def test_today(self):
ts_from_string = Timestamp('today')
ts_from_method = Timestamp.today()
ts_datetime = datetime.today()
ts_from_string_tz = Timestamp('today', tz='US/Eastern')
ts_from_method_tz = Timestamp.today(tz='US/Eastern')
# Check that the delta between the times is less than 1s (arbitrarily
# small)
delta = Timedelta(seconds=1)
assert abs(ts_from_method - ts_from_string) < delta
assert abs(ts_datetime - ts_from_method) < delta
assert abs(ts_from_method_tz - ts_from_string_tz) < delta
assert (abs(ts_from_string_tz.tz_localize(None) -
ts_from_method_tz.tz_localize(None)) < delta)
def test_today(self):
ts_from_string = Timestamp('today')
ts_from_method = Timestamp.today()
ts_datetime = datetime.today()
ts_from_string_tz = Timestamp('today', tz='US/Eastern')
ts_from_method_tz = Timestamp.today(tz='US/Eastern')
# Check that the delta between the times is less than 1s (arbitrarily
# small)
delta = Timedelta(seconds=1)
assert abs(ts_from_method - ts_from_string) < delta
assert abs(ts_datetime - ts_from_method) < delta
assert abs(ts_from_method_tz - ts_from_string_tz) < delta
assert (abs(
ts_from_string_tz.tz_localize(None) -
ts_from_method_tz.tz_localize(None)) < delta)
def _netcdf_add_brew_hist(xds, msg, key='history'):
from pandas import Timestamp
now = Timestamp.today().strftime('%Y-%m-%dT%H:%M')
prefix = f'\n[DataBrewery@{now}] '
msg = prefix + msg
if key not in xds.attrs:
xds.attrs[key] = msg
elif xds.attrs[key] == '':
xds.attrs[key] = msg
else:
xds.attrs[key] += '; ' + msg
return xds
def next_month(self):
# additionally, will return True if the viewed month
# is the current month, and False otherwise
this_month = Timestamp.today().replace(day=1).floor('D')
month = DateOffset(months=1)
cur_view = Timestamp(config.year,config.month,1)
next_view = cur_view + month
config.year = next_view.year
config.month = next_view.month
self.redraw_cal()
return (next_view == this_month)
def setUpClass(cls):
super(TestYahooOptions, cls).setUpClass()
_skip_if_no_lxml()
# aapl has monthlies
cls.aapl = web.Options('aapl', 'yahoo')
d = (Timestamp.today() + pd.offsets.MonthBegin(1)).normalize()
cls.year = d.year
cls.month = d.month
cls.expiry = d
cls.expiry2 = d + pd.offsets.MonthBegin(1)
cls.dirpath = tm.get_data_path()
cls.html1 = os.path.join(cls.dirpath, 'yahoo_options1.html')
cls.html2 = os.path.join(cls.dirpath, 'yahoo_options2.html')
cls.html3 = os.path.join(cls.dirpath, 'yahoo_options3.html') #Empty table GH#22
cls.data1 = cls.aapl._option_frames_from_url(cls.html1)['puts']
def setUpClass(cls):
super(TestYahooOptions, cls).setUpClass()
_skip_if_no_lxml()
# aapl has monthlies
cls.aapl = web.Options('aapl', 'yahoo')
d = (Timestamp.today() + pd.offsets.MonthBegin(1)).normalize()
cls.year = d.year
cls.month = d.month
cls.expiry = d
cls.expiry2 = d + pd.offsets.MonthBegin(1)
cls.dirpath = tm.get_data_path()
cls.html1 = os.path.join(cls.dirpath, 'yahoo_options1.html')
cls.html2 = os.path.join(cls.dirpath, 'yahoo_options2.html')
cls.html3 = os.path.join(cls.dirpath,
'yahoo_options3.html') #Empty table GH#22
cls.data1 = cls.aapl._option_frames_from_url(cls.html1)['puts']
def _add_history(self, xds):
from pandas import Timestamp
version = ".{__version__}" if __version__ else ""
now = Timestamp.today().strftime("%Y-%m-%dT%H:%M")
prefix = f"[OceanDataTools{version}@{now}] "
msg = prefix + self.msg
if "history" not in xds.attrs:
xds.attrs["history"] = msg
elif xds.attrs["history"] == "":
xds.attrs["history"] = msg
else:
hist = xds.attrs["history"].split(";")
hist = [h.strip() for h in hist]
xds.attrs["history"] = "; ".join(hist + [msg])
return xds
def __init__(self, date):
self.date = date
self.cur_mood = config.visible_cal
self.rate = config.mood_data.get_rate(self.cur_mood, self.date)
super().__init__(text=str(self.date.day))
today = Timestamp.today().floor('D')
if self.date > today:
self.disabled = True
if isnull(self.rate): return
if config.mood_data.is_negative(self.cur_mood):
self.rate = 10 - self.rate
self.background_color = self.rate_to_color()
self.color = [0.976, 0.969, 0.929, 1]
def init():
global mood_data
