Final document

e66f93ce · 李明杰 · 2e09f51f · e66f93ce · e66f93ce · e66f93ce
Commit e66f93ce authored Mar 25, 2019 by 李明杰
37 changed files
--- a/bottom_function/data/STXINGKA.TTF
+++ b/bottom_function/data/STXINGKA.TTF
--- a/bottom_function/data/fromat_error_data.csv
+++ b/bottom_function/data/fromat_error_data.csv
+,num,data
--- a/bottom_function/data/msyh.ttf
+++ b/bottom_function/data/msyh.ttf
--- a/bottom_function/data/sortLog.sh
+++ b/bottom_function/data/sortLog.sh
+#!/bin/bash
+
+#set env
+export PATH=$PATH:/bin:/sbin:/usr/sbin
+
+
+function sortfile() {
+
+logfile1=/home/work/semantic_platform_DAS/bottom_function/data
+logfile1_1=${logfile1}/semantic/m1
+logfile1_2=${logfile1}/semantic/m1_1
+logfile2=/home/work/logfile/ctoc/unisound
+logfile2_1=${logfile1}/semantic/m2
+logfile3=${logfile1}/semantic/unorder/unisoundCtoC_logfile_`date +%Y%m%d`
+
+logfile4=${logfile1}/semantic/order/semantic.txt
+logfile5=${logfile1}/semantic/order/semantic_`date +%Y%m%d`
+
+[ -d "${logfile1_1}" ] || mkdir -p "${logfile1_1}"
+[ -d "${logfile1_2}" ] || mkdir -p "${logfile1_2}"
+[ -d "${logfile2_1}" ] || mkdir -p "${logfile2_1}"
+[ -d "${logfile1}/semantic/unorder" ] || mkdir -p "${logfile1}/semantic/unorder"
+[ -d "${logfile1}/semantic/order" ] || mkdir -p "${logfile1}/semantic/order"
+
+[ -f "${logfile3}" ] || touch "${logfile3}"
+[ -f "${logfile4}" ] || touch "${logfile4}"
+[ -f "${logfile5}" ] || touch "${logfile5}"
+
+[ -f "${logfile1_2}/unisoundCtoC_logfile_`date +%Y%m%d`" ] || touch "${logfile1_2}/unisoundCtoC_logfile_`date +%Y%m%d`"
+[ -f "${logfile2_1}/unisoundCtoC_logfile_`date +%Y%m%d`" ] || touch "${logfile2_1}/unisoundCtoC_logfile_`date +%Y%m%d`"
+
+
+rsync -av root@10.7.19.130::testlogs ${logfile1_1} --password-file=/etc/rsync.password >/dev/null 
+touch /tmp/temp
+temp=/tmp/temp
+diff ${logfile1_2}/unisoundCtoC_logfile_`date +%Y%m%d` ${logfile1_1}/unisoundCtoC_logfile_`date +%Y%m%d` | grep  ">"|awk -F "> " '{print $2}' > ${temp}
+rsync -av ${logfile1_1}/unisoundCtoC_logfile_`date +%Y%m%d` ${logfile1_2}/unisoundCtoC_logfile_`date +%Y%m%d` >/dev/null
+cat ${temp} >>${logfile1_2}/unisoundCtoC_logfile_`date +%Y%m%d`
+cat /dev/null>${logfile3}
+cat ${temp} >> ${logfile3}
+
+
+diff ${logfile2_1}/unisoundCtoC_logfile_`date +%Y%m%d` ${logfile2}/unisoundCtoC_logfile_`date +%Y%m%d` | grep  ">"|awk -F "> " '{print $2}' > ${temp}
+rsync -av ${logfile2}/unisoundCtoC_logfile_`date +%Y%m%d` ${logfile2_1}/unisoundCtoC_logfile_`date +%Y%m%d` >/dev/null
+cat ${temp} >>${logfile2_1}/unisoundCtoC_logfile_`date +%Y%m%d`
+cat ${temp} >> ${logfile3}
+#sed 'N;s#\n#,#g' ${logfile3}
+
+
+if [ -s ${logfile3} ]
+then
+	paste -sd ',\n' ${logfile3} | sort -u > ${logfile4}
+else
+	echo 0
+	exit 1
+fi	
+
+lastEndTime=`tail -1 ${logfile5} |awk -F "(])|( request)" '{print $2} '`
+newStartTime=`head -1 ${logfile4} |awk -F "(])|( request)" '{print $2} '`
+t1=`date -d "${lastEndTime}" +%s`
+t2=`date -d "${newStartTime}" +%s`
+
+if [ ! -s ${logfile5} ] 
+then
+        cat ${logfile4}>>${logfile5}
+else
+	[ ${t1} -lt ${t2} ] && cat ${logfile4}>>${logfile5}||echo "no new logfiles"
+fi
+find ${logfile1}/semantic/order -name "unisoundCtoC_logfile_*" -type f -mtime +30|xargs rm -f
+echo ${logfile4}
+}
+sortfile
+
--- a/bottom_function/normalization.py
+++ b/bottom_function/normalization.py
@@ -9,7 +9,7 @@ import jieba.posseg as psg
 def get_stopword_list():
    # 停用词表存储路径，每一行为一个词，按行读取进行加载
    # 进行编码转换确保匹配准确率
-    stop_word_path = './data/HGDstopwords.txt'
+    stop_word_path = '/home/work/semantic_platform_DAS/bottom_function/data/HGDstopwords.txt'
    stopword_list = [sw.replace('\n', '') for sw in open(stop_word_path, encoding='UTF-8').readlines()]
    return stopword_list

@@ -40,7 +40,7 @@ def word_filter(seg_list, pos=False):
        if not flag.startswith('n'):
            continue
        # 过滤停用词表中的词，以及长度为<2的词
-        if not word in stopword_list and len(word) > 0:
+        if not word in stopword_list and len(word) > 2:
            filter_list.append(word)

