#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of the Cortix toolkit environment
# https://cortix.org
'''
Phase *history* container. When you think of a phase value, think of that value at
a specific point in time. This container holds the historic data of a phase;
its species and quantities. This implementation treats access of time stamps within
a tolerance. All searches for time stamped values are subjected to an approximation
of the time stamp to avoid storing values too close to each other in time, and/or to
return the closest value in time searched or no value if none can be found according
to the tolerance.
Background
----------
TODO: ATTENTION:
The species (list of Species) AND quantities (list of Quantity) data members
have ARBITRARY density values either at an arbitrary point in the history or at
no point in the history. This needs to be removed in the future to avoid confusion.
To obtain history values, associated to the phase, at a particular point in time,
use the GetValue() method to access the history data frame (pandas) via columns and
rows. ALERT: The corresponding values in species and quantities are OVERRIDEN and NOT to
be used through the phase interface.
Author: Valmor F. de Almeida dealmeidav@ornl.gov; vfda
Sat Sep 5 01:26:53 EDT 2015
Cortix: a program for system-level modules coupling, execution, and analysis.
'''
import os, io
from copy import deepcopy
import time
import datetime
import numpy as np
import pandas
import matplotlib
matplotlib.use('Agg', warn=False)
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.ticker import MultipleLocator
from matplotlib.ticker import ScalarFormatter
from cortix.support.species import Species
from cortix.support.quantity import Quantity
[docs]class PhaseNew:
'''
Phase `history` container. A `Phase` consists of `Species` and `Quantities`
varying with time. This container is meant to reproduce the basic idea of a
material phase.
'''
[docs] def __init__(self, name = None,
time_stamp = None,
time_unit = None,
species = None,
quantities = None
):
#TODO
'''
Sometimes an empty Phase object is created by user code. This case needs
adequate logic for None types.
Note on usage: when passing quantities, do set the value argument explicitly
to help define the type and avoid set_value() errors with Pandas. This is
to be investigated later. Also, the usage of a DataFrame needs to be re-evaluated.
Maybe better to use a Quantity object and a Species object with a Pandas Series
history as a value to avoid the existance of a value in Quantity and a value
in Phase that are not in sync.
'''
if not name:
self.name = self.__class__.__name__
else:
self.name = name
if time_stamp is None:
time_stamp = 0.0 # default type is float
else:
assert isinstance(time_stamp, float)
self.__time_stamp = time_stamp
if time_unit is None:
self.__time_unit = 's' # second
else:
assert isinstance(time_unit, str)
self.__time_unit = time_unit
if species is not None:
assert isinstance(species, list)
for each_species in species:
assert isinstance(each_species, Species)
if quantities is not None:
assert isinstance(quantities, list)
for quant in quantities:
assert isinstance(quant, Quantity)
# List of species and quantities objects; columns of data frame are named
# by objects.
# A new object held by a Phase() object
self.__species = deepcopy(species)
# A new object held by a Phase() object
self.__quantities = deepcopy(quantities)
names = list()
if species is not None:
for each_species in self.__species:
names.append(each_species.name)
if quantities is not None:
for quant in self.__quantities:
names.append(quant.name)
quant.value = 0.0 # clear these values
# todo: eliminate them from Quantity in the future
# Table data phase without data type assigned; this is left to the user
# Time stamps will always be float
self.__df = pandas.DataFrame( index=[float(time_stamp)], columns=names )
# This is meant to be the value of species concentration; a float type
if species is not None:
for each_species in species:
self.__df.loc[time_stamp, each_species.name] = 0.0
if quantities is not None:
for quant in quantities:
self.__df.loc[time_stamp, quant.name] = quant.value
#self.__df.fillna( 0.0, inplace=True ) # dtype defaults to float
return
[docs] def has_time_stamp(self, try_time_stamp):
'''
Checks to see if try_time_stamp exists in the phase history.
