{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Capability Correlations and Time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the second part of the analysis we will focus on how the global capabilities change with time. This part looks to answer one main question that can be divided into other severall research questions. \n",
"- How does the research and innovation in a certain field change over time? \n",
"- Are there any chronological gaps in the research throughout the years? \n",
"[...]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents\n",
"- [1. Data Extraction](#one)\n",
" - [1.1. The Years](#one-one)\n",
" - [1.2. Co-occurence matrix for the years](#one-two)\n",
" - [1.2.1. Getting the labels](#one-two-one)\n",
" - [1.2.2. Function](#one-two-two)\n",
"- [2. Analysis](#two)\n",
" - [2.1. Year Profiles](#two-one)\n",
" - [2.2. Correlation Matrix](#two-two)\n",
" - [2.2.1. Considerations](#two-two-one)\n",
" - [2.2.2. Final co-ocurrence matrix](#two-two-two)\n",
" - [2.2.3. Heatmap Clustering](#two-two-three)\n",
" - [2.3. Correlation Over Time](#two-three)\n",
" - [2.4. Research details over time](#two-four)\n",
" - [2.4.1. Outputs](#two-four-one)\n",
" - [2.4.2. Processing technologies](#two-four-two)\n",
" - [2.4.3. Feedstock](#two-four-three)\n",
" - [2.5. Contextual Relationships](#two-five)\n",
" - [2.5.1. US Regular Conventional Gas Price](#two-five-one)\n",
" - [2.5.2. Sugar Cost](#two-five-two) \n",
" - [2.6. In depth year comparison](#two-six)\n",
" - [2.6.1. Visualizing the differences](#two-six-one)\n",
" - [2.6.2. Understanding the differences](#two-six-two)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data Extraction "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by importing all of the external libraries that will be useful during the analysis. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from py2neo import Graph\n",
"import numpy as np \n",
"from pandas import DataFrame\n",
"import itertools\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import json\n",
"import math\n",
"import pandas as pd\n",
"import plotly \n",
"import plotly.graph_objs as go\n",
"import qgrid\n",
"from scipy import stats, spatial\n",
"from sklearn.cluster.bicluster import SpectralBiclustering\n",
"import operator\n",
"from IPython.display import display, HTML\n",
"\n",
"# connection to Neo4j\n",
"local_connection_url = \"http://localhost:7474/db/data\"\n",
"connection_to_graph = Graph(local_connection_url)\n",
"\n",
"# plotly credentials\n",
"plotly_config = json.load(open('plotly_config.json'))\n",
"plotly.tools.set_credentials_file(username=plotly_config['username'], api_key=plotly_config['key'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1. The Years "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not all years in the Neo4j databse contain technological assets. For this reason, two lists will be created. A completely chronological one and a database one. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The database list starts in 1938, ends in 2019 and contains 38 years.\n",
"The real list starts in 1938, ends in 2019 and contains 82 years.\n"
]
}
],
"source": [
"# query years\n",
"years_available_q = \"\"\" MATCH (n:Asset)\n",
" WITH n.year as YEAR\n",
" RETURN YEAR, count(YEAR)\n",
" ORDER BY YEAR ASC \"\"\"\n",
"\n",
"# create a list with the years where records exist\n",
"years_available = DataFrame(connection_to_graph.data(years_available_q)).as_matrix()[:, 0][:-1]\n",
"years_available = [int(year) for year in years_available]\n",
"\n",
"# create a pure range list\n",
"first_year = int(years_available[0])\n",
"last_year = int(years_available[-1])\n",
"real_years = range(first_year, last_year + 1, 1)\n",
"\n",
"# give information \n",
"print 'The database list starts in {}, ends in {} and contains {} years.'.format(years_available[0], years_available[-1], len(years_available))\n",
"print 'The real list starts in {}, ends in {} and contains {} years.'.format(real_years[0], real_years[-1], len(real_years))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have all of the years available, we can start building the technological capability matrixes, with a similar process to what was previsouly done. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2. Co-occurence matrix for the years "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2.1. Getting the labels "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by importing a few methods from the previous notebook. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def find_index(something, in_list):\n",
" return in_list.index(something)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first get all of the axis that our matrixes will take.\n",
"\n",
"We start by designing two queries that will help us get all of the labels of the matrix. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The labels of the non intersecting part: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The axis list has 289 terms.\n"
]
}
],
"source": [
"q_noInter_axis = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
" MATCH (a:Asset)-[:CONTAINS]->(out:Output)\n",
" MATCH (a:Asset)-[:CONTAINS]->(pt:ProcessingTech)\n",
" RETURN fs.term, pt.term, out.term, count(a)\n",
" \"\"\"\n",
"\n",
"feedstocks = np.unique(DataFrame(connection_to_graph.data(q_noInter_axis)).as_matrix()[:, 1]).tolist()\n",
"proc_tech = np.unique(DataFrame(connection_to_graph.data(q_noInter_axis)).as_matrix()[:, 2]).tolist()\n",
"output = np.unique(DataFrame(connection_to_graph.data(q_noInter_axis)).as_matrix()[:, 3]).tolist()\n",
"\n",
"axis_names = feedstocks + proc_tech + output \n",
"print 'The axis list has {} terms.'.format(len(axis_names))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The labels of the intersecting part:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The axis list has 342 terms.\n"
]
}
],
"source": [
"q_Inter_axis = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:{})\n",
" MATCH (a:Asset)-[:CONTAINS]->(t:{})\n",
" WHERE fs<>t \n",
" RETURN fs.term, t.term, count(a)\n",
" \"\"\"\n",
"process_variables = ['Feedstock', 'Output', 'ProcessingTech']\n",
"\n",
"# Extra labels that only appear in non-intersection queries\n",
"for category in process_variables:\n",
" data_no_intersections = DataFrame(connection_to_graph.data(q_Inter_axis.format(category, category))).as_matrix()\n",
" for column_number in range(1,3):\n",
" column = data_no_intersections[:, column_number]\n",
" for name in column:\n",
" if name not in axis_names:\n",
" axis_names.append(name)\n",
"\n",
"print 'The axis list has {} terms.'.format(len(axis_names)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1.2.2. Function "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by creating a function that given a certain year, returns the year's capability matrix. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_year_matrix(year, normalization=True):\n",
" \n",
" # define queries\n",
" q1 = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
" MATCH (a:Asset)-[:CONTAINS]->(out:Output)\n",
" MATCH (a:Asset)-[:CONTAINS]->(pt:ProcessingTech)\n",
" WHERE a.year = \"{}\"\n",
" RETURN fs.term, pt.term, out.term, count(a)\n",
" \"\"\".format(year)\n",
" \n",
" process_variables = ['Feedstock', 'Output', 'ProcessingTech']\n",
" \n",
" q2 = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:{})\n",
" MATCH (a:Asset)-[:CONTAINS]->(t:{})\n",
" WHERE fs<>t AND a.year = \"{}\"\n",
" RETURN fs.term, t.term, count(a)\n",
" \"\"\"\n",
" q3 = \"\"\"\n",
" MATCH (n:Asset)\n",
" WITH n.year as YEAR\n",
" RETURN YEAR, count(YEAR)\n",
" ORDER BY YEAR ASC\n",
" \"\"\"\n",
" \n",
" raw_data_q3 = DataFrame(connection_to_graph.data(q3)).as_matrix()\n",
" index_of_year = list(raw_data_q3[:, 0]).index('{}'.format(year))\n",
" total_documents = raw_data_q3[index_of_year, 1]\n",
"\n",
" \n",
" \n",
" \n",
" # get data\n",
" data_q1 = DataFrame(connection_to_graph.data(q1)).as_matrix()\n",
" \n",
" # create matrix\n",
" year_matrix = np.zeros([len(axis_names), len(axis_names)])\n",
" \n",
" # for no intersections data\n",
" for row in data_q1:\n",
" # the last column is the frequency (count)\n",
" frequency = row[0]\n",
" indexes = [find_index(element, axis_names) for element in row[1::]]\n",
" # add frequency value to matrix position not inter\n",
" for pair in itertools.combinations(indexes, 2):\n",
" year_matrix[pair[0], pair[1]] += frequency\n",
" year_matrix[pair[1], pair[0]] += frequency\n",
" \n",
" # for intersecting data\n",
" for category in process_variables:\n",
" process_data = DataFrame(connection_to_graph.data(q2.format(category, category, year))).as_matrix()\n",
" for row in process_data:\n",
" frequency = row[0]\n",
" indexes = [find_index(element, axis_names) for element in row[1::]]\n",
" # add frequency value to matrix position inter\n",
" for pair in itertools.combinations(indexes, 2):\n",
" year_matrix[pair[0], pair[1]] += frequency / 2 # Divided by two because query not optimized\n",
" year_matrix[pair[1], pair[0]] += frequency / 2 # Divided by two because query not optimized\n",
" \n",
" # normalize\n",
" norm_year_matrix = year_matrix / total_documents\n",
" \n",
" # dynamic return \n",
" if normalization == True:\n",
" return norm_year_matrix\n",
" else: \n",
" return year_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We finally test our function with the year 2016. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The matrix from 2017 has shape (342, 342) a max value of 0.229850746269, a min value of 0.0 and a mean of 0.000223745334066.\n"
]
}
],
"source": [
"year = 2017\n",
"print 'The matrix from {} has shape {} a max value of {}, a min value of {} and a mean of {}.'.format(year, get_year_matrix(year).shape, np.amax(get_year_matrix(year)), np.amin(get_year_matrix(year)), np.mean(get_year_matrix(year)))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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nyJei8lhIiQ9d+wiA3PYr71ovb2A9/x/8lT9YqesLLHD5xhOldkgpIY36YisT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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## call functions\n",
"colors = 'BuPu_r'\n",
"year_in_focus = 2016\n",
"# create a subplot\n",
"plt.subplots(2,1,figsize=(17,17))\n",
"\n",
"# first heatmap\n",
"plt.subplot(121)\n",