    return filter_list

--- a/chat_function_analysis.py
+++ b/chat_function_analysis.py
+#coding=utf-8
+
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
+import matplotlib.pyplot as plt
+from sklearn.manifold import MDS
+from sklearn.metrics.pairwise import cosine_similarity
+import random
+from matplotlib.font_manager import FontProperties
+from sklearn.cluster import KMeans
+from sklearn import metrics
+from collections import Counter
+from scipy.cluster.hierarchy import ward, dendrogram
+import bottom_function.normalization as normalization
+import bottom_function.m_SQL as qb
+
+# import json
+# from flask import Flask
+# from flask import request
+# from flask_cors import CORS
+
+
+class Culter:
+
+    def __init__(self, start_time, end_time):
+        self.start_time = start_time
+        self.end_time = end_time
+
+        csv_data = pd.DataFrame()
+        self.chat_data = pd.DataFrame()
+        tablename = "semantic_data_table"
+        db = qb.Schema(host="localhost", user="560193", password="jay560193", mysqlName="semantic_data_schema",
+                       port="3306")
+        csv_data = db.getData(tableName=tablename, startTime=start_time, endTime=end_time)
+        self.chat_data = csv_data[(csv_data['domain'] == 'chat')]
+
+        # self.chat_data.drop_duplicates(subset=['query'], inplace=True)
+        # self.chat_data.dropna(subset=['query'], inplace=True)
+
+        self.out_data = ''  # 写入分析结果
+        self.feature_names = []
+        self.f_sse = []
+        self.feature_matrix = np.matrix([])
+
+    def build_feature_matrix(self, documents, feature_type, ngram_range, min_df, max_df):
+
+        feature_type = feature_type.lower().strip()
+        if feature_type == 'binary':
+            vectorizer = CountVectorizer(binary=True,
+                                         max_df=max_df, ngram_range=ngram_range)
+        elif feature_type == 'frequency':
+            vectorizer = CountVectorizer(binary=False, min_df=min_df,
+                                         max_df=max_df, ngram_range=ngram_range)
+        elif feature_type == 'tfidf':
+            vectorizer = TfidfVectorizer(token_pattern=r"(?u)\b\w+\b", max_df=max_df)
+        else:
+            raise Exception("Wrong feature type entered. Possible values: 'binary', 'frequency', 'tfidf'")
+
+        feature_matrix = vectorizer.fit_transform(documents).astype(float)
+
+        return vectorizer, feature_matrix
+
+    def feature_extraction_data(self):
+
+        chat_one = self.chat_data['query'].tolist()
+
+        norm_chat_one = normalization.normalize_corpus(chat_one, pos=False)
+
+        # 提取 tf-idf 特征
+        vectorizer, self.feature_matrix = self.build_feature_matrix(norm_chat_one, feature_type='tfidf', min_df=0.2,
+                                                                    max_df=0.90,
+                                                                    ngram_range=(1, 2))
+
+        # 查看特征数量)
+        self.out_data = '聚类分析结果：\n' + '**' * 30
+        self.out_data = self.out_data + '\n特征数量：\n' + str(self.feature_matrix.shape)
+
+        # 获取特征名字
+        self.feature_names = vectorizer.get_feature_names()
+
+        # 打印某些特征
+        self.out_data = self.out_data + '\n部分特征：\n' + ', '.join(self.feature_names[:5])
+
+    def get_cluster_data(self, clustering_obj, m_data, feature_names, num_clusters, topn_features):
+        cluster_data = {}
+
+        # 获取cluster的center
+        ordered_centroids = clustering_obj.cluster_centers_.argsort()[:, ::-1]
+        # 获取每个cluster的关键特征
+        # 获取每个cluster的query
+        for cluster_num in range(num_clusters):
+            cluster_data[cluster_num] = {}
+            cluster_data[cluster_num]['cluster_num'] = cluster_num
+            key_features = [feature_names[index]
+                            for index
+                            in ordered_centroids[cluster_num, :topn_features]]
+            cluster_data[cluster_num]['key_features'] = key_features
+
+            c_datas = m_data[m_data['Cluster'] == cluster_num]['query'].values.tolist()
+            cluster_data[cluster_num]['query'] = c_datas
+
+        return cluster_data
+
+    def print_cluster_data(self, cluster_data):
+        self.out_data = self.out_data + '\n\n聚类详细数据：\n'
+
+        for cluster_num, cluster_details in cluster_data.items():
+            self.out_data = self.out_data + '\nCluster {} details:\n'.format(cluster_num)
+
+            self.out_data = self.out_data + '-' * 20
+            self.out_data = self.out_data + '\nKey features:\n'
+            self.out_data = self.out_data + ', '.join(cluster_details['key_features'])
+
+            self.out_data = self.out_data + '\ndata in this cluster:\n'
+            self.out_data = self.out_data + ', '.join(cluster_details['query'])
+            self.out_data = self.out_data + '\n' + '=' * 40
+
+    def plot_clusters(self, feature_matrix, cluster_data, m_data, plot_size):
+        def generate_random_color():  # generate random color for clusters
+            color = '#%06x' % random.randint(0, 0xFFFFFF)
+            return color
+
+        # define markers for clusters
+        markers = ['o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd']
+        # build cosine distance matrix
+        cosine_distance = 1 - cosine_similarity(feature_matrix)
+        # dimensionality reduction using MDS
+        mds = MDS(n_components=2, dissimilarity="precomputed",
+                  random_state=1)
+        # get coordinates of clusters in new low-dimensional space
+        plot_positions = mds.fit_transform(cosine_distance)
+        x_pos, y_pos = plot_positions[:, 0], plot_positions[:, 1]
+        # build cluster plotting data
+        cluster_color_map = {}
+        cluster_name_map = {}
+        # print(cluster_data)
+        for cluster_num, cluster_details in cluster_data.items():
+            # assign cluster features to unique label
+            cluster_color_map[cluster_num] = generate_random_color()
+            cluster_name_map[cluster_num] = ', '.join(cluster_details['key_features'][:5]).strip()
+        # map each unique cluster label with its coordinates and books
+        cluster_plot_frame = pd.DataFrame({'x': x_pos,
+                                           'y': y_pos,
+                                           'label': m_data['Cluster'].values.tolist(),
+                                           'query': m_data['query'].values.tolist()
+                                           })
+        grouped_plot_frame = cluster_plot_frame.groupby('label')
+        # set plot figure size and axes
+
+        plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
+        matplotlib.rcParams['font.family'] = 'sans-serif'
+        plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
+        fig, ax = plt.subplots(figsize=plot_size)
+        ax.margins(0.05)
+        # plot each cluster using co-ordinates and  titles
+        for cluster_num, cluster_frame in grouped_plot_frame:
+            marker = markers[cluster_num] if cluster_num < len(markers) \
+                else np.random.choice(markers, size=1)[0]
+            ax.plot(cluster_frame['x'], cluster_frame['y'],
+                    marker=marker, linestyle='', ms=12,
+                    label=cluster_name_map[cluster_num],
+                    color=cluster_color_map[cluster_num], mec='none')
+            ax.set_aspect('auto')
+            ax.tick_params(axis='x', which='both', bottom=False, top=False,
+                           labelbottom='off')
+            ax.tick_params(axis='y', which='both', left=False, top=False,
+                           labelleft=False)
+        fontP = FontProperties()
+        fontP.set_size(23)
+        ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.01), fancybox=True,
+                  shadow=True, ncol=5, numpoints=1, prop=fontP)
+        # add labels as the film titles
+        for index in range(len(cluster_plot_frame)):
+            ax.text(cluster_plot_frame.ix[index]['x'], cluster_plot_frame.ix[index]['y'],
+                    cluster_plot_frame.ix[index]['query'], size=20)
+            # show the plot
+        plt.title(self.start_time + ' to ' + self.end_time + 'chat data cluster point set', fontsize=25)
+        path = '/roobo/soft/phpmyadmin/cluster_point.jpg'
+        plt.savefig(path)
+        return 'http://120.79.171.145:8000/cluster_point.jpg'
+
+    def k_means(self, feature_matrix):
+        f_sse = []
+        num_clusters = []
+        for i in range(2, 21):
+            km = KMeans(n_clusters=i, max_iter=10000)
+            km.fit(feature_matrix)
+            clusters = km.labels_
+            num_matrix = feature_matrix.todense()
+            sse = metrics.calinski_harabaz_score(num_matrix, clusters)
+            num_clusters.append(i)
+            f_sse.append(sse)
+
+        pd_see = pd.Series(f_sse, index=num_clusters)
+        pct_see = pd_see.pct_change()
+
+        fig, ax = plt.subplots(figsize=(10, 8))
+        ax.plot(num_clusters, f_sse, 'o-', c='orangered', label='clustering quality')
+        plt.legend(loc=2)
+        plt.xticks(num_clusters)
+        ax.set_xlabel("cluster number")
+
+        ax.set_ylabel("coefficient")
+
+        ax1 = ax.twinx()
+        ax1.plot(pct_see.values, 'o-', c='blue', label='gradient change')
+        ax1.set_ylabel("gradient")
+        plt.legend(loc=1)
+
+        plt.title(self.start_time + " to " + self.end_time + " the analysis of clusters with different numbers", fontsize=12)
+        path = '/roobo/soft/phpmyadmin/choice_num.jpg'
+        plt.savefig(path)
+
+        # input_num = input('输入最优聚类数目：')
+        # best_num = int(input_num)
+
+        self.f_sse = f_sse
+        return 'http://120.79.171.145:8000/choice_num.jpg'
+
+    def k_means_cluster(self, best_num):
+
+        self.out_data = self.out_data + '\n' + "=" * 20
+        self.out_data = self.out_data + "\n\n聚类效果分析：\n"
+        self.out_data = self.out_data + "\n聚类数目为：" + str(best_num)
+
+        f_sse = self.f_sse
+        sse = f_sse[best_num]
+        km = KMeans(n_clusters=best_num, max_iter=10000)
+        km.fit(self.feature_matrix)
+        clusters = km.labels_
+        self.chat_data['Cluster'] = clusters
+
+        # 获取每个cluster的数量
+        c = Counter(clusters)
+
+        sort_c = sorted(c.items(), key=lambda c: c[0], reverse=False)
+        c.clear()
+        for key, value in sort_c:
+            c[key] = value
+
+        self.out_data = self.out_data + '\nCalinski-Harabasz分数:' + str(sse)
+        self.out_data = self.out_data + '\n每个特征的数据量：\n'
+        self.out_data = self.out_data + (str(c.items()))
+        self.out_data = self.out_data + '\n' + "=" * 20
+        cluster_data = self.get_cluster_data(clustering_obj=km,
+                                             m_data=self.chat_data,
+                                             feature_names=self.feature_names,
+                                             num_clusters=best_num,
+                                             topn_features=5)
+
+        self.print_cluster_data(cluster_data)
+
+        path = self.plot_clusters(feature_matrix=self.feature_matrix, cluster_data=cluster_data, m_data=self.chat_data,
+                                  plot_size=(40, 25))
+        return path
+
+    def ward_hierarchical_clustering(self, feature_matrix):
+        cosine_distance = 1 - cosine_similarity(feature_matrix)
+        linkage_matrix = ward(cosine_distance)
+        return linkage_matrix
+
+    def plot_hierarchical_clusters(self, linkage_matrix, m_data, figure_size):
+        # set size
+        fig, ax = plt.subplots(figsize=figure_size)
+        m_titles = m_data['query'].values.tolist()
+
+        # plot dendrogram
+        ax = dendrogram(linkage_matrix, orientation="left", labels=m_titles)
+        plt.tick_params(axis='x',
+                        which='both',
+                        bottom=False,
+                        top=False,
+                        labelbottom=False)
+        plt.tight_layout()
+        plt.title(self.start_time + ' to ' + self.end_time + 'chat data ward hierachical clusters',fontsize=12)
+        path = '/roobo/soft/phpmyadmin/hierachical_clusters.jpg'
+        plt.savefig(path)
+        return 'http://120.79.171.145:8000/hierachical_clusters.jpg'
+
+
+# app = Flask(__name__)
+# CORS(app, supports_credentials=True)
+#
+# data_cluster = Culter(start_time="2018-12-01 00:00:00", end_time="2018-12-02 00:00:00")
+#
+#
+# @app.route('/SPDAS/chat_function_analysis/choice1', methods=['POST'])
+# def choice():
+#     param = ({"time": "2018-12-01 00:00:00/2018-12-02 00:00:00"})
+#     return json.JSONEncoder().encode(param)
+#
+#
+# @app.route('/SPDAS/chat_function_analysis/choice2', methods=['POST'])
+# def choice_form():
+#     # 需要从request对象读取表单内容：
+#     data = request.get_data()
+#     json_re = json.loads(data)
+#
+#     m_time = json_re['time']
+#     str_time = str(m_time)
+#     m_time = str_time.split('/')
+#     starttime = m_time[0]
+#     endtime = m_time[1]
+#     data_cluster = Culter(start_time=starttime, end_time=endtime)
+#     data_cluster.feature_extraction_data()
+#     image_path = data_cluster.k_means(data_cluster.feature_matrix)
+#     path = ({"num_image": image_path})
+#     return json.JSONEncoder().encode(path)
+#
+#
+# @app.route('/SPDAS/chat_function_analysis/chat1', methods=['POST'])
+# def chat():
+#     param = ({"best_num": "2"})
+#     return json.JSONEncoder().encode(param)
+#
+#
+# @app.route('/SPDAS/chat_function_analysis/chat2', methods=['POST'])
+# def chat_form():
+#     # 需要从request对象读取表单内容：
+#     data = request.get_data()
+#     json_re = json.loads(data)
+#     bestnum = json_re['best_num']
+#     image_path1 = data_cluster.k_means_cluster(best_num=bestnum)
+#
+#     linkage_matrix = data_cluster.ward_hierarchical_clustering(data_cluster.feature_matrix)
+#
+#     image_path2 = data_cluster.plot_hierarchical_clusters(linkage_matrix=linkage_matrix, m_data=data_cluster.chat_data,
+#                                                           figure_size=(35, 40))
+#     with open("/roobo/soft/phpmyadmin/chat_function_data.txt", 'w') as file:
+#         file.writelines(data_cluster.out_data)
+#
+#     path = ({"cluster_point": image_path1, "ward_image": image_path2})
+#     return json.JSONEncoder().encode(path)
+#
+#
+# if __name__ == '__main__':
+#     app.run(debug=True, host='10.7.19.129', port=5000)
--- a/domain_structure_analysis.py
+++ b/domain_structure_analysis.py
@@ -5,10 +5,11 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import bottom_function.data_read as dr
-import json
-from flask import Flask
-from flask import request
-from flask_cors import CORS
+
+# import json
+# from flask import Flask
+# from flask import request
+# from flask_cors import CORS


 def data_statistics_plot(datatype, starttime, endtime, graphtype):
@@ -24,7 +25,7 @@ def data_statistics_plot(datatype, starttime, endtime, graphtype):
    # m_data.dropna(inplace=True)
    m_data.sort_values(ascending=False, inplace=True)
    m_data = m_data[m_data.values != 0]
-    fig = plt.figure(figsize=(10, 6))
+    plt.figure(figsize=(10, 8))

