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loaddata.jl
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loaddata.jl
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using DataFrames
using CSV
using CairoMakie
import Images
using ColorTypes
using Colors
using Dates
#https://blog.csdn.net/hfy1237/article/details/124663893 繪圖介紹
#畫圖參數
struct PlotConfig
#axis 常用參數
#標題
title::String
titlealign::Symbol
titlecolor::Symbol
titlefont::Symbol
titlegap::Float64
titlesize::Float64
subtitle::String
subtitlefont::Symbol
subtitlecolor::Symbol
subtitlegap::Int
subtitlelineheight::Int
subtitlesize::Float64
subvisible::Bool
spinewidth::Float64
rightspinevisible::Bool
rightspinecolor::Symbol
leftspinevisible::Bool
leftspinecolor::Symbol
bottomspinecolor::Symbol
bottomspinevisible::Bool
flip_ylable::Bool
resolution::Tuple{Int64, Int64}
figure_padding::Tuple{Int64, Int64,Int64, Int64}
backgroundcolor::ColorTypes.RGB{Float64}
font::Symbol
fontsize::Float64
cycle::Vector{Symbol} #line
color::Symbol
linestyle::Symbol
linewidth::Float64
visible::Bool
overdraw::Bool
transparency::Bool
xticks::UnitRange{Int64}
yticks::UnitRange{Int64}
xticksmirrored::Bool
yticksmirrored::Bool
xminorticksvisible::Bool
yminorticksvisible::Bool
xminortickalign::Int
yminortickalign::Int
xtickalign::Int
ytickalign::Int
end
# instantiate PlotConfig
default_config = PlotConfig(
"房子\n統計", #title::String
:center, #titlealign
:black, #titlecolor
:blod, #titlefont :italic,:bold ,:bold_italic,:italic,:regular
4.0, #titlegap
20.0, #titlesize
"count/年度", #subtitle::String
:regular , #subtitlefont::Symbol
:black , #subtitlecolor::Symbol
1 , #subtitlegap
1 , #subtitlelineheight
10.0 , #subtitlesize::Float64
true , # subvisible::Bool
1.5, #spinewidth::Float64
true , #rightspinevisible::Bool
:black , #rightspinecolor::Symbol
true , #leftspinevisible::Bool
:black , #leftspinecolor::Symbol
:black , #bottomspinecolor::Symbol
true , #bottomspinevisible::Bool
true , #flip_ylable::Bool
(800,600) , #resolution
(5,5,5,5) , #figure_padding
Images.RGB(0.9, 0.9, 0.9) , # backgroundcolor
:regular , #font::Symbol
20.0 , #fontsize::Float64
[:solid, :dash, :dot] , #cycle::Vector{Symbol}
:dark , #color::Symbol
:dot , #linestyle::Symbol
1.5 , #linewidth::Float64
true , #visible::Bool
false , #overdraw::Bool
true , #transparency::Bool
0:10 , #xticks::UnitRange{Int64}
0:10 , #yticks::UnitRange{Int64}
true, #xticksmirrored::Bool
true, #yticksmirrored::Bool
true, #xminorticksvisible::Bool
true, #yminorticksvisible::Bool
1, #xminortickalign::Int
1, #yminortickalign::Int
2, #xtickalign::Int
5 #ytickalign::Int
)
joinpath(@__DIR__,"air_pollution.csv")
# 指定CSV檔案的路
path=@__DIR__
file_path = joinpath(@__DIR__,"air_pollution.csv")
# 使用CSV.File()函數讀取CSV檔案並轉換為DataFrame
df = DataFrame(CSV.File(file_path))
#可畫圖片 1. 各國家城市數量
#2.各國家平均PM2.5 對年度
#3.預測2024 各城市的PM2.5 和各國家的PM2.5
#1. 各國家城市數量
# 使用groupby和combine函數進行組合和計數
result = combine(groupby(df, :country), nrow)
sorted_result = sort(result, :nrow, rev=true)
