corems.mass_spectrum.calc.MassSpectrumCalc
1__author__ = "Yuri E. Corilo" 2__date__ = "Jun 27, 2019" 3 4from numpy import power, multiply, sqrt, array, mean 5from corems.mass_spectrum.calc.NoiseCalc import NoiseThresholdCalc 6from corems.mass_spectrum.calc.PeakPicking import PeakPicking 7 8 9class MassSpecCalc(PeakPicking, NoiseThresholdCalc): 10 """Class for Mass Spectrum Calculations 11 12 Class including numerical calculations related to mass spectrum class 13 Inherited PeakPicking and NoiseThresholdCalc ensuring its methods are 14 available to the instantiated mass spectrum class object 15 16 Parameters 17 ------- 18 mass_spectrum : MassSpectrum 19 CoreMS mass spectrum object 20 21 Attributes 22 -------- 23 All Attributes are derivative from the MassSpecBase Class 24 25 Methods 26 -------- 27 * check_mspeaks(). 28 Check if the mspeaks attribute is populated 29 * sort_by_abundance(). 30 Sort the mspeaks by abundance 31 * percentile_assigned(report_error=False). 32 Calculate the percentage of assigned peaks 33 * resolving_power_calc(B, T). 34 Calculate the resolving power 35 * number_average_molecular_weight(profile=False). 36 Calculate the number average molecular weight 37 * weight_average_molecular_weight(profile=False). 38 Calculate the weight average molecular weight 39 """ 40 41 def percentage_assigned( 42 self, report_error: bool = False, mute_output: bool = False 43 ): 44 """Percentage of peaks which are assigned 45 46 Calculates the percentage and relative abundance of assigned peaks in the spectrum. 47 Includes protection against division by zero with explicit handling of edge cases. 48 49 Parameters 50 ----------- 51 report_error: bool, optional 52 Report the RMS error of the assigned peaks. Default is False. 53 mute_output: bool, optional 54 Override the verbose setting. Default is False. 55 If True, the function will silence results 56 57 Returns 58 ------- 59 tuple 60 If report_error is False: 61 (assigned_count, unassigned_count, total_percent, total_relative_abundance) 62 If report_error is True: 63 (assigned_count, unassigned_count, total_percent, total_relative_abundance, rms_error) 64 where rms_error is None if no assigned peaks exist 65 66 Notes 67 ----- 68 Edge cases are handled with explicit reporting: 69 - If no peaks detected: returns (0, 0, 0.0, 0.0[, None]) with message 70 - If no abundance data: returns (i, j, 0.0, 0.0[, None]) with message 71 - If no assigned peaks but peaks exist: returns with rms_error=None and explanatory message 72 """ 73 verbose = self.parameters.mass_spectrum.verbose_processing 74 assign_abun = 0 75 not_assign_abun = 0 76 i = 0 77 j = 0 78 if report_error: 79 error = [] 80 for mspeak in self.sort_by_abundance(): 81 if mspeak.is_assigned: 82 i += 1 83 assign_abun += mspeak.abundance 84 if report_error: 85 error.append(mspeak.best_molecular_formula_candidate.mz_error) 86 87 else: 88 j += 1 89 not_assign_abun += mspeak.abundance 90 91 # Protect against division by zero 92 total_peaks = i + j 93 total_abundance = assign_abun + not_assign_abun 94 95 # Handle edge cases 96 if total_peaks == 0: 97 if verbose and not mute_output: 98 print("No peaks detected in spectrum") 99 if report_error: 100 return i, j, 0.0, 0.0, None 101 else: 102 return i, j, 0.0, 0.0 103 104 if total_abundance == 0: 105 if verbose and not mute_output: 106 print("No abundance data detected in spectrum") 107 if report_error: 108 return i, j, 0.0, 0.0, None 109 else: 110 return i, j, 0.0, 0.0 111 112 total_percent = (i / total_peaks * 100) if total_peaks > 0 else 0.0 113 total_relative_abundance = ( 114 (assign_abun / total_abundance * 100) if total_abundance > 0 else 0.0 115 ) 116 117 if report_error: 118 rms_error = None 119 if i > 0: 120 rms_error = sqrt(mean(array(error) ** 2)) 121 if verbose and not mute_output: 122 if i == 0: 123 print( 124 "No assigned peaks detected - cannot calculate RMS error. %i unassigned peaks, total = %.2f %%, relative abundance = %.2f %%" 125 % (j, total_percent, total_relative_abundance) 126 ) 127 else: 128 print( 129 "%i assigned peaks and %i unassigned peaks, total = %.2f %%, relative abundance = %.2f %%, RMS error (best candidate) (ppm) = %.3f" 130 % (i, j, total_percent, total_relative_abundance, rms_error) 131 ) 132 return i, j, total_percent, total_relative_abundance, rms_error 133 134 else: 135 if verbose and not mute_output: 136 print( 137 "%i assigned peaks and %i unassigned peaks , total = %.2f %%, relative abundance = %.2f %%" 138 % ( 139 i, 140 j, 141 total_percent, 142 total_relative_abundance, 143 ) 144 ) 145 return i, j, total_percent, total_relative_abundance 146 147 def percentile_assigned( 148 self, report_error: bool = False, mute_output: bool = False 149 ): 150 """Deprecated: Use percentage_assigned() instead. 151 152 This method is deprecated and will be removed in a future version. 153 The function returns a percentage, not a percentile, so the name has been corrected. 154 155 Parameters 156 ----------- 157 report_error: bool, optional 158 Report the error of the assigned peaks. Default is False. 159 mute_output: bool, optional 160 Override the verbose setting. Default is False. 161 162 Returns 163 ------- 164 tuple 165 Refer to percentage_assigned() for return value details. 166 """ 167 import warnings 168 169 warnings.warn( 170 "percentile_assigned() is deprecated and will be removed in a future version. " 171 "Use percentage_assigned() instead, as the function returns a percentage, not a percentile.", 172 DeprecationWarning, 173 stacklevel=2, 174 ) 175 return self.percentage_assigned( 176 report_error=report_error, mute_output=mute_output 177 ) 178 179 def resolving_power_calc(self, B: float, T: float): 180 """Calculate the theoretical resolving power 181 182 Calls on the MSPeak object function to calculate the resolving power of a peak, this calcs for all peaks in a spectrum. 183 184 Parameters 185 ----------- 186 T : float 187 transient time 188 B : float 189 Magnetic Filed Strength (Tesla) 190 191 References 192 ---------- 193 1. Marshall et al. (Mass Spectrom Rev. 1998 Jan-Feb;17(1):1-35.) 194 DOI: 10.1002/(SICI)1098-2787(1998)17:1<1::AID-MAS1>3.0.CO;2-K 195 196 """ 197 198 self.check_mspeaks() 199 return array([mspeak.resolving_power_calc(B, T) for mspeak in self.mspeaks]) 200 201 def _f_to_mz(self): 202 """Ledford equation for converting frequency(Hz) to m/z 203 204 Returns 205 ---------- 206 mz_domain : numpy array 207 m/z domain after conversion from frequency 208 """ 209 Aterm, Bterm, Cterm = self.Aterm, self.Bterm, self.Cterm 210 # Check if the Bterm of Ledford equation scales with the ICR trap voltage or not then Bterm = Bterm*trap_voltage 211 212 if Cterm == 0: 213 if Bterm == 0: 214 # uncalibrated data 215 mz_domain = Aterm / self.freq_exp_profile 216 217 else: 218 mz_domain = (Aterm / (self.freq_exp_profile)) + ( 219 Bterm / power((self.freq_exp_profile), 2) 220 ) 221 222 # @will I need you insight here, not sure what is the inverted ledford equation that Bruker refers to 223 else: 224 mz_domain = ( 225 (Aterm / self.freq_exp_profile) 226 + (Bterm / power(self.freq_exp_profile, 2)) 227 + Cterm 228 ) 229 230 return mz_domain 231 232 def _f_to_mz_bruker(self): 233 """Frequency to m/z conversion (Bruker) 234 Bruker equations for converting frequency (Hz) to m/z, 235 nOmega acquisition is not yet implemented here. 236 However, nOmega should work for commerical Bruker 2xR systems as A Term is automatically defined for 2X or 1X by the instrument 237 238 239 Returns 240 ---------- 241 numpy.array(float) 242 m/z domain after conversion from frequency 243 """ 244 Aterm, Bterm, Cterm = self.Aterm, self.Bterm, self.Cterm 245 # Check if the Bterm of Ledford equation scales with the ICR trap voltage or not then Bterm = Bterm*trap_voltage 246 if Cterm == 0: 247 if Bterm == 0: 248 # uncalibrated data 249 return Aterm / self.freq_exp_profile 250 251 else: 252 # calc2 253 return Aterm / (self.freq_exp_profile + Bterm) 254 255 # @will I need you insight here, not sure what is the inverted ledford equation that Bruker refers to 256 else: 257 diff = Aterm * Aterm 258 259 # this sign(diff + 4) changes on older aquistion software 260 diff = diff + 4 * Cterm * (self.freq_exp_profile - Bterm) 261 diff = sqrt(diff) 262 diff = -Aterm + diff 263 # calc3 264 return (2 * Cterm) / diff 265 return diff / 2 * (self.freq_exp_profile - Bterm) 266 267 def number_average_molecular_weight(self, profile: bool = False): 268 """Average molecular weight calculation 269 270 Parameters 271 ---------- 272 profile : bool, optional 273 is data profile or centroid mode. The default is False (e.g. Centroid data) 274 275 Returns 276 ------- 277 float 278 The average molecular weight. 279 280 """ 281 # mode is profile or centroid data 282 if profile: 283 a = multiply(self.mz_exp_profile, self.abundance_profile) 284 b = self.abundance_profile 285 return a.sum() / b.sum() 286 287 else: 288 return sum(self.mz_exp * self.abundance) / sum(self.abundance) 289 290 def weight_average_molecular_weight(self, profile: bool = False): 291 """ 292 Weighted Average molecular weight calculation 293 294 295 Returns 296 ------- 297 float 298 The weight average molecular weight. 299 300 """ 301 302 # implement from MassSpectralPeaks objs 303 304 if profile: 305 a = multiply(power(self.mz_exp_profile, 2), self.abundance_profile) 306 b = self.mz_exp_profile * self.abundance_profile 307 return a.sum() / b.sum() 308 309 else: 310 return sum(power(self.mz_exp, 2) * self.abundance) / sum( 311 self.mz_exp * self.abundance 312 )
class
MassSpecCalc(corems.mass_spectrum.calc.PeakPicking.PeakPicking, corems.mass_spectrum.calc.NoiseCalc.NoiseThresholdCalc):
10class MassSpecCalc(PeakPicking, NoiseThresholdCalc): 11 """Class for Mass Spectrum Calculations 12 13 Class including numerical calculations related to mass spectrum class 14 Inherited PeakPicking and NoiseThresholdCalc ensuring its methods are 15 available to the instantiated mass spectrum class object 16 17 Parameters 18 ------- 19 mass_spectrum : MassSpectrum 20 CoreMS mass spectrum object 21 22 Attributes 23 -------- 24 All Attributes are derivative from the MassSpecBase Class 25 26 Methods 27 -------- 28 * check_mspeaks(). 29 Check if the mspeaks attribute is populated 30 * sort_by_abundance(). 31 Sort the mspeaks by abundance 32 * percentile_assigned(report_error=False). 33 Calculate the percentage of assigned peaks 34 * resolving_power_calc(B, T). 35 Calculate the resolving power 36 * number_average_molecular_weight(profile=False). 37 Calculate the number average molecular weight 38 * weight_average_molecular_weight(profile=False). 39 Calculate the weight average molecular weight 40 """ 41 42 def percentage_assigned( 43 self, report_error: bool = False, mute_output: bool = False 44 ): 45 """Percentage of peaks which are assigned 46 47 Calculates the percentage and relative abundance of assigned peaks in the spectrum. 