diff --git a/doodling.R b/doodling.R
@@ -0,0 +1,43 @@
+source('/Users/hannah/Dropbox/Westneat Lab/Eartheater Project/Code/genfunctions.R')
+source('/Users/hannah/Dropbox/Westneat Lab/Eartheater Project/Code/Read shapes.R')
+
+# Aligning vectors --------------------------------------------------------
+
+mouth.S <- graphAlignPeaksGG('./Measurements/S_mouthgape.csv', col=2, main='Strike', xlab="")
+mouth.E <- graphAlignPeaksGG('./Measurements/E_mouthgape.csv', col=5, main='Ejection')
+mouth.W <- graphAlignPeaksGG('./Measurements/W_mouthgape.csv', col=4, main='Winnowing', xlab="")
+branch.S <- graphAlignPeaksGG('./Measurements/S_branch.csv', col=2, main='Strike')
+branch.W <- graphAlignPeaksGG('./Measurements/W_branch.csv', col=5, main='Winnowing')
+branch.E <- graphAlignPeaksGG('./Measurements/E_branch.csv', col=4, main='Ejection')
+
+multiplot(branch.S, branch.W, branch.E)
+multiplot(mouth.S, mouth.W, mouth.E)
+
+strike.mouth <- read.csv('./Measurements/S_mouthgape.csv')[,-1]
+strike.branch <- read.csv('./Measurements/S_branch.csv')[,-1]
+strike.premax <- read.csv('./Measurements/S_premax.csv')[,-1]
+mouth.test <- strike.mouth[2,]
+mouth.test <- mouth.test[is.na(mouth.test)==FALSE]
+branch.test <- strike.branch[2,]
+branch.test <- branch.test[is.na(branch.test)==FALSE]
+premax.test <- strike.premax[2,]
+premax.test <- premax.test[is.na(premax.test)==FALSE]
+
+frame.index <- seq(1, length(mouth.test), 1)
+frame.index <- (frame.index-1)*1/100
+
+
+splinefit <- smooth.spline(frame.index, mouth.test) # smooth vector
+mouth.test <- predict(splinefit, data=frame.index)$y
+
+
+splinefit2 <- smooth.spline(frame.index, branch.test) # smooth vector
+branch.test <- predict(splinefit2, data=frame.index)$y
+
+df.test <- data.frame(mouth.test=mouth.test, branch.test=branch.test, premax=premax.test, frame.index=frame.index)
+
+p <- ggplot(data=df.test, aes(x=frame.index, y=mouth.test)) + 
+  geom_line(data=df.test, aes(x=frame.index, y=mouth.test), lwd=1, col='blue')
+p <- p + geom_line(data=df.test, aes(c=frame.index, y=branch.test), lwd=1, lty=2, col='red')
+p <- p + geom_line(data=df.test, aes(c=frame.index, y=premax), lwd=1, lty=3, col='purple', alpha=1)
+p
diff --git a/eye_size.m b/eye_size.m
@@ -1,28 +1,37 @@
-function[pixels] = eye_size(imagename, threshold)
+function[closest] = eye_size(imagename, threshold)
 image = imread(imagename);
-greyimage = rgb2gray(image);
+greyimage = image(:,:,1);
 imdim = size(greyimage);
 
 % set threshold; if above then turn white
-
+% threshold = 30;
 for i = 1:imdim(1)
     for j = 1:imdim(2)
         if greyimage(i, j) > threshold
-            greyimage(i, j) = 255;
-        else
             greyimage(i, j) = 0;
+        else
+            greyimage(i, j) = 1;
         end
     end
 end
-BW2 = bwmorph(greyimage,'remove');
+
+greyimage = logical(greyimage);
+% BW2 = bwmorph(greyimage,'remove');
 alternate = bwmorph(greyimage, 'majority', Inf);
-BW3 = bwmorph(alternate, 'remove');
-figure; imshow(image);
-figure; imshow(greyimage);
-% figure; imshow(BW2);
-figure; imshow(alternate);
-figure; imshow(BW3);
+alternate = bwmorph(alternate, 'clean', Inf);
+alternate = bwmorph(alternate, 'fill', Inf);
+alternate = bwareafilt(alternate, [1500 7000]);
+
+
+stats = regionprops('table', alternate, 'Centroid','Extent', 'Area', 'PixelList');
+extenty = cat(1, stats.Extent);
+areas = cat(1, stats.Area);
+value = 0.74;
 
