#! /opt/rh/rh-python35/root/usr/bin/python3.5 """The Tab Nanny despises ambiguous indentation. She knows no mercy. tabnanny -- Detection of ambiguous indentation For the time being this module is intended to be called as a script. However it is possible to import it into an IDE and use the function check() described below. Warning: The API provided by this module is likely to change in future releases; such changes may not be backward compatible. """ # Released to the public domain, by Tim Peters, 15 April 1998. # XXX Note: this is now a standard library module. # XXX The API needs to undergo changes however; the current code is too # XXX script-like. This will be addressed later. __version__ = "6" import os import sys import getopt import tokenize if not hasattr(tokenize, 'NL'): raise ValueError("tokenize.NL doesn't exist -- tokenize module too old") __all__ = ["check", "NannyNag", "process_tokens"] verbose = 0 filename_only = 0 def errprint(*args): sep = "" for arg in args: sys.stderr.write(sep + str(arg)) sep = " " sys.stderr.write("\n") def main(): global verbose, filename_only try: opts, args = getopt.getopt(sys.argv[1:], "qv") except getopt.error as msg: errprint(msg) return for o, a in opts: if o == '-q': filename_only = filename_only + 1 if o == '-v': verbose = verbose + 1 if not args: errprint("Usage:", sys.argv[0], "[-v] file_or_directory ...") return for arg in args: check(arg) class NannyNag(Exception): """ Raised by tokeneater() if detecting an ambiguous indent. Captured and handled in check(). """ def __init__(self, lineno, msg, line): self.lineno, self.msg, self.line = lineno, msg, line def get_lineno(self): return self.lineno def get_msg(self): return self.msg def get_line(self): return self.line def check(file): """check(file_or_dir) If file_or_dir is a directory and not a symbolic link, then recursively descend the directory tree named by file_or_dir, checking all .py files along the way. If file_or_dir is an ordinary Python source file, it is checked for whitespace related problems. The diagnostic messages are written to standard output using the print statement. """ if os.path.isdir(file) and not os.path.islink(file): if verbose: print("%r: listing directory" % (file,)) names = os.listdir(file) for name in names: fullname = os.path.join(file, name) if (os.path.isdir(fullname) and not os.path.islink(fullname) or os.path.normcase(name[-3:]) == ".py"): check(fullname) return try: f = tokenize.open(file) except OSError as msg: errprint("%r: I/O Error: %s" % (file, msg)) return if verbose > 1: print("checking %r ..." % file) try: process_tokens(tokenize.generate_tokens(f.readline)) except tokenize.TokenError as msg: errprint("%r: Token Error: %s" % (file, msg)) return except IndentationError as msg: errprint("%r: Indentation Error: %s" % (file, msg)) return except NannyNag as nag: badline = nag.get_lineno() line = nag.get_line() if verbose: print("%r: *** Line %d: trouble in tab city! ***" % (file, badline)) print("offending line: %r" % (line,)) print(nag.get_msg()) else: if ' ' in file: file = '"' + file + '"' if filename_only: print(file) else: print(file, badline, repr(line)) return finally: f.close() if verbose: print("%r: Clean bill of health." % (file,)) class Whitespace: # the characters used for space and tab S, T = ' \t' # members: # raw # the original string # n # the number of leading whitespace characters in raw # nt # the number of tabs in raw[:n] # norm # the normal form as a pair (count, trailing), where: # count # a tuple such that raw[:n] contains count[i] # instances of S * i + T # trailing # the number of trailing spaces in raw[:n] # It's A Theorem that m.indent_level(t) == # n.indent_level(t) for all t >= 1 iff m.norm == n.norm. # is_simple # true iff raw[:n] is of the form (T*)(S*) def __init__(self, ws): self.raw = ws S, T = Whitespace.S, Whitespace.T count = [] b = n = nt = 0 for ch in self.raw: if ch == S: n = n + 1 b = b + 1 elif ch == T: n = n + 1 nt = nt + 1 if b >= len(count): count = count + [0] * (b - len(count) + 1) count[b] = count[b] + 1 b = 0 else: break self.n = n self.nt = nt self.norm = tuple(count), b self.is_simple = len(count) <= 1 # return length of longest contiguous run of spaces (whether or not # preceding a tab) def longest_run_of_spaces(self): count, trailing = self.norm return max(len(count)-1, trailing) def indent_level(self, tabsize): # count, il = self.norm # for i in range(len(count)): # if count[i]: # il = il + (i//tabsize + 1)*tabsize * count[i] # return il # quicker: # il = trailing + sum (i//ts + 1)*ts*count[i] = # trailing + ts * sum (i//ts + 1)*count[i] = # trailing + ts * sum i//ts*count[i] + count[i] = # trailing + ts * [(sum i//ts*count[i]) + (sum count[i])] = # trailing + ts * [(sum i//ts*count[i]) + num_tabs] # and note that i//ts*count[i] is 0 when i < ts count, trailing = self.norm il = 0 for i in range(tabsize, len(count)): il = il + i//tabsize * count[i] return trailing + tabsize * (il + self.nt) # return true iff self.indent_level(t) == other.indent_level(t) # for all t >= 1 def equal(self, other): return self.norm == other.norm # return a list of tuples (ts, i1, i2) such that # i1 == self.indent_level(ts) != other.indent_level(ts) == i2. # Intended to be used after not self.equal(other) is known, in which # case it will return at least one witnessing tab size. def not_equal_witness(self, other): n = max(self.longest_run_of_spaces(), other.longest_run_of_spaces()) + 1 a = [] for ts in range(1, n+1): if self.indent_level(ts) != other.indent_level(ts): a.append( (ts, self.indent_level(ts), other.indent_level(ts)) ) return a # Return True iff self.indent_level(t) < other.indent_level(t) # for all t >= 1. # The algorithm is due to Vincent Broman. # Easy to prove it's correct. # XXXpost that. # Trivial to prove n is sharp (consider T vs ST). # Unknown whether there's a faster general way. I suspected so at # first, but no longer. # For the special (but common!) case where M and N are both of the # form (T*)(S*), M.less(N) iff M.len() < N.len() and # M.num_tabs() <= N.num_tabs(). Proof is easy but kinda long-winded. # XXXwrite that up. # Note that M is of the form (T*)(S*) iff len(M.norm[0]) <= 1. def less(self, other): if self.n >= other.n: return False if self.is_simple and other.is_simple: return self.nt <= other.nt n = max(self.longest_run_of_spaces(), other.longest_run_of_spaces()) + 1 # the self.n >= other.n test already did it for ts=1 for ts in range(2, n+1): if self.indent_level(ts) >= other.indent_level(ts): return False return True # return a list of tuples (ts, i1, i2) such that # i1 == self.indent_level(ts) >= other.indent_level(ts) == i2. # Intended to be used after not self.less(other) is known, in which # case it will return at least one witnessing tab size. def not_less_witness(self, other): n = max(self.longest_run_of_spaces(), other.longest_run_of_spaces()) + 1 a = [] for ts in range(1, n+1): if self.indent_level(ts) >= other.indent_level(ts): a.append( (ts, self.indent_level(ts), other.indent_level(ts)) ) return a def format_witnesses(w): firsts = (str(tup[0]) for tup in w) prefix = "at tab size" if len(w) > 1: prefix = prefix + "s" return prefix + " " + ', '.join(firsts) def process_tokens(tokens): INDENT = tokenize.INDENT DEDENT = tokenize.DEDENT NEWLINE = tokenize.NEWLINE JUNK = tokenize.COMMENT, tokenize.NL indents = [Whitespace("")] check_equal = 0 for (type, token, start, end, line) in tokens: if type == NEWLINE: # a program statement, or ENDMARKER, will eventually follow, # after some (possibly empty) run of tokens of the form # (NL | COMMENT)* (INDENT | DEDENT+)? # If an INDENT appears, setting check_equal is wrong, and will # be undone when we see the INDENT. check_equal = 1 elif type == INDENT: check_equal = 0 thisguy = Whitespace(token) if not indents[-1].less(thisguy): witness = indents[-1].not_less_witness(thisguy) msg = "indent not greater e.g. " + format_witnesses(witness) raise NannyNag(start[0], msg, line) indents.append(thisguy) elif type == DEDENT: # there's nothing we need to check here! what's important is # that when the run of DEDENTs ends, the indentation of the # program statement (or ENDMARKER) that triggered the run is # equal to what's left at the top of the indents stack # Ouch! This assert triggers if the last line of the source # is indented *and* lacks a newline -- then DEDENTs pop out # of thin air. # assert check_equal # else no earlier NEWLINE, or an earlier INDENT check_equal = 1 del indents[-1] elif check_equal and type not in JUNK: # this is the first "real token" following a NEWLINE, so it # must be the first token of the next program statement, or an # ENDMARKER; the "line" argument exposes the leading whitespace # for this statement; in the case of ENDMARKER, line is an empty # string, so will properly match the empty string with which the # "indents" stack was seeded check_equal = 0 thisguy = Whitespace(line) if not indents[-1].equal(thisguy): witness = indents[-1].not_equal_witness(thisguy) msg = "indent not equal e.g. " + format_witnesses(witness) raise NannyNag(start[0], msg, line) if __name__ == '__main__': main()

