OH\n"
+      ]
+    }
+  ]
+}
diff --git a/JediMasters/DWave Challenge/FoodBankClientDeliveryData.txt b/JediMasters/DWave Challenge/FoodBankClientDeliveryData.txt
new file mode 100644
index 0000000..5f154b7
--- /dev/null
+++ b/JediMasters/DWave Challenge/FoodBankClientDeliveryData.txt	
@@ -0,0 +1,2258 @@
+between_9_47 = 736
+between_47_9 = 736
+between_9_41 = 340
+between_41_9 = 340
+between_0_1 = 1712
+between_1_0 = 1712
+between_0_2 = 463
+between_2_0 = 463
+between_0_3 = 2371
+between_3_0 = 2371
+between_0_4 = 1421
+between_4_0 = 1421
+between_0_5 = 1137
+between_5_0 = 1137
+between_0_6 = 893
+between_6_0 = 893
+between_0_7 = 210
+between_7_0 = 210
+between_0_8 = 160
+between_8_0 = 160
+between_0_9 = 2208
+between_9_0 = 2208
+between_0_10 = 653
+between_10_0 = 653
+between_0_11 = 569
+between_11_0 = 569
+between_0_12 = 935
+between_12_0 = 935
+between_0_13 = 834
+between_13_0 = 834
+between_0_14 = 490
+between_14_0 = 490
+between_0_15 = 366
+between_15_0 = 366
+between_0_16 = 1388
+between_16_0 = 1388
+between_0_17 = 832
+between_17_0 = 832
+between_0_18 = 1235
+between_18_0 = 1235
+between_0_19 = 817
+between_19_0 = 817
+between_0_20 = 1156
+between_20_0 = 1156
+between_0_21 = 248
+between_21_0 = 248
+between_0_22 = 720
+between_22_0 = 720
+between_0_23 = 2091
+between_23_0 = 2091
+between_0_24 = 1029
+between_24_0 = 1029
+between_0_25 = 2293
+between_25_0 = 2293
+between_0_26 = 1285
+between_26_0 = 1285
+between_0_27 = 966
+between_27_0 = 966
+between_0_28 = 1284
+between_28_0 = 1284
+between_0_29 = 1164
+between_29_0 = 1164
+between_0_30 = 565
+between_30_0 = 565
+between_0_31 = 1589
+between_31_0 = 1589
+between_0_32 = 659
+between_32_0 = 659
+between_0_33 = 792
+between_33_0 = 792
+between_0_34 = 2674
+between_34_0 = 2674
+between_0_35 = 875
+between_35_0 = 875
+between_0_36 = 1202
+between_36_0 = 1202
+between_0_37 = 368
+between_37_0 = 368
+between_0_38 = 1417
+between_38_0 = 1417
+between_0_39 = 281
+between_39_0 = 281
+between_0_40 = 797
+between_40_0 = 797
+between_0_41 = 1907
+between_41_0 = 1907
+between_0_42 = 1335
+between_42_0 = 1335
+between_0_43 = 690
+between_43_0 = 690
+between_0_44 = 2711
+between_44_0 = 2711
+between_0_45 = 651
+between_45_0 = 651
+between_0_46 = 903
+between_46_0 = 903
+between_0_47 = 1470
+between_47_0 = 1470
+between_1_2 = 1297
+between_2_1 = 1297
+between_1_3 = 755
+between_3_1 = 755
+between_1_4 = 821
+between_4_1 = 821
+between_1_5 = 2535
+between_5_1 = 2535
+between_1_6 = 2358
+between_6_1 = 2358
+between_1_7 = 1884
+between_7_1 = 1884
+between_1_8 = 1846
+between_8_1 = 1846
+between_1_9 = 926
+between_9_1 = 926
+between_1_10 = 1553
+between_10_1 = 1553
+between_1_11 = 1699
+between_11_1 = 1699
+between_1_12 = 1401
+between_12_1 = 1401
+between_1_13 = 1163
+between_13_1 = 1163
+between_1_14 = 1773
+between_14_1 = 1773
+between_1_15 = 1446
+between_15_1 = 1446
+between_1_16 = 2802
+between_16_1 = 2802
+between_1_17 = 2297
+between_17_1 = 2297
+between_1_18 = 2649
+between_18_1 = 2649
+between_1_19 = 1942
+between_19_1 = 1942
+between_1_20 = 1648
+between_20_1 = 1648
+between_1_21 = 1470
+between_21_1 = 1470
+between_1_22 = 1379
+between_22_1 = 1379
+between_1_23 = 1143
+between_23_1 = 1143
+between_1_24 = 1249
+between_24_1 = 1249
+between_1_25 = 727
+between_25_1 = 727
+between_1_26 = 2628
+between_26_1 = 2628
+between_1_27 = 2363
+between_27_1 = 2363
+between_1_28 = 481
+between_28_1 = 481
+between_1_29 = 2484
+between_29_1 = 2484
+between_1_30 = 2174
+between_30_1 = 2174
+between_1_31 = 1480
+between_31_1 = 1480
+between_1_32 = 1875
+between_32_1 = 1875
+between_1_33 = 960
+between_33_1 = 960
+between_1_34 = 1290
+between_34_1 = 1290
+between_1_35 = 2241
+between_35_1 = 2241
+between_1_36 = 2589
+between_36_1 = 2589
+between_1_37 = 2059
+between_37_1 = 2059
+between_1_38 = 1339
+between_38_1 = 1339
+between_1_39 = 1636
+between_39_1 = 1636
+between_1_40 = 1006
+between_40_1 = 1006
+between_1_41 = 663
+between_41_1 = 663
+between_1_42 = 2615
+between_42_1 = 2615
+between_1_43 = 2249
+between_43_1 = 2249
+between_1_44 = 1447
+between_44_1 = 1447
+between_1_45 = 1964
+between_45_1 = 1964
+between_1_46 = 1694
+between_46_1 = 1694
+between_1_47 = 918
+between_47_1 = 918
+between_2_3 = 1959
+between_3_2 = 1959
+between_2_4 = 969
+between_4_2 = 969
+between_2_5 = 1348
+between_5_2 = 1348
+between_2_6 = 1113
+between_6_2 = 1113
+between_2_7 = 672
+between_7_2 = 672
+between_2_8 = 519
+between_8_2 = 519
+between_2_9 = 1778
+between_9_2 = 1778
+between_2_10 = 445
+between_10_2 = 445
+between_2_11 = 584
+between_11_2 = 584
+between_2_12 = 615
+between_12_2 = 615
+between_2_13 = 483
+between_13_2 = 483
+between_2_14 = 556
+between_14_2 = 556
+between_2_15 = 344
+between_15_2 = 344
+between_2_16 = 1599
+between_16_2 = 1599
+between_2_17 = 1052
+between_17_2 = 1052
+between_2_18 = 1447
+between_18_2 = 1447
+between_2_19 = 836
+between_19_2 = 836
+between_2_20 = 863
+between_20_2 = 863
+between_2_21 = 262
+between_21_2 = 262
+between_2_22 = 345
+between_22_2 = 345
+between_2_23 = 1681
+between_23_2 = 1681
+between_2_24 = 619
+between_24_2 = 619
+between_2_25 = 1881
+between_25_2 = 1881
+between_2_26 = 1496
+between_26_2 = 1496
+between_2_27 = 1191
+between_27_2 = 1191
+between_2_28 = 872
+between_28_2 = 872
+between_2_29 = 1335
+between_29_2 = 1335
+between_2_30 = 887
+between_30_2 = 887
+between_2_31 = 1213
+between_31_2 = 1213
+between_2_32 = 724
+between_32_2 = 724
+between_2_33 = 339
+between_33_2 = 339
+between_2_34 = 2244
+between_34_2 = 2244
+between_2_35 = 1068
+between_35_2 = 1068
+between_2_36 = 1414
+between_36_2 = 1414
+between_2_37 = 733
+between_37_2 = 733
+between_2_38 = 1007
+between_38_2 = 1007
+between_2_39 = 349
+between_39_2 = 349
+between_2_40 = 514
+between_40_2 = 514
+between_2_41 = 1477
+between_41_2 = 1477
+between_2_42 = 1466
+between_42_2 = 1466
+between_2_43 = 962
+between_43_2 = 962
+between_2_44 = 2304
+between_44_2 = 2304
+between_2_45 = 735
+between_45_2 = 735
+between_2_46 = 709
+between_46_2 = 709
+between_2_47 = 1057
+between_47_2 = 1057
+between_3_4 = 1168
+between_4_3 = 1168
+between_3_5 = 2921
+between_5_3 = 2921
+between_3_6 = 2813
+between_6_3 = 2813
+between_3_7 = 2639
+between_7_3 = 2639
+between_3_8 = 2467
+between_8_3 = 2467
+between_3_9 = 552
+between_9_3 = 552
+between_3_10 = 1957
+between_10_3 = 1957
+between_3_11 = 2189
+between_11_3 = 2189
+between_3_12 = 1713
+between_12_3 = 1713
+between_3_13 = 1695
+between_13_3 = 1695
+between_3_14 = 2279
+between_14_3 = 2279
+between_3_15 = 2202
+between_15_3 = 2202
+between_3_16 = 3166
+between_16_3 = 3166
+between_3_17 = 2752
+between_17_3 = 2752
+between_3_18 = 3013
+between_18_3 = 3013
+between_3_19 = 2247
+between_19_3 = 2247
+between_3_20 = 1889
+between_20_3 = 1889
+between_3_21 = 2130
+between_21_3 = 2130
+between_3_22 = 1876
+between_22_3 = 1876
+between_3_23 = 1010
+between_23_3 = 1010
+between_3_24 = 1530
+between_24_3 = 1530
+between_3_25 = 163
+between_25_3 = 163
+between_3_26 = 2993
+between_26_3 = 2993
+between_3_27 = 2810
+between_27_3 = 2810
+between_3_28 = 1141
+between_28_3 = 1141
+between_3_29 = 2848
+between_29_3 = 2848
+between_3_30 = 2789
+between_30_3 = 2789
+between_3_31 = 1490
+between_31_3 = 1490
+between_3_32 = 2365
+between_32_3 = 2365
+between_3_33 = 1621
+between_33_3 = 1621
+between_3_34 = 536
+between_34_3 = 536
+between_3_35 = 2688
+between_35_3 = 2688
+between_3_36 = 2999
+between_36_3 = 2999
+between_3_37 = 2680
+between_37_3 = 2680
+between_3_38 = 1465
+between_38_3 = 1465
+between_3_39 = 2274
+between_39_3 = 2274
+between_3_40 = 1761
+between_40_3 = 1761
+between_3_41 = 649
+between_41_3 = 649
+between_3_42 = 2979
+between_42_3 = 2979
+between_3_43 = 2787
+between_43_3 = 2787
+between_3_44 = 693
+between_44_3 = 693
+between_3_45 = 2470
+between_45_3 = 2470
+between_3_46 = 2006
+between_46_3 = 2006
+between_3_47 = 1087
+between_47_3 = 1087
+between_4_5 = 1880
+between_5_4 = 1880
+between_4_6 = 1744
+between_6_4 = 1744
+between_4_7 = 1630
+between_7_4 = 1630
+between_4_8 = 1403
+between_8_4 = 1403
+between_4_9 = 823
+between_9_4 = 823
+between_4_10 = 913
+between_10_4 = 913
+between_4_11 = 1085
+between_11_4 = 1085
+between_4_12 = 672
+between_12_4 = 672
+between_4_13 = 540
+between_13_4 = 540
+between_4_14 = 1163
+between_14_4 = 1163
+between_4_15 = 1225
+between_15_4 = 1225
+between_4_16 = 2125
+between_16_4 = 2125
+between_4_17 = 1683
+between_17_4 = 1683
+between_4_18 = 1973
+between_18_4 = 1973
+between_4_19 = 1206
+between_19_4 = 1206
+between_4_20 = 919
+between_20_4 = 919
+between_4_21 = 1201
+between_21_4 = 1201
+between_4_22 = 760
+between_22_4 = 760
+between_4_23 = 792
+between_23_4 = 792
+between_4_24 = 489
+between_24_4 = 489
+between_4_25 = 991
+between_25_4 = 991
+between_4_26 = 1952
+between_26_4 = 1952
+between_4_27 = 1749
+between_27_4 = 1749
+between_4_28 = 392
+between_28_4 = 392
+between_4_29 = 1807
+between_29_4 = 1807
+between_4_30 = 1673
+between_30_4 = 1673
+between_4_31 = 696
+between_31_4 = 696
+between_4_32 = 1261
+between_32_4 = 1261
+between_4_33 = 679
+between_33_4 = 679
+between_4_34 = 1289
+between_34_4 = 1289
+between_4_35 = 1628
+between_35_4 = 1628
+between_4_36 = 1958
+between_36_4 = 1958
+between_4_37 = 1596
+between_37_4 = 1596
+between_4_38 = 524
+between_38_4 = 524
+between_4_39 = 1158
+between_39_4 = 1158
+between_4_40 = 922
+between_40_4 = 922
+between_4_41 = 522
+between_41_4 = 522
+between_4_42 = 1938
+between_42_4 = 1938
+between_4_43 = 1671
+between_43_4 = 1671
+between_4_44 = 1349
+between_44_4 = 1349
+between_4_45 = 1354
+between_45_4 = 1354
+between_4_46 = 965
+between_46_4 = 965
+between_4_47 = 102
+between_47_4 = 102
+between_5_6 = 284
+between_6_5 = 284
+between_5_7 = 1208
+between_7_5 = 1208
