--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/loompy_to_tsv.py	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+
+"""Converts a loompy file to tsv file(s). Each layer becomes a new file."""
+
+import argparse
+
+import loompy
+
+parser = argparse.ArgumentParser(description="Loompy file converter flags")
+parser.add_argument('--version', action='version', version='%(prog)s 0.1.0',
+                    help="Displays tool version")
+parser.add_argument("-f", "--file", help="loom file to import")
+args = parser.parse_args()
+
+file = args.file
+
+matrices = []
+allcols = []
+colstrings = []
+allrows = []
+
+# Build background info for all attributes and layers
+loompyfile = loompy.connect(file)
+row_attributes = loompyfile.ra.keys()  # List of row attributes
+for row in row_attributes:  # Each list represents rownames for row_attributes
+    c_row = loompyfile.ra[row]
+    c_row = [str(r) for r in c_row]
+    allrows.append(c_row)
+col_attributes = loompyfile.ca.keys()  # List of column attributes
+for col in col_attributes:  # each list represents colnames for col_attributes
+    c_col = loompyfile.ca[col]
+    c_col = [str(c) for c in c_col]
+    allcols.append(c_col)
+layers = loompyfile.layers.keys()  # List of layers
+for layer in layers:  # List with each element being a loompy layer
+    c_layer = loompyfile[layer]
+    c_layer = c_layer[:, :]
+    c_layer = c_layer.astype(str)
+    matrices.append(c_layer)
+
+# Create column attribute output
+with open("attributes/col_attr.tsv", "w") as colout:
+    col_attributes = "\t".join(col_attributes) + "\n"
+    colout.write(col_attributes)
+    for length in range(0, len(c_col)):
+        attributestring = ""
+        for col in allcols:
+            attributestring = attributestring + col[length] + "\t"
+        while attributestring[-1] == "\t":
+            attributestring = attributestring[:-1]
+        colout.write(attributestring)
+        colout.write("\n")
+# Create row attribute output
+with open("attributes/row_attr.tsv", "w") as rowout:
+    row_attributes = "\t".join(row_attributes) + "\n"
+    rowout.write(row_attributes)
+    for length in range(0, len(c_row)):
+        attributestring = ""
+        for row in allrows:
+            attributestring = attributestring + row[length] + "\t"
+        while attributestring[-1] == "\t":
+            attributestring = attributestring[:-1]
+        rowout.write(attributestring)
+        rowout.write("\n")
+
+# Build output files for each layer
+for x in range(0, len(layers)):
+    # Output file name generation
+    if layers[x] in layers[0: x]:  # Different output names if layers have same names somehow
+        repeats = layers[0, x].count(layer[x])
+        outputname = "output/" + layers[x] + repeats + ".tsv"
+    elif layers[x] == "":  # Empty layer name
+        outputname = "output/mainmatrix.tsv"
+    else:
+        outputname = "output/" + str(layers[x]) + ".tsv"  # Usual case
+# Matrix output
+    with open(outputname, "w") as outputmatrix:
+        for line in matrices[x]:
+            line = "\t".join(line)
+            line += "\n"
+            line = line
+            outputmatrix.write(line)

--- a/macros.xml	Thu Dec 12 09:23:54 2019 -0500
+++ b/macros.xml	Mon Jan 06 13:45:39 2020 -0500
@@ -15,13 +15,19 @@
         </citations>
     </xml>
     <xml name="version_command">
-        <version_command><![CDATA[python -c "import anndata as ad;print('anndata version: %s' % ad.__version__)"]]></version_command>
+        <version_command><![CDATA[python -c "import anndata as ad;print('anndata version: %s' % ad.__version__); import loompy;print('\nloompy version: %s' % loompy.__version__)"]]></version_command>
     </xml>
     <token name="@CMD@"><![CDATA[
 cat '$script_file' &&
 python '$script_file'
     ]]>
     </token>
+    <token name="@LOOMCMD@"><![CDATA[
+mkdir ./output &&
+mkdir ./attributes &&
+python '$__tool_directory__/loompy_to_tsv.py' -f '${hd5_format.input}'
+    ]]>
+    </token>
     <token name="@CMD_imports@"><![CDATA[
 import anndata as ad
     ]]>
@@ -33,15 +39,27 @@
 
 AnnData stores a data matrix `X` together with annotations of observations `obs`, variables `var` and unstructured annotations `uns`.
 
