dataimport.py

import numpy as np
import matplotlib.pyplot as plt
import datetime
import matplotlib.dates


def containsNumber(value):
    for character in value:
        if character.isdigit():
            return True
    return False


# import data from notes and put it in a list
with open("liftingpostcovid.txt", "r") as data:
    rawNotes = [(line.strip()).split() for line in data]


k = 0
while k < len(rawNotes):
    if rawNotes[k] == "Front" and rawNotes[k + 1] == "Squat":
        rawNotes[k : k + 1] = ["".join(rawNotes[k : k + 1])]
    k = k + 1

print(rawNotes[0:5])
flatNotes = [x for note in rawNotes for x in note]
print(flatNotes[0:5])

# initialize benchWeight and benchReps arrays


def masterFunc(liftName):
    weight = []
    reps = []
    liftDate = []
    i = 0

    def findDate(j):
        # Finds corresponding date for a particular set
        while j > 0:
            if "/" in flatNotes[j - 1]:
                newDate = datetime.datetime.strptime(flatNotes[j - 1], "%m/%d/%y")
                newDate = matplotlib.dates.date2num(newDate)
                j = 0
            else:
                j = j - 1
        return newDate

    # create 3 arrays, for lift weight, reps performed, and date performed
    while i < len(flatNotes):
        # make sure entry is in valid form
        if liftName in flatNotes[i]:
            if not containsNumber(flatNotes[i + 1]) or not containsNumber(
                flatNotes[i + 2]
            ):
                i = i + 2
                print("oops", flatNotes[i], matplotlib.dates.num2date(findDate(i)))
            # add value for rep and weight to list
            else:
                weight = np.append(weight, flatNotes[i + 1])
                reps = np.append(reps, flatNotes[i + 2])
                j = i
                # add corresponding date value to list
                liftDate = np.append(liftDate, findDate(j))

        i = i + 1

    ######### do error test and print line for which iti doesnt work and date and stufdf here

    # find the hardest set for each day
    x = 0

    # grab only value of highest reps performed that day, discard lighter sets
    highestReps = []
    while x < len(reps):
        if "," in reps[x]:
            newReps = [int(x) for x in reps[x].split(",")]
            highestReps = np.append(highestReps, (max(newReps)))

            x = x + 1
        elif "x" in reps[x]:
            newReps = [reps[x][2 : len(reps[x])]]
            x = x + 1
            highestReps = np.append(highestReps, newReps)
        else:
            newReps = reps[x]
            x = x + 1
            highestReps = np.append(highestReps, newReps)

    weight = weight.astype(float)
    highestReps = highestReps.astype(int)
    # print(highestReps)

    # creating empty rep max table
    repidx = [i for i in range(1, 11)]
    repweight = [0 for i in range(1, 11)]
    table = np.array(list(zip(repidx, repweight)))

    n = 0
    while n < len(highestReps):
        highestReps[n] = min(highestReps[n], 10)
        idx = highestReps[n] - 1
        if weight[n] > repweight[idx]:
            repweight[idx] = weight[n]
        n = n + 1
    table = np.array(list(zip(repidx, repweight)))

    highestReps = highestReps.astype(int)
    # calculate the estimated 1 rep max based on hardest set of each day

    #    print(highestReps)
    #    print(weight)
    e1rm = weight * (36 / (37 - highestReps))  # Bryzcki formula
    #    e1rm = weight*highestReps**.1 #Lombardi formula
    #    print(e1rm)

    # plotting stuff
    plt.figure()
    matplotlib.pyplot.plot_date(liftDate, e1rm)
    plt.xlabel("Date")
    plt.ylabel("Estimated " + str(liftName) + "1 Rep Max")
    ax = plt.gca()
    n = 7  # Keeps every 7th label
    [
        l.set_visible(False)
        for (i, l) in enumerate(ax.xaxis.get_ticklabels())
        if i % n != 0
    ]
    plt.grid(True)
    plt.suptitle(liftName)
    plt.savefig(str(liftName) + " e1RM")

    fig, ax = plt.subplots(1, 1)
    ax.set_title(liftName)
    data = table
    column_labels = ["Reps", "Weight"]
    ax.axis("tight")
    ax.axis("off")
    ax.table(cellText=data, colLabels=column_labels, loc="center")

    return


masterFunc("Bench")
# masterFunc("Deadlift")
# masterFunc("Ohp")
# masterFunc("Squat")