def _generic_plot(output_dir: str, master: skbio.OrdinationResults,
metadata: qiime2.Metadata,
other_pcoa: skbio.OrdinationResults, plot_name,
custom_axes: str=None):
mf = metadata.to_dataframe()
if other_pcoa is None:
procrustes = None
else:
procrustes = [other_pcoa]
viz = Emperor(master, mf, procrustes=procrustes, remote='.')
if custom_axes is not None:
viz.custom_axes = custom_axes
if other_pcoa:
viz.procrustes_names = ['reference', 'other']
html = viz.make_emperor(standalone=True)
viz.copy_support_files(output_dir)
with open(os.path.join(output_dir, 'emperor.html'), 'w') as fh:
fh.write(html)
index = os.path.join(TEMPLATES, 'index.html')
q2templates.render(index, output_dir, context={'plot_name': plot_name})
def _generic_plot(output_dir: str, master: skbio.OrdinationResults,
metadata: qiime2.Metadata,
other_pcoa: skbio.OrdinationResults, plot_name,
custom_axes: str=None,
feature_metadata: qiime2.Metadata=None):
mf = metadata.to_dataframe()
if feature_metadata is not None:
feature_metadata = feature_metadata.to_dataframe()
if other_pcoa is None:
procrustes = None
else:
procrustes = [other_pcoa]
viz = Emperor(master, mf, feature_mapping_file=feature_metadata,
procrustes=procrustes, remote='.')
if custom_axes is not None:
viz.custom_axes = custom_axes
if other_pcoa:
viz.procrustes_names = ['reference', 'other']
html = viz.make_emperor(standalone=True)
viz.copy_support_files(output_dir)
with open(os.path.join(output_dir, 'emperor.html'), 'w') as fh:
fh.write(html)
index = os.path.join(TEMPLATES, 'index.html')
q2templates.render(index, output_dir, context={'plot_name': plot_name})
def plot(output_dir: str, pcoa: skbio.OrdinationResults,
metadata: qiime2.Metadata, custom_axis: str=None) -> None:
mf = metadata.to_dataframe()
viz = Emperor(pcoa, mf, remote='.')
if custom_axis is not None:
# put custom_axis inside a list to workaround the type system not
# supporting lists of types
html = viz.make_emperor(standalone=True, custom_axes=[custom_axis])
else:
html = viz.make_emperor(standalone=True)
viz.copy_support_files(output_dir)
with open(os.path.join(output_dir, 'emperor.html'), 'w') as fh:
fh.write(html)
index = os.path.join(TEMPLATES, 'index.html')
q2templates.render(index, output_dir)
def plot(output_dir: str,
pcoa: skbio.OrdinationResults,
metadata: qiime2.Metadata,
custom_axis: str = None) -> None:
mf = metadata.to_dataframe()
viz = Emperor(pcoa, mf, remote='.')
if custom_axis is not None:
# put custom_axis inside a list to workaround the type system not
# supporting lists of types
html = viz.make_emperor(standalone=True, custom_axes=[custom_axis])
else:
html = viz.make_emperor(standalone=True)
viz.copy_support_files(output_dir)
with open(os.path.join(output_dir, 'emperor.html'), 'w') as fh:
fh.write(html)
index = os.path.join(TEMPLATES, 'index.html')
q2templates.render(index, output_dir)
def _generate_ordination_results_summary(files, metadata, out_dir):
# Magic number [0] -> there is only one plain text file and it is the
# ordination results
ord_res = OrdinationResults.read(files['plain_text'][0])
md_df = pd.DataFrame.from_dict(metadata, orient='index')
emp = Emperor(ord_res, md_df, remote="emperor_support_files")
html_summary_fp = join(out_dir, 'index.html')
esf_dp = join(out_dir, 'emperor_support_files')
makedirs(esf_dp)
with open(html_summary_fp, 'w') as f:
f.write(emp.make_emperor(standalone=True))
emp.copy_support_files(esf_dp)
return html_summary_fp, esf_dp
def plot(output_dir: str, sample_metadata: qiime.Metadata,
pcoa: skbio.OrdinationResults) -> None:
mf = sample_metadata.to_dataframe()
output = join(output_dir, 'emperor-required-resources/')
viz = Emperor(pcoa, mf, remote=output)
with open(join(output_dir, 'index.html'), 'w') as f:
# correct the path
html = viz.make_emperor(standalone=True)
viz.copy_support_files(output_dir)
f.write(html)
return None
def generic_plot(output_dir: str,
master: skbio.OrdinationResults,
metadata: qiime2.Metadata,
other_pcoa: skbio.OrdinationResults,
plot_name: str,
info: str = None,
custom_axes: str = None,
settings: dict = None,
ignore_missing_samples: bool = False,
feature_metadata: qiime2.Metadata = None):
mf = metadata.to_dataframe()
if feature_metadata is not None:
feature_metadata = feature_metadata.to_dataframe()
if other_pcoa is None:
procrustes = None
else:
procrustes = [other_pcoa]
viz = Emperor(master,
mf,
feature_mapping_file=feature_metadata,
ignore_missing_samples=ignore_missing_samples,
procrustes=procrustes,
remote='.')
