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def walk_level(path, level=1):
"""Like os.walk, but takes `level` kwarg that indicates how deep the recursion will go.
TODO: refactor `level`->`depth`
path (str): Root path to begin file tree traversal (walk)
level (int, optional): Depth of file tree to halt recursion at.
None = full recursion to as deep as it goes
0 = nonrecursive, just provide a list of files at the root level of the tree
1 = one level of depth deeper in the tree
>>> root = os.path.dirname(__file__)
>>> all((os.path.join(base,d).count('/')==(root.count('/')+1)) for (base, dirs, files) in walk_level(root, level=0) for d in dirs)
if isinstance(level, NoneType):
level = float('inf')
path = path.rstrip(os.path.sep)
if os.path.isdir(path):
root_level = path.count(os.path.sep)
for root, dirs, files in os.walk(path):
yield root, dirs, files
if root.count(os.path.sep) >= root_level + level:
del dirs[:]
elif os.path.isfile(path):
yield os.path.dirname(path), [], [os.path.basename(path)]
raise RuntimeError("Can't find a valid folder or file for path {0}".format(repr(path)))
def find_files(path, ext='', level=None, verbosity=0):
"""Recursively find all files in the indicated directory with the indicated file name extension
path (str):
ext (str): File name extension. Only file paths that ".endswith()" this string will be returned
level (int, optional): Depth of file tree to halt recursion at.
None = full recursion to as deep as it goes
0 = nonrecursive, just provide a list of files at the root level of the tree
1 = one level of depth deeper in the tree
list of dicts: dict keys are { 'path', 'name', 'bytes', 'created', 'modified', 'accessed', 'permissions' }
path (str): Full, absolute paths to file beneath the indicated directory and ending with `ext`
name (str): File name only (everythin after the last slash in the path)
size (int): File size in bytes
created (datetime): File creation timestamp from file system
modified (datetime): File modification timestamp from file system
accessed (datetime): File access timestamp from file system
permissions (int): File permissions bytes as a chown-style integer with a maximum of 4 digits
e.g.: 777 or 1755
>>> sorted(d['name'] for d in find_files(os.path.dirname(__file__), ext='.py', level=0))[0]
path = path or './'
files_in_queue = []
if verbosity:
print 'Preprocessing files to estimate pb.ETA'
# if verbosity:
# widgets = [pb.Counter(), '/%d bytes for all files: ' % file_bytes, pb.Percentage(), ' ', pb.RotatingMarker(), ' ', pb.Bar(),' ', pb.ETA()]
# i, pbar = 0, pb.ProgressBar(widgets=widgets, maxval=file_bytes)
# print pbar
# pbar.start()
for dir_path, dir_names, filenames in walk_level(path, level=level):
for fn in filenames:
if ext and not fn.lower().endswith(ext):
files_in_queue += [{'name': fn, 'path': os.path.join(dir_path, fn)}]
files_in_queue[-1]['size'] = os.path.getsize(files_in_queue[-1]['path'])
files_in_queue[-1]['accessed'] = datetime.datetime.fromtimestamp(os.path.getatime(files_in_queue[-1]['path']))
files_in_queue[-1]['modified'] = datetime.datetime.fromtimestamp(os.path.getmtime(files_in_queue[-1]['path']))
files_in_queue[-1]['created'] = datetime.datetime.fromtimestamp(os.path.getctime(files_in_queue[-1]['path']))
# file_bytes += files_in_queue[-1]['size']
if verbosity > 1:
print files_in_queue
return files_in_queue
def flatten_csv(path='.', ext='csv', date_parser=parse_date, verbosity=0, output_ext=None):
"""Load all CSV files in the given path, write .flat.csv files, return `DataFrame`s
path (str): file or folder to retrieve CSV files and `pandas.DataFrame`s from
ext (str): file name extension (to filter files by)
date_parser (function): if the MultiIndex can be interpretted as a datetime, this parser will be used
dict of DataFrame: { file_path: flattened_data_frame }
date_parser = date_parser or (lambda x: x)
dotted_ext, dotted_output_ext = None, None
if ext != None and output_ext != None:
dotted_ext = ('' if ext.startswith('.') else '.') + ext
dotted_output_ext = ('' if output_ext.startswith('.') else '.') + output_ext
table = {}
for file_properties in find_files(path, ext=ext or '', verbosity=verbosity):
file_path = file_properties['path']
if output_ext and (dotted_output_ext + '.') in file_path:
df = pd.DataFrame.from_csv(file_path, parse_dates=False)
df = flatten_dataframe(df)
if dotted_ext != None and dotted_output_ext != None:
df.to_csv(file_path[:-len(dotted_ext)] + dotted_output_ext + dotted_ext)
table[file_path] = df
return table
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