fix: device_count;modbus;告警恢复;dgiot_serial_client支持校验位；sendemail

apps/dgiot_evidence/priv/python/drawxnqx.py

@@ -111,8 +111,10 @@ def calculate(data_x, parameters):
         datay.append(parameters[2] + parameters[1] * x + parameters[0] * x * x)
     return datay
 
+
 """完成函数的绘制"""
 
+
 def draw(flow1, head1, headparameters, power1, powerparameters, effect, effectparameters, params):
     fm = math.ceil(max(flow1))
     hm = math.ceil(max(head1))
@@ -122,78 +124,93 @@ def draw(flow1, head1, headparameters, power1, powerparameters, effect, effectpa
     nm = math.ceil(max(effect))
     nmin = math.floor(min(effect))
 
-    fig = plt.figure(figsize=(9, 5))
-    host = HostAxes(fig, [0.15, 0.1, 0.65, 0.8])
-    par1 = ParasiteAxes(host, sharex=host)
-    par2 = ParasiteAxes(host, sharex=host)
-    host.parasites.append(par1)
-    host.parasites.append(par2)
-    host.set_ylabel('效率(E)(%)', color="blue")
-    host.set_xlabel('流量(Q)(m3/h)')
-    host.axis['right'].set_visible(False)
-    par1.axis['right'].set_visible(True)
-    par1.set_ylabel('功率(P)(kW)', color="red")
-
-    par1.axis['right'].major_ticklabels.set_visible(True)
-    par1.axis['right'].label.set_visible(True)
-
-    par2.set_ylabel('效率(E)(%)', color="blue")
-    offset = (60, 0)
-    new_axisline = par2._grid_helper.new_fixed_axis
-    par2.axis['right2'] = new_axisline(loc='right', axes=par2, offset=offset)
+    # 图片大小 像素
+    # 注意这里的宽度和高度的单位是英寸，1英寸=100像素，所以要除以100
+    # plt.figure(figsize=(宽度 高度))
+    fig = plt.figure(figsize=(11, 5.5))
+    # HostAxes(figure,[ 左，下，宽，高 ]）
+    host = HostAxes(fig, [0.2, 0.1, 0.65, 0.8])
     fig.add_axes(host)
-    host.set_xlim(0, fm + 1)
-    # plt.xticks(range(0, fm + 1, 1))
-    host.set_ylim(0, hm + 5)
-
-    host.set_xlabel('流量(Q)(m3/h)')
-    host.set_ylabel('功率(P)(kW)', color="red")
-    host.set_ylabel('扬程(H)(m)', color="black")
     x = np.linspace(0, fm, 500)
-    y = headparameters[0] * x ** 2 + headparameters[1] * x + headparameters[2]
-    p1, = host.plot(x, y, label="HQ拟合曲线", color="black")
-    host.scatter(flow1, head1, c='k', label="HQ离散数据")
-    x1 = np.linspace(0, fm, 500)
-    y1 = powerparameters[0] * x ** 2 + powerparameters[1] * x + powerparameters[2]
-    p2, = par1.plot(x, y1, label="PQ拟合曲线", color="red")
-    par1.scatter(flow1, power1, c='r', label="PQ离散数据")
-    x2 = np.linspace(0, fm, 500)
-    y2 = effectparameters[0] * x ** 2 + effectparameters[1] * x + effectparameters[2]
-    p3, = par2.plot(x, y2, label="EQ拟合曲线", color="blue")
-    par2.scatter(flow1, effect, c='b', label="EQ离散数据")
-    par1.set_ylim(0, pm * 2)
-    par2.set_ylim(0, nm + 5)
-    # host.legend()
-    par2.axis['right2'].major_ticklabels.set_color(p3.get_color())  # 刻度值颜色
-    par2.axis['right2'].set_axisline_style('-|>', size=1.5)  # 轴的形状色
 