if custom_axes is not None:
viz.custom_axes = custom_axes
if other_pcoa:
viz.procrustes_names = ['reference', 'other']
viz.info = info
viz.settings = settings
html = viz.make_emperor(standalone=True)
viz.copy_support_files(output_dir)
with open(os.path.join(output_dir, 'emperor.html'), 'w') as fh:
fh.write(html)
index = os.path.join(TEMPLATES, 'index.html')
q2templates.render(index, output_dir, context={'plot_name': plot_name})
def emperor_output(sklearn_output, full_file_list, eigenvalues, percent_variance, output_file, new_files = None):
print("Made it to Emperor Function!")
#read in sklearn output and format accordingly for emperor intake
eigvals = pd.Series(data = eigenvalues)
samples = pd.DataFrame(data = sklearn_output, index = full_file_list)
p_explained = pd.Series(data = percent_variance)
ores = OrdinationResults(long_method_name = "principal component analysis", short_method_name = "pcoa", eigvals = eigvals, samples = samples, proportion_explained = p_explained)
#this first part is for the global metadata file
global_metadata = pd.read_csv(config.PATH_TO_ORIGINAL_MAPPING_FILE, sep = "\t")
global_metadata_headers = global_metadata.columns.tolist()
global_metadata.rename(columns = {'filename': 'SampleID'}, inplace = True)
global_metadata["type"] = "Global Data"
global_metadata.set_index("SampleID", inplace = True)
common = global_metadata
#this part is for the user uploaded metadata file
if new_files != None:
metadata_uploaded = pd.DataFrame({"SampleID": new_files, "type":["Your Data"] * len(new_files)})
for item in global_metadata_headers:
metadata_uploaded[item] = ["Your Data"] * len(new_files)
metadata_uploaded.set_index("SampleID", inplace = True)
common = pd.concat([global_metadata, metadata_uploaded])
#so you need to align the metadata and the files contained within the ordination file BEFORE feeding it into the Emperor thing otherwise it doesn't like to output results
final_metadata, unused = common.align(samples, join = "right", axis = 0)
#call stuff to ouput an emperor plot
emp = Emperor(ores, final_metadata, remote = True)
# create an output directory
os.makedirs(output_file, exist_ok=True)
with open(os.path.join(output_file, 'index.html'), 'w') as f:
f.write(emp.make_emperor(standalone = True))
emp.copy_support_files(output_file)
def emperor_output(sklearn_output,
full_file_list,
eigenvalues,
percent_variance,
output_file,
new_files=[]):
eigvals = pd.Series(data=eigenvalues)
samples = pd.DataFrame(data=sklearn_output, index=full_file_list)
samples.index.rename("SampleID", inplace=True)
p_explained = pd.Series(data=percent_variance)
ores = OrdinationResults(long_method_name="principal component analysis",
short_method_name="pcoa",
eigvals=eigvals,
samples=samples,
proportion_explained=p_explained)
#read in all sample metadata
df = pd.read_table(config.PATH_TO_ORIGINAL_MAPPING_FILE)
df.rename(columns={"filename": "SampleID"}, inplace=True)
df.set_index("SampleID", inplace=True)
#handling the case in which the pca is a projection
if len(new_files) != 0:
df["Type"] = "Global"
new_meta = pd.DataFrame({"SampleID": new_files, "Type": "Your Data"})
new_meta.set_index("SampleID", inplace=True)
df = pd.concat([df, new_meta], axis=0, join="outer")
final_metadata, unused = df.align(samples, join="right", axis=0)
#call stuff to ouput an emperor plot
emp = Emperor(ores, final_metadata, remote=True)
