From cceb1d1ec0d4ad5e99fa7f433c81ebcf0d344ca5 Mon Sep 17 00:00:00 2001 From: Marty Oehme Date: Sat, 22 Jun 2024 23:11:49 +0200 Subject: [PATCH] initial commit --- data/nuclear_explosions.csv | 2052 ++++++++++++++ notebooks/groupby_keep_zero-values.qmd | 54 + notebooks/sns_objects-style.qmd | 75 + nuclear_explosions.qmd | 382 +++ poetry.lock | 3392 ++++++++++++++++++++++++ pyproject.toml | 32 + 6 files changed, 5987 insertions(+) create mode 100644 data/nuclear_explosions.csv create mode 100644 notebooks/groupby_keep_zero-values.qmd create mode 100644 notebooks/sns_objects-style.qmd create mode 100644 nuclear_explosions.qmd create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/data/nuclear_explosions.csv b/data/nuclear_explosions.csv new file mode 100644 index 0000000..ea76b98 --- /dev/null +++ b/data/nuclear_explosions.csv @@ -0,0 +1,2052 @@ 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+19960127,1996,96001,FRANCE,FANGATAUFA,WTN,-22.236,-138.815,0,0,0,0,120,WR,XOUTHOS,UG +19960608,1996,96002,CHINA,LOP NOR,HFS,41.65,88.76,6.3,0,0,30,120,WR,NA,UG +19960729,1996,96003,CHINA,LOP NOR,HFS,41.69,88.35,5.3,0,0,3,12,WR,NA,UG +19980511,1998,98001,INDIA,POKHRAN,HFS,27.07,71.7,5.3,0,0,0,20,WR,SHAKTI 1-3,UG +19980513,1998,98003,INDIA,POKHRAN,NRD,27.07,71.7,0,0,0,0,1,WR,NA,UG +19980528,1998,98004,PAKIST,CHAGAI,HFS,28.9,64.89,0,0,0,0,35,WR,NA,UG +19980530,1998,98005,PAKIST,KHARAN,HFS,28.49,63.78,5,0,0,0,18,WR,NA,UG diff --git a/notebooks/groupby_keep_zero-values.qmd b/notebooks/groupby_keep_zero-values.qmd new file mode 100644 index 0000000..d5259cf --- /dev/null +++ b/notebooks/groupby_keep_zero-values.qmd @@ -0,0 +1,54 @@ + + +The following is a simple groupby, counting the len of country rows per date: + +```{python} +# | label: fig-percountry-drop +# | fig-cap: "Nuclear explosions by country, 1945-98" +per_country = df.group_by(pl.col("date", "country")).agg(pl.len()).sort("date") + +g = sns.lineplot(data=per_country, x="date", y="len", hue="country") +g.set_xlabel("Year") +g.set_ylabel("Count") +plt.setp( + g.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor" +) # ensure rotated right-anchor +plt.show() +``` + +This works well to group generally, but there is an issue: +If there is a year where a country did not have any entries at all, +the resulting df will not have `Date | Cty | 0` but instead will not have an entry at all. + +This can be desirable for some applications, but for example if we then +draw a line plot based on this it would interpolate between the +country values and **not drop the line down to 0 for the years where a country does not have an entry**. + +We can fix it by first doing a cross product of all keys we always want to have a row for. +Then we do the group by but supply it to a left-join on this cross product. + +End result is we keep all the rows from the cross-product, but we still aggregate and have a len +column as before. For those where we don't have a len value we finally just fill in a 0 instead. + +```{python} +# | label: fig-percountry-keep +# | fig-cap: "Nuclear explosions by country, 1945-98" +keys = df.select("date").unique().join(df.select("country").unique(), how="cross") +per_country = keys.join( + df.group_by(["date", "country"], maintain_order=True).len(), + on=["date", "country"], + how="left", + coalesce=True, +).with_columns(pl.col("len").fill_null(0)) + +g = sns.lineplot(data=per_country, x="date", y="len", hue="country") +g.set_xlabel("Year") +g.set_ylabel("Count") +plt.setp( + g.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor" +) # ensure rotated right-anchor +plt.show() +``` + +A more nicely function-based solution (though using the same solution under the hood) can be found +here: https://github.com/pola-rs/polars/issues/15997#issuecomment-2089362557 diff --git a/notebooks/sns_objects-style.qmd b/notebooks/sns_objects-style.qmd new file mode 100644 index 0000000..b3ceb47 --- /dev/null +++ b/notebooks/sns_objects-style.qmd @@ -0,0 +1,75 @@ + + constructed with seaborn object-style plots instead. +These kind of plots are much more structured for the workflow I use and the way I think about plotting, +clearly delineating between a plot; +some visual on the plot; +some statistical transformation; +some movement, labeling or scaling operation. +They are also, however, fairly new and still considered experimental. + +They also don't allow *quite* the customization that the