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gradio-demo

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  1. README.md +506 -13
  2. Seoul.csv +0 -0
  3. Washington.csv +1441 -0
  4. app.py +230 -0
  5. readme_files/gradio.svg +23 -0
  6. readme_files/screenshot.gif +0 -0
README.md CHANGED
@@ -1,13 +1,506 @@
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- ---
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- title: Gradio Demo
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- emoji: 🐠
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- colorFrom: indigo
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- colorTo: gray
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- sdk: gradio
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- sdk_version: 3.29.0
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- app_file: app.py
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- pinned: false
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- license: apache-2.0
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # **Gradio-Demo**
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+
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+ ## - **Gradio**
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+ [<img src="readme_files/gradio.svg" alt="gradio" width=300>](https://gradio.app)<br>
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+ Here's the gradio github link: https://github.com/gradio-app/gradio
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+
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+ Gradio is open-source python library. It can make demo for machine learning model.
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+
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+ ### Gradio is useful for:
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+
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+ - **Demoing** your machine learning models for clients/collaborators/users/students.
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+
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+ - **Deploying** your models quickly with automatic shareable links and getting feedback on model performance.
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+
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+ - **Debugging** your model interactively during development using built-in manipulation and interpretation tools.
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+
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+ We can use various components for making demos.
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+
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+ This project provides demo for people who want to use gradio library.
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+
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+ ## - **Demo Description**
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+
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+ This demo shows 2022 year weather in table and graph by selecting the month, day, weather elements, and location.
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+
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+ ### **Input components**
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+
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+ Month, day : dropbox<br>
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+ weather elements : checkboxGroup<br>
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+ location : radio<br>
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+ precipitation : checkbox
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+
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+ Precipitation data has few valid data, so we made a separate component without drawing a graph.
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+
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+ ### **Output components**
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+
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+ Weather table : dataframe<br>
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+ Weather graph : plots
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+
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+ We used pandas and matplotlib python library when we make dataframe and plots.
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+
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+ Humidity is represented by a bar graph and other weather elements are represented by a curved line graphs.
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+
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+ ### **Function**
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+
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+ In demo, we made two functions which names are dataSearch and showOutput.
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+
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+ - dataSearch()
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+
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+ This function provides obtaining data about the month, day, weather element, and location entered by the user.
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+
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+ The columns in the csv file are composed of location, date, time, percipitation, wind, humidity, air pressure, and temperature. We took csv files from weather data opening portal.
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+
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+ ```python
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+ def dataSearch(month, day, weather_elements, location, precipitation):
55
+ if location=='Seoul':
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+ df = pd.read_csv('Seoul.csv')
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+ elif location=='Washington':
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+ df = pd.read_csv('Washington.csv')
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+
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+ if precipitation:
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+ weather_elements.append('precipitation')
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+
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+ if day in ['1','2','3','4','5','6','7','8','9']:
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+ today = '2022-'+month +'-0'+day
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+ else:
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+ today = '2022-'+month+'-'+day
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+
68
+ df1 = df[df.date == today]
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+ columns = ['location', 'date', 'time'] + weather_elements
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+ df2 = df1.loc[:, columns]
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+
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+ return df2
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+ ```
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+
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+ - showOutput()
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+
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+ This function represents data obtained from dataSearch() by dataframe and graphs.
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+
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+ It replaced month with numeric form and the maximum number of plots increased to 2 depending on the weather elements selected.