    if graphtype == 'pie':
        e = []
@@ -60,49 +61,49 @@ def data_statistics_plot(datatype, starttime, endtime, graphtype):
        plt.xticks(rotation=45)
        plt.ylabel('Number', fontsize=12, labelpad=5)
        for x, y in zip(range(len(num_list)), num_list):
-            plt.text(x, y, '%d' % y, ha='left', va='center', fontsize=9)
+            plt.text(x, y, '%d' % y, ha='left', va='center', fontsize=12)
    else:
        m_data.plot(kind=graphtype, use_index=True)
    plt.title(str(starttime) + ' to ' + str(endtime) + ' semantic domain analysis of ' + graphtype + ' graph',
-              fontsize=12)
+              fontsize=15)
    plt.tight_layout(5)
    path = '/roobo/soft/phpmyadmin/plot_domain.jpg'
    plt.savefig(path)
-    return path
-
-
-app = Flask(__name__)
-CORS(app, supports_credentials=True)
-
-
-@app.route('/SPDAS/domain_structure_analysis1', methods=['POST'])
-def domain():
-    param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
-              "time": "2018-12-01 00:00:00/2018-12-02 00:00:00",
-              "graph_type": [{"value": "bar"}, {"value": "pie"}]})
-    return json.JSONEncoder().encode(param)
-
-
-@app.route('/SPDAS/domain_structure_analysis2', methods=['POST'])
-def domain_form():
-    # 需要从request对象读取表单内容：
-    data = request.get_data()
-    json_re = json.loads(data)
-    print(json_re)
-    datatype = json_re['data_type']
-    m_time = json_re['time']
-    graphtype = json_re['graph_type']
-    str_time = str(m_time)
-    m_time = str_time.split('/')
-    starttime = m_time[0]
-    endtime = m_time[1]
-    image_path = data_statistics_plot(datatype=datatype, starttime=starttime, endtime=endtime, graphtype=graphtype)
-    path = ({"domain_image": image_path})
-    return json.JSONEncoder().encode(path)
+    return 'http://120.79.171.145:8000/plot_domain.jpg'


-if __name__ == '__main__':
-    app.run(debug=True, host='10.7.19.129', port=5000)
+# app = Flask(__name__)
+# CORS(app, supports_credentials=True)
+#
+#
+# @app.route('/SPDAS/domain_structure_analysis1', methods=['POST'])
+# def domain():
+#     param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
+#               "time": "2018-12-01 00:00:00/2018-12-02 00:00:00",
+#               "graph_type": [{"value": "bar"}, {"value": "pie"}]})
+#     return json.JSONEncoder().encode(param)
+#
+#
+# @app.route('/SPDAS/domain_structure_analysis2', methods=['POST'])
+# def domain_form():
+#     # 需要从request对象读取表单内容：
+#     data = request.get_data()
+#     json_re = json.loads(data)
+#     print(json_re)
+#     datatype = json_re['data_type']
+#     m_time = json_re['time']
+#     graphtype = json_re['graph_type']
+#     str_time = str(m_time)
+#     m_time = str_time.split('/')
+#     starttime = m_time[0]
+#     endtime = m_time[1]
+#     image_path = data_statistics_plot(datatype=datatype, starttime=starttime, endtime=endtime, graphtype=graphtype)
+#     path = ({"domain_image": image_path})
+#     return json.JSONEncoder().encode(path)
+#
+#
+# if __name__ == '__main__':
+#     app.run(debug=True, host='10.7.19.129', port=5000)

 # str_time = str('2018.12.01 00:00:00/2018.12.02 00:00:00')
 # m_time = str_time.split('/')

--- a/processing_unisound_logfile.log
+++ b/processing_unisound_logfile.log
--- a/bottom_function/processing_unisound_logfile.py
+++ b/bottom_function/processing_unisound_logfile.py
-# -*- coding: utf-8 -*-
+# -*- coding: utf-8 -*-
 # author：Li Mingjie time:2019/1/24
 # Brief:process unisound logfile

@@ -9,13 +9,16 @@ import threading
 import bottom_function.m_SQL as qb
 import datetime as dt
 import paramiko
+import os
+import time
+import bottom_function.data_read as dr


 class timing_processing:
    def __init__(self):
        self.data = pd.DataFrame()
        self.datetime = pd.Timestamp("2019-01-01 00:00:00")
-        self.db = qb.Schema(host="localhost", user="root", password="560193", mysqlName="semantic_data_schema",
+        self.db = qb.Schema(host="localhost", user="560193", password="jay560193", mysqlName="semantic_data_schema",
                            port="3306")
        self.gree_list = ["aircleaner", "airconditioner", "airconditionerfan", "airsteward", "curtain",
                          "dehumidifier", "disinfection", "fanner", "furnace", "humidifier", "playcontrol",
@@ -27,7 +30,10 @@ class timing_processing:
                             "recipe", "science", "sound", "sports", "stock", "translate", "weather"]

    def data_storage(self):
-        data = open('./data/unisound_logfile', 'r', encoding='utf-8').readlines()
+        os.system("sh /home/work/semantic_platform_DAS/bottom_function/data/sortLog.sh")
+        data = open('/home/work/semantic_platform_DAS/bottom_function/data/semantic/order/semantic.txt', 'r', encoding='utf-8').readlines()
+        if len(data) == 0:
+            print("data is null")

        datetime_data = []
        macwifi_data = []
@@ -41,6 +47,8 @@ class timing_processing:
        costtime_data = []

        error_data = []
+        error_num = []
+        n = 1
        for line_data in data:
            if line_data == '':
                continue
@@ -56,7 +64,10 @@ class timing_processing:
            # data1 = data1.replace('\\', '')
            data1 = data1.replace('response:', '"response":').replace('costtime:', ',"costtime":"')
            data1 = data1.replace('\t', '')
-            js_data = json.loads(data1)
+            try:
+                js_data = json.loads(data1)
+            except:
+                error_data.append(line_data)

            try:
                dom = 'null'
@@ -118,40 +129,46 @@ class timing_processing:
                costtime_data.append(m_cost)
            except:
                error_data.append(line_data)
+                error_num.append(n)
+                n += 1

        outdata = pd.DataFrame(
            {'datetime': datetime_data, 'mac_wifi': macwifi_data, 'mac_voice': macvoice_data,
             'query': query_data, 'classify': classify_data, 'code': code_data,
             'domain': domain_data, 'intent': intent_data, 'response_data': response_data,
             'cost_time_ms': costtime_data})
-        errordata = pd.DataFrame({'data': error_data})
+        #errordata = pd.DataFrame({'num': error_num, 'data': error_data})

        outdata['datetime'] = pd.to_datetime(outdata['datetime'])
        outdata = outdata.sort_values(by=['datetime'])
-        self.datetime = dt.datetime.strftime(outdata['datetime'][0], "%Y-%m-%d %H ")
+        # print(outdata['datetime'])
+        self.datetime = time.strftime("%Y-%m-%d %H ", time.localtime(time.time()))
        outdata = outdata.set_index('datetime')
        self.data = outdata
+
        control_error_data = outdata[(outdata['classify'] == 'control') & (outdata['code'] != 0)]
        application_error_data = outdata[(outdata['classify'] == 'application') & (outdata['code'] != 0)]
-        control_error_data.drop_duplicates(subset='query', keep='first', inplace=True)
-        application_error_data.drop_duplicates(subset='query', keep='first', inplace=True)
+
+        # control_error_data.drop_duplicates(subset='query', keep='first', inplace=True)
+        # application_error_data.drop_duplicates(subset='query', keep='first', inplace=True)

        self.db.dataframeToMysql(data=outdata, tableName="semantic_data_table")
-        self.db.dataframeToMysql(data=errordata, tableName="error_format_data")
        self.db.dataframeToMysql(data=control_error_data, tableName="control_error_data")
        self.db.dataframeToMysql(data=application_error_data, tableName="application_error_data")
+        #errordata.to_csv('./bottom_function/data/fromat_error_data.csv')

-        print('storage the data to SQL is complete')
+        # self.domain_data_to_statistics(control_data, data_type="control")
+        # self.domain_data_to_statistics(application_data, data_type="application")
+        # self.costtime_data_to_statistics(outdata)

-        # timer=threading.Timer(3600,data_storage)
-        # timer.start()
+        print('storage the data to SQL is complete')

    def domain_data_to_statistics(self, data, data_type):

        print('Start domain data classification:')
        data_dict_domain = {}
        if data_type == 'control':
-            data_dict_domain = {"datetime": pd.Timestamp(2019, 1, 1), "aircleaner": 0, "airconditioner": 0,
+            data_dict_domain = {"datetime": self.datetime, "aircleaner": 0, "airconditioner": 0,
                                "airconditionerfan": 0, "airsteward": 0, "curtain": 0, "dehumidifier": 0,
                                "disinfection": 0, "fanner": 0, "furnace": 0, "humidifier": 0, "playcontrol": 0,
                                "refrigerator": 0, "ricecooker": 0,
@@ -163,7 +180,7 @@ class timing_processing:
            domain_list = self.gree_list

        elif data_type == 'application':
-            data_dict_domain = {"datetime": pd.Timestamp(2019, 1, 1), "almanac": 0, "ancient_poem": 0, "astro": 0,
+            data_dict_domain = {"datetime": self.datetime, "almanac": 0, "ancient_poem": 0, "astro": 0,
                                "baike": 0, "chat": 0, "chengyu": 0, "common_qa": 0, "finance": 0, "fm": 0, "food": 0,
                                "general_question_answering": 0, "history": 0, "holiday": 0, "joke": 0, "music": 0,
                                "news": 0, "recipe": 0, "science": 0, "sound": 0, "sports": 0, "stock": 0,
@@ -186,7 +203,7 @@ class timing_processing:
            if domain_data not in domain_list:
                if data_type == 'control':
                    self.gree_list.append(domain_data)
-                if data_type == 'control':
+                if data_type == 'application':
                    self.tencent_list.append(domain_data)
                self.db.setAddField(tableName=table_name, field=domain_data)

@@ -205,7 +222,7 @@ class timing_processing:
        gree_list = self.gree_list
        tencent_list = self.tencent_list

