├── README.md
├── src
    ├── Algorithm9.py
    ├── GridData9.py
    ├── Network9.py
    ├── georgia.py
    ├── grid_K.py
    ├── grid_gen.py
    ├── grid_reg.py
    ├── grid_stab.py
    └── kinghouse.py
└── synthetic
    ├── grid_0h.txt
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    └── gridtest_8n.txt


/README.md:
--------------------------------------------------------------------------------
  1 | # Overview
  2 | 
  3 | This software aims to delineate spatial regimes (geographically connected regions with varying coefficients across regions) in the context of linear regression models. Please refer to our paper for more details:    
  4 | Hao Guo, Andre Python & Yu Liu (2023) Extending regionalization algorithms to explore spatial process heterogeneity, International Journal of Geographical Information Science, 37:11, 2319-2344, DOI: 10.1080/13658816.2023.2266493
  5 | 
  6 | # Errata and Notes
  7 | - p.2321 Although the objective in Equation 1 does make sense, we mean to write $$\mathcal{L}(\mathcal{R})=\sum_{j=1}^p \sum_{1\le i_1<i_2\le n} I\[u_{i_1}, u_{i_2} \in R_j\]  \Vert\mathbf{x}\_{i_1}-\mathbf{x}\_{i_2}\Vert$$, used by the p-regions problem, for example.  
  8 | 
  9 | - p.2326 The time complexity for two-stage K-Models algorithm (first stage) should be $$O(Km(m^2+n)+m^2n)$$ for each iteration.  
 10 | 
 11 | - p.2328 In Algorithm 3, line 8 should be "Find $$d_i\in \{1,...,p\}$$ such that $$u_i\in R_{d_i}$$"; line 10 should be $$r_i =\mathop{\arg\min}\limits_{1\le j \le p}\vert y_i-f(\mathbf{x}_i, \boldsymbol{\theta}_j)\vert$$.
 12 | 
 13 | - p.2329 Just like in AZP, the Sherman-Morrison formula (see note 8) can also be applied to the Regional-K-Models algorithm, yielding $$O(pmn+n|E|+m(m+n))$$ complexity for each iteration.
 14 | 
 15 | - p.2331 Equation 12 should be $$\text{NMI}(\mathcal{R},\mathcal{R}') = \frac{2I(\mathcal{R},\mathcal{R}')}{H(\mathcal{R})+H(\mathcal{R'})}$$. The reported NMI values are correct. 
 16 | 
 17 | - p.2336 "For multiple sales at the same location, we only retain the latest record." This involves ambiguity as several pairs of sales have the same location and date. One possible solution is to retain the sale with higher price. We have reproduced the King County experiment in this setting, verifying that this issue does not affect the main finding (lower SSR for K-Models than Skater-reg).   
 18 | 
 19 | # Environment & Dependencies
 20 | 
 21 | The software is built on Python 3.9 (or higher versions) as well as several Python packages.
 22 | 
 23 | - Necessary
 24 |   - numpy, matplotlib, networkx
 25 |   - scikit-learn
 26 |   - geopandas
 27 |   - libpysal, mgwr, spopt, spreg
 28 |   - xlrd, xlwt
 29 | 
 30 | - Optional
 31 |   - statsmodels (significance testing of linear equations)  
 32 | 
 33 | # File Structure
 34 | 
 35 | ## Core Modules
 36 | 
 37 | - ``src/Algorithm9.py`` : Implementation of the regime delineation algorithms, including the two-stage K-Models (KM),  AZP, Regional-K-Models (RKM). We also incorporate the interfaces of GWR-Skater (GSK, from mgwr/spopt) and Skater regression (SKR, from spreg) for comparison.  
 38 | - ``src/Network9.py`` : tool functions dealing with the adjacency network, region contiguity, etc.
 39 | - ``src/GridData9.py`` : generating and handling simulated grid data.
 40 | 
 41 | ## Simulated Data
 42 | 
 43 | - ``synthetic/grid_*.txt`` : three synthetic datasets on 25\*25 grid, each with 50 simulations (```dataid```  0-49: *Rectangular*; 50-99: *Voronoi*; 100-149: *Arbitrary*). Each simulation contains three samples under different noise levels ('h' for high, 'm' for medium, 'l' for low).
 44 | 
 45 | We also include several simulations on 10\*10 grid for debugging codes (``synthetic/gridtest_*.txt``).  
 46 | 
 47 | ## Scripts for Regime Delineation Experiments
 48 | 
 49 | ### Synthetic Data
 50 | 
 51 | - ``src/grid_gen.py`` : data generation
 52 | - ``src/grid_reg.py`` : spatial regime delineation with five considered algorithms
 53 | - ``src/grid_K.py`` : experiment on the choice of parameter K (number of micro-clusters) in two-stage K-Models  
 54 | - ``src/grid_stab.py`` : experiment on the algorithm stability of two-stage K-Models, AZP and Regional-K-Models
 55 | 
 56 | ### Real Data
 57 | 
 58 | - ``src/georgia.py`` : spatial regime delineation on the Georgia Dataset, using five considered algorithms
 59 | - ``src/kinghouse.py``: spatial regime delineation on the King County house price Dataset, using two-stage K-Models and Skater-reg
 60 | 
 61 | # Function Interfaces
 62 | 
 63 | ## Algorithm9
 64 | 
 65 | ```Python
 66 | kmodels(Xarr, Yarr, K, w, max_iter=10000, min_size=None, init_stoc_step=True, verbose=False)
 67 | ```
 68 | 
 69 | The first stage (partition stage) of the two-stage K-Models algorithm for spatial regime delineation.
 70 | 
 71 | - **Parameters:**
 72 |   - **Xarr** (``numpy.ndarray``): 2D array of explanatory variables with size $n\times m$. $n$ is the number of spatial units, $m$ is the number of features (If the model includes an intercept, there should be a feature where all units has value 1).
 73 |   - **Yarr** (``numpy.ndarray``): 1D array of the response variable with size $n$.  
 74 |   - **K** (``int``): number of micro-clusters in the partition stage.
 75 |   - **w** (``pysal.weights.W``): spatial contiguity matrix of the $n$ areal units. The order of units must be consistent with ```Xarr``` and ```Yarr```.
 76 |   - **max_iter** (``int``): maximum number of iterations.  
 77 |   - **min_size** (``int``): minimum number of spatial units in each micro-cluster. If ``None``, the default value of $m$ is used.
 78 |   - **init_stoc_step** (``bool``): the way of region growing in initialization. If ``False``, all neighboring units of current region are merged into it. Otherwise, a randomly chosen neighboring unit is assigned into it in each step. This increases the diversity of initial solutions, yet may be time-consuming for large datasets.
 79 |   - **verbose** (``bool``): If ``True``, there will be additional output in the algorithm process.  
 80 | - **Returns:**  
 81 |   - **label** (``list``): the region label for each unit, with length $n$. The $u$ th unit is assigned to the region with index ``label[u]``. The region indices are integers from 0 to $K-1$.
 82 |   - **iters** (``int``): the number of iterations.
 83 | 
 84 | The second stage (merge stage) of the two-stage K-Models algorithm is implemented in two functions. ``split_components`` breaks disconnected regions into branches, and ``greedy_merge`` merges the branches to achieve the required number of regions. They should be called sequentially.
 85 | 
 86 | ```Python
 87 | split_components(w, clabel)
 88 | ```
 89 | 
 90 | - **Parameters**:
 91 |   - **w**: see ```kmodels```.
 92 |   - **clabel** (```list```): ```label``` from ```kmodels```.
 93 | - **Returns**:
 94 |   - **rlabel** (```list```): the region label after split components, with length $n$.
 95 | 
 96 | ```Python
 97 | greedy_merge(Xarr, Yarr, n_regions, w, label, min_size=None, verbose=False)
 98 | ```
 99 | 
100 | - **Parameters**:
101 |   - **Xarr**, **Yarr**, **w**, **verbose**: see ```kmodels```.
102 |   - **n_regions** (```int```): the number of output regions.
103 |   - **label** (```list```): ```rlabel``` from ```split_components```.
104 |   - **min_size** (```int```): minimum number of units in each output region. If ``None``, the default value of $m$ is used. It may be different from ``min_size`` in ```kmodels```.
105 | 
106 | - **Returns**:
107 |   - **rlabel** (``list``): the final region label for each unit, with length $n$. The region indices are integers from 0 to ```n_regions-1```.
108 |   - **coeffs** (``list``): estimated coefficients for each region. ``coeff[r]`` is a list of length $m$, containing $m$ coefficients corresponding to the features in ```Xarr```.  
109 |   - **merges** (``int``): the number of merge operations.
110 | 
111 | ```Python
112 | azp(Xarr, Yarr, n_regions, w, max_iter=10000, min_size=None, init_stoc_step=True)
113 | ```
114 | 
115 | AZP algorithm for spatial regime delineation.
116 | 
117 | - **Parameters**:  
118 |   - **Xarr**, **Yarr**, **w**, **max_iter**, **init_stoc_step**: see ```kmodels```.
119 |   - **n_regions**, **min_size**: see ```greedy_merge```.
120 | - **Returns**:  
121 |   - **label**: same as ```rlabel``` in ```greedy_merge```.  
122 |   - **coeffs**: see ```greedy_merge```.  
123 |   - **iters** (``int``): the number of iterations.
124 | 
125 | ```Python
126 | region_k_models(Xarr, Yarr, n_regions, w, max_iter=10000, min_size=None, init_stoc_step=True):
127 | ```
128 | 
129 | Regional-K-Models algorithm for spatial regime delineation.
130 | 
131 | - **Parameters**:  
132 |   The same with ```azp```.
133 | - **Returns**:  
134 |   The same with ```azp```.
135 | 
136 | ```Python
137 | gwr_skater(Xarr, Yarr, n_regions, w, coord, min_size=None)
138 | ```
139 | 
140 | GWR-Skater for spatial regime delineation.
141 | 
142 | - **Parameters**:  
143 |   - **Xarr**, **Yarr**, **n_regions**, **w**, **min_size**: see ```azp```.
144 |   - **coord** (```list```): coordinates of the spatial units. Each of the $n$ elements is a coordinate pair (tuple of length 2) indicating the position of a unit.
145 | - **Returns**:
146 |   - **label**, **coeffs**: see ```azp```.
147 | 
148 | ```Python
149 | skater_reg(Xarr, Yarr, n_regions, w, min_size=None, verbose=0)
150 | ```
151 | 
152 | Skater regression for spatial regime delineation. 
153 | 
154 | - **Parameters**:  
155 |   - **Xarr**, **Yarr**, **n_regions**, **w**, **min_size**: see ```azp```.
156 |   - **verbose** (```int```): see document of [spreg.skater_reg](https://pysal.org/spreg/notebooks/skater_reg.html).
157 | - **Returns**:
158 |   - **label**, **coeffs**: see ```azp```.
159 | 
160 | # Reproduction & Replication Guide
161 | 
162 | ## Experiments on Synthetic Data
163 | 
164 | ### 1. Data Generation
165 | 
166 | Simulated data used in our experiments are provided in ```synthetic/grid_*.txt```. The file structure for each sample is: the array of $x_1$, $x_2$, $y$, region index, $\beta_1$, and $\beta_2$. Each array contains 25 lines, and each line has 25 numbers, corresponding to 25*25 grid cells. These files can be read by ```grid_reg.py``` automatically for spatial regime delineation.
167 | 
168 | If you use provided data, go straightly to step 2. Otherwise, use the script ```grid_gen.py``` to generate new data samples. Open ```grid_gen.py``` and change the following parameters if needed:
169 | 
170 | - **Side** : number of cells in each row and column of the grid.
171 | - **repeat** : number of simulations in each dataset (*Rectangular*, *Voronoi*, *Arbitrary*).  
172 | - **min_region** : minimum number of units in each region.  
173 | 
174 | Then run the script. Files named ```grid_*.txt``` will be produced. Note that three samples with different noise levels are generated in each simulation. Move these files to the ```synthetic``` directory for future use. The script will also show the latent regions of one simulation for each dataset, which produced **Figure 2**.  
175 | 
176 | ### 2. Spatial Regime Delineation
177 | 
178 | Use the script ```grid_reg.py``` to perform spatial regime delineation with the five considered algorithms. Open ```grid_reg.py``` and change the following parameters if needed:
179 | 
180 | - **Side** : number of cells in each side of the grid. Should be consistent with input data.
181 | - **ids, idt** : index range of input data. The code will process data with id from ```ids``` to ```idt```-1.  
182 | - **prefix** : 'edis_' or 'edistest_'. Should be consistent with input data.  
183 | - **micro_clusters** : the parameter K in the two-stage K-Models algorithm.  
184 | - **min_region** : minimum number of units in each region.  
185 | - **recordnum** : the number for this experiment, used to label output files.  
186 | 
187 | The script will generate a log file named ```RG(recordnum).txt```.  It records five lines for each sample, which is the summary for KM, AZP, RKM, GSK, SKR, respectively. Each line includes: total sum of squared residuals (SSR); number of true positives (TP), false negatives (FN), false positives (FP), true negatives (TN), Rand index; entropy of true regions, entropy of reconstructed regions, mutual information (MI), normalized mutual information (NMI); mean absolute error (MAE) of $\beta_0$, $\beta_1$, $\beta_2$; execution time; number of iterations and merge operations (if applicable). Please refer to our paper for detailed explanation of these metrics. **Table 1**, **Table 2**, and **Table A1** are summarized from these results.
188 | 
189 | For each ```dataid```, the program outputs:
190 | 
191 | - A TXT file named ```result_(recordnum)_(dataid).txt```. The result of low noise data comes first, then medium and high noise, each contains results from five algorithms. For each algorithm, the file first records map of the delineated regions (a ```Side*Side``` matrix containing region index of each unit), then the estimated coefficients (the $r$-th line is the intercept and linear coefficients for the $r$-th region). **Figure 4** is a visualization of the delineated regions recorded in these files.
192 | 
193 | - A PNG file named with ```recordnum```, ```dataid``` and time, visualizing the region delineation at each noise level.  
194 | 
195 | ### 3. Algorithm Stability
196 | 
197 | To reproduce **Figure A1a**, run the two-stage K-Models, AZP, and Regional-K-Models algorithms repeatedly on the same sample of data, using the script ```grid_stab.py```. The usage is the same with ```grid_reg.py``` except that ```dataid``` and ```repeat``` (number of repeated runs) need to be specified rather than ```ids``` and ```idt```.
198 | 
199 | ### 4. The Effect of $K$
200 | 
201 | To reproduce **Figure A1b**, run the two-stage K-Models algorithm with different $K$ values, using the script ```grid_K.py```. The usage is the same with ```grid_reg.py``` except that  ```idlist``` (a list of ```dataid```) needs to be specified rather than ```ids``` and ```idt```. You may also change ```mclist```, which specify the range of $K$ values.
202 | 
203 | ## Real Data: Georgia Dataset
204 | 
205 | The Georgia dataset is available as sample data of the [MGWR software](https://sgsup.asu.edu/sparc/multiscale-gwr). We aggregated polygons belong to the same county into multi-polygons, making it easy to generate PySAL spatial weights.
206 | 
207 | The script ```src/georgia.py``` run the five considered algorithms on the Georgia dataset. You may change the following parameters:
208 | 
209 | - **pmin, pmax** : the range of number of regions. The code runs the algorithms for each $p$ in [```pmin```,```pmax```].  
210 | - **Kfac** : the $K$ parameter in the two-stage K-Models is calculated as ```Kfac```$\times p$.  
211 | - **runs** : number of repetitions. Increasing this parameter may find better solution, with the cost of more computation time.  
212 | - **numid** : number of the experiment, used to label output files.  
213 | - **min_region** : minimum number of units for each region. Should be at least 5 in this analysis for valid significance test.  
214 | 
215 | After running the script, results are recorded in two output files:
216 | 
217 | - ```Georgia_(numid).txt``` : the log file. It records the results at each number of regions $p$ (from ```pmin``` to ```pmax```). For each $p$, in the order of KM, AZP, RKM, GSK, and SKR, it first lists total SSR, execution time, number of iterations and merge operations (if applicable) for each run. Then for the best solution (with the lowest SSR), it records the coefficients and F-test results. Each line contains: region index, number of units, estimated coefficients, the F-value and p-value of the overall F-test. **Figure 5** visualizes the total SSR from the five considered algorithm at different $p$ values.  
218 | - ```Georgia_(numid).xls``` : an Excel workbook. For each county, it records the region it belongs to, as well as the estimated coefficients of that region. For each of the five algorithms, only the best solution is recorded.
219 | 
220 | To visualize the results, join the output table ```Georgia_(numid).xls``` with the polygon Shapefile via the ```Area_key/AreaKey``` field with any standard GIS software (e.g. QGIS). After that, **Figure 6** and **Figure 7** can be rendered in a GIS environment using the joined Shapefile.  
221 | 
222 | ## Real Data: King County Dataset  
223 | 
224 | The King County house price dataset is available from [GeoDa center](https://geodacenter.github.io/data-and-lab//KingCounty-HouseSales2015/). The data processing procedure is described in our paper.   
225 | 
226 | The script ```src/kinghouse.py``` run the two-stage K-Models and Skater regression  algorithms on the King County dataset. You may change the parameters including **pmin, pmax**, **Kfac**, **runs**, **numid**, and **min_region**. The meanings of these parameters are the same with those in ```src/georgia.py```. Note that ```min_region``` should be at least 18 in this analysis for valid significance test.  
227 | 
228 | After running the script, results are recorded in two output files:
229 | 
230 | - ```Kinghouse_(numid).txt``` : the log file. The file structure is the same with the log file for the Georgia dataset.  
231 | - ```Kinghouse_(numid).xls``` : an Excel workbook. For each house location, it records the region it belongs to.  
232 | 
233 | To visualize the results, join the output table ```Kinghouse_(numid).xls``` with the point Shapefile via the ```id``` field with any standard GIS software (e.g. QGIS). After that, **Figure 8** can be rendered in a GIS environment using the joined Shapefile.  
234 | 


--------------------------------------------------------------------------------
/src/Algorithm9.py:
--------------------------------------------------------------------------------
  1 | from Network9 import *
  2 | from sklearn import preprocessing, metrics
  3 | import numpy as np
  4 | import geopandas
  5 | from mgwr import gwr
  6 | from mgwr.sel_bw import Sel_BW
  7 | from spopt.region import Skater
  8 | import spreg
  9 | from spreg.skater_reg import Skater_reg
 10 | 
 11 | 
 12 | def kmodels(Xarr, Yarr, K, w,  max_iter=10000, min_size=None, init_stoc_step=True, verbose=False):
 13 |     # w: pysal.weights.W object
 14 |     if min_size is None:
 15 |         min_size = Xarr.shape[1]
 16 |     units = np.arange(w.n).astype(int)
 17 |     label = init_zones(w, K, min_size, stoc_step=init_stoc_step)
 18 | 
 19 |     # iteration
 20 |     iters = 0
 21 |     regions = [units[label == r].tolist() for r in range(K)]
 22 |     coeffs = fit_equations(Xarr, Yarr, regions)
 23 |     closest = np.array(closest_equation(Xarr, Yarr, coeffs))
 24 |     moves = units[closest != label]
 25 |     while len(moves) > 0 and iters < max_iter:
 26 |         # make move and update assignments, coeffs, closest, candidates
 27 |         for u in moves:
 28 |             donor_region = units[label == label[u]].tolist()
 29 |             if len(donor_region) <= min_size:
 30 |                 continue
 31 |             label[u] = closest[u]
 32 |         regions = [units[label == r].tolist() for r in range(K)]
 33 |         coeffs = fit_equations(Xarr, Yarr, regions)
 34 |         closest = np.array(closest_equation(Xarr, Yarr, coeffs))
 35 |         candidate_moves = units[closest != label]
 36 |         moves = []
 37 |         for u in candidate_moves:
 38 |             donor_region = units[label == label[u]].tolist()
 39 |             if len(donor_region) > min_size:
 40 |                 moves.append(u)
 41 |         iters += 1
 42 |         if verbose and iters%10 == 0:
 43 |             print(iters, len(moves), regression_error(regions,Xarr,Yarr), moves)
 44 |     return label, iters
 45 | 
 46 | 
 47 | def split_components(w, clabel):
 48 |     g = weights_to_graph(w)
 49 |     units = np.arange(w.n).astype(int)
 50 |     clusters = [units[clabel == r].tolist() for r in set(clabel)]
 51 |     rid = 0
 52 |     rlabel = np.array([-1] * w.n).astype(int)
 53 | 
 54 |     for c in clusters:
 55 |         clus = g.subgraph(c)
 56 |         regs = networkx.connected_components(clus)
 57 |         for r in regs:
 58 |             for u in r:
 59 |                 rlabel[u] = rid
 60 |             rid += 1
 61 |     return rlabel
 62 | 
 63 | 
 64 | def greedy_merge(Xarr, Yarr, n_regions, w, label, min_size=None, verbose=False):
 65 |     if min_size is None:
 66 |         min_size = Xarr.shape[1]
 67 |     units = np.arange(w.n).astype(int)
 68 |     reg_label = list(set(label))
 69 |     reg_label.sort()
 70 |     regions = [units[label == r].tolist() for r in reg_label]
 71 |     regtree = dict()
 72 | 
 73 |     for rid in range(len(regions)):
 74 |         curnode = Node(reg_label[rid],regions[rid])
 75 |         curnode.err = region_error(Xarr,Yarr,curnode.units)
 76 |         regtree[reg_label[rid]] = curnode
 77 |     for cid in regtree.keys():
 78 |         regtree[cid].links = set([label[u] for u in region_neighbors(regtree[cid].units, w)])
 79 | 
 80 |     merges = 0
 81 |     # merge segment regions first, to enforce minimum size constraint
 82 |     fragments = [p for p in regtree.keys() if len(regtree[p].units) < min_size]
 83 |     while len(fragments) > 0:
 84 |         p = np.random.choice(fragments)
 85 |         min_derr = inf  # min increase of error
 86 |         minq = -1
 87 |         for q in regtree[p].links:
 88 |             derr = delta_err(regtree, p, q, Xarr, Yarr)
 89 |             if derr < min_derr:
 90 |                 min_derr = derr
 91 |                 minq = q
 92 |         merge_node(regtree,p,minq,Xarr,Yarr)
 93 |         merges += 1
 94 |         fragments = [p for p in regtree.keys() if len(regtree[p].units) < min_size]
 95 |         if verbose and len(regtree.keys()) % 10 == 0:
 96 |             print(len(regtree.keys()), minq, min_derr)
 97 |     if len(regtree.keys()) < n_regions:
 98 |         raise RuntimeError("Failed to achieve required number of regions.")
 99 | 
100 |     while len(regtree.keys()) > n_regions:
101 |         min_derr = inf
102 |         minp,minq = -1,-1
103 |         for p in regtree.keys():
104 |             for q in regtree[p].links:
105 |                 if q <= p:
106 |                     continue
107 |                 derr = delta_err(regtree, p, q, Xarr, Yarr)
108 |                 if derr < min_derr:
109 |                     min_derr = derr
110 |                     minp = p
111 |                     minq = q
112 |         merge_node(regtree,minp,minq,Xarr,Yarr)
113 |         merges += 1
114 |         if verbose and len(regtree.keys()) % 10 == 0:
115 |             print(len(regtree.keys()), minp, minq, min_derr)
116 | 
117 |     regions = [regtree[k].units for k in regtree.keys()]
118 |     rlabel = region_to_label(w.n,regions)
119 |     coeffs = fit_equations(Xarr, Yarr, regions)
120 |     return rlabel, coeffs, merges
121 | 
122 | 
123 | def azp(Xarr, Yarr, n_regions, w, max_iter=10000, min_size=None, init_stoc_step=True):
124 |     # w: pysal.weights.W object
125 |     # min_size: lower bound for region size, default: #params
126 |     if min_size is None:
127 |         min_size = Xarr.shape[1]
128 |     k = n_regions
129 |     units = np.arange(w.n).astype(int)
130 |     label = init_zones(w, k, min_size, init_stoc_step)
131 |     regions = [units[label == r].tolist() for r in range(k)]
132 |     xtxinvs = init_xtxinv(Xarr, regions)
133 | 
134 |     # iteration
135 |     g = weights_to_graph(w)
136 |     iters = 0
137 | 
138 |     while True:
139 |         stable = True
140 |         region_list = list(range(k))
141 |         while len(region_list) > 0 and iters < max_iter:
142 |             r = np.random.choice(region_list)
143 |             region_list.remove(r)
144 |             moves = region_neighbors(regions[r], w)
145 |             valid_moves = batch_check_AZP(moves, units, label, r, g, w, Xarr, Yarr, xtxinvs, min_size)
146 |             if len(valid_moves) == 0:
147 |                 continue
148 |             stable = False
149 |             u = np.random.choice(valid_moves)
150 |             xtxinvs[label[u]] = sherman_morrison(xtxinvs[label[u]], Xarr[u].reshape(-1), add=False)
151 |             xtxinvs[r] = sherman_morrison(xtxinvs[r], Xarr[u].reshape(-1), add=True)
152 |             label[u] = r
153 |             regions = [units[label == r].tolist() for r in range(k)]
154 |             # print(iters,evaluation_func(regions,Xarr,Yarr,0))
155 |         iters += 1
156 |         if stable or iters >= max_iter:
157 |             break
158 |     coeffs = fit_equations(Xarr, Yarr, regions)
159 |     return label, coeffs, iters
160 | 
161 | 
162 | def region_k_models(Xarr, Yarr, n_regions, w, max_iter=10000, min_size=None, init_stoc_step=True):
163 |     # w: pysal.weights.W object
164 |     if min_size is None:
165 |         min_size = Xarr.shape[1]
166 |     k = n_regions
167 |     units = np.arange(w.n).astype(int)
168 |     label = init_zones(w, k, min_size, init_stoc_step)
169 | 
170 |     # iteration
171 |     g = weights_to_graph(w)
172 |     iters = 0
173 |     regions = [units[label == r].tolist() for r in range(k)]
174 |     coeffs = fit_equations(Xarr, Yarr, regions)
175 |     closest = np.array(closest_equation(Xarr, Yarr, coeffs))
176 |     moves = units[closest != label]
177 |     valid_moves = batch_check_RKM(moves, units, label, closest, g, w, min_size)
178 |     while valid_moves and iters < max_iter:
179 |         # make move and update assignments, coeffs, closest, candidates
180 |         u = np.random.choice(valid_moves)
181 |         label[u] = closest[u]
182 |         regions = [units[label == r].tolist() for r in range(k)]
183 |         coeffs = fit_equations(Xarr, Yarr, regions)
184 |         closest = np.array(closest_equation(Xarr, Yarr, coeffs))
185 |         moves = units[closest != label]
186 |         valid_moves = batch_check_RKM(moves, units, label, closest, g, w, min_size)
187 |         iters += 1
188 |     coeffs = fit_equations(Xarr, Yarr, regions)
189 |     return label, coeffs, iters
190 | 
191 | 
192 | def gwr_skater(Xarr, Yarr, n_regions, w, coord, min_size=None):
193 |     # Initialization
194 |     nobs = Xarr.shape[0]
195 |     nvar = Xarr.shape[1] - 1
196 | 
197 |     # gwr
198 |     coeff = [None for u in range(nobs)]
199 |     Xprm = np.asarray([Xarr[u,1:] for u in range(nobs)])
200 |     Xprm = Xprm.reshape((nobs, nvar))
201 |     Yprm = np.asarray([Yarr[u] for u in range(nobs)])
202 |     Yprm = Yprm.reshape((nobs,1))
203 |     bw = Sel_BW(coord, Yprm, Xprm, fixed=False, kernel='bisquare').search(criterion='AICc')
204 |     # for debug
205 |     # print(f"bw:{bw}")
206 | 
207 |     coord = np.array(coord)
208 |     model = gwr.GWR(coord, Yprm, Xprm, bw=bw, fixed=False, kernel='bisquare')
209 |     results = model.fit()
210 |     for u in range(nobs):
211 |          coeff[u] = results.params[u]
212 |     # for debug
213 |     # print("GWR finished")
214 | 
215 |     # pre-processing
216 |     X = np.asarray([coeff[u] for u in range(nobs)])
217 |     X1 = preprocessing.StandardScaler().fit_transform(X)
218 | 
219 |     # SKATER regionalization
220 |     fields = ['intercept']+[f'slope{v}' for v in range(nvar)]
221 |     pd = geopandas.GeoDataFrame(X1, columns=fields, dtype=float)
222 |     # Use default configurations
223 |     spconfig = dict(dissimilarity=metrics.pairwise.manhattan_distances, affinity=None, reduction=np.sum, center=np.mean)
224 |     model = Skater(pd, w, attrs_name=fields, n_clusters=n_regions, floor=min_size, trace=False, spanning_forest_kwds=spconfig)
225 |     model.solve()
226 | 
227 |     label = model.labels_
228 |     units = np.arange(w.n).astype(int)
229 |     regions = [units[label == r].tolist() for r in range(n_regions)]
230 |     coeffs = fit_equations(Xarr, Yarr, regions)
231 |     return label, coeffs
232 | 
233 | 
234 | def skater_reg(Xarr, Yarr, n_regions, w, min_size=None):
235 |     nobs = Xarr.shape[0]
236 |     nvar = Xarr.shape[1] - 1
237 | 
238 |     Xreg = np.asarray([Xarr[u, 1:] for u in range(nobs)])
239 |     Xreg = Xreg.reshape((nobs, nvar))
240 |     Yreg = np.asarray([Yarr[u] for u in range(nobs)])
241 |     Yreg = Yreg.reshape((nobs, 1))
242 |     results = Skater_reg().fit(n_clusters=n_regions, W=w, data=Xreg,
243 |               data_reg={'reg': spreg.OLS, 'y': Yreg, 'x': Xreg}, quorum=min_size)
244 | 
245 |     label = results.current_labels_
246 |     units = np.arange(w.n).astype(int)
247 |     regions = [units[label == r].tolist() for r in range(n_regions)]
248 |     coeffs = fit_equations(Xarr, Yarr, regions)
249 |     return label, coeffs
250 | 
251 | 


--------------------------------------------------------------------------------
/src/GridData9.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | import math
  3 | from Network9 import weights_to_graph, regression_error, region_neighbors
  4 | from libpysal import weights
  5 | from sklearn.metrics import mean_absolute_error
  6 | import networkx
  7 | 
  8 | # General
  9 | 
 10 | def Pos_Encode(r,c,side):
 11 |     return r*side+c
 12 | 
 13 | 
 14 | def Pos_Decode(code,side):
 15 |     return code//side, code%side
 16 | 
 17 | 
 18 | def GetNeighbors(pos,side):
 19 |     nlist = []
 20 |     if pos+side < side*side:
 21 |         nlist.append(pos + side)
 22 |     if pos-side >= 0:
 23 |         nlist.append(pos - side)
 24 |     if pos % side != side - 1:
 25 |         nlist.append(pos + 1)
 26 |     if pos % side != 0:
 27 |         nlist.append(pos - 1)
 28 |     return nlist
 29 | 
 30 | 
 31 | def GetAllNeighbors(side):
 32 |     neighbors =[]
 33 |     for pos in range(side*side):
 34 |         neighbors.append(GetNeighbors(pos,side))
 35 |     return neighbors
 36 | 
 37 | 
 38 | def GetCoord(side):
 39 |     return [(pos // side, pos % side) for pos in range(side * side)]
 40 | 
 41 | 
 42 | # Grid Regionlization Evaluation
 43 | # Xarr (Side*Side, Variables(including constant)); Yarr (Side*Side)
 44 | # Label (Side*Side); Coeff (Side*Side, Variables(including constant))
 45 | class Grid_Region_Metrics:
 46 |     def __init__(self, Xarr, Yarr, true_label, pred_label, true_coeff, pred_coeff):
 47 |         self.X = Xarr
 48 |         self.Y = Yarr
 49 |         self.N = len(self.X)
 50 |         self.label = true_label
 51 |         self.rlabel = np.asarray(pred_label, dtype=int)
 52 |         units = np.arange(self.N).astype(int)
 53 |         self.reg = [units[self.label == r].tolist() for r in set(self.label)]
 54 |         self.rreg = [units[self.rlabel == r].tolist() for r in set(self.rlabel)]
 55 |         self.coeff = true_coeff
 56 |         self.rcoeff = pred_coeff
 57 | 
 58 |         self.SSR()
 59 |         self.Rand()
 60 |         self.Mutual_Info()
 61 |         self.Coeff_MAE()
 62 |         return
 63 | 
 64 |     def SSR(self):
 65 |         self.ssr = regression_error(self.rreg, self.X, self.Y)
 66 |         return
 67 | 
 68 |     def Rand(self):
 69 |         tp = fn = fp = tn = 0
 70 |         for i in range(self.N):
 71 |             for j in range(i + 1, self.N):
 72 |                 if self.label[i] == self.label[j]:
 73 |                     if self.rlabel[i] == self.rlabel[j]:
 74 |                         tp += 1
 75 |                     else:
 76 |                         fn += 1
 77 |                 else:
 78 |                     if self.rlabel[i] == self.rlabel[j]:
 79 |                         fp += 1
 80 |                     else:
 81 |                         tn += 1
 82 |         npair = self.N * (self.N - 1) // 2
 83 |         assert npair == tp + fn + fp + tn
 84 |         self.tp, self.fn, self.fp, self.tn = tp, fn, fp, tn
 85 |         self.randi = (tp + tn) / npair
 86 |         return
 87 | 
 88 |     def Mutual_Info(self):
 89 |         n = sum([len(r) for r in self.reg])
 90 |         assert n == sum([len(r) for r in self.rreg])
 91 |         h = -sum([(len(r) / n) * math.log2(len(r) / n) for r in self.reg])
 92 |         hr = -sum([(len(r) / n) * math.log2(len(r) / n) for r in self.rreg])
 93 |         reg_set = [set(r) for r in self.reg]
 94 |         rreg_set = [set(r) for r in self.rreg]
 95 |         mi = 0
 96 |         for r in reg_set:
 97 |             for rr in rreg_set:
 98 |                 n_int = len(r.intersection(rr))
 99 |                 if n_int == 0:
100 |                     continue
101 |                 mi += (n_int / n) * math.log2(n * n_int / (len(r) * len(rr)))
102 |         nmi = 2 * mi / (h + hr)
103 |         self.h, self.hr, self.mi, self.nmi = h, hr, mi, nmi
104 |         return
105 | 
106 |     def Coeff_MAE(self):