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+
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+ ```python
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+ def showOutput(month, day, weather_elements, location, precipitation):
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+ if month=='January':
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+ month = '01'
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+ elif month=='February':
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+ month = '02'
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+ elif month=='March':
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+ month = '03'
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+ elif month=='April':
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+ month = '04'
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+ elif month=='May':
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+ month = '05'
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+ elif month=='June':
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+ month = '06'
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+ elif month=='July':
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+ month = '07'
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+ elif month=='August':
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+ month = '08'
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+ elif month=='September':
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+ month = '09'
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+ elif month=='October':
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+ month = '10'
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+ elif month=='November':
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+ month = '11'
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+ elif month=='December':
106
+ month = '12'
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+
108
+ weatherTable = dataSearch(month, day, weather_elements, location, precipitation)
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+
110
+ if precipitation:
111
+ weather_elements.remove('precipitation')
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+
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+ if day in ['1','2','3','4','5','6','7','8','9']:
114
+ xname = '2022-'+month +'-0'+day
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+ else:
116
+ xname = '2022-'+month+'-'+day
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+
118
+ y_value=[0]*len(weather_elements)
119
+
120
+ for i in range(len(weather_elements)):
121
+ y_value[i] = weatherTable[weather_elements[i]]
122
+
123
+ x_value = weatherTable['time']
124
+
125
+ if 'humidity' in weather_elements:
126
+ humidity_index = weather_elements.index('humidity')
127
+ if weather_elements[humidity_index] != weather_elements[-1]:
128
+ temp = weather_elements[humidity_index]
129
+ weather_elements[humidity_index] = weather_elements[-1]
130
+ weather_elements[-1] = temp
131
+
132
+
133
+ if len(weather_elements) == 1:
134
+ weatherPlot = plt.figure(figsize=(10,10))
135
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
136
+ plt.xlabel(xname,labelpad=5, fontsize=15)
137
+ plt.ylabel(weather_elements[0], labelpad=15, fontsize=15)
138
+ plt.xticks(size=10, rotation=45)
139
+
140
+ plt.bar(x_value, y_value[-1], color='skyblue', label='1st data')
141
+ plt.legend(loc = "upper left")
142
+
143
+ elif len(weather_elements) == 2:
144
+ weatherPlot, ax1 = plt.subplots(figsize=(10,10))
145
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
146
+ plt.xticks(size=10, rotation=45)
147
+
148
