def show_time(request): fig = plt.figure() ax = fig.add_subplot(121) data = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data = pd.DataFrame(data) dt_jy = data.groupby('주야') dtjy = dt_jy['발생년'].count() labels1 = ['야', '주'] ax = dtjy.plot(kind='pie', title='사고유형', figsize=(18, 6), fontsize=17, autopct='%.1f%%') ax = fig.add_subplot(122) data_df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data = pd.DataFrame(data_df) data["발생시"].unique() data_si = data["발생시"].value_counts() data_si.shape[0] for i in range(data_si.shape[0]): si = sns.countplot(data=data, x="발생시") for i in range(data_si.shape[0]): si.text(x=i, y=data_si[i], s=data_si[i], horizontalalignment='center') buf = io.BytesIO() canvas = FigureCanvasAgg(fig) canvas.print_png(buf) response = HttpResponse(buf.getvalue(), content_type="image/png") response['Content-Length'] = str(len(response.content)) return response
def road_shape(request): fig = plt.figure() ax = fig.add_subplot(121) df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv", encoding='utf-8') df = pd.DataFrame(df) data3=df['도로형태'].value_counts() ax = data3.plot(kind='pie', figsize=(15, 10), legend=False, autopct='%1.2f%%', fontsize=15) plt.xticks(rotation=0) ax = fig.add_subplot(122) df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv", encoding='utf-8') df = pd.DataFrame(df) data4 = df[df["가해자법규위반"] == "과속"].loc[:, ["가해자법규위반", "도로형태"]].value_counts() ax = data4.plot(kind='bar', figsize=(15, 7), legend=False, fontsize=15) plt.xticks(rotation=0) buf = io.BytesIO() canvas = FigureCanvasAgg(fig) canvas.print_png(buf) fig.clear() response = HttpResponse(buf.getvalue(), content_type="image/png") response['Content-Length'] = str(len(response.content)) return response
def patients_chart(request): fig = plt.figure() ax = fig.add_subplot(111) data = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv", encoding='utf-8') df = pd.DataFrame(data) chung_df = df.loc[df['발생지시도'] == '충남', ['발생년', '사망자수', '부상자수', '중상자수', '경상자수']] chung_group = chung_df.groupby(["발생년"])[["사망자수", "부상자수", "중상자수", "경상자수"]].sum() color = "red", "pink", "sandybrown", "peachpuff" ax = chung_group.plot(kind='bar', title='환자유형', figsize=(12, 5), legend=True, fontsize=18, color=color) ax.set_xlabel('발생년도', fontsize=12) # x축 정보 표시 plt.xticks(rotation=45) # 가로축 텍스트 회전 (세로로 보이던거 수정) ax.set_ylabel('사고수', fontsize=12) # y축 정보 표시 ax.legend(['사망자수', '부상자수', '경상자수', '중상자수'], fontsize=12) buf = io.BytesIO() canvas = FigureCanvasAgg(fig) canvas.print_png(buf) fig.clear() response = HttpResponse(buf.getvalue(), content_type="image/png") response['Content-Length'] = str(len(response.content)) return response
def law_violations(request): fig = plt.figure() ax = fig.add_subplot(121) df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv", encoding='utf-8') data1 = df['가해자법규위반'].value_counts() ax = data1.plot(kind='pie', figsize=(15, 10), legend=False, autopct='%1.2f%%', fontsize=12) plt.xticks(rotation=0) ax = fig.add_subplot(122) df = pd.read_csv('C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv', encoding='utf-8') data2 = df['가해자법규위반'].value_counts() ax1 = data2.plot(kind='bar', figsize=(25, 7), fontsize=9, color='pink') ax1.set_xlabel('') plt.xticks(rotation=360) # 가로축 텍스트 회전 (세로로 보이던거 수정) ax1.set_ylabel('') buf = io.BytesIO() canvas = FigureCanvasAgg(fig) canvas.print_png(buf) fig.clear() response = HttpResponse(buf.getvalue(), content_type="image/png") response['Content-Length'] = str(len(response.content)) return response
