Zero-to-hero to Hevy

README.md

From Zero To Hero to Hevy

I had been using Zero To Hero for years to track my workouts. The app has not seen development in a while.

You can backup your ZTH data to a Realm database via the app. This .zth file can be exported to a JSON with the attached Node.js script (you need npm install realm).

Then the JSON can be post-processed into a CSV file following the Strong app format (I took this sample).

The resulting CSV can be imported via the Hevy Import/Export menu.

json_to_strong_csv.py

import csv
import json
from datetime import datetime, timedelta
from functools import lru_cache


def read_json(path):
    with open(path, "r") as f:
        return json.load(f)


def write_csv(path, data):
    with open(path, "w") as f:
        writer = csv.writer(f)
        writer.writerows(data)


def populate_csv_from_json(data):
    rows = [
        [
            "Date",
            "Workout Name",
            "Duration",
            "Exercise Name",
            "Set Order",
            "Weight",
            "Reps",
            "Distance",
            "Seconds",
            "Notes",
            "Workout Notes",
            "RPE",
        ]
    ]
    class Duration:
        def __init__(self, *timestamps):
            self.timestamps = list(timestamps)
        def append(self, timestamp):
            if timestamp:
                self.timestamps.append(timestamp)
        def __str__(self):
            dt = timedelta(milliseconds = max(self.timestamps) - min(self.timestamps))
            mm, ss = divmod(dt.total_seconds(), 60)
            hh, mm = divmod(mm, 60)
            if hh > 1:
                hh = 0
            if hh == 0 and mm == 0:
                return "1h 00m"
            return "%dh %02dm" % (hh, mm)

    for workout in data["Workout"]:
        timestamp = workout["date"] or 0
        date = datetime.fromtimestamp(timestamp / 1000).strftime("%Y-%m-%d %H:%M:%S")
        workout_name = workout["name"]
        duration = Duration(timestamp)
        for exercise in workout["exerciseList"]:
            for set_order, movement in enumerate(exercise["movementSetList"], 1):
                row = []
                duration.append(movement["date"])
                exercise_name = movement_map(movement["movement"])
                weight = movement["performedWeight"]
                reps = movement["reps"]
                row.extend(
                    [
                        date,
                        workout_name,
                        duration,
                        exercise_name,
                        set_order,
                        weight,
                        reps,
                        "",
                        "",
                        "",
                        "",
                        "",
                    ]
                )
                rows.append(row)
        
    for workout in data["CustomProgramData"]:
        ...
    # for exercise in data["Exercise"]:
    #     ...
    return rows


@lru_cache(maxsize=None)
def movement_map(key):
    return {
        "1653663211431": "Butterfly reverse",
        "1693643373232": "Push-up",
        "1693645906260": "Pike push-ups",
        "BARBELLROW": "Barbell row",
        "BEHINDTHENECKPRESS": "Behind the neck press",
        "BENCHPRESS": "Bench press",
        "BENCHPRESS_DECLINE": "Declined bench press",
        "BENCHPRESS_INCLINE": "Inclined bench press",
        "BENCHPRESS_INCLINE_DUMBBELL": "Inclined bench press (dumbbell)",
        "BICEPS_CURL_HAMMER_DUMBBELL": "Biceps hammer curl (dumbbell)",
        "CALFRAISE": "Seated calf raise",
        "CALFRAISE_STANDING": "Standing calf raise",
        "CURL_BARBELL": "Barbell curl",
        "CURL_DUMBBELL": "Dumbbell curl",
        "DEADLIFT": "Deadlift",
        "DEADLIFT_ROMANIAN": "Romanian deadlift",
        "FLYS_DUMBBELL": "Dumbbell fly",
        "FLYS_INCLINE_CABLE": "Incline cable fly",
        "LEGPRESS": "Leg press",
        "LEG_CURL": "Leg curl",
        "LEG_EXTENSION": "Leg extension",
        "LUNGE_BACKSTEP": "Backstep lunge",
        "OVERHEADPRESS": "Overhead press",
        "PEC_FLY_MACHINE": "Pec fly machine",
        "PULLDOWN_LATERAL": "Lateral pull down",
        "PULLUP": "Pull-up",
        "PULL_FACE": "Face pull",
        "ROW_CABLE_SEATED": "Seated cable row",
        "ROW_DUMBBELL": "Row (dumbbell)",
        "ROW_PENDLAY": "Row (pendlay)",
        "SHOULDER_PRESS_SEATED_DUMBBELL": "Seated shoulder press (dumbbell)",
        "SHOULDER_RAISE_SIDE_LATERAL": "Lateral side raise",
        "SKULLCRUSHERS": "Skullcrushers",
        "SQUAT": "Squat",
        "SQUAT_FRONT": "Front squat",
        "TRICEPS_DIPS": "Dips",
        "TRICEP_EXTENSION_CABLE": "Cable tricep extension",
        "TRICEP_PUSHDOWNS": "Tricep pushdowns",
    }.get(key, key)


def main():
    data = read_json("exported_data.json")
    rows = populate_csv_from_json(data)
    write_csv("exported_data.csv", rows)


if __name__ == "__main__":
    main()

zth_to_json.js

const Realm = require('realm');
const fs = require('fs');

// Function to export entire Realm database to JSON
async function exportRealmToJSON() {
  // Specify the file path for your Realm database
  const realmFile = './backup.zth';

  // Configure Realm with the specified file path
  const realm = await Realm.open({
    path: realmFile,
    readOnly: true,
  });

  // Get all objects from all available object types
  const allObjects = {};
  realm.schema.forEach((objectType) => {
    allObjects[objectType.name] = realm.objects(objectType.name);
  });

  // Convert to JSON
  const jsonData = JSON.stringify(allObjects, null, 2);

  // Print or save the JSON string as needed
  console.log(jsonData);

  // If you want to save to a file
  const exportFilePath = 'exported_data.json';
  fs.writeFileSync(exportFilePath, jsonData, 'utf-8');

  // Close the Realm instance
  realm.close();
}

// Call the function to export
exportRealmToJSON();