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import
re
import
uuid
from
pathlib
import
Path
from
uuid
import
UUID
import
numpy
as
np
import
pytest
from
deepdiff
import
DeepDiff
from
freezegun
import
freeze_time
from
diffpy
.
utils
.
diffraction_objects
import
XQUANTITIES
,
DiffractionObject
@
pytest
.
mark
.
parametrize
(
"do_args_1, do_args_2, expected_equality, wavelength_warning_expected"
,
[
# Test when __eq__ returns True and False
(
# C1: Identical args, expect equality
{
"name"
:
"same"
,
"scat_quantity"
:
"x-ray"
,
"wavelength"
:
0.71
,
"xtype"
:
"q"
,
"xarray"
:
np
.
array
([
1.0
,
2.0
]),
"yarray"
:
np
.
array
([
100.0
,
200.0
]),
"metadata"
: {
"thing1"
:
1
},
},
{
"name"
:
"same"
,
"scat_quantity"
:
"x-ray"
,
"wavelength"
:
0.71
,
"xtype"
:
"q"
,
"xarray"
:
np
.
array
([
1.0
,
2.0
]),
"yarray"
:
np
.
array
([
100.0
,
200.0
]),
"metadata"
: {
"thing1"
:
1
},
},
True
,
False
,
),
(
# C2: Different names, expect inequality
{
"name"
:
"something"
,
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
{
"name"
:
"something else"
,
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
False
,
True
,
),
(
# C3: One without wavelength, expect inequality
{
"wavelength"
:
0.71
,
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
{
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
False
,
True
,
),
(
# C4: Different wavelength values, expect inequality
{
"wavelength"
:
0.71
,
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
{
"wavelength"
:
0.711
,
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
False
,
False
,
),
(
# C5: Different scat_quantity, expect inequality
{
"scat_quantity"
:
"x-ray"
,
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
{
"scat_quantity"
:
"neutron"
,
"xtype"
:
"tth"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
False
,
True
,
),
(
# C6: Different q xarray values, expect inequality
{
"xtype"
:
"q"
,
"xarray"
:
np
.
array
([
1.0
,
2.0
]),
"yarray"
:
np
.
array
([
100.0
,
200.0
]),
},
{
"xtype"
:
"q"
,
"xarray"
:
np
.
array
([
3.0
,
4.0
]),
"yarray"
:
np
.
array
([
100.0
,
200.0
]),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
False
,
True
,
),
(
# C7: Different metadata, expect inequality
{
"xtype"
:
"q"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
0
,
"thing2"
:
"thing2"
},
},
{
"xtype"
:
"q"
,
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"metadata"
: {
"thing1"
:
1
,
"thing2"
:
"thing2"
},
},
False
,
True
,
),
],
)
def
test_diffraction_objects_equality
(
do_args_1
,
do_args_2
,
expected_equality
,
wavelength_warning_expected
,
wavelength_warning_msg
,
):
if
wavelength_warning_expected
:
with
pytest
.
warns
(
UserWarning
,
match
=
re
.
escape
(
wavelength_warning_msg
)
):
do_1
=
DiffractionObject
(
**
do_args_1
)
do_2
=
DiffractionObject
(
**
do_args_2
)
else
:
do_1
=
DiffractionObject
(
**
do_args_1
)
do_2
=
DiffractionObject
(
**
do_args_2
)
assert
(
do_1
==
do_2
)
==
expected_equality
@
pytest
.
mark
.
parametrize
(
"xtype, expected_xarray"
,
[
# Test whether on_xtype returns the correct xarray values.
# C1: tth to tth, expect no change in xarray value
# 1. "tth" provided, expect tth
(
"tth"
,
np
.
array
([
30
,
60
])),
# 2. "2theta" provided, expect tth
(
"2theta"
,
np
.
array
([
30
,
60
])),
# C2: "q" provided, expect q converted from tth
(
"q"
,
np
.
array
([
0.51764
,
1
])),
# C3: "d" provided, expect d converted from tth
(
"d"
,
np
.
array
([
12.13818
,
6.28319
])),
],
)
def
test_on_xtype
(
xtype
,
expected_xarray
,
do_minimal_tth
):
do
=
do_minimal_tth
actual_xrray
,
actual_yarray
=
do
.
on_xtype
(
xtype
)
assert
np
.
