\n",
"Reviewers: Jennifer Ding Research Application Manager \n",
"The Alan Turing Institute
Eirini Zormpa Community Manager of Open Collaboration The Alan Turing Institute
\n",
"Date: October 6, 2022
\n",
"\n",
"## Background\n",
"In the summer of 2010, researchers from The International Group for Historic Aircraft Recovery excavated, in fragments, a small semi-opaque cosmetic jar from the Pacific island of Nikumaroro. They were searching for evidence to support their hypothesis that Amelia Earhart and her navigator Fred Noonan perished there in 1937, after very nearly succeeding in their pathbreaking mission to circumnavigate the globe by air at its widest point, the equator.\n",
"\n",
"My interest in glass artifacts from the island ultimately led to a personal quest to become a resident glass expert of the team. In partnership with archaeologists Thomas King and Bill Lockhart, and chemist Greg George, we produced a [paper](https://www.academia.edu/40823470/A_Freckle_In_Time_or_a_Fly_in_the_Ointment) that summarized our findings. By then, our work on the jar had already generated some [press](https://www.nbcnews.com/id/wbna47623025).\n",
"\n",
"As we continued our research in 2013, Greg George and I worked with EAG Laboratories to determine the chemical composition of the Nikumaroro jar and a sibling glass jar, used as a control, that I had purchased on eBay and had named the clear facsimile. While the two jars were similar in most respects, interestingly, the clear facsimile jar was transparent, while the Nikumaroro jar was semi-opaque. Both jars were manufactured by the Hazel-Atlas Glass Company of Wheeling, West Virginia, but EAG Laboratories' [ICP-MS analysis](https://tighar.org/Projects/Earhart/Archives/Research/ResearchPapers/freckleintime/Document_02_Facsimileclearjarreport.pdf) revealed them to have considerably different chemical profiles.\n",
"\n",
"\n",
"## Data \n",
"In the social sciences, \"data\" often mean a collection of facts. In industry, however, we often consider data to be a collection of numbers and vectors, and we use computational tools such as statistics and machine learning to aid in their analysis. As part of an eCornell machine learning course, in 2022 I had the opportunity to revisit my work on the Nikumaroro jar as a learning exercise, and to use the data returned by EAG Laboratories to attempt to solve a novel machine learning classification problem I had proposed.\n",
"\n",
"Thus was my interest in analyzing the Nikumaroro jar and comparing it with its sibling rekindled. Instead of using the jars' data as a collection of facts, I now wanted to use them to study machine learning classification. \n",
"\n",
"By analyzing the weight percent of the chemical elements of which each jar's glass was made, plus refractive index, machine learning can build a model that will predict a pre-defined class to which it believes a given glass sample belongs. We may then assess the skill of the model by comparing all predictions to the actual correct classes for each sample. The correct class of the Nikumaroro jar and of the clear facsimile is that of a **container.**\n",
"\n",
"Two sets of measurements for the purposes of machine learning are not of much value, however, because machine learning models require a full range of known examples upon which to 'train' a model to recognize examples it has never seen before. \n",
"\n",
"EAG Laboratories had been thorough, and for the purposes of our 2013 analysis, their data was ample enough. For the purposes of machine learning, however, the data, while eagerly anticipated by our team, was paltry. Where would I find the data upon which to train a machine learning model? I did not need to search very far for my answer. British forensic scientists ``Ian W. Evett`` and ``Ernest J. Spiehler`` assembled a database of glass samples in 1987 for the purposes of solving crimes, such as break-ins. Their paper was first presented at the 1987 conference of the KBS (Knowledge-Based Systems) in Goverment and is titled:
\"Rule Induction in Forensic Science.\" http://gpbib.cs.ucl.ac.uk/gp-html/evett_1987_rifs.html.
Their database is available at the University of California Machine Learning Repository [here](http://archive.ics.uci.edu/ml/datasets/Glass+Identification).\n",
"\n",
"The weight percent of eight chemical elements and refractive index comprise the features of their database and each sample is represented by a target variable called Type. The types of glass represented in the sample are: Window Float, Window Non-Float, Vehicle Float, Container, Tableware, and Headlamp. There are no Vehicle Non-Float types represented in the data.\n",
"\n",
"My two glass samples, as luck would have it, were independently measured for most of the same elements as found in the U.K. database, with a few caveats:\n",
"\n",
"* Because silicon was measured in my lab data as 'Matrix,' with no actual number stated, I estimated the silicon for both containers based on the average for containers in the database (72.37). \n",
"* Because K (potassium) was measured at 980 ppm for the clear facsimile, rather than by wt%, I assigned it a value of 50% of the wt% of the Nikumaroro jar: .5 X .24 = .12. \n",
"* Because Fe (iron) is at very low wt% levels in the 1987 database, and the levels in my data are listed at very low parts per million (ppm), I assigned to the Nikumaroro jar and to the clear facsimile a wt% of Fe of .02 and .01, respectively.\n",
"* Refractive index was not measured for either the Nikumaroro jar or the clear facsimile. Since the clear facsimile is completely transparent, I assigned it the minimum refractive index of containers from the 1987 dataset. Since the Nikumaroro jar is semi-opaque, I assigned it the maximum refractive index of containers from the 1987 dataset. \n",
"\n",
"These educated guessed were necessary to ensure the ability of the machine learning algorithm to compare the data from EAG Laboratories with the training dataset from the U.K.*\n",
"\n",
"#### *Note: In a later section of this story, we will use a technique to evaluate whether or not the decision to supply a few of the values for the jars makes a significant difference in our machine learning algorithm's ability to predict the class of the two jar samples.\n",
"\n",
"\n",
"## Research Agenda\n",
"There are three main research questions I wish to answer in my machine learning classification problem. I follow each of these questions with a rationale that explains the derived benefits in answering each question.
