def test_metadata_save(self):
local = path.dirname(__file__)
t = np.arange(12).reshape(3,4) #set up a test data file with mixed metadata
t = Data(t)
t.column_headers = ["1","2","3","4"]
metitems = [True,1,0.2,{"a":1, "b":"abc"},(1,2),np.arange(3),[1,2,3], "abc", #all types accepted
r"\\abc\cde", 1e-20, #extra tests
[1,(1,2),"abc"], #list with different types
[[[1]]], #nested list
None, #None value
]
metnames = ["t"+str(i) for i in range(len(metitems))]
for k,v in zip(metnames,metitems):
t[k] = v
t.save(path.join(local, "mixedmetatest.dat"))
tl = Data(path.join(local, "mixedmetatest.txt")) #will change extension to txt if not txt or tdi, is this what we want?
t2 = self.d4.clone #check that python tdi save is the same as labview tdi save
t2.save(path.join(local, "mixedmetatest2.txt"))
t2l = Data(path.join(local, "mixedmetatest2.txt"))
for orig, load in [(t,tl), (t2, t2l)]:
for k in ['Loaded as', 'TDI Format']:
orig[k]=load[k]
self.assertTrue(np.allclose(orig.data, load.data))
self.assertTrue(orig.column_headers==load.column_headers)
self.res=load.metadata^orig.metadata
self.assertTrue(load.metadata==orig.metadata,"Metadata not the same on round tripping to disc")
os.remove(path.join(local, "mixedmetatest.txt")) #clear up
os.remove(path.join(local, "mixedmetatest2.txt"))
def setUp(self):
self.d = Data(path.join(path.dirname(__file__), "CoreTest.dat"),
setas="xy")
self.d2 = Data(
path.join(__home__, "..", "sample-data", "TDI_Format_RT.txt"))
self.d3 = Data(
path.join(__home__, "..", "sample-data", "New-XRay-Data.dql"))
def test_extra_plots(self):
x = np.random.uniform(-np.pi, np.pi, size=5001)
y = np.random.uniform(-np.pi, np.pi, size=5001)
z = (np.cos(4 * np.sqrt(x**2 + y**2)) *
np.exp(-np.sqrt(x**2 + y**2) / 3.0))**2
self.d2 = Data(x, y, z, column_headers=["X", "Y", "Z"], setas="xyz")
self.d2.contour_xyz(projection="2d") #
self.assertEqual(len(plt.get_fignums()), 1,
"Setting Data.fig by integer failed.")
plt.close("all")
X, Y, Z = self.d2.griddata(xlim=(-np.pi, np.pi), ylim=(-np.pi, np.pi))
plt.imshow(Z)
self.assertEqual(len(plt.get_fignums()), 1,
"Setting Data.fig by integer failed.")
plt.imshow(Z)
plt.close("all")
x, y = np.meshgrid(np.linspace(-np.pi, np.pi, 10),
np.linspace(-np.pi, np.pi, 10))
z = np.zeros_like(x)
w = np.cos(np.sqrt(x**2 + y**2))
q = np.arctan2(x, y)
u = np.abs(w) * np.cos(q)
v = np.abs(w) * np.sin(q)
self.d3 = Data(x.ravel(),
y.ravel(),
z.ravel(),
u.ravel(),
v.ravel(),
w.ravel(),
setas="xyzuvw")
self.d3.plot()
self.assertEqual(len(plt.get_fignums()), 1,
"Setting Data.fig by integer failed.")
plt.close("all")
def setUp(self):
self.d1 = Data(path.join(self.datadir, "OVF1.ovf"))
self.d2 = Data(path.join(self.datadir, "TDI_Format_RT.txt"))
self.d3 = Data(path.join(self.datadir, "New-XRay-Data.dql"))
self.d4 = Data(np.column_stack([np.ones(100),
np.ones(100) * 2]),
setas="xy")
def test_Properties(self):
fldr=SF.DataFolder(self.datadir,debug=False,recursive=False)
self.assertEqual(fldr.mindepth,0,"Minimum depth of flat group n ot equal to zero.")
fldr/="Loaded as"
grps=list(fldr.lsgrp)
skip=0 if hyperspy_ok else 1
self.assertEqual(len(grps),26-skip,"Length of lsgrp not as expected: {} not 25".format(len(grps)))
fldr.debug=True
self.fldr=fldr
self.assertTrue(fldr["XRDFile"][0].debug,"Setting debug on folder failed!")
fldr.debug=False
fldr["QDFile"].group("Byapp")
self.assertEqual(fldr.trunkdepth,1,"Trunkdepth failed")
self.assertEqual(fldr.mindepth,1,"mindepth attribute of folder failed.")