+ ax1.bar(x_value, y_value[-1], color='skyblue', label='1st data')
149
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
150
+ ax1.set_ylabel(weather_elements[1], labelpad=15, fontsize=15)
151
+ ax1.legend(loc='upper left')
152
+
153
+ ax1_sub = ax1.twinx()
154
+ ax1_sub.plot(x_value, y_value[0], color='red', marker = "o", label='2nd data')
155
+ ax1_sub.set_ylabel(weather_elements[0], labelpad=25, fontsize=15, rotation=270)
156
+ ax1_sub.legend(loc='upper right')
157
+
158
+ elif len(weather_elements) == 3:
159
+ weatherPlot, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,15), constrained_layout=True)
160
+
161
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label='1st data')
162
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
163
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
164
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
165
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
166
+
167
+ ax1_sub = ax1.twinx()
168
+ line2 = ax1_sub.plot(x_value, y_value[1], color='blue', marker = "o", label='2nd data')
169
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
170
+
171
+ lines = line1 + line2
172
+ labels = [l.get_label() for l in lines]
173
+ ax1.legend(lines, labels, loc='upper left')
174
+
175
+ ax2.bar(x_value, y_value[-1], color='skyblue', label='3rd data')
176
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
177
+ ax2.set_ylabel(weather_elements[-1], labelpad=15, fontsize=15)
178
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
179
+ ax2.legend(loc='upper right')
180
+
181
+ elif len(weather_elements) == 4:
182
+ weatherPlot, (ax1, ax2) = plt.subplots(2,1, figsize=(10,15), constrained_layout=True)
183
+
184
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label='1st data')
185
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
186
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
187
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
188
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
189
+
190
+ ax1_sub = ax1.twinx()
191
+ line2 = ax1_sub.plot(x_value, y_value[1], color='blue', marker = "o", label='2nd data')
192
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
193
+
194
+ lines = line1 + line2
195
+ labels = [l.get_label() for l in lines]
196
+ ax1.legend(lines, labels, loc='upper left')
197
+
198
+ ax2.bar(x_value, y_value[-1], color='skyblue', label='3rd data')
199
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
200
+ ax2.set_ylabel(weather_elements[-1], labelpad=15, fontsize=15)
201
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
202
+ ax2.legend(loc='upper left')
203
+
204
+ ax2_sub = ax2.twinx()
205
+ ax2_sub.plot(x_value, y_value[2], color='gray', marker = "o", label='4th data')
206
+ ax2_sub.set_ylabel(weather_elements[2], labelpad=25, fontsize=15, rotation=270)
207
+ ax2_sub.legend(loc='upper right')
208
+
209
+
210
+ else:
211
+ if len(weather_elements) == 1:
212
+ weatherPlot = plt.figure(figsize=(10,10))
213
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
214
+ plt.xlabel(xname,labelpad=5, fontsize=15)
215
+ plt.ylabel(weather_elements[0], labelpad=15, fontsize=15)
216
+ plt.xticks(size=10, rotation=45)
217
+ plt.plot(x_value, y_value[0], color='red', marker='o', label='1st data')
218
+ plt.legend(loc = "upper left")
219
+
220
+ elif len(weather_elements) == 2:
221
+ weatherPlot, ax1 = plt.subplots(figsize=(10,10))
222
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
223
+ plt.xticks(size=10, rotation=45)
224
+
225
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker='o', label='1st data')
226
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
227
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
