Miladiouss

import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
from torch.utils.data import Dataset, DataLoader
import numpy as np

# Transformations
RC = transforms.RandomCrop(32, padding=4)
RHF = transforms.RandomHorizontalFlip()
RVF = transforms.RandomVerticalFlip()

Miladiouss

from pathlib import Path
import yaml

here = Path(__file__).resolve().parent

with open(here / './config.yaml', 'r') as stream:
    config_dict = yaml.safe_load(stream)


class Struct:

Miladiouss

import plotly.graph_objects as go
fig = go.Figure()

xdata = [(1 - 1/i) for i in range(1, 15)]

fig.add_trace(go.Scatter(
    x=xdata, y=len(xdata)*[0], mode='markers', marker_size=10, marker_color='red'
))
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False, 

Miladiouss

import plotly.graph_objects as go
from time import sleep
import numpy as np

fig = go.FigureWidget()
# Display in Jupyter:
display(fig)
sleep(.25)

fig.add_trace(go.Scatter(x=[0], y=[0]))

Miladiouss

from astropy.modeling.polynomial import SIP
import itertools
from astropy.io.fits.header import Header

def get_sip_coeffs(hdr: Header  , kind='A') -> dict:
    """Return a SIP polynomial coefficients for the selected
     coefficient type: (A, B, AP, BP).

    Parameters
    ----------

Miladiouss

# Science
import numpy as np
from astropy.units import Unit

# Notebook
from IPython.display import Markdown
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

# Handy functions

Miladiouss

import plotly.graph_objects as go
import numpy as np
from scipy.special import gamma
from pathlib import Path

here = Path(__file__).parent
filename = Path(__file__).stem

xdata = np.linspace(-5, 5, 1000)
ydata = gamma(xdata)

Miladiouss

import numpy as np

from pathlib import Path

# AstroPy
import astropy
from astropy.coordinates import SkyCoord, GCRS, ICRS, GeocentricTrueEcliptic, Galactic
import astropy.units as u
from astropy.io import fits
from astropy.wcs import WCS

Miladiouss

"""
Author: Miladious
Latest Modification Date: Oct 11, 2019
About: 
Creating a publication quality figure using matplotlib requires a lot of tweaks.
In this gist, I show the main tweaks for creating publication quality plots. 
This is an example how one would plot astronomical images in python. 
Feel free to modify to your field's standards.
"""

Miladiouss

import numpy as np
from matplotlib import pyplot as plt
import torch


class HistogramTransform(object):
    """
    Transforms the distribution of the input tensor to match that
    of the list of template histograms corresponding to each channel.