INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Add a dataset and add it to the UI | def open(self, path):
"""Add a dataset and add it to the UI"""
logger.debug("open dataset: %r", path)
if path.startswith("http") or path.startswith("ws"):
dataset = vaex.open(path, thread_mover=self.call_in_main_thread)
else:
dataset = vaex.open(path)
self... |
basic support for evaluate at server at least to run some unittest do not expect this to work from strings | def evaluate(self, expression, i1=None, i2=None, out=None, selection=None, delay=False):
expression = _ensure_strings_from_expressions(expression)
"""basic support for evaluate at server, at least to run some unittest, do not expect this to work from strings"""
result = self.server._call_dataset... |
Decorator to transparantly accept delayed computation. | def delayed(f):
'''Decorator to transparantly accept delayed computation.
Example:
>>> delayed_sum = ds.sum(ds.E, binby=ds.x, limits=limits,
>>> shape=4, delay=True)
>>> @vaex.delayed
>>> def total_sum(sums):
>>> return sums.sum()
>>> sum_of_sums = total_sum(delay... |
Find all columns that this selection depends on for df ds | def _depending_columns(self, ds):
'''Find all columns that this selection depends on for df ds'''
depending = set()
for expression in self.expressions:
expression = ds._expr(expression) # make sure it is an expression
depending |= expression.variables()
if self.p... |
TODO: doc + server side implementation | def limits(self, value, square=False):
"""TODO: doc + server side implementation"""
if isinstance(value, six.string_types):
import re
match = re.match(r"(\d*)(\D*)", value)
if match is None:
raise ValueError("do not understand limit specifier %r, examp... |
Plot the subspace using sane defaults to get a quick look at the data. | def plot(self, grid=None, size=256, limits=None, square=False, center=None, weight=None, weight_stat="mean", figsize=None,
aspect="auto", f="identity", axes=None, xlabel=None, ylabel=None,
group_by=None, group_limits=None, group_colors='jet', group_labels=None, group_count=None,
v... |
Plot the subspace using sane defaults to get a quick look at the data. | def plot1d(self, grid=None, size=64, limits=None, weight=None, figsize=None, f="identity", axes=None, xlabel=None, ylabel=None, **kwargs):
"""Plot the subspace using sane defaults to get a quick look at the data.
:param grid: A 2d numpy array with the counts, if None it will be calculated using limits ... |
Returns a bounded subspace ( SubspaceBounded ) with limits given by Subspace. limits_sigma () | def bounded_by_sigmas(self, sigmas=3, square=False):
"""Returns a bounded subspace (SubspaceBounded) with limits given by Subspace.limits_sigma()
:rtype: SubspaceBounded
"""
bounds = self.limits_sigma(sigmas=sigmas, square=square)
return SubspaceBounded(self, bounds) |
Helper function for returning tasks results result when immediate is True otherwise the task itself which is a promise | def _task(self, task, progressbar=False):
"""Helper function for returning tasks results, result when immediate is True, otherwise the task itself, which is a promise"""
if self.delay:
# should return a task or a promise nesting it
return self.executor.schedule(task)
else... |
Sort table by given column number. | def sort(self, Ncol, order):
"""Sort table by given column number.
"""
self.emit(QtCore.SIGNAL("layoutAboutToBeChanged()"))
if Ncol == 0:
print("by name")
# get indices, sorted by pair name
sortlist = list(zip(self.pairs, list(range(len(self.pairs)))))... |
: param DatasetLocal dataset: dataset to export: param str path: path for file: param lis [ str ] column_names: list of column names to export or None for all columns: param str byteorder: = for native < for little endian and > for big endian: param bool shuffle: export rows in random order: param bool selection: expor... | def export_hdf5_v1(dataset, path, column_names=None, byteorder="=", shuffle=False, selection=False, progress=None, virtual=True):
"""
:param DatasetLocal dataset: dataset to export
:param str path: path for file
:param lis[str] column_names: list of column names to export or None for all columns
:pa... |
Read header data from Gadget data file filename with Gadget file type gtype. Returns offsets of positions and velocities. | def getinfo(filename, seek=None):
"""Read header data from Gadget data file 'filename' with Gadget file
type 'gtype'. Returns offsets of positions and velocities."""