allclose
(
actual_xrray
,
expected_xarray
)
assert
np
.
allclose
(
actual_yarray
,
np
.
array
([
1
,
2
]))
def
test_init_invalid_xtype
():
with
pytest
.
raises
(
ValueError
,
match
=
re
.
escape
(
f"I don't know how to handle the xtype, 'invalid_type'. "
f"Please rerun specifying an xtype from
{
*
XQUANTITIES
,
}
"
),
):
return
DiffractionObject
(
xarray
=
np
.
empty
(
0
),
yarray
=
np
.
empty
(
0
),
xtype
=
"invalid_type"
,
wavelength
=
1.54
,
)
@
pytest
.
mark
.
parametrize
(
"org_do_args, target_do_args, scale_inputs, expected"
,
[
# Test whether the original y-array is scaled as expected
(
# C1: none of q, tth, d, provided, expect to scale on the maximal
# intensity from each object
{
"xarray"
:
np
.
array
([
0.1
,
0.2
,
0.3
]),
"yarray"
:
np
.
array
([
1
,
2
,
3
]),
"xtype"
:
"q"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"xarray"
:
np
.
array
([
0.05
,
0.1
,
0.2
,
0.3
]),
"yarray"
:
np
.
array
([
5
,
10
,
20
,
30
]),
"xtype"
:
"q"
,
"wavelength"
:
2
*
np
.
pi
,
},
{},
{
"xtype"
:
"q"
,
"yarray"
:
np
.
array
([
10
,
20
,
30
])},
),
(
# C2: Same x-arrays
# x-value has exact matches at tth=60 (y=60) and tth=60 (y=6),
# for original and target diffraction objects,
# expect original y-array to multiply by 6/60=1/10
{
"xarray"
:
np
.
array
([
10
,
15
,
25
,
30
,
60
,
140
]),
"yarray"
:
np
.
array
([
10
,
20
,
25
,
30
,
60
,
100
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"xarray"
:
np
.
array
([
10
,
20
,
25
,
30
,
60
,
140
]),
"yarray"
:
np
.
array
([
2
,
3
,
4
,
5
,
6
,
7
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"tth"
:
60
},
{
"xtype"
:
"tth"
,
"yarray"
:
np
.
array
([
1
,
2
,
2.5
,
3
,
6
,
10
])},
),
(
# C3: Different x-arrays with same length,
# x-value has closest match at q=0.12 (y=10) and q=0.14 (y=1)
# for original and target diffraction objects,
# expect original y-array to multiply by 1/10
{
"xarray"
:
np
.
array
([
0.12
,
0.24
,
0.31
,
0.4
]),
"yarray"
:
np
.
array
([
10
,
20
,
40
,
60
]),
"xtype"
:
"q"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"xarray"
:
np
.
array
([
0.14
,
0.24
,
0.31
,
0.4
]),
"yarray"
:
np
.
array
([
1
,
3
,
4
,
5
]),
"xtype"
:
"q"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"q"
:
0.1
},
{
"xtype"
:
"q"
,
"yarray"
:
np
.
array
([
1
,
2
,
4
,
6
])},
),
(
# C4: Different x-array lengths
# x-value has closest matches at tth=61 (y=50) and tth=62 (y=5),
# for original and target diffraction objects,
# expect original y-array to multiply by 5/50=1/10
{
"xarray"
:
np
.
array
([
10
,
25
,
30.1
,
40.2
,
61
,
120
,
140
]),
"yarray"
:
np
.
array
([
10
,
20
,
30
,
40
,
50
,
60
,
100
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"xarray"
:
np
.
array
([
20
,
25.5
,
32
,
45
,
50
,
62
,
100
,
125
,
140
]),
"yarray"
:
np
.
array
([
1.1
,
2
,
3
,
3.5
,
4
,
5
,
10
,
12
,
13
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"tth"
:
60
},
{
"xtype"
:
"tth"
,
"yarray"
:
np
.
array
([
1
,
2
,
3
,
4
,
5
,
6
,
10
])},
),
(
# C5.1: Reuse test case from C1, none of q, tth, d, provided, but
# include an offset, expect scaled y-array in C1 to shift up by 2
{
"xarray"
:
np
.
array
([
0.1
,
0.2
,
0.3
]),
"yarray"
:
np
.
array
([
1
,
2
,
3
]),
"xtype"
:
"q"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"xarray"
:
np
.
