from fastai.gen_doc.nbdoc import *
from fastai.core import * 

show_doc(has_arg)

has_arg(download_url,'url')

has_arg(index_row,'x')

has_arg(index_row,'a')

show_doc(ifnone)

param,alt_param = None,5
ifnone(param,alt_param)

param,alt_param = None,[1,2,3]
ifnone(param,alt_param)

show_doc(is1d)

two_d_array = np.arange(12).reshape(6,2)
print( two_d_array )
print( is1d(two_d_array) )

is1d(two_d_array.flatten())

show_doc(is_listy)

some_data = [1,2,3]
is_listy(some_data)

some_data = (1,2,3)
is_listy(some_data)

some_data = 1024
print( is_listy(some_data) )

print( is_listy( [some_data] ) )

some_data = dict([('a',1),('b',2),('c',3)])
print( some_data )
print( some_data.keys() )

print( is_listy(some_data) )
print( is_listy(some_data.keys()) )

print( is_listy(list(some_data.keys())) )

show_doc(is_tuple)

print( is_tuple( [1,2,3] ) )

print( is_tuple( (1,2,3) ) )

show_doc(arange_of)

arange_of([5,6,7])

type(arange_of([5,6,7]))

show_doc(array)

array([1,2,3])

def data_gen():
    i = 100.01
    while i<200:
        yield i
        i += 1.

ex_data_gen = data_gen()
for _ in range(5):
    print(next(ex_data_gen))

array(ex_data_gen)

ex_data_gen_int = data_gen()

array(ex_data_gen_int,dtype=int)  #Cast output to int array

show_doc(arrays_split)

data_a = np.arange(15)
data_b = np.arange(15)[::-1]

mask_a = (data_a > 10)
print(data_a)
print(data_b)
print(mask_a)

arrays_split(mask_a,data_a)

np.vstack([data_a,data_b]).transpose().shape

arrays_split(mask_a,np.vstack([data_a,data_b]).transpose()) #must match on dimension 0

show_doc(chunks)

data = [0,1,2,3,4,5,6,7,8,9]
for chunk in chunks(data, 2):
    print(chunk)

for chunk in chunks(data, 3):
    print(chunk)

show_doc(df_names_to_idx)

ex_df = pd.DataFrame.from_dict({"a":[1,1,1],"b":[2,2,2]})
print(ex_df)

df_names_to_idx('b',ex_df)

show_doc(extract_kwargs)

key_word_args = {"a":2,"some_list":[1,2,3],"param":'mean'}
key_word_args

(extracted_val,remainder) = extract_kwargs(['param'],key_word_args)
print( extracted_val,remainder )

show_doc(idx_dict)

idx_dict(['a','b','c'])

show_doc(index_row)

data = [0,1,2,3,4,5,6,7,8,9]
index_row(data,4)

index_row(pd.Series(data),7)

data_df = pd.DataFrame([data[::-1],data]).transpose()
data_df

index_row(data_df,7)

show_doc(listify)

to_match = np.arange(12)
listify('a',to_match)

listify('a',5)

listify(77.1,3)

listify( (1,2,3) )

listify((1,2,3),('a','b','c'))

show_doc(random_split)

data = np.arange(20).reshape(10,2)
data.tolist()

random_split(0.20,data.tolist())

random_split(0.20,pd.DataFrame(data))

show_doc(range_of)

range_of([5,4,3])

range_of(np.arange(10)[::-1])

show_doc(series2cat)

data_df = pd.DataFrame.from_dict({"a":[1,1,1,2,2,2],"b":['f','e','f','g','g','g']})
data_df

data_df['b']

series2cat(data_df,'b')
data_df['b']

series2cat(data_df,'a')
data_df['a']

show_doc(split_kwargs_by_func)

key_word_args = {'url':'http://fast.ai','dest':'./','new_var':[1,2,3],'testvalue':42}
split_kwargs_by_func(key_word_args,download_url)

show_doc(to_int)

to_int(3.1415)

data = [1.2,3.4,7.25]
to_int(data)

show_doc(uniqueify)

uniqueify( pd.Series(data=['a','a','b','b','f','g']) )

show_doc(download_url)

show_doc(find_classes)

show_doc(join_path)

show_doc(join_paths)

show_doc(loadtxt_str)

show_doc(save_texts)

show_doc(num_cpus)

show_doc(parallel)

def my_func(value, index):
    print("Index: {}, Value: {}".format(index, value))
 
my_array = [i*2 for i in range(5)]
parallel(my_func, my_array, max_workers=3)

show_doc(partition)

show_doc(partition_by_cores)

show_doc(ItemBase, title_level=3)

show_doc(ItemBase.apply_tfms)

show_doc(ItemBase.show)

show_doc(Category, title_level=3)

show_doc(EmptyLabel, title_level=3)

show_doc(MultiCategory, title_level=3)

show_doc(FloatItem)

show_doc(camel2snake)

camel2snake('DeviceDataLoader')

show_doc(even_mults)

# from 1 to 10 in 5 steps
t = np.linspace(1, 10, 5)
t

for i in range(len(t) - 1):
    print(t[i+1] - t[i])

t = even_mults(1, 10, 5)
t

# notice how each number is a multiple of its predecessor
for i in range(len(t) - 1):
    print(t[i+1] / t[i])

show_doc(func_args)

func_args(download_url)

func_args(np.linspace)

show_doc(noop)

# object is returned as-is
noop([1,2,3])

show_doc(one_hot)

y = [1, 4, 4, 5, 7, 9, 2, 4, 0]

jekyll_note("""y is zero-indexed, therefore its first element (1) belongs to class 2, its second element (4) to class 5 and so on.""")

len(y)

for label in y:
    print(one_hot(label, 10))

show_doc(show_some)

# select 3 elements from a list
some_data = show_some([10, 20, 30, 40, 50], 3) 
some_data

type(some_data) 

# the separator can be changed
some_data = show_some([10, 20, 30, 40, 50], 3, sep = '---') 
some_data

some_data[:-3]

class Any(object):
    def __init__(self, data):
        self.data = data
    def __len__(self):
        return len(self.data)
    def __getitem__(self,i):
        return self.data[i]
 
some_other_data = Any('nice')
show_some(some_other_data, 2)

show_doc(subplots)

show_doc(text2html_table)

show_doc(is_dict)