\n",
"**1) What can Python's Matplotlib and Seaborn graphing capabilities reveal about the similarities and/or dissimilarities between the Nikumaroro jar, clear facsimile, and the glass samples in the 1987 database?**\n",
"\n",
"Rationale: Before tackling the machine learning problem, we need to learn whether and how the Nikumaroro jar and the clear facsimile, which precede the samples in the U.K. database by many decades, differ from the samples in that U.K. database. If the difference is too great, it may not be possible to use machine learning to classify the Nikumaroro jar and the clear facsimile at all.\n",
"\n",
" **2) What do the correlations between elements for the different types of glass in the 1987 database reveal about late 20th century glassmaking, as compared with early 20th century glassmaking?**\n",
"\n",
"Rationale: The Nikumaroro jar and the clear facsimile precede the samples in the U.K. database by many decades. During these many decades, the recipes used in the glassmaking batches may have changed considerably. It is always fascinating to observe historical changes made manifest in data, but beyond this historical interest, the graphs that may be built from these correlations may also provide visual explanations for some difficulties a machine learning model may be having with classifying the Nikumaroro jar and the clear facsimile.\n",
"\n",
" **3) Using machine learning to train a model on the 1987 database, could that model be used to classify the type (container) of one or both of the older samples unseen by the model?**\n",
"\n",
"Rationale: A successful classification of the Nikumaroro jar may suggest that it is not quite so unusual as we had supposed in our earlier research. The U.K. database was never built to classify highly unusual or historical glass samples. It was built to classify ordinary glass samples retrieved from break-ins that occurred in the 1980s. Successful classification would strengthen the argument, often expressed but without evidence, that the jar is not a rare item likely to be from the 1930s, but of far more recent provenance, too recent to have possibly been brought to the island in 1937 by Amelia Earhart. \n",
"\n",
"The outcome of this classification problem has the potential to disverify or perhaps weaken a part of our [hypothesis](https://www.academia.edu/9015080/Amelia_Earhart_on_Nikumaroro_A_Summary_of_the_Evidence) that Earhart and her navigator, Fred Noonan, perished on Nikumaroro. Efforts to disverify are an important element of scientific research, for as [Richard Feynman once said](https://faculty.mtsac.edu/cbriggs/Biol-1%20Readings%20Cargo%20Cult%20Science.pdf), \"The first principle is that you must not fool yourself — and you are the easiest person to fool.\" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First, before we start to analyze these questions, we will read in the data file and create a simple report.\n",
"\n",
"Our first task is to read in a copy of the 1987 glass.data file that has been uploaded to Zenodo.org. Then, we can assign the names of the variables in this file to a variable called names. Notice I am carefully avoiding the variable name 'Type' because it is a reserved word in Python. (This was obviously not true in 1987 when the database was built, because Python did not exist then.) Using that reserved word will co-opt the type function, thus disabling my ability to check the types of specific variables. Instead, I will use the variable name 'GlassType' to contain the various types of glass.\n",
"\n",
"I also include a docstring explaining what these variables mean and their general significance to glassmaking."