array
([
0.05
,
0.1
,
0.2
,
0.3
]),
"yarray"
:
np
.
array
([
5
,
10
,
20
,
30
]),
"xtype"
:
"q"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"offset"
:
2
},
{
"xtype"
:
"q"
,
"yarray"
:
np
.
array
([
12
,
22
,
32
])},
),
(
# C5.2: Reuse test case from C4, but include an offset, expect
# scaled y-array in C4 to shift up by 2
{
"xarray"
:
np
.
array
([
10
,
25
,
30.1
,
40.2
,
61
,
120
,
140
]),
"yarray"
:
np
.
array
([
10
,
20
,
30
,
40
,
50
,
60
,
100
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"xarray"
:
np
.
array
([
20
,
25.5
,
32
,
45
,
50
,
62
,
100
,
125
,
140
]),
"yarray"
:
np
.
array
([
1.1
,
2
,
3
,
3.5
,
4
,
5
,
10
,
12
,
13
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"tth"
:
60
,
"offset"
:
2
},
{
"xtype"
:
"tth"
,
"yarray"
:
np
.
array
([
3
,
4
,
5
,
6
,
7
,
8
,
12
])},
),
],
)
def
test_scale_to
(
org_do_args
,
target_do_args
,
scale_inputs
,
expected
):
original_do
=
DiffractionObject
(
**
org_do_args
)
target_do
=
DiffractionObject
(
**
target_do_args
)
scaled_do
=
original_do
.
scale_to
(
target_do
,
**
scale_inputs
)
# Check the intensity data is the same as expected
assert
np
.
allclose
(
scaled_do
.
on_xtype
(
expected
[
"xtype"
])[
1
],
expected
[
"yarray"
]
)
@
pytest
.
mark
.
parametrize
(
"org_do_args, target_do_args, scale_inputs"
,
[
# Test expected errors produced from scale_to() with invalid inputs
(
# C2: tth and d both provided, expect ValueError
{
"xarray"
:
np
.
array
([
10
,
25
,
30.1
,
40.2
,
61
,
120
,
140
]),
"yarray"
:
np
.
array
([
10
,
20
,
30
,
40
,
50
,
60
,
100
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"xarray"
:
np
.
array
([
20
,
25.5
,
32
,
45
,
50
,
62
,
100
,
125
,
140
]),
"yarray"
:
np
.
array
([
1.1
,
2
,
3
,
3.5
,
4
,
5
,
10
,
12
,
13
]),
"xtype"
:
"tth"
,
"wavelength"
:
2
*
np
.
pi
,
},
{
"tth"
:
60
,
"d"
:
10
,
},
),
],
)
def
test_scale_to_bad
(
org_do_args
,
target_do_args
,
scale_inputs
):
original_do
=
DiffractionObject
(
**
org_do_args
)
target_do
=
DiffractionObject
(
**
target_do_args
)
with
pytest
.
raises
(
ValueError
,
match
=
"You must specify none or exactly one of 'q', 'tth', or 'd'. "
"Please provide either none or one value."
,
):
original_do
.
scale_to
(
target_do
,
**
scale_inputs
)
@
pytest
.
mark
.
parametrize
(
"do_args, get_array_index_inputs, expected_index"
,
[
# Test get_array_index() returns the expected index given xtype and
# value
(
# C1: Target value is in the xarray and xtype is identical, expect
# exact index match
{
"wavelength"
:
4
*
np
.
pi
,
"xarray"
:
np
.
array
([
30.005
,
60
]),
"yarray"
:
np
.
array
([
1
,
2
]),
"xtype"
:
"tth"
,
},
{
"xtype"
:
"tth"
,
"value"
:
30.005
,
},
0
,
),
# C2: Target value lies in the array, expect the closest index
(
# 1. xtype(tth) is equal to self._input_xtype(tth)
{
"wavelength"
:
4
*
np
.
pi
,
"xarray"
:
np
.
array
([
30
,
60
]),
"yarray"
:
np
.
array
([
1
,
2
]),
"xtype"
:
"tth"
,
},
{
"xtype"
:
"tth"
,
"value"
:
45
,
},
0
,
),
(
# 2. use default xtype(equal to self._input_xtype)
{
"wavelength"
:
4
*
np
.
pi
,
"xarray"
:
np
.
