def register_pandas_datetime_converter_if_needed():
# based on https://github.com/pandas-dev/pandas/pull/17710
global _registered
if not _registered:
from pandas.tseries import converter
converter.register()
_registered = True
def plot(self, date1: str, date2: str, data='Global Horiz'):
df = self.loading()
converter.register()
d1 = datetime.strptime(date1, '%Y-%m-%d')
d2 = datetime.strptime(date2, '%Y-%m-%d')
df2plot = df.loc[d1:d2]
sns.set(style="darkgrid")
f, ax = plt.subplots(figsize=(10, 5))
sns.lineplot(x=df2plot.index, y=df2plot[data])
# Removing top and right borders
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
# Finalize the plot
sns.despine(bottom=True)
plt.setp(f.axes,
xticks=[],
xlabel='Interval\nfrom: {0}\nto: {1}'.format(date1, date2),
ylabel='Solar Irradiation (W/m²)')
plt.tight_layout(h_pad=2)
plt.savefig("output.png")
print('Image saved.')
def register_pandas_datetime_converter_if_needed():
# based on https://github.com/pandas-dev/pandas/pull/17710
global _registered
if not _registered:
from pandas.tseries import converter
converter.register()
_registered = True
def test_old_import_warns(self):
with tm.assert_produces_warning(FutureWarning) as w:
from pandas.tseries import converter
converter.register()
assert len(w)
assert ('pandas.plotting.register_matplotlib_converters' in
str(w[0].message))
def test_old_import_warns(self):
with tm.assert_produces_warning(FutureWarning) as w:
from pandas.tseries import converter
converter.register()
assert len(w)
assert ('pandas.plotting.register_matplotlib_converters' in
str(w[0].message))
def main():
# NB: https://github.com/pydata/xarray/issues/1661#issuecomment-339525582
from pandas.tseries import converter
converter.register()
p = parse_cmdline()
common.set_logger(logging.DEBUG if p.verbose else logging.INFO,
p.log,
loggers={"FCDR_HIRS", "typhon"})
plot(p)
def test_date2num_dst_pandas():
# Test for github issue #3896, but in date2num around DST transitions
# with a timezone-aware pandas date_range object.
pd = pytest.importorskip('pandas')
from pandas.tseries import converter
converter.register()
def tz_convert(*args):
return pd.DatetimeIndex.tz_convert(*args).astype(object)
_test_date2num_dst(pd.date_range, tz_convert)
def test_date2num_dst_pandas():
# Test for github issue #3896, but in date2num around DST transitions
# with a timezone-aware pandas date_range object.
pd = pytest.importorskip('pandas')
from pandas.tseries import converter
converter.register()
def tz_convert(*args):
return pd.DatetimeIndex.tz_convert(*args).astype(object)
_test_date2num_dst(pd.date_range, tz_convert)
def heatmap(*args, classes=None, cmap=DEFAULT_DISCRETE_CMAP, **kws):
"""j24.visualization.heatmap wrapper for sounding data"""
# This is needed because of a bug in pandas 0.21
from pandas.tseries import converter
converter.register()
fig, ax = vis.heatmap(*args, cmap='jet', **kws)
fmt_m2km(ax.yaxis)
ax.set_ylabel('Height, km')
if classes is not None:
vis.class_colors(classes, ax=ax, cmap=cmap)
return fig, ax
def register_pandas_datetime_converter_if_needed():
# based on https://github.com/pandas-dev/pandas/pull/17710
global _registered
if not _registered:
try:
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
except ImportError:
# register_matplotlib_converters new in pandas 0.22
from pandas.tseries import converter
converter.register()
_registered = True
def register_pandas_datetime_converter_if_needed():
# based on https://github.com/pandas-dev/pandas/pull/17710
global _registered
if not _registered:
try:
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
except ImportError:
# register_matplotlib_converters new in pandas 0.22
from pandas.tseries import converter
converter.register()
_registered = True
import pandas as pd
import numpy
import matplotlib.pyplot as plt
import argparse
from pandas.tseries import converter
converter.register()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--assessments',
type=str,
default="assessments.csv",
help="CSV file input")
parser.add_argument('--parcel',
type=str,
help="The parcel to compare",
required=False)
args = parser.parse_args()
df = pd.read_csv(args.assessments)
df['Sale Date'] = pd.to_datetime(df['Sale Date'], format='%m/%d/%Y')
parcel = df.loc[df['Parcel Id'] == args.parcel]
ax = df.plot(kind='scatter', x='Lot Area', y='County Land Value')
parcel.plot(kind='scatter',
x='Lot Area',
y='County Land Value',
color='red',
ax=ax)
ax = df.plot(kind='scatter',
from .sankey import * # noqa (API import)
from .stats import * # noqa (API import)
from .tabular import * # noqa (API import)
from .renderer import MPLRenderer
mpl_ge_150 = LooseVersion(mpl.__version__) >= '1.5.0'
if pd:
try:
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
except ImportError:
from pandas.tseries import converter
converter.register()
def set_style(key):
"""
Select a style by name, e.g. set_style('default'). To revert to the
previous style use the key 'unset' or False.
"""
if key is None:
return
elif not key or key in ['unset', 'backup']:
if 'backup' in styles:
plt.rcParams.update(styles['backup'])
else:
raise Exception('No style backed up to restore')
elif key not in styles:
import datetime
import numpy as np
from pandas.util.decorators import cache_readonly
import pandas.core.common as com
from pandas.core.index import MultiIndex
from pandas.core.series import Series, remove_na
from pandas.tseries.index import DatetimeIndex
from pandas.tseries.period import PeriodIndex
from pandas.tseries.frequencies import get_period_alias, get_base_alias
from pandas.tseries.offsets import DateOffset
try: # mpl optional
import pandas.tseries.converter as conv
conv.register()
except ImportError:
pass
def _get_standard_kind(kind):
return {'density' : 'kde'}.get(kind, kind)
def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
diagonal='hist', marker='.', **kwds):
"""
Draw a matrix of scatter plots.
Parameters
----------
alpha : amount of transparency applied
"""
Functions for generating nice lidar profile graphs
"""
# get matplotlib with agg backend for drawing graphs on servers with
# no display
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['figure.figsize'] = (15, 7)
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
# see here: https://matplotlib.org/faq/howto_faq.html#plot-numpy-datetime64-values
from pandas.tseries import converter as pdtc
pdtc.register()
# useful stuff
import xml, rasppy
import datetime as dt
import numpy as np
import xarray as xr
from io import BytesIO
# matplotlib tools for calculating tickmark locations -- I'm using
# these to find nice windbarb intervals
from matplotlib.ticker import MaxNLocator
from matplotlib.dates import AutoDateLocator, num2date
# and this is needed to help monkey patch the barbs function
from dateutil.rrule import (rrule, MO, TU, WE, TH, FR, SA, SU, YEARLY,
MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY,
SECONDLY)
MICROSECONDLY = SECONDLY + 1