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"ID: an integer that identifies the 1987 sample number. This column should always be droppped in any analysis.\n",
"Ref_ix: Refractive index. There are many scientific definitions of refractive index, but in basic terms,\n",
"refractive index refers to how much the path of light is refracted when it enters the glass. The higher the \n",
"refractive index, the less transparent the glass is.\n",
"Na: Sodium oxide reduces the temperature at which silica melts to around 1500-1650 Celsius. A lower temperature\n",
"equates to a more efficient and less costly manufacturing process.\n",
"Mg: Magnesium oxide is used as a reducing agent in glassmaking. It reduces sulfates, which are used to fine the\n",
"glass, and other impurities, such as iron.\n",
"Al: Aluminum oxide is added to increase durability and heat resistance and to reduce crystallization.\n",
"Si: Silicon oxide. The main constituent (matrix) of glass is silica sand.\n",
"K: Potassium oxide is used for strengthening and fining the glass.\n",
"Ca: Calcium oxide is considered a stabilizer. It is used for increasing the chemical durability of glass, and \n",
"also to lower the melting point of the silica. Calcium oxide can also be used to raise refractive index.\n",
"Ba: Barium oxide. Barium was, and is, employed in glass manufacture to add luster or brilliance to glass, \n",
"and also to raise the refractive index.[1] Museum glass, salt shakers, shot glasses, and microscopic slides \n",
"are often high in barium. The Nikumaroro jar is also high in barium.\n",
"Fe: Iron oxide can be used as a colorant or it can exist by chance as an impurity.\n",
"GlassType: This is the integer assigned in 1987 to signify the type of glass of the sample. This column should\n",
"only appear in the validation dataset; it should be dropped from the training dataset.\n",
"\"\"\"\n",
"import urllib.request\n",
"with urllib.request.urlopen(\"https://zenodo.org/record/6913745/files/glass.data\") as f:\n",
" html=f.read().decode('utf-8')\n",
" with open('glass.csv', 'w') as out:\n",
" out.write(html)\n",
"\n",
"filename='glass.csv'\n",
"\n",
"names=['ID','Ref_ix','Na','Mg','Al','Si','K','Ca','Ba','Fe','GlassType']\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The three questions of the research agenda (listed above) are quite different. It will be convenient to modify the dataset in different ways for each problem. Therefore, we will read in the file twice to create two Pandas dataframes of the glass data, thus allowing us to have separate data pipelines to keep the machine learning question separate from the other two. The pandas library in Python provides a convenient function, read_csv, for this purpose. One of the columns is imported as an object, which I will coax to be an integer for more reliable data processing."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"from pandas import read_csv\n",
"dataset = read_csv(filename, header=None, sep=',',names=names)\n",
"dataset_ml = read_csv(\n",
" filename, header=None, sep=',',names=names)\n",
"# Convert clunky object formats to ints\n",
"dataset['GlassType'] = dataset['GlassType'].astype('string')\n",
"dataset['GlassType'] = dataset['GlassType'].astype('int')\n",
"dataset_ml['GlassType'] = dataset['GlassType'].astype('string')\n",
"dataset_ml['GlassType'] = dataset['GlassType'].astype('int')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we will provide the list of variables in the file with descriptions of their counts and data types. I could use the info() method for this purpose, but an anonymous user on [StackOverflow](https://stackoverflow.com/questions/64067424/how-to-convert-df-info-into-data-frame-df-info) has provided a function, which I have adopted, to give this information a more attractive display. It would be useful as well to have a simple statistical report of this dataset, which is provided by the describe method. Notice that the columns ID and GlassType, as supplied from the 1987 study, are integers, but they are also categorical variables. I will therefore drop these columns from my simple statistical report because they would be meaningless in terms of the measures of central tendency (mean, max, etc.). However, because GlassType will be very important to the rest of this code, I will take a copy of the dataset prior to making this report, to ensure I do not drop this variable from the original Pandas dataframe."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
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"4 Al 214 float64\n",
"5 Si 214 float64\n",
"6 K 214 float64\n",
"7 Ca 214 float64\n",
"8 Ba 214 float64\n",
"9 Fe 214 float64\n",
"10 GlassType 214 int64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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Ref_ix
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Na
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Mg
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Al
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Si
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K
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Ba
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214.000000
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214.000000
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mean
\n",
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1.518365
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13.407850
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2.684533
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1.444907
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72.650935
