Update article and rename to index.qmd
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@ -1,7 +1,5 @@
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---
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title: Nuclear Explosions
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author: Marty Oehme
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output-dir: out
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references:
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- type: techreport
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id: Bergkvist2000
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@ -18,16 +16,6 @@ references:
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title: "Nuclear Explosions 1945 - 1998"
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page: 1-42
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issn: 1104-9154
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format:
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html:
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toc: true
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code-fold: true
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typst:
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toc: true
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echo: false
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docx:
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toc: true
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echo: false
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---
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```{python}
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@ -43,7 +31,21 @@ from matplotlib import pyplot as plt
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sns.set_theme(style="darkgrid")
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sns.set_context("notebook")
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cp=sns.color_palette()
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country_colors = {
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"US": cp[0],
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"USSR": cp[3],
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"France": cp[6],
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"UK": cp[5],
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"China": cp[4],
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"India": cp[1],
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"Pakistan": cp[2],
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}
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```
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```{python}
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# | label: data-prep
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# | echo: false
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schema_overrides = (
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{
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col: pl.Categorical
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@ -53,13 +55,31 @@ schema_overrides = (
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| {col: pl.String for col in ["year", "name"]}
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)
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cty_alias = {
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"PAKIST": "Pakistan",
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"FRANCE": "France",
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"CHINA": "China",
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"INDIA": "India",
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"USA": "US",
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}
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def cty_replace(name: str) -> str:
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if name in cty_alias:
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return cty_alias[name]
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return name
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df = (
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pl.read_csv(
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"data/nuclear_explosions.csv",
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schema_overrides=schema_overrides,
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null_values=["NA"],
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)
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.with_columns(date=pl.col("year").str.strptime(pl.Date, "%Y"))
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.with_columns(
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date=pl.col("year").str.strptime(pl.Date, "%Y"),
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country=pl.col("country").map_elements(cty_replace, return_dtype=pl.String),
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)
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.with_columns(year=pl.col("date").dt.year().cast(pl.Int32))
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)
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```
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@ -69,7 +89,7 @@ df = (
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The following is a re-creation and expansion of some of the graphs found in the
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@Bergkvist2000 produced report on nuclear explosions between 1945 and 1998. It
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is primarily a reproduction of key plots from the original report.
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Additionally, it serves as a exercise in plotting with the python library
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Additionally, it serves as an exercise in plotting with the python library
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seaborn and the underlying matplotlib. Lastly, it approaches some less well
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tread territory for data science in the python universe as it uses the python
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library polars-rs for data loading and transformation. All the code used to
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@ -77,9 +97,9 @@ transform the data and create the plots is available directly within the full
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text document, and separately as well. PDF and Docx formats are available with
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the plotting results only.
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Their original purpose was the collection of a long list of all the nuclear
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explosions occurring between those years, as well as analysing the responsible
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nations, tracking the types and purposes of the explosions, as well as
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The authors' original purpose was the collection of a long list of all the
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nuclear explosions occurring between those years, as well as analysing the
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responsible nations, tracking the types and purposes of the explosions and
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connecting the rise and fall of nuclear explosion numbers to historical events
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throughout.
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@ -90,13 +110,13 @@ throughout.
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## Nuclear devices
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There are two main kinds of nuclear device: those based entirely, on fission,
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or the splitting of heavy atomic nucleii (previously known as atomic devices)
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or the splitting of heavy atomic nuclei (previously known as atomic devices)
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and those in which the main energy is obtained by means of fusion, or of -light
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atomic nucleii (hydrogen or thermonuclear devices). A fusion explosion must
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atomic nuclei (hydrogen or thermonuclear devices). A fusion explosion must
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however be initiated with the help of a fission device. The strength of a
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fusion explosion can be practically unlimited. The explosive power of a
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nuclear explosion is expressed in ktlotons, (kt) or megatons (Mt), which
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correspond to 1000 and i million'tonnes, of conventional explosive (TNT),
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nuclear explosion is expressed in kilotons, (kt) or megatons (Mt), which
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correspond to 1000 and 1 million tonnes, of conventional explosive (TNT),
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respectively.
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[@Bergkvist2000, 6]
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@ -108,8 +128,9 @@ each country had explode, seen in @tbl-yields.
