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running_analysis
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Fix notebook

main
Jacob Windle 2023-05-01 22:04:44 -04:00
parent a3edd796bf
commit 3e6cfd397e
1 changed files with 448 additions and 11 deletions
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459
RunningAnalysis → RunningAnalysis.jl
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@ -13,6 +13,8 @@ begin
using Dates
using Plots
using Statistics
using StatsBase
using StatsPlots
end
# ╔═╡ 9fee4ffb-83c0-46df-b84a-ef902a5e99ab
@ -42,7 +44,6 @@ begin
# Start with some average time in each stage. Timestamp should be in a more usable
# format.
metrics.ParsedTimestamp = DateTime.(metrics.Timestamp, dateformat"y-m-d H:M:S")
# Equivalent: metrics[:ParsedTimestamp], metrics[:, :ParsedTimestamp]
describe(metrics)
end
@ -151,15 +152,24 @@ md"""
begin
@pyimport garmin_fit_sdk
fit_files = readdir("fit_files"; join=true)
fit_files = readdir("./fit_files"; join=true)
function decode_fit_file(fp::AbstractString)::Tuple{Any,Any}
stream = garmin_fit_sdk.Stream.from_file(fp)
decoder = garmin_fit_sdk.Decoder(stream)
decoder.read()
try
decoder.read()
finally
stream.close()
end
end
decoded_fit_files = [dc[1] for dc in decode_fit_file.(fit_files)]
decoded = []
for file ∈ fit_files
push!(decoded, decode_fit_file(file))
end
decoded_fit_files = [dc[1] for dc in decoded]
end
# ╔═╡ 065c7811-0203-44fd-bb88-49946d674fd8
@ -177,17 +187,193 @@ md"""
# ╔═╡ 39a46471-663e-4395-b4bd-2380932d7189
begin
decoded_fit_files[1]["record_mesgs"]
function extract_record_and_timestamp(fit_file::Dict{Any, Any})::Tuple{DateTime, Vector{Dict{Any, Any}}}
function string_keys(d::Dict{Any,Any})::Dict{String,Any}
Dict([k => v for (k, v) in d if k isa AbstractString])
end
function extract_record_and_timestamp(fit_file::Dict{Any, Any})
ts = DateTime(fit_file["activity_mesgs"][1]["timestamp"])
records = filter(x -> x isa AbstractString)
ts, fit_file["record_mesgs"]
messages = string_keys.(fit_file["record_mesgs"])
try
ts, DataFrame(messages)
catch
nothing
end
end
extract_record_and_timestamp.(decoded_fit_files)
decoded_files = extract_record_and_timestamp.(decoded_fit_files)
end
# ╔═╡ ed9e2e1f-cebe-4a0b-969a-93d344578a28
md"""
## Mapping my data to DataFrame
- Extract the data needed
- Write function that pulls data out of PyDict
"""
# ╔═╡ b7854b76-eb0e-4d78-9bd6-9b0258a725ba
describe(decoded_files[1][2])
# ╔═╡ 3e1d6c7b-41b5-435a-88f0-b0f3ab442662
begin
# A generic graph_run function
function graph_run(decoded_file::Tuple{DateTime, DataFrame})
label = "Run on $(decoded_file[1])"
df = decoded_file[2]
l = @layout [a b; c d]
p = plot(df."timestamp", df."heart_rate"; title="Heart Rate", ylabel="BPM", xticks=nothing)
p2 = plot(df.timestamp, df.altitude; title="Altitude", xticks=nothing)
p3 = scatter(df.timestamp, df.cadence, title="Cadence", xticks=nothing)
p4 = plot(df.timestamp, df.speed; title="Speed", xticks=nothing)
plot(p, p2, p3, p4, layout = l)
end
end
# ╔═╡ 2f4cdc08-27a1-4e92-8709-8021cae771c6
graph_run(decoded_files[14])
# ╔═╡ a9d1b364-65d5-44bc-965f-61a5f2b47a36
md"""
## Graphing Runs
- Show speed, cadence, heartrate, and altitude
- Trail runs appear to have varied cadence, road runs are more stable
"""
# ╔═╡ 0c5d6cdb-7255-4712-92bc-eb3f3182d7eb
graph_run(decoded_files[19])
# ╔═╡ e4461dae-f648-4c34-a963-17dfb3f0220a
md"""
## Trends across Training
- What was my average heartrate for each run?
- Was my average heartrate effected by sleep?
"""
# ╔═╡ d1588e94-a450-4d8c-9811-69873deed252
begin
function calc_avgs(data::Union{Nothing, Tuple{DateTime, DataFrame}})
if isnothing(data)
return nothing
end
try
return (timestamp=data[1], mean_hr=mean(data[2].heart_rate), mean_cadence=mean(data[2].cadence), mean_alt=mean(data[2].altitude))
catch
return nothing
end
end
avg_heartrates = DataFrame(filter(f -> !isnothing(f), calc_avgs.(decoded_files)))
ps = scatter(avg_heartrates.timestamp, avg_heartrates.mean_hr, title="Avg Heartrate over Time", label="hr", xlabel="Timestamp", ylabel="Beats per minute", ylims=(115, 160))
hspan!(ps, [145, maximum(avg_heartrates.mean_hr)]; color=:red, alpha=0.2, label="endurance run")
hspan!(ps, [minimum(avg_heartrates.mean_hr), 145]; color=:green, alpha=0.2, label="recovery run")
end
# ╔═╡ 0b6bce6e-22e5-4a8d-95f4-4708deb07cdd
md"""
## Determining if Heartrate was affected by sleep
- Will need to use the workouts to determine which days were endurance runs
- Find sleep data for same day
- Stitch together to see results
"""