# can cange file locations here:
mood_data = Mood("data/rates.csv", "data/weights.json")
# indicates which mood is viewed in the calendar widget
global visible_cal
visible_cal = 'overall' # can change default here
# indicate which month is currently being viewed:
date = Timestamp.today()
global year
year = date.year
global month
month = date.month
def test_class_ops_dateutil(self):
def compare(x, y):
assert (int(np.round(Timestamp(x).value / 1e9)) == int(
np.round(Timestamp(y).value / 1e9)))
compare(Timestamp.now(), datetime.now())
compare(Timestamp.now('UTC'), datetime.now(tzutc()))
compare(Timestamp.utcnow(), datetime.utcnow())
compare(Timestamp.today(), datetime.today())
current_time = calendar.timegm(datetime.now().utctimetuple())
compare(Timestamp.utcfromtimestamp(current_time),
datetime.utcfromtimestamp(current_time))
compare(Timestamp.fromtimestamp(current_time),
datetime.fromtimestamp(current_time))
date_component = datetime.utcnow()
time_component = (date_component + timedelta(minutes=10)).time()
compare(Timestamp.combine(date_component, time_component),
datetime.combine(date_component, time_component))
def test_class_ops_dateutil(self):
def compare(x, y):
assert (int(np.round(Timestamp(x).value / 1e9)) ==
int(np.round(Timestamp(y).value / 1e9)))
compare(Timestamp.now(), datetime.now())
compare(Timestamp.now('UTC'), datetime.now(tzutc()))
compare(Timestamp.utcnow(), datetime.utcnow())
compare(Timestamp.today(), datetime.today())
current_time = calendar.timegm(datetime.now().utctimetuple())
compare(Timestamp.utcfromtimestamp(current_time),
datetime.utcfromtimestamp(current_time))
compare(Timestamp.fromtimestamp(current_time),
datetime.fromtimestamp(current_time))
date_component = datetime.utcnow()
time_component = (date_component + timedelta(minutes=10)).time()
compare(Timestamp.combine(date_component, time_component),
datetime.combine(date_component, time_component))
def test_class_ops_pytz(self):
def compare(x, y):
assert int((Timestamp(x).value - Timestamp(y).value) / 1e9) == 0
compare(Timestamp.now(), datetime.now())
compare(Timestamp.now("UTC"), datetime.now(timezone("UTC")))
compare(Timestamp.utcnow(), datetime.utcnow())
compare(Timestamp.today(), datetime.today())
current_time = calendar.timegm(datetime.now().utctimetuple())
compare(
Timestamp.utcfromtimestamp(current_time),
datetime.utcfromtimestamp(current_time),
)
compare(Timestamp.fromtimestamp(current_time),
datetime.fromtimestamp(current_time))
date_component = datetime.utcnow()
time_component = (date_component + timedelta(minutes=10)).time()
compare(
Timestamp.combine(date_component, time_component),
datetime.combine(date_component, time_component),
)
def counts_data(self):
ref_date = Timestamp.today().strftime('%Y-%m-%d')
if ref_date in storage.keys():
return storage[ref_date]['counts'].copy()
conn = create_engine(self.connection_string)
cdata = read_sql('SELECT * FROM app__scholar_attrition__counts', conn)
sdata = read_sql('SELECT * FROM app__scholar_attrition__similarities', conn)
sdata = sdata.set_index(['school_name_row', 'grade_row', 'school_name_col', 'grade_col'])['similarity'].\
unstack(['school_name_col', 'grade_col'])
sdata.index.names = [r.replace('_row', '') for r in sdata.index.names]
sdata.columns.names = [c.replace('_col', '') for c in sdata.columns.names]
conn.dispose()
for k in storage.keys():
_ = storage.pop(k)
storage[ref_date] = {}
storage[ref_date]['counts'] = cdata.copy()
storage[ref_date]['similarities'] = sdata.copy()
return cdata
def test_class_ops_pytz(self):
def compare(x, y):
assert int((Timestamp(x).value - Timestamp(y).value) / 1e9) == 0
compare(Timestamp.now(), datetime.now())
compare(Timestamp.now("UTC"), datetime.now(timezone("UTC")))
compare(Timestamp.utcnow(), datetime.utcnow())
compare(Timestamp.today(), datetime.today())
current_time = calendar.timegm(datetime.now().utctimetuple())
msg = "timezone-aware Timestamp with UTC"
with tm.assert_produces_warning(FutureWarning, match=msg):
# GH#22451
ts_utc = Timestamp.utcfromtimestamp(current_time)
compare(
ts_utc,
datetime.utcfromtimestamp(current_time),
)
compare(