RE-INVENTING THE TELEVISION NEWS BUSINESS*

A revolution in video storytelling

Creating entirely new & cost-effective production methods

From the world leaders in video production training and the creators of Character Driven Storytelling™

*and every other business that uses video

WHAT WE DO

Over the past 35 years, we have designed, built or restructured some of the most powerful news and journalism companies in the world.

We replace the traditional TV news ‘crew’ with one highly trained journalist, working alone with nothing but an iPhone.

No more TV news ‘crews’, no editors and no field producers.

This is television news done the way newspaper journalism is done – one reporter with their electronic pad and pencil.

In doing this, we can cut the cost of production by as much as 75% while increasing ratings and audience engagement.

In the place of conventional TV news ‘packages’ – ie, reporter stand up, interview, b-roll, man on the street, we marry great journalism with Netflix and Hollywood storytelling.

It’s a combination that works.

We have taken most of our clients to #1 in their respective markets.

And it’s not just for news. Any company, any profit, any NGO and anyone else who is online needs to tell their story in compelling yet cost-effective video. We can teach you to do that. Either in person or virtually.

EXAMPLES OF WHAT WE CAN TEACH YOUR STAFF TO PRODUCE

ITAY HOD

Itay Hod, MMJ with KPIX/CBS in San Francisco, took the 5-Day Intensive Video Storytelling Bootcamp in 2018.

Because he works alone, with only an iPhone, he was able to embed himself with a homeless family.

Here’s the story he produced in a one-day turn.

KIET DO

Kiet Do, an MMJ with KPIX/CBS in San Francisco, took the 5-Day Intensive Video Storytelling Bootcamp in 2021.

Here is a story he produced, all on his own, with only an iPhone and in a one-day turn.

TAYLOR SCHAUB

Taylor Schaub, an MMJ with Spectrum News 1 in LA, took the 5-Day Intensive Video Storytelling Bootcamp in 2023.

Here is a story he turned in only one day, using only an iPhone. It was the first video story he ever did and it was nominated for an Emmy.

THE BOOTCAMP

How do we convert stations and whole networks to working in this way?

Since 1988, we have run intensive 5-Day Video Storytelling Bootcamps

We have done these all over the world.

These are hands-on bootcamps, and participants learn an entirely new way of creating TV news stories.

-We shoot at a 3:1 ratio or lower, so turnaround times are fast.

-We go directly from camera to timelilne and edit – no written scripts. We work in the medium of pictures and sound.

-We are entirely character-driven.

-We are driven by pictures and real events.

-We are focused almost entirely on ’the viewer experience’.

Since 1988, more than 70,000 journalists around the world have taken our bootcamps, either in person on virtualy.

Case Studies

CBS News

We have started to work with CBS News, bringing our ideas of character-driven storytelling to one of the most successful and biggest networks in the United States. Since beginning to work with them ratings have climbed and more importantly, audience engagement is through the ceiling.

Learn More

New York Times Television

We started New York Times Television in 1990 and it was the first paper to be brought into the world of TV. It quickly became one of the most successful non-fiction production companies in the United States. The series and documentaries we produced won many awards including multiple Emmys.

Learn More

The BBC

We have been working with the BBC since the year 2000 helping to convert their national news network to our visual storytelling technique. Most recently we have trained teams from their sports, documentaries, and comedy divisions to make character-driven stories using only smartphones.

Learn More

Spectrum News

For the past five years we have worked with Spectrum News to introduce and train their journalists on visual, character driven storytelling using smartphones helping to create a different kind of local news for their network of 24-Hour News Stations across the United States.

Learn More

The United Nations

In 2006, we were approached by the United Nations. Rather than rely on news outlets, it would be much easier to train the field operatives to produce their own stories. We spent two years working with the UN, training more than 100 of their staff in bootcamps in Geneva and Nairobi.

Learn More

The Newark Star-Ledger

We trained 50 print reporters at the paper to shoot and tell their own stories, in conjunction with their print work. We built a TV newsroom in their existing print newsroom – you could not ask for a better set and they began to live stream their stories in conjunction with their print work.

