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brutasse/graphite-api
graphite_api/functions.py
_getPercentile
def _getPercentile(points, n, interpolate=False): """ Percentile is calculated using the method outlined in the NIST Engineering Statistics Handbook: http://www.itl.nist.gov/div898/handbook/prc/section2/prc252.htm """ sortedPoints = sorted(not_none(points)) if len(sortedPoints) == 0: return None fractionalRank = (n/100.0) * (len(sortedPoints) + 1) rank = int(fractionalRank) rankFraction = fractionalRank - rank if not interpolate: rank += int(math.ceil(rankFraction)) if rank == 0: percentile = sortedPoints[0] elif rank - 1 == len(sortedPoints): percentile = sortedPoints[-1] else: percentile = sortedPoints[rank - 1] # Adjust for 0-index if interpolate: if rank != len(sortedPoints): # if a next value exists nextValue = sortedPoints[rank] percentile = percentile + rankFraction * (nextValue - percentile) return percentile
python
def _getPercentile(points, n, interpolate=False): """ Percentile is calculated using the method outlined in the NIST Engineering Statistics Handbook: http://www.itl.nist.gov/div898/handbook/prc/section2/prc252.htm """ sortedPoints = sorted(not_none(points)) if len(sortedPoints) == 0: return None fractionalRank = (n/100.0) * (len(sortedPoints) + 1) rank = int(fractionalRank) rankFraction = fractionalRank - rank if not interpolate: rank += int(math.ceil(rankFraction)) if rank == 0: percentile = sortedPoints[0] elif rank - 1 == len(sortedPoints): percentile = sortedPoints[-1] else: percentile = sortedPoints[rank - 1] # Adjust for 0-index if interpolate: if rank != len(sortedPoints): # if a next value exists nextValue = sortedPoints[rank] percentile = percentile + rankFraction * (nextValue - percentile) return percentile
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brutasse/graphite-api
graphite_api/functions.py
nPercentile
def nPercentile(requestContext, seriesList, n): """Returns n-percent of each series in the seriesList.""" assert n, 'The requested percent is required to be greater than 0' results = [] for s in seriesList: # Create a sorted copy of the TimeSeries excluding None values in the # values list. s_copy = TimeSeries(s.name, s.start, s.end, s.step, sorted(not_none(s))) if not s_copy: continue # Skip this series because it is empty. perc_val = _getPercentile(s_copy, n) if perc_val is not None: name = 'nPercentile(%s, %g)' % (s_copy.name, n) point_count = int((s.end - s.start)/s.step) perc_series = TimeSeries(name, s_copy.start, s_copy.end, s_copy.step, [perc_val] * point_count) perc_series.pathExpression = name results.append(perc_series) return results
python
def nPercentile(requestContext, seriesList, n): """Returns n-percent of each series in the seriesList.""" assert n, 'The requested percent is required to be greater than 0' results = [] for s in seriesList: # Create a sorted copy of the TimeSeries excluding None values in the # values list. s_copy = TimeSeries(s.name, s.start, s.end, s.step, sorted(not_none(s))) if not s_copy: continue # Skip this series because it is empty. perc_val = _getPercentile(s_copy, n) if perc_val is not None: name = 'nPercentile(%s, %g)' % (s_copy.name, n) point_count = int((s.end - s.start)/s.step) perc_series = TimeSeries(name, s_copy.start, s_copy.end, s_copy.step, [perc_val] * point_count) perc_series.pathExpression = name results.append(perc_series) return results
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brutasse/graphite-api
graphite_api/functions.py
averageOutsidePercentile
def averageOutsidePercentile(requestContext, seriesList, n): """ Removes functions lying inside an average percentile interval """ averages = [safeAvg(s) for s in seriesList] if n < 50: n = 100 - n lowPercentile = _getPercentile(averages, 100 - n) highPercentile = _getPercentile(averages, n) return [s for s in seriesList if not lowPercentile < safeAvg(s) < highPercentile]
python
def averageOutsidePercentile(requestContext, seriesList, n): """ Removes functions lying inside an average percentile interval """ averages = [safeAvg(s) for s in seriesList] if n < 50: n = 100 - n lowPercentile = _getPercentile(averages, 100 - n) highPercentile = _getPercentile(averages, n) return [s for s in seriesList if not lowPercentile < safeAvg(s) < highPercentile]
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brutasse/graphite-api
graphite_api/functions.py
removeBetweenPercentile
def removeBetweenPercentile(requestContext, seriesList, n): """ Removes lines who do not have an value lying in the x-percentile of all the values at a moment """ if n < 50: n = 100 - n transposed = list(zip_longest(*seriesList)) lowPercentiles = [_getPercentile(col, 100-n) for col in transposed] highPercentiles = [_getPercentile(col, n) for col in transposed] return [l for l in seriesList if sum([not lowPercentiles[index] < val < highPercentiles[index] for index, val in enumerate(l)]) > 0]
python
def removeBetweenPercentile(requestContext, seriesList, n): """ Removes lines who do not have an value lying in the x-percentile of all the values at a moment """ if n < 50: n = 100 - n transposed = list(zip_longest(*seriesList)) lowPercentiles = [_getPercentile(col, 100-n) for col in transposed] highPercentiles = [_getPercentile(col, n) for col in transposed] return [l for l in seriesList if sum([not lowPercentiles[index] < val < highPercentiles[index] for index, val in enumerate(l)]) > 0]
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brutasse/graphite-api
graphite_api/functions.py
removeAboveValue
def removeAboveValue(requestContext, seriesList, n): """ Removes data above the given threshold from the series or list of series provided. Values above this threshold are assigned a value of None. """ for s in seriesList: s.name = 'removeAboveValue(%s, %g)' % (s.name, n) s.pathExpression = s.name for (index, val) in enumerate(s): if val is None: continue if val > n: s[index] = None return seriesList
python
def removeAboveValue(requestContext, seriesList, n): """ Removes data above the given threshold from the series or list of series provided. Values above this threshold are assigned a value of None. """ for s in seriesList: s.name = 'removeAboveValue(%s, %g)' % (s.name, n) s.pathExpression = s.name for (index, val) in enumerate(s): if val is None: continue if val > n: s[index] = None return seriesList
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Removes data above the given threshold from the series or list of series provided. Values above this threshold are assigned a value of None.
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train
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brutasse/graphite-api
graphite_api/functions.py
removeBelowPercentile
def removeBelowPercentile(requestContext, seriesList, n): """ Removes data below the nth percentile from the series or list of series provided. Values below this percentile are assigned a value of None. """ for s in seriesList: s.name = 'removeBelowPercentile(%s, %g)' % (s.name, n) s.pathExpression = s.name try: percentile = nPercentile(requestContext, [s], n)[0][0] except IndexError: continue for (index, val) in enumerate(s): if val is None: continue if val < percentile: s[index] = None return seriesList
python
def removeBelowPercentile(requestContext, seriesList, n): """ Removes data below the nth percentile from the series or list of series provided. Values below this percentile are assigned a value of None. """ for s in seriesList: s.name = 'removeBelowPercentile(%s, %g)' % (s.name, n) s.pathExpression = s.name try: percentile = nPercentile(requestContext, [s], n)[0][0] except IndexError: continue for (index, val) in enumerate(s): if val is None: continue if val < percentile: s[index] = None return seriesList
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brutasse/graphite-api
graphite_api/functions.py
sortByName
def sortByName(requestContext, seriesList, natural=False): """ Takes one metric or a wildcard seriesList. Sorts the list of metrics by the metric name using either alphabetical order or natural sorting. Natural sorting allows names containing numbers to be sorted more naturally, e.g: - Alphabetical sorting: server1, server11, server12, server2 - Natural sorting: server1, server2, server11, server12 """ if natural: return list(sorted(seriesList, key=lambda x: paddedName(x.name))) else: return list(sorted(seriesList, key=lambda x: x.name))
python
def sortByName(requestContext, seriesList, natural=False): """ Takes one metric or a wildcard seriesList. Sorts the list of metrics by the metric name using either alphabetical order or natural sorting. Natural sorting allows names containing numbers to be sorted more naturally, e.g: - Alphabetical sorting: server1, server11, server12, server2 - Natural sorting: server1, server2, server11, server12 """ if natural: return list(sorted(seriesList, key=lambda x: paddedName(x.name))) else: return list(sorted(seriesList, key=lambda x: x.name))
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Takes one metric or a wildcard seriesList. Sorts the list of metrics by the metric name using either alphabetical order or natural sorting. Natural sorting allows names containing numbers to be sorted more naturally, e.g: - Alphabetical sorting: server1, server11, server12, server2 - Natural sorting: server1, server2, server11, server12
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brutasse/graphite-api
graphite_api/functions.py
sortByTotal
def sortByTotal(requestContext, seriesList): """ Takes one metric or a wildcard seriesList. Sorts the list of metrics by the sum of values across the time period specified. """ return list(sorted(seriesList, key=safeSum, reverse=True))
python
def sortByTotal(requestContext, seriesList): """ Takes one metric or a wildcard seriesList. Sorts the list of metrics by the sum of values across the time period specified. """ return list(sorted(seriesList, key=safeSum, reverse=True))
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brutasse/graphite-api
graphite_api/functions.py
useSeriesAbove
def useSeriesAbove(requestContext, seriesList, value, search, replace): """ Compares the maximum of each series against the given `value`. If the series maximum is greater than `value`, the regular expression search and replace is applied against the series name to plot a related metric. e.g. given useSeriesAbove(ganglia.metric1.reqs,10,'reqs','time'), the response time metric will be plotted only when the maximum value of the corresponding request/s metric is > 10 Example:: &target=useSeriesAbove(ganglia.metric1.reqs,10,"reqs","time") """ newSeries = [] for series in seriesList: newname = re.sub(search, replace, series.name) if safeMax(series) > value: n = evaluateTarget(requestContext, newname) if n is not None and len(n) > 0: newSeries.append(n[0]) return newSeries
python
def useSeriesAbove(requestContext, seriesList, value, search, replace): """ Compares the maximum of each series against the given `value`. If the series maximum is greater than `value`, the regular expression search and replace is applied against the series name to plot a related metric. e.g. given useSeriesAbove(ganglia.metric1.reqs,10,'reqs','time'), the response time metric will be plotted only when the maximum value of the corresponding request/s metric is > 10 Example:: &target=useSeriesAbove(ganglia.metric1.reqs,10,"reqs","time") """ newSeries = [] for series in seriesList: newname = re.sub(search, replace, series.name) if safeMax(series) > value: n = evaluateTarget(requestContext, newname) if n is not None and len(n) > 0: newSeries.append(n[0]) return newSeries
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brutasse/graphite-api
graphite_api/functions.py
mostDeviant
def mostDeviant(requestContext, seriesList, n): """ Takes one metric or a wildcard seriesList followed by an integer N. Draws the N most deviant metrics. To find the deviants, the standard deviation (sigma) of each series is taken and ranked. The top N standard deviations are returned. Example:: &target=mostDeviant(server*.instance*.memory.free, 5) Draws the 5 instances furthest from the average memory free. """ deviants = [] for series in seriesList: mean = safeAvg(series) if mean is None: continue square_sum = sum([(value - mean) ** 2 for value in series if value is not None]) sigma = safeDiv(square_sum, safeLen(series)) if sigma is None: continue deviants.append((sigma, series)) return [series for sig, series in sorted(deviants, # sort by sigma key=itemgetter(0), reverse=True)][:n]
python
def mostDeviant(requestContext, seriesList, n): """ Takes one metric or a wildcard seriesList followed by an integer N. Draws the N most deviant metrics. To find the deviants, the standard deviation (sigma) of each series is taken and ranked. The top N standard deviations are returned. Example:: &target=mostDeviant(server*.instance*.memory.free, 5) Draws the 5 instances furthest from the average memory free. """ deviants = [] for series in seriesList: mean = safeAvg(series) if mean is None: continue square_sum = sum([(value - mean) ** 2 for value in series if value is not None]) sigma = safeDiv(square_sum, safeLen(series)) if sigma is None: continue deviants.append((sigma, series)) return [series for sig, series in sorted(deviants, # sort by sigma key=itemgetter(0), reverse=True)][:n]
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train
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brutasse/graphite-api
graphite_api/functions.py
stdev
def stdev(requestContext, seriesList, points, windowTolerance=0.1): """ Takes one metric or a wildcard seriesList followed by an integer N. Draw the Standard Deviation of all metrics passed for the past N datapoints. If the ratio of null points in the window is greater than windowTolerance, skip the calculation. The default for windowTolerance is 0.1 (up to 10% of points in the window can be missing). Note that if this is set to 0.0, it will cause large gaps in the output anywhere a single point is missing. Example:: &target=stdev(server*.instance*.threads.busy,30) &target=stdev(server*.instance*.cpu.system,30,0.0) """ # For this we take the standard deviation in terms of the moving average # and the moving average of series squares. for seriesIndex, series in enumerate(seriesList): stdevSeries = TimeSeries("stdev(%s,%d)" % (series.name, int(points)), series.start, series.end, series.step, []) stdevSeries.pathExpression = "stdev(%s,%d)" % (series.name, int(points)) validPoints = 0 currentSum = 0 currentSumOfSquares = 0 for index, newValue in enumerate(series): # Mark whether we've reached our window size - dont drop points # out otherwise if index < points: bootstrapping = True droppedValue = None else: bootstrapping = False droppedValue = series[index - points] # Track non-None points in window if not bootstrapping and droppedValue is not None: validPoints -= 1 if newValue is not None: validPoints += 1 # Remove the value that just dropped out of the window if not bootstrapping and droppedValue is not None: currentSum -= droppedValue currentSumOfSquares -= droppedValue**2 # Add in the value that just popped in the window if newValue is not None: currentSum += newValue currentSumOfSquares += newValue**2 if ( validPoints > 0 and float(validPoints) / points >= windowTolerance ): try: deviation = math.sqrt(validPoints * currentSumOfSquares - currentSum**2) / validPoints except ValueError: deviation = None stdevSeries.append(deviation) else: stdevSeries.append(None) seriesList[seriesIndex] = stdevSeries return seriesList
python
def stdev(requestContext, seriesList, points, windowTolerance=0.1): """ Takes one metric or a wildcard seriesList followed by an integer N. Draw the Standard Deviation of all metrics passed for the past N datapoints. If the ratio of null points in the window is greater than windowTolerance, skip the calculation. The default for windowTolerance is 0.1 (up to 10% of points in the window can be missing). Note that if this is set to 0.0, it will cause large gaps in the output anywhere a single point is missing. Example:: &target=stdev(server*.instance*.threads.busy,30) &target=stdev(server*.instance*.cpu.system,30,0.0) """ # For this we take the standard deviation in terms of the moving average # and the moving average of series squares. for seriesIndex, series in enumerate(seriesList): stdevSeries = TimeSeries("stdev(%s,%d)" % (series.name, int(points)), series.start, series.end, series.step, []) stdevSeries.pathExpression = "stdev(%s,%d)" % (series.name, int(points)) validPoints = 0 currentSum = 0 currentSumOfSquares = 0 for index, newValue in enumerate(series): # Mark whether we've reached our window size - dont drop points # out otherwise if index < points: bootstrapping = True droppedValue = None else: bootstrapping = False droppedValue = series[index - points] # Track non-None points in window if not bootstrapping and droppedValue is not None: validPoints -= 1 if newValue is not None: validPoints += 1 # Remove the value that just dropped out of the window if not bootstrapping and droppedValue is not None: currentSum -= droppedValue currentSumOfSquares -= droppedValue**2 # Add in the value that just popped in the window if newValue is not None: currentSum += newValue currentSumOfSquares += newValue**2 if ( validPoints > 0 and float(validPoints) / points >= windowTolerance ): try: deviation = math.sqrt(validPoints * currentSumOfSquares - currentSum**2) / validPoints except ValueError: deviation = None stdevSeries.append(deviation) else: stdevSeries.append(None) seriesList[seriesIndex] = stdevSeries return seriesList
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Takes one metric or a wildcard seriesList followed by an integer N. Draw the Standard Deviation of all metrics passed for the past N datapoints. If the ratio of null points in the window is greater than windowTolerance, skip the calculation. The default for windowTolerance is 0.1 (up to 10% of points in the window can be missing). Note that if this is set to 0.0, it will cause large gaps in the output anywhere a single point is missing. Example:: &target=stdev(server*.instance*.threads.busy,30) &target=stdev(server*.instance*.cpu.system,30,0.0)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2659-L2728
brutasse/graphite-api
graphite_api/functions.py
secondYAxis
def secondYAxis(requestContext, seriesList): """ Graph the series on the secondary Y axis. """ for series in seriesList: series.options['secondYAxis'] = True series.name = 'secondYAxis(%s)' % series.name return seriesList
python
def secondYAxis(requestContext, seriesList): """ Graph the series on the secondary Y axis. """ for series in seriesList: series.options['secondYAxis'] = True series.name = 'secondYAxis(%s)' % series.name return seriesList
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Graph the series on the secondary Y axis.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2731-L2738
brutasse/graphite-api
graphite_api/functions.py
holtWintersForecast
def holtWintersForecast(requestContext, seriesList): """ Performs a Holt-Winters forecast using the series as input data. Data from one week previous to the series is used to bootstrap the initial forecast. """ previewSeconds = 7 * 86400 # 7 days # ignore original data and pull new, including our preview newContext = requestContext.copy() newContext['startTime'] = (requestContext['startTime'] - timedelta(seconds=previewSeconds)) previewList = evaluateTokens(newContext, requestContext['args'][0]) results = [] for series in previewList: analysis = holtWintersAnalysis(series) predictions = analysis['predictions'] windowPoints = previewSeconds // predictions.step result = TimeSeries("holtWintersForecast(%s)" % series.name, predictions.start + previewSeconds, predictions.end, predictions.step, predictions[windowPoints:]) result.pathExpression = result.name results.append(result) return results
python
def holtWintersForecast(requestContext, seriesList): """ Performs a Holt-Winters forecast using the series as input data. Data from one week previous to the series is used to bootstrap the initial forecast. """ previewSeconds = 7 * 86400 # 7 days # ignore original data and pull new, including our preview newContext = requestContext.copy() newContext['startTime'] = (requestContext['startTime'] - timedelta(seconds=previewSeconds)) previewList = evaluateTokens(newContext, requestContext['args'][0]) results = [] for series in previewList: analysis = holtWintersAnalysis(series) predictions = analysis['predictions'] windowPoints = previewSeconds // predictions.step result = TimeSeries("holtWintersForecast(%s)" % series.name, predictions.start + previewSeconds, predictions.end, predictions.step, predictions[windowPoints:]) result.pathExpression = result.name results.append(result) return results
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Performs a Holt-Winters forecast using the series as input data. Data from one week previous to the series is used to bootstrap the initial forecast.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2854-L2876
brutasse/graphite-api
graphite_api/functions.py
holtWintersConfidenceBands
def holtWintersConfidenceBands(requestContext, seriesList, delta=3): """ Performs a Holt-Winters forecast using the series as input data and plots upper and lower bands with the predicted forecast deviations. """ previewSeconds = 7 * 86400 # 7 days # ignore original data and pull new, including our preview newContext = requestContext.copy() newContext['startTime'] = (requestContext['startTime'] - timedelta(seconds=previewSeconds)) previewList = evaluateTokens(newContext, requestContext['args'][0]) results = [] for series in previewList: analysis = holtWintersAnalysis(series) data = analysis['predictions'] windowPoints = previewSeconds // data.step forecast = TimeSeries(data.name, data.start + previewSeconds, data.end, data.step, data[windowPoints:]) forecast.pathExpression = data.pathExpression data = analysis['deviations'] windowPoints = previewSeconds // data.step deviation = TimeSeries(data.name, data.start + previewSeconds, data.end, data.step, data[windowPoints:]) deviation.pathExpression = data.pathExpression seriesLength = len(forecast) i = 0 upperBand = list() lowerBand = list() while i < seriesLength: forecast_item = forecast[i] deviation_item = deviation[i] i = i + 1 if forecast_item is None or deviation_item is None: upperBand.append(None) lowerBand.append(None) else: scaled_deviation = delta * deviation_item upperBand.append(forecast_item + scaled_deviation) lowerBand.append(forecast_item - scaled_deviation) upperName = "holtWintersConfidenceUpper(%s)" % series.name lowerName = "holtWintersConfidenceLower(%s)" % series.name upperSeries = TimeSeries(upperName, forecast.start, forecast.end, forecast.step, upperBand) lowerSeries = TimeSeries(lowerName, forecast.start, forecast.end, forecast.step, lowerBand) upperSeries.pathExpression = series.pathExpression lowerSeries.pathExpression = series.pathExpression results.append(lowerSeries) results.append(upperSeries) return results
python
def holtWintersConfidenceBands(requestContext, seriesList, delta=3): """ Performs a Holt-Winters forecast using the series as input data and plots upper and lower bands with the predicted forecast deviations. """ previewSeconds = 7 * 86400 # 7 days # ignore original data and pull new, including our preview newContext = requestContext.copy() newContext['startTime'] = (requestContext['startTime'] - timedelta(seconds=previewSeconds)) previewList = evaluateTokens(newContext, requestContext['args'][0]) results = [] for series in previewList: analysis = holtWintersAnalysis(series) data = analysis['predictions'] windowPoints = previewSeconds // data.step forecast = TimeSeries(data.name, data.start + previewSeconds, data.end, data.step, data[windowPoints:]) forecast.pathExpression = data.pathExpression data = analysis['deviations'] windowPoints = previewSeconds // data.step deviation = TimeSeries(data.name, data.start + previewSeconds, data.end, data.step, data[windowPoints:]) deviation.pathExpression = data.pathExpression seriesLength = len(forecast) i = 0 upperBand = list() lowerBand = list() while i < seriesLength: forecast_item = forecast[i] deviation_item = deviation[i] i = i + 1 if forecast_item is None or deviation_item is None: upperBand.append(None) lowerBand.append(None) else: scaled_deviation = delta * deviation_item upperBand.append(forecast_item + scaled_deviation) lowerBand.append(forecast_item - scaled_deviation) upperName = "holtWintersConfidenceUpper(%s)" % series.name lowerName = "holtWintersConfidenceLower(%s)" % series.name upperSeries = TimeSeries(upperName, forecast.start, forecast.end, forecast.step, upperBand) lowerSeries = TimeSeries(lowerName, forecast.start, forecast.end, forecast.step, lowerBand) upperSeries.pathExpression = series.pathExpression lowerSeries.pathExpression = series.pathExpression results.append(lowerSeries) results.append(upperSeries) return results
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Performs a Holt-Winters forecast using the series as input data and plots upper and lower bands with the predicted forecast deviations.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2879-L2932
brutasse/graphite-api
graphite_api/functions.py
holtWintersAberration
def holtWintersAberration(requestContext, seriesList, delta=3): """ Performs a Holt-Winters forecast using the series as input data and plots the positive or negative deviation of the series data from the forecast. """ results = [] for series in seriesList: confidenceBands = holtWintersConfidenceBands(requestContext, [series], delta) lowerBand = confidenceBands[0] upperBand = confidenceBands[1] aberration = list() for i, actual in enumerate(series): if actual is None: aberration.append(0) elif upperBand[i] is not None and actual > upperBand[i]: aberration.append(actual - upperBand[i]) elif lowerBand[i] is not None and actual < lowerBand[i]: aberration.append(actual - lowerBand[i]) else: aberration.append(0) newName = "holtWintersAberration(%s)" % series.name results.append(TimeSeries(newName, series.start, series.end, series.step, aberration)) return results
python
def holtWintersAberration(requestContext, seriesList, delta=3): """ Performs a Holt-Winters forecast using the series as input data and plots the positive or negative deviation of the series data from the forecast. """ results = [] for series in seriesList: confidenceBands = holtWintersConfidenceBands(requestContext, [series], delta) lowerBand = confidenceBands[0] upperBand = confidenceBands[1] aberration = list() for i, actual in enumerate(series): if actual is None: aberration.append(0) elif upperBand[i] is not None and actual > upperBand[i]: aberration.append(actual - upperBand[i]) elif lowerBand[i] is not None and actual < lowerBand[i]: aberration.append(actual - lowerBand[i]) else: aberration.append(0) newName = "holtWintersAberration(%s)" % series.name results.append(TimeSeries(newName, series.start, series.end, series.step, aberration)) return results
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Performs a Holt-Winters forecast using the series as input data and plots the positive or negative deviation of the series data from the forecast.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2935-L2960
brutasse/graphite-api
graphite_api/functions.py
holtWintersConfidenceArea
def holtWintersConfidenceArea(requestContext, seriesList, delta=3): """ Performs a Holt-Winters forecast using the series as input data and plots the area between the upper and lower bands of the predicted forecast deviations. """ bands = holtWintersConfidenceBands(requestContext, seriesList, delta) results = areaBetween(requestContext, bands) for series in results: series.name = series.name.replace('areaBetween', 'holtWintersConfidenceArea') return results
python
def holtWintersConfidenceArea(requestContext, seriesList, delta=3): """ Performs a Holt-Winters forecast using the series as input data and plots the area between the upper and lower bands of the predicted forecast deviations. """ bands = holtWintersConfidenceBands(requestContext, seriesList, delta) results = areaBetween(requestContext, bands) for series in results: series.name = series.name.replace('areaBetween', 'holtWintersConfidenceArea') return results
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Performs a Holt-Winters forecast using the series as input data and plots the area between the upper and lower bands of the predicted forecast deviations.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2963-L2974
brutasse/graphite-api
graphite_api/functions.py
linearRegressionAnalysis
def linearRegressionAnalysis(series): """ Returns factor and offset of linear regression function by least squares method. """ n = safeLen(series) sumI = sum([i for i, v in enumerate(series) if v is not None]) sumV = sum([v for i, v in enumerate(series) if v is not None]) sumII = sum([i * i for i, v in enumerate(series) if v is not None]) sumIV = sum([i * v for i, v in enumerate(series) if v is not None]) denominator = float(n * sumII - sumI * sumI) if denominator == 0: return None else: factor = (n * sumIV - sumI * sumV) / denominator / series.step offset = sumII * sumV - sumIV * sumI offset = offset / denominator - factor * series.start return factor, offset
python
def linearRegressionAnalysis(series): """ Returns factor and offset of linear regression function by least squares method. """ n = safeLen(series) sumI = sum([i for i, v in enumerate(series) if v is not None]) sumV = sum([v for i, v in enumerate(series) if v is not None]) sumII = sum([i * i for i, v in enumerate(series) if v is not None]) sumIV = sum([i * v for i, v in enumerate(series) if v is not None]) denominator = float(n * sumII - sumI * sumI) if denominator == 0: return None else: factor = (n * sumIV - sumI * sumV) / denominator / series.step offset = sumII * sumV - sumIV * sumI offset = offset / denominator - factor * series.start return factor, offset
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Returns factor and offset of linear regression function by least squares method.
