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Merge pull request #2 from php1ic/fwf_read
Utilise read_fwf() from pandas
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‎README.md‎

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@@ -14,7 +14,12 @@ All data should, however, be accurate.
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## Mass tables
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The data files released by the papers linked below are used to create the mass tables read by this code
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The data files released by the papers linked below are used to create the mass tables read by this code.
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There was no AME data published in 1997, but the 1995 AME matches the 1997 NUBASE according to section 4, "The tables" on P31 of [these proceedings](https://www.google.co.uk/books/edition/Atomic_Physics_at_Accelerators_Mass_Spec/3AbsCAAAQBAJ?hl=en).
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As a result the 1997 NUBASE data is referred to as being from 1995 for simplicity when merging data.
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- [AME1983](https://doi.org/10.1016/0375-9474(85)90283-0)
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- [AME1993](https://doi.org/10.1016/0375-9474(93)90024-R)
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- [AME1995](https://doi.org/10.1016/0375-9474(95)00445-9) + [NUBASE1997](https://doi.org/10.1016/S0375-9474(97)00482-X)
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- [AME2003](https://doi.org/10.1016/j.nuclphysa.2003.11.002) + [NUBASE2003](https://doi.org/10.1016/j.nuclphysa.2003.11.001)
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- [AME2012](https://doi.org/10.1088/1674-1137/36/12/002) + [NUBASE2012](https://doi.org/10.1088/1674-1137/36/12/001)
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- [AME2016](https://doi.org/10.1088/1674-1137/41/3/030002) + [NUBASE2016](https://doi.org/10.1088/1674-1137/41/3/030001)
@@ -30,3 +35,102 @@ The package is available on the Python Package Index so can be installed via pip
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pip install nuclearmasses
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```
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Or you can clone the latest version from github
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```bash
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git clone https://github.com/php1ic/nuclearmasses
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```
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## Usage
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Once installed or cloned, the data is available as a single dataframe indexed on the mass table year
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```python
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>>> from nuclearmasses.mass_table import MassTable
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>>> df = MassTable().full_data
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```
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You can then interrogate, or extract, what ever information you want.
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For example, how has the mass excess and it's accuracy changed overtime for 190Re according to the AME
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```python
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>>> df[(df['A'] == 190) & (df['Symbol'] == 'Re')][['AMEMassExcess', 'AMEMassExcessError']]
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AMEMassExcess AMEMassExcessError
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TableYear
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1983 -35536.605 200.029
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1993 -35557.789 145.549
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1995 -35568.032 212.151
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2003 -35566.326 149.248
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2012 -35634.992 70.542
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2016 -35635.830 70.852
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2020 -35583.015 4.870
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```
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Or how do the mass excess of gold vary across the isotropic chain according to NUBASE in the most recent table for both experimentally measured and theoretical values
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```python
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>>> df.query("TableYear == 2020 and Symbol == 'Au'")[['A', 'NUBASEMassExcess', 'NUBASEMassExcessError', 'Experimental']]
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A NUBASEMassExcess NUBASEMassExcessError Experimental
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TableYear
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2020 168 2530.0 400.0 False
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2020 169 -1790.0 300.0 False
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2020 170 -3700.0 200.0 False
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2020 171 -7562.0 21.0 True
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2020 172 -9320.0 60.0 True
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2020 173 -12832.0 23.0 True
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2020 174 -14060.0 100.0 False
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2020 175 -17400.0 40.0 True
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2020 176 -18520.0 30.0 True
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2020 177 -21546.0 10.0 True
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2020 178 -22303.0 10.0 True
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2020 179 -24989.0 12.0 True
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2020 180 -25626.0 5.0 True
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2020 181 -27871.0 20.0 True
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2020 182 -28304.0 19.0 True
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2020 183 -30191.0 9.0 True
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2020 184 -30319.0 22.0 True
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2020 185 -31858.1 2.6 True
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2020 186 -31715.0 21.0 True
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2020 187 -33029.0 22.0 True
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2020 188 -32371.3 2.7 True
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2020 189 -33582.0 20.0 True
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2020 190 -32834.0 3.0 True
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2020 191 -33798.0 5.0 True
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2020 192 -32772.0 16.0 True
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2020 193 -33405.0 9.0 True
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2020 194 -32211.9 2.1 True
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2020 195 -32567.1 1.1 True
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2020 196 -31138.7 3.0 True
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2020 197 -31139.8 0.5 True
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2020 198 -29580.8 0.5 True
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2020 199 -29093.8 0.5 True
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2020 200 -27240.0 27.0 True
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2020 201 -26401.0 3.0 True
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2020 202 -24353.0 23.0 True
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2020 203 -23143.0 3.0 True
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2020 204 -20390.0 200.0 False
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2020 205 -18570.0 200.0 False
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2020 206 -14190.0 300.0 False
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2020 207 -10640.0 300.0 False
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2020 208 -5910.0 300.0 False
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2020 209 -2230.0 400.0 False
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2020 210 2680.0 400.0 False
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```
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## Contributing
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If you have ideas for additional functionality or find bugs please create an [issue](https://github.com/php1ic/nuclearmasses/issues) or better yet a [pull request](https://github.com/php1ic/nuclearmasses/pulls).
