From eca4d62fda6f43c7c004175b2c8237bad7a8b517 Mon Sep 17 00:00:00 2001 From: seanebum Date: Mon, 28 Feb 2022 14:47:02 -0500 Subject: [PATCH 01/25] small doc update --- .../.doctrees/classes/spatialpy.core.doctree | Bin 1331264 -> 1332244 bytes docs/build/html/.doctrees/environment.pickle | Bin 451357 -> 451496 bytes .../html/_modules/spatialpy/core/domain.html | 16 +++++++--------- .../html/_modules/spatialpy/core/result.html | 9 ++++++++- docs/build/html/classes/spatialpy.core.html | 18 ++++++++++++++++-- docs/build/html/searchindex.js | 2 +- spatialpy/core/result.py | 9 ++++++++- 7 files changed, 40 insertions(+), 14 deletions(-) diff --git a/docs/build/html/.doctrees/classes/spatialpy.core.doctree b/docs/build/html/.doctrees/classes/spatialpy.core.doctree index 372e3575c3e4f429d3d05f097810bae7bb61ab31..bf7b07074ae5c19792745e55d45c0be383267d20 100644 GIT binary patch delta 1550 zcmeHHOK1~O6wP}x$z&#N+DWP?QTl|G82mKdxX3~(MJj#@U0J)SX=Z9h+nJJ|gwonT z?5Zf38J_MUA_xHmX?!T6Rs_Lrbmu~(sUQe$+zFcEO)`xYM3*W!FwDK@+#$%GM#5=GTh&3r1BN&!%R0IMkwr*qTln5IBlk8FU%QY4j5 z0yv(X7@JOLfR7s)U5N#gIg&LdGgB&=NXFu6&CsDr&p<&|OT~@)L9X}B(cT46)GjGnCyA#gE7r%G(x4a0p$1TJ^i+&ydwyWiDB3)xjv-sK8Z?uz$NvG%-|m9U7`MXPsX#&rp1M7m`c%VTK-7^lq? ztB?GZkDel;J^h|-(B~Nhwl($kTO4E_)FxwRB5-ygK>KP^gpG1-6wWShO#jq=09z$4 z!cKGThoRkX6YM7PCNSH|d&`@ovSrk93ey|w-XJ~Ub7t5>zM}(p&hDapbHWz3iaJfq zX8F$1nqcdpd4l#ajO8Mx(HTi*cX&BG@b4|znIrQ5CHu2~J9h45|ad&c|JFQT;r**h5aFYt8rN~gdc^Zv_UMeD;*V>umc2zBK-_L}cX9Qy{D C3KRhV delta 614 zcmZvZPiWI{6vy-XCHW<3(xgdNR_Mh!=LSU=cUAnu`~xjIdQuO18$?%85fyJ8wA71;Hhljns?2)8oDG_rv3T>$i%nYsIoh zJnHeD;E5i)FYTv>&XYaRcw}ycvMecZZb(Ywc_uw2#DQs1a$v+W)VODLH+wWc z33GkK!9qgX@C!y3Oa_N`TLajn@^xbQ97nj7az&R#>Sqt(y@MiS^(jOy2yUy)w;DySMX+hhxdg1Q*M-8Xa{hEy}D;?oY&5Vhw)E%kUO4)DqE z5p1ag8-MR$UQF%^ae~*~$kRDvmg%&#cbCnt9d654=?ubYG4SsA?xl$6-@N%;1D@U3HcgGH_%J!vY%!ys&(bO%hCe7iL+~pSj05t!&#{d8T diff --git a/docs/build/html/.doctrees/environment.pickle b/docs/build/html/.doctrees/environment.pickle index ebf24ccd3f558a6b6b25240a2d882c35a423f221..4cc145587402db263ae760cb8c88a89995ca84e7 100644 GIT binary patch delta 3017 zcmb7GeQZz55w3Q&MvntkZodo*_LQqw;RT-pPSLoI;SfzE-x{q`3bD{y# zrbTjo=l;$)zjN-r@7?=OT`u|2uS(|XEcBa}5_zV;9Z;fCZ?rBNR${6W2v0lQp@?^O z#;688-h))LR2f5oDQ_hHvgMkz@kr-O)~*Kas+5wlwe@`*eeC{3TUZ*soc2khc4c{# z-SVY2zdXu$+D%_w+eS~=%4xT)4NB?R>U^Wut$L&MY*i8cyDb}id6}NjgrWu=;py3# z&9ui>Nqg-j^wp|tdd8YZU#zs#kiDEbtxa^no`w8ZXxv&$BUYKt*~;jmwUVY;t@Ns` zIPE*4&EvCn`dyp(k&@x+dd$xIZ)KmLCo3yym-TT`ikG=#%cG_C>_^JIY0snP>P&j4 zdJT0{ndl9)Z`*Q2%V(;sbh5e%+uIfwT?3ShG!jpr|$e}zSx@Efd9Chc7R*+aaMn!f;xr{p-c(A z*}I#TE4lQub$Ri=J{#a*Rs7hV*L0(6AR!F;)V4gjcxwf7Xh7mk`vN-qo-3c#8IV~R z90uC$DWf}vuFz{fU2gp_%3brVqQ~}>$4`vRh(WwwIjI}(xefx|)>}qT4Hwg=dmFIp zbM&b_dhYYyPQbyc_(#4&x)JnaIA{K<>I{8u{5s6X`vdnS!S4&Lgd9HO9Y!(T9jV$p zoemGeJbgFZhmq9>{6ScV&)#}ghQim2U^W%5Bz}?#g#h#Hk`V^7c=ih-`2}r0QAJ>N z7c8(%y};pkFSrpt?}ader%%AUeQ zjr(CQ!twnufN*C&>_K>PNaURz;$klj!Wd^*U>N15{Oc}*0g+9pAnPOd- zp7}>Ad)fykoh?gt^PvwibVg1x8^)HHiX?Li8JzgJF|kM)e(>NZ?1x^2OMcjeP^jXj zF0iN$*Hmc2(FzULBeZL<1>xHov>{y6U>yRC!v?s*HjYD^Nxa)#Squg9*!Rbw7$57O zj>F#o26Yu~av4BSZ2*qGpxU2&^_=x{+A9TKbk*+B%*1!Pg{<0dBINKQ75> zi<0x6bYynMLa)DGk)t){rKC9jEIG3b$Q2`(&;FDHE7Am?V{kUGC!3@SQ=`Fo2qRd_ z6H-aJ@EtZdFY+AHF0Qp%ZJM8Evh!l%l_qKIw)EjqeNz&+DcZm@vfQQwWtUeD1@Y%= zmJcnjDPLOKlud4yr@VoXTa8WEJm`z~L$+qw;|)e1^-bUa!G{uplNdXtM%^La zrbZg1Cf2So~*^UMD8Gs;*glBWy*SDU$I#Yc86y*;i;uvzcoo{ zzuiF^FnDwesZDPSO@tNv0@CSPQv4;%q<|eAf-HR~w-K{$9p6aoy7l&E3|YDu(y&U-cCtn%iaSX< z5)0djIZfOSq8ZdIW%Zkg30(&|iEX)3DVrMw&izB4N_DHm3tJ-Z!x1D=GpvksB z?B6}VbI$Lcd+y`=>92B6|1x)>qJXHp!Ex6?+FX!JkK)f?P^yZCoSsPhm4any=drVC z=RQjJy(c9lHSYfS*xB@N@4xF?TDN}oQMPYo-THYn~uIK{v$3^wDs?0=HmSXz2 zSx<+HwY1w@L;q>kK^}eAl1X2%XlO@SIo(oHN`GHkLY<{~^sSP5L_K{_q@nMY8EK@% zKo6H?(7ECix(E5!%qjHQQfKlhQP;`&653K?rWT8y#>~3^p6CCnU$&IdvC@q78TOQJ zen@#A)~u&HEn2$GoUb`0){C>mzN(n#D=lU)i8c5za=#^qmTlG3`APj91YQDlTO5X8N28{Ns)EP4UrUoK?PANZZDZ z^uVy44q0>Q^{!63JdsZCRO#d0-DW^zQ~X5F8}gvFFChkWu1QaCe7b?HTqNrF69ayk zy*ie`DilECD}#2RO;fmk-?|`-Fy1ZT!)}-&qTOd$MK9Rd`5w^9#94NR6R$Ur zWOl>>1e3~oA%yU~UYJJE^?@BB&<75Lr9K!zu=Wc$(Jy>=`-QJsprXfo4`&M+m!Qhg1xG z&kioR?%%O{4rph~%|xYj>XMR5#98&-W}~Cf!TvM?S`7bUL^NY?6voiHJSyffIbdAY z_?&f&K|3w~Jb8m}RfGN60izh(G6pWWjDKs{Cr)Tf+c!187Q|1h#6EUHdwOH^5mu#R z_Z`rtZH(?)=h+8N7?HI!vOB%yAvYIGVQ;u`F@GFi+m&>gNNFKNy;fPD3%s&SmX0|_ zp^artfKFx%vM~MOmr8bI0@N~Vlv(zh3DC%lX_eXGUSkSZnbXMNy3e^qk2D?_M=R`s zE`%!{=s?Kw;+`(Chz!eK*pAi)A3TMy*9Q#<-|;~`!m;YPJock1m+ zl2!eg zWJ^^%KhK_dQp!u-rcX+;r7LXP*tHs|Fu6uyJB+?q7HvC}%}m=RlJkthc8ODDZ)not z4bJ;!`I}?1T}sq#m)y6dyD_vF>QS~ zTptR4-8CI`O>^@R1ipcLa5q@SbG?A|%EH)8?Tu(lA z+{<{mndp#BX(j~-Rn5eLFtwlLtUqR})fZs2K@$qQrXA6c&|PRITalCQ3J1OnVZ=@NPR0=oNwfxl?Hi>Q^)c8!5*)yH=4A-OdC#Rl#? ziUKPBsI3n9OD3lf@p=iGcaWs`A;W2a>S}n~h{=iycXApN)D#!AG|3u#K27 z=m$2w*NMwE@;-h-*{O>pGe_iX>Source code for spatialpy.core.domain
         #vertices
         obj.vertices = mesh_obj.points
         # triangles
-        if 'triangle' in mesh_obj.cells:
-            obj.triangles = mesh_obj.cells['triangle']
+        triangles = list(filter(lambda cell: cell.type == "triangle", mesh_obj.cells))
+        if triangles:
+            obj.triangles = triangles[0].data
         #tetrahedrons
-        if 'tetra' in mesh_obj.cells:
-            obj.tetrahedrons = mesh_obj.cells['tetra']
+        tetras = list(filter(lambda cell: cell.type == "tetra", mesh_obj.cells))
+        if tetras:
+            obj.tetrahedrons = tetras[0].data
         # volume
         obj.calculate_vol()
         if not numpy.count_nonzero(obj.vol):
@@ -835,16 +837,12 @@ 
Source code for spatialpy.core.domain
         :returns: SpatialPy Domain object created from the mesh file.
         :rtype: spatialpy.Domain.Domain
         """
-        try:
-            import pygmsh # pylint: disable=import-outside-toplevel
-        except ImportError as err:
-            raise DomainError("The python package 'pygmsh' is not installed.") from err
         try:
             import meshio # pylint: disable=import-outside-toplevel
         except ImportError as err:
             raise DomainError("The python package 'meshio' is not installed.") from err
 
-        return cls.import_meshio_object(meshio.msh_io.read(filename))

+        return cls.import_meshio_object(meshio.read(filename))
[docs] def read_stochss_subdomain_file(self, filename, type_ids=None): """ diff --git a/docs/build/html/_modules/spatialpy/core/result.html b/docs/build/html/_modules/spatialpy/core/result.html index 4256e5b5..1eff9884 100644 --- a/docs/build/html/_modules/spatialpy/core/result.html +++ b/docs/build/html/_modules/spatialpy/core/result.html @@ -671,7 +671,14 @@

Source code for spatialpy.core.result

         given, that value is used to index into the timespan, and that time point is returned \
         as a 1D array with size (number of voxel).
 
-        :param property_name: A string describing the property to be returned.
+        :param property_name: A string describing the property to be returned.  Can be one of: {
+            'v' : velocity,
+            'rho': density,
+            'mass': mass,
+            'id': type_id,
+            'type': type as str,
+            'bvf_phi': boundary volume fraction,
+            'nu': viscosity}
         :type property_name: str
 
         :param timepoints: timespan index to be returned.  Default is None
diff --git a/docs/build/html/classes/spatialpy.core.html b/docs/build/html/classes/spatialpy.core.html
index a5249b96..7e561e2a 100644
--- a/docs/build/html/classes/spatialpy.core.html
+++ b/docs/build/html/classes/spatialpy.core.html
@@ -1643,7 +1643,14 @@ 
Submodules
 
Parameters

 
    
-
  • property_name (str) – A string describing the property to be returned.
    • 
+
  • property_name (str) – A string describing the property to be returned.  Can be one of: {
+‘v’ : velocity,
+‘rho’: density,
+‘mass’: mass,
+‘id’: type_id,
+‘type’: type as str,
+‘bvf_phi’: boundary volume fraction,
+‘nu’: viscosity}
    • 
 
  • timepoints (int) – timespan index to be returned.  Default is None
    • 
 

 

@@ -3825,7 +3832,14 @@ 
Submodules
 
Parameters

 
    
-
  • property_name (str) – A string describing the property to be returned.
    • 
+
  • property_name (str) – A string describing the property to be returned.  Can be one of: {
+‘v’ : velocity,
+‘rho’: density,
+‘mass’: mass,
+‘id’: type_id,
+‘type’: type as str,
+‘bvf_phi’: boundary volume fraction,
+‘nu’: viscosity}
    • 
 
  • timepoints (int) – timespan index to be returned.  Default is None
    • 
 

 

diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 4a0be063..6ffa4df0 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
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package","spatialpy.core package","spatialpy.solvers package","spatialpy.stochss package","Documentation for SpatialPy 1.0"],titleterms:{"0":4,"1":4,api:4,boundarycondit:1,build_express:2,cleanup:1,content:[0,1,2,3],copi:4,core:1,datafunct:1,document:4,domain:1,exampl:4,geometri:1,get:4,indic:4,initialcondit:1,issu:4,model:1,modul:[0,1,2,3],packag:[0,1,2,3],paramet:1,reaction:1,refer:4,report:4,result:1,solver:2,spatialpi:[0,1,2,3,4],spatialpyerror:1,speci:1,stochss:3,stochss_export:3,submodul:[1,2,3],subpackag:0,tabl:4,vtkreader:1}})
\ No newline at end of file
diff --git a/spatialpy/core/result.py b/spatialpy/core/result.py
index fbc06f5f..9f766c8e 100644
--- a/spatialpy/core/result.py
+++ b/spatialpy/core/result.py
@@ -578,7 +578,14 @@ def get_property(self, property_name, timepoints=None):
         given, that value is used to index into the timespan, and that time point is returned \
         as a 1D array with size (number of voxel).
 
-        :param property_name: A string describing the property to be returned.
+        :param property_name: A string describing the property to be returned.  Can be one of: {
+            'v' : velocity,
+            'rho': density,
+            'mass': mass,
+            'id': type_id,
+            'type': type as str,
+            'bvf_phi': boundary volume fraction,
+            'nu': viscosity}
         :type property_name: str
 
         :param timepoints: timespan index to be returned.  Default is None

From 3372f964158676ef467b0ee2a185e6eff44b6fcc Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Tue, 1 Mar 2022 12:34:44 -0500
Subject: [PATCH 02/25] Fixed start here notebook name.

---
 docs/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/index.rst b/docs/index.rst
index ef9b1b78..450e6250 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -20,7 +20,7 @@ The latest version of SpatialPy can be found on `PyPI `_ for more information on how to build and simulate your models with SpatialPy. For an example of how to use SpatialPy to simulate a spatial stochastic reaction-diffusion system, see the `3D Cylinder Demo `_. We also provide examples of how to use SpatialPy to simulate physics (`Gravity Demo `_) and fluid flow (`Weir Model `_).
+See our `Example Notebook - Start Here `_ for more information on how to build and simulate your models with SpatialPy. For an example of how to use SpatialPy to simulate a spatial stochastic reaction-diffusion system, see the `3D Cylinder Demo `_. We also provide examples of how to use SpatialPy to simulate physics (`Gravity Demo `_) and fluid flow (`Weir Model `_).
 
 
 Reporting Issues

From a2585ede8e312b1766e0e1eca228db25127fce77 Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Tue, 1 Mar 2022 12:42:02 -0500
Subject: [PATCH 03/25] Fixed doc strings for boundary conditions.

---
 spatialpy/core/boundarycondition.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spatialpy/core/boundarycondition.py b/spatialpy/core/boundarycondition.py
index 55fb0bd0..1cef0911 100644
--- a/spatialpy/core/boundarycondition.py
+++ b/spatialpy/core/boundarycondition.py
@@ -59,7 +59,7 @@ class BoundaryCondition():
     and applies to deterministic simulation. **BoundaryCondition not yet implemeneted for Stochastic Species**.
     :type deterministic: bool
 
-    :param target: Set **target** to properties, can be 'nu' 'rho' or 'v' or species name
+    :param target: Set **target** to properties, can be 'nu' 'rho' or 'v' or species name \
     If species name, determinstic must also be set to True/False.
     :type target: str
 

From 05ad212046e846c2e796569a75b57b9d683b6774 Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Tue, 1 Mar 2022 12:43:15 -0500
Subject: [PATCH 04/25] Fixed doc strings for boundary conditions.

---
 spatialpy/core/boundarycondition.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spatialpy/core/boundarycondition.py b/spatialpy/core/boundarycondition.py
index 1cef0911..71657209 100644
--- a/spatialpy/core/boundarycondition.py
+++ b/spatialpy/core/boundarycondition.py
@@ -31,7 +31,7 @@ class BoundaryCondition():
          - type_id: type (subdomain) of the partciles
     Targets (one of the following must be set):
         property: (str), 'nu', 'rho','v'
-        species: (str) name of a chemical species.
+        species: (str) name of a chemical species. \
                    Must also set deterministic=True/False flag.
 
     :param xmin: x-axis coordinate lower bound of **condition**

From 86c3647b3c2bb44b262ce70a5e0200fe073d1aa2 Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Tue, 1 Mar 2022 14:15:25 -0500
Subject: [PATCH 05/25] Added a missing raises doc tag for data functions.

---
 spatialpy/core/datafunction.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/spatialpy/core/datafunction.py b/spatialpy/core/datafunction.py
index f63b375f..97d589c5 100644
--- a/spatialpy/core/datafunction.py
+++ b/spatialpy/core/datafunction.py
@@ -23,6 +23,8 @@ class DataFunction():
 
     :param name: Name of the Data Function.
     :type name: str
+
+    :raises DataFunctionError: If a name is not provided.
     """
 
     def __init__(self, name=None):

From 4ca44ad5949a4f6c027598caf55865f6b583c4ea Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Tue, 1 Mar 2022 15:31:46 -0500
Subject: [PATCH 06/25] Updated docs in domain.  updated docs.

---
 docs/build/html/.buildinfo                    |    2 +-
 .../.doctrees/classes/spatialpy.core.doctree  |  Bin 1332244 -> 1462682 bytes
 .../html/.doctrees/classes/spatialpy.doctree  |  Bin 5787 -> 5949 bytes
 .../classes/spatialpy.solvers.doctree         |  Bin 225708 -> 234328 bytes
 .../classes/spatialpy.stochss.doctree         |  Bin 13469 -> 13763 bytes
 docs/build/html/.doctrees/environment.pickle  |  Bin 451496 -> 452171 bytes
 docs/build/html/.doctrees/index.doctree       |  Bin 15874 -> 16082 bytes
 docs/build/html/_modules/collections.html     |  435 +++++--
 docs/build/html/_modules/index.html           |    2 +-
 .../html/_modules/plotly/offline/offline.html |    2 +-
 .../html/_modules/scipy/spatial/kdtree.html   | 1071 ++++++++---------
 .../spatialpy/core/boundarycondition.html     |    6 +-
 .../html/_modules/spatialpy/core/cleanup.html |    2 +-
 .../_modules/spatialpy/core/datafunction.html |    4 +-
 .../html/_modules/spatialpy/core/domain.html  |   53 +-
 .../_modules/spatialpy/core/geometry.html     |    2 +-
 .../spatialpy/core/initialcondition.html      |    2 +-
 .../html/_modules/spatialpy/core/model.html   |    2 +-
 .../_modules/spatialpy/core/parameter.html    |    2 +-
 .../_modules/spatialpy/core/reaction.html     |    2 +-
 .../html/_modules/spatialpy/core/result.html  |    2 +-
 .../spatialpy/core/spatialpyerror.html        |    2 +-
 .../html/_modules/spatialpy/core/species.html |    2 +-
 .../_modules/spatialpy/core/vtkreader.html    |    2 +-
 .../spatialpy/solvers/build_expression.html   |    2 +-
 .../_modules/spatialpy/solvers/solver.html    |    2 +-
 .../spatialpy/stochss/stochss_export.html     |    2 +-
 docs/build/html/_sources/index.rst.txt        |    2 +-
 docs/build/html/_static/basic.css             |    6 +-
 docs/build/html/_static/doctools.js           |    5 +-
 docs/build/html/_static/language_data.js      |    2 +-
 docs/build/html/_static/pygments.css          |    8 +-
 docs/build/html/_static/searchtools.js        |   23 +-
 docs/build/html/classes/spatialpy.core.html   |  934 +++++++++-----
 docs/build/html/classes/spatialpy.html        |   17 +-
 .../build/html/classes/spatialpy.solvers.html |   51 +-
 .../build/html/classes/spatialpy.stochss.html |   21 +-
 docs/build/html/genindex.html                 |   58 +-
 docs/build/html/index.html                    |   31 +-
 docs/build/html/objects.inv                   |  Bin 2547 -> 2622 bytes
 docs/build/html/py-modindex.html              |    2 +-
 docs/build/html/search.html                   |    7 +-
 docs/build/html/searchindex.js                |    2 +-
 spatialpy/core/domain.py                      |   51 +-
 44 files changed, 1673 insertions(+), 1148 deletions(-)

diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo
index ab4bd335..8940d805 100644
--- a/docs/build/html/.buildinfo
+++ b/docs/build/html/.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: 9eb3e1c2ecc65d7c728e939f73f722a8
+config: dbb3a5f1ac404bdea5afd00c4aced27b
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/build/html/.doctrees/classes/spatialpy.core.doctree b/docs/build/html/.doctrees/classes/spatialpy.core.doctree
index bf7b07074ae5c19792745e55d45c0be383267d20..7de450f71e8738e3e5183e5d5f205297b527fd81 100644
GIT binary patch
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diff --git a/docs/build/html/_modules/collections.html b/docs/build/html/_modules/collections.html
index 54bcd065..e044d857 100644
--- a/docs/build/html/_modules/collections.html
+++ b/docs/build/html/_modules/collections.html
@@ -107,17 +107,30 @@ 
Source code for collections
 
 '''
 
-__all__ = ['deque', 'defaultdict', 'namedtuple', 'UserDict', 'UserList',
-            'UserString', 'Counter', 'OrderedDict', 'ChainMap']
+__all__ = [
+    'ChainMap',
+    'Counter',
+    'OrderedDict',
+    'UserDict',
+    'UserList',
+    'UserString',
+    'defaultdict',
+    'deque',
+    'namedtuple',
+]
 
 import _collections_abc
-from operator import itemgetter as _itemgetter, eq as _eq
-from keyword import iskeyword as _iskeyword
-import sys as _sys
 import heapq as _heapq
-from _weakref import proxy as _proxy
-from itertools import repeat as _repeat, chain as _chain, starmap as _starmap
+import sys as _sys
+
+from itertools import chain as _chain
+from itertools import repeat as _repeat
+from itertools import starmap as _starmap
+from keyword import iskeyword as _iskeyword
+from operator import eq as _eq
+from operator import itemgetter as _itemgetter
 from reprlib import recursive_repr as _recursive_repr
+from _weakref import proxy as _proxy
 
 try:
     from _collections import deque
@@ -147,6 +160,7 @@ 
Source code for collections
         return obj
     raise AttributeError(f'module {__name__!r} has no attribute {name!r}')
 
+
 ################################################################################
 ### OrderedDict
 ################################################################################
@@ -384,7 +398,25 @@ 
Source code for collections
         '''
         if isinstance(other, OrderedDict):
             return dict.__eq__(self, other) and all(map(_eq, self, other))
-        return dict.__eq__(self, other)

+        return dict.__eq__(self, other)
+
+    def __ior__(self, other):
+        self.update(other)
+        return self
+
+    def __or__(self, other):
+        if not isinstance(other, dict):
+            return NotImplemented
+        new = self.__class__(self)
+        new.update(other)
+        return new
+
+    def __ror__(self, other):
+        if not isinstance(other, dict):
+            return NotImplemented
+        new = self.__class__(other)
+        new.update(self)
+        return new

 
 
 try:
@@ -474,18 +506,23 @@ 
Source code for collections
     # Variables used in the methods and docstrings
     field_names = tuple(map(_sys.intern, field_names))
     num_fields = len(field_names)
-    arg_list = repr(field_names).replace("'", "")[1:-1]
+    arg_list = ', '.join(field_names)
+    if num_fields == 1:
+        arg_list += ','
     repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')'
     tuple_new = tuple.__new__
     _dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip
 
     # Create all the named tuple methods to be added to the class namespace
 
-    s = f'def __new__(_cls, {arg_list}): return _tuple_new(_cls, ({arg_list}))'
-    namespace = {'_tuple_new': tuple_new, '__name__': f'namedtuple_{typename}'}
-    # Note: exec() has the side-effect of interning the field names
-    exec(s, namespace)
-    __new__ = namespace['__new__']
+    namespace = {
+        '_tuple_new': tuple_new,
+        '__builtins__': {},
+        '__name__': f'namedtuple_{typename}',
+    }
+    code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))'
+    __new__ = eval(code, namespace)
+    __new__.__name__ = '__new__'
     __new__.__doc__ = f'Create new instance of {typename}({arg_list})'
     if defaults is not None:
         __new__.__defaults__ = defaults
@@ -522,8 +559,14 @@ 
Source code for collections
         return _tuple(self)
 
     # Modify function metadata to help with introspection and debugging
-    for method in (__new__, _make.__func__, _replace,
-                   __repr__, _asdict, __getnewargs__):
+    for method in (
+        __new__,
+        _make.__func__,
+        _replace,
+        __repr__,
+        _asdict,
+        __getnewargs__,
+    ):
         method.__qualname__ = f'{typename}.{method.__name__}'
 
     # Build-up the class namespace dictionary
@@ -533,8 +576,6 @@ 
Source code for collections
         '__slots__': (),
         '_fields': field_names,
         '_field_defaults': field_defaults,
-        # alternate spelling for backward compatibility
-        '_fields_defaults': field_defaults,
         '__new__': __new__,
         '_make': _make,
         '_replace': _replace,
@@ -641,7 +682,7 @@ 
Source code for collections
         >>> c = Counter(a=4, b=2)                   # a new counter from keyword args
 
         '''
-        super(Counter, self).__init__()
+        super().__init__()
         self.update(iterable, **kwds)
 
     def __missing__(self, key):
@@ -725,7 +766,8 @@ 
Source code for collections
                     for elem, count in iterable.items():
                         self[elem] = count + self_get(elem, 0)
                 else:
-                    super(Counter, self).update(iterable) # fast path when counter is empty
+                    # fast path when counter is empty
+                    super().update(iterable)
             else:
                 _count_elements(self, iterable)
         if kwds:
@@ -772,13 +814,14 @@ 
Source code for collections
 
     def __repr__(self):
         if not self:
-            return '%s()' % self.__class__.__name__
+            return f'{self.__class__.__name__}()'
         try:
-            items = ', '.join(map('%r: %r'.__mod__, self.most_common()))
-            return '%s({%s})' % (self.__class__.__name__, items)
+            # dict() preserves the ordering returned by most_common()
+            d = dict(self.most_common())
         except TypeError:
             # handle case where values are not orderable
-            return '{0}({1!r})'.format(self.__class__.__name__, dict(self))
+            d = dict(self)
+        return f'{self.__class__.__name__}({d!r})'
 
     # Multiset-style mathematical operations discussed in:
     #       Knuth TAOCP Volume II section 4.6.3 exercise 19
@@ -999,7 +1042,7 @@ 
Source code for collections
     def __iter__(self):
         d = {}
         for mapping in reversed(self.maps):
-            d.update(mapping)                   # reuses stored hash values if possible
+            d.update(dict.fromkeys(mapping))    # reuses stored hash values if possible
         return iter(d)
 
     def __contains__(self, key):
@@ -1043,7 +1086,7 @@ 
Source code for collections
         try:
             del self.maps[0][key]
         except KeyError:
-            raise KeyError('Key not found in the first mapping: {!r}'.format(key))
+            raise KeyError(f'Key not found in the first mapping: {key!r}')
 
     def popitem(self):
         'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.'
@@ -1057,12 +1100,31 @@ 
Source code for collections
         try:
             return self.maps[0].pop(key, *args)
         except KeyError:
-            raise KeyError('Key not found in the first mapping: {!r}'.format(key))
+            raise KeyError(f'Key not found in the first mapping: {key!r}')
 
     def clear(self):
         'Clear maps[0], leaving maps[1:] intact.'
         self.maps[0].clear()
 
+    def __ior__(self, other):
+        self.maps[0].update(other)
+        return self
+
+    def __or__(self, other):
+        if not isinstance(other, _collections_abc.Mapping):
+            return NotImplemented
+        m = self.copy()
+        m.maps[0].update(other)
+        return m
+
+    def __ror__(self, other):
+        if not isinstance(other, _collections_abc.Mapping):
+            return NotImplemented
+        m = dict(other)
+        for child in reversed(self.maps):
+            m.update(child)
+        return self.__class__(m)
+
 
 ################################################################################
 ### UserDict
@@ -1071,38 +1133,29 @@ 
Source code for collections
 class UserDict(_collections_abc.MutableMapping):
 
     # Start by filling-out the abstract methods
-    def __init__(*args, **kwargs):
-        if not args:
-            raise TypeError("descriptor '__init__' of 'UserDict' object "
-                            "needs an argument")
-        self, *args = args
-        if len(args) > 1:
-            raise TypeError('expected at most 1 arguments, got %d' % len(args))
-        if args:
-            dict = args[0]
-        elif 'dict' in kwargs:
-            dict = kwargs.pop('dict')
-            import warnings
-            warnings.warn("Passing 'dict' as keyword argument is deprecated",
-                          DeprecationWarning, stacklevel=2)
-        else:
-            dict = None
+    def __init__(self, dict=None, /, **kwargs):
         self.data = {}
         if dict is not None:
             self.update(dict)
         if kwargs:
             self.update(kwargs)
-    __init__.__text_signature__ = '($self, dict=None, /, **kwargs)'
 
-    def __len__(self): return len(self.data)
+    def __len__(self):
+        return len(self.data)
+
     def __getitem__(self, key):
         if key in self.data:
             return self.data[key]
         if hasattr(self.__class__, "__missing__"):
             return self.__class__.__missing__(self, key)
         raise KeyError(key)
-    def __setitem__(self, key, item): self.data[key] = item
-    def __delitem__(self, key): del self.data[key]
+
+    def __setitem__(self, key, item):
+        self.data[key] = item
+
+    def __delitem__(self, key):
+        del self.data[key]
+
     def __iter__(self):
         return iter(self.data)
 
@@ -1111,7 +1164,30 @@ 
Source code for collections
         return key in self.data
 
     # Now, add the methods in dicts but not in MutableMapping
-    def __repr__(self): return repr(self.data)
+    def __repr__(self):
+        return repr(self.data)
+
+    def __or__(self, other):
+        if isinstance(other, UserDict):
+            return self.__class__(self.data | other.data)
+        if isinstance(other, dict):
+            return self.__class__(self.data | other)
+        return NotImplemented
+
+    def __ror__(self, other):
+        if isinstance(other, UserDict):
+            return self.__class__(other.data | self.data)
+        if isinstance(other, dict):
+            return self.__class__(other | self.data)
+        return NotImplemented
+
+    def __ior__(self, other):
+        if isinstance(other, UserDict):
+            self.data |= other.data
+        else:
+            self.data |= other
+        return self
+
     def __copy__(self):
         inst = self.__class__.__new__(self.__class__)
         inst.__dict__.update(self.__dict__)
@@ -1140,13 +1216,13 @@ 
Source code for collections
         return d
 
 
-
 ################################################################################
 ### UserList
 ################################################################################
 
 class UserList(_collections_abc.MutableSequence):
     """A more or less complete user-defined wrapper around list objects."""
+
     def __init__(self, initlist=None):
         self.data = []
         if initlist is not None:
@@ -1157,35 +1233,60 @@ 
Source code for collections
                 self.data[:] = initlist.data[:]
             else:
                 self.data = list(initlist)
-    def __repr__(self): return repr(self.data)
-    def __lt__(self, other): return self.data <  self.__cast(other)
-    def __le__(self, other): return self.data <= self.__cast(other)
-    def __eq__(self, other): return self.data == self.__cast(other)
-    def __gt__(self, other): return self.data >  self.__cast(other)
-    def __ge__(self, other): return self.data >= self.__cast(other)
+
+    def __repr__(self):
+        return repr(self.data)
+
+    def __lt__(self, other):
+        return self.data < self.__cast(other)
+
+    def __le__(self, other):
+        return self.data <= self.__cast(other)
+
+    def __eq__(self, other):
+        return self.data == self.__cast(other)
+
+    def __gt__(self, other):
+        return self.data > self.__cast(other)
+
+    def __ge__(self, other):
+        return self.data >= self.__cast(other)
+
     def __cast(self, other):
         return other.data if isinstance(other, UserList) else other
-    def __contains__(self, item): return item in self.data
-    def __len__(self): return len(self.data)
+
+    def __contains__(self, item):
+        return item in self.data
+
+    def __len__(self):
+        return len(self.data)
+
     def __getitem__(self, i):
         if isinstance(i, slice):
             return self.__class__(self.data[i])
         else:
             return self.data[i]
-    def __setitem__(self, i, item): self.data[i] = item
-    def __delitem__(self, i): del self.data[i]
+
+    def __setitem__(self, i, item):
+        self.data[i] = item
+
+    def __delitem__(self, i):
+        del self.data[i]
+
     def __add__(self, other):
         if isinstance(other, UserList):
             return self.__class__(self.data + other.data)
         elif isinstance(other, type(self.data)):
             return self.__class__(self.data + other)
         return self.__class__(self.data + list(other))
+
     def __radd__(self, other):
         if isinstance(other, UserList):
             return self.__class__(other.data + self.data)
         elif isinstance(other, type(self.data)):
             return self.__class__(other + self.data)
         return self.__class__(list(other) + self.data)
+
     def __iadd__(self, other):
         if isinstance(other, UserList):
             self.data += other.data
@@ -1194,28 +1295,53 @@ 
Source code for collections
         else:
             self.data += list(other)
         return self
+
     def __mul__(self, n):
-        return self.__class__(self.data*n)
+        return self.__class__(self.data * n)
+
     __rmul__ = __mul__
+
     def __imul__(self, n):
         self.data *= n
         return self
+
     def __copy__(self):
         inst = self.__class__.__new__(self.__class__)
         inst.__dict__.update(self.__dict__)
         # Create a copy and avoid triggering descriptors
         inst.__dict__["data"] = self.__dict__["data"][:]
         return inst
-    def append(self, item): self.data.append(item)
-    def insert(self, i, item): self.data.insert(i, item)
-    def pop(self, i=-1): return self.data.pop(i)
-    def remove(self, item): self.data.remove(item)
-    def clear(self): self.data.clear()
-    def copy(self): return self.__class__(self)
-    def count(self, item): return self.data.count(item)
-    def index(self, item, *args): return self.data.index(item, *args)
-    def reverse(self): self.data.reverse()
-    def sort(self, /, *args, **kwds): self.data.sort(*args, **kwds)
+
+    def append(self, item):
+        self.data.append(item)
+
+    def insert(self, i, item):
+        self.data.insert(i, item)
+
+    def pop(self, i=-1):
+        return self.data.pop(i)
+
+    def remove(self, item):
+        self.data.remove(item)
+
+    def clear(self):
+        self.data.clear()
+
+    def copy(self):
+        return self.__class__(self)
+
+    def count(self, item):
+        return self.data.count(item)
+
+    def index(self, item, *args):
+        return self.data.index(item, *args)
+
+    def reverse(self):
+        self.data.reverse()
+
+    def sort(self, /, *args, **kwds):
+        self.data.sort(*args, **kwds)
+
     def extend(self, other):
         if isinstance(other, UserList):
             self.data.extend(other.data)
@@ -1223,12 +1349,12 @@ 
Source code for collections
             self.data.extend(other)
 
 
-
 ################################################################################
 ### UserString
 ################################################################################
 
 class UserString(_collections_abc.Sequence):
+
     def __init__(self, seq):
         if isinstance(seq, str):
             self.data = seq
@@ -1236,12 +1362,25 @@ 
Source code for collections
             self.data = seq.data[:]
         else:
             self.data = str(seq)
-    def __str__(self): return str(self.data)
-    def __repr__(self): return repr(self.data)
-    def __int__(self): return int(self.data)
-    def __float__(self): return float(self.data)
-    def __complex__(self): return complex(self.data)
-    def __hash__(self): return hash(self.data)
+
+    def __str__(self):
+        return str(self.data)
+
+    def __repr__(self):
+        return repr(self.data)
+
+    def __int__(self):
+        return int(self.data)
+
+    def __float__(self):
+        return float(self.data)
+
+    def __complex__(self):
+        return complex(self.data)
+
+    def __hash__(self):
+        return hash(self.data)
+
     def __getnewargs__(self):
         return (self.data[:],)
 
@@ -1249,18 +1388,22 @@ 
Source code for collections
         if isinstance(string, UserString):
             return self.data == string.data
         return self.data == string
+
     def __lt__(self, string):
         if isinstance(string, UserString):
             return self.data < string.data
         return self.data < string
+
     def __le__(self, string):
         if isinstance(string, UserString):
             return self.data <= string.data
         return self.data <= string
+
     def __gt__(self, string):
         if isinstance(string, UserString):
             return self.data > string.data
         return self.data > string
+
     def __ge__(self, string):
         if isinstance(string, UserString):
             return self.data >= string.data
@@ -1271,105 +1414,191 @@ 
Source code for collections
             char = char.data
         return char in self.data
 
-    def __len__(self): return len(self.data)
-    def __getitem__(self, index): return self.__class__(self.data[index])
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        return self.__class__(self.data[index])
+
     def __add__(self, other):
         if isinstance(other, UserString):
             return self.__class__(self.data + other.data)
         elif isinstance(other, str):
             return self.__class__(self.data + other)
         return self.__class__(self.data + str(other))
+
     def __radd__(self, other):
         if isinstance(other, str):
             return self.__class__(other + self.data)
         return self.__class__(str(other) + self.data)
+
     def __mul__(self, n):
-        return self.__class__(self.data*n)
+        return self.__class__(self.data * n)
+
     __rmul__ = __mul__
+
     def __mod__(self, args):
         return self.__class__(self.data % args)
+
     def __rmod__(self, template):
         return self.__class__(str(template) % self)
+
     # the following methods are defined in alphabetical order:
-    def capitalize(self): return self.__class__(self.data.capitalize())
+    def capitalize(self):
+        return self.__class__(self.data.capitalize())
+
     def casefold(self):
         return self.__class__(self.data.casefold())
+
     def center(self, width, *args):
         return self.__class__(self.data.center(width, *args))
+
     def count(self, sub, start=0, end=_sys.maxsize):
         if isinstance(sub, UserString):
             sub = sub.data
         return self.data.count(sub, start, end)
+
+    def removeprefix(self, prefix, /):
+        if isinstance(prefix, UserString):
+            prefix = prefix.data
+        return self.__class__(self.data.removeprefix(prefix))
+
+    def removesuffix(self, suffix, /):
+        if isinstance(suffix, UserString):
+            suffix = suffix.data
+        return self.__class__(self.data.removesuffix(suffix))
+
     def encode(self, encoding='utf-8', errors='strict'):
         encoding = 'utf-8' if encoding is None else encoding
         errors = 'strict' if errors is None else errors
         return self.data.encode(encoding, errors)
+
     def endswith(self, suffix, start=0, end=_sys.maxsize):
         return self.data.endswith(suffix, start, end)
+
     def expandtabs(self, tabsize=8):
         return self.__class__(self.data.expandtabs(tabsize))
+
     def find(self, sub, start=0, end=_sys.maxsize):
         if isinstance(sub, UserString):
             sub = sub.data
         return self.data.find(sub, start, end)
+
     def format(self, /, *args, **kwds):
         return self.data.format(*args, **kwds)
+
     def format_map(self, mapping):
         return self.data.format_map(mapping)
+
     def index(self, sub, start=0, end=_sys.maxsize):
         return self.data.index(sub, start, end)
-    def isalpha(self): return self.data.isalpha()
-    def isalnum(self): return self.data.isalnum()
-    def isascii(self): return self.data.isascii()
-    def isdecimal(self): return self.data.isdecimal()
-    def isdigit(self): return self.data.isdigit()
-    def isidentifier(self): return self.data.isidentifier()
-    def islower(self): return self.data.islower()
-    def isnumeric(self): return self.data.isnumeric()
-    def isprintable(self): return self.data.isprintable()
-    def isspace(self): return self.data.isspace()
-    def istitle(self): return self.data.istitle()
-    def isupper(self): return self.data.isupper()
-    def join(self, seq): return self.data.join(seq)
+
+    def isalpha(self):
+        return self.data.isalpha()
+
+    def isalnum(self):
+        return self.data.isalnum()
+
+    def isascii(self):
+        return self.data.isascii()
+
+    def isdecimal(self):
+        return self.data.isdecimal()
+
+    def isdigit(self):
+        return self.data.isdigit()
+
+    def isidentifier(self):
+        return self.data.isidentifier()
+
+    def islower(self):
+        return self.data.islower()
+
+    def isnumeric(self):
+        return self.data.isnumeric()
+
+    def isprintable(self):
+        return self.data.isprintable()
+
+    def isspace(self):
+        return self.data.isspace()
+
+    def istitle(self):
+        return self.data.istitle()
+
+    def isupper(self):
+        return self.data.isupper()
+
+    def join(self, seq):
+        return self.data.join(seq)
+
     def ljust(self, width, *args):
         return self.__class__(self.data.ljust(width, *args))
-    def lower(self): return self.__class__(self.data.lower())
-    def lstrip(self, chars=None): return self.__class__(self.data.lstrip(chars))
+
+    def lower(self):
+        return self.__class__(self.data.lower())
+
+    def lstrip(self, chars=None):
+        return self.__class__(self.data.lstrip(chars))
+
     maketrans = str.maketrans
+
     def partition(self, sep):
         return self.data.partition(sep)
+
     def replace(self, old, new, maxsplit=-1):
         if isinstance(old, UserString):
             old = old.data
         if isinstance(new, UserString):
             new = new.data
         return self.__class__(self.data.replace(old, new, maxsplit))
+
     def rfind(self, sub, start=0, end=_sys.maxsize):
         if isinstance(sub, UserString):
             sub = sub.data
         return self.data.rfind(sub, start, end)
+
     def rindex(self, sub, start=0, end=_sys.maxsize):
         return self.data.rindex(sub, start, end)
+
     def rjust(self, width, *args):
         return self.__class__(self.data.rjust(width, *args))
+
     def rpartition(self, sep):
         return self.data.rpartition(sep)
+
     def rstrip(self, chars=None):
         return self.__class__(self.data.rstrip(chars))
+
     def split(self, sep=None, maxsplit=-1):
         return self.data.split(sep, maxsplit)
+
     def rsplit(self, sep=None, maxsplit=-1):
         return self.data.rsplit(sep, maxsplit)
-    def splitlines(self, keepends=False): return self.data.splitlines(keepends)
+
+    def splitlines(self, keepends=False):
+        return self.data.splitlines(keepends)
+
     def startswith(self, prefix, start=0, end=_sys.maxsize):
         return self.data.startswith(prefix, start, end)
-    def strip(self, chars=None): return self.__class__(self.data.strip(chars))
-    def swapcase(self): return self.__class__(self.data.swapcase())
-    def title(self): return self.__class__(self.data.title())
+
+    def strip(self, chars=None):
+        return self.__class__(self.data.strip(chars))
+
+    def swapcase(self):
+        return self.__class__(self.data.swapcase())
+
+    def title(self):
+        return self.__class__(self.data.title())
+
     def translate(self, *args):
         return self.__class__(self.data.translate(*args))
-    def upper(self): return self.__class__(self.data.upper())
-    def zfill(self, width): return self.__class__(self.data.zfill(width))
+
+    def upper(self):
+        return self.__class__(self.data.upper())
+
+    def zfill(self, width):
+        return self.__class__(self.data.zfill(width))
 

 
           

@@ -1389,7 +1618,7 @@ 
Source code for collections
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html
index d1f4d7cf..bf0e1e5c 100644
--- a/docs/build/html/_modules/index.html
+++ b/docs/build/html/_modules/index.html
@@ -128,7 +128,7 @@ 
All modules for which code is available

       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/plotly/offline/offline.html b/docs/build/html/_modules/plotly/offline/offline.html
index f45be670..3a9247a1 100644
--- a/docs/build/html/_modules/plotly/offline/offline.html
+++ b/docs/build/html/_modules/plotly/offline/offline.html
@@ -950,7 +950,7 @@ 
Source code for plotly.offline.offline
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/scipy/spatial/kdtree.html b/docs/build/html/_modules/scipy/spatial/kdtree.html
index e4fe7acf..6992b244 100644
--- a/docs/build/html/_modules/scipy/spatial/kdtree.html
+++ b/docs/build/html/_modules/scipy/spatial/kdtree.html
@@ -94,8 +94,8 @@ 
Source code for scipy.spatial.kdtree
 # Copyright Anne M. Archibald 2008
 # Released under the scipy license
 import numpy as np
-from heapq import heappush, heappop
-import scipy.sparse
+import warnings
+from .ckdtree import cKDTree, cKDTreeNode
 
 __all__ = ['minkowski_distance_p', 'minkowski_distance',
            'distance_matrix',
@@ -103,8 +103,7 @@ 
Source code for scipy.spatial.kdtree
 
 
 def minkowski_distance_p(x, y, p=2):
-    """
-    Compute the pth power of the L**p distance between two arrays.
+    """Compute the pth power of the L**p distance between two arrays.
 
