def test_no_version_raises(monkeypatch):
name = "fakemodule"
module = types.ModuleType(name)
sys.modules[name] = module
monkeypatch.setitem(VERSIONS, name, "1.0.0")
with pytest.raises(ImportError, match="Can't determine .* fakemodule"):
import_optional_dependency(name)
def test_import_optional():
match = "Missing optional dependency 'notapackage'. Use conda or pip to install notapackage."
with pytest.raises(ImportError, match=match) as exc_info:
import_optional_dependency("notapackage")
# The original exception should be there as context:
assert isinstance(exc_info.value.__context__, ImportError)
result = import_optional_dependency("notapackage", errors="ignore")
assert result is None
def get_plotly_figure(clear=True, fig=None, **kwargs):
"""
Get the figure where to plot.
Parameters
----------
clear : bool
If False the figure provided in the `fig` parameters is used.
fig : plotly figure
If provided, and clear is not True, it will be used for plotting
kwargs : any
Keywords arguments to be passed to the plotly figure constructor.
Returns
-------
Plotly figure instance
"""
go = import_optional_dependency("plotly.graph_objects", errors="ignore")
if go is None:
raise ImportError(
"Plotly is not installed. Uee pip or conda to install it")
if clear or fig is None:
# create a figure
return go.Figure()
# a figure already exists - if several we take the last
return fig
def test_bad_version(monkeypatch):
name = "fakemodule"
module = types.ModuleType(name)
module.__version__ = "0.9.0"
sys.modules[name] = module
monkeypatch.setitem(VERSIONS, name, "1.0.0")
match = "SpectroChemPy requires .*1.0.0.* of .fakemodule.*'0.9.0'"
with pytest.raises(ImportError, match=match):
import_optional_dependency("fakemodule")
# Test min_version parameter
result = import_optional_dependency("fakemodule", min_version="0.8")
assert result is module
with tm.assert_produces_warning(UserWarning):
result = import_optional_dependency("fakemodule", errors="warn")
assert result is None
module.__version__ = "1.0.0" # exact match is OK
result = import_optional_dependency("fakemodule")
assert result is module
def test_compare_scikit_learn():
try:
import_optional_dependency("scikit-learn")
except ImportError:
return
from sklearn.decomposition import PCA as sklPCA
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
pcas = sklPCA(n_components=2)
pcas.fit(X)
pca = PCA(NDDataset(X))
pca.printev(n_pc=2)
assert_array_almost_equal(pca._sv.data, pcas.singular_values_)
assert_array_almost_equal(pca.ev_ratio.data,
pcas.explained_variance_ratio_ * 100.0)
dataset = NDDataset.read("irdata/nh4y-activation.spg")
X1 = dataset.copy().data
pcas = sklPCA(n_components=5, svd_solver="full")
pcas.fit(X1)
X2 = NDDataset(dataset.copy())
pca = PCA(X2)
pca.printev(n_pc=5)
assert_array_almost_equal(pca._sv.data[:5], pcas.singular_values_[:5], 4)
assert_array_almost_equal(pca.ev_ratio.data[:5],
pcas.explained_variance_ratio_[:5] * 100.0, 4)
show()
def test_submodule(monkeypatch):
# Create a fake module with a submodule
name = "fakemodule"
module = types.ModuleType(name)
module.__version__ = "0.9.0"
sys.modules[name] = module
sub_name = "submodule"
submodule = types.ModuleType(sub_name)
setattr(module, sub_name, submodule)
sys.modules[f"{name}.{sub_name}"] = submodule
monkeypatch.setitem(VERSIONS, name, "1.0.0")
match = "SpectroChemPy requires .*1.0.0.* of .fakemodule.*'0.9.0'"
with pytest.raises(ImportError, match=match):
import_optional_dependency("fakemodule.submodule")
with tm.assert_produces_warning(UserWarning):
result = import_optional_dependency("fakemodule.submodule",
errors="warn")
assert result is None
module.__version__ = "1.0.0" # exact match is OK
result = import_optional_dependency("fakemodule.submodule")
assert result is submodule
from scipy.optimize import minimize, differential_evolution, least_squares
from spectrochempy.optional import import_optional_dependency
from spectrochempy.core import error_
from spectrochempy.core.dataset.nddataset import NDDataset, Coord
__all__ = [
"coverages_vs_time",
"concentrations_vs_time",
"modify_rate",
"modify_surface_kinetics",
"fit_to_concentrations",
"PFR",
]
ct = import_optional_dependency("cantera", errors="ignore")
def _cantera_is_not_available():
if ct is None:
error_(
"Missing optional dependency 'cantera'. Use conda or pip to install cantera."
