def filtering_test():
arr = np.array([[0.2, 0.3, 0.4], [0.1, 0.3, 0.6], [0.6, 0.3, 0.6]])
cols = ['a', 'b', 'c']
index = ['one', 'two', 'three']
name = 'test_table'
t = Table(arr, cols, index, name)
# filter by equality
t2 = t.filter_on_column('c', 0.6)
arr2 = np.array([[0.1, 0.3, 0.6], [0.6, 0.3, 0.6]])
cols2 = ['a', 'b', 'c']
index2 = ['two', 'three']
name2 = 'test_table'
assert (t2.shape == arr2.shape and np.all(t2.arr == arr2)
and t2.cols == cols2 and t2.index == index2 and t2.name == name2)
# filter by < 0.6
t2 = t.filter_on_column('c', 0.6, operator.lt)
arr2 = np.array([[0.2, 0.3, 0.4]])
cols2 = ['a', 'b', 'c']
index2 = ['one']
name2 = 'test_table'
assert (t2.shape == arr2.shape and np.all(t2.arr == arr2)
and t2.cols == cols2 and t2.index == index2 and t2.name == name2)
# filter by < 0.6 and omit column
t2 = t.filter_on_column('c', 0.6, operator.lt, omit=True)
arr2 = np.array([[0.2, 0.3]])
cols2 = ['a', 'b']
index2 = ['one']
name2 = 'test_table'
assert (t2.shape == arr2.shape and np.all(t2.arr == arr2)
and t2.cols == cols2 and t2.index == index2 and t2.name == name2)
def collect(seq, func, kwargs=None, with_index=False, index_col_name=None):
"""
Take a sequence seq of arguments to function func.
- func should return a Table or an ndarray.
- kwargs is a dictionary of arguments that will be passed to func.
Return the outputs of func concatenated vertically into an np.array
(thereby making copies of the collected data).
If the outputs are Tables, concatenate the arrays and return a Table.
If with_index is True, append index column to outputs.
If the outputs are Tables, index_col_name must be provided for this purpose.
"""
all_results = []
cols = None
for index, image in enumerate(seq):
results = func(image, **kwargs) if kwargs else func(image)
if results != None and results.shape[0] > 0:
from synthetic.table import Table
if isinstance(results, Table):
cols = results.cols
results = results.arr
if with_index:
all_results.append(append_index_column(results, index))
else:
all_results.append(results)
ret = np.array([])
if len(all_results) > 0:
ret = np.vstack(all_results)
if cols:
assert (index_col_name)
ret = Table(ret, list(cols + [index_col_name]))
return ret
def filtering_test():
arr = np.array([
[0.2, 0.3, 0.4],
[0.1, 0.3, 0.6],
[0.6, 0.3, 0.6]])
cols = ['a','b','c']
index = ['one','two','three']
name = 'test_table'
t = Table(arr, cols, index, name)
# filter by equality
t2 = t.filter_on_column('c',0.6)
arr2 = np.array([
[0.1, 0.3, 0.6],
[0.6, 0.3, 0.6]])
cols2 = ['a','b','c']
index2 = ['two','three']
name2 = 'test_table'
assert(t2.shape == arr2.shape and np.all(t2.arr == arr2) and t2.cols == cols2 and t2.index == index2 and t2.name == name2)
# filter by < 0.6
t2 = t.filter_on_column('c',0.6,operator.lt)
arr2 = np.array([
[0.2, 0.3, 0.4]])
cols2 = ['a','b','c']
index2 = ['one']
name2 = 'test_table'
assert(t2.shape == arr2.shape and np.all(t2.arr == arr2) and t2.cols == cols2 and t2.index == index2 and t2.name == name2)
# filter by < 0.6 and omit column
t2 = t.filter_on_column('c',0.6,operator.lt,omit=True)
arr2 = np.array([
[0.2, 0.3]])
cols2 = ['a','b']
index2 = ['one']
name2 = 'test_table'
assert(t2.shape == arr2.shape and np.all(t2.arr == arr2) and t2.cols == cols2 and t2.index == index2 and t2.name == name2)
def overall_and_subset_test():
arr = np.array([
[0.2, 0.3, 0.4],
[0.1, 0.3, 0.6],
[0.6, 0.3, 0.6]])
cols = ['a','b','c']
index = ['one','two','three']
name = 'test_table'
t = Table(arr, cols, index, name)
# test init
assert(t.shape == arr.shape and np.all(t.arr == arr) and t.cols == cols and t.index == index and t.name == name)
# test copy
t2 = t.copy()
assert(t2.shape == arr.shape and np.all(t2.arr == arr) and t2.cols == cols and t2.index == index and t2.name == name)
# change values in the copy and make sure original is unmodified
t2.arr[0,:] = 1
t2.index[0] = 'one_m'
t2.cols[0] = 'big A'
t2.name = 'modified_table'
