def setup_rfdir(self, path, num_procs):
"""
Creates the directory path and copies required files from
``self.base_dir`` into path:type path: string
:param path: folder_name
:param num_procs: number of processors per :program:`ADCIRC` run
"""
mkdir(path)
copy(self.save_dir+'/fort.13', path)
# crete sybolic links from fort.* files to path
inputs1 = glob.glob(self.grid_dir+'/fort.1*')
inputs2 = glob.glob(self.grid_dir+'/fort.2*')
inputs0 = glob.glob(self.grid_dir+'/fort.01*')
inputs = inputs0 + inputs1 + inputs2
if self.grid_dir+'/fort.13' in inputs:
inputs.remove(self.grid_dir+'/fort.13')
if not self.grid_dir+'/fort.019' in inputs:
if self.grid_dir+'/fort.015' in inputs:
inputs.remove(self.grid_dir+'/fort.015')
for fid in inputs:
if os.path.exists(path+'/'+fid.rpartition('/')[-1]):
os.remove(path+'/'+fid.rpartition('/')[-1])
os.symlink(fid, path+'/'+fid.rpartition('/')[-1])
#copy(fid, path+'/'+fid.rpartition('/')[-1])
if not os.path.exists(path+'/adcprep'):
os.symlink(self.base_dir+'/adcprep', path+'/adcprep')
prep.write_1(path, num_procs)
prep.write_2(path, num_procs)
prep.write_5(path, num_procs)
def make_plots(self,
points,
domain,
save=True,
show=False,
bathymetry=False):
"""
Plots ``mesh``, ``station_locations``, ``basis_functions``,
``random_fields``, ``mean_field``, ``station_data``, and
save in save_dir/figs
.. todo:: this uses bv_array everywhere. I might want to change this
later when I go to the nested mesh approach
"""
mkdir(self.save_dir + '/figs')
domain.get_Triangulation(self.save_dir, save, show)
domain.plot_bathymetry(self.save_dir, save, show)
domain.plot_station_locations(self.save_dir, bathymetry, save, show)
bv_array = tmm.get_basis_vec_array(self.basis_dir)
self.plot_basis_functions(domain, bv_array, save, show)
self.plot_random_fields(domain, points, bv_array, save, show)
self.plot_mean_field(domain, points, bv_array, save, show)
self.plot_station_data(save, show)
def setup_rfdir(self, path, num_procs):
"""
Creates the directory path and copies required files from
``self.base_dir`` into path:type path: string
:param path: folder_name
:param num_procs: number of processors per :program:`ADCIRC` run
"""
mkdir(path)
copy(self.save_dir + '/fort.13', path)
# crete sybolic links from fort.* files to path
inputs1 = glob.glob(self.grid_dir + '/fort.1*')
inputs2 = glob.glob(self.grid_dir + '/fort.2*')
inputs0 = glob.glob(self.grid_dir + '/fort.01*')
inputs = inputs0 + inputs1 + inputs2
if self.grid_dir + '/fort.13' in inputs:
inputs.remove(self.grid_dir + '/fort.13')
if not self.grid_dir + '/fort.019' in inputs:
if self.grid_dir + '/fort.015' in inputs:
inputs.remove(self.grid_dir + '/fort.015')
for fid in inputs:
if os.path.exists(path + '/' + fid.rpartition('/')[-1]):
os.remove(path + '/' + fid.rpartition('/')[-1])
os.symlink(fid, path + '/' + fid.rpartition('/')[-1])
#copy(fid, path+'/'+fid.rpartition('/')[-1])
if not os.path.exists(path + '/adcprep'):
os.symlink(self.base_dir + '/adcprep', path + '/adcprep')
prep.write_1(path, num_procs)
prep.write_2(path, num_procs)
prep.write_5(path, num_procs)
def get_Triangulation(domain, path=None, save=True, show=False, ics=1,
ext='.png'):
"""
:param domain: :class:`~polyadcirc.run_framework.domain`
:type path: string or None
:param path: directory to store plots
:type save: boolean
:param save: flag for whether or not to save plots
:type show: boolean
:param show: flag for whether or not to show plots
:param int ics: coordinate system (1 cartisian, 2 polar)
:param string ext: file extesion
:returns: :class:`matplotlib.tri.Triangulation`
"""
x = np.array([n.x for n in domain.node.itervalues()])
y = np.array([n.y for n in domain.node.itervalues()])
triangles = np.array([e-1 for e in domain.element.itervalues()])
triangulation = tri.Triangulation(x, y, triangles)
plt.figure()
if path == None:
path = os.getcwd()
if not os.path.exists(path+'/figs'):
fm.mkdir(path+'/figs')
if save or show:
plt.triplot(triangulation, 'g-')
plt.title('grid')
plt.gca().set_aspect('equal')
add_2d_axes_labels(ics)
save_show(path+'/figs/grid', save, show, ext)
domain.triangulation = triangulation
return triangulation
def setup_landuse_folder(self, class_num, manningsn_value=1,
folder_name=None):
"""
Set up a single landuse with name landuse_class_num
:param int class_num: land classification number for this folder
:param int manningsn_value: Manning's *n* value for this land
classification
:param string folder_name: folder name relative to ``self.base_dir``
:rtype: string
:returns: file name of bash script for this land class
"""
if folder_name is None:
folder_name = 'landuse_'+'{:=02d}'.format(class_num)
print 'Setting up folder -- '+folder_name+'...'
# create a folder for this land-use classification
fm.mkdir(os.path.join(self.basis_dir, folder_name))
# cp self.file_name folder_name
fm.copy(os.path.join(self.basis_dir, self.file_name),
os.path.join(self.basis_dir, folder_name))
# create *.in files
self.create_griddata_input_files(folder_name)
# create *.sh files
script_name = self.create_bash_script(folder_name)
# create the *.table file needed for grid_all_data
self.setup_tables_single_value(class_num, manningsn_value,
os.path.join(self.basis_dir,
folder_name))
return script_name
def make_plots(self,
points,
domain,
save=True,
show=False,
bathymetry=False,
ext='.eps',
ics=2):
"""
Plots ``mesh``, ``station_locations``, ``basis_functions``,
``random_fields``, ``mean_field``, ``station_data``, and
save in save_dir/figs
"""
mkdir(os.path.join(self.save_dir, 'figs'))
domain.get_Triangulation(self.save_dir, save, show, ext, ics)
domain.plot_bathymetry(self.save_dir, save, show, ext, ics)
domain.plot_station_locations(self.save_dir, bathymetry, save, show,
ext, ics)
bv_dict = tmm.get_basis_vectors(self.basis_dir)
self.plot_basis_functions(domain,
tmm.get_basis_vec_array(self.basis_dir),
save, show, ext, ics)
self.plot_random_fields(domain, points, bv_dict, save, show, ext, ics)
self.plot_mean_field(domain, points, bv_dict, save, show, ext, ics)
self.plot_station_data(save, show, ext)
def nts_line_data(nts_data,
keys=None,
path=None,
save=True,
show=False,
ext='.png'):
"""
Plot the non timeseries data in ``data`` with ``points`` as the x axis. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys.
