| 1 | """ |
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| 2 | Merge a list of .sww files together into a single file. |
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| 3 | """ |
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| 4 | |
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| 5 | import numpy as num |
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| 6 | from anuga.utilities.numerical_tools import ensure_numeric |
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| 7 | |
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| 8 | from Scientific.IO.NetCDF import NetCDFFile |
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| 9 | from anuga.config import netcdf_mode_r, netcdf_mode_w, netcdf_mode_a |
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| 10 | from anuga.config import netcdf_float, netcdf_float32, netcdf_int |
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| 11 | from anuga.file.sww import SWW_file, Write_sww |
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| 12 | |
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| 13 | def sww_merge(domain_global_name, np, verbose=False): |
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| 14 | |
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| 15 | output = domain_global_name+".sww" |
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| 16 | swwfiles = [ domain_global_name+"_P"+str(np)+"_"+str(v)+".sww" for v in range(np)] |
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| 17 | |
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| 18 | _sww_merge(swwfiles, output, verbose) |
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| 19 | |
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| 20 | |
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| 21 | def sww_merge_parallel(domain_global_name, np, verbose=False, delete_old=False): |
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| 22 | |
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| 23 | output = domain_global_name+".sww" |
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| 24 | swwfiles = [ domain_global_name+"_P"+str(np)+"_"+str(v)+".sww" for v in range(np)] |
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| 25 | |
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| 26 | _sww_merge_parallel(swwfiles, output, verbose, delete_old) |
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| 27 | |
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| 28 | |
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| 29 | def _sww_merge(swwfiles, output, verbose=False): |
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| 30 | """ |
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| 31 | Merge a list of sww files into a single file. |
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| 32 | |
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| 33 | May be useful for parallel runs. Note that colinear points and |
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| 34 | edges are not merged: there will essentially be multiple meshes within |
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| 35 | the one sww file. |
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| 36 | |
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| 37 | The sww files to be merged must have exactly the same timesteps. Note |
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| 38 | that some advanced information and custom quantities may not be |
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| 39 | exported. |
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| 40 | |
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| 41 | swwfiles is a list of .sww files to merge. |
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| 42 | output is the output filename, including .sww extension. |
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| 43 | verbose True to log output information |
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| 44 | """ |
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| 45 | |
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| 46 | if verbose: |
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| 47 | print "MERGING SWW Files" |
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| 48 | |
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| 49 | static_quantities = ['elevation'] |
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| 50 | dynamic_quantities = ['stage', 'xmomentum', 'ymomentum'] |
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| 51 | |
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| 52 | first_file = True |
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| 53 | tri_offset = 0 |
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| 54 | for filename in swwfiles: |
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| 55 | if verbose: |
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| 56 | print 'Reading file ', filename, ':' |
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| 57 | |
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| 58 | fid = NetCDFFile(filename, netcdf_mode_r) |
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| 59 | |
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| 60 | |
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| 61 | |
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| 62 | tris = fid.variables['volumes'][:] |
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| 63 | |
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| 64 | if first_file: |
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| 65 | times = fid.variables['time'][:] |
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| 66 | x = [] |
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| 67 | y = [] |
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| 68 | out_tris = list(tris) |
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| 69 | out_s_quantities = {} |
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| 70 | out_d_quantities = {} |
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| 71 | |
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| 72 | |
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| 73 | xllcorner = fid.xllcorner |
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| 74 | yllcorner = fid.yllcorner |
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| 75 | |
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| 76 | order = fid.order |
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| 77 | xllcorner = fid.xllcorner; |
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| 78 | yllcorner = fid.yllcorner ; |
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| 79 | zone = fid.zone; |
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| 80 | false_easting = fid.false_easting; |
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| 81 | false_northing = fid.false_northing; |
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| 82 | datum = fid.datum; |
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| 83 | projection = fid.projection; |
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| 84 | |
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| 85 | |
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| 86 | for quantity in static_quantities: |
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| 87 | out_s_quantities[quantity] = [] |
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| 88 | |
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| 89 | # Quantities are stored as a 2D array of timesteps x data. |
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| 90 | for quantity in dynamic_quantities: |
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| 91 | out_d_quantities[quantity] = [ [] for _ in range(len(times))] |
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| 92 | |
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| 93 | description = 'merged:' + getattr(fid, 'description') |
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| 94 | first_file = False |
