# Data Types ## Contributing If you're writing data types for local tools, all data types can be added directly to your Galaxy instance, but if you're publishing tools to the Galaxy Toolshed we strongly recommend adding all referenced data types upstream to Galaxy itself. Basic information on contributing to Galaxy can be found in the [CONTRIBUTING documentation](https://github.com/galaxyproject/galaxy/blob/dev/CONTRIBUTING.md). The rest of this document contains the basics of how to create and register data types in Galaxy, but we've noticed some common problems with simple implementations that can cause serious problems on production servers. When adding new data types to Galaxy, please copy this production checklist into your PR and update it with your answers to help use avoid these problems in the future. ``` ## Production Data Types Checklist - Do any of your ``sniff``, ``set_meta``, ``set_peek`` implementations for data types potentially use an unbounded amount of memory? This will happen most often if they read a whole file into memory. This is not allowed even if you would expect these files to typically be small. - [ ] No. - Does your ``sniff`` implementation read through the entirety of the file? This is never allowed for production data types. - [ ] No. - Do either of your ``set_meta`` or ``set_peek`` implementations read through the entirety of the file? This is generally discouraged, but if you feel it substantially improves the usability of the datatype, please indicate why below. - [ ] No. - [ ] Yes. *Replace THIS sentence with the reason.* - Does your datatype include metadata elements? In general, metadata should not take the place of say a data type viewer or visualization, metadata should be used by the tools that consume this datatype. - [ ] No. - [ ] Yes. *Replace THIS sentence with how tools will consume this metadata.* ``` ## Adding a New Data Type (Subclassed) This specification describes the Galaxy source code changes required to add support for a new data type. Every Galaxy dataset is associated with a datatype which can be determined by the file extension (or format in the history item). Within Galaxy, supported datatypes are contained in the `galaxy.datatypes.registry:Registry` class, which has the responsibility of mapping extensions to datatype instances. At start up this registry is initialized with data type values from the `datatypes_conf.xml` file. All data type classes are a subclass of the `galaxy.datatypes.data:Data` class. We'll pretend to add a new datatype format named `Foobar` whose associated file extension is `foo` to our local Galaxy instance as a way to provide the details for adding support for new data types. Our example `Foobar` data type will be a subclass of `galaxy.datatypes.tabular.Tabular`. ### Step 1: Register Data Type We'll add the new data type to the `` tag section of the `datatypes_conf.xml` file. Sample `` tag attributes in this section are: ```xml ``` where - `extension` - the data type's Dataset file extension (e.g., `ab1`, `bed`, `gff`, `qual`, etc.). The extension must consist only of lowercase letters, numbers, `_`, `-`, and `.`. - `type` - the path to the class for that data type. - `mimetype` - if present (it's optional), the data type's mime type - `display_in_upload` - if present (it's optional and defaults to False), the associated file extension will be displayed in the "File Format" select list in the "Upload File from your computer" tool in the "Get Data" tool section of the tool panel. **Note:** If you do not wish to add extended functionality to a new datatype, but simply want to restrict the output of a set of tools to be used in another set of tools, you can add the flag `subclass="True"` to the datatype definition line. Example: ```xml ``` ### Step 2: Sniffer Galaxy tools are configured to automatically set the data type of an output dataset. However, in some scenarios, Galaxy will attempt to determine the data type of a file using a sniffer (e.g., uploading a file from a local disk with 'Auto-detect' selected in the File Format select list). The order in which Galaxy attempts to determine data types is critical because some formats are much more loosely defined than others. The order in which the sniffer for each data type is applied to the file should be most rigidly defined formats first followed by less and less rigidly defined formats, with the most loosely defined format last, and then a default format associated with the file if none of the data type sniffers were successful. The order in which data type sniffers are applied to files is implicit in the `` tag set section of the `datatypes_conf.xml` file. We'll assume that the format of our Foobar data type is fairly rigidly defined, so it can be placed closer to the start of the sniff order. ```xml ``` ### Step 3: Data Type Class We'll now