smart guessing optional
This commit is contained in:
abelvm
commit
df68d2454e
3
.brackets.json
Normal file
3
.brackets.json
Normal file
@ -0,0 +1,3 @@
|
||||
{
|
||||
"sbruchmann.staticpreview.basepath": "/home/carto/Projects/crankshaft/"
|
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}
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7
NEWS.md
7
NEWS.md
@ -1,3 +1,10 @@
|
||||
0.4.0 (2016-08-30)
|
||||
------------------
|
||||
* Add CDB_Contour
|
||||
* Add CDB_PIA
|
||||
* Add CDB_Densify
|
||||
* Add CDB_TINmap
|
||||
|
||||
0.3.1 (2016-08-18)
|
||||
------------------
|
||||
* Fix Voronoi projection issue
|
||||
|
||||
@ -15,11 +15,12 @@ shall be performed by the designated *Release Manager*.
|
||||
1. Generate an upgrade path from the previous to the next release by copying the generated release file. E.g:
|
||||
|
||||
```shell
|
||||
cp release/cranckshaft--X.Y.Z.sql release/cranckshaft--A.B.C--X.Y.Z.sql
|
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cp release/crankshaft--X.Y.Z.sql release/crankshaft--A.B.C--X.Y.Z.sql
|
||||
```
|
||||
NOTE: you can rely on this thanks to the compatibility checks.
|
||||
|
||||
TODO: automate this step [#94](https://github.com/CartoDB/crankshaft/issues/94)
|
||||
2. Update the [NEWS.md](https://github.com/CartoDB/crankshaft/blob/master/NEWS.md) file
|
||||
1. Commit and push the generated files.
|
||||
1. Tag the release:
|
||||
|
||||
@ -29,7 +30,6 @@ shall be performed by the designated *Release Manager*.
|
||||
```
|
||||
1. Deploy and test in staging
|
||||
1. Deploy and test in production
|
||||
2. Update the [NEWS.md](https://github.com/CartoDB/crankshaft/blob/master/NEWS.md) file
|
||||
1. Merge back into develop
|
||||
|
||||
|
||||
|
||||
33
doc/13_PIA.md
Normal file
33
doc/13_PIA.md
Normal file
@ -0,0 +1,33 @@
|
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## Pole of inaccessibility (PIA)
|
||||
|
||||
Function to find the [PIA](https://en.wikipedia.org/wiki/Pole_of_inaccessibility) from a given polygon and tolerance, following the quadtree approach by [Vladimir Agafonkin](https://github.com/mourner) described [here](https://github.com/mapbox/polylabel)
|
||||
|
||||
|
||||
|
||||
### CDB_PIA (polygon geometry, tolerance numeric DEFAULT 1.0)
|
||||
|
||||
#### Arguments
|
||||
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| polygon | geometry | Target polygon |
|
||||
| tolerance | numeric | Threshold to decide to take a cell into account |
|
||||
|
||||
### Returns
|
||||
|
||||
| Column Name | Type | Description |
|
||||
|-------------|------|-------------|
|
||||
| point | geometry| Pole of inaccessibility |
|
||||
|
||||
|
||||
#### Example Usage
|
||||
|
||||
```sql
|
||||
with a as(
|
||||
select st_geomfromtext('POLYGON((-432540.453078056 4949775.20452642,-432329.947920966 4951361.232584,-431245.028163694 4952223.31516671,-429131.071033529 4951768.00415574,-424622.07505895 4952843.13503987,-423688.327170174 4953499.20752423,-424086.294349759 4954968.38274191,-423068.388925945 4954378.63345336,-423387.653225542 4953355.67417084,-420594.869840519 4953781.00230592,-416026.095299382 4951484.06849063,-412483.018546414 4951024.5410983,-410490.399661215 4954502.24032205,-408186.197521284 4956398.91417441,-407627.262358013 4959300.94633864,-406948.770061627 4959874.85407739,-404949.583326472 4959047.74518163,-402570.908447199 4953743.46829807,-400971.358683991 4952193.11680804,-403533.488084088 4949649.89857885,-406335.177028373 4950193.19571096,-407790.456731515 4952391.46015616,-412060.672398345 4950381.2389307,-410716.93482498 4949156.7509561,-408464.162289794 4943912.8940387,-409350.599394983 4942819.84896006,-408087.791091424 4942451.6711778,-407274.045613725 4940572.4807777,-404446.196589102 4939976.71501489,-402422.964843936 4940450.3670813,-401010.654464241 4939054.8061663,-397647.247369412 4940679.80737878,-395658.413346901 4940528.84765185,-395536.852462953 4938829.79565997,-394268.923462818 4938003.7277717,-393388.720249116 4934757.80596815,-392393.301362444 4934326.71675815,-392573.527618037 4932323.40974412,-393464.640141837 4931903.10653605,-393085.597275686 4931094.7353605,-398426.261165985 4929156.87541607,-398261.174361137 4926238.00816416,-394045.059966834 4925765.18668498,-392982.960705174 4926391.81893628,-393090.272694301 4927176.84692181,-391648.240010564 4924626.06386961,-391889.914625075 4923086.14787613,-394345.177314013 4923235.086036,-395550.878718795 4917812.79243978,-399009.463978251 4912927.7157945,-398948.794855767 4911941.91010796,-398092.636652078 4911806.57392519,-401991.601817112 4911722.9204501,-406225.972607907 4914505.47286319,-411104.994569885 4912569.26941163,-412925.513522316 4913030.3608866,-414630.148884835 4914436.69169949,-414207.691417276 4919205.78028405,-418306.141109809 4917994.9580478,-424184.700779621 4918938.12432889,-426816.961458921 4923664.37379373,-420956.324227126 4923381.98014807,-420186.661267781 4924286.48693378,-420943.411166194 4926812.76394433,-419779.45457046 4928527.43466337,-419768.767899344 4930681.94459216,-421911.668097113 4930432.40620397,-423482.386112205 4933451.28047252,-427272.814773717 4934151.56473242,-427144.908678797 4939731.77191996,-428982.125554848 4940522.84445172,-428986.133056516 4942437.17281266,-431237.792396792 4947309.68284815,-432476.889648814 4947791.74800037,-432540.453078056 4949775.20452642))', 3857) as g
|
||||
),
|
||||
b as (
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||||
select ST_Transform(g, 4326) as g from a
|
||||
)
|
||||
SELECT st_astext(CDB_PIA(g)) from b;
|
||||
```
|
||||
35
doc/14_densify.md
Normal file
35
doc/14_densify.md
Normal file
@ -0,0 +1,35 @@
|
||||
## Densify function
|
||||
|
||||
Iterative densification of a set of scattered points using Delaunay triangulation. The new points are located at the centroids of the grid cells and have as assigned value the barycentric average value of the cell's vertex.
|
||||
|
||||
### CDB_Densify(geomin geometry[], colin numeric[], iterations integer)
|
||||
|
||||
#### Arguments
|
||||
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| geomin | geometry[] | Array of points geometries |
|
||||
| colin | numeric[] | Array of points' values |
|
||||
| iterations | integer | Number of iterations |
|
||||
|
||||
### Returns
|
||||
|
||||
Returns a table object
|
||||
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| geomout | geometry | Geometries of new dataset of points|
|
||||
| colout | numeric | Values of points|
|
||||
|
||||
#### Example Usage
|
||||
|
||||
```sql
|
||||
with data as (
|
||||
select
|
||||
ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
|
||||
ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
|
||||
)
|
||||
select CDB_Densify(geomin, colin, 2) from data;
|
||||
```
|
||||
|
||||
|
||||
36
doc/15_tinmap.md
Normal file
36
doc/15_tinmap.md
Normal file
@ -0,0 +1,36 @@
|
||||
## TINMAP function
|
||||
|
||||
Generates a fake contour map, in the form of a TIN map, from a set of scattered points.Depends on **CDB_Densify**.
|
||||
|
||||
Its iterative nature lets the user smooth the final result as much as desired, but with a exponential time cost increase.
|
||||
|
||||
### CDB_TINmap(geomin geometry[], colin numeric[], iterations integer)
|
||||
|
||||
#### Arguments
|
||||
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| geomin | geometry[] | Array of points geometries |
|
||||
| colin | numeric[] | Array of points' values |
|
||||
| iterations | integer | Number of iterations |
|
||||
|
||||
### Returns
|
||||
|
||||
Returns a table object
|
||||
|
||||
| Name | Type | Description |
|
||||
|------|------|-------------|
|
||||
| geomout | geometry | Geometries of new dataset of polygons|
|
||||
| colout | numeric | Values of each cell|
|
||||
|
||||
#### Example Usage
|
||||
|
||||
```sql
|
||||
with data as (
|
||||
select
|
||||
ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
|
||||
ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
|
||||
)
|
||||
select CDB_TINmap(geomin, colin, 2) from data;
|
||||
```
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
## Contour maps
|
||||
|
||||
Function to generate a contour map from an scatter dataset of points, using one of three methos:
|
||||
Function to generate a contour map from an scatter dataset of points, using one of these three methods:
|
||||
|
||||
* [Nearest neighbor](https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation)
|
||||
* [Barycentric](https://en.wikipedia.org/wiki/Barycentric_coordinate_system)
|
||||
|
||||
1948
release/crankshaft--0.3.1--0.4.0.sql
Normal file
1948
release/crankshaft--0.3.1--0.4.0.sql
Normal file
File diff suppressed because it is too large
Load Diff
1948
release/crankshaft--0.4.0.sql
Normal file
1948
release/crankshaft--0.4.0.sql
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,5 +1,5 @@
|
||||
comment = 'CartoDB Spatial Analysis extension'
|
||||
default_version = '0.3.1'
|
||||
default_version = '0.4.0'
|
||||
requires = 'plpythonu, postgis'
|
||||
superuser = true
|
||||
schema = cdb_crankshaft
|
||||
|
||||
5
release/python/0.4.0/crankshaft/crankshaft/__init__.py
Normal file
5
release/python/0.4.0/crankshaft/crankshaft/__init__.py
Normal file
@ -0,0 +1,5 @@
|
||||
"""Import all modules"""
|
||||
import crankshaft.random_seeds
|
||||
import crankshaft.clustering
|
||||
import crankshaft.space_time_dynamics
|
||||
import crankshaft.segmentation
|
||||
@ -0,0 +1,3 @@
|
||||
"""Import all functions from for clustering"""
|
||||
from moran import *
|
||||
from kmeans import *
|
||||
@ -0,0 +1,18 @@
|
||||
from sklearn.cluster import KMeans
|
||||
import plpy
|
||||
|
||||
def kmeans(query, no_clusters, no_init=20):
|
||||
data = plpy.execute('''select array_agg(cartodb_id order by cartodb_id) as ids,
|
||||
array_agg(ST_X(the_geom) order by cartodb_id) xs,
|
||||
array_agg(ST_Y(the_geom) order by cartodb_id) ys from ({query}) a
|
||||
where the_geom is not null
|
||||
'''.format(query=query))
|
||||
|
||||
xs = data[0]['xs']
|
||||
ys = data[0]['ys']
|
||||
ids = data[0]['ids']
|
||||
|
||||
km = KMeans(n_clusters= no_clusters, n_init=no_init)
|
||||
labels = km.fit_predict(zip(xs,ys))
|
||||
return zip(ids,labels)
|
||||
|
||||
262
release/python/0.4.0/crankshaft/crankshaft/clustering/moran.py
Normal file
262
release/python/0.4.0/crankshaft/crankshaft/clustering/moran.py
Normal file
@ -0,0 +1,262 @@
|
||||
"""
|
||||
Moran's I geostatistics (global clustering & outliers presence)
|
||||
"""
|
||||
|
||||
# TODO: Fill in local neighbors which have null/NoneType values with the
|
||||
# average of the their neighborhood
|
||||
|
||||
import pysal as ps
|
||||
import plpy
|
||||
from collections import OrderedDict
|
||||
|
||||
# crankshaft module
|
||||
import crankshaft.pysal_utils as pu
|
||||
|
||||
# High level interface ---------------------------------------
|
||||
|
||||
def moran(subquery, attr_name,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I (global)
|
||||
Implementation building neighbors with a PostGIS database and Moran's I
|
||||
core clusters with PySAL.
|
||||
Andy Eschbacher
|
||||
"""
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr_name),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
|
||||
plpy.notice('** Query: %s' % query)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(2)
|
||||
plpy.notice('** Query returned with %d rows' % len(result))
|
||||
except plpy.SPIError:
|
||||
plpy.error('Error: areas of interest query failed, check input parameters')
|
||||
plpy.notice('** Query failed: "%s"' % query)
|
||||
plpy.notice('** Error: %s' % plpy.SPIError)
|
||||
return pu.empty_zipped_array(2)
|
||||
|
||||
## collect attributes
|
||||
attr_vals = pu.get_attributes(result)
|
||||
|
||||
## calculate weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
## calculate moran global
|
||||
moran_global = ps.esda.moran.Moran(attr_vals, weight,
|
||||
permutations=permutations)
|
||||
|
||||
return zip([moran_global.I], [moran_global.EI])
|
||||
|
||||
def moran_local(subquery, attr,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I implementation for PL/Python
|
||||
Andy Eschbacher
|
||||
"""
|
||||
|
||||
# geometries with attributes that are null are ignored
|
||||
# resulting in a collection of not as near neighbors
|
||||
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(5)
|
||||
except plpy.SPIError:
|
||||
plpy.error('Error: areas of interest query failed, check input parameters')
|
||||
plpy.notice('** Query failed: "%s"' % query)
|
||||
return pu.empty_zipped_array(5)
|
||||
|
||||
attr_vals = pu.get_attributes(result)
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local(attr_vals, weight,
|
||||
permutations=permutations)
|
||||
|
||||
# find quadrants for each geometry
|
||||
quads = quad_position(lisa.q)
|
||||
|
||||
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
|
||||
|
||||
def moran_rate(subquery, numerator, denominator,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Rate (global)
|
||||
Andy Eschbacher
|
||||
"""
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", numerator),
|
||||
("attr2", denominator)
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
|
||||
plpy.notice('** Query: %s' % query)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(2)
|
||||
plpy.notice('** Query returned with %d rows' % len(result))
|
||||
except plpy.SPIError:
|
||||
plpy.error('Error: areas of interest query failed, check input parameters')
|
||||
plpy.notice('** Query failed: "%s"' % query)
|
||||
plpy.notice('** Error: %s' % plpy.SPIError)
|
||||
return pu.empty_zipped_array(2)
|
||||
|
||||
## collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
## calculate moran global rate
|
||||
lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
|
||||
permutations=permutations)
|
||||
|
||||
return zip([lisa_rate.I], [lisa_rate.EI])
|
||||
|
||||
def moran_local_rate(subquery, numerator, denominator,
|
||||
w_type, num_ngbrs, permutations, geom_col, id_col):
|
||||
"""
|
||||
Moran's I Local Rate
|
||||
Andy Eschbacher
|
||||
"""
|
||||
# geometries with values that are null are ignored
|
||||
# resulting in a collection of not as near neighbors
|
||||
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("numerator", numerator),
|
||||
("denominator", denominator),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(5)
|
||||
except plpy.SPIError:
|
||||
plpy.error('Error: areas of interest query failed, check input parameters')
|
||||
plpy.notice('** Query failed: "%s"' % query)
|
||||
plpy.notice('** Error: %s' % plpy.SPIError)
|
||||
return pu.empty_zipped_array(5)
|
||||
|
||||
## collect attributes
|
||||
numer = pu.get_attributes(result, 1)
|
||||
denom = pu.get_attributes(result, 2)
|
||||
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local_Rate(numer, denom, weight,
|
||||
permutations=permutations)
|
||||
|
||||
# find quadrants for each geometry
|
||||
quads = quad_position(lisa.q)
|
||||
|
||||
return zip(lisa.Is, quads, lisa.p_sim, weight.id_order, lisa.y)
|
||||
|
||||
def moran_local_bv(subquery, attr1, attr2,
|
||||
permutations, geom_col, id_col, w_type, num_ngbrs):
|
||||
"""
|
||||
Moran's I (local) Bivariate (untested)
|
||||
"""
|
||||
plpy.notice('** Constructing query')
|
||||
|
||||
qvals = OrderedDict([("id_col", id_col),
|
||||
("attr1", attr1),
|
||||
("attr2", attr2),
|
||||
("geom_col", geom_col),
|
||||
("subquery", subquery),
|
||||
("num_ngbrs", num_ngbrs)])
|
||||
|
||||
query = pu.construct_neighbor_query(w_type, qvals)
|
||||
|
||||
try:
|
||||
result = plpy.execute(query)
|
||||
# if there are no neighbors, exit
|
||||
if len(result) == 0:
|
||||
return pu.empty_zipped_array(4)
|
||||
except plpy.SPIError:
|
||||
plpy.error("Error: areas of interest query failed, " \
|
||||
"check input parameters")
|
||||
plpy.notice('** Query failed: "%s"' % query)
|
||||
return pu.empty_zipped_array(4)
|
||||
|
||||
## collect attributes
|
||||
attr1_vals = pu.get_attributes(result, 1)
|
||||
attr2_vals = pu.get_attributes(result, 2)
|
||||
|
||||
# create weights
|
||||
weight = pu.get_weight(result, w_type, num_ngbrs)
|
||||
|
||||
# calculate LISA values
|
||||
lisa = ps.esda.moran.Moran_Local_BV(attr1_vals, attr2_vals, weight,
|
||||
permutations=permutations)
|
||||
|
||||
plpy.notice("len of Is: %d" % len(lisa.Is))
|
||||
|
||||
# find clustering of significance
|
||||
lisa_sig = quad_position(lisa.q)
|
||||
|
||||
plpy.notice('** Finished calculations')
|
||||
|
||||
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
|
||||
|
||||
# Low level functions ----------------------------------------
|
||||
|
||||
def map_quads(coord):
|
||||
"""
|
||||
Map a quadrant number to Moran's I designation
|
||||
HH=1, LH=2, LL=3, HL=4
|
||||
Input:
|
||||
@param coord (int): quadrant of a specific measurement
|
||||
Output:
|
||||
classification (one of 'HH', 'LH', 'LL', or 'HL')
|
||||
"""
|
||||
if coord == 1:
|
||||
return 'HH'
|
||||
elif coord == 2:
|
||||
return 'LH'
|
||||
elif coord == 3:
|
||||
return 'LL'
|
||||
elif coord == 4:
|
||||
return 'HL'
|
||||
else:
|
||||
return None
|
||||
|
||||
def quad_position(quads):
|
||||
"""
|
||||
Produce Moran's I classification based of n
|
||||
Input:
|
||||
@param quads ndarray: an array of quads classified by
|
||||
1-4 (PySAL default)
|
||||
Output:
|
||||
@param list: an array of quads classied by 'HH', 'LL', etc.