assert(t.shape == arr.shape and np.all(t.arr == arr) and t.cols == cols and t.index == index and t.name == name)
## subset the first and last rows of t2
arr2 = np.array([
[1, 1, 1],
[0.6, 0.3, 0.6]])
index2 = ['one_m','three']
cols2 = ['big A','b','c']
name2 = 'modified_table'
# by list of indices
t3 = t2.row_subset([0,2])
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr([0,2])
assert(np.all(t3_arr==arr2))
# by list of names
t3 = t2.row_subset(['one_m','three'])
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(['one_m','three'])
assert(np.all(t3_arr==arr2))
# by array of indices
t3 = t2.row_subset(np.array([0,2]))
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(np.array([0,2]))
assert(np.all(t3_arr==arr2))
# by array of names
t3 = t2.row_subset(np.array(['one_m','three']))
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(['one_m','three'])
assert(np.all(t3_arr==arr2))
# subset just the middle row of t2
arr2 = np.array([
[0.1, 0.3, 0.6]])
cols2 = ['big A','b','c']
index2 = ['two']
name2 = 'modified_table'
# by index
t3 = t2.row_subset(1)
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(1)
assert(np.all(t3_arr==arr2))
# by name
t3 = t2.row_subset('two')
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr('two')
assert(np.all(t3_arr==arr2.squeeze()))
## subset the first and last columns of t2
arr2 = np.array([
[1, 1],
[0.1, 0.6],
[0.6, 0.6]])
index2 = ['one_m','two','three']
cols2 = ['big A','c']
name2 = 'modified_table'
# by list of indices
t3 = t2.subset([0,2])
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr([0,2])
assert(np.all(t3_arr==arr2))
# by list of names
t3 = t2.subset(['big A','c'])
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(['big A','c'])
assert(np.all(t3_arr==arr2))
# by array of indices
t3 = t2.subset(np.array([0,2]))
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(np.array([0,2]))
assert(np.all(t3_arr==arr2))
# by array of names
t3 = t2.subset(np.array(['big A','c']))
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(np.array(['big A','c']))
assert(np.all(t3_arr==arr2))
## subset just the middle column of t2
arr2 = np.array([
[1],
[0.3],
[0.3]])
index2 = ['one_m','two','three']
cols2 = ['b']
name2 = 'modified_table'
# by index
t3 = t2.subset(1)
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(np.array(['b']))
assert(np.all(t3_arr==arr2.squeeze()))
# by name
t3 = t2.subset('b')
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(['b'])
assert(np.all(t3_arr==arr2.squeeze()))
## row_subset without index
t2 = Table(arr, cols, None, name)
# subset the first and last rows of t2
arr2 = np.array([
[0.2, 0.3, 0.4],
[0.6, 0.3, 0.6]])
index2 = None
cols2 = ['a','b','c']
name2 = 'test_table'
# by list of indices
t3 = t2.row_subset([0,2])
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr([0,2])
assert(np.all(t3_arr==arr2))
# by array of indices
t3 = t2.row_subset(np.array([0,2]))
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(np.array([0,2]))
assert(np.all(t3_arr==arr2))
## subset just the middle row of t2
arr2 = np.array([
[0.1, 0.3, 0.6]])
cols2 = ['a','b','c']
name2 = 'test_table'
# by index
t3 = t2.row_subset(1)
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(1)
assert(np.all(t3_arr==arr2.squeeze()))
## subset just the middle value of t2
arr2 = np.array([
[0.3]])
cols2 = ['b']
name2 = 'test_table'
# by index
t3 = t2.row_subset(1).subset(1)
assert(t3.shape == arr2.shape and np.all(t3.arr == arr2) and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset(1).subset_arr(1)
assert(np.all(t3_arr==np.array([0.3])))
def overall_and_subset_test():
arr = np.array([[0.2, 0.3, 0.4], [0.1, 0.3, 0.6], [0.6, 0.3, 0.6]])
cols = ['a', 'b', 'c']
index = ['one', 'two', 'three']
name = 'test_table'
t = Table(arr, cols, index, name)
# test init
assert (t.shape == arr.shape and np.all(t.arr == arr) and t.cols == cols
and t.index == index and t.name == name)
# test copy
t2 = t.copy()
