diff --git a/autotest/t007_test.py b/autotest/t007_test.py index 4045298a7d..b9f068bb6c 100644 --- a/autotest/t007_test.py +++ b/autotest/t007_test.py @@ -1,4 +1,6 @@ # Test export module +import sys +sys.path.append('..') import copy import glob import os @@ -138,6 +140,49 @@ def test_export_output(): arr_mask = arr.mask[0] assert np.array_equal(ibound_mask, arr_mask) +def test_write_shapefile(): + from flopy.utils.reference import SpatialReference + from flopy.export.shapefile_utils import shp2recarray + from flopy.export.shapefile_utils import write_grid_shapefile, write_grid_shapefile2 + + sr = SpatialReference(delr=np.ones(10) *1.1, # cell spacing along model rows + delc=np.ones(10) *1.1, # cell spacing along model columns + epsg=26715, + lenuni=1 # MODFLOW length units + ) + outshp1 = os.path.join(tpth, 'junk.shp') + outshp2 = os.path.join(tpth, 'junk2.shp') + write_grid_shapefile(outshp1, sr, array_dict={}) + write_grid_shapefile2(outshp2, sr, array_dict={}) + + for outshp in [outshp1, outshp2]: + # check that pyshp reads integers + # this only check that row/column were recorded as "N" + # not how they will be cast by python or numpy + import shapefile as sf + sfobj = sf.Reader(outshp) + for f in sfobj.fields: + if f[0] == 'row' or f[0] == 'column': + assert f[1] == 'N' + recs = list(sfobj.records()) + for r in recs[0]: + assert isinstance(r, int) + + # check that row and column appear as integers in recarray + ra = shp2recarray(outshp) + assert np.issubdtype(ra.dtype['row'], np.integer) + assert np.issubdtype(ra.dtype['column'], np.integer) + + try: # check that fiona reads integers + import fiona + with fiona.open(outshp) as src: + meta = src.meta + assert 'int' in meta['schema']['properties']['row'] + assert 'int' in meta['schema']['properties']['column'] + except: + pass + + def test_export_array(): try: @@ -847,7 +892,8 @@ def build_sfr_netcdf(): # for namfile in ["fhb.nam"]: # export_netcdf(namfile) #test_freyberg_export() - test_export_array() + #test_export_array() + test_write_shapefile() #test_wkt_parse() #test_get_rc_from_node_coordinates() pass diff --git a/autotest/t009_test.py b/autotest/t009_test.py index ffe7d4f5d0..5e777a4e36 100644 --- a/autotest/t009_test.py +++ b/autotest/t009_test.py @@ -49,14 +49,34 @@ } def create_sfr_data(): - - dtype = np.dtype([('iseg', int), ('ireach', int)]) + dtype = np.dtype([('k', int), + ('i', int), + ('j', int), + ('iseg', int), + ('ireach', int)]) r = create_empty_recarray(27, dtype=dtype) + r['i'] = [3, 4, 5, + 7, 8, 9, + 0, 1, 2, + 4, 4, 5, + 0, 0, 0, + 3, 4, 5, + 0, 1, 2, + 4, 5, 6, + 2, 2, 2] + r['j'] = [0, 1, 2, + 6, 6, 6, + 6, 6, 6, + 3, 4, 5, + 9, 8, 7, + 6, 6, 6, + 0, 0, 0, + 6, 6, 6, + 9, 8, 7] r['iseg'] = sorted(list(range(1, 10)) * 3) r['ireach'] = [1, 2, 3] * 9 - dtype = np.dtype([('nseg', int), ('outseg', int)]) - d = create_empty_recarray(9, dtype=dtype) + d = create_empty_recarray(9, dtype=np.dtype([('nseg', int), ('outseg', int)])) d['nseg'] = range(1, 10) d['outseg'] = [4, 0, 6, 8, 3, 8, 1, 2, 8] return r, d @@ -251,6 +271,38 @@ def test_const(): assert sfr.const == 1.486 * 86400. assert True +def test_export(): + fm = flopy.modflow + m = fm.Modflow() + dis = fm.ModflowDis(m, 1, 10, 10, lenuni=2, itmuni=4) + m.sr = SpatialReference(delr=m.dis.delr.array, delc=m.dis.delc.array) + m.sr.write_shapefile(os.path.join(outpath, 'grid.shp')) + r, d = create_sfr_data() + sfr = flopy.modflow.ModflowSfr2(m, reach_data=r, segment_data={0: d}) + sfr.segment_data[0]['flow'][-1] = 1e4 + sfr.stress_period_data.export(os.path.join(outpath, 'sfr.shp'), sparse=True) + sfr.export_linkages(os.path.join(outpath, 'linkages.shp')) + sfr.export_outlets(os.path.join(outpath, 'outlets.shp')) + sfr.export_transient_variable(os.path.join(outpath, 'inlets.shp'), + 'flow') + + from flopy.export.shapefile_utils import shp2recarray + ra = shp2recarray(os.path.join(outpath, 'inlets.shp')) + assert ra.flow0[0] == 1e4 + ra = shp2recarray(os.path.join(outpath, 'outlets.shp')) + assert ra.iseg[0] + ra.ireach[0] == 5 + ra = shp2recarray(os.path.join(outpath, 'linkages.shp')) + crds = np.array(list(ra.geometry[2].coords)) + assert np.array_equal(crds, np.array([[2.5, 4.5], [3.5, 5.5]])) + ra = shp2recarray(os.path.join(outpath, 'sfr.shp')) + assert ra.iseg0.sum() == sfr.reach_data.iseg.sum() + assert ra.ireach0.sum() == sfr.reach_data.ireach.sum() + y = np.concatenate([np.array(g.exterior)[:, 1] for g in ra.geometry]) + x = np.concatenate([np.array(g.exterior)[:, 0] for g in ra.geometry]) + assert (x.min(), y.min(), x.max(), y.max()) == m.sr.bounds + assert ra[(ra.iseg0 == 2) & (ra.ireach0 == 1)]['geometry'][0].bounds \ + == (6.0, 2.0, 7.0, 3.0) + def test_example(): m = flopy.modflow.Modflow.load('test1ss.nam', version='mf2005', exe_name='mf2005.exe', @@ -397,12 +449,13 @@ def test_sfr_plot(): pass if __name__ == '__main__': - #test_sfr() + test_sfr() test_sfr_renumbering() - #test_example() - #test_transient_example() - #test_sfr_plot() - #test_assign_layers() - #test_SfrFile() - #test_const() + test_example() + test_export() + test_transient_example() + test_sfr_plot() + test_assign_layers() + test_SfrFile() + test_const() pass diff --git a/autotest/t024_test.py b/autotest/t024_test.py index 34f2823cf4..31f0bbf9f1 100644 --- a/autotest/t024_test.py +++ b/autotest/t024_test.py @@ -104,7 +104,8 @@ def test_properties_check(): if __name__ == '__main__': - # for mfnam in testmodels: - # checker_on_load(mfnam) + print('numpy version: {}'.format(np.__version__)) + for mfnam in testmodels: + checker_on_load(mfnam) test_bcs_check() - #test_properties_check() + test_properties_check() diff --git a/autotest/t031_test.py b/autotest/t031_test.py index 69db5325a0..d7b8aebd2f 100644 --- a/autotest/t031_test.py +++ b/autotest/t031_test.py @@ -22,6 +22,11 @@ for f in mffiles: shutil.copy(f, os.path.join(path, os.path.split(f)[1])) +def ra_slice(ra, cols): + raslice = np.column_stack([ra[c] for c in cols]) + dtype = [(str(d[0]), d[1]) for d in ra.dtype.descr if d[0] in cols] + return np.array([tuple(r) for r in raslice], + dtype=dtype).view(np.recarray) def test_mpsim(): model_ws = path diff --git a/autotest/t034_test.py b/autotest/t034_test.py index 24f25a0c9e..f67b4f80ee 100644 --- a/autotest/t034_test.py +++ b/autotest/t034_test.py @@ -45,10 +45,33 @@ def test_create(): finf = np.loadtxt(os.path.join(datpth, 'finf.dat')) finf = np.reshape(finf, (m.nper, m.nrow, m.ncol)) + finf = {i: finf[i] for i in range(finf.shape[0])} extwc = np.loadtxt(os.path.join(datpth, 'extwc.dat')) - uzgag = {-68: [-68], + uzgag = {-68: [], + 65: [3, 6, 1], + 66: [6, 3, 2], + 67: [10, 5, 3]} + uzf = flopy.modflow.ModflowUzf1(m, + nuztop=1, iuzfopt=1, irunflg=1, ietflg=1, + ipakcb=0, + iuzfcb2=61, + # binary output of recharge and groundwater discharge + ntrail2=25, nsets=20, + surfdep=1.0, uzgag=uzgag, + iuzfbnd=m.bas6.ibound.array, + irunbnd=irunbnd, + vks=vks, + finf=finf, + eps=3.5, + thts=0.3, + pet=5.000000E-08, + extdp=15., + extwc=extwc + ) + assert uzf.uzgag == uzgag + uzgag2 = {-68: [-68], 65: [3, 6, 65, 1], 66: [6, 3, 66, 2], 67: [10, 5, 67, 3]} @@ -58,8 +81,8 @@ def test_create(): ipakcb=0, iuzfcb2=61, # binary output of recharge and groundwater discharge - ntrail2=25, nsets=20, nuzgag=4, - surfdep=1.0, uzgag=uzgag, + ntrail2=25, nsets=20, + surfdep=1.0, uzgag=uzgag2, iuzfbnd=m.bas6.ibound.array, irunbnd=irunbnd, vks=vks, @@ -70,13 +93,36 @@ def test_create(): extdp=15., extwc=extwc ) + assert uzf.uzgag == uzgag + uzgaglist = [[-68], + [3, 6, 65, 1], + [6, 3, 66, 2], + [10, 5, 67, 3]] + uzf = flopy.modflow.ModflowUzf1(m, + nuztop=1, iuzfopt=1, irunflg=1, ietflg=1, + ipakcb=0, + iuzfcb2=61, + # binary output of recharge and groundwater discharge + ntrail2=25, nsets=20, + surfdep=1.0, uzgag=uzgaglist, + iuzfbnd=m.bas6.ibound.array, + irunbnd=irunbnd, + vks=vks, + finf=finf, + eps=3.5, + thts=0.3, + pet=5.000000E-08, + extdp=15., + extwc=extwc + ) + assert uzf.uzgag == uzgag m.write_input() - + uzf2 = flopy.modflow.ModflowUzf1.load(os.path.join(cpth, uzf.file_name[0]), m) + assert uzf2.uzgag == uzgag m2 = flopy.modflow.Modflow.load('UZFtest2.nam', version='mf2005', exe_name='mf2005', verbose=True, - model_ws=os.path.split(gpth)[0]) - + model_ws=os.path.split(gpth)[0], forgive=False) # verify that all of the arrays in the created UZF package are the same # as those in the loaded example attrs = [attr for attr in dir(uzf) diff --git a/autotest/t049_test.py b/autotest/t049_test.py index f37f5c5af8..8b701d31e9 100644 --- a/autotest/t049_test.py +++ b/autotest/t049_test.py @@ -133,6 +133,8 @@ def test_pathline_plot(): run = False lpth = pth + nampath = os.path.join(lpth, mfnam) + assert os.path.exists(nampath), "namefile {} doesn't exist.".format(nampath) # load the modflow files for model map m = flopy.modflow.Modflow.load(mfnam, model_ws=lpth, verbose=True, forgive=False, exe_name=mf2005_exe) diff --git a/docs/version_changes.md b/docs/version_changes.md index 0d959b949c..25930e84b0 100644 --- a/docs/version_changes.md +++ b/docs/version_changes.md @@ -7,6 +7,8 @@ FloPy Changes * MODFLOW 6 identifiers are now zero based * Added remove_package method in MFSimulation and MFModel that removes MODFLOW 6 packages from the existing simulation/model * Flopy code for MODFLOW 6 generalized to support different model types +* Added `ModflowSfr2.export_transient_variable` method to export shapefiles of segment data variables, with stress period data as attributes +* Added support for UZF package gages * Bug fixes: * Fixed issue with default settings for MODFLOW 5 SUB package `dp` dataset. @@ -17,6 +19,10 @@ FloPy Changes * IMS file name conflicts now automatically resolved * Fixed issue with passing in numpy ndarrays arrays as layered data * Doc string formatting for MODFLOW 6 packages fixed to make doc strings easier to read + * UZF package: fixed issues with handling of finf, pet, extdp and extwc arrays. + * SFR package: fixed issue with reading stress period data where not all segments are listed for periods > 0. + * `SpatialReference.write_gridSpec` was not converting the model origin coordinates to model length units. + * shorted integer field lengths written to shapefiles to 18 characters; some readers may misinterpret longer field lengths as float dtypes. ### Version 3.2.8 * Added `has_package(name)` method to see if a package exists. This feature goes nicely with `get_package(name)` method. @@ -25,6 +31,11 @@ FloPy Changes * Installation: Added dfn files required by MODFLOW 6 functionality to MANIFEST.in so that they are included in the distribution. * SFR2 package: Fixed issue reading transient data when `ISFOPT` is 4 or 5 for the first stress period. +### Version 3.2.7 - develop +* Added SFR2 package functionarlity + * `export_inlets` method to write shapefile showing locations where external flows are entering the stream network. + + ### Version 3.2.7 * Added beta support for MODFLOW 6 See [here](./mf6.md) for more information. * Added support for retrieving time series from binary cell-by-cell files. Cell-by-cell time series are accessed in the same way they are accessed for heads and concentrations but a text string is required. diff --git a/examples/Notebooks/flopy3_uzf_example.ipynb b/examples/Notebooks/flopy3_uzf_example.ipynb index 2142defcc5..5c99c2d853 100644 --- a/examples/Notebooks/flopy3_uzf_example.ipynb +++ b/examples/Notebooks/flopy3_uzf_example.ipynb @@ -24,20 +24,18 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "3.5.2 | packaged by conda-forge | (default, Jan 12 2017, 05:36:40) \n", - "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.54)]\n", - "numpy version: 1.11.3\n", - "matplotlib version: 2.0.0\n", - "pandas version: 0.19.2\n", - "flopy version: 3.2.6\n" + "3.6.4 | packaged by conda-forge | (default, Dec 23 2017, 16:54:01) \n", + "[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]\n", + "numpy version: 1.14.0\n", + "matplotlib version: 2.1.2\n", + "pandas version: 0.22.0\n", + "flopy version: 3.2.8\n" ] } ], @@ -71,9 +69,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "#Set name of MODFLOW exe\n", @@ -86,12 +82,10 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "path = 'data'\n", + "path = 'temp'\n", "if not os.path.isdir(path):\n", " os.mkdir(path)\n", "gpth = os.path.join('..', 'data', 'mf2005_test', 'UZFtest2.*')\n", @@ -109,9 +103,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "m = flopy.modflow.Modflow.load('UZFtest2.nam', version='mf2005', exe_name=exe_name, \n", @@ -129,27 +121,25 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['data/UZFtest2.uzf1',\n", - " 'data/UZFtest2.uzf2',\n", - " 'data/UZFtest2.uzf3',\n", - " 'data/UZFtest2.uzf4',\n", - " 'data/UZFtest2.sg2',\n", - " 'data/UZFtest2.sg3',\n", - " 'data/UZFtest2.sg4',\n", - " 'data/UZFtest2.sg5',\n", - " 'data/UZFtest2.sg6',\n", - " 'data/UZFtest2.sg7',\n", - " 'data/UZFtest2.sg8',\n", - " 'data/UZFtest2dv.sg9',\n", - " 'data/UZFtest2.sg1',\n", - " 'data/UZFtest2.uzfot']" + "['temp/UZFtest2.uzfot',\n", + " 'temp/UZFtest2.uzf1',\n", + " 'temp/UZFtest2.uzf2',\n", + " 'temp/UZFtest2.uzf3',\n", + " 'temp/UZFtest2.uzf4',\n", + " 'temp/UZFtest2.sg1',\n", + " 'temp/UZFtest2.sg2',\n", + " 'temp/UZFtest2.sg3',\n", + " 'temp/UZFtest2.sg4',\n", + " 'temp/UZFtest2.sg5',\n", + " 'temp/UZFtest2.sg6',\n", + " 'temp/UZFtest2.sg7',\n", + " 'temp/UZFtest2.sg8',\n", + " 'temp/UZFtest2dv.sg9']" ] }, "execution_count": 5, @@ -164,9 +154,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "rm = [True if '.uz' in f else False for f in m.external_fnames]" @@ -175,22 +163,20 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['data/UZFtest2.sg2',\n", - " 'data/UZFtest2.sg3',\n", - " 'data/UZFtest2.sg4',\n", - " 'data/UZFtest2.sg5',\n", - " 'data/UZFtest2.sg6',\n", - " 'data/UZFtest2.sg7',\n", - " 'data/UZFtest2.sg8',\n", - " 'data/UZFtest2dv.sg9',\n", - " 'data/UZFtest2.sg1']" + "['temp/UZFtest2.sg1',\n", + " 'temp/UZFtest2.sg2',\n", + " 'temp/UZFtest2.sg3',\n", + " 