107 |         self.coeff_mae=[]
108 |         for v in range(self.X.shape[1]):
109 |             coeffv = [self.coeff[u][v] for u in range(self.N)]
110 |             rcoeffv = [self.rcoeff[self.rlabel[u]][v] for u in range(self.N)]
111 |             self.coeff_mae.append(mean_absolute_error(coeffv,rcoeffv))
112 |         return
113 | 
114 |     def result_full_str(self):
115 |         str = f'{self.ssr:.4f} '
116 |         str += f'{self.tp:d} {self.fn:d} {self.fp:d} {self.tn:d} {self.randi:.4f} '
117 |         str += f'{self.h:.4f} {self.hr:.4f} {self.mi:.4f} {self.nmi:.4f} '
118 |         str += ' '.join([f'{mae:.4f}' for mae in self.coeff_mae])
119 |         return str
120 | 
121 |     def result_str(self):
122 |         str = f'{self.ssr:.4f} {self.randi:.4f} {self.nmi:.4f} '
123 |         str += ' '.join([f'{mae:.4f}' for mae in self.coeff_mae])
124 |         return str
125 | 
126 | # Grid Data Simulation
127 | 
128 | # data[0] Xarr (Side*Side)*Variables
129 | # data[1] Yarr (Side*Side)
130 | # data[2] Coeff (Side*Side)*Variables
131 | 
132 | 
133 | def dist(r1,c1,r2,c2):
134 |     return math.sqrt((r1-r2)*(r1-r2)+(c1-c2)*(c1-c2))
135 | 
136 | 
137 | def simulate_zone(side, zonemap, valarr, bias):
138 |     # zonemap: side*side; valarr: d*zones
139 |     Xarr = np.asarray([[1]+[np.random.rand() for d in range(len(valarr)-1)]
140 |                          for u in range(side*side)])
141 |     coeff = np.asarray([[valarr[d][zonemap[u//side][u % side]]
142 |                          for d in range(len(valarr))] for u in range(side*side)])
143 |     Yarr = np.asarray([np.inner(coeff[u], Xarr[u])+bias*np.random.randn()
144 |                          for u in range(side*side)])
145 |     return Xarr, Yarr, coeff
146 | 
147 | 
148 | def generate_regular_zones(side,zones):
149 |     width = int(round(side/zones))
150 |     zonemap = [[r//width for c in range(side)] for r in range(side)]
151 |     return zonemap
152 | 
153 | 
154 | def generate_voronoi_zones(side,zones,min_size):
155 |     zonemap = [[-1 for c in range(side)] for r in range(side)]
156 |     w = weights.lat2W(side,side)
157 |     g = weights_to_graph(w)
158 |     while True:
159 |         seeds = np.random.choice(range(side*side),zones,replace=False)
160 |         for r in range(side):
161 |             for c in range(side):
162 |                 dists = [dist(r, c, seeds[i]//side, seeds[i]%side) for i in range(zones)]
163 |                 zonemap[r][c] = dists.index(min(dists))
164 |         valid = True
165 |         for i in range(zones):
166 |             zone = [p for p in range(side*side) if zonemap[p//side][p%side]==i]
167 |             if not networkx.is_connected(g.subgraph(zone)):
168 |                 valid = False
169 |             if len(zone) < min_size:
170 |                 valid = False
171 |         if valid:
172 |             break
173 |     return zonemap
174 | 
175 | 
176 | def generate_random_zones(side, zones, min_size):
177 |     w = weights.lat2W(side, side)
178 |     units = np.arange(w.n).astype(int)
179 |     while True:
180 |         seeds = np.random.choice(units, size=zones, replace=False)
181 |         label = np.array([-1] * w.n).astype(int)
182 |         for i, seed in enumerate(seeds):
183 |             label[seed] = i
184 |         to_assign = units[label == -1]
185 | 
186 |         while to_assign.size > 0:
187 |             for rid in range(zones):
188 |                 region = units[label == rid]
189 |                 neighbors = region_neighbors(region, w)
190 |                 neighbors = [j for j in neighbors if j in to_assign]
191 |                 if len(neighbors) > 0:
192 |                     u = np.random.choice(neighbors)
193 |                     label[u] = rid
194 |             to_assign = units[label == -1]
195 | 
196 |         regions = [units[label == r].tolist() for r in range(zones)]
197 |         if min([len(region) for region in regions]) >= min_size:
198 |             break
199 | 
200 |     zonemap = [[label[Pos_Encode(r, c, side)] for c in range(side)] for r in range(side)]
201 |     return zonemap
202 | 
203 | # Input/Output
204 | 
205 | def reg_pic(side, label, coeffs=None,dim=-1):
206 |     if dim == -1:
207 |         arr = np.asarray([[-1 for c in range(side)] for r in range(side)])
208 |         # Show shape of zones
209 |         for u in range(len(label)):
210 |             r,c = Pos_Decode(u,side)
211 |             arr[r][c] = label[u]
212 |     else:
213 |         # Show coefficients
214 |         arr = np.asarray([[-1.0 for c in range(side)] for r in range(side)])
215 |         for u in range(len(label)):
216 |             r,c = Pos_Decode(u,side)
217 |             arr[r][c] = coeffs[label[u]][dim]
218 |     return arr
219 | 
220 | 
221 | def input_data(i, side, nvar):
222 |     Xarr = np.asarray([[1.0]+[0.0]*nvar for u in range(side*side)])
223 |     Yarr = np.asarray([-1.0 for u in range(side*side)])
224 |     label = np.asarray([-1 for u in range(side*side)])
225 |     coeff = np.asarray([[0.0]*(nvar+1) for u in range(side * side)])
226 |     # Read Xarr
227 |     for v in range(nvar):
228 |         for r in range(side):
229 |             line = i.readline().split()
230 |             for c in range(side):
231 |                 Xarr[Pos_Encode(r,c,side)][v+1] = float(line[c])
232 |         line = i.readline()
233 |     # Read Yarr
234 |     for r in range(side):
235 |         line = i.readline().split()
236 |         for c in range(side):
237 |             Yarr[Pos_Encode(r, c, side)] = float(line[c])
238 |     line = i.readline()
239 |     # Read label
240 |     for r in range(side):
241 |         line = i.readline().split()
242 |         for c in range(side):
243 |             label[Pos_Encode(r, c, side)] = int(line[c])
244 |     line = i.readline()
245 |     # Read Coeff
246 |     for v in range(nvar):
247 |         for r in range(side):
248 |             line = i.readline().split()
249 |             for c in range(side):
250 |                 coeff[Pos_Encode(r,c,side)][v+1] = float(line[c])
251 |         line = i.readline()
252 |     return Xarr, Yarr, label, coeff
253 | 
254 | 
255 | def output_data(o, side, Xarr, Yarr, coeff=None, regmap=None):
256 |     variables = Xarr.shape[1]
257 | 
258 |     # X
259 |     for v in range(1,variables):
260 |         for r in range(side):
261 |             for c in range(side):
262 |                 u = Pos_Encode(r,c,side)
263 |                 o.write('%.6f' % Xarr[u][v]+" ")
264 |             o.write("\n")
265 |         o.write("\n")
266 |     # Y
267 |     for r in range(side):
268 |         for c in range(side):
269 |             u = Pos_Encode(r, c, side)
270 |             o.write('%.6f' % Yarr[u]+" ")
271 |         o.write("\n")
272 |     o.write("\n")
273 |     # regmap
274 |     if regmap is not None:
275 |         for r in range(side):
276 |             for c in range(side):
277 |                 o.write(str(regmap[r][c]) + " ")
278 |             o.write("\n")
279 |         o.write("\n")
280 |     # coeff
281 |     if coeff is not None:
282 |         for v in range(1, variables):
283 |             for r in range(side):
284 |                 for c in range(side):
285 |                     u = Pos_Encode(r, c, side)
286 |                     o.write('%.1f' % coeff[u][v] + " ")
287 |                 o.write("\n")
288 |             o.write("\n")
289 |     return
290 | 
291 | 
292 | def output_result(o, side, rlabel, coeffs, method):
293 |     o.write(method + "\n")
294 |     regmap = reg_pic(side, rlabel)
295 |     for r in range(side):
296 |         for c in range(side):
297 |             o.write(str(regmap[r][c])+" ")
298 |         o.write("\n")
299 |     o.write("\n")
300 |     for z in range(len(coeffs)):
301 |         for entry in range(len(coeffs[z])):
302 |             o.write('%.4f' % coeffs[z][entry]+" ")
303 |         o.write("\n")
304 |     o.write("\n")
305 |     return
306 | 
307 | 
308 | def output_coeff(o, coeffs):
309 |     for z in range(len(coeffs)):
310 |         for entry in range(len(coeffs[z])):
311 |             o.write('%.4f' % coeffs[z][entry]+" ")
312 |         o.write("\n")
313 |     o.write("\n")
314 |     return
315 | 


--------------------------------------------------------------------------------
/src/Network9.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | from sklearn import linear_model
  3 | import statsmodels.api as sm
  4 | import networkx
  5 | import copy
  6 | 
  7 | inf = 1e10
  8 | 
  9 | 
 10 | # General
 11 | def region_error(Xarr,Yarr,region):
 12 |     variables = Xarr.shape[1] - 1
 13 |     reg = linear_model.LinearRegression()
 14 |     nsize = len(region)
 15 |     if nsize == 0:
 16 |         return 0
 17 |     Xreg = np.asarray([Xarr[u, 1:] for u in region])
 18 |     Xreg = Xreg.reshape((nsize, variables))
 19 |     Yreg = np.asarray([Yarr[u] for u in region])
 20 |     Yreg = Yreg.reshape(nsize)
 21 | 
 22 |     reg.fit(Xreg, Yreg)
 23 |     coeff = [reg.intercept_] + list(reg.coef_)
 24 | 
 25 |     err = 0
 26 |     for u in region:
 27 |         err += (np.inner(coeff, Xarr[u, :])-Yarr[u])**2
 28 |     return err
 29 | 
 30 | 
 31 | def regression_error(regions, Xarr, Yarr):
 32 |     ssr = 0
 33 |     for r in regions:
 34 |         ssr += region_error(Xarr,Yarr,r)
 35 |     return ssr
 36 | 
 37 | 
 38 | def init_zones(w, n_regions, min_size, stoc_step=True, max_attempt=50):
 39 |     units = np.arange(w.n).astype(int)
 40 |     trial = 0
 41 |     while trial < max_attempt:
 42 |         label = init_zones_generation(w, n_regions, stoc_step)
 43 |         regions = [units[label == r].tolist() for r in range(n_regions)]
 44 |         if min([len(region) for region in regions]) >= min_size:
 45 |             break
 46 |         trial += 1
 47 |     if trial == max_attempt:
 48 |         raise RuntimeError("Initial zoning failed. Please check minimum size constraint.")
 49 |     return label
 50 | 
 51 | 
 52 | def init_zones_generation(w, n_regions, stoc_step):
 53 |     # w: libpysal.weights.W
 54 |     units = np.arange(w.n).astype(int)
 55 |     k = n_regions
 56 | 
 57 |     seeds = np.random.choice(units, size=k, replace=False)
 58 |     label = np.array([-1] * w.n).astype(int)
 59 |     for i, seed in enumerate(seeds):
 60 |         label[seed] = i
 61 |     to_assign = units[label == -1]
 62 | 
 63 |     while to_assign.size > 0:
 64 |         for rid in range(k):
 65 |             region = units[label == rid]
 66 |             neighbors = region_neighbors(region, w)
 67 |             neighbors = [j for j in neighbors if j in to_assign]
 68 |             if len(neighbors) > 0:
 69 |                 if stoc_step:
 70 |                     u = np.random.choice(neighbors)
 71 |                     label[u] = rid
 72 |                 else:
 73 |                     for u in neighbors:
 74 |                         label[u] = rid
 75 |         to_assign = units[label == -1]
 76 |     return label
 77 | 
 78 | 
 79 | def is_neighbor(unit, region, w):
 80 |     if unit in region:
 81 |         return False
 82 |     for member in region:
 83 |         if unit in w[member]:
 84 |             return True
 85 |     return False
 86 | 
 87 | 
 88 | def region_neighbors(region, w):
 89 |     # Get neighboring units for members of a region.
 90 |     n_list = []
 91 |     for member in region:
 92 |         n_list.extend(w[member])
 93 |     n_set = set(n_list).difference(set(region))
 94 |     return n_set
 95 | 
 96 | 
 97 | def weights_to_graph(w):
 98 |     # transform a PySAL W to a networkx graph
 99 |     g = networkx.Graph()
100 |     for ego, alters in w.neighbors.items():
101 |         for alter in alters:
102 |             g.add_edge(ego, alter)
103 |     return g
104 | 
105 | 
106 | def fit_equations(Xarr,Yarr,regions):
107 |     # Xarr: (n_samples, n_units, n_variables)
108 |     variables = Xarr.shape[1] - 1
109 |     reg = linear_model.LinearRegression()
110 |     coeffs = []
111 |     for region in regions:
112 |         if len(region) == 0:
113 |             coeffs.append([inf]+[0 for i in range(variables)])
114 |             continue
115 |         nsize = len(region)
116 | 
117 |         Xreg = np.asarray([Xarr[u, 1:] for u in region])
118 |         Xreg = Xreg.reshape((nsize, variables))
119 |         Yreg = np.asarray([Yarr[u] for u in region])
120 |         Yreg = Yreg.reshape(nsize)
121 | 
122 |         reg.fit(Xreg, Yreg)
123 |         coeffs.append([reg.intercept_] + list(reg.coef_))
124 |     return np.array(coeffs)
125 | 
126 | 
127 | def closest_equation(Xarr, Yarr, coeffs):
128 |     units = Xarr.shape[0]
129 |     closest = []
130 |     for u in range(units):
131 |         reg_err = [np.abs(Yarr[u] - np.inner(coeffs[z], Xarr[u, :])) for z in range(len(coeffs))]
132 |         closest.append(reg_err.index(min(reg_err)))
133 |     return closest
134 | 
135 | 
136 | def region_to_label(N, regions):
137 |     label = np.array([-1] * N).astype(int)
138 |     for r in range(len(regions)):
139 |         for u in regions[r]:
140 |             label[u] = r
141 |     return label
142 | 
143 | 
144 | # Explicit Methods
145 | def contiguity_check(unit, from_region, to_region, g, w):
146 |     # first check if area has a neighbor in destination
147 |     if not is_neighbor(unit, to_region, w):
148 |         return False
149 |     # check if moving area would break source connectivity
150 |     new_source = [j for j in from_region if j != unit]
151 |     if len(new_source) == 0 or networkx.is_connected(g.subgraph(new_source)):
152 |         return True
153 |     else:
154 |         return False
155 | 
156 | 
157 | def accuracy_check(u, from_region, to_region, inv_source, inv_target, Xarr, Yarr):
158 |     ps, pd = from_region, to_region
159 |     ns = copy.copy(from_region)
160 |     ns.remove(u)
161 |     nd = copy.copy(to_region)
162 |     nd.append(u)
163 |     psinv, pdinv = inv_source, inv_target
164 |     v = Xarr[u].reshape((-1))
165 |     nsinv = sherman_morrison(psinv, v, add=False)
166 |     ndinv = sherman_morrison(pdinv, v, add=True)
167 |     err_prev = linreg_err(Xarr, Yarr, ps, psinv) + linreg_err(Xarr, Yarr, pd, pdinv)
168 |     err_new = linreg_err(Xarr, Yarr, ns, nsinv) + linreg_err(Xarr, Yarr, nd, ndinv)
169 |     return err_new <= err_prev
170 | 
171 | 
172 | def batch_check_RKM(moves, units, label, closest, g, w, min_size):
173 |     valid_moves = []
174 |     for u in moves:
175 |         from_region = units[label == label[u]]
176 |         if len(from_region) <= min_size:
177 |             continue
178 |         to_region = units[label == closest[u]]
179 |         if contiguity_check(u, from_region, to_region, g, w):
180 |             valid_moves.append(u)
181 |     return valid_moves
182 | 
183 | 
184 | def batch_check_AZP(moves, units, label, target, g, w, Xarr, Yarr, xtxinvs, min_size):
185 |     valid_moves = []
186 |     for u in moves:
187 |         from_region = units[label == label[u]].tolist()
188 |         if len(from_region) <= min_size:
189 |             continue
190 |         to_region = units[label == target].tolist()
191 |         # connectivity O(kn); sherman-morrison update O(kn)
192 |         if accuracy_check(u, from_region, to_region, xtxinvs[label[u]],
193 |                              xtxinvs[target], Xarr, Yarr):
194 |             if contiguity_check(u, from_region, to_region, g, w):
195 |                 valid_moves.append(u)
196 |     return valid_moves
197 | 
198 | 
199 | def linreg_err(Xarr,Yarr,region,xtxinv=None):
200 |     variables = Xarr.shape[1]
201 |     nsize = len(region)
202 |     if nsize == 0:
203 |         return 0
204 |     Xreg = np.asarray([Xarr[u] for u in region])
205 |     Xreg = Xreg.reshape((nsize, variables))
206 |     Xreg_t = np.transpose(Xreg)
207 |     Yreg = np.asarray([Yarr[u] for u in region])
208 |     Yreg = Yreg.reshape(nsize)
209 |     if xtxinv is None:
210 |         xtxinv = np.linalg.inv(np.matmul(Xreg_t, Xreg))
211 |     lin_coeff = np.matmul(xtxinv,np.matmul(Xreg_t, Yreg))
212 |     err = 0
213 |     for u in region:
214 |         err += (np.inner(lin_coeff, Xarr[u, :]) - Yarr[u]) ** 2
215 |     return err
216 | 
217 | 
218 | def sherman_morrison(xtxinv, v, add=True):
219 |     m = v.shape[0]
220 |     w = np.matmul(xtxinv, v)
221 |     k = np.matmul(v, w)
222 |     w = w.reshape((m,1))
223 |     if add:
224 |         res = xtxinv - np.matmul(w, np.transpose(w))/(1 + k)
225 |     else:
226 |         res = xtxinv + np.matmul(w, np.transpose(w))/(1 - k)
227 |     return res
228 | 
229 | 
230 | def init_xtxinv(Xarr, regions):
231 |     xtxinvs = []
232 |     for region in regions:
233 |         Xreg = np.asarray([Xarr[u] for u in region])
234 |         Xreg_t = np.transpose(Xreg)
235 |         xtxinv = np.linalg.inv(np.matmul(Xreg_t, Xreg))
236 |         xtxinvs.append(xtxinv)
237 |     return xtxinvs
238 | 
239 | 
240 | 
241 | # Implicit Methods
242 | class Node:
243 |     def __init__ (self, id_: int , units_:list):
244 |         self.id = id_
245 |         self.units = units_  # index in Xarr,Yarr,coord,Neighbors
246 |         self.links = set()  # id in graph
247 |         self.err = 0
248 | 
249 |     def __str__(self):
250 |         return str(self.id)+" "+str(self.units)+" "+str(self.links)+" "+str(self.err)
251 | 
252 | 
253 | def merge_node(g:dict, k1, k2, Xarr, Yarr):
254 |     assert k2 in g[k1].links and k1 in g[k2].links
255 |     g[k1].units += g[k2].units
256 |     g[k1].err = region_error(Xarr,Yarr,g[k1].units)
257 |     g[k1].links = g[k1].links.union(g[k2].links).difference({k1,k2})
258 |     g.pop(k2)
259 |     for k in g[k1].links:
260 |         if k2 in g[k].links:
261 |             g[k].links.remove(k2)
262 |         g[k].links.add(k1)
263 |     return g
264 | 
265 | 
266 | def delta_err(g:dict, k1, k2, Xarr, Yarr):
267 |     virnode = Node(-1,g[k1].units + g[k2].units)
268 |     virnode.err = region_error(Xarr, Yarr, virnode.units)
269 |     return virnode.err - g[k1].err - g[k2].err
270 | 
271 | 
272 | def Test_Equations(regions,Xarr,Yarr,log):
273 |     zone_id = 0
274 |     for zone in regions:
275 |         zone_id += 1
276 |         variables = Xarr.shape[1] - 1
277 |         zsize = len(zone)
278 |         if zsize <= 4:
279 |             log.write(str(zone_id) + " " + str(zsize) + '\n')
280 |             continue
281 | 
282 |         X = np.asarray([Xarr[u, 1:] for u in zone])
283 |         X = X.reshape((zsize, variables))
284 |         X = sm.add_constant(X)
285 |         Y = np.asarray([Yarr[u] for u in zone])
286 |         Y = Y.reshape(zsize)
287 | 
288 |         results = sm.OLS(Y, X).fit()
289 |         # print(results.summary())
290 |         f_test = results.f_test(np.identity(len(results.params))[1:, :])
291 |         log.write(str(zone_id) + " " + str(zsize) + " " + str(results.params)
292 |                   + " " + str(f_test.fvalue) + " " + str(f_test.pvalue)+'\n')
293 |     log.write('\n')
294 |     return
295 | 
296 | 
297 | 


--------------------------------------------------------------------------------
/src/georgia.py:
--------------------------------------------------------------------------------
  1 | from Algorithm9 import *
  2 | import copy
  3 | import datetime
  4 | import xlrd
  5 | import xlwt
  6 | import libpysal
  7 | 
  8 | cmp = "bwr"
  9 | pmin = 2
 10 | pmax = 10
 11 | Kfac = 2
 12 | runs = 10
 13 | numid = 7
 14 | min_region = 5
 15 | 
 16 | log = open("Georgia_"+str(numid)+".txt", 'w')
 17 | log.write(str(datetime.datetime.now().ctime())+'\n')
 18 | log.write("Method: KModels AZP RegKModels GWRSkater SkaterReg \n")
 19 | log.write(f"pmin: {pmin} pmax: {pmax} Kfac: {Kfac} repeat: {runs}\n")
 20 | 
 21 | data = xlrd.open_workbook("../georgia/GData_utm.xls")
 22 | table = data.sheets()[1]
 23 | NCounty = table.nrows - 1
 24 | NVar = 3
 25 | print(NCounty)
 26 | AreaIndex = {}
 27 | IndexArea = []
 28 | X = []
 29 | Y = []
 30 | coord = []
 31 | 
 32 | for r in range(1, NCounty+1):
 33 |     Areaid = table.cell_value(r, 0)
 34 |     PerBach = table.cell_value(r, 1)
 35 |     PerRural = table.cell_value(r, 2)
 36 |     PerFB = table.cell_value(r, 3)
 37 |     PerBlack = table.cell_value(r, 4)
 38 |     coordX = table.cell_value(r, 5)
 39 |     coordY = table.cell_value(r, 6)
 40 |     AreaIndex[Areaid] = r-1
 41 |     IndexArea.append(Areaid)
 42 |     X.append([PerRural,PerFB,PerBlack])
 43 |     Y.append([PerBach])
 44 |     coord.append((coordX,coordY))
 45 | 
 46 | Xarr = preprocessing.StandardScaler().fit_transform(np.array(X))
 47 | Yarr = preprocessing.StandardScaler().fit_transform(np.array(Y))
 48 | Xarr = np.array([[1]+list(datapoint) for datapoint in Xarr])
 49 | Xarr = Xarr.reshape((NCounty, NVar+1))
 50 | Yarr = Yarr.reshape(NCounty)
 51 | 
 52 | w = libpysal.weights.Rook.from_shapefile("../Georgia/Aggre.shp",idVariable="AreaId")
 53 | units = np.arange(w.n).astype(int)
 54 | 
 55 | outxls = xlwt.Workbook(encoding='utf-8')
 56 | 
 57 | for n_regions in range(pmin, pmax+1):
 58 |     micro_clusters = Kfac*n_regions
 59 |     print(f"{n_regions} Regions")
 60 | 
 61 |     params = outxls.add_sheet(f'R{n_regions}')
 62 |     params.write(0, 0, label='Area_Key')
 63 |     params.write(0, 1, label='Const_GWR')
 64 |     params.write(0, 2, label='Rural_GWR')
 65 |     params.write(0, 3, label='FB_GWR')
 66 |     params.write(0, 4, label='Black_GWR')
 67 |     params.write(0, 5, label='Zone_KM')
 68 |     params.write(0, 6, label='Const_KM')
 69 |     params.write(0, 7, label='Rural_KM')
 70 |     params.write(0, 8, label='FB_KM')
 71 |     params.write(0, 9, label='Black_KM')
 72 |     params.write(0, 10, label='Zone_AZP')
 73 |     params.write(0, 11, label='Const_AZP')
 74 |     params.write(0, 12, label='Rural_AZP')
 75 |     params.write(0, 13, label='FB_AZP')
 76 |     params.write(0, 14, label='Black_AZP')
 77 |     params.write(0, 15, label='Zone_RKM')
 78 |     params.write(0, 16, label='Const_RKM')
 79 |     params.write(0, 17, label='Rural_RKM')
 80 |     params.write(0, 18, label='FB_RKM')
 81 |     params.write(0, 19, label='Black_RKM')
 82 |     params.write(0, 20, label='Zone_GSK')
 83 |     params.write(0, 21, label='Const_GSK')
 84 |     params.write(0, 22, label='Rural_GSK')
 85 |     params.write(0, 23, label='FB_GSK')
 86 |     params.write(0, 24, label='Black_GSK')
 87 |     params.write(0, 25, label='Zone_SKR')
 88 |     params.write(0, 26, label='Const_SKR')
 89 |     params.write(0, 27, label='Rural_SKR')
 90 |     params.write(0, 28, label='FB_SKR')
 91 |     params.write(0, 29, label='Black_SKR')
 92 | 
 93 |     for u in range(NCounty):
 94 |         params.write(u + 1, 0, label=int(IndexArea[u]))
 95 | 
 96 |     # GWR_coeff
 97 |     Xprm = np.asarray([Xarr[u, 1:] for u in range(NCounty)])
 98 |     Yprm = np.asarray([Yarr[u] for u in range(NCounty)])
 99 |     Xprm = Xprm.reshape((NCounty, NVar))
100 |     Yprm = Yprm.reshape((NCounty, 1))
101 |     bw = Sel_BW(coord, Yprm, Xprm, fixed=False, kernel='bisquare').search(criterion='AICc')
102 |     coord = np.array(coord)
103 |     model = gwr.GWR(coord, Yprm, Xprm, bw=bw, fixed=False, kernel='bisquare')
104 |     results = model.fit()
105 |     for u in range(NCounty):
106 |         for v in range(NVar + 1):
107 |             params.write(u + 1, 1 + v, label=results.params[u][v])
108 | 
109 |     # KModels
110 |     bestres = None
111 |     bestssr = 1e10
112 |     for run in range(runs):
113 |         st = datetime.datetime.now()
114 |         clabel, iters = kmodels(Xarr, Yarr, micro_clusters, w)
115 |         slabel = split_components(w, clabel)
116 |         rlabel, rcoeff, merges = greedy_merge(Xarr, Yarr, n_regions, w, slabel, min_size=min_region)
117 |         ed = datetime.datetime.now()
118 |         regions = [units[rlabel == r].tolist() for r in set(rlabel)]
119 |         ssr = regression_error(regions, Xarr, Yarr)
120 |         log.write(f'{ssr} {ed - st} {iters} {merges}\n')
121 |         if ssr < bestssr:
122 |             bestres = (copy.copy(regions), copy.copy(rcoeff))
123 |             bestssr = ssr
124 |     print(f'{bestssr}')
125 |     log.write('\n')
126 |     breg, bcoeff = bestres
127 |     for zoneid in range(len(breg)):
128 |         z = breg[zoneid]
129 |         for u in z:
130 |             params.write(u + 1, 5, label=zoneid)
131 |             for v in range(NVar + 1):
132 |                 params.write(u + 1, 6 + v, label=bcoeff[zoneid][v])
133 |     Test_Equations(breg, Xarr, Yarr, log)
134 | 
135 |     # AZP
136 |     bestres = None
137 |     bestssr = 1e10
138 |     for run in range(runs):
139 |         st = datetime.datetime.now()
140 |         rlabel, rcoeff, iters = azp(Xarr, Yarr, n_regions, w, min_size=min_region)
141 |         ed = datetime.datetime.now()
142 |         regions = [units[rlabel == r].tolist() for r in set(rlabel)]
143 |         ssr = regression_error(regions, Xarr, Yarr)
144 |         log.write(f'{ssr} {ed - st} {iters}\n')
145 |         if ssr < bestssr:
146 |             bestres = (copy.copy(regions), copy.copy(rcoeff))
147 |             bestssr = ssr
148 |     print(f'{bestssr}')
149 |     log.write('\n')
150 |     breg, bcoeff = bestres
151 |     for zoneid in range(len(breg)):
152 |         z = breg[zoneid]
153 |         for u in z:
154 |             params.write(u + 1, 10, label=zoneid)
155 |             for v in range(NVar + 1):
156 |                 params.write(u + 1, 11 + v, label=bcoeff[zoneid][v])
157 |     Test_Equations(breg, Xarr, Yarr, log)
158 | 
159 |     # Region-K-Models
160 |     bestres = None
161 |     bestssr = 1e10
162 |     for run in range(runs):
163 |         st = datetime.datetime.now()
164 |         rlabel, rcoeff, iters = region_k_models(Xarr, Yarr, n_regions, w, min_size=min_region)
165 |         ed = datetime.datetime.now()
166 |         regions = [units[rlabel == r].tolist() for r in set(rlabel)]
167 |         ssr = regression_error(regions, Xarr, Yarr)
168 |         log.write(f'{ssr} {ed - st} {iters}\n')
169 |         if ssr < bestssr:
170 |             bestres = (copy.copy(regions), copy.copy(rcoeff))
171 |             bestssr = ssr
172 | 
173 |     print(f'{bestssr}')
174 |     log.write('\n')
175 |     breg, bcoeff = bestres
176 |     for zoneid in range(len(breg)):
177 |         z = breg[zoneid]
178 |         for u in z:
179 |             params.write(u + 1, 15, label=zoneid)
180 |             for v in range(NVar + 1):
181 |                 params.write(u + 1, 16 + v, label=bcoeff[zoneid][v])
182 |     Test_Equations(breg, Xarr, Yarr, log)
183 | 
184 |     # GWR_Skater
185 |     st = datetime.datetime.now()
186 |     rlabel, rcoeff = gwr_skater(Xarr, Yarr, n_regions, w, coord, min_size=min_region)
187 |     ed = datetime.datetime.now()
188 |     regions = [units[rlabel == r].tolist() for r in set(rlabel)]
189 |     ssr = regression_error(regions, Xarr, Yarr)
190 |     print(f'{ssr}')
191 |     log.write(f'{ssr} {ed - st}\n\n')
192 |     for zoneid in range(len(regions)):
193 |         z = regions[zoneid]
194 |         for u in z:
195 |             params.write(u + 1, 20, label=zoneid)
196 |             for v in range(NVar + 1):
197 |                 params.write(u + 1, 21 + v, label=rcoeff[zoneid][v])
198 |     Test_Equations(regions, Xarr, Yarr, log)
199 | 
200 |     # SkaterReg
201 |     st = datetime.datetime.now()
202 |     rlabel, rcoeff = skater_reg(Xarr, Yarr, n_regions, w, min_size=min_region)
203 |     ed = datetime.datetime.now()
204 |     regions = [units[rlabel == r].tolist() for r in set(rlabel)]
205 |     ssr = regression_error(regions, Xarr, Yarr)
206 |     print(f'{ssr}')
207 |     log.write(f'{ssr} {ed - st}\n\n')
208 |     for zoneid in range(len(regions)):
209 |         z = regions[zoneid]
210 |         for u in z:
211 |             params.write(u + 1, 25, label=zoneid)
212 |             for v in range(NVar + 1):
213 |                 params.write(u + 1, 26 + v, label=rcoeff[zoneid][v])
214 |     Test_Equations(regions, Xarr, Yarr, log)
215 | 
216 | log.close()
217 | outxls.save(f'Georgia_{numid}.xls')
218 | 


--------------------------------------------------------------------------------
/src/grid_K.py:
--------------------------------------------------------------------------------
 1 | from GridData9 import *
 2 | from Algorithm9 import *
 3 | import datetime
 4 | from matplotlib import pyplot as plt
 5 | import libpysal
 6 | 
 7 | # Test Mode
 8 | #Side = 10
 9 | #idlist = [0,3,6]
10 | #n_regions = 5
11 | #prefix = 'gridtest_'
12 | #mclist = range(n_regions, 15)
13 | #min_region = 5
14 | 
15 | # Run Mode
16 | Side = 25
17 | idlist = range(150)
18 | n_regions = 5
19 | prefix = 'grid_'
20 | min_region = 10
21 | mclist = range(10, 31)
22 | 
23 | recordnum = 66
24 | # Zones params
25 | nvar = 2
26 | plt.rcParams['figure.figsize'] = (10.0, 8.0)  # inches; 3 row: 10,10 5 row: 10,14
27 | shrink_ratio = 1  # 3 row:0.81 5 row: 0.98
28 | title = ['low noise', 'medium noise', 'high noise']
29 | noiselevel = ['l', 'm', 'h']
30 | 
31 | log = open("RG"+str(recordnum)+".txt", 'w')
32 | log.write(str(datetime.datetime.now().ctime())+'\n')
33 | log.write("Hyper\n")
34 | log.write("Side: "+str(Side)+" Data: "+str(idlist)+"\n")
35 | log.write("Regions:"+str(n_regions)+" Micro_clusters:"+str(mclist)+"\n")
36 | log.write("Method: KModels\n")
37 | 
38 | for dataid in idlist:
39 |     for micro_clusters in mclist:
40 |         ofile = open(f"result_{recordnum}_{dataid}_{micro_clusters}.txt", "w")
41 |         fig, axes = plt.subplots(2, len(noiselevel), figsize=(10, 8))
42 |         for noi in range(len(noiselevel)):
43 |             print(f"Data {dataid}{noiselevel[noi]} {micro_clusters}")
44 |             simdata = open("../synthetic/" + prefix + str(dataid) + noiselevel[noi]+".txt")
45 |             Xarr, Yarr, label, coeff = input_data(simdata, Side, nvar)
46 |             coord = GetCoord(Side)
47 |             w = libpysal.weights.lat2W(Side, Side)
48 | 
49 |             axes[0, noi].set_xticks([])
50 |             axes[0, noi].set_yticks([])
51 |             axes[0, noi].set_title(title[noi])
52 |             regmap = label.reshape((Side, Side))
53 |             axes[0, noi].imshow(regmap)
54 | 
55 |             # KModels
56 |             st = datetime.datetime.now()
57 |             clabel, iters = kmodels(Xarr, Yarr, micro_clusters, w)
58 |             slabel = split_components(w, clabel)
59 |             rlabel, rcoeff, merges = greedy_merge(Xarr, Yarr, n_regions, w, slabel, min_size=min_region)
60 |             ed = datetime.datetime.now()
61 |             metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
62 |             print(metrics.result_str() + " " + str(ed - st) + " " + str(iters) + " " + str(merges))
63 |             log.write(metrics.result_full_str() + " " + str(ed - st) + " " + str(iters) + " " + str(merges) + '\n')
64 |             output_result(ofile, Side, rlabel, rcoeff, "KModels")
65 |             axes[1, noi].set_title("KModels "+title[noi])
66 |             axes[1, noi].set_xticks([])  # 去掉x轴
67 |             axes[1, noi].set_yticks([])  # 去掉y轴
68 |             axes[1, noi].imshow(reg_pic(Side, rlabel, dim=-1))
69 | 
70 |         plt.tight_layout()
71 |         fig.savefig('RG'+str(recordnum) + '-' + str(dataid) + '-' + str(micro_clusters) +'-'+
72 |                     str(datetime.datetime.now().strftime('%y%m%d%H%M%S')) + '.png')
73 |         plt.close()
74 |         ofile.close()
75 |         log.write("\n")
76 | 
77 | log.close()
78 | 


--------------------------------------------------------------------------------
/src/grid_gen.py:
--------------------------------------------------------------------------------
 1 | import random
 2 | from GridData9 import *
 3 | from matplotlib import pyplot as plt
 4 | from matplotlib.colors import LinearSegmentedColormap
 5 | 
 6 | Side = 25
 7 | repeat = 1
 8 | bias_y = [0, 0.1, 0.2, 0.3]
 9 | shape_label = ["r","v","a"]
10 | noise_label = ["n","l","m","h"]
11 | zonenum = 5
12 | min_region = 10
13 | prefix = 'gridtest_'
14 | colors = ["#aec7e8", "#ffbb78", "#98df8a", "#ff9896", "#c5b0d5"]
15 | cmp = LinearSegmentedColormap.from_list("newcmp", colors)
16 | 
17 | fig, axes = plt.subplots(1, 3, figsize=(12, 6))
18 | 
19 | for shapeid in range(len(shape_label)):
20 |     shape = shape_label[shapeid]
21 |     print("Shape:" + shape)
22 |     for r in range(repeat):
23 |         print("Run " + str(r))
24 | 
25 |         if shape == 'r':
26 |             zonemap = generate_regular_zones(Side, zonenum)
27 |         elif shape == 'v':
28 |             zonemap = generate_voronoi_zones(Side, zonenum, min_size=min_region)
29 |         elif shape == 'a':
30 |             zonemap = generate_random_zones(Side, zonenum, min_size=min_region)
31 |         else:
32 |             raise KeyError("Generation approach not supported.")