-        all_data_dict = {"datetime": pd.Timestamp(2019, 1, 1), "0~500ms": "0", "500~1000ms": "0", "1000~2000ms": "0",
+        all_data_dict = {"datetime": self.datetime, "0~500ms": "0", "500~1000ms": "0", "1000~2000ms": "0",
                         "2000~3000ms": "0", "3000~5000ms": "0", "morethan5000ms": "0", }

        all_tencent_dict = {}
@@ -213,8 +230,8 @@ class timing_processing:
        gree_data_dict = {}
        tencent_data_dict = {}

-        tencent_data = data[data['classify'] == 'tencent']
-        gree_data = data[data['classify'] == 'gree']
+        tencent_data = data[data['classify'] == 'application']
+        gree_data = data[data['classify'] == 'control']
        for dom1 in tencent_list:
            num1 = tencent_data.loc[(tencent_data['cost_time_ms'] >= 0) & (tencent_data['cost_time_ms'] < 500) & (
                    tencent_data['domain'] == dom1), ['domain', 'cost_time_ms']].domain.count()
@@ -374,17 +391,33 @@ class timing_processing:

    def run(self):
        self.data_storage()
-        controldata = self.data[(self.data['classify'] == 'gree') & (self.data['code'] == 0)]
-        applicationdata = self.data[(self.data['classify'] == 'tencent') & (self.data['code'] == 0)]
+        pr_datetime = time.strftime("%Y-%m-%d %H ", time.localtime(time.time() - 3600))
+        pr_datetime = str(pd.to_datetime(pr_datetime))
+        nw_datetime = str(pd.to_datetime(self.datetime))
+        print("时间为：### %s" % pr_datetime)
+        print("时间为：### %s" % nw_datetime)
+        cdata = dr.read_data(datatype='control', starttime=pr_datetime, endtime=nw_datetime)
+        adata = dr.read_data(datatype='application', starttime=pr_datetime, endtime=nw_datetime)
+        print("all****%d\n" % (len(cdata)))
+        print("all****%d\n" % (len(adata)))
+        controldata = cdata[cdata['code'] == 0]
+        applicationdata = adata[adata['code'] == 0]
+        print("****%d\n" % (len(controldata)))
+        print("****%d\n" % (len(applicationdata)))
        self.domain_data_to_statistics(data=controldata, data_type="control")
        self.domain_data_to_statistics(data=applicationdata, data_type="application")
        self.costtime_data_to_statistics(data=self.data)


-# TP = timing_processing()
-# TP.run()
-# timer = threading.Timer(20, TP.run)
-# timer.start()
+def load_run():
+    print ("*****datetime:")
+    print(time.strftime("%Y-%m-%d %H ", time.localtime(time.time())))
+    print ("*****\n")
+    TP = timing_processing()
+    TP.run()
+
+
+load_run()

 # st = pd.Timestamp("2018-12-01 00:00:00")
 # et = pd.Timestamp("2019-01-01 00:00:00")
@@ -404,5 +437,5 @@ class timing_processing:
 #     TP.domain_data_to_statistics(data=applicationdata, data_type="application")
 #     TP.costtime_data_to_statistics(data=data)

-TP = timing_processing()
-TP.data_storage()
+# TP = timing_processing()
+# TP.data_storage()
--- a/response_analysis.py
+++ b/response_analysis.py
@@ -2,49 +2,49 @@
 # author：Li Mingjie time:2019/3/7
 # Brief:

-import json
-from flask import Flask
-from flask import request
-from flask_cors import CORS
-import response_time_analysis
-import response_error_analysis
-
-app = Flask(__name__)
-CORS(app, supports_credentials=True)
-
-
-@app.route('/SPDAS/response_analysis1', methods=['POST'])
-def domain():
-    param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
-              "effect_type": [{"value": "cost_time"}, {"value": "response_error"}],
-              "time": "2018-12-01 00:00:00/2018-12-02 00:00:00"})
-    return json.JSONEncoder().encode(param)
-
-
-@app.route('/SPDAS/response_analysis2', methods=['POST'])
-def domain_form():
-    # 需要从request对象读取表单内容：
-    data = request.get_data()
-    json_re = json.loads(data)
-    datatype = json_re['data_type']
-
-    effecttype = json_re['effect_type']
-    m_time = json_re['time']
-    str_time = str(m_time)
-    m_time = str_time.split('/')
-    starttime = m_time[0]
-    endtime = m_time[1]
-    if effecttype == 'cost_time':
-        image_path = response_time_analysis.cost_time_plot(datatype=datatype, starttime=starttime, endtime=endtime)
-        path = ({"response_image": image_path})
-        return json.JSONEncoder().encode(path)
-    else:
-        image_path = response_error_analysis.error_data_statistics_plot(datatype=datatype, starttime=starttime,
-                                                                        endtime=endtime,
-                                                                        graphtype='bar')
-        path = ({"response_image": image_path})
-        return json.JSONEncoder().encode(path)
-
-
-if __name__ == '__main__':
-    app.run(debug=True, host='10.7.19.129', port=5000)
+# import json
+# from flask import Flask
+# from flask import request
+# from flask_cors import CORS
+# import response_time_analysis
+# import response_error_analysis
+#
+# app = Flask(__name__)
+# CORS(app, supports_credentials=True)
+#
+#
+# @app.route('/SPDAS/response_analysis1', methods=['POST'])
+# def domain():
+#     param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
+#               "effect_type": [{"value": "cost_time"}, {"value": "response_error"}],
+#               "time": "2018-12-01 00:00:00/2018-12-02 00:00:00"})
+#     return json.JSONEncoder().encode(param)
+#
+#
+# @app.route('/SPDAS/response_analysis2', methods=['POST'])
+# def domain_form():
+#     # 需要从request对象读取表单内容：
+#     data = request.get_data()
+#     json_re = json.loads(data)
+#     datatype = json_re['data_type']
+#
+#     effecttype = json_re['effect_type']
+#     m_time = json_re['time']
+#     str_time = str(m_time)
+#     m_time = str_time.split('/')
+#     starttime = m_time[0]
+#     endtime = m_time[1]
+#     if effecttype == 'cost_time':
+#         image_path = response_time_analysis.cost_time_plot(datatype=datatype, starttime=starttime, endtime=endtime)
+#         path = ({"response_image": image_path})
+#         return json.JSONEncoder().encode(path)
+#     else:
+#         image_path = response_error_analysis.error_data_statistics_plot(datatype=datatype, starttime=starttime,
+#                                                                         endtime=endtime,
+#                                                                         graphtype='bar')
+#         path = ({"response_image": image_path})
+#         return json.JSONEncoder().encode(path)
+#
+#
+# if __name__ == '__main__':
+#     app.run(debug=True, host='10.7.19.129', port=5000)
--- a/response_error_analysis.py
+++ b/response_error_analysis.py
@@ -4,15 +4,19 @@

 import pandas as pd
 import matplotlib.pyplot as plt
-import sys
 import bottom_function.data_read as dr
-import cgi


 def error_data_statistics_plot(datatype, starttime, endtime, graphtype):
+    if datatype == 'control':
+        datatype = 'error_control'
+    else:
+        datatype = 'error_application'
    csv_data = pd.DataFrame()
    csv_data = dr.read_data(datatype=datatype, starttime=starttime, endtime=endtime)
+    print(len(csv_data))
    csv_data.drop_duplicates(subset='query', keep='first', inplace=True)
+    csv_data = csv_data.reset_index(drop=True)
    error_dict = {}
    for i in range(len(csv_data)):
        error_code = "error " + str(csv_data.ix[i]['code'])
@@ -21,7 +25,7 @@ def error_data_statistics_plot(datatype, starttime, endtime, graphtype):
        else:
            error_dict.update({error_code: 1})

-    fig = plt.figure(figsize=(10, 6))
+    plt.figure(figsize=(10, 8))
    if graphtype == 'pie':
        e = []
        code_other = 0
@@ -54,10 +58,9 @@ def error_data_statistics_plot(datatype, starttime, endtime, graphtype):
        for x, y in zip(range(len(num_list)), num_list):
            plt.text(x, y, '%d' % y, ha='center', va='bottom', fontsize=9)

-    plt.title(str(starttime) + ' to ' + str(endtime) + ' semantic domain analysis of ' + graphtype + ' graph',
-              fontsize=12)
+    plt.title(str(starttime) + ' to ' + str(endtime) + ' ' + datatype + ' response analysis of ' + graphtype + ' graph',
+              fontsize=15)
    plt.tight_layout(5)
    path = '/roobo/soft/phpmyadmin/response_error.jpg'
    plt.savefig(path)
-    return path
-
+    return 'http://120.79.171.145:8000/response_error.jpg'
--- a/response_time_analysis.py
+++ b/response_time_analysis.py
@@ -4,13 +4,14 @@

 import matplotlib.pyplot as plt
 import bottom_function.data_read as dr
+import numpy as np


 def cost_time_plot(datatype, starttime, endtime):
    cost_time_dict = dr.read_cost_time_data(datatype=datatype, starttime=starttime, endtime=endtime)
    y_max = 0
    xl_list = list(cost_time_dict.keys())
-    fig = plt.figure(figsize=(10, 6))
+    plt.figure(figsize=(10, 8))

    for x in range(6):
        lab_list = []
@@ -26,6 +27,8 @@ def cost_time_plot(datatype, starttime, endtime):

        plt.bar(x_list, y_list, width=0.2, color=['r', 'g', 'b'])
        for i, j, lab in zip(x_list, y_list, lab_list):
+            if j == 0:
+                continue
            plt.text(i, j, lab, ha='center', va='bottom', fontsize=9, rotation=70)
    # plt.rcParams['font.family'] = ['sans-serif']
    # plt.rcParams['font.sans-serif'] = ['SimHei']
@@ -33,13 +36,13 @@ def cost_time_plot(datatype, starttime, endtime):
    plt.ylim(0, y_max * 1.2)
    plt.xticks(np.arange(0.5, 6, 1), xl_list)
    if datatype == 'application':
-        plt.title(str(starttime) + ' to ' + str(endtime) + ' cost time analysis of application resources', fontsize=12)
+        plt.title(str(starttime) + ' to ' + str(endtime) + ' cost time analysis of application resources', fontsize=15)
    elif datatype == 'control':
-        plt.title(str(starttime) + ' to ' + str(endtime) + ' cost time analysis of control resources', fontsize=12)
+        plt.title(str(starttime) + ' to ' + str(endtime) + ' cost time analysis of control resources', fontsize=15)
    else:
-        plt.title(str(starttime) + ' to ' + str(endtime) + ' cost time analysis of all resources', fontsize=12)
+        plt.title(str(starttime) + ' to ' + str(endtime) + ' cost time analysis of all resources', fontsize=15)
    plt.tight_layout(5)
    path = '/roobo/soft/phpmyadmin/response_time.jpg'
    plt.savefig(path)
-    return path
+    return 'http://120.79.171.145:8000/response_time.jpg'