# create an output directory
os.makedirs(output_file, exist_ok=True)
with open(os.path.join(output_file, 'index.html'), 'w') as f:
f.write(emp.make_emperor(standalone=True))
emp.copy_support_files(output_file)
def create_emperor_visual(args, pcfile):
"""
Sample .pc file
# Eigvals 4
# 0.2705559825337763 0.07359266496720843 0.02997793703738496 0.0
#
# Proportion explained 4
# 0.7231669539538659 0.19670525434062255 0.0801277917055116 0.0
#
# Species 0 0
#
# Site 4 4
# ICM_LCY_Bv6--LCY_0001_2003_05_11 -0.04067063044757823 -0.09380781760926289 0.13680474645584195 0.0
# ICM_LCY_Bv6--LCY_0003_2003_05_04 -0.11521436634022217 -0.15957409396683217 -0.10315005726535573 0.0
# ICM_LCY_Bv6--LCY_0005_2003_05_16 0.4268532792747924 0.06657577342833808 -0.02212569426459717 0.0
# ICM_LCY_Bv6--LCY_0007_2003_05_04 -0.2709682824869916 0.18680613814775715 -0.011528994925888972 0.0
#
# Biplot 0 0
#
# Site constraints 0 0
"""
#print PCoA_result
from emperor import Emperor
from skbio import OrdinationResults
#load metadata
mf = load_mf(args.map_fp)
# must read from file (scikit-bio version 0.5.1 http://scikit-bio.org/docs/0.5.1/generated/generated/skbio.stats.ordination.OrdinationResults.html
res = OrdinationResults.read(pcfile)
emp = Emperor(res, mf)
#pcoa_outdir = os.path.join(args.basedir,'views', 'tmp',args.prefix+'_pcoa3d')
pcoa_outdir = os.path.join(args.basedir, args.prefix + '_pcoa3d')
print('OUT?', pcoa_outdir, args.basedir)
os.makedirs(pcoa_outdir, mode=0o777, exist_ok=True)
with open(os.path.join(pcoa_outdir, 'index.html'), 'w') as f:
f.write(emp.make_emperor(standalone=True))
emp.copy_support_files(pcoa_outdir)
def create_emperor_visual(args, pcfile):
"""
Sample .pc file
# Eigvals 4
# 0.2705559825337763 0.07359266496720843 0.02997793703738496 0.0
#
# Proportion explained 4
# 0.7231669539538659 0.19670525434062255 0.0801277917055116 0.0
#
# Species 0 0
#
# Site 4 4
# ICM_LCY_Bv6--LCY_0001_2003_05_11 -0.04067063044757823 -0.09380781760926289 0.13680474645584195 0.0
# ICM_LCY_Bv6--LCY_0003_2003_05_04 -0.11521436634022217 -0.15957409396683217 -0.10315005726535573 0.0
# ICM_LCY_Bv6--LCY_0005_2003_05_16 0.4268532792747924 0.06657577342833808 -0.02212569426459717 0.0
# ICM_LCY_Bv6--LCY_0007_2003_05_04 -0.2709682824869916 0.18680613814775715 -0.011528994925888972 0.0
#
# Biplot 0 0
#
# Site constraints 0 0
"""
#print PCoA_result
from emperor import Emperor
from skbio import OrdinationResults
#load metadata
mf = load_mf(args.map_fp)
# must read from file (scikit-bio version 0.5.1 http://scikit-bio.org/docs/0.5.1/generated/generated/skbio.stats.ordination.OrdinationResults.html
res = OrdinationResults.read(pcfile)
emp = Emperor(res, mf)
pcoa_outdir = os.path.join(args.basedir,'views', 'tmp',args.prefix+'_pcoa3d')
print('OUT?',pcoa_outdir,args.basedir)
os.makedirs(pcoa_outdir, exist_ok=True)
with open(os.path.join(pcoa_outdir, 'index.html'), 'w') as f:
f.write(emp.make_emperor(standalone=True))
emp.copy_support_files(pcoa_outdir)
coords = (np.random.randn(N, 10) * 1000).tolist()
pct_var = pd.Series(1/np.exp(np.arange(10)))
pct_var = pct_var / pct_var.sum()
md_headers = ['SampleID', 'DOB', 'Strings']
metadata = []
for _id in coords_ids:
metadata.append([_id, ''.join(sample(set(categories), 1)), ''.join(choice(
ascii_letters) for x in range(10))])
samples = pd.DataFrame(index=coords_ids, data=coords)
mf = pd.DataFrame(data=metadata, columns=md_headers)
mf.set_index('SampleID', inplace=True)
