# means = np.array([(-d, -d), (d, -d), (-d, d), (d, d)])
covs = ( # covariance for logspace (D=2)
np.array(
[
[[1, 0], [0, 1]],
[[0.5, 0.15], [0.15, 0.5]],
[[1.5, -1], [-1, 1.5]],
[[1.2, -0.6], [-0.6, 1.2]],
]
)
* sig
)
means = ILRTransform().inverse_transform(means) # compositional means (D=3)
size = 2000 # logo @ 10000
pts = [random_composition(mean=M, cov=C, size=size) for M, C in zip(means, covs)]
T = ILRTransform()
to_log = T.transform
from_log = T.inverse_transform
df = pd.DataFrame(np.vstack(pts))
df.columns = ["SiO2", "MgO", "FeO"]
df["Sample"] = np.repeat(np.arange(df.columns.size + 1), size).flatten()
chem = ["MgO", "SiO2", "FeO"]
#######################################################################################
#
fig, ax = plt.subplots(
2, 2, figsize=(10, 10 * np.sqrt(3) / 2), subplot_kw=dict(projection="ternary")
)
ax = ax.flat
def setUp(self):
self.static = np.array(
[[0, 1, 1], [0, np.nan, 1], [np.nan, 0, 1], [0, 1, np.nan], [1, np.nan, 0]]
)
self.rdata = random_composition(size=200, missing="MCAR")
def setUp(self):
self.data = random_composition(size=200, missing="MNAR")
.. note:: This example and features in this module are currently incomplete and a work in progress.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyrolite.comp.impute import EMCOMP
from pyrolite.util.synthetic import random_composition, random_cov_matrix
from pyrolite.plot import pyroplot
np.random.seed(41)
########################################################################################
sample_data = random_composition(
size=1000,
D=4,
cov=random_cov_matrix(3, sigmas=[0.1, 0.3, 0.15]),
propnan=0.1,
missing="MNAR",
missingcols=3,
)
########################################################################################
imputed_data, p0, niter = EMCOMP(sample_data,
threshold=np.nanpercentile(sample_data,
10,
axis=0),
tol=0.01)
imputed_data = pd.DataFrame(imputed_data, columns=["A", "B", "C", "D"])
########################################################################################
fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12, 5))
ax[0].set_title("Original Data")
ax[1].set_title("New Imputed Data")
def setUp(self):
self.fig, self.ax = plt.subplots(1)
self.els = REE()
self.arr = random_composition(size=10, D=len(self.els))
def setUp(self):
self.comp3d = random_composition(size=100, D=3)
self.T = ILRTransform()
self.comp2d = self.T.transform(self.comp3d)
def setUp(self):
self.rdata = pd.DataFrame(
random_composition(size=200, D=4, missing="MCAR"),
columns=["MgO", "SiO2", "CaO", "TiO2"],
)