33 |         if r == 0:
34 |             axes[shapeid].set_xticks([])
35 |             axes[shapeid].set_yticks([])
36 |             im = axes[shapeid].imshow(zonemap, cmap=cmp)
37 | 
38 |         valarr = [[0, 0, 0, 0, 0],[-2, -1, 0, 1, 2],[-2, -1, 0, 1, 2]]
39 |         random.shuffle(valarr[1])
40 |         random.shuffle(valarr[2])
41 | 
42 |         for noi in range(len(bias_y)):
43 |             data = simulate_zone(Side, zonemap, valarr, bias_y[noi])
44 |             Xarr, Yarr, coeff = data[0], data[1], data[2]
45 |             ofile = open(f'{prefix}{repeat*shapeid+r:d}{noise_label[noi]}.txt', "w")
46 |             output_data(ofile,Side,Xarr,Yarr,coeff,zonemap)
47 |             ofile.close()
48 | 
49 | plt.show()
50 | 
51 | 
52 | 


--------------------------------------------------------------------------------
/src/grid_reg.py:
--------------------------------------------------------------------------------
  1 | from GridData9 import *
  2 | from Algorithm9 import *
  3 | import datetime
  4 | from matplotlib import pyplot as plt
  5 | import libpysal
  6 | 
  7 | # Test Mode
  8 | #Side = 10
  9 | #ids, idt = 0, 9
 10 | #n_regions = 5
 11 | #prefix = 'gridtest_'
 12 | #micro_clusters = 10
 13 | #min_region = 5
 14 | 
 15 | # Run Mode
 16 | Side = 25
 17 | ids, idt = 0, 150
 18 | n_regions = 5
 19 | prefix = 'grid_'
 20 | micro_clusters = 20
 21 | min_region = 10
 22 | 
 23 | recordnum = 70
 24 | 
 25 | # Zones params
 26 | nvar = 2
 27 | plt.rcParams['figure.figsize'] = (10.0, 16.0)  # inches; 3 row: 10,10 5 row: 10,14
 28 | shrink_ratio = 1  # 3 row:0.81 5 row: 0.98
 29 | title = ['low noise', 'medium noise', 'high noise']
 30 | noiselevel = ['l', 'm', 'h']
 31 | 
 32 | log = open("RG"+str(recordnum)+".txt", 'w')
 33 | log.write(str(datetime.datetime.now().ctime())+'\n')
 34 | log.write("Rebuild\n")
 35 | log.write("Side: "+str(Side)+" Data: "+str(ids)+"-"+str(idt-1)+"\n")
 36 | log.write("Regions:"+str(n_regions)+" Micro_clusters:"+str(micro_clusters)+"\n")
 37 | log.write("Method: KModels AZP Reg_KModels GWR_Skater Skater_Reg\n")
 38 | 
 39 | for dataid in range(ids, idt):
 40 |     ofile = open("result_" + str(recordnum) + "_" + str(dataid) + ".txt", "w")
 41 |     fig, axes = plt.subplots(6, len(noiselevel), figsize=(10, 16))
 42 |     for noi in range(len(noiselevel)):
 43 |         print("Data " + str(dataid) + str(noiselevel[noi]))
 44 |         simdata = open("../synthetic/" + prefix + str(dataid) + noiselevel[noi]+".txt")
 45 |         Xarr, Yarr, label, coeff = input_data(simdata, Side, nvar)
 46 |         coord = GetCoord(Side)
 47 |         w = libpysal.weights.lat2W(Side, Side)
 48 | 
 49 |         axes[0, noi].set_xticks([])
 50 |         axes[0, noi].set_yticks([])
 51 |         axes[0, noi].set_title(title[noi])
 52 |         regmap = label.reshape((Side, Side))
 53 |         axes[0, noi].imshow(regmap)
 54 | 
 55 |         # KModels
 56 |         st = datetime.datetime.now()
 57 |         clabel, iters = kmodels(Xarr, Yarr, micro_clusters, w)
 58 |         slabel = split_components(w, clabel)
 59 |         rlabel, rcoeff, merges = greedy_merge(Xarr, Yarr, n_regions, w, slabel, min_size=min_region)
 60 |         ed = datetime.datetime.now()
 61 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
 62 |         print(metrics.result_str() + " " + str(ed - st) + " " + str(iters) + " " + str(merges))
 63 |         log.write(metrics.result_full_str() + " " + str(ed - st) + " " + str(iters) + " " + str(merges) + '\n')
 64 |         output_result(ofile, Side, rlabel, rcoeff, "KModels")
 65 |         axes[1, noi].set_title("KModels "+title[noi])
 66 |         axes[1, noi].set_xticks([])  # 去掉x轴
 67 |         axes[1, noi].set_yticks([])  # 去掉y轴
 68 |         axes[1, noi].imshow(reg_pic(Side, rlabel, dim=-1))
 69 | 
 70 |         # AZP
 71 |         st = datetime.datetime.now()
 72 |         rlabel, rcoeff, iters = azp(Xarr, Yarr, n_regions, w, min_size=min_region)
 73 |         ed = datetime.datetime.now()
 74 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
 75 |         print(metrics.result_str() + " " + str(ed - st) + " " + str(iters))
 76 |         log.write(metrics.result_full_str() + " " + str(ed - st) + " " + str(iters) + '\n')
 77 |         output_result(ofile, Side, rlabel, rcoeff, "AZP")
 78 |         axes[2, noi].set_title("AZP " + title[noi])
 79 |         axes[2, noi].set_xticks([])  # 去掉x轴
 80 |         axes[2, noi].set_yticks([])  # 去掉y轴
 81 |         axes[2, noi].imshow(reg_pic(Side, rlabel, dim=-1))
 82 | 
 83 |         # Region-K-Models
 84 |         st = datetime.datetime.now()
 85 |         rlabel, rcoeff, iters = region_k_models(Xarr, Yarr, n_regions, w, min_size=min_region)
 86 |         ed = datetime.datetime.now()
 87 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
 88 |         print(metrics.result_str() + " " + str(ed - st) + " " + str(iters))
 89 |         log.write(metrics.result_full_str() + " " + str(ed - st) + " " + str(iters) + '\n')
 90 |         output_result(ofile, Side, rlabel, rcoeff, "RegionKModels")
 91 |         axes[3, noi].set_title("RegKModels "+title[noi])
 92 |         axes[3, noi].set_xticks([])  # 去掉x轴
 93 |         axes[3, noi].set_yticks([])  # 去掉y轴
 94 |         axes[3, noi].imshow(reg_pic(Side, rlabel, dim=-1))
 95 | 
 96 |         # GWR_Skater
 97 |         st = datetime.datetime.now()
 98 |         rlabel, rcoeff = gwr_skater(Xarr, Yarr, n_regions, w, coord, min_size=min_region)
 99 |         ed = datetime.datetime.now()
100 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
101 |         print(metrics.result_str() + " " + str(ed - st))
102 |         log.write(metrics.result_full_str() + " " + str(ed - st) + '\n')
103 |         output_result(ofile, Side, rlabel, rcoeff, "GWR_Skater")
104 |         axes[4, noi].set_title("GSK "+title[noi])
105 |         axes[4, noi].set_xticks([])  # 去掉x轴
106 |         axes[4, noi].set_yticks([])  # 去掉y轴
107 |         axes[4, noi].imshow(reg_pic(Side, rlabel, dim=-1))
108 | 
109 |         # Skater_reg
110 |         st = datetime.datetime.now()
111 |         rlabel, rcoeff = skater_reg(Xarr, Yarr, n_regions, w, min_size=min_region)
112 |         ed = datetime.datetime.now()
113 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
114 |         print(metrics.result_str() + " " + str(ed - st))
115 |         log.write(metrics.result_full_str() + " " + str(ed - st) +'\n')
116 |         output_result(ofile, Side, rlabel, rcoeff, "Skater_Reg")
117 |         axes[5, noi].set_title("SKR " + title[noi])
118 |         axes[5, noi].set_xticks([])  # 去掉x轴
119 |         axes[5, noi].set_yticks([])  # 去掉y轴
120 |         axes[5, noi].imshow(reg_pic(Side, rlabel, dim=-1))
121 |         log.write("\n")
122 | 
123 |     plt.tight_layout()
124 |     fig.savefig('RG'+str(recordnum) + '-' + str(dataid) + '-' +
125 |                 str(datetime.datetime.now().strftime('%y%m%d%H%M%S')) + '.png')
126 |     plt.clf()
127 |     ofile.close()
128 | 
129 | log.close()
130 | 


--------------------------------------------------------------------------------
/src/grid_stab.py:
--------------------------------------------------------------------------------
  1 | from GridData9 import *
  2 | from Algorithm9 import *
  3 | import datetime
  4 | from matplotlib import pyplot as plt
  5 | import libpysal
  6 | 
  7 | # Test Mode
  8 | # Side = 10
  9 | # dataid = 0
 10 | # n_regions = 5
 11 | # prefix = 'gridtest_'
 12 | # micro_clusters = 10
 13 | # min_region = 5
 14 | # repeat = 3
 15 | 
 16 | # Run Mode
 17 | Side = 25
 18 | dataid = 4
 19 | n_regions = 5
 20 | prefix = 'grid_'
 21 | micro_clusters = 20
 22 | min_region = 10
 23 | repeat = 50
 24 | 
 25 | recordnum = 67
 26 | 
 27 | # Zones params
 28 | nvar = 2
 29 | plt.rcParams['figure.figsize'] = (10.0, 10.0)  # inches; 3 row: 10,10 5 row: 10,14
 30 | shrink_ratio = 1  # 3 row:0.81 5 row: 0.98
 31 | title = ['low noise', 'medium noise', 'high noise']
 32 | noiselevel = ['l', 'm', 'h']
 33 | 
 34 | log = open("RG"+str(recordnum)+".txt", 'w')
 35 | log.write(str(datetime.datetime.now().ctime())+'\n')
 36 | log.write("Stability\n")
 37 | log.write(f"Side: {Side} Data: {dataid} Repeat: {repeat}\n")
 38 | log.write("Regions:"+str(n_regions)+" Micro_clusters:"+str(micro_clusters)+"\n")
 39 | log.write("Method: KModels AZP Reg_KModels\n")
 40 | 
 41 | for rep in range(repeat):
 42 |     ofile = open(f"result_{recordnum}_{dataid}_R{rep}.txt", "w")
 43 |     fig, axes = plt.subplots(4, len(noiselevel), figsize=(10, 10))
 44 |     for noi in range(len(noiselevel)):
 45 |         print(f"Data {dataid}{noiselevel[noi]} R{rep}")
 46 |         simdata = open("../synthetic/" + prefix + str(dataid) + noiselevel[noi]+".txt")
 47 |         Xarr, Yarr, label, coeff = input_data(simdata, Side, nvar)
 48 |         coord = GetCoord(Side)
 49 |         w = libpysal.weights.lat2W(Side, Side)
 50 | 
 51 |         axes[0, noi].set_xticks([])
 52 |         axes[0, noi].set_yticks([])
 53 |         axes[0, noi].set_title(title[noi])
 54 |         regmap = label.reshape((Side, Side))
 55 |         axes[0, noi].imshow(regmap)
 56 | 
 57 |         # KModels
 58 |         st = datetime.datetime.now()
 59 |         clabel, iters = kmodels(Xarr, Yarr, micro_clusters, w)
 60 |         slabel = split_components(w, clabel)
 61 |         rlabel, rcoeff, merges = greedy_merge(Xarr, Yarr, n_regions, w, slabel, min_size=min_region)
 62 |         ed = datetime.datetime.now()
 63 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
 64 |         print(metrics.result_str() + " " + str(ed - st) + " " + str(iters) + " " + str(merges))
 65 |         log.write(metrics.result_full_str() + " " + str(ed - st) + " " + str(iters) + " " + str(merges) + '\n')
 66 |         output_result(ofile, Side, rlabel, rcoeff, "KModels")
 67 |         axes[1, noi].set_title("KModels "+title[noi])
 68 |         axes[1, noi].set_xticks([])  # 去掉x轴
 69 |         axes[1, noi].set_yticks([])  # 去掉y轴
 70 |         axes[1, noi].imshow(reg_pic(Side, rlabel, dim=-1))
 71 | 
 72 |         # AZP
 73 |         st = datetime.datetime.now()
 74 |         rlabel, rcoeff, iters = azp(Xarr, Yarr, n_regions, w, min_size=min_region)
 75 |         ed = datetime.datetime.now()
 76 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
 77 |         print(metrics.result_str() + " " + str(ed - st) + " " + str(iters))
 78 |         log.write(metrics.result_full_str() + " " + str(ed - st) + " " + str(iters) + '\n')
 79 |         output_result(ofile, Side, rlabel, rcoeff, "AZP")
 80 |         axes[2, noi].set_title("AZP " + title[noi])
 81 |         axes[2, noi].set_xticks([])  # 去掉x轴
 82 |         axes[2, noi].set_yticks([])  # 去掉y轴
 83 |         axes[2, noi].imshow(reg_pic(Side, rlabel, dim=-1))
 84 | 
 85 |         # Region-K-Models
 86 |         st = datetime.datetime.now()
 87 |         rlabel, rcoeff, iters = region_k_models(Xarr, Yarr, n_regions, w, min_size=min_region)
 88 |         ed = datetime.datetime.now()
 89 |         metrics = Grid_Region_Metrics(Xarr, Yarr, label, rlabel, coeff, rcoeff)
 90 |         print(metrics.result_str() + " " + str(ed - st) + " " + str(iters))
 91 |         log.write(metrics.result_full_str() + " " + str(ed - st) + " " + str(iters) + '\n')
 92 |         output_result(ofile, Side, rlabel, rcoeff, "RegionKModels")
 93 |         axes[3, noi].set_title("RegKModels "+title[noi])
 94 |         axes[3, noi].set_xticks([])  # 去掉x轴
 95 |         axes[3, noi].set_yticks([])  # 去掉y轴
 96 |         axes[3, noi].imshow(reg_pic(Side, rlabel, dim=-1))
 97 | 
 98 |         log.write("\n")
 99 | 
100 |     plt.tight_layout()
101 |     fig.savefig(f'RG{recordnum}-{dataid}-R{rep}-'+
102 |                 str(datetime.datetime.now().strftime('%y%m%d%H%M%S')) + '.png')
103 |     plt.clf()
104 |     ofile.close()
105 | 
106 | log.close()


--------------------------------------------------------------------------------
/src/kinghouse.py:
--------------------------------------------------------------------------------
  1 | from Algorithm9 import *
  2 | from GridData9 import output_coeff
  3 | import networkx
  4 | import datetime
  5 | import xlrd
  6 | import xlwt
  7 | import libpysal
  8 | 
  9 | cmp = "bwr"
 10 | pmin = 5
 11 | pmax = 5
 12 | Kfac = 2
 13 | numid = 1
 14 | min_region = 20
 15 | 
 16 | log = open("Kinghouse_"+str(numid)+".txt", 'w')
 17 | log.write(str(datetime.datetime.now().ctime())+'\n')
 18 | log.write("Method: KModels SkaterReg \n")
 19 | log.write(f"pmin: {pmin} pmax: {pmax} Kfac: {Kfac}\n")
 20 | 
 21 | data = xlrd.open_workbook("../hedonic/kc_house_r5.xls")
 22 | table = data.sheets()[0]
 23 | nobs = table.nrows - 1
 24 | nvar = 16
 25 | print(nobs, nvar)
 26 | AreaIndex = {}
 27 | IndexArea = []
 28 | X = []
 29 | Y = []
 30 | coord = []
 31 | 
 32 | for r in range(1, nobs+1):
 33 |     Areaid = table.cell_value(r, 0)
 34 |     logprice = table.cell_value(r, 2)
 35 |     bedroom = table.cell_value(r, 3)
 36 |     bathroom = table.cell_value(r, 4)
 37 |     sqft_liv = table.cell_value(r, 5)
 38 |     sqft_lot = table.cell_value(r, 6)
 39 |     floors = table.cell_value(r, 7)
 40 |     renovated = table.cell_value(r, 8)
 41 |     age = table.cell_value(r, 9)
 42 |     age2 = table.cell_value(r, 10)
 43 |     sqft_liv15 = table.cell_value(r, 11)
 44 |     sqft_lot15 = table.cell_value(r, 12)
 45 |     viewd = table.cell_value(r, 13)
 46 |     condn = table.cell_value(r, 14)
 47 |     avggrade = table.cell_value(r, 15)
 48 |     abvavgrd = table.cell_value(r, 16)
 49 |     greatgrd = table.cell_value(r, 17)
 50 |     distnn = table.cell_value(r, 18)
 51 |     coordX = table.cell_value(r, 19)
 52 |     coordY = table.cell_value(r, 20)
 53 |     AreaIndex[Areaid] = r-1
 54 |     IndexArea.append(Areaid)
 55 |     X.append([bedroom, bathroom, sqft_liv, sqft_lot, floors, renovated, age, age2,
 56 |               sqft_liv15, sqft_lot15, viewd, condn, avggrade, abvavgrd, greatgrd, distnn])
 57 |     Y.append([logprice])
 58 |     coord.append((coordX,coordY))
 59 | 
 60 | Xarr = preprocessing.StandardScaler().fit_transform(np.array(X))
 61 | Yarr = preprocessing.StandardScaler().fit_transform(np.array(Y))
 62 | Xarr = np.array([[1]+list(datapoint) for datapoint in Xarr])
 63 | Xarr = Xarr.reshape((nobs, nvar+1))
 64 | Yarr = Yarr.reshape(nobs)
 65 | 
 66 | knn = libpysal.weights.KNN.from_shapefile("../hedonic/kc_house_utm_r5.shp", k=18)
 67 | w = knn.symmetrize()
 68 | print(networkx.is_connected(weights_to_graph(w)))
 69 | units = np.arange(w.n).astype(int)
 70 | 
 71 | outxls = xlwt.Workbook(encoding='utf-8')
 72 | 
 73 | for n_regions in range(pmin, pmax+1):
 74 |     micro_clusters = Kfac*n_regions
 75 |     print(f"{n_regions} Regions")
 76 | 
 77 |     params = outxls.add_sheet(f'R{n_regions}')
 78 |     params.write(0, 0, label='Area_Key')
 79 |     params.write(0, 1, label='Zone_KM')
 80 |     params.write(0, 2, label='Zone_SKR')
 81 |     for u in range(nobs):
 82 |         params.write(u + 1, 0, label=int(IndexArea[u]))
 83 | 
 84 |     # KModels
 85 |     st = datetime.datetime.now()
 86 |     clabel, iters = kmodels(Xarr, Yarr, micro_clusters, w, init_stoc_step=False, verbose=True)
 87 |     print(f"km_finish {datetime.datetime.now()-st}")
 88 |     slabel = split_components(w, clabel)
 89 |     print(f"sl_finish {datetime.datetime.now()-st}")
 90 |     rlabel, rcoeff, merges = greedy_merge(Xarr, Yarr, n_regions, w, slabel, min_size=min_region, verbose=True)
 91 |     ed = datetime.datetime.now()
 92 |     regions = [units[rlabel == r].tolist() for r in set(rlabel)]
 93 |     ssr = regression_error(regions, Xarr, Yarr)
 94 |     log.write(f'{ssr} {ed - st} {iters} {merges}\n')
 95 |     print(f'{ssr} {ed - st} {iters} {merges}')
 96 |     for zoneid in range(len(regions)):
 97 |         z = regions[zoneid]
 98 |         for u in z:
 99 |             params.write(u + 1, 1, label=zoneid)
100 |     output_coeff(log, rcoeff)
101 |     Test_Equations(regions, Xarr, Yarr, log)
102 | 
103 |     '''
104 |     # GWR_Skater
105 |     st = datetime.datetime.now()
106 |     rlabel, rcoeff = gwr_skater(Xarr, Yarr, n_regions, w, coord, min_size=min_region)
107 |     ed = datetime.datetime.now()
108 |     regions = [units[rlabel == r].tolist() for r in set(rlabel)]
109 |     ssr = regression_error(regions, Xarr, Yarr)
110 |     print(f'{ssr}')
111 |     log.write(f'{ssr} {ed - st}\n')
112 |     for zoneid in range(len(regions)):
113 |         z = regions[zoneid]
114 |         for u in z:
115 |             params.write(u + 1, 20, label=zoneid)
116 |             for v in range(nvar + 1):
117 |                 params.write(u + 1, 21 + v, label=rcoeff[zoneid][v])
118 |     output_coeff(log, rcoeff)
119 |     Test_Equations(regions, Xarr, Yarr, log)
120 |     
121 |     DNF in 30 min
122 |     '''
123 | 
124 |     # SkaterReg
125 |     st = datetime.datetime.now()
126 |     rlabel, rcoeff = skater_reg(Xarr, Yarr, n_regions, w, min_size=min_region)
127 |     ed = datetime.datetime.now()
128 |     regions = [units[rlabel == r].tolist() for r in set(rlabel)]
129 |     ssr = regression_error(regions, Xarr, Yarr)
130 |     print(f'{ssr}')
131 |     log.write(f'{ssr} {ed - st}\n\n')
132 |     for zoneid in range(len(regions)):
133 |         z = regions[zoneid]
134 |         for u in z:
135 |             params.write(u + 1, 2, label=zoneid)
136 |     output_coeff(log, rcoeff)
137 |     Test_Equations(regions, Xarr, Yarr, log)
138 | 
139 | log.close()
140 | outxls.save(f'kc_house_{numid}.xls')


--------------------------------------------------------------------------------
/synthetic/gridtest_0h.txt:
--------------------------------------------------------------------------------
 1 | 0.513779 0.809392 0.206208 0.117965 0.810811 0.800774 0.446916 0.628059 0.970805 0.418974 
 2 | 0.630016 0.272677 0.724966 0.604589 0.737761 0.553525 0.940133 0.119075 0.899666 0.520357 
 3 | 0.540103 0.156567 0.353946 0.152132 0.777149 0.673309 0.591285 0.413465 0.351616 0.972849 
 4 | 0.943271 0.931344 0.869882 0.619933 0.413012 0.216956 0.869478 0.306785 0.701634 0.478613 
 5 | 0.045289 0.315545 0.305932 0.728173 0.011997 0.513763 0.003605 0.211351 0.063995 0.998458 
 6 | 0.937413 0.307395 0.028939 0.181711 0.927429 0.668097 0.615688 0.901988 0.887100 0.702140 
 7 | 0.558434 0.655746 0.507911 0.084475 0.596083 0.183209 0.614087 0.695977 0.334613 0.731355 
 8 | 0.792059 0.641835 0.955856 0.706767 0.931670 0.482119 0.864267 0.082036 0.876541 0.209772 
 9 | 0.618958 0.231046 0.967656 0.880558 0.281131 0.729467 0.357436 0.663434 0.684779 0.424216 
10 | 0.768187 0.335849 0.321030 0.010283 0.964144 0.121142 0.178237 0.299631 0.165040 0.479819 
11 | 
12 | 0.631887 0.259515 0.770093 0.523926 0.854872 0.384034 0.409522 0.445691 0.323287 0.245081 
13 | 0.716895 0.656977 0.503010 0.772517 0.982942 0.235374 0.808552 0.444130 0.430420 0.024143 
14 | 0.200153 0.630433 0.077077 0.747454 0.347177 0.004538 0.892227 0.774413 0.512056 0.054031 
15 | 0.409257 0.841360 0.412991 0.470967 0.457104 0.222908 0.312359 0.515664 0.828513 0.842827 
16 | 0.368964 0.647081 0.649278 0.832482 0.776184 0.103601 0.822498 0.952391 0.534084 0.282021 
17 | 0.288949 0.435061 0.316751 0.939977 0.356245 0.049115 0.703765 0.585404 0.771766 0.175919 
18 | 0.932183 0.586575 0.082762 0.523645 0.004339 0.475281 0.880968 0.291936 0.783159 0.197005 
19 | 0.873746 0.817069 0.824274 0.952584 0.915314 0.909849 0.957056 0.848742 0.671097 0.167549 
20 | 0.479156 0.316489 0.063634 0.406277 0.792499 0.137233 0.828374 0.140366 0.470347 0.969531 
21 | 0.110810 0.844691 0.828859 0.001272 0.364066 0.369802 0.662028 0.480847 0.731607 0.253199 
22 | 
23 | -0.880918 0.444687 -1.516925 -1.420479 -1.320190 -0.343646 -0.313069 -0.038961 0.681197 0.308289 
24 | -1.000327 -0.980397 -0.253091 -1.165014 -1.415399 -0.143196 -1.211573 -0.513534 0.343468 0.287617 
25 | -0.692602 -0.042890 -0.459724 -0.355423 -1.167953 -0.823642 -0.472771 -0.199139 -0.782324 -0.982109 
26 | -0.739370 -1.112212 -0.961894 -0.837528 -0.675432 0.202885 -1.041851 -0.311313 -0.384622 -0.652741 
27 | 0.447863 -0.050158 -0.040533 -0.689409 0.843130 -0.683648 0.537195 0.477209 0.452019 -1.619458 
28 | -1.706390 -0.056010 0.782671 0.507462 -1.635231 -1.429401 -0.840029 -1.143135 -0.874346 -0.588250 
29 | 0.173363 0.441050 1.036690 -0.452444 0.396778 -0.036004 0.163319 1.038300 0.112727 0.698984 
30 | 0.658270 0.775856 1.011994 0.484138 1.219434 -0.412849 0.707937 -1.169626 0.858749 0.372973 
31 | 0.912175 0.483207 0.171303 0.194265 1.129427 0.725961 1.646019 0.781742 1.037729 2.314961 
32 | 0.234614 1.584147 1.447469 -0.270281 0.403365 1.030551 1.166352 0.923538 1.397712 0.226153 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
50 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
51 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
52 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
53 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
54 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_0l.txt:
--------------------------------------------------------------------------------
 1 | 0.708862 0.859950 0.212070 0.552091 0.762532 0.681456 0.026965 0.322686 0.665737 0.162918 
 2 | 0.873542 0.381843 0.980948 0.110765 0.384853 0.389311 0.418297 0.299341 0.969775 0.941478 
 3 | 0.767742 0.297549 0.929789 0.263320 0.476337 0.901324 0.340002 0.015684 0.138584 0.701809 
 4 | 0.477940 0.821565 0.749109 0.823840 0.931205 0.256630 0.696665 0.209148 0.770601 0.302994 
 5 | 0.569937 0.510557 0.186067 0.771730 0.748405 0.158722 0.471154 0.193854 0.060833 0.561209 
 6 | 0.179216 0.157969 0.959688 0.345315 0.776213 0.702174 0.745319 0.550347 0.952733 0.086654 
 7 | 0.673948 0.327601 0.223445 0.324337 0.984514 0.684072 0.194618 0.190413 0.725292 0.089283 
 8 | 0.399064 0.347932 0.658267 0.132353 0.495103 0.438592 0.550836 0.377098 0.938897 0.132420 
 9 | 0.052736 0.864505 0.724242 0.975063 0.379771 0.073506 0.741692 0.095042 0.408956 0.501165 
10 | 0.358530 0.078833 0.210375 0.884168 0.090526 0.638670 0.890105 0.968707 0.353703 0.770031 
11 | 
12 | 0.030680 0.532877 0.666625 0.482949 0.661736 0.553333 0.767283 0.291082 0.843495 0.521412 
13 | 0.196929 0.600514 0.075297 0.699472 0.961155 0.747203 0.619768 0.747554 0.250491 0.337066 
14 | 0.987807 0.215361 0.098854 0.278856 0.938561 0.062840 0.220016 0.171829 0.787537 0.506552 
15 | 0.832929 0.674076 0.501338 0.458013 0.497788 0.920087 0.153353 0.512893 0.877085 0.146905 
16 | 0.997307 0.434037 0.319443 0.769504 0.035933 0.734356 0.835901 0.944251 0.672748 0.941634 
17 | 0.909477 0.812857 0.428602 0.472561 0.153806 0.082091 0.701680 0.943710 0.632183 0.516678 
18 | 0.967934 0.810984 0.781888 0.702642 0.290173 0.745240 0.291658 0.520221 0.415261 0.980917 
19 | 0.632050 0.593861 0.391160 0.733391 0.079130 0.676483 0.833012 0.672350 0.077855 0.257503 
20 | 0.183183 0.998131 0.639726 0.309077 0.652552 0.881292 0.777910 0.632338 0.566688 0.478673 
21 | 0.932210 0.833847 0.415248 0.200342 0.769351 0.730779 0.137082 0.604296 0.070912 0.728476 
22 | 
23 | 0.657639 -0.070370 -1.066866 -0.485684 -0.711627 -0.415973 -1.481483 -0.259134 -0.955540 -0.933613 
24 | 0.387900 -0.790889 0.935749 -1.398421 -1.534792 -1.111045 -0.912640 -1.099813 0.446538 0.331296 
25 | -0.867200 -0.276445 -0.969260 -0.424905 -0.476838 -1.020571 -0.392097 -0.028552 -0.238239 -0.750056 
26 | -0.544568 -0.900576 -0.791586 -0.799760 -0.797780 -0.285537 -0.812849 -0.083209 -0.661185 -0.267841 
27 | -0.072916 -0.546971 0.041399 -0.681894 -1.579972 0.460646 -0.037525 0.447875 0.590928 -0.318715 
28 | 0.646774 0.629618 -1.399026 -0.253427 -1.213436 -1.375664 -0.974810 -0.227710 -1.285008 0.305743 
29 | 0.313408 0.084603 -0.302173 0.010778 1.734990 0.836116 -0.011575 -0.034345 1.037763 -0.755892 
30 | 0.056166 0.064627 0.736467 -0.285321 0.907252 0.040469 0.247855 -0.011985 1.758874 -0.234709 
31 | 0.395757 1.973524 1.425392 0.593221 1.094356 1.850775 1.548055 1.267615 1.271381 0.967892 
32 | 1.787321 1.645005 0.913465 0.351341 1.504968 1.462705 0.202416 1.223153 0.127131 1.446301 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
50 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
51 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
52 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
53 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
54 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_0m.txt:
--------------------------------------------------------------------------------
 1 | 0.664202 0.060151 0.767985 0.617831 0.907988 0.912119 0.545040 0.506095 0.757303 0.594484 
 2 | 0.755126 0.663675 0.200336 0.057615 0.367959 0.577706 0.141362 0.610647 0.169689 0.130049 
 3 | 0.453390 0.101050 0.331314 0.781272 0.240594 0.703693 0.518031 0.064885 0.169269 0.906925 
 4 | 0.847436 0.281028 0.387578 0.846359 0.779905 0.984043 0.252904 0.056612 0.712472 0.740648 
 5 | 0.158719 0.216090 0.235949 0.551484 0.284754 0.987187 0.169640 0.633117 0.418473 0.808141 
 6 | 0.202204 0.320217 0.304743 0.137613 0.734949 0.626027 0.696662 0.095233 0.532877 0.695151 
 7 | 0.364630 0.363031 0.731528 0.571893 0.946442 0.282650 0.814954 0.829472 0.554217 0.360017 
 8 | 0.700757 0.696794 0.197425 0.954178 0.378481 0.704266 0.616347 0.894136 0.106123 0.444347 
 9 | 0.664260 0.569073 0.718638 0.431428 0.407343 0.801764 0.830864 0.700177 0.726185 0.834980 
10 | 0.665059 0.568759 0.852018 0.634605 0.524762 0.718304 0.706466 0.705156 0.270932 0.479362 
11 | 
12 | 0.352041 0.676797 0.685430 0.894430 0.240345 0.783637 0.069790 0.685380 0.724111 0.344743 
13 | 0.734630 0.888201 0.089888 0.257310 0.587424 0.022737 0.991469 0.307749 0.640766 0.875418 
14 | 0.522419 0.296468 0.031135 0.722653 0.727515 0.087292 0.204356 0.317522 0.029247 0.279170 
15 | 0.070616 0.440140 0.680511 0.160446 0.051739 0.647822 0.237983 0.577038 0.887464 0.337170 
16 | 0.346202 0.729451 0.859943 0.876204 0.730135 0.696292 0.089601 0.236781 0.332401 0.973882 
17 | 0.237890 0.316560 0.721522 0.451940 0.165668 0.633767 0.931931 0.726465 0.584268 0.481306 
18 | 0.878795 0.056872 0.464803 0.516199 0.773272 0.483737 0.447121 0.092933 0.377318 0.859069 
19 | 0.458620 0.765405 0.800901 0.454655 0.022790 0.345610 0.541205 0.166033 0.623901 0.333164 
20 | 0.699360 0.223655 0.869894 0.850000 0.936970 0.938837 0.301804 0.576172 0.814512 0.296896 
21 | 0.041654 0.450852 0.448247 0.263676 0.703569 0.156730 0.173260 0.693190 0.588707 0.653428 
22 | 
23 | 0.122266 -1.228676 -0.811915 -0.890950 0.728146 -0.509634 0.470697 -0.880527 -0.785223 -0.386043 
24 | -0.613795 -1.153416 0.079296 -0.370140 -1.069124 0.720681 -1.770700 -0.208311 -1.161741 -1.832353 
25 | -0.383547 -0.091966 -0.100133 -0.625185 -0.189148 -0.468631 -0.736390 -0.052622 -0.038203 -0.944541 
26 | -0.491607 0.139647 -0.646135 -0.713924 -1.021596 -1.149316 0.034383 -0.101096 -1.069921 -0.893857 
27 | 0.187008 0.268538 0.261626 -0.361941 0.105201 -1.340494 -0.680815 -0.626288 -0.583010 -0.860188 
28 | -0.106568 -0.394171 0.248181 -0.123915 -1.367955 -1.078698 -0.472849 0.639401 -0.179402 -1.468456 
29 | 0.000342 0.699007 0.862338 0.774974 1.390601 0.286036 1.058522 1.758127 0.691083 -0.212197 
