abcccc/reportGen/test.py

import sys
import numpy as np
import processing
import doc
import datetime
import os
import numpy as np
from matplotlib import pyplot as plt
import docx
import pandas
import openpyxl
import shutil


def scinot(num: float, n: int) -> str:
    ret = ""
    if num < 0:
        ret += "-"
        num = -num
    r = 0
    if num > 10:
        while num > 10:
            num = num / 10
            r += 1
    if num < 1:
        while num < 1:
            if num == 0:
                break
            num = num * 10
            r -= 1
    ret += str(int(num))
    if n > 0:
        ret += "."
    while n > 0:
        num = num - int(num)
        num = num * 10
        ret += str(int(num))
        n -= 1
    if r != 0:
        ret += "e"
        if r > 0:
            ret += "+"
        ret += str(r)
    return ret


# 0 光功率 float
# 1 黑体距离
# 2 低温温度
# 3 高温温度
# 4 测试时钟
# 5 帧频
# 6 环境温度
# 7 环境湿度
# 8 像元大小
# 9 积分时间
# 10 行数
# 11 列数
# 12 截止波长
# 13 GPOL值
# 14 探测器编号
# 15 NETD阈值
# 16 G因子
# 17 相邻阈值
# 18 相邻方向
# 19 低温文件
# 20 高温文件
# 21 参考电压 8192对应的电压值


if __name__ == "__main__":
    args = sys.argv

    os.chdir("reportGen/"+ args[14 +1])
    t = datetime.datetime.now()
    fpath = (
       
        str(t.year)
        + "-"
        + str(t.month)
        + "-"
        + str(t.day)
        + "-"
        + str(t.hour)
        + "-"
        + str(t.minute)
        + "-"
        + str(t.second)
    )
    os.mkdir(fpath)
    os.chdir(fpath)

    rows = int(float(args[10 +1]))
    cols = int(float(args[11 +1]))

    lows = np.frombuffer(open("../"+args[19 +1], "rb").read(), dtype=np.uint16).reshape(
        100, rows, cols
    ).astype(np.float64)/4/8192*float(args[21+1])
    vn1 = np.average(lows, axis=0)
    vnt1 = np.std(lows,axis=0)
    highs = np.frombuffer(open("../"+args[20 +1], "rb").read(), dtype=np.uint16).reshape(
        100, rows, cols
    ).astype(np.float64)/4/8192*float(args[21+1])
    vn2 = np.average(highs,axis=0)

    plt.cla()
    plt.clf()
    plt.imshow(vn1, cmap="gist_rainbow")
    plt.colorbar()
    plt.savefig("p7.png", bbox_inches="tight", pad_inches=0.3)

    plt.cla()
    plt.clf()
    plt.imshow(vn2, cmap="gist_rainbow")
    plt.colorbar()
    plt.savefig("p8.png", bbox_inches="tight", pad_inches=0.3)

    plt.cla()
    plt.clf()
    plt.imshow(vnt1, cmap="gist_rainbow")
    plt.colorbar()
    plt.savefig("p9.png", bbox_inches="tight", pad_inches=0.3)

    # plt.figure(dpi=192, figsize=(16, 12))

    min, max, hist0, hist1 = processing.getDualHist(vn1, vn2)
    x = np.linspace(min, max, 256)
    plt.cla()
    plt.clf()
    plt.bar(x, hist0, color="red", width=(x[1] - x[0]) * 1.01)
    plt.title("VT1直方图", fontproperties="SimHei", fontsize=20)
    # plt.show()
    plt.savefig("p10.png", bbox_inches="tight", pad_inches=0.3)

    plt.cla()
    plt.clf()
    plt.bar(x, hist1, color="red", width=(x[1] - x[0]) * 1.01)
    plt.title("VT2直方图", fontproperties="SimHei", fontsize=20)
    # plt.show()
    plt.savefig("p11.png", bbox_inches="tight", pad_inches=0.3)

    min, max, hist = processing.getHist(vnt1)
    x = np.linspace(min, max, 256)
    plt.cla()
    plt.clf()
    plt.bar(x, hist, color="red", width=(x[1] - x[0]) * 1.01)
    plt.title("VnT1直方图", fontproperties="SimHei", fontsize=20)
    # plt.show()
    plt.savefig("P12.png", bbox_inches="tight", pad_inches=0.3)

