xyzshop/plot/refill/algorithm.py

from .DataStream import EMchPosmap
import numpy as np
import logging

logger = logging.getLogger('algorithm')


def calc_chratios(data, pos_map, start, end, window, left_len, right_len):
    view = data[[pos_map.succ_count, pos_map.fail_count, pos_map.commit_count], :]
    all = view[:, start:end]

    view = np.cumsum(all, axis=1)
    succ = view[0, :]
    fail = view[1, :]
    commit = view[2, :] + 0.0000001
    y = succ / commit
    y = y.ravel()

    if window is not None:
        y = np.convolve(y, window, 'same')
    y = y[left_len:end - start - right_len]

    cur = all[:, left_len:end - start - right_len]
    sums = np.sum(cur, axis=1)
    succs = int(sums[0])
    fails = int(sums[1])
    commits = int(sums[1])

    return succs, commits, y

def calc_cov_chratios(data, pos_map, start, end, window, left_len,right_len = 0):
    view = data[[pos_map.succ_count, pos_map.fail_count, pos_map.commit_count], :]

    sum_view = view[:, start + left_len:end - right_len]
    sums = np.sum(sum_view, axis=1)
    succs = int(sums[0])
    fails = int(sums[1])
    commits = int(sums[2])

    view = view[:, start:end]
    succ = view[0, :]
    fail = view[1, :]
    if window is not None:
        succ = np.convolve(succ, window, 'same')
        fail = np.convolve(fail, window, 'same')
    commit = succ + fail + 0.0000001
    y = succ / commit
    y = y[left_len:end - start - right_len]

    return succs, commits, y


def calc_chspeed_ratio(data, pos_map, start, end, period):
    dim = pos_map.dim()
    view = data[:, start:end]
    sum_view = view.reshape((dim, -1, period))
    sums = np.sum(sum_view, axis=2)

    succs = sums[pos_map.succ_count]
    commits = sums[pos_map.commit_count]
    ratios = succs / (commits + 0.000001)
    return commits, succs, ratios

def calc_cov_chsuccs(data, pos_map, start, end, window, left_len,right_len):
    view = data[[pos_map.succ_count, pos_map.fail_count, pos_map.commit_count], :]
    view = view[:, start:end]

    cur = view[:, left_len:end - start - right_len]
    sums = np.sum(cur, axis=1)
    succs = int(sums[0])
    fails = int(sums[1])

    succ   = view[0, :]
    commit = view[2, :]
    succ   = np.convolve(succ, window, 'same')
    commit = np.convolve(commit, window, 'same')
    succ   = succ[left_len:end - start - right_len]
    commit = commit[left_len:end - start - right_len]
    return succ, commit, succs, (succs + fails)


def calc_chspeed(data, pos_map, start, end):
    view = data[[pos_map.commit_count], :]
    view = view[:, start:end]
    speed = np.sum(view, axis=1)
    return int(speed[0])


def calc_chratio(data, pos_map, start, end):
    view = data[[pos_map.succ_count, pos_map.fail_count, pos_map.succ_periods, pos_map.fail_periods, pos_map.commit_count], :]
    view = view[:, start:end]

    sums = np.sum(view, axis=1)
    succs = sums[0]
    fails = sums[1]
    succ_periods = sums[2]
    fail_periods = sums[3]
    commit_count = int(sums[4])

    all = int(succs + fails)
    ratio = succs / (commit_count + 0.00001)
    back_time = (succ_periods + fail_periods) / (succs + fails + 0.00001)
    succ_time = (succ_periods) / (succs + 0.00001)
    return round(ratio, 5), commit_count, int(back_time),int(succ_time)

def calc_commit(data, pos_map, start, end):
    view = data[[pos_map.commit_count], :]
    view = view[:, start:end]

    sums = np.sum(view, axis=1)
    commit_count = int(sums[0])

    return commit_count


def calc_mchratios(data, pos_map, start, end, window, left_len, right_len):
    view = data[[pos_map.succ_count, pos_map.fail_count, pos_map.submit_count], :]

    sum_view = view[:, start + left_len:end - right_len]
    sums = np.sum(sum_view, axis=1)
    succs = int(sums[0])
    fails = int(sums[1])
    commits = int(sums[2])