30 | 0.699485 0.618126 -0.516336 1.883671 0.565306 1.232068 0.402488 1.695128 -0.526052 0.714279 
31 | 1.226556 0.715628 1.712165 1.850741 1.827565 2.028936 0.563454 0.887332 1.598002 0.401469 
32 | 0.080170 0.778885 0.869795 0.832563 1.240891 0.362155 0.156554 1.424203 1.373771 1.056434 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
50 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
51 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
52 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
53 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
54 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_0n.txt:
--------------------------------------------------------------------------------
 1 | 0.707509 0.455632 0.404357 0.874732 0.626675 0.612625 0.029512 0.969659 0.486899 0.665607 
 2 | 0.565035 0.993057 0.952355 0.009327 0.409753 0.565369 0.716988 0.998687 0.850748 0.920761 
 3 | 0.057329 0.012041 0.903702 0.408576 0.647961 0.801997 0.275986 0.987029 0.719120 0.261318 
 4 | 0.776395 0.139832 0.343337 0.006584 0.458998 0.712175 0.133870 0.393707 0.195639 0.740155 
 5 | 0.189603 0.899051 0.967181 0.078376 0.682792 0.552785 0.569291 0.448254 0.785678 0.875815 
 6 | 0.000626 0.370330 0.314242 0.138177 0.335223 0.896683 0.435339 0.160004 0.211648 0.529943 
 7 | 0.780978 0.753106 0.673573 0.439301 0.585879 0.993873 0.593563 0.358321 0.643531 0.955206 
 8 | 0.528656 0.661835 0.185885 0.673956 0.683056 0.745702 0.658550 0.942577 0.652369 0.226573 
 9 | 0.338025 0.693900 0.667453 0.865219 0.907282 0.510824 0.402380 0.380799 0.417811 0.452378 
10 | 0.453372 0.702017 0.825302 0.640252 0.663854 0.243677 0.878877 0.910207 0.855591 0.511098 
11 | 
12 | 0.678449 0.552847 0.024764 0.751312 0.266411 0.082058 0.091061 0.577391 0.204175 0.111551 
13 | 0.646933 0.491493 0.374354 0.768436 0.558430 0.723330 0.168445 0.711173 0.905511 0.518110 
14 | 0.956043 0.418276 0.524216 0.444539 0.881797 0.570823 0.748947 0.272955 0.654740 0.813516 
15 | 0.842932 0.130055 0.800598 0.889120 0.962923 0.260060 0.223260 0.541793 0.846644 0.188515 
16 | 0.011319 0.360645 0.922785 0.495327 0.274077 0.719510 0.277619 0.485111 0.549398 0.866357 
17 | 0.170853 0.352284 0.729443 0.725825 0.012976 0.943555 0.256741 0.063024 0.331171 0.147026 
18 | 0.689466 0.728235 0.861325 0.917098 0.627560 0.365171 0.153990 0.049738 0.401077 0.004012 
19 | 0.287738 0.738492 0.538057 0.036843 0.183430 0.262530 0.806220 0.577931 0.633396 0.873351 
20 | 0.279240 0.234591 0.340807 0.358639 0.778412 0.783127 0.022761 0.985127 0.282808 0.230462 
21 | 0.772629 0.992146 0.215050 0.080776 0.369054 0.328236 0.019340 0.867319 0.176006 0.638509 
22 | 
23 | -0.649390 -0.650063 0.354829 -0.627892 0.093854 0.448510 -0.152610 -0.185123 0.078550 0.442504 
24 | -0.728830 0.010072 0.203646 -1.527545 -0.707107 -0.881291 0.380097 -0.423659 -0.960273 -0.115460 
25 | -0.057329 -0.012041 -0.903702 -0.408576 -0.647961 -0.801997 -0.275986 -0.987029 -0.719120 -0.261318 
26 | -0.776395 -0.139832 -0.343337 -0.006584 -0.458998 -0.712175 -0.133870 -0.393707 -0.195639 -0.740155 
27 | -0.367888 -1.437458 -1.011577 0.338575 -1.091507 -0.386061 -0.860963 -0.411398 -1.021958 -0.885272 
28 | 0.169601 -0.388376 0.100960 0.449471 -0.657469 -0.849812 -0.613936 -0.256983 -0.092124 -0.912860 
29 | 0.872491 0.777978 0.485821 -0.038496 0.544198 1.622575 1.033136 0.666904 0.885985 1.906401 
30 | 0.769574 0.585178 -0.166286 1.311069 1.182682 1.228874 0.510881 1.307222 0.671341 -0.420205 
31 | 0.558481 0.469182 0.681614 0.717277 1.556824 1.566253 0.045522 1.970254 0.565616 0.460924 
32 | 1.545259 1.984293 0.430099 0.161553 0.738109 0.656472 0.038680 1.734638 0.352012 1.277017 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
50 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
51 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
52 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
53 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
54 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_1h.txt:
--------------------------------------------------------------------------------
 1 | 0.473445 0.151897 0.031031 0.757350 0.788227 0.973607 0.348036 0.651894 0.472485 0.389897 
 2 | 0.741674 0.358455 0.345463 0.053117 0.005246 0.659544 0.094531 0.566478 0.291798 0.595782 
 3 | 0.373039 0.394089 0.696938 0.029298 0.349557 0.137940 0.384753 0.955699 0.197074 0.019321 
 4 | 0.093161 0.930699 0.692830 0.973025 0.441592 0.216492 0.022330 0.714921 0.812233 0.000920 
 5 | 0.018935 0.697834 0.514853 0.219556 0.177855 0.505366 0.981979 0.820568 0.235083 0.658711 
 6 | 0.419303 0.854792 0.148037 0.656621 0.911307 0.213355 0.441204 0.014300 0.901464 0.683746 
 7 | 0.727083 0.753417 0.684222 0.872575 0.319421 0.190754 0.510945 0.203899 0.552479 0.490680 
 8 | 0.877739 0.604175 0.015068 0.770329 0.274979 0.140444 0.999582 0.591065 0.522366 0.997042 
 9 | 0.608933 0.134302 0.587695 0.589940 0.428741 0.327224 0.725320 0.585694 0.115997 0.593136 
10 | 0.026961 0.430523 0.398542 0.775492 0.971790 0.306009 0.667604 0.557624 0.919717 0.653103 
11 | 
12 | 0.496023 0.625676 0.432991 0.483219 0.116105 0.570000 0.219051 0.998687 0.418355 0.545785 
13 | 0.966985 0.230062 0.767061 0.761725 0.019821 0.610519 0.698694 0.364631 0.712291 0.675442 
14 | 0.872297 0.173077 0.945920 0.594864 0.646330 0.180572 0.941184 0.963953 0.152960 0.725292 
15 | 0.486506 0.637736 0.730257 0.384469 0.593459 0.419636 0.244722 0.276018 0.072000 0.057680 
16 | 0.890697 0.112248 0.924884 0.488202 0.164000 0.036732 0.131130 0.407613 0.830379 0.823597 
17 | 0.462442 0.274631 0.360993 0.894517 0.739634 0.605729 0.608962 0.981705 0.570987 0.863279 
18 | 0.185329 0.876623 0.336746 0.330172 0.816307 0.073556 0.891693 0.657355 0.676166 0.495379 
19 | 0.917373 0.506304 0.013428 0.336706 0.956993 0.793194 0.536151 0.052997 0.337335 0.443852 
20 | 0.379777 0.886176 0.705333 0.373322 0.883157 0.693741 0.160845 0.800942 0.263570 0.751112 
21 | 0.235397 0.167450 0.914888 0.952719 0.445468 0.869217 0.533980 0.211679 0.661427 0.559810 
22 | 
23 | 0.531815 1.480108 0.466975 1.238323 0.689075 1.626495 0.117509 1.759913 0.672238 1.356143 
24 | 2.003484 0.919229 1.214137 0.746272 -0.408321 1.719803 1.011802 1.107265 1.252300 1.675211 
25 | 0.886223 0.817515 1.250691 -0.211345 1.053212 0.361811 0.218548 1.732241 0.767457 0.411193 
26 | 0.543505 1.747554 1.435652 2.312299 0.711863 0.371567 0.013312 1.146183 1.444130 -0.012013 
27 | -0.874719 -1.143220 -0.992359 -0.851043 -0.848533 -0.409607 -1.009028 -1.018738 -0.807468 -1.489019 
28 | -0.940326 -1.055378 -0.459810 -1.462062 -1.985183 -0.974366 -0.727236 -1.174645 -1.158381 -1.607030 
29 | -0.499604 -1.604642 -0.447552 -0.861447 -1.864248 -0.050263 -1.982861 -0.483176 -1.759307 -1.040345 
30 | -2.091397 -0.965941 -0.512091 -0.582409 -2.113850 -1.199532 -1.048829 0.514856 -0.738112 -0.842941 
31 | -0.248186 1.821963 0.401485 -0.250462 1.344227 0.723120 -1.339400 0.340977 0.378608 0.165752 
32 | -0.098668 -1.153532 1.486349 0.203885 -1.370060 0.667777 -0.818222 -0.619482 -0.693824 -0.266833 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
48 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
55 | 
56 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
57 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
61 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
63 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_1l.txt:
--------------------------------------------------------------------------------
 1 | 0.774215 0.145939 0.124120 0.321055 0.555585 0.308118 0.939961 0.062750 0.131453 0.349918 
 2 | 0.828357 0.448600 0.824732 0.290004 0.894343 0.378092 0.252085 0.796734 0.013514 0.957504 
 3 | 0.953392 0.543525 0.523632 0.191338 0.933360 0.234664 0.281852 0.317305 0.955209 0.976922 
 4 | 0.999065 0.207832 0.892025 0.130676 0.467845 0.268267 0.118604 0.489324 0.035889 0.003440 
 5 | 0.874234 0.780798 0.354221 0.573311 0.848818 0.692236 0.123262 0.379768 0.422859 0.753819 
 6 | 0.347438 0.849198 0.257110 0.956521 0.561788 0.897341 0.432454 0.148665 0.670008 0.464586 
 7 | 0.483606 0.088822 0.661975 0.570658 0.804623 0.614713 0.530157 0.655335 0.129472 0.428190 
 8 | 0.081567 0.132670 0.485595 0.124768 0.296098 0.170538 0.994412 0.510938 0.247716 0.525119 
 9 | 0.245981 0.881562 0.902549 0.203855 0.351407 0.792944 0.823352 0.293514 0.240686 0.744248 
10 | 0.054772 0.741960 0.425771 0.620913 0.781736 0.331053 0.303178 0.228244 0.258473 0.153139 
11 | 
12 | 0.088197 0.683231 0.325451 0.672643 0.775438 0.968823 0.005563 0.588349 0.419561 0.132908 
13 | 0.329106 0.046942 0.328460 0.873980 0.337615 0.723600 0.565203 0.473697 0.019502 0.006947 
14 | 0.736171 0.971757 0.082556 0.394729 0.800223 0.840749 0.670746 0.350769 0.011199 0.210084 
15 | 0.655396 0.913267 0.781936 0.569448 0.153986 0.377125 0.517347 0.049446 0.701935 0.404022 
16 | 0.867829 0.651353 0.651529 0.524961 0.482537 0.617106 0.056997 0.826515 0.084005 0.608181 
17 | 0.863174 0.397492 0.305609 0.264744 0.520543 0.109639 0.900457 0.283518 0.300551 0.751549 
18 | 0.423339 0.773189 0.942742 0.030708 0.077039 0.043984 0.460744 0.812692 0.727651 0.439078 
19 | 0.548953 0.308105 0.902142 0.849613 0.173508 0.087099 0.471714 0.124264 0.091492 0.744746 
20 | 0.581550 0.394928 0.143844 0.377435 0.568371 0.677685 0.208907 0.456142 0.165398 0.718938 
21 | 0.245818 0.866515 0.576135 0.004805 0.562489 0.461686 0.683820 0.731720 0.718841 0.654271 
22 | 
23 | 0.722882 0.825339 0.517289 0.970004 1.157313 1.429938 0.917956 0.681954 0.480411 0.585057 
24 | 1.103233 0.515428 1.101185 0.852270 1.344475 1.133111 0.884341 1.151366 0.194309 1.062509 
25 | 1.943132 1.186071 0.964529 0.473168 1.905321 0.562103 0.545010 0.736253 1.726274 1.905494 
26 | 2.152678 0.442376 1.891524 0.305025 1.124736 0.587727 0.302601 1.225724 0.089632 -0.063571 
27 | -1.732728 -1.318939 -1.047917 -1.004278 -1.022357 -1.381807 -0.163241 -1.222836 -0.581804 -1.409855 
28 | -1.157637 -1.242936 -0.499584 -1.151445 -1.136211 -0.975912 -1.214695 -0.374016 -0.978013 -1.087612 
29 | -0.790834 -1.573852 -1.962704 -0.012726 -0.061089 -0.267528 -0.885796 -1.867647 -1.363782 -0.801371 
30 | -1.037593 -0.725168 -1.622493 -1.911781 -0.122829 -0.082383 -0.775871 -0.310792 -0.126812 -1.518354 
31 | 0.802215 -1.007642 -1.481247 0.070662 0.380217 -0.214596 -1.121358 0.323336 -0.081252 0.141090 
32 | 0.377543 0.169463 0.320648 -1.267722 -0.328788 0.366830 0.872647 1.023441 0.778873 0.952119 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
48 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
55 | 
56 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
57 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
61 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
63 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_1m.txt:
--------------------------------------------------------------------------------
 1 | 0.208135 0.191636 0.736291 0.638868 0.549883 0.946099 0.049373 0.473370 0.898262 0.720004 
 2 | 0.149190 0.361596 0.280280 0.811725 0.011668 0.599170 0.486738 0.907867 0.851931 0.487684 
 3 | 0.204665 0.917965 0.104303 0.511265 0.560764 0.439924 0.736765 0.541473 0.033217 0.079742 
 4 | 0.022466 0.968141 0.154924 0.201584 0.391153 0.759234 0.441843 0.891908 0.994943 0.269405 
 5 | 0.819795 0.070266 0.409677 0.724502 0.789421 0.380765 0.266553 0.366779 0.461955 0.872631 
 6 | 0.250788 0.585304 0.403173 0.611201 0.026768 0.248705 0.136530 0.666667 0.963270 0.217535 
 7 | 0.812386 0.688616 0.498006 0.757784 0.522941 0.369709 0.031992 0.470335 0.445118 0.289185 
 8 | 0.007362 0.798145 0.856649 0.071421 0.793449 0.658945 0.215209 0.020313 0.488157 0.897008 
 9 | 0.547170 0.868697 0.439013 0.437646 0.206428 0.572570 0.064815 0.241121 0.597569 0.510186 
10 | 0.221672 0.455772 0.022420 0.342068 0.455439 0.376643 0.560333 0.756168 0.771082 0.847224 
11 | 
12 | 0.228548 0.158861 0.352726 0.956883 0.371731 0.578616 0.222583 0.464048 0.242021 0.404608 
13 | 0.763881 0.821053 0.456129 0.782969 0.079036 0.491022 0.419784 0.470172 0.707279 0.758139 
14 | 0.729379 0.709981 0.399103 0.875136 0.642273 0.764640 0.913175 0.133985 0.977795 0.345962 
15 | 0.369126 0.714375 0.778872 0.726021 0.312874 0.403725 0.195549 0.239651 0.414145 0.007021 
16 | 0.808937 0.288261 0.016090 0.220768 0.370772 0.089339 0.276657 0.839388 0.456143 0.753930 
17 | 0.552453 0.232311 0.846877 0.130101 0.179806 0.190320 0.558399 0.840697 0.420728 0.826897 
18 | 0.592240 0.664408 0.439640 0.457765 0.109506 0.449591 0.837245 0.031947 0.703467 0.672737 
19 | 0.702477 0.829039 0.551549 0.051037 0.215026 0.316947 0.510823 0.479820 0.387099 0.872423 
20 | 0.981114 0.943658 0.271144 0.428227 0.871754 0.477852 0.583449 0.372858 0.415913 0.799144 
21 | 0.315259 0.781346 0.457629 0.506025 0.456869 0.294472 0.217082 0.992776 0.577845 0.146824 
22 | 
23 | 0.196308 0.093094 0.909257 1.824203 1.204770 1.800045 0.211173 0.695770 1.087615 0.762090 
24 | 1.272713 1.199949 0.598134 1.459968 0.334971 0.904977 0.968176 1.488660 1.543115 1.449649 
25 | 0.239590 1.665651 0.333692 0.743634 1.152817 0.772734 1.612506 1.137949 -0.306281 -0.250393 
26 | 0.103682 2.075168 0.254971 0.373884 0.822711 1.394001 0.807735 2.086790 2.192941 0.539164 
27 | -1.659472 -0.385293 -0.278021 -0.907900 -1.314811 -0.521410 -0.673490 -1.160340 -1.256126 -1.375908 
28 | -0.528724 -0.725638 -1.344747 -0.924095 -0.321206 -0.764262 -1.048749 -1.840622 -1.217551 -0.723053 
29 | -1.329008 -1.287380 -0.855719 -0.907809 -0.242219 -0.852777 -1.211411 0.228183 -1.361050 -1.057566 
30 | -1.367478 -1.589949 -0.907742 -0.489109 -0.479537 -0.691223 -1.210042 -1.264427 -0.562023 -1.876708 
31 | 0.560471 -0.014925 -0.315026 0.118836 1.109346 -0.433525 0.943616 0.245115 -0.431398 0.611486 
32 | 0.287414 0.732479 0.952343 0.288662 -0.088887 -0.362575 -0.820012 0.541459 -0.517082 -1.314910 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
48 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
55 | 
56 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
57 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
61 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
63 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_1n.txt:
--------------------------------------------------------------------------------
 1 | 0.197559 0.304962 0.714359 0.090654 0.968611 0.608721 0.427198 0.644422 0.292007 0.964240 
 2 | 0.396334 0.932144 0.851455 0.793392 0.203958 0.734259 0.803572 0.598491 0.604099 0.947167 
 3 | 0.938423 0.836953 0.752161 0.422229 0.725002 0.117408 0.328912 0.407148 0.529623 0.794625 
 4 | 0.033770 0.486793 0.259088 0.485927 0.867190 0.730375 0.677324 0.147427 0.142101 0.490502 
 5 | 0.194334 0.023884 0.711281 0.840841 0.544462 0.528794 0.045583 0.764574 0.056624 0.914159 
 6 | 0.128046 0.104099 0.919220 0.088073 0.626339 0.405039 0.557259 0.778344 0.492041 0.175234 
 7 | 0.185541 0.534614 0.754140 0.032964 0.474014 0.475200 0.166358 0.950117 0.958772 0.391745 
 8 | 0.488780 0.095820 0.286606 0.643621 0.328065 0.946727 0.990872 0.527092 0.830972 0.169224 
 9 | 0.771922 0.447814 0.904410 0.551609 0.996125 0.929256 0.805447 0.294677 0.616133 0.169704 
10 | 0.657723 0.768519 0.158891 0.994026 0.336936 0.469694 0.212372 0.186754 0.367276 0.693161 
11 | 
12 | 0.462374 0.487113 0.898347 0.097724 0.993628 0.228191 0.373712 0.272276 0.029834 0.070170 
13 | 0.264227 0.771700 0.959660 0.904809 0.547454 0.633096 0.808982 0.868018 0.286905 0.791507 
14 | 0.428514 0.196533 0.861818 0.358241 0.273964 0.804310 0.859047 0.047044 0.088144 0.763361 
15 | 0.329216 0.261651 0.015574 0.786415 0.055505 0.908549 0.990062 0.796052 0.531832 0.222817 
16 | 0.161219 0.230999 0.959570 0.251965 0.878263 0.822118 0.163071 0.026686 0.090260 0.255745 
17 | 0.548202 0.586903 0.069754 0.567368 0.115894 0.626534 0.722267 0.699475 0.342863 0.375437 
18 | 0.289500 0.918185 0.214720 0.772352 0.783956 0.488623 0.815085 0.337219 0.181030 0.667953 
19 | 0.520002 0.399580 0.624265 0.530924 0.280580 0.262556 0.774668 0.233754 0.372596 0.962278 
20 | 0.183898 0.076107 0.526139 0.965645 0.628582 0.997431 0.265313 0.179815 0.842910 0.393888 
21 | 0.153479 0.933781 0.835882 0.965423 0.155719 0.778219 0.205694 0.562194 0.065372 0.358369 
22 | 
23 | 0.659933 0.792075 1.612706 0.188377 1.962239 0.836912 0.800910 0.916699 0.321841 1.034410 
24 | 0.660561 1.703844 1.811115 1.698201 0.751413 1.367355 1.612555 1.466509 0.891004 1.738674 
25 | 1.876845 1.673906 1.504321 0.844458 1.450005 0.234816 0.657824 0.814296 1.059246 1.589249 
26 | 0.067540 0.973587 0.518176 0.971854 1.734380 1.460751 1.354647 0.294855 0.284203 0.981005 
27 | -0.355553 -0.254883 -1.670851 -1.092806 -1.422725 -1.350912 -0.208655 -0.791260 -0.146884 -1.169905 
28 | -0.676248 -0.691003 -0.988974 -0.655441 -0.742233 -1.031573 -1.279526 -1.477819 -0.834904 -0.550671 
29 | -0.579000 -1.836370 -0.429440 -1.544703 -1.567913 -0.977247 -1.630169 -0.674437 -0.362059 -1.335906 
30 | -1.040004 -0.799160 -1.248530 -1.061849 -0.561159 -0.525112 -1.549336 -0.467508 -0.745193 -1.924556 
31 | -1.176047 -0.743413 -0.756543 0.828072 -0.735086 0.136350 -1.080269 -0.229723 0.453553 0.448368 
32 | -1.008488 0.330523 1.353982 -0.057206 -0.362433 0.617051 -0.013356 0.750881 -0.603807 -0.669584 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
46 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
47 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
48 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
55 | 
56 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
57 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
58 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
59 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
61 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
63 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_2h.txt:
--------------------------------------------------------------------------------
 1 | 0.876083 0.186146 0.945371 0.514102 0.309468 0.142057 0.379302 0.633159 0.316727 0.440985 
 2 | 0.247177 0.441158 0.882976 0.927986 0.484746 0.469614 0.658234 0.678885 0.152757 0.781696 
 3 | 0.289477 0.931340 0.508184 0.872901 0.867328 0.682737 0.639323 0.355099 0.290834 0.609844 
 4 | 0.512569 0.290098 0.590599 0.567560 0.678616 0.189020 0.208914 0.848473 0.524897 0.980787 
 5 | 0.828869 0.384245 0.263085 0.263455 0.288476 0.701152 0.560040 0.186293 0.696018 0.692712 
 6 | 0.419932 0.554138 0.898370 0.407821 0.025192 0.126697 0.181717 0.916692 0.241765 0.078490 
 7 | 0.933113 0.673015 0.744153 0.981022 0.076025 0.368836 0.654540 0.675039 0.041645 0.468013 
 8 | 0.773584 0.856313 0.436658 0.322452 0.920874 0.362115 0.629000 0.417419 0.523503 0.145854 
 9 | 0.160042 0.720910 0.049122 0.781614 0.537668 0.593656 0.891321 0.439879 0.746572 0.982746 
10 | 0.885906 0.191629 0.141277 0.943630 0.748411 0.034883 0.120043 0.249488 0.733876 0.331062 
11 | 
12 | 0.399645 0.292054 0.946646 0.465417 0.255037 0.183432 0.596886 0.654492 0.465973 0.298426 
13 | 0.771016 0.429121 0.099642 0.474684 0.971175 0.336777 0.913791 0.070853 0.439527 0.039993 
14 | 0.724112 0.425468 0.940615 0.933412 0.602157 0.876577 0.172441 0.111448 0.151508 0.834764 
15 | 0.483501 0.289357 0.426977 0.125120 0.328369 0.954919 0.374334 0.749283 0.235684 0.495329 
16 | 0.678252 0.101487 0.781000 0.475872 0.404230 0.383830 0.803043 0.112373 0.730676 0.683569 
17 | 0.319036 0.830575 0.445808 0.977153 0.154060 0.729352 0.246851 0.915765 0.500401 0.993202 
18 | 0.135902 0.579577 0.735111 0.546007 0.212076 0.804704 0.501411 0.476903 0.752918 0.840004 
19 | 0.541408 0.687184 0.578490 0.399266 0.642574 0.175127 0.757047 0.404029 0.224764 0.214410 
20 | 0.070476 0.694547 0.617800 0.119610 0.846271 0.346904 0.124082 0.149735 0.111281 0.569106 
21 | 0.693353 0.122758 0.607200 0.660137 0.276823 0.519041 0.957657 0.267349 0.324004 0.218418 
22 | 
23 | -0.754841 0.349455 0.492818 0.349676 -0.329784 0.515697 0.203363 0.087342 0.420213 -0.192724 
24 | 0.649160 -0.359521 -1.796655 -0.677847 1.227873 -0.444899 0.627079 -1.645031 0.736066 -1.648631 
25 | -1.890197 -1.507979 -2.635372 -2.918023 -1.961546 -2.411276 -1.340148 -0.738867 -0.655247 -2.453849 
26 | -1.427154 -0.839735 -1.294994 -1.083543 -1.425495 -1.915036 -0.523175 -2.395687 -0.719919 -2.585162 
27 | 1.852139 1.263369 0.869672 0.935913 0.888129 1.938647 1.790792 0.911049 2.675190 2.035097 
28 | 1.095136 1.994373 2.575240 1.900850 -0.110011 1.492025 1.008798 2.348879 0.607753 0.970313 
29 | 0.550325 -0.164324 0.185308 0.205229 -0.011055 0.434253 -0.101073 -0.204847 -0.132759 0.138550 
30 | -0.068555 -0.011060 -0.063097 -0.097824 0.064761 0.066700 0.133438 0.023934 -0.378538 0.094537 
31 | 0.084267 0.434607 -0.871890 0.944111 -0.617783 0.417005 1.110527 0.586597 0.627218 -0.127459 
32 | 0.575369 -0.021414 -0.008328 0.455066 0.683873 -0.366130 -1.327366 0.301745 0.047691 0.097038 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
46 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
50 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
63 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
64 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_2l.txt:
--------------------------------------------------------------------------------
 1 | 0.411976 0.309438 0.035855 0.212980 0.010545 0.189601 0.364251 0.243318 0.287430 0.046052 
 2 | 0.408184 0.705273 0.775116 0.500916 0.091504 0.748798 0.395494 0.590512 0.164732 0.623015 
 3 | 0.088288 0.153016 0.987479 0.481527 0.866913 0.929062 0.326798 0.503125 0.899347 0.195823 
 4 | 0.943922 0.918644 0.108415 0.905025 0.950539 0.415545 0.875125 0.697023 0.125140 0.835015 
 5 | 0.707294 0.026826 0.610853 0.190886 0.966933 0.624501 0.858877 0.100865 0.284384 0.380071 
 6 | 0.189446 0.232361 0.422588 0.465123 0.827788 0.746280 0.535990 0.768325 0.569061 0.763396 
 7 | 0.087439 0.982796 0.153427 0.603780 0.140439 0.822898 0.536039 0.921843 0.444655 0.912084 
 8 | 0.664491 0.528525 0.536150 0.766629 0.643804 0.696781 0.984134 0.001587 0.497765 0.145608 
 9 | 0.551393 0.432620 0.510812 0.603629 0.857238 0.734339 0.402746 0.896385 0.281842 0.524577 
10 | 0.498821 0.924426 0.910804 0.248884 0.645271 0.907662 0.945903 0.498886 0.428734 0.866191 
11 | 
12 | 0.619735 0.971368 0.204330 0.877430 0.238494 0.720087 0.647009 0.403384 0.944455 0.826755 
13 | 0.489558 0.124452 0.230555 0.780001 0.354701 0.961424 0.883710 0.900862 0.113205 0.004815 
14 | 0.602137 0.250216 0.772349 0.187589 0.575361 0.335465 0.212294 0.469349 0.394989 0.351885 
15 | 0.302099 0.129683 0.568392 0.392975 0.832611 0.629671 0.540555 0.606775 0.102665 0.310781 
16 | 0.248397 0.465376 0.896853 0.367346 0.649743 0.656562 0.136795 0.156774 0.733559 0.842708 
17 | 0.762524 0.813073 0.385202 0.362691 0.531671 0.538783 0.259374 0.627115 0.832140 0.580926 
18 | 0.321524 0.290752 0.781732 0.099324 0.270991 0.874418 0.598504 0.922804 0.051803 0.690068 
19 | 0.909563 0.766441 0.157220 0.365594 0.068942 0.971392 0.619350 0.602439 0.785024 0.929563 
20 | 0.368236 0.442240 0.782507 0.869254 0.453627 0.316309 0.836220 0.416981 0.068855 0.733866 
21 | 0.223814 0.804876 0.872583 0.507167 0.813834 0.286706 0.751816 0.329645 0.547360 0.456315 
22 | 
23 | 0.255924 1.265215 0.550173 1.124316 0.471438 1.111457 0.642997 0.489303 1.214975 1.661705 
24 | 0.167810 -1.204756 -1.180195 0.666529 0.448992 0.472386 0.917748 0.639212 0.039619 -1.180524 
25 | -1.302043 -0.659263 -2.689438 -0.900218 -2.091572 -1.525805 -0.805589 -1.417387 -1.724669 -0.982633 
26 | -1.314653 -1.063727 -1.180854 -1.618077 -2.602719 -1.730321 -1.710704 -1.937177 -0.471665 -1.609731 
27 | 1.561893 0.470664 2.073708 0.799556 2.660537 1.973577 1.759352 0.288975 1.293730 1.557716 
28 | 1.263017 1.178855 1.305801 1.276721 2.135885 2.191916 1.161365 2.275643 2.094426 2.166972 
29 | -0.036364 0.096017 -0.083912 -0.054158 0.154531 -0.172165 0.023997 -0.129540 -0.065346 0.037287 
30 | -0.132571 -0.030940 -0.071289 0.086089 -0.040382 -0.094368 0.005923 0.062174 0.148775 0.004639 
31 | 0.040059 0.020994 -0.100754 -0.285460 0.337055 0.536724 -0.469007 0.583105 0.096815 -0.174151 
32 | 0.208419 0.136507 -0.119474 -0.177936 -0.276783 0.664803 0.173156 0.197172 -0.087562 0.590171 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
46 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
50 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
63 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
64 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_2m.txt:
--------------------------------------------------------------------------------
 1 | 0.105733 0.432191 0.924292 0.755202 0.120959 0.383939 0.748933 0.782457 0.491399 0.598024 
 2 | 0.574679 0.910416 0.153615 0.457463 0.258575 0.325870 0.136812 0.862130 0.660507 0.173406 
 3 | 0.837288 0.812336 0.083685 0.360516 0.435786 0.073244 0.674190 0.459065 0.074586 0.381354 
 4 | 0.495822 0.602938 0.352463 0.594818 0.923410 0.755310 0.075502 0.515786 0.064100 0.332823 
 5 | 0.990813 0.903905 0.516156 0.338073 0.980196 0.528483 0.151041 0.736711 0.790039 0.139396 
 6 | 0.997987 0.458166 0.267527 0.846762 0.998824 0.421244 0.718940 0.481702 0.400387 0.211933 
 7 | 0.616368 0.445631 0.824310 0.226737 0.485331 0.921331 0.833841 0.114048 0.338026 0.976331 
 8 | 0.539057 0.624869 0.687009 0.622443 0.675225 0.086710 0.595216 0.945528 0.043155 0.460746 
 9 | 0.095205 0.556948 0.494375 0.372169 0.587419 0.186161 0.823223 0.281284 0.333786 0.197734 
10 | 0.365139 0.332564 0.869425 0.183391 0.881812 0.851078 0.923723 0.223201 0.295320 0.126908 
11 | 
12 | 0.345952 0.433013 0.168679 0.018340 0.378070 0.095418 0.018726 0.840307 0.386597 0.116616 
13 | 0.257272 0.957368 0.469942 0.796441 0.964571 0.629251 0.340497 0.346300 0.693109 0.549756 
14 | 0.275879 0.542439 0.253057 0.502862 0.432710 0.835071 0.351787 0.496663 0.128674 0.983148 
15 | 0.724394 0.543278 0.319675 0.285389 0.134713 0.333866 0.839709 0.158466 0.299141 0.129017 
16 | 0.204436 0.162964 0.082811 0.345366 0.934956 0.279722 0.379877 0.346106 0.656077 0.837670 
17 | 0.089248 0.260067 0.472879 0.759347 0.120037 0.456445 0.210925 0.757790 0.553625 0.650343 
18 | 0.639651 0.505611 0.912604 0.916784 0.554488 0.439891 0.199633 0.409261 0.469897 0.046282 
19 | 0.103387 0.378373 0.475135 0.431079 0.149880 0.955719 0.528638 0.739546 0.040345 0.272939 
20 | 0.190638 0.748432 0.510957 0.874665 0.515066 0.980787 0.498452 0.470103 0.753800 0.461953 
21 | 0.234394 0.898990 0.727874 0.714167 0.094129 0.267698 0.105790 0.535085 0.032017 0.204673 
22 | 
23 | 0.346143 -0.304779 -1.522770 -1.748590 0.455614 -0.536574 -1.249972 -0.106963 -0.331337 -0.988440 
24 | -0.863089 0.227303 0.702561 0.847458 1.343171 0.897093 0.312341 -1.253273 0.118546 0.912605 
25 | -1.455220 -1.900035 -0.499341 -1.524538 -1.266174 -1.520421 -1.633213 -1.680992 -0.096114 -2.508816 
26 | -1.843255 -1.716369 -0.831932 -1.242396 -1.047016 -1.343930 -1.568224 -0.755922 -0.522317 -0.630851 
27 | 1.937147 2.154187 0.831360 1.092988 2.982835 1.349656 0.891883 1.523887 2.406306 0.884232 
28 | 1.639136 1.201590 1.183264 2.409988 1.765157 1.305844 1.630991 1.791687 1.182246 1.149366 
29 | 0.299006 -0.116967 0.103174 -0.415160 -0.043377 -0.295314 0.043568 -0.232906 -0.031990 -0.140345 