     For efficiency, this function computes the L**p distance but does
     not extract the pth root. If `p` is 1 or infinity, this is equal to
@@ -146,8 +145,7 @@ 
Source code for scipy.spatial.kdtree
 
 
 def minkowski_distance(x, y, p=2):
-    """
-    Compute the L**p distance between two arrays.
+    """Compute the L**p distance between two arrays.
 
     Parameters
     ----------
@@ -173,7 +171,7 @@ 
Source code for scipy.spatial.kdtree
         return minkowski_distance_p(x, y, p)**(1./p)
 
 
-class Rectangle(object):
+class Rectangle:
     """Hyperrectangle class.
 
     Represents a Cartesian product of intervals.
@@ -192,8 +190,7 @@ 
Source code for scipy.spatial.kdtree
         return np.prod(self.maxes-self.mins)
 
     def split(self, d, split):
-        """
-        Produce two hyperrectangles by splitting.
+        """Produce two hyperrectangles by splitting.
 
         In general, if you need to compute maximum and minimum
         distances to the children, it can be done more efficiently
@@ -217,7 +214,8 @@ 
Source code for scipy.spatial.kdtree
 
     def min_distance_point(self, x, p=2.):
         """
-        Return the minimum distance between input and points in the hyperrectangle.
+        Return the minimum distance between input and points in the
+        hyperrectangle.
 
         Parameters
         ----------
@@ -227,7 +225,10 @@ 
Source code for scipy.spatial.kdtree
             Input.
 
         """
-        return minkowski_distance(0, np.maximum(0,np.maximum(self.mins-x,x-self.maxes)),p)
+        return minkowski_distance(
+            0, np.maximum(0, np.maximum(self.mins-x, x-self.maxes)),
+            p
+        )
 
     def max_distance_point(self, x, p=2.):
         """
@@ -241,7 +242,7 @@ 
Source code for scipy.spatial.kdtree
             Input.
 
         """
-        return minkowski_distance(0, np.maximum(self.maxes-x,x-self.mins),p)
+        return minkowski_distance(0, np.maximum(self.maxes-x, x-self.mins), p)
 
     def min_distance_rectangle(self, other, p=2.):
         """
@@ -255,7 +256,12 @@ 
Source code for scipy.spatial.kdtree
             Input.
 
         """
-        return minkowski_distance(0, np.maximum(0,np.maximum(self.mins-other.maxes,other.mins-self.maxes)),p)
+        return minkowski_distance(
+            0,
+            np.maximum(0, np.maximum(self.mins-other.maxes,
+                                     other.mins-self.maxes)),
+            p
+        )
 
     def max_distance_rectangle(self, other, p=2.):
         """
@@ -269,38 +275,46 @@ 
Source code for scipy.spatial.kdtree
             Input.
 
         """
-        return minkowski_distance(0, np.maximum(self.maxes-other.mins,other.maxes-self.mins),p)
+        return minkowski_distance(
+            0, np.maximum(self.maxes-other.mins, other.maxes-self.mins), p)
 
 
-
[docs]class KDTree(object):
-    """
-    kd-tree for quick nearest-neighbor lookup
+
[docs]class KDTree(cKDTree):
+    """kd-tree for quick nearest-neighbor lookup.
 
-    This class provides an index into a set of k-D points which
-    can be used to rapidly look up the nearest neighbors of any point.
+    This class provides an index into a set of k-dimensional points
+    which can be used to rapidly look up the nearest neighbors of any
+    point.
 
     Parameters
     ----------
-    data : (N,K) array_like
-        The data points to be indexed. This array is not copied, and
-        so modifying this data will result in bogus results.
-    leafsize : int, optional
+    data : array_like, shape (n,m)
+        The n data points of dimension m to be indexed. This array is
+        not copied unless this is necessary to produce a contiguous
+        array of doubles, and so modifying this data will result in
+        bogus results. The data are also copied if the kd-tree is built
+        with copy_data=True.
+    leafsize : positive int, optional
         The number of points at which the algorithm switches over to
-        brute-force.  Has to be positive.
-
-    Raises
-    ------
-    RuntimeError
-        The maximum recursion limit can be exceeded for large data
-        sets.  If this happens, either increase the value for the `leafsize`
-        parameter or increase the recursion limit by::
-
-            >>> import sys
-            >>> sys.setrecursionlimit(10000)
-
-    See Also
-    --------
-    cKDTree : Implementation of `KDTree` in Cython
+        brute-force.  Default: 10.
+    compact_nodes : bool, optional
+        If True, the kd-tree is built to shrink the hyperrectangles to
+        the actual data range. This usually gives a more compact tree that
+        is robust against degenerated input data and gives faster queries
+        at the expense of longer build time. Default: True.
+    copy_data : bool, optional
+        If True the data is always copied to protect the kd-tree against
+        data corruption. Default: False.
+    balanced_tree : bool, optional
+        If True, the median is used to split the hyperrectangles instead of
+        the midpoint. This usually gives a more compact tree and
+        faster queries at the expense of longer build time. Default: True.
+    boxsize : array_like or scalar, optional
+        Apply a m-d toroidal topology to the KDTree.. The topology is generated
+        by :math:`x_i + n_i L_i` where :math:`n_i` are integers and :math:`L_i`
+        is the boxsize along i-th dimension. The input data shall be wrapped
+        into :math:`[0, L_i)`. A ValueError is raised if any of the data is
+        outside of this bound.
 
     Notes
     -----
@@ -323,27 +337,45 @@ 
Source code for scipy.spatial.kdtree
     significantly faster than brute force. High-dimensional nearest-neighbor
     queries are a substantial open problem in computer science.
 
-    The tree also supports all-neighbors queries, both with arrays of points
-    and with other kd-trees. These do use a reasonably efficient algorithm,
-    but the kd-tree is not necessarily the best data structure for this
-    sort of calculation.
+    Attributes
+    ----------
+    data : ndarray, shape (n,m)
+        The n data points of dimension m to be indexed. This array is
+        not copied unless this is necessary to produce a contiguous
+        array of doubles. The data are also copied if the kd-tree is built
+        with `copy_data=True`.
+    leafsize : positive int
+        The number of points at which the algorithm switches over to
+        brute-force.
+    m : int
+        The dimension of a single data-point.
+    n : int
+        The number of data points.
+    maxes : ndarray, shape (m,)
+        The maximum value in each dimension of the n data points.
+    mins : ndarray, shape (m,)
+        The minimum value in each dimension of the n data points.
+    size : int
+        The number of nodes in the tree.
 
     """
-    def __init__(self, data, leafsize=10):
-        self.data = np.asarray(data)
-        if self.data.dtype.kind == 'c':
-            raise TypeError("KDTree does not work with complex data")
 
-        self.n, self.m = np.shape(self.data)
-        self.leafsize = int(leafsize)
-        if self.leafsize < 1:
-            raise ValueError("leafsize must be at least 1")
-        self.maxes = np.amax(self.data,axis=0)
-        self.mins = np.amin(self.data,axis=0)
+
[docs]    class node:
+        @staticmethod
+        def _create(ckdtree_node=None):
+            """Create either an inner or leaf node, wrapping a cKDTreeNode instance"""
+            if ckdtree_node is None:
+                return KDTree.node(ckdtree_node)
+            elif ckdtree_node.split_dim == -1:
+                return KDTree.leafnode(ckdtree_node)
+            else:
+                return KDTree.innernode(ckdtree_node)
 
-        self.tree = self.__build(np.arange(self.n), self.maxes, self.mins)
+        def __init__(self, ckdtree_node=None):
+            if ckdtree_node is None:
+                ckdtree_node = cKDTreeNode()
+            self._node = ckdtree_node
 
-
[docs]    class node(object):
         def __lt__(self, other):
             return id(self) < id(other)
 
@@ -360,327 +392,227 @@ 
Source code for scipy.spatial.kdtree
             return id(self) == id(other)

 
 
[docs]    class leafnode(node):
-        def __init__(self, idx):
-            self.idx = idx
-            self.children = len(idx)

+        @property
+        def idx(self):
+            return self._node.indices
+
+        @property
+        def children(self):
+            return self._node.children

 
 
[docs]    class innernode(node):
-        def __init__(self, split_dim, split, less, greater):
-            self.split_dim = split_dim
-            self.split = split
-            self.less = less
-            self.greater = greater
-            self.children = less.children+greater.children

-
-    def __build(self, idx, maxes, mins):
-        if len(idx) <= self.leafsize:
-            return KDTree.leafnode(idx)
-        else:
-            data = self.data[idx]
-            # maxes = np.amax(data,axis=0)
-            # mins = np.amin(data,axis=0)
-            d = np.argmax(maxes-mins)
-            maxval = maxes[d]
-            minval = mins[d]
-            if maxval == minval:
-                # all points are identical; warn user?
-                return KDTree.leafnode(idx)
-            data = data[:,d]
-
-            # sliding midpoint rule; see Maneewongvatana and Mount 1999
-            # for arguments that this is a good idea.
-            split = (maxval+minval)/2
-            less_idx = np.nonzero(data <= split)[0]
-            greater_idx = np.nonzero(data > split)[0]
-            if len(less_idx) == 0:
-                split = np.amin(data)
-                less_idx = np.nonzero(data <= split)[0]
-                greater_idx = np.nonzero(data > split)[0]
-            if len(greater_idx) == 0:
-                split = np.amax(data)
-                less_idx = np.nonzero(data < split)[0]
-                greater_idx = np.nonzero(data >= split)[0]
-            if len(less_idx) == 0:
-                # _still_ zero? all must have the same value
-                if not np.all(data == data[0]):
-                    raise ValueError("Troublesome data array: %s" % data)
-                split = data[0]
-                less_idx = np.arange(len(data)-1)
-                greater_idx = np.array([len(data)-1])
-
-            lessmaxes = np.copy(maxes)
-            lessmaxes[d] = split
-            greatermins = np.copy(mins)
-            greatermins[d] = split
-            return KDTree.innernode(d, split,
-                    self.__build(idx[less_idx],lessmaxes,mins),
-                    self.__build(idx[greater_idx],maxes,greatermins))
-
-    def __query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
-
-        side_distances = np.maximum(0,np.maximum(x-self.maxes,self.mins-x))
-        if p != np.inf:
-            side_distances **= p
-            min_distance = np.sum(side_distances)
-        else:
-            min_distance = np.amax(side_distances)
-
-        # priority queue for chasing nodes
-        # entries are:
-        #  minimum distance between the cell and the target
-        #  distances between the nearest side of the cell and the target
-        #  the head node of the cell
-        q = [(min_distance,
-              tuple(side_distances),
-              self.tree)]
-        # priority queue for the nearest neighbors
-        # furthest known neighbor first
-        # entries are (-distance**p, i)
-        neighbors = []
-
-        if eps == 0:
-            epsfac = 1
-        elif p == np.inf:
-            epsfac = 1/(1+eps)
-        else:
-            epsfac = 1/(1+eps)**p
-
-        if p != np.inf and distance_upper_bound != np.inf:
-            distance_upper_bound = distance_upper_bound**p
-
-        while q:
-            min_distance, side_distances, node = heappop(q)
-            if isinstance(node, KDTree.leafnode):
-                # brute-force
-                data = self.data[node.idx]
-                ds = minkowski_distance_p(data,x[np.newaxis,:],p)
-                for i in range(len(ds)):
-                    if ds[i] < distance_upper_bound:
-                        if len(neighbors) == k:
-                            heappop(neighbors)
-                        heappush(neighbors, (-ds[i], node.idx[i]))
-                        if len(neighbors) == k:
-                            distance_upper_bound = -neighbors[0][0]
-            else:
-                # we don't push cells that are too far onto the queue at all,
-                # but since the distance_upper_bound decreases, we might get
-                # here even if the cell's too far
-                if min_distance > distance_upper_bound*epsfac:
-                    # since this is the nearest cell, we're done, bail out
-                    break
-                # compute minimum distances to the children and push them on
-                if x[node.split_dim] < node.split:
-                    near, far = node.less, node.greater
-                else:
-                    near, far = node.greater, node.less
-
-                # near child is at the same distance as the current node
-                heappush(q,(min_distance, side_distances, near))
-
-                # far child is further by an amount depending only
-                # on the split value
-                sd = list(side_distances)
-                if p == np.inf:
-                    min_distance = max(min_distance, abs(node.split-x[node.split_dim]))
-                elif p == 1:
-                    sd[node.split_dim] = np.abs(node.split-x[node.split_dim])
-                    min_distance = min_distance - side_distances[node.split_dim] + sd[node.split_dim]
-                else:
-                    sd[node.split_dim] = np.abs(node.split-x[node.split_dim])**p
-                    min_distance = min_distance - side_distances[node.split_dim] + sd[node.split_dim]
-
-                # far child might be too far, if so, don't bother pushing it
-                if min_distance <= distance_upper_bound*epsfac:
-                    heappush(q,(min_distance, tuple(sd), far))
-
-        if p == np.inf:
-            return sorted([(-d,i) for (d,i) in neighbors])
-        else:
-            return sorted([((-d)**(1./p),i) for (d,i) in neighbors])
+        def __init__(self, ckdtreenode):
+            assert isinstance(ckdtreenode, cKDTreeNode)
+            super().__init__(ckdtreenode)
+            self.less = KDTree.node._create(ckdtreenode.lesser)
+            self.greater = KDTree.node._create(ckdtreenode.greater)
+
+        @property
+        def split_dim(self):
+            return self._node.split_dim
+
+        @property
+        def split(self):
+            return self._node.split
+
+        @property
+        def children(self):
+            return self._node.children

+
+    @property
+    def tree(self):
+        if not hasattr(self, "_tree"):
+            self._tree = KDTree.node._create(super().tree)
+
+        return self._tree
+
+    def __init__(self, data, leafsize=10, compact_nodes=True, copy_data=False,
+                 balanced_tree=True, boxsize=None):
+        data = np.asarray(data)
+        if data.dtype.kind == 'c':
+            raise TypeError("KDTree does not work with complex data")
 
-
[docs]    def query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
-        """
-        Query the kd-tree for nearest neighbors
+        # Note KDTree has different default leafsize from cKDTree
+        super().__init__(data, leafsize, compact_nodes, copy_data,
+                         balanced_tree, boxsize)
+
+
[docs]    def query(
+            self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf, workers=1):
+        r"""Query the kd-tree for nearest neighbors.
 
         Parameters
         ----------
         x : array_like, last dimension self.m
             An array of points to query.
-        k : int, optional
-            The number of nearest neighbors to return.
+        k : int or Sequence[int], optional
+            Either the number of nearest neighbors to return, or a list of the
+            k-th nearest neighbors to return, starting from 1.
         eps : nonnegative float, optional
             Return approximate nearest neighbors; the kth returned value
             is guaranteed to be no further than (1+eps) times the
             distance to the real kth nearest neighbor.
         p : float, 1<=p<=infinity, optional
             Which Minkowski p-norm to use.
-            1 is the sum-of-absolute-values "Manhattan" distance
-            2 is the usual Euclidean distance
-            infinity is the maximum-coordinate-difference distance
+            1 is the sum-of-absolute-values distance ("Manhattan" distance).
+            2 is the usual Euclidean distance.
+            infinity is the maximum-coordinate-difference distance.
+            A large, finite p may cause a ValueError if overflow can occur.
         distance_upper_bound : nonnegative float, optional
             Return only neighbors within this distance. This is used to prune
             tree searches, so if you are doing a series of nearest-neighbor
             queries, it may help to supply the distance to the nearest neighbor
             of the most recent point.
+        workers : int, optional
+            Number of workers to use for parallel processing. If -1 is given
+            all CPU threads are used. Default: 1.
+
+            .. versionadded:: 1.6.0
 
         Returns
         -------
         d : float or array of floats
             The distances to the nearest neighbors.
-            If x has shape tuple+(self.m,), then d has shape tuple if
-            k is one, or tuple+(k,) if k is larger than one. Missing
-            neighbors (e.g. when k > n or distance_upper_bound is
-            given) are indicated with infinite distances.  If k is None,
-            then d is an object array of shape tuple, containing lists
-            of distances. In either case the hits are sorted by distance
-            (nearest first).
+            If ``x`` has shape ``tuple+(self.m,)``, then ``d`` has shape
+            ``tuple+(k,)``.
+            When k == 1, the last dimension of the output is squeezed.
+            Missing neighbors are indicated with infinite distances.
+            Hits are sorted by distance (nearest first).
+
+            .. deprecated:: 1.6.0
+               If ``k=None``, then ``d`` is an object array of shape ``tuple``,
+               containing lists of distances. This behavior is deprecated and
+               will be removed in SciPy 1.8.0, use ``query_ball_point``
+               instead.
+
         i : integer or array of integers
-            The locations of the neighbors in self.data. i is the same
-            shape as d.
+            The index of each neighbor in ``self.data``.
+            ``i`` is the same shape as d.
+            Missing neighbors are indicated with ``self.n``.
 
         Examples
         --------
-        >>> from scipy import spatial
+
+        >>> import numpy as np
+        >>> from scipy.spatial import KDTree
         >>> x, y = np.mgrid[0:5, 2:8]
-        >>> tree = spatial.KDTree(list(zip(x.ravel(), y.ravel())))
-        >>> tree.data
-        array([[0, 2],
-               [0, 3],
-               [0, 4],
-               [0, 5],
-               [0, 6],
-               [0, 7],
-               [1, 2],
-               [1, 3],
-               [1, 4],
-               [1, 5],
-               [1, 6],
-               [1, 7],
-               [2, 2],
-               [2, 3],
-               [2, 4],
-               [2, 5],
-               [2, 6],
-               [2, 7],
-               [3, 2],
-               [3, 3],
-               [3, 4],
-               [3, 5],
-               [3, 6],
-               [3, 7],
-               [4, 2],
-               [4, 3],
-               [4, 4],
-               [4, 5],
-               [4, 6],
-               [4, 7]])
-        >>> pts = np.array([[0, 0], [2.1, 2.9]])
-        >>> tree.query(pts)
-        (array([ 2.        ,  0.14142136]), array([ 0, 13]))
-        >>> tree.query(pts[0])
-        (2.0, 0)
+        >>> tree = KDTree(np.c_[x.ravel(), y.ravel()])
+
+        To query the nearest neighbours and return squeezed result, use
+
+        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=1)
+        >>> print(dd, ii, sep='\n')
+        [2.         0.2236068]
+        [ 0 13]
+
+        To query the nearest neighbours and return unsqueezed result, use
+
+        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1])
+        >>> print(dd, ii, sep='\n')
+        [[2.        ]
+         [0.2236068]]
+        [[ 0]
+         [13]]
+
+        To query the second nearest neighbours and return unsqueezed result,
+        use
+
+        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[2])
+        >>> print(dd, ii, sep='\n')
+        [[2.23606798]
+         [0.80622577]]
+        [[ 6]
+         [19]]
+
+        To query the first and second nearest neighbours, use
+
+        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=2)
+        >>> print(dd, ii, sep='\n')
+        [[2.         2.23606798]
+         [0.2236068  0.80622577]]
+        [[ 0  6]
+         [13 19]]
+
+        or, be more specific
+
+        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2])
+        >>> print(dd, ii, sep='\n')
+        [[2.         2.23606798]
+         [0.2236068  0.80622577]]
+        [[ 0  6]
+         [13 19]]
 
         """
         x = np.asarray(x)
         if x.dtype.kind == 'c':
             raise TypeError("KDTree does not work with complex data")
-        if np.shape(x)[-1] != self.m:
-            raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x)))
-        if p < 1:
-            raise ValueError("Only p-norms with 1<=p<=infinity permitted")
-        retshape = np.shape(x)[:-1]
-        if retshape != ():
-            if k is None:
-                dd = np.empty(retshape,dtype=object)
-                ii = np.empty(retshape,dtype=object)
-            elif k > 1:
-                dd = np.empty(retshape+(k,),dtype=float)
-                dd.fill(np.inf)
-                ii = np.empty(retshape+(k,),dtype=int)
-                ii.fill(self.n)
-            elif k == 1:
-                dd = np.empty(retshape,dtype=float)
-                dd.fill(np.inf)
-                ii = np.empty(retshape,dtype=int)
-                ii.fill(self.n)
-            else:
-                raise ValueError("Requested %s nearest neighbors; acceptable numbers are integers greater than or equal to one, or None")
-            for c in np.ndindex(retshape):
-                hits = self.__query(x[c], k=k, eps=eps, p=p, distance_upper_bound=distance_upper_bound)
-                if k is None:
-                    dd[c] = [d for (d,i) in hits]
-                    ii[c] = [i for (d,i) in hits]
-                elif k > 1:
-                    for j in range(len(hits)):
-                        dd[c+(j,)], ii[c+(j,)] = hits[j]
-                elif k == 1:
-                    if len(hits) > 0:
-                        dd[c], ii[c] = hits[0]
-                    else:
-                        dd[c] = np.inf
-                        ii[c] = self.n
-            return dd, ii
-        else:
-            hits = self.__query(x, k=k, eps=eps, p=p, distance_upper_bound=distance_upper_bound)
-            if k is None:
-                return [d for (d,i) in hits], [i for (d,i) in hits]
-            elif k == 1:
-                if len(hits) > 0:
-                    return hits[0]
-                else:
-                    return np.inf, self.n
-            elif k > 1:
-                dd = np.empty(k,dtype=float)
-                dd.fill(np.inf)
-                ii = np.empty(k,dtype=int)
-                ii.fill(self.n)
-                for j in range(len(hits)):
-                    dd[j], ii[j] = hits[j]
-                return dd, ii
-            else:
-                raise ValueError("Requested %s nearest neighbors; acceptable numbers are integers greater than or equal to one, or None")

-
-    def __query_ball_point(self, x, r, p=2., eps=0):
-        R = Rectangle(self.maxes, self.mins)
-
-        def traverse_checking(node, rect):
-            if rect.min_distance_point(x, p) > r / (1. + eps):
-                return []
-            elif rect.max_distance_point(x, p) < r * (1. + eps):
-                return traverse_no_checking(node)
-            elif isinstance(node, KDTree.leafnode):
-                d = self.data[node.idx]
-                return node.idx[minkowski_distance(d, x, p) <= r].tolist()
-            else:
-                less, greater = rect.split(node.split_dim, node.split)
-                return traverse_checking(node.less, less) + \
-                       traverse_checking(node.greater, greater)
 
-        def traverse_no_checking(node):
-            if isinstance(node, KDTree.leafnode):
-                return node.idx.tolist()
-            else:
-                return traverse_no_checking(node.less) + \
-                       traverse_no_checking(node.greater)
+        if k is None:
+            # k=None, return all neighbors
+            warnings.warn(
+                "KDTree.query with k=None is deprecated and will be removed "
+                "in SciPy 1.8.0. Use KDTree.query_ball_point instead.",
+                DeprecationWarning)
+
+            # Convert index query to a lists of distance and index,
+            # sorted by distance
+            def inds_to_hits(point, neighbors):
+                dist = minkowski_distance(point, self.data[neighbors], p)
+                hits = sorted([(d, i) for d, i in zip(dist, neighbors)])
+                return [d for d, i in hits], [i for d, i in hits]
+
+            x = np.asarray(x, dtype=np.float64)
+            inds = super().query_ball_point(
+                x, distance_upper_bound, p, eps, workers)
+
+            if isinstance(inds, list):
+                return inds_to_hits(x, inds)
+
+            dists = np.empty_like(inds)
+            for idx in np.ndindex(inds.shape):
+                dists[idx], inds[idx] = inds_to_hits(x[idx], inds[idx])
+
+            return dists, inds
 
-        return traverse_checking(self.tree, R)
+        d, i = super().query(x, k, eps, p, distance_upper_bound, workers)
+        if isinstance(i, int):
+            i = np.intp(i)
+        return d, i

 
-
[docs]    def query_ball_point(self, x, r, p=2., eps=0):
+
[docs]    def query_ball_point(self, x, r, p=2., eps=0, workers=1,
+                         return_sorted=None, return_length=False):
         """Find all points within distance r of point(s) x.
 
         Parameters
         ----------
         x : array_like, shape tuple + (self.m,)
             The point or points to search for neighbors of.
-        r : positive float
-            The radius of points to return.
+        r : array_like, float
+            The radius of points to return, must broadcast to the length of x.
         p : float, optional
             Which Minkowski p-norm to use.  Should be in the range [1, inf].
+            A finite large p may cause a ValueError if overflow can occur.
         eps : nonnegative float, optional
             Approximate search. Branches of the tree are not explored if their
             nearest points are further than ``r / (1 + eps)``, and branches are
             added in bulk if their furthest points are nearer than
             ``r * (1 + eps)``.
+        workers : int, optional
+            Number of jobs to schedule for parallel processing. If -1 is given
+            all processors are used. Default: 1.
+
+            .. versionadded:: 1.6.0
+        return_sorted : bool, optional
+            Sorts returned indicies if True and does not sort them if False. If
+            None, does not sort single point queries, but does sort
+            multi-point queries which was the behavior before this option
+            was added.
+
+            .. versionadded:: 1.6.0
+        return_length: bool, optional
+            Return the number of points inside the radius instead of a list
+            of the indices.
+
+            .. versionadded:: 1.6.0
 
         Returns
         -------
@@ -701,7 +633,7 @@ 
Source code for scipy.spatial.kdtree
         >>> x, y = np.mgrid[0:5, 0:5]
         >>> points = np.c_[x.ravel(), y.ravel()]
         >>> tree = spatial.KDTree(points)
-        >>> tree.query_ball_point([2, 0], 1)
+        >>> sorted(tree.query_ball_point([2, 0], 1))
         [5, 10, 11, 15]
 
         Query multiple points and plot the results:
@@ -719,20 +651,13 @@ 
Source code for scipy.spatial.kdtree
         x = np.asarray(x)
         if x.dtype.kind == 'c':
             raise TypeError("KDTree does not work with complex data")
-        if x.shape[-1] != self.m:
-            raise ValueError("Searching for a %d-dimensional point in a "
-                             "%d-dimensional KDTree" % (x.shape[-1], self.m))
-        if len(x.shape) == 1:
-            return self.__query_ball_point(x, r, p, eps)
-        else:
-            retshape = x.shape[:-1]
-            result = np.empty(retshape, dtype=object)
-            for c in np.ndindex(retshape):
-                result[c] = self.__query_ball_point(x[c], r, p=p, eps=eps)
-            return result

+        return super().query_ball_point(
+            x, r, p, eps, workers, return_sorted, return_length)

 
 
[docs]    def query_ball_tree(self, other, r, p=2., eps=0):
-        """Find all pairs of points whose distance is at most r
+        """
+        Find all pairs of points between `self` and `other` whose distance is
+        at most r.
 
         Parameters
         ----------
@@ -755,54 +680,33 @@ 
Source code for scipy.spatial.kdtree
             For each element ``self.data[i]`` of this tree, ``results[i]`` is a
             list of the indices of its neighbors in ``other.data``.
 
-        """
-        results = [[] for i in range(self.n)]
-
-        def traverse_checking(node1, rect1, node2, rect2):
-            if rect1.min_distance_rectangle(rect2, p) > r/(1.+eps):
-                return
-            elif rect1.max_distance_rectangle(rect2, p) < r*(1.+eps):
-                traverse_no_checking(node1, node2)
-            elif isinstance(node1, KDTree.leafnode):
-                if isinstance(node2, KDTree.leafnode):
-                    d = other.data[node2.idx]
-                    for i in node1.idx:
-                        results[i] += node2.idx[minkowski_distance(d,self.data[i],p) <= r].tolist()
-                else:
-                    less, greater = rect2.split(node2.split_dim, node2.split)
-                    traverse_checking(node1,rect1,node2.less,less)
-                    traverse_checking(node1,rect1,node2.greater,greater)
-            elif isinstance(node2, KDTree.leafnode):
-                less, greater = rect1.split(node1.split_dim, node1.split)
-                traverse_checking(node1.less,less,node2,rect2)
-                traverse_checking(node1.greater,greater,node2,rect2)
-            else:
-                less1, greater1 = rect1.split(node1.split_dim, node1.split)
-                less2, greater2 = rect2.split(node2.split_dim, node2.split)
-                traverse_checking(node1.less,less1,node2.less,less2)
-                traverse_checking(node1.less,less1,node2.greater,greater2)
-                traverse_checking(node1.greater,greater1,node2.less,less2)
-                traverse_checking(node1.greater,greater1,node2.greater,greater2)
-
-        def traverse_no_checking(node1, node2):
-            if isinstance(node1, KDTree.leafnode):
-                if isinstance(node2, KDTree.leafnode):
-                    for i in node1.idx:
-                        results[i] += node2.idx.tolist()
-                else:
-                    traverse_no_checking(node1, node2.less)
-                    traverse_no_checking(node1, node2.greater)
-            else:
-                traverse_no_checking(node1.less, node2)
-                traverse_no_checking(node1.greater, node2)
+        Examples
+        --------
+        You can search all pairs of points between two kd-trees within a distance:
 
-        traverse_checking(self.tree, Rectangle(self.maxes, self.mins),
-                          other.tree, Rectangle(other.maxes, other.mins))
-        return results

+        >>> import matplotlib.pyplot as plt
+        >>> import numpy as np
+        >>> from scipy.spatial import KDTree
+        >>> rng = np.random.default_rng()
+        >>> points1 = rng.random((15, 2))
+        >>> points2 = rng.random((15, 2))
+        >>> plt.figure(figsize=(6, 6))
+        >>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14)
+        >>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14)
+        >>> kd_tree1 = KDTree(points1)
+        >>> kd_tree2 = KDTree(points2)
+        >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
+        >>> for i in range(len(indexes)):
+        ...     for j in indexes[i]:
+        ...         plt.plot([points1[i, 0], points2[j, 0]],
+        ...             [points1[i, 1], points2[j, 1]], "-r")
+        >>> plt.show()
 
-
[docs]    def query_pairs(self, r, p=2., eps=0):
-        """
-        Find all pairs of points within a distance.
+        """
+        return super().query_ball_tree(other, r, p, eps)

+
+
[docs]    def query_pairs(self, r, p=2., eps=0, output_type='set'):
+        """Find all pairs of points in `self` whose distance is at most r.
 
         Parameters
         ----------
@@ -816,177 +720,190 @@ 
Source code for scipy.spatial.kdtree
             if their nearest points are further than ``r/(1+eps)``, and
             branches are added in bulk if their furthest points are nearer
             than ``r * (1+eps)``.  `eps` has to be non-negative.
+        output_type : string, optional
+            Choose the output container, 'set' or 'ndarray'. Default: 'set'
+
+            .. versionadded:: 1.6.0
 
         Returns
         -------
-        results : set
+        results : set or ndarray
             Set of pairs ``(i,j)``, with ``i < j``, for which the corresponding
-            positions are close.
+            positions are close. If output_type is 'ndarray', an ndarry is
+            returned instead of a set.
+
+        Examples
+        --------
+        You can search all pairs of points in a kd-tree within a distance:
+
+        >>> import matplotlib.pyplot as plt
+        >>> import numpy as np
+        >>> from scipy.spatial import KDTree
+        >>> rng = np.random.default_rng()
+        >>> points = rng.random((20, 2))
+        >>> plt.figure(figsize=(6, 6))
+        >>> plt.plot(points[:, 0], points[:, 1], "xk", markersize=14)
+        >>> kd_tree = KDTree(points)
+        >>> pairs = kd_tree.query_pairs(r=0.2)
+        >>> for (i, j) in pairs:
+        ...     plt.plot([points[i, 0], points[j, 0]],
+        ...             [points[i, 1], points[j, 1]], "-r")
+        >>> plt.show()
 
         """
-        results = set()
-
-        def traverse_checking(node1, rect1, node2, rect2):
-            if rect1.min_distance_rectangle(rect2, p) > r/(1.+eps):
-                return
-            elif rect1.max_distance_rectangle(rect2, p) < r*(1.+eps):
-                traverse_no_checking(node1, node2)
-            elif isinstance(node1, KDTree.leafnode):
-                if isinstance(node2, KDTree.leafnode):
-                    # Special care to avoid duplicate pairs
-                    if id(node1) == id(node2):
-                        d = self.data[node2.idx]
-                        for i in node1.idx:
-                            for j in node2.idx[minkowski_distance(d,self.data[i],p) <= r]:
-                                if i < j:
-                                    results.add((i,j))
-                    else:
-                        d = self.data[node2.idx]
-                        for i in node1.idx:
-                            for j in node2.idx[minkowski_distance(d,self.data[i],p) <= r]:
-                                if i < j:
-                                    results.add((i,j))
-                                elif j < i:
-                                    results.add((j,i))
-                else:
-                    less, greater = rect2.split(node2.split_dim, node2.split)
-                    traverse_checking(node1,rect1,node2.less,less)
-                    traverse_checking(node1,rect1,node2.greater,greater)
-            elif isinstance(node2, KDTree.leafnode):
-                less, greater = rect1.split(node1.split_dim, node1.split)
-                traverse_checking(node1.less,less,node2,rect2)
-                traverse_checking(node1.greater,greater,node2,rect2)
-            else:
-                less1, greater1 = rect1.split(node1.split_dim, node1.split)
-                less2, greater2 = rect2.split(node2.split_dim, node2.split)
-                traverse_checking(node1.less,less1,node2.less,less2)
-                traverse_checking(node1.less,less1,node2.greater,greater2)
-
-                # Avoid traversing (node1.less, node2.greater) and
-                # (node1.greater, node2.less) (it's the same node pair twice
-                # over, which is the source of the complication in the
-                # original KDTree.query_pairs)
-                if id(node1) != id(node2):
-                    traverse_checking(node1.greater,greater1,node2.less,less2)
-
-                traverse_checking(node1.greater,greater1,node2.greater,greater2)
-
-        def traverse_no_checking(node1, node2):
-            if isinstance(node1, KDTree.leafnode):
-                if isinstance(node2, KDTree.leafnode):
-                    # Special care to avoid duplicate pairs
-                    if id(node1) == id(node2):
-                        for i in node1.idx:
-                            for j in node2.idx:
-                                if i < j:
-                                    results.add((i,j))
-                    else:
-                        for i in node1.idx:
-                            for j in node2.idx:
-                                if i < j:
-                                    results.add((i,j))
-                                elif j < i:
-                                    results.add((j,i))
-                else:
-                    traverse_no_checking(node1, node2.less)
-                    traverse_no_checking(node1, node2.greater)
-            else:
-                # Avoid traversing (node1.less, node2.greater) and
-                # (node1.greater, node2.less) (it's the same node pair twice
-                # over, which is the source of the complication in the
-                # original KDTree.query_pairs)
-                if id(node1) == id(node2):
-                    traverse_no_checking(node1.less, node2.less)
-                    traverse_no_checking(node1.less, node2.greater)
-                    traverse_no_checking(node1.greater, node2.greater)
-                else:
-                    traverse_no_checking(node1.less, node2)
-                    traverse_no_checking(node1.greater, node2)
-
-        traverse_checking(self.tree, Rectangle(self.maxes, self.mins),
-                          self.tree, Rectangle(self.maxes, self.mins))
-        return results

-
-
[docs]    def count_neighbors(self, other, r, p=2.):
-        """
-        Count how many nearby pairs can be formed.
+        return super().query_pairs(r, p, eps, output_type)

+
+
[docs]    def count_neighbors(self, other, r, p=2., weights=None, cumulative=True):
+        """Count how many nearby pairs can be formed.
 
-        Count the number of pairs (x1,x2) can be formed, with x1 drawn
-        from self and x2 drawn from ``other``, and where
+        Count the number of pairs ``(x1,x2)`` can be formed, with ``x1`` drawn
+        from ``self`` and ``x2`` drawn from ``other``, and where
         ``distance(x1, x2, p) <= r``.
-        This is the "two-point correlation" described in Gray and Moore 2000,
-        "N-body problems in statistical learning", and the code here is based
-        on their algorithm.
+
+        Data points on ``self`` and ``other`` are optionally weighted by the
+        ``weights`` argument. (See below)
+
+        This is adapted from the "two-point correlation" algorithm described by
+        Gray and Moore [1]_.  See notes for further discussion.
 
         Parameters
         ----------
-        other : KDTree instance
-            The other tree to draw points from.
+        other : KDTree
+            The other tree to draw points from, can be the same tree as self.
         r : float or one-dimensional array of floats
             The radius to produce a count for. Multiple radii are searched with
             a single tree traversal.
-        p : float, 1<=p<=infinity, optional
-            Which Minkowski p-norm to use
+            If the count is non-cumulative(``cumulative=False``), ``r`` defines
+            the edges of the bins, and must be non-decreasing.
+        p : float, optional
+            1<=p<=infinity.
+            Which Minkowski p-norm to use.
+            Default 2.0.
+            A finite large p may cause a ValueError if overflow can occur.
+        weights : tuple, array_like, or None, optional
+            If None, the pair-counting is unweighted.
+            If given as a tuple, weights[0] is the weights of points in
+            ``self``, and weights[1] is the weights of points in ``other``;
+            either can be None to indicate the points are unweighted.
+            If given as an array_like, weights is the weights of points in
+            ``self`` and ``other``. For this to make sense, ``self`` and
+            ``other`` must be the same tree. If ``self`` and ``other`` are two
+            different trees, a ``ValueError`` is raised.
+            Default: None
+
+            .. versionadded:: 1.6.0
+        cumulative : bool, optional
+            Whether the returned counts are cumulative. When cumulative is set
+            to ``False`` the algorithm is optimized to work with a large number
+            of bins (>10) specified by ``r``. When ``cumulative`` is set to
+            True, the algorithm is optimized to work with a small number of
+            ``r``. Default: True
+
+            .. versionadded:: 1.6.0
 
         Returns
         -------
-        result : int or 1-D array of ints
-            The number of pairs. Note that this is internally stored in a numpy
-            int, and so may overflow if very large (2e9).
+        result : scalar or 1-D array
+            The number of pairs. For unweighted counts, the result is integer.
+            For weighted counts, the result is float.
+            If cumulative is False, ``result[i]`` contains the counts with
+            ``(-inf if i == 0 else r[i-1]) < R <= r[i]``
+
+        Notes
+        -----
+        Pair-counting is the basic operation used to calculate the two point
+        correlation functions from a data set composed of position of objects.
+
+        Two point correlation function measures the clustering of objects and
+        is widely used in cosmology to quantify the large scale structure
+        in our Universe, but it may be useful for data analysis in other fields
+        where self-similar assembly of objects also occur.
+
+        The Landy-Szalay estimator for the two point correlation function of
+        ``D`` measures the clustering signal in ``D``. [2]_
+
+        For example, given the position of two sets of objects,
+
+        - objects ``D`` (data) contains the clustering signal, and
+
+        - objects ``R`` (random) that contains no signal,
+
+        .. math::
+
+             \\xi(r) = \\frac{<D, D> - 2 f <D, R> + f^2<R, R>}{f^2<R, R>},
+
+        where the brackets represents counting pairs between two data sets
+        in a finite bin around ``r`` (distance), corresponding to setting
+        `cumulative=False`, and ``f = float(len(D)) / float(len(R))`` is the
+        ratio between number of objects from data and random.
+
+        The algorithm implemented here is loosely based on the dual-tree
+        algorithm described in [1]_. We switch between two different
+        pair-cumulation scheme depending on the setting of ``cumulative``.
+        The computing time of the method we use when for
+        ``cumulative == False`` does not scale with the total number of bins.
+        The algorithm for ``cumulative == True`` scales linearly with the
+        number of bins, though it is slightly faster when only
+        1 or 2 bins are used. [5]_.
+
+        As an extension to the naive pair-counting,
+        weighted pair-counting counts the product of weights instead
+        of number of pairs.
+        Weighted pair-counting is used to estimate marked correlation functions
+        ([3]_, section 2.2),
+        or to properly calculate the average of data per distance bin
+        (e.g. [4]_, section 2.1 on redshift).
+
+        .. [1] Gray and Moore,
+               "N-body problems in statistical learning",
+               Mining the sky, 2000,
+               https://arxiv.org/abs/astro-ph/0012333
+
+        .. [2] Landy and Szalay,
+               "Bias and variance of angular correlation functions",
+               The Astrophysical Journal, 1993,
+               http://adsabs.harvard.edu/abs/1993ApJ...412...64L
+
+        .. [3] Sheth, Connolly and Skibba,
+               "Marked correlations in galaxy formation models",
+               Arxiv e-print, 2005,
+               https://arxiv.org/abs/astro-ph/0511773
+
+        .. [4] Hawkins, et al.,
+               "The 2dF Galaxy Redshift Survey: correlation functions,
+               peculiar velocities and the matter density of the Universe",
+               Monthly Notices of the Royal Astronomical Society, 2002,
+               http://adsabs.harvard.edu/abs/2003MNRAS.346...78H
+
+        .. [5] https://github.com/scipy/scipy/pull/5647#issuecomment-168474926
+
+        Examples
+        --------
+        You can count neighbors number between two kd-trees within a distance:
+
+        >>> import numpy as np
+        >>> from scipy.spatial import KDTree
+        >>> rng = np.random.default_rng()
+        >>> points1 = rng.random((5, 2))
+        >>> points2 = rng.random((5, 2))
+        >>> kd_tree1 = KDTree(points1)
+        >>> kd_tree2 = KDTree(points2)
+        >>> kd_tree1.count_neighbors(kd_tree2, 0.2)
+        1
+
+        This number is same as the total pair number calculated by
+        `query_ball_tree`:
+
+        >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
+        >>> sum([len(i) for i in indexes])
+        1
 
         """
-        def traverse(node1, rect1, node2, rect2, idx):
-            min_r = rect1.min_distance_rectangle(rect2,p)
-            max_r = rect1.max_distance_rectangle(rect2,p)
-            c_greater = r[idx] > max_r
-            result[idx[c_greater]] += node1.children*node2.children
-            idx = idx[(min_r <= r[idx]) & (r[idx] <= max_r)]
-            if len(idx) == 0:
-                return
-
-            if isinstance(node1,KDTree.leafnode):
-                if isinstance(node2,KDTree.leafnode):
-                    ds = minkowski_distance(self.data[node1.idx][:,np.newaxis,:],
-                                  other.data[node2.idx][np.newaxis,:,:],
-                                  p).ravel()
-                    ds.sort()
-                    result[idx] += np.searchsorted(ds,r[idx],side='right')
-                else:
-                    less, greater = rect2.split(node2.split_dim, node2.split)
-                    traverse(node1, rect1, node2.less, less, idx)
-                    traverse(node1, rect1, node2.greater, greater, idx)
-            else:
-                if isinstance(node2,KDTree.leafnode):
-                    less, greater = rect1.split(node1.split_dim, node1.split)
-                    traverse(node1.less, less, node2, rect2, idx)
-                    traverse(node1.greater, greater, node2, rect2, idx)
-                else:
-                    less1, greater1 = rect1.split(node1.split_dim, node1.split)
-                    less2, greater2 = rect2.split(node2.split_dim, node2.split)
-                    traverse(node1.less,less1,node2.less,less2,idx)
-                    traverse(node1.less,less1,node2.greater,greater2,idx)
-                    traverse(node1.greater,greater1,node2.less,less2,idx)
-                    traverse(node1.greater,greater1,node2.greater,greater2,idx)
-
-        R1 = Rectangle(self.maxes, self.mins)
-        R2 = Rectangle(other.maxes, other.mins)
-        if np.shape(r) == ():
-            r = np.array([r])
-            result = np.zeros(1,dtype=int)
-            traverse(self.tree, R1, other.tree, R2, np.arange(1))
-            return result[0]
-        elif len(np.shape(r)) == 1:
-            r = np.asarray(r)
-            n, = r.shape
-            result = np.zeros(n,dtype=int)
-            traverse(self.tree, R1, other.tree, R2, np.arange(n))
-            return result
-        else:
-            raise ValueError("r must be either a single value or a one-dimensional array of values")

+        return super().count_neighbors(other, r, p, weights, cumulative)

 
-
[docs]    def sparse_distance_matrix(self, other, max_distance, p=2.):
-        """
-        Compute a sparse distance matrix
+
[docs]    def sparse_distance_matrix(
+            self, other, max_distance, p=2., output_type='dok_matrix'):
+        """Compute a sparse distance matrix.
 