)
return ct is None
def coverages_vs_time(surface, t, returnNDDataset=False):
"""
Returns the surface coverages at time(s) t.
Parameters
def to_xarray(self):
"""
Convert a NDDataset instance to an `~xarray.DataArray` object.
Warning: the xarray library must be available.
Returns
-------
object
A axrray.DataArray object.
"""
# Information about DataArray from the DataArray docstring
#
# Attributes
# ----------
# dims: tuple
# Dimension names associated with this array.
# values: np.ndarray
# Access or modify DataArray values as a numpy array.
# coords: dict-like
# Dictionary of DataArray objects that label values along each dimension.
# name: str or None
# Name of this array.
# attrs: OrderedDict
# Dictionary for holding arbitrary metadata.
# Init docstring
#
# Parameters
# ----------
# data: array_like
# Values for this array. Must be an ``numpy.ndarray``, ndarray like,
# or castable to an ``ndarray``.
# coords: sequence or dict of array_like objects, optional
# Coordinates (tick labels) to use for indexing along each dimension.
# If dict-like, should be a mapping from dimension names to the
# corresponding coordinates. If sequence-like, should be a sequence
# of tuples where the first element is the dimension name and the
# second element is the corresponding coordinate array_like object.
# dims: str or sequence of str, optional
# Name(s) of the data dimension(s). Must be either a string (only
# for 1D data) or a sequence of strings with length equal to the
# number of dimensions. If this argument is omitted, dimension names
# are taken from ``coords`` (if possible) and otherwise default to
# ``['dim_0', ... 'dim_n']``.
# name: str or None, optional
# Name of this array.
# attrs: dict_like or None, optional
# Attributes to assign to the new instance. By default, an empty
# attribute dictionary is initialized.
# encoding: dict_like or None, optional
# Dictionary specifying how to encode this array's data into a
# serialized format like netCDF4. Currently used keys (for netCDF)
# include '_FillValue', 'scale_factor', 'add_offset', 'dtype',
# 'units' and 'calendar' (the later two only for datetime arrays).
# Unrecognized keys are ignored.
xr = import_optional_dependency("xarray")
if xr is None:
return
x, y = self.x, self.y
tx = x.title
if y:
ty = y.title
da = xr.DataArray(
np.array(self.data, dtype=np.float64),
coords=[(ty, y.data), (tx, x.data)],
)
da.attrs["units"] = self.units
else:
da = xr.DataArray(
np.array(self.data, dtype=np.float64),
coords=[(tx, x.data)],
)
da.attrs["units"] = self.units
da.attrs["title"] = self.title
return da
def show_versions(file=sys.stdout):
"""print the versions of spectrochempy and its dependencies
Parameters
----------
file : file-like, optional
print to the given file-like object. Defaults to sys.stdout.
