# Path: covest/constants.py
# MAX_EXP = 200
# GRID_DEPTH = 3
# INF = float('inf')
# VERBOSE = True
# PLOT_LOG_SCALE = True
# USE_BIGFLOAT = False
# STEP = 1.1
# OPTIMIZATION_METHOD = 'L-BFGS-B'
# INITIAL_GRID_COUNT = 20
# INITIAL_GRID_STEP = 3
# DEFAULT_ERR_SCALE = 1
# DEFAULT_K = 21
# DEFAULT_READ_LENGTH = 100
# DEFAULT_REPEAT_MODEL = 0
# DEFAULT_MIN_SINGLECOPY_RATIO = 0.3
# AUTO_SAMPLE_TARGET_COVERAGE = 12
# AUTO_TRIM_PRECISION = 6
# NOISE_THRESHOLD = 10**-6
# MAX_ERRORS = 8
# MAX_NOTRIM = 25
#     DEFAULT_THREAD_COUNT = cpu_count()
#     DEFAULT_THREAD_COUNT = 2
# 
# Path: covest/utils.py
# def estimate_p(cc, alpha):
#     return (cc * (alpha - 1)) / (alpha * cc - alpha - cc)
# 
# def kmer_to_read_coverage(coverage, k, r):
#     return coverage * r / (r - k + 1)
# 
# def fix_coverage(coverage):
#     return inverse(lambda c: (c - c * exp(-c)) / (1 - exp(-c) - c * exp(-c)))(coverage)
# 
# def verbose_print(message):
#     if not constants.VERBOSE:
#         return
#     sys.stderr.write(message + "\n")


import random
from collections import defaultdict
from covest_poisson import poisson_dist
from math import exp, floor, ceil
from scipy.stats import binom
from covest import constants
from .utils import estimate_p, kmer_to_read_coverage, fix_coverage, verbose_print
def get_trim(hist, ignore_last=False):
    hist = remove_noise(hist)
    ss = float(sum(hist.values()))
    if ignore_last:
        ss -= hist[max(hist)]
    s = 0.0
    trim = max(hist)
    for i, h in sorted(hist.items()):
        s += h
        r = s / ss
        r = round(r, constants.AUTO_TRIM_PRECISION)
        if r >= 1:
            trim = i
            break
    return trim


def trim_hist(hist, threshold):
    if threshold >= max(hist):
        return hist, 0
    h = {k: v for k, v in hist.items() if k < threshold}
    tail = sum(v for k, v in hist.items() if k >= threshold)
    # remove 0 elements
    return {k: v for k, v in h.items() if v > 0}, tail


def process_histogram(hist, k, r, trim=None, sample_factor=None, max_notrim=constants.MAX_NOTRIM):
    hist = dict(hist)
    tail = 0
    if sample_factor is not None and sample_factor > 1:

# Path: covest/constants.py
# MAX_EXP = 200
# GRID_DEPTH = 3
# INF = float('inf')
# VERBOSE = True
# PLOT_LOG_SCALE = True
# USE_BIGFLOAT = False
# STEP = 1.1
# OPTIMIZATION_METHOD = 'L-BFGS-B'
# INITIAL_GRID_COUNT = 20
# INITIAL_GRID_STEP = 3
# DEFAULT_ERR_SCALE = 1
# DEFAULT_K = 21
# DEFAULT_READ_LENGTH = 100
# DEFAULT_REPEAT_MODEL = 0
# DEFAULT_MIN_SINGLECOPY_RATIO = 0.3
# AUTO_SAMPLE_TARGET_COVERAGE = 12
# AUTO_TRIM_PRECISION = 6
# NOISE_THRESHOLD = 10**-6
# MAX_ERRORS = 8
# MAX_NOTRIM = 25
#     DEFAULT_THREAD_COUNT = cpu_count()
#     DEFAULT_THREAD_COUNT = 2
# 
# Path: covest/utils.py
# def estimate_p(cc, alpha):
#     return (cc * (alpha - 1)) / (alpha * cc - alpha - cc)
# 
# def kmer_to_read_coverage(coverage, k, r):
#     return coverage * r / (r - k + 1)
# 
# def fix_coverage(coverage):
#     return inverse(lambda c: (c - c * exp(-c)) / (1 - exp(-c) - c * exp(-c)))(coverage)
# 
# def verbose_print(message):
#     if not constants.VERBOSE:
#         return
#     sys.stderr.write(message + "\n")


# Path: covest/histogram.py
import random
from collections import defaultdict
from covest_poisson import poisson_dist
from math import exp, floor, ceil
from scipy.stats import binom
from covest import constants
from .utils import estimate_p, kmer_to_read_coverage, fix_coverage, verbose_print

def get_trim(hist, ignore_last=False):
    hist = remove_noise(hist)
    ss = float(sum(hist.values()))
    if ignore_last:
        ss -= hist[max(hist)]
    s = 0.0
    trim = max(hist)
    for i, h in sorted(hist.items()):
        s += h
        r = s / ss
        r = round(r, constants.AUTO_TRIM_PRECISION)
        if r >= 1:
            trim = i
            break
    return trim


def trim_hist(hist, threshold):
    if threshold >= max(hist):
        return hist, 0
    h = {k: v for k, v in hist.items() if k < threshold}
    tail = sum(v for k, v in hist.items() if k >= threshold)
    # remove 0 elements
    return {k: v for k, v in h.items() if v > 0}, tail


def process_histogram(hist, k, r, trim=None, sample_factor=None, max_notrim=constants.MAX_NOTRIM):
    hist = dict(hist)
    tail = 0
    if sample_factor is not None and sample_factor > 1:

        verbose_print('Sampling histogram {}x...'.format(sample_factor))