Parameters
----------
try_time_stamp:
'''
time_stamp = self.__get_time_stamp( try_time_stamp )
if time_stamp is not None:
return True
else:
return False
def __get_time_unit(self):
'''
Returns the time unit of the `Phase.`
Returns
-------
time_unit: str
'''
return self.__time_unit
time_unit = property(__get_time_unit,None,None,None)
def __get_time_stamps(self):
'''
Get all time stamps in the index of the data frame.
Returns
-------
time_stamps: list
'''
return list(self.__df.index) # return all time stamps
time_stamps = property(__get_time_stamps, None, None, None)
def __get_species_list(self):
'''
Returns every single species in the phase history.
Returns
-------
species: list
'''
return self.__species
species = property(__get_species_list, None, None, None)
[docs] def GetQuantities(self):
'''
Returns the list of `Quantities`. The values in each `Quantity` are
synchronized with the `Phase` data frame.
Returns
-------
quantities: list
'''
for quant in self.__quantities:
tmp = self.GetQuantity(quant.name) # handy way to synchronize the whole list
return self.__quantities
quantities = property(GetQuantities, None, None, None)
def __get_actors(self):
'''
Returns a list of names of all the actors in the phase history.
Returns
-------
list(self.__df.colums): list
'''
return list(self.__df.columns) # return all names in order
actors = property(__get_actors, None, None, None)
def __get_df(self):
'''
Die hard access.
'''
return self.__df
df = property(__get_df,None,None,None)
[docs] def get_species(self, name):
'''
Returns the species specified by name if it exists,
or None if it doesn't.
Parameters
----------
name: str
Returns
-------
specie: str
'''
for species in self.__species:
if species.name == name:
return species
return None
[docs] def get_species_concentration(self, name, try_time_stamp=None):
'''
Returns the species concentration at `try_time_stamp`.
Parameters
----------
name: str
try_time_time_stamp: float
Returns
-------
concentration: float
'''
return self.get_value(name,try_time_stamp)
[docs] def set_species_id(self, name, val):
'''
Sets the flag of a species "name" equal to val.
Parameters
----------
name: str
val: int
'''
for species in self.__species:
if species.name == name:
species.flag = val
return
[docs] def get_quantity(self, name, try_time_stamp=None):
'''
Get the quantity `name` at a point in time closest to
`try_time_stamp` up to a tolerance. If no time stamp is passed,
the value at the last time stamp is returned.
Parameters
----------
name: str
try_time_stamp: float, int or None
Time stamp of desired quantity value. Default: None, returns the
value at the last time stamp.
Returns
-------
quant.value: float or int or other
'''
assert name in self.__df.columns, 'name %r not in %r'%\
(name,self.__df.columns)
time_stamp = self.__get_time_stamp( try_time_stamp )
for quant in self.__quantities:
if quant.name == name:
quant.value = self.__df.loc[time_stamp, name] # labels' access mode
return quant # return quantity syncronized with the phase
[docs] def get_quantity_history(self, name):
'''
Create a Quantity `name` history. This will create a fully qualified
Quantity object and return to the caller. The function is typically
needed for data output to a file through `pickle`. Since the value
attribute of a quantity can be any data structure, a time-series is
built on the fly and stored in the value attribute. In addition the
time unit is added to the final return value as a tuple.
Parameters
----------
name: str
Returns
-------
quant_history: tuple(Quantity,str)
'''
assert name in self.__df.columns, 'name %r not in %r'%\
(name,self.__df.columns)
for quant in self.__quantities:
if quant.name == name:
quant_history = deepcopy(quant)
quant_history.value = self.__df[name] # whole data frame index series
return (quant_history,self.__time_unit) # return tuple
[docs] def add_single_species(self, new_species):
'''
Adds a new specie object to the phase history. See species.py for
more details on the Species class.
