Welcome to pypsbuilder documentation!

Installation

In the moment you need to have Python and needed modules installed locally and pypsbuilder must be installed from source. Just follow these steps:

1. Easiest way to install Python is to use Anaconda/ Miniconda/ Miniforge distribution. Download it and follow installation steps.

2. Download latest version of pypsbuilder and unzip to folder of your choice.

3. Use conda to create an environment from an environment.yml file. Open the terminal, change directory where you unzip the source and execute following command:
$ conda env create -f environment.yml
Activate the new environment and install from current directory:
$ conda activate pyps
(pyps) $ pip install .

Alternatively, you can use pip to install pypsbuilder:

(pyps) $ pip install pypsbuilder

Upgrade to latest version

You can anytime upgrade your existing pypsbuilder to the latest released version:

(pyps) $ pip install --upgrade pypsbuilder

or to latest master version at github:

(pyps) $ pip install --upgrade https://github.com/ondrolexa/pypsbuilder/archive/master.zip

Pseudosection builders tutorial

The pypsbuilder provides three builders ptbuilder, txbuilder and pxbuilder to create P-T, T-X and P-X pseudosections.

Before you can successfully run any builder, you have to prepare working directory, which contain THERMOCALC and drawpd executables, preferences file, thermodynamic dataset and a-x file. Builders will work only with certain setting, like calcmode must be set to 1, no ask for scripts etc. Builders validate settings and gives warning if some action is needed.

The only special need is to place special tags for bulk composition, ptguesses and dogmin in your scriptfile, to manage starting guesses and dogmin runs.

You have to add following comment lines to your script file to line where normally starting guesses should be placed (definitely before last * and before standard or samecoding guesses).:

%{PSBGUESS-BEGIN}
%{PSBGUESS-END}

Scriptfile modifications for TC3.4

For older version of THERMOCALC you need add two other commented blocks. For dogmin replace existing dogmin script with:

%{PSBDOGMIN-BEGIN}
dogmin no
%{PSBDOGMIN-END}

and for bulk composition place before and after existing setbulk script(s) these tags:

%{PSBBULK-BEGIN}
setbulk ....
%{PSBBULK-END}

Scriptfile modifications for TC3.5

For latest THERMOCALC the tags are slightly different. You should enclosed calculation part as:

%{PSBCALC-BEGIN}
calcP 4 12
calcT 600 1050 9
calctatp no
with  plc q aug hb sph g ep
zeromodeisopleth g
%{PSBCALC-END}

and for bulk composition place before and after existing bulk scripts these tags:

%{PSBBULK-BEGIN}
bulk  H2O   SiO2   Al2O3 CaO    MgO    FeOt  K2O   Na2O  TiO2  O
bulk  4.781 50.052 9.106 12.391 11.192 8.943 0.139 2.078 0.705 0.612
%{PSBBULK-END}

If you are not sure, which scripts should be set on and off, you can check example scriptfiles in examples/avgpelite or examples/avgpelite_34 directory.

Using tcinit to setup working directory

For TC3.5 you can use tcinit script to initialize working directory. It will download the latest 3.5 version of THERMOCALC, appropriate dataset and a-x file according to user selection.

New P-T pseudosection project

Use the terminal or an Anaconda Prompt, activate the pyps environment and run ptbuilder:

$ conda activate pyps
(pyps) $ ptbuilder

To create the new project (File->New project), you have to select working directory. ptbuilder automatically execute THERMOCALC, check settings in your script file and initialize project. Available phases are automatically populated to Phases list and default P-T range from scriptfile is set.

Create invariant point

In Phases list you select phases which should be in stable assemblage, while in lower pane you select two phases for which modal proportion should be zero. Than just click either Calc P or Calc T and invariant point will appear on diagram and in the list of invariant points in lower right part of window.

Create univariant line

Similarly, you can create univariant line, when only one phase is selected to have zero modal proportion. In addition ptbuilder allows you to create univariant lines based on already calculated invariant points. Right-click on invariant points will show context menu with possible choices of univariant lines passing trough this point and which are not yet calculated. Note, that selecting offered univariant line phases from context menu, the starting guesses from invariant point will be used in subsequent calculation. Hit Calc T or Calc P according to dp/dT of univariant line. Once calculated, result is added to diagram and to the list of univariant lines in upper right part of the window. Within this list you can define begin and end by selecting appropriate invariant points. If you allow autoconnection on Settings pane and both invariants points are already calculated, the begin and end is set automatically.

By default, ptbuilder use 50 steps to calculate univariant lines. You can change it in Settings pane. When you need to calculate some short univariant lines you can zoom into this part of pseudosection and hit one of the Calc buttons. Active region (possibly extended, check Extend view range to calculation range setting) will be used as computational P-T range. Moreover, you can manually add univariant line to simply connect two invariant points by straight line. For ‘’Manual’’ addition of both invariant point or univariant line present phases and zero mode phases have to be properly selected. Manually added lines or points are shown in italics in lists. Unconnected univariant lines are shown in bold.

When univariant line cannot be calculated by single THERMOCALC calculation (either due to starting guesses or high curvature), you can merge partially calculated segments using ‘’+’’ button before calculation. Instead of overwriting of existing line, newly calculated part will be merged with existing one. To remove selected part of already calculated line, you can zoom to that part and right-click highlighted name and choose ‘’Remove nodes’’.

Double-clicking any univariant line or invariant point in the list will highlight that line/point on diagram marked by calculated points.

Note that double-click name of univariant (or invariant) line will populate Modes and Full output panes at the bottom of application, so you can always check what is going on along lines. Double-clicking of tabs heading open outputs in larger separate window.

Starting guesses

ptbuilder stores all relevant information for each point or line already calculated. If you need to update starting guesses during construction of pseudosection, just choose invariant point or univariant line from which the starting guesses should be copied and click Set ptguess button. ptbuilder stores new starting guesses to your script file, so next calculation will use it. You can any time check and/or modify your script file with integrated editor on Script file pane. The Log pane always shows standard output of last THERMOCALC execution.

Phase out and phase stable lines

Double click on any phase in Phases list will highlight all univariant lines with zero modal proportion of selected phase and all phase present univariant lines.

Manual invariant points or univariant lines

Manual button allows to add user-defined point or line. You need to select stable phases and zero mode phases accordingly. For manual univariant line begin and end invariant point must be specified. For manual invariant point, you can either specify position of point by clicking on diagram by mouse or when more than two univariant lines passing trough that point already exists, calculated intersection could be used.

Searching for invariant points

To find out, what phase is appearing or disappearing along exiting univariant line, you can activate that line and click the Search button. Builder list possible (only found ones, if ptguesses are not appropriate, only metastable invariant points could be offered) solutions ordered along univariant line direction. The already calculated invariant points are marked. If there is one already calculated invariant point, the ptguesses from that point are used.

Dogmin

Gmin button runs THERMOCALC dogmin script, which tries to calculate phase equilibria between all possible subsets of a list of selected phases. The pressure and temperature is indicated by clicking on the diagram. Maximum variance to be considered (higher max variance -> fewer phases in smallest assemblage) is set in spin widget next to Gmin button. Ranked the equilibria in order of stability by comparing the Gibbs energies of each assemblage are shown in Modes pane. On Dogmin pane you can use Select button to select found assemblage in Phases and Set guesses to use ptguess of found solution.

Finished pseudosection should contain topologically correct set of univariant lines and invariant points. Topology could be checked by creating areas (Tools>Show areas or Ctrl-A) of stable assemblages. If there are some topological or geometrical problems to create areas, corresponding IDs are reported on ‘’Mode’’ tab.

Double crossing univariant lines

Common problem is that you realize during construction of pseudosection, that some univariant lines are double crossing and therefore two invariant points and two separated segments of univariant lines with identical assemblage and zero mode phase(s) exists. The pypsbuilders cannot have those “identical” points and lines in single project an pseudosection has be split in two parts by defining axes limits on Settings pane. Split line should be placed approximately like on the figure.

The all parts of single pseudosection could be post-processed and visualized by psexplorers simulataneously as single pseudosection.

When you have already calculated invariant points and lines and you want to use them in separate project, create new project, set axes limits properly and use File>Import > Import into range to read it.

T-X and P-X pseudosections

You can create T-X and P-X pseudosection using exactly same steps like for P-T pseudosection using txbuilder or pxbuilder.

Pseudosection explorers tutorial

psexplorers provides several post-processing methods and visualizations of already constructed pseudosections. You can also create isopleths diagrams or do calculations along paths.