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https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2977-L2995
brutasse/graphite-api
graphite_api/functions.py
linearRegression
def linearRegression(requestContext, seriesList, startSourceAt=None, endSourceAt=None): """ Graphs the liner regression function by least squares method. Takes one metric or a wildcard seriesList, followed by a quoted string with the time to start the line and another quoted string with the time to end the line. The start and end times are inclusive (default range is from to until). See ``from / until`` in the render\_api_ for examples of time formats. Datapoints in the range is used to regression. Example:: &target=linearRegression(Server.instance01.threads.busy,'-1d') &target=linearRegression(Server.instance*.threads.busy, "00:00 20140101","11:59 20140630") """ from .app import evaluateTarget results = [] sourceContext = requestContext.copy() if startSourceAt is not None: sourceContext['startTime'] = parseATTime(startSourceAt) if endSourceAt is not None: sourceContext['endTime'] = parseATTime(endSourceAt) sourceList = [] for series in seriesList: source = evaluateTarget(sourceContext, series.pathExpression) sourceList.extend(source) for source, series in zip(sourceList, seriesList): newName = 'linearRegression(%s, %s, %s)' % ( series.name, int(epoch(sourceContext['startTime'])), int(epoch(sourceContext['endTime']))) forecast = linearRegressionAnalysis(source) if forecast is None: continue factor, offset = forecast values = [offset + (series.start + i * series.step) * factor for i in range(len(series))] newSeries = TimeSeries(newName, series.start, series.end, series.step, values) newSeries.pathExpression = newSeries.name results.append(newSeries) return results
python
def linearRegression(requestContext, seriesList, startSourceAt=None, endSourceAt=None): """ Graphs the liner regression function by least squares method. Takes one metric or a wildcard seriesList, followed by a quoted string with the time to start the line and another quoted string with the time to end the line. The start and end times are inclusive (default range is from to until). See ``from / until`` in the render\_api_ for examples of time formats. Datapoints in the range is used to regression. Example:: &target=linearRegression(Server.instance01.threads.busy,'-1d') &target=linearRegression(Server.instance*.threads.busy, "00:00 20140101","11:59 20140630") """ from .app import evaluateTarget results = [] sourceContext = requestContext.copy() if startSourceAt is not None: sourceContext['startTime'] = parseATTime(startSourceAt) if endSourceAt is not None: sourceContext['endTime'] = parseATTime(endSourceAt) sourceList = [] for series in seriesList: source = evaluateTarget(sourceContext, series.pathExpression) sourceList.extend(source) for source, series in zip(sourceList, seriesList): newName = 'linearRegression(%s, %s, %s)' % ( series.name, int(epoch(sourceContext['startTime'])), int(epoch(sourceContext['endTime']))) forecast = linearRegressionAnalysis(source) if forecast is None: continue factor, offset = forecast values = [offset + (series.start + i * series.step) * factor for i in range(len(series))] newSeries = TimeSeries(newName, series.start, series.end, series.step, values) newSeries.pathExpression = newSeries.name results.append(newSeries) return results
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L2998-L3044
brutasse/graphite-api
graphite_api/functions.py
drawAsInfinite
def drawAsInfinite(requestContext, seriesList): """ Takes one metric or a wildcard seriesList. If the value is zero, draw the line at 0. If the value is above zero, draw the line at infinity. If the value is null or less than zero, do not draw the line. Useful for displaying on/off metrics, such as exit codes. (0 = success, anything else = failure.) Example:: drawAsInfinite(Testing.script.exitCode) """ for series in seriesList: series.options['drawAsInfinite'] = True series.name = 'drawAsInfinite(%s)' % series.name return seriesList
python
def drawAsInfinite(requestContext, seriesList): """ Takes one metric or a wildcard seriesList. If the value is zero, draw the line at 0. If the value is above zero, draw the line at infinity. If the value is null or less than zero, do not draw the line. Useful for displaying on/off metrics, such as exit codes. (0 = success, anything else = failure.) Example:: drawAsInfinite(Testing.script.exitCode) """ for series in seriesList: series.options['drawAsInfinite'] = True series.name = 'drawAsInfinite(%s)' % series.name return seriesList
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Takes one metric or a wildcard seriesList. If the value is zero, draw the line at 0. If the value is above zero, draw the line at infinity. If the value is null or less than zero, do not draw the line. Useful for displaying on/off metrics, such as exit codes. (0 = success, anything else = failure.) Example:: drawAsInfinite(Testing.script.exitCode)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3047-L3065
brutasse/graphite-api
graphite_api/functions.py
lineWidth
def lineWidth(requestContext, seriesList, width): """ Takes one metric or a wildcard seriesList, followed by a float F. Draw the selected metrics with a line width of F, overriding the default value of 1, or the &lineWidth=X.X parameter. Useful for highlighting a single metric out of many, or having multiple line widths in one graph. Example:: &target=lineWidth(server01.instance01.memory.free,5) """ for series in seriesList: series.options['lineWidth'] = width return seriesList
python
def lineWidth(requestContext, seriesList, width): """ Takes one metric or a wildcard seriesList, followed by a float F. Draw the selected metrics with a line width of F, overriding the default value of 1, or the &lineWidth=X.X parameter. Useful for highlighting a single metric out of many, or having multiple line widths in one graph. Example:: &target=lineWidth(server01.instance01.memory.free,5) """ for series in seriesList: series.options['lineWidth'] = width return seriesList
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Takes one metric or a wildcard seriesList, followed by a float F. Draw the selected metrics with a line width of F, overriding the default value of 1, or the &lineWidth=X.X parameter. Useful for highlighting a single metric out of many, or having multiple line widths in one graph. Example:: &target=lineWidth(server01.instance01.memory.free,5)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3068-L3085
brutasse/graphite-api
graphite_api/functions.py
dashed
def dashed(requestContext, seriesList, dashLength=5): """ Takes one metric or a wildcard seriesList, followed by a float F. Draw the selected metrics with a dotted line with segments of length F If omitted, the default length of the segments is 5.0 Example:: &target=dashed(server01.instance01.memory.free,2.5) """ for series in seriesList: series.name = 'dashed(%s, %g)' % (series.name, dashLength) series.options['dashed'] = dashLength return seriesList
python
def dashed(requestContext, seriesList, dashLength=5): """ Takes one metric or a wildcard seriesList, followed by a float F. Draw the selected metrics with a dotted line with segments of length F If omitted, the default length of the segments is 5.0 Example:: &target=dashed(server01.instance01.memory.free,2.5) """ for series in seriesList: series.name = 'dashed(%s, %g)' % (series.name, dashLength) series.options['dashed'] = dashLength return seriesList
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Takes one metric or a wildcard seriesList, followed by a float F. Draw the selected metrics with a dotted line with segments of length F If omitted, the default length of the segments is 5.0 Example:: &target=dashed(server01.instance01.memory.free,2.5)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3088-L3103
brutasse/graphite-api
graphite_api/functions.py
timeStack
def timeStack(requestContext, seriesList, timeShiftUnit, timeShiftStart, timeShiftEnd): """ Takes one metric or a wildcard seriesList, followed by a quoted string with the length of time (See ``from / until`` in the render\_api_ for examples of time formats). Also takes a start multiplier and end multiplier for the length of time- Create a seriesList which is composed the original metric series stacked with time shifts starting time shifts from the start multiplier through the end multiplier. Useful for looking at history, or feeding into averageSeries or stddevSeries. Example:: # create a series for today and each of the previous 7 days &target=timeStack(Sales.widgets.largeBlue,"1d",0,7) """ # Default to negative. parseTimeOffset defaults to + if timeShiftUnit[0].isdigit(): timeShiftUnit = '-' + timeShiftUnit delta = parseTimeOffset(timeShiftUnit) # if len(seriesList) > 1, they will all have the same pathExpression, # which is all we care about. series = seriesList[0] results = [] timeShiftStartint = int(timeShiftStart) timeShiftEndint = int(timeShiftEnd) for shft in range(timeShiftStartint, timeShiftEndint): myContext = requestContext.copy() innerDelta = delta * shft myContext['startTime'] = requestContext['startTime'] + innerDelta myContext['endTime'] = requestContext['endTime'] + innerDelta for shiftedSeries in evaluateTarget(myContext, series.pathExpression): shiftedSeries.name = 'timeShift(%s, %s, %s)' % (shiftedSeries.name, timeShiftUnit, shft) shiftedSeries.pathExpression = shiftedSeries.name shiftedSeries.start = series.start shiftedSeries.end = series.end results.append(shiftedSeries) return results
python
def timeStack(requestContext, seriesList, timeShiftUnit, timeShiftStart, timeShiftEnd): """ Takes one metric or a wildcard seriesList, followed by a quoted string with the length of time (See ``from / until`` in the render\_api_ for examples of time formats). Also takes a start multiplier and end multiplier for the length of time- Create a seriesList which is composed the original metric series stacked with time shifts starting time shifts from the start multiplier through the end multiplier. Useful for looking at history, or feeding into averageSeries or stddevSeries. Example:: # create a series for today and each of the previous 7 days &target=timeStack(Sales.widgets.largeBlue,"1d",0,7) """ # Default to negative. parseTimeOffset defaults to + if timeShiftUnit[0].isdigit(): timeShiftUnit = '-' + timeShiftUnit delta = parseTimeOffset(timeShiftUnit) # if len(seriesList) > 1, they will all have the same pathExpression, # which is all we care about. series = seriesList[0] results = [] timeShiftStartint = int(timeShiftStart) timeShiftEndint = int(timeShiftEnd) for shft in range(timeShiftStartint, timeShiftEndint): myContext = requestContext.copy() innerDelta = delta * shft myContext['startTime'] = requestContext['startTime'] + innerDelta myContext['endTime'] = requestContext['endTime'] + innerDelta for shiftedSeries in evaluateTarget(myContext, series.pathExpression): shiftedSeries.name = 'timeShift(%s, %s, %s)' % (shiftedSeries.name, timeShiftUnit, shft) shiftedSeries.pathExpression = shiftedSeries.name shiftedSeries.start = series.start shiftedSeries.end = series.end results.append(shiftedSeries) return results
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3106-L3151
brutasse/graphite-api
graphite_api/functions.py
timeShift
def timeShift(requestContext, seriesList, timeShift, resetEnd=True, alignDST=False): """ Takes one metric or a wildcard seriesList, followed by a quoted string with the length of time (See ``from / until`` in the render\_api_ for examples of time formats). Draws the selected metrics shifted in time. If no sign is given, a minus sign ( - ) is implied which will shift the metric back in time. If a plus sign ( + ) is given, the metric will be shifted forward in time. Will reset the end date range automatically to the end of the base stat unless resetEnd is False. Example case is when you timeshift to last week and have the graph date range set to include a time in the future, will limit this timeshift to pretend ending at the current time. If resetEnd is False, will instead draw full range including future time. Because time is shifted by a fixed number of seconds, comparing a time period with DST to a time period without DST, and vice-versa, will result in an apparent misalignment. For example, 8am might be overlaid with 7am. To compensate for this, use the alignDST option. Useful for comparing a metric against itself at a past periods or correcting data stored at an offset. Example:: &target=timeShift(Sales.widgets.largeBlue,"7d") &target=timeShift(Sales.widgets.largeBlue,"-7d") &target=timeShift(Sales.widgets.largeBlue,"+1h") """ # Default to negative. parseTimeOffset defaults to + if timeShift[0].isdigit(): timeShift = '-' + timeShift delta = parseTimeOffset(timeShift) myContext = requestContext.copy() myContext['startTime'] = requestContext['startTime'] + delta myContext['endTime'] = requestContext['endTime'] + delta if alignDST: reqStartDST = localDST(requestContext['startTime']) reqEndDST = localDST(requestContext['endTime']) myStartDST = localDST(myContext['startTime']) myEndDST = localDST(myContext['endTime']) dstOffset = timedelta(hours=0) # If the requestContext is entirely in DST, and we are entirely # NOT in DST if ( (reqStartDST and reqEndDST) and (not myStartDST and not myEndDST) ): dstOffset = timedelta(hours=1) # Or if the requestContext is entirely NOT in DST, and we are # entirely in DST elif ( (not reqStartDST and not reqEndDST) and (myStartDST and myEndDST) ): dstOffset = timedelta(hours=-1) # Otherwise, we don't do anything, because it would be visually # confusing myContext['startTime'] += dstOffset myContext['endTime'] += dstOffset results = [] if not seriesList: return results # if len(seriesList) > 1, they will all have the same pathExpression, # which is all we care about. series = seriesList[0] for shiftedSeries in evaluateTarget(myContext, series.pathExpression): shiftedSeries.name = 'timeShift(%s, %s)' % (shiftedSeries.name, timeShift) if resetEnd: shiftedSeries.end = series.end else: shiftedSeries.end = ( shiftedSeries.end - shiftedSeries.start + series.start) shiftedSeries.start = series.start results.append(shiftedSeries) return results
python
def timeShift(requestContext, seriesList, timeShift, resetEnd=True, alignDST=False): """ Takes one metric or a wildcard seriesList, followed by a quoted string with the length of time (See ``from / until`` in the render\_api_ for examples of time formats). Draws the selected metrics shifted in time. If no sign is given, a minus sign ( - ) is implied which will shift the metric back in time. If a plus sign ( + ) is given, the metric will be shifted forward in time. Will reset the end date range automatically to the end of the base stat unless resetEnd is False. Example case is when you timeshift to last week and have the graph date range set to include a time in the future, will limit this timeshift to pretend ending at the current time. If resetEnd is False, will instead draw full range including future time. Because time is shifted by a fixed number of seconds, comparing a time period with DST to a time period without DST, and vice-versa, will result in an apparent misalignment. For example, 8am might be overlaid with 7am. To compensate for this, use the alignDST option. Useful for comparing a metric against itself at a past periods or correcting data stored at an offset. Example:: &target=timeShift(Sales.widgets.largeBlue,"7d") &target=timeShift(Sales.widgets.largeBlue,"-7d") &target=timeShift(Sales.widgets.largeBlue,"+1h") """ # Default to negative. parseTimeOffset defaults to + if timeShift[0].isdigit(): timeShift = '-' + timeShift delta = parseTimeOffset(timeShift) myContext = requestContext.copy() myContext['startTime'] = requestContext['startTime'] + delta myContext['endTime'] = requestContext['endTime'] + delta if alignDST: reqStartDST = localDST(requestContext['startTime']) reqEndDST = localDST(requestContext['endTime']) myStartDST = localDST(myContext['startTime']) myEndDST = localDST(myContext['endTime']) dstOffset = timedelta(hours=0) # If the requestContext is entirely in DST, and we are entirely # NOT in DST if ( (reqStartDST and reqEndDST) and (not myStartDST and not myEndDST) ): dstOffset = timedelta(hours=1) # Or if the requestContext is entirely NOT in DST, and we are # entirely in DST elif ( (not reqStartDST and not reqEndDST) and (myStartDST and myEndDST) ): dstOffset = timedelta(hours=-1) # Otherwise, we don't do anything, because it would be visually # confusing myContext['startTime'] += dstOffset myContext['endTime'] += dstOffset results = [] if not seriesList: return results # if len(seriesList) > 1, they will all have the same pathExpression, # which is all we care about. series = seriesList[0] for shiftedSeries in evaluateTarget(myContext, series.pathExpression): shiftedSeries.name = 'timeShift(%s, %s)' % (shiftedSeries.name, timeShift) if resetEnd: shiftedSeries.end = series.end else: shiftedSeries.end = ( shiftedSeries.end - shiftedSeries.start + series.start) shiftedSeries.start = series.start results.append(shiftedSeries) return results
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Takes one metric or a wildcard seriesList, followed by a quoted string with the length of time (See ``from / until`` in the render\_api_ for examples of time formats). Draws the selected metrics shifted in time. If no sign is given, a minus sign ( - ) is implied which will shift the metric back in time. If a plus sign ( + ) is given, the metric will be shifted forward in time. Will reset the end date range automatically to the end of the base stat unless resetEnd is False. Example case is when you timeshift to last week and have the graph date range set to include a time in the future, will limit this timeshift to pretend ending at the current time. If resetEnd is False, will instead draw full range including future time. Because time is shifted by a fixed number of seconds, comparing a time period with DST to a time period without DST, and vice-versa, will result in an apparent misalignment. For example, 8am might be overlaid with 7am. To compensate for this, use the alignDST option. Useful for comparing a metric against itself at a past periods or correcting data stored at an offset. Example:: &target=timeShift(Sales.widgets.largeBlue,"7d") &target=timeShift(Sales.widgets.largeBlue,"-7d") &target=timeShift(Sales.widgets.largeBlue,"+1h")
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3158-L3243
brutasse/graphite-api
graphite_api/functions.py
timeSlice
def timeSlice(requestContext, seriesList, startSliceAt, endSliceAt='now'): """ Takes one metric or a wildcard metric, followed by a quoted string with the time to start the line and another quoted string with the time to end the line. The start and end times are inclusive. See ``from / until`` in the render api for examples of time formats. Useful for filtering out a part of a series of data from a wider range of data. Example:: &target=timeSlice(network.core.port1,"00:00 20140101","11:59 20140630") &target=timeSlice(network.core.port1,"12:00 20140630","now") """ results = [] start = epoch(parseATTime(startSliceAt)) end = epoch(parseATTime(endSliceAt)) for slicedSeries in seriesList: slicedSeries.name = 'timeSlice(%s, %s, %s)' % (slicedSeries.name, int(start), int(end)) curr = epoch(requestContext["startTime"]) for i, v in enumerate(slicedSeries): if v is None or curr < start or curr > end: slicedSeries[i] = None curr += slicedSeries.step results.append(slicedSeries) return results
python
def timeSlice(requestContext, seriesList, startSliceAt, endSliceAt='now'): """ Takes one metric or a wildcard metric, followed by a quoted string with the time to start the line and another quoted string with the time to end the line. The start and end times are inclusive. See ``from / until`` in the render api for examples of time formats. Useful for filtering out a part of a series of data from a wider range of data. Example:: &target=timeSlice(network.core.port1,"00:00 20140101","11:59 20140630") &target=timeSlice(network.core.port1,"12:00 20140630","now") """ results = [] start = epoch(parseATTime(startSliceAt)) end = epoch(parseATTime(endSliceAt)) for slicedSeries in seriesList: slicedSeries.name = 'timeSlice(%s, %s, %s)' % (slicedSeries.name, int(start), int(end)) curr = epoch(requestContext["startTime"]) for i, v in enumerate(slicedSeries): if v is None or curr < start or curr > end: slicedSeries[i] = None curr += slicedSeries.step results.append(slicedSeries) return results
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Takes one metric or a wildcard metric, followed by a quoted string with the time to start the line and another quoted string with the time to end the line. The start and end times are inclusive. See ``from / until`` in the render api for examples of time formats. Useful for filtering out a part of a series of data from a wider range of data. Example:: &target=timeSlice(network.core.port1,"00:00 20140101","11:59 20140630") &target=timeSlice(network.core.port1,"12:00 20140630","now")
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3246-L3275
brutasse/graphite-api
graphite_api/functions.py
constantLine
def constantLine(requestContext, value): """ Takes a float F. Draws a horizontal line at value F across the graph. Example:: &target=constantLine(123.456) """ name = "constantLine(%s)" % str(value) start = int(epoch(requestContext['startTime'])) end = int(epoch(requestContext['endTime'])) step = int((end - start) / 2.0) series = TimeSeries(str(value), start, end, step, [value, value, value]) series.pathExpression = name return [series]
python
def constantLine(requestContext, value): """ Takes a float F. Draws a horizontal line at value F across the graph. Example:: &target=constantLine(123.456) """ name = "constantLine(%s)" % str(value) start = int(epoch(requestContext['startTime'])) end = int(epoch(requestContext['endTime'])) step = int((end - start) / 2.0) series = TimeSeries(str(value), start, end, step, [value, value, value]) series.pathExpression = name return [series]
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Takes a float F. Draws a horizontal line at value F across the graph. Example:: &target=constantLine(123.456)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3278-L3295
brutasse/graphite-api
graphite_api/functions.py
aggregateLine
def aggregateLine(requestContext, seriesList, func='avg'): """ Takes a metric or wildcard seriesList and draws a horizontal line based on the function applied to each series. Note: By default, the graphite renderer consolidates data points by averaging data points over time. If you are using the 'min' or 'max' function for aggregateLine, this can cause an unusual gap in the line drawn by this function and the data itself. To fix this, you should use the consolidateBy() function with the same function argument you are using for aggregateLine. This will ensure that the proper data points are retained and the graph should line up correctly. Example:: &target=aggregateLine(server01.connections.total, 'avg') &target=aggregateLine(server*.connections.total, 'avg') """ t_funcs = {'avg': safeAvg, 'min': safeMin, 'max': safeMax} if func not in t_funcs: raise ValueError("Invalid function %s" % func) results = [] for series in seriesList: value = t_funcs[func](series) if value is not None: name = 'aggregateLine(%s, %g)' % (series.name, value) else: name = 'aggregateLine(%s, None)' % (series.name) [series] = constantLine(requestContext, value) series.name = name series.pathExpression = series.name results.append(series) return results
python
def aggregateLine(requestContext, seriesList, func='avg'): """ Takes a metric or wildcard seriesList and draws a horizontal line based on the function applied to each series. Note: By default, the graphite renderer consolidates data points by averaging data points over time. If you are using the 'min' or 'max' function for aggregateLine, this can cause an unusual gap in the line drawn by this function and the data itself. To fix this, you should use the consolidateBy() function with the same function argument you are using for aggregateLine. This will ensure that the proper data points are retained and the graph should line up correctly. Example:: &target=aggregateLine(server01.connections.total, 'avg') &target=aggregateLine(server*.connections.total, 'avg') """ t_funcs = {'avg': safeAvg, 'min': safeMin, 'max': safeMax} if func not in t_funcs: raise ValueError("Invalid function %s" % func) results = [] for series in seriesList: value = t_funcs[func](series) if value is not None: name = 'aggregateLine(%s, %g)' % (series.name, value) else: name = 'aggregateLine(%s, None)' % (series.name) [series] = constantLine(requestContext, value) series.name = name series.pathExpression = series.name results.append(series) return results
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3298-L3335
brutasse/graphite-api
graphite_api/functions.py
verticalLine
def verticalLine(requestContext, ts, label=None, color=None): """ Takes a timestamp string ts. Draws a vertical line at the designated timestamp with optional 'label' and 'color'. Supported timestamp formats include both relative (e.g. -3h) and absolute (e.g. 16:00_20110501) strings, such as those used with ``from`` and ``until`` parameters. When set, the 'label' will appear in the graph legend. Note: Any timestamps defined outside the requested range will raise a 'ValueError' exception. Example:: &target=verticalLine("12:3420131108","event","blue") &target=verticalLine("16:00_20110501","event") &target=verticalLine("-5mins") """ ts = int(epoch(parseATTime(ts, requestContext['tzinfo']))) start = int(epoch(requestContext['startTime'])) end = int(epoch(requestContext['endTime'])) if ts < start: raise ValueError("verticalLine(): timestamp %s exists " "before start of range" % ts) elif ts > end: raise ValueError("verticalLine(): timestamp %s exists " "after end of range" % ts) start = end = ts step = 1.0 series = TimeSeries(label, start, end, step, [1.0, 1.0]) series.options['drawAsInfinite'] = True if color: series.color = color return [series]
python
def verticalLine(requestContext, ts, label=None, color=None): """ Takes a timestamp string ts. Draws a vertical line at the designated timestamp with optional 'label' and 'color'. Supported timestamp formats include both relative (e.g. -3h) and absolute (e.g. 16:00_20110501) strings, such as those used with ``from`` and ``until`` parameters. When set, the 'label' will appear in the graph legend. Note: Any timestamps defined outside the requested range will raise a 'ValueError' exception. Example:: &target=verticalLine("12:3420131108","event","blue") &target=verticalLine("16:00_20110501","event") &target=verticalLine("-5mins") """ ts = int(epoch(parseATTime(ts, requestContext['tzinfo']))) start = int(epoch(requestContext['startTime'])) end = int(epoch(requestContext['endTime'])) if ts < start: raise ValueError("verticalLine(): timestamp %s exists " "before start of range" % ts) elif ts > end: raise ValueError("verticalLine(): timestamp %s exists " "after end of range" % ts) start = end = ts step = 1.0 series = TimeSeries(label, start, end, step, [1.0, 1.0]) series.options['drawAsInfinite'] = True if color: series.color = color return [series]
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Takes a timestamp string ts. Draws a vertical line at the designated timestamp with optional 'label' and 'color'. Supported timestamp formats include both relative (e.g. -3h) and absolute (e.g. 16:00_20110501) strings, such as those used with ``from`` and ``until`` parameters. When set, the 'label' will appear in the graph legend. Note: Any timestamps defined outside the requested range will raise a 'ValueError' exception. Example:: &target=verticalLine("12:3420131108","event","blue") &target=verticalLine("16:00_20110501","event") &target=verticalLine("-5mins")
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3338-L3373
brutasse/graphite-api
graphite_api/functions.py
threshold
def threshold(requestContext, value, label=None, color=None): """ Takes a float F, followed by a label (in double quotes) and a color. (See ``bgcolor`` in the render\_api_ for valid color names & formats.) Draws a horizontal line at value F across the graph. Example:: &target=threshold(123.456, "omgwtfbbq", "red") """ [series] = constantLine(requestContext, value) if label: series.name = label if color: series.color = color return [series]
python
def threshold(requestContext, value, label=None, color=None): """ Takes a float F, followed by a label (in double quotes) and a color. (See ``bgcolor`` in the render\_api_ for valid color names & formats.) Draws a horizontal line at value F across the graph. Example:: &target=threshold(123.456, "omgwtfbbq", "red") """ [series] = constantLine(requestContext, value) if label: series.name = label if color: series.color = color return [series]
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Takes a float F, followed by a label (in double quotes) and a color. (See ``bgcolor`` in the render\_api_ for valid color names & formats.) Draws a horizontal line at value F across the graph. Example:: &target=threshold(123.456, "omgwtfbbq", "red")
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3376-L3393
brutasse/graphite-api
graphite_api/functions.py
transformNull
def transformNull(requestContext, seriesList, default=0, referenceSeries=None): """ Takes a metric or wildcard seriesList and replaces null values with the value specified by `default`. The value 0 used if not specified. The optional referenceSeries, if specified, is a metric or wildcard series list that governs which time intervals nulls should be replaced. If specified, nulls are replaced only in intervals where a non-null is found for the same interval in any of referenceSeries. This method compliments the drawNullAsZero function in graphical mode, but also works in text-only mode. Example:: &target=transformNull(webapp.pages.*.views,-1) This would take any page that didn't have values and supply negative 1 as a default. Any other numeric value may be used as well. """ def transform(v, d): if v is None: return d else: return v if referenceSeries: defaults = [default if any(v is not None for v in x) else None for x in zip_longest(*referenceSeries)] else: defaults = None for series in seriesList: if referenceSeries: series.name = "transformNull(%s,%g,referenceSeries)" % ( series.name, default) else: series.name = "transformNull(%s,%g)" % (series.name, default) series.pathExpression = series.name if defaults: values = [transform(v, d) for v, d in zip_longest(series, defaults)] else: values = [transform(v, default) for v in series] series.extend(values) del series[:len(values)] return seriesList
python
def transformNull(requestContext, seriesList, default=0, referenceSeries=None): """ Takes a metric or wildcard seriesList and replaces null values with the value specified by `default`. The value 0 used if not specified. The optional referenceSeries, if specified, is a metric or wildcard series list that governs which time intervals nulls should be replaced. If specified, nulls are replaced only in intervals where a non-null is found for the same interval in any of referenceSeries. This method compliments the drawNullAsZero function in graphical mode, but also works in text-only mode. Example:: &target=transformNull(webapp.pages.*.views,-1) This would take any page that didn't have values and supply negative 1 as a default. Any other numeric value may be used as well. """ def transform(v, d): if v is None: return d else: return v if referenceSeries: defaults = [default if any(v is not None for v in x) else None for x in zip_longest(*referenceSeries)] else: defaults = None for series in seriesList: if referenceSeries: series.name = "transformNull(%s,%g,referenceSeries)" % ( series.name, default) else: series.name = "transformNull(%s,%g)" % (series.name, default) series.pathExpression = series.name if defaults: values = [transform(v, d) for v, d in zip_longest(series, defaults)] else: values = [transform(v, default) for v in series] series.extend(values) del series[:len(values)] return seriesList
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Takes a metric or wildcard seriesList and replaces null values with the value specified by `default`. The value 0 used if not specified. The optional referenceSeries, if specified, is a metric or wildcard series list that governs which time intervals nulls should be replaced. If specified, nulls are replaced only in intervals where a non-null is found for the same interval in any of referenceSeries. This method compliments the drawNullAsZero function in graphical mode, but also works in text-only mode. Example:: &target=transformNull(webapp.pages.*.views,-1) This would take any page that didn't have values and supply negative 1 as a default. Any other numeric value may be used as well.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3396-L3440
brutasse/graphite-api
graphite_api/functions.py
isNonNull