+between_5_8 = 980
+between_8_5 = 980
+between_5_9 = 2574
+between_9_5 = 2574
+between_5_10 = 1047
+between_10_5 = 1047
+between_5_11 = 835
+between_11_5 = 835
+between_5_12 = 1207
+between_12_5 = 1207
+between_5_13 = 1380
+between_13_5 = 1380
+between_5_14 = 849
+between_14_5 = 849
+between_5_15 = 1482
+between_15_5 = 1482
+between_5_16 = 255
+between_16_5 = 255
+between_5_17 = 330
+between_17_5 = 330
+between_5_18 = 102
+between_18_5 = 102
+between_5_19 = 723
+between_19_5 = 723
+between_5_20 = 1290
+between_20_5 = 1290
+between_5_21 = 1320
+between_21_5 = 1320
+between_5_22 = 1201
+between_22_5 = 1201
+between_5_23 = 2333
+between_23_5 = 2333
+between_5_24 = 1395
+between_24_5 = 1395
+between_5_25 = 2817
+between_25_5 = 2817
+between_5_26 = 152
+between_26_5 = 152
+between_5_27 = 182
+between_27_5 = 182
+between_5_28 = 2108
+between_28_5 = 2108
+between_5_29 = 112
+between_29_5 = 112
+between_5_30 = 619
+between_30_5 = 619
+between_5_31 = 1723
+between_31_5 = 1723
+between_5_32 = 661
+between_32_5 = 661
+between_5_33 = 1574
+between_33_5 = 1574
+between_5_34 = 3040
+between_34_5 = 3040
+between_5_35 = 292
+between_35_5 = 292
+between_5_36 = 87.5
+between_36_5 = 87.5
+between_5_37 = 819
+between_37_5 = 819
+between_5_38 = 1678
+between_38_5 = 1678
+between_5_39 = 1007
+between_39_5 = 1007
+between_5_40 = 1865
+between_40_5 = 1865
+between_5_41 = 2273
+between_41_5 = 2273
+between_5_42 = 200
+between_42_5 = 200
+between_5_43 = 449
+between_43_5 = 449
+between_5_44 = 2993
+between_44_5 = 2993
+between_5_45 = 653
+between_45_5 = 653
+between_5_46 = 1036
+between_46_5 = 1036
+between_5_47 = 1835
+between_47_5 = 1835
+between_6_7 = 963
+between_7_6 = 963
+between_6_8 = 735
+between_8_6 = 735
+between_6_9 = 2467
+between_9_6 = 2467
+between_6_10 = 873
+between_10_6 = 873
+between_6_11 = 661
+between_11_6 = 661
+between_6_12 = 1101
+between_12_6 = 1101
+between_6_13 = 1206
+between_13_6 = 1206
+between_6_14 = 643
+between_14_6 = 643
+between_6_15 = 1241
+between_15_6 = 1241
+between_6_16 = 534
+between_16_6 = 534
+between_6_17 = 64.9
+between_17_6 = 64.9
+between_6_18 = 381
+between_18_6 = 381
+between_6_19 = 673
+between_19_6 = 673
+between_6_20 = 1184
+between_20_6 = 1184
+between_6_21 = 1078
+between_21_6 = 1078
+between_6_22 = 1027
+between_22_6 = 1027
+between_6_23 = 2227
+between_23_6 = 2227
+between_6_24 = 1289
+between_24_6 = 1289
+between_6_25 = 2710
+between_25_6 = 2710
+between_6_26 = 431
+between_26_6 = 431
+between_6_27 = 112
+between_27_6 = 112
+between_6_28 = 1934
+between_28_6 = 1934
+between_6_29 = 307
+between_29_6 = 307
+between_6_30 = 373
+between_30_6 = 373
+between_6_31 = 1616
+between_31_6 = 1616
+between_6_32 = 486
+between_32_6 = 486
+between_6_33 = 1401
+between_33_6 = 1401
+between_6_34 = 2933
+between_34_6 = 2933
+between_6_35 = 130
+between_35_6 = 130
+between_6_36 = 349
+between_36_6 = 349
+between_6_37 = 574
+between_37_6 = 574
+between_6_38 = 1572
+between_38_6 = 1572
+between_6_39 = 765
+between_39_6 = 765
+between_6_40 = 1623
+between_40_6 = 1623
+between_6_41 = 2166
+between_41_6 = 2166
+between_6_42 = 481
+between_42_6 = 481
+between_6_43 = 204
+between_43_6 = 204
+between_6_44 = 2887
+between_44_6 = 2887
+between_6_45 = 447
+between_45_6 = 447
+between_6_46 = 930
+between_46_6 = 930
+between_6_47 = 1729
+between_47_6 = 1729
+between_7_8 = 261
+between_8_7 = 261
+between_7_9 = 2417
+between_9_7 = 2417
+between_7_10 = 862
+between_10_7 = 862
+between_7_11 = 778
+between_11_7 = 778
+between_7_12 = 1143
+between_12_7 = 1143
+between_7_13 = 1043
+between_13_7 = 1043
+between_7_14 = 675
+between_14_7 = 675
+between_7_15 = 443
+between_15_7 = 443
+between_7_16 = 1457
+between_16_7 = 1457
+between_7_17 = 901
+between_17_7 = 901
+between_7_18 = 1304
+between_18_7 = 1304
+between_7_19 = 1026
+between_19_7 = 1026
+between_7_20 = 1365
+between_20_7 = 1365
+between_7_21 = 431
+between_21_7 = 431
+between_7_22 = 929
+between_22_7 = 929
+between_7_23 = 2300
+between_23_7 = 2300
+between_7_24 = 1238
+between_24_7 = 1238
+between_7_25 = 2476
+between_25_7 = 2476
+between_7_26 = 1354
+between_26_7 = 1354
+between_7_27 = 1035
+between_27_7 = 1035
+between_7_28 = 1468
+between_28_7 = 1468
+between_7_29 = 1230
+between_29_7 = 1230
+between_7_30 = 614
+between_30_7 = 614
+between_7_31 = 1798
+between_31_7 = 1798
+between_7_32 = 835
+between_32_7 = 835
+between_7_33 = 1001
+between_33_7 = 1001
+between_7_34 = 2883
+between_34_7 = 2883
+between_7_35 = 978
+between_35_7 = 978
+between_7_36 = 1272
+between_36_7 = 1272
+between_7_37 = 381
+between_37_7 = 381
+between_7_38 = 1626
+between_38_7 = 1626
+between_7_39 = 490
+between_39_7 = 490
+between_7_40 = 873
+between_40_7 = 873
+between_7_41 = 2116
+between_41_7 = 2116
+between_7_42 = 1404
+between_42_7 = 1404
+between_7_43 = 759
+between_43_7 = 759
+between_7_44 = 2920
+between_44_7 = 2920
+between_7_45 = 733
+between_45_7 = 733
+between_7_46 = 1112
+between_46_7 = 1112
+between_7_47 = 1679
+between_47_7 = 1679
+between_8_9 = 2174
+between_9_8 = 2174
+between_8_10 = 619
+between_10_8 = 619
+between_8_11 = 534
+between_11_8 = 534
+between_8_12 = 900
+between_12_8 = 900
+between_8_13 = 864
+between_13_8 = 864
+between_8_14 = 407
+between_14_8 = 407
+between_8_15 = 526
+between_15_8 = 526
+between_8_16 = 1228
+between_16_8 = 1228
+between_8_17 = 672
+between_17_8 = 672
+between_8_18 = 1076
+between_18_8 = 1076
+between_8_19 = 775
+between_19_8 = 775
+between_8_20 = 1122
+between_20_8 = 1122
+between_8_21 = 381
+between_21_8 = 381
+between_8_22 = 685
+between_22_8 = 685
+between_8_23 = 2057
+between_23_8 = 2057
+between_8_24 = 995
+between_24_8 = 995
+between_8_25 = 2417
+between_25_8 = 2417
+between_8_26 = 1125
+between_26_8 = 1125
+between_8_27 = 807
+between_27_8 = 807
+between_8_28 = 1381
+between_28_8 = 1381
+between_8_29 = 1004
+between_29_8 = 1004
+between_8_30 = 405
+between_30_8 = 405
+between_8_31 = 1555
+between_31_8 = 1555
+between_8_32 = 567
+between_32_8 = 567
+between_8_42 = 1175
+between_42_8 = 1175
+between_8_33 = 848
+between_33_8 = 848
+between_8_34 = 2640
+between_34_8 = 2640
+between_8_35 = 715
+between_35_8 = 715
+between_8_36 = 1043
+between_36_8 = 1043
+between_8_37 = 214
+between_37_8 = 214
+between_8_38 = 1383
+between_38_8 = 1383
+between_8_39 = 250
+between_39_8 = 250
+between_8_40 = 956
+between_40_8 = 956
+between_8_41 = 1873
+between_41_8 = 1873
+between_8_43 = 530
+between_43_8 = 530
+between_8_44 = 2677
+between_44_8 = 2677
+between_8_45 = 502
+between_45_8 = 502
+between_8_46 = 868
+between_46_8 = 868
+between_8_47 = 1436
+between_47_8 = 1436
+between_9_10 = 1606
+between_10_9 = 1606
+between_9_11 = 1838
+between_11_9 = 1838
+between_9_12 = 1362
+between_12_9 = 1362
+between_9_13 = 1344
+between_13_9 = 1344
+between_9_14 = 1928
+between_14_9 = 1928
+between_9_15 = 2039
+between_15_9 = 2039
+between_9_16 = 2815
+between_16_9 = 2815
+between_10_13 = 373
+between_13_10 = 373
+between_9_17 = 2401
+between_17_9 = 2401
+between_9_18 = 2663
+between_18_9 = 2663
+between_9_19 = 1896
+between_19_9 = 1896
+between_9_20 = 1460
+between_20_9 = 1460
+between_9_21 = 2030
+between_21_9 = 2030
+between_9_22 = 1525
+between_22_9 = 1525
+between_9_23 = 486
+between_23_9 = 486
+between_9_24 = 1179
+between_24_9 = 1179
+between_9_25 = 450
+between_25_9 = 450
+between_9_26 = 2642
+between_26_9 = 2642
+between_9_27 = 2459
+between_27_9 = 2459
+between_9_28 = 963
+between_28_9 = 963
+between_9_29 = 2497
+between_29_9 = 2497
+between_9_30 = 2438
+between_30_9 = 2438
+between_9_31 = 1026
+between_31_9 = 1026
+between_9_32 = 2014
+between_32_9 = 2014
+between_9_33 = 1483
+between_33_9 = 1483
+between_9_34 = 475
+between_34_9 = 475
+between_9_35 = 2337
+between_35_9 = 2337
+between_9_36 = 2648
+between_36_9 = 2648
+between_9_37 = 2361
+between_37_9 = 2361
+between_9_38 = 1114
+between_38_9 = 1114
+between_9_39 = 1923
+between_39_9 = 1923
+between_9_40 = 1628
+between_40_9 = 1628
+between_9_42 = 2628
+between_42_9 = 2628
+between_9_43 = 2436
+between_43_9 = 2436
+between_9_44 = 535
+between_44_9 = 535
+between_9_45 = 2119
+between_45_9 = 2119
+between_9_46 = 1655
+between_46_9 = 1655
+between_10_11 = 213
+between_11_10 = 213
+between_10_12 = 338
+between_12_10 = 338
+between_10_14 = 376
+between_14_10 = 376
+between_10_15 = 760
+between_15_10 = 760
+between_10_16 = 1311
+between_16_10 = 1311
+between_10_17 = 811
+between_17_10 = 811
+between_10_18 = 1158
+between_18_10 = 1158
+between_10_19 = 392
+between_19_10 = 392
+between_10_20 = 520
+between_20_10 = 520
+between_10_21 = 586
+between_21_10 = 586
+between_10_22 = 193
+between_22_10 = 193
+between_10_23 = 1494
+between_23_10 = 1494
+between_10_24 = 432
+between_24_10 = 432
+between_10_25 = 1854
+between_25_10 = 1854
+between_10_26 = 1137
+between_26_10 = 1137
+between_10_27 = 877
+between_27_10 = 877
+between_10_28 = 1062
+between_28_10 = 1062
+between_10_29 = 993
+between_29_10 = 993
+between_10_30 = 843
+between_30_10 = 843
+between_10_31 = 952
+between_31_10 = 952
+between_10_32 = 389
+between_32_10 = 389
+between_10_33 = 594
+between_33_10 = 594
+between_10_34 = 2077
+between_34_10 = 2077
+between_10_35 = 755
+between_35_10 = 755
+between_10_36 = 1103
+between_36_10 = 1103
+between_10_37 = 811
+between_37_10 = 811
+between_10_38 = 820
+between_38_10 = 820
+between_10_39 = 373
+between_39_10 = 373
+between_10_40 = 920
+between_40_10 = 920
+between_10_41 = 1310
+between_41_10 = 1310
+between_10_42 = 1124
+between_42_10 = 1124
+between_10_43 = 841
+between_43_10 = 841
+between_10_44 = 2114
+between_44_10 = 2114
+between_10_45 = 524
+between_45_10 = 524
+between_10_46 = 266
+between_46_10 = 266
+between_10_47 = 873
+between_47_10 = 873
+between_11_12 = 476
+between_12_11 = 476
+between_11_13 = 545