other plots do, +and seem either a little buggy or I have not fully understood them yet in regards to ticks and labels. + +```{python} +# | label: fig-groundlevel-so +# | fig-cap: "Nuclear explosions, 1945-98" +import seaborn.objects as so +import matplotlib.dates as mdates + +above_cat = pl.Series( + [ + "ATMOSPH", + "AIRDROP", + "TOWER", + "BALLOON", + "SURFACE", + "BARGE", + "ROCKET", + "SPACE", + "SHIP", + "WATERSUR", + "WATER SU", + ] +) +df_groundlevel = ( + df.with_columns( + above_ground=pl.col("type").map_elements( + lambda x: True if x in above_cat else False, return_dtype=bool + )) + .group_by(pl.col("year", "country", "above_ground")) + .agg(count=pl.len()) + .sort("year") +) + +fig, ax = plt.subplots() +ax.xaxis.set_tick_params(rotation=90) + +from seaborn import axes_style +p = ( + so.Plot(df_groundlevel, x="year", y="count", color="country") + .add( + so.Bars(), + so.Stack(), + data=df_groundlevel.filter(pl.col("above_ground") == True).sort("country"), + ) + .add( + so.Bars(), + so.Stack(), + data=df_groundlevel.filter(pl.col("above_ground") == False).with_columns( + count=pl.col("count") * -1 + ).sort("country"), + ) + .label(x="Year", y="Count") + .scale( + x=so.Continuous().tick(locator=mdates.YearLocator(base=5), minor=4).label(like="{x:.0f}"), + # x=so.Nominal().tick(locator=mdates.YearLocator(base=5), minor=4), # this might work in the future + ) + .theme({ + **axes_style("darkgrid"), + "xtick.bottom": True, + "ytick.left": True + }) + .on(ax) + .plot() +) +``` diff --git a/nuclear_explosions.qmd b/nuclear_explosions.qmd new file mode 100644 index 0000000..559fb9a --- /dev/null +++ b/nuclear_explosions.qmd @@ -0,0 +1,382 @@ +--- +title: Nuclear Explosions +author: Marty Oehme +output-dir: out +references: + - type: techreport + id: Bergkvist2000 + author: + - family: Bergkvist + given: Nils-Olov + - family: Ferm + given: Ragnhild + issued: + date-parts: + - - 2000 + - 7 + - 1 + title: "Nuclear Explosions 1945 - 1998" + page: 1-42 + issn: 1104-9154 +format: + html: + toc: true + code-fold: true + typst: + toc: true + echo: false + docx: + toc: true + echo: false +--- + +```{python} +#| label: setup +#| echo: false +import matplotlib.dates as mdates +import numpy as np +import pandas as pd +import polars as pl +import polars.selectors as cs +import seaborn as sns +from matplotlib import pyplot as plt + +sns.set_theme(style="darkgrid") +sns.set_context("notebook") + +schema_overrides = ( + { + col: pl.Categorical + for col in ["type", "name", "purpose", "country", "source", "region"] + } + | {col: pl.Float64 for col in ["magnitude_body", "magnitude_surface"]} + | {col: pl.String for col in ["year", "name"]} +) + +df = ( + pl.read_csv( + "data/nuclear_explosions.csv", + schema_overrides=schema_overrides, + null_values=["NA"], + ) + .with_columns(date=pl.col("year").str.strptime(pl.Date, "%Y")) + .with_columns(year=pl.col("date").dt.year().cast(pl.Int32)) +) +``` + +## Introduction + +The following is a re-creation and expansion of some of the graphs found in the +@Bergkvist2000 produced report on nuclear explosions between 1945 and 1998. It +is primarily a reproduction of key plots from the original report. +Additionally, it serves as a exercise in plotting with the python library +seaborn and the underlying matplotlib. Lastly, it approaches some less well +tread territory for data science in the python universe as it uses the python +library polars-rs for data loading and transformation. All the code used to +transform the data and create the plots is available directly within the full +text document, and separately as well. PDF and Docx formats are available with +the plotting results only. + +Their original purpose was the collection of a long list of all the nuclear +explosions occurring between those years, as well as analysing the responsible +nations, tracking the types and purposes of the explosions, as well as +connecting the rise and fall of nuclear explosion numbers to historical events +throughout. + +## Total numbers + +::: {.callout-note} + +## Nuclear devices + +There are two main kinds of nuclear device: those based entirely, on fission, +or the splitting of heavy atomic nucleii (previously known as atomic devices) +and those in which the main energy is obtained by means of fusion, or of -light +atomic nucleii (hydrogen or thermonuclear devices). A fusion explosion must +however be initiated with the help