assert (t2.shape == arr.shape and np.all(t2.arr == arr) and t2.cols == cols
and t2.index == index and t2.name == name)
# change values in the copy and make sure original is unmodified
t2.arr[0, :] = 1
t2.index[0] = 'one_m'
t2.cols[0] = 'big A'
t2.name = 'modified_table'
assert (t.shape == arr.shape and np.all(t.arr == arr) and t.cols == cols
and t.index == index and t.name == name)
## subset the first and last rows of t2
arr2 = np.array([[1, 1, 1], [0.6, 0.3, 0.6]])
index2 = ['one_m', 'three']
cols2 = ['big A', 'b', 'c']
name2 = 'modified_table'
# by list of indices
t3 = t2.row_subset([0, 2])
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr([0, 2])
assert (np.all(t3_arr == arr2))
# by list of names
t3 = t2.row_subset(['one_m', 'three'])
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(['one_m', 'three'])
assert (np.all(t3_arr == arr2))
# by array of indices
t3 = t2.row_subset(np.array([0, 2]))
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(np.array([0, 2]))
assert (np.all(t3_arr == arr2))
# by array of names
t3 = t2.row_subset(np.array(['one_m', 'three']))
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(['one_m', 'three'])
assert (np.all(t3_arr == arr2))
# subset just the middle row of t2
arr2 = np.array([[0.1, 0.3, 0.6]])
cols2 = ['big A', 'b', 'c']
index2 = ['two']
name2 = 'modified_table'
# by index
t3 = t2.row_subset(1)
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(1)
assert (np.all(t3_arr == arr2))
# by name
t3 = t2.row_subset('two')
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr('two')
assert (np.all(t3_arr == arr2.squeeze()))
## subset the first and last columns of t2
arr2 = np.array([[1, 1], [0.1, 0.6], [0.6, 0.6]])
index2 = ['one_m', 'two', 'three']
cols2 = ['big A', 'c']
name2 = 'modified_table'
# by list of indices
t3 = t2.subset([0, 2])
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr([0, 2])
assert (np.all(t3_arr == arr2))
# by list of names
t3 = t2.subset(['big A', 'c'])
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(['big A', 'c'])
assert (np.all(t3_arr == arr2))
# by array of indices
t3 = t2.subset(np.array([0, 2]))
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(np.array([0, 2]))
assert (np.all(t3_arr == arr2))
# by array of names
t3 = t2.subset(np.array(['big A', 'c']))
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(np.array(['big A', 'c']))
assert (np.all(t3_arr == arr2))
## subset just the middle column of t2
arr2 = np.array([[1], [0.3], [0.3]])
index2 = ['one_m', 'two', 'three']
cols2 = ['b']
name2 = 'modified_table'
# by index
t3 = t2.subset(1)
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(np.array(['b']))
assert (np.all(t3_arr == arr2.squeeze()))
# by name
t3 = t2.subset('b')
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.subset_arr(['b'])
assert (np.all(t3_arr == arr2.squeeze()))
## row_subset without index
t2 = Table(arr, cols, None, name)
# subset the first and last rows of t2
arr2 = np.array([[0.2, 0.3, 0.4], [0.6, 0.3, 0.6]])
index2 = None
cols2 = ['a', 'b', 'c']
name2 = 'test_table'
# by list of indices
t3 = t2.row_subset([0, 2])
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr([0, 2])
assert (np.all(t3_arr == arr2))
# by array of indices
t3 = t2.row_subset(np.array([0, 2]))
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(np.array([0, 2]))
assert (np.all(t3_arr == arr2))
## subset just the middle row of t2
arr2 = np.array([[0.1, 0.3, 0.6]])
cols2 = ['a', 'b', 'c']
name2 = 'test_table'
# by index
t3 = t2.row_subset(1)
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset_arr(1)
assert (np.all(t3_arr == arr2.squeeze()))
## subset just the middle value of t2
arr2 = np.array([[0.3]])
cols2 = ['b']
name2 = 'test_table'
# by index
t3 = t2.row_subset(1).subset(1)
assert (t3.shape == arr2.shape and np.all(t3.arr == arr2)
and t3.cols == cols2 and t3.index == index2 and t3.name == name2)
t3_arr = t2.row_subset(1).subset_arr(1)
assert (np.all(t3_arr == np.array([0.3])))