+between_13_11 = 545
+between_11_14 = 166
+between_14_11 = 166
+between_11_15 = 846
+between_15_11 = 846
+between_11_16 = 1103
+between_16_11 = 1103
+between_11_17 = 598
+between_17_11 = 598
+between_11_18 = 950
+between_18_11 = 950
+between_11_19 = 254
+between_19_11 = 254
+between_11_34 = 2309
+between_34_11 = 2309
+between_11_20 = 588
+between_20_11 = 588
+between_11_21 = 672
+between_21_11 = 672
+between_11_22 = 367
+between_22_11 = 367
+between_11_23 = 1631
+between_23_11 = 1631
+between_11_24 = 643
+between_24_11 = 643
+between_11_25 = 2086
+between_25_11 = 2086
+between_11_26 = 929
+between_26_11 = 929
+between_11_27 = 664
+between_27_11 = 664
+between_11_28 = 1274
+between_28_11 = 1274
+between_11_29 = 785
+between_29_11 = 785
+between_11_30 = 629
+between_30_11 = 629
+between_11_31 = 1020
+between_31_11 = 1020
+between_11_32 = 175
+between_32_11 = 175
+between_11_33 = 740
+between_33_11 = 740
+between_11_35 = 542
+between_35_11 = 542
+between_11_36 = 890
+between_36_11 = 890
+between_11_37 = 618
+between_37_11 = 618
+between_11_38 = 965
+between_38_11 = 965
+between_11_39 = 289
+between_39_11 = 289
+between_11_40 = 1095
+between_40_11 = 1095
+between_11_41 = 1542
+between_41_11 = 1542
+between_11_42 = 916
+between_42_11 = 916
+between_11_43 = 627
+between_43_11 = 627
+between_11_44 = 2259
+between_44_11 = 2259
+between_11_45 = 311
+between_45_11 = 311
+between_11_46 = 334
+between_46_11 = 334
+between_11_47 = 1104
+between_47_11 = 1104
+between_12_13 = 257
+between_13_12 = 257
+between_12_14 = 639
+between_14_12 = 639
+between_12_15 = 997
+between_15_12 = 997
+between_12_16 = 1453
+between_16_12 = 1453
+between_12_17 = 1039
+between_17_12 = 1039
+between_13_24 = 166
+between_24_13 = 166
+between_12_18 = 1301
+between_18_12 = 1301
+between_12_19 = 534
+between_19_12 = 534
+between_12_20 = 248
+between_20_12 = 248
+between_12_21 = 823
+between_21_12 = 823
+between_12_22 = 266
+between_22_12 = 266
+between_12_23 = 1177
+between_23_12 = 1177
+between_12_24 = 188
+between_24_12 = 188
+between_12_25 = 1610
+between_25_12 = 1610
+between_12_26 = 1280
+between_26_12 = 1280
+between_12_27 = 1097
+between_27_12 = 1097
+between_12_28 = 947
+between_28_12 = 947
+between_12_29 = 1135
+between_29_12 = 1135
+between_12_30 = 1106
+between_30_12 = 1106
+between_12_31 = 669
+between_31_12 = 669
+between_12_32 = 652
+between_32_12 = 652
+between_12_33 = 545
+between_33_12 = 545
+between_12_34 = 1833
+between_34_12 = 1833
+between_12_35 = 975
+between_35_12 = 975
+between_12_36 = 1286
+between_36_12 = 1286
+between_12_37 = 1091
+between_37_12 = 1091
+between_12_38 = 503
+between_38_12 = 503
+between_12_39 = 656
+between_39_12 = 656
+between_12_40 = 930
+between_40_12 = 930
+between_12_41 = 1066
+between_41_12 = 1066
+between_12_42 = 1266
+between_42_12 = 1266
+between_12_43 = 1104
+between_43_12 = 1104
+between_12_44 = 1797
+between_44_12 = 1797
+between_12_45 = 787
+between_45_12 = 787
+between_12_46 = 293
+between_46_12 = 293
+between_12_47 = 629
+between_47_12 = 629
+between_13_14 = 623
+between_14_13 = 623
+between_13_15 = 799
+between_15_13 = 799
+between_13_16 = 1648
+between_16_13 = 1648
+between_13_17 = 1143
+between_17_13 = 1143
+between_13_18 = 1495
+between_18_13 = 1495
+between_13_19 = 762
+between_19_13 = 762
+between_13_20 = 504
+between_20_13 = 504
+between_13_21 = 715
+between_21_13 = 715
+between_13_22 = 220
+between_22_13 = 220
+between_13_23 = 1237
+between_23_13 = 1237
+between_13_25 = 1528
+between_25_13 = 1528
+between_13_26 = 1474
+between_26_13 = 1474
+between_13_27 = 1209
+between_27_13 = 1209
+between_13_28 = 690
+between_28_13 = 690
+between_13_29 = 1330
+between_29_13 = 1330
+between_13_30 = 1133
+between_30_13 = 1133
+between_14_35 = 563
+between_35_14 = 563
+between_13_31 = 769
+between_31_13 = 769
+between_13_32 = 720
+between_32_13 = 720
+between_13_33 = 299
+between_33_13 = 299
+between_13_34 = 1816
+between_34_13 = 1816
+between_13_35 = 1087
+between_35_13 = 1087
+between_13_36 = 1435
+between_36_13 = 1435
+between_13_37 = 1056
+between_37_13 = 1056
+between_13_38 = 563
+between_38_13 = 563
+between_13_39 = 618
+between_39_13 = 618
+between_13_40 = 683
+between_40_13 = 683
+between_13_41 = 1049
+between_41_13 = 1049
+between_13_42 = 1461
+between_42_13 = 1461
+between_13_43 = 1131
+between_43_13 = 1131
+between_13_44 = 1876
+between_44_13 = 1876
+between_13_45 = 814
+between_45_13 = 814
+between_13_46 = 550
+between_46_13 = 550
+between_13_47 = 629
+between_47_13 = 629
+between_14_15 = 798
+between_15_14 = 798
+between_14_16 = 1095
+between_16_14 = 1095
+between_14_17 = 581
+between_17_14 = 581
+between_14_18 = 942
+between_18_14 = 942
+between_14_19 = 394
+between_19_14 = 394
+between_14_20 = 752
+between_20_14 = 752
+between_14_21 = 622
+between_21_14 = 622
+between_14_22 = 444
+between_22_14 = 444
+between_14_23 = 1801
+between_23_14 = 1801
+between_14_24 = 753
+between_24_14 = 753
+between_14_25 = 2175
+between_25_14 = 2175
+between_14_26 = 941
+between_26_14 = 941
+between_14_27 = 678
+between_27_14 = 678
+between_14_28 = 1344
+between_28_14 = 1344
+between_14_29 = 796
+between_29_14 = 796
+between_14_30 = 517
+between_30_14 = 517
+between_14_31 = 1185
+between_31_14 = 1185
+between_14_32 = 186
+between_32_14 = 186
+between_14_33 = 810
+between_33_14 = 810
+between_14_34 = 2398
+between_34_14 = 2398
+between_14_36 = 909
+between_36_14 = 909
+between_14_37 = 459
+between_37_14 = 459
+between_14_38 = 1127
+between_38_14 = 1127
+between_14_39 = 210
+between_39_14 = 210
+between_14_40 = 1066
+between_40_14 = 1066
+between_14_41 = 1631
+between_41_14 = 1631
+between_14_42 = 927
+between_42_14 = 927
+between_14_43 = 515
+between_43_14 = 515
+between_14_44 = 2421
+between_44_14 = 2421
+between_14_45 = 198
+between_45_14 = 198
+between_14_46 = 498
+between_46_14 = 498
+between_14_47 = 1194
+between_47_14 = 1194
+between_15_16 = 1726
+between_16_15 = 1726
+between_15_17 = 1179
+between_17_15 = 1179
+between_15_18 = 1573
+between_18_15 = 1573
+between_15_19 = 1096
+between_19_15 = 1096
+between_15_20 = 1181
+between_20_15 = 1181
+between_15_21 = 175
+between_21_15 = 175
+between_15_22 = 716
+between_22_15 = 716
+between_15_23 = 2010
+between_23_15 = 2010
+between_15_24 = 958
+between_24_15 = 958
+between_15_25 = 2075
+between_25_15 = 2075
+between_15_26 = 1623
+between_26_15 = 1623
+between_15_47 = 1320
+between_47_15 = 1320
+between_15_27 = 1318
+between_27_15 = 1318
+between_15_28 = 1066
+between_28_15 = 1066
+between_15_29 = 1483
+between_29_15 = 1483
+between_15_30 = 930
+between_30_15 = 930
+between_15_31 = 1553
+between_31_15 = 1553
+between_15_32 = 962
+between_32_15 = 962
+between_15_33 = 633
+between_33_15 = 633
+between_15_34 = 2507
+between_34_15 = 2507
+between_15_35 = 1194
+between_35_15 = 1194
+between_15_36 = 1540
+between_36_15 = 1540
+between_15_37 = 733
+between_37_15 = 733
+between_15_38 = 1346
+between_38_15 = 1346
+between_15_39 = 587
+between_39_15 = 587
+between_15_40 = 436
+between_40_15 = 436
+between_15_41 = 1669
+between_41_15 = 1669
+between_15_42 = 1640
+between_42_15 = 1640
+between_15_43 = 1055
+between_43_15 = 1055
+between_15_44 = 2567
+between_44_15 = 2567
+between_15_45 = 963
+between_45_15 = 963
+between_15_46 = 1027
+between_46_15 = 1027
+between_16_17 = 582
+between_17_16 = 582
+between_16_18 = 162
+between_18_16 = 162
+between_16_19 = 933
+between_19_16 = 933
+between_16_20 = 1536
+between_20_16 = 1536
+between_16_21 = 1571
+between_21_16 = 1571
+between_16_22 = 1456
+between_22_16 = 1456
+between_16_23 = 2579
+between_23_16 = 2579
+between_16_24 = 1641
+between_24_16 = 1641
+between_16_25 = 3062
+between_25_16 = 3062
+between_16_26 = 163
+between_26_16 = 163
+between_28_34 = 1434
+between_34_28 = 1434
+between_16_27 = 433
+between_27_16 = 433
+between_16_40 = 2116
+between_40_16 = 2116
+between_16_28 = 2363
+between_28_16 = 2363
+between_16_29 = 322
+between_29_16 = 322
+between_16_30 = 870
+between_30_16 = 870
+between_16_31 = 1969
+between_31_16 = 1969
+between_16_32 = 916
+between_32_16 = 916
+between_16_33 = 1829
+between_33_16 = 1829
+between_16_34 = 3286
+between_34_16 = 3286
+between_16_35 = 543
+between_35_16 = 543
+between_16_36 = 217
+between_36_16 = 217
+between_16_37 = 1071
+between_37_16 = 1071
+between_16_38 = 1924
+between_38_16 = 1924
+between_16_39 = 1258
+between_39_16 = 1258
+between_16_41 = 2518
+between_41_16 = 2518
+between_16_42 = 180
+between_42_16 = 180
+between_16_43 = 701
+between_43_16 = 701
+between_16_44 = 3239
+between_44_16 = 3239
+between_18_25 = 2909
+between_25_18 = 2909
+between_16_45 = 904
+between_45_16 = 904
+between_16_46 = 1282
+between_46_16 = 1282
+between_16_47 = 2081
+between_47_16 = 2081
+between_17_18 = 428
+between_18_17 = 428
+between_17_19 = 612
+between_19_17 = 612
+between_17_20 = 1123
+between_20_17 = 1123
+between_17_21 = 1017
+between_21_17 = 1017
+between_17_22 = 966
+between_22_17 = 966
+between_17_23 = 2166
+between_23_17 = 2166
+between_17_24 = 1228
+between_24_17 = 1228
+between_17_25 = 2649
+between_25_17 = 2649
+between_17_26 = 477
+between_26_17 = 477
+between_17_27 = 159
+between_27_17 = 159
+between_17_28 = 1873
+between_28_17 = 1873
+between_17_29 = 353
+between_29_17 = 353
+between_17_30 = 312
+between_30_17 = 312
+between_17_31 = 1555
+between_31_17 = 1555
+between_17_32 = 425
+between_32_17 = 425
+between_17_33 = 1340
+between_33_17 = 1340
+between_17_34 = 2872
+between_34_17 = 2872
+between_17_35 = 112
+between_35_17 = 112
+between_17_36 = 395
+between_36_17 = 395
+between_17_37 = 513
+between_37_17 = 513
+between_17_38 = 1511
+between_38_17 = 1511
+between_17_39 = 704
+between_39_17 = 704
+between_17_40 = 1562
+between_40_17 = 1562
+between_17_41 = 2105
+between_41_17 = 2105