self.assertEqual(fldr.depth,2,"depth attribute failed.")
fldr=SF.DataFolder(self.datadir,debug=False,recursive=False)
fldr+=Data()
skip=1 if hyperspy_ok else 2
self.assertEqual(len(list(fldr.loaded)),1,"loaded attribute failed {}".format(len(list(fldr.loaded))))
self.assertEqual(len(list(fldr.not_empty)),len(fldr)-skip,"not_empty attribute failed.")
fldr-="Untitled"
self.assertFalse(fldr.is_empty,"fldr.is_empty failed")
fldr=SF.DataFolder(self.datadir,debug=False,recursive=False)
objects=copy(fldr.objects)
fldr.objects=dict(objects)
self.assertTrue(isinstance(fldr.objects,regexpDict),"Folder objects not reset to regexp dictionary")
fldr.objects=objects
self.assertTrue(isinstance(fldr.objects,regexpDict),"Setting Folder objects mangled type")
fldr.type=Data()
self.assertTrue(issubclass(fldr.type,Data),"Settin type by instance of class failed")
def test_scale(self):
x=np.linspace(-5,5,101)
y=np.sin(x)
orig=Data(x+np.random.normal(size=101,scale=0.025),y+np.random.normal(size=101,scale=0.01))
orig.setas="xy"
XTests=[[(0,0,0.5),(0,2,-0.1)],
[(0,0,0.5)],
[(0,2,-0.2)]]
YTests=[[(1,1,0.5),(1,2,-0.1)],
[(1,1,0.5)],
[(1,2,-0.2)]]
for xmode,xdata,xtests in zip(["linear","scale","offset"],[x*2+0.2,x*2,x+0.2],XTests):
for ymode,ydata,ytests in zip(["linear","scale","offset"],[y*2+0.2,y*2,y+0.2],YTests):
to_scale=Data(xdata+np.random.normal(size=101,scale=0.025),ydata+np.random.normal(size=101,scale=0.01))
to_scale.setas="xy"
to_scale.scale(orig,xmode=xmode,ymode=ymode)
transform=to_scale["Transform"]
t_err=to_scale["Transform Err"]
for i,j,v in xtests+ytests:
self.assertLessEqual(np.abs(transform[i,j]-v),5*t_err[i,j],"Failed to get correct trandorm factor for {}:{} ({} vs {}".format(xmode,ymode,transform[i,j],v))
to_scale=Data(x*5+0.1+np.random.normal(size=101,scale=0.025),y*0.5+0.1+0.5*x+np.random.normal(size=101,scale=0.01))
to_scale.setas="xy"
to_scale.scale(orig,xmode="affine")
a_tranform=np.array([[0.2,0.,-0.02],[-0.2, 2.,-0.17]])
t_delta=np.abs(to_scale["Transform"]-a_tranform)
t_in_range=t_delta<to_scale["Transform Err"]*5
self.assertTrue(np.all(t_in_range),"Failed to produce correct affine scaling {} vs {}".format(to_scale["Transform"],a_tranform))
def test_clip(self):
x=np.linspace(0,np.pi*10,1001)
y=np.sin(x)
z=np.cos(x)
d=Data(x,y,z,setas="xyz")
d.clip((-0.1,0.2),"Column 2")
self.assertTrue((d.z.min()>=-0.1) and (d.z.max()<=0.2),"Clip with a column specified failed.")
d=Data(x,y,z,setas="xyz")
d.clip((-0.5,0.7))
self.assertTrue((d.y.min()>=-0.5) and (d.y.max()<=0.7),"Clip with no column specified failed.")
def test_Base_Operators(self):
fldr=SF.DataFolder(self.datadir,debug=False,recursive=False)
for d in fldr:
_=d["Loaded as"]
fldr=baseFolder(fldr)
fl=len(fldr)
d=Data(np.ones((100,5)))
fldr+=d
self.assertEqual(fl+1,len(fldr),"Failed += operator on DataFolder")
fldr2=fldr+fldr
self.assertEqual((fl+1)*2,len(fldr2),"Failed + operator with DataFolder on DataFolder")
fldr-="Untitled"
self.assertEqual(len(fldr),fl,"Failed to remove Untitled-0 from DataFolder by name.")
fldr-="New-XRay-Data.dql"
self.assertEqual(fl-1,len(fldr),"Failed to remove NEw Xray data by name.")
del fldr["1449 37.0 kx.emd"]
fldr/="Loaded as"
self.assertEqual(len(fldr["QDFile"]),4,"Failoed to group folder by Loaded As metadata with /= opeator.")