         Computes a distance matrix between two KDTrees, leaving as zero
         any distance greater than max_distance.
@@ -997,50 +914,60 @@ 
Source code for scipy.spatial.kdtree
 
         max_distance : positive float
 
-        p : float, optional
+        p : float, 1<=p<=infinity
+            Which Minkowski p-norm to use.
+            A finite large p may cause a ValueError if overflow can occur.
+
+        output_type : string, optional
+            Which container to use for output data. Options: 'dok_matrix',
+            'coo_matrix', 'dict', or 'ndarray'. Default: 'dok_matrix'.
+
+            .. versionadded:: 1.6.0
 
         Returns
         -------
-        result : dok_matrix
-            Sparse matrix representing the results in "dictionary of keys" format.
+        result : dok_matrix, coo_matrix, dict or ndarray
+            Sparse matrix representing the results in "dictionary of keys"
+            format. If a dict is returned the keys are (i,j) tuples of indices.
+            If output_type is 'ndarray' a record array with fields 'i', 'j',
+            and 'v' is returned,
 
-        """
-        result = scipy.sparse.dok_matrix((self.n,other.n))
-
-        def traverse(node1, rect1, node2, rect2):
-            if rect1.min_distance_rectangle(rect2, p) > max_distance:
-                return
-            elif isinstance(node1, KDTree.leafnode):
-                if isinstance(node2, KDTree.leafnode):
-                    for i in node1.idx:
-                        for j in node2.idx:
-                            d = minkowski_distance(self.data[i],other.data[j],p)
-                            if d <= max_distance:
-                                result[i,j] = d
-                else:
-                    less, greater = rect2.split(node2.split_dim, node2.split)
-                    traverse(node1,rect1,node2.less,less)
-                    traverse(node1,rect1,node2.greater,greater)
-            elif isinstance(node2, KDTree.leafnode):
-                less, greater = rect1.split(node1.split_dim, node1.split)
-                traverse(node1.less,less,node2,rect2)
-                traverse(node1.greater,greater,node2,rect2)
-            else:
-                less1, greater1 = rect1.split(node1.split_dim, node1.split)
-                less2, greater2 = rect2.split(node2.split_dim, node2.split)
-                traverse(node1.less,less1,node2.less,less2)
-                traverse(node1.less,less1,node2.greater,greater2)
-                traverse(node1.greater,greater1,node2.less,less2)
-                traverse(node1.greater,greater1,node2.greater,greater2)
-        traverse(self.tree, Rectangle(self.maxes, self.mins),
-                 other.tree, Rectangle(other.maxes, other.mins))
+        Examples
+        --------
+        You can compute a sparse distance matrix between two kd-trees:
+
+        >>> import numpy as np
+        >>> from scipy.spatial import KDTree
+        >>> rng = np.random.default_rng()
+        >>> points1 = rng.random((5, 2))
+        >>> points2 = rng.random((5, 2))
+        >>> kd_tree1 = KDTree(points1)
+        >>> kd_tree2 = KDTree(points2)
+        >>> sdm = kd_tree1.sparse_distance_matrix(kd_tree2, 0.3)
+        >>> sdm.toarray()
+        array([[0.        , 0.        , 0.12295571, 0.        , 0.        ],
+           [0.        , 0.        , 0.        , 0.        , 0.        ],
+           [0.28942611, 0.        , 0.        , 0.2333084 , 0.        ],
+           [0.        , 0.        , 0.        , 0.        , 0.        ],
+           [0.24617575, 0.29571802, 0.26836782, 0.        , 0.        ]])
+
+        You can check distances above the `max_distance` are zeros:
+
+        >>> from scipy.spatial import distance_matrix
+        >>> distance_matrix(points1, points2)
+        array([[0.56906522, 0.39923701, 0.12295571, 0.8658745 , 0.79428925],
+           [0.37327919, 0.7225693 , 0.87665969, 0.32580855, 0.75679479],
+           [0.28942611, 0.30088013, 0.6395831 , 0.2333084 , 0.33630734],
+           [0.31994999, 0.72658602, 0.71124834, 0.55396483, 0.90785663],
+           [0.24617575, 0.29571802, 0.26836782, 0.57714465, 0.6473269 ]])
 
-        return result

+        """
+        return super().sparse_distance_matrix(
+            other, max_distance, p, output_type)

 
 
 def distance_matrix(x, y, p=2, threshold=1000000):
-    """
-    Compute the distance matrix.
+    """Compute the distance matrix.
 
     Returns the matrix of all pair-wise distances.
 
@@ -1109,7 +1036,7 @@ 
Source code for scipy.spatial.kdtree
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/boundarycondition.html b/docs/build/html/_modules/spatialpy/core/boundarycondition.html
index 514b800d..cb78923f 100644
--- a/docs/build/html/_modules/spatialpy/core/boundarycondition.html
+++ b/docs/build/html/_modules/spatialpy/core/boundarycondition.html
@@ -124,7 +124,7 @@ 
Source code for spatialpy.core.boundarycondition

          - type_id: type (subdomain) of the partciles
     Targets (one of the following must be set):
         property: (str), 'nu', 'rho','v'
-        species: (str) name of a chemical species.
+        species: (str) name of a chemical species. \
                    Must also set deterministic=True/False flag.
 
     :param xmin: x-axis coordinate lower bound of **condition**
@@ -152,7 +152,7 @@ 
Source code for spatialpy.core.boundarycondition

     and applies to deterministic simulation. **BoundaryCondition not yet implemeneted for Stochastic Species**.
     :type deterministic: bool
 
-    :param target: Set **target** to properties, can be 'nu' 'rho' or 'v' or species name
+    :param target: Set **target** to properties, can be 'nu' 'rho' or 'v' or species name \
     If species name, determinstic must also be set to True/False.
     :type target: str
 
@@ -277,7 +277,7 @@ 
Source code for spatialpy.core.boundarycondition

       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/cleanup.html b/docs/build/html/_modules/spatialpy/core/cleanup.html
index 81d264d1..e5d3fe39 100644
--- a/docs/build/html/_modules/spatialpy/core/cleanup.html
+++ b/docs/build/html/_modules/spatialpy/core/cleanup.html
@@ -199,7 +199,7 @@ 
Source code for spatialpy.core.cleanup
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/datafunction.html b/docs/build/html/_modules/spatialpy/core/datafunction.html
index 97cecec1..f4275e2c 100644
--- a/docs/build/html/_modules/spatialpy/core/datafunction.html
+++ b/docs/build/html/_modules/spatialpy/core/datafunction.html
@@ -116,6 +116,8 @@ 
Source code for spatialpy.core.datafunction
 
     :param name: Name of the Data Function.
     :type name: str
+
+    :raises DataFunctionError: If a name is not provided.
     """
 
     def __init__(self, name=None):
@@ -157,7 +159,7 @@ 
Source code for spatialpy.core.datafunction
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/domain.html b/docs/build/html/_modules/spatialpy/core/domain.html
index dd647154..b7adf18a 100644
--- a/docs/build/html/_modules/spatialpy/core/domain.html
+++ b/docs/build/html/_modules/spatialpy/core/domain.html
@@ -225,10 +225,10 @@ 
Source code for spatialpy.core.domain
 
 
[docs]    def compile_prep(self):
         """
-        Generate the domain list of type ids and check for invalid type_ids and rho values
+        Generate the domains list of type ids and check for invalid type_ids and rho values
         in preperation of compiling the simulation files.
 
-        :raises DomainError: If a type_id is not set or rh for a particle is 0.
+        :raises DomainError: If a type_id is not set or rho=0 for a particle.
         """
         if self.type_id.tolist().count(None) > 0:
             raise DomainError(f"Particles must be assigned a type_id.")
@@ -243,7 +243,7 @@ 
Source code for spatialpy.core.domain
         Add a single point particle to the domain space.
 
         :param point: Spatial coordinate vertices of point to be added
-        :type point: tuple(float, float, float) or tuple(float, float)
+        :type point: tuple(float, float, float)
 
         :param vol: Default volume of particle to be added
         :type vol: float
@@ -251,19 +251,19 @@ 
Source code for spatialpy.core.domain
         :param mass: Default mass of particle to be added
         :type mass: float
 
-        :param type_id: Particle type ID of particle to be created
+        :param type_id: Particle type ID of particle to be craddedeated
         :type type_id: str | int
 
-        :param nu: Default viscosity of particle to be created
+        :param nu: Default viscosity of particle to be added
         :type nu: float
 
         :param fixed: True if particle is spatially fixed, else False
         :type fixed: bool
 
-        :param c: Default artificial speed of sound of particle to be created
+        :param c: Default artificial speed of sound of particle to be added
         :type c: float
 
-        :param rho: Default density of particle to be created
+        :param rho: Default density of particle to be added
         :type rho: float
 
         :raises DomainError: Type_id is 0 or type_id contains an invalid character.
@@ -297,9 +297,9 @@ 
Source code for spatialpy.core.domain
         """
         Add a type definition to the domain. By default, all regions are set to type 0.
 
-        :param geometry_ivar: an instance of a 'spatialpy.Geometry' subclass.  The 'inside()' method
+        :param geometry_ivar: an instance of a :py:class:`Geometry` subclass.  The 'inside()' method
                    of this object will be used to assign properties to points.
-        :type geometry_ivar: spatialpy.Geometry.Geometry
+        :type geometry_ivar: spatialpy.geometry.Geometry
 
         :param type_id: The identifier for this type.
         :type type_id: str | int
@@ -394,8 +394,7 @@ 
Source code for spatialpy.core.domain
         :param zmax: Maximum z value of the bounding box (defaults to Domain.zlim[1]).
         :type zmax: float
 
-        :param kwargs: Key word arguments for Domain.add_point.
-        :type kwargs: dict
+        :param \**kwargs: addition keyword arguments passed to :py:meth:`Domain.add_point`.
 
         :returns: The number of particles that were created within this geometry.
         :rtype: int
@@ -509,10 +508,10 @@ 
Source code for spatialpy.core.domain
         Get distance between 2 domain vertices.
 
         :param start: Starting point
-        :type start: tuple(float, float, float) or tuple(float, float)
+        :type start: tuple(float, float, float)
 
         :param end: Ending point
-        :type end: tuple(float, float, float) or tuple(float, float)
+        :type end: tuple(float, float, float)
 
         :returns: a distance measurement between start and end point
         :rtype: float
@@ -524,7 +523,7 @@ 
Source code for spatialpy.core.domain
         Find the nearest vertex of a given point in the domain.
 
         :param point: Target source point
-        :type point: tuple(float, float, float) or tuple(float, float)
+        :type point: tuple(float, float, float)
 
         :returns: The coordinates of the nearest vertex to the source point.
         :rtype: tuple(float, float, float) or tuple(float, float)
@@ -948,20 +947,13 @@ 
Source code for spatialpy.core.domain
         :type c: float
 
         :param rho: default density of particles to be created.
-        :type rho:
+        :type rho: float
 
         :param fixed: spatially fixed flag of particles to be created. Defaults to false.
         :type fixed: bool
 
-        :param rho0: background density for the system. Defaults to 1.0
-        :type rho0: float
-
-        :param c0: speed of sound for the system. Defaults to 10
-        :type c0: float
-
-        :param P0: background pressure for the system. Defaults to 10
-        :type P0: float
-
+        :param \**kwargs: addition keyword arguments passed to :py:class:`Domain`.
+        
         :returns: Uniform 3D SpatialPy Domain object.
         :rtype: spatialpy.Domain.Domain
         """
@@ -1014,19 +1006,12 @@ 
Source code for spatialpy.core.domain
         :type c: float
 
         :param rho: default density of particles to be created.
-        :type rho:
+        :type rho: float
 
         :param fixed: spatially fixed flag of particles to be created. Defaults to false.
         :type fixed: bool
 
-        :param rho0: background density for the system. Defaults to 1.0
-        :type rho0: float
-
-        :param c0: speed of sound for the system. Defaults to 10
-        :type c0: float
-
-        :param P0: background pressure for the system. Defaults to 10
-        :type P0: float
+        :param \**kwargs: addition keyword arguments passed to :py:class:`Domain`.
 
         :returns: Uniform 2D SpatialPy Domain object.
         :rtype: spatialpy.Domain.Domain
@@ -1065,7 +1050,7 @@ 
Source code for spatialpy.core.domain
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/geometry.html b/docs/build/html/_modules/spatialpy/core/geometry.html
index bbdc26dc..df0a2100 100644
--- a/docs/build/html/_modules/spatialpy/core/geometry.html
+++ b/docs/build/html/_modules/spatialpy/core/geometry.html
@@ -201,7 +201,7 @@ 
Source code for spatialpy.core.geometry
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/initialcondition.html b/docs/build/html/_modules/spatialpy/core/initialcondition.html
index 13d3e41e..cb3c93a8 100644
--- a/docs/build/html/_modules/spatialpy/core/initialcondition.html
+++ b/docs/build/html/_modules/spatialpy/core/initialcondition.html
@@ -294,7 +294,7 @@ 
Source code for spatialpy.core.initialcondition
<
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/model.html b/docs/build/html/_modules/spatialpy/core/model.html
index 5f5db543..53d202c1 100644
--- a/docs/build/html/_modules/spatialpy/core/model.html
+++ b/docs/build/html/_modules/spatialpy/core/model.html
@@ -856,7 +856,7 @@ 
Source code for spatialpy.core.model
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/parameter.html b/docs/build/html/_modules/spatialpy/core/parameter.html
index a1bd41b0..c3259fed 100644
--- a/docs/build/html/_modules/spatialpy/core/parameter.html
+++ b/docs/build/html/_modules/spatialpy/core/parameter.html
@@ -181,7 +181,7 @@ 
Source code for spatialpy.core.parameter
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/reaction.html b/docs/build/html/_modules/spatialpy/core/reaction.html
index 3525a238..f3f0cadd 100644
--- a/docs/build/html/_modules/spatialpy/core/reaction.html
+++ b/docs/build/html/_modules/spatialpy/core/reaction.html
@@ -386,7 +386,7 @@ 
Source code for spatialpy.core.reaction
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/result.html b/docs/build/html/_modules/spatialpy/core/result.html
index 1eff9884..f99c972e 100644
--- a/docs/build/html/_modules/spatialpy/core/result.html
+++ b/docs/build/html/_modules/spatialpy/core/result.html
@@ -992,7 +992,7 @@ 
Source code for spatialpy.core.result
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/spatialpyerror.html b/docs/build/html/_modules/spatialpy/core/spatialpyerror.html
index 2cf15b1b..c511a67e 100644
--- a/docs/build/html/_modules/spatialpy/core/spatialpyerror.html
+++ b/docs/build/html/_modules/spatialpy/core/spatialpyerror.html
@@ -207,7 +207,7 @@ 
Source code for spatialpy.core.spatialpyerror
Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/species.html b/docs/build/html/_modules/spatialpy/core/species.html
index 0de7a3d4..edd18741 100644
--- a/docs/build/html/_modules/spatialpy/core/species.html
+++ b/docs/build/html/_modules/spatialpy/core/species.html
@@ -192,7 +192,7 @@ 
Source code for spatialpy.core.species
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/core/vtkreader.html b/docs/build/html/_modules/spatialpy/core/vtkreader.html
index 68768261..589bbff2 100644
--- a/docs/build/html/_modules/spatialpy/core/vtkreader.html
+++ b/docs/build/html/_modules/spatialpy/core/vtkreader.html
@@ -306,7 +306,7 @@ 
Source code for spatialpy.core.vtkreader
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/solvers/build_expression.html b/docs/build/html/_modules/spatialpy/solvers/build_expression.html
index 6aaf4746..20b81fe4 100644
--- a/docs/build/html/_modules/spatialpy/solvers/build_expression.html
+++ b/docs/build/html/_modules/spatialpy/solvers/build_expression.html
@@ -865,7 +865,7 @@ 
Source code for spatialpy.solvers.build_expression
Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/solvers/solver.html b/docs/build/html/_modules/spatialpy/solvers/solver.html
index c4967386..a4d18b10 100644
--- a/docs/build/html/_modules/spatialpy/solvers/solver.html
+++ b/docs/build/html/_modules/spatialpy/solvers/solver.html
@@ -724,7 +724,7 @@ 
Source code for spatialpy.solvers.solver
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_modules/spatialpy/stochss/stochss_export.html b/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
index bb8ca688..01aa00a1 100644
--- a/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
+++ b/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
@@ -363,7 +363,7 @@ 
Source code for spatialpy.stochss.stochss_export

       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.1.1
+      Powered by Sphinx 4.4.0
       & Alabaster 0.7.12
       
     

diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
index ef9b1b78..450e6250 100644
--- a/docs/build/html/_sources/index.rst.txt
+++ b/docs/build/html/_sources/index.rst.txt
@@ -20,7 +20,7 @@ The latest version of SpatialPy can be found on `PyPI `_ for more information on how to build and simulate your models with SpatialPy. For an example of how to use SpatialPy to simulate a spatial stochastic reaction-diffusion system, see the `3D Cylinder Demo `_. We also provide examples of how to use SpatialPy to simulate physics (`Gravity Demo `_) and fluid flow (`Weir Model `_).
+See our `Example Notebook - Start Here `_ for more information on how to build and simulate your models with SpatialPy. For an example of how to use SpatialPy to simulate a spatial stochastic reaction-diffusion system, see the `3D Cylinder Demo `_. We also provide examples of how to use SpatialPy to simulate physics (`Gravity Demo `_) and fluid flow (`Weir Model `_).
 
 
 Reporting Issues
diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css
index 912859b5..bf18350b 100644
--- a/docs/build/html/_static/basic.css
+++ b/docs/build/html/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -731,8 +731,9 @@ dl.glossary dt {
 
 .classifier:before {
     font-style: normal;
-    margin: 0.5em;
+    margin: 0 0.5em;
     content: ":";
+    display: inline-block;
 }
 
 abbr, acronym {
@@ -756,6 +757,7 @@ span.pre {
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
+    white-space: nowrap;
 }
 
 div[class*="highlight-"] {
diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js
index 8cbf1b16..e509e483 100644
--- a/docs/build/html/_static/doctools.js
+++ b/docs/build/html/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for all documentation.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -264,6 +264,9 @@ var Documentation = {
   hideSearchWords : function() {
     $('#searchbox .highlight-link').fadeOut(300);
     $('span.highlighted').removeClass('highlighted');
+    var url = new URL(window.location);
+    url.searchParams.delete('highlight');
+    window.history.replaceState({}, '', url);
   },
 
   /**
diff --git a/docs/build/html/_static/language_data.js b/docs/build/html/_static/language_data.js
index 863704b3..ebe2f03b 100644
--- a/docs/build/html/_static/language_data.js
+++ b/docs/build/html/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/build/html/_static/pygments.css b/docs/build/html/_static/pygments.css
index 1b945229..87f8bd12 100644
--- a/docs/build/html/_static/pygments.css
+++ b/docs/build/html/_static/pygments.css
@@ -1,7 +1,7 @@
-pre { line-height: 125%; margin: 0; }
-td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
-td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
 .highlight { background: #f8f8f8; }
diff --git a/docs/build/html/_static/searchtools.js b/docs/build/html/_static/searchtools.js
index 8eb14218..2d778593 100644
--- a/docs/build/html/_static/searchtools.js
+++ b/docs/build/html/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -276,19 +276,28 @@ var Search = {
           setTimeout(function() {
             displayNextItem();
           }, 5);
-        } else {
+        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
           $.ajax({url: requestUrl,
                   dataType: "text",
                   complete: function(jqxhr, textstatus) {
                     var data = jqxhr.responseText;
                     if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
+                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
+                      if (summary) {
+                        listItem.append(summary);
+                      }
                     }
                     Search.output.append(listItem);
                     setTimeout(function() {
                       displayNextItem();
                     }, 5);
                   }});
+        } else {
+          // no source available, just display title
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
         }
       }
       // search finished, update title and status message
@@ -319,7 +328,9 @@ var Search = {
     var results = [];
 
     for (var prefix in objects) {
-      for (var name in objects[prefix]) {
+      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
+        var match = objects[prefix][iMatch];
+        var name = match[4];
         var fullname = (prefix ? prefix + '.' : '') + name;
         var fullnameLower = fullname.toLowerCase()
         if (fullnameLower.indexOf(object) > -1) {
@@ -333,7 +344,6 @@ var Search = {
           } else if (parts[parts.length - 1].indexOf(object) > -1) {
             score += Scorer.objPartialMatch;
           }
-          var match = objects[prefix][name];
           var objname = objnames[match[1]][2];
           var title = titles[match[0]];
           // If more than one term searched for, we require other words to be
@@ -492,6 +502,9 @@ var Search = {
    */
   makeSearchSummary : function(htmlText, keywords, hlwords) {
     var text = Search.htmlToText(htmlText);
+    if (text == "") {
+      return null;
+    }
     var textLower = text.toLowerCase();
     var start = 0;
     $.each(keywords, function() {
diff --git a/docs/build/html/classes/spatialpy.core.html b/docs/build/html/classes/spatialpy.core.html
index 7e561e2a..bbb262c3 100644
--- a/docs/build/html/classes/spatialpy.core.html
+++ b/docs/build/html/classes/spatialpy.core.html
@@ -4,7 +4,8 @@
 
   
     
-    
+    
+
     spatialpy.core package — SpatialPy 1.0 documentation
     
     
@@ -13,6 +14,7 @@
     
     
     
+    
     
     
     
@@ -103,12 +105,12 @@ 
Quick search

 
           

             
-  

+  

 
spatialpy.core package

-

+

 
Submodules

-

-

+

+

 
spatialpy.core.boundarycondition module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -124,11 +126,11 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.boundarycondition.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]

+class spatialpy.core.boundarycondition.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]
 
Bases: object

 
Set spatial regions of the domain where a property of
 particles are held constant (updated each simulation step)

-

+

 
Conditions (one or more of the following must be set):

+

 
spatialpy.core.cleanup module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -233,8 +224,8 @@ 
Submodules
+

+

 
spatialpy.core.datafunction module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -250,13 +241,16 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.datafunction.DataFunction(name=None)[source]

+class spatialpy.core.datafunction.DataFunction(name=None)[source]
 
Bases: object

 
Abstract class used to constuct the data function.

 

 
Parameters

 
name (str) – Name of the Data Function.

 

+
Raises

+
DataFunctionError – If a name is not provided.

+

 

 

 

@@ -281,8 +275,8 @@ 
Submodules
+

+

 
spatialpy.core.domain module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -298,7 +292,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.domain.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]

+class spatialpy.core.domain.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]
 
Bases: object

 
Domain class for SpatialPy.  A domain defines points and attributes of a regional space for simulation.

 

@@ -322,14 +316,14 @@ 
Submodules
 
Parameters

 
    
-
  • point (tuple(float, float, float) or tuple(float, float)) – Spatial coordinate vertices of point to be added
    • 
+
  • point (tuple(float, float, float)) – Spatial coordinate vertices of point to be added
    • 
 
  • vol (float) – Default volume of particle to be added
    • 
 
  • mass (float) – Default mass of particle to be added
    • 
-
  • type_id (str | int) – Particle type ID of particle to be created
    • 
-
  • nu (float) – Default viscosity of particle to be created
    • 
+
  • type_id (str | int) – Particle type ID of particle to be craddedeated
    • 
+
  • nu (float) – Default viscosity of particle to be added
    • 
 
  • fixed (bool) – True if particle is spatially fixed, else False
    • 
-
  • c (float) – Default artificial speed of sound of particle to be created
    • 
-
  • rho (float) – Default density of particle to be created
    • 
+
  • c (float) – Default artificial speed of sound of particle to be added
    • 
+
  • rho (float) – Default density of particle to be added
    • 
 

 

 
Raises

@@ -350,7 +344,7 @@ 
Submodules
 
Parameters

-
point (tuple(float, float, float) or tuple(float, float)) – Target source point

+
point (tuple(float, float, float)) – Target source point

 

 
Returns

 
The coordinates of the nearest vertex to the source point.

@@ -364,11 +358,11 @@ 
Submodules
 

 compile_prep()[source]

-
Generate the domain list of type ids and check for invalid type_ids and rho values
+
Generate the domains list of type ids and check for invalid type_ids and rho values
 in preperation of compiling the simulation files.

 

 
Raises

-
DomainError – If a type_id is not set or rh for a particle is 0.

+
DomainError – If a type_id is not set or rho=0 for a particle.

 

 

 

@@ -389,7 +383,7 @@ 
Submodules
 

-classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

+classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
 
Create a filled 2D domain

 

 
Parameters

@@ -402,11 +396,9 @@ 
SubmodulesDomain.

 
 

 
Returns

@@ -420,7 +412,7 @@ 
Submodules
 

-classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

+classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
 
Create a filled 3D domain

 

 
Parameters

@@ -435,11 +427,9 @@ 
SubmodulesDomain.

 
 

 
Returns

@@ -458,8 +448,8 @@ 
Submodules
 
Parameters

 
    
-
  • start (tuple(float, float, float) or tuple(float, float)) – Starting point
    • 
-
  • end (tuple(float, float, float) or tuple(float, float)) – Ending point
    • 
+
  • start (tuple(float, float, float)) – Starting point
    • 
+
  • end (tuple(float, float, float)) – Ending point
    • 
 

 

 
Returns

@@ -489,7 +479,7 @@ 
SubmodulesDomain.add_point().

 
 

 
Returns

@@ -609,7 +599,7 @@ 
Submodules
 

-classmethod import_meshio_object(mesh_obj)[source]

+classmethod import_meshio_object(mesh_obj)[source]
 
Import a python meshio mesh object.

 

 
Parameters

@@ -652,7 +642,7 @@ 
Submodules
 

-classmethod read_msh_file(filename)[source]

+classmethod read_msh_file(filename)[source]
 
Read a Gmsh style .msh file

 

 
Parameters

@@ -669,7 +659,7 @@ 
Submodules
 

-classmethod read_stochss_domain(filename)[source]

+classmethod read_stochss_domain(filename)[source]
 
Read a StochSS Domain (.domn) file or pull a StochSS Domain from a StochSS Spatial Model (.smdl) file.

 

 
Parameters

@@ -703,7 +693,7 @@ 
Submodules
 

-classmethod read_xml_mesh(filename)[source]

+classmethod read_xml_mesh(filename)[source]
 
Read a FEniCS/dolfin style XML mesh file

 

 
Parameters

@@ -725,7 +715,7 @@ 
Submodules
 
Parameters

 

+

 
spatialpy.core.geometry module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -767,7 +757,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.geometry.Geometry[source]

+class spatialpy.core.geometry.Geometry[source]
 
Bases: object

 
Geometry class provides a method for tagging parts of the spatial domain as separate parts.

 

@@ -793,7 +783,7 @@ 
Submodules
 

-class spatialpy.core.geometry.GeometryAll[source]

+class spatialpy.core.geometry.GeometryAll[source]
 
Bases: spatialpy.core.geometry.Geometry

 
Mark all particles.

 

@@ -819,7 +809,7 @@ 
Submodules
 

-class spatialpy.core.geometry.GeometryExterior[source]

+class spatialpy.core.geometry.GeometryExterior[source]
 
Bases: spatialpy.core.geometry.Geometry

 
Mark particles that are on the edge of the domain.
 only works for meshes that define triangles and tetrahedrons.

@@ -846,7 +836,7 @@ 
Submodules
 

-class spatialpy.core.geometry.GeometryInterior[source]

+class spatialpy.core.geometry.GeometryInterior[source]
 
Bases: spatialpy.core.geometry.Geometry

 
Mark particles that are not on the edge of the domain.
 Only works for meshes that define triangles and tetrahedrons.

@@ -871,8 +861,8 @@ 
Submodules
+

+

 
spatialpy.core.initialcondition module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -888,7 +878,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.initialcondition.InitialCondition[source]

+class spatialpy.core.initialcondition.InitialCondition[source]
 
Bases: object

 
Class used to defined initial conditions in SpatialPy.
 SubClasses must implement the ‘apply(model)’ method, which
@@ -903,7 +893,7 @@ 
Submodules
 

-class spatialpy.core.initialcondition.PlaceInitialCondition(species, count, location)[source]

+class spatialpy.core.initialcondition.PlaceInitialCondition(species, count, location)[source]
 
Bases: spatialpy.core.initialcondition.InitialCondition

 
Class used to defined the place initial condition in SpatialPy.

 

@@ -930,7 +920,7 @@ 
Submodules
 

-class spatialpy.core.initialcondition.ScatterInitialCondition(species, count, types=None)[source]

+class spatialpy.core.initialcondition.ScatterInitialCondition(species, count, types=None)[source]
 
Bases: spatialpy.core.initialcondition.InitialCondition

 
Class used to defined the scatter initial condition in SpatialPy.

 

@@ -957,7 +947,7 @@ 
Submodules
 

-class spatialpy.core.initialcondition.UniformInitialCondition(species, count, types=None)[source]

+class spatialpy.core.initialcondition.UniformInitialCondition(species, count, types=None)[source]
 
Bases: spatialpy.core.initialcondition.InitialCondition

 
Class used to defined the uniform initial condition in SpatialPy.

 

@@ -982,8 +972,8 @@ 
Submodules
+

+

 
spatialpy.core.model module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -999,7 +989,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.model.Model(name='spatialpy')[source]

+class spatialpy.core.model.Model(name='spatialpy')[source]
 
Bases: object

 
Representation of a spatial biochemical model.

 

@@ -1330,7 +1320,7 @@ 
Submodules
 

-reserved_names = ['vol', 't']

+reserved_names = ['vol', 't']
 

 
 

@@ -1425,7 +1415,7 @@ 
Submodules
 

-special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']

+special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']
 

 
 

@@ -1475,8 +1465,8 @@ 
Submodules
+

+

 
spatialpy.core.parameter module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -1492,7 +1482,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.parameter.Parameter(name=None, expression=None)[source]

+class spatialpy.core.parameter.Parameter(name=None, expression=None)[source]
 
Bases: object

 
Model of a rate paramter.
 A parameter can be given as a String expression (function) or directly as a scalar value.
@@ -1508,8 +1498,8 @@ 
Submodules
+

+

 
spatialpy.core.reaction module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -1525,7 +1515,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.reaction.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]

+class spatialpy.core.reaction.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]
 
Bases: object

 
Models a biochemical reaction. A reaction conatains dictionaries of species (reactants and products)     and parameters. The reaction’s propensity function needs to be evaluable and result in a     non-negative scalar value in the namespace defined by the union of its Reactant, Product and     Parameter dictionaries.  For mass-action, zeroth, first     and second order reactions are supported, attempting to used higher orders will result in an error.

 

@@ -1593,8 +1583,8 @@ 
Submodules
+

+

 
spatialpy.core.result module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -1610,7 +1600,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.result.Result(model=None, result_dir=None)[source]

+class spatialpy.core.result.Result(model=None, result_dir=None)[source]
 
Bases: object

 
Result object for a URDME simulation.

 

@@ -1828,8 +1818,8 @@ 
Submodules
+

+

 
spatialpy.core.spatialpyerror module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -1845,104 +1835,104 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-exception spatialpy.core.spatialpyerror.BoundaryConditionError[source]

+exception spatialpy.core.spatialpyerror.BoundaryConditionError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Base class for exceptions in the boundarycondition module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.DataFunctionError[source]

+exception spatialpy.core.spatialpyerror.DataFunctionError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in the datafunction module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.DomainError[source]

+exception spatialpy.core.spatialpyerror.DomainError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in the domain module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.GeometryError[source]

+exception spatialpy.core.spatialpyerror.GeometryError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in the geometry module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.InitialConditionError[source]

+exception spatialpy.core.spatialpyerror.InitialConditionError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in initailcondition module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.ModelError[source]

+exception spatialpy.core.spatialpyerror.ModelError[source]
 
Bases: Exception

 
Class for exceptions in the model module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.ParameterError[source]

+exception spatialpy.core.spatialpyerror.ParameterError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in parameter module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.ReactionError[source]

+exception spatialpy.core.spatialpyerror.ReactionError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in reaction module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.ResultError[source]

+exception spatialpy.core.spatialpyerror.ResultError[source]
 
Bases: Exception

 
Class for exceptions in the results module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.SimulationError[source]

+exception spatialpy.core.spatialpyerror.SimulationError[source]
 
Bases: Exception

 
Class for exceptions in the simulation module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.SimulationTimeout[source]

+exception spatialpy.core.spatialpyerror.SimulationTimeout[source]
 
Bases: spatialpy.core.spatialpyerror.SimulationError

 
Exception raised for timeout errors.

 

 
 

 

-exception spatialpy.core.spatialpyerror.SpeciesError[source]

+exception spatialpy.core.spatialpyerror.SpeciesError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in the species module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.VTKReaderError[source]

+exception spatialpy.core.spatialpyerror.VTKReaderError[source]
 
Bases: Exception

 
Bass class for exceptions in the vtkreader module.

 

 
 

 

-exception spatialpy.core.spatialpyerror.VTKReaderIOError(message)[source]

+exception spatialpy.core.spatialpyerror.VTKReaderIOError(message)[source]
 
Bases: spatialpy.core.spatialpyerror.VTKReaderError

 
Exception raised for I/O errors.

 

 
-

-

+
+

 
spatialpy.core.species module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -1958,7 +1948,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.species.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]

+class spatialpy.core.species.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]
 
Bases: object

 
Model of a biochemical species. Must be assigned a diffusion coefficent.

 

@@ -1988,8 +1978,8 @@ 
Submodules
+

+

 
spatialpy.core.vtkreader module

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -2005,7 +1995,7 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.vtkreader.VTKReader(filename=None, debug=False)[source]

+class spatialpy.core.vtkreader.VTKReader(filename=None, debug=False)[source]
 
Bases: object

 
VTKReader.py: SpatialPy minimal VTK legacy file reader.
 Reference: https://vtk.org/wp-content/uploads/2015/04/file-formats.pdf

@@ -2100,8 +2090,8 @@ 
Submodules
+

+

 
Module contents

 
SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -2117,11 +2107,11 @@ 
Submoduleshttp://www.gnu.org/licenses/>.

 

 

-class spatialpy.core.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]

+class spatialpy.core.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]
 
Bases: object

 
Set spatial regions of the domain where a property of
 particles are held constant (updated each simulation step)

-

+

 
Conditions (one or more of the following must be set):

 
 

 

-class spatialpy.core.DataFunction(name=None)[source]

+class spatialpy.core.DataFunction(name=None)[source]
 
Bases: object

 
Abstract class used to constuct the data function.

 

 
Parameters

 
name (str) – Name of the Data Function.

 

+
Raises

+
DataFunctionError – If a name is not provided.

+

 

 

 

@@ -2219,14 +2201,14 @@ 
Submodules
 

-exception spatialpy.core.DataFunctionError[source]

+exception spatialpy.core.DataFunctionError[source]

 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in the datafunction module.

 

 
 

 

-class spatialpy.core.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]

+class spatialpy.core.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]
 
Bases: object

 
Domain class for SpatialPy.  A domain defines points and attributes of a regional space for simulation.

 

@@ -2250,14 +2232,14 @@ 
Submodules
 
Parameters

 
    
-
  • point (tuple(float, float, float) or tuple(float, float)) – Spatial coordinate vertices of point to be added
    • 
+
  • point (tuple(float, float, float)) – Spatial coordinate vertices of point to be added
    • 
 
  • vol (float) – Default volume of particle to be added
    • 
 
  • mass (float) – Default mass of particle to be added
    • 
-
  • type_id (str | int) – Particle type ID of particle to be created
    • 
-
  • nu (float) – Default viscosity of particle to be created
    • 
+
  • type_id (str | int) – Particle type ID of particle to be craddedeated
    • 
+
  • nu (float) – Default viscosity of particle to be added
    • 
 
  • fixed (bool) – True if particle is spatially fixed, else False
    • 
-
  • c (float) – Default artificial speed of sound of particle to be created
    • 
-
  • rho (float) – Default density of particle to be created
    • 
+
  • c (float) – Default artificial speed of sound of particle to be added
    • 
+
  • rho (float) – Default density of particle to be added
    • 
 

 

 
Raises

@@ -2278,7 +2260,7 @@ 
Submodules
 
Parameters

-
point (tuple(float, float, float) or tuple(float, float)) – Target source point

+
point (tuple(float, float, float)) – Target source point

 

 
Returns

 
The coordinates of the nearest vertex to the source point.

@@ -2292,11 +2274,11 @@ 
Submodules
 

 compile_prep()[source]

-
Generate the domain list of type ids and check for invalid type_ids and rho values
+
Generate the domains list of type ids and check for invalid type_ids and rho values
 in preperation of compiling the simulation files.

 

 
Raises

-
DomainError – If a type_id is not set or rh for a particle is 0.

+
DomainError – If a type_id is not set or rho=0 for a particle.

 

 

 

@@ -2317,7 +2299,7 @@ 
Submodules
 

-classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

+classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
 
Create a filled 2D domain

 

 
Parameters

@@ -2330,11 +2312,9 @@ 
SubmodulesDomain.

 
 

 
Returns

@@ -2348,7 +2328,7 @@ 
Submodules
 

-classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

+classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
 
Create a filled 3D domain

 

 
Parameters

@@ -2363,11 +2343,9 @@ 
SubmodulesDomain.

 
 

 
Returns

@@ -2386,8 +2364,8 @@ 
Submodules
 
Parameters

 
    
-
  • start (tuple(float, float, float) or tuple(float, float)) – Starting point
    • 
-
  • end (tuple(float, float, float) or tuple(float, float)) – Ending point
    • 
+
  • start (tuple(float, float, float)) – Starting point
    • 
+
  • end (tuple(float, float, float)) – Ending point
    • 
 

 

 
Returns

@@ -2417,7 +2395,7 @@ 
SubmodulesDomain.add_point().

 
 

 
Returns

@@ -2537,7 +2515,7 @@ 
Submodules
 

-classmethod import_meshio_object(mesh_obj)[source]

+classmethod import_meshio_object(mesh_obj)[source]
 
Import a python meshio mesh object.

 

 
Parameters

@@ -2580,7 +2558,7 @@ 
Submodules
 

-classmethod read_msh_file(filename)[source]

+classmethod read_msh_file(filename)[source]
 
Read a Gmsh style .msh file

 

 
Parameters

@@ -2597,7 +2575,7 @@ 
Submodules
 

-classmethod read_stochss_domain(filename)[source]

+classmethod read_stochss_domain(filename)[source]
 
Read a StochSS Domain (.domn) file or pull a StochSS Domain from a StochSS Spatial Model (.smdl) file.

 

 
Parameters

@@ -2631,7 +2609,7 @@ 
Submodules
 

-classmethod read_xml_mesh(filename)[source]

+classmethod read_xml_mesh(filename)[source]
 
Read a FEniCS/dolfin style XML mesh file

 

 
Parameters

@@ -2653,7 +2631,7 @@ 
Submodules
 
Parameters

 

 
 

 

-class spatialpy.core.Geometry[source]

+class spatialpy.core.Geometry[source]
 
Bases: object

 
Geometry class provides a method for tagging parts of the spatial domain as separate parts.

 

@@ -2713,7 +2691,7 @@ 
Submodules
 

-class spatialpy.core.GeometryAll[source]

+class spatialpy.core.GeometryAll[source]
 
Bases: spatialpy.core.geometry.Geometry

 
Mark all particles.

 

@@ -2739,14 +2717,14 @@ 
Submodules
 

-exception spatialpy.core.GeometryError[source]

+exception spatialpy.core.GeometryError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in the geometry module.

 

 
 

 

-class spatialpy.core.GeometryExterior[source]

+class spatialpy.core.GeometryExterior[source]
 
Bases: spatialpy.core.geometry.Geometry

 
Mark particles that are on the edge of the domain.
 only works for meshes that define triangles and tetrahedrons.

@@ -2773,7 +2751,7 @@ 
Submodules
 

-class spatialpy.core.GeometryInterior[source]

+class spatialpy.core.GeometryInterior[source]
 
Bases: spatialpy.core.geometry.Geometry

 
Mark particles that are not on the edge of the domain.
 Only works for meshes that define triangles and tetrahedrons.

@@ -2800,7 +2778,7 @@ 
Submodules
 

-class spatialpy.core.InitialCondition[source]

+class spatialpy.core.InitialCondition[source]
 
Bases: object

 
Class used to defined initial conditions in SpatialPy.
 SubClasses must implement the ‘apply(model)’ method, which
@@ -2815,43 +2793,46 @@ 
Submodules
 

-exception spatialpy.core.InitialConditionError[source]

+exception spatialpy.core.InitialConditionError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in initailcondition module.

 

 
 

 

-class spatialpy.core.KDTree(data, leafsize=10)[source]

-
Bases: object

-
kd-tree for quick nearest-neighbor lookup

-
This class provides an index into a set of k-D points which
-can be used to rapidly look up the nearest neighbors of any point.

+class spatialpy.core.KDTree(data, leafsize=10, compact_nodes=True, copy_data=False, balanced_tree=True, boxsize=None)[source]
+
Bases: scipy.spatial.ckdtree.cKDTree

+
kd-tree for quick nearest-neighbor lookup.

+
This class provides an index into a set of k-dimensional points
+which can be used to rapidly look up the nearest neighbors of any
+point.

 

 
Parameters

 
    
-
  • data ((N,K) array_like) – The data points to be indexed. This array is not copied, and
-so modifying this data will result in bogus results.
    • 
-
  • leafsize (int, optional) – The number of points at which the algorithm switches over to
-brute-force.  Has to be positive.
    • 
+
  • data (array_like, shape (n,m)) – The n data points of dimension m to be indexed. This array is
+not copied unless this is necessary to produce a contiguous
+array of doubles, and so modifying this data will result in
+bogus results. The data are also copied if the kd-tree is built
+with copy_data=True.
    • 
+
  • leafsize (positive int, optional) – The number of points at which the algorithm switches over to
+brute-force.  Default: 10.
    • 
+
  • compact_nodes (bool, optional) – If True, the kd-tree is built to shrink the hyperrectangles to
+the actual data range. This usually gives a more compact tree that
+is robust against degenerated input data and gives faster queries
+at the expense of longer build time. Default: True.
    • 
+
  • copy_data (bool, optional) – If True the data is always copied to protect the kd-tree against
+data corruption. Default: False.
    • 
+
  • balanced_tree (bool, optional) – If True, the median is used to split the hyperrectangles instead of
+the midpoint. This usually gives a more compact tree and
+faster queries at the expense of longer build time. Default: True.
    • 
+
  • boxsize (array_like or scalar, optional) – Apply a m-d toroidal topology to the KDTree.. The topology is generated
+by \(x_i + n_i L_i\) where \(n_i\) are integers and \(L_i\)
+is the boxsize along i-th dimension. The input data shall be wrapped
+into \([0, L_i)\). A ValueError is raised if any of the data is
+outside of this bound.
    • 
 

 

-
Raises

-
RuntimeError – The maximum recursion limit can be exceeded for large data
-    sets.  If this happens, either increase the value for the leafsize
-    parameter or increase the recursion limit by::
-    
-        >>> import sys
-        >>> sys.setrecursionlimit(10000)

-

-

-

-
See also

-

-
cKDTree
Implementation of KDTree in Cython

-

 

-

 
Notes

 
The algorithm used is described in Maneewongvatana and Mount 1999.
 The general idea is that the kd-tree is a binary tree, each of whose
@@ -2868,153 +2849,414 @@ 
Submodules
+

+data

+
The n data points of dimension m to be indexed. This array is
+not copied unless this is necessary to produce a contiguous
+array of doubles. The data are also copied if the kd-tree is built
+with copy_data=True.