DESC = '=I4sII' # struct formatting string
HEAD = '=I6I6dddii6iiiddddii6ii60xI' # struct formatting string
keys... |
: param DatasetLocal dataset: dataset to export: param str path: path for file: param lis [ str ] column_names: list of column names to export or None for all columns: param str byteorder: = for native < for little endian and > for big endian: param bool shuffle: export rows in random order: param bool selection: expor... | def _export(dataset_input, dataset_output, random_index_column, path, column_names=None, byteorder="=", shuffle=False, selection=False, progress=None, virtual=True, sort=None, ascending=True):
"""
:param DatasetLocal dataset: dataset to export
:param str path: path for file
:param lis[str] column_names:... |
: param DatasetLocal dataset: dataset to export: param str path: path for file: param lis [ str ] column_names: list of column names to export or None for all columns: param bool shuffle: export rows in random order: param bool selection: export selection or not: param progress: progress callback that gets a progress f... | def export_fits(dataset, path, column_names=None, shuffle=False, selection=False, progress=None, virtual=True, sort=None, ascending=True):
"""
:param DatasetLocal dataset: dataset to export
:param str path: path for file
:param lis[str] column_names: list of column names to export or None for all column... |
clear the cursor | def clear(self, event):
"""clear the cursor"""
if self.useblit:
self.background = (
self.canvas.copy_from_bbox(self.canvas.figure.bbox))
for line in self.vlines + self.hlines:
line.set_visible(False)
self.ellipse.set_visible(False) |
Used for unittesting to make sure the plots are all done | def _wait(self):
"""Used for unittesting to make sure the plots are all done"""
logger.debug("will wait for last plot to finish")
self._plot_event = threading.Event()
self.queue_update._wait()
self.queue_replot._wait()
self.queue_redraw._wait()
qt_app = QtCore.QCo... |
Each layer has it s own ranges_grid computed now unless something went wrong But all layers are shown with the same ranges ( self. state. ranges_viewport ) If any of the ranges is None take the min/ max of each layer | def _update_step2(self, layers):
"""Each layer has it's own ranges_grid computed now, unless something went wrong
But all layers are shown with the same ranges (self.state.ranges_viewport)
If any of the ranges is None, take the min/max of each layer
"""
logger.info("done with ran... |
Generates a list with start end stop indices of length parts [ ( 0 length/ parts )... (.. length ) ] | def subdivide(length, parts=None, max_length=None):
"""Generates a list with start end stop indices of length parts, [(0, length/parts), ..., (.., length)]"""
if max_length:
i1 = 0
done = False
while not done:
i2 = min(length, i1 + max_length)
# print i1, i2
... |
Open document by the default handler of the OS could be a url opened by a browser a text file by an editor etc | def os_open(document):
"""Open document by the default handler of the OS, could be a url opened by a browser, a text file by an editor etc"""
osname = platform.system().lower()
if osname == "darwin":
os.system("open \"" + document + "\"")
if osname == "linux":
cmd = "xdg-open \"" + docum... |
Flexible writing where f can be a filename or f object if filename closed after writing | def write_to(f, mode):
"""Flexible writing, where f can be a filename or f object, if filename, closed after writing"""
if hasattr(f, 'write'):
yield f
else:
f = open(f, mode)
yield f
f.close() |
Combines all masks from a list of arrays and logically ors them into a single mask | def _split_and_combine_mask(arrays):
'''Combines all masks from a list of arrays, and logically ors them into a single mask'''
masks = [np.ma.getmaskarray(block) for block in arrays if np.ma.isMaskedArray(block)]
arrays = [block.data if np.ma.isMaskedArray(block) else block for block in arrays]
mask = None
if mask... |
Plot conting contours on 2D grid. | def plot2d_contour(self, x=None, y=None, what="count(*)", limits=None, shape=256,
selection=None, f="identity", figsize=None,
xlabel=None, ylabel=None,
aspect="auto", levels=None, fill=False,
colorbar=False, colorbar_label=None,
... |
: param DatasetLocal dataset: dataset to export: param str path: path for file: param lis [ str ] column_names: list of column names to export or None for all columns: param str byteorder: = for native < for little endian and > for big endian: param bool shuffle: export rows in random order: param bool selection: expor... | def _export_table(dataset, column_names=None, byteorder="=", shuffle=False, selection=False, progress=None, virtual=True, sort=None, ascending=True):
"""
:param DatasetLocal dataset: dataset to export
:param str path: path for file
:param lis[str] column_names: list of column names to export or None for... |
Evaluates expression and drop the result usefull for benchmarking since vaex is usually lazy | def nop(self, expression, progress=False, delay=False):
"""Evaluates expression, and drop the result, usefull for benchmarking, since vaex is usually lazy"""
expression = _ensure_string_from_expression(expression)
def map(ar):
pass
def reduce(a, b):
pass
r... |
Estimate the mutual information between and x and y on a grid with shape mi_shape and mi_limits possibly on a grid defined by binby. | def mutual_information(self, x, y=None, mi_limits=None, mi_shape=256, binby=[], limits=None, shape=default_shape, sort=False, selection=False, delay=False):
"""Estimate the mutual information between and x and y on a grid with shape mi_shape and mi_limits, possibly on a grid defined by binby.