of a fission device. The strength of a +fusion explosion can be practically unlimited. The explosive power of a +nuclear explosion is expressed in ktlotons, (kt) or megatons (Mt), which +correspond to 1000 and i million'tonnes, of conventional explosive (TNT), +respectively. + +[@Bergkvist2000, 6] +::: + +We begin by investigating a table containing all the absolute counts and yields +each country had explode, seen in @tbl-yields. + +```{python} +# | label: tbl-yields +# | tbl-cap: "Total number and yields of explosions" + +from great_tables import GT, md + +df_yields = ( + df.select(["country", "id_no", "yield_lower", "yield_upper"]) + .with_columns(yield_avg=pl.mean_horizontal(pl.col(["yield_lower", "yield_upper"]))) + .group_by("country") + .agg( + pl.col("id_no").len().alias("count"), + pl.col("yield_avg").sum(), + ) + # .with_columns(country=pl.col("country").cast(pl.String).str.to_titlecase()) + .sort("count", descending=True) +) + +( + GT(df_yields) + .tab_source_note( + source_note="Source: Author's elaboration based on Bergkvist and Ferm (2000)." + ) + .tab_spanner(label="Totals", columns=["count", "yield_avg"]) + .tab_stub(rowname_col="country") + .tab_stubhead(label="Country") + .cols_label( + count="Count", + yield_avg="Yield in kt", + ) + .fmt_integer(columns="count") + .fmt_number(columns="yield_avg", decimals=1) +) +``` + +## Numbers over time + +When investigating the nuclear explosions in the world, let us first start by +looking at how many explosions occurred each year in total. This hides the +specific details of who was responsible and which types were involved but +instead paints a much stronger picture of the overall dimension of nuclear +testing, as can be seen in @fig-total. + +```{python} +# | label: fig-total +# | fig-cap: "Total Nuclear explosions, 1945-98" +per_year = df.group_by(pl.col("year")).agg(pl.len()).sort("year") +with sns.axes_style( + "darkgrid", {"xtick.bottom": True, "ytick.left": True} +): + g = sns.barplot(data=per_year, x="year", y="len", order=range(1945, 1999), width=1) + g.set_xlabel("Year") + g.set_ylabel("Count") + plt.setp( + g.get_xticklabels(), + rotation=90, + ha="right", + va="center", + rotation_mode="anchor", + ) # ensure rotated right-anchor + g.set_xticks(g.get_xticks(), minor=True) # enable minor ticks every entry + g.set_xticks(g.get_xticks()[::2]) # enable major ticks every 2nd entry + plt.show() +del per_year +``` + +As we can see, the numbers of explosions rise increasingly towards 1957 and +sharply until 1958, before dropping off for a year in 1959. The reasons for +this drop are not entirely clear, but it is very likely that the data are +simply missing for these years. + + + +There is another, very steep, rise in 1962 with over 175 recorded explosions, +before an even sharper drop-off the following year down to just 50 explosions. + +Afterwards the changes appear less sharp and the changes remain between 77 and +24 explosions per year, with a slight downward tendency. + +While these numbers show the overall proliferation of nuclear power, let us now +instead turn to the contributions by individual countries. A split in the number +of explosions over time by country can be seen in @fig-percountry. + +```{python} +# | label: fig-percountry +# | fig-cap: "Nuclear explosions by country, 1945-98" +keys = df.select("date").unique().join(df.select("country").unique(), how="cross") +per_country = keys.join( + df.group_by(["date", "country"], maintain_order=True).len(), + on=["date", "country"], + how="left", + coalesce=True, +).with_columns(pl.col("len").fill_null(0)) + +g = sns.lineplot(data=per_country, x="date", y="len", hue="country") +g.set_xlabel("Year") +g.set_ylabel("Count") +plt.setp( + g.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor" +) # ensure rotated right-anchor +plt.show() +del per_country +``` + +Once again we can see the visibly steep ramp-up to 1962, though it becomes +clear that this was driven both by the USSR and the US. Of course the graph +also makes visible the sheer unmatched number of explosions emenating from both +of the countries, with only France catching up to the US numbers and China +ultimately overtaking them in the 1990s. + +However, here it also becomes more clear how the UK was responsible for some +early explosions in the late 1950s and early 1960s already, as well as the rise +in France's nuclear testing from the early 1960s onwards to around 1980, before +slowly decreasing in intensity afterwards. + +Let us turn to a cross-cut through the explosions in @fig-groundlevel, focusing +on the number of explosions that have occurred underground and above-ground +respectively.