# Sasdata
A package for importing and exporting reduced small angle scattering data.
The data loaders provided are usable as a standalone package, or in conjunction with the sasview analysis package.
## Install
The easiest way to use sasdata is by using [SasView](http://www.sasview.org).
View the latest release on the [sasdata pypi page](https://pypi.org/project/sasdata/) and install using `pip install sasdata`.
To run sasdata from the source, create a python environment using python 3.10 or higher and install all dependencies
- Using a [python virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/)::
$ python -m venv sasdata
$ .\sasdata\Scripts\activate (Windows) -or- source sasdata/bin/activate (Mac/Linux)
(sasdata) $ python -m pip install -e .
- Using any [anaconda](https://www.anaconda.com/) distribution::
$ conda create -n sasdata
$ conda activate sasdata
(sasdata) $ python -m pip install -e .
## Data Formats
The `Loader()` class is directly callable so a transient call can be made to the class or, for cases where repeated calls
are necessary, the `Loader()` instance can be assigned to a python variable.
The `Loader.load()` method accepts a string or list of strings to load a single or multiple data sets simultaneously. The
strings passed to `load()` can be any combination of file path representations, or URIs. A list of `Data1D/Data2D`
objects is returned. An optional `format` parameter can be passed to specify the expected file extension associated with
a reader. If format is passed, it must either be a single value, or a list of values of the same length as the file path list.
- Load `format` options include:
- `.xml`: [canSAS XML](https://www.cansas.org/formats/canSAS1d/1.1/doc/index.html) format
- `.h5`, `.hdf`, `.hdf5`, `.nxs`: [NXcanSAS](https://manual.nexusformat.org/classes/applications/NXcanSAS.html) format
- `.txt`: Multi-column ascii format
- `.csv`: Comma delimited text format
- `.ses`, `.sesans`: [Multi-column SESANS](https://www.sasview.org/docs/user/qtgui/MainWindow/data_formats_help.html#d-sesans-format) data
- `.dat`: [2D NIST](https://github.com/sansigormacros/ncnrsansigormacros/wiki/NCNROutput2D_QxQy) format
- `.abs`, `.cor`: 1D NIST format for SAS and USAS
- `.pdh`: Anton Paar reduced SAXS format
The `save()` method accepts 3 arguments; the file path to save the file as, a `Data1D` or `Data2D` object, and, optionally,
a file extension. If an extension is passed to `save`, any file extension in the file path will be superseded. If no file
extension is given in the filename or format, a ValueError will be thrown.
- Save `format` options include:
- `.xml`: for the canSAS XML format
- `.h5`: for the NXcanSAS format
- `.txt`: for the multi-column ascii format
- `.csv`: for a comma delimited text format
Save argument examples and data output:
| filename | format | saved file name | saved file format |
|--------------|--------|-----------------|-------------------|
| 'mydata' | '.csv' | mydata.csv | CSV format |
| 'mydata.xml' | None | mydata.xml | canSAS XML format |
| 'mydata.xml' | '.csv' | mydata.xml.csv | CSV format |
| 'mydata' | None | - | raise ValueError |
More information on the recognized data formats is available on the
[sasview website](https://www.sasview.org/docs/user/qtgui/MainWindow/data_formats_help.html).
## Example Data
A number of data files are included with this package available in `sasdata.example_data`.
- Each subdirectory has a specific type of data.
- `1d_data`: 1-dimensional SAS data. A few examples are `apoferritin.txt` which is SANS from apoferritin, 3 files starting with `AOT_` that are contrast variations for a mircroemulsion, and `latex_smeared.xml` with SANS and USANS data for spherical latex particles.
- `2d_data`: 2-dimensional SAS data. Examples include 3 `P123_....dat` files for a polymer concentration series.
- `convertibles_files`: A series of data sets that can be converted via the data conversion tool in the `sasdata.file_converter` package.
- `dls_data`: *NOTE* Not loadable by sasdata. Two example DLS data sets that will be loadable in a future release.
- `image_data`: Image file loadable from `sasdata.dataloader.readers.tiff_reader`. The files are all the same image, but in different image formats.
- `sesans_data`: SESANS data sets. `sphere_isis.ses` is spin-echo SANS from a sample with spherical particles.