.. note:: This only applies to 1D nts data (``irtype`` = 1)
:type nts_data: :class:`dict`
:param nts_data: ``nts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param list keys: list of types of ADCIRC output data to plot
:type path: string or None
:param path: directory to store plots
:type save: bool
:param save: flag for whether or not to save plots
:type show: bool
:param show: flag for whether or not to show plots
:param string ext: file extension
"""
if path is None:
path = os.getcwd()
if keys is None:
keys = nts_data.keys()
s_keys = list()
for k in keys:
if not (f15.filetype[k][0]) and f15.filetype[k][1] == 1:
s_keys.append(k)
keys = s_keys
fm.mkdir(os.path.join(path, 'figs', 'nts'))
for k in keys:
fig = plt.figure()
x = np.arange(nts_data[k].shape[0])
ax = fig.add_subplot(111)
ax.set_xlim((np.min(x), np.max(x)))
ax.set_ylim((np.min(nts_data[k]), np.max(nts_data[k])))
segs = list()
for j in xrange(nts_data[k].shape[1]):
segs.append(zip(x, nts_data[k][..., j]))
line_segs = LineCollection(segs, linestyles='solid')
line_segs.set_array(np.arange(nts_data[k].shape[1]))
ax.add_collection(line_segs)
colorbar(line_segs)
fig.title(k)
fig.xlabel('node number')
save_show(os.path.join(path, 'figs', 'nts', k), save, show, ext)
def nts_line_data(nts_data, keys=None, path=None, save=True, show=False,
ext='.png'):
"""
Plot the non timeseries data in ``data`` with ``points`` as the x axis. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys.
.. note:: This only applies to 1D nts data (``irtype`` = 1)
:type nts_data: :class:`dict`
:param nts_data: ``nts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param list() keys: list of types of ADCIRC output data to plot
:type path: string or None
:param path: directory to store plots
:type save: boolean
:param save: flag for whether or not to save plots
:type show: boolean
:param show: flag for whether or not to show plots
:param string ext: file extension
"""
if path == None:
path = os.getcwd()
if keys == None:
keys = nts_data.keys()
s_keys = list()
for k in keys:
if not(f15.filetype[k][0]) and f15.filetype[k][1] == 1:
s_keys.append(k)
keys = s_keys
fm.mkdir(path+'/figs/nts')
for k in keys:
fig = plt.figure()
x = np.arange(nts_data[k].shape[0])
ax = fig.add_subplot(111)
ax.set_xlim((np.min(x),np.max(x)))
ax.set_ylim((np.min(nts_data[k]),np.max(nts_data[k])))
segs = list()
for j in xrange(nts_data[k].shape[1]):
segs.append(zip(x, nts_data[k][...,j]))
line_segs = LineCollection(segs, linestyles = 'solid')
line_segs.set_array(np.arange(nts_data[k].shape[1]))
ax.add_collection(line_segs)
colorbar(line_segs, fig)
fig.title(k)
fig.xlabel('node number')
save_show(path+'/figs/nts/'+k, save, show, ext)
def link_random_field_directories(self):
"""
Assumes that the pre-preped ``RF_directory`` is ``RF_directory_1``.
In each of the ``RF_directory_*`` create the ``PE****`` folders copy
over the ``fort.13`` and then link the ``fort.019``, ``fort.18``,
``fort.15``, fort.14`` into the ``PE****`` folder. Also link
``metis_graph.txt`` and ``partmesh.txt`` into the ``RF_directory``.
:param int num_procs: number of processes per padcirc run
"""
# get a list of all RF_dirs
rf_dirs = glob.glob(os.path.join(self.save_dir, 'RF_directory_*'))
link_rf_files = ['metis_graph.txt', 'partmesh.txt']
# remove the first RF_dir from the list and save the name as a vairbale
prime_rf_dir = os.path.join(self.save_dir, 'RF_directory_1')
rf_dirs.remove(prime_rf_dir)
# create lists of PE directories and files to link
PE_dirs = glob.glob(os.path.join(prime_rf_dir, 'PE*'))
link_inputs = ['fort.019', 'fort.18', 'fort.15', 'fort.14']
if not os.path.exists(os.path.join(prime_rf_dir, 'fort.019')):
link_inputs.remove('fort.019')
for rf_dir in rf_dirs:
# link rf files
for rf_file in link_rf_files:
if os.path.exists(os.path.join(rf_dir, rf_file)):
os.remove(os.path.join(rf_dir, rf_file))
os.symlink(os.path.join(prime_rf_dir, rf_file),
os.path.join(rf_dir, rf_file))
for PE_dir in PE_dirs:
# create the PE* directories
my_PE_dir = os.path.join(rf_dir, os.path.basename(PE_dir))
if not os.path.exists(my_PE_dir):
mkdir(my_PE_dir)
# link files into the PE* directories
for l_input in link_inputs:
if os.path.exists(os.path.join(my_PE_dir, l_input)):
os.remove(os.path.join(my_PE_dir, l_input))
os.symlink(os.path.join(PE_dir, l_input),
os.path.join(my_PE_dir, l_input))
# copy fort.13 into the PE* directories
if os.path.exists(os.path.join(my_PE_dir, 'fort.13')):
os.remove(os.path.join(my_PE_dir, 'fort.13'))
shutil.copy(os.path.join(PE_dir, 'fort.13'),
os.path.join(my_PE_dir, 'fort.13'))
def link_random_field_directories(self):
"""
Assumes that the pre-preped ``RF_directory`` is ``RF_directory_1``.
In each of the ``RF_directory_*`` create the ``PE****`` folders copy
over the ``fort.13`` and then link the ``fort.019``, ``fort.18``,
``fort.15``, fort.14`` into the ``PE****`` folder. Also link
``metis_graph.txt`` and ``partmesh.txt`` into the ``RF_directory``.