Parameters
----------
new_species: obj
'''
assert isinstance(new_species, Species)
assert new_species.name not in list(self.__df.columns), \
'new_species: %r exists. Current names: %r' % \
(new_species, self.__df.columns)
species_formulae = [specie.formula_name for specie in self.__species]
assert new_species.formula_name not in species_formulae
self.__species.append(new_species)
new_name = new_species.name
col = pandas.DataFrame( index=list(self.__df.index), columns=[new_name] )
tmp = self.__df
df = tmp.join(col, how='outer')
self.__df = df.fillna(0.0) # for species have float as default
[docs] def add_quantity(self, new_quant):
'''
Adds a new quantity object to the dataframe. See quantity.py for more
details on the quantity class.
Parameters
----------
new_quant: object
'''
assert isinstance(new_quant, Quantity)
assert new_quant.name not in list(self.__df.columns), \
'quantity: %r exists. Current names: %r' % \
(new_quant, self.__df.columns)
quant_formal_names = [quant.formal_name for quant in self.__quantities]
assert new_quant.formal_name not in quant_formal_names
self.__quantities.append(new_quant)
new_name = new_quant.name
# create a col with object data type; user must fill out column
col = pandas.DataFrame( index=list( self.__df.index), columns=[new_name],
dtype=object )
tmp = self.__df
df = tmp.join(col, how='outer')
#self.__df = df.fillna(new_quant.value)
[docs] def add_row(self, try_time_stamp, row_values):
'''
Adds a row to the `DataFrame`, with a `timestamp` equal to `try_time_stamp` and
row values equal to `row_values`. The length of `row_values` must match the
number of columns in the data frame.
Parameters
----------
try_time_stamp: float
row_values: list
'''
assert try_time_stamp not in self.__df.index, 'already used time_stamp: %r'%\
(try_time_stamp)
assert isinstance(row_values, list)
time_stamp = self.__get_time_stamp( try_time_stamp )
assert time_stamp is None, 'already used time_stamp: %r'%(try_time_stamp)
time_stamp = try_time_stamp
assert len(row_values) == self.__df.columns.size
# create a row with object data type; users row_values data define data type
row = pandas.DataFrame( index=[time_stamp],
columns=list( self.__df.columns ), dtype=object )
for (col,v) in zip(row.columns, row_values):
row.loc[time_stamp,col] = v
frames = [self.__df, row]
self.__df = pandas.concat(frames)
return
[docs] def get_row(self, try_time_stamp=None):
'''
Returns an entire row of the phase dataframe. A row is a series of
values that are all at the same time stamp.
Parameters
----------
try_time_stamp: float
Returns
-------
list(self.__df.loc[time_stamp, :]): list
'''
time_stamp = self.__get_time_stamp( try_time_stamp )
assert time_stamp is not None, 'missing try_time_stamp: %r'%(try_time_stamp)
return list(self.__df.loc[time_stamp, :])
[docs] def get_column(self, actor):
'''
Returns an entire column of data. A column is the entire history
of data associated with a specific actor.
Parameters
----------
actor: str
Returns
-------
list(self.__df.loc[:, actor]): list
'''
assert isinstance(actor, str)
assert actor in self.__df.columns, 'actor %r not in %r'% \
(actor,self.__df.columns)
return list(self.__df.loc[:, actor])
[docs] def scale_row(self, try_time_stamp, value):
'''
Multiplies all of the data in a row (except time stamp) by a scalar
value.
Parameters
----------
try_time_stamp: float
value: float
'''
assert isinstance(try_time_stamp, int) or isinstance(try_time_stamp, float)
time_stamp = self.__get_time_stamp( try_time_stamp )
assert time_stamp is not None, 'missing try_time_stamp: %r'%(try_time_stamp)
#assert isinstance(value, int) or isinstance(value, float)
self.__df.loc[time_stamp, :] *= value
return
[docs] def ClearHistory(self, value=0.0):
'''
Set species and quantities of history to a given value
(default to zero value), all time stamps are preserved.