30 | -0.096090 0.122125 0.256591 -0.087862 0.544558 0.183188 -0.028826 0.101043 -0.499437 0.011817 
31 | 0.151166 0.039074 -0.049900 -0.648420 0.331939 -0.234940 0.354150 -0.248913 -0.348224 0.167830 
32 | 0.122239 -0.516585 0.206276 -0.480617 0.736733 0.302913 0.548288 -0.041850 0.073719 0.140358 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
46 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
50 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
63 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
64 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_2n.txt:
--------------------------------------------------------------------------------
 1 | 0.298771 0.534816 0.495742 0.931730 0.902147 0.990540 0.930113 0.523775 0.591828 0.692993 
 2 | 0.725422 0.507597 0.118845 0.218397 0.272960 0.718804 0.134171 0.000790 0.755557 0.636678 
 3 | 0.840666 0.331534 0.455097 0.876633 0.384413 0.921464 0.623664 0.724193 0.787350 0.109572 
 4 | 0.627300 0.388073 0.596033 0.174063 0.427125 0.977244 0.774589 0.706261 0.455841 0.011611 
 5 | 0.148819 0.699530 0.337631 0.033551 0.671652 0.334829 0.342330 0.086492 0.567035 0.919906 
 6 | 0.523299 0.229053 0.512313 0.456722 0.568560 0.926639 0.879640 0.316222 0.490681 0.330901 
 7 | 0.227569 0.508657 0.315485 0.010124 0.354123 0.124709 0.257629 0.923138 0.190783 0.978683 
 8 | 0.120943 0.119678 0.294916 0.887268 0.437034 0.949041 0.409952 0.024568 0.599444 0.245468 
 9 | 0.012138 0.076929 0.907365 0.677466 0.883392 0.368196 0.682524 0.540459 0.816775 0.080191 
10 | 0.200936 0.187764 0.731969 0.188202 0.410951 0.186860 0.768826 0.862702 0.598963 0.408320 
11 | 
12 | 0.947671 0.323685 0.819179 0.918178 0.266781 0.070512 0.136432 0.410196 0.838708 0.005272 
13 | 0.947621 0.967656 0.273310 0.439779 0.846904 0.769326 0.304228 0.390582 0.777720 0.675657 
14 | 0.431298 0.473459 0.954921 0.986065 0.684829 0.301918 0.134286 0.458781 0.208359 0.416029 
15 | 0.806179 0.620056 0.892067 0.427166 0.615360 0.169217 0.152493 0.331286 0.406233 0.203261 
16 | 0.897034 0.969707 0.135219 0.778526 0.073487 0.394674 0.241754 0.944143 0.784947 0.947553 
17 | 0.204830 0.734659 0.612908 0.326059 0.775099 0.320455 0.183585 0.784506 0.445413 0.069155 
18 | 0.418795 0.112694 0.440649 0.342062 0.473856 0.974652 0.897138 0.604931 0.055471 0.967084 
19 | 0.554337 0.446248 0.157673 0.681794 0.186240 0.330076 0.818897 0.346245 0.981575 0.067759 
20 | 0.471478 0.366982 0.409137 0.630693 0.666786 0.098586 0.233891 0.612144 0.616820 0.322759 
21 | 0.582238 0.859017 0.887090 0.495854 0.607841 0.578917 0.692655 0.354582 0.833476 0.811206 
22 | 
23 | 1.297800 -0.422262 0.646875 -0.027104 -1.270732 -1.840057 -1.587362 -0.227158 0.493760 -1.375442 
24 | 0.444398 0.920118 0.308930 0.442765 1.147887 0.101044 0.340115 0.779583 0.044326 0.077957 
25 | -1.703261 -1.278452 -2.364940 -2.848763 -1.754071 -1.525300 -0.892236 -1.641754 -1.204068 -0.941629 
26 | -2.239659 -1.628185 -2.380167 -1.028396 -1.657845 -1.315679 -1.079576 -1.368833 -1.268307 -0.418133 
27 | 1.194671 2.368767 0.810481 0.845628 1.416791 1.064333 0.926414 1.117126 1.919016 2.787366 
28 | 1.251428 1.192765 1.637535 1.239503 1.912220 2.173734 1.942865 1.416951 1.426774 0.730957 
29 | 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 
30 | 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 
31 | -0.459340 -0.290053 0.498228 0.046773 0.216606 0.269610 0.448633 -0.071685 0.199955 -0.242567 
32 | -0.381302 -0.671253 -0.155121 -0.307652 -0.196890 -0.392057 0.076171 0.508120 -0.234512 -0.402886 
33 | 
34 | 0 0 0 0 0 0 0 0 0 0 
35 | 0 0 0 0 0 0 0 0 0 0 
36 | 1 1 1 1 1 1 1 1 1 1 
37 | 1 1 1 1 1 1 1 1 1 1 
38 | 2 2 2 2 2 2 2 2 2 2 
39 | 2 2 2 2 2 2 2 2 2 2 
40 | 3 3 3 3 3 3 3 3 3 3 
41 | 3 3 3 3 3 3 3 3 3 3 
42 | 4 4 4 4 4 4 4 4 4 4 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
46 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
49 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
50 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
51 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
52 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
60 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
61 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
62 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
63 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
64 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_3h.txt:
--------------------------------------------------------------------------------
 1 | 0.911423 0.551251 0.116624 0.125386 0.626914 0.016793 0.671938 0.372160 0.098997 0.000767 
 2 | 0.320518 0.440364 0.038474 0.396103 0.930827 0.360011 0.257684 0.551214 0.843519 0.641971 
 3 | 0.895374 0.675347 0.223048 0.458308 0.314696 0.650886 0.403278 0.851003 0.981578 0.779111 
 4 | 0.622367 0.340694 0.573062 0.559459 0.986826 0.005331 0.169870 0.987659 0.931112 0.509275 
 5 | 0.989188 0.865992 0.821816 0.898679 0.365332 0.230637 0.195630 0.669100 0.480514 0.871977 
 6 | 0.684973 0.468560 0.982860 0.455540 0.829036 0.768163 0.244592 0.846634 0.822816 0.858909 
 7 | 0.465134 0.953385 0.064427 0.503876 0.144636 0.160906 0.415777 0.703128 0.821617 0.402242 
 8 | 0.401150 0.130475 0.310517 0.335458 0.866812 0.232972 0.211110 0.213848 0.020197 0.794479 
 9 | 0.747691 0.702287 0.828419 0.824905 0.003654 0.489213 0.658088 0.006861 0.290927 0.434293 
10 | 0.438599 0.341570 0.411959 0.274320 0.893207 0.668917 0.460840 0.472194 0.196929 0.985040 
11 | 
12 | 0.779649 0.835374 0.596620 0.506629 0.104007 0.280230 0.084779 0.870717 0.288253 0.746448 
13 | 0.387771 0.282733 0.119150 0.228690 0.936908 0.443682 0.738316 0.639580 0.256904 0.675126 
14 | 0.391505 0.911236 0.223555 0.837322 0.990636 0.513993 0.172190 0.984204 0.449776 0.878318 
15 | 0.433502 0.045337 0.796228 0.139617 0.732255 0.011297 0.469358 0.262515 0.794353 0.640030 
16 | 0.265309 0.419443 0.043776 0.664859 0.397710 0.169116 0.232079 0.169454 0.360212 0.920243 
17 | 0.220442 0.134944 0.515611 0.095556 0.955974 0.458825 0.913453 0.556177 0.970326 0.878225 
18 | 0.415905 0.863284 0.381104 0.541973 0.071947 0.236566 0.186930 0.626338 0.364438 0.409809 
19 | 0.647832 0.234986 0.700272 0.504492 0.442625 0.899564 0.702754 0.860259 0.728060 0.827225 
20 | 0.070898 0.871044 0.402856 0.943536 0.817596 0.912508 0.104994 0.923561 0.487567 0.245280 
21 | 0.186265 0.620379 0.551999 0.183635 0.755057 0.881700 0.800909 0.849841 0.947230 0.225003 
22 | 
23 | -0.330975 0.039695 -0.469386 -0.225218 0.272051 -0.500559 0.869255 -0.928705 -0.432927 -1.597637 
24 | -0.080211 -0.100197 -0.155224 0.214984 -0.495567 -0.188377 -1.479529 -0.149546 1.585433 -0.509275 
25 | 0.304041 -0.221338 -0.358652 0.164592 -1.660220 0.460347 0.765366 -0.249870 0.052546 0.421970 
26 | -0.012468 -0.220147 -0.300970 0.129792 0.491260 0.416971 -0.583520 -0.121867 0.110044 -0.448714 
27 | 0.623675 0.556770 0.618621 0.141643 0.116336 -0.034581 0.423464 0.036634 -0.336761 -0.381406 
28 | 0.784150 0.504605 -0.536224 0.830459 0.407659 -0.749047 -0.205381 0.074028 -0.004974 -0.422587 
29 | 0.051670 -0.772821 0.213983 -0.549818 -0.468847 1.022588 -0.129944 -0.335620 0.083541 0.036190 
30 | 0.422045 0.038696 0.797182 0.404361 -1.090229 -0.558725 -0.110836 -0.289464 1.264804 -0.427527 
31 | -1.017903 0.155976 -0.761900 0.118354 1.774194 0.384768 0.110032 0.856963 0.510846 -0.310200 
32 | -0.686521 0.789918 0.527928 -0.043174 -0.365313 0.510562 0.345321 0.546127 0.605851 -1.333473 
33 | 
34 | 2 2 2 2 2 3 3 3 3 3 
35 | 2 2 2 2 2 3 3 3 3 0 
36 | 2 2 2 2 3 3 3 3 0 0 
37 | 2 2 2 2 3 3 3 0 0 0 
38 | 2 2 2 3 3 3 0 0 0 0 
39 | 2 2 4 3 3 0 0 0 0 0 
40 | 4 4 4 4 0 0 0 0 0 1 
41 | 4 4 4 4 4 0 0 0 1 1 
42 | 4 4 4 4 4 4 0 1 1 1 
43 | 4 4 4 4 4 4 1 1 1 1 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 
46 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 
47 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 0.0 
48 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 
49 | 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 
50 | 1.0 1.0 -2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 
51 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 -1.0 
52 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 -1.0 -1.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 -1.0 -1.0 -1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 
58 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 
59 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 
61 | -1.0 -1.0 2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 
62 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
63 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 0.0 1.0 1.0 1.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_3l.txt:
--------------------------------------------------------------------------------
 1 | 0.613801 0.744157 0.567106 0.680480 0.542481 0.857936 0.353706 0.997195 0.919195 0.101564 
 2 | 0.337759 0.824527 0.038073 0.902470 0.669558 0.323881 0.413435 0.977872 0.469168 0.944186 
 3 | 0.348136 0.348394 0.506627 0.663298 0.834083 0.644501 0.141544 0.686581 0.346751 0.470832 
 4 | 0.245202 0.816519 0.853761 0.488708 0.095563 0.017310 0.103096 0.280909 0.322753 0.324857 
 5 | 0.509472 0.035711 0.800906 0.255741 0.589016 0.530432 0.645357 0.480556 0.695228 0.335107 
 6 | 0.741911 0.299846 0.628955 0.438385 0.926540 0.683717 0.905604 0.175268 0.179709 0.665550 
 7 | 0.113892 0.726785 0.058296 0.976442 0.468561 0.219345 0.358131 0.178230 0.554934 0.237567 
 8 | 0.133170 0.828647 0.759892 0.619403 0.139477 0.153939 0.435538 0.330134 0.685382 0.413926 
 9 | 0.904785 0.161810 0.404556 0.587989 0.082717 0.046327 0.935242 0.880227 0.433610 0.772534 
10 | 0.154678 0.432639 0.191702 0.368925 0.575315 0.933896 0.178629 0.697756 0.937281 0.214643 
11 | 
12 | 0.300615 0.327206 0.551441 0.288580 0.622363 0.238151 0.775406 0.449957 0.119410 0.791537 
13 | 0.493021 0.409696 0.470084 0.059579 0.141146 0.239143 0.448705 0.884057 0.683354 0.162258 
14 | 0.381261 0.921344 0.429820 0.864494 0.275946 0.199028 0.073435 0.419423 0.489824 0.386994 
15 | 0.470922 0.720839 0.510045 0.394279 0.823367 0.299738 0.207793 0.538299 0.706427 0.171859 
16 | 0.889152 0.471537 0.132281 0.902881 0.941020 0.691733 0.242268 0.971133 0.747936 0.820220 
17 | 0.070067 0.297263 0.411747 0.362735 0.565295 0.799200 0.614315 0.624325 0.956954 0.274176 
18 | 0.838206 0.538305 0.931006 0.282419 0.781439 0.256222 0.649831 0.414698 0.419680 0.042481 
19 | 0.995105 0.755937 0.279118 0.117297 0.408083 0.706832 0.581391 0.628567 0.131898 0.033695 
20 | 0.097757 0.298970 0.095102 0.597865 0.387418 0.013194 0.823863 0.978422 0.637766 0.852605 
21 | 0.182661 0.857127 0.709668 0.841200 0.001499 0.729028 0.173599 0.594014 0.788987 0.425198 
22 | 
23 | 0.383408 0.382844 0.135149 0.565042 -0.171095 1.144921 -0.776346 1.179007 1.624555 -1.470777 
24 | -0.120783 0.524449 -0.682682 0.814024 0.502006 0.164654 -0.125257 -0.060715 -0.479279 0.102988 
25 | -0.052465 -0.543447 0.106457 -0.248257 1.008268 0.823397 0.166983 0.561982 0.054422 0.033813 
26 | -0.212022 0.097517 0.327575 -0.020550 -1.707314 -0.511298 -0.058236 -0.128878 0.079916 0.132338 
27 | -0.471948 -0.472591 0.478728 -1.269285 -0.766006 -0.313032 -0.015613 -0.205481 0.035132 0.055020 
28 | 0.544955 -0.008947 -0.328524 0.078777 0.655870 -0.057937 -0.155041 0.130766 0.069073 0.183097 
29 | 1.565973 -0.154677 1.651710 -1.323381 0.014920 -0.146572 -0.078009 0.104360 0.110951 -0.215030 
30 | 1.638586 -0.021217 -0.946970 -1.007210 0.378643 0.044937 0.037557 -0.042884 -0.461056 -0.432893 
31 | -1.803356 0.278520 -0.782288 -0.056741 0.554938 -0.198464 -0.085535 0.144487 0.195166 -0.006450 
32 | 0.049389 0.818366 0.935308 1.003633 -1.141418 -0.263633 -0.112897 -0.189897 -0.199367 0.182478 
33 | 
34 | 2 2 2 2 2 3 3 3 3 3 
35 | 2 2 2 2 2 3 3 3 3 0 
36 | 2 2 2 2 3 3 3 3 0 0 
37 | 2 2 2 2 3 3 3 0 0 0 
38 | 2 2 2 3 3 3 0 0 0 0 
39 | 2 2 4 3 3 0 0 0 0 0 
40 | 4 4 4 4 0 0 0 0 0 1 
41 | 4 4 4 4 4 0 0 0 1 1 
42 | 4 4 4 4 4 4 0 1 1 1 
43 | 4 4 4 4 4 4 1 1 1 1 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 
46 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 
47 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 0.0 
48 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 
49 | 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 
50 | 1.0 1.0 -2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 
51 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 -1.0 
52 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 -1.0 -1.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 -1.0 -1.0 -1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 
58 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 
59 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 
61 | -1.0 -1.0 2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 
62 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
63 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 0.0 1.0 1.0 1.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_3m.txt:
--------------------------------------------------------------------------------
 1 | 0.601352 0.424198 0.955131 0.737238 0.531151 0.764792 0.373839 0.729140 0.456638 0.728006 
 2 | 0.552122 0.945603 0.569651 0.512799 0.305855 0.213449 0.646936 0.658258 0.003854 0.968419 
 3 | 0.613868 0.034777 0.757028 0.994673 0.063139 0.911703 0.683176 0.092333 0.790567 0.462538 
 4 | 0.588508 0.953297 0.819433 0.136370 0.474082 0.852767 0.358226 0.034532 0.254974 0.409081 
 5 | 0.594634 0.625670 0.713662 0.551026 0.524472 0.392053 0.189006 0.921594 0.044120 0.616349 
 6 | 0.436799 0.915755 0.590914 0.674076 0.096182 0.799791 0.825884 0.365561 0.856457 0.633374 
 7 | 0.631333 0.076634 0.047317 0.488062 0.686763 0.925829 0.197102 0.114772 0.965210 0.820570 
 8 | 0.773869 0.430912 0.689300 0.229768 0.719546 0.381588 0.506143 0.391142 0.874509 0.560310 
 9 | 0.867278 0.396572 0.706862 0.823297 0.203288 0.926030 0.492525 0.454333 0.778495 0.861872 
10 | 0.302742 0.816557 0.801593 0.904950 0.199300 0.813929 0.509457 0.822480 0.322330 0.560025 
11 | 
12 | 0.524697 0.090680 0.095812 0.919728 0.101993 0.718980 0.389474 0.353220 0.097618 0.598738 
13 | 0.426926 0.263499 0.379516 0.469828 0.154822 0.103229 0.242870 0.652003 0.226778 0.113773 
14 | 0.475690 0.842726 0.068215 0.507253 0.896481 0.942119 0.363267 0.585672 0.524013 0.597787 
15 | 0.019546 0.711299 0.271607 0.304338 0.588906 0.714318 0.924354 0.026672 0.215817 0.070696 
16 | 0.390003 0.528221 0.735127 0.059061 0.483528 0.436097 0.770310 0.626265 0.083424 0.343814 
17 | 0.027096 0.877329 0.415890 0.618916 0.212224 0.786450 0.822110 0.633371 0.494347 0.239118 
18 | 0.168492 0.769659 0.014612 0.331032 0.302113 0.860613 0.411097 0.588122 0.780992 0.163000 
19 | 0.349371 0.724881 0.008812 0.663868 0.814471 0.107319 0.837878 0.322965 0.098522 0.220758 
20 | 0.416297 0.317107 0.666167 0.230611 0.697067 0.278759 0.835056 0.979374 0.463152 0.238144 
21 | 0.276717 0.120217 0.149830 0.175006 0.096052 0.665463 0.084102 0.905548 0.278626 0.533884 
22 | 
23 | -0.277360 0.350630 1.150188 -0.125030 0.470598 0.301792 -0.129856 0.590443 0.952173 0.189389 
24 | 0.228026 0.711640 0.299490 0.277430 0.073267 0.437327 0.896806 0.235690 -0.225801 0.233530 
25 | 0.209800 -1.098766 0.988439 0.540872 -1.620874 -0.048552 0.556293 -0.777401 0.049012 -0.200250 
26 | 0.462091 -0.117173 0.540800 0.050783 -0.230259 0.594429 -1.151459 0.238927 0.639893 -0.047554 
27 | 0.059572 -0.161087 0.040451 0.484937 -0.230543 0.339043 -0.171452 0.098911 -0.093011 0.191662 
28 | 0.316715 0.300789 -0.478117 0.068044 -0.402389 -0.090648 0.000495 -0.082803 -0.205399 0.134905 
29 | -0.635576 1.425294 -0.035684 -0.336759 0.385831 -0.152447 -0.266507 -0.195462 0.227777 -0.488053 
30 | -0.984873 0.977287 -1.371262 1.267407 0.096223 -0.100168 -0.152021 -0.112928 -0.973547 -0.305370 
31 | -1.128372 -0.316790 -0.116955 -1.232709 0.866662 -1.535332 0.112366 0.286809 -0.424014 -0.453546 
32 | 0.074408 -1.274924 -1.751140 -1.601485 -0.044099 -0.346052 -0.355135 0.045420 -0.341102 0.130285 
33 | 
34 | 2 2 2 2 2 3 3 3 3 3 
35 | 2 2 2 2 2 3 3 3 3 0 
36 | 2 2 2 2 3 3 3 3 0 0 
37 | 2 2 2 2 3 3 3 0 0 0 
38 | 2 2 2 3 3 3 0 0 0 0 
39 | 2 2 4 3 3 0 0 0 0 0 
40 | 4 4 4 4 0 0 0 0 0 1 
41 | 4 4 4 4 4 0 0 0 1 1 
42 | 4 4 4 4 4 4 0 1 1 1 
43 | 4 4 4 4 4 4 1 1 1 1 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 
46 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 
47 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 0.0 
48 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 
49 | 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 
50 | 1.0 1.0 -2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 
51 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 -1.0 
52 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 -1.0 -1.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 -1.0 -1.0 -1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 
58 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 
59 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 
61 | -1.0 -1.0 2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 
62 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
63 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 0.0 1.0 1.0 1.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_3n.txt:
--------------------------------------------------------------------------------
 1 | 0.810212 0.411306 0.761949 0.432318 0.080810 0.209411 0.072215 0.895528 0.650736 0.561076 
 2 | 0.347900 0.121670 0.130250 0.469317 0.335476 0.852355 0.050308 0.280990 0.352684 0.111828 
 3 | 0.647541 0.106172 0.079301 0.032754 0.583649 0.389333 0.797566 0.490666 0.495148 0.804609 
 4 | 0.125156 0.180440 0.901157 0.491630 0.808647 0.606633 0.053490 0.184005 0.822509 0.512119 
 5 | 0.449740 0.013710 0.850182 0.725773 0.440446 0.041511 0.268580 0.847135 0.494882 0.343334 
 6 | 0.478289 0.201525 0.453598 0.382964 0.184165 0.226411 0.998600 0.903039 0.192341 0.681292 
 7 | 0.949850 0.800844 0.479443 0.942018 0.909644 0.612700 0.839003 0.962420 0.679972 0.430761 
 8 | 0.445574 0.076641 0.821705 0.205935 0.754881 0.409815 0.759750 0.066929 0.117693 0.289600 
 9 | 0.895146 0.557830 0.679324 0.635288 0.243736 0.527352 0.678285 0.342100 0.255738 0.637763 
10 | 0.300206 0.794364 0.592745 0.329940 0.327024 0.455345 0.636543 0.485579 0.022468 0.092325 
11 | 
12 | 0.045777 0.720386 0.292533 0.125001 0.535518 0.788778 0.413116 0.047350 0.387500 0.351283 
13 | 0.780279 0.816204 0.122466 0.627196 0.884906 0.246485 0.865785 0.913326 0.528708 0.089574 
14 | 0.952567 0.883376 0.897911 0.826263 0.578038 0.006662 0.747565 0.211556 0.360544 0.582640 
15 | 0.641354 0.472402 0.055540 0.209859 0.977267 0.113347 0.119731 0.259419 0.652772 0.452416 
16 | 0.169630 0.261864 0.365773 0.112132 0.621086 0.357488 0.276369 0.613045 0.125817 0.852626 
17 | 0.954005 0.275993 0.859399 0.272997 0.822728 0.687609 0.628882 0.823058 0.701413 0.664638 
18 | 0.888774 0.885752 0.257988 0.527542 0.490180 0.198852 0.078088 0.101513 0.367698 0.197048 
19 | 0.637751 0.868753 0.486074 0.087229 0.413563 0.904113 0.401242 0.255556 0.382186 0.169651 
20 | 0.791941 0.488630 0.116564 0.180873 0.929467 0.165876 0.526290 0.083788 0.911835 0.333897 
21 | 0.659403 0.240830 0.729157 0.604115 0.716877 0.679130 0.258358 0.708258 0.305798 0.604343 
22 | 
23 | 0.764435 -0.309079 0.469415 0.307317 -0.454708 -1.158734 -0.681803 1.696355 0.526472 0.419587 
24 | -0.432379 -0.694534 0.007785 -0.157879 -0.549430 1.211740 -1.630954 -1.264670 -0.352048 0.000000 
25 | -0.305026 -0.777203 -0.818610 -0.793509 0.011223 0.765341 0.100002 0.558222 0.000000 0.000000 
26 | -0.516198 -0.291962 0.845617 0.281771 -0.337240 0.986572 -0.132482 0.000000 0.000000 0.000000 
27 | 0.280110 -0.248153 0.484410 1.227283 -0.361282 -0.631956 0.000000 0.000000 0.000000 0.000000 
28 | -0.475716 -0.074468 0.811602 0.219935 -1.277127 0.000000 0.000000 0.000000 0.000000 0.000000 
29 | -0.122152 0.169815 -0.442910 -0.828951 0.000000 0.000000 0.000000 0.000000 0.000000 -0.233712 
30 | 0.384353 1.584223 -0.671263 -0.237411 -0.682636 0.000000 0.000000 0.000000 0.264494 -0.119948 
31 | -0.206410 -0.138401 -1.125520 -0.908830 1.371462 -0.722952 0.000000 -0.258312 0.656097 -0.303866 
32 | 0.718394 -1.107068 0.272824 0.548350 0.779704 0.447570 -0.378185 0.222679 0.283330 0.512018 
33 | 
34 | 2 2 2 2 2 3 3 3 3 3 
35 | 2 2 2 2 2 3 3 3 3 0 
36 | 2 2 2 2 3 3 3 3 0 0 
37 | 2 2 2 2 3 3 3 0 0 0 
38 | 2 2 2 3 3 3 0 0 0 0 
39 | 2 2 4 3 3 0 0 0 0 0 
40 | 4 4 4 4 0 0 0 0 0 1 
41 | 4 4 4 4 4 0 0 0 1 1 
42 | 4 4 4 4 4 4 0 1 1 1 
43 | 4 4 4 4 4 4 1 1 1 1 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 
46 | 1.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 
47 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 0.0 0.0 
48 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 
49 | 1.0 1.0 1.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 
50 | 1.0 1.0 -2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 
51 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 -1.0 
52 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 -1.0 -1.0 
53 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 -1.0 -1.0 -1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 -2.0 
57 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 
58 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 
59 | -1.0 -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 
60 | -1.0 -1.0 -1.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 
61 | -1.0 -1.0 2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 0.0 
62 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
63 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
64 | 2.0 2.0 2.0 2.0 2.0 2.0 0.0 1.0 1.0 1.0 
65 | 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_4h.txt:
--------------------------------------------------------------------------------
 1 | 0.647651 0.288104 0.232674 0.395852 0.103877 0.714444 0.478575 0.676312 0.023342 0.474802 
 2 | 0.100681 0.264091 0.606519 0.120659 0.698956 0.392290 0.938439 0.226810 0.302736 0.291185 
 3 | 0.616990 0.695619 0.423119 0.268309 0.447956 0.572032 0.816964 0.486576 0.718391 0.430899 
 4 | 0.318462 0.772628 0.247821 0.374896 0.907541 0.209194 0.896013 0.148015 0.904448 0.565128 
 5 | 0.206550 0.450712 0.879770 0.517179 0.135169 0.153304 0.885891 0.103792 0.506945 0.873918 
 6 | 0.271892 0.054381 0.420021 0.504011 0.790956 0.801900 0.321672 0.207308 0.755556 0.182301 
 7 | 0.399756 0.719599 0.937958 0.013382 0.017908 0.917085 0.508049 0.223268 0.846676 0.991768 
 8 | 0.160096 0.653189 0.394205 0.105055 0.110021 0.194738 0.605334 0.658483 0.121536 0.216912 
 9 | 0.948897 0.609126 0.700482 0.539767 0.157110 0.841658 0.288251 0.223691 0.452055 0.302566 
10 | 0.325783 0.218430 0.734318 0.646016 0.523485 0.606155 0.057161 0.550764 0.977687 0.613262 
11 | 
12 | 0.858913 0.913492 0.727866 0.873630 0.783306 0.553160 0.902447 0.257446 0.300836 0.652890 
13 | 0.875431 0.729871 0.394557 0.075838 0.412957 0.821117 0.231987 0.006016 0.274586 0.297509 
14 | 0.464728 0.304162 0.203525 0.839170 0.644902 0.644344 0.378993 0.223125 0.946699 0.460760 
15 | 0.998612 0.143016 0.759604 0.026735 0.752104 0.908810 0.197112 0.207538 0.489578 0.681509 
16 | 0.689220 0.742237 0.955247 0.125380 0.116368 0.251110 0.824617 0.190380 0.731168 0.278219 
17 | 0.644569 0.803471 0.336306 0.853183 0.001825 0.721687 0.621645 0.670811 0.462540 0.374254 
18 | 0.487595 0.707267 0.766433 0.728692 0.778784 0.572754 0.579515 0.296223 0.198005 0.700198 
19 | 0.478770 0.085024 0.614601 0.931109 0.727638 0.396618 0.135882 0.710792 0.417606 0.784857 
20 | 0.809990 0.548691 0.952256 0.769618 0.083202 0.707473 0.817686 0.016740 0.456425 0.083920 
21 | 0.748309 0.665025 0.766366 0.785273 0.890923 0.639181 0.560675 0.307437 0.700895 0.638245 
22 | 
23 | -1.924348 -1.715403 -1.670723 -1.153535 -1.255550 -0.982660 -1.506909 -1.160453 -0.840532 -1.417526 
24 | 1.542830 -0.809297 -1.495322 -0.267964 -1.499213 -1.718384 -0.339910 0.034370 -0.989648 -0.773537 
25 | -0.135849 -0.625019 -0.394464 0.803273 -0.873250 -1.282974 -1.174327 -0.638672 -2.002002 -1.211398 
26 | 1.066993 -1.119895 0.659148 -0.465849 -0.914947 1.332643 -1.565750 -0.554708 -1.562729 0.722597 
27 | 0.954270 1.445467 -0.101735 -0.340828 0.016641 0.418942 -0.262904 -0.198322 1.487273 1.527376 
28 | 1.334023 1.829683 -0.662614 0.491144 -1.805882 -0.216606 -1.629689 -0.772895 1.395393 0.430274 
29 | 1.230368 1.599111 2.131485 1.960375 1.614069 -1.558393 -1.049027 -0.286913 1.827115 1.411744 
30 | 0.440596 0.525665 0.942431 1.270239 0.541876 -1.024533 -0.385800 -1.502363 -0.048846 0.186295 
31 | 1.716369 0.602515 1.669620 1.259718 0.585242 1.135187 -1.441869 -0.218448 0.809643 0.583528 
32 | 1.678823 0.843943 1.639043 1.202250 1.547603 1.172688 0.280009 -0.588519 2.097607 1.080413 
33 | 
34 | 1 1 1 1 1 1 1 1 1 1 
35 | 3 1 1 1 1 1 1 1 1 1 
36 | 3 3 3 3 1 1 1 1 1 1 
37 | 3 3 3 3 3 3 3 1 1 4 
38 | 3 3 3 3 3 3 3 0 4 4 
39 | 2 3 3 3 3 3 0 0 4 4 
40 | 2 2 2 3 3 0 0 0 4 4 
41 | 2 2 2 2 2 0 0 0 4 4 
42 | 2 2 2 2 2 2 0 0 4 4 
43 | 2 2 2 2 2 2 2 0 4 4 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
46 | -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
47 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 2.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 
50 | 1.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 
51 | 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 0.0 2.0 2.0 
52 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 2.0 2.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 2.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 2.0 2.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
57 | 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
58 | 2.0 2.0 2.0 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
59 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -1.0 -1.0 0.0 
60 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 0.0 0.0 
61 | 1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 0.0 0.0 
62 | 1.0 1.0 1.0 2.0 2.0 -2.0 -2.0 -2.0 0.0 0.0 
63 | 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 -2.0 0.0 0.0 
64 | 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 
65 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_4l.txt:
--------------------------------------------------------------------------------
 1 | 0.827826 0.177203 0.607803 0.647910 0.082336 0.716030 0.844867 0.213972 0.913910 0.574020 
 2 | 0.871472 0.200424 0.943931 0.153926 0.091089 0.044189 0.415204 0.196483 0.755049 0.898461 
 3 | 0.244341 0.353171 0.705722 0.770206 0.788942 0.688314 0.763157 0.951896 0.464413 0.686949 
 4 | 0.725407 0.559697 0.970484 0.681962 0.457358 0.488022 0.137483 0.014114 0.547052 0.545960 
 5 | 0.883285 0.951473 0.987530 0.532355 0.266002 0.072823 0.345042 0.685331 0.745732 0.578171 
 6 | 0.207758 0.371639 0.713766 0.351986 0.957800 0.176948 0.834575 0.029584 0.537588 0.460549 
 7 | 0.920086 0.419633 0.761915 0.502695 0.705423 0.899258 0.130621 0.907825 0.850515 0.160292 
 8 | 0.995238 0.712484 0.977712 0.755463 0.726990 0.214289 0.673979 0.878225 0.427392 0.422878 