48 Includes protection against division by zero with explicit handling of edge cases. 49 50 Parameters 51 ----------- 52 report_error: bool, optional 53 Report the RMS error of the assigned peaks. Default is False. 54 mute_output: bool, optional 55 Override the verbose setting. Default is False. 56 If True, the function will silence results 57 58 Returns 59 ------- 60 tuple 61 If report_error is False: 62 (assigned_count, unassigned_count, total_percent, total_relative_abundance) 63 If report_error is True: 64 (assigned_count, unassigned_count, total_percent, total_relative_abundance, rms_error) 65 where rms_error is None if no assigned peaks exist 66 67 Notes 68 ----- 69 Edge cases are handled with explicit reporting: 70 - If no peaks detected: returns (0, 0, 0.0, 0.0[, None]) with message 71 - If no abundance data: returns (i, j, 0.0, 0.0[, None]) with message 72 - If no assigned peaks but peaks exist: returns with rms_error=None and explanatory message 73 """ 74 verbose = self.parameters.mass_spectrum.verbose_processing 75 assign_abun = 0 76 not_assign_abun = 0 77 i = 0 78 j = 0 79 if report_error: 80 error = [] 81 for mspeak in self.sort_by_abundance(): 82 if mspeak.is_assigned: 83 i += 1 84 assign_abun += mspeak.abundance 85 if report_error: 86 error.append(mspeak.best_molecular_formula_candidate.mz_error) 87 88 else: 89 j += 1 90 not_assign_abun += mspeak.abundance 91 92 # Protect against division by zero 93 total_peaks = i + j 94 total_abundance = assign_abun + not_assign_abun 95 96 # Handle edge cases 97 if total_peaks == 0: 98 if verbose and not mute_output: 99 print("No peaks detected in spectrum") 100 if report_error: 101 return i, j, 0.0, 0.0, None 102 else: 103 return i, j, 0.0, 0.0 104 105 if total_abundance == 0: 106 if verbose and not mute_output: 107 print("No abundance data detected in spectrum") 108 if report_error: 109 return i, j, 0.0, 0.0, None 110 else: 111 return i, j, 0.0, 0.0 112 113 total_percent = (i / total_peaks * 100) if total_peaks > 0 else 0.0 114 total_relative_abundance = ( 115 (assign_abun / total_abundance * 100) if total_abundance > 0 else 0.0 116 ) 117 118 if report_error: 119 rms_error = None 120 if i > 0: 121 rms_error = sqrt(mean(array(error) ** 2)) 122 if verbose and not mute_output: 123 if i == 0: 124 print( 125 "No assigned peaks detected - cannot calculate RMS error. %i unassigned peaks, total = %.2f %%, relative abundance = %.2f %%" 126 % (j, total_percent, total_relative_abundance) 127 ) 128 else: 129 print( 130 "%i assigned peaks and %i unassigned peaks, total = %.2f %%, relative abundance = %.2f %%, RMS error (best candidate) (ppm) = %.3f" 131 % (i, j, total_percent, total_relative_abundance, rms_error) 132 ) 133 return i, j, total_percent, total_relative_abundance, rms_error 134 135 else: 136 if verbose and not mute_output: 137 print( 138 "%i assigned peaks and %i unassigned peaks , total = %.2f %%, relative abundance = %.2f %%" 139 % ( 140 i, 141 j, 142 total_percent, 143 total_relative_abundance, 144 ) 145 ) 146 return i, j, total_percent, total_relative_abundance 147 148 def percentile_assigned( 149 self, report_error: bool = False, mute_output: bool = False 150 ): 151 """Deprecated: Use percentage_assigned() instead. 152 153 This method is deprecated and will be removed in a future version. 154 The function returns a percentage, not a percentile, so the name has been corrected. 155 156 Parameters 157 ----------- 158 report_error: bool, optional 159 Report the error of the assigned peaks. Default is False. 