It provides four command-line scipts psgrid, psshow and psiso for quick visualizations. For options, check built-in help, e.g.:

$ psshow -h

To show pseudosection, you need to provide project file:

$ psshow -b /some/path/project.ptb

Using psexplorers in Python (or Jupyter notebook)

To access all options of psexplorer it is suggested to use Python API. You need to activate the pyps environment and run python interpreter:

$ conda activate pyps
$ python

To use psexplorer we need to import appropriate class, which contains most of the methods to work with pseudosection.

PTPS class for P-T pseudosection constructed with ptbuilder

TXPS class for T-X pseudosection constructed with txbuilder

PXPS class for P-X pseudosection constructed with pxbuilder

All following commands must be executed in Python interpreter.

from pypsbuilder import PTPS

The second step is to create instance of pseudosection using existing project file.

pt = PTPS('/some/path/project.ptb')

We can check, whether the pseudosection already contains gridded calculations. If not, we can use calculate_composition method to calculate compositional variations on grid. The resulting data are stored in project file. Note that any new modifications of the project by pypsbuilder* will discard compositional variations on grid and must be calculated again.

if not pt.gridded:
    pt.calculate_composition(nx=50, ny=50)

Gridding 1/1: 100%|██████████| 2500/2500 [03:10<00:00, 13.12it/s]
Grid search done. 0 empty points left.

Visualize pseudosection

To show pseudosection, we can use show method

pt.show()

The keyword arguments cmap and out could be used to modify colormap and highlight zero mode lines across pseudosection.

pt.show(cmap='viridis', out=['g', 'chl', 'ep', 'law'])

The keyword arguments bulk and label set whether the bulk composition is shown on figure and whether the fields are labeled by phases.

pt.show(cmap='viridis', bulk=True, label=True)

The pseudosection identify method could be used to identify stable assemblage for given p and T conditions. Note that returned key (Python frozenset) is used to identify stable assemblage in many PTPS methods.

key = pt.identify(550, 13)
print(key)

frozenset({'sph', 'pa', 'q', 'g', 'mu', 'H2O', 'bi'})

Access data and variables stored in project

The calculated data are usually accessed using stable assemblage key (see above). Theera are three groups of data stored 1) at invariant points, 2) along univariant lines and 3) on grid covering multivariate fields. To see data coverage and all available variables, you can use show_data method. When no variable (or expression) is provided, method will show available variables.

pt.show_data(key, 'g')

Missing expression argument. Available variables for phase g are:
mode x z m f xMgX xFeX xMnX xCaX xAlY xFe3Y H2O SiO2 Al2O3 CaO MgO FeO K2O Na2O TiO2 MnO O factor G H S V rho
Available end-members for g: kho gr alm py spss

Once variable is provided, the all available data are shown.

pt.show_data(key, 'g', 'xCaX')

For data on the grid you can visualize them for all diagram in once using show_grid method.

pt.show_grid('g', 'xCaX')

To create isopleths diagram you can use isopleths method. Note that contours are created separately for each stable assemblage allowing proper geometry of isopleths.

pt.isopleths('g', 'xCaX', N=14)

pt.isopleths('chl')

Missing expression argument. Available variables for phase chl are:
mode x y f m QAl Q1 Q4 xMgM1 xMnM1 xFeM1 xAlM1 xMgM23 xMnM23 xFeM23 xMgM4 xFeM4 xFe3M4 xAlM4 xSiT2 xAlT2 H2O SiO2 Al2O3 CaO MgO FeO K2O Na2O TiO2 MnO O factor G H S V rho
Available end-members for chl: ames mmchl ochl1 f3clin afchl ochl4 clin daph

pt.isopleths('chl', 'mode')

Calculations along PT paths

PTPS allows you to evaluate equilibria along user-defined PT path. PT path is defined by series of points (path is interpolated) and method collect_ptpath do actual calculations. It runs THERMOCALC with ptguesses obtained from existing calculations.

t = [380, 480, 580, 640, 500]
p = [7, 12, 15, 9, 5.5]
pa = pt.collect_ptpath(t, p)

Calculating: 100%|██████████| 100/100 [00:03<00:00, 25.86it/s]

You can see phase modes along PT path using show_path_modes method.

pt.show_path_modes(pa, exclude=['H2O'])

or show value of user-defined expression shown as colored strip on PT space.

pt.show_path_data(pa, 'g', 'mode')

Extra

show_status method shows status of calculations on the grid. Possible failed calculations are shown.

pt.show_status()

Do you want to know execution time of THERMOCALC on individual grid points? Check show_delta method.

pt.show_delta(pointsec=True)

For full description of Python API check: Python API.

Command line scripts

Before any further calculations you can check and draw your pseudosection using psshow command which construct finished areas within your project. It has few options to label pseudosection with assamblages or highlight out phase lines.

$ psshow -h
usage: psshow [-h] [-o OUT [OUT ...]] [-l] [--origwd] [-b] [--cmap CMAP]
          [--alpha ALPHA] [--connect] [--high HIGH]
          [--tolerance TOLERANCE]
          project [project ...]

Draw pseudosection from project file

positional arguments:
  project               builder project file(s)

optional arguments:
  -h, --help            show this help message and exit
  -o OUT [OUT ...], --out OUT [OUT ...]
                        highlight out lines for given phases
  -l, --label           show area labels
  --origwd              use stored original working directory
  -b, --bulk            show bulk composition on figure
  --cmap CMAP           name of the colormap
  --alpha ALPHA         alpha of colormap
  --connect             whether mouse click echo stable assemblage
  --high HIGH           highlight field defined by set of phases
  --tolerance TOLERANCE
                        tolerance to simplify univariant lines

To draw pseudosection with marked epidote-out and chlorite-out lines execute:

$ psshow '/path/to/project.ptb' -o ep chl

Draw isopleths diagrams

To create isopleths diagrams the pseudoction should be gridded at first (In other case only values from univariant lines and invariant points are used and interpolated accross areas). Command psgrid will do all calculations and result are saved afterwards, so next time results are automatically loaded. Be aware that calculations takes some time.

$ psgrid -h
usage: psgrid [-h] [--nx NX] [--ny NY] [--origwd] [--tolerance TOLERANCE]
              project [project ...]

Calculate compositions in grid

positional arguments:
  project               builder project file(s)

optional arguments:
  -h, --help            show this help message and exit
  --nx NX               number of T steps
  --ny NY               number of P steps
  --origwd              use stored original working directory
  --tolerance TOLERANCE
                        tolerance to simplify univariant lines

For gridding pseudosection with grid 50x50 run following command:

$ psgrid '/path/to/project.ptb' --nx 50 --ny 50
            Gridding: 100%|█████████████████████████████| 2500/2500 [01:30<00:00, 27.62it/s]
            Grid search done. 0 empty grid points left.

Once gridded you can draw isopleths diagrams using psiso command:

            $ psiso -h
usage: psiso [-h] [-e EXPR] [-f] [--origwd] [-o OUT [OUT ...]] [--nosplit]
             [-b] [--step STEP] [--ncont NCONT] [--colors COLORS]
             [--cmap CMAP] [--smooth SMOOTH] [--labelkey LABELKEY]
             [--high HIGH] [--tolerance TOLERANCE]
             project [project ...] phase

Draw isopleth diagrams

positional arguments:
  project               builder project file(s)
  phase                 phase used for contouring

optional arguments:
  -h, --help            show this help message and exit
  -e EXPR, --expr EXPR  expression evaluated to calculate values
  -f, --filled          filled contours
  --origwd              use stored original working directory
  -o OUT [OUT ...], --out OUT [OUT ...]
                        highlight out lines for given phases
  --nosplit             controls whether the underlying contour is removed or
                        not
  -b, --bulk            show bulk composition on figure
  --step STEP           contour step
  --ncont NCONT         number of contours
  --colors COLORS       color for all levels
  --cmap CMAP           name of the colormap
  --smooth SMOOTH       smoothness of the approximation
  --labelkey LABELKEY   label contours in field defined by set of phases
  --high HIGH           highlight field defined by set of phases
  --tolerance TOLERANCE
                        tolerance to simplify univariant lines

Following example shows isopleths of garnet mode:

$ psiso '/path/to/project.ptb' -f g -e mode

If the expression argument is not provided, the psexplorer shows list of all calculated variables available for given phase.