    # 获取差值，根据平均值判断正负，再确保结果为正
    dimage = vn2 - vn1
    if np.average(dimage) < 0:
        dimage = -dimage

    plt.cla()
    plt.clf()
    plt.imshow(dimage, cmap="gist_rainbow")
    plt.colorbar()
    plt.clim(np.average(dimage) / 2, np.max(dimage))
    plt.savefig("p2.png", bbox_inches="tight", pad_inches=0.3)

    min, max, hist = processing.getHist(dimage)
    x = np.linspace(min, max, 256)
    plt.cla()
    plt.clf()
    plt.bar(x, hist, color="red", width=(x[1] - x[0]) * 1.01)
    plt.title("摆幅V直方图", fontproperties="SimHei", fontsize=20)
    # plt.show()
    plt.savefig("p5.png", bbox_inches="tight", pad_inches=0.3)
    # 差值除以噪声， 确保为正数
    snr = dimage / (vnt1 + 0.00001)

    plt.cla()
    plt.clf()
    plt.imshow(snr, cmap="gist_rainbow")
    plt.colorbar()
    plt.clim(np.average(snr) / 2, np.max(snr))
    plt.savefig("p3.png", bbox_inches="tight", pad_inches=0.3)

    min, max, hist = processing.getHist(snr)
    x = np.linspace(min, max, 256)
    plt.cla()
    plt.clf()
    plt.bar(x, hist, color="red", width=(x[1] - x[0]) * 1.01)
    plt.title("SNR直方图", fontproperties="SimHei", fontsize=20)
    # plt.show()
    plt.savefig("p6.png", bbox_inches="tight", pad_inches=0.3)

    # stephen = 5.673*np.power(10,-12) #
    Ad = np.power(float(args[8 +1]), 2) / 100000000  # 像元尺寸
    p = float(args[0 +1])  # 光功率(light power)
    tao = float(args[9 +1]) / 1000  # 积分时间(IT)ms
    # Dn图像生成
    imageD = snr * np.sqrt(Ad / tao / 2) / p
    plt.cla()
    plt.clf()
    plt.imshow(imageD, cmap="gist_rainbow")
    plt.colorbar()
    plt.clim(np.average(imageD) / 2, np.max(imageD))
    plt.savefig("p1.png", bbox_inches="tight", pad_inches=0.3)

    min, max, hist = processing.getHist(imageD)
    x = np.linspace(min, max, 256)
    plt.cla()
    plt.clf()
    plt.bar(x, hist, color="red", width=(x[1] - x[0]) * 1.01)
    plt.title("Dn直方图", fontproperties="SimHei", fontsize=20)
    # plt.show()
    plt.savefig("p4.png", bbox_inches="tight", pad_inches=0.3)

    maskd, maskh = processing.getValidPoint(dimage, vnt1)

    d = []
    h = []
    for i in range(dimage.shape[0]):
        for j in range(dimage.shape[1]):
            if maskd[i, j] > 0:
                d.append([i, j])
            if maskh[i, j] > 0:
                h.append([i, j])
    d = np.array(d)
    h = np.array(h)
    plt.imsave("坏点.png", maskd, cmap="gray")
    plt.imsave("热点.png", maskh, cmap="gray")
    f1 = open("坏点.txt", "w")
    f2 = open("热点.txt", "w")
    for i in range(d.shape[0]):
        f1.write(str(i) + ":" + str(d[i, 0]) + "," + str(d[i, 1]) + "\n")
    for i in range(h.shape[0]):
        f2.write(str(i) + ":" + str(h[i, 0]) + "," + str(h[i, 1]) + "\n")
    f1.close()
    f2.close()

    plt.cla()
    plt.clf()

    ax = plt.gca()
    ax.xaxis.set_ticks_position("top")
    # ax.invert_yaxis()
    # plt.figure(dpi=192, figsize=(16, 12))
    plt.xlim(0, cols)
    plt.ylim(rows, 0)
    plt.scatter(d[:, 1], d[:, 0], color="blue", s=2)
    plt.scatter(h[:, 1], h[:, 0], color="red", s=2)
    plt.title(
        "死像元(蓝):"
        + str(int(np.sum(maskd)))
        + ";过热像元(红):"
        + str(int(np.sum(maskh))),
        fontproperties="SimHei",
        fontsize=20,
    )
    plt.savefig("p13.png")

    imagedirs = []

    envs = []
    rsts = []

    envs.append(args[0 +1])
    envs.append(args[1 +1] + "cm")
    envs.append(args[8 +1] + "um")
    envs.append(args[12 +1] + "um")
    envs.append(args[13 +1] + "V")
    envs.append(args[9 +1] + "ms")
    envs.append(args[11 +1] + "x" + args[10 +1])
    envs.append(args[2 +1] + "℃")
    envs.append(args[3 +1] + "℃")
    envs.append(args[4 +1] + "MHz")
    envs.append(args[5 +1])
    envs.append(args[6 +1] + "℃")
    envs.append(args[7 +1] + "%RH")

