338rajesh

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from os import path, makedirs

CSD = path.dirname(__file__)


def rotate_points(xy: np.ndarray, theta: float = 0.0, pivot_point: tuple[float, float] = (0.0, 0.0), ):
    """ Rotate the points `x` and `y` by specified angle about the point (xc, yc). """

338rajesh

from os import path, listdir
import cv2
import imageio
import argparse
from tqdm import tqdm


class MovieMaker:
    """ Helper definitions for making videos """

338rajesh

"""
    It prepares and saves .npy array to the specified path.
"""

import os
import numpy as np
import sys
from PIL import Image
from tqdm import tqdm
import argparse

338rajesh

import numpy as np

def remove_outliers(arr, col_indices=None, k=1.5):
    """
        Removes outliers from the given 2D array using inter-quartile range.

    arr: np.ndarray
        a 2D numpy array

    col_indices=[0,]

338rajesh

import numpy as np

# shuffling arrays concurrently


def shuffle_arrays(*arr, order=1):
    num_rows = arr[0].shape[0]
    assert all([i.shape[0] == num_rows for i in arr])
    indicies = np.array(list(range(num_rows)), dtype=np.int64)

338rajesh

import numpy as np
import matplotlib.pyplot as plt
import os
from scipy import integrate

CFD = os.path.dirname(__file__)


def get_chunks(x: np.ndarray[float], y: np.ndarray[float], pivot: float = 0.0):
	sort_indices = np.argsort(x)

338rajesh

import numpy as np
import matplotlib.pyplot as plt

def clip_histogram(data, get_indices=False, **hist_kwargs):
    n, bns = np.histogram(data, **hist_kwargs)
    bin_pairs = [(bns[i], bns[i+1]) for i in range(len(bns)-1)]  
    new_data = []
    indices = []
    for (i, (bnl, bnu)) in enumerate(bin_pairs):
        bin_width = bnu - bnl

338rajesh

import time    
import os


class ProgressBar:
    def __init__(self, num_iters, header=None) -> None:
        self.t0 = time.time()
        self.n = num_iters
        self.term_cols = (os.get_terminal_size().columns)*0.25
        

338rajesh

import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import os

num_chunks = 6
img_size = (64, 64)
container_ID = "CIRCLES_VF10_RS10_R5"
cfd = os.path.dirname(__vsc_ipynb_file__)
container_dir = os.path.join(cfd, "containers", "rve", f"container_{container_ID}")

338rajesh

h5open(data_set_path, "w") do h5file_id
    attributes(h5file_id)["eqrad"] = 1.0
    attributes(h5file_id)["inclusion_volume_fraction"] = 32.0
    attributes(h5file_id)["rve_xlb"] = bbox[1]
    attributes(h5file_id)["rve_ylb"] = bbox[2]
    attributes(h5file_id)["rve_xub"] = bbox[3]
    attributes(h5file_id)["rve_yub"] = bbox[4]
end