def save(self, node, key, val, type='data'):
'''
obj_key The variable that points to the object being stored
obj_type The type of the object being stored
id The configuration number being used
keys The variables in the object
vals and their values
'''
# If a groupname is supplied, open/create it and enter that group
if type == 'group' and pl.is_string_like(key):
try:
node = self.h5f.getNode(node, key)
except:
node = self.h5f.createGroup(node, key, filters=self.FILTERS)
# If an id is supplied, make a group for it and enter that group
elif type == 'id' and pl.is_numlike(key):
try:
node = self.h5f.getNode(node, "i%d" % key)
except:
node = self.h5f.createGroup(node,
"i%d" % key,
filters=self.FILTERS)
# When data is supplied, add it (and overwrite any data that was there before)
elif type == 'data':
if key == None:
key = 'None'
elif not pl.is_string_like(key):
error(self, 'This should not happen...')
try:
self.h5f.removeNode(node, key)
except:
pass
if issubclass(val.__class__, np.ndarray):
# Empty tuples not supported by pytables. Fix by reshaping to (1,)
if val.shape == ():
val = val.reshape((1, ))
atom = tb.Atom.from_dtype(val.dtype)
new_node = self.h5f.createCArray(node,
key,
atom,
val.shape,
filters=self.FILTERS)
new_node[:] = val[:]
else:
self.h5f.createArray(node, key, val)
else:
error(self, 'Hmm? Either \'type\' or \'key\' is of shitty format.')
node = self.h5f.root
return node
def save(self, node, key, val, type='data'):
'''
obj_key The variable that points to the object being stored
obj_type The type of the object being stored
id The configuration number being used
keys The variables in the object
vals and their values
'''
# If a groupname is supplied, open/create it and enter that group
if type=='group' and pl.is_string_like(key):
try:
node = self.h5f.getNode(node, key)
except:
node = self.h5f.createGroup(node, key, filters=self.FILTERS)
# If an id is supplied, make a group for it and enter that group
elif type=='id' and pl.is_numlike(key):
try:
node = self.h5f.getNode(node, "i%d"%key)
except:
node = self.h5f.createGroup(node, "i%d"%key, filters=self.FILTERS)
# When data is supplied, add it (and overwrite any data that was there before)
elif type=='data':
if key == None:
key = 'None'
elif not pl.is_string_like(key):
error(self, 'This should not happen...')
try:
self.h5f.removeNode(node, key)
except:
pass
if issubclass(val.__class__, np.ndarray):
# Empty tuples not supported by pytables. Fix by reshaping to (1,)
if val.shape == ():
val = val.reshape((1,))
atom = tb.Atom.from_dtype(val.dtype)
new_node = self.h5f.createCArray(node, key, atom, val.shape, filters=self.FILTERS)
new_node[:] = val[:]
else:
self.h5f.createArray(node, key, val)
else:
error(self, 'Hmm? Either \'type\' or \'key\' is of shitty format.')
node = self.h5f.root
return node
def read_text_pyfusion(files, target='^Shot .*', ph_dtype=None, plot=pl.isinteractive(), ms=100, hold=0, debug=0, quiet=1, exception = Exception):
""" Accepts a file or a list of files, returns a list of structured arrays
See merge ds_list to merge and convert types (float -> pyfusion.prec_med
"""
st = seconds(); last_update=seconds()
file_list = files
if len(np.shape(files)) == 0: file_list = [file_list]
f='f8'
if ph_dtype == None: ph_dtype = [('p12',f),('p23',f),('p34',f),('p45',f),('p56',f)]
#ph_dtype = [('p12',f)]
ds_list =[]
comment_list =[]
count = 0
for (i,filename) in enumerate(file_list):
if seconds() - last_update > 30:
last_update = seconds()
print('reading {n}/{t}: {f}'
.format(f=filename, n=i, t=len(file_list)))
try:
if pl.is_string_like(target):
skip = 1+find_data(filename, target,debug=debug)
else:
skip = target
if quiet == 0:
print('{t:.1f} sec, loading data from line {s} of {f}'
.format(t = seconds()-st, s=skip, f=filename))