"sns.heatmap(get_year_matrix(year_in_focus, normalization=False) , cmap=colors, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
"plt.title('Capability Matrix Absolute: {}'.format(year_in_focus))\n",
"\n",
"# second heatmap\n",
"plt.subplot(122)\n",
"sns.heatmap(get_year_matrix(year_in_focus, normalization=True) , cmap=colors, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
"plt.title('Capability Matrix Normalized: {}'.format(year_in_focus))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Analysis "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to analyse the correlation of the years between themselves, we will need to transform each year matrix into a list. Since the matrix is symmetrical, we will only need the upper triangle. For programming reasons, we have designed our own upper triangulization matrix. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_list_from(matrix):\n",
" only_valuable = []\n",
" extension = 1\n",
" for row_number in range(matrix.shape[0]):\n",
" only_valuable.append(matrix[row_number, extension:matrix.shape[0]].tolist()) # numpy functions keep 0s so I hard coded it. \n",
" extension += 1 \n",
" return [element for column in only_valuable for element in column ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1. Year Profiles "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's visualize the correlation between two years and their capability arrays. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The pearson correlation index between the two years is: 0.902580296089 (P-value of 0.0)\n"
]
}
],
"source": [
"# apply functions to both countries\n",
"a_list = get_list_from(get_year_matrix(2012, normalization=True))\n",
"b_list = get_list_from(get_year_matrix(2013, normalization=True))\n",
"\n",
"# create a matrix where each row is a list of a country\n",
"corelation = np.vstack((a_list, b_list))\n",
"\n",
"# plot the matrix \n",
"plt.subplots(1,1,figsize=(20, 5))\n",
"plt.subplot(111)\n",
"sns.heatmap(corelation, cmap='flag_r', cbar=None, square=False, yticklabels=['2012', '2013'], xticklabels=False)\n",
"plt.yticks(rotation=0)\n",
"plt.title('Year Capability List Visualization', size=15)\n",
"plt.show()\n",
"\n",
"print 'The pearson correlation index between the two years is: {} (P-value of {})'.format(stats.pearsonr(a_list, b_list)[0], stats.pearsonr(a_list, b_list)[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is already apparent that these two consecutive years are highly correlated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2. Correlation Matrix "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.2.1. Considerations "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As previously done with countries, a year correlation matrix will be built. \n",
"\n",
"We first define the scope of the matrix."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1938, 1975, 1980, 1981, 1983, 1985, 1986, 1988, 1989, 1990, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019]\n"
]
}
],
"source": [
"number_of_years = len(years_available)\n",
"years_in_matrix = years_available\n",
"years_correlation = np.zeros([number_of_years, number_of_years])\n",
"print years_in_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we build the matrix"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
}
],
"source": [
"data = np.load('Data/year_capability_dict.npy').item()\n",
"for row in range(number_of_years):\n",
" print 'Processing year {} / {} ({})\\r'.format(row + 1, number_of_years, years_in_matrix[row]),\n",
" year_1_list = data[years_in_matrix[row]]\n",
" for column in range(number_of_years):\n",
" \n",
" year_2_list = data[years_in_matrix[column]]\n",
"\n",
" years_correlation[row, column] = stats.pearsonr(year_1_list, year_2_list)[0]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ouXNBfdHG+sX0dZXERdQHJQ6SRzYkSdJA2dmQJEkD1UoQWxl3TBl+UUScGhH1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\nL61oz99l5tsqpt8XOBi4LDOv6DHNg4B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W8yIyIiLfEpEnRORfIvKiiGwRkcszMZ+dCcxh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\ntMobGy6Xy+VyuaZV/wM3afTxPmzUIQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplots(1,1,figsize=(9, 9))\n",
"plt.subplot(111)\n",
"sns.heatmap(years_correlation, cbar=False, square=True, yticklabels=years_in_matrix, xticklabels=years_in_matrix)\n",
"plt.title('Years Correlation Matrix: Unordered', size=13)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There seems to be a lot of data missing. \n",
"\n",
"Let's plot the amount of records in our databse over time to get a better sense on how to approach the problem."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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SLh1kMcfHetgGaQ4704mbc+2112revHnKzMzUFVdcoSuuuKLFD83OztbTTz/d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\nw4cPw+l0AgB++OEHFBcXo7y8HIsWLYKmaaH53G4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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# get all of the data\n",
"data = DataFrame(connection_to_graph.data(years_available_q)).as_matrix()\n",
"raw = [int(a) for a in data[:-1, 0]]\n",
"timeline = range(min(raw), max(raw))\n",
"qtties = []\n",
"\n",
"# build a timeline and number of records. \n",
"for year in timeline:\n",
" if year not in raw:\n",
" qtties.append(0)\n",
" else: \n",
" idx = find_index(str(year), list(data[:, 0]))\n",
" qtties.append(data[idx, 1])\n",
" \n",
"amountOfRecords = np.column_stack((timeline, qtties))\n",
"\n",
"# plot the graph\n",
"plt.style.use('seaborn-darkgrid')\n",
"plt.subplots(1,1,figsize=(16, 5))\n",
"plt.subplot(111)\n",
"plt.title(\"Number of assets over time\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Number of Available assets\")\n",
"plt.plot(timeline, qtties)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.2.2. Final co-ocurrence matrix "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To counteract the fact that our dataset is not uniformily distributed across the years, we will only consider the last 15 years. [2004-2018]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"number_of_years = 22\n",
"years_in_matrix = years_available[:-1][-number_of_years:]\n",
"years_correlation = np.zeros([number_of_years, number_of_years])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now rebuild and plot the heatmaop of correlations. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"data": {
"image/png": 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/Q3EGRw8DXPlWubEs6zuXGcsCqLUOGM0zOXoYIGRwIH5n0Oyoy6gYp9E8k6N9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\npk6dCg8PD7S3t6O0tNSh2+iPPPIIjhw5gmHDhmHKlCkO1XM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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = np.load('Data/year_capability_dict.npy').item()\n",
"for row in range(number_of_years):\n",
" print 'Processing year {} / {} ({})\\r'.format(row + 1, number_of_years, years_in_matrix[row]),\n",
" year_1_list = data[years_in_matrix[row]]\n",
" for column in range(number_of_years): \n",
" year_2_list = data[years_in_matrix[column]]\n",
" years_correlation[row, column] = stats.pearsonr(year_1_list, year_2_list)[0]\n",
"\n",
"plt.subplots(1,1,figsize=(8, 8))\n",
"plt.subplot(111)\n",
"sns.heatmap(years_correlation, cbar=False, square=True, yticklabels=years_in_matrix, xticklabels=years_in_matrix)\n",
"plt.title('Years Correlation Matrix: Unordered, last 15 years', size=13)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.2.3. Heatmap Clustering "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us reorder the heatmap according to hierarchical clustering. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Rof79+1+2nTfffFMvv/yyV2NyVjYAAC6Y5jHn5+drwoQJ+uabb3TnnXd6NSYzZgAAXDDN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\nVFRUpISEBB0+fFiFhYUaPny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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the clustermap\n",
"a = sns.clustermap(years_correlation, figsize=(8, 8), xticklabels = years_in_matrix, yticklabels=years_in_matrix)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3. Correlation Over Time "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us see how related is each year in our matrx with the one before it. In this way we might more easily detect discripancies. \n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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shBIHAAAAADZCiQMAAAAAG6HEAQAAAICNUOIAAAAAwEYocQAAAABg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\nm+7KbB4AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# remove first year\n",
"advanced_timeline = years_in_matrix[1::]\n",
"corr_with_pre = []\n",
"\n",
"row = 1\n",
"col = 0\n",
"for year in advanced_timeline:\n",
" corr_with_pre.append(years_correlation[row, col])\n",
" row = row + 1\n",
" col = col + 1\n",
"\n",
"plt.subplots(1,1,figsize=(15,7))\n",
"sns.barplot(np.arange(len(corr_with_pre)), corr_with_pre )\n",
"plt.xticks(np.arange(len(corr_with_pre)), advanced_timeline, rotation=90, fontsize=11)\n",
"plt.title('Correlation of year with previous year')\n",
"plt.ylabel('Pearson Correlation Index')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some years, such as 2006 or 2007 appear to have very low correlations with the years after. There seems to be an overall tendency of augmenting correlation with the years. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4. Research details over time "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following part of the analysis wil focus on how certain process variables (Feedstocks, Processing Technologies and Outputs) evolve over time.\n",
"\n",
"This can help in answering questions such as for example:\n",
"\n",
"- Is the focus on a certain processing technology constant over time?\n",
"- Is this evolution correlated with other external factors? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by creating a function such as: \n",
"\n",
"f(term, type of process variable) = [array with the number of records containing the term in each year]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"from __future__ import division\n",
"def get_records_of(startYear, endYear, term, process_type):\n",
" \n",
" # make query \n",
" yearRangeQuery = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:{})\n",
" WHERE fs.term = \"{}\"\n",
