def setUp(self):
self.cols = ["MgO", "SiO2", "CaO"]
# can run into interesting singular matrix errors with bivariate random data
self.tridf = test_df(cols=self.cols, index_length=100)
self.bidf = self.tridf.loc[:, self.cols[:2]]
self.multidf = test_df(cols=REE(), index_length=100)
def setUp(self):
self.X = test_df(index_length=20).apply(close, axis=1)
iris = sklearn.datasets.load_iris()
self.data, self.target = iris["data"], iris["target"]
svc = SVC_pipeline(probability=True)
self.gs = svc.fit(self.data, self.target)
self.clf = self.gs.best_estimator_
def test_LambdaTransformer(self):
"""Test the LambdaTransformer transfomer."""
df = test_df(cols=REE()).apply(close, axis=1)
tmr = LambdaTransformer()
for input in [df]:
with self.subTest(input=input):
out = tmr.transform(input)
def setUp(self):
self.df = test_df()
self.df.loc[:, "Title"] = [
"Title {}".format(x) for x in self.df.index.values
]
self.ser = test_ser()
self.ser.loc["Title"] = "Test_title"
def setUp(self):
self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"]
self.df = test_df(cols=self.cols)
nans = 10
self.df.iloc[
np.random.randint(1, 10, size=nans),
np.random.randint(1, len(self.cols), size=nans),
] = np.nan
def setUp(self):
self.X = test_df(index_length=20).apply(close, axis=1)
self.gs = test_classifier()
self.y = np.ones(self.X.index.size)
self.y[4:] += 1
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
self.X, self.y, test_size=0.3, stratify=self.y)
self.gs.fit(self.X_train, self.y_train)
self.clf = self.gs.best_estimator_
def setUp(self):
cols = [
"MgO",
"SiO2",
"CaO",
"FeO",
"Ti",
"Hf",
"Zr",
"H2O",
] + pyrolite.geochem.REE()
self.df = test_df(index_length=4, cols=cols)
self.df = renormalise(self.df)
including the Total Alkali-Silica (TAS) classification.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyrolite.util.classification import Geochemistry
from pyrolite.util.synthetic import test_df, random_cov_matrix
# sphinx_gallery_thumbnail_number = 2
########################################################################################
# We'll first generate some synthetic data to play with:
#
mean = np.array([49, 11, 15, 4, 0.5, 4, 1.5])
df = (test_df(
cols=["SiO2", "CaO", "MgO", "FeO", "TiO2", "Na2O", "K2O"],
mean=mean,
index_length=100,
seed=82,
) * mean.sum())
df.head(3)
########################################################################################
# We can visualise how this chemistry corresponds to the TAS diagram:
#
from pyrolite.util.classification import Geochemistry
df["TotalAlkali"] = df["Na2O"] + df["K2O"]
cm = Geochemistry.TAS()
fig, ax = plt.subplots(1)
ax.scatter(df["SiO2"], df["TotalAlkali"], c="k", alpha=0.2)
the compositional space. The commonly used log-transformations include the
Additive Log-Ratio (:func:`~pyrolite.comp.pyrocomp.ALR`), Centred Log-Ratio
(:func:`~pyrolite.comp.pyrocomp.CLR`), and Isometric Log-Ratio
(:func:`~pyrolite.comp.pyrocomp.ILR`) [#ref_1]_ [#ref_2]_.
This example will show you how to access and use some of these functions in pyrolite.