:param int num_procs: number of processes per padcirc run
"""
# get a list of all RF_dirs
rf_dirs = glob.glob(os.path.join(self.save_dir, 'RF_directory_*'))
link_rf_files = ['metis_graph.txt', 'partmesh.txt']
# remove the first RF_dir from the list and save the name as a vairbale
prime_rf_dir = os.path.join(self.save_dir, 'RF_directory_1')
rf_dirs.remove(prime_rf_dir)
# create lists of PE directories and files to link
PE_dirs = glob.glob(os.path.join(prime_rf_dir, 'PE*'))
link_inputs = ['fort.019', 'fort.18', 'fort.15', 'fort.14']
if not os.path.exists(os.path.join(prime_rf_dir, 'fort.019')):
link_inputs.remove('fort.019')
for rf_dir in rf_dirs:
# link rf files
for rf_file in link_rf_files:
if os.path.exists(os.path.join(rf_dir, rf_file)):
os.remove(os.path.join(rf_dir, rf_file))
os.symlink(os.path.join(prime_rf_dir, rf_file),
os.path.join(rf_dir, rf_file))
for PE_dir in PE_dirs:
# create the PE* directories
my_PE_dir = os.path.join(rf_dir, os.path.basename(PE_dir))
if not os.path.exists(my_PE_dir):
mkdir(my_PE_dir)
# link files into the PE* directories
for l_input in link_inputs:
if os.path.exists(os.path.join(my_PE_dir, l_input)):
os.remove(os.path.join(my_PE_dir, l_input))
os.symlink(os.path.join(PE_dir, l_input),
os.path.join(my_PE_dir, l_input))
# copy fort.13 into the PE* directories
if os.path.exists(os.path.join(my_PE_dir, 'fort.13')):
os.remove(os.path.join(my_PE_dir, 'fort.13'))
shutil.copy(os.path.join(PE_dir, 'fort.13'),
os.path.join(my_PE_dir, 'fort.13'))
def __init__(self, path, node_num=0, element_num=0,
node=None, element=None):
"""
Initialization
"""
super(subdomain, self).__init__(path, node_num, element_num, node,
element)
# figure out where the script dir for the ncsu subdomain code is
for sys_path in sys.path:
potential_file_list = glob.glob(os.path.join(sys_path, 'py'))
if potential_file_list:
self.script_dir = potential_file_list[0]
break
fm.mkdir(path)
#: flag for shape of subdomain (0 ellipse, 1 circle)
self.flag = None
def setup_rfdir(self, path, num_procs):
"""
Creates the directory path and copies required files from
``self.base_dir`` into
:param string path: folder_name
:param int num_procs: number of processors per :program:`ADCIRC` run
"""
mkdir(path)
copy(os.path.join(self.save_dir, 'fort.13'), path)
# crete sybolic links from fort.* files to path
inputs1 = glob.glob(os.path.join(self.grid_dir, 'fort.1*'))
inputs2 = glob.glob(os.path.join(self.grid_dir, 'fort.2*'))
inputs0 = glob.glob(os.path.join(self.grid_dir, 'fort.01*'))
inputs = inputs0 + inputs1 + inputs2
if os.path.join(self.grid_dir, 'fort.13') in inputs:
inputs.remove(os.path.join(self.grid_dir, 'fort.13'))
if not os.path.join(self.grid_dir, 'fort.019') in inputs:
if os.path.join(self.grid_dir, 'fort.015') in inputs:
inputs.remove(os.path.join(self.grid_dir, 'fort.015'))
else:
sub_files = ['bv.nodes', 'py.140', 'py.141']
sub_files = [os.path.join(self.grid_dir, sf) for sf in sub_files]
inputs.extend(sub_files)
for fid in inputs:
rf_fid = os.path.join(path, fid.rpartition('/')[-1])
if os.path.exists(rf_fid):
if os.path.islink(rf_fid):
os.unlink(rf_fid)
else:
os.remove(rf_fid)
os.symlink(fid, rf_fid)
if not os.path.exists(os.path.join(path, 'padcirc')):
os.symlink(os.path.join(self.base_dir, 'padcirc'),
os.path.join(path, 'padcirc'))
if not os.path.exists(os.path.join(path, 'adcprep')):
os.symlink(os.path.join(self.base_dir, 'adcprep'),
os.path.join(path, 'adcprep'))
prep.write_1(path, num_procs)
prep.write_2(path, num_procs)
prep.write_5(path, num_procs)
def setup_rfdir(self, path, num_procs):
"""
Creates the directory path and copies required files from
``self.base_dir`` into
:param string path: folder_name
:param int num_procs: number of processors per :program:`ADCIRC` run
"""
mkdir(path)
copy(os.path.join(self.save_dir, 'fort.13'), path)
# crete sybolic links from fort.* files to path
inputs1 = glob.glob(os.path.join(self.grid_dir, 'fort.1*'))
inputs2 = glob.glob(os.path.join(self.grid_dir, 'fort.2*'))
inputs0 = glob.glob(os.path.join(self.grid_dir, 'fort.01*'))
inputs = inputs0 + inputs1 + inputs2
if os.path.join(self.grid_dir, 'fort.13') in inputs:
inputs.remove(os.path.join(self.grid_dir, 'fort.13'))
if not os.path.join(self.grid_dir, 'fort.019') in inputs:
if os.path.join(self.grid_dir, 'fort.015') in inputs:
inputs.remove(os.path.join(self.grid_dir, 'fort.015'))
else:
sub_files = ['bv.nodes', 'py.140', 'py.141']
sub_files = [os.path.join(self.grid_dir, sf) for sf in sub_files]
inputs.extend(sub_files)
for fid in inputs:
rf_fid = os.path.join(path, fid.rpartition('/')[-1])
if os.path.exists(rf_fid):
if os.path.islink(rf_fid):
os.unlink(rf_fid)
else:
os.remove(rf_fid)
os.symlink(fid, rf_fid)
if not os.path.exists(os.path.join(path, 'padcirc')):
os.symlink(os.path.join(self.base_dir, 'padcirc'),
os.path.join(path, 'padcirc'))
if not os.path.exists(os.path.join(path, 'adcprep')):
os.symlink(os.path.join(self.base_dir, 'adcprep'),
os.path.join(path, 'adcprep'))
prep.write_1(path, num_procs)
prep.write_2(path, num_procs)
prep.write_5(path, num_procs)
def make_plots(self, points, domain, save=True, show=False,
bathymetry=False, ext='.eps', ics=2):
"""
Plots ``mesh``, ``station_locations``, ``basis_functions``,
``random_fields``, ``mean_field``, ``station_data``, and
save in save_dir/figs
"""
mkdir(os.path.join(self.save_dir, 'figs'))
domain.get_Triangulation(self.save_dir, save, show, ext, ics)
domain.plot_bathymetry(self.save_dir, save, show, ext, ics)
