From 6b4db6638d35ef9e6998d4ac9e56f9c511a397ff Mon Sep 17 00:00:00 2001 From: Brian Cheung Date: Fri, 11 Aug 2023 12:16:59 -0400 Subject: [PATCH] Update apidocs --- apidoc/routines/io.rst | 4 +- docs/.buildinfo | 2 +- docs/_sources/routines/io.rst.txt | 4 +- docs/_static/basic.css | 18 ++++++ docs/_static/documentation_options.js | 2 +- docs/genindex.html | 22 ++++--- docs/index.html | 14 ++--- docs/logging/index.html | 17 +++--- docs/objects.inv | Bin 1043 -> 1066 bytes docs/py-modindex.html | 12 ++-- docs/routines/best_intermediates.html | 20 +++---- docs/routines/general.html | 12 ++-- docs/routines/index.html | 14 ++--- docs/routines/io.html | 80 +++++++++++++++++--------- docs/routines/matrices.html | 48 ++++++++-------- docs/routines/modelling.html | 16 +++--- docs/routines/plotting.html | 18 +++--- docs/search.html | 12 ++-- docs/searchindex.js | 2 +- 19 files changed, 185 insertions(+), 132 deletions(-) diff --git a/apidoc/routines/io.rst b/apidoc/routines/io.rst index f20ea8f..9e610a3 100644 --- a/apidoc/routines/io.rst +++ b/apidoc/routines/io.rst @@ -2,7 +2,7 @@ IO Utilities ============ -Routines for reading and writing demand modelling files, particularly matrices and TMG Toolbox network packages. +Routines for reading and writing demand modelling files, particularly matrices and TMGToolbox network packages. Matrices -------- @@ -38,5 +38,7 @@ Fortran format Network Packages (NWP) ---------------------- +For more information on the TMGToolbox Network Package format, please visit https://tmg.utoronto.ca/doc/1.6/tmgtoolbox/input_output/ExportNetworkPackage.html + .. automodule:: balsa.routines.io.nwp :members: diff --git a/docs/.buildinfo b/docs/.buildinfo index 29ea089..1ea85a3 100644 --- a/docs/.buildinfo +++ b/docs/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: e9c765a95887a75c9a2e673fd39b858c +config: 2d7548e41818fc7ff042dc792b902925 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_sources/routines/io.rst.txt b/docs/_sources/routines/io.rst.txt index f20ea8f..9e610a3 100644 --- a/docs/_sources/routines/io.rst.txt +++ b/docs/_sources/routines/io.rst.txt @@ -2,7 +2,7 @@ IO Utilities ============ -Routines for reading and writing demand modelling files, particularly matrices and TMG Toolbox network packages. +Routines for reading and writing demand modelling files, particularly matrices and TMGToolbox network packages. Matrices -------- @@ -38,5 +38,7 @@ Fortran format Network Packages (NWP) ---------------------- +For more information on the TMGToolbox Network Package format, please visit https://tmg.utoronto.ca/doc/1.6/tmgtoolbox/input_output/ExportNetworkPackage.html + .. automodule:: balsa.routines.io.nwp :members: diff --git a/docs/_static/basic.css b/docs/_static/basic.css index 6157296..b97662d 100644 --- a/docs/_static/basic.css +++ b/docs/_static/basic.css @@ -670,6 +670,16 @@ dd { margin-left: 30px; } +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + dl > dd:last-child, dl > dd:last-child > :last-child { margin-bottom: 0; @@ -738,6 +748,14 @@ abbr, acronym { cursor: help; } +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + /* -- code displays --------------------------------------------------------- */ pre { diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index 4ecdb06..b9b6d11 100644 --- a/docs/_static/documentation_options.js +++ b/docs/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '1.4.0', + VERSION: '1.4.2', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/genindex.html b/docs/genindex.html index 792498b..54709ff 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -7,7 +7,7 @@ - Index — wsp-balsa 1.4.0 documentation + Index — wsp-balsa 1.4.2 documentation @@ -27,15 +27,15 @@ - + - - - + + + @@ -558,10 +558,12 @@

O

P

@@ -586,10 +588,10 @@

R

  • read_nwp_node_attributes() (in module balsa.routines.io.nwp)
    • - - +
    • read_nwp_traffic_results_at_countpost() (in module balsa.routines.io.nwp)
    • read_nwp_transit_line_attributes() (in module balsa.routines.io.nwp) @@ -601,6 +603,8 @@

      R

    • read_nwp_transit_segment_results() (in module balsa.routines.io.nwp)
    • read_nwp_transit_station_results() (in module balsa.routines.io.nwp) +
      • +
    • read_nwp_transit_vehicles() (in module balsa.routines.io.nwp)
    • read_omx() (in module balsa.routines.io.omx)
      • @@ -701,7 +705,7 @@