[^aboveground] + +[^aboveground]: Detonations counted as above ground are made up of atmospheric, +airdrop, tower, balloon, barge or ship, rocket and water surface detonations. +Any other detonation is counted as below ground, primarily taking place in +tunnels, shafts and galleries. + +```{python} +# | label: fig-groundlevel +# | fig-cap: "Nuclear explosions above and below ground, 1945-98" +from polars import Boolean + +above_cat = pl.Series( + [ + "ATMOSPH", + "AIRDROP", + "TOWER", + "BALLOON", + "SURFACE", + "BARGE", + "ROCKET", + "SPACE", + "SHIP", + "WATERSUR", + "WATER SU", + ] +) +df_groundlevel = ( + df.with_columns( + above_ground=pl.col("type").map_elements( + lambda x: True if x in above_cat else False, return_dtype=Boolean + ) + ) + .group_by(pl.col("date", "country", "above_ground")) + .agg(count=pl.len()) + .sort("date") +) + +with sns.axes_style("darkgrid", {"xtick.bottom": True, "ytick.left": True}): + for above_ground in [True, False]: + g = sns.histplot( + data=df_groundlevel.filter( + pl.col("above_ground") == above_ground + ).with_columns( + count=pl.col("count") * (1 if above_ground else -1), + ), + x="date", + weights="count", + hue="country", + multiple="stack", + binwidth=365, + ) + + g.xaxis.set_major_locator(mdates.YearLocator(base=5)) + g.xaxis.set_minor_locator(mdates.YearLocator()) + plt.setp( + g.get_xticklabels(), rotation=90, ha="right", va="top", rotation_mode="anchor" + ) + # FIXME get dynamic range for yticks instead of hardcoding + g.set_yticks(np.arange(-130, 140, 20)) + g.set_yticks(np.arange(-130, 140, 10), minor=True) + plt.show() +del df_groundlevel +``` + +This plot paints a different picture yet again: while overall the number of +explosions still rise and fall with some early sharp spikes, we can see a clear +shift from above-ground to underground tests, starting with the year 1962. + +## Locations + +Finally, let's view a map of the world with the explosions marked. + +```{python} +# | label: fig-worldmap +# | fig-cap: "World map of nuclear explosions, 1945-98" +import folium +import geopandas as gpd +from shapely.geometry import Point + +def set_style() -> pl.Expr: + return ( + pl.when(pl.col("country") == "USSR") + .then(pl.lit({"color": "red"}, allow_object=True)) + .otherwise(pl.lit({"color": "blue"}, allow_object=True)) + ) + + +geom = [Point(xy) for xy in zip(df["longitude"], df["latitude"])] +# df_pd = df.with_columns(style=set_style()).to_pandas().set_index("date") +df_pd = df.with_columns().to_pandas().set_index("date") +gdf = gpd.GeoDataFrame( + df_pd, + crs="EPSG:4326", + geometry=gpd.points_from_xy(x=df_pd["longitude"], y=df_pd["latitude"]), +) +del df_pd + +country_colors = { + "USA": "darkblue", + "USSR": "darkred", + "FRANCE": "pink", + "UK": "black", + "CHINA": "purple", + "INDIA": "orange", + "PAKIST": "green", +} + +m = folium.Map(tiles="cartodb positron") +for country in country_colors.keys(): + fg = folium.FeatureGroup(name=country, show=True).add_to(m) + folium.GeoJson( + gdf[gdf["country"].str.contains(country)], + name="Nuclear Explosions", + marker=folium.Circle(radius=3, fill_opacity=0.4), + style_function=lambda x: { + "color": country_colors[x["properties"]["country"]], + "radius": ( + x["properties"]["magnitude_body"] + if x["properties"]["magnitude_body"] > 0 + else 1.0 + ) + * 10, + }, + tooltip=folium.GeoJsonTooltip(fields=["year", "country", "type"]), + highlight_function=lambda x: {"fillOpacity": 0.8}, + popup=folium.GeoJsonPopup( + fields=[ + "year", + "country", + "region", + "source", + "latitude", + "longitude", + "magnitude_body", + "magnitude_surface", + "depth", + "yield_lower", + "yield_upper", + "purpose", + "name", + "type", + ] + ), + ).add_to(fg) +folium.LayerControl().add_to(m) +m +``` + +That is all for now. +There are undoubtedly more explorations to undertake, +but this is it for the time being. + + diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..908ee87 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3392 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "anyio" +version = "4.4.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = 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