tImprove docstring for irregularPacking and add `quality` parameter - Granular.jl - Julia package for granular dynamics simulation
(HTM) git clone git://src.adamsgaard.dk/Granular.jl
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---
(DIR) commit 0a7f6cb04c2d44712d6e05f8db0803e53c20e489
(DIR) parent 7b7df1fcc13b749f31cf95cb962a2c80b5cc9134
(HTM) Author: Anders Damsgaard <andersd@riseup.net>
Date: Fri, 22 Dec 2017 11:50:10 -0500
Improve docstring for irregularPacking and add `quality` parameter
Diffstat:
M src/packing.jl | 30 ++++++++++++++++++------------
1 file changed, 18 insertions(+), 12 deletions(-)
---
(DIR) diff --git a/src/packing.jl b/src/packing.jl
t@@ -93,7 +93,9 @@ end
export irregularPacking!
"""
irregularPacking!(simulation[, radius_max, radius_min, sample_limit,
- thickness, seed, plot_during_packing, verbose)
+ padding_factor, binary_radius_search,
+ binary_sampling_quality, thickness, seed,
+ plot_during_packing, verbose)
Generate a dense disc packing in 2D using Poisson disc sampling with O(N)
complexity, as described by [Robert Bridson (2007) "Fast Poisson disk sampling
t@@ -107,6 +109,15 @@ in arbitrary dimensions"](https://doi.org/10.1145/1278780.1278807). The
* `radius_min::Real`: smallest grain radius to use.
* `sample_limit::Integer=30`: number of points to sample around each grain
before giving up.
+* `padding_factor::Real=0.`: if positive and `binary_radius_search = false`, try to
+ add an occasional grain from the current active grain
+ (`radius_max*padding_factor`).
+* `binary_radius_search::Bool=false`: use a binary radius-sampling procedure to
+ fit the largest possible grains into the packing. This option will create
+ the highest packing density.
+* `binary_sampling_quality::Real=100.`: the quality to enforce during the binary
+ radius search when `binary_radius_search = true`. Larger values create
+ denser packings but take longer to complete.
* `seed::Integer`: seed value to the pseudo-random number generator.
* `plot_during_packing::Bool=false`: produce successive plots as the packing is
generated. Requires gnuplot (default).
t@@ -116,8 +127,9 @@ function irregularPacking!(simulation::Simulation;
radius_max::Real=.1,
radius_min::Real=.1,
sample_limit::Integer=30,
- padding_factor::Real=2.,
+ padding_factor::Real=0.,
binary_radius_search::Bool=false,
+ binary_sampling_quality::Real=100.,
thickness::Real=1.,
seed::Integer=1,
plot_during_packing::Bool=false,
t@@ -215,8 +227,6 @@ function irregularPacking!(simulation::Simulation;
# to a value as large as possible
if binary_radius_search
- radius_not_found = true
-
# first test the maximum radius. If unsuccessful, iteratively
# find the optimal radius using binary searches
if !checkForContacts(simulation, grid, x_candidate, r_candidate)
t@@ -226,20 +236,16 @@ function irregularPacking!(simulation::Simulation;
r_R = radius_max
# size of radius sampling step
- dr = (r_R - r_L)/25.
-
- while radius_not_found
+ dr = (r_R - r_L)/binary_sampling_quality
- # 2. If L > R, the search terminates as unsuccessful
- if r_L > r_R
- radius_not_found = false
- break
- end
+ # 2. If L > R, the search terminates as unsuccessful
+ while r_L < r_R
# 3. Set r to the middle of the current range
r_candidate = (r_L + r_R)/2.0
x_candidate = getPositionDistancedFromPoint(T, x_active,
r_active + r_candidate)
+ #println("[$r_L, \t $r_candidate, \t $r_R]")
# 4. If r < target, set L to r+dr and go to step 2
if checkForContacts(simulation, grid, x_candidate,