array
([
30
,
60
]),
"yarray"
:
np
.
array
([
1
,
2
]),
"xtype"
:
"tth"
,
},
{
"xtype"
:
None
,
"value"
:
45
,
},
0
,
),
(
# 3. xtype(q) is different from self._input_xtype(tth)
{
"wavelength"
:
4
*
np
.
pi
,
"xarray"
:
np
.
array
([
30
,
60
]),
"yarray"
:
np
.
array
([
1
,
2
]),
"xtype"
:
"tth"
,
},
{
"xtype"
:
"q"
,
"value"
:
0.5
,
},
1
,
),
# C3: Target value out of the range, expect the closest index
(
# 1. Test with xtype of "q"
{
"wavelength"
:
4
*
np
.
pi
,
"xarray"
:
np
.
array
([
0.25
,
0.5
,
0.71
]),
"yarray"
:
np
.
array
([
1
,
2
,
3
]),
"xtype"
:
"q"
,
},
{
"xtype"
:
"q"
,
"value"
:
0.1
,
},
0
,
),
(
# 2. Test with xtype of "tth"
{
"wavelength"
:
4
*
np
.
pi
,
"xarray"
:
np
.
array
([
30
,
60
]),
"yarray"
:
np
.
array
([
1
,
2
]),
"xtype"
:
"tth"
,
},
{
"xtype"
:
"tth"
,
"value"
:
63
,
},
1
,
),
],
)
def
test_get_array_index
(
do_args
,
get_array_index_inputs
,
expected_index
):
do
=
DiffractionObject
(
**
do_args
)
actual_index
=
do
.
get_array_index
(
get_array_index_inputs
[
"value"
],
get_array_index_inputs
[
"xtype"
]
)
assert
actual_index
==
expected_index
def
test_get_array_index_bad
():
# empty array in DiffractionObject
do
=
DiffractionObject
(
wavelength
=
2
*
np
.
pi
,
xarray
=
np
.
array
([]),
yarray
=
np
.
array
([]),
xtype
=
"tth"
,
)
with
pytest
.
raises
(
ValueError
,
match
=
re
.
escape
(
"The 'tth' array is empty. Please ensure it is initialized."
),
):
do
.
get_array_index
(
xtype
=
"tth"
,
xvalue
=
30
)
# non-existing xtype
do
=
DiffractionObject
(
wavelength
=
4
*
np
.
pi
,
xarray
=
np
.
array
([
30
,
60
]),
yarray
=
np
.
array
([
1
,
2
]),
xtype
=
"tth"
,
)
non_existing_xtype
=
"non_existing_xtype"
with
pytest
.
raises
(
ValueError
,
match
=
re
.
escape
(
f"I don't know how to handle the xtype, '
{
non_existing_xtype
}
'. "
f"Please rerun specifying an xtype from
{
*
XQUANTITIES
,
}
"
),
):
do
.
get_array_index
(
xtype
=
non_existing_xtype
,
xvalue
=
30
)
def
test_dump
(
tmp_path
,
mocker
):
x
,
y
=
np
.
linspace
(
0
,
5
,
6
),
np
.
linspace
(
0
,
5
,
6
)
directory
=
Path
(
tmp_path
)
file
=
directory
/
"testfile"
with
pytest
.
warns
(
RuntimeWarning
,
match
=
"divide by zero encountered in divide"
):
do
=
DiffractionObject
(
wavelength
=
1.54
,
name
=
"test"
,
scat_quantity
=
"x-ray"
,
xarray
=
np
.
array
(
x
),
yarray
=
np
.
array
(
y
),
xtype
=
"q"
,
metadata
=
{
"thing1"
:
1
,
"thing2"
:
"thing2"
,
"package_info"
: {
"package2"
:
"3.4.5"
},
},
)
mocker
.
patch
(
"importlib.metadata.version"
,
return_value
=
"3.3.0"
)
with
freeze_time
(
"2012-01-14"
):
do
.
dump
(
file
,
"q"
)
with
open
(
file
,
"r"
)
as
f
:
actual
=
f
.