-pixels = bwconncomp(alternate);
+diffo = abs(value-extenty);
+[idx idx] = min(diffo);
+closest = areas(idx);
+% imshow(alternate); hold on; plot(stats.Centroid(idx,1), stats.Centroid(idx,2), 'b*'); hold off
+imshow(image); hold on; plot(stats.Centroid(idx,1), stats.Centroid(idx,2), 'c*'); hold off
 
 end
diff --git a/eye_size_playing.m b/eye_size_playing.m
@@ -0,0 +1,128 @@
+%% MESSIN ABOOT
+
+k = 3;
+m = 5;
+datedir = dir('*-16');
+trialdir = dir([datedir(k).name, '/Intervals/*Intervals']);
+jpgdir = dir([datedir(k).name, '/Intervals/', trialdir(m).name, '/*.jpg']);
+pixel.areas = [];
+
+
+for i = 24:length(jpgdir)
+    imagename = sprintf('%s%s%s%s%s', datedir(k).name, '/Intervals/', trialdir(m).name, '/', jpgdir(i).name);
+    pixel.areas(i) = eye_size(imagename, 40); hold on;
+    pause
+end
+
+
+
+
+%% trying to threshold good ;-;
+
+k = 3;
+m = 2;
+datedir = dir('*-16');
+trialdir = dir([datedir(k).name, '/Intervals/*Intervals']);
+jpgdir = dir([datedir(k).name, '/Intervals/', trialdir(m).name, '/*.jpg']);
+pixel.areas = [];
+imagename = sprintf('%s%s%s%s%s', datedir(k).name, '/Intervals/', trialdir(m).name, '/', jpgdir(1).name);
+
+image = imread(imagename);
+greyimage = rgb2gray(image);
+imdim = size(greyimage);
+
+% set threshold; if above then turn white
+threshold = 50;
+for i = 1:imdim(1)
+    for j = 1:imdim(2)
+        if greyimage(i, j) > threshold
+            greyimage(i, j) = 0;
+        else
+            greyimage(i, j) = 1;
+        end
+    end
+end
+
+greyimage = logical(greyimage);
+% BW2 = bwmorph(greyimage,'remove');
+alternate = bwmorph(greyimage, 'majority', Inf);
+alternate = bwmorph(alternate, 'clean', Inf);
+alternate = bwmorph(alternate, 'fill', Inf);
+alternate = bwareafilt(alternate, [1000 7000]);
+
+
+stats = regionprops('table', alternate, 'Centroid','Extent', 'Area', 'PixelList');
+imshow(image); hold on; plot(stats.Centroid(:,1), stats.Centroid(:,2), 'c*'); hold off
+imshow(alternate);
+variances = [];
+means = [];
+for i = 1:height(stats)
+    centroid = stats.Centroid(i,:);
+    pixels = stats.PixelList{i};
+    centroid_dist = [];
+    for j = 1:length(pixels)
+        centroid_dist(j) = get_distance(centroid, pixels(j,:));
+    end
+    figure; hist(centroid_dist);
+    variances(i) = var(centroid_dist);
+    means(i) = mean(centroid_dist);
+%     pause
+end
+
+
+
+extenty = cat(1, stats.Extent);
+areas = cat(1, stats.Area);
+value = 0.75;
+
+diffo = abs(value-extenty);
+[idx idx] = min(diffo);
+closest = areas(idx);
+
+imshow(image); hold on; plot(stats.Centroid(idx,1), stats.Centroid(idx,2), 'c*'); hold off
+
+%% color segmentation?
+k = 3;
+m = 2;
+datedir = dir('*-16');
+trialdir = dir([datedir(k).name, '/Intervals/*Intervals']);
+jpgdir = dir([datedir(k).name, '/Intervals/', trialdir(m).name, '/*.jpg']);
+pixel.areas = [];
+imagename = sprintf('%s%s%s%s%s', datedir(k).name, '/Intervals/', trialdir(m).name, '/', jpgdir(1).name);
+
+image = imread(imagename);
+greyimage = image(:,:,1);
+imdim = size(greyimage);
+
+for i = 1:3
+    figure; imshow(image(:,:,i));
+end
+
+cform = makecform('srgb2lab');
+lab_he = applycform(image,cform);
+ab = double(lab_he(:,:,2:3));
+nrows = size(ab,1);
+ncols = size(ab,2);
+ab = reshape(ab,nrows*ncols,2);
+
+nColors = 3;
+% repeat the clustering 3 times to avoid local minima
+[cluster_idx, cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...
