commit c4d6a764ed1b1a326cc868f3c4b9b20b46a52177 (tree)
parent 8e08fe70e6f374b65b3e6f6865e00d62396948f4
Author: Motiejus Jakštys <desired.mta@gmail.com>
Date: Tue, 26 May 2020 14:00:37 +0300
add layer of direction
remove layer of indirection
Diffstat:
2 files changed, 8 insertions(+), 8 deletions(-)
diff --git a/II/Referatas/Makefile b/II/Referatas/Makefile
@@ -8,17 +8,17 @@ mj-referatas.pdf: mj-referatas.tex version.tex bib.bib zeimena-pretty.pdf \
latexmk -g -pdf $<
define algo2img
-db/.faux_$(1)-$(2)-$(3): $(2).sql db/.faux_ready
- ./managedb -- --echo-all -v ON_ERROR_STOP=1 -v src=$(1) -v tolerance=$(3) -v tbl=$(1)_$(2)_$(3) -f $(2).sql
+db/.faux_$(1)-$(2)-%: $(2).sql db/.faux_ready
+ ./managedb -- --echo-all -v ON_ERROR_STOP=1 -v src=$(1) -v tolerance=$$* -v tbl=$(1)_$(2)_$$* -f $(2).sql
touch $$@
-$(1)-$(2)-$(3).pdf: layer2img.py db/.faux_$(1)-$(2)-$(3)
- ./layer2img.py --table=$(1)_$(2)_$(3) --size=52x74 --outfile $(1)-$(2)-$(3).pdf
+$(1)-$(2)-%.pdf: layer2img.py db/.faux_$(1)-$(2)-%
+ ./layer2img.py --table=$(1)_$(2)_$$* --size=52x74 --outfile $(1)-$(2)-$$*.pdf
endef
-$(eval $(call algo2img,sinewave,douglas,5))
-$(foreach t,$(TOLERANCES),$(eval $(call algo2img,zeimena,douglas,$(t))))
-$(foreach t,$(TOLERANCES),$(eval $(call algo2img,zeimena,visvalingam,$(t))))
+$(eval $(call algo2img,sinewave,douglas))
+$(eval $(call algo2img,zeimena,douglas))
+$(eval $(call algo2img,zeimena,visvalingam))
sinewave.gpkg: sinewave.py
./sinewave.py
diff --git a/II/Referatas/mj-referatas.tex b/II/Referatas/mj-referatas.tex
@@ -220,7 +220,7 @@ of the least developed aspects of automatic line generalization, according to
\cite{miuller1995generalization}. {\WM} encoded this process to an algorithm.
Imagine there are two small bends close to each other, similar to
-figure~\ref{pic:example-bend} on page~\pageref{pic:example-bend}, and one needs
+figure~\ref{pic:sinewave} on page~\pageref{pic:sinewave}, and one needs
to generalize it. The bends are too large to ignore replace them with a
straight line, but too small to retain both and retain their complexity.