+

+
Type

+
ndarray, shape (n,m)

+

+

+

+
+

+

+leafsize

+
The number of points at which the algorithm switches over to
+brute-force.

+

+
Type

+
positive int

+

+

+

+
+

+

+m

+
The dimension of a single data-point.

+

+
Type

+
int

+

+

+

+
+

+

+n

+
The number of data points.

+

+
Type

+
int

+

+

+

+
+

+

+maxes

+
The maximum value in each dimension of the n data points.

+

+
Type

+
ndarray, shape (m,)

+

+

+

+
+

+

+mins

+
The minimum value in each dimension of the n data points.

+

+
Type

+
ndarray, shape (m,)

+

+

+

+
+

+

+size

+
The number of nodes in the tree.

+

+
Type

+
int

+

+

+

+
 

 

-count_neighbors(other, r, p=2.0)[source]

+count_neighbors(other, r, p=2.0, weights=None, cumulative=True)[source]
 
Count how many nearby pairs can be formed.

-
Count the number of pairs (x1,x2) can be formed, with x1 drawn
-from self and x2 drawn from other, and where
-distance(x1, x2, p) <= r.
-This is the “two-point correlation” described in Gray and Moore 2000,
-“N-body problems in statistical learning”, and the code here is based
-on their algorithm.

+
Count the number of pairs (x1,x2) can be formed, with x1 drawn
+from self and x2 drawn from other, and where
+distance(x1, x2, p) <= r.

+
Data points on self and other are optionally weighted by the
+weights argument. (See below)

+
This is adapted from the “two-point correlation” algorithm described by
+Gray and Moore 1.  See notes for further discussion.

 

 
Parameters

 
    
-
  • other (KDTree instance) – The other tree to draw points from.
    • 
+
  • other (KDTree) – The other tree to draw points from, can be the same tree as self.
    • 
 
  • r (float or one-dimensional array of floats) – The radius to produce a count for. Multiple radii are searched with
-a single tree traversal.
    • 
-
  • p (float, 1<=p<=infinity, optional) – Which Minkowski p-norm to use
    • 
+a single tree traversal.
+If the count is non-cumulative(cumulative=False), r defines
+the edges of the bins, and must be non-decreasing.
    
+
  • p (float, optional) – 1<=p<=infinity.
+Which Minkowski p-norm to use.
+Default 2.0.
+A finite large p may cause a ValueError if overflow can occur.
    • 
+
  • weights (tuple, array_like, or None, optional) – 
    If None, the pair-counting is unweighted.
+If given as a tuple, weights[0] is the weights of points in
+self, and weights[1] is the weights of points in other;
+either can be None to indicate the points are unweighted.
+If given as an array_like, weights is the weights of points in
+self and other. For this to make sense, self and
+other must be the same tree. If self and other are two
+different trees, a ValueError is raised.
+Default: None
    
+
    
+
    New in version 1.6.0.
    
+
    
+
    • 
+
  • cumulative (bool, optional) – 
    Whether the returned counts are cumulative. When cumulative is set
+to False the algorithm is optimized to work with a large number
+of bins (>10) specified by r. When cumulative is set to
+True, the algorithm is optimized to work with a small number of
+r. Default: True
    
+
    
+
    New in version 1.6.0.
    
+
    
+
    • 
 

 

 
Returns

-
result – The number of pairs. Note that this is internally stored in a numpy
-int, and so may overflow if very large (2e9).

+
result – The number of pairs. For unweighted counts, the result is integer.
+For weighted counts, the result is float.
+If cumulative is False, result[i] contains the counts with
+(-inf if i == 0 else r[i-1]) < R <= r[i]

 

 
Return type

-
int or 1-D array of ints

+
scalar or 1-D array

 

 

+
Notes

+
Pair-counting is the basic operation used to calculate the two point
+correlation functions from a data set composed of position of objects.

+
Two point correlation function measures the clustering of objects and
+is widely used in cosmology to quantify the large scale structure
+in our Universe, but it may be useful for data analysis in other fields
+where self-similar assembly of objects also occur.

+
The Landy-Szalay estimator for the two point correlation function of
+D measures the clustering signal in D. 2

+
For example, given the position of two sets of objects,

+
    
+
  • objects D (data) contains the clustering signal, and
    • 
+
  • objects R (random) that contains no signal,
    • 
+

+

+\[\xi(r) = \frac{<D, D> - 2 f <D, R> + f^2<R, R>}{f^2<R, R>},\]

+
where the brackets represents counting pairs between two data sets
+in a finite bin around r (distance), corresponding to setting
+cumulative=False, and f = float(len(D)) / float(len(R)) is the
+ratio between number of objects from data and random.

+
The algorithm implemented here is loosely based on the dual-tree
+algorithm described in 1. We switch between two different
+pair-cumulation scheme depending on the setting of cumulative.
+The computing time of the method we use when for
+cumulative == False does not scale with the total number of bins.
+The algorithm for cumulative == True scales linearly with the
+number of bins, though it is slightly faster when only
+1 or 2 bins are used. 5.

+
As an extension to the naive pair-counting,
+weighted pair-counting counts the product of weights instead
+of number of pairs.
+Weighted pair-counting is used to estimate marked correlation functions
+(3, section 2.2),
+or to properly calculate the average of data per distance bin
+(e.g. 4, section 2.1 on redshift).

+

+
1(1,2)

+
Gray and Moore,
+“N-body problems in statistical learning”,
+Mining the sky, 2000,
+https://arxiv.org/abs/astro-ph/0012333

+

+
2

+
Landy and Szalay,
+“Bias and variance of angular correlation functions”,
+The Astrophysical Journal, 1993,
+http://adsabs.harvard.edu/abs/1993ApJ…412…64L

+

+
3

+
Sheth, Connolly and Skibba,
+“Marked correlations in galaxy formation models”,
+Arxiv e-print, 2005,
+https://arxiv.org/abs/astro-ph/0511773

+

+
4

+
Hawkins, et al.,
+“The 2dF Galaxy Redshift Survey: correlation functions,
+peculiar velocities and the matter density of the Universe”,
+Monthly Notices of the Royal Astronomical Society, 2002,
+http://adsabs.harvard.edu/abs/2003MNRAS.346…78H

+

+
5

+
https://github.com/scipy/scipy/pull/5647#issuecomment-168474926

+

+

+
Examples

+
You can count neighbors number between two kd-trees within a distance:

+
>>> import numpy as np
+>>> from scipy.spatial import KDTree
+>>> rng = np.random.default_rng()
+>>> points1 = rng.random((5, 2))
+>>> points2 = rng.random((5, 2))
+>>> kd_tree1 = KDTree(points1)
+>>> kd_tree2 = KDTree(points2)
+>>> kd_tree1.count_neighbors(kd_tree2, 0.2)
+1
+

+

+
This number is same as the total pair number calculated by
+query_ball_tree:

+
>>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
+>>> sum([len(i) for i in indexes])
+1
+

+

 

 
 

 

-class innernode(split_dim, split, less, greater)[source]

+class innernode(ckdtreenode)[source]
 
Bases: scipy.spatial.kdtree.KDTree.node

+

+

+property children

+

+
+

+

+property split

+

+
+

+

+property split_dim

+

+
 

 
 

 

-class leafnode(idx)[source]

+class leafnode(ckdtree_node=None)[source]
 
Bases: scipy.spatial.kdtree.KDTree.node

+

+

+property children

+

+
+

+

+property idx

+

+
 

 
 

 

-class node[source]

+class node(ckdtree_node=None)[source]
 
Bases: object

 

 
 

 

-query(x, k=1, eps=0, p=2, distance_upper_bound=inf)[source]

-
Query the kd-tree for nearest neighbors

+query(x, k=1, eps=0, p=2, distance_upper_bound=inf, workers=1)[source]
+
Query the kd-tree for nearest neighbors.

 

 
Parameters

 
    
 
  • x (array_like, last dimension self.m) – An array of points to query.
    • 
-
  • k (int, optional) – The number of nearest neighbors to return.
    • 
+
  • k (int or Sequence[int], optional) – Either the number of nearest neighbors to return, or a list of the
+k-th nearest neighbors to return, starting from 1.
    • 
 
  • eps (nonnegative float, optional) – Return approximate nearest neighbors; the kth returned value
 is guaranteed to be no further than (1+eps) times the
 distance to the real kth nearest neighbor.
    • 
 
  • p (float, 1<=p<=infinity, optional) – Which Minkowski p-norm to use.
-1 is the sum-of-absolute-values “Manhattan” distance
-2 is the usual Euclidean distance
-infinity is the maximum-coordinate-difference distance
    • 
+1 is the sum-of-absolute-values distance (“Manhattan” distance).
+2 is the usual Euclidean distance.
+infinity is the maximum-coordinate-difference distance.
+A large, finite p may cause a ValueError if overflow can occur.
    
 
  • distance_upper_bound (nonnegative float, optional) – Return only neighbors within this distance. This is used to prune
 tree searches, so if you are doing a series of nearest-neighbor
 queries, it may help to supply the distance to the nearest neighbor
 of the most recent point.
    • 
+
  • workers (int, optional) – 
    Number of workers to use for parallel processing. If -1 is given
+all CPU threads are used. Default: 1.
    
+
    
+
    New in version 1.6.0.
    
+
    
+
    • 
 

 

 
Returns

-
    
+
      
 
    • d (float or array of floats) – The distances to the nearest neighbors.
-If x has shape tuple+(self.m,), then d has shape tuple if
-k is one, or tuple+(k,) if k is larger than one. Missing
-neighbors (e.g. when k > n or distance_upper_bound is
-given) are indicated with infinite distances.  If k is None,
-then d is an object array of shape tuple, containing lists
-of distances. In either case the hits are sorted by distance
-(nearest first).
      • 
-
    • i (integer or array of integers) – The locations of the neighbors in self.data. i is the same
-shape as d.
      • 
+If x has shape tuple+(self.m,), then d has shape
+tuple+(k,).
+When k == 1, the last dimension of the output is squeezed.
+Missing neighbors are indicated with infinite distances.
+Hits are sorted by distance (nearest first).
      
+
      
+
      Deprecated since version 1.6.0: If k=None, then d is an object array of shape tuple,
+containing lists of distances. This behavior is deprecated and
+will be removed in SciPy 1.8.0, use query_ball_point
+instead.
      
+
      
+
+
    • i (integer or array of integers) – The index of each neighbor in self.data.
+i is the same shape as d.
+Missing neighbors are indicated with self.n.
      • 
 
    
 
    
 
    
 

 
Examples

-
>>> from scipy import spatial
+
>>> import numpy as np
+>>> from scipy.spatial import KDTree
 >>> x, y = np.mgrid[0:5, 2:8]
->>> tree = spatial.KDTree(list(zip(x.ravel(), y.ravel())))
->>> tree.data
-array([[0, 2],
-       [0, 3],
-       [0, 4],
-       [0, 5],
-       [0, 6],
-       [0, 7],
-       [1, 2],
-       [1, 3],
-       [1, 4],
-       [1, 5],
-       [1, 6],
-       [1, 7],
-       [2, 2],
-       [2, 3],
-       [2, 4],
-       [2, 5],
-       [2, 6],
-       [2, 7],
-       [3, 2],
-       [3, 3],
-       [3, 4],
-       [3, 5],
-       [3, 6],
-       [3, 7],
-       [4, 2],
-       [4, 3],
-       [4, 4],
-       [4, 5],
-       [4, 6],
-       [4, 7]])
->>> pts = np.array([[0, 0], [2.1, 2.9]])
->>> tree.query(pts)
-(array([ 2.        ,  0.14142136]), array([ 0, 13]))
->>> tree.query(pts[0])
-(2.0, 0)
+>>> tree = KDTree(np.c_[x.ravel(), y.ravel()])
+

+

+
To query the nearest neighbours and return squeezed result, use

+
>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=1)
+>>> print(dd, ii, sep='\n')
+[2.         0.2236068]
+[ 0 13]
+

+

+
To query the nearest neighbours and return unsqueezed result, use

+
>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1])
+>>> print(dd, ii, sep='\n')
+[[2.        ]
+ [0.2236068]]
+[[ 0]
+ [13]]
+

+

+
To query the second nearest neighbours and return unsqueezed result,
+use

+
>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[2])
+>>> print(dd, ii, sep='\n')
+[[2.23606798]
+ [0.80622577]]
+[[ 6]
+ [19]]
+

+

+
To query the first and second nearest neighbours, use

+
>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=2)
+>>> print(dd, ii, sep='\n')
+[[2.         2.23606798]
+ [0.2236068  0.80622577]]
+[[ 0  6]
+ [13 19]]
+

+

+
or, be more specific

+
>>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2])
+>>> print(dd, ii, sep='\n')
+[[2.         2.23606798]
+ [0.2236068  0.80622577]]
+[[ 0  6]
+ [13 19]]
 

 

 

 
 

 

-query_ball_point(x, r, p=2.0, eps=0)[source]

+query_ball_point(x, r, p=2.0, eps=0, workers=1, return_sorted=None, return_length=False)[source]
 
Find all points within distance r of point(s) x.

 

 
Parameters

 
    
 
  • x (array_like, shape tuple + (self.m,)) – The point or points to search for neighbors of.
    • 
-
  • r (positive float) – The radius of points to return.
    • 
-
  • p (float, optional) – Which Minkowski p-norm to use.  Should be in the range [1, inf].
    • 
+
  • r (array_like, float) – The radius of points to return, must broadcast to the length of x.
    • 
+
  • p (float, optional) – Which Minkowski p-norm to use.  Should be in the range [1, inf].
+A finite large p may cause a ValueError if overflow can occur.
    • 
 
  • eps (nonnegative float, optional) – Approximate search. Branches of the tree are not explored if their
 nearest points are further than r / (1 + eps), and branches are
 added in bulk if their furthest points are nearer than
 r * (1 + eps).
    • 
+
  • workers (int, optional) – 
    Number of jobs to schedule for parallel processing. If -1 is given
+all processors are used. Default: 1.
    
+
    
+
    New in version 1.6.0.
    
+
    
+
    • 
+
  • return_sorted (bool, optional) – 
    Sorts returned indicies if True and does not sort them if False. If
+None, does not sort single point queries, but does sort
+multi-point queries which was the behavior before this option
+was added.
    
+
    
+
    New in version 1.6.0.
    
+
    
+
    • 
+
  • return_length (bool, optional) – 
    Return the number of points inside the radius instead of a list
+of the indices.
    
+
    
+
    New in version 1.6.0.
    
+
    
+
    • 
 

 

 
Returns

@@ -3035,7 +3277,7 @@ 
Submodules>>> x, y = np.mgrid[0:5, 0:5]
 >>> points = np.c_[x.ravel(), y.ravel()]
 >>> tree = spatial.KDTree(points)
->>> tree.query_ball_point([2, 0], 1)
+>>> sorted(tree.query_ball_point([2, 0], 1))
 [5, 10, 11, 15]
 

 

@@ -3055,7 +3297,8 @@ 
Submodules
 

 query_ball_tree(other, r, p=2.0, eps=0)[source]

-
Find all pairs of points whose distance is at most r

+
Find all pairs of points between self and other whose distance is
+at most r.

 

 
Parameters

 

 

+
Examples

+
You can search all pairs of points between two kd-trees within a distance:

+
>>> import matplotlib.pyplot as plt
+>>> import numpy as np
+>>> from scipy.spatial import KDTree
+>>> rng = np.random.default_rng()
+>>> points1 = rng.random((15, 2))
+>>> points2 = rng.random((15, 2))
+>>> plt.figure(figsize=(6, 6))
+>>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14)
+>>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14)
+>>> kd_tree1 = KDTree(points1)
+>>> kd_tree2 = KDTree(points2)
+>>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
+>>> for i in range(len(indexes)):
+...     for j in indexes[i]:
+...         plt.plot([points1[i, 0], points2[j, 0]],
+...             [points1[i, 1], points2[j, 1]], "-r")
+>>> plt.show()
+

+

 

 
 

 

-query_pairs(r, p=2.0, eps=0)[source]

-
Find all pairs of points within a distance.

+query_pairs(r, p=2.0, eps=0, output_type='set')[source]
+
Find all pairs of points in self whose distance is at most r.

 

 
Parameters

 

 

 
Returns

 
results – Set of pairs (i,j), with i < j, for which the corresponding
-positions are close.

+positions are close. If output_type is ‘ndarray’, an ndarry is
+returned instead of a set.

 

 
Return type

-
set

+
set or ndarray

 

 

+
Examples

+
You can search all pairs of points in a kd-tree within a distance:

+
>>> import matplotlib.pyplot as plt
+>>> import numpy as np
+>>> from scipy.spatial import KDTree
+>>> rng = np.random.default_rng()
+>>> points = rng.random((20, 2))
+>>> plt.figure(figsize=(6, 6))
+>>> plt.plot(points[:, 0], points[:, 1], "xk", markersize=14)
+>>> kd_tree = KDTree(points)
+>>> pairs = kd_tree.query_pairs(r=0.2)
+>>> for (i, j) in pairs:
+...     plt.plot([points[i, 0], points[j, 0]],
+...             [points[i, 1], points[j, 1]], "-r")
+>>> plt.show()
+

+

 

 
 

 

-sparse_distance_matrix(other, max_distance, p=2.0)[source]

-
Compute a sparse distance matrix

+sparse_distance_matrix(other, max_distance, p=2.0, output_type='dok_matrix')[source]
+
Compute a sparse distance matrix.

 
Computes a distance matrix between two KDTrees, leaving as zero
 any distance greater than max_distance.

 

@@ -3116,23 +3403,66 @@ 
Submodules
    
 
  • other (KDTree) – 
    • 
 
  • max_distance (positive float) – 
    • 
-
  • p (float, optional) – 
    • 
+
  • p (float, 1<=p<=infinity) – Which Minkowski p-norm to use.
+A finite large p may cause a ValueError if overflow can occur.
    • 
+
  • output_type (string, optional) – 
    Which container to use for output data. Options: ‘dok_matrix’,
+‘coo_matrix’, ‘dict’, or ‘ndarray’. Default: ‘dok_matrix’.
    
+
    
+
    New in version 1.6.0.
    
+
    
+
    • 
 

 

 
Returns

-
result – Sparse matrix representing the results in “dictionary of keys” format.

+
result – Sparse matrix representing the results in “dictionary of keys”
+format. If a dict is returned the keys are (i,j) tuples of indices.
+If output_type is ‘ndarray’ a record array with fields ‘i’, ‘j’,
+and ‘v’ is returned,

 

 
Return type

-
dok_matrix

+
dok_matrix, coo_matrix, dict or ndarray

 

 

+
Examples

+
You can compute a sparse distance matrix between two kd-trees:

+
>>> import numpy as np
+>>> from scipy.spatial import KDTree
+>>> rng = np.random.default_rng()
+>>> points1 = rng.random((5, 2))
+>>> points2 = rng.random((5, 2))
+>>> kd_tree1 = KDTree(points1)
+>>> kd_tree2 = KDTree(points2)
+>>> sdm = kd_tree1.sparse_distance_matrix(kd_tree2, 0.3)
+>>> sdm.toarray()
+array([[0.        , 0.        , 0.12295571, 0.        , 0.        ],
+   [0.        , 0.        , 0.        , 0.        , 0.        ],
+   [0.28942611, 0.        , 0.        , 0.2333084 , 0.        ],
+   [0.        , 0.        , 0.        , 0.        , 0.        ],
+   [0.24617575, 0.29571802, 0.26836782, 0.        , 0.        ]])
+

+

+
You can check distances above the max_distance are zeros:

+
>>> from scipy.spatial import distance_matrix
+>>> distance_matrix(points1, points2)
+array([[0.56906522, 0.39923701, 0.12295571, 0.8658745 , 0.79428925],
+   [0.37327919, 0.7225693 , 0.87665969, 0.32580855, 0.75679479],
+   [0.28942611, 0.30088013, 0.6395831 , 0.2333084 , 0.33630734],
+   [0.31994999, 0.72658602, 0.71124834, 0.55396483, 0.90785663],
+   [0.24617575, 0.29571802, 0.26836782, 0.57714465, 0.6473269 ]])
+

+

 
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diff --git a/docs/build/html/.doctrees/environment.pickle b/docs/build/html/.doctrees/environment.pickle
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+

+

+property tree

+

+
 
 
 

 

-class spatialpy.core.Model(name='spatialpy')[source]

+class spatialpy.core.Model(name='spatialpy')[source]
 
Bases: object

 
Representation of a spatial biochemical model.

 

@@ -3463,7 +3793,7 @@ 
Submodules
 

-reserved_names = ['vol', 't']

+reserved_names = ['vol', 't']
 

 
 

@@ -3558,7 +3888,7 @@ 
Submodules
 

-special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']

+special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']
 

 
 

@@ -3589,14 +3919,14 @@ 
Submodules
 

-exception spatialpy.core.ModelError[source]

+exception spatialpy.core.ModelError[source]
 
Bases: Exception

 
Class for exceptions in the model module.

 

 
 

 

-class spatialpy.core.OrderedDict[source]

+class spatialpy.core.OrderedDict[source]
 
Bases: dict

 
Dictionary that remembers insertion order

 

@@ -3617,12 +3947,12 @@ 
Submodules
 

-items() → a set-like object providing a view on D’s items

+items() → a set-like object providing a view on D's items
 

 
 

 

-keys() → a set-like object providing a view on D’s keys

+keys() → a set-like object providing a view on D's keys
 

 
 

@@ -3634,7 +3964,7 @@ 
Submodules
 

-pop(k[, d]) → v, remove specified key and return the corresponding

+pop(k[, d]) → v, remove specified key and return the corresponding
 
value.  If key is not found, d is returned if given, otherwise KeyError
 is raised.

 

@@ -3655,7 +3985,7 @@ 
Submodules
 

-update([E, ]**F) → None.  Update D from dict/iterable E and F.

+update([E, ]**F) → None.  Update D from dict/iterable E and F.
 
If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k]
 If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v
 In either case, this is followed by: for k in F:  D[k] = F[k]

@@ -3663,14 +3993,14 @@ 
Submodules
 

-values() → an object providing a view on D’s values

+values() → an object providing a view on D's values
 

 
 

 
 

 

-class spatialpy.core.Parameter(name=None, expression=None)[source]

+class spatialpy.core.Parameter(name=None, expression=None)[source]
 
Bases: object

 
Model of a rate paramter.
 A parameter can be given as a String expression (function) or directly as a scalar value.
@@ -3688,14 +4018,14 @@ 
Submodules
 

-exception spatialpy.core.ParameterError[source]

+exception spatialpy.core.ParameterError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in parameter module.

 

 
 

 

-class spatialpy.core.PlaceInitialCondition(species, count, location)[source]

+class spatialpy.core.PlaceInitialCondition(species, count, location)[source]
 
Bases: spatialpy.core.initialcondition.InitialCondition

 
Class used to defined the place initial condition in SpatialPy.

 

@@ -3722,7 +4052,7 @@ 
Submodules
 

-class spatialpy.core.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]

+class spatialpy.core.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]
 
Bases: object

 
Models a biochemical reaction. A reaction conatains dictionaries of species (reactants and products)     and parameters. The reaction’s propensity function needs to be evaluable and result in a     non-negative scalar value in the namespace defined by the union of its Reactant, Product and     Parameter dictionaries.  For mass-action, zeroth, first     and second order reactions are supported, attempting to used higher orders will result in an error.

 

@@ -3792,14 +4122,14 @@ 
Submodules
 

-exception spatialpy.core.ReactionError[source]

+exception spatialpy.core.ReactionError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in reaction module.

 

 
 

 

-class spatialpy.core.Result(model=None, result_dir=None)[source]

+class spatialpy.core.Result(model=None, result_dir=None)[source]
 
Bases: object

 
Result object for a URDME simulation.

 

@@ -4019,14 +4349,14 @@ 
Submodules
 

-exception spatialpy.core.ResultError[source]

+exception spatialpy.core.ResultError[source]
 
Bases: Exception

 
Class for exceptions in the results module.

 

 
 

 

-class spatialpy.core.ScatterInitialCondition(species, count, types=None)[source]

+class spatialpy.core.ScatterInitialCondition(species, count, types=None)[source]
 
Bases: spatialpy.core.initialcondition.InitialCondition

 
Class used to defined the scatter initial condition in SpatialPy.

 

@@ -4053,21 +4383,21 @@ 
Submodules
 

-exception spatialpy.core.SimulationError[source]

+exception spatialpy.core.SimulationError[source]
 
Bases: Exception

 
Class for exceptions in the simulation module.

 

 
 

 

-exception spatialpy.core.SimulationTimeout[source]

+exception spatialpy.core.SimulationTimeout[source]
 
Bases: spatialpy.core.spatialpyerror.SimulationError

 
Exception raised for timeout errors.

 

 
 

 

-class spatialpy.core.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]

+class spatialpy.core.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]
 
Bases: object

 
Model of a biochemical species. Must be assigned a diffusion coefficent.

 

@@ -4099,14 +4429,14 @@ 
Submodules
 

-exception spatialpy.core.SpeciesError[source]

+exception spatialpy.core.SpeciesError[source]
 
Bases: spatialpy.core.spatialpyerror.ModelError

 
Class for exceptions in the species module.

 

 
 

 

-class spatialpy.core.UniformInitialCondition(species, count, types=None)[source]

+class spatialpy.core.UniformInitialCondition(species, count, types=None)[source]
 
Bases: spatialpy.core.initialcondition.InitialCondition

 
Class used to defined the uniform initial condition in SpatialPy.

 

@@ -4133,7 +4463,7 @@ 
Submodules
 

-class spatialpy.core.VTKReader(filename=None, debug=False)[source]

+class spatialpy.core.VTKReader(filename=None, debug=False)[source]
 
Bases: object

 
VTKReader.py: SpatialPy minimal VTK legacy file reader.
 Reference: https://vtk.org/wp-content/uploads/2015/04/file-formats.pdf

@@ -4230,14 +4560,14 @@ 
Submodules
 

-exception spatialpy.core.VTKReaderError[source]

+exception spatialpy.core.VTKReaderError[source]
 
Bases: Exception

 
Bass class for exceptions in the vtkreader module.

 

 
 

 

-exception spatialpy.core.VTKReaderIOError(message)[source]

+exception spatialpy.core.VTKReaderIOError(message)[source]
 
Bases: spatialpy.core.spatialpyerror.VTKReaderError

 
Exception raised for I/O errors.

 

@@ -4278,7 +4608,7 @@ 
Submodules
 

-class spatialpy.core.combinations(iterable, r)

+class spatialpy.core.combinations(iterable, r)
 
Bases: object

 
Return successive r-length combinations of elements in the iterable.

 
combinations(range(4), 3) –> (0,1,2), (0,1,3), (0,2,3), (1,2,3)

@@ -4333,7 +4663,7 @@ 
Submodules
-
image (default=None |’png’ |’jpeg’ |’svg’ |’webp’) – This parameter sets
the format of the image to be downloaded, if we choose to download an
+
image (default=None |’png’ |’jpeg’ |’svg’ |’webp’) – This parameter sets
the format of the image to be downloaded, if we choose to download an
 image. This parameter has a default value of None indicating that no
 image should be downloaded. Please note: for higher resolution images
 and more export options, consider using plotly.io.write_image. See
@@ -4370,7 +4700,7 @@ 
Submodulesiplot([{'x': [1, 2, 3], 'y': [5, 2, 7]}], image='png')
 `

 
animation_opts Example:
-```
+```
 from plotly.offline import iplot
 figure = {‘data’: [{‘x’: [0, 1], ‘y’: [0, 1]}],

 

@@ -4387,11 +4717,11 @@ 
Submodules```

+```

 

 
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-
+ + @@ -4419,7 +4749,7 @@

SubmodulesSphinx 4.1.1 + Powered by Sphinx 4.4.0 & Alabaster 0.7.12 | diff --git a/docs/build/html/classes/spatialpy.html b/docs/build/html/classes/spatialpy.html index 2c1be40a..424282e2 100644 --- a/docs/build/html/classes/spatialpy.html +++ b/docs/build/html/classes/spatialpy.html @@ -4,7 +4,8 @@ - + + spatialpy package — SpatialPy 1.0 documentation @@ -101,9 +102,9 @@

Quick search

-
+

spatialpy package

-
+

Subpackages

    @@ -140,8 +141,8 @@

    Subpackages +

+

Module contents

SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -155,8 +156,8 @@

Subpackageshttp://www.gnu.org/licenses/>.

-

-
+ +
@@ -184,7 +185,7 @@

SubpackagesSphinx 4.1.1 + Powered by Sphinx 4.4.0 & Alabaster 0.7.12 | diff --git a/docs/build/html/classes/spatialpy.solvers.html b/docs/build/html/classes/spatialpy.solvers.html index 6399a06c..f20b5841 100644 --- a/docs/build/html/classes/spatialpy.solvers.html +++ b/docs/build/html/classes/spatialpy.solvers.html @@ -4,7 +4,8 @@ - + + spatialpy.solvers package — SpatialPy 1.0 documentation @@ -103,12 +104,12 @@

Quick search

-
+

spatialpy.solvers package

-
+

Submodules

-
-
+
+

spatialpy.solvers.build_expression module

SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -124,7 +125,7 @@

Submoduleshttp://www.gnu.org/licenses/>.

-class spatialpy.solvers.build_expression.BuildExpression(blacklist=None, namespace=None, sanitize=False)[source]
+class spatialpy.solvers.build_expression.BuildExpression(blacklist=None, namespace=None, sanitize=False)[source]

Bases: object

Accepts an expression string to validate and convert. Allows for pre-flight syntax and namespace validations, @@ -158,7 +159,7 @@

Submodules
-class ValidationVisitor(namespace=None, blacklist=None, sanitize=False)[source]
+class ValidationVisitor(namespace=None, blacklist=None, sanitize=False)[source]

Bases: ast.NodeTransformer

A subclass of ast.NodeTransformer used to sanitize spatialpy expresions.

@@ -172,7 +173,7 @@

Submodules
-visit_Assign(node: _ast.Assign)[source]
+visit_Assign(node: ast.Assign)[source]

Visitor function for ast.Assign nodes.

Parameters
@@ -334,7 +335,7 @@

Submodules
-classmethod map_operator(operator)[source]
+classmethod map_operator(operator)[source]

Map operator strings with built expressions.

Parameters
@@ -345,7 +346,7 @@

Submodules
-operator_map = {'!': <class '_ast.Not'>, '!=': <class '_ast.NotEq'>, '%': <class '_ast.Mod'>, '&': <class '_ast.BitAnd'>, '*': <class '_ast.Mult'>, '**': <class '_ast.Pow'>, '+': <class '_ast.Add'>, '-': <class '_ast.Sub'>, '/': <class '_ast.Div'>, '//': <class '_ast.FloorDiv'>, ':=': <class '_ast.Assign'>, '<': <class '_ast.Lt'>, '<<': <class '_ast.LShift'>, '<=': <class '_ast.LtE'>, '=': <class '_ast.Assign'>, '==': <class '_ast.Eq'>, '>': <class '_ast.Gt'>, '>=': <class '_ast.GtE'>, '>>': <class '_ast.RShift'>, '@': <class '_ast.MatMult'>, '^': <class '_ast.BitXor'>, 'and': <class '_ast.And'>, 'or': <class '_ast.Or'>, '|': <class '_ast.BitOr'>}
+operator_map = {'!': <class 'ast.Not'>, '!=': <class 'ast.NotEq'>, '%': <class 'ast.Mod'>, '&': <class 'ast.BitAnd'>, '*': <class 'ast.Mult'>, '**': <class 'ast.Pow'>, '+': <class 'ast.Add'>, '-': <class 'ast.Sub'>, '/': <class 'ast.Div'>, '//': <class 'ast.FloorDiv'>, ':=': <class 'ast.Assign'>, '<': <class 'ast.Lt'>, '<<': <class 'ast.LShift'>, '<=': <class 'ast.LtE'>, '=': <class 'ast.Assign'>, '==': <class 'ast.Eq'>, '>': <class 'ast.Gt'>, '>=': <class 'ast.GtE'>, '>>': <class 'ast.RShift'>, '@': <class 'ast.MatMult'>, '^': <class 'ast.BitXor'>, 'and': <class 'ast.And'>, 'or': <class 'ast.Or'>, '|': <class 'ast.BitOr'>}

@@ -415,17 +416,17 @@

Submodules
-class spatialpy.solvers.build_expression.CppConverter(tree)[source]
+class spatialpy.solvers.build_expression.CppConverter(tree)[source]

Bases: spatialpy.solvers.build_expression.ExpressionConverter

Converts an ast.AST to a C++ expression string.

-class CppExpressionTransformer[source]
+class CppExpressionTransformer[source]

Bases: ast.NodeTransformer

A subclass of ast.NodeTransformer used to convert ast.AST expresions to C++ expressions.

-visit_BinOp(node: _ast.BinOp)[source]
+visit_BinOp(node: ast.BinOp)[source]

Visitor function for ast.BinOp nodes.

Parameters
@@ -482,7 +483,7 @@

Submodules
-class spatialpy.solvers.build_expression.ExpressionConverter(tree)[source]
+class spatialpy.solvers.build_expression.ExpressionConverter(tree)[source]

Bases: ast.NodeVisitor

A subclass of ast.NodeVisitor used to convert spatialpy expresions.

@@ -492,7 +493,7 @@

Submodules
-classmethod convert_str(expression)[source]
+classmethod convert_str(expression)[source]

Convert ‘^’ to python pow operator.

Parameters
@@ -820,7 +821,7 @@

Submodules
-class spatialpy.solvers.build_expression.ExpressionResults(invalid_names=None, invalid_operators=None, is_valid=True)[source]
+class spatialpy.solvers.build_expression.ExpressionResults(invalid_names=None, invalid_operators=None, is_valid=True)[source]

Bases: object

Container struct for returning the results of expression validation. Any expression items which indicate an invalid expression are listed on an ExpressionResults instance. @@ -841,7 +842,7 @@

Submodules
-class spatialpy.solvers.build_expression.PythonConverter(tree)[source]
+class spatialpy.solvers.build_expression.PythonConverter(tree)[source]

Bases: spatialpy.solvers.build_expression.ExpressionConverter

Converts an ast.AST to a Python expression string.

@@ -868,8 +869,8 @@

Submodules +

+

spatialpy.solvers.solver module

SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -885,7 +886,7 @@

Submoduleshttp://www.gnu.org/licenses/>.

-class spatialpy.solvers.solver.Solver(model, debug_level=0)[source]
+class spatialpy.solvers.solver.Solver(model, debug_level=0)[source]

Bases: object

SpatialPy solver object.

@@ -946,8 +947,8 @@

Submodules +

+

Module contents

SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -961,8 +962,8 @@

Submoduleshttp://www.gnu.org/licenses/>.

-

-
+ + @@ -990,7 +991,7 @@

SubmodulesSphinx 4.1.1 + Powered by Sphinx 4.4.0 & Alabaster 0.7.12 | diff --git a/docs/build/html/classes/spatialpy.stochss.html b/docs/build/html/classes/spatialpy.stochss.html index 2cb25e06..4026adfb 100644 --- a/docs/build/html/classes/spatialpy.stochss.html +++ b/docs/build/html/classes/spatialpy.stochss.html @@ -4,7 +4,8 @@ - + + spatialpy.stochss package — SpatialPy 1.0 documentation @@ -101,12 +102,12 @@

Quick search

-
+

spatialpy.stochss package

-
+

Submodules

-
-
+
+

spatialpy.stochss.stochss_export module

SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -138,8 +139,8 @@

Submodules +

+

Module contents

SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -153,8 +154,8 @@

Submoduleshttp://www.gnu.org/licenses/>.

-

-
+ + @@ -178,7 +179,7 @@

SubmodulesSphinx 4.1.1 + Powered by Sphinx 4.4.0 & Alabaster 0.7.12 | diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index b01ffea2..f9cb72e9 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -101,7 +101,9 @@

Index

| G | I | K + | L | M + | N | O | P | Q @@ -232,6 +234,12 @@

C

+
  • children (spatialpy.core.KDTree.innernode property) + +
  • cleanup_build_files() (in module spatialpy.core) @@ -267,11 +275,11 @@

    C

  • combinations (class in spatialpy.core) -
    • -
  • compile() (spatialpy.solvers.solver.Solver method)
    • @@ -4077,7 +4077,7 @@

    Submodules
    Parameters
    @@ -4091,7 +4091,7 @@

    Submodules
    Parameters
    @@ -4115,7 +4115,7 @@

    Submodules
    Parameters
    -

    model (spatialpy.Model) – Target SpatialPy Model for annotation.

    +

    model (spatialpy.core.model.Model) – Target SpatialPy Model for annotation.

    From 0186da35216335acb28e5f980e3eb1b0bd0fa9f0 Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Wed, 2 Mar 2022 11:32:23 -0500 Subject: [PATCH 16/25] Updated docs for results. --- spatialpy/core/result.py | 52 +++++++++++++++++++++------------------- 1 file changed, 28 insertions(+), 24 deletions(-) diff --git a/spatialpy/core/result.py b/spatialpy/core/result.py index 9f766c8e..a8408d8f 100644 --- a/spatialpy/core/result.py +++ b/spatialpy/core/result.py @@ -312,25 +312,25 @@ def get_species(self, species, timepoints=None, concentration=False, determinist one or all timepoints. Returns a numpy array containing the species population/concentration values. :param species: A species in string or dictionary form to retreive information about - :type species: str | dict + :type species: str | spatialpy.core.species.Species :param timepoints: A time point where the information should be retreived from. If 'timepoints' is None (default), a matrix of dimension: (number of timepoints) x (number of voxels) is returned. If an integer value is given, that value is used to index into the timespan, and that time point is returned - as a 1D array with size (number of voxel). - :type timepoints: int (default None) + as a 1D array with size (number of voxel). Defaults to None + :type timepoints: int :param concentration: Whether or not the species is a concentration (True) or population (False) If concentration is False (default), the integer, raw, trajectory data is returned. - If set to True, the concentration (=copy_number/volume) is returned. - :type concentration: bool (default False) + If set to True, the concentration (=copy_number/volume) is returned. Defaults to False + :type concentration: bool - :param deterministic: Whether or not the species is deterministic (True) or stochastic (False) - :type deterministic: bool (default False) + :param deterministic: Whether or not the species is deterministic (True) or stochastic (False). Defaults to False + :type deterministic: bool - :param debug: Whether or not debug information should be printed - :type debug: bool (default False) + :param debug: Whether or not debug information should be printed. Defaults to False + :type debug: bool :returns: A numpy array containing population/concentration values for target species across specified timepoints. Defaults to all timepoints. @@ -340,7 +340,7 @@ def get_species(self, species, timepoints=None, concentration=False, determinist """ num_voxel = self.model.domain.get_num_voxels() - if isinstance(species,str): + if isinstance(species, str): spec_name = species else: spec_name = species.name @@ -404,11 +404,13 @@ def plot_species(self, species, t_ndx=None, t_val=None, concentration=False, :param deterministic: Whether or not to plot the data as deterministic :type deterministic: bool - :param width: Width in pixels of output plot box - :type width: int (default 500) + :param width: Width in pixels of output plot box or for matplotlib inches of output plot box. \ + Defaults to 500 (Plotly) or 6.4 (MatPlotLib) + :type width: int - :param height: Height in pixels of output plot box - :type height: int (default 500) + :param height: Height in pixels of output plot box or for matplotlib inches of output plot box. \ + Defaults to 500 (Plotly) or 4.8 (MatPlotLib) + :type height: int :param colormap: colormap to use. Plotly specification, valid values: "Plotly3","Jet","Blues","YlOrRd", "PuRd","BuGn","YlOrBr","PuBuGn","BuPu","YlGnBu", "PuBu","GnBu","YlGn","Greens","Reds", @@ -450,7 +452,7 @@ def plot_species(self, species, t_ndx=None, t_val=None, concentration=False, :returns: A dictionary containing data for a plotly figure of species output trajectory :rtype: dict - :raises ResultsError: unable to plot species for given time + :raises ResultError: unable to plot species for given time """ time_index_list = self.get_timespan() @@ -594,7 +596,7 @@ def get_property(self, property_name, timepoints=None): :returns: a numpy array of target property values across timepoints, defaults to all timepoints. :rtype: numpy.ndarray - :raises ResultsError: Could not get data for given timepoints. + :raises ResultError: Could not get data for given timepoints. """ l_time = len(self.get_timespan()) - 1 @@ -646,11 +648,13 @@ def plot_property(self, property_name, t_ndx=None, t_val=None, p_ndx=0, width=No :param p_ndx: The property index of the results to be plotted :type p_ndx: int - :param width: Width in pixels of output plot box or for matplotlib inches of output plot box - :type width: int (default 500) + :param width: Width in pixels of output plot box or for matplotlib inches of output plot box. \ + Defaults to 500 (Plotly) or 6.4 (MatPlotLib) + :type width: int - :param height: Height in pixels of output plot box or for matplotlib inches of output plot box - :type height: int (default 500) + :param height: Height in pixels of output plot box or for matplotlib inches of output plot box. \ + Defaults to 500 (Plotly) or 4.8 (MatPlotLib) + :type height: int :param colormap: colormap to use. Plotly specification, valid values: "Plotly3","Jet","Blues","YlOrRd", "PuRd","BuGn","YlOrBr","PuBuGn","BuPu","YlGnBu", "PuBu","GnBu","YlGn","Greens","Reds", @@ -842,9 +846,9 @@ def export_to_csv(self, folder_name=None): The columns of modelname_mesh.csv are: 'Voxel ID', 'X', 'Y', 'Z', 'Type', 'Volume', 'Mass', 'Viscosity' The columns of modelname_species_S.csv: 'Time', 'Voxel 0', Voxel 1', ... 'Voxel N'. - :type folder_name: str (default current working directory) - :param folder_name: A path where the vtk files will be written, created if non-existant. - If no path is provided current working directory is used. + :type folder_name: str + :param folder_name: A path where the vtk files will be written, created if non-existant. \ + Defaults current working directory """ if not folder_name: folder_name = os.path.abspath(os.getcwd()) @@ -873,7 +877,7 @@ def export_to_csv(self, folder_name=None): for voxel in range(num_vox): writer.writerow([voxel] + data[:,voxel].tolist()) - def export_to_vtk(self, timespan, folder_name=None): + def __export_to_vtk(self, timespan, folder_name=None): """ Write the trajectory to a collection of vtk files. The exported data is #molecules/volume, where the volume unit is implicit from the mesh dimension. From aaa16063c78be6444f3d93668fc20f79b532fbd1 Mon Sep 17 00:00:00 2001 From: Bryan Rumsey Date: Wed, 2 Mar 2022 11:35:30 -0500 Subject: [PATCH 17/25] Updated docs html files. --- .../.doctrees/classes/spatialpy.core.doctree | Bin 1458314 -> 1446171 bytes docs/build/html/.doctrees/environment.pickle | Bin 452584 -> 452444 bytes .../html/_modules/spatialpy/core/result.html | 54 +++++++------ docs/build/html/classes/spatialpy.core.html | 74 +++++++----------- docs/build/html/genindex.html | 6 -- docs/build/html/objects.inv | Bin 2622 -> 2612 bytes docs/build/html/searchindex.js | 2 +- 7 files changed, 58 insertions(+), 78 deletions(-) diff --git a/docs/build/html/.doctrees/classes/spatialpy.core.doctree b/docs/build/html/.doctrees/classes/spatialpy.core.doctree index 11541e34bdb14c03a2441f7db5c9240219b7d037..4e2cff48587a27bda8d8722f798f1eec70b92cac 100644 GIT binary patch delta 162156 zcmc${d7Mwx{|D}#neo2&{hrm#2xE*9O|~(UrBE1Avd$=xvKBG68EMfXib`bZjZ+kc 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U9BTf_^Z?JzF7SDpc3n>Se;g=I`2YX_ diff --git a/docs/build/html/_modules/spatialpy/core/result.html b/docs/build/html/_modules/spatialpy/core/result.html index f99c972e..1d4c4b2f 100644 --- a/docs/build/html/_modules/spatialpy/core/result.html +++ b/docs/build/html/_modules/spatialpy/core/result.html @@ -405,25 +405,25 @@

    Source code for spatialpy.core.result

             one or all timepoints. Returns a numpy array containing the species population/concentration values.
 