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| 95 | else: |
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| 96 | for tri in tris: |
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| 97 | # Advance new tri indices to point at newly appended points. |
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| 98 | verts = [vertex+tri_offset for vertex in tri] |
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| 99 | out_tris.append(verts) |
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| 100 | |
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| 101 | num_pts = fid.dimensions['number_of_points'] |
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| 102 | tri_offset += num_pts |
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| 103 | |
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| 104 | if verbose: |
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| 105 | print ' new triangle index offset is ', tri_offset |
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| 106 | |
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| 107 | x.extend(list(fid.variables['x'][:])) |
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| 108 | y.extend(list(fid.variables['y'][:])) |
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| 109 | |
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| 110 | # Grow the list of static quantities associated with the x,y points |
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| 111 | for quantity in static_quantities: |
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| 112 | out_s_quantities[quantity].extend(fid.variables[quantity][:]) |
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| 113 | |
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| 114 | #Collate all dynamic quantities according to their timestep |
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| 115 | for quantity in dynamic_quantities: |
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| 116 | time_chunks = fid.variables[quantity][:] |
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| 117 | for i, time_chunk in enumerate(time_chunks): |
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| 118 | out_d_quantities[quantity][i].extend(time_chunk) |
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| 119 | |
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| 120 | # Mash all points into a single big list |
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| 121 | points = [[xx, yy] for xx, yy in zip(x, y)] |
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| 122 | |
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| 123 | points = num.asarray(points).astype(netcdf_float32) |
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| 124 | |
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| 125 | fid.close() |
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| 126 | |
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| 127 | #--------------------------- |
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| 128 | # Write out the SWW file |
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| 129 | #--------------------------- |
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| 130 | |
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| 131 | if verbose: |
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| 132 | print 'Writing file ', output, ':' |
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| 133 | fido = NetCDFFile(output, netcdf_mode_w) |
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| 134 | sww = Write_sww(static_quantities, dynamic_quantities) |
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| 135 | sww.store_header(fido, times, |
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| 136 | len(out_tris), |
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| 137 | len(points), |
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| 138 | description=description, |
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| 139 | sww_precision=netcdf_float32) |
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| 140 | |
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| 141 | |
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| 142 | |
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| 143 | from anuga.coordinate_transforms.geo_reference import Geo_reference |
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| 144 | geo_reference = Geo_reference() |
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| 145 | |
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| 146 | sww.store_triangulation(fido, points, out_tris, points_georeference=geo_reference) |
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| 147 | |
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| 148 | fido.order = order |
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| 149 | fido.xllcorner = xllcorner; |
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| 150 | fido.yllcorner = yllcorner ; |
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| 151 | fido.zone = zone; |
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| 152 | fido.false_easting = false_easting; |
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| 153 | fido.false_northing = false_northing; |
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| 154 | fido.datum = datum; |
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| 155 | fido.projection = projection; |
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| 156 | |
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| 157 | sww.store_static_quantities(fido, verbose=verbose, **out_s_quantities) |
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| 158 | |
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| 159 | # Write out all the dynamic quantities for each timestep |
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| 160 | for q in dynamic_quantities: |
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| 161 | q_values = out_d_quantities[q] |
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| 162 | for i, time_slice in enumerate(q_values): |
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| 163 | fido.variables[q][i] = num.array(time_slice, netcdf_float32) |
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| 164 | |
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| 165 | # This updates the _range values |
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| 166 | q_range = fido.variables[q + Write_sww.RANGE][:] |
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| 167 | q_values_min = num.min(q_values) |
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| 168 | if q_values_min < q_range[0]: |
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| 169 | fido.variables[q + Write_sww.RANGE][0] = q_values_min |
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| 170 | q_values_max = num.max(q_values) |
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| 171 | if q_values_max > q_range[1]: |
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| 172 | fido.variables[q + Write_sww.RANGE][1] = q_values_max |
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| 173 | |
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| 174 | |
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| 175 | fido.close() |
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| 176 | |
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| 177 | |
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| 178 | def _sww_merge_parallel(swwfiles, output, verbose=False, delete_old=False): |
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| 179 | """ |
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| 180 | Merge a list of sww files into a single file. |
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| 181 | |
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| 182 | Use to merge files created by parallel runs. |
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| 183 | |