    view = view[:, start:end]
    all = np.cumsum(view, axis=1)
    succ = all[0, :]
    commit = all[2, :] + 0.0000001
    y = succ / commit
    y = y.ravel()
    if window is not None:
        y = np.convolve(y, window, 'same')
    y = y[left_len:end - start - right_len]

    return succs, commits, y

def calc_mchratios_val(data, pos_map, start, end):
    view = data[[pos_map.succ_count, pos_map.fail_count, pos_map.submit_count], :]
    view = view[:, start:end]

    sums = np.sum(view, axis=1)
    succs = sums[0]
    fails = sums[1]
    ratio = succs / (succs + fails + 0.0000001)

    return int(succs), int(succs + fails), round(ratio,5)

def calc_cov_mchratios(data, pos_map, start, end, window, left_len, right_len):
    view = data[[pos_map.succ_count, pos_map.fail_count, pos_map.submit_count], :]

    sum_view = view[:, start + left_len:end - right_len]
    sums = np.sum(sum_view, axis=1)
    succs = sums[0]
    fails = sums[1]

    view = view[:, start:end]
    succ = view[0, :]
    fail = view[1, :]
    succ = np.convolve(succ, window, 'same')
    fail = np.convolve(fail, window, 'same')
    commit = succ + fail + 0.0000001
    y = succ / commit
    y = y[left_len:end - start - right_len]

    return int(succs), int(succs + fails), y


def calc_morder_send(data, pos_map: type(EMchPosmap), start: int, end: int):
    view = data[:, start:end]
    sums = np.sum(view, axis=1)

    all_return = sums[pos_map.succ_mch_amounts] + sums[pos_map.fail_mch_amounts] + 0.0000001
    ratio = sums[pos_map.succ_mch_amounts] / all_return

    send_count = sums[pos_map.submit_count] - sums[pos_map.succ_count] - sums[pos_map.fail_count]
    send_amounts = sums[pos_map.submit_amounts] - sums[pos_map.succ_mch_amounts] - sums[pos_map.fail_mch_amounts]
    lack_amounts = send_amounts * ratio

    return send_count, sums[pos_map.submit_count], sums[pos_map.succ_count], sums[pos_map.fail_count], sums[pos_map.submit_amounts], \
           sums[pos_map.succ_mch_amounts], sums[pos_map.fail_mch_amounts], send_amounts, lack_amounts


def calc_morder_lack(data, pos_map: type(EMchPosmap), start: int, end: int):
    view = data[:, start:end]
    sums = np.sum(view, axis=1)

    all_return = sums[pos_map.succ_count] + sums[pos_map.fail_count] + 0.0000001
    ratio = sums[pos_map.succ_count] / all_return

    send_count = sums[pos_map.submit_count] - sums[pos_map.succ_count] - sums[pos_map.fail_count]
    send_amounts = sums[pos_map.submit_amounts] - sums[pos_map.succ_mch_amounts] - sums[pos_map.fail_mch_amounts]
    lack_amounts = send_amounts * ratio
    logger.info("send_count=%d send_amounts=%.4f ratio=%.4f lack_amounts=%.4f", send_count, send_amounts, ratio, lack_amounts)

    return send_amounts, lack_amounts


# 用于计算成功率及利润率
# succ_count, fail_count, succ_ratio, profit,profit_ratio
def calc_mch_profit(data, pos_map: type(EMchPosmap), start: int, end: int):
    view = data[:, start:end]
    sums = np.sum(view, axis=1)

    submit_count = sums[pos_map.submit_count]
    succ_count = sums[pos_map.succ_count]
    fail_count = sums[pos_map.fail_count]
    succ_ratio = succ_count / (succ_count + fail_count + 0.0000001)

    ch_amounts = sums[pos_map.succ_ch_amounts]
    mch_amounts = sums[pos_map.succ_mch_amounts]
    profit = mch_amounts - ch_amounts

    return int(submit_count), int(succ_count), int(fail_count), round(succ_ratio, 5), round(profit, 3)


def calc_cov_netfail(data, pos_map, start, end, window):
    view = data[[pos_map.succ_count, pos_map.fail_count], :]
    view = view[:, start:end]

    sums = np.sum(view, axis=1)
    succs = int(sums[0])
    fails = int(sums[1])

    succ = view[0, :]
    fail = view[1, :]
    succ = np.convolve(succ, window, 'same')
    fail = np.convolve(fail, window, 'same')

    fail = fail / (fail + succ + 0.0000001)
    return fail, fails, (succs + fails)