|
||||
"""
|
||||
return [map_quads(q) for q in quads]
|
||||
@ -0,0 +1,2 @@
|
||||
"""Import all functions for pysal_utils"""
|
||||
from crankshaft.pysal_utils.pysal_utils import *
|
||||
@ -0,0 +1,188 @@
|
||||
"""
|
||||
Utilities module for generic PySAL functionality, mainly centered on
|
||||
translating queries into numpy arrays or PySAL weights objects
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
import pysal as ps
|
||||
|
||||
def construct_neighbor_query(w_type, query_vals):
|
||||
"""Return query (a string) used for finding neighbors
|
||||
@param w_type text: type of neighbors to calculate ('knn' or 'queen')
|
||||
@param query_vals dict: values used to construct the query
|
||||
"""
|
||||
|
||||
if w_type.lower() == 'knn':
|
||||
return knn(query_vals)
|
||||
else:
|
||||
return queen(query_vals)
|
||||
|
||||
## Build weight object
|
||||
def get_weight(query_res, w_type='knn', num_ngbrs=5):
|
||||
"""
|
||||
Construct PySAL weight from return value of query
|
||||
@param query_res dict-like: query results with attributes and neighbors
|
||||
"""
|
||||
# if w_type.lower() == 'knn':
|
||||
# row_normed_weights = [1.0 / float(num_ngbrs)] * num_ngbrs
|
||||
# weights = {x['id']: row_normed_weights for x in query_res}
|
||||
# else:
|
||||
# weights = {x['id']: [1.0 / len(x['neighbors'])] * len(x['neighbors'])
|
||||
# if len(x['neighbors']) > 0
|
||||
# else [] for x in query_res}
|
||||
|
||||
neighbors = {x['id']: x['neighbors'] for x in query_res}
|
||||
print 'len of neighbors: %d' % len(neighbors)
|
||||
|
||||
built_weight = ps.W(neighbors)
|
||||
built_weight.transform = 'r'
|
||||
|
||||
return built_weight
|
||||
|
||||
def query_attr_select(params):
|
||||
"""
|
||||
Create portion of SELECT statement for attributes inolved in query.
|
||||
@param params: dict of information used in query (column names,
|
||||
table name, etc.)
|
||||
"""
|
||||
|
||||
attr_string = ""
|
||||
template = "i.\"%(col)s\"::numeric As attr%(alias_num)s, "
|
||||
|
||||
if 'time_cols' in params:
|
||||
## if markov analysis
|
||||
attrs = params['time_cols']
|
||||
|
||||
for idx, val in enumerate(attrs):
|
||||
attr_string += template % {"col": val, "alias_num": idx + 1}
|
||||
else:
|
||||
## if moran's analysis
|
||||
attrs = [k for k in params
|
||||
if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')]
|
||||
|
||||
for idx, val in enumerate(sorted(attrs)):
|
||||
attr_string += template % {"col": params[val], "alias_num": idx + 1}
|
||||
|
||||
return attr_string
|
||||
|
||||
def query_attr_where(params):
|
||||
"""
|
||||
Construct where conditions when building neighbors query
|
||||
Create portion of WHERE clauses for weeding out NULL-valued geometries
|
||||
Input: dict of params:
|
||||
{'subquery': ...,
|
||||
'numerator': 'data1',
|
||||
'denominator': 'data2',
|
||||
'': ...}
|
||||
Output: 'idx_replace."data1" IS NOT NULL AND idx_replace."data2" IS NOT NULL'
|
||||
Input:
|
||||
{'subquery': ...,
|
||||
'time_cols': ['time1', 'time2', 'time3'],
|
||||
'etc': ...}
|
||||
Output: 'idx_replace."time1" IS NOT NULL AND idx_replace."time2" IS NOT
|
||||
NULL AND idx_replace."time3" IS NOT NULL'
|
||||
"""
|
||||
attr_string = []
|
||||
template = "idx_replace.\"%s\" IS NOT NULL"
|
||||
|
||||
if 'time_cols' in params:
|
||||
## markov where clauses
|
||||
attrs = params['time_cols']
|
||||
# add values to template
|
||||
for attr in attrs:
|
||||
attr_string.append(template % attr)
|
||||
else:
|
||||
## moran where clauses
|
||||
|
||||
# get keys
|
||||
attrs = sorted([k for k in params
|
||||
if k not in ('id_col', 'geom_col', 'subquery', 'num_ngbrs', 'subquery')])
|
||||
# add values to template
|
||||
for attr in attrs:
|
||||
attr_string.append(template % params[attr])
|
||||
|
||||
if len(attrs) == 2:
|
||||
attr_string.append("idx_replace.\"%s\" <> 0" % params[attrs[1]])
|
||||
|
||||
out = " AND ".join(attr_string)
|
||||
|
||||
return out
|
||||
|
||||
def knn(params):
|
||||
"""SQL query for k-nearest neighbors.
|
||||
@param vars: dict of values to fill template
|
||||
"""
|
||||
|
||||
attr_select = query_attr_select(params)
|
||||
attr_where = query_attr_where(params)
|
||||
|
||||
replacements = {"attr_select": attr_select,
|
||||
"attr_where_i": attr_where.replace("idx_replace", "i"),
|
||||
"attr_where_j": attr_where.replace("idx_replace", "j")}
|
||||
|
||||
query = "SELECT " \
|
||||
"i.\"{id_col}\" As id, " \
|
||||
"%(attr_select)s" \
|
||||
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
|
||||
"FROM ({subquery}) As j " \
|
||||
"WHERE " \
|
||||
"i.\"{id_col}\" <> j.\"{id_col}\" AND " \
|
||||
"%(attr_where_j)s " \
|
||||
"ORDER BY " \
|
||||
"j.\"{geom_col}\" <-> i.\"{geom_col}\" ASC " \
|
||||
"LIMIT {num_ngbrs})" \
|
||||
") As neighbors " \
|
||||
"FROM ({subquery}) As i " \
|
||||
"WHERE " \
|
||||
"%(attr_where_i)s " \
|
||||
"ORDER BY i.\"{id_col}\" ASC;" % replacements
|
||||
|
||||
return query.format(**params)
|
||||
|
||||
## SQL query for finding queens neighbors (all contiguous polygons)
|
||||
def queen(params):
|
||||
"""SQL query for queen neighbors.
|
||||
@param params dict: information to fill query
|
||||
"""
|
||||
attr_select = query_attr_select(params)
|
||||
attr_where = query_attr_where(params)
|
||||
|
||||
replacements = {"attr_select": attr_select,
|
||||
"attr_where_i": attr_where.replace("idx_replace", "i"),
|
||||
"attr_where_j": attr_where.replace("idx_replace", "j")}
|
||||
|
||||
query = "SELECT " \
|
||||
"i.\"{id_col}\" As id, " \
|
||||
"%(attr_select)s" \
|
||||
"(SELECT ARRAY(SELECT j.\"{id_col}\" " \
|
||||
"FROM ({subquery}) As j " \
|
||||
"WHERE i.\"{id_col}\" <> j.\"{id_col}\" AND " \
|
||||
"ST_Touches(i.\"{geom_col}\", j.\"{geom_col}\") AND " \
|
||||
"%(attr_where_j)s)" \
|
||||
") As neighbors " \
|
||||
"FROM ({subquery}) As i " \
|
||||
"WHERE " \
|
||||
"%(attr_where_i)s " \
|
||||
"ORDER BY i.\"{id_col}\" ASC;" % replacements
|
||||
|
||||
return query.format(**params)
|
||||
|
||||
## to add more weight methods open a ticket or pull request
|
||||
|
||||
def get_attributes(query_res, attr_num=1):
|
||||
"""
|
||||
@param query_res: query results with attributes and neighbors
|
||||
@param attr_num: attribute number (1, 2, ...)
|
||||
"""
|
||||
return np.array([x['attr' + str(attr_num)] for x in query_res], dtype=np.float)
|
||||
|
||||
def empty_zipped_array(num_nones):
|
||||
"""
|
||||
prepare return values for cases of empty weights objects (no neighbors)
|
||||
Input:
|
||||
@param num_nones int: number of columns (e.g., 4)
|
||||
Output:
|
||||
[(None, None, None, None)]
|
||||
"""
|
||||
|
||||
return [tuple([None] * num_nones)]
|
||||
11
release/python/0.4.0/crankshaft/crankshaft/random_seeds.py
Normal file
11
release/python/0.4.0/crankshaft/crankshaft/random_seeds.py
Normal file
@ -0,0 +1,11 @@
|
||||
"""Random seed generator used for non-deterministic functions in crankshaft"""
|
||||
import random
|
||||
import numpy
|
||||
|
||||
def set_random_seeds(value):
|
||||
"""
|
||||
Set the seeds of the RNGs (Random Number Generators)
|
||||
used internally.
|
||||
"""
|
||||
random.seed(value)
|
||||
numpy.random.seed(value)
|
||||
@ -0,0 +1 @@
|
||||
from segmentation import *
|
||||
@ -0,0 +1,176 @@
|
||||
"""
|
||||
Segmentation creation and prediction
|
||||
"""
|
||||
|
||||
import sklearn
|
||||
import numpy as np
|
||||
import plpy
|
||||
from sklearn.ensemble import GradientBoostingRegressor
|
||||
from sklearn import metrics
|
||||
from sklearn.cross_validation import train_test_split
|
||||
|
||||
# Lower level functions
|
||||
#----------------------
|
||||
|
||||
def replace_nan_with_mean(array):
|
||||
"""
|
||||
Input:
|
||||
@param array: an array of floats which may have null-valued entries
|
||||
Output:
|
||||
array with nans filled in with the mean of the dataset
|
||||
"""
|
||||
# returns an array of rows and column indices
|
||||
indices = np.where(np.isnan(array))
|
||||
|
||||
# iterate through entries which have nan values
|
||||
for row, col in zip(*indices):
|
||||
array[row, col] = np.mean(array[~np.isnan(array[:, col]), col])
|
||||
|
||||
return array
|
||||
|
||||
def get_data(variable, feature_columns, query):
|
||||
"""
|
||||
Fetch data from the database, clean, and package into
|
||||
numpy arrays
|
||||
Input:
|
||||
@param variable: name of the target variable
|
||||
@param feature_columns: list of column names
|
||||
@param query: subquery that data is pulled from for the packaging
|
||||
Output:
|
||||
prepared data, packaged into NumPy arrays
|
||||
"""
|
||||
|
||||
columns = ','.join(['array_agg("{col}") As "{col}"'.format(col=col) for col in feature_columns])
|
||||
|
||||
try:
|
||||
data = plpy.execute('''SELECT array_agg("{variable}") As target, {columns} FROM ({query}) As a'''.format(
|
||||
variable=variable,
|
||||
columns=columns,
|
||||
query=query))
|
||||
except Exception, e:
|
||||
plpy.error('Failed to access data to build segmentation model: %s' % e)
|
||||
|
||||
# extract target data from plpy object
|
||||
target = np.array(data[0]['target'])
|
||||
|
||||
# put n feature data arrays into an n x m array of arrays
|
||||
features = np.column_stack([np.array(data[0][col], dtype=float) for col in feature_columns])
|
||||
|
||||
return replace_nan_with_mean(target), replace_nan_with_mean(features)
|
||||
|
||||
# High level interface
|
||||
# --------------------
|
||||
|
||||
def create_and_predict_segment_agg(target, features, target_features, target_ids, model_parameters):
|
||||
"""
|
||||
Version of create_and_predict_segment that works on arrays that come stright form the SQL calling
|
||||
the function.
|
||||
|
||||
Input:
|
||||
@param target: The 1D array of lenth NSamples containing the target variable we want the model to predict
|
||||
@param features: Thw 2D array of size NSamples * NFeatures that form the imput to the model
|
||||
@param target_ids: A 1D array of target_ids that will be used to associate the results of the prediction with the rows which they come from
|
||||
@param model_parameters: A dictionary containing parameters for the model.
|
||||
"""
|
||||
|
||||
clean_target = replace_nan_with_mean(target)
|
||||
clean_features = replace_nan_with_mean(features)
|
||||
target_features = replace_nan_with_mean(target_features)
|
||||
|
||||
model, accuracy = train_model(clean_target, clean_features, model_parameters, 0.2)
|
||||
prediction = model.predict(target_features)
|
||||
accuracy_array = [accuracy]*prediction.shape[0]
|
||||
return zip(target_ids, prediction, np.full(prediction.shape, accuracy_array))
|
||||
|
||||
|
||||
|
||||
def create_and_predict_segment(query, variable, target_query, model_params):
|
||||
"""
|
||||
generate a segment with machine learning
|
||||
Stuart Lynn
|
||||
"""
|
||||
|
||||
## fetch column names
|
||||
try:
|
||||
columns = plpy.execute('SELECT * FROM ({query}) As a LIMIT 1 '.format(query=query))[0].keys()
|
||||
except Exception, e:
|
||||
plpy.error('Failed to build segmentation model: %s' % e)
|
||||
|
||||
## extract column names to be used in building the segmentation model
|
||||
feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
|
||||
## get data from database
|
||||
target, features = get_data(variable, feature_columns, query)
|
||||
|
||||
model, accuracy = train_model(target, features, model_params, 0.2)
|
||||
cartodb_ids, result = predict_segment(model, feature_columns, target_query)
|
||||
accuracy_array = [accuracy]*result.shape[0]
|
||||
return zip(cartodb_ids, result, accuracy_array)
|
||||
|
||||
|
||||
def train_model(target, features, model_params, test_split):
|
||||
"""
|
||||
Train the Gradient Boosting model on the provided data and calculate the accuracy of the model
|
||||
Input:
|
||||
@param target: 1D Array of the variable that the model is to be trianed to predict
|
||||
@param features: 2D Array NSamples * NFeatures to use in trining the model
|
||||
@param model_params: A dictionary of model parameters, the full specification can be found on the
|
||||
scikit learn page for [GradientBoostingRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)
|
||||
@parma test_split: The fraction of the data to be withheld for testing the model / calculating the accuray
|
||||
"""
|
||||
features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=test_split)
|
||||
model = GradientBoostingRegressor(**model_params)
|
||||
model.fit(features_train, target_train)
|
||||
accuracy = calculate_model_accuracy(model, features, target)
|
||||
return model, accuracy
|
||||
|
||||
def calculate_model_accuracy(model, features, target):
|
||||
"""
|
||||
Calculate the mean squared error of the model prediction
|
||||
Input:
|
||||
@param model: model trained from input features
|
||||
@param features: features to make a prediction from
|
||||
@param target: target to compare prediction to
|
||||
Output:
|
||||
mean squared error of the model prection compared to the target
|
||||
"""
|
||||
prediction = model.predict(features)
|
||||
return metrics.mean_squared_error(prediction, target)
|
||||
|
||||
def predict_segment(model, features, target_query):
|
||||
"""
|
||||
Use the provided model to predict the values for the new feature set
|
||||
Input:
|
||||
@param model: The pretrained model
|
||||
@features: A list of features to use in the model prediction (list of column names)
|
||||
@target_query: The query to run to obtain the data to predict on and the cartdb_ids associated with it.
