natcap.invest.utils

InVEST specific code utils.

class natcap.invest.utils.ThreadFilter(thread_name)

Bases: Filter

Filters out log messages issued by the given thread.

Any log messages generated by a thread with the name matching the threadname provided to the constructor will be excluded.

filter(record)

Filter the given log record.

Parameters

record (log record) – The log record to filter.

Returns

True if the record should be included, false if not.

natcap.invest.utils.array_equals_nodata(array, nodata)

Check for the presence of nodata values in array.

The comparison supports numpy.nan nodata values.

Parameters
  • array (numpy array) – the array to mask for nodata values.

  • nodata (number) – the nodata value to check for. Supports numpy.nan.

Returns

A boolean numpy array with values of 1 where array is equal to nodata and 0 otherwise.

natcap.invest.utils.build_file_registry(base_file_path_list, file_suffix)

Combine file suffixes with key names, base filenames, and directories.

Parameters
  • base_file_tuple_list (list) – a list of (dict, path) tuples where the dictionaries have a ‘file_key’: ‘basefilename’ pair, or ‘file_key’: list of ‘basefilename’s. ‘path’ indicates the file directory path to prepend to the basefile name.

  • file_suffix (string) – a string to append to every filename, can be empty string

Returns

dictionary of ‘file_keys’ from the dictionaries in base_file_tuple_list mapping to full file paths with suffixes or lists of file paths with suffixes depending on the original type of the ‘basefilename’ pair.

Raises
  • ValueError if there are duplicate file keys or duplicate file paths.

  • ValueError if a path is not a string or a list of strings.

natcap.invest.utils.build_lookup_from_csv(table_path, key_field, column_list=None, to_lower=True)

Read a CSV table into a dictionary indexed by key_field.

Creates a dictionary from a CSV whose keys are unique entries in the CSV table under the column named by key_field and values are dictionaries indexed by the other columns in table_path including key_field whose values are the values on that row of the CSV table.

If an entire row is NA/NaN (including key_field) then it is dropped from the table and a warning is given of the dropped rows.

Parameters
  • table_path (string) – path to a CSV file containing at least the header key_field

  • key_field – (string): a column in the CSV file at table_path that can uniquely identify each row in the table and sets the row index.

  • column_list (list) – a list of column names to subset from the CSV file, default=None

  • to_lower (bool) – if True, converts all unicode in the CSV, including headers and values to lowercase, otherwise uses raw string values. default=True.

Returns

a dictionary of the form {key_field_0: {csv_header_0: value0, csv_header_1: value1…}, key_field_1: {csv_header_0: valuea, csv_header_1: valueb…}}

if to_lower all strings including key_fields and values are converted to lowercase unicode.

Return type

lookup_dict (dict)

Raises
  • ValueError – If ValueError occurs during conversion to dictionary.

  • KeyError – If key_field is not present during set_index call.

natcap.invest.utils.capture_gdal_logging()

Context manager for logging GDAL errors with python logging.

GDAL error messages are logged via python’s logging system, at a severity that corresponds to a log level in logging. Error messages are logged with the osgeo.gdal logger.

Parameters

None

Returns

None

natcap.invest.utils.create_coordinate_transformer(base_ref, target_ref, osr_axis_mapping_strategy=0)

Create a spatial reference coordinate transformation function.

Parameters
  • base_ref (osr spatial reference) – A defined spatial reference to transform FROM

  • target_ref (osr spatial reference) – A defined spatial reference to transform TO

  • osr_axis_mapping_strategy (int) – OSR axis mapping strategy for SpatialReference objects. Defaults to utils.DEFAULT_OSR_AXIS_MAPPING_STRATEGY. This parameter should not be changed unless you know what you are doing.

Returns

An OSR Coordinate Transformation object

natcap.invest.utils.exponential_decay_kernel_raster(expected_distance, kernel_filepath)

Create a raster-based exponential decay kernel.

The raster created will be a tiled GeoTiff, with 256x256 memory blocks.

Parameters
  • expected_distance (int or float) – The distance (in pixels) of the kernel’s radius, the distance at which the value of the decay function is equal to 1/e.

  • kernel_filepath (string) – The path to the file on disk where this kernel should be stored. If this file exists, it will be overwritten.

Returns

None

natcap.invest.utils.has_utf8_bom(textfile_path)

Determine if the text file has a UTF-8 byte-order marker.

Parameters

textfile_path (str) – The path to a file on disk.

Returns

A bool indicating whether the textfile has a BOM. If True, a BOM is present.

natcap.invest.utils.log_to_file(logfile, exclude_threads=None, logging_level=0, log_fmt='%(asctime)s (%(name)s) %(module)s.%(funcName)s(%(lineno)d) %(levelname)s %(message)s', date_fmt=None)

Log all messages within this context to a file.

Parameters
  • logfile (string) – The path to where the logfile will be written. If there is already a file at this location, it will be overwritten.

  • exclude_threads=None (list) – If None, logging from all threads will be included in the log. If a list, it must be a list of string thread names that should be excluded from logging in this file.

  • logging_level=logging.NOTSET (int) – The logging threshold. Log messages with a level less than this will be automatically excluded from the logfile. The default value (logging.NOTSET) will cause all logging to be captured.

  • log_fmt=LOG_FMT (string) – The logging format string to use. If not provided, utils.LOG_FMT will be used.