Learn More

Mcgraw Hill

We spent two years with McGraw Hill, training more than 150 of their staffers, making them completely video literate. McGraw/Hill media properties we transit included Business Week, Aviation Week, (what was the name of the architecture magazine), and JD Power and Associates.

Learn More

Voice of America

In 1990, we were approached by The Voice of America, the official broadcasting agency for the United States Government. When we met with VOA, they were only a short wave radio broadcaster, but working with them, we took them into television, launching VOA-TV.

Learn More

Oyster Yachts

British based Oyster Yachts makes some of the finest yachts in the world. Like every other company, they had to find a way to feed the never-ending video demands of social media – sites like Instagram and TikTok. We trained the Oyster staff to tell their own stories, using only iPhones.

Learn More

Scottish Environmental Protection Agency

We were approached by SEPA, the Scottish Environmental Protection Agency because they had to continually find a way to ‘feed the media beast’. The result was that SEPA was able to tell their own stories, whenever they wanted, and at almost no additional cost.

Learn More

Michael on Media

Michael Rosenblum has been writing about the media since 1988. His work and ideas have appeared in The Guardian, The Huffington Post, Ilkeston Life and many other publications.

He has been blogging regularly for the past 35 years on this subject. Having taught media studies at Columbia University, NYU and now the University of Oxford, he is considered an expert on this subject.

Continue reading this post or look back at previous posts.

Do You Have Questions About Learning Video Skills?

If you would like to know more about our courses contact us and one of our training advisors will be happy to call you.

Your IP : 216.73.216.103

RE-INVENTING THE TELEVISION NEWS BUSINESS*

A revolution in video storytelling

Creating entirely new & cost-effective production methods

From the world leaders in video production training and the creators of Character Driven Storytelling™

*and every other business that uses video

WHAT WE DO

Over the past 35 years, we have designed, built or restructured some of the most powerful news and journalism companies in the world.

We replace the traditional TV news ‘crew’ with one highly trained journalist, working alone with nothing but an iPhone.

No more TV news ‘crews’, no editors and no field producers.

This is television news done the way newspaper journalism is done – one reporter with their electronic pad and pencil.

In doing this, we can cut the cost of production by as much as 75% while increasing ratings and audience engagement.

In the place of conventional TV news ‘packages’ – ie, reporter stand up, interview, b-roll, man on the street, we marry great journalism with Netflix and Hollywood storytelling.

It’s a combination that works.

We have taken most of our clients to #1 in their respective markets.

And it’s not just for news. Any company, any profit, any NGO and anyone else who is online needs to tell their story in compelling yet cost-effective video. We can teach you to do that. Either in person or virtually.

EXAMPLES OF WHAT WE CAN TEACH YOUR STAFF TO PRODUCE

ITAY HOD

Itay Hod, MMJ with KPIX/CBS in San Francisco, took the 5-Day Intensive Video Storytelling Bootcamp in 2018.

Because he works alone, with only an iPhone, he was able to embed himself with a homeless family.

Here’s the story he produced in a one-day turn.

KIET DO

Kiet Do, an MMJ with KPIX/CBS in San Francisco, took the 5-Day Intensive Video Storytelling Bootcamp in 2021.

Here is a story he produced, all on his own, with only an iPhone and in a one-day turn.

TAYLOR SCHAUB

Taylor Schaub, an MMJ with Spectrum News 1 in LA, took the 5-Day Intensive Video Storytelling Bootcamp in 2023.

Here is a story he turned in only one day, using only an iPhone. It was the first video story he ever did and it was nominated for an Emmy.

THE BOOTCAMP

How do we convert stations and whole networks to working in this way?

Since 1988, we have run intensive 5-Day Video Storytelling Bootcamps

We have done these all over the world.

These are hands-on bootcamps, and participants learn an entirely new way of creating TV news stories.

-We shoot at a 3:1 ratio or lower, so turnaround times are fast.

-We go directly from camera to timelilne and edit – no written scripts. We work in the medium of pictures and sound.

-We are entirely character-driven.

-We are driven by pictures and real events.

-We are focused almost entirely on ’the viewer experience’.

Since 1988, more than 70,000 journalists around the world have taken our bootcamps, either in person on virtualy.

Case Studies

CBS News

New York Times Television

The BBC

Spectrum News

The United Nations

The Newark Star-Ledger

Mcgraw Hill

Voice of America

Oyster Yachts

Scottish Environmental Protection Agency

Michael on Media