Docstrings for the lasio package¶
Reading LAS files¶
-
lasio.
read
(file_ref, **kwargs)[source]¶ Read a LAS file.
Note that only versions 1.2 and 2.0 of the LAS file specification are fully supported. There is partial support for reading LAS 3.0 files.
Parameters: file_ref (file-like object or
str
) – either a filename, an open file object, or a string containing the contents of a file.Keyword Arguments: - ignore_header_errors (bool) – ignore LASHeaderErrors (False by default)
- ignore_comments (sequence/str) – ignore lines beginning with these
characters e.g.
("#", '"')
in header sections. - ignore_data_comments (str) – ignore lines beginning with this character in data sections only.
- mnemonic_case (str) – ‘preserve’: keep the case of HeaderItem mnemonics ‘upper’: convert all HeaderItem mnemonics to uppercase ‘lower’: convert all HeaderItem mnemonics to lowercase
- ignore_data (bool) – if True, do not read in any of the actual data, just the header metadata. False by default.
- engine (str) – “normal”: parse data section with normal Python reader (quite slow); “numpy”: parse data section with numpy.genfromtxt (fast). By default the engine is “numpy”.
- use_normal_engine_for_wrapped (bool) – if header metadata indicates that the file is wrapped, always use the ‘normal’ engine. Default is True. The only reason you should use False is if speed is a very high priority and you had files with metadata that incorrectly indicates they are wrapped.
- read_policy (str or list) – Apply regular expression substitutions for common errors in
fixed-width formatted data sections. If you do not want any such substitutions
to applied, pass
read_policy=()
. - null_policy (str or list) – see https://lasio.readthedocs.io/en/latest/data-section.html#handling-invalid-data-indicators-automatically
- accept_regexp_sub_recommendations (bool) – Accept recommendations to auto- matically remove read substitutions (applied by the default read_policy) which look for numeric run-on errors involving hyphens. This avoids incorrect parsing of dates such as ‘2018-05-22’ as three separate columns containing ‘2018’, ‘-5’ and ‘-22’. The read substitutions are applied only if the inspection code of the data section finds a hyphen in every line. The only circumstance where this should be manually set to False is where you have very problematic fixed-column-width data sections involving negative values.
- index_unit (str) – Optionally force-set the index curve’s unit to “m” or “ft”
- dtypes ("auto", dict or list) – specify the data types for each curve in the ~ASCII data section. If “auto”, each curve will be converted to floats if possible and remain as str if not. If a dict you can specify only the curve mnemonics you want to convert as a key. If a list, please specify data types for each curve in order. Note that the conversion currently only occurs via numpy.ndarray.astype() and therefore only a few simple casts will work e.g. int, float, str.
- encoding (str) – character encoding to open file_ref with, using
io.open()
(this is handled bylasio.reader.open_with_codecs()
) - encoding_errors (str) – ‘strict’, ‘replace’ (default), ‘ignore’ - how to
handle errors with encodings (see
this section
of the standard library’s
codecs
module for more information) (this is handled bylasio.reader.open_with_codecs()
) - autodetect_encoding (str or bool) – default True to use chardet to detect encoding.
Note if set to False several common encodings will be tried but
chardet won’t be used.
(this is handled by
lasio.reader.open_with_codecs()
) - autodetect_encoding_chars (int/None) – number of chars to read from LAS
file for auto-detection of encoding.
(this is handled by
lasio.reader.open_with_codecs()
)
Returns: a
lasio.LASFile
object representing the fileThe documented arguments above are combined from these methods:
lasio.reader.open_with_codecs()
- manage issues relate to character encodingslasio.LASFile.read()
- control how NULL values and errors are handled during parsing
-
class
lasio.
LASFile
(file_ref=None, **read_kwargs)[source]¶ LAS file object.
Keyword Arguments: - file_ref (file-like object or
str
) – either a filename, an open file object, or a string containing the contents of a file. - ignore_header_errors (bool) – ignore LASHeaderErrors (False by default)
- ignore_comments (sequence/str) – ignore lines beginning with these
characters e.g.
("#", '"')
in header sections. - ignore_data_comments (str) – ignore lines beginning with this character in data sections only.
