| [8399] | 1 | """ Random utilities for reading sww file data and for plotting (in ipython, or |
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| 2 | in scripts) |
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| 3 | |
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| 4 | Functionality of note: |
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| 5 | |
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| 6 | util.get_outputs -- read the data from a single sww file into a single object |
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| 7 | |
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| 8 | util.combine_outputs -- read the data from a list of sww files into a single object |
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| 9 | |
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| 10 | util.near_transect -- for finding the indices of points 'near' to a given |
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| 11 | line, and assigning these points a coordinate along that line. |
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| 12 | This is useful for plotting outputs which are 'almost' along a |
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| 13 | transect (e.g. a channel cross-section) -- see example below |
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| 14 | |
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| 15 | util.sort_sww_filenames -- match sww filenames by a wildcard, and order |
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| 16 | them according to their 'time'. This means that |
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| 17 | they can be stuck together using 'combine_outputs' correctly |
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| 18 | |
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| 19 | util.triangle_areas -- compute the areas of every triangle in a get_outputs object [ must be vertex-based] |
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| 20 | |
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| 21 | util.water_volume -- compute the water volume at every time step in an sww file (needs both vertex and centroid value input). |
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| 22 | |
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| 23 | Here is an example ipython session which uses some of these functions: |
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| 24 | |
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| 25 | > import util |
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| 26 | > from matplotlib import pyplot as pyplot |
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| 27 | > p=util.get_output('myfile.sww',minimum_allowed_height=0.01) |
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| 28 | > p2=util.get_centroids(p,velocity_extrapolation=True) |
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| 29 | > xxx=util.near_transect(p,[95., 85.], [120.,68.],tol=2.) # Could equally well use p2 |
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| 30 | > pyplot.ion() # Interactive plotting |
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| 31 | > pyplot.scatter(xxx[1],p.vel[140,xxx[0]],color='red') # Plot along the transect |
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| 32 | |
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| 33 | FIXME: TODO -- Convert to a single function 'get_output', which can either take a |
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| 34 | single filename, a list of filenames, or a wildcard defining a number of |
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| 35 | filenames, and ensure that in each case, the output will be as desired. |
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| 36 | |
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| 37 | """ |
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| 38 | from Scientific.IO.NetCDF import NetCDFFile |
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| 39 | import numpy |
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| 40 | |
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| 41 | class combine_outputs: |
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| 42 | """ |
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| 43 | Read in a list of filenames, and combine all their outputs into a single object. |
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| 44 | e.g.: |
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| 45 | |
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| 46 | p = util.combine_outputs(['file1.sww', 'file1_time_10000.sww', 'file1_time_20000.sww'], 0.01) |
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| 47 | |
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| 48 | will make an object p which has components p.x,p.y,p.time,p.stage, .... etc, |
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| 49 | where the values of stage / momentum / velocity from the sww files are concatenated as appropriate. |
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| 50 | |
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| 51 | This is nice for interactive interrogation of model outputs, or for sticking together outputs in scripts |
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| 52 | |
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| 53 | WARNING: It is easy to use lots of memory, if the sww files are large. |
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| 54 | |
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| 55 | Note: If you want the centroid values, then you could subsequently use: |
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| 56 | |
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| 57 | p2 = util.get_centroids(p,velocity_extrapolation=False) |
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| 58 | |
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| 59 | which would make an object p2 that is like p, but holds information at centroids |
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| 60 | """ |
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| 61 | def __init__(self, filename_list, minimum_allowed_height=1.0e-03): |
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| 62 | # |
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| 63 | # Go through the sww files in 'filename_list', and combine them into one object. |
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| 64 | # |
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| 65 | |