If sort i... |
Count the number of non - NaN values ( or all if expression is None or * ). | def count(self, expression=None, binby=[], limits=None, shape=default_shape, selection=False, delay=False, edges=False, progress=None):
"""Count the number of non-NaN values (or all, if expression is None or "*").
Example:
>>> df.count()
330000
>>> df.count("*")
330000.... |
Return the first element of a binned expression where the values each bin are sorted by order_expression. | def first(self, expression, order_expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, edges=False, progress=None):
"""Return the first element of a binned `expression`, where the values each bin are sorted by `order_expression`.
Example:
>>> import vaex
... |
Calculate the mean for expression possibly on a grid defined by binby. | def mean(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None, edges=False):
"""Calculate the mean for expression, possibly on a grid defined by binby.
Example:
>>> df.mean("x")
-0.067131491264005971
>>> df.mean("(x**2+y**2)*... |
Calculate the sum for the given expression possible on a grid defined by binby | def sum(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None, edges=False):
"""Calculate the sum for the given expression, possible on a grid defined by binby
Example:
>>> df.sum("L")
304054882.49378014
>>> df.sum("L", binby=... |
Calculate the standard deviation for the given expression possible on a grid defined by binby | def std(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None):
"""Calculate the standard deviation for the given expression, possible on a grid defined by binby
>>> df.std("vz")
110.31773397535071
>>> df.std("vz", binby=["(x**2+y**2)... |
Calculate the covariance cov [ x y ] between and x and y possibly on a grid defined by binby. | def covar(self, x, y, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None):
"""Calculate the covariance cov[x,y] between and x and y, possibly on a grid defined by binby.
Example:
>>> df.covar("x**2+y**2+z**2", "-log(-E+1)")
array(52.69461456005138)
... |
Calculate the correlation coefficient cov [ x y ]/ ( std [ x ] * std [ y ] ) between and x and y possibly on a grid defined by binby. | def correlation(self, x, y=None, binby=[], limits=None, shape=default_shape, sort=False, sort_key=np.abs, selection=False, delay=False, progress=None):
"""Calculate the correlation coefficient cov[x,y]/(std[x]*std[y]) between and x and y, possibly on a grid defined by binby.
Example:
>>> df.co... |
Calculate the covariance matrix for x and y or more expressions possibly on a grid defined by binby. | def cov(self, x, y=None, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None):
"""Calculate the covariance matrix for x and y or more expressions, possibly on a grid defined by binby.
Either x and y are expressions, e.g:
>>> df.cov("x", "y")
Or only... |
Calculate the minimum and maximum for expressions possibly on a grid defined by binby. | def minmax(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None):
"""Calculate the minimum and maximum for expressions, possibly on a grid defined by binby.
Example:
>>> df.minmax("x")
array([-128.293991, 271.365997])
>>> d... |
Calculate the minimum for given expressions possibly on a grid defined by binby. | def min(self, expression, binby=[], limits=None, shape=default_shape, selection=False, delay=False, progress=None, edges=False):
"""Calculate the minimum for given expressions, possibly on a grid defined by binby.
Example:
>>> df.min("x")
array(-128.293991)
>>> df.min(["x", "y... |
Calculate the median possibly on a grid defined by binby. | def median_approx(self, expression, percentage=50., binby=[], limits=None, shape=default_shape, percentile_shape=256, percentile_limits="minmax", selection=False, delay=False):
"""Calculate the median , possibly on a grid defined by binby.