 9 | 0.052314 0.108654 0.534556 0.674108 0.133612 0.231482 0.839818 0.910578 0.585930 0.083639 
10 | 0.057701 0.237072 0.784755 0.030229 0.176402 0.321125 0.742355 0.633256 0.147707 0.785073 
11 | 
12 | 0.708220 0.195311 0.500979 0.694511 0.472827 0.679423 0.177785 0.054720 0.064497 0.739832 
13 | 0.485385 0.112569 0.360913 0.336957 0.926388 0.860651 0.615785 0.943640 0.636661 0.655129 
14 | 0.943724 0.331667 0.334936 0.975715 0.856385 0.636737 0.794187 0.894744 0.601381 0.378214 
15 | 0.627944 0.917420 0.577671 0.432160 0.409033 0.195759 0.289110 0.625547 0.442012 0.322183 
16 | 0.462788 0.762308 0.893589 0.967858 0.856518 0.595034 0.372545 0.740940 0.311576 0.959838 
17 | 0.820766 0.269173 0.985308 0.116614 0.860519 0.543968 0.149217 0.442427 0.793701 0.320269 
18 | 0.114024 0.649270 0.720609 0.527585 0.645179 0.977907 0.459635 0.507274 0.584037 0.277560 
19 | 0.383981 0.153894 0.714317 0.372142 0.440793 0.025332 0.556462 0.499656 0.677432 0.141910 
20 | 0.697857 0.425729 0.296785 0.549054 0.950065 0.843518 0.980276 0.444504 0.073952 0.745846 
21 | 0.945240 0.472277 0.749130 0.998669 0.745297 0.186253 0.235196 0.870230 0.252118 0.968346 
22 | 
23 | -1.512247 -0.312951 -1.219270 -1.305147 -0.325356 -1.289802 -0.964029 -0.166691 -0.942517 -1.292250 
24 | -0.813861 -0.426305 -1.312122 -0.440688 -0.975490 -0.811650 -0.991336 -1.263272 -1.445008 -1.523755 
25 | 1.380542 -0.166107 -0.862240 0.599916 -1.624007 -1.324438 -1.651208 -1.743281 -0.923023 -1.048436 
26 | -0.354591 0.926726 -0.775289 -0.524993 -0.062506 -0.741514 0.193829 -0.512313 -0.767946 1.050301 
27 | -0.828073 -0.490769 -0.093472 0.889290 1.159468 1.052791 -0.039765 -1.709766 1.578270 1.200289 
28 | 1.116315 -0.206157 0.607178 -0.440086 -0.306451 0.697310 -0.458674 -0.916483 1.151913 0.989332 
29 | 1.054852 1.089264 1.448894 -0.010620 -0.251398 -1.886660 -0.913819 -1.125843 1.470658 0.158394 
30 | 1.404699 0.681424 1.816925 0.979745 1.234810 -0.266047 -1.198482 -1.057715 0.751721 0.764369 
31 | 0.499342 0.477442 0.772922 0.996587 1.263257 1.104041 -1.914175 -0.858875 1.002033 0.208663 
32 | 0.992705 0.787571 1.623085 0.967680 0.903542 0.358755 0.938373 -1.729432 0.380775 1.586578 
33 | 
34 | 1 1 1 1 1 1 1 1 1 1 
35 | 3 1 1 1 1 1 1 1 1 1 
36 | 3 3 3 3 1 1 1 1 1 1 
37 | 3 3 3 3 3 3 3 1 1 4 
38 | 3 3 3 3 3 3 3 0 4 4 
39 | 2 3 3 3 3 3 0 0 4 4 
40 | 2 2 2 3 3 0 0 0 4 4 
41 | 2 2 2 2 2 0 0 0 4 4 
42 | 2 2 2 2 2 2 0 0 4 4 
43 | 2 2 2 2 2 2 2 0 4 4 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
46 | -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
47 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 2.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 
50 | 1.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 
51 | 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 0.0 2.0 2.0 
52 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 2.0 2.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 2.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 2.0 2.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
57 | 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
58 | 2.0 2.0 2.0 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
59 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -1.0 -1.0 0.0 
60 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 0.0 0.0 
61 | 1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 0.0 0.0 
62 | 1.0 1.0 1.0 2.0 2.0 -2.0 -2.0 -2.0 0.0 0.0 
63 | 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 -2.0 0.0 0.0 
64 | 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 
65 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_4m.txt:
--------------------------------------------------------------------------------
 1 | 0.275290 0.156036 0.298461 0.177910 0.096264 0.175871 0.248355 0.104086 0.010686 0.476735 
 2 | 0.996719 0.456376 0.094668 0.797353 0.959969 0.020013 0.649320 0.590626 0.835329 0.260322 
 3 | 0.429588 0.699756 0.946887 0.018214 0.172898 0.337531 0.144345 0.842353 0.088630 0.915682 
 4 | 0.013647 0.720174 0.661169 0.284187 0.725646 0.050208 0.168461 0.959183 0.679251 0.414071 
 5 | 0.996182 0.416080 0.460284 0.293355 0.993640 0.129474 0.251837 0.112507 0.259913 0.303065 
 6 | 0.279067 0.592071 0.833584 0.588059 0.416418 0.371042 0.177209 0.250254 0.632198 0.392511 
 7 | 0.138525 0.805941 0.920804 0.109778 0.174873 0.272880 0.090684 0.214658 0.822594 0.388449 
 8 | 0.048104 0.252756 0.722536 0.544355 0.621524 0.131192 0.739886 0.468035 0.911992 0.353699 
 9 | 0.720020 0.314685 0.402129 0.464870 0.055222 0.074381 0.285076 0.754078 0.182778 0.141059 
10 | 0.820636 0.174394 0.358135 0.460257 0.239362 0.664099 0.692429 0.666369 0.395323 0.444789 
11 | 
12 | 0.196245 0.077173 0.377178 0.988190 0.123788 0.716348 0.262901 0.491597 0.918981 0.616926 
13 | 0.412419 0.647333 0.831008 0.403780 0.301732 0.810208 0.337070 0.156173 0.241281 0.696245 
14 | 0.580266 0.257422 0.307086 0.223422 0.489098 0.329529 0.640106 0.521451 0.275798 0.292742 
15 | 0.643602 0.938970 0.279842 0.380822 0.615143 0.260040 0.135653 0.712144 0.019402 0.881646 
16 | 0.333257 0.407546 0.155685 0.796189 0.415207 0.527562 0.502271 0.297529 0.185752 0.626979 
17 | 0.345669 0.338377 0.177570 0.178790 0.332859 0.919123 0.075453 0.466905 0.019091 0.697732 
18 | 0.799190 0.071881 0.825711 0.115467 0.518117 0.293336 0.473349 0.078631 0.127634 0.899737 
19 | 0.434600 0.701289 0.873423 0.420809 0.889966 0.557014 0.014225 0.054746 0.431335 0.327705 
20 | 0.624578 0.988872 0.962642 0.048027 0.272606 0.025128 0.342294 0.575672 0.421953 0.098084 
21 | 0.431081 0.766772 0.219796 0.768503 0.381185 0.887085 0.315971 0.746951 0.057041 0.989171 
22 | 
23 | 0.046522 -0.188741 -0.401831 -1.334421 -0.061693 -0.694152 -0.262300 -0.783196 -0.840390 -0.918966 
24 | -1.367878 -0.746379 -0.936933 -1.091484 -0.959121 -1.056958 -0.943762 -0.910069 -0.902772 -1.037150 
25 | 0.270103 -1.088951 -1.175080 0.273066 -0.940271 -0.678518 -0.688897 -1.193561 -0.716260 -1.533395 
26 | 1.327345 0.445513 -0.612671 0.332491 0.030418 0.317366 -0.068611 -1.918551 -0.591141 0.513784 
27 | -1.107555 -0.074933 -0.595023 1.055227 -1.191194 1.025447 0.644098 -0.825584 0.332651 0.706330 
28 | 0.389208 -0.445886 -1.291386 -0.797605 -0.389936 0.814467 -0.325271 -0.763450 1.401275 0.771827 
29 | 0.999234 0.547259 1.776269 -0.352839 0.825310 -0.411913 -0.897171 -0.101172 1.429028 1.057506 
30 | 0.453747 0.685510 1.603748 1.210132 1.845544 -1.109099 -0.159089 0.049827 1.400611 0.535862 
31 | 0.971737 1.174242 1.285269 0.593151 0.629653 -0.242554 -0.834219 -1.189240 0.488572 0.586390 
32 | 1.207891 1.037429 0.529470 1.427369 0.784673 1.452251 1.152704 -1.513460 0.965180 1.205230 
33 | 
34 | 1 1 1 1 1 1 1 1 1 1 
35 | 3 1 1 1 1 1 1 1 1 1 
36 | 3 3 3 3 1 1 1 1 1 1 
37 | 3 3 3 3 3 3 3 1 1 4 
38 | 3 3 3 3 3 3 3 0 4 4 
39 | 2 3 3 3 3 3 0 0 4 4 
40 | 2 2 2 3 3 0 0 0 4 4 
41 | 2 2 2 2 2 0 0 0 4 4 
42 | 2 2 2 2 2 2 0 0 4 4 
43 | 2 2 2 2 2 2 2 0 4 4 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
46 | -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
47 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 2.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 
50 | 1.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 
51 | 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 0.0 2.0 2.0 
52 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 2.0 2.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 2.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 2.0 2.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
57 | 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
58 | 2.0 2.0 2.0 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
59 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -1.0 -1.0 0.0 
60 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 0.0 0.0 
61 | 1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 0.0 0.0 
62 | 1.0 1.0 1.0 2.0 2.0 -2.0 -2.0 -2.0 0.0 0.0 
63 | 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 -2.0 0.0 0.0 
64 | 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 
65 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_4n.txt:
--------------------------------------------------------------------------------
 1 | 0.147459 0.703573 0.380370 0.282048 0.353885 0.423528 0.137751 0.346786 0.928054 0.769553 
 2 | 0.891157 0.767308 0.278946 0.000242 0.210904 0.115488 0.198477 0.286863 0.481351 0.019921 
 3 | 0.823222 0.738897 0.432670 0.407549 0.083641 0.924006 0.616018 0.797723 0.574092 0.564615 
 4 | 0.781983 0.432630 0.142930 0.093747 0.481660 0.510990 0.646458 0.890775 0.231236 0.415576 
 5 | 0.343016 0.114436 0.161103 0.955779 0.278157 0.906544 0.387289 0.224707 0.189926 0.654958 
 6 | 0.417980 0.828210 0.547959 0.291814 0.548793 0.612069 0.477147 0.784123 0.387867 0.287521 
 7 | 0.022523 0.392906 0.516538 0.506974 0.717147 0.663611 0.286302 0.100208 0.345683 0.406134 
 8 | 0.924738 0.601592 0.091990 0.814663 0.744106 0.390320 0.954243 0.335963 0.497501 0.187795 
 9 | 0.740007 0.852862 0.346703 0.167199 0.154842 0.931972 0.584068 0.871057 0.170203 0.409270 
10 | 0.282367 0.394497 0.104599 0.760880 0.394538 0.341927 0.131289 0.033096 0.449323 0.531381 
11 | 
12 | 0.385661 0.652863 0.646739 0.946480 0.180738 0.687999 0.440312 0.764022 0.002990 0.221535 
13 | 0.823528 0.422380 0.312869 0.371859 0.389242 0.038552 0.849402 0.836866 0.882517 0.648558 
14 | 0.737629 0.170492 0.547895 0.443460 0.917890 0.689793 0.102392 0.020141 0.742848 0.432786 
15 | 0.409183 0.148369 0.901902 0.179930 0.149495 0.429811 0.890680 0.314702 0.081494 0.152844 
16 | 0.808258 0.099858 0.931254 0.375089 0.805229 0.134819 0.822270 0.192587 0.430396 0.576134 
17 | 0.604928 0.741705 0.758371 0.835454 0.362155 0.599339 0.534606 0.361254 0.953848 0.568098 
18 | 0.924222 0.938037 0.245686 0.962122 0.523716 0.925576 0.813232 0.479304 0.790268 0.313105 
19 | 0.150458 0.239047 0.063166 0.094937 0.331943 0.003089 0.631297 0.044287 0.109184 0.163667 
20 | 0.392336 0.256164 0.920208 0.069024 0.095247 0.632728 0.726269 0.606785 0.982070 0.584522 
21 | 0.491064 0.921491 0.441424 0.261967 0.911789 0.065382 0.714949 0.976670 0.035841 0.812691 
22 | 
23 | -0.533121 -1.356436 -1.027109 -1.228528 -0.534623 -1.111527 -0.578063 -1.110808 -0.931043 -0.991088 
24 | -0.135256 -1.189687 -0.591815 -0.372102 -0.600145 -0.154040 -1.047879 -1.123729 -1.363868 -0.668479 
25 | -0.171186 -1.136810 0.230450 0.071821 -1.001532 -1.613799 -0.718410 -0.817864 -1.316939 -0.997401 
26 | -0.745601 -0.568521 1.517943 0.172367 -0.664330 -0.162358 0.488444 -1.205477 -0.312730 0.831153 
27 | 0.930484 -0.029154 1.540303 -1.161379 1.054143 -1.543448 0.869962 -0.385174 0.379853 1.309916 
28 | 1.022908 -0.173009 0.420825 1.087279 -0.373276 -0.025462 -1.069211 -0.722508 0.775733 0.575041 
29 | 0.946745 1.330943 0.762224 0.910296 -0.386863 -1.851152 -1.626465 -0.958608 0.691367 0.812269 
30 | 1.075196 0.840639 0.155156 0.909601 1.076049 -0.006178 -1.262593 -0.088575 0.995002 0.375591 
31 | 1.132343 1.109026 1.266911 0.236223 0.250088 1.564699 -1.452537 -1.213570 0.340406 0.818540 
32 | 0.773431 1.315988 0.546022 1.022847 1.306327 0.407310 0.846239 -1.953341 0.898646 1.062762 
33 | 
34 | 1 1 1 1 1 1 1 1 1 1 
35 | 3 1 1 1 1 1 1 1 1 1 
36 | 3 3 3 3 1 1 1 1 1 1 
37 | 3 3 3 3 3 3 3 1 1 4 
38 | 3 3 3 3 3 3 3 0 4 4 
39 | 2 3 3 3 3 3 0 0 4 4 
40 | 2 2 2 3 3 0 0 0 4 4 
41 | 2 2 2 2 2 0 0 0 4 4 
42 | 2 2 2 2 2 2 0 0 4 4 
43 | 2 2 2 2 2 2 2 0 4 4 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
46 | -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
47 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
48 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 2.0 
49 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 
50 | 1.0 -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 
51 | 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 0.0 2.0 2.0 
52 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 2.0 2.0 
53 | 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 2.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 2.0 2.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
57 | 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
58 | 2.0 2.0 2.0 2.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
59 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -1.0 -1.0 0.0 
60 | 2.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 0.0 0.0 
61 | 1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 0.0 0.0 
62 | 1.0 1.0 1.0 2.0 2.0 -2.0 -2.0 -2.0 0.0 0.0 
63 | 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 -2.0 0.0 0.0 
64 | 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 -2.0 0.0 0.0 
65 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -2.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_5h.txt:
--------------------------------------------------------------------------------
 1 | 0.057458 0.539643 0.015440 0.512341 0.539770 0.112846 0.745823 0.736305 0.321549 0.964658 
 2 | 0.870558 0.655548 0.557170 0.657797 0.571386 0.694397 0.350233 0.235573 0.715617 0.970249 
 3 | 0.964508 0.409181 0.395236 0.417556 0.849599 0.531939 0.606097 0.773783 0.043903 0.148771 
 4 | 0.708000 0.207302 0.042266 0.787011 0.914640 0.657414 0.062529 0.757643 0.854750 0.058398 
 5 | 0.970101 0.657577 0.836193 0.764797 0.970535 0.216909 0.030873 0.498672 0.822244 0.705254 
 6 | 0.733418 0.834820 0.464061 0.408673 0.874067 0.600269 0.335970 0.181442 0.875927 0.268387 
 7 | 0.774850 0.986690 0.169378 0.506629 0.289212 0.896426 0.040121 0.480335 0.270125 0.948477 
 8 | 0.585308 0.292036 0.106127 0.024306 0.280134 0.760942 0.405581 0.919908 0.291828 0.556175 
 9 | 0.204854 0.671114 0.294414 0.579274 0.910375 0.117768 0.416995 0.910190 0.608061 0.355595 
10 | 0.138689 0.316821 0.331372 0.331790 0.431696 0.360145 0.311965 0.489640 0.851763 0.836826 
11 | 
12 | 0.981636 0.168564 0.053173 0.645876 0.955200 0.847213 0.320253 0.732287 0.180902 0.534791 
13 | 0.297735 0.689983 0.011706 0.566613 0.116250 0.216436 0.726977 0.792047 0.115135 0.365562 
14 | 0.958950 0.803245 0.607492 0.468164 0.906418 0.000834 0.110421 0.266423 0.211934 0.225086 
15 | 0.707426 0.528219 0.934618 0.942666 0.607370 0.868240 0.384212 0.014319 0.614704 0.888553 
16 | 0.124653 0.514662 0.841422 0.555203 0.241709 0.306344 0.128897 0.702849 0.759336 0.407244 
17 | 0.532570 0.105035 0.312950 0.796324 0.396795 0.715721 0.340751 0.042016 0.389227 0.313242 
18 | 0.477487 0.133244 0.792718 0.636643 0.292225 0.532308 0.379958 0.654235 0.344007 0.938881 
19 | 0.003142 0.591128 0.892521 0.217936 0.333349 0.741610 0.423043 0.799611 0.775082 0.358118 
20 | 0.102159 0.003108 0.824430 0.881079 0.793112 0.303246 0.124590 0.383611 0.468532 0.687325 
21 | 0.494105 0.469205 0.986859 0.498143 0.351820 0.715051 0.178990 0.951943 0.165801 0.346580 
22 | 
23 | -1.948447 0.147521 0.070269 -1.377938 -1.681884 -0.052053 -1.144953 -0.432802 -0.325976 -1.134805 
24 | -0.319804 -0.378022 1.047004 -0.683514 0.256800 0.373877 0.324791 1.066326 -0.793598 -1.201626 
25 | -0.903062 -0.538223 -0.364606 -0.909630 -0.012138 -0.597086 -0.055708 0.112951 0.595831 -0.024877 
26 | -0.945016 -0.506967 -1.605334 0.257464 0.282481 -0.134634 0.005659 -0.276997 1.534945 1.868905 
27 | -0.703588 -0.146374 -0.015729 0.100993 -0.972722 0.102720 -0.042563 0.365103 0.698144 0.862043 
28 | 0.020364 -0.905801 -0.171873 0.235388 -0.641797 -0.050839 0.488438 0.181731 1.434821 0.088703 
29 | -0.134383 -0.665288 -0.019560 0.234298 0.333257 1.565540 -0.507975 0.505240 0.410776 1.324320 
30 | -0.084616 0.661202 0.368948 0.393559 -0.199447 0.573113 0.077676 1.064849 -0.167636 1.104801 
31 | -0.046556 -0.669864 0.814030 0.418953 -0.391430 0.116149 0.873568 1.302440 0.795699 -0.181407 
32 | 0.772002 0.440750 0.550184 -0.272246 -0.204876 -0.266678 0.138797 -0.008135 1.396184 1.721675 
33 | 
34 | 4 4 4 4 4 3 0 0 0 0 
35 | 4 4 4 4 4 3 3 0 0 0 
36 | 4 4 4 4 3 3 3 3 0 0 
37 | 4 4 4 1 3 3 3 3 2 0 
38 | 1 1 1 1 1 3 2 2 2 2 
39 | 1 1 1 1 1 2 2 2 2 2 
40 | 1 1 1 1 1 2 2 2 2 2 
41 | 1 1 1 1 1 2 2 2 2 2 
42 | 1 1 1 1 1 2 2 2 2 2 
43 | 1 1 1 1 1 2 2 2 2 2 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 0.0 -2.0 -2.0 -2.0 -2.0 
46 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
47 | 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 -2.0 
48 | 1.0 1.0 1.0 -1.0 0.0 0.0 0.0 0.0 2.0 -2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 2.0 2.0 2.0 2.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
53 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
54 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 2.0 2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 2.0 2.0 
59 | -2.0 -2.0 -2.0 1.0 0.0 0.0 0.0 0.0 -1.0 2.0 
60 | 1.0 1.0 1.0 1.0 1.0 0.0 -1.0 -1.0 -1.0 -1.0 
61 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_5l.txt:
--------------------------------------------------------------------------------
 1 | 0.432777 0.313756 0.711043 0.455108 0.945869 0.621160 0.124275 0.056396 0.419519 0.108260 
 2 | 0.519242 0.421607 0.509055 0.489959 0.071101 0.301552 0.814184 0.596772 0.371171 0.708314 
 3 | 0.334076 0.620165 0.555218 0.301352 0.969512 0.491548 0.506879 0.352995 0.124041 0.557164 
 4 | 0.909722 0.027851 0.358551 0.767345 0.425305 0.404105 0.390128 0.608353 0.418207 0.290026 
 5 | 0.028800 0.089755 0.696021 0.230301 0.313915 0.644825 0.704793 0.284833 0.745750 0.864318 
 6 | 0.039072 0.862114 0.473942 0.730043 0.302025 0.775215 0.447190 0.912327 0.421798 0.730910 
 7 | 0.739060 0.515223 0.701011 0.278045 0.329260 0.505074 0.210598 0.326934 0.157894 0.872312 
 8 | 0.916296 0.682146 0.764551 0.127732 0.935731 0.859121 0.299562 0.667160 0.593069 0.899961 
 9 | 0.789012 0.489935 0.248238 0.289642 0.286706 0.383153 0.754987 0.801295 0.977357 0.700879 
10 | 0.525044 0.229702 0.140054 0.116343 0.502590 0.134944 0.927325 0.746048 0.218982 0.440817 
11 | 
12 | 0.180217 0.269353 0.845241 0.093897 0.572742 0.970210 0.465621 0.196691 0.838351 0.559272 
13 | 0.709588 0.247726 0.379731 0.310346 0.954493 0.108641 0.564456 0.657699 0.158043 0.509340 
14 | 0.609145 0.416920 0.857142 0.264607 0.311745 0.324964 0.342131 0.850928 0.969126 0.565187 
15 | 0.943276 0.651004 0.519780 0.212274 0.025127 0.942598 0.006855 0.639695 0.751474 0.624033 
16 | 0.692644 0.859149 0.680724 0.013869 0.479037 0.366754 0.506111 0.754484 0.739172 0.655897 
17 | 0.245469 0.836983 0.618425 0.125020 0.588217 0.417419 0.742827 0.919805 0.588266 0.473573 
18 | 0.771962 0.417737 0.668318 0.774606 0.212084 0.183513 0.937804 0.109877 0.153233 0.959591 
19 | 0.134250 0.092903 0.999590 0.925666 0.091183 0.035475 0.569095 0.536405 0.305008 0.866689 
20 | 0.660471 0.006005 0.041245 0.070751 0.374155 0.115750 0.103959 0.409782 0.232754 0.208627 
21 | 0.780644 0.073868 0.147700 0.463470 0.964094 0.747259 0.393450 0.858266 0.572230 0.607034 
22 | 
23 | 0.087317 -0.269234 -0.787958 0.135290 -0.149534 -0.131718 0.562897 0.192573 0.879982 0.850409 
24 | -0.850547 -0.064188 -0.259261 -0.213623 -1.681552 0.076261 -0.147487 0.137550 -0.449845 -0.420238 
25 | -0.895783 -0.103542 -1.249031 -0.255985 -0.048264 0.018603 -0.044820 -0.231003 1.691915 0.023668 
26 | -1.292693 -1.224744 -0.569754 -0.617665 0.032278 0.163707 -0.089445 0.052036 0.082985 0.710961 
27 | 0.458477 0.635394 -0.055914 -0.316192 0.214039 -0.015256 0.838315 -0.372206 0.743476 1.036613 
28 | 0.296171 0.045404 0.221382 -0.595997 0.182782 1.037729 0.140994 0.742449 0.229729 0.891502 
29 | 0.129142 -0.093370 0.056555 0.495395 -0.135050 0.751239 -0.371857 0.424454 0.163402 0.518711 
30 | -0.626401 -0.577518 0.164683 0.868712 -0.799914 1.808519 0.079969 0.604340 0.822346 0.865694 
31 | -0.214898 -0.564571 -0.087273 0.003818 -0.054402 0.814880 1.343415 1.095096 1.801944 1.269250 
32 | 0.150076 -0.161299 0.049568 0.414641 0.571064 -0.434186 1.360604 0.718315 -0.176834 0.207132 
33 | 
34 | 4 4 4 4 4 3 0 0 0 0 
35 | 4 4 4 4 4 3 3 0 0 0 
36 | 4 4 4 4 3 3 3 3 0 0 
37 | 4 4 4 1 3 3 3 3 2 0 
38 | 1 1 1 1 1 3 2 2 2 2 
39 | 1 1 1 1 1 2 2 2 2 2 
40 | 1 1 1 1 1 2 2 2 2 2 
41 | 1 1 1 1 1 2 2 2 2 2 
42 | 1 1 1 1 1 2 2 2 2 2 
43 | 1 1 1 1 1 2 2 2 2 2 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 0.0 -2.0 -2.0 -2.0 -2.0 
46 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
47 | 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 -2.0 
48 | 1.0 1.0 1.0 -1.0 0.0 0.0 0.0 0.0 2.0 -2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 2.0 2.0 2.0 2.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
53 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
54 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 2.0 2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 2.0 2.0 
59 | -2.0 -2.0 -2.0 1.0 0.0 0.0 0.0 0.0 -1.0 2.0 
60 | 1.0 1.0 1.0 1.0 1.0 0.0 -1.0 -1.0 -1.0 -1.0 
61 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_5m.txt:
--------------------------------------------------------------------------------
 1 | 0.865829 0.966406 0.128998 0.192592 0.772297 0.624195 0.440634 0.254781 0.097124 0.664304 
 2 | 0.557040 0.876499 0.780241 0.868843 0.544621 0.437677 0.069076 0.611063 0.305755 0.565570 
 3 | 0.394256 0.210029 0.242069 0.433665 0.413029 0.402390 0.059441 0.260856 0.581993 0.981201 
 4 | 0.136799 0.500525 0.298247 0.763590 0.146943 0.583502 0.262035 0.065179 0.808611 0.736965 
 5 | 0.592761 0.951248 0.989475 0.257922 0.129990 0.441655 0.513612 0.151377 0.446232 0.361556 
 6 | 0.734087 0.093356 0.005047 0.141247 0.670206 0.212659 0.513438 0.565514 0.763519 0.788929 
 7 | 0.597463 0.709617 0.128740 0.045325 0.246992 0.600123 0.685487 0.258230 0.381038 0.654313 
 8 | 0.279923 0.883747 0.673382 0.509537 0.184063 0.810945 0.997035 0.408734 0.918512 0.152567 
 9 | 0.304353 0.357997 0.773449 0.200366 0.826214 0.497918 0.214579 0.641810 0.326458 0.824188 
10 | 0.489276 0.357887 0.458318 0.089892 0.542525 0.924028 0.643705 0.401697 0.620824 0.499020 
11 | 
12 | 0.241156 0.697822 0.998895 0.882853 0.348439 0.440438 0.527540 0.321269 0.529683 0.751226 
13 | 0.596645 0.284220 0.803820 0.147148 0.707670 0.623661 0.811648 0.095930 0.248796 0.089043 
14 | 0.659493 0.288254 0.062372 0.931210 0.704358 0.610005 0.085373 0.849445 0.995621 0.924524 
15 | 0.622359 0.226658 0.551814 0.307972 0.203106 0.118051 0.097376 0.572990 0.465568 0.537193 
16 | 0.174511 0.581815 0.372413 0.385880 0.087691 0.021148 0.574760 0.977985 0.425269 0.716327 
17 | 0.205308 0.480248 0.110034 0.722304 0.344640 0.715604 0.544224 0.747472 0.241671 0.241978 
18 | 0.100186 0.152505 0.552838 0.695338 0.681608 0.489915 0.544465 0.964812 0.565558 0.075262 
19 | 0.905770 0.107921 0.854520 0.912345 0.954908 0.449365 0.926976 0.955902 0.478231 0.143513 
20 | 0.761810 0.095199 0.378282 0.440243 0.022009 0.434007 0.298414 0.361670 0.768045 0.263025 
21 | 0.701503 0.119274 0.913902 0.006199 0.944034 0.013049 0.746340 0.348921 0.039456 0.619511 
22 | 
23 | 0.239374 -0.622819 -1.704765 -1.487319 0.228833 0.035697 0.087171 -0.073336 0.711579 0.293663 
24 | -0.431120 0.295512 -1.146323 0.609630 -0.522538 0.047850 0.432103 -0.951582 -0.129309 -0.789244 
25 | -0.948660 -0.463193 0.358234 -1.387407 0.066634 0.380252 -0.016829 0.223210 0.850295 0.089831 
26 | -1.229361 -0.069811 -0.647727 -0.609467 0.084717 0.236070 0.141284 0.224476 1.002202 0.007038 
27 | -0.436931 -0.522995 -0.402353 -0.011397 0.337224 -0.073326 0.333598 -0.614062 0.270834 0.328140 
28 | -0.477710 0.349082 0.652261 0.341864 -0.145136 -0.458559 0.521389 0.248087 1.549419 1.275165 
29 | -0.392058 -0.526363 0.439528 0.531427 0.609261 0.701386 0.705251 -0.434511 0.453065 1.100117 
30 | 0.243626 -0.905656 0.342934 0.205775 0.587798 1.068976 1.071848 -0.268711 1.125199 0.000458 
31 | 0.395727 -0.634590 -0.391805 0.326074 -0.758431 0.424576 0.134586 0.925707 -0.117755 1.301578 
32 | 0.405129 -0.454182 0.180803 0.159619 0.053541 2.092095 0.500851 0.307414 1.544092 0.617735 
33 | 
34 | 4 4 4 4 4 3 0 0 0 0 
35 | 4 4 4 4 4 3 3 0 0 0 
36 | 4 4 4 4 3 3 3 3 0 0 
37 | 4 4 4 1 3 3 3 3 2 0 
38 | 1 1 1 1 1 3 2 2 2 2 
39 | 1 1 1 1 1 2 2 2 2 2 
40 | 1 1 1 1 1 2 2 2 2 2 
41 | 1 1 1 1 1 2 2 2 2 2 
42 | 1 1 1 1 1 2 2 2 2 2 
43 | 1 1 1 1 1 2 2 2 2 2 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 0.0 -2.0 -2.0 -2.0 -2.0 
46 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
47 | 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 -2.0 
48 | 1.0 1.0 1.0 -1.0 0.0 0.0 0.0 0.0 2.0 -2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 2.0 2.0 2.0 2.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
53 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
54 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 2.0 2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 2.0 2.0 
59 | -2.0 -2.0 -2.0 1.0 0.0 0.0 0.0 0.0 -1.0 2.0 
60 | 1.0 1.0 1.0 1.0 1.0 0.0 -1.0 -1.0 -1.0 -1.0 
61 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_5n.txt:
--------------------------------------------------------------------------------
 1 | 0.216260 0.051076 0.853016 0.606490 0.286009 0.521279 0.456836 0.359431 0.063134 0.384015 
 2 | 0.465298 0.597853 0.338659 0.486004 0.667946 0.751929 0.412402 0.422602 0.512833 0.929338 
 3 | 0.121606 0.248634 0.169583 0.958747 0.447020 0.805699 0.438779 0.771229 0.376815 0.706066 
 4 | 0.111728 0.818053 0.005112 0.252371 0.933203 0.216420 0.047003 0.043865 0.553776 0.753421 
 5 | 0.928182 0.221205 0.349004 0.349966 0.378194 0.339911 0.703459 0.019253 0.049612 0.442132 
 6 | 0.436298 0.550240 0.399043 0.943630 0.113043 0.813904 0.640160 0.204545 0.059588 0.554373 
 7 | 0.900931 0.190239 0.543822 0.440008 0.044543 0.424097 0.994311 0.640961 0.856722 0.823912 
 8 | 0.932466 0.707388 0.513518 0.669287 0.933914 0.100222 0.107851 0.074764 0.896345 0.726826 
 9 | 0.332364 0.872620 0.348105 0.435993 0.177966 0.302498 0.130955 0.595927 0.669070 0.123148 
10 | 0.738859 0.179986 0.990849 0.123394 0.319153 0.544936 0.923355 0.612745 0.718255 0.531145 
11 | 
12 | 0.370129 0.266051 0.391156 0.301886 0.251672 0.954026 0.398977 0.747493 0.372182 0.716693 
13 | 0.281894 0.570424 0.105181 0.970306 0.687307 0.265296 0.593241 0.097312 0.791895 0.148323 
14 | 0.180322 0.046192 0.691880 0.955861 0.781447 0.900073 0.007519 0.890135 0.646390 0.568735 
15 | 0.761438 0.415729 0.801510 0.263263 0.900747 0.480775 0.986857 0.047174 0.769923 0.124226 
16 | 0.438990 0.261600 0.241155 0.068292 0.948376 0.835314 0.574288 0.139380 0.434147 0.120680 
17 | 0.411756 0.669265 0.714252 0.533449 0.129437 0.559571 0.055138 0.275873 0.328155 0.666556 
18 | 0.398332 0.223850 0.537929 0.666826 0.755738 0.313912 0.990648 0.638372 0.880051 0.179252 
19 | 0.935753 0.813058 0.989239 0.043069 0.263337 0.178245 0.898443 0.061001 0.575176 0.599593 
20 | 0.619536 0.198424 0.188581 0.692883 0.337992 0.744705 0.143537 0.941810 0.554087 0.319507 
21 | 0.166649 0.805075 0.250323 0.001102 0.544408 0.901553 0.898907 0.407755 0.351523 0.895929 
22 | 
23 | -0.523999 -0.481026 0.070705 0.002718 -0.217336 0.000000 -0.115718 0.776124 0.618097 0.665354 
24 | -0.098491 -0.542995 0.128297 -1.454607 -0.706668 0.000000 0.000000 -0.650581 0.558123 -1.562031 
25 | -0.239038 0.156251 -1.214176 -0.952976 0.000000 0.000000 0.000000 0.000000 0.539151 -0.274661 
26 | -1.411149 -0.013406 -1.597908 0.010891 0.000000 0.000000 0.000000 0.000000 0.337628 -1.258390 