160 mute_output: bool, optional 161 Override the verbose setting. Default is False. 162 163 Returns 164 ------- 165 tuple 166 Refer to percentage_assigned() for return value details. 167 """ 168 import warnings 169 170 warnings.warn( 171 "percentile_assigned() is deprecated and will be removed in a future version. " 172 "Use percentage_assigned() instead, as the function returns a percentage, not a percentile.", 173 DeprecationWarning, 174 stacklevel=2, 175 ) 176 return self.percentage_assigned( 177 report_error=report_error, mute_output=mute_output 178 ) 179 180 def resolving_power_calc(self, B: float, T: float): 181 """Calculate the theoretical resolving power 182 183 Calls on the MSPeak object function to calculate the resolving power of a peak, this calcs for all peaks in a spectrum. 184 185 Parameters 186 ----------- 187 T : float 188 transient time 189 B : float 190 Magnetic Filed Strength (Tesla) 191 192 References 193 ---------- 194 1. Marshall et al. (Mass Spectrom Rev. 1998 Jan-Feb;17(1):1-35.) 195 DOI: 10.1002/(SICI)1098-2787(1998)17:1<1::AID-MAS1>3.0.CO;2-K 196 197 """ 198 199 self.check_mspeaks() 200 return array([mspeak.resolving_power_calc(B, T) for mspeak in self.mspeaks]) 201 202 def _f_to_mz(self): 203 """Ledford equation for converting frequency(Hz) to m/z 204 205 Returns 206 ---------- 207 mz_domain : numpy array 208 m/z domain after conversion from frequency 209 """ 210 Aterm, Bterm, Cterm = self.Aterm, self.Bterm, self.Cterm 211 # Check if the Bterm of Ledford equation scales with the ICR trap voltage or not then Bterm = Bterm*trap_voltage 212 213 if Cterm == 0: 214 if Bterm == 0: 215 # uncalibrated data 216 mz_domain = Aterm / self.freq_exp_profile 217 218 else: 219 mz_domain = (Aterm / (self.freq_exp_profile)) + ( 220 Bterm / power((self.freq_exp_profile), 2) 221 ) 222 223 # @will I need you insight here, not sure what is the inverted ledford equation that Bruker refers to 224 else: 225 mz_domain = ( 226 (Aterm / self.freq_exp_profile) 227 + (Bterm / power(self.freq_exp_profile, 2)) 228 + Cterm 229 ) 230 231 return mz_domain 232 233 def _f_to_mz_bruker(self): 234 """Frequency to m/z conversion (Bruker) 235 Bruker equations for converting frequency (Hz) to m/z, 236 nOmega acquisition is not yet implemented here. 237 However, nOmega should work for commerical Bruker 2xR systems as A Term is automatically defined for 2X or 1X by the instrument 238 239 240 Returns 241 ---------- 242 numpy.array(float) 243 m/z domain after conversion from frequency 244 """ 245 Aterm, Bterm, Cterm = self.Aterm, self.Bterm, self.Cterm 246 # Check if the Bterm of Ledford equation scales with the ICR trap voltage or not then Bterm = Bterm*trap_voltage 247 if Cterm == 0: 248 if Bterm == 0: 249 # uncalibrated data 250 return Aterm / self.freq_exp_profile 251 252 else: 253 # calc2 254 return Aterm / (self.freq_exp_profile + Bterm) 255 256 # @will I need you insight here, not sure what is the inverted ledford equation that Bruker refers to 257 else: 258 diff = Aterm * Aterm 259 260 # this sign(diff + 4) changes on older aquistion software 261 diff = diff + 4 * Cterm * (self.freq_exp_profile - Bterm) 262 diff = sqrt(diff) 263 diff = -Aterm + diff 264 # calc3 265 return (2 * Cterm) / diff 266 return diff / 2 * (self.freq_exp_profile - Bterm) 267 268 def number_average_molecular_weight(self, profile: bool = False): 269 """Average molecular weight calculation 270 271 Parameters 272 ---------- 273 profile : bool, optional 274 is data profile or centroid mode. The default is False (e.g. Centroid data) 275 276 Returns 277 ------- 278 float 279 The average molecular weight. 