fitnotes-graph

fitnotes_graph.py

# Given FitNotes CSV export, plot estimated 1RM
#
# Usage: fitnotes_graph.py <FILE>
# <FILE> is exported csv
# Assumes format:
# ['Date', 'Exercise', 'Category', 'Weight (lbs)', 'Reps', 'Distance', 
#   'Distance Unit', 'Time', 'Comment']
import csv
from datetime import datetime
from dateutil.relativedelta import relativedelta
import itertools
import matplotlib.pyplot as plt
import os
import sys

filepath = sys.argv[1]

# Structure
# categories = {
#   "Legs": {
#      "Standing Calf Raise": [ ... ], ...
#   }, ...
# }
categories = {}

# 1RM Estimate Table:
brzycki = dict(((1, .100), (2, .95), (3, .90), (4, .88), (5, .86), (6, .83), \
	(7, .80), (8, .78), (9, .76), (10, .75), (11, .72), (12, .70)))

# Populate categories of exercises
with open(filepath) as f:
	fitnotes = csv.reader(f, delimiter=',')
	for i, row in enumerate(fitnotes):
		if i == 0:
			continue
		date, exercise, category, weight, reps, distance, dunit, \
			time, comment = row
		# Skip exercises with no weights (like ab crunches)
		if float(weight) == 0.0:
			continue
		if category not in categories:
			categories[category] = {}
		exercises = categories[category]
		if exercise not in exercises:
			exercises[exercise] = []

		reps = int(reps)
		one_rm = None
		# Compute 1RM estimate using brzycki table
		if reps in brzycki:
			one_rm = int(float(weight) / brzycki[reps])
		else:
			# Approx. 1RM beyond 12 reps by using value for 12 reps
			one_rm = int(float(weight) / brzycki[12])
		date = datetime.strptime(date, "%Y-%m-%d")

		# If there's already an exercise recorded for same date,
		# then keep best rep (want unique dates for each exercise)
		# Assumes CSV file is sorted by date
		if len(exercises[exercise]) > 0 and \
			exercises[exercise][-1][0] == date:
			if reps > exercises[exercise][-1][2]:
				exercises[exercise].pop()
				exercises[exercise].append( \
					(date, weight, reps, one_rm))
		else:
				exercises[exercise].append( \
					(date, weight, reps, one_rm))

def saveExercisePlot(category, exercise):
	print(category, exercise)
	x = [item[0] for item in categories[category][exercise]]
	y = [item[3] for item in categories[category][exercise]]

	plt.plot(x, y, linestyle='--')
	plt.gcf().autofmt_xdate()

	def datespan(startDate, endDate, delta):
		currentDate = startDate
		while currentDate < endDate:
			yield currentDate
			currentDate += delta

	month_delta = relativedelta(months=+1)
	x_month = []
	y_month = []
	# Kind of a brute-force way to chunk data by month - want to get
	# best 1RM by month
	for x_date in datespan(x[0], x[-1] + month_delta, delta=month_delta):
		print (x_date)
		best_1rm = 0
		best_date = None
		for item in categories[category][exercise]:
			if item[3] is None:
				continue
			if item[0].month == x_date.month and item[0].year == x_date.year:
				if best_1rm < item[3]:
					best_1rm = item[3]
					best_date = item[0]
		if best_date is not None:
			x_month.append(best_date)
			y_month.append(best_1rm)

	if len(x_month) > 0:
		plt.plot(x_month, y_month, marker='o')

	category_name = ''.join(e for e in category if e.isalnum())
	if not os.path.exists("out/" + category_name):
		os.makedirs("out/" + category_name)
	exercise_name = ''.join(e for e in exercise if e.isalnum())
	plt.savefig("out/" + category_name + "/" + exercise_name + ".png")
	plt.gcf().clear()

if not os.path.exists("out"):
	os.makedirs("out")

for c_key in categories.keys():
	for e_key in categories[c_key].keys():
		saveExercisePlot(c_key, e_key)