def notebook_extension(*, _inline_js=True):
"""Enable ipywidgets, holoviews, and asyncio notebook integration."""
if not in_ipynb():
raise RuntimeError('"adaptive.notebook_extension()" may only be run '
'from a Jupyter notebook.')
global _async_enabled, _holoviews_enabled, _ipywidgets_enabled
# Load holoviews
try:
_holoviews_enabled = False # After closing a notebook the js is gone
if not _holoviews_enabled:
import holoviews
holoviews.notebook_extension('bokeh',
logo=False,
inline=_inline_js)
_holoviews_enabled = True
except ModuleNotFoundError:
warnings.warn("holoviews is not installed; plotting "
"is disabled.", RuntimeWarning)
# Load ipywidgets
try:
if not _ipywidgets_enabled:
import ipywidgets
_ipywidgets_enabled = True
except ModuleNotFoundError:
warnings.warn("ipywidgets is not installed; live_info "
"is disabled.", RuntimeWarning)
# Enable asyncio integration
if not _async_enabled:
get_ipython().magic('gui asyncio')
_async_enabled = True
def init_notebook(mpl=True):
# Enable inline plotting in the notebook
if mpl:
try:
get_ipython().enable_matplotlib(gui='inline')
except NameError:
pass
print('Populated the namespace with:\n' +
', '.join(init_mooc_nb) +
'\nfrom code/edx_components:\n' +
', '.join(edx_components.__all__) +
'\nfrom code/functions:\n' +
', '.join(functions.__all__))
holoviews.notebook_extension('matplotlib')
Store.renderers['matplotlib'].fig = 'svg'
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.usetex'] = True
latex_packs = [r'\usepackage{amsmath}',
r'\usepackage{amssymb}'
r'\usepackage{bm}']
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.latex.preamble'] = latex_packs
# Set plot style.
options = Store.options(backend='matplotlib')
options.Contours = Options('style', linewidth=2, color='k')
options.Contours = Options('plot', aspect='square')
options.HLine = Options('style', linestyle='--', color='b', linewidth=2)
options.VLine = Options('style', linestyle='--', color='r', linewidth=2)
options.Image = Options('style', cmap='RdBu_r')
options.Image = Options('plot', title_format='{label}')
options.Path = Options('style', linewidth=1.2, color='k')
options.Path = Options('plot', aspect='square', title_format='{label}')
options.Curve = Options('style', linewidth=2, color='k')
options.Curve = Options('plot', aspect='square', title_format='{label}')
options.Overlay = Options('plot', show_legend=False, title_format='{label}')
options.Layout = Options('plot', title_format='{label}')
options.Surface = Options('style', cmap='RdBu_r', rstride=1, cstride=1, lw=0.2)
options.Surface = Options('plot', azimuth=20, elevation=8)
# Turn off a bogus holoviews warning.
# Temporary solution to ignore the warnings
warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
module_dir = os.path.dirname(__file__)
matplotlib.rc_file(os.path.join(module_dir, "matplotlibrc"))
np.set_printoptions(precision=2, suppress=True,
formatter={'complexfloat': pretty_fmt_complex})
# Patch a bug in holoviews
if holoviews.__version__.release <= (1, 4, 3):
from patch_holoviews import patch_all
patch_all()
def setUp(self):
notebook_extension(*BACKENDS)
Store.current_backend = 'matplotlib'
Store.renderers['matplotlib'] = mpl.MPLRenderer.instance()
if bokeh:
Store.renderers['bokeh'] = bokeh.BokehRenderer.instance()
OutputSettings.options = OrderedDict(OutputSettings.defaults.items())
super(TestOutputUtil, self).setUp()
def setUp(self):
notebook_extension(*BACKENDS)
Store.current_backend = 'matplotlib'
Store.renderers['matplotlib'] = mpl.MPLRenderer.instance()
if bokeh:
Store.renderers['bokeh'] = bokeh.BokehRenderer.instance()
OutputSettings.options = OrderedDict(OutputSettings.defaults.items())
super(TestOutputUtil, self).setUp()
def init_notebook():
# Enable inline plotting in the notebook
try:
get_ipython().enable_matplotlib(gui='inline')
except NameError:
pass
print('Populated the namespace with:\n' + ', '.join(__all__))
holoviews.notebook_extension('matplotlib')