+    # 流量
+    host.set_xlim(0, fm + 1)
+    host.set_xlabel('流量(Q)(m3/h)')
+
+    # 扬程
+    par2 = ParasiteAxes(host, sharex=host)
+    host.parasites.append(par2)
+    par2.set_ylabel('扬程(H)(m)', color="blue")
+    offset2 = (-45, 0)
+    new_axisline2 = par2._grid_helper.new_fixed_axis
+    par2.axis['right2'] = new_axisline2(loc='left', axes=par2, offset=offset2)
+    y2 = headparameters[0] * x ** 2 + headparameters[1] * x + headparameters[2]
+    # p3, = par2.plot(x, y2, label="HQ拟合曲线", color="blue")
+    p3, = par2.plot(x, y2, color="blue")
+    par2.scatter(flow1, head1, marker='o', c='b', label="扬程")
+    par2.set_ylim(0, hm + 5)
+    par2.axis['right2'].major_ticklabels.set_color(p3.get_color())  # 刻度值颜色
+    # par2.axis['right2'].set_axisline_style('-|>', size=1.5)  # 轴的形状色
+
+    # 功率
+    par1 = ParasiteAxes(host, sharex=host)
+    host.parasites.append(par1)
+    par1.set_ylabel('功率(P)(kW)', color="red")
+    offset1 = (-90, 0)
+    new_axisline1 = par1._grid_helper.new_fixed_axis
+    par1.axis['right'] = new_axisline1(loc='left', axes=par1, offset=offset1)
+    y1 = powerparameters[0] * x ** 2 + powerparameters[1] * x + powerparameters[2]
+    p2, = par1.plot(x, y1, color="red")
+    par1.scatter(flow1, power1, marker='s', c='r', label="功率")
+    par1.set_ylim(0, pm * 2)
     par1.axis['right'].major_ticklabels.set_color(p2.get_color())  # 刻度值颜色
+    # par1.axis['right2'].set_axisline_style('-|>', size=1.5)  # 轴的形状色
+
+    # 效率
+    host.set_ylabel('效率(E)(%)', color="black")
+    y = effectparameters[0] * x ** 2 + effectparameters[1] * x + effectparameters[2]
+    p1, = host.plot(x, y, color="black")
+    host.scatter(flow1, effect, marker='*', c='k', label="效率")
+    host.set_ylim(0, nm + 5)
+    host.axis['right'].set_visible(True)  # 刻度值
+
+    #
     if 'dgiot_testing_equipment_flowSet' in params:
         flowSet = float(params['dgiot_testing_equipment_flowSet'])
         par2.axvline(flowSet, color='orange')
-        flowSetclose = find_close(flow1, flowSet)
-        x_begin = flowSetclose
-        x_end = flowSetclose
-        fepoints = [t for t in zip(flow1, effect) if x_begin <= t[0] <= x_end]
-        fhpoints = [t for t in zip(flow1, head1) if x_begin <= t[0] <= x_end]
-        fppoints = [t for t in zip(flow1, power1) if x_begin <= t[0] <= x_end]
-        if len(fepoints) > 0:
-            par2.text(fepoints[0][0], fepoints[0][1] + 0.5,
-                      ("流量" + str(fepoints[0][0]) + " m3/h", "效率" + str(fepoints[0][1]) + " %"),
-                      ha='center', color='b')
-            host.text(fhpoints[0][0], fhpoints[0][1] + 1,
-                      ("流量" + str(fhpoints[0][0]) + " m3/h", "扬程" + str(fhpoints[0][1]) + " m"),
-                      ha='center', color='k')
-            par1.text(fppoints[0][0], fppoints[0][1] + 0.1,
-                      ("流量" + str(fppoints[0][0]) + " m3/h", "功率" + str(fppoints[0][1]) + " kW"),
-                      ha='center', color='r')
+
+        effectpoint = round(np.interp(flowSet, x, y), 3)
+        host.plot(flowSet, effectpoint, marker='*', color="orange")
+        host.text(flowSet, effectpoint * 0.9,
+                  str(effectpoint),
+                  ha='center', color='k')
+
+        headpoint = round(np.interp(flowSet, x, y2), 3)
+        par2.plot(flowSet, headpoint, marker='o', c='orange')
+        par2.text(flowSet, headpoint * 0.9,
+                  str(headpoint),
+                  ha='center', color='b')
+
+        powerpoint = round(np.interp(flowSet, x, y1), 3)
+        par1.plot(flowSet, powerpoint, marker='s', c='orange')
+        par1.text(flowSet, powerpoint * 0.9,
+                  str(powerpoint),
+                  ha='center', color='r')
 