+between_17_42 = 527
+between_42_17 = 527
+between_17_43 = 143
+between_43_17 = 143
+between_17_44 = 2826
+between_44_17 = 2826
+between_17_45 = 386
+between_45_17 = 386
+between_17_46 = 869
+between_46_17 = 869
+between_17_47 = 1668
+between_47_17 = 1668
+between_18_19 = 780
+between_19_18 = 780
+between_18_20 = 1383
+between_20_18 = 1383
+between_18_21 = 1418
+between_21_18 = 1418
+between_18_22 = 1303
+between_22_18 = 1303
+between_18_23 = 2426
+between_23_18 = 2426
+between_18_24 = 1488
+between_24_18 = 1488
+between_18_26 = 67.5
+between_26_18 = 67.5
+between_18_27 = 280
+between_27_18 = 280
+between_18_28 = 2210
+between_28_18 = 2210
+between_18_29 = 169
+between_29_18 = 169
+between_18_30 = 717
+between_30_18 = 717
+between_18_31 = 1815
+between_31_18 = 1815
+between_18_32 = 763
+between_32_18 = 763
+between_18_33 = 1676
+between_33_18 = 1676
+between_18_34 = 3133
+between_34_18 = 3133
+between_18_35 = 390
+between_35_18 = 390
+between_18_36 = 50.9
+between_36_18 = 50.9
+between_18_36 = 59
+between_36_18 = 59
+between_18_37 = 918
+between_37_18 = 918
+between_19_25 = 2143
+between_25_19 = 2143
+between_18_38 = 1771
+between_38_18 = 1771
+between_18_39 = 1105
+between_39_18 = 1105
+between_18_40 = 1963
+between_40_18 = 1963
+between_18_41 = 2365
+between_41_18 = 2365
+between_18_42 = 180
+between_42_18 = 180
+between_18_43 = 548
+between_43_18 = 548
+between_18_44 = 3086
+between_44_18 = 3086
+between_18_45 = 751
+between_45_18 = 751
+between_18_46 = 1129
+between_46_18 = 1129
+between_18_47 = 1928
+between_47_18 = 1928
+between_19_20 = 626
+between_20_19 = 626
+between_19_21 = 923
+between_21_19 = 923
+between_19_22 = 583
+between_22_19 = 583
+between_19_23 = 1669
+between_23_19 = 1669
+between_19_24 = 722
+between_24_19 = 722
+between_19_26 = 758
+between_26_19 = 758
+between_19_27 = 670
+between_27_19 = 670
+between_19_41 = 1599
+between_41_19 = 1599
+between_19_28 = 1452
+between_28_19 = 1452
+between_19_29 = 614
+between_29_19 = 614
+between_19_30 = 735
+between_30_19 = 735
+between_19_31 = 1058
+between_31_19 = 1058
+between_19_32 = 256
+between_32_19 = 256
+between_19_33 = 983
+between_33_19 = 983
+between_19_34 = 2366
+between_34_19 = 2366
+between_19_35 = 548
+between_35_19 = 548
+between_19_36 = 773
+between_36_19 = 773
+between_19_37 = 771
+between_37_19 = 771
+between_19_38 = 1014
+between_38_19 = 1014
+between_19_39 = 539
+between_39_19 = 539
+between_19_40 = 1346
+between_40_19 = 1346
+between_19_42 = 763
+between_42_19 = 763
+between_19_43 = 689
+between_43_19 = 689
+between_20_21 = 1004
+between_21_20 = 1004
+between_19_44 = 2329
+between_44_19 = 2329
+between_19_45 = 417
+between_45_19 = 417
+between_19_46 = 372
+between_46_19 = 372
+between_19_47 = 1162
+between_47_19 = 1162
+between_20_22 = 510
+between_22_20 = 510
+between_20_23 = 1048
+between_23_20 = 1048
+between_20_24 = 435
+between_24_20 = 435
+between_20_25 = 1786
+between_25_20 = 1786
+between_20_26 = 1360
+between_26_20 = 1360
+between_20_27 = 1176
+between_27_20 = 1176
+between_20_28 = 1193
+between_28_20 = 1193
+between_20_29 = 1215
+between_29_20 = 1215
+between_20_30 = 1209
+between_30_20 = 1209
+between_20_31 = 438
+between_31_20 = 438
+between_20_32 = 755
+between_32_20 = 755
+between_20_33 = 792
+between_33_20 = 792
+between_22_40 = 746
+between_40_22 = 746
+between_20_34 = 1786
+between_34_20 = 1786
+between_20_35 = 1055
+between_35_20 = 1055
+between_20_36 = 1365
+between_36_20 = 1365
+between_20_37 = 1200
+between_37_20 = 1200
+between_20_38 = 461
+between_38_20 = 461
+between_20_39 = 873
+between_39_20 = 873
+between_20_40 = 1176
+between_40_20 = 1176
+between_20_41 = 1242
+between_41_20 = 1242
+between_20_42 = 1346
+between_42_20 = 1346
+between_20_43 = 1196
+between_43_20 = 1196
+between_20_44 = 1708
+between_44_20 = 1708
+between_20_45 = 890
+between_45_20 = 890
+between_20_46 = 259
+between_46_20 = 259
+between_20_47 = 817
+between_47_20 = 817
+between_21_22 = 544
+between_22_21 = 544
+between_21_23 = 1915
+between_23_21 = 1915
+between_21_24 = 853
+between_24_21 = 853
+between_21_25 = 2048
+between_25_21 = 2048
+between_21_26 = 1461
+between_26_21 = 1461
+between_21_27 = 1156
+between_27_21 = 1156
+between_21_28 = 1039
+between_28_21 = 1039
+between_21_29 = 1322
+between_29_21 = 1322
+between_21_30 = 787
+between_30_21 = 787
+between_21_31 = 1448
+between_31_21 = 1448
+between_21_32 = 790
+between_32_21 = 790
+between_21_33 = 605
+between_33_21 = 605
+between_21_34 = 2498
+between_34_21 = 2498
+between_21_35 = 1033
+between_35_21 = 1033
+between_21_36 = 1379
+between_36_21 = 1379
+between_21_37 = 595
+between_37_21 = 595
+between_21_38 = 1241
+between_38_21 = 1241
+between_21_39 = 415
+between_39_21 = 415
+between_21_40 = 549
+between_40_21 = 549
+between_21_41 = 1642
+between_41_21 = 1642
+between_21_42 = 1478
+between_42_21 = 1478
+between_21_43 = 912
+between_43_21 = 912
+between_21_44 = 2559
+between_44_21 = 2559
+between_21_45 = 801
+between_45_21 = 801
+between_21_46 = 855
+between_46_21 = 855
+between_21_47 = 1312
+between_47_21 = 1312
+between_22_23 = 1414
+between_23_22 = 1414
+between_22_24 = 352
+between_24_22 = 352
+between_22_25 = 1774
+between_25_22 = 1774
+between_22_26 = 1296
+between_26_22 = 1296
+between_22_27 = 1030
+between_27_22 = 1030
+between_22_28 = 887
+between_28_22 = 887
+between_22_29 = 1152
+between_29_22 = 1152
+between_22_30 = 954
+between_30_22 = 954
+between_22_31 = 946
+between_31_22 = 946
+between_22_32 = 542
+between_32_22 = 542
+between_22_33 = 420
+between_33_22 = 420
+between_22_34 = 1997
+between_34_22 = 1997
+between_22_35 = 909
+between_35_22 = 909
+between_22_36 = 1257
+between_36_22 = 1257
+between_22_37 = 878
+between_37_22 = 878
+between_22_38 = 740
+between_38_22 = 740
+between_22_39 = 440
+between_39_22 = 440
+between_22_41 = 1230
+between_41_22 = 1230
+between_22_42 = 1282
+between_42_22 = 1282
+between_22_43 = 952
+between_43_22 = 952
+between_22_44 = 2034
+between_44_22 = 2034
+between_22_45 = 636
+between_45_22 = 636
+between_22_46 = 436
+between_46_22 = 436
+between_22_47 = 792
+between_47_22 = 792
+between_23_24 = 1139
+between_24_23 = 1139
+between_23_25 = 907
+between_25_23 = 907
+between_23_26 = 2406
+between_26_23 = 2406
+between_23_27 = 2223
+between_27_23 = 2223
+between_23_28 = 1181
+between_28_23 = 1181
+between_23_29 = 2261
+between_29_23 = 2261
+between_23_30 = 2255
+between_30_23 = 2255
+between_23_31 = 612
+between_31_23 = 612
+between_23_32 = 1801
+between_32_23 = 1801
+between_23_33 = 1458
+between_33_23 = 1458
+between_23_34 = 707
+between_34_23 = 707
+between_23_35 = 2101
+between_35_23 = 2101
+between_23_36 = 2412
+between_36_23 = 2412
+between_23_37 = 2249
+between_37_23 = 2249
+between_23_38 = 701
+between_38_23 = 701
+between_23_39 = 1811
+between_39_23 = 1811
+between_23_40 = 1710
+between_40_23 = 1710
+between_23_41 = 483
+between_41_23 = 483
+between_23_42 = 2392
+between_42_23 = 2392
+between_23_43 = 2242
+between_43_23 = 2242
+between_23_44 = 629
+between_44_23 = 629
+between_23_45 = 1936
+between_45_23 = 1936
+between_23_46 = 1305
+between_46_23 = 1305
+between_23_47 = 691
+between_47_23 = 691
+between_24_25 = 1426
+between_25_24 = 1426
+between_24_26 = 1468
+between_26_24 = 1468
+between_24_27 = 1284
+between_27_24 = 1284
+between_24_28 = 776
+between_28_24 = 776
+between_24_29 = 1323
+between_29_24 = 1323
+between_24_30 = 1264
+between_30_24 = 1264
+between_24_31 = 661
+between_31_24 = 661
+between_24_32 = 818
+between_32_24 = 818
+between_24_33 = 438
+between_33_24 = 438
+between_24_34 = 1649
+between_34_24 = 1649
+between_24_35 = 1163
+between_35_24 = 1163
+between_24_36 = 1473
+between_36_24 = 1473
+between_24_37 = 1187
+between_37_24 = 1187
+between_24_38 = 455
+between_38_24 = 455
+between_24_39 = 749
+between_39_24 = 749
+between_24_40 = 822
+between_40_24 = 822
+between_24_41 = 882
+between_41_24 = 882
+between_24_42 = 1454
+between_42_24 = 1454
+between_24_43 = 1262
+between_43_24 = 1262
+between_24_44 = 1710
+between_44_24 = 1710
+between_24_45 = 945
+between_45_24 = 945
+between_24_46 = 481
+between_46_24 = 481
+between_24_47 = 445
+between_47_24 = 445
+between_25_26 = 2890
+between_26_25 = 2890
+between_25_27 = 2706
+between_27_25 = 2706
+between_25_28 = 1064
+between_28_25 = 1064
+between_25_29 = 2745
+between_29_25 = 2745
+between_25_30 = 2685
+between_30_25 = 2685
+between_25_31 = 1387
+between_31_25 = 1387
+between_25_32 = 2261
+between_32_25 = 2261
+between_25_33 = 1544
+between_33_25 = 1544
+between_25_34 = 518
+between_34_25 = 518
+between_25_35 = 2585
+between_35_25 = 2585
+between_25_36 = 2895
+between_36_25 = 2895
+between_25_37 = 2609
+between_37_25 = 2609
+between_25_38 = 1362
+between_38_25 = 1362
+between_25_39 = 2171
+between_39_25 = 2171
+between_25_40 = 1697
+between_40_25 = 1697
+between_25_41 = 546
+between_41_25 = 546
+between_25_42 = 2876
+between_42_25 = 2876
+between_25_43 = 2683
+between_43_25 = 2683
+between_25_44 = 675
+between_44_25 = 675
+between_25_45 = 2367
+between_45_25 = 2367
+between_25_46 = 1903
+between_46_25 = 1903
+between_25_47 = 984
+between_47_25 = 984
+between_26_27 = 329
+between_27_26 = 329
+between_26_28 = 2202
+between_28_26 = 2202
+between_26_29 = 151
+between_29_26 = 151
+between_26_30 = 766
+between_30_26 = 766
+between_26_31 = 1795
+between_31_26 = 1795
+between_26_32 = 755
+between_32_26 = 755
+between_26_33 = 1668
+between_33_26 = 1668
+between_26_34 = 3112
+between_34_26 = 3112
+between_27_41 = 2162
+between_41_27 = 2162
+between_26_35 = 439
+between_35_26 = 439
+between_26_36 = 116
+between_36_26 = 116
+between_26_37 = 967
+between_37_26 = 967
+between_26_38 = 1750
+between_38_26 = 1750
+between_26_39 = 1154
+between_39_26 = 1154
+between_26_40 = 2012
