emon-tools 0.1.2

A numpy subclass to read emoncms PHPFINA feeds as numpy array

emon_tools

emon-tools is a Python library that provides tools and APIs for interacting with EmonCMS and processing time-series data. It is designed to simplify data retrieval, analysis, and validation, making it easier to work with energy monitoring data.

Installation

Global Installation

To install all modules and dependencies globally:

1. Via Pip

pip install emon-tools

Module-Specific Installation

You can install specific modules and their dependencies as needed. For example:

• To enable pandas time-series output functionality:

pip install emon-tools[time_series]

• To include graph plotting capabilities:

pip install emon-tools[plot]

Modules

emon_tools is modular, allowing you to install and use individual components as needed.

1. emon_fina

The emon_fina module facilitates the analysis and processing of time-series data, particularly from PhpFina file formats.

Features

• Data Reading: Efficiently read data from PhpFina file formats.
• Time-Series Analysis: Compute daily statistics such as min, max, mean, and more.
• Filtering: Validate and filter data based on custom thresholds.
• Utilities: Timestamp manipulation and interval computation tools.

PhpFina File Structure

PhpFina is a lightweight binary file format used by EmonCMS for storing time-series data. Each PhpFina feed consists of two files:

1. .dat File: Contains the actual time-series data values, stored as binary floats. Each value corresponds to a specific timestamp based on the feed's start time and interval.

2. .meta File: Contains metadata about the feed. Its structure includes:

• Offset 0-7: Reserved for future use or ignored by the library.
• Offset 8-15: Contains two 4-byte little-endian integers:
  • interval: The time interval (in seconds) between consecutive data points.
  • start_time: The Unix timestamp of the first data point.
• Computed Values:
  • npoints: The total number of data points, calculated as data_size // 4 (where each data point is 4 bytes).
  • end_time: Computed as start_time + npoints * interval - interval.

Usage Examples:

The examples below demonstrate how to retrieve and analyze data from PhpFina timeseries .dat files. For additional examples, refer to the emon_fina Jupiter NoteBook.

Retrieving data

1. Initialize FinaData:

This initializes the FinaData class, allowing you to interact with the time-series data files:

from emon_tools.emon_fina import FinaData

fdf = FinaData(
    feed_id=1,
    data_dir="/path/to/phpfina/files
)

Access metadata of the .meta file:

print(fdf.meta)
# Example Output:
# {
#     "interval": 10,
#     "start_time": 1575981140,
#     "npoints": 4551863,
#     "end_time": 1621499760
# }

2. Retrieve Values:

Retrieve specific ranges of data values from the .dat file based on time intervals or date ranges.

1. 1D NumPy Array by time window:

Extract values starting from a specific timestamp and within a time window:

values = fdf.get_fina_values(
    start=fr.meta.start_time,
    step=10,
    window=8 * 24 * 3600
)

2. 1D NumPy Array by datetime interval:

Extract values within a specific date range:

ts = fdf.get_fina_values_by_date(
    start_date='2019-12-12 00:00:00',
    end_date='2019-12-13 00:00:00',
    step=10
)

3. 2D Time-Series NumPy Array by time window:

Retrieve a 2D array containing timestamps and corresponding values:

ts = fdf.get_fina_time_series(
    start=fr.meta.start_time,
    step=10,
    window=8 * 24 * 3600
)

4. 2D Time-Series NumPy Array by datetime interval:

Retrieve a 2D array of timestamps and values for a specific date range:

ts = fdf.get_fina_time_series_by_date(
    start_date='2019-12-12 00:00:00',
    end_date='2019-12-13 00:00:00',
    step=10
)

5. Pandas DataFrame Time-Series:

Convert time-series data into a Pandas DataFrame for easier manipulation:

FinaDataFrame initialization:

from emon_tools.fina_time_series import FinaDataFrame

fdf = FinaDataFrame(
    feed_id=1,
    data_dir="/path/to/phpfina/files
)

ts = fdf.get_fina_df_time_series(
    start=fdf.meta.start_time,
    step=10,
    window=8 * 24 * 3600
)

# Or by date_range

ts = fdf.get_fina_time_series_by_date(
    start_date='2019-12-12 00:00:00',
    end_date='2019-12-13 00:00:00',
    step=10
)

Access metadata of the .meta file:

print(fdf.meta)
# Example Output:
# {
#     "interval": 10,
#     "start_time": 1575981140,
#     "npoints": 4551863,
#     "end_time": 1621499760
# }

3. Plotting Data:

Visualize the retrieved time-series data:

from emon_tools.fina_plot import PlotData

PlotData.plot(data=ts)

Compute Daily Statistics

1. Initialize FinaStats:

This initializes the FinaStats class for statistical computations:

from emon_tools.emon_fina import FinaStats
from emon_tools.emon_fina import StatsType

stats = FinaStats(
    feed_id=1,
    data_dir="/path/to/phpfina/files
)

Access metadata of the .meta file:

print(stats.meta)
# Example Output:
# {
#     "interval": 10,
#     "start_time": 1575981140,
#     "npoints": 4551863,
#     "end_time": 1621499760
# }

2. Integrity Statistics:

Analyze the integrity of the .dat file by computing the presence of valid and missing data:

# Compute daily statistics
daily_stats = stats.get_stats(stats_type=StatsType.INTEGRITY)

3. Value Statistics:

Compute daily statistics (e.g., min, max, mean) for data values:

# Compute daily statistics
daily_stats = stats.get_stats(stats_type=StatsType.VALUES)

4. Filtered Value Statistics:

Restrict statistical calculations to a specific value range:

# Compute daily statistics
daily_stats = stats.get_stats(
    stats_type=StatsType.VALUES,
    min_value=-50,
    max_value=50
)

5. Windowed Statistics:

Limit statistics to a specific time window:

# Compute daily statistics
daily_stats = stats.get_stats(
    start_time=1575981140,
    steps_window=8 * 24 * 3600,
    stats_type=StatsType.VALUES,
    min_value=-50,
    max_value=50
)

2. emon_api

The emon_api module is Emoncms python api module, used to interract with Emoncms server instance.

Features

• Data Reading: Efficiently read data from Emoncms.
• Data Writing: Set Inputs, feeds or values

Usage Examples:

...

Running Tests

To ensure everything is functioning correctly, run the test suite:

pytest -v

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch.
3. Submit a pull request with a clear description.

License

This project is licensed under the MIT License. See LICENSE for more details.