interested in the [[package dependency graph|calibcant.svg]] generated
with Yu-Jie Lin's [PDepGraph.py][]:
- $ IGNORED=matplotlib,scipy,numpy,pyyaml,h5py,python,eselect-python
- $ python PDepGraph.py -o calibcant.dot -D "$IGNORED" calibcant
- $ dot -T svg -o calibcant.svg calibcant.dot
+ $ IGNORED=matplotlib,scipy,numpy,pyyaml,h5py,python,eselect-python
+ $ python PDepGraph.py -o calibcant.dot -D "$IGNORED" calibcant
+ $ dot -T svg -o calibcant.svg calibcant.dot
Thermal calibration requires three separate measurements: photodiode
sensitivity (via surface bumps), fluid temperature (estimated, or via
thermocouple), and thermal vibration (watching the cantilever vibrate
-in far from the surface). The calibcant package takes [[repeated
-measurements|statistics.png]] of each of these parameters to allow
-estimation of statistical uncertainty:
+in far from the surface). The calibcant package takes repeated
+measurements ([[!ltio statistics.png]]) of each of these parameters to
+allow estimation of statistical uncertainty:
- $ calibcant-analyze.py calibcant/examples/calibration.h5
+ $ calibcant-analyze.py calibcant/examples/calibration.h5
...
... variable (units) : mean +/- std. dev. (relative error)
... cantilever k (N/m) : 0.0629167 +/- 0.00439057 (0.0697838)
which leads to more accurate and reproducible spring constant
estimates.
-[[!img bump.png alt="Surface bump for photodiode sensitivity"
- title="Surface bump for photodiode sensitivity" ]]
-
+[[!img vibration.png alt="Thermal vibration measurement"
+ title="Thermal vibration measurement" ]]
Finally, all data and analysis results are stored in the standard,
portable [[HDF5]] file format, so it's easy to reanalyze earlier