"""
########################################################################################
# First let's create some example data:
#
from pyrolite.util.synthetic import test_df, random_cov_matrix
df = test_df(
index_length=100,
cov=random_cov_matrix(sigmas=[0.1, 0.05, 0.3, 0.6], dim=4, seed=32),
seed=32,
)
df.describe()
########################################################################################
# Let's have a look at some of the log-transforms, which can be accessed directly from
# your dataframes (via :class:`pyrolite.comp.pyrocomp`), after you've imported
# :mod:`pyrolite.comp`. Note that the transformations will return *new* dataframes,
# rather than modify their inputs. For example:
#
import pyrolite.comp
lr_df = df.pyrocomp.CLR() # using a centred log-ratio transformation
########################################################################################
# The transformations are implemented such that the column names generally make it
# evident which transformations have been applied:
def setUp(self):
self.df = test_df(cols=["Si", "Mg", "MgO", "CaO", "Li", "B"])
def setUp(self):
self.df = test_df().apply(close, axis=1)
def setUp(self):
self.df0 = test_df()
self.others = [test_df()] * 4
including the Total Alkali-Silica (TAS) classification.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyrolite.util.classification import TAS
from pyrolite.util.synthetic import test_df, random_cov_matrix
# sphinx_gallery_thumbnail_number = 2
########################################################################################
# We'll first generate some synthetic data to play with:
#
df = (
test_df(
cols=["SiO2", "Na2O", "K2O", "Al2O3"],
mean=[0.5, 0.04, 0.05, 0.4],
index_length=100,
seed=49,
)
* 100
)
df.head(3)
########################################################################################
# We can visualise how this chemistry corresponds to the TAS diagram:
#
import pyrolite.plot
df["Na2O + K2O"] = df["Na2O"] + df["K2O"]
cm = TAS()
fig, ax = plt.subplots(1)
def setUp(self):
self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"]
self.df = test_df(cols=self.cols)
def setUp(self):
self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"]
self.d = len(self.cols)
self.n = 10
self.df = test_df(cols=self.cols, index_length=self.n)
def setUp(self):
self.cols = ["SiO2", "CaO", "MgO", "FeO", "TiO2"]
self.df = test_df(cols=self.cols)
self.df = self.df.apply(lambda x: x / np.sum(x), axis="columns")
"""
Break down a list of phases into oxide components.
"""
atoms = set()
# get atoms which are not O
for p in phases:
atoms |= set(get_cations(p))
print(atoms)
oxides = [simple_oxides(a)[1] for a in atoms]
return oxides
from pyrolite.util.synthetic import test_df, random_cov_matrix
X = test_df(
cols=["cpx", "olivine", "plag"],
cov=random_cov_matrix(dim=2, sigmas=np.array([0.5, 0.7])),
)
component_matrix(components=X.columns,
phases=["CaAl2Si2O8", "Mg2Si2O4", "CaMgSi2O6"])
mixed = X @ components.pyrochem.to_weight().pyrocomp.renormalise(scale=1)
mixed
def unmix_compositions(X, components, bdl=10 * -5):
x = X.copy()
c = components.copy()
c = c.pyrochem.to_weight().pyrocomp.renormalise(scale=1)
# c[c == 0] = bdl; clr
"""
Geochemical Indexes and Selectors
==================================
"""
import pyrolite.geochem
import pandas as pd
pd.set_option("precision", 3) # smaller outputs
########################################################################################
from pyrolite.util.synthetic import test_df
df = test_df(
cols=["CaO", "MgO", "SiO2", "FeO", "Mn", "Ti", "La", "Lu", "Mg/Fe"])
########################################################################################
df.head(2).pyrochem.oxides
########################################################################################
df.head(2).pyrochem.elements
########################################################################################
df.head(2).pyrochem.REE
########################################################################################
df.head(2).pyrochem.compositional
########################################################################################
def setUp(self):
self.data = test_df(cols=["SiO2", "CaO", "MgO"],
index_length=20).values
def setUp(self):
self.df = test_df().apply(close, axis=1)
self.group = (self.df["MgO"] > 0.21).apply(np.int)
represents the best of both worlds. A version inspired by similar existing
visualisations is implemented with :func:`~pyrolite.plot.pyroplot.heatscatter`.