|
||||
"""
|
||||
|
||||
batch_size = 1000
|
||||
joined_features = ','.join(['"{0}"::numeric'.format(a) for a in features])
|
||||
|
||||
try:
|
||||
cursor = plpy.cursor('SELECT Array[{joined_features}] As features FROM ({target_query}) As a'.format(
|
||||
joined_features=joined_features,
|
||||
target_query=target_query))
|
||||
except Exception, e:
|
||||
plpy.error('Failed to build segmentation model: %s' % e)
|
||||
|
||||
results = []
|
||||
|
||||
while True:
|
||||
rows = cursor.fetch(batch_size)
|
||||
if not rows:
|
||||
break
|
||||
batch = np.row_stack([np.array(row['features'], dtype=float) for row in rows])
|
||||
|
||||
#Need to fix this. Should be global mean. This will cause weird effects
|
||||
batch = replace_nan_with_mean(batch)
|
||||
prediction = model.predict(batch)
|
||||
results.append(prediction)
|
||||
|
||||
try:
|
||||
cartodb_ids = plpy.execute('''SELECT array_agg(cartodb_id ORDER BY cartodb_id) As cartodb_ids FROM ({0}) As a'''.format(target_query))[0]['cartodb_ids']
|
||||
except Exception, e:
|
||||
plpy.error('Failed to build segmentation model: %s' % e)
|
||||
|
||||
return cartodb_ids, np.concatenate(results)
|
||||
@ -0,0 +1,2 @@
|
||||
"""Import all functions from clustering libraries."""
|
||||
from markov import *
|
||||
@ -0,0 +1,189 @@
|
||||
"""
|
||||
Spatial dynamics measurements using Spatial Markov
|
||||
"""
|
||||
|
||||
|
||||
import numpy as np
|
||||
import pysal as ps
|
||||
import plpy
|
||||
import crankshaft.pysal_utils as pu
|
||||
|
||||
def spatial_markov_trend(subquery, time_cols, num_classes=7,
|
||||
w_type='knn', num_ngbrs=5, permutations=0,
|
||||
geom_col='the_geom', id_col='cartodb_id'):
|
||||
"""
|
||||
Predict the trends of a unit based on:
|
||||
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
|
||||
2. average class of its neighbors
|
||||
|
||||
Inputs:
|
||||
@param subquery string: e.g., SELECT the_geom, cartodb_id,
|
||||
interesting_time_column FROM table_name
|
||||
@param time_cols list of strings: list of strings of column names
|
||||
@param num_classes (optional): number of classes to break distribution
|
||||
of values into. Currently uses quantile bins.
|
||||
@param w_type string (optional): weight type ('knn' or 'queen')
|
||||
@param num_ngbrs int (optional): number of neighbors (if knn type)
|
||||
@param permutations int (optional): number of permutations for test
|
||||
stats
|
||||
@param geom_col string (optional): name of column which contains the
|
||||
geometries
|
||||
@param id_col string (optional): name of column which has the ids of
|
||||
the table
|
||||
|
||||
Outputs:
|
||||
@param trend_up float: probablity that a geom will move to a higher
|
||||
class
|
||||
@param trend_down float: probablity that a geom will move to a lower
|
||||
class
|
||||
@param trend float: (trend_up - trend_down) / trend_static
|
||||
@param volatility float: a measure of the volatility based on
|
||||
probability stddev(prob array)
|
||||
"""
|
||||
|
||||
if len(time_cols) < 2:
|
||||
plpy.error('More than one time column needs to be passed')
|
||||
|
||||
qvals = {"id_col": id_col,
|
||||
"time_cols": time_cols,
|
||||
"geom_col": geom_col,
|
||||
"subquery": subquery,
|
||||
"num_ngbrs": num_ngbrs}
|
||||
|
||||
try:
|
||||
query_result = plpy.execute(
|
||||
pu.construct_neighbor_query(w_type, qvals)
|
||||
)
|
||||
if len(query_result) == 0:
|
||||
return zip([None], [None], [None], [None], [None])
|
||||
except plpy.SPIError, err:
|
||||
plpy.debug('Query failed with exception %s: %s' % (err, pu.construct_neighbor_query(w_type, qvals)))
|
||||
plpy.error('Query failed, check the input parameters')
|
||||
return zip([None], [None], [None], [None], [None])
|
||||
|
||||
## build weight
|
||||
weights = pu.get_weight(query_result, w_type)
|
||||
weights.transform = 'r'
|
||||
|
||||
## prep time data
|
||||
t_data = get_time_data(query_result, time_cols)
|
||||
|
||||
plpy.debug('shape of t_data %d, %d' % t_data.shape)
|
||||
plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
|
||||
plpy.debug('first num elements: %f' % t_data[0, 0])
|
||||
|
||||
sp_markov_result = ps.Spatial_Markov(t_data,
|
||||
weights,
|
||||
k=num_classes,
|
||||
fixed=False,
|
||||
permutations=permutations)
|
||||
|
||||
## get lag classes
|
||||
lag_classes = ps.Quantiles(
|
||||
ps.lag_spatial(weights, t_data[:, -1]),
|
||||
k=num_classes).yb
|
||||
|
||||
## look up probablity distribution for each unit according to class and lag class
|
||||
prob_dist = get_prob_dist(sp_markov_result.P,
|
||||
lag_classes,
|
||||
sp_markov_result.classes[:, -1])
|
||||
|
||||
## find the ups and down and overall distribution of each cell
|
||||
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist,
|
||||
sp_markov_result.classes[:, -1])
|
||||
|
||||
## output the results
|
||||
return zip(trend, trend_up, trend_down, volatility, weights.id_order)
|
||||
|
||||
def get_time_data(markov_data, time_cols):
|
||||
"""
|
||||
Extract the time columns and bin appropriately
|
||||
"""
|
||||
num_attrs = len(time_cols)
|
||||
return np.array([[x['attr' + str(i)] for x in markov_data]
|
||||
for i in range(1, num_attrs+1)], dtype=float).transpose()
|
||||
|
||||
## not currently used
|
||||
def rebin_data(time_data, num_time_per_bin):
|
||||
"""
|
||||
Convert an n x l matrix into an (n/m) x l matrix where the values are
|
||||
reduced (averaged) for the intervening states:
|
||||
1 2 3 4 1.5 3.5
|
||||
5 6 7 8 -> 5.5 7.5
|
||||
9 8 7 6 8.5 6.5
|
||||
5 4 3 2 4.5 2.5
|
||||
|
||||
if m = 2, the 4 x 4 matrix is transformed to a 2 x 4 matrix.
|
||||
|
||||
This process effectively resamples the data at a longer time span n
|
||||
units longer than the input data.
|
||||
For cases when there is a remainder (remainder(5/3) = 2), the remaining
|
||||
two columns are binned together as the last time period, while the
|
||||
first three are binned together for the first period.
|
||||
|
||||
Input:
|
||||
@param time_data n x l ndarray: measurements of an attribute at
|
||||
different time intervals
|
||||
@param num_time_per_bin int: number of columns to average into a new
|
||||
column
|
||||
Output:
|
||||
ceil(n / m) x l ndarray of resampled time series
|
||||
"""
|
||||
|
||||
if time_data.shape[1] % num_time_per_bin == 0:
|
||||
## if fit is perfect, then use it
|
||||
n_max = time_data.shape[1] / num_time_per_bin
|
||||
else:
|
||||
## fit remainders into an additional column
|
||||
n_max = time_data.shape[1] / num_time_per_bin + 1
|
||||
|
||||
return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
|
||||
for i in range(n_max)]).T
|
||||
|
||||
def get_prob_dist(transition_matrix, lag_indices, unit_indices):
|
||||
"""
|
||||
Given an array of transition matrices, look up the probability
|
||||
associated with the arrangements passed
|
||||
|
||||
Input:
|
||||
@param transition_matrix ndarray[k,k,k]:
|
||||
@param lag_indices ndarray:
|
||||
@param unit_indices ndarray:
|
||||
|
||||
Output:
|
||||
Array of probability distributions
|
||||
"""
|
||||
|
||||
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])]
|
||||
for i in range(len(lag_indices))])
|
||||
|
||||
def get_prob_stats(prob_dist, unit_indices):
|
||||
"""
|
||||
get the statistics of the probability distributions
|
||||
|
||||
Outputs:
|
||||
@param trend_up ndarray(float): sum of probabilities for upward
|
||||
movement (relative to the unit index of that prob)
|
||||
@param trend_down ndarray(float): sum of probabilities for downward
|
||||
movement (relative to the unit index of that prob)
|
||||
@param trend ndarray(float): difference of upward and downward
|
||||
movements
|
||||
"""
|
||||
|
||||
num_elements = len(unit_indices)
|
||||
trend_up = np.empty(num_elements, dtype=float)
|
||||
trend_down = np.empty(num_elements, dtype=float)
|
||||
trend = np.empty(num_elements, dtype=float)
|
||||
|
||||
for i in range(num_elements):
|
||||
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
|
||||
trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
|
||||
if prob_dist[i, unit_indices[i]] > 0.0:
|
||||
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
|
||||
else:
|
||||
trend[i] = None
|
||||
|
||||
## calculate volatility of distribution
|
||||
volatility = prob_dist.std(axis=1)
|
||||
|
||||
return trend_up, trend_down, trend, volatility
|
||||
49
release/python/0.4.0/crankshaft/setup.py
Normal file
49
release/python/0.4.0/crankshaft/setup.py
Normal file
@ -0,0 +1,49 @@
|
||||
|
||||
"""
|
||||
CartoDB Spatial Analysis Python Library
|
||||
See:
|
||||
https://github.com/CartoDB/crankshaft
|
||||
"""
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name='crankshaft',
|
||||
|
||||
version='0.0.0',
|
||||
|
||||
description='CartoDB Spatial Analysis Python Library',
|
||||
|
||||
url='https://github.com/CartoDB/crankshaft',
|
||||
|
||||
author='Data Services Team - CartoDB',
|
||||
author_email='dataservices@cartodb.com',
|
||||
|
||||
license='MIT',
|
||||
|
||||
classifiers=[
|
||||
'Development Status :: 3 - Alpha',
|
||||
'Intended Audience :: Mapping comunity',
|
||||
'Topic :: Maps :: Mapping Tools',
|
||||
'License :: OSI Approved :: MIT License',
|
||||
'Programming Language :: Python :: 2.7',
|
||||
],
|
||||
|
||||
keywords='maps mapping tools spatial analysis geostatistics',
|
||||
|
||||
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
|
||||
|
||||
extras_require={
|
||||
'dev': ['unittest'],
|
||||
'test': ['unittest', 'nose', 'mock'],
|
||||
},
|
||||
|
||||
# The choice of component versions is dictated by what's
|
||||
# provisioned in the production servers.
|
||||
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
|
||||
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
|
||||
|
||||
requires=['pysal', 'numpy', 'sklearn'],
|
||||
|
||||
test_suite='test'
|
||||
)
|
||||
49
release/python/0.4.0/crankshaft/setup.py-r
Normal file
49
release/python/0.4.0/crankshaft/setup.py-r
Normal file
@ -0,0 +1,49 @@
|
||||
|
||||
"""
|
||||
CartoDB Spatial Analysis Python Library
|
||||
See:
|
||||
https://github.com/CartoDB/crankshaft
|
||||
"""
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name='crankshaft',
|
||||
|
||||
version='0.0.0',
|
||||
|
||||
description='CartoDB Spatial Analysis Python Library',
|
||||
|
||||
url='https://github.com/CartoDB/crankshaft',
|
||||
|
||||
author='Data Services Team - CartoDB',
|
||||
author_email='dataservices@cartodb.com',
|
||||
|
||||
license='MIT',
|
||||
|
||||
classifiers=[
|
||||
'Development Status :: 3 - Alpha',
|
||||
'Intended Audience :: Mapping comunity',
|
||||
'Topic :: Maps :: Mapping Tools',
|
||||
'License :: OSI Approved :: MIT License',
|
||||
'Programming Language :: Python :: 2.7',
|
||||
],
|
||||
|
||||
keywords='maps mapping tools spatial analysis geostatistics',
|
||||
|
||||
packages=find_packages(exclude=['contrib', 'docs', 'tests']),
|
||||
|
||||
extras_require={
|
||||
'dev': ['unittest'],
|
||||
'test': ['unittest', 'nose', 'mock'],
|
||||
},
|
||||
|
||||
# The choice of component versions is dictated by what's
|
||||
# provisioned in the production servers.
|
||||
# IMPORTANT NOTE: please don't change this line. Instead issue a ticket to systems for evaluation.
|
||||
install_requires=['joblib==0.8.3', 'numpy==1.6.1', 'scipy==0.14.0', 'pysal==1.11.2', 'scikit-learn==0.14.1'],
|
||||
|
||||
requires=['pysal', 'numpy', 'sklearn'],
|
||||
|
||||
test_suite='test'
|
||||
)
|
||||
1
release/python/0.4.0/crankshaft/test/fixtures/kmeans.json
vendored
Normal file
1
release/python/0.4.0/crankshaft/test/fixtures/kmeans.json
vendored
Normal file
@ -0,0 +1 @@
|
||||
[{"xs": [9.917239463463458, 9.042767302696836, 10.798929825304187, 8.763751051762995, 11.383882954810852, 11.018206993460897, 8.939526075734316, 9.636159342565252, 10.136336896960058, 11.480610059427342, 12.115011910725082, 9.173267848893428, 10.239300931201738, 8.00012512174072, 8.979962292282131, 9.318376124429575, 10.82259513754284, 10.391747171927115, 10.04904588886165, 9.96007160443463, -0.78825626804569, -0.3511819898577426, -1.2796410003764271, -0.3977049391203402, 2.4792311265774667, 1.3670311632092624, 1.2963504112955613, 2.0404844103073025, -1.6439708506073223, 0.39122885445645805, 1.026031821452462, -0.04044477160482201, -0.7442346929085072, -0.34687120826243034, -0.23420359971379054, -0.5919629143336708, -0.202903054395391, -0.1893399644841902, 1.9331834251176807, -0.12321054392851609], "ys": [8.735627063679981, 9.857615954045011, 10.81439096759407, 10.586727233537191, 9.232919976568622, 11.54281262696508, 8.392787912674466, 9.355119689665944, 9.22380703532752, 10.542142541823122, 10.111980619367035, 10.760836265570738, 8.819773453269804, 10.25325722424816, 9.802077905695608, 8.955420161552611, 9.833801181904477, 10.491684241001613, 12.076108669877556, 11.74289693140474, -0.5685725015474191, -0.5715728344759778, -0.20180907868635137, 0.38431336480089595, -0.3402202083684184, -2.4652736827783586, 0.08295159401756182, 0.8503818775816505, 0.6488691600321166, 0.5794762568230527, -0.6770063922144103, -0.6557616416449478, -1.2834289177624947, 0.1096318195532717, -0.38986922166834853, -1.6224497706950238, 0.09429787743230483, 0.4005097316394031, -0.508002811195673, -1.2473463371366507], "ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39]}]
|
||||
1
release/python/0.4.0/crankshaft/test/fixtures/markov.json
vendored
Normal file
1
release/python/0.4.0/crankshaft/test/fixtures/markov.json
vendored
Normal file
@ -0,0 +1 @@
|
||||
