Module pyCHNOSZ.fun
Expand source code
import os
from contextlib import contextmanager
from IPython.display import Image, display
import pandas as pd
import numpy as np
import re
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import copy
import statistics
import pkg_resources
import decimal
import chemparse
import rpy2.rinterface_lib.callbacks
import logging
rpy2.rinterface_lib.callbacks.logger.setLevel(logging.ERROR) # will display errors, but not warnings
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
from rpy2.robjects.lib import grdevices
pandas2ri.activate()
CHNOSZ = importr("CHNOSZ")
grdev = importr('grDevices')
NumberTypes = (int, float, complex)
@contextmanager
def __r_inline_plot(width=600, height=520, dpi=150, plot_it=True):
"""
Display R plots inline.
Parameters
----------
width, height : numeric, default 600 by 520
Width and height of the plot.
dpi : numeric, default 150
Resolution of the plot.
plot_it : bool, default True
Render the plot?
"""
with grdevices.render_to_bytesio(grdevices.png,
width=width,
height=height,
res=dpi) as b:
yield
data = b.getvalue()
if plot_it:
display(Image(data=data, format='png', embed=True))
class R_output(object):
def capture_r_output(self):
"""
Capture and create a list of R console messages
"""
# Record output #
self.stdout = []
self.stderr = []
# Dummy functions #
def add_to_stdout(line): self.stdout.append(line)
def add_to_stderr(line): self.stderr.append(line)
# Keep the old functions #
self.stdout_orig = rpy2.rinterface_lib.callbacks.consolewrite_print
self.stderr_orig = rpy2.rinterface_lib.callbacks.consolewrite_warnerror
# Set the call backs #
rpy2.rinterface_lib.callbacks.consolewrite_print = add_to_stdout
rpy2.rinterface_lib.callbacks.consolewrite_warnerror = add_to_stderr
def __flatten_list(_2d_list):
flat_list = []
# Iterate through the outer list
for element in _2d_list:
if type(element) is list:
# If the element is of type list, iterate through the sublist
for item in element:
flat_list.append(item)
else:
flat_list.append(element)
return flat_list
def __save_figure(fig, save_as, save_format, save_scale, plot_width, plot_height, ppi):
if isinstance(save_format, str) and save_format not in ['png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json', 'html']:
raise Exception("{}".format(save_format)+" is an unrecognized "
"save format. Supported formats include 'png', "
"'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', "
"'json', or 'html'")
if isinstance(save_format, str):
if not isinstance(save_as, str):
save_as = "newplot"
if save_format=="html":
fig.write_html(save_as+".html")
print("Saved figure as {}".format(save_as)+".html")
save_format = 'png'
elif save_format in ['pdf', 'eps', 'json']:
pio.write_image(fig, save_as+"."+save_format, format=save_format, scale=save_scale,
width=plot_width*ppi, height=plot_height*ppi)
print("Saved figure as {}".format(save_as)+"."+save_format)
save_format = "png"
else:
pio.write_image(fig, save_as+"."+save_format, format=save_format, scale=save_scale,
width=plot_width*ppi, height=plot_height*ppi)
print("Saved figure as {}".format(save_as)+"."+save_format)
else:
save_format = "png"
return save_as, save_format
def html_chemname_format(name):
"""
Format a chemical formula to display subscripts and superscripts in HTML
(e.g., Plotly plots)
Example, "CH3COO-" becomes "CH<sub>3</sub>COO<sup>-</sup>"
Parameters
----------
name : str
A chemical formula.
Returns
-------
A formatted chemical formula string.
"""
p = re.compile(r'(?P<sp>[-+]\d*?$)')
name = p.sub(r'<sup>\g<sp></sup>', name)
charge = re.search(r'<.*$', name)
name_no_charge = re.match(r'(?:(?!<|$).)*', name).group(0)
mapping = {"0": "<sub>0</sub>", "1": "<sub>1</sub>", "2": "<sub>2</sub>", "3": "<sub>3</sub>", "4": "<sub>4</sub>",
"5": "<sub>5</sub>", "6": "<sub>6</sub>", "7": "<sub>7</sub>", "8": "<sub>8</sub>", "9": "<sub>9</sub>",
".":"<sub>.</sub>"}
name_no_charge_formatted = "".join([mapping.get(x) or x for x in list(name_no_charge)])
if charge != None:
name = name_no_charge_formatted + charge.group(0)
else:
name = name_no_charge_formatted
return name
def __seq(start, end, by=None, length_out=None):
"""
Mimic the seq() function in base R.
"""
len_provided = True if (length_out is not None) else False
by_provided = True if (by is not None) else False
if (not by_provided) & (not len_provided):
raise ValueError('At least by or n_points must be provided')
width = end - start
eps = pow(10.0, -14)
if by_provided:
if (abs(by) < eps):
raise ValueError('by must be non-zero.')
absby = abs(by)
if absby - width < eps:
length_out = int(width / absby)
else:
# by is too great, we assume by is actually length_out
length_out = int(by)
by = width / (by - 1)
else:
length_out = int(length_out)
by = width / (length_out - 1)
out = [float(start)]*length_out
for i in range(1, length_out):
out[i] += by * i
if abs(start + by * length_out - end) < eps:
out.append(end)
return out
def solubility(iaq=None, in_terms_of=None, dissociate=False, find_IS=False,
messages=True, **kwargs):
"""
Python wrapper for the solubility() function in CHNOSZ.
Calculate chemical activities of aqueous species in equilibrium with a mineral or gas.
Parameters
----------
iaq : str, int, or list of str or int
Name(s) of aqueous species (if str). If int, the index of the aqueous
species in the OBIGT database.
in_terms_of : str, optional
Express the total solubility in terms of moles of this species.
dissociate : bool, default False
Does the mineral undergo a dissociation reaction?
find_IS : bool, default False
Find the equilibrium ionic strength by iteration?
messages : bool, default True
Display messages from CHNOSZ?
**kwargs : named arguments
Arguments for `affinity` or `mosaic` (i.e. plotting variables).
Returns
-------
s : rpy2.ListVector
Output from `solubility`.
"""
args = {'dissociate':dissociate, 'find_IS':find_IS}
if in_terms_of != None: args["in_terms_of"] = in_terms_of
args["iaq"] = _convert_to_RVector(iaq, force_Rvec=True)
for key, value in kwargs.items():
if isinstance(value, list):
value = ro.FloatVector(value)
args.update({key:value})
capture = R_output()
capture.capture_r_output()
s = CHNOSZ.solubility(**args)
if messages:
for line in capture.stderr: print(line)
return s
def retrieve(elements=None, ligands=None, state=None, T=None, P="Psat",
add_charge=True, hide_groups=True, must_have=None, messages=True):
"""
Python wrapper for the retrieve() function in CHNOSZ.
Retrieve species in the database containing one or more chemical elements.
Parameters
----------
elements : str, list of str, or tuple of str
Elements in a chemical system. If `elements` is a string, retrieve
species containing that element.
E.g., `retrieve("Au")` will return all species containing Au.
If `elements` is a list, retrieve species that have all of the elements
in the list.
E.g., `retrieve(["Au", "Cl"])` will return all species that have both
Au and Cl.
If `elements` is a tuple, retrieve species relevant to the system,
including charged species.
E.g., `retrieve(("Au", "Cl"))` will return species that have Au
and/or Cl, including charged species, but no other elements.
ligands : str, list of str, or tuple of str, optional
Elements present in any ligands.
state : str, list of str, or tuple of str, optional
Filter the result on these state(s).
T : float, optional
Temperature where DeltaG0 of species must not be NA
P : float or "Psat", default "Psat"
Pressure where DeltaG0 of species must not be NA
add_charge : bool, default True
Add charge to the system?
hide_groups : bool, default True
Exclude groups from the result?
must_have : str or list of str, optional
Retrieved species must have the element(s).
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
out: list or dict
A list of the OBIGT indices of retrieved chemical species
"""
if elements == None:
elements = ro.r("NULL")
elif isinstance(elements, list) or isinstance(elements, str):
elements = _convert_to_RVector(elements, force_Rvec=True)
elif isinstance(elements, tuple):
elements = ro.ListVector({chr(ord('`')+i):l for i,l in zip(range(1, len(elements)+1), elements)})
else:
pass
if ligands == None:
ligands = ro.r("NULL")
elif isinstance(ligands, list) or isinstance(ligands, str):
ligands = _convert_to_RVector(ligands, force_Rvec=True)
elif isinstance(ligands, tuple):
ligands = ro.ListVector({chr(ord('`')+i):l for i,l in zip(range(1, len(ligands)+1), ligands)})
else:
pass
if state == None:
state = ro.r("NULL")
elif isinstance(state, list) or isinstance(state, str):
state = _convert_to_RVector(state, force_Rvec=True)
elif isinstance(state, tuple):
state = ro.ListVector({chr(ord('`')+i):l for i,l in zip(range(1, len(state)+1), state)})
else:
pass
if T == None:
T = ro.r("NULL")
if P == None:
P = ro.r("NULL")
args = {'elements':elements, 'ligands':ligands, 'state':state, 'T':T, 'P':P,
'add_charge':add_charge, 'hide_groups':hide_groups}
capture = R_output()
capture.capture_r_output()
out = CHNOSZ.retrieve(**args)
if messages:
for line in capture.stderr: print(line)
out = list(out)
if isinstance(must_have, str):
must_have = [must_have]
if isinstance(must_have, list):
df = info(out, messages=False)
formulas = df["formula"].apply(chemparse.parse_formula)
keep_ind = []
for i,formula in enumerate(formulas):
if all(elem in formula.keys() for elem in must_have):
keep_ind.append(i)
keep_ind = list(set(keep_ind))
out = [out[i] for i in keep_ind]
return out
def animation(basis_args={}, species_args={}, affinity_args={},
equilibrate_args=None, diagram_args={},
anim_var="T", anim_range=[0, 350, 8],
messages=False):
"""
Produce an animated interactive affinity, activity, or predominance diagram.
Parameters
----------
basis_args : dict
Dictionary of options for defining basis species (see `basis`) in the
animated diagram.
Example: basis_args={'species':['CO2', 'O2', 'H2O', 'H+']}
species_args : dict
Dictionary of options for defining species (see `species`) in the
animated diagram.
Example: species_args={'species':['CO2', 'HCO3-', 'CO3-2']}
affinity_args : dict
Dictionary of options for defining the affinity calculation (see
`affinity`).
Example: affinity_args={"pH":[2, 12, 100]}
Example: affinity_args={"pH":[2, 12, 100], "P":[2000, 4000, 100]}
equilibrate_args : dict or None, default None
Dictionary of options for defining equilibration calculation
(see `equilibrate`). If None, plots output from `affinity`.
Example: equilibrate_args={"balance":1}
diagram_args : dict
Dictionary of options for diagramming (see `diagram`). Diagram option
`interactive` is set to True.
Example: diagram_args={"alpha":True}
anim_var : str, default "T"
Variable that changes with each frame of animation.
anim_range : list of numeric, default [0, 350, 8]
The first two numbers in the list are the starting and ending
values for `anim_var`. The third number in the list is the desired
number of animation frames.
messages : bool, default True
Display messages from CHNOSZ?
Returns
-------
An interactive animated plot.
"""
# cap number of frames in animation. Remove limitation after more testing.
if isinstance(anim_range, list):
if len(anim_range) == 3:
if anim_range[2] > 30:
raise Exception("anim_range is limited to 30 frames.")
else:
raise Exception("anim_range must be a list with three values: starting "
"value of anim_var, stopping value, and number of "
"frames in the animation")
else:
raise Exception("anim_range must be a list with three values: starting "
"value of anim_var, stopping value, and number of "
"frames in the animation")
if isinstance(basis_args, dict):
if "species" not in basis_args.keys():
raise Exception("basis_args needs a list of basis species for 'species'. "
"Example: basis_args={'species':['CO2', 'O2', 'H2O', 'H+']}")
else:
raise Exception("basis_args needs to be a Python dictionary with a key "
"called 'species' (additional keys are optional). "
"Example: basis_args={'species':['CO2', 'O2', 'H2O', 'H+']}")
if isinstance(species_args, dict):
if "species" not in species_args.keys():
raise Exception("species_args needs a list of species for 'species'. "
"Example: species_args={'species':['CO2', 'HCO3-', 'CO3-2']}")
else:
raise Exception("species_args needs to be a Python dictionary with a key "
"called 'species' (additional keys are optional). "
"Example: species_args={'species':['CO2', 'HCO3-', 'CO3-2']}")
basis_sp = basis_args["species"]
sp = species_args["species"]
if isinstance(sp[0], (int, np.integer)):
sp = [info(s, messages=False)["name"].values[0] for s in sp]
basis_out = basis(**basis_args)
if "logact" in species_args.keys():
mod_species_logact = copy.copy(species_args['logact'])
del species_args['logact']
else:
mod_species_logact = []
species_out = species(**species_args)
if len(mod_species_logact)>0:
for i in range(0, len(mod_species_logact)) :
species_out = species(species_args["species"][i], mod_species_logact[i])
dfs = []
dmaps = []
dmaps_names = []
if len(anim_range) == 2:
anim_res = 8
anim_range = anim_range + [anim_res]
elif len(anim_range) == 3:
anim_res = anim_range[2]
anim_range = [anim_range[0], anim_range[1]]
zvals = __seq(anim_range[0], anim_range[1], length_out=anim_res)
if "messages" not in affinity_args.keys():
affinity_args["messages"] = messages
if "messages" not in diagram_args.keys():
diagram_args["messages"] = messages
if "plot_it" not in diagram_args.keys():
diagram_args["plot_it"] = False
diagram_args["interactive"] = True
for z in zvals:
if anim_var in basis_out.index:
basis_out = basis(anim_var, z)
elif anim_var in list(species_out["name"]):
species_out = species(anim_var, z)
else:
affinity_args[anim_var] = z
aeout = affinity(**affinity_args)
if equilibrate_args != None:
equilibrate_args["aout"] = aeout
if "messages" not in equilibrate_args.keys():
equilibrate_args["messages"] = messages
aeout = equilibrate(**equilibrate_args)
aeout_args = aeout.rx2("args")
xvar = aeout_args.names[0]
xrange = list(aeout_args[0])
res_default = 256 # default affinity resolution
if len(xrange) == 3:
xres = int(xrange[2])
else:
xres = res_default
diagram_args["eout"] = aeout
df = diagram(**diagram_args)
df[anim_var] = z
dfs.append(df)
if 'pred' not in df.columns:
# affinity/activity plot
is_predom_plot = False
else:
# predominance plot
is_predom_plot = True
yvar = aeout_args.names[1]
yrange = list(aeout_args[1])
if len(yrange) == 3:
yres = int(yrange[2])
else:
yres = res_default
data = np.array(df.pred)
shape = (xres, yres)
dmap = data.reshape(shape)
dmaps.append(dmap)
data = np.array(df.prednames)
shape = (xres, yres)
dmap_names = data.reshape(shape)
dmaps_names.append(dmap_names)
xvals = __seq(xrange[0], xrange[1], length_out=xres)
if not is_predom_plot:
if 'loga.equil' not in aeout.names:
yvar = "A/(2.303RT)"
else:
yvar = "log a"
if "alpha" in diagram_args.keys():
if diagram_args["alpha"]:
yvar = "alpha"
df_c = pd.concat(dfs)
fig = px.line(df_c, x=xvar, y="value", color='variable', template="simple_white",
width=500, height=400, animation_frame=anim_var,
labels=dict(value=yvar, x=xvar),
)
if "annotation" in diagram_args.keys():
if "annotation_coords" not in diagram_args.keys():
diagram_args["annotation_coords"] = [0, 0]
fig.add_annotation(x=diagram_args["annotation_coords"][0],
y=diagram_args["annotation_coords"][1],
xref="paper",
yref="paper",
align='left',
text=diagram_args["annotation"],
bgcolor="rgba(255, 255, 255, 0.5)",
showarrow=False)
if 'main' in diagram_args.keys():
fig.update_layout(title={'text':diagram_args["main"], 'x':0.5, 'xanchor':'center'})
fig.update_layout(legend_title=None)
config = {'displaylogo': False,
'modeBarButtonsToRemove': ['resetScale2d', 'toggleSpikelines']}
fig.show(config=config)
return
else:
yvals = __seq(yrange[0], yrange[1], length_out=yres)
unit_dict = {"P":"bar", "T":"°C", "pH":"", "Eh":"volts", "IS":"mol/kg"}
if any([anim_var in basis_out.index, anim_var in list(species_out["name"])]) and anim_var not in unit_dict.keys():
unit_dict[anim_var] = "logact "+anim_var
for s in basis_sp:
unit_dict[s] = "logact "+s
xlab = xvar+", "+unit_dict[xvar]
if xvar in basis_sp:
xlab = unit_dict[xvar]
if xvar == "pH":
xlab = "pH"
if is_predom_plot:
ylab = yvar+", "+unit_dict[yvar]
if yvar in basis_sp:
ylab = unit_dict[yvar]
if yvar == "pH":
yvar = "pH"
frames = []
slider_steps = []
annotations = []
cst_data = []
heatmaps = []
# i is a frame in the animation
for i in range(0, len(zvals)):
annotations_i = []
for s in sp:
if s in set(dfs[i]["prednames"]):
# if an annotation should appear, create one for this frame
df_s = dfs[i].loc[dfs[i]["prednames"]==s,]
namex = df_s[xvar].mean()
namey = df_s[yvar].mean()
a = go.layout.Annotation(
x=namex,
y=namey,
xref="x",
yref="y",
text=html_chemname_format(s),
bgcolor="rgba(255, 255, 255, 0.5)",
showarrow=False,
)
else:
# if an annotation shouldn't appear, make an invisible annotation
# (workaround for a plotly bug where annotations won't clear in an animation)
namex = statistics.mean(xvals)
namey = statistics.mean(yvals)
a = go.layout.Annotation(
x=namex,
y=namey,
xref="x",
yref="y",
text="",
bgcolor="rgba(255, 255, 255, 0)",
showarrow=False,
)
annotations_i.append(a)
# allows adding a custom annotation; append to frame
if "annotation" in diagram_args.keys():
if "annotation_coords" not in diagram_args.keys():
diagram_args["annotation_coords"] = [0, 0]
custom_annotation = go.layout.Annotation(
x=diagram_args["annotation_coords"][0],
y=diagram_args["annotation_coords"][1],
xref="paper",
yref="paper",
align='left',
text=diagram_args["annotation"],
bgcolor="rgba(255, 255, 255, 0.5)",
showarrow=False,
)
annotations_i.append(custom_annotation)
annotations.append(annotations_i)
if 'ylab' in diagram_args.keys():
ylab = diagram_args["ylab"]
hover_ylab = ylab+': %{y} '
else:
ylab = html_chemname_format(ylab)
hover_ylab = yvar+': %{y} '+unit_dict[yvar]
if 'xlab' in diagram_args.keys():
xlab = diagram_args["xlab"]
hover_xlab = xlab+': %{x} '
else:
xlab = html_chemname_format(xlab)
hover_xlab = xvar+': %{x} '+unit_dict[xvar]
heatmaps_i = go.Heatmap(z=dmaps[i], x=xvals, y=yvals, zmin=0, zmax=len(sp)-1,
customdata=dmaps_names[i],
hovertemplate=hover_xlab+'<br>'+hover_ylab+'<br>Region: %{customdata}<extra></extra>')
heatmaps.append(heatmaps_i)
frame = go.Frame(data=[heatmaps_i],
name=str(i),
layout=go.Layout(annotations=annotations_i))
frames.append(frame)
slider_step = dict(
method='animate',
label=zvals[i],
value=i,
args=[
[i],
dict(
frame=dict(duration=300, redraw=True),
mode='immediate',
transition=dict(duration=0)
)
]
)
slider_steps.append(slider_step)
fig = go.Figure(
data = heatmaps[0],
layout=go.Layout(
# title="Frame 0",
title_x=0.5,
width=500, height=500,
annotations=annotations[0],
sliders=[dict(
active=0,
yanchor='top',
xanchor='left',
currentvalue=dict(
font=dict(size=12),
prefix='{}: '.format(anim_var),
suffix=' '+unit_dict[anim_var],
visible=True,
xanchor='right'
),
transition=dict(duration=0, easing='cubic-in-out'),
pad=dict(b=10, t=50),
len=0.9,
x=0.1,
y=0,
steps=slider_steps
)],
updatemenus=[dict(
type="buttons",
buttons=[dict(label="Play",
method="animate",
args=[None, {"fromcurrent":True}]),
dict(label="Pause",
method="animate",
args=[[None],
{"frame": {"duration": 0, "redraw": True},
"mode": "immediate",
"transition": {"duration": 0}}],
)],
direction="left",
pad={"r": 10, "t": 87},
showactive=False,
x=0.1,
xanchor="right",
y=0,
yanchor="top",
)]
),
frames=frames
)
fig.update_traces(dict(showscale=False,
colorscale='viridis'),
selector={'type':'heatmap'})
fig.update_layout(
xaxis_title=xlab,
yaxis_title=ylab,
xaxis={"range":[list(dfs[0][xvar])[0], list(dfs[0][xvar])[-1]]},
yaxis={"range":[list(dfs[0][yvar])[0], list(dfs[0][yvar])[-1]]},
margin={"t": 60, "r":60},
)
if 'main' in diagram_args.keys():
fig.update_layout(title={'text':diagram_args['main'], 'x':0.5, 'xanchor':'center'})
config = {'displaylogo': False,
'modeBarButtonsToRemove': ['zoom2d', 'pan2d', 'zoomIn2d', 'zoomOut2d',
'autoScale2d', 'toggleSpikelines',
'hoverClosestCartesian', 'hoverCompareCartesian']}
fig.show(config=config)
def diagram_interactive(data, title=None,
annotation=None, annotation_coords=[0, 0],
balance=None, xlab=None, ylab=None, colormap="viridis",
width=600, height=520, alpha=False, messages=True,
plot_it=True, save_as=None, save_format=None, save_scale=1):
"""
Produce an interactive diagram.