# this little bit to determine layout of data
# very inefficient to read twice, but in a hurry!
txt = np.loadtxt(fname=filename, skiprows=skip-1, dtype=str,
delimiter='FOOBARWOOBAR')
header_toks = txt[0].split()
# is the first character of the 2nd last a digit?
if header_toks[-2][0] in '0123456789':
if pyfusion.VERBOSE > 0:
print('found new header including number of phases')
n_phases = int(header_toks[-2])
ph_dtype = [('p{n}{np1}'.format(n=n,np1=n+1), f) for n in range(n_phases)]
if 'frlow' in header_toks: # add the two extra fields
fs_dtype= [ ('shot','i8'), ('t_mid','f8'),
('_binary_svs','i8'),
('freq','f8'), ('amp', 'f8'), ('a12','f8'),
('p', 'f8'), ('H','f8'),
('frlow','f8'), ('frhigh', 'f8'),('phases',ph_dtype)]
else:
fs_dtype= [ ('shot','i8'), ('t_mid','f8'),
('_binary_svs','i8'),
('freq','f8'), ('amp', 'f8'), ('a12','f8'),
('p', 'f8'), ('H','f8'), ('phases',ph_dtype)]
ds_list.append(
np.loadtxt(fname=filename, skiprows = skip,
dtype= fs_dtype)
)
count += 1
comment_list.append(filename)
except ValueError, info:
print('Conversion error while reading {f} with loadtxt - {info}'.format(f=filename, info=info))
def save_nodes(self,filename,path='./results/'):
"""Saves the contents of a Nodes instance to a npz file."""
attribute = dir(self[0])
save_str = []
#Determine which attributes to be saved
for attribute in dir(self[0]):
if attribute[0]=='_':
continue
elif is_numlike(getattr(self[0],attribute)) or is_string_like(getattr(self[0],attribute)):
save_str.append(attribute + '=' + 'np.array([self[i].'+attribute+' for i in np.arange(len(self))])')
#Write save file
eval('np.savez(path+filename,'+','.join(save_str)+')')
print 'Saved nodes to file: ', path+filename
sys.stdout.flush()
def scatter_classic(x, y, s=None, c='b'):
"""
SCATTER_CLASSIC(x, y, s=None, c='b')
Make a scatter plot of x versus y. s is a size (in data coords) and
can be either a scalar or an array of the same length as x or y. c is
a color and can be a single color format string or an length(x) array
of intensities which will be mapped by the colormap jet.
If size is None a default size will be used
Copied from older version of matplotlib -- removed in version 0.9.1
for whatever reason.
"""
self = gca()
if not self._hold: self.cla()
if is_string_like(c):
c = [c] * len(x)
elif not iterable(c):
c = [c] * len(x)
else:
norm = normalize()
norm(c)
c = cm.jet(c)
if s is None:
s = [abs(0.015 * (amax(y) - amin(y)))] * len(x)
elif not iterable(s):
s = [s] * len(x)
if len(c) != len(x):
raise ValueError, 'c and x are not equal lengths'
if len(s) != len(x):
raise ValueError, 's and x are not equal lengths'
patches = []
for thisX, thisY, thisS, thisC in zip(x, y, s, c):
circ = Circle(
(thisX, thisY),
radius=thisS,
)
circ.set_facecolor(thisC)
self.add_patch(circ)
patches.append(circ)
self.autoscale_view()
return patches
def save_nodes(self, filename, path="./results/"):
"""Saves the contents of a Nodes instance to a npz file."""
attribute = dir(self[0])
save_str = []
# Determine which attributes to be saved
for attribute in dir(self[0]):
if attribute[0] == "_":
continue
elif is_numlike(getattr(self[0], attribute)) or is_string_like(getattr(self[0], attribute)):
save_str.append(attribute + "=" + "array([self[i]." + attribute + " for i in arange(len(self))])")
# Write save file
eval("np.savez(path+filename," + ",".join(save_str) + ")")
print "Saved nodes to file: ", path + filename
sys.stdout.flush()
def scatter_classic(x, y, s=None, c='b'):
"""
SCATTER_CLASSIC(x, y, s=None, c='b')
Make a scatter plot of x versus y. s is a size (in data coords) and
can be either a scalar or an array of the same length as x or y. c is
a color and can be a single color format string or an length(x) array
of intensities which will be mapped by the colormap jet.