228
+
229
+ ax1_sub = ax1.twinx()
230
+ line2 = ax1_sub.plot(x_value, y_value[1], color='skyblue', marker='o', label='2nd data')
231
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
232
+
233
+ lines = line1 + line2
234
+ labels = [l.get_label() for l in lines]
235
+ ax1.legend(lines, labels, loc='upper left')
236
+
237
+ elif len(weather_elements) == 3:
238
+ weatherPlot, (ax1, ax2) = plt.subplots(2,1, figsize=(10,15), constrained_layout=True)
239
+
240
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label='1st data')
241
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
242
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
243
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
244
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
245
+
246
+ ax1_sub = ax1.twinx()
247
+ line2 = ax1_sub.plot(x_value, y_value[1], color='skyblue', marker = "o", label='2nd data')
248
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
249
+
250
+ lines = line1 + line2
251
+ labels = [l.get_label() for l in lines]
252
+ ax1.legend(lines, labels, loc='upper left')
253
+
254
+ ax2.plot(x_value, y_value[2], color='gray', marker = "o", label='3rd data')
255
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
256
+ ax2.set_ylabel(weather_elements[2], labelpad=15, fontsize=15)
257
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
258
+ ax2.legend(loc='upper left')
259
+
260
+ return [weatherTable, weatherPlot]
261
+ ```
262
+ ## - Demo Usage
263
+
264
+ Before you implement demo, you have to install gradio, pandas, and matplotlib library.
265
+
266
+ ```bash
267
+ pip install gradio pandas matplotlib
268
+ ```
269
+
270
+ Run the code below as a Python script or in a Jupyter Notebook or google colab.
271
+
272
+ We provided demo code as a .ipynb file.
273
+
274
+ ```python
275
+ import gradio as gr
276
+ import pandas as pd
277
+ import matplotlib.pyplot as plt
278
+
279
+ # search weather data that is in csv file
280
+ def dataSearch(month, day, weather_elements, location, precipitation):
281
+ if location=='Seoul':
282
+ df = pd.read_csv('Seoul.csv')
283
+ elif location=='Washington':
284
+ df = pd.read_csv('Washington.csv')
285
+
286
+ if precipitation:
287
+ weather_elements.append('precipitation')
288
+
289
+ if day in ['1','2','3','4','5','6','7','8','9']:
290
+ today = '2022-'+month +'-0'+day
291
+ else:
292
+ today = '2022-'+month+'-'+day
293
+
294
+ df1 = df[df.date == today]
295
+ columns = ['location', 'date', 'time'] + weather_elements
296
+ df2 = df1.loc[:, columns]
297
+
298
+ return df2
299
+
300
+ # show weather data in plot using matplotlib
301
+ def showOutput(month, day, weather_elements, location, precipitation):
302
+ if month=='January':
303
+ month = '01'
304
+ elif month=='February':
305
+ month = '02'
306
+ elif month=='March':
307
+ month = '03'
308
+ elif month=='April':
309
+ month = '04'
310
+ elif month=='May':
311
+ month = '05'
312
+ elif month=='June':
313
+ month = '06'
314
+ elif month=='July':
315
+ month = '07'
316
+ elif month=='August':
317
+ month = '08'
318
+ elif month=='September':
319
+ month = '09'
320
+ elif month=='October':
321
+ month = '10'
322
+ elif month=='November':
323
+ month = '11'
324
+ elif month=='December':
325
+ month = '12'
326
+
327
+ weatherTable = dataSearch(month, day, weather_elements, location, precipitation)
328
+
329
+ if precipitation:
330
+ weather_elements.remove('precipitation')
331
+
332
+ if day in ['1','2','3','4','5','6','7','8','9']:
333
+ xname = '2022-'+month +'-0'+day
334
+ else:
335
+ xname = '2022-'+month+'-'+day
336
+
337
+ y_value=[0]*len(weather_elements)
338
+
339
+ for i in range(len(weather_elements)):
340