--- a/run.log
+++ b/run.log
+nohup: 忽略输入
+*****datetime:
+2019-03-22 18 
+*****
+
+ * Serving Flask app "run" (lazy loading)
+ * Environment: production
+   WARNING: Do not use the development server in a production environment.
+   Use a production WSGI server instead.
+ * Debug mode: on
+ * Running on http://10.7.19.129:5000/ (Press CTRL+C to quit)
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 150-974-631
+120.237.155.119 - - [22/Mar/2019 18:56:38] "GET /SPDAS/user_portrait_analysis1 HTTP/1.1" 405 -
+120.237.155.119 - - [22/Mar/2019 18:56:38] "GET /favicon.ico HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:42] "GET /SPDAS HTTP/1.1" 200 -
+120.237.155.119 - - [22/Mar/2019 18:56:42] "GET /static/css/app.1e732aece1c20bddd293529e2b191477.css HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:42] "GET /static/js/manifest.0a5f072fcf6561c1e6d5.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:42] "GET /static/js/app.c3cb6b75bb652b44d7a2.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:42] "GET /static/js/vendor.e324bda0bd56a2f46d94.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:45] "GET /SPDAS HTTP/1.1" 200 -
+120.237.155.119 - - [22/Mar/2019 18:56:45] "GET /static/css/app.1e732aece1c20bddd293529e2b191477.css HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:45] "GET /static/js/manifest.0a5f072fcf6561c1e6d5.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:45] "GET /static/js/vendor.e324bda0bd56a2f46d94.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:45] "GET /static/js/app.c3cb6b75bb652b44d7a2.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:45] "GET /static/js/vendor.e324bda0bd56a2f46d94.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:45] "GET /static/js/app.c3cb6b75bb652b44d7a2.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:46] "GET /SPDAS HTTP/1.1" 200 -
+120.237.155.119 - - [22/Mar/2019 18:56:46] "GET /static/css/app.1e732aece1c20bddd293529e2b191477.css HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:46] "GET /static/js/manifest.0a5f072fcf6561c1e6d5.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:46] "GET /static/js/vendor.e324bda0bd56a2f46d94.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:46] "GET /static/js/app.c3cb6b75bb652b44d7a2.js HTTP/1.1" 404 -
+120.237.155.119 - - [22/Mar/2019 18:56:46] "GET /static/js/app.c3cb6b75bb652b44d7a2.js HTTP/1.1" 404 -
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+*****datetime:
+2019-03-22 19 
+*****
+
+{'data_type': 'all', 'effect_type': 'cost_time', 'time': '2019-01-03 00:00:00/2019-01-04 00:00:00'}
+SELECT * FROM cost_time_data WHERE datetime >= "2019-01-03 00:00:00" and datetime< "2019-01-04 00:00:00"
+2019-03-23 08:12:43,568 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'
+2019-03-23 08:12:43,568 INFO sqlalchemy.engine.base.Engine {}
+2019-03-23 08:12:43,570 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'
+2019-03-23 08:12:43,570 INFO sqlalchemy.engine.base.Engine {}
+2019-03-23 08:12:43,571 INFO sqlalchemy.engine.base.Engine SELECT DATABASE()
+2019-03-23 08:12:43,571 INFO sqlalchemy.engine.base.Engine {}
+2019-03-23 08:12:43,572 INFO sqlalchemy.engine.base.Engine show collation where `Charset` = 'utf8mb4' and `Collation` = 'utf8mb4_bin'
+2019-03-23 08:12:43,572 INFO sqlalchemy.engine.base.Engine {}
+2019-03-23 08:12:43,573 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS CHAR(60)) AS anon_1
+2019-03-23 08:12:43,573 INFO sqlalchemy.engine.base.Engine {}
+2019-03-23 08:12:43,574 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS CHAR(60)) AS anon_1
+2019-03-23 08:12:43,574 INFO sqlalchemy.engine.base.Engine {}
+2019-03-23 08:12:43,575 INFO sqlalchemy.engine.base.Engine SELECT CAST('test collated returns' AS CHAR CHARACTER SET utf8mb4) COLLATE utf8mb4_bin AS anon_1
+2019-03-23 08:12:43,575 INFO sqlalchemy.engine.base.Engine {}
+2019-03-23 08:12:43,576 INFO sqlalchemy.engine.base.Engine SELECT * FROM cost_time_data WHERE datetime >= "2019-01-03 00:00:00" and datetime< "2019-01-04 00:00:00"
+2019-03-23 08:12:43,576 INFO sqlalchemy.engine.base.Engine {}
+/usr/local/python3/lib/python3.6/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.
+  (prop.get_family(), self.defaultFamily[fontext]))
+120.237.155.119 - - [23/Mar/2019 08:12:43] "POST /SPDAS/response_analysis2 HTTP/1.1" 200 -
--- a/run.py
+++ b/run.py
+# -*- coding: utf-8 -*-
+# author：Li Mingjie time:2019/3/9
+# Brief:
+
+import domain_structure_analysis as DS
+import time_series_analysis as TS
+import user_portrait_analysis as UP
+import chat_function_analysis as CF
+import response_time_analysis as RT
+import response_error_analysis as RE
+import second_test as ST
+import json
+from flask import Flask, render_template
+from flask import request
+from flask_cors import CORS
+from flask_caching import Cache
+import time
+import base64
+import random
+
+app = Flask(__name__)
+CORS(app, supports_credentials=True)
+# cache = Cache(app, config={'CACHE_TYPE': 'simple'})
+
+print("*****datetime:")
+now_time = time.strftime("%b %d %Y %H:%M:%S", time.localtime(time.time()))
+print(now_time)
+print("*****\n")
+
+users = {
+    "admin": "spdas!23"
+}
+
+
+def gen_token(uid):
+    token = base64.b64encode((':'.join([str(uid), str(random.random()), str(now_time)])).encode('utf-8'))
+    print("BASE64加密串:\n" + str(token, 'utf-8'))
+    # users[uid].append(token)
+    return token
+
+
+@app.route('/SPDAS', methods=['GET'])
+def index():
+    return render_template('index.html')
+
+
+@app.route('/SPDAS/login', methods=['POST'])
+def login():
+    data = request.get_data()
+    json_re = json.loads(data)
+    username = json_re['user']
+    password = json_re['password']
+    if username in users.keys() and password == users[username]:
+        return gen_token(username)
+        # gen_token(username)
+        #return 'yes'
+    else:
+        return "error"
+
+
+@app.route('/SPDAS/domain_structure_analysis1', methods=['POST'])
+def domain():
+    param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
+              "time": "2019-01-01 00:00:00/2019-01-02 00:00:00",
+              "graph_type": [{"value": "bar"}, {"value": "pie"}],
+              "image_path": "http://120.79.171.145:8000/plot_domain1.jpg"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/domain_structure_analysis2', methods=['POST'])
+def domain_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    print(json_re)
+    datatype = json_re['data_type']
+    m_time = json_re['time']
+    graphtype = json_re['graph_type']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    image_path = DS.data_statistics_plot(datatype=datatype, starttime=starttime, endtime=endtime, graphtype=graphtype)
+    path = ({"domain_image": image_path})
+    return json.JSONEncoder().encode(path)
+
+
+@app.route('/SPDAS/time_series_analysis1', methods=['POST'])
+def time():
+    param = ({"time_type": [{"value": "hour", "id": 1}, {"value": "day", "id": 2},
+                            {"value": "month", "id": 3}, {"value": "year", "id": 4}],
+              "time": "2019-01-01 00:00:00/2019-01-02 00:00:00",
+              "graph_type": [{"value": "bar"}, {"value": "pie"}],
+              "image_path": "http://120.79.171.145:8000/plot_time1.jpg"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/time_series_analysis2', methods=['POST'])
+def time_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    print(json_re)
+    timetype = json_re['time_type']
+    m_time = json_re['time']
+    graphtype = json_re['graph_type']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    image_path = TS.datetime_data_plot(timetype=timetype, starttime=starttime, endtime=endtime, graphtype=graphtype)
+    path = ({"time_image": image_path})
+    return json.JSONEncoder().encode(path)
+
+
+@app.route('/SPDAS/user_portrait_analysis1', methods=['POST'])
+def user():
+    param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "chat"}, {"value": "all"}],
+              "time": "2019-01-01 00:00:00/2019-01-02 00:00:00",
+              "image_path": "http://120.79.171.145:8000/plot_user1.jpg"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/user_portrait_analysis2', methods=['POST'])
+def user_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    print(json_re)
+    datatype = json_re['data_type']
+    m_time = json_re['time']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    image_path = UP.portrait_plot(datatype=datatype, starttime=starttime, endtime=endtime)
+    path = {"user_image": image_path}
+    return json.JSONEncoder().encode(path)
+
+
+@app.route('/SPDAS/chat_function_analysis/choice1', methods=['POST'])
+def choice():
+    param = (
+        {"time": "2018-12-01 00:00:00/2018-12-28 20:00:00", "image_path": "http://120.79.171.145:8000/choice_num1.jpg"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/chat_function_analysis/choice2', methods=['POST'])
+# @cache.cached(timeout=60 * 30, key_prefix='choice_form')
+def choice_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    print(json_re)
+    m_time = json_re['time']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    data_cluster = CF.Culter(start_time=starttime, end_time=endtime)
+    data_cluster.feature_extraction_data()
+    image_path = data_cluster.k_means(data_cluster.feature_matrix)
+    path = ({"num_image": image_path})
+    return json.JSONEncoder().encode(path)
+
+
+@app.route('/SPDAS/chat_function_analysis/chat1', methods=['POST'])
+def chat():
+    param = ({"time": "2018-12-01 00:00:00/2018-12-28 20:00:00", "best_num": "1",
+              "image1_path": "http://120.79.171.145:8000/hierachical_clusters1.jpg",
+              "image2_path": "http://120.79.171.145:8000/cluster_point1.jpg"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/chat_function_analysis/chat2', methods=['POST'])
+def chat_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    m_time = json_re['time']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    data_cluster = CF.Culter(start_time=starttime, end_time=endtime)
+    data_cluster.feature_extraction_data()
+    image_path = data_cluster.k_means(data_cluster.feature_matrix)
+    charnum = json_re['best_num']
+    bestnum = int(charnum)
+    print(bestnum)
+    image_path1 = data_cluster.k_means_cluster(best_num=bestnum)
+    linkage_matrix = data_cluster.ward_hierarchical_clustering(data_cluster.feature_matrix)
+
+    image_path2 = data_cluster.plot_hierarchical_clusters(linkage_matrix=linkage_matrix, m_data=data_cluster.chat_data,
+                                                          figure_size=(35, 40))
+    with open("/roobo/soft/phpmyadmin/chat_function_data.txt", 'w') as file:
+        file.writelines(data_cluster.out_data)
+    path = ({"cluster_point": image_path2, "ward_image": image_path1})
+    return json.JSONEncoder().encode(path)
+
+
+@app.route('/SPDAS/response_analysis1', methods=['POST'])
+def response():
+    param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
+              "effect_type": [{"value": "cost_time"}, {"value": "response_error"}],
+              "time": "2019-01-01 00:00:00/2019-01-02 00:00:00",
+              "image_path": "http://120.79.171.145:8000/response_time1.jpg"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/response_analysis2', methods=['POST'])
+def response_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    print(json_re)
+    datatype = json_re['data_type']
+    effecttype = json_re['effect_type']
+    m_time = json_re['time']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    if effecttype == 'cost_time':
+        image_path = RT.cost_time_plot(datatype=datatype, starttime=starttime, endtime=endtime)
+    else:
+        image_path = RE.error_data_statistics_plot(datatype=datatype, starttime=starttime, endtime=endtime,
+                                                   graphtype='bar')
+    path = ({"response_image": image_path})
+    print(image_path)
+    return json.JSONEncoder().encode(path)
+
+
+@app.route('/SPDAS/second_test1', methods=['POST'])
+def second():
+    param = ({"data_type": [{"value": "error_control"}, {"value": "error_application"}],
+              "time": "2019-01-01 00:00:00/2019-01-02 00:00:00",
+              "graph_type": [{"value": "bar"}, {"value": "pie"}],
+              "image_path": "http://120.79.171.145:8000/response_error1.jpg"})
+    return json.JSONEncoder().encode(param)
+
+
+@app.route('/SPDAS/second_test2', methods=['POST'])
+def second_form():
+    # 需要从request对象读取表单内容：
+    data = request.get_data()
+    json_re = json.loads(data)
+    print(json_re)
+    datatype = json_re['data_type']
+    m_time = json_re['time']
+    str_time = str(m_time)
+    m_time = str_time.split('/')
+    starttime = m_time[0]
+    endtime = m_time[1]
+    graphtype = json_re['graph_type']
+
+    image_path = ST.second_test_plot(datatype=datatype, starttime=starttime, endtime=endtime, graphtype=graphtype)
+    path = ({"test_image": image_path})
+    return json.JSONEncoder().encode(path)
+
+
+if __name__ == '__main__':
+    app.run(debug=True, host='10.7.19.129', port=5000)
--- a/second_test.py
+++ b/second_test.py
@@ -9,9 +9,11 @@ import pandas as pd
 import bottom_function.m_SQL as qb
 import bottom_function.data_read as dr
 import json
-from flask import Flask
-from flask import request
-from flask_cors import CORS
+
+
+# from flask import Flask
+# from flask import request
+# from flask_cors import CORS