"""
print("\nINSTALLED VERSIONS", file=file)
print("------------------", file=file)
for key, val in get_sys_info():
print(f"{key}: {val}", file=file)
print(file=file)
# dependancies - TODO: update this list upon changes in env_template.yml
deps = ("""
quadprog,
brukeropusreader,
quaternion,
cantera,
colorama,
dill,
ipython,
jinja2,
matplotlib,
numba,
numpy,
pint,
requests,
scipy,
tqdm,
traitlets,
traittypes,
xlrd,
pyyaml,
ipywidgets,
ipympl,
setuptools,
setuptools_scm,
git,
jupyterlab,
nodejs,
pytest,
pytest-doctestplus,
pytest-flake8,
pytest-mock,
pyfakefs,
scikit-image,
coverage,
black,
pre-commit,
cffconvert,
mamba,
jupytext,
sphinx,
sphinx_rtd_theme,
autodocsumm,
sphinx-gallery,
nbsphinx,
jupyter_sphinx,
json5,
sphinx-copybutton,
numpydoc,
pandoc,
conda-build,
conda-verify,
anaconda-client,
xarray,
scikit-learn,
dash,
dash-bootstrap-components,
dash,
daq,
jupyter-dash,
plotly,
pip,
autodoc_traits,
dash_defer_js_import,
dash-ace,
spectrochempy
""".replace("\n", "").replace(" ", "").split(","))
for dep in deps:
mod = optional.import_optional_dependency(dep, errors="ignore")
try:
print(
f"{dep}: "
f"{optional.get_module_version(mod) if mod is not None else None}",
file=file,
)
except ImportError:
print(f"{dep}: (Can't determine version string)", file=file)
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from traitlets import Dict, HasTraits, Instance, Union, default, TraitError
from spectrochempy.utils.plots import get_figure, _Axes, _Axes3D
from spectrochempy.utils import pathclean
from spectrochempy.core.dataset.meta import Meta
from spectrochempy.core import preferences, plot_preferences, error_
from spectrochempy.core.plotters.plot1d import plot_1D
from spectrochempy.core.plotters.plot2d import plot_2D
from spectrochempy.core.plotters.plot3d import plot_3D
from spectrochempy.optional import import_optional_dependency
go = import_optional_dependency("plotly.graph_objects", errors="ignore")
HAS_PLOTLY = go is not None
# from spectrochempy.utils import deprecated
# ======================================================================================================================
# Management of the preferences for datasets
# ======================================================================================================================
class PreferencesSet(Meta):
"""
Preferences setting.
"""
def __getitem__(self, key):
def download_iris():
"""
Upload the classical `IRIS` dataset.
The `IRIS` dataset is a classical example for machine learning.It is downloaded from
the [UCI distant repository](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)
Returns
-------
dataset
The `IRIS` dataset.
See Also
--------
read : Read data from experimental data.
"""
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
try:
connection = True
response = requests.get(url, stream=True, timeout=10)
except OSError:
error_(
"OSError: Cannot connect to the UCI repository. Try Scikit-Learn")
connection = False
if connection: # Download data
txtdata = ""
for rd in response.iter_content():
txtdata += rd.decode("utf8")
fil = StringIO(txtdata)
try:
data = np.loadtxt(fil, delimiter=",", usecols=range(4))
fil.seek(0)
labels = np.loadtxt(fil, delimiter=",", usecols=(4, ), dtype="|S")
labels = list((lab.decode("utf8") for lab in labels))
except Exception:
raise OSError("can't read JCAMP file")
coordx = Coord(
labels=[
"sepal_length", "sepal width", "petal_length", "petal_width"
],
title="features",
)
coordy = Coord(labels=labels, title="samples")
new = NDDataset(
data,
coordset=[coordy, coordx],
title="size",
name="`IRIS` Dataset",
units="cm",
)
new.history = "Loaded from UC Irvine machine learning repository"
return new
else:
# Cannot download - use the scikit-learn dataset (if scikit-learn is installed)
sklearn = import_optional_dependency("sklearn", errors="ignore")
if sklearn is None:
raise OSError("Failed in uploading the `IRIS` dataset!")
else:
from sklearn import datasets
data = datasets.load_iris()
coordx = Coord(
labels=[
"sepal_length", "sepal width", "petal_length", "petal_width"
],
title="features",
)
labels = [data.target_names[i] for i in data.target]
coordy = Coord(labels=labels, title="samples")
new = NDDataset(
data.data,
coordset=[coordy, coordx],
title="size",
name="`IRIS` Dataset",
units="cm",
)
new.history = "Loaded from scikit-learn datasets"
return new