+                                      'Replicates',3);
+pixel_labels = reshape(cluster_idx,nrows,ncols);
+imshow(pixel_labels,[]), title('image labeled by cluster index');
+
+
+%% taking reds
+mkdir('testreds');
+dir1 = dir('02-26-16/Intervals/*Intervals');
+
+for i = 1:length(dir1)
+    dir2 = dir(['02-26-16/Intervals/', dir1(i).name, '/*.jpg']);
+    mkdir(['testreds/', dir1(i).name]);
+    for j = 1:length(dir2)
+        img = imread(dir2(j).name);
+        savename = sprintf('%s%s%s%s', 'testreds/', dir1(i).name, '/', dir2(j).name);
+        imwrite(img(:,:,1), savename);
+    end
+end
diff --git a/genfunctions.R b/genfunctions.R
diff --git a/geophagine_character_matrix b/geophagine_character_matrix
@@ -0,0 +1,233 @@
+# import csv
+
+# def LF2010(filename):
+# 	rd = {}
+# 	with open(filename, 'rU') as f:
+# 		reader = csv.DictReader(f)
+# 		# reader is now an iterator of row dictionaries with keys as the column names
+# 		for row in reader:
+# 			# whatever order you want!
+# 			rd[ row['Taxon'] ] = [ 
+# 				row['Tissue sample'], row['Voucher'], 
+# 				row['Collection locality'], row['ND4'], row['Cytochrome b'], 
+# 				row['16S'], row['RAG2'], row['S7 NaN Intron 1']
+# 				]
+# 	return rd
+
+import csv
+LF2010 = open('/Users/hannah/Dropbox/Westneat Lab/Eartheater Project/Data/Phylogenetics/LF2010_GenBank.csv', 'rU')
+
+csvread = csv.reader(LF2010)
+
+species = []
+ND4 = []
+CytB = []
+_16S = []
+RAG2 = []
+S7_Intron1 = []
+for row in csvread:
+	species.append(row[0])
+	ND4.append(row[4])
+	CytB.append(row[5])
+	_16S.append(row[6])
+	RAG2.append(row[7])
+	S7_Intron1.append(row[8])
+
+LF2010.close()
+
+from dendropy.interop import genbank
+ND4_dna = genbank.GenBankDna(ids=ND4, verify=False)
+ND4_char_matrix = ND4_dna.generate_char_matrix(label_components=["organism", "accession"], label_component_separator="_")
+
+CytB_dna = genbank.GenBankDna(ids=CytB, verify=False)
+CytB_char_matrix = CytB_dna.generate_char_matrix(label_components=["organism", "accession"], label_component_separator="_")
+
+_16S_dna = genbank.GenBankDna(ids=_16S, verify=False)
+_16S_char_matrix = _16S_dna.generate_char_matrix(label_components=["organism", "accession"], label_component_separator="_")
+
+RAG2_dna = genbank.GenBankDna(ids=RAG2, verify=False)
+RAG2_char_matrix = RAG2_dna.generate_char_matrix(label_components=["organism", "accession"], label_component_separator="_")
+
+S7_Intron1_dna = genbank.GenBankDna(ids=S7_Intron1, verify=False)
+S7_Intron1_char_matrix = S7_Intron1_dna.generate_char_matrix(label_components=["organism", "accession"], label_component_separator="_")
+
+ND4_char_matrix.write(
+        path='ND4_geophagini.nex',
+        schema='nexus',
+        suppress_taxa_blocks=None,
+        suppress_block_titles=None,
+        file_comments=[],
+        preamble_blocks=[],
+        supplemental_blocks=[],
+        allow_multiline_comments=True,
+        suppress_leaf_taxon_labels=True,
+        suppress_leaf_node_labels=True,
+        suppress_internal_taxon_labels=True,
+        suppress_internal_node_labels=True,
+        suppress_rooting=True,
+        suppress_edge_lengths=True,
+        unquoted_underscores=False,
+        preserve_spaces=False,
+        store_tree_weights=False,
+        translate_tree_taxa=None,
+        suppress_annotations=True,
+        annotations_as_nhx=False,
+        suppress_item_comments=False,
+        node_label_element_separator=' ',
+        node_label_compose_fn=None,
+        edge_label_compose_fn=None,
+        real_value_format_specifier='',
+        ignore_unrecognized_keyword_arguments=True,
+        )
+
+CytB_char_matrix.write(
+        path='CytB_geophagini.nex',
+        schema='nexus',
+        suppress_taxa_blocks=None,
+        suppress_block_titles=None,
+        file_comments=[],
+        preamble_blocks=[],
+        supplemental_blocks=[],
+        allow_multiline_comments=True,
+        suppress_leaf_taxon_labels=True,
+        suppress_leaf_node_labels=True,
+        suppress_internal_taxon_labels=True,
+        suppress_internal_node_labels=True,
+        suppress_rooting=True,