 def second_test(text, classify):
@@ -55,10 +57,14 @@ def second_test_plot(datatype, starttime, endtime, graphtype):
    csv_data = dr.read_data(datatype=datatype, starttime=starttime, endtime=endtime)
    if datatype == 'error_control':
        table_name = "control_error_data"
+        classsify = 'control'
    elif datatype == 'error_application':
        table_name = "application_error_data"
+        classsify = 'application'
    db = qb.Schema(host="localhost", user="560193", password="jay560193", mysqlName="semantic_data_schema", port="3306")
-    classsify = csv_data.ix[0]['classify']
+    csv_data.drop_duplicates(subset='query', keep='first', inplace=True)
+    csv_data = csv_data.rest_index(drop=True)
+
    error_dict = {}
    for i in range(len(csv_data)):
        # query = str(csv_data.ix[i]['query'].encode('utf-8').decode('utf-8-sig'))
@@ -78,7 +84,7 @@ def second_test_plot(datatype, starttime, endtime, graphtype):
        else:
            error_dict.update({error_code: 1})

-    fig = plt.figure(figsize=(10, 6))
+    plt.figure(figsize=(10, 8))
    if graphtype == 'pie':
        e = []
        code_other = 0
@@ -117,43 +123,42 @@ def second_test_plot(datatype, starttime, endtime, graphtype):