-.. image:: https://falexwolf.de/img/scanpy/anndata.svg 
+.. image:: https://falexwolf.de/img/scanpy/anndata.svg
 
 
-AnnData stores observations (samples) of variables (features) in the rows of a matrix. This is the convention of the modern classics 
-of statistics (`Hastie et al., 2009 <https://web.stanford.edu/~hastie/ElemStatLearn/>`__)  and machine learning (Murphy, 2012), the convention of dataframes both in R and Python and the established statistics 
+AnnData stores observations (samples) of variables (features) in the rows of a matrix. This is the convention of the modern classics
+of statistics (`Hastie et al., 2009 <https://web.stanford.edu/~hastie/ElemStatLearn/>`__)  and machine learning (Murphy, 2012), the convention of dataframes both in R and Python and the established statistics
 and machine learning packages in Python (statsmodels, scikit-learn).
 
 More details on the `AnnData documentation
 <https://anndata.readthedocs.io/en/latest/anndata.AnnData.html>`__
+
+
+**Loom data**
+
+Loom files are an efficient file format for very large omics datasets, consisting of a main matrix, optional additional layers, a variable number of row and column annotations, and sparse graph objects.
+
+.. image:: https://linnarssonlab.org/loompy/_images/Loom_components.png
+
+
+Loom files to store single-cell gene expression data: the main matrix contains the actual expression values (one column per cell, one row per gene); row and column annotations contain metadata for genes
+and cells, such as Name, Chromosome, Position (for genes), and Strain, Sex, Age (for cells).
+
     ]]>
     </token>
     <xml name="params_chunk_X">

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/modify_loom.py	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+"""This program adds layers, row attributes or column attributes for loom files"""
+
+import argparse
+
+import loompy
+import numpy as np
+
+parser = argparse.ArgumentParser(description="Loompy file converter flags")
+parser.add_argument('--VERSION', action='version', version='%(prog)s 0.1.0',
+                    help="Displays tool version")
+parser.add_argument('--file', '-f',
+                    help="Loom file to which data will be added")
+parser.add_argument('--rowfile', '-r', help="File of row attributes & values")
+parser.add_argument('--colfile', '-c',
+                    help="File of column attributes and values")
+parser.add_argument('--layers', '-l', nargs='*',
+                    help="Input tsv files. First file becomes main layer.")
+parser.add_argument('--add', '-a', choices=["rows", "cols", "layers"],
+                    help="Selects rows, columns or layers to be added to file")
+args = parser.parse_args()
+
+lfile = args.file
+if args.rowfile:
+    rowfile = args.rowfile
+if args.colfile:
+    colfile = args.colfile
+if args.layers:
+    alllayers = args.layers
+addselect = args.add
+# Check proper flags for chosen attributes are being added
+if addselect == "cols" and not args.colfile:
+    raise Exception("To add column attributes, column flag and file must be provided")
+if addselect == "rows" and not args.rowfile:
+    raise Exception("To add row attributes, row flag and file must be provided")
+if addselect == "layers" and not args.layers:
+    raise Exception("To add layers, a layer flag and file(s) must be provided")
+
+layernames = []
+rowdict = {}
+coldict = {}
+
+with loompy.connect(lfile) as loomfile:
+    # Loom file dimensions
+    nrow = loomfile.shape[0]
+    ncol = loomfile.shape[1]
+    if addselect == "layers":
+        layernames = []
+        # Generate layer names based on file names
+        for x in range(0, len(alllayers)):
+            layer = alllayers[x]
+            layer = layer.split("/")[-1].split(".")[-2]  # Takes away path, takes off extension
+            layernames.append(layer)
+        # Add in the layers themselves
+        for layer in range(0, len(alllayers)):
+            matrix = ""
+            with open(alllayers[layer], "r") as infile:
+                rows = 0
+                count = 0
+                for line in infile:
+                    if count == 0:
+                        cols = len(line.split("\t"))
+                        if cols != ncol:
+                            raise Exception("Dimensions of new matrix incorrect for this loom file. New matrices must be %d by %d" % (nrow, ncol))
+                    matrix = matrix + line + "\t"
+                    rows += 1
+                if rows != nrow:
+                    raise Exception("Dimensions of new matrix incorrect for this loom file. New matrices must be %d by %d")
+            matrix = matrix.split("\t")
+            matrix = [float(n) for n in matrix[:-1]]
+            matrix = np.asarray(matrix).reshape(nrow, ncol)
+            loomfile[layernames[layer]] = matrix
+    elif addselect == "rows":
+        with open(rowfile, "r") as rows:
+            count = 0
+            for line in rows:
+                line = line.strip().split("\t")
+                if count == 0:  # First time through
+                    row_attributes = line
+                    for x in row_attributes:
+                        rowdict[x] = []
+                    count += 1
+                else:
+                    for x in range(0, len(line)):
+                        rowdict[row_attributes[x]].append(line[x])
+        for x in row_attributes:
+            if len(rowdict[x]) != nrow:
+                raise Exception("Incorrect length of row. Row length must be: %d" % nrow)
+            loomfile.ra[x] = rowdict[x]
+    elif addselect == "cols":
+        with open(colfile, "r") as cols:
+            count = 0
+            for line in cols:
+                line = line.replace('\"', "")
+                line = line.replace(' ', "")
+                line = line.strip().split("\t")
+                if count == 0:  # First time through
+                    col_attributes = line
+                    for x in col_attributes:
+                        coldict[x] = []
+                    count += 1
+                else:
+                    for x in range(0, len(line)):
+                        coldict[col_attributes[x]].append(line[x])
+        for y in col_attributes:
+            if len(coldict[y]) != ncol:
+                raise Exception("Incorrect length of column. Column length must be: %d" % ncol)
+            loomfile.ca[y] = coldict[y]