domain.plot_station_locations(self.save_dir, bathymetry, save, show,
ext, ics)
bv_dict = tmm.get_basis_vectors(self.basis_dir)
self.plot_basis_functions(domain,
tmm.get_basis_vec_array(self.basis_dir),
save, show, ext, ics)
self.plot_random_fields(domain, points, bv_dict, save, show, ext, ics)
self.plot_mean_field(domain, points, bv_dict, save, show, ext, ics)
self.plot_station_data(save, show, ext)
def get_Triangulation(domain,
path=None,
save=True,
show=False,
ics=1,
ext='.png',
title=False):
"""
:param domain: :class:`~polyadcirc.run_framework.domain`
:type path: string or None
:param path: directory to store plots
:type save: bool
:param save: flag for whether or not to save plots
:type show: bool
:param show: flag for whether or not to show plots
:param int ics: coordinate system (1 cartisian, 2 polar)
:param string ext: file extesion
:rtype: :class:`matplotlib.tri.Triangulation`
:returns: triangulation of ``domain``
"""
x = np.array([n.x for n in domain.node.itervalues()])
y = np.array([n.y for n in domain.node.itervalues()])
triangles = np.array([e - 1 for e in domain.element.itervalues()])
triangulation = tri.Triangulation(x, y, triangles)
plt.figure()
if path is None:
path = os.getcwd()
fig_folder = os.path.join(path, 'figs')
if not os.path.exists(fig_folder):
fm.mkdir(fig_folder)
if save or show:
plt.triplot(triangulation, 'g-')
if title:
plt.title('grid')
plt.gca().set_aspect('equal')
add_2d_axes_labels(ics=ics)
save_show(os.path.join(fig_folder, 'grid'), save, show, ext)
domain.triangulation = triangulation
return triangulation
def setup_folder(self, folder_name = 'temp'):
"""
Set up a single folder with name folder_name
:param string folder_name: folder name relative to ``self.base_dir``
:rtype: string
:returns: file name of bash script for this land class
"""
print 'Setting up folder -- '+folder_name+'...'
# create a folder for this land-use classification
fm.mkdir(self.basis_dir+'/'+folder_name)
# cp self.file_name folder_name
fm.copy(self.basis_dir+'/'+self.file_name, self.basis_dir+'/'+folder_name)
# create *.in files
self.create_griddata_input_files(folder_name)
# create *.sh files
script_name = self.create_bash_script(folder_name)
# create the *.table file needed for grid_all_data
self.setup_tables(self.basis_dir+'/'+folder_name)
return script_name
def compare(basis_dir=None, default=0.012):
"""
Create a set of diagnostic plots in basis_dir/figs
:param string basis_dir: directory containing the test folder and landuse
folders
:param float default: default Manning's *n*
"""
if basis_dir is None:
basis_dir = os.getcwd()
tables = tm.read_tables(os.path.join(basis_dir, 'test'))
domain = dom.domain(basis_dir)
domain.read_spatial_grid()
fm.mkdir(os.path.join(basis_dir, 'figs'))
old_files = glob.glob(os.path.join(basis_dir, 'figs', '*.png'))
for fid in old_files:
os.remove(fid)
domain.get_Triangulation(path=basis_dir)
original = f13.read_nodal_attr_dict(os.path.join(basis_dir, 'test'))
original = tmm.dict_to_array(original, default, domain.node_num)
weights = np.array(tables[0].land_classes.values())
lim = (np.min(weights), np.max(weights))
bv_dict = tmm.get_basis_vectors(basis_dir)
combo = tmm.combine_basis_vectors(weights, bv_dict, default,
domain.node_num)
bv_array = tmm.get_basis_vec_array(basis_dir,
domain.node_num)
plt.basis_functions(domain, bv_array, path=basis_dir)
plt.field(domain, original, 'original', clim=lim, path=basis_dir)
plt.field(domain, combo, 'reconstruction', clim=lim, path=basis_dir)
plt.field(domain, original-combo, 'difference', path=basis_dir)
combo_array = tmm.combine_bv_array(weights, bv_array)
plt.field(domain, combo_array, 'combo_array', clim=lim, path=basis_dir)
plt.field(domain, original-combo_array, 'diff_ori_array', path=basis_dir)
plt.field(domain, combo-combo_array, 'diff_com_array', path=basis_dir)
combo_bv = tmm.combine_basis_vectors(np.ones(weights.shape),
bv_dict, default, domain.node_num)
plt.field(domain, combo_bv, 'combo_bv', path=basis_dir)
def make_plots(self, points, domain, save=True, show=False,
bathymetry=False):
"""
Plots ``mesh``, ``station_locations``, ``basis_functions``,
``random_fields``, ``mean_field``, ``station_data``, and
save in save_dir/figs
.. todo:: this uses bv_array everywhere. I might want to change this
later when I go to the nested mesh approach
"""
mkdir(self.save_dir+'/figs')
domain.get_Triangulation(self.save_dir, save, show)
domain.plot_bathymetry(self.save_dir, save, show)
domain.plot_station_locations(self.save_dir, bathymetry, save, show)
bv_array = tmm.get_basis_vec_array(self.basis_dir)
self.plot_basis_functions(domain, bv_array, save, show)
self.plot_random_fields(domain, points, bv_array, save, show)
self.plot_mean_field(domain, points, bv_array, save, show)
self.plot_station_data(save, show)
def __init__(self,
path,
node_num=0,
element_num=0,
node=None,
element=None):
"""
Initialization
"""
super(subdomain, self).__init__(path, node_num, element_num, node,
element)
# figure out where the script dir for the ncsu subdomain code is
for sys_path in sys.path:
potential_file_list = glob.glob(os.path.join(sys_path, 'py'))
if potential_file_list:
self.script_dir = potential_file_list[0]
break
fm.mkdir(path)
#: flag for shape of subdomain (0 ellipse, 1 circle)
self.flag = None
def station_data(ts_data,
time_obs,
keys=None,
stations=None,
path=None,
save=True,
show=False,
ext='.png'):
"""
Plot the station data in ``ts_data`` with ``time_obs`` as the x axis. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys.