280 281 """ 282 # mode is profile or centroid data 283 if profile: 284 a = multiply(self.mz_exp_profile, self.abundance_profile) 285 b = self.abundance_profile 286 return a.sum() / b.sum() 287 288 else: 289 return sum(self.mz_exp * self.abundance) / sum(self.abundance) 290 291 def weight_average_molecular_weight(self, profile: bool = False): 292 """ 293 Weighted Average molecular weight calculation 294 295 296 Returns 297 ------- 298 float 299 The weight average molecular weight. 300 301 """ 302 303 # implement from MassSpectralPeaks objs 304 305 if profile: 306 a = multiply(power(self.mz_exp_profile, 2), self.abundance_profile) 307 b = self.mz_exp_profile * self.abundance_profile 308 return a.sum() / b.sum() 309 310 else: 311 return sum(power(self.mz_exp, 2) * self.abundance) / sum( 312 self.mz_exp * self.abundance 313 )
Class for Mass Spectrum Calculations
Class including numerical calculations related to mass spectrum class Inherited PeakPicking and NoiseThresholdCalc ensuring its methods are available to the instantiated mass spectrum class object
Parameters
- mass_spectrum (MassSpectrum): CoreMS mass spectrum object
Attributes
- All Attributes are derivative from the MassSpecBase Class
Methods
- check_mspeaks(). Check if the mspeaks attribute is populated
- sort_by_abundance(). Sort the mspeaks by abundance
- percentile_assigned(report_error=False). Calculate the percentage of assigned peaks
- resolving_power_calc(B, T). Calculate the resolving power
- number_average_molecular_weight(profile=False). Calculate the number average molecular weight
- weight_average_molecular_weight(profile=False). Calculate the weight average molecular weight
def
percentage_assigned(self, report_error: bool = False, mute_output: bool = False):
42 def percentage_assigned( 43 self, report_error: bool = False, mute_output: bool = False 44 ): 45 """Percentage of peaks which are assigned 46 47 Calculates the percentage and relative abundance of assigned peaks in the spectrum. 48 Includes protection against division by zero with explicit handling of edge cases. 49 50 Parameters 51 ----------- 52 report_error: bool, optional 53 Report the RMS error of the assigned peaks. Default is False. 54 mute_output: bool, optional 55 Override the verbose setting. Default is False. 56 If True, the function will silence results 57 58 Returns 59 ------- 60 tuple 61 If report_error is False: 62 (assigned_count, unassigned_count, total_percent, total_relative_abundance) 63 If report_error is True: 64 (assigned_count, unassigned_count, total_percent, total_relative_abundance, rms_error) 65 where rms_error is None if no assigned peaks exist 66 67 Notes 68 ----- 69 Edge cases are handled with explicit reporting: 70 - If no peaks detected: returns (0, 0, 0.0, 0.0[, None]) with message 71 - If no abundance data: returns (i, j, 0.0, 0.0[, None]) with message 72 - If no assigned peaks but peaks exist: returns with rms_error=None and explanatory message 73 """ 74 verbose = self.parameters.mass_spectrum.verbose_processing 75 assign_abun = 0 76 not_assign_abun = 0 77 i = 0 78 j = 0 79 if report_error: 80 error = [] 81 for mspeak in self.sort_by_abundance(): 82 if mspeak.is_assigned: 83 i += 1 84 assign_abun += mspeak.abundance 85 if report_error: 86 error.append(mspeak.best_molecular_formula_candidate.mz_error) 87 88 else: 89 j += 1 90 not_assign_abun += mspeak.abundance 91 92 # Protect against division by zero 93 total_peaks = i + j 94 total_abundance = assign_abun + not_assign_abun 95 96 # Handle edge cases 97 if total_peaks == 0: 98 if verbose and not mute_output: 99 print("No peaks