- mnemonic_case (str) – ‘preserve’: keep the case of HeaderItem mnemonics ‘upper’: convert all HeaderItem mnemonics to uppercase ‘lower’: convert all HeaderItem mnemonics to lowercase
- ignore_data (bool) – if True, do not read in any of the actual data, just the header metadata. False by default.
- engine (str) – “normal”: parse data section with normal Python reader (quite slow); “numpy”: parse data section with numpy.genfromtxt (fast). By default the engine is “numpy”.
- use_normal_engine_for_wrapped (bool) – if header metadata indicates that the file is wrapped, always use the ‘normal’ engine. Default is True. The only reason you should use False is if speed is a very high priority and you had files with metadata that incorrectly indicates they are wrapped.
- read_policy (str or list) – Apply regular expression substitutions for common errors in
fixed-width formatted data sections. If you do not want any such substitutions
to applied, pass
read_policy=()
. - null_policy (str or list) – see https://lasio.readthedocs.io/en/latest/data-section.html#handling-invalid-data-indicators-automatically
- accept_regexp_sub_recommendations (bool) – Accept recommendations to auto- matically remove read substitutions (applied by the default read_policy) which look for numeric run-on errors involving hyphens. This avoids incorrect parsing of dates such as ‘2018-05-22’ as three separate columns containing ‘2018’, ‘-5’ and ‘-22’. The read substitutions are applied only if the inspection code of the data section finds a hyphen in every line. The only circumstance where this should be manually set to False is where you have very problematic fixed-column-width data sections involving negative values.
- index_unit (str) – Optionally force-set the index curve’s unit to “m” or “ft”
- dtypes ("auto", dict or list) – specify the data types for each curve in the ~ASCII data section. If “auto”, each curve will be converted to floats if possible and remain as str if not. If a dict you can specify only the curve mnemonics you want to convert as a key. If a list, please specify data types for each curve in order. Note that the conversion currently only occurs via numpy.ndarray.astype() and therefore only a few simple casts will work e.g. int, float, str.
- encoding (str) – character encoding to open file_ref with, using
io.open()
(this is handled bylasio.reader.open_with_codecs()
) - encoding_errors (str) – ‘strict’, ‘replace’ (default), ‘ignore’ - how to
handle errors with encodings (see
this section
of the standard library’s
codecs
module for more information) (this is handled bylasio.reader.open_with_codecs()
) - autodetect_encoding (str or bool) – default True to use chardet to detect encoding.
Note if set to False several common encodings will be tried but
chardet won’t be used.
(this is handled by
lasio.reader.open_with_codecs()
) - autodetect_encoding_chars (int/None) – number of chars to read from LAS
file for auto-detection of encoding.
(this is handled by
lasio.reader.open_with_codecs()
)
The documented arguments above are combined from these methods:
lasio.reader.open_with_codecs()
- manage issues relate to character encodingslasio.LASFile.read()
- control how NULL values and errors are handled during parsing
- file_ref (file-like object or
-
LASFile.
read
(file_ref, ignore_header_errors=False, ignore_comments=('#', ), ignore_data_comments='#', mnemonic_case='upper', ignore_data=False, engine='numpy', use_normal_engine_for_wrapped=True, read_policy='default', null_policy='strict', accept_regexp_sub_recommendations=True, index_unit=None, dtypes='auto', **kwargs)[source]¶ Read a LAS file.
Parameters: file_ref (file-like object or
str
) – either a filename, an open file object, or a string containing the contents of a file.Keyword Arguments: - ignore_header_errors (bool) – ignore LASHeaderErrors (False by default)
- ignore_comments (sequence/str) – ignore lines beginning with these
characters e.g.
("#", '"')
in header sections. - ignore_data_comments (str) – ignore lines beginning with this character in data sections only.
- mnemonic_case (str) – ‘preserve’: keep the case of HeaderItem mnemonics ‘upper’: convert all HeaderItem mnemonics to uppercase ‘lower’: convert all HeaderItem mnemonics to lowercase
- ignore_data (bool) – if True, do not read in any of the actual data, just the header metadata. False by default.
- engine (str) – “normal”: parse data section with normal Python reader (quite slow); “numpy”: parse data section with numpy.genfromtxt (fast). By default the engine is “numpy”.