Parameters
----------
value: float
'''
assert isinstance(value, int) or isinstance(value, float)
self.__df.loc[:, :] = value
return
[docs] def ResetHistory(self, try_time_stamp=None, value=None):
'''
Set species and quantities of history to a given value
(default to zero value) only one time stamp is preserved (default to
last time stamp).
Parameters
----------
try_time_stamp: float
value: float
'''
if value is not None:
assert isinstance(value, int) or isinstance(value, float) or \
isinstance(value, np.ndarray)
if try_time_stamp is not None:
assert isinstance(try_time_stamp, int) or isinstance(try_time_stamp, float)
time_stamp = self.__get_time_stamp( try_time_stamp )
assert time_stamp is not None, 'missing try_time_stamp: %r'%(try_time_stamp)
values = self.GetRow(time_stamp) # save values
columns = list(self.__df.columns)
assert len(columns) == len(values), 'FATAL: oops internal error.'
self.__df = pandas.DataFrame( index=[time_stamp], columns=columns )
self.__df.fillna( 0.0, inplace=True )
if value is None:
for v in values:
idx = values.index(v)
self.__df.loc[time_stamp, columns[idx]] = v # restore values
else:
self.__df.loc[time_stamp, :] = value # set user-given value
return
[docs] def get_value(self, actor, try_time_stamp=None):
'''
Returns the value associated with a specified actor at a specified
time stamp.
Parameters
----------
actor: str
try_time_stamp: float
Default is None which returns the last time stamp.
Returns
-------
self.__df.loc[time_stamp, actor]: any
'''
assert isinstance(actor, str)
assert actor in self.__df.columns, 'actor %r not in %r'% \
(actor,self.__df.columns)
if try_time_stamp is not None:
assert isinstance(try_time_stamp, int) or isinstance(try_time_stamp, float)
time_stamp = self.__get_time_stamp( try_time_stamp )
assert time_stamp is not None, 'missing try_time_stamp: %r'%(try_time_stamp)
return self.__df.loc[time_stamp, actor]
[docs] def set_value(self, actor, value, try_time_stamp=None):
'''
'''
assert isinstance(actor, str)
assert actor in self.__df.columns
if try_time_stamp is not None:
assert isinstance(try_time_stamp, int) or isinstance(try_time_stamp, float)
time_stamp = self.__get_time_stamp( try_time_stamp )
assert time_stamp is not None, 'missing try_time_stamp: %r'%(try_time_stamp)
# Note: user value could have a different type than other column values.
# If there is a type change, this will not be checked; user has been advised.
self.__df.loc[time_stamp, actor] = value
return
[docs] def write_html(self, fileName):
'''
Convert the `Phase` container into an HTML file.
Parameters
---------
fileName: str
'''
assert isinstance(fileName, str)
tmp = pandas.DataFrame(self.__df)
column_names = tmp.columns
species_names = [species.name for species in self.__species]
quantity_names = [quantity.name for quantity in self.__quantities]
for col in column_names:
if col in species_names:
idx = species_names.index(col)
species = self.__species[idx]
tmp.rename(columns={col: species.formula_name}, inplace=True)
elif col in quantity_names:
idx = quantity_names.index(col)
quant = self.__quantities[idx]
tmp.rename( columns={ col: col + '[' + quant.unit + ']'},
inplace=True )
else:
assert False, 'oops fatal.'