Parameters
----------
data : rpy2.ListVector
Output from `equilibrate` or `affinity`.
title : str, optional
Title of the plot.
annotation : str, optional
Annotation to add to the plot.
annotation_coords : list of numeric, default [0, 0], optional
Coordinates of annotation, where 0,0 is bottom left and 1,1 is top
right.
balance : str or numeric, optional
How to balance the transformations.
xlab, ylab : str
Custom x and y axes labels.
width, height : numeric, default 600 by 520
Width and height of the plot.
alpha : bool or str (balance), default False
For speciation diagrams, plot degree of formation instead of
activities?
messages : bool, default True
Display messages from CHNOSZ?
plot_it : bool, default True
Show the plot?
save_as : str, optional
For interactive plots only (`interactive`=True). Provide a filename to
save this figure. Filetype of saved figure is determined by
`save_format`.
Note: interactive plots can be saved by clicking the 'Download plot'
button in the plot's toolbar.
save_format : str, default "png"
For interactive plots only (`interactive`=True). Desired format of saved
or downloaded figure. Can be 'png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf',
'eps', 'json', or 'html'. If 'html', an interactive plot will be saved.
Only 'png', 'svg', 'jpeg', and 'webp' can be downloaded with the
'download as' button in the toolbar of an interactive plot.
save_scale : numeric, default 1
For interactive plots only (`interactive`=True). Multiply
title/legend/axis/canvas sizes by this factor when saving the figure.
Returns
-------
An interactive plot.
"""
basis_sp = list(data.rx2("basis").index)
xyvars = list(data.rx2("vars"))
xyvals = list(data.rx2("vals"))
if 'loga.equil' not in data.names:
calc_type = "a"
else:
calc_type = "e"
data = equilibrate(data, balance=balance, messages=messages)
if calc_type=="a":
out_vals = data.rx2("values")
out_units = "A/(2.303RT)"
nsp = len(out_vals)
lsp = out_vals[0].size
flat_out_vals = np.concatenate(tuple(arr.transpose() for arr in out_vals), axis=0).reshape(nsp, lsp).tolist()
df = pd.DataFrame(flat_out_vals)
df["n.balance"] = list(data.rx2("n.balance"))
# divide values by balance
df = df.apply(lambda row: row/row["n.balance"], axis=1)
df = df.drop(["n.balance"], axis=1)
sp = list(info([int(val) for val in list(out_vals.names)], messages=False)["name"])
elif calc_type=="e":
out_vals = data.rx2("loga.equil")
out_units = "log a"
nsp = len(out_vals)
lsp = out_vals[0].size
flat_out_vals = np.concatenate(tuple(arr.transpose() for arr in out_vals), axis=0).reshape(nsp, lsp).tolist()
df = pd.DataFrame(flat_out_vals)
sp = list(info([int(val) for val in list(data.rx2("values").names)], messages=False)["name"])
df.index = sp
df = df.transpose()
if alpha and len(xyvars) == 1:
df = df.applymap(lambda x: 10**x)
df = df[sp].div(df[sp].sum(axis=1), axis=0)
xvar = xyvars[0]
xvals = [float(val) for val in xyvals[0]]
if len(xyvars) == 1:
df[xvar] = xvals
if not isinstance(ylab, str):
if alpha:
ylab = "alpha"
else:
ylab = out_units
ylab = html_chemname_format(ylab)
df = pd.melt(df, id_vars=xyvars, value_vars=sp)
elif len(xyvars)==2:
# predominance plot
yvar = xyvars[1]
yvals = [float(val) for val in xyvals[1]]
df["pred"] = df.idxmax(axis=1)
df["prednames"] = df["pred"]
xvals_full = xvals*len(yvals)
yvals_full = __flatten_list([[y]*len(xvals) for y in yvals])
df[xvar] = xvals_full
df[yvar] = yvals_full
unit_dict = {"P":"bar", "T":"°C", "pH":"", "Eh":"volts", "IS":"mol/kg"}
for s in basis_sp:
unit_dict[s] = "logact "+s
if not isinstance(xlab, str):
xlab = xvar+", "+unit_dict[xvar]
if xvar == "pH":
xlab = "pH"
if xvar in basis_sp:
xlab = unit_dict[xvar]
xlab = html_chemname_format(xlab)
if len(xyvars) == 1:
df["variable"] = df["variable"].apply(html_chemname_format)
fig = px.line(df, x=xvar, y="value", color='variable', template="simple_white",
width=width, height=height,
labels=dict(value=ylab, x=xlab), render_mode='svg',
)
fig.update_layout(xaxis_title=xlab,
yaxis_title=ylab,
legend_title=None)
if isinstance(title, str):
fig.update_layout(title={'text':title, 'x':0.5, 'xanchor':'center'})
if isinstance(annotation, str):
fig.add_annotation(
x=annotation_coords[0],
y=annotation_coords[1],
text=annotation,
showarrow=False,
xref="paper",
yref="paper",
align='left',
bgcolor="rgba(255, 255, 255, 0.5)")
save_as, save_format = __save_figure(fig, save_as, save_format, save_scale,
plot_width=width, plot_height=height, ppi=1)
config = {'displaylogo': False,
'modeBarButtonsToRemove': ['resetScale2d', 'toggleSpikelines'],
'toImageButtonOptions': {
'format': save_format, # one of png, svg, jpeg, webp
'filename': save_as,
'height': height,
'width': width,
'scale': save_scale,
},
}
if len(xyvars) == 2:
mappings = {'pred': {s:lab for s,lab in zip(sp,range(0,len(sp)))}}
df.replace(mappings, inplace=True)
data = np.array(df.pred)
shape = (len(xvals), len(yvals))
dmap = data.reshape(shape)
data = np.array(df.prednames)
shape = (len(xvals), len(yvals))
dmap_names = data.reshape(shape)
if not isinstance(ylab, str):
ylab = yvar+", "+unit_dict[yvar]
if yvar in basis_sp:
ylab = unit_dict[yvar]
if yvar == "pH":
ylab = "pH"
ylab = html_chemname_format(ylab)
fig = px.imshow(dmap, width=width, height=height, aspect="auto",
labels=dict(x=xlab, y=ylab, color="region"),
x=xvals, y=yvals, template="simple_white",
)
fig.update(data=[{'customdata': dmap_names,
'hovertemplate': xlab+': %{x}<br>'+ylab+': %{y}<br>Region: %{customdata}<extra></extra>'}])
if colormap == 'none':
colormap = [[0, 'white'], [1, 'white']]
fig.update_traces(dict(showscale=False,
coloraxis=None,
colorscale=colormap),
selector={'type':'heatmap'})
fig.update_yaxes(autorange=True)
if isinstance(title, str):
fig.update_layout(title={'text':title, 'x':0.5, 'xanchor':'center'})
for s in sp:
if s in set(df["prednames"]):
df_s = df.loc[df["prednames"]==s,]
namex = df_s[xvar].mean()
namey = df_s[yvar].mean()
fig.add_annotation(x=namex, y=namey,
text=html_chemname_format(s),
bgcolor="rgba(255, 255, 255, 0.5)",
showarrow=False)
if isinstance(annotation, str):
fig.add_annotation(
x=annotation_coords[0],
y=annotation_coords[1],
text=annotation,
showarrow=False,
xref="paper",
yref="paper",
align='left',
bgcolor="rgba(255, 255, 255, 0.5)")
save_as, save_format = __save_figure(fig, save_as, save_format, save_scale,
plot_width=width, plot_height=height, ppi=1)
config = {'displaylogo': False,
'modeBarButtonsToRemove': ['zoom2d', 'pan2d', 'zoomIn2d', 'zoomOut2d',
'autoScale2d', 'resetScale2d', 'toggleSpikelines',
'hoverClosestCartesian', 'hoverCompareCartesian'],
'toImageButtonOptions': {
'format': save_format, # one of png, svg, jpeg, webp
'filename': save_as,
'height': height,
'width': width,
'scale': save_scale,
},
}
if plot_it:
fig.show(config=config)
return df, fig
def _convert_to_RVector(value, force_Rvec=True):
"""
Convert a value or list into an R vector of the appropriate type.
Parameters
----------
value : numeric or str, or list of numeric or str
Value to be converted.
force_Rvec : bool, default True
If `value` is not a list, force conversion into a R vector?
False will return an int, float, or str if value is non-list.
True will always return an R vector.
Returns
-------
int, float, str, an rpy2 R vector
A value or R vector of an appropriate data type.
"""
if not isinstance(value, list) and not force_Rvec:
return value
elif not isinstance(value, list) and force_Rvec:
value = [value]
else:
pass
if all(isinstance(x, bool) for x in value):
return ro.BoolVector(value)
elif all(isinstance(x, (int, np.integer)) for x in value):
return ro.IntVector(value)
elif all(isinstance(x, (int, np.integer, float, np.float)) for x in value):
return ro.FloatVector(value)
else:
return ro.StrVector(value)
def water(property=None, T=298.15, P="Psat", P1=True, messages=True):
"""
Python wrapper for the water() function in CHNOSZ.
Calculate thermodynamic and electrostatic properties of water.
Parameters
----------
property : str or list of str, optional
Computational setting or property(s) to calculate. To set a water model,
use 'SUPCRT92' (default) or 'SUPCRT', 'IAPWS95' or 'IAPWS', or 'DEW'.
To calculate a property, use 'A', 'G', 'S', 'U', etc. See
http://chnosz.net/manual/water.html for the complete catalog of
properties that can be calculated, their units, and their availability
in water models.
T : numeric, default 298.15
Temperature (K)
P : numeric, default "Psat"
Pressure (bar), or Psat for vapor-liquid saturation.
P1 : bool, default True
Output pressure of 1 bar below 100 °C instead of calculated values of Psat?
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
out : float or dict
Calculated value of desired water property. If `property` is a list,
returns a dictionary of calculated values.
"""
if property == None:
property = ro.r("NULL")
elif isinstance(property, list):
property = _convert_to_RVector(property, force_Rvec=True)
else:
pass
args = {'property':property, 'T':T, 'P':P, 'P1':P1}
capture = R_output()
capture.capture_r_output()
out = CHNOSZ.water(**args)
if messages:
for line in capture.stderr: print(line)
if property not in ["SUPCRT92", "SUPCRT", "IAPWS95", "IAPWS", "DEW"]:
return ro.conversion.rpy2py(out)
def entropy(formula, messages=True):
"""
Python wrapper for the entropy() function in CHNOSZ.
Calculate the standard molal entropy of elements in a compound.
Parameters
----------
formula : str, int, or list of str or int
Supply a chemical formula (e.g. "CH4"), a species index, or a list of
formulas or indices.
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
out : float or list of float
Standard molal entropy of elements in the formula in cal/(mol K).
Returns a list if `formula` is a list.
"""
formula_R = _convert_to_RVector(formula, force_Rvec=False)
capture = R_output()
capture.capture_r_output()
out = CHNOSZ.entropy(formula_R)
if messages:
for line in capture.stderr: print(line)
out = list(out)
if not isinstance(formula, list):
out = out[0]
return out
def mass(formula, messages=True):
"""
Python wrapper for the mass() function in CHNOSZ.
Calculate the mass of the sum of elements in a formula.
Parameters
----------
formula : str, int, or list of str or int
Supply a chemical formula (e.g. "CH4"), a species index, or a list of
formulas or indices.
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
out : float or list of float
Molar mass of the sum of elements in the formula, in g/mol.
Returns a list if `formula` is a list.
"""
formula_R = _convert_to_RVector(formula, force_Rvec=False)
capture = R_output()
capture.capture_r_output()
out = CHNOSZ.mass(formula_R)
if messages:
for line in capture.stderr: print(line)
out = list(out)
if not isinstance(formula, list):
out = out[0]
return out
def zc(formula, messages=True):
"""
Python wrapper for the ZC() function in CHNOSZ.
Calculate the average oxidation state of carbon (ZC) in a molecule.
Parameters
----------
formula : str or list of str
Chemical formula(s) of molecules.
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
out : float or list of float
The average oxidation state of carbon in the formula.
Returns a list if `formula` is a list.
"""
formula_R = _convert_to_RVector(formula, force_Rvec=False)
capture = R_output()
capture.capture_r_output()
out = CHNOSZ.ZC(formula_R)
if messages:
for line in capture.stderr: print(line)
out = list(out)
if not isinstance(formula, list):
out = out[0]
return out
def makeup(formula, multiplier=1, sum=False, count_zero=False, messages=True):
"""
Python wrapper for the makeup() function in CHNOSZ.
Parse a formula into a dictionary of elements and their counts.
Parameters
----------
formula : str or list of str
Chemical formula or a list of chemical formulas.
multiplier : numeric, default 1
Multiplier for the elemental counts in each formula.
sum : bool, default False
Add together the elemental counts in all formulas?
Ignored if `formula` is not a list.
count_zero : bool, default False
Include zero counts for an element if it is not in this formula, but is
found in another formula in the list? Ensures that the dictionaries
returned for each formula have the same length.
Ignored if `formula` is not a list.
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
out : dict
Dictionary of elements and their counts in the formula(s).
"""
formula_R = _convert_to_RVector(formula, force_Rvec=False)
args = {'formula':formula_R, "multiplier":multiplier,
"sum":sum, "count.zero":count_zero}
capture = R_output()
capture.capture_r_output()
out = CHNOSZ.makeup(**args)
if messages:
for line in capture.stderr: print(line)
if isinstance(out, ro.ListVector):
out_dict = {}
for i, molecule in enumerate(formula):
molecule_dict = {}
if not count_zero:
out_names = out[i].names
else:
out_names = out[i].names[0]
for ii, elem in enumerate(out_names):
molecule_dict[elem] = out[i][ii]
out_dict[molecule] = molecule_dict
else:
out_dict = {}
if not sum or not isinstance(formula, list):
out_names = out.names
else:
out_names = out.names[0]
for i, elem in enumerate(out_names):
out_dict[elem] = out[i]
return out_dict
def seq2aa(protein, sequence, messages=True):
"""
Python wrapper for the seq2aa() function in CHNOSZ.
Returns a data frame of amino acid composition corresponding to the provided
sequence.
Parameters
----------
protein : str
Protein name with an underscore, e.g., 'LYSC_CHICK'
sequence : str
Amino acid sequence of the protein.
messages : bool, default True
Display messages from CHNOSZ?
Returns
-------
Pandas dataframe
Amino acid composition of protein.
"""
args = {'protein':protein, 'sequence':sequence}
capture = R_output()
capture.capture_r_output()
pout = CHNOSZ.seq2aa(**args)
if messages:
for line in capture.stderr: print(line)
return ro.conversion.rpy2py(pout)
def add_protein(aa, messages=True):
"""
Python wrapper for the add.protein() function in CHNOSZ.
Add proteins to the OBIGT thermodynamic database.
Parameters
----------
aa : Pandas dataframe
Amino acid composition of protein(s) from `seq2aa`.
messages : bool, default True
Display messages from CHNOSZ?
Returns
-------
list of int
List of protein indices, iprotein.
"""
aa = ro.conversion.py2rpy(aa)
args = {'aa':aa}
capture = R_output()
capture.capture_r_output()
apout = CHNOSZ.add_protein(**args)
if messages:
for line in capture.stderr: print(line)
return list(apout)
def equilibrate(aout, balance=None, loga_balance=None, ispecies=None,
normalize=False, messages=True):
"""
Python wrapper for the equilibrate() function in CHNOSZ.
Calculate equilibrium chemical activities of species from the affinities
of formation of the species at unit activity.
Parameters
----------
aout : rpy2.ListVector
Output from `affinity`.
balance : str or numeric, optional
How to balance the transformations.
loga_balance : numeric or list of numeric, optional
Logarithm of total activity of balanced quantity.
ispecies : numeric, optional
Which species to include.
normalize : bool, default False
Normalize the molar formulas of species by the balancing coefficients?
messages : bool, default True
Display messages from CHNOSZ?
Returns
-------
eout : rpy2.ListVector
Output from `equilibrate`.
"""
# stay.normal is an unused argument in equilibrate()?
args = {'aout':aout, 'normalize':normalize}
if balance != None: args['balance'] = balance
if loga_balance != None: args['loga.balance'] = loga_balance
if ispecies != None: args['ispecies'] = _convert_to_RVector(ispecies)
capture = R_output()
capture.capture_r_output()
eout = CHNOSZ.equilibrate(**args)
if messages:
for line in capture.stderr: print(line)
return eout
def diagram(eout, ptype='auto', alpha=False, normalize=False,
as_residue=False, balance=None, groups=None,
xrange=None, mar=None, yline=None, side=[1,2,3,4],
ylog=True, xlim=None, ylim=None, xlab=None, ylab=None,
cex=None, cex_names=None, cex_axis=None,
lty=None, lwd=None, dotted=None,
spline_method=None, contour_method=None, levels=None,
col=None, col_names=None, fill=None,
fill_NA="gray80", limit_water=None,
names=None, format_names=True, bold=False, italic=False,
font=None, family=None, adj=0.5,
dx=0, dy=0, srt=0, min_area=0,
main=None, legend_x=None,
add=False, plot_it=True, tplot=True,
annotation=None, annotation_coords=[0,0],
width=600, height=520, dpi=150,
messages=True, interactive=False, save_as=None, save_format=None,
save_scale=1, fig_out=False):
"""
Python wrapper for the diagram() function in CHNOSZ.
Plot equilibrium chemical activity (1-D speciation) or equal-activity
(2-D predominance) diagrams as a function of chemical activities of
basis species, temperature and/or pressure.
Parameters
----------
eout : rpy2.ListVector
Output from `equilibrate` or `affinity`.
ptype : str, default 'auto'
Type of plot, or name of basis species whose activity to plot.
alpha : bool or str (balance), default False
For speciation diagrams, plot degree of formation instead of
activities?
normalize : bool, default False
Divide chemical affinities by balance coefficients (rescale to whole
formulas)?
as_residue : bool, default False
Divide chemical affinities by balance coefficients (no rescaling)?
balance : str or numeric, optional
How to balance the transformations.
groups : rpy2.ListVector of numeric, optional
Groups of species to consider as a single effective species.
xrange : numeric, optional
Range of x-values between which predominance field boundaries are
plotted.
mar : numeric, optional
Margins of plot frame.
yline : numeric, optional
Margin line on which to plot the y-axis name.
side : numeric, optional
Which sides of plot to draw axes.
xlim : numeric, optional
Limits of x-axis.
ylim : numeric, optional
Limits of y-axis.
xlab : str, optional
Label to use for x-axis.
ylab : str, optional
Label to use for y-axis.
ylog : bool, optional
Use a logarithmic y-axis (on 1D degree diagrams)?
cex : numeric, optional
Character expansion (scaling relative to current).
cex_names : numeric, optional
Character expansion factor to be used for names of species on plots.
cex_axis : numeric, optional
Character expansion factor for names of axes.
lty : numeric, optional
Line types to be used in plots.
lwd : numeric, optional
Line width.
dotted : numeric, optional
How often to skip plotting points on predominance field boundaries (to
gain the effect of dotted or dashed boundary lines).
spline_method : str, optional
Method used in splinefun.
contour_method : str, optional
Labelling method used in contour (use None for no labels).
levels : numeric, optional
Levels at which to draw contour lines.
col : str, optional
Color of activity lines (1D diagram) or predominance field boundaries
(2D diagram).
col_names : str, optional
Colors for labels of species.
fill : str, optional
Colors used to fill predominance fields.
fill_NA : str, optional
Color for grid points with NA values.
limit_water : None or bool, optional
Set NaN values beyond water stability limits?
names : str, optional
Names of species for activity lines or predominance fields.
format_names : bool, default True
Apply formatting to chemical formulas?
bold : bool, default False
Use bold formatting for names?
italic : bool, default False
Use italic formatting for names?
font : str, optional
Font type for names (has no effect if format.names is TRUE).
family : str, optional
Font family for names.
adj : numeric or list, default 0.5
Adjustment for line labels.
dx : numeric or list, default 0
X offset for line or field labels.
dy : numeric or list, default 0
Y offset for line or field labels.
srt : numeric, default 0
Rotation for line labels.
min_area : numeric, default 0
Minimum area of fields that should be labeled, expressed as a fraction
of the total plot area.
main : str, optional
A main title for the plot; None means to plot no title.
legend_x : str, optional
Description of legend placement passed to legend.
add : bool, default False
Add to current plot?
plot_it : bool, default True
Make a plot?
tplot : bool, default True
Set up plot with thermo.plot.new?
annotation : str, optional
Annotation to add to the plot. Interactive plots only (`interactive`
is set to True).
annotation_coords : list of numeric, default [0, 0], optional
Coordinates of annotation, where 0,0 is bottom left and 1,1 is top
right. Interactive plots only (`interactive` is set to True).
width, height : numeric, default 600 by 520
Width and height of the plot.
dpi : numeric, default 150
Resolution of the plot.
messages : bool, default True
Display messages from CHNOSZ?
interactive : bool, default False
Display an interactive plot?
save_as : str, optional
For interactive plots only (`interactive`=True). Provide a filename to
save this figure. Filetype of saved figure is determined by
`save_format`.
Note: interactive plots can be saved by clicking the 'Download plot'
button in the plot's toolbar.
save_format : str, default "png"
For interactive plots only (`interactive`=True). Desired format of saved
or downloaded figure. Can be 'png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf',
'eps', 'json', or 'html'. If 'html', an interactive plot will be saved.