+ y_value[i] = weatherTable[weather_elements[i]]
341
+
342
+ x_value = weatherTable['time']
343
+
344
+ if 'humidity' in weather_elements:
345
+ humidity_index = weather_elements.index('humidity')
346
+ if weather_elements[humidity_index] != weather_elements[-1]:
347
+ temp = weather_elements[humidity_index]
348
+ weather_elements[humidity_index] = weather_elements[-1]
349
+ weather_elements[-1] = temp
350
+
351
+
352
+ if len(weather_elements) == 1:
353
+ weatherPlot = plt.figure(figsize=(10,10))
354
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
355
+ plt.xlabel(xname,labelpad=5, fontsize=15)
356
+ plt.ylabel(weather_elements[0], labelpad=15, fontsize=15)
357
+ plt.xticks(size=10, rotation=45)
358
+
359
+ plt.bar(x_value, y_value[-1], color='skyblue', label='1st data')
360
+ plt.legend(loc = "upper left")
361
+
362
+ elif len(weather_elements) == 2:
363
+ weatherPlot, ax1 = plt.subplots(figsize=(10,10))
364
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
365
+ plt.xticks(size=10, rotation=45)
366
+
367
+ ax1.bar(x_value, y_value[-1], color='skyblue', label='1st data')
368
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
369
+ ax1.set_ylabel(weather_elements[1], labelpad=15, fontsize=15)
370
+ ax1.legend(loc='upper left')
371
+
372
+ ax1_sub = ax1.twinx()
373
+ ax1_sub.plot(x_value, y_value[0], color='red', marker = "o", label='2nd data')
374
+ ax1_sub.set_ylabel(weather_elements[0], labelpad=25, fontsize=15, rotation=270)
375
+ ax1_sub.legend(loc='upper right')
376
+
377
+ elif len(weather_elements) == 3:
378
+ weatherPlot, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,15), constrained_layout=True)
379
+
380
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label='1st data')
381
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
382
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
383
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
384
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
385
+
386
+ ax1_sub = ax1.twinx()
387
+ line2 = ax1_sub.plot(x_value, y_value[1], color='blue', marker = "o", label='2nd data')
388
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
389
+
390
+ lines = line1 + line2
391
+ labels = [l.get_label() for l in lines]
392
+ ax1.legend(lines, labels, loc='upper left')
393
+
394
+ ax2.bar(x_value, y_value[-1], color='skyblue', label='3rd data')
395
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
396
+ ax2.set_ylabel(weather_elements[-1], labelpad=15, fontsize=15)
397
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
398
+ ax2.legend(loc='upper right')
399
+
400
+ elif len(weather_elements) == 4:
401
+ weatherPlot, (ax1, ax2) = plt.subplots(2,1, figsize=(10,15), constrained_layout=True)
402
+
403
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label='1st data')
404
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
405
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
406
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
407
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
408
+
409
+ ax1_sub = ax1.twinx()
410
+ line2 = ax1_sub.plot(x_value, y_value[1], color='blue', marker = "o", label='2nd data')
411
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
412
+
413
+ lines = line1 + line2
414
+ labels = [l.get_label() for l in lines]
415
+ ax1.legend(lines, labels, loc='upper left')
416
+
417
+ ax2.bar(x_value, y_value[-1], color='skyblue', label='3rd data')
418
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
419
+ ax2.set_ylabel(weather_elements[-1], labelpad=15, fontsize=15)
420
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
421
+ ax2.legend(loc='upper left')
422
+
423
+ ax2_sub = ax2.twinx()
424