'temp/UZFtest2.sg4',\n", + " 'temp/UZFtest2.sg5',\n", + " 'temp/UZFtest2.sg6',\n", + " 'temp/UZFtest2.sg7',\n", + " 'temp/UZFtest2.sg8',\n", + " 'temp/UZFtest2dv.sg9']" ] }, "execution_count": 7, @@ -206,9 +192,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "m.external_binflag = [f for i, f in enumerate(m.external_binflag) if not rm[i]]\n", @@ -227,15 +211,13 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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lXq8HwPz8fGeZmyn1PVu0y8vLDAaDzvL6/T7z8/P0+/3OMle2z0wzzdy9mdPwHK0kFbNo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\nVpKKWbSSVOz/A27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+ "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -261,15 +243,13 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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zapgkqVI9W/FIUqM1uOIx8UhSzQTNnlzgUJskqVJWPJJUR1Y8kiSVw4pHkmoo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\nOmuLH/R9i/aJxeu1xftz2/a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\n", 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NsuKRpOJkq+/VZuKRpAL5WARJkmpixSNJJWrxUJsVjySpUVY8klSahOjPnQuK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\nrJovsDAAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -325,78 +303,44 @@ "linecollection = modelmap.plot_grid()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `finf` array \n", + "* load infiltration rates from a file into a 3D array\n", + "* `finf` can be submitted to FloPy as a 3D array, list of 2D arrays, list of numeric values, or single numeric value" + ] + }, { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " loading iuzfbnd array...\n", - " loading irunbnd array...\n", - " loading vks array...\n", - " loading eps array...\n", - " loading thts array...\n", - "stress period 1:\n", - " loading finf array...\n", - " loading pet array...\n", - " loading extdp array...\n", - " loading extwc array...\n", - "stress period 2:\n", - " loading finf array...\n", - " loading pet array...\n", - "stress period 3:\n", - " loading finf array...\n", - "stress period 4:\n", - " loading finf array...\n", - "stress period 5:\n", - " loading finf array...\n", - "stress period 6:\n", - " loading finf array...\n", - "stress period 7:\n", - " loading finf array...\n", - "stress period 8:\n", - " loading finf array...\n", - "stress period 9:\n", - " loading finf array...\n", - "stress period 10:\n", - " loading finf array...\n", - "stress period 11:\n", - " loading finf array...\n", - "stress period 12:\n", - " loading finf array...\n" - ] + "data": { + "text/plain": [ + "(15, 10, 1, 12)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "m2 = flopy.modflow.Modflow.load('UZFtest2.nam', version='mf2005', exe_name='mf2005', \n", - " model_ws=path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `finf` array \n", - "* load infiltration rates from a file into a 3D array\n", - "* `finf` can be submitted to FloPy as a 3D array, list of 2D arrays, list of numeric values, or single numeric value" + "m.nrow_ncol_nlay_nper" ] }, { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -405,9 +349,9 @@ }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -416,7 +360,8 @@ ], "source": [ "finf = np.loadtxt(os.path.join('..', 'data', 'uzf_examples', 'finf.dat'))\n", - "finf = np.reshape(finf, (m2.nper, m2.nrow, m2.ncol))\n", + "finf = np.reshape(finf, (m.nper, m.nrow, m.ncol))\n", + "finf = {i: finf[i] for i in range(finf.shape[0])}\n", "plt.imshow(finf[0], interpolation='none')\n", "plt.colorbar()" ] @@ -424,14 +369,12 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'Average infiltration rate, inches per year')" ] }, "execution_count": 14, @@ -440,9 +383,9 @@ }, { "data": { - "image/png": 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QEuOimTQ2yWcdxiV7DrHmg/187ZwpjE6K88kxAa45JYcogZUldldgIo+TonMP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\n5STGRvPpIbTfGuPEcWOTmDd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+ "image/png": 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cQM+b70rh2U3VNLd1khQ/rLEFxhg/c/IXOkpVbdJXGGpu62TdnoN8db5/yk570zPD+KN9jczxY5E7Y8zwOeksfkZEPuP3SIzPvbfrAB1dGtD+gR75ro8XqTHGhDYnieAW3MmgRUQaRKRRRGw4SBgo2lZHXEwUBZMC/408Z3QiSXHRNoTUmDDgZNRQSiACMb7nLjs92q9lp/sTFSXMcNkiNcaEA2/VR/M9P0/q6xG4EM1Q1DS2Urq3MaDzB46W70qhdG8jNm/QmNDm7Y7gu7gnjv1PH68pcI5fIjI+8fb2/UDgykr0Jd+VymPvlVPT2EZWakLQ4jDGeNdvIlDVJZ6fCwMXjvGVom11pCXGcvyEtKDF0DNyaEt1gyUCY0KYk85iE2Z6yk7PmzaW6ACUne6P1RwyJjxYIohAO+uaqTrUGtT+AYD0UXG4UhMsERgT4iwRRKA1ZYEtO+1NniuFLZYIjAlpg04EIjJeROL9EYzxjaKyOnJGJzIxQGWnvckfn8L2miY6urqDHYoxph9DuSP4K1AqIr/0dTBm+Lq6lbe272f+tAw8FV6DKt+VQntXN7vqbJEaY0LVoKuBqep54v6EOc4P8Zhh2lR5iMbWzqD3D/TIy3KXmtiyt5HpWTY30ZhQ5OiOQETmi8j1nt8zgEmq+oFfIzNDUrTNvb7zGVMDW3a6P1PHJRETJWy1GcbGhKwBE4GI/Bj3qmLLPJvigL85eN9DIlIjIpt7bRsjIqtFZJvnp5Wl9LGisjqOG5/K2OTQ6MaJj4lmSmYSpdXWYWxMqHJyR3AFcBnQDKCqVYCTe/yHgYuO2nYn8LKqTgde9jw3PnK4vZO1u+uZPz00moV65LtSrQqpMSHMSSJo9ywyrwAikuTkwKr6BnDgqM2XA494fn8EWOQwTuPA+7sO0t7VHTL9Az3yXClU1rfQ0NoR7FCMMX1wkgiWi8j/Aeki8jXcK5U9OMTzZalqNYDn57ghHsf0YU1ZHXHRUZwyaUywQ/mEnhnGH9ldgTEhyUkZ6l+KyPlAA5AH/IeqrvZ3YCKyBHfROyZOnOjv00WEom11zD1mNIlxgS877U3++I8XqSkIsSRljHHWWfwLVV2tqneo6u2qulpEfjHE8+0TkfGe444HavrbUVUfUNUCVS3IzMwc4ulGjv1NbXxY3RBy/QMAE9ISSEmIsbUJjAlRTpqGzu9j28VDPN/TwHWe368DnhriccxR3vKUnQ61/gEAESHflWI1h4wJUd4WpvmmiGwC8kRkY6/HTmDjQAcWkceAtz3vrxCRrwJ3AeeLyDbcCeYu3/xnmDVldaQkxDArO3hlp73Js0VqjAlZ3voI/g78C/g5nxzm2aiqR48G+hRVvbafl851Hp5xQlV5c1sdZ0wNbtlpb/JcqTS27qHqUCvZ6YnBDscY00u/dwSqekhVd6nqtaq6G2jBPYQ0WUSs9zaE7DlwmMr6lpCoNtqfY4+sTWD9BMaEGiedxZd6mnJ2Aq8Du3DfKZgQ8eY2d9npUOwf6DHjyGpl1k9gTKhx0ln8U+A04CNVnYy7aWeNX6Myg7KmrI4JaQlMznA01y8oUhNiyU5PtA5jY0KQk0TQoar7gSgRiVLVV4ET/RyXcain7PS8ECk77Y2NHDImNDkpQ10vIsnAG8CjIlIDdPo3LOPUB1WHONTSEZLzB46W50rh9Y9qae/sJi7GFsczJlQ4+Wu8HDgM3Ao8D2wHLvVnUMa5Is+ylGdMDf1EkD8+lc5uZXttU7BDMcb04jURiEg08JSqdqtqp6o+oqr3epqKTAhYU1ZHviuFzJTQKDvtTf6RkUPWPGRMKPGaCFS1CzgsIqE5S2mEa+3o4v1dB0N62GhvkzOSiI0WttgQUmNCipM+glZgk4isxrMmAYCqLvVbVMaR4l0Hae/sZl4Y9A8AxEZHMW2cdRgbE2qcJIJnPQ8TQlatq+RHq9yLv/1g5Sa+f1E+i+ZkBzmqgeW7Unh7e2i1LK5aV8k9L2ylqr6FCemJ3HFhXlhcS2N8xUkZ6kcG2scE1qp1lSxbuYmWji4Aqg+1smzlJoCQ/wDLd6Xw5LpKDh3uIG1UbLDD+dS1rKxvCZtraYyv2Bi+MHTPC1uPfHD1aOno4p4XtgYpIufyPB3GoVKSOpyvpTG+YokgDFXVtwxqeyjJd328SE0oCOdraYyvWCIII6rK39/dQ3+FnCeEQVXPrNR40kfFhkwicKUl9Lk9HK6lMb4yYB+BiMwA7gCO6b2/qp7jx7jMUZraOvnByk08vaGKvKxkdh84TGtH95HXE2OjuePCvCBG6IyIkJeVEjJNQzMnpFJ9qPUT28LlWhrjK05GDRUCfwD+CHQNsK8jInIrcCPustabgOtVtdX7u0auLdUN3PToWnbtb+b2C2bwrbOn8fSGqrAd6ZLvSmFFSQXd3UpUENdPqGlopahsP3Ny06luaGXvoVaS42P46aKZYXMtjfEFJ4mgU1V/76sTikg2sBQ4TlVbRGQ58HngYV+dI1KoKo+/X86Pn/6AtMRY/v610zhtyljAPaIlXD+s8sen0tzeRWV9C7ljRgUtjvteKaOjq5tff+5EJmUk8bW/FLO+vJ5LZo8PWkzGBIOTPoJ/isi3RGS8iIzpeQzzvDFAoojEAKOAqmEeL+I0t3Xy3eUbuHPlJk6eNIZnly44kgTCXd6RtQmC1zy0e38zj723h8+dnMskT/nuxXNzqG1s4/WPaoMWlzHB4OSOoGex+Tt6bVNgylBOqKqVIvJLYA/uVc9eVNUXh3KsSFW6t4FvPbqWXXXNfPf8Gdy0cFrILkE5FHlZH9ccuuB4V1Bi+NXqj4iJFpaeO/3ItoX548hIjmN5cTnnHpsVlLiMCYYB7whUdXIfjyElAQARGY27oulkYAKQJCJf7GO/JSJSLCLFtbUj4