+between_40_26 = 2012
+between_26_41 = 2345
+between_41_26 = 2345
+between_26_42 = 118
+between_42_26 = 118
+between_26_43 = 597
+between_43_26 = 597
+between_26_44 = 3065
+between_44_26 = 3065
+between_26_45 = 800
+between_45_26 = 800
+between_26_46 = 1108
+between_46_26 = 1108
+between_26_47 = 1907
+between_47_26 = 1907
+between_27_28 = 1937
+between_28_27 = 1937
+between_27_29 = 205
+between_29_27 = 205
+between_27_30 = 451
+between_30_27 = 451
+between_27_31 = 1612
+between_31_27 = 1612
+between_27_32 = 489
+between_32_27 = 489
+between_27_33 = 1403
+between_33_27 = 1403
+between_27_34 = 2929
+between_34_27 = 2929
+between_27_35 = 127
+between_35_27 = 127
+between_27_36 = 246
+between_36_27 = 246
+between_27_37 = 651
+between_37_27 = 651
+between_27_38 = 1567
+between_38_27 = 1567
+between_27_39 = 843
+between_39_27 = 843
+between_27_40 = 1701
+between_40_27 = 1701
+between_27_42 = 379
+between_42_27 = 379
+between_27_43 = 282
+between_43_27 = 282
+between_28_35 = 1816
+between_35_28 = 1816
+between_27_44 = 2882
+between_44_27 = 2882
+between_27_45 = 481
+between_45_27 = 481
+between_27_46 = 925
+between_46_27 = 925
+between_27_47 = 1724
+between_47_27 = 1724
+between_28_29 = 2059
+between_29_28 = 2059
+between_28_30 = 1748
+between_30_28 = 1748
+between_28_31 = 1085
+between_31_28 = 1085
+between_28_32 = 1449
+between_32_28 = 1449
+between_28_33 = 535
+between_33_28 = 535
+between_28_36 = 2164
+between_36_28 = 2164
+between_28_37 = 1594
+between_37_28 = 1594
+between_28_38 = 867
+between_38_28 = 867
+between_28_39 = 1211
+between_39_28 = 1211
+between_28_40 = 688
+between_40_28 = 688
+between_28_41 = 625
+between_41_28 = 625
+between_28_42 = 2189
+between_42_28 = 2189
+between_28_43 = 1824
+between_43_28 = 1824
+between_28_44 = 1494
+between_44_28 = 1494
+between_28_45 = 1539
+between_45_28 = 1539
+between_28_46 = 1249
+between_46_28 = 1249
+between_28_47 = 492
+between_47_28 = 492
+between_29_30 = 642
+between_30_29 = 642
+between_29_31 = 1649
+between_31_29 = 1649
+between_29_32 = 609
+between_32_29 = 609
+between_29_33 = 1523
+between_33_29 = 1523
+between_29_34 = 2966
+between_34_29 = 2966
+between_29_35 = 293
+between_35_29 = 293
+between_29_36 = 163
+between_36_29 = 163
+between_29_37 = 843
+between_37_29 = 843
+between_29_38 = 1605
+between_38_29 = 1605
+between_29_39 = 990
+between_39_29 = 990
+between_29_40 = 1846
+between_40_29 = 1846
+between_29_41 = 2199
+between_41_29 = 2199
+between_29_42 = 158
+between_42_29 = 158
+between_29_43 = 473
+between_43_29 = 473
+between_29_44 = 2920
+between_44_29 = 2920
+between_29_45 = 654
+between_45_29 = 654
+between_29_46 = 963
+between_46_29 = 963
+between_29_47 = 1762
+between_47_29 = 1762
+between_30_31 = 1647
+between_31_30 = 1647
+between_30_32 = 479
+between_32_30 = 479
+between_30_33 = 1215
+between_33_30 = 1215
+between_30_34 = 2909
+between_34_30 = 2909
+between_34_41 = 811
+between_41_34 = 811
+between_30_35 = 389
+between_35_30 = 389
+between_30_36 = 683
+between_36_30 = 683
+between_30_37 = 227
+between_37_30 = 227
+between_30_38 = 1594
+between_38_30 = 1594
+between_30_39 = 539
+between_39_30 = 539
+between_30_40 = 1363
+between_40_30 = 1363
+between_30_41 = 2142
+between_41_30 = 2142
+between_30_42 = 815
+between_42_30 = 815
+between_30_43 = 170
+between_43_30 = 170
+between_30_44 = 2918
+between_44_30 = 2918
+between_30_45 = 318
+between_45_30 = 318
+between_30_46 = 961
+between_46_30 = 961
+between_30_47 = 1704
+between_47_30 = 1704
+between_31_32 = 1190
+between_32_31 = 1190
+between_31_33 = 933
+between_33_31 = 933
+between_31_34 = 1350
+between_34_31 = 1350
+between_31_35 = 1490
+between_35_31 = 1490
+between_31_36 = 1801
+between_36_31 = 1801
+between_31_37 = 1635
+between_37_31 = 1635
+between_31_38 = 210
+between_38_31 = 210
+between_31_39 = 1309
+between_39_31 = 1309
+between_31_40 = 1317
+between_40_31 = 1317
+between_31_41 = 922
+between_41_31 = 922
+between_31_42 = 1781
+between_42_31 = 1781
+between_31_43 = 1631
+between_43_31 = 1631
+between_31_44 = 1272
+between_44_31 = 1272
+between_31_45 = 1326
+between_45_31 = 1326
+between_31_46 = 694
+between_46_31 = 694
+between_31_47 = 593
+between_47_31 = 593
+between_32_33 = 915
+between_33_32 = 915
+between_32_34 = 2484
+between_34_32 = 2484
+between_32_35 = 368
+between_35_32 = 368
+between_32_36 = 716
+between_36_32 = 716
+between_32_37 = 517
+between_37_32 = 517
+between_32_38 = 1140
+between_38_32 = 1140
+between_32_39 = 380
+between_39_32 = 380
+between_32_40 = 1236
+between_40_32 = 1236
+between_32_41 = 1717
+between_41_32 = 1717
+between_32_42 = 743
+between_42_32 = 743
+between_32_43 = 479
+between_43_32 = 479
+between_32_44 = 2464
+between_44_32 = 2464
+between_32_45 = 163
+between_45_32 = 163
+between_32_46 = 507
+between_46_32 = 507
+between_32_47 = 1280
+between_47_32 = 1280
+between_33_34 = 1955
+between_34_33 = 1955
+between_33_35 = 1283
+between_35_33 = 1283
+between_33_36 = 1631
+between_36_33 = 1631
+between_33_37 = 1061
+between_37_33 = 1061
+between_33_38 = 733
+between_38_33 = 733
+between_33_39 = 678
+between_39_33 = 678
+between_33_40 = 388
+between_40_33 = 388
+between_33_41 = 1188
+between_41_33 = 1188
+between_33_42 = 1656
+between_42_33 = 1656
+between_33_43 = 1290
+between_43_33 = 1290
+between_33_44 = 2016
+between_44_33 = 2016
+between_33_45 = 1006
+between_45_33 = 1006
+between_33_46 = 839
+between_46_33 = 839
+between_33_47 = 769
+between_47_33 = 769
+between_34_35 = 2809
+between_35_34 = 2809
+between_34_36 = 3119
+between_36_34 = 3119
+between_34_37 = 2832
+between_37_34 = 2832
+between_34_38 = 1398
+between_38_34 = 1398
+between_34_39 = 2395
+between_39_34 = 2395
+between_34_40 = 2100
+between_40_34 = 2100
+between_34_42 = 3100
+between_42_34 = 3100
+between_34_43 = 2907
+between_43_34 = 2907
+between_34_44 = 160
+between_44_34 = 160
+between_34_45 = 2591
+between_45_34 = 2591
+between_34_46 = 2042
+between_46_34 = 2042
+between_34_47 = 1208
+between_47_34 = 1208
+between_35_36 = 351
+between_36_35 = 351
+between_35_37 = 570
+between_37_35 = 570
+between_35_38 = 1445
+between_38_35 = 1445
+between_35_39 = 720
+between_39_35 = 720
+between_35_40 = 1578
+between_40_35 = 1578
+between_35_41 = 2040
+between_41_35 = 2040
+between_35_42 = 450
+between_42_35 = 450
+between_35_43 = 220
+between_43_35 = 220
+between_35_44 = 2761
+between_44_35 = 2761
+between_35_45 = 366
+between_45_35 = 366
+between_35_46 = 803
+between_46_35 = 803
+between_35_47 = 1603
+between_47_35 = 1603
+between_36_37 = 884
+between_37_36 = 884
+between_36_38 = 1755
+between_38_36 = 1755
+between_36_39 = 1064
+between_39_36 = 1064
+between_36_40 = 1922
+between_40_36 = 1922
+between_36_41 = 2350
+between_41_36 = 2350
+between_36_42 = 229
+between_42_36 = 229
+between_36_43 = 514
+between_43_36 = 514
+between_36_44 = 3070
+between_44_36 = 3070
+between_36_45 = 711
+between_45_36 = 711
+between_36_46 = 1113
+between_46_36 = 1113
+between_36_47 = 1912
+between_47_36 = 1912
+between_37_38 = 1575
+between_38_37 = 1575
+between_37_39 = 442
+between_39_37 = 442
+between_37_40 = 1166
+between_40_37 = 1166
+between_37_41 = 2065
+between_41_37 = 2065
+between_37_42 = 1015
+between_42_37 = 1015
+between_37_43 = 371
+between_43_37 = 371
+between_37_44 = 2869
+between_44_37 = 2869
+between_37_45 = 355
+between_45_37 = 355
+between_37_46 = 951
+between_46_37 = 951
+between_37_47 = 1628
+between_47_37 = 1628
+between_38_39 = 1137
+between_39_38 = 1137
+between_38_40 = 1118
+between_40_38 = 1118
+between_38_41 = 818
+between_41_38 = 818
+between_38_42 = 1736
+between_42_38 = 1736
+between_38_43 = 1586
+between_43_38 = 1586
+between_38_44 = 1321
+between_44_38 = 1321
+between_38_45 = 1281
+between_45_38 = 1281
+between_38_46 = 649
+between_46_38 = 649
+between_38_47 = 424
+between_47_38 = 424
+between_39_40 = 859
+between_40_39 = 859
+between_39_41 = 1627
+between_41_39 = 1627
+between_39_42 = 1120
+between_42_39 = 1120
+between_39_43 = 614
+between_43_39 = 614
+between_39_44 = 2432
+between_44_39 = 2432
+between_39_45 = 390
+between_45_39 = 390
+between_39_46 = 623
+between_46_39 = 623
+between_39_47 = 1190
+between_47_39 = 1190
+between_40_41 = 1290
+between_41_40 = 1290
+between_40_42 = 1976
+between_42_40 = 1976
+between_40_43 = 1472
+between_43_40 = 1472
+between_40_44 = 2159
+between_44_40 = 2159
+between_40_45 = 1245
+between_45_40 = 1245
+between_40_46 = 1223
+between_46_40 = 1223
+between_40_47 = 1020
+between_47_40 = 1020
+between_41_42 = 2332
+between_42_41 = 2332
+between_41_43 = 2140
+between_43_41 = 2140
+between_41_44 = 871
+between_44_41 = 871
+between_41_45 = 1823
+between_45_41 = 1823
+between_41_46 = 1360
+between_46_41 = 1360
+between_41_47 = 440
+between_47_41 = 440
+between_42_43 = 648
+between_43_42 = 648
+between_42_44 = 3053
+between_44_42 = 3053
+between_42_45 = 851
+between_45_42 = 851
+between_42_46 = 1096
+between_46_42 = 1096
+between_42_47 = 1895
+between_47_42 = 1895
+between_43_44 = 2901
+between_44_43 = 2901
+between_43_45 = 316
+between_45_43 = 316
+between_43_46 = 944
+between_46_43 = 944
+between_43_47 = 1702
+between_47_43 = 1702
+between_44_45 = 2596
+between_45_44 = 2596
+between_44_46 = 1965
+between_46_44 = 1965
+between_44_47 = 1268
+between_47_44 = 1268
+between_45_46 = 643
+between_46_45 = 643
+between_45_47 = 1387
+between_47_45 = 1387
+between_46_47 = 920
+between_47_46 = 920
diff --git a/JediMasters/DWave Challenge/FoodBankHouseLookup.txt b/JediMasters/DWave Challenge/FoodBankHouseLookup.txt
new file mode 100644
index 0000000..a49bdb0
--- /dev/null
+++ b/JediMasters/DWave Challenge/FoodBankHouseLookup.txt	
@@ -0,0 +1,48 @@
+0,Columbus OH,1961 Cleveland Ave
+1,Columbus OH,1944 Frank Dr
+2,Columbus OH,3432 Main St
+3,Columbus OH,78 High Point St.
