docs: Add skeleton for documentation
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@ -12,3 +12,4 @@ plot.py
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__pycache__
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*.egg-info
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build
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*.pdf
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{
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"spellright.language": [
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"de",
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"en"
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],
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"spellright.documentTypes": [
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"markdown",
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"latex",
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"plaintext"
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]
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}
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MD = $(wildcard src/*.md)
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DOTS = $(wildcard src/*.dot)
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ASYS = $(wildcard src/*.asy)
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PYS = $(wildcard src/img_*.py)
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PDFS = $(MD:src/%.md=out/%.pdf)
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IMG_PDFS = $(ASYS:src/%.asy=img/%.pdf) $(PYS:src/img_%.py=img/%.pdf) $(DOTS:src/%.dot=img/%.pdf)
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IMGS = $(IMG_PDFS)
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TEMPLATE = eisvogel
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PDF_ENGINE = xelatex
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PANDOC = pandoc
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PANDOC_OPTIONS = -F panflute -F pandoc-citeproc --pdf-engine=$(PDF_ENGINE) --template $(TEMPLATE) -N --standalone --listings
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GRAPHVIZ = dot
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GRAPHVIZ_OPTIONS = -Tpdf
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ASY = asy
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ASY_OPTIONS = -noV -f pdf
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PYTHON = python
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PYTHON_OPTIONS =
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mkfile_path := $(abspath $(lastword $(MAKEFILE_LIST)))
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current_dir := $(notdir $(patsubst %/,%,$(dir $(mkfile_path))))
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.PHONY: clean all default
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all: $(PDFS)
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default: all
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out/%.pdf: src/%.md $(IMGS) Makefile
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$(PANDOC) $(PANDOC_OPTIONS) -o $@ $<
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img/%.pdf: src/%.dot
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$(GRAPHVIZ) $(GRAPHVIZ_OPTIONS) -o $@ $<
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img/%.pdf: src/img_%.py
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$(PYTHON) $(PYTHON_OPTIONS) $< $@
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img/%.pdf: src/%.asy
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$(ASY) $(ASY_OPTIONS) -o $@ $<
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watch:
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watchexec -w src -w data -w filters -w Makefile make all
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clean:
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-rm $(PDFS) $(IMGS)
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@ -0,0 +1,147 @@
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from panflute import *
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import tempfile
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import sys
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from jinja2 import Template, Environment, PackageLoader, select_autoescape
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import contextlib
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import io
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import hashlib
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from dateutil.parser import parse as dateparse
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from functools import partial
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import subprocess as SP
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import panflute as pf
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import os
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import csv
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import datetime
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import re
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def remove_pound(elem, doc):
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if type(elem) == Str:
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return Str(elem.text.lstrip("#"))
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def fix_color(elem, doc):
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if type(elem) == MetaMap:
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for k in elem.content:
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if k.endswith("-color"):
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elem[k] = elem[k].walk(remove_pound)
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def update_date(elem, doc):
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if type(elem) == MetaMap:
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datefmt = doc.get_metadata('datefmt', "%Y-%m-%d")
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today = datetime.date.today().strftime(datefmt)
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date = dateparse(doc.get_metadata('date', today)).date()
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elem['date'] = MetaInlines(Str(date.strftime(datefmt)))
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return elem
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def csv_table(elem, doc):
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if type(elem) == Para and len(elem.content) == 1 and type(elem.content[0]) == Image:
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elem = elem.content[0]
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ext = os.path.splitext(elem.url)[1][1:]
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if ext == "csv":
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caption = elem.content
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has_header = elem.attributes.get(
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'has-header', "false").lower() == "true"
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with open(elem.url) as f:
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reader = csv.reader(f)
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body = []
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for row in reader:
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cells = [TableCell(Plain(Str(x))) for x in row]
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body.append(TableRow(*cells))
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header = body.pop(0) if has_header else None
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ret = Table(*body, header=header, caption=caption)
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return ret
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def code_refs(elem, doc):
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if type(elem) == Cite:
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label = elem.content[0]
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if type(label) == Str:
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label = label.text
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filename = re.findall(r"^\[@lst:(.*)\]$", label) or [None]
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if filename[0] in doc.inc_files:
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return [RawInline("\\hyperref[{}]{{{}}}".format(filename[0], filename[0]), format="tex")]
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def include_code(elem, doc):
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if type(elem) == CodeBlock:
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if "include" in elem.attributes:
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filepath = elem.attributes.pop("include")
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filename = os.path.split(filepath)[-1]
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try:
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elem.text += elem.text + \
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open(filepath, encoding="utf-8").read()
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elem.attributes['caption'] = filename
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doc.inc_files.append(filename)
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except Exception as e:
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elem.text += "Error: {}".format(e)
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return [RawBlock("\\label{{{}}}".format(filename), format="tex"), elem]
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def py_eval(options, data, element, doc):
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out_buffer = io.StringIO()
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with contextlib.redirect_stdout(out_buffer):
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exec(data, doc.pyenv)
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out_buffer.seek(0)
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return convert_text(out_buffer.read())
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def jinja_py_filt(doc, file):
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env = {}
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code = open(file, encoding="utf-8").read()