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0.497056
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8.956963
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0.499270
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1.423153
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0.497219
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0.097439
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min
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1.511150
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10.730000
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0.000000
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0.290000
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69.810000
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5.430000
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0.000000
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12.907500
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2.115000
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1.190000
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72.280000
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8.240000
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0.000000
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13.300000
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3.480000
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1.360000
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72.790000
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0.555000
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8.600000
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0.000000
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0.000000
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1.519157
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13.825000
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3.600000
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1.630000
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73.087500
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0.610000
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9.172500
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1.533930
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17.380000
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4.490000
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3.500000
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75.410000
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6.210000
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16.190000
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3.150000
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0.510000
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" Ref_ix Na Mg Al Si K \\\n",
"count 214.000000 214.000000 214.000000 214.000000 214.000000 214.000000 \n",
"mean 1.518365 13.407850 2.684533 1.444907 72.650935 0.497056 \n",
"std 0.003037 0.816604 1.442408 0.499270 0.774546 0.652192 \n",
"min 1.511150 10.730000 0.000000 0.290000 69.810000 0.000000 \n",
"25% 1.516522 12.907500 2.115000 1.190000 72.280000 0.122500 \n",
"50% 1.517680 13.300000 3.480000 1.360000 72.790000 0.555000 \n",
"75% 1.519157 13.825000 3.600000 1.630000 73.087500 0.610000 \n",
"max 1.533930 17.380000 4.490000 3.500000 75.410000 6.210000 \n",
"\n",
" Ca Ba Fe \n",
"count 214.000000 214.000000 214.000000 \n",
"mean 8.956963 0.175047 0.057009 \n",
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"min 5.430000 0.000000 0.000000 \n",
"25% 8.240000 0.000000 0.000000 \n",
"50% 8.600000 0.000000 0.000000 \n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def infoOut(data,details=False):\n",
" dfInfo = data.columns.to_frame(name='Column')\n",
" dfInfo['Non-Null Count'] = data.notna().sum()\n",
" dfInfo['Dtype'] = data.dtypes\n",
" dfInfo.reset_index(drop=True,inplace=True)\n",
" if details:\n",
" rangeIndex = (dfInfo['Non-Null Count'].min(),dfInfo['Non-Null Count'].min())\n",
" totalColumns = dfInfo['Column'].count()\n",
" dtypesCount = dfInfo['Dtype'].value_counts()\n",
" totalMemory = dfInfo.memory_usage().sum()\n",
" return dfInfo, rangeIndex, totalColumns, dtypesCount, totalMemory\n",
" else:\n",
" return dfInfo\n",
"display(infoOut(dataset))\n",
"temp=dataset.copy()\n",
"display(temp.iloc[:,1:-1].describe())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are no nulls in the dataset. The column types are all numeric. All 214 samples of the database have been included."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a dictionary of glass types.\n",
"We need a way to translate the numerical glass types found in the 1987 database to their English equivalents. This is accomplished by creating a Python dictionary.\n",
"\n",
"Note we could have omitted Vehicle Non-Float from this dictionary, since there are no examples in the data of this type; however, for the sake of clarity we will retain it.\n",
"\n",
"We include also an exhibit that lists the counts for the various glass types."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
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\n",
" \n",
"
\n",
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\n",
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Counts
\n",
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GlassType
\n",
"
\n",
"
\n",
" \n",
" \n",
"
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Window Non-Float
\n",
"
76
\n",
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\n",
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Window Float
\n",
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70
\n",
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Headlamp