    rsts.append(str(int(np.sum(maskd))))
    rsts.append(str(int(np.sum(maskh))))
    rsts.append(
        format(
            np.sum(vnt1 * (1 - np.sign(maskd + maskh)))
            / np.sum(1 - np.sign(maskd + maskh)),
            ".5f",
        )
        + "V"
    )
    rsts.append(
        scinot(
            np.sum(dimage * (1 - np.sign(maskd + maskh)))
            / np.sum(1 - np.sign(maskd + maskh))
            / p,
            2,
        )
    )
    rsts.append(
        scinot(
            np.sum(imageD * (1 - np.sign(maskd + maskh)))
            / np.sum(1 - np.sign(maskd + maskh)),
            2,
        )
    )
    avg = np.sum(dimage * (1 - np.sign(maskd + maskh))) / np.sum(
        1 - np.sign(maskd + maskh)
    )
    nu = np.sqrt(
        np.sum(np.power((dimage - avg) * (1 - np.sign(maskd + maskh)), 2))
        / np.sum(1 - np.sign(maskd + maskh))
    )
    rsts.append(format(nu / avg * 100, "5f") + "%")
    rsts.append(format(np.average(1 - np.sign(maskd + maskh)) * 100, ".5f") + "%")

    rsts.append(
        format(
            np.sum(dimage * (1 - np.sign(maskd + maskh)))
            / np.sum(1 - np.sign(maskd + maskh)),
            ".3f",
        )
        + "V"
    )
    rsts.append(
        format(
            np.sum(vn1 * (1 - np.sign(maskd + maskh)))
            / np.sum(1 - np.sign(maskd + maskh)),
            ".3f",
        )
        + "V"
    )
    rsts.append(
        format(
            np.sum(vn2 * (1 - np.sign(maskd + maskh)))
            / np.sum(1 - np.sign(maskd + maskh)),
            ".3f",
        )
        + "V"
    )

    temp = float(args[3 +1]) - float(args[2 +1])
    netd0 = vnt1 / (dimage + 0.000001) * (1 - np.sign(maskd + maskh)) * temp * 1000
    netd1 = (
        np.sum(vnt1 * (1 - np.sign(maskd + maskh)))
        / np.sum(dimage * (1 - np.sign(maskd + maskh)))
        * temp
        * 1000
    )
    print("temp = ", temp, "netd = ", netd1, "netd0 max = ", np.max(netd0))
    rsts.append(
        format(np.sum(netd0) / np.sum(1 - np.sign(maskd + maskh)), ".3f") + "mK"
    )
    rsts.append(
        str(int(np.sum((netd0 * (1 - np.sign(maskd + maskh))) > float(args[15 +1]))))
    )

    Glamda = (
        float(args[16 +1])
        * np.sum(imageD * (1 - np.sign(maskd + maskh)))
        / np.sum(1 - np.sign(maskd + maskh))
    )

    plt.cla()
    plt.clf()
    plt.imshow(netd0, cmap="gist_rainbow")
    plt.colorbar()
    plt.clim(np.min(netd0), np.average(netd0) * 2)
    plt.savefig("p3.png", bbox_inches="tight", pad_inches=0.3)

    min, max, hist = processing.getHist(netd0)
    x = np.linspace(min, max, 256)
    plt.cla()
    plt.clf()
    plt.bar(x, hist, color="red", width=(x[1] - x[0]) * 1.01)
    plt.title("netd直方图", fontproperties="SimHei", fontsize=20)
    # plt.show()
    plt.savefig("p6.png", bbox_inches="tight", pad_inches=0.3)

    rsts.append(scinot(Glamda, 3))
    rsts.append(format(netd1, ".3f") + "mK")

    temp = float(args[3]) - float(args[2])

    imagedirs.append("p1.png")
    imagedirs.append("p2.png")
    imagedirs.append("p3.png")
    imagedirs.append("p4.png")
    imagedirs.append("p5.png")
    imagedirs.append("p6.png")
    imagedirs.append("p7.png")
    imagedirs.append("p8.png")
    imagedirs.append("p9.png")
    imagedirs.append("p10.png")
    imagedirs.append("p11.png")
    imagedirs.append("p12.png")
    imagedirs.append("p13.png")
    # imagedirs.append("p1.png")
    r = int(float(args[17 +1]))
    dir = int(float(args[18 +1]))
    clus = processing.calcCluster(r, dir, np.sign(maskd + maskh))

    # self.model = docx.Document(self.pwd+"/model.docx")

    doc.rewrite("../../",args[14+1], envs, rsts, clus, imagedirs, type=1)

    today = datetime.date.today()
    # data = {}