+4,Columbus OH,31 York Drive
+5,Columbus OH,9070 Ramblewood Ave.
+6,Columbus OH,98 Galvin Street
+7,Columbus OH,51 Columbia Street
+8,Columbus OH,8 Old Jackson Avenue
+9,Columbus OH,11 High Noon St.
+10,Columbus OH,7085 Myers St.
+11,Columbus OH,5 South Greystone Street
+12,Columbus OH,82 Paris Hill St.
+13,Columbus OH,9980 Brookside Dr.
+14,Columbus OH,23 S. Woodside Road
+15,Columbus OH,7798 Brown Drive
+16,Columbus OH,70 Coffee Ave.
+17,Columbus OH,7 Marvon Road
+18,Columbus OH,737 Pacific Street
+19,Columbus OH,23 Oak Meadow Drive
+20,Columbus OH,44 NW. San Pablo St.
+21,Columbus OH,8858 Canal Lane
+22,Columbus OH,417 Theatre Lane
+23,Columbus OH,16 Park Ave.
+24,Columbus OH,821 Garfield Rd.
+25,Columbus OH,977 Elizabeth Lane
+26,Columbus OH,9697 Bowman Drive
+27,Columbus OH,32 Southampton Street
+28,Columbus OH,7750 John Drive
+29,Columbus OH,8043 N. Glen Ridge Dr.
+30,Columbus OH,9304 Brickyard St.
+31,Columbus OH,7297 North Park Road
+32,Columbus OH,7678 Clinton Road
+33,Columbus OH,953 South Fieldstone Drive
+34,Columbus OH,69 Buckingham St.
+35,Columbus OH,3 Hillcrest Court
+36,Columbus OH,685 Oakland Court
+37,Columbus OH,7152 Augusta St.
+38,Columbus OH,349 W. Rockaway Drive
+39,Columbus OH,26 Walnut Drive
+40,Columbus OH,287 Military Dr.
+41,Columbus OH,64 Illinois Lane
+42,Columbus OH,7655 Catherine Road
+43,Columbus OH,92 South Country Club Rd.
+44,Columbus OH,8891 Vine Street
+45,Columbus OH,192 West Albany St.
+46,Columbus OH,19 Center Street
+47,Columbus OH,51 Lexington Dr.
diff --git a/JediMasters/DWave Challenge/FoodBankRouteDeliveryOptimization.py b/JediMasters/DWave Challenge/FoodBankRouteDeliveryOptimization.py
new file mode 100644
index 0000000..5182346
--- /dev/null
+++ b/JediMasters/DWave Challenge/FoodBankRouteDeliveryOptimization.py	
@@ -0,0 +1,145 @@
+from dwave_qbsolv import QBSolv
+from dwave.system import LeapHybridSampler
+import numpy as np
+import matplotlib.pyplot as plt
+import re
+#from mpl_toolkits.basemap import Basemap
+
+# Number of houses in our food delivery problem
+node_count = 48  # Number of houses
+salesmen_count = 8  # Number of volunteers
+# Number_Routes = (int)(node_count/salesmen_count)
+
+
+def key(a, b):
+    """key returns a key for QUBO dictionary."""
+    return a * node_count + b
+
+
+# Tunable parameters.
+A = 8500
+B = 1
+chainstrength = 4500
+numruns = 100
+
+
+def food_bank_distances():
+    """food_bank_distances loads nodes and edges from food bank files
+    and returns distance matrix, where rows and columns represent a cities and each entry is a distance.
+    """
+    fn = "FoodBankClientDeliveryData.txt"
+    try:
+        with open(fn, "r") as myfile:
+            distance_text = myfile.readlines()
+            myfile.close()
+    except IOError:
+        print(f"{fn} file is missing")
+        exit(1)
+    distances = [[0 for z in range(node_count)]
+                 for y in range(node_count)]
+    for i in distance_text:
+        if re.search("^between", i):
+            m = re.search("^between_(\d+)_(\d+) = (\d+)", i)
+            house_a = int(m.group(1))
+            house_b = int(m.group(2))
+            distances[house_a][house_b] = distances[house_b][house_a] = int(
+                m.group(3))
+    return distances
+
+
+def qubo(distances):
+    """qubo generates and returns QUBO dictionary"""
+    q = {}
+    for i in range(node_count*node_count):
+        for j in range(node_count*node_count):
+            q.update({(i, j): 0})
+
+    # Constraint that each row has exactly one 1, constant = N*A
+    for v in range(node_count):
+        for j in range(node_count):
+            q[(key(v, j), key(v, j))] += -1*A
+            for k in range(j+1, node_count):
+                q[(key(v, j), key(v, k))] += 2*A
+                q[(key(v, k), key(v, j))] += 2*A
+
+    # Constraint that each col has exactly one 1
+    for j in range(node_count):
+        for v in range(node_count):
+            q[(key(v, j), key(v, j))] += -1*A
+            for w in range(v+1, node_count):
+                q[(key(v, j), key(w, j))] += 2*A
+                q[(key(w, j), key(v, j))] += 2*A
+
+    # Objective that minimizes distance
+    for u in range(node_count):
+        for v in range(node_count):
+            if u != v:
+                for j in range(node_count):
+                    q[(key(u, j), key(
+                        v, (j+1) % node_count))] += B*distances[u][v]
+
+    return q
+
+
+distances = food_bank_distances()
+Q = qubo(distances)
+
+# Run the QUBO using qbsolv (classically solving)
+resp = QBSolv().sample_qubo(Q)
+
+# Quantum Solution
+
+#from dwave_qbsolv import QBSolv
+#from dwave.system.samplers import DWaveSampler
+#from dwave.system.composites import EmbeddingComposite
+#subqubo_size = 30
+#sampler = EmbeddingComposite(DWaveSampler())
+#resp = QBSolv().sample_qubo(Q, solver=sampler, solver_limit=subqubo_size)
+
+# # Use LeapHybridSampler() for faster QPU access
+# sampler = LeapHybridSampler()
+# resp = sampler.sample_qubo(Q)
+
+# First solution is the lowest energy solution found
+sample = next(iter(resp))
+
+# Display energy for best solution found
+print('Energy: ', next(iter(resp.data())).energy)
+
+# Print route for solution found
+route = [-1]*node_count
+for node in sample:
+    if sample[node] > 0:
+        j = node % node_count
+        v = (node-j)/node_count
+        route[j] = int(v)
+
+# Compute and display total mileage
+mileage = 0
+for i in range(node_count):
+    mileage += distances[route[i]][route[(i+1) % node_count]]
+
+print('Mileage: ', mileage)
+
+# Print route:
+
+houses = [',']*node_count
+cities = [',']*node_count
+
+with open('FoodBankHouseLookup.txt', "r") as myfile:
+    address_text = myfile.readlines()
+    myfile.close()
+
+for i in address_text:
+    index, city, house = i.split(',')
+    cities[int(index)] = city.rstrip()
+    houses[int(index)] = house.rstrip()
+
+output = open('Food_Bank_Delivery.route.offline', 'w')
+
+r_no = 1
+for i in range(node_count):
+    if(i % (int)(node_count/salesmen_count) == 0):
+        output.write('\n' + 'Route ' + str(r_no) + '\n')
+        r_no = r_no + 1
+    output.write(houses[route[i]] + ',' + cities[route[i]] + '\n')
diff --git a/JediMasters/DWave Challenge/Food_Bank_Delivery.route.txt b/JediMasters/DWave Challenge/Food_Bank_Delivery.route.txt
new file mode 100644
index 0000000..25cd856
--- /dev/null
+++ b/JediMasters/DWave Challenge/Food_Bank_Delivery.route.txt	
@@ -0,0 +1,64 @@
+
+Route 1
+3432 Main St,Columbus OH
+9070 Ramblewood Ave.,Columbus OH
+7655 Catherine Road,Columbus OH
+7 Marvon Road,Columbus OH
+23 S. Woodside Road,Columbus OH
+5 South Greystone Street,Columbus OH
+
+Route 2
+8 Old Jackson Avenue,Columbus OH
+1961 Cleveland Ave,Columbus OH
+287 Military Dr.,Columbus OH
+7152 Augusta St.,Columbus OH
+92 South Country Club Rd.,Columbus OH
+82 Paris Hill St.,Columbus OH
+
+Route 3
+78 High Point St.,Columbus OH
+977 Elizabeth Lane,Columbus OH
+26 Walnut Drive,Columbus OH
+8858 Canal Lane,Columbus OH
+9304 Brickyard St.,Columbus OH
+19 Center Street,Columbus OH
+
+Route 4
+953 South Fieldstone Drive,Columbus OH
+8043 N. Glen Ridge Dr.,Columbus OH
+32 Southampton Street,Columbus OH
+7678 Clinton Road,Columbus OH
+737 Pacific Street,Columbus OH
+7798 Brown Drive,Columbus OH
+
+Route 5
+7297 North Park Road,Columbus OH
+7085 Myers St.,Columbus OH
+44 NW. San Pablo St.,Columbus OH
+349 W. Rockaway Drive,Columbus OH
+98 Galvin Street,Columbus OH
+685 Oakland Court,Columbus OH
+
+Route 6
+3 Hillcrest Court,Columbus OH
+9697 Bowman Drive,Columbus OH
+51 Columbia Street,Columbus OH
+9980 Brookside Dr.,Columbus OH
+23 Oak Meadow Drive,Columbus OH
+70 Coffee Ave.,Columbus OH
+
+Route 7
+192 West Albany St.,Columbus OH
+64 Illinois Lane,Columbus OH
+31 York Drive,Columbus OH
+11 High Noon St.,Columbus OH
+69 Buckingham St.,Columbus OH
+8891 Vine Street,Columbus OH
+
+Route 8
+7750 John Drive,Columbus OH
+16 Park Ave.,Columbus OH
+1944 Frank Dr,Columbus OH
+821 Garfield Rd.,Columbus OH
+51 Lexington Dr.,Columbus OH
+417 Theatre Lane,Columbus OH
diff --git a/JediMasters/DWave Challenge/Presentation .pdf b/JediMasters/DWave Challenge/Presentation .pdf
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diff --git a/JediMasters/DWave Challenge/README.md b/JediMasters/DWave Challenge/README.md
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+We have provided a brief explanation of the TSP Algorithm used for the food bank solution below :
+
+## Mapping Traveling Salesman Problem into QUBO (Quadratic Unconstrained Binary Optimization)
+In order to find approximately optimal solutions of Traveling Salesman Problem we map the problem into QUBO model.  
+A route is represented as a matrix. For example:
+
+<pre>
+Visit Order / City  A B C D  
+1                   1 0 0 0  
+2                   0 0 0 1  
+3                   0 1 0 0  
+4                   0 0 1 0  
+</pre>
+
+There are 4 cities in this example: A, B, C, D. This matrix represents a route where city A is visited first, then D, then B and then C. Optionally, salesman returns to city A at the end. The row number represents the order of a city in a travel route. To make this route valid only one `1` should be allowed in every row and column.
+
+In our case annealing process can be thought as a process that assignes binary values `0` or `1` to the matrix entries in order to find an approximately optimal Traveling Salesman route. Annealing process is going to try all possible routes, thus trying all kind of combinations of `0` and `1` in the matrix.