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| 66 | for i, filename in enumerate(filename_list): |
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| 67 | print i, filename |
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| 68 | # Store output from filename |
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| 69 | p_tmp = get_output(filename, minimum_allowed_height) |
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| 70 | if(i==0): |
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| 71 | # Create self |
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| 72 | p1=p_tmp |
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| 73 | else: |
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| 74 | # Append extra data to self |
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| 75 | # Note that p1.x, p1.y, p1.vols, p1.elev should not change |
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| 76 | assert (p1.x == p_tmp.x).all() |
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| 77 | assert (p1.y == p_tmp.y).all() |
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| 78 | assert (p1.vols ==p_tmp.vols).all() |
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| 79 | p1.time = numpy.append(p1.time, p_tmp.time) |
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| 80 | p1.stage = numpy.append(p1.stage, p_tmp.stage, axis=0) |
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| 81 | p1.xmom = numpy.append(p1.xmom, p_tmp.xmom, axis=0) |
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| 82 | p1.ymom = numpy.append(p1.ymom, p_tmp.ymom, axis=0) |
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| 83 | p1.xvel = numpy.append(p1.xvel, p_tmp.xvel, axis=0) |
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| 84 | p1.yvel = numpy.append(p1.yvel, p_tmp.yvel, axis=0) |
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| 85 | p1.vel = numpy.append(p1.vel, p_tmp.vel, axis=0) |
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| 86 | |
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| 87 | self.x, self.y, self.time, self.vols, self.elev, self.stage, self.xmom, \ |
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| 88 | self.ymom, self.xvel, self.yvel, self.vel, self.minimum_allowed_height = \ |
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| 89 | p1.x, p1.y, p1.time, p1.vols, p1.elev, p1.stage, p1.xmom, p1.ymom, p1.xvel,\ |
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| 90 | p1.yvel, p1.vel, p1.minimum_allowed_height |
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| 91 | |
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| 92 | #################### |
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| 93 | |
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| 94 | def sort_sww_filenames(sww_wildcard): |
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| 95 | # Function to take a 'wildcard' sww filename, |
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| 96 | # and return a list of all filenames of this type, |
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| 97 | # sorted by their time. |
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| 98 | # This can then be used efficiently in 'combine_outputs' |
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| 99 | # if you have many filenames starting with the same pattern |
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| 100 | import glob |
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| 101 | filenames=glob.glob(sww_wildcard) |
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| 102 | |
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| 103 | # Extract time from filenames |
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| 104 | file_time=range(len(filenames)) # Predefine |
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| 105 | |
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| 106 | for i,filename in enumerate(filenames): |
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| 107 | filesplit=filename.rsplit('_time_') |
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| 108 | if(len(filesplit)>1): |
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| 109 | file_time[i]=int(filesplit[1].split('_0.sww')[0]) |
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| 110 | else: |
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| 111 | file_time[i]=0 |
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| 112 | |
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| 113 | name_and_time=zip(file_time,filenames) |
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| 114 | name_and_time.sort() # Sort by file_time |
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| 115 | |
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| 116 | output_times, output_names = zip(*name_and_time) |
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| 117 | |
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| 118 | return list(output_names) |
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| 119 | |
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| 120 | ############## |
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| 121 | |
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| 122 | class get_output: |
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| 123 | """Read in data from an .sww file in a convenient form |
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| 124 | e.g. |
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| 125 | p = util.get_output('channel3.sww', minimum_allowed_height=0.01) |
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| 126 | |
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| 127 | p then contains most relevant information as e.g., p.stage, p.elev, p.xmom, etc |
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| 128 | """ |
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| 129 | def __init__(self, filename, minimum_allowed_height=1.0e-03): |
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| 130 | self.x, self.y, self.time, self.vols, self.stage, \ |
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| 131 | self.elev, self.xmom, self.ymom, \ |
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| 132 | self.xvel, self.yvel, self.vel, self.minimum_allowed_height = \ |
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| 133 | read_output(filename, minimum_allowed_height) |
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| 134 | |
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| 135 | |
