def test_user_missing_values(self):
data = "A, B, C\n0, 0., 0j\n1, N/A, 1j\n-9, 2.2, N/A\n3, -99, 3j"
basekwargs = dict(dtype=None, delimiter=",", names=True)
mdtype = [("A", int), ("B", float), ("C", complex)]
#
test = np.mafromtxt(StringIO(data), missing_values="N/A", **basekwargs)
control = ma.array(
[(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 0)],
dtype=mdtype,
)
assert_equal(test, control)
#
basekwargs["dtype"] = mdtype
test = np.mafromtxt(StringIO(data), missing_values={0: -9, 1: -99, 2: -999j}, **basekwargs)
control = ma.array(
[(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype,
)
assert_equal(test, control)
#
test = np.mafromtxt(StringIO(data), missing_values={0: -9, "B": -99, "C": -999j}, **basekwargs)
control = ma.array(
[(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype,
)
assert_equal(test, control)
def test_user_missing_values(self):
datastr ="A, B, C\n0, 0., 0j\n1, N/A, 1j\n-9, 2.2, N/A\n3, -99, 3j"
data = StringIO.StringIO(datastr)
basekwargs = dict(dtype=None, delimiter=',', names=True, missing='N/A')
mdtype = [('A', int), ('B', float), ('C', complex)]
#
test = np.mafromtxt(data, **basekwargs)
control = ma.array([( 0, 0.0, 0j), (1, -999, 1j),
( -9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 0)],
dtype=mdtype)
assert_equal(test, control)
#
data.seek(0)
test = np.mafromtxt(data,
missing_values={0:-9, 1:-99, 2:-999j}, **basekwargs)
control = ma.array([( 0, 0.0, 0j), (1, -999, 1j),
( -9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
#
data.seek(0)
test = np.mafromtxt(data,
missing_values={0:-9, 'B':-99, 'C':-999j},
**basekwargs)
control = ma.array([( 0, 0.0, 0j), (1, -999, 1j),
( -9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
def test_user_missing_values(self):
data = "A, B, C\n0, 0., 0j\n1, N/A, 1j\n-9, 2.2, N/A\n3, -99, 3j"
basekwargs = dict(dtype=None, delimiter=",", names=True,)
mdtype = [('A', int), ('B', float), ('C', complex)]
#
test = np.mafromtxt(StringIO(data), missing_values="N/A",
**basekwargs)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
(-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 0)],
dtype=mdtype)
assert_equal(test, control)
#
basekwargs['dtype'] = mdtype
test = np.mafromtxt(StringIO(data),
missing_values={0:-9, 1:-99, 2:-999j}, **basekwargs)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
(-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
#
test = np.mafromtxt(StringIO(data),
missing_values={0:-9, 'B':-99, 'C':-999j},
**basekwargs)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
(-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
def test_withmissing(self):
data = StringIO('A,B\n0,1\n2,N/A')
kwargs = dict(delimiter=",", missing_values="N/A", names=True)
err = np.seterr(invalid='ignore')
try:
test = np.mafromtxt(data, dtype=None, **kwargs)
finally:
np.seterr(**err)
control = ma.array([(0, 1), (2, -1)],
mask=[(False, False), (False, True)],
dtype=[('A', np.int), ('B', np.int)])
assert_equal(test, control)
assert_equal(test.mask, control.mask)
#
data.seek(0)
err = np.seterr(invalid='ignore')
try:
test = np.mafromtxt(data, **kwargs)
finally:
np.seterr(**err)
control = ma.array([(0, 1), (2, -1)],
mask=[(False, False), (False, True)],
dtype=[('A', np.float), ('B', np.float)])
assert_equal(test, control)
assert_equal(test.mask, control.mask)
def test_withmissing(self):
data = StringIO.StringIO('A,B\n0,1\n2,N/A')