def chart_hap(request): fig = plt.figure() ax = fig.add_subplot(122) data_df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data_df = pd.DataFrame(data_df) selectdata3 = data_df.groupby('발생년') selectdata4 = selectdata3['발생년'].count() labels2 = ['2015', '2016', '2017', '2018', '2019'] ax = selectdata4.plot(kind='pie', title='사고유형', figsize=(18, 6), fontsize=17, autopct='%.1f%%') ax = fig.add_subplot(121) data = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data = pd.DataFrame(data) selectdata5 = data.groupby('발생년') selectdata6 = selectdata5['발생년'].count() x = np.arange(5) years = ['2015', '2016', '2017', '2018', '2019'] values = selectdata6 plt.bar(x, values) plt.xticks(x, years) buf = io.BytesIO() canvas = FigureCanvasAgg(fig) canvas.print_png(buf) fig.clear() response = HttpResponse(buf.getvalue(), content_type="image/png") response['Content-Length'] = str(len(response.content)) return response
def show_images(images, labels, preds): plt.figure(figsize=(8, 4)) for i, image in enumerate(images): plt.subplot(1, 6, i + 1, xticks=[], yticks=[]) image = image.numpy().transpose((1, 2, 0)) mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) image = image * std + mean image = np.clip(image, 0., 1.) plt.imshow(image) col = 'green' if preds[i] != labels[i]: col = 'red' plt.xlabel(f'{class_names[int(labels[i].numpy())]}') plt.ylabel(f'{class_names[int(preds[i].numpy())]}', color=col) plt.tight_layout() plt.show()
def chart_hap2(request): fig = plt.figure() ax = fig.add_subplot(212) # ax=도화지 data_df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data_df = pd.DataFrame(data_df) df1 = data_df.loc[data_df['발생지시도'] == '충남', ['발생년', '사고유형_대분류']] df1_group = df1.groupby("사고유형_대분류")['발생년'].count() colors = ['#ffc000', '#8fd9b6', '#d395d0'] wedgeprops = {'width': 0.7, 'edgecolor': 'w', 'linewidth': 5} ax = df1_group.plot(kind='pie', title='사고유형', figsize=(18, 6), fontsize=17, autopct='%.1f%%', colors=colors, wedgeprops=wedgeprops) ax.set_ylabel('', fontsize=5) ax = fig.add_subplot(122) # ax=도화지 data_df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data_df = pd.DataFrame(data_df) df3 = data_df.loc[data_df['발생지시도'] == '충남', ['발생년', '가해자_당사자종별']] df3_group = df3.groupby("가해자_당사자종별")['발생년'].count() colors1 = ['thistle', 'mistyrose', 'lightpink'] ax2 = df3_group.plot(kind='pie', title='가해자차종', figsize=(18, 6), fontsize=17, autopct='%.1f%%', colors=colors1) ax2.set_ylabel('', fontsize=5) ax = fig.add_subplot(121) # ax=도화지 data_df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data_df = pd.DataFrame(data_df) df5 = data_df.loc[data_df['발생지시도'] == '충남', ['발생년', '피해자_당사자종별']] df5_group = df5.groupby("피해자_당사자종별")['발생년'].count() df5_group = df5_group.sort_values(ascending=False) colors2 = ['darkturquoise', 'cadetblue', 'powderblue', 'lightblue', 'skyblue', 'lightskyblue', 'lightsteelblue', 'aliceblue', 'linen'] ax3 = df5_group.plot(kind='pie', title='피해자유형', figsize=(18, 6), fontsize=12, autopct='%.1f%%', colors=colors2) ax3.set_ylabel('', fontsize=5) ax3.set_ylabel('', fontsize=5) buf = io.BytesIO() canvas = FigureCanvasAgg(fig) canvas.print_png(buf) fig.clear() response = HttpResponse(buf.getvalue(), content_type="image/png") response['Content-Length'] = str(len(response.content)) return response