add the `Foobar` class to `lib/galaxy/datatypes/tabular.py`. Keep in mind that your new data type class should be placed in a file that is appropriate (based on its superclass), and that the file will need to be imported by `lib/galaxy/datatypes/registry.py`. You will need to include a `file_ext` attribute to your class and create any necessary functions to override the functions in your new data type's superclass (in our example, the `galaxy.datatypes.tabular.Tabular` class). In our example below, we have set our class's `file_ext` attribute to "foo" and we have overridden the `__init__()`, `init_meta()` and `sniff()` functions. It is important to override functions (especially the meta data and sniff functions) if the attributes of your new class differ from those of its superclass. Note: sniff functions are not required to be included in new data type classes, but if the sniff function is missing, Galaxy will call the superclass method. ```python from galaxy.datatypes.sniff import get_headers from galaxy.datatypes.tabular import Tabular class Foobar(Tabular): """Tab delimited data in foo format""" file_ext = "foo" MetadataElement(name="columns", default=3, desc="Number of columns", readonly=True) def __init__(self, **kwd): """Initialize foobar datatype""" Tabular.__init__(self, **kwd) self.do_something_else() def init_meta(self, dataset, copy_from=None): Tabular.init_meta(self, dataset, copy_from=copy_from) if elems_len == 8: try: map(int, [hdr[6], hdr[7]]) proceed = True except: pass def sniff(self, filename): headers = get_headers(filename, '\t') try: if len(headers) < 2: return False for hdr in headers: if len(hdr) > 1 and hdr[0] and not hdr[0].startswith('#'): if len(hdr) != 8: return False try: map(int, [hdr[6], hdr[7]]) except: return False # Do other necessary checking here... except: return False # If we haven't yet returned False, then... return True ... ``` That should be it! If all of your code is functionally correct you should now have support for your new data type within your Galaxy instance. ## Adding a New Data Type (completely new) ### Basic Datatypes In this [real life example](https://github.com/galaxyproject/galaxy/blob/dev/lib/galaxy/datatypes/sequence.py), we'll add a datatype named `Genbank`, to support GenBank files. First, we'll set up a file named `sequence.py` in `lib/galaxy/datatypes/sequence.py`. This file could contain some of the standard sequence types, though we'll only implement GenBank. ```python """ Classes for all common sequence formats """ import logging from galaxy.datatypes import data log = logging.getLogger(__name__) class Genbank(data.Text): """Class representing a Genbank sequence""" file_ext = "genbank" ``` This is all you need to get started with a datatype. Now, load it into your `datatypes_conf.xml` by adding the following line: ```xml ``` and start up your server, the datatype will be available. ### Adding a Sniffer Datatypes can be "sniffed", their formats can be automatically detected from their contents. For GenBank files that's extremely easy to do, the first 5 characters will be `LOCUS`, according to section 3.4.4 of the [specification](ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt). To implement this in our tool we first have to add the relevant sniffing code to our `Genbank` class in `sequence.py`. ```python def sniff(self, filename): header = open(filename).read(5) return header == 'LOCUS' ``` and then we have to register the sniffer in `datatypes_conf.xml`. ```xml ``` Once that's done, restart your server and try uploading a `genbank` file. You'll notice that the filetype is automatically detected as `genbank` once the upload is done. ### More Features One of the useful things your datatype can do is provide metadata. This is done by adding metadata entries inside your class like this: ```python class Genbank(data.Text): file_ext = "genbank" MetadataElement(name="number_of_sequences", default=0, desc="Number of sequences", readonly=True, visible=True, optional=True, no_value=0) ``` Here we have a `MetadataElement`, accessible in methods with a dataset parameter from `dataset.metadata.number_of_sequences`. There are a couple relevant functions you'll want to override here: * ```python set_peek(self, dataset, is_multi_byte=False) ``` * ```python set_meta(self, dataset, **kwd) ``` The `set_peek` function is used to determine the blurb of text that will appear to users above the preview (first 5 lines of the file, the file peek), informing them about metadata of a sequence. For `genbank` files, we're probably interested in how many genome/records are contained within a file. To do that, we need to count the number of times the word LOCUS appears as the first five characters of a line. We'll write a function named `_count_genbank_sequences`. ```python def _count_genbank_sequences(self, filename): count = 0 with