    plt.title(
        str(starttime) + ' to ' + str(endtime) + ' second test of error response analysis with ' + graphtype + ' graph',
-        fontsize=12)
+        fontsize=15)
    plt.tight_layout(5)
    path = '/roobo/soft/phpmyadmin/second_test.jpg'
    plt.savefig(path)
-    return path
-
-
-app = Flask(__name__)
-CORS(app, supports_credentials=True)
-
-
-@app.route('/SPDAS/second_test1', methods=['POST'])
-def domain():
-    param = ({"data_type": [{"value": "error_control"}, {"value": "error_application"}],
-              "time": "2018-12-01 00:00:00/2018-12-02 00:00:00",
-              "graph_type": [{"value": "bar"}, {"value": "pie"}]})
-    return json.JSONEncoder().encode(param)
-
-
-@app.route('/SPDAS/second_test2', methods=['POST'])
-def domain_form():
-    # 需要从request对象读取表单内容：
-    data = request.get_data()
-    json_re = json.loads(data)
-    datatype = json_re['data_type']
-
-    m_time = json_re['time']
-    str_time = str(m_time)
-    m_time = str_time.split('/')
-    starttime = m_time[0]
-    endtime = m_time[1]
-    graphtype = json_re['graph_type']
-
-    image_path = second_test_plot(datatype=datatype, starttime=starttime, endtime=endtime, graphtype=graphtype)
-    path = ({"test_image": image_path})
-    return json.JSONEncoder().encode(path)
-
-
-if __name__ == '__main__':
-    app.run(debug=True, host='10.7.19.129', port=5000)
+    return 'http://120.79.171.145:8000/second_test.jpg'
+
+# app = Flask(__name__)
+# CORS(app, supports_credentials=True)
+#
+#
+# @app.route('/SPDAS/second_test1', methods=['POST'])
+# def domain():
+#     param = ({"data_type": [{"value": "error_control"}, {"value": "error_application"}],
+#               "time": "2018-12-01 00:00:00/2018-12-02 00:00:00",
+#               "graph_type": [{"value": "bar"}, {"value": "pie"}]})
+#     return json.JSONEncoder().encode(param)
+#
+#
+# @app.route('/SPDAS/second_test2', methods=['POST'])
+# def domain_form():
+#     # 需要从request对象读取表单内容：
+#     data = request.get_data()
+#     json_re = json.loads(data)
+#     datatype = json_re['data_type']
+#
+#     m_time = json_re['time']
+#     str_time = str(m_time)
+#     m_time = str_time.split('/')
+#     starttime = m_time[0]
+#     endtime = m_time[1]
+#     graphtype = json_re['graph_type']
+#
+#     image_path = second_test_plot(datatype=datatype, starttime=starttime, endtime=endtime, graphtype=graphtype)
+#     path = ({"test_image": image_path})
+#     return json.JSONEncoder().encode(path)
+#
+#
+# if __name__ == '__main__':
+#     app.run(debug=True, host='10.7.19.129', port=5000)
--- a/static/css/app.9c4162e157000096832f7467b0da7465.css
+++ b/static/css/app.9c4162e157000096832f7467b0da7465.css
--- a/static/fonts/ionicons.99ac330.woff
+++ b/static/fonts/ionicons.99ac330.woff
--- a/static/fonts/ionicons.d535a25.ttf
+++ b/static/fonts/ionicons.d535a25.ttf
--- a/static/img/bg_login.e334716.png
+++ b/static/img/bg_login.e334716.png
--- a/static/img/fullstack.e51bafb.jpg
+++ b/static/img/fullstack.e51bafb.jpg
--- a/static/img/ionicons.a2c4a26.svg
+++ b/static/img/ionicons.a2c4a26.svg
--- a/static/img/logo.1e6719b.png
+++ b/static/img/logo.1e6719b.png
--- a/static/js/0.929eb24a6140a29a71e9.js
+++ b/static/js/0.929eb24a6140a29a71e9.js
--- a/static/js/1.7bc85965315ad5f725e9.js
+++ b/static/js/1.7bc85965315ad5f725e9.js
+webpackJsonp([1],{"+ZrS":function(e,t){},"0Kq5":function(e,t){},"8zSp":function(e,t){},LE12:function(e,t,a){"use strict";Object.defineProperty(t,"__esModule",{value:!0});var 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--- a/static/js/6.2eabc7342bc8bb97de0c.js
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+webpackJsonp([6],{SwKW:function(t,e,a){"use strict";Object.defineProperty(e,"__esModule",{value:!0});var i=a("PJh5"),n=a.n(i),o=a("VkeE"),m=a("6d20"),d={name:"domain",data:function(){return{domainForm:{domainSelect:"",domainChartSelect:""},domainGetInitTime:[],ruleDomainForm:{domainSelected:[{required:!0,trigger:"change",message:"请选择类型"}],domainChartSelected:[{required:!0,trigger:"change",message:"请选择图表类型"}],domainGetDates:[{required:!0,type:"date",trigger:"change",message:"请选择日期和时间"}]},selectTime:"",getDatesOptions:{disabledDate:function(t){return t<n()("2018-12-21 01:00:00","YYYY-MM-DD HH:mm:ss").toDate()||t.valueOf()>Date.now()}},data_type:[],graph_type:[],chartList:[],loading1:!1,loading2:!1,domainImage:"",isBtnLoading:!1,isImgLoading:!0}},mounted:function(){var t=this;this.$api.post(o.e).then(function(e){t.domainImage=e.data.image,t.data_type=e.data.data_type,t.graph_type=e.data.graph_type,t.domainImage=e.data.image_path,t.domainForm.domainSelect=e.data.data_type[2].value,t.domainForm.domainChartSelect=e.data.graph_type[0].value;var a=e.data.time.split("/"),i=n()(a[0],"YYYY-MM-DD HH:mm:ss").toDate(),o=n()(a[1],"YYYY-MM-DD HH:mm:ss").toDate();t.domainGetInitTime=[i,o],t.isImgLoading=!1}).catch(function(e){t.isImgLoading=!0})},filters:{kindsChange:function(t){return m.c[t]},chartChange:function(t){return m.a[t]}},methods:{handleSubmit:function(){var t=this;this.$refs.domainForm.validate(function(e){if(e){if(!1===t.isBtnLoading){t.isBtnLoading=!0,t.isImgLoading=!0;var a={time:n()(t.domainGetInitTime[0]).format("YYYY-MM-DD HH:mm:ss")+"/"+n()(t.domainGetInitTime[1]).format("YYYY-MM-DD HH:mm:ss"),data_type:t.domainForm.domainSelect,graph_type:t.domainForm.domainChartSelect};t.$api.post(o.f,a).then(function(e){console.log("domain",e.data),t.domainImage=e.data.domain_image+"?t="+(new Date).getTime(),200===e.status&&(t.isBtnLoading=!1,t.isImgLoading=!1)}).catch(function(e){console.log(e),t.isBtnLoading=!1,t.isImgLoading=!0})}t.$Message.success("Success")}else t.$Message.error("failed")})},imgBig:function(){console.log("1111"),console.log("imgWidth:",document.getElementsByTagName("img")[0].width);var t=document.getElementsByTagName("img")[0],e=t.length;console.log("imgLen:",e);for(var a=0;a<e;a++)t.width<600&&(t[a].width=2*t[a].width)}}},r={render:function(){var t=this,e=t.$createElement,a=t._self._c||e;return a("div",[a("Row",{staticStyle:{"margin-top":"10px"},attrs:{gutter:20}},[a("i-col",{attrs:{span:"24",md:24,lg:24}},[a("Card",{attrs:{shadow:""}},[a("i-form",{ref:"domainForm",attrs:{inline:"",model:t.domainForm,rules:t.ruleDomainForm}},[a("Form-item",{attrs:{prop:"data_type"}},[a("i-select",{staticStyle:{width:"120px"},attrs:{clearable:""},model:{value:t.domainForm.domainSelect,callback:function(e){t.$set(t.domainForm,"domainSelect",e)},expression:"domainForm.domainSelect"}},t._l(t.data_type,function(e){return a("i-option",{key:e.index,attrs:{value:e.value}},[t._v(t._s(t._f("kindsChange")(e.value))+"\n              ")])}),1)],1),t._v(" "),a("Form-item",{attrs:{prop:"graph_type"}},[a("i-select",{staticStyle:{width:"100px"},attrs:{clearable:""},model:{value:t.domainForm.domainChartSelect,callback:function(e){t.$set(t.domainForm,"domainChartSelect",e)},expression:"domainForm.domainChartSelect"}},t._l(t.graph_type,function(e){return a("i-option",{key:e.index,attrs:{value:e.value}},[t._v(t._s(t._f("chartChange")(e.value))+"\n              ")])}),1)],1),t._v(" "),a("Form-item",{attrs:{prop:"time"}},[a("Date-picker",{staticStyle:{width:"300px"},attrs:{confirm:"",options:t.getDatesOptions,type:"datetimerange",format:"yyyy-MM-dd HH:mm:ss",placeholder:"请选择开始时间和结束时间"},model:{value:t.domainGetInitTime,callback:function(e){t.domainGetInitTime=e},expression:"domainGetInitTime"}})],1),t._v(" "),a("Form-item",[a("i-button",{attrs:{type:"primary",loading:t.isBtnLoading},on:{click:t.handleSubmit}},[t._v("确定")])],1)],1),t._v(" "),a("div",{staticClass:"img-div loading"},[a("viewer",[a("img",{attrs:{src:t.domainImage},on:{click:t.imgBig}})]),t._v(" "),t.isImgLoading?a("Spin",{attrs:{fix:""}},[t._v("加载中...")]):t._e()],1)],1)],1)],1)],1)},staticRenderFns:[]};var s=a("VU/8")(d,r,!1,function(t){a("dDHT")},"data-v-0e6c7106",null);e.default=s.exports},dDHT:function(t,e){}});
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--- a/static/js/7.931938d5b9ce23d1c7c9.js
+++ b/static/js/7.931938d5b9ce23d1c7c9.js
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\ No newline at end of file
--- a/static/js/app.d34b7f33f5584be04934.js
+++ b/static/js/app.d34b7f33f5584be04934.js
+webpackJsonp([9],{"+skl":function(e,n){},B774:function(e,n){},N1kN:function(e,n){},NHnr:function(e,n,t){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=t("7+uW"),a={render:function(){var e=this.$createElement,n=this._self._c||e;return n("div",{attrs:{id:"app"}},[n("router-view")],1)},staticRenderFns:[]};var o=t("VU/8")({name:"App"},a,!1,function(e){t("B774")},null,null).exports,s=t("/ocq");r.default.use(s.a);var u=new s.a({routes:[{path:"/",redirect:"/login"},{path:"/login",name:"login",component:function(){return t.e(2).then(t.bind(null,"vdVF"))}},{path:"/SPDAS",name:"homePage",component:function(){return t.e(1).then(t.bind(null,"LE12"))},redirect:"/SPDAS/domain",meta:{requireAuth:!0},children:[{path:"/SPDAS/domain",name:"domain",component:function(){return Promise.all([t.e(0),t.e(6)]).then(t.bind(null,"SwKW"))},meta:{requireAuth:!0}},{path:"/SPDAS/seriesTime",name:"seriesTime",component:function(){return Promise.all([t.e(0),t.e(5)]).then(t.bind(null,"6NKR"))},meta:{requireAuth:!0}},{path:"/SPDAS/user",name:"user",component:function(){return Promise.all([t.e(0),t.e(7)]).then(t.bind(null,"ZeSV"))},meta:{requireAuth:!0}},{path:"/SPDAS/chat",name:"chat",component:function(){return Promise.all([t.e(0),t.e(4)]).then(t.bind(null,"M2d4"))},meta:{requireAuth:!0}},{path:"/SPDAS/responsive",name:"responsive",component:function(){return Promise.all([t.e(0),t.e(3)]).then(t.bind(null,"fKh/"))},meta:{requireAuth:!0}}]}]});u.beforeEach(function(e,n,t){var r=sessionStorage.getItem("token");console.log("token:",r),e.meta.requireAuth?(console.log(123),r?t():t({path:"/login"})):t()});var i=u,c=(t("j1ja"),t("//Fk")),l=t.n(c),p=t("mtWM"),d=t.n(p),f=t("VkeE"),m=this,h="http://120.79.171.145:5000/SPDAS",_=d.a.create();_.defaults.baseURL=h,_.defaults.timeout=6e5,_.defaults.headers.post["Content-Type"]="application/x-www-form-urlencoded",_.defaults.headers.post["X-Request-With"]="XMLHttpRequest",_.defaults.headers.post.login_token="",_.interceptors.request.use(function(e){var n="";try{n=JSON.parse(localStorage.getItem(f.p))}catch(e){localStorage.removeItem(f.p),console.log(e)}return n&&(e.headers.login_token=n),e},function(e){return m.$Message.error("网络错误，请稍后再试"),l.a.reject(e)}),_.interceptors.response.use(function(e){return e.data&&200!==e.status?(m.$Message.error("登录信息过期，请重新登录！"),i.replace({name:"login"}),l.a.reject(e)):l.a.resolve(e)},function(e){if(e.response)switch(e.response.status){case 401:localStorage.removeItem(f.p),m.$Message.error("请重新授权登录"),i.replace({name:"login"});break;case 404:m.$Message.error("请求页面不存在");break;case 500:m.$Message.error("服务器错误，请稍后再试")}else m.$Message.error("请求超时，请稍后重试");return l.a.reject(e)});var g=_,S=t("BTaQ"),A=t.n(S),v=(t("+skl"),t("EAZf")),y=t.n(v);t("N1kN");r.default.use(A.a),r.default.config.productionTip=!1,r.default.prototype.$api=g,r.default.use(y.a,{defaultOptions:{zIndex:9999}}),new r.default({el:"#app",router:i,components:{App:o},template:"<App/>"})},VkeE:function(e,n,t){"use strict";t.d(n,"p",function(){return r}),t.d(n,"g",function(){return a}),t.d(n,"e",function(){return o}),t.d(n,"f",function(){return s}),t.d(n,"l",function(){return u}),t.d(n,"m",function(){return i}),t.d(n,"n",function(){return c}),t.d(n,"o",function(){return l}),t.d(n,"c",function(){return p}),t.d(n,"d",function(){return d}),t.d(n,"a",function(){return f}),t.d(n,"b",function(){return m}),t.d(n,"h",function(){return h}),t.d(n,"i",function(){return _}),t.d(n,"j",function(){return g}),t.d(n,"k",function(){return S});var r="",a="/login",o="/domain_structure_analysis1",s="/domain_structure_analysis2",u="/time_series_analysis1",i="/time_series_analysis2",c="/user_portrait_analysis1",l="/user_portrait_analysis2",p="/chat_function_analysis/choice1",d="/chat_function_analysis/choice2",f="/chat_function_analysis/chat1",m="/chat_function_analysis/chat2",h="/response_analysis1",_="/response_analysis2",g="/second_test1",S="/second_test2"}},["NHnr"]);
\ No newline at end of file
--- a/static/js/manifest.ffddd6eb6c14b28fe19d.js
+++ b/static/js/manifest.ffddd6eb6c14b28fe19d.js
+!function(e){var n=window.webpackJsonp;window.webpackJsonp=function(r,c,a){for(var f,i,u,s=0,l=[];s<r.length;s++)i=r[s],t[i]&&l.push(t[i][0]),t[i]=0;for(f in c)Object.prototype.hasOwnProperty.call(c,f)&&(e[f]=c[f]);for(n&&n(r,c,a);l.length;)l.shift()();if(a)for(s=0;s<a.length;s++)u=o(o.s=a[s]);return u};var r={},t={10:0};function o(n){if(r[n])return r[n].exports;var t=r[n]={i:n,l:!1,exports:{}};return e[n].call(t.exports,t,t.exports,o),t.l=!0,t.exports}o.e=function(e){var n=t[e];if(0===n)return new Promise(function(e){e()});if(n)return n[2];var r=new Promise(function(r,o){n=t[e]=[r,o]});n[2]=r;var c=document.getElementsByTagName("head")[0],a=document.createElement("script");a.type="text/javascript",a.charset="utf-8",a.async=!0,a.timeout=12e4,o.nc&&a.setAttribute("nonce",o.nc),a.src=o.p+"static/js/"+e+"."+{0:"929eb24a6140a29a71e9",1:"7bc85965315ad5f725e9",2:"9e073194c7971f80ffb1",3:"ac8f0baf4212298750c4",4:"0b2eb810526861297b2f",5:"49d9f21e16f136fa71c7",6:"2eabc7342bc8bb97de0c",7:"931938d5b9ce23d1c7c9"}[e]+".js";var f=setTimeout(i,12e4);function i(){a.onerror=a.onload=null,clearTimeout(f);var n=t[e];0!==n&&(n&&n[1](new Error("Loading chunk "+e+" failed.")),t[e]=void 0)}return a.onerror=a.onload=i,c.appendChild(a),r},o.m=e,o.c=r,o.d=function(e,n,r){o.o(e,n)||Object.defineProperty(e,n,{configurable:!1,enumerable:!0,get:r})},o.n=function(e){var n=e&&e.__esModule?function(){return e.default}:function(){return e};return o.d(n,"a",n),n},o.o=function(e,n){return Object.prototype.hasOwnProperty.call(e,n)},o.p="./",o.oe=function(e){throw console.error(e),e}}([]);
\ No newline at end of file
--- a/static/js/vendor.e324bda0bd56a2f46d94.js
+++ b/static/js/vendor.e324bda0bd56a2f46d94.js
--- a/templates/index.html
+++ b/templates/index.html
+<!DOCTYPE html><html><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><title>语义数据分析平台</title><style>html,body{
+        padding: 0;
+        margin: 0;
+        height: 100%;
+        overflow:hidden;
+      }</style><link href=./static/css/app.9c4162e157000096832f7467b0da7465.css rel=stylesheet></head><body><div id=app></div><script type=text/javascript src=./static/js/manifest.ffddd6eb6c14b28fe19d.js></script><script type=text/javascript src=./static/js/vendor.e324bda0bd56a2f46d94.js></script><script type=text/javascript src=./static/js/app.d34b7f33f5584be04934.js></script></body></html>
\ No newline at end of file
--- a/time_series_analysis.py
+++ b/time_series_analysis.py
@@ -5,10 +5,11 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import datetime as dt
 import bottom_function.data_read as dr
-import json
-from flask import Flask
-from flask import request
-from flask_cors import CORS
+
+# import json
+# from flask import Flask
+# from flask import request
+# from flask_cors import CORS