:type ts_data: :class:`dict`
:param ts_data: ``ts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:type time_obs: :class:`dict`
:param time_obs: ``time_obs`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param list keys: list of types of ADCIRC output data to plot
:param dict stations: dictonary of lists of stations to plot
:type path: string or None
:param path: directory to store plots
:type save: bool
:param save: flag for whether or not to save plots
:type show: bool
:param show: flag for whether or not to show plots
:param string ext: file extension
"""
if path is None:
path = os.getcwd()
if keys is None:
keys = ts_data.keys()
s_keys = list()
for k in keys:
if f15.filetype[k][0]:
s_keys.append(k)
keys = s_keys
for k in keys:
fm.mkdir(os.path.join(path, 'figs', k))
if stations[k] is None:
stations = xrange(ts_data[k].shape[0])
for i in stations:
fig = plt.figure()
x = time_obs[k]
num_plts = f15.filetype[k][1]
if num_plts == 1:
ax = fig.add_subplot(111)
ax.set_xlim((np.min(x), np.max(x)))
ax.set_ylim((np.min(ts_data[k]), np.max(ts_data[k])))
segs = list()
for j in xrange(ts_data[k].shape[2]):
segs.append(zip(x, ts_data[k][i, ..., j]))
line_segs = LineCollection(segs, linestyles='solid')
line_segs.set_array(np.arange(ts_data[k].shape[2]))
ax.add_collection(line_segs)
ax.set_xlabel('time')
colorbar(line_segs)
elif num_plts == 2:
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.set_xlim((np.min(x), np.max(x)))
ax1.set_ylim(
(np.min(ts_data[k][:, :,
0, :]), np.max(ts_data[k][:, :, 0, :])))
ax2.set_xlim((np.min(x), np.max(x)))
ax2.set_ylim(
(np.min(ts_data[k][:, :,
1, :]), np.max(ts_data[k][:, :, 1, :])))
segs1 = list()
segs2 = list()
for j in xrange(ts_data[k].shape[-1]):
segs1.append(zip(x, ts_data[k][i, :, 0, j]))
segs2.append(zip(x, ts_data[k][i, :, 1, j]))
line_segs1 = LineCollection(segs1, linestyles='solid')
line_segs1.set_array(np.arange(ts_data[k].shape[-1]))
ax1.add_collection(line_segs1)
ax1.set_ylabel('u (m/s)')
ax1.set_xlabel('time')
line_segs2 = LineCollection(segs2, linestyles='solid')
line_segs2.set_array(np.arange(ts_data[k].shape[-1]))
ax2.add_collection(line_segs2)
ax2.set_xlabel('time')
ax2.set_ylabel('v (m/s)')
colorbar(line_segs2)
save_show(os.path.join(path, 'figs', k, 'station' + str(i)), save,
show, ext)
def ts_pcolor(ts_data,
time_obs,
domain,
keys=None,
points=None,
path=None,
save=True,
show=False,
ics=1,
ext='.png'):
"""
Plot the timeseries data in ``ts_data`` as a series of 2D color plots. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys and which runs in points.
.. note:: This only applies to 1D nts data (``irtype`` = 1)
:type ts_data: :class:`dict`
:param ts_data: ``ts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:type time_obs: :class:``dict``
:param time_obs: ``time_obs`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param domain: :class:`~polyadcirc.run_framework.domain`
:param list keys: list of types of ADCIRC output data to plot
:param list points: list of runs or points to plot
:type path: string or None
:param path: directory to store plots
:type save: bool
:param save: flag for whether or not to save plots
:type show: bool
:param show: flag for whether or not to show plots
:param int ics: polar coordinate option (1 = cart coords, 2 = polar)
:param string ext: file extension
"""
if path is None:
path = os.getcwd()
if keys is None:
keys = ts_data.keys()
s_keys = list()
for k in keys:
if not (f15.filetype[k][0]) and f15.filetype[k][1] == 1:
s_keys.append(k)
keys = s_keys
if points is None:
points = (0, ts_data[keys[0]].shape[-1] - 1)
fm.mkdir(os.path.join(path, 'figs', 'ts'))
for k in keys:
fig_folder = os.path.join(path, 'figs', 'ts', k)
fm.mkdir(fig_folder)
clim = (np.min(ts_data[k]), np.max(ts_data[k]))
for j in points:
subfig_folder = os.path.join(fig_folder, 'run' + str(j))
fm.mkdir(subfig_folder)
for i, t in enumerate(time_obs[k]):
plt.figure()
add_2d_axes_labels(ics=ics)
plt.tripcolor(domain.triangulation,
ts_data[k][:, i, j],
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
colorbar()
plt.title('time = ' + str(t))
save_show(os.path.join(subfig_folder, str(i)), save, show, ext)
if len(points) == 2:
fm.mkdir(os.path.join(path, 'figs', 'ts', k, 'diff'))
diff = ts_data[k][:, :, points[-1]] - ts_data[k][:, :, points[0]]
cdiff = (np.min(diff), np.max(diff))
for i, t in enumerate(time_obs[k]):
plt.figure(figsize=(6, 9))
plt.subplot(311)
plt.tripcolor(domain.triangulation,
ts_data[k][:, i, points[0]],
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + str(points[0]))
plt.subplot(312)
plt.tripcolor(domain.triangulation,
ts_data[k][:, i, points[-1]],
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + str(points[-1]))
plt.subplot(313)
plt.tripcolor(domain.triangulation,
diff[:, i],
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(cdiff[0], cdiff[1])
cb = colorbar()
add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + 'diff')
plt.tight_layout()
plt.suptitle('time = ' + str(t))
save_show(os.path.join(path, 'figs', 'ts', k, 'diff', str(i)),
save, show, ext)
def ts_quiver(ts_data,
time_obs,
domain,
keys=None,
points=None,
path=None,
save=True,
show=False,
ics=1,
ext='.png'):
"""
Plot the timeseries data in ``ts_data`` as a series of 2D quiver plots. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys and which runs in points.