xuaqrL8/XIW/XYNja2d/O3GU1l67vSISgIASfExTBwzKmgjhz6oOsRT66u4Yd5kslI/HjUUGx3FFXOyeXlLDfub2oISmzHB4GSpylgRWSoiKzyPm0VkOFNCzwN2qmqtqnYAK4Ezjt5JVR9Q1QJVLcjMzBzG6cLD4fZOblu+ge89sZG5x4zmuaULwqK09FDluYI3cuieF7aSlhjL18+a+qnXFhfk0tmtrFpvrZVm5HDSR/B7YC7wO89jrmfbUO0BThORUeJeUutcYMswjhf2tu5t5LL71/Dk+kq+c950/nLDqWFRVno4jnWlsLOumdYOnwxEc+ydHft5bWst3zx7KmmJn/4+MyMrhRNy0igsLke1vxkbxkQWJ4ngZFW9TlVf8TyuB04e6glV9V1gBbAW99DRKOCBoR4v3BUWl3P5b4uoP9zB3756Kt85b0bENQX1Jc+VSrdCWU3gFqlRVe5+vpSs1HiuO31Sv/stLsildG8jmytDY66DMf7mJBF0iciRe2gRmcIw5xOo6o9VNV9VZ6rql1R1xDXI9jQF3bFiI3NyR/PcLfOZFybrCvhC/viemkOB6yd4aUsNa/fUc8u5M0iMi+53v0tPmEB8TBSFJeUBi82YYHIyaugO4FUR2QEI7hnG1/s1qgi3bV8j33p0LWW1TSw9dzq3RGCH8EAmjU0iPiaKrQHqJ+jqVu55oZQpGUlcU5Djdd+0xFguPN7FU+ur+MFnjiUhtv+kYUwkcFKG+mURmQ7k4U4EpSPxG7yvPFFSwY9WbSYpPpq/3nBqWCw67w/RUcL0rOSA3RE8ua6Sj/Y18dt/O4mY6IFvhK8pyOXpDVW8tGUfl8yeEIAIjQmefhOBiJyjqq+IyJVHvTRVRFDVlX6OLaK0tHfx46c3s7y4glMnj+Hea+d8YujiSJTvSg3I5K22zi5+vfojZmWncfFMZ/MWzpg6luz0RJYXV1giMBHP2x3BWcArwKV9vKa4h30aB8pq3E1B22qa+PY507jl3OmOvpVGup6aQ/ub2hib7L9RUo++s4fK+hbuumqW49pGUVHCVSdlc9+rZVQfamF8mlUjNZGr30Sgqj/2/PoTVd3Z+zURmezXqCLIk+sq+OGTm0mMjeaR60/hzBmRPyfCqZ5SE1v3NnLGNP8kgqa2Tu5/tYwzpo5l/iA746+em8u9r5Sxcm0lNy2c5pf4jAkFTr6WPtHHthW+DiTStHZ08f0VG7n18Q3MzE7juVsWWBI4SiAWqXnwzR0caG7nexfl45624tzEsaM4bcoYm1NgIp63PoJ84Hgg7ah+glRgZDdu96H3AuiZKfFEC1Q3tHHTwqncet4MawrqQ2ZKPGOT4vw2w3h/Uxt/fGMHF890cWJu+pCOsXhuLrcVbuD9XQc5ZfJway0aE5q8fTrlAZcA6bj7CXoeJwFf839o4aNnAfTK+hYUqGlso7qhjSVnTuaOC/MtCXiRPz6FrX66I7j/1TJaOrq47YKhLzJz8SwXyfExFBbbnAITufr9hFLVpzyziC9R1et7PZaq6lsBjDHk9bUAOsCzG/cGIZrwkpeVytZ9jXR1+7bppfzAYR59Zw+L5+YybVzykI8zKi6Gz84az7Obqmlu6/RhhMaEDidfVdeJyE0i8jsReajn4ffIwogtgD50+a4UWju62XPgsE+P+78vbQOBW86bPvDOA7jm5BwOt3fx7KZqH0RmTOhxkgj+CriAC4HXgRwgNFYeDxH9LXRuC6APrKfUhC9nGG/d28jKdRVcd/oxPvl/cNLE0UzJTGJFcYUPojMm9DhJBNNU9d+BZlV9BPgsMMu/YYWX28+fwdHjUWwBdGemj0tBBLZU++67xS9f3EpyXAzfOts3Qz5FhKvn5vDergPsqmv2yTGNCSVOEkGH52e9iMwE0oBJfosoDI1LS0CB0aNiESA7PZGfXzkrbNcUDqTEuGgmjU3yWYdxye6DrP5wH18/awqjk+J8ckyAq07KIUpgRYndFZjI46To3AOeVcV+BDwNJAP/7teowszy4nJSE2J4e9m5VqBsCPJdKT5Zv1hV+cXzpWQkx3P9PN/OecxKTeCsGZmsKKng1vNHRqlwM3J4vSMQkSigQVUPquobqjpFVcep6v8FKL6Qd6ilg+c37+XyE7MtCQxRniuF3QcOc7h9eKNyXvuolvd2HmDpudNIinfyHWdwFhfksrehlaKyOp8f25hg8poIVLUbuDlAsYSlZzZW0dbZzeIBShub/uW7UlGFbfuGvkhNd7dy9/NbyR2TyOdPnujD6D527rHjSB8Va3MKTMRx0kewWkRuF5FcERnT8xjOSUUk3bP+camIbBGR04dzvGAqLK4gLyuFWdlpwQ4lbOW7ehapGXrz0D83VrGluoHbzs8jLsY/E/jiY6JZdGI2L364j/rD7X45hzHB4OQv5gbgJuANoMTzKB7meX8DPK+q+cAJhOmaxdv2NbK+vJ7FBTmDrmNjPjZxzCgSY6OHXHOovbOb/3nxI/JdKVx2gn9LRi8uyKG9s5unN9ji9iZyOEkEx6rq5N4P4LihnlBEUoEzgT8BqGq7qtYP9XjBVFhSQUyUcIWNDhqWqChhhmvopSYeLy5nz4HDfP+ifMdlpofq+AlpHDc+lUKbU2AiiJNE0Fc5ieGUmJgC1AJ/FpF1IvKgiCQdvZOILBGRYhEprq31/+Ilg9XR1c3KtZWckz/Or7X0R4r8rBRK9zYOusrn4fZO7n15G6dMGsPZeYGp7rq4IIdNlYd8MtLJmFDQbyIQEZeIzAUSRWSOiJzkeZwNjBrGOWNwF677varOAZqBO4/eSVUfUNUCVS3IzAy98s2vba2lrqmNxQW5wQ4lIuSPT+FAczu1TYNbBfXPa3ZR29jG9y7KC1jz3KITs4mLjrK7AhMxvI2xuxD4Cu6SEr/qtb0R+MEwzlkBVKjqu57nK+gjEYS6wuJyMpLjA/YtNNL1LFJTWt3IuBRnVc4PNrfzh9e2c96x4yiYFLgS0aOT4jjvuHGsWl/JnRfn+61z2phA8VZ99BFVXQh8RVUX9npcNpz1ilV1L1AuIj31F84FPhzq8YKhrqmNV0pruPKkbGKtxLRP9CxSM5h+gj+8vp2m9k5uD0Ipj8VzcznQ3M4rpTUBP7cxvuZtYZrv9vV7D1X91dHbBuHbwKMiEgfsAK4fxrECbtW6Sjq7lcVzbe6Ar4xJimNcSrzjkUPVh1p4+K1dXDEn+0gRKWnmAAAaWklEQVQSCaQF0zMYlxLPipJyLprpCvj5jfElb01DKf46qaquBwr8dXx/UlUKiys4ITed6Vl+u0QjUp4rxfFcgt+8tI1uVW49b4afo+pbTHQUV83N4YE3dlDT2Oq4OcuYUORt8fr/CmQg4WJT5SG27mvkZ1fMDHYoESfflcIjb++ms6vb66puZTVNLC8u58unTyJ3zHDGLQzP4rk5/P617Ty5tpKvnzU1aHEYM1zemoa+p6p3i8h9wKfG9KnqUr9GFqIKiyuIj4niUj9PXBqJ8l2ptHd2s2t/M9PG9X+39avVW0mIjebmc3xTZnqopmQmM/eY0RSWVLDkzCk2qdCELW89nT0duMV8PKO492PEae3o4qn1lVw000VqQmyww4k4R0YOeekn2FBez3Ob9nLjgilkhMD8jcVzcyiraWJ9eVjOiTQG8J4IPuf5me4ZQfSJRyCCCzUvfriPhtZOrrG5A34xbVwy0VHideTQPS9sZUxSHF9b4Nsy00P12dnjSYyNZrnNKTBhzFsimCsixwA3iMjo3gXnhlt0LlwVFpeTnZ7I6VPGBjuUiJQQG83kjKR+Vysr2lZHUVkdNy2cRkqI3JGlJMRy8SwXz2yooqW9K9jhGDMk3hLBH4DngXw+3Sw03KJzYaeqvoWisjqumpvj93o2I1meK4Wt+z49ckhVufuFUrLTE/nCqf4pMz1Ui+fm0tjWyQsf7A12KMYMibcJZfeq6rHAQ54FaXoXnpsSwBhDwhMlFahicwf87FhXCuUHWmhq++QiNf/avJeNFYf4znnTQ24BoFMnjyF3TCKFJbZOgQlPA06LVdVviki0iEwQkYk9j0AEFypUlRVrKzh9ytigDlccCfL6mGHc2dXNL1/YyvRxyVx5Uugl4qgoYfHcXNaU7af8wOFgh2PMoA2YCETkZmAfsBp41vN4xs9xhZT3dh5g9/7DtgpZAPQsUtM7EawoqWBHXTO3X5gXsmsFXzU3BxF4Yq11Gpvw46RQzneAPFU9XlVneR6z/R1YKCksqSA5PoaLZ44PdigRL2d0IsnxMUdmGLd2dPG/L21jzsR0LjguK8jR9S87PZF5UzNYUVJBd/fgSmkbE2xOEkE5cMjfgYSqprZOnt1YzSWzx5MYF1pt05FIRJiRlXxkLsFf3t7F3oZWvn9RfshP2FpckEPFwRbe2bE/2KEYMyjeag312AG8JiLPAkeKxQ+z6FzYeG5jNS0dXbbuQADlj0/l2Y3VHGrp4LevbuesGZmcFgZDdi883kVKQgyFJRWcMS0j2OEY45iTO4I9uPsH4nAXout5jAiFJeVMyUzipInpwQ5lxGjr6OJQSwcn/NeLHGrpoGDS6GCH5EhCbDSXnTCBf22upqG1I9jhAO5KufPueoXJdz7LvLteYdW6ymCHZELQgHcEI7n43I7aJt7fdTAsmiUixap1lfxzY/Untv3u1e3kjh7FojBYG3pxQS6PvruHZzdWc+0pwR1ct2pdJctWbqKlwz3RrbK+hWUrNwGExbU0geNtqcr/9fz8p4g8ffQjcCEGz4qSCqIErjzJ/mgC5Z4XttLe2f2JbS0dXdzzwtYgRTQ4J+SkMSMrmeXFwZ9TcM8LW48kgR7hdC1N4Hi7I/ir5+cv/XFiEYnGPUO5UlUv8cc5hqOrW1m5tpKz88aRlWq15gOlqr5lUNtDjYh7TsHPnttCWU2j1yqq/hbu19IEjreZxSWen6/39fDBuW8BtvjgOH7x5rZa9ja02kziAJuQnjio7aFo0ZxsoqOEwpLgzSnYtq+x3zkXrjT7YmM+KSgL7opIDvBZ4MFgnN+JwuIKRo+K5dxjQ3fseiS648I8Eo8qIZEYG80dQViXeKgyU+JZmDeOlWsr6ezqHvgNPvZESQWX3b+GhNgo4vpY4Kezq5st1c5WgjMjQ7BWXv9f4HtA4P9KHKg/3M7qD/dx+YnZxMXY4vSBtGhONj+/chbZ6YkI7olaP79yVth1bl5TkENtYxuvf1QbsHO2tHfxvRUbuK1wA7Nz0nj5trO5++rZn7iWNy+cioiw6Ldr+Md7e1C1yW/G2TwCAEQkSVWbh3tCEbkEqFHVEhE528t+S4AlABMnBnb0xVPrq2jv6rZ1B4Jk0ZzssPvgP9rC/HFkJMdRWFwRkLvKspombnp0LR/VNPLtc6Zxy7nTiYmO6vNafmXeZG59fD13rtzEuzsP8NNFM0mKd/xRYCKQk1pDZ4jIh3ja80XkBBH53TDOOQ+4TER2Af8AzhGRvx29k6o+oKoFqlqQmZk5jNMNXmFJOcdPSOW4CakBPa+JHLHRUSw6MZuXS/dxoLndr+d6cl0Fl91fRG1TG49cfwq3XZDndc3njOR4937nz+Cp9ZVcen/RkZIeZmRy0u7xa+BCYD+Aqm4AzhzqCVV1marmqOok4PPAK6r6xaEez9c+rGpgc2WDdRKbYVtckEtHl/ptEldrRxd3PrGRWx/fwMzsNJ5buoAzZzj70hQVJXz73Ok8euNpNLZ2sui3a1j+frk1FY1QjhrAVfXoQdERuxRTYUk5cdFRXH5ieDdNmODLc6VwQk4ay4t9/wG7vbbJ3c7/fjk3LZzK3288dUijgU6fOpbnli5g7jGj+d4TG7mtcAOH2zsHfqOJKI6KzonIGYCKSJyI3I6Phn2q6muhNIegvbObp9ZXcf5xWYxOigt2OCYCXF2QS+neRj6o8l3Ty1PrK7n0viJqGtt4+PqTuePCfK9NQQPJTInnLzecyq3nzeDJdZVcdv8aPtrX/7rRJvI4+dfzDeAmIBuoAE70PI84r3jac6+2dQeMj1w2ewJxMVEU+mCmcWtHF8tWbuKWf6zn+AmpPLt0PmfnjfNBlBAdJdxy3nQe/eqp1B/u4LL7i3wSswkPTlYoq1PVL6hqlqqOU9UvqmpE1tldXlxBVmo8Z04PbOe0iVxpo2K58HgXq9ZX0dox9BbVHbVNXPG7t3jsvT188+ypPPa10xif5vtJdmdMy+C5W+Zz0sTR3LFiI7ctt6aikWDAMWMicm8fmw8Bxar6lO9DCo6ahlZe21rDN86aGrKrYJnwdE1BDv/cUMVLW/ZxyewJg37/0xuqWPbERuJiovjz9Sez0Ed3Af0Zl5LAX796Kve+vI17X9nGxop6fveFk5ieNWKKDo84TpqGEnA3B23zPGYDY4Cv9hSmiwQr11XSrXC1jRYyPnbG1AwmpCVQWDy4khOtHV388MlNLH1snXuNhqUL/J4EekRHCbeeP4O/3nAqBw+3c9n9a3giiCUzjH85SQTTgHNU9T5VvQ84DzgWuAK4wJ/BBYqqUlhcTsExo5mSmRzscEyEiY4Srpqbw5vbaqk+5Kzg2866Zq783Vs8+u4evn7WFP6x5LSg1FuaPz2D55Yu4ITcNG4r3MD3VmygpT1iBw2OWE4SQTaQ1Ot5EjBBVbvotWJZOFu7p57ttc22OL3xm6vn5tCtsHLtwHMKntlYxaX3FVF1qIU/XVfAsouPJXYYo4KGa1xqAo/eeBpLz5lGYUkFi367hrKapqDFY3zPyb+uu4H1IvJnEXkYWAf8UkSSgJf8GVygrCgpJzE2ms8Oof3WGCeOGZvEqZPHUOhlTkFrRxf/vmozN/99HTOyknl26YKQKXoYHSV894I8/nLDKdQ1tXHZ/UU8uc6aiiKFk1FDfwLOAFZ5HvNV9UFVbVbVO/wdoL+1tHfxzw3VfGbWeJKt3orxo8UFuezaf5ji3Qc/9dru/c1c/Ye3+Os7u1