def getFormattedResult(self, specs=None, decimalmark='.', sciformat=1):
"""Formatted result:
1. If the result is not floatable, return it without being formatted
2. If the analysis specs has hidemin or hidemax enabled and the
result is out of range, render result as '<min' or '>max'
3. If the result is floatable, render it to the correct precision
specs param is optional. A dictionary as follows:
{'min': <min_val>,
'max': <max_val>,
'error': <error>,
'hidemin': <hidemin_val>,
'hidemax': <hidemax_val>}
:param sciformat: 1. The sci notation has to be formatted as aE^+b
2. The sci notation has to be formatted as a·10^b
3. As 2, but with super html entity for exp
4. The sci notation has to be formatted as a·10^b
5. As 4, but with super html entity for exp
By default 1
"""
result = self.getResult()
service = self.getService()
# 1. If the result is not floatable, return it without being formatted
try:
result = float(result)
except:
return result
# 2. If the analysis specs has enabled hidemin or hidemax and the
# result is out of range, render result as '<min' or '>max'
belowmin = False
abovemax = False
if not specs:
uid = self.getServiceUID()
specs = self.aq_parent.getResultsRangeDict().get(uid, {})
hidemin = specs.get('hidemin', '')
hidemax = specs.get('hidemax', '')
try:
belowmin = hidemin and result < float(hidemin) or False
except:
belowmin = False
pass
try:
abovemax = hidemax and result > float(hidemax) or False
except:
abovemax = False
pass
# 2.1. If result is below min and hidemin enabled, return '<min'
if belowmin:
return formatDecimalMark('< %s' % hidemin, decimalmark)
# 2.2. If result is above max and hidemax enabled, return '>max'
if abovemax:
return formatDecimalMark('> %s' % hidemax, decimalmark)
# 3. If the result is floatable, render it to the correct precision
return formatDecimalMark(format_numeric_result(self, result, sciformat), decimalmark)
def _analysis_data(self, analysis):
""" Returns a dict that represents the analysis
"""
decimalmark = analysis.aq_parent.aq_parent.getDecimalMark()
keyword = analysis.getKeyword()
service = analysis.getService()
andict = {'obj': analysis,
'id': analysis.id,
'title': analysis.Title(),
'keyword': keyword,
'scientific_name': service.getScientificName(),
'accredited': service.getAccredited(),
'point_of_capture': to_utf8(POINTS_OF_CAPTURE.getValue(service.getPointOfCapture())),
'category': to_utf8(service.getCategoryTitle()),
'result': analysis.getResult(),
'unit': to_utf8(service.getUnit()),
'formatted_unit': format_supsub(to_utf8(service.getUnit())),
'capture_date': analysis.getResultCaptureDate(),
'request_id': analysis.aq_parent.getId(),
'formatted_result': '',
'uncertainty': analysis.getUncertainty(),
'formatted_uncertainty': '',
'retested': analysis.getRetested(),
'remarks': to_utf8(analysis.getRemarks()),
'resultdm': to_utf8(analysis.getResultDM()),
'outofrange': False,
'type': analysis.portal_type,
'reftype': analysis.getReferenceType() \
if hasattr(analysis, 'getReferenceType')
else None,
'worksheet': None,
'specs': {},
'formatted_specs': ''}
andict['refsample'] = analysis.getSample().id \
if analysis.portal_type == 'Analysis' \
else '%s - %s' % (analysis.aq_parent.id, analysis.aq_parent.Title())