fldr=SF.DataFolder(self.datadir,debug=False,recursive=False)
for d in fldr:
_=d["Loaded as"]
fldr=baseFolder(fldr)
fldr2=SF.DataFolder(path.join(self.datadir,"NLIV"),pattern="*.txt")
fldr2.group(lambda x:"zero" if x["iterator"]%2==0 else "one")
fldr3=fldr+fldr2
self.assertEqual(fldr3.shape,(47, {'one': (9, {}), 'zero': (7, {})}),"Adding two DataFolders with groups failed")
fldr4=fldr3-fldr2
fldr4.prune()
self.assertEqual(fldr4.shape,fldr.shape,"Failed to subtract one DataFolder from another :{}".format(fldr4.shape))
del fldr2["one"]
self.assertEqual(fldr2.shape,(0, {'zero': (7, {})}),"Delitem with group failed")
fldr2.key=path.basename(fldr2.key)
self.assertEqual(repr(fldr2),"DataFolder(NLIV) with pattern ('*.txt',) has 0 files and 1 groups\n\tDataFolder(zero) with pattern ['*.txt'] has 7 files and 0 groups","Representation methods failed")
self.fldr=SF.DataFolder(self.datadir,debug=False,recursive=False)
names=list(self.fldr.ls)[::2]
self.fldr-=names
self.assertEqual(len(self.fldr),23,"Failed to delete from a sequence")
try:
self.fldr-0.34
except TypeError:
pass
else:
self.assertTrue(False,"Failed to throw a TypeError when subtracting a float")
try:
self.fldr-Data()
except RuntimeError:
pass
else:
self.assertTrue(False,"Failed to throw a RuntimeError when subtracting a non-member")
try:
self.fldr-"Wiggle"
except RuntimeError:
pass
else:
self.assertTrue(False,"Failed to throw a RuntimeError when subtracting a non-member")
def test_metadata_save(self):
local = path.dirname(__file__)
t = np.arange(12).reshape(
3, 4) #set up a test data file with mixed metadata
t = Data(t)
t.column_headers = ["1", "2", "3", "4"]
metitems = [
True,
1,
0.2,
{
"a": 1,
"b": "abc"
},
(1, 2),
np.arange(3),
[1, 2, 3],
"abc", #all types accepted
r"\\abc\cde",
1e-20, #extra tests
[1, (1, 2), "abc"], #list with different types
[[[1]]] #nested list
]
metnames = ["t" + str(i) for i in range(len(metitems))]
for k, v in zip(metnames, metitems):
t[k] = v
t.save(path.join(local, "mixedmetatest.dat"))
tl = Data(
path.join(local, "mixedmetatest.txt")
) #will change extension to txt if not txt or tdi, is this what we want?
t2 = self.d4.clone #check that python tdi save is the same as labview tdi save
t2.save(path.join(local, "mixedmetatest2.txt"))
t2l = Data(path.join(local, "mixedmetatest2.txt"))
for orig, load in [(t, tl), (t2, t2l)]:
self.assertTrue(np.allclose(orig.data, load.data))
self.assertTrue(orig.column_headers == load.column_headers)
self.assertTrue(
all([i in load.metadata.keys() for i in orig.metadata.keys()]))
for k in orig.metadata.keys():
if isinstance(orig[k], np.ndarray):
self.assertTrue(np.allclose(load[k], orig[k]))
elif isinstance(orig[k], float) and np.isnan(orig[k]):
self.assertTrue(np.isnan(load[k]))
else:
self.assertTrue(
load[k] == orig[k],
"Not equal for metadata: {}".format(load[k]))
os.remove(path.join(local, "mixedmetatest.txt")) #clear up
os.remove(path.join(local, "mixedmetatest2.txt"))
def test_constructor(self):
"""Constructor Tests"""
d = Data()
self.assertTrue(d.shape == (1, 0), "Bare constructor failed")
d = Data(self.d)
self.assertTrue(np.all(d.data == self.d.data),
"Constructor from DataFile failed")
d = Data([np.ones(100), np.zeros(100)])
self.assertTrue(d.shape == (100, 2),
"Constructor from iterable list of nd array failed")
d = Data([np.ones(100), np.zeros(100)], ["X", "Y"])
self.assertTrue(
d.column_headers == ["X", "Y"],
"Failed to set column headers in constructor: {}".format(
d.column_headers))
def test_len(self):
# Check that length of the column is the same as length of the data
self.assertEqual(len(Data()),0,"Empty DataFile not length zero")
self.assertEqual(len(self.d.column(0)),len(self.d),"Column 0 length not equal to DataFile length")
self.assertEqual(len(self.d),self.d.data.shape[0],"DataFile length not equal to data.shape[0]")
