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)