# Which analysis specs must be used?
# Try first with those defined at AR Publish Specs level
if analysis.portal_type == 'ReferenceAnalysis':
# The analysis is a Control or Blank. We might use the
# reference results instead other specs
uid = analysis.getServiceUID()
specs = analysis.aq_parent.getResultsRangeDict().get(uid, {})
elif analysis.portal_type == 'DuplicateAnalysis':
specs = analysis.getAnalysisSpecs()
else:
ar = analysis.aq_parent
specs = ar.getPublicationSpecification()
if not specs or keyword not in specs.getResultsRangeDict():
specs = analysis.getAnalysisSpecs()
specs = specs.getResultsRangeDict().get(keyword, {}) \
if specs else {}
andict['specs'] = specs
scinot = self.context.bika_setup.getScientificNotationReport()
andict['formatted_result'] = analysis.getFormattedResult(
specs=specs, sciformat=int(scinot), decimalmark=decimalmark)
fs = ''
if specs.get('min', None) and specs.get('max', None):
fs = '%s - %s' % (specs['min'], specs['max'])
elif specs.get('min', None):
fs = '> %s' % specs['min']
elif specs.get('max', None):
fs = '< %s' % specs['max']
andict['formatted_specs'] = formatDecimalMark(fs, decimalmark)
andict['formatted_uncertainty'] = format_uncertainty(
analysis,
analysis.getResult(),
decimalmark=decimalmark,
sciformat=int(scinot))
# Out of range?
if specs:
adapters = getAdapters((analysis, ), IResultOutOfRange)
bsc = getToolByName(self.context, "bika_setup_catalog")
for name, adapter in adapters:
ret = adapter(specification=specs)
if ret and ret['out_of_range']:
andict['outofrange'] = True
break
return andict
def format_uncertainty(analysis, result, decimalmark='.', sciformat=1):
"""
Returns the formatted uncertainty according to the analysis, result
and decimal mark specified following these rules:
If the "Calculate precision from uncertainties" is enabled in
the Analysis service, and
a) If the the non-decimal number of digits of the result is above
the service's ExponentialFormatPrecision, the uncertainty will
be formatted in scientific notation. The uncertainty exponential
value used will be the same as the one used for the result. The
uncertainty will be rounded according to the same precision as
the result.
Example:
Given an Analysis with an uncertainty of 37 for a range of
results between 30000 and 40000, with an
ExponentialFormatPrecision equal to 4 and a result of 32092,
this method will return 0.004E+04
b) If the number of digits of the integer part of the result is
below the ExponentialFormatPrecision, the uncertainty will be
formatted as decimal notation and the uncertainty will be
rounded one position after reaching the last 0 (precision
calculated according to the uncertainty value).
Example:
Given an Analysis with an uncertainty of 0.22 for a range of
results between 1 and 10 with an ExponentialFormatPrecision
equal to 4 and a result of 5.234, this method will return 0.2
If the "Calculate precision from Uncertainties" is disabled in the
analysis service, the same rules described above applies, but the
precision used for rounding the uncertainty is not calculated from
the uncertainty neither the result. The fixed length precision is
used instead.
For further details, visit
https://jira.bikalabs.com/browse/LIMS-1334
If the result is not floatable or no uncertainty defined, returns
an empty string.
The default decimal mark '.' will be replaced by the decimalmark
specified.
:param analysis: the analysis from which the uncertainty, precision
and other additional info have to be retrieved
:param result: result of the analysis. Used to retrieve and/or
calculate the precision and/or uncertainty
:param decimalmark: decimal mark to use. By default '.'
:param sciformat: 1. The sci notation has to be formatted as aE^+b
2. The sci notation has to be formatted as ax10^b
3. As 2, but with super html entity for exp
4. The sci notation has to be formatted as a·10^b
5. As 4, but with super html entity for exp
By default 1
:return: the formatted uncertainty
"""
try:
result = float(result)
except ValueError:
return ""
objres = None
try:
objres = float(analysis.getResult())
except ValueError:
pass
service = analysis.getService()
uncertainty = None
if result == objres:
# To avoid problems with DLs
uncertainty = analysis.getUncertainty()
else:
uncertainty = analysis.getUncertainty(result)
if uncertainty is None or uncertainty == 0:
return ""
# Scientific notation?
# Get the default precision for scientific notation
threshold = service.getExponentialFormatPrecision()