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.usetex'] = True
# Set plot style.
options = Store.options(backend='matplotlib')
options.Contours = Options('style', linewidth=2, color='k')
options.Contours = Options('plot', aspect='square')
options.HLine = Options('style', linestyle='--', color='b', linewidth=2)
options.VLine = Options('style', linestyle='--', color='r', linewidth=2)
options.Image = Options('style', cmap='RdBu_r')
options.Image = Options('plot', title_format='{label}')
options.Path = Options('style', linewidth=1.2, color='k')
options.Path = Options('plot', aspect='square', title_format='{label}')
options.Curve = Options('style', linewidth=2, color='k')
options.Curve = Options('plot', aspect='square', title_format='{label}')
options.Overlay = Options('plot', show_legend=False, title_format='{label}')
options.Layout = Options('plot', title_format='{label}')
options.Surface = Options('style', cmap='RdBu_r', rstride=1, cstride=1, lw=0.2)
options.Surface = Options('plot', azimuth=20, elevation=8)
# Turn off a bogus holoviews warning.
# Temporary solution to ignore the warnings
warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
module_dir = os.path.dirname(__file__)
matplotlib.rc_file(os.path.join(module_dir, "matplotlibrc"))
np.set_printoptions(precision=2, suppress=True,
formatter={'complexfloat': pretty_fmt_complex})
# In order to make the notebooks readable through nbviewer we want to hide
# the code by default. However the same code is executed by the students,
# and in that case we don't want to hide the code. So we check if the code
# is executed by one of the mooc developers. Here we do by simply checking
# for some files that belong to the internal mooc repository, but are not
# published. This is a temporary solution, and should be improved in the
# long run.
developer = os.path.exists(os.path.join(module_dir, os.path.pardir,
'scripts'))
display_html(display.HTML(nb_html_header +
(hide_outside_ipython if developer else '')))
# Patch a bug in holoviews
from patch_holoviews import patch_all
patch_all()
def init_notebook():
print_information()
check_versions()
code_dir = os.path.dirname(os.path.realpath(__file__))
hv_css = os.path.join(code_dir, 'hv_widgets_settings.css')
holoviews.plotting.widgets.SelectionWidget.css = hv_css
holoviews.notebook_extension('matplotlib')
# Enable inline plotting in the notebook
get_ipython().enable_matplotlib(gui='inline')
Store.renderers['matplotlib'].fig = 'svg'
Store.renderers['matplotlib'].dpi = 100
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.usetex'] = True
latex_packs = [r'\usepackage{amsmath}',
r'\usepackage{amssymb}'
r'\usepackage{bm}']
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.latex.preamble'] = \
latex_packs
# Set plot style.
options = Store.options(backend='matplotlib')
options.Contours = Options('style', linewidth=2, color='k')
options.Contours = Options('plot', aspect='square')
options.HLine = Options('style', linestyle='--', color='b', linewidth=2)
options.VLine = Options('style', linestyle='--', color='r', linewidth=2)
options.Image = Options('style', cmap='RdBu_r')
options.Image = Options('plot', title_format='{label}')
options.Path = Options('style', linewidth=1.2, color='k')
options.Path = Options('plot', aspect='square', title_format='{label}')
options.Curve = Options('style', linewidth=2, color='k')
options.Curve = Options('plot', aspect='square', title_format='{label}')
options.Overlay = Options('plot', show_legend=False, title_format='{label}')
options.Layout = Options('plot', title_format='{label}')
options.Surface = Options('style', cmap='RdBu_r', rstride=2, cstride=2,
lw=0.2, edgecolors='k')
options.Surface = Options('plot', azimuth=20, elevation=8)
# Set slider label formatting
for dimension_type in [float, np.float64, np.float32]:
holoviews.Dimension.type_formatters[dimension_type] = pretty_fmt_complex
matplotlib.rc_file(os.path.join(code_dir, "matplotlibrc"))
np.set_printoptions(precision=2, suppress=True,
formatter={'complexfloat': pretty_fmt_complex})
def init_notebook():
print_information()
check_versions()
code_dir = os.path.dirname(os.path.realpath(__file__))
hv_css = os.path.join(code_dir, 'hv_widgets_settings.css')
holoviews.plotting.widgets.SelectionWidget.css = hv_css
holoviews.notebook_extension('matplotlib')
# Enable inline plotting in the notebook
get_ipython().enable_matplotlib(gui='inline')
Store.renderers['matplotlib'].fig = 'svg'
Store.renderers['matplotlib'].dpi = 100
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.usetex'] = True
latex_packs = [r'\usepackage{amsmath}',
r'\usepackage{amssymb}'
r'\usepackage{bm}']