ly5hQe//rpZIdg6e0F0zN57pYFzMxO49bHN3DnExuHNRrKhAan95utQDVwAJgmIkMuMRFq/rW5mqa2TmsWMn73mVkukuKiPzU+/7lN1VxybxHlB1p48MsF/OAzwW0KGkhWagJ/v/FUbl44jceLy1n02zVsr7WmonDmZPjojbgXkckB1gOnAW8D5/g3tMAoLK5g4phRnDp5TLBDMRFuVFwMs7LTKCyuoLC4gvFpCUzNTOLNsv2cmJvO/f82h5zRo4IdpiMx0VHcfmEeJ08ew62Pr+fS+4r4+ZWzUHUvkVlV38KE9ETuuDAv7CvJjgRO2kJuAU4G3lHVhSKSD0TEgvblBw7z9o793Hb+DFuc3vjdqnWVrC2vp6eHoOpQK1WHWjl7RgYPfPnksFz74qwZmTy3dAFLH1vHLf9YT3SU0NXt/i+srG9h2cpNAJYMQpyTf3mtqtoKICLxqloKhM9yUV6sKKlABK6yuQMmAO55YSvtnZ9ei2lbTXNYJoEerrQE/v61U0mOjzmSBHq0dHRxzwtbgxSZccrJHUGFiKTj7iheLSIHgSr/huV/3d3KipIK5k/LCKv1cE34iuTF5GOio2hu67sURVV9C6pqd90hzMmooStUtV5V/xP4d+BPwCJ/B+Zvb+/YT2V9C4ttFTITIP194YiULyL9/XcocPFv3uTBN3dQ2xgRU48ijtdEICJRIrK557mqvq6qT6uqf5dcCoDC4nJSE2K44LjQGKdtIt8dF+aRGBv9iW2JsdHccWFEtLT2+d+XEBvF1XNzSIiN5qfPbuG0n7/MjY+8z/Ob9/bZTGaCw2vTkKp2i8gGEZmoqnt8cUIRyQX+ArhwL17/gKr+xhfHdqqhtYN/bd7L4gL3P1BjAqGnwzRSR9UM9N+3bV8jK9ZW8OTaSl7aUsPoUbFcfmI2V8/N4fgJqdZ0FEQy0MpJIvIK7lFD7wFHFq9X1cuGdEKR8cB4VV0rIilACbBIVT/s7z0FBQVaXFw8lNP16dF3d/PDJzfz1E3zOCE33WfHNcYMrLOrmzfL6lhRXMHqD/fR3tVNviuFq+fmsGhONhnJ8cEOMWKISImqFgy0n5POYp8OFVXVatyT01DVRhHZgrueUb+JwNcKiyvIy0phdk5aoE5pjPGIiY5iYd44FuaNo/5wO//cUMWKkgp++uwW7vpXKQvzx3H13BwW5o0L69FU4WTARKCqr4vIMcB0VX1JREYBPmlPEZFJwBzgXV8cz4lt+xpZX17Pjz57rN2KGhNk6aPi+NLpk/jS6ZP4aF8jK0oqWLm2ktUf7mNMUhyLPE1Hx01IDXaoEc1J09DXgCXAGFWdKiLTgT+o6rnDOrFIMvA68DNVXdnH60s852XixIlzd+/ePZzTHfH/ntvCQ0U7eecH59otqDEhqLOrmze21bKixN101NGlHDc+lavn5nD5iRMYa3+3jjltGnKSCNYDpwDvquocz7ZNqjprGMHFAs8AL6jqrwba31d9BB1d3Zz+81eYMzGdP355wGtjjAmyg83tPO1pOtpUeYjYaOGc/HFcPTeXhpZ2frV6W0R2vPuKL/sI2lS1vacZRURiAO/Zw3tggnsuwhYnScCXXt9aS11TG9fY3AFjwsLopDiuO2MS150xidK9DawormDV+kpe+GDfJ/azchbD46Qn5nUR+QGQKCLnA4XAP4dxznnAl4BzRGS95/GZYRzPscKScjKS4zg7zxanNybc5LtS+dElx/H2snMZkxT3qddbOrr4xfOlQYgs/DlJBHcCtcAm4OvAc8CPhnpCVS1SVVHV2ap6oufx3FCP51RdUxsvb6nhijnZIV3i1xjjXWx0FAeb+57TWn2olW/+rYSXt+yjs8smrDnlpGnocuAvqvpHfwfjT6vWVdLZrVZSwpgIMCE9kco+ajQlxUfz3s4D/GvzXjKS47nyJPeooxlZKUGIMnw4+Wp8GfCRiPxVRD7r6SMIK6ruAnMn5KbbPwhjIkB/5Tp+tmgWby87lwe+NJeTJqbzUNFOLvj1G1x2fxF/fXsX9YfDvjqOXww4agiOjPK5GPgcMB9Yrao3+jm2I4Y7amhTxSEuvb+Iny6ayRdPO8aHkRljgmXVusoBy3Xsb2pj1foqCovLKd3bSFx0FOcfl8XVc3NYMD2DmAhvJvbZ8NFeB4wFLgKuBxaoasB6XIebCP591WaWF5fz3g/PIy0x1oeRGWPCxQdVh1hRUsFT66s40NzOuJR4rjgpm8Vzc5g2LjJbCnw2fFRELgI+DywEXgMeBK4ZboCB0trRxVPrK7lopsuSgDEj2PET0jh+QhrLLj6WV0prWFFSwYNv7uT/Xt/BCbnpLJ6bw6WzJ5A2auR9Tjhp7/8K8A/g66oadsXEV3+4j4bWThbPtU5iYwzExURx0UwXF810UdvYxlPrKyksruBHqzbzk2c+5ILjslhckMv8aRlER42MMjROag19vvdzEZkH/Juq3uS3qHygp/2wsr6FaBFqG1qDHZIxJsRkpsRz44IpfHX+ZD6oaqCwuJynNlTxzMZqslLjufKkHK6em8PUzGRHfRK+FMjzOe0sPhH4N9xNQjuBlap6n18i6sNg+whWratk2cpNtHR0HdmWGBvNz6+cZbMOjTFetXV28coWd9PRax/V0tWtHDMmkapDrXR0ffx56c/PFF99hg27s1hEZuDuG7gW2A88DtyuqgEfdjPYRDDvrlf6HGOcnZ7ImjvP8WVoxpgIVtPQyqr1ldz9/FY6uz/9WRkTJUzOSPL5eXfWNfd5vsF+hvmis7gUeBO4VFXLPAe91XEEQRTJi4QbYwJnXGoCS86cys+f67t0RWe3Mj0r2efn3VbT1Od2f32GeUsEV+G+I3hVRJ7H3WEcFj0n/c06jJRFwo0xgdXfZ0p2eiK/+8Jcn5+vv1YNf32G9TubQlWfVNXPAfm4h43eCmSJyO9F5AK/ROMjkb5IuDEmsAL9mRLo8zkZNdQMPAo8KiJjgMW4C9G96JeIfCDSFwk3xgRWoD9TAn0+xzOLg8nXi9cbY8xI4LSzOLILbRhjjBmQJQJjjBnhLBEYY8wIZ4nAGGNGOEsExhgzwoXFqCERqQV2BzmMDKAuyDEMRTjGHY4xQ3jGHY4xQ3jGHYyYj3GydkxYJIJQICLFToZhhZpwjDscY4bwjDscY4bwjDuUY7amIWOMGeEsERhjzAhnicC5B4IdwBCFY9zhGDOEZ9zhGDOEZ9whG7P1ERhjzAhndwTGGDPCWSJwQER2icgmEVkvIiFZ/U5EHhKRGhHZ3GvbGBFZLSLbPD9HBzPGvvQT93+KSKXneq8Xkc8EM8ajiUiuiLwqIltE5AMRucWzPWSvt5eYQ/1aJ4jIeyKywRP3f3m2TxaRdz3X+nERiQt2rL15ifthEdnZ63qfGOxYwZqGHBGRXUCBqobsuGURORNoAv6iqjM92+4GDqjqXSJyJzBaVb8fzDiP1k/c/wk0qeovgxlbf0RkPDBeVdeKSApQAiwCvkKIXm8vMV9DaF9rAZJUtUlEYoEi4Bbgu7jXTv+HiPwB2KCqvw9mrL15ifsbwDOquiKoAR7F7ggihKq+ARw4avPlwCOe3x/B/YcfUvqJO6SparWqrvX83ghsAbIJ4evtJeaQpm496zbGeh4KnAP0fJiG1LUGr3GHJEsEzijwooiUiMiSYAczCFmqWg3uDwJgXJDjGYybRWSjp+koZJpYjiYik4A5wLuEyfU+KmYI8WstItEish6oAVYD24F6Ve307FJBCCa1o+NW1Z7r/TPP9f61iMQHMcQjLBE4M09VTwIuBm7yNGcY//k9MBU4EagG/ie44fRNRJKBJ4DvqGpDsONxoo+YQ/5aq2qXqp4I5ACnAMf2tVtgoxrY0XGLyExgGe7lf08GxgAh0XRoicABVa3y/KwBnsT9jzEc7PO0Dfe0EdcEOR5HVHWf54+oG/gjIXi9Pe2+TwCPqupKz+aQvt59xRwO17qHqtbjXj/9NCBdRHqW2s0BqoIV10B6xX2Rp4lOVbUN+DMhcr0tEQxARJI8nWuISBJwAbDZ+7tCxtPAdZ7frwOeCmIsjvV8mHpcQYhdb09H4J+ALar6q14vhez17i/mMLjWmSKS7vk9ETgPd//Gq8DVnt1C6lpDv3GX9vqiILj7NULietuooQGIyBTcdwEAMcDfVfVnQQypTyLyGHA27gqH+4AfA6uA5cBEYA+wWFVDqmO2n7jPxt1UocAu4Os9be+hQETmA28Cm4Buz+Yf4G5zD8nr7SXmawntaz0bd2dwNO4vrstV9Seev8t/4G5eWQd80fMtOyR4ifsVIBMQYD3wjV6dykFjicAYY0Y4axoyxpgRzhKBMcaMcJYIjDFmhLNEYIwxI5wlAmOMGeEsEZiwJSJje1Vx3HtUFc23/HzuCSIy5MJhInK2iDzjy5iMGaqYgXcxJjSp6n7cY+ADXrHUM9v86gF3NCYM2B2BiUgi0uT5ebaIvC4iy0XkIxG5S0S+4KkVv0lEpnr2yxSRJ0Tkfc9j3gDHnySeNRRE5CsislJEnvfUx7+7n/dcJCKlIlIEXNlr+yki8paIrPP8zPNsf7N3vXoRWSMis0XkrF53Put6Zr4bM1SWCMxIcALuWvCzgC8BM1T1FOBB4NuefX4D/FpVTwau8rw2GCcCn/Oc43Miktv7RRFJwF3L51JgAeDq9XIpcKaqzgH+A/h/nu0P4l7jABGZAcSr6kbgduAmT0GzBUDLIGM15hMsEZiR4H1Psa823CWMX/Rs3wRM8vx+HnC/p2zw00DqIL9pv6yqh1S1FfgQOOao1/OBnaq6Td3T+f/W67U0oNBzh/Fr4HjP9kLgEk+xuBuAhz3b1wC/EpGlQHqvcszGDIn1EZiRoHcNmu5ez7v5+G8gCjhdVYf67br3Obro+2+rv3ou/w28qqpXeNYKeA1AVQ+LyGrcC95cAxR4tt8lIs8CnwHeEZHzVLV0iHEbY3cExni8CNzc86Snbd7Tfv8XHxy/FJjc0yeBu9hbjzSg0vP7V45634PAvbjvag54YpqqqptU9RdAMe67DWOGzBKBMW5LgQLPylEf4l5bFtyVRIfdBu9pMloCPOvpLN7d6+W7gZ+LyBrc1Sp7v68EaMBdu77Hd0Rks4hs8MT2r+HGZ0Y2qz5qjBcicg/wV08nbTDOPwF3U1G+Z/EYY3zOEoExIUpEvgz8DPiuqhYGOx4TuSwRGGPMCGd9BMYYM8JZIjDGmBHOEoExxoxwlgiMMWaEs0RgjDEjnCUCY4wZ4f4/7KOABSo8OeEAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -450,8 +393,8 @@ } ], "source": [ - "plt.plot(m2.dis.perlen.array.cumsum()/864600, \n", - " [a.mean() * 86400 * 365 * 12 for a in finf[:, :, :]], marker='o')\n", + "plt.plot(m.dis.perlen.array.cumsum()/864600, \n", + " [a.mean() * 86400 * 365 * 12 for a in finf.values()], marker='o')\n", "plt.xlabel('Time, in days')\n", "plt.ylabel('Average infiltration rate, inches per year')" ] @@ -466,14 +409,12 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -482,9 +423,9 @@ }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -511,9 +452,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "uzgag = {-68: [-68],\n", @@ -532,18 +471,16 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "uzf = flopy.modflow.ModflowUzf1(m,\n", " nuztop=1, iuzfopt=1, irunflg=1, ietflg=1,\n", " ipakcb=0, \n", " iuzfcb2=61,# binary output of recharge and groundwater discharge\n", - " ntrail2=25, nsets=20, nuzgag=4,\n", + " ntrail2=25, nsets=20,\n", " surfdep=1.0, uzgag=uzgag,\n", - " iuzfbnd=m2.bas6.ibound.array, \n", + " iuzfbnd=m.bas6.ibound.array, \n", " irunbnd=irunbnd, \n", " vks=vks, # saturated vertical hydraulic conductivity of the uz\n", " finf=finf, #infiltration rates\n", @@ -558,9 +495,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "m.write_input()" @@ -576,22 +511,20 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "FloPy is using the following executable to run the model: /Users/jdhughes/.local/bin/mf2005\n", + "FloPy is using the following executable to run the model: /Users/aleaf/Documents/software/pymake_exes/mf2005\n", "\n", " MODFLOW-2005 \n", " U.S. GEOLOGICAL SURVEY MODULAR FINITE-DIFFERENCE GROUND-WATER FLOW MODEL\n", " Version 1.12.00 2/3/2017 \n", "\n", " Using NAME file: UZFtest2.nam \n", - " Run start date and time (yyyy/mm/dd hh:mm:ss): 2017/11/28 19:12:56\n", + " Run start date and time (yyyy/mm/dd hh:mm:ss): 2018/02/15 15:58:59\n", "\n", " Solving: Stress period: 1 Time step: 1 Ground-Water Flow Eqn.