.. note:: This only applies to 2D nts data (``irtype`` = 2)
:type ts_data: :class:`dict`
:param ts_data: ``ts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:type time_obs: :class:`dict`
:param time_obs: ``time_obs`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param domain: :class:`~polyadcirc.run_framework.domain`
:param list keys: list of types of ADCIRC output data to plot
:param list points: list of runs or points to plot
:type path: string or None
:param path: directory to store plots
:type save: bool
:param save: flag for whether or not to save plots
:type show: bool
:param show: flag for whether or not to show plots
:param int ics: polar coordinate option (1 = cart coords, 2 = polar
:param string ext: file extension
.. todo:: maybe add plt.streamplot, but this requires a regularly spaced
grid and is therefore more expenseive to do
"""
if path is None:
path = os.getcwd()
if keys is None:
keys = ts_data.keys()
s_keys = list()
for k in keys:
if not f15.filetype[k][0] and f15.filetype[k][1] == 2:
s_keys.append(k)
keys = s_keys
if points is None:
points = (0, ts_data[keys[0]].shape[-1] - 1)
fig_folder = os.path.join(path, 'figs', 'ts_quiver')
fm.mkdir(fig_folder)
x = domain.array_x()
y = domain.array_y()
for k in keys:
fm.mkdir(os.path.join(fig_folder, k))
mag = np.sqrt(
pow(ts_data[k][:, :, 0, :], 2) + pow(ts_data[k][:, :, 1, :], 2))
clim = (np.min(mag), np.max(mag))
for j in points:
fm.mkdir(os.path.join(fig_folder, k, 'run' + str(j)))
for i, t in enumerate(time_obs[k]):
plt.figure()
add_2d_axes_labels(ics=ics)
plt.quiver(x, y, ts_data[k][:, i, 0, j],
ts_data[k][:, i, 1, j], mag[:, i, j])
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
colorbar()
plt.title('time = ' + str(t))
save_show(os.path.join(fig_folder, k, 'run' + str(j), str(i)),
save, show, ext)
if len(points) == 2:
fm.mkdir(os.path.join(fig_folder, 'diff'))
diff = ts_data[k][:, :, :, points[-1]] - ts_data[k][:, :, :,
points[0]]
mag_diff = np.sqrt(pow(diff[:, :, 0], 2) + pow(diff[:, :, 1], 2))
cdiff = (np.min(mag_diff), np.max(mag_diff))
for i, t in enumerate(time_obs[k]):
plt.figure(figsize=(6, 9))
ax1 = plt.subplot(311)
plt.quiver(x, y, ts_data[k][:, i, 0, points[0]],
ts_data[k][:, i, 1, points[0]], mag[:, i,
points[0]])
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + str(points[0]))
ax2 = plt.subplot(312)
plt.quiver(x, y, ts_data[k][:, i, 0, points[-1]],
ts_data[k][:, i, 1, points[-1]], mag[:, i,
points[-1]])
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + str(points[-1]))
ax3 = plt.subplot(313)
plt.quiver(x, y, diff[:, i, 0], diff[:, i, 1], mag_diff[:, i])
plt.gca().set_aspect('equal')
plt.clim(cdiff[0], cdiff[1])
cb = colorbar()
add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + 'diff')
plt.tight_layout()
plt.suptitle('time = ' + str(t))
save_show(os.path.join(fig_folder, 'diff', str(i)), save, show,
ext)
def nts_contour(nts_data,
domain,
keys=None,
points=None,
path=None,
save=True,
show=False,
ics=1,
ext='.png'):
"""
Plot the non timeseries data in ``nts_data`` as a set of 2D contour plots.
To plot only a subset of this data list which ADCIRC Output types to plot
in keys, and which runs in ``points``.
.. note:: This only applies to 1D nts data (``irtype`` = 1)
:type nts_data: :class:`dict`
:param nts_data: ``nts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param domain: :class:`~polyadcirc.run_framework.domain`
:param list keys: list of types of ADCIRC output data to plot
:param list points: list of runs to plot
:type path: string or None
:param path: directory to store plots
:type save: bool
:param save: flag for whether or not to save plots
:type show: bool
:param show: flag for whether or not to show plots
:param int ics: polar coordinate option (1 = cart coords, 2 = polar
:param string ext: file extension
"""
if path is None:
path = os.getcwd()
if keys is None:
keys = nts_data.keys()
s_keys = list()
for k in keys:
if not f15.filetype[k][0] and f15.filetype[k][1] == 1:
s_keys.append(k)
keys = s_keys
if points is None:
points = (0, nts_data[keys[0]].shape[-1] - 1)
fig_folder = os.path.join(path, 'figs', 'nts_contour')
fm.mkdir(fig_folder)
for k in keys:
for j in points:
plt.figure()
plt.tricontour(domain.triangulation,
nts_data[k][:, j],
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
colorbar()
add_2d_axes_labels(ics=ics)
plt.title(k + '_' + str(j))
save_show(os.path.join(fig_folder, k + '_' + str(j) + '_contour'),
save, show, ext)
if len(points) == 2:
plt.figure(figsize=(6, 9))
plt.subplot(311)
plt.tricontour(domain.triangulation,
nts_data[k][:, points[0]],
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
cb = colorbar()
#add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + str(points[0]))
plt.subplot(312)
plt.tricontour(domain.triangulation,
nts_data[k][:, points[-1]],
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
cb = colorbar()
#add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + str(points[-1]))
plt.subplot(313)
diff = nts_data[k][:, points[-1]] - nts_data[k][:, points[0]]
plt.tricontour(domain.triangulation,
diff,
shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
cb = colorbar()
add_2d_axes_labels(ics=ics)
cb.set_label(k + '_' + 'diff')
save_show(os.path.join(fig_folder, k + '_diff_contour'), save,
show, ext)
def nts_contour(nts_data, domain, keys=None, points=None, path=None,
save=True, show=False, ics=1, ext='.png'):
"""
Plot the non timeseries data in ``nts_data`` as a set of 2D contour plots.
To plot only a subset of this data list which ADCIRC Output types to plot
in keys, and which runs in ``points``.
.. note:: This only applies to 1D nts data (``irtype`` = 1)
:type nts_data: :class:`dict`
:param nts_data: ``nts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param domain: :class:`~polyadcirc.run_framework.domain`
:param list() keys: list of types of ADCIRC output data to plot
:param list() points: list of runs to plot
:type path: string or None
:param path: directory to store plots
:type save: boolean
:param save: flag for whether or not to save plots
:type show: boolean
:param show: flag for whether or not to show plots
:param int ics: polar coordinate option (1 = cart coords, 2 = polar
:param string ext: file extension
"""
if path == None:
path = os.getcwd()
if keys == None:
keys = nts_data.keys()
s_keys = list()
for k in keys:
if not f15.filetype[k][0] and f15.filetype[k][1] == 1:
s_keys.append(k)
keys = s_keys
if points == None:
points = (0, nts_data[keys[0]].shape[-1]-1)
fm.mkdir(path+'/figs/nts_contour')
for k in keys:
for j in points:
plt.figure()
plt.tricontour(domain.triangulation, nts_data[k][:,j],
shading='gouraud', cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
colorbar()
add_2d_axes_labels(ics)
plt.title(k+'_'+str(j))
save_show(path+'/figs/nts_contour/'+k+'_'+str(j)+'_contour', save,
show, ext)
if len(points) == 2:
plt.figure(figsize=(6,9))
plt.subplot(311)
plt.tricontour(domain.triangulation, nts_data[k][:,points[0]],
shading='gouraud', cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
cb = colorbar()
#add_2d_axes_labels(ics)
cb.set_label(k+'_'+str(points[0]))
plt.subplot(312)
plt.tricontour(domain.triangulation, nts_data[k][:,points[-1]],
shading='gouraud', cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
cb = colorbar()
#add_2d_axes_labels(ics)
cb.set_label(k+'_'+str(points[-1]))
plt.subplot(313)
diff = nts_data[k][:,points[-1]] - nts_data[k][:,points[0]]
plt.tricontour(domain.triangulation, diff, shading='gouraud',
cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
cb = colorbar()
add_2d_axes_labels(ics)
cb.set_label(k+'_'+'diff')
save_show(path+'/figs/nts_contour/'+k+'_diff_contour', save, show,
ext)
def ts_quiver(ts_data, time_obs, domain, keys=None, points=None,
path=None, save=True, show=False, ics=1, ext='.png'):
"""
Plot the timeseries data in ``ts_data`` as a series of 2D quiver plots. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys and which runs in points.