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```{python}
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# | label: tbl-yields
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# | tbl-cap: "Total number and yields of explosions"
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# | output: asis
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from great_tables import GT, md
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from great_tables import GT
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df_yields = (
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df.select(["country", "id_no", "yield_lower", "yield_upper"])
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pl.col("id_no").len().alias("count"),
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pl.col("yield_avg").sum(),
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)
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# .with_columns(country=pl.col("country").cast(pl.String).str.to_titlecase())
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.with_columns(yield_per_ex=pl.col("yield_avg") / pl.col("count"))
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.sort("count", descending=True)
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)
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(
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us_row = df_yields.filter(pl.col("country") == "US")
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yields_above_us = df_yields.filter(
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pl.col("yield_per_ex") > us_row["yield_per_ex"]
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).sort("yield_per_ex", descending=True)
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assert len(yields_above_us) == 3, "Yield per explosion desc needs updating!"
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tab=(
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GT(df_yields)
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.tab_source_note(
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source_note="Source: Author's elaboration based on Bergkvist and Ferm (2000)."
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.tab_spanner(label="Totals", columns=["count", "yield_avg"])
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.tab_stub(rowname_col="country")
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.tab_stubhead(label="Country")
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.cols_label(
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count="Count",
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yield_avg="Yield in kt",
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)
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.cols_label(count="Count", yield_avg="Yield in kt", yield_per_ex="Yield average")
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.fmt_integer(columns="count")
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.fmt_number(columns="yield_avg", decimals=1)
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.fmt_number(columns="yield_per_ex", decimals=1)
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)
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del df_yields
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tab
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```
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It is interesting to note that while the US undoubtedly had the highest raw
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number of explosions, it did not, in fact, output the highest estimated
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detonation yields.
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In fact, `{python} len(yields_above_us)` countries have a higher average
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explosion yield per detonation than the US:
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`{python} yields_above_us[0]["country"].item()` leads with an average of
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`{python} f"{yields_above_us[0]['yield_per_ex'].item():.2f}"` kt,
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before
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`{python} yields_above_us[1]["country"].item()` with an average of
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`{python} f"{yields_above_us[1]['yield_per_ex'].item():.2f}"` kt.
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## Numbers over time
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When investigating the nuclear explosions in the world, let us first start by
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looking at how many explosions occurred each year in total. This hides the
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specific details of who was responsible and which types were involved but
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instead paints a much stronger picture of the overall dimension of nuclear
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testing, as can be seen in @fig-total.
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In the examination of global nuclear detonations, our initial focus shall be
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quantifying the annual incidence of the events in aggregate. While it obscures
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the specific details of the responsible nations and which diversity of types
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tested, it instead paints a much stronger picture of the overall abstracted
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dimension of nuclear testing throughout history, as depicted in @fig-total.
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```{python}
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# | label: fig-total
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@ -171,17 +209,29 @@ with sns.axes_style(
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del per_year
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```
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As we can see, the numbers of explosions rise increasingly towards 1957 and
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sharply until 1958, before dropping off for a year in 1959. The reasons for
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this drop are not entirely clear, but it is very likely that the data are
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simply missing for these years.
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<!-- FIXME: The reasons for this are a non-proliferation pact, in article -->
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As we can see, the number of explosions rises increasingly towards 1957 and
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sharply until 1958, before dropping off for a year in 1959. The reason for this
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drop should primarily be found in the start of the 'Treaty of Test Ban' which
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put limits and restraints on the testing of above-ground nuclear armaments, as
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discussed in the original article. Above all the contract signals the
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prohibition of radioactive debris to fall beyond a nation's respective
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territorial bounds.
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However, this contract should perhaps not be viewed as the only reason: With
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political and cultural shifts throughout the late 1950s and early 1960s
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increasingly focusing on the fallout and horror of nuclear warfare a burgeoning
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public opposition to nuclear testing and instead a push towards disarmament was
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taking hold. The increased focus on the space race between the US and USSR may
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have detracted from the available funds, human resources and agenda attention
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for nuclear testing. Lastly, with nuclear testing policies strongly shaped by
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the political dynamics of the Cold War, a period of improved diplomatic
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relations such as the late 1950s prior to the Cuban missile crisis may directly
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affect the output of nuclear testing facilities between various powers.
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<!-- TODO: Extract exact numbers from data on-the-fly -->
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There is another, very steep, rise in 1962 with over 175 recorded explosions,
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before an even sharper drop-off the following year down to just 50 explosions.
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Afterwards the changes appear less sharp and the changes remain between 77 and
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Afterward the changes appear less sharp and the changes remain between 77 and
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24 explosions per year, with a slight downward tendency.
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While these numbers show the overall proliferation of nuclear power, let us now
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@ -191,6 +241,7 @@ of explosions over time by country can be seen in @fig-percountry.