+ ax2_sub.plot(x_value, y_value[2], color='gray', marker = "o", label='4th data')
425
+ ax2_sub.set_ylabel(weather_elements[2], labelpad=25, fontsize=15, rotation=270)
426
+ ax2_sub.legend(loc='upper right')
427
+
428
+
429
+ else:
430
+ if len(weather_elements) == 1:
431
+ weatherPlot = plt.figure(figsize=(10,10))
432
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
433
+ plt.xlabel(xname,labelpad=5, fontsize=15)
434
+ plt.ylabel(weather_elements[0], labelpad=15, fontsize=15)
435
+ plt.xticks(size=10, rotation=45)
436
+ plt.plot(x_value, y_value[0], color='red', marker='o', label='1st data')
437
+ plt.legend(loc = "upper left")
438
+
439
+ elif len(weather_elements) == 2:
440
+ weatherPlot, ax1 = plt.subplots(figsize=(10,10))
441
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
442
+ plt.xticks(size=10, rotation=45)
443
+
444
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker='o', label='1st data')
445
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
446
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
447
+
448
+ ax1_sub = ax1.twinx()
449
+ line2 = ax1_sub.plot(x_value, y_value[1], color='skyblue', marker='o', label='2nd data')
450
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
451
+
452
+ lines = line1 + line2
453
+ labels = [l.get_label() for l in lines]
454
+ ax1.legend(lines, labels, loc='upper left')
455
+
456
+ elif len(weather_elements) == 3:
457
+ weatherPlot, (ax1, ax2) = plt.subplots(2,1, figsize=(10,15), constrained_layout=True)
458
+
459
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label='1st data')
460
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
461
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
462
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
463
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
464
+
465
+ ax1_sub = ax1.twinx()
466
+ line2 = ax1_sub.plot(x_value, y_value[1], color='skyblue', marker = "o", label='2nd data')
467
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
468
+
469
+ lines = line1 + line2
470
+ labels = [l.get_label() for l in lines]
471
+ ax1.legend(lines, labels, loc='upper left')
472
+
473
+ ax2.plot(x_value, y_value[2], color='gray', marker = "o", label='3rd data')
474
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
475
+ ax2.set_ylabel(weather_elements[2], labelpad=15, fontsize=15)
476
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
477
+ ax2.legend(loc='upper left')
478
+
479
+ return [weatherTable, weatherPlot]
480
+
481
+ output1 = gr.Dataframe()
482
+ output2 = gr.Plot()
483
+
484
+ # make gradio interface
485
+ demo = gr.Interface(
486
+ fn=showOutput,
487
+ inputs=[
488
+ gr.Dropdown(["January", "February", "March", "April", "May","June",
489
+ "July", "August", "September", "October", "November", "December"],label="Month", info="Select Months"),
490
+ gr.Dropdown(["1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
491
+ "11", "12", "13", "14", "15", "16", "17", "18", "19", "20",
492
+ "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31"], label="Day", info="Select Day"),
493
+ gr.CheckboxGroup(["temperature(˚C)", "wind(m/s)", "humidity(%)", "air_pressure(hPa)"],
494
+ label="Weather element", info="Choose weather element"),
495
+ gr.Radio(["Washington", "Seoul"], label="Location", info="Choose location"),
496
+ gr.Checkbox(label="precipation?")],
497
+ outputs=[output1,output2]
498
+ )
499
+
500
+ if __name__=="__main__":
501
+ demo.launch()
502
+ ```
503
+
504
+ The demo below will appear automatically within the Jupyter Notebook, or pop in a browser on http://localhost:7860 if running from a script:
505
+
506
+ ![weather data demo](readme_files/screenshot.gif)
Seoul.csv ADDED