tmp.to_html(fileName)
return
def __str__(self):
s = '\n\t **Phase()**: name=%s;' + \
'\n\t time unit: %s;' + \
'\n\t *quantities*: %s;' + \
'\n\t *species*: %s;' + \
'\n\t *history* # time_stamps=%s;' + \
'\n\t *history end* @%s;' + \
'\n%s'
return s % (self.name,
self.__time_unit,
self.__quantities,
self.__species,
len(self.__df.index),
self.__df.index[-1],
self.__df.loc[self.__df.index[-1], :] )
def __repr__(self):
s = '\n\t **Phase()**: name=%s;' + \
'\n\t time unit: %s;' + \
'\n\t *quantities*: %s;' + \
'\n\t *species*: %s;' + \
'\n\t *history* # time_stamps=%s;' + \
'\n\t *history end* @%s;' + \
'\n%s'
return s % (self.name,
self.__time_unit,
self.__quantities,
self.__species,
len(self.__df.index),
self.__df.index[-1],
self.__df.loc[self.__df.index[-1], :] )
def __get_time_stamp(self, try_time_stamp=None):
'''
Helper method for finding the closest time stamp to `try_time_stamp`
in the phase history. The pandas index container used for storing
float data type time stamps will return the nearest time stamp up to a
tolerance. Whether the time index has one value, this function will
inspect for the proximity to that value.
Parameters
----------
try_time_stamp: float, int or None
Default: None will return the last time stamp.
Returns
-------
self.__df.index[loc]: float or None
Will return None if no time stamp within tolerance is found.
'''
import numpy as np
tol = 1.0e-3
if try_time_stamp is None:
return self.__df.index[-1]
else:
time_stamps = np.array(self.__df.index)
if time_stamps.size >= 2:
tol = 1.0e-3 * np.diff(time_stamps).mean() # 1e-3 * the mean delta t
try: # abs(index_value - try_time_stamp) <= tolerance
loc = self.__df.index.get_loc( try_time_stamp, method='nearest',
tolerance=tol )
except KeyError: # no value found withing tol
return None
else:
return self.__df.index[loc]
[docs] def plot_species(self, name, scaling=[1.0,1.0] , title=None, xlabel='Time [s]',
ylabel='y', legend='no-legend', filename_tag=None, figsize=[6,5], dpi=100 ):
fig,ax=plt.subplots(1,figsize=figsize)
x = np.array( [t for t in self.__df.index] )
x *= float(scaling[0])
y = np.array( self.get_column(name),dtype=np.float64 )
y *= float(scaling[1])
yformatter = ScalarFormatter(useMathText=True,useOffset=True)
yformatter.set_powerlimits((15, 5))
ax.yaxis.set_major_formatter(yformatter)
ax.plot(x, y, 'b-', label=legend)
ax.set_xlabel(r''+xlabel,fontsize=16)
ax.set_ylabel(r''+ylabel,fontsize=16,color='black')
ax.tick_params(axis='y',labelsize=14)
ax.tick_params(axis='x',labelsize=14)
ax.legend(loc='best',fontsize=12)
if title:
ax.set_title(title)
elif self.get_species(name).info:
ax.set_title(self.get_species(name).info,fontsize=14)
ax.grid(True)
fig_name = name+'-'+self.name+'-phase-plot-'
if filename_tag:
fig_name += filename_tag
fig.savefig(fig_name+'.png', dpi=dpi, fomat='png')
plt.close(fig)
return
[docs] def plot( self, name='phase-plot-name', time_unit='s', legend=None,