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exec(code, env)
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return env['main'](doc)
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def prepare(doc):
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doc.inc_files = []
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doc.env = Environment()
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doc.pyenv = {}
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filters = {'py': partial(jinja_py_filt, doc)}
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doc.env.filters.update(filters)
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def process_templates(elem, doc):
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if type(elem) == CodeBlock:
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if elem.classes == ["@"]:
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args = {'meta': doc.get_metadata()}
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return convert_text(doc.env.from_string(elem.text).render(args))
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def yaml_filt(elem, doc):
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tags = {
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'eval': py_eval,
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}
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return yaml_filter(elem, doc, tags=tags, strict_yaml=True)
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def checkboxes(elem, doc):
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if type(elem) in [Para, Plain]:
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val = re.findall(r"^\[([xX]|\ )\] (.*)$", stringify(elem))
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if val:
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val = val[0][0].lower() == "x"
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else:
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return elem
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marker = {
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True: RawInline("$\\boxtimes$", format="latex"),
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False: RawInline("$\\square$", format="latex"),
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}[val]
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cont = []
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if val:
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cont += elem.content[2:]
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else:
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cont += elem.content[4:]
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return Plain(marker, Space, *cont)
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return elem
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def main(doc=None):
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f = [process_templates, update_date, csv_table, include_code,
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fix_color, code_refs, yaml_filt, checkboxes]
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return run_filters(f, prepare=prepare, doc=doc)
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if __name__ == "__main__":
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main()
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@ -0,0 +1,58 @@
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---
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# Metadata
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title: ED_LRR
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author:
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- Daniel Seiller <earthnuker@gmail.com>
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subtitle: 'Elite Dangerous: Long-Range Router'
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# Formating
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toc: true
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lang: en
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colorlinks: true
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papersize: a4
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numbersections: true
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#Panflute options
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panflute-filters: [multifilter]
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panflute-path: 'filters'
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#Template options
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titlepage: true
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toc-own-page: false
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---
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# How it works
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## `stars.csv` format
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### Columns
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| Name | Content |
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| --------- | ------------------------------------------------------------------- |
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| id | unique ID-Number (not to be confused with EDSM id or id64) |
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| star_type | Type of Star |
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| system | Name of System |
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| body | Name of Star |
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| mult | Jump Range Multiplier (1.5 for White Dwarfs, 4.0 for Neutron Stars) |
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| distance | Distance from arrival in Ls |
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| x,y,z | Position in Galactic Coordinates with Sol at (0,0,0) |
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## Routing Algorithms
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### Breadth-First Search (BFS)
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### A*-Search
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### Greedy Search
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## Optimizations
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## Routing Graphs
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# Usage
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## Preprocessing Data
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## Plotting a Route
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# [Changelog](https://gitlab.com/Earthnuker/ed_lrr/blob/pyqt_gui/CHANGELOG.md)
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import sys
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import pylab as PL
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import numpy as np
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from scipy.spatial.ckdtree import cKDTree
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import heapq
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exit()
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def vec(a, b):
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return b - a
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def bfs(points):
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return
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def in_ellipse(p, f1, f2, r, offset=0):
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df = ((f1 - f2) ** 2).sum(0) ** 0.5
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d_f1 = ((p - f1) ** 2).sum(1) ** 0.5
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d_f2 = ((p - f2) ** 2).sum(1) ** 0.5
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return (d_f1 + d_f2) < (df * (1 + r))
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num_points = 100000
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p_orig = np.random.normal(0, 10, size=(num_points, 2))
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tree = cKDTree(p_orig)
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f1 = np.array([0, -30])
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f2 = -f1 # np.random.normal(0, 20, (3,))
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# r = 2 ** ((n / cnt) - cnt)
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mask = in_ellipse(p_orig, f1, f2, 0.1)
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p = p_orig[mask]
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p_orig = p_orig[~mask]
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colors = np.random.random(p.shape[0])
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fig = PL.gcf()
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PL.scatter(
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p_orig[:, 0],
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p_orig[:, 1],
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marker=".",
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s=0.2,
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edgecolor="None",
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c=[(0.0, 0.0, 0.0)],
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alpha=0.75,
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rasterized=True,
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)
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PL.scatter(
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p[:, 0], p[:, 1], marker="s", s=0.2, edgecolor="None", c=colors, rasterized=True
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)
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PL.plot(f1[0], f1[1], "r.", label="Source")
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PL.plot(f2[0], f2[1], "g.", label="Destination")
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max_v = max(p_orig[:, 0].max(), p_orig[:, 1].max(), f1[0], f1[1], f2[0], f2[1]) + 2
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min_v = min(p_orig[:, 0].min(), p_orig[:, 1].min(), f1[0], f1[1], f2[0], f2[1]) - 2
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PL.xlim(min_v, max_v)
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PL.ylim(min_v, max_v)
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PL.legend()
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PL.savefig(sys.argv[1], dpi=1200)
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