[[0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 0], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 1], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 2], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 3], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 4], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 5], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 6], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 7], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 8], [0.19047619047619049, 0.16, 0.0, 0.32594478059941379, 9], [-0.23529411764705882, 0.0, 0.19047619047619047, 0.31356338348865387, 10], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 11], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 12], [0.027777777777777783, 0.11111111111111112, 0.088888888888888892, 0.30339641183779581, 13], [0.03125, 0.030303030303030304, 0.0, 0.3850273981640871, 14], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 15], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 16], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 17], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 18], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 19], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 20], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 21], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 22], [-0.16666666666666663, 0.18181818181818182, 0.27272727272727271, 0.20246415864836445, 23], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 24], [0.1875, 0.23999999999999999, 0.12, 0.23731835158706122, 25], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 26], [-0.043478260869565216, 0.0, 0.041666666666666664, 0.37950991789118999, 27], [0.22222222222222221, 0.18181818181818182, 0.0, 0.31701083225750354, 28], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 29], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 30], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 31], [0.030303030303030304, 0.078947368421052627, 0.052631578947368418, 0.33560628561957595, 32], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 33], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 34], [0.0, 0.10000000000000001, 0.10000000000000001, 0.30331501776206204, 35], [-0.054054054054054057, 0.0, 0.05128205128205128, 0.37488547451276033, 36], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 37], [-0.22222222222222224, 0.13333333333333333, 0.26666666666666666, 0.22310934040908681, 38], [-0.0625, 0.095238095238095233, 0.14285714285714285, 0.28634850244519822, 39], [0.034482758620689655, 0.0625, 0.03125, 0.35388469167230169, 40], [0.11111111111111112, 0.10000000000000001, 0.0, 0.35213633723318016, 41], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 42], [0.0, 0.0, 0.0, 0.40000000000000002, 43], [0.0, 0.065217391304347824, 0.065217391304347824, 0.33605067580764519, 44], [0.078947368421052641, 0.073170731707317083, 0.0, 0.36451788667842738, 45], [0.052631578947368425, 0.090909090909090912, 0.045454545454545456, 0.33352611505171165, 46], [-0.20512820512820512, 0.0, 0.1702127659574468, 0.32172013908826891, 47]]
|
||||
52
release/python/0.4.0/crankshaft/test/fixtures/moran.json
vendored
Normal file
52
release/python/0.4.0/crankshaft/test/fixtures/moran.json
vendored
Normal file
@ -0,0 +1,52 @@
|
||||
[[0.9319096128346788, "HH"],
|
||||
[-1.135787401862846, "HL"],
|
||||
[0.11732030672508517, "LL"],
|
||||
[0.6152779669180425, "LL"],
|
||||
[-0.14657336660125297, "LH"],
|
||||
[0.6967858120189607, "LL"],
|
||||
[0.07949310115714454, "HH"],
|
||||
[0.4703198759258987, "HH"],
|
||||
[0.4421125200498064, "HH"],
|
||||
[0.5724288737143592, "LL"],
|
||||
[0.8970743435692062, "LL"],
|
||||
[0.18327334401918674, "LL"],
|
||||
[-0.01466729201304962, "HL"],
|
||||
[0.3481559372544409, "LL"],
|
||||
[0.06547094736902978, "LL"],
|
||||
[0.15482141569329988, "HH"],
|
||||
[0.4373841193538136, "HH"],
|
||||
[0.15971286468915544, "LL"],
|
||||
[1.0543588860308968, "HH"],
|
||||
[1.7372866900020818, "HH"],
|
||||
[1.091998586053999, "LL"],
|
||||
[0.1171572584252222, "HH"],
|
||||
[0.08438455015300014, "LL"],
|
||||
[0.06547094736902978, "LL"],
|
||||
[0.15482141569329985, "HH"],
|
||||
[1.1627044812890683, "HH"],
|
||||
[0.06547094736902978, "LL"],
|
||||
[0.795275137550483, "HH"],
|
||||
[0.18562939195219, "LL"],
|
||||
[0.3010757406693439, "LL"],
|
||||
[2.8205795942839376, "HH"],
|
||||
[0.11259190602909264, "LL"],
|
||||
[-0.07116352791516614, "HL"],
|
||||
[-0.09945240794119009, "LH"],
|
||||
[0.18562939195219, "LL"],
|
||||
[0.1832733440191868, "LL"],
|
||||
[-0.39054253768447705, "HL"],
|
||||
[-0.1672071289487642, "HL"],
|
||||
[0.3337669247916343, "HH"],
|
||||
[0.2584386102554792, "HH"],
|
||||
[-0.19733845476322634, "HL"],
|
||||
[-0.9379282899805409, "LH"],
|
||||
[-0.028770969951095866, "LH"],
|
||||
[0.051367269430983485, "LL"],
|
||||
[-0.2172548045913472, "LH"],
|
||||
[0.05136726943098351, "LL"],
|
||||
[0.04191046803899837, "LL"],
|
||||
[0.7482357030403517, "HH"],
|
||||
[-0.014585767863118111, "LH"],
|
||||
[0.5410013139159929, "HH"],
|
||||
[1.0223932668429925, "LL"],
|
||||
[1.4179402898927476, "LL"]]
|
||||
54
release/python/0.4.0/crankshaft/test/fixtures/neighbors.json
vendored
Normal file
54
release/python/0.4.0/crankshaft/test/fixtures/neighbors.json
vendored
Normal file
@ -0,0 +1,54 @@
|
||||
[
|
||||
{"neighbors": [48, 26, 20, 9, 31], "id": 1, "value": 0.5},
|
||||
{"neighbors": [30, 16, 46, 3, 4], "id": 2, "value": 0.7},
|
||||
{"neighbors": [46, 30, 2, 12, 16], "id": 3, "value": 0.2},
|
||||
{"neighbors": [18, 30, 23, 2, 52], "id": 4, "value": 0.1},
|
||||
{"neighbors": [47, 40, 45, 37, 28], "id": 5, "value": 0.3},
|
||||
{"neighbors": [10, 21, 41, 14, 37], "id": 6, "value": 0.05},
|
||||
{"neighbors": [8, 17, 43, 25, 12], "id": 7, "value": 0.4},
|
||||
{"neighbors": [17, 25, 43, 22, 7], "id": 8, "value": 0.7},
|
||||
{"neighbors": [39, 34, 1, 26, 48], "id": 9, "value": 0.5},
|
||||
{"neighbors": [6, 37, 5, 45, 49], "id": 10, "value": 0.04},
|
||||
{"neighbors": [51, 41, 29, 21, 14], "id": 11, "value": 0.08},
|
||||
{"neighbors": [44, 46, 43, 50, 3], "id": 12, "value": 0.2},
|
||||
{"neighbors": [45, 23, 14, 28, 18], "id": 13, "value": 0.4},
|
||||
{"neighbors": [41, 29, 13, 23, 6], "id": 14, "value": 0.2},
|
||||
{"neighbors": [36, 27, 32, 33, 24], "id": 15, "value": 0.3},
|
||||
{"neighbors": [19, 2, 46, 44, 28], "id": 16, "value": 0.4},
|
||||
{"neighbors": [8, 25, 43, 7, 22], "id": 17, "value": 0.6},
|
||||
{"neighbors": [23, 4, 29, 14, 13], "id": 18, "value": 0.3},
|
||||
{"neighbors": [42, 16, 28, 26, 40], "id": 19, "value": 0.7},
|
||||
{"neighbors": [1, 48, 31, 26, 42], "id": 20, "value": 0.8},
|
||||
{"neighbors": [41, 6, 11, 14, 10], "id": 21, "value": 0.1},
|
||||
{"neighbors": [25, 50, 43, 31, 44], "id": 22, "value": 0.4},
|
||||
{"neighbors": [18, 13, 14, 4, 2], "id": 23, "value": 0.1},
|
||||
{"neighbors": [33, 49, 34, 47, 27], "id": 24, "value": 0.3},
|
||||
{"neighbors": [43, 8, 22, 17, 50], "id": 25, "value": 0.4},
|
||||
{"neighbors": [1, 42, 20, 31, 48], "id": 26, "value": 0.6},
|
||||
{"neighbors": [32, 15, 36, 33, 24], "id": 27, "value": 0.3},
|
||||
{"neighbors": [40, 45, 19, 5, 13], "id": 28, "value": 0.8},
|
||||
{"neighbors": [11, 51, 41, 14, 18], "id": 29, "value": 0.3},
|
||||
{"neighbors": [2, 3, 4, 46, 18], "id": 30, "value": 0.1},
|
||||
{"neighbors": [20, 26, 1, 50, 48], "id": 31, "value": 0.9},
|
||||
{"neighbors": [27, 36, 15, 49, 24], "id": 32, "value": 0.3},
|
||||
{"neighbors": [24, 27, 49, 34, 32], "id": 33, "value": 0.4},
|
||||
{"neighbors": [47, 9, 39, 40, 24], "id": 34, "value": 0.3},
|
||||
{"neighbors": [38, 51, 11, 21, 41], "id": 35, "value": 0.3},
|
||||
{"neighbors": [15, 32, 27, 49, 33], "id": 36, "value": 0.2},
|
||||
{"neighbors": [49, 10, 5, 47, 24], "id": 37, "value": 0.5},
|
||||
{"neighbors": [35, 21, 51, 11, 41], "id": 38, "value": 0.4},
|
||||
{"neighbors": [9, 34, 48, 1, 47], "id": 39, "value": 0.6},
|
||||
{"neighbors": [28, 47, 5, 9, 34], "id": 40, "value": 0.5},
|
||||
{"neighbors": [11, 14, 29, 21, 6], "id": 41, "value": 0.4},
|
||||
{"neighbors": [26, 19, 1, 9, 31], "id": 42, "value": 0.2},
|
||||
{"neighbors": [25, 12, 8, 22, 44], "id": 43, "value": 0.3},
|
||||
{"neighbors": [12, 50, 46, 16, 43], "id": 44, "value": 0.2},
|
||||
{"neighbors": [28, 13, 5, 40, 19], "id": 45, "value": 0.3},
|
||||
{"neighbors": [3, 12, 44, 2, 16], "id": 46, "value": 0.2},
|
||||
{"neighbors": [34, 40, 5, 49, 24], "id": 47, "value": 0.3},
|
||||
{"neighbors": [1, 20, 26, 9, 39], "id": 48, "value": 0.5},
|
||||
{"neighbors": [24, 37, 47, 5, 33], "id": 49, "value": 0.2},
|
||||
{"neighbors": [44, 22, 31, 42, 26], "id": 50, "value": 0.6},
|
||||
{"neighbors": [11, 29, 41, 14, 21], "id": 51, "value": 0.01},
|
||||
{"neighbors": [4, 18, 29, 51, 23], "id": 52, "value": 0.01}
|
||||
]
|
||||
1
release/python/0.4.0/crankshaft/test/fixtures/neighbors_markov.json
vendored
Normal file
1
release/python/0.4.0/crankshaft/test/fixtures/neighbors_markov.json
vendored
Normal file
File diff suppressed because one or more lines are too long
13
release/python/0.4.0/crankshaft/test/helper.py
Normal file
13
release/python/0.4.0/crankshaft/test/helper.py
Normal file
@ -0,0 +1,13 @@
|
||||
import unittest
|
||||
|
||||
from mock_plpy import MockPlPy
|
||||
plpy = MockPlPy()
|
||||
|
||||
import sys
|
||||
sys.modules['plpy'] = plpy
|
||||
|
||||
import os
|
||||
|
||||
def fixture_file(name):
|
||||
dir = os.path.dirname(os.path.realpath(__file__))
|
||||
return os.path.join(dir, 'fixtures', name)
|
||||
52
release/python/0.4.0/crankshaft/test/mock_plpy.py
Normal file
52
release/python/0.4.0/crankshaft/test/mock_plpy.py
Normal file
@ -0,0 +1,52 @@
|
||||
import re
|
||||
|
||||
class MockCursor:
|
||||
def __init__(self, data):
|
||||
self.cursor_pos = 0
|
||||
self.data = data
|
||||
|
||||
def fetch(self, batch_size):
|
||||
batch = self.data[self.cursor_pos : self.cursor_pos + batch_size]
|
||||
self.cursor_pos += batch_size
|
||||
return batch
|
||||
|
||||
|
||||
class MockPlPy:
|
||||
def __init__(self):
|
||||
self._reset()
|
||||
|
||||
def _reset(self):
|
||||
self.infos = []
|
||||
self.notices = []
|
||||
self.debugs = []
|
||||
self.logs = []
|
||||
self.warnings = []
|
||||
self.errors = []
|
||||
self.fatals = []
|
||||
self.executes = []
|
||||
self.results = []
|
||||
self.prepares = []
|
||||
self.results = []
|
||||
|
||||
def _define_result(self, query, result):
|
||||
pattern = re.compile(query, re.IGNORECASE | re.MULTILINE)
|
||||
self.results.append([pattern, result])
|
||||
|
||||
def notice(self, msg):
|
||||
self.notices.append(msg)
|
||||
|
||||
def debug(self, msg):
|
||||
self.notices.append(msg)
|
||||
|
||||
def info(self, msg):
|
||||
self.infos.append(msg)
|
||||
|
||||
def cursor(self, query):
|
||||
data = self.execute(query)
|
||||
return MockCursor(data)
|
||||
|
||||
def execute(self, query): # TODO: additional arguments
|
||||
for result in self.results:
|
||||
if result[0].match(query):
|
||||
return result[1]
|
||||
return []
|
||||
38
release/python/0.4.0/crankshaft/test/test_cluster_kmeans.py
Normal file
38
release/python/0.4.0/crankshaft/test/test_cluster_kmeans.py
Normal file
@ -0,0 +1,38 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import plpy, fixture_file
|
||||
import numpy as np
|
||||
import crankshaft.clustering as cc
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
|
||||
class KMeansTest(unittest.TestCase):
|
||||
"""Testing class for Moran's I functions"""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
self.cluster_data = json.loads(open(fixture_file('kmeans.json')).read())
|
||||
self.params = {"subquery": "select * from table",
|
||||
"no_clusters": "10"
|
||||
}
|
||||
|
||||
def test_kmeans(self):
|
||||
data = self.cluster_data
|
||||
plpy._define_result('select' ,data)
|
||||
clusters = cc.kmeans('subquery', 2)
|
||||
labels = [a[1] for a in clusters]
|
||||
c1 = [a for a in clusters if a[1]==0]
|
||||
c2 = [a for a in clusters if a[1]==1]
|
||||
|
||||
self.assertEqual(len(np.unique(labels)),2)
|
||||
self.assertEqual(len(c1),20)
|
||||
self.assertEqual(len(c2),20)
|
||||
|
||||
@ -0,0 +1,88 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import plpy, fixture_file
|
||||
|
||||
import crankshaft.clustering as cc
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
|
||||
class MoranTest(unittest.TestCase):
|
||||
"""Testing class for Moran's I functions"""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"attr1": "andy",
|
||||
"attr2": "jay_z",
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
self.params_markov = {"id_col": "cartodb_id",
|
||||
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
self.neighbors_data = json.loads(open(fixture_file('neighbors.json')).read())
|
||||
self.moran_data = json.loads(open(fixture_file('moran.json')).read())
|
||||
|
||||
def test_map_quads(self):
|
||||
"""Test map_quads"""
|
||||
self.assertEqual(cc.map_quads(1), 'HH')
|
||||
self.assertEqual(cc.map_quads(2), 'LH')
|
||||
self.assertEqual(cc.map_quads(3), 'LL')
|
||||
self.assertEqual(cc.map_quads(4), 'HL')
|
||||
self.assertEqual(cc.map_quads(33), None)
|
||||
self.assertEqual(cc.map_quads('andy'), None)
|
||||
|
||||
def test_quad_position(self):
|
||||
"""Test lisa_sig_vals"""
|
||||
|
||||
quads = np.array([1, 2, 3, 4], np.int)
|
||||
|
||||
ans = np.array(['HH', 'LH', 'LL', 'HL'])
|
||||
test_ans = cc.quad_position(quads)
|
||||
|
||||
self.assertTrue((test_ans == ans).all())
|
||||
|
||||
def test_moran_local(self):
|
||||
"""Test Moran's I local"""
|
||||
data = [ { 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local('subquery', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
expected = self.moran_data
|
||||
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_val, exp_val)
|
||||
self.assertEqual(res_quad, exp_quad)
|
||||
|
||||
def test_moran_local_rate(self):
|
||||
"""Test Moran's I rate"""
|
||||
data = [ { 'id': d['id'], 'attr1': d['value'], 'attr2': 1, 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