" AND (toInteger(a.year)>={} AND toInteger(a.year)<={}) \n",
" AND NOT a.year = \"Null\"\n",
" RETURN a.year, count(a)\n",
" ORDER BY a.year \"\"\".format(process_type, term, startYear, endYear)\n",
" \n",
" # extract matrix\n",
" rawQuery = DataFrame(connection_to_graph.data(yearRangeQuery)).as_matrix()\n",
" \n",
" # create matrix to store years, docs and total docs\n",
" normalTimeline = np.arange(startYear, endYear + 1)\n",
" completeMatrix = np.transpose(np.vstack((normalTimeline, normalTimeline, normalTimeline, normalTimeline))) \n",
" completeMatrix[:, 1::] = 0 \n",
"\n",
" # add number of docs found by query to matrix\n",
" for i in range(len(rawQuery[:, 0])):\n",
" for j in range(len(completeMatrix[:, 0])):\n",
" if int(rawQuery[i, 0]) == completeMatrix[j, 0]:\n",
" completeMatrix[j, 1] = rawQuery[i, 1] \n",
" \n",
" # add total number of docs in that year to matrix\n",
" for i in range(len(completeMatrix[:, 0])):\n",
" for j in range(len(amountOfRecords[:, 0])):\n",
" if completeMatrix[i, 0] == amountOfRecords[j, 0]:\n",
" completeMatrix[i, 2] = amountOfRecords[j, 1]\n",
"\n",
" # create a list of the normalized results \n",
" normalizedRecords = []\n",
" for i in range(len(completeMatrix[:, 0])):\n",
" if completeMatrix[i, 2] != 0:\n",
" normalizedRecords.append(float(completeMatrix[i, 1])/float(completeMatrix[i, 2]))\n",
" else:\n",
" normalizedRecords.append(0)\n",
" \n",
" # return a dictionnary for easy access to all variables\n",
" result = {}\n",
" result['range'] = completeMatrix[:, 0].tolist()\n",
" result['nominal'] = completeMatrix[:, 1].tolist()\n",
" result['total'] = completeMatrix[:, 2].tolist() \n",
" result['normalized'] = normalizedRecords\n",
" \n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that the function is built, we can plot virtually any evolution. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.4.1. Outputs \n",
"\n",
"Let us see the evolution of records of biogas Vs. ethanol as an example."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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FRERERESk1lBIFRERERERkVojYiH1u+++Y/To0fs8/sEHH3DxxRczYsQI/vOf/0Tq8iIi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\nIiIiIpahJlVEREREREQs4/8Bo4qozh/TjtsAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"listOfOutputs = ['biogas', 'ethanol', 'biodiesel']\n",
"start_year = 1990\n",
"end_year = 2017\n",
"\n",
"# plot the graph\n",
"plt.style.use('seaborn-darkgrid')\n",
"plt.subplots(1,1,figsize=(16, 5))\n",
"plt.subplot(111)\n",
"plt.title(\"Evolution of Records with focus on Output\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Normalized Quantity\")\n",
"\n",
"for name in listOfOutputs:\n",
" nameData = get_records_of(start_year,end_year,name, 'Output')\n",
" plt.plot(nameData['range'], nameData['normalized'], label=name)\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.4.2. Processing technologies \n",
"\n",
"Let us develop the same procedure for some processign technologies."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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BgwZs2rQJuPsM78CBA7l27RrVq1dnzZo1htf/97//PTAWf39/goODDcXC5s2bDe+NHDmS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qsoC9tR515E/fMQAj6PRr/3YwzBzbjS+Scyn/4aY7nm0psnxK2oNeQ2Hk1Wy+m4nNuJ+955jo7o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\nQ4mJiUrPnj0Vi8WinD17VunQo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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"listOfProcTech = ['fermentation','enzymatic hydrolysis','hydrolysis' ]\n",
"start_year = 1990\n",
"end_year = 2017\n",
"\n",
"# plot the graph\n",
"plt.style.use('seaborn-darkgrid')\n",
"plt.subplots(1,1,figsize=(16, 5))\n",
"plt.subplot(111)\n",
"plt.title(\"Evolution of Records with focus on Processing Technologies\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Normalized Quantity\")\n",
"\n",
"for name in listOfProcTech:\n",
" nameData = get_records_of(start_year,end_year,name, 'ProcessingTech')\n",
" plt.plot(nameData['range'], nameData['normalized'], label=name)\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.4.3. Feedstock \n",
"\n",
"Let us develop the same procedure for feedstock."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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b2LTpO9atW+291rBhI7Zv30aTJs348cdtAOTk5DBjxhvMm7cIgBEj7sHj8VCv\nXn3atm3Pww8/htvtZtas/1K7dh2fxakkVbwKnEUczXNWqpIaE24mNtLsbVkjIiIiIiKh7eKLL+HF\nF59h+fKlWCwWTCYTTqcTgJtuuoUJE55gxYovqVkzAbPZTExMDOef35q77voXJpMZq9XK4cPp9Op1\nDT/88D133z2M/Pw8LrnkMqKjY3wWp1+SVLfbzZNPPsmOHTsIDw9n4sSJ1KtXz3t95syZLFq0CIPB\nwF133UXPnj39EYacpLTs4qpoUgXtZ0okWSNIUyVVRERERKRKaNeuPbNnf1TmtXXrVjNs2J2ce+55\nbNy4gSNHDmMwGJgwYUqZz//3vx/wW5x+SVKXLVtGYWEhc+fOZfPmzUyZMoXp06cDYLfbeffdd1m6\ndCn5+flcd911SlJDRElVNKUSlVSAlLhI/szI92dIIiIiIiISAMnJtZk8eTwmkwm328399z8YtFj8\nkqR+//33dO3aFYA2bdqwfft277WoqChSUlLIz88nPz+/wlNkJXBK9pdWupIaG8mGPRl4PB59jiIi\nIiIiVVj9+g144423gx0G4KckNScnB4vF4v3dZDLhcrkwm4tvl5ycTO/evSkqKuLOO+8scwyLJQKz\n2eSP8HzCZDISHx8d7DB8KtNRhNlooEmdapiMFSedDROt5G/aDxFhxEeHByDCM9+ZOK8kNGhuiT9o\nXok/aF6Jv2huVR1+SVItFgu5ubne391utzdBXblyJYcOHWL58uUA3HbbbbRr145WrVqVGiMnJ7T3\nOsbHR5OZmRfsMHzq9/QcalnCybZXbglvXJgRgB17M2ie6JueSGe7M3FeSWjQ3BJ/0LwSf9C8En/R\n3AotCQnl5w9Gf9ywXbt2rFy5EoDNmzfTtGlT77W4uDgiIyMJDw8nIiICq9WK3W73RxhyktLsBd7+\np5WhNjQiIiIiIuJrfqmk9uzZkzVr1jBo0CA8Hg+TJk3i7bffpm7dulx++eWsXbuWAQMGYDQaadeu\nHRdffLE/wpCTZLM7aH9OXKWfn2yN/Ot1akMjIiIiIiK+4Zck1Wg0Mn78+FKPNWrUyPvzfffdx333\n3eePW8spchW5Sc9xnFQlNS7KTKTZqDY0IiIiIiJnCYfDwdKl/+Oaa67z2z38stxXqp6DOQ7cnsq3\nnwEwGAwkx0WqkioiIiIicpY4evQICxfO9+s9/FJJlaon7STbz5RIjo3QnlQRERERkZP0xw+H+X3T\nYZ+O2aBdTeq3rVnu9VtvvYnnn38FqzWWXr0uZ+rUN2jWrDm33jqYiy7qxC+//ITdnkXjxk0ZPXos\nW7duZtq0lzCbzURGRjJx4tPaKTEZAAAgAElEQVS8++5M/vjjd95++y3697+RKVPGk5WVBcD99z9E\no0aNT/t9KEkV4Ni+0uSTqKSWPP9HW7Y/QhIRERERER/q2rUbGzaso1atRJKTU/juuw2Eh4eTlJSC\n1WrlpZdew+12M2TIANLTD7Fq1Td0796DAQP+yerVK7Hbs7n55lvZtWsn//rX7bz22itccMFF9O17\nA3v3/smkSeOYPn3GacepJFWAYyf0JlpPrpKaZI0gq8BFXmER0eGh29dWRERERCSU1G974qqnP3Tr\ndhnvvDOTxMQk7rjjbj755EPcbg89elzBTz9tZ+zY0URHR5Ofn4/L5WLIkH/x7rsz+c9/hpOQUIsW\nLVridBZ6x9u9eyebNn3H8uVLAcjO9k3XFu1JFaC4/UzNmHDCzSc3JUoqr9qXKiIiIiIS2ho2bMyB\nA/v5+ecf6dTpYvLz81m9+hvCwswcOnSQceMmcccd9+BwFODxeFi6dDG9el3N1Klv0KBBQxYsSMVg\nMOLxuAGoV68+Awb8k2nT3mTChCn84x9X+SROVVIFKK6kJp/kflQ4toc1ze6gUc0YX4clIiIiIiI+\n1LbtBdhsBzAajbRp044//thNixYteeedmdxzz+0YDAZSUmpz+HA6557bkilTJhIVFYXBYGDUqMeo\nVq0aTqeL1157hZtvvpUpUyawYEEqeXm53HrrHT6J0eDxeDw+GcnH0tNDe59jfHw0mZl5wQ7DZ/rN\n+JbmiVYmXX3uSb3uULaD3m9u4OHLG3NDmxQ/RXf2ONPmlYQOzS3xB80r8QfNK/EXza3QkpBgLfea\nlvsKbo+HtGzHSR+aBFDTEo7ZaNAJvyIiIiIi4hNKUoUjuYU4izyntNzXaDCQFBuhPakiIiIiIuIT\nSlLFWwU9lUoqQFJsJGlKUkVERERExAeUpIo3wUw6hUoqQLI1Qst9RURERETEJ5SkymlXUpNjIzmc\nW0ihy+3LsERERERE5CykJFWw2QuIizQTHW46pdeXVGAPZquaKiIiIiIip0dJqmCzF5B0ilVUOFaB\n1eFJIiIiIiJyupSkCja745RO9i2RHFf82jTtSxURERERkdOkJPUs5/F4SLMXnPJ+VIBESwRGAxxQ\nJVVERERERE6TktSzXFaBi3yn+5RP9gUwm4zUjAlXGxoRERERETltSlLPciWJ5elUUkterzY0IiIi\nIiJyupSknuWOtZ859UoqFJ/wq0qqiIiIiIicLiWpZ7mSE3lP53RfKK6kHswppMjt8UVYIiIiIiJy\nllKSepaz2R1EhRmJizSf1jjJsREUuT2k52jJr4iIiIiInDolqWe5kpN9DQbDaY2THBf513hKUkVE\nRERE5NQpST3LFfdIPb2lvgDJ1uIx1IZGREREREROh5LUs1yaveC02s+UKBlDlVQRERERETkdSlLP\nYnmFRWQVuHxSSY0MM1EtKsx7EJOIiIiIiMipUJJ6FrN5e6SefiUVStrQqJIqIiIiIiKnrsIktV+/\nfsyaNYvMzMxAxCMBVJJQnm77mRLJsZGqpIqIiIiIyGmpMEmdNWsWYWFh3HXXXYwYMYK1a9cGIi4J\ngAP+qKRmO/B41CtVREREREROTYVJamxsLIMHD+app57CaDQycuRI+vfvz5dffhmI+MSP0uwFhJkM\n1IgJ98l4KbGROFxuMvKdPhlPRERERETOPuaKnvD+++/z2WefYbFY6N+/P1OmTMHlcjFgwAB69uwZ\niBjFT2x2B0nWCIyn2SO1RMmyYVtWAdWjfZP4ioiIiIjI2aXCJPXQoUM8//zznHPOOd7HwsLCGD9+\nvF8DE/8rbj/jm/2ocGzZsM3u4Lxknw0rIiIiIiJnkXKX+xYVFVFYWMiuXbtISkqisLAQh8PBzTff\nDEDbtm0DFqT4h83u8Nl+VMDbykaHJ4mIiIiIyKkqt5I6b948Xn/9dQ4fPsyVV16Jx+PBaDTSvn37\nQMYnflLocnM4t9CnlVRrpJmYcJPa0IiIiIiIyCkrN0kdMGAAAwYM4JNPPuGGG24IZEwSAAezixNJ\nX1ZSi8dTGxoRERERETl15SapH3/8Mf3792fPnj288MILpa498MADfg9M/OtY+xnfVVLhWBsaERER\nERGRU1FukpqUlARAw4YNSz1u8NFJsBJcaX5KUlNiI9m8P8unY4qIiIiIyNmj3IOTunbtCsC2bdvo\n27ev99/atWsDFpz4j83uwGiAWhbftopJio0gx1FEdoHLp+OKiIiIiMjZodxK6vvvv8/06dPJyspi\n6dKl3scbNWoUkMDEv9LsBSRYIjCbyv2e4pT8/YRfa6TFp2OLiIiIiMiZr9wkdfDgwQwePJjXX3+d\nu+66K5AxSQD4uv1Mib/3Sm1aS0mqiIiIiIicnHKT1BI33XQTixcvprCw0PvYdddd59egxP/S7AW0\nqh3n83FLWtqk6YRfERERERE5BRUmqXfffTe1atUiOTkZ0MFJZwKX28PBnEK/VFKrR4cRYTZiU69U\nERERERE5BRUmqR6Ph+eeey4QsUiAHM5xUOT2eKuevmQwGEi0RpCWrUqqiIiIiIicvAqT1GbNmrFl\nyxbOPfdc72Ph4Sc+EdbtdvPkk0+yY8cOwsPDmThxIvXq1fNe/+abb3j11VfxeDycd955jB07VhXa\nACqpcqb4oZJaPG6kKqkiIiIiInJKKkxSv/32W1asWOH93WAwsHz58hO+ZtmyZRQWFjJ37lw2b97M\nlClTmD59OgA5OTk8++yzvPvuu1SvXp233nqLjIwMqlevfppvRSrL9td+UX9UUovHjWDHzhy/jC0i\nIiIiIme2CpPUBQsWnPSg33//vbfPaps2bdi+fbv32g8//EDTpk15+umn2bt3L/3791eCGmBpf1U5\nk6z+qaQmx0aSke+kwFlEZJjJL/cQEREREZEzU4VJ6vLly/nggw9wOp14PB4yMzNZuHDhCV+Tk5OD\nxXKs/YjJZMLlcmE2m8nIyGDDhg3Mnz+f6OhoBg8eTJs2bWjQoEGpMSyWCMzm0E1wTCYj8fHRwQ7j\nlBx1uKgRE05SgtUv4zdKigUgFwNJVfRvFCxVeV5JaNPcEn/QvBJ/0LwSf9HcqjoqTFJfeuklxo8f\nz4cffkiHDh1Ys2ZNhYNaLBZyc3O9v7vdbszm4lvFx8dz/vnnk5CQAED79u35+eefj0tSc3JCe09j\nfHw0mZl5wQ7jlOxJzyXRGuG3+GPNxfuLf92fSY0wo1/ucaaqyvNKQpvmlviD5pX4g+aV+IvmVmhJ\nOEHBrMIMolatWrRt2xaAfv36cejQoQpv2K5dO1auXAnA5s2badq0qffaeeedx6+//srRo0dxuVxs\n2bKFxo0bVzim+I7NXuCX9jMlSsbW4UkiIiIiInKyKqykhoWFsXHjRlwuF6tWrSIjI6PCQXv27Mma\nNWsYNGgQHo+HSZMm8fbbb1O3bl0uv/xyRo4cybBhwwC48sorSyWx4l8ej4e0bAddG9Xw2z1qWiIw\nGQ2k2dWGRkRERERETk6FSeq4cePYvXs3w4cP5+WXX2b48OEVDmo0Ghk/fnypxxo1auT9uXfv3vTu\n3fsUwpXTdTTPicPl9msl1Ww0kGgJVyVVREREREROWoVJalFRkbfH6aOPPur3gMS/0vzcfqZEUmwk\ntixVUkVERERE5ORUmKSOGDECg8GA2+1m37591KtXjzlz5gQiNvGDkuqmPyupJeNv/DPTr/cQERER\nEZEzT4VJ6ty5c70/2+12Hn/8cb8GJP5l+6uSmhyASurh3EJcRW7MJp3wKyIiIiIilXNS2YPVamXv\n3r3+ikUCIM3uwBJhwhJR4fcTpyU5NgK3Bw6GeCshEREREREJLRVmKgMHDsRgMODxeDh69CidOnUK\nRFziJ8XtZ/xbRYVje17T7A5qx0X5/X4iIiIiInJmqDBJfeGFF7w/R0REULNmTb8GJP5lsztIifN/\nkpryV5JqUxsaERERERE5CSdMUrdu3coHH3zA/v37SUxM5MYbb+Srr76iWbNmtGrVKlAxig/Z7AVc\ncE6c3++TaI34635a7isiIiIiIpVXbpK6cuVKpk2bxr///W9q167NH3/8wcSJE7FYLLz77ruBjFF8\nJLvARW5hkd/bzwCEm43UjAlXGxoRERERETkp5Sap//3vf3nzzTeJj48HoGHDhixbtoxdu3ZhMBgC\nFqD4zrGTff3bfqZEcmwEtmxVUkVEREREpPLKPd3X4/F4E9QSXbp0wWQy+T0o8Y+SpbeBqKSW3CdN\ne1JFREREROQklJukOhwOnE5nqcd69OhBUVGR34MS/0gLQiX1YLYDt8cTkPuJiIiIiEjVV26Ses01\n1zB69GiysrIAyMzMZMyYMVx99dUBC058y2Z3EGE2Ui0qLCD3S4qNxFnk4UhuYUDuJyIiIiIiVV+5\ne1KHDBnC7NmzGThwINnZ2VitVm666SZuuummQMYnPlTcIzUiYHuKj7WhcZBgCUz1VkREREREqrYT\ntqAZMmQIQ4YMCVQs4mc2e0HA9qMCJP21rDjNXkCrlNiA3VdERERERKqucpf7ypknze4I2H5UgOS/\nEuIDakMjIiIiIiKVpCT1LFHgLCIj3+lNHAMhOtxEXKSZNLWhERERERGRSip3ue+BAwfKfVFKSopf\nghH/SfO2nwns3tCk2Ehvf1YREREREZGKlJukjhgxAig+1Tc3N5cmTZqwc+dOatasyaeffhqwAMU3\nbNl/tZ+xBq6SCsVtaPZk5Af0niIiIiIiUnWVu9x37ty5zJ07l8aNG7NkyRLefvttvvjiCxITEwMZ\nX0gyFGTAkd+CHcZJsQWxkppmL8CjXqkiIiIiIlIJFe5JTUtLw2KxABAdHU16errfgwp1UVtmYH7n\nSnAXBTuUSrNlFWAyGgLeCiY5NoJ8p5usAldA7ysiIiIiIlXTCVvQAHTp0oWbbrqJli1bsnXrVnr0\n6BGIuEJaUbXGGPIzMB/5CVfC+cEOp1Js9gISrRGYjIHpkVqi5KCmNHsB8VFhAb23iIiIiIhUPRUm\nqSNGjGD79u3s2bOH6667jubNmwcirpDmTLkIgLD966tMkhro9jMlSu55wO6geaI14PcXEREREZGq\npcLlvgcPHmTWrFnMmzePrVu3smXLlkDEFdLclhQ88fUJO7A+2KFUms1eQFIA28+USPpbJVVERERE\nRKQiFSapjz/+ONdffz1Op5P27dvz1FNPBSKukOep25kw27fgcQc7lAq5itwczi0k2Rr4SmpcpJmo\nMKP34CYREREREZETqTBJLSgooFOnThgMBho2bEhEROATnVDkrtsZY0EGpqOhf8rvwRwHbs+x/aGB\nZDAYvCf8ioiIiIiIVKTCJDUiIoJVq1bhdrvZvHkz4eHhgYgr5HnqdgaoEkt+04LUfqZEcmyEKqki\nIiIiIlIpFSapEyZMIDU1lYyMDGbOnMm4ceMCEVfoi69HUUwSYQc2BDuSCh3IKq5ipsQFvpIKxRVc\nVVJFRERERKQyKjzdd/369bz44ove32fNmsUtt9ziz5iqBoMBZ0pHwvavA48HDIFt7XIy0uwODEBi\nEPakQnGSmlXgIrfQRUx4hVNORERERETOYhVWUseNG8cjjzyC2118QNCKFSv8HlRV4UzpiCnvIKas\n34MdygnZ7AXUtIQTZqrw4/aLkjY0WvIrIiIiIiIVqTBradmyJW3btmX48OEUFGjJ5t85UzoAhPyS\nX1u2gyRrcJb6gtrQiIiIiIhI5VW49tJgMDBw4ECsViu33nqrt6IqUFStMe6oGoQd2EBBixuDHU65\n0uwFtEi0Bu3+qqSKiIiIiEhlVVhJrV+/PgC9evXirrvuYseOHf6OqeowGHCmdAjpSqrb4+FgtsNb\nzQyGGjHhhJkMqqSKiIiIiEiFyk1SXS4XAGPGjKGwsJDCwkI6duzIhg2hm5AFgzO5A6bsvRiz9wc7\nlDIdyS3EWeTxVjODwWgwkGhVGxoREREREalYuct9H374YZ5//nmuvPJKDAYDHo8HKF7+u3z58oAF\nGOoKUzoCxf1SHc2uD3I0xytpP5McpPYzJdSGRkREREREKqPcJPX5558HdJpvRYpqNMcdEUfYgQ0h\nmaSm/VW9DGYlteT+a3/PCGoMIiIiIiIS+spNUgcOHIihnN6fH374od8CqnKMJpzJFxJ2YH2wIymT\n7a/qZXIQ96RC8Qm/h3MLcbjcRJiD0wpHRERERERCX7lJ6gsvvBDIOKo0Z3IHIv5YhiH3EJ6YWsEO\np5S0bAdxkWaiwkxBjaOkknsw20HdalFBjUVKc+S52PblPlr2qE1kTFiwwxERERGRs1y5SWrt2rUB\n2LNnD0uWLMHpdAJw6NAhxo8fH5joqghn7b/2pdq+pbDx1UGOpjSbvSDoVVQ4Vsm12QuUpIaYnRsO\nsfu7dKLjw2nRLSXY4YiIiIjIWa7CdZcjR44EYNOmTezbt4/MzEy/B1XVuGq2xGOOJjwEl/za7A6S\ngrwfFfDGoMOTQou7yM3ujYcA2LP5iPeANBERERGRYKkwSY2OjubOO+8kMTGRKVOmcPjw4UDEVbWY\nwnAmtw+5fakejwdbVmhUUhMtERgNqA1NiNn3Uwb52U5SmseTfbiAjAN5wQ5JRERERM5yFSapBoOB\n9PR0cnNzycvLIy9P/xNbFmdKB8xHfsFQEDon2GbluyhwuYPefgbAbDKSYIlQJTXE7Fx/CEv1CC7s\n2wCjycCeLUeCHZKIiIiInOUqTFLvvfdevvzyS/r06UOPHj3o1KlThYO63W6eeOIJBg4cyJAhQ9iz\nZ0+Zzxk2bBhz5sw5tchDjLOkX6ptY5AjOcaW/dfJvtbgL/eF4sOTVEkNHRkHcjn8Zw6NOtQiItpM\ncrN49m47grtIS35FREREJHjKPTipxIUXXsiFF14IwOWXX16pQZctW0ZhYSFz585l8+bNTJkyhenT\np5d6zksvvYTdbj+FkEOTs1ZrPKYIwvavp7DBP4IdDnBsaW0oLPeF4jY0W/ZnBTsM+ctvGw5hCjPS\noG1NAOq1rsH+nzI4uNtOcpO4IEcnIiIiImerCiupL774IhdffDFdunTx/qvI999/T9euXQFo06YN\n27dvL3V9yZIlGAwG73POCOZInIltCLNtCHYkXiVLa0Ph4CQorqQeynbgcleuUncgbz8zdryOy+3y\nc2RnH0eukz+3HqF+mxqERxV/V5XcNI6wSBN7NmvfuYiIiIgET4WV1K+//pqvvvqK8PDwSg+ak5OD\nxWLx/m4ymXC5XJjNZn799VcWLVrEK6+8wquvvlruGBZLBGZzcHt7nojJZCQ+PrrUY8aGXTGueYH4\nqCKIsAYpsmOOOoqICTdRNykWg8EQ7HBomBhLkQcKjUZqxlfchuatnanM2TWHhNga3Hzu0ABEGHxl\nzSt/2Pztn7hdHtr2rFfqfo0uqMXOjQeJiYogLCJ0//uTkxeouSVnF80r8QfNK/EXza2qo8IktUWL\nFjgcjpNKUi0WC7m5ud7f3W43ZnPxrebPn8/BgwcZOnQo+/fvJywsjNq1a3PJJZeUGiMnJ7T3LsbH\nR5OZWfoQqbDqFxDvcZO7YxXOupcGJ7C/2ZOeQ6I1gqys/GCHAkBcWHGivGNfJtGcuJrq8Xj4au/X\nALy+9XU6xl9CrahEf4cYdGXNK19zF3nYvnI/tRpaMUYZSt0v+dw4fllj46d1+6nXpqZf45DACsTc\nkrOP5pX4g+aV+IvmVmhJSCi/qFfhct8mTZrQpUsXLr/8crp3716pfant2rVj5cqVAGzevJmmTZt6\nr40aNYqPP/6Y2bNn07dvX2655ZbjEtSqypl0AR6jmfD9odGK5kCItJ8pkWwtjsVWiRN+f8/eTVq+\njX82uhmPx81rP7/s7/DOGgd+ySAvq5AmHY5P+mueYyEmPpw/dMqviIiIiARJhZXUxYsXs3z5cmJj\nYys9aM+ePVmzZg2DBg3C4/EwadIk3n77berWrVvpw5eqpLBoXAnnh8y+1LRsB61SKv+5+VvJ3ti0\nSpzwu/bQKgD61ruBKFMUM359g2/T13NRQke/xng2+G3DIaLjw0luHn/cNYPRQN3WNfhlpY387EKi\nrJVfQSEiIiIi4gsVJqkpKSlERUWd1HJfo9HI+PHjSz3WqFGj457373//u9JjVhXOlI5EbfkvuPLB\nXPG+S3/JLXRhL3CFVCU1MsxE9eiwSlVS1x1aQ/O4FtSIrEn/BjeydP//eOXH55nZ9T3CTaFxEFRV\nlJmWR/rv2bT6Rx2MxrL3KddrXYOfv7Hx57ajNOucFOAIRURERORsV+Fy37S0NHr27MnAgQMZOHAg\ngwYNCkRcVZYzpSMGt5OwtE1BjaOk/UyonOxbIik2ssIk9ajjCD9n/kinxIsBCDeF85/zHuRA3n7m\n7H4vEGGesXaWtJ25IKHc58QmRFGtdjR/asmviIiIiARBhZXUyZMnExkZOtW4UOdMbo8HA2EHNuCs\nc3HQ4ihpPxNKlVQobkPzW3ruCZ+z7tAaADrXOtaiqF3N9lyW3IMPds2mR8oV1I6p49c4z0SF+S72\nbDlC3VbViYg+8X/69VrXYPPivWQdyieuVvBWBIiIiIjI2afCSuqYMWOoXbt2qX9SPk9EHK6a5xF2\nILj7UksqqcmhVkm1RnIw24HHU/7pvmsPriYxKomG1tJLxIef+2/CjGam/vTCCV8vZft902GKnG6a\ndKz4lOS659fAYIQ9qqaKiIiISIBVmKRGR0czadIk5syZw9y5c5k7d24g4qrSnCkdCDv4PRQVBi2G\nNHsBYSYD1WNC6+Cb5NgIHC43R/OcZV4vKCrg+8Pf0rlWl+N6u9aMTOCWJrfzbfp6Vh38JhDhnjHc\nbg87Nxwiob6V+KSK+4NFWsJIbBTHn1uO4HH75wsBgyMLy8oxRPz8EYbCbL/cQ0RERESqngqT1LZt\n2xIbG8uRI0dIT08nPT09EHFVac6UDhhcBZgPbQ1aDAeyHCRZIzAayj4cJ1iS/lp+nFbOvtTvD2+k\n0F1I58SuZV7vW+96Glob8+pPL5HvUp+ryrLtyCQ3w0HjDrUq/Zp6rWuQl1XI4T9zfB+Quwjr0nuJ\n2jaL2BUPUGNmG6xfDCf89y+D+uWOiIiIiARfhUnqvffeS8uWLYmIiKB58+bce++9gYirSnOmdAAg\n7EDw+qWmZYdWj9QSKXHFy49t5bShWXdwNTHmGFpVb1PmdZPRzP0tHyK94BCzd77ttzjPNDs3HCIq\nNoza51ar9GtqnxuPOdzolyW/MeunEPHnV2R3m0zG9Z9R0GIQ4ftWE7f4X9SYdQGWbx7DnPY9aFm3\niIiIyFmnwiT1+eefJzU1lbCwMObPn8/TTz8diLiqNE9UDVzVmgZ1X6rN7gjJJLUkprJO+HV73Kw7\ntIaLEjoSZgwrd4yW1c7nqjpX8/HvH/J79m6/xXqmsB/K5+AuO40uqoXRVPnKujncRO1zq7F3+1GK\nnG6fxRPx66dE/zCd/POGUNByCK6kC8i55CmO3LKJrN6zKKzTlcifP6TavD5Uf68L0RuexZSxy2f3\nFxEREZHQVmGSunHjRl555RVuueUWpk6dynfffReIuKo8Z0oHwmwbwe0K+L0dLjdHcgtDrv0MgCXC\njCXCVGYldUfWz2QUHqVTrS4VjnNH87uJMcfw8o/P6RClCvy24RBGs4GG7ctvO1Oeem1q4CwowvZr\npk9iMR/agnXFgxSmdCCn67jSF01hFNbvQfYVr3Hk1s3YL3+Roti6RH/3CtU/6Eb8x72J2jIDQ562\nHIiIiIicySpMUl0uF253cRXF4/Ecd5iNlM2Z0gGjMwfz4Z8Cfu+D2SUn+4ZeJRWK4yqrkrr24CqM\nBhMXJXSqcIy48HiGNRvO1qOb+fLAEn+EeUYoLHCxZ/Nh6p5fnciY8qvT5anVIJZIS5hPlvwacg8R\n+79huKNqYr/iDTCVf6iXJ9yKo3l/svrM4ejQb8np/Di4i7CsHkuNWe2JW3gTETtSwal9ySIiIiJn\nmgr7pPbq1Ysbb7yR1q1bs3XrVnr16hWIuKq8Y/tSN+Cq1Sqg9y5JAEOxkgqQZI0os5K69uBqWlVr\nTWx4bKXG6XXONfxv3yLe+HkanWt1wRJm9XWoVd4fPxzGVeimcYeK286UxWgyULdVdXZuOIQjz1Vh\nf9VyFTmIW3IHxoIMMvp9hie6ZqVf6rYkk9/2TvLb3onpyA4if/2UiF8/JXbZfXjM0TgaXklBs344\n63QB4ynGJyIiIiIho8JK6q233sqECRNo164d48eP55ZbbglAWFWf25JMUWy9oByeVHJyblWqpB7I\n28/vObvplFjxUt8SRoOR+897kKzCLGb8+qavw6zyPG4PO9cfosY5MVSvHXPK49RrXQN3kYe924+e\nYiAeLN88Rljad2R3f5GihPNOOZaiGs3I7fQIR29eR2bfTyho2pfwPcuJX3gTNWZdSMyqsZgPbdGB\nSyIiIiJVWLllh/nz5x/32E8//cRPP/3Edddd59egzhSFKR2J+P0L8LjBUOH3AT5zwO7AZIBa1tCs\npCbHRZJbWER2gQtrZPEUXHdwNQCdK7Ef9e+axDWjT71+zN8zj6vq9KZpXHOfx1tVpe3MIueog5aX\n1z6tceKTo4lNiOTPLUdofFHlW9iUiNw2i6ifPyT3gvtwNLnmtGLxMhhxpnTEmdKRnEvGE75nBZE7\nUonaPpvorTNwxTfC0awfBU374o6t65t7ioiIiEhAlJuk7tpV+jRNj8dDamoqkZGRSlIryVm7I1G/\nzMV09FeKagQueUqzF5BgicBsDM39w8mxJW1oCrBGWgBYe2g19SwNqB1T56TH+1fTO/jatoIXtz/L\ntM5vYjKYfBpvVfXb+kNEWsKo3aLybWfKYjAYqNe6BtuW7SfnaAGW6pWv0IftW4Nl9ZM46vckr8OD\npxVHuUwRFDa8isKGV2EoyCRi1+dE/JpKzIZnidnwLM7kCylo2hdH42vwRJ7e30JERERE/K/c8t7I\nkSO9//r378/333/PpQEQ7ycAACAASURBVJdeyoIFCwIZX5X2932pgVTcfiY0q6gASd42NMX7UnOc\n2Ww9uvmkq6glLGEWhp/7b3Zk/czivQt9FmdVln2kgLTfsmh0YQIm8+lX8eu2rgHAn1srv+TXaP+T\n2CV3UhTfkOyerwRkNYEnMp6C8waT1XceR4asJ6fjIxgKsrB+M5oab7cj9vNb+T/2zjs8jurs2/ds\n70Wr3qsly0XukrvpvZMQCMlLSEIaCQmBhEBCSKGnvhBSgBeSfAmBFErophk3uWK5y5JsyeqSpZW2\n15nvj5VXFu62VlqZua9rNbMzZ+acLZo9v3mapvFViPgTPhYZGRkZGRkZGZlT47izxr/97W986Utf\n4pZbbuGBBx7AZDKNxbjOCERzHlFT1pjHpXa5AnEhmIwcakkFWN9bS1SKsuAk4lE/zjnZ5zMjZRZP\n1f8eZ/AUYyfPIBrX9aBQChTPPXn33CNhtGlJKzTTsqXvxEr+hLxYX78ZkHBd/DSSZuyTWomWXPyz\nb8V5/bv0f/ot/NNvRtVbh/Wtr+J4Zhbmd7+Dev8H41ImSkZGRkZGRkZG5ugcVaR2d3dz8803s3Hj\nRv75z39y1llnjeW4zgwEgXBWdcySOkaJXCKiRI87uS2pdr0arUoRF6lruldh09iosFWe8jkFQeC2\nqXfgi/j40+4nRmuoE5JwMErz5gPkTrGjN5982ZmjUVDlwN0XwNnuPXZDScTy7m0o+/fgOv8Jorbi\nURvDKSEIRNOm4F34I/o/v56By/9BqPgiNHvfHEq4NBvTintQdayPxY/LyMjIyMjIyMiMK0eNSb3k\nkkvQaDTU1NTw05/+dMS+X/7ylwkf2JlCOKcGXcNLKAf3jclk/YAnSFQiqS2pgiCQadbS5QoSESOs\n613L4sylpx1LWmAq5NNFN/Dc3r9ycd5lTEupGqURTyxatvQRDkYpqzm1sjNHI3eKnc2vtdBS10dK\n7tE9KgwbfoN275t4Ft5LOH/pqI7htFEoCectIpy3CJY+gGb/+2gbXkG3+3n02/9M1JRNsPQygpOu\nJJI6FeS60DIyMjIyMjIyY85RReoTT3yyrVGjRTi7BgB1R+2YiNSDcZ7JbEmF4TI0W/u34I14Tjke\n9ePcWHoT73a8zW+2P8ofFz2L6hNWN1OSJBrXdWPPMZCSe+plZ46ERq8iu9zG/m39VF2Yh0J5uCOG\nZu8bGDf8ikD5tfirvjyq/Y86Kl084ZIn5EGz7220DS+j3/o0hi1/jGUILrucYNmVRO0l4z1aGRkZ\nGRkZGZlPDEedwc+bN28sx3HGErWVIOpTUXesI1B5Q8L760zyGqkHybJqqe/xsKZnHWqFhtmpo/N9\n06v03Fr5be7d/ANebP4nnyq+flTOO1Ho2evC1Rtg3tVFCAmwAhZUOWjb4aS7yUXWJNuIfcq+XViW\n30Y4fQbuZQ9NKCukpDERLL+aYPnVCAFnLENwwysYNvwG44ZfE06dSrDsCoJllyOaT6+kj4yMjIyM\njIyMzLEZu+Kdn1QEgXB29Zhl+D0oUpPZ3RdiItrpD7GmeyWzHXPQq/Sjdu6FGUuoSVvAsw1P0xvo\nHbXzTgQaanvQGlXkTUtJyPkzy6xo9EpatvSN2C4EnFhf/yKixozr4qdAldzfv2Mh6ewEptzI4JUv\n0H/TBjyL7gOFCtPa+3H8pRrbf65Ct+1ZBN+B8R6qjIyMjIyMjMwZiSxSx4BQdjVKdxsKV1vC++p0\nBUkxxBITJTOZFi0KbTdd/s7Tyup7JARB4NYp3yEqRfj9rv8d1XMnMx5nkI76AYrnjE7ZmSOhVCnI\nm5pC++4BwsFobGM0jOXNr6LwdOG66ElEY2ZC+h4PRGMm/qovMfCpV+m7cRXe6u8hBF2YP/whjmdn\nY33ls2h3vYAQdI33UJOKgCdMd5OLhnXdDHb7xns4MjIyMjIyMhOMT1bA3jgRj0vtrCVouTahfXW5\nAknv6guQZdahMu0CoCZ94aifP9uQw2dL/odnGp7kotxLmZtWPep9JBtN63oQBCgZpbIzR6NghoOm\nDb2073RSODMV4+qfomlfjeucXxPJnJXQvscT0VqIb8638M35Fsq+3WgbXkbX8DKW925HWvEDQgVn\nESy9gmDhuaAePc+AZCYSiuLq8TPQ7Wcw/vAR9A6X9VEoBWZclE/JvLSEuKDLyMjIyMjInHnIInUM\niKaUI2qtqDvWESxPrEjtdAWZlDa6CXMSQaZFi8q0i3R1Cam6tIT0cV3xDSxvf5PHdvyKpxb/FY1S\nk5B+Ro2QF2HXckhbBCfp/hwJRdm3uZecyXYM1sS+TkeeCaNdS0tdHxXa5Ri2PYOv6ssEKz6V0H6T\niaijAp+jAl/191B1f4S24WW0jf9Fu/dNRLWRUNH5BMuuJJS3BJSjVwZovBBFCU9fYIQQHez243EG\nYai6llKtwJKuI6vchjVDjy1Dj96iYcsbrWx+tYWefS7mXFmIRif/7MjIyMjIyMgcG3m2MBYolISz\n5iU8LlWSJLrdQZaUOBLaz2igVHtQ6FtJFa5JWB8apZZvTrmd72/4Ds/v+xufK/1Cwvo6XVTdH2Fe\n/k1Ug82kmHPxLPwRoeKLTzj5UMvWfkL+0S87cyQEQaCgysGuFR0InkcJ5S/Bu+CehPeblAgCkcxZ\nRDJn4V14L+qO2phgbXoN3Z4XEbU2giWXEJx0BeGsalCcXpmlRCNJEgFP+DAx6urxE43E1KgggClF\nhy3TQEGVA2uGHmumAaNdi0Jx+Pd18Y1l1K/uYts7bTg7fMy/roSUnOS/kSYjIyMjIyMzfsgidYwI\nZ1ejbV6OwtuNaEyMkOj3hQlGxKQvPwOw4cBaBEFCHZya0H7mplWzNPNs/tb4Z87JPp9sQ5JlZhUj\nGDY9jmHDrxGNmUQu+hWsfxLrm18hlLsIz6KfEHWUH/MUkiTRWNuNLVNPasHR65eOJoWlEXZ+AHui\nF5F//h3wCSv1c0QUSsK5CwnnLsSz5OdoWj9Eu+cldHteRL/zbwxqy9lnuoFA5gKU1jSUagUqjWJo\nqRz5XB1bJtI9Nhw81FXXFxemId+wq67OpMaaoadkXjrWDAPWDD2WdD0q9YnHPAsKgYrFWaQWmKh9\nYS/vPbmL6RfkUlaTIbv/ysjIyMicsYhREe9ACK8ziEIhoNGr0BiUaPSqhP/GnwnIM8sxIpwdi4lU\nd6wnWHZZQvqYKOVnANZ0r0QlpuByJ8bV91C+Xnkb63treXzHr7l/zqNJc1FQDLZgeedbqLs2EZh0\nFZ4lP8eakcVA4dXodvwN47pHsD9/Pv5pN+GbdzuS1nrE8/Q2uxns9jPnysKxeW0RP7nrvka65vPs\nEq4mT2dPfJ8TDaWGYME59OgW0KG8k45t7fR3Dbn97vAAnhM7zZBwPShaVRolyo8/HyF2FajUw9sO\nbleqFPTt9dCxdyAuRr3OYLwflUaBJV1PzmQb1gwDtgw9lgw9OuPouSqn5ps57+tT2PCffWx5vZXe\nfW7mXlWERn9m/AxFwiKNtd34XCGMdi0muxbj0EOtTW4LuoyMjIzMqREORvH0B/H2B/D0B+MPb38A\n32AISTrycQrVkGjVq9DolUMC9pB1vQrtIaL24EOl/eSI2zNjdjABiKRNQ1IZYsmTEiZSY5POZBep\ngWiATQc2kK5YRJcrePwDTpM0XRo3lX2R3+9+jDU9K1mYsSThfR4TSUK7+5+YVv4IBCWu8x4nOOnK\n4f0KFYFp/0Ow7HKMtY+g3/p/6BpewltzF4HJ14Ew0orVWNuDRq8kf/oYuHlLEub3v4e6dyv5s7LY\nWCsy2O3DmmFIfN8TADEq0dfqoX2Xk/ZdA3Eh6MizMW2WndyCKLbdT6Gof42wwoK37DN4Cy8ngpZI\nKEokJBINi8PLcJRoSCQSFoeXYZGAJ3xIm9gyGhaPOz5BAJNDhz3bQOHMVGwZeqyZeow2LcIRXHVH\nG61BxcLPlrJnbTdb32rj7Sd2MP/TJTjyxsYDIBFIkkTbDid1b7XiGwih0iiIhEZ+FlqDKi5YjXYN\nxhQdJrsGo12LwapBoUzubOwyMjIyn1QOhsHEhOdBERqIPw8e4n0EQ9f7FC2OPBP5VVpMKTqMdg1I\nEPJHCPqjhHwRQv6DjyghfwSvM4izw0vIHz3m77mg4HBxewSBqzGo0BpU2LMMY/L7nghkkTpWKFSE\ns+YmNC61K14jNbndfTcf2EhQDFJmmMfr3hDhqIg6wZO0qwo/xZttr/HYjl8zyzF3VOuyngxCwIn5\ng7vQNr1GKLsG97m/RTQf2QVZ0tnxLHuQwJTPYlp5L+b370S34//hWfxTIpmzAfANBmnf7WTSgsyT\ncsE8VfQf/QHdnhfxVt9JdsUChPVbaKnrY/r5n1yRGglF6W500b57gI76AUK+CAqlQEaJhYrFmWRX\n2NCbh5NZRQvug+rPY6l9mLTGnyC2/w7v3O8QqLzhtJIsSaIUF6zDonZY5KZnmxF0Asox+J4cC0EQ\nKF+QSWq+idrnm3jvqd1MPz+XSQsmnvvvQJePLa/vp2efG2uGnmU3l5NWaCbki004PM4gXmfM1evg\nBKRtpxNJHL61LijAYIkJVmOKFqNNiyll2AqrNaom3PsiIyMjM5GIRkR8g6G4APUeahF1BkeIRkEA\ngzV2szGn0h7znEkZum6naEclOWA0LBIKRAj5ooeI2SFB+zGBG/CEcfX4Cfmjw6UBD2HWZQWUzkts\n1YdEIYvUMSScXY1x3SMIASdSAlwkO11BzFoVJm1yf6xre1ZhUBmYnjKDV6V9dLuD5NoSKxpVChXf\nnnont9V+jf/X+CxfrvhaQvs7EurWDzG/+x0U/n488+/GP+MrJ5RIJ5I2lYGr/o224WWMa36G/d9X\nEKj4FJ6aH9C4PgQSlFYn/gKkbnkf49oHCJRcim/2t9AJApmlVlrq+ph2bu6EvVN3KgS8YTrrB2jf\nNUB3k4toWEStU5JdbiO7wkZmmfWYLp5RWzGuC/+IqmszxrX3Y/7wHvR1T+Kr/j7B0ksOs5afCIJC\nQK1VHrVfm83AwEDy1Cx15Jpi7r8v7qPuzVZ69rmYd3UxWkNyX78Agr4IO95rp2lDD2qtklmXFVA8\nOw2FMvY/oDWq0RrVpOQebiEWoxJ+dyg2CRoSrwcfnfUDBDwj78or1YpD3Ic1hwlZlUZ2JZaRkZE5\nHtGIiLs3QP8+L92trtiNxCFB+nG3XKVagWlIfGaWWjCmxCyippSY90uiatEf2r9erUFvPrnjxKhI\nKHBQyEaJBKOkFp7kSZKI5J8NnEEcGpcaKr5g1M/f6QokvRVVlETW9qxmbmoNOdbYBK7LlXiRCjAt\npYoLci7mhX1/5/zciygwFSa8TwAiAYy1D2Goe4qIvYyBS/5MJO0kE0YJAsFJVxIsPA/jpsfQb/kT\nisZ32NfzR7LLHRhtif3clc4mLG9/g6hjMu5zfhXPOlxQ5aBzzyC9LW7SiywJHcN44+4L0LF7gPZd\nTvr2e5Ck2N3Uotmp5FTYSSs0nbTbZiRzFoNX/gtNy3sYax/E8vbXCG+pwjv/bsK5o18/ONnQ6FUs\nuL6UxnU91L3ZyvIndlDz6WJS85PzR1UUJfZu7GX7u+2E/RGK56Yz9ZyckxLWCqWA0RYTmke6tRQJ\nRWPW14Hg0CRqWMT27HMd7kpsjLkSWzP0ZJRYSC+2jGossYyMjMxEI+ANM9jlw9npY7DLz0CXD1dv\nYIQXi9aowpSixZFvomBIgBqHLKI6k3pCerAolAp0RsUZ8xsgi9QxJJwxA0mpRd2xLiEitcsVJNua\n3PGo9YO76Q/2sSBjEVmG2FgPJnwaC26p+Dqru1fy2x2/4JfzHkv4RUh5YCeW5d9E1V+Pf9pNeObf\nA+rTEOQaI975d+GffB0dLz5PMKRmpvdh1C2fI1xw1ugN/BCEoAvL6zeDQsXgxf8H6mHX3uwKGyqN\ngpa6vjNOpEqihLPDS/uuAdp3D+Dq8QNgy9QzeWk2OZNt2LIMp/8dEgRChecQyl+Gds+LGNc9iu3l\n6wjlL8VTczfRtCmj8GqSF0EQKKvJwJFnYu3zTbz/9G6mnpNLxaLMpLLO9za7+ei1/Qx0+UgrNDPz\nknxsmaPv5q7SKGNlfTIOv05IkkRwyJXYe1C8DoTw9Ado2+5k36YDANiyDGSUWMgosZBaYB6TUAAZ\nGRmZsUYUJTwHAgx0+YYeMUEacIfjbfQWNbZMA1nlNmyZerKL7EgqCbVO9kJJdmSROpYotYQzZqLu\nqB31U0uSRKcrwOy8I2eATRbWdK9EISipTluAThGz/o1F8qSD2LUpfKn8q/xmx6O817Gcc3LOT0xH\nkoi+7imMax9C0loZvPQvhArOHrXTR62FbAtchtXmJlvfgPrVzxEsPB/PonsRrYWj1g9iFPPyW1G6\nWhi8/DlES+6I3SqNktwpdtq2O5l1ScG4xzueLtGISO8+N+27nXTsGsDvDiMoIK3ATPHFeeRUxOJP\nEoJCSbDiWoKll6Lf/hcMG/+XlBcuIDDpKrzV30O05CWm3yQhJcfIeV+vZNPLzWxb3kZvs5t51xSN\n+x1h32CIrW+1sn9bPwarhvmfLiF3qn1c7rILgoDOqEZnVOP4mCuxGI3dVOluctHd5KJhbTf1q7pQ\nqARS801kFMdEqy3beMR6tjIyMjLJTCgQiVtFDz5c3cM1vBVKAUuaLnadyzQMPfRoP/YbkmyhLzJH\nRxapY0w4uxrDpscQQm4kzei5tLmDEbyhaNJn9l3bs4pp9ulYNDGrW5pJM6aWVIBL8i/njbZXeWLX\n/1KdvgCTenQziyo8HZjfvR1N2yqCRRfgPusRJP3oZt7ta/Xg7PAx67JiBma/i77uKQwbf0vK38/G\nN/Mr+GZ/c4TF81QxrnsYbct7uJc+QDhn/hHbFFQ5aP6oj476AfKmppx2n2NNKBCha88g7bsG6GoY\nJByMolQryCyzklNhI6vcNrZxkiod/hm3EJh8HYbNT6Cvewpt42v4p30e3+xvIekn3nt8omh0Kmo+\nXUJaUS9b3tjP8t/tiD0fh5iaaFikfnUXuz7sBEmiclk2FYszkzYGVKEUcOSZcOSZqFyWTSQUpbfZ\nHRet295pZ9s77Wj0StKLLKQPWVpNKdoJ6dYmIyNzZiJJEl5nMG4VHezyMdDpwzsQirfRGFTYMmM1\nvA8KUnOaLuGxojJjiyxSx5hw9nyEjb9F1blxVN0zh8vPJG9Maqevg73uJr5W8c34tkyzjk732FlS\nAZSCku9MvZOvrf4izzY8ya2V3xm1c2saX8X8wfcRoiHcyx6OZWxNwASwobYHtU5JQZUDlEr8s75O\nsPxqjGsfxLjpMXS7/4l34Y8Ill5+yv1r97yEYfMT+KfcSGDq54/aLq3Igt6spqWub8KIVL8rNOTG\n66R3nxsxKqE1qsidaidnsp30Ysu4u0hKWive+T/AP+0mDBt+FStFtOt5/DO/hq/qS6NyEyIZEQSB\n0nnpQ+6/jXzwf7uZcnYOk5dkjYn7ryRJdOwaYMubrXidQXIr7VRdmJc4C3qCUGmUZE2ykTXJBkDA\nE6Znr4uuJhc9TS7adjoBMNo0ccGaUWw5zOogIyMjkygioSiDPX4GuvwxMTr0iASHYu8FMDt0pOQa\nKZ6Thi3LgDXTgN48MWNGZU4OWaSOMeHMWUgKFZqOdaMqUofLzySvJXVtzyoA5mcsim/LsmjZ2e0e\n87FMslZwef5VvNT8by7IuZgya/lpnU8IuTGtvBfd7n8STq/Cfd5jRG3FozTakfhdIdp2OCmrSR+R\nyVU0ZuI+97f4p9yIaeW9WN7+BqFtf8Gz5GdEUytPqg9Vz1bM732XUFY1nsU/PWZbhUIgf7qDPWu7\nCXrDST/J7W1xs+LZesSIhMmhpWx+BjmTbaTkmpLSDVI0ZeE561H8VbdgrH0I47pH0G17Ft/c2wlU\nfgYUZ+Zl3J5l4LyvTWHTK81sf7ed3mY31dcWozMl7vvl6vHz0ev76W5yYUnXs/QL5WQUnxmx1jqT\nmvzpDvKnO5AkCU9fcMjKOkjbDjmeVUZGJrFIooSr109vs4cDLW6cnT48fYF4Vl2VVoEtw0BBVSq2\nTH1MkKbrk9Z7RSbxnJmzm2RGbSCSNn3U41IngiV1bfdqCkyF5BqHY+syLTreaziAKEkoxviu2M3l\nt/Bh1/v8ZscveGz+H1GcQtkPAFXnBizv3IbC3YZ3zm345nz7tOpdHo+mDb1IknTUsjORrLkMXPsq\nul3/wFj7MPYXLiQw5XN4q+84odJHgq8XyxtfRNQ7cF34R1BqjntMQZWD+tVdtO5wJnU9Lu9AkDXP\nNWK0allwQymWNN2EuRsbTSnDdfHTqDo3Ylp7P+YVd6Gv+xPemrsIFV+UEIv9eKPWKqm+tpj0Ygsf\nvdrC27/bQfWnikddOIb8EXa830Hjuh5UGgUzL8mnZG56vKTMmYYgCJhTdZhTdZRWp4+MZ90rx7PK\nyMicPmJUYqDLR2+zm95mNwda3IT8sTqeerMae46RvKkpcUFqtGmTKlmezPgji9RxIJxdjb7uKQj7\nTy/T6yF0ugJoVQps+uS0YnnCHrb0b+ZTRdeP2J5l0RIRJQ54QqSbx1Zgm9UWvjL5Vh6q+xmvt/6X\nS/OvOLkTRMMYNv4Gw6bHEM25DFz1HyJZcxIz2INdRkT2buwhq8yKKeUYVnOFksCUzxIsuQTD+l+i\n3/4XtA0v4635HoHKzx69Pms0iPXNW1AEnAxc/RKSIfWExmXN1GNN19Oy5UDSitRIKMrqvzciRiQW\nfrYUS1riyx4lgkjWHAau+g+a5uUY1z6I9c1bCGfMxLvgHsLZNeM9vFFHEASKZ6fhyDWy5h9NrHi2\nnspl2VQuyz5twSSJEvs+OsC25W0EfRGK56Qx9ZyccU/WNNYcMZ61xUN30yA9x4lnlZGRkYHY/KS/\n3RsTpM1uDuz3xEtmmVK05Ey2k1ZoJrXQjNGmmTA3iGXGD1mkjgPh7BoMH/0edffmUauF2OUKkmVJ\n3gQYG3priUpRFqQvGrH9YKKnTldgzEUqwHnZF/J66395qv73LM5cilVjO6HjlAN7MS//JuqeOgIV\nn8az+CejmgjraLTtcBLwRCiryTih9pLOhnfJzwhMuQHTynsxr7gb/fb/h2fJz+J1e4cbS5g+/CHq\nzg24zn/ipGq5CoJAfpWDbcvb8PQHji2gxwFJktjwYjMDXT4W31g2YQVqHEEgVHQ+oYKz0e3+F4b1\nv8D24rUEC87BO/8uoo7J4z3CUceaYeC8r1Wy+dUWdr7fwYFmN9WfKkZvPr6l/0gc2B8rKePs8JGa\nb2LJ5/OxZxtHedQTE5VGSVaZlayyWLb4g/GsB5MwHRrPWlSVRm6VfeL/T8nIyJwUkVCUvlZv3FLa\n3+aJZ9q1pOspmOEgrSAmSg2WU7tOy3yykUXqOBDOmouEgLqjdtREaqcrkNTxqGt6VmHV2JhsH1nz\nMdMyXIamKmfsxyUIArdN+S63rLqJJ3f/njum/+DYB0gSup1/w7TqJ0hKDYMX/IFQ6aVjM1igobYb\nsyOWYv1kiDomM3jFC2iaXsO0+qfYXryGQNkVeBf8ENGUBYBu+5/R73wO36xbCZZdftJjK6hKYds7\nbbTU9THlrHH4MI/Brg87ad3ez/Tzc+OJZM4IFCoClZ8hMOkK9FufwbDpcez/OJ9gxafwzvsuojm5\nPofTRaVRMu/qYtKLLGz675D777XFZJaeeOktvyvE1rdj31O9RU31tcXkT09J2ht8ycBh8az9Qbob\nY/GsO1d3sH1FO5llVspq0skstcouezJJjyRJsVhICUCKx0UKAiiUchz2kQj5IxzY74m77va3+5BE\nCUGIxbKXzEuPidICU9LnppCZGMgidRyQtBYiqVNQd6wbtXN2uoJMzhj7Mg0nQkSMsK5nLQszFqMU\nRrqZHmpJHS+KzCVcW/QZnt/7Ny7Mu5Sp9mlHbCf4+zC/dyfa5rcJ5S7Gfc6v4gJvLOhv89Df5mXm\nJfmnNgkUBEKll9JfcA6GzY9j+OgPaPctxzvnW0RSp2Ba+WOChefirfneKY3PYNWSXmimpa6PymXZ\nSTPp79g9wPZ328mfnkL5oszxHk5iUOnxz/o6gcrrMWx6HP22Z9E2vIx/2k34Zt96QrHIE4nCmanY\nc4ysfb6JD/+yh8lLsphyVs4xY0ijEZE9a7rZtaIDMSoxeUkWFUuyRiQfkzk+giBgdugwO2LxrBqF\nio/e3U/Thh5W/rUhloysJoPCGamodfJ7K3PiiFGJnr0uWur6ONDiJhoR4+IRKSYsgZHbYn9GbJek\nj2+TiDc7eOwxUGkVaA1qtEYVWoNqaKlGM7SuMw6va40q1Fpl0vzejSYBb5gDQ1bS3mYPA90+kGLh\nASk5RsoXZcbcd/NM8v+6TEKQReo4Ec6pQb/9rxANnVBimmPhD0cZ8IfjVslkY5uzDk/EzYKMxYft\nM2iUWHUqusa4DM3H+XzpF3ivYzm/3f4L/rDwaZQfy5iqaX4X83t3IAQH8Sz8Mf6qL8IpJlo6VRpq\nY0ldCmecWJzoUVHr8VXfSWDydZhW/QRT7UMAROyluM/939N6XflVDja+1Ex/mxdH3ujWnz0VBnv8\n1P6rCXu2gTlXFp2RE4lDkXR2vAt/hH/6zRjX/xL9lj+h2/kcvtnfgMW3jvfwRhVrup5zvzKZj17f\nz64VnfQ2u6n5dMkR3co66gfY8vp+PP1BsitszLgoL+lc0icqBouGKWfFasi273TSUNvNR6/tZ9s7\nbRTNTKW0JgOzQ36vZY6MJEk4O3y01PXRuq2PgCcSK6821YGIBAIIEFsevH7Ht8Wexy/rh7QZ3ibE\nj489PWT/0I5Dj5fEmMUw6A0T9EUIeMIMdvsJesNxV9aPo1AKMdF6iKAdFrjq+HZN/LkyKa21vsFQ\nTJC2xGJKXb0x44FSrcCRZ2TKsmzSCs2k5BrljLsyY4IsUseJcHY1hrqnUPXUEcmae1rn6opn9k3O\nicCa7lWoFRrm7uXbJQAAIABJREFUpB75dWZZdONqSQXQqwx8Y/Jt3PfRPbzU8m+uKboutiPsx7T2\n5+i3/ZlISjmuK/4+LvF+AU+Y1u39FM9JG7U7lqIlH9fFT6Nu/TAmZKrvRNKeXtbU3Cl2Pnq1hZa6\nvnEXqUFfhNV/a0ClVrDw+rJPVCkN0ZyD+5xf4ZvxZYy1D2Na+yBS/Quolj5KJHveeA9v1FBplMy9\nsijm/vtKM8t/t4N51xbHYyldvX62vNFKV8Mg5lQdS/5n0km5BsucOEqVIu4S3NfmobG2h6YNvTTU\n9pA1yUpZTQYZJRbZFVgGAI8zyP66Plrq+nAfCKBQCmSV2yiocpA1yYoj1cTAgG+8hxlHkiSiYZGg\nd1jABr2RoeXI585OLyFfJJ7J9kiodcoRQlZjUMUSwQkxAS0IQlx0HxTUw+tCvM3I50PrikOPP9K5\nhttHIxL9bR56mz14nbG5pFqrJLXARMGMVNIKzdizDShVn5zfT5nkISEiVRRF7rvvPurr69FoNPz8\n5z+noKAgvv/ZZ5/ltddeA2Dp0qXceuuZdYf/RAhnxSaK6o51py1SDwq8ZCw/I0kSa3tWMcsxG73K\ncMQ2mRYtLf3+MR7Z4SzOXMa8tBqeaXiSpVlnk+nuwrz8m6icjfiqvoy35vugGp8bAXs39iJGj152\n5nQI5y0hnLdkVM6l0anIrrDRuq2fGRfljdvdYjEqUftCE77BEMtuLsdg/WQmbYg6JuO65FnUrSux\nrvg+thevwT/tJrw1d4HmzEkSVFDlICXHyNrnG1n5lz1xt+6Gtd0oVQqqLsqjrDo9Ka0XZyKOXBOO\na01MvyCPvRt6aNrQw4d/2RMreVOTHnMFlt2sP3EEfRFat/ezv66PA/s9AKQVmilfmEnuFDsaffLa\nTQRBQKVRotIoMdpPbK4lRkVC/mhMxMYF7eGi1usM4uzwIonDrsqHxswOr8eWh66PBlqDitRCM2Xz\nYzGl1kyDXGpKJilIyBXhnXfeIRQK8fzzz7NlyxYeeughfv/73wPQ2trKK6+8wj//+U8UCgXXX389\n5557LhUVFYkYStIi6R1E7JPQdNTin316Ir1rSKQmY+KkFk8zHb52riv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ObIk7//KbeO2N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\nuONdFPRLOuO01k9bQqcE4a6heFf96SE2z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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"listOfFeed = ['sugar','wood','paper', 'algae', 'waste']\n",
"start_year = 1990\n",
"end_year = 2017\n",
"\n",
"# plot the graph\n",
"plt.style.use('seaborn-darkgrid')\n",
"plt.subplots(1,1,figsize=(16, 5))\n",
"plt.subplot(111)\n",
"plt.title(\"Evolution of Records with focus on Feedstocks\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Normalized Quantity\")\n",
"\n",
"for name in listOfFeed:\n",
" nameData = get_records_of(start_year,end_year,name, 'Feedstock')\n",
" plt.plot(nameData['range'], nameData['normalized'], label=name)\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.5. Contextual Relationships \n",
"\n",
"#### 2.5.1. US Regular Conventional Gas Price "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by comparing the evolution of the outputs above studied with the average oil price per gallon found in the [following](https://fred.stlouisfed.org/series/GASREGCOVM#0) website.\n",
"\n",
"We import the data, and convert monthly prices to yearly averages with the bellow code. \n",
"\n",
"- [Price per gallon in US dollars](https://fred.stlouisfed.org/series/GASREGCOVM#0)\n",
"- [Price per barrel inflation adjusted in US dollars](https://inflationdata.com/Inflation/Inflation_Rate/Historical_Oil_Prices_Table.asp)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# get price per gallon in US dollars\n",
"oil_data = pd.read_csv('Data/GasData.csv', delimiter=',', header=None).as_matrix()[1::, :]\n",
"gallon = []\n",
"oil_years = list(set([int(e[0:4]) for e in oil_data[:, 0]]))[:-1]\n",
"for year in oil_years:\n",