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| 184 | The sww files to be merged must have exactly the same timesteps. |
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| 185 | |
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| 186 | Note that some advanced information and custom quantities may not be |
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| 187 | exported. |
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| 188 | |
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| 189 | swwfiles is a list of .sww files to merge. |
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| 190 | output is the output filename, including .sww extension. |
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| 191 | verbose True to log output information |
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| 192 | """ |
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| 193 | |
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| 194 | if verbose: |
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| 195 | print "MERGING SWW Files" |
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| 196 | |
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| 197 | |
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| 198 | first_file = True |
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| 199 | tri_offset = 0 |
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| 200 | for filename in swwfiles: |
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| 201 | if verbose: |
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| 202 | print 'Reading file ', filename, ':' |
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| 203 | |
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| 204 | fid = NetCDFFile(filename, netcdf_mode_r) |
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| 205 | |
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| 206 | if first_file: |
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| 207 | |
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| 208 | times = fid.variables['time'][:] |
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| 209 | n_steps = len(times) |
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| 210 | number_of_timesteps = fid.dimensions['number_of_timesteps'] |
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| 211 | #print n_steps, number_of_timesteps |
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| 212 | starttime = int(fid.starttime) |
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| 213 | |
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| 214 | out_s_quantities = {} |
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| 215 | out_d_quantities = {} |
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| 216 | |
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| 217 | |
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| 218 | xllcorner = fid.xllcorner |
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| 219 | yllcorner = fid.yllcorner |
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| 220 | |
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| 221 | number_of_global_triangles = int(fid.number_of_global_triangles) |
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| 222 | number_of_global_nodes = int(fid.number_of_global_nodes) |
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| 223 | |
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| 224 | order = fid.order |
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| 225 | xllcorner = fid.xllcorner; |
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| 226 | yllcorner = fid.yllcorner ; |
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| 227 | zone = fid.zone; |
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| 228 | false_easting = fid.false_easting; |
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| 229 | false_northing = fid.false_northing; |
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| 230 | datum = fid.datum; |
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| 231 | projection = fid.projection; |
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| 232 | |
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| 233 | g_volumes = num.zeros((number_of_global_triangles,3),num.int) |
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| 234 | g_x = num.zeros((number_of_global_nodes,),num.float32) |
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| 235 | g_y = num.zeros((number_of_global_nodes,),num.float32) |
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| 236 | |
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| 237 | g_points = num.zeros((number_of_global_nodes,2),num.float32) |
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| 238 | |
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| 239 | quantities = ['elevation', 'stage', 'xmomentum', 'ymomentum'] |
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| 240 | static_quantities = [] |
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| 241 | dynamic_quantities = [] |
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| 242 | |
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| 243 | for quantity in quantities: |
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| 244 | # Test if elevation is static |
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| 245 | if n_steps == fid.variables[quantity].shape[0]: |
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| 246 | dynamic_quantities.append(quantity) |
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| 247 | else: |
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| 248 | static_quantities.append(quantity) |
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| 249 | |
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| 250 | for quantity in static_quantities: |
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| 251 | out_s_quantities[quantity] = num.zeros((number_of_global_nodes,),num.float32) |
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| 252 | |
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| 253 | # Quantities are stored as a 2D array of timesteps x data. |
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| 254 | for quantity in dynamic_quantities: |
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| 255 | out_d_quantities[quantity] = \ |
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| 256 | num.zeros((n_steps,number_of_global_nodes),num.float32) |
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| 257 | |
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| 258 | description = 'merged:' + getattr(fid, 'description') |
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| 259 | first_file = False |
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| 260 | |
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| 261 | |
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| 262 | # Read in from files and add to global arrays |
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| 263 | |
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| 264 | tri_l2g = fid.variables['tri_l2g'][:] |
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| 265 | node_l2g = fid.variables['node_l2g'][:] |
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| 266 | tri_full_flag = fid.variables['tri_full_flag'][:] |
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| 267 | volumes = num.array(fid.variables['volumes'][:],dtype=num.int) |
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| 268 | l_volumes = num.zeros_like(volumes) |
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| 269 | l_old_volumes = num.zeros_like(volumes) |
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| 270 | |
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| 271 | |
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| 272 | # Change the local node ids to global id in the |
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| 273 | # volume array |
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| 274 | |