NOTE: this value is approximated by calculating the cumulative ... |
Calculate the percentile given by percentage possibly on a grid defined by binby. | def percentile_approx(self, expression, percentage=50., binby=[], limits=None, shape=default_shape, percentile_shape=1024, percentile_limits="minmax", selection=False, delay=False):
"""Calculate the percentile given by percentage, possibly on a grid defined by binby.
NOTE: this value is approximated by... |
Calculate the [ min max ] range for expression containing approximately a percentage of the data as defined by percentage. | def limits_percentage(self, expression, percentage=99.73, square=False, delay=False):
"""Calculate the [min, max] range for expression, containing approximately a percentage of the data as defined
by percentage.
The range is symmetric around the median, i.e., for a percentage of 90, this gives ... |
Calculate the [ min max ] range for expression as described by value which is 99. 7% by default. | def limits(self, expression, value=None, square=False, selection=None, delay=False, shape=None):
"""Calculate the [min, max] range for expression, as described by value, which is '99.7%' by default.
If value is a list of the form [minvalue, maxvalue], it is simply returned, this is for convenience when... |
Calculate/ estimate the mode. | def mode(self, expression, binby=[], limits=None, shape=256, mode_shape=64, mode_limits=None, progressbar=False, selection=None):
"""Calculate/estimate the mode."""
if len(binby) == 0:
raise ValueError("only supported with binby argument given")
else:
# todo, fix progress... |
Viz 1d 2d or 3d in a Jupyter notebook | def plot_widget(self, x, y, z=None, grid=None, shape=256, limits=None, what="count(*)", figsize=None,
f="identity", figure_key=None, fig=None, axes=None, xlabel=None, ylabel=None, title=None,
show=True, selection=[None, True], colormap="afmhot", grid_limits=None, normalize="norma... |
Count non missing value for expression on an array which represents healpix data. | def healpix_count(self, expression=None, healpix_expression=None, healpix_max_level=12, healpix_level=8, binby=None, limits=None, shape=default_shape, delay=False, progress=None, selection=None):
"""Count non missing value for expression on an array which represents healpix data.
:param expression: Exp... |
Viz data in 2d using a healpix column. | def healpix_plot(self, healpix_expression="source_id/34359738368", healpix_max_level=12, healpix_level=8, what="count(*)", selection=None,
grid=None,
healpix_input="equatorial", healpix_output="galactic", f=None,
colormap="afmhot", grid_limits=None, image_s... |
Use at own risk requires ipyvolume | def plot3d(self, x, y, z, vx=None, vy=None, vz=None, vwhat=None, limits=None, grid=None, what="count(*)", shape=128, selection=[None, True], f=None,
vcount_limits=None,
smooth_pre=None, smooth_post=None, grid_limits=None, normalize="normalize", colormap="afmhot",
figure_key=... |
Gives direct access to the columns only ( useful for tab completion ). | def col(self):
"""Gives direct access to the columns only (useful for tab completion).
Convenient when working with ipython in combination with small DataFrames, since this gives tab-completion.
Columns can be accesed by there names, which are attributes. The attribues are currently expression... |
Return the size in bytes the whole DataFrame requires ( or the selection ) respecting the active_fraction. | def byte_size(self, selection=False, virtual=False):
"""Return the size in bytes the whole DataFrame requires (or the selection), respecting the active_fraction."""
bytes_per_row = 0
N = self.count(selection=selection)
extra = 0
for column in list(self.get_column_names(virtual=vi... |
Return the numpy dtype for the given expression if not a column the first row will be evaluated to get the dtype. | def dtype(self, expression, internal=False):
"""Return the numpy dtype for the given expression, if not a column, the first row will be evaluated to get the dtype."""
expression = _ensure_string_from_expression(expression)
if expression in self.variables:
return np.float64(1).dtype
... |
Gives a Pandas series object containing all numpy dtypes of all columns ( except hidden ). | def dtypes(self):
"""Gives a Pandas series object containing all numpy dtypes of all columns (except hidden)."""
from pandas import Series
return Series({column_name:self.dtype(column_name) for column_name in self.get_column_names()}) |
Return if a column is a masked ( numpy. ma ) column. | def is_masked(self, column):
'''Return if a column is a masked (numpy.ma) column.'''
column = _ensure_string_from_expression(column)
if column in self.columns:
return np.ma.isMaskedArray(self.columns[column])
return False |
Returns the unit ( an astropy. unit. Units object ) for the expression. | def unit(self, expression, default=None):
"""Returns the unit (an astropy.unit.Units object) for the expression.