Only 'png', 'svg', 'jpeg', and 'webp' can be downloaded with the
'download as' button in the toolbar of an interactive plot.
save_scale : numeric, default 1
For interactive plots only (`interactive`=True). Multiply
title/legend/axis/canvas sizes by this factor when saving the figure.
fig_out : bool, default False
Function output is a plotly figure? Ignored if `interactive` is False.
Returns
-------
a : rpy2.ListVector
Output from `diagram`.
args : dict
Dictionary of arguments supplied to `diagram`.
"""
if interactive:
df, fig = diagram_interactive(data=eout, title=main, annotation=annotation,
annotation_coords=annotation_coords,
balance=balance,
xlab=xlab, ylab=ylab,
colormap=fill,
width=width, height=height,
alpha=alpha, plot_it=plot_it,
save_as=save_as, save_format=save_format,
save_scale=save_scale, messages=messages)
if fig_out:
return df, fig
else:
return df
args = {'eout':eout, 'type':ptype, 'alpha':alpha, 'normalize':normalize,
'as.residue':as_residue, 'ylog':ylog, 'fill.NA':fill_NA, 'format.names':format_names,
'bold':bold, 'italic':italic, 'adj':adj, 'dx':dx, 'dy':dy, 'srt':srt,
'min.area':min_area, 'plot.it':plot_it, 'tplot':tplot}
if balance != None: args["balance"] = balance
if groups != None: args["groups"] = groups
if xrange != None: args["xrange"] = _convert_to_RVector(xrange)
if mar != None: args["mar"] = _convert_to_RVector(mar)
if yline != None: args["yline"] = _convert_to_RVector(yline)
if side != None: args["side"] = _convert_to_RVector(side)
if xlim != None: args["xlim"] = _convert_to_RVector(xlim)
if ylim != None: args["ylim"] = _convert_to_RVector(ylim)
if xlab != None: args["xlab"] = xlab
if ylab != None: args["ylab"] = ylab
if cex != None: args["cex"] = _convert_to_RVector(cex)
if cex_names != None: args["cex.names"] = _convert_to_RVector(cex_names)
if cex_axis != None: args["cex.axis"] = _convert_to_RVector(cex_axis)
if lty != None: args["lty"] = _convert_to_RVector(lty)
if lwd != None: args["lwd"] = _convert_to_RVector(lwd)
if dotted != None: args["dotted"] = convert_to_RVector(dotted)
if spline_method != None: args["spline.method"] = spline_method
if contour_method != None: args["contour.method"] = contour_method
if levels != None: args["levels"] = _convert_to_RVector(levels)
if col != None: args["col"] = _convert_to_RVector(col)
if col_names != None: args["col.names"] = _convert_to_RVector(col_names)
if fill != None: args["fill"] = _convert_to_RVector(fill)
if limit_water != None: args["limit.water"] = limit_water
if names != None: args["names"] = _convert_to_RVector(names)
if font != None: args["font"] = _convert_to_RVector(font)
if family != None: args["family"] = _convert_to_RVector(family)
if isinstance(adj, list):
args["adj"] = _convert_to_RVector(adj)
if isinstance(dx, list):
args["dx"] = _convert_to_RVector(dx)
if isinstance(dy, list):
args["dy"] = _convert_to_RVector(dy)
if main != None: args["main"] = main
if legend_x != None: args["legend.x"] = legend_x
capture = R_output()
capture.capture_r_output()
with __r_inline_plot(width=width, height=height, dpi=dpi, plot_it=plot_it):
if isinstance(add, bool):
if add: # add='True' does not work with the current pyCHNOSZ framework
raise Exception("The argument 'add' must be assigned the output of the previous diagram(s).")
else:
a = CHNOSZ.diagram(**args)
elif isinstance(add, list):
args.update({'add':True})
for to_add in add:
CHNOSZ.diagram(**to_add)
a = CHNOSZ.diagram(**args)
else:
CHNOSZ.diagram(**add)
args.update({'add':True})
a = CHNOSZ.diagram(**args)
if messages:
for line in capture.stderr: print(line)
return a, args
def affinity(property=None, sout=None, exceed_Ttr=False,
exceed_rhomin=False, return_buffer=False, return_sout=False,
balance="PBB", iprotein=None, loga_protein=-3, transect=None,
messages=True, **kwargs):
"""
Python wrapper for the affinity() function in CHNOSZ.
Calculate the chemical affinities of formation reactions of species.
Parameters
----------
property : str, default 'A'
The property to be calculated. Default is A, for chemical affinity of
formation reactions of species of interest.
sout : list
Output from `subcrt`.
exceed_Ttr : bool, default False
Allow subcrt to compute properties for phases beyond their transition
temperature?
exceed_rhomin : bool, default False
Allow subcrt to compute properties of species in the HKF model below
0.35 g cm-3?
return_buffer : bool, default False
If TRUE, and a buffer has been associated with one or more basis
species in the system, return the values of the activities of the basis
species calculated using the buffer. Default is FALSE.
return_sout : bool, default False
Return only the values calculated with subcrt?
balance : str, default 'PBB'
This argument is used to identify a conserved basis species (or PBB) in
a chemical activity buffer.
iprotein : numeric, optional
Indices of proteins in thermo$protein for which to calculate
properties.
loga_protein : numeric, default -3
Logarithms of activities of proteins identified in iprotein.
transect : bool, optional
Force a transect calculation, even for three or fewer values of the
variables?
messages : bool, default True
Display messages from CHNOSZ?
**kwargs : numeric, zero or more named arguments
Used to identify the variables of interest in the calculations. E.g.,
pH, T, P, Eh, ...
Returns
-------
a : rpy2.ListVector
Output from `affinity`.
"""
args = {'exceed_Ttr':exceed_Ttr, 'exceed_rhomin':exceed_rhomin,
'return_buffer':return_buffer, 'return_sout':return_sout,
'balance':balance, 'loga_protein':loga_protein}
if property != None: args["property"] = property
if sout != None: args["sout"] = sout
if iprotein != None: args["iprotein"] = _convert_to_RVector(iprotein)
if transect != None: args["transect"] = transect
for key, value in kwargs.items():
if isinstance(value, list):
value = ro.FloatVector(value)
args.update({key:value})
capture = R_output()
capture.capture_r_output()
a = CHNOSZ.affinity(**args)
if messages:
for line in capture.stderr: print(line)
return a
def species(species=None, state=None, delete=False, add=False,
index_return=False, messages=True):
"""
Python wrapper for the species() function in CHNOSZ.
Define the species of interest in a system; modify their physical states
and logarithms of activities.
Parameters
----------
species : str, int, or list of str or int
Names or formulas of species to add to the species definition; int,
rownumbers of species in OBIGT to modify or delete.
state : str or list of str, optional
physical states; numeric, logarithms of activities or fugacities.
delete : bool, default False
Delete the species identified by numeric values of species (or all
species if that argument is missing)?
add : bool, default False
Delete a previous species definition instead of adding to it?
index_return : bool, default False
return the affected rownumbers of species in the OBIGT database instead
of the normal output of `species`?
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
pd.DataFrame
Pandas dataframe containing a stoichometric matrix of reactions to form
species from basis species defined by `basis`.
"""
args={}
if species != None:
args["species"] = _convert_to_RVector(species, force_Rvec=False)
if state != None:
args["state"] = _convert_to_RVector(state, force_Rvec=False)
args["add"] = add
args["delete"] = delete
args["index.return"] = index_return
capture = R_output()
capture.capture_r_output()
sout = CHNOSZ.species(**args)
if messages:
for line in capture.stderr: print(line)
return ro.conversion.rpy2py(sout)
def basis(species=None, state=None, logact=None, delete=False, messages=True):
"""
Python wrapper for the basis() function in CHNOSZ.
Define the basis species of a chemical system.
Parameters
----------
species : str, int, or list of str or int
Names or formulas of species, or numeric, indices of species.
state : str or list of str, optional
Physical states or names of buffers.
logact : numeric or list of numeric, optional
Logarithms of activities or fugacities.
delete : bool, default False
Delete the current basis species definition?
messages : bool, default True
Display messages from CHNOSZ?
Returns
----------
pd.DataFrame
Pandas dataframe containing a stoichometric matrix of basis species'
elemental composition.
"""
args={}
if species != None:
args["species"] = _convert_to_RVector(species, force_Rvec=False)
if state != None:
args["state"] = _convert_to_RVector(state, force_Rvec=False)
if logact != None: args["logact"] = _convert_to_RVector(logact)
args["delete"] = delete
capture = R_output()
capture.capture_r_output()
bout = CHNOSZ.basis(**args)
if messages:
for line in capture.stderr: print(line)
return ro.conversion.rpy2py(bout)
def reset(messages=True):
"""
Python wrapper for the reset() function in CHNOSZ.
Reset all of the data used in CHNOSZ to default values. This includes the
computational settings, thermodynamic database, and system settings
(chemical species).
Parameters
----------
messages : bool, default True
Print messages from CHNOSZ?
"""
capture = R_output()
capture.capture_r_output()
CHNOSZ.reset()
if messages:
for line in capture.stderr: print(line)
def add_OBIGT(file, species=None, force=True, messages=True):
"""
Python wrapper for the add.OBIGT() function in CHNOSZ.
Add or overwrite species in the OBIGT thermodynamic database by supplying
a comma separated value (csv) file with custom data.
Parameters
----------
file : str
Path to a file.
species : str, optional
Names of species to load from file.
force : bool, default True
Force replacement of already existing species?
messages : bool, default True
Print messages from CHNOSZ?
Returns
----------
a : list of int
A list of OBIGT database indices (ispecies) that have been added or
modified.
"""
add_obigt_builtins = ["SUPCRT92", "AS04", "DEW", "OldAA",
"AkDi", "SLOP98", "Berman_cr", "biotic_aq",
"H2O_aq", "inorganic_aq", "inorganic_cr",
"inorganic_gas", "organic_aq", "organic_cr",
"organic_gas", "organic_liq"]
if isinstance(file, str):
if file in add_obigt_builtins:
capture = R_output()
capture.capture_r_output()
args={'file':file}
if species != None:
if not isinstance(species, list):
args["species"] = species
else:
args["species"] = _convert_to_RVector(species)
ispecies = CHNOSZ.add_OBIGT(**args)
if messages:
for line in capture.stderr: print(line)
return list(ispecies)
elif ".csv" not in file[-4:] or ".CSV" in file[-4:]:
raise Exception("File must be in .csv format")
else:
df = pd.read_csv(file, keep_default_na=False, na_values=['NA']) # keep_default_na=False keeps NA in data file instead of converting them to NaN. NaNs cause errors when converting to an R dataframe with rpy2 3.4.5.
if df.shape[0] == 0:
raise Exception("file is empty")
elif isinstance(file, pd.DataFrame):
df = file
if df.shape[0] == 0:
raise Exception("file is empty")
else:
raise Exception("file parameter must be either the name of a .csv file "
"to import, or a Pandas dataframe of thermodynamic data.")
OBIGT_cols = ['name', 'abbrv', 'formula', 'state',
'ref1', 'ref2', 'date', 'E_units',
'G', 'H', 'S', 'Cp', 'V',
'a1.a', 'a2.b', 'a3.c', 'a4.d',
'c1.e', 'c2.f', 'omega.lambda', 'z.T']
if all(col in df.columns for col in OBIGT_cols):
df_mod_OBIGT = df[OBIGT_cols]
if species != None:
if isinstance(species, list):
df_mod_OBIGT = df_mod_OBIGT[df_mod_OBIGT["name"].isin(species)]
else:
raise Exception("species must be a list of names of species to load from file.")
if not force:
t = thermo()
df_mod_OBIGT = df_mod_OBIGT[~df_mod_OBIGT["name"].isin(t.OBIGT.name)]
if df_mod_OBIGT.shape[0] == 0:
raise Exception("No species to add while force=False")
else:
msg = "The file must contain all of the columns: {}".format(str(OBIGT_cols))
raise Exception(msg)
numeric_cols = ['G', 'H', 'S', 'Cp', 'V',
'a1.a', 'a2.b', 'a3.c', 'a4.d',
'c1.e', 'c2.f', 'omega.lambda', 'z.T']
df_mod_OBIGT[numeric_cols] = df_mod_OBIGT[numeric_cols].apply(pd.to_numeric)
return mod_OBIGT(df_mod_OBIGT, messages=messages)
def mod_OBIGT(*args, messages=True, **kwargs):
"""
Python wrapper for the mod.OBIGT() function in CHNOSZ.
Modify species in the OBIGT thermodynamic database. Optionally, supply a
Pandas dataframe containing custom data.
Parameters
----------
*args : str, numeric, or Pandas dataframe
Species names or thermodynamic database index numbers to modify. If a
Pandas dataframe, database entries to add or modify.
*kwargs : str or numeric
Properties of species to modify in the thermodynamic database.
messages : bool, default True
Print messages from CHNOSZ?
Returns
----------
list of int
A list of OBIGT database indices (ispecies) that have been added or
modified.
"""
capture = R_output()
capture.capture_r_output()
if isinstance(args[0], pd.DataFrame):
arg_list = list(args)
arg_list[0] = ro.conversion.py2rpy(arg_list[0])
args = tuple(arg_list)
else:
pass
ispecies = CHNOSZ.mod_OBIGT(*args, **kwargs)
if messages:
for line in capture.stderr: print(line)
return list(ispecies)
def info(species, state=None, check_it=True, messages=True):
"""
Python wrapper for the info() function in CHNOSZ.
Search for species by name or formula, retrieve their thermodynamic
properties and parameters, and add proteins to the thermodynamic database.
Parameters
----------
species : str, int, or list of str or int
Name or formula of species, or numeric, rownumber of species in the
OBIGT database.
state : str or list of str, optional
State(s) of species.
check_it : bool, default True
Check GHS and EOS parameters for self-consistency?
messages : bool, default True
Print messages from CHNOSZ?
Returns
----------
pd.DataFrame or list of int
Returns a Pandas dataframe if supplied a list of OBIGT database indices
(ispecies), or a list of ispecies if given a list of species names or
formulas.
"""
args = {}
output_is_df = False
args["species"] = _convert_to_RVector(species, force_Rvec=False)
if not isinstance(species, list): species = [species]
if all(isinstance(x, (int, np.integer)) for x in species):
output_is_df = True
if state != None:
args["state"] = _convert_to_RVector(state, force_Rvec=False)
args["check.it"] = check_it
capture = R_output()
capture.capture_r_output()
a = CHNOSZ.info(**args)
if messages:
for line in capture.stderr: print(line)
if output_is_df:
return ro.conversion.rpy2py(a)
else:
return list(a)
def subcrt(species, coeff=None, state=None,
property=["logK", "G", "H", "S", "V", "Cp"],
T=None, P=None, grid=None,
convert=True, exceed_Ttr=False, exceed_rhomin=False,
logact=None, autobalance=True, IS=None, messages=True,
show=True):
"""
Python wrapper for the subcrt() function in CHNOSZ.
Calculate the standard molal thermodynamic properties of one or more
species or a reaction between species as a function of temperature and
pressure.
Parameters
----------
species : str, int, or list of str or int
Name or formula of species, or numeric, rownumber of species in the
OBIGT database.
coeff : numeric or list of numeric, optional
Reaction coefficients on species.
state : str or list of str, optional
State(s) of species.
property : str or list of str, optional
Property(s) to calculate.
T : numeric or list of numeric, optional
Temperature(s) of the calculation.
P : numeric, list of numeric, or str if 'Psat', default 'Psat'
Pressure(s) of the calculation.
grid : str, default None
Type of PxT grid to produce (None, the default, means no gridding).
exceed_Ttr : bool, default False
Calculate Gibbs energies of mineral phases and other species beyond
their transition temperatures?
exceed_rhomin : bool, default False
Return properties of species in the HKF model below 0.35 g cm-3?
logact : numeric or list of numeric, optional
Logarithms of activities of species in reaction.
convert : bool, default True
Are input and output units of T and P those of the user (True) (see
T_units), or are they Kelvin and bar (False)?
autobalance : bool, default True
Attempt to automatically balance reaction with basis species?
IS : numeric or list of numeric, optional
Ionic strength(s) at which to calculate adjusted molal properties,
mol kg^-1.
messages : bool, default True
Print messages from CHNOSZ?
show : bool, default True
Display CHNOSZ tables?
Returns
----------
out : object of class SubcrtOutput
An object that stores the output of `subcrt`.
"""
single_species = False
args = {'species': _convert_to_RVector(species, force_Rvec=False)}
if coeff != None:
args["coeff"] = _convert_to_RVector(coeff)
else:
single_species = True
if state != None:
args["state"] = _convert_to_RVector(state, force_Rvec=False)
args["property"] = _convert_to_RVector(property)
if T != None:
args['T'] = _convert_to_RVector(T, force_Rvec=False)
if P != None:
args['P'] = _convert_to_RVector(P, force_Rvec=False)
if grid != None: args['grid'] = grid # grid is either 'T' or 'P'
args['convert'] = convert
args['exceed.Ttr'] = exceed_Ttr
args['exceed.rhomin'] = exceed_rhomin
if logact != None: args["logact"] = _convert_to_RVector(logact)
args['autobalance'] = autobalance
if IS != None:
args["IS"] = _convert_to_RVector(IS, force_Rvec=False)
capture = R_output()
capture.capture_r_output()
a = CHNOSZ.subcrt(**args)
if messages:
for line in capture.stderr: print(line)
if "warnings" in a.names:
warn = a.rx2("warnings")[0] # subcrt's list includes warnings only if they appear
else:
warn = None
if "polymorphs" in a.names:
poly = a.rx2("polymorphs") # subcrt's list includes polymorphs only if they appear
else:
poly = None
if not single_species:
out_dict = {"reaction":ro.conversion.rpy2py(a[0]),
"out":ro.conversion.rpy2py(a[1])} # the extra [0] is important
else:
out_dict = {"species":ro.conversion.rpy2py(a[0]), "out":{}}
i=0
for df in a[1]:
out_dict["out"][out_dict["species"].name[i]] = ro.conversion.rpy2py(df)
i += 1
if isinstance(warn, str):
out_dict["warnings"] = warn
if isinstance(poly, pd.DataFrame):
out_dict["polymorphs"] = poly
out = SubcrtOutput(out_dict)
if show:
for table in out.__dict__.keys():
if not isinstance(out[table], dict):
display(out[table])
else:
for subtable in out[table].keys():
print("\n"+subtable+":") # species name
display(out[table][subtable])
return out
class SubcrtOutput(object):
"""
Stores the output of a `subcrt` calculation.
Attributes
----------
reaction : pd.DataFrame
Pandas dataframe summary of the reaction. Only present if a reaction is
specified.
species : pd.Dataframe
Pandas dataframe summary of species. Only present if a reaction is not
specified.
out : pd.Dataframe
Pandas dataframe of `subcrt` output.
warnings : pd.Dataframe
Pandas dataframe of calculation warnings. Only present if warnings were
generated.
"""
def __init__(self, args):
for k in args:
setattr(self, k, args[k])
def __getitem__(self, item):
return getattr(self, item)
class thermo:
"""
Python wrapper for the thermo() object in CHNOSZ.
See the original CHNOSZ documentation for in-depth descriptions of each
attribute: https://chnosz.net/manual/thermo.html
Attributes
----------
OBIGT : pd.DataFrame
A thermodynamic database of standard molal thermodynamic properties and
equations of state parameters of species.
basis : pd.DataFrame
Initially `None`, reserved for a dataframe written by basis upon
definition of the basis species.
buffer : pd.DataFrame
Contains definitions of buffers of chemical activity.
element : pd.DataFrame
Containins the thermodynamic properties of elements taken from Cox et
al., 1989, Wagman et al., 1982, and (for Am, Pu, Np, Cm) Thoenen et al.,
2014.
groups : pd.DataFrame
A dataframe with 22 columns for the amino acid sidechain, backbone and
protein backbone groups ([Ala]..[Tyr],[AABB],[UPBB]) whose rows
correspond to the elements C, H, N, O, S. It is used to quickly
calculate the chemical formulas of proteins that are selected using the
iprotein argument in `affinity`.
opar : dict
Stores parameters of the last plot generated in pyCHNOSZ. If a plot has
not yet been generated, `opar` is `None`.
opt : dict
Dictionary of computational settings.
protein : pd.DataFrame
Amino acid compositions of selected proteins.
refs : pd.DataFrame
References for thermodynamic data.
stoich : pd.DataFrame
A precalculated stoichiometric matrix for the default database.
species : pd.DataFrame
Initially `None`, reserved for a dataframe generated by species to
define the species of interest.
"""
def __init__(self, messages=True, **kwargs):
args = {}
for key, value in kwargs.items():
if isinstance(value, list) or isinstance(value, str) or isinstance(value, NumberTypes):
value = _convert_to_RVector(value, force_Rvec=False)
elif isinstance(value, pd.DataFrame):
value = ro.conversion.py2rpy(value)
else:
pass
args.update({key:value})
capture = R_output()
capture.capture_r_output()
t = CHNOSZ.thermo(**args)
if messages:
for line in capture.stderr: print(line)
for i, name in enumerate(t.names):
if isinstance(t[i], ro.DataFrame) or isinstance(t[i], ro.Matrix):
attr = ro.conversion.rpy2py(t[i])
elif isinstance(t[i], ro.ListVector):
attr = {}
for ii, subname in enumerate(t[i].names):
attr[subname] = list(t[i][ii])
elif isinstance(t[i], ro.FloatVector) or isinstance(t[i], ro.IntVector) or isinstance(t[i], ro.StrVector):
attr = list(t[i])
if len(t[i]) == 1:
attr = attr[0]
elif isinstance(t[i], rpy2.rinterface_lib.sexp.NULLType):
attr = None
else:
attr = t[i]
setattr(self, name, attr)
def __getitem__(self, item):
return getattr(self, item)
def unicurve(logK, species, coeff, state, pressures=1, temperatures=25, IS=0,
minT=0.1, maxT=100, minP=1, maxP=500, tol=None,
solve="T", width=600, height=520, dpi=90, plot_it=True,
messages=True, show=True):
"""
Solve for temperatures or pressures of equilibration for a given logK
value and produce a plot.