      T

      - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

      diff --git a/docs/index.html b/docs/index.html index 238b55d..f846ab6 100644 --- a/docs/index.html +++ b/docs/index.html @@ -10,7 +10,7 @@ - wsp-balsa: Common modelling tools — wsp-balsa 1.4.0 documentation + wsp-balsa: Common modelling tools — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -332,7 +332,7 @@

      wsp-balsa: Common modelling toolsIO Utilities
    • Modelling Utilities
        @@ -455,7 +455,7 @@

        wsp-balsa: Common modelling tools

        - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

        diff --git a/docs/logging/index.html b/docs/logging/index.html index 567ed60..35b7389 100644 --- a/docs/logging/index.html +++ b/docs/logging/index.html @@ -10,7 +10,7 @@ - Logging — wsp-balsa 1.4.0 documentation + Logging — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -343,9 +343,8 @@

        Logging
        -class balsa.logging.LogFormats(value)#
        -

        An enumeration.

        -
        +class balsa.logging.LogFormats(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)# +
        @@ -502,7 +501,7 @@

        Logging

        - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

        diff --git a/docs/objects.inv b/docs/objects.inv index 6962298040e69454c8fce7cd1b31fbd76f336d54..ca766f67f1afb480f531168b6dc0272dff109656 100644 GIT binary patch delta 959 zcmV;w13>(f2&xE>K>;$6LOg%Xj@vj4fbV$<0o`ldPLFe4U>5~C*g@Mp3yMT5LiFQG zawplRU)r*rc;eWJ?Vgf|)1Ix6NQdvog)3b4+)IdIwX6&P~0*=`}8vP;3 zwttZAW_M|qeRc9L=M9UYLU7GOAWuVtYtt7eK%>0`zr;54P&gm!6+nO7J28h5%3a|d zr{MiMAOj&V3Cmzn6^{cXL~1Gs)m?1!((oUfq9ouZJDssX&zg8&$5ZlJa3#z!Oic;-F89QKg|GrvZMJU^&di zDVK3)3>pmvAH@@K?*RA#o&&|A>eca>4NSpk1=pho2LJxFl@b6kp8 z<^=|GnGXnCXf%>q9;ikUmciN1WL0y4tp$d}00oOw7g>eD0yb8q zr^)qJ;TO7N=&*(O=KNs=92{AU)yhL$frbG*Py~9lE8{B5wb(1IOT>ep=Za%_3Z;&} zfr#!>?n)aAw@QCdt4bBHx-Am-=q^?i? z1$)Ky+#PeNyv{6gXq_B#bE|Ykz#=`*zV4Fvtvf<1omxky$E5TitDbzk{py3qh(d++ zp*ah6KJmj!tkSMTODn`b5zQ>e%N~6azXAGrMQF(nP~CraODmd}kY(IZXqE1dcxzj{ z21XKt`R5`J6(#7;na`l}?4M)8o$m z0xzrEA>FxSHGa#tK6?zR9dG)!@qe)UiP4v)z)Tj3NzcsvKwMewy^3o{}kh(KI h1nT8Dd2LnCm;lW4eLp+WSTgyZ;w4LOg##kJ~B`fbaPgNPX9&-FMt;rB;=?>gv7rj0hgHD8RUY zyGeh2$H8{ujbkUadrAT`AHy(AX4nc7#Kxdf+9_5X9xZ020rr8_WAC-gV83~Z*1St| z>wnmKwLRC{d`0yMy%jN32x&wJ?4gfvsrrN&7}8hZ=jd)c6zJn|2M~W>Fq^^%<+kuB zIe5Pe$U+E0!8};h#r*&Uk=qKwP3zmdbTVpmw=%TTk$c2AMVUT%M4oE{_o#a;qjnHB zTL_VB7r|S|w1^No(b>qozm&TYs{`cAAx$}l6QgS*jFXp(W zCQz~$8LxkE4>Ag;W?I!{hM%SIGl5n1JMH*>j&>er_^MLh6aaraZnJ#41<5a9_j3{R ztRQF3vw;AUEpV^JjDsNUO;rt0EkJv)z`-f*FpA0u$~Z%5b=0opTl&JcA^>*~U(xRt zz`#uEY%guo?ar?H+2>nQ}UXaLP$H=g;R52MW7To zP5)Sf$ZHjNGds4AR!%78K~)v+aB6DX|XR{+}&xiOzhIX_EUNz2b z9((*?-@Psn&Du`@O7*TMR%zxA!v z{?YcScJdf|N_n6U(uPDYrE{~{Q!Cs - Python Module Index — wsp-balsa 1.4.0 documentation + Python Module Index — wsp-balsa 1.4.2 documentation @@ -27,15 +27,15 @@ - + - - - + + + @@ -432,7 +432,7 @@

        Python Module Index

        - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

        diff --git a/docs/routines/best_intermediates.html b/docs/routines/best_intermediates.html index 935bec0..de2d031 100644 --- a/docs/routines/best_intermediates.html +++ b/docs/routines/best_intermediates.html @@ -10,7 +10,7 @@ - Triple-Index Utilities — wsp-balsa 1.4.0 documentation + Triple-Index Utilities — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -362,7 +362,7 @@