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```{python}
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# | label: fig-percountry
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# | fig-cap: "Nuclear explosions by country, 1945-98"
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keys = df.select("date").unique().join(df.select("country").unique(), how="cross")
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per_country = keys.join(
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df.group_by(["date", "country"], maintain_order=True).len(),
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coalesce=True,
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).with_columns(pl.col("len").fill_null(0))
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g = sns.lineplot(data=per_country, x="date", y="len", hue="country")
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g = sns.lineplot(data=per_country, x="date", y="len", hue="country", palette=country_colors)
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g.set_xlabel("Year")
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g.set_ylabel("Count")
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plt.setp(
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Once again we can see the visibly steep ramp-up to 1962, though it becomes
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clear that this was driven both by the USSR and the US. Of course the graph
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also makes visible the sheer unmatched number of explosions emenating from both
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also makes visible the sheer unmatched number of explosions emanating from both
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of the countries, with only France catching up to the US numbers and China
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ultimately overtaking them in the 1990s.
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However, here it also becomes more clear how the UK was responsible for some
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early explosions in the late 1950s and early 1960s already, as well as the rise
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in France's nuclear testing from the early 1960s onwards to around 1980, before
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slowly decreasing in intensity afterwards.
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slowly decreasing in intensity afterward.
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Let us turn to a cross-cut through the explosions in @fig-groundlevel, focusing
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on the number of explosions that have occurred underground and above-ground
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hue="country",
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multiple="stack",
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binwidth=365,
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palette=country_colors,
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)
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g.xaxis.set_major_locator(mdates.YearLocator(base=5))
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## Locations
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Finally, let's view a map of the world with the explosions marked.
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Finally, let's view a map of the world with the explosions marked, separated by country.
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::: {.content-visible when-format="html"}
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Hovering over individual explosions will show their year
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while a click will open more information in a panel.
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:::
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The map can be seen in @fig-worldmap.
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```{python}
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# | label: fig-worldmap
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# | fig-cap: "World map of nuclear explosions, 1945-98"
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# | label: worldmap-setup
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# | output: false
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import folium
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import geopandas as gpd
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from shapely.geometry import Point
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def set_style() -> pl.Expr:
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return (
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pl.when(pl.col("country") == "USSR")
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.then(pl.lit({"color": "red"}, allow_object=True))
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.otherwise(pl.lit({"color": "blue"}, allow_object=True))
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)
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geom = [Point(xy) for xy in zip(df["longitude"], df["latitude"])]
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# df_pd = df.with_columns(style=set_style()).to_pandas().set_index("date")
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df_pd = df.with_columns().to_pandas().set_index("date")
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gdf = gpd.GeoDataFrame(
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df_pd,
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)
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del df_pd
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country_colors = {
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"USA": "darkblue",
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"USSR": "darkred",
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"FRANCE": "pink",
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"UK": "black",
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"CHINA": "purple",
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"INDIA": "orange",
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"PAKIST": "green",
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}
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def rgb_to_hex(rgb: tuple[float,float,float]) -> str:
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return "#" + "".join([format(int(c*255), '02x') for c in rgb])
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m = folium.Map(tiles="cartodb positron")
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for country in country_colors.keys():
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fg = folium.FeatureGroup(name=country, show=True).add_to(m)
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folium.GeoJson(
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gdf[gdf["country"].str.contains(country)],
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gdf[gdf["country"] == country],
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name="Nuclear Explosions",
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marker=folium.Circle(radius=3, fill_opacity=0.4),
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style_function=lambda x: {
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"color": country_colors[x["properties"]["country"]],
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"color": rgb_to_hex(country_colors[x["properties"]["country"]]),
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"radius": (
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x["properties"]["magnitude_body"]
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if x["properties"]["magnitude_body"] > 0
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),
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).add_to(fg)
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folium.LayerControl().add_to(m)
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```
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::: {#fig-worldmap}
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:::: {.content-visible when-format="html"}
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```{python}
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# | label: worldmap-html
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m
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```
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That is all for now.
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There are undoubtedly more explorations to undertake,
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but this is it for the time being.
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::::
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<!-- Ideas TODO:
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- do not just use 'count' of explosions but yields
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- compare number to yields for ctrys
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- count up total number per country in table
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-->
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:::: {.content-visible unless-format="html" width=80%}
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```{python}
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# | label: worldmap-non-html
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# ENSURE SELENIUM IS INSTALLED
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m.png_enabled = True
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m
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```
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::::
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World map of nuclear explosions, 1945-98
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:::
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While there are undoubtedly more aspects of the data that provide interesting
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patterns for analysis, this shall be the extent of review for the time being
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for this reproduction.
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We can see how the combination of python polars and seaborn makes the process
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relatively approachable, understandable and, combined with the rendering output
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by quarto, fully reproducible.
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Additionally, we can see how additional projects can be included to produce
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interactive graphs and maps with tools such as folium and geopandas.
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## References
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::: {#refs}
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:::
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