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.latex.preamble'] = \
latex_packs
# Set plot style.
options = Store.options(backend='matplotlib')
options.Contours = Options('style', linewidth=2, color='k')
options.Contours = Options('plot', aspect='square')
options.HLine = Options('style', linestyle='--', color='b', linewidth=2)
options.VLine = Options('style', linestyle='--', color='r', linewidth=2)
options.Image = Options('style', cmap='RdBu_r')
options.Image = Options('plot', title_format='{label}')
options.Path = Options('style', linewidth=1.2, color='k')
options.Path = Options('plot', aspect='square', title_format='{label}')
options.Curve = Options('style', linewidth=2, color='k')
options.Curve = Options('plot', aspect='square', title_format='{label}')
options.Overlay = Options('plot', show_legend=False, title_format='{label}')
options.Layout = Options('plot', title_format='{label}')
options.Surface = Options('style', cmap='RdBu_r', rstride=2, cstride=2,
lw=0.2, edgecolors='k')
options.Surface = Options('plot', azimuth=20, elevation=8)
# Set slider label formatting
for dimension_type in [float, np.float64, np.float32]:
holoviews.Dimension.type_formatters[dimension_type] = pretty_fmt_complex
matplotlib.rc_file(os.path.join(code_dir, "matplotlibrc"))
np.set_printoptions(precision=2, suppress=True,
formatter={'complexfloat': pretty_fmt_complex})
def manage_display_import():
global MPLRenderer
import matplotlib as mpl
if os.environ.get('DISPLAY', '') == '':
log.info('no display found. Using non-interactive Agg backend')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
mpl.use('Agg')
from holoviews.plotting.mpl import MPLRenderer # noqa
if is_interactive():
from holoviews import notebook_extension
notebook_extension("bokeh")
def notebook_extension():
get_ipython().magic('gui asyncio')
try:
import holoviews as hv
return hv.notebook_extension('bokeh')
except ModuleNotFoundError:
warnings.warn(
"The holoviews package is not installed so plotting"
"will not work.", RuntimeWarning)
def notebook_extension():
if not in_ipynb():
raise RuntimeError('"adaptive.notebook_extension()" may only be run '
'from a Jupyter notebook.')
global _plotting_enabled
_plotting_enabled = False
try:
import ipywidgets
import holoviews
holoviews.notebook_extension('bokeh')
_plotting_enabled = True
except ModuleNotFoundError:
warnings.warn("holoviews and (or) ipywidgets are not installed; plotting "
"is disabled.", RuntimeWarning)
global _async_enabled
get_ipython().magic('gui asyncio')
_async_enabled = True
def interactive_crop(
video_path,
frame=0,
):
"""
Loads and displays a frame for a video to be used for cropping.
Cropping automatically updated using holoviews stream object.
