Misc. Utilities

API

Utilities used within urbansim that don’t yet have a better home.

urbansim.utils.misc.charts_dir()

Return the directory for the chart configuration files (used by the website).

urbansim.utils.misc.column_list(tables, columns)

Take a list of tables and a list of column names and return the columns that are present in the tables.

Parameters:

tables : sequence of _DataFrameWrapper or _TableFuncWrapper

Could also be sequence of modified pandas.DataFrames, the important thing is that they have .name and .columns attributes.

columns : sequence of str

The column names of interest.

Returns:

cols : list

Lists of column names available in the tables.

urbansim.utils.misc.column_map(tables, columns)

Take a list of tables and a list of column names and resolve which columns come from which table.

Parameters:

tables : sequence of _DataFrameWrapper or _TableFuncWrapper

Could also be sequence of modified pandas.DataFrames, the important thing is that they have .name and .columns attributes.

columns : sequence of str

The column names of interest.

Returns:

col_map : dict

Maps table names to lists of column names.

urbansim.utils.misc.compute_range(travel_data, attr, travel_time_attr, dist, agg=<function sum>)

Compute a zone-based accessibility query using the urbansim format travel data dataframe.

Parameters:

travel_data : dataframe

The dataframe of urbansim format travel data. Has from_zone_id as first index, to_zone_id as second index, and different impedances between zones as columns.

attr : series

The attr to aggregate. Should be indexed by zone_id and the values will be aggregated.

travel_time_attr : string

The column name in travel_data to use as the impedance.

dist : float

The max distance to aggregate up to

agg : function, optional, np.sum by default

The numpy function to use for aggregation

urbansim.utils.misc.config(fname)

Return the config path for the file with the given filename.

urbansim.utils.misc.configs_dir()

Return the directory for the model configuration files.

urbansim.utils.misc.data_dir()

Return the directory for the input data.

urbansim.utils.misc.df64bitto32bit(tbl)

Convert a Pandas dataframe from 64 bit types to 32 bit types to save memory or disk space.

Parameters:tbl : The dataframe to convert Returns:The converted dataframe

urbansim.utils.misc.edits_dir()

Return the directory for the editable files (used by the website).

urbansim.utils.misc.get_run_number()

Get a run number for this execution of the model system, for identifying the output hdf5 files).

Returns:The integer number for this run of the model system.

urbansim.utils.misc.maps_dir()

Return the directory for the map configuration files (used by the website).

urbansim.utils.misc.models_dir()

Return the directory for the model configuration files (used by the website).

urbansim.utils.misc.naicsname(val)

This function maps NAICS (job codes) from number to name.

urbansim.utils.misc.numpymat2df(mat)

Sometimes (though not very often) it is useful to convert a numpy matrix which has no column names to a Pandas dataframe for use of the Pandas functions. This method converts a 2D numpy matrix to Pandas dataframe with default column headers.

Parameters:

mat : The numpy matrix

Returns:

A pandas dataframe with the same data as the input matrix but with columns

named x0, x1, ... x[n-1] for the number of columns.

urbansim.utils.misc.pandasdfsummarytojson(df, ndigits=3)

Convert the result of a

Parameters:

df : The result of a Pandas describe operation.

ndigits : int, optional - The number of significant digits to round to.

Returns:

A json object which captures the describe. Keys are field names and

values are dictionaries with all of the indexes returned by the Pandas

describe.

urbansim.utils.misc.reindex(series1, series2)

This reindexes the first series by the second series. This is an extremely common operation that does not appear to be in Pandas at this time. If anyone knows of an easier way to do this in Pandas, please inform the UrbanSim developers.

The canonical example would be a parcel series which has an index which is parcel_ids and a value which you want to fetch, let’s say it’s land_area. Another dataset, let’s say of buildings has a series which indicate the parcel_ids that the buildings are located on, but which does not have land_area. If you pass parcels.land_area as the first series and buildings.parcel_id as the second series, this function returns a series which is indexed by buildings and has land_area as values and can be added to the buildings dataset.

In short, this is a join on to a different table using a foreign key stored in the current table, but with only one attribute rather than for a full dataset.

This is very similar to the pandas “loc” function or “reindex” function, but neither of those functions return the series indexed on the current table. In both of those cases, the series would be indexed on the foreign table and would require a second step to change the index.

urbansim.utils.misc.reports_dir()

Return the directory for the report configuration files (used by the website).

urbansim.utils.misc.runs_dir()

Return the directory for the run output.

urbansim.utils.misc.series64bitto32bit(s)

Convert a Pandas series from 64 bit types to 32 bit types to save memory or disk space.

Parameters:s : The series to convert Returns:The converted series

urbansim.utils.misc.signif(val)

Convert a statistical significance to its ascii representation - this should be the same representation created in R.

urbansim.utils.misc.simulations_dir()

Return the directory for the simulation configuration files (used by the website).