minerals = ['rhodium', 'platinum', 'gold', 'ruthenium']
mf['subject'] = np.random.randint(low=0, high=len(minerals), size=N)
mf['subject'] = mf['subject'].apply(lambda x: minerals[x])
res = OrdinationResults(short_method_name='PC', long_method_name='Principal '
'Coordinates Analysis', eigvals=pct_var,
samples=samples, proportion_explained=pct_var)
viz = Emperor(res, mf, remote=get_emperor_support_files_dir())
with open('new-emperor.html', 'w') as f:
f.write(viz.make_emperor(standalone=True))
def main(arguments):
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=textwrap.dedent(
'''additional information: output of taxsum, betadiv and pcoa are written in STDOUT
If you use microbiomeutils in your work, please cite:
Tremblay, Julien
microbiomeutils 0.9 : Microbiome utilities
https://github.com/jtremblay/microbiomeutils
Thank you.'''))
subparsers = parser.add_subparsers(title='subcommands',
description='valid subcommands',
help='additional help',
dest="command")
parser_bd = subparsers.add_parser('betadiv')
parser_bd.add_argument('-i',
'--infile-feature-table',
help="Input file",
type=argparse.FileType('r'))
parser_bd.add_argument("-m",
"--metric",
help="Diversity metric (default: bray-curtis)",
choices=["bray-curtis", "weighted-unifrac"],
default="bray-curtis")
parser_bd.add_argument("-t",
"--infile-tree",
help="Tree file (for weighted uniFrac)",
type=argparse.FileType('r'))
#parser_bd.set_defaults(func=betadiv)
parser_ts = subparsers.add_parser('taxsum')
parser_ts.add_argument('-i',
'--infile-feature-table',
help="Input file",
type=argparse.FileType('r'))
parser_ts.add_argument("-t",
"--sumtype",
help="Summary type (default: absolute)",
choices=["absolute", "relative"],
default="absolute")
parser_ts.add_argument("-l",
"--level",
help="Level <int> 1 to 7",
choices=["1", "2", "3", "4", "5", "6", "7", "8"],
default="3")
#parser_bd.set_defaults(func=taxsum)
parser_ts = subparsers.add_parser('pcoa')
parser_ts.add_argument('-i',
'--infile-distance-matrix',
help="Input file",
type=argparse.FileType('r'))
parser_ts = subparsers.add_parser('emperor')
parser_ts.add_argument('-i',
'--infile-coords',
help="Input file",
type=argparse.FileType('r'))
parser_ts.add_argument('-m',
'--mapping-file',
help="Mapping file",
type=argparse.FileType('r'))
parser_ts.add_argument('-o', '--outdir', help="Output directory")
args = parser.parse_args(arguments)
if args.command == 'betadiv':
infile_feature_table = os.path.abspath(args.infile_feature_table.name)
sys.stderr.write("[betadiv]\n")
if args.infile_tree is None and args.metric == "weighted-unifrac":
raise ValueError(
'weighted-unifrac needs a tree supplied. --infile-tree needed')
if args.metric == "bray-curtis":
betadiv(infile_feature_table, args.metric)
else:
betadiv(infile_feature_table, args.metric, args.infile_tree.name)
elif args.command == 'taxsum':
infile_feature_table = os.path.abspath(args.infile_feature_table.name)
sys.stderr.write("[taxsum]\n")
taxsum(infile_feature_table, args.sumtype, args.level)
elif args.command == 'pcoa':
sys.stderr.write("[pcoa]\n")
infile_distance_matrix = os.path.abspath(
args.infile_distance_matrix.name)
ord_res = do_pcoa(infile_distance_matrix)
elif args.command == 'emperor':
sys.stderr.write("[emperor]\n")
metadata = pd.read_csv(args.mapping_file,
sep='\t',
index_col='#SampleID',
dtype={'#SampleID': 'string'})
ordination = OrdinationResults.read(args.infile_coords)