  • date_fmt (string) – The logging date format string to use. If not provided, ISO8601 format will be used.

Yields

handler

An instance of logging.FileHandler that

represents the file that is being written to.

Returns

None

natcap.invest.utils.make_directories(directory_list)

Create directories in directory_list if they do not already exist.

natcap.invest.utils.make_suffix_string(args, suffix_key)

Make an InVEST appropriate suffix string.

Creates an InVEST appropriate suffix string given the args dictionary and suffix key. In general, prepends an ‘_’ when necessary and generates an empty string when necessary.

Parameters
  • args (dict) – the classic InVEST model parameter dictionary that is passed to execute.

  • suffix_key (string) – the key used to index the base suffix.

Returns

If suffix_key is not in args, or args[‘suffix_key’] is “”

return “”,

If args[‘suffix_key’] starts with ‘_’ return args[‘suffix_key’]

else return ‘_’+`args[‘suffix_key’]`

natcap.invest.utils.matches_format_string(test_string, format_string)

Assert that a given string matches a given format string.

This means that the given test string could be derived from the given format string by replacing replacement fields with any text. For example, the string ‘Value “foo” is invalid.’ matches the format string ‘Value “{value}” is invalid.’

Parameters
  • test_string (str) – string to test.

  • format_string (str) – format string, which may contain curly-brace delimited replacement fields

Returns

True if test_string matches format_string, False if not.

natcap.invest.utils.mean_pixel_size_and_area(pixel_size_tuple)

Convert to mean and raise Exception if they are not close.

Parameter:
pixel_size_tuple (tuple): a 2 tuple indicating the x/y size of a

pixel.

Returns

tuple of (mean absolute average of pixel_size, area of pixel size)

Raises

ValueError if the dimensions of pixel_size_tuple are not almost – square.

natcap.invest.utils.prepare_workspace(workspace, name, logging_level=0, exclude_threads=None)

Prepare the workspace.

natcap.invest.utils.read_csv_to_dataframe(path, to_lower=False, sep=None, encoding=None, engine='python', **kwargs)

Return a dataframe representation of the CSV.

Wrapper around pandas.read_csv that standardizes the column names by stripping leading/trailing whitespace and optionally making all lowercase. This helps avoid common errors caused by user-supplied CSV files with column names that don’t exactly match the specification. Strips leading/trailing whitespace from data entries as well.

Parameters
  • path (string) – path to a CSV file

  • to_lower (bool) – if True, convert all column names to lowercase

  • sep – separator to pass to pandas.read_csv. Defaults to None, which lets the Python engine infer the separator (if engine=’python’).

  • encoding (string) – name of encoding codec to pass to pandas.read_csv. Defaults to None. Setting engine=’python’ when encoding=None allows a lot of non-UTF8 encodings to be read without raising an error. Any special characters in other encodings may get replaced with the replacement character. If encoding=None, and the file begins with a BOM, the encoding gets set to ‘utf-8-sig’; otherwise the BOM causes an error.

  • engine (string) – kwarg for pandas.read_csv: ‘c’, ‘python’, or None. Defaults to ‘python’ (see note about encoding).

  • **kwargs – any kwargs that are valid for pandas.read_csv

Returns

pandas.DataFrame with the contents of the given CSV

natcap.invest.utils.reclassify_raster(raster_path_band, value_map, target_raster_path, target_datatype, target_nodata, error_details)

A wrapper function for calling pygeoprocessing.reclassify_raster.

This wrapper function is helpful when added as a TaskGraph.task so a better error message can be provided to the users if a pygeoprocessing.ReclassificationMissingValuesError is raised.

Parameters
  • raster_path_band (tuple) – a tuple including file path to a raster and the band index to operate over. ex: (path, band_index)

  • value_map (dictionary) – a dictionary of values of {source_value: dest_value, …} where source_value’s type is the same as the values in base_raster_path at band band_index. Must contain at least one value.

  • target_raster_path (string) – target raster output path; overwritten if it exists

  • target_datatype (gdal type) – the numerical type for the target raster

  • target_nodata (numerical type) – the nodata value for the target raster Must be the same type as target_datatype

  • error_details (dict) –

    a dictionary with key value pairs that provide more context for a raised pygeoprocessing.ReclassificationMissingValuesError. keys must be {‘raster_name’, ‘column_name’, ‘table_name’}. Values each key represent:

    ’raster_name’ - string for the raster name being reclassified ‘column_name’ - name of the table column that value_map dictionary keys came from. ‘table_name’ - table name that value_map came from.

Returns

None

Raises
  • ValueError if values_required is True and a pixel value from

  • raster_path_band` is not a key in value_map

natcap.invest.utils.sandbox_tempdir(suffix='', prefix='tmp', dir=None)

Create a temporary directory for this context and clean it up on exit.

Parameters are identical to those for tempfile.mkdtemp().

When the context manager exits, the created temporary directory is recursively removed.

Parameters
  • suffix='' (string) – a suffix for the name of the directory.

  • prefix='tmp' (string) – the prefix to use for the directory name.

  • dir=None (string or None) – If a string, a directory that should be the parent directory of the new temporary directory. If None, tempfile will determine the appropriate tempdir to use as the parent folder.

Yields

sandbox (string) – The path to the new folder on disk.

Returns

None