Parameters
----------
logK : numeric
Logarithm (base 10) of an equilibrium constant.
species : str, int, or list of str or int
Name or formula of species, or numeric, rownumber of species in the
OBIGT database.
coeff : numeric or list of numeric, optional
Reaction coefficients on species.
state : str or list of str, optional
State(s) of species.
pressures, temperatures : numeric or list of numeric
Pressures (if solving for temperature) or temperatures (if solving for
pressures) to resolve with `logK`. Pressures are in bars and
temperatures are in degrees Celcius.
IS : numeric or list of numeric, optional
Ionic strength(s) at which to calculate adjusted molal properties,
mol kg^-1.
minT, maxT : numeric
Minimum and maximum temperatures (degrees Celcius) to search within for
a solution for the given pressure(s) and logK. Ignored if solving for
pressure.
minP, maxP : numeric
Minimum and maximum pressures (bars) to search within for
a solution for the given temperatures(s) and logK. Ignored if solving
for temperature.
tol : float
Tolerance for finding a temperature or pressure that converges on the
given logK. Will attempt to find a solution that satisfies logK plus or
minus `tol`. By default, tol is equal to 1/(10^(n+2)) where n is the
number of decimal places in logK, with a maximum default tolerance of
1e-5.
solve : "T" or "P"
Solve for temperature or pressure?
width, height : numeric, default 600 by 520
Width and height of the plot.
dpi : numeric, default 90
Resolution of the plot.
plot_it : bool, default True
Show the plot?
messages : bool, default True
Display messages from CHNOSZ?
show : bool, default True
Display CHNOSZ tables?
Returns
-------
out : object of class SubcrtOutput
An object that stores the output of `subcrt` along the univariant curve.
"""
if tol == None:
d = decimal.Decimal(str(logK))
n_decimals = abs(d.as_tuple().exponent)
tol = float("0."+"".join(n_decimals*["0"])+"01")
if tol > 0.00001:
tol = 0.00001
species = _convert_to_RVector(species, force_Rvec=False)
state = _convert_to_RVector(state, force_Rvec=False)
coeff = _convert_to_RVector(coeff, force_Rvec=False)
pressures = _convert_to_RVector(pressures, force_Rvec=False)
temperatures = _convert_to_RVector(temperatures, force_Rvec=False)
capture = R_output()
capture.capture_r_output()
r_univariant = pkg_resources.resource_string(
__name__, 'univariant.r').decode("utf-8")
ro.r(r_univariant)
if solve=="T":
a = ro.r.uc_solveT(logK=logK,
species=species,
coeff=coeff,
state=state,
pressures=pressures,
IS=IS,
minT=minT,
maxT=maxT,
tol=tol)
if plot_it:
with __r_inline_plot(width=width, height=height, dpi=dpi, plot_it=plot_it):
ro.r.create_output_plot_T(logK=logK,
species=species,
coeff=coeff,
state=state,
pressures=pressures,
minT=minT,
maxT=maxT)
elif solve=="P":
a = ro.r.uc_solveP(logK=logK,
species=species,
state=state,
coeff=coeff,
temperatures=temperatures,
IS=IS,
minP=minP,
maxP=maxP,
tol=tol)
if plot_it:
with __r_inline_plot(width=width, height=height, dpi=dpi, plot_it=plot_it):
ro.r.create_output_plot_P(logK=logK,
species=species,
state=state,
coeff=coeff,
temperatures=temperatures,
minP=minP,
maxP=maxP)
if messages:
for line in capture.stderr: print(line)
if len(a) == 3:
warn = a[2][0] # subcrt's list includes warnings only if they appear
else:
warn = None
out_dict = {"reaction":ro.conversion.rpy2py(a[0]),
"out":ro.conversion.rpy2py(a[1])} # the extra [0] is important
if warn != None:
out_dict["warnings"] = warn
out = SubcrtOutput(out_dict)
if show:
for table in out.__dict__.keys():
if not isinstance(out[table], dict):
display(out[table])
else:
for subtable in out[table].keys():
print("\n"+subtable+":") # species name
display(out[table][subtable])
return out
def univariant_TP(logK, species, coeff, state, Trange, Prange, IS=0,
tol=None, title=None, res=10, width=500, height=400,
show=False, messages=False, plot_it=True):
"""
Solve for temperatures or pressures of equilibration for a given logK
value and produce a plot with temperature and pressure axes.
Parameters
----------
logK : numeric
Logarithm (base 10) of an equilibrium constant.
species : str, int, or list of str or int
Name or formula of species, or numeric, rownumber of species in the
OBIGT database.
coeff : numeric or list of numeric, optional
Reaction coefficients on species.
state : str or list of str, optional
State(s) of species.
Trange : list of two numeric
List containing the minimum and maximum temperature, in degrees C, to
solve for the specified logK value. Does not necessarily correspond to
temperature axis range in the resulting plot.
Prange : list of two numeric
List containing the minimum and maximum pressure, in bars, to
solve for the specified logK value. Does not necessarily correspond to
pressure axis range in the resulting plot.
IS : numeric or list of numeric, optional
Ionic strength(s) at which to calculate adjusted molal properties,
mol kg^-1.
tol : float
Tolerance for finding a temperature or pressure that converges on the
given logK. Will attempt to find a solution that satisfies logK plus or
minus `tol`. By default, tol is equal to 1/(10^(n+2)) where n is the
number of decimal places in logK, with a maximum default tolerance of
1e-5.
width, height : numeric, default 500 by 400
Width and height of the plot.
messages : bool, default True
Display messages from CHNOSZ?
show : bool, default True
Display CHNOSZ tables?
plot_it : bool, default True
Show the plot?
Returns
-------
out : object of class SubcrtOutput
An object that stores the output of `subcrt` along the univariant curve.
"""
if not isinstance(logK, list):
logK = [logK]
fig = go.Figure()
output = []
for this_logK in logK:
if tol == None:
d = decimal.Decimal(str(this_logK))
n_decimals = abs(d.as_tuple().exponent)
tol = float("0."+"".join(n_decimals*["0"])+"01")
if tol > 0.00001:
tol = 0.00001
out = unicurve(solve="T",
logK=this_logK,
species=species,
state=state,
coeff=coeff,
pressures=list(np.linspace(start=Prange[0], stop=Prange[1], num=res)),
minT=Trange[0],
maxT=Trange[1],
IS=IS,
tol=tol,
show=show,
plot_it=False, messages=messages)
if not out["out"]["T"].isnull().all():
fig.add_trace(go.Scatter(
x=out["out"]["T"],
y=out["out"]["P"],
mode="lines+markers",
name="logK="+str(this_logK),
text = ["logK="+str(this_logK) for i in range(0, len(out["out"]["T"]))],
hovertemplate = '%{text}<br>T, °C=%{x:.2f}<br>P, bar=%{y:.2f}<extra></extra>',
))
else:
print("Could not find any T or P values in this range that correspond to a logK value of {}".format(this_logK))
output.append(out)
if title == None:
react_grid = output[0]["reaction"]
react_grid["name"] = [name if name != "water" else "H2O" for name in react_grid["name"]] # replace any "water" with "H2O" in the written reaction
reactants = " + ".join([(str(-int(react_grid["coeff"][i]) if isinstance(react_grid["coeff"][i], (int, np.integer)) else -react_grid["coeff"][i])+" " if -react_grid["coeff"][i] != 1 else "") + html_chemname_format(react_grid["name"][i]) for i in range(0, len(react_grid["name"])) if react_grid["coeff"][i] < 0])
products = " + ".join([(str(int(react_grid["coeff"][i]) if isinstance(react_grid["coeff"][i], (int, np.integer)) else react_grid["coeff"][i])+" " if react_grid["coeff"][i] != 1 else "") + html_chemname_format(react_grid["name"][i]) for i in range(0, len(react_grid["name"])) if react_grid["coeff"][i] > 0])
title = reactants + " = " + products
fig.update_layout(template="simple_white",
title=str(title),
xaxis_title="T, °C",
yaxis_title="P, bar",
width=width,
height=height,
hoverlabel=dict(bgcolor="white"),
)
config = {'displaylogo': False,
'modeBarButtonsToRemove': ['resetScale2d', 'toggleSpikelines']}
if plot_it:
fig.show(config=config)
return output
def syslab(system=["K2O", "Al2O3", "SiO2", "H2O"], dash="-"):
"""
Python wrapper for the syslab() function in CHNOSZ.
Formats the given thermodynamic components and adds intervening en dashes.
Parameters
----------
system : list of str, default ["K2O", "Al2O3", "SiO2", "H2O"]
Thermodynamic components.
dash : str, default "-"
Character to use for dash between components.
Returns
-------
A formatted string representing the thermodynamic system.
"""
return dash.join([html_chemname_format(sp)for sp in system])
def ratlab(top="K+", bottom="H+", molality=False):
"""
Python wrapper for the ratlab() function in CHNOSZ.
Produces a expression for the activity ratio between the ions in the top and
bottom arguments. The default is a ratio with H+, i.e.
(activity of the ion) / [(activity of H+) ^ (charge of the ion)]
Parameters
----------
top : str, default "K+"
The ion in the numerator of the ratio.
bottom : str, default "H+"
The ion in the denominator of the ratio.
molality : bool, default False
Use molality (m) instead of activity (a) for aqueous species?
Returns
-------
A formatted string representing the activity ratio.
"""
top_formula = chemparse.parse_formula(top)
if "+" in top_formula.keys():
top_charge = top_formula["+"]
elif "-" in top_formula.keys():
top_charge = top_formula["-"]
else:
raise Exception("Cannot create an ion ratio involving one or more neutral species.")
bottom_formula = chemparse.parse_formula(bottom)
if "+" in bottom_formula.keys():
bottom_charge = bottom_formula["+"]
elif "-" in bottom_formula.keys():
top_charge = bottom_formula["-"]
else:
raise Exception("Cannot create an ion ratio involving one or more neutral species.")
if top_charge.is_integer():
top_charge = int(top_charge)
if bottom_charge.is_integer():
bottom_charge = int(bottom_charge)
if top_charge != 1:
top_charge = "<sup>"+str(top_charge)+"</sup>"
else:
top_charge = ""
if bottom_charge != 1:
bottom_charge = "<sup>"+str(bottom_charge)+"</sup>"
else:
bottom_charge = ""
if molality:
sym = "m"
else:
sym = "a"
return "log("+sym+bottom_charge+"<sub>"+html_chemname_format(top)+"</sub>/"+sym+top_charge+"<sub>"+html_chemname_format(bottom)+"</sub>)"Functions
def add_OBIGT(file, species=None, force=True, messages=True)-
Python wrapper for the add.OBIGT() function in CHNOSZ. Add or overwrite species in the OBIGT thermodynamic database by supplying a comma separated value (csv) file with custom data.
Parameters
file:str- Path to a file.
species:str, optional- Names of species to load from file.
force:bool, defaultTrue- Force replacement of already existing species?
messages:bool, defaultTrue- Print messages from CHNOSZ?
Returns
a:listofint- A list of OBIGT database indices (ispecies) that have been added or modified.
Expand source code
def add_OBIGT(file, species=None, force=True, messages=True): """ Python wrapper for the add.OBIGT() function in CHNOSZ. Add or overwrite species in the OBIGT thermodynamic database by supplying a comma separated value (csv) file with custom data. Parameters ---------- file : str Path to a file. species : str, optional Names of species to load from file. force : bool, default True Force replacement of already existing species? messages : bool, default True Print messages from CHNOSZ? Returns ---------- a : list of int A list of OBIGT database indices (ispecies) that have been added or modified. """ add_obigt_builtins = ["SUPCRT92", "AS04", "DEW", "OldAA", "AkDi", "SLOP98", "Berman_cr", "biotic_aq", "H2O_aq", "inorganic_aq", "inorganic_cr", "inorganic_gas", "organic_aq", "organic_cr", "organic_gas", "organic_liq"] if isinstance(file, str): if file in add_obigt_builtins: capture = R_output() capture.capture_r_output() args={'file':file} if species != None: if not isinstance(species, list): args["species"] = species else: args["species"] = _convert_to_RVector(species) ispecies = CHNOSZ.add_OBIGT(**args) if messages: for line in capture.stderr: print(line) return list(ispecies) elif ".csv" not in file[-4:] or ".CSV" in file[-4:]: raise Exception("File must be in .csv format") else: df = pd.read_csv(file, keep_default_na=False, na_values=['NA']) # keep_default_na=False keeps NA in data file instead of converting them to NaN. NaNs cause errors when converting to an R dataframe with rpy2 3.4.5. if df.shape[0] == 0: raise Exception("file is empty") elif isinstance(file, pd.DataFrame): df = file if df.shape[0] == 0: raise Exception("file is empty") else: raise Exception("file parameter must be either the name of a .csv file " "to import, or a Pandas dataframe of thermodynamic data.") OBIGT_cols = ['name', 'abbrv', 'formula', 'state', 'ref1', 'ref2', 'date', 'E_units', 'G', 'H', 'S', 'Cp', 'V', 'a1.a', 'a2.b', 'a3.c', 'a4.d', 'c1.e', 'c2.f', 'omega.lambda', 'z.T'] if all(col in df.columns for col in OBIGT_cols): df_mod_OBIGT = df[OBIGT_cols] if species != None: if isinstance(species, list): df_mod_OBIGT = df_mod_OBIGT[df_mod_OBIGT["name"].isin(species)] else: raise Exception("species must be a list of names of species to load from file.") if not force: t = thermo() df_mod_OBIGT = df_mod_OBIGT[~df_mod_OBIGT["name"].isin(t.OBIGT.name)] if df_mod_OBIGT.shape[0] == 0: raise Exception("No species to add while force=False") else: msg = "The file must contain all of the columns: {}".format(str(OBIGT_cols)) raise Exception(msg) numeric_cols = ['G', 'H', 'S', 'Cp', 'V', 'a1.a', 'a2.b', 'a3.c', 'a4.d', 'c1.e', 'c2.f', 'omega.lambda', 'z.T'] df_mod_OBIGT[numeric_cols] = df_mod_OBIGT[numeric_cols].apply(pd.to_numeric) return mod_OBIGT(df_mod_OBIGT, messages=messages) def add_protein(aa, messages=True)-
Python wrapper for the add.protein() function in CHNOSZ. Add proteins to the OBIGT thermodynamic database.
Parameters
aa:Pandas dataframe- Amino acid composition of protein(s) from
seq2aa(). messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
listofint- List of protein indices, iprotein.
Expand source code
def add_protein(aa, messages=True): """ Python wrapper for the add.protein() function in CHNOSZ. Add proteins to the OBIGT thermodynamic database. Parameters ---------- aa : Pandas dataframe Amino acid composition of protein(s) from `seq2aa`. messages : bool, default True Display messages from CHNOSZ? Returns ------- list of int List of protein indices, iprotein. """ aa = ro.conversion.py2rpy(aa) args = {'aa':aa} capture = R_output() capture.capture_r_output() apout = CHNOSZ.add_protein(**args) if messages: for line in capture.stderr: print(line) return list(apout) def affinity(property=None, sout=None, exceed_Ttr=False, exceed_rhomin=False, return_buffer=False, return_sout=False, balance='PBB', iprotein=None, loga_protein=-3, transect=None, messages=True, **kwargs)-
Python wrapper for the affinity() function in CHNOSZ. Calculate the chemical affinities of formation reactions of species.
Parameters
property:str, default'A'- The property to be calculated. Default is A, for chemical affinity of formation reactions of species of interest.
sout:list- Output from
subcrt(). exceed_Ttr:bool, defaultFalse- Allow subcrt to compute properties for phases beyond their transition temperature?
exceed_rhomin:bool, defaultFalse- Allow subcrt to compute properties of species in the HKF model below 0.35 g cm-3?
return_buffer:bool, defaultFalse- If TRUE, and a buffer has been associated with one or more basis species in the system, return the values of the activities of the basis species calculated using the buffer. Default is FALSE.
return_sout:bool, defaultFalse- Return only the values calculated with subcrt?
balance:str, default'PBB'- This argument is used to identify a conserved basis species (or PBB) in a chemical activity buffer.
iprotein:numeric, optional- Indices of proteins in thermo$protein for which to calculate properties.
loga_protein:numeric, default-3- Logarithms of activities of proteins identified in iprotein.
transect:bool, optional- Force a transect calculation, even for three or fewer values of the variables?
messages:bool, defaultTrue- Display messages from CHNOSZ?
**kwargs:numeric, zeroormore named arguments- Used to identify the variables of interest in the calculations. E.g., pH, T, P, Eh, …
Returns
a:rpy2.ListVector- Output from
affinity().
Expand source code
def affinity(property=None, sout=None, exceed_Ttr=False, exceed_rhomin=False, return_buffer=False, return_sout=False, balance="PBB", iprotein=None, loga_protein=-3, transect=None, messages=True, **kwargs): """ Python wrapper for the affinity() function in CHNOSZ. Calculate the chemical affinities of formation reactions of species. Parameters ---------- property : str, default 'A' The property to be calculated. Default is A, for chemical affinity of formation reactions of species of interest. sout : list Output from `subcrt`. exceed_Ttr : bool, default False Allow subcrt to compute properties for phases beyond their transition temperature? exceed_rhomin : bool, default False Allow subcrt to compute properties of species in the HKF model below 0.35 g cm-3? return_buffer : bool, default False If TRUE, and a buffer has been associated with one or more basis species in the system, return the values of the activities of the basis species calculated using the buffer. Default is FALSE. return_sout : bool, default False Return only the values calculated with subcrt? balance : str, default 'PBB' This argument is used to identify a conserved basis species (or PBB) in a chemical activity buffer. iprotein : numeric, optional Indices of proteins in thermo$protein for which to calculate properties. loga_protein : numeric, default -3 Logarithms of activities of proteins identified in iprotein. transect : bool, optional Force a transect calculation, even for three or fewer values of the variables? messages : bool, default True Display messages from CHNOSZ? **kwargs : numeric, zero or more named arguments Used to identify the variables of interest in the calculations. E.g., pH, T, P, Eh, ... Returns ------- a : rpy2.ListVector Output from `affinity`. """ args = {'exceed_Ttr':exceed_Ttr, 'exceed_rhomin':exceed_rhomin, 'return_buffer':return_buffer, 'return_sout':return_sout, 'balance':balance, 'loga_protein':loga_protein} if property != None: args["property"] = property if sout != None: args["sout"] = sout if iprotein != None: args["iprotein"] = _convert_to_RVector(iprotein) if transect != None: args["transect"] = transect for key, value in kwargs.items(): if isinstance(value, list): value = ro.FloatVector(value) args.update({key:value}) capture = R_output() capture.capture_r_output() a = CHNOSZ.affinity(**args) if messages: for line in capture.stderr: print(line) return a def animation(basis_args={}, species_args={}, affinity_args={}, equilibrate_args=None, diagram_args={}, anim_var='T', anim_range=[0, 350, 8], messages=False)-
Produce an animated interactive affinity, activity, or predominance diagram.
Parameters
basis_args:dict- Dictionary of options for defining basis species (see
basis()) in the animated diagram. Example: basis_args={'species':['CO2', 'O2', 'H2O', 'H+']} species_args:dict- Dictionary of options for defining species (see
species()) in the animated diagram. Example: species_args={'species':['CO2', 'HCO3-', 'CO3-2']} affinity_args:dict- Dictionary of options for defining the affinity calculation (see
affinity()). Example: affinity_args={"pH":[2, 12, 100]} Example: affinity_args={"pH":[2, 12, 100], "P":[2000, 4000, 100]} equilibrate_args:dictorNone, defaultNone- Dictionary of options for defining equilibration calculation
(see
equilibrate()). If None, plots output fromaffinity(). Example: equilibrate_args={"balance":1} diagram_args:dict- Dictionary of options for diagramming (see
diagram()). Diagram optioninteractiveis set to True. Example: diagram_args={"alpha":True} anim_var:str, default"T"- Variable that changes with each frame of animation.
anim_range:listofnumeric, default[0, 350, 8]- The first two numbers in the list are the starting and ending
values for
anim_var. The third number in the list is the desired number of animation frames. messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
An interactive animated plot.
