2 Analyze the Summary.htm file produced by GERALD
6 from htsworkflow.pipelines.runfolder import ElementTree
7 from htsworkflow.util.ethelp import indent, flatten
11 Extract some useful information from the Summary.htm file
16 class LaneResultSummary(object):
18 Parse the LaneResultSummary table out of Summary.htm
19 Mostly for the cluster number
21 LANE_RESULT_SUMMARY = 'LaneResultSummary'
23 'LaneYield': 'lane_yield',
24 'Cluster': 'cluster', # Raw
25 'ClusterPF': 'cluster_pass_filter',
26 'AverageFirstCycleIntensity': 'average_first_cycle_intensity',
27 'PercentIntensityAfter20Cycles': 'percent_intensity_after_20_cycles',
28 'PercentPassFilterClusters': 'percent_pass_filter_clusters',
29 'PercentPassFilterAlign': 'percent_pass_filter_align',
30 'AverageAlignmentScore': 'average_alignment_score',
31 'PercentErrorRate': 'percent_error_rate'
34 def __init__(self, html=None, xml=None):
37 self.lane_yield = None
39 self.cluster_pass_filter = None
40 self.average_first_cycle_intensity = None
41 self.percent_intensity_after_20_cycles = None
42 self.percent_pass_filter_clusters = None
43 self.percent_pass_filter_align = None
44 self.average_alignment_score = None
45 self.percent_error_rate = None
48 self.set_elements_from_html(html)
50 self.set_elements(xml)
52 def set_elements_from_html(self, data):
53 if not len(data) in (8,10):
54 raise RuntimeError("Summary.htm file format changed")
56 # same in pre-0.3.0 Summary file and 0.3 summary file
57 self.lane = int(data[0])
60 parsed_data = [ parse_mean_range(x) for x in data[1:] ]
61 # this is the < 0.3 Pipeline version
62 self.cluster = parsed_data[0]
63 self.average_first_cycle_intensity = parsed_data[1]
64 self.percent_intensity_after_20_cycles = parsed_data[2]
65 self.percent_pass_filter_clusters = parsed_data[3]
66 self.percent_pass_filter_align = parsed_data[4]
67 self.average_alignment_score = parsed_data[5]
68 self.percent_error_rate = parsed_data[6]
70 parsed_data = [ parse_mean_range(x) for x in data[2:] ]
71 # this is the >= 0.3 summary file
72 self.lane_yield = data[1]
73 self.cluster = parsed_data[0]
74 self.cluster_pass_filter = parsed_data[1]
75 self.average_first_cycle_intensity = parsed_data[2]
76 self.percent_intensity_after_20_cycles = parsed_data[3]
77 self.percent_pass_filter_clusters = parsed_data[4]
78 self.percent_pass_filter_align = parsed_data[5]
79 self.average_alignment_score = parsed_data[6]
80 self.percent_error_rate = parsed_data[7]
82 def get_elements(self):
83 lane_result = ElementTree.Element(
84 Summary.LaneResultSummary.LANE_RESULT_SUMMARY,
85 {'lane': str(self.lane), 'end': str(self.end)})
86 for tag, variable_name in Summary.LaneResultSummary.TAGS.items():
87 value = getattr(self, variable_name)
90 # it looks like a sequence
91 elif type(value) in (types.TupleType, types.ListType):
92 element = make_mean_range_element(
98 element = ElementTree.SubElement(lane_result, tag)
102 def set_elements(self, tree):
103 if tree.tag != Summary.LaneResultSummary.LANE_RESULT_SUMMARY:
104 raise ValueError('Expected %s' % (
105 Summary.LaneResultSummary.LANE_RESULT_SUMMARY))
106 self.lane = int(tree.attrib['lane'])
107 # default to the first end, for the older summary files
108 # that are single ended
109 self.end = int(tree.attrib.get('end', 0))
110 tags = Summary.LaneResultSummary.TAGS
111 for element in list(tree):
113 variable_name = tags[element.tag]
114 setattr(self, variable_name,
115 parse_summary_element(element))
117 logging.warn('Unrecognized tag %s' % (element.tag,))
119 def __init__(self, filename=None, xml=None):
120 # lane results is a list of 1 or 2 ends containing
121 # a dictionary of all the lanes reported in this
123 self.lane_results = [{}]
125 if filename is not None:
126 self._extract_lane_results(filename)
128 self.set_elements(xml)
130 def __getitem__(self, key):
131 return self.lane_results[key]
134 return len(self.lane_results)
136 def _flattened_row(self, row):
138 flatten the children of a <tr>...</tr>
140 return [flatten(x) for x in row.getchildren() ]
142 def _parse_table(self, table):
144 assumes the first line is the header of a table,
145 and that the remaining rows are data
147 rows = table.getchildren()
150 data.append(self._flattened_row(r))
153 def _extract_named_tables(self, pathname):
155 extract all the 'named' tables from a Summary.htm file
156 and return as a dictionary
158 Named tables are <h2>...</h2><table>...</table> pairs
159 The contents of the h2 tag is considered to the name
162 # tree = ElementTree.parse(pathname).getroot()
163 # hack for 1.1rc1, this should be removed when possible.