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## Known issues
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- The half life from the NUBASE data is stored as the individual elements, a column with the value in seconds would be useful
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```python
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>>> df[(df['A'] == 14) & (df['Symbol'] == 'C')][['HalfLifeValue', 'HalfLifeUnit', 'HalfLifeError']]
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HalfLifeValue HalfLifeUnit HalfLifeError
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TableYear
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1983 NaN <NA> NaN
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1993 NaN <NA> NaN
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1995 5.73 ky 0.04
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2003 5.70 ky 0.03
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2012 5.70 ky 0.03
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2016 5.70 ky 0.03
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2020 5.70 ky 0.03
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```
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- The decay mode field from the NUBASE data is stored 'as-is' from the file.
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It looks like it can be split on the ';' character for isotopes where there is more than one mode.
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A dictionary of {decay mode: fraction} may be the best way to store all of this information.
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- Information from anything other than the ground state of an isotope is ignored when parsing the NUABSE file.
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The selection of what is and what is not included appears random to me which is why I simply ignored for the moment.

‎nuclearmasses/ame_mass_file.py‎

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"""Storage for variable line positions."""
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from nuclearmasses.parse import Parse
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from nuclearmasses.element_converter import ElementConverter
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class AMEMassFile(Parse):
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class AMEMassFile(ElementConverter):
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"""Easy access to where the variables are in the AME mass file."""
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def __init__(self, year: int):
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"""Setup up the values."""
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super().__init__()
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if year < 2020:
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self.HEADER = 39
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self.FOOTER = None
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self.START_A = 16
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self.END_A = 19
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self.START_Z = 11
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self.END_Z = 14
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self.START_ME = 29
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self.END_ME = 41
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self.START_DME = 42
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self.END_DME = 53
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self.START_BE_PER_A = 54
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self.END_BE_PER_A = 64
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self.START_DBE_PER_A = 65
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self.END_DBE_PER_A = 72
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self.START_BETA_DECAY_ENERGY = 76
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self.END_BETA_DECAY_ENERGY = 86
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self.START_DBETA_DECAY_ENERGY = 87
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self.END_DBETA_DECAY_ENERGY = 95
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self.START_MICRO_U = 100
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self.END_MICRO_U = 112
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self.START_MICRO_DU = 113
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self.END_MICRO_DU = 125
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else:
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self.HEADER = 36
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self.FOOTER = None
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self.START_A = 16
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self.END_A = 19
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self.START_Z = 11
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self.END_Z = 14
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self.START_ME = 29
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self.END_ME = 42
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self.START_DME = 43
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self.END_DME = 53
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self.START_BE_PER_A = 56
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self.END_BE_PER_A = 66
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self.START_DBE_PER_A = 69
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self.END_DBE_PER_A = 77
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self.START_BETA_DECAY_ENERGY = 82
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self.END_BETA_DECAY_ENERGY = 93
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self.START_DBETA_DECAY_ENERGY = 95
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self.END_DBETA_DECAY_ENERGY = 104
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self.START_MICRO_U = 110
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self.END_MICRO_U = 120
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self.START_MICRO_DU = 124
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self.END_MICRO_DU = 135
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match year:
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case 1983:
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self.HEADER = 35
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self.FOOTER = 0
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self.START_Z = 11
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self.END_Z = 14
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self.START_A = 16
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self.END_A = 19
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self.START_ME = 29
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self.END_ME = 39
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self.START_DME = 41
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self.END_DME = 48
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self.START_BE_PER_A = 49
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self.END_BE_PER_A = 59
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self.START_DBE_PER_A = 61
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self.END_DBE_PER_A = 68
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self.START_BETA_DECAY_ENERGY = 76
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self.END_BETA_DECAY_ENERGY = 85
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self.START_DBETA_DECAY_ENERGY = 87
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self.END_DBETA_DECAY_ENERGY = 94
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self.START_AM = 97
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self.END_AM = 99
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self.START_MICRO_U = 100
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self.END_MICRO_U = 110
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self.START_MICRO_DU = 113
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self.END_MICRO_DU = 120
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case 1993:
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self.HEADER = 40
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self.FOOTER = 0
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self.START_Z = 11