test = np.mafromtxt(data, dtype=None, delimiter=',', missing='N/A',
names=True)
control = ma.array([(0, 1), (2, -1)],
mask=[(False, False), (False, True)],
dtype=[('A', np.int), ('B', np.int)])
assert_equal(test, control)
assert_equal(test.mask, control.mask)
#
data.seek(0)
test = np.mafromtxt(data, delimiter=',', missing='N/A', names=True)
control = ma.array([(0, 1), (2, -1)],
mask=[[False, False], [False, True]],)
assert_equal(test, control)
assert_equal(test.mask, control.mask)
def test_fancy_dtype_alt(self):
"Check that a nested dtype isn't MIA"
data = StringIO.StringIO('1,2,3.0\n4,5,6.0\n')
fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
test = np.mafromtxt(data, dtype=fancydtype, delimiter=',')
control = ma.array([(1,(2,3.0)),(4,(5,6.0))], dtype=fancydtype)
assert_equal(test, control)
def test_fancy_dtype_alt(self):
"Check that a nested dtype isn't MIA"
data = StringIO.StringIO('1,2,3.0\n4,5,6.0\n')
fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
test = np.mafromtxt(data, dtype=fancydtype, delimiter=',')
control = ma.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=fancydtype)
assert_equal(test, control)
def test_fancy_dtype_alt(self):
"Check that a nested dtype isn't MIA"
data = StringIO("1,2,3.0\n4,5,6.0\n")
fancydtype = np.dtype([("x", int), ("y", [("t", int), ("s", float)])])
test = np.mafromtxt(data, dtype=fancydtype, delimiter=",")
control = ma.array([(1, (2, 3.0)), (4, (5, 6.0))], dtype=fancydtype)
assert_equal(test, control)
def test_withmissing_float(self):
data = StringIO("A,B\n0,1.5\n2,-999.00")
test = np.mafromtxt(data, dtype=None, delimiter=",", missing_values="-999.0", names=True)
control = ma.array(
[(0, 1.5), (2, -1.0)], mask=[(False, False), (False, True)], dtype=[("A", np.int), ("B", np.float)]
)
assert_equal(test, control)
assert_equal(test.mask, control.mask)
def test_withmissing(self):
data = StringIO("A,B\n0,1\n2,N/A")
kwargs = dict(delimiter=",", missing_values="N/A", names=True)
test = np.mafromtxt(data, dtype=None, **kwargs)
control = ma.array(
[(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[("A", np.int), ("B", np.int)]
)
assert_equal(test, control)
assert_equal(test.mask, control.mask)
#
data.seek(0)
test = np.mafromtxt(data, **kwargs)
control = ma.array(
[(0, 1), (2, -1)], mask=[(False, False), (False, True)], dtype=[("A", np.float), ("B", np.float)]
)
assert_equal(test, control)
assert_equal(test.mask, control.mask)
def test_withmissing_float(self):
data = StringIO.StringIO('A,B\n0,1.5\n2,-999.00')
test = np.mafromtxt(data, dtype=None, delimiter=',', missing='-999.0',
names=True,)
control = ma.array([(0, 1.5), (2, -1.)],
mask=[(False, False), (False, True)],
dtype=[('A', np.int), ('B', np.float)])
assert_equal(test, control)
assert_equal(test.mask, control.mask)
def test_withmissing_float(self):
data = StringIO('A,B\n0,1.5\n2,-999.00')
test = np.mafromtxt(data, dtype=None, delimiter=',',
missing_values='-999.0', names=True,)
control = ma.array([(0, 1.5), (2, -1.)],
mask=[(False, False), (False, True)],
dtype=[('A', np.int), ('B', np.float)])
assert_equal(test, control)
assert_equal(test.mask, control.mask)
def test_user_missing_values(self):
data = "A, B, C\n0, 0., 0j\n1, N/A, 1j\n-9, 2.2, N/A\n3, -99, 3j"
basekwargs = dict(dtype=None, delimiter=",", names=True,)
mdtype = [('A', int), ('B', float), ('C', complex)]