Timestamp.fromtimestamp(current_time), datetime.fromtimestamp(current_time)
)
compare(
# Support tz kwarg in Timestamp.fromtimestamp
Timestamp.fromtimestamp(current_time, "UTC"),
datetime.fromtimestamp(current_time, utc),
)
compare(
# Support tz kwarg in Timestamp.fromtimestamp
Timestamp.fromtimestamp(current_time, tz="UTC"),
datetime.fromtimestamp(current_time, utc),
)
date_component = datetime.utcnow()
time_component = (date_component + timedelta(minutes=10)).time()
compare(
Timestamp.combine(date_component, time_component),
datetime.combine(date_component, time_component),
)
from pandas import Timestamp from pandas.core.tools.datetimes import DatetimeScalar from typing import Union from datetime import date, datetime from numpy.core import datetime64 Date = Union[DatetimeScalar, Timestamp, date, datetime64] TODAY = Timestamp.today().date()
fin_data['Home Department Description'] =fin_data.groupby(['Supervisor ID','EEOC Job Classification','Job Title Description'])['Home Department Description'].bfill().ffill() # In[61]: # Column that indicates whether this employee is still in Family Services or has been terminated(left) fin_data['Left'] = np.where(fin_data['Termination Date'].notnull(), 1, 0) # In[62]: # Fill null with today's timestamp from pandas import Timestamp fin_data['Termination Date'] = fin_data['Termination Date'].fillna(Timestamp.today()) # In[63]: # Years of service in timedelta fin_data['timedelta'] = fin_data['Termination Date'] - fin_data['Hire Date'] # In[64]: # Converting Years of service in timedelta into number of years fin_data['timedelta'] = (fin_data['timedelta'].dt.days)/365
def isFinished(self):
retval = Timestamp.today() > self.endTime
if retval is True:
print(" ====> Terminate Time [{0}] Is Reached <====".format(
self.endTime))
return retval
def update_plot(self):
today = Timestamp.today().strftime('%Y-%m-%d')
today_date = to_datetime(today)
df = self.counts_data
weights = self.similarities_data.xs(self.school, level='school_name', axis=1)
df_all = df[df['school_name'].eq(self.school)]
present_academic_year = df['academic_year'].unique()[0]
tooltips = '''
<div style="font-size: 12px;">
<span style="font-weight: bold;">@school_name</span> (@school_type, @school_age years old)<br>
Grade @grade enrollment, as of @reference_date_string<br>
<br>
<span style="font-weight: bold;">Attrition</span>: @exit_count_present_cumm (@exit_percent_present_cumm{'0.0%'} change from October 1)<br>
<span style="font-weight: bold;">Backfill</span>: @enter_count_present_cumm (@enter_percent_present_cumm{'0.0%'} change from October 1)<br>
<span style="font-weight: bold;">Current total enrollment</span>: @total_enrollment
</div>
'''
ticker_ref = DataFrame({'month': date_range('2014-10-01', '2015-06-01', freq='MS')})
ticker_ref['marker'] = ticker_ref['month']
ticker_ref['month'] = ticker_ref['month'].apply(lambda x: x.strftime('%B'))
ticker_ref = ticker_ref.set_index('marker')['month'].to_dict()
x_range1d = Range1d(-0.05, 290.0)
y_index = max(
-df_all['exit_percent_present_cumm'].min(),
df_all['enter_percent_present_cumm'].max(),
-df_all['exit_percent_past_cumm'].min(),
df_all['enter_percent_past_cumm'].max()
)
y_range1d = Range1d(-y_index - 0.005, y_index + 0.005)
plots = []
focus_cols = ['enter_percent_present_cumm', 'exit_percent_present_cumm']
unique_grades = sorted(df_all['grade'].unique().astype('int'))
for grade in unique_grades:
grade_weight = weights[grade]
benchmark_df = DataFrame(columns=focus_cols)
df_copy = df[df['academic_year'].eq(present_academic_year)].copy().set_index(['school_name', 'grade'])
for rid, label in sorted(ticker_ref.items(), reverse=True):
df_copy = df_copy[df_copy['reference_date'].le(rid)]
df_ave = df_copy.groupby(level=['school_name', 'grade'])[focus_cols].tail(1).\
mul(grade_weight, axis=0).sum() / grade_weight.sum()
df_ave.name = label
benchmark_df = benchmark_df.append(df_ave)
benchmark_df = benchmark_df.reset_index()
benchmark_df['reference_date'] = benchmark_df['index'].map({y: x for x, y in ticker_ref.items()})
benchmark_df['reference_id'] = benchmark_df['reference_date'].apply(lambda x: x.toordinal()) - \
