# HG changeset patch
# User greg
# Date 1496754128 14400
# Node ID 22cae2172406fd20c73e9246118cf99fe529e027
# Parent  9831319a19fb8b636a312666eea289fe4296c000
Uploaded

diff -r 9831319a19fb -r 22cae2172406 ks_distribution.R
--- a/ks_distribution.R	Wed May 31 12:14:32 2017 -0400
+++ b/ks_distribution.R	Tue Jun 06 09:02:08 2017 -0400
@@ -64,7 +64,7 @@
     return(results)
 }
 
-plot_ks<-function(kaks_input, output, pi, mu, var, max_ks)
+plot_ks<-function(kaks_input, output, pi, mu, var)
 {
     # Start PDF device driver to save charts to output.
     pdf(file=output, bg="white")
@@ -72,8 +72,8 @@
     max_ks <- max(kaks$Ks, na.rm=TRUE)
     # Change bin width
     max_bin_range <- as.integer(max_ks / 0.05)
-    bin <- 0.05 * seq(0, max_bin_range)
-    kaks <- kaks[kaks$Ks<max_ks,];
+    bin <- 0.05 * seq(0, (max_bin_range + 1 ))
+    kaks <- kaks[kaks$Ks<max_ks,]
     h.kst <- hist(kaks$Ks, breaks=bin, plot=F)
     nc <- h.kst$counts
     vx <- h.kst$mids
@@ -81,11 +81,11 @@
     # Set margin for plot bottom, left top, right.
     par(mai=c(0.5, 0.5, 0, 0))
     # Plot dimension in inches.
-    par(pin=c(2.5, 2.5))
-    g <- calculate_fitted_density(pi, mu, var)
-    h <- ntot * 2.5 / sum(g)
-    vx <- seq(1, 100) * 0.02
-    ymax <- max(nc) + 5
+    par(pin=c(3.0, 3.0))
+    g <- calculate_fitted_density(pi, mu, var, max_ks)
+    h <- ntot * 1.5 / sum(g)
+    vx <- seq(1, 100) * (max_ks / 100)
+    ymax <- max(nc)
     barplot(nc, space=0.25, offset=0, width=0.04, xlim=c(0, max_ks), ylim=c(0, ymax), col="lightpink1", border="lightpink3")
     # Add x-axis.
     axis(1)
@@ -96,13 +96,13 @@
     }
 }
 
-calculate_fitted_density <- function(pi, mu, var)
+calculate_fitted_density <- function(pi, mu, var, max_ks)
 {
     comp <- length(pi)
     var <- var/mu^2
     mu <- log(mu)
     # Calculate lognormal density.
-    vx <- seq(1, 100) * 0.02
+    vx <- seq(1, 100) * (max_ks / 100)
     fx <- matrix(0, 100, comp)
     for (i in 1:100)
     {
@@ -122,9 +122,42 @@
 
 # Set pi, mu, var.
 items <- get_pi_mu_var(components_data, num_components)
-pi <- items[1:3]
-mu <- items[4:6]
-var <- items[7:9]
+if (num_components == 1)
+{
+	pi <- items[1]
+	mu <- items[2]
+	var <- items[3]
+}
+if (num_components == 2)
+{
+	pi <- items[1:2]
+	mu <- items[3:4]
+	var <- items[5:6]
+}
+if (num_components == 3)
+{
+	pi <- items[1:3]
+	mu <- items[4:6]
+	var <- items[7:9]
+}
+if (num_components == 4)
+{
+	pi <- items[1:4]
+	mu <- items[5:8]
+	var <- items[9:12]
+}
+if (num_components == 5)
+{
+	pi <- items[1:5]
+	mu <- items[6:10]
+	var <- items[11:15]
+}
+if (num_components == 6)
+{
+	pi <- items[1:6]
+	mu <- items[7:12]
+	var <- items[13:18]
+}
 