- use_normal_engine_for_wrapped (bool) – if header metadata indicates that the file is wrapped, always use the ‘normal’ engine. Default is True. The only reason you should use False is if speed is a very high priority and you had files with metadata that incorrectly indicates they are wrapped.
- read_policy (str or list) – Apply regular expression substitutions for common errors in
fixed-width formatted data sections. If you do not want any such substitutions
to applied, pass
read_policy=()
. - null_policy (str or list) – see https://lasio.readthedocs.io/en/latest/data-section.html#handling-invalid-data-indicators-automatically
- accept_regexp_sub_recommendations (bool) – Accept recommendations to auto- matically remove read substitutions (applied by the default read_policy) which look for numeric run-on errors involving hyphens. This avoids incorrect parsing of dates such as ‘2018-05-22’ as three separate columns containing ‘2018’, ‘-5’ and ‘-22’. The read substitutions are applied only if the inspection code of the data section finds a hyphen in every line. The only circumstance where this should be manually set to False is where you have very problematic fixed-column-width data sections involving negative values.
- index_unit (str) – Optionally force-set the index curve’s unit to “m” or “ft”
- dtypes ("auto", dict or list) – specify the data types for each curve in the ~ASCII data section. If “auto”, each curve will be converted to floats if possible and remain as str if not. If a dict you can specify only the curve mnemonics you want to convert as a key. If a list, please specify data types for each curve in order. Note that the conversion currently only occurs via numpy.ndarray.astype() and therefore only a few simple casts will work e.g. int, float, str.
- encoding (str) – character encoding to open file_ref with, using
io.open()
(this is handled bylasio.reader.open_with_codecs()
) - encoding_errors (str) – ‘strict’, ‘replace’ (default), ‘ignore’ - how to
handle errors with encodings (see
this section
of the standard library’s
codecs
module for more information) (this is handled bylasio.reader.open_with_codecs()
) - autodetect_encoding (str or bool) – default True to use chardet to detect encoding.
Note if set to False several common encodings will be tried but
chardet won’t be used.
(this is handled by
lasio.reader.open_with_codecs()
) - autodetect_encoding_chars (int/None) – number of chars to read from LAS
file for auto-detection of encoding.
(this is handled by
lasio.reader.open_with_codecs()
)
-
lasio.
open_file
(file_ref, **encoding_kwargs)[source]¶ Open a file if necessary.
If
autodetect_encoding=True
thenchardet
needs to be installed, or else anImportError
will be raised.Parameters: file_ref (file-like object, str) – either a filename, an open file object, or a string containing the contents of a file. See
lasio.reader.open_with_codecs()
for keyword arguments that can be used here.Returns: tuple of an open file-like object, and the encoding that was used to decode it (if it were read from disk).
-
lasio.reader.
open_with_codecs
(filename, encoding=None, encoding_errors='replace', autodetect_encoding=True, autodetect_encoding_chars=4000)[source]¶ Read Unicode data from file.
Parameters: filename (str) – path to file
Keyword Arguments: - encoding (str) – character encoding to open file_ref with, using
io.open()
. - encoding_errors (str) – ‘strict’, ‘replace’ (default), ‘ignore’ - how to
handle errors with encodings (see
this section
of the standard library’s
codecs
module for more information) - autodetect_encoding (str or bool) – default True to use chardet to detect encoding. Note if set to False several common encodings will be tried but chardet won’t be used.
- autodetect_encoding_chars (int/None) – number of chars to read from LAS file for auto-detection of encoding.
Returns: a unicode or string object
This function is called by
lasio.reader.open_file()
.- encoding (str) – character encoding to open file_ref with, using
-
lasio.reader.
get_encoding
(auto, raw)[source]¶ Automatically detect character encoding.
Parameters: Returns: A string specifying the character encoding.
-
LASFile.
match_raw_section
(pattern, re_func='match', flags=<RegexFlag.IGNORECASE: 2>)[source]¶ Find raw section with a regular expression.
Parameters: pattern (str) – regular expression (you need to include the tilde)
Keyword Arguments: - re_func (str) – either “match” or “search”, see python
re
module. - flags (int) – flags for
re.compile()
Returns: dict
Intended for internal use only.