" addition = 0\n",
" months = 0\n",
" for row in oil_data:\n",
" if str(year) in row[0]:\n",
" addition += float(row[1])\n",
" months += 1\n",
" average = addition / months\n",
" gallon.append(average)\n",
"\n",
"\n",
"\n",
"# get price per barrel data \n",
"barrel = pd.read_csv('Data/GasDataNormalized.csv', delimiter=';', header=None).as_matrix()[:, 1].tolist()\n",
"\n",
"oil_index = {'gallon':gallon, 'barrel':barrel}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Relationship Over Time**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us visualize how the evolution of the price of gas relates to the normalized quantity of assets over time, in a chronological graph. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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7du2YP38+AAsWLKB9+/ahCC1kfvnlZwB27dqJ2+0iLe04du3aiWma5OSsqXhc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UPX6aJT7MveS5faXUmJaxDyzqqVLb04jSXHFmORLUxuM830UNcmuS1NXc2qZmfZC/SGjShYtR7X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\nAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# define subplots\n",
"fig, ax1 = plt.subplots(figsize=(15,7))\n",
"listOfOutputs = ['biogas', 'bioplastic', 'butanol']\n",
"colors = ['b', 'y', 'g']\n",
"start_year = 1990\n",
"end_year = 2017\n",
"price_type = 'barrel'\n",
"\n",
"# first axis\n",
"\n",
"for position, outputName in enumerate(listOfOutputs):\n",
" nameData = get_records_of(start_year, end_year, outputName, 'Output')\n",
" ax1.plot(nameData['range'], nameData['normalized'], label=outputName, color=colors[position], ls='--', alpha=0.5) \n",
" \n",
"\n",
"ax1.set_xlabel('Years')\n",
"ax1.set_ylabel('Number of relative records')\n",
"ax1.tick_params('y')\n",
"ax1.set_title('Oil Price Vs. Asset Quantity')\n",
"ax1.legend(loc=2, frameon=True)\n",
"ax1.grid(False)\n",
"\n",
"# second axis\n",
"ax2 = ax1.twinx()\n",
"ax2.plot(oil_years,oil_index[price_type], color='r', label='Oil Price')\n",
"ax2.set_ylabel('Price of {} of oil $US'.format(price_type), color='r')\n",
"ax2.tick_params('y', colors='r')\n",
"ax2.legend(loc=1, frameon=True)\n",
"\n",
"# expose\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Scatter Visualization**\n",
"\n",
"To study this relationship in a more in depth fashio we create a process that given a certain term gives us the relationship with the price of gas. "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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DAZdffjkmTZqEPn36nPLrl156aZv3TUFBAX7605/CZDLhRz/6ERYsWIArr7wSZrMZI0eO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\nZQCGOyIiIiIiogzw/wGvwczYL/2cjwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pearson Correlation Index: 0.8445465111603638\n",
"P-value: 1.6031894575347735e-08\n"
]
}
],
"source": [
"outPutToCompare = 'butanol'\n",
"typeOfProcessVariable = 'Output'\n",
"price_type = 'gallon'\n",
"\n",
"\n",
"data = get_records_of(1990, 2017, outPutToCompare, typeOfProcessVariable)['normalized']\n",
"\n",
"fig, ax1 = plt.subplots(figsize=(15,7))\n",
"sns.regplot(np.asarray(oil_index[price_type]), np.asarray(data) ,fit_reg=True, marker=\"+\", color = 'g')\n",
"plt.title('Gas price relation with quantity of Assets: {}'.format(outPutToCompare))\n",
"plt.xlabel('Price of {} of oil in US$ in Year'.format(price_type))\n",
"plt.ylabel('Quantity of Asset {} in Year'.format(outPutToCompare))\n",
"\n",
"plt.show()\n",
"\n",
"correlationIndexes = stats.pearsonr(np.asarray(oil_index[price_type]), np.asarray(get_records_of(1990, 2017, outPutToCompare, 'Output')['normalized']))\n",
"print 'Pearson Correlation Index: ', correlationIndexes[0]\n",
"print 'P-value: ', correlationIndexes[1]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above graph each datapoint corresponds to a year. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Ranking of Most Related Outputs**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The relationship between relative number of documents and price of oil over time:\n",
"TOP 10:\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Output Name | \n",
" P-value | \n",
" Pearson Correlation Index | \n",
"
\n",
" \n",
" \n",
" \n",
" 7 | \n",
" butanol | \n",
" 1.603189e-08 | \n",
" 0.844547 | \n",
"
\n",
" \n",
" 19 | \n",
" bioplastic | \n",
" 2.463734e-07 | \n",
" 0.804599 | \n",
"
\n",
" \n",
" 1 | \n",
" biodiesel | \n",
" 7.978637e-07 | \n",
" 0.784034 | \n",
"
\n",
" \n",
" 21 | \n",
" fatty acid ethyl ester | \n",
" 1.427601e-06 | \n",
" 0.772960 | \n",
"
\n",
" \n",
" 30 | \n",
" adipic acid | \n",
" 1.048009e-05 | \n",
" 0.729790 | \n",
"
\n",
" \n",
" 3 | \n",
" bioethanol | \n",
" 2.862862e-05 | \n",
" 0.704439 | \n",
"
\n",
" \n",
" 9 | \n",
" syng | \n",
" 3.649295e-05 | \n",
" 0.697899 | \n",
"
\n",
" \n",
" 15 | \n",
" biobutanol | \n",
" 5.140385e-05 | \n",
" 0.688369 | \n",
"
\n",
" \n",
" 8 | \n",
" cellulosic ethanol | \n",
" 1.301892e-04 | \n",
" 0.660616 | \n",
"
\n",
" \n",
" 14 | \n",
" biopolymers | \n",
" 3.263515e-04 | \n",
" 0.630094 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Output Name P-value Pearson Correlation Index\n",
"7 butanol 1.603189e-08 0.844547\n",
"19 bioplastic 2.463734e-07 0.804599\n",
"1 biodiesel 7.978637e-07 0.784034\n",
"21 fatty acid ethyl ester 1.427601e-06 0.772960\n",
"30 adipic acid 1.048009e-05 0.729790\n",
"3 bioethanol 2.862862e-05 0.704439\n",
"9 syng 3.649295e-05 0.697899\n",
"15 biobutanol 5.140385e-05 0.688369\n",
"8 cellulosic ethanol 1.301892e-04 0.660616\n",
"14 biopolymers 3.263515e-04 0.630094"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"term_names_query = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:Output)\n",
" WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
" AND NOT a.year = \"Null\"\n",
" RETURN fs.term, count(a)\n",
" ORDER BY count(a) DESC\"\"\"\n",
"oil_type = 'gallon'\n",
"term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
"correlations = []\n",
"p_values = []\n",
"for term in term_names:\n",
" data = get_records_of(1990, 2017, term, 'Output')['normalized']\n",
" correlations.append(stats.pearsonr(data, oil_index[oil_type])[0])\n",
" p_values.append(stats.pearsonr(data, oil_index[oil_type])[1])\n",
"\n",
"oilDataFrame = pd.DataFrame(\n",
" {'Output Name': term_names,\n",
" 'Pearson Correlation Index': correlations,\n",
" 'P-value': p_values\n",
" })\n",
"oilDataFrame = oilDataFrame.sort_values('Pearson Correlation Index', ascending=False)\n",
"\n",
"\n",
"\n",
"print 'The relationship between relative number of documents and price of oil over time:'\n",
"top = 10\n",
"\n",
"print 'TOP {}:'.format(top)\n",
"display(oilDataFrame[:top]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Negative Correlations**"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The relationship between relative number of documents and price of oil over time:\n",
"BOTTOM -10:\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Output Name | \n",
" P-value | \n",
" Pearson Correlation Index | \n",
"
\n",
" \n",
" \n",
" \n",
" 16 | \n",
" naphtha | \n",
" 0.677733 | \n",
" 0.082145 | \n",
"
\n",
" \n",
" 44 | \n",
" biodiesel blending | \n",
" 0.683315 | \n",
" 0.080645 | \n",
"
\n",
" \n",
" 45 | \n",
" ethanol blending | \n",
" 0.683315 | \n",
" 0.080645 | \n",
"
\n",
" \n",
" 18 | \n",
" renewable diesel | \n",
" 0.716673 | \n",
" 0.071767 | \n",
"
\n",
" \n",
" 11 | \n",
" renewable fuel | \n",
" 0.944557 | \n",
" 0.013770 | \n",
"
\n",
" \n",
" 17 | \n",
" succinic acid | \n",
" 0.956893 | \n",
" 0.010703 | \n",
"
\n",
" \n",
" 40 | \n",
" rdif | \n",
" 0.629618 | \n",
" -0.095276 | \n",
"
\n",
" \n",
" 34 | \n",
" electricity from biomass | \n",
" 0.456174 | \n",
" -0.146748 | \n",
"
\n",
" \n",
" 5 | \n",
" gasoline | \n",
" 0.371514 | \n",
" -0.175570 | \n",
"
\n",
" \n",
" 10 | \n",
" pellets | \n",
" 0.268436 | \n",
" -0.216520 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Output Name P-value Pearson Correlation Index\n",
"16 naphtha 0.677733 0.082145\n",
"44 biodiesel blending 0.683315 0.080645\n",
"45 ethanol blending 0.683315 0.080645\n",
"18 renewable diesel 0.716673 0.071767\n",
"11 renewable fuel 0.944557 0.013770\n",
"17 succinic acid 0.956893 0.010703\n",
"40 rdif 0.629618 -0.095276\n",
"34 electricity from biomass 0.456174 -0.146748\n",
"5 gasoline 0.371514 -0.175570\n",
"10 pellets 0.268436 -0.216520"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"term_names_query = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:Output)\n",
" WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
" AND NOT a.year = \"Null\"\n",
" RETURN fs.term, count(a)\n",
" ORDER BY count(a) DESC\"\"\"\n",
"oil_type = 'gallon'\n",
"term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
"correlations = []\n",
"p_values = []\n",
"for term in term_names:\n",
" data = get_records_of(1990, 2017, term, 'Output')['normalized']\n",
" correlations.append(stats.pearsonr(data, oil_index[oil_type])[0])\n",
" p_values.append(stats.pearsonr(data, oil_index[oil_type])[1])\n",
"\n",
"oilDataFrame = pd.DataFrame(\n",
" {'Output Name': term_names,\n",
" 'Pearson Correlation Index': correlations,\n",
" 'P-value': p_values\n",
" })\n",
"oilDataFrame = oilDataFrame.sort_values('Pearson Correlation Index', ascending=False)\n",
"\n",
"\n",
"\n",
"print 'The relationship between relative number of documents and price of oil over time:'\n",
"bottom = -10\n",
"\n",
"print 'BOTTOM {}:'.format(bottom)\n",
"display(oilDataFrame[bottom:]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.5.2. Sugar Cost "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this part we will make the same analysis but taking an example of a feedstock: sugar. \n",
"\n",
"Data was obtained [here.](http://databank.worldbank.org/data/reports.aspx?source=global-economic-monitor-commodities#)\n",
"\n",
"We start by importing the data. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"sugar_data = pd.read_csv('Data/Sugar_Price.csv', delimiter=';', header=None).as_matrix()\n",
"sugar = {}\n",
"sugar['years'] = [int(e) for e in sugar_data[:, 0]]\n",
"sugar['nominal'] = [e for e in sugar_data[:, 1]]\n",
"sugar['real'] = [e for e in sugar_data[:, 2]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Relationship Over Time**\n",
"\n",