.. note:: This only applies to 2D nts data (``irtype`` = 2)
:type ts_data: :class:`dict`
:param ts_data: ``ts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:type time_obs: :class:`dict`
:param time_obs: ``time_obs`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param domain: :class:`~polyadcirc.run_framework.domain`
:param list() keys: list of types of ADCIRC output data to plot
:param list() points: list of runs or points to plot
:type path: string or None
:param path: directory to store plots
:type save: boolean
:param save: flag for whether or not to save plots
:type show: boolean
:param show: flag for whether or not to show plots
:param int ics: polar coordinate option (1 = cart coords, 2 = polar
.. todo:: maybe add plt.streamplot, but this requires a regularly spaced
grid and is therefore more expenseive to do
"""
if path == None:
path = os.getcwd()
if keys == None:
keys = ts_data.keys()
s_keys = list()
for k in keys:
if not f15.filetype[k][0] and f15.filetype[k][1] == 2:
s_keys.append(k)
keys = s_keys
if points == None:
points = (0, ts_data[keys[0]].shape[-1]-1)
fm.mkdir(path+'/figs/ts_quiver')
x = domain.array_x()
y = domain.array_y()
for k in keys:
fm.mkdir(path+'/figs/ts_quiver/'+k)
mag = np.sqrt(pow(ts_data[k][:,:,0,:],2)+pow(ts_data[k][:,:,1,:],2))
clim = (np.min(mag),np.max(mag))
for j in points:
fm.mkdir(path+'/figs/ts_quiver/'+k+'/run'+str(j))
for i, t in enumerate(time_obs[k]):
plt.figure()
add_2d_axes_labels(ics)
plt.quiver(x, y, ts_data[k][:,i,0,j], ts_data[k][:,i,1,j],
mag[:,i,j])
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
colorbar()
plt.title('time = '+str(t))
save_show(path+'/figs/ts_quiver/'+k+'/run'+str(j)+'/'+str(i),
save, show, ext)
if len(points) == 2:
fm.mkdir(path+'/figs/ts_quiver/'+k+'/diff')
diff = ts_data[k][:,:,:,points[-1]] - ts_data[k][:,:,:,points[0]]
mag_diff = np.sqrt(pow(diff[:,:,0],2)+pow(diff[:,:,1],2))
cdiff = (np.min(mag_diff), np.max(mag_diff))
for i, t in enumerate(time_obs[k]):
plt.figure(figsize=(6,9))
ax1 = plt.subplot(311)
plt.quiver(x, y, ts_data[k][:,i,0,points[0]],
ts_data[k][:,i,1,points[0]], mag[:,i,points[0]])
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics)
cb.set_label(k+'_'+str(points[0]))
ax2 = plt.subplot(312)
plt.quiver(x, y, ts_data[k][:,i,0,points[-1]],
ts_data[k][:,i,1,points[-1]], mag[:,i,points[-1]])
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics)
cb.set_label(k+'_'+str(points[-1]))
ax3 = plt.subplot(313)
plt.quiver(x, y, diff[:,i,0], diff[:,i,1], mag_diff[:,i])
plt.gca().set_aspect('equal')
plt.clim(cdiff[0], cdiff[1])
cb = colorbar()
add_2d_axes_labels(ics)
cb.set_label(k+'_'+'diff')
plt.tight_layout()
plt.suptitle('time = '+str(t))
save_show(path+'/figs/ts_quiver/'+k+'/diff/'+str(i), save,
show, ext)
def ts_pcolor(ts_data, time_obs, domain, keys=None, points=None,
path=None, save=True, show=False, ics=1, ext='.png'):
"""
Plot the timeseries data in ``ts_data`` as a series of 2D color plots. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys and which runs in points.
.. note:: This only applies to 1D nts data (``irtype`` = 1)
:type ts_data: :class:`dict`
:param ts_data: ``ts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:type time_obs: :class:``dict``
:param time_obs: ``time_obs`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param domain: :class:`~polyadcirc.run_framework.domain`
:param list() keys: list of types of ADCIRC output data to plot
:param list() points: list of runs or points to plot
:type path: string or None
:param path: directory to store plots
:type save: boolean
:param save: flag for whether or not to save plots
:type show: boolean
:param show: flag for whether or not to show plots
:param int ics: polar coordinate option (1 = cart coords, 2 = polar)
:param string ext: file extension
"""
if path == None:
path = os.getcwd()
if keys == None:
keys = ts_data.keys()
s_keys = list()
for k in keys:
if not(f15.filetype[k][0]) and f15.filetype[k][1] == 1:
s_keys.append(k)
keys = s_keys
if points == None:
points = (0, ts_data[keys[0]].shape[-1]-1)
fm.mkdir(path+'/figs/ts')
for k in keys:
fm.mkdir(path+'/figs/ts/'+k)
clim = (np.min(ts_data[k]),np.max(ts_data[k]))
for j in points:
fm.mkdir(path+'/figs/ts/'+k+'/run'+str(j))
for i, t in enumerate(time_obs[k]):
plt.figure()
add_2d_axes_labels(ics)
plt.tripcolor(domain.triangulation, ts_data[k][:,i,j],
shading='gouraud', cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
colorbar()
plt.title('time = '+str(t))
save_show(path+'/figs/ts/'+k+'/run'+str(j)+'/'+str(i),
save, show, ext)
if len(points) == 2:
fm.mkdir(path+'/figs/ts/'+k+'/diff')
diff = ts_data[k][:,:,points[-1]] - ts_data[k][:,:,points[0]]
cdiff = (np.min(diff), np.max(diff))
for i, t in enumerate(time_obs[k]):
plt.figure(figsize=(6,9))
ax1 = plt.subplot(311)
plt.tripcolor(domain.triangulation, ts_data[k][:,i,points[0]],
shading='gouraud', cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics)
cb.set_label(k+'_'+str(points[0]))
ax2 = plt.subplot(312)
plt.tripcolor(domain.triangulation, ts_data[k][:,i,points[-1]],
