diff --git a/CHANGELOG.md b/CHANGELOG.md index b59a7c7..399d5a1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -73,3 +73,8 @@ ## 0.7.4 - Added start and end parameters to plotting functions. + +## 0.8.0 + +// TODO: says what you added to the pacakge +- Added \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 4bf79e1..20f65f4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. 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hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"}, @@ -842,6 +1317,7 @@ version = "6.0.1" description = "YAML parser and emitter for Python" optional = false python-versions = ">=3.6" +groups = ["main", "dev"] files = [ {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"}, {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"}, @@ -861,6 +1337,7 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, + {file = 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"sha256:0ff4183f8f42cd8fa3acea16c45205521a4ef28f73c6391d8a25e92893134f2e"}, @@ -908,7 +1386,7 @@ files = [ [package.extras] docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov ; platform_python_implementation != \"PyPy\"", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] [[package]] @@ -917,6 +1395,7 @@ version = "1.16.0" description = "Python 2 and 3 compatibility utilities" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" +groups = ["main"] files = [ {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"}, {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"}, @@ -928,6 +1407,7 @@ version = "0.6.3" description = "Extract data from python stack frames and tracebacks for informative displays" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"}, {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"}, @@ -947,6 +1427,7 @@ version = "8.2.3" description = "Retry code until it succeeds" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"}, {file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"}, @@ -961,6 +1442,8 @@ version = "2.0.1" description = "A lil' TOML parser" optional = false python-versions = ">=3.7" +groups = ["dev"] +markers = "python_version < \"3.11\"" files = [ {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"}, {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, @@ -972,6 +1455,7 @@ version = "5.14.2" description = "Traitlets Python configuration system" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "traitlets-5.14.2-py3-none-any.whl", hash = "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"}, {file = "traitlets-5.14.2.tar.gz", hash = "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9"}, @@ -987,6 +1471,7 @@ version = "6.0.12.20240311" description = "Typing stubs for PyYAML" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "types-PyYAML-6.0.12.20240311.tar.gz", hash = "sha256:a9e0f0f88dc835739b0c1ca51ee90d04ca2a897a71af79de9aec5f38cb0a5342"}, {file = "types_PyYAML-6.0.12.20240311-py3-none-any.whl", hash = "sha256:b845b06a1c7e54b8e5b4c683043de0d9caf205e7434b3edc678ff2411979b8f6"}, @@ -998,6 +1483,7 @@ version = "4.10.0" description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" +groups = ["main", "dev"] files = [ {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"}, {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"}, @@ -1009,6 +1495,7 @@ version = "2024.1" description = "Provider of IANA time zone data" optional = false python-versions = ">=2" +groups = ["main"] files = [ {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"}, {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"}, @@ -1020,6 +1507,7 @@ version = "20.25.1" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "virtualenv-20.25.1-py3-none-any.whl", hash = "sha256:961c026ac520bac5f69acb8ea063e8a4f071bcc9457b9c1f28f6b085c511583a"}, {file = "virtualenv-20.25.1.tar.gz", hash = "sha256:e08e13ecdca7a0bd53798f356d5831434afa5b07b93f0abdf0797b7a06ffe197"}, @@ -1032,7 +1520,7 @@ platformdirs = ">=3.9.1,<5" [package.extras] docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] -test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""] [[package]] name = "wcwidth" @@ -1040,12 +1528,13 @@ version = "0.2.13" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, ] [metadata] -lock-version = "2.0" -python-versions = "^3.9" -content-hash = "c5ea2c2e57fb887ed9121c44dbdf21fc13a8fb7103ad7cba9f4974d315e366a7" +lock-version = "2.1" +python-versions = ">3.10,<4.0" +content-hash = "4fae891d438674d4f618f4eaf7c2c9c3998c72fa2aad326e7849a4e8fbc1f2ef" diff --git a/pyproject.toml b/pyproject.toml index 8085b39..28d8a02 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,18 +1,19 @@ [tool.poetry] name = "meteaudata" -version = "0.7.4" +version = "0.8.0" description = "A lightweight package for tracking metadata about time series to create repeatable data pipelines." authors = ["Jean-David Therrien "] license = "CC-BY4" readme = "README.md" [tool.poetry.dependencies] -python = "^3.9" +python = ">3.10,<4.0" pandas = ">=1.4" ipython = "^8.2" pyyaml = "^6.0.1" pydantic = "^2.0" plotly = "^5.21.0" +matplotlib = "3.10.1" [tool.poetry.group.dev.dependencies] mypy = "^1.9.0" diff --git a/src/meteaudata/__init__.py b/src/meteaudata/__init__.py index b1695c8..43fceaa 100644 --- a/src/meteaudata/__init__.py +++ b/src/meteaudata/__init__.py @@ -4,8 +4,14 @@ from meteaudata.processing_steps.univariate.interpolate import ( # noqa: F401 linear_interpolation, ) +from meteaudata.processing_steps.univariate.remove_duplicates import ( + remove_duplicates, # noqa: F401 +) from meteaudata.processing_steps.univariate.replace import replace_ranges # noqa: F401 from meteaudata.processing_steps.univariate.resample import resample # noqa: F401 +from meteaudata.processing_steps.univariate.select_time_range import ( + select_time_range, # noqa: F401 +) from meteaudata.processing_steps.univariate.subset import subset # noqa: F401 from meteaudata.types import ( # noqa: F401 DataProvenance, diff --git a/src/meteaudata/processing_steps/multivariate/average.py b/src/meteaudata/processing_steps/multivariate/average.py index 7049914..3768887 100644 --- a/src/meteaudata/processing_steps/multivariate/average.py +++ b/src/meteaudata/processing_steps/multivariate/average.py @@ -2,6 +2,7 @@ from typing import Optional import pandas as pd + from meteaudata.types import ( DataProvenance, FunctionInfo, diff --git a/src/meteaudata/processing_steps/univariate/remove_duplicates.py b/src/meteaudata/processing_steps/univariate/remove_duplicates.py new file mode 100644 index 0000000..2f633b9 --- /dev/null +++ b/src/meteaudata/processing_steps/univariate/remove_duplicates.py @@ -0,0 +1,76 @@ +import datetime +from typing import Literal + +import pandas as pd + +from meteaudata.types import ( + FunctionInfo, + Parameters, + ProcessingStep, + ProcessingType, +) + + +def remove_duplicates( + input_series: list[pd.Series], + *args, + keep: Literal["first", "last", False] = "first", + **kwargs, +) -> list[tuple[pd.Series, list[ProcessingStep]]]: + """ + A processing function to remove duplicate sample points from time series data. + + The function checks for duplicates and retains only the desired occurrence of each duplicate. + first -> keeps first encountered row with that index + last -> keeps last encountered value with that index + False -> removes all duplicated values + + Args: + input_series (list[pd.Series]): List of input time series to be processed. + + Returns: + list[tuple[pd.Series, list[ProcessingStep]]]: Time series with duplicates removed, including metadata about the processing steps. + """ + + func_info = FunctionInfo( + name="remove_duplicates", + version="0.1", + author="Loes Verhaeghe", + reference="Loes Verhaeghe with the help of chat gpt", + ) + + processing_step = ProcessingStep( + type=ProcessingType.RESAMPLING, + parameters=Parameters(keep=keep), + function_info=func_info, + description="A processing function to remove duplicate sample points from time