def isNonNull(requestContext, seriesList): """ Takes a metric or wild card seriesList and counts up how many non-null values are specified. This is useful for understanding which metrics have data at a given point in time (ie, to count which servers are alive). Example:: &target=isNonNull(webapp.pages.*.views) Returns a seriesList where 1 is specified for non-null values, and 0 is specified for null values. """ def transform(v): if v is None: return 0 else: return 1 for series in seriesList: series.name = "isNonNull(%s)" % (series.name) series.pathExpression = series.name values = [transform(v) for v in series] series.extend(values) del series[:len(values)] return seriesList
python
def isNonNull(requestContext, seriesList): """ Takes a metric or wild card seriesList and counts up how many non-null values are specified. This is useful for understanding which metrics have data at a given point in time (ie, to count which servers are alive). Example:: &target=isNonNull(webapp.pages.*.views) Returns a seriesList where 1 is specified for non-null values, and 0 is specified for null values. """ def transform(v): if v is None: return 0 else: return 1 for series in seriesList: series.name = "isNonNull(%s)" % (series.name) series.pathExpression = series.name values = [transform(v) for v in series] series.extend(values) del series[:len(values)] return seriesList
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Takes a metric or wild card seriesList and counts up how many non-null values are specified. This is useful for understanding which metrics have data at a given point in time (ie, to count which servers are alive). Example:: &target=isNonNull(webapp.pages.*.views) Returns a seriesList where 1 is specified for non-null values, and 0 is specified for null values.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3443-L3470
brutasse/graphite-api
graphite_api/functions.py
identity
def identity(requestContext, name, step=60): """ Identity function: Returns datapoints where the value equals the timestamp of the datapoint. Useful when you have another series where the value is a timestamp, and you want to compare it to the time of the datapoint, to render an age Example:: &target=identity("The.time.series") This would create a series named "The.time.series" that contains points where x(t) == t. Accepts optional second argument as 'step' parameter (default step is 60 sec) """ start = int(epoch(requestContext["startTime"])) end = int(epoch(requestContext["endTime"])) values = range(start, end, step) series = TimeSeries(name, start, end, step, values) series.pathExpression = 'identity("%s")' % name return [series]
python
def identity(requestContext, name, step=60): """ Identity function: Returns datapoints where the value equals the timestamp of the datapoint. Useful when you have another series where the value is a timestamp, and you want to compare it to the time of the datapoint, to render an age Example:: &target=identity("The.time.series") This would create a series named "The.time.series" that contains points where x(t) == t. Accepts optional second argument as 'step' parameter (default step is 60 sec) """ start = int(epoch(requestContext["startTime"])) end = int(epoch(requestContext["endTime"])) values = range(start, end, step) series = TimeSeries(name, start, end, step, values) series.pathExpression = 'identity("%s")' % name return [series]
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Identity function: Returns datapoints where the value equals the timestamp of the datapoint. Useful when you have another series where the value is a timestamp, and you want to compare it to the time of the datapoint, to render an age Example:: &target=identity("The.time.series") This would create a series named "The.time.series" that contains points where x(t) == t. Accepts optional second argument as 'step' parameter (default step is 60 sec)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3473-L3496
brutasse/graphite-api
graphite_api/functions.py
countSeries
def countSeries(requestContext, *seriesLists): """ Draws a horizontal line representing the number of nodes found in the seriesList. Example:: &target=countSeries(carbon.agents.*.*) """ if not seriesLists or not any(seriesLists): series = constantLine(requestContext, 0).pop() series.pathExpression = "countSeries()" else: seriesList, start, end, step = normalize(seriesLists) name = "countSeries(%s)" % formatPathExpressions(seriesList) values = (int(len(row)) for row in zip_longest(*seriesList)) series = TimeSeries(name, start, end, step, values) series.pathExpression = name return [series]
python
def countSeries(requestContext, *seriesLists): """ Draws a horizontal line representing the number of nodes found in the seriesList. Example:: &target=countSeries(carbon.agents.*.*) """ if not seriesLists or not any(seriesLists): series = constantLine(requestContext, 0).pop() series.pathExpression = "countSeries()" else: seriesList, start, end, step = normalize(seriesLists) name = "countSeries(%s)" % formatPathExpressions(seriesList) values = (int(len(row)) for row in zip_longest(*seriesList)) series = TimeSeries(name, start, end, step, values) series.pathExpression = name return [series]
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Draws a horizontal line representing the number of nodes found in the seriesList. Example:: &target=countSeries(carbon.agents.*.*)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3499-L3519
brutasse/graphite-api
graphite_api/functions.py
group
def group(requestContext, *seriesLists): """ Takes an arbitrary number of seriesLists and adds them to a single seriesList. This is used to pass multiple seriesLists to a function which only takes one. """ seriesGroup = [] for s in seriesLists: seriesGroup.extend(s) return seriesGroup
python
def group(requestContext, *seriesLists): """ Takes an arbitrary number of seriesLists and adds them to a single seriesList. This is used to pass multiple seriesLists to a function which only takes one. """ seriesGroup = [] for s in seriesLists: seriesGroup.extend(s) return seriesGroup
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Takes an arbitrary number of seriesLists and adds them to a single seriesList. This is used to pass multiple seriesLists to a function which only takes one.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3522-L3532
brutasse/graphite-api
graphite_api/functions.py
mapSeries
def mapSeries(requestContext, seriesList, mapNode): """ Short form: ``map()``. Takes a seriesList and maps it to a list of sub-seriesList. Each sub-seriesList has the given mapNode in common. Example (note: This function is not very useful alone. It should be used with :py:func:`reduceSeries`):: mapSeries(servers.*.cpu.*,1) => [ servers.server1.cpu.*, servers.server2.cpu.*, ... servers.serverN.cpu.* ] """ metaSeries = {} keys = [] for series in seriesList: key = series.name.split(".")[mapNode] if key not in metaSeries: metaSeries[key] = [series] keys.append(key) else: metaSeries[key].append(series) return [metaSeries[k] for k in keys]
python
def mapSeries(requestContext, seriesList, mapNode): """ Short form: ``map()``. Takes a seriesList and maps it to a list of sub-seriesList. Each sub-seriesList has the given mapNode in common. Example (note: This function is not very useful alone. It should be used with :py:func:`reduceSeries`):: mapSeries(servers.*.cpu.*,1) => [ servers.server1.cpu.*, servers.server2.cpu.*, ... servers.serverN.cpu.* ] """ metaSeries = {} keys = [] for series in seriesList: key = series.name.split(".")[mapNode] if key not in metaSeries: metaSeries[key] = [series] keys.append(key) else: metaSeries[key].append(series) return [metaSeries[k] for k in keys]
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Short form: ``map()``. Takes a seriesList and maps it to a list of sub-seriesList. Each sub-seriesList has the given mapNode in common. Example (note: This function is not very useful alone. It should be used with :py:func:`reduceSeries`):: mapSeries(servers.*.cpu.*,1) => [ servers.server1.cpu.*, servers.server2.cpu.*, ... servers.serverN.cpu.* ]
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3535-L3562
brutasse/graphite-api
graphite_api/functions.py
reduceSeries
def reduceSeries(requestContext, seriesLists, reduceFunction, reduceNode, *reduceMatchers): """ Short form: ``reduce()``. Takes a list of seriesLists and reduces it to a list of series by means of the reduceFunction. Reduction is performed by matching the reduceNode in each series against the list of reduceMatchers. The each series is then passed to the reduceFunction as arguments in the order given by reduceMatchers. The reduceFunction should yield a single series. The resulting list of series are aliased so that they can easily be nested in other functions. **Example**: Map/Reduce asPercent(bytes_used,total_bytes) for each server. Assume that metrics in the form below exist:: servers.server1.disk.bytes_used servers.server1.disk.total_bytes servers.server2.disk.bytes_used servers.server2.disk.total_bytes servers.server3.disk.bytes_used servers.server3.disk.total_bytes ... servers.serverN.disk.bytes_used servers.serverN.disk.total_bytes To get the percentage of disk used for each server:: reduceSeries(mapSeries(servers.*.disk.*,1), "asPercent",3,"bytes_used","total_bytes") => alias(asPercent(servers.server1.disk.bytes_used, servers.server1.disk.total_bytes), "servers.server1.disk.reduce.asPercent"), alias(asPercent(servers.server2.disk.bytes_used, servers.server2.disk.total_bytes), "servers.server2.disk.reduce.asPercent"), ... alias(asPercent(servers.serverN.disk.bytes_used, servers.serverN.disk.total_bytes), "servers.serverN.disk.reduce.asPercent") In other words, we will get back the following metrics:: servers.server1.disk.reduce.asPercent, servers.server2.disk.reduce.asPercent, ... servers.serverN.disk.reduce.asPercent .. seealso:: :py:func:`mapSeries` """ metaSeries = {} keys = [] for seriesList in seriesLists: for series in seriesList: nodes = series.name.split('.') node = nodes[reduceNode] reduceSeriesName = '.'.join( nodes[0:reduceNode]) + '.reduce.' + reduceFunction if node in reduceMatchers: if reduceSeriesName not in metaSeries: metaSeries[reduceSeriesName] = [None] * len(reduceMatchers) keys.append(reduceSeriesName) i = reduceMatchers.index(node) metaSeries[reduceSeriesName][i] = series for key in keys: metaSeries[key] = app.functions[reduceFunction]( requestContext, *[[s] for s in metaSeries[key]])[0] metaSeries[key].name = key return [metaSeries[key] for key in keys]
python
def reduceSeries(requestContext, seriesLists, reduceFunction, reduceNode, *reduceMatchers): """ Short form: ``reduce()``. Takes a list of seriesLists and reduces it to a list of series by means of the reduceFunction. Reduction is performed by matching the reduceNode in each series against the list of reduceMatchers. The each series is then passed to the reduceFunction as arguments in the order given by reduceMatchers. The reduceFunction should yield a single series. The resulting list of series are aliased so that they can easily be nested in other functions. **Example**: Map/Reduce asPercent(bytes_used,total_bytes) for each server. Assume that metrics in the form below exist:: servers.server1.disk.bytes_used servers.server1.disk.total_bytes servers.server2.disk.bytes_used servers.server2.disk.total_bytes servers.server3.disk.bytes_used servers.server3.disk.total_bytes ... servers.serverN.disk.bytes_used servers.serverN.disk.total_bytes To get the percentage of disk used for each server:: reduceSeries(mapSeries(servers.*.disk.*,1), "asPercent",3,"bytes_used","total_bytes") => alias(asPercent(servers.server1.disk.bytes_used, servers.server1.disk.total_bytes), "servers.server1.disk.reduce.asPercent"), alias(asPercent(servers.server2.disk.bytes_used, servers.server2.disk.total_bytes), "servers.server2.disk.reduce.asPercent"), ... alias(asPercent(servers.serverN.disk.bytes_used, servers.serverN.disk.total_bytes), "servers.serverN.disk.reduce.asPercent") In other words, we will get back the following metrics:: servers.server1.disk.reduce.asPercent, servers.server2.disk.reduce.asPercent, ... servers.serverN.disk.reduce.asPercent .. seealso:: :py:func:`mapSeries` """ metaSeries = {} keys = [] for seriesList in seriesLists: for series in seriesList: nodes = series.name.split('.') node = nodes[reduceNode] reduceSeriesName = '.'.join( nodes[0:reduceNode]) + '.reduce.' + reduceFunction if node in reduceMatchers: if reduceSeriesName not in metaSeries: metaSeries[reduceSeriesName] = [None] * len(reduceMatchers) keys.append(reduceSeriesName) i = reduceMatchers.index(node) metaSeries[reduceSeriesName][i] = series for key in keys: metaSeries[key] = app.functions[reduceFunction]( requestContext, *[[s] for s in metaSeries[key]])[0] metaSeries[key].name = key return [metaSeries[key] for key in keys]
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Short form: ``reduce()``. Takes a list of seriesLists and reduces it to a list of series by means of the reduceFunction. Reduction is performed by matching the reduceNode in each series against the list of reduceMatchers. The each series is then passed to the reduceFunction as arguments in the order given by reduceMatchers. The reduceFunction should yield a single series. The resulting list of series are aliased so that they can easily be nested in other functions. **Example**: Map/Reduce asPercent(bytes_used,total_bytes) for each server. Assume that metrics in the form below exist:: servers.server1.disk.bytes_used servers.server1.disk.total_bytes servers.server2.disk.bytes_used servers.server2.disk.total_bytes servers.server3.disk.bytes_used servers.server3.disk.total_bytes ... servers.serverN.disk.bytes_used servers.serverN.disk.total_bytes To get the percentage of disk used for each server:: reduceSeries(mapSeries(servers.*.disk.*,1), "asPercent",3,"bytes_used","total_bytes") => alias(asPercent(servers.server1.disk.bytes_used, servers.server1.disk.total_bytes), "servers.server1.disk.reduce.asPercent"), alias(asPercent(servers.server2.disk.bytes_used, servers.server2.disk.total_bytes), "servers.server2.disk.reduce.asPercent"), ... alias(asPercent(servers.serverN.disk.bytes_used, servers.serverN.disk.total_bytes), "servers.serverN.disk.reduce.asPercent") In other words, we will get back the following metrics:: servers.server1.disk.reduce.asPercent, servers.server2.disk.reduce.asPercent, ... servers.serverN.disk.reduce.asPercent .. seealso:: :py:func:`mapSeries`
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3565-L3638
brutasse/graphite-api
graphite_api/functions.py
applyByNode
def applyByNode(requestContext, seriesList, nodeNum, templateFunction, newName=None): """ Takes a seriesList and applies some complicated function (described by a string), replacing templates with unique prefixes of keys from the seriesList (the key is all nodes up to the index given as `nodeNum`). If the `newName` parameter is provided, the name of the resulting series will be given by that parameter, with any "%" characters replaced by the unique prefix. Example:: &target=applyByNode(servers.*.disk.bytes_free,1, "divideSeries(%.disk.bytes_free,sumSeries(%.disk.bytes_*))") Would find all series which match `servers.*.disk.bytes_free`, then trim them down to unique series up to the node given by nodeNum, then fill them into the template function provided (replacing % by the prefixes). """ from .app import evaluateTarget prefixes = set() for series in seriesList: prefix = '.'.join(series.name.split('.')[:nodeNum + 1]) prefixes.add(prefix) results = [] for prefix in sorted(prefixes): target = templateFunction.replace('%', prefix) for resultSeries in evaluateTarget(requestContext, target): if newName: resultSeries.name = newName.replace('%', prefix) resultSeries.pathExpression = prefix resultSeries.start = series.start resultSeries.end = series.end results.append(resultSeries) return results
python
def applyByNode(requestContext, seriesList, nodeNum, templateFunction, newName=None): """ Takes a seriesList and applies some complicated function (described by a string), replacing templates with unique prefixes of keys from the seriesList (the key is all nodes up to the index given as `nodeNum`). If the `newName` parameter is provided, the name of the resulting series will be given by that parameter, with any "%" characters replaced by the unique prefix. Example:: &target=applyByNode(servers.*.disk.bytes_free,1, "divideSeries(%.disk.bytes_free,sumSeries(%.disk.bytes_*))") Would find all series which match `servers.*.disk.bytes_free`, then trim them down to unique series up to the node given by nodeNum, then fill them into the template function provided (replacing % by the prefixes). """ from .app import evaluateTarget prefixes = set() for series in seriesList: prefix = '.'.join(series.name.split('.')[:nodeNum + 1]) prefixes.add(prefix) results = [] for prefix in sorted(prefixes): target = templateFunction.replace('%', prefix) for resultSeries in evaluateTarget(requestContext, target): if newName: resultSeries.name = newName.replace('%', prefix) resultSeries.pathExpression = prefix resultSeries.start = series.start resultSeries.end = series.end results.append(resultSeries) return results
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3641-L3677
brutasse/graphite-api
graphite_api/functions.py
groupByNode
def groupByNode(requestContext, seriesList, nodeNum, callback): """ Takes a serieslist and maps a callback to subgroups within as defined by a common node. Example:: &target=groupByNode(ganglia.by-function.*.*.cpu.load5,2,"sumSeries") Would return multiple series which are each the result of applying the "sumSeries" function to groups joined on the second node (0 indexed) resulting in a list of targets like:: sumSeries(ganglia.by-function.server1.*.cpu.load5), sumSeries(ganglia.by-function.server2.*.cpu.load5),... """ return groupByNodes(requestContext, seriesList, callback, nodeNum)
python
def groupByNode(requestContext, seriesList, nodeNum, callback): """ Takes a serieslist and maps a callback to subgroups within as defined by a common node. Example:: &target=groupByNode(ganglia.by-function.*.*.cpu.load5,2,"sumSeries") Would return multiple series which are each the result of applying the "sumSeries" function to groups joined on the second node (0 indexed) resulting in a list of targets like:: sumSeries(ganglia.by-function.server1.*.cpu.load5), sumSeries(ganglia.by-function.server2.*.cpu.load5),... """ return groupByNodes(requestContext, seriesList, callback, nodeNum)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3680-L3697
brutasse/graphite-api
graphite_api/functions.py
groupByNodes
def groupByNodes(requestContext, seriesList, callback, *nodes): """ Takes a serieslist and maps a callback to subgroups within as defined by multiple nodes. Example:: &target=groupByNodes(ganglia.server*.*.cpu.load*,"sumSeries",1,4) Would return multiple series which are each the result of applying the "sumSeries" function to groups joined on the nodes' list (0 indexed) resulting in a list of targets like:: sumSeries(ganglia.server1.*.cpu.load5), sumSeries(ganglia.server1.*.cpu.load10), sumSeries(ganglia.server1.*.cpu.load15), sumSeries(ganglia.server2.*.cpu.load5), sumSeries(ganglia.server2.*.cpu.load10), sumSeries(ganglia.server2.*.cpu.load15), ... """ from .app import app metaSeries = {} keys = [] if isinstance(nodes, int): nodes = [nodes] for series in seriesList: key = '.'.join(series.name.split(".")[n] for n in nodes) if key not in metaSeries: metaSeries[key] = [series] keys.append(key) else: metaSeries[key].append(series) for key in metaSeries: metaSeries[key] = app.functions[callback](requestContext, metaSeries[key])[0] metaSeries[key].name = key return [metaSeries[key] for key in keys]
python
def groupByNodes(requestContext, seriesList, callback, *nodes): """ Takes a serieslist and maps a callback to subgroups within as defined by multiple nodes. Example:: &target=groupByNodes(ganglia.server*.*.cpu.load*,"sumSeries",1,4) Would return multiple series which are each the result of applying the "sumSeries" function to groups joined on the nodes' list (0 indexed) resulting in a list of targets like:: sumSeries(ganglia.server1.*.cpu.load5), sumSeries(ganglia.server1.*.cpu.load10), sumSeries(ganglia.server1.*.cpu.load15), sumSeries(ganglia.server2.*.cpu.load5), sumSeries(ganglia.server2.*.cpu.load10), sumSeries(ganglia.server2.*.cpu.load15), ... """ from .app import app metaSeries = {} keys = [] if isinstance(nodes, int): nodes = [nodes] for series in seriesList: key = '.'.join(series.name.split(".")[n] for n in nodes) if key not in metaSeries: metaSeries[key] = [series] keys.append(key) else: metaSeries[key].append(series) for key in metaSeries: metaSeries[key] = app.functions[callback](requestContext, metaSeries[key])[0] metaSeries[key].name = key return [metaSeries[key] for key in keys]
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3700-L3737
brutasse/graphite-api
graphite_api/functions.py
exclude
def exclude(requestContext, seriesList, pattern): """ Takes a metric or a wildcard seriesList, followed by a regular expression in double quotes. Excludes metrics that match the regular expression. Example:: &target=exclude(servers*.instance*.threads.busy,"server02") """ regex = re.compile(pattern) return [s for s in seriesList if not regex.search(s.name)]
python
def exclude(requestContext, seriesList, pattern): """ Takes a metric or a wildcard seriesList, followed by a regular expression in double quotes. Excludes metrics that match the regular expression. Example:: &target=exclude(servers*.instance*.threads.busy,"server02") """ regex = re.compile(pattern) return [s for s in seriesList if not regex.search(s.name)]
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3740-L3750
brutasse/graphite-api
graphite_api/functions.py
smartSummarize
def smartSummarize(requestContext, seriesList, intervalString, func='sum'): """ Smarter experimental version of summarize. """ results = [] delta = parseTimeOffset(intervalString) interval = to_seconds(delta) # Adjust the start time to fit an entire day for intervals >= 1 day requestContext = requestContext.copy() tzinfo = requestContext['tzinfo'] s = requestContext['startTime'] if interval >= DAY: requestContext['startTime'] = datetime(s.year, s.month, s.day, tzinfo=tzinfo) elif interval >= HOUR: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, tzinfo=tzinfo) elif interval >= MINUTE: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, s.minute, tzinfo=tzinfo) paths = [] for series in seriesList: paths.extend(pathsFromTarget(requestContext, series.pathExpression)) data_store = fetchData(requestContext, paths) for series in seriesList: # XXX: breaks with summarize(metric.{a,b}) # each series.pathExpression == metric.{a,b} newSeries = evaluateTarget(requestContext, series.pathExpression, data_store)[0] series[0:len(series)] = newSeries series.start = newSeries.start series.end = newSeries.end series.step = newSeries.step for series in seriesList: buckets = {} # {timestamp: [values]} timestamps = range(int(series.start), int(series.end), int(series.step)) datapoints = zip_longest(timestamps, series) # Populate buckets for timestamp, value in datapoints: # ISSUE: Sometimes there is a missing timestamp in datapoints when # running a smartSummary if not timestamp: continue bucketInterval = int((timestamp - series.start) / interval) if bucketInterval not in buckets: buckets[bucketInterval] = [] if value is not None: buckets[bucketInterval].append(value) newValues = [] for timestamp in range(series.start, series.end, interval): bucketInterval = int((timestamp - series.start) / interval) bucket = buckets.get(bucketInterval, []) if bucket: if func == 'avg': newValues.append(float(sum(bucket)) / float(len(bucket))) elif func == 'last': newValues.append(bucket[len(bucket)-1]) elif func == 'max': newValues.append(max(bucket)) elif func == 'min': newValues.append(min(bucket)) else: newValues.append(sum(bucket)) else: newValues.append(None) newName = "smartSummarize(%s, \"%s\", \"%s\")" % (series.name, intervalString, func) alignedEnd = series.start + (bucketInterval * interval) + interval newSeries = TimeSeries(newName, series.start, alignedEnd, interval, newValues) newSeries.pathExpression = newName results.append(newSeries) return results
python
def smartSummarize(requestContext, seriesList, intervalString, func='sum'): """ Smarter experimental version of summarize. """ results = [] delta = parseTimeOffset(intervalString) interval = to_seconds(delta) # Adjust the start time to fit an entire day for intervals >= 1 day requestContext = requestContext.copy() tzinfo = requestContext['tzinfo'] s = requestContext['startTime'] if interval >= DAY: requestContext['startTime'] = datetime(s.year, s.month, s.day, tzinfo=tzinfo) elif interval >= HOUR: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, tzinfo=tzinfo) elif interval >= MINUTE: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, s.minute, tzinfo=tzinfo) paths = [] for series in seriesList: paths.extend(pathsFromTarget(requestContext, series.pathExpression)) data_store = fetchData(requestContext, paths) for series in seriesList: # XXX: breaks with summarize(metric.{a,b}) # each series.pathExpression == metric.{a,b} newSeries = evaluateTarget(requestContext, series.pathExpression, data_store)[0] series[0:len(series)] = newSeries series.start = newSeries.start series.end = newSeries.end series.step = newSeries.step for series in seriesList: buckets = {} # {timestamp: [values]} timestamps = range(int(series.start), int(series.end), int(series.step)) datapoints = zip_longest(timestamps, series) # Populate buckets for timestamp, value in datapoints: # ISSUE: Sometimes there is a missing timestamp in datapoints when # running a smartSummary if not timestamp: continue bucketInterval = int((timestamp - series.start) / interval) if bucketInterval not in buckets: buckets[bucketInterval] = [] if value is not None: buckets[bucketInterval].append(value) newValues = [] for timestamp in range(series.start, series.end, interval): bucketInterval = int((timestamp - series.start) / interval) bucket = buckets.get(bucketInterval, []) if bucket: if func == 'avg': newValues.append(float(sum(bucket)) / float(len(bucket))) elif func == 'last': newValues.append(bucket[len(bucket)-1]) elif func == 'max': newValues.append(max(bucket)) elif func == 'min': newValues.append(min(bucket)) else: newValues.append(sum(bucket)) else: newValues.append(None) newName = "smartSummarize(%s, \"%s\", \"%s\")" % (series.name, intervalString, func) alignedEnd = series.start + (bucketInterval * interval) + interval newSeries = TimeSeries(newName, series.start, alignedEnd, interval, newValues) newSeries.pathExpression = newName results.append(newSeries) return results
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3767-L3854
brutasse/graphite-api
graphite_api/functions.py
summarize
def summarize(requestContext, seriesList, intervalString, func='sum', alignToFrom=False): """ Summarize the data into interval buckets of a certain size. By default, the contents of each interval bucket are summed together. This is useful for counters where each increment represents a discrete event and retrieving a "per X" value requires summing all the events in that interval. Specifying 'avg' instead will return the mean for each bucket, which can be more useful when the value is a gauge that represents a certain value in time. 'max', 'min' or 'last' can also be specified. By default, buckets are calculated by rounding to the nearest interval. This works well for intervals smaller than a day. For example, 22:32 will end up in the bucket 22:00-23:00 when the interval=1hour. Passing alignToFrom=true will instead create buckets starting at the from time. In this case, the bucket for 22:32 depends on the from time. If from=6:30 then the 1hour bucket for 22:32 is 22:30-23:30. Example:: # total errors per hour &target=summarize(counter.errors, "1hour") # new users per week &target=summarize(nonNegativeDerivative(gauge.num_users), "1week") # average queue size per hour &target=summarize(queue.size, "1hour", "avg") # maximum queue size during each hour &target=summarize(queue.size, "1hour", "max") # 2010 Q1-4 &target=summarize(metric, "13week", "avg", true)&from=midnight+20100101 """ results = [] delta = parseTimeOffset(intervalString) interval = to_seconds(delta) for series in seriesList: buckets = {} timestamps = range(int(series.start), int(series.end) + 1, int(series.step)) datapoints = zip_longest(timestamps, series) for timestamp, value in datapoints: if timestamp is None: continue if alignToFrom: bucketInterval = int((timestamp - series.start) / interval) else: bucketInterval = timestamp - (timestamp % interval) if bucketInterval not in buckets: buckets[bucketInterval] = [] if value is not None: buckets[bucketInterval].append(value) if alignToFrom: newStart = series.start newEnd = series.end else: newStart = series.start - (series.start % interval) newEnd = series.end - (series.end % interval) + interval newValues = [] for timestamp in range(newStart, newEnd, interval): if alignToFrom: newEnd = timestamp bucketInterval = int((timestamp - series.start) / interval) else: bucketInterval = timestamp - (timestamp % interval) bucket = buckets.get(bucketInterval, []) if bucket: if func == 'avg': newValues.append(float(sum(bucket)) / float(len(bucket))) elif func == 'last': newValues.append(bucket[len(bucket)-1]) elif func == 'max': newValues.append(max(bucket)) elif func == 'min': newValues.append(min(bucket)) else: newValues.append(sum(bucket)) else: newValues.append(None) if alignToFrom: newEnd += interval newName = "summarize(%s, \"%s\", \"%s\"%s)" % ( series.name, intervalString, func, alignToFrom and ", true" or "") newSeries = TimeSeries(newName, newStart, newEnd, interval, newValues) newSeries.pathExpression = newName results.append(newSeries) return results
python
def summarize(requestContext, seriesList, intervalString, func='sum', alignToFrom=False): """ Summarize the data into interval buckets of a certain size. By default, the contents of each interval bucket are summed together. This is useful for counters where each increment represents a discrete event and retrieving a "per X" value requires summing all the events in that interval. Specifying 'avg' instead will return the mean for each bucket, which can be more useful when the value is a gauge that represents a certain value in time. 'max', 'min' or 'last' can also be specified. By default, buckets are calculated by rounding to the nearest interval. This works well for intervals smaller than a day. For example, 22:32 will end up in the bucket 22:00-23:00 when the interval=1hour. Passing alignToFrom=true will instead create buckets starting at the from time. In this case, the bucket for 22:32 depends on the from time. If from=6:30 then the 1hour bucket for 22:32 is 22:30-23:30. Example:: # total errors per hour &target=summarize(counter.errors, "1hour") # new users per week &target=summarize(nonNegativeDerivative(gauge.num_users), "1week") # average queue size per hour &target=summarize(queue.size, "1hour", "avg") # maximum queue size during each hour &target=summarize(queue.size, "1hour", "max") # 2010 Q1-4 &target=summarize(metric, "13week", "avg", true)&from=midnight+20100101 """ results = [] delta = parseTimeOffset(intervalString) interval = to_seconds(delta) for series in seriesList: buckets = {} timestamps = range(int(series.start), int(series.end) + 1, int(series.step)) datapoints = zip_longest(timestamps, series) for timestamp, value in datapoints: if timestamp is None: continue if alignToFrom: bucketInterval = int((timestamp - series.start) / interval) else: bucketInterval = timestamp - (timestamp % interval) if bucketInterval not in buckets: buckets[bucketInterval] = [] if value is not None: buckets[bucketInterval].append(value) if alignToFrom: newStart = series.start newEnd = series.end else: newStart = series.start - (series.start % interval) newEnd = series.end - (series.end % interval) + interval newValues = [] for timestamp in range(newStart, newEnd, interval): if alignToFrom: newEnd = timestamp bucketInterval = int((timestamp - series.start) / interval) else: bucketInterval = timestamp - (timestamp % interval) bucket = buckets.get(bucketInterval, []) if bucket: if func == 'avg': newValues.append(float(sum(bucket)) / float(len(bucket))) elif func == 'last': newValues.append(bucket[len(bucket)-1]) elif func == 'max': newValues.append(max(bucket)) elif func == 'min': newValues.append(min(bucket)) else: newValues.append(sum(bucket)) else: newValues.append(None) if alignToFrom: newEnd += interval newName = "summarize(%s, \"%s\", \"%s\"%s)" % ( series.name, intervalString, func, alignToFrom and ", true" or "") newSeries = TimeSeries(newName, newStart, newEnd, interval, newValues) newSeries.pathExpression = newName results.append(newSeries) return results