shading='gouraud', cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(clim[0], clim[1])
cb = colorbar()
#add_2d_axes_labels(ics)
cb.set_label(k+'_'+str(points[-1]))
ax3 = plt.subplot(313)
plt.tripcolor(domain.triangulation, diff[:,i] ,
shading='gouraud', cmap=plt.cm.jet)
plt.gca().set_aspect('equal')
plt.clim(cdiff[0], cdiff[1])
cb = colorbar()
add_2d_axes_labels(ics)
cb.set_label(k+'_'+'diff')
plt.tight_layout()
plt.suptitle('time = '+str(t))
save_show(path+'/figs/ts/'+k+'/diff/'+str(i), save, show, ext)
# Specify run parameter folders
adcirc_dir = '/h1/lgraham/workspace'
grid_dir = adcirc_dir + '/ADCIRC_landuse/Katrina_small/inputs'
save_dir = adcirc_dir + '/ADCIRC_landuse/Katrina_small/runs/output_test'
basis_dir = adcirc_dir +'/ADCIRC_landuse/Katrina_small/landuse_basis/gap/shelf_test'
# load in the small katrina mesh
domain = dom.domain(grid_dir)
domain.update()
# load in basis vectors for domain
bv_dict = tmm.get_basis_vectors(basis_dir)
# create the shelf basis vector dictonary
shelf_limits = [0, 50] #[0, 100] [50, 100]
shelf_bv = tmm.create_shelf(domain, shelf_limits, bv_dict)
# write this out to an appropriately numbered basis vector mesh in the correct
# basis_dir
# get list of landuse folder names
folders = glob.glob(basis_dir+'/landuse_*')
# create new folder
folder_name = basis_dir+'/landuse_'+'{:=02d}'.format(len(folders))
fm.mkdir(folder_name)
# copy a fort.13 file to that folder
fm.copy(save_dir+'/fort.13', folder_name+'/fort.13')
f13.update_mann(shelf_bv, folder_name)
def station_data(ts_data, time_obs, keys=None, stations = None, path=None,
save=True, show=False, ext='.png'):
"""
Plot the station data in ``ts_data`` with ``time_obs`` as the x axis. To
plot only a subset of this data list which ADCIRC Output types to plot in
keys.
:type ts_data: :class:`dict`
:param ts_data: ``ts_data`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:type time_obs: :class:`dict`
:param time_obs: ``time_obs`` from
:class:`~polyadcirc.run_framework.random_manningsn.runSet`
:param list() keys: list of types of ADCIRC output data to plot
:param dict stations: dictonary of lists of stations to plot
:type path: string or None
:param path: directory to store plots
:type save: boolean
:param save: flag for whether or not to save plots
:type show: boolean
:param show: flag for whether or not to show plots
:param string ext: file extension
"""
if path == None:
path = os.getcwd()
if keys == None:
keys = ts_data.keys()
s_keys = list()
for k in keys:
if f15.filetype[k][0]:
s_keys.append(k)
keys = s_keys
for k in keys:
fm.mkdir(path+'/figs/'+k)
if stations[k] == None:
stations = xrange(ts_data[k].shape[0])
for i in stations:
fig = plt.figure()
x = time_obs[k]
num_plts = f15.filetype[k][1]
if num_plts == 1:
ax = fig.add_subplot(111)
ax.set_xlim((np.min(x),np.max(x)))
ax.set_ylim((np.min(ts_data[k]),np.max(ts_data[k])))
segs = list()
for j in xrange(ts_data[k].shape[2]):
segs.append(zip(x, ts_data[k][i,...,j]))
line_segs = LineCollection(segs, linestyles = 'solid')
line_segs.set_array(np.arange(ts_data[k].shape[2]))
ax.add_collection(line_segs)
ax.set_xlabel('time')
colorbar(line_segs, fig)
elif num_plts == 2:
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.set_xlim((np.min(x),np.max(x)))
ax1.set_ylim((np.min(ts_data[k][:,:,0,:]),
np.max(ts_data[k][:,:,0,:])))
ax2.set_xlim((np.min(x),np.max(x)))
ax2.set_ylim((np.min(ts_data[k][:,:,1,:]),
np.max(ts_data[k][:,:,1,:])))
segs1 = list()
segs2 = list()
for j in xrange(ts_data[k].shape[-1]):
segs1.append(zip(x, ts_data[k][i,:,0,j]))
segs2.append(zip(x, ts_data[k][i,:,1,j]))
line_segs1 = LineCollection(segs1, linestyles = 'solid')
line_segs1.set_array(np.arange(ts_data[k].shape[-1]))
ax1.add_collection(line_segs1)
ax1.set_ylabel('u (m/s)')
ax1.set_xlabel('time')
line_segs2 = LineCollection(segs2, linestyles = 'solid')
line_segs2.set_array(np.arange(ts_data[k].shape[-1]))
ax2.add_collection(line_segs2)
ax2.set_xlabel('time')
ax2.set_ylabel('v (m/s)')
colorbar(line_segs2, fig)
save_show(path+'/figs/'+k+'/station'+str(i), save, show, ext)
import polyadcirc.pyADCIRC.fort13_management as f13
import glob
# Specify run parameter folders
adcirc_dir = '/h1/lgraham/workspace'
grid_dir = adcirc_dir + '/ADCIRC_landuse/Katrina_small/inputs'
save_dir = adcirc_dir + '/ADCIRC_landuse/Katrina_small/runs/output_test'
basis_dir = adcirc_dir + '/ADCIRC_landuse/Katrina_small/landuse_basis/gap/shelf_test'
# load in the small katrina mesh
domain = dom.domain(grid_dir)
domain.update()
# load in basis vectors for domain
bv_dict = tmm.get_basis_vectors(basis_dir)
# create the shelf basis vector dictonary
shelf_limits = [0, 50]
shelf_bv = tmm.create_shelf(domain, shelf_limits, bv_dict)
# write this out to an appropriately numbered basis vector mesh in the correct
# basis_dir
# get list of landuse folder names
folders = glob.glob(basis_dir + '/landuse_*')
# create new folder
folder_name = basis_dir + '/landuse_' + '{:=02d}'.format(len(folders))
fm.mkdir(folder_name)
# copy a fort.13 file to that folder
fm.copy(save_dir + '/fort.13', folder_name + '/fort.13')
f13.update_mann(shelf_bv, folder_name)
