Package API¶

nist_elem¶

class tofsim.nist_elem.Sustancias(elementos='', threshold=0.0001, isotopes=True)¶

Bases: dict

Object describing a list of substances.

Examples

>>> elementos = ['N2', 'H2O', 'Ar^{3+}', 'Ar^{+}', 'Ar^2+', 'UF6']
>>> m = Sustancias(elementos, threshold=1.e-3)
>>> print(m)
l                     M          P
-------------  --------  ---------
1H2-16O^{+}     18.0106  99.7341
1H2-18O^{+}     20.0148   0.204953
14N2^{+}        28.0061  99.2733
14N-15N^{+}     29.0032   0.72535
36Ar^{3+}       35.9675   0.3365
36Ar^{+}        35.9675   0.3365
36Ar^{2+}       35.9675   0.3365
40Ar^{3+}       39.9624  99.6003
40Ar^{+}        39.9624  99.6003
40Ar^{2+}       39.9624  99.6003
19F6-235U^{+}  349.034    0.7204
19F6-238U^{+}  352.041   99.2742

To get help use:

>>> m.show_help()
Create an object Sustancias as:
#
  Sustancias(<elemento o sustancia>)
#
Fields in mass have the meaning:
#
 Z:  str  Atomic Number
 S:  str  Atomic Symbol
 A:  str  Mass Number
 M:  str  Relative Atomic Mass
 P:  str  Isotopic Composition
 W:  str  Standard Atomic Weight
 q:  str  Charge

add(sustancias)¶

Returns a dictionary with all the information on the substances added.

Parameters

sustancias (str) –

Comma separated list of substances to be added, por instance:

m = add(‘H20,O2,N2,UF5,UF6’)

copy()¶: Copy the object and returns a new one.

get_lista(cols=None)¶

Return a list with the cols

Parameters: cols (tuple/list or None) – A list of string selecting the fields to included, chosen from “A,L,l,M,P,S,Z”

list(key=None)¶

Return a list of elements, optionally filtered with a criteria given by key

Parameters

key (None or function) –

if key=None returns all elements
otherwise key has to be a function returning True or False.

Examples

>>> list(key = lambda x:  x['P'] > 0.40)  # returns True if population greater than 40%
>>> list(key = lambda x:  x['M'] > 50)    # returns True if Mass smaller than than 50 AMU
>>> list(key = lambda x:  x['q'] > 2)     # returns True if charge greater than 2

loadconf(fname)¶

Load substances from file

Parameters: fname (str or Path() object) – File with definition of substances to add

File should have configuration format as:

[34S1]
S=S
L=$^{34}\mathrm{S}$
P=4.25
M=33.9678669
A=34
Z=16

[32S1]
S=S
L=$^{32}\mathrm{S}$
P=94.99
M=31.972071
A=32
Z=16

remove(sustancias, criteria='fragment')¶

Remove a fragment

Parameters

sustancias (str or list of strings) – substance or fragment to remove
criteria (str) – either ‘fragment’ (the default value) or ‘substance’:

For the choices of ‘criteria’, we use:

‘fragment’: e.g: Ar^{2+}, SF6^++, Removes exactly the fragment given
‘substance’: e.g: Ar, SF6, Xe, Remove all fragments with independently of the charge

saveconf(fname=None)¶

Save the substances to a file in a format that may be later read

Parameters

self (type) – description
fname (None or str or pathlib.Path()) – Name of the file. If None prints to screen

show_help()¶: Show help on use of Object Sustancias

sort(order=None, reverse=False)¶

Sort the substances according to the specified order. The sort is stable.

Parameters

self (type) – description
order (str) – desired order. One of ‘ALMPSZq’
reverse (bool) – If True will be reversed

to_latex(cols=None, headers=None, standalone=False)¶

Convert to LaTeX table using tabulate package

Parameters

cols (list) – Substance characteristics to include.
headers (str) – String to write before data.
standalone (bool) – If True make a standalone document that may be compiled by LaTeX.

to_pdf(cols=None, headers=None, latexcommand='pdflatex', output='tmp.pdf')¶

Convert to pdf format using latex.

Export to a latex file and compiles it.

Parameters

self (type) – description
cols (str) – Substance characteristics to include
headers (str) – Description to include before data
latexcommand (str) – command to compile to pdf
output (str) – Name of the output file

to_table(cols=None, headers=(), tablefmt='simple', floatfmt='g', numalign='decimal', stralign='left', missingval='')¶

Format the list of sustances in Table format.