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pyrolite.plot import pyroplot
np.random.seed(12)
########################################################################################
# First we'll create some example data
from pyrolite.util.synthetic import test_df, random_cov_matrix
df = test_df(
index_length=1000,
cov=random_cov_matrix(sigmas=np.random.rand(4) * 2, dim=4, seed=12),
seed=12,
)
########################################################################################
# We can compare a minimal :func:`~pyrolite.plot.pyroplot.heatscatter` plot to other
# visualisations for the same data:
#
from pyrolite.util.plot import share_axes
fig, ax = plt.subplots(3, 4, figsize=(12, 9))
ax = ax.flat
share_axes(ax[:4], which="xy")
share_axes(ax[4:8], which="xy")
share_axes(ax[8:], which="xy")
def setUp(self):
self.df = test_df().applymap(str)
def setUp(self):
self.cols = ["MgO", "SiO2", "CaO"]
# can run into interesting singular matrix errors with bivariate random data
self.tridf = test_df(cols=self.cols, index_length=100)
def setUp(self):
self.ser = test_ser()
self.df = test_df()
def setUp(self):
self.df = test_df().pyrocomp.renormalise()
One of pyrolite's strengths is converting mixed elemental and oxide data to a new
form. The simplest way to perform this is by using the
:func:`~pyrolite.geochem.transform.convert_chemistry` function. Note that by default
pyrolite assumes that data are in the same units.
"""
import pyrolite.geochem
import pandas as pd
pd.set_option("precision", 3) # smaller outputs
########################################################################################
# Here we create some synthetic data to work with, which has some variables in Wt% and
# some in ppm. Notably some elements are present in more than one column (Ca, Na):
#
from pyrolite.util.synthetic import test_df
df = test_df(cols=["MgO", "SiO2", "FeO", "CaO", "Na2O", "Te", "K", "Na"]) * 100
df.pyrochem.elements *= 100 # elements in ppm
########################################################################################
df.head(2)
########################################################################################
# As the units are heterogeneous, we'll need to convert the data frame to a single set of
# units (here we use Wt%):
#
df.pyrochem.elements = df.pyrochem.elements.pyrochem.scale('ppm',
'wt%') # ppm to wt%
########################################################################################
# We can transform this chemical data to a new set of compositional variables.
# Here we i) convert CaO to Ca, ii) aggregate Na2O and Na to Na and iii) calculate
# mass ratios for Na/Te and MgO/SiO2.
# Note that you can also use this function to calculate mass ratios:
#
"""
Unit Scaling
=============
"""
import pyrolite.geochem
import pandas as pd
pd.set_option("precision", 3) # smaller outputs
########################################################################################
from pyrolite.util.synthetic import test_df
df = test_df(
cols=['CaO', 'MgO', 'SiO2', 'FeO', 'Ni', 'Ti', 'La', 'Lu', 'Mg/Fe'])
########################################################################################
cols = ["Ni", "NiO", "La", "La2O3"]
df.head(2).pyrochem.convert_chemistry(to=cols)[cols] # these are in ppm!
def setUp(self):
self.df = test_df()
.. note:: This tutorial is a work in progress and will be gradually updated.
In this tutorial we will illustrate some straightfoward formatting for your plots which
will allow for greater customisation as needed. As :mod:`pyrolite` heavily uses
and exposes the API of :mod:`matplotlib` for the visualisation components
(and also :mod:`mpltern` for ternary diagrams), you should also check out their
documentation pages for more in-depth guides, examples and API documentation.
"""
#######################################################################################
# First let's pull in a simple dataset to use throughout these examples:
#
from pyrolite.util.synthetic import test_df
# sphinx_gallery_thumbnail_number = 7
df = test_df(cols=["SiO2", "CaO", "MgO", "Al2O3", "TiO2", "27Al", "d11B"])
#######################################################################################
# Basic Figure and Axes Settings
# ------------------------------
#
# :mod:`matplotlib` makes it relatively straightfoward to customise most settings for
# your figures and axes. These settings can be defined at creation (e.g. in a call to
# :func:`~matplotlib.pyplot.subplots`), or they can be defined after you've created an
# axis (with the methods :code:`ax.set_<parameter>()`). For example:
#
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1)
ax.set_xlabel("My X Axis Label")
ax.set_title("My Axis Title", fontsize=12)