# Current result precision is above the threshold?
sig_digits = get_significant_digits(result)
negative = sig_digits < 0
sign = '-' if negative else ''
sig_digits = abs(sig_digits)
sci = sig_digits >= threshold and sig_digits > 0
formatted = ''
if sci:
# Scientific notation
# 3.2014E+4
if negative == True:
res = float(uncertainty)*(10**sig_digits)
else:
res = float(uncertainty)/(10**sig_digits)
res = float(str("%%.%sf" % (sig_digits-1)) % res)
res = int(res) if res.is_integer() else res
if sciformat in [2,3,4,5]:
if sciformat == 2:
# ax10^b or ax10^-b
formatted = "%s%s%s%s" % (res,"x10^",sign,sig_digits)
elif sciformat == 3:
# ax10<super>b</super> or ax10<super>-b</super>
formatted = "%s%s%s%s%s" % (res,"x10<sup>",sign,sig_digits,"</sup>")
elif sciformat == 4:
# ax10^b or ax10^-b
formatted = "%s%s%s%s" % (res,"·10^",sign,sig_digits)
elif sciformat == 5:
# ax10<super>b</super> or ax10<super>-b</super>
formatted = "%s%s%s%s%s" % (res,"·10<sup>",sign,sig_digits,"</sup>")
else:
# Default format: aE^+b
sig_digits = "%02d" % sig_digits
formatted = "%s%s%s%s" % (res,"e",sign,sig_digits)
#formatted = str("%%.%se" % sig_digits) % uncertainty
else:
# Decimal notation
prec = analysis.getPrecision(result)
prec = prec if prec else ''
formatted = str("%%.%sf" % prec) % uncertainty
return formatDecimalMark(formatted, decimalmark)
def _analysis_data(self, analysis, decimalmark=None):
keyword = analysis.getKeyword()
service = analysis.getService()
andict = {'obj': analysis,
'id': analysis.id,
'title': analysis.Title(),
'keyword': keyword,
'scientific_name': service.getScientificName(),
'accredited': service.getAccredited(),
'point_of_capture': to_utf8(POINTS_OF_CAPTURE.getValue(service.getPointOfCapture())),
'category': to_utf8(service.getCategoryTitle()),
'result': analysis.getResult(),
'isnumber': isnumber(analysis.getResult()),
'unit': to_utf8(service.getUnit()),
'formatted_unit': format_supsub(to_utf8(service.getUnit())),
'capture_date': analysis.getResultCaptureDate(),
'request_id': analysis.aq_parent.getId(),
'formatted_result': '',
'uncertainty': analysis.getUncertainty(),
'formatted_uncertainty': '',
'retested': analysis.getRetested(),
'remarks': to_utf8(analysis.getRemarks()),
'resultdm': to_utf8(analysis.getResultDM()),
'outofrange': False,
'type': analysis.portal_type,
'reftype': analysis.getReferenceType() \
if hasattr(analysis, 'getReferenceType')
else None,
'worksheet': None,
'specs': {},
'formatted_specs': ''}
if analysis.portal_type == 'DuplicateAnalysis':
andict['reftype'] = 'd'
ws = analysis.getBackReferences('WorksheetAnalysis')
andict['worksheet'] = ws[0].id if ws and len(ws) > 0 else None
andict['worksheet_url'] = ws[0].absolute_url if ws and len(ws) > 0 else None
andict['refsample'] = analysis.getSample().id \
if analysis.portal_type == 'Analysis' \
else '%s - %s' % (analysis.aq_parent.id, analysis.aq_parent.Title())
# Which analysis specs must be used?
# Try first with those defined at AR Publish Specs level
if analysis.portal_type == 'ReferenceAnalysis':
# The analysis is a Control or Blank. We might use the
# reference results instead other specs
uid = analysis.getServiceUID()
specs = analysis.aq_parent.getResultsRangeDict().get(uid, {})
elif analysis.portal_type == 'DuplicateAnalysis':
specs = analysis.getAnalysisSpecs();
else:
ar = analysis.aq_parent
specs = ar.getPublicationSpecification()
if not specs or keyword not in specs.getResultsRangeDict():
specs = analysis.getAnalysisSpecs()
specs = specs.getResultsRangeDict().get(keyword, {}) \
if specs else {}
andict['specs'] = specs
scinot = self.context.bika_setup.getScientificNotationReport()
andict['formatted_result'] = analysis.getFormattedResult(specs=specs, sciformat=int(scinot), decimalmark=decimalmark)
fs = ''
if specs.get('min', None) and specs.get('max', None):
fs = '%s - %s' % (specs['min'], specs['max'])
elif specs.get('min', None):
fs = '> %s' % specs['min']
elif specs.get('max', None):
fs = '< %s' % specs['max']
andict['formatted_specs'] = formatDecimalMark(fs, decimalmark)
andict['formatted_uncertainty'] = format_uncertainty(analysis, analysis.getResult(), decimalmark=decimalmark, sciformat=int(scinot))