Example
>>> import vaex
>>> ds = vaex.example()
>>> df.unit("x")
Unit("kpc")
>>> df.unit("x*L")
Unit("km kpc2 / s")
:param expressi... |
Find a set of columns ( names ) which have the ucd or part of the ucd. | def ucd_find(self, ucds, exclude=[]):
"""Find a set of columns (names) which have the ucd, or part of the ucd.
Prefixed with a ^, it will only match the first part of the ucd.
Example
>>> df.ucd_find('pos.eq.ra', 'pos.eq.dec')
['RA', 'DEC']
>>> df.ucd_find('pos.eq.ra',... |
Each DataFrame has a directory where files are stored for metadata etc. | def get_private_dir(self, create=False):
"""Each DataFrame has a directory where files are stored for metadata etc.
Example
>>> import vaex
>>> ds = vaex.example()
>>> vaex.get_private_dir()
'/Users/users/breddels/.vaex/dfs/_Users_users_breddels_vaex-testing_data_helmi-... |
Return the internal state of the DataFrame in a dictionary | def state_get(self):
"""Return the internal state of the DataFrame in a dictionary
Example:
>>> import vaex
>>> df = vaex.from_scalars(x=1, y=2)
>>> df['r'] = (df.x**2 + df.y**2)**0.5
>>> df.state_get()
{'active_range': [0, 1],
'column_names': ['x', 'y',... |
Sets the internal state of the df | def state_set(self, state, use_active_range=False):
"""Sets the internal state of the df
Example:
>>> import vaex
>>> df = vaex.from_scalars(x=1, y=2)
>>> df
# x y r
0 1 2 2.23607
>>> df['r'] = (df.x**2 + df.y**2)**0.5
>>>... |
Load a state previously stored by: meth: DataFrame. state_store see also: meth: DataFrame. state_set. | def state_load(self, f, use_active_range=False):
"""Load a state previously stored by :meth:`DataFrame.state_store`, see also :meth:`DataFrame.state_set`."""
state = vaex.utils.read_json_or_yaml(f)
self.state_set(state, use_active_range=use_active_range) |
Removes the file with the virtual column etc it does not change the current virtual columns etc. | def remove_virtual_meta(self):
"""Removes the file with the virtual column etc, it does not change the current virtual columns etc."""
dir = self.get_private_dir(create=True)
path = os.path.join(dir, "virtual_meta.yaml")
try:
if os.path.exists(path):
os.remove... |
Writes virtual columns variables and their ucd description and units. | def write_virtual_meta(self):
"""Writes virtual columns, variables and their ucd,description and units.
The default implementation is to write this to a file called virtual_meta.yaml in the directory defined by
:func:`DataFrame.get_private_dir`. Other implementation may store this in the DataFr... |
Will read back the virtual column etc written by: func: DataFrame. write_virtual_meta. This will be done when opening a DataFrame. | def update_virtual_meta(self):
"""Will read back the virtual column etc, written by :func:`DataFrame.write_virtual_meta`. This will be done when opening a DataFrame."""
import astropy.units
try:
path = os.path.join(self.get_private_dir(create=False), "virtual_meta.yaml")
... |
Writes all meta data ucd description and units | def write_meta(self):
"""Writes all meta data, ucd,description and units
The default implementation is to write this to a file called meta.yaml in the directory defined by
:func:`DataFrame.get_private_dir`. Other implementation may store this in the DataFrame file itself.
(For instance ... |
Generate a Subspaces object based on a custom list of expressions or all possible combinations based on dimension | def subspaces(self, expressions_list=None, dimensions=None, exclude=None, **kwargs):
"""Generate a Subspaces object, based on a custom list of expressions or all possible combinations based on
dimension
:param expressions_list: list of list of expressions, where the inner list defines the subsp... |
Set the variable to an expression or value defined by expression_or_value. | def set_variable(self, name, expression_or_value, write=True):
"""Set the variable to an expression or value defined by expression_or_value.