$ psiso '/path/to/project.ptb' -f g
Missing expression argument. Available variables for phase g are:
mode x z m f xMgX xFeX xMnX xCaX xAlY xFe3Y H2O SiO2 Al2O3 CaO MgO FeO K2O
Na2O TiO2 MnO O factor G H S V rho

To draw isopleths of almandine garnet proportion you can use expression from a-x file alm = x + (-m) x + (-x) z:

$ psiso '/path/to/project.ptb' -f g -e 'x-m*x-x*z'

or use variable xFeX:

$ psiso tutorial.ptb -f g -e xFeX

If you need to label contour lines, you can use labelkey option to define field, where contour labels are plotted.

$ psiso '/path/to/project.ptb' g -e mode --labelkey "H2O bi g mu pa pl q ru"
            --step 0.005 --colors m

Another example of some other options.

$ psiso tutorial.ptb -f g -e mode --step 0.005 --high "H2O bi g mu pa pl q ru"
--out chl ep --cmap YlGnBu_r

Python API

pypsbuilder expose several classes which could be used in Python sctipts, interactively or in Jupyter notebooks. Check included jupyter notebook to see how it works.

Here you can find auto-generated documentation of main classes, methods and functions.

psexplorer module

`PTPS`	Class to postprocess ptbuilder project
`TXPS`	Class to postprocess txbuilder project
`PXPS`	Class to postprocess pxbuilder project

class pypsbuilder.psexplorer.PTPS(*args, **kwargs)

Bases: PS

Class to postprocess ptbuilder project

calculate_composition(nx=50, ny=50)

Method to calculate compositional variations on grid.

A compositions are calculated for stable assemblages in regular grid covering pT range of pseudosection. A stable assemblage is identified from constructed divariant fields. Results are stored in grid property as GridData instance. A property all_data_keys is updated.

Before any grid point calculation, ptguesses are updated from nearest invariant point. If calculation fails, nearest solution from univariant line is used to update ptguesses. Finally, if solution is still not found, the method fix_solutions is called and neigbouring grid calculations are used to provide ptguess.

Parameters:

nx (int) – Number of grid points along x direction (T)
ny (int) – Number of grid points along y direction (p)

fix_solutions()

Method try to find solution for grid points with failed status.

Ptguesses are used from successfully calculated neighboring points until solution is find. Otherwise ststus remains failed.

collect_ptpath(tpath, ppath, N=100, kind='quadratic')

Method to collect THERMOCALC calculations along defined PT path.

PT path is interpolated from provided points using defined method. For each point THERMOCALC seek for solution using ptguess from nearest GridData point.

Parameters:

tpath (numpy.array) – 1D array of temperatures for given PT path
ppath (numpy.array) – 1D array of pressures for given PT path
N (int) – Number of calculation steps. Default 100.
kind (str) – Kind of interpolation. See scipy.interpolate.interp1d

Returns:

returns instance of PTpath class storing all calculations: along PT path.

Return type:

PTpath

show_path_data(ptpath, phase, expr=None, label=False, pathwidth=4, allpath=True, skiplabels=0, labelfs=6)

Show values of expression for given phase calculated along PTpath.

It plots colored strip on PT space. Strips arenot drawn accross fields, where ‘phase’ is not present.

Parameters:

ptpath (PTpath) – Results obtained by collect_ptpath method.
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6
pathwidth (int) – Width of colored strip. Default 4.
allpath (bool) – Whether to plot full PT path (dashed line).

show_path_modes(ptpath, exclude=[], cmap='tab20')

Show stacked area diagram of phase modes along PT path

Parameters:

ptpath (PTpath) – Results obtained by collect_ptpath method.
exclude (list) – List of phases to exclude. Included phases area normalized to 100%.
cmap (str) – matplotlib colormap. Default ‘tab20’

collect_all_data_keys()

Collect all phases and variables calculated on grid.

Result is stored in all_data_keys property as dictionary of dictionaries.

Example

To get list of all variables calculated for phase ‘g’ or end-member ‘g(alm)’ use:

>>> pt.all_data_keys['g']
['mode', 'x', 'z', 'm', 'f', 'xMgX', 'xFeX', 'xMnX', 'xCaX', 'xAlY',
'xFe3Y', 'H2O', 'SiO2', 'Al2O3', 'CaO', 'MgO', 'FeO', 'K2O', 'Na2O',
'TiO2', 'MnO', 'O', 'factor', 'G', 'H', 'S', 'V', 'rho']
>>> pt.all_data_keys['g(alm)']
['ideal', 'gamma', 'activity', 'prop', 'mu', 'RTlna']

collect_data(key, phase, expr, which=7)

Convinient function to retrieve values of expression for given phase for user-defined combination of results of divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

collect_grid_data(key, phase, expr)

Retrieve values of expression for given phase for all GridData points within divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

collect_inv_data(key, phase, expr)

Retrieve value of variables based expression for given phase for all invariant points surrounding divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

collect_uni_data(key, phase, expr)

Retrieve values of expression for given phase for all univariant lines surrounding divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

common_grid_and_masks(**kwargs): Initialize common grid and mask for all partial grids

create_masks(): Update grid masks from existing divariant fields

property endmembers: Returns dictionary with phases and their end-members names

gendrawpd(export_areas=True)

Method to write drawpd file

Parameters:: export_areas (bool) – Whether to include constructed areas. Default True.

get_grids(phase, expr=None)

Convinient function to get dictionary of grids.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

get_nearest_grid_data(x, y)

Retrieve nearest results from GridData to point.

Parameters:

x (float) – x-coordinate of point
y – y-coordiante of point

get_section_id(x, y): Return index of pseudosection and grid containing point

gidentify(label=False, skiplabels=0, labelfs=6)

Visual version of identify method. PT point is provided by mouse click.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

ginput_path(label=False, skiplabels=0, labelfs=6)

Collect Path data by mouse digitizing.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

glabel(label=False, skiplabels=0, labelfs=6)

Return formatted string of assamblage at PT point provided by mouse click.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

property gridded: True when compositional grid(s) is calculated, otherwise False

identify(x, y)

Return key (frozenset) of divariant field for given temperature and pressure.

Parameters:

x (float) – x coord
y (float) – y coord

invs_from_unilist(ix, unilist)

Return set of IDs of invariant points associated with unilines. lines.

Parameters:: unilist (iterable) – list of (section_id, uni_id) pairs
Returns:: set of associated invariant points
Return type:: set

isopleths(phase, expr=None, **kwargs)

Method to draw compositional isopleths.

Isopleths are drawn as contours for values evaluated from provided expression. Individual divariant fields are contoured separately, so final plot allows sharp changes accross univariant lines. Within divariant field the selected interpolation is used.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
N (int) – Max number of contours. Default 10.
step (int) – Step between contour levels. If defined, N is ignored. Default None.
cdf (bool) – When True contour levels are percentile based. Default False.
alpha (float) – Alpha value for filled contours. Default 1
levels (list) – User-defined contour levels. If defined, N and step is ignored.
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points. Default 7 (all data)
colorbar (bool) – Whether to show colorbar. Default True
method – Interpolation method. Default is ‘rbf’, other option is ‘quadratic’, which uses least-square fit to quadratic surface or ‘spline’ for bivariate spline interpolation.
rbf_func – Default ‘thin_plate’. See scipy.interpolation.Rbf
smooth (int) – Values greater than zero increase the smoothness of the approximation. 0 is for interpolation (default), the function will always go through the nodal points.
epsilon (int) – Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).
degree (int) – Degrees of the bivariate spline. Default is 3.
refine (int) – Degree of grid refinement. Default 1
filter_outliers (bool) – Whether to filter outliers. Defaut False.
filled (bool) – Whether to contours should be filled. Defaut True.
filled_over (bool) – Whether to overlay contourline over filled contours. Defaut False.
out (str or list) – Highligt zero-mode lines for given phases.
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used for contours. Default ‘viridis’.
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
labelkeys (frozenset or list) – Keys of divariant fields where contours should be labeled.
nosplit (bool) – Controls whether the contour underlying labels are removed or not. Defaut True
gradient (bool) – Whether the first derivate of values should be used. Default False.
dt (bool) – Whether the gradient should be calculated along temperature or pressure. Default True.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

isopleths_vector(phase, expr=None, **kwargs)

Method to draw vectorized compositional isopleths.