result = cc.moran_local_rate('subquery', 'numerator', 'denominator', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
print 'result == None? ', result == None
|
||||
result = [(row[0], row[1]) for row in result]
|
||||
expected = self.moran_data
|
||||
for ([res_val, res_quad], [exp_val, exp_quad]) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_val, exp_val)
|
||||
|
||||
def test_moran(self):
|
||||
"""Test Moran's I global"""
|
||||
data = [{ 'id': d['id'], 'attr1': d['value'], 'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1235)
|
||||
result = cc.moran('table', 'value', 'knn', 5, 99, 'the_geom', 'cartodb_id')
|
||||
print 'result == None?', result == None
|
||||
result_moran = result[0][0]
|
||||
expected_moran = np.array([row[0] for row in self.moran_data]).mean()
|
||||
self.assertAlmostEqual(expected_moran, result_moran, delta=10e-2)
|
||||
142
release/python/0.4.0/crankshaft/test/test_pysal_utils.py
Normal file
142
release/python/0.4.0/crankshaft/test/test_pysal_utils.py
Normal file
@ -0,0 +1,142 @@
|
||||
import unittest
|
||||
|
||||
import crankshaft.pysal_utils as pu
|
||||
from crankshaft import random_seeds
|
||||
|
||||
|
||||
class PysalUtilsTest(unittest.TestCase):
|
||||
"""Testing class for utility functions related to PySAL integrations"""
|
||||
|
||||
def setUp(self):
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"attr1": "andy",
|
||||
"attr2": "jay_z",
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
|
||||
self.params_array = {"id_col": "cartodb_id",
|
||||
"time_cols": ["_2013_dec", "_2014_jan", "_2014_feb"],
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
|
||||
def test_query_attr_select(self):
|
||||
"""Test query_attr_select"""
|
||||
|
||||
ans = "i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, "
|
||||
|
||||
ans_array = "i.\"_2013_dec\"::numeric As attr1, " \
|
||||
"i.\"_2014_jan\"::numeric As attr2, " \
|
||||
"i.\"_2014_feb\"::numeric As attr3, "
|
||||
|
||||
self.assertEqual(pu.query_attr_select(self.params), ans)
|
||||
self.assertEqual(pu.query_attr_select(self.params_array), ans_array)
|
||||
|
||||
def test_query_attr_where(self):
|
||||
"""Test pu.query_attr_where"""
|
||||
|
||||
ans = "idx_replace.\"andy\" IS NOT NULL AND " \
|
||||
"idx_replace.\"jay_z\" IS NOT NULL AND " \
|
||||
"idx_replace.\"jay_z\" <> 0"
|
||||
|
||||
ans_array = "idx_replace.\"_2013_dec\" IS NOT NULL AND " \
|
||||
"idx_replace.\"_2014_jan\" IS NOT NULL AND " \
|
||||
"idx_replace.\"_2014_feb\" IS NOT NULL"
|
||||
|
||||
self.assertEqual(pu.query_attr_where(self.params), ans)
|
||||
self.assertEqual(pu.query_attr_where(self.params_array), ans_array)
|
||||
|
||||
def test_knn(self):
|
||||
"""Test knn neighbors constructor"""
|
||||
|
||||
ans = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
"FROM (SELECT * FROM a_list) As j " \
|
||||
"WHERE " \
|
||||
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
|
||||
"j.\"andy\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" <> 0 " \
|
||||
"ORDER BY " \
|
||||
"j.\"the_geom\" <-> i.\"the_geom\" ASC " \
|
||||
"LIMIT 321)) As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"andy\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" <> 0 " \
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
ans_array = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"_2013_dec\"::numeric As attr1, " \
|
||||
"i.\"_2014_jan\"::numeric As attr2, " \
|
||||
"i.\"_2014_feb\"::numeric As attr3, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
"FROM (SELECT * FROM a_list) As j " \
|
||||
"WHERE i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
|
||||
"j.\"_2013_dec\" IS NOT NULL AND " \
|
||||
"j.\"_2014_jan\" IS NOT NULL AND " \
|
||||
"j.\"_2014_feb\" IS NOT NULL " \
|
||||
"ORDER BY j.\"the_geom\" <-> i.\"the_geom\" ASC " \
|
||||
"LIMIT 321)) As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"_2013_dec\" IS NOT NULL AND " \
|
||||
"i.\"_2014_jan\" IS NOT NULL AND " \
|
||||
"i.\"_2014_feb\" IS NOT NULL "\
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
self.assertEqual(pu.knn(self.params), ans)
|
||||
self.assertEqual(pu.knn(self.params_array), ans_array)
|
||||
|
||||
def test_queen(self):
|
||||
"""Test queen neighbors constructor"""
|
||||
|
||||
ans = "SELECT i.\"cartodb_id\" As id, " \
|
||||
"i.\"andy\"::numeric As attr1, " \
|
||||
"i.\"jay_z\"::numeric As attr2, " \
|
||||
"(SELECT ARRAY(SELECT j.\"cartodb_id\" " \
|
||||
"FROM (SELECT * FROM a_list) As j " \
|
||||
"WHERE " \
|
||||
"i.\"cartodb_id\" <> j.\"cartodb_id\" AND " \
|
||||
"ST_Touches(i.\"the_geom\", " \
|
||||
"j.\"the_geom\") AND " \
|
||||
"j.\"andy\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" IS NOT NULL AND " \
|
||||
"j.\"jay_z\" <> 0)" \
|
||||
") As neighbors " \
|
||||
"FROM (SELECT * FROM a_list) As i " \
|
||||
"WHERE i.\"andy\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" IS NOT NULL AND " \
|
||||
"i.\"jay_z\" <> 0 " \
|
||||
"ORDER BY i.\"cartodb_id\" ASC;"
|
||||
|
||||
self.assertEqual(pu.queen(self.params), ans)
|
||||
|
||||
def test_construct_neighbor_query(self):
|
||||
"""Test construct_neighbor_query"""
|
||||
|
||||
# Compare to raw knn query
|
||||
self.assertEqual(pu.construct_neighbor_query('knn', self.params),
|
||||
pu.knn(self.params))
|
||||
|
||||
def test_get_attributes(self):
|
||||
"""Test get_attributes"""
|
||||
|
||||
## need to add tests
|
||||
|
||||
self.assertEqual(True, True)
|
||||
|
||||
def test_get_weight(self):
|
||||
"""Test get_weight"""
|
||||
|
||||
self.assertEqual(True, True)
|
||||
|
||||
def test_empty_zipped_array(self):
|
||||
"""Test empty_zipped_array"""
|
||||
ans2 = [(None, None)]
|
||||
ans4 = [(None, None, None, None)]
|
||||
self.assertEqual(pu.empty_zipped_array(2), ans2)
|
||||
self.assertEqual(pu.empty_zipped_array(4), ans4)
|
||||
64
release/python/0.4.0/crankshaft/test/test_segmentation.py
Normal file
64
release/python/0.4.0/crankshaft/test/test_segmentation.py
Normal file
@ -0,0 +1,64 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
from helper import plpy, fixture_file
|
||||
import crankshaft.segmentation as segmentation
|
||||
import json
|
||||
|
||||
class SegmentationTest(unittest.TestCase):
|
||||
"""Testing class for Moran's I functions"""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
|
||||
def generate_random_data(self,n_samples,random_state, row_type=False):
|
||||
x1 = random_state.uniform(size=n_samples)
|
||||
x2 = random_state.uniform(size=n_samples)
|
||||
x3 = random_state.randint(0, 4, size=n_samples)
|
||||
|
||||
y = x1+x2*x2+x3
|
||||
cartodb_id = range(len(x1))
|
||||
|
||||
if row_type:
|
||||
return [ {'features': vals} for vals in zip(x1,x2,x3)], y
|
||||
else:
|
||||
return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
|
||||
|
||||
def test_replace_nan_with_mean(self):
|
||||
test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
|
||||
|
||||
def test_create_and_predict_segment(self):
|
||||
n_samples = 1000
|
||||
|
||||
random_state_train = np.random.RandomState(13)
|
||||
random_state_test = np.random.RandomState(134)
|
||||
training_data = self.generate_random_data(n_samples, random_state_train)
|
||||
test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
|
||||
|
||||
|
||||
ids = [{'cartodb_ids': range(len(test_data))}]
|
||||
rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
|
||||
|
||||
plpy._define_result('select \* from \(select \* from training\) a limit 1',rows)
|
||||
plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
|
||||
plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
|
||||
plpy._define_result('.*select \* from test.*' ,test_data)
|
||||
|
||||
model_parameters = {'n_estimators': 1200,
|
||||
'max_depth': 3,
|
||||
'subsample' : 0.5,
|
||||
'learning_rate': 0.01,
|
||||
'min_samples_leaf': 1}
|
||||
|
||||
result = segmentation.create_and_predict_segment(
|
||||
'select * from training',
|
||||
'target',
|
||||
'select * from test',
|
||||
model_parameters)
|
||||
|
||||
prediction = [r[1] for r in result]
|
||||
|
||||
accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
|
||||
|
||||
self.assertEqual(len(result),len(test_data))
|
||||
self.assertTrue( result[0][2] < 0.01)
|
||||
self.assertTrue( accuracy < 0.5*np.mean(test_y) )
|
||||
324
release/python/0.4.0/crankshaft/test/test_space_time_dynamics.py
Normal file
324
release/python/0.4.0/crankshaft/test/test_space_time_dynamics.py
Normal file
@ -0,0 +1,324 @@
|
||||
import unittest
|
||||
import numpy as np
|
||||
|
||||
import unittest
|
||||
|
||||
|
||||
# from mock_plpy import MockPlPy
|
||||
# plpy = MockPlPy()
|
||||
#
|
||||
# import sys
|
||||
# sys.modules['plpy'] = plpy
|
||||
from helper import plpy, fixture_file
|
||||
|
||||
import crankshaft.space_time_dynamics as std
|
||||
from crankshaft import random_seeds
|
||||
import json
|
||||
|
||||
class SpaceTimeTests(unittest.TestCase):
|
||||
"""Testing class for Markov Functions."""
|
||||
|
||||
def setUp(self):
|
||||
plpy._reset()
|
||||
self.params = {"id_col": "cartodb_id",
|
||||
"time_cols": ['dec_2013', 'jan_2014', 'feb_2014'],
|
||||
"subquery": "SELECT * FROM a_list",
|
||||
"geom_col": "the_geom",
|
||||
"num_ngbrs": 321}
|
||||
self.neighbors_data = json.loads(open(fixture_file('neighbors_markov.json')).read())
|
||||
self.markov_data = json.loads(open(fixture_file('markov.json')).read())
|
||||
|
||||
self.time_data = np.array([i * np.ones(10, dtype=float) for i in range(10)]).T
|
||||
|
||||
self.transition_matrix = np.array([
|
||||
[[ 0.96341463, 0.0304878 , 0.00609756, 0. , 0. ],
|
||||
[ 0.06040268, 0.83221477, 0.10738255, 0. , 0. ],
|
||||
[ 0. , 0.14 , 0.74 , 0.12 , 0. ],
|
||||
[ 0. , 0.03571429, 0.32142857, 0.57142857, 0.07142857],
|
||||
[ 0. , 0. , 0. , 0.16666667, 0.83333333]],
|
||||
[[ 0.79831933, 0.16806723, 0.03361345, 0. , 0. ],
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0.00537634, 0.06989247, 0.8655914 , 0.05913978, 0. ],
|
||||
[ 0. , 0. , 0.06372549, 0.90196078, 0.03431373],
|
||||
[ 0. , 0. , 0. , 0.19444444, 0.80555556]],
|
||||
[[ 0.84693878, 0.15306122, 0. , 0. , 0. ],
|
||||
[ 0.08133971, 0.78947368, 0.1291866 , 0. , 0. ],
|
||||
[ 0.00518135, 0.0984456 , 0.79274611, 0.0984456 , 0.00518135],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0. , 0. , 0. , 0.10204082, 0.89795918]],
|
||||
[[ 0.8852459 , 0.09836066, 0. , 0.01639344, 0. ],
|
||||
[ 0.03875969, 0.81395349, 0.13953488, 0. , 0.00775194],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0.02339181, 0.12865497, 0.75438596, 0.09356725],
|
||||
[ 0. , 0. , 0. , 0.09661836, 0.90338164]],
|
||||
[[ 0.33333333, 0.66666667, 0. , 0. , 0. ],
|
||||
[ 0.0483871 , 0.77419355, 0.16129032, 0.01612903, 0. ],
|
||||
[ 0.01149425, 0.16091954, 0.74712644, 0.08045977, 0. ],
|
||||
[ 0. , 0.01036269, 0.06217617, 0.89637306, 0.03108808],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]]]
|
||||
)
|
||||
|
||||
def test_spatial_markov(self):
|
||||
"""Test Spatial Markov."""
|
||||
data = [ { 'id': d['id'],
|
||||
'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'],
|
||||
'neighbors': d['neighbors'] } for d in self.neighbors_data]
|
||||
print(str(data[0]))
|
||||
plpy._define_result('select', data)
|
||||
random_seeds.set_random_seeds(1234)
|
||||
|
||||
result = std.spatial_markov_trend('subquery', ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'], 5, 'knn', 5, 0, 'the_geom', 'cartodb_id')
|
||||
|
||||
self.assertTrue(result != None)
|
||||
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
|
||||
print result[0]
|
||||
expected = self.markov_data
|
||||
for ([res_trend, res_up, res_down, res_vol, res_id],
|
||||
[exp_trend, exp_up, exp_down, exp_vol, exp_id]
|
||||
) in zip(result, expected):
|
||||
self.assertAlmostEqual(res_trend, exp_trend)
|
||||
|
||||
def test_get_time_data(self):
|
||||
"""Test get_time_data"""
|
||||
data = [ { 'attr1': d['y1995'],
|
||||
'attr2': d['y1996'],
|
||||
'attr3': d['y1997'],
|
||||
'attr4': d['y1998'],
|
||||
'attr5': d['y1999'],
|
||||
'attr6': d['y2000'],
|
||||
'attr7': d['y2001'],
|
||||
'attr8': d['y2002'],
|
||||
'attr9': d['y2003'],
|
||||
'attr10': d['y2004'],
|
||||
'attr11': d['y2005'],
|
||||
'attr12': d['y2006'],
|
||||
'attr13': d['y2007'],
|
||||
'attr14': d['y2008'],
|
||||
'attr15': d['y2009'] } for d in self.neighbors_data]
|
||||
|
||||
result = std.get_time_data(data, ['y1995', 'y1996', 'y1997', 'y1998', 'y1999', 'y2000', 'y2001', 'y2002', 'y2003', 'y2004', 'y2005', 'y2006', 'y2007', 'y2008', 'y2009'])
|
||||
|
||||
## expected was prepared from PySAL example:
|
||||
### f = ps.open(ps.examples.get_path("usjoin.csv"))
|
||||
### pci = np.array([f.by_col[str(y)] for y in range(1995, 2010)]).transpose()
|
||||
### rpci = pci / (pci.mean(axis = 0))
|
||||
|
||||
expected = np.array([[ 0.87654416, 0.863147, 0.85637567, 0.84811668, 0.8446154, 0.83271652
|
||||
, 0.83786314, 0.85012593, 0.85509656, 0.86416612, 0.87119375, 0.86302631
|
||||
, 0.86148267, 0.86252252, 0.86746356],
|
||||
[ 0.9188951, 0.91757931, 0.92333258, 0.92517289, 0.92552388, 0.90746978
|
||||
, 0.89830489, 0.89431991, 0.88924794, 0.89815176, 0.91832091, 0.91706054
|
||||
, 0.90139505, 0.87897455, 0.86216858],
|
||||
[ 0.82591007, 0.82548596, 0.81989793, 0.81503235, 0.81731522, 0.78964559
|
||||
, 0.80584442, 0.8084998, 0.82258551, 0.82668196, 0.82373724, 0.81814804
|
||||
, 0.83675961, 0.83574199, 0.84647177],
|
||||
[ 1.09088176, 1.08537689, 1.08456418, 1.08415404, 1.09898841, 1.14506948
|
||||
, 1.12151133, 1.11160697, 1.10888621, 1.11399806, 1.12168029, 1.13164797
|
||||
, 1.12958508, 1.11371818, 1.09936775],
|
||||
[ 1.10731446, 1.11373944, 1.13283638, 1.14472559, 1.15910025, 1.16898201
|
||||
, 1.17212488, 1.14752303, 1.11843284, 1.11024964, 1.11943471, 1.11736468
|
||||
, 1.10863242, 1.09642516, 1.07762337],
|
||||