Example
>>> df.set_variable("a", 2.)
>>> df.set_variable("b", "a**2")
>>> df.get_variable("b")
'a**2'
>>> df.evaluate_var... |
Evaluates the variable given by name. | def evaluate_variable(self, name):
"""Evaluates the variable given by name."""
if isinstance(self.variables[name], six.string_types):
# TODO: this does not allow more than one level deep variable, like a depends on b, b on c, c is a const
value = eval(self.variables[name], expres... |
Internal use ignores the filter | def _evaluate_selection_mask(self, name="default", i1=None, i2=None, selection=None, cache=False):
"""Internal use, ignores the filter"""
i1 = i1 or 0
i2 = i2 or len(self)
scope = scopes._BlockScopeSelection(self, i1, i2, selection, cache=cache)
return scope.evaluate(name) |
Return a list of [ ( column_name ndarray )... ) ] pairs where the ndarray corresponds to the evaluated data | def to_items(self, column_names=None, selection=None, strings=True, virtual=False):
"""Return a list of [(column_name, ndarray), ...)] pairs where the ndarray corresponds to the evaluated data
:param column_names: list of column names, to export, when None DataFrame.get_column_names(strings=strings, vi... |
Return a dict containing the ndarray corresponding to the evaluated data | def to_dict(self, column_names=None, selection=None, strings=True, virtual=False):
"""Return a dict containing the ndarray corresponding to the evaluated data
:param column_names: list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtual) is used
:pa... |
Return a copy of the DataFrame if selection is None it does not copy the data it just has a reference | def to_copy(self, column_names=None, selection=None, strings=True, virtual=False, selections=True):
"""Return a copy of the DataFrame, if selection is None, it does not copy the data, it just has a reference
:param column_names: list of column names, to copy, when None DataFrame.get_column_names(string... |
Return a pandas DataFrame containing the ndarray corresponding to the evaluated data | def to_pandas_df(self, column_names=None, selection=None, strings=True, virtual=False, index_name=None):
"""Return a pandas DataFrame containing the ndarray corresponding to the evaluated data
If index is given, that column is used for the index of the dataframe.
Example
>>> df_pan... |
Returns an arrow Table object containing the arrays corresponding to the evaluated data | def to_arrow_table(self, column_names=None, selection=None, strings=True, virtual=False):
"""Returns an arrow Table object containing the arrays corresponding to the evaluated data
:param column_names: list of column names, to export, when None DataFrame.get_column_names(strings=strings, virtual=virtua... |
Returns a astropy table object containing the ndarrays corresponding to the evaluated data | def to_astropy_table(self, column_names=None, selection=None, strings=True, virtual=False, index=None):
"""Returns a astropy table object containing the ndarrays corresponding to the evaluated data
:param column_names: list of column names, to export, when None DataFrame.get_column_names(strings=string... |
Validate an expression ( may throw Exceptions ) | def validate_expression(self, expression):
"""Validate an expression (may throw Exceptions)"""
# return self.evaluate(expression, 0, 2)
vars = set(self.get_column_names()) | set(self.variables.keys())
funcs = set(expression_namespace.keys())
return vaex.expresso.validate_expressi... |
Add an in memory array as a column. | def add_column(self, name, f_or_array):
"""Add an in memory array as a column."""
if isinstance(f_or_array, (np.ndarray, Column)):
data = ar = f_or_array
# it can be None when we have an 'empty' DataFrameArrays
if self._length_original is None:
self._l... |
Renames a column not this is only the in memory name this will not be reflected on disk | def rename_column(self, name, new_name, unique=False, store_in_state=True):
"""Renames a column, not this is only the in memory name, this will not be reflected on disk"""
new_name = vaex.utils.find_valid_name(new_name, used=[] if not unique else list(self))
data = self.columns.get(name)
... |
Add a healpix ( in memory ) column based on a longitude and latitude | def add_column_healpix(self, name="healpix", longitude="ra", latitude="dec", degrees=True, healpix_order=12, nest=True):
"""Add a healpix (in memory) column based on a longitude and latitude
:param name: Name of column
:param longitude: longitude expression
:param latitude: latitude exp... |
Propagates uncertainties ( full covariance matrix ) for a set of virtual columns. | def propagate_uncertainties(self, columns, depending_variables=None, cov_matrix='auto',
covariance_format="{}_{}_covariance",
uncertainty_format="{}_uncertainty"):
"""Propagates uncertainties (full covariance matrix) for a set of virtual columns.