df_all['reference_date'].min().toordinal()
source_df = df_all[df_all['grade'].eq(grade)]
source_df_rev = source_df.sort_values('reference_id', ascending=False)
source_df_trunc = source_df.loc[source_df['reference_date'].le(today_date), :]
source = ColumnDataSource(source_df)
source_trunc = ColumnDataSource(source_df_trunc)
patch_source = ColumnDataSource(dict(
x_past=source_df['reference_id'].tolist() + source_df_rev['reference_id'].tolist(),
y_past=source_df['enter_percent_past_cumm'].tolist() + source_df_rev['exit_percent_past_cumm'].tolist()
))
plot1 = Plot(
x_range=x_range1d,
y_range=y_range1d,
min_border_bottom=5,
min_border_top=10,
min_border_right=10,
plot_width=700,
plot_height=150,
title=None,
title_text_font_size='0pt',
title_text_color='grey',
outline_line_alpha=0.0)
plot1.add_layout(
LinearAxis(
axis_label='Grade ' + str(grade), axis_label_text_font_size='9pt', minor_tick_line_alpha=0.0,
axis_label_text_color='grey',
axis_line_alpha=0.1, major_tick_line_alpha=0.1, major_label_text_color='grey',
major_label_text_font_size='7pt', formatter=NumeralTickFormatter(format='0%')
), 'left')
patch = Patch(x='x_past', y='y_past', fill_color='#AFAFAD', fill_alpha=0.25, line_alpha=0.0)
plot1.add_glyph(patch_source, patch)
line1 = Line(
x='reference_id', y='enter_percent_present_cumm', line_width=2, line_color='#f7910b', line_alpha=1.0)
plot1.add_glyph(source_trunc, line1)
line2 = Line(
x='reference_id', y='exit_percent_present_cumm', line_width=2, line_color='#f7910b', line_alpha=1.0)
plot1.add_glyph(source_trunc, line2)
line_h = Line(x='reference_id', y=0, line_width=1, line_color='black', line_alpha=0.1)
line_renderer = GlyphRenderer(data_source=source, glyph=line_h, name='line')
plot1.add_glyph(source, line_h)
for ind, series in benchmark_df.iterrows():
x = series['reference_id']
y_enter = series['enter_percent_present_cumm']
y_exit = series['exit_percent_present_cumm']
label = series['index']
line = Segment(x0=x, x1=x, y0=-y_index, y1=y_index, line_width=1, line_color='#165788', line_alpha=0.1)
plot1.add_glyph(line)
linec1 = Segment(
x0=x - 3, x1=x + 3, y0=y_enter, y1=y_enter, line_width=1, line_color='#ed2939', line_alpha=1.0)
plot1.add_glyph(linec1)
linec2 = Segment(
x0=x - 3, x1=x + 3, y0=y_exit, y1=y_exit, line_width=1, line_color='#ed2939', line_alpha=1.0)
plot1.add_glyph(linec2)
text = Text(x=x+3, y=-y_index, text=[label], text_font_size='8pt', text_color='grey', text_alpha=0.5)
plot1.add_glyph(text)
hover_tool = HoverTool(
plot=plot1, renderers=[line_renderer], tooltips=tooltips, always_active=False, mode='vline',
point_policy='follow_mouse', line_policy='prev')
crosshair_tool = CrosshairTool(plot=plot1, dimensions=['height'])
zoom_tool = BoxZoomTool(plot=plot1, dimensions=['width'])
reset_tool = ResetTool(plot=plot1)
save_tool = PreviewSaveTool(plot=plot1)
pan_tool = PanTool(plot=plot1, dimensions=['width'])
help_tool = HelpTool(plot=plot1, help_tooltip='App help page', redirect='http://data.successacademies.org/blog/')
plot1.tools.extend([hover_tool, zoom_tool, pan_tool, reset_tool, save_tool, help_tool, crosshair_tool])
plot1.renderers.extend([line_renderer])
plots.append([plot1])
self.plot.children = plots
authority = randomCompliantString(4)
if verbosity > 1:
print('No authority given, random generated authority is "{}" '.format(
authority))
if number is None:
number = randomCompliantString(5)
# number = str(randint(1, 99999)).zfill(5)
if verbosity > 1:
print(
'No number given, random generated number is "{}" '.format(number))
if birth_year is None and birth_date is None:
year = Timestamp.today().year
birth_year = year - randint(19, 99)
if verbosity > 1:
print(
'No birth_year and birth_date given, random generated birth_year is "{}" '
.format(birth_year))
if birth_month is None and birth_date is None:
birth_month = randint(1, 12)
if verbosity > 1:
print(
'No birth_month and birth_date given, random generated birth_month is "{}" '
.format(birth_month))
def get(self):
return Timestamp.today().round(self.round)
def _download(self,
start=None,
end=None,
inseason=False,
vars='ALL',
stns=260,
interval='daily'):