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| 275 | # FIXME SR: Surely we can knock up a numpy way of doing this |
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| 276 | #for i in range(len(l_volumes)): |
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| 277 | # g_n0 = node_l2g[volumes[i,0]] |
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| 278 | # g_n1 = node_l2g[volumes[i,1]] |
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| 279 | # g_n2 = node_l2g[volumes[i,2]] |
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| 280 | # |
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| 281 | # l_old_volumes[i,:] = [g_n0,g_n1,g_n2] |
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| 282 | |
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| 283 | g_n0 = node_l2g[volumes[:,0]].reshape(-1,1) |
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| 284 | g_n1 = node_l2g[volumes[:,1]].reshape(-1,1) |
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| 285 | g_n2 = node_l2g[volumes[:,2]].reshape(-1,1) |
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| 286 | |
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| 287 | #print g_n0.shape |
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| 288 | l_volumes = num.hstack((g_n0,g_n1,g_n2)) |
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| 289 | |
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| 290 | #assert num.allclose(l_volumes, l_old_volumes) |
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| 291 | |
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| 292 | # Just pick out the full triangles |
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| 293 | ftri_l2g = num.compress(tri_full_flag, tri_l2g) |
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| 294 | |
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| 295 | #print l_volumes |
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| 296 | #print tri_full_flag |
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| 297 | #print tri_l2g |
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| 298 | #print ftri_l2g |
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| 299 | |
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| 300 | fg_volumes = num.compress(tri_full_flag,l_volumes,axis=0) |
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| 301 | g_volumes[ftri_l2g] = fg_volumes |
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| 302 | |
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| 303 | |
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| 304 | |
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| 305 | |
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| 306 | #g_x[node_l2g] = fid.variables['x'] |
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| 307 | #g_y[node_l2g] = fid.variables['y'] |
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| 308 | |
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| 309 | g_points[node_l2g,0] = fid.variables['x'] |
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| 310 | g_points[node_l2g,1] = fid.variables['y'] |
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| 311 | |
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| 312 | |
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| 313 | #print number_of_timesteps |
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| 314 | |
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| 315 | |
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| 316 | # FIXME SR: It seems that some of the "ghost" node quantity values |
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| 317 | # are being storded. We should only store those nodes which are associated with |
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| 318 | # full triangles. So we need an index array of "full" nodes, ie those in |
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| 319 | # full triangles |
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| 320 | |
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| 321 | #use numpy.compress and numpy.unique to get "full nodes |
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| 322 | |
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| 323 | f_volumes = num.compress(tri_full_flag,volumes,axis=0) |
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| 324 | fl_nodes = num.unique(f_volumes) |
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| 325 | f_node_l2g = node_l2g[fl_nodes] |
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| 326 | |
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| 327 | #print len(node_l2g) |
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| 328 | #print len(fl_nodes) |
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| 329 | |
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| 330 | # Read in static quantities |
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| 331 | for quantity in static_quantities: |
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| 332 | #out_s_quantities[quantity][node_l2g] = \ |
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| 333 | # num.array(fid.variables[quantity],dtype=num.float32) |
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| 334 | q = fid.variables[quantity] |
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| 335 | print quantity, q.shape |
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| 336 | out_s_quantities[quantity][f_node_l2g] = \ |
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| 337 | num.array(q,dtype=num.float32)[fl_nodes] |
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| 338 | |
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| 339 | |
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| 340 | #Collate all dynamic quantities according to their timestep |
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| 341 | for quantity in dynamic_quantities: |
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| 342 | q = fid.variables[quantity] |
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| 343 | #print q.shape |
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| 344 | for i in range(n_steps): |
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| 345 | #out_d_quantities[quantity][i][node_l2g] = \ |
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| 346 | # num.array(q[i],dtype=num.float32) |
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| 347 | out_d_quantities[quantity][i][f_node_l2g] = \ |
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| 348 | num.array(q[i],dtype=num.float32)[fl_nodes] |
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| 349 | |
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| 350 | |
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| 351 | |
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| 352 | |
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| 353 | fid.close() |
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| 354 | |
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| 355 | |
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| 356 | #--------------------------- |
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| 357 | # Write out the SWW file |
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| 358 | #--------------------------- |
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| 359 | #print g_points.shape |
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| 360 | |
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| 361 | #print number_of_global_triangles |
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| 362 | #print number_of_global_nodes |
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| 363 | |
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| 364 | |
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| 365 | if verbose: |
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| 366 | print 'Writing file ', output, ':' |
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| 367 | fido = NetCDFFile(output, netcdf_mode_w) |