         :param species: A species in string or dictionary form to retreive information about
-        :type species: str | dict
+        :type species: str | spatialpy.core.species.Species
 
         :param timepoints: A time point where the information should be retreived from.
             If 'timepoints' is None (default), a matrix of dimension:
             (number of timepoints) x (number of voxels) is returned.
             If an integer value is given, that value is used to index into the timespan, and that time point is returned
-            as a 1D array with size (number of voxel).
-        :type timepoints: int (default None)
+            as a 1D array with size (number of voxel).  Defaults to None
+        :type timepoints: int
 
         :param concentration: Whether or not the species is a concentration (True) or population (False)
             If concentration is False (default), the integer, raw, trajectory data is returned.
-            If set to True, the concentration (=copy_number/volume) is returned.
-        :type concentration: bool (default False)
+            If set to True, the concentration (=copy_number/volume) is returned.  Defaults to False
+        :type concentration: bool
 
-        :param deterministic: Whether or not the species is deterministic (True) or stochastic (False)
-        :type deterministic: bool (default False)
+        :param deterministic: Whether or not the species is deterministic (True) or stochastic (False).  Defaults to False
+        :type deterministic: bool
 
-        :param debug: Whether or not debug information should be printed
-        :type debug: bool (default False)
+        :param debug: Whether or not debug information should be printed. Defaults to False
+        :type debug: bool
 
         :returns: A numpy array containing population/concentration values for target species across specified
                     timepoints.  Defaults to all timepoints.
@@ -433,7 +433,7 @@ 
    Source code for spatialpy.core.result
             """
         num_voxel = self.model.domain.get_num_voxels()
 
-        if isinstance(species,str):
+        if isinstance(species, str):
             spec_name = species
         else:
             spec_name = species.name
@@ -497,11 +497,13 @@ 
    Source code for spatialpy.core.result
             :param deterministic: Whether or not to plot the data as deterministic
         :type deterministic: bool
 
-        :param width: Width in pixels of output plot box
-        :type width: int (default 500)
+        :param width: Width in pixels of output plot box or for matplotlib inches of output plot box. \
+        Defaults to 500 (Plotly) or 6.4 (MatPlotLib)
+        :type width: int
 
-        :param height: Height in pixels of output plot box
-        :type height: int (default 500)
+        :param height: Height in pixels of output plot box or for matplotlib inches of output plot box. \
+        Defaults to 500 (Plotly) or 4.8 (MatPlotLib)
+        :type height: int
 
         :param colormap: colormap to use.  Plotly specification, valid values: "Plotly3","Jet","Blues","YlOrRd",
                 "PuRd","BuGn","YlOrBr","PuBuGn","BuPu","YlGnBu", "PuBu","GnBu","YlGn","Greens","Reds",
@@ -543,7 +545,7 @@ 
    Source code for spatialpy.core.result
             :returns: A dictionary containing data for a plotly figure of species output trajectory
         :rtype: dict
 
-        :raises ResultsError: unable to plot species for given time
+        :raises ResultError: unable to plot species for given time
         """
         time_index_list = self.get_timespan()
 
@@ -687,7 +689,7 @@ 
    Source code for spatialpy.core.result
             :returns: a numpy array of target property values across timepoints, defaults to all timepoints.
         :rtype: numpy.ndarray
 
-        :raises ResultsError: Could not get data for given timepoints.
+        :raises ResultError: Could not get data for given timepoints.
         """
 
         l_time = len(self.get_timespan()) - 1
@@ -739,11 +741,13 @@ 
    Source code for spatialpy.core.result
             :param p_ndx: The property index of the results to be plotted
         :type p_ndx: int
 
-        :param width: Width in pixels of output plot box or for matplotlib inches of output plot box
-        :type width: int (default 500)
+        :param width: Width in pixels of output plot box or for matplotlib inches of output plot box. \
+        Defaults to 500 (Plotly) or 6.4 (MatPlotLib)
+        :type width: int
 
-        :param height: Height in pixels of output plot box or for matplotlib inches of output plot box
-        :type height: int (default 500)
+        :param height: Height in pixels of output plot box or for matplotlib inches of output plot box. \
+        Defaults to 500 (Plotly) or 4.8 (MatPlotLib)
+        :type height: int
 
         :param colormap: colormap to use.  Plotly specification, valid values: "Plotly3","Jet","Blues","YlOrRd",
                 "PuRd","BuGn","YlOrBr","PuBuGn","BuPu","YlGnBu", "PuBu","GnBu","YlGn","Greens","Reds",
@@ -935,9 +939,9 @@ 
    Source code for spatialpy.core.result
             The columns of modelname_mesh.csv are: 'Voxel ID', 'X', 'Y', 'Z', 'Type', 'Volume', 'Mass', 'Viscosity'
         The columns of modelname_species_S.csv: 'Time', 'Voxel 0', Voxel 1', ... 'Voxel N'.
 
-        :type folder_name: str (default current working directory)
-        :param folder_name: A path where the vtk files will be written, created if non-existant.
-        If no path is provided current working directory is used.
+        :type folder_name: str
+        :param folder_name: A path where the vtk files will be written, created if non-existant. \
+        Defaults current working directory
         """
         if not folder_name:
             folder_name = os.path.abspath(os.getcwd())
@@ -966,13 +970,13 @@ 
    Source code for spatialpy.core.result
                     for voxel in range(num_vox):
                     writer.writerow([voxel] + data[:,voxel].tolist())
    
 
-
    [docs]    def export_to_vtk(self, timespan, folder_name=None):
+    def __export_to_vtk(self, timespan, folder_name=None):
         """
         Write the trajectory to a collection of vtk files.
         The exported data is #molecules/volume, where the volume unit is implicit from the mesh dimension.
         Not currently implemented.
         """
-        raise ResultError("Not implemented.")
    
+        raise ResultError("Not implemented.")
    
 
    
 
           
    
diff --git a/docs/build/html/classes/spatialpy.core.html b/docs/build/html/classes/spatialpy.core.html
index ab866213..a42f0c99 100644
--- a/docs/build/html/classes/spatialpy.core.html
+++ b/docs/build/html/classes/spatialpy.core.html
@@ -1613,18 +1613,9 @@ 
    Submodules
 
    Parameters
    
-
    folder_name (str (default current working directory)) – A path where the vtk files will be written, created if non-existant.
    
+
    folder_name (str) – A path where the vtk files will be written, created if non-existant.         Defaults current working directory
    
 
    
 
-
    If no path is provided current working directory is used.
    
-
-
-
    
-
    
-export_to_vtk(timespan, folder_name=None)[source]
    
-
    Write the trajectory to a collection of vtk files.
-The exported data is #molecules/volume, where the volume unit is implicit from the mesh dimension.
-Not currently implemented.
    
 
    
 
 
    
@@ -1652,7 +1643,7 @@ 
    Submodules
    numpy.ndarray
    
 
 
    Raises
    
-
    ResultsError – Could not get data for given timepoints.
    
+
    ResultError – Could not get data for given timepoints.
    
 
    
 
    
 
@@ -1665,17 +1656,17 @@ 
    Submodules
 
    Parameters
    
 
      
-
    • species (str | dict) – A species in string or dictionary form to retreive information about
      • 
-
    • timepoints (int (default None)) – A time point where the information should be retreived from.
+
    • species (str | spatialpy.core.species.Species) – A species in string or dictionary form to retreive information about
      • 
+
    • timepoints (int) – A time point where the information should be retreived from.
 If ‘timepoints’ is None (default), a matrix of dimension:
 (number of timepoints) x (number of voxels) is returned.
 If an integer value is given, that value is used to index into the timespan, and that time point is returned
-as a 1D array with size (number of voxel).
      • 
-
    • concentration (bool (default False)) – Whether or not the species is a concentration (True) or population (False)
+as a 1D array with size (number of voxel).  Defaults to None
      • 
+
    • concentration (bool) – Whether or not the species is a concentration (True) or population (False)
 If concentration is False (default), the integer, raw, trajectory data is returned.
-If set to True, the concentration (=copy_number/volume) is returned.
      • 
-
    • deterministic (bool (default False)) – Whether or not the species is deterministic (True) or stochastic (False)
      • 
-
    • debug (bool (default False)) – Whether or not debug information should be printed
      • 
+If set to True, the concentration (=copy_number/volume) is returned.  Defaults to False
      
+
    • deterministic (bool) – Whether or not the species is deterministic (True) or stochastic (False).  Defaults to False
      • 
+
    • debug (bool) – Whether or not debug information should be printed. Defaults to False
      • 
 
    
 
    
 
    Returns
    
@@ -1716,8 +1707,8 @@ 
    Submodules
    dict
    
 
 
    Raises
    
-
    ResultsError – unable to plot species for given time
    
+
    ResultError – unable to plot species for given time
    
 
    
 
 
@@ -4143,18 +4134,9 @@ 
    Submodules
 
    Parameters
    
-
    folder_name (str (default current working directory)) – A path where the vtk files will be written, created if non-existant.
    
+
    folder_name (str) – A path where the vtk files will be written, created if non-existant.         Defaults current working directory
    
 
    
 
-
    If no path is provided current working directory is used.
    
-
-
-
    
-
    
-export_to_vtk(timespan, folder_name=None)[source]
    
-
    Write the trajectory to a collection of vtk files.
-The exported data is #molecules/volume, where the volume unit is implicit from the mesh dimension.
-Not currently implemented.
    
 
    
 
 
    
@@ -4182,7 +4164,7 @@ 
    Submodules
    numpy.ndarray
    
 
 
    Raises
    
-
    ResultsError – Could not get data for given timepoints.
    
+
    ResultError – Could not get data for given timepoints.
    
 
    
 
    
 
@@ -4195,17 +4177,17 @@ 
    Submodules
 
    Parameters
    
 
      
-
    • species (str | dict) – A species in string or dictionary form to retreive information about
      • 
-
    • timepoints (int (default None)) – A time point where the information should be retreived from.
+
    • species (str | spatialpy.core.species.Species) – A species in string or dictionary form to retreive information about
      • 
+
    • timepoints (int) – A time point where the information should be retreived from.
 If ‘timepoints’ is None (default), a matrix of dimension:
 (number of timepoints) x (number of voxels) is returned.
 If an integer value is given, that value is used to index into the timespan, and that time point is returned
-as a 1D array with size (number of voxel).
      • 
-
    • concentration (bool (default False)) – Whether or not the species is a concentration (True) or population (False)
+as a 1D array with size (number of voxel).  Defaults to None
      • 
+
    • concentration (bool) – Whether or not the species is a concentration (True) or population (False)
 If concentration is False (default), the integer, raw, trajectory data is returned.
-If set to True, the concentration (=copy_number/volume) is returned.
      • 
-
    • deterministic (bool (default False)) – Whether or not the species is deterministic (True) or stochastic (False)
      • 
-
    • debug (bool (default False)) – Whether or not debug information should be printed
      • 
+If set to True, the concentration (=copy_number/volume) is returned.  Defaults to False
      
+
    • deterministic (bool) – Whether or not the species is deterministic (True) or stochastic (False).  Defaults to False
      • 
+
    • debug (bool) – Whether or not debug information should be printed. Defaults to False
      • 
 
    
 
    
 
    Returns
    
@@ -4246,8 +4228,8 @@ 
    Submodules
    dict
    
 
 
    Raises
    
-
    ResultsError – unable to plot species for given time
    
+
    ResultError – unable to plot species for given time
    
 
    
 
 
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index f9cb72e9..61d2a048 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -400,12 +400,6 @@ 
    E
    
 
       
-      
  • export_to_vtk() (spatialpy.core.Result method)
-
-      
    • 
   
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
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jyuIGlE4TRwHN0C>qO#raM*o?=XYH>)xi
Date: Wed, 2 Mar 2022 11:48:05 -0500
Subject: [PATCH 18/25] Updated docs for species.

---
 spatialpy/core/species.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/spatialpy/core/species.py b/spatialpy/core/species.py
index 11a63b53..c1f03142 100644
--- a/spatialpy/core/species.py
+++ b/spatialpy/core/species.py
@@ -28,7 +28,7 @@ class Species():
     :param diffusion_coefficient: Non-constant coefficient of diffusion for Species.
     :type diffusion_coefficient: float
 
-    :param restrict_to: Set the diffusion coefficient to zero for 'species' in all types not in 'listOfTypes'.
+    :param restrict_to: Set the diffusion coefficient to zero for 'species' in all types not in 'listOfTypes'. \
     This effectively restricts the movement of 'species' to the types specified in 'listOfTypes'.
     :type restrict_to: int, str, list of ints or list of strs
     """
@@ -71,7 +71,7 @@ def set_diffusion_coefficient(self, diffusion_coefficient):
         :param diffusion_coefficient: Non-constant coefficient of diffusion for Species.
         :type diffusion_coefficient: float
 
-        :raises SpeciesError: If diffusion_coefficient is negative.
+        :raises SpeciesError: If diffusion_coefficient is negative or not a valid type.
         """
         if not (isinstance(diffusion_coefficient, (Parameter, float, int)) or \
                             type(diffusion_coefficient).__name__ == 'Parameter'):

From 93511d476de4402bbc9d5cae8a2eca20a11e073f Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Wed, 2 Mar 2022 11:50:49 -0500
Subject: [PATCH 19/25] Updated docs html files.

---
 .../.doctrees/classes/spatialpy.core.doctree  | Bin 1446171 -> 1448363 bytes
 docs/build/html/.doctrees/environment.pickle  | Bin 452444 -> 452466 bytes
 .../html/_modules/spatialpy/core/species.html |   4 ++--
 docs/build/html/classes/spatialpy.core.html   |  12 ++++--------
 4 files changed, 6 insertions(+), 10 deletions(-)

diff --git a/docs/build/html/.doctrees/classes/spatialpy.core.doctree b/docs/build/html/.doctrees/classes/spatialpy.core.doctree
index 4e2cff48587a27bda8d8722f798f1eec70b92cac..426c00af159c036612e9d66115d5137a0a593398 100644
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diff --git a/docs/build/html/_modules/spatialpy/core/species.html b/docs/build/html/_modules/spatialpy/core/species.html
index edd18741..38cfe11e 100644
--- a/docs/build/html/_modules/spatialpy/core/species.html
+++ b/docs/build/html/_modules/spatialpy/core/species.html
@@ -121,7 +121,7 @@ 
    Source code for spatialpy.core.species
         :param diffusion_coefficient: Non-constant coefficient of diffusion for Species.
     :type diffusion_coefficient: float
 
-    :param restrict_to: Set the diffusion coefficient to zero for 'species' in all types not in 'listOfTypes'.
+    :param restrict_to: Set the diffusion coefficient to zero for 'species' in all types not in 'listOfTypes'. \
     This effectively restricts the movement of 'species' to the types specified in 'listOfTypes'.
     :type restrict_to: int, str, list of ints or list of strs
     """
@@ -164,7 +164,7 @@ 
    Source code for spatialpy.core.species
             :param diffusion_coefficient: Non-constant coefficient of diffusion for Species.
         :type diffusion_coefficient: float
 
-        :raises SpeciesError: If diffusion_coefficient is negative.
+        :raises SpeciesError: If diffusion_coefficient is negative or not a valid type.
         """
         if not (isinstance(diffusion_coefficient, (Parameter, float, int)) or \
                             type(diffusion_coefficient).__name__ == 'Parameter'):
diff --git a/docs/build/html/classes/spatialpy.core.html b/docs/build/html/classes/spatialpy.core.html
index a42f0c99..cbc65089 100644
--- a/docs/build/html/classes/spatialpy.core.html
+++ b/docs/build/html/classes/spatialpy.core.html
@@ -1948,12 +1948,10 @@ 
    Submodules
      
 
    • name (str) – Name of the Species
      • 
 
    • diffusion_coefficient (float) – Non-constant coefficient of diffusion for Species.
      • 
-
    • restrict_to – Set the diffusion coefficient to zero for ‘species’ in all types not in ‘listOfTypes’.
      • 
+
    • restrict_to (int, str, list of ints or list of strs) – Set the diffusion coefficient to zero for ‘species’ in all types not in ‘listOfTypes’.     This effectively restricts the movement of ‘species’ to the types specified in ‘listOfTypes’.
      • 
 
    
 
 
-
    This effectively restricts the movement of ‘species’ to the types specified in ‘listOfTypes’.
-:type restrict_to: int, str, list of ints or list of strs
    
 
    
 
    
 set_diffusion_coefficient(diffusion_coefficient)[source]
    
@@ -1963,7 +1961,7 @@ 
    Submodules
    diffusion_coefficient (float) – Non-constant coefficient of diffusion for Species.
    
 
 
    Raises
    
-
    SpeciesError – If diffusion_coefficient is negative.
    
+
    SpeciesError – If diffusion_coefficient is negative or not a valid type.
    
 
    
 
    
 
@@ -4389,12 +4387,10 @@ 
    Submodules
      
 
    • name (str) – Name of the Species
      • 
 
    • diffusion_coefficient (float) – Non-constant coefficient of diffusion for Species.
      • 
-
    • restrict_to – Set the diffusion coefficient to zero for ‘species’ in all types not in ‘listOfTypes’.
      • 
+
    • restrict_to (int, str, list of ints or list of strs) – Set the diffusion coefficient to zero for ‘species’ in all types not in ‘listOfTypes’.     This effectively restricts the movement of ‘species’ to the types specified in ‘listOfTypes’.
      • 
 
    
 
 
-
    This effectively restricts the movement of ‘species’ to the types specified in ‘listOfTypes’.
-:type restrict_to: int, str, list of ints or list of strs
    
 
    
 
    
 set_diffusion_coefficient(diffusion_coefficient)[source]
    
@@ -4404,7 +4400,7 @@ 
    Submodules
    diffusion_coefficient (float) – Non-constant coefficient of diffusion for Species.
    
 
 
    Raises
    
-
    SpeciesError – If diffusion_coefficient is negative.
    
+
    SpeciesError – If diffusion_coefficient is negative or not a valid type.
    
 
    
 
    
 

From 5f417171a004d2b739c9e44f7bace06c52ad4b5c Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Wed, 2 Mar 2022 12:08:45 -0500
Subject: [PATCH 20/25] Updated docs for stochss export.

---
 spatialpy/stochss/stochss_export.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/spatialpy/stochss/stochss_export.py b/spatialpy/stochss/stochss_export.py
index 3bd47f6c..02ecc484 100644
--- a/spatialpy/stochss/stochss_export.py
+++ b/spatialpy/stochss/stochss_export.py
@@ -200,13 +200,17 @@ def export(model, path=None, return_stochss_model=False):
     """
     SpatialPy model to StochSS converter
 
-    Args:
-        spatialpy_model : spatialpy.Model
-            SpatialPy model to be converted to StochSS
-        filename : str
-            Path to the exported stochss model
-        return_stochss_model : bool
-            Whether or not to return the model
+    :param model: SpatialPy model to be converted to StochSS.
+    :type model: spatialpy.core.model.Model
+
+    :param filename: Path to the exported stochss model.
+    :type filename: str
+
+    :param return_stochss_model: Whether or not to return the model.
+    :type return_stochss_model: bool
+
+    :returns: StochSS model dict if return_stochss_model is True else path to StochSS model file.
+    :rtype: dict | str
     """
     _ = model.compile_prep()
     if path is None:

From dd077f1717d85f0e15b503f367fcb825856a8c35 Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Wed, 2 Mar 2022 12:11:35 -0500
Subject: [PATCH 21/25] Updated docs html files.

---
 .../classes/spatialpy.stochss.doctree         | Bin 13763 -> 16109 bytes
 docs/build/html/.doctrees/environment.pickle  | Bin 452466 -> 452624 bytes
 .../spatialpy/stochss/stochss_export.html     |  18 +++++++++++-------
 .../build/html/classes/spatialpy.stochss.html |  15 +++++++++------
 docs/build/html/searchindex.js                |   2 +-
 5 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/docs/build/html/.doctrees/classes/spatialpy.stochss.doctree b/docs/build/html/.doctrees/classes/spatialpy.stochss.doctree
index 7bb1d6ca14abe6cb21b84eb0911a4574198d4746..4c7328a903f201e048f78574fcc7d2281c50b83b 100644
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diff --git a/docs/build/html/.doctrees/environment.pickle b/docs/build/html/.doctrees/environment.pickle
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diff --git a/docs/build/html/_modules/spatialpy/stochss/stochss_export.html b/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
index 01aa00a1..03309955 100644
--- a/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
+++ b/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
@@ -293,13 +293,17 @@ 
    Source code for spatialpy.stochss.stochss_export
    
     """
     SpatialPy model to StochSS converter
 
-    Args:
-        spatialpy_model : spatialpy.Model
-            SpatialPy model to be converted to StochSS
-        filename : str
-            Path to the exported stochss model
-        return_stochss_model : bool
-            Whether or not to return the model
+    :param model: SpatialPy model to be converted to StochSS.
+    :type model: spatialpy.core.model.Model
+
+    :param filename: Path to the exported stochss model.
+    :type filename: str
+
+    :param return_stochss_model: Whether or not to return the model.
+    :type return_stochss_model: bool
+
+    :returns: StochSS model dict if return_stochss_model is True else path to StochSS model file.
+    :rtype: dict | str
     """
     _ = model.compile_prep()
     if path is None:
diff --git a/docs/build/html/classes/spatialpy.stochss.html b/docs/build/html/classes/spatialpy.stochss.html
index 4026adfb..87f7ecc8 100644
--- a/docs/build/html/classes/spatialpy.stochss.html
+++ b/docs/build/html/classes/spatialpy.stochss.html
@@ -128,14 +128,17 @@ 
    Submodules
 
    Parameters
    
 
    
 
    
+
    Returns
    
+
    StochSS model dict if return_stochss_model is True else path to StochSS model file.
    
+
    
+
    Return type
    
+
    dict | str
    
+
    
 
 
 
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 8c96f62d..c7334be8 100644
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+++ b/docs/build/html/searchindex.js
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\ No newline at end of file

From e9edd0289939ffd0c88e291e72bf9c4c90412b52 Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Wed, 2 Mar 2022 12:13:36 -0500
Subject: [PATCH 22/25] Updated docs for solver.

---
 spatialpy/solvers/solver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spatialpy/solvers/solver.py b/spatialpy/solvers/solver.py
index 6f30f0c6..0387165f 100644
--- a/spatialpy/solvers/solver.py
+++ b/spatialpy/solvers/solver.py
@@ -49,7 +49,7 @@ class Solver:
     SpatialPy solver object.
 
     :param model: Target model of solver simulation.
-    :type model: spatialpy.Model.Model
+    :type model: spatialpy.core.model.Model
 
     :param debug_level: Target level of debugging.
     :type debug_level: int

From 7d7f806711f0f4d228d2419225424385b110d96b Mon Sep 17 00:00:00 2001
From: Bryan Rumsey 
Date: Wed, 2 Mar 2022 12:17:22 -0500
Subject: [PATCH 23/25] Updated docs html files.

---
 .../classes/spatialpy.solvers.doctree         | Bin 234328 -> 234343 bytes
 docs/build/html/.doctrees/environment.pickle  | Bin 452624 -> 452629 bytes
 .../_modules/spatialpy/solvers/solver.html    |   2 +-
 .../build/html/classes/spatialpy.solvers.html |   2 +-
 docs/build/html/searchindex.js                |   2 +-
 5 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/build/html/.doctrees/classes/spatialpy.solvers.doctree b/docs/build/html/.doctrees/classes/spatialpy.solvers.doctree
index 9e89df7f6dcc3cbda0ce94e66a821c17eb6f5e42..06e22d16f19863511d1ddaeafc92aeb8b128e9a3 100644
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z!)F?#w*#RTO&6rtVb5~u=q9qHWkfQ>@koa@IySource code for spatialpy.solvers.solver
         SpatialPy solver object.
 
     :param model: Target model of solver simulation.
-    :type model: spatialpy.Model.Model
+    :type model: spatialpy.core.model.Model
 
     :param debug_level: Target level of debugging.
     :type debug_level: int
diff --git a/docs/build/html/classes/spatialpy.solvers.html b/docs/build/html/classes/spatialpy.solvers.html
index f20b5841..c3b84ac1 100644
--- a/docs/build/html/classes/spatialpy.solvers.html
+++ b/docs/build/html/classes/spatialpy.solvers.html
@@ -892,7 +892,7 @@ 
    Submodules
 
    Parameters
    
 
    
 
    
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index c7334be8..a6ef7e16 100644
--- a/docs/build/html/searchindex.js
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\ No newline at end of file

From a60534dd22bfbfdd46171e4694b8f96ee8177b1f Mon Sep 17 00:00:00 2001
From: seanebum 
Date: Mon, 7 Mar 2022 14:06:12 -0500
Subject: [PATCH 24/25] update to domain docs

---
 docs/build/html/.buildinfo                    |    2 +-
 .../.doctrees/classes/spatialpy.core.doctree  |  Bin 1448363 -> 1306308 bytes
 .../html/.doctrees/classes/spatialpy.doctree  |  Bin 5949 -> 5787 bytes
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 .../classes/spatialpy.stochss.doctree         |  Bin 16109 -> 15653 bytes
 docs/build/html/.doctrees/environment.pickle  |  Bin 452629 -> 451550 bytes
 docs/build/html/.doctrees/index.doctree       |  Bin 16082 -> 15839 bytes
 docs/build/html/_modules/collections.html     |  435 ++-----
 docs/build/html/_modules/index.html           |    2 +-
 .../html/_modules/plotly/offline/offline.html |    2 +-
 .../html/_modules/scipy/spatial/kdtree.html   | 1071 +++++++++--------
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 .../html/_modules/spatialpy/core/species.html |    2 +-
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 .../spatialpy/stochss/stochss_export.html     |    2 +-
 docs/build/html/_static/basic.css             |    6 +-
 docs/build/html/_static/doctools.js           |    5 +-
 docs/build/html/_static/language_data.js      |    2 +-
 docs/build/html/_static/pygments.css          |    8 +-
 docs/build/html/_static/searchtools.js        |   23 +-
 docs/build/html/classes/spatialpy.core.html   |  834 ++++---------
 docs/build/html/classes/spatialpy.html        |   17 +-
 .../build/html/classes/spatialpy.solvers.html |   51 +-
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 docs/build/html/genindex.html                 |   58 +-
 docs/build/html/index.html                    |   29 +-
 docs/build/html/objects.inv                   |  Bin 2612 -> 2537 bytes
 docs/build/html/py-modindex.html              |    2 +-
 docs/build/html/search.html                   |    7 +-
 docs/build/html/searchindex.js                |    2 +-
 spatialpy/core/domain.py                      |    6 +-
 43 files changed, 1022 insertions(+), 1599 deletions(-)

diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo
index 8940d805..ab4bd335 100644
--- a/docs/build/html/.buildinfo
+++ b/docs/build/html/.buildinfo
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diff --git a/docs/build/html/.doctrees/classes/spatialpy.core.doctree b/docs/build/html/.doctrees/classes/spatialpy.core.doctree
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diff --git a/docs/build/html/.doctrees/environment.pickle b/docs/build/html/.doctrees/environment.pickle
index 229ce013bfb21eda6fd99e21e7ff525363c1ebeb..ad915c636278faf58adb7226bac89bb8d7fc7b7d 100644
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diff --git a/docs/build/html/.doctrees/index.doctree b/docs/build/html/.doctrees/index.doctree
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GIT binary patch
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nQfmFVyC%577BLx%X9dV&YE=VWH47+~lG#qGvHxSource code for collections
     
 '''
 
-__all__ = [
-    'ChainMap',
-    'Counter',
-    'OrderedDict',
-    'UserDict',
-    'UserList',
-    'UserString',
-    'defaultdict',
-    'deque',
-    'namedtuple',
-]
+__all__ = ['deque', 'defaultdict', 'namedtuple', 'UserDict', 'UserList',
+            'UserString', 'Counter', 'OrderedDict', 'ChainMap']
 
 import _collections_abc
-import heapq as _heapq
-import sys as _sys
-
-from itertools import chain as _chain
-from itertools import repeat as _repeat
-from itertools import starmap as _starmap
+from operator import itemgetter as _itemgetter, eq as _eq
 from keyword import iskeyword as _iskeyword
-from operator import eq as _eq
-from operator import itemgetter as _itemgetter
-from reprlib import recursive_repr as _recursive_repr
+import sys as _sys
+import heapq as _heapq
 from _weakref import proxy as _proxy
+from itertools import repeat as _repeat, chain as _chain, starmap as _starmap
+from reprlib import recursive_repr as _recursive_repr
 
 try:
     from _collections import deque
@@ -160,7 +147,6 @@ 
    Source code for collections
             return obj
     raise AttributeError(f'module {__name__!r} has no attribute {name!r}')
 
-
 ################################################################################
 ### OrderedDict
 ################################################################################
@@ -398,25 +384,7 @@ 
    Source code for collections
             '''
         if isinstance(other, OrderedDict):
             return dict.__eq__(self, other) and all(map(_eq, self, other))
-        return dict.__eq__(self, other)
-
-    def __ior__(self, other):
-        self.update(other)
-        return self
-
-    def __or__(self, other):
-        if not isinstance(other, dict):
-            return NotImplemented
-        new = self.__class__(self)
-        new.update(other)
-        return new
-
-    def __ror__(self, other):
-        if not isinstance(other, dict):
-            return NotImplemented
-        new = self.__class__(other)
-        new.update(self)
-        return new
    
+        return dict.__eq__(self, other)
    
 
 
 try:
@@ -506,23 +474,18 @@ 
    Source code for collections
         # Variables used in the methods and docstrings
     field_names = tuple(map(_sys.intern, field_names))
     num_fields = len(field_names)
-    arg_list = ', '.join(field_names)
-    if num_fields == 1:
-        arg_list += ','
+    arg_list = repr(field_names).replace("'", "")[1:-1]
     repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')'
     tuple_new = tuple.__new__
     _dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip
 
     # Create all the named tuple methods to be added to the class namespace
 
-    namespace = {
-        '_tuple_new': tuple_new,
-        '__builtins__': {},
-        '__name__': f'namedtuple_{typename}',
-    }
-    code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))'
-    __new__ = eval(code, namespace)
-    __new__.__name__ = '__new__'
+    s = f'def __new__(_cls, {arg_list}): return _tuple_new(_cls, ({arg_list}))'
+    namespace = {'_tuple_new': tuple_new, '__name__': f'namedtuple_{typename}'}
+    # Note: exec() has the side-effect of interning the field names
+    exec(s, namespace)
+    __new__ = namespace['__new__']
     __new__.__doc__ = f'Create new instance of {typename}({arg_list})'
     if defaults is not None:
         __new__.__defaults__ = defaults
@@ -559,14 +522,8 @@ 
    Source code for collections
             return _tuple(self)
 
     # Modify function metadata to help with introspection and debugging
-    for method in (
-        __new__,
-        _make.__func__,
-        _replace,
-        __repr__,
-        _asdict,
-        __getnewargs__,
-    ):
+    for method in (__new__, _make.__func__, _replace,
+                   __repr__, _asdict, __getnewargs__):
         method.__qualname__ = f'{typename}.{method.__name__}'
 
     # Build-up the class namespace dictionary
@@ -576,6 +533,8 @@ 
    Source code for collections
             '__slots__': (),
         '_fields': field_names,
         '_field_defaults': field_defaults,
+        # alternate spelling for backward compatibility
+        '_fields_defaults': field_defaults,
         '__new__': __new__,
         '_make': _make,
         '_replace': _replace,
@@ -682,7 +641,7 @@ 
    Source code for collections
             >>> c = Counter(a=4, b=2)                   # a new counter from keyword args
 
         '''
-        super().__init__()
+        super(Counter, self).__init__()
         self.update(iterable, **kwds)
 
     def __missing__(self, key):
@@ -766,8 +725,7 @@ 
    Source code for collections
                         for elem, count in iterable.items():
                         self[elem] = count + self_get(elem, 0)
                 else:
-                    # fast path when counter is empty
-                    super().update(iterable)
+                    super(Counter, self).update(iterable) # fast path when counter is empty
             else:
                 _count_elements(self, iterable)
         if kwds:
@@ -814,14 +772,13 @@ 
    Source code for collections
     
     def __repr__(self):
         if not self:
-            return f'{self.__class__.__name__}()'
+            return '%s()' % self.__class__.__name__
         try:
-            # dict() preserves the ordering returned by most_common()
-            d = dict(self.most_common())
+            items = ', '.join(map('%r: %r'.__mod__, self.most_common()))
+            return '%s({%s})' % (self.__class__.__name__, items)
         except TypeError:
             # handle case where values are not orderable
-            d = dict(self)
-        return f'{self.__class__.__name__}({d!r})'
+            return '{0}({1!r})'.format(self.__class__.__name__, dict(self))
 
     # Multiset-style mathematical operations discussed in:
     #       Knuth TAOCP Volume II section 4.6.3 exercise 19
@@ -1042,7 +999,7 @@ 
    Source code for collections
         def __iter__(self):
         d = {}
         for mapping in reversed(self.maps):
-            d.update(dict.fromkeys(mapping))    # reuses stored hash values if possible
+            d.update(mapping)                   # reuses stored hash values if possible
         return iter(d)
 
     def __contains__(self, key):
@@ -1086,7 +1043,7 @@ 
    Source code for collections
             try:
             del self.maps[0][key]
         except KeyError:
-            raise KeyError(f'Key not found in the first mapping: {key!r}')
+            raise KeyError('Key not found in the first mapping: {!r}'.format(key))
 
     def popitem(self):
         'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.'
@@ -1100,31 +1057,12 @@ 
    Source code for collections
             try:
             return self.maps[0].pop(key, *args)
         except KeyError:
-            raise KeyError(f'Key not found in the first mapping: {key!r}')
+            raise KeyError('Key not found in the first mapping: {!r}'.format(key))
 
     def clear(self):
         'Clear maps[0], leaving maps[1:] intact.'
         self.maps[0].clear()
 
-    def __ior__(self, other):
-        self.maps[0].update(other)
-        return self
-
-    def __or__(self, other):
-        if not isinstance(other, _collections_abc.Mapping):
-            return NotImplemented
-        m = self.copy()
-        m.maps[0].update(other)
-        return m
-
-    def __ror__(self, other):
-        if not isinstance(other, _collections_abc.Mapping):
-            return NotImplemented
-        m = dict(other)
-        for child in reversed(self.maps):
-            m.update(child)
-        return self.__class__(m)
-
 
 ################################################################################
 ### UserDict
@@ -1133,29 +1071,38 @@ 
    Source code for collections
     class UserDict(_collections_abc.MutableMapping):
 
     # Start by filling-out the abstract methods
-    def __init__(self, dict=None, /, **kwargs):
+    def __init__(*args, **kwargs):
+        if not args:
+            raise TypeError("descriptor '__init__' of 'UserDict' object "
+                            "needs an argument")
+        self, *args = args
+        if len(args) > 1:
+            raise TypeError('expected at most 1 arguments, got %d' % len(args))
+        if args:
+            dict = args[0]
+        elif 'dict' in kwargs:
+            dict = kwargs.pop('dict')
+            import warnings
+            warnings.warn("Passing 'dict' as keyword argument is deprecated",
+                          DeprecationWarning, stacklevel=2)
+        else:
+            dict = None
         self.data = {}
         if dict is not None:
             self.update(dict)
         if kwargs:
             self.update(kwargs)
+    __init__.__text_signature__ = '($self, dict=None, /, **kwargs)'
 
-    def __len__(self):
-        return len(self.data)
-
+    def __len__(self): return len(self.data)
     def __getitem__(self, key):
         if key in self.data:
             return self.data[key]
         if hasattr(self.__class__, "__missing__"):
             return self.__class__.__missing__(self, key)
         raise KeyError(key)
-
-    def __setitem__(self, key, item):
-        self.data[key] = item
-
-    def __delitem__(self, key):
-        del self.data[key]
-
+    def __setitem__(self, key, item): self.data[key] = item
+    def __delitem__(self, key): del self.data[key]
     def __iter__(self):
         return iter(self.data)
 
@@ -1164,30 +1111,7 @@ 
    Source code for collections
             return key in self.data
 
     # Now, add the methods in dicts but not in MutableMapping
-    def __repr__(self):
-        return repr(self.data)
-
-    def __or__(self, other):
-        if isinstance(other, UserDict):
-            return self.__class__(self.data | other.data)
-        if isinstance(other, dict):
-            return self.__class__(self.data | other)
-        return NotImplemented
-
-    def __ror__(self, other):
-        if isinstance(other, UserDict):
-            return self.__class__(other.data | self.data)
-        if isinstance(other, dict):
-            return self.__class__(other | self.data)
-        return NotImplemented
-
-    def __ior__(self, other):
-        if isinstance(other, UserDict):
-            self.data |= other.data
-        else:
-            self.data |= other
-        return self
-
+    def __repr__(self): return repr(self.data)
     def __copy__(self):
         inst = self.__class__.__new__(self.__class__)
         inst.__dict__.update(self.__dict__)
@@ -1216,13 +1140,13 @@ 
    Source code for collections
             return d
 
 
+
 ################################################################################
 ### UserList
 ################################################################################
 
 class UserList(_collections_abc.MutableSequence):
     """A more or less complete user-defined wrapper around list objects."""
-
     def __init__(self, initlist=None):
         self.data = []
         if initlist is not None:
@@ -1233,60 +1157,35 @@ 
    Source code for collections
                     self.data[:] = initlist.data[:]
             else:
                 self.data = list(initlist)
-
-    def __repr__(self):
-        return repr(self.data)
-
-    def __lt__(self, other):
-        return self.data < self.__cast(other)
-
-    def __le__(self, other):
-        return self.data <= self.__cast(other)
-
-    def __eq__(self, other):
-        return self.data == self.__cast(other)
-
-    def __gt__(self, other):
-        return self.data > self.__cast(other)
-
-    def __ge__(self, other):
-        return self.data >= self.__cast(other)
-
+    def __repr__(self): return repr(self.data)
+    def __lt__(self, other): return self.data <  self.__cast(other)
+    def __le__(self, other): return self.data <= self.__cast(other)
+    def __eq__(self, other): return self.data == self.__cast(other)
+    def __gt__(self, other): return self.data >  self.__cast(other)
+    def __ge__(self, other): return self.data >= self.__cast(other)
     def __cast(self, other):
         return other.data if isinstance(other, UserList) else other
-
-    def __contains__(self, item):
-        return item in self.data
-
-    def __len__(self):
-        return len(self.data)
-
+    def __contains__(self, item): return item in self.data
+    def __len__(self): return len(self.data)
     def __getitem__(self, i):
         if isinstance(i, slice):
             return self.__class__(self.data[i])
         else:
             return self.data[i]
-
-    def __setitem__(self, i, item):
-        self.data[i] = item
-
-    def __delitem__(self, i):
-        del self.data[i]
-
+    def __setitem__(self, i, item): self.data[i] = item
+    def __delitem__(self, i): del self.data[i]
     def __add__(self, other):
         if isinstance(other, UserList):
             return self.__class__(self.data + other.data)
         elif isinstance(other, type(self.data)):
             return self.__class__(self.data + other)
         return self.__class__(self.data + list(other))
-
     def __radd__(self, other):
         if isinstance(other, UserList):
             return self.__class__(other.data + self.data)
         elif isinstance(other, type(self.data)):
             return self.__class__(other + self.data)
         return self.__class__(list(other) + self.data)
-
     def __iadd__(self, other):
         if isinstance(other, UserList):
             self.data += other.data
@@ -1295,53 +1194,28 @@ 
    Source code for collections
             else:
             self.data += list(other)
         return self
-
     def __mul__(self, n):
-        return self.__class__(self.data * n)
-
+        return self.__class__(self.data*n)
     __rmul__ = __mul__
-
     def __imul__(self, n):
         self.data *= n
         return self
-
     def __copy__(self):
         inst = self.__class__.__new__(self.__class__)
         inst.__dict__.update(self.__dict__)
         # Create a copy and avoid triggering descriptors
         inst.__dict__["data"] = self.__dict__["data"][:]
         return inst
-
-    def append(self, item):
-        self.data.append(item)
-
-    def insert(self, i, item):
-        self.data.insert(i, item)
-
-    def pop(self, i=-1):
-        return self.data.pop(i)
-
-    def remove(self, item):
-        self.data.remove(item)
-
-    def clear(self):
-        self.data.clear()
-
-    def copy(self):
-        return self.__class__(self)
-
-    def count(self, item):
-        return self.data.count(item)
-
-    def index(self, item, *args):
-        return self.data.index(item, *args)
-
-    def reverse(self):
-        self.data.reverse()
-
-    def sort(self, /, *args, **kwds):
-        self.data.sort(*args, **kwds)
-
+    def append(self, item): self.data.append(item)
+    def insert(self, i, item): self.data.insert(i, item)
+    def pop(self, i=-1): return self.data.pop(i)
+    def remove(self, item): self.data.remove(item)
+    def clear(self): self.data.clear()
+    def copy(self): return self.__class__(self)
+    def count(self, item): return self.data.count(item)
+    def index(self, item, *args): return self.data.index(item, *args)
+    def reverse(self): self.data.reverse()
+    def sort(self, /, *args, **kwds): self.data.sort(*args, **kwds)
     def extend(self, other):
         if isinstance(other, UserList):
             self.data.extend(other.data)
@@ -1349,12 +1223,12 @@ 
    Source code for collections
                 self.data.extend(other)
 
 
+
 ################################################################################
 ### UserString
 ################################################################################
 
 class UserString(_collections_abc.Sequence):
-
     def __init__(self, seq):
         if isinstance(seq, str):
             self.data = seq
@@ -1362,25 +1236,12 @@ 
    Source code for collections
                 self.data = seq.data[:]
         else:
             self.data = str(seq)
-
-    def __str__(self):
-        return str(self.data)
-
-    def __repr__(self):
-        return repr(self.data)
-
-    def __int__(self):
-        return int(self.data)
-
-    def __float__(self):
-        return float(self.data)
-
-    def __complex__(self):
-        return complex(self.data)
-
-    def __hash__(self):
-        return hash(self.data)
-
+    def __str__(self): return str(self.data)
+    def __repr__(self): return repr(self.data)
+    def __int__(self): return int(self.data)
+    def __float__(self): return float(self.data)
+    def __complex__(self): return complex(self.data)
+    def __hash__(self): return hash(self.data)
     def __getnewargs__(self):
         return (self.data[:],)
 
@@ -1388,22 +1249,18 @@ 
    Source code for collections
             if isinstance(string, UserString):
             return self.data == string.data
         return self.data == string
-
     def __lt__(self, string):
         if isinstance(string, UserString):
             return self.data < string.data
         return self.data < string
-
     def __le__(self, string):
         if isinstance(string, UserString):
             return self.data <= string.data
         return self.data <= string
-
     def __gt__(self, string):
         if isinstance(string, UserString):
             return self.data > string.data
         return self.data > string
-
     def __ge__(self, string):
         if isinstance(string, UserString):
             return self.data >= string.data
@@ -1414,191 +1271,105 @@ 
    Source code for collections
                 char = char.data
         return char in self.data
 