nrows=2, ncols=2, dpi=200):
num_var = len(self.__df.columns)
if num_var == 0:
return
today = datetime.datetime.today().strftime("%d%b%y %H:%M:%S")
lead_name = name
fig_num = None
# Loop over variables and assign to the dashboards
i_dash = 0
for i_var in range(num_var):
# if multiple of nrows*ncols start new dashboard
if i_var % (nrows*ncols) == 0:
if i_var != 0: # flush any current figure
fig_name = lead_name+'-'+self.name+'-phase-plot-' + \
str(i_dash).zfill(2)
fig.savefig(fig_name+'.png', dpi=dpi, fomat='png')
plt.close(fig_num)
#pickle.dump( fig, open(fig_name+'.pickle','wb') )
i_dash += 1
fig_num = str(np.random.random()) + '.' + str(i_dash)
fig = plt.figure(num=fig_num)
gs = gridspec.GridSpec(nrows, ncols)
# gs.update(left=0.08, right=0.98, wspace=0.4, hspace=0.4)
gs.update(left=0.11, right=0.98, wspace=0.4, hspace=0.5)
axlst = list()
nPlotsNeeded = num_var - i_var
count = 0
for i in range(nrows):
for j in range(ncols):
axlst.append(fig.add_subplot(gs[i, j]))
count += 1
if count == nPlotsNeeded:
break
if count == nPlotsNeeded:
break
axes = np.array(axlst)
text = today + ': Cortix.Phase.Plot'
fig.text(.5, .95, text, horizontalalignment='center', fontsize=14)
axs = axes.flat
axId = 0
# end of: if i_var % nrows*ncols == 0: # if a multiple of nrows*ncols
# start a new dashboard
ax = axs[axId]
axId += 1
col_name = self.__df.columns[i_var]
species = self.get_species(col_name)
if species:
varName = species.formula_name
else:
quant = self.get_quantity(col_name)
varName = quant.formal_name
# sanity check
if i_var <= len(self.__species):
assert self.__species[i_var].name == self.__df.columns[i_var]
else:
assert self.__quantities[i_var].name == self.__df.columns[i_var]
varUnit = 'g/L'
'''
if varUnit == 'gram':
varUnit = 'g'
if varUnit == 'gram/min':
varUnit = 'g/min'
if varUnit == 'gram/s':
varUnit = 'g/s'
if varUnit == 'gram/m3':
varUnit = 'g/m3'
if varUnit == 'gram/L':
varUnit = 'g/L'
if varUnit == 'sec':
varUnit = 's'
'''
varLegend = legend
varScale = 'linear-linear'
assert varScale == 'log' or varScale == 'linear' or varScale == 'log-linear' \
or varScale == 'linear-log' or varScale == 'linear-linear' or \
varScale == 'log-log'
time_unit = 's'
if time_unit == 'minute':
time_unit = 'min'
x = np.array( [i for i in self.__df.index] )
if (varScale == 'linear' or varScale == 'linear-linear' or \
varScale == 'linear-log') and x.max() >= 60.0:
x /= 60.0
if time_unit == 'min':
time_unit = 'h'
if time_unit == 'second' or time_unit=='s':
time_unit = 'min'
y = np.array( self.__df[col_name] ) # convert to numpy ndarray
'''
if (y.max() >= 1e3 or y.min() <= -1e3) and varScale != 'linear-log' and \
varScale != 'log-log' and varScale != 'log':
y /= 1e3
if varUnit == 'gram' or varUnit == 'g':
varUnit = 'kg'
if varUnit == 'L':
varUnit = 'kL'
if varUnit == 'cc':
varUnit = 'L'
if varUnit == 'Ci':
varUnit = 'kCi'
if varUnit == 'W':
varUnit = 'kW'
if varUnit == 'gram/min' or varUnit == 'g/min':