#
err = np.seterr(invalid='ignore')
try:
test = np.mafromtxt(StringIO(data), missing_values="N/A",
**basekwargs)
finally:
np.seterr(**err)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
(-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 0)],
dtype=mdtype)
assert_equal(test, control)
#
basekwargs['dtype'] = mdtype
err = np.seterr(invalid='ignore')
try:
test = np.mafromtxt(StringIO(data),
missing_values={0:-9, 1:-99, 2:-999j},
**basekwargs)
finally:
np.seterr(**err)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
(-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
#
err = np.seterr(invalid='ignore')
try:
test = np.mafromtxt(StringIO(data),
missing_values={0:-9, 'B':-99, 'C':-999j},
**basekwargs)
finally:
np.seterr(**err)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j),
(-9, 2.2, -999j), (3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
def test_withmissing(self):
data = StringIO.StringIO('A,B\n0,1\n2,N/A')
test = np.mafromtxt(data,
dtype=None,
delimiter=',',
missing='N/A',
names=True)
control = ma.array([(0, 1), (2, -1)],
mask=[(False, False), (False, True)],
dtype=[('A', np.int), ('B', np.int)])
assert_equal(test, control)
assert_equal(test.mask, control.mask)
#
data.seek(0)
test = np.mafromtxt(data, delimiter=',', missing='N/A', names=True)
control = ma.array(
[(0, 1), (2, -1)],
mask=[[False, False], [False, True]],
)
assert_equal(test, control)
assert_equal(test.mask, control.mask)
def test_user_missing_values(self):
datastr = "A, B, C\n0, 0., 0j\n1, N/A, 1j\n-9, 2.2, N/A\n3, -99, 3j"
data = StringIO.StringIO(datastr)
basekwargs = dict(dtype=None, delimiter=',', names=True, missing='N/A')
mdtype = [('A', int), ('B', float), ('C', complex)]
#
test = np.mafromtxt(data, **basekwargs)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j),
(3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (0, 0, 1), (0, 0, 0)],
dtype=mdtype)
assert_equal(test, control)
#
data.seek(0)
test = np.mafromtxt(data,
missing_values={
0: -9,
1: -99,
2: -999j
},
**basekwargs)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j),
(3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
#
data.seek(0)
test = np.mafromtxt(data,
missing_values={
0: -9,
'B': -99,
'C': -999j
},
**basekwargs)
control = ma.array([(0, 0.0, 0j), (1, -999, 1j), (-9, 2.2, -999j),
(3, -99, 3j)],
mask=[(0, 0, 0), (0, 1, 0), (1, 0, 1), (0, 1, 0)],
dtype=mdtype)
assert_equal(test, control)
ll_corner = arcpy.Point(-180., -90.0) # to position the bottom left corner
dx_dy = 0.5
nodata = '-9999'
#
# ---- create the basic workspace parameters, create masked arrays ----
#
out_file = r'c:\Data\ascii_samples\avg_yr.tif'
folder = r'C:\Data\ascii_samples'
arcpy.env.workspace = folder
ascii_files = arcpy.ListFiles("*.asc")
a_s = [folder + '\{}'.format(i) for i in ascii_files]
arrays = []
for arr in a_s[:1]:
a = np.mafromtxt(arr, dtype='int32', comments='#',
delimiter=' ', skip_header=6,
missing_values=nodata, usemask=True)
value_to_nodata = int(a.get_fill_value())
out = a.view(np.ndarray, fill_value=value_to_nodata)
r = arcpy.NumPyArrayToRaster(out, ll_corner, dx_dy, dx_dy)
out_file = arr.replace(".asc", ".tif")
if save_output:
r.save(out_file)
del r
# ----------------------------------------------------------------------
# __main__ .... code section
if __name__ == "__main__":
"""Optionally...
: - print the script source name.
: - run the _demo