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self.END_Z = 14
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self.START_A = 16
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self.END_A = 19
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self.START_ME = 29
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self.END_ME = 39
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self.START_DME = 41
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self.END_DME = 48
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self.START_BE_PER_A = 49
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self.END_BE_PER_A = 59
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self.START_DBE_PER_A = 61
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self.END_DBE_PER_A = 68
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self.START_BETA_DECAY_ENERGY = 76
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self.END_BETA_DECAY_ENERGY = 85
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self.START_DBETA_DECAY_ENERGY = 87
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self.END_DBETA_DECAY_ENERGY = 94
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self.START_AM = 97
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self.END_AM = 99
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self.START_MICRO_U = 100
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self.END_MICRO_U = 110
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self.START_MICRO_DU = 112
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self.END_MICRO_DU = 120
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case 1995:
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self.HEADER = 39
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self.FOOTER = 0
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self.START_Z = 11
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self.END_Z = 14
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self.START_A = 16
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self.END_A = 19
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self.START_ME = 29
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self.END_ME = 39
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self.START_DME = 41
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self.END_DME = 48
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self.START_BE_PER_A = 49
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self.END_BE_PER_A = 59
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self.START_DBE_PER_A = 61
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self.END_DBE_PER_A = 68
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self.START_BETA_DECAY_ENERGY = 76
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self.END_BETA_DECAY_ENERGY = 85
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self.START_DBETA_DECAY_ENERGY = 87
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self.END_DBETA_DECAY_ENERGY = 94
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self.START_AM = 97
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self.END_AM = 99
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self.START_MICRO_U = 100
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self.END_MICRO_U = 110
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self.START_MICRO_DU = 112
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self.END_MICRO_DU = 120
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case 2020:
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self.HEADER = 36
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self.FOOTER = 0
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self.START_Z = 11
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self.END_Z = 14
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self.START_A = 16
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self.END_A = 19
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self.START_ME = 29
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self.END_ME = 42
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self.START_DME = 43
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self.END_DME = 53
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self.START_BE_PER_A = 56
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self.END_BE_PER_A = 66
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self.START_DBE_PER_A = 69
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self.END_DBE_PER_A = 77
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self.START_BETA_DECAY_ENERGY = 82
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self.END_BETA_DECAY_ENERGY = 93
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self.START_DBETA_DECAY_ENERGY = 95
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self.END_DBETA_DECAY_ENERGY = 104
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self.START_AM = 106
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self.END_AM = 109
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self.START_MICRO_U = 110
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self.END_MICRO_U = 120
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self.START_MICRO_DU = 124
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self.END_MICRO_DU = 135
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case _:
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self.HEADER = 39
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self.FOOTER = 0
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self.START_Z = 11
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self.END_Z = 14
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self.START_A = 16
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self.END_A = 19
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self.START_ME = 29
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self.END_ME = 41
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self.START_DME = 42
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self.END_DME = 53
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self.START_BE_PER_A = 54
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self.END_BE_PER_A = 64
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self.START_DBE_PER_A = 65
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self.END_DBE_PER_A = 72
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self.START_BETA_DECAY_ENERGY = 76
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self.END_BETA_DECAY_ENERGY = 86
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self.START_DBETA_DECAY_ENERGY = 87
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self.END_DBETA_DECAY_ENERGY = 95
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self.START_AM = 96
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self.END_AM = 99
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self.START_MICRO_U = 100
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self.END_MICRO_U = 112
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self.START_MICRO_DU = 113
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self.END_MICRO_DU = 120
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self.column_limits = [
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(self.START_Z, self.END_Z),
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(self.START_A, self.END_A),
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(self.START_ME, self.END_ME),
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(self.START_DME, self.END_DME),
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(self.START_BE_PER_A, self.END_BE_PER_A),
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(self.START_DBE_PER_A, self.END_DBE_PER_A),
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(self.START_BETA_DECAY_ENERGY, self.END_BETA_DECAY_ENERGY),
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(self.START_DBETA_DECAY_ENERGY, self.END_DBETA_DECAY_ENERGY),
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(self.START_AM, self.END_AM),
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(self.START_MICRO_U, self.END_MICRO_U),
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(self.START_MICRO_DU, self.END_MICRO_DU),
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]

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