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Summarize the data into interval buckets of a certain size. By default, the contents of each interval bucket are summed together. This is useful for counters where each increment represents a discrete event and retrieving a "per X" value requires summing all the events in that interval. Specifying 'avg' instead will return the mean for each bucket, which can be more useful when the value is a gauge that represents a certain value in time. 'max', 'min' or 'last' can also be specified. By default, buckets are calculated by rounding to the nearest interval. This works well for intervals smaller than a day. For example, 22:32 will end up in the bucket 22:00-23:00 when the interval=1hour. Passing alignToFrom=true will instead create buckets starting at the from time. In this case, the bucket for 22:32 depends on the from time. If from=6:30 then the 1hour bucket for 22:32 is 22:30-23:30. Example:: # total errors per hour &target=summarize(counter.errors, "1hour") # new users per week &target=summarize(nonNegativeDerivative(gauge.num_users), "1week") # average queue size per hour &target=summarize(queue.size, "1hour", "avg") # maximum queue size during each hour &target=summarize(queue.size, "1hour", "max") # 2010 Q1-4 &target=summarize(metric, "13week", "avg", true)&from=midnight+20100101
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3857-L3963
brutasse/graphite-api
graphite_api/functions.py
hitcount
def hitcount(requestContext, seriesList, intervalString, alignToInterval=False): """ Estimate hit counts from a list of time series. This function assumes the values in each time series represent hits per second. It calculates hits per some larger interval such as per day or per hour. This function is like summarize(), except that it compensates automatically for different time scales (so that a similar graph results from using either fine-grained or coarse-grained records) and handles rarely-occurring events gracefully. """ results = [] delta = parseTimeOffset(intervalString) interval = to_seconds(delta) if alignToInterval: requestContext = requestContext.copy() tzinfo = requestContext['tzinfo'] s = requestContext['startTime'] if interval >= DAY: requestContext['startTime'] = datetime(s.year, s.month, s.day, tzinfo=tzinfo) elif interval >= HOUR: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, tzinfo=tzinfo) elif interval >= MINUTE: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, s.minute, tzinfo=tzinfo) # Gather all paths first, then the data paths = [] for series in seriesList: paths.extend(pathsFromTarget(requestContext, series.pathExpression)) data_store = fetchData(requestContext, paths) for series in seriesList: newSeries = evaluateTarget(requestContext, series.pathExpression, data_store)[0] intervalCount = int((series.end - series.start) / interval) series[0:len(series)] = newSeries series.start = newSeries.start series.end = newSeries.start + ( intervalCount * interval) + interval series.step = newSeries.step for series in seriesList: step = int(series.step) bucket_count = int(math.ceil( float(series.end - series.start) / interval)) buckets = [[] for _ in range(bucket_count)] newStart = int(series.end - bucket_count * interval) for i, value in enumerate(series): if value is None: continue start_time = int(series.start + i * step) start_bucket, start_mod = divmod(start_time - newStart, interval) end_time = start_time + step end_bucket, end_mod = divmod(end_time - newStart, interval) if end_bucket >= bucket_count: end_bucket = bucket_count - 1 end_mod = interval if start_bucket == end_bucket: # All of the hits go to a single bucket. if start_bucket >= 0: buckets[start_bucket].append(value * (end_mod - start_mod)) else: # Spread the hits among 2 or more buckets. if start_bucket >= 0: buckets[start_bucket].append( value * (interval - start_mod)) hits_per_bucket = value * interval for j in range(start_bucket + 1, end_bucket): buckets[j].append(hits_per_bucket) if end_mod > 0: buckets[end_bucket].append(value * end_mod) newValues = [] for bucket in buckets: if bucket: newValues.append(sum(bucket)) else: newValues.append(None) newName = 'hitcount(%s, "%s"%s)' % (series.name, intervalString, alignToInterval and ", true" or "") newSeries = TimeSeries(newName, newStart, series.end, interval, newValues) newSeries.pathExpression = newName results.append(newSeries) return results
python
def hitcount(requestContext, seriesList, intervalString, alignToInterval=False): """ Estimate hit counts from a list of time series. This function assumes the values in each time series represent hits per second. It calculates hits per some larger interval such as per day or per hour. This function is like summarize(), except that it compensates automatically for different time scales (so that a similar graph results from using either fine-grained or coarse-grained records) and handles rarely-occurring events gracefully. """ results = [] delta = parseTimeOffset(intervalString) interval = to_seconds(delta) if alignToInterval: requestContext = requestContext.copy() tzinfo = requestContext['tzinfo'] s = requestContext['startTime'] if interval >= DAY: requestContext['startTime'] = datetime(s.year, s.month, s.day, tzinfo=tzinfo) elif interval >= HOUR: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, tzinfo=tzinfo) elif interval >= MINUTE: requestContext['startTime'] = datetime(s.year, s.month, s.day, s.hour, s.minute, tzinfo=tzinfo) # Gather all paths first, then the data paths = [] for series in seriesList: paths.extend(pathsFromTarget(requestContext, series.pathExpression)) data_store = fetchData(requestContext, paths) for series in seriesList: newSeries = evaluateTarget(requestContext, series.pathExpression, data_store)[0] intervalCount = int((series.end - series.start) / interval) series[0:len(series)] = newSeries series.start = newSeries.start series.end = newSeries.start + ( intervalCount * interval) + interval series.step = newSeries.step for series in seriesList: step = int(series.step) bucket_count = int(math.ceil( float(series.end - series.start) / interval)) buckets = [[] for _ in range(bucket_count)] newStart = int(series.end - bucket_count * interval) for i, value in enumerate(series): if value is None: continue start_time = int(series.start + i * step) start_bucket, start_mod = divmod(start_time - newStart, interval) end_time = start_time + step end_bucket, end_mod = divmod(end_time - newStart, interval) if end_bucket >= bucket_count: end_bucket = bucket_count - 1 end_mod = interval if start_bucket == end_bucket: # All of the hits go to a single bucket. if start_bucket >= 0: buckets[start_bucket].append(value * (end_mod - start_mod)) else: # Spread the hits among 2 or more buckets. if start_bucket >= 0: buckets[start_bucket].append( value * (interval - start_mod)) hits_per_bucket = value * interval for j in range(start_bucket + 1, end_bucket): buckets[j].append(hits_per_bucket) if end_mod > 0: buckets[end_bucket].append(value * end_mod) newValues = [] for bucket in buckets: if bucket: newValues.append(sum(bucket)) else: newValues.append(None) newName = 'hitcount(%s, "%s"%s)' % (series.name, intervalString, alignToInterval and ", true" or "") newSeries = TimeSeries(newName, newStart, series.end, interval, newValues) newSeries.pathExpression = newName results.append(newSeries) return results
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Estimate hit counts from a list of time series. This function assumes the values in each time series represent hits per second. It calculates hits per some larger interval such as per day or per hour. This function is like summarize(), except that it compensates automatically for different time scales (so that a similar graph results from using either fine-grained or coarse-grained records) and handles rarely-occurring events gracefully.
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L3966-L4066
brutasse/graphite-api
graphite_api/functions.py
timeFunction
def timeFunction(requestContext, name, step=60): """ Short Alias: time() Just returns the timestamp for each X value. T Example:: &target=time("The.time.series") This would create a series named "The.time.series" that contains in Y the same value (in seconds) as X. A second argument can be provided as a step parameter (default is 60 secs) """ start = int(epoch(requestContext["startTime"])) end = int(epoch(requestContext["endTime"])) delta = timedelta(seconds=step) when = requestContext["startTime"] values = [] while when < requestContext["endTime"]: values.append(epoch(when)) when += delta series = TimeSeries(name, start, end, step, values) series.pathExpression = name return [series]
python
def timeFunction(requestContext, name, step=60): """ Short Alias: time() Just returns the timestamp for each X value. T Example:: &target=time("The.time.series") This would create a series named "The.time.series" that contains in Y the same value (in seconds) as X. A second argument can be provided as a step parameter (default is 60 secs) """ start = int(epoch(requestContext["startTime"])) end = int(epoch(requestContext["endTime"])) delta = timedelta(seconds=step) when = requestContext["startTime"] values = [] while when < requestContext["endTime"]: values.append(epoch(when)) when += delta series = TimeSeries(name, start, end, step, values) series.pathExpression = name return [series]
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Short Alias: time() Just returns the timestamp for each X value. T Example:: &target=time("The.time.series") This would create a series named "The.time.series" that contains in Y the same value (in seconds) as X. A second argument can be provided as a step parameter (default is 60 secs)
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L4069-L4098
brutasse/graphite-api
graphite_api/functions.py
sinFunction
def sinFunction(requestContext, name, amplitude=1, step=60): """ Short Alias: sin() Just returns the sine of the current time. The optional amplitude parameter changes the amplitude of the wave. Example:: &target=sin("The.time.series", 2) This would create a series named "The.time.series" that contains sin(x)*2. A third argument can be provided as a step parameter (default is 60 secs). """ delta = timedelta(seconds=step) when = requestContext["startTime"] values = [] while when < requestContext["endTime"]: values.append(math.sin(epoch(when))*amplitude) when += delta series = TimeSeries( name, int(epoch(requestContext["startTime"])), int(epoch(requestContext["endTime"])), step, values) series.pathExpression = 'sin({0})'.format(name) return [series]
python
def sinFunction(requestContext, name, amplitude=1, step=60): """ Short Alias: sin() Just returns the sine of the current time. The optional amplitude parameter changes the amplitude of the wave. Example:: &target=sin("The.time.series", 2) This would create a series named "The.time.series" that contains sin(x)*2. A third argument can be provided as a step parameter (default is 60 secs). """ delta = timedelta(seconds=step) when = requestContext["startTime"] values = [] while when < requestContext["endTime"]: values.append(math.sin(epoch(when))*amplitude) when += delta series = TimeSeries( name, int(epoch(requestContext["startTime"])), int(epoch(requestContext["endTime"])), step, values) series.pathExpression = 'sin({0})'.format(name) return [series]
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Short Alias: sin() Just returns the sine of the current time. The optional amplitude parameter changes the amplitude of the wave. Example:: &target=sin("The.time.series", 2) This would create a series named "The.time.series" that contains sin(x)*2. A third argument can be provided as a step parameter (default is 60 secs).
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L4101-L4129
brutasse/graphite-api
graphite_api/functions.py
randomWalkFunction
def randomWalkFunction(requestContext, name, step=60): """ Short Alias: randomWalk() Returns a random walk starting at 0. This is great for testing when there is no real data in whisper. Example:: &target=randomWalk("The.time.series") This would create a series named "The.time.series" that contains points where x(t) == x(t-1)+random()-0.5, and x(0) == 0. Accepts an optional second argument as step parameter (default step is 60 sec). """ delta = timedelta(seconds=step) when = requestContext["startTime"] values = [] current = 0 while when < requestContext["endTime"]: values.append(current) current += random.random() - 0.5 when += delta return [TimeSeries( name, int(epoch(requestContext["startTime"])), int(epoch(requestContext["endTime"])), step, values)]
python
def randomWalkFunction(requestContext, name, step=60): """ Short Alias: randomWalk() Returns a random walk starting at 0. This is great for testing when there is no real data in whisper. Example:: &target=randomWalk("The.time.series") This would create a series named "The.time.series" that contains points where x(t) == x(t-1)+random()-0.5, and x(0) == 0. Accepts an optional second argument as step parameter (default step is 60 sec). """ delta = timedelta(seconds=step) when = requestContext["startTime"] values = [] current = 0 while when < requestContext["endTime"]: values.append(current) current += random.random() - 0.5 when += delta return [TimeSeries( name, int(epoch(requestContext["startTime"])), int(epoch(requestContext["endTime"])), step, values)]
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Short Alias: randomWalk() Returns a random walk starting at 0. This is great for testing when there is no real data in whisper. Example:: &target=randomWalk("The.time.series") This would create a series named "The.time.series" that contains points where x(t) == x(t-1)+random()-0.5, and x(0) == 0. Accepts an optional second argument as step parameter (default step is 60 sec).
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train
https://github.com/brutasse/graphite-api/blob/0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff/graphite_api/functions.py#L4146-L4175
opencobra/memote
memote/experimental/medium.py
Medium.validate
def validate(self, model, checks=[]): """Use a defined schema to validate the medium table format.""" custom = [ check_partial(reaction_id_check, frozenset(r.id for r in model.reactions)) ] super(Medium, self).validate(model=model, checks=checks + custom)
python
def validate(self, model, checks=[]): """Use a defined schema to validate the medium table format.""" custom = [ check_partial(reaction_id_check, frozenset(r.id for r in model.reactions)) ] super(Medium, self).validate(model=model, checks=checks + custom)
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/medium.py#L48-L54
opencobra/memote
memote/experimental/medium.py
Medium.apply
def apply(self, model): """Set the defined medium on the given model.""" model.medium = {row.exchange: row.uptake for row in self.data.itertuples(index=False)}
python
def apply(self, model): """Set the defined medium on the given model.""" model.medium = {row.exchange: row.uptake for row in self.data.itertuples(index=False)}
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Set the defined medium on the given model.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/medium.py#L56-L59
opencobra/memote
memote/suite/results/result.py
MemoteResult.add_environment_information
def add_environment_information(meta): """Record environment information.""" meta["timestamp"] = datetime.utcnow().isoformat(" ") meta["platform"] = platform.system() meta["release"] = platform.release() meta["python"] = platform.python_version() meta["packages"] = get_pkg_info("memote")
python
def add_environment_information(meta): """Record environment information.""" meta["timestamp"] = datetime.utcnow().isoformat(" ") meta["platform"] = platform.system() meta["release"] = platform.release() meta["python"] = platform.python_version() meta["packages"] = get_pkg_info("memote")
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/results/result.py#L46-L52
opencobra/memote
memote/support/helpers.py
find_transported_elements
def find_transported_elements(rxn): """ Return a dictionary showing the amount of transported elements of a rxn. Collects the elements for each metabolite participating in a reaction, multiplies the amount by the metabolite's stoichiometry in the reaction and bins the result according to the compartment that metabolite is in. This produces a dictionary of dictionaries such as this ``{'p': {'C': -1, 'H': -4}, c: {'C': 1, 'H': 4}}`` which shows the transported entities. This dictionary is then simplified to only include the non-zero elements of one single compartment i.e. showing the precise elements that are transported. Parameters ---------- rxn : cobra.Reaction Any cobra.Reaction containing metabolites. """ element_dist = defaultdict() # Collecting elements for each metabolite. for met in rxn.metabolites: if met.compartment not in element_dist: # Multiplication by the metabolite stoichiometry. element_dist[met.compartment] = \ {k: v * rxn.metabolites[met] for (k, v) in iteritems(met.elements)} else: x = {k: v * rxn.metabolites[met] for (k, v) in iteritems(met.elements)} y = element_dist[met.compartment] element_dist[met.compartment] = \ {k: x.get(k, 0) + y.get(k, 0) for k in set(x) | set(y)} delta_dict = defaultdict() # Simplification of the resulting dictionary of dictionaries. for elements in itervalues(element_dist): delta_dict.update(elements) # Only non-zero values get included in the returned delta-dict. delta_dict = {k: abs(v) for (k, v) in iteritems(delta_dict) if v != 0} return delta_dict
python
def find_transported_elements(rxn): """ Return a dictionary showing the amount of transported elements of a rxn. Collects the elements for each metabolite participating in a reaction, multiplies the amount by the metabolite's stoichiometry in the reaction and bins the result according to the compartment that metabolite is in. This produces a dictionary of dictionaries such as this ``{'p': {'C': -1, 'H': -4}, c: {'C': 1, 'H': 4}}`` which shows the transported entities. This dictionary is then simplified to only include the non-zero elements of one single compartment i.e. showing the precise elements that are transported. Parameters ---------- rxn : cobra.Reaction Any cobra.Reaction containing metabolites. """ element_dist = defaultdict() # Collecting elements for each metabolite. for met in rxn.metabolites: if met.compartment not in element_dist: # Multiplication by the metabolite stoichiometry. element_dist[met.compartment] = \ {k: v * rxn.metabolites[met] for (k, v) in iteritems(met.elements)} else: x = {k: v * rxn.metabolites[met] for (k, v) in iteritems(met.elements)} y = element_dist[met.compartment] element_dist[met.compartment] = \ {k: x.get(k, 0) + y.get(k, 0) for k in set(x) | set(y)} delta_dict = defaultdict() # Simplification of the resulting dictionary of dictionaries. for elements in itervalues(element_dist): delta_dict.update(elements) # Only non-zero values get included in the returned delta-dict. delta_dict = {k: abs(v) for (k, v) in iteritems(delta_dict) if v != 0} return delta_dict
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Return a dictionary showing the amount of transported elements of a rxn. Collects the elements for each metabolite participating in a reaction, multiplies the amount by the metabolite's stoichiometry in the reaction and bins the result according to the compartment that metabolite is in. This produces a dictionary of dictionaries such as this ``{'p': {'C': -1, 'H': -4}, c: {'C': 1, 'H': 4}}`` which shows the transported entities. This dictionary is then simplified to only include the non-zero elements of one single compartment i.e. showing the precise elements that are transported. Parameters ---------- rxn : cobra.Reaction Any cobra.Reaction containing metabolites.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L81-L120
opencobra/memote
memote/support/helpers.py
find_transport_reactions
def find_transport_reactions(model): """ Return a list of all transport reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- A transport reaction is defined as follows: 1. It contains metabolites from at least 2 compartments and 2. at least 1 metabolite undergoes no chemical reaction, i.e., the formula and/or annotation stays the same on both sides of the equation. A notable exception is transport via PTS, which also contains the following restriction: 3. The transported metabolite(s) are transported into a compartment through the exchange of a phosphate group. An example of transport via PTS would be pep(c) + glucose(e) -> glucose-6-phosphate(c) + pyr(c) Reactions similar to transport via PTS (referred to as "modified transport reactions") follow a similar pattern: A(x) + B-R(y) -> A-R(y) + B(y) Such modified transport reactions can be detected, but only when a formula field exists for all metabolites in a particular reaction. If this is not the case, transport reactions are identified through annotations, which cannot detect modified transport reactions. """ transport_reactions = [] transport_rxn_candidates = set(model.reactions) - set(model.boundary) \ - set(find_biomass_reaction(model)) transport_rxn_candidates = set( [rxn for rxn in transport_rxn_candidates if len(rxn.compartments) >= 2] ) # Add all labeled transport reactions sbo_matches = set([rxn for rxn in transport_rxn_candidates if rxn.annotation is not None and 'sbo' in rxn.annotation and rxn.annotation['sbo'] in TRANSPORT_RXN_SBO_TERMS]) if len(sbo_matches) > 0: transport_reactions += list(sbo_matches) # Find unlabeled transport reactions via formula or annotation checks for rxn in transport_rxn_candidates: # Check if metabolites have formula field rxn_mets = set([met.formula for met in rxn.metabolites]) if (None not in rxn_mets) and (len(rxn_mets) != 0): if is_transport_reaction_formulae(rxn): transport_reactions.append(rxn) elif is_transport_reaction_annotations(rxn): transport_reactions.append(rxn) return set(transport_reactions)
python
def find_transport_reactions(model): """ Return a list of all transport reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- A transport reaction is defined as follows: 1. It contains metabolites from at least 2 compartments and 2. at least 1 metabolite undergoes no chemical reaction, i.e., the formula and/or annotation stays the same on both sides of the equation. A notable exception is transport via PTS, which also contains the following restriction: 3. The transported metabolite(s) are transported into a compartment through the exchange of a phosphate group. An example of transport via PTS would be pep(c) + glucose(e) -> glucose-6-phosphate(c) + pyr(c) Reactions similar to transport via PTS (referred to as "modified transport reactions") follow a similar pattern: A(x) + B-R(y) -> A-R(y) + B(y) Such modified transport reactions can be detected, but only when a formula field exists for all metabolites in a particular reaction. If this is not the case, transport reactions are identified through annotations, which cannot detect modified transport reactions. """ transport_reactions = [] transport_rxn_candidates = set(model.reactions) - set(model.boundary) \ - set(find_biomass_reaction(model)) transport_rxn_candidates = set( [rxn for rxn in transport_rxn_candidates if len(rxn.compartments) >= 2] ) # Add all labeled transport reactions sbo_matches = set([rxn for rxn in transport_rxn_candidates if rxn.annotation is not None and 'sbo' in rxn.annotation and rxn.annotation['sbo'] in TRANSPORT_RXN_SBO_TERMS]) if len(sbo_matches) > 0: transport_reactions += list(sbo_matches) # Find unlabeled transport reactions via formula or annotation checks for rxn in transport_rxn_candidates: # Check if metabolites have formula field rxn_mets = set([met.formula for met in rxn.metabolites]) if (None not in rxn_mets) and (len(rxn_mets) != 0): if is_transport_reaction_formulae(rxn): transport_reactions.append(rxn) elif is_transport_reaction_annotations(rxn): transport_reactions.append(rxn) return set(transport_reactions)
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Return a list of all transport reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- A transport reaction is defined as follows: 1. It contains metabolites from at least 2 compartments and 2. at least 1 metabolite undergoes no chemical reaction, i.e., the formula and/or annotation stays the same on both sides of the equation. A notable exception is transport via PTS, which also contains the following restriction: 3. The transported metabolite(s) are transported into a compartment through the exchange of a phosphate group. An example of transport via PTS would be pep(c) + glucose(e) -> glucose-6-phosphate(c) + pyr(c) Reactions similar to transport via PTS (referred to as "modified transport reactions") follow a similar pattern: A(x) + B-R(y) -> A-R(y) + B(y) Such modified transport reactions can be detected, but only when a formula field exists for all metabolites in a particular reaction. If this is not the case, transport reactions are identified through annotations, which cannot detect modified transport reactions.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L124-L181
opencobra/memote
memote/support/helpers.py
is_transport_reaction_formulae
def is_transport_reaction_formulae(rxn): """ Return boolean if a reaction is a transport reaction (from formulae). Parameters ---------- rxn: cobra.Reaction The metabolic reaction under investigation. """ # Collecting criteria to classify transporters by. rxn_reactants = set([met.formula for met in rxn.reactants]) rxn_products = set([met.formula for met in rxn.products]) # Looking for formulas that stay the same on both side of the reaction. transported_mets = \ [formula for formula in rxn_reactants if formula in rxn_products] # Collect information on the elemental differences between # compartments in the reaction. delta_dicts = find_transported_elements(rxn) non_zero_array = [v for (k, v) in iteritems(delta_dicts) if v != 0] # Excluding reactions such as oxidoreductases where no net # transport of Hydrogen is occurring, but rather just an exchange of # electrons or charges effecting a change in protonation. if set(transported_mets) != set('H') and list( delta_dicts.keys() ) == ['H']: pass # All other reactions for which the amount of transported elements is # not zero, which are not part of the model's exchange nor # biomass reactions, are defined as transport reactions. # This includes reactions where the transported metabolite reacts with # a carrier molecule. elif sum(non_zero_array): return True
python
def is_transport_reaction_formulae(rxn): """ Return boolean if a reaction is a transport reaction (from formulae). Parameters ---------- rxn: cobra.Reaction The metabolic reaction under investigation. """ # Collecting criteria to classify transporters by. rxn_reactants = set([met.formula for met in rxn.reactants]) rxn_products = set([met.formula for met in rxn.products]) # Looking for formulas that stay the same on both side of the reaction. transported_mets = \ [formula for formula in rxn_reactants if formula in rxn_products] # Collect information on the elemental differences between # compartments in the reaction. delta_dicts = find_transported_elements(rxn) non_zero_array = [v for (k, v) in iteritems(delta_dicts) if v != 0] # Excluding reactions such as oxidoreductases where no net # transport of Hydrogen is occurring, but rather just an exchange of # electrons or charges effecting a change in protonation. if set(transported_mets) != set('H') and list( delta_dicts.keys() ) == ['H']: pass # All other reactions for which the amount of transported elements is # not zero, which are not part of the model's exchange nor # biomass reactions, are defined as transport reactions. # This includes reactions where the transported metabolite reacts with # a carrier molecule. elif sum(non_zero_array): return True
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Return boolean if a reaction is a transport reaction (from formulae). Parameters ---------- rxn: cobra.Reaction The metabolic reaction under investigation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L184-L217
opencobra/memote
memote/support/helpers.py
is_transport_reaction_annotations
def is_transport_reaction_annotations(rxn): """ Return boolean if a reaction is a transport reaction (from annotations). Parameters ---------- rxn: cobra.Reaction The metabolic reaction under investigation. """ reactants = set([(k, tuple(v)) for met in rxn.reactants for k, v in iteritems(met.annotation) if met.id != "H" and k is not None and k != 'sbo' and v is not None]) products = set([(k, tuple(v)) for met in rxn.products for k, v in iteritems(met.annotation) if met.id != "H" and k is not None and k != 'sbo' and v is not None]) # Find intersection between reactant annotations and # product annotations to find common metabolites between them, # satisfying the requirements for a transport reaction. Reactions such # as those involving oxidoreductases (where no net transport of # Hydrogen is occurring, but rather just an exchange of electrons or # charges effecting a change in protonation) are excluded. transported_mets = reactants & products if len(transported_mets) > 0: return True
python
def is_transport_reaction_annotations(rxn): """ Return boolean if a reaction is a transport reaction (from annotations). Parameters ---------- rxn: cobra.Reaction The metabolic reaction under investigation. """ reactants = set([(k, tuple(v)) for met in rxn.reactants for k, v in iteritems(met.annotation) if met.id != "H" and k is not None and k != 'sbo' and v is not None]) products = set([(k, tuple(v)) for met in rxn.products for k, v in iteritems(met.annotation) if met.id != "H" and k is not None and k != 'sbo' and v is not None]) # Find intersection between reactant annotations and # product annotations to find common metabolites between them, # satisfying the requirements for a transport reaction. Reactions such # as those involving oxidoreductases (where no net transport of # Hydrogen is occurring, but rather just an exchange of electrons or # charges effecting a change in protonation) are excluded. transported_mets = reactants & products if len(transported_mets) > 0: return True
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Return boolean if a reaction is a transport reaction (from annotations). Parameters ---------- rxn: cobra.Reaction The metabolic reaction under investigation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L220-L246
opencobra/memote
memote/support/helpers.py
find_converting_reactions
def find_converting_reactions(model, pair): """ Find all reactions which convert a given metabolite pair. Parameters ---------- model : cobra.Model The metabolic model under investigation. pair: tuple or list A pair of metabolite identifiers without compartment suffix. Returns ------- frozenset The set of reactions that have one of the pair on their left-hand side and the other on the right-hand side. """ first = set(find_met_in_model(model, pair[0])) second = set(find_met_in_model(model, pair[1])) hits = list() for rxn in model.reactions: # FIXME: Use `set.issubset` much more idiomatic. if len(first & set(rxn.reactants)) > 0 and len( second & set(rxn.products)) > 0: hits.append(rxn) elif len(first & set(rxn.products)) > 0 and len( second & set(rxn.reactants)) > 0: hits.append(rxn) return frozenset(hits)
python
def find_converting_reactions(model, pair): """ Find all reactions which convert a given metabolite pair. Parameters ---------- model : cobra.Model The metabolic model under investigation. pair: tuple or list A pair of metabolite identifiers without compartment suffix. Returns ------- frozenset The set of reactions that have one of the pair on their left-hand side and the other on the right-hand side. """ first = set(find_met_in_model(model, pair[0])) second = set(find_met_in_model(model, pair[1])) hits = list() for rxn in model.reactions: # FIXME: Use `set.issubset` much more idiomatic. if len(first & set(rxn.reactants)) > 0 and len( second & set(rxn.products)) > 0: hits.append(rxn) elif len(first & set(rxn.products)) > 0 and len( second & set(rxn.reactants)) > 0: hits.append(rxn) return frozenset(hits)
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Find all reactions which convert a given metabolite pair. Parameters ---------- model : cobra.Model The metabolic model under investigation. pair: tuple or list A pair of metabolite identifiers without compartment suffix. Returns ------- frozenset The set of reactions that have one of the pair on their left-hand side and the other on the right-hand side.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L249-L278
opencobra/memote
memote/support/helpers.py
find_biomass_reaction
def find_biomass_reaction(model): """ Return a list of the biomass reaction(s) of the model. This function identifies possible biomass reactions using two steps: 1. Return reactions that include the SBO annotation "SBO:0000629" for biomass. If no reactions can be identifies this way: 2. Look for the ``buzzwords`` "biomass", "growth" and "bof" in reaction IDs. 3. Look for metabolite IDs or names that contain the ``buzzword`` "biomass" and obtain the set of reactions they are involved in. 4. Remove boundary reactions from this set. 5. Return the union of reactions that match the buzzwords and of the reactions that metabolites are involved in that match the buzzword. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Identified biomass reactions. """ sbo_matches = set([rxn for rxn in model.reactions if rxn.annotation is not None and 'sbo' in rxn.annotation and rxn.annotation['sbo'] == 'SBO:0000629']) if len(sbo_matches) > 0: return list(sbo_matches) buzzwords = ['biomass', 'growth', 'bof'] buzzword_matches = set([rxn for rxn in model.reactions if any( string in rxn.id.lower() for string in buzzwords)]) biomass_met = [] for met in model.metabolites: if 'biomass' in met.id.lower() or ( met.name is not None and 'biomass' in met.name.lower()): biomass_met.append(met) if biomass_met == 1: biomass_met_matches = set( biomass_met.reactions ) - set(model.boundary) else: biomass_met_matches = set() return list(buzzword_matches | biomass_met_matches)