        Triple-Index Utilities#

        -balsa.routines.best_intermediates.best_intermediate_subset_zones(pk_subset_cost: Series, pk_table: DataFrame, kq_table: DataFrame, cost_col: str, *, n_subset: int = 1, n_final: int = 1, add_pkq_cost: ndarray | None = None, flag_array: ndarray | None = None, maximize_subset: bool = True, maximize_final: bool = True, null_index: int = 0, other_columns: bool = True, intermediate_name: str = 'intermediate_zone', availability_column: str = 'available', n_threads: int = 1, squeeze: bool = True) → DataFrame | Dict[int, DataFrame]#
        +balsa.routines.best_intermediates.best_intermediate_subset_zones(pk_subset_cost: Series, pk_table: DataFrame, kq_table: DataFrame, cost_col: str, *, n_subset: int = 1, n_final: int = 1, add_pkq_cost: ndarray[Any, dtype[ScalarType]] = None, flag_array: ndarray[Any, dtype[ScalarType]] = None, maximize_subset: bool = True, maximize_final: bool = True, null_index: int = 0, other_columns: bool = True, intermediate_name: str = 'intermediate_zone', availability_name: str = 'available', n_threads: int = 1, squeeze: bool = True) → DataFrame | Dict[int, DataFrame]#

        Numba-accelerated.

        Triple-index operation for two matrices, finding the most- or least-cost intermediate zones from a subset. Takes a first leg matrix (“pk”) and a second leg matrix (“kq”) to produce a combined “pq” matrix with the best @@ -406,7 +406,7 @@ in the “pk” and “kq” tables. The result table will be of the shape ((p, q), A | E + 3)

      • intermediate_name (str, optional) – Defaults to 'intermediate_zone'. Name of the column in the result table containing the selected intermediate zone.

        • -

      • availability_column (str, optional) – Defaults to 'available'. Name of the column in the result table +

      • availability_name (str, optional) – Defaults to 'available'. Name of the column in the result table containing a flag whether ANY intermediate zone was found to be available.

      • n_threads (int, optional) – Defaults to 1. Number of threads to use.

      • squeeze (bool, optional) – Defaults to True. If n_final == 1 and squeeze=True, a single DataFrame @@ -433,7 +433,7 @@

        -balsa.routines.best_intermediates.best_intermediate_zones(pk_table: DataFrame, kq_table: DataFrame, cost_col: str, *, n: int = 1, add_pkq_cost: ndarray | None = None, flag_array: ndarray | None = None, maximize: bool = True, null_index: int = 0, other_columns: bool = True, intermediate_name: str = 'intermediate_zone', availability_column: str = 'available', n_threads: int = 1, squeeze: bool = True) → DataFrame | Dict[int, DataFrame]#
        +balsa.routines.best_intermediates.best_intermediate_zones(pk_table: DataFrame, kq_table: DataFrame, cost_col: str, *, n: int = 1, add_pkq_cost: ndarray[Any, dtype[ScalarType]] = None, flag_array: ndarray[Any, dtype[ScalarType]] = None, maximize: bool = True, null_index: int = 0, other_columns: bool = True, intermediate_name: str = 'intermediate_zone', availability_name: str = 'available', n_threads: int = 1, squeeze: bool = True) → DataFrame | Dict[int, DataFrame]#

        Numba-accelerated.

        Triple-index operation for two matrices, finding the most- or least-cost intermediate zones. Takes a first leg matrix (“pk”) and a second leg matrix (“kq”) to produce a combined “pq” matrix with the best intermediate “k”. @@ -471,7 +471,7 @@ in the “pk” and “kq” tables. The result table will be of the shape ((p, q), A | E + 3)

      • intermediate_name (str, optional) – Defaults to 'intermediate_zone'. Name of the column in the result table containing the selected intermediate zone.

        • -

      • availability_column (str, optional) – Defaults to 'available'. Name of the column in the result table +

      • availability_name (str, optional) – Defaults to 'available'. Name of the column in the result table containing a flag whether ANY intermediate zone was found to be available.

      • n_threads (int, optional) – Defaults to 1. Number of threads to use.

      • squeeze (bool, optional) – Defaults to True. If n == 1 and squeeze=True, a single DataFrame is @@ -590,7 +590,7 @@

        - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

        diff --git a/docs/routines/general.html b/docs/routines/general.html index 8b8e20b..e21354c 100644 --- a/docs/routines/general.html +++ b/docs/routines/general.html @@ -10,7 +10,7 @@ - General Utilities — wsp-balsa 1.4.0 documentation + General Utilities — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -531,7 +531,7 @@

        - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

        diff --git a/docs/routines/index.html b/docs/routines/index.html index fa8fb83..8900b2a 100644 --- a/docs/routines/index.html +++ b/docs/routines/index.html @@ -10,7 +10,7 @@ - Routines — wsp-balsa 1.4.0 documentation + Routines — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -369,7 +369,7 @@

        RoutinesIO Utilities

      • Modelling Utilities
          @@ -485,7 +485,7 @@

          Routines

          - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

          diff --git a/docs/routines/io.html b/docs/routines/io.html index b78e71d..55b0c3e 100644 --- a/docs/routines/io.html +++ b/docs/routines/io.html @@ -10,7 +10,7 @@ - IO Utilities — wsp-balsa 1.4.0 documentation + IO Utilities — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -360,7 +360,7 @@

          IO Utilities#

          -

          Routines for reading and writing demand modelling files, particularly matrices and TMG Toolbox network packages.