 def datetime_data_plot(timetype, starttime, endtime, graphtype):
@@ -63,49 +64,49 @@ def datetime_data_plot(timetype, starttime, endtime, graphtype):
            index_data[i] = dt.datetime.strftime(index_data[i], "%Y")
        all_data.index = index_data

-    fig = plt.figure(figsize=(16, 6))
-    all_data.plot(kind=graphtype, stacked=True, use_index=True)
+    # fig = plt.figure(figsize=(16, 6))
+    all_data.plot(kind=graphtype, stacked=True, use_index=True,figsize=(10, 8))
    plt.xticks(rotation=45)

    plt.title(str(starttime) + ' to ' + str(
        endtime) + ' ' + timetype + ' datetime domain analysis of ' + graphtype + ' graph',
-              fontsize=10)
+              fontsize=15)
    plt.tight_layout(5)
    path = '/roobo/soft/phpmyadmin/plot_time.jpg'
    plt.savefig(path)
-    return path
-
-
-app = Flask(__name__)
-CORS(app, supports_credentials=True)
-
-
-@app.route('/SPDAS/time_series_analysis1', methods=['POST'])
-def domain():
-    param = ({"time_type": [{"value": "hour", "id": 1}, {"value": "day", "id": 2},
-                            {"value": "month", "id": 3}, {"value": "year", "id": 4}],
-              "time": "2018-12-01 00:00:00/2018-12-02 00:00:00",
-              "graph_type": [{"value": "bar"}, {"value": "pie"}]})
-    return json.JSONEncoder().encode(param)
-
-
-@app.route('/SPDAS/time_series_analysis2', methods=['POST'])
-def domain_form():
-    # 需要从request对象读取表单内容：
-    data = request.get_data()
-    json_re = json.loads(data)
-    print(json_re)
-    timetype = json_re['time_type']
-    m_time = json_re['time']
-    graphtype = json_re['graph_type']
-    str_time = str(m_time)
-    m_time = str_time.split('/')
-    starttime = m_time[0]
-    endtime = m_time[1]
-    image_path = datetime_data_plot(timetype=timetype, starttime=starttime, endtime=endtime, graphtype=graphtype)
-    path = ({"time_image": image_path})
-    return json.JSONEncoder().encode(path)
+    return 'http://120.79.171.145:8000/plot_time.jpg'


-if __name__ == '__main__':
-    app.run(debug=True, host='10.7.19.129', port=5000)
+# app = Flask(__name__)
+# CORS(app, supports_credentials=True)
+#
+#
+# @app.route('/SPDAS/time_series_analysis1', methods=['POST'])
+# def domain():
+#     param = ({"time_type": [{"value": "hour", "id": 1}, {"value": "day", "id": 2},
+#                             {"value": "month", "id": 3}, {"value": "year", "id": 4}],
+#               "time": "2018-12-01 00:00:00/2018-12-02 00:00:00",
+#               "graph_type": [{"value": "bar"}, {"value": "pie"}]})
+#     return json.JSONEncoder().encode(param)
+#
+#
+# @app.route('/SPDAS/time_series_analysis2', methods=['POST'])
+# def domain_form():
+#     # 需要从request对象读取表单内容：
+#     data = request.get_data()
+#     json_re = json.loads(data)
+#     print(json_re)
+#     timetype = json_re['time_type']
+#     m_time = json_re['time']
+#     graphtype = json_re['graph_type']
+#     str_time = str(m_time)
+#     m_time = str_time.split('/')
+#     starttime = m_time[0]
+#     endtime = m_time[1]
+#     image_path = datetime_data_plot(timetype=timetype, starttime=starttime, endtime=endtime, graphtype=graphtype)
+#     path = ({"time_image": image_path})
+#     return json.JSONEncoder().encode(path)
+#
+#
+# if __name__ == '__main__':
+#     app.run(debug=True, host='10.7.19.129', port=5000)
--- a/timing.py
+++ b/timing.py
+# -*- coding: utf-8 -*-
+# author：Li Mingjie time:2019/3/14
+# Brief:
+import time
+import threading
+
+
+def run():
+    datetime = time.localtime(time.time())
+    print(datetime)
+
+
+run()
--- a/user_portrait_analysis.py
+++ b/user_portrait_analysis.py
@@ -7,10 +7,12 @@ import cv2
 import matplotlib.pyplot as plt
 import bottom_function.data_read as dr
 from bottom_function import normalization as norm
-import json
-from flask import Flask
-from flask import request
-from flask_cors import CORS
+
+
+# import json
+# from flask import Flask
+# from flask import request
+# from flask_cors import CORS


 def portrait_plot(datatype, starttime, endtime):
@@ -24,11 +26,11 @@ def portrait_plot(datatype, starttime, endtime):

    cut_text = norm.remove_special_characters(query_data)

-    color_mask = cv2.imread('./bottom_function/data/gree_logo.jpg')
+    color_mask = cv2.imread('/home/work/semantic_platform_DAS/bottom_function/data/gree_logo.jpg')

    cloud = WordCloud(
        # 设置字体，不指定就会出现乱码
-        font_path=" C:\\Windows\\Fonts\\STXINGKA.TTF",
+        font_path="./bottom_function/data/msyh.ttf",
        # font_path=path.join(d,'simsun.ttc'),
        font_step=1,
        width=720,
@@ -48,41 +50,44 @@ def portrait_plot(datatype, starttime, endtime):
    # wCloud.to_file('../data/word_cloud/cloud.jpg')

    plt.imshow(wCloud, interpolation='bilinear')
+
    plt.axis('off')
-    til = datatype + ' ' + starttime + endtime + 'user portrait of word cloud'
+    til = starttime + ' to ' + endtime + ' ' + datatype + ' user portrait of word cloud'
    plt.title(til)
    path = '/roobo/soft/phpmyadmin/plot_user.jpg'
-    plt.savefig(path)
-    return path
-
-
-app = Flask(__name__)
-CORS(app, supports_credentials=True)
-
-
-@app.route('/SPDAS/user_portrait_analysis1', methods=['POST'])
-def domain():
-    param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
-              "time": "2019.01.01 00:00:00/2019.01.02 00:00:00"})
-    return json.JSONEncoder().encode(param)
-
-
-@app.route('/SPDAS/user_portrait_analysis2', methods=['POST'])
-def domain_form():
-    # 需要从request对象读取表单内容：
-    data = request.get_data()
-    json_re = json.loads(data)
-
-    datatype = json_re['data_type']
-    m_time = json_re['time']
-    str_time = str(m_time)
-    m_time = str_time.split('/')
-    starttime = m_time[0]
-    endtime = m_time[1]
-    image_path = portrait_plot(datatype=datatype, starttime=starttime, endtime=endtime)
-    path = {"user_image": image_path}
-    return json.JSONEncoder().encode(path)
-
-
-if __name__ == '__main__':
-    app.run(debug=True, host='10.7.19.129', port=5000)
+    wCloud.to_file(path)
+    # plt.savefig(path)
+    return 'http://120.79.171.145:8000/plot_user.jpg'
+
+# portrait_plot(datatype='all', starttime='2018-12-28 00:00:00', endtime='2018-12-29 00:00:00')
+
+# app = Flask(__name__)
+# CORS(app, supports_credentials=True)
+#
+#
+# @app.route('/SPDAS/user_portrait_analysis1', methods=['POST'])
+# def domain():
+#     param = ({"data_type": [{"value": "control"}, {"value": "application"}, {"value": "all"}],
+#               "time": "2019.01.01 00:00:00/2019.01.02 00:00:00"})
+#     return json.JSONEncoder().encode(param)
+#
+#
+# @app.route('/SPDAS/user_portrait_analysis2', methods=['POST'])
+# def domain_form():
+#     # 需要从request对象读取表单内容：
+#     data = request.get_data()
+#     json_re = json.loads(data)
+#
+#     datatype = json_re['data_type']
+#     m_time = json_re['time']
+#     str_time = str(m_time)
+#     m_time = str_time.split('/')
+#     starttime = m_time[0]
+#     endtime = m_time[1]
+#     image_path = portrait_plot(datatype=datatype, starttime=starttime, endtime=endtime)
+#     path = {"user_image": image_path}
+#     return json.JSONEncoder().encode(path)
+#
+#
+# if __name__ == '__main__':
+#     app.run(debug=True, host='10.7.19.129', port=5000)