# Import the necessary modules (optional and not included in the
# installation of pastas).
try:
import requests
except ImportError:
raise ImportError('The module requests could not be imported. '
'Please install through:'
'>>> pip install requests'
'or:'
'>>> conda install requests')
from io import StringIO
if start is None:
start = Timestamp(Timestamp.today().year, 1, 1)
else:
start = to_datetime(start)
if end is None:
end = Timestamp.today()
else:
end = to_datetime(end)
if not isinstance(vars, list):
if isinstance(vars, ndarray):
vars = list(vars)
else:
vars = [vars]
if not isinstance(stns, list):
if isinstance(stns, ndarray):
stns = list(stns)
else:
stns = [stns]
# convert possible integers to string
stns = [str(i) for i in stns]
if interval.startswith('hour') and 'RD' in vars:
message = 'Interval can not be hourly for rainfall-stations'
raise (ValueError(message))
if 'RD' in vars and len(vars) > 1:
message = 'Only daily precipitation can be downloaded from ' \
'rainfall-stations'
raise (ValueError(message))
if interval.startswith('hour'):
# hourly data from meteorological stations
url = 'http://projects.knmi.nl/klimatologie/uurgegevens/getdata_uur.cgi'
elif 'RD' in vars:
# daily data from rainfall-stations
url = 'http://projects.knmi.nl/klimatologie/monv/reeksen/getdata_rr.cgi'
else:
# daily data from meteorological stations
url = 'http://projects.knmi.nl/klimatologie/daggegevens/getdata_dag.cgi'
vars = ":".join(vars)
stns = ":".join(stns)
if interval.startswith('hour'):
data = {
'start': start.strftime('%Y%m%d') + '01',
'end': end.strftime('%Y%m%d') + '24',
'vars': vars,
'stns': stns,
}
else:
data = {
'start': start.strftime('%Y%m%d'),
'end': end.strftime('%Y%m%d'),
'inseason': str(int(inseason)),
'vars': vars,
'stns': stns,
}
result = requests.get(url, params=data).text
f = StringIO(result)
self.readdata(f)
from bokeh.models import Plot, ColumnDataSource
from bokeh.models.glyphs import Rect
from bokeh.models.axes import LinearAxis, CategoricalAxis
from bokeh.models.ranges import FactorRange, DataRange1d
from bokeh.models.widgets.tables import DataTable, TableColumn
API_KEY = '<INSERT VALID API KEY HERE>'
ARTICLE_URI = 'http://api.nytimes.com/svc/search/v2/articlesearch.json'
SHARED_URI = 'http://api.nytimes.com/svc/mostpopular/v2/mostshared/{section}/30.json'
EMAILED_URI = 'http://api.nytimes.com/svc/mostpopular/v2/mostemailed/{section}/30.json'
VIEWED_URI = 'http://api.nytimes.com/svc/mostpopular/v2/mostviewed/{section}/30.json'
curstate().autoadd = False
# set up metadata for API query
today = Timestamp.today().strftime('%Y%m%d')
today_minus_thirty = (Timestamp.today().date() -
to_timedelta('30 days')).strftime('%Y%m%d')
page = 0
query_params = {
'q': '"climate change"',
'begin_date': today_minus_thirty,
'end_date': today,
'sort': 'newest',
'hl': True,
'page': page,
'api-key': API_KEY
}
# pull all data for specified time period