read
()
expected
=
(
"[DiffractionObject]
\n
"
"name = test
\n
"
"wavelength = 1.54
\n
"
"scat_quantity = x-ray
\n
"
"thing1 = 1
\n
"
"thing2 = thing2
\n
"
"package_info = {'package2': '3.4.5', 'diffpy.utils': '3.3.0'}
\n
"
"creation_time = 2012-01-14 00:00:00
\n
\n
"
"#### start data
\n
0.000000000000000000e+00 0.000000000000000000e+00
\n
"
"1.000000000000000000e+00 1.000000000000000000e+00
\n
"
"2.000000000000000000e+00 2.000000000000000000e+00
\n
"
"3.000000000000000000e+00 3.000000000000000000e+00
\n
"
"4.000000000000000000e+00 4.000000000000000000e+00
\n
"
"5.000000000000000000e+00 5.000000000000000000e+00
\n
"
)
assert
actual
==
expected
@
pytest
.
mark
.
parametrize
(
(
"do_init_args, expected_do_dict, "
"divide_by_zero_warning_expected, wavelength_warning_expected"
),
[
# Test __dict__ of DiffractionObject instance initialized with valid
# arguments
(
# C1: Instantiate DO with empty arrays, expect it to be a valid DO,
# but with everything empty
{
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"xtype"
:
"tth"
,
},
{
"_all_arrays"
:
np
.
array
([]),
"_input_xtype"
:
"tth"
,
"metadata"
: {},
"name"
:
""
,
"scat_quantity"
:
""
,
"qmin"
:
np
.
float64
(
np
.
inf
),
"qmax"
:
np
.
float64
(
0.0
),
"tthmin"
:
np
.
float64
(
np
.
inf
),
"tthmax"
:
np
.
float64
(
0.0
),
"dmin"
:
np
.
float64
(
np
.
inf
),
"dmax"
:
np
.
float64
(
0.0
),
"wavelength"
:
None
,
},
False
,
True
,
),
(
# C2: Instantiate just DO with empty array like in C1 but with
# wavelength, xtype, name, and metadata expect a valid DO with
# empty arrays, but with some non-array attributes
{
"xarray"
:
np
.
empty
(
0
),
"yarray"
:
np
.
empty
(
0
),
"xtype"
:
"tth"
,
"name"
:
"test_name"
,
"wavelength"
:
1.54
,
"metadata"
: {
"item_1"
:
"1"
,
"item_2"
:
"2"
},
},
{
"_all_arrays"
:
np
.
array
([]),
"_input_xtype"
:
"tth"
,
"metadata"
: {
"item_1"
:
"1"
,
"item_2"
:
"2"
},
"name"
:
"test_name"
,
"scat_quantity"
:
""
,
"qmin"
:
np
.
float64
(
np
.
inf
),
"qmax"
:
np
.
float64
(
0.0
),
"tthmin"
:
np
.
float64
(
np
.
inf
),
"tthmax"
:
np
.
float64
(
0.0
),
"dmin"
:
np
.
float64
(
np
.
inf
),
"dmax"
:
np
.
float64
(
0.0
),
"wavelength"
:
1.54
,
},
False
,
False
,
),
(
# C3: Minimum arguments provided for init with non-empty values
# for xarray and yarray and wavelength expect all attributes set
# without None
{
"xarray"
:
np
.
array
([
0.0
,
90.0
,
180.0
]),
"yarray"
:
np
.
array
([
1.0
,
2.0
,
3.0
]),
"xtype"
:
"tth"
,
"wavelength"
:
4.0
*
np
.
pi
,
},
{
"_all_arrays"
:
np
.
array
(
[
[
1.0
,
0.0
,
0.0
,
np
.
float64
(
np
.
inf
)],
[
2.0
,
1.0
/
np
.
sqrt
(
2
),
90.0
,
np
.
sqrt
(
2
)
*
2
*
np
.
pi
],
[
3.0
,
1.0
,
180.0
,
1.0
*
2
*
np
.
pi
],
]
),
"metadata"
: {},
"_input_xtype"
:
"tth"
,
"name"
:
""
,
"scat_quantity"
:
""
,
"qmin"
:
np
.
float64
(
0.0
),
"qmax"
:
np
.
float64
(
1.0
),
"tthmin"
:
np
.
float64
(
0.0
),
"tthmax"
:
np
.
float64
(
180.0
),
"dmin"
:
np
.
float64
(
2
*
np
.
pi
),
"dmax"
:
np
.
float64
(
np
.
inf
),
"wavelength"
:
4.0
*
np
.