If size is None a default size will be used
Copied from older version of matplotlib -- removed in version 0.9.1
for whatever reason.
"""
self = gca()
if not self._hold: self.cla()
if is_string_like(c):
c = [c]*len(x)
elif not iterable(c):
c = [c]*len(x)
else:
norm = normalize()
norm(c)
c = cm.jet(c)
if s is None:
s = [abs(0.015*(amax(y)-amin(y)))]*len(x)
elif not iterable(s):
s = [s]*len(x)
if len(c)!=len(x):
raise ValueError, 'c and x are not equal lengths'
if len(s)!=len(x):
raise ValueError, 's and x are not equal lengths'
patches = []
for thisX, thisY, thisS, thisC in zip(x,y,s,c):
circ = Circle( (thisX, thisY),
radius=thisS,
)
circ.set_facecolor(thisC)
self.add_patch(circ)
patches.append(circ)
self.autoscale_view()
return patches
def plot_complex_image(image, plot_title='', textsize=18, scale=False):
limit = 6 * median(abs(image))
clf()
if is_string_like(plot_title):
figtext(0.5,
0.95,
plot_title,
size=1.5 * textsize,
horizontalalignment='center')
pass
subplot(221)
title("Real")
if scale:
imshow(image.real,
interpolation='nearest',
vmin=image.imag.min(),
vmax=image.imag.max())
else:
imshow(image.real, interpolation='nearest')
pass
colorbar()
subplot(222)
title("Imag")
imshow(image.imag, interpolation='nearest')
colorbar()
subplot(223)
title("Abs")
if scale:
imshow(abs(image), interpolation='nearest', vmax=image.imag.max())
else:
imshow(abs(image), interpolation='nearest')
pass
colorbar()
subplot(224)
title("Phase")
imshow(angle(image), interpolation='nearest')
colorbar()
pass
_var_defaults="""
shot_range=[27233]
plot=True
exception=Exception
diag_name='HMP01'
dev_name="LHD"
"""
exec(_var_defaults)
from pyfusion.utils import process_cmd_line_args
exec(process_cmd_line_args())
if pl.is_string_like(shot_range):
print('loading from file %s ' %(shot_range))
shot_range = np.loadtxt(shot_range)
device = pyfusion.getDevice(dev_name)
for shot in shot_range:
try:
data = device.acq.getdata(shot, diag_name)
#data = pyfusion.load_channel(shot,chan_name)
sig=data.signal
sigac=sig-np.average(sig*np.blackman(len(sig)))
fs = abs(np.fft.fft(sigac*np.blackman(len(sig))))
if plot:
pl.semilogy(fs[0:2000],hold=0)
pl.title('shot %d, %s' % (shot, diag_name))
_var_defaults = """
shot_range=[27233]
plot=True
exception=Exception
diag_name='HMP01'
dev_name="LHD"
"""
exec(_var_defaults)
from pyfusion.utils import process_cmd_line_args
exec(process_cmd_line_args())
if pl.is_string_like(shot_range):
print('loading from file %s ' % (shot_range))
shot_range = np.loadtxt(shot_range)
device = pyfusion.getDevice(dev_name)
for shot in shot_range:
try:
data = device.acq.getdata(shot, diag_name)
#data = pyfusion.load_channel(shot,chan_name)
sig = data.signal
sigac = sig - np.average(sig * np.blackman(len(sig)))
fs = abs(np.fft.fft(sigac * np.blackman(len(sig))))
if plot:
pl.semilogy(fs[0:2000], hold=0)
pl.title('shot %d, %s' % (shot, diag_name))
def extract(self, dictionary = False, varnames=None, inds = None, limit=None,strict=0, debug=0):
""" extract the listed variables into the dictionary (local by default)
selecting those at indices <inds> (all be default
variables must be strings, either an array, or separated by commas
if the dictionary is False, return them in a tuple instead
Note: returning a list requires you to make the order consistent
if varnames is None - extract all.
e.g. if da is a dictionary or arrays
da = DA('mydata.npz')
da.extract('shot,beta')
plot(shot,beta)
(shot,beta,n_e) = da.extract(['shot','beta','n_e'], \
inds=np.where(da['beta']>3)[0])
# makes a tuple of 3 arrays of data for high beta.
Note syntax of where()! It is evaluted in your variable space.
to extract one var, need trailing "," (tuple notation) e.g.