Isopleths are drawn as lines for values evaluated from provided expression. Individual divariant fields are contoured separately, so final plot allows sharp changes accross univariant lines. Within divariant field the selected interpolation is used.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
N (int) – Max number of contours. Default 10.
step (int) – Step between contour levels. If defined, N is ignored. Default None.
cdf (bool) – When True contour levels are percentile based. Default False.
levels (list) – User-defined contour levels. If defined, N and step is ignored.
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points. Default 7 (all data)
colorbar (bool) – Whether to show colorbar. Default True
method – Interpolation method. Default is ‘rbf’, other option is ‘quadratic’, which uses least-square fit to quadratic surface.
rbf_func – Default ‘thin_plate’. See scipy.interpolation.Rbf
smooth (int) – Values greater than zero increase the smoothness of the approximation. 0 is for interpolation (default), the function will always go through the nodal points.
epsilon (int) – Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).
degree (int) – Degrees of the bivariate spline. Default is 3.
refine (int) – Degree of grid refinement. Default 1
filter_outliers (bool) – Whether to filter outliers. Defaut False.
out (str or list) – Highligt zero-mode lines for given phases.
overlay (bool) – Whether to show assemblage fields. Default True
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used isopleths coloring. Default ‘viridis’.
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
lw (float) – Linewidth of isopleths. Default 1.0
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
color – matplotlib color for isopleths. If None cmap is used. Default None.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

property keys: Returns set of all existing multivariant fields. Fields are identified by frozenset of present phases called key.

merge_data(phase, expr, which=7)

Returns merged data obtained by collect_data method for all divariant fields.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

property name: Get project directory name.

overlap_isopleths(*args, **kwargs)

Function to overlaped isopleths ranges.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
levels (tuple) – tuple of min an max values for isopleth band
alpha (float) – Alpha value for filled contours. Default 1
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

Example

>>> pt.overlap_isopleths(
        'g', 'xMgX', (0.07, 0.13),
        'g', 'xFeX', (0.51, 0.65),
        'g', 'xCaX', (0.06, 0.15),
        'g', 'xMnX', (0.16, 0.27),
    )

property phases: Returns set of all phases present in pseudosection

remove_grid_data(key, phase, expr)

Remove calculated data from grid and mark as failed

Note: Click on all data to be discarded by mouse and finish be Enter

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

save()

Save gridded copositions and constructed divariant fields into psbuilder project file.

Note that once project is edited with psbuilder, calculated compositions are removed and need to be recalculated using PTPS.calculate_composition method.

save_tab(comps, tabfile=None)

Export gridded values to Perple_X tab format. Could be used in pywerami.

Note: Gridded values are interpolated from raw results

Parameters:

comps (list) –
List of (phase, expr) tuples. phase (str): Phase or end-member named expr (str): Expression to evaluate. It could use any

variable existing for given phase. Check all_data_keys property for possible variables.
tabfile (str) – filename for tabfile. Default pseudosection name.tab

search_composition(*args, interpolation=None, label=False, skiplabels=0, labelfs=6, geterror=False, getpt=False, fig=None, fig_kw={}, isopleths=False, which=7, smooth=0, alpha=0.5, lw=2)

Function to plot root squared error of calculated and estimated values.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
val (float) – searched value
interpolation (str) – matplotlib imshow interpolation method. Default None.
label (bool) – Whether to label divariant fields. Default False.
geterror (bool) – When True, calculated RMSE array is returned. Otherwise error is plotted. Deafult False
getpt (bool) – When True return tuple of (p, T, err) where error is minimal. Default False
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
isopleths (bool) – When True, searched isopleths are shown. Default False
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

Example

>>> pt.search_composition(
        'g', 'xMgX', 0.076,
        'g', 'xFeX', 0.525,
        'g', 'xCaX', 0.126,
        'g', 'xMnX', 0.268
    )

show(**kwargs)

Method to draw PT pseudosection.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
out (str or list) – Highligt zero-mode lines for given phases.
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used to divariant fields coloring. Colors are based on variance. Default ‘Purples’.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
alpha (float) – alpha value for colors. Default 0.6
connect (bool) – Whether mouse click echo stable assemblage to STDOUT. Default False.
show_vertices (bool) – Whether to show vertices of drawn areas. Default False.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

show_data(key, phase, expr=None, which=7)

Convinient function to show values of expression for given phase for user-defined combination of results of divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

show_delta(label=False, pointsec=False, skiplabels=0, labelfs=6)

Shows THERMOCALC execution time for all grid points.

Parameters:

pointsec (bool) – Whether to show points/sec or secs/point. Default False.
label (bool) – Whether to label divariant fields. Default False.

show_grid(phase, expr=None, interpolation=None, label=False, skiplabels=0, labelfs=6)

Convinient function to show values of expression for given phase only from Grid Data.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
interpolation (str) – matplotlib imshow interpolation method. Default None.
label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

show_status(label=False, skiplabels=0, labelfs=6): Shows status of grid calculations

property variance: Returns dictionary of variances

class pypsbuilder.psexplorer.TXPS(*args, **kwargs)

Bases: PS

Class to postprocess txbuilder project

calculate_composition(nx=50, ny=50)

Method to calculate compositional variations on grid.

A compositions are calculated for stable assemblages in regular grid covering pT range of pseudosection. A stable assemblage is identified from constructed divariant fields. Results are stored in grid property as GridData instance. A property all_data_keys is updated.

Before any grid point calculation, ptguesses are updated from nearest invariant point. If calculation fails, nearest solution from univariant line is used to update ptguesses. Finally, if solution is still not found, the method fix_solutions is called and neigbouring grid calculations are used to provide ptguess.

Parameters:

nx (int) – Number of grid points along x direction (T)
ny (int) – Number of grid points along y direction (p)

fix_solutions()

Method try to find solution for grid points with failed status.

Ptguesses are used from successfully calculated neighboring points until solution is find. Otherwise ststus remains failed.

collect_all_data_keys()

Collect all phases and variables calculated on grid.

Result is stored in all_data_keys property as dictionary of dictionaries.

Example

To get list of all variables calculated for phase ‘g’ or end-member ‘g(alm)’ use:

>>> pt.all_data_keys['g']
['mode', 'x', 'z', 'm', 'f', 'xMgX', 'xFeX', 'xMnX', 'xCaX', 'xAlY',
'xFe3Y', 'H2O', 'SiO2', 'Al2O3', 'CaO', 'MgO', 'FeO', 'K2O', 'Na2O',
'TiO2', 'MnO', 'O', 'factor', 'G', 'H', 'S', 'V', 'rho']
>>> pt.all_data_keys['g(alm)']
['ideal', 'gamma', 'activity', 'prop', 'mu', 'RTlna']

collect_data(key, phase, expr, which=7)

Convinient function to retrieve values of expression for given phase for user-defined combination of results of divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

collect_grid_data(key, phase, expr)

Retrieve values of expression for given phase for all GridData points within divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

collect_inv_data(key, phase, expr)

Retrieve value of variables based expression for given phase for all invariant points surrounding divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

collect_uni_data(key, phase, expr)

Retrieve values of expression for given phase for all univariant lines surrounding divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

common_grid_and_masks(**kwargs): Initialize common grid and mask for all partial grids

create_masks(): Update grid masks from existing divariant fields

property endmembers: Returns dictionary with phases and their end-members names

gendrawpd(export_areas=True)

Method to write drawpd file

Parameters:: export_areas (bool) – Whether to include constructed areas. Default True.

get_grids(phase, expr=None)

Convinient function to get dictionary of grids.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

get_nearest_grid_data(x, y)

Retrieve nearest results from GridData to point.

Parameters:

x (float) – x-coordinate of point
y – y-coordiante of point

get_section_id(x, y): Return index of pseudosection and grid containing point

gidentify(label=False, skiplabels=0, labelfs=6)

Visual version of identify method. PT point is provided by mouse click.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

ginput_path(label=False, skiplabels=0, labelfs=6)

Collect Path data by mouse digitizing.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

glabel(label=False, skiplabels=0, labelfs=6)

Return formatted string of assamblage at PT point provided by mouse click.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

property gridded: True when compositional grid(s) is calculated, otherwise False

identify(x, y)

Return key (frozenset) of divariant field for given temperature and pressure.

Parameters:

x (float) – x coord
y (float) – y coord

invs_from_unilist(ix, unilist)

Return set of IDs of invariant points associated with unilines. lines.

Parameters:: unilist (iterable) – list of (section_id, uni_id) pairs
Returns:: set of associated invariant points
Return type:: set

isopleths(phase, expr=None, **kwargs)

Method to draw compositional isopleths.