Expand source code
def animation(basis_args={}, species_args={}, affinity_args={}, equilibrate_args=None, diagram_args={}, anim_var="T", anim_range=[0, 350, 8], messages=False): """ Produce an animated interactive affinity, activity, or predominance diagram. Parameters ---------- basis_args : dict Dictionary of options for defining basis species (see `basis`) in the animated diagram. Example: basis_args={'species':['CO2', 'O2', 'H2O', 'H+']} species_args : dict Dictionary of options for defining species (see `species`) in the animated diagram. Example: species_args={'species':['CO2', 'HCO3-', 'CO3-2']} affinity_args : dict Dictionary of options for defining the affinity calculation (see `affinity`). Example: affinity_args={"pH":[2, 12, 100]} Example: affinity_args={"pH":[2, 12, 100], "P":[2000, 4000, 100]} equilibrate_args : dict or None, default None Dictionary of options for defining equilibration calculation (see `equilibrate`). If None, plots output from `affinity`. Example: equilibrate_args={"balance":1} diagram_args : dict Dictionary of options for diagramming (see `diagram`). Diagram option `interactive` is set to True. Example: diagram_args={"alpha":True} anim_var : str, default "T" Variable that changes with each frame of animation. anim_range : list of numeric, default [0, 350, 8] The first two numbers in the list are the starting and ending values for `anim_var`. The third number in the list is the desired number of animation frames. messages : bool, default True Display messages from CHNOSZ? Returns ------- An interactive animated plot. """ # cap number of frames in animation. Remove limitation after more testing. if isinstance(anim_range, list): if len(anim_range) == 3: if anim_range[2] > 30: raise Exception("anim_range is limited to 30 frames.") else: raise Exception("anim_range must be a list with three values: starting " "value of anim_var, stopping value, and number of " "frames in the animation") else: raise Exception("anim_range must be a list with three values: starting " "value of anim_var, stopping value, and number of " "frames in the animation") if isinstance(basis_args, dict): if "species" not in basis_args.keys(): raise Exception("basis_args needs a list of basis species for 'species'. " "Example: basis_args={'species':['CO2', 'O2', 'H2O', 'H+']}") else: raise Exception("basis_args needs to be a Python dictionary with a key " "called 'species' (additional keys are optional). " "Example: basis_args={'species':['CO2', 'O2', 'H2O', 'H+']}") if isinstance(species_args, dict): if "species" not in species_args.keys(): raise Exception("species_args needs a list of species for 'species'. " "Example: species_args={'species':['CO2', 'HCO3-', 'CO3-2']}") else: raise Exception("species_args needs to be a Python dictionary with a key " "called 'species' (additional keys are optional). " "Example: species_args={'species':['CO2', 'HCO3-', 'CO3-2']}") basis_sp = basis_args["species"] sp = species_args["species"] if isinstance(sp[0], (int, np.integer)): sp = [info(s, messages=False)["name"].values[0] for s in sp] basis_out = basis(**basis_args) if "logact" in species_args.keys(): mod_species_logact = copy.copy(species_args['logact']) del species_args['logact'] else: mod_species_logact = [] species_out = species(**species_args) if len(mod_species_logact)>0: for i in range(0, len(mod_species_logact)) : species_out = species(species_args["species"][i], mod_species_logact[i]) dfs = [] dmaps = [] dmaps_names = [] if len(anim_range) == 2: anim_res = 8 anim_range = anim_range + [anim_res] elif len(anim_range) == 3: anim_res = anim_range[2] anim_range = [anim_range[0], anim_range[1]] zvals = __seq(anim_range[0], anim_range[1], length_out=anim_res) if "messages" not in affinity_args.keys(): affinity_args["messages"] = messages if "messages" not in diagram_args.keys(): diagram_args["messages"] = messages if "plot_it" not in diagram_args.keys(): diagram_args["plot_it"] = False diagram_args["interactive"] = True for z in zvals: if anim_var in basis_out.index: basis_out = basis(anim_var, z) elif anim_var in list(species_out["name"]): species_out = species(anim_var, z) else: affinity_args[anim_var] = z aeout = affinity(**affinity_args) if equilibrate_args != None: equilibrate_args["aout"] = aeout if "messages" not in equilibrate_args.keys(): equilibrate_args["messages"] = messages aeout = equilibrate(**equilibrate_args) aeout_args = aeout.rx2("args") xvar = aeout_args.names[0] xrange = list(aeout_args[0]) res_default = 256 # default affinity resolution if len(xrange) == 3: xres = int(xrange[2]) else: xres = res_default diagram_args["eout"] = aeout df = diagram(**diagram_args) df[anim_var] = z dfs.append(df) if 'pred' not in df.columns: # affinity/activity plot is_predom_plot = False else: # predominance plot is_predom_plot = True yvar = aeout_args.names[1] yrange = list(aeout_args[1]) if len(yrange) == 3: yres = int(yrange[2]) else: yres = res_default data = np.array(df.pred) shape = (xres, yres) dmap = data.reshape(shape) dmaps.append(dmap) data = np.array(df.prednames) shape = (xres, yres) dmap_names = data.reshape(shape) dmaps_names.append(dmap_names) xvals = __seq(xrange[0], xrange[1], length_out=xres) if not is_predom_plot: if 'loga.equil' not in aeout.names: yvar = "A/(2.303RT)" else: yvar = "log a" if "alpha" in diagram_args.keys(): if diagram_args["alpha"]: yvar = "alpha" df_c = pd.concat(dfs) fig = px.line(df_c, x=xvar, y="value", color='variable', template="simple_white", width=500, height=400, animation_frame=anim_var, labels=dict(value=yvar, x=xvar), ) if "annotation" in diagram_args.keys(): if "annotation_coords" not in diagram_args.keys(): diagram_args["annotation_coords"] = [0, 0] fig.add_annotation(x=diagram_args["annotation_coords"][0], y=diagram_args["annotation_coords"][1], xref="paper", yref="paper", align='left', text=diagram_args["annotation"], bgcolor="rgba(255, 255, 255, 0.5)", showarrow=False) if 'main' in diagram_args.keys(): fig.update_layout(title={'text':diagram_args["main"], 'x':0.5, 'xanchor':'center'}) fig.update_layout(legend_title=None) config = {'displaylogo': False, 'modeBarButtonsToRemove': ['resetScale2d', 'toggleSpikelines']} fig.show(config=config) return else: yvals = __seq(yrange[0], yrange[1], length_out=yres) unit_dict = {"P":"bar", "T":"°C", "pH":"", "Eh":"volts", "IS":"mol/kg"} if any([anim_var in basis_out.index, anim_var in list(species_out["name"])]) and anim_var not in unit_dict.keys(): unit_dict[anim_var] = "logact "+anim_var for s in basis_sp: unit_dict[s] = "logact "+s xlab = xvar+", "+unit_dict[xvar] if xvar in basis_sp: xlab = unit_dict[xvar] if xvar == "pH": xlab = "pH" if is_predom_plot: ylab = yvar+", "+unit_dict[yvar] if yvar in basis_sp: ylab = unit_dict[yvar] if yvar == "pH": yvar = "pH" frames = [] slider_steps = [] annotations = [] cst_data = [] heatmaps = [] # i is a frame in the animation for i in range(0, len(zvals)): annotations_i = [] for s in sp: if s in set(dfs[i]["prednames"]): # if an annotation should appear, create one for this frame df_s = dfs[i].loc[dfs[i]["prednames"]==s,] namex = df_s[xvar].mean() namey = df_s[yvar].mean() a = go.layout.Annotation( x=namex, y=namey, xref="x", yref="y", text=html_chemname_format(s), bgcolor="rgba(255, 255, 255, 0.5)", showarrow=False, ) else: # if an annotation shouldn't appear, make an invisible annotation # (workaround for a plotly bug where annotations won't clear in an animation) namex = statistics.mean(xvals) namey = statistics.mean(yvals) a = go.layout.Annotation( x=namex, y=namey, xref="x", yref="y", text="", bgcolor="rgba(255, 255, 255, 0)", showarrow=False, ) annotations_i.append(a) # allows adding a custom annotation; append to frame if "annotation" in diagram_args.keys(): if "annotation_coords" not in diagram_args.keys(): diagram_args["annotation_coords"] = [0, 0] custom_annotation = go.layout.Annotation( x=diagram_args["annotation_coords"][0], y=diagram_args["annotation_coords"][1], xref="paper", yref="paper", align='left', text=diagram_args["annotation"], bgcolor="rgba(255, 255, 255, 0.5)", showarrow=False, ) annotations_i.append(custom_annotation) annotations.append(annotations_i) if 'ylab' in diagram_args.keys(): ylab = diagram_args["ylab"] hover_ylab = ylab+': %{y} ' else: ylab = html_chemname_format(ylab) hover_ylab = yvar+': %{y} '+unit_dict[yvar] if 'xlab' in diagram_args.keys(): xlab = diagram_args["xlab"] hover_xlab = xlab+': %{x} ' else: xlab = html_chemname_format(xlab) hover_xlab = xvar+': %{x} '+unit_dict[xvar] heatmaps_i = go.Heatmap(z=dmaps[i], x=xvals, y=yvals, zmin=0, zmax=len(sp)-1, customdata=dmaps_names[i], hovertemplate=hover_xlab+'<br>'+hover_ylab+'<br>Region: %{customdata}<extra></extra>') heatmaps.append(heatmaps_i) frame = go.Frame(data=[heatmaps_i], name=str(i), layout=go.Layout(annotations=annotations_i)) frames.append(frame) slider_step = dict( method='animate', label=zvals[i], value=i, args=[ [i], dict( frame=dict(duration=300, redraw=True), mode='immediate', transition=dict(duration=0) ) ] ) slider_steps.append(slider_step) fig = go.Figure( data = heatmaps[0], layout=go.Layout( # title="Frame 0", title_x=0.5, width=500, height=500, annotations=annotations[0], sliders=[dict( active=0, yanchor='top', xanchor='left', currentvalue=dict( font=dict(size=12), prefix='{}: '.format(anim_var), suffix=' '+unit_dict[anim_var], visible=True, xanchor='right' ), transition=dict(duration=0, easing='cubic-in-out'), pad=dict(b=10, t=50), len=0.9, x=0.1, y=0, steps=slider_steps )], updatemenus=[dict( type="buttons", buttons=[dict(label="Play", method="animate", args=[None, {"fromcurrent":True}]), dict(label="Pause", method="animate", args=[[None], {"frame": {"duration": 0, "redraw": True}, "mode": "immediate", "transition": {"duration": 0}}], )], direction="left", pad={"r": 10, "t": 87}, showactive=False, x=0.1, xanchor="right", y=0, yanchor="top", )] ), frames=frames ) fig.update_traces(dict(showscale=False, colorscale='viridis'), selector={'type':'heatmap'}) fig.update_layout( xaxis_title=xlab, yaxis_title=ylab, xaxis={"range":[list(dfs[0][xvar])[0], list(dfs[0][xvar])[-1]]}, yaxis={"range":[list(dfs[0][yvar])[0], list(dfs[0][yvar])[-1]]}, margin={"t": 60, "r":60}, ) if 'main' in diagram_args.keys(): fig.update_layout(title={'text':diagram_args['main'], 'x':0.5, 'xanchor':'center'}) config = {'displaylogo': False, 'modeBarButtonsToRemove': ['zoom2d', 'pan2d', 'zoomIn2d', 'zoomOut2d', 'autoScale2d', 'toggleSpikelines', 'hoverClosestCartesian', 'hoverCompareCartesian']} fig.show(config=config) def basis(species=None, state=None, logact=None, delete=False, messages=True)-
Python wrapper for the basis() function in CHNOSZ. Define the basis species of a chemical system.
Parameters
species:str, int,orlistofstrorint- Names or formulas of species, or numeric, indices of species.
state:strorlistofstr, optional- Physical states or names of buffers.
logact:numericorlistofnumeric, optional- Logarithms of activities or fugacities.
delete:bool, defaultFalse- Delete the current basis species definition?
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
pd.DataFrame- Pandas dataframe containing a stoichometric matrix of basis species' elemental composition.
Expand source code
def basis(species=None, state=None, logact=None, delete=False, messages=True): """ Python wrapper for the basis() function in CHNOSZ. Define the basis species of a chemical system. Parameters ---------- species : str, int, or list of str or int Names or formulas of species, or numeric, indices of species. state : str or list of str, optional Physical states or names of buffers. logact : numeric or list of numeric, optional Logarithms of activities or fugacities. delete : bool, default False Delete the current basis species definition? messages : bool, default True Display messages from CHNOSZ? Returns ---------- pd.DataFrame Pandas dataframe containing a stoichometric matrix of basis species' elemental composition. """ args={} if species != None: args["species"] = _convert_to_RVector(species, force_Rvec=False) if state != None: args["state"] = _convert_to_RVector(state, force_Rvec=False) if logact != None: args["logact"] = _convert_to_RVector(logact) args["delete"] = delete capture = R_output() capture.capture_r_output() bout = CHNOSZ.basis(**args) if messages: for line in capture.stderr: print(line) return ro.conversion.rpy2py(bout) def diagram(eout, ptype='auto', alpha=False, normalize=False, as_residue=False, balance=None, groups=None, xrange=None, mar=None, yline=None, side=[1, 2, 3, 4], ylog=True, xlim=None, ylim=None, xlab=None, ylab=None, cex=None, cex_names=None, cex_axis=None, lty=None, lwd=None, dotted=None, spline_method=None, contour_method=None, levels=None, col=None, col_names=None, fill=None, fill_NA='gray80', limit_water=None, names=None, format_names=True, bold=False, italic=False, font=None, family=None, adj=0.5, dx=0, dy=0, srt=0, min_area=0, main=None, legend_x=None, add=False, plot_it=True, tplot=True, annotation=None, annotation_coords=[0, 0], width=600, height=520, dpi=150, messages=True, interactive=False, save_as=None, save_format=None, save_scale=1, fig_out=False)-
Python wrapper for the diagram() function in CHNOSZ. Plot equilibrium chemical activity (1-D speciation) or equal-activity (2-D predominance) diagrams as a function of chemical activities of basis species, temperature and/or pressure.
Parameters
eout:rpy2.ListVector- Output from
equilibrate()oraffinity(). ptype:str, default'auto'- Type of plot, or name of basis species whose activity to plot.
alpha:boolorstr (balance), defaultFalse- For speciation diagrams, plot degree of formation instead of activities?
normalize:bool, defaultFalse- Divide chemical affinities by balance coefficients (rescale to whole formulas)?
as_residue:bool, defaultFalse- Divide chemical affinities by balance coefficients (no rescaling)?
balance:strornumeric, optional- How to balance the transformations.
groups:rpy2.ListVectorofnumeric, optional- Groups of species to consider as a single effective species.
xrange:numeric, optional- Range of x-values between which predominance field boundaries are plotted.
mar:numeric, optional- Margins of plot frame.
yline:numeric, optional- Margin line on which to plot the y-axis name.
side:numeric, optional- Which sides of plot to draw axes.
xlim:numeric, optional- Limits of x-axis.
ylim:numeric, optional- Limits of y-axis.
xlab:str, optional- Label to use for x-axis.
ylab:str, optional- Label to use for y-axis.
ylog:bool, optional- Use a logarithmic y-axis (on 1D degree diagrams)?
cex:numeric, optional- Character expansion (scaling relative to current).
cex_names:numeric, optional- Character expansion factor to be used for names of species on plots.
cex_axis:numeric, optional- Character expansion factor for names of axes.
lty:numeric, optional- Line types to be used in plots.
lwd:numeric, optional- Line width.
dotted:numeric, optional- How often to skip plotting points on predominance field boundaries (to gain the effect of dotted or dashed boundary lines).
spline_method:str, optional- Method used in splinefun.
contour_method:str, optional- Labelling method used in contour (use None for no labels).
levels:numeric, optional- Levels at which to draw contour lines.
col:str, optional- Color of activity lines (1D diagram) or predominance field boundaries (2D diagram).
col_names:str, optional- Colors for labels of species.
fill:str, optional- Colors used to fill predominance fields.
fill_NA:str, optional- Color for grid points with NA values.
limit_water:Noneorbool, optional- Set NaN values beyond water stability limits?
names:str, optional- Names of species for activity lines or predominance fields.
format_names:bool, defaultTrue- Apply formatting to chemical formulas?
bold:bool, defaultFalse- Use bold formatting for names?
italic:bool, defaultFalse- Use italic formatting for names?
font:str, optional- Font type for names (has no effect if format.names is TRUE).
family:str, optional- Font family for names.
adj:numericorlist, default0.5- Adjustment for line labels.
dx:numericorlist, default0- X offset for line or field labels.
dy:numericorlist, default0- Y offset for line or field labels.
srt:numeric, default0- Rotation for line labels.
min_area:numeric, default0- Minimum area of fields that should be labeled, expressed as a fraction of the total plot area.
main:str, optional- A main title for the plot; None means to plot no title.
legend_x:str, optional- Description of legend placement passed to legend.
add:bool, defaultFalse- Add to current plot?
plot_it:bool, defaultTrue- Make a plot?
tplot:bool, defaultTrue- Set up plot with thermo.plot.new?
annotation:str, optional- Annotation to add to the plot. Interactive plots only (
interactiveis set to True). annotation_coords:listofnumeric, default[0, 0], optional- Coordinates of annotation, where 0,0 is bottom left and 1,1 is top
right. Interactive plots only (
interactiveis set to True). width,height:numeric, default600 by 520- Width and height of the plot.
dpi:numeric, default150- Resolution of the plot.
messages:bool, defaultTrue- Display messages from CHNOSZ?
interactive:bool, defaultFalse- Display an interactive plot?
save_as:str, optional- For interactive plots only (
interactive=True). Provide a filename to save this figure. Filetype of saved figure is determined bysave_format. Note: interactive plots can be saved by clicking the 'Download plot' button in the plot's toolbar. save_format:str, default"png"- For interactive plots only (
interactive=True). Desired format of saved or downloaded figure. Can be 'png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json', or 'html'. If 'html', an interactive plot will be saved. Only 'png', 'svg', 'jpeg', and 'webp' can be downloaded with the 'download as' button in the toolbar of an interactive plot. save_scale:numeric, default1- For interactive plots only (
interactive=True). Multiply title/legend/axis/canvas sizes by this factor when saving the figure. fig_out:bool, defaultFalse- Function output is a plotly figure? Ignored if
interactiveis False.
Returns
Expand source code
def diagram(eout, ptype='auto', alpha=False, normalize=False, as_residue=False, balance=None, groups=None, xrange=None, mar=None, yline=None, side=[1,2,3,4], ylog=True, xlim=None, ylim=None, xlab=None, ylab=None, cex=None, cex_names=None, cex_axis=None, lty=None, lwd=None, dotted=None, spline_method=None, contour_method=None, levels=None, col=None, col_names=None, fill=None, fill_NA="gray80", limit_water=None, names=None, format_names=True, bold=False, italic=False, font=None, family=None, adj=0.5, dx=0, dy=0, srt=0, min_area=0, main=None, legend_x=None, add=False, plot_it=True, tplot=True, annotation=None, annotation_coords=[0,0], width=600, height=520, dpi=150, messages=True, interactive=False, save_as=None, save_format=None, save_scale=1, fig_out=False): """ Python wrapper for the diagram() function in CHNOSZ. Plot equilibrium chemical activity (1-D speciation) or equal-activity (2-D predominance) diagrams as a function of chemical activities of basis species, temperature and/or pressure. Parameters ---------- eout : rpy2.ListVector Output from `equilibrate` or `affinity`. ptype : str, default 'auto' Type of plot, or name of basis species whose activity to plot. alpha : bool or str (balance), default False For speciation diagrams, plot degree of formation instead of activities? normalize : bool, default False Divide chemical affinities by balance coefficients (rescale to whole formulas)? as_residue : bool, default False Divide chemical affinities by balance coefficients (no rescaling)? balance : str or numeric, optional How to balance the transformations. groups : rpy2.ListVector of numeric, optional Groups of species to consider as a single effective species. xrange : numeric, optional Range of x-values between which predominance field boundaries are plotted. mar : numeric, optional Margins of plot frame. yline : numeric, optional Margin line on which to plot the y-axis name. side : numeric, optional Which sides of plot to draw axes. xlim : numeric, optional Limits of x-axis. ylim : numeric, optional Limits of y-axis. xlab : str, optional Label to use for x-axis. ylab : str, optional Label to use for y-axis. ylog : bool, optional Use a logarithmic y-axis (on 1D degree diagrams)? cex : numeric, optional Character expansion (scaling relative to current). cex_names : numeric, optional Character expansion factor to be used for names of species on plots. cex_axis : numeric, optional Character expansion factor for names of axes. lty : numeric, optional Line types to be used in plots. lwd : numeric, optional Line width. dotted : numeric, optional How often to skip plotting points on predominance field boundaries (to gain the effect of dotted or dashed boundary lines). spline_method : str, optional Method used in splinefun. contour_method : str, optional Labelling method used in contour (use None for no labels). levels : numeric, optional Levels at which to draw contour lines. col : str, optional Color of activity lines (1D diagram) or predominance field boundaries (2D diagram). col_names : str, optional Colors for labels of species. fill : str, optional Colors used to fill predominance fields. fill_NA : str, optional Color for grid points with NA values. limit_water : None or bool, optional Set NaN values beyond water stability limits? names : str, optional Names of species for activity lines or predominance fields. format_names : bool, default True Apply formatting to chemical formulas? bold : bool, default False Use bold formatting for names? italic : bool, default False Use italic formatting for names? font : str, optional Font type for names (has no effect if format.names is TRUE). family : str, optional Font family for names. adj : numeric or list, default 0.5 Adjustment for line labels. dx : numeric or list, default 0 X offset for line or field labels. dy : numeric or list, default 0 Y offset for line or field labels. srt : numeric, default 0 Rotation for line labels. min_area : numeric, default 0 Minimum area of fields that should be labeled, expressed as a fraction of the total plot area. main : str, optional A main title for the plot; None means to plot no title. legend_x : str, optional Description of legend placement passed to legend. add : bool, default False Add to current plot? plot_it : bool, default True Make a plot? tplot : bool, default True Set up plot with thermo.plot.new? annotation : str, optional Annotation to add to the plot. Interactive plots only (`interactive` is set to True). annotation_coords : list of numeric, default [0, 0], optional Coordinates of annotation, where 0,0 is bottom left and 1,1 is top right. Interactive plots only (`interactive` is set to True). width, height : numeric, default 600 by 520 Width and height of the plot. dpi : numeric, default 150 Resolution of the plot. messages : bool, default True Display messages from CHNOSZ? interactive : bool, default False Display an interactive plot? save_as : str, optional For interactive plots only (`interactive`=True). Provide a filename to save this figure. Filetype of saved figure is determined by `save_format`. Note: interactive plots can be saved by clicking the 'Download plot' button in the plot's toolbar. save_format : str, default "png" For interactive plots only (`interactive`=True). Desired format of saved or downloaded figure. Can be 'png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json', or 'html'. If 'html', an interactive plot will be saved. Only 'png', 'svg', 'jpeg', and 'webp' can be downloaded with the 'download as' button in the toolbar of an interactive plot. save_scale : numeric, default 1 For interactive plots only (`interactive`=True). Multiply title/legend/axis/canvas sizes by this factor when saving the figure. fig_out : bool, default False Function output is a plotly figure? Ignored if `interactive` is False. Returns ------- a : rpy2.ListVector Output from `diagram`. args : dict Dictionary of arguments supplied to `diagram`. """ if interactive: df, fig = diagram_interactive(data=eout, title=main, annotation=annotation, annotation_coords=annotation_coords, balance=balance, xlab=xlab, ylab=ylab, colormap=fill, width=width, height=height, alpha=alpha, plot_it=plot_it, save_as=save_as, save_format=save_format, save_scale=save_scale, messages=messages) if fig_out: return df, fig else: return df args = {'eout':eout, 'type':ptype, 'alpha':alpha, 'normalize':normalize, 'as.residue':as_residue, 'ylog':ylog, 'fill.NA':fill_NA, 'format.names':format_names, 'bold':bold, 'italic':italic, 'adj':adj, 'dx':dx, 'dy':dy, 'srt':srt, 'min.area':min_area, 'plot.it':plot_it, 'tplot':tplot} if balance != None: args["balance"] = balance if groups != None: args["groups"] = groups if xrange != None: args["xrange"] = _convert_to_RVector(xrange) if mar != None: args["mar"] = _convert_to_RVector(mar) if yline != None: args["yline"] = _convert_to_RVector(yline) if side != None: args["side"] = _convert_to_RVector(side) if xlim != None: args["xlim"] = _convert_to_RVector(xlim) if ylim != None: args["ylim"] = _convert_to_RVector(ylim) if xlab != None: args["xlab"] = xlab if ylab != None: args["ylab"] = ylab if cex != None: args["cex"] = _convert_to_RVector(cex) if cex_names != None: args["cex.names"] = _convert_to_RVector(cex_names) if cex_axis != None: args["cex.axis"] = _convert_to_RVector(cex_axis) if lty != None: args["lty"] = _convert_to_RVector(lty) if lwd != None: args["lwd"] = _convert_to_RVector(lwd) if dotted != None: args["dotted"] = convert_to_RVector(dotted) if spline_method != None: args["spline.method"] = spline_method if contour_method != None: args["contour.method"] = contour_method if levels != None: args["levels"] = _convert_to_RVector(levels) if col != None: args["col"] = _convert_to_RVector(col) if col_names != None: args["col.names"] = _convert_to_RVector(col_names) if fill != None: args["fill"] = _convert_to_RVector(fill) if limit_water != None: args["limit.water"] = limit_water if names != None: args["names"] = _convert_to_RVector(names) if font != None: args["font"] = _convert_to_RVector(font) if family != None: args["family"] = _convert_to_RVector(family) if isinstance(adj, list): args["adj"] = _convert_to_RVector(adj) if isinstance(dx, list): args["dx"] = _convert_to_RVector(dx) if isinstance(dy, list): args["dy"] = _convert_to_RVector(dy) if main != None: args["main"] = main if legend_x != None: args["legend.x"] = legend_x capture = R_output() capture.capture_r_output() with __r_inline_plot(width=width, height=height, dpi=dpi, plot_it=plot_it): if isinstance(add, bool): if add: # add='True' does not work with the current pyCHNOSZ framework raise Exception("The argument 'add' must be assigned the output of the previous diagram(s).") else: a = CHNOSZ.diagram(**args) elif isinstance(add, list): args.update({'add':True}) for to_add in add: CHNOSZ.diagram(**to_add) a = CHNOSZ.diagram(**args) else: CHNOSZ.diagram(**add) args.update({'add':True}) a = CHNOSZ.diagram(**args) if messages: for line in capture.stderr: print(line) return a, args def diagram_interactive(data, title=None, annotation=None, annotation_coords=[0, 0], balance=None, xlab=None, ylab=None, colormap='viridis', width=600, height=520, alpha=False, messages=True, plot_it=True, save_as=None, save_format=None, save_scale=1)-
Produce an interactive diagram.