          +

          Routines for reading and writing demand modelling files, particularly matrices and TMGToolbox network packages.

          Matrices#

          @@ -388,7 +388,7 @@

          Matrices
          -balsa.routines.io.common.expand_array(a: ndarray, n: ndarray, *, axis: int | None = None) → ndarray#
          +balsa.routines.io.common.expand_array(a: ndarray, n: ndarray, *, axis: int = None) → ndarray#

          Expands an array across all dimensions by a set amount

          Parameters:
          @@ -451,7 +451,7 @@

          Matrices
          -balsa.routines.io.inro.read_emx(file: str | FileIO | Path, *, zones: int | Iterable[int] | Index | None = None, tall: bool = False) → ndarray | DataFrame | Series#
          +balsa.routines.io.inro.read_emx(file: str | FileIO | Path, *, zones: int | Iterable[int] | Index = None, tall: bool = False) → ndarray | DataFrame | Series#

          Reads an “internal” Emme matrix (found in <Emme Project>/Database/emmemat); with an ‘.emx’ extension. This data format does not contain information about zones. Its size is determined by the dimensions of the Emmebank (Emmebank.dimensions['centroids']), regardless of the number of zones actually used in all scenarios.

          @@ -559,7 +559,7 @@

          OMX format
          -balsa.routines.io.omx.read_omx(src_fp: str | PathLike, *, tables: Iterable[str] | None = None, mapping: str | None = None, tall: bool = False, raw: bool = False, squeeze: bool = True) → DataFrame | Series | ndarray | Dict[str, DataFrame | Series | ndarray]#
          +balsa.routines.io.omx.read_omx(src_fp: str | PathLike, *, tables: Iterable[str] = None, mapping: str = None, tall: bool = False, raw: bool = False, squeeze: bool = True) → DataFrame | Series | ndarray | Dict[str, DataFrame | Series | ndarray]#

          Reads Open Matrix (OMX) files. An OMX file can contain multiple matrices, so this function typically returns a Dict.

          @@ -584,7 +584,7 @@

          OMX format
          -balsa.routines.io.omx.to_omx(dst_fp: str | PathLike, tables: Dict[str, DataFrame | Series | ndarray], *, zone_index: Index | None = None, title: str = '', descriptions: Dict[str, str] | None = None, attrs: Dict[str, Dict] | None = None, mapping_name: str = 'zone_numbers')#
          +balsa.routines.io.omx.to_omx(dst_fp: str | PathLike, tables: Dict[str, DataFrame | Series | ndarray], *, zone_index: Index = None, title: str = '', descriptions: Dict[str, str] = None, attrs: Dict[str, Dict] = None, mapping_name: str = 'zone_numbers')#

          Creates a new (or overwrites an old) OMX file with a collection of matrices.

          Parameters:
          @@ -612,7 +612,7 @@

          OMX format

          Fortran format#

          -balsa.routines.io.fortran.read_fortran_rectangle(file: str | FileIO | Path, n_columns: int, *, zones: int | Iterable[int] | Index | None = None, tall: bool = False, reindex_rows: bool = False, fill_value: int | float | None = None) → ndarray | DataFrame | Series#
          +balsa.routines.io.fortran.read_fortran_rectangle(file: str | FileIO | Path, n_columns: int, *, zones: int | Iterable[int] | Index = None, tall: bool = False, reindex_rows: bool = False, fill_value: int | float = None) → ndarray | DataFrame | Series#

          Reads a FORTRAN-friendly .bin file (a.k.a. ‘simple binary format’) which is known to NOT be square. Also works with square matrices.

          This file format is an array of 4-bytes, where each row is prefaced by an integer referring to the 1-based @@ -643,7 +643,7 @@

          OMX format
          -balsa.routines.io.fortran.read_fortran_square(file: str | FileIO | Path, *, zones: int | Iterable[int] | Index | None = None, tall: bool = False) → ndarray | DataFrame | Series#
          +balsa.routines.io.fortran.read_fortran_square(file: str | FileIO | Path, *, zones: int | Iterable[int] | Index = None, tall: bool = False) → ndarray | DataFrame | Series#

          Reads a FORTRAN-friendly .bin file (a.k.a. ‘simple binary format’) which is known to be square.