Isopleths are drawn as contours for values evaluated from provided expression. Individual divariant fields are contoured separately, so final plot allows sharp changes accross univariant lines. Within divariant field the selected interpolation is used.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
N (int) – Max number of contours. Default 10.
step (int) – Step between contour levels. If defined, N is ignored. Default None.
cdf (bool) – When True contour levels are percentile based. Default False.
alpha (float) – Alpha value for filled contours. Default 1
levels (list) – User-defined contour levels. If defined, N and step is ignored.
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points. Default 7 (all data)
colorbar (bool) – Whether to show colorbar. Default True
method – Interpolation method. Default is ‘rbf’, other option is ‘quadratic’, which uses least-square fit to quadratic surface or ‘spline’ for bivariate spline interpolation.
rbf_func – Default ‘thin_plate’. See scipy.interpolation.Rbf
smooth (int) – Values greater than zero increase the smoothness of the approximation. 0 is for interpolation (default), the function will always go through the nodal points.
epsilon (int) – Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).
degree (int) – Degrees of the bivariate spline. Default is 3.
refine (int) – Degree of grid refinement. Default 1
filter_outliers (bool) – Whether to filter outliers. Defaut False.
filled (bool) – Whether to contours should be filled. Defaut True.
filled_over (bool) – Whether to overlay contourline over filled contours. Defaut False.
out (str or list) – Highligt zero-mode lines for given phases.
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used for contours. Default ‘viridis’.
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
labelkeys (frozenset or list) – Keys of divariant fields where contours should be labeled.
nosplit (bool) – Controls whether the contour underlying labels are removed or not. Defaut True
gradient (bool) – Whether the first derivate of values should be used. Default False.
dt (bool) – Whether the gradient should be calculated along temperature or pressure. Default True.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

isopleths_vector(phase, expr=None, **kwargs)

Method to draw vectorized compositional isopleths.

Isopleths are drawn as lines for values evaluated from provided expression. Individual divariant fields are contoured separately, so final plot allows sharp changes accross univariant lines. Within divariant field the selected interpolation is used.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
N (int) – Max number of contours. Default 10.
step (int) – Step between contour levels. If defined, N is ignored. Default None.
cdf (bool) – When True contour levels are percentile based. Default False.
levels (list) – User-defined contour levels. If defined, N and step is ignored.
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points. Default 7 (all data)
colorbar (bool) – Whether to show colorbar. Default True
method – Interpolation method. Default is ‘rbf’, other option is ‘quadratic’, which uses least-square fit to quadratic surface.
rbf_func – Default ‘thin_plate’. See scipy.interpolation.Rbf
smooth (int) – Values greater than zero increase the smoothness of the approximation. 0 is for interpolation (default), the function will always go through the nodal points.
epsilon (int) – Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).
degree (int) – Degrees of the bivariate spline. Default is 3.
refine (int) – Degree of grid refinement. Default 1
filter_outliers (bool) – Whether to filter outliers. Defaut False.
out (str or list) – Highligt zero-mode lines for given phases.
overlay (bool) – Whether to show assemblage fields. Default True
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used isopleths coloring. Default ‘viridis’.
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
lw (float) – Linewidth of isopleths. Default 1.0
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
color – matplotlib color for isopleths. If None cmap is used. Default None.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

property keys: Returns set of all existing multivariant fields. Fields are identified by frozenset of present phases called key.

merge_data(phase, expr, which=7)

Returns merged data obtained by collect_data method for all divariant fields.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

property name: Get project directory name.

overlap_isopleths(*args, **kwargs)

Function to overlaped isopleths ranges.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
levels (tuple) – tuple of min an max values for isopleth band
alpha (float) – Alpha value for filled contours. Default 1
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

Example

>>> pt.overlap_isopleths(
        'g', 'xMgX', (0.07, 0.13),
        'g', 'xFeX', (0.51, 0.65),
        'g', 'xCaX', (0.06, 0.15),
        'g', 'xMnX', (0.16, 0.27),
    )

property phases: Returns set of all phases present in pseudosection

remove_grid_data(key, phase, expr)

Remove calculated data from grid and mark as failed

Note: Click on all data to be discarded by mouse and finish be Enter

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

save()

Save gridded copositions and constructed divariant fields into psbuilder project file.

Note that once project is edited with psbuilder, calculated compositions are removed and need to be recalculated using PTPS.calculate_composition method.

save_tab(comps, tabfile=None)

Export gridded values to Perple_X tab format. Could be used in pywerami.

Note: Gridded values are interpolated from raw results

Parameters:

comps (list) –
List of (phase, expr) tuples. phase (str): Phase or end-member named expr (str): Expression to evaluate. It could use any

variable existing for given phase. Check all_data_keys property for possible variables.
tabfile (str) – filename for tabfile. Default pseudosection name.tab

search_composition(*args, interpolation=None, label=False, skiplabels=0, labelfs=6, geterror=False, getpt=False, fig=None, fig_kw={}, isopleths=False, which=7, smooth=0, alpha=0.5, lw=2)

Function to plot root squared error of calculated and estimated values.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
val (float) – searched value
interpolation (str) – matplotlib imshow interpolation method. Default None.
label (bool) – Whether to label divariant fields. Default False.
geterror (bool) – When True, calculated RMSE array is returned. Otherwise error is plotted. Deafult False
getpt (bool) – When True return tuple of (p, T, err) where error is minimal. Default False
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
isopleths (bool) – When True, searched isopleths are shown. Default False
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

Example

>>> pt.search_composition(
        'g', 'xMgX', 0.076,
        'g', 'xFeX', 0.525,
        'g', 'xCaX', 0.126,
        'g', 'xMnX', 0.268
    )

show(**kwargs)

Method to draw PT pseudosection.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
out (str or list) – Highligt zero-mode lines for given phases.
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used to divariant fields coloring. Colors are based on variance. Default ‘Purples’.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
alpha (float) – alpha value for colors. Default 0.6
connect (bool) – Whether mouse click echo stable assemblage to STDOUT. Default False.
show_vertices (bool) – Whether to show vertices of drawn areas. Default False.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

show_data(key, phase, expr=None, which=7)

Convinient function to show values of expression for given phase for user-defined combination of results of divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

show_delta(label=False, pointsec=False, skiplabels=0, labelfs=6)

Shows THERMOCALC execution time for all grid points.

Parameters:

pointsec (bool) – Whether to show points/sec or secs/point. Default False.
label (bool) – Whether to label divariant fields. Default False.

show_grid(phase, expr=None, interpolation=None, label=False, skiplabels=0, labelfs=6)

Convinient function to show values of expression for given phase only from Grid Data.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
interpolation (str) – matplotlib imshow interpolation method. Default None.
label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

show_status(label=False, skiplabels=0, labelfs=6): Shows status of grid calculations

property variance: Returns dictionary of variances

class pypsbuilder.psexplorer.PXPS(*args, **kwargs)

Bases: PS

Class to postprocess pxbuilder project

calculate_composition(nx=50, ny=50)

Method to calculate compositional variations on grid.

A compositions are calculated for stable assemblages in regular grid covering pT range of pseudosection. A stable assemblage is identified from constructed divariant fields. Results are stored in grid property as GridData instance. A property all_data_keys is updated.

Before any grid point calculation, ptguesses are updated from nearest invariant point. If calculation fails, nearest solution from univariant line is used to update ptguesses. Finally, if solution is still not found, the method fix_solutions is called and neigbouring grid calculations are used to provide ptguess.

Parameters:

nx (int) – Number of grid points along x direction (T)
ny (int) – Number of grid points along y direction (p)

fix_solutions()

Method try to find solution for grid points with failed status.

Ptguesses are used from successfully calculated neighboring points until solution is find. Otherwise ststus remains failed.

collect_all_data_keys()

Collect all phases and variables calculated on grid.

Result is stored in all_data_keys property as dictionary of dictionaries.