# Check that self.column_headers returns the right length
self.assertEqual(len(self.d.column_headers),self.d.data.shape[1],"Length of column_headers not equal to data.shape[1]")
def test_indexing(self):
#Check all the indexing possibilities
data=np.array(self.d.data)
colname=self.d.column_headers[0]
self.assertTrue(all(self.d.column(colname)==self.d[:,0]),"Failed direct indexing versus column method")
self.assertTrue(all(self.d[:,0]==data[:,0]),"Failed direct idnexing versusus direct array index")
self.assertTrue(all(self.d[:,[0,1]]==data),"Failed direct list indexing")
self.assertTrue(all(self.d[::2,:]==data[::2]),"Failed slice indexing rows")
self.assertTrue(all(self.d[colname]==data[:,0]),"Failed direct indexing by column name")
self.assertTrue(all(self.d[:,colname]==data[:,0]),"Failed fallback indexing by column name")
self.assertEqual(self.d[25,1],645.0,"Failed direct single cell index")
self.assertEqual(self.d[25,"Y-Data"],645.0,"Failoed single cell index direct")
self.assertEqual(self.d["Y-Data",25],645.0,"Failoed single cell fallback index order")
self.d["X-Dat"]=[11,12,13,14,15]
self.assertEqual(self.d["X-Dat",2],13,"Failed indexing of metadata lists with tuple")
self.assertEqual(self.d["X-Dat"][2],13,"Failed indexing of metadata lists with double indices")
d=Data(np.ones((10,10)))
d[0,0]=5 #Index by tuple into data
d["Column_1",0]=6 # Index by column name, row into data
d[0,"Column_2"]=7 #Index by row, column name into data
d["Column_3"]=[1,2,3,4] # Create a metadata
d["Column_3",2]=2 # Index existing metadata via tuple
d.metadata[0,5]=10
d[0,5]=12 # Even if tuple, index metadata if already existing.
self.assertTrue(np.all(d[0]==np.array([5,6,7,1,1,1,1,1,1,1])),"setitem on Data to index into Data.data failed.\n{}".format(d[0]))
self.assertEqual(d.metadata["Column_3"],[1,2,2,4],"Tuple indexing into metadata Failed.")
self.assertEqual(d.metadata[0,5],12,"Indexing of pre-existing metadta keys rather than Data./data failed.")
self.assertEqual(d.metadata[1],[1, 2, 2, 4],"Indexing metadata by integer failed.")
def hysteresis(self, mask=None):
"""Make a hysteresis loop of the average intensity in the given images
Keyword Argument:
mask(ndarray or list):
boolean array of same size as an image or imarray or list of
masks for each image. If True then don't include that area in
the intensity averaging.
Returns
-------
hyst(Data):
'Field', 'Intensity', 2 column array
"""
hyst = np.column_stack((self.fields, np.zeros(len(self))))
for i in range(len(self)):
im = self[i]
if isinstance(mask, np.ndarray) and len(mask.shape) == 2:
hyst[i, 1] = np.average(im[np.invert(mask.astype(bool))])
elif isinstance(mask, np.ndarray) and len(mask.shape) == 3:
hyst[i,
1] = np.average(im[np.invert(mask[i, :, :].astype(bool))])
elif isinstance(mask, (tuple, list)):
hyst[i, 1] = np.average(im[np.invert(mask[i])])
else:
hyst[i, 1] = np.average(im)
d = Data(hyst, setas="xy")
d.column_headers = ["Field", "Intensity"]
return d
def test_threshold(self):
#set up some zigzag data
#mins at 0,100,200,300,400, max at 50, 150, 250, 350 and zeros in between
ar = np.zeros((400, 2))
ar[:, 0] = np.arange(0, len(ar))
for i in range(4):
ar[i * 100:i * 100 + 50, 1] = np.linspace(-1, 1, 50)
for i in range(4):
ar[i * 100 + 50:i * 100 + 100, 1] = np.linspace(1, -1, 50)
d = Data(ar, setas='xy')
self.assertTrue(
len(
d.threshold(0, rising=True, falling=False, all_vals=True) ==
4))
self.assertTrue(
len(
d.threshold(0, rising=False, falling=True, all_vals=True) ==
4))
self.assertTrue(
len(
d.threshold(0,
interpolate=False,
rising=False,
falling=True,
all_vals=True) == 4))
self.assertTrue(d.threshold(0, all_vals=True)[1] == 124.5)
def test_functions(self):
#Test section:
self.s1 = self.d1.section(z=(12, 13))
self.assertTrue(142.710 < self.d2.mean("Temp") < 142.711,
"Failed on the mean test.")