(allbeta,) = D54.extract('beta',locals())
which can be abbreviated to
allbeta, = D54.extract('beta',locals())
"""
start_mem = report_mem(msg='extract')
if debug == 0: debug = self.debug
if varnames == None: varnames = self.da.keys() # all variables
if pl.is_string_like(varnames):
varlist = varnames.split(',')
else: varlist = varnames
val_tuple = ()
if inds == None:
inds = np.arange(self.len)
if (len(np.shape(inds))==2):
inds = inds[0] # trick to catch when you forget [0] on where
if limit != None and len(inds)> abs(limit):
if limit<0:
print('repeatably' ),
np.random.seed(0) # if positive, should be random
# negative will repeat the sequence
else: print('randomly'),
print('decimating from sample of {n} and'.format(n=len(inds))),
ir = np.where(np.random.random(len(inds))
< float(abs(limit))/len(inds))[0]
inds = inds[ir]
if len(inds)<500: print('*** {n} is a very small number to extract????'
.format(n=len(inds)))
if self.verbose>0:
print('extracting a sample of {n} '.format(n=len(inds)))
for k in varlist:
if k in self.keys:
debug_(debug,key='extract')
if hasattr(self.da[k],'keys'):
allvals = self.da[k]
else:
allvals = np.array(self.da[k])
if len(np.shape(allvals)) == 0:
sel_vals = allvals
else:
sel_vals = allvals[inds]
if dictionary == False:
val_tuple += (sel_vals,)
else:
dictionary.update({k: sel_vals})
else: print('variable {k} not found in {ks}'.
format(k=k, ks = np.sort(self.da.keys())))
report_mem(start_mem)
if dictionary == False:
return(val_tuple)
def sp(ds, x=None, y=None, sz=None, col=None, decimate=0, ind = None, nomode=None,
size_scale=None, dot_size=30, hold=0, seed=None, colorbar=None, legend=True, marker='o'):
""" Scatter plot front end, size_scale
x, y, sz, col can be keys or variables (of matching size to ds)
decimate = 0.1 selects 10% of the input, -0.1 uses a fixed key.
"""
def size_val(marker_size):
if size_scale<0:
return(-size_scale*np.exp(np.sqrt(marker_size/(dot_size/20))))
else:
return(size_scale*(np.sqrt(marker_size/dot_size)))
if type(ds) == type({}): keys = np.sort(ds.keys())
elif type(ds) == np.ndarray: keys = np.sort(ds.dtype.names)
else: raise ValueError(
'First argument must be a dictionary of arrays or an '
'array read from loadtxt')
if x == None: x = keys[0]
if y == None: y = keys[1]
if col == None: col = keys[2]
# deal with the indices first, so we can consider indexing x,y earlier
if ind == None:
if pl.is_string_like(x):
lenx = len(ds[x])
else:
lenx = len(x)
ind = np.arange(lenx)
if seed != None: np.random.seed(seed)
if decimate != 0:
if decimate<0: np.random.seed(0) # fixed seed for decimate<0
ind = ind[(np.where(np.random.rand(len(ind))<abs(decimate)))[0]]
else: # decimate if very long array and decimate == 0
if (len(ind) > 2e4): # 2e5 for scatter, 1e5 for plot
print('Decimating automatically as data length too long [{0}]'
.format(len(ind)))
ind = ind[np.where(np.random.rand(len(ind))<(2e4/len(ind)))[0]]
if pl.is_string_like(x):
x_string = x
x = ds[x]
else:
x_string = ''
if pl.is_string_like(y):
y_string = y
y = ds[y]
else:
y_string = ''
size_string = '<size>'
color_string = '<color>'
if pl.is_string_like(col):
if np.any(np.array(keys)== col):
color_string = col
col=ds[col][ind]
else: col = col # colour is hardwired