27 | -0.489192 0.040396 -0.107849 -0.281674 0.570183 0.000000 0.832631 -0.100875 -0.334923 0.763585 
28 | -0.024542 0.119025 0.315209 -0.410181 0.016395 1.068238 1.225181 0.133218 -0.208980 0.442189 
29 | -0.502600 0.033611 -0.005893 0.226818 0.711195 0.534282 0.997975 0.643550 0.833393 1.468573 
30 | 0.003287 0.105670 0.475721 -0.626218 -0.670577 0.022199 -0.682741 0.088528 1.217514 0.854058 
31 | 0.287171 -0.674196 -0.159524 0.256890 0.160026 -0.139708 0.118373 0.250044 0.784052 -0.073211 
32 | -0.572210 0.625090 -0.740527 -0.122292 0.225254 0.188319 0.947804 0.817735 1.084987 0.166362 
33 | 
34 | 4 4 4 4 4 3 0 0 0 0 
35 | 4 4 4 4 4 3 3 0 0 0 
36 | 4 4 4 4 3 3 3 3 0 0 
37 | 4 4 4 1 3 3 3 3 2 0 
38 | 1 1 1 1 1 3 2 2 2 2 
39 | 1 1 1 1 1 2 2 2 2 2 
40 | 1 1 1 1 1 2 2 2 2 2 
41 | 1 1 1 1 1 2 2 2 2 2 
42 | 1 1 1 1 1 2 2 2 2 2 
43 | 1 1 1 1 1 2 2 2 2 2 
44 | 
45 | 1.0 1.0 1.0 1.0 1.0 0.0 -2.0 -2.0 -2.0 -2.0 
46 | 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
47 | 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 -2.0 
48 | 1.0 1.0 1.0 -1.0 0.0 0.0 0.0 0.0 2.0 -2.0 
49 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 2.0 2.0 2.0 2.0 
50 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
53 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
54 | -1.0 -1.0 -1.0 -1.0 -1.0 2.0 2.0 2.0 2.0 2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 2.0 2.0 2.0 2.0 
57 | -2.0 -2.0 -2.0 -2.0 -2.0 0.0 0.0 2.0 2.0 2.0 
58 | -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 0.0 2.0 2.0 
59 | -2.0 -2.0 -2.0 1.0 0.0 0.0 0.0 0.0 -1.0 2.0 
60 | 1.0 1.0 1.0 1.0 1.0 0.0 -1.0 -1.0 -1.0 -1.0 
61 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
62 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
63 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
64 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
65 | 1.0 1.0 1.0 1.0 1.0 -1.0 -1.0 -1.0 -1.0 -1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_6h.txt:
--------------------------------------------------------------------------------
 1 | 0.160260 0.213169 0.902474 0.220238 0.635601 0.916422 0.098416 0.525126 0.192629 0.585927 
 2 | 0.180484 0.559161 0.697005 0.124483 0.867494 0.106143 0.941673 0.722149 0.921662 0.776100 
 3 | 0.660313 0.225604 0.326993 0.133998 0.166391 0.573423 0.799928 0.270690 0.636693 0.432711 
 4 | 0.410486 0.210852 0.843802 0.366343 0.455239 0.555487 0.277169 0.092521 0.614703 0.796913 
 5 | 0.942894 0.668152 0.014338 0.793389 0.515089 0.507518 0.885700 0.915729 0.002626 0.096739 
 6 | 0.982783 0.153091 0.916055 0.078152 0.975957 0.185088 0.530878 0.945789 0.633203 0.935457 
 7 | 0.421445 0.491857 0.178149 0.434866 0.881710 0.570950 0.141993 0.787756 0.842291 0.292930 
 8 | 0.644965 0.920856 0.058190 0.122086 0.336469 0.383733 0.374803 0.288680 0.568073 0.971732 
 9 | 0.647851 0.802236 0.476627 0.350195 0.204972 0.739231 0.974114 0.998940 0.860401 0.410375 
10 | 0.341538 0.778551 0.772496 0.180758 0.998807 0.269918 0.164032 0.165359 0.205564 0.598410 
11 | 
12 | 0.003392 0.981764 0.456077 0.630161 0.962999 0.728702 0.304846 0.831420 0.607554 0.825183 
13 | 0.894604 0.760228 0.592493 0.164939 0.366864 0.396279 0.510331 0.481252 0.362401 0.732267 
14 | 0.284596 0.921412 0.131041 0.255067 0.366427 0.667434 0.469487 0.426474 0.243115 0.506550 
15 | 0.532000 0.553966 0.048599 0.980210 0.114414 0.090428 0.388153 0.872229 0.880277 0.765594 
16 | 0.234896 0.771356 0.245799 0.110966 0.467710 0.160440 0.818667 0.248157 0.866508 0.676336 
17 | 0.086977 0.583123 0.447839 0.655190 0.214969 0.438435 0.220194 0.827384 0.136926 0.680033 
18 | 0.192286 0.275663 0.134045 0.860029 0.195309 0.903179 0.061848 0.918160 0.628825 0.846142 
19 | 0.883697 0.051301 0.788422 0.702813 0.512174 0.733950 0.886313 0.590706 0.723832 0.924215 
20 | 0.104776 0.489100 0.885022 0.934105 0.750900 0.935826 0.022931 0.736843 0.674617 0.271424 
21 | 0.626006 0.475468 0.824892 0.608044 0.942333 0.078495 0.589633 0.815618 0.266096 0.403043 
22 | 
23 | -0.200404 -1.934713 -1.191334 -1.793202 0.242076 0.172020 0.022723 0.951929 -0.206642 0.016844 
24 | -2.385808 -0.745264 0.381038 -0.021602 0.242442 -0.270338 0.330019 0.088692 -0.291921 -0.361112 
25 | -0.851789 -1.990036 -0.264174 -0.013133 -0.336530 0.360740 -0.152159 0.103527 -0.228379 -0.577686 
26 | -1.480829 -1.443929 0.580626 -0.745852 -0.339841 -0.251354 0.198581 0.260656 0.026635 -1.032995 
27 | -1.278025 -2.141247 -0.095315 0.554364 0.169419 -0.344296 -0.111442 -0.106485 0.326139 0.429496 
28 | -1.370535 -1.587976 0.483214 0.027664 0.143945 -0.236421 -0.205520 -1.090346 -1.508866 -0.799942 
29 | -0.507115 1.199582 0.044878 -0.740036 0.940766 -0.188302 0.118239 -0.680397 -1.270977 0.017064 
30 | -2.171985 2.110885 1.146561 1.389379 1.343723 -0.118080 0.426385 -0.302092 0.012667 -1.015714 
31 | -0.620691 2.468461 3.143442 2.904939 1.774975 3.642313 -2.355004 -1.089548 -1.120037 -0.334468 
32 | -1.412984 2.206698 3.424503 2.303248 4.201265 1.334220 1.235188 0.860124 -0.287545 -0.340906 
33 | 
34 | 2 2 2 2 4 4 4 3 3 3 
35 | 2 4 4 4 4 3 4 3 3 3 
36 | 2 2 4 4 4 3 3 3 3 3 
37 | 2 2 4 4 3 3 3 3 3 0 
38 | 2 2 4 4 4 3 3 3 3 0 
39 | 2 2 4 3 3 3 0 0 0 0 
40 | 2 1 4 4 4 3 3 0 0 0 
41 | 2 1 1 1 1 0 0 0 0 0 
42 | 2 1 1 1 1 1 0 0 0 0 
43 | 2 1 1 1 1 1 1 0 0 0 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 
46 | -1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 
47 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -2.0 
49 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 
50 | -1.0 -1.0 1.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 -2.0 
51 | -1.0 2.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
52 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
53 | -1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 
54 | -1.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 
57 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 -1.0 0.0 0.0 0.0 
58 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
59 | -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 
60 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 1.0 
61 | -2.0 -2.0 -1.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 
62 | -2.0 2.0 -1.0 -1.0 -1.0 0.0 0.0 1.0 1.0 1.0 
63 | -2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 
64 | -2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
65 | -2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_6l.txt:
--------------------------------------------------------------------------------
 1 | 0.183717 0.268986 0.075931 0.798335 0.495500 0.620131 0.415772 0.642890 0.475814 0.870782 
 2 | 0.032562 0.272116 0.152382 0.749093 0.372516 0.547132 0.679044 0.372464 0.777598 0.949172 
 3 | 0.664643 0.731888 0.150577 0.981024 0.607362 0.024010 0.975488 0.526487 0.550407 0.898789 
 4 | 0.805684 0.890373 0.423181 0.316890 0.958792 0.915364 0.445176 0.485245 0.321646 0.335449 
 5 | 0.655466 0.715813 0.495137 0.029313 0.502910 0.670367 0.042960 0.109611 0.734254 0.448084 
 6 | 0.761013 0.351005 0.921772 0.605846 0.858047 0.536395 0.702062 0.460571 0.627321 0.158184 
 7 | 0.229754 0.494363 0.930603 0.718512 0.663628 0.359462 0.232965 0.741123 0.167700 0.420399 
 8 | 0.972594 0.706037 0.829429 0.031048 0.355072 0.710970 0.104121 0.651266 0.219975 0.739546 
 9 | 0.434654 0.011839 0.161329 0.007515 0.925663 0.133584 0.108190 0.333068 0.810618 0.430112 
10 | 0.027255 0.860549 0.927447 0.772170 0.518106 0.698593 0.932846 0.130374 0.282808 0.231463 
11 | 
12 | 0.343513 0.247267 0.106319 0.013126 0.820879 0.552444 0.099864 0.119368 0.576920 0.522661 
13 | 0.416718 0.136638 0.161376 0.964779 0.762492 0.312210 0.807861 0.973455 0.847106 0.804896 
14 | 0.131422 0.057971 0.566109 0.329464 0.881742 0.509368 0.266413 0.229667 0.744853 0.443735 
15 | 0.038683 0.610181 0.049169 0.638929 0.421073 0.195304 0.710765 0.047678 0.555419 0.497192 
16 | 0.510729 0.584230 0.390802 0.901440 0.372408 0.442694 0.046333 0.067709 0.310472 0.529241 
17 | 0.239349 0.918162 0.968068 0.113864 0.503846 0.187747 0.609318 0.090592 0.463931 0.685942 
18 | 0.936541 0.373669 0.530746 0.680594 0.454945 0.174149 0.156492 0.435623 0.869917 0.108754 
19 | 0.890796 0.720925 0.153517 0.465397 0.778112 0.806207 0.969653 0.081593 0.053955 0.175063 
20 | 0.016594 0.831055 0.977308 0.208214 0.599973 0.417016 0.444579 0.856724 0.255544 0.110444 
21 | 0.335161 0.297457 0.438769 0.424944 0.117314 0.881681 0.512882 0.426936 0.282716 0.672010 
22 | 
23 | -0.884498 -0.716017 -0.349545 -0.925398 -0.364847 0.201930 0.189556 -0.007011 -0.122963 -0.134871 
24 | -0.759359 0.108961 0.023108 -0.346177 -0.290671 -0.022355 -0.098530 0.050415 0.076189 -0.225188 
25 | -0.825305 -0.974673 -0.405821 0.639538 -0.274934 -0.115385 -0.099081 0.095641 -0.009841 0.030289 
26 | -0.877451 -2.082120 0.320838 -0.425914 -0.061882 -0.125674 0.126142 0.176259 0.071971 -0.140167 
27 | -1.692029 -1.819780 0.158792 -0.850288 -0.018421 0.146751 -0.191355 0.090185 -0.029456 -0.492267 
28 | -1.243490 -2.101084 -0.000606 0.021084 -0.133118 -0.027048 -0.695239 -0.634810 -0.803245 0.561732 
29 | -1.892330 1.752172 0.506975 0.022276 0.227188 -0.058750 -0.081129 -1.026720 0.459859 -0.641994 
30 | -2.760681 2.963535 1.844074 1.090749 2.238113 -0.763122 0.730764 -1.168708 -0.377172 -1.281405 
31 | -0.357265 1.728596 2.261208 0.395315 2.977044 1.133315 0.241510 0.215046 -1.392243 -0.843189 
32 | -0.554272 2.362673 2.772273 2.207355 1.231811 3.064620 2.778485 0.075349 -0.258108 0.134226 
33 | 
34 | 2 2 2 2 4 4 4 3 3 3 
35 | 2 4 4 4 4 3 4 3 3 3 
36 | 2 2 4 4 4 3 3 3 3 3 
37 | 2 2 4 4 3 3 3 3 3 0 
38 | 2 2 4 4 4 3 3 3 3 0 
39 | 2 2 4 3 3 3 0 0 0 0 
40 | 2 1 4 4 4 3 3 0 0 0 
41 | 2 1 1 1 1 0 0 0 0 0 
42 | 2 1 1 1 1 1 0 0 0 0 
43 | 2 1 1 1 1 1 1 0 0 0 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 
46 | -1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 
47 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -2.0 
49 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 
50 | -1.0 -1.0 1.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 -2.0 
51 | -1.0 2.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
52 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
53 | -1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 
54 | -1.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 
57 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 -1.0 0.0 0.0 0.0 
58 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
59 | -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 
60 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 1.0 
61 | -2.0 -2.0 -1.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 
62 | -2.0 2.0 -1.0 -1.0 -1.0 0.0 0.0 1.0 1.0 1.0 
63 | -2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 
64 | -2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
65 | -2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_6m.txt:
--------------------------------------------------------------------------------
 1 | 0.819323 0.549744 0.252434 0.792436 0.812244 0.456814 0.310782 0.086702 0.309334 0.736849 
 2 | 0.230966 0.521943 0.898476 0.792452 0.131714 0.195035 0.670538 0.317694 0.006241 0.597776 
 3 | 0.552255 0.082245 0.180263 0.310178 0.664573 0.772199 0.350099 0.602943 0.011422 0.654531 
 4 | 0.957027 0.530909 0.274021 0.681137 0.954748 0.694173 0.211672 0.243272 0.865338 0.108443 
 5 | 0.948014 0.062378 0.754361 0.680846 0.415261 0.618609 0.628264 0.957431 0.740074 0.112685 
 6 | 0.302192 0.312645 0.871412 0.122307 0.983909 0.421239 0.578567 0.455931 0.576518 0.125147 
 7 | 0.775072 0.167677 0.075986 0.578337 0.565249 0.962712 0.826819 0.292927 0.851704 0.368132 
 8 | 0.798750 0.677743 0.438612 0.555677 0.486408 0.233901 0.306615 0.937384 0.219168 0.611280 
 9 | 0.484280 0.458058 0.557114 0.822622 0.888769 0.051644 0.817770 0.653335 0.933757 0.657829 
10 | 0.907783 0.324808 0.138691 0.244561 0.581713 0.593470 0.450782 0.552190 0.511780 0.786638 
11 | 
12 | 0.098773 0.239816 0.752393 0.196116 0.914223 0.295461 0.278825 0.045782 0.493223 0.024282 
13 | 0.276513 0.659593 0.182331 0.148605 0.838135 0.110729 0.773345 0.041811 0.805823 0.862289 
14 | 0.474371 0.236954 0.359937 0.037775 0.941519 0.946417 0.901362 0.928295 0.095191 0.664175 
15 | 0.725468 0.637616 0.748961 0.120792 0.243341 0.374948 0.665898 0.388372 0.684247 0.891697 
16 | 0.345888 0.731736 0.335467 0.121999 0.200387 0.761705 0.693466 0.307080 0.887919 0.817877 
17 | 0.175753 0.609470 0.715147 0.397949 0.809335 0.579365 0.771659 0.628371 0.159004 0.667343 
18 | 0.483878 0.443128 0.679060 0.371861 0.362612 0.364856 0.078270 0.892889 0.681222 0.445976 
19 | 0.608644 0.278337 0.299665 0.638150 0.718228 0.160858 0.672196 0.074997 0.255929 0.710240 
20 | 0.256229 0.946294 0.650312 0.393859 0.832330 0.559547 0.302995 0.277660 0.182519 0.999666 
21 | 0.894839 0.753977 0.257646 0.752170 0.646769 0.519377 0.622040 0.669023 0.434538 0.211911 
22 | 
23 | -1.080520 -1.079178 -1.570684 -1.287823 0.263363 -0.135681 0.100600 -0.092421 0.328648 -0.311350 
24 | -0.362644 -0.153343 0.637501 0.485306 -0.421734 -0.552438 0.088697 -0.005174 0.053109 -0.224697 
25 | -1.407972 -0.764838 0.044992 0.071961 -0.074093 -0.140324 0.200916 -0.064518 -0.121249 -0.035263 
26 | -2.516265 -1.948377 -0.653609 0.394707 -0.020996 0.078412 0.367387 -0.090503 -0.348487 0.774585 
27 | -1.819569 -1.547285 0.225588 0.651725 0.078998 0.048167 -0.157122 0.124778 -0.098536 0.369426 
28 | -0.302898 -1.149566 0.330866 -0.080351 0.111317 0.202923 -0.533234 -0.535026 -0.943694 0.634185 
29 | -1.449532 1.312403 -0.876734 0.246073 -0.138902 0.129407 0.165340 0.694085 -0.850173 -0.425583 
30 | -1.837771 1.816233 1.534179 2.298902 2.331917 -0.339146 0.023107 -1.880181 -0.191398 -0.386183 
31 | -0.908263 2.701629 2.176081 2.603978 3.539196 1.119525 -1.346492 -0.857081 -1.434115 -0.241947 
32 | -2.691359 2.048056 1.059796 2.243653 2.432514 2.332112 1.829591 -0.403312 -0.624239 -1.275131 
33 | 
34 | 2 2 2 2 4 4 4 3 3 3 
35 | 2 4 4 4 4 3 4 3 3 3 
36 | 2 2 4 4 4 3 3 3 3 3 
37 | 2 2 4 4 3 3 3 3 3 0 
38 | 2 2 4 4 4 3 3 3 3 0 
39 | 2 2 4 3 3 3 0 0 0 0 
40 | 2 1 4 4 4 3 3 0 0 0 
41 | 2 1 1 1 1 0 0 0 0 0 
42 | 2 1 1 1 1 1 0 0 0 0 
43 | 2 1 1 1 1 1 1 0 0 0 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 
46 | -1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 
47 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -2.0 
49 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 
50 | -1.0 -1.0 1.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 -2.0 
51 | -1.0 2.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
52 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
53 | -1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 
54 | -1.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 
57 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 -1.0 0.0 0.0 0.0 
58 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
59 | -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 
60 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 1.0 
61 | -2.0 -2.0 -1.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 
62 | -2.0 2.0 -1.0 -1.0 -1.0 0.0 0.0 1.0 1.0 1.0 
63 | -2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 
64 | -2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
65 | -2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_6n.txt:
--------------------------------------------------------------------------------
 1 | 0.109353 0.919624 0.411863 0.540163 0.198901 0.872028 0.240974 0.608726 0.584121 0.810882 
 2 | 0.713960 0.187353 0.430272 0.165867 0.532633 0.715826 0.458126 0.687567 0.370415 0.144083 
 3 | 0.254063 0.204713 0.190149 0.393492 0.597699 0.151490 0.260802 0.101225 0.886084 0.413397 
 4 | 0.653511 0.653479 0.271408 0.545883 0.488178 0.176527 0.460888 0.698403 0.631553 0.442464 
 5 | 0.281343 0.237111 0.362524 0.030036 0.057032 0.117835 0.227462 0.581336 0.224639 0.826283 
 6 | 0.355711 0.294940 0.043046 0.097155 0.936401 0.041658 0.040778 0.830598 0.452531 0.978393 
 7 | 0.722996 0.835924 0.207041 0.830287 0.372206 0.237231 0.082702 0.773219 0.210049 0.663021 
 8 | 0.921677 0.345142 0.644548 0.766522 0.091305 0.428421 0.904037 0.947281 0.360041 0.423141 
 9 | 0.665757 0.522275 0.054660 0.189573 0.233090 0.983462 0.049610 0.653491 0.811907 0.912241 
10 | 0.234846 0.592385 0.172731 0.870834 0.096617 0.309692 0.950653 0.864985 0.046944 0.095184 
11 | 
12 | 0.535898 0.683018 0.627729 0.179253 0.265237 0.287888 0.209649 0.000110 0.551496 0.929228 
13 | 0.467473 0.117106 0.986354 0.286761 0.244336 0.130191 0.101267 0.329847 0.427456 0.767746 
14 | 0.312088 0.222012 0.370315 0.209278 0.603116 0.077456 0.930195 0.569770 0.095620 0.730704 
15 | 0.271807 0.762202 0.912533 0.773159 0.805139 0.767673 0.573854 0.309414 0.982768 0.642357 
16 | 0.239983 0.169702 0.693075 0.170490 0.790889 0.211549 0.147284 0.854223 0.789369 0.081376 
17 | 0.102900 0.312147 0.078966 0.101737 0.374550 0.781264 0.247040 0.683543 0.693847 0.313180 
18 | 0.492930 0.472875 0.034655 0.097732 0.313557 0.450865 0.481679 0.857053 0.923759 0.899693 
19 | 0.209564 0.992458 0.397930 0.953456 0.391841 0.825678 0.984210 0.240834 0.435947 0.452815 
20 | 0.325457 0.451537 0.674423 0.439690 0.882399 0.643664 0.379330 0.855791 0.906073 0.214835 
21 | 0.554442 0.114351 0.929763 0.697925 0.827794 0.059322 0.153552 0.315564 0.824016 0.177298 
22 | 
23 | -1.181149 -2.285661 -1.667322 -0.898668 -0.066335 0.584140 0.031325 0.000000 0.000000 0.000000 
24 | -1.648905 0.070246 -0.556082 -0.120893 0.288297 0.000000 0.356859 0.000000 0.000000 0.000000 
25 | -0.878240 -0.648736 -0.180165 0.184214 -0.005417 0.000000 0.000000 0.000000 0.000000 0.000000 
26 | -1.197125 -2.177882 -0.641125 -0.227276 0.000000 0.000000 0.000000 0.000000 0.000000 -0.242571 
27 | -0.761309 -0.576514 -0.330551 -0.140454 -0.733858 0.000000 0.000000 0.000000 0.000000 -1.571189 
28 | -0.561512 -0.919233 -0.035920 0.000000 0.000000 0.000000 0.165484 -0.977653 -0.211215 -1.643606 
29 | -1.708856 2.617599 0.172385 0.732556 0.058648 0.000000 0.000000 -0.689385 0.503660 -0.426348 
30 | -1.340805 2.675199 2.084955 3.439956 0.966291 -0.031163 -0.823864 -1.653728 -0.284136 -0.393468 
31 | -1.316671 1.947624 1.458166 1.258526 2.230979 3.254252 0.280110 -0.451191 -0.717741 -1.609647 
32 | -1.343729 1.413472 2.204987 3.137518 1.848823 0.738028 2.208409 -1.414406 0.730128 -0.013071 
33 | 
34 | 2 2 2 2 4 4 4 3 3 3 
35 | 2 4 4 4 4 3 4 3 3 3 
36 | 2 2 4 4 4 3 3 3 3 3 
37 | 2 2 4 4 3 3 3 3 3 0 
38 | 2 2 4 4 4 3 3 3 3 0 
39 | 2 2 4 3 3 3 0 0 0 0 
40 | 2 1 4 4 4 3 3 0 0 0 
41 | 2 1 1 1 1 0 0 0 0 0 
42 | 2 1 1 1 1 1 0 0 0 0 
43 | 2 1 1 1 1 1 1 0 0 0 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 
46 | -1.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 
47 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -2.0 
49 | -1.0 -1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -2.0 
50 | -1.0 -1.0 1.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 -2.0 
51 | -1.0 2.0 1.0 1.0 1.0 0.0 0.0 -2.0 -2.0 -2.0 
52 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 -2.0 
53 | -1.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 -2.0 
54 | -1.0 2.0 2.0 2.0 2.0 2.0 2.0 -2.0 -2.0 -2.0 
55 | 
56 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 
57 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 -1.0 0.0 0.0 0.0 
58 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
59 | -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 
60 | -2.0 -2.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 1.0 
61 | -2.0 -2.0 -1.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 
62 | -2.0 2.0 -1.0 -1.0 -1.0 0.0 0.0 1.0 1.0 1.0 
63 | -2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 
64 | -2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 1.0 
65 | -2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.0 1.0 1.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_7h.txt:
--------------------------------------------------------------------------------
 1 | 0.365798 0.451266 0.340408 0.705555 0.286748 0.609897 0.463627 0.060956 0.831352 0.918223 
 2 | 0.618816 0.612237 0.339117 0.244799 0.134202 0.901004 0.797713 0.134079 0.142317 0.301861 
 3 | 0.777232 0.354638 0.164860 0.515916 0.419461 0.876901 0.550611 0.309747 0.001588 0.661509 
 4 | 0.979909 0.823152 0.721171 0.414730 0.557351 0.049024 0.081489 0.774453 0.041127 0.938114 
 5 | 0.625956 0.191410 0.700730 0.063087 0.437437 0.668867 0.182530 0.976854 0.195570 0.747154 
 6 | 0.382807 0.340131 0.225175 0.019344 0.604834 0.469570 0.374425 0.810368 0.038658 0.414279 
 7 | 0.472492 0.490965 0.277926 0.732784 0.848841 0.588084 0.318415 0.580829 0.883301 0.955163 
 8 | 0.042978 0.907368 0.999270 0.455890 0.464349 0.934579 0.276249 0.031096 0.012434 0.689179 
 9 | 0.023508 0.131672 0.017594 0.745307 0.115597 0.998334 0.476707 0.053117 0.440576 0.850350 
10 | 0.606375 0.964110 0.929011 0.064168 0.018092 0.570047 0.129536 0.560138 0.111745 0.490919 
11 | 
12 | 0.768917 0.052341 0.811197 0.251629 0.555217 0.274168 0.623532 0.274836 0.038264 0.313597 
13 | 0.757453 0.718082 0.265636 0.600881 0.309060 0.801594 0.781955 0.498969 0.178981 0.219535 
14 | 0.064783 0.223339 0.265438 0.541059 0.351233 0.062003 0.799301 0.805117 0.834425 0.541008 
15 | 0.962570 0.368179 0.600551 0.849812 0.612448 0.124473 0.170306 0.156934 0.433922 0.553410 
16 | 0.592616 0.839289 0.662562 0.586167 0.422436 0.849470 0.507466 0.825845 0.018308 0.419905 
17 | 0.890475 0.786230 0.691770 0.451722 0.547857 0.651463 0.085747 0.670476 0.537775 0.547200 
18 | 0.146933 0.811135 0.380125 0.735712 0.653919 0.452713 0.191776 0.157773 0.884545 0.898643 
19 | 0.229153 0.109872 0.462321 0.022859 0.851588 0.371734 0.122780 0.133367 0.891928 0.053826 
20 | 0.963483 0.653989 0.979742 0.546999 0.169933 0.260119 0.030973 0.292494 0.769211 0.608654 
21 | 0.341807 0.068362 0.921455 0.305448 0.029340 0.107886 0.480251 0.583273 0.315102 0.384313 
22 | 
23 | 0.984707 -0.211005 1.074904 -0.049541 0.354576 1.042407 1.355981 0.741309 -0.093884 0.687655 
24 | 0.327059 0.338255 0.187496 -0.003956 0.543279 0.940857 2.022834 1.822365 0.166598 0.303313 
25 | -0.927191 -0.197835 0.144148 0.061147 -0.041089 -1.388558 1.487307 1.905189 2.146099 1.082896 
26 | 0.013898 -1.142123 0.357493 0.434332 0.185493 -0.497980 0.345000 0.703135 0.830921 1.241074 
27 | -0.475095 0.949229 -0.744417 -0.963339 0.246702 -2.236797 0.158980 1.100355 1.052060 0.646219 
28 | 0.582493 -1.422217 -1.268200 -0.676741 0.139832 -1.363200 -1.082944 0.837496 0.133220 0.589843 
29 | 0.706003 -0.428485 -0.731683 -0.167010 -2.297012 -1.706022 0.239712 0.303845 0.846286 0.863752 
30 | -0.281306 1.400668 1.287640 1.089881 -1.262870 0.928812 0.367240 -0.288440 0.631298 0.592336 
31 | -2.211765 -0.788808 -1.172268 -2.041719 -0.647269 0.486274 0.926414 0.038755 0.301773 0.380485 
32 | -1.769974 -1.898859 -2.754013 -0.915249 -0.347323 0.162567 0.341003 0.982901 0.303340 0.693847 
33 | 
34 | 2 2 2 2 0 0 0 0 0 0 
35 | 2 2 2 2 0 0 0 0 0 0 
36 | 2 2 2 2 2 3 0 0 0 0 
37 | 2 2 2 2 2 3 0 1 0 0 
38 | 2 2 2 4 4 3 1 1 1 1 
39 | 2 4 4 4 4 3 3 1 1 1 
40 | 4 4 4 4 3 3 1 1 1 1 
41 | 4 4 4 4 3 1 1 1 1 1 
42 | 4 3 3 3 3 1 1 1 1 1 
43 | 3 3 3 3 3 1 1 1 1 1 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
46 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 1.0 0.0 0.0 
49 | -1.0 -1.0 -1.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 
50 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 
51 | 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 
52 | 2.0 2.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
53 | 2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 2.0 2.0 2.0 
59 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 0.0 2.0 2.0 
60 | 1.0 1.0 1.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 
61 | 1.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 
62 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 
63 | -2.0 -2.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 0.0 
64 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_7l.txt:
--------------------------------------------------------------------------------
 1 | 0.878646 0.426216 0.580833 0.736168 0.728194 0.052402 0.474528 0.836901 0.988627 0.098065 
 2 | 0.864806 0.069241 0.005279 0.784801 0.827307 0.151907 0.994476 0.756926 0.540747 0.586085 
 3 | 0.456008 0.311351 0.492734 0.802731 0.282466 0.733522 0.839766 0.588030 0.516955 0.665259 
 4 | 0.351469 0.596744 0.789112 0.370935 0.252461 0.326148 0.317709 0.337936 0.910313 0.841614 
 5 | 0.521283 0.676161 0.249618 0.218393 0.957576 0.519727 0.224193 0.071448 0.031842 0.804336 
 6 | 0.739779 0.144108 0.334636 0.056458 0.159186 0.511150 0.333157 0.918040 0.289155 0.257754 
 7 | 0.063620 0.462895 0.003960 0.337266 0.733714 0.609687 0.340005 0.090020 0.528196 0.772984 
 8 | 0.368614 0.804503 0.633648 0.886491 0.961338 0.710597 0.397266 0.064521 0.557901 0.815485 
 9 | 0.616739 0.877517 0.130317 0.249533 0.033004 0.872927 0.085727 0.057359 0.477728 0.495904 
10 | 0.666880 0.571301 0.892718 0.756431 0.678994 0.574127 0.905915 0.827094 0.198548 0.187919 
11 | 
12 | 0.884870 0.741652 0.653793 0.941143 0.164074 0.822308 0.117634 0.943268 0.044625 0.295523 
13 | 0.290250 0.536873 0.939664 0.894464 0.762874 0.656377 0.157997 0.926686 0.454769 0.749257 
14 | 0.236961 0.330153 0.243938 0.756736 0.247720 0.601238 0.149107 0.589968 0.968759 0.451399 
15 | 0.860892 0.697864 0.470538 0.182623 0.687014 0.106310 0.247683 0.097383 0.777115 0.880129 
16 | 0.358204 0.661244 0.370660 0.302067 0.238673 0.191779 0.762050 0.664538 0.027948 0.364507 
17 | 0.931381 0.779696 0.910681 0.443212 0.330085 0.050670 0.926630 0.935722 0.317501 0.848016 
18 | 0.995075 0.328781 0.122687 0.473677 0.845005 0.173296 0.600681 0.995497 0.959495 0.342706 
19 | 0.633943 0.917192 0.637207 0.275054 0.386988 0.638054 0.178440 0.437558 0.355910 0.561775 
20 | 0.478952 0.779988 0.859574 0.253949 0.821812 0.030977 0.146801 0.919791 0.683920 0.533853 
21 | 0.871732 0.918737 0.919508 0.681664 0.794264 0.777687 0.780162 0.476310 0.436922 0.328907 
22 | 
23 | -0.077239 0.326220 0.007670 0.178921 0.227702 1.624801 0.082087 1.956090 0.080612 0.554138 
24 | -0.516420 0.298715 0.881168 0.266429 1.496426 1.230844 0.373896 1.683975 0.693748 1.565107 
25 | -0.258345 0.186888 -0.240552 -0.170105 -0.244316 -1.924733 0.079660 1.215669 2.053288 0.957836 
26 | 0.439954 0.091180 -0.320462 -0.022663 0.300723 -0.729559 0.393247 0.205168 1.451061 1.742053 
27 | -0.216487 -0.218150 0.100498 -0.215006 1.346429 -1.149189 0.222633 0.107485 0.030479 0.997888 
28 | 0.179382 -1.202699 -1.038280 -0.713752 -0.508525 -0.977318 -1.662854 0.930460 0.393302 0.121947 
29 | -2.025168 0.203904 -0.204085 -0.297118 -2.326014 -1.258112 0.302637 -0.041505 0.590057 0.628708 
30 | -0.453150 -0.262586 -0.084117 1.274579 -2.204743 0.630997 0.467286 0.288739 0.317336 0.808610 
31 | 0.465157 -2.509523 -1.154453 -0.929108 -0.956777 0.985999 0.079165 0.077580 0.406469 0.505629 
32 | -2.224123 -2.263783 -2.732747 -2.237102 -2.025819 0.517167 1.189707 0.883158 0.326308 0.277593 
33 | 
34 | 2 2 2 2 0 0 0 0 0 0 
35 | 2 2 2 2 0 0 0 0 0 0 
36 | 2 2 2 2 2 3 0 0 0 0 
37 | 2 2 2 2 2 3 0 1 0 0 
38 | 2 2 2 4 4 3 1 1 1 1 
39 | 2 4 4 4 4 3 3 1 1 1 