self.assertTrue(
round(self.d2.span("Temp")[0], 1) == 4.3
and round(self.d2.span("Temp")[1], 1) == 291.6,
"Span test failed.")
f = self.d2.split(lambda r: r["Temp"] < 150)
self.assertTrue(len(f[0]) == 838, "Split failed to work.")
self.assertEqual(
len(
self.d3.threshold(2000,
rising=True,
falling=True,
all_vals=True)), 5, "Threshold failure.")
self.d4.add(0, 1, "Add")
self.d4.subtract(1, 0, header="Subtract")
self.d4.multiply(0, 1, header="Multiply")
self.d4.divide(0, 1, header="Divide")
self.d4.diffsum(0, 1, header="Diffsum")
self.assertTrue(
np.all(self.d4[0] == np.array([-0.5, -1, -3, 3, -1, 2])),
"Test column ops failed.")
d = Data(np.zeros((100, 1)))
d.add(0, 1.0)
self.assertEqual(np.sum(d[:, 0]), 100., "Add with a flot didn't work")
d.add(0, np.ones(100))
self.assertEqual(np.sum(d[:, 0]), 200., "Add with an array failed.")
def norm_group(pos, _, **kargs):
"""Takes the drain current for each file in group and builds an analysis file and works out the mean drain"""
if "signal" in kargs:
signal = kargs["signal"]
else:
signal = "fluo"
lfit = kargs["lfit"]
rfit = kargs["rfit"]
posfile = Data()
posfile.metadata = pos[0].metadata
posfile = posfile & pos[0].column(0)
posfile.column_headers = ["Energy"]
for f in pos:
print(str(f["run"]) + str(f.find_col(signal)))
posfile = posfile & f.column(signal)
posfile.add_column(lambda r: np.mean(r[1:]), "mean drain")
ec = posfile.find_col("Energy")
md = posfile.find_col("mean drain")
linearfit = scipy.poly1d(
posfile.polyfit(ec, md, 1, lambda x, y: lfit[0] <= x <= lfit[1]))
posfile.add_column(lambda r: r[md] - linearfit(r[ec]), "minus linear")
highend = posfile.mean("minus", lambda r: rfit[0] <= r[ec] <= rfit[1])
ml = posfile.find_col("minus linear")
posfile.add_column(lambda r: r[ml] / highend, "normalised")
if "group_key" in kargs:
posfile[kargs["group_key"]] = pos.key
return posfile
def test_Operators(self):
self.setUp()
fldr = self.fldr
fl = len(fldr)
d = Data(np.ones((100, 5)))
fldr += d
self.assertEqual(fl + 1, len(fldr), "Failed += operator on DataFolder")
fldr2 = fldr + fldr
self.assertEqual((fl + 1) * 2, len(fldr2),
"Failed + operator with DataFolder on DataFolder")
fldr -= "Untitled"
self.assertEqual(
len(fldr), fl,
"Failed to remove Untitled-0 from DataFolder by name.")
fldr -= "New-XRay-Data.dql"
self.assertEqual(fl - 1, len(fldr),
"Failed to remove NEw Xray data by name.")
fldr += "New-XRay-Data.dql"
self.assertEqual(len(fldr), fl,
"Failed += oeprator with string on DataFolder")
fldr /= "Loaded as"
self.assertEqual(
len(fldr["QDFile"]), 4,
"Failoed to group folder by Loaded As metadata with /= opeator.")
fldr.flatten()
def Fit(self):
"""Run the fitting code."""
