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Type 'q()' to quit R. > pkgname <- "FastLZeroSpikeInference" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('FastLZeroSpikeInference') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("FastLZeroSpikeInference") > ### * FastLZeroSpikeInference > > flush(stderr()); flush(stdout()) > > ### Name: FastLZeroSpikeInference > ### Title: FastLZeroSpikeInference: FastLZeroSpikeInference: A package for > ### estimating spike times from calcium imaging data using an L0 penalty > ### Aliases: FastLZeroSpikeInference FastLZeroSpikeInference-package > > ### ** Examples > > > sim <- simulate_ar1(n = 500, gam = 0.95, poisMean = 0.009, sd = 0.05, seed = 1) > plot(sim) > > ## Fits for a single tuning parameter > > # AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) Output: Number of spikes 2 Settings: Data length 500 Model type ar1 Gamma 0.95 Lambda 1 > > # compute fitted values from prev. fit > fit <- estimate_calcium(fit) > plot(fit) > > # or > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, estimate_calcium = TRUE) > plot(fit) > > # Constrained AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, + estimate_calcium = TRUE) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, estimate_calcium = TRUE) Output: Number of spikes 2 Settings: Data length 500 Model type ar1-pos-constrained Gamma 0.95 Lambda 1 > plot(fit) > > > > > cleanEx() > nameEx("estimate_calcium") > ### * estimate_calcium > > flush(stderr()); flush(stdout()) > > ### Name: estimate_calcium > ### Title: Estimate underlying calcium concentration based on estimated > ### spikes > ### Aliases: estimate_calcium > > ### ** Examples > > > sim <- simulate_ar1(n = 500, gam = 0.95, poisMean = 0.009, sd = 0.05, seed = 1) > plot(sim) > > ## Fits for a single tuning parameter > > # AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) Output: Number of spikes 2 Settings: Data length 500 Model type ar1 Gamma 0.95 Lambda 1 > > # compute fitted values from prev. fit > fit <- estimate_calcium(fit) > plot(fit) > > # or > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, estimate_calcium = TRUE) > plot(fit) > > # Constrained AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, + estimate_calcium = TRUE) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, estimate_calcium = TRUE) Output: Number of spikes 2 Settings: Data length 500 Model type ar1-pos-constrained Gamma 0.95 Lambda 1 > plot(fit) > > > > > cleanEx() > nameEx("estimate_spike_paths") > ### * estimate_spike_paths > > flush(stderr()); flush(stdout()) > > ### Name: estimate_spike_paths > ### Title: Estimate spike train, underlying calcium concentration, and > ### changepoints based on a fluorescence trace. Automatic tuning > ### parameter selection within a range of values [lambda_min, lambda_max] > ### Aliases: estimate_spike_paths > > ### ** Examples > > > sim <- simulate_ar1(n = 500, gam = 0.95, poisMean = 0.009, sd = 0.05, seed = 1) > plot(sim) > > ## Fits for tuning parameters between [0.1, 10] > fits <- estimate_spike_paths(dat = sim$fl, gam = 0.95, lambda_min = 0.1, lambda_max = 10) ARFPOP.cpp:163:7: runtime error: load of value 45, which is not a valid value for type 'bool' #0 0x7f4320da091f in ARFPOP(double*, int, double, double, double*, int*, double*, int*, bool*, int*, bool*, double) /data/gannet/ripley/R/packages/tests-gcc-SAN/FastLZeroSpikeInference/src/ARFPOP.cpp:163 #1 0x58efbc in do_dotCode /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1845 #2 0x62d766 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7136 #3 0x675027 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:748 #4 0x67a100 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1918 #5 0x67c4f7 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1844 #6 0x64c745 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7104 #7 0x675027 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:748 #8 0x67a100 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1918 #9 0x67c4f7 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1844 #10 0x67569f in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:871 #11 0x681779 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2991 #12 0x675ac0 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:823 #13 0x6f765d in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264 #14 0x6f7cf0 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:316 #15 0x6f7e34 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1137 #16 0x6f7e82 in Rf_mainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1144 #17 0x41b418 in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29 #18 0x7f4331436b74 in __libc_start_main (/lib64/libc.so.6+0x27b74) #19 0x41dc1d in _start (/data/gannet/ripley/R/gcc-SAN/bin/exec/R+0x41dc1d) > print(fits) Output: Number of tuning values used 3 Settings: Data length 500 Model type ar1 Gamma 0.95 > plot(fits) > print(fits$path_fits[[1]]) Call: estimate_spikes(dat = dat, gam = gam, lambda = lambda_min, constraint = constraint, estimate_calcium = EPS) Output: Number of spikes 2 Settings: Data length 500 Model type ar1 Gamma 0.95 Lambda 0.1 > plot(fits$path_fits[[1]]) > > ## Fits for a single tuning parameter > > # AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) Output: Number of spikes 2 Settings: Data length 500 Model type ar1 Gamma 0.95 Lambda 1 > > # compute fitted values from prev. fit > fit <- estimate_calcium(fit) > plot(fit) > > # or > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, estimate_calcium = TRUE) > plot(fit) > > # Constrained AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, + estimate_calcium = TRUE) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, estimate_calcium = TRUE) Output: Number of spikes 2 Settings: Data length 500 Model type ar1-pos-constrained Gamma 0.95 Lambda 1 > plot(fit) > > > > > cleanEx() > nameEx("estimate_spikes") > ### * estimate_spikes > > flush(stderr()); flush(stdout()) > > ### Name: estimate_spikes > ### Title: Estimate spike train, underlying calcium concentration, and > ### changepoints based on a fluorescence trace. > ### Aliases: estimate_spikes > > ### ** Examples > > > sim <- simulate_ar1(n = 500, gam = 0.95, poisMean = 0.009, sd = 0.05, seed = 1) > plot(sim) > > ## Fits for a single tuning parameter > > # AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1) Output: Number of spikes 2 Settings: Data length 500 Model type ar1 Gamma 0.95 Lambda 1 > > # compute fitted values from prev. fit > fit <- estimate_calcium(fit) > plot(fit) > > # or > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, estimate_calcium = TRUE) > plot(fit) > > # Constrained AR(1) model > fit <- estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, + estimate_calcium = TRUE) > print(fit) Call: estimate_spikes(dat = sim$fl, gam = 0.95, lambda = 1, constraint = TRUE, estimate_calcium = TRUE) Output: Number of spikes 2 Settings: Data length 500 Model type ar1-pos-constrained Gamma 0.95 Lambda 1 > plot(fit) > > > > > cleanEx() > nameEx("plot.simdata") > ### * plot.simdata > > flush(stderr()); flush(stdout()) > > ### Name: plot.simdata > ### Title: Plot simulated data > ### Aliases: plot.simdata > > ### ** Examples > > > sim <- simulate_ar1(n = 500, gam = 0.998, poisMean = 0.009, sd = 0.05, seed = 1) > plot(sim) > > > > > cleanEx() > nameEx("simulate_ar1") > ### * simulate_ar1 > > flush(stderr()); flush(stdout()) > > ### Name: simulate_ar1 > ### Title: Simulate fluorescence trace based on simple AR(1) generative > ### model > ### Aliases: simulate_ar1 > > ### ** Examples > > sim <- simulate_ar1(n = 500, gam = 0.998, poisMean = 0.009, sd = 0.05, seed = 1) > plot(sim) > > > > ### *