+Without constraints, most paths will not be valid. For example,
+<pre>
+Visit Order / City  A B C D  
+1                   0 1 0 0  
+2                   0 1 0 0  
+3                   0 0 0 1  
+4                   0 1 0 0  
+</pre>
+is not a valid path, because city B is visited 3 times. Every city must be visited exactly once.
+Also, 
+<pre>
+Visit Order / City  A B C D  
+1                   1 1 1 0  
+2                   0 0 0 0  
+3                   0 0 0 1  
+4                   0 0 0 0  
+</pre>
+is not a valid path, because cities A, B and C are all visited in step one. Only one city can be visited in a single step.
+
+In order to make annealing process to only consider valid paths we are going to apply constraints to our function.
+
+The first constraint is to make every city appear exactly once in a route:
+<pre>
+A Σ<sub>city</sub>(1 - Σ<sub>step</sub>(x<sub>city,step</sub>))
+</pre>
+Here **A** is some constant, the first sum runs across columns in the matrix above (ensuring every city gets this constraint applied to it), the second sum runs accross all binary values in a given column (ensuring every city is visited exactly once). Minimum of this function occurres when every city gets included exactly once into a route.
+
+The second constraint is to make exactly one city to be visited in one step of the route:
+<pre>
+A Σ<sub>step</sub>(1 - Σ<sub>city</sub>(x<sub>city,step</sub>))
+</pre>
+Here **A** is the same constant as above, the first sum runs across rows in the matrix above (ensuring every step gets this constraint applied to), the second sum runs accross all binary values in a given row (ensuring exactly one city is visited in a given step). Minimum of this function occurres when every step involves visiting exactly one city.
+
+To this point we can generate all valid paths. Now we need to minimize sum of weights of nodes (distances between cities) on possible valid paths. To do that we add this term to our function:
+<pre>
+B Σ<sub>city_u,city_v</sub>(W<sub>city_u,city_v</sub> Σ<sub>step</sub>(x<sub>city_u,step</sub>x<sub>city_v,step+1</sub>))
+</pre>
+Here **W<sub>u,v</sub>** is the length between two cities (weight of an edge). **B** must be small enough to ensure path being valid is more important than path being optimal:
+<pre>
+0 < B max(W<sub>u,v</sub>) < A
+</pre>
diff --git a/JediMasters/IBM_Galaxy/Code b/JediMasters/IBM_Galaxy/Code
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+++ b/JediMasters/IBM_Galaxy/Code
@@ -0,0 +1 @@
+
diff --git a/JediMasters/IBM_Galaxy/IBMQ_Quantum image processing .pptx b/JediMasters/IBM_Galaxy/IBMQ_Quantum image processing .pptx
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diff --git a/JediMasters/IBM_Galaxy/frqi_qkt.py b/JediMasters/IBM_Galaxy/frqi_qkt.py
new file mode 100644
index 0000000..07e523c
--- /dev/null
+++ b/JediMasters/IBM_Galaxy/frqi_qkt.py
@@ -0,0 +1,81 @@
+# This file contains the implementation of the FRQI algorithm in Qiskit
+
+import qiskit as qk
+from qiskit import Aer,execute
+from math import pi, sqrt, acos
+from utils import r_num, g_num, b_num
+
+# this function takes the results for the measurements and returns the angles it finds
+def probs(results, num_shots):
+
+	prob = {}
+	
+	# create every possible basis vector and add the estimated prob of that vector being measured
+	chars = ["0", "1"]
+	for c in chars:
+		for d in chars:
+			for e in chars:
+				state = c + d + e
+				if (state in results):
+					prob[state] = results[state]/num_shots
+				else:
+					prob[state] = 0.
+
+	# using the probability of every pixel being 0, estimate the angle stored
+	angles = []
+	for c in chars:
+		for d in chars:
+			zero = "0" + c + d
+			one = "1" + c + d
+			zero_prob = prob[zero]/(prob[zero] + prob[one])
+			# prob(|0>) = cos^2(theta), so to solve for theta need to do inverse
+			angles.append(acos(sqrt(zero_prob)))
+	
+	return angles
+
+# this function creates the FRQI state for the image
+def run(angles, num_shots):
+
+	qr = qk.QuantumRegister(3)
+	cr = qk.ClassicalRegister(3)
+
+	# create a circuit
+	qc= qk.QuantumCircuit(qr,cr)
+
+	#Creating the Hadamard State
+	qc.h(qr[0])
+	qc.h(qr[1])
+
+	i = 0
+	# for every angle, add to the circuit the encoding of that angle
+	for ang in angles:
+
+		# to make sure we are transforming the correct vector, need to NOT certain qubits
+		qc.x(qr[0])
+
+		if(i%2 == 0):
+			qc.x(qr[1])
+
+		# The C^2-Ry operation
+		qc.cu3(ang,0,0,qr[0],qr[2])
+		qc.cx(qr[0],qr[1])
+		qc.cu3(-ang,0,0,qr[1],qr[2])
+		qc.cx(qr[0],qr[1])
+		qc.cu3(ang,0,0,qr[1],qr[2])
+
+		i += 1
+
+	qc.barrier(qr)
+	qc.measure(qr,cr)
+
+	# run the circuit
+	backend_sim = Aer.get_backend('qasm_simulator')
+	job_sim = execute(qc, backend_sim, shots=num_shots)
+	result_sim = job_sim.result()
+
+	# get the dictionary that contains number of times each outcome was measured
+	counts = result_sim.get_counts(qc)
+
+	new_angles = probs(counts, num_shots)
+
+	return new_angles
\ No newline at end of file
diff --git a/JediMasters/IBM_Galaxy/ibmq_galaxy_test.ipynb b/JediMasters/IBM_Galaxy/ibmq_galaxy_test.ipynb
new file mode 100644
index 0000000..aa7e1c9
--- /dev/null
+++ b/JediMasters/IBM_Galaxy/ibmq_galaxy_test.ipynb
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Galaxy Classification with QIMP (FRQI) on IBM Q \n",
+    "\n",
+    "## Group: JediMasters\n",
+    "\n",
+    "#### FRQI: \n",
+    "\n",
+    "The algorithm transforms a state with all qubits in the |0⟩ state to the FRQI state, which stores an RGB image. Once the image has been stored and the state is measured a number of times to attempt to recover the RGB values in the image. \n",
+    "There are three main phases for the algorithm: \n",
+    "\n",
+    "1.\tThe first phase involves converting an array of RGB triplets, which represent an image, into angles in [0, π/2]. \n",
+    "\n",
+    "2.\tThe  angles are then sent to a function which uses two operations to store them in a quantum state, keeping track of which angle represents a color in which pixel of the image. The algorithm starts with 2n + 1 qubits in the |0⟩ state. The first operation applies Hadamard gates to 2n of the qubits. The second operation is a succession of rotations about the Y axis by 2θi controlled by 2n qubits. CNOT and controlled y-rotation gates are applied to accomplish rotating by 2θ only the basis vectors corresponding to the pixels whose colour is being encoded in the state. Once all the colours have been encoded, the state is finalised.\n",
+    "\n",
+    "3.\tRecovery of the image: this is done by repeatedly measuring the state to get estimates for the probability of measure |0⟩ for each pixel and using that to recover θi. The θ values are then converted back to RGB values and the image is recovered.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import IBMQ, QuantumCircuit, ClassicalRegister, QuantumRegister\n",
+    "from qiskit import execute, QuantumRegister\n",
+    "from qiskit.qasm import pi\n",
+    "from qiskit.tools.visualization import plot_histogram, circuit_drawer\n",
+    "from qiskit import execute, Aer, BasicAer\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "configrc.store_credentials:WARNING:2020-07-22 12:01:57,632: Credentials already present. Set overwrite=True to overwrite.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import quantum_edge_detection as qed\n",
+    "\n",
+    "from qiskit import IBMQ\n",
+    "IBMQ.save_account('5410725d63b096bb909fb3208b6367a7b2e7f094acfe6fce597e4d84f1149a4f6f8a7f38532dc5335a5074a5dbaef3def1c0a995f0b11f1de411266ead2cf551')\n",
+    "\n",
+    "#IBMQ.load_account('5410725d63b096bb909fb3208b6367a7b2e7f094acfe6fce597e4d84f1149a4f6f8a7f38532dc5335a5074a5dbaef3def1c0a995f0b11f1de411266ead2cf551')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anc = QuantumRegister(1, \"anc\")\n",
+    "img = QuantumRegister(11, \"img\")\n",
+    "anc2 = QuantumRegister(1, \"anc2\")\n",
+    "c = ClassicalRegister(12)\n",
+    "\n",
+    "qc = QuantumCircuit(anc, img, anc2, c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING! APOGEE environment variable SDSS_LOCAL_SAS_MIRROR not set\n",
+      "WARNING! Gaia environment variable GAIA_TOOLS_DATA not set\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras import utils\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import pylab as plt\n",
+    "\n",
+    "\n",
+    "from astroNN.datasets import galaxy10\n",
+    "from astroNN.datasets.galaxy10 import galaxy10cls_lookup, galaxy10_confusion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Galaxy10.h5: 100%|█████████▉| 210M/210M [04:36<00:00, 784kB/s]    "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloaded Galaxy10 successfully to /home/sgangoly/.astroNN/datasets/Galaxy10.h5\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Galaxy10.h5: 210MB [04:36, 759kB/s]                           \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load images and labels (will download automatically at the first time)\n",
+    "# First time downloading location will be ~/.astroNN/datasets/\n",
+    "images, labels = galaxy10.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===================Data Inspection===================\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===============Data Inspection Finished===============\n"
+     ]
+    }
+   ],
+   "source": [
+    "# To convert the labels to categorical 10 classes\n",
+    "labels = utils.to_categorical(labels, 10)\n",
+    "\n",
+    "# Select 10 of the images to inspect\n",
+    "img = None\n",
+    "plt.ion()\n",
+    "print('===================Data Inspection===================')\n",
+    "for counter, i in enumerate(range(np.random.randint(0, labels.shape[0], size=10).shape[0])):\n",
+    "    img = plt.imshow(images[i])\n",
+    "    plt.title('Class {}: {} \\n Random Demo images {} of 10'.format(np.argmax(labels[i]), galaxy10cls_lookup(labels[i]), counter+1))\n",
+    "    plt.draw()\n",
+    "    plt.pause(2.)\n",
+    "plt.close('all')\n",
+    "print('===============Data Inspection Finished===============')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "imageNames = [\"refdisk.jpg\", \"ecross.jpg\", \"disk4.jpg\"]\n",
+    "imageNum1 = 0\n",
+    "imageNum2 = 2\n",
+    "\n",
+    "image1 = Image.open(imageNames[imageNum1]).convert('LA')\n",
+    "image2 = Image.open(imageNames[imageNum2]).convert('LA')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fs = !ls *.png\n",
+    "\n",
+    "import IPython.display as dp\n",
+    "\n",
+    "# create list of image objects\n",
+    "images = []\n",
+    "for ea in fs:\n",
+    "    images.append(dp.Image(filename=ea, format='png'))\n",
+    "\n",
+    "# display all images\n",
+    "for ea in images:\n",
+    "    dp.display_png(ea)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "from resizeimage import resizeimage\n",
+    "def image_normalization(image):\n",
+    "    image = resizeimage.resize_cover(image, [32, 32])\n",
+    "    w, h = 32, 32\n",
+    "    image = np.array([[image.getpixel((x,y))[0] for x in range(w)] for y in range(h)])\n",
+    "\n",
+    "    # 2-dimentional data convert to 1-dimentional array\n",
+    "    image = image.flatten()\n",
+    "    # change type\n",
+    "    image = image.astype('float64')\n",
+    "    # Normalization(0~pi/2)\n",
+    "    image /= 255.0\n",
+    "    generated_image = np.arcsin(image)\n",
+    "\n",
+    "    return generated_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image1 = image_normalization(image1)\n",
+    "image2 = image_normalization(image2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import utils\n",
+    "import frqi_qkt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "\n",
+    "\t# use 10 shots\n",
+    "\tif (len(sys.argv) == 1):\n",
+    "\t\tnum_shots = 10\n",
+    "\telse:\n",
+    "\t\tnum_shots = int(sys.argv[1])\n",
+    "\n",
+    "\timage = utils.get_image()\n",
+    "\tsize = len(image)\n",
+    "\n",
+    "\tangles = []\n",
+    "\t# convert rgb pixel values into angles in [0, pi/2]\n",
+    "\tfor i in range(size):\n",
+    "\t\tfor j in range(size):\n",
+    "\t\t\tangles.append((utils.rgb_to_theta(image[i][j])))\n",
+    "\n",