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| 136 | def read_output(filename, minimum_allowed_height): |
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| 137 | # Input: The name of an .sww file to read data from, |
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| 138 | # e.g. read_sww('channel3.sww') |
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| 139 | # |
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| 140 | # Purpose: To read the sww file, and output a number of variables as arrays that |
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| 141 | # we can then manipulate (e.g. plot, interrogate) |
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| 142 | # |
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| 143 | # Output: x, y, time, stage, elev, xmom, ymom, xvel, yvel, vel |
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| 144 | |
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| 145 | # Import modules |
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| 146 | |
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| 147 | |
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| 148 | |
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| 149 | # Open ncdf connection |
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| 150 | fid=NetCDFFile(filename) |
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| 151 | |
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| 152 | |
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| 153 | # Read variables |
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| 154 | x=fid.variables['x'] |
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| 155 | x=x.getValue() |
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| 156 | y=fid.variables['y'] |
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| 157 | y=y.getValue() |
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| 158 | |
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| 159 | stage=fid.variables['stage'] |
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| 160 | stage=stage.getValue() |
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| 161 | |
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| 162 | elev=fid.variables['elevation'] |
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| 163 | elev=elev.getValue() |
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| 164 | |
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| 165 | xmom=fid.variables['xmomentum'] |
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| 166 | xmom=xmom.getValue() |
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| 167 | |
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| 168 | ymom=fid.variables['ymomentum'] |
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| 169 | ymom=ymom.getValue() |
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| 170 | |
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| 171 | time=fid.variables['time'] |
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| 172 | time=time.getValue() |
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| 173 | |
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| 174 | vols=fid.variables['volumes'] |
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| 175 | vols=vols.getValue() |
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| 176 | |
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| 177 | |
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| 178 | # Calculate velocity = momentum/depth |
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| 179 | xvel=xmom*0.0 |
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| 180 | yvel=ymom*0.0 |
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| 181 | |
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| 182 | for i in range(xmom.shape[0]): |
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| 183 | xvel[i,:]=xmom[i,:]/(stage[i,:]-elev+1.0e-06)*(stage[i,:]> elev+minimum_allowed_height) |
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| 184 | yvel[i,:]=ymom[i,:]/(stage[i,:]-elev+1.0e-06)*(stage[i,:]> elev+minimum_allowed_height) |
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| 185 | |
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| 186 | vel = (xvel**2+yvel**2)**0.5 |
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| 187 | |
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| 188 | return x, y, time, vols, stage, elev, xmom, ymom, xvel, yvel, vel, minimum_allowed_height |
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| 189 | |
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| 190 | ############## |
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| 191 | |
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| 192 | |
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| 193 | |
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| 194 | class get_centroids: |
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| 195 | """ |
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| 196 | Extract centroid values from the output of get_output. |
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| 197 | e.g. |
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| 198 | p = util.get_output('my_sww.sww', minimum_allowed_height=0.01) # vertex values |
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| 199 | pc=util.get_centroids(p, velocity_extrapolation=True) # centroid values |
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| 200 | """ |
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| 201 | def __init__(self,p, velocity_extrapolation=False): |
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| 202 | self.time, self.x, self.y, self.stage, self.xmom,\ |
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| 203 | self.ymom, self.elev, self.xvel, \ |
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| 204 | self.yvel, self.vel= \ |
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| 205 | get_centroid_values(p, velocity_extrapolation) |
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| 206 | |
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| 207 | |
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| 208 | def get_centroid_values(p, velocity_extrapolation): |
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| 209 | # Input: p is the result of e.g. p=util.get_output('mysww.sww'). See the get_output class defined above |
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| 210 | # Output: Values of x, y, Stage, xmom, ymom, elev, xvel, yvel, vel at centroids |