else:
if col == None: col='b'
else:
col = np.array(col)[ind]
color_string = ''
if nomode == None:
if hasattr(col, 'dtype'):
col_dtype = col.dtype
minint = np.iinfo(col_dtype).min
nomode = minint
else:
if len(col) != 0:
nomode = dp.iinfo(col[0]).min
else:
nomode = dp.iinfo(col).min
w_not_nomode = np.where(nomode != col)[0]
# shrink ind further to avoid displaying unidentified modes
ind = ind[w_not_nomode]
col = col[w_not_nomode]
if sz == None: sz=20 * np.ones(len(x))
if pl.is_string_like(sz):
size_string = sz # size scale is the value giving a dot size of dot_size
sz=ds[sz]
else: # must be a number or an array
sz = np.array(sz)
if size_scale==None: size_scale = max(sz[ind])
if size_scale<0: # negative is a log scale
sz=dot_size/20*(np.log(sz[ind]/-size_scale))**2
else:
sz=dot_size*(sz[ind]/size_scale) # squarung may make sense, but too big
if max(sz)>1000:
if pl.isinteractive():
inp=raw_input('huge circles, radius~ {szm:.3g}, Y/y to continue'
.format(szm = max(sz.astype(float)))) # bug! shouldn't need asfloat
if inp.upper() != 'Y': raise ValueError
else:
warn('reducing symbol size')
sz=200/max(sz) * sz
debug_(debug,3)
if hold==0: pl.clf()
coll = pl.scatter(x[ind],y[ind],sz,col, hold=hold,marker=marker,label='')
# pl.legend(coll # can't select an element out of a CircleCollection
sizes = coll.get_sizes()
max_size=max(sizes)
big=matplotlib.collections.CircleCollection([max_size])
med=matplotlib.collections.CircleCollection([max_size/10])
sml=matplotlib.collections.CircleCollection([max_size/100])
if legend == True:
pl.legend([big,med,sml],
[("%s=%.3g" % (size_string,size_val(max_size))),
("%.3g" % (size_val(max_size/10))),
("%.3g" % (size_val(max_size/100)))])
pl.xlabel(x_string)
pl.ylabel(y_string)
pl.title('size=%s, colour=%s' % (size_string, color_string))
if colorbar == None and len(col) > 1:
colorbar = True
if colorbar: pl.colorbar()
def sp(ds,
x=None,
y=None,
sz=None,
col=None,
decimate=0,
ind=None,
nomode=None,
size_scale=None,
dot_size=30,
hold=0,
seed=None,
colorbar=None,
legend=True,
marker='o'):
""" Scatter plot front end, size_scale
x, y, sz, col can be keys or variables (of matching size to ds)
decimate = 0.1 selects 10% of the input, -0.1 uses a fixed key.
"""
def size_val(marker_size):
if size_scale < 0:
return (-size_scale * np.exp(np.sqrt(marker_size /
(dot_size / 20))))
else:
return (size_scale * (np.sqrt(marker_size / dot_size)))
if type(ds) == type({}): keys = np.sort(ds.keys())
elif type(ds) == np.ndarray: keys = np.sort(ds.dtype.names)
else:
raise ValueError('First argument must be a dictionary of arrays or an '
'array read from loadtxt')
if x == None: x = keys[0]
if y == None: y = keys[1]
if col == None: col = keys[2]
# deal with the indices first, so we can consider indexing x,y earlier
if ind == None:
if pl.is_string_like(x):
lenx = len(ds[x])
else:
lenx = len(x)
ind = np.arange(lenx)
if seed != None: np.random.seed(seed)
if decimate != 0:
if decimate < 0: np.random.seed(0) # fixed seed for decimate<0
ind = ind[(np.where(np.random.rand(len(ind)) < abs(decimate)))[0]]
else: # decimate if very long array and decimate == 0
if (len(ind) > 2e4): # 2e5 for scatter, 1e5 for plot
print('Decimating automatically as data length too long [{0}]'.
