| FROM ubuntu:16.04 as base | |
| USER root | |
| ## Setting default environment variables | |
| ENV WEB_ROOT=/web_root | |
| # Root project folder | |
| ENV ARCHES_ROOT=${WEB_ROOT}/arches | |
| ENV WHEELS=/wheels | |
| ENV PYTHONUNBUFFERED=1 |
| FROM ubuntu:16.04 as base | |
| USER root | |
| ## Setting default environment variables | |
| ENV WEB_ROOT=/web_root | |
| ENV DOCKER_DIR=/docker | |
| # Root project folder | |
| ENV ARCHES_ROOT=${WEB_ROOT}/arches | |
| ENV WHEELS=/wheels | |
| ENV PYTHONUNBUFFERED=1 |
| def test_jsonld_concept_match_no_label(self): | |
| dt = self.DT.get_instance("concept") | |
| # from the thesaurus that should be loaded into Arches, | |
| # the following concept value should have a key of 43d75450-7282-4754-af63-02e13032b73a | |
| jf = { | |
| "@id": "http://localhost:8000/concepts/86be632e-0dad-4d88-b5da-3d65875d6239", | |
| "@type": [ | |
| "http://www.cidoc-crm.org/cidoc-crm/E55_Type" | |
| ] | |
| } |
When any resource is requested with a "?graph=true" parameter, it engages the pycallgraph object to start tracking, stopping at the end of the processing and then exports a .png of the callgraph to the CALLGRAPH_DIR (or "ROOT_DIR/callgraphs" if CALLGRAPH_DIR is not set).
It adds the requisite dependencies to the Dockerfile: apt-get install graphviz pip install pycallgraph
| Full command: run_arches | |
| Command: run_arches | |
| Testing if database server is up... | |
| Database server is up | |
| Testing if Elasticsearch is up... | |
| Elasticsearch is up | |
| Checking if database dev exists... | |
| Database dev does not exists yet, starting setup... | |
| Current work directory: /web_root/arches |
| (fork arches repo and clone your fork locally) | |
| $ cd arches | |
| $ cp docker-compose.yml docker-compose-local.py | |
| (edit it in the manner described at the bottom of https://github.com/archesproject/arches/blob/master/docker/Readme.md#arches-core-development) | |
| $ docker-compose -f docker-compose-local.yml run --entrypoint /bin/bash arches | |
| (It will pull images and build the arches image, and then put you into a bash shell on the arches container) |
| from xml.etree import ElementTree as ET | |
| import re | |
| def format_t(time_string, frac_sep = ".", output_frag_sep = "."): | |
| h = m = secs = ms = 0 | |
| frags = time_string.split(":") | |
| spart = frags[-1].split(frac_sep) | |
| if len(spart) == 1: | |
| s = int(spart[0]) | |
| elif len(spart) == 2: |
| Some equivalent string formatting things to give you an idea of the basics | |
| "I love {favourite} pie, but hate {hated} ones.".format(favourite = "apple", hated = "pear") | |
| fruit = {"favourite": "apple", "hated": "pear"} | |
| "I love {favourite} pie, but hate {hated} ones.".format(**fruit) | |
| fruit = {"favourite": "apple", "hated": "pear"} | |
| "I love {favourite} pie, but hate {hated} ones.".format(**fruit) |
| # 'doc' is ALTO XML, parsed into an etree xml obj. | |
| def get_illustration_coords(doc, component="PrintSpace"): | |
| page = doc.find("Layout/Page") | |
| illustrations = doc.findall('Layout/Page/{0}/ComposedBlock[@TYPE="Illustration"]/GraphicalElement'.format(component)) | |
| pageh, pagew = int(page.attrib['HEIGHT']), int(page.attrib['WIDTH']) | |
| images = [] | |
| for img in illustrations: | |
| x,y = map(int, [img.attrib['HPOS'], img.attrib['VPOS']]) | |
| h,w = map(int, [img.attrib['HEIGHT'], img.attrib['WIDTH']]) |
| for xmlfile in `find OCR -name *xml` | |
| do | |
| echo "Processing `basename $xmlfile`" | |
| mkdir -p text/`dirname ${xmlfile#OCR/}` | |
| xsltproc --output text/${xmlfile#OCR/}.txt alto2txt.xslt $xmlfile | |
| done |