pi
,
},
True
,
False
,
),
(
# C4: Same as C3, but with an optional scat_quantity argument,
# expect non-empty string for scat_quantity
{
"xarray"
:
np
.
array
(
[
np
.
inf
,
2
*
np
.
sqrt
(
2
)
*
np
.
pi
,
2
*
np
.
pi
]
),
"yarray"
:
np
.
array
([
1.0
,
2.0
,
3.0
]),
"xtype"
:
"d"
,
"wavelength"
:
4.0
*
np
.
pi
,
"scat_quantity"
:
"x-ray"
,
},
{
"_all_arrays"
:
np
.
array
(
[
[
1.0
,
0.0
,
0.0
,
np
.
float64
(
np
.
inf
)],
[
2.0
,
1.0
/
np
.
sqrt
(
2
),
90.0
,
np
.
sqrt
(
2
)
*
2
*
np
.
pi
],
[
3.0
,
1.0
,
180.0
,
1.0
*
2
*
np
.
pi
],
]
),
"metadata"
: {},
"_input_xtype"
:
"d"
,
"name"
:
""
,
"scat_quantity"
:
"x-ray"
,
"qmin"
:
np
.
float64
(
0.0
),
"qmax"
:
np
.
float64
(
1.0
),
"tthmin"
:
np
.
float64
(
0.0
),
"tthmax"
:
np
.
float64
(
180.0
),
"dmin"
:
np
.
float64
(
2
*
np
.
pi
),
"dmax"
:
np
.
float64
(
np
.
inf
),
"wavelength"
:
4.0
*
np
.
pi
,
},
False
,
False
,
),
],
)
def
test_init_valid
(
do_init_args
,
expected_do_dict
,
divide_by_zero_warning_expected
,
wavelength_warning_expected
,
wavelength_warning_msg
,
):
if
divide_by_zero_warning_expected
:
with
pytest
.
warns
(
RuntimeWarning
,
match
=
"divide by zero encountered in divide"
):
actual_do_dict
=
DiffractionObject
(
**
do_init_args
).
__dict__
elif
wavelength_warning_expected
:
with
pytest
.
warns
(
UserWarning
,
match
=
re
.
escape
(
wavelength_warning_msg
)
):
actual_do_dict
=
DiffractionObject
(
**
do_init_args
).
__dict__
else
:
actual_do_dict
=
DiffractionObject
(
**
do_init_args
).
__dict__
diff
=
DeepDiff
(
actual_do_dict
,
expected_do_dict
,
ignore_order
=
True
,
significant_digits
=
13
,
exclude_paths
=
"root['_uuid']"
,
)
assert
diff
==
{}
@
pytest
.
mark
.
parametrize
(
"do_init_args, expected_error_msg"
,
[
# Test expected error messages when 3 required arguments not provided
# in DiffractionObject init
(
# C1: No arguments provided, expect 3 required positional
# arguments error
{},
(
"missing 3 required positional arguments: "
"'xarray', 'yarray', and 'xtype'"
),
),
(
# C2: Only xarray and yarray provided, expect 1 required
# positional argument error
{
"xarray"
:
np
.
array
([
0.0
,
90.0
]),
"yarray"
:
np
.
array
([
0.0
,
90.0
])},
"missing 1 required positional argument: 'xtype'"
,
),
],
)
def
test_init_invalid_args
(
do_init_args
,
expected_error_msg
,
):
with
pytest
.
raises
(
TypeError
,
match
=
expected_error_msg
):
DiffractionObject
(
**
do_init_args
)
def
test_all_array_getter
(
do_minimal_tth
):
actual_do
=
do_minimal_tth
print
(
actual_do
.
all_arrays
)
expected_all_arrays
=
[
[
1
,
0.51763809
,
30
,
12.13818192
],
[
2
,
1
,
60
,
6.28318531
],
]
assert
np
.
allclose
(
actual_do
.
all_arrays
,
expected_all_arrays
)
def
test_all_array_setter
(
do_minimal
):
do
=
do_minimal
with
pytest
.
raises
(
AttributeError
,
match
=
"Direct modification of attribute 'all_arrays' is not allowed. "
"Please use 'input_data' to modify 'all_arrays'."