Parameters
data:rpy2.ListVector- Output from
equilibrate()oraffinity(). title:str, optional- Title of the plot.
annotation:str, optional- Annotation to add to the plot.
annotation_coords:listofnumeric, default[0, 0], optional- Coordinates of annotation, where 0,0 is bottom left and 1,1 is top right.
balance:strornumeric, optional- How to balance the transformations.
xlab,ylab:str- Custom x and y axes labels.
width,height:numeric, default600 by 520- Width and height of the plot.
alpha:boolorstr (balance), defaultFalse- For speciation diagrams, plot degree of formation instead of activities?
messages:bool, defaultTrue- Display messages from CHNOSZ?
plot_it:bool, defaultTrue- Show the plot?
save_as:str, optional- For interactive plots only (
interactive=True). Provide a filename to save this figure. Filetype of saved figure is determined bysave_format. Note: interactive plots can be saved by clicking the 'Download plot' button in the plot's toolbar. save_format:str, default"png"- For interactive plots only (
interactive=True). Desired format of saved or downloaded figure. Can be 'png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json', or 'html'. If 'html', an interactive plot will be saved. Only 'png', 'svg', 'jpeg', and 'webp' can be downloaded with the 'download as' button in the toolbar of an interactive plot. save_scale:numeric, default1- For interactive plots only (
interactive=True). Multiply title/legend/axis/canvas sizes by this factor when saving the figure.
Returns
An interactive plot.
Expand source code
def diagram_interactive(data, title=None, annotation=None, annotation_coords=[0, 0], balance=None, xlab=None, ylab=None, colormap="viridis", width=600, height=520, alpha=False, messages=True, plot_it=True, save_as=None, save_format=None, save_scale=1): """ Produce an interactive diagram. Parameters ---------- data : rpy2.ListVector Output from `equilibrate` or `affinity`. title : str, optional Title of the plot. annotation : str, optional Annotation to add to the plot. annotation_coords : list of numeric, default [0, 0], optional Coordinates of annotation, where 0,0 is bottom left and 1,1 is top right. balance : str or numeric, optional How to balance the transformations. xlab, ylab : str Custom x and y axes labels. width, height : numeric, default 600 by 520 Width and height of the plot. alpha : bool or str (balance), default False For speciation diagrams, plot degree of formation instead of activities? messages : bool, default True Display messages from CHNOSZ? plot_it : bool, default True Show the plot? save_as : str, optional For interactive plots only (`interactive`=True). Provide a filename to save this figure. Filetype of saved figure is determined by `save_format`. Note: interactive plots can be saved by clicking the 'Download plot' button in the plot's toolbar. save_format : str, default "png" For interactive plots only (`interactive`=True). Desired format of saved or downloaded figure. Can be 'png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json', or 'html'. If 'html', an interactive plot will be saved. Only 'png', 'svg', 'jpeg', and 'webp' can be downloaded with the 'download as' button in the toolbar of an interactive plot. save_scale : numeric, default 1 For interactive plots only (`interactive`=True). Multiply title/legend/axis/canvas sizes by this factor when saving the figure. Returns ------- An interactive plot. """ basis_sp = list(data.rx2("basis").index) xyvars = list(data.rx2("vars")) xyvals = list(data.rx2("vals")) if 'loga.equil' not in data.names: calc_type = "a" else: calc_type = "e" data = equilibrate(data, balance=balance, messages=messages) if calc_type=="a": out_vals = data.rx2("values") out_units = "A/(2.303RT)" nsp = len(out_vals) lsp = out_vals[0].size flat_out_vals = np.concatenate(tuple(arr.transpose() for arr in out_vals), axis=0).reshape(nsp, lsp).tolist() df = pd.DataFrame(flat_out_vals) df["n.balance"] = list(data.rx2("n.balance")) # divide values by balance df = df.apply(lambda row: row/row["n.balance"], axis=1) df = df.drop(["n.balance"], axis=1) sp = list(info([int(val) for val in list(out_vals.names)], messages=False)["name"]) elif calc_type=="e": out_vals = data.rx2("loga.equil") out_units = "log a" nsp = len(out_vals) lsp = out_vals[0].size flat_out_vals = np.concatenate(tuple(arr.transpose() for arr in out_vals), axis=0).reshape(nsp, lsp).tolist() df = pd.DataFrame(flat_out_vals) sp = list(info([int(val) for val in list(data.rx2("values").names)], messages=False)["name"]) df.index = sp df = df.transpose() if alpha and len(xyvars) == 1: df = df.applymap(lambda x: 10**x) df = df[sp].div(df[sp].sum(axis=1), axis=0) xvar = xyvars[0] xvals = [float(val) for val in xyvals[0]] if len(xyvars) == 1: df[xvar] = xvals if not isinstance(ylab, str): if alpha: ylab = "alpha" else: ylab = out_units ylab = html_chemname_format(ylab) df = pd.melt(df, id_vars=xyvars, value_vars=sp) elif len(xyvars)==2: # predominance plot yvar = xyvars[1] yvals = [float(val) for val in xyvals[1]] df["pred"] = df.idxmax(axis=1) df["prednames"] = df["pred"] xvals_full = xvals*len(yvals) yvals_full = __flatten_list([[y]*len(xvals) for y in yvals]) df[xvar] = xvals_full df[yvar] = yvals_full unit_dict = {"P":"bar", "T":"°C", "pH":"", "Eh":"volts", "IS":"mol/kg"} for s in basis_sp: unit_dict[s] = "logact "+s if not isinstance(xlab, str): xlab = xvar+", "+unit_dict[xvar] if xvar == "pH": xlab = "pH" if xvar in basis_sp: xlab = unit_dict[xvar] xlab = html_chemname_format(xlab) if len(xyvars) == 1: df["variable"] = df["variable"].apply(html_chemname_format) fig = px.line(df, x=xvar, y="value", color='variable', template="simple_white", width=width, height=height, labels=dict(value=ylab, x=xlab), render_mode='svg', ) fig.update_layout(xaxis_title=xlab, yaxis_title=ylab, legend_title=None) if isinstance(title, str): fig.update_layout(title={'text':title, 'x':0.5, 'xanchor':'center'}) if isinstance(annotation, str): fig.add_annotation( x=annotation_coords[0], y=annotation_coords[1], text=annotation, showarrow=False, xref="paper", yref="paper", align='left', bgcolor="rgba(255, 255, 255, 0.5)") save_as, save_format = __save_figure(fig, save_as, save_format, save_scale, plot_width=width, plot_height=height, ppi=1) config = {'displaylogo': False, 'modeBarButtonsToRemove': ['resetScale2d', 'toggleSpikelines'], 'toImageButtonOptions': { 'format': save_format, # one of png, svg, jpeg, webp 'filename': save_as, 'height': height, 'width': width, 'scale': save_scale, }, } if len(xyvars) == 2: mappings = {'pred': {s:lab for s,lab in zip(sp,range(0,len(sp)))}} df.replace(mappings, inplace=True) data = np.array(df.pred) shape = (len(xvals), len(yvals)) dmap = data.reshape(shape) data = np.array(df.prednames) shape = (len(xvals), len(yvals)) dmap_names = data.reshape(shape) if not isinstance(ylab, str): ylab = yvar+", "+unit_dict[yvar] if yvar in basis_sp: ylab = unit_dict[yvar] if yvar == "pH": ylab = "pH" ylab = html_chemname_format(ylab) fig = px.imshow(dmap, width=width, height=height, aspect="auto", labels=dict(x=xlab, y=ylab, color="region"), x=xvals, y=yvals, template="simple_white", ) fig.update(data=[{'customdata': dmap_names, 'hovertemplate': xlab+': %{x}<br>'+ylab+': %{y}<br>Region: %{customdata}<extra></extra>'}]) if colormap == 'none': colormap = [[0, 'white'], [1, 'white']] fig.update_traces(dict(showscale=False, coloraxis=None, colorscale=colormap), selector={'type':'heatmap'}) fig.update_yaxes(autorange=True) if isinstance(title, str): fig.update_layout(title={'text':title, 'x':0.5, 'xanchor':'center'}) for s in sp: if s in set(df["prednames"]): df_s = df.loc[df["prednames"]==s,] namex = df_s[xvar].mean() namey = df_s[yvar].mean() fig.add_annotation(x=namex, y=namey, text=html_chemname_format(s), bgcolor="rgba(255, 255, 255, 0.5)", showarrow=False) if isinstance(annotation, str): fig.add_annotation( x=annotation_coords[0], y=annotation_coords[1], text=annotation, showarrow=False, xref="paper", yref="paper", align='left', bgcolor="rgba(255, 255, 255, 0.5)") save_as, save_format = __save_figure(fig, save_as, save_format, save_scale, plot_width=width, plot_height=height, ppi=1) config = {'displaylogo': False, 'modeBarButtonsToRemove': ['zoom2d', 'pan2d', 'zoomIn2d', 'zoomOut2d', 'autoScale2d', 'resetScale2d', 'toggleSpikelines', 'hoverClosestCartesian', 'hoverCompareCartesian'], 'toImageButtonOptions': { 'format': save_format, # one of png, svg, jpeg, webp 'filename': save_as, 'height': height, 'width': width, 'scale': save_scale, }, } if plot_it: fig.show(config=config) return df, fig def entropy(formula, messages=True)-
Python wrapper for the entropy() function in CHNOSZ. Calculate the standard molal entropy of elements in a compound.
Parameters
formula:str, int,orlistofstrorint- Supply a chemical formula (e.g. "CH4"), a species index, or a list of formulas or indices.
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
out:floatorlistoffloat- Standard molal entropy of elements in the formula in cal/(mol K).
Returns a list if
formulais a list.
Expand source code
def entropy(formula, messages=True): """ Python wrapper for the entropy() function in CHNOSZ. Calculate the standard molal entropy of elements in a compound. Parameters ---------- formula : str, int, or list of str or int Supply a chemical formula (e.g. "CH4"), a species index, or a list of formulas or indices. messages : bool, default True Display messages from CHNOSZ? Returns ---------- out : float or list of float Standard molal entropy of elements in the formula in cal/(mol K). Returns a list if `formula` is a list. """ formula_R = _convert_to_RVector(formula, force_Rvec=False) capture = R_output() capture.capture_r_output() out = CHNOSZ.entropy(formula_R) if messages: for line in capture.stderr: print(line) out = list(out) if not isinstance(formula, list): out = out[0] return out def equilibrate(aout, balance=None, loga_balance=None, ispecies=None, normalize=False, messages=True)-
Python wrapper for the equilibrate() function in CHNOSZ. Calculate equilibrium chemical activities of species from the affinities of formation of the species at unit activity.
Parameters
aout:rpy2.ListVector- Output from
affinity(). balance:strornumeric, optional- How to balance the transformations.
loga_balance:numericorlistofnumeric, optional- Logarithm of total activity of balanced quantity.
ispecies:numeric, optional- Which species to include.
normalize:bool, defaultFalse- Normalize the molar formulas of species by the balancing coefficients?
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
eout:rpy2.ListVector- Output from
equilibrate().
Expand source code
def equilibrate(aout, balance=None, loga_balance=None, ispecies=None, normalize=False, messages=True): """ Python wrapper for the equilibrate() function in CHNOSZ. Calculate equilibrium chemical activities of species from the affinities of formation of the species at unit activity. Parameters ---------- aout : rpy2.ListVector Output from `affinity`. balance : str or numeric, optional How to balance the transformations. loga_balance : numeric or list of numeric, optional Logarithm of total activity of balanced quantity. ispecies : numeric, optional Which species to include. normalize : bool, default False Normalize the molar formulas of species by the balancing coefficients? messages : bool, default True Display messages from CHNOSZ? Returns ------- eout : rpy2.ListVector Output from `equilibrate`. """ # stay.normal is an unused argument in equilibrate()? args = {'aout':aout, 'normalize':normalize} if balance != None: args['balance'] = balance if loga_balance != None: args['loga.balance'] = loga_balance if ispecies != None: args['ispecies'] = _convert_to_RVector(ispecies) capture = R_output() capture.capture_r_output() eout = CHNOSZ.equilibrate(**args) if messages: for line in capture.stderr: print(line) return eout def html_chemname_format(name)-
Format a chemical formula to display subscripts and superscripts in HTML (e.g., Plotly plots) Example, "CH3COO-" becomes "CH3COO-"
Parameters
name:str- A chemical formula.
Returns
A formatted chemical formula string.
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def html_chemname_format(name): """ Format a chemical formula to display subscripts and superscripts in HTML (e.g., Plotly plots) Example, "CH3COO-" becomes "CH<sub>3</sub>COO<sup>-</sup>" Parameters ---------- name : str A chemical formula. Returns ------- A formatted chemical formula string. """ p = re.compile(r'(?P<sp>[-+]\d*?$)') name = p.sub(r'<sup>\g<sp></sup>', name) charge = re.search(r'<.*$', name) name_no_charge = re.match(r'(?:(?!<|$).)*', name).group(0) mapping = {"0": "<sub>0</sub>", "1": "<sub>1</sub>", "2": "<sub>2</sub>", "3": "<sub>3</sub>", "4": "<sub>4</sub>", "5": "<sub>5</sub>", "6": "<sub>6</sub>", "7": "<sub>7</sub>", "8": "<sub>8</sub>", "9": "<sub>9</sub>", ".":"<sub>.</sub>"} name_no_charge_formatted = "".join([mapping.get(x) or x for x in list(name_no_charge)]) if charge != None: name = name_no_charge_formatted + charge.group(0) else: name = name_no_charge_formatted return name def info(species, state=None, check_it=True, messages=True)-
Python wrapper for the info() function in CHNOSZ. Search for species by name or formula, retrieve their thermodynamic properties and parameters, and add proteins to the thermodynamic database.
Parameters
species:str, int,orlistofstrorint- Name or formula of species, or numeric, rownumber of species in the OBIGT database.
state:strorlistofstr, optional- State(s) of species.
check_it:bool, defaultTrue- Check GHS and EOS parameters for self-consistency?
messages:bool, defaultTrue- Print messages from CHNOSZ?
Returns
pd.DataFrameorlistofint- Returns a Pandas dataframe if supplied a list of OBIGT database indices (ispecies), or a list of ispecies if given a list of species names or formulas.
Expand source code
def info(species, state=None, check_it=True, messages=True): """ Python wrapper for the info() function in CHNOSZ. Search for species by name or formula, retrieve their thermodynamic properties and parameters, and add proteins to the thermodynamic database. Parameters ---------- species : str, int, or list of str or int Name or formula of species, or numeric, rownumber of species in the OBIGT database. state : str or list of str, optional State(s) of species. check_it : bool, default True Check GHS and EOS parameters for self-consistency? messages : bool, default True Print messages from CHNOSZ? Returns ---------- pd.DataFrame or list of int Returns a Pandas dataframe if supplied a list of OBIGT database indices (ispecies), or a list of ispecies if given a list of species names or formulas. """ args = {} output_is_df = False args["species"] = _convert_to_RVector(species, force_Rvec=False) if not isinstance(species, list): species = [species] if all(isinstance(x, (int, np.integer)) for x in species): output_is_df = True if state != None: args["state"] = _convert_to_RVector(state, force_Rvec=False) args["check.it"] = check_it capture = R_output() capture.capture_r_output() a = CHNOSZ.info(**args) if messages: for line in capture.stderr: print(line) if output_is_df: return ro.conversion.rpy2py(a) else: return list(a) def makeup(formula, multiplier=1, sum=False, count_zero=False, messages=True)-
Python wrapper for the makeup() function in CHNOSZ. Parse a formula into a dictionary of elements and their counts.
Parameters
formula:strorlistofstr- Chemical formula or a list of chemical formulas.
multiplier:numeric, default1- Multiplier for the elemental counts in each formula.
sum:bool, defaultFalse- Add together the elemental counts in all formulas?
Ignored if
formulais not a list. count_zero:bool, defaultFalse- Include zero counts for an element if it is not in this formula, but is
found in another formula in the list? Ensures that the dictionaries
returned for each formula have the same length.
Ignored if
formulais not a list. messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
out:dict- Dictionary of elements and their counts in the formula(s).
Expand source code
def makeup(formula, multiplier=1, sum=False, count_zero=False, messages=True): """ Python wrapper for the makeup() function in CHNOSZ. Parse a formula into a dictionary of elements and their counts. Parameters ---------- formula : str or list of str Chemical formula or a list of chemical formulas. multiplier : numeric, default 1 Multiplier for the elemental counts in each formula. sum : bool, default False Add together the elemental counts in all formulas? Ignored if `formula` is not a list. count_zero : bool, default False Include zero counts for an element if it is not in this formula, but is found in another formula in the list? Ensures that the dictionaries returned for each formula have the same length. Ignored if `formula` is not a list. messages : bool, default True Display messages from CHNOSZ? Returns ---------- out : dict Dictionary of elements and their counts in the formula(s). """ formula_R = _convert_to_RVector(formula, force_Rvec=False) args = {'formula':formula_R, "multiplier":multiplier, "sum":sum, "count.zero":count_zero} capture = R_output() capture.capture_r_output() out = CHNOSZ.makeup(**args) if messages: for line in capture.stderr: print(line) if isinstance(out, ro.ListVector): out_dict = {} for i, molecule in enumerate(formula): molecule_dict = {} if not count_zero: out_names = out[i].names else: out_names = out[i].names[0] for ii, elem in enumerate(out_names): molecule_dict[elem] = out[i][ii] out_dict[molecule] = molecule_dict else: out_dict = {} if not sum or not isinstance(formula, list): out_names = out.names else: out_names = out.names[0] for i, elem in enumerate(out_names): out_dict[elem] = out[i] return out_dict def mass(formula, messages=True)-
Python wrapper for the mass() function in CHNOSZ. Calculate the mass of the sum of elements in a formula.