open(filename) as gbk: for line in gbk: if line[0:5] == 'LOCUS': count += 1 return count ``` Which we'll call in our `set_meta` function, since we're setting metadata about the file. ```python def set_meta(self, dataset, **kwd): dataset.metadata.number_of_sequences = self._count_genbank_sequences(dataset.get_file_name()) ``` Now we'll need to make use of this in our `set_peek` override: ```python def set_peek(self, dataset, is_multi_byte=False): if not dataset.dataset.purged: # Add our blurb if (dataset.metadata.number_of_sequences == 1): dataset.blurb = "1 sequence" else: dataset.blurb = "%s sequences" % dataset.metadata.number_of_sequences # Get standard text peek from dataset dataset.peek = data.get_file_peek(dataset.get_file_name(), is_multi_byte=is_multi_byte) else: dataset.peek = 'file does not exist' dataset.blurb = 'file purged from disk' ``` This function will be called during metadata setting. Try uploading a multi record `genbank` file and testing it out. If you don't have a multi-record `genbank` file, simply concatenate a single file together a couple times and upload that. By now you should have a complete GenBank parser in `sequence.py` that looks about like the following: ```python from galaxy.datatypes import data from galaxy.datatypes.metadata import MetadataElement import logging log = logging.getLogger(__name__) class Genbank(data.Text): file_ext = "genbank" MetadataElement(name="number_of_sequences", default=0, desc="Number of sequences", readonly=True, visible=True, optional=True, no_value=0) def set_peek(self, dataset, is_multi_byte=False): if not dataset.dataset.purged: # Add our blurb if (dataset.metadata.number_of_sequences == 1): dataset.blurb = "1 sequence" else: dataset.blurb = "%s sequences" % dataset.metadata.number_of_sequences # Get dataset.peek = data.get_file_peek(dataset.get_file_name(), is_multi_byte=is_multi_byte) else: dataset.peek = 'file does not exist' dataset.blurb = 'file purged from disk' def get_mime(self): return 'text/plain' def sniff(self, filename): header = open(filename).read(5) return header == 'LOCUS' def set_meta(self, dataset, **kwd): """ Set the number of sequences in dataset. """ dataset.metadata.number_of_sequences = self._count_genbank_sequences(dataset.get_file_name()) def _count_genbank_sequences(self, filename): """ This is not a perfect definition, but should suffice for general usage. It fails to detect any errors that would result in parsing errors like incomplete files. """ # Specification for the genbank file format can be found in # ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt # in section 3.4.4 LOCUS Format count = 0 with open(filename) as gbk: for line in gbk: if line[0:5] == 'LOCUS': count += 1 return count ``` ## Composite Datatypes Composite datatypes can be used as a more structured way to contain individual history items which are composed of multiple files. The `Rgenetics` package for Galaxy has been implemented using Composite Datatypes; for real-life examples, examine the configuration files (particularly `lib/galaxy/datatypes/genetics.py`) included with the distribution. In composite datatypes, there is one "primary" data file and any number of specified "component" files. The primary data file is a base dataset object and the component files are all located in a directory associated with that base dataset. There are two types of composite datatypes: basic and auto_primary_file. In **basic** composite datatypes, the primary data file must be written by tools or provided directly during upload. In **auto_primary_file** composite datatypes, the primary data file is generated automatically by the Framework; all tool-writable or uploadable files are stored in the directory associated with the primary file. ### Creating Composite Datatypes A datatype can be set to composite by setting the `composite_type` flag. There are 3 valid options: - None (not a composite datatype) - `basic` - `auto_primary_file` ### Defining Basic Composite Datatypes The example below defines a basic composite datatype which is composed of 2 component files along with a tool-written or user-uploaded primary file. In this example the primary file is a report summarizing the results. The two component files (`results.txt`, `results.dat`) contain the results; `results.txt` is a text file and is handled as such during upload whereas `results.dat` is flagged as binary, allowing a binary file to be uploaded for that component. During upload, 3 sets of file upload/paste interfaces are provided to the user. A file must be provided for the primary (index) file as well as `results.txt`; `results.dat` is flagged as optional so may be left blank. ```python class BasicComposite(Text): composite_type = 'basic' def __init__ (self, **kwd): Text. __init__ (self, **kwd) self.add_composite_file('results.txt') self.add_composite_file('results.dat', is_binary = True, optional = True) ``` These files can be specified on the command line in the following fashion: ```xml someTool.sh '$input1' '${os.path.join($input1.extra_files_path, 'results.txt')}' '${os.path.join($input1.extra_files_path, 'results.dat')}' '$output1' ``` If a tool is aware of the file names for a datatype, then only `input1.extra_files_path` needs to be provided. There are cases when it is desirable for the composite filenames to have varying names, but be of a similar form; for an example of this see `Rgenetics` below. ### Defining Auto Primary File Composite Datatypes The example below defines an auto primary file composite datatype which is composed of 2 component files along with a framework generated file. In this example the primary file is an html page containing download links to the individual result files. The two component files (`results.txt`, `results.dat`) contain the results; `results.txt` is a text file and is handled as such during upload whereas `results.dat` is flagged as binary, allowing a binary file to be uploaded for that component. During upload, 2 sets of file upload/paste interfaces are provided to the user. A file must be provided for `results.txt` whereas `results.dat` is flagged as optional so may be left blank. In this composite type, the primary file is generated using the `generate_primary_file` method specified in the datatype definition. The `generate_primary_file` method here loops through the defined components and creates a link to each. ```python class AutoPrimaryComposite(Text): composite_type = 'auto_primary_file' def __init__ (self, **kwd): Text. __init__ (self, **kwd) self.add_composite_file('results.txt') self.add_composite_file('results.dat', is_binary=True, optional=True) def generate_primary_file(self, dataset=None): rval = ['Files for Composite Dataset (%s)

This composite dataset is composed of the following files:

    ' % (self.file_ext)] for composite_name, composite_file in self.get_composite_files(dataset=dataset).iteritems(): opt_text = '' if composite_file.optional: opt_text = ' (optional)' rval.append('
  • %s%s' % (composite_name, composite_name, opt_text)) rval.append('
') return "\n".join(rval) ``` These files can be specified on the command line in the following fashion: ```xml someTool.sh ${os.path.join($input1.extra_files_path, 'results.txt')} ${os.path.join($input1.extra_files_path, 'results.dat')} $output1 ``` ### Advanced Topics: `Rgenetics` Example `Rgenetics` datatypes are defined as composite datatypes. The tools in this package rely heavily on the contents of a filename for analysis and reporting. In this case it is desirable for the filenames of the components to vary slightly, but maintain a common form. To do this, we use a special metadata parameter that can only be set at dataset creation (i.e. upload). This example uses the metadata parameter **base_name** to specify part of the components' filenames. ```python class Lped(Text): MetadataElement(name="base_name", desc="base name for all transformed versions of this genetic dataset", default="galaxy", readonly=True, set_in_upload=True) composite_type = 'auto_primary_file' file_ext="lped" def __init__(self, **kwd): Rgenetics.__init__(self, **kwd) self.add_composite_file('%s.ped', description='Pedigree File', substitute_name_with_metadata='base_name', is_binary=True) self.add_composite_file('%s.map', description='Map File', substitute_name_with_metadata='base_name', is_binary=True) def generate_primary_file(self, dataset=None): rval = ['Files for Composite Dataset (%s)

This composite dataset is composed of the following files:

    ' % (self.file_ext)] for composite_name, composite_file in self.get_composite_files(dataset=dataset).iteritems(): opt_text = '' if composite_file.optional: opt_text = ' (optional)' rval.append('
  • %s%s' % (composite_name, composite_name, opt_text)) rval.append('
') return "\n".join(rval) ``` The file specified as `%s.ped` is found at `${os.path.join($input1.extra_files_path, '%s.ped' % input1.metadata.base_name)}`. It should be noted that changing the datatype of datasets which use this substitution method will cause an error if the metadata parameter 'base_name' does not exist in a datatype that the dataset is set to. This is because the value within 'base_name' will be lost -- if the datatype is set back to the original datatype, the default metadata value will be used and the filenames might not match the basename. For this reason, users are not allowed to change the datatype of dataset between a composite datatype and any other datatype. This can be enforced by setting the `allow_datatype_change` attribute of a datatype class to `False`. ## Galaxy Tool Shed - Data Types **Note:** These are deprecated and shouldn't be used. If you find old documentation recommending these, please remove it.