- re_func (str) – either “match” or “search”, see python
-
lasio.reader.
read_data_section_iterative_normal_engine
(file_obj, line_nos, regexp_subs, value_null_subs, ignore_data_comments, n_columns, dtypes, line_splitter)[source]¶ Read data section into memory.
Parameters: - file_obj – file-like object open for reading at the beginning of the section
- line_nos (tuple) – the first and last line no of the section to read
- regexp_subs (list) – each item should be a tuple of the pattern and substitution string for a call to re.sub() on each line of the data section. See defaults.py READ_SUBS and NULL_SUBS for examples.
- value_null_subs (list) – list of numerical values to be replaced by numpy.nan values.
- ignore_data_comments (str) – lines beginning with this character will be ignored
- n_columns (int) – expected number of columns
- dtypes (list, "auto", False) – list of expected data types for each column, (each data type can be specified as e.g. int, float, str, datetime). If you specify ‘auto’, then this function will attempt to convert each column to a float and if that fails, the column will be returned as a string. If you specify False, no conversion of data types will be attempt at all.
- line_splitter (function) – This function is dynamically configured to split data lines on the configured delimiter
Returns: generator which yields the data as a 1D ndarray for each column at a time.
-
lasio.reader.
read_data_section_iterative_numpy_engine
(file_obj, line_nos)[source]¶ Read data section into memory.
Parameters: - file_obj – file-like object open for reading at the beginning of the section
- line_nos (tuple) – the first and last line no of the section to read
Returns: A numpy ndarray.
-
lasio.reader.
get_substitutions
(read_policy, null_policy)[source]¶ Parse read and null policy definitions into a list of regexp and value substitutions.
Parameters: - read_policy (str, list, or substitution) – either (1) a string defined in defaults.READ_POLICIES; (2) a list of substitutions as defined by the keys of defaults.READ_SUBS; or (3) a list of actual substitutions similar to the values of defaults.READ_SUBS. You can mix (2) and (3) together if you want.
- null_policy (str, list, or sub) – as for read_policy but for defaults.NULL_POLICIES and defaults.NULL_SUBS
Returns: regexp_subs, value_null_subs, version_NULL - two lists and a bool. The first list is pairs of regexp patterns and substrs, and the second list is just a list of floats or integers. The bool is whether or not ‘NULL’ was located as a substitution.
The default READ_POLICIES are
- comma-decimal-mark : in numbers replace a comma divider with a decimal
- run-on(-) : separate 2 numbers that run together on the negative sign
- run-on(.) : replace numbers with 2 or more decimals or a NaN and a decimal with 2 NaNs
-
class
lasio.reader.
SectionParser
(title, version=1.2)[source]¶ Parse lines from header sections.
Parameters: title (str) – title line of section. Used to understand different order formatting across the special sections ~C, ~P, ~W, and ~V, depending on version 1.2 or 2.0. Keyword Arguments: version (float) – version to parse according to. Default is 1.2.
-
lasio.reader.
read_header_line
(line, pattern=None, section_name=None)[source]¶ Read a line from a LAS header section.
The line is parsed with a regular expression – see LAS file specs for more details, but it should basically be in the format:
name.unit value : descr
Parameters: Returns: A dictionary with keys ‘name’, ‘unit’, ‘value’, and ‘descr’, each containing a string as value.
-
class
lasio.
HeaderItem
(mnemonic='', unit='', value='', descr='', data=None)[source]¶ Dictionary/namedtuple-style object for a LAS header line.
Parameters: These arguments are available for use as either items or attributes of the object.
-
HeaderItem.
set_session_mnemonic_only
(value)[source]¶ Set the mnemonic for session use.
See source comments for
lasio.HeaderItem.__init__
for a more in-depth explanation.
-
class
lasio.
CurveItem
(mnemonic='', unit='', value='', descr='', data=None)[source]¶ Dictionary/namedtuple-style object for a LAS curve.
See
lasio.HeaderItem`
for the (keyword) arguments.Keyword Arguments: data (array-like, 1-D) – the curve’s data.
-
class
lasio.
SectionItems
(*args, **kwargs)[source] Variant of a
list
which is used to represent a LAS section.
Reading data¶
-
LASFile.
__getitem__
(key)[source]¶ Provide access to curve data.
Parameters: key (str, int) – either a curve mnemonic or the column index. Returns: 1D numpy.ndarray
(the data for the curve)
-
LASFile.