- To directly access this data via a python prompt, `import data_path from sasdata` returns the absolute path to `sasdata.example_data`
## Usage
### Accessing example data
(sasdata) $ python
>>> from sasdata import data_path
>>> data = Loader().load(os.path.join(data_path, '1d_data', 'apoferritin.txt'))
### Loading and saving data sets using a fixed Loader instance:
(sasdata) $ python
>>> from sasdata.dataloader.loader import Loader
>>> loader_module = Loader()
>>> loaded_data_sets = loader_module.load(path="/path/to/file.ext")
>>> loaded_data_set = loaded_data_sets[0]
>>> loader_module.save(path='/path/to/new/file.ext', data=loaded_data_set, format=None)
### Loading and saving data sets using a transient Loader instance (more scriptable):
(sasdata) $ python
>>> from sasdata.dataloader.loader import Loader
>>> loaded_data_sets = Loader().load(path="/path/to/file.ext")
>>> Loader().save(path='/path/to/new/file.ext', data=loaded_data_sets[0], format=None)
Raw data
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"description": "# Sasdata\n\nA package for importing and exporting reduced small angle scattering data.\n\nThe data loaders provided are usable as a standalone package, or in conjunction with the sasview analysis package.\n\n## Install\nThe easiest way to use sasdata is by using [SasView](http://www.sasview.org).\n\nView the latest release on the [sasdata pypi page](https://pypi.org/project/sasdata/) and install using `pip install sasdata`.\n\nTo run sasdata from the source, create a python environment using python 3.10 or higher and install all dependencies\n - Using a [python virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/)::\n\n $ python -m venv sasdata\n $ .\\sasdata\\Scripts\\activate (Windows) -or- source sasdata/bin/activate (Mac/Linux)\n (sasdata) $ python -m pip install -e .\n \n - Using any [anaconda](https://www.anaconda.com/) distribution::\n \n $ conda create -n sasdata\n $ conda activate sasdata\n (sasdata) $ python -m pip install -e .\n\n## Data Formats\n\nThe `Loader()` class is directly callable so a transient call can be made to the class or, for cases where repeated calls\nare necessary, the `Loader()` instance can be assigned to a python variable.\n\nThe `Loader.load()` method accepts a string or list of strings to load a single or multiple data sets simultaneously. The\nstrings passed to `load()` can be any combination of file path representations, or URIs. A list of `Data1D/Data2D`\nobjects is returned. An optional `format` parameter can be passed to specify the expected file extension associated with \na reader. If format is passed, it must either be a single value, or a list of values of the same length as the file path list.\n\n- Load `format` options include:\n - `.xml`: [canSAS XML](https://www.cansas.org/formats/canSAS1d/1.1/doc/index.html) format\n - `.h5`, `.hdf`, `.hdf5`, `.nxs`: [NXcanSAS](https://manual.nexusformat.org/classes/applications/NXcanSAS.html) format\n - `.txt`: Multi-column ascii format\n - `.csv`: Comma delimited text format\n - `.ses`, `.sesans`: [Multi-column SESANS](https://www.sasview.org/docs/user/qtgui/MainWindow/data_formats_help.html#d-sesans-format) data\n - `.dat`: [2D NIST](https://github.com/sansigormacros/ncnrsansigormacros/wiki/NCNROutput2D_QxQy) format\n - `.abs`, `.cor`: 1D NIST format for SAS and USAS\n - `.pdh`: Anton Paar reduced SAXS format\n\nThe `save()` method accepts 3 arguments; the file path to save the file as, a `Data1D` or `Data2D` object, and, optionally, \na file extension. If an extension is passed to `save`, any file extension in the file path will be superseded. If no file\nextension is given in the filename or format, a ValueError will be thrown.\n\n- Save `format` options include:\n - `.xml`: for the canSAS XML format\n - `.h5`: for the NXcanSAS format\n - `.txt`: for the multi-column ascii format\n - `.csv`: for a comma delimited text format\n\nSave argument examples and data output:\n\n| filename | format | saved file name | saved file format |\n|--------------|--------|-----------------|-------------------|\n| 'mydata' | '.csv' | mydata.csv | CSV format |\n| 'mydata.xml' | None | mydata.xml | canSAS XML format |\n| 'mydata.xml' | '.csv' | mydata.xml.csv | CSV format |\n| 'mydata' | None | - | raise ValueError |\n\nMore information on the recognized data formats is available on the \n[sasview website](https://www.sasview.org/docs/user/qtgui/MainWindow/data_formats_help.html).\n\n## Example Data\n\nA number of data files are included with this package available in `sasdata.example_data`.\n\n- Each subdirectory has a specific type of data.\n - `1d_data`: 1-dimensional SAS data. A few examples are `apoferritin.txt` which is SANS from apoferritin, 3 files starting with `AOT_` that are contrast variations for a mircroemulsion, and `latex_smeared.xml` with SANS and USANS data for spherical latex particles.\n - `2d_data`: 2-dimensional SAS data. Examples include 3 `P123_....dat` files for a polymer concentration series.\n - `convertibles_files`: A series of data sets that can be converted via the data conversion tool in the `sasdata.file_converter` package.\n - `dls_data`: *NOTE* Not loadable by sasdata. Two example DLS data sets that will be loadable in a future release.\n - `image_data`: Image file loadable from `sasdata.dataloader.readers.tiff_reader`. The files are all the same image, but in different image formats.\n - `sesans_data`: SESANS data sets. `sphere_isis.ses` is spin-echo SANS from a sample with spherical particles.\n- To directly access this data via a python prompt, `import data_path from sasdata` returns the absolute path to `sasdata.example_data`\n\n## Usage\n\n### Accessing example data\n\n (sasdata) $ python\n >>> from sasdata import data_path\n >>> data = Loader().load(os.path.join(data_path, '1d_data', 'apoferritin.txt'))\n\n### Loading and saving data sets using a fixed Loader instance:\n\n (sasdata) $ python\n >>> from sasdata.dataloader.loader import Loader\n >>> loader_module = Loader()\n >>> loaded_data_sets = loader_module.load(path=\"/path/to/file.ext\")\n >>> loaded_data_set = loaded_data_sets[0]\n >>> loader_module.save(path='/path/to/new/file.ext', data=loaded_data_set, format=None)\n\n### Loading and saving data sets using a transient Loader instance (more scriptable):\n\n (sasdata) $ python\n >>> from sasdata.dataloader.loader import Loader\n >>> loaded_data_sets = Loader().load(path=\"/path/to/file.ext\")\n >>> Loader().save(path='/path/to/new/file.ext', data=loaded_data_sets[0], format=None)\n",
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