,
):
do
.
all_arrays
=
np
.
empty
((
4
,
4
))
def
test_uuid_getter
(
do_minimal
):
do
=
do_minimal
assert
hasattr
(
do
,
"uuid"
)
assert
isinstance
(
do
.
uuid
,
UUID
)
assert
len
(
str
(
do
.
uuid
))
==
36
def
test_uuid_getter_with_mock
(
mocker
,
do_minimal
):
mocker
.
patch
.
object
(
DiffractionObject
,
"uuid"
,
new_callable
=
lambda
:
UUID
(
"d67b19c6-3016-439f-81f7-cf20a04bee87"
),
)
do
=
do_minimal
assert
do
.
uuid
==
UUID
(
"d67b19c6-3016-439f-81f7-cf20a04bee87"
)
def
test_uuid_setter_error
(
do_minimal
):
do
=
do_minimal
with
pytest
.
raises
(
AttributeError
,
match
=
(
"Direct modification of attribute 'uuid' is not allowed. "
"Please use 'input_data' to modify 'uuid'."
),
):
do
.
uuid
=
uuid
.
uuid4
()
def
test_xarray_yarray_length_mismatch
():
with
pytest
.
raises
(
ValueError
,
match
=
"'xarray' and 'yarray' are different lengths. "
"They must correspond to each other and have the same length. Please "
"re-initialize 'DiffractionObject'with valid 'xarray' and 'yarray's"
,
):
DiffractionObject
(
xarray
=
np
.
array
([
1.0
,
2.0
]),
yarray
=
np
.
array
([
0.0
,
0.0
,
0.0
]),
xtype
=
"tth"
,
wavelength
=
1.54
,
)
def
test_input_xtype_getter
(
do_minimal
):
do
=
do_minimal
assert
do
.
input_xtype
==
"tth"
def
test_input_xtype_setter_error
(
do_minimal
):
do
=
do_minimal
with
pytest
.
raises
(
AttributeError
,
match
=
"Direct modification of attribute 'input_xtype' is not allowed. "
"Please use 'input_data' to modify 'input_xtype'."
,
):
do
.
input_xtype
=
"q"
def
test_copy_object
(
do_minimal
):
do
=
do_minimal
do_copy
=
do
.
copy
()
assert
do
==
do_copy
assert
id
(
do
)
!=
id
(
do_copy
)
@
pytest
.
mark
.
parametrize
(
"operation, starting_yarray, scalar_value, expected_yarray"
,
[
# Test scalar addition, subtraction, multiplication, and division to
# y-values by adding a scalar value
# C1: Test scalar addition to y-values (intensity), expect no change
# to x-values (q, tth, d)
(
# 1. Add 5
"add"
,
np
.
array
([
1.0
,
2.0
]),
5
,
np
.
array
([
6.0
,
7.0
]),
),
(
# 2. Add 5.1
"add"
,
np
.
array
([
1.0
,
2.0
]),
5.1
,
np
.
array
([
6.1
,
7.1
]),
),
# C2: Test scalar subtraction to y-values (intensity), expect no
# change to x-values (q, tth, d)
(
# 1. Subtract 1
"sub"
,
np
.
array
([
1.0
,
2.0
]),
1
,
np
.
array
([
0.0
,
1.0
]),
),
(
# 2. Subtract 0.5
"sub"
,
np
.
array
([
1.0
,
2.0
]),
0.5
,
np
.
array
([
0.5
,
1.5
]),
),
# C3: Test scalar multiplication to y-values (intensity), expect no
# change to x-values (q, tth, d)
(
# 1. Multiply by 2
"mul"
,
np
.
array
([
1.0
,
2.0
]),
2
,
np
.
array
([
2.0
,
4.0
]),
),
(
# 2. Multiply by 2.5
"mul"
,
np
.
array
([
1.0
,
2.0
]),
2.5
,
np
.
array
([
2.5
,
5.0
]),
),
# C4: Test scalar division to y-values (intensity), expect no change
# to x-values (q, tth, d)
(
# 1. Divide by 2
"div"
,
np
.
array
([
1.0
,
2.0
]),
2
,
np
.
array
([
0.5
,
1.0
]),
),
(
# 2. Divide by 2.5
"div"
,
np
.
array
([
1.0
,
2.0
]),
2.5
,
np
.
array
([
0.4
,
0.8
]),
),
],
)
def
test_scalar_operations
(
operation
,
starting_yarray
,
scalar_value
,
expected_yarray
,
do_minimal_tth
):
do
=
do_minimal_tth
expected_xarray_constant
=
np
.