The diff for this file is too large to render. See raw diff
 
Washington.csv ADDED
@@ -0,0 +1,1441 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ location,date,time,precipitation(mm),air_pressure(hPa),temperature(˚C),wind(m/s),humidity(%)
2
+ Washington,2022-01-01,6:00:00 AM,1,999.4,13.9,6,93.1
3
+ Washington,2022-01-01,12:00:00 PM,0,998.7,13.9,4,96.2
4
+ Washington,2022-01-01,6:00:00 PM,8,994.7,15,3,93.1
5
+ Washington,2022-01-02,12:00:00 AM,0,991,15.6,6,96.2
6
+ Washington,2022-01-02,6:00:00 AM,9,991.3,12.8,0,96.1
7
+ Washington,2022-01-02,12:00:00 PM,1,992.3,13.3,0,96.8
8
+ Washington,2022-01-02,6:00:00 PM,0,994.3,16.7,4,86.3
9
+ Washington,2022-01-03,12:00:00 AM,0,1001,12.2,14,64
10
+ Washington,2022-01-03,6:00:00 PM,10,1005.4,-1.1,17,65.7
11
+ Washington,2022-01-04,12:00:00 AM,0,1012.1,-0.6,7,53.4
12
+ Washington,2022-01-04,6:00:00 AM,0,1016.5,-2.8,10,62.8
13
+ Washington,2022-01-04,12:00:00 PM,0,1018.8,-8.3,0,91.8
14
+ Washington,2022-01-04,6:00:00 PM,0,1018.8,-1.1,6,74.8
15
+ Washington,2022-01-05,12:00:00 AM,0,1015.5,-2.2,4,84.8
16
+ Washington,2022-01-05,6:00:00 AM,0,1011.4,-3.3,6,84.1
17
+ Washington,2022-01-05,12:00:00 PM,0,1005.7,-1.1,5,81.2
18
+ Washington,2022-01-05,6:00:00 PM,0,999,2.2,8,75.4
19
+ Washington,2022-01-06,12:00:00 AM,0,996.7,0.6,4,88.4
20
+ Washington,2022-01-06,6:00:00 AM,0,996.7,1.7,3,88.5
21
+ Washington,2022-01-06,12:00:00 PM,0,1001.7,-1.1,0,74.8
22
+ Washington,2022-01-06,6:00:00 PM,0,1003,5,7,42.5
23
+ Washington,2022-01-07,12:00:00 AM,0,1001.7,2.8,5,51.9
24
+ Washington,2022-01-07,6:00:00 AM,1,998,0,8,88.3
25
+ Washington,2022-01-07,12:00:00 PM,6,1002.7,-2.8,15,68.4
26
+ Washington,2022-01-07,6:00:00 PM,0,1007.1,-2.2,22,50.5
27
+ Washington,2022-01-08,12:00:00 AM,0,1014.5,-4.4,12,54.5
28
+ Washington,2022-01-08,6:00:00 AM,0,1019.2,-6.1,5,59
29
+ Washington,2022-01-08,12:00:00 PM,0,1023.9,-7.8,5,64.6
30
+ Washington,2022-01-08,6:00:00 PM,0,1025.2,-1.1,0,44.4
31
+ Washington,2022-01-09,12:00:00 AM,0,1024.5,-1.7,4,46.4
32
+ Washington,2022-01-09,6:00:00 AM,0,1020.5,-1.1,9,50.8
33
+ Washington,2022-01-09,12:00:00 PM,0,1014.5,-0.6,7,58.2
34
+ Washington,2022-01-09,6:00:00 PM,3,1009.4,2.2,4,92.4
35
+ Washington,2022-01-10,12:00:00 AM,5,1006.1,4.4,6,96.5
36
+ Washington,2022-01-10,6:00:00 AM,0,1008.7,4.4,21,64.5
37
+ Washington,2022-01-10,12:00:00 PM,0,1012.1,-1.1,17,40.6
38
+ Washington,2022-01-10,6:00:00 PM,0,1012.4,1.1,9,39.7
39
+ Washington,2022-01-11,12:00:00 AM,0,1013.8,-0.6,17,49
40
+ Washington,2022-01-11,6:00:00 AM,0,1016.1,-3.3,8,47.8
41
+ Washington,2022-01-11,12:00:00 PM,0,1022.2,-5.6,8,56.8
42
+ Washington,2022-01-11,6:00:00 PM,0,1023.2,-3.3,9,36.4
43
+ Washington,2022-01-12,12:00:00 AM,0,1022.5,-5,4,45.4
44
+ Washington,2022-01-12,6:00:00 AM,0,1018.8,-6.7,4,61.8
45
+ Washington,2022-01-12,12:00:00 PM,0,1013.1,-3.9,11,57.3
46
+ Washington,2022-01-12,6:00:00 PM,0,1008.4,6.1,8,36.1
47
+ Washington,2022-01-13,12:00:00 AM,0,1007.4,5.6,5,42.6
48
+ Washington,2022-01-13,6:00:00 AM,0,1006.4,0.6,0,69.1
49
+ Washington,2022-01-14,12:00:00 AM,0,1000,6.7,4,48.8
50
+ Washington,2022-01-14,6:00:00 AM,0,998.7,0.6,3,75.1
51
+ Washington,2022-01-14,12:00:00 PM,0,999,0.6,6,77.9
52
+ Washington,2022-01-14,6:00:00 PM,0,1000.4,6.7,14,65
53
+ Washington,2022-01-15,12:00:00 AM,0,1004.4,4.4,11,59.5
54
+ Washington,2022-01-15,6:00:00 AM,0,1007.7,-0.6,12,53.4
55
+ Washington,2022-01-15,12:00:00 PM,0,1012.1,-4.4,10,49.8
56
+ Washington,2022-01-15,6:00:00 PM,0,1015.5,-3.3,10,33.2
57
+ Washington,2022-01-16,12:00:00 AM,0,1016.5,-5,9,35.9
58
+ Washington,2022-01-16,6:00:00 AM,0,1016.8,-8.3,6,48.7
59
+ Washington,2022-01-16,12:00:00 PM,0,1016.1,-9.4,5,55.3
60
+ Washington,2022-01-16,6:00:00 PM,0,1009.8,-5.6,8,52
61
