Taskwarrior modifier .isnt for “not equal”

Edited my “someday” report:

report.sd.filter=status:pending sprint:s sprint.isnt:srv

sprint:s seems to catch srv too, which I don’t want. Not anymore. Also Taskwarrior - FAQ is the list of such modifiers.

Attribute modifiers make filters more precise.  Supported modifiers are:

  Modifiers         Example            Equivalent           Meaning
  ----------------  -----------------  -------------------  -------------------------
                    due:today          due = today          Fuzzy match
  not               due.not:today      due != today         Fuzzy non-match
  before, below     due.before:today   due < tomorrow       Exact date comparison
  after, above      due.after:today    due > tomorrow       Exact date comparison
  none              project.none:      project == ''        Empty
  any               project.any:       project !== ''       Not empty
  is, equals        project.is:x       project == x         Exact match
  isnt              project.isnt:x     project !== x        Exact non-match
  has, contains     desc.has:Hello     desc ~ Hello         Pattern match
  hasnt,            desc.hasnt:Hello   desc !~ Hello        Pattern non-match
  startswith, left  desc.left:Hel      desc ~ '^Hel'        Beginning match
  endswith, right   desc.right:llo     desc ~ 'llo$'        End match
  word              desc.word:Hello    desc ~ '\bHello\b'   Boundaried word match
  noword            desc.noword:Hello  desc !~ '\bHello\b'  Boundaried word non-match

Intellij idea

In intellij idea you can set more options for each breakpoint after right-clicking on it; especially “disable until breakpoint X is hit”, where X can be disabled.

Keras2 eager execution

.. is not there by default all the time; the hard-to-find answer for this is adding model.run_eagerly=True after model.compile().

Tensorflow / python Dataset iterator

Of course, the following also works:

[x[1][1]['mycast'] for x in dataset.enumerate(5).__iter__()]

Tensorflow padding

… add what you tell it to add, even if you’ve use tf.one_hot() on the data before. Then you get weird zeros in the result of the one hot encoding.