\n", " Solving: Stress period: 2 Time step: 1 Ground-Water Flow Eqn.\n", @@ -697,7 +630,13 @@ " Solving: Stress period: 8 Time step: 11 Ground-Water Flow Eqn.\n", " Solving: Stress period: 8 Time step: 12 Ground-Water Flow Eqn.\n", " Solving: Stress period: 8 Time step: 13 Ground-Water Flow Eqn.\n", - " Solving: Stress period: 8 Time step: 14 Ground-Water Flow Eqn.\n", + " Solving: Stress period: 8 Time step: 14 Ground-Water Flow Eqn.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " Solving: Stress period: 8 Time step: 15 Ground-Water Flow Eqn.\n", " Solving: Stress period: 9 Time step: 1 Ground-Water Flow Eqn.\n", " Solving: Stress period: 9 Time step: 2 Ground-Water Flow Eqn.\n", @@ -759,8 +698,8 @@ " Solving: Stress period: 12 Time step: 13 Ground-Water Flow Eqn.\n", " Solving: Stress period: 12 Time step: 14 Ground-Water Flow Eqn.\n", " Solving: Stress period: 12 Time step: 15 Ground-Water Flow Eqn.\n", - " Run end date and time (yyyy/mm/dd hh:mm:ss): 2017/11/28 19:12:59\n", - " Elapsed run time: 2.865 Seconds\n", + " Run end date and time (yyyy/mm/dd hh:mm:ss): 2018/02/15 15:59:03\n", + " Elapsed run time: 3.102 Seconds\n", "\n", " Normal termination of simulation\n" ] @@ -792,194 +731,198 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(1, 1, b' GW ET', 10, 15, -1, 4, 2628000.0, 2628000.0, 2628000.0, b'', b'', b'', b'')\n", - "(1, 1, b' UZF RECHARGE', 10, 15, -1, 4, 2628000.0, 2628000.0, 2628000.0, b'', b'', b'', b'')\n", - "(1, 1, b' SURFACE LEAKAGE', 10, 15, -1, 4, 2628000.0, 2628000.0, 2628000.0, b'', b'', b'', b'')\n", - "(1, 1, b' HORT+DUNN', 10, 15, -1, 4, 2628000.0, 2628000.0, 2628000.0, b'', b'', b'', b'')\n", - "(1, 1, b' STORAGE CHANGE', 10, 15, -1, 4, 2628000.0, 2628000.0, 2628000.0, b'', b'', b'', b'')\n", - "(1, 2, b' GW ET', 10, 15, -1, 4, 82713.0703125, 82713.0703125, 2710713.0, b'', b'', b'', b'')\n", - "(1, 2, b' UZF RECHARGE', 10, 15, -1, 4, 82713.0703125, 82713.0703125, 2710713.0, b'', b'', b'', b'')\n", - "(1, 2, b' SURFACE 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MBCLyF2PMVKAA6z7BIyLyuQf7rgT+T0S+N8bEAmuNMZ8DNwFfisgfjTEPYPVIur/Jv0FT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\n7z7GP77JYNbZ3Zjix0FEqvl6J8bw3u3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AauBr4E3gVtplks26yonArG28VRZB0hdA60t6\nArg+328EVpeyLwCcnVf/Y8BOSbspE54cr2zTsglYI6lV4+YcYCXwO/BRREzlsfcCG3AisB5xIjBr\nq9aamaksz9A+V04Dro6IX2btOyLpEHAT5Ur/hg6fL2BrRIz+Y2UZS5g9WOfBO+sZ90OanZrDwAOt\nBeX8sZJWRMRERDxBqXy7CvgROKuy7yhwX5ZxRtKlWW0WSu35i3Ns4A7KNIVmPeFEYHZqHgSuzMHe\nY5T5ZAG2SZqU9CnwB/AGcBT4U2Uy8u3ALuAY8LGkSeB52nca48CzlBLJX9KuRmvWdX581KzP/Jip\n9ZvvCMzMGs53BGZmDec7AjOzhnMiMDNrOCcCM7OGcyIwM2s4JwIzs4ZzIjAza7i/AS0oweews2Vd\nAAAAAElFTkSuQmCC\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1073,14 +1012,12 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'Volume for whole model, in cubic feet')" ] }, "execution_count": 23, @@ -1089,9 +1026,9 @@ }, { "data": { - "image/png": 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URERExNeUAIp4EN/YPKgKYL0CRgmgiIiIiJ8pARTxIBzbCD5D20DUa+8U0CO/uZSIiIhI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YfxwQ9+cN0B7k6jaRoefvhh3H333fje976HF154Aa+//joefPBBHD16FMPDw9sio0muWq22Kti7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\nkiRJUkf8B6ByUn/D+0hWAAAAAElFTkSuQmCC\n", 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\n", 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Xp9K5djPlbxfxnlZKnyrLm5sr+7jdKtPbjf63oFSNMk+eSTka5R/Hzg0KvRst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\n00Vpfl5VZ/bye2vrkvGO9mPGyLzofvHbr3Wn2Ygu4rFtb56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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1901,9 +1856,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -1911,7 +1864,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1925,7 +1878,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/flopy/export/shapefile_utils.py b/flopy/export/shapefile_utils.py index f7bb24c098..8bdbf52259 100755 --- a/flopy/export/shapefile_utils.py +++ b/flopy/export/shapefile_utils.py @@ -42,7 +42,7 @@ def write_gridlines_shapefile(filename, sr): "importing shapefile - try pip install pyshp") wr = shapefile.Writer(shapeType=shapefile.POLYLINE) - wr.field("number", "N", 20, 0) + wr.field("number", "N", 18, 0) for i, line in enumerate(sr.get_grid_lines()): wr.poly([line]) wr.record(i) @@ -91,9 +91,9 @@ def write_grid_shapefile(filename, sr, array_dict, nan_val=-1.0e9): assert array.shape == (sr.nrow, sr.ncol) array[np.where(np.isnan(array))] = nan_val if array.dtype in [np.int, np.int32, np.int64]: - wr.field(name, "N", 20, 0) + wr.field(name, "N", 18, 0) else: - wr.field(name, "N", 20, 12) + wr.field(name, "N", 18, 12) arrays.append(array) for i in range(sr.nrow): @@ -315,8 +315,8 @@ def enforce_10ch_limit(names): def get_pyshp_field_info(dtypename): """Get pyshp dtype information for a given numpy dtype. """ - fields = {'int': ('N', 20, 0), - ' 1: + f_sfr.write('{:.0f} {:.0f} {:.0f} '.format(self.nstrail, self.isuzn, self.nsfrsets)) if self.nstrm < 0 or self.transroute: @@ -1678,6 +1698,37 @@ def export_outlets(self, f, **kwargs): geoms = [Point(x, y) for x, y in zip(x0, y0)] recarray2shp(rd, geoms, f, **kwargs) + def export_transient_variable(self, f, varname, **kwargs): + """Export point shapefile showing locations with + a given segment_data variable applied. For example, segments + where streamflow is entering or leaving the upstream end of a stream segment (FLOW) + or where RUNOFF is applied. Cell centroids of the first reach of segments with + non-zero terms of varname are exported; values of varname are exported by + stress period in the attribute fields (e.g. flow0, flow1, flow2... for FLOW + in stress periods 0, 1, 2... + + Parameters + ---------- + f : str, filename + varname : str + Variable in SFR Package dataset 6a (see SFR package documentation) + + """ + from flopy.utils.geometry import Point + from flopy.export.shapefile_utils import recarray2shp + + rd = self.reach_data + if np.min(rd.outreach) == np.max(rd.outreach): + self.set_outreaches() + ra = self.get_variable_by_stress_period(varname.lower()) + + # get the cell centers for each reach + m = self.parent + x0 = m.sr.xcentergrid[ra.i, ra.j] + y0 = m.sr.ycentergrid[ra.i, ra.j] + geoms = [Point(x, y) for x, y in zip(x0, y0)] + recarray2shp(ra, geoms, f, **kwargs) + @staticmethod def ftype(): return 'SFR' @@ -2654,9 +2705,10 @@ def _parse_1c(line, reachinput, transroute): nsfrsets = int(line.pop(0)) if nstrm < 0: isfropt = int(line.pop(0)) - nstrail = int(line.pop(0)) - isuzn = int(line.pop(0)) - nsfrsets = int(line.pop(0)) + if isfropt > 1: + nstrail = int(line.pop(0)) + isuzn = int(line.pop(0)) + nsfrsets = int(line.pop(0)) irtflg, numtim, weight, flwtol = na, na, na, na if nstrm < 0 or transroute: diff --git a/flopy/modflow/mfuzf1.py b/flopy/modflow/mfuzf1.py index 16222a3f92..9a9de1e00f 100644 --- a/flopy/modflow/mfuzf1.py +++ b/flopy/modflow/mfuzf1.py @@ -12,7 +12,7 @@ import numpy as np from ..utils.flopy_io import _pop_item, line_parse, read_nwt_options from ..pakbase import Package -from ..utils import Util2d +from ..utils import Util2d, Transient2d class ModflowUzf1(Package): @@ -85,11 +85,6 @@ class ModflowUzf1(Package): waves allowed within an unsaturated zone cell is equal to NTRAIL2*NSETS2. An error will occur if the number of waves in a cell exceeds this value. (default is 20) - nuzgag : integer - equal to the number of cells (one per vertical column) that will be - specified for printing detailed information on the unsaturated zone - water budget and water content. A gage also may be used to print - the budget summed over all model cells. (default is 0) surfdep : float The average height of undulations, D (Figure 1 in UZF documentation), in the land surface altitude. (default is 1.0) @@ -178,6 +173,14 @@ class ModflowUzf1(Package): * h < CELTOP-EXTDP, ET is zero; * CELTOP-EXTDP < h < CELTOP-EXTDP+SMOOTHINT, ET is smoothed; CELTOP-EXTDP+SMOOTHINT < h, ET is equal to potential ET. + uzgage : dict of lists or list of lists + Dataset 8 in UZF Package documentation. Each entry in the dict + is keyed by iftunit. + Dict of lists: If iftunit is negative, the list is empty. + If iftunit is positive, the list includes [IUZROW, IUZCOL, IUZOPT] + List of lists: + Lists follow the format described in the documentation: + [[IUZROW, IUZCOL, IFTUNIT, IUZOPT]] or [[-IFTUNIT]] netflux : list of [Unitrech (int), Unitdis (int)] (MODFLOW-NWT version 1.1 and MODFLOW-2005 1.12 or later) An optional character variable. When NETFLUX is specified, @@ -193,19 +196,31 @@ class ModflowUzf1(Package): are the sum of discharge values for the UZF, SFR2, and LAK packages. Values are averaged over the period between output times. -[NETFLUX unitrech unitdis] - finf : float - used to define the infiltration rates (LT-1) at land surface for each + [NETFLUX unitrech unitdis] + finf : float, 2-D array, or dict of {kper:value} + where kper is the zero-based stress period + to assign a value to. Value should be cast-able to Util2d instance + can be a scalar, list, or ndarray is the array value is constant in + time. + Used to define the infiltration rates (LT-1) at land surface for each vertical column of cells. If FINF is specified as being greater than the vertical hydraulic conductivity then FINF is set equal to the vertical unsaturated hydraulic conductivity. Excess water is routed to streams or lakes when IRUNFLG is not zero, and if SFR2 or LAK3 is active. (default is 1.0E-8) - pet : float - used to define the ET demand rates (L1T-1) within the ET extinction + pet : float, 2-D array, or dict of {kper:value} + where kper is the zero-based stress period + to assign a value to. Value should be cast-able to Util2d instance + can be a scalar, list, or ndarray is the array value is constant in + time. + Used to define the ET demand rates (L1T-1) within the ET extinction depth interval for each vertical column of cells. (default is 5.0E-8) - extdp : float - used to define the ET extinction depths. The quantity of ET removed + extdp : float, 2-D array, or dict of {kper:value} + where kper is the zero-based stress period + to assign a value to. Value should be cast-able to Util2d instance + can be a scalar, list, or ndarray is the array value is constant in + time. + Used to define the ET extinction depths. The quantity of ET removed from a cell is limited by the volume of water stored in the unsaturated zone above the extinction depth. If ground water is within the ET extinction depth, then the rate removed is based @@ -213,8 +228,12 @@ class ModflowUzf1(Package): the ET extinction depth. The linear decrease is the same method used in the Evapotranspiration Package (McDonald and Harbaugh, 1988, chap. 10). (default is 15.0) - extwc : float - used to define the extinction water content below which ET cannot be + extwc : float, 2-D array, or dict of {kper:value} + where kper is the zero-based stress period + to assign a value to. Value should be cast-able to Util2d instance + can be a scalar, list, or ndarray is the array value is constant in + time. + Used to define the extinction water content below which ET cannot be removed from the unsaturated zone. EXTWC must have a value between (THTS-Sy) and THTS, where Sy is the specific yield specified in either the LPF or BCF Package. (default is 0.1) @@ -243,6 +262,11 @@ class ModflowUzf1(Package): Attributes ---------- + nuzgag : integer + equal to the number of cells (one per vertical column) that will be + specified for printing detailed information on the unsaturated zone + water budget and water content. A gage also may be used to print + the budget summed over all model cells. (default is 0) Methods ------- @@ -265,7 +289,7 @@ class ModflowUzf1(Package): def __init__(self, model, nuztop=1, iuzfopt=0, irunflg=0, ietflg=0, ipakcb=None, - iuzfcb2=None, ntrail2=10, nsets=20, nuzgag=0, + iuzfcb2=None, ntrail2=10, nsets=20, surfdep=1.0, iuzfbnd=1, irunbnd=0, vks=1.0E-6, eps=3.5, thts=0.35, thtr=0.15, thti=0.20, @@ -311,6 +335,15 @@ def __init__(self, model, ipos = 3 if uzgag is not None: + # convert to dict + if isinstance(uzgag, list): + d = {} + for l in uzgag: + if len(l) > 1: + d[l[2]] = [l[0], l[1], l[3]] + else: + d[-np.abs(l[0])] = [] + uzgag = d for key, value in uzgag.items(): fname = filenames[ipos] iu = abs(key) @@ -320,6 +353,12 @@ def __init__(self, model, extension=uzgagext, package=ModflowUzf1.ftype()) ipos += 1 + # handle case where iftunit is listed in the values + # (otherwise, iftunit will be written instead of iuzopt) + if len(value) == 4: + uzgag[key] = value[:2] + value[-1:] + elif len(value) == 1: + uzgag[-np.abs(key)] = [] # Fill namefile items name = [ModflowUzf1.ftype()] @@ -383,7 +422,6 @@ def __init__(self, model, if iuzfopt > 0: self.ntrail2 = ntrail2 self.nsets = nsets - self.nuzgag = nuzgag self.surfdep = surfdep # Data Set 2 @@ -422,50 +460,42 @@ def __init__(self, model, name='thti') # Data Set 8 - # [IUZROW] [IUZCOL] IFTUNIT [IUZOPT] - self.uzgag = uzgag - if uzgag is not None: - if len(uzgag) != nuzgag: - print( - "WARNING!\nItem 8 doesn't correspond with NUZGAG.