self.Discard().Normalise().offset_correct()
chi2 = self.p0.shape[0] > 1
method = getattr(self, self.method)
if not chi2: # Single fit mode, consider whether to plot and save etc
fit = method(
self.model,
p0=self.p0,
result=True,
header="Fit",
output="report",
)
if self.show_plot:
self.plot_results()
if self.save_fit:
self.save(False)
if self.report:
print(fit.fit_report())
return fit
d = Data(self)
fit = d.lmfit(
self.model, p0=self.p0, result=True, header="Fit", output="data"
)
if self.show_plot:
fit.plot(multiple="panels", capsize=3)
fit.yscale = "log" # Adjust y scale for chi^2
fit.tight_layout()
if self.save_fit:
fit.filename = None
fit.save(False)
def import_moke(filename, roi=[-np.inf, np.inf], normalise=True):
"""
Returns moke data from a file
----------------------------------------------------
Params:
- filename of data file (str)
- region of interest - lower and upper bound of roi (2-long array)
- normalise = True/False
Returns:
- x and y data for MOKE in ROI
----------------------------------------------------
"""
lower = roi[0]
upper = roi[1]
d = Data(filename)
d.setas(x='Field(T)', y='MOKE Signal') # Set axes
if normalise == True:
d.y = normalise_moke(d.y)
d.del_rows('Field(T)', (lower, upper),
invert=True) # Delete rows except for ROI
return d.x, d.y
def test_sg_filter(self):
x=np.linspace(0,10*np.pi,1001)
y=np.sin(x)+np.random.normal(size=1001,scale=0.05)
d=Data(x,y,column_headers=["Time","Signal"],setas="xy")
d.SG_Filter(order=1,result=True)
d.setas="x.y"
d.y=d.y-np.cos(x)
self.assertAlmostEqual(d.y[5:-5].mean(), 0,places=2,msg="Failed to differentiate correctly")
def test_csvfile(self):
self.csv = Data(path.join(self.datadir, "working", "CSVFile_test.dat"),
filetype="JustNumbers",
column_headers=["Q", "I", "dI"],
setas="xye")
self.assertEqual(self.csv.shape, (167, 3),
"Failed to load CSVFile from text")
def hist(im, *args, **kargs):
"""Pass through to :py:func:`matplotlib.pyplot.hist` function."""
counts, edges = np.histogram(im.ravel(), *args, **kargs)
centres = (edges[1:] + edges[:-1]) / 2
new = Data(np.column_stack((centres, counts)))
new.column_headers = ["Intensity", "Frequency"]
new.setas = "xy"
return new
def test_grouping(self):
fldr4=SF.DataFolder()
x=np.linspace(-np.pi,np.pi,181)
for phase in np.linspace(0,1.0,5):
for amplitude in np.linspace(1,2,6):
for frequency in np.linspace(1,2,5):
y=amplitude*np.sin(frequency*x+phase*np.pi)
d=Data(x,y,setas="xy",column_headers=["X","Y"])
d["frequency"]=frequency
d["amplitude"]=amplitude
d["phase"]=phase
d["params"]=[phase,frequency,amplitude]
d.filename="test/{amplitude}/{phase}/{frequency}.dat".format(**d)
fldr4+=d
fldr4.unflatten()
self.assertEqual(fldr4.mindepth,3,"Unflattened DataFolder had wrong mindepth.")
self.assertEqual(fldr4.shape, (~~fldr4).shape,"Datafodler changed shape on flatten/unflatten")
fldr5=fldr4.select(amplitude=1.4,recurse=True)
fldr5.prune()
pruned=(0,
{'test': (0,
{'1.4': (0,
{'0.0': (5, {}),
'0.25': (5, {}),
'0.5': (5, {}),
'0.75': (5, {}),
'1.0': (5, {})})})})
selected=(0,
{'test': (0,
{'1.4': (0,
{'0.25': (1, {}), '0.5': (1, {}), '0.75': (1, {}), '1.0': (1, {})})})})
self.assertEqual(fldr5.shape,pruned,"Folder pruning gave an unxpected shape.")
self.assertEqual(fldr5[("test","1.4","0.5",0,"phase")],0.5,"Multilevel indexing of tree failed.")
shape=(~(~fldr4).select(amplitude=1.4).select(frequency=1).select(phase__gt=0.2)).shape
self.fldr4=fldr4
self.assertEqual(shape, selected,"Multi selects and inverts failed.")
g=(~fldr4)/10
self.assertEqual(g.shape,(0,{'Group 0': (15, {}),'Group 1': (15, {}),'Group 2': (15, {}),'Group 3': (15, {}),'Group 4': (15, {}),
'Group 5': (15, {}),'Group 6': (15, {}),'Group 7': (15, {}),'Group 8': (15, {}),'Group 9': (15, {})}),"Dive by int failed.")
g["Group 6"]-=5
self.assertEqual(g.shape,(0,{'Group 0': (15, {}),'Group 1': (15, {}),'Group 2': (15, {}),'Group 3': (15, {}),'Group 4': (15, {}),
'Group 5': (15, {}),'Group 6': (14, {}),'Group 7': (15, {}),'Group 8': (15, {}),'Group 9': (15, {})}),"Sub by int failed.")
remove=g["Group 3"][4]
g["Group 3"]-=remove
self.assertEqual(g.shape,(0,{'Group 0': (15, {}),'Group 1': (15, {}),'Group 2': (15, {}),'Group 3': (14, {}),'Group 4': (15, {}),
'Group 5': (15, {}),'Group 6': (14, {}),'Group 7': (15, {}),'Group 8': (15, {}),'Group 9': (15, {})}),"Sub by object failed.")
d=fldr4["test",1.0,1.0].gather(0,1)
self.assertEqual(d.shape,(181,6),"Gather seems have failed.")