python
def find_biomass_reaction(model): """ Return a list of the biomass reaction(s) of the model. This function identifies possible biomass reactions using two steps: 1. Return reactions that include the SBO annotation "SBO:0000629" for biomass. If no reactions can be identifies this way: 2. Look for the ``buzzwords`` "biomass", "growth" and "bof" in reaction IDs. 3. Look for metabolite IDs or names that contain the ``buzzword`` "biomass" and obtain the set of reactions they are involved in. 4. Remove boundary reactions from this set. 5. Return the union of reactions that match the buzzwords and of the reactions that metabolites are involved in that match the buzzword. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Identified biomass reactions. """ sbo_matches = set([rxn for rxn in model.reactions if rxn.annotation is not None and 'sbo' in rxn.annotation and rxn.annotation['sbo'] == 'SBO:0000629']) if len(sbo_matches) > 0: return list(sbo_matches) buzzwords = ['biomass', 'growth', 'bof'] buzzword_matches = set([rxn for rxn in model.reactions if any( string in rxn.id.lower() for string in buzzwords)]) biomass_met = [] for met in model.metabolites: if 'biomass' in met.id.lower() or ( met.name is not None and 'biomass' in met.name.lower()): biomass_met.append(met) if biomass_met == 1: biomass_met_matches = set( biomass_met.reactions ) - set(model.boundary) else: biomass_met_matches = set() return list(buzzword_matches | biomass_met_matches)
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Return a list of the biomass reaction(s) of the model. This function identifies possible biomass reactions using two steps: 1. Return reactions that include the SBO annotation "SBO:0000629" for biomass. If no reactions can be identifies this way: 2. Look for the ``buzzwords`` "biomass", "growth" and "bof" in reaction IDs. 3. Look for metabolite IDs or names that contain the ``buzzword`` "biomass" and obtain the set of reactions they are involved in. 4. Remove boundary reactions from this set. 5. Return the union of reactions that match the buzzwords and of the reactions that metabolites are involved in that match the buzzword. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Identified biomass reactions.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L282-L333
opencobra/memote
memote/support/helpers.py
find_demand_reactions
def find_demand_reactions(model): u""" Return a list of demand reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines demand reactions as: -- 'unbalanced network reactions that allow the accumulation of a compound' -- reactions that are chiefly added during the gap-filling process -- as a means of dealing with 'compounds that are known to be produced by the organism [..] (i) for which no information is available about their fractional distribution to the biomass or (ii) which may only be produced in some environmental conditions -- reactions with a formula such as: 'met_c -> ' Demand reactions differ from exchange reactions in that the metabolites are not removed from the extracellular environment, but from any of the organism's compartments. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ try: extracellular = find_compartment_id_in_model(model, 'e') except KeyError: extracellular = None return find_boundary_types(model, 'demand', extracellular)
python
def find_demand_reactions(model): u""" Return a list of demand reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines demand reactions as: -- 'unbalanced network reactions that allow the accumulation of a compound' -- reactions that are chiefly added during the gap-filling process -- as a means of dealing with 'compounds that are known to be produced by the organism [..] (i) for which no information is available about their fractional distribution to the biomass or (ii) which may only be produced in some environmental conditions -- reactions with a formula such as: 'met_c -> ' Demand reactions differ from exchange reactions in that the metabolites are not removed from the extracellular environment, but from any of the organism's compartments. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ try: extracellular = find_compartment_id_in_model(model, 'e') except KeyError: extracellular = None return find_boundary_types(model, 'demand', extracellular)
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u""" Return a list of demand reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines demand reactions as: -- 'unbalanced network reactions that allow the accumulation of a compound' -- reactions that are chiefly added during the gap-filling process -- as a means of dealing with 'compounds that are known to be produced by the organism [..] (i) for which no information is available about their fractional distribution to the biomass or (ii) which may only be produced in some environmental conditions -- reactions with a formula such as: 'met_c -> ' Demand reactions differ from exchange reactions in that the metabolites are not removed from the extracellular environment, but from any of the organism's compartments. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L337-L373
opencobra/memote
memote/support/helpers.py
find_sink_reactions
def find_sink_reactions(model): u""" Return a list of sink reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines sink reactions as: -- 'similar to demand reactions' but reversible, thus able to supply the model with metabolites -- reactions that are chiefly added during the gap-filling process -- as a means of dealing with 'compounds that are produced by nonmetabolic cellular processes but that need to be metabolized' -- reactions with a formula such as: 'met_c <-> ' Sink reactions differ from exchange reactions in that the metabolites are not removed from the extracellular environment, but from any of the organism's compartments. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ try: extracellular = find_compartment_id_in_model(model, 'e') except KeyError: extracellular = None return find_boundary_types(model, 'sink', extracellular)
python
def find_sink_reactions(model): u""" Return a list of sink reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines sink reactions as: -- 'similar to demand reactions' but reversible, thus able to supply the model with metabolites -- reactions that are chiefly added during the gap-filling process -- as a means of dealing with 'compounds that are produced by nonmetabolic cellular processes but that need to be metabolized' -- reactions with a formula such as: 'met_c <-> ' Sink reactions differ from exchange reactions in that the metabolites are not removed from the extracellular environment, but from any of the organism's compartments. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ try: extracellular = find_compartment_id_in_model(model, 'e') except KeyError: extracellular = None return find_boundary_types(model, 'sink', extracellular)
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u""" Return a list of sink reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines sink reactions as: -- 'similar to demand reactions' but reversible, thus able to supply the model with metabolites -- reactions that are chiefly added during the gap-filling process -- as a means of dealing with 'compounds that are produced by nonmetabolic cellular processes but that need to be metabolized' -- reactions with a formula such as: 'met_c <-> ' Sink reactions differ from exchange reactions in that the metabolites are not removed from the extracellular environment, but from any of the organism's compartments. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L377-L412
opencobra/memote
memote/support/helpers.py
find_exchange_rxns
def find_exchange_rxns(model): u""" Return a list of exchange reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines exchange reactions as: -- reactions that 'define the extracellular environment' -- 'unbalanced, extra-organism reactions that represent the supply to or removal of metabolites from the extra-organism "space"' -- reactions with a formula such as: 'met_e -> ' or ' -> met_e' or 'met_e <=> ' Exchange reactions differ from demand reactions in that the metabolites are removed from or added to the extracellular environment only. With this the uptake or secretion of a metabolite is modeled, respectively. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ try: extracellular = find_compartment_id_in_model(model, 'e') except KeyError: extracellular = None return find_boundary_types(model, 'exchange', extracellular)
python
def find_exchange_rxns(model): u""" Return a list of exchange reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines exchange reactions as: -- reactions that 'define the extracellular environment' -- 'unbalanced, extra-organism reactions that represent the supply to or removal of metabolites from the extra-organism "space"' -- reactions with a formula such as: 'met_e -> ' or ' -> met_e' or 'met_e <=> ' Exchange reactions differ from demand reactions in that the metabolites are removed from or added to the extracellular environment only. With this the uptake or secretion of a metabolite is modeled, respectively. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ try: extracellular = find_compartment_id_in_model(model, 'e') except KeyError: extracellular = None return find_boundary_types(model, 'exchange', extracellular)
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u""" Return a list of exchange reactions. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- [1] defines exchange reactions as: -- reactions that 'define the extracellular environment' -- 'unbalanced, extra-organism reactions that represent the supply to or removal of metabolites from the extra-organism "space"' -- reactions with a formula such as: 'met_e -> ' or ' -> met_e' or 'met_e <=> ' Exchange reactions differ from demand reactions in that the metabolites are removed from or added to the extracellular environment only. With this the uptake or secretion of a metabolite is modeled, respectively. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L416-L450
opencobra/memote
memote/support/helpers.py
find_interchange_biomass_reactions
def find_interchange_biomass_reactions(model, biomass=None): """ Return the set of all transport, boundary, and biomass reactions. These reactions are either pseudo-reactions, or incorporated to allow metabolites to pass between compartments. Some tests focus on purely metabolic reactions and hence exclude this set. Parameters ---------- model : cobra.Model The metabolic model under investigation. biomass : list or cobra.Reaction, optional A list of cobrapy biomass reactions. """ boundary = set(model.boundary) transporters = find_transport_reactions(model) if biomass is None: biomass = set(find_biomass_reaction(model)) return boundary | transporters | biomass
python
def find_interchange_biomass_reactions(model, biomass=None): """ Return the set of all transport, boundary, and biomass reactions. These reactions are either pseudo-reactions, or incorporated to allow metabolites to pass between compartments. Some tests focus on purely metabolic reactions and hence exclude this set. Parameters ---------- model : cobra.Model The metabolic model under investigation. biomass : list or cobra.Reaction, optional A list of cobrapy biomass reactions. """ boundary = set(model.boundary) transporters = find_transport_reactions(model) if biomass is None: biomass = set(find_biomass_reaction(model)) return boundary | transporters | biomass
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Return the set of all transport, boundary, and biomass reactions. These reactions are either pseudo-reactions, or incorporated to allow metabolites to pass between compartments. Some tests focus on purely metabolic reactions and hence exclude this set. Parameters ---------- model : cobra.Model The metabolic model under investigation. biomass : list or cobra.Reaction, optional A list of cobrapy biomass reactions.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L453-L473
opencobra/memote
memote/support/helpers.py
run_fba
def run_fba(model, rxn_id, direction="max", single_value=True): """ Return the solution of an FBA to a set objective function. Parameters ---------- model : cobra.Model The metabolic model under investigation. rxn_id : string A string containing the reaction ID of the desired FBA objective. direction: string A string containing either "max" or "min" to specify the direction of the desired FBA objective function. single_value: boolean Indicates whether the results for all reactions are gathered from the solver, or only the result for the objective value. Returns ------- cobra.solution The cobra solution object for the corresponding FBA problem. """ model.objective = model.reactions.get_by_id(rxn_id) model.objective_direction = direction if single_value: try: return model.slim_optimize() except Infeasible: return np.nan else: try: solution = model.optimize() return solution except Infeasible: return np.nan
python
def run_fba(model, rxn_id, direction="max", single_value=True): """ Return the solution of an FBA to a set objective function. Parameters ---------- model : cobra.Model The metabolic model under investigation. rxn_id : string A string containing the reaction ID of the desired FBA objective. direction: string A string containing either "max" or "min" to specify the direction of the desired FBA objective function. single_value: boolean Indicates whether the results for all reactions are gathered from the solver, or only the result for the objective value. Returns ------- cobra.solution The cobra solution object for the corresponding FBA problem. """ model.objective = model.reactions.get_by_id(rxn_id) model.objective_direction = direction if single_value: try: return model.slim_optimize() except Infeasible: return np.nan else: try: solution = model.optimize() return solution except Infeasible: return np.nan
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Return the solution of an FBA to a set objective function. Parameters ---------- model : cobra.Model The metabolic model under investigation. rxn_id : string A string containing the reaction ID of the desired FBA objective. direction: string A string containing either "max" or "min" to specify the direction of the desired FBA objective function. single_value: boolean Indicates whether the results for all reactions are gathered from the solver, or only the result for the objective value. Returns ------- cobra.solution The cobra solution object for the corresponding FBA problem.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L476-L511
opencobra/memote
memote/support/helpers.py
close_boundaries_sensibly
def close_boundaries_sensibly(model): """ Return a cobra model with all boundaries closed and changed constraints. In the returned model previously fixed reactions are no longer constrained as such. Instead reactions are constrained according to their reversibility. This is to prevent the FBA from becoming infeasible when trying to solve a model with closed exchanges and one fixed reaction. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- cobra.Model A cobra model with all boundary reactions closed and the constraints of each reaction set according to their reversibility. """ for rxn in model.reactions: if rxn.reversibility: rxn.bounds = -1, 1 else: rxn.bounds = 0, 1 for boundary in model.boundary: boundary.bounds = (0, 0)
python
def close_boundaries_sensibly(model): """ Return a cobra model with all boundaries closed and changed constraints. In the returned model previously fixed reactions are no longer constrained as such. Instead reactions are constrained according to their reversibility. This is to prevent the FBA from becoming infeasible when trying to solve a model with closed exchanges and one fixed reaction. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- cobra.Model A cobra model with all boundary reactions closed and the constraints of each reaction set according to their reversibility. """ for rxn in model.reactions: if rxn.reversibility: rxn.bounds = -1, 1 else: rxn.bounds = 0, 1 for boundary in model.boundary: boundary.bounds = (0, 0)
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Return a cobra model with all boundaries closed and changed constraints. In the returned model previously fixed reactions are no longer constrained as such. Instead reactions are constrained according to their reversibility. This is to prevent the FBA from becoming infeasible when trying to solve a model with closed exchanges and one fixed reaction. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- cobra.Model A cobra model with all boundary reactions closed and the constraints of each reaction set according to their reversibility.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L514-L541
opencobra/memote
memote/support/helpers.py
metabolites_per_compartment
def metabolites_per_compartment(model, compartment_id): """ Identify all metabolites that belong to a given compartment. Parameters ---------- model : cobra.Model The metabolic model under investigation. compartment_id : string Model specific compartment identifier. Returns ------- list List of metabolites belonging to a given compartment. """ return [met for met in model.metabolites if met.compartment == compartment_id]
python
def metabolites_per_compartment(model, compartment_id): """ Identify all metabolites that belong to a given compartment. Parameters ---------- model : cobra.Model The metabolic model under investigation. compartment_id : string Model specific compartment identifier. Returns ------- list List of metabolites belonging to a given compartment. """ return [met for met in model.metabolites if met.compartment == compartment_id]
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Identify all metabolites that belong to a given compartment. Parameters ---------- model : cobra.Model The metabolic model under investigation. compartment_id : string Model specific compartment identifier. Returns ------- list List of metabolites belonging to a given compartment.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L572-L590
opencobra/memote
memote/support/helpers.py
largest_compartment_id_met
def largest_compartment_id_met(model): """ Return the ID of the compartment with the most metabolites. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- string Compartment ID of the compartment with the most metabolites. """ # Sort compartments by decreasing size and extract the largest two. candidate, second = sorted( ((c, len(metabolites_per_compartment(model, c))) for c in model.compartments), reverse=True, key=itemgetter(1))[:2] # Compare the size of the compartments. if candidate[1] == second[1]: raise RuntimeError("There is a tie for the largest compartment. " "Compartment {} and {} have equal amounts of " "metabolites.".format(candidate[0], second[0])) else: return candidate[0]
python
def largest_compartment_id_met(model): """ Return the ID of the compartment with the most metabolites. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- string Compartment ID of the compartment with the most metabolites. """ # Sort compartments by decreasing size and extract the largest two. candidate, second = sorted( ((c, len(metabolites_per_compartment(model, c))) for c in model.compartments), reverse=True, key=itemgetter(1))[:2] # Compare the size of the compartments. if candidate[1] == second[1]: raise RuntimeError("There is a tie for the largest compartment. " "Compartment {} and {} have equal amounts of " "metabolites.".format(candidate[0], second[0])) else: return candidate[0]
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Return the ID of the compartment with the most metabolites. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- string Compartment ID of the compartment with the most metabolites.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L593-L618
opencobra/memote
memote/support/helpers.py
find_compartment_id_in_model
def find_compartment_id_in_model(model, compartment_id): """ Identify a model compartment by looking up names in COMPARTMENT_SHORTLIST. Parameters ---------- model : cobra.Model The metabolic model under investigation. compartment_id : string Memote internal compartment identifier used to access compartment name shortlist to look up potential compartment names. Returns ------- string Compartment identifier in the model corresponding to compartment_id. """ if compartment_id not in COMPARTMENT_SHORTLIST.keys(): raise KeyError("{} is not in the COMPARTMENT_SHORTLIST! Make sure " "you typed the ID correctly, if yes, update the " "shortlist manually.".format(compartment_id)) if len(model.compartments) == 0: raise KeyError( "It was not possible to identify the " "compartment {}, since the " "model has no compartments at " "all.".format(COMPARTMENT_SHORTLIST[compartment_id][0]) ) if compartment_id in model.compartments.keys(): return compartment_id for name in COMPARTMENT_SHORTLIST[compartment_id]: for c_id, c_name in model.compartments.items(): if c_name.lower() == name: return c_id if compartment_id == 'c': return largest_compartment_id_met(model)
python
def find_compartment_id_in_model(model, compartment_id): """ Identify a model compartment by looking up names in COMPARTMENT_SHORTLIST. Parameters ---------- model : cobra.Model The metabolic model under investigation. compartment_id : string Memote internal compartment identifier used to access compartment name shortlist to look up potential compartment names. Returns ------- string Compartment identifier in the model corresponding to compartment_id. """ if compartment_id not in COMPARTMENT_SHORTLIST.keys(): raise KeyError("{} is not in the COMPARTMENT_SHORTLIST! Make sure " "you typed the ID correctly, if yes, update the " "shortlist manually.".format(compartment_id)) if len(model.compartments) == 0: raise KeyError( "It was not possible to identify the " "compartment {}, since the " "model has no compartments at " "all.".format(COMPARTMENT_SHORTLIST[compartment_id][0]) ) if compartment_id in model.compartments.keys(): return compartment_id for name in COMPARTMENT_SHORTLIST[compartment_id]: for c_id, c_name in model.compartments.items(): if c_name.lower() == name: return c_id if compartment_id == 'c': return largest_compartment_id_met(model)
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L621-L661
opencobra/memote
memote/support/helpers.py
find_met_in_model
def find_met_in_model(model, mnx_id, compartment_id=None): """ Return specific metabolites by looking up IDs in METANETX_SHORTLIST. Parameters ---------- model : cobra.Model The metabolic model under investigation. mnx_id : string Memote internal MetaNetX metabolite identifier used to map between cross-references in the METANETX_SHORTLIST. compartment_id : string, optional ID of the specific compartment where the metabolites should be found. Defaults to returning matching metabolites from all compartments. Returns ------- list cobra.Metabolite(s) matching the mnx_id. """ def compare_annotation(annotation): """ Return annotation IDs that match to METANETX_SHORTLIST references. Compares the set of METANETX_SHORTLIST references for a given mnx_id and the annotation IDs stored in a given annotation dictionary. """ query_values = set(utils.flatten(annotation.values())) ref_values = set(utils.flatten(METANETX_SHORTLIST[mnx_id])) return query_values & ref_values # Make sure that the MNX ID we're looking up exists in the metabolite # shortlist. if mnx_id not in METANETX_SHORTLIST.columns: raise ValueError( "{} is not in the MetaNetX Shortlist! Make sure " "you typed the ID correctly, if yes, update the " "shortlist by updating and re-running the script " "generate_mnx_shortlists.py.".format(mnx_id) ) candidates = [] # The MNX ID used in the model may or may not be tagged with a compartment # tag e.g. `MNXM23141_c` vs. `MNXM23141`, which is tested with the # following regex. # If the MNX ID itself cannot be found as an ID, we try all other # identifiers that are provided by our shortlist of MetaNetX' mapping # table. regex = re.compile('^{}(_[a-zA-Z0-9]+)?$'.format(mnx_id)) if model.metabolites.query(regex): candidates = model.metabolites.query(regex) elif model.metabolites.query(compare_annotation, attribute='annotation'): candidates = model.metabolites.query( compare_annotation, attribute='annotation' ) else: for value in METANETX_SHORTLIST[mnx_id]: if value: for ident in value: regex = re.compile('^{}(_[a-zA-Z0-9]+)?$'.format(ident)) if model.metabolites.query(regex, attribute='id'): candidates.extend( model.metabolites.query(regex, attribute='id')) # Return a list of all possible candidates if no specific compartment ID # is provided. # Otherwise, just return the candidate in one specific compartment. Raise # an exception if there are more than one possible candidates for a given # compartment. if compartment_id is None: print("compartment_id = None?") return candidates else: candidates_in_compartment = \ [cand for cand in candidates if cand.compartment == compartment_id] if len(candidates_in_compartment) == 0: raise RuntimeError("It was not possible to identify " "any metabolite in compartment {} corresponding to " "the following MetaNetX identifier: {}." "Make sure that a cross-reference to this ID in " "the MetaNetX Database exists for your " "identifier " "namespace.".format(compartment_id, mnx_id)) elif len(candidates_in_compartment) > 1: raise RuntimeError("It was not possible to uniquely identify " "a single metabolite in compartment {} that " "corresponds to the following MetaNetX " "identifier: {}." "Instead these candidates were found: {}." "Check that metabolite compartment tags are " "correct. Consider switching to a namespace scheme " "where identifiers are truly " "unique.".format(compartment_id, mnx_id, utils.get_ids( candidates_in_compartment )) ) else: return candidates_in_compartment
python
def find_met_in_model(model, mnx_id, compartment_id=None): """ Return specific metabolites by looking up IDs in METANETX_SHORTLIST. Parameters ---------- model : cobra.Model The metabolic model under investigation. mnx_id : string Memote internal MetaNetX metabolite identifier used to map between cross-references in the METANETX_SHORTLIST. compartment_id : string, optional ID of the specific compartment where the metabolites should be found. Defaults to returning matching metabolites from all compartments. Returns ------- list cobra.Metabolite(s) matching the mnx_id. """ def compare_annotation(annotation): """ Return annotation IDs that match to METANETX_SHORTLIST references. Compares the set of METANETX_SHORTLIST references for a given mnx_id and the annotation IDs stored in a given annotation dictionary. """ query_values = set(utils.flatten(annotation.values())) ref_values = set(utils.flatten(METANETX_SHORTLIST[mnx_id])) return query_values & ref_values # Make sure that the MNX ID we're looking up exists in the metabolite # shortlist. if mnx_id not in METANETX_SHORTLIST.columns: raise ValueError( "{} is not in the MetaNetX Shortlist! Make sure " "you typed the ID correctly, if yes, update the " "shortlist by updating and re-running the script " "generate_mnx_shortlists.py.".format(mnx_id) ) candidates = [] # The MNX ID used in the model may or may not be tagged with a compartment # tag e.g. `MNXM23141_c` vs. `MNXM23141`, which is tested with the # following regex. # If the MNX ID itself cannot be found as an ID, we try all other # identifiers that are provided by our shortlist of MetaNetX' mapping # table. regex = re.compile('^{}(_[a-zA-Z0-9]+)?$'.format(mnx_id)) if model.metabolites.query(regex): candidates = model.metabolites.query(regex) elif model.metabolites.query(compare_annotation, attribute='annotation'): candidates = model.metabolites.query( compare_annotation, attribute='annotation' ) else: for value in METANETX_SHORTLIST[mnx_id]: if value: for ident in value: regex = re.compile('^{}(_[a-zA-Z0-9]+)?$'.format(ident)) if model.metabolites.query(regex, attribute='id'): candidates.extend( model.metabolites.query(regex, attribute='id')) # Return a list of all possible candidates if no specific compartment ID # is provided. # Otherwise, just return the candidate in one specific compartment. Raise # an exception if there are more than one possible candidates for a given # compartment. if compartment_id is None: print("compartment_id = None?") return candidates else: candidates_in_compartment = \ [cand for cand in candidates if cand.compartment == compartment_id] if len(candidates_in_compartment) == 0: raise RuntimeError("It was not possible to identify " "any metabolite in compartment {} corresponding to " "the following MetaNetX identifier: {}." "Make sure that a cross-reference to this ID in " "the MetaNetX Database exists for your " "identifier " "namespace.".format(compartment_id, mnx_id)) elif len(candidates_in_compartment) > 1: raise RuntimeError("It was not possible to uniquely identify " "a single metabolite in compartment {} that " "corresponds to the following MetaNetX " "identifier: {}." "Instead these candidates were found: {}." "Check that metabolite compartment tags are " "correct. Consider switching to a namespace scheme " "where identifiers are truly " "unique.".format(compartment_id, mnx_id, utils.get_ids( candidates_in_compartment )) ) else: return candidates_in_compartment
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Return specific metabolites by looking up IDs in METANETX_SHORTLIST. Parameters ---------- model : cobra.Model The metabolic model under investigation. mnx_id : string Memote internal MetaNetX metabolite identifier used to map between cross-references in the METANETX_SHORTLIST. compartment_id : string, optional ID of the specific compartment where the metabolites should be found. Defaults to returning matching metabolites from all compartments. Returns ------- list cobra.Metabolite(s) matching the mnx_id.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L664-L764
opencobra/memote
memote/support/helpers.py
find_bounds
def find_bounds(model): """ Return the median upper and lower bound of the metabolic model. Bounds can vary from model to model. Cobrapy defaults to (-1000, 1000) but this may not be the case for merged or autogenerated models. In these cases, this function is used to iterate over all the bounds of all the reactions and find the median bound values in the model, which are then used as the 'most common' bounds. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ lower_bounds = np.asarray([rxn.lower_bound for rxn in model.reactions], dtype=float) upper_bounds = np.asarray([rxn.upper_bound for rxn in model.reactions], dtype=float) lower_bound = np.nanmedian(lower_bounds[lower_bounds != 0.0]) upper_bound = np.nanmedian(upper_bounds[upper_bounds != 0.0]) if np.isnan(lower_bound): LOGGER.warning("Could not identify a median lower bound.") lower_bound = -1000.0 if np.isnan(upper_bound): LOGGER.warning("Could not identify a median upper bound.") upper_bound = 1000.0 return lower_bound, upper_bound
python
def find_bounds(model): """ Return the median upper and lower bound of the metabolic model. Bounds can vary from model to model. Cobrapy defaults to (-1000, 1000) but this may not be the case for merged or autogenerated models. In these cases, this function is used to iterate over all the bounds of all the reactions and find the median bound values in the model, which are then used as the 'most common' bounds. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ lower_bounds = np.asarray([rxn.lower_bound for rxn in model.reactions], dtype=float) upper_bounds = np.asarray([rxn.upper_bound for rxn in model.reactions], dtype=float) lower_bound = np.nanmedian(lower_bounds[lower_bounds != 0.0]) upper_bound = np.nanmedian(upper_bounds[upper_bounds != 0.0]) if np.isnan(lower_bound): LOGGER.warning("Could not identify a median lower bound.") lower_bound = -1000.0 if np.isnan(upper_bound): LOGGER.warning("Could not identify a median upper bound.") upper_bound = 1000.0 return lower_bound, upper_bound
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Return the median upper and lower bound of the metabolic model. Bounds can vary from model to model. Cobrapy defaults to (-1000, 1000) but this may not be the case for merged or autogenerated models. In these cases, this function is used to iterate over all the bounds of all the reactions and find the median bound values in the model, which are then used as the 'most common' bounds. Parameters ---------- model : cobra.Model The metabolic model under investigation.