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29
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\n",
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Vehicle Float
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17
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\n",
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Container
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13
\n",
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Tableware
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9
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" Counts\n",
"GlassType \n",
"Window Non-Float 76\n",
"Window Float 70\n",
"Headlamp 29\n",
"Vehicle Float 17\n",
"Container 13\n",
"Tableware 9"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
" dict = {1: 'Window Float',\n",
" 2: 'Window Non-Float', \n",
" 3: 'Vehicle Float',\n",
" 4: 'Vehicle Non-Float',\n",
" 5: 'Container',\n",
" 6: 'Tableware',\n",
" 7: 'Headlamp'}\n",
"\n",
"vc=temp.replace({\"GlassType\": dict},inplace=True)\n",
"vc=temp['GlassType'].value_counts().rename_axis('unique_values').reset_index(name='Counts')\n",
"vc.set_index('unique_values',inplace=True)\n",
"vc.rename_axis(['GlassType'],inplace=True)\n",
"display(vc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What do the types of glass represent in everyday life?\n",
"The terms \"float\" and \"non-float\" are glass industry terms. They describe the process by which window glass is made. Prior to 1959, all windows were made with non-float processes. These commonly involved grinding and polishing sheets of plate glass, which were cast on an iron surface. The result was not the perfectly smooth and clear glass we know today but rather was somewhat more translucent and sometimes ['wavy'](https://brennancorp.com/blog/why-is-the-glass-in-my-windows-wavy/). Today, this kind of window glass is prized for its artistic appearance and historical significance, if not for its lack of clarity. Float glass is made by floating molten glass on a bed of molten tin under controlled environmental conditions in a bath chamber. An excellent video describing this process is [here:](https://www.youtube.com/watch?v=pMKHs_FF0bw&t=200s)\n",
"\n",
"If a glass sample is Window Float, in all probability it was manufactured no earlier than 1959. Window Non-Float samples are probably older than 1959. \n",
"\n",
"Vehicle Float glass describes the type of glass that has been used for vehicle windshields since 1959.\n",
"\n",
"Headlamps are vehicle lamps, which illuminate the road ahead.\n",
"\n",
"Container glass describes commercial product containers, whether for bottles or jars.\n",
"\n",
"Tableware glass describes glassware for the table or any glass table item used in dining or the serving of food."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create some utility functions for plotting graphs from the 1987 dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before using machine learning on the data, there is much we can learn about the two jars' likeness to glass samples in the 1987 database, simply by observing counts based on specific criteria. By comparing each jar with its \"nearest neighbors,\" based on measurements for three elements in the periodic table, we may gain a sense of which type of glass in the database each jar most closely resembles. \n",
"\n",
"We will therefore write functions to create three graphical reports, one for each of the three elements magnesium, calcium, and barium. Each of the three reports will display three things: \n",
"1) The complete range of counts for each glass type. (This graph will be repeated for each report.) \n",
"2) The range of counts for all glass types within 0.15 above and below the elemental measurement of the clear facsimile. \n",
"3) The range of counts for all glass types within 0.15 above and below the elemental measurement of the Nikumaroro jar.\n",
"\n",
"The key function, numgroups, requires an element from the periodic table so that it can look up parameters in the included dictionary. This dictionary contains the measurements obtained from the lab for each element for the clear facsimile and for the Nikumaroro jar. Low and High parameter values for each element are also required, even when the complete range of counts is to be displayed. These will be passed to the function as hard-coded values that are either \n",
"> a. exactly 0.15 above and below the measurement for each jar; or \n",
"> b. the lowest and highest measured values in the database for each element.\n",
"\n",
"Last, the jar_spec parameter is simply the type of jar to be analyzed, clear facsimile or Nikumaroro jar. The jar_spec is used to create the title for each graph.\n",
"\n",
"If the graph's input parameters result in a graph with a single type of glass, no graph is created. (Bar graphs with but a single bar are, after all, neither very attractive nor useful!) Instead, a message is displayed that mirrors the text that accompanies the other graphs.\n",
"\n",
"If the number of glass types returned by the criteria is equal to zero, we cause an error to be thrown with int('d'), which attempts to convert the letter 'd' to an integer, rather than waiting for the error that would have been thrown naturally by the plot statement. If we had waited for the plot statement to throw the error, useless information about the size of the graph (which cannot be drawn in this case) is displayed. In the case of this error, a simple message is printed to inform us that no relevant glass samples were found in the database.\n",
"\n",