"Let us see the evolution of Sugar prices side by side with the evolution of certain feedstocks in our database. "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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6atc+/aC+xo9/lrfffgOn00l6ejo33XQ7gYCfnTt3MHHi27jdbpYs+RqAN9+cSIsWF9Kt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aUkQZcuTrp3j8/iSJ6Ro0BRSHr6iViHUmFNm9qYO7c6TqdMUZHBCy/kEAyaRX1M0XFof2UgAfdX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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# define subplots\n",
"fig, ax1 = plt.subplots(figsize=(15,7))\n",
"feedstock_list = ['sugar', 'wood', 'sugarcane', 'sugar beet', 'cellulosic sugars']\n",
"colors = ['gold', 'mediumblue', 'm', 'green', 'k']\n",
"\n",
"start_year = 1990\n",
"end_year = 2017\n",
"sugar_price_type = 'real'\n",
"\n",
"# first axis\n",
"for position,feedstock in enumerate(feedstock_list):\n",
" data = get_records_of(start_year, end_year, feedstock, 'Feedstock')\n",
" ax1.plot(data['range'], data['normalized'], label=feedstock, ls='--', color=colors[position])\n",
" \n",
"ax1.set_xlabel('Years')\n",
"ax1.set_ylabel('Relative number of records')\n",
"ax1.tick_params('y')\n",
"ax1.set_title('Sugar Prices Vs. Asset Quantity')\n",
"ax1.legend(loc=3, frameon=True)\n",
"ax1.grid(False)\n",
"\n",
"# second axis\n",
"ax2 = ax1.twinx()\n",
"ax2.plot(sugar['years'], sugar[sugar_price_type], color='r', label='Sugar Price', ls='-')\n",
"ax2.set_ylabel('Price per kilo of sugar in $US (inflation adjusted)', color='r')\n",
"ax2.tick_params('y', colors='r')\n",
"ax2.legend(loc=1, frameon=True)\n",
"\n",
"# expose\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Scatter Example**\n",
"\n",
"Let us see a scatter plot where each point is a year and the x and y axis correpond to the price of sugar and quantity of assets respectively. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Ql6cHvV27jKM0JUlgwAB/0LvoIg2S1MiTRbighT5N0zB79mzs2bMHFosF8+bN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\nx8cOnF0D5doAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"outPutToCompare = 'sugarcane'\n",
"typeOfProcessVariable = 'Feedstock'\n",
"price_type = 'real'\n",
"\n",
"\n",
"data = get_records_of(1990, 2017, outPutToCompare, typeOfProcessVariable)['normalized']\n",
"\n",
"fig, ax1 = plt.subplots(figsize=(15,7))\n",
"sns.regplot(np.asarray(sugar[price_type]), np.asarray(data) ,fit_reg=True, marker=\"+\", color = 'b')\n",
"plt.title('Sugar price relation with quantity of Assets: {}'.format(outPutToCompare))\n",
"plt.xlabel('Price of sugar US$ per kilo in Year ({})'.format(price_type))\n",
"plt.ylabel('Quantity of Asset {} in Year'.format(outPutToCompare))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Most Related Feedstocks**\n",
"\n",
"Which are the feedstocks who are more related to the price of sugar per kilo in what regards the number of records? "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The relationship between relative number of documents and price per kilo of sugar:\n",
"TOP 10:\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Feedstock Name | \n",
" P-value | \n",
" Pearson Correlation Index | \n",
"
\n",
" \n",
" \n",
" \n",
" 26 | \n",
" sugarcane | \n",
" 6.074365e-07 | \n",
" 0.789014 | \n",
"
\n",
" \n",
" 107 | \n",
" cellulosic sugars | \n",
" 1.263220e-06 | \n",
" 0.775341 | \n",
"
\n",
" \n",
" 43 | \n",
" jatropha | \n",
" 1.521222e-06 | \n",
" 0.771713 | \n",
"
\n",
" \n",
" 34 | \n",
" sorghum | \n",
" 3.083429e-06 | \n",
" 0.757299 | \n",
"
\n",
" \n",
" 64 | \n",
" dry biomass | \n",
" 3.454736e-06 | \n",
" 0.754884 | \n",
"
\n",
" \n",
" 75 | \n",
" beets | \n",
" 4.105286e-06 | \n",
" 0.751165 | \n",
"
\n",
" \n",
" 99 | \n",
" dedicated energy crops | \n",
" 6.915371e-06 | \n",
" 0.739525 | \n",
"
\n",
" \n",
" 1 | \n",
" algae | \n",
" 1.103076e-05 | \n",
" 0.728562 | \n",
"
\n",
" \n",
" 100 | \n",
" hybrid poplar | \n",
" 1.490683e-05 | \n",
" 0.721206 | \n",
"
\n",
" \n",
" 25 | \n",
" soy | \n",
" 2.631077e-05 | \n",
" 0.706675 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Feedstock Name P-value Pearson Correlation Index\n",
"26 sugarcane 6.074365e-07 0.789014\n",
"107 cellulosic sugars 1.263220e-06 0.775341\n",
"43 jatropha 1.521222e-06 0.771713\n",
"34 sorghum 3.083429e-06 0.757299\n",
"64 dry biomass 3.454736e-06 0.754884\n",
"75 beets 4.105286e-06 0.751165\n",
"99 dedicated energy crops 6.915371e-06 0.739525\n",
"1 algae 1.103076e-05 0.728562\n",
"100 hybrid poplar 1.490683e-05 0.721206\n",
"25 soy 2.631077e-05 0.706675"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"term_names_query = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
" WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
" AND NOT a.year = \"Null\"\n",
" RETURN fs.term, count(a)\n",
" ORDER BY count(a) DESC\"\"\"\n",
"price_type = 'nominal'\n",
"term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
"correlations = []\n",
"p_values = []\n",
"for term in term_names:\n",
" data = get_records_of(1990, 2017, term, 'Feedstock')['normalized']\n",
" correlations.append(stats.pearsonr(data, sugar[price_type])[0])\n",
" p_values.append(stats.pearsonr(data, sugar[price_type])[1])\n",
"\n",
"sugarDataframe = pd.DataFrame(\n",
" {'Feedstock Name': term_names,\n",
" 'Pearson Correlation Index': correlations,\n",
" 'P-value': p_values\n",
" })\n",
"sugarDataframe = sugarDataframe.sort_values('Pearson Correlation Index', ascending=False)\n",
"\n",
"\n",
"\n",
"print 'The relationship between relative number of documents and price per kilo of sugar:'\n",
"top = 10\n",
"\n",
"print 'TOP {}:'.format(top)\n",
"display(sugarDataframe[:top]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Negative Correlations**"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The relationship between relative number of documents and price per kilo of sugar:\n",
"Bottom 10:\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Feedstock Name | \n",
" P-value | \n",
" Pearson Correlation Index | \n",
"
\n",
" \n",
" \n",
" \n",
" 93 | \n",
" wood fuel | \n",
" 0.336664 | \n",
" -0.188530 | \n",
"
\n",
" \n",
" 72 | \n",
" waste oil | \n",
" 0.329527 | \n",
" -0.191282 | \n",
"
\n",
" \n",
" 136 | \n",
" particle board | \n",
" 0.289545 | \n",
" -0.207425 | \n",
"
\n",
" \n",
" 156 | \n",
" durum | \n",
" 0.279399 | \n",
" -0.211741 | \n",
"
\n",
" \n",
" 178 | \n",
" citrus residues | \n",
" 0.260452 | \n",
" -0.220080 | \n",
"
\n",
" \n",
" 123 | \n",
" beef tallow | \n",
" 0.240798 | \n",
" -0.229158 | \n",
"
\n",
" \n",
" 53 | \n",
" sawdust | \n",
" 0.223542 | \n",
" -0.237542 | \n",
"
\n",
" \n",
" 138 | \n",
" trap grease | \n",
" 0.212719 | \n",
" -0.243025 | \n",
"
\n",
" \n",
" 79 | \n",
" wood waste | \n",
" 0.210637 | \n",
" -0.244102 | \n",
"
\n",
" \n",
" 5 | \n",
" wood | \n",
" 0.137684 | \n",
" -0.287685 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Feedstock Name P-value Pearson Correlation Index\n",
"93 wood fuel 0.336664 -0.188530\n",
"72 waste oil 0.329527 -0.191282\n",
"136 particle board 0.289545 -0.207425\n",
"156 durum 0.279399 -0.211741\n",
"178 citrus residues 0.260452 -0.220080\n",
"123 beef tallow 0.240798 -0.229158\n",
"53 sawdust 0.223542 -0.237542\n",
"138 trap grease 0.212719 -0.243025\n",
"79 wood waste 0.210637 -0.244102\n",
"5 wood 0.137684 -0.287685"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"term_names_query = \"\"\" MATCH (a:Asset)-[:CONTAINS]->(fs:Feedstock)\n",
" WHERE (toInteger(a.year)>=1990 AND toInteger(a.year)<=2017) \n",
" AND NOT a.year = \"Null\"\n",
" RETURN fs.term, count(a)\n",
" ORDER BY count(a) DESC\"\"\"\n",
"price_type = 'nominal'\n",
"term_names = list(DataFrame(connection_to_graph.data(term_names_query)).as_matrix()[:, 1].tolist())\n",
"correlations = []\n",
"p_values = []\n",
"for term in term_names:\n",
" data = get_records_of(1990, 2017, term, 'Feedstock')['normalized']\n",
" correlations.append(stats.pearsonr(data, sugar[price_type])[0])\n",
" p_values.append(stats.pearsonr(data, sugar[price_type])[1])\n",
"\n",
"sugarDataframe = pd.DataFrame(\n",
" {'Feedstock Name': term_names,\n",
" 'Pearson Correlation Index': correlations,\n",
" 'P-value': p_values\n",
" })\n",
"sugarDataframe = sugarDataframe.sort_values('Pearson Correlation Index', ascending=False)\n",
"\n",
"\n",
"\n",
"print 'The relationship between relative number of documents and price per kilo of sugar:'\n",
"bottom = -10\n",
"\n",
"print 'Bottom {}:'.format(bottom * -1)\n",
"display(sugarDataframe[bottom:]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NON SERIES TIME ANALYSIS IS A LIMITATION.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.6. In depth year comparison \n",
"\n",
"In this part of the analysis the goal is two understand what exact capabilities differ from year to year. More exactly, how does one particular capability evolve over the course of two or more years. \n",
"\n",
"For example, if in year X1, Y1% of the assets related to sugar, what is the percentage Y2% in year X2? \n",
"\n",
"\n",
"#### 2.6.1. Visualizing the differences "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us visualize two different years side by side. "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
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GzX3KtD8xtwYL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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## call functions\n",
"first_year = 2017\n",
"second_year = 2010\n",
"colors = 'BuPu_r'\n",
"fst_year_matrix = get_year_matrix(first_year, normalization=False)\n",
"scnd_year_matrix = get_year_matrix(second_year, normalization=False)\n",
"\n",
"# create a subplot\n",
"plt.subplots(2,1,figsize=(17,17))\n",
"\n",
"# first heatmap\n",
"plt.subplot(121)\n",
"sns.heatmap(fst_year_matrix, cmap=colors, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
"plt.title('Capability Matrix: {}'.format(first_year))\n",
"\n",
"# second heatmap\n",
"plt.subplot(122)\n",
"sns.heatmap(scnd_year_matrix, cmap=colors, cbar=None, square=True, xticklabels=False, yticklabels=False)\n",
"plt.title('Capability Matrix: {}'.format(second_year))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Due to the very high number of rows, visualization is rather hard. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to create a matrix of absolute diferences between the two examples, for this, we start by subtracting them:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"cap_diff = np.absolute(fst_year_matrix - scnd_year_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we plot these differences. "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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RpUw2Owow4kpEREREKcGIKxEREVHKZHVwVt1GXLnMJfnB+pMtsqVKVV8TGvMl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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplots(1,1,figsize=(13, 13))\n",