          This file format is an array of 4-bytes, where each row is prefaced by an integer referring to the 1-based positional index that FORTRAN uses. The rest of the data are in 4-byte floats. To read this, the number of columns @@ -668,7 +668,7 @@

          OMX format
          -balsa.routines.io.fortran.to_fortran(matrix: ndarray | DataFrame | Series, file: str | FileIO | Path, *, n_columns: int | None = None, min_index: int = 1, force_square: bool = True)#
          +balsa.routines.io.fortran.to_fortran(matrix: ndarray | DataFrame | Series, file: str | FileIO | Path, *, n_columns: int = None, min_index: int = 1, force_square: bool = True)#

          Writes a FORTRAN-friendly .bin file (a.k.a. ‘simple binary format’), in a square format.

          Parameters:
          @@ -688,8 +688,16 @@

          OMX format -

          Network Packages (NWP)#

          +
          +

          Network Packages (NWP)#

          +

          For more information on the TMGToolbox Network Package format, please visit https://tmg.utoronto.ca/doc/1.6/tmgtoolbox/input_output/ExportNetworkPackage.html

          +
          +
          +balsa.routines.io.nwp.parse_tmg_ncs_line_id(s: Series) → Tuple[Series, Series]#
          +

          A function to parse line IDs based on TMG Network Coding Standard conventions. Returns pandas Series objects +corresponding to the parsed operator and route IDs

          +
          +
          balsa.routines.io.nwp.process_emme_eng_notation_series(s: ~pandas.core.series.Series, *, to_dtype=<class 'float'>) → Series#
          @@ -734,7 +742,7 @@

          OMX format +balsa.routines.io.nwp.read_nwp_link_attributes(nwp_fp: str | PathLike, *, attributes: str | List[str] = None, **kwargs) → DataFrame#

          A function to read link attributes from a Network Package file (exported from Emme using the TMG Toolbox).

          Parameters:
          @@ -753,7 +761,7 @@

          OMX format
          -balsa.routines.io.nwp.read_nwp_node_attributes(nwp_fp: str | PathLike, *, attributes: str | List[str] | None = None, **kwargs) → DataFrame#
          +balsa.routines.io.nwp.read_nwp_node_attributes(nwp_fp: str | PathLike, *, attributes: str | List[str] = None, **kwargs) → DataFrame#

          A function to read node attributes from a Network Package file (exported from Emme using the TMG Toolbox).

          Parameters:
          @@ -806,7 +814,7 @@

          OMX format
          -balsa.routines.io.nwp.read_nwp_transit_line_attributes(nwp_fp: str | PathLike, *, attributes: str | List[str] | None = None, **kwargs) → DataFrame#
          +balsa.routines.io.nwp.read_nwp_transit_line_attributes(nwp_fp: str | PathLike, *, attributes: str | List[str] = None, **kwargs) → DataFrame#

          A function to read transit line attributes from a Network Package file (exported from Emme using the TMG Toolbox).

          @@ -844,16 +852,16 @@

          OMX format
          -balsa.routines.io.nwp.read_nwp_transit_result_summary(nwp_fp: str | PathLike) → DataFrame#
          +balsa.routines.io.nwp.read_nwp_transit_result_summary(nwp_fp: str | PathLike, *, parse_line_id: bool = True) → DataFrame#

          A function to read and summarize the transit assignment boardings and max volumes from a Network Package file (exported from Emme using the TMG Toolbox) by operator and route.

          -
          -

          Note

          -

          Transit line names in Emme must adhere to the TMG NCS16 for this function to work properly.

          -
          Parameters:
          -

          nwp_fp (str | PathLike) – File path to the network package.

          +
            +

          • nwp_fp (str | PathLike) – File path to the network package.

            • +

          • parse_line_id (bool, optional) – Defaults to True. Option to parse operator and route IDs from line IDs. +Please note that transit line IDs must adhere to the TMG NCS16 for this option to work properly.

            • +
          Returns:

          pd.DataFrame

          @@ -898,6 +906,24 @@

          OMX format +
          +balsa.routines.io.nwp.read_nwp_transit_vehicles(nwp_fp: str | PathLike) → DataFrame#
          +

          A function to read the transit vehicles from a Network Package file (exported from Emme using the TMG Toolbox) +into DataFrames.

          +
          +
          Parameters:
          +

          nwp_fp (str | PathLike) – File path to the network package.

          +
          +
          Returns:
          +

          DataFrame containing the transit vehicles.

