Welcome to daylio-parser’s documentation!

Installation

Via pip:

pip3 install --user daylio-parser

Via Pipenv:

pipenv install daylio-parser

Via Poetry:

poetry add daylio-parser

Config

daylio-parser comes with a default config that works for the default Daylio setup after installing the app. That is, there’s just 5 moods, called awful, bad, meh, good, rad.

Each mood has its class:

class Mood
name: str

Name of the mood, must correspond with mood name in the exported CSV.

level: int

Assigned numeral level for the mood (higher = better mood). The allowed levels are 1 to 5.

color: str

Any hex color.

boundaries: Tuple[float, float]

A tuple with lower and upper bound for the mood. Any average mood that falls withing these boundaries will be colored using the Mood.color.

The whole mood config for your app will be constructed using the MoodConfig class.

class MoodConfig(mood_list=None, color_palette=None)

Creates a config with mood_list. If the mood list isn’t provided, DEFAULT_MOODS will be used. All moods are automatically colored using color_palette and boundaries are also calculated. Each boundary is exactly 1 in size, with the first one and the last one being only 0.5 in size.

Parameters

  • mood_list (List[Tuple[int, str]]) – A list of moods with (level, name)

  • color_palette (List[str]) – A list of colors (hex values or common names)

from_list(mood_list, color_palette=None) → None

Updates the config with a new list of moods.

Parameters

  • mood_list (List[Tuple[str, str]]) – A list of moods with (level, name)

  • color_palette (List[str]) – A list of colors (hex values or common names)

static from_file(path) → MoodConfig

Loads the MoodConfig from a JSON file. The file structure is:

{
    "moods": [(level, name), ...],
    "colors": [value, ...],
}
get(mood_name) → Mood

Returns a Mood by its name.

Raises MoodNotFound if the mood_name doesn’t exist.

Parameters

mood_name (str) – Mood name

exception MoodNotFound

Raised in cases when attempting to retrieve a non-existing Mood.

Parser

class Entry

A class that holds data for an entry in the diary. One day can have multiple entries.

datetime: datetime.datetime
mood: Mood
activities: List[str]
notes: str
class Parser(config=None)

Parser for the CSV file. If config is not provided, a default one will be created.

Parameters

config (MoodConfig) – MoodConfig for the parser

load_csv(path) → List[Entry]

Load entries from a CSV file.

Parameters

path (str) – Path to the CSV file

load_from_buffer(f) → List[Entry]

Load entries from a file like object containing CSV data.

Parameters

f – A file-like object

PlotData

class PlotData(entries, config=None)

A class that provides some data for easier plotting.

Parameters

  • entries (List[Entry]) – A list of parsed entries

  • config (MoodConfig) – MoodConfig for the parser (if none is provided, a default one will be created)

split_into_bands(moods) → numpy.ma.MaskedArray
Parameters

moods (numpy.ndarray) – An array of mood values

Return type

numpy.ma.MaskedArray

Splits input moods into bands, given their boundaries. See Mood.boundaries.

interpolate(avg_moods=None, interpolate_steps=360)

Interpolates moods to make a smooth chart. Returns an array of dates and an array of moods.

Parameters

  • avg_moods – Average moods to iterate over. If not provided, these are generated by Stats.average_moods()

  • interpolate_steps (int) – Number of steps for one day (midnight to midnight)

Return type

Tuple[numpy.ndarray, numpy.ndarray]

Stats

class MoodPeriod

This class represents a period of moods.

start_date: datetime.date
end_date: datetime.date
duration: int

Length of the period as a number of days.

avg_mood: float

Average mood for the whole period.

class Stats(entries, config=None)

A class for computing various stats from the entries.

Parameters

  • entries (List[Entry]) – A list of parsed entries

  • config (MoodConfig) – MoodConfig for the parser (if none is provided, a default one will be created)

average_moods() → List[Tuple[datetime.date, float]]

Computes average mood for each day.

activity_moods() → Dict[str, Tuple[float, float]]

Computes average mood and standard deviation for each activity. The returned dict has mood name as a key and (mean, std) as value.

mean() → Tuple[float, float]

Returns (mean, std) for all entries.

rolling_mean(N=5)

Computes a rolling mean for the entries.

Parameters

N (int) – Window size

find_high_periods(threshold=4, min_duration=4) → List[MoodPeriod]

Find all periods of high moods.

Parameters

  • threshold (float) – Find moods higher than this

  • min_duration (int) – Find periods longer than this

find_low_periods(threshold=3, min_duration=5) → List[MoodPeriod]

Find all periods of low moods.

Parameters

  • threshold (float) – Find moods higher than this

  • min_duration (int) – Find periods longer than this