format(len(ind)))
ind = ind[np.where(np.random.rand(len(ind)) < (2e4 / len(ind)))[0]]
if pl.is_string_like(x):
x_string = x
x = ds[x]
else:
x_string = ''
if pl.is_string_like(y):
y_string = y
y = ds[y]
else:
y_string = ''
size_string = '<size>'
color_string = '<color>'
if pl.is_string_like(col):
if np.any(np.array(keys) == col):
color_string = col
col = ds[col][ind]
else:
col = col # colour is hardwired
else:
if col == None: col = 'b'
else:
col = np.array(col)[ind]
color_string = ''
if nomode == None:
if hasattr(col, 'dtype'):
col_dtype = col.dtype
minint = np.iinfo(col_dtype).min
nomode = minint
else:
if len(col) != 0:
nomode = dp.iinfo(col[0]).min
else:
nomode = dp.iinfo(col).min
w_not_nomode = np.where(nomode != col)[0]
# shrink ind further to avoid displaying unidentified modes
ind = ind[w_not_nomode]
col = col[w_not_nomode]
if sz == None: sz = 20 * np.ones(len(x))
if pl.is_string_like(sz):
size_string = sz # size scale is the value giving a dot size of dot_size
sz = ds[sz]
else: # must be a number or an array
sz = np.array(sz)
if size_scale == None: size_scale = max(sz[ind])
if size_scale < 0: # negative is a log scale
sz = dot_size / 20 * (np.log(sz[ind] / -size_scale))**2
else:
sz = dot_size * (sz[ind] / size_scale
) # squarung may make sense, but too big
if max(sz) > 1000:
if pl.isinteractive():
inp = raw_input(
'huge circles, radius~ {szm:.3g}, Y/y to continue'.format(
szm=max(sz.astype(float)))) # bug! shouldn't need asfloat
if inp.upper() != 'Y': raise ValueError
else:
warn('reducing symbol size')
sz = 200 / max(sz) * sz
debug_(debug, 3)
if hold == 0: pl.clf()
coll = pl.scatter(x[ind],
y[ind],
sz,
col,
hold=hold,
marker=marker,
label='')
# pl.legend(coll # can't select an element out of a CircleCollection
sizes = coll.get_sizes()
max_size = max(sizes)
big = matplotlib.collections.CircleCollection([max_size])
med = matplotlib.collections.CircleCollection([max_size / 10])
sml = matplotlib.collections.CircleCollection([max_size / 100])
if legend == True:
pl.legend([big, med, sml],
[("%s=%.3g" % (size_string, size_val(max_size))),
("%.3g" % (size_val(max_size / 10))),
("%.3g" % (size_val(max_size / 100)))])
pl.xlabel(x_string)
pl.ylabel(y_string)
pl.title('size=%s, colour=%s' % (size_string, color_string))
if colorbar == None and len(col) > 1:
colorbar = True
if colorbar: pl.colorbar()
def __save__(self, root_node, self_key, conf, new_conf):
'''
self_key The variable in the calling class that points to self
db The database
depth The recursive level'''
import mynumpy as np
from gfuncs import is_builtin_type, is_np_type
# Process the optional arguments
# opts = {'is_subclass':False}
# opts = processArgs(args, opts)
db = Configurable.__db__
# Start by adding the key for this class, if supplied.
if self_key != None and pl.is_string_like(self_key):
new_conf[self_key] = {}
new_conf = new_conf[self_key]
new_conf['class_type'] = self.__class__
class_node = db.save(root_node,
self.__class__.__name__,
None,
type='group')
if conf != None:
conf = conf[self_key]
# Then add the configuration number
# Check if the data already exists (in which case a new slot is not needed)
# If this is the first configuration, we'll __save__ the data at the first slot
# If one or more configurations exist, and the 'is_new' flag is given, we
# select a slot with equal contents, if such a slot exists. If not, a new
# slot is created and the data is saved there.
# To ensure that no data is stored twice, this search must be performed
# each time.
was_equal, id = self.__find_new_slot__(root_node, conf)
new_conf['id'] = id
new_conf['class_attr'] = {}
new_conf = new_conf['class_attr']
id_node = db.save(class_node, id, None, type='id')
# Iterate over all class instance variables
for key, val in self.__dict__.iteritems():
vc = val.__class__
# print key
#if key=='Xd':
# print 'hello'
# Skip empty types
if val == None:
pass
# If the variable is another configurable class, call its __save__()
if issubclass(val.__class__, Configurable):
if vc == np.Ndarray:
db.save(id_node, key, val, type='data')
if key not in new_conf:
new_conf[key] = val.__class__
else:
val.__save__(root_node, key, conf, new_conf)
# Save normal datatypes to the database
if is_builtin_type(vc) or is_np_type(vc):
if key == None:
key = 'None'
db.save(id_node, key, val, type='data')
if key not in new_conf:
new_conf[key] = val.__class__
# NOTE: Be careful with this one. Prolly try to avoid these types
elif type(val) == tuple or type(val) == list:
if type(val) == tuple:
tw = TupleWrapper()
else:
tw = ListWrapper()
tw.len = val.__len__()
for i in range(0, tw.len):
if val[i] == None:
val[i] = 'None'
setattr(tw, 'i%d' % i, val[i])
tw.__save__(root_node, key, conf, new_conf)
elif type(val) == dict:
dw = DictWrapper()
for subkey in val:
if subkey == None:
setattr(dw, 'None', val[subkey])
else:
setattr(dw, subkey, val[subkey])
dw.__save__(root_node, key, conf, new_conf)
def extract(self,
dictionary=False,
varnames=None,
inds=None,
limit=None,
strict=0,
debug=0):
""" extract the listed variables into the dictionary (local by default)
selecting those at indices <inds> (all be default
variables must be strings, either an array, or separated by commas
if the dictionary is False, return them in a tuple instead
Note: returning a list requires you to make the order consistent
if varnames is None - extract all.