Parameters
formula:str, int,orlistofstrorint- Supply a chemical formula (e.g. "CH4"), a species index, or a list of formulas or indices.
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
out:floatorlistoffloat- Molar mass of the sum of elements in the formula, in g/mol.
Returns a list if
formulais a list.
Expand source code
def mass(formula, messages=True): """ Python wrapper for the mass() function in CHNOSZ. Calculate the mass of the sum of elements in a formula. Parameters ---------- formula : str, int, or list of str or int Supply a chemical formula (e.g. "CH4"), a species index, or a list of formulas or indices. messages : bool, default True Display messages from CHNOSZ? Returns ---------- out : float or list of float Molar mass of the sum of elements in the formula, in g/mol. Returns a list if `formula` is a list. """ formula_R = _convert_to_RVector(formula, force_Rvec=False) capture = R_output() capture.capture_r_output() out = CHNOSZ.mass(formula_R) if messages: for line in capture.stderr: print(line) out = list(out) if not isinstance(formula, list): out = out[0] return out def mod_OBIGT(*args, messages=True, **kwargs)-
Python wrapper for the mod.OBIGT() function in CHNOSZ. Modify species in the OBIGT thermodynamic database. Optionally, supply a Pandas dataframe containing custom data.
Parameters
*args:str, numeric,orPandas dataframe- Species names or thermodynamic database index numbers to modify. If a Pandas dataframe, database entries to add or modify.
*kwargs:strornumeric- Properties of species to modify in the thermodynamic database.
messages:bool, defaultTrue- Print messages from CHNOSZ?
Returns
listofint- A list of OBIGT database indices (ispecies) that have been added or modified.
Expand source code
def mod_OBIGT(*args, messages=True, **kwargs): """ Python wrapper for the mod.OBIGT() function in CHNOSZ. Modify species in the OBIGT thermodynamic database. Optionally, supply a Pandas dataframe containing custom data. Parameters ---------- *args : str, numeric, or Pandas dataframe Species names or thermodynamic database index numbers to modify. If a Pandas dataframe, database entries to add or modify. *kwargs : str or numeric Properties of species to modify in the thermodynamic database. messages : bool, default True Print messages from CHNOSZ? Returns ---------- list of int A list of OBIGT database indices (ispecies) that have been added or modified. """ capture = R_output() capture.capture_r_output() if isinstance(args[0], pd.DataFrame): arg_list = list(args) arg_list[0] = ro.conversion.py2rpy(arg_list[0]) args = tuple(arg_list) else: pass ispecies = CHNOSZ.mod_OBIGT(*args, **kwargs) if messages: for line in capture.stderr: print(line) return list(ispecies) def ratlab(top='K+', bottom='H+', molality=False)-
Python wrapper for the ratlab() function in CHNOSZ. Produces a expression for the activity ratio between the ions in the top and bottom arguments. The default is a ratio with H+, i.e. (activity of the ion) / [(activity of H+) ^ (charge of the ion)]
Parameters
top:str, default"K+"- The ion in the numerator of the ratio.
bottom:str, default"H+"- The ion in the denominator of the ratio.
molality:bool, defaultFalse- Use molality (m) instead of activity (a) for aqueous species?
Returns
A formatted string representing the activity ratio.
Expand source code
def ratlab(top="K+", bottom="H+", molality=False): """ Python wrapper for the ratlab() function in CHNOSZ. Produces a expression for the activity ratio between the ions in the top and bottom arguments. The default is a ratio with H+, i.e. (activity of the ion) / [(activity of H+) ^ (charge of the ion)] Parameters ---------- top : str, default "K+" The ion in the numerator of the ratio. bottom : str, default "H+" The ion in the denominator of the ratio. molality : bool, default False Use molality (m) instead of activity (a) for aqueous species? Returns ------- A formatted string representing the activity ratio. """ top_formula = chemparse.parse_formula(top) if "+" in top_formula.keys(): top_charge = top_formula["+"] elif "-" in top_formula.keys(): top_charge = top_formula["-"] else: raise Exception("Cannot create an ion ratio involving one or more neutral species.") bottom_formula = chemparse.parse_formula(bottom) if "+" in bottom_formula.keys(): bottom_charge = bottom_formula["+"] elif "-" in bottom_formula.keys(): top_charge = bottom_formula["-"] else: raise Exception("Cannot create an ion ratio involving one or more neutral species.") if top_charge.is_integer(): top_charge = int(top_charge) if bottom_charge.is_integer(): bottom_charge = int(bottom_charge) if top_charge != 1: top_charge = "<sup>"+str(top_charge)+"</sup>" else: top_charge = "" if bottom_charge != 1: bottom_charge = "<sup>"+str(bottom_charge)+"</sup>" else: bottom_charge = "" if molality: sym = "m" else: sym = "a" return "log("+sym+bottom_charge+"<sub>"+html_chemname_format(top)+"</sub>/"+sym+top_charge+"<sub>"+html_chemname_format(bottom)+"</sub>)" def reset(messages=True)-
Python wrapper for the reset() function in CHNOSZ. Reset all of the data used in CHNOSZ to default values. This includes the computational settings, thermodynamic database, and system settings (chemical species).
Parameters
messages:bool, defaultTrue- Print messages from CHNOSZ?
Expand source code
def reset(messages=True): """ Python wrapper for the reset() function in CHNOSZ. Reset all of the data used in CHNOSZ to default values. This includes the computational settings, thermodynamic database, and system settings (chemical species). Parameters ---------- messages : bool, default True Print messages from CHNOSZ? """ capture = R_output() capture.capture_r_output() CHNOSZ.reset() if messages: for line in capture.stderr: print(line) def retrieve(elements=None, ligands=None, state=None, T=None, P='Psat', add_charge=True, hide_groups=True, must_have=None, messages=True)-
Python wrapper for the retrieve() function in CHNOSZ. Retrieve species in the database containing one or more chemical elements.
Parameters
elements:str, listofstr,ortupleofstr-
Elements in a chemical system. If
elementsis a string, retrieve species containing that element.E.g.,
retrieve("Au")will return all species containing Au.If
elementsis a list, retrieve species that have all of the elements in the list.E.g.,
retrieve(["Au", "Cl"])will return all species that have both Au and Cl.If
elementsis a tuple, retrieve species relevant to the system, including charged species.E.g.,
retrieve(("Au", "Cl"))will return species that have Au and/or Cl, including charged species, but no other elements. ligands:str, listofstr,ortupleofstr, optional- Elements present in any ligands.
state:str, listofstr,ortupleofstr, optional- Filter the result on these state(s).
T:float, optional- Temperature where DeltaG0 of species must not be NA
P:floator"Psat", default"Psat"- Pressure where DeltaG0 of species must not be NA
add_charge:bool, defaultTrue- Add charge to the system?
hide_groups:bool, defaultTrue- Exclude groups from the result?
must_have:strorlistofstr, optional- Retrieved species must have the element(s).
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
out:listordict- A list of the OBIGT indices of retrieved chemical species
Expand source code
def retrieve(elements=None, ligands=None, state=None, T=None, P="Psat", add_charge=True, hide_groups=True, must_have=None, messages=True): """ Python wrapper for the retrieve() function in CHNOSZ. Retrieve species in the database containing one or more chemical elements. Parameters ---------- elements : str, list of str, or tuple of str Elements in a chemical system. If `elements` is a string, retrieve species containing that element. E.g., `retrieve("Au")` will return all species containing Au. If `elements` is a list, retrieve species that have all of the elements in the list. E.g., `retrieve(["Au", "Cl"])` will return all species that have both Au and Cl. If `elements` is a tuple, retrieve species relevant to the system, including charged species. E.g., `retrieve(("Au", "Cl"))` will return species that have Au and/or Cl, including charged species, but no other elements. ligands : str, list of str, or tuple of str, optional Elements present in any ligands. state : str, list of str, or tuple of str, optional Filter the result on these state(s). T : float, optional Temperature where DeltaG0 of species must not be NA P : float or "Psat", default "Psat" Pressure where DeltaG0 of species must not be NA add_charge : bool, default True Add charge to the system? hide_groups : bool, default True Exclude groups from the result? must_have : str or list of str, optional Retrieved species must have the element(s). messages : bool, default True Display messages from CHNOSZ? Returns ---------- out: list or dict A list of the OBIGT indices of retrieved chemical species """ if elements == None: elements = ro.r("NULL") elif isinstance(elements, list) or isinstance(elements, str): elements = _convert_to_RVector(elements, force_Rvec=True) elif isinstance(elements, tuple): elements = ro.ListVector({chr(ord('`')+i):l for i,l in zip(range(1, len(elements)+1), elements)}) else: pass if ligands == None: ligands = ro.r("NULL") elif isinstance(ligands, list) or isinstance(ligands, str): ligands = _convert_to_RVector(ligands, force_Rvec=True) elif isinstance(ligands, tuple): ligands = ro.ListVector({chr(ord('`')+i):l for i,l in zip(range(1, len(ligands)+1), ligands)}) else: pass if state == None: state = ro.r("NULL") elif isinstance(state, list) or isinstance(state, str): state = _convert_to_RVector(state, force_Rvec=True) elif isinstance(state, tuple): state = ro.ListVector({chr(ord('`')+i):l for i,l in zip(range(1, len(state)+1), state)}) else: pass if T == None: T = ro.r("NULL") if P == None: P = ro.r("NULL") args = {'elements':elements, 'ligands':ligands, 'state':state, 'T':T, 'P':P, 'add_charge':add_charge, 'hide_groups':hide_groups} capture = R_output() capture.capture_r_output() out = CHNOSZ.retrieve(**args) if messages: for line in capture.stderr: print(line) out = list(out) if isinstance(must_have, str): must_have = [must_have] if isinstance(must_have, list): df = info(out, messages=False) formulas = df["formula"].apply(chemparse.parse_formula) keep_ind = [] for i,formula in enumerate(formulas): if all(elem in formula.keys() for elem in must_have): keep_ind.append(i) keep_ind = list(set(keep_ind)) out = [out[i] for i in keep_ind] return out def seq2aa(protein, sequence, messages=True)-
Python wrapper for the seq2aa() function in CHNOSZ. Returns a data frame of amino acid composition corresponding to the provided sequence.
Parameters
protein:str- Protein name with an underscore, e.g., 'LYSC_CHICK'
sequence:str- Amino acid sequence of the protein.
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
Pandas dataframe- Amino acid composition of protein.
Expand source code
def seq2aa(protein, sequence, messages=True): """ Python wrapper for the seq2aa() function in CHNOSZ. Returns a data frame of amino acid composition corresponding to the provided sequence. Parameters ---------- protein : str Protein name with an underscore, e.g., 'LYSC_CHICK' sequence : str Amino acid sequence of the protein. messages : bool, default True Display messages from CHNOSZ? Returns ------- Pandas dataframe Amino acid composition of protein. """ args = {'protein':protein, 'sequence':sequence} capture = R_output() capture.capture_r_output() pout = CHNOSZ.seq2aa(**args) if messages: for line in capture.stderr: print(line) return ro.conversion.rpy2py(pout) def solubility(iaq=None, in_terms_of=None, dissociate=False, find_IS=False, messages=True, **kwargs)-
Python wrapper for the solubility() function in CHNOSZ. Calculate chemical activities of aqueous species in equilibrium with a mineral or gas.
Parameters
iaq:str, int,orlistofstrorint- Name(s) of aqueous species (if str). If int, the index of the aqueous species in the OBIGT database.
in_terms_of:str, optional- Express the total solubility in terms of moles of this species.
dissociate:bool, defaultFalse- Does the mineral undergo a dissociation reaction?
find_IS:bool, defaultFalse- Find the equilibrium ionic strength by iteration?
messages:bool, defaultTrue- Display messages from CHNOSZ?
**kwargs:named arguments- Arguments for
affinity()ormosaic(i.e. plotting variables).
Returns
s:rpy2.ListVector- Output from
solubility().
Expand source code
def solubility(iaq=None, in_terms_of=None, dissociate=False, find_IS=False, messages=True, **kwargs): """ Python wrapper for the solubility() function in CHNOSZ. Calculate chemical activities of aqueous species in equilibrium with a mineral or gas. Parameters ---------- iaq : str, int, or list of str or int Name(s) of aqueous species (if str). If int, the index of the aqueous species in the OBIGT database. in_terms_of : str, optional Express the total solubility in terms of moles of this species. dissociate : bool, default False Does the mineral undergo a dissociation reaction? find_IS : bool, default False Find the equilibrium ionic strength by iteration? messages : bool, default True Display messages from CHNOSZ? **kwargs : named arguments Arguments for `affinity` or `mosaic` (i.e. plotting variables). Returns ------- s : rpy2.ListVector Output from `solubility`. """ args = {'dissociate':dissociate, 'find_IS':find_IS} if in_terms_of != None: args["in_terms_of"] = in_terms_of args["iaq"] = _convert_to_RVector(iaq, force_Rvec=True) for key, value in kwargs.items(): if isinstance(value, list): value = ro.FloatVector(value) args.update({key:value}) capture = R_output() capture.capture_r_output() s = CHNOSZ.solubility(**args) if messages: for line in capture.stderr: print(line) return s def species(species=None, state=None, delete=False, add=False, index_return=False, messages=True)-
Python wrapper for the species() function in CHNOSZ. Define the species of interest in a system; modify their physical states and logarithms of activities.
Parameters
species:str, int,orlistofstrorint- Names or formulas of species to add to the species definition; int, rownumbers of species in OBIGT to modify or delete.
state:strorlistofstr, optional- physical states; numeric, logarithms of activities or fugacities.
delete:bool, defaultFalse- Delete the species identified by numeric values of species (or all species if that argument is missing)?
add:bool, defaultFalse- Delete a previous species definition instead of adding to it?
index_return:bool, defaultFalse- return the affected rownumbers of species in the OBIGT database instead
of the normal output of
species()? messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
pd.DataFrame- Pandas dataframe containing a stoichometric matrix of reactions to form
species from basis species defined by
basis().
Expand source code
def species(species=None, state=None, delete=False, add=False, index_return=False, messages=True): """ Python wrapper for the species() function in CHNOSZ. Define the species of interest in a system; modify their physical states and logarithms of activities. Parameters ---------- species : str, int, or list of str or int Names or formulas of species to add to the species definition; int, rownumbers of species in OBIGT to modify or delete. state : str or list of str, optional physical states; numeric, logarithms of activities or fugacities. delete : bool, default False Delete the species identified by numeric values of species (or all species if that argument is missing)? add : bool, default False Delete a previous species definition instead of adding to it? index_return : bool, default False return the affected rownumbers of species in the OBIGT database instead of the normal output of `species`? messages : bool, default True Display messages from CHNOSZ? Returns ---------- pd.DataFrame Pandas dataframe containing a stoichometric matrix of reactions to form species from basis species defined by `basis`. """ args={} if species != None: args["species"] = _convert_to_RVector(species, force_Rvec=False) if state != None: args["state"] = _convert_to_RVector(state, force_Rvec=False) args["add"] = add args["delete"] = delete args["index.return"] = index_return capture = R_output() capture.capture_r_output() sout = CHNOSZ.species(**args) if messages: for line in capture.stderr: print(line) return ro.conversion.rpy2py(sout) def subcrt(species, coeff=None, state=None, property=['logK', 'G', 'H', 'S', 'V', 'Cp'], T=None, P=None, grid=None, convert=True, exceed_Ttr=False, exceed_rhomin=False, logact=None, autobalance=True, IS=None, messages=True, show=True)-
Python wrapper for the subcrt() function in CHNOSZ. Calculate the standard molal thermodynamic properties of one or more species or a reaction between species as a function of temperature and pressure.
Parameters
species:str, int,orlistofstrorint- Name or formula of species, or numeric, rownumber of species in the OBIGT database.
coeff:numericorlistofnumeric, optional- Reaction coefficients on species.
state:strorlistofstr, optional- State(s) of species.
property:strorlistofstr, optional- Property(s) to calculate.
T:numericorlistofnumeric, optional- Temperature(s) of the calculation.
P:numeric, listofnumeric,orstr if 'Psat', default'Psat'- Pressure(s) of the calculation.
grid:str, defaultNone- Type of PxT grid to produce (None, the default, means no gridding).
exceed_Ttr:bool, defaultFalse- Calculate Gibbs energies of mineral phases and other species beyond their transition temperatures?
exceed_rhomin:bool, defaultFalse- Return properties of species in the HKF model below 0.35 g cm-3?
logact:numericorlistofnumeric, optional- Logarithms of activities of species in reaction.
convert:bool, defaultTrue- Are input and output units of T and P those of the user (True) (see T_units), or are they Kelvin and bar (False)?
autobalance:bool, defaultTrue- Attempt to automatically balance reaction with basis species?
IS:numericorlistofnumeric, optional- Ionic strength(s) at which to calculate adjusted molal properties, mol kg^-1.
messages:bool, defaultTrue- Print messages from CHNOSZ?
show:bool, defaultTrue- Display CHNOSZ tables?
Returns
out:objectofclass SubcrtOutput- An object that stores the output of
subcrt().
Expand source code
def subcrt(species, coeff=None, state=None, property=["logK", "G", "H", "S", "V", "Cp"], T=None, P=None, grid=None, convert=True, exceed_Ttr=False, exceed_rhomin=False, logact=None, autobalance=True, IS=None, messages=True, show=True): """ Python wrapper for the subcrt() function in CHNOSZ. Calculate the standard molal thermodynamic properties of one or more species or a reaction between species as a function of temperature and pressure. Parameters ---------- species : str, int, or list of str or int Name or formula of species, or numeric, rownumber of species in the OBIGT database. coeff : numeric or list of numeric, optional Reaction coefficients on species. state : str or list of str, optional State(s) of species. property : str or list of str, optional Property(s) to calculate. T : numeric or list of numeric, optional Temperature(s) of the calculation. P : numeric, list of numeric, or str if 'Psat', default 'Psat' Pressure(s) of the calculation. grid : str, default None Type of PxT grid to produce (None, the default, means no gridding). exceed_Ttr : bool, default False Calculate Gibbs energies of mineral phases and other species beyond their transition temperatures? exceed_rhomin : bool, default False Return properties of species in the HKF model below 0.35 g cm-3? logact : numeric or list of numeric, optional Logarithms of activities of species in reaction. convert : bool, default True Are input and output units of T and P those of the user (True) (see T_units), or are they Kelvin and bar (False)? autobalance : bool, default True Attempt to automatically balance reaction with basis species? IS : numeric or list of numeric, optional Ionic strength(s) at which to calculate adjusted molal properties, mol kg^-1. messages : bool, default True Print messages from CHNOSZ? show : bool, default True Display CHNOSZ tables? Returns ---------- out : object of class SubcrtOutput An object that stores the output of `subcrt`. """ single_species = False args = {'species': _convert_to_RVector(species, force_Rvec=False)} if coeff != None: args["coeff"] = _convert_to_RVector(coeff) else: single_species = True if state != None: args["state"] = _convert_to_RVector(state, force_Rvec=False) args["property"] = _convert_to_RVector(property) if T != None: args['T'] = _convert_to_RVector(T, force_Rvec=False) if P != None: args['P'] = _convert_to_RVector(P, force_Rvec=False) if grid != None: args['grid'] = grid # grid is either 'T' or 'P' args['convert'] = convert args['exceed.Ttr'] = exceed_Ttr args['exceed.rhomin'] = exceed_rhomin if logact != None: args["logact"] = _convert_to_RVector(logact) args['autobalance'] = autobalance if IS != None: args["IS"] = _convert_to_RVector(IS, force_Rvec=False) capture = R_output() capture.capture_r_output() a = CHNOSZ.subcrt(**args) if messages: for line in capture.stderr: print(line) if "warnings" in a.names: warn = a.rx2("warnings")[0] # subcrt's list includes warnings only if they appear else: warn = None if "polymorphs" in a.names: poly = a.rx2("polymorphs") # subcrt's list includes polymorphs only if they appear else: poly = None if not single_species: out_dict = {"reaction":ro.conversion.rpy2py(a[0]), "out":ro.conversion.rpy2py(a[1])} # the extra [0] is important else: out_dict = {"species":ro.conversion.rpy2py(a[0]), "out":{}} i=0 for df in a[1]: out_dict["out"][out_dict["species"].name[i]] = ro.conversion.rpy2py(df) i += 1 if isinstance(warn, str): out_dict["warnings"] = warn if isinstance(poly, pd.DataFrame): out_dict["polymorphs"] = poly out = SubcrtOutput(out_dict) if show: for table in out.__dict__.keys(): if not isinstance(out[table], dict): display(out[table]) else: for subtable in out[table].keys(): print("\n"+subtable+":") # species name display(out[table][subtable]) return out def syslab(system=['K2O', 'Al2O3', 'SiO2', 'H2O'], dash='-')-
Python wrapper for the syslab() function in CHNOSZ. Formats the given thermodynamic components and adds intervening en dashes.
Parameters
system:listofstr, default["K2O", "Al2O3", "SiO2", "H2O"]- Thermodynamic components.
dash:str, default"-"- Character to use for dash between components.
Returns
A formatted string representing the thermodynamic system.