"The other function, ticks_restrict_to_integer, is a utility function that controls the number of tick marks on the y-axis of each graph. The y-axis represents the count. Because the scale of counts varies with each graph, it was necessary to make the y-axes more uniform with one another by having this function."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.ticker import MultipleLocator\n",
"def ticks_restrict_to_integer(axis):\n",
" \"\"\"Restrict the ticks on the given axis to be at least integer;\n",
" that is, no half ticks at 1.5 for example.\n",
" \"\"\"\n",
" major_tick_locs = axis.get_majorticklocs()\n",
" if len(major_tick_locs) < 2 or major_tick_locs[1] - major_tick_locs[0] < 1:\n",
" axis.set_major_locator(MultipleLocator(1)) \n",
"\n",
"def numgroups(element,low,high,jar_spec):\n",
" \"\"\"\n",
" non-fruitful function. Outputs a graph if the number of types of\n",
" glass within the DataFrame is > 1. Otherwise, the function prints\n",
" a message, for aesthetic reasons.\n",
"\n",
" parameters:\n",
" element: an element in the periodic table\n",
" low: A minimum value for the measured element, which acts as the \n",
" minimum value to allow into the dataset sample\n",
" high: A maximum value for the measured element, which acts as the \n",
" maximum value to allow into the dataset sample\n",
" jar_spec: A value of either 'clear facsimile' or 'Nikumaroro jar', used\n",
" in the title of the graph. \n",
" \n",
" However, when the function is called to obtain the complete distri-\n",
" bution of counts, the value of jar_spec is set equal to None. \n",
" \n",
" Preconditions:\n",
" element is a string with value of: 'Mg' or 'Ca' or 'Ba'\n",
" \n",
" Low cannnot be greater than high.\n",
" \"\"\"\n",
" jar_spec='full range of ' + element + ' measurements in the database' if jar_spec==None else jar_spec\n",
" rangedict = {'Ba':['Barium',[.37,.74]],\n",
" 'Mg':['Magnesium',[2.4,4.3]],\n",
" 'Ca':['Calcium',[3.6,8.5]]}\n",
" facsimile_ref=rangedict[element][1][0]\n",
" artifact_ref=rangedict[element][1][1]\n",
" element_full_name=rangedict[element][0]\n",
" sampl = dataset[(dataset[element] >= low) & (\n",
" dataset[element] <= high)]\n",
" number_groups=sampl['GlassType'].value_counts().shape[0]\n",
" titletext1=\"Count of Glass Types in the 1987 database \\n with a range of measured values of \" + \\\n",
" element_full_name + \" \\n between \" + str(low) + \" and \" + str(high) + \" wt%. \\n \" + \\\n",
" \"This is the range of counts for the \" + jar_spec.upper()\n",
"\n",
" if 'clear' in jar_spec or 'Niku' in jar_spec:\n",
" #Tolerance provides information in the report about the range of values considered in the graph.\n",
" tolerance=round((high-low)/2,2)\n",
" titletext2=\" that fall within a tolerance of +-\" + str(tolerance) + ' of its measurement.'\n",
" else:\n",
" tolerance=0\n",
" titletext2=\".\"\n",
" \n",
" if low>high:\n",
" print('Low value must be smaller than the high value. Try again.')\n",
" return\n",
" \n",
" if number_groups==1:\n",
" print(titletext1 + titletext2)\n",
" print('There is only ONE sample Type.')\n",
" print(sampl['GlassType'].values[0],'=',sampl.shape[0])\n",
" print('reference: Clear facsimile jar =',facsimile_ref,'wt%'\n",
" '\\n Nikumaroro jar =',artifact_ref,'wt%')\n",
" else:\n",
" print('reference: Clear facsimile jar =',facsimile_ref,'wt%'\n",
" '\\n Nikumaroro jar =',artifact_ref,'wt%')\n",
" try:\n",
" vc=sampl['GlassType'].value_counts()\n",
" int('d') if len(vc)==0 else int('1')\n",
" \n",
" vc.plot(\n",
" kind='bar', figsize=(2.4*number_groups, 6), rot=0, cmap='Spectral');\n",
" plt.xlabel(\"Glass Type\", labelpad=14,fontsize=16,rotation=0)\n",
" plt.ylabel(\"Count of Type\", labelpad=70, \n",
" fontsize=16,rotation=0)\n",
" plt.xticks(fontsize=14)\n",
" plt.yticks(fontsize=14)\n",
" plt.figtext(0.5, 1.0, titletext1+titletext2, wrap=True, horizontalalignment='center', fontsize=16) \n",
" ax = plt.subplot()\n",
" ticks_restrict_to_integer(ax.yaxis)\n",
" plt.show()\n",
" except Exception as e: \n",
" #print(e)\n",
" print('For the ' + jar_spec + ', no glass samples in the U.K. data are in the range of ' + str(low) +\n",
" \" and \" + str(high) + ' wt% for ' + element_full_name + '.')\n",
" print()\n",
" return"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What types of glass in the 1987 database are most similar to the Nikumaroro jar and clear facsimile in terms of magnesium, barium or calcium content?\n",
"To review what was stated above, examining Mg, Ba, and Ca individually in the U.K. database, we can perform the following steps to produce a report: \n",
"1. Obtain the complete distribution of counts in the 1987 database by restricting the element's values to the range between its minimum and maximum wt% values. \n",
"2. See the distribution of counts in the 1987 database that results from setting the range of measured wt% for the element to a tolerance 0.15 wt% above and below its measurement for the ```clear facsimile```. \n",
"3. See the distribution of counts in the 1987 database that results from setting the range of measured wt% for the element to a tolerance 0.15 wt% above and below its measurement for the ```Nikumaroro jar```.\n",
"4. Repeat steps 1 to 3 for the next element until all elements have been reported."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Report for Magnesium"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference: Clear facsimile jar = 2.4 wt%\n",
" Nikumaroro jar = 4.3 wt%\n"
]
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference: Clear facsimile jar = 2.4 wt%\n",
" Nikumaroro jar = 4.3 wt%\n",
"For the Nikumaroro jar, no glass samples in the U.K. data are in the range of 4.15 and 4.45 wt% for Magnesium.\n",
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"import random\n",