40 | 4 4 4 4 3 3 1 1 1 1 
41 | 4 4 4 4 3 1 1 1 1 1 
42 | 4 3 3 3 3 1 1 1 1 1 
43 | 3 3 3 3 3 1 1 1 1 1 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
46 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 1.0 0.0 0.0 
49 | -1.0 -1.0 -1.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 
50 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 
51 | 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 
52 | 2.0 2.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
53 | 2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 2.0 2.0 2.0 
59 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 0.0 2.0 2.0 
60 | 1.0 1.0 1.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 
61 | 1.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 
62 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 
63 | -2.0 -2.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 0.0 
64 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_7m.txt:
--------------------------------------------------------------------------------
 1 | 0.429380 0.234861 0.026142 0.297395 0.672330 0.367869 0.375267 0.054707 0.087045 0.256253 
 2 | 0.882681 0.025696 0.698595 0.006497 0.372329 0.908188 0.158694 0.810045 0.547025 0.577168 
 3 | 0.637351 0.682767 0.657805 0.383761 0.789361 0.127513 0.826568 0.819440 0.615969 0.856964 
 4 | 0.725731 0.329794 0.735444 0.235373 0.445560 0.362102 0.195688 0.291282 0.121429 0.656735 
 5 | 0.161965 0.012987 0.004376 0.980396 0.207035 0.524886 0.205745 0.161191 0.033474 0.895232 
 6 | 0.685844 0.515081 0.879226 0.324154 0.955994 0.859779 0.024345 0.679781 0.452237 0.723063 
 7 | 0.856716 0.657131 0.694060 0.935484 0.055689 0.437002 0.041908 0.992591 0.689579 0.183414 
 8 | 0.306053 0.007537 0.478727 0.207563 0.765213 0.957216 0.570518 0.331902 0.629192 0.533523 
 9 | 0.842906 0.384546 0.475688 0.806969 0.604928 0.046385 0.034608 0.227284 0.270502 0.313071 
10 | 0.244762 0.005957 0.295086 0.286189 0.289009 0.552581 0.744265 0.569632 0.561946 0.961751 
11 | 
12 | 0.716914 0.146695 0.112579 0.103189 0.721740 0.084663 0.870434 0.031869 0.144649 0.133577 
13 | 0.116617 0.549813 0.705896 0.415940 0.148349 0.834773 0.931106 0.795384 0.130831 0.292769 
14 | 0.171595 0.234442 0.283311 0.365823 0.435973 0.457402 0.399887 0.838934 0.542230 0.545533 
15 | 0.170741 0.819672 0.677578 0.504368 0.079249 0.949261 0.574201 0.602808 0.908149 0.848619 
16 | 0.365014 0.868606 0.879859 0.406246 0.559060 0.110540 0.004382 0.944530 0.365494 0.545314 
17 | 0.732397 0.065357 0.182183 0.042929 0.126028 0.108275 0.436624 0.028255 0.690510 0.151747 
18 | 0.161388 0.891975 0.530102 0.248141 0.395935 0.062764 0.783821 0.707550 0.093115 0.691898 
19 | 0.231157 0.054911 0.472161 0.114023 0.150132 0.633709 0.487052 0.123144 0.129725 0.090950 
20 | 0.958915 0.980713 0.807519 0.680708 0.219046 0.453097 0.197534 0.265843 0.908148 0.790178 
21 | 0.325834 0.620512 0.163425 0.244850 0.867680 0.776957 0.847600 0.010350 0.221766 0.365159 
22 | 
23 | 0.052346 0.092561 0.084609 -0.384076 1.727577 0.392242 1.798509 0.015725 0.498683 0.437773 
24 | -0.633928 0.649126 -0.077228 0.037784 0.355254 1.656976 1.669364 1.728434 0.359310 0.242244 
25 | -0.594384 -0.482744 -0.302846 0.306602 -0.410346 -0.589477 0.683558 1.694390 0.921783 1.300002 
26 | -0.786298 0.530633 0.084165 0.333220 -0.419867 -1.449545 1.293709 0.394188 1.850929 1.835321 
27 | 0.311120 1.097086 0.556627 1.085218 -0.706931 -1.114734 0.171632 0.254775 -0.050569 0.730828 
28 | 0.073368 0.928426 1.578704 0.688108 1.546143 -1.688084 -0.557278 0.592123 0.354949 0.794178 
29 | 1.305329 -0.892073 0.570118 1.273887 -0.469199 -1.141326 0.060268 1.157823 0.498054 -0.018497 
30 | -0.028190 -0.157992 0.255621 0.494050 -1.764423 0.975477 0.666610 0.653347 0.841406 0.957615 
31 | -0.538264 -1.169723 -1.634697 -2.361489 -1.235635 0.342633 0.049624 -0.031477 0.136501 0.330786 
32 | -0.716090 -0.579140 -0.925433 -0.878921 -1.769114 0.557538 0.517664 0.431086 0.613516 1.078157 
33 | 
34 | 2 2 2 2 0 0 0 0 0 0 
35 | 2 2 2 2 0 0 0 0 0 0 
36 | 2 2 2 2 2 3 0 0 0 0 
37 | 2 2 2 2 2 3 0 1 0 0 
38 | 2 2 2 4 4 3 1 1 1 1 
39 | 2 4 4 4 4 3 3 1 1 1 
40 | 4 4 4 4 3 3 1 1 1 1 
41 | 4 4 4 4 3 1 1 1 1 1 
42 | 4 3 3 3 3 1 1 1 1 1 
43 | 3 3 3 3 3 1 1 1 1 1 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
46 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 1.0 0.0 0.0 
49 | -1.0 -1.0 -1.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 
50 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 
51 | 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 
52 | 2.0 2.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
53 | 2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 2.0 2.0 2.0 
59 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 0.0 2.0 2.0 
60 | 1.0 1.0 1.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 
61 | 1.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 
62 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 
63 | -2.0 -2.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 0.0 
64 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_7n.txt:
--------------------------------------------------------------------------------
 1 | 0.185801 0.893281 0.736792 0.885494 0.363296 0.062411 0.655345 0.266038 0.403705 0.214962 
 2 | 0.844217 0.445090 0.959466 0.115405 0.942066 0.782242 0.014871 0.070336 0.893126 0.472191 
 3 | 0.875180 0.807781 0.587470 0.023007 0.633803 0.457856 0.655354 0.042767 0.707713 0.032940 
 4 | 0.723906 0.621752 0.661417 0.415175 0.622568 0.337496 0.321640 0.639236 0.440717 0.046271 
 5 | 0.314121 0.418864 0.819099 0.864432 0.298075 0.392476 0.302082 0.853738 0.576234 0.732885 
 6 | 0.421709 0.480215 0.637789 0.729212 0.797428 0.476473 0.437741 0.315657 0.965206 0.853790 
 7 | 0.980032 0.822844 0.561549 0.581278 0.210818 0.107225 0.232031 0.976235 0.345613 0.459653 
 8 | 0.778115 0.572044 0.341080 0.275032 0.613934 0.949427 0.262886 0.931733 0.185644 0.857800 
 9 | 0.809949 0.703317 0.738688 0.194910 0.416123 0.033183 0.648859 0.571435 0.583472 0.186493 
10 | 0.120033 0.258014 0.957205 0.021682 0.482567 0.500999 0.116097 0.103430 0.097426 0.208207 
11 | 
12 | 0.478725 0.226650 0.422151 0.483565 0.298598 0.226105 0.078362 0.294247 0.115190 0.339469 
13 | 0.080709 0.642481 0.221863 0.412356 0.500971 0.549148 0.837408 0.536874 0.921763 0.931414 
14 | 0.319628 0.913330 0.278635 0.550168 0.851215 0.319234 0.642728 0.684314 0.010657 0.209888 
15 | 0.876518 0.655525 0.794921 0.623660 0.747043 0.225569 0.599579 0.408520 0.603298 0.788693 
16 | 0.506256 0.941946 0.985420 0.149762 0.775611 0.371095 0.279189 0.359700 0.801675 0.422932 
17 | 0.684292 0.191794 0.658217 0.133384 0.672714 0.211945 0.872280 0.847574 0.239320 0.486862 
18 | 0.309499 0.670258 0.340894 0.214010 0.829481 0.459780 0.183610 0.028735 0.905335 0.873323 
19 | 0.206885 0.223399 0.636744 0.199795 0.575266 0.827277 0.287089 0.683433 0.463893 0.893262 
20 | 0.992514 0.206269 0.661667 0.708296 0.651691 0.599533 0.105498 0.856025 0.304781 0.992695 
21 | 0.024086 0.291379 0.874488 0.818596 0.024802 0.524692 0.869362 0.211032 0.741875 0.715774 
22 | 
23 | 0.292924 -0.666631 -0.314640 -0.401929 0.597196 0.452210 0.156724 0.588495 0.230379 0.678938 
24 | -0.763509 0.197391 -0.737602 0.296951 1.001942 1.098296 1.674816 1.073749 1.843526 1.862829 
25 | -0.555552 0.105549 -0.308835 0.527161 0.217412 -1.234946 1.285457 1.368628 0.021315 0.419777 
26 | 0.152612 0.033773 0.133505 0.208484 0.124475 -0.900561 1.199159 0.639236 1.206596 1.577386 
27 | 0.192135 0.523082 0.166321 1.429340 -0.955072 -1.156046 0.302082 0.853738 0.576234 0.732885 
28 | 0.262583 0.576842 -0.040856 1.191657 0.249427 -1.164890 -1.747762 0.315657 0.965206 0.853790 
29 | 1.341066 0.305173 0.441312 0.734535 -1.251116 -0.674231 0.232031 0.976235 0.345613 0.459653 
30 | 1.142461 0.697288 -0.591326 0.150474 -1.803135 0.949427 0.262886 0.931733 0.185644 0.857800 
31 | -0.365131 -1.612902 -2.139042 -1.098116 -1.483936 0.033183 0.648859 0.571435 0.583472 0.186493 
32 | -0.264152 -0.807408 -2.788899 -0.861960 -0.989935 0.500999 0.116097 0.103430 0.097426 0.208207 
33 | 
34 | 2 2 2 2 0 0 0 0 0 0 
35 | 2 2 2 2 0 0 0 0 0 0 
36 | 2 2 2 2 2 3 0 0 0 0 
37 | 2 2 2 2 2 3 0 1 0 0 
38 | 2 2 2 4 4 3 1 1 1 1 
39 | 2 4 4 4 4 3 3 1 1 1 
40 | 4 4 4 4 3 3 1 1 1 1 
41 | 4 4 4 4 3 1 1 1 1 1 
42 | 4 3 3 3 3 1 1 1 1 1 
43 | 3 3 3 3 3 1 1 1 1 1 
44 | 
45 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
46 | -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 
47 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 0.0 0.0 0.0 
48 | -1.0 -1.0 -1.0 -1.0 -1.0 -2.0 0.0 1.0 0.0 0.0 
49 | -1.0 -1.0 -1.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 
50 | -1.0 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 
51 | 2.0 2.0 2.0 2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 
52 | 2.0 2.0 2.0 2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
53 | 2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
54 | -2.0 -2.0 -2.0 -2.0 -2.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
57 | 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 2.0 2.0 
58 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 2.0 2.0 2.0 
59 | 1.0 1.0 1.0 1.0 1.0 -1.0 2.0 0.0 2.0 2.0 
60 | 1.0 1.0 1.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 
61 | 1.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 
62 | -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 0.0 0.0 0.0 0.0 
63 | -2.0 -2.0 -2.0 -2.0 -1.0 0.0 0.0 0.0 0.0 0.0 
64 | -2.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
65 | -1.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_8h.txt:
--------------------------------------------------------------------------------
 1 | 0.892392 0.039619 0.561827 0.702318 0.126076 0.546668 0.727974 0.310856 0.147106 0.475644 
 2 | 0.376162 0.000191 0.736548 0.122347 0.693905 0.261648 0.921732 0.590400 0.106894 0.143762 
 3 | 0.834377 0.279759 0.390111 0.279724 0.337079 0.920507 0.909780 0.706628 0.404716 0.674197 
 4 | 0.546508 0.755363 0.439964 0.787029 0.683945 0.958721 0.810946 0.351027 0.591478 0.700278 
 5 | 0.476039 0.597173 0.180226 0.527021 0.365732 0.956576 0.228907 0.031008 0.707048 0.410398 
 6 | 0.400194 0.257630 0.917133 0.020733 0.574434 0.569262 0.475753 0.031667 0.273270 0.257580 
 7 | 0.735873 0.005194 0.662884 0.528395 0.566505 0.452972 0.242211 0.299215 0.534549 0.450144 
 8 | 0.054336 0.864311 0.112795 0.999278 0.154396 0.298722 0.632742 0.227564 0.462362 0.016834 
 9 | 0.757548 0.504222 0.522216 0.278276 0.216171 0.957690 0.812201 0.459491 0.945435 0.428031 
10 | 0.957374 0.246014 0.088025 0.739422 0.498889 0.194983 0.665220 0.950980 0.602346 0.056440 
11 | 
12 | 0.663372 0.675023 0.051601 0.273710 0.148336 0.942145 0.433950 0.227531 0.295468 0.962804 
13 | 0.888262 0.017381 0.031419 0.089858 0.952251 0.574626 0.442575 0.315540 0.812552 0.416851 
14 | 0.487608 0.741958 0.427729 0.440384 0.398348 0.759284 0.760075 0.969975 0.205891 0.858071 
15 | 0.488660 0.824412 0.164929 0.216605 0.359518 0.956815 0.764405 0.123607 0.260509 0.715052 
16 | 0.934519 0.915135 0.813654 0.572910 0.029882 0.881639 0.086202 0.406202 0.421519 0.946225 
17 | 0.269025 0.859367 0.238124 0.554956 0.795296 0.088200 0.193044 0.896328 0.610792 0.371807 
18 | 0.714475 0.901942 0.175786 0.589480 0.398767 0.296691 0.761204 0.909452 0.000848 0.834266 
19 | 0.185709 0.086580 0.270046 0.138049 0.893308 0.290089 0.296734 0.189949 0.541486 0.436788 
20 | 0.830621 0.931018 0.842495 0.221734 0.132282 0.390039 0.370899 0.826186 0.479471 0.695051 
21 | 0.532340 0.224013 0.487317 0.834321 0.417178 0.663808 0.580016 0.477839 0.481286 0.805075 
22 | 
23 | -1.670841 -0.706706 -1.031880 -1.552459 0.236159 -2.342905 1.615460 0.745004 0.397664 1.624203 
24 | -1.278765 -0.350927 -0.205022 -0.091862 -2.484987 -1.774739 2.398608 1.512098 0.460528 0.793782 
25 | -1.710016 -1.470752 -1.578974 -0.681387 -0.893165 -2.644621 -2.639033 2.655826 0.924456 2.332525 
26 | -0.761345 -1.392095 -0.372700 -0.724140 -0.523453 -2.253343 2.424488 0.861995 1.373083 1.723371 
27 | -1.874479 -1.128445 -1.778358 -0.892952 0.117685 -3.112198 -1.004393 -0.621726 1.646031 1.284515 
28 | -0.710178 1.142013 -0.942820 1.330564 0.945755 1.068106 0.609224 0.275435 1.357878 1.019084 
29 | 0.363732 1.873739 -0.602532 0.514684 0.747787 0.479782 -0.351623 1.122925 1.225873 1.965084 
30 | 0.592942 -0.607647 0.306052 -0.980456 0.061736 -0.022578 0.833337 0.586344 0.326524 0.523393 
31 | 0.621682 0.875505 1.082973 0.669677 0.538419 0.649112 1.192080 0.207633 0.818989 1.169242 
32 | 0.530812 0.153280 -0.106422 1.183407 0.632204 0.450991 0.750402 0.483307 0.672195 0.094361 
33 | 
34 | 1 1 1 1 1 1 2 2 2 2 
35 | 1 1 3 3 3 1 2 2 2 2 
36 | 1 1 3 3 3 1 1 2 2 2 
37 | 3 3 3 3 3 1 2 2 2 2 
38 | 3 3 3 3 3 1 1 1 2 2 
39 | 3 0 3 0 0 4 4 4 2 2 
40 | 0 0 0 0 0 4 4 2 2 2 
41 | 0 0 0 0 4 4 4 4 4 2 
42 | 0 0 0 4 4 4 4 4 4 2 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 2.0 2.0 2.0 2.0 
46 | -2.0 -2.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
47 | -2.0 -2.0 0.0 0.0 0.0 -2.0 -2.0 2.0 2.0 2.0 
48 | 0.0 0.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
49 | 0.0 0.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 2.0 2.0 
50 | 0.0 -1.0 0.0 -1.0 -1.0 1.0 1.0 1.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 2.0 
53 | -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 
57 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
58 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 -1.0 1.0 1.0 1.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
60 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 1.0 1.0 
61 | -2.0 2.0 -2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
62 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 1.0 1.0 1.0 
63 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
64 | 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 
65 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_8l.txt:
--------------------------------------------------------------------------------
 1 | 0.873730 0.038469 0.623745 0.628325 0.143759 0.932439 0.839341 0.244758 0.558846 0.681577 
 2 | 0.351831 0.959476 0.749290 0.423778 0.741357 0.727244 0.237488 0.833407 0.001861 0.775595 
 3 | 0.870134 0.262862 0.125476 0.015130 0.679708 0.567810 0.020909 0.542983 0.894474 0.861152 
 4 | 0.013190 0.708506 0.578354 0.654014 0.823136 0.301176 0.503519 0.481494 0.695832 0.611330 
 5 | 0.376744 0.820808 0.423376 0.916363 0.503842 0.019160 0.791963 0.023738 0.725447 0.038318 
 6 | 0.548859 0.917081 0.684274 0.058948 0.753272 0.427701 0.226100 0.241385 0.087833 0.431846 
 7 | 0.489389 0.125688 0.499152 0.937683 0.227907 0.298576 0.630855 0.522689 0.543117 0.095252 
 8 | 0.671116 0.767900 0.097638 0.201565 0.452453 0.771117 0.125202 0.995079 0.006785 0.440862 
 9 | 0.908719 0.330512 0.029888 0.387689 0.532294 0.037117 0.135500 0.644217 0.078680 0.921877 
10 | 0.336417 0.682116 0.023491 0.200972 0.555739 0.705437 0.952762 0.075668 0.530548 0.224270 
11 | 
12 | 0.970467 0.086517 0.828196 0.491752 0.086033 0.354147 0.021429 0.682797 0.371188 0.618881 
13 | 0.318945 0.986510 0.775565 0.212507 0.436727 0.248106 0.922176 0.329620 0.924493 0.670185 
14 | 0.654251 0.928209 0.482852 0.757633 0.633927 0.282956 0.742198 0.725244 0.937762 0.329553 
15 | 0.050779 0.852853 0.053979 0.501946 0.073974 0.344517 0.617673 0.069328 0.047569 0.631909 
16 | 0.415899 0.890546 0.268882 0.551929 0.619178 0.373341 0.304564 0.742597 0.120590 0.050560 
17 | 0.884219 0.068236 0.932819 0.363591 0.219167 0.589683 0.298293 0.557048 0.643007 0.306479 
18 | 0.300345 0.592251 0.886475 0.497539 0.516457 0.066271 0.455706 0.330931 0.564608 0.699509 
19 | 0.406004 0.634099 0.255311 0.589903 0.587821 0.235125 0.882063 0.022983 0.651300 0.609197 
20 | 0.871764 0.164796 0.206550 0.681640 0.594236 0.598803 0.683185 0.670613 0.372797 0.380929 
21 | 0.123664 0.080631 0.405840 0.910594 0.449245 0.033864 0.770277 0.102336 0.417158 0.352134 
22 | 
23 | -2.886870 -0.177707 -2.063831 -1.729781 -0.313074 -2.119772 1.771301 1.151150 1.289579 2.163668 
24 | -1.066638 -2.848062 -1.520142 -0.336918 -0.870249 -1.715389 1.344793 2.028712 0.866837 2.331909 
25 | -2.450438 -1.566065 -1.015357 -1.530120 -1.221066 -1.517259 -0.834886 1.798018 2.825084 1.968706 
26 | -0.270998 -1.834451 -0.091058 -0.756410 -0.252278 -0.895039 1.690393 0.976990 1.425419 1.867219 
27 | -1.022544 -1.774958 -0.731437 -1.139369 -1.313038 -0.462379 -1.870633 -0.709783 1.364622 -0.038292 
28 | -1.853809 -0.986315 -1.823335 0.472355 -0.346357 0.398116 0.393126 0.354362 0.841790 1.178987 
29 | 0.209016 1.142076 1.426831 -0.022542 0.591740 0.262561 0.663692 1.374977 1.565260 0.903817 
30 | 0.076547 0.464169 0.255188 1.038074 0.361878 0.864647 0.028361 0.860310 0.073090 1.663757 
31 | 0.664856 -0.009343 0.387471 0.558498 0.536823 0.098389 -0.044692 0.639287 -0.049333 2.100139 
32 | 0.306105 0.618632 0.231703 0.102270 0.760789 0.698685 1.091937 -0.006366 0.634472 0.193668 
33 | 
34 | 1 1 1 1 1 1 2 2 2 2 
35 | 1 1 3 3 3 1 2 2 2 2 
36 | 1 1 3 3 3 1 1 2 2 2 
37 | 3 3 3 3 3 1 2 2 2 2 
38 | 3 3 3 3 3 1 1 1 2 2 
39 | 3 0 3 0 0 4 4 4 2 2 
40 | 0 0 0 0 0 4 4 2 2 2 
41 | 0 0 0 0 4 4 4 4 4 2 
42 | 0 0 0 4 4 4 4 4 4 2 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 2.0 2.0 2.0 2.0 
46 | -2.0 -2.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
47 | -2.0 -2.0 0.0 0.0 0.0 -2.0 -2.0 2.0 2.0 2.0 
48 | 0.0 0.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
49 | 0.0 0.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 2.0 2.0 
50 | 0.0 -1.0 0.0 -1.0 -1.0 1.0 1.0 1.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 2.0 
53 | -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 
57 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
58 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 -1.0 1.0 1.0 1.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
60 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 1.0 1.0 
61 | -2.0 2.0 -2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
62 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 1.0 1.0 1.0 
63 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
64 | 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 
65 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_8m.txt:
--------------------------------------------------------------------------------
 1 | 0.415475 0.516665 0.594621 0.657451 0.732753 0.860094 0.851586 0.677889 0.333272 0.201648 
 2 | 0.356168 0.516842 0.720543 0.402317 0.523893 0.954617 0.470477 0.922429 0.126311 0.758836 
 3 | 0.103391 0.066540 0.038209 0.809940 0.351090 0.611297 0.904523 0.552133 0.538939 0.201110 
 4 | 0.264897 0.312322 0.407993 0.652759 0.572809 0.037545 0.194594 0.088367 0.838180 0.551204 
 5 | 0.378057 0.330237 0.081916 0.926408 0.662806 0.825512 0.846147 0.135565 0.941916 0.528366 
 6 | 0.821461 0.413985 0.060622 0.740700 0.649747 0.455979 0.550063 0.500490 0.436974 0.817003 
 7 | 0.919368 0.515761 0.679827 0.259095 0.403545 0.619856 0.332357 0.818765 0.208820 0.621694 
 8 | 0.730362 0.432875 0.839929 0.711724 0.879183 0.227055 0.065568 0.308257 0.163014 0.895340 
 9 | 0.376351 0.000273 0.073197 0.534560 0.838344 0.569204 0.430809 0.955530 0.389629 0.436682 
10 | 0.305603 0.664602 0.844190 0.790397 0.523152 0.068740 0.867646 0.419977 0.429741 0.685830 
11 | 
12 | 0.029464 0.364890 0.365335 0.355285 0.815670 0.654734 0.105385 0.580447 0.807533 0.244915 
13 | 0.482535 0.611414 0.689180 0.089972 0.119747 0.111352 0.470480 0.939775 0.387181 0.848367 
14 | 0.433612 0.529258 0.004596 0.087597 0.242304 0.813871 0.026692 0.177232 0.060149 0.106511 
15 | 0.339509 0.938443 0.232266 0.224941 0.105794 0.354346 0.301792 0.338156 0.256400 0.227053 
16 | 0.053916 0.116630 0.999822 0.881924 0.801429 0.584871 0.767014 0.795606 0.338583 0.439317 
17 | 0.197125 0.458918 0.573011 0.356566 0.469410 0.006263 0.058691 0.460140 0.847486 0.042163 
18 | 0.566456 0.095928 0.982763 0.344745 0.776714 0.986484 0.619630 0.491486 0.678986 0.017509 
19 | 0.700696 0.533654 0.052970 0.504071 0.473881 0.728404 0.112829 0.181966 0.558404 0.986014 
20 | 0.165265 0.942596 0.273387 0.944641 0.904765 0.455023 0.649482 0.298242 0.430811 0.348385 
21 | 0.244001 0.776280 0.006498 0.278335 0.581175 0.829443 0.745993 0.725846 0.842197 0.201212 
22 | 
23 | -0.980619 -1.471762 -1.682954 -1.912465 -2.168541 -2.241119 2.024512 2.274388 1.237709 0.777156 
24 | -1.332526 -1.898419 -1.371146 -0.283023 -0.245937 -1.849300 1.460916 2.769661 0.711946 2.572386 
25 | -0.634798 -0.614532 -0.503726 0.043418 -0.365067 -2.211371 -1.613886 0.964275 1.291451 0.672330 
26 | -0.608462 -1.833957 -0.591570 -0.631738 0.013077 -0.419371 0.653518 0.402341 2.133145 1.461136 
27 | -0.236498 -0.469391 -1.961481 -1.588710 -1.570101 -2.319722 -2.348765 -1.170521 2.508682 1.415385 
28 | -0.521736 0.595381 -0.985581 -0.190704 0.132431 0.329979 0.364935 0.473321 1.644417 1.800886 
29 | 0.557219 -0.522094 1.353573 0.082872 1.116439 0.462182 0.534495 1.743564 0.859382 1.218529 
30 | 0.802221 0.558603 -0.710835 0.027455 0.890119 -0.059101 -0.340798 0.162826 0.347744 2.680431 
31 | 0.357771 1.697884 0.073324 0.517901 0.712660 0.471653 0.345654 1.011578 0.566813 1.250350 
32 | 0.215904 0.718840 0.698565 0.797901 0.301617 -0.245355 0.479580 0.348767 0.596166 0.767453 
33 | 
34 | 1 1 1 1 1 1 2 2 2 2 
35 | 1 1 3 3 3 1 2 2 2 2 
36 | 1 1 3 3 3 1 1 2 2 2 
37 | 3 3 3 3 3 1 2 2 2 2 
38 | 3 3 3 3 3 1 1 1 2 2 
39 | 3 0 3 0 0 4 4 4 2 2 
40 | 0 0 0 0 0 4 4 2 2 2 
41 | 0 0 0 0 4 4 4 4 4 2 
42 | 0 0 0 4 4 4 4 4 4 2 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 2.0 2.0 2.0 2.0 
46 | -2.0 -2.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
47 | -2.0 -2.0 0.0 0.0 0.0 -2.0 -2.0 2.0 2.0 2.0 
48 | 0.0 0.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
49 | 0.0 0.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 2.0 2.0 
50 | 0.0 -1.0 0.0 -1.0 -1.0 1.0 1.0 1.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 2.0 
53 | -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 
57 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
58 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 -1.0 1.0 1.0 1.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
60 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 1.0 1.0 
61 | -2.0 2.0 -2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
62 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 1.0 1.0 1.0 
63 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
64 | 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 
65 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------
/synthetic/gridtest_8n.txt:
--------------------------------------------------------------------------------
 1 | 0.761869 0.885580 0.250887 0.599395 0.007077 0.266319 0.450233 0.693652 0.451610 0.478567 
 2 | 0.229342 0.264011 0.472135 0.222472 0.401623 0.315255 0.315952 0.040081 0.998217 0.934218 
 3 | 0.467790 0.410736 0.900516 0.132366 0.724854 0.843791 0.793594 0.965850 0.268015 0.511307 
 4 | 0.896749 0.814845 0.262069 0.357728 0.171811 0.538908 0.785899 0.459304 0.452817 0.224343 
 5 | 0.323924 0.065914 0.262380 0.447688 0.377559 0.167095 0.259520 0.646478 0.432367 0.559570 
 6 | 0.181805 0.015468 0.009678 0.889331 0.955621 0.430231 0.968845 0.413834 0.013315 0.497656 
 7 | 0.790276 0.078793 0.859545 0.849013 0.010463 0.038315 0.504791 0.599647 0.775380 0.195860 
 8 | 0.505674 0.023766 0.739120 0.595180 0.052822 0.235562 0.829378 0.307611 0.853499 0.229238 
 9 | 0.140535 0.366484 0.085675 0.190219 0.162441 0.630890 0.804844 0.379912 0.262877 0.491527 
10 | 0.030187 0.703671 0.975636 0.156939 0.093301 0.329019 0.352048 0.542696 0.956103 0.914777 
11 | 
12 | 0.389415 0.376961 0.968623 0.470952 0.008983 0.088046 0.344297 0.123720 0.217276 0.601876 
13 | 0.079922 0.598184 0.248089 0.530009 0.010677 0.330039 0.612889 0.781526 0.640680 0.354885 
14 | 0.698868 0.182063 0.530993 0.195725 0.464466 0.689002 0.758868 0.579881 0.801988 0.160832 
15 | 0.107502 0.852724 0.562359 0.278219 0.379754 0.533926 0.030191 0.041608 0.366145 0.158315 
16 | 0.832448 0.667751 0.856002 0.143448 0.690407 0.386408 0.002688 0.337316 0.036101 0.036314 
17 | 0.485894 0.220681 0.515190 0.401785 0.151044 0.929538 0.950021 0.032859 0.941586 0.415697 
18 | 0.824120 0.740633 0.938524 0.462515 0.516176 0.440860 0.321708 0.440869 0.895012 0.044499 
19 | 0.718496 0.578633 0.773906 0.135592 0.196111 0.771926 0.956217 0.184296 0.462474 0.620939 
20 | 0.409555 0.182868 0.204805 0.615040 0.993404 0.228362 0.303578 0.393180 0.143702 0.947385 
21 | 0.537652 0.782028 0.993274 0.256678 0.523263 0.572544 0.643679 0.624509 0.356555 0.982399 
22 | 
23 | -1.913153 -2.148120 -1.470396 -1.669741 -0.023138 -0.620683 1.244763 1.511024 1.120497 1.559010 
24 | -0.538605 -1.126206 -0.496179 -1.060018 -0.021354 -0.960549 1.244794 0.861687 2.637113 2.223321 
25 | -1.634448 -1.003535 -1.061986 -0.391449 -0.928932 -2.376585 -2.346056 2.511581 1.338018 1.183445 
26 | -0.215003 -1.705448 -1.124719 -0.556439 -0.759508 -1.611742 1.601989 0.960216 1.271779 0.607000 
27 | -1.664896 -1.335502 -1.712004 -0.286896 -1.380814 -0.720598 -0.521727 -1.630273 0.900834 1.155454 
28 | -0.971788 0.425894 -1.030379 -0.085760 -0.653534 0.430231 0.968845 0.413834 0.968216 1.411010 
29 | 0.857965 1.402472 1.017504 0.076018 1.021889 0.038315 0.504791 1.640163 2.445773 0.436219 
30 | 0.931318 1.133500 0.808692 -0.323995 0.052822 0.235562 0.829378 0.307611 0.853499 1.079415 
31 | 0.678575 -0.000747 0.323936 0.190219 0.162441 0.630890 0.804844 0.379912 0.262877 1.930439 
32 | 0.030187 0.703671 0.975636 0.156939 0.093301 0.329019 0.352048 0.542696 0.956103 0.914777 
33 | 
34 | 1 1 1 1 1 1 2 2 2 2 
35 | 1 1 3 3 3 1 2 2 2 2 
36 | 1 1 3 3 3 1 1 2 2 2 
37 | 3 3 3 3 3 1 2 2 2 2 
38 | 3 3 3 3 3 1 1 1 2 2 
39 | 3 0 3 0 0 4 4 4 2 2 
40 | 0 0 0 0 0 4 4 2 2 2 
41 | 0 0 0 0 4 4 4 4 4 2 
42 | 0 0 0 4 4 4 4 4 4 2 
43 | 4 4 4 4 4 4 4 4 4 4 
44 | 
45 | -2.0 -2.0 -2.0 -2.0 -2.0 -2.0 2.0 2.0 2.0 2.0 
46 | -2.0 -2.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
47 | -2.0 -2.0 0.0 0.0 0.0 -2.0 -2.0 2.0 2.0 2.0 
48 | 0.0 0.0 0.0 0.0 0.0 -2.0 2.0 2.0 2.0 2.0 
49 | 0.0 0.0 0.0 0.0 0.0 -2.0 -2.0 -2.0 2.0 2.0 
50 | 0.0 -1.0 0.0 -1.0 -1.0 1.0 1.0 1.0 2.0 2.0 
51 | -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 2.0 2.0 2.0 
52 | -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 2.0 
53 | -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 
54 | 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 
55 | 
56 | -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 1.0 1.0 1.0 1.0 
57 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
58 | -1.0 -1.0 -2.0 -2.0 -2.0 -1.0 -1.0 1.0 1.0 1.0 
59 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 1.0 1.0 1.0 1.0 
60 | -2.0 -2.0 -2.0 -2.0 -2.0 -1.0 -1.0 -1.0 1.0 1.0 
61 | -2.0 2.0 -2.0 2.0 2.0 0.0 0.0 0.0 1.0 1.0 
62 | 2.0 2.0 2.0 2.0 2.0 0.0 0.0 1.0 1.0 1.0 
63 | 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 
64 | 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 
65 | 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
66 | 
67 | 


--------------------------------------------------------------------------------