def chart_casualtie(request): fig = plt.figure() ax = fig.add_subplot(121) # ax=도화지 data_df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data_df = pd.DataFrame(data_df) df4 = data_df.loc[data_df['발생지시도']=='충남',['발생년','발생지시군구']] df4_group = df4.groupby("발생지시군구")['발생년'].count() df4_group = df4_group.sort_values(ascending=True) color = 'rosybrown' ax = df4_group.plot(kind='bar', title='시/군별 사고수', figsize=(12, 5), fontsize=13, color=color) ax.set_xlabel('', fontsize=12) # x축 정보 표시 plt.xticks(rotation=45) # 가로축 텍스트 회전 (세로로 보이던거 수정) ax.set_ylabel('', fontsize=12) # y축 정보 표시 ax = fig.add_subplot(122) # ax=도화지 data_df = pd.read_csv("C:/djangowork1/accidentsite/static/어린이 사망교통사고 정보(2015~2019년).csv") data_df = pd.DataFrame(data_df) df2 = data_df.loc[data_df['발생지시도']=='충남',['발생년','요일']] df2_group = df2.groupby("요일")["발생년"].count() df2_group = df2_group.sort_values(ascending=False) color = 'tan' ax5 = df2_group.plot(kind='bar', title='요일별 사고수', figsize=(12, 5), fontsize=18, color=color) ax5.set_xlabel('', fontsize=12) # x축 정보 표시 plt.xticks(rotation=360) # 가로축 텍스트 회전 (세로로 보이던거 수정) ax5.set_ylabel('', fontsize=12) # y축 정보 표시 buf = io.BytesIO() canvas = FigureCanvasAgg(fig) canvas.print_png(buf) fig.clear() response = HttpResponse(buf.getvalue(), content_type="image/png") response['Content-Length'] = str(len(response.content)) return response
if size == 1: for line in content: k_array = line.split(" ") file_graph = str(sys.argv[2]) if os.path.exists(file_graph): # if file exist with open(file_graph) as f: content = f.readlines() # read each line content = [x.strip() for x in content] ratio = NULL size = len(content) if size == 1: for line in content: ratio = line.split(" ") list_ratio = [] for i in ratio: list_ratio.append(float(i)) list_k_array = [] for i in k_array: list_k_array.append(float(i)) plt.clf() plt.figure(figsize=(16, 10)) plt.ylabel("Ratio") plt.xlabel("k_degree") plt.plot(list_k_array, list_ratio, 'r--') plt.title("Graph friend 1000 10 100") plt.savefig("ratio_fakedataset.png", dpi=120)
model.add(Dense(output_size, activation='sigmoid')) model.compile(optimizer='adam', loss='mse') #------------------------------------------------------训练------------------------------------------------- epochs = 15 batch_size = 128 history = model.fit(x_train, x_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, x_test)) #-----------------------------------------------------查看解码效果-------------------------------------------- decoded_imgs = model.predict(x_test) n = 10 plt.figure(figsize=(20, 6)) for i in range(n): # 原图 ax = plt.subplot(3, n, i+1) plt.imshow(x_test[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) # 解码效果图 ax = plt.subplot(3, n, i+n+1) plt.imshow(decoded_imgs[i].reshape(28, 28)) plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False)
rc('font', family='NanumBarunGothic') wordcloud = WordCloud( font_path='/content/CookieRun Regular.ttf', # 맥에선 한글폰트 설정 잘해야함. background_color='white', # 배경 색깔 정하기 colormap='Accent_r', # 폰트 색깔 정하기 width=800, height=800) wordcloud_words = wordcloud.generate_from_frequencies(words) array = wordcloud.to_array() print(type(array)) # numpy.ndarray print(array.shape) # (800, 800, 3) fig = plt.figure(figsize=(10, 10)) plt.imshow(array, interpolation="bilinear") plt.axis('off') plt.show() fig.savefig('business_anlytics_worldcloud.png') for sentence in lists: morphs.append(twitter.pos(sentence)) print(morphs) noun_adj_adv_list = [] for sentence in morphs: for word, tag in sentence: if tag in ['Noun'] and ("것" not in word) and ("내" not in word) and ( "나" not in word) and ("수" not in word) and ( "게" not in word) and ("말" not in word): noun_adj_adv_list.append(word)