... |
Convert cartesian to polar coordinates | def add_virtual_columns_cartesian_to_polar(self, x="x", y="y", radius_out="r_polar", azimuth_out="phi_polar",
propagate_uncertainties=False,
radians=False):
"""Convert cartesian to polar coordinates
:param x: ... |
Concert velocities from a cartesian to a spherical coordinate system | def add_virtual_columns_cartesian_velocities_to_spherical(self, x="x", y="y", z="z", vx="vx", vy="vy", vz="vz", vr="vr", vlong="vlong", vlat="vlat", distance=None):
"""Concert velocities from a cartesian to a spherical coordinate system
TODO: errors
:param x: name of x column (input)
:... |
Convert cartesian to polar velocities. | def add_virtual_columns_cartesian_velocities_to_polar(self, x="x", y="y", vx="vx", radius_polar=None, vy="vy", vr_out="vr_polar", vazimuth_out="vphi_polar",
propagate_uncertainties=False,):
"""Convert cartesian to polar velocities.
:param x:
... |
Convert cylindrical polar velocities to Cartesian. | def add_virtual_columns_polar_velocities_to_cartesian(self, x='x', y='y', azimuth=None, vr='vr_polar', vazimuth='vphi_polar', vx_out='vx', vy_out='vy', propagate_uncertainties=False):
""" Convert cylindrical polar velocities to Cartesian.
:param x:
:param y:
:param azimuth: Optional exp... |
Rotation in 2d. | def add_virtual_columns_rotation(self, x, y, xnew, ynew, angle_degrees, propagate_uncertainties=False):
"""Rotation in 2d.
:param str x: Name/expression of x column
:param str y: idem for y
:param str xnew: name of transformed x column
:param str ynew:
:param float angle... |
Convert spherical to cartesian coordinates. | def add_virtual_columns_spherical_to_cartesian(self, alpha, delta, distance, xname="x", yname="y", zname="z",
propagate_uncertainties=False,
center=[0, 0, 0], center_name="solar_position", radians=False):
"""Co... |
Convert cartesian to spherical coordinates. | def add_virtual_columns_cartesian_to_spherical(self, x="x", y="y", z="z", alpha="l", delta="b", distance="distance", radians=False, center=None, center_name="solar_position"):
"""Convert cartesian to spherical coordinates.
:param x:
:param y:
:param z:
:param alpha:
:p... |
Add aitoff ( https:// en. wikipedia. org/ wiki/ Aitoff_projection ) projection | def add_virtual_columns_aitoff(self, alpha, delta, x, y, radians=True):
"""Add aitoff (https://en.wikipedia.org/wiki/Aitoff_projection) projection
:param alpha: azimuth angle
:param delta: polar angle
:param x: output name for x coordinate
:param y: output name for y coordinate
... |
Add a virtual column to the DataFrame. | def add_virtual_column(self, name, expression, unique=False):
"""Add a virtual column to the DataFrame.
Example:
>>> df.add_virtual_column("r", "sqrt(x**2 + y**2 + z**2)")
>>> df.select("r < 10")
:param: str name: name of virtual column
:param: expression: expression f... |
Deletes a virtual column from a DataFrame. | def delete_virtual_column(self, name):
"""Deletes a virtual column from a DataFrame."""
del self.virtual_columns[name]
self.signal_column_changed.emit(self, name, "delete") |
Add a variable to to a DataFrame. | def add_variable(self, name, expression, overwrite=True, unique=True):
"""Add a variable to to a DataFrame.
A variable may refer to other variables, and virtual columns and expression may refer to variables.
Example
>>> df.add_variable('center', 0)
>>> df.add_virtual_column('x... |
Deletes a variable from a DataFrame. | def delete_variable(self, name):
"""Deletes a variable from a DataFrame."""
del self.variables[name]
self.signal_variable_changed.emit(self, name, "delete") |
Return a shallow copy a DataFrame with the last n rows. | def tail(self, n=10):
"""Return a shallow copy a DataFrame with the last n rows."""