+        suppress_edge_lengths=True,
+        unquoted_underscores=False,
+        preserve_spaces=False,
+        store_tree_weights=False,
+        translate_tree_taxa=None,
+        suppress_annotations=True,
+        annotations_as_nhx=False,
+        suppress_item_comments=False,
+        node_label_element_separator=' ',
+        node_label_compose_fn=None,
+        edge_label_compose_fn=None,
+        real_value_format_specifier='',
+        ignore_unrecognized_keyword_arguments=True,
+        )
+
+_16S_char_matrix.write(
+        path='16S_geophagini.nex',
+        schema='nexus',
+        suppress_taxa_blocks=None,
+        suppress_block_titles=None,
+        file_comments=[],
+        preamble_blocks=[],
+        supplemental_blocks=[],
+        allow_multiline_comments=True,
+        suppress_leaf_taxon_labels=True,
+        suppress_leaf_node_labels=True,
+        suppress_internal_taxon_labels=True,
+        suppress_internal_node_labels=True,
+        suppress_rooting=True,
+        suppress_edge_lengths=True,
+        unquoted_underscores=False,
+        preserve_spaces=False,
+        store_tree_weights=False,
+        translate_tree_taxa=None,
+        suppress_annotations=True,
+        annotations_as_nhx=False,
+        suppress_item_comments=False,
+        node_label_element_separator=' ',
+        node_label_compose_fn=None,
+        edge_label_compose_fn=None,
+        real_value_format_specifier='',
+        ignore_unrecognized_keyword_arguments=True,
+        )
+
+RAG2_char_matrix.write(
+        path='RAG2_geophagini.nex',
+        schema='nexus',
+        suppress_taxa_blocks=None,
+        suppress_block_titles=None,
+        file_comments=[],
+        preamble_blocks=[],
+        supplemental_blocks=[],
+        allow_multiline_comments=True,
+        suppress_leaf_taxon_labels=True,
+        suppress_leaf_node_labels=True,
+        suppress_internal_taxon_labels=True,
+        suppress_internal_node_labels=True,
+        suppress_rooting=True,
+        suppress_edge_lengths=True,
+        unquoted_underscores=False,
+        preserve_spaces=False,
+        store_tree_weights=False,
+        translate_tree_taxa=None,
+        suppress_annotations=True,
+        annotations_as_nhx=False,
+        suppress_item_comments=False,
+        node_label_element_separator=' ',
+        node_label_compose_fn=None,
+        edge_label_compose_fn=None,
+        real_value_format_specifier='',
+        ignore_unrecognized_keyword_arguments=True,
+        )
+
+S7_Intron1_char_matrix.write(
+        path='S7_Intron1_geophagini.nex',
+        schema='nexus',
+        suppress_taxa_blocks=None,
+        suppress_block_titles=None,
+        file_comments=[],
+        preamble_blocks=[],
+        supplemental_blocks=[],
+        allow_multiline_comments=True,
+        suppress_leaf_taxon_labels=True,
+        suppress_leaf_node_labels=True,
+        suppress_internal_taxon_labels=True,
+        suppress_internal_node_labels=True,
+        suppress_rooting=True,
+        suppress_edge_lengths=True,
+        unquoted_underscores=False,
+        preserve_spaces=False,
+        store_tree_weights=False,
+        translate_tree_taxa=None,
+        suppress_annotations=True,
+        annotations_as_nhx=False,
+        suppress_item_comments=False,
+        node_label_element_separator=' ',
+        node_label_compose_fn=None,
+        edge_label_compose_fn=None,
+        real_value_format_specifier='',
+        ignore_unrecognized_keyword_arguments=True,
+        )
+
+
+# print(char_matrix.as_string("nexus"))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# from Bio import SeqIO
+
+# for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
+#     print(seq_record.id)
+#     print(repr(seq_record.seq))
+#     print(len(seq_record))
diff --git a/get_distance.m b/get_distance.m
@@ -0,0 +1,3 @@
+function [dist] = get_distance(p1, p2)
+dist = sqrt((p1(1)-p2(1))^2 + (p1(2)-p2(2))^2);
+end
diff --git a/image_analysis_trials.R b/image_analysis_trials.R
@@ -0,0 +1,2 @@
+source('http://bioconductor.org/biocLite.R')
+biocLite('EBImage')