Args:
video_path (str): Path to the video
frame (int): The index of the frame to be used for cropping
Returns:
image, stream
"""
hv.notebook_extension("bokeh")
cap = cv2.VideoCapture(video_path)
cap.set(cv2.CAP_PROP_POS_FRAMES, frame)
_, frame = cap.read()
print(frame.shape)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# frame = frame[::-1, :] # inverse y axis for plotting
cap.release()
image = hv.Image(
(np.arange(frame.shape[1]), np.arange(frame.shape[0]), frame))
image.opts(
width=frame.shape[1],
height=frame.shape[0],
cmap="gray",
colorbar=True,
toolbar="below",
title="First Frame. Crop if Desired",
)
box = hv.Polygons([])
box.opts(alpha=0.5)
box_stream = streams.BoxEdit(source=box, num_objects=1)
return (image * box), box_stream
import os
import sys
sys.path.insert(0, os.path.abspath('..')) # to get adaptive on the path
import adaptive
import holoviews
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
from PIL import Image, ImageDraw
holoviews.notebook_extension('matplotlib')
def create_and_run_learner():
def ring(xy):
import numpy as np
x, y = xy
a = 0.2
return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)
learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])
adaptive.runner.simple(learner, goal=lambda l: l.loss() < 0.01)
return learner
def plot_learner_and_save(learner, fname):
fig, ax = plt.subplots()
tri = learner.ip().tri
triang = mtri.Triangulation(*tri.points.T, triangles=tri.vertices)
ax.triplot(triang, c='k', lw=0.8)
ax.imshow(learner.plot().Image.I.data, extent=(-0.5, 0.5, -0.5, 0.5))
import collections
import threading
from concurrent.futures import ThreadPoolExecutor
import warnings
import glob
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt # noqa: F401
import matplotlib.cbook
import holoviews as hv
from holoviews import opts
hv.extension("bokeh")
hv.notebook_extension(width=90) # For showing wide plots
# Get rid of matplotlib deprecation warnings.
warnings.filterwarnings("ignore", category=matplotlib.cbook.mplDeprecation)
# +
def load_config(path: str) -> typing.Dict:
"""Loads run metadata."""
with open(f"{path}/meta.json", "r") as f:
try:
config = json.load(f)
return config
except Exception as e: # noqa: F841
print(f"Error parsing {path}")
# coding: utf-8
# # System of 2 particles
# In[2]:
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
import holoviews as hv
hv.notebook_extension('matplotlib')
get_ipython().magic('matplotlib inline')
# In[3]:
m = 1 #particle mass
n = 2 #number of particles
h = .01 #time step
L = 100 #box size
eps = 1
sigma = 0.01
# In[73]:
pos0 = np.random.rand(n, 2) # one particle each row: [x y ]
print(pos0)
vel0 = np.array([[1, 1], [-2, -1]]) #one particle each row: [px py]
mom0 = m * vel0
print(mom0, vel0)
def init_notebook(mpl=True):
# Enable inline plotting in the notebook
if mpl:
try:
get_ipython().enable_matplotlib(gui='inline')
except NameError:
pass
print('Populated the namespace with:\n' + ', '.join(init_mooc_nb) +
'\nfrom code/edx_components:\n' + ', '.join(edx_components.__all__) +
'\nfrom code/functions:\n' + ', '.join(functions.__all__))
holoviews.notebook_extension('matplotlib')
Store.renderers['matplotlib'].fig = 'svg'
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.usetex'] = True
latex_packs = [
r'\usepackage{amsmath}', r'\usepackage{amssymb}'
r'\usepackage{bm}'
]
holoviews.plotting.mpl.MPLPlot.fig_rcparams[
'text.latex.preamble'] = latex_packs
# Set plot style.
options = Store.options(backend='matplotlib')
options.Contours = Options('style', linewidth=2, color='k')
options.Contours = Options('plot', aspect='square')
options.HLine = Options('style', linestyle='--', color='b', linewidth=2)
options.VLine = Options('style', linestyle='--', color='r', linewidth=2)
options.Image = Options('style', cmap='RdBu_r')
options.Image = Options('plot', title_format='{label}')
options.Path = Options('style', linewidth=1.2, color='k')
options.Path = Options('plot', aspect='square', title_format='{label}')
options.Curve = Options('style', linewidth=2, color='k')
options.Curve = Options('plot', aspect='square', title_format='{label}')
options.Overlay = Options('plot',
show_legend=False,
title_format='{label}')
options.Layout = Options('plot', title_format='{label}')
options.Surface = Options('style',
cmap='RdBu_r',
rstride=1,
cstride=1,
lw=0.2)
options.Surface = Options('plot', azimuth=20, elevation=8)