__setitem__
(key, value)[source]¶ Append a curve.
Parameters: See
lasio.LASFile.append_curve_item()
orlasio.LASFile.append_curve()
for more details.
-
LASFile.
get_curve
(mnemonic)[source]¶ Return CurveItem object.
Parameters: mnemonic (str) – the name of the curve Returns: lasio.CurveItem
(not just the data array)
-
LASFile.
df
()[source]¶ Return data as a
pandas.DataFrame
structure.The first Curve of the LASFile object is used as the pandas DataFrame’s index.
-
LASFile.
version
¶ Header information from the Version (~V) section.
Returns: lasio.SectionItems
object.
-
LASFile.
well
¶ Header information from the Well (~W) section.
Returns: lasio.SectionItems
object.
-
LASFile.
curves
¶ Curve information and data from the Curves (~C) and data section..
Returns: lasio.SectionItems
object.
-
LASFile.
curvesdict
¶ Curve information and data from the Curves (~C) and data section..
Returns: dict
-
LASFile.
params
¶ Header information from the Parameter (~P) section.
Returns: lasio.SectionItems
object.
-
LASFile.
other
¶ Header information from the Other (~O) section.
Returns: str
-
LASFile.
index
¶ Return data from the first column of the LAS file data (depth/time).
-
LASFile.
depth_m
¶ Return the index as metres.
-
LASFile.
depth_ft
¶ Return the index as feet.
-
LASFile.
data
¶
Reading and modifying header data¶
-
class
lasio.
SectionItems
(*args, **kwargs)[source]¶ Variant of a
list
which is used to represent a LAS section.-
get
(mnemonic, default='', add=False)[source]¶ Get an item, with a default value for the situation when it is missing.
Parameters: - mnemonic (str) – mnemonic of item to retrieve
- default (str, HeaderItem, or CurveItem) – default to provide
if mnemonic is missing from the section. If a string is
provided, it will be used as the
value
attribute of a new HeaderItem or thedescr
attribute of a new CurveItem. - add (bool) – if True, the returned HeaderItem/CurveItem will also be appended to the SectionItems. By default this is not done.
Returns: item from the section, if it is in there, or a new item, if it is not. If a CurveItem is returned, the
data
attribute will containnumpy.nan
values.Return type:
-
set_item
(key, newitem)[source]¶ Replace an item by comparison of session mnemonics.
Parameters: - key (str) – the item mnemonic (or HeaderItem with mnemonic) you want to replace.
- newitem (HeaderItem) – the new item
If key is not present, it appends newitem.
-
Modifying data¶
-
LASFile.
set_data
(array_like, names=None, truncate=False)[source]¶ Set the data for the LAS; actually sets data on individual curves.
Parameters: array_like (array_like or
pandas.DataFrame
) – 2-D data arrayKeyword Arguments: - names (list, optional) – used to replace the names of the existing
lasio.CurveItem
objects. - truncate (bool) – remove any columns which are not included in the Curves (~C) section.
Note: you can pass a
pandas.DataFrame
to this method. If you do this, the index of the DataFrame will be used as the first curve in the LAS file (i.e. it will not be discarded).- names (list, optional) – used to replace the names of the existing
-
LASFile.
set_data_from_df
(df, **kwargs)[source]¶ Set the LAS file data from a
pandas.DataFrame
.Parameters: df (pandas.DataFrame) – curve mnemonics are the column names. The depth column for the curves must be the index of the DataFrame. Keyword arguments are passed to
lasio.LASFile.set_data()
.
-
LASFile.
append_curve
(mnemonic, data, unit='', descr='', value='')[source]¶ Add a curve.
Parameters: - mnemonic (str) – the curve mnemonic
- data (1D ndarray) – the curve data
Keyword Arguments:
-
LASFile.
insert_curve
(ix, mnemonic, data, unit='', descr='', value='')[source]¶ Insert a curve.
Parameters: Keyword Arguments:
-
LASFile.
delete_curve
(mnemonic=None, ix=None)[source]¶ Delete a curve.
Keyword Arguments: The index takes precedence over the mnemonic.
-
LASFile.
append_curve_item
(curve_item)[source]¶ Add a CurveItem.