self.assertTrue(np.all(fldr4["test",1.0,1.0].slice_metadata("phase")==
np.ones(5)),"Slice metadata failure.")
d=(~fldr4).extract("phase","frequency","amplitude","params")
self.assertEqual(d.shape,(150,6),"Extract failed to produce data of correct shape.")
self.assertEqual(d.column_headers,['phase', 'frequency', 'amplitude', 'params', 'params', 'params'],"Exctract failed to get correct column headers.")
p=fldr4["test",1.0,1.0]
p=SF.PlotFolder(p)
p.plot()
self.assertEqual(len(plt.get_fignums()),1,"Failed to generate a single plot for PlotFolder.")
plt.close("all")
def test_apply(self):
self.app=Data(np.zeros((100,1)),setas="y")
self.app.apply(lambda r:r.i[0],header="Counter")
def calc(r,omega=1.0,k=1.0):
return np.sin(r.y*omega)
self.app.apply(calc,replace=False,header="Sin",_extra={"omega":0.1},k=1.0)
self.app.apply(lambda r:r.__class__([r[1],r[0]]),replace=True,header=["Index","Sin"])
self.app.setas="xy"
self.assertAlmostEqual(self.app.integrate(output="result"),18.87616564214,msg="Integrate after aplies failed.")
def setUp(self):
x_data = np.linspace(-10, 10, 101)
y_data = 0.01 * x_data**2 + 0.3 * x_data - 2
y_data *= np.random.normal(size=101, loc=1.0, scale=0.01)
x_data += np.random.normal(size=101, scale=0.02)
self.data = Data(x_data, y_data, column_headers=["X", "Y"])
self.data.setas = "xy"
def setUp(self):
"""Create a test data set."""
x = np.linspace(1, 10, 10)
y = 2 * x - 3
dy = np.abs(y / 100)
z = x + 4
dz = np.abs(z / 100)
self.data = Data(
np.column_stack((x, y, dy, z, dz)),
column_headers=["Tine", "Signal 1", "d1", "Signal 2", "d2"])
def profile_line(img, src=None, dst=None, linewidth=1, order=1, mode="constant", cval=0.0, constrain=True, **kargs):
"""Wrapper for sckit-image method of the same name to get a line_profile.
Parameters:
img(ImageArray): Image data to take line section of
src, dst (2-tuple of int or float): start and end of line profile. If the co-ordinates
are given as intergers then they are assumed to be pxiel co-ordinates, floats are
assumed to be real-space co-ordinates using the embedded metadata.
linewidth (int): the wideth of the profile to be taken.
order (int 1-3): Order of interpolation used to find image data when not aligned to a point
mode (str): How to handle data outside of the image.
cval (float): The constant value to assume for data outside of the image is mode is "constant"
constrain (bool): Ensure the src and dst are within the image (default True).
Returns:
A :py:class:`Stoner.Data` object containing the line profile data and the metadata from the image.
"""
scale = img.get("MicronsPerPixel", 1.0)
r, c = img.shape
if src is None and dst is None:
if "x" in kargs:
src = (kargs["x"], 0)
dst = (kargs["x"], r)
if "y" in kargs:
src = (0, kargs["y"])
dst = (c, kargs["y"])
if isinstance(src, float):
src = (src, src)
if isinstance(dst, float):
dst = (dst, dst)
dst = _scale(dst, scale)
src = _scale(src, scale)
if not istuple(src, int, int):
raise ValueError("src co-ordinates are not a 2-tuple of ints.")
if not istuple(dst, int, int):
raise ValueError("dst co-ordinates are not a 2-tuple of ints.")
if constrain:
fix = lambda x, mx: int(round(sorted([0, x, mx])[1]))
r, c = img.shape
src = list(src)
src = (fix(src[0], r), fix(src[1], c))
dst = (fix(dst[0], r), fix(dst[1], c))
result = measure.profile_line(img, src, dst, linewidth, order, mode, cval)
points = measure.profile._line_profile_coordinates(src, dst, linewidth)[:, :, 0]
ret = Data()
ret.data = points.T
ret.setas = "xy"
ret &= np.sqrt(ret.x ** 2 + ret.y ** 2) * scale
ret &= result
ret.column_headers = ["X", "Y", "Distance", "Intensity"]
ret.setas = "..xy"
ret.metadata = img.metadata.copy()
return ret
def test_deltions(self):
ch=["{}-Data".format(chr(x)) for x in range(65,91)]
data=np.zeros((100,26))
metadata=OrderedDict([("Key 1",True),("Key 2",12),("Key 3","Hellow world")])
self.dd=Data(metadata)
self.dd.data=data
self.dd.column_headers=ch
self.dd.setas="3.x3.y3.z"
self.repr_string="""=========================== ======== ======= ======== ======= ======== ========
TDI Format 1.5 D-Data .... H-Data .... Y-Data Z-Data
index 3 (x) 7 (y) 24 25
=========================== ======== ======= ======== ======= ======== ========
Key 1{Boolean}= True 0 0 0 0
Key 2{I32}= 12 0 ... 0 ... 0 0
Key 3{String}= Hellow world 0 ... 0 ... 0 0
Stoner.class{String}= Data 0 ... 0 ... 0 0
... 0 ... 0 ... 0 0"""
self.assertEqual("\n".join(repr(self.dd).split("\n")[:9]),self.repr_string,"Representation with interesting columns failed.")