     # 解决使用matplotliblib画图的时候出现中文或者是负号无法显示的情况
     mpl.rcParams['font.sans-serif'] = ['SimHei']
     mpl.rcParams['axes.unicode_minus'] = False
 
-    plt.title("性能曲线拟合数据")
-    plt.legend(loc=9, bbox_to_anchor=(-0.142, 1.1), borderaxespad=0., fontsize=8)
+    plt.title("泵性能曲线")
+
+    # 自定义 图例顺序
+    handles, labels = host.get_legend_handles_labels()
+    handles = [handles[1], handles[2], handles[0]]
+    labels = [labels[1], labels[2], labels[0]]
+    # print(handles)
+    # print(labels)
+
+    plt.legend(handles, labels, loc=9, bbox_to_anchor=(1.1, 1.1), borderaxespad=0., fontsize=12)
     # 获取当前时间
     # localtime = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
     filepath = base64.b64decode(params['path']).decode("utf-8")
@@ -203,6 +220,7 @@ def draw(flow1, head1, headparameters, power1, powerparameters, effect, effectpa
     # plt.show()
     return (filename)
 
+
 def find_close(arr, e):
     low = 0
     high = len(arr) - 1
@@ -223,6 +241,7 @@ def find_close(arr, e):
 
     return arr[idx]
 
+
 def main(argv):
     params = json.loads(base64.b64decode(argv).decode("utf-8"))
     # 流量