 # Plot the output.
-plot_ks(opt$kaks_input, opt$output, pi, mu, var, max_ks)
+plot_ks(opt$kaks_input, opt$output, pi, mu, var)
diff -r 9831319a19fb -r 22cae2172406 test-data/components.tabular
--- a/test-data/components.tabular	Wed May 31 12:14:32 2017 -0400
+++ b/test-data/components.tabular	Tue Jun 06 09:02:08 2017 -0400
@@ -1,7 +1,2 @@
 species	n	number_comp	lnL	AIC	BIC	mean	variance	porportion
-species1	1160	1	-1404.9900	2813.98	2824.09	0.4426	0.1293	1.00
-species1	1160	2	-1353.6550	2717.31	2742.59	0.7376	0.1672	0.61
-						0.2000	0.0069	0.39
-species1	1160	3	-1323.7480	2663.50	2703.94	0.1214	0.0002	0.10
-						0.7759	0.1663	0.57
-						0.2428	0.0070	0.33
+species1	3	1	-3.4750	6.95	6.95	3.1183	5.7732	1.00
diff -r 9831319a19fb -r 22cae2172406 test-data/kaks_input1.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/kaks_input1.tabular	Tue Jun 06 09:02:08 2017 -0400
@@ -0,0 +1,4 @@
+SEQ1	SEQ2	Ka	Ks	Ka\Ks
+contig_241;	contig_241	1.5312	7.1619	0.2138
+contig_300	contig_300;	0.8653	3.7872	0.2285
+contig_586	contig_586;	1.7791	1.1181	1.5912
diff -r 9831319a19fb -r 22cae2172406 test-data/rates.tabular
--- a/test-data/rates.tabular	Wed May 31 12:14:32 2017 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,101 +0,0 @@
-SEQ1	SEQ2	Ka	Ks	Ka\Ks
-species1_10079	species1_10080	0.1194	0.9782	0.1221
-species1_10818	species1_10817	0.0194	0.9541	0.0203
-species1_4505	species1_4506	0.0379	0.2411	0.1571
-species1_2890	species1_2889	0.0212	0.8520	0.0249
-species1_3386	species1_3385	0.0002	0.1698	0.0010
-species1_2266	species1_2265	0.0881	0.1515	0.5814
-species1_7052	species1_7051	0.0514	0.8172	0.0630
-species1_3374	species1_3373	0.0034	0.4496	0.0075
-species1_688	species1_687	0.1420	0.2970	0.4782
-species1_10669	species1_10670	0.0003	0.3216	0.0010
-species1_9810	species1_9809	0.1490	0.7782	0.1914
-species1_11440	species1_11439	0.0083	0.2177	0.0380
-species1_2713	species1_2714	0.1227	0.2133	0.5755
-species1_4698	species1_4697	0.1021	0.2207	0.4624
-species1_10939	species1_10940	0.0861	0.7844	0.1098
-species1_22	species1_21	0.1368	0.2515	0.5437
-species1_7149	species1_7150	0.0817	0.9899	0.0825
-species1_5139	species1_5140	0.1167	0.2669	0.4373
-species1_3430	species1_3429	0.0803	0.9931	0.0808
-species1_10208	species1_10207	0.0764	0.1098	0.6957
-species1_8410	species1_8409	0.0165	0.8114	0.0204
-species1_2452	species1_2451	0.0629	0.4523	0.1391
-species1_289	species1_290	0.0560	0.1590	0.3526
-species1_10028	species1_10027	0.0927	1.3078	0.0708
-species1_8242	species1_8241	0.0037	0.1390	0.0264
-species1_5661	species1_5662	0.2040	0.5982	0.3410
-species1_85	species1_86	0.0391	0.1012	0.3866
-species1_1660	species1_1659	0.0408	0.3674	0.1111
-species1_616	species1_615	0.0078	0.1315	0.0595
-species1_8043	species1_8044	0.0637	0.3891	0.1637
-species1_1465	species1_1466	0.1186	0.1725	0.6876
-species1_7761	species1_7762	0.2544	0.3513	0.7242
-species1_299	species1_300	0.1407	0.5325	0.2643
-species1_3563	species1_3564	0.0732	0.1679	0.4360
-species1_4847	species1_4848	0.4327	0.6173	0.7009
-species1_3344	species1_3343	0.0642	0.5842	0.1098
-species1_262	species1_261	0.1786	1.5710	0.1137