# the remote argument refers to where the support files will be located
# relative to the plot itself i.e. index.html.
emp = Emperor(ordination, metadata, remote='.')
output_folder = args.outdir # new folder where data will be saved
# create an output directory
os.makedirs(output_folder, exist_ok=True)
with open(os.path.join(output_folder, 'index.html'), 'w') as f:
f.write(emp.make_emperor(standalone=True))
emp.copy_support_files(output_folder)
class Empress():
def __init__(self,
tree,
table,
sample_metadata,
feature_metadata=None,
ordination=None,
ignore_missing_samples=False,
filter_missing_features=False,
resource_path=None,
filter_unobserved_features_from_phylogeny=True):
"""Visualize a phylogenetic tree
Use this object to interactively display a phylogenetic tree using the
Empress GUI.
Parameters
----------
tree: bp.Tree:
The phylogenetic tree to visualize.
table: pd.DataFrame:
The matrix to visualize paired with the phylogenetic tree.
sample_metadata: pd.DataFrame
DataFrame object with the metadata associated to the samples in the
``ordination`` object, should have an index set and it should match
the identifiers in the ``ordination`` object.
feature_metadata: pd.DataFrame, optional
DataFrame object with the metadata associated to the names of
tips and/or internal nodes in the ``tree`` object, should have an
index set and it should match at least one of these nodes' names.
ordination: skbio.OrdinationResults, optional
Object containing the computed values for an ordination method in
scikit-bio. Currently supports skbio.stats.ordination.PCoA and
skbio.stats.ordination.RDA results.
ignore_missing_samples: bool, optional (default False)
If True, pads missing samples (i.e. samples in the table but not
the metadata) with placeholder metadata. If False, raises a
DataMatchingError if any such samples exist. (Note that in either
case, samples in the metadata but not in the table are filtered
out; and if no samples are shared between the table and metadata, a
DataMatchingError is raised regardless.) This is analogous to the
ignore_missing_samples flag in Emperor.
filter_missing_features: bool, optional (default False)
If True, filters features from the table that aren't present as
tips in the tree. If False, raises a DataMatchingError if any such
features exist. (Note that in either case, features in the tree but
not in the table are preserved.)
resource_path: str, optional
Load the resources from a user-specified remote location. If set to
None resources are loaded from the current directory.
filter_unobserved_features_from_phylogeny: bool, optional
If True, filters features from the phylogeny that aren't present as
features in feature table. features in feature table. Otherwise,
the phylogeny is not filtered.
Attributes
----------
tree:
Phylogenetic tree.
table:
Contingency matrix for the phylogeny.
samples:
Sample metadata.
features:
Feature metadata.
ordination:
Ordination matrix to visualize simultaneously with the tree.
base_url:
Base path to the remote resources.
"""
self.tree = tree
self.table = table
self.samples = sample_metadata.copy()
if feature_metadata is not None:
self.features = feature_metadata.copy()
else:
self.features = None
self.ordination = ordination
self.base_url = resource_path
if self.base_url is None:
self.base_url = './'
self._validate_and_match_data(
ignore_missing_samples, filter_missing_features,
filter_unobserved_features_from_phylogeny)
if self.ordination is not None:
# Note that tip-level metadata is the only "feature metadata" we
# send to Emperor, because internal nodes in the tree should not
# correspond to features in the table (and thus to arrows in a
# biplot).
self._emperor = Emperor(
self.ordination,
mapping_file=self.samples,
feature_mapping_file=self.tip_md,
ignore_missing_samples=ignore_missing_samples,
remote='./emperor-resources')
else:
self._emperor = None
def _validate_and_match_data(self, ignore_missing_samples,
filter_missing_features,
filter_unobserved_features_from_phylogeny):
# remove unobserved features from the phylogeny
if filter_unobserved_features_from_phylogeny:
self.tree = self.tree.shear(set(self.table.columns))
# extract balance parenthesis
self._bp_tree = list(self.tree.B)
self.tree = Tree.from_tree(to_skbio_treenode(self.tree))
fill_missing_node_names(self.tree)
# Note that the feature_table we get from QIIME 2 (as an argument to
# this function) is set up such that the index describes sample IDs and
# the columns describe feature IDs. We transpose this table before
# sending it to tools.match_inputs() and keep using the transposed
# table for the rest of this visualizer.
self.table, self.samples, self.tip_md, self.int_md = match_inputs(
self.tree, self.table.T, self.samples, self.features,
ignore_missing_samples, filter_missing_features)
def copy_support_files(self, target=None):
"""Copies the support files to a target directory
If an ordination is included Emperor's support files will also be
copied over (in a directory named emperor-resources).
Parameters
----------
target : str
The path where resources should be copied to. By default it copies
the files to ``self.base_url``.
"""
if target is None:
target = self.base_url
# copy the required resources
copytree(SUPPORT_FILES, os.path.join(target, 'support_files'))
if self._emperor is not None:
self._emperor.copy_support_files(
os.path.join(target, 'emperor-resources'))
def __str__(self):
return self.make_empress()
def make_empress(self):
"""Build an empress plot
Returns
-------
str
Formatted empress plot.
Notes
-----
Once you generate the plot (and write it to a HTML file in a given
directory) you will need to copy the support files (the JS/CSS/etc.
code needed to view the visualization) to the same directory by calling
the ``copy_support_files`` method.
See Also
--------
empress.core.Empress.copy_support_files
"""
main_template = self._get_template()
# _process_data does a lot of munging to the coordinates data and
# _to_dict puts the data into a dictionary-like object for consumption
data = self._to_dict()
plot = main_template.render(data)
return plot
def _to_dict(self):
"""Convert processed data into a dictionary
Returns
-------
dict
A dictionary describing the plots contained in the ordination
object and the sample + feature metadata.
"""
# Compute coordinates resulting from layout algorithm(s)
# TODO: figure out implications of screen size
layout_to_coordsuffix, default_layout = self.tree.coords(4020, 4020)
tree_data = {}
names_to_keys = {}
for i, node in enumerate(self.tree.postorder(include_self=True), 1):
tree_data[i] = {
'name': node.name,
'color': [0.75, 0.75, 0.75],
'sampVal': 1,
'visible': True,
'single_samp': False
}
# Add coordinate data from all layouts for this node
for layoutsuffix in layout_to_coordsuffix.values():
xcoord = "x" + layoutsuffix
ycoord = "y" + layoutsuffix
tree_data[i][xcoord] = getattr(node, xcoord)
tree_data[i][ycoord] = getattr(node, ycoord)
# Hack: it isn't mentioned above, but we need start pos info for
# circular layout. The start pos for the other layouts is the
# parent xy coordinates so we need only need to specify the start
# for circular layout.
tree_data[i]["xc0"] = node.xc0
tree_data[i]["yc0"] = node.yc0
# Also add vertical bar coordinate info for the rectangular layout,
# and start point & arc coordinate info for the circular layout
if not node.is_tip():
tree_data[i]["highestchildyr"] = node.highest_child_yr
tree_data[i]["lowestchildyr"] = node.lowest_child_yr
if not node.is_root():
tree_data[i]["arcx0"] = node.arcx0
tree_data[i]["arcy0"] = node.arcy0
tree_data[i]["arcstartangle"] = node.highest_child_clangle
tree_data[i]["arcendangle"] = node.lowest_child_clangle
if node.name in names_to_keys:
names_to_keys[node.name].append(i)
else:
names_to_keys[node.name] = [i]
names = []
for node in self.tree.preorder(include_self=True):
names.append(node.name)
# Convert sample metadata to a JSON-esque format
sample_data = self.samples.to_dict(orient='index')