"plt.subplot(111)\n",
"sns.heatmap(cap_diff, cmap=colors, square=True, yticklabels=False, xticklabels=False)\n",
"plt.title('Differences between {} and {}: Normalized Differences'.format(first_year, second_year), size=13)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There seem to be some areas where differences clearly exist. Let us investigate these areas in a more in depth fashion. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.6.2. Understanding the differences "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's understand what exact capability pairs are the most 'popular' in each year. \n",
"\n",
"We start by creating a function that returns given a year X, the most popular capability pairs of that year as absolute numbers and percentage of total documents. "
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def get_top_hits(yearMatrix, year):\n",
" \"\"\"\n",
" The function prints the top occurences if fed a matrix of occurences, it also prints other types of valuable info.\n",
" WARNING: Percentages are shown as 0 to 1. \n",
" \"\"\"\n",
" \n",
" # list where all the values and indexes of matrix are stored\n",
" top = 10\n",
" values = []\n",
" indexes = []\n",
" no_duplicates = np.triu(yearMatrix, 1)\n",
" total_documents = np.sum(no_duplicates)\n",
" matrix_axis_names = axis_names\n",
" \n",
" \n",
" # loop through the matrix\n",
" for row_n in range(yearMatrix.shape[0]):\n",
" for col_n in range(yearMatrix.shape[1]):\n",
" values.append(no_duplicates[row_n, col_n])\n",
" indexes.append((row_n, col_n))\n",
" \n",
" \n",
" # order the indexes and get the top\n",
" Z = [indexes for _,indexes in sorted(zip(values,indexes))]\n",
" extremes = Z[-top :]\n",
" \n",
" \n",
" # create dataframe\n",
" term_Dataframe = pd.DataFrame(\n",
" {'First Term': [matrix_axis_names[e[0]] for e in extremes],\n",
" 'Second Term': [matrix_axis_names[e[1]] for e in extremes],\n",
" 'Number of Documents': [int(no_duplicates[e[0], e[1]]) for e in extremes], \n",
" 'Percentage' : [no_duplicates[e[0], e[1]] / float(total_documents) for e in extremes], \n",
" })\n",
" \n",
" # prepare dataframe\n",
" term_Dataframe = term_Dataframe[['First Term', 'Second Term','Number of Documents', 'Percentage']]\n",
" term_Dataframe = term_Dataframe.sort_values('Number of Documents', ascending=False)\n",
" \n",
" \n",
" # print everything\n",
" print 'The top hits for the {} matrix: '.format(year)\n",
" display(HTML(term_Dataframe.to_html(index=False)))\n",
" \n",
" \n",
" print 'The total number of documents is {}.'.format(int(total_documents))\n",
" print 'Note: Percentages are as 0-1 in this table. '"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us use this function to try to understand each year."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The top hits for the 2017 matrix: \n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" First Term | \n",
" Second Term | \n",
" Number of Documents | \n",
" Percentage | \n",
"
\n",
" \n",
" \n",
" \n",
" ethanol | \n",
" fermentation | \n",
" 154 | \n",
" 0.017566 | \n",
"
\n",
" \n",
" biogas | \n",
" anaerobic digestion | \n",
" 137 | \n",
" 0.015627 | \n",
"
\n",
" \n",
" bio-oil | \n",
" pyrolysis | \n",
" 101 | \n",
" 0.011520 | \n",
"
\n",
" \n",
" bioethanol | \n",
" fermentation | \n",
" 76 | \n",
" 0.008669 | \n",
"
\n",
" \n",
" ethanol | \n",
" hydrolysis | \n",
" 76 | \n",
" 0.008669 | \n",
"
\n",
" \n",
" sugar | \n",
" ethanol | \n",
" 60 | \n",
" 0.006844 | \n",
"
\n",
" \n",
" waste | \n",
" ethanol | \n",
" 58 | \n",
" 0.006616 | \n",
"
\n",
" \n",
" sugar | \n",
" fermentation | \n",
" 57 | \n",
" 0.006502 | \n",
"
\n",
" \n",
" waste | \n",
" biogas | \n",
" 53 | \n",
" 0.006045 | \n",
"
\n",
" \n",
" biogas | \n",
" fermentation | \n",
" 53 | \n",
" 0.006045 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The total number of documents is 8767.\n",
"Note: Percentages are as 0-1 in this table. \n"
]
}
],
"source": [
"get_top_hits(fst_year_matrix, first_year)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The top hits for the 2010 matrix: \n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" First Term | \n",
" Second Term | \n",
" Number of Documents | \n",
" Percentage | \n",
"
\n",
" \n",
" \n",
" \n",
" ethanol | \n",
" fermentation | \n",
" 319 | \n",
" 0.022040 | \n",
"
\n",
" \n",
" ethanol | \n",
" hydrolysis | \n",
" 225 | \n",
" 0.015545 | \n",
"
\n",
" \n",
" biodiesel | \n",
" transesterification | \n",
" 168 | \n",
" 0.011607 | \n",
"
\n",
" \n",
" biogas | \n",
" anaerobic digestion | \n",
" 152 | \n",
" 0.010502 | \n",
"
\n",
" \n",
" biodiesel | \n",
" catalysis | \n",
" 131 | \n",
" 0.009051 | \n",
"
\n",
" \n",
" bioethanol | \n",
" fermentation | \n",
" 120 | \n",
" 0.008291 | \n",
"
\n",
" \n",
" sugar | \n",
" ethanol | \n",
" 106 | \n",
" 0.007323 | \n",
"
\n",
" \n",
" sugar | \n",
" fermentation | \n",
" 102 | \n",
" 0.007047 | \n",
"
\n",
" \n",
" bioethanol | \n",
" hydrolysis | \n",
" 95 | \n",
" 0.006563 | \n",
"
\n",
" \n",
" ethanol | \n",
" enzymatic hydrolysis | \n",
" 85 | \n",
" 0.005873 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The total number of documents is 14474.\n",
"Note: Percentages are as 0-1 in this table. \n"
]
}
],
"source": [
"get_top_hits(scnd_year_matrix, second_year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can make two observations: \n",
"- These two particular years have generally the same term pairs in their top table. \n",
"- However, the percentages can differ greatly. \n",
"\n",
"*Note: There is a high difference in number of documents. *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us now create a side by side comparison. "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
" First Term | \n",
" Second Term | \n",
" 2017 Percentage | \n",
" 2010 Percentage | \n",
" Difference in % | \n",
"
\n",
" \n",
" \n",
" \n",
" bio-oil | \n",
" pyrolysis | \n",
" 0.011520 | \n",
" 0.002625 | \n",
" 0.008895 | \n",
"
\n",
" \n",
" biodiesel | \n",
" transesterification | \n",
" 0.003878 | \n",
" 0.011607 | \n",
" 0.007729 | \n",
"
\n",
" \n",
" biodiesel | \n",
" catalysis | \n",
" 0.001825 | \n",
" 0.009051 | \n",
" 0.007226 | \n",
"
\n",
" \n",
" ethanol | \n",
" hydrolysis | \n",
" 0.008669 | \n",
" 0.015545 | \n",
" 0.006876 | \n",
"
\n",
" \n",
" biogas | \n",
" anaerobic digestion | \n",
" 0.015627 | \n",
" 0.010502 | \n",
" 0.005125 | \n",
"
\n",
" \n",
" ethanol | \n",
" anaerobic digestion | \n",
" 0.005133 | \n",
" 0.000553 | \n",
" 0.004580 | \n",
"
\n",
" \n",
" ethanol | \n",
" fermentation | \n",
" 0.017566 | \n",
" 0.022040 | \n",
" 0.004474 | \n",
"
\n",
" \n",
" vegetable oil | \n",
" transesterification | \n",
" 0.000570 | \n",
" 0.005044 | \n",
" 0.004473 | \n",
"
\n",
" \n",
" syng | \n",
" gasification | \n",
" 0.004563 | \n",
" 0.000345 | \n",
" 0.004217 | \n",
"
\n",
" \n",
" ethanol | \n",
" catalysis | \n",
" 0.000798 | \n",
" 0.004974 | \n",
" 0.004176 | \n",
"
\n",
" \n",
" ethanol | \n",
" transesterification | \n",
" 0.001825 | \n",
" 0.005596 | \n",
" 0.003771 | \n",
"
\n",
" \n",
" methanol | \n",
" catalysis | \n",
" 0.000114 | \n",
" 0.003869 | \n",
" 0.003755 | \n",
"
\n",
" \n",
" biodiesel | \n",
" solvents | \n",
" 0.005703 | \n",
" 0.002073 | \n",
" 0.003631 | \n",
"
\n",
" \n",
" waste | \n",
" anaerobic digestion | \n",
" 0.005817 | \n",
" 0.002556 | \n",
" 0.003261 | \n",
"
\n",
" \n",
" gasoline | \n",
" fermentation | \n",
" 0.000342 | \n",
" 0.003454 | \n",
" 0.003112 | \n",
"
\n",
" \n",
" vegetable oil | \n",
" catalysis | \n",
" 0.000913 | \n",
" 0.004007 | \n",
" 0.003095 | \n",
"
\n",
" \n",
" methanol | \n",
" transesterification | \n",
" 0.001711 | \n",
" 0.004629 | \n",
" 0.002918 | \n",
"
\n",
" \n",
" bioethanol | \n",
" anaerobic digestion | \n",
" 0.002966 | \n",
" 0.000069 | \n",
" 0.002897 | \n",
"
\n",
" \n",
" waste | \n",
" bio-oil | \n",
" 0.003764 | \n",
" 0.000898 | \n",
" 0.002866 | \n",
"
\n",
" \n",
" vegetable oil | \n",
" biodiesel | \n",
" 0.002509 | \n",
" 0.005320 | \n",
" 0.002810 | \n",
"
\n",
" \n",
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentages are as 0-1 in this table for easy viz.\n"
]
}
],
"source": [
"# list where all the values and indexes of matrix are stored\n",
"frst_perc = fst_year_matrix / np.sum(np.triu(fst_year_matrix, 1)) # half only \n",
"scnd_perc = scnd_year_matrix / np.sum(np.triu(scnd_year_matrix, 1)) \n",
"differences = frst_perc - scnd_perc\n",
"differences = np.absolute(differences)\n",
"values = []\n",
"indexes = []\n",
"no_duplicates = np.triu(differences, 1)\n",
"matrix_axis_names = axis_names\n",
"\n",
"\n",
"top = 20\n",
"\n",
"# loop through the matrix\n",
"for row_n in range(differences.shape[0]):\n",
" for col_n in range(differences.shape[1]):\n",
" values.append(no_duplicates[row_n, col_n])\n",
" indexes.append((row_n, col_n))\n",
"\n",
"# print the table \n",
"Z = [indexes for _,indexes in sorted(zip(values,indexes))]\n",
"extremes = list(reversed(Z[-top:]))\n",
"\n",
"\n",
"term_Dataframe = pd.DataFrame(\n",
" {'First Term': [matrix_axis_names[e[0]] for e in extremes],\n",
" 'Second Term': [matrix_axis_names[e[1]] for e in extremes],\n",
" '{} Percentage'.format(first_year): [frst_perc[e[0], e[1]] for e in extremes], \n",
" '{} Percentage'.format(second_year): [scnd_perc[e[0], e[1]] for e in extremes], \n",
" 'Difference in %': [no_duplicates[e[0], e[1]] for e in extremes]\n",
" })\n",
"\n",
"term_Dataframe = term_Dataframe[['First Term', 'Second Term', '{} Percentage'.format(first_year), '{} Percentage'.format(second_year), 'Difference in %']]\n",
"\n",
"\n",
"display(HTML(term_Dataframe.to_html(index=False)))\n",
"print 'Percentages are as 0-1 in this table for easy viz.'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With this visualization we can easily compare the term pairs and see their evolution over the course of the years. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
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