Example

To get list of all variables calculated for phase ‘g’ or end-member ‘g(alm)’ use:

>>> pt.all_data_keys['g']
['mode', 'x', 'z', 'm', 'f', 'xMgX', 'xFeX', 'xMnX', 'xCaX', 'xAlY',
'xFe3Y', 'H2O', 'SiO2', 'Al2O3', 'CaO', 'MgO', 'FeO', 'K2O', 'Na2O',
'TiO2', 'MnO', 'O', 'factor', 'G', 'H', 'S', 'V', 'rho']
>>> pt.all_data_keys['g(alm)']
['ideal', 'gamma', 'activity', 'prop', 'mu', 'RTlna']

collect_data(key, phase, expr, which=7)

Convinient function to retrieve values of expression for given phase for user-defined combination of results of divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

collect_grid_data(key, phase, expr)

Retrieve values of expression for given phase for all GridData points within divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

collect_inv_data(key, phase, expr)

Retrieve value of variables based expression for given phase for all invariant points surrounding divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

collect_uni_data(key, phase, expr)

Retrieve values of expression for given phase for all univariant lines surrounding divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

common_grid_and_masks(**kwargs): Initialize common grid and mask for all partial grids

create_masks(): Update grid masks from existing divariant fields

property endmembers: Returns dictionary with phases and their end-members names

gendrawpd(export_areas=True)

Method to write drawpd file

Parameters:: export_areas (bool) – Whether to include constructed areas. Default True.

get_grids(phase, expr=None)

Convinient function to get dictionary of grids.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

get_nearest_grid_data(x, y)

Retrieve nearest results from GridData to point.

Parameters:

x (float) – x-coordinate of point
y – y-coordiante of point

get_section_id(x, y): Return index of pseudosection and grid containing point

gidentify(label=False, skiplabels=0, labelfs=6)

Visual version of identify method. PT point is provided by mouse click.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

ginput_path(label=False, skiplabels=0, labelfs=6)

Collect Path data by mouse digitizing.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

glabel(label=False, skiplabels=0, labelfs=6)

Return formatted string of assamblage at PT point provided by mouse click.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

property gridded: True when compositional grid(s) is calculated, otherwise False

identify(x, y)

Return key (frozenset) of divariant field for given temperature and pressure.

Parameters:

x (float) – x coord
y (float) – y coord

invs_from_unilist(ix, unilist)

Return set of IDs of invariant points associated with unilines. lines.

Parameters:: unilist (iterable) – list of (section_id, uni_id) pairs
Returns:: set of associated invariant points
Return type:: set

isopleths(phase, expr=None, **kwargs)

Method to draw compositional isopleths.

Isopleths are drawn as contours for values evaluated from provided expression. Individual divariant fields are contoured separately, so final plot allows sharp changes accross univariant lines. Within divariant field the selected interpolation is used.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
N (int) – Max number of contours. Default 10.
step (int) – Step between contour levels. If defined, N is ignored. Default None.
cdf (bool) – When True contour levels are percentile based. Default False.
alpha (float) – Alpha value for filled contours. Default 1
levels (list) – User-defined contour levels. If defined, N and step is ignored.
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points. Default 7 (all data)
colorbar (bool) – Whether to show colorbar. Default True
method – Interpolation method. Default is ‘rbf’, other option is ‘quadratic’, which uses least-square fit to quadratic surface or ‘spline’ for bivariate spline interpolation.
rbf_func – Default ‘thin_plate’. See scipy.interpolation.Rbf
smooth (int) – Values greater than zero increase the smoothness of the approximation. 0 is for interpolation (default), the function will always go through the nodal points.
epsilon (int) – Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).
degree (int) – Degrees of the bivariate spline. Default is 3.
refine (int) – Degree of grid refinement. Default 1
filter_outliers (bool) – Whether to filter outliers. Defaut False.
filled (bool) – Whether to contours should be filled. Defaut True.
filled_over (bool) – Whether to overlay contourline over filled contours. Defaut False.
out (str or list) – Highligt zero-mode lines for given phases.
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used for contours. Default ‘viridis’.
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
labelkeys (frozenset or list) – Keys of divariant fields where contours should be labeled.
nosplit (bool) – Controls whether the contour underlying labels are removed or not. Defaut True
gradient (bool) – Whether the first derivate of values should be used. Default False.
dt (bool) – Whether the gradient should be calculated along temperature or pressure. Default True.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

isopleths_vector(phase, expr=None, **kwargs)

Method to draw vectorized compositional isopleths.

Isopleths are drawn as lines for values evaluated from provided expression. Individual divariant fields are contoured separately, so final plot allows sharp changes accross univariant lines. Within divariant field the selected interpolation is used.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
N (int) – Max number of contours. Default 10.
step (int) – Step between contour levels. If defined, N is ignored. Default None.
cdf (bool) – When True contour levels are percentile based. Default False.
levels (list) – User-defined contour levels. If defined, N and step is ignored.
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points. Default 7 (all data)
colorbar (bool) – Whether to show colorbar. Default True
method – Interpolation method. Default is ‘rbf’, other option is ‘quadratic’, which uses least-square fit to quadratic surface.
rbf_func – Default ‘thin_plate’. See scipy.interpolation.Rbf
smooth (int) – Values greater than zero increase the smoothness of the approximation. 0 is for interpolation (default), the function will always go through the nodal points.
epsilon (int) – Adjustable constant for gaussian or multiquadrics functions - defaults to approximate average distance between nodes (which is a good start).
degree (int) – Degrees of the bivariate spline. Default is 3.
refine (int) – Degree of grid refinement. Default 1
filter_outliers (bool) – Whether to filter outliers. Defaut False.
out (str or list) – Highligt zero-mode lines for given phases.
overlay (bool) – Whether to show assemblage fields. Default True
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used isopleths coloring. Default ‘viridis’.
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
lw (float) – Linewidth of isopleths. Default 1.0
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
color – matplotlib color for isopleths. If None cmap is used. Default None.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

property keys: Returns set of all existing multivariant fields. Fields are identified by frozenset of present phases called key.

merge_data(phase, expr, which=7)

Returns merged data obtained by collect_data method for all divariant fields.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

property name: Get project directory name.

overlap_isopleths(*args, **kwargs)

Function to overlaped isopleths ranges.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
levels (tuple) – tuple of min an max values for isopleth band
alpha (float) – Alpha value for filled contours. Default 1
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

Example

>>> pt.overlap_isopleths(
        'g', 'xMgX', (0.07, 0.13),
        'g', 'xFeX', (0.51, 0.65),
        'g', 'xCaX', (0.06, 0.15),
        'g', 'xMnX', (0.16, 0.27),
    )

property phases: Returns set of all phases present in pseudosection

remove_grid_data(key, phase, expr)

Remove calculated data from grid and mark as failed

Note: Click on all data to be discarded by mouse and finish be Enter

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.

save()

Save gridded copositions and constructed divariant fields into psbuilder project file.

Note that once project is edited with psbuilder, calculated compositions are removed and need to be recalculated using PTPS.calculate_composition method.

save_tab(comps, tabfile=None)

Export gridded values to Perple_X tab format. Could be used in pywerami.

Note: Gridded values are interpolated from raw results

Parameters:

comps (list) –
List of (phase, expr) tuples. phase (str): Phase or end-member named expr (str): Expression to evaluate. It could use any

variable existing for given phase. Check all_data_keys property for possible variables.
tabfile (str) – filename for tabfile. Default pseudosection name.tab

search_composition(*args, interpolation=None, label=False, skiplabels=0, labelfs=6, geterror=False, getpt=False, fig=None, fig_kw={}, isopleths=False, which=7, smooth=0, alpha=0.5, lw=2)

Function to plot root squared error of calculated and estimated values.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
val (float) – searched value
interpolation (str) – matplotlib imshow interpolation method. Default None.
label (bool) – Whether to label divariant fields. Default False.
geterror (bool) – When True, calculated RMSE array is returned. Otherwise error is plotted. Deafult False
getpt (bool) – When True return tuple of (p, T, err) where error is minimal. Default False
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
isopleths (bool) – When True, searched isopleths are shown. Default False
which (int) – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

Example

>>> pt.search_composition(
        'g', 'xMgX', 0.076,
        'g', 'xFeX', 0.525,
        'g', 'xCaX', 0.126,
        'g', 'xMnX', 0.268
    )

show(**kwargs)

Method to draw PT pseudosection.

Parameters:

label (bool) – Whether to label divariant fields. Default False.
out (str or list) – Highligt zero-mode lines for given phases.
high (frozenset or list) – Highlight divariant fields identified by key(s).
cmap (str) – matplotlib colormap used to divariant fields coloring. Colors are based on variance. Default ‘Purples’.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6
bulk (bool) – Whether to show bulk composition on top of diagram. Default False.
alpha (float) – alpha value for colors. Default 0.6
connect (bool) – Whether mouse click echo stable assemblage to STDOUT. Default False.
show_vertices (bool) – Whether to show vertices of drawn areas. Default False.
fig (Figure) – If not None, axes are added to fig and returned. Default None
fig_kw – dict passed to subplots method.
ax (Axes) – Axes to be used. Default None
filename – If not None, figure is saved to file
save_kw – dict passed to savefig method.
show (bool) – When False, Axes are returned, otherwise plot is shown. Default True

show_data(key, phase, expr=None, which=7)

Convinient function to show values of expression for given phase for user-defined combination of results of divariant field identified by key.