apps/dgiot_evidence/priv/python/drawxnqx_new.py

@@ -1,276 +0,0 @@
-import matplotlib.pyplot as plt
-from pylab import mpl
-import math
-import numpy as np
-from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
-import time
-import sys
-import base64
-import json
-
-"""完成拟合曲线参数计算前相应变量的计算"""
-
-
-def polynomial_fitting(data_x, data_y):
-    size = len(data_x)
-    i = 0
-    sum_x = 0
-    sum_sqare_x = 0
-    sum_third_power_x = 0
-    sum_four_power_x = 0
-    sum_y = 0
-    sum_xy = 0
-    sum_sqare_xy = 0
-    while i < size:
-        sum_x += data_x[i]
-        sum_y += data_y[i]
-        sum_sqare_x += math.pow(data_x[i], 2)
-        sum_third_power_x += math.pow(data_x[i], 3)
-        sum_four_power_x += math.pow(data_x[i], 4)
-        sum_xy += data_x[i] * data_y[i]
-        sum_sqare_xy += math.pow(data_x[i], 2) * data_y[i]
-        i += 1;
-    return [[size, sum_x, sum_sqare_x, sum_y]
-        , [sum_x, sum_sqare_x, sum_third_power_x, sum_xy]
-        , [sum_sqare_x, sum_third_power_x, sum_four_power_x, sum_sqare_xy]]
-
-
-"""完成拟合曲线参数的计算
- 其中解方程的时候，利用高斯消元法计算相应的参数值
-"""
-
-
-def calculate_parameter(data):
-    # i用来控制列元素，line是一行元素,j用来控制循环次数,datas用来存储局部变量。保存修改后的值
-    i = 0;
-    j = 0;
-    line_size = len(data)
-
-    # 将行列式变换为上三角行列式
-    while j < line_size - 1:
-        line = data[j]
-        temp = line[j]
-        templete = []
-        for x in line:
-            x = x / temp
-            templete.append(x)
-        data[j] = templete
-        # flag标志应该进行消元的行数
-        flag = j + 1
-        while flag < line_size:
-            templete1 = []
-            temp1 = data[flag][j]
-            i = 0
-            for x1 in data[flag]:
-                if x1 != 0:
-                    x1 = x1 - (temp1 * templete[i])
-                    templete1.append(x1)
-                else:
-                    templete1.append(0)
-                i += 1
-            data[flag] = templete1
-            flag += 1
-        j += 1
-
-    # 求相应的参数值
-    parameters = []
-    i = line_size - 1
-    # j标识减去元素个数
-    # flag_rol标识除那一列
-    flag_j = 0
-    rol_size = len(data[0])
-    flag_rol = rol_size - 2
-    # 获得解的个数
-    while i >= 0:
-        operate_line = data[i]
-        if i == line_size - 1:
-            parameter = operate_line[rol_size - 1] / operate_line[flag_rol]
-            parameters.append(parameter)
-        else:
-            flag_j = (rol_size - flag_rol - 2)
-            temp2 = operate_line[rol_size - 1]
-            # result_flag为访问已求出解的标志
-            result_flag = 0
-            while flag_j > 0:
-                temp2 -= operate_line[flag_rol + flag_j] * parameters[result_flag]
-                result_flag += 1
-                flag_j -= 1
-            parameter = temp2 / operate_line[flag_rol]
-            parameters.append(parameter)
-        flag_rol -= 1
-        i -= 1
-    return parameters
-
-
-"""计算拟合曲线的值"""
-
-
-def calculate(data_x, parameters):
-    datay = []
-    for x in data_x:
-        datay.append(parameters[2] + parameters[1] * x + parameters[0] * x * x)
-    return datay
-
-
-"""完成函数的绘制"""
-
-
-def draw(flow1, head1, headparameters, power1, powerparameters, effect, effectparameters, params):
-    fm = math.ceil(max(flow1))
-    hm = math.ceil(max(head1))
-    hmin = math.floor(min(head1))
-    pm = math.ceil(max(power1))
-    pmin = math.floor(min(power1))
-    nm = math.ceil(max(effect))
-    nmin = math.floor(min(effect))
-
-    # 图片大小 像素
-    # 注意这里的宽度和高度的单位是英寸，1英寸=100像素，所以要除以100
-    # plt.figure(figsize=(宽度 高度))
-    fig = plt.figure(figsize=(11, 5.5))
-    # HostAxes(figure,[ 左，下，宽，高 ]）
-    host = HostAxes(fig, [0.2, 0.1, 0.65, 0.8])
-    fig.add_axes(host)
-    x = np.linspace(0, fm, 500)
-
-    # 流量
-    host.set_xlim(0, fm + 1)
-    host.set_xlabel('流量(Q)(m3/h)')
-
-    # 扬程
-    par2 = ParasiteAxes(host, sharex=host)
-    host.parasites.append(par2)
-    par2.set_ylabel('扬程(H)(m)', color="blue")
-    offset2 = (-45, 0)
-    new_axisline2 = par2._grid_helper.new_fixed_axis
-    par2.axis['right2'] = new_axisline2(loc='left', axes=par2, offset=offset2)
-    y2 = headparameters[0] * x ** 2 + headparameters[1] * x + headparameters[2]
-    # p3, = par2.plot(x, y2, label="HQ拟合曲线", color="blue")
-    p3, = par2.plot(x, y2, color="blue")