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| 211 | #import numpy |
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| 212 | |
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| 213 | # Make 3 arrays, each containing one index of a vertex of every triangle. |
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| 214 | l=len(p.vols) |
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| 215 | vols0=numpy.zeros(l, dtype='int') |
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| 216 | vols1=numpy.zeros(l, dtype='int') |
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| 217 | vols2=numpy.zeros(l, dtype='int') |
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| 218 | |
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| 219 | # FIXME: 22/2/12/ - I think this loop is slow, should be able to do this |
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| 220 | # another way |
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| 221 | for i in range(l): |
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| 222 | vols0[i]=p.vols[i][0] |
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| 223 | vols1[i]=p.vols[i][1] |
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| 224 | vols2[i]=p.vols[i][2] |
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| 225 | |
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| 226 | # Then use these to compute centroid averages |
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| 227 | x_cent=(p.x[vols0]+p.x[vols1]+p.x[vols2])/3.0 |
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| 228 | y_cent=(p.y[vols0]+p.y[vols1]+p.y[vols2])/3.0 |
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| 229 | |
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| 230 | stage_cent=(p.stage[:,vols0]+p.stage[:,vols1]+p.stage[:,vols2])/3.0 |
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| 231 | elev_cent=(p.elev[vols0]+p.elev[vols1]+p.elev[vols2])/3.0 |
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| 232 | |
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| 233 | # Here, we need to treat differently the case of momentum extrapolation or |
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| 234 | # velocity extrapolation |
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| 235 | if velocity_extrapolation: |
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| 236 | xvel_cent=(p.xvel[:,vols0]+p.xvel[:,vols1]+p.xvel[:,vols2])/3.0 |
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| 237 | yvel_cent=(p.yvel[:,vols0]+p.yvel[:,vols1]+p.yvel[:,vols2])/3.0 |
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| 238 | |
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| 239 | # Now compute momenta |
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| 240 | xmom_cent=stage_cent*0.0 |
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| 241 | ymom_cent=stage_cent*0.0 |
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| 242 | |
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| 243 | t=len(p.time) |
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| 244 | |
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| 245 | for i in range(t): |
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| 246 | xmom_cent[i,:]=xvel_cent[i,:]*(stage_cent[i,:]-elev_cent+1.0e-06)*\ |
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| 247 | (stage_cent[i,:]>elev_cent+p.minimum_allowed_height) |
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| 248 | ymom_cent[i,:]=yvel_cent[i,:]*(stage_cent[i,:]-elev_cent+1.0e-06)*\ |
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| 249 | (stage_cent[i,:]>elev_cent+p.minimum_allowed_height) |
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| 250 | |
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| 251 | else: |
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| 252 | xmom_cent=(p.xmom[:,vols0]+p.xmom[:,vols1]+p.xmom[:,vols2])/3.0 |
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| 253 | ymom_cent=(p.ymom[:,vols0]+p.ymom[:,vols1]+p.ymom[:,vols2])/3.0 |
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| 254 | |
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| 255 | # Now compute velocities |
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| 256 | xvel_cent=stage_cent*0.0 |
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| 257 | yvel_cent=stage_cent*0.0 |
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| 258 | |
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| 259 | t=len(p.time) |
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| 260 | |
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| 261 | for i in range(t): |
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| 262 | xvel_cent[i,:]=xmom_cent[i,:]/(stage_cent[i,:]-elev_cent+1.0e-06)*(stage_cent[i,:]>elev_cent+p.minimum_allowed_height) |
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| 263 | yvel_cent[i,:]=ymom_cent[i,:]/(stage_cent[i,:]-elev_cent+1.0e-06)*(stage_cent[i,:]>elev_cent+p.minimum_allowed_height) |
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| 264 | |
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| 265 | |
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| 266 | # Compute velocity |
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| 267 | vel_cent=(xvel_cent**2 + yvel_cent**2)**0.5 |
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| 268 | |
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| 269 | return p.time, x_cent, y_cent, stage_cent, xmom_cent,\ |
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| 270 | ymom_cent, elev_cent, xvel_cent, yvel_cent, vel_cent |
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| 271 | |
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| 272 | # Make plot of stage over time. |
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| 273 | #pylab.close() |
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| 274 | #pylab.ion() |
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| 275 | #pylab.plot(time, stage[:,1]) |
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| 276 | #for i in range(201): |
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| 277 | # pylab.plot(time,stage[:,i]) |
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| 278 | |
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| 279 | # Momentum should be 0. |
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| 280 | #print 'Momentum max/min is', xmom.max() , xmom.min() |
