Turns out integers are used as is in `random.seed()`, but strings are
hashed (in the version 1 protocol), and the hashing isn't stable. So
instead we hack around this by using `ord()` on each character and
concatenating the digits. Yucky, but probably better than using numpy
and putting this tool on the list of tools that inherit galaxy's
environment (or adding a requirement tag and resolving with conda ...).
random.seed has changed in python 3, and while there's backwards
compatibility using `version=1`, random.randint and random.sample have
also changed. The most striaghtforward change without adding additional
dependencies then was to copy those methods from the python 2.7 source
code.
Also:
- Remove deprecated `interpreter` attribute of `<command>`
- Single-quote text and data parameters inside the command
- Fix wrong `name` attribute of `output` in 3 tests
- Linting and cleanups
This does undo an attempt that Nate made to auto sniff fastq.gz files when they can't be converted anyway, but that was kind of broken in its own way.
From Gitter:
jmchilton: natefoo "# Link mode can't decompress anyway, so enable sniffing for keep-compressed datatypes even when auto_decompress is enabled" Did you test this hack? I get the thought process but it seems like it results in uploaded fastq.gz files defaulting to fastqcssanger.gz? Maybe it didn't at some point though?
Nate Coraor: ummmmmmmmmmmm maybe probably not
John Chilton: I have four fixes for compressed datatypes that all fall apart because of this hack... can I just use the configured sniff order or do you want me to try to preserve this and write a test case
Nate Coraor: nah go ahead and de-hack
John Chilton: There is a good argument to be made for making the behavior more consistent anyway right? Cool thanks
Previously sniffing would happen on the original file (before carriage returns and tabular spaces were converted) if in_place was false and on the converted file if it was true.
Allows describing hierarchical data in JSON or inferring structure from archives or directories.
Datasets or archive sources can be specified via uploads, URLs, paths (if admin && allow_path_paste), library_import_dir/user_library_import_dir, and/or FTP imports. Unlike existing API endpoints, a mix of these on a per file basis is allowed and they work seemlessly between libraries and histories.
Supported "archives" include gzip, zip, bagit directories, bagit achives (with fetching and validations of downloads).
The existing upload API endpoint is quite rough to work with both in terms of adding parameters (e.g. the file type and dbkey hanlding in 4563 was difficult to implement, terribly hacky, and should seemingly have been trivial) and in terms of building requests (one needs to build a tool form - not describe sensible inputs in JSON). This API is built to be intelligable from an API standpoint instead of being constrained to the older style tool form. Additionally it built with hierarchical data in mind in a way that would not be easy at all enhancing the tool form components we don't even render.
This implements 5159 though much simpler YAML descriptions of data libraries should be possible basically as the API descriptions. We can replace the data library script in Ephemeris https://github.com/galaxyproject/ephemeris/blob/master/ephemeris/setup_data_libraries.py with one that converts a simple YAML file into an API call and allows many new options for free.
In future PRs I'll add filtering options to this and it will serve as the backend to 4733.
strings can not be written to byte mode file handlers.
writing resulted in:
TypeError: a bytes-like object is required, not 'str'
Alternative would be to make string byte.
There seems to be no UnvalidatedValue class anymore. So I removed these
checks which seems to make the tool functional again.
Note that there is one more instance of such a check in the Galaxy
sources (in tools/parameters/basic.py). I guess this can also be
removed?
mvdbeek