"\n",
"dataset.replace({\"GlassType\": dict},inplace=True)\n",
"element='Mg'\n",
"Mgdict={'clear facsimile':[2.25, 2.55],'Nikumaroro jar':[4.15,4.45]}\n",
"low=0\n",
"high=dataset[element].max()\n",
"\n",
"#Run report for all glass types\n",
"numgroups(element,low,high,None)\n",
"\n",
"#Run reports for the clear facsimile and Nikumaroro jar\n",
"for jar_type in Mgdict:\n",
" low=Mgdict[jar_type][0]\n",
" high=Mgdict[jar_type][1]\n",
" numgroups(element,low,high,jar_type)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Magnesium Analysis\n",
"Magnesium: \n",
"The clear facsimile jar has 2.4 wt% Mg. There are only two glass types, tableware and non-float windows, with values between 2.25 and 2.55 wt% Mg. Magnesium, if it were the only feature, would seem to predict the clear facsimile jar to be in the window or the tableware family, a close cousin to the container family. The Nikumaroro jar has 4.3 wt% Mg. Checking back to the report we created with the describe() method, this value is well above the 75th percentile. We know from the literature on glassmaking that any Mg measurement from a modern glass sample that is above 3.5 wt% is likely to be a window, and not a container.[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Report for Calcium"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference: Clear facsimile jar = 3.6 wt%\n",
" Nikumaroro jar = 8.5 wt%\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference: Clear facsimile jar = 3.6 wt%\n",
" Nikumaroro jar = 8.5 wt%\n",
"For the clear facsimile, no glass samples in the U.K. data are in the range of 3.45 and 3.75 wt% for Calcium.\n",
"\n",
"reference: Clear facsimile jar = 3.6 wt%\n",
" Nikumaroro jar = 8.5 wt%\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dataset.replace({\"GlassType\": dict},inplace=True)\n",
"element='Ca'\n",
"Cadict={'clear facsimile':[3.45, 3.75],'Nikumaroro jar':[8.35,8.65]}\n",
"low=0\n",
"high=dataset[element].max()\n",
"\n",
"#Run report for all glass types\n",
"numgroups(element,low,high,None)\n",
"\n",
"#Run reports for the clear facsimile and Nikumaroro jar\n",
"for jar_type in Cadict:\n",
" low=Cadict[jar_type][0]\n",
" high=Cadict[jar_type][1]\n",
" numgroups(element,low,high,jar_type)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calcium Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calcium: \n",
"The clear facsimile jar has 3.6 wt% Ca. We can see from the report we created with the describe() method that this is off-scale low, a value less than all the samples in the database. No glass samples are in the range of 3.45% and 3.75% weight calcium. The Nikumaroro jar has 8.5 wt% Ca. This is close to the mean in the database (8.96) for all glass types, but no containers are displayed on the graph within +- .15 of the calcium value measured on the Nikumaroro jar."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Report for Barium"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference: Clear facsimile jar = 0.37 wt%\n",
" Nikumaroro jar = 0.74 wt%\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dataset.replace({\"GlassType\": dict},inplace=True)\n",
"element='Ba'\n",
"Badict={'clear facsimile':[0.22, 0.52],'Nikumaroro jar':[0.59,0.89]}\n",
"low=0\n",
"high=dataset[element].max()\n",
"\n",
"#Run report for all glass types\n",
"numgroups(element,low,high,None)\n",
"\n",
"#Run reports for the clear facsimile and Nikumaroro jar\n",
"for jar_type in Badict:\n",
" low=Badict[jar_type][0]\n",
" high=Badict[jar_type][1]\n",
" numgroups(element,low,high,jar_type)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Barium Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Barium: \n",
"The ratio of 2:1 for the element Barium in the two glass samples (.74 for the Nikumaroro jar and .37 for the clear facsimile jar) suggests that the glass maker, Hazel-Atlas, used a recipe. This is not uncommon in the glass industry. \n",
"For the clear facsimile, one window, one container and one headlamp have measurements of barium in the database that are within +- 0.15 of its measured value of barium.\n",
"For the Nikumaroro jar, eight headlamps and one window have measurements of barium in the database that are within +-0.15 of its measured value of barium.\n",
"\n",
"Conclusion: We have not yet applied machine learning to the identification of the Nikumaroro jar or the clear facsimile, but it would appear from the weight percent of some of the key ingredients from these jars that windows, of the float or non-float variety, and, to a lesser extent, headlamps, are strong candidates for how the 1987 database might predict their identity, if machine learning were employed as a predictive tool to do this. Nevertheless, some containers did appear in the bar graphs for the clear facsimile, indicating that there is at least a slight resemblance between that jar and containers from the U.K. database. The results are not exactly encouraging to the success of a machine learning model, but they are not discouraging enough to dampen curiosity at discovering what machine learning might be able to do with the data from the Nikumaroro jar and the clear facsimile."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Turning the question around: If one were to subset the 1987 database only for containers, how would that database compare with the two jars? Both of the jars are, of course, containers."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Container summary report:\n"
]
},
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Ref_ix
\n",
"
Na
\n",
"
Mg
\n",
"
Al
\n",
"
Si
\n",
"
K
\n",
"
Ca
\n",
"
Ba
\n",
"
Fe
\n",
"
\n",
" \n",
" \n",
"
\n",
"
count
\n",
"
13.000000
\n",
"
