initial

analyze.py

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+from pathlib import Path
+from datetime import datetime
+from collections import Counter
+from functools import reduce
+import re
+import string
+
+def parse_foods_file():
+    path = Path.home() / 'projects' / 'open-journal' / 'foods'
+    text = path.read_text()
+    foods, recipes = text.split('---')
+
+    def parse_macro(macro):
+        if macro == '...':
+            return ('INVALID', 0.0)
+
+        name, value = macro.split()
+        value = float(value.removesuffix('g').removesuffix('kcal'))
+        return (name, value)
+
+    foods = {
+        macros[0]: dict(parse_macro(macro) for macro in macros[1:])
+        for macros in [food.split('\n') for food in foods.strip().split('\n\n')]
+    }
+
+    def combine_values(fst, snd):
+        result = fst.copy()
+        for k,v in snd.items():
+            if k in fst:
+                result[k] += v
+            else:
+                result[k] = v
+        return result
+
+    def evaluate_ingredients(ingredients):
+        result = {}
+
+        total_weight = 0.0
+        for ingredient in ingredients:
+            k,v = parse_macro(ingredient)
+            if k == 'TOTAL':
+                result[k] = v
+                break
+            else:
+                total_weight += v
+
+
+            food = foods[k]
+
+            for kk,vv in food.items():
+                if kk not in result:
+                    result[kk] = 0.0
+
+                result[kk] += vv * (v/100.0)
+
+        if 'TOTAL' not in result:
+            result['TOTAL'] = total_weight
+
+        return result
+
+    recipes = {
+        ingredients[0]: evaluate_ingredients(ingredients[1:])
+        for ingredients in [
+            recipe.split('\n') for recipe in recipes.strip().split('\n\n')
+        ]
+    }
+
+    def get_calories_from_macros(mm):
+        calories = 0.0
+        for k,v in mm.items():
+            calories += v * {
+                'Carbs': 4,
+                'Fat': 8,
+                'Protein': 4
+            }.get(k, 0.0)
+        return calories
+
+    #for k,v in foods.items():
+    #    print(round(v.get('Energy') - get_calories_from_macros(v)), k)
+
+    return foods, recipes
+
+foods, recipes = parse_foods_file()
+
+
+entry_re = re.compile(r'^(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}) ', re.MULTILINE)
+diet_re = re.compile(r'@diet (\d+g) ([a-zA-Z]+)')
+
+total_entries = 0
+total_words = 0
+word_frequency = Counter()
+
+total_csv = [['day', 'entries', 'words']]
+daily_csv = [['day', 'entries', 'words', 'calories']]
+entry_csv = [['timestamp', 'words']]
+words_csv = [['word', 'count']]
+
+diet_csv = [[
+    'timestamp', 'name', 'grams', 'calories', 'carbs', 'fat', 'protein',
+    'saturated_fat', 'sugar', 'fiber'
+]]
+
+for fpath in sorted((Path.home() / 'workspace' / 'journal').glob('*.md')):
+    day = fpath.stem
+    header, *tmp = entry_re.split(fpath.read_text())
+    entries = list(zip(tmp[::2], tmp[1::2]))
+
+    daily_entries = len(entries)
+    daily_words = 0
+    daily_calories = 0.0
+
+    for (timestamp, content) in sorted(entries, key=lambda x: x[0]):
+        timestamp = int(datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S').timestamp())
+
+        content = '\n'.join(
+            part.replace('\n', ' ')
+            for part in content.split('\n\n')
+        )
+
+        for diet in diet_re.finditer(content):
+            value, name = diet.groups()
+            value = float(value.removesuffix('g'))
+
+            if name in recipes:
+                food = recipes[name]
+
+                if value == 0.0:
+                    value = food['TOTAL']
+
+                food = {k: v*(value/food['TOTAL']) for k,v in food.items()}
+            elif name in foods:
+                if value == 0.0:
+                    value = 100
+
+                food = {k: v*(value/100.0) for k,v in foods[name].items()}
+            else:
+                breakpoint()
+                print(f'ERROR: Invalid diet entry: {content}')
+                continue
+            
+
+            diet_csv.append((
+                timestamp,
+                name,
+                value,
+                round(food.get('Energy', 0.0), 2),
+                round(food.get('Carbs', 0.0), 2),
+                round(food.get('Fat', 0.0), 2),
+                round(food.get('Protein', 0.0), 2),
+                round(food.get('SaturatedFat', 0.0), 2),
+                round(food.get('Sugar', 0.0), 2),
+                round(food.get('Fiber', 0.0), 2),
+            ))
+
+            daily_calories += food.get('Energy', 0.0)
+
+        words = ''.join(
+            c if c in string.ascii_letters+"'" else ' '
+            for c in content.lower()
+        ).split()
+
+        word_frequency.update(words)
+
+        entry_words = len(words)
+        daily_words += entry_words
+
+        entry_csv.append([timestamp, entry_words])
+
+    daily_csv.append([day, daily_entries, daily_words, daily_calories])
+
+    total_entries += daily_entries
+    total_words += daily_words
+
+    total_csv.append([day, total_entries, total_words])
+
+words_csv += word_frequency.most_common()
+
+def write_csv(fname, csv):
+    with open(fname, 'w') as fp:
+        fp.write('\n'.join(','.join(str(x) for x in row) for row in csv))
+
+write_csv('total.csv', total_csv)
+write_csv('daily.csv', daily_csv)
+write_csv('entry.csv', entry_csv)
+write_csv('words.csv', words_csv)
+write_csv('diet.csv', diet_csv)