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/helpers.py#L781-L809
opencobra/memote
memote/suite/reporting/report.py
Report.render_html
def render_html(self): """Render an HTML report.""" return self._template.safe_substitute( report_type=self._report_type, results=self.render_json() )
python
def render_html(self): """Render an HTML report.""" return self._template.safe_substitute( report_type=self._report_type, results=self.render_json() )
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Render an HTML report.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/reporting/report.py#L80-L85
opencobra/memote
memote/suite/reporting/report.py
Report.compute_score
def compute_score(self): """Calculate the overall test score using the configuration.""" # LOGGER.info("Begin scoring") cases = self.get_configured_tests() | set(self.result.cases) scores = DataFrame({"score": 0.0, "max": 1.0}, index=sorted(cases)) self.result.setdefault("score", dict()) self.result["score"]["sections"] = list() # Calculate the scores for each test individually. for test, result in iteritems(self.result.cases): # LOGGER.info("Calculate score for test: '%s'.", test) # Test metric may be a dictionary for a parametrized test. metric = result["metric"] if hasattr(metric, "items"): result["score"] = test_score = dict() total = 0.0 for key, value in iteritems(metric): value = 1.0 - value total += value test_score[key] = value # For some reason there are parametrized tests without cases. if len(metric) == 0: metric = 0.0 else: metric = total / len(metric) else: metric = 1.0 - metric scores.at[test, "score"] = metric scores.loc[test, :] *= self.config["weights"].get(test, 1.0) score = 0.0 maximum = 0.0 # Calculate the scores for each section considering the individual test # case scores. for section_id, card in iteritems( self.config['cards']['scored']['sections'] ): # LOGGER.info("Calculate score for section: '%s'.", section_id) cases = card.get("cases", None) if cases is None: continue card_score = scores.loc[cases, "score"].sum() card_total = scores.loc[cases, "max"].sum() # Format results nicely to work immediately with Vega Bar Chart. section_score = {"section": section_id, "score": card_score / card_total} self.result["score"]["sections"].append(section_score) # Calculate the final score for the entire model. weight = card.get("weight", 1.0) score += card_score * weight maximum += card_total * weight self.result["score"]["total_score"] = score / maximum
python
def compute_score(self): """Calculate the overall test score using the configuration.""" # LOGGER.info("Begin scoring") cases = self.get_configured_tests() | set(self.result.cases) scores = DataFrame({"score": 0.0, "max": 1.0}, index=sorted(cases)) self.result.setdefault("score", dict()) self.result["score"]["sections"] = list() # Calculate the scores for each test individually. for test, result in iteritems(self.result.cases): # LOGGER.info("Calculate score for test: '%s'.", test) # Test metric may be a dictionary for a parametrized test. metric = result["metric"] if hasattr(metric, "items"): result["score"] = test_score = dict() total = 0.0 for key, value in iteritems(metric): value = 1.0 - value total += value test_score[key] = value # For some reason there are parametrized tests without cases. if len(metric) == 0: metric = 0.0 else: metric = total / len(metric) else: metric = 1.0 - metric scores.at[test, "score"] = metric scores.loc[test, :] *= self.config["weights"].get(test, 1.0) score = 0.0 maximum = 0.0 # Calculate the scores for each section considering the individual test # case scores. for section_id, card in iteritems( self.config['cards']['scored']['sections'] ): # LOGGER.info("Calculate score for section: '%s'.", section_id) cases = card.get("cases", None) if cases is None: continue card_score = scores.loc[cases, "score"].sum() card_total = scores.loc[cases, "max"].sum() # Format results nicely to work immediately with Vega Bar Chart. section_score = {"section": section_id, "score": card_score / card_total} self.result["score"]["sections"].append(section_score) # Calculate the final score for the entire model. weight = card.get("weight", 1.0) score += card_score * weight maximum += card_total * weight self.result["score"]["total_score"] = score / maximum
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Calculate the overall test score using the configuration.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/reporting/report.py#L114-L164
opencobra/memote
memote/support/sbo.py
find_components_without_sbo_terms
def find_components_without_sbo_terms(model, components): """ Find model components that are not annotated with any SBO terms. Parameters ---------- model : cobra.Model The metabolic model under investigation. components : {"metabolites", "reactions", "genes"} A string denoting `cobra.Model` components. Returns ------- list The components without any SBO term annotation. """ return [elem for elem in getattr(model, components) if elem.annotation is None or 'sbo' not in elem.annotation]
python
def find_components_without_sbo_terms(model, components): """ Find model components that are not annotated with any SBO terms. Parameters ---------- model : cobra.Model The metabolic model under investigation. components : {"metabolites", "reactions", "genes"} A string denoting `cobra.Model` components. Returns ------- list The components without any SBO term annotation. """ return [elem for elem in getattr(model, components) if elem.annotation is None or 'sbo' not in elem.annotation]
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Find model components that are not annotated with any SBO terms. Parameters ---------- model : cobra.Model The metabolic model under investigation. components : {"metabolites", "reactions", "genes"} A string denoting `cobra.Model` components. Returns ------- list The components without any SBO term annotation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/sbo.py#L27-L45
opencobra/memote
memote/support/sbo.py
check_component_for_specific_sbo_term
def check_component_for_specific_sbo_term(items, term): r""" Identify model components that lack a specific SBO term(s). Parameters ---------- items : list A list of model components i.e. reactions to be checked for a specific SBO term. term : str or list of str A string denoting a valid SBO term matching the regex '^SBO:\d{7}$' or a list containing such string elements. Returns ------- list The components without any or that specific SBO term annotation. """ # check for multiple allowable SBO terms if isinstance(term, list): return [elem for elem in items if elem.annotation is None or 'sbo' not in elem.annotation or not any(i in elem.annotation['sbo'] for i in term)] else: return [elem for elem in items if elem.annotation is None or 'sbo' not in elem.annotation or term not in elem.annotation['sbo']]
python
def check_component_for_specific_sbo_term(items, term): r""" Identify model components that lack a specific SBO term(s). Parameters ---------- items : list A list of model components i.e. reactions to be checked for a specific SBO term. term : str or list of str A string denoting a valid SBO term matching the regex '^SBO:\d{7}$' or a list containing such string elements. Returns ------- list The components without any or that specific SBO term annotation. """ # check for multiple allowable SBO terms if isinstance(term, list): return [elem for elem in items if elem.annotation is None or 'sbo' not in elem.annotation or not any(i in elem.annotation['sbo'] for i in term)] else: return [elem for elem in items if elem.annotation is None or 'sbo' not in elem.annotation or term not in elem.annotation['sbo']]
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r""" Identify model components that lack a specific SBO term(s). Parameters ---------- items : list A list of model components i.e. reactions to be checked for a specific SBO term. term : str or list of str A string denoting a valid SBO term matching the regex '^SBO:\d{7}$' or a list containing such string elements. Returns ------- list The components without any or that specific SBO term annotation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/sbo.py#L48-L77
opencobra/memote
memote/support/thermodynamics.py
get_smallest_compound_id
def get_smallest_compound_id(compounds_identifiers): """ Return the smallest KEGG compound identifier from a list. KEGG identifiers may map to compounds, drugs or glycans prefixed respectively with "C", "D", and "G" followed by at least 5 digits. We choose the lowest KEGG identifier with the assumption that several identifiers are due to chirality and that the lower one represents the more common form. Parameters ---------- compounds_identifiers : list A list of mixed KEGG identifiers. Returns ------- str The KEGG compound identifier with the smallest number. Raises ------ ValueError When compound_identifiers contains no KEGG compound identifiers. """ return min((c for c in compounds_identifiers if c.startswith("C")), key=lambda c: int(c[1:]))
python
def get_smallest_compound_id(compounds_identifiers): """ Return the smallest KEGG compound identifier from a list. KEGG identifiers may map to compounds, drugs or glycans prefixed respectively with "C", "D", and "G" followed by at least 5 digits. We choose the lowest KEGG identifier with the assumption that several identifiers are due to chirality and that the lower one represents the more common form. Parameters ---------- compounds_identifiers : list A list of mixed KEGG identifiers. Returns ------- str The KEGG compound identifier with the smallest number. Raises ------ ValueError When compound_identifiers contains no KEGG compound identifiers. """ return min((c for c in compounds_identifiers if c.startswith("C")), key=lambda c: int(c[1:]))
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Return the smallest KEGG compound identifier from a list. KEGG identifiers may map to compounds, drugs or glycans prefixed respectively with "C", "D", and "G" followed by at least 5 digits. We choose the lowest KEGG identifier with the assumption that several identifiers are due to chirality and that the lower one represents the more common form. Parameters ---------- compounds_identifiers : list A list of mixed KEGG identifiers. Returns ------- str The KEGG compound identifier with the smallest number. Raises ------ ValueError When compound_identifiers contains no KEGG compound identifiers.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/thermodynamics.py#L37-L64
opencobra/memote
memote/support/thermodynamics.py
map_metabolite2kegg
def map_metabolite2kegg(metabolite): """ Return a KEGG compound identifier for the metabolite if it exists. First see if there is an unambiguous mapping to a single KEGG compound ID provided with the model. If not, check if there is any KEGG compound ID in a list of mappings. KEGG IDs may map to compounds, drugs and glycans. KEGG compound IDs are sorted so we keep the lowest that is there. If none of this works try mapping to KEGG via the CompoundMatcher by the name of the metabolite. If the metabolite cannot be mapped at all we simply map it back to its own ID. Parameters ---------- metabolite : cobra.Metabolite The metabolite to be mapped to its KEGG compound identifier. Returns ------- None If the metabolite could not be mapped. str The smallest KEGG compound identifier that was found. """ logger.debug("Looking for KEGG compound identifier for %s.", metabolite.id) kegg_annotation = metabolite.annotation.get("kegg.compound") if kegg_annotation is None: # TODO (Moritz Beber): Currently name matching is very slow and # inaccurate. We disable it until there is a better solution. # if metabolite.name: # # The compound matcher uses regular expression and chokes # # with a low level error on `[` in the name, for example. # df = compound_matcher.match(metabolite.name) # try: # return df.loc[df["score"] > threshold, "CID"].iat[0] # except (IndexError, AttributeError): # logger.warning( # "Could not match the name %r to any kegg.compound " # "annotation for metabolite %s.", # metabolite.name, metabolite.id # ) # return # else: logger.warning("No kegg.compound annotation for metabolite %s.", metabolite.id) return if isinstance(kegg_annotation, string_types) and \ kegg_annotation.startswith("C"): return kegg_annotation elif isinstance(kegg_annotation, Iterable): try: return get_smallest_compound_id(kegg_annotation) except ValueError: return logger.warning( "No matching kegg.compound annotation for metabolite %s.", metabolite.id ) return
python
def map_metabolite2kegg(metabolite): """ Return a KEGG compound identifier for the metabolite if it exists. First see if there is an unambiguous mapping to a single KEGG compound ID provided with the model. If not, check if there is any KEGG compound ID in a list of mappings. KEGG IDs may map to compounds, drugs and glycans. KEGG compound IDs are sorted so we keep the lowest that is there. If none of this works try mapping to KEGG via the CompoundMatcher by the name of the metabolite. If the metabolite cannot be mapped at all we simply map it back to its own ID. Parameters ---------- metabolite : cobra.Metabolite The metabolite to be mapped to its KEGG compound identifier. Returns ------- None If the metabolite could not be mapped. str The smallest KEGG compound identifier that was found. """ logger.debug("Looking for KEGG compound identifier for %s.", metabolite.id) kegg_annotation = metabolite.annotation.get("kegg.compound") if kegg_annotation is None: # TODO (Moritz Beber): Currently name matching is very slow and # inaccurate. We disable it until there is a better solution. # if metabolite.name: # # The compound matcher uses regular expression and chokes # # with a low level error on `[` in the name, for example. # df = compound_matcher.match(metabolite.name) # try: # return df.loc[df["score"] > threshold, "CID"].iat[0] # except (IndexError, AttributeError): # logger.warning( # "Could not match the name %r to any kegg.compound " # "annotation for metabolite %s.", # metabolite.name, metabolite.id # ) # return # else: logger.warning("No kegg.compound annotation for metabolite %s.", metabolite.id) return if isinstance(kegg_annotation, string_types) and \ kegg_annotation.startswith("C"): return kegg_annotation elif isinstance(kegg_annotation, Iterable): try: return get_smallest_compound_id(kegg_annotation) except ValueError: return logger.warning( "No matching kegg.compound annotation for metabolite %s.", metabolite.id ) return
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Return a KEGG compound identifier for the metabolite if it exists. First see if there is an unambiguous mapping to a single KEGG compound ID provided with the model. If not, check if there is any KEGG compound ID in a list of mappings. KEGG IDs may map to compounds, drugs and glycans. KEGG compound IDs are sorted so we keep the lowest that is there. If none of this works try mapping to KEGG via the CompoundMatcher by the name of the metabolite. If the metabolite cannot be mapped at all we simply map it back to its own ID. Parameters ---------- metabolite : cobra.Metabolite The metabolite to be mapped to its KEGG compound identifier. Returns ------- None If the metabolite could not be mapped. str The smallest KEGG compound identifier that was found.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/thermodynamics.py#L67-L126
opencobra/memote
memote/support/thermodynamics.py
translate_reaction
def translate_reaction(reaction, metabolite_mapping): """ Return a mapping from KEGG compound identifiers to coefficients. Parameters ---------- reaction : cobra.Reaction The reaction whose metabolites are to be translated. metabolite_mapping : dict An existing mapping from cobra.Metabolite to KEGG compound identifier that may already contain the metabolites in question or will have to be extended. Returns ------- dict The stoichiometry of the reaction given as a mapping from metabolite KEGG identifier to coefficient. """ # Transport reactions where the same metabolite occurs in different # compartments should have been filtered out but just to be sure, we add # coefficients in the mapping. stoichiometry = defaultdict(float) for met, coef in iteritems(reaction.metabolites): kegg_id = metabolite_mapping.setdefault(met, map_metabolite2kegg(met)) if kegg_id is None: continue stoichiometry[kegg_id] += coef return dict(stoichiometry)
python
def translate_reaction(reaction, metabolite_mapping): """ Return a mapping from KEGG compound identifiers to coefficients. Parameters ---------- reaction : cobra.Reaction The reaction whose metabolites are to be translated. metabolite_mapping : dict An existing mapping from cobra.Metabolite to KEGG compound identifier that may already contain the metabolites in question or will have to be extended. Returns ------- dict The stoichiometry of the reaction given as a mapping from metabolite KEGG identifier to coefficient. """ # Transport reactions where the same metabolite occurs in different # compartments should have been filtered out but just to be sure, we add # coefficients in the mapping. stoichiometry = defaultdict(float) for met, coef in iteritems(reaction.metabolites): kegg_id = metabolite_mapping.setdefault(met, map_metabolite2kegg(met)) if kegg_id is None: continue stoichiometry[kegg_id] += coef return dict(stoichiometry)
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Return a mapping from KEGG compound identifiers to coefficients. Parameters ---------- reaction : cobra.Reaction The reaction whose metabolites are to be translated. metabolite_mapping : dict An existing mapping from cobra.Metabolite to KEGG compound identifier that may already contain the metabolites in question or will have to be extended. Returns ------- dict The stoichiometry of the reaction given as a mapping from metabolite KEGG identifier to coefficient.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/thermodynamics.py#L129-L158
opencobra/memote
memote/support/thermodynamics.py
find_thermodynamic_reversibility_index
def find_thermodynamic_reversibility_index(reactions): u""" Return the reversibility index of the given reactions. To determine the reversibility index, we calculate the reversibility index ln_gamma (see [1]_ section 3.5) of each reaction using the eQuilibrator API [2]_. Parameters ---------- reactions: list of cobra.Reaction A list of reactions for which to calculate the reversibility index. Returns ------- tuple list of cobra.Reaction, index pairs A list of pairs of reactions and their reversibility indexes. list of cobra.Reaction A list of reactions which contain at least one metabolite that could not be mapped to KEGG on the basis of its annotation. list of cobra.Reaction A list of reactions for which it is not possible to calculate the standard change in Gibbs free energy potential. Reasons of failure include that participating metabolites cannot be broken down with the group contribution method. list of cobra.Reaction A list of reactions that are not chemically or redox balanced. References ---------- .. [1] Elad Noor, Arren Bar-Even, Avi Flamholz, Yaniv Lubling, Dan Davidi, Ron Milo; An integrated open framework for thermodynamics of reactions that combines accuracy and coverage, Bioinformatics, Volume 28, Issue 15, 1 August 2012, Pages 2037–2044, https://doi.org/10.1093/bioinformatics/bts317 .. [2] https://pypi.org/project/equilibrator-api/ """ incomplete_mapping = [] problematic_calculation = [] reversibility_indexes = [] unbalanced = [] metabolite_mapping = {} for rxn in reactions: stoich = translate_reaction(rxn, metabolite_mapping) if len(stoich) < len(rxn.metabolites): incomplete_mapping.append(rxn) continue try: # Remove protons from stoichiometry. if "C00080" in stoich: del stoich["C00080"] eq_rxn = Reaction(stoich, rxn.id) except KeyError: incomplete_mapping.append(rxn) continue if eq_rxn.check_full_reaction_balancing(): try: ln_rev_index = eq_rxn.reversibility_index() # TODO (Moritz Beber): Which exceptions can we expect here? except Exception: problematic_calculation.append(rxn) continue reversibility_indexes.append((rxn, ln_rev_index)) else: unbalanced.append(rxn) reversibility_indexes.sort(key=lambda p: abs(p[1]), reverse=True) return ( reversibility_indexes, incomplete_mapping, problematic_calculation, unbalanced )
python
def find_thermodynamic_reversibility_index(reactions): u""" Return the reversibility index of the given reactions. To determine the reversibility index, we calculate the reversibility index ln_gamma (see [1]_ section 3.5) of each reaction using the eQuilibrator API [2]_. Parameters ---------- reactions: list of cobra.Reaction A list of reactions for which to calculate the reversibility index. Returns ------- tuple list of cobra.Reaction, index pairs A list of pairs of reactions and their reversibility indexes. list of cobra.Reaction A list of reactions which contain at least one metabolite that could not be mapped to KEGG on the basis of its annotation. list of cobra.Reaction A list of reactions for which it is not possible to calculate the standard change in Gibbs free energy potential. Reasons of failure include that participating metabolites cannot be broken down with the group contribution method. list of cobra.Reaction A list of reactions that are not chemically or redox balanced. References ---------- .. [1] Elad Noor, Arren Bar-Even, Avi Flamholz, Yaniv Lubling, Dan Davidi, Ron Milo; An integrated open framework for thermodynamics of reactions that combines accuracy and coverage, Bioinformatics, Volume 28, Issue 15, 1 August 2012, Pages 2037–2044, https://doi.org/10.1093/bioinformatics/bts317 .. [2] https://pypi.org/project/equilibrator-api/ """ incomplete_mapping = [] problematic_calculation = [] reversibility_indexes = [] unbalanced = [] metabolite_mapping = {} for rxn in reactions: stoich = translate_reaction(rxn, metabolite_mapping) if len(stoich) < len(rxn.metabolites): incomplete_mapping.append(rxn) continue try: # Remove protons from stoichiometry. if "C00080" in stoich: del stoich["C00080"] eq_rxn = Reaction(stoich, rxn.id) except KeyError: incomplete_mapping.append(rxn) continue if eq_rxn.check_full_reaction_balancing(): try: ln_rev_index = eq_rxn.reversibility_index() # TODO (Moritz Beber): Which exceptions can we expect here? except Exception: problematic_calculation.append(rxn) continue reversibility_indexes.append((rxn, ln_rev_index)) else: unbalanced.append(rxn) reversibility_indexes.sort(key=lambda p: abs(p[1]), reverse=True) return ( reversibility_indexes, incomplete_mapping, problematic_calculation, unbalanced )
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u""" Return the reversibility index of the given reactions. To determine the reversibility index, we calculate the reversibility index ln_gamma (see [1]_ section 3.5) of each reaction using the eQuilibrator API [2]_. Parameters ---------- reactions: list of cobra.Reaction A list of reactions for which to calculate the reversibility index. Returns ------- tuple list of cobra.Reaction, index pairs A list of pairs of reactions and their reversibility indexes. list of cobra.Reaction A list of reactions which contain at least one metabolite that could not be mapped to KEGG on the basis of its annotation. list of cobra.Reaction A list of reactions for which it is not possible to calculate the standard change in Gibbs free energy potential. Reasons of failure include that participating metabolites cannot be broken down with the group contribution method. list of cobra.Reaction A list of reactions that are not chemically or redox balanced. References ---------- .. [1] Elad Noor, Arren Bar-Even, Avi Flamholz, Yaniv Lubling, Dan Davidi, Ron Milo; An integrated open framework for thermodynamics of reactions that combines accuracy and coverage, Bioinformatics, Volume 28, Issue 15, 1 August 2012, Pages 2037–2044, https://doi.org/10.1093/bioinformatics/bts317 .. [2] https://pypi.org/project/equilibrator-api/
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/thermodynamics.py#L161-L234
opencobra/memote
memote/support/consistency.py
check_stoichiometric_consistency
def check_stoichiometric_consistency(model): """ Verify the consistency of the model's stoichiometry. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- See [1]_ section 3.1 for a complete description of the algorithm. .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245. """ problem = model.problem # The transpose of the stoichiometric matrix N.T in the paper. stoich_trans = problem.Model() internal_rxns = con_helpers.get_internals(model) metabolites = set(met for rxn in internal_rxns for met in rxn.metabolites) LOGGER.info("model '%s' has %d internal reactions", model.id, len(internal_rxns)) LOGGER.info("model '%s' has %d internal metabolites", model.id, len(metabolites)) stoich_trans.add([problem.Variable(m.id, lb=1) for m in metabolites]) stoich_trans.update() con_helpers.add_reaction_constraints( stoich_trans, internal_rxns, problem.Constraint) # The objective is to minimize the metabolite mass vector. stoich_trans.objective = problem.Objective( Zero, direction="min", sloppy=True) stoich_trans.objective.set_linear_coefficients( {var: 1. for var in stoich_trans.variables}) status = stoich_trans.optimize() if status == OPTIMAL: return True elif status == INFEASIBLE: return False else: raise RuntimeError( "Could not determine stoichiometric consistencty." " Solver status is '{}'" " (only optimal or infeasible expected).".format(status))
python
def check_stoichiometric_consistency(model): """ Verify the consistency of the model's stoichiometry. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- See [1]_ section 3.1 for a complete description of the algorithm. .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245. """ problem = model.problem # The transpose of the stoichiometric matrix N.T in the paper. stoich_trans = problem.Model() internal_rxns = con_helpers.get_internals(model) metabolites = set(met for rxn in internal_rxns for met in rxn.metabolites) LOGGER.info("model '%s' has %d internal reactions", model.id, len(internal_rxns)) LOGGER.info("model '%s' has %d internal metabolites", model.id, len(metabolites)) stoich_trans.add([problem.Variable(m.id, lb=1) for m in metabolites]) stoich_trans.update() con_helpers.add_reaction_constraints( stoich_trans, internal_rxns, problem.Constraint) # The objective is to minimize the metabolite mass vector. stoich_trans.objective = problem.Objective( Zero, direction="min", sloppy=True) stoich_trans.objective.set_linear_coefficients( {var: 1. for var in stoich_trans.variables}) status = stoich_trans.optimize() if status == OPTIMAL: return True elif status == INFEASIBLE: return False else: raise RuntimeError( "Could not determine stoichiometric consistencty." " Solver status is '{}'" " (only optimal or infeasible expected).".format(status))
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Verify the consistency of the model's stoichiometry. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- See [1]_ section 3.1 for a complete description of the algorithm. .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L63-L110
opencobra/memote
memote/support/consistency.py
find_unconserved_metabolites
def find_unconserved_metabolites(model): """ Detect unconserved metabolites. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- See [1]_ section 3.2 for a complete description of the algorithm. .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245. """ problem = model.problem stoich_trans = problem.Model() internal_rxns = con_helpers.get_internals(model) metabolites = set(met for rxn in internal_rxns for met in rxn.metabolites) # The binary variables k[i] in the paper. k_vars = list() for met in metabolites: # The element m[i] of the mass vector. m_var = problem.Variable(met.id) k_var = problem.Variable("k_{}".format(met.id), type="binary") k_vars.append(k_var) stoich_trans.add([m_var, k_var]) # This constraint is equivalent to 0 <= k[i] <= m[i]. stoich_trans.add(problem.Constraint( k_var - m_var, ub=0, name="switch_{}".format(met.id))) stoich_trans.update() con_helpers.add_reaction_constraints( stoich_trans, internal_rxns, problem.Constraint) # The objective is to maximize the binary indicators k[i], subject to the # above inequality constraints. stoich_trans.objective = problem.Objective( Zero, sloppy=True, direction="max") stoich_trans.objective.set_linear_coefficients( {var: 1. for var in k_vars}) status = stoich_trans.optimize() if status == OPTIMAL: # TODO: See if that could be a Boolean test `bool(var.primal)`. return set([model.metabolites.get_by_id(var.name[2:]) for var in k_vars if var.primal < 0.8]) else: raise RuntimeError( "Could not compute list of unconserved metabolites." " Solver status is '{}' (only optimal expected).".format(status))
python