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, index):
-        return self.__class__(self.data[index])
-
+    def __len__(self): return len(self.data)
+    def __getitem__(self, index): return self.__class__(self.data[index])
     def __add__(self, other):
         if isinstance(other, UserString):
             return self.__class__(self.data + other.data)
         elif isinstance(other, str):
             return self.__class__(self.data + other)
         return self.__class__(self.data + str(other))
-
     def __radd__(self, other):
         if isinstance(other, str):
             return self.__class__(other + self.data)
         return self.__class__(str(other) + self.data)
-
     def __mul__(self, n):
-        return self.__class__(self.data * n)
-
+        return self.__class__(self.data*n)
     __rmul__ = __mul__
-
     def __mod__(self, args):
         return self.__class__(self.data % args)
-
     def __rmod__(self, template):
         return self.__class__(str(template) % self)
-
     # the following methods are defined in alphabetical order:
-    def capitalize(self):
-        return self.__class__(self.data.capitalize())
-
+    def capitalize(self): return self.__class__(self.data.capitalize())
     def casefold(self):
         return self.__class__(self.data.casefold())
-
     def center(self, width, *args):
         return self.__class__(self.data.center(width, *args))
-
     def count(self, sub, start=0, end=_sys.maxsize):
         if isinstance(sub, UserString):
             sub = sub.data
         return self.data.count(sub, start, end)
-
-    def removeprefix(self, prefix, /):
-        if isinstance(prefix, UserString):
-            prefix = prefix.data
-        return self.__class__(self.data.removeprefix(prefix))
-
-    def removesuffix(self, suffix, /):
-        if isinstance(suffix, UserString):
-            suffix = suffix.data
-        return self.__class__(self.data.removesuffix(suffix))
-
     def encode(self, encoding='utf-8', errors='strict'):
         encoding = 'utf-8' if encoding is None else encoding
         errors = 'strict' if errors is None else errors
         return self.data.encode(encoding, errors)
-
     def endswith(self, suffix, start=0, end=_sys.maxsize):
         return self.data.endswith(suffix, start, end)
-
     def expandtabs(self, tabsize=8):
         return self.__class__(self.data.expandtabs(tabsize))
-
     def find(self, sub, start=0, end=_sys.maxsize):
         if isinstance(sub, UserString):
             sub = sub.data
         return self.data.find(sub, start, end)
-
     def format(self, /, *args, **kwds):
         return self.data.format(*args, **kwds)
-
     def format_map(self, mapping):
         return self.data.format_map(mapping)
-
     def index(self, sub, start=0, end=_sys.maxsize):
         return self.data.index(sub, start, end)
-
-    def isalpha(self):
-        return self.data.isalpha()
-
-    def isalnum(self):
-        return self.data.isalnum()
-
-    def isascii(self):
-        return self.data.isascii()
-
-    def isdecimal(self):
-        return self.data.isdecimal()
-
-    def isdigit(self):
-        return self.data.isdigit()
-
-    def isidentifier(self):
-        return self.data.isidentifier()
-
-    def islower(self):
-        return self.data.islower()
-
-    def isnumeric(self):
-        return self.data.isnumeric()
-
-    def isprintable(self):
-        return self.data.isprintable()
-
-    def isspace(self):
-        return self.data.isspace()
-
-    def istitle(self):
-        return self.data.istitle()
-
-    def isupper(self):
-        return self.data.isupper()
-
-    def join(self, seq):
-        return self.data.join(seq)
-
+    def isalpha(self): return self.data.isalpha()
+    def isalnum(self): return self.data.isalnum()
+    def isascii(self): return self.data.isascii()
+    def isdecimal(self): return self.data.isdecimal()
+    def isdigit(self): return self.data.isdigit()
+    def isidentifier(self): return self.data.isidentifier()
+    def islower(self): return self.data.islower()
+    def isnumeric(self): return self.data.isnumeric()
+    def isprintable(self): return self.data.isprintable()
+    def isspace(self): return self.data.isspace()
+    def istitle(self): return self.data.istitle()
+    def isupper(self): return self.data.isupper()
+    def join(self, seq): return self.data.join(seq)
     def ljust(self, width, *args):
         return self.__class__(self.data.ljust(width, *args))
-
-    def lower(self):
-        return self.__class__(self.data.lower())
-
-    def lstrip(self, chars=None):
-        return self.__class__(self.data.lstrip(chars))
-
+    def lower(self): return self.__class__(self.data.lower())
+    def lstrip(self, chars=None): return self.__class__(self.data.lstrip(chars))
     maketrans = str.maketrans
-
     def partition(self, sep):
         return self.data.partition(sep)
-
     def replace(self, old, new, maxsplit=-1):
         if isinstance(old, UserString):
             old = old.data
         if isinstance(new, UserString):
             new = new.data
         return self.__class__(self.data.replace(old, new, maxsplit))
-
     def rfind(self, sub, start=0, end=_sys.maxsize):
         if isinstance(sub, UserString):
             sub = sub.data
         return self.data.rfind(sub, start, end)
-
     def rindex(self, sub, start=0, end=_sys.maxsize):
         return self.data.rindex(sub, start, end)
-
     def rjust(self, width, *args):
         return self.__class__(self.data.rjust(width, *args))
-
     def rpartition(self, sep):
         return self.data.rpartition(sep)
-
     def rstrip(self, chars=None):
         return self.__class__(self.data.rstrip(chars))
-
     def split(self, sep=None, maxsplit=-1):
         return self.data.split(sep, maxsplit)
-
     def rsplit(self, sep=None, maxsplit=-1):
         return self.data.rsplit(sep, maxsplit)
-
-    def splitlines(self, keepends=False):
-        return self.data.splitlines(keepends)
-
+    def splitlines(self, keepends=False): return self.data.splitlines(keepends)
     def startswith(self, prefix, start=0, end=_sys.maxsize):
         return self.data.startswith(prefix, start, end)
-
-    def strip(self, chars=None):
-        return self.__class__(self.data.strip(chars))
-
-    def swapcase(self):
-        return self.__class__(self.data.swapcase())
-
-    def title(self):
-        return self.__class__(self.data.title())
-
+    def strip(self, chars=None): return self.__class__(self.data.strip(chars))
+    def swapcase(self): return self.__class__(self.data.swapcase())
+    def title(self): return self.__class__(self.data.title())
     def translate(self, *args):
         return self.__class__(self.data.translate(*args))
-
-    def upper(self):
-        return self.__class__(self.data.upper())
-
-    def zfill(self, width):
-        return self.__class__(self.data.zfill(width))
+    def upper(self): return self.__class__(self.data.upper())
+    def zfill(self, width): return self.__class__(self.data.zfill(width))
 
    
 
           
    
@@ -1618,7 +1389,7 @@ 
    Source code for collections
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html
index bf0e1e5c..d1f4d7cf 100644
--- a/docs/build/html/_modules/index.html
+++ b/docs/build/html/_modules/index.html
@@ -128,7 +128,7 @@ 
    All modules for which code is available
    
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by     Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/plotly/offline/offline.html b/docs/build/html/_modules/plotly/offline/offline.html
index 3a9247a1..f45be670 100644
--- a/docs/build/html/_modules/plotly/offline/offline.html
+++ b/docs/build/html/_modules/plotly/offline/offline.html
@@ -950,7 +950,7 @@ 
    Source code for plotly.offline.offline
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/scipy/spatial/kdtree.html b/docs/build/html/_modules/scipy/spatial/kdtree.html
index 6992b244..e4fe7acf 100644
--- a/docs/build/html/_modules/scipy/spatial/kdtree.html
+++ b/docs/build/html/_modules/scipy/spatial/kdtree.html
@@ -94,8 +94,8 @@ 
    Source code for scipy.spatial.kdtree
     # Copyright Anne M. Archibald 2008
 # Released under the scipy license
 import numpy as np
-import warnings
-from .ckdtree import cKDTree, cKDTreeNode
+from heapq import heappush, heappop
+import scipy.sparse
 
 __all__ = ['minkowski_distance_p', 'minkowski_distance',
            'distance_matrix',
@@ -103,7 +103,8 @@ 
    Source code for scipy.spatial.kdtree
     
 
 def minkowski_distance_p(x, y, p=2):
-    """Compute the pth power of the L**p distance between two arrays.
+    """
+    Compute the pth power of the L**p distance between two arrays.
 
     For efficiency, this function computes the L**p distance but does
     not extract the pth root. If `p` is 1 or infinity, this is equal to
@@ -145,7 +146,8 @@ 
    Source code for scipy.spatial.kdtree
     
 
 def minkowski_distance(x, y, p=2):
-    """Compute the L**p distance between two arrays.
+    """
+    Compute the L**p distance between two arrays.
 
     Parameters
     ----------
@@ -171,7 +173,7 @@ 
    Source code for scipy.spatial.kdtree
             return minkowski_distance_p(x, y, p)**(1./p)
 
 
-class Rectangle:
+class Rectangle(object):
     """Hyperrectangle class.
 
     Represents a Cartesian product of intervals.
@@ -190,7 +192,8 @@ 
    Source code for scipy.spatial.kdtree
             return np.prod(self.maxes-self.mins)
 
     def split(self, d, split):
-        """Produce two hyperrectangles by splitting.
+        """
+        Produce two hyperrectangles by splitting.
 
         In general, if you need to compute maximum and minimum
         distances to the children, it can be done more efficiently
@@ -214,8 +217,7 @@ 
    Source code for scipy.spatial.kdtree
     
     def min_distance_point(self, x, p=2.):
         """
-        Return the minimum distance between input and points in the
-        hyperrectangle.
+        Return the minimum distance between input and points in the hyperrectangle.
 
         Parameters
         ----------
@@ -225,10 +227,7 @@ 
    Source code for scipy.spatial.kdtree
                 Input.
 
         """
-        return minkowski_distance(
-            0, np.maximum(0, np.maximum(self.mins-x, x-self.maxes)),
-            p
-        )
+        return minkowski_distance(0, np.maximum(0,np.maximum(self.mins-x,x-self.maxes)),p)
 
     def max_distance_point(self, x, p=2.):
         """
@@ -242,7 +241,7 @@ 
    Source code for scipy.spatial.kdtree
                 Input.
 
         """
-        return minkowski_distance(0, np.maximum(self.maxes-x, x-self.mins), p)
+        return minkowski_distance(0, np.maximum(self.maxes-x,x-self.mins),p)
 
     def min_distance_rectangle(self, other, p=2.):
         """
@@ -256,12 +255,7 @@ 
    Source code for scipy.spatial.kdtree
                 Input.
 
         """
-        return minkowski_distance(
-            0,
-            np.maximum(0, np.maximum(self.mins-other.maxes,
-                                     other.mins-self.maxes)),
-            p
-        )
+        return minkowski_distance(0, np.maximum(0,np.maximum(self.mins-other.maxes,other.mins-self.maxes)),p)
 
     def max_distance_rectangle(self, other, p=2.):
         """
@@ -275,46 +269,38 @@ 
    Source code for scipy.spatial.kdtree
                 Input.
 
         """
-        return minkowski_distance(
-            0, np.maximum(self.maxes-other.mins, other.maxes-self.mins), p)
+        return minkowski_distance(0, np.maximum(self.maxes-other.mins,other.maxes-self.mins),p)
 
 
-
    [docs]class KDTree(cKDTree):
-    """kd-tree for quick nearest-neighbor lookup.
+
    [docs]class KDTree(object):
+    """
+    kd-tree for quick nearest-neighbor lookup
 
-    This class provides an index into a set of k-dimensional points
-    which can be used to rapidly look up the nearest neighbors of any
-    point.
+    This class provides an index into a set of k-D points which
+    can be used to rapidly look up the nearest neighbors of any point.
 
     Parameters
     ----------
-    data : array_like, shape (n,m)
-        The n data points of dimension m to be indexed. This array is
-        not copied unless this is necessary to produce a contiguous
-        array of doubles, and so modifying this data will result in
-        bogus results. The data are also copied if the kd-tree is built
-        with copy_data=True.
-    leafsize : positive int, optional
+    data : (N,K) array_like
+        The data points to be indexed. This array is not copied, and
+        so modifying this data will result in bogus results.
+    leafsize : int, optional
         The number of points at which the algorithm switches over to
-        brute-force.  Default: 10.
-    compact_nodes : bool, optional
-        If True, the kd-tree is built to shrink the hyperrectangles to
-        the actual data range. This usually gives a more compact tree that
-        is robust against degenerated input data and gives faster queries
-        at the expense of longer build time. Default: True.
-    copy_data : bool, optional
-        If True the data is always copied to protect the kd-tree against
-        data corruption. Default: False.
-    balanced_tree : bool, optional
-        If True, the median is used to split the hyperrectangles instead of
-        the midpoint. This usually gives a more compact tree and
-        faster queries at the expense of longer build time. Default: True.
-    boxsize : array_like or scalar, optional
-        Apply a m-d toroidal topology to the KDTree.. The topology is generated
-        by :math:`x_i + n_i L_i` where :math:`n_i` are integers and :math:`L_i`
-        is the boxsize along i-th dimension. The input data shall be wrapped
-        into :math:`[0, L_i)`. A ValueError is raised if any of the data is
-        outside of this bound.
+        brute-force.  Has to be positive.
+
+    Raises
+    ------
+    RuntimeError
+        The maximum recursion limit can be exceeded for large data
+        sets.  If this happens, either increase the value for the `leafsize`
+        parameter or increase the recursion limit by::
+
+            >>> import sys
+            >>> sys.setrecursionlimit(10000)
+
+    See Also
+    --------
+    cKDTree : Implementation of `KDTree` in Cython
 
     Notes
     -----
@@ -337,45 +323,27 @@ 
    Source code for scipy.spatial.kdtree
         significantly faster than brute force. High-dimensional nearest-neighbor
     queries are a substantial open problem in computer science.
 
-    Attributes
-    ----------
-    data : ndarray, shape (n,m)
-        The n data points of dimension m to be indexed. This array is
-        not copied unless this is necessary to produce a contiguous
-        array of doubles. The data are also copied if the kd-tree is built
-        with `copy_data=True`.
-    leafsize : positive int
-        The number of points at which the algorithm switches over to
-        brute-force.
-    m : int
-        The dimension of a single data-point.
-    n : int
-        The number of data points.
-    maxes : ndarray, shape (m,)
-        The maximum value in each dimension of the n data points.
-    mins : ndarray, shape (m,)
-        The minimum value in each dimension of the n data points.
-    size : int
-        The number of nodes in the tree.
+    The tree also supports all-neighbors queries, both with arrays of points
+    and with other kd-trees. These do use a reasonably efficient algorithm,
+    but the kd-tree is not necessarily the best data structure for this
+    sort of calculation.
 
     """
+    def __init__(self, data, leafsize=10):
+        self.data = np.asarray(data)
+        if self.data.dtype.kind == 'c':
+            raise TypeError("KDTree does not work with complex data")
 
-
    [docs]    class node:
-        @staticmethod
-        def _create(ckdtree_node=None):
-            """Create either an inner or leaf node, wrapping a cKDTreeNode instance"""
-            if ckdtree_node is None:
-                return KDTree.node(ckdtree_node)
-            elif ckdtree_node.split_dim == -1:
-                return KDTree.leafnode(ckdtree_node)
-            else:
-                return KDTree.innernode(ckdtree_node)
+        self.n, self.m = np.shape(self.data)
+        self.leafsize = int(leafsize)
+        if self.leafsize < 1:
+            raise ValueError("leafsize must be at least 1")
+        self.maxes = np.amax(self.data,axis=0)
+        self.mins = np.amin(self.data,axis=0)
 
-        def __init__(self, ckdtree_node=None):
-            if ckdtree_node is None:
-                ckdtree_node = cKDTreeNode()
-            self._node = ckdtree_node
+        self.tree = self.__build(np.arange(self.n), self.maxes, self.mins)
 
+
    [docs]    class node(object):
         def __lt__(self, other):
             return id(self) < id(other)
 
@@ -392,227 +360,327 @@ 
    Source code for scipy.spatial.kdtree
                 return id(self) == id(other)
    
 
 
    [docs]    class leafnode(node):
-        @property
-        def idx(self):
-            return self._node.indices
-
-        @property
-        def children(self):
-            return self._node.children
    
+        def __init__(self, idx):
+            self.idx = idx
+            self.children = len(idx)
    
 
 
    [docs]    class innernode(node):
-        def __init__(self, ckdtreenode):
-            assert isinstance(ckdtreenode, cKDTreeNode)
-            super().__init__(ckdtreenode)
-            self.less = KDTree.node._create(ckdtreenode.lesser)
-            self.greater = KDTree.node._create(ckdtreenode.greater)
-
-        @property
-        def split_dim(self):
-            return self._node.split_dim
-
-        @property
-        def split(self):
-            return self._node.split
-
-        @property
-        def children(self):
-            return self._node.children
    
-
-    @property
-    def tree(self):
-        if not hasattr(self, "_tree"):
-            self._tree = KDTree.node._create(super().tree)
-
-        return self._tree
-
-    def __init__(self, data, leafsize=10, compact_nodes=True, copy_data=False,
-                 balanced_tree=True, boxsize=None):
-        data = np.asarray(data)
-        if data.dtype.kind == 'c':
-            raise TypeError("KDTree does not work with complex data")
-
-        # Note KDTree has different default leafsize from cKDTree
-        super().__init__(data, leafsize, compact_nodes, copy_data,
-                         balanced_tree, boxsize)
+        def __init__(self, split_dim, split, less, greater):
+            self.split_dim = split_dim
+            self.split = split
+            self.less = less
+            self.greater = greater
+            self.children = less.children+greater.children
    
+
+    def __build(self, idx, maxes, mins):
+        if len(idx) <= self.leafsize:
+            return KDTree.leafnode(idx)
+        else:
+            data = self.data[idx]
+            # maxes = np.amax(data,axis=0)
+            # mins = np.amin(data,axis=0)
+            d = np.argmax(maxes-mins)
+            maxval = maxes[d]
+            minval = mins[d]
+            if maxval == minval:
+                # all points are identical; warn user?
+                return KDTree.leafnode(idx)
+            data = data[:,d]
+
+            # sliding midpoint rule; see Maneewongvatana and Mount 1999
+            # for arguments that this is a good idea.
+            split = (maxval+minval)/2
+            less_idx = np.nonzero(data <= split)[0]
+            greater_idx = np.nonzero(data > split)[0]
+            if len(less_idx) == 0:
+                split = np.amin(data)
+                less_idx = np.nonzero(data <= split)[0]
+                greater_idx = np.nonzero(data > split)[0]
+            if len(greater_idx) == 0:
+                split = np.amax(data)
+                less_idx = np.nonzero(data < split)[0]
+                greater_idx = np.nonzero(data >= split)[0]
+            if len(less_idx) == 0:
+                # _still_ zero? all must have the same value
+                if not np.all(data == data[0]):
+                    raise ValueError("Troublesome data array: %s" % data)
+                split = data[0]
+                less_idx = np.arange(len(data)-1)
+                greater_idx = np.array([len(data)-1])
+
+            lessmaxes = np.copy(maxes)
+            lessmaxes[d] = split
+            greatermins = np.copy(mins)
+            greatermins[d] = split
+            return KDTree.innernode(d, split,
+                    self.__build(idx[less_idx],lessmaxes,mins),
+                    self.__build(idx[greater_idx],maxes,greatermins))
+
+    def __query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
+
+        side_distances = np.maximum(0,np.maximum(x-self.maxes,self.mins-x))
+        if p != np.inf:
+            side_distances **= p
+            min_distance = np.sum(side_distances)
+        else:
+            min_distance = np.amax(side_distances)
+
+        # priority queue for chasing nodes
+        # entries are:
+        #  minimum distance between the cell and the target
+        #  distances between the nearest side of the cell and the target
+        #  the head node of the cell
+        q = [(min_distance,
+              tuple(side_distances),
+              self.tree)]
+        # priority queue for the nearest neighbors
+        # furthest known neighbor first
+        # entries are (-distance**p, i)
+        neighbors = []
+
+        if eps == 0:
+            epsfac = 1
+        elif p == np.inf:
+            epsfac = 1/(1+eps)
+        else:
+            epsfac = 1/(1+eps)**p
+
+        if p != np.inf and distance_upper_bound != np.inf:
+            distance_upper_bound = distance_upper_bound**p
+
+        while q:
+            min_distance, side_distances, node = heappop(q)
+            if isinstance(node, KDTree.leafnode):
+                # brute-force
+                data = self.data[node.idx]
+                ds = minkowski_distance_p(data,x[np.newaxis,:],p)
+                for i in range(len(ds)):
+                    if ds[i] < distance_upper_bound:
+                        if len(neighbors) == k:
+                            heappop(neighbors)
+                        heappush(neighbors, (-ds[i], node.idx[i]))
+                        if len(neighbors) == k:
+                            distance_upper_bound = -neighbors[0][0]
+            else:
+                # we don't push cells that are too far onto the queue at all,
+                # but since the distance_upper_bound decreases, we might get
+                # here even if the cell's too far
+                if min_distance > distance_upper_bound*epsfac:
+                    # since this is the nearest cell, we're done, bail out
+                    break
+                # compute minimum distances to the children and push them on
+                if x[node.split_dim] < node.split:
+                    near, far = node.less, node.greater
+                else:
+                    near, far = node.greater, node.less
+
+                # near child is at the same distance as the current node
+                heappush(q,(min_distance, side_distances, near))
+
+                # far child is further by an amount depending only
+                # on the split value
+                sd = list(side_distances)
+                if p == np.inf:
+                    min_distance = max(min_distance, abs(node.split-x[node.split_dim]))
+                elif p == 1:
+                    sd[node.split_dim] = np.abs(node.split-x[node.split_dim])
+                    min_distance = min_distance - side_distances[node.split_dim] + sd[node.split_dim]
+                else:
+                    sd[node.split_dim] = np.abs(node.split-x[node.split_dim])**p
+                    min_distance = min_distance - side_distances[node.split_dim] + sd[node.split_dim]
+
+                # far child might be too far, if so, don't bother pushing it
+                if min_distance <= distance_upper_bound*epsfac:
+                    heappush(q,(min_distance, tuple(sd), far))
+
+        if p == np.inf:
+            return sorted([(-d,i) for (d,i) in neighbors])
+        else:
+            return sorted([((-d)**(1./p),i) for (d,i) in neighbors])
 
-
    [docs]    def query(
-            self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf, workers=1):
-        r"""Query the kd-tree for nearest neighbors.
+
    [docs]    def query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
+        """
+        Query the kd-tree for nearest neighbors
 
         Parameters
         ----------
         x : array_like, last dimension self.m
             An array of points to query.
-        k : int or Sequence[int], optional
-            Either the number of nearest neighbors to return, or a list of the
-            k-th nearest neighbors to return, starting from 1.
+        k : int, optional
+            The number of nearest neighbors to return.
         eps : nonnegative float, optional
             Return approximate nearest neighbors; the kth returned value
             is guaranteed to be no further than (1+eps) times the
             distance to the real kth nearest neighbor.
         p : float, 1<=p<=infinity, optional
             Which Minkowski p-norm to use.
-            1 is the sum-of-absolute-values distance ("Manhattan" distance).
-            2 is the usual Euclidean distance.
-            infinity is the maximum-coordinate-difference distance.
-            A large, finite p may cause a ValueError if overflow can occur.
+            1 is the sum-of-absolute-values "Manhattan" distance
+            2 is the usual Euclidean distance
+            infinity is the maximum-coordinate-difference distance
         distance_upper_bound : nonnegative float, optional
             Return only neighbors within this distance. This is used to prune
             tree searches, so if you are doing a series of nearest-neighbor
             queries, it may help to supply the distance to the nearest neighbor
             of the most recent point.
-        workers : int, optional
-            Number of workers to use for parallel processing. If -1 is given
-            all CPU threads are used. Default: 1.
-
-            .. versionadded:: 1.6.0
 
         Returns
         -------
         d : float or array of floats
             The distances to the nearest neighbors.
-            If ``x`` has shape ``tuple+(self.m,)``, then ``d`` has shape
-            ``tuple+(k,)``.
-            When k == 1, the last dimension of the output is squeezed.
-            Missing neighbors are indicated with infinite distances.
-            Hits are sorted by distance (nearest first).
-
-            .. deprecated:: 1.6.0
-               If ``k=None``, then ``d`` is an object array of shape ``tuple``,
-               containing lists of distances. This behavior is deprecated and
-               will be removed in SciPy 1.8.0, use ``query_ball_point``
-               instead.
-
+            If x has shape tuple+(self.m,), then d has shape tuple if
+            k is one, or tuple+(k,) if k is larger than one. Missing
+            neighbors (e.g. when k > n or distance_upper_bound is
+            given) are indicated with infinite distances.  If k is None,
+            then d is an object array of shape tuple, containing lists
+            of distances. In either case the hits are sorted by distance
+            (nearest first).
         i : integer or array of integers
-            The index of each neighbor in ``self.data``.
-            ``i`` is the same shape as d.
-            Missing neighbors are indicated with ``self.n``.
+            The locations of the neighbors in self.data. i is the same
+            shape as d.
 
         Examples
         --------
-
-        >>> import numpy as np
-        >>> from scipy.spatial import KDTree
+        >>> from scipy import spatial
         >>> x, y = np.mgrid[0:5, 2:8]
-        >>> tree = KDTree(np.c_[x.ravel(), y.ravel()])
-
-        To query the nearest neighbours and return squeezed result, use
-
-        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=1)
-        >>> print(dd, ii, sep='\n')
-        [2.         0.2236068]
-        [ 0 13]
-
-        To query the nearest neighbours and return unsqueezed result, use
-
-        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1])
-        >>> print(dd, ii, sep='\n')
-        [[2.        ]
-         [0.2236068]]
-        [[ 0]
-         [13]]
-
-        To query the second nearest neighbours and return unsqueezed result,
-        use
-
-        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[2])
-        >>> print(dd, ii, sep='\n')
-        [[2.23606798]
-         [0.80622577]]
-        [[ 6]
-         [19]]
-
-        To query the first and second nearest neighbours, use
-
-        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=2)
-        >>> print(dd, ii, sep='\n')
-        [[2.         2.23606798]
-         [0.2236068  0.80622577]]
-        [[ 0  6]
-         [13 19]]
-
-        or, be more specific
-
-        >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2])
-        >>> print(dd, ii, sep='\n')
-        [[2.         2.23606798]
-         [0.2236068  0.80622577]]
-        [[ 0  6]
-         [13 19]]
+        >>> tree = spatial.KDTree(list(zip(x.ravel(), y.ravel())))
+        >>> tree.data
+        array([[0, 2],
+               [0, 3],
+               [0, 4],
+               [0, 5],
+               [0, 6],
+               [0, 7],
+               [1, 2],
+               [1, 3],
+               [1, 4],
+               [1, 5],
+               [1, 6],
+               [1, 7],
+               [2, 2],
+               [2, 3],
+               [2, 4],
+               [2, 5],
+               [2, 6],
+               [2, 7],
+               [3, 2],
+               [3, 3],
+               [3, 4],
+               [3, 5],
+               [3, 6],
+               [3, 7],
+               [4, 2],
+               [4, 3],
+               [4, 4],
+               [4, 5],
+               [4, 6],
+               [4, 7]])
+        >>> pts = np.array([[0, 0], [2.1, 2.9]])
+        >>> tree.query(pts)
+        (array([ 2.        ,  0.14142136]), array([ 0, 13]))
+        >>> tree.query(pts[0])
+        (2.0, 0)
 
         """
         x = np.asarray(x)
         if x.dtype.kind == 'c':
             raise TypeError("KDTree does not work with complex data")
+        if np.shape(x)[-1] != self.m:
+            raise ValueError("x must consist of vectors of length %d but has shape %s" % (self.m, np.shape(x)))
+        if p < 1:
+            raise ValueError("Only p-norms with 1<=p<=infinity permitted")
+        retshape = np.shape(x)[:-1]
+        if retshape != ():
+            if k is None:
+                dd = np.empty(retshape,dtype=object)
+                ii = np.empty(retshape,dtype=object)
+            elif k > 1:
+                dd = np.empty(retshape+(k,),dtype=float)
+                dd.fill(np.inf)
+                ii = np.empty(retshape+(k,),dtype=int)
+                ii.fill(self.n)
+            elif k == 1:
+                dd = np.empty(retshape,dtype=float)
+                dd.fill(np.inf)
+                ii = np.empty(retshape,dtype=int)
+                ii.fill(self.n)
+            else:
+                raise ValueError("Requested %s nearest neighbors; acceptable numbers are integers greater than or equal to one, or None")
+            for c in np.ndindex(retshape):
+                hits = self.__query(x[c], k=k, eps=eps, p=p, distance_upper_bound=distance_upper_bound)
+                if k is None:
+                    dd[c] = [d for (d,i) in hits]
+                    ii[c] = [i for (d,i) in hits]
+                elif k > 1:
+                    for j in range(len(hits)):
+                        dd[c+(j,)], ii[c+(j,)] = hits[j]
+                elif k == 1:
+                    if len(hits) > 0:
+                        dd[c], ii[c] = hits[0]
+                    else:
+                        dd[c] = np.inf
+                        ii[c] = self.n
+            return dd, ii
+        else:
+            hits = self.__query(x, k=k, eps=eps, p=p, distance_upper_bound=distance_upper_bound)
+            if k is None:
+                return [d for (d,i) in hits], [i for (d,i) in hits]
+            elif k == 1:
+                if len(hits) > 0:
+                    return hits[0]
+                else:
+                    return np.inf, self.n
+            elif k > 1:
+                dd = np.empty(k,dtype=float)
+                dd.fill(np.inf)
+                ii = np.empty(k,dtype=int)
+                ii.fill(self.n)
+                for j in range(len(hits)):
+                    dd[j], ii[j] = hits[j]
+                return dd, ii
+            else:
+                raise ValueError("Requested %s nearest neighbors; acceptable numbers are integers greater than or equal to one, or None")
    
+
+    def __query_ball_point(self, x, r, p=2., eps=0):
+        R = Rectangle(self.maxes, self.mins)
+
+        def traverse_checking(node, rect):
+            if rect.min_distance_point(x, p) > r / (1. + eps):
+                return []
+            elif rect.max_distance_point(x, p) < r * (1. + eps):
+                return traverse_no_checking(node)
+            elif isinstance(node, KDTree.leafnode):
+                d = self.data[node.idx]
+                return node.idx[minkowski_distance(d, x, p) <= r].tolist()
+            else:
+                less, greater = rect.split(node.split_dim, node.split)
+                return traverse_checking(node.less, less) + \
+                       traverse_checking(node.greater, greater)
 
-        if k is None:
-            # k=None, return all neighbors
-            warnings.warn(
-                "KDTree.query with k=None is deprecated and will be removed "
-                "in SciPy 1.8.0. Use KDTree.query_ball_point instead.",
-                DeprecationWarning)
-
-            # Convert index query to a lists of distance and index,
-            # sorted by distance
-            def inds_to_hits(point, neighbors):
-                dist = minkowski_distance(point, self.data[neighbors], p)
-                hits = sorted([(d, i) for d, i in zip(dist, neighbors)])
-                return [d for d, i in hits], [i for d, i in hits]
-
-            x = np.asarray(x, dtype=np.float64)
-            inds = super().query_ball_point(
-                x, distance_upper_bound, p, eps, workers)
-
-            if isinstance(inds, list):
-                return inds_to_hits(x, inds)
-
-            dists = np.empty_like(inds)
-            for idx in np.ndindex(inds.shape):
-                dists[idx], inds[idx] = inds_to_hits(x[idx], inds[idx])
-
-            return dists, inds
+        def traverse_no_checking(node):
+            if isinstance(node, KDTree.leafnode):
+                return node.idx.tolist()
+            else:
+                return traverse_no_checking(node.less) + \
+                       traverse_no_checking(node.greater)
 
-        d, i = super().query(x, k, eps, p, distance_upper_bound, workers)
-        if isinstance(i, int):
-            i = np.intp(i)
-        return d, i
    
+        return traverse_checking(self.tree, R)
 
-
    [docs]    def query_ball_point(self, x, r, p=2., eps=0, workers=1,
-                         return_sorted=None, return_length=False):
+
    [docs]    def query_ball_point(self, x, r, p=2., eps=0):
         """Find all points within distance r of point(s) x.
 
         Parameters
         ----------
         x : array_like, shape tuple + (self.m,)
             The point or points to search for neighbors of.
-        r : array_like, float
-            The radius of points to return, must broadcast to the length of x.
+        r : positive float
+            The radius of points to return.
         p : float, optional
             Which Minkowski p-norm to use.  Should be in the range [1, inf].
-            A finite large p may cause a ValueError if overflow can occur.
         eps : nonnegative float, optional
             Approximate search. Branches of the tree are not explored if their
             nearest points are further than ``r / (1 + eps)``, and branches are
             added in bulk if their furthest points are nearer than
             ``r * (1 + eps)``.
-        workers : int, optional
-            Number of jobs to schedule for parallel processing. If -1 is given
-            all processors are used. Default: 1.
-
-            .. versionadded:: 1.6.0
-        return_sorted : bool, optional
-            Sorts returned indicies if True and does not sort them if False. If
-            None, does not sort single point queries, but does sort
-            multi-point queries which was the behavior before this option
-            was added.
-
-            .. versionadded:: 1.6.0
-        return_length: bool, optional
-            Return the number of points inside the radius instead of a list
-            of the indices.
-
-            .. versionadded:: 1.6.0
 
         Returns
         -------
@@ -633,7 +701,7 @@ 
    Source code for scipy.spatial.kdtree
             >>> x, y = np.mgrid[0:5, 0:5]
         >>> points = np.c_[x.ravel(), y.ravel()]
         >>> tree = spatial.KDTree(points)
-        >>> sorted(tree.query_ball_point([2, 0], 1))
+        >>> tree.query_ball_point([2, 0], 1)
         [5, 10, 11, 15]
 
         Query multiple points and plot the results:
@@ -651,13 +719,20 @@ 
    Source code for scipy.spatial.kdtree
             x = np.asarray(x)
         if x.dtype.kind == 'c':
             raise TypeError("KDTree does not work with complex data")
-        return super().query_ball_point(
-            x, r, p, eps, workers, return_sorted, return_length)
    
+        if x.shape[-1] != self.m:
+            raise ValueError("Searching for a %d-dimensional point in a "
+                             "%d-dimensional KDTree" % (x.shape[-1], self.m))
+        if len(x.shape) == 1:
+            return self.__query_ball_point(x, r, p, eps)
+        else:
+            retshape = x.shape[:-1]
+            result = np.empty(retshape, dtype=object)
+            for c in np.ndindex(retshape):
+                result[c] = self.__query_ball_point(x[c], r, p=p, eps=eps)
+            return result
    
 
 
    [docs]    def query_ball_tree(self, other, r, p=2., eps=0):
-        """
-        Find all pairs of points between `self` and `other` whose distance is
-        at most r.
+        """Find all pairs of points whose distance is at most r
 
         Parameters
         ----------
@@ -680,33 +755,54 @@ 
    Source code for scipy.spatial.kdtree
                 For each element ``self.data[i]`` of this tree, ``results[i]`` is a
             list of the indices of its neighbors in ``other.data``.
 
-        Examples
-        --------
-        You can search all pairs of points between two kd-trees within a distance:
-
-        >>> import matplotlib.pyplot as plt
-        >>> import numpy as np
-        >>> from scipy.spatial import KDTree
-        >>> rng = np.random.default_rng()
-        >>> points1 = rng.random((15, 2))
-        >>> points2 = rng.random((15, 2))
-        >>> plt.figure(figsize=(6, 6))
-        >>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14)
-        >>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14)
-        >>> kd_tree1 = KDTree(points1)
-        >>> kd_tree2 = KDTree(points2)
-        >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
-        >>> for i in range(len(indexes)):
-        ...     for j in indexes[i]:
-        ...         plt.plot([points1[i, 0], points2[j, 0]],
-        ...             [points1[i, 1], points2[j, 1]], "-r")
-        >>> plt.show()
-
         """
-        return super().query_ball_tree(other, r, p, eps)
    
+        results = [[] for i in range(self.n)]
+
+        def traverse_checking(node1, rect1, node2, rect2):
+            if rect1.min_distance_rectangle(rect2, p) > r/(1.+eps):
+                return
+            elif rect1.max_distance_rectangle(rect2, p) < r*(1.+eps):
+                traverse_no_checking(node1, node2)
+            elif isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    d = other.data[node2.idx]
+                    for i in node1.idx:
+                        results[i] += node2.idx[minkowski_distance(d,self.data[i],p) <= r].tolist()
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse_checking(node1,rect1,node2.less,less)
+                    traverse_checking(node1,rect1,node2.greater,greater)
+            elif isinstance(node2, KDTree.leafnode):
+                less, greater = rect1.split(node1.split_dim, node1.split)
+                traverse_checking(node1.less,less,node2,rect2)
+                traverse_checking(node1.greater,greater,node2,rect2)
+            else:
+                less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                traverse_checking(node1.less,less1,node2.less,less2)
+                traverse_checking(node1.less,less1,node2.greater,greater2)
+                traverse_checking(node1.greater,greater1,node2.less,less2)
+                traverse_checking(node1.greater,greater1,node2.greater,greater2)
+
+        def traverse_no_checking(node1, node2):
+            if isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    for i in node1.idx:
+                        results[i] += node2.idx.tolist()
+                else:
+                    traverse_no_checking(node1, node2.less)
+                    traverse_no_checking(node1, node2.greater)
+            else:
+                traverse_no_checking(node1.less, node2)
+                traverse_no_checking(node1.greater, node2)
 
-
    [docs]    def query_pairs(self, r, p=2., eps=0, output_type='set'):
-        """Find all pairs of points in `self` whose distance is at most r.
+        traverse_checking(self.tree, Rectangle(self.maxes, self.mins),
+                          other.tree, Rectangle(other.maxes, other.mins))
+        return results
    
+
+
    [docs]    def query_pairs(self, r, p=2., eps=0):
+        """
+        Find all pairs of points within a distance.
 
         Parameters
         ----------
@@ -720,190 +816,177 @@ 
    Source code for scipy.spatial.kdtree
                 if their nearest points are further than ``r/(1+eps)``, and
             branches are added in bulk if their furthest points are nearer
             than ``r * (1+eps)``.  `eps` has to be non-negative.
-        output_type : string, optional
-            Choose the output container, 'set' or 'ndarray'. Default: 'set'
-
-            .. versionadded:: 1.6.0
 
         Returns
         -------
-        results : set or ndarray
+        results : set
             Set of pairs ``(i,j)``, with ``i < j``, for which the corresponding
-            positions are close. If output_type is 'ndarray', an ndarry is
-            returned instead of a set.
-
-        Examples
-        --------
-        You can search all pairs of points in a kd-tree within a distance:
-
-        >>> import matplotlib.pyplot as plt
-        >>> import numpy as np
-        >>> from scipy.spatial import KDTree
-        >>> rng = np.random.default_rng()
-        >>> points = rng.random((20, 2))
-        >>> plt.figure(figsize=(6, 6))
-        >>> plt.plot(points[:, 0], points[:, 1], "xk", markersize=14)
-        >>> kd_tree = KDTree(points)
-        >>> pairs = kd_tree.query_pairs(r=0.2)
-        >>> for (i, j) in pairs:
-        ...     plt.plot([points[i, 0], points[j, 0]],
-        ...             [points[i, 1], points[j, 1]], "-r")
-        >>> plt.show()
+            positions are close.
 
         """
-        return super().query_pairs(r, p, eps, output_type)
    
-
-
    [docs]    def count_neighbors(self, other, r, p=2., weights=None, cumulative=True):
-        """Count how many nearby pairs can be formed.
+        results = set()
+
+        def traverse_checking(node1, rect1, node2, rect2):
+            if rect1.min_distance_rectangle(rect2, p) > r/(1.+eps):
+                return
+            elif rect1.max_distance_rectangle(rect2, p) < r*(1.+eps):
+                traverse_no_checking(node1, node2)
+            elif isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    # Special care to avoid duplicate pairs
+                    if id(node1) == id(node2):
+                        d = self.data[node2.idx]
+                        for i in node1.idx:
+                            for j in node2.idx[minkowski_distance(d,self.data[i],p) <= r]:
+                                if i < j:
+                                    results.add((i,j))
+                    else:
+                        d = self.data[node2.idx]
+                        for i in node1.idx:
+                            for j in node2.idx[minkowski_distance(d,self.data[i],p) <= r]:
+                                if i < j:
+                                    results.add((i,j))
+                                elif j < i:
+                                    results.add((j,i))
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse_checking(node1,rect1,node2.less,less)
+                    traverse_checking(node1,rect1,node2.greater,greater)
+            elif isinstance(node2, KDTree.leafnode):
+                less, greater = rect1.split(node1.split_dim, node1.split)
+                traverse_checking(node1.less,less,node2,rect2)
+                traverse_checking(node1.greater,greater,node2,rect2)
+            else:
+                less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                traverse_checking(node1.less,less1,node2.less,less2)
+                traverse_checking(node1.less,less1,node2.greater,greater2)
+
+                # Avoid traversing (node1.less, node2.greater) and
+                # (node1.greater, node2.less) (it's the same node pair twice
+                # over, which is the source of the complication in the
+                # original KDTree.query_pairs)
+                if id(node1) != id(node2):
+                    traverse_checking(node1.greater,greater1,node2.less,less2)
+
+                traverse_checking(node1.greater,greater1,node2.greater,greater2)
+
+        def traverse_no_checking(node1, node2):
+            if isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    # Special care to avoid duplicate pairs
+                    if id(node1) == id(node2):
+                        for i in node1.idx:
+                            for j in node2.idx:
+                                if i < j:
+                                    results.add((i,j))
+                    else:
+                        for i in node1.idx:
+                            for j in node2.idx:
+                                if i < j:
+                                    results.add((i,j))
+                                elif j < i:
+                                    results.add((j,i))
+                else:
+                    traverse_no_checking(node1, node2.less)
+                    traverse_no_checking(node1, node2.greater)
+            else:
+                # Avoid traversing (node1.less, node2.greater) and
+                # (node1.greater, node2.less) (it's the same node pair twice
+                # over, which is the source of the complication in the
+                # original KDTree.query_pairs)
+                if id(node1) == id(node2):
+                    traverse_no_checking(node1.less, node2.less)
+                    traverse_no_checking(node1.less, node2.greater)
+                    traverse_no_checking(node1.greater, node2.greater)
+                else:
+                    traverse_no_checking(node1.less, node2)
+                    traverse_no_checking(node1.greater, node2)
+
+        traverse_checking(self.tree, Rectangle(self.maxes, self.mins),
+                          self.tree, Rectangle(self.maxes, self.mins))
+        return results
    
+
+
    [docs]    def count_neighbors(self, other, r, p=2.):
+        """
+        Count how many nearby pairs can be formed.
 
-        Count the number of pairs ``(x1,x2)`` can be formed, with ``x1`` drawn
-        from ``self`` and ``x2`` drawn from ``other``, and where
+        Count the number of pairs (x1,x2) can be formed, with x1 drawn
+        from self and x2 drawn from ``other``, and where
         ``distance(x1, x2, p) <= r``.
-
-        Data points on ``self`` and ``other`` are optionally weighted by the
-        ``weights`` argument. (See below)
-
-        This is adapted from the "two-point correlation" algorithm described by
-        Gray and Moore [1]_.  See notes for further discussion.
+        This is the "two-point correlation" described in Gray and Moore 2000,
+        "N-body problems in statistical learning", and the code here is based
+        on their algorithm.
 