series", + run_datetime=datetime.datetime.now(), + requires_calibration=False, + input_series_names=[str(col.name) for col in input_series], + suffix="DEDUPLICATED", + ) + + outputs = [] + + for col in input_series: + col = col.copy() + col_name = col.name + signal, _ = str(col_name).split("_") + + # Ensure the series has a proper datetime index + if not isinstance(col.index, pd.DatetimeIndex): + raise IndexError( + f"Series {col.name} has index type {type(col.index)}. Please provide pd.DatetimeIndex." + ) + + # Remove duplicate values while keeping the first occurrence + filtered_col = col.loc[~col.index.duplicated(keep=keep)] + + # Update the series name with the processing step suffix + new_name = f"{signal}_{processing_step.suffix}" + filtered_col.name = new_name + + # Append the filtered series along with the processing step metadata + outputs.append((filtered_col, [processing_step])) + + return outputs diff --git a/src/meteaudata/processing_steps/univariate/resample.py b/src/meteaudata/processing_steps/univariate/resample.py index d00dd9c..3b5f9b5 100644 --- a/src/meteaudata/processing_steps/univariate/resample.py +++ b/src/meteaudata/processing_steps/univariate/resample.py @@ -39,7 +39,7 @@ def resample( for col in input_series: col = col.copy() col_name = col.name - signal, _ = str(col_name).split("_") + signal = "_".join(str(col_name).split("_")[:-1]) if not isinstance(col.index, (pd.DatetimeIndex, pd.TimedeltaIndex)): raise IndexError( f"Series {col.name} has index type {type(col.index)}. Please provide either pd.DatetimeIndex or pd.TimedeltaIndex" diff --git a/src/meteaudata/processing_steps/univariate/select_time_range.py b/src/meteaudata/processing_steps/univariate/select_time_range.py new file mode 100644 index 0000000..100f7d5 --- /dev/null +++ b/src/meteaudata/processing_steps/univariate/select_time_range.py @@ -0,0 +1,76 @@ +import datetime + +import pandas as pd + +from meteaudata.types import ( + FunctionInfo, + Parameters, + ProcessingStep, + ProcessingType, +) + + +def select_time_range( + input_series: list[pd.Series], start_time: str, end_time: str, *args, **kwargs +) -> list[tuple[pd.Series, list[ProcessingStep]]]: + """ + A processing function to filter time series data within a specified time range. + + The function accepts a start and end time, and filters the data accordingly. + + Args: + input_series (list[pd.Series]): List of input time series to be processed. + start_time (str): Start of the time range (e.g., "2023-10-01 00:00:00"). + end_time (str): End of the time range (e.g., "2023-10-20 00:00:00"). + + Returns: + list[tuple[pd.Series, list[ProcessingStep]]]: Filtered time series with metadata about the processing steps. + """ + + func_info = FunctionInfo( + name="select_time_range", + version="0.1", + author="Loes Verhaeghe", + reference="Loes Verhaeghe with the help of chat gpt", + ) + + parameters = Parameters(start_time=start_time, end_time=end_time) + + processing_step = ProcessingStep( + type=ProcessingType.FILTERING, + parameters=parameters, + function_info=func_info, + description="A processing function to select data between a specific time range", + run_datetime=datetime.datetime.now(), + requires_calibration=False, + input_series_names=[str(col.name) for col in input_series], + suffix="TIME-SLICE", + ) + + outputs = [] + + start_time = pd.to_datetime(start_time) + end_time = pd.to_datetime(end_time) + + for col in input_series: + col = col.copy() + col_name = col.name + signal, _ = str(col_name).split("_") + + # Ensure the series has a proper datetime index + if not isinstance(col.index, pd.DatetimeIndex): + raise IndexError( + f"Series {col.name} has index type {type(col.index)}. Please provide pd.DatetimeIndex." + ) + + # Filter the data based on the given time range + filtered_col = col[(col.index >= start_time) & (col.index <= end_time)] + + # Update the series name with the processing step suffix + new_name = f"{signal}_{processing_step.suffix}" + filtered_col.name = new_name + + # Append the filtered series along with