Expand source code
def syslab(system=["K2O", "Al2O3", "SiO2", "H2O"], dash="-"): """ Python wrapper for the syslab() function in CHNOSZ. Formats the given thermodynamic components and adds intervening en dashes. Parameters ---------- system : list of str, default ["K2O", "Al2O3", "SiO2", "H2O"] Thermodynamic components. dash : str, default "-" Character to use for dash between components. Returns ------- A formatted string representing the thermodynamic system. """ return dash.join([html_chemname_format(sp)for sp in system]) def unicurve(logK, species, coeff, state, pressures=1, temperatures=25, IS=0, minT=0.1, maxT=100, minP=1, maxP=500, tol=None, solve='T', width=600, height=520, dpi=90, plot_it=True, messages=True, show=True)-
Solve for temperatures or pressures of equilibration for a given logK value and produce a plot.
Parameters
logK:numeric- Logarithm (base 10) of an equilibrium constant.
species:str, int,orlistofstrorint- Name or formula of species, or numeric, rownumber of species in the OBIGT database.
coeff:numericorlistofnumeric, optional- Reaction coefficients on species.
state:strorlistofstr, optional- State(s) of species.
pressures,temperatures:numericorlistofnumeric- Pressures (if solving for temperature) or temperatures (if solving for
pressures) to resolve with
logK. Pressures are in bars and temperatures are in degrees Celcius. IS:numericorlistofnumeric, optional- Ionic strength(s) at which to calculate adjusted molal properties, mol kg^-1.
minT,maxT:numeric- Minimum and maximum temperatures (degrees Celcius) to search within for a solution for the given pressure(s) and logK. Ignored if solving for pressure.
minP,maxP:numeric- Minimum and maximum pressures (bars) to search within for a solution for the given temperatures(s) and logK. Ignored if solving for temperature.
tol:float- Tolerance for finding a temperature or pressure that converges on the
given logK. Will attempt to find a solution that satisfies logK plus or
minus
tol. By default, tol is equal to 1/(10^(n+2)) where n is the number of decimal places in logK, with a maximum default tolerance of 1e-5. solve:"T"or"P"- Solve for temperature or pressure?
width,height:numeric, default600 by 520- Width and height of the plot.
dpi:numeric, default90- Resolution of the plot.
plot_it:bool, defaultTrue- Show the plot?
messages:bool, defaultTrue- Display messages from CHNOSZ?
show:bool, defaultTrue- Display CHNOSZ tables?
Returns
out:objectofclass SubcrtOutput- An object that stores the output of
subcrt()along the univariant curve.
Expand source code
def unicurve(logK, species, coeff, state, pressures=1, temperatures=25, IS=0, minT=0.1, maxT=100, minP=1, maxP=500, tol=None, solve="T", width=600, height=520, dpi=90, plot_it=True, messages=True, show=True): """ Solve for temperatures or pressures of equilibration for a given logK value and produce a plot. Parameters ---------- logK : numeric Logarithm (base 10) of an equilibrium constant. species : str, int, or list of str or int Name or formula of species, or numeric, rownumber of species in the OBIGT database. coeff : numeric or list of numeric, optional Reaction coefficients on species. state : str or list of str, optional State(s) of species. pressures, temperatures : numeric or list of numeric Pressures (if solving for temperature) or temperatures (if solving for pressures) to resolve with `logK`. Pressures are in bars and temperatures are in degrees Celcius. IS : numeric or list of numeric, optional Ionic strength(s) at which to calculate adjusted molal properties, mol kg^-1. minT, maxT : numeric Minimum and maximum temperatures (degrees Celcius) to search within for a solution for the given pressure(s) and logK. Ignored if solving for pressure. minP, maxP : numeric Minimum and maximum pressures (bars) to search within for a solution for the given temperatures(s) and logK. Ignored if solving for temperature. tol : float Tolerance for finding a temperature or pressure that converges on the given logK. Will attempt to find a solution that satisfies logK plus or minus `tol`. By default, tol is equal to 1/(10^(n+2)) where n is the number of decimal places in logK, with a maximum default tolerance of 1e-5. solve : "T" or "P" Solve for temperature or pressure? width, height : numeric, default 600 by 520 Width and height of the plot. dpi : numeric, default 90 Resolution of the plot. plot_it : bool, default True Show the plot? messages : bool, default True Display messages from CHNOSZ? show : bool, default True Display CHNOSZ tables? Returns ------- out : object of class SubcrtOutput An object that stores the output of `subcrt` along the univariant curve. """ if tol == None: d = decimal.Decimal(str(logK)) n_decimals = abs(d.as_tuple().exponent) tol = float("0."+"".join(n_decimals*["0"])+"01") if tol > 0.00001: tol = 0.00001 species = _convert_to_RVector(species, force_Rvec=False) state = _convert_to_RVector(state, force_Rvec=False) coeff = _convert_to_RVector(coeff, force_Rvec=False) pressures = _convert_to_RVector(pressures, force_Rvec=False) temperatures = _convert_to_RVector(temperatures, force_Rvec=False) capture = R_output() capture.capture_r_output() r_univariant = pkg_resources.resource_string( __name__, 'univariant.r').decode("utf-8") ro.r(r_univariant) if solve=="T": a = ro.r.uc_solveT(logK=logK, species=species, coeff=coeff, state=state, pressures=pressures, IS=IS, minT=minT, maxT=maxT, tol=tol) if plot_it: with __r_inline_plot(width=width, height=height, dpi=dpi, plot_it=plot_it): ro.r.create_output_plot_T(logK=logK, species=species, coeff=coeff, state=state, pressures=pressures, minT=minT, maxT=maxT) elif solve=="P": a = ro.r.uc_solveP(logK=logK, species=species, state=state, coeff=coeff, temperatures=temperatures, IS=IS, minP=minP, maxP=maxP, tol=tol) if plot_it: with __r_inline_plot(width=width, height=height, dpi=dpi, plot_it=plot_it): ro.r.create_output_plot_P(logK=logK, species=species, state=state, coeff=coeff, temperatures=temperatures, minP=minP, maxP=maxP) if messages: for line in capture.stderr: print(line) if len(a) == 3: warn = a[2][0] # subcrt's list includes warnings only if they appear else: warn = None out_dict = {"reaction":ro.conversion.rpy2py(a[0]), "out":ro.conversion.rpy2py(a[1])} # the extra [0] is important if warn != None: out_dict["warnings"] = warn out = SubcrtOutput(out_dict) if show: for table in out.__dict__.keys(): if not isinstance(out[table], dict): display(out[table]) else: for subtable in out[table].keys(): print("\n"+subtable+":") # species name display(out[table][subtable]) return out def univariant_TP(logK, species, coeff, state, Trange, Prange, IS=0, tol=None, title=None, res=10, width=500, height=400, show=False, messages=False, plot_it=True)-
Solve for temperatures or pressures of equilibration for a given logK value and produce a plot with temperature and pressure axes.
Parameters
logK:numeric- Logarithm (base 10) of an equilibrium constant.
species:str, int,orlistofstrorint- Name or formula of species, or numeric, rownumber of species in the OBIGT database.
coeff:numericorlistofnumeric, optional- Reaction coefficients on species.
state:strorlistofstr, optional- State(s) of species.
Trange:listoftwo numeric- List containing the minimum and maximum temperature, in degrees C, to solve for the specified logK value. Does not necessarily correspond to temperature axis range in the resulting plot.
Prange:listoftwo numeric- List containing the minimum and maximum pressure, in bars, to solve for the specified logK value. Does not necessarily correspond to pressure axis range in the resulting plot.
IS:numericorlistofnumeric, optional- Ionic strength(s) at which to calculate adjusted molal properties, mol kg^-1.
tol:float- Tolerance for finding a temperature or pressure that converges on the
given logK. Will attempt to find a solution that satisfies logK plus or
minus
tol. By default, tol is equal to 1/(10^(n+2)) where n is the number of decimal places in logK, with a maximum default tolerance of 1e-5. width,height:numeric, default500 by 400- Width and height of the plot.
messages:bool, defaultTrue- Display messages from CHNOSZ?
show:bool, defaultTrue- Display CHNOSZ tables?
plot_it:bool, defaultTrue- Show the plot?
Returns
out:objectofclass SubcrtOutput- An object that stores the output of
subcrt()along the univariant curve.
Expand source code
def univariant_TP(logK, species, coeff, state, Trange, Prange, IS=0, tol=None, title=None, res=10, width=500, height=400, show=False, messages=False, plot_it=True): """ Solve for temperatures or pressures of equilibration for a given logK value and produce a plot with temperature and pressure axes. Parameters ---------- logK : numeric Logarithm (base 10) of an equilibrium constant. species : str, int, or list of str or int Name or formula of species, or numeric, rownumber of species in the OBIGT database. coeff : numeric or list of numeric, optional Reaction coefficients on species. state : str or list of str, optional State(s) of species. Trange : list of two numeric List containing the minimum and maximum temperature, in degrees C, to solve for the specified logK value. Does not necessarily correspond to temperature axis range in the resulting plot. Prange : list of two numeric List containing the minimum and maximum pressure, in bars, to solve for the specified logK value. Does not necessarily correspond to pressure axis range in the resulting plot. IS : numeric or list of numeric, optional Ionic strength(s) at which to calculate adjusted molal properties, mol kg^-1. tol : float Tolerance for finding a temperature or pressure that converges on the given logK. Will attempt to find a solution that satisfies logK plus or minus `tol`. By default, tol is equal to 1/(10^(n+2)) where n is the number of decimal places in logK, with a maximum default tolerance of 1e-5. width, height : numeric, default 500 by 400 Width and height of the plot. messages : bool, default True Display messages from CHNOSZ? show : bool, default True Display CHNOSZ tables? plot_it : bool, default True Show the plot? Returns ------- out : object of class SubcrtOutput An object that stores the output of `subcrt` along the univariant curve. """ if not isinstance(logK, list): logK = [logK] fig = go.Figure() output = [] for this_logK in logK: if tol == None: d = decimal.Decimal(str(this_logK)) n_decimals = abs(d.as_tuple().exponent) tol = float("0."+"".join(n_decimals*["0"])+"01") if tol > 0.00001: tol = 0.00001 out = unicurve(solve="T", logK=this_logK, species=species, state=state, coeff=coeff, pressures=list(np.linspace(start=Prange[0], stop=Prange[1], num=res)), minT=Trange[0], maxT=Trange[1], IS=IS, tol=tol, show=show, plot_it=False, messages=messages) if not out["out"]["T"].isnull().all(): fig.add_trace(go.Scatter( x=out["out"]["T"], y=out["out"]["P"], mode="lines+markers", name="logK="+str(this_logK), text = ["logK="+str(this_logK) for i in range(0, len(out["out"]["T"]))], hovertemplate = '%{text}<br>T, °C=%{x:.2f}<br>P, bar=%{y:.2f}<extra></extra>', )) else: print("Could not find any T or P values in this range that correspond to a logK value of {}".format(this_logK)) output.append(out) if title == None: react_grid = output[0]["reaction"] react_grid["name"] = [name if name != "water" else "H2O" for name in react_grid["name"]] # replace any "water" with "H2O" in the written reaction reactants = " + ".join([(str(-int(react_grid["coeff"][i]) if isinstance(react_grid["coeff"][i], (int, np.integer)) else -react_grid["coeff"][i])+" " if -react_grid["coeff"][i] != 1 else "") + html_chemname_format(react_grid["name"][i]) for i in range(0, len(react_grid["name"])) if react_grid["coeff"][i] < 0]) products = " + ".join([(str(int(react_grid["coeff"][i]) if isinstance(react_grid["coeff"][i], (int, np.integer)) else react_grid["coeff"][i])+" " if react_grid["coeff"][i] != 1 else "") + html_chemname_format(react_grid["name"][i]) for i in range(0, len(react_grid["name"])) if react_grid["coeff"][i] > 0]) title = reactants + " = " + products fig.update_layout(template="simple_white", title=str(title), xaxis_title="T, °C", yaxis_title="P, bar", width=width, height=height, hoverlabel=dict(bgcolor="white"), ) config = {'displaylogo': False, 'modeBarButtonsToRemove': ['resetScale2d', 'toggleSpikelines']} if plot_it: fig.show(config=config) return output def water(property=None, T=298.15, P='Psat', P1=True, messages=True)-
Python wrapper for the water() function in CHNOSZ. Calculate thermodynamic and electrostatic properties of water.
Parameters
property:strorlistofstr, optional- Computational setting or property(s) to calculate. To set a water model, use 'SUPCRT92' (default) or 'SUPCRT', 'IAPWS95' or 'IAPWS', or 'DEW'. To calculate a property, use 'A', 'G', 'S', 'U', etc. See http://chnosz.net/manual/water.html for the complete catalog of properties that can be calculated, their units, and their availability in water models.
T:numeric, default298.15- Temperature (K)
P:numeric, default"Psat"- Pressure (bar), or Psat for vapor-liquid saturation.
P1:bool, defaultTrue- Output pressure of 1 bar below 100 °C instead of calculated values of Psat?
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
out:floatordict- Calculated value of desired water property. If
propertyis a list, returns a dictionary of calculated values.
Expand source code
def water(property=None, T=298.15, P="Psat", P1=True, messages=True): """ Python wrapper for the water() function in CHNOSZ. Calculate thermodynamic and electrostatic properties of water. Parameters ---------- property : str or list of str, optional Computational setting or property(s) to calculate. To set a water model, use 'SUPCRT92' (default) or 'SUPCRT', 'IAPWS95' or 'IAPWS', or 'DEW'. To calculate a property, use 'A', 'G', 'S', 'U', etc. See http://chnosz.net/manual/water.html for the complete catalog of properties that can be calculated, their units, and their availability in water models. T : numeric, default 298.15 Temperature (K) P : numeric, default "Psat" Pressure (bar), or Psat for vapor-liquid saturation. P1 : bool, default True Output pressure of 1 bar below 100 °C instead of calculated values of Psat? messages : bool, default True Display messages from CHNOSZ? Returns ---------- out : float or dict Calculated value of desired water property. If `property` is a list, returns a dictionary of calculated values. """ if property == None: property = ro.r("NULL") elif isinstance(property, list): property = _convert_to_RVector(property, force_Rvec=True) else: pass args = {'property':property, 'T':T, 'P':P, 'P1':P1} capture = R_output() capture.capture_r_output() out = CHNOSZ.water(**args) if messages: for line in capture.stderr: print(line) if property not in ["SUPCRT92", "SUPCRT", "IAPWS95", "IAPWS", "DEW"]: return ro.conversion.rpy2py(out) def zc(formula, messages=True)-
Python wrapper for the ZC() function in CHNOSZ. Calculate the average oxidation state of carbon (ZC) in a molecule.
Parameters
formula:strorlistofstr- Chemical formula(s) of molecules.
messages:bool, defaultTrue- Display messages from CHNOSZ?
Returns
out:floatorlistoffloat- The average oxidation state of carbon in the formula.
Returns a list if
formulais a list.
Expand source code
def zc(formula, messages=True): """ Python wrapper for the ZC() function in CHNOSZ. Calculate the average oxidation state of carbon (ZC) in a molecule. Parameters ---------- formula : str or list of str Chemical formula(s) of molecules. messages : bool, default True Display messages from CHNOSZ? Returns ---------- out : float or list of float The average oxidation state of carbon in the formula. Returns a list if `formula` is a list. """ formula_R = _convert_to_RVector(formula, force_Rvec=False) capture = R_output() capture.capture_r_output() out = CHNOSZ.ZC(formula_R) if messages: for line in capture.stderr: print(line) out = list(out) if not isinstance(formula, list): out = out[0] return out
Classes
class R_output-
Expand source code
class R_output(object): def capture_r_output(self): """ Capture and create a list of R console messages """ # Record output # self.stdout = [] self.stderr = [] # Dummy functions # def add_to_stdout(line): self.stdout.append(line) def add_to_stderr(line): self.stderr.append(line) # Keep the old functions # self.stdout_orig = rpy2.rinterface_lib.callbacks.consolewrite_print self.stderr_orig = rpy2.rinterface_lib.callbacks.consolewrite_warnerror # Set the call backs # rpy2.rinterface_lib.callbacks.consolewrite_print = add_to_stdout rpy2.rinterface_lib.callbacks.consolewrite_warnerror = add_to_stderrMethods
def capture_r_output(self)-
Capture and create a list of R console messages
Expand source code
def capture_r_output(self): """ Capture and create a list of R console messages """ # Record output # self.stdout = [] self.stderr = [] # Dummy functions # def add_to_stdout(line): self.stdout.append(line) def add_to_stderr(line): self.stderr.append(line) # Keep the old functions # self.stdout_orig = rpy2.rinterface_lib.callbacks.consolewrite_print self.stderr_orig = rpy2.rinterface_lib.callbacks.consolewrite_warnerror # Set the call backs # rpy2.rinterface_lib.callbacks.consolewrite_print = add_to_stdout rpy2.rinterface_lib.callbacks.consolewrite_warnerror = add_to_stderr
class SubcrtOutput (args)-
Stores the output of a
subcrt()calculation.Attributes
reaction:pd.DataFrame- Pandas dataframe summary of the reaction. Only present if a reaction is specified.
species:pd.Dataframe- Pandas dataframe summary of species. Only present if a reaction is not specified.
out:pd.Dataframe- Pandas dataframe of
subcrt()output. warnings:pd.Dataframe- Pandas dataframe of calculation warnings. Only present if warnings were generated.
Expand source code
class SubcrtOutput(object): """ Stores the output of a `subcrt` calculation. Attributes ---------- reaction : pd.DataFrame Pandas dataframe summary of the reaction. Only present if a reaction is specified. species : pd.Dataframe Pandas dataframe summary of species. Only present if a reaction is not specified. out : pd.Dataframe Pandas dataframe of `subcrt` output. warnings : pd.Dataframe Pandas dataframe of calculation warnings. Only present if warnings were generated. """ def __init__(self, args): for k in args: setattr(self, k, args[k]) def __getitem__(self, item): return getattr(self, item) class thermo (messages=True, **kwargs)-
Python wrapper for the thermo() object in CHNOSZ. See the original CHNOSZ documentation for in-depth descriptions of each attribute: https://chnosz.net/manual/thermo.html
Attributes
OBIGT:pd.DataFrame- A thermodynamic database of standard molal thermodynamic properties and equations of state parameters of species.
basis:pd.DataFrame- Initially
None, reserved for a dataframe written by basis upon definition of the basis species. buffer:pd.DataFrame- Contains definitions of buffers of chemical activity.
element:pd.DataFrame- Containins the thermodynamic properties of elements taken from Cox et al., 1989, Wagman et al., 1982, and (for Am, Pu, Np, Cm) Thoenen et al., 2014.
groups:pd.DataFrame- A dataframe with 22 columns for the amino acid sidechain, backbone and
protein backbone groups ([Ala]..[Tyr],[AABB],[UPBB]) whose rows
correspond to the elements C, H, N, O, S. It is used to quickly
calculate the chemical formulas of proteins that are selected using the
iprotein argument in
affinity(). opar:dict- Stores parameters of the last plot generated in pyCHNOSZ. If a plot has
not yet been generated,
oparisNone. opt:dict- Dictionary of computational settings.
protein:pd.DataFrame- Amino acid compositions of selected proteins.
refs:pd.DataFrame- References for thermodynamic data.
stoich:pd.DataFrame- A precalculated stoichiometric matrix for the default database.
species:pd.DataFrame- Initially
None, reserved for a dataframe generated by species to define the species of interest.
Expand source code
class thermo: """ Python wrapper for the thermo() object in CHNOSZ. See the original CHNOSZ documentation for in-depth descriptions of each attribute: https://chnosz.net/manual/thermo.html Attributes ---------- OBIGT : pd.DataFrame A thermodynamic database of standard molal thermodynamic properties and equations of state parameters of species. basis : pd.DataFrame Initially `None`, reserved for a dataframe written by basis upon definition of the basis species. buffer : pd.DataFrame Contains definitions of buffers of chemical activity. element : pd.DataFrame Containins the thermodynamic properties of elements taken from Cox et al., 1989, Wagman et al., 1982, and (for Am, Pu, Np, Cm) Thoenen et al., 2014. groups : pd.DataFrame A dataframe with 22 columns for the amino acid sidechain, backbone and protein backbone groups ([Ala]..[Tyr],[AABB],[UPBB]) whose rows correspond to the elements C, H, N, O, S. It is used to quickly calculate the chemical formulas of proteins that are selected using the iprotein argument in `affinity`. opar : dict Stores parameters of the last plot generated in pyCHNOSZ. If a plot has not yet been generated, `opar` is `None`. opt : dict Dictionary of computational settings. protein : pd.DataFrame Amino acid compositions of selected proteins. refs : pd.DataFrame References for thermodynamic data. stoich : pd.DataFrame A precalculated stoichiometric matrix for the default database. species : pd.DataFrame Initially `None`, reserved for a dataframe generated by species to define the species of interest. """ def __init__(self, messages=True, **kwargs): args = {} for key, value in kwargs.items(): if isinstance(value, list) or isinstance(value, str) or isinstance(value, NumberTypes): value = _convert_to_RVector(value, force_Rvec=False) elif isinstance(value, pd.DataFrame): value = ro.conversion.py2rpy(value) else: pass args.update({key:value}) capture = R_output() capture.capture_r_output() t = CHNOSZ.thermo(**args) if messages: for line in capture.stderr: print(line) for i, name in enumerate(t.names): if isinstance(t[i], ro.DataFrame) or isinstance(t[i], ro.Matrix): attr = ro.conversion.rpy2py(t[i]) elif isinstance(t[i], ro.ListVector): attr = {} for ii, subname in enumerate(t[i].names): attr[subname] = list(t[i][ii]) elif isinstance(t[i], ro.FloatVector) or isinstance(t[i], ro.IntVector) or isinstance(t[i], ro.StrVector): attr = list(t[i]) if len(t[i]) == 1: attr = attr[0] elif isinstance(t[i], rpy2.rinterface_lib.sexp.NULLType): attr = None else: attr = t[i] setattr(self, name, attr) def __getitem__(self, item): return getattr(self, item)