         Parameters
         ----------
-        other : KDTree
-            The other tree to draw points from, can be the same tree as self.
+        other : KDTree instance
+            The other tree to draw points from.
         r : float or one-dimensional array of floats
             The radius to produce a count for. Multiple radii are searched with
             a single tree traversal.
-            If the count is non-cumulative(``cumulative=False``), ``r`` defines
-            the edges of the bins, and must be non-decreasing.
-        p : float, optional
-            1<=p<=infinity.
-            Which Minkowski p-norm to use.
-            Default 2.0.
-            A finite large p may cause a ValueError if overflow can occur.
-        weights : tuple, array_like, or None, optional
-            If None, the pair-counting is unweighted.
-            If given as a tuple, weights[0] is the weights of points in
-            ``self``, and weights[1] is the weights of points in ``other``;
-            either can be None to indicate the points are unweighted.
-            If given as an array_like, weights is the weights of points in
-            ``self`` and ``other``. For this to make sense, ``self`` and
-            ``other`` must be the same tree. If ``self`` and ``other`` are two
-            different trees, a ``ValueError`` is raised.
-            Default: None
-
-            .. versionadded:: 1.6.0
-        cumulative : bool, optional
-            Whether the returned counts are cumulative. When cumulative is set
-            to ``False`` the algorithm is optimized to work with a large number
-            of bins (>10) specified by ``r``. When ``cumulative`` is set to
-            True, the algorithm is optimized to work with a small number of
-            ``r``. Default: True
-
-            .. versionadded:: 1.6.0
+        p : float, 1<=p<=infinity, optional
+            Which Minkowski p-norm to use
 
         Returns
         -------
-        result : scalar or 1-D array
-            The number of pairs. For unweighted counts, the result is integer.
-            For weighted counts, the result is float.
-            If cumulative is False, ``result[i]`` contains the counts with
-            ``(-inf if i == 0 else r[i-1]) < R <= r[i]``
-
-        Notes
-        -----
-        Pair-counting is the basic operation used to calculate the two point
-        correlation functions from a data set composed of position of objects.
-
-        Two point correlation function measures the clustering of objects and
-        is widely used in cosmology to quantify the large scale structure
-        in our Universe, but it may be useful for data analysis in other fields
-        where self-similar assembly of objects also occur.
-
-        The Landy-Szalay estimator for the two point correlation function of
-        ``D`` measures the clustering signal in ``D``. [2]_
-
-        For example, given the position of two sets of objects,
-
-        - objects ``D`` (data) contains the clustering signal, and
-
-        - objects ``R`` (random) that contains no signal,
-
-        .. math::
-
-             \\xi(r) = \\frac{<D, D> - 2 f <D, R> + f^2<R, R>}{f^2<R, R>},
-
-        where the brackets represents counting pairs between two data sets
-        in a finite bin around ``r`` (distance), corresponding to setting
-        `cumulative=False`, and ``f = float(len(D)) / float(len(R))`` is the
-        ratio between number of objects from data and random.
-
-        The algorithm implemented here is loosely based on the dual-tree
-        algorithm described in [1]_. We switch between two different
-        pair-cumulation scheme depending on the setting of ``cumulative``.
-        The computing time of the method we use when for
-        ``cumulative == False`` does not scale with the total number of bins.
-        The algorithm for ``cumulative == True`` scales linearly with the
-        number of bins, though it is slightly faster when only
-        1 or 2 bins are used. [5]_.
-
-        As an extension to the naive pair-counting,
-        weighted pair-counting counts the product of weights instead
-        of number of pairs.
-        Weighted pair-counting is used to estimate marked correlation functions
-        ([3]_, section 2.2),
-        or to properly calculate the average of data per distance bin
-        (e.g. [4]_, section 2.1 on redshift).
-
-        .. [1] Gray and Moore,
-               "N-body problems in statistical learning",
-               Mining the sky, 2000,
-               https://arxiv.org/abs/astro-ph/0012333
-
-        .. [2] Landy and Szalay,
-               "Bias and variance of angular correlation functions",
-               The Astrophysical Journal, 1993,
-               http://adsabs.harvard.edu/abs/1993ApJ...412...64L
-
-        .. [3] Sheth, Connolly and Skibba,
-               "Marked correlations in galaxy formation models",
-               Arxiv e-print, 2005,
-               https://arxiv.org/abs/astro-ph/0511773
-
-        .. [4] Hawkins, et al.,
-               "The 2dF Galaxy Redshift Survey: correlation functions,
-               peculiar velocities and the matter density of the Universe",
-               Monthly Notices of the Royal Astronomical Society, 2002,
-               http://adsabs.harvard.edu/abs/2003MNRAS.346...78H
-
-        .. [5] https://github.com/scipy/scipy/pull/5647#issuecomment-168474926
-
-        Examples
-        --------
-        You can count neighbors number between two kd-trees within a distance:
-
-        >>> import numpy as np
-        >>> from scipy.spatial import KDTree
-        >>> rng = np.random.default_rng()
-        >>> points1 = rng.random((5, 2))
-        >>> points2 = rng.random((5, 2))
-        >>> kd_tree1 = KDTree(points1)
-        >>> kd_tree2 = KDTree(points2)
-        >>> kd_tree1.count_neighbors(kd_tree2, 0.2)
-        1
-
-        This number is same as the total pair number calculated by
-        `query_ball_tree`:
-
-        >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
-        >>> sum([len(i) for i in indexes])
-        1
+        result : int or 1-D array of ints
+            The number of pairs. Note that this is internally stored in a numpy
+            int, and so may overflow if very large (2e9).
 
         """
-        return super().count_neighbors(other, r, p, weights, cumulative)
    
+        def traverse(node1, rect1, node2, rect2, idx):
+            min_r = rect1.min_distance_rectangle(rect2,p)
+            max_r = rect1.max_distance_rectangle(rect2,p)
+            c_greater = r[idx] > max_r
+            result[idx[c_greater]] += node1.children*node2.children
+            idx = idx[(min_r <= r[idx]) & (r[idx] <= max_r)]
+            if len(idx) == 0:
+                return
+
+            if isinstance(node1,KDTree.leafnode):
+                if isinstance(node2,KDTree.leafnode):
+                    ds = minkowski_distance(self.data[node1.idx][:,np.newaxis,:],
+                                  other.data[node2.idx][np.newaxis,:,:],
+                                  p).ravel()
+                    ds.sort()
+                    result[idx] += np.searchsorted(ds,r[idx],side='right')
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse(node1, rect1, node2.less, less, idx)
+                    traverse(node1, rect1, node2.greater, greater, idx)
+            else:
+                if isinstance(node2,KDTree.leafnode):
+                    less, greater = rect1.split(node1.split_dim, node1.split)
+                    traverse(node1.less, less, node2, rect2, idx)
+                    traverse(node1.greater, greater, node2, rect2, idx)
+                else:
+                    less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                    less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                    traverse(node1.less,less1,node2.less,less2,idx)
+                    traverse(node1.less,less1,node2.greater,greater2,idx)
+                    traverse(node1.greater,greater1,node2.less,less2,idx)
+                    traverse(node1.greater,greater1,node2.greater,greater2,idx)
+
+        R1 = Rectangle(self.maxes, self.mins)
+        R2 = Rectangle(other.maxes, other.mins)
+        if np.shape(r) == ():
+            r = np.array([r])
+            result = np.zeros(1,dtype=int)
+            traverse(self.tree, R1, other.tree, R2, np.arange(1))
+            return result[0]
+        elif len(np.shape(r)) == 1:
+            r = np.asarray(r)
+            n, = r.shape
+            result = np.zeros(n,dtype=int)
+            traverse(self.tree, R1, other.tree, R2, np.arange(n))
+            return result
+        else:
+            raise ValueError("r must be either a single value or a one-dimensional array of values")
    
 
-
    [docs]    def sparse_distance_matrix(
-            self, other, max_distance, p=2., output_type='dok_matrix'):
-        """Compute a sparse distance matrix.
+
    [docs]    def sparse_distance_matrix(self, other, max_distance, p=2.):
+        """
+        Compute a sparse distance matrix
 
         Computes a distance matrix between two KDTrees, leaving as zero
         any distance greater than max_distance.
@@ -914,60 +997,50 @@ 
    Source code for scipy.spatial.kdtree
     
         max_distance : positive float
 
-        p : float, 1<=p<=infinity
-            Which Minkowski p-norm to use.
-            A finite large p may cause a ValueError if overflow can occur.
-
-        output_type : string, optional
-            Which container to use for output data. Options: 'dok_matrix',
-            'coo_matrix', 'dict', or 'ndarray'. Default: 'dok_matrix'.
-
-            .. versionadded:: 1.6.0
+        p : float, optional
 
         Returns
         -------
-        result : dok_matrix, coo_matrix, dict or ndarray
-            Sparse matrix representing the results in "dictionary of keys"
-            format. If a dict is returned the keys are (i,j) tuples of indices.
-            If output_type is 'ndarray' a record array with fields 'i', 'j',
-            and 'v' is returned,
-
-        Examples
-        --------
-        You can compute a sparse distance matrix between two kd-trees:
-
-        >>> import numpy as np
-        >>> from scipy.spatial import KDTree
-        >>> rng = np.random.default_rng()
-        >>> points1 = rng.random((5, 2))
-        >>> points2 = rng.random((5, 2))
-        >>> kd_tree1 = KDTree(points1)
-        >>> kd_tree2 = KDTree(points2)
-        >>> sdm = kd_tree1.sparse_distance_matrix(kd_tree2, 0.3)
-        >>> sdm.toarray()
-        array([[0.        , 0.        , 0.12295571, 0.        , 0.        ],
-           [0.        , 0.        , 0.        , 0.        , 0.        ],
-           [0.28942611, 0.        , 0.        , 0.2333084 , 0.        ],
-           [0.        , 0.        , 0.        , 0.        , 0.        ],
-           [0.24617575, 0.29571802, 0.26836782, 0.        , 0.        ]])
-
-        You can check distances above the `max_distance` are zeros:
-
-        >>> from scipy.spatial import distance_matrix
-        >>> distance_matrix(points1, points2)
-        array([[0.56906522, 0.39923701, 0.12295571, 0.8658745 , 0.79428925],
-           [0.37327919, 0.7225693 , 0.87665969, 0.32580855, 0.75679479],
-           [0.28942611, 0.30088013, 0.6395831 , 0.2333084 , 0.33630734],
-           [0.31994999, 0.72658602, 0.71124834, 0.55396483, 0.90785663],
-           [0.24617575, 0.29571802, 0.26836782, 0.57714465, 0.6473269 ]])
+        result : dok_matrix
+            Sparse matrix representing the results in "dictionary of keys" format.
 
         """
-        return super().sparse_distance_matrix(
-            other, max_distance, p, output_type)
    
+        result = scipy.sparse.dok_matrix((self.n,other.n))
+
+        def traverse(node1, rect1, node2, rect2):
+            if rect1.min_distance_rectangle(rect2, p) > max_distance:
+                return
+            elif isinstance(node1, KDTree.leafnode):
+                if isinstance(node2, KDTree.leafnode):
+                    for i in node1.idx:
+                        for j in node2.idx:
+                            d = minkowski_distance(self.data[i],other.data[j],p)
+                            if d <= max_distance:
+                                result[i,j] = d
+                else:
+                    less, greater = rect2.split(node2.split_dim, node2.split)
+                    traverse(node1,rect1,node2.less,less)
+                    traverse(node1,rect1,node2.greater,greater)
+            elif isinstance(node2, KDTree.leafnode):
+                less, greater = rect1.split(node1.split_dim, node1.split)
+                traverse(node1.less,less,node2,rect2)
+                traverse(node1.greater,greater,node2,rect2)
+            else:
+                less1, greater1 = rect1.split(node1.split_dim, node1.split)
+                less2, greater2 = rect2.split(node2.split_dim, node2.split)
+                traverse(node1.less,less1,node2.less,less2)
+                traverse(node1.less,less1,node2.greater,greater2)
+                traverse(node1.greater,greater1,node2.less,less2)
+                traverse(node1.greater,greater1,node2.greater,greater2)
+        traverse(self.tree, Rectangle(self.maxes, self.mins),
+                 other.tree, Rectangle(other.maxes, other.mins))
+
+        return result
    
 
 
 def distance_matrix(x, y, p=2, threshold=1000000):
-    """Compute the distance matrix.
+    """
+    Compute the distance matrix.
 
     Returns the matrix of all pair-wise distances.
 
@@ -1036,7 +1109,7 @@ 
    Source code for scipy.spatial.kdtree
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/boundarycondition.html b/docs/build/html/_modules/spatialpy/core/boundarycondition.html
index b5a19163..4a7889e4 100644
--- a/docs/build/html/_modules/spatialpy/core/boundarycondition.html
+++ b/docs/build/html/_modules/spatialpy/core/boundarycondition.html
@@ -277,7 +277,7 @@ 
    Source code for spatialpy.core.boundarycondition
    
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/cleanup.html b/docs/build/html/_modules/spatialpy/core/cleanup.html
index e5d3fe39..81d264d1 100644
--- a/docs/build/html/_modules/spatialpy/core/cleanup.html
+++ b/docs/build/html/_modules/spatialpy/core/cleanup.html
@@ -199,7 +199,7 @@ 
    Source code for spatialpy.core.cleanup
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/datafunction.html b/docs/build/html/_modules/spatialpy/core/datafunction.html
index b0205924..3e2840cb 100644
--- a/docs/build/html/_modules/spatialpy/core/datafunction.html
+++ b/docs/build/html/_modules/spatialpy/core/datafunction.html
@@ -159,7 +159,7 @@ 
    Source code for spatialpy.core.datafunction
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/domain.html b/docs/build/html/_modules/spatialpy/core/domain.html
index 8dd8345d..7b608992 100644
--- a/docs/build/html/_modules/spatialpy/core/domain.html
+++ b/docs/build/html/_modules/spatialpy/core/domain.html
@@ -394,7 +394,7 @@ 
    Source code for spatialpy.core.domain
             :param zmax: Maximum z value of the bounding box (defaults to Domain.zlim[1]).
         :type zmax: float
 
-        :param \**kwargs: addition keyword arguments passed to :py:meth:`Domain.add_point`.
+        :param \**kwargs: Additional keyword arguments passed to :py:meth:`Domain.add_point`.
 
         :returns: The number of particles that were created within this geometry.
         :rtype: int
@@ -952,7 +952,7 @@ 
    Source code for spatialpy.core.domain
             :param fixed: spatially fixed flag of particles to be created. Defaults to false.
         :type fixed: bool
 
-        :param \**kwargs: addition keyword arguments passed to :py:class:`Domain`.
+        :param \**kwargs: Additional keyword arguments passed to :py:class:`Domain`.
         
         :returns: Uniform 3D SpatialPy Domain object.
         :rtype: spatialpy.core.domain.Domain
@@ -1011,7 +1011,7 @@ 
    Source code for spatialpy.core.domain
             :param fixed: spatially fixed flag of particles to be created. Defaults to false.
         :type fixed: bool
 
-        :param \**kwargs: addition keyword arguments passed to :py:class:`Domain`.
+        :param \**kwargs: Additional keyword arguments passed to :py:class:`Domain`.
 
         :returns: Uniform 2D SpatialPy Domain object.
         :rtype: spatialpy.core.domain.Domain
@@ -1050,7 +1050,7 @@ 
    Source code for spatialpy.core.domain
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/geometry.html b/docs/build/html/_modules/spatialpy/core/geometry.html
index bb7f3ee7..8ca065ff 100644
--- a/docs/build/html/_modules/spatialpy/core/geometry.html
+++ b/docs/build/html/_modules/spatialpy/core/geometry.html
@@ -201,7 +201,7 @@ 
    Source code for spatialpy.core.geometry
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/initialcondition.html b/docs/build/html/_modules/spatialpy/core/initialcondition.html
index 07abcaec..9dc0c791 100644
--- a/docs/build/html/_modules/spatialpy/core/initialcondition.html
+++ b/docs/build/html/_modules/spatialpy/core/initialcondition.html
@@ -297,7 +297,7 @@ 
    Source code for spatialpy.core.initialcondition
    <
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/model.html b/docs/build/html/_modules/spatialpy/core/model.html
index fa0027d2..6e155948 100644
--- a/docs/build/html/_modules/spatialpy/core/model.html
+++ b/docs/build/html/_modules/spatialpy/core/model.html
@@ -856,7 +856,7 @@ 
    Source code for spatialpy.core.model
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/parameter.html b/docs/build/html/_modules/spatialpy/core/parameter.html
index c3259fed..a1bd41b0 100644
--- a/docs/build/html/_modules/spatialpy/core/parameter.html
+++ b/docs/build/html/_modules/spatialpy/core/parameter.html
@@ -181,7 +181,7 @@ 
    Source code for spatialpy.core.parameter
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/reaction.html b/docs/build/html/_modules/spatialpy/core/reaction.html
index e917abbd..3c4d5a34 100644
--- a/docs/build/html/_modules/spatialpy/core/reaction.html
+++ b/docs/build/html/_modules/spatialpy/core/reaction.html
@@ -386,7 +386,7 @@ 
    Source code for spatialpy.core.reaction
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/result.html b/docs/build/html/_modules/spatialpy/core/result.html
index 1d4c4b2f..aa473c0b 100644
--- a/docs/build/html/_modules/spatialpy/core/result.html
+++ b/docs/build/html/_modules/spatialpy/core/result.html
@@ -996,7 +996,7 @@ 
    Source code for spatialpy.core.result
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/spatialpyerror.html b/docs/build/html/_modules/spatialpy/core/spatialpyerror.html
index c511a67e..2cf15b1b 100644
--- a/docs/build/html/_modules/spatialpy/core/spatialpyerror.html
+++ b/docs/build/html/_modules/spatialpy/core/spatialpyerror.html
@@ -207,7 +207,7 @@ 
    Source code for spatialpy.core.spatialpyerror
    Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/species.html b/docs/build/html/_modules/spatialpy/core/species.html
index 38cfe11e..8429f830 100644
--- a/docs/build/html/_modules/spatialpy/core/species.html
+++ b/docs/build/html/_modules/spatialpy/core/species.html
@@ -192,7 +192,7 @@ 
    Source code for spatialpy.core.species
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/core/vtkreader.html b/docs/build/html/_modules/spatialpy/core/vtkreader.html
index 589bbff2..68768261 100644
--- a/docs/build/html/_modules/spatialpy/core/vtkreader.html
+++ b/docs/build/html/_modules/spatialpy/core/vtkreader.html
@@ -306,7 +306,7 @@ 
    Source code for spatialpy.core.vtkreader
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/solvers/build_expression.html b/docs/build/html/_modules/spatialpy/solvers/build_expression.html
index 20b81fe4..6aaf4746 100644
--- a/docs/build/html/_modules/spatialpy/solvers/build_expression.html
+++ b/docs/build/html/_modules/spatialpy/solvers/build_expression.html
@@ -865,7 +865,7 @@ 
    Source code for spatialpy.solvers.build_expression
    Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/solvers/solver.html b/docs/build/html/_modules/spatialpy/solvers/solver.html
index 06c34b1f..5c84fcbc 100644
--- a/docs/build/html/_modules/spatialpy/solvers/solver.html
+++ b/docs/build/html/_modules/spatialpy/solvers/solver.html
@@ -724,7 +724,7 @@ 
    Source code for spatialpy.solvers.solver
           ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_modules/spatialpy/stochss/stochss_export.html b/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
index 03309955..cead8e00 100644
--- a/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
+++ b/docs/build/html/_modules/spatialpy/stochss/stochss_export.html
@@ -367,7 +367,7 @@ 
    Source code for spatialpy.stochss.stochss_export
    
       ©Copyright (C) 2017-2022.
       
       |
-      Powered by Sphinx 4.4.0
+      Powered by Sphinx 4.1.1
       & Alabaster 0.7.12
       
     
    
diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css
index bf18350b..912859b5 100644
--- a/docs/build/html/_static/basic.css
+++ b/docs/build/html/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -731,9 +731,8 @@ dl.glossary dt {
 
 .classifier:before {
     font-style: normal;
-    margin: 0 0.5em;
+    margin: 0.5em;
     content: ":";
-    display: inline-block;
 }
 
 abbr, acronym {
@@ -757,7 +756,6 @@ span.pre {
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
-    white-space: nowrap;
 }
 
 div[class*="highlight-"] {
diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js
index e509e483..8cbf1b16 100644
--- a/docs/build/html/_static/doctools.js
+++ b/docs/build/html/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for all documentation.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -264,9 +264,6 @@ var Documentation = {
   hideSearchWords : function() {
     $('#searchbox .highlight-link').fadeOut(300);
     $('span.highlighted').removeClass('highlighted');
-    var url = new URL(window.location);
-    url.searchParams.delete('highlight');
-    window.history.replaceState({}, '', url);
   },
 
   /**
diff --git a/docs/build/html/_static/language_data.js b/docs/build/html/_static/language_data.js
index ebe2f03b..863704b3 100644
--- a/docs/build/html/_static/language_data.js
+++ b/docs/build/html/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/build/html/_static/pygments.css b/docs/build/html/_static/pygments.css
index 87f8bd12..1b945229 100644
--- a/docs/build/html/_static/pygments.css
+++ b/docs/build/html/_static/pygments.css
@@ -1,7 +1,7 @@
-pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+pre { line-height: 125%; margin: 0; }
+td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
+td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
 .highlight { background: #f8f8f8; }
diff --git a/docs/build/html/_static/searchtools.js b/docs/build/html/_static/searchtools.js
index 2d778593..8eb14218 100644
--- a/docs/build/html/_static/searchtools.js
+++ b/docs/build/html/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -276,28 +276,19 @@ var Search = {
           setTimeout(function() {
             displayNextItem();
           }, 5);
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
+        } else {
           $.ajax({url: requestUrl,
                   dataType: "text",
                   complete: function(jqxhr, textstatus) {
                     var data = jqxhr.responseText;
                     if (data !== '' && data !== undefined) {
-                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
-                      if (summary) {
-                        listItem.append(summary);
-                      }
+                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
                     }
                     Search.output.append(listItem);
                     setTimeout(function() {
                       displayNextItem();
                     }, 5);
                   }});
-        } else {
-          // no source available, just display title
-          Search.output.append(listItem);
-          setTimeout(function() {
-            displayNextItem();
-          }, 5);
         }
       }
       // search finished, update title and status message
@@ -328,9 +319,7 @@ var Search = {
     var results = [];
 
     for (var prefix in objects) {
-      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
-        var match = objects[prefix][iMatch];
-        var name = match[4];
+      for (var name in objects[prefix]) {
         var fullname = (prefix ? prefix + '.' : '') + name;
         var fullnameLower = fullname.toLowerCase()
         if (fullnameLower.indexOf(object) > -1) {
@@ -344,6 +333,7 @@ var Search = {
           } else if (parts[parts.length - 1].indexOf(object) > -1) {
             score += Scorer.objPartialMatch;
           }
+          var match = objects[prefix][name];
           var objname = objnames[match[1]][2];
           var title = titles[match[0]];
           // If more than one term searched for, we require other words to be
@@ -502,9 +492,6 @@ var Search = {
    */
   makeSearchSummary : function(htmlText, keywords, hlwords) {
     var text = Search.htmlToText(htmlText);
-    if (text == "") {
-      return null;
-    }
     var textLower = text.toLowerCase();
     var start = 0;
     $.each(keywords, function() {
diff --git a/docs/build/html/classes/spatialpy.core.html b/docs/build/html/classes/spatialpy.core.html
index cbc65089..0b5b39b2 100644
--- a/docs/build/html/classes/spatialpy.core.html
+++ b/docs/build/html/classes/spatialpy.core.html
@@ -4,8 +4,7 @@
 
   
     
-    
-
+    
     spatialpy.core package — SpatialPy 1.0 documentation
     
     
@@ -14,7 +13,6 @@
     
     
     
-    
     
     
     
@@ -105,12 +103,12 @@ 
    Quick search
    
 
           
    
             
-  
    
+  
    
 
    spatialpy.core package
    
-
    
+
    
 
    Submodules
    
-
    
-
    
+
    
+
    
 
    spatialpy.core.boundarycondition module
    
 
    SpatialPy is a Python 3 package for simulation of
 spatial deterministic/stochastic reaction-diffusion-advection problems
@@ -126,7 +124,7 @@ 
    Submoduleshttp://www.gnu.org/licenses/>.
    
 
    
 
    
-class spatialpy.core.boundarycondition.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]
    
+class spatialpy.core.boundarycondition.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]

    Bases: object

    Set spatial regions of the domain where a property of particles are held constant (updated each simulation step)

    @@ -175,8 +173,8 @@

    Submodules + +

    spatialpy.core.cleanup module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -224,8 +222,8 @@

    Submodules +

    +

    spatialpy.core.datafunction module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -241,7 +239,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.datafunction.DataFunction(name=None)[source]
    +class spatialpy.core.datafunction.DataFunction(name=None)[source]

    Bases: object

    Abstract class used to constuct the data function.

    @@ -272,8 +270,8 @@

    Submodules +

    +

    spatialpy.core.domain module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -289,7 +287,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.domain.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]
    +class spatialpy.core.domain.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]

    Bases: object

    Domain class for SpatialPy. A domain defines points and attributes of a regional space for simulation.

    @@ -380,7 +378,7 @@

    Submodules
    -classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
    +classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

    Create a filled 2D domain

    Parameters
    @@ -395,7 +393,7 @@

    SubmodulesDomain.

    +

  • **kwargs – Additional keyword arguments passed to Domain.

    • Returns
    @@ -409,7 +407,7 @@

    Submodules
    -classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
    +classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

    Create a filled 3D domain

    Parameters
    @@ -426,7 +424,7 @@

    SubmodulesDomain.

    +

  • **kwargs – Additional keyword arguments passed to Domain.

    • Returns
    @@ -476,7 +474,7 @@

    SubmodulesDomain.add_point().

    +

  • **kwargs – Additional keyword arguments passed to Domain.add_point().

    • Returns
    @@ -596,7 +594,7 @@

    Submodules
    -classmethod import_meshio_object(mesh_obj)[source]
    +classmethod import_meshio_object(mesh_obj)[source]

    Import a python meshio mesh object.

    Parameters
    @@ -639,7 +637,7 @@

    Submodules
    -classmethod read_msh_file(filename)[source]
    +classmethod read_msh_file(filename)[source]

    Read a Gmsh style .msh file

    Parameters
    @@ -656,7 +654,7 @@

    Submodules
    -classmethod read_stochss_domain(filename)[source]
    +classmethod read_stochss_domain(filename)[source]

    Read a StochSS Domain (.domn) file or pull a StochSS Domain from a StochSS Spatial Model (.smdl) file.

    Parameters
    @@ -690,7 +688,7 @@

    Submodules
    -classmethod read_xml_mesh(filename)[source]
    +classmethod read_xml_mesh(filename)[source]

    Read a FEniCS/dolfin style XML mesh file

    Parameters
    @@ -737,8 +735,8 @@

    Submodules +

    +

    spatialpy.core.geometry module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -754,7 +752,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.geometry.Geometry[source]
    +class spatialpy.core.geometry.Geometry[source]

    Bases: object

    Geometry class provides a method for tagging parts of the spatial domain as separate parts.

    @@ -780,7 +778,7 @@

    Submodules
    -class spatialpy.core.geometry.GeometryAll[source]
    +class spatialpy.core.geometry.GeometryAll[source]

    Bases: spatialpy.core.geometry.Geometry

    Mark all particles.

    @@ -806,7 +804,7 @@

    Submodules
    -class spatialpy.core.geometry.GeometryExterior[source]
    +class spatialpy.core.geometry.GeometryExterior[source]

    Bases: spatialpy.core.geometry.Geometry

    Mark particles that are on the edge of the domain. only works for domains that define triangles and tetrahedrons.

    @@ -833,7 +831,7 @@

    Submodules
    -class spatialpy.core.geometry.GeometryInterior[source]
    +class spatialpy.core.geometry.GeometryInterior[source]

    Bases: spatialpy.core.geometry.Geometry

    Mark particles that are not on the edge of the domain. Only works for domains that define triangles and tetrahedrons.

    @@ -858,8 +856,8 @@

    Submodules +

    +

    spatialpy.core.initialcondition module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -875,7 +873,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.initialcondition.InitialCondition[source]
    +class spatialpy.core.initialcondition.InitialCondition[source]

    Bases: object

    Class used to defined initial conditions in SpatialPy. SubClasses must implement the ‘apply(model)’ method, which @@ -895,7 +893,7 @@

    Submodules
    -class spatialpy.core.initialcondition.PlaceInitialCondition(species, count, location)[source]
    +class spatialpy.core.initialcondition.PlaceInitialCondition(species, count, location)[source]

    Bases: spatialpy.core.initialcondition.InitialCondition

    Class used to defined the place initial condition in SpatialPy.

    @@ -922,7 +920,7 @@

    Submodules
    -class spatialpy.core.initialcondition.ScatterInitialCondition(species, count, types=None)[source]
    +class spatialpy.core.initialcondition.ScatterInitialCondition(species, count, types=None)[source]

    Bases: spatialpy.core.initialcondition.InitialCondition

    Class used to defined the scatter initial condition in SpatialPy.

    @@ -949,7 +947,7 @@

    Submodules
    -class spatialpy.core.initialcondition.UniformInitialCondition(species, count, types=None)[source]
    +class spatialpy.core.initialcondition.UniformInitialCondition(species, count, types=None)[source]

    Bases: spatialpy.core.initialcondition.InitialCondition

    Class used to defined the uniform initial condition in SpatialPy.

    @@ -974,8 +972,8 @@

    Submodules +

    +

    spatialpy.core.model module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -991,7 +989,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.model.Model(name='spatialpy')[source]
    +class spatialpy.core.model.Model(name='spatialpy')[source]

    Bases: object

    Representation of a spatial biochemical model.

    @@ -1322,7 +1320,7 @@

    Submodules
    -reserved_names = ['vol', 't']
    +reserved_names = ['vol', 't']

    @@ -1416,7 +1414,7 @@

    Submodules
    -special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']
    +special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']

    @@ -1466,8 +1464,8 @@

    Submodules +

    +

    spatialpy.core.parameter module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -1483,7 +1481,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.parameter.Parameter(name=None, expression=None)[source]
    +class spatialpy.core.parameter.Parameter(name=None, expression=None)[source]

    Bases: object

    Model of a rate paramter. A parameter can be given as a String expression (function) or directly as a scalar value. @@ -1499,8 +1497,8 @@

    Submodules +

    +

    spatialpy.core.reaction module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -1516,7 +1514,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.reaction.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]
    +class spatialpy.core.reaction.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]

    Bases: object

    Models a biochemical reaction. A reaction conatains dictionaries of species (reactants and products) and parameters. The reaction’s propensity function needs to be evaluable and result in a non-negative scalar value in the namespace defined by the union of its Reactant, Product and Parameter dictionaries. For mass-action, zeroth, first and second order reactions are supported, attempting to used higher orders will result in an error.

    @@ -1584,8 +1582,8 @@

    Submodules +

    +

    spatialpy.core.result module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -1601,7 +1599,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.result.Result(model=None, result_dir=None)[source]
    +class spatialpy.core.result.Result(model=None, result_dir=None)[source]

    Bases: object

    Result object for a URDME simulation.

    @@ -1810,8 +1808,8 @@

    Submodules +

    +

    spatialpy.core.spatialpyerror module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -1827,104 +1825,104 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -exception spatialpy.core.spatialpyerror.BoundaryConditionError[source]
    +exception spatialpy.core.spatialpyerror.BoundaryConditionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Base class for exceptions in the boundarycondition module.

    -exception spatialpy.core.spatialpyerror.DataFunctionError[source]
    +exception spatialpy.core.spatialpyerror.DataFunctionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the datafunction module.

    -exception spatialpy.core.spatialpyerror.DomainError[source]
    +exception spatialpy.core.spatialpyerror.DomainError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the domain module.

    -exception spatialpy.core.spatialpyerror.GeometryError[source]
    +exception spatialpy.core.spatialpyerror.GeometryError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the geometry module.

    -exception spatialpy.core.spatialpyerror.InitialConditionError[source]
    +exception spatialpy.core.spatialpyerror.InitialConditionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in initailcondition module.

    -exception spatialpy.core.spatialpyerror.ModelError[source]
    +exception spatialpy.core.spatialpyerror.ModelError[source]

    Bases: Exception

    Class for exceptions in the model module.

    -exception spatialpy.core.spatialpyerror.ParameterError[source]
    +exception spatialpy.core.spatialpyerror.ParameterError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in parameter module.

    -exception spatialpy.core.spatialpyerror.ReactionError[source]
    +exception spatialpy.core.spatialpyerror.ReactionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in reaction module.

    -exception spatialpy.core.spatialpyerror.ResultError[source]
    +exception spatialpy.core.spatialpyerror.ResultError[source]

    Bases: Exception

    Class for exceptions in the results module.

    -exception spatialpy.core.spatialpyerror.SimulationError[source]
    +exception spatialpy.core.spatialpyerror.SimulationError[source]

    Bases: Exception

    Class for exceptions in the simulation module.

    -exception spatialpy.core.spatialpyerror.SimulationTimeout[source]
    +exception spatialpy.core.spatialpyerror.SimulationTimeout[source]

    Bases: spatialpy.core.spatialpyerror.SimulationError

    Exception raised for timeout errors.

    -exception spatialpy.core.spatialpyerror.SpeciesError[source]
    +exception spatialpy.core.spatialpyerror.SpeciesError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the species module.

    -exception spatialpy.core.spatialpyerror.VTKReaderError[source]
    +exception spatialpy.core.spatialpyerror.VTKReaderError[source]

    Bases: Exception

    Bass class for exceptions in the vtkreader module.

    -exception spatialpy.core.spatialpyerror.VTKReaderIOError(message)[source]
    +exception spatialpy.core.spatialpyerror.VTKReaderIOError(message)[source]

    Bases: spatialpy.core.spatialpyerror.VTKReaderError

    Exception raised for I/O errors.

    - -
    +

    +

    spatialpy.core.species module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -1940,7 +1938,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.species.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]
    +class spatialpy.core.species.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]

    Bases: object

    Model of a biochemical species. Must be assigned a diffusion coefficent.

    @@ -1968,8 +1966,8 @@

    Submodules +

    +

    spatialpy.core.vtkreader module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -1985,7 +1983,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.vtkreader.VTKReader(filename=None, debug=False)[source]
    +class spatialpy.core.vtkreader.VTKReader(filename=None, debug=False)[source]

    Bases: object

    VTKReader.py: SpatialPy minimal VTK legacy file reader. Reference: https://vtk.org/wp-content/uploads/2015/04/file-formats.pdf

    @@ -2080,8 +2078,8 @@

    Submodules +

    +

    Module contents

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -2097,7 +2095,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.core.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]
    +class spatialpy.core.BoundaryCondition(xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, type_id=None, deterministic=True, target=None, value=None, model=None)[source]

    Bases: object

    Set spatial regions of the domain where a property of particles are held constant (updated each simulation step)

    @@ -2148,14 +2146,14 @@

    Submodules
    -exception spatialpy.core.BoundaryConditionError[source]
    +exception spatialpy.core.BoundaryConditionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Base class for exceptions in the boundarycondition module.

    -class spatialpy.core.DataFunction(name=None)[source]
    +class spatialpy.core.DataFunction(name=None)[source]

    Bases: object

    Abstract class used to constuct the data function.

    @@ -2188,14 +2186,14 @@

    Submodules
    -exception spatialpy.core.DataFunctionError[source]
    +exception spatialpy.core.DataFunctionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the datafunction module.

    -class spatialpy.core.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]
    +class spatialpy.core.Domain(numpoints, xlim, ylim, zlim, rho0=1.0, c0=10, P0=None, gravity=None)[source]

    Bases: object

    Domain class for SpatialPy. A domain defines points and attributes of a regional space for simulation.

    @@ -2286,7 +2284,7 @@

    Submodules
    -classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
    +classmethod create_2D_domain(xlim, ylim, nx, ny, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

    Create a filled 2D domain

    Parameters
    @@ -2301,7 +2299,7 @@

    SubmodulesDomain.

    +

  • **kwargs – Additional keyword arguments passed to Domain.

    • Returns
    @@ -2315,7 +2313,7 @@

    Submodules
    -classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]
    +classmethod create_3D_domain(xlim, ylim, zlim, nx, ny, nz, type_id=1, mass=1.0, nu=1.0, rho=None, c=0, fixed=False, **kwargs)[source]

    Create a filled 3D domain

    Parameters
    @@ -2332,7 +2330,7 @@

    SubmodulesDomain.

    +

  • **kwargs – Additional keyword arguments passed to Domain.

    • Returns
    @@ -2382,7 +2380,7 @@

    SubmodulesDomain.add_point().

    +

  • **kwargs – Additional keyword arguments passed to Domain.add_point().

    • Returns
    @@ -2502,7 +2500,7 @@

    Submodules
    -classmethod import_meshio_object(mesh_obj)[source]
    +classmethod import_meshio_object(mesh_obj)[source]

    Import a python meshio mesh object.

    Parameters
    @@ -2545,7 +2543,7 @@

    Submodules
    -classmethod read_msh_file(filename)[source]
    +classmethod read_msh_file(filename)[source]

    Read a Gmsh style .msh file

    Parameters
    @@ -2562,7 +2560,7 @@

    Submodules
    -classmethod read_stochss_domain(filename)[source]
    +classmethod read_stochss_domain(filename)[source]

    Read a StochSS Domain (.domn) file or pull a StochSS Domain from a StochSS Spatial Model (.smdl) file.

    Parameters
    @@ -2596,7 +2594,7 @@

    Submodules
    -classmethod read_xml_mesh(filename)[source]
    +classmethod read_xml_mesh(filename)[source]

    Read a FEniCS/dolfin style XML mesh file

    Parameters
    @@ -2645,14 +2643,14 @@

    Submodules
    -exception spatialpy.core.DomainError[source]
    +exception spatialpy.core.DomainError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the domain module.

    -class spatialpy.core.Geometry[source]
    +class spatialpy.core.Geometry[source]

    Bases: object

    Geometry class provides a method for tagging parts of the spatial domain as separate parts.

    @@ -2678,7 +2676,7 @@

    Submodules
    -class spatialpy.core.GeometryAll[source]
    +class spatialpy.core.GeometryAll[source]

    Bases: spatialpy.core.geometry.Geometry

    Mark all particles.

    @@ -2704,14 +2702,14 @@

    Submodules
    -exception spatialpy.core.GeometryError[source]
    +exception spatialpy.core.GeometryError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the geometry module.

    -class spatialpy.core.GeometryExterior[source]
    +class spatialpy.core.GeometryExterior[source]

    Bases: spatialpy.core.geometry.Geometry

    Mark particles that are on the edge of the domain. only works for domains that define triangles and tetrahedrons.

    @@ -2738,7 +2736,7 @@

    Submodules
    -class spatialpy.core.GeometryInterior[source]
    +class spatialpy.core.GeometryInterior[source]

    Bases: spatialpy.core.geometry.Geometry

    Mark particles that are not on the edge of the domain. Only works for domains that define triangles and tetrahedrons.

    @@ -2765,7 +2763,7 @@

    Submodules
    -class spatialpy.core.InitialCondition[source]
    +class spatialpy.core.InitialCondition[source]

    Bases: object

    Class used to defined initial conditions in SpatialPy. SubClasses must implement the ‘apply(model)’ method, which @@ -2785,46 +2783,43 @@

    Submodules
    -exception spatialpy.core.InitialConditionError[source]
    +exception spatialpy.core.InitialConditionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in initailcondition module.

    -class spatialpy.core.KDTree(data, leafsize=10, compact_nodes=True, copy_data=False, balanced_tree=True, boxsize=None)[source]
    -

    Bases: scipy.spatial.ckdtree.cKDTree

    -

    kd-tree for quick nearest-neighbor lookup.

    -

    This class provides an index into a set of k-dimensional points -which can be used to rapidly look up the nearest neighbors of any -point.

    +class spatialpy.core.KDTree(data, leafsize=10)[source] +

    Bases: object

    +

    kd-tree for quick nearest-neighbor lookup

    +

    This class provides an index into a set of k-D points which +can be used to rapidly look up the nearest neighbors of any point.

    Parameters
      -

    • data (array_like, shape (n,m)) – The n data points of dimension m to be indexed. This array is -not copied unless this is necessary to produce a contiguous -array of doubles, and so modifying this data will result in -bogus results. The data are also copied if the kd-tree is built -with copy_data=True.

      • -

    • leafsize (positive int, optional) – The number of points at which the algorithm switches over to -brute-force. Default: 10.

      • -

    • compact_nodes (bool, optional) – If True, the kd-tree is built to shrink the hyperrectangles to -the actual data range. This usually gives a more compact tree that -is robust against degenerated input data and gives faster queries -at the expense of longer build time. Default: True.

      • -

    • copy_data (bool, optional) – If True the data is always copied to protect the kd-tree against -data corruption. Default: False.

      • -

    • balanced_tree (bool, optional) – If True, the median is used to split the hyperrectangles instead of -the midpoint. This usually gives a more compact tree and -faster queries at the expense of longer build time. Default: True.

      • -

    • boxsize (array_like or scalar, optional) – Apply a m-d toroidal topology to the KDTree.. The topology is generated -by \(x_i + n_i L_i\) where \(n_i\) are integers and \(L_i\) -is the boxsize along i-th dimension. The input data shall be wrapped -into \([0, L_i)\). A ValueError is raised if any of the data is -outside of this bound.

      • +

    • data ((N,K) array_like) – The data points to be indexed. This array is not copied, and +so modifying this data will result in bogus results.

      • +

    • leafsize (int, optional) – The number of points at which the algorithm switches over to +brute-force. Has to be positive.

    +
    Raises
    +

    RuntimeError – The maximum recursion limit can be exceeded for large data + sets. If this happens, either increase the value for the leafsize + parameter or increase the recursion limit by:: + + >>> import sys + >>> sys.setrecursionlimit(10000)

    +
    +
    +
    +

    See also

    +
    +
    cKDTree

    Implementation of KDTree in Cython

    +
    +

    Notes

    The algorithm used is described in Maneewongvatana and Mount 1999. The general idea is that the kd-tree is a binary tree, each of whose @@ -2841,414 +2836,153 @@

    Submodules -
    -data
    -

    The n data points of dimension m to be indexed. This array is -not copied unless this is necessary to produce a contiguous -array of doubles. The data are also copied if the kd-tree is built -with copy_data=True.

    -
    -
    Type
    -

    ndarray, shape (n,m)

    -
    -
    -

    - -
    -
    -leafsize
    -

    The number of points at which the algorithm switches over to -brute-force.

    -
    -
    Type
    -

    positive int

    -
    -
    -
    - -
    -
    -m
    -

    The dimension of a single data-point.

    -
    -
    Type
    -

    int

    -
    -
    -
    - -
    -
    -n
    -

    The number of data points.

    -
    -
    Type
    -

    int

    -
    -
    -
    - -
    -
    -maxes
    -

    The maximum value in each dimension of the n data points.

    -
    -
    Type
    -

    ndarray, shape (m,)

    -
    -
    -
    - -
    -
    -mins
    -

    The minimum value in each dimension of the n data points.

    -
    -
    Type
    -

    ndarray, shape (m,)

    -
    -
    -
    - -
    -
    -size
    -

    The number of nodes in the tree.

    -
    -
    Type
    -

    int

    -
    -
    -
    - +

    The tree also supports all-neighbors queries, both with arrays of points +and with other kd-trees. These do use a reasonably efficient algorithm, +but the kd-tree is not necessarily the best data structure for this +sort of calculation.

    -count_neighbors(other, r, p=2.0, weights=None, cumulative=True)[source]
    +count_neighbors(other, r, p=2.0)[source]

    Count how many nearby pairs can be formed.

    -

    Count the number of pairs (x1,x2) can be formed, with x1 drawn -from self and x2 drawn from other, and where -distance(x1, x2, p) <= r.

    -

    Data points on self and other are optionally weighted by the -weights argument. (See below)

    -

    This is adapted from the “two-point correlation” algorithm described by -Gray and Moore 1. See notes for further discussion.

    +

    Count the number of pairs (x1,x2) can be formed, with x1 drawn +from self and x2 drawn from other, and where +distance(x1, x2, p) <= r. +This is the “two-point correlation” described in Gray and Moore 2000, +“N-body problems in statistical learning”, and the code here is based +on their algorithm.

    Parameters
      -

    • other (KDTree) – The other tree to draw points from, can be the same tree as self.

      • +

    • other (KDTree instance) – The other tree to draw points from.

    • r (float or one-dimensional array of floats) – The radius to produce a count for. Multiple radii are searched with -a single tree traversal. -If the count is non-cumulative(cumulative=False), r defines -the edges of the bins, and must be non-decreasing.

      • -

    • p (float, optional) – 1<=p<=infinity. -Which Minkowski p-norm to use. -Default 2.0. -A finite large p may cause a ValueError if overflow can occur.

      • -

    • weights (tuple, array_like, or None, optional) –

      If None, the pair-counting is unweighted. -If given as a tuple, weights[0] is the weights of points in -self, and weights[1] is the weights of points in other; -either can be None to indicate the points are unweighted. -If given as an array_like, weights is the weights of points in -self and other. For this to make sense, self and -other must be the same tree. If self and other are two -different trees, a ValueError is raised. -Default: None

      -
      -

      New in version 1.6.0.

      -
      -

      • -

    • cumulative (bool, optional) –

      Whether the returned counts are cumulative. When cumulative is set -to False the algorithm is optimized to work with a large number -of bins (>10) specified by r. When cumulative is set to -True, the algorithm is optimized to work with a small number of -r. Default: True

      -
      -

      New in version 1.6.0.

      -
      -

      • +a single tree traversal.

      +

    • p (float, 1<=p<=infinity, optional) – Which Minkowski p-norm to use

    Returns
    -

    result – The number of pairs. For unweighted counts, the result is integer. -For weighted counts, the result is float. -If cumulative is False, result[i] contains the counts with -(-inf if i == 0 else r[i-1]) < R <= r[i]

    +

    result – The number of pairs. Note that this is internally stored in a numpy +int, and so may overflow if very large (2e9).

    Return type
    -

    scalar or 1-D array

    -
    -
    -

    Notes

    -

    Pair-counting is the basic operation used to calculate the two point -correlation functions from a data set composed of position of objects.

    -

    Two point correlation function measures the clustering of objects and -is widely used in cosmology to quantify the large scale structure -in our Universe, but it may be useful for data analysis in other fields -where self-similar assembly of objects also occur.