          +
          +
          Return type:
          +

          pd.DataFrame

          +
          +
          +

          +

          @@ -976,7 +1002,8 @@

          OMX formatNetwork Packages (NWP) @@ -1034,7 +1062,7 @@

          OMX format

          - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

          diff --git a/docs/routines/matrices.html b/docs/routines/matrices.html index f90a264..3c5cb7d 100644 --- a/docs/routines/matrices.html +++ b/docs/routines/matrices.html @@ -10,7 +10,7 @@ - Matrix Utilities — wsp-balsa 1.4.0 documentation + Matrix Utilities — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -362,21 +362,21 @@

          Matrix Utilities#

          -balsa.routines.matrices.aggregate_matrix(matrix: ~pandas.core.frame.DataFrame | ~pandas.core.series.Series, *, groups: ~pandas.core.series.Series | ~numpy.ndarray | None = None, row_groups: ~pandas.core.series.Series | ~numpy.ndarray | None = None, col_groups: ~pandas.core.series.Series | ~numpy.ndarray | None = None, aggfunc: ~typing.Callable[[~typing.Iterable[int | float]], int | float] = <function sum>) → DataFrame | Series#
          +balsa.routines.matrices.aggregate_matrix(matrix: ~pandas.core.frame.DataFrame | ~pandas.core.series.Series, *, groups: ~pandas.core.series.Series | ~numpy.ndarray[~typing.Any, ~numpy.dtype[~numpy._typing._generic_alias.ScalarType]] = None, row_groups: ~pandas.core.series.Series | ~numpy.ndarray[~typing.Any, ~numpy.dtype[~numpy._typing._generic_alias.ScalarType]] = None, col_groups: ~pandas.core.series.Series | ~numpy.ndarray[~typing.Any, ~numpy.dtype[~numpy._typing._generic_alias.ScalarType]] = None, aggfunc: ~typing.Callable[[~typing.Iterable[int | float]], int | float] = <function sum>) → DataFrame | Series#

          Aggregates a matrix based on mappings provided for each axis, using a specified aggregation function.

          Parameters:

          • matrix (pandas.DataFrame | pandas.Series) – Matrix data to aggregate. DataFrames and Series with 2-level indices are supported

            • -

          • groups (pandas.Series | numpy.ndarray, optional) – Syntactic sugar to specify both row_groups and +

          • groups (pandas.Series | NDArray, optional) – Syntactic sugar to specify both row_groups and col_groups to use the same grouping series.

            • -

          • row_groups (pandas.Series | numpy.ndarray, optional) – Groups for the rows. If aggregating a DataFrame, +

          • row_groups (pandas.Series | NDArray, optional) – Groups for the rows. If aggregating a DataFrame, this must match the index of the matrix. For a “tall” matrix, this series can match either the “full” index of the series, or it can match the first level of the matrix (it would be the same as if aggregating a DataFrame). Alternatively, an array can be provided, but it must be the same length as the DataFrame’s index, or the full length of the Series.

            • -

          • col_groups (pandas.Series | numpy.ndarray, optional) – Groups for the columns. If aggregating a DataFrame, +

          • col_groups (pandas.Series | NDArray, optional) – Groups for the columns. If aggregating a DataFrame, this must match the columns of the matrix. For a “tall” matrix, this series can match either the “full” index of the series, or it can match the second level of the matrix (it would be the same as if aggregating a DataFrame). Alternatively, an array can be provided, but it must be the same length as the DataFrame’s @@ -529,7 +529,7 @@

            -balsa.routines.matrices.disaggregate_matrix(matrix: DataFrame, *, mapping: Series | None = None, proportions: Series | None = None, row_mapping: Series | None = None, row_proportions: Series | None = None, col_mapping: Series | None = None, col_proportions: Series | None = None) → DataFrame#
            +balsa.routines.matrices.disaggregate_matrix(matrix: DataFrame, *, mapping: Series = None, proportions: Series = None, row_mapping: Series = None, row_proportions: Series = None, col_mapping: Series = None, col_proportions: Series = None) → DataFrame#

            Split multiple rows and columns in a matrix all at once. The cells in the matrix MUST be numeric, but the row and column labels do not.

            @@ -746,13 +746,13 @@
            -balsa.routines.matrices.matrix_balancing_1d(m: ndarray, a: ndarray, axis: int) → ndarray#
            +balsa.routines.matrices.matrix_balancing_1d(m: ndarray[Any, dtype[ScalarType]], a: ndarray[Any, dtype[ScalarType]], axis: int) → ndarray[Any, dtype[ScalarType]]#

            Balances a matrix using a single constraint.

            Parameters:
              -

            • m (numpy.ndarray) – The matrix (a 2-dimensional ndarray) to be balanced

              • -

            • a (numpy.ndarray) – The totals vector (a 1-dimensional ndarray) constraint

              • +

            • m (NDArray) – The matrix (a 2-dimensional ndarray) to be balanced

              • +

            • a (NDArray) – The totals vector (a 1-dimensional ndarray) constraint

            • axis (int) – Direction to constrain (0 = along columns, 1 = along rows)

            @@ -760,22 +760,22 @@

            A balanced matrix

            Return type:
            -

            numpy.ndarray

            +

            NDArray

            -balsa.routines.matrices.matrix_balancing_2d(m: ndarray | DataFrame, a: ndarray, b: ndarray, *, totals_to_use: str = 'raise', max_iterations: int = 1000, rel_error: float = 0.0001, n_procs: int = 1) → Tuple[ndarray | DataFrame, float, int]#
            +balsa.routines.matrices.matrix_balancing_2d(m: ndarray[Any, dtype[ScalarType]] | DataFrame, a: ndarray[Any, dtype[ScalarType]], b: ndarray[Any, dtype[ScalarType]], *, totals_to_use: str = 'raise', max_iterations: int = 1000, rel_error: float = 0.0001, n_threads: int = 1) → Tuple[ndarray[Any, dtype[ScalarType]] | DataFrame, float, int]#

            Balances a two-dimensional matrix using iterative proportional fitting.