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/addlayer1.tsv	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,9 @@
+-4.38397705861083	14.292813312163	-3.50113245239144	0.822254333007829	1.00150289648448	2.43591204698924	-0.356677013622828	-6.11947364969354	2.56281003005924	6.63909009032078	-0.78946499862756	4.69845868698249	0.555316773542253	1.14493347970535	-2.13170480396421	0.671242754395511	-1.24598424970653	2.35762885939	0.808249063291586	2.13338650412044	-0.442400711567299	0.185262494040311	2.11898976841304	1.33325569029047	-1.59297725345935	-0.966752781646448	3.58792591654639	1.66406580309397	0.63327385793388	-0.308925411126234	1.62030751470511	-0.551680746030133	0.481109445913889	-1.29522624182828	-1.2921145302749	1.35092954319461	0.357971803776218	0.602387478388324	-0.86450428384588	-0.253516849675122	-0.191991523300002	-1.23045693386899	1.10601149579208	0.474927307151347	0.0295127518668576	0.347320109574423	-0.124263697792595	-1.1355438083115	-0.408955132576027	0.652951279673313	-0.560853895759609	-1.50956633618222	-0.372228084801999	0.649248591501578	1.24501120925882	-0.333165552309464	-0.632289365875825	-0.489136074968853	0.606241184362324	-0.0697756202083343	0.723366081694609	0.269156075893411	-1.1927509857456	1.82685437466378	0.112075843817834	-0.393322613665142	1.28166028199521	-1.44150233543534	0.439168555592787	0.344017904319427	1.81395743926678	0.508805925557063	0.848238740356503	1.11258006953971	-0.54997200023266	0.809927913686388	-0.738427841761447	-0.453122730649697	-0.486373667035062	0.746360065063862	0.700920021470684	-0.313589192332652	-0.906138725835488	-0.661793483747946	-0.271124869295733	-0.00930238204411152	0.3215219758547	-1.25380192942129	-1.55728763598058	0.0952110940501214	0.00839453406532392	-0.0541448293428013	0.18477775291979	-0.62373646202135	-0.0772377796590389	-0.662366716574923	-1.03592409403641	-0.89249106169138	-0.717710251591651	0.776880357163071
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Binary file test-data/addloomout2.loom has changed

Binary file test-data/addloomout3.loom has changed

Binary file test-data/addtest.loom has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cols.tsv	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,101 @@
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Binary file test-data/converted.loom.test has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/finallayer.tsv	Mon Jan 06 13:45:39 2020 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/firstlayer.tsv	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,9 @@
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Binary file test-data/loomtest.loom has changed