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| 368 | sww = Write_sww(static_quantities, dynamic_quantities) |
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| 369 | sww.store_header(fido, starttime, |
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| 370 | number_of_global_triangles, |
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| 371 | number_of_global_nodes, |
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| 372 | description=description, |
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| 373 | sww_precision=netcdf_float32) |
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| 374 | |
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| 375 | |
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| 376 | |
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| 377 | from anuga.coordinate_transforms.geo_reference import Geo_reference |
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| 378 | geo_reference = Geo_reference() |
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| 379 | |
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| 380 | sww.store_triangulation(fido, g_points, g_volumes, points_georeference=geo_reference) |
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| 381 | |
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| 382 | fido.order = order |
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| 383 | fido.xllcorner = xllcorner; |
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| 384 | fido.yllcorner = yllcorner ; |
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| 385 | fido.zone = zone; |
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| 386 | fido.false_easting = false_easting; |
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| 387 | fido.false_northing = false_northing; |
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| 388 | fido.datum = datum; |
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| 389 | fido.projection = projection; |
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| 390 | |
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| 391 | sww.store_static_quantities(fido, verbose=verbose, **out_s_quantities) |
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| 392 | |
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| 393 | # Write out all the dynamic quantities for each timestep |
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| 394 | |
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| 395 | for i in range(n_steps): |
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| 396 | fido.variables['time'][i] = times[i] |
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| 397 | |
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| 398 | for q in dynamic_quantities: |
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| 399 | q_values = out_d_quantities[q] |
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| 400 | for i in range(n_steps): |
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| 401 | fido.variables[q][i] = q_values[i] |
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| 402 | |
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| 403 | # This updates the _range values |
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| 404 | q_range = fido.variables[q + Write_sww.RANGE][:] |
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| 405 | q_values_min = num.min(q_values) |
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| 406 | if q_values_min < q_range[0]: |
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| 407 | fido.variables[q + Write_sww.RANGE][0] = q_values_min |
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| 408 | q_values_max = num.max(q_values) |
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| 409 | if q_values_max > q_range[1]: |
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| 410 | fido.variables[q + Write_sww.RANGE][1] = q_values_max |
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| 411 | |
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| 412 | |
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| 413 | #print out_s_quantities |
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| 414 | #print out_d_quantities |
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| 415 | |
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| 416 | #print g_x |
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| 417 | #print g_y |
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| 418 | |
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| 419 | #print g_volumes |
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| 420 | |
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| 421 | fido.close() |
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| 422 | |
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| 423 | if delete_old: |
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| 424 | import os |
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| 425 | for filename in swwfiles: |
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| 426 | |
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| 427 | if verbose: |
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| 428 | print 'Deleting file ', filename, ':' |
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| 429 | os.remove(filename) |
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| 430 | |
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| 431 | if __name__ == "__main__": |
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| 432 | |
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| 433 | import argparse |
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| 434 | from anuga.anuga_exceptions import ANUGAError |
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| 435 | |
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| 436 | |
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| 437 | parser = argparse.ArgumentParser(description='Merge sww files created from parallel run') |
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| 438 | parser.add_argument('-np', type=int, default = 4, |
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| 439 | help='number of processors used to produce sww files') |
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| 440 | parser.add_argument('-f', type=str, default="domain", |
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| 441 | help='domain global name') |
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| 442 | parser.add_argument('-v', nargs='?', type=bool, const=True, default=False, |
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| 443 | help='verbosity') |
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| 444 | parser.add_argument('-delete_old', nargs='?', type=bool, const=True, default=False, |
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| 445 | help='Flag to delete the input files') |
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| 446 | args = parser.parse_args() |
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| 447 | |
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| 448 | np = args.np |
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| 449 | domain_global_name = args.f |
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| 450 | verbose = args.v |
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| 451 | delete_old = args.delete_old |
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| 452 | |
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| 453 | |
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| 454 | try: |
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| 455 | sww_merge_parallel(domain_global_name, np, verbose, delete_old) |
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| 456 | except: |
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| 457 | msg = 'ERROR: When merging sww files %s '% domain_global_name |
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| 458 | print msg |
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| 459 | raise |
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