[ 1.42269757, 1.42118434, 1.44273502, 1.43577571, 1.44400684, 1.44184737
|
||||
, 1.44782832, 1.41978227, 1.39092208, 1.4059372, 1.40788646, 1.44052766
|
||||
, 1.45241216, 1.43306098, 1.4174431 ],
|
||||
[ 1.13073885, 1.13110513, 1.11074708, 1.13364636, 1.13088149, 1.10888138
|
||||
, 1.11856629, 1.13062931, 1.11944984, 1.12446239, 1.11671008, 1.10880034
|
||||
, 1.08401709, 1.06959206, 1.07875225],
|
||||
[ 1.04706124, 1.04516831, 1.04253372, 1.03239987, 1.02072545, 0.99854316
|
||||
, 0.9880258, 0.99669587, 0.99327676, 1.01400905, 1.03176742, 1.040511
|
||||
, 1.01749645, 0.9936394, 0.98279746],
|
||||
[ 0.98996986, 1.00143564, 0.99491, 1.00188408, 1.00455845, 0.99127006
|
||||
, 0.97925917, 0.9683482, 0.95335147, 0.93694787, 0.94308213, 0.92232874
|
||||
, 0.91284091, 0.89689833, 0.88928858],
|
||||
[ 0.87418391, 0.86416601, 0.84425695, 0.8404494, 0.83903044, 0.8578708
|
||||
, 0.86036185, 0.86107306, 0.8500772, 0.86981998, 0.86837929, 0.87204141
|
||||
, 0.86633032, 0.84946077, 0.83287146],
|
||||
[ 1.14196118, 1.14660262, 1.14892712, 1.14909594, 1.14436624, 1.14450183
|
||||
, 1.12349752, 1.12596664, 1.12213996, 1.1119989, 1.10257792, 1.10491258
|
||||
, 1.11059842, 1.10509795, 1.10020097],
|
||||
[ 0.97282463, 0.96700147, 0.96252588, 0.9653878, 0.96057687, 0.95831051
|
||||
, 0.94480909, 0.94804195, 0.95430286, 0.94103989, 0.92122519, 0.91010201
|
||||
, 0.89280392, 0.89298243, 0.89165385],
|
||||
[ 0.94325468, 0.96436902, 0.96455242, 0.95243009, 0.94117647, 0.9480927
|
||||
, 0.93539182, 0.95388718, 0.94597005, 0.96918424, 0.94781281, 0.93466815
|
||||
, 0.94281559, 0.96520315, 0.96715441],
|
||||
[ 0.97478408, 0.98169225, 0.98712809, 0.98474769, 0.98559897, 0.98687073
|
||||
, 0.99237486, 0.98209969, 0.9877653, 0.97399471, 0.96910087, 0.98416665
|
||||
, 0.98423613, 0.99823861, 0.99545704],
|
||||
[ 0.85570269, 0.85575915, 0.85986132, 0.85693406, 0.8538012, 0.86191535
|
||||
, 0.84981451, 0.85472102, 0.84564835, 0.83998883, 0.83478547, 0.82803648
|
||||
, 0.8198736, 0.82265395, 0.8399404 ],
|
||||
[ 0.87022047, 0.85996258, 0.85961813, 0.85689572, 0.83947136, 0.82785597
|
||||
, 0.86008789, 0.86776298, 0.86720209, 0.8676334, 0.89179317, 0.94202108
|
||||
, 0.9422231, 0.93902708, 0.94479184],
|
||||
[ 0.90134907, 0.90407738, 0.90403991, 0.90201769, 0.90399238, 0.90906632
|
||||
, 0.92693339, 0.93695966, 0.94242697, 0.94338265, 0.91981796, 0.91108804
|
||||
, 0.90543476, 0.91737138, 0.94793657],
|
||||
[ 1.1977611, 1.18222564, 1.18439158, 1.18267865, 1.19286723, 1.20172869
|
||||
, 1.21328691, 1.22624778, 1.22397075, 1.23857042, 1.24419893, 1.23929384
|
||||
, 1.23418676, 1.23626739, 1.26754398],
|
||||
[ 1.24919678, 1.25754773, 1.26991161, 1.28020651, 1.30625667, 1.34790023
|
||||
, 1.34399863, 1.32575181, 1.30795492, 1.30544841, 1.30303302, 1.32107766
|
||||
, 1.32936244, 1.33001241, 1.33288462],
|
||||
[ 1.06768004, 1.03799276, 1.03637303, 1.02768449, 1.03296093, 1.05059016
|
||||
, 1.03405057, 1.02747623, 1.03162734, 0.9961416, 0.97356208, 0.94241549
|
||||
, 0.92754547, 0.92549227, 0.92138102],
|
||||
[ 1.09475614, 1.11526796, 1.11654299, 1.13103948, 1.13143264, 1.13889622
|
||||
, 1.12442212, 1.13367018, 1.13982256, 1.14029944, 1.11979401, 1.10905389
|
||||
, 1.10577769, 1.11166825, 1.09985155],
|
||||
[ 0.76530058, 0.76612841, 0.76542451, 0.76722683, 0.76014284, 0.74480073
|
||||
, 0.76098396, 0.76156903, 0.76651952, 0.76533288, 0.78205934, 0.76842416
|
||||
, 0.77487118, 0.77768683, 0.78801192],
|
||||
[ 0.98391336, 0.98075816, 0.98295341, 0.97386015, 0.96913803, 0.97370819
|
||||
, 0.96419154, 0.97209861, 0.97441313, 0.96356162, 0.94745352, 0.93965462
|
||||
, 0.93069645, 0.94020973, 0.94358232],
|
||||
[ 0.83561828, 0.82298088, 0.81738502, 0.81748588, 0.80904801, 0.80071489
|
||||
, 0.83358256, 0.83451613, 0.85175032, 0.85954307, 0.86790024, 0.87170334
|
||||
, 0.87863799, 0.87497981, 0.87888675],
|
||||
[ 0.98845573, 1.02092428, 0.99665283, 0.99141823, 0.99386619, 0.98733195
|
||||
, 0.99644997, 0.99669587, 1.02559097, 1.01116651, 0.99988024, 0.97906749
|
||||
, 0.99323123, 1.00204939, 0.99602148],
|
||||
[ 1.14930913, 1.15241949, 1.14300962, 1.14265542, 1.13984683, 1.08312397
|
||||
, 1.05192626, 1.04230892, 1.05577278, 1.08569751, 1.12443486, 1.08891079
|
||||
, 1.08603695, 1.05997314, 1.02160943],
|
||||
[ 1.11368269, 1.1057147, 1.11893431, 1.13778669, 1.1432272, 1.18257029
|
||||
, 1.16226243, 1.16009196, 1.14467789, 1.14820235, 1.12386598, 1.12680236
|
||||
, 1.12357937, 1.1159258, 1.12570828],
|
||||
[ 1.30379431, 1.30752186, 1.31206366, 1.31532267, 1.30625667, 1.31210239
|
||||
, 1.29989156, 1.29203193, 1.27183516, 1.26830786, 1.2617743, 1.28656675
|
||||
, 1.29734097, 1.29390205, 1.29345446],
|
||||
[ 0.83953719, 0.82701448, 0.82006005, 0.81188876, 0.80294864, 0.78772975
|
||||
, 0.82848011, 0.8259679, 0.82435705, 0.83108634, 0.84373784, 0.83891093
|
||||
, 0.84349247, 0.85637272, 0.86539395],
|
||||
[ 1.23450087, 1.2426022, 1.23537935, 1.23581293, 1.24522626, 1.2256767
|
||||
, 1.21126648, 1.19377804, 1.18355337, 1.19674434, 1.21536573, 1.23653297
|
||||
, 1.27962009, 1.27968392, 1.25907738],
|
||||
[ 0.9769662, 0.97400719, 0.98035944, 0.97581531, 0.95543282, 0.96480308
|
||||
, 0.94686376, 0.93679073, 0.92540049, 0.92988835, 0.93442917, 0.92100464
|
||||
, 0.91475304, 0.90249622, 0.9021363 ],
|
||||
[ 0.84986886, 0.8986851, 0.84295997, 0.87280534, 0.85659368, 0.88937573
|
||||
, 0.894401, 0.90448993, 0.95495898, 0.92698333, 0.94745352, 0.92562488
|
||||
, 0.96635366, 1.02520312, 1.0394296 ],
|
||||
[ 1.01922808, 1.00258203, 1.00974428, 1.00303417, 0.99765073, 1.00759019
|
||||
, 0.99192968, 0.99747298, 0.99550759, 0.97583768, 0.9610168, 0.94779638
|
||||
, 0.93759089, 0.93353431, 0.94121705],
|
||||
[ 0.86367411, 0.85558932, 0.85544346, 0.85103025, 0.84336613, 0.83434854
|
||||
, 0.85813595, 0.84667961, 0.84374558, 0.85951183, 0.87194227, 0.89455097
|
||||
, 0.88283929, 0.90349491, 0.90600675],
|
||||
[ 1.00947534, 1.00411055, 1.00698819, 0.99513687, 0.99291086, 1.00581626
|
||||
, 0.98850522, 0.99291168, 0.98983209, 0.97511924, 0.96134615, 0.96382634
|
||||
, 0.95011401, 0.9434686, 0.94637765],
|
||||
[ 1.05712571, 1.05459419, 1.05753012, 1.04880786, 1.05103857, 1.04800023
|
||||
, 1.03024941, 1.04200483, 1.0402554, 1.03296979, 1.02191682, 1.02476275
|
||||
, 1.02347523, 1.02517684, 1.04359571],
|
||||
[ 1.07084189, 1.06669497, 1.07937623, 1.07387988, 1.0794043, 1.0531801
|
||||
, 1.07452771, 1.09383478, 1.1052447, 1.10322136, 1.09167939, 1.08772756
|
||||
, 1.08859544, 1.09177338, 1.1096083 ],
|
||||
[ 0.86719222, 0.86628896, 0.86675156, 0.86425632, 0.86511809, 0.86287327
|
||||
, 0.85169796, 0.85411285, 0.84886336, 0.84517414, 0.84843858, 0.84488343
|
||||
, 0.83374329, 0.82812044, 0.82878599],
|
||||
[ 0.88389211, 0.92288667, 0.90282398, 0.91229186, 0.92023286, 0.92652175
|
||||
, 0.94278865, 0.93682452, 0.98655146, 0.992237, 0.9798497, 0.93869677
|
||||
, 0.96947771, 1.00362626, 0.98102351],
|
||||
[ 0.97082064, 0.95320233, 0.94534081, 0.94215593, 0.93967, 0.93092109
|
||||
, 0.92662519, 0.93412152, 0.93501274, 0.92879506, 0.92110542, 0.91035556
|
||||
, 0.90430364, 0.89994694, 0.90073864],
|
||||
[ 0.95861858, 0.95774543, 0.98254811, 0.98919472, 0.98684824, 0.98882205
|
||||
, 0.97662234, 0.95601578, 0.94905385, 0.94934888, 0.97152609, 0.97163004
|
||||
, 0.9700702, 0.97158948, 0.95884908],
|
||||
[ 0.83980439, 0.84726737, 0.85747, 0.85467221, 0.8556751, 0.84818516
|
||||
, 0.85265681, 0.84502402, 0.82645665, 0.81743586, 0.83550406, 0.83338919
|
||||
, 0.83511679, 0.82136617, 0.80921874],
|
||||
[ 0.95118156, 0.9466212, 0.94688098, 0.9508583, 0.9512441, 0.95440787
|
||||
, 0.96364363, 0.96804412, 0.97136214, 0.97583768, 0.95571724, 0.96895368
|
||||
, 0.97001634, 0.97082733, 0.98782366],
|
||||
[ 1.08910044, 1.08248968, 1.08492895, 1.08656923, 1.09454249, 1.10558188
|
||||
, 1.1214086, 1.12292577, 1.13021031, 1.13342735, 1.14686068, 1.14502975
|
||||
, 1.14474747, 1.14084037, 1.16142926],
|
||||
[ 1.06336033, 1.07365823, 1.08691496, 1.09764846, 1.11669863, 1.11856702
|
||||
, 1.09764283, 1.08815849, 1.08044313, 1.09278827, 1.07003204, 1.08398066
|
||||
, 1.09831768, 1.09298232, 1.09176125],
|
||||
[ 0.79772065, 0.78829196, 0.78581151, 0.77615922, 0.77035744, 0.77751194
|
||||
, 0.79902974, 0.81437881, 0.80788828, 0.79603865, 0.78966436, 0.79949807
|
||||
, 0.80172182, 0.82168155, 0.85587911],
|
||||
[ 1.0052447, 1.00007696, 1.00475899, 1.00613942, 1.00639561, 1.00162979
|
||||
, 0.99860739, 1.00814981, 1.00574316, 0.99030032, 0.97682565, 0.97292596
|
||||
, 0.96519561, 0.96173403, 0.95890284],
|
||||
[ 0.95808419, 0.9382568, 0.9654441, 0.95561201, 0.96987289, 0.96608031
|
||||
, 0.99727185, 1.00781194, 1.03484236, 1.05333619, 1.0983263, 1.1704974
|
||||
, 1.17025154, 1.18730553, 1.14242645]])
|
||||
|
||||
self.assertTrue(np.allclose(result, expected))
|
||||
self.assertTrue(type(result) == type(expected))
|
||||
self.assertTrue(result.shape == expected.shape)
|
||||
|
||||
def test_rebin_data(self):
|
||||
"""Test rebin_data"""
|
||||
## sample in double the time (even case since 10 % 2 = 0):
|
||||
## (0+1)/2, (2+3)/2, (4+5)/2, (6+7)/2, (8+9)/2
|
||||
## = 0.5, 2.5, 4.5, 6.5, 8.5
|
||||
ans_even = np.array([(i + 0.5) * np.ones(10, dtype=float)
|
||||
for i in range(0, 10, 2)]).T
|
||||
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 2), ans_even))
|
||||
|
||||
## sample in triple the time (uneven since 10 % 3 = 1):
|
||||
## (0+1+2)/3, (3+4+5)/3, (6+7+8)/3, (9)/1
|
||||
## = 1, 4, 7, 9
|
||||
ans_odd = np.array([i * np.ones(10, dtype=float)
|
||||
for i in (1, 4, 7, 9)]).T
|
||||
self.assertTrue(np.array_equal(std.rebin_data(self.time_data, 3), ans_odd))
|
||||
|
||||
def test_get_prob_dist(self):
|
||||
"""Test get_prob_dist"""
|
||||
lag_indices = np.array([1, 2, 3, 4])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
])
|
||||
result = std.get_prob_dist(self.transition_matrix, lag_indices, unit_indices)
|
||||
|
||||
self.assertTrue(np.array_equal(result, answer))
|
||||
|
||||
def test_get_prob_stats(self):
|
||||
"""Test get_prob_stats"""
|
||||
|
||||
probs = np.array([
|
||||
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
|
||||
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
|
||||
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
|
||||
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
|
||||
])
|
||||
unit_indices = np.array([1, 3, 2, 4])
|
||||
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
|
||||
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
|
||||
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
|
||||
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
|
||||
|
||||
result = std.get_prob_stats(probs, unit_indices)
|
||||
result_up = result[0]
|
||||
result_down = result[1]
|
||||
result_trend = result[2]
|
||||
result_volatility = result[3]
|
||||
|
||||
self.assertTrue(np.allclose(result_up, answer_up))
|
||||
self.assertTrue(np.allclose(result_down, answer_down))
|
||||
self.assertTrue(np.allclose(result_trend, answer_trend))
|
||||
self.assertTrue(np.allclose(result_volatility, answer_volatility))
|
||||
@ -1,5 +1,5 @@
|
||||
comment = 'CartoDB Spatial Analysis extension'
|
||||
default_version = '0.3.1'
|
||||
default_version = '0.4.0'
|
||||
requires = 'plpythonu, postgis'
|
||||
superuser = true
|
||||
schema = cdb_crankshaft
|
||||
|
||||
123
src/pg/sql/13_PIA.sql
Normal file
123
src/pg/sql/13_PIA.sql
Normal file
@ -0,0 +1,123 @@
|
||||
-- Based on:
|
||||
-- https://github.com/mapbox/polylabel/blob/master/index.js
|
||||
-- https://sites.google.com/site/polesofinaccessibility/
|
||||
-- Requires: https://github.com/CartoDB/cartodb-postgresql
|
||||
|
||||
-- Based on:
|
||||
-- https://github.com/mapbox/polylabel/blob/master/index.js
|
||||
-- https://sites.google.com/site/polesofinaccessibility/
|
||||
-- Requires: https://github.com/CartoDB/cartodb-postgresql
|
||||
|
||||
CREATE OR REPLACE FUNCTION CDB_PIA(
|
||||
IN polygon geometry,
|
||||
IN tolerance numeric DEFAULT 1.0
|
||||
)
|
||||
RETURNS geometry AS $$
|
||||
DECLARE
|
||||
env geometry[];
|
||||
cells geometry[];
|
||||
cell geometry;
|
||||
best_c geometry;
|
||||
best_d numeric;
|
||||
test_d numeric;
|
||||
test_mx numeric;
|
||||
test_h numeric;
|
||||
test_cells geometry[];
|
||||
width numeric;
|
||||
height numeric;
|
||||
h numeric;
|
||||
i integer;
|
||||
n integer;
|
||||
sqr numeric;
|
||||
p geometry;
|
||||
BEGIN
|
||||
sqr := |/2;
|
||||
polygon := ST_Transform(polygon, 3857);
|
||||
|
||||
-- grid #0 cell size
|
||||
height := ST_YMax(polygon) - ST_YMin(polygon);
|
||||
width := ST_XMax(polygon) - ST_XMin(polygon);
|
||||
h := 0.5*LEAST(height, width);
|
||||
|
||||
-- grid #0
|
||||
with c1 as(
|
||||
SELECT cdb_crankshaft.CDB_RectangleGrid(polygon, h, h) as c
|
||||
)
|
||||
SELECT array_agg(c) INTO cells FROM c1;
|
||||
|
||||
-- 1st guess: centroid
|
||||
best_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(Polygon));
|
||||
|
||||
-- looping the loop
|
||||
n := array_length(cells,1);
|
||||
i := 1;
|
||||
LOOP
|
||||
|
||||
EXIT WHEN i > n;
|
||||
|
||||
cell := cells[i];
|
||||
i := i+1;
|
||||
|
||||
-- cell side size, it's square
|
||||
test_h := ST_XMax(cell) - ST_XMin(cell) ;
|
||||
|
||||
-- check distance
|
||||
test_d := cdb_crankshaft._Signed_Dist(polygon, ST_Centroid(cell));
|
||||
IF test_d > best_d THEN
|
||||
best_d := test_d;
|
||||
best_c := cells[i];
|
||||
END IF;
|
||||
|
||||
-- longest distance within the cell
|
||||
test_mx := test_d + (test_h/2 * sqr);
|
||||
|
||||
-- if the cell has no chance to contains the desired point, continue
|
||||
CONTINUE WHEN test_mx - best_d <= tolerance;
|
||||
|
||||
-- resample the cell
|
||||
with c1 as(
|
||||
SELECT cdb_crankshaft.CDB_RectangleGrid(cell, test_h/2, test_h/2) as c
|
||||
)
|
||||