    -

    The Landy-Szalay estimator for the two point correlation function of -D measures the clustering signal in D. 2

    -

    For example, given the position of two sets of objects,

    -
      -

    • objects D (data) contains the clustering signal, and

      • -

    • objects R (random) that contains no signal,

      • -
    -
    -\[\xi(r) = \frac{<D, D> - 2 f <D, R> + f^2<R, R>}{f^2<R, R>},\]
    -

    where the brackets represents counting pairs between two data sets -in a finite bin around r (distance), corresponding to setting -cumulative=False, and f = float(len(D)) / float(len(R)) is the -ratio between number of objects from data and random.

    -

    The algorithm implemented here is loosely based on the dual-tree -algorithm described in 1. We switch between two different -pair-cumulation scheme depending on the setting of cumulative. -The computing time of the method we use when for -cumulative == False does not scale with the total number of bins. -The algorithm for cumulative == True scales linearly with the -number of bins, though it is slightly faster when only -1 or 2 bins are used. 5.

    -

    As an extension to the naive pair-counting, -weighted pair-counting counts the product of weights instead -of number of pairs. -Weighted pair-counting is used to estimate marked correlation functions -(3, section 2.2), -or to properly calculate the average of data per distance bin -(e.g. 4, section 2.1 on redshift).

    -
    -
    1(1,2)
    -

    Gray and Moore, -“N-body problems in statistical learning”, -Mining the sky, 2000, -https://arxiv.org/abs/astro-ph/0012333

    -
    -
    2
    -

    Landy and Szalay, -“Bias and variance of angular correlation functions”, -The Astrophysical Journal, 1993, -http://adsabs.harvard.edu/abs/1993ApJ…412…64L

    -
    -
    3
    -

    Sheth, Connolly and Skibba, -“Marked correlations in galaxy formation models”, -Arxiv e-print, 2005, -https://arxiv.org/abs/astro-ph/0511773

    -
    -
    4
    -

    Hawkins, et al., -“The 2dF Galaxy Redshift Survey: correlation functions, -peculiar velocities and the matter density of the Universe”, -Monthly Notices of the Royal Astronomical Society, 2002, -http://adsabs.harvard.edu/abs/2003MNRAS.346…78H

    -
    -
    5
    -

    https://github.com/scipy/scipy/pull/5647#issuecomment-168474926

    +

    int or 1-D array of ints

    -

    Examples

    -

    You can count neighbors number between two kd-trees within a distance:

    -
    >>> import numpy as np
->>> from scipy.spatial import KDTree
->>> rng = np.random.default_rng()
->>> points1 = rng.random((5, 2))
->>> points2 = rng.random((5, 2))
->>> kd_tree1 = KDTree(points1)
->>> kd_tree2 = KDTree(points2)
->>> kd_tree1.count_neighbors(kd_tree2, 0.2)
-1
-
    -
    -

    This number is same as the total pair number calculated by -query_ball_tree:

    -
    >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
->>> sum([len(i) for i in indexes])
-1
-
    -
    -class innernode(ckdtreenode)[source]
    +class innernode(split_dim, split, less, greater)[source]

    Bases: scipy.spatial.kdtree.KDTree.node

    -
    -
    -property children
    -
    - -
    -
    -property split
    -
    - -
    -
    -property split_dim
    -
    -
    -class leafnode(ckdtree_node=None)[source]
    +class leafnode(idx)[source]

    Bases: scipy.spatial.kdtree.KDTree.node

    -
    -
    -property children
    -
    - -
    -
    -property idx
    -
    -
    -class node(ckdtree_node=None)[source]
    +class node[source]

    Bases: object

    -query(x, k=1, eps=0, p=2, distance_upper_bound=inf, workers=1)[source]
    -

    Query the kd-tree for nearest neighbors.

    +query(x, k=1, eps=0, p=2, distance_upper_bound=inf)[source] +

    Query the kd-tree for nearest neighbors

    Parameters

    • x (array_like, last dimension self.m) – An array of points to query.

      • -

    • k (int or Sequence[int], optional) – Either the number of nearest neighbors to return, or a list of the -k-th nearest neighbors to return, starting from 1.

      • +

    • k (int, optional) – The number of nearest neighbors to return.

    • eps (nonnegative float, optional) – Return approximate nearest neighbors; the kth returned value is guaranteed to be no further than (1+eps) times the distance to the real kth nearest neighbor.

    • p (float, 1<=p<=infinity, optional) – Which Minkowski p-norm to use. -1 is the sum-of-absolute-values distance (“Manhattan” distance). -2 is the usual Euclidean distance. -infinity is the maximum-coordinate-difference distance. -A large, finite p may cause a ValueError if overflow can occur.

      • +1 is the sum-of-absolute-values “Manhattan” distance +2 is the usual Euclidean distance +infinity is the maximum-coordinate-difference distance

    • distance_upper_bound (nonnegative float, optional) – Return only neighbors within this distance. This is used to prune tree searches, so if you are doing a series of nearest-neighbor queries, it may help to supply the distance to the nearest neighbor of the most recent point.

      • -

    • workers (int, optional) –

      Number of workers to use for parallel processing. If -1 is given -all CPU threads are used. Default: 1.

      -
      -

      New in version 1.6.0.

      -
      -

    Returns
    -

      +

      • d (float or array of floats) – The distances to the nearest neighbors. -If x has shape tuple+(self.m,), then d has shape -tuple+(k,). -When k == 1, the last dimension of the output is squeezed. -Missing neighbors are indicated with infinite distances. -Hits are sorted by distance (nearest first).

        -
        -

        Deprecated since version 1.6.0: If k=None, then d is an object array of shape tuple, -containing lists of distances. This behavior is deprecated and -will be removed in SciPy 1.8.0, use query_ball_point -instead.

        -
        -
        • -

      • i (integer or array of integers) – The index of each neighbor in self.data. -i is the same shape as d. -Missing neighbors are indicated with self.n.

        • +If x has shape tuple+(self.m,), then d has shape tuple if +k is one, or tuple+(k,) if k is larger than one. Missing +neighbors (e.g. when k > n or distance_upper_bound is +given) are indicated with infinite distances. If k is None, +then d is an object array of shape tuple, containing lists +of distances. In either case the hits are sorted by distance +(nearest first).

        +

      • i (integer or array of integers) – The locations of the neighbors in self.data. i is the same +shape as d.

    Examples

    -
    >>> import numpy as np
->>> from scipy.spatial import KDTree
+
    >>> from scipy import spatial
 >>> x, y = np.mgrid[0:5, 2:8]
->>> tree = KDTree(np.c_[x.ravel(), y.ravel()])
-
    
-
    
-
    To query the nearest neighbours and return squeezed result, use
    
-
    >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=1)
->>> print(dd, ii, sep='\n')
-[2.         0.2236068]
-[ 0 13]
-
    
-
    
-
    To query the nearest neighbours and return unsqueezed result, use
    
-
    >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1])
->>> print(dd, ii, sep='\n')
-[[2.        ]
- [0.2236068]]
-[[ 0]
- [13]]
-
    
-
    
-
    To query the second nearest neighbours and return unsqueezed result,
-use
    
-
    >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[2])
->>> print(dd, ii, sep='\n')
-[[2.23606798]
- [0.80622577]]
-[[ 6]
- [19]]
-
    
-
    
-
    To query the first and second nearest neighbours, use
    
-
    >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=2)
->>> print(dd, ii, sep='\n')
-[[2.         2.23606798]
- [0.2236068  0.80622577]]
-[[ 0  6]
- [13 19]]
-
    
-
    
-
    or, be more specific
    
-
    >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2])
->>> print(dd, ii, sep='\n')
-[[2.         2.23606798]
- [0.2236068  0.80622577]]
-[[ 0  6]
- [13 19]]
+>>> tree = spatial.KDTree(list(zip(x.ravel(), y.ravel())))
+>>> tree.data
+array([[0, 2],
+       [0, 3],
+       [0, 4],
+       [0, 5],
+       [0, 6],
+       [0, 7],
+       [1, 2],
+       [1, 3],
+       [1, 4],
+       [1, 5],
+       [1, 6],
+       [1, 7],
+       [2, 2],
+       [2, 3],
+       [2, 4],
+       [2, 5],
+       [2, 6],
+       [2, 7],
+       [3, 2],
+       [3, 3],
+       [3, 4],
+       [3, 5],
+       [3, 6],
+       [3, 7],
+       [4, 2],
+       [4, 3],
+       [4, 4],
+       [4, 5],
+       [4, 6],
+       [4, 7]])
+>>> pts = np.array([[0, 0], [2.1, 2.9]])
+>>> tree.query(pts)
+(array([ 2.        ,  0.14142136]), array([ 0, 13]))
+>>> tree.query(pts[0])
+(2.0, 0)
    -query_ball_point(x, r, p=2.0, eps=0, workers=1, return_sorted=None, return_length=False)[source]
    +query_ball_point(x, r, p=2.0, eps=0)[source]

    Find all points within distance r of point(s) x.

    Parameters

    • x (array_like, shape tuple + (self.m,)) – The point or points to search for neighbors of.

      • -

    • r (array_like, float) – The radius of points to return, must broadcast to the length of x.

      • -

    • p (float, optional) – Which Minkowski p-norm to use. Should be in the range [1, inf]. -A finite large p may cause a ValueError if overflow can occur.

      • +

    • r (positive float) – The radius of points to return.

      • +

    • p (float, optional) – Which Minkowski p-norm to use. Should be in the range [1, inf].

    • eps (nonnegative float, optional) – Approximate search. Branches of the tree are not explored if their nearest points are further than r / (1 + eps), and branches are added in bulk if their furthest points are nearer than r * (1 + eps).

      • -

    • workers (int, optional) –

      Number of jobs to schedule for parallel processing. If -1 is given -all processors are used. Default: 1.

      -
      -

      New in version 1.6.0.

      -
      -

      • -

    • return_sorted (bool, optional) –

      Sorts returned indicies if True and does not sort them if False. If -None, does not sort single point queries, but does sort -multi-point queries which was the behavior before this option -was added.

      -
      -

      New in version 1.6.0.

      -
      -

      • -

    • return_length (bool, optional) –

      Return the number of points inside the radius instead of a list -of the indices.

      -
      -

      New in version 1.6.0.

      -
      -

    Returns
    @@ -3269,7 +3003,7 @@

    Submodules>>> x, y = np.mgrid[0:5, 0:5] >>> points = np.c_[x.ravel(), y.ravel()] >>> tree = spatial.KDTree(points) ->>> sorted(tree.query_ball_point([2, 0], 1)) +>>> tree.query_ball_point([2, 0], 1) [5, 10, 11, 15]

    @@ -3289,8 +3023,7 @@

    Submodules
    query_ball_tree(other, r, p=2.0, eps=0)[source]
    -

    Find all pairs of points between self and other whose distance is -at most r.

    +

    Find all pairs of points whose distance is at most r

    Parameters
    -

    Examples

    -

    You can search all pairs of points between two kd-trees within a distance:

    -
    >>> import matplotlib.pyplot as plt
->>> import numpy as np
->>> from scipy.spatial import KDTree
->>> rng = np.random.default_rng()
->>> points1 = rng.random((15, 2))
->>> points2 = rng.random((15, 2))
->>> plt.figure(figsize=(6, 6))
->>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14)
->>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14)
->>> kd_tree1 = KDTree(points1)
->>> kd_tree2 = KDTree(points2)
->>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
->>> for i in range(len(indexes)):
-...     for j in indexes[i]:
-...         plt.plot([points1[i, 0], points2[j, 0]],
-...             [points1[i, 1], points2[j, 1]], "-r")
->>> plt.show()
-
    -
    -query_pairs(r, p=2.0, eps=0, output_type='set')[source]
    -

    Find all pairs of points in self whose distance is at most r.

    +query_pairs(r, p=2.0, eps=0)[source] +

    Find all pairs of points within a distance.

    Parameters
    Returns

    results – Set of pairs (i,j), with i < j, for which the corresponding -positions are close. If output_type is ‘ndarray’, an ndarry is -returned instead of a set.

    +positions are close.

    Return type
    -

    set or ndarray

    +

    set

    -

    Examples

    -

    You can search all pairs of points in a kd-tree within a distance:

    -
    >>> import matplotlib.pyplot as plt
->>> import numpy as np
->>> from scipy.spatial import KDTree
->>> rng = np.random.default_rng()
->>> points = rng.random((20, 2))
->>> plt.figure(figsize=(6, 6))
->>> plt.plot(points[:, 0], points[:, 1], "xk", markersize=14)
->>> kd_tree = KDTree(points)
->>> pairs = kd_tree.query_pairs(r=0.2)
->>> for (i, j) in pairs:
-...     plt.plot([points[i, 0], points[j, 0]],
-...             [points[i, 1], points[j, 1]], "-r")
->>> plt.show()
-
    -
    -sparse_distance_matrix(other, max_distance, p=2.0, output_type='dok_matrix')[source]
    -

    Compute a sparse distance matrix.

    +sparse_distance_matrix(other, max_distance, p=2.0)[source] +

    Compute a sparse distance matrix

    Computes a distance matrix between two KDTrees, leaving as zero any distance greater than max_distance.

    @@ -3395,66 +3084,23 @@

    Submodules

    • other (KDTree) –

    • max_distance (positive float) –

      • -

    • p (float, 1<=p<=infinity) – Which Minkowski p-norm to use. -A finite large p may cause a ValueError if overflow can occur.

      • -

    • output_type (string, optional) –

      Which container to use for output data. Options: ‘dok_matrix’, -‘coo_matrix’, ‘dict’, or ‘ndarray’. Default: ‘dok_matrix’.

      -
      -

      New in version 1.6.0.

      -
      -

      • +

    • p (float, optional) –

    Returns
    -

    result – Sparse matrix representing the results in “dictionary of keys” -format. If a dict is returned the keys are (i,j) tuples of indices. -If output_type is ‘ndarray’ a record array with fields ‘i’, ‘j’, -and ‘v’ is returned,

    +

    result – Sparse matrix representing the results in “dictionary of keys” format.

    Return type
    -

    dok_matrix, coo_matrix, dict or ndarray

    +

    dok_matrix

    -

    Examples

    -

    You can compute a sparse distance matrix between two kd-trees:

    -
    >>> import numpy as np
->>> from scipy.spatial import KDTree
->>> rng = np.random.default_rng()
->>> points1 = rng.random((5, 2))
->>> points2 = rng.random((5, 2))
->>> kd_tree1 = KDTree(points1)
->>> kd_tree2 = KDTree(points2)
->>> sdm = kd_tree1.sparse_distance_matrix(kd_tree2, 0.3)
->>> sdm.toarray()
-array([[0.        , 0.        , 0.12295571, 0.        , 0.        ],
-   [0.        , 0.        , 0.        , 0.        , 0.        ],
-   [0.28942611, 0.        , 0.        , 0.2333084 , 0.        ],
-   [0.        , 0.        , 0.        , 0.        , 0.        ],
-   [0.24617575, 0.29571802, 0.26836782, 0.        , 0.        ]])
-
    -
    -

    You can check distances above the max_distance are zeros:

    -
    >>> from scipy.spatial import distance_matrix
->>> distance_matrix(points1, points2)
-array([[0.56906522, 0.39923701, 0.12295571, 0.8658745 , 0.79428925],
-   [0.37327919, 0.7225693 , 0.87665969, 0.32580855, 0.75679479],
-   [0.28942611, 0.30088013, 0.6395831 , 0.2333084 , 0.33630734],
-   [0.31994999, 0.72658602, 0.71124834, 0.55396483, 0.90785663],
-   [0.24617575, 0.29571802, 0.26836782, 0.57714465, 0.6473269 ]])
-
    -

    -
    -
    -property tree
    -
    -
    -class spatialpy.core.Model(name='spatialpy')[source]
    +class spatialpy.core.Model(name='spatialpy')[source]

    Bases: object

    Representation of a spatial biochemical model.

    @@ -3785,7 +3431,7 @@

    Submodules
    -reserved_names = ['vol', 't']
    +reserved_names = ['vol', 't']

    @@ -3879,7 +3525,7 @@

    Submodules
    -special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']
    +special_characters = ['[', ']', '+', '-', '*', '/', '.', '^']

    @@ -3910,14 +3556,14 @@

    Submodules
    -exception spatialpy.core.ModelError[source]
    +exception spatialpy.core.ModelError[source]

    Bases: Exception

    Class for exceptions in the model module.

    -class spatialpy.core.OrderedDict[source]
    +class spatialpy.core.OrderedDict[source]

    Bases: dict

    Dictionary that remembers insertion order

    @@ -3938,12 +3584,12 @@

    Submodules
    -items() → a set-like object providing a view on D's items
    +items() → a set-like object providing a view on D’s items

    -keys() → a set-like object providing a view on D's keys
    +keys() → a set-like object providing a view on D’s keys
    @@ -3955,7 +3601,7 @@

    Submodules
    -pop(k[, d]) → v, remove specified key and return the corresponding
    +pop(k[, d]) → v, remove specified key and return the corresponding

    value. If key is not found, d is returned if given, otherwise KeyError is raised.

    @@ -3976,7 +3622,7 @@

    Submodules
    -update([E, ]**F) → None.  Update D from dict/iterable E and F.
    +update([E, ]**F) → None.  Update D from dict/iterable E and F.

    If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

    @@ -3984,14 +3630,14 @@

    Submodules
    -values() → an object providing a view on D's values
    +values() → an object providing a view on D’s values

    -class spatialpy.core.Parameter(name=None, expression=None)[source]
    +class spatialpy.core.Parameter(name=None, expression=None)[source]

    Bases: object

    Model of a rate paramter. A parameter can be given as a String expression (function) or directly as a scalar value. @@ -4009,14 +3655,14 @@

    Submodules
    -exception spatialpy.core.ParameterError[source]
    +exception spatialpy.core.ParameterError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in parameter module.

    -class spatialpy.core.PlaceInitialCondition(species, count, location)[source]
    +class spatialpy.core.PlaceInitialCondition(species, count, location)[source]

    Bases: spatialpy.core.initialcondition.InitialCondition

    Class used to defined the place initial condition in SpatialPy.

    @@ -4043,7 +3689,7 @@

    Submodules
    -class spatialpy.core.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]
    +class spatialpy.core.Reaction(name='', reactants=None, products=None, propensity_function=None, rate=None, annotation=None, restrict_to=None)[source]

    Bases: object

    Models a biochemical reaction. A reaction conatains dictionaries of species (reactants and products) and parameters. The reaction’s propensity function needs to be evaluable and result in a non-negative scalar value in the namespace defined by the union of its Reactant, Product and Parameter dictionaries. For mass-action, zeroth, first and second order reactions are supported, attempting to used higher orders will result in an error.

    @@ -4113,14 +3759,14 @@

    Submodules
    -exception spatialpy.core.ReactionError[source]
    +exception spatialpy.core.ReactionError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in reaction module.

    -class spatialpy.core.Result(model=None, result_dir=None)[source]
    +class spatialpy.core.Result(model=None, result_dir=None)[source]

    Bases: object

    Result object for a URDME simulation.

    @@ -4331,14 +3977,14 @@

    Submodules
    -exception spatialpy.core.ResultError[source]
    +exception spatialpy.core.ResultError[source]

    Bases: Exception

    Class for exceptions in the results module.

    -class spatialpy.core.ScatterInitialCondition(species, count, types=None)[source]
    +class spatialpy.core.ScatterInitialCondition(species, count, types=None)[source]

    Bases: spatialpy.core.initialcondition.InitialCondition

    Class used to defined the scatter initial condition in SpatialPy.

    @@ -4365,21 +4011,21 @@

    Submodules
    -exception spatialpy.core.SimulationError[source]
    +exception spatialpy.core.SimulationError[source]

    Bases: Exception

    Class for exceptions in the simulation module.

    -exception spatialpy.core.SimulationTimeout[source]
    +exception spatialpy.core.SimulationTimeout[source]

    Bases: spatialpy.core.spatialpyerror.SimulationError

    Exception raised for timeout errors.

    -class spatialpy.core.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]
    +class spatialpy.core.Species(name=None, diffusion_coefficient=None, restrict_to=None)[source]

    Bases: object

    Model of a biochemical species. Must be assigned a diffusion coefficent.

    @@ -4409,14 +4055,14 @@

    Submodules
    -exception spatialpy.core.SpeciesError[source]
    +exception spatialpy.core.SpeciesError[source]

    Bases: spatialpy.core.spatialpyerror.ModelError

    Class for exceptions in the species module.

    -class spatialpy.core.UniformInitialCondition(species, count, types=None)[source]
    +class spatialpy.core.UniformInitialCondition(species, count, types=None)[source]

    Bases: spatialpy.core.initialcondition.InitialCondition

    Class used to defined the uniform initial condition in SpatialPy.

    @@ -4443,7 +4089,7 @@

    Submodules
    -class spatialpy.core.VTKReader(filename=None, debug=False)[source]
    +class spatialpy.core.VTKReader(filename=None, debug=False)[source]

    Bases: object

    VTKReader.py: SpatialPy minimal VTK legacy file reader. Reference: https://vtk.org/wp-content/uploads/2015/04/file-formats.pdf

    @@ -4540,14 +4186,14 @@

    Submodules
    -exception spatialpy.core.VTKReaderError[source]
    +exception spatialpy.core.VTKReaderError[source]

    Bases: Exception

    Bass class for exceptions in the vtkreader module.

    -exception spatialpy.core.VTKReaderIOError(message)[source]
    +exception spatialpy.core.VTKReaderIOError(message)[source]

    Bases: spatialpy.core.spatialpyerror.VTKReaderError

    Exception raised for I/O errors.

    @@ -4588,7 +4234,7 @@

    Submodules
    -class spatialpy.core.combinations(iterable, r)
    +class spatialpy.core.combinations(iterable, r)

    Bases: object

    Return successive r-length combinations of elements in the iterable.

    combinations(range(4), 3) –> (0,1,2), (0,1,3), (0,2,3), (1,2,3)

    @@ -4643,7 +4289,7 @@

    Submodules -
    image (default=None |’png’ |’jpeg’ |’svg’ |’webp’) – This parameter sets

    the format of the image to be downloaded, if we choose to download an +

    image (default=None |’png’ |’jpeg’ |’svg’ |’webp’) – This parameter sets

    the format of the image to be downloaded, if we choose to download an image. This parameter has a default value of None indicating that no image should be downloaded. Please note: for higher resolution images and more export options, consider using plotly.io.write_image. See @@ -4680,7 +4326,7 @@

    Submodulesiplot([{'x': [1, 2, 3], 'y': [5, 2, 7]}], image='png') `

    animation_opts Example: -``` +``` from plotly.offline import iplot figure = {‘data’: [{‘x’: [0, 1], ‘y’: [0, 1]}],

    @@ -4697,11 +4343,11 @@

    Submodules```

    +```

    - - + + @@ -4729,7 +4375,7 @@

    SubmodulesSphinx 4.4.0 + Powered by Sphinx 4.1.1 & Alabaster 0.7.12 | diff --git a/docs/build/html/classes/spatialpy.html b/docs/build/html/classes/spatialpy.html index 424282e2..2c1be40a 100644 --- a/docs/build/html/classes/spatialpy.html +++ b/docs/build/html/classes/spatialpy.html @@ -4,8 +4,7 @@ - - + spatialpy package — SpatialPy 1.0 documentation @@ -102,9 +101,9 @@

    Quick search

    -
    +

    spatialpy package

    -
    +

    Subpackages

      @@ -141,8 +140,8 @@

      Subpackages +

    +

    Module contents

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -156,8 +155,8 @@

    Subpackageshttp://www.gnu.org/licenses/>.

    -

    -
    +
    + @@ -185,7 +184,7 @@

    SubpackagesSphinx 4.4.0 + Powered by Sphinx 4.1.1 & Alabaster 0.7.12 | diff --git a/docs/build/html/classes/spatialpy.solvers.html b/docs/build/html/classes/spatialpy.solvers.html index c3b84ac1..a0aecbb1 100644 --- a/docs/build/html/classes/spatialpy.solvers.html +++ b/docs/build/html/classes/spatialpy.solvers.html @@ -4,8 +4,7 @@ - - + spatialpy.solvers package — SpatialPy 1.0 documentation @@ -104,12 +103,12 @@

    Quick search

    -
    +

    spatialpy.solvers package

    -
    +

    Submodules

    -
    -
    +
    +

    spatialpy.solvers.build_expression module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -125,7 +124,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.solvers.build_expression.BuildExpression(blacklist=None, namespace=None, sanitize=False)[source]
    +class spatialpy.solvers.build_expression.BuildExpression(blacklist=None, namespace=None, sanitize=False)[source]

    Bases: object

    Accepts an expression string to validate and convert. Allows for pre-flight syntax and namespace validations, @@ -159,7 +158,7 @@

    Submodules
    -class ValidationVisitor(namespace=None, blacklist=None, sanitize=False)[source]
    +class ValidationVisitor(namespace=None, blacklist=None, sanitize=False)[source]

    Bases: ast.NodeTransformer

    A subclass of ast.NodeTransformer used to sanitize spatialpy expresions.

    @@ -173,7 +172,7 @@

    Submodules
    -visit_Assign(node: ast.Assign)[source]
    +visit_Assign(node: _ast.Assign)[source]

    Visitor function for ast.Assign nodes.

    Parameters
    @@ -335,7 +334,7 @@

    Submodules
    -classmethod map_operator(operator)[source]
    +classmethod map_operator(operator)[source]

    Map operator strings with built expressions.

    Parameters
    @@ -346,7 +345,7 @@

    Submodules
    -operator_map = {'!': <class 'ast.Not'>, '!=': <class 'ast.NotEq'>, '%': <class 'ast.Mod'>, '&': <class 'ast.BitAnd'>, '*': <class 'ast.Mult'>, '**': <class 'ast.Pow'>, '+': <class 'ast.Add'>, '-': <class 'ast.Sub'>, '/': <class 'ast.Div'>, '//': <class 'ast.FloorDiv'>, ':=': <class 'ast.Assign'>, '<': <class 'ast.Lt'>, '<<': <class 'ast.LShift'>, '<=': <class 'ast.LtE'>, '=': <class 'ast.Assign'>, '==': <class 'ast.Eq'>, '>': <class 'ast.Gt'>, '>=': <class 'ast.GtE'>, '>>': <class 'ast.RShift'>, '@': <class 'ast.MatMult'>, '^': <class 'ast.BitXor'>, 'and': <class 'ast.And'>, 'or': <class 'ast.Or'>, '|': <class 'ast.BitOr'>}
    +operator_map = {'!': <class '_ast.Not'>, '!=': <class '_ast.NotEq'>, '%': <class '_ast.Mod'>, '&': <class '_ast.BitAnd'>, '*': <class '_ast.Mult'>, '**': <class '_ast.Pow'>, '+': <class '_ast.Add'>, '-': <class '_ast.Sub'>, '/': <class '_ast.Div'>, '//': <class '_ast.FloorDiv'>, ':=': <class '_ast.Assign'>, '<': <class '_ast.Lt'>, '<<': <class '_ast.LShift'>, '<=': <class '_ast.LtE'>, '=': <class '_ast.Assign'>, '==': <class '_ast.Eq'>, '>': <class '_ast.Gt'>, '>=': <class '_ast.GtE'>, '>>': <class '_ast.RShift'>, '@': <class '_ast.MatMult'>, '^': <class '_ast.BitXor'>, 'and': <class '_ast.And'>, 'or': <class '_ast.Or'>, '|': <class '_ast.BitOr'>}

    @@ -416,17 +415,17 @@

    Submodules
    -class spatialpy.solvers.build_expression.CppConverter(tree)[source]
    +class spatialpy.solvers.build_expression.CppConverter(tree)[source]

    Bases: spatialpy.solvers.build_expression.ExpressionConverter

    Converts an ast.AST to a C++ expression string.

    -class CppExpressionTransformer[source]
    +class CppExpressionTransformer[source]

    Bases: ast.NodeTransformer

    A subclass of ast.NodeTransformer used to convert ast.AST expresions to C++ expressions.

    -visit_BinOp(node: ast.BinOp)[source]
    +visit_BinOp(node: _ast.BinOp)[source]

    Visitor function for ast.BinOp nodes.

    Parameters
    @@ -483,7 +482,7 @@

    Submodules
    -class spatialpy.solvers.build_expression.ExpressionConverter(tree)[source]
    +class spatialpy.solvers.build_expression.ExpressionConverter(tree)[source]

    Bases: ast.NodeVisitor

    A subclass of ast.NodeVisitor used to convert spatialpy expresions.

    @@ -493,7 +492,7 @@

    Submodules
    -classmethod convert_str(expression)[source]
    +classmethod convert_str(expression)[source]

    Convert ‘^’ to python pow operator.

    Parameters
    @@ -821,7 +820,7 @@

    Submodules
    -class spatialpy.solvers.build_expression.ExpressionResults(invalid_names=None, invalid_operators=None, is_valid=True)[source]
    +class spatialpy.solvers.build_expression.ExpressionResults(invalid_names=None, invalid_operators=None, is_valid=True)[source]

    Bases: object

    Container struct for returning the results of expression validation. Any expression items which indicate an invalid expression are listed on an ExpressionResults instance. @@ -842,7 +841,7 @@

    Submodules
    -class spatialpy.solvers.build_expression.PythonConverter(tree)[source]
    +class spatialpy.solvers.build_expression.PythonConverter(tree)[source]

    Bases: spatialpy.solvers.build_expression.ExpressionConverter

    Converts an ast.AST to a Python expression string.

    @@ -869,8 +868,8 @@

    Submodules +

    +

    spatialpy.solvers.solver module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -886,7 +885,7 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -class spatialpy.solvers.solver.Solver(model, debug_level=0)[source]
    +class spatialpy.solvers.solver.Solver(model, debug_level=0)[source]

    Bases: object

    SpatialPy solver object.

    @@ -947,8 +946,8 @@

    Submodules +

    +

    Module contents

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -962,8 +961,8 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -

    - +
    + @@ -991,7 +990,7 @@

    SubmodulesSphinx 4.4.0 + Powered by Sphinx 4.1.1 & Alabaster 0.7.12 | diff --git a/docs/build/html/classes/spatialpy.stochss.html b/docs/build/html/classes/spatialpy.stochss.html index 87f7ecc8..b422fed0 100644 --- a/docs/build/html/classes/spatialpy.stochss.html +++ b/docs/build/html/classes/spatialpy.stochss.html @@ -4,8 +4,7 @@ - - + spatialpy.stochss package — SpatialPy 1.0 documentation @@ -102,12 +101,12 @@

    Quick search

    -
    +

    spatialpy.stochss package

    -
    +

    Submodules

    -
    -
    +
    +

    spatialpy.stochss.stochss_export module

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -142,8 +141,8 @@

    Submodules +

    +

    Module contents

    SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems @@ -157,8 +156,8 @@

    Submoduleshttp://www.gnu.org/licenses/>.

    -

    - +
    + @@ -182,7 +181,7 @@

    SubmodulesSphinx 4.4.0 + Powered by Sphinx 4.1.1 & Alabaster 0.7.12 | diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html index 61d2a048..f15d74f7 100644 --- a/docs/build/html/genindex.html +++ b/docs/build/html/genindex.html @@ -101,9 +101,7 @@

    Index

    | G | I | K - | L | M - | N | O | P | Q @@ -234,12 +232,6 @@

    C

    -
  • children (spatialpy.core.KDTree.innernode property) - -
  • cleanup_build_files() (in module spatialpy.core) @@ -276,10 +268,10 @@

    C

  • combinations (class in spatialpy.core)
    • - - +
    • compile_prep() (spatialpy.core.Domain method)
        @@ -324,8 +316,6 @@

        C

        D

        - + -

        U

        @@ -1331,7 +1287,7 @@

        W

        ©Copyright (C) 2017-2022. | - Powered by Sphinx 4.4.0 + Powered by Sphinx 4.1.1 & Alabaster 0.7.12 diff --git a/docs/build/html/index.html b/docs/build/html/index.html index bdf46565..fdf6b500 100644 --- a/docs/build/html/index.html +++ b/docs/build/html/index.html @@ -4,8 +4,7 @@ - - + Documentation for SpatialPy 1.0 — SpatialPy 1.0 documentation @@ -91,23 +90,23 @@

        Quick search

        -
        +

        Documentation for SpatialPy 1.0

        SpatialPy is a Python 3 package for simulation of spatial deterministic/stochastic reaction-diffusion-advection problems embedded in Lagrangian reference frame particle based fluid dynamics domain

        This package is intended to replace the PyURDME software https://github.com/pyurdme/pyurdme and will feature both a NSM solver for RDME simulation on static domains and a sSSA-SDPD particle based fluid dynamics solver as described in the publication “A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection–diffusion–reaction problems” by Drawert, Jacob, Li, Yi, Petzold https://www.sciencedirect.com/science/article/pii/S0021999118307101

        -
        +

        Getting a copy of SpatialPy

        The latest version of SpatialPy can be found on PyPI. The source code is available on GitHub. SpatialPy is licensed under the GNU General Public License version 3.

        -

        Please register as a user! SpatialPy's development is funded by NIH grant 2R01EB014877, and to continue support, we need to prove SpatialPy has users. Please fill out our short registration form!

        -
        +

        Please register as a user! SpatialPy's development is funded by NIH grant 2R01EB014877, and to continue support, we need to prove SpatialPy has users. Please fill out our short registration form!

        +

        Examples

        See our Example Notebook - Start Here for more information on how to build and simulate your models with SpatialPy. For an example of how to use SpatialPy to simulate a spatial stochastic reaction-diffusion system, see the 3D Cylinder Demo. We also provide examples of how to use SpatialPy to simulate physics (Gravity Demo) and fluid flow (Weir Model).

        -
        -
        +
        +

        Reporting Issues

        If you find any problem with SpatialPy or this documentation, please report it using the GitHub issue tracker for the project. You can also contact the main author, Dr. Brian Drawert, directly with questions and suggestions.

        - -
        +
        +

        Documentation

        API reference

        @@ -124,16 +123,16 @@

        Documentation +

        +
        + @@ -157,7 +156,7 @@

        Indices and tablesSphinx 4.4.0 + Powered by Sphinx 4.1.1 & Alabaster 0.7.12 | diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv index f335ae26546f40e1c8858cf9394845d73fa8c58f..df1b09ba2e90d19b38829327fd1e4b2c2a8ac311 100644 GIT binary patch delta 2441 zcmV;433m3h6zLO?dVfoE+c*}5_x=^Cn%P>@k6rINvAZkPlf>0ZvSmReBw-8^XaH2K z{`G?gDUlRKxrGCIk&-Nm-@)VZ5|2YA7o5}FFj48{XH`%|iwBmQg*^O{bE5U){>O!X zL-l{OB!&?#56fIAy4;zP|7-AB7jb{b3{^%5uI<5c%_`z`ZhvL>825KNXZA?*o#k_3 z6s5~Q9=_O@TNRqKC)rC@Ra8}?h*artru8@_8-UyqGpzZ%~>@Lp5}bJ26E-*oj}!&=f8si@Wswxnh!3IO*}DWM#Z zzT{N;QvYM<1Aj5(x4OzB>)gdGOCn?H*=0ehC`!VjD(3-9QWTjKtTIuR3#^>*yynEv zY%e&1$Avw~WP6)T=?elVO366QY~V@=PN>2qW~vc1MP1YV^pF+DzQrJa1|kcl4XJXP zt*QA=X_futyNBhFHrO!Ev+vC8GD#eigQ!)Jt(!y>qJJHR-2p&bYC`c?wY6_vK*-SN znPz`e5V5LDduhK=u0cR^kTfgk286?;9ac)AOjc68VE1)wABvaOJWp@8f$*EU4I&LBk<0hQFm-JSlXgS+Udm?-?QoAKzTlVD)CWW_AXP>H~~(z)CJ z&bC0_=AY=^_a&4B5CXL<-1@O^JxOvJO+?JQMmK9?D9hZ=a-wof4<_UR#o8!QKGTCn z@tC0{42CRNDfTooBBND-LP{ZlDA#f*oPW(C1>KOE8x%2IrzqN<@H#GAK6P*0H(sDe z1|nxXahtt>D^#Pr_+_d;J0p}EOV%R7L{N#Mj@!KG@%#viR3Kng2@@H%M_4nHj@;^M zafp!nlj`OQzquzJz}YO^Zj?=qx!%WB&hxgXU5*0?2N()>Xo%rUgY#Q%z{+|au79@8 z_E_BVMPMN!c9q+qCsn{Sd4U+8C|9g>MibDbdZK-?u_tO1%R5ELtqm6le!@|(&8BuX zt1K6Evtjl?Tn297h?e_Wv5inAFk~(e<3r}#mp^QJFQUdIM@CqaQHmV=Sc4hb-Eb-Q!@~*?lC;q&>jGyxIdUXn&V?NQF4J z2T-7adzAb$o}9?e-4_A7x`%MAxqF1Z!|{+-?{hN9@qG@)7{AZC2>m-l7SUo4&m)@ZyaHkLHFpv5fIC_jWBtre_ zLpa3kIfNoSpF<{K1GtMst#EXia5wt}IK)AGfemhEq`!THiw-oL*MGfB#X8={$nb~& zy5EObmTxQ10bnOlBb1n8L`7l1kEdw7(8z6V9S=(})) z8Li7hI_d+r@dvKl+SzIqHw^D5n7E<%kp*!B_p(%6!CDl;ZF|N-*>1gb}4?sadujDL3J_Bc3mZXb*C=$=yncjj{_B1!hCFyrnB7TyF98}IQ?+FHaS z1HKPT^E-P34fJo^4e_vF)fs@1KOr+fr}*KKfduTqx&asZ0M3Aoe9+VIf`kF5HU1u@i*(16zZ zNxDu!#@OvI(bJG27W^?Vz>Kd2-?xjDm*cki)7Ew+cX-gqB#YmCBmO3Z_?) z5sLc|L30GFcVMqM$)7pXW-h_kE_L4`_+Cmov-Xc1B)Hvm>F$qH&Aup7X=kQ+Gugy9 zx?UOlx}j02Wty?W)n9-C34Kmcp;KQajLQ)bJAn4@5`Xp6m}0u97*h+sU7GuG1=C4x zJ(p#<*s`4PSv3>h^u|P+i?1kV)k~GWCKOYR%qJud*Pk-ODkd8JPY5O(?@wt~`W1Vj zSkVWzpH=UXb0#&LH+&xdnpN(1Gqc>|ta|@7Gt13ey^q&_#tti(Xl6ex_$JBblQ1)1 zil6FoR)4)G@qJdkm6>-uwW`;%>V2JEFxfiSNi%HdYWzIv$uD9hoTv>1lFo3)wGfls z@Cw^039rl5ks8(JJzD!H1**<1_PR}Y3K<@1zSe%>TT|`Ve4~9xI#|yVVV5WRq?@$I zci^+HEVLhjFr|+~L|!!`UC)+q=`(8qLl(9zY%?Q;S delta 2516 zcmV;_2`l#L6SNeNdVkAu+c*}6_kIdh&1}u-%dU5w*xi-tN#bfH*|H!KvM>e-Gyp16 zKm7m%Ns$yqxrHC}A|+WAzk|!cAuj(>GQl~`3=^eJKC7H6nm@42tmN^Rj1#R__dl-c zH&p*eOJW$|@|a{o(PU=|{;$zzeZ>78GgKKNxV9II4J%1)=YLlAfN_7PGiI+e-$`C_ zqbN=Oc=%#pZneiAxiIdk4~s^5I|Uc*Z0K`N?sgDt4pi5$RvR7xmE zq=$^ky43#|`hP$S`K>Cmz&iIaNkL>tJ^L(36-7ZiO} zn(hTh@VKxynRIW1DLo*7qL7T!)H<$&;DpLuV5S;DQ`9xxPY-F{^(_YZGZ2|GZAh8X zbVJQ|O3U=8x_ej#X@f1}JpInhE|tVVIfz>3>81%Z-hbO+*c|}$OpPxdE1&I~0|@E+ zJk{)P3L=(OVTbmCat#8Sqoiq0w;&uQ?65)#WzvG`9TTb8{Eudc=qS1H6-G3k9oTtV z=v`_@3`Oy@5!p^_T(B5Wb+u_c5?SU$!Rwv^@U--CS+9Ku<~EAATs_I*{_M=NE|yNysmai<~XcCFzs#2Cdkm7zD2CYJocs3hAlo-0~= zNE2_50IZgrnHbD8X9a|NzM&CH!nS2cW%&UHY=5Vp;m%lqFy!E$?f?`kL8KCdfq2(n z6;($-9I83#hS)V-@D{^4(lpT*LX?C-LNCtS?WaoH#ZkN7E{G)_07Y!u?e@|A(Rx|P z1hBrp-g)X4_6LLX&s~5=F?DY+l*K$tT`Jd(03Xl&V3pntjcby2;Jc=6WH4=5?XJ! zg$FL9Lqf|4^hJlmIijP%YO1KFYESdDw3Q8=MmER_P)SYP)9G(IdWxQkiNbf?j7K+} z1OvORYGzS}N(9c8&OQEjwgs{_{si}aU4KGJ04`9;+?^ZMttUxFgNca!mPwID2qnqv z`ZOrU3}8YYQLL>J#S1-Z6ptBNz+lLN6=F|QBT`!CD5Mk;h;l85!r3U2(=DmEK@t5p zMbY+zS7FiessG@FM8l2?boP#w?-=f;bp!8!#oNIKYu&j?(5Rx&&^ z2vdlkF`=6ASI|K!a<`H%D+y?khSa{Mn~HJoskP^KXG*sgu>-pgV{lbHFMo(Zwo74& z!@^q@-JElot7#EqL=@`A!+`w9U?Pzd1#(;y#$`~SH z$r&RoEJtI+1)cJasgR6~5fqrXF-iUzPmYwvF*E|s<`~1VNgWgP9gc@g@i-@gGCj`0 zn1qjWE+X&aoC*zW=q!*S9eMJAxU?${UqwZqY8!qw~-;1C7z1va>u zk^1%tF51y>TK7H`t9YLx!#x71ejj6D8u%%2g!00D7p00HGr@I-D&z;?3RdlCYWZm+ zK+XKL0hn@rfJf=)2Y*nsioOp=h|&5yq@vz)8-3u~?VYXHVbkzofC-z5cPxk+co3z+ z3f7_!YTGjwN_IO9M(A%R$N(YkITfSGRh^19xYujRWw0uDz`==ghgg(G_nZoNGM_^c zL2^ii33n&3@FsxBc)LGoD-jEH_#rUP?`w{1pnuzPjEDW2)_(|${OPC>I>rwUjZDB2 z>qcB?0?vqyOtd&dqxA42FjjMZ5ss0dkMTIk_lOKt%3fk)gtPs?9~xV|JU?9MKO)5u z{O`?+>ZI?1Rqt33LHz^`D6OBR>lkE=-2M_h4jCfBp8^BK_*(FNyGeQ3Z<}w;xNiFm zuQeWN&0~O*cYp4K$AO{CV`O0GHUr5wyVlC?is+XUM(hN- zeix~qrYy$uiYaUUM^LkRUcq#fN6%%mTs*Uk@I`G#eCSOXZ3bVSFKRDV_?od8Yi2%U z@_0SV41a4e((Hf6V6^%EoXt|d;w%(v^nvXcwfD$5lbS8-K97GbYVLQlu(`)Y?fus* zY;M`=eZ2lNc36XvR`&A--z3<45@z9{_^B!uwf7{xFKTaXmYq+ntIeYJzAkPs+CJCG zra#ct_;u7%y@?fZp*AuQb%i@^3$e)cudtI6|9_gSJ8Dpy4`^*F1**<1_PR`X4(XGc zueG25)?E8F-)NJhz4R;+HaXE})uaQy1D}0mrA_`zF0$$WN!i`E>is{-Bzqyx^ltiq za30kiA+E;%fAU3Wd%8Hw?RV{J;$-qNz3KRQdanUj6He36pJ~a;oE~ZzCmYHguKm>C z{Y}BWy*quavDy2^gUG6amZmPython Module Index

        ©Copyright (C) 2017-2022. | - Powered by Sphinx 4.4.0 + Powered by Sphinx 4.1.1 & Alabaster 0.7.12 diff --git a/docs/build/html/search.html b/docs/build/html/search.html index d6fae452..0fca5605 100644 --- a/docs/build/html/search.html +++ b/docs/build/html/search.html @@ -86,14 +86,13 @@

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