Parameters: curve_item (lasio.CurveItem) –
-
LASFile.
insert_curve_item
(ix, curve_item)[source]¶ Insert a CurveItem.
Parameters: - ix (int) – position to insert CurveItem i.e. 0 for start
- curve_item (lasio.CurveItem) –
-
LASFile.
update_start_stop_step
(STRT=None, STOP=None, STEP=None, fmt='%.5f')[source]¶ Configure or change STRT, STOP, and STEP values on the LASFile object.
Keyword Arguments: - STOP, STEP (STRT,) – value to set on the relevant header item in the ~Well section - can be any data type.
- fmt (str) – Python format string for formatting the STRT/STOP/STEP value in the situation where any of those keyword arguments are None
If STRT/STOP/STEP are not passed to this method, they will be automatically calculated from the index curve.
Writing data out¶
-
LASFile.
write
(file_ref, **kwargs)[source]¶ Write LAS file to disk.
Parameters: file_ref (open file-like object or str
) – either a file-like object open for writing, or a filename.All
**kwargs
are passed tolasio.writer.write()
– please check the docstring of that function for more keyword arguments you can use here!Examples
>>> import lasio >>> las = lasio.read("tests/examples/sample.las") >>> with open('test_output.las', mode='w') as f: ... las.write(f, version=2.0) # <-- this method
-
lasio.writer.
write
(las, file_object, version=None, wrap=None, STRT=None, STOP=None, STEP=None, fmt='%.5f', column_fmt=None, len_numeric_field=None, lhs_spacer=' ', spacer=' ', data_width=79, header_width=60, data_section_header='~ASCII', mnemonics_header=False)[source]¶ Write a LAS files.
Parameters: - las (
lasio.LASFile
) – - file_object (file-like object open for writing) – output
- version (float or None) – version of written file, either 1.2 or 2.
If this is None,
las.version.VERS.value
will be used. - wrap (bool or None) – whether to wrap the output data section.
If this is None,
las.version.WRAP.value
will be used. - STRT (float or None) – value to use as STRT (note the data will not be clipped). If this is None, the data value in the first column, first row will be used.
- STOP (float or None) – value to use as STOP (note the data will not be clipped). If this is None, the data value in the first column, last row will be used.
- STEP (float or None) – value to use as STEP (note the data will not be resampled and/or interpolated). If this is None, the STEP will be estimated from the first two rows of the first column.
- fmt (str) – Python string formatting operator for numeric data to be used.
- column_fmt (dict or None) – use this to set a different format string
for specific columns from the data ndarray. E.g. to use
'%.3f'
for the depth column and'%.2f'
for all the other columns, you would usefmt='%.2f', column_fmt={0: '%.3f'}
. - len_numeric_field (int) – width of each numeric field column (must be greater than than all the formatted numeric values in the file). If it is None, the maximum necessary value will be used automatically (i.e. all columns will have the same width). If it is -1, then the columns will not have consistent widths. You can combine -1 with the fmt keyword argument to control column widths closely.
- data_width (79) – width of data field in characters
- lhs_spacer (str) – string which goes on left hand side of data section - by default it is ” “.
- spacer (str) – string which goes between each column of the data section
- data_section_header (str) – default “~ASCII”
- mnemonics_header (bool) – include mnemonic curve names in the data_section_header at the top of data section
Creating an output file is not the only side-effect of this function. It will also modify the STRT, STOP and STEP HeaderItems so that they correctly reflect the ~Data section’s units and the actual first, last, and interval values.
However, passing a version to this write() function only changes the version of the object written to. Example: las.write(myfile, version=2). Lasio’s internal-las-object version will remain separate and defined by las.version.VERS.value
You should avoid calling this function directly - instead use the
lasio.LASFile.write()
method.- las (
-
lasio.writer.
get_formatter_function
(order, left_width=None, middle_width=None)[source]¶ Create function to format a LAS header item for output.
Parameters: order – format of item, either ‘descr:value’ or ‘value:descr’
Keyword Arguments: Returns: A function which takes a header item (e.g.
lasio.HeaderItem
) as its single argument and which in turn returns a string which is the correctly formatted LAS header line.
