def day_of_year(self):
"""Calculates the number of days since the beginning of the year.
Returns:
Tensor of int32 type with elements in range [1, 366]. January 1st yields
"1".
## Example
```python
dt = dates.from_tuples([(2019, 1, 25), (2020, 3, 2)])
dt.day_of_year() # [25, 62]
```
"""
if self._day_of_year is None:
cumul_days_in_month_nonleap = tf.math.cumsum(
_DAYS_IN_MONTHS_NON_LEAP, exclusive=True)
cumul_days_in_month_leap = tf.math.cumsum(_DAYS_IN_MONTHS_LEAP,
exclusive=True)
days_before_month_non_leap = tf.gather(cumul_days_in_month_nonleap,
self.month() - 1)
days_before_month_leap = tf.gather(cumul_days_in_month_leap,
self.month() - 1)
days_before_month = tf.where(date_utils.is_leap_year(self.year()),
days_before_month_leap,
days_before_month_non_leap)
self._day_of_year = days_before_month + self.day()
return self._day_of_year
def test_is_leap_year(self):
years = np.array([
1600, 1700, 1800, 1900, 1901, 1903, 1904, 1953, 2000, 2020, 2025,
2100
])
expected = np.array([
True, False, False, False, False, False, True, False, True, True,
False, False
])
self.assertAllEqual(expected, date_utils.is_leap_year(years))
def _leap_days_since_start(date_tensor):
"""Calculates the number of leap days until the supplied dates."""
year = date_tensor.year()
month = date_tensor.month()
day = date_tensor.day()
leap_years_since_0 = year // 4 - year // 100 + year // 400
is_leap_year = du.is_leap_year(year)
needs_adjustment = (is_leap_year & ((month < 2) | (tf.equal(month, 2) &
(day < 29))))
return leap_years_since_0 - tf.where(needs_adjustment, 1, 0)
def from_year_month_day_tensors(cls,
years,
months,
days,
validate=True):
"""Creates DateTensor from tensors of years, months and days.
Args:
years: Tensor of int32 type. Elements should be positive.
months: Tensor of int32 type of same shape as `year`. Elements should be
in range `[1, 12]`.
days: Tensor of int32 type of same shape as `year`. Elements should be in
range `[1, 31]` and represent valid dates together with corresponding
elements of `month` and `year` Tensors.
validate: Whether to validate the dates.
Returns:
DateTensor object.
Example:
```python
years = tf.constant([2015, 2017], dtype=tf.int32)
months = tf.constant([4, 12], dtype=tf.int32)
days = tf.constant([15, 30], dtype=tf.int32)
date_tensor = DateTensor.from_year_month_day_tensors(years, months, days)
```
"""
years = tf.convert_to_tensor(years, tf.int32)
months = tf.convert_to_tensor(months, tf.int32)
days = tf.convert_to_tensor(days, tf.int32)
control_deps = []
if validate:
control_deps.append(tf.debugging.assert_positive(years))
control_deps.append(
tf.debugging.assert_greater_equal(months, Month.JANUARY.value))
control_deps.append(
tf.debugging.assert_less_equal(months, Month.DECEMBER.value))
control_deps.append(tf.debugging.assert_positive(days))
is_leap = date_utils.is_leap_year(years)
days_in_months = tf.constant(_days_in_months, tf.int32)
max_days = tf.gather(days_in_months,
months + 12 * tf.dtypes.cast(is_leap, np.int32))
control_deps.append(tf.debugging.assert_less_equal(days, max_days))
with tf.compat.v1.control_dependencies(control_deps):
# Ensure years, months, days themselves are under control_deps.
years = tf.identity(years)
months = tf.identity(months)
days = tf.identity(days)
with tf.compat.v1.control_dependencies(control_deps):
ordinals = date_utils.year_month_day_to_ordinal(years, months, days)
return DateTensor(ordinals, years, months, days)
def adjust_day(year, month, day):
is_leap = date_utils.is_leap_year(year)
days_in_months = tf.constant(_DAYS_IN_MONTHS, tf.int32)
max_days = tf.gather(
days_in_months, month + 12 * tf.dtypes.cast(is_leap, np.int32))
return tf.math.minimum(day, max_days)
def _num_days_in_month(month, year):
"""Returns number of days in a given month of a given year."""
days_in_months = tf.constant(_DAYS_IN_MONTHS_COMBINED, tf.int32)
is_leap = date_utils.is_leap_year(year)
return tf.gather(days_in_months,
month + 12 * tf.dtypes.cast(is_leap, tf.int32))