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def calculate_derivs(positions, t, m_sun, m_planet, G):
    # Unpack positions and velocities
    asteroid_x = positions[0]
    asteroid_vx = positions[1]
    asteroid_y = positions[2]
    asteroid_vy = positions[3]

    sun_x = positions[0]
    sun_vx = positions[1]
    sun_y = positions[2]

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npools = 4
inputs = []
for i in range(npools):
    inputs.append((rs[i::npools], T_perps_0[i::npools], T_pars_0[i::npools],
                   vs[i::npools], ns[i::npools]))

# multiprocessing pool
with Pool(npools) as p:
    anis = p.map(single_tani, inputs)

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import matplotlib
matplotlib.use('qt5agg')
import matplotlib.pyplot as plt
import matplotlib.widgets as mwidgets

from datetime import datetime, timedelta
import numpy as np
import astropy.units as u
import astropy.constants as const
from astropy.visualization import quantity_support

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from parfive import Downloader
for i in range(10, 20):
    dl = Downloader()
    dl.enqueue_file(f"https://spdf.gsfc.nasa.gov/pub/data/psp/fields/l2/mag_rtn_1min/2018/psp_fld_l2_mag_rtn_1min_201811{i:02}_v01.cdf", path="./")
    result = dl.download()
    if len(result.errors):
        raise result.errors[0][2]

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MPL code of conduct (CoC)

Purpose of meeting

• To decide on a process for selecting CoC for matplotlib

So far

• Proposed CoC was opened at matplotlib/matplotlib#18538

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self.m.meta['CTYPE1'] = 'CRLN-CEA'
self.m.meta['CTYPE2'] = 'CRLT-CEA'
self.m.meta['CDELT1'] = np.abs(self.m.meta['CDELT1'])
self.m.meta['CDELT2'] = 180 / np.pi * self.m.meta['CDELT2']
self.m.meta['CRVAL1'] = 0.0
self.m.meta['CUNIT1'] = 'deg'
self.m.meta['CUNIT2'] = 'deg'
self.m.meta['date-obs'] = parse_time(self.m.meta['t_start']).isot

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import matplotlib.pyplot as plt
from reproject import reproject_interp

import sunpy.data.sample
import sunpy.map


map_aia = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)
map_hmi = sunpy.map.Map(sunpy.data.sample.HMI_LOS_IMAGE)
map_hmi.plot_settings['cmap'] = "hmimag"

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import matplotlib.pyplot as plt

import astropy.units as u
from astropy.coordinates import SkyCoord
from matplotlib.patches import Rectangle

import sunpy.data.sample
import sunpy.map

aia_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)

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def load_data(files):
    # Number of data points for each variable and time
    npoints = 360 * 180
    # Variable names
    variables = ['lons', 'lats', 'b_all', 'b_feet', 'b_ss']

    # Empty array to store data
    all_data = np.zeros((len(variables), len(files), npoints)) * np.nan

    dtimes = []

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