total_nrow = sum(sorted_result.nrow)
#篩選出>100的 依序排序
filtered_result = filter(row -> row.nrow > 100, result)
# 使用eachrow迭代DataFrame的行，並根據條件收集索引
filtered_indexes = Int[]
for (index, row) in enumerate(eachrow(result))
if row.nrow > 100
push!(filtered_indexes, index)
end
end
df_100=filter(:country => x -> x in collect(filtered_result.country) ,df)
#畫圖
f=Figure(resolution=(1000,800),figure_padding = (5, 50, 5, 5),backgroundcolor=default_config.backgroundcolor,font=20)
ax=Axis(f[1,1],title=default_config.title,titlecolor=Images.RGB(0.2, 0.1, 0.1),titlefont=:bold_italic,titlegap=8.0,titlesize=20,titlelineheight=1
,subtitle=default_config.subtitle,aspect= DataAspect())
lines!(ax, parse.(Int,names(df_100[1,3:end])),collect(df_100[1,3:end]),transparency=true)
#test
# collect(df_100[1,3:end])
######
# 按国家对数据进行分组，并计算每个国家出现的次数
country_counts = combine(groupby(df_100, :country), nrow => :count)
# 提取国家名称和对应的计数
countries = country_counts.country
counts = country_counts.count
# 绘制折线图
# ax=Axis(f[1,1],
# xticks=0:10,
# yticks=0:10,
# xticksmirrored=true,
# yticksmirrored=true,
# xminorticksvisible=true,
# yminorticksvisible=true,
# xminortickalign=1,
# yminortickalign=1,
# )
#ax = Axis(f[1, 1], xlabel = "Country", ylabel = "Count")
# 使用 barplot! 函数绘制柱状图
label_vector = collect(countries)
colors_length=length(countries)
#各城市出現次數+-------------------------------------------------
fig = Figure(backgroundcolor=Images.RGB(1.0, 1.0, 0.94))
#算出顏色~
color_length=round(0.5/colors_length,digits=3)
colors = collect(map(i -> RGB(i * color_length, i * color_length, 0.5 + i * color_length), 1:colors_length))
#訂定y軸~
maximum(counts)
# 确定输入整数的位数
num_digits_upper = floor(log10(maximum(counts))) + 1
num_digits_down = floor(log10(minimum(counts))) + 1
# 将输入的整数除以 10 的 (位数 - 1) 次方，并向上取整，然后再乘以 10 的 (位数 - 1) 次方
output_integer_upper = Int(ceil(maximum(counts) / 10^(num_digits_upper - 1)) * 10^(num_digits_upper- 1))
output_integer_down = Int(floor(minimum(counts) / 10^(num_digits_down - 1)) * 10^(num_digits_down - 1))
#先不要取下限y
yticks=0:Int(output_integer_upper/10 ):output_integer_upper
country_index = 1:length(countries)
# 使用 barplot! 函数绘制柱状图
#barplot!(ax, country_index, counts, xticks = (country_index, countries),color=colors)
# 根据 counts 的值对 countries 进行排序
sorted_indices = sortperm(counts, rev=true)
sorted_countries = countries[sorted_indices]
sorted_counts = counts[sorted_indices]
# 绘制柱形图 #AXIS
ax = Axis(fig[1, 1],xticks=(1:colors_length,sorted_countries ),title="城市數量",titlecolor=Images.RGB(0.2, 0.1, 0.1),titlesize=30,xticklabelsize=16,xticklabelfont=:bold,xtickcolor= Images.RGB(0.504,0.504,1.004),
yticksmirrored=true,
yminorticksvisible=true,
yticks=yticks,xticklabelrotation=45
)
barplot!(ax,sorted_counts, xticks=(1:length(sorted_countries), sorted_countries), color=colors)
today_date = today()
CairoMakie.save(joinpath(@__DIR__,"$(today_date)_count&country.png"), fig)
# 创建一个 Figure 对象
#----------------------------------------
using Statistics
year_array=[]
for i in ["2017","2018","2019","2020","2021","2022","2023"]