varUnit = 'kg/min'
if varUnit == 'gram/s' or varUnit == 'g/s':
varUnit = 'kg/s'
if varUnit == 'gram/m3' or varUnit == 'g/m3':
varUnit = 'kg/m3'
if varUnit == 'gram/L' or varUnit == 'g/L':
varUnit = 'kg/L'
if varUnit == 'W/L':
varUnit = 'kW/L'
if varUnit == 'Ci/L':
varUnit = 'kCi/L'
if varUnit == '':
varUnit = 'x1e3'
if varUnit == 'L/min':
varUnit = 'kL/min'
if varUnit == 'Pa':
varUnit = 'kPa'
if varUnit == 's':
varUnit = 'ks'
if varUnit == 'm':
varUnit = 'km'
if varUnit == 'm/s':
varUnit = 'km/s'
if (y.max() < 1e-6 and y.min() > -1e-6) and varScale != 'linear-log' and \
varScale != 'log-log' and varScale != 'log':
y *= 1e9
if varUnit == 'gram' or varUnit == 'g':
varUnit = 'ng'
if varUnit == 'cc':
varUnit = 'n-cc'
if varUnit == 'L':
varUnit = 'nL'
if varUnit == 'W':
varUnit = 'nW'
if varUnit == 'Ci':
varUnit = 'nCi'
if varUnit == 'gram/min' or varUnit == 'g/min':
varUnit = 'ng/min'
if varUnit == 'gram/s' or varUnit == 'g/s':
varUnit = 'ng/s'
if varUnit == 'gram/m3' or varUnit == 'g/m3':
varUnit = 'ng/m3'
if varUnit == 'gram/L' or varUnit == 'g/L':
varUnit = 'ng/L'
if varUnit == 'W/L':
varUnit = 'nW/L'
if varUnit == 'Ci/L':
varUnit = 'nCi/L'
if varUnit == 'L/min':
varUnit = 'nL/min'
if varUnit == 'Pa':
varUnit = 'nPa'
if varUnit == 's':
varUnit = 'ns'
if varUnit == 'm/s':
varUnit = 'nm/s'
if (y.max() >= 1e-6 and y.max() < 1e-3) or \
(y.min() > -1e-3 and y.min() <= -1e-6) and varScale != 'linear-log' and \
varScale != 'log-log' and varScale != 'log':
y *= 1e6
if varUnit == 'gram' or varUnit == 'g':
varUnit = 'ug'
if varUnit == 'cc':
varUnit = 'u-cc'
if varUnit == 'L':
varUnit = 'uL'
if varUnit == 'W':
varUnit = 'uW'
if varUnit == 'Ci':
varUnit = 'uCi'
if varUnit == 'gram/min' or varUnit == 'g/min':
varUnit = 'ug/min'
if varUnit == 'gram/s' or varUnit == 'g/s':
varUnit = 'ug/s'
if varUnit == 'gram/m3' or varUnit == 'g/m3':
varUnit = 'ug/m3'
if varUnit == 'gram/L' or varUnit == 'g/L':
varUnit = 'ug/L'
if varUnit == 'W/L':
varUnit = 'uW/L'
if varUnit == 'Ci/L':
varUnit = 'uCi/L'
if varUnit == 'L/min':
varUnit = 'uL/min'
if varUnit == 'Pa':
varUnit = 'uPa'
if varUnit == 's':
varUnit = 'us'
if varUnit == 'm/s':
varUnit = 'um/s'
if (y.max() >= 1e-3 and y.max() < 1e-1) or \
(y.min() <= -1e-3 and y.min() > -1e-1) and varScale != 'linear-log' and \
varScale != 'log-log' and varScale != 'log':
y *= 1e3
if varUnit == 'gram' or varUnit == 'g':
varUnit = 'mg'
if varUnit == 'cc':
varUnit = 'm-cc'
if varUnit == 'L':
varUnit = 'mL'
if varUnit == 'W':
varUnit = 'mW'
if varUnit == 'Ci':
varUnit = 'mCi'
if varUnit == 'gram/min' or varUnit == 'g/min':
varUnit = 'mg/min'
if varUnit == 'gram/s' or varUnit == 'g/s':
varUnit = 'mg/s'
if varUnit == 'gram/m3' or varUnit == 'g/m3':
varUnit = 'mg/m3'
if varUnit == 'gram/L' or varUnit == 'g/L':
varUnit = 'mg/L'
if varUnit == 'W/L':
varUnit = 'mW/L'
if varUnit == 'Ci/L':
varUnit = 'mCi/L'
if varUnit == 'L/min':
