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Add legend to linear fits
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1 changed files with 8 additions and 8 deletions
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@ -48,12 +48,10 @@ class Replicate():
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self.logger.info(msg.format(round(r_squared, 4)))
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self.logger.info(' slope: {}'.format(slope))
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if slope < 0:
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self.logger.warning(' Slope is negative. Will use absolute '
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'value for further calculations!')
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self.logger.info(' Slope is negative. Will use absolute '
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'value for further calculations!')
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self.logger.info(' intercept: {}'.format(slope))
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return {'slope': slope,
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'intercept': intercept,
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'r_value': r_value,
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@ -95,8 +93,8 @@ class Measurement():
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self.logger.info('Average slope: {} ± {}'.format(self.avg_slope,
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self.avg_slope_err))
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if self.avg_slope < 0:
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self.logger.warning('Avererage slope is negative. Will use '
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'absolute value for further calculations!')
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self.logger.info('Avererage slope is negative. Will use '
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'absolute value for further calculations!')
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self.logger.info('-----')
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def get_results(self):
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@ -150,8 +148,10 @@ class Experiment():
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if self.fit_to_replicates:
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for r in m.replicates:
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self.raw_kinetic_data['x'].append(m.concentration)
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self.raw_kinetic_data['y'].append(np.absolute(r.fitresult['slope']))
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self.raw_kinetic_data['yerr'].append(r.fitresult['std_err'])
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self.raw_kinetic_data['y'].append(
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np.absolute(r.fitresult['slope']))
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self.raw_kinetic_data['yerr'].append(
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r.fitresult['std_err'])
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else:
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# extract relevant data for kinetics calculation
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