SELECT array_agg(c) INTO test_cells FROM c1;
|
||||
|
||||
-- concat the new cells to the former array
|
||||
cells := cells || test_cells;
|
||||
|
||||
-- prepare next iteration
|
||||
n := array_length(cells,1);
|
||||
|
||||
END LOOP;
|
||||
|
||||
RETURN ST_transform(ST_Centroid(best_c), 4326);
|
||||
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
|
||||
|
||||
-- signed distance point to polygon with holes
|
||||
-- negative is the point is out the polygon
|
||||
CREATE OR REPLACE FUNCTION _Signed_Dist(
|
||||
IN polygon geometry,
|
||||
IN point geometry
|
||||
)
|
||||
RETURNS numeric AS $$
|
||||
DECLARE
|
||||
i integer;
|
||||
within integer;
|
||||
holes integer;
|
||||
dist numeric;
|
||||
BEGIN
|
||||
dist := 1e999;
|
||||
SELECT LEAST(dist, ST_distance(point, ST_ExteriorRing(polygon))::numeric) INTO dist;
|
||||
SELECT CASE WHEN ST_Within(point,polygon) THEN 1 ELSE -1 END INTO within;
|
||||
SELECT ST_NumInteriorRings(polygon) INTO holes;
|
||||
IF holes > 0 THEN
|
||||
FOR i IN 1..holes
|
||||
LOOP
|
||||
SELECT LEAST(dist, ST_distance(point, ST_InteriorRingN(polygon, i))::numeric) INTO dist;
|
||||
END LOOP;
|
||||
END IF;
|
||||
dist := dist * within::numeric;
|
||||
RETURN dist;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
67
src/pg/sql/14_densify.sql
Normal file
67
src/pg/sql/14_densify.sql
Normal file
@ -0,0 +1,67 @@
|
||||
--
|
||||
-- Iterative densification of a set of points using Delaunay triangulation
|
||||
-- the new points have as assigned value the average value of the 3 vertex (centroid)
|
||||
--
|
||||
-- @param geomin - array of geometries (points)
|
||||
--
|
||||
-- @param colin - array of numeric values in that points
|
||||
--
|
||||
-- @param iterations - integer, number of iterations
|
||||
--
|
||||
--
|
||||
-- Returns: TABLE(geomout geometry, colout numeric)
|
||||
--
|
||||
--
|
||||
CREATE OR REPLACE FUNCTION CDB_Densify(
|
||||
IN geomin geometry[],
|
||||
IN colin numeric[],
|
||||
IN iterations integer
|
||||
)
|
||||
RETURNS TABLE(geomout geometry, colout numeric) AS $$
|
||||
DECLARE
|
||||
geotemp geometry[];
|
||||
coltemp numeric[];
|
||||
i integer;
|
||||
gs geometry[];
|
||||
g geometry;
|
||||
vertex geometry[];
|
||||
va numeric;
|
||||
vb numeric;
|
||||
vc numeric;
|
||||
center geometry;
|
||||
centerval numeric;
|
||||
tmp integer;
|
||||
BEGIN
|
||||
geotemp := geomin;
|
||||
coltemp := colin;
|
||||
FOR i IN 1..iterations
|
||||
LOOP
|
||||
-- generate TIN
|
||||
WITH a as (SELECT unnest(geotemp) AS e),
|
||||
b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a),
|
||||
c as (SELECT (ST_Dump(t)).geom AS v FROM b)
|
||||
SELECT array_agg(v) INTO gs FROM c;
|
||||
-- loop cells
|
||||
FOREACH g IN ARRAY gs
|
||||
LOOP
|
||||
-- append centroid
|
||||
SELECT ST_Centroid(g) INTO center;
|
||||
geotemp := array_append(geotemp, center);
|
||||
-- retrieve the value of each vertex
|
||||
WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v)
|
||||
SELECT array_agg(v) INTO vertex FROM a;
|
||||
WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c)
|
||||
SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]);
|
||||
WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c)
|
||||
SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]);
|
||||
WITH a AS(SELECT unnest(geotemp) as geo, unnest(coltemp) as c)
|
||||
SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]);
|
||||
-- calc the value at the center
|
||||
centerval := (va + vb + vc) / 3;
|
||||
-- append the value
|
||||
coltemp := array_append(coltemp, centerval);
|
||||
END LOOP;
|
||||
END LOOP;
|
||||
RETURN QUERY SELECT unnest(geotemp ) as geomout, unnest(coltemp ) as colout;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
43
src/pg/sql/15_tinmap.sql
Normal file
43
src/pg/sql/15_tinmap.sql
Normal file
@ -0,0 +1,43 @@
|
||||
CREATE OR REPLACE FUNCTION CDB_TINmap(
|
||||
IN geomin geometry[],
|
||||
IN colin numeric[],
|
||||
IN iterations integer
|
||||
)
|
||||
RETURNS TABLE(geomout geometry, colout numeric) AS $$
|
||||
DECLARE
|
||||
p geometry[];
|
||||
vals numeric[];
|
||||
gs geometry[];
|
||||
g geometry;
|
||||
vertex geometry[];
|
||||
centerval numeric;
|
||||
va numeric;
|
||||
vb numeric;
|
||||
vc numeric;
|
||||
coltemp numeric[];
|
||||
BEGIN
|
||||
SELECT array_agg(dens.geomout), array_agg(dens.colout) INTO p, vals FROM cdb_crankshaft.CDB_Densify(geomin, colin, iterations) dens;
|
||||
WITH a as (SELECT unnest(p) AS e),
|
||||
b as (SELECT ST_DelaunayTriangles(ST_Collect(a.e),0.001, 0) AS t FROM a),
|
||||
c as (SELECT (ST_Dump(t)).geom AS v FROM b)
|
||||
SELECT array_agg(v) INTO gs FROM c;
|
||||
FOREACH g IN ARRAY gs
|
||||
LOOP
|
||||
-- retrieve the vertex of each triangle
|
||||
WITH a AS (SELECT (ST_DumpPoints(g)).geom AS v)
|
||||
SELECT array_agg(v) INTO vertex FROM a;
|
||||
-- retrieve the value of each vertex
|
||||
WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c)
|
||||
SELECT c INTO va FROM a WHERE ST_Equals(geo, vertex[1]);
|
||||
WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c)
|
||||
SELECT c INTO vb FROM a WHERE ST_Equals(geo, vertex[2]);
|
||||
WITH a AS(SELECT unnest(p) as geo, unnest(vals) as c)
|
||||
SELECT c INTO vc FROM a WHERE ST_Equals(geo, vertex[3]);
|
||||
-- calc the value at the center
|
||||
centerval := (va + vb + vc) / 3;
|
||||
-- append the value
|
||||
coltemp := array_append(coltemp, centerval);
|
||||
END LOOP;
|
||||
RETURN QUERY SELECT unnest(gs) as geomout, unnest(coltemp ) as colout;
|
||||
END;
|
||||
$$ language plpgsql IMMUTABLE;
|
||||
7
src/pg/test/expected/13_pia_test.out
Normal file
7
src/pg/test/expected/13_pia_test.out
Normal file
@ -0,0 +1,7 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
st_astext
|
||||
-------------------------------------------
|
||||
POINT(-3.67484492582767 40.4395084885993)
|
||||
(1 row)
|
||||
|
||||
50
src/pg/test/expected/14_densify_test.out
Normal file
50
src/pg/test/expected/14_densify_test.out
Normal file
@ -0,0 +1,50 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
cdb_densify
|
||||
-----------------------------------------------------------------
|
||||
(01010000001361C3D32B6501403255302AA9B34440,7.0)
|
||||
(01010000002497FF907EFB0040F085C954C1B04440,8.0)
|
||||
(0101000000A167B3EA73350140E4141DC9E5AF4440,1.0)
|
||||
(010100000062A1D634EF38014014D044D8F0B44440,2.0)
|
||||
(010100000052B81E85EB510140EEEBC03923B24440,3.0)
|
||||
(0101000000C286A757CA3201409D8026C286AF4440,5.0)
|
||||
(01010000007DD0B359F5390140F38E537424AF4440,6.0)
|
||||
(0101000000D237691A140D0140014EEFE2FDB44440,4.0)
|
||||
(01010000003312B4DCAC14014047F8F1AAE1B14440,4.3333333333333333)
|
||||
(010100000048C0FBBD27290140A9BBC5D646B34440,2.3333333333333333)
|
||||
(01010000001DEBE2361A400140F79A0B4953B24440,2.0000000000000000)
|
||||
(010100000098933D2F02500140115B676994B34440,4.0000000000000000)
|
||||
(01010000004BA3DC90595001405C456C9DA5B14440,5.3333333333333333)
|
||||
(0101000000D1FA8198714001404285107D64B04440,3.3333333333333333)
|
||||
(01010000004AEA043411360140D2B687AA85AF4440,4.0000000000000000)
|
||||
(0101000000CCA4736BBF2201402B8716D9CEAF4440,6.3333333333333333)
|
||||
(0101000000832C1EF13E2101402609AF4A0FB04440,4.6666666666666667)
|
||||
(010100000063A009D8BF090140BEEE38F68AB24440,5.4444444444444444)
|
||||
(010100000019AE5D3CF8180140A5008D2162B34440,3.5555555555555555)
|
||||
(01010000007E88BE590E250140EB9DA83067B44440,2.7777777777777778)
|
||||
(0101000000C05105B65D3B014044A2D0B2EEB34440,2.7777777777777778)
|
||||
(010100000005329D125F4F01401C8049790FB44440,4.3333333333333333)
|
||||
(010100000054E45F2DB3570140BBDE724420B34440,4.6666666666666667)
|
||||
(0101000000E53EEA4DD05701407FD7C9557BB24440,5.1111111111111111)
|
||||
(01010000004A9CC694D34F01402B63A5BE7BB14440,6.1111111111111111)
|
||||
(0101000000DD24068195430140317345DA64B04440,4.8888888888888889)
|
||||
(010100000033E768B7D23A014002994E89AFAF4440,4.4444444444444444)
|
||||
(010100000083C0CAA145360140CAEC55A065AF4440,5.0000000000000000)
|
||||
(010100000004549A09D52F01403E3230057EAF4440,5.7777777777777778)
|
||||
(010100000024040D72661D0140B0DEBBE0E6AF4440,6.7777777777777778)
|
||||
(01010000007CCD85A42915014017B22F2835B04440,6.3333333333333333)
|
||||
(0101000000F5F145CA781001401F2DCE18E6B04440,5.6666666666666667)
|
||||
(0101000000F10DE756572701402134E4146CB14440,3.6666666666666667)
|
||||
(01010000008894DB45FA290140F8C4EB987EB24440,2.8888888888888889)
|
||||
(0101000000AABF5E61C13D0140E6E512830FB34440,2.7777777777777778)
|
||||
(0101000000581215F9574B0140FDF5BB4EAEB24440,3.0000000000000000)
|
||||
(0101000000EA6C9F19754B0140C0EE126009B24440,3.4444444444444444)
|
||||
(01010000001383C0CAA145014088CC827674B14440,3.5555555555555555)
|
||||
(0101000000D0B02B40EE350140C90D9905EDB04440,3.3333333333333333)
|
||||
(010100000051DA1B7C613201406F36F4DA1DB04440,3.0000000000000000)
|
||||
(0101000000EA6E133D52390140FD1AE7FAEFAF4440,2.7777777777777778)
|
||||
(01010000003A487527C5340140C76EEE11A6AF4440,3.3333333333333333)
|
||||
(0101000000BADB448F542E01403AB4C876BEAF4440,4.1111111111111111)
|
||||
(0101000000FB12176D7B280140BDE1A0F9EBAF4440,4.0000000000000000)
|
||||
(44 rows)
|
||||
|
||||
83
src/pg/test/expected/15_tinmap_test.out
Normal file
83
src/pg/test/expected/15_tinmap_test.out
Normal file
@ -0,0 +1,83 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
cdb_tinmap
|
||||
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
(01030000000100000004000000D237691A140D0140014EEFE2FDB444402497FF907EFB0040F085C954C1B0444063A009D8BF090140BEEE38F68AB24440D237691A140D0140014EEFE2FDB44440,5.8148148148148148)
|
||||
(01030000000100000004000000D237691A140D0140014EEFE2FDB4444063A009D8BF090140BEEE38F68AB2444019AE5D3CF8180140A5008D2162B34440D237691A140D0140014EEFE2FDB44440,4.3333333333333333)
|
||||
(01030000000100000004000000D237691A140D0140014EEFE2FDB4444019AE5D3CF8180140A5008D2162B344407E88BE590E250140EB9DA83067B44440D237691A140D0140014EEFE2FDB44440,3.4444444444444444)
|
||||
(01030000000100000004000000D237691A140D0140014EEFE2FDB444407E88BE590E250140EB9DA83067B4444062A1D634EF38014014D044D8F0B44440D237691A140D0140014EEFE2FDB44440,2.9259259259259259)
|
||||
(0103000000010000000400000062A1D634EF38014014D044D8F0B444407E88BE590E250140EB9DA83067B44440C05105B65D3B014044A2D0B2EEB3444062A1D634EF38014014D044D8F0B44440,2.5185185185185185)
|
||||
(0103000000010000000400000062A1D634EF38014014D044D8F0B44440C05105B65D3B014044A2D0B2EEB3444005329D125F4F01401C8049790FB4444062A1D634EF38014014D044D8F0B44440,3.0370370370370370)
|
||||
(0103000000010000000400000062A1D634EF38014014D044D8F0B4444005329D125F4F01401C8049790FB444401361C3D32B6501403255302AA9B3444062A1D634EF38014014D044D8F0B44440,4.4444444444444444)
|
||||
(010300000001000000040000001361C3D32B6501403255302AA9B3444005329D125F4F01401C8049790FB4444098933D2F02500140115B676994B344401361C3D32B6501403255302AA9B34440,5.1111111111111111)
|
||||
(010300000001000000040000001361C3D32B6501403255302AA9B3444098933D2F02500140115B676994B3444054E45F2DB3570140BBDE724420B344401361C3D32B6501403255302AA9B34440,5.2222222222222222)
|
||||
(010300000001000000040000001361C3D32B6501403255302AA9B3444054E45F2DB3570140BBDE724420B34440E53EEA4DD05701407FD7C9557BB244401361C3D32B6501403255302AA9B34440,5.5925925925925926)
|
||||
(010300000001000000040000001361C3D32B6501403255302AA9B34440E53EEA4DD05701407FD7C9557BB244404A9CC694D34F01402B63A5BE7BB144401361C3D32B6501403255302AA9B34440,6.0740740740740741)
|
||||
(010300000001000000040000007DD0B359F5390140F38E537424AF44404A9CC694D34F01402B63A5BE7BB14440DD24068195430140317345DA64B044407DD0B359F5390140F38E537424AF4440,5.6666666666666667)
|
||||
(010300000001000000040000007DD0B359F5390140F38E537424AF4440DD24068195430140317345DA64B0444033E768B7D23A014002994E89AFAF44407DD0B359F5390140F38E537424AF4440,5.1111111111111111)
|
||||
(010300000001000000040000007DD0B359F5390140F38E537424AF444033E768B7D23A014002994E89AFAF44404AEA043411360140D2B687AA85AF44407DD0B359F5390140F38E537424AF4440,4.8148148148148148)
|
||||
(010300000001000000040000007DD0B359F5390140F38E537424AF44404AEA043411360140D2B687AA85AF4440C286A757CA3201409D8026C286AF44407DD0B359F5390140F38E537424AF4440,5.0000000000000000)
|
||||
(010300000001000000040000007DD0B359F5390140F38E537424AF4440C286A757CA3201409D8026C286AF444004549A09D52F01403E3230057EAF44407DD0B359F5390140F38E537424AF4440,5.5925925925925926)
|
||||
(010300000001000000040000007DD0B359F5390140F38E537424AF444004549A09D52F01403E3230057EAF4440CCA4736BBF2201402B8716D9CEAF44407DD0B359F5390140F38E537424AF4440,6.0370370370370370)
|
||||
(010300000001000000040000007DD0B359F5390140F38E537424AF4440CCA4736BBF2201402B8716D9CEAF444024040D72661D0140B0DEBBE0E6AF44407DD0B359F5390140F38E537424AF4440,6.3703703703703704)
|
||||
(0103000000010000000400000024040D72661D0140B0DEBBE0E6AF4440CCA4736BBF2201402B8716D9CEAF4440832C1EF13E2101402609AF4A0FB0444024040D72661D0140B0DEBBE0E6AF4440,5.9259259259259259)
|
||||
(0103000000010000000400000024040D72661D0140B0DEBBE0E6AF4440832C1EF13E2101402609AF4A0FB044407CCD85A42915014017B22F2835B0444024040D72661D0140B0DEBBE0E6AF4440,5.9259259259259259)
|
||||
(0103000000010000000400000024040D72661D0140B0DEBBE0E6AF44407CCD85A42915014017B22F2835B044402497FF907EFB0040F085C954C1B0444024040D72661D0140B0DEBBE0E6AF4440,7.0370370370370370)
|
||||
(010300000001000000040000002497FF907EFB0040F085C954C1B044407CCD85A42915014017B22F2835B04440F5F145CA781001401F2DCE18E6B044402497FF907EFB0040F085C954C1B04440,6.6666666666666667)
|
||||
(010300000001000000040000002497FF907EFB0040F085C954C1B04440F5F145CA781001401F2DCE18E6B0444063A009D8BF090140BEEE38F68AB244402497FF907EFB0040F085C954C1B04440,6.3703703703703704)
|
||||
(0103000000010000000400000063A009D8BF090140BEEE38F68AB24440F5F145CA781001401F2DCE18E6B044403312B4DCAC14014047F8F1AAE1B1444063A009D8BF090140BEEE38F68AB24440,5.1481481481481481)
|
||||
