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