varUnit = 'mL/min'
if varUnit == 'Pa':
varUnit = 'mPa'
if varUnit == 's':
varUnit = 'ms'
if varUnit == 'm/s':
varUnit = 'mm/s'
'''
ax.set_xlabel('Time [' + time_unit + ']', fontsize=9)
ax.set_ylabel(varName + ' [' + varUnit + ']', fontsize=9)
'''
ymax = y.max()
dy = ymax * .1
ymax += dy
ymin = y.min()
ymin -= dy
if abs(ymin - ymax) <= 1.e-4:
ymin = -1.0
ymax = 1.0
ax.set_ylim(ymin, ymax)
'''
if ncols >= 4:
for l in ax.get_xticklabels():
l.set_fontsize(8)
else:
for l in ax.get_xticklabels():
l.set_fontsize(10)
for l in ax.get_yticklabels():
l.set_fontsize(10)
if time_unit == 'h' and x.max() - x.min() <= 5.0:
majorLocator = MultipleLocator(1.0)
minorLocator = MultipleLocator(0.5)
ax.xaxis.set_major_locator(majorLocator)
ax.xaxis.set_minor_locator(minorLocator)
if varScale == 'log' or varScale == 'log-log':
ax.set_xscale('log')
ax.set_yscale('log')
positiveX = x > 0.0
x = np.extract(positiveX, x)
y = np.extract(positiveX, y)
positiveY = y > 0.0
x = np.extract(positiveY, x)
y = np.extract(positiveY, y)
if y.size > 0:
if y.min() > 0.0 and y.max() > y.min():
ymax = y.max()
dy = ymax * .1
ymax += dy
ymin = y.min()
ymin -= dy
if ymin < 0.0 or ymin > ymax / 1000.0:
ymin = ymax / 1000.0
ax.set_ylim(ymin, ymax)
else:
ax.set_ylim(1.0, 10.0)
else:
ax.set_ylim(1.0, 10.0)
if varScale == 'log-linear':
ax.set_xscale('log')
positiveX = x > 0.0 # True if > 0.0
x = np.extract(positiveX, x)
y = np.extract(positiveX, y)
if varScale == 'linear-log':
ax.set_yscale('log')
positiveY = y > 0.0 # True if > 0.0
x = np.extract(positiveY, x)
y = np.extract(positiveY, y)
#assert x.size == y.size, 'size error; stop.'
if y.size > 0:
if y.min() > 0.0 and y.max() > y.min():
ymax = y.max()
dy = ymax * .1
ymax += dy
ymin = y.min()
ymin -= dy
if ymin < 0.0 or ymin > ymax / 1000.0:
ymin = ymax / 1000.0
ax.set_ylim(y.min(), ymax)
else:
ax.set_ylim(1.0, 10.0)
else:
ax.set_ylim(1.0, 10.0)
# ...................
# make the plot here
yformatter = ScalarFormatter(useMathText=True,useOffset=True)
yformatter.set_powerlimits((15, 5))
ax.yaxis.set_major_formatter(yformatter)
if varLegend:
ax.plot(x, y, 's-', color='black', linewidth=0.5, markersize=2,
markeredgecolor='black', label=varLegend)
else:
ax.plot(x, y, 's-', color='black', linewidth=0.5, markersize=2,
markeredgecolor='black')
# ...................
if species.info:
ax.set_title(species.info,fontsize=8)
if varLegend:
ax.legend(loc='best', prop={'size': 7})
ax.grid()
# end of: for i_var in range(num_var):
fig_name = name+'-'+self.name+'-phase-plot-' + str(i_dash).zfill(2)
fig.savefig(fig_name+'.png', dpi=dpi, fomat='png')
plt.close(fig_num)
#pickle.dump( fig, open(fig_name+'.pickle','wb') )
return
if __name__ == '__main__':
tbp_org = Species( name='TBP', formula_name='(C4H9O)_3PO(o)',
phase_name='organic', atoms=['12*C','27*H','4*O','P'] )
quant = Quantity( name='volume' )
phase = PhaseNew(name='solvent',species=[tbp_org],quantities=[quant])
print(phase)