average_without_missing = mean(skipmissing(df_100[!,Symbol(i)]))
push!(year_array,average_without_missing )
end
fig = Figure(backgroundcolor=Images.RGB(1.0, 1.0, 0.94))
#算出顏色~
year_xtick=["2017","2018","2019","2020","2021","2022","2023"]
colors_length=length(["2017","2018","2019","2020","2021","2022","2023"])
color_length=round(0.5/colors_length,digits=3)
colors = collect(map(i -> Images.RGB(i * color_length, i * color_length, 0.5 + i * color_length), 1:colors_length))
#訂定y軸~
# 确定输入整数的位数
num_digits_upper = floor(log10(maximum(year_array))) + 1
num_digits_down = floor(log10(minimum(year_array))) + 1
# 将输入的整数除以 10 的 (位数 - 1) 次方，并向上取整，然后再乘以 10 的 (位数 - 1) 次方
output_integer_upper = Int(ceil(maximum(year_array) / 10^(num_digits_upper - 1)) * 10^(num_digits_upper- 1))
output_integer_down = Int(floor(minimum(year_array) / 10^(num_digits_down - 1)) * 10^(num_digits_down - 1))
#先不要取下限y
yticks=0:Int(output_integer_upper/10 ):output_integer_upper
years_int = parse.(Int, year_xtick)
ax = Axis(fig[1, 1],xticks=(years_int ),title="PM2.5",titlecolor=Images.RGB(0.2, 0.1, 0.1),titlesize=30,xticklabelsize=16,xticklabelfont=:bold,xtickcolor= Images.RGB(0.504,0.504,1.004)
)
#箭頭座標
arrow_path = BezierPath([
MoveTo(Point(0, 0)),
LineTo(Point(0.3, -0.3)),
LineTo(Point(0.15, -0.3)),
LineTo(Point(0.15, -1)),
LineTo(Point(0, -0.98)),
LineTo(Point(-0.15, -1)),
LineTo(Point(-0.15, -0.3)),
LineTo(Point(-0.3, -0.3)),
ClosePath()
])
for (index, i) in enumerate(year_array)
if index ==1
scatter!(ax,years_int[index],i, markersize=30,color=colors[index],marker = arrow_path,rotations=3/2*pi)
else
y=(i-year_array[index-1])
x=1.8*(output_integer_upper/ output_integer_down)/length(years_int)
angle_radians = atan(y / x)
scatter!(ax,years_int[index],i, markersize=30,color=colors[index],marker = arrow_path,rotations=3/2*pi+angle_radians)
end
end
today_date = today()
CairoMakie.save(joinpath(@__DIR__,"$(today_date)_PM&year.png"), fig)
#-------------------------------------------
#畫標示 確認角度~ done
# 前三多的國家 分成兩邊 預測國家標籤
#dropmiss
df=dropmissing(df_100)
counts = sort(combine(groupby(df, :country), nrow => :count),:count,rev=true)
df_country=collect(counts[1:3,1])
#篩選前三個國家
# 使用filter函数筛选出"country"列的值为指定国家的行
filtered_df = filter(row -> row.country in df_country, df)
using Random
# 获取DataFrame的行数
n_rows = nrow(filtered_df )
random_indices = shuffle(MersenneTwister(1234),1:n_rows)
shuffled_df = filtered_df[random_indices, :]
# 将DataFrame分成训练集和测试集的比例（这里假设为7:3）
train_ratio = 0.7
test_ratio = 1.0 - train_ratio
split_index = Int(floor(train_ratio * n_rows))
# 分割DataFrame为训练集和测试集
train_df = shuffled_df[1:split_index, :]
test_df = shuffled_df[(split_index + 1):end, :]
println("训练集大小：", nrow(train_df))
println("测试集大小：", nrow(test_df))
using Flux: onehotbatch, onecold, crossentropy, throttle
using Base.Iterators: repeated
using Statistics: mean
#准备数据
println("準備數據")
X_train =train_df[!,3:end]
X_test =train_df[!,2]
Y_train = test_df[!,3:end]
Y_test = test_df[!,2]
#分類任務 用 onehotbatch