# Turn off a bogus holoviews warning.
# Temporary solution to ignore the warnings
warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
module_dir = os.path.dirname(__file__)
matplotlib.rc_file(os.path.join(module_dir, "matplotlibrc"))
np.set_printoptions(precision=2,
suppress=True,
formatter={'complexfloat': pretty_fmt_complex})
# Patch a bug in holoviews
if holoviews.__version__.release <= (1, 4, 3):
from patch_holoviews import patch_all
patch_all()
from holoviews import notebook_extension
from holoviews.element.comparison import ComparisonTestCase
from holoviews import Store
from holoviews.util import output, opts, OutputSettings
from holoviews.core import OrderedDict
from holoviews.core.options import OptionTree
from holoviews.plotting import mpl
try:
from holoviews.plotting import bokeh
except:
bokeh = None
BACKENDS = ['matplotlib'] + (['bokeh'] if bokeh else [])
notebook_extension(*BACKENDS)
class TestOutputUtil(ComparisonTestCase):
def setUp(self):
Store.current_backend = 'matplotlib'
Store.renderers['matplotlib'] = mpl.MPLRenderer.instance()
if bokeh:
Store.renderers['bokeh'] = bokeh.BokehRenderer.instance()
OutputSettings.options = OrderedDict(OutputSettings.defaults.items())
super(TestOutputUtil, self).setUp()
def tearDown(self):
Store.renderers['matplotlib'] = mpl.MPLRenderer.instance()
def init_notebook():
print_information()
check_versions()
holoviews.notebook_extension("matplotlib")
holoviews.output(widget_location='bottom')
# Enable inline plotting in the notebook
get_ipython().enable_matplotlib(gui="inline")
Store.renderers["matplotlib"].fig = "svg"
Store.renderers["matplotlib"].dpi = 100
holoviews.plotting.mpl.MPLPlot.fig_rcparams["text.usetex"] = False
latex_packs = [
r"\usepackage{amsmath}", r"\usepackage{amssymb}"
r"\usepackage{bm}"
]
holoviews.plotting.mpl.MPLPlot.fig_rcparams[
"text.latex.preamble"] = latex_packs
# Set plot style.
options = Store.options(backend="matplotlib")
options.Contours = Options("style", linewidth=2, color="k")
options.Contours = Options("plot", padding=0, aspect="square")
options.HLine = Options("style", linestyle="--", color="b", linewidth=2)
options.VLine = Options("style", linestyle="--", color="r", linewidth=2)
options.Image = Options("style", cmap="RdBu_r")
options.Image = Options("plot", padding=0, title="{label}")
options.Path = Options("style", linewidth=1.2, color="black")
options.Path = Options("plot", padding=0, aspect="square", title="{label}")
options.Curve = Options("style", linewidth=2, color="black")
options.Curve = Options("plot",
padding=0,
aspect="square",
title="{label}")
options.Overlay = Options("plot",
padding=0,
show_legend=False,
title="{label}")
options.Layout = Options("plot", title="{label}")
options.Surface = Options("style",
cmap="RdBu_r",
rstride=2,
cstride=2,
lw=0.2,
edgecolor="black")
options.Surface = Options("plot", azimuth=20, elevation=8)
# Set slider label formatting
for dimension_type in [float, np.float64, np.float32]:
holoviews.Dimension.type_formatters[
dimension_type] = lambda x: pretty_fmt_complex(x, 4)
code_dir = os.path.dirname(os.path.realpath(__file__))
matplotlib.rc_file(os.path.join(code_dir, "matplotlibrc"))
np.set_printoptions(precision=2,
suppress=True,
formatter={"complexfloat": pretty_fmt_complex})
# Silence Kwant warnings from color scale overflow
warnings.filterwarnings("ignore",
category=RuntimeWarning,
message="The plotted data contains")
import os
import subprocess
from parfile import *
# display
# (M2,P)
# (3,4) | (3,8) | (3,12)
# (2,4) | (2,8) | (2,12)
# (1,4) | (1,8) | (1,12)
# => M2[i//3] and P[i%3]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
hv.notebook_extension("matplotlib")
