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%matplotlib inline
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import firecloud.api as fc
import pandas as pd
from StringIO import StringIO
from ggplot import *
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ws1=fc.get_entities('broad-project','mw-pathseq-dev For PROJECT',"sample_set").json()
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sample_set=filter(lambda x:x["name"]==u'low_alignment_76',ws)[0]["attributes"]
print(sample_set)
samples=map(lambda x: x["entityName"],sample_set["samples"]["items"])
samples
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In [25]:
temp=fc.get_entities('broad-project','mw-pathseq-dev For PROJECT',"sample").json()
ws=filter(lambda x: x["name"] in samples, temp)
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def get_top_pathseq_contam(wsi):
url=wsi.get('attributes').get('pathseq_WGS_scores')
url_metrics=wsi.get('attributes').get('pathseq_WGS_filter_metrics')
aligned=wsi.get('attributes').get('Aligned')
if aligned is None:
return None
content=!gsutil cat {url}
content_metrics=!gsutil cat {url_metrics}
data=pd.read_csv(StringIO("\n".join(content)),sep="\t")
data_metrics=pd.read_csv(StringIO("\n".join(content_metrics)),sep="\t",comment='#',header=True)
genus=data[data.type=='genus']
top_genus=genus.sort(columns='reads',ascending=False).iloc[0]
return {"top_genus_name":top_genus['name'],
"top_genus_reads":top_genus['reads'],
"percent_in_top_genus":top_genus['reads']/data_metrics.PRIMARY_READS[0],
"percent_passing_filters":data_metrics.FINAL_TOTAL_READS[0]/ data_metrics.PRIMARY_READS[0],
"percent_aligned_in_original":float(aligned)/100
}
In [28]:
res=pd.DataFrame.from_records(
filter(lambda x: x is not None,
map(lambda i: get_top_pathseq_contam(ws[i]),
range(len(ws)))))
res['percent_aligned_in_original']=res['percent_aligned_in_original'].convert_objects(convert_numeric=True)
res['top_genus_reads']=res['top_genus_reads'].convert_objects(convert_numeric=True)
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res
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In [31]:
p=ggplot(res,aes(x='percent_aligned_in_original',y='percent_in_top_genus',label='top_genus_name'))+ geom_point()
p.save("test.pdf")