e.g. if da is a dictionary or arrays
da = DA('mydata.npz')
da.extract('shot,beta')
plot(shot,beta)
(shot,beta,n_e) = da.extract(['shot','beta','n_e'], \
inds=np.where(da['beta']>3)[0])
# makes a tuple of 3 arrays of data for high beta.
Note syntax of where()! It is evaluted in your variable space.
to extract one var, need trailing "," (tuple notation) e.g.
(allbeta,) = D54.extract('beta',locals())
which can be abbreviated to
allbeta, = D54.extract('beta',locals())
"""
start_mem = report_mem(msg='extract')
if debug == 0: debug = self.debug
if varnames == None: varnames = self.da.keys() # all variables
if pl.is_string_like(varnames):
varlist = varnames.split(',')
else:
varlist = varnames
val_tuple = ()
if inds == None:
inds = np.arange(self.len)
if (len(np.shape(inds)) == 2):
inds = inds[0] # trick to catch when you forget [0] on where
if limit != None and len(inds) > abs(limit):
if limit < 0:
print('repeatably'),
np.random.seed(0) # if positive, should be random
# negative will repeat the sequence
else:
print('randomly'),
print('decimating from sample of {n} and'.format(n=len(inds))),
ir = np.where(
np.random.random(len(inds)) < float(abs(limit)) / len(inds))[0]
inds = inds[ir]
if len(inds) < 500:
print('*** {n} is a very small number to extract????'.format(
n=len(inds)))
if self.verbose > 0:
print('extracting a sample of {n} '.format(n=len(inds)))
for k in varlist:
if k in self.keys:
debug_(debug, key='extract')
if hasattr(self.da[k], 'keys'):
allvals = self.da[k]
else:
allvals = np.array(self.da[k])
if len(np.shape(allvals)) == 0:
sel_vals = allvals
else:
sel_vals = allvals[inds]
if dictionary == False:
val_tuple += (sel_vals, )
else:
dictionary.update({k: sel_vals})
else:
print('variable {k} not found in {ks}'.format(
k=k, ks=np.sort(self.da.keys())))
report_mem(start_mem)
if dictionary == False:
return (val_tuple)
def __save__(self, root_node, self_key, conf, new_conf):
'''
self_key The variable in the calling class that points to self
db The database
depth The recursive level'''
import mynumpy as np
from gfuncs import is_builtin_type, is_np_type
# Process the optional arguments
# opts = {'is_subclass':False}
# opts = processArgs(args, opts)
db = Configurable.__db__
# Start by adding the key for this class, if supplied.
if self_key != None and pl.is_string_like(self_key):
new_conf[self_key] = {}
new_conf = new_conf[self_key]
new_conf['class_type'] = self.__class__
class_node = db.save(root_node, self.__class__.__name__, None, type='group')
if conf != None:
conf = conf[self_key]