# x = np.arange(0, np.pi, 0.1)
# y = np.arange(0, 2*np.pi, 0.1)
# X, Y = np.meshgrid(x, y)
# Z = np.cos(X) * np.sin(Y) * 10
# colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 1)] # R -> G -> B
# n_bins = [4, 6, 10, 100] # Discretizes the interpolation into bins
# cmap_name = 'my_list'
# # fig, ax = plt.subplots(1, 1, figsize=(6, 6))
# vmax=25
# cm = LinearSegmentedColormap.from_list(cmap_name, [(0, 'blue'),
# (0.2/vmax, 'red'),
# (0.6/vmax, 'green'),
# (2./vmax, 'black'),
# (6./vmax, 'yellow'),
def init_notebook():
# Enable inline plotting in the notebook
try:
get_ipython().enable_matplotlib(gui='inline')
except NameError:
pass
print('Populated the namespace with:\n' + ', '.join(init_mooc_nb) +
'\nfrom code/edx_components:\n' + ', '.join(edx_components.__all__) +
'\nfrom code/functions:\n' + ', '.join(functions.__all__))
holoviews.notebook_extension('matplotlib')
holoviews.plotting.mpl.MPLPlot.fig_rcparams['text.usetex'] = True
# Set plot style.
options = Store.options(backend='matplotlib')
options.Contours = Options('style', linewidth=2, color='k')
options.Contours = Options('plot', aspect='square')
options.HLine = Options('style', linestyle='--', color='b', linewidth=2)
options.VLine = Options('style', linestyle='--', color='r', linewidth=2)
options.Image = Options('style', cmap='RdBu_r')
options.Image = Options('plot', title_format='{label}')
options.Path = Options('style', linewidth=1.2, color='k')
options.Path = Options('plot', aspect='square', title_format='{label}')
options.Curve = Options('style', linewidth=2, color='k')
options.Curve = Options('plot', aspect='square', title_format='{label}')
options.Overlay = Options('plot',
show_legend=False,
title_format='{label}')
options.Layout = Options('plot', title_format='{label}')
options.Surface = Options('style',
cmap='RdBu_r',
rstride=1,
cstride=1,
lw=0.2)
options.Surface = Options('plot', azimuth=20, elevation=8)
# Turn off a bogus holoviews warning.
# Temporary solution to ignore the warnings
warnings.filterwarnings('ignore', r'All-NaN (slice|axis) encountered')
module_dir = os.path.dirname(__file__)
matplotlib.rc_file(os.path.join(module_dir, "matplotlibrc"))
np.set_printoptions(precision=2,
suppress=True,
formatter={'complexfloat': pretty_fmt_complex})
# In order to make the notebooks readable through nbviewer we want to hide
# the code by default. However the same code is executed by the students,
# and in that case we don't want to hide the code. So we check if the code
# is executed by one of the mooc developers. Here we do by simply checking
# for some files that belong to the internal mooc repository, but are not
# published. This is a temporary solution, and should be improved in the
# long run.
developer = os.path.exists(
os.path.join(module_dir, os.path.pardir, 'scripts'))
display_html(
display.HTML(nb_html_header +
(hide_outside_ipython if developer else '')))
# Patch a bug in holoviews
from patch_holoviews import patch_all
patch_all()
def init_imports():
"""Initialize speedups and bokeh"""
speedups.enable()
hv.notebook_extension('bokeh')
import numpy as np
from netCDF4 import Dataset
import time
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from ipyleaflet import Map, ImageOverlay, Marker, TileLayer
import os
from pyproj import Proj, transform
import holoviews as hv
hv.notebook_extension()
class OLCIprocessing:
def __init__(self, ProductName, OutputName=True):
'''ProductName; contains the path to the Sentinel-3 product that is being processed/of interest.