            Parameters:
              -

            • m (numpy.ndarray | pandas.DataFrame) – The matrix (a 2-dimensional ndarray) to be balanced. If a DataFrame +

            • m (NDArray | pandas.DataFrame) – The matrix (a 2-dimensional ndarray) to be balanced. If a DataFrame is supplied, the output will be returned as a DataFrame.

              • -

            • a (numpy.ndarray) – The row totals (a 1-dimensional ndarray) to use for balancing

              • -

            • b (numpy.ndarray) – The column totals (a 1-dimensional ndarray) to use for balancing

              • +

            • a (NDArray) – The row totals (a 1-dimensional ndarray) to use for balancing

              • +

            • b (NDArray) – The column totals (a 1-dimensional ndarray) to use for balancing

            • totals_to_use (str, optional) – Defaults to 'raise'. Describes how to scale the row and column totals if their sums do not match. Must be one of [‘rows’, ‘columns’, ‘average’, ‘raise’]. - rows: scales the columns totals so that their sums matches the row totals @@ -784,26 +784,26 @@ - raise: raises an Exception if the sums of the row and column totals do not match

            • max_iterations (int, optional) – Defaults to 1000. Maximum number of iterations

            • rel_error (float, optional) – Defaults to 1.0E-4. Relative error stopping criteria

              • -

            • n_procs (int, optional) – Defaults to 1. Number of processors for parallel computation. (Not used)

              • +

            • n_threads (int, optional) – Defaults to 1. Number of processors for parallel computation. (Not used)

            Returns:

            The balanced matrix, residual, and n_iterations

            Return type:
            -

            Tuple[numpy.ndarray | pandas.DataFrame, float, int]

            +

            Tuple[NDArray | pandas.DataFrame, float, int]

            -balsa.routines.matrices.matrix_bucket_rounding(m: ndarray | DataFrame, *, decimals: int = 0) → ndarray | DataFrame#
            +balsa.routines.matrices.matrix_bucket_rounding(m: ndarray[Any, dtype[ScalarType]] | DataFrame, *, decimals: int = 0) → ndarray[Any, dtype[ScalarType]] | DataFrame#

            Bucket rounds to the given number of decimals.

            Parameters:
              -

            • m (numpy.ndarray | pandas.DataFrame) – The matrix to be rounded

              • +

            • m (NDArray | pandas.DataFrame) – The matrix to be rounded

            • decimals (int, optional) – Defaults to 0. Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point.

            @@ -812,7 +812,7 @@

            The rounded matrix

            Return type:
            -

            numpy.ndarray | pandas.DataFrame

            +

            NDArray | pandas.DataFrame

            @@ -941,7 +941,7 @@

            - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

            diff --git a/docs/routines/modelling.html b/docs/routines/modelling.html index d7b15e5..a8d0519 100644 --- a/docs/routines/modelling.html +++ b/docs/routines/modelling.html @@ -10,7 +10,7 @@ - Modelling Utilities — wsp-balsa 1.4.0 documentation + Modelling Utilities — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -405,7 +405,7 @@
            -balsa.routines.modelling.distance_matrix(x0: ndarray | Series, y0: ndarray | Series, *, labels0: Iterable | Index | None = None, tall: bool = False, x1: Series | ndarray | None = None, y1: Series | ndarray | None = None, labels1: Series | ndarray | None = None, method: str = 'EUCLIDEAN', **kwargs) → Series | DataFrame | ndarray#
            +balsa.routines.modelling.distance_matrix(x0: ndarray | Series, y0: ndarray | Series, *, labels0: Iterable | Index = None, tall: bool = False, x1: ndarray | Series = None, y1: ndarray | Series = None, labels1: ndarray | Series = None, method: str = 'EUCLIDEAN', **kwargs) → Series | DataFrame | ndarray#

            Fastest method of computing a distance matrix from vectors of coordinates, using the NumExpr package. Supports several equations for computing distances.

            Accepts two or four vectors of x-y coordinates. If only two vectors are provided (x0, y0), the result will be the @@ -470,7 +470,7 @@

            -balsa.routines.modelling.tlfd(values: ndarray | Series, *, bin_start: int = 0, bin_end: int = 200, bin_step: int = 2, weights: Series | ndarray | None = None, intrazonal: Series | ndarray | None = None, label_type: str = 'MULTI', include_top: bool = False) → Series#
            +balsa.routines.modelling.tlfd(values: ndarray | Series, *, bin_start: int = 0, bin_end: int = 200, bin_step: int = 2, weights: ndarray | Series = None, intrazonal: ndarray | Series = None, label_type: str = 'MULTI', include_top: bool = False) → Series#

            Generates a Trip Length Frequency Distribution (i.e. a histogram) from given data. Produces a “pretty” Pandas object suitable for charting.