This is a wrapper around the tabulate function from the tabulate package: https://pypi.python.org/pypi/tabulate. Available formats are: fancy_grid, grid, plain, psql, rst, simple, tsv latex, latex_raw, latex_booktabs, mediawiki, orgtbl, pipe, html

Options tablefmt, floatfmt, numalign, stralign, missingval are passed to package tabulate, if available.

Parameters

cols (list) – A list of strings with the characteristics to include. Must be in ALMPSZ
headers (str) – String to include before data
tablefmt (str) – Format of table
floatfmt (str) – Format used to write numbers
numalign (str) – Alignment for numbers
stralign (str) – Alignment for strings
missingval (str) – Value to write when values are missing

to_text()¶: Convenience function to format as a simple table

tofsim.nist_elem.analyze_substance(subst, threshold=0.0001, fragments=False, isotopes=True)¶

Devuelve todas las combinaciones con posibles isotopos (y su poblacion) de la sustancia con la formula dada.

Parameters

subst (str) – substance to include (H2O, CO, SF6, N2, …)
threshold (float) – Minimum abundance that has to have an isotope to be included
fragments (bool) – If True includes all fragments. For instance from ‘CO’ -> ‘CO’, ‘C’, ‘O’
isotopes (bool) – If True includes all isotope combinations

Examples:

analyze_substance('H2O').keys() =  ['1H1-2H1-16O1', '1H2-17O1', '1H2-16O1', '1H2-18O1']
analyze_substance('CO').keys() =
       ['13C1-17O1', '13C1-18O1', '12C1-16O1', '12C1-18O1', '13C1-16O1', '12C1-17O1']

tofsim.nist_elem.format_listmass(rows, end='\n', sep=' ', header='', footer='')¶

Simple formatting of a list of substances. It is intended to use with the output of get_lista()

Parameters

rows (list) – List where each row is a list with data on a single element
end (str) – End of line string
sep (str) – string used as separator within a line
header (str) – Header to add before the data
footer (str) – Footer to add after the data

tofsim.nist_elem.loadmass(fname)¶

Load masses from configuration file

Parameters: fname (str or Path()) – File to read masses from
Returns: a dictionary with the elements read
Return type: dict

tofsim.nist_elem.make_label(ss, fmt='key')¶

Construct the label for a given substance

Parameters

ss (str) – Array of tuples [(A1,S1), (A2,S2), …] for all atoms in the substance

fmt (str) –

Indicates the format of labels:

'key'         -> "14N2"
'Latex'       -> "$^{14}N_{2}$"
'latexsimple' -> "$N_{2}$"
'latexdoc'    -> r"\ce{^{14}N_{2}}"
'mass'        -> "28"

tofsim.nist_elem.mass2conf(masses)¶

Format masses for a configuration file

Parameters: masses (dict) – Information on substance(s)
Returns: string with the format of a configuration file
Return type: str

tofsim.nist_elem.read_nist_data(threshold=0)¶

Read data from file datafile and fills the dictionary __NIST_ELEMENTS__ with all the data

Parameters: threshold (float) – Add all elements whose abundance is higher than threshold

tof¶

class tofsim.tof.Peaks¶

Bases: dict

Simple object describing spectra peaks for the TOF.

It is essentially a dictionary, with a few added convenience methods

Each peak is described by a dictionary whose key is the label of the fragment.

The values are:

‘index’: tuple
has the form (im, i0, ip) with the indexes of the peak (i0) and the two positions where the width is obtained.

‘position’: float
is value of the coordinate where the peak is located, in microseconds.

‘height’: float
is the value of the peak.

‘width’: float
is the width of the peak in microseconds.

tolist()¶: Returns a list with the data describing the peaks. The form is: [‘Substance’, ‘index’, ‘position’, ‘height’, ‘width’]

class tofsim.tof.ToF(substances=None, **kwds)¶

Bases: object

The ToF object defines and handles all aspects of a Time-of-Flight spectrometer

The following parameters related to the construction and operation are included: Distances, voltages, working temperature, particle velocity fields and dispersion, time duration and size of the ionizing beam are also included.