-
lasio.writer.
get_section_order_function
(section, version, order_definitions={1.2: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['descr:value', ('value:descr', ['STRT', 'STOP', 'STEP', 'NULL', 'strt', 'stop', 'step', 'null'])]}, 2.0: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['value:descr']}, 2.1: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['value:descr']}, 3.0: {'Curves': ['value:descr'], 'Parameter': ['value:descr'], 'Version': ['value:descr'], 'Well': ['value:descr']}})[source]¶ Get a function that returns the order per the mnemonic and section.
Parameters: Keyword Arguments: order_definitions (dict) – see source of defaults.py for more information
Returns: A function which takes a mnemonic (str) as its only argument, and in turn returns the order ‘value:descr’ or ‘descr:value’.
-
lasio.writer.
get_section_widths
(section_name, items, version, order_func)[source]¶ Find minimum section widths fitting the content in items.
Parameters: - section_name (str) – either ‘version’, ‘well’, ‘curves’, or ‘params’
- items (SectionItems) – section items
- version (float) – either 1.2 or 2.0
- order_func (func) – see
lasio.writer.get_section_order_function()
-
LASFile.
to_csv
(file_ref, mnemonics=True, units=True, units_loc='line', **kwargs)[source]¶ Export to a CSV file.
Parameters: file_ref (open file-like object or
str
) – either a file-like object open for writing, or a filename.Keyword Arguments: - mnemonics (list, True, False) – write mnemonics as a header line at the start. If list, use the supplied items as mnemonics. If True, use the curve mnemonics.
- units (list, True, False) – as for mnemonics.
- units_loc (str or None) – either ‘line’, ‘[]’ or ‘()’. ‘line’ will put units on the line following the mnemonics (good for WellCAD). ‘[]’ and ‘()’ will put the units in either brackets or parentheses following the mnemonics, on the single header line (better for Excel)
- **kwargs – passed to
csv.writer
. Note that iflineterminator
is not specified here, then it will be sent tocsv.writer
aslineterminator='\\n'
.
Defaults¶
Custom exceptions¶
Test data¶
-
lasio.examples.
open
(filename, **kwargs)[source]¶ Open an example LAS file from lasio’s test suite.
Parameters: filename (str) – forward-slash separated filename of a LAS file from lasio’s test suite, starting from the “tests/examples” subfolder e.g. “1001178549.las” or “2.0/sample_2.0.las” Other keyword arguments are passed to lasio.LASFile. If lasio has been installed locally from source, then the local version of the example file will be opened. If lasio has not been installed from source then the LAS file will be downloaded from GitHub.
Returns: LASFile object
-
lasio.examples.
open_github_example
(filename, url_prefix='https://raw.githubusercontent.com/kinverarity1/lasio/master/tests/examples/', **kwargs)[source]¶ Open an example LAS file from lasio’s test suite on GitHub
Parameters: filename (str) – forward-slash separated filename of a LAS file from lasio’s test suite, starting from the “tests/examples” subfolder e.g. “1001178549.las” or “2.0/sample_2.0.las” Other keyword arguments are passed to lasio.LASFile.
Returns: LASFile object
-
lasio.examples.
open_local_example
(filename, **kwargs)[source]¶ Open an example LAS file from lasio’s test suite.
Parameters: filename (str) – forward-slash separated filename of a LAS file from lasio’s test suite, starting from the “tests/examples” subfolder e.g. “1001178549.las” or “2.0/sample_2.0.las” Other keyword arguments are passed to lasio.LASFile. If lasio has not been installed from source then an exception will be raised.
Returns: LASFile object
Logging¶
-
lasio.
add_logging_level
(levelName, levelNum, methodName=None)[source]¶ Add a new logging level to current logger.
Comprehensively adds a new logging level to the logging module and the currently configured logging class.
levelName becomes an attribute of the logging module with the value levelNum. methodName becomes a convenience method for both logging itself and the class returned by logging.getLoggerClass() (usually just logging.Logger). If methodName is not specified, levelName.lower() is used.
To avoid accidental clobberings of existing attributes, this method will raise an AttributeError if the level name is already an attribute of the logging module or if the method name is already present
Example
>>> add_logging_level('TRACE', logging.DEBUG - 5) >>> logging.getLogger(__name__).setLevel("TRACE") >>> logging.getLogger(__name__).trace('that worked') >>> logging.trace('so did this') >>> logging.TRACE 5