def find_unconserved_metabolites(model): """ Detect unconserved metabolites. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- See [1]_ section 3.2 for a complete description of the algorithm. .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245. """ problem = model.problem stoich_trans = problem.Model() internal_rxns = con_helpers.get_internals(model) metabolites = set(met for rxn in internal_rxns for met in rxn.metabolites) # The binary variables k[i] in the paper. k_vars = list() for met in metabolites: # The element m[i] of the mass vector. m_var = problem.Variable(met.id) k_var = problem.Variable("k_{}".format(met.id), type="binary") k_vars.append(k_var) stoich_trans.add([m_var, k_var]) # This constraint is equivalent to 0 <= k[i] <= m[i]. stoich_trans.add(problem.Constraint( k_var - m_var, ub=0, name="switch_{}".format(met.id))) stoich_trans.update() con_helpers.add_reaction_constraints( stoich_trans, internal_rxns, problem.Constraint) # The objective is to maximize the binary indicators k[i], subject to the # above inequality constraints. stoich_trans.objective = problem.Objective( Zero, sloppy=True, direction="max") stoich_trans.objective.set_linear_coefficients( {var: 1. for var in k_vars}) status = stoich_trans.optimize() if status == OPTIMAL: # TODO: See if that could be a Boolean test `bool(var.primal)`. return set([model.metabolites.get_by_id(var.name[2:]) for var in k_vars if var.primal < 0.8]) else: raise RuntimeError( "Could not compute list of unconserved metabolites." " Solver status is '{}' (only optimal expected).".format(status))
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Detect unconserved metabolites. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- See [1]_ section 3.2 for a complete description of the algorithm. .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L113-L165
opencobra/memote
memote/support/consistency.py
find_inconsistent_min_stoichiometry
def find_inconsistent_min_stoichiometry(model, atol=1e-13): """ Detect inconsistent minimal net stoichiometries. Parameters ---------- model : cobra.Model The metabolic model under investigation. atol : float, optional Values below the absolute tolerance are treated as zero. Expected to be very small but larger than zero. Notes ----- See [1]_ section 3.3 for a complete description of the algorithm. References ---------- .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245. """ if check_stoichiometric_consistency(model): return set() Model, Constraint, Variable, Objective = con_helpers.get_interface(model) unconserved_mets = find_unconserved_metabolites(model) LOGGER.info("model has %d unconserved metabolites", len(unconserved_mets)) internal_rxns = con_helpers.get_internals(model) internal_mets = set( met for rxn in internal_rxns for met in rxn.metabolites) get_id = attrgetter("id") reactions = sorted(internal_rxns, key=get_id) metabolites = sorted(internal_mets, key=get_id) stoich, met_index, rxn_index = con_helpers.stoichiometry_matrix( metabolites, reactions) left_ns = con_helpers.nullspace(stoich.T) # deal with numerical instabilities left_ns[np.abs(left_ns) < atol] = 0.0 LOGGER.info("nullspace has dimension %d", left_ns.shape[1]) inc_minimal = set() (problem, indicators) = con_helpers.create_milp_problem( left_ns, metabolites, Model, Variable, Constraint, Objective) LOGGER.debug(str(problem)) cuts = list() for met in unconserved_mets: row = met_index[met] if (left_ns[row] == 0.0).all(): LOGGER.debug("%s: singleton minimal unconservable set", met.id) # singleton set! inc_minimal.add((met,)) continue # expect a positive mass for the unconserved metabolite problem.variables[met.id].lb = 1e-3 status = problem.optimize() while status == "optimal": LOGGER.debug("%s: status %s", met.id, status) LOGGER.debug("sum of all primal values: %f", sum(problem.primal_values.values())) LOGGER.debug("sum of binary indicators: %f", sum(var.primal for var in indicators)) solution = [model.metabolites.get_by_id(var.name[2:]) for var in indicators if var.primal > 0.2] LOGGER.debug("%s: set size %d", met.id, len(solution)) inc_minimal.add(tuple(solution)) if len(solution) == 1: break cuts.append(con_helpers.add_cut( problem, indicators, len(solution) - 1, Constraint)) status = problem.optimize() LOGGER.debug("%s: last status %s", met.id, status) # reset problem.variables[met.id].lb = 0.0 problem.remove(cuts) cuts.clear() return inc_minimal
python
def find_inconsistent_min_stoichiometry(model, atol=1e-13): """ Detect inconsistent minimal net stoichiometries. Parameters ---------- model : cobra.Model The metabolic model under investigation. atol : float, optional Values below the absolute tolerance are treated as zero. Expected to be very small but larger than zero. Notes ----- See [1]_ section 3.3 for a complete description of the algorithm. References ---------- .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245. """ if check_stoichiometric_consistency(model): return set() Model, Constraint, Variable, Objective = con_helpers.get_interface(model) unconserved_mets = find_unconserved_metabolites(model) LOGGER.info("model has %d unconserved metabolites", len(unconserved_mets)) internal_rxns = con_helpers.get_internals(model) internal_mets = set( met for rxn in internal_rxns for met in rxn.metabolites) get_id = attrgetter("id") reactions = sorted(internal_rxns, key=get_id) metabolites = sorted(internal_mets, key=get_id) stoich, met_index, rxn_index = con_helpers.stoichiometry_matrix( metabolites, reactions) left_ns = con_helpers.nullspace(stoich.T) # deal with numerical instabilities left_ns[np.abs(left_ns) < atol] = 0.0 LOGGER.info("nullspace has dimension %d", left_ns.shape[1]) inc_minimal = set() (problem, indicators) = con_helpers.create_milp_problem( left_ns, metabolites, Model, Variable, Constraint, Objective) LOGGER.debug(str(problem)) cuts = list() for met in unconserved_mets: row = met_index[met] if (left_ns[row] == 0.0).all(): LOGGER.debug("%s: singleton minimal unconservable set", met.id) # singleton set! inc_minimal.add((met,)) continue # expect a positive mass for the unconserved metabolite problem.variables[met.id].lb = 1e-3 status = problem.optimize() while status == "optimal": LOGGER.debug("%s: status %s", met.id, status) LOGGER.debug("sum of all primal values: %f", sum(problem.primal_values.values())) LOGGER.debug("sum of binary indicators: %f", sum(var.primal for var in indicators)) solution = [model.metabolites.get_by_id(var.name[2:]) for var in indicators if var.primal > 0.2] LOGGER.debug("%s: set size %d", met.id, len(solution)) inc_minimal.add(tuple(solution)) if len(solution) == 1: break cuts.append(con_helpers.add_cut( problem, indicators, len(solution) - 1, Constraint)) status = problem.optimize() LOGGER.debug("%s: last status %s", met.id, status) # reset problem.variables[met.id].lb = 0.0 problem.remove(cuts) cuts.clear() return inc_minimal
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Detect inconsistent minimal net stoichiometries. Parameters ---------- model : cobra.Model The metabolic model under investigation. atol : float, optional Values below the absolute tolerance are treated as zero. Expected to be very small but larger than zero. Notes ----- See [1]_ section 3.3 for a complete description of the algorithm. References ---------- .. [1] Gevorgyan, A., M. G Poolman, and D. A Fell. "Detection of Stoichiometric Inconsistencies in Biomolecular Models." Bioinformatics 24, no. 19 (2008): 2245.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L170-L246
opencobra/memote
memote/support/consistency.py
detect_energy_generating_cycles
def detect_energy_generating_cycles(model, metabolite_id): u""" Detect erroneous energy-generating cycles for a a single metabolite. The function will first build a dissipation reaction corresponding to the input metabolite. This reaction is then set as the objective for optimization, after closing all exchanges. If the reaction was able to carry flux, an erroneous energy-generating cycle must be present. In this case a list of reactions with a flux greater than zero is returned. Otherwise, the function returns False. Parameters ---------- model : cobra.Model The metabolic model under investigation. metabolite_id : str The identifier of an energy metabolite. Notes ----- "[...] energy generating cycles (EGC) [...] charge energy metabolites without a source of energy. [...] To efficiently identify the existence of diverse EGCs, we first add a dissipation reaction to the metabolic network for each metabolite used to transmit cellular energy; e.g., for ATP, the irreversible reaction ATP + H2O → ADP + P + H+ is added. These dissipation reactions close any existing energy-generating cycles, thereby converting them to type-III pathways. Fluxes through any of the dissipation reactions at steady state indicate the generation of energy through the metabolic network. Second, all uptake reactions are constrained to zero. The sum of the fluxes through the energy dissipation reactions is now maximized using FBA. For a model without EGCs, these reactions cannot carry any flux without the uptake of nutrients. [1]_." References ---------- .. [1] Fritzemeier, C. J., Hartleb, D., Szappanos, B., Papp, B., & Lercher, M. J. (2017). Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. PLoS Computational Biology, 13(4), 1–14. http://doi.org/10.1371/journal.pcbi.1005494 """ main_comp = helpers.find_compartment_id_in_model(model, 'c') met = helpers.find_met_in_model(model, metabolite_id, main_comp)[0] dissipation_rxn = Reaction('Dissipation') if metabolite_id in ['MNXM3', 'MNXM63', 'MNXM51', 'MNXM121', 'MNXM423']: # build nucleotide-type dissipation reaction dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM2', main_comp)[0]: -1, helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 1, helpers.find_met_in_model(model, 'MNXM9', main_comp)[0]: 1, }) elif metabolite_id in ['MNXM6', 'MNXM10']: # build nicotinamide-type dissipation reaction dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 1 }) elif metabolite_id in ['MNXM38', 'MNXM208', 'MNXM191', 'MNXM223', 'MNXM7517', 'MNXM12233', 'MNXM558']: # build redox-partner-type dissipation reaction dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 2 }) elif metabolite_id == 'MNXM21': dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM2', main_comp)[0]: -1, helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 1, helpers.find_met_in_model(model, 'MNXM26', main_comp)[0]: 1, }) elif metabolite_id == 'MNXM89557': dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM2', main_comp)[0]: -1, helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 2, helpers.find_met_in_model(model, 'MNXM15', main_comp)[0]: 1, }) dissipation_product = helpers.find_met_in_model( model, ENERGY_COUPLES[metabolite_id], main_comp)[0] dissipation_rxn.add_metabolites( {met: -1, dissipation_product: 1}) helpers.close_boundaries_sensibly(model) model.add_reactions([dissipation_rxn]) model.objective = dissipation_rxn solution = model.optimize(raise_error=True) if solution.objective_value > 0.0: return solution.fluxes[solution.fluxes.abs() > 0.0].index. \ drop(["Dissipation"]).tolist() else: return []
python
def detect_energy_generating_cycles(model, metabolite_id): u""" Detect erroneous energy-generating cycles for a a single metabolite. The function will first build a dissipation reaction corresponding to the input metabolite. This reaction is then set as the objective for optimization, after closing all exchanges. If the reaction was able to carry flux, an erroneous energy-generating cycle must be present. In this case a list of reactions with a flux greater than zero is returned. Otherwise, the function returns False. Parameters ---------- model : cobra.Model The metabolic model under investigation. metabolite_id : str The identifier of an energy metabolite. Notes ----- "[...] energy generating cycles (EGC) [...] charge energy metabolites without a source of energy. [...] To efficiently identify the existence of diverse EGCs, we first add a dissipation reaction to the metabolic network for each metabolite used to transmit cellular energy; e.g., for ATP, the irreversible reaction ATP + H2O → ADP + P + H+ is added. These dissipation reactions close any existing energy-generating cycles, thereby converting them to type-III pathways. Fluxes through any of the dissipation reactions at steady state indicate the generation of energy through the metabolic network. Second, all uptake reactions are constrained to zero. The sum of the fluxes through the energy dissipation reactions is now maximized using FBA. For a model without EGCs, these reactions cannot carry any flux without the uptake of nutrients. [1]_." References ---------- .. [1] Fritzemeier, C. J., Hartleb, D., Szappanos, B., Papp, B., & Lercher, M. J. (2017). Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. PLoS Computational Biology, 13(4), 1–14. http://doi.org/10.1371/journal.pcbi.1005494 """ main_comp = helpers.find_compartment_id_in_model(model, 'c') met = helpers.find_met_in_model(model, metabolite_id, main_comp)[0] dissipation_rxn = Reaction('Dissipation') if metabolite_id in ['MNXM3', 'MNXM63', 'MNXM51', 'MNXM121', 'MNXM423']: # build nucleotide-type dissipation reaction dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM2', main_comp)[0]: -1, helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 1, helpers.find_met_in_model(model, 'MNXM9', main_comp)[0]: 1, }) elif metabolite_id in ['MNXM6', 'MNXM10']: # build nicotinamide-type dissipation reaction dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 1 }) elif metabolite_id in ['MNXM38', 'MNXM208', 'MNXM191', 'MNXM223', 'MNXM7517', 'MNXM12233', 'MNXM558']: # build redox-partner-type dissipation reaction dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 2 }) elif metabolite_id == 'MNXM21': dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM2', main_comp)[0]: -1, helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 1, helpers.find_met_in_model(model, 'MNXM26', main_comp)[0]: 1, }) elif metabolite_id == 'MNXM89557': dissipation_rxn.add_metabolites({ helpers.find_met_in_model(model, 'MNXM2', main_comp)[0]: -1, helpers.find_met_in_model(model, 'MNXM1', main_comp)[0]: 2, helpers.find_met_in_model(model, 'MNXM15', main_comp)[0]: 1, }) dissipation_product = helpers.find_met_in_model( model, ENERGY_COUPLES[metabolite_id], main_comp)[0] dissipation_rxn.add_metabolites( {met: -1, dissipation_product: 1}) helpers.close_boundaries_sensibly(model) model.add_reactions([dissipation_rxn]) model.objective = dissipation_rxn solution = model.optimize(raise_error=True) if solution.objective_value > 0.0: return solution.fluxes[solution.fluxes.abs() > 0.0].index. \ drop(["Dissipation"]).tolist() else: return []
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u""" Detect erroneous energy-generating cycles for a a single metabolite. The function will first build a dissipation reaction corresponding to the input metabolite. This reaction is then set as the objective for optimization, after closing all exchanges. If the reaction was able to carry flux, an erroneous energy-generating cycle must be present. In this case a list of reactions with a flux greater than zero is returned. Otherwise, the function returns False. Parameters ---------- model : cobra.Model The metabolic model under investigation. metabolite_id : str The identifier of an energy metabolite. Notes ----- "[...] energy generating cycles (EGC) [...] charge energy metabolites without a source of energy. [...] To efficiently identify the existence of diverse EGCs, we first add a dissipation reaction to the metabolic network for each metabolite used to transmit cellular energy; e.g., for ATP, the irreversible reaction ATP + H2O → ADP + P + H+ is added. These dissipation reactions close any existing energy-generating cycles, thereby converting them to type-III pathways. Fluxes through any of the dissipation reactions at steady state indicate the generation of energy through the metabolic network. Second, all uptake reactions are constrained to zero. The sum of the fluxes through the energy dissipation reactions is now maximized using FBA. For a model without EGCs, these reactions cannot carry any flux without the uptake of nutrients. [1]_." References ---------- .. [1] Fritzemeier, C. J., Hartleb, D., Szappanos, B., Papp, B., & Lercher, M. J. (2017). Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal. PLoS Computational Biology, 13(4), 1–14. http://doi.org/10.1371/journal.pcbi.1005494
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L277-L369
opencobra/memote
memote/support/consistency.py
find_stoichiometrically_balanced_cycles
def find_stoichiometrically_balanced_cycles(model): u""" Find metabolic reactions in stoichiometrically balanced cycles (SBCs). Identify forward and reverse cycles by closing all exchanges and using FVA. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- "SBCs are artifacts of metabolic reconstructions due to insufficient constraints (e.g., thermodynamic constraints and regulatory constraints) [1]_." They are defined by internal reactions that carry flux in spite of closed exchange reactions. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ helpers.close_boundaries_sensibly(model) fva_result = flux_variability_analysis(model, loopless=False) return fva_result.index[ (fva_result["minimum"] <= (-1 + TOLERANCE_THRESHOLD)) | (fva_result["maximum"] >= (1 - TOLERANCE_THRESHOLD)) ].tolist()
python
def find_stoichiometrically_balanced_cycles(model): u""" Find metabolic reactions in stoichiometrically balanced cycles (SBCs). Identify forward and reverse cycles by closing all exchanges and using FVA. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- "SBCs are artifacts of metabolic reconstructions due to insufficient constraints (e.g., thermodynamic constraints and regulatory constraints) [1]_." They are defined by internal reactions that carry flux in spite of closed exchange reactions. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203 """ helpers.close_boundaries_sensibly(model) fva_result = flux_variability_analysis(model, loopless=False) return fva_result.index[ (fva_result["minimum"] <= (-1 + TOLERANCE_THRESHOLD)) | (fva_result["maximum"] >= (1 - TOLERANCE_THRESHOLD)) ].tolist()
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u""" Find metabolic reactions in stoichiometrically balanced cycles (SBCs). Identify forward and reverse cycles by closing all exchanges and using FVA. Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- "SBCs are artifacts of metabolic reconstructions due to insufficient constraints (e.g., thermodynamic constraints and regulatory constraints) [1]_." They are defined by internal reactions that carry flux in spite of closed exchange reactions. References ---------- .. [1] Thiele, I., & Palsson, B. Ø. (2010, January). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. Nature Publishing Group. http://doi.org/10.1038/nprot.2009.203
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L400-L431
opencobra/memote
memote/support/consistency.py
find_orphans
def find_orphans(model): """ Return metabolites that are only consumed in reactions. Metabolites that are involved in an exchange reaction are never considered to be orphaned. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ exchange = frozenset(model.exchanges) return [ met for met in model.metabolites if (len(met.reactions) > 0) and all( (not rxn.reversibility) and (rxn not in exchange) and (rxn.metabolites[met] < 0) for rxn in met.reactions ) ]
python
def find_orphans(model): """ Return metabolites that are only consumed in reactions. Metabolites that are involved in an exchange reaction are never considered to be orphaned. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ exchange = frozenset(model.exchanges) return [ met for met in model.metabolites if (len(met.reactions) > 0) and all( (not rxn.reversibility) and (rxn not in exchange) and (rxn.metabolites[met] < 0) for rxn in met.reactions ) ]
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Return metabolites that are only consumed in reactions. Metabolites that are involved in an exchange reaction are never considered to be orphaned. Parameters ---------- model : cobra.Model The metabolic model under investigation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L434-L454
opencobra/memote
memote/support/consistency.py
find_metabolites_not_produced_with_open_bounds
def find_metabolites_not_produced_with_open_bounds(model): """ Return metabolites that cannot be produced with open exchange reactions. A perfect model should be able to produce each and every metabolite when all medium components are available. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Those metabolites that could not be produced. """ mets_not_produced = list() helpers.open_exchanges(model) for met in model.metabolites: with model: exch = model.add_boundary( met, type="irrex", reaction_id="IRREX", lb=0, ub=1000) solution = helpers.run_fba(model, exch.id) if np.isnan(solution) or solution < TOLERANCE_THRESHOLD: mets_not_produced.append(met) return mets_not_produced
python
def find_metabolites_not_produced_with_open_bounds(model): """ Return metabolites that cannot be produced with open exchange reactions. A perfect model should be able to produce each and every metabolite when all medium components are available. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Those metabolites that could not be produced. """ mets_not_produced = list() helpers.open_exchanges(model) for met in model.metabolites: with model: exch = model.add_boundary( met, type="irrex", reaction_id="IRREX", lb=0, ub=1000) solution = helpers.run_fba(model, exch.id) if np.isnan(solution) or solution < TOLERANCE_THRESHOLD: mets_not_produced.append(met) return mets_not_produced
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Return metabolites that cannot be produced with open exchange reactions. A perfect model should be able to produce each and every metabolite when all medium components are available. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Those metabolites that could not be produced.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L493-L520
opencobra/memote
memote/support/consistency.py
find_metabolites_not_consumed_with_open_bounds
def find_metabolites_not_consumed_with_open_bounds(model): """ Return metabolites that cannot be consumed with open boundary reactions. When all metabolites can be secreted, it should be possible for each and every metabolite to be consumed in some form. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Those metabolites that could not be consumed. """ mets_not_consumed = list() helpers.open_exchanges(model) for met in model.metabolites: with model: exch = model.add_boundary( met, type="irrex", reaction_id="IRREX", lb=-1000, ub=0) solution = helpers.run_fba(model, exch.id, direction="min") if np.isnan(solution) or abs(solution) < TOLERANCE_THRESHOLD: mets_not_consumed.append(met) return mets_not_consumed
python
def find_metabolites_not_consumed_with_open_bounds(model): """ Return metabolites that cannot be consumed with open boundary reactions. When all metabolites can be secreted, it should be possible for each and every metabolite to be consumed in some form. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Those metabolites that could not be consumed. """ mets_not_consumed = list() helpers.open_exchanges(model) for met in model.metabolites: with model: exch = model.add_boundary( met, type="irrex", reaction_id="IRREX", lb=-1000, ub=0) solution = helpers.run_fba(model, exch.id, direction="min") if np.isnan(solution) or abs(solution) < TOLERANCE_THRESHOLD: mets_not_consumed.append(met) return mets_not_consumed
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Return metabolites that cannot be consumed with open boundary reactions. When all metabolites can be secreted, it should be possible for each and every metabolite to be consumed in some form. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- list Those metabolites that could not be consumed.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L523-L550
opencobra/memote
memote/support/consistency.py
find_reactions_with_unbounded_flux_default_condition
def find_reactions_with_unbounded_flux_default_condition(model): """ Return list of reactions whose flux is unbounded in the default condition. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- tuple list A list of reactions that in default modeling conditions are able to carry flux as high/low as the systems maximal and minimal bounds. float The fraction of the amount of unbounded reactions to the amount of non-blocked reactions. list A list of reactions that in default modeling conditions are not able to carry flux at all. """ try: fva_result = flux_variability_analysis(model, fraction_of_optimum=1.0) except Infeasible as err: LOGGER.error("Failed to find reactions with unbounded flux " "because '{}'. This may be a bug.".format(err)) raise Infeasible("It was not possible to run flux variability " "analysis on the model. Make sure that the model " "can be solved! Check if the constraints are not " "too strict.") # Per reaction (row) the flux is below threshold (close to zero). conditionally_blocked = fva_result.loc[ fva_result.abs().max(axis=1) < TOLERANCE_THRESHOLD ].index.tolist() small, large = helpers.find_bounds(model) # Find those reactions whose flux is close to or outside of the median # upper or lower bound, i.e., appears unconstrained. unlimited_flux = fva_result.loc[ np.isclose(fva_result["maximum"], large, atol=TOLERANCE_THRESHOLD) | (fva_result["maximum"] > large) | np.isclose(fva_result["minimum"], small, atol=TOLERANCE_THRESHOLD) | (fva_result["minimum"] < small) ].index.tolist() try: fraction = len(unlimited_flux) / \ (len(model.reactions) - len(conditionally_blocked)) except ZeroDivisionError: LOGGER.error("Division by Zero! Failed to calculate the " "fraction of unbounded reactions. Does this model " "have any reactions at all?") raise ZeroDivisionError("It was not possible to calculate the " "fraction of unbounded reactions to " "un-blocked reactions. This may be because" "the model doesn't have any reactions at " "all or that none of the reactions can " "carry a flux larger than zero!") return unlimited_flux, fraction, conditionally_blocked
python
def find_reactions_with_unbounded_flux_default_condition(model): """ Return list of reactions whose flux is unbounded in the default condition. Parameters ---------- model : cobra.Model The metabolic model under investigation. Returns ------- tuple list A list of reactions that in default modeling conditions are able to carry flux as high/low as the systems maximal and minimal bounds. float The fraction of the amount of unbounded reactions to the amount of non-blocked reactions. list A list of reactions that in default modeling conditions are not able to carry flux at all. """ try: fva_result = flux_variability_analysis(model, fraction_of_optimum=1.0) except Infeasible as err: LOGGER.error("Failed to find reactions with unbounded flux " "because '{}'. This may be a bug.".format(err)) raise Infeasible("It was not possible to run flux variability " "analysis on the model. Make sure that the model " "can be solved! Check if the constraints are not " "too strict.") # Per reaction (row) the flux is below threshold (close to zero). conditionally_blocked = fva_result.loc[ fva_result.abs().max(axis=1) < TOLERANCE_THRESHOLD ].index.tolist() small, large = helpers.find_bounds(model) # Find those reactions whose flux is close to or outside of the median # upper or lower bound, i.e., appears unconstrained. unlimited_flux = fva_result.loc[ np.isclose(fva_result["maximum"], large, atol=TOLERANCE_THRESHOLD) | (fva_result["maximum"] > large) | np.isclose(fva_result["minimum"], small, atol=TOLERANCE_THRESHOLD) | (fva_result["minimum"] < small) ].index.tolist() try: fraction = len(unlimited_flux) / \ (len(model.reactions) - len(conditionally_blocked)) except ZeroDivisionError: LOGGER.error("Division by Zero! Failed to calculate the " "fraction of unbounded reactions. Does this model " "have any reactions at all?") raise ZeroDivisionError("It was not possible to calculate the " "fraction of unbounded reactions to " "un-blocked reactions. This may be because" "the model doesn't have any reactions at " "all or that none of the reactions can " "carry a flux larger than zero!") return unlimited_flux, fraction, conditionally_blocked
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/consistency.py#L553-L612
opencobra/memote
memote/experimental/tabular.py
read_tabular
def read_tabular(filename, dtype_conversion=None): """ Read a tabular data file which can be CSV, TSV, XLS or XLSX. Parameters ---------- filename : str or pathlib.Path The full file path. May be a compressed file. dtype_conversion : dict Column names as keys and corresponding type for loading the data. Please take a look at the `pandas documentation <https://pandas.pydata.org/pandas-docs/stable/io.html#specifying-column-data-types>`__ for detailed explanations. Returns ------- pandas.DataFrame The data table. """ if dtype_conversion is None: dtype_conversion = {} name, ext = filename.split(".", 1) ext = ext.lower() # Completely empty columns are interpreted as float by default. dtype_conversion["comment"] = str if "csv" in ext: df = pd.read_csv(filename, dtype=dtype_conversion, encoding="utf-8") elif "tsv" in ext: df = pd.read_table(filename, dtype=dtype_conversion, encoding="utf-8") elif "xls" in ext or "xlsx" in ext: df = pd.read_excel(filename, dtype=dtype_conversion, encoding="utf-8") # TODO: Add a function to parse ODS data into a pandas data frame. else: raise ValueError("Unknown file format '{}'.".format(ext)) return df