Parameters:

key (frozenset) – Key identifying divariant field
phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
which – Bitopt defining from where data are collected. 0 bit - invariant points, 1 bit - uniariant lines and 2 bit - GridData points

show_delta(label=False, pointsec=False, skiplabels=0, labelfs=6)

Shows THERMOCALC execution time for all grid points.

Parameters:

pointsec (bool) – Whether to show points/sec or secs/point. Default False.
label (bool) – Whether to label divariant fields. Default False.

show_grid(phase, expr=None, interpolation=None, label=False, skiplabels=0, labelfs=6)

Convinient function to show values of expression for given phase only from Grid Data.

Parameters:

phase (str) – Phase or end-member named
expr (str) – Expression to evaluate. It could use any variable existing for given phase. Check all_data_keys property for possible variables.
interpolation (str) – matplotlib imshow interpolation method. Default None.
label (bool) – Whether to label divariant fields. Default False.
skiplabels (float) – Minimal area fraction of fields to be labelled
labelfs (float) – Size of label font. Default 6

show_status(label=False, skiplabels=0, labelfs=6): Shows status of grid calculations

property variance: Returns dictionary of variances

tcapi module

`TC35API`
`TC34API`

class pypsbuilder.tcapi.TC35API(workdir, tcexe, drexe, encoding='mac-roman')

Bases: TCAPI

parse_logfile(**kwargs)

Parser for THERMOCALC 3.50beta output.

It parses the outputs of THERMOCALC after calculation.

Parameters:

output (str) – When not None, used as content of logfile. Default None.
resic (str) – When not None, used as content of icfile. Default None.
get_phases (bool) – When true returns also tuple (phases, out, calcs). Default False

Returns:

Result of parsing. ‘ok’, ‘nir’ (nothing in range) or ‘bombed’. results (TCResultSet): Results of TC calculation. output (str): Full nonparsed THERMOCALC output.

Return type:

status (str)

Example

Parse output after univariant line calculation in P-T pseudosection:

>>> tc = TCAPI('pat/to/dir')
>>> status, result, output = tc.parse_logfile()

update_scriptfile(**kwargs)

Method to update scriptfile.

This method is used to programatically edit scriptfile.

Kwargs:

calcs: List of lines defining fully hands-off calculations. Default None. get_old_calcs: When True method returns existing calcs lines

before possible modification. Default False.

guesses: List of lines defining ptguesses. If None guesses: are not modified. Default None.
get_old_guesses: When True method returns existing ptguess lines: before possible modification. Default False.

bulk: List of lines defining bulk composition. Default None. xsteps: Number of compositional steps between two bulks.

Default 20.

calc_t(phases, out, **kwargs)

Method to run THERMOCALC to find univariant line using Calc T at P strategy.

Parameters:

phases (set) – Set of present phases
out (set) – Set of single zero mode phase
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_p(phases, out, **kwargs)

Method to run THERMOCALC to find univariant line using Calc P at T strategy.

Parameters:

phases (set) – Set of present phases
out (set) – Set of single zero mode phase
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_pt(phases, out, **kwargs)

Method to run THERMOCALC to find invariant point.

Parameters:

phases (set) – Set of present phases
out (set) – Set of two zero mode phases
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_tx(phases, out, **kwargs)

Method to run THERMOCALC for T-X pseudosection calculations.

Parameters:

phases (set) – Set of present phases
out (set) – Set of zero mode phases
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
xvals (tuple) – range for X variable
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_px(phases, out, **kwargs)

Method to run THERMOCALC for p-X pseudosection calculations.

Parameters:

phases (set) – Set of present phases
out (set) – Set of zero mode phases
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
xvals (tuple) – range for X variable
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_assemblage(phases, p, t, onebulk=None)

Method to run THERMOCALC to calculate compositions of stable assemblage.

Parameters:

phases (set) – Set of present phases
p (float) – Temperature for calculation
t (float) – Pressure for calculation

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

dogmin(phases, p, t, variance, doglevel=1, onebulk=None)

Run THERMOCALC dogmin session.

Parameters:: variance (int) – Maximum variance to be considered
Returns:: THERMOCALC standard output
Return type:: str

calc_variance(phases)

Get variance of assemblage.

Parameters:: phases (set) – Set of present phases
Returns:: variance
Return type:: int

property axfile

Path to used a-x file.

Type:: pathlib.Path

property csvfile

Path to csv file.

Type:: pathlib.Path

property dataset

Version identification of thermodynamic dataset in use.

Type:: str

property datasetfile

Path to dataset file.

Type:: pathlib.Path

property drawpdfile

Path to drawpd file.

Type:: pathlib.Path

property drfile

Path to -dr output file.

Type:: pathlib.Path

property icfile

Path to ic file.

Type:: pathlib.Path

property itfile

Path to it file.

Type:: pathlib.Path

property logfile

Path to THERMOCALC log file.

Type:: pathlib.Path

property ofile

Path to project output file.

Type:: pathlib.Path

parse_dogmin(): Dogmin parser.

property prefsfile

Path to THERMOCALC prefs file.

Type:: pathlib.Path

rundr(): Method to run drawpd.

runtc(instr='kill\n\n')

Low-level method to actually run THERMOCALC.

Parameters:: instr (str) – String to be passed to standard input for session.
Returns:: THERMOCALC standard output
Return type:: str

property scriptfile

Path to scriptfile.

Type:: pathlib.Path

property tcnewversion

False for THERMOCALC older than 3.5.

Type:: bool

property tcversion

THERMOCALC version string

Type:: str

class pypsbuilder.tcapi.TC34API(workdir, tcexe, drexe, encoding='mac-roman')

Bases: TCAPI

parse_logfile(**kwargs)

Parser for THERMOCALC 3.4x output.

It parses the outputs of THERMOCALC after calculation.

Parameters:

output (str) – When not None, used as content of logfile. Default None.
resic (str) – When not None, used as content of icfile. Default None.
get_phases (bool) – When true returns also tuple (phases, out, calcs). Default False

Returns:

Result of parsing. ‘ok’, ‘nir’ (nothing in range) or ‘bombed’. results (TCResultSet): Results of TC calculation. output (str): Full nonparsed THERMOCALC output.

Return type:

status (str)

Example

Parse output after univariant line calculation in P-T pseudosection:

>>> tc = TCAPI('pat/to/dir')
>>> status, result, output = tc.parse_logfile()

update_scriptfile(**kwargs)

Method to update scriptfile.

This methodcould be used to read or update ptguess or dogmin settings.

Parameters:

guesses (list) – List of lines defining ptguesses. If None guesses are not modified. Default None.
get_old_guesses (bool) – When True method returns existing ptguess before possible modification. Default False.
dogmin (str) – Argument of dogmin script. Could be ‘no’ or ‘yes’ or ‘yes X’, where X is log level. When None no modification is done. Default None.
which (set) – Set of phases used for dogmin.
bulk (list) – Bulk composition. Default None.
xvals (tuple) – x values for compositions. Default (0, 1)
xsteps (int) – Number of compositional steps between two bulks. Default 20.
p (float) – Pressure for dogmin calculation
T (float) – Temperature for dogmin calculation

property axfile

Path to used a-x file.

Type:: pathlib.Path

property csvfile

Path to csv file.

Type:: pathlib.Path

property dataset

Version identification of thermodynamic dataset in use.

Type:: str

property datasetfile

Path to dataset file.

Type:: pathlib.Path

property drawpdfile

Path to drawpd file.

Type:: pathlib.Path

property drfile

Path to -dr output file.

Type:: pathlib.Path

property icfile

Path to ic file.

Type:: pathlib.Path

property itfile

Path to it file.

Type:: pathlib.Path

property logfile

Path to THERMOCALC log file.

Type:: pathlib.Path

property ofile

Path to project output file.

Type:: pathlib.Path

parse_dogmin(): Dogmin parser.

property prefsfile

Path to THERMOCALC prefs file.

Type:: pathlib.Path

rundr(): Method to run drawpd.

runtc(instr='kill\n\n')

Low-level method to actually run THERMOCALC.