OutputName; will be the name of the later on generated png file. If set to True, this function will set this variable to 'S3A_' followed by the date and time of the products retrieval.'''
self.prodName = ProductName
if OutputName == True:
self.out = self.prodName[44:47] + '_' + self.prodName[60:75]
else:
self.out = OutputName
def importNetCDF(self, NumBand=21):
'''
This function imports the variables (radiance and coordinates) of a Sentinel-3 OLCI EFR Product
NumBand; The number of bands that want to be imported. Currently just increasing band numbers are supported, i.e. if NumBand is set to 13, the band Oa01 - Oa13 are imported. Note: CalcRGB method requires the first 10 bands.
'''
import os
import sys
import holoviews
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
from PIL import Image, ImageDraw
sys.path.insert(0, os.path.abspath("..")) # to get adaptive on the path
import adaptive # noqa: E402, isort:skip
holoviews.notebook_extension("matplotlib")
def create_and_run_learner():
def ring(xy):
import numpy as np
x, y = xy
a = 0.2
return x + np.exp(-((x**2 + y**2 - 0.75**2)**2) / a**4)
learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])
adaptive.runner.simple(learner, goal=lambda l: l.loss() < 0.01)
return learner
def plot_learner_and_save(learner, fname):
fig, ax = plt.subplots()
tri = learner.interpolator(scaled=True).tri
sys.path.append(minian_path)
import itertools as itt
import numpy as np
import xarray as xr
import holoviews as hv
import pandas as pd
from holoviews.operation.datashader import datashade, regrid
from minian.cross_registration import (calculate_centroids,
calculate_centroid_distance,
calculate_mapping, group_by_session,
resolve_mapping, fill_mapping)
from minian.motion_correction import estimate_shifts, apply_shifts
from minian.utilities import open_minian, open_minian_mf
from minian.visualization import AlignViewer
hv.notebook_extension('bokeh', width=100)
# # Allign Videos
# ## open datasets
# In[4]:
minian_ds = open_minian_mf(dpath, id_dims, pattern=f_pattern, backend='zarr')
# ## estimate shifts
# In[5]:
temps = minian_ds['Y'].max('frame').compute().rename('temps')
shifts = estimate_shifts(temps, max_sh=param_t_dist, dim='session').compute()
from holoviews import notebook_extension
from holoviews.element.comparison import ComparisonTestCase
from holoviews import Store
from holoviews.util import output, opts, OutputSettings
from holoviews.core import OrderedDict
from holoviews.core.options import OptionTree
from holoviews.plotting import mpl
try:
from holoviews.plotting import bokeh
except:
bokeh = None
BACKENDS = ['matplotlib'] + (['bokeh'] if bokeh else [])
notebook_extension(*BACKENDS)
class TestOutputUtil(ComparisonTestCase):
def setUp(self):
Store.current_backend = 'matplotlib'
Store.renderers['matplotlib'] = mpl.MPLRenderer.instance()
if bokeh:
Store.renderers['bokeh'] = bokeh.BokehRenderer.instance()
OutputSettings.options = OrderedDict(OutputSettings.defaults.items())
super(TestOutputUtil, self).setUp()
def tearDown(self):
Store.renderers['matplotlib'] = mpl.MPLRenderer.instance()
if bokeh:
import pandas as pd
import holoviews as hv
import geoviews as gv
import geoviews.feature as gf
import geoviews.tile_sources as gts
import geopandas
from bokeh.palettes import YlOrBr3 as palette
import cartopy
from cartopy import crs as ccrs
from bokeh.tile_providers import STAMEN_TONER
from bokeh.models import WMTSTileSource
hv.notebook_extension('bokeh')
DATA_PATH = "data/"
MAP_PATH = DATA_PATH + "world.geo.json/countries/"
COUNTRY_CODE_DATA = DATA_PATH + "country-codes/data/country-codes.csv"
try:
from_day = int(sys.argv[1])
to_day = int(sys.argv[2])
out_name = "./results/" + str(from_day) + "to" + str(to_day)
except:
print("Arguemnts should be like :")
print("python scraper.py 'From' 'to'")
def isNaN(num):
return num != num
import numpy as np
import holoviews as hv
hv.notebook_extension("bokeh")
xs = np.linspace(0,np.pi*4,100)
data = (xs,np.sin(xs))
hv.Curve(data)