detected in spectrum") 100 if report_error: 101 return i, j, 0.0, 0.0, None 102 else: 103 return i, j, 0.0, 0.0 104 105 if total_abundance == 0: 106 if verbose and not mute_output: 107 print("No abundance data detected in spectrum") 108 if report_error: 109 return i, j, 0.0, 0.0, None 110 else: 111 return i, j, 0.0, 0.0 112 113 total_percent = (i / total_peaks * 100) if total_peaks > 0 else 0.0 114 total_relative_abundance = ( 115 (assign_abun / total_abundance * 100) if total_abundance > 0 else 0.0 116 ) 117 118 if report_error: 119 rms_error = None 120 if i > 0: 121 rms_error = sqrt(mean(array(error) ** 2)) 122 if verbose and not mute_output: 123 if i == 0: 124 print( 125 "No assigned peaks detected - cannot calculate RMS error. %i unassigned peaks, total = %.2f %%, relative abundance = %.2f %%" 126 % (j, total_percent, total_relative_abundance) 127 ) 128 else: 129 print( 130 "%i assigned peaks and %i unassigned peaks, total = %.2f %%, relative abundance = %.2f %%, RMS error (best candidate) (ppm) = %.3f" 131 % (i, j, total_percent, total_relative_abundance, rms_error) 132 ) 133 return i, j, total_percent, total_relative_abundance, rms_error 134 135 else: 136 if verbose and not mute_output: 137 print( 138 "%i assigned peaks and %i unassigned peaks , total = %.2f %%, relative abundance = %.2f %%" 139 % ( 140 i, 141 j, 142 total_percent, 143 total_relative_abundance, 144 ) 145 ) 146 return i, j, total_percent, total_relative_abundance
Percentage of peaks which are assigned
Calculates the percentage and relative abundance of assigned peaks in the spectrum. Includes protection against division by zero with explicit handling of edge cases.
Parameters
- report_error (bool, optional): Report the RMS error of the assigned peaks. Default is False.
- mute_output (bool, optional): Override the verbose setting. Default is False. If True, the function will silence results
Returns
- tuple: If report_error is False: (assigned_count, unassigned_count, total_percent, total_relative_abundance) If report_error is True: (assigned_count, unassigned_count, total_percent, total_relative_abundance, rms_error) where rms_error is None if no assigned peaks exist
Notes
Edge cases are handled with explicit reporting:
- If no peaks detected: returns (0, 0, 0.0, 0.0[, None]) with message
- If no abundance data: returns (i, j, 0.0, 0.0[, None]) with message
- If no assigned peaks but peaks exist: returns with rms_error=None and explanatory message
def
percentile_assigned(self, report_error: bool = False, mute_output: bool = False):
148 def percentile_assigned( 149 self, report_error: bool = False, mute_output: bool = False 150 ): 151 """Deprecated: Use percentage_assigned() instead. 152 153 This method is deprecated and will be removed in a future version. 154 The function returns a percentage, not a percentile, so the name has been corrected. 155 156 Parameters 157 ----------- 158 report_error: bool, optional 159 Report the error of the assigned peaks. Default is False. 160 mute_output: bool, optional 161 Override the verbose setting. Default is False. 162 163 Returns 164 ------- 165 tuple 166 Refer to percentage_assigned() for return value details. 167 """ 168 import warnings 169 170 warnings.warn( 171 "percentile_assigned() is deprecated and will be removed in a future version. " 172 "Use percentage_assigned() instead, as the function returns a percentage, not a percentile.", 173 DeprecationWarning, 174 stacklevel=2, 175 ) 176 return self.percentage_assigned( 177 report_error=report_error, mute_output=mute_output 178 )
Deprecated: Use percentage_assigned() instead.