# Out of range?
if specs:
adapters = getAdapters((analysis, ), IResultOutOfRange)
bsc = getToolByName(self.context, "bika_setup_catalog")
for name, adapter in adapters:
ret = adapter(specification=specs)
if ret and ret['out_of_range']:
andict['outofrange'] = True
break
return andict
def format_numeric_result(analysis, result, decimalmark='.', sciformat=1):
"""
Returns the formatted number part of a results value. This is
responsible for deciding the precision, and notation of numeric
values in accordance to the uncertainty. If a non-numeric
result value is given, the value will be returned unchanged.
The following rules apply:
If the "Calculate precision from uncertainties" is enabled in
the Analysis service, and
a) If the non-decimal number of digits of the result is above
the service's ExponentialFormatPrecision, the result will
be formatted in scientific notation.
Example:
Given an Analysis with an uncertainty of 37 for a range of
results between 30000 and 40000, with an
ExponentialFormatPrecision equal to 4 and a result of 32092,
this method will return 3.2092E+04
b) If the number of digits of the integer part of the result is
below the ExponentialFormatPrecision, the result will be
formatted as decimal notation and the resulta will be rounded
in accordance to the precision (calculated from the uncertainty)
Example:
Given an Analysis with an uncertainty of 0.22 for a range of
results between 1 and 10 with an ExponentialFormatPrecision
equal to 4 and a result of 5.234, this method will return 5.2
If the "Calculate precision from Uncertainties" is disabled in the
analysis service, the same rules described above applies, but the
precision used for rounding the result is not calculated from
the uncertainty. The fixed length precision is used instead.
For further details, visit
https://jira.bikalabs.com/browse/LIMS-1334
The default decimal mark '.' will be replaced by the decimalmark
specified.
:param analysis: the analysis from which the uncertainty, precision
and other additional info have to be retrieved
:param result: result to be formatted.
:param decimalmark: decimal mark to use. By default '.'
:param sciformat: 1. The sci notation has to be formatted as aE^+b
2. The sci notation has to be formatted as ax10^b
3. As 2, but with super html entity for exp
4. The sci notation has to be formatted as a·10^b
5. As 4, but with super html entity for exp
By default 1
:return: the formatted result
"""
try:
result = float(result)
except ValueError:
return result
service = analysis.getService()
# Scientific notation?
# Get the default precision for scientific notation
threshold = service.getExponentialFormatPrecision()
# Current result precision is above the threshold?
sig_digits = get_significant_digits(result)
negative = sig_digits < 0
sign = '-' if negative else ''
sig_digits = abs(sig_digits)
sci = sig_digits >= threshold
formatted = ''
if sci:
# Scientific notation
if sciformat in [2,3,4,5]:
if negative == True:
res = float(result)*(10**sig_digits)
else:
res = float(result)/(10**sig_digits)
res = float(str("%%.%sf" % (sig_digits-1)) % res)
res = int(res) if res.is_integer() else res
if sciformat == 2:
# ax10^b or ax10^-b
formatted = "%s%s%s%s" % (res,"x10^",sign,sig_digits)
elif sciformat == 3:
# ax10<super>b</super> or ax10<super>-b</super>
formatted = "%s%s%s%s%s" % (res,"x10<sup>",sign,sig_digits,"</sup>")
elif sciformat == 4:
# ax10^b or ax10^-b
formatted = "%s%s%s%s" % (res,"·10^",sign,sig_digits)
elif sciformat == 5:
# ax10<super>b</super> or ax10<super>-b</super>
formatted = "%s%s%s%s%s" % (res,"·10<sup>",sign,sig_digits,"</sup>")
else:
# Default format: aE^+b
formatted = str("%%.%se" % sig_digits) % result
else:
# Decimal notation
prec = analysis.getPrecision(result)
prec = prec if prec else ''
formatted = str("%%.%sf" % prec) % result
formatted = str(int(float(formatted))) if float(formatted).is_integer() else formatted
return formatDecimalMark(formatted, decimalmark)