164 file_body = open(pathname).read()
165 file_body = file_body.replace('CHASTITY<=', 'CHASTITY<=')
166 tree = ElementTree.fromstring(file_body)
167 body = tree.find('body')
169 for i in range(len(body)):
170 if body[i].tag == 'h2' and body[i+1].tag == 'table':
171 # we have an interesting table
172 name = flatten(body[i])
174 data = self._parse_table(table)
178 def _extract_lane_results(self, pathname):
179 tables = self._extract_named_tables(pathname)
180 table_names = [ ('Lane Results Summary', 0),
181 ('Lane Results Summary : Read 1', 0),
182 ('Lane Results Summary : Read 2', 1),]
183 for name, end in table_names:
184 if tables.has_key(name):
185 self._extract_lane_results_for_end(tables, name, end)
187 def _extract_lane_results_for_end(self, tables, table_name, end):
189 extract the Lane Results Summary table
191 # parse lane result summary
192 lane_summary = tables[table_name]
193 # this is version 1 of the summary file
194 if len(lane_summary[-1]) == 8:
196 headers = lane_summary[0]
197 # grab the lane by lane data
198 lane_summary = lane_summary[1:]
200 # this is version 2 of the summary file
201 if len(lane_summary[-1]) == 10:
202 # lane_summary[0] is a different less specific header row
203 headers = lane_summary[1]
204 lane_summary = lane_summary[2:10]
205 # after the last lane, there's a set of chip wide averages
207 # append an extra dictionary if needed
208 if len(self.lane_results) < (end + 1):
209 self.lane_results.append({})
211 for r in lane_summary:
212 lrs = Summary.LaneResultSummary(html=r)
214 self.lane_results[lrs.end][lrs.lane] = lrs
216 def get_elements(self):
217 summary = ElementTree.Element(Summary.SUMMARY,
218 {'version': unicode(Summary.XML_VERSION)})
219 for end in self.lane_results:
220 for lane in end.values():
221 summary.append(lane.get_elements())
224 def set_elements(self, tree):
225 if tree.tag != Summary.SUMMARY:
226 return ValueError("Expected %s" % (Summary.SUMMARY,))
227 xml_version = int(tree.attrib.get('version', 0))
228 if xml_version > Summary.XML_VERSION:
229 logging.warn('Summary XML tree is a higher version than this class')
230 for element in list(tree):
231 lrs = Summary.LaneResultSummary()
232 lrs.set_elements(element)
233 if len(self.lane_results) < (lrs.end + 1):
234 self.lane_results.append({})
235 self.lane_results[lrs.end][lrs.lane] = lrs
237 def is_paired_end(self):
238 return len(self.lane_results) == 2
242 Debugging function, report current object
248 Convert a value to int if its an int otherwise a float.
252 except ValueError, e:
256 def parse_mean_range(value):
258 Parse values like 123 +/- 4.5
260 if value.strip() == 'unknown':
263 average, pm, deviation = value.split()
265 raise RuntimeError("Summary.htm file format changed")
266 return tonumber(average), tonumber(deviation)
268 def make_mean_range_element(parent, name, mean, deviation):
270 Make an ElementTree subelement <Name mean='mean', deviation='deviation'/>
272 element = ElementTree.SubElement(parent, name,
273 { 'mean': unicode(mean),
274 'deviation': unicode(deviation)})
277 def parse_mean_range_element(element):
279 Grab mean/deviation out of element
281 return (tonumber(element.attrib['mean']),
282 tonumber(element.attrib['deviation']))
284 def parse_summary_element(element):
286 Determine if we have a simple element or a mean/deviation element
288 if len(element.attrib) > 0:
289 return parse_mean_range_element(element)