This method is deprecated and will be removed in a future version. The function returns a percentage, not a percentile, so the name has been corrected.
Parameters
- report_error (bool, optional): Report the error of the assigned peaks. Default is False.
- mute_output (bool, optional): Override the verbose setting. Default is False.
Returns
- tuple: Refer to percentage_assigned() for return value details.
def
resolving_power_calc(self, B: float, T: float):
180 def resolving_power_calc(self, B: float, T: float): 181 """Calculate the theoretical resolving power 182 183 Calls on the MSPeak object function to calculate the resolving power of a peak, this calcs for all peaks in a spectrum. 184 185 Parameters 186 ----------- 187 T : float 188 transient time 189 B : float 190 Magnetic Filed Strength (Tesla) 191 192 References 193 ---------- 194 1. Marshall et al. (Mass Spectrom Rev. 1998 Jan-Feb;17(1):1-35.) 195 DOI: 10.1002/(SICI)1098-2787(1998)17:1<1::AID-MAS1>3.0.CO;2-K 196 197 """ 198 199 self.check_mspeaks() 200 return array([mspeak.resolving_power_calc(B, T) for mspeak in self.mspeaks])
Calculate the theoretical resolving power
Calls on the MSPeak object function to calculate the resolving power of a peak, this calcs for all peaks in a spectrum.
Parameters
- T (float): transient time
- B (float): Magnetic Filed Strength (Tesla)
References
- Marshall et al. (Mass Spectrom Rev. 1998 Jan-Feb;17(1):1-35.) DOI: 10.1002/(SICI)1098-2787(1998)17:1<1::AID-MAS1>3.0.CO;2-K
def
number_average_molecular_weight(self, profile: bool = False):
268 def number_average_molecular_weight(self, profile: bool = False): 269 """Average molecular weight calculation 270 271 Parameters 272 ---------- 273 profile : bool, optional 274 is data profile or centroid mode. The default is False (e.g. Centroid data) 275 276 Returns 277 ------- 278 float 279 The average molecular weight. 280 281 """ 282 # mode is profile or centroid data 283 if profile: 284 a = multiply(self.mz_exp_profile, self.abundance_profile) 285 b = self.abundance_profile 286 return a.sum() / b.sum() 287 288 else: 289 return sum(self.mz_exp * self.abundance) / sum(self.abundance)
Average molecular weight calculation
Parameters
- profile (bool, optional): is data profile or centroid mode. The default is False (e.g. Centroid data)
Returns
- float: The average molecular weight.
def
weight_average_molecular_weight(self, profile: bool = False):
291 def weight_average_molecular_weight(self, profile: bool = False): 292 """ 293 Weighted Average molecular weight calculation 294 295 296 Returns 297 ------- 298 float 299 The weight average molecular weight. 300 301 """ 302 303 # implement from MassSpectralPeaks objs 304 305 if profile: 306 a = multiply(power(self.mz_exp_profile, 2), self.abundance_profile) 307 b = self.mz_exp_profile * self.abundance_profile 308 return a.sum() / b.sum() 309 310 else: 311 return sum(power(self.mz_exp, 2) * self.abundance) / sum( 312 self.mz_exp * self.abundance 313 )
Weighted Average molecular weight calculation
Returns
- float: The weight average molecular weight.
Inherited Members
- corems.mass_spectrum.calc.PeakPicking.PeakPicking
- prepare_peak_picking_data
- cut_mz_domain_peak_picking
- legacy_cut_mz_domain_peak_picking
- extrapolate_axis
- extrapolate_axes_for_pp
- do_peak_picking
- find_minima
- linear_fit_calc
- calculate_resolving_power
- cal_minima
- calc_centroid
- get_threshold
- algebraic_quadratic
- find_apex_fit_quadratic
- check_prominence
- use_the_max
- calc_centroid_legacy