array
(
[[
0.51763809
,
30.0
,
12.13818192
], [
1.0
,
60.0
,
6.28318531
]]
)
assert
np
.
allclose
(
do
.
all_arrays
[:, [
1
,
2
,
3
]],
expected_xarray_constant
)
assert
np
.
allclose
(
do
.
all_arrays
[:,
0
],
starting_yarray
)
if
operation
==
"add"
:
do_right_op
=
do
+
scalar_value
do_left_op
=
scalar_value
+
do
elif
operation
==
"sub"
:
do_right_op
=
do
-
scalar_value
do_left_op
=
scalar_value
-
do
elif
operation
==
"mul"
:
do_right_op
=
do
*
scalar_value
do_left_op
=
scalar_value
*
do
elif
operation
==
"div"
:
do_right_op
=
do
/
scalar_value
do_left_op
=
scalar_value
/
do
assert
np
.
allclose
(
do_right_op
.
all_arrays
[:,
0
],
expected_yarray
)
assert
np
.
allclose
(
do_left_op
.
all_arrays
[:,
0
],
expected_yarray
)
# Ensure x-values are unchanged
assert
np
.
allclose
(
do_right_op
.
all_arrays
[:, [
1
,
2
,
3
]],
expected_xarray_constant
)
assert
np
.
allclose
(
do_left_op
.
all_arrays
[:, [
1
,
2
,
3
]],
expected_xarray_constant
)
@
pytest
.
mark
.
parametrize
(
(
"operation, expected_do_1_all_arrays_with_y_modified, "
"expected_do_2_all_arrays_with_y_modified"
),
[
# Test addition, subtraction, multiplication, and division of two DO
# objects
(
# Test addition of two DO objects, expect combined yarray values
"add"
,
np
.
array
(
[
[
2.0
,
0.51763809
,
30.0
,
12.13818192
],
[
4.0
,
1.0
,
60.0
,
6.28318531
],
]
),
np
.
array
(
[
[
2.0
,
0.51763809
,
30.0
,
12.13818192
],
[
4.0
,
1.0
,
60.0
,
6.28318531
],
]
),
),
(
# Test subtraction of two DO objects, expect differences in yarray
# values
"sub"
,
np
.
array
(
[
[
0.0
,
0.51763809
,
30.0
,
12.13818192
],
[
0.0
,
1.0
,
60.0
,
6.28318531
],
]
),
np
.
array
(
[
[
0.0
,
0.51763809
,
30.0
,
12.13818192
],
[
0.0
,
1.0
,
60.0
,
6.28318531
],
]
),
),
(
# Test multiplication of two DO objects, expect multiplication in
# yarray values
"mul"
,
np
.
array
(
[
[
1.0
,
0.51763809
,
30.0
,
12.13818192
],
[
4.0
,
1.0
,
60.0
,
6.28318531
],
]
),
np
.
array
(
[
[
1.0
,
0.51763809
,
30.0
,
12.13818192
],
[
4.0
,
1.0
,
60.0
,
6.28318531
],
]
),
),
(
# Test division of two DO objects, expect division in yarray values
"div"
,
np
.
array
(
[
[
1.0
,
0.51763809
,
30.0
,
12.13818192
],
[
1.0
,
1.0
,
60.0
,
6.28318531
],
]
),
np
.
array
(
[
[
1.0
,
0.51763809
,
30.0
,
12.13818192
],
[
1.0
,
1.0
,
60.0
,
6.28318531
],
]
),
),
],
)
def
test_binary_operator_on_do
(
operation
,
expected_do_1_all_arrays_with_y_modified
,
expected_do_2_all_arrays_with_y_modified
,
do_minimal_tth
,
):
do_1
=
do_minimal_tth
do_2
=
do_minimal_tth
assert
np
.
allclose
(
do_1
.
all_arrays
,
np
.
array
(
[
[
1.0
,
0.51763809
,
30.0
,
12.13818192
],
[
2.0
,
1.0
,
60.0
,
6.28318531
],
]
),
)
assert
np
.
allclose
(
do_2
.
all_arrays
,
np
.
array
(
[
[
1.0
,
0.51763809
,
30.0
,
12.13818192
],
[
2.0
,
1.0
,
60.0
,
6.28318531
],
]
),
)
if
operation
==
"add"
:
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