del self.dd["Key 1"]
self.assertEqual(len(self.dd.metadata),3,"Deletion of metadata failed.")
del self.dd[20:30]
self.assertEqual(self.dd.shape,(90,26),"Deleting rows directly failed.")
self.dd.del_column("Q")
self.assertEqual(self.dd.shape,(90,25),"Deleting rows directly failed.")
self.dd%=3
self.assertEqual(self.dd.shape,(90,24),"Deleting rows directly failed.")
self.dd.setas="x..y..z"
self.dd.del_column(self.dd.setas.not_set)
self.assertEqual(self.dd.shape,(90,3),"Deleting rows directly failed.")
self.dd.mask[50,1]=True
self.dd.del_column()
self.assertEqual(self.dd.shape,(90,2),"Deletion of masked rows failed.")
self.d5=Data(np.ones((10,10)))
self.d5.column_headers=["A"]*5+["B"]*5
self.d5.del_column("A",duplicates=True)
self.assertEqual(self.d5.shape,(10,6),"Failed to delete columns with duplicates True and col specified.")
self.d5=Data(np.ones((10,10)))
self.d5.column_headers=list("ABCDEFGHIJ")
self.d5.setas="..x..y"
self.d5.del_column(True)
self.assertEqual(self.d5.column_headers,["C","F"],"Failed to delete columns with col=True")
def LoadData(self, data_item_number, filename):
"""LoadData(self, data_item_number, filename) --> none
Loads the data from filename into the data_item_number.
"""
try:
datafile = Data(str(filename),
debug=True) # does all the hard work here
except Exception as e:
ShowWarningDialog(
self.parent,
"Could not load the file: " + filename +
" \nPlease check the format.\n\n Stoner.Data" +
" gave the following error:\n" + str(e),
)
else:
# For the freak case of only one data point
try:
if datafile.setas.cols["axes"] == 0:
self.x_col = datafile.find_col(self.x_col)
self.y_col = datafile.find_col(self.y_col)
self.e_col = datafile.find_col(self.e_col)
datafile.etsas(x=self.x_col, y=self.y_col, e=self.e_col)
else:
self.x_col = datafile.setas.cols["xcol"]
self.y_col = datafile.setas.cols["ycol"][0]
if len(datafile.setas.cols["yerr"]) > 0:
self.e_col = datafile.setas.cols["yerr"][0]
else:
datafile.add_column(np.ones(len(datafile)))
datafile.setas[-1] = "e"
except Exception as e:
ShowWarningDialog(
self.parent,
"The data file does not contain" +
"all the columns specified in the opions\n" + e.message,
)
# Okay now we have showed a dialog lets bail out ...
return
# The data is set by the default Template.__init__ function, neat hu
# Know the loaded data goes into *_raw so that they are not
# changed by the transforms
datafile.y = np.where(datafile.y == 0.0, 1e-8, datafile.y)
self.data[data_item_number].x_raw = datafile.x
self.data[data_item_number].y_raw = datafile.y
self.data[data_item_number].error_raw = datafile.e
# Run the commands on the data - this also sets the x,y, error memebers
# of that data item.
self.data[data_item_number].run_command()
# Send an update that new data has been loaded
self.SendUpdateDataEvent()
def test_Properties(self):
self.setUp()
fldr=self.fldr
fldr/="Loaded as"
fldr["QDFile"].group("Byapp")
self.assertEqual(fldr.mindepth,1,"mindepth attribute of folder failed.")
self.assertEqual(fldr.depth,2,"depth attribute failed.")
self.setUp()
fldr=self.fldr
fldr+=Data()
self.assertEqual(len(list(fldr.loaded)),1,"loaded attribute failed {}".format(len(list(fldr.loaded))))
self.assertEqual(len(list(fldr.not_empty)),len(fldr)-1,"not_empty attribute failed.")
fldr-="Untitled"