13.000000
\n",
"
13.000000
\n",
"
13.000000
\n",
"
13.000000
\n",
"
13.000000
\n",
"
13.000000
\n",
"
13.000000
\n",
"
13.000000
\n",
"
\n",
"
\n",
"
mean
\n",
"
1.518928
\n",
"
12.827692
\n",
"
0.773846
\n",
"
2.033846
\n",
"
72.366154
\n",
"
1.470000
\n",
"
10.123846
\n",
"
0.187692
\n",
"
0.060769
\n",
"
\n",
"
\n",
"
std
\n",
"
0.003345
\n",
"
0.777037
\n",
"
0.999146
\n",
"
0.693920
\n",
"
1.282319
\n",
"
2.138695
\n",
"
2.183791
\n",
"
0.608251
\n",
"
0.155588
\n",
"
\n",
"
\n",
"
min
\n",
"
1.513160
\n",
"
11.030000
\n",
"
0.000000
\n",
"
1.400000
\n",
"
69.890000
\n",
"
0.130000
\n",
"
5.870000
\n",
"
0.000000
\n",
"
0.000000
\n",
"
\n",
"
\n",
"
25%
\n",
"
1.516660
\n",
"
12.730000
\n",
"
0.000000
\n",
"
1.560000
\n",
"
72.180000
\n",
"
0.380000
\n",
"
9.700000
\n",
"
0.000000
\n",
"
0.000000
\n",
"
\n",
"
\n",
"
50%
\n",
"
1.519940
\n",
"
12.970000
\n",
"
0.000000
\n",
"
1.760000
\n",
"
72.690000
\n",
"
0.580000
\n",
"
11.270000
\n",
"
0.000000
\n",
"
0.000000
\n",
"
\n",
"
\n",
"
75%
\n",
"
1.521190
\n",
"
13.270000
\n",
"
1.710000
\n",
"
2.170000
\n",
"
73.390000
\n",
"
0.970000
\n",
"
11.530000
\n",
"
0.000000
\n",
"
0.000000
\n",
"
\n",
"
\n",
"
max
\n",
"
1.523690
\n",
"
14.010000
\n",
"
2.680000
\n",
"
3.500000
\n",
"
73.880000
\n",
"
6.210000
\n",
"
12.500000
\n",
"
2.200000
\n",
"
0.510000
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Ref_ix Na Mg Al Si K \\\n",
"count 13.000000 13.000000 13.000000 13.000000 13.000000 13.000000 \n",
"mean 1.518928 12.827692 0.773846 2.033846 72.366154 1.470000 \n",
"std 0.003345 0.777037 0.999146 0.693920 1.282319 2.138695 \n",
"min 1.513160 11.030000 0.000000 1.400000 69.890000 0.130000 \n",
"25% 1.516660 12.730000 0.000000 1.560000 72.180000 0.380000 \n",
"50% 1.519940 12.970000 0.000000 1.760000 72.690000 0.580000 \n",
"75% 1.521190 13.270000 1.710000 2.170000 73.390000 0.970000 \n",
"max 1.523690 14.010000 2.680000 3.500000 73.880000 6.210000 \n",
"\n",
" Ca Ba Fe \n",
"count 13.000000 13.000000 13.000000 \n",
"mean 10.123846 0.187692 0.060769 \n",
"std 2.183791 0.608251 0.155588 \n",
"min 5.870000 0.000000 0.000000 \n",
"25% 9.700000 0.000000 0.000000 \n",
"50% 11.270000 0.000000 0.000000 \n",
"75% 11.530000 0.000000 0.000000 \n",
"max 12.500000 2.200000 0.510000 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Mg : min to max: 0.0 to 2.68\n",
"reference: Clear facsimile jar = 2.4 wt%\n",
" Nikumaroro jar= 4.3 wt%\n",
"\n",
"Ca : min to max: 5.87 to 12.5\n",
"reference: Clear facsimile jar = 3.6 wt%\n",
" Nikumaroro jar= 8.5 wt%\n",
"\n",
"Ba : min to max: 0.0 to 2.2\n",
"reference: Clear facsimile jar = 0.37 wt%\n",
" Nikumaroro jar= 0.74 wt%\n"
]
}
],
"source": [
"sampcont = dataset[dataset['GlassType'].isin(['Container'])]\n",
"sampcont=sampcont.drop(['ID', 'GlassType'], axis=1)\n",
"print('Container summary report:')\n",
"display(sampcont.describe())\n",
"rangedict = {'Ba':['Barium',[.37,.74]],\n",
" 'Mg':['Magnesium',[2.4,4.3]],\n",
" 'Ca':['Calcium',[3.6,8.5]]}\n",
"ELEMENTS=['Mg','Ca','Ba']\n",
"for element in ELEMENTS:\n",
" print()\n",
" print(\n",
" element,': min to max:',sampcont[element].min(),'to'\n",
" ,sampcont[element].max())\n",
"\n",
" facsimile_ref=rangedict[element][1][0]\n",
" artifact_ref=rangedict[element][1][1]\n",
" print('reference: Clear facsimile jar =',facsimile_ref,'wt%'\n",
" '\\n Nikumaroro jar=',artifact_ref,'wt%')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we restrict the U.K. database to only containers, the comparisons with the Nikumaroro jar and the clear facsimile enter into sharper relief. The clear facsimile has a high magnesium content for a container but this is still within range of the lowest and highest values within the U.K. database. The jar from Nikumaroro has a magnesium content much higher than that of the highest nearest neighbor in the U.K. database.\n",
"\n",
"The clear facsimile has a calcium measurement lower than that of all the containers in the U.K. database. This indicates, perhaps, that calcium was not as heavily used in glassmaking in the early part of the twentieth century. The Nikumaroro jar has a calcium content, 8.5, that is lower than the 25th percentile. This is a low value for a container, especially when one considers that the mean for containers in the U.K. database, 10.12, is skewed toward the higher end of the range.\n",
"\n",
"The clear facsimile jar and the Nikumaroro jar are within the range of minimum to maximum in barium weight percentage for containers; however, most of the measured values of barium in the database are zero. The measured values of barium for the clear facsimile and the Nikumaroro jar are still far higher than those usually found in the U.K. database."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### What do the correlations between elements for the different types of glass in the 1987 database reveal about late 20th century glassmaking, as compared with early 20th century glassmaking?\n",
"To answer this question, we can generate custom diverging colormaps from the U.K. database (excluding our samples of clear facsimile and Nikumaroro jar). The correlation colormaps, also known as heatmaps, show, for example, which elements of the glass most strongly correlate with refractive index, which may be considered synonymous with brilliance. \n",
"\n",
"To keep the number of graphs to a manageable amount, we will restrict them to the glass types of Window Float, Window Non-Float and Containers. These three heatmaps provide a good summary of the correlations of ingredients in glassmaking of the late twentieth century."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
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