the processing step metadata + outputs.append((filtered_col, [processing_step])) + + return outputs diff --git a/src/meteaudata/timeseries_stats.py b/src/meteaudata/timeseries_stats.py new file mode 100644 index 0000000..597dd95 --- /dev/null +++ b/src/meteaudata/timeseries_stats.py @@ -0,0 +1,23 @@ +from meteaudata.types import ( + TimeSeries, +) + + +def check_missing_values(input_ts: TimeSeries) -> int: + """ + A function to count the missing values in a time series. + + The function checks for any missing (NaN) values in the input series. + + Args: + input_ts (TimeSeries): input time series to be processed. + + Returns: + int: The number of NaN values in the Time Series data + """ + + data = input_ts.series + + missing_count = data.isnull().sum() + + return missing_count diff --git a/src/meteaudata/types.py b/src/meteaudata/types.py index b202d6d..d4f87bb 100644 --- a/src/meteaudata/types.py +++ b/src/meteaudata/types.py @@ -9,6 +9,7 @@ from pathlib import Path from typing import Any, Optional, Protocol, Union +import matplotlib.pyplot as plt import numpy as np import pandas as pd import plotly.graph_objects as go @@ -545,6 +546,8 @@ def plot( ) return fig + # TODO: implement plot_mpl for the time_series object. + def remove_duplicated_steps(self): steps = self.processing_steps new_steps = [] @@ -1012,6 +1015,45 @@ def plot( ) return fig + def plot_mpl( + self, + ts_names: list[str], + title: Optional[str] = None, + y_axis: Optional[str] = None, + x_axis: Optional[str] = None, + ) -> None: + """ + Renders the selected time series in a matplotlib figure. + """ + # Set default titles if not provided + if not title: + title = f"Time series plot of {self.name}" + if not y_axis: + y_axis = f"{self.name} ({self.units})" + if not x_axis: + x_axis = "Time" + + # Create a figure and axis + fig = plt.figure(figsize=(12, 6)) + + # Loop through time series names and plot each one + for ts_name in ts_names: + ts = self.time_series[ts_name].series + # Assuming the time series `ts` has a pandas Series structure + plt.plot(ts, label=ts_name) + + # Add title and labels + plt.title(title) + plt.xlabel(x_axis) + plt.ylabel(y_axis) + + # Show legend + plt.legend() + + # Display the plot + plt.tight_layout() + return fig + def build_dependency_graph(self, ts_name: str) -> list[dict[str, Any]]: """ Build a data structure that represents all the processig steps and their dependencies for a given time series. @@ -1564,6 +1606,8 @@ def plot( ) return fig + # TODO: implement plot_mpl for the dataset object. + def __eq__(self, other): if not isinstance(other, Dataset): return False diff --git a/tests/test_metEAUdata.py b/tests/test_metEAUdata.py index 5c3387c..a5caaa7 100644 --- a/tests/test_metEAUdata.py +++ b/tests/test_metEAUdata.py @@ -6,8 +6,10 @@ from meteaudata.processing_steps.univariate import ( interpolate, prediction, + remove_duplicates, replace, resample, + select_time_range, subset, ) from meteaudata.types import DataProvenance, Dataset, Signal, TimeSeries @@ -91,9 +93,21 @@ def sample_dataset(): reason="sensor calibration procedure", replace_with=np.nan, ) + # pd.date_range(start="2020-01-01", freq="6min", periods=100),) signal = signal.process( [f"{signal_name}_RESAMPLED#1"], interpolate.linear_interpolation ) + signal = signal.process( + [f"{signal_name}_LIN-INT#1"], + select_time_range.select_time_range, + "2020-01-01 00:00:00", + "2020-01-01 02:00:00", + ) + signal = signal.process( + [f"{signal_name}_TIME-SLICE#1"], + remove_duplicates.remove_duplicates, + keep="last", + ) return dataset diff --git a/tests/test_snippets.py b/tests/test_snippets.py deleted file mode 100644 index 36d31d2..0000000 --- a/tests/test_snippets.py +++ /dev/null @@ -1,11 +0,0 @@ -from snippets import create_dataset, create_signal - - -def test_create_signal(): - print("We got here! It means the file ran with no errors") - assert True - - -def test_create_dataset(): - print("We got here! It means the file ran with no errors") - assert True