\nNUZGAG set to 0") - self.nuzgag = 0 - self.uzgag = [] - else: - self.uzgag = uzgag + # {IFTUNIT: [IUZROW, IUZCOL, IUZOPT]} + self._uzgag = uzgag # Dataset 9, 11, 13 and 15 will be written automatically in the write_file function # Data Set 10 # [FINF (NCOL, NROW)] – U2DREL - self.finf = [] - for i, a in enumerate(self._2list(finf)): - b = Util2d(model, (nrow, ncol), np.float32, a, - name='finf_' + str(i + 1)) - self.finf.append(b) + + self.finf = Transient2d(model, (nrow, ncol), np.float32, + finf, name='finf') if ietflg > 0: - # Data Set 12 - # [PET (NCOL, NROW)] – U2DREL - self.pet = [] - for i, a in enumerate(self._2list(pet)): - b = Util2d(model, (nrow, ncol), np.float32, a, - name='pet_' + str(i + 1)) - self.pet.append(b) - # Data Set 14 - # [EXTDP (NCOL, NROW)] – U2DREL - self.extdp = [] - for i, a in enumerate(self._2list(extdp)): - b = Util2d(model, (nrow, ncol), np.float32, a, - name='extdp_' + str(i + 1)) - self.extdp.append(b) - # Data Set 16 - # [EXTWC (NCOL, NROW)] – U2DREL - if iuzfopt > 0: - self.extwc = [] - for i, a in enumerate(self._2list(extwc)): - b = Util2d(model, (nrow, ncol), np.float32, a, - name='extwc_' + str(i + 1)) - self.extwc.append(b) + self.pet = Transient2d(model, (nrow, ncol), np.float32, + pet, name='pet') + self.extdp = Transient2d(model, (nrow, ncol), np.float32, + extdp, name='extdp') + self.extwc = Transient2d(model, (nrow, ncol), np.float32, + extwc, name='extwc') self.parent.add_package(self) + def __setattr__(self, key, value): + if key == "uzgag": + print('Uzgag must be set by the constructor; \ + modifying this attribute requires creating a new ModflowUzf1 instance') + else: + super(ModflowUzf1, self).__setattr__(key, value) + + @property + def nuzgag(self): + if self.uzgag is None: + return 0 + else: + return len(self.uzgag) + + @property + def uzgag(self): + return self._uzgag + def _2list(self, arg): # input as a 3D array if isinstance(arg, np.ndarray) and len(arg.shape) == 3: @@ -582,49 +612,30 @@ def write_file(self,f=None): values[0] += 1 values[1] += 1 comment = ' #IUZROW IUZCOL IFTUNIT IUZOPT' + values.insert(2, iftunit) for v in values: f_uzf.write('{:10d}'.format(v)) f_uzf.write('{}\n'.format(comment)) else: comment = ' #IFTUNIT' - for v in values: - f_uzf.write('{:10d}'.format(v)) + f_uzf.write('{:10d}'.format(iftunit)) f_uzf.write('{}\n'.format(comment)) + + def write_transient(name): + invar, var = self.__dict__[name].get_kper_entry(n) + + comment = ' #{} for stress period '.format(name) + str(n + 1) + f_uzf.write('{0:10d}{1:20s}\n'.format(invar, comment)) + if (invar >= 0): + f_uzf.write(var) + for n in range(nper): - comment = ' #NUZF1 for stress period ' + str(n + 1) - if n < len(self.finf): - nuzf1 = 1 - else: - nuzf1 = -1 - f_uzf.write('{0:10d}{1:20s}\n'.format(nuzf1, comment)) - if n < len(self.finf): - f_uzf.write(self.finf[n].get_file_entry()) - comment = ' #NUZF2 for stress period ' + str(n + 1) + write_transient('finf') if self.ietflg > 0: - if n < len(self.pet): - nuzf2 = 1 - else: - nuzf2 = -1 - f_uzf.write('{0:10d}{1:20s}\n'.format(nuzf2, comment)) - if n < len(self.pet): - f_uzf.write(self.pet[n].get_file_entry()) - comment = ' #NUZF3 for stress period ' + str(n + 1) - if n < len(self.extdp): - nuzf3 = 1 - else: - nuzf3 = -1 - f_uzf.write('{0:10d}{1:20s}\n'.format(nuzf3, comment)) - if n < len(self.extdp): - f_uzf.write(self.extdp[n].get_file_entry()) - comment = ' #NUZF4 for stress period ' + str(n + 1) + write_transient('pet') + write_transient('extdp') if self.iuzfopt > 0: - if n < len(self.extwc): - nuzf4 = 1 - else: - nuzf4 = -1 - f_uzf.write('{0:10d}{1:20s}\n'.format(nuzf4, comment)) - if n < len(self.extwc): - f_uzf.write(self.extwc[n].get_file_entry()) + write_transient('extwc') f_uzf.close() @staticmethod @@ -682,18 +693,18 @@ def load(f, model, ext_unit_dict=None, check=False): nuztop, iuzfopt, irunflg, ietflg, ipakcb, iuzfcb2, \ ntrail2, nsets2, nuzgag, surfdep = _parse1(line) - arrays = {'finf': [], + arrays = {'finf': {}, # datasets 10, 12, 14, 16 are lists of util2d arrays - 'pet': [], - 'extdp': [], - 'extwc': []} + 'pet': {}, + 'extdp': {}, + 'extwc': {}} def load_util2d(name, dtype, per=None): print(' loading {} array...'.format(name)) if per is not None: - arrays[name].append( + arrays[name][per] =\ Util2d.load(f, model, (nrow, ncol), dtype, name, - ext_unit_dict)) + ext_unit_dict) else: arrays[name] = Util2d.load(f, model, (nrow, ncol), dtype, name, ext_unit_dict) @@ -798,7 +809,7 @@ def load_util2d(name, dtype, per=None): nuztop=nuztop, iuzfopt=iuzfopt, irunflg=irunflg, ietflg=ietflg, ipakcb=ipakcb, iuzfcb2=iuzfcb2, - ntrail2=ntrail2, nsets=nsets2, nuzgag=nuzgag, + ntrail2=ntrail2, nsets=nsets2, surfdep=surfdep, uzgag=uzgag, specifythtr=specifythtr, specifythti=specifythti, nosurfleak=nosurfleak, unitnumber=unitnumber, diff --git a/flopy/utils/check.py b/flopy/utils/check.py index b0f79cd5a4..a0d7d640b7 100644 --- a/flopy/utils/check.py +++ b/flopy/utils/check.py @@ -296,21 +296,16 @@ def _list_spd_check_violations(self, stress_period_data, criteria, col=None, values, and description of error for each row in stress_period_data where criteria=True. """ inds_col = ['k', 'i', 'j'] if self.structured else ['node'] - + #inds = stress_period_data[criteria][inds_col]\ + # .reshape(stress_period_data[criteria].shape + (-1,)) + #inds = np.atleast_2d(np.squeeze(inds.tolist())) inds = stress_period_data[criteria] a = inds[inds_col[0]] if len(inds_col) > 1: for n in inds_col[1:]: a = np.concatenate((a, inds[n])) inds = a.view(int) - - #inds = stress_period_data[criteria] - #inds = inds[inds_col] - #inds = inds.copy() - #inds = inds.view(int) inds = inds.reshape(stress_period_data[criteria].shape + (-1,)) - #inds = stress_period_data[criteria][inds_col].view(int)\ - # .reshape(stress_period_data[criteria].shape + (-1,)) if col is not None: v = stress_period_data[criteria][col] diff --git a/flopy/utils/recarray_utils.py b/flopy/utils/recarray_utils.py index 574c0d58a7..8de5083aed 100644 --- a/flopy/utils/recarray_utils.py +++ b/flopy/utils/recarray_utils.py @@ -2,8 +2,7 @@ def create_empty_recarray(length, dtype, default_value=0): r = np.zeros(length, dtype=dtype) - #if default_value != 0: - # r[:] = default_value + assert isinstance(dtype, np.dtype), "dtype argument must be an instance of np.dtype, not list." for name in dtype.names: dt = dtype.fields[name][0] if 'float' in str(dt): diff --git a/flopy/utils/reference.py b/flopy/utils/reference.py index 73ffad4870..3de6442a55 100755 --- a/flopy/utils/reference.py +++ b/flopy/utils/reference.py @@ -890,7 +890,8 @@ def write_gridSpec(self, filename): f = open(filename, 'w') f.write( "{0:10d} {1:10d}\n".format(self.delc.shape[0], self.delr.shape[0])) - f.write("{0:15.6E} {1:15.6E} {2:15.6E}\n".format(self.xul, self.yul, + f.write("{0:15.6E} {1:15.6E} {2:15.6E}\n".format(self.xul * self.length_multiplier, + self.yul * self.length_multiplier, self.rotation)) for r in self.delr: @@ -984,7 +985,7 @@ def plot_array(self, a, ax=None, **kwargs): def export_array(self, filename, a, nodata=-9999, fieldname='value', **kwargs): - """Write a numpy array to Arc Ascii grid + """Write a numpy array to Arc Ascii grid or shapefile with the model reference. Parameters @@ -1077,6 +1078,9 @@ def export_array(self, filename, a, nodata=-9999, Affine.rotation(self.rotation) * \ Affine.scale(dxdy, -dxdy) + a = a.copy() + if a.dtype.name == 'int64': + a = a.astype('int32') meta = {'count': 1, 'width': a.shape[1], 'height': a.shape[0],