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| 281 | |
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| 282 | #pylab.gca().set_aspect('equal') |
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| 283 | #pylab.scatter(x,y,c=elev,edgecolors='none') |
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| 284 | #pylab.colorbar() |
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| 285 | # |
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| 286 | #n=xvel.shape[0]-1 |
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| 287 | #pylab.quiver(x,y,xvel[n,:],yvel[n,:]) |
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| 288 | #pylab.savefig('Plot1.png') |
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| 289 | |
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| 290 | def animate_1D(time, var, x, ylab=' '): #, x=range(var.shape[1]), vmin=var.min(), vmax=var.max()): |
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| 291 | # Input: time = one-dimensional time vector; |
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| 292 | # var = array with first dimension = len(time) ; |
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| 293 | # x = (optional) vector width dimension equal to var.shape[1]; |
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| 294 | |
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| 295 | import pylab |
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| 296 | import numpy |
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| 297 | |
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| 298 | |
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| 299 | |
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| 300 | pylab.close() |
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| 301 | pylab.ion() |
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| 302 | |
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| 303 | # Initial plot |
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| 304 | vmin=var.min() |
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| 305 | vmax=var.max() |
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| 306 | line, = pylab.plot( (x.min(), x.max()), (vmin, vmax), 'o') |
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| 307 | |
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| 308 | # Lots of plots |
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| 309 | for i in range(len(time)): |
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| 310 | line.set_xdata(x) |
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| 311 | line.set_ydata(var[i,:]) |
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| 312 | pylab.draw() |
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| 313 | pylab.xlabel('x') |
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| 314 | pylab.ylabel(ylab) |
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| 315 | pylab.title('time = ' + str(time[i])) |
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| 316 | |
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| 317 | return |
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| 318 | |
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| 319 | def near_transect(p, point1, point2, tol=1.): |
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| 320 | # Function to get the indices of points in p less than 'tol' from the line |
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| 321 | # joining (x1,y1), and (x2,y2) |
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| 322 | # p comes from util.get_output('mysww.sww') |
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| 323 | # |
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| 324 | # e.g. |
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| 325 | # import util |
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| 326 | # from matplotlib import pyplot |
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| 327 | # p=util.get_output('merewether_1m.sww',0.01) |
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| 328 | # p2=util.get_centroids(p,velocity_extrapolation=True) |
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| 329 | # #xxx=transect_interpolate.near_transect(p,[95., 85.], [120.,68.],tol=2.) |
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| 330 | # xxx=util.near_transect(p,[95., 85.], [120.,68.],tol=2.) |
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| 331 | # pyplot.scatter(xxx[1],p.vel[140,xxx[0]],color='red') |
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| 332 | |
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| 333 | x1=point1[0] |
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| 334 | y1=point1[1] |
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| 335 | |
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| 336 | x2=point2[0] |
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| 337 | y2=point2[1] |
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| 338 | |
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| 339 | # Find line equation a*x + b*y + c = 0 |
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| 340 | # based on y=gradient*x +intercept |
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| 341 | if x1!=x2: |
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| 342 | gradient= (y2-y1)/(x2-x1) |
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| 343 | intercept = y1 - gradient*x1 |
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| 344 | |
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| 345 | a = -gradient |
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| 346 | b = 1. |
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| 347 | c = -intercept |
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| 348 | else: |
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| 349 | #print 'FIXME: Still need to treat 0 and infinite gradients' |
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| 350 | #assert 0==1 |
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| 351 | a=1. |
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| 352 | b=0. |
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| 353 | c=-x2 |
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| 354 | |
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| 355 | # Distance formula |
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| 356 | inv_denom = 1./(a**2 + b**2)**0.5 |
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| 357 | distp = abs(p.x*a + p.y*b + c)*inv_denom |