-species1_3725	species1_3726	0.1883	0.8863	0.2125
-species1_779	species1_780	0.0725	0.2543	0.2849
-species1_1818	species1_1817	0.0681	0.2312	0.2946
-species1_11023	species1_11024	0.1603	0.6655	0.2408
-species1_9653	species1_9654	0.0007	0.7308	0.0010
-species1_6805	species1_6806	0.1090	0.9048	0.1205
-species1_10221	species1_10222	0.0522	0.1314	0.3975
-species1_6011	species1_6012	0.0099	0.9179	0.0108
-species1_5208	species1_5207	0.0931	0.6077	0.1532
-species1_10692	species1_10691	0.0537	0.1868	0.2876
-species1_10036	species1_10035	0.2450	0.4664	0.5254
-species1_1382	species1_1381	0.0682	0.1241	0.5495
-species1_1350	species1_1349	0.0129	0.4414	0.0293
-species1_6889	species1_6890	0.0195	0.7789	0.0251
-species1_4426	species1_4425	0.0399	0.3048	0.1309
-species1_7405	species1_7406	0.0443	1.3004	0.0341
-species1_11014	species1_11013	0.0643	0.9027	0.0712
-species1_8683	species1_8684	0.1321	0.6680	0.1977
-species1_5287	species1_5288	0.0882	0.3480	0.2534
-species1_11671	species1_11672	0.1101	0.2895	0.3802
-species1_551	species1_552	0.0165	0.2045	0.0809
-species1_3396	species1_3395	0.3309	1.6105	0.2055
-species1_5398	species1_5397	0.0004	0.4077	0.0010
-species1_7959	species1_7960	0.0498	0.4343	0.1146
-species1_11030	species1_11029	0.0999	0.2613	0.3825
-species1_10250	species1_10249	0.0486	0.1441	0.3372
-species1_6254	species1_6253	0.6399	0.8894	0.7195
-species1_5438	species1_5437	0.0613	0.2224	0.2757
-species1_5728	species1_5727	0.0788	0.1384	0.5697
-species1_8814	species1_8813	0.1076	1.8253	0.0590
-species1_6351	species1_6352	0.2963	0.8001	0.3704
-species1_11253	species1_11254	0.1359	0.7304	0.1860
-species1_11373	species1_11374	0.0924	0.1261	0.7325
-species1_4903	species1_4904	0.1021	1.3915	0.0734
-species1_4326	species1_4325	0.0275	0.1845	0.1492
-species1_9047	species1_9048	0.0648	2.3728	0.0273
-species1_7487	species1_7488	0.0488	0.2406	0.2029
-species1_1125	species1_1126	0.0813	0.3900	0.2085
-species1_9140	species1_9139	0.1643	0.7126	0.2306
-species1_3355	species1_3356	0.0262	0.9844	0.0266
-species1_7068	species1_7067	0.0368	0.1055	0.3492
-species1_8341	species1_8342	0.0356	0.6112	0.0583
-species1_4871	species1_4872	0.0728	0.2256	0.3227
-species1_4408	species1_4407	0.1148	0.7863	0.1460
-species1_10007	species1_10008	0.1071	0.5462	0.1960
-species1_236	species1_235	0.0834	0.2601	0.3207
-species1_4571	species1_4572	0.0745	1.0106	0.0737
-species1_897	species1_898	0.0916	0.9281	0.0987
-species1_5161	species1_5162	0.0707	0.4099	0.1725
-species1_6899	species1_6900	0.0814	0.6724	0.1210
-species1_5749	species1_5750	0.0325	0.3209	0.1013
-species1_2843	species1_2844	0.0327	0.8263	0.0396
-species1_653	species1_654	0.0382	0.8706	0.0439
-species1_8698	species1_8697	0.2628	2.2612	0.1162
-species1_405	species1_406	0.1135	0.1936	0.5861
-species1_4782	species1_4781	0.0341	1.1289	0.0302
-species1_42	species1_41	0.1711	0.4618	0.3706
-species1_1330	species1_1329	0.1074	0.7932	0.1354
-species1_11127	species1_11128	0.1866	1.6162	0.1155
-species1_7639	species1_7640	0.0787	0.4300	0.1830
-species1_7061	species1_7062	0.0571	0.3851	0.1483
-species1_56	species1_55	0.0260	0.6935	0.0375
-species1_417	species1_418	0.0272	0.8148	0.0334