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/rows.tsv	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,10 @@
+Gene	Protein	Testing	Testing2
+0	0	3	15
+1	1	4	16
+2	2	5	17
+3	3	6	18
+4	4	7	19
+5	5	8	20
+6	6	9	21
+7	7	10	22
+8	8	11	23

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/secondlayer.tsv	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,9 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tsv_to_loompy.py	Mon Jan 06 13:45:39 2020 -0500
@@ -0,0 +1,109 @@
+#!/usr/bin/env python
+"""This module converts a tsv file into a binary loom file"""
+
+import argparse
+import os
+
+import loompy
+import numpy as np
+
+parser = argparse.ArgumentParser(description="Loompy file converter flags")
+parser.add_argument('--VERSION', action='version', version='%(prog)s 0.1.0',
+                    help="Displays tool version")
+parser.add_argument('--rowfile', '-r', help="File of row attributes & values")
+parser.add_argument('--colfile', '-c',
+                    help="File of column attributes and values")
+parser.add_argument('--output', '-o', help="Output file name")
+parser.add_argument('--files', '-f', nargs='*',
+                    help="Input tsv files. First file becomes main layer.")
+args = parser.parse_args()
+
+colsfile = args.colfile
+rowsfile = args.rowfile
+if args.output:
+    filename = args.output
+else:
+    filename = "converted.loom"
+alldata = args.files
+alayers = []
+layernames = []
+rowdict = {}
+coldict = {}
+
+#  Creates dictionary based on row file
+#  For each attribute:
+#  Attribute: [attribute values]
+with open(rowsfile, "r") as rows:
+    count = 0
+    for line in rows:
+        line = line.strip().split("\t")
+        if count == 0:  # First time through
+            row_attributes = line
+            for x in row_attributes:
+                rowdict[x] = []
+            count += 1
+        else:
+            for x in range(0, len(line)):
+                rowdict[row_attributes[x]].append(line[x])
+#  Same as above, but for columns
+with open(colsfile, "r") as cols:
+    count = 0
+    for line in cols:
+        line = line.replace('\"', "")
+        line = line.replace(' ', "")
+        line = line.strip().split("\t")
+        if count == 0:  # First time through
+            col_attributes = line
+            for x in col_attributes:
+                coldict[x] = []
+            count += 1
+        else:
+            for x in range(0, len(line)):
+                coldict[col_attributes[x]].append(line[x])
+#  Finding dimensions for the loom layers
+rowshape = len(rowdict[list(rowdict.keys())[0]])
+colshape = len(coldict[list(coldict.keys())[0]])
+
+#  Creates a list with each element being entire matrix of
+#  each layer file as floats
+for file in range(0, len(alldata)):
+    layer = alldata[file][:-4]
+    layer = layer.split("/")[-1]
+    if layer == "":
+        raise Exception("Please only use named files")
+    layernames.append(layer)
+    cfile = alldata[file]
+    with open(cfile, "r") as tsv:
+        cmatrix = []
+        for line in tsv:
+            line = line.strip().split("\t")
+            line = [float(i) for i in line]
+            cmatrix += line
+        alayers.append(cmatrix)
+
+#  Loompy cannot overwright existing files. If somehow it finds
+#  a second file with the same name, it must be deleted
+if os.path.isfile(filename):
+    os.remove(filename)
+#  To create the file properly, the first row and column attributes must be
+#  added separately in the form of individual dictionaries
+row_attrs = {row_attributes[0]: np.asarray(rowdict[row_attributes[0]])}
+col_attrs = {col_attributes[0]: np.asarray(coldict[col_attributes[0]])}
+matrix = np.asarray(alayers[0])
+matrix = matrix.astype(float)
+matrix = matrix.reshape(rowshape, colshape)
+#  Creation of initial loom file
+if "loom" not in filename[-5:]:
+    filename = filename + ".loom"
+loompy.create(filename, matrix, row_attrs, col_attrs)
+#  Adding all row and column attributes, then all layers
+with loompy.connect(filename) as loomfile:
+    for x in row_attributes:
+        loomfile.ra[x] = rowdict[x]
+    for y in col_attributes:
+        loomfile.ca[y] = coldict[y]
+    for z in range(1, len(alayers)):
+        matrix = np.asarray(alayers[z])
+        matrix = matrix.astype(float)
+        matrix = matrix.reshape(rowshape, colshape)
+        loomfile[layernames[z]] = matrix