# Convert feature metadata, similarly to how we handle sample metadata.
# If the user passed in feature metadata, self.features won't be None.
# (We don't actually use any data from self.features at this point in
# the program since it hasn't had taxonomy splitting / matching / etc.
# done.)
if self.features is not None:
# If we're in this block, we know that self.tip_md and self.int_md
# are both DataFrames. They have identical columns, so we can just
# use self.tip_md.columns when setting feature_metadata_columns.
# (We don't use self.features.columns because stuff like taxonomy
# splitting will have changed the columns from what they initially
# were in some cases.)
feature_metadata_columns = list(self.tip_md.columns)
# Calling .to_dict() on an empty DataFrame just gives you {}, so
# this is safe even if there is no tip or internal node metadata.
# (...At least one of these DFs should be populated, though, since
# none of the feature IDs matching up would have caused an error.)
tip_md_json = self.tip_md.to_dict(orient='index')
int_md_json = self.int_md.to_dict(orient='index')
else:
feature_metadata_columns = []
tip_md_json = {}
int_md_json = {}
# TODO: Empress is currently storing all metadata as strings. This is
# memory intensive and won't scale well. We should convert all numeric
# data/compress metadata.
# This is used in biom-table. Currently this is only used to ignore
# null data (i.e. NaN and "unknown") and also determines sorting order.
# The original intent is to signal what columns are
# discrete/continuous. type of sample metadata (n - number, o - object)
sample_data_type = self.samples.dtypes.to_dict()
sample_data_type = {
k: 'n' if pd.api.types.is_numeric_dtype(v) else 'o'
for k, v in sample_data_type.items()
}
# create a mapping of observation ids and the samples that contain them
obs_data = {}
feature_table = (self.table > 0)
for _, series in feature_table.iteritems():
sample_ids = series[series].index.tolist()
obs_data[series.name] = sample_ids
data_to_render = {
'base_url': './support_files',
'tree': self._bp_tree,
'tree_data': tree_data,
'names_to_keys': names_to_keys,
'sample_data': sample_data,
'sample_data_type': sample_data_type,
'tip_metadata': tip_md_json,
'int_metadata': int_md_json,
'feature_metadata_columns': feature_metadata_columns,
'obs_data': obs_data,
'names': names,
'layout_to_coordsuffix': layout_to_coordsuffix,
'default_layout': default_layout,
'emperor_div': '',
'emperor_require_logic': '',
'emperor_style': '',
'emperor_base_dependencies': '',
'emperor_classes': ''
}
if self._emperor is not None:
data_to_render.update(self._scavenge_emperor())
return data_to_render
def _get_template(self, standalone=False):
"""Get the jinja template object
Parameters
----------
standalone: bool, optional
Whether or not the generated plot will load resources locally
(``True``), or from a specified URL (``False``).
Returns
-------
jinja2.Template
Template where the plot is created.
"""
# based on: http://stackoverflow.com/a/6196098
env = Environment(loader=FileSystemLoader(TEMPLATES))
return env.get_template('empress-template.html')
def _scavenge_emperor(self):
# can't make this 50vw because one of the plot containers has some
# padding that makes the divs stack on top of each other
self._emperor.width = '48vw'
self._emperor.height = '100vh; float: right'
# make the background white so it matches Empress
self._emperor.set_background_color('white')
self._emperor.set_axes(color='black')
html = self._emperor.make_emperor(standalone=True)
html = html.split('\n')
# The following line references will be replace with API calls to the
# Emperor object, however those are not implemented yet
emperor_base_dependencies = html[6]
# line 14 is where the CSS includes start, but it is surrounded by
# unnecessary tags so we strip those out
style = '\n'.join([
line.strip().replace("'", '').replace(',', '')
for line in html[14:20]
])
# main divs for emperor
emperor_div = '\n'.join(html[39:44])
# main js script for emperor
emperor_require_logic = '\n'.join(html[45:-3])
# once everything is loaded replace the callback tag for custom JS
with open(SELECTION_CALLBACK_PATH) as f:
selection_callback = f.read()
emperor_require_logic = emperor_require_logic.replace(
'/*__select_callback__*/', selection_callback)
emperor_data = {
'emperor_div': emperor_div,
'emperor_require_logic': emperor_require_logic,
'emperor_style': style,
'emperor_base_dependencies': emperor_base_dependencies,
'emperor_classes': 'combined-plot-container'
}
return emperor_data