# ╔═╡ 4561f42b-2658-4c74-b642-6b2f92cd3598
begin
# Copy the existing dataframe.
hr_sleep_df = deepcopy(avg_heartrates)
# Get the days that we have an endurance run
my_endurance_days = workouts[:, :WorkoutDay]
my_endurance_selector = [d in my_endurance_days for d in Date.(hr_sleep_df.timestamp)]
my_endurance_days_df = hr_sleep_df[my_endurance_selector, :]
my_endurance_days_df.Date = Date.(my_endurance_days_df.timestamp)
# sleep_hours = metrics[metrics.Type .== "Sleep Hours", [:Value, :ParsedTimestamp]]
sleep_on_endurance_days = [d in my_endurance_days for d in Date.(sleep_hours.ParsedTimestamp)]
sleep_on_endurance_days_df = sleep_hours[sleep_on_endurance_days, :]
sleep_on_endurance_days_df.Date = Date.(sleep_on_endurance_days_df.ParsedTimestamp)
sleep_hr_df = leftjoin(my_endurance_days_df, sleep_on_endurance_days_df; on = :Date)
ns = scatter(sleep_hr_df.mean_hr, sleep_hr_df.Value; xlims=(120, 160), title="Heartrate over Sleep", xlabel="BPM", ylabel="Hours Slept")
vspan!(ns, [140, maximum(sleep_hr_df.mean_hr)]; color=:red, alpha=0.2, label="endurance run")
vspan!(ns, [minimum(sleep_hr_df.mean_hr), 140]; color=:green, alpha=0.2, label="recovery run")
end
# ╔═╡ 27a59d46-f838-4d8f-9c66-a7edc1eea8dd
md"""
## Findings
- No discernible relationship
- Appears to have clusters, left half is recovery, right half is endurance
## Next Question
- Does Cadence Affect HR?
"""
# ╔═╡ 504ed9c8-ff54-4a25-84b9-d832fc435486
begin
cadence_hr_df = deepcopy(avg_heartrates)
# cadence_hr_df.Date = Date.(cadence_hr_df.timestamp)
scatter(cadence_hr_df.mean_hr, cadence_hr_df.mean_cadence; xlabel="HR", ylabel="Cadence", title="HR vs. Cadence")
end
# ╔═╡ 873bcfa3-a989-426a-8402-6b81a8bbb46a
md"""
## Findings
- No relationship, because I always ran with the same cadence!
## Next Question
- Does altitude affect HR?
"""
# ╔═╡ b10b4931-e60f-4791-8dae-d6d58cd71e66
begin
altitude_hr_df = deepcopy(avg_heartrates)
scatter(altitude_hr_df.mean_hr, altitude_hr_df.mean_alt; xlabel="HR", ylabel="Mean Altitude", title="HR vs. Altitude")
end
# ╔═╡ b14e7de2-b91f-40d9-8aa8-237d82f25286
md"""
# Findings
- Altitude does appear to affect heart rate.
- Majority of runs were not at altitude, show normal distribution
- Runs at higher altitudes appear to cluster at higher HR
# Next Questions
- Do my data follow the normal distribution?
"""
# ╔═╡ 1fd645ee-0680-4de8-b1eb-1c94c2069ae9
begin
d = fit(UnitRangeTransform, avg_heartrates.mean_hr)
hr_normalized = StatsBase.transform(d, avg_heartrates.mean_hr)
avg_heartrates.hr_normalized = hr_normalized
@df avg_heartrates histogram(:mean_hr; bins=25, title="HR Histogram")
end
# ╔═╡ c3286275-3cff-4771-9ae6-78a960ba0484
md"""
## Findings
- Kind of normally distributed, but have two clusters
- Recovery vs. Endurance again!
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
@ -198,6 +384,8 @@ Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
[compat]
CSV = "~0.10.9"
@ -205,6 +393,8 @@ Conda = "~1.8.0"
DataFrames = "~1.5.0"
Plots = "~1.38.10"
PyCall = "~1.95.1"
StatsBase = "~0.33.21"
StatsPlots = "~0.15.4"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
@ -213,15 +403,45 @@ PLUTO_MANIFEST_TOML_CONTENTS = """
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@ -248,6 +468,12 @@ git-tree-sha1 = "4b859a208b2397a7a623a03449e4636bdb17bcf2"
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@ -260,6 +486,12 @@ git-tree-sha1 = "f84967c4497e0e1955f9a582c232b02847c5f589"
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@ -345,6 +577,12 @@ git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6"
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@ -353,6 +591,28 @@ uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
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@ -391,6 +669,12 @@ version = "0.9.20"
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[[deps.OffsetArrays]]
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deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "887579a3eb005446d514ab7aeac5d1d027658b8f"
@ -750,6 +1091,12 @@ deps = ["Artifacts", "Libdl"]
uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15"
version = "10.40.0+0"
[[deps.PDMats]]
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deps = ["Dates", "SnoopPrecompile"]
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@ -830,6 +1177,12 @@ git-tree-sha1 = "0c03844e2231e12fda4d0086fd7cbe4098ee8dc5"
uuid = "ea2cea3b-5b76-57ae-a6ef-0a8af62496e1"
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deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
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@ -838,6 +1191,12 @@ uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
deps = ["SHA", "Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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@ -867,6 +1226,18 @@ git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7"
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@ -886,6 +1257,10 @@ version = "1.3.18"
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@ -922,6 +1297,17 @@ git-tree-sha1 = "ef28127915f4229c971eb43f3fc075dd3fe91880"
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@ -938,16 +1324,38 @@ git-tree-sha1 = "d1bf48bfcc554a3761a133fe3a9bb01488e06916"
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@ -1027,6 +1435,18 @@ git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
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@ -1268,5 +1688,22 @@ version = "1.4.1+0"
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