Examples

tof_parameters= {
  's': 0.7,                    # Distance from center to first plate in cm
  'd': 2.54,                   # Distance between plates in cm (second stage)
  'D': 100.,                   # Distance of free flight
  'Vs': 500,                   # Potential Es in eV
  'Vd': 1900,                  # Potential Ed in eV
  'ds': 0.05,                  # Radio del spot del laser in cm
  'dt': 0.008,                 # Laser-pulse duration
  't0': 0.,                    # Offset in time of TOF (experimental)
  'r0': 1.e3,                  # Aperture radius after extraction
  }

# We can create the object with the parameters desired
T = ToF(['Ar', 'N2', 'CO2'],**tof_parameters)

# We can also load parameters from a file
T.load_conf_file('tof.conf')

T.signal()     # Calculate the signals

# Plot the signals
T.make_plot(fname='tof2.png', negative=True, show_legend=True, show_all=True)

# Just printing the object gives all the information on construction and
# condition parameters as well as the substances being simulated
print(T)

add_substances(substances, threshold=0.001)¶

Add substances to be simulated.

Parameters

substances (str) – Comma-separated string, or list of strings, each substance may have the form SF5^{+}, SF5^{++}, SF5^+, S^2+
threshold (float) – Minimum abundance of a given isotope to be included (default 1.e-3).

calc_signal(particles, Time)¶: Calculo de los histogramas para generar la señal de un grupo de partículas

data_to_array(unpack=False)¶

Conversion from signal to “numpy arrays”, sorted by time

Keyword Arguments: unpack – (default False)

get_statistics_peaks(substances='all', fwidth=0.36787944117144233)¶

Find peaks

Parameters

substances ('all' or list) –
- if ‘all’ find the peak for every fragment in the TOF
- if substance is a list of strings, each element must be a fragment in TOF
fwidth (float) – fraction of the maximum at which to evaluate the full width. For instance, fwidth = 0.5 corresponds to FWHM

get_tof_parameters(explain=False)¶

Returns the parameters of TOF in a dict.

Parameters

self (type) – description
explain (bool) – If True add an ascii graphic with the diagram of the TOF

load_conf_file(fname)¶: Load a configuration file, update tof parameters, and also return masses if present.

make_plot(especies=None, fname=None, **kvars)¶

Plot the signals in a “standard” form.

Parameters

especies (dict) – masses to plot. If no present uses simulated fragments
fname (string or None (default None)) – if not None -> Save the figure to “fname”
kvars (Optional) –
- negative = True/False : if True -> Negative signal
- graph_all=`True/False` (or show_all): if True -> Plot individual species
- show_legend=`True/False` : if True -> Show the legend

mean_times(x0=0.0)¶

Evaluate the ‘main’ time of each species, given as if generated in ideal conditions:

At time t=0
At rest (null initial velocity)
At a distance x0 from the center of the extraction plates (s = 0)

remove_substances(sustancias)¶

Remove substances from the simulation

Parameters

self (type) – description
sustancias (str or list of strings) – substances or fragments to remove

save_conf_file(fname, masas={})¶: Save configuration data for tof and masses to a file

save_data(fname)¶: Save the signal to a data file

set_initial_distribution()¶: Initial velocity and position distributions

signal(particles=None)¶

Evaluate the signal that would produce the particles in the ToF

Parameters

particles (dict each item is a dictionary that should have) –
- key: is the label of the mass
- ’M’: Mass (in AMU)
- ’P’: (float in range 0 to 1) Abundance
- ’L’: an (optional, possibly formatted) label, otherwise the key is used
particles is None => Usa los fragments (If) –

Returns

times – Sets the object variable self.times and also returns its value

Return type

numpy array

time_of_extract(x_0, v_0)¶: Evaluates the time taken to extract a particle of mass M=1 and charge q=1 with initial velocity v_0 and position x_0 from the extraction plates.

time_of_flight(x_0, v_0)¶: Evaluates the time-of-flight of a particle of mass M=1 and charge q=1 with initial velocity v_0 and position x_0.

time_of_flight_bibliog(x_0, v_0)¶: Alternative evaluatation of time-of-flight of a particle of mass M=1 and charge q=1 with initial velocity v_0 and position x_0 (from bibliography).

tofsim.tof.get_one_peak(x, y, fwidth=0.36787944117144233)¶

Returns features of a single peak. Returns a list with: - Position 0: indexes of position of:

lower half height

center

upper half height

Position 1: values of x
Position 2: values of y