# Then add the configuration number
# Check if the data already exists (in which case a new slot is not needed)
# If this is the first configuration, we'll __save__ the data at the first slot
# If one or more configurations exist, and the 'is_new' flag is given, we
# select a slot with equal contents, if such a slot exists. If not, a new
# slot is created and the data is saved there.
# To ensure that no data is stored twice, this search must be performed
# each time.
was_equal, id = self.__find_new_slot__(root_node, conf)
new_conf['id'] = id
new_conf['class_attr'] = {}
new_conf = new_conf['class_attr']
id_node = db.save(class_node, id, None, type='id')
# Iterate over all class instance variables
for key,val in self.__dict__.iteritems():
vc = val.__class__
# print key
#if key=='Xd':
# print 'hello'
# Skip empty types
if val == None:
pass
# If the variable is another configurable class, call its __save__()
if issubclass(val.__class__, Configurable):
if vc == np.Ndarray:
db.save(id_node, key, val, type='data')
if key not in new_conf:
new_conf[key] = val.__class__
else:
val.__save__(root_node, key, conf, new_conf)
# Save normal datatypes to the database
if is_builtin_type(vc) or is_np_type(vc):
if key == None:
key = 'None'
db.save(id_node, key, val, type='data')
if key not in new_conf:
new_conf[key] = val.__class__
# NOTE: Be careful with this one. Prolly try to avoid these types
elif type(val) == tuple or type(val) == list:
if type(val) == tuple:
tw = TupleWrapper()
else:
tw = ListWrapper()
tw.len = val.__len__()
for i in range(0,tw.len):
if val[i] == None:
val[i] = 'None'
setattr(tw, 'i%d'%i, val[i])
tw.__save__(root_node, key, conf, new_conf)
elif type(val) == dict:
dw = DictWrapper()
for subkey in val:
if subkey == None:
setattr(dw, 'None', val[subkey])
else:
setattr(dw, subkey, val[subkey])
dw.__save__(root_node, key, conf, new_conf)
def read_text_pyfusion(files,
target='^Shot .*',
ph_dtype=None,
plot=pl.isinteractive(),
ms=100,
hold=0,
debug=0,
quiet=1,
exception=Exception):
""" Accepts a file or a list of files, returns a list of structured arrays
See merge ds_list to merge and convert types (float -> pyfusion.prec_med
"""
st = seconds()
last_update = seconds()
file_list = files
if len(np.shape(files)) == 0: file_list = [file_list]
f = 'f8'
if ph_dtype == None:
ph_dtype = [('p12', f), ('p23', f), ('p34', f), ('p45', f), ('p56', f)]
#ph_dtype = [('p12',f)]
ds_list = []
comment_list = []
count = 0
for (i, filename) in enumerate(file_list):
if seconds() - last_update > 30:
last_update = seconds()
print('reading {n}/{t}: {f}'.format(f=filename,
n=i,
t=len(file_list)))
try:
if pl.is_string_like(target):
skip = 1 + find_data(filename, target, debug=debug)
else:
skip = target
if quiet == 0:
print('{t:.1f} sec, loading data from line {s} of {f}'.format(
t=seconds() - st, s=skip, f=filename))
# this little bit to determine layout of data
# very inefficient to read twice, but in a hurry!
txt = np.loadtxt(fname=filename,
skiprows=skip - 1,
dtype=str,
delimiter='FOOBARWOOBAR')
header_toks = txt[0].split()
# is the first character of the 2nd last a digit?
if header_toks[-2][0] in '0123456789':
if pyfusion.VERBOSE > 0:
print('found new header including number of phases')
n_phases = int(header_toks[-2])
ph_dtype = [('p{n}{np1}'.format(n=n, np1=n + 1), f)
for n in range(n_phases)]
if 'frlow' in header_toks: # add the two extra fields
fs_dtype = [('shot', 'i8'), ('t_mid', 'f8'),
('_binary_svs', 'i8'), ('freq', 'f8'),
('amp', 'f8'), ('a12', 'f8'), ('p', 'f8'),
('H', 'f8'), ('frlow', 'f8'), ('frhigh', 'f8'),
('phases', ph_dtype)]
else:
fs_dtype = [('shot', 'i8'), ('t_mid', 'f8'),
('_binary_svs', 'i8'), ('freq', 'f8'),
('amp', 'f8'), ('a12', 'f8'), ('p', 'f8'),
('H', 'f8'), ('phases', ph_dtype)]
ds_list.append(
np.loadtxt(fname=filename, skiprows=skip, dtype=fs_dtype))
count += 1
comment_list.append(filename)
except ValueError, info:
print('Conversion error while reading {f} with loadtxt - {info}'.
format(f=filename, info=info))