            @@ -601,7 +601,7 @@

            - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

            diff --git a/docs/routines/plotting.html b/docs/routines/plotting.html index f2a177b..5f0da0d 100644 --- a/docs/routines/plotting.html +++ b/docs/routines/plotting.html @@ -10,7 +10,7 @@ - Plotting Utilities — wsp-balsa 1.4.0 documentation + Plotting Utilities — wsp-balsa 1.4.2 documentation @@ -30,15 +30,15 @@ - + - - - + + + @@ -362,7 +362,7 @@

            Plotting Utilities#

            -balsa.routines.plotting.convergence_boxplot(targets: DataFrame, results: DataFrame, filter_func: Callable[[Series], Series], *, adjust_target: bool = True, percentage: bool = True, band: Tuple[float, float] | None = None, simple_labels: bool = True, ax: Axes | None = None, fp: str | PathLike | None = None, title: str | None = None) → Axes#
            +balsa.routines.plotting.convergence_boxplot(targets: DataFrame, results: DataFrame, filter_func: Callable[[Series], Series], *, adjust_target: bool = True, percentage: bool = True, band: Tuple[float, float] = None, simple_labels: bool = True, ax: Axes = None, fp: str | PathLike = None, title: str = None) → Axes#

            Measures convergence of constrained location-choice models (such as work-location choice). Can be used to produce multiple box plots for different sub-sets of zones, usually based on size.

            @@ -388,7 +388,7 @@
            -balsa.routines.plotting.location_summary(model: DataFrame, target: DataFrame, ensemble_names: Series, *, title: str = '', fp: str | PathLike | None = None, dpi: int = 150, district_name: str = 'Ensemble') → Axes#
            +balsa.routines.plotting.location_summary(model: DataFrame, target: DataFrame, ensemble_names: Series, *, title: str = '', fp: str | PathLike = None, dpi: int = 150, district_name: str = 'Ensemble') → Axes#

            Creates a compound plot showing total attractions to specified locations

            Parameters:
            @@ -410,7 +410,7 @@
            -balsa.routines.plotting.trumpet_diagram(counts: Series, model_volume: Series, *, categories: Series | List[Series] | None = None, category_colours: Dict[str | tuple, str] | None = None, category_markers: Dict[str | tuple, str] | None = None, label_format: str | None = None, title: str = '', y_bounds: Tuple[float, float] = (-2, 2), ax: Axes | None = None, x_label: str = 'Count volume', legend: bool = True, **kwargs) → Axes#
            +balsa.routines.plotting.trumpet_diagram(counts: Series, model_volume: Series, *, categories: Series | List[Series] = None, category_colours: Dict[str | tuple, str] = None, category_markers: Dict[str | tuple, str] = None, label_format: str = None, title: str = '', y_bounds: Tuple[float, float] = (-2, 2), ax: Axes = None, x_label: str = 'Count volume', legend: bool = True, **kwargs) → Axes#

            Plots an auto volumes “trumpet” diagram of relative error vs. target count, and will draw min/max error curves based on FHWA guidelines. Can be used to plot different categories of count locations.

            @@ -545,7 +545,7 @@

            - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

            diff --git a/docs/search.html b/docs/search.html index b25d2c6..5198b89 100644 --- a/docs/search.html +++ b/docs/search.html @@ -6,7 +6,7 @@ - Search - wsp-balsa 1.4.0 documentation + Search - wsp-balsa 1.4.2 documentation @@ -26,15 +26,15 @@ - + - - - + + + @@ -386,7 +386,7 @@

            Search

            - Created using Sphinx 7.0.1. + Created using Sphinx 7.1.2.

            diff --git a/docs/searchindex.js b/docs/searchindex.js index edc31ab..691c7b4 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["index", "logging/index", "routines/best_intermediates", "routines/general", "routines/index", "routines/io", "routines/matrices", "routines/modelling", "routines/plotting"], "filenames": ["index.rst", "logging\\index.rst", "routines\\best_intermediates.rst", "routines\\general.rst", "routines\\index.rst", "routines\\io.rst", "routines\\matrices.rst", "routines\\modelling.rst", "routines\\plotting.rst"], "titles": ["wsp-balsa: Common modelling tools", "Logging", "Triple-Index Utilities", "General Utilities", "Routines", "IO Utilities", "Matrix Utilities", "Modelling Utilities", "Plotting Utilities"], "terms": {"i": [0, 1, 2, 3, 5, 6, 7, 8], "collect": [0, 1, 4, 5], "function": [0, 1, 2, 4, 5, 6, 7], "python": [0, 1, 3, 5], "facilit": 0, "travel": 0, "demand": [0, 5], "forecast": 0, "applic": 0, 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