-    par2.scatter(flow1, head1, marker='o', c='b', label="扬程")
-    par2.set_ylim(0, hm + 5)
-    par2.axis['right2'].major_ticklabels.set_color(p3.get_color())  # 刻度值颜色
-    # par2.axis['right2'].set_axisline_style('-|>', size=1.5)  # 轴的形状色
-
-    # 功率
-    par1 = ParasiteAxes(host, sharex=host)
-    host.parasites.append(par1)
-    par1.set_ylabel('功率(P)(kW)', color="red")
-    offset1 = (-90, 0)
-    new_axisline1 = par1._grid_helper.new_fixed_axis
-    par1.axis['right'] = new_axisline1(loc='left', axes=par1, offset=offset1)
-    y1 = powerparameters[0] * x ** 2 + powerparameters[1] * x + powerparameters[2]
-    p2, = par1.plot(x, y1, color="red")
-    par1.scatter(flow1, power1, marker='s', c='r', label="功率")
-    par1.set_ylim(0, pm * 2)
-    par1.axis['right'].major_ticklabels.set_color(p2.get_color())  # 刻度值颜色
-    # par1.axis['right2'].set_axisline_style('-|>', size=1.5)  # 轴的形状色
-
-    # 效率
-    host.set_ylabel('效率(E)(%)', color="black")
-    y = effectparameters[0] * x ** 2 + effectparameters[1] * x + effectparameters[2]
-    p1, = host.plot(x, y, color="black")
-    host.scatter(flow1, effect, marker='*', c='k', label="效率")
-    host.set_ylim(0, nm + 5)
-    host.axis['right'].set_visible(True)  # 刻度值
-
-    #
-    if 'dgiot_testing_equipment_flowSet' in params:
-        flowSet = float(params['dgiot_testing_equipment_flowSet'])
-        par2.axvline(flowSet, color='orange')
-
-        effectpoint = round(np.interp(flowSet, x, y), 3)
-        host.plot(flowSet, effectpoint, marker='*', color="orange")
-        host.text(flowSet, effectpoint * 0.9,
-                  str(effectpoint),
-                  ha='center', color='k')
-
-        headpoint = round(np.interp(flowSet, x, y2), 3)
-        par2.plot(flowSet, headpoint, marker='o', c='orange')
-        par2.text(flowSet, headpoint * 0.9,
-                  str(headpoint),
-                  ha='center', color='b')
-
-        powerpoint = round(np.interp(flowSet, x, y1), 3)
-        par1.plot(flowSet, powerpoint, marker='s', c='orange')
-        par1.text(flowSet, powerpoint * 0.9,
-                  str(powerpoint),
-                  ha='center', color='r')
-
-    # 解决使用matplotliblib画图的时候出现中文或者是负号无法显示的情况
-    mpl.rcParams['font.sans-serif'] = ['SimHei']
-    mpl.rcParams['axes.unicode_minus'] = False
-
-    plt.title("泵性能曲线")
-
-    # 自定义 图例顺序
-    handles, labels = host.get_legend_handles_labels()
-    handles = [handles[1], handles[2], handles[0]]
-    labels = [labels[1], labels[2], labels[0]]
-    # print(handles)
-    # print(labels)
-
-    plt.legend(handles, labels, loc=9, bbox_to_anchor=(1.1, 1.1), borderaxespad=0., fontsize=12)
-    # 获取当前时间
-    # localtime = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    filepath = base64.b64decode(params['path']).decode("utf-8")
-    filename = filepath + params['name']
-    # print(filename)
-    plt.savefig(filename)
-    # plt.show()
-    return (filename)
-
-
-def find_close(arr, e):
-    low = 0
-    high = len(arr) - 1
-    idx = -1
-
-    while low <= high:
-        mid = int((low + high) / 2)
-        if e == arr[mid] or mid == low:
-            idx = mid
-            break
-        elif e > arr[mid]:
-            low = mid
-        elif e < arr[mid]:
-            high = mid
-
-    if idx + 1 < len(arr) and abs(e - arr[idx]) > abs(e - arr[idx + 1]):
-        idx += 1
-
-    return arr[idx]
-
-
-def main(argv):
-    params = json.loads(base64.b64decode(argv).decode("utf-8"))
-    # 流量
-    flow = params['data']['flow']
-    # 扬程
-    head = params['data']['head']
-    # 功率
-    power = params['data']['power']
-    # 效率
-    effect = params['data']['effect']
-
-    headdata = polynomial_fitting(flow, head)
-    headparameters = calculate_parameter(headdata)
-
-    powerdata = polynomial_fitting(flow, power)
-    powerparameters = calculate_parameter(powerdata)
-
-    effectdata = polynomial_fitting(flow, effect)
-    effectparameters = calculate_parameter(effectdata)
-
-    filename = draw(flow, head, headparameters, power, powerparameters, effect, effectparameters, params)
-    after_base64 = base64.b64encode(filename.encode('utf-8')).decode('ascii')
-    print(after_base64)
-
-
-def exit():
-    os._exit(0)
-
-
-if __name__ == "__main__":
-    main(
-        "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")