N = len(self)
# self.cat(i1=max(0, N-n), i2=min(len(self), N))
return self[max(0, N - n):min(len(self), N)] |
Display the first and last n elements of a DataFrame. | def head_and_tail_print(self, n=5):
"""Display the first and last n elements of a DataFrame."""
from IPython import display
display.display(display.HTML(self._head_and_tail_table(n))) |
Give a description of the DataFrame. | def describe(self, strings=True, virtual=True, selection=None):
"""Give a description of the DataFrame.
>>> import vaex
>>> df = vaex.example()[['x', 'y', 'z']]
>>> df.describe()
x y z
dtype float64 float64 float64
co... |
Display the DataFrame from row i1 till i2 | def cat(self, i1, i2, format='html'):
"""Display the DataFrame from row i1 till i2
For format, see https://pypi.org/project/tabulate/
:param int i1: Start row
:param int i2: End row.
:param str format: Format to use, e.g. 'html', 'plain', 'latex'
"""
from IPytho... |
Set the current row and emit the signal signal_pick. | def set_current_row(self, value):
"""Set the current row, and emit the signal signal_pick."""
if (value is not None) and ((value < 0) or (value >= len(self))):
raise IndexError("index %d out of range [0,%d]" % (value, len(self)))
self._current_row = value
self.signal_pick.emi... |
Return a list of column names | def get_column_names(self, virtual=True, strings=True, hidden=False, regex=None):
"""Return a list of column names
Example:
>>> import vaex
>>> df = vaex.from_scalars(x=1, x2=2, y=3, s='string')
>>> df['r'] = (df.x**2 + df.y**2)**2
>>> df.get_column_names()
['x'... |
Sets the active_fraction set picked row to None and remove selection. | def set_active_fraction(self, value):
"""Sets the active_fraction, set picked row to None, and remove selection.
TODO: we may be able to keep the selection, if we keep the expression, and also the picked row
"""
if value != self._active_fraction:
self._active_fraction = valu... |
Sets the active_fraction set picked row to None and remove selection. | def set_active_range(self, i1, i2):
"""Sets the active_fraction, set picked row to None, and remove selection.
TODO: we may be able to keep the selection, if we keep the expression, and also the picked row
"""
logger.debug("set active range to: %r", (i1, i2))
self._active_fracti... |
Return a DataFrame where all columns are trimmed by the active range. | def trim(self, inplace=False):
'''Return a DataFrame, where all columns are 'trimmed' by the active range.
For the returned DataFrame, df.get_active_range() returns (0, df.length_original()).
{note_copy}
:param inplace: {inplace}
:rtype: DataFrame
'''
df = self... |
Returns a DataFrame containing only rows indexed by indices | def take(self, indices):
'''Returns a DataFrame containing only rows indexed by indices
{note_copy}
Example:
>>> import vaex, numpy as np
>>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd']), x=np.arange(1,5))
>>> df.take([0,2])
# s x
0 a... |
Return a DataFrame containing only the filtered rows. | def extract(self):
'''Return a DataFrame containing only the filtered rows.
{note_copy}
The resulting DataFrame may be more efficient to work with when the original DataFrame is
heavily filtered (contains just a small number of rows).
If no filtering is applied, it returns a t... |
Returns a DataFrame with a random set of rows | def sample(self, n=None, frac=None, replace=False, weights=None, random_state=None):
'''Returns a DataFrame with a random set of rows
{note_copy}
Provide either n or frac.
Example:
>>> import vaex, numpy as np
>>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd'])... |
Returns a list containing random portions of the DataFrame. | def split_random(self, frac, random_state=None):
'''Returns a list containing random portions of the DataFrame.
{note_copy}
Example:
>>> import vaex, import numpy as np
>>> np.random.seed(111)
>>> df = vaex.from_arrays(x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> fo... |
Returns a list containing ordered subsets of the DataFrame. | def split(self, frac):
'''Returns a list containing ordered subsets of the DataFrame.
{note_copy}
Example:
>>> import vaex
>>> df = vaex.from_arrays(x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> for dfs in df.split(frac=0.3):
... print(dfs.x.values)
...
... |
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