python
def read_tabular(filename, dtype_conversion=None): """ Read a tabular data file which can be CSV, TSV, XLS or XLSX. Parameters ---------- filename : str or pathlib.Path The full file path. May be a compressed file. dtype_conversion : dict Column names as keys and corresponding type for loading the data. Please take a look at the `pandas documentation <https://pandas.pydata.org/pandas-docs/stable/io.html#specifying-column-data-types>`__ for detailed explanations. Returns ------- pandas.DataFrame The data table. """ if dtype_conversion is None: dtype_conversion = {} name, ext = filename.split(".", 1) ext = ext.lower() # Completely empty columns are interpreted as float by default. dtype_conversion["comment"] = str if "csv" in ext: df = pd.read_csv(filename, dtype=dtype_conversion, encoding="utf-8") elif "tsv" in ext: df = pd.read_table(filename, dtype=dtype_conversion, encoding="utf-8") elif "xls" in ext or "xlsx" in ext: df = pd.read_excel(filename, dtype=dtype_conversion, encoding="utf-8") # TODO: Add a function to parse ODS data into a pandas data frame. else: raise ValueError("Unknown file format '{}'.".format(ext)) return df
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/tabular.py#L25-L60
opencobra/memote
memote/suite/cli/reports.py
snapshot
def snapshot(model, filename, pytest_args, exclusive, skip, solver, experimental, custom_tests, custom_config): """ Take a snapshot of a model's state and generate a report. MODEL: Path to model file. Can also be supplied via the environment variable MEMOTE_MODEL or configured in 'setup.cfg' or 'memote.ini'. """ model_obj, sbml_ver, notifications = api.validate_model( model) if model_obj is None: LOGGER.critical( "The model could not be loaded due to the following SBML errors.") utils.stdout_notifications(notifications) api.validation_report(model, notifications, filename) sys.exit(1) if not any(a.startswith("--tb") for a in pytest_args): pytest_args = ["--tb", "no"] + pytest_args # Add further directories to search for tests. pytest_args.extend(custom_tests) config = ReportConfiguration.load() # Update the default test configuration with custom ones (if any). for custom in custom_config: config.merge(ReportConfiguration.load(custom)) model_obj.solver = solver _, results = api.test_model(model_obj, sbml_version=sbml_ver, results=True, pytest_args=pytest_args, skip=skip, exclusive=exclusive, experimental=experimental) with open(filename, "w", encoding="utf-8") as file_handle: LOGGER.info("Writing snapshot report to '%s'.", filename) file_handle.write(api.snapshot_report(results, config))
python
def snapshot(model, filename, pytest_args, exclusive, skip, solver, experimental, custom_tests, custom_config): """ Take a snapshot of a model's state and generate a report. MODEL: Path to model file. Can also be supplied via the environment variable MEMOTE_MODEL or configured in 'setup.cfg' or 'memote.ini'. """ model_obj, sbml_ver, notifications = api.validate_model( model) if model_obj is None: LOGGER.critical( "The model could not be loaded due to the following SBML errors.") utils.stdout_notifications(notifications) api.validation_report(model, notifications, filename) sys.exit(1) if not any(a.startswith("--tb") for a in pytest_args): pytest_args = ["--tb", "no"] + pytest_args # Add further directories to search for tests. pytest_args.extend(custom_tests) config = ReportConfiguration.load() # Update the default test configuration with custom ones (if any). for custom in custom_config: config.merge(ReportConfiguration.load(custom)) model_obj.solver = solver _, results = api.test_model(model_obj, sbml_version=sbml_ver, results=True, pytest_args=pytest_args, skip=skip, exclusive=exclusive, experimental=experimental) with open(filename, "w", encoding="utf-8") as file_handle: LOGGER.info("Writing snapshot report to '%s'.", filename) file_handle.write(api.snapshot_report(results, config))
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/cli/reports.py#L89-L119
opencobra/memote
memote/suite/cli/reports.py
history
def history(location, model, filename, deployment, custom_config): """Generate a report over a model's git commit history.""" callbacks.git_installed() LOGGER.info("Initialising history report generation.") if location is None: raise click.BadParameter("No 'location' given or configured.") try: repo = git.Repo() except git.InvalidGitRepositoryError: LOGGER.critical( "The history report requires a git repository in order to check " "the model's commit history.") sys.exit(1) LOGGER.info("Obtaining history of results from " "the deployment branch {}.".format(deployment)) repo.git.checkout(deployment) try: manager = managers.SQLResultManager(repository=repo, location=location) except (AttributeError, ArgumentError): manager = managers.RepoResultManager( repository=repo, location=location) config = ReportConfiguration.load() # Update the default test configuration with custom ones (if any). for custom in custom_config: config.merge(ReportConfiguration.load(custom)) LOGGER.info("Tracing the commit history.") history = managers.HistoryManager(repository=repo, manager=manager) history.load_history(model, skip={deployment}) LOGGER.info("Composing the history report.") report = api.history_report(history, config=config) with open(filename, "w", encoding="utf-8") as file_handle: file_handle.write(report)
python
def history(location, model, filename, deployment, custom_config): """Generate a report over a model's git commit history.""" callbacks.git_installed() LOGGER.info("Initialising history report generation.") if location is None: raise click.BadParameter("No 'location' given or configured.") try: repo = git.Repo() except git.InvalidGitRepositoryError: LOGGER.critical( "The history report requires a git repository in order to check " "the model's commit history.") sys.exit(1) LOGGER.info("Obtaining history of results from " "the deployment branch {}.".format(deployment)) repo.git.checkout(deployment) try: manager = managers.SQLResultManager(repository=repo, location=location) except (AttributeError, ArgumentError): manager = managers.RepoResultManager( repository=repo, location=location) config = ReportConfiguration.load() # Update the default test configuration with custom ones (if any). for custom in custom_config: config.merge(ReportConfiguration.load(custom)) LOGGER.info("Tracing the commit history.") history = managers.HistoryManager(repository=repo, manager=manager) history.load_history(model, skip={deployment}) LOGGER.info("Composing the history report.") report = api.history_report(history, config=config) with open(filename, "w", encoding="utf-8") as file_handle: file_handle.write(report)
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/cli/reports.py#L144-L175
opencobra/memote
memote/suite/cli/reports.py
diff
def diff(models, filename, pytest_args, exclusive, skip, solver, experimental, custom_tests, custom_config): """ Take a snapshot of all the supplied models and generate a diff report. MODELS: List of paths to two or more model files. """ if not any(a.startswith("--tb") for a in pytest_args): pytest_args = ["--tb", "no"] + pytest_args # Add further directories to search for tests. pytest_args.extend(custom_tests) config = ReportConfiguration.load() # Update the default test configuration with custom ones (if any). for custom in custom_config: config.merge(ReportConfiguration.load(custom)) # Build the diff report specific data structure diff_results = dict() model_and_model_ver_tuple = list() for model_path in models: try: model_filename = os.path.basename(model_path) diff_results.setdefault(model_filename, dict()) model, model_ver, notifications = api.validate_model(model_path) if model is None: head, tail = os.path.split(filename) report_path = os.path.join( head, '{}_structural_report.html'.format(model_filename)) api.validation_report( model_path, notifications, report_path) LOGGER.critical( "The model {} could not be loaded due to SBML errors " "reported in {}.".format(model_filename, report_path)) continue model.solver = solver model_and_model_ver_tuple.append((model, model_ver)) except (IOError, SBMLError): LOGGER.debug(exc_info=True) LOGGER.warning("An error occurred while loading the model '%s'. " "Skipping.", model_filename) # Abort the diff report unless at least two models can be loaded # successfully. if len(model_and_model_ver_tuple) < 2: LOGGER.critical( "Out of the %d provided models only %d could be loaded. Please, " "check if the models that could not be loaded are valid SBML. " "Aborting.", len(models), len(model_and_model_ver_tuple)) sys.exit(1) # Running pytest in individual processes to avoid interference partial_test_diff = partial(_test_diff, pytest_args=pytest_args, skip=skip, exclusive=exclusive, experimental=experimental) pool = Pool(min(len(models), cpu_count())) results = pool.map(partial_test_diff, model_and_model_ver_tuple) for model_path, result in zip(models, results): model_filename = os.path.basename(model_path) diff_results[model_filename] = result with open(filename, "w", encoding="utf-8") as file_handle: LOGGER.info("Writing diff report to '%s'.", filename) file_handle.write(api.diff_report(diff_results, config))
python
def diff(models, filename, pytest_args, exclusive, skip, solver, experimental, custom_tests, custom_config): """ Take a snapshot of all the supplied models and generate a diff report. MODELS: List of paths to two or more model files. """ if not any(a.startswith("--tb") for a in pytest_args): pytest_args = ["--tb", "no"] + pytest_args # Add further directories to search for tests. pytest_args.extend(custom_tests) config = ReportConfiguration.load() # Update the default test configuration with custom ones (if any). for custom in custom_config: config.merge(ReportConfiguration.load(custom)) # Build the diff report specific data structure diff_results = dict() model_and_model_ver_tuple = list() for model_path in models: try: model_filename = os.path.basename(model_path) diff_results.setdefault(model_filename, dict()) model, model_ver, notifications = api.validate_model(model_path) if model is None: head, tail = os.path.split(filename) report_path = os.path.join( head, '{}_structural_report.html'.format(model_filename)) api.validation_report( model_path, notifications, report_path) LOGGER.critical( "The model {} could not be loaded due to SBML errors " "reported in {}.".format(model_filename, report_path)) continue model.solver = solver model_and_model_ver_tuple.append((model, model_ver)) except (IOError, SBMLError): LOGGER.debug(exc_info=True) LOGGER.warning("An error occurred while loading the model '%s'. " "Skipping.", model_filename) # Abort the diff report unless at least two models can be loaded # successfully. if len(model_and_model_ver_tuple) < 2: LOGGER.critical( "Out of the %d provided models only %d could be loaded. Please, " "check if the models that could not be loaded are valid SBML. " "Aborting.", len(models), len(model_and_model_ver_tuple)) sys.exit(1) # Running pytest in individual processes to avoid interference partial_test_diff = partial(_test_diff, pytest_args=pytest_args, skip=skip, exclusive=exclusive, experimental=experimental) pool = Pool(min(len(models), cpu_count())) results = pool.map(partial_test_diff, model_and_model_ver_tuple) for model_path, result in zip(models, results): model_filename = os.path.basename(model_path) diff_results[model_filename] = result with open(filename, "w", encoding="utf-8") as file_handle: LOGGER.info("Writing diff report to '%s'.", filename) file_handle.write(api.diff_report(diff_results, config))
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/cli/reports.py#L226-L287
opencobra/memote
memote/suite/results/history_manager.py
HistoryManager.build_branch_structure
def build_branch_structure(self, model, skip): """Inspect and record the repo's branches and their history.""" self._history = dict() self._history["commits"] = commits = dict() self._history["branches"] = branches = dict() for branch in self._repo.refs: LOGGER.debug(branch.name) if branch.name in skip: continue branches[branch.name] = branch_history = list() latest = branch.commit history = [latest] + list(latest.iter_parents()) for commit in history: # Find model in committed files. if not is_modified(model, commit): LOGGER.info( "The model was not modified in commit '{}'. " "Skipping.".format(commit)) continue branch_history.append(commit.hexsha) if commit.hexsha not in commits: commits[commit.hexsha] = sub = dict() sub["timestamp"] = commit.authored_datetime.isoformat(" ") sub["author"] = commit.author.name sub["email"] = commit.author.email LOGGER.debug("%s", json.dumps(self._history, indent=2))
python
def build_branch_structure(self, model, skip): """Inspect and record the repo's branches and their history.""" self._history = dict() self._history["commits"] = commits = dict() self._history["branches"] = branches = dict() for branch in self._repo.refs: LOGGER.debug(branch.name) if branch.name in skip: continue branches[branch.name] = branch_history = list() latest = branch.commit history = [latest] + list(latest.iter_parents()) for commit in history: # Find model in committed files. if not is_modified(model, commit): LOGGER.info( "The model was not modified in commit '{}'. " "Skipping.".format(commit)) continue branch_history.append(commit.hexsha) if commit.hexsha not in commits: commits[commit.hexsha] = sub = dict() sub["timestamp"] = commit.authored_datetime.isoformat(" ") sub["author"] = commit.author.name sub["email"] = commit.author.email LOGGER.debug("%s", json.dumps(self._history, indent=2))
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/results/history_manager.py#L65-L90
opencobra/memote
memote/suite/results/history_manager.py
HistoryManager.load_history
def load_history(self, model, skip={"gh-pages"}): """ Load the entire results history into memory. Could be a bad idea in a far future. """ if self._history is None: self.build_branch_structure(model, skip) self._results = dict() all_commits = list(self._history["commits"]) for commit in all_commits: try: self._results[commit] = self.manager.load(commit) except (IOError, NoResultFound) as err: LOGGER.error("Could not load result '%s'.", commit) LOGGER.debug("%s", str(err))
python
def load_history(self, model, skip={"gh-pages"}): """ Load the entire results history into memory. Could be a bad idea in a far future. """ if self._history is None: self.build_branch_structure(model, skip) self._results = dict() all_commits = list(self._history["commits"]) for commit in all_commits: try: self._results[commit] = self.manager.load(commit) except (IOError, NoResultFound) as err: LOGGER.error("Could not load result '%s'.", commit) LOGGER.debug("%s", str(err))
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/results/history_manager.py#L100-L116
opencobra/memote
memote/suite/results/history_manager.py
HistoryManager.get_result
def get_result(self, commit, default=MemoteResult()): """Return an individual result from the history if it exists.""" assert self._results is not None, \ "Please call the method `load_history` first." return self._results.get(commit, default)
python
def get_result(self, commit, default=MemoteResult()): """Return an individual result from the history if it exists.""" assert self._results is not None, \ "Please call the method `load_history` first." return self._results.get(commit, default)
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/suite/results/history_manager.py#L118-L122
opencobra/memote
memote/support/matrix.py
absolute_extreme_coefficient_ratio
def absolute_extreme_coefficient_ratio(model): """ Return the maximum and minimum absolute, non-zero coefficients. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) abs_matrix = np.abs(s_matrix) return abs_matrix.max(), abs_matrix[abs_matrix > 0].min()
python
def absolute_extreme_coefficient_ratio(model): """ Return the maximum and minimum absolute, non-zero coefficients. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) abs_matrix = np.abs(s_matrix) return abs_matrix.max(), abs_matrix[abs_matrix > 0].min()
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Return the maximum and minimum absolute, non-zero coefficients. Parameters ---------- model : cobra.Model The metabolic model under investigation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/matrix.py#L29-L43
opencobra/memote
memote/support/matrix.py
number_independent_conservation_relations
def number_independent_conservation_relations(model): """ Return the number of conserved metabolite pools. This number is given by the left null space of the stoichiometric matrix. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) ln_matrix = con_helpers.nullspace(s_matrix.T) return ln_matrix.shape[1]
python
def number_independent_conservation_relations(model): """ Return the number of conserved metabolite pools. This number is given by the left null space of the stoichiometric matrix. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) ln_matrix = con_helpers.nullspace(s_matrix.T) return ln_matrix.shape[1]
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Return the number of conserved metabolite pools. This number is given by the left null space of the stoichiometric matrix. Parameters ---------- model : cobra.Model The metabolic model under investigation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/matrix.py#L46-L62
opencobra/memote
memote/support/matrix.py
matrix_rank
def matrix_rank(model): """ Return the rank of the model's stoichiometric matrix. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) return con_helpers.rank(s_matrix)
python
def matrix_rank(model): """ Return the rank of the model's stoichiometric matrix. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) return con_helpers.rank(s_matrix)
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Return the rank of the model's stoichiometric matrix. Parameters ---------- model : cobra.Model The metabolic model under investigation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/matrix.py#L65-L78
opencobra/memote
memote/support/matrix.py
degrees_of_freedom
def degrees_of_freedom(model): """ Return the degrees of freedom, i.e., number of "free variables". Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- This specifically refers to the dimensionality of the (right) null space of the stoichiometric matrix, as dim(Null(S)) corresponds directly to the number of free variables in the system [1]_. The formula used calculates this using the rank-nullity theorem [2]_. References ---------- .. [1] Fukuda, K. & Terlaky, T. Criss-cross methods: A fresh view on pivot algorithms. Mathematical Programming 79, 369-395 (1997). .. [2] Alama, J. The Rank+Nullity Theorem. Formalized Mathematics 15, (2007). """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) return s_matrix.shape[1] - matrix_rank(model)
python
def degrees_of_freedom(model): """ Return the degrees of freedom, i.e., number of "free variables". Parameters ---------- model : cobra.Model The metabolic model under investigation. Notes ----- This specifically refers to the dimensionality of the (right) null space of the stoichiometric matrix, as dim(Null(S)) corresponds directly to the number of free variables in the system [1]_. The formula used calculates this using the rank-nullity theorem [2]_. References ---------- .. [1] Fukuda, K. & Terlaky, T. Criss-cross methods: A fresh view on pivot algorithms. Mathematical Programming 79, 369-395 (1997). .. [2] Alama, J. The Rank+Nullity Theorem. Formalized Mathematics 15, (2007). """ s_matrix, _, _ = con_helpers.stoichiometry_matrix( model.metabolites, model.reactions ) return s_matrix.shape[1] - matrix_rank(model)
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/matrix.py#L81-L109
opencobra/memote
memote/experimental/config.py
ExperimentConfiguration.load
def load(self, model): """ Load all information from an experimental configuration file. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ self.load_medium(model) self.load_essentiality(model) self.load_growth(model) # self.load_experiment(config.config.get("growth"), model) return self
python
def load(self, model): """ Load all information from an experimental configuration file. Parameters ---------- model : cobra.Model The metabolic model under investigation. """ self.load_medium(model) self.load_essentiality(model) self.load_growth(model) # self.load_experiment(config.config.get("growth"), model) return self
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Load all information from an experimental configuration file. Parameters ---------- model : cobra.Model The metabolic model under investigation.
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/config.py#L67-L81
opencobra/memote
memote/experimental/config.py
ExperimentConfiguration.validate
def validate(self): """Validate the configuration file.""" validator = Draft4Validator(self.SCHEMA) if not validator.is_valid(self.config): for err in validator.iter_errors(self.config): LOGGER.error(str(err.message)) validator.validate(self.config)
python
def validate(self): """Validate the configuration file.""" validator = Draft4Validator(self.SCHEMA) if not validator.is_valid(self.config): for err in validator.iter_errors(self.config): LOGGER.error(str(err.message)) validator.validate(self.config)
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train
https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/config.py#L83-L89
opencobra/memote
memote/experimental/config.py
ExperimentConfiguration.load_medium
def load_medium(self, model): """Load and validate all media.""" media = self.config.get("medium") if media is None: return definitions = media.get("definitions") if definitions is None or len(definitions) == 0: return path = self.get_path(media, join("data", "experimental", "media")) for medium_id, medium in iteritems(definitions): if medium is None: medium = dict() filename = medium.get("filename") if filename is None: filename = join(path, "{}.csv".format(medium_id)) elif not isabs(filename): filename = join(path, filename) tmp = Medium(identifier=medium_id, obj=medium, filename=filename) tmp.load() tmp.validate(model) self.media[medium_id] = tmp
python
def load_medium(self, model): """Load and validate all media.""" media = self.config.get("medium") if media is None: return definitions = media.get("definitions") if definitions is None or len(definitions) == 0: return path = self.get_path(media, join("data", "experimental", "media")) for medium_id, medium in iteritems(definitions): if medium is None: medium = dict() filename = medium.get("filename") if filename is None: filename = join(path, "{}.csv".format(medium_id)) elif not isabs(filename): filename = join(path, filename) tmp = Medium(identifier=medium_id, obj=medium, filename=filename) tmp.load() tmp.validate(model) self.media[medium_id] = tmp
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/config.py#L91-L111
opencobra/memote
memote/experimental/config.py
ExperimentConfiguration.load_essentiality
def load_essentiality(self, model): """Load and validate all data files.""" data = self.config.get("essentiality") if data is None: return experiments = data.get("experiments") if experiments is None or len(experiments) == 0: return path = self.get_path(data, join("data", "experimental", "essentiality")) for exp_id, exp in iteritems(experiments): if exp is None: exp = dict() filename = exp.get("filename") if filename is None: filename = join(path, "{}.csv".format(exp_id)) elif not isabs(filename): filename = join(path, filename) experiment = EssentialityExperiment( identifier=exp_id, obj=exp, filename=filename) if experiment.medium is not None: assert experiment.medium in self.media, \ "Experiment '{}' has an undefined medium '{}'.".format( exp_id, experiment.medium) experiment.medium = self.media[experiment.medium] experiment.load() experiment.validate(model) self.essentiality[exp_id] = experiment
python
def load_essentiality(self, model): """Load and validate all data files.""" data = self.config.get("essentiality") if data is None: return experiments = data.get("experiments") if experiments is None or len(experiments) == 0: return path = self.get_path(data, join("data", "experimental", "essentiality")) for exp_id, exp in iteritems(experiments): if exp is None: exp = dict() filename = exp.get("filename") if filename is None: filename = join(path, "{}.csv".format(exp_id)) elif not isabs(filename): filename = join(path, filename) experiment = EssentialityExperiment( identifier=exp_id, obj=exp, filename=filename) if experiment.medium is not None: assert experiment.medium in self.media, \ "Experiment '{}' has an undefined medium '{}'.".format( exp_id, experiment.medium) experiment.medium = self.media[experiment.medium] experiment.load() experiment.validate(model) self.essentiality[exp_id] = experiment
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/config.py#L113-L140
opencobra/memote
memote/experimental/config.py
ExperimentConfiguration.load_growth
def load_growth(self, model): """Load and validate all data files.""" data = self.config.get("growth") if data is None: return experiments = data.get("experiments") if experiments is None or len(experiments) == 0: return path = self.get_path(data, join("data", "experimental", "growth")) for exp_id, exp in iteritems(experiments): if exp is None: exp = dict() filename = exp.get("filename") if filename is None: filename = join(path, "{}.csv".format(exp_id)) elif not isabs(filename): filename = join(path, filename) growth = GrowthExperiment( identifier=exp_id, obj=exp, filename=filename) if growth.medium is not None: assert growth.medium in self.media, \ "Growth-experiment '{}' has an undefined medium '{}'." \ "".format(exp_id, growth.medium) growth.medium = self.media[growth.medium] growth.load() growth.validate(model) self.growth[exp_id] = growth
python
def load_growth(self, model): """Load and validate all data files.""" data = self.config.get("growth") if data is None: return experiments = data.get("experiments") if experiments is None or len(experiments) == 0: return path = self.get_path(data, join("data", "experimental", "growth")) for exp_id, exp in iteritems(experiments): if exp is None: exp = dict() filename = exp.get("filename") if filename is None: filename = join(path, "{}.csv".format(exp_id)) elif not isabs(filename): filename = join(path, filename) growth = GrowthExperiment( identifier=exp_id, obj=exp, filename=filename) if growth.medium is not None: assert growth.medium in self.media, \ "Growth-experiment '{}' has an undefined medium '{}'." \ "".format(exp_id, growth.medium) growth.medium = self.media[growth.medium] growth.load() growth.validate(model) self.growth[exp_id] = growth
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/config.py#L142-L169
opencobra/memote
memote/experimental/config.py
ExperimentConfiguration.get_path
def get_path(self, obj, default): """Return a relative or absolute path to experimental data.""" path = obj.get("path") if path is None: path = join(self._base, default) if not isabs(path): path = join(self._base, path) return path
python
def get_path(self, obj, default): """Return a relative or absolute path to experimental data.""" path = obj.get("path") if path is None: path = join(self._base, default) if not isabs(path): path = join(self._base, path) return path
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/experimental/config.py#L171-L178
opencobra/memote
memote/support/annotation.py
find_components_without_annotation
def find_components_without_annotation(model, components): """ Find model components with empty annotation attributes. Parameters ---------- model : cobra.Model A cobrapy metabolic model. components : {"metabolites", "reactions", "genes"} A string denoting `cobra.Model` components. Returns ------- list The components without any annotation. """ return [elem for elem in getattr(model, components) if elem.annotation is None or len(elem.annotation) == 0]
python
def find_components_without_annotation(model, components): """ Find model components with empty annotation attributes. Parameters ---------- model : cobra.Model A cobrapy metabolic model. components : {"metabolites", "reactions", "genes"} A string denoting `cobra.Model` components. Returns ------- list The components without any annotation. """ return [elem for elem in getattr(model, components) if elem.annotation is None or len(elem.annotation) == 0]
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Find model components with empty annotation attributes. Parameters ---------- model : cobra.Model A cobrapy metabolic model. components : {"metabolites", "reactions", "genes"} A string denoting `cobra.Model` components. Returns ------- list The components without any annotation.
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https://github.com/opencobra/memote/blob/276630fcd4449fb7b914186edfd38c239e7052df/memote/support/annotation.py#L125-L143