Parameters:: instr (str) – String to be passed to standard input for session.
Returns:: THERMOCALC standard output
Return type:: str

property scriptfile

Path to scriptfile.

Type:: pathlib.Path

property tcnewversion

False for THERMOCALC older than 3.5.

Type:: bool

property tcversion

THERMOCALC version string

Type:: str

calc_t(phases, out, **kwargs)

Method to run THERMOCALC to find univariant line using Calc T at P strategy.

Parameters:

phases (set) – Set of present phases
out (set) – Set of single zero mode phase
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_p(phases, out, **kwargs)

Method to run THERMOCALC to find univariant line using Calc P at T strategy.

Parameters:

phases (set) – Set of present phases
out (set) – Set of single zero mode phase
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_pt(phases, out, **kwargs)

Method to run THERMOCALC to find invariant point.

Parameters:

phases (set) – Set of present phases
out (set) – Set of two zero mode phases
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_tx(phases, out, **kwargs)

Method to run THERMOCALC for T-X pseudosection calculations.

Parameters:

phases (set) – Set of present phases
out (set) – Set of zero mode phases
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_px(phases, out, **kwargs)

Method to run THERMOCALC for P-X pseudosection calculations.

Parameters:

phases (set) – Set of present phases
out (set) – Set of zero mode phases
prange (tuple) – Temperature range for calculation
trange (tuple) – Pressure range for calculation
steps (int) – Number of steps

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

calc_assemblage(phases, p, t)

Method to run THERMOCALC to calculate compositions of stable assemblage.

Parameters:

phases (set) – Set of present phases
p (float) – Temperature for calculation
t (float) – Pressure for calculation

Returns:

(tcout, ans) standard output and input for THERMOCALC run. Input ans could be used to reproduce calculation.

Return type:

tuple

dogmin(phases, p, t, variance, doglevel=1, onebulk=None)

Run THERMOCALC dogmin session.

Parameters:: variance (int) – Maximum variance to be considered
Returns:: THERMOCALC standard output
Return type:: str

calc_variance(phases)

Get variance of assemblage.

Parameters:: phases (set) – Set of present phases
Returns:: variance
Return type:: int

psclasses module

`InvPoint`	Class to store invariant point
`UniLine`	Class to store univariant line
`Dogmin`
`TCResult`
`TCResultSet`
`PTsection`	P-T pseudosection class
`TXsection`	T-X pseudosection class
`PXsection`	P-X pseudosection class

class pypsbuilder.psclasses.InvPoint(**kwargs)

Bases: PseudoBase

Class to store invariant point

id

Invariant point identification

Type:: int

phases

set of present phases

Type:: set

out

set of zero mode phases

Type:: set

cmd

THERMOCALC standard input to calculate this point

Type:: str

variance

variance

Type:: int

x

Array of x coordinates (even if only one, it is stored as array)

Type:: numpy.array

y

Array of x coordinates (even if only one, it is stored as array)

Type:: numpy.array

results

List of results dicts with data and ptgues keys.

Type:: list

output

Full THERMOCALC output

Type:: str

manual

True when inavariant point is user-defined and not calculated

Type:: bool

shape(): Return shapely Point representing invariant point.

all_unilines(): Return four tuples (phases, out) indicating possible four univariant lines passing trough this invariant point

annotation(state=0): str: String representation of ID with possible zermo mode phase.

datakeys(phase=None)

list: Get list of variables for phase.

Parameters:: phase (str) – name of phase

label(excess={}): str: full label with space delimeted phases - zero mode phase.

ptguess(**kwargs)

list: Get stored ptguesses.

InvPoint has just single ptguess, but for UniLine idx need to be specified. If omitted, the middle point from calculated ones is used.

Parameters:: idx (int) – index which guesses to get.

class pypsbuilder.psclasses.UniLine(**kwargs)

Bases: PseudoBase

Class to store univariant line

id

Invariant point identification

Type:: int

phases

set of present phases

Type:: set

out

set of zero mode phase

Type:: set

cmd

THERMOCALC standard input to calculate this point

Type:: str

variance

variance

Type:: int

_x

Array of x coordinates (all calculated)

Type:: numpy.array

_y

Array of x coordinates (all calculated)

Type:: numpy.array

results

List of results dicts with data and ptgues keys.

Type:: list

output

Full THERMOCALC output

Type:: str

manual

True when inavariant point is user-defined and not calculated

Type:: bool

begin

id of invariant point defining begining of the line. 0 for no begin

Type:: int

end

id of invariant point defining end of the line. 0 for no end

Type:: int

used

slice indicating which point on calculated line are between begin and end

Type:: slice

shape(ratio=None, tolerance=None)

Return shapely LineString representing univariant line.

This method is using trimmed points.

Parameters:

ratio – y-coordinate multiplier to scale coordinates. Default None
tolerance – tolerance x coordinates. Simplified object will be within
None (the tolerance distance of the original geometry. Default)

contains_inv(ip)

Check whether invariant point theoretically belong to univariant line.

Parameters:: ip (InvPoint) – Invariant point
Returns:: True for yes, False for no. Note that metastability is not checked.
Return type:: bool

get_label_point(): Returns coordinate tuple of labeling point for univariant line.

annotation(state=0): str: String representation of ID with possible zermo mode phase.

datakeys(phase=None)

list: Get list of variables for phase.

Parameters:: phase (str) – name of phase

label(excess={}): str: full label with space delimeted phases - zero mode phase.

ptguess(**kwargs)

list: Get stored ptguesses.

InvPoint has just single ptguess, but for UniLine idx need to be specified. If omitted, the middle point from calculated ones is used.

Parameters:: idx (int) – index which guesses to get.

class pypsbuilder.psclasses.Dogmin(**kwargs)

Bases: object

label(excess={}): str: full label with space delimeted phases.

annotation(show_out=False, excess={}): str: String representation of ID with possible zermo mode phase.

class pypsbuilder.psclasses.TCResult(T, p, variance=0, c=0, data={}, ptguess=['']): Bases: object

class pypsbuilder.psclasses.TCResultSet(results): Bases: object

class pypsbuilder.psclasses.PTsection(**kwargs)

Bases: SectionBase

P-T pseudosection class

getidinv(inv=None): Return id of either new or existing invariant point

getiduni(uni=None): Return id of either new or existing univariant line

class pypsbuilder.psclasses.TXsection(**kwargs)

Bases: SectionBase

T-X pseudosection class

getidinv(inv=None): Return id of either new or existing invariant point

getiduni(uni=None): Return id of either new or existing univariant line

class pypsbuilder.psclasses.PXsection(**kwargs)

Bases: SectionBase

P-X pseudosection class

getidinv(inv=None): Return id of either new or existing invariant point

getiduni(uni=None): Return id of either new or existing univariant line

Credits

Development Lead

Ondrej Lexa <lexa.ondrej@gmail.com>

Contributors

None yet. Why not be the first?

THERMOCALC is a thermodynamic calculation program (Powell & Holland 1988) that uses an internally-consistent thermodynamic dataset (Holland & Powell, 1998, 2011) to undertake thermobarometry and phase diagram calculations for metamorphic rocks. However, using THERMOCALC to create a diagram is quite laborious; each curve must be calculated by hand, and the Schreinemaker’s analysis must be done manually. The curves must be built up one by one, and manually combined.

pypsbuilder is developed with the idea to make this tedious process much easier and more enjoyable while keeping the concept to force users understand the Phase Rule, Schreinemaker’s analysis, and how variance changes across field boundaries.

Check Pseudosection builders tutorial and Pseudosection explorers tutorial or watch video below to see pypsbuilder in action.

Welcome to pypsbuilder documentation!

Installation

Upgrade to latest version

Pseudosection builders tutorial

Scriptfile modifications for TC3.4

Scriptfile modifications for TC3.5

Using tcinit to setup working directory

New P-T pseudosection project

Create invariant point

Create univariant line

Starting guesses

Phase out and phase stable lines

Manual invariant points or univariant lines

Searching for invariant points

Dogmin

Double crossing univariant lines

T-X and P-X pseudosections

Pseudosection explorers tutorial

Using psexplorers in Python (or Jupyter notebook)

Visualize pseudosection

Access data and variables stored in project

Calculations along PT paths

Extra

Command line scripts

Draw isopleths diagrams

Python API

psexplorer module

tcapi module

psclasses module

Credits

Development Lead

Contributors

Indices and tables