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| 358 | |
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| 359 | near_points = (distp<tol).nonzero()[0] |
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| 360 | |
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| 361 | # Now find a 'local' coordinate for the point, projected onto the line |
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| 362 | # g1 = unit vector parallel to the line |
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| 363 | # g2 = vector joining (x1,y1) and (p.x,p.y) |
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| 364 | g1x = x2-x1 |
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| 365 | g1y = y2-y1 |
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| 366 | g1_norm = (g1x**2 + g1y**2)**0.5 |
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| 367 | g1x=g1x/g1_norm |
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| 368 | g1y=g1x/g1_norm |
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| 369 | |
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| 370 | g2x = p.x[near_points] - x1 |
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| 371 | g2y = p.y[near_points] - y1 |
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| 372 | |
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| 373 | # Dot product = projected distance == a local coordinate |
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| 374 | local_coord = g1x*g2x + g1y*g2y |
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| 375 | |
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| 376 | return near_points, local_coord |
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| 377 | |
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| 378 | ######################## |
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| 379 | # TRIANGLE AREAS, WATER VOLUME |
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| 380 | def triangle_areas(p, subset='null'): |
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| 381 | # Compute areas of triangles in p -- assumes p contains vertex information |
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| 382 | # subset = vector of centroid indices to include in the computation. |
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| 383 | |
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| 384 | if(subset=='null'): |
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| 385 | subset=range(len(p.vols[:,0])) |
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| 386 | |
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| 387 | x0=p.x[p.vols[subset,0]] |
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| 388 | x1=p.x[p.vols[subset,1]] |
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| 389 | x2=p.x[p.vols[subset,2]] |
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| 390 | |
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| 391 | y0=p.y[p.vols[subset,0]] |
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| 392 | y1=p.y[p.vols[subset,1]] |
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| 393 | y2=p.y[p.vols[subset,2]] |
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| 394 | |
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| 395 | # Vectors for cross-product |
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| 396 | v1_x=x0-x1 |
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| 397 | v1_y=y0-y1 |
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| 398 | # |
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| 399 | v2_x=x2-x1 |
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| 400 | v2_y=y2-y1 |
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| 401 | # Area |
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| 402 | area=(v1_x*v2_y-v1_y*v2_x)*0.5 |
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| 403 | area=abs(area) |
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| 404 | return area |
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| 405 | |
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| 406 | ### |
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| 407 | |
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| 408 | def water_volume(p,p2, per_unit_area=False, subset='null'): |
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| 409 | # Compute the water volume from p(vertex values) and p2(centroid values) |
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| 410 | |
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| 411 | if(subset=='null'): |
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| 412 | subset=range(len(p2.x)) |
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| 413 | |
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| 414 | l=len(p2.time) |
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| 415 | area=triangle_areas(p, subset=subset) |
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| 416 | |
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| 417 | total_area=area.sum() |
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| 418 | volume=p2.time*0. |
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| 419 | |
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| 420 | # This accounts for how volume is measured in ANUGA |
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| 421 | # Compute in 2 steps to reduce precision error (important sometimes) |
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| 422 | for i in range(l): |
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| 423 | #volume[i]=((p2.stage[i,subset]-p2.elev[subset])*(p2.stage[i,subset]>p2.elev[subset])*area).sum() |
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| 424 | volume[i]=((p2.stage[i,subset])*(p2.stage[i,subset]>p2.elev[subset])*area).sum() |
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| 425 | volume[i]=volume[i]+((-p2.elev[subset])*(p2.stage[i,subset]>p2.elev[subset])*area).sum() |
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| 426 | |
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| 427 | if(per_unit_area): |
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| 428 | volume=volume/total_area |
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| 429 | |
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| 430 | return volume |
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| 431 | |
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| 432 | |
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| 433 | |
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