(0103000000010000000400000063A009D8BF090140BEEE38F68AB244403312B4DCAC14014047F8F1AAE1B1444019AE5D3CF8180140A5008D2162B3444063A009D8BF090140BEEE38F68AB24440,4.4444444444444444)
|
||||
(0103000000010000000400000019AE5D3CF8180140A5008D2162B344403312B4DCAC14014047F8F1AAE1B144408894DB45FA290140F8C4EB987EB2444019AE5D3CF8180140A5008D2162B34440,3.5925925925925926)
|
||||
(0103000000010000000400000019AE5D3CF8180140A5008D2162B344408894DB45FA290140F8C4EB987EB2444048C0FBBD27290140A9BBC5D646B3444019AE5D3CF8180140A5008D2162B34440,2.9259259259259259)
|
||||
(0103000000010000000400000019AE5D3CF8180140A5008D2162B3444048C0FBBD27290140A9BBC5D646B344407E88BE590E250140EB9DA83067B4444019AE5D3CF8180140A5008D2162B34440,2.8888888888888889)
|
||||
(010300000001000000040000007E88BE590E250140EB9DA83067B4444048C0FBBD27290140A9BBC5D646B34440C05105B65D3B014044A2D0B2EEB344407E88BE590E250140EB9DA83067B44440,2.6296296296296296)
|
||||
(01030000000100000004000000C05105B65D3B014044A2D0B2EEB3444048C0FBBD27290140A9BBC5D646B34440AABF5E61C13D0140E6E512830FB34440C05105B65D3B014044A2D0B2EEB34440,2.6296296296296296)
|
||||
(01030000000100000004000000C05105B65D3B014044A2D0B2EEB34440AABF5E61C13D0140E6E512830FB3444098933D2F02500140115B676994B34440C05105B65D3B014044A2D0B2EEB34440,3.1851851851851852)
|
||||
(01030000000100000004000000C05105B65D3B014044A2D0B2EEB3444098933D2F02500140115B676994B3444005329D125F4F01401C8049790FB44440C05105B65D3B014044A2D0B2EEB34440,3.7037037037037037)
|
||||
(0103000000010000000400000098933D2F02500140115B676994B34440AABF5E61C13D0140E6E512830FB34440581215F9574B0140FDF5BB4EAEB2444098933D2F02500140115B676994B34440,3.2592592592592593)
|
||||
(0103000000010000000400000098933D2F02500140115B676994B34440581215F9574B0140FDF5BB4EAEB2444054E45F2DB3570140BBDE724420B3444098933D2F02500140115B676994B34440,3.8888888888888889)
|
||||
(0103000000010000000400000054E45F2DB3570140BBDE724420B34440581215F9574B0140FDF5BB4EAEB24440E53EEA4DD05701407FD7C9557BB2444054E45F2DB3570140BBDE724420B34440,4.2592592592592593)
|
||||
(01030000000100000004000000E53EEA4DD05701407FD7C9557BB24440581215F9574B0140FDF5BB4EAEB2444052B81E85EB510140EEEBC03923B24440E53EEA4DD05701407FD7C9557BB24440,3.7037037037037037)
|
||||
(01030000000100000004000000E53EEA4DD05701407FD7C9557BB2444052B81E85EB510140EEEBC03923B244404BA3DC90595001405C456C9DA5B14440E53EEA4DD05701407FD7C9557BB24440,4.4814814814814815)
|
||||
(01030000000100000004000000E53EEA4DD05701407FD7C9557BB244404BA3DC90595001405C456C9DA5B144404A9CC694D34F01402B63A5BE7BB14440E53EEA4DD05701407FD7C9557BB24440,5.5185185185185185)
|
||||
(010300000001000000040000004A9CC694D34F01402B63A5BE7BB144404BA3DC90595001405C456C9DA5B144401383C0CAA145014088CC827674B144404A9CC694D34F01402B63A5BE7BB14440,5.0000000000000000)
|
||||
(010300000001000000040000004A9CC694D34F01402B63A5BE7BB144401383C0CAA145014088CC827674B14440DD24068195430140317345DA64B044404A9CC694D34F01402B63A5BE7BB14440,4.8518518518518518)
|
||||
(01030000000100000004000000DD24068195430140317345DA64B044401383C0CAA145014088CC827674B14440D1FA8198714001404285107D64B04440DD24068195430140317345DA64B04440,3.9259259259259259)
|
||||
(01030000000100000004000000DD24068195430140317345DA64B04440D1FA8198714001404285107D64B0444033E768B7D23A014002994E89AFAF4440DD24068195430140317345DA64B04440,4.2222222222222222)
|
||||
(0103000000010000000400000033E768B7D23A014002994E89AFAF4440D1FA8198714001404285107D64B04440EA6E133D52390140FD1AE7FAEFAF444033E768B7D23A014002994E89AFAF4440,3.5185185185185185)
|
||||
(0103000000010000000400000033E768B7D23A014002994E89AFAF4440EA6E133D52390140FD1AE7FAEFAF4440A167B3EA73350140E4141DC9E5AF444033E768B7D23A014002994E89AFAF4440,2.7407407407407407)
|
||||
(0103000000010000000400000033E768B7D23A014002994E89AFAF4440A167B3EA73350140E4141DC9E5AF44403A487527C5340140C76EEE11A6AF444033E768B7D23A014002994E89AFAF4440,2.9259259259259259)
|
||||
(0103000000010000000400000033E768B7D23A014002994E89AFAF44403A487527C5340140C76EEE11A6AF44404AEA043411360140D2B687AA85AF444033E768B7D23A014002994E89AFAF4440,3.9259259259259259)
|
||||
(010300000001000000040000004AEA043411360140D2B687AA85AF44403A487527C5340140C76EEE11A6AF4440C286A757CA3201409D8026C286AF44404AEA043411360140D2B687AA85AF4440,4.1111111111111111)
|
||||
(01030000000100000004000000C286A757CA3201409D8026C286AF44403A487527C5340140C76EEE11A6AF4440BADB448F542E01403AB4C876BEAF4440C286A757CA3201409D8026C286AF4440,4.1481481481481481)
|
||||
(01030000000100000004000000C286A757CA3201409D8026C286AF4440BADB448F542E01403AB4C876BEAF444004549A09D52F01403E3230057EAF4440C286A757CA3201409D8026C286AF4440,4.9629629629629630)
|
||||
(0103000000010000000400000004549A09D52F01403E3230057EAF4440BADB448F542E01403AB4C876BEAF4440FB12176D7B280140BDE1A0F9EBAF444004549A09D52F01403E3230057EAF4440,4.6296296296296296)
|
||||
(0103000000010000000400000004549A09D52F01403E3230057EAF4440FB12176D7B280140BDE1A0F9EBAF4440CCA4736BBF2201402B8716D9CEAF444004549A09D52F01403E3230057EAF4440,5.3703703703703704)
|
||||
(01030000000100000004000000CCA4736BBF2201402B8716D9CEAF4440FB12176D7B280140BDE1A0F9EBAF4440832C1EF13E2101402609AF4A0FB04440CCA4736BBF2201402B8716D9CEAF4440,5.0000000000000000)
|
||||
(01030000000100000004000000832C1EF13E2101402609AF4A0FB04440FB12176D7B280140BDE1A0F9EBAF4440F10DE756572701402134E4146CB14440832C1EF13E2101402609AF4A0FB04440,4.1111111111111111)
|
||||
(01030000000100000004000000832C1EF13E2101402609AF4A0FB04440F10DE756572701402134E4146CB14440F5F145CA781001401F2DCE18E6B04440832C1EF13E2101402609AF4A0FB04440,4.6666666666666667)
|
||||
(01030000000100000004000000832C1EF13E2101402609AF4A0FB04440F5F145CA781001401F2DCE18E6B044407CCD85A42915014017B22F2835B04440832C1EF13E2101402609AF4A0FB04440,5.5555555555555556)
|
||||
(01030000000100000004000000F5F145CA781001401F2DCE18E6B04440F10DE756572701402134E4146CB144403312B4DCAC14014047F8F1AAE1B14440F5F145CA781001401F2DCE18E6B04440,4.5555555555555556)
|
||||
(010300000001000000040000003312B4DCAC14014047F8F1AAE1B14440F10DE756572701402134E4146CB144408894DB45FA290140F8C4EB987EB244403312B4DCAC14014047F8F1AAE1B14440,3.6296296296296296)
|
||||
(010300000001000000040000008894DB45FA290140F8C4EB987EB24440F10DE756572701402134E4146CB144401DEBE2361A400140F79A0B4953B244408894DB45FA290140F8C4EB987EB24440,2.8518518518518519)
|
||||
(010300000001000000040000008894DB45FA290140F8C4EB987EB244401DEBE2361A400140F79A0B4953B24440AABF5E61C13D0140E6E512830FB344408894DB45FA290140F8C4EB987EB24440,2.5555555555555556)
|
||||
(010300000001000000040000008894DB45FA290140F8C4EB987EB24440AABF5E61C13D0140E6E512830FB3444048C0FBBD27290140A9BBC5D646B344408894DB45FA290140F8C4EB987EB24440,2.6666666666666667)
|
||||
(01030000000100000004000000AABF5E61C13D0140E6E512830FB344401DEBE2361A400140F79A0B4953B24440581215F9574B0140FDF5BB4EAEB24440AABF5E61C13D0140E6E512830FB34440,2.5925925925925926)
|
||||
(01030000000100000004000000581215F9574B0140FDF5BB4EAEB244401DEBE2361A400140F79A0B4953B24440EA6C9F19754B0140C0EE126009B24440581215F9574B0140FDF5BB4EAEB24440,2.8148148148148148)
|
||||
(01030000000100000004000000581215F9574B0140FDF5BB4EAEB24440EA6C9F19754B0140C0EE126009B2444052B81E85EB510140EEEBC03923B24440581215F9574B0140FDF5BB4EAEB24440,3.1481481481481481)
|
||||
(0103000000010000000400000052B81E85EB510140EEEBC03923B24440EA6C9F19754B0140C0EE126009B244404BA3DC90595001405C456C9DA5B1444052B81E85EB510140EEEBC03923B24440,3.9259259259259259)
|
||||
(010300000001000000040000004BA3DC90595001405C456C9DA5B14440EA6C9F19754B0140C0EE126009B244401383C0CAA145014088CC827674B144404BA3DC90595001405C456C9DA5B14440,4.1111111111111111)
|
||||
(010300000001000000040000001383C0CAA145014088CC827674B14440EA6C9F19754B0140C0EE126009B244401DEBE2361A400140F79A0B4953B244401383C0CAA145014088CC827674B14440,3.0000000000000000)
|
||||
(010300000001000000040000001383C0CAA145014088CC827674B144401DEBE2361A400140F79A0B4953B24440D0B02B40EE350140C90D9905EDB044401383C0CAA145014088CC827674B14440,2.9629629629629629)
|
||||
(010300000001000000040000001383C0CAA145014088CC827674B14440D0B02B40EE350140C90D9905EDB04440D1FA8198714001404285107D64B044401383C0CAA145014088CC827674B14440,3.4074074074074074)
|
||||
(01030000000100000004000000D1FA8198714001404285107D64B04440D0B02B40EE350140C90D9905EDB0444051DA1B7C613201406F36F4DA1DB04440D1FA8198714001404285107D64B04440,3.2222222222222222)
|
||||
(01030000000100000004000000D1FA8198714001404285107D64B0444051DA1B7C613201406F36F4DA1DB04440EA6E133D52390140FD1AE7FAEFAF4440D1FA8198714001404285107D64B04440,3.0370370370370370)
|
||||
(01030000000100000004000000EA6E133D52390140FD1AE7FAEFAF444051DA1B7C613201406F36F4DA1DB04440A167B3EA73350140E4141DC9E5AF4440EA6E133D52390140FD1AE7FAEFAF4440,2.2592592592592593)
|
||||
(01030000000100000004000000A167B3EA73350140E4141DC9E5AF444051DA1B7C613201406F36F4DA1DB04440BADB448F542E01403AB4C876BEAF4440A167B3EA73350140E4141DC9E5AF4440,2.7037037037037037)
|
||||
(01030000000100000004000000A167B3EA73350140E4141DC9E5AF4440BADB448F542E01403AB4C876BEAF44403A487527C5340140C76EEE11A6AF4440A167B3EA73350140E4141DC9E5AF4440,2.8148148148148148)
|
||||
(01030000000100000004000000BADB448F542E01403AB4C876BEAF444051DA1B7C613201406F36F4DA1DB04440FB12176D7B280140BDE1A0F9EBAF4440BADB448F542E01403AB4C876BEAF4440,3.7037037037037037)
|
||||
(01030000000100000004000000FB12176D7B280140BDE1A0F9EBAF444051DA1B7C613201406F36F4DA1DB04440F10DE756572701402134E4146CB14440FB12176D7B280140BDE1A0F9EBAF4440,3.5555555555555556)
|
||||
(01030000000100000004000000F10DE756572701402134E4146CB1444051DA1B7C613201406F36F4DA1DB04440D0B02B40EE350140C90D9905EDB04440F10DE756572701402134E4146CB14440,3.3333333333333333)
|
||||
(01030000000100000004000000F10DE756572701402134E4146CB14440D0B02B40EE350140C90D9905EDB044401DEBE2361A400140F79A0B4953B24440F10DE756572701402134E4146CB14440,3.0000000000000000)
|
||||
(77 rows)
|
||||
|
||||
7
src/pg/test/sql/13_pia_test.sql
Normal file
7
src/pg/test/sql/13_pia_test.sql
Normal file
@ -0,0 +1,7 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
|
||||
with a as(
|
||||
select st_geomfromtext('POLYGON((-432540.453078056 4949775.20452642,-432329.947920966 4951361.232584,-431245.028163694 4952223.31516671,-429131.071033529 4951768.00415574,-424622.07505895 4952843.13503987,-423688.327170174 4953499.20752423,-424086.294349759 4954968.38274191,-423068.388925945 4954378.63345336,-423387.653225542 4953355.67417084,-420594.869840519 4953781.00230592,-416026.095299382 4951484.06849063,-412483.018546414 4951024.5410983,-410490.399661215 4954502.24032205,-408186.197521284 4956398.91417441,-407627.262358013 4959300.94633864,-406948.770061627 4959874.85407739,-404949.583326472 4959047.74518163,-402570.908447199 4953743.46829807,-400971.358683991 4952193.11680804,-403533.488084088 4949649.89857885,-406335.177028373 4950193.19571096,-407790.456731515 4952391.46015616,-412060.672398345 4950381.2389307,-410716.93482498 4949156.7509561,-408464.162289794 4943912.8940387,-409350.599394983 4942819.84896006,-408087.791091424 4942451.6711778,-407274.045613725 4940572.4807777,-404446.196589102 4939976.71501489,-402422.964843936 4940450.3670813,-401010.654464241 4939054.8061663,-397647.247369412 4940679.80737878,-395658.413346901 4940528.84765185,-395536.852462953 4938829.79565997,-394268.923462818 4938003.7277717,-393388.720249116 4934757.80596815,-392393.301362444 4934326.71675815,-392573.527618037 4932323.40974412,-393464.640141837 4931903.10653605,-393085.597275686 4931094.7353605,-398426.261165985 4929156.87541607,-398261.174361137 4926238.00816416,-394045.059966834 4925765.18668498,-392982.960705174 4926391.81893628,-393090.272694301 4927176.84692181,-391648.240010564 4924626.06386961,-391889.914625075 4923086.14787613,-394345.177314013 4923235.086036,-395550.878718795 4917812.79243978,-399009.463978251 4912927.7157945,-398948.794855767 4911941.91010796,-398092.636652078 4911806.57392519,-401991.601817112 4911722.9204501,-406225.972607907 4914505.47286319,-411104.994569885 4912569.26941163,-412925.513522316 4913030.3608866,-414630.148884835 4914436.69169949,-414207.691417276 4919205.78028405,-418306.141109809 4917994.9580478,-424184.700779621 4918938.12432889,-426816.961458921 4923664.37379373,-420956.324227126 4923381.98014807,-420186.661267781 4924286.48693378,-420943.411166194 4926812.76394433,-419779.45457046 4928527.43466337,-419768.767899344 4930681.94459216,-421911.668097113 4930432.40620397,-423482.386112205 4933451.28047252,-427272.814773717 4934151.56473242,-427144.908678797 4939731.77191996,-428982.125554848 4940522.84445172,-428986.133056516 4942437.17281266,-431237.792396792 4947309.68284815,-432476.889648814 4947791.74800037,-432540.453078056 4949775.20452642))', 3857) as g
|
||||
)
|
||||
SELECT st_astext(cdb_crankshaft.CDB_PIA(g)) from a;
|
||||
9
src/pg/test/sql/14_densify_test.sql
Normal file
9
src/pg/test/sql/14_densify_test.sql
Normal file
@ -0,0 +1,9 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
|
||||
with data as (
|
||||
select
|
||||
ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
|
||||
ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
|
||||
)
|
||||
select cdb_crankshaft.CDB_Densify(geomin, colin, 2) from data;
|
||||
9
src/pg/test/sql/15_tinmap_test.sql
Normal file
9
src/pg/test/sql/15_tinmap_test.sql
Normal file
@ -0,0 +1,9 @@
|
||||
SET client_min_messages TO WARNING;
|
||||
\set ECHO none
|
||||
|
||||
with data as (
|
||||
select
|
||||
ARRAY[7.0,8.0,1.0,2.0,3.0,5.0,6.0,4.0] as colin,
|
||||
ARRAY[ST_GeomFromText('POINT(2.1744 41.4036)'),ST_GeomFromText('POINT(2.1228 41.3809)'),ST_GeomFromText('POINT(2.1511 41.3742)'),ST_GeomFromText('POINT(2.1528 41.4136)'),ST_GeomFromText('POINT(2.165 41.3917)'),ST_GeomFromText('POINT(2.1498 41.3713)'),ST_GeomFromText('POINT(2.1533 41.3683)'),ST_GeomFromText('POINT(2.131386 41.413998)')] as geomin
|
||||
)
|
||||
select cdb_crankshaft.CDB_TINmap(geomin, colin, 2) from data;
|
||||
Loading…
Reference in New Issue
Block a user