+    "\tnew_angles = frqi_qkt.run(angles, num_shots)\n",
+    "\n",
+    "\tim = []\n",
+    "\ti = 0\n",
+    "\t# convert recovered angles into rgb values\n",
+    "\tfor ang in new_angles:\n",
+    "\n",
+    "\t\trgb = utils.theta_to_rgb(ang)\n",
+    "\t\tif (i % 2 == 0):\n",
+    "\t\t\trow = []\n",
+    "\n",
+    "\t\trow.append(rgb)\n",
+    "\n",
+    "\t\tif (i % 2 != 0):\n",
+    "\t\t\tim.append(row)\n",
+    "\n",
+    "\t\ti += 1\n",
+    "\n",
+    "\t# image using the recovered RGB values\n",
+    "\tnew_image = utils.make_image(im)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ref: https://qiskit.org/textbook/ch-machine-learning/machine-learning-qiskit-pytorch.html\n",
+    "\n",
+    "import torch\n",
+    "from torch.autograd import Function\n",
+    "#from torchvision import datasets, transforms\n",
+    "import torch.optim as optim\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "import qiskit\n",
+    "from qiskit.visualization import *"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a \"Quantum Class\" with Qiskit \n",
+    "Hard code the circuit for simplicity and use a  RY− rotation by the angle  θ  to train the output of our circuit. \n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "measure the output in the  z− basis -->  calculate the  $σ_{z}$  expectation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class QuantumCircuit:\n",
+    "    \"\"\" \n",
+    "    This class provides a simple interface for interaction \n",
+    "    with the quantum circuit \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    def __init__(self, n_qubits, backend, shots):\n",
+    "        # --- Circuit definition ---\n",
+    "        self._circuit = qiskit.QuantumCircuit(n_qubits)\n",
+    "        \n",
+    "        all_qubits = [i for i in range(n_qubits)]\n",
+    "        self.theta = qiskit.circuit.Parameter('theta')\n",
+    "        \n",
+    "        self._circuit.h(all_qubits)\n",
+    "        self._circuit.barrier()\n",
+    "        self._circuit.ry(self.theta, all_qubits)\n",
+    "        \n",
+    "        self._circuit.measure_all()\n",
+    "        # ---------------------------\n",
+    "\n",
+    "        self.backend = backend\n",
+    "        self.shots = shots\n",
+    "    \n",
+    "    def run(self, thetas):\n",
+    "        job = qiskit.execute(self._circuit, \n",
+    "                             self.backend, \n",
+    "                             shots = self.shots,\n",
+    "                             parameter_binds = [{self.theta: theta} for theta in thetas])\n",
+    "        result = job.result().get_counts(self._circuit)\n",
+    "        \n",
+    "        counts = np.array(list(result.values()))\n",
+    "        states = np.array(list(result.keys())).astype(float)\n",
+    "        \n",
+    "        # Compute probabilities for each state\n",
+    "        probabilities = counts / self.shots\n",
+    "        # Get state expectation\n",
+    "        expectation = np.sum(states * probabilities)\n",
+    "        \n",
+    "        return np.array([expectation])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected value for rotation pi 0.58\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐ ░ ┌───────────┐ ░ ┌─┐\n",
+       "   q_0: ┤ H ├─░─┤ RY(theta) ├─░─┤M├\n",
+       "        └───┘ ░ └───────────┘ ░ └╥┘\n",
+       "meas_0: ═════════════════════════╩═\n",
+       "                                   </pre>"
+      ],
+      "text/plain": [
+       "        ┌───┐ ░ ┌───────────┐ ░ ┌─┐\n",
+       "   q_0: ┤ H ├─░─┤ RY(theta) ├─░─┤M├\n",
+       "        └───┘ ░ └───────────┘ ░ └╥┘\n",
+       "meas_0: ═════════════════════════╩═\n",
+       "                                   "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulator = qiskit.Aer.get_backend('qasm_simulator')\n",
+    "\n",
+    "circuit = QuantumCircuit(1, simulator, 100)\n",
+    "print('Expected value for rotation pi {}'.format(circuit.run([np.pi])[0]))\n",
+    "circuit._circuit.draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a \"Quantum-Classical Class\" with PyTorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class HybridFunction(Function):\n",
+    "    \"\"\" Hybrid quantum - classical function definition \"\"\"\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def forward(ctx, input, quantum_circuit, shift):\n",
+    "        \"\"\" Forward pass computation \"\"\"\n",
+    "        ctx.shift = shift\n",
+    "        ctx.quantum_circuit = quantum_circuit\n",
+    "\n",
+    "        expectation_z = ctx.quantum_circuit.run(input[0].tolist())\n",
+    "        result = torch.tensor([expectation_z])\n",
+    "        ctx.save_for_backward(input, result)\n",
+    "\n",
+    "        return result\n",
+    "        \n",
+    "    @staticmethod\n",
+    "    def backward(ctx, grad_output):\n",
+    "        \"\"\" Backward pass computation \"\"\"\n",
+    "        input, expectation_z = ctx.saved_tensors\n",
+    "        input_list = np.array(input.tolist())\n",
+    "        \n",
+    "        shift_right = input_list + np.ones(input_list.shape) * ctx.shift\n",
+    "        shift_left = input_list - np.ones(input_list.shape) * ctx.shift\n",
+    "        \n",
+    "        gradients = []\n",
+    "        for i in range(len(input_list)):\n",
+    "            expectation_right = ctx.quantum_circuit.run(shift_right[i])\n",
+    "            expectation_left  = ctx.quantum_circuit.run(shift_left[i])\n",
+    "            \n",
+    "            gradient = torch.tensor([expectation_right]) - torch.tensor([expectation_left])\n",
+    "            gradients.append(gradient)\n",
+    "        gradients = np.array([gradients]).T\n",
+    "        return torch.tensor([gradients]).float() * grad_output.float(), None, None\n",
+    "\n",
+    "class Hybrid(nn.Module):\n",
+    "    \"\"\" Hybrid quantum - classical layer definition \"\"\"\n",
+    "    \n",
+    "    def __init__(self, backend, shots, shift):\n",
+    "        super(Hybrid, self).__init__()\n",
+    "        self.quantum_circuit = QuantumCircuit(1, backend, shots)\n",
+    "        self.shift = shift\n",
+    "        \n",
+    "    def forward(self, input):\n",
+    "        return HybridFunction.apply(input, self.quantum_circuit, self.shift)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Train the data\n",
+    "from torchvision import datasets, transforms\n",
+    "images = 8\n",
+    "\n",
+    "X_train = datasets.galaxy10(root='./data', train=True, download=True,\n",
+    "                         transform=transforms.Compose([transforms.ToTensor()]))\n",
+    "\n",
+    "# Leaving only labels 0 and 1 \n",
+    "idx = np.append(np.where(X_train.targets == 0)[0][:images], \n",
+    "                np.where(X_train.targets == 1)[0][:images])\n",
+    "\n",
+    "X_train.data = X_train.data[idx]\n",
+    "X_train.targets = X_train.targets[idx]\n",
+    "\n",
+    "train_loader = torch.utils.data.DataLoader(X_train, batch_size=1, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'train_loader' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-69-c1ea5a466891>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'train_loader' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "images = 6\n",
+    "\n",
+    "data_iter = iter(train_loader)\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=img, figsize=(10, 3))\n",
+    "\n",
+    "while img > 0:\n",
+    "    images, targets = data_iter.__next__()\n",
+    "\n",
+    "    axes[images - 1].imshow(images[0].numpy().squeeze(), cmap='gray')\n",
+    "    axes[images - 1].set_xticks([])\n",
+    "    axes[images - 1].set_yticks([])\n",
+    "    axes[images - 1].set_title(\"Labeled: {}\".format(targets.item()))\n",
+    "    \n",
+    "    images -= 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/JediMasters/IBM_Galaxy/utils.py b/JediMasters/IBM_Galaxy/utils.py
new file mode 100644
index 0000000..fbede1d
--- /dev/null
+++ b/JediMasters/IBM_Galaxy/utils.py
@@ -0,0 +1,117 @@
+# This file contains functions which have to do with manipulating RGB images
+from PIL import Image
+import numpy as np
+from math import pi, floor
+
+# the number of possible RGB values used (default would be 256)
+r_num = 16
+g_num = r_num ** 2
+b_num = r_num ** 3
+
+# this function doubles the size of an image with 4 colors, because a 2x2 pixel image is very small
+def double_size(im):
+
+	rows = len(im)
+	cols = len(im[0])
+
+	new_im = np.zeros(((rows * 2, cols * 2, 3)))
+
+	r = im[0]
+	first = r[0]
+	second = r[int(cols/2)]
+
+	for i in range(rows):
+		for j in range(cols):
+			for k in range(3):
+				new_im[i][j][k] = first[k]
+		for j in range(cols, cols * 2):
+			for k in range(3):
+				new_im[i][j][k] = second[k]
+
+	r = im[int(rows/2)]
+	first = r[0]
+	second = r[int(cols/2)]
+
+	for i in range(rows, rows * 2):
+		for j in range(cols):
+			for k in range(3):
+				new_im[i][j][k] = first[k]
+		for j in range(cols, cols * 2):
+			for k in range(3):
+				new_im[i][j][k] = second[k]
+
+	return new_im
+
+# given an rgb image as a regular python list, converts it into a numpy array image
+def make_image(rgbs):
+
+	rows = len(rgbs)
+	cols = len(rgbs[0])
+
+	new_im = np.zeros(((rows, cols, 3)))
+
+	# convert to numpy array and scale the image back up
+	for i in range(rows):
+		for j in range(cols):
+			for k in range(3):
+				new_im[i][j][k] = rgbs[i][j][k]
+
+	new_im = scale_up(new_im)
+
+	return new_im
+
+# This function displays an image and saves it to the current directory
+def show_image(pixels):
+
+	# Create a PIL image from the NumPy array
+	image = Image.fromarray(pixels.astype('uint8'), 'RGB')
+
+	image.show()
+
+# This function takes an image as a list of RGB values and converts it to a numpy array of RGB values and scales up the values
+def scale_up(image):
+
+	rows = len(image)
+	cols = len(image[0])
+	scale = 256/r_num
+	new_im = np.zeros(((rows, cols, 3)))
+
+	for i in range(rows):
+		for j in range(rows):
+			for k in range(3):
+				new_im[i][j][k] = image[i][j][k] * scale
+
+	return new_im
+
+# this function takes an RGB triplet and converts it to an angle in [0, pi/2]
+def rgb_to_theta(rgb):
+
+	r, g, b = rgb
+	theta = (pi/2) * ((r/r_num) + (g/g_num) + (b/b_num))
+	return theta
+
+# this function takes an angle and converts it to an RGB triplett
+def theta_to_rgb(theta):
+
+	r = floor(theta/(pi/(r_num * 2)))
+	theta = theta - r*(pi/(r_num * 2))
+
+	g = floor(theta/(pi/(g_num * 2)))
+	theta = theta - g*(pi/(g_num * 2))
+
+	b = floor(theta/(pi/(b_num * 2)))
+
+	return [r,g,b]
+
+# this function has the image which will be stored and recovered
+def get_image():
+
+	pixels = np.array([[[17, 17, 17], [240, 17, 17]], [[17, 240, 17], [17, 17, 240]]])
+	scale = 256/r_num
+
+	for i in range(2):
+		for j in range(2):
+			for k in range(3):
+				pixels[i][j][k] = floor(pixels[i][j][k] / scale)
+
+	return pixels
\ No newline at end of file
diff --git a/JediMasters/README.md b/JediMasters/README.md
index 9c558e3..cdac2aa 100644
--- a/JediMasters/README.md
+++ b/JediMasters/README.md
@@ -1 +1,29 @@
-.
+# Solutions for Hackathon 2020
+
+We attempted two problems from two different hardware providers as part of this hackathon.
+
+As part of the DWave challenge, we attempted to find an efficient way of generating volunteer food delivery routes for food banks, serving the less fortunate segments of the society, using a modified version of the Multiple Travelling Salesman problem and DWave Leap Hybrid Solver.
+
+[Food Bank Delivery Solution](https://github.com/tina-seb/Hackathon2020/blob/master/JediMasters/DWave%20Challenge/CDL_Hackathon_2020_FoodBankDeliveryScheduling.ipynb)
+
+As part of the IBM Challenge, we attempted the Galaxy classification with FRQI utilizing Quantum Image Classification.
+
+[Galaxy Classification](https://github.com/tina-seb/Hackathon2020/blob/master/JediMasters/IBM_Galaxy/ibmq_galaxy_test.ipynb)
+
+## Credits
+Santanu Ganguly  
+Eunice Ofori-Addo  
+Tina Sebastian  
+Eduard Smetanin
+
+## License
+ 
+The MIT License (MIT)
+
+Copyright (c) 2020 Santanu Ganguly, Eunice Ofori-Addo, Tina Sebastian, Eduard Smetanin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.