+ Washington,2022-01-17,12:00:00 AM,11,996.3,-1.7,11,88.8
62
+ Washington,2022-01-17,6:00:00 AM,19,975.2,3.9,0,95.9
63
+ Washington,2022-01-17,12:00:00 PM,1,976.2,0.6,11,88.4
64
+ Washington,2022-01-17,6:00:00 PM,0,981.6,2.2,17,51.7
65
+ Washington,2022-01-18,12:00:00 AM,0,989.6,1.1,12,47.2
66
+ Washington,2022-01-18,6:00:00 AM,0,996,0.6,14,58
67
+ Washington,2022-01-18,12:00:00 PM,0,1002.7,1.1,6,56
68
+ Washington,2022-01-18,6:00:00 PM,0,1008.1,2.2,14,54.2
69
+ Washington,2022-01-19,12:00:00 AM,0,1010.4,-2.2,0,71.2
70
+ Washington,2022-01-19,6:00:00 AM,0,1010.4,-2.8,5,71.7
71
+ Washington,2022-01-19,12:00:00 PM,0,1008.7,0,8,63.5
72
+ Washington,2022-01-19,6:00:00 PM,0,1005.7,7.2,13,43.4
73
+ Washington,2022-01-20,12:00:00 AM,0,1005.1,8.9,8,43.6
74
+ Washington,2022-01-20,6:00:00 AM,2,1005.1,3.9,3,92.5
75
+ Washington,2022-01-20,12:00:00 PM,8,1007.4,5,6,88.7
76
+ Washington,2022-01-20,6:00:00 PM,3,1011.8,1.7,13,58.3
77
+ Washington,2022-01-21,12:00:00 AM,0,1015.5,-0.6,13,44.9
78
+ Washington,2022-01-21,6:00:00 AM,0,1018.5,-4.4,10,49.8
79
+ Washington,2022-01-21,12:00:00 PM,0,1021.8,-7.8,11,53.5
80
+ Washington,2022-01-21,6:00:00 PM,0,1022.2,-6.1,11,46.9
81
+ Washington,2022-01-22,12:00:00 AM,0,1021.8,-5.6,8,49.9
82
+ Washington,2022-01-22,6:00:00 AM,0,1020.2,-7.2,7,51.1
83
+ Washington,2022-01-22,12:00:00 PM,0,1019.2,-8.9,7,51
84
+ Washington,2022-01-22,6:00:00 PM,0,1016.1,-2.2,6,29.1
85
+ Washington,2022-01-23,12:00:00 AM,0,1011.8,-5,0,54.3
86
+ Washington,2022-01-23,6:00:00 AM,0,1008.1,-4.4,0,47.4
87
+ Washington,2022-01-23,12:00:00 PM,0,1005.4,-7.8,0,73.5
88
+ Washington,2022-01-23,6:00:00 PM,0,1000,3.3,8,33.9
89
+ Washington,2022-01-24,12:00:00 AM,0,997.3,1.1,0,49.5
90
+ Washington,2022-01-24,6:00:00 AM,0,998.7,1.7,3,58.3
91
+ Washington,2022-01-24,12:00:00 PM,0,1002.4,-2.2,5,62.9
92
+ Washington,2022-01-24,6:00:00 PM,0,1000,3.3,9,43.9
93
+ Washington,2022-01-25,12:00:00 AM,0,998,2.8,7,59.1
94
+ Washington,2022-01-25,6:00:00 AM,0,998,1.1,4,58.6
95
+ Washington,2022-01-25,12:00:00 PM,0,1000.4,-1.7,0,78.1
96
+ Washington,2022-01-25,6:00:00 PM,0,1003.7,6.7,10,55
97
+ Washington,2022-01-26,12:00:00 AM,0,1009.8,0.6,14,47
98
+ Washington,2022-01-26,6:00:00 AM,0,1012.8,-3.3,9,50.1
99
+ Washington,2022-01-26,12:00:00 PM,0,1016.1,-7.8,0,64.6
100
+ Washington,2022-01-26,6:00:00 PM,0,1017.1,-1.1,7,37.1
101
+ Washington,2022-01-27,12:00:00 AM,0,1018.8,-3.3,6,43.7
102
+ Washington,2022-01-27,6:00:00 AM,0,1020.5,-8.9,3,64.3
103
+ Washington,2022-01-27,12:00:00 PM,0,1020.2,-11.7,0,76.5
104
+ Washington,2022-01-27,6:00:00 PM,0,1016.8,0,10,40.9
105
+ Washington,2022-01-28,12:00:00 AM,0,1013.8,1.1,5,41.3
106
+ Washington,2022-01-28,6:00:00 AM,0,1010.8,-0.6,0,53.4
107
+ Washington,2022-01-28,12:00:00 PM,0,1008.7,-0.6,0,60.9
108
+ Washington,2022-01-28,6:00:00 PM,0,1006.4,2.8,5,66.6
109
+ Washington,2022-01-29,12:00:00 AM,3,1004.1,0.6,7,91.6
110
+ Washington,2022-01-29,6:00:00 AM,1,1001,-0.6,12,85
111
+ Washington,2022-01-29,12:00:00 PM,0,1000.4,-5,13,71.2
112
+ Washington,2022-01-29,6:00:00 PM,0,1001,-4.4,21,41.2
113
+ Washington,2022-01-30,12:00:00 AM,0,1002.7,-6.1,12,41.1
114
+ Washington,2022-01-30,6:00:00 AM,0,1005.7,-7.8,10,48.8
115
+ Washington,2022-01-30,12:00:00 PM,0,1007.7,-11.1,0,63.3
116
+ Washington,2022-01-30,6:00:00 PM,0,1006.7,-2.8,9,40.4
117
+ Washington,2022-01-31,12:00:00 AM,0,1007.1,-1.7,0,50.6
118
+ Washington,2022-01-31,6:00:00 AM,0,1009.4,-2.8,0,50.3
119
+ Washington,2022-01-31,12:00:00 PM,0,1011.8,-5,3,64.8
120
+ Washington,2022-01-31,6:00:00 PM,0,1014.5,2.2,3,45.7
121
+ Washington,2022-02-01,12:00:00 AM,0,1016.1,-0.6,7,53.4
122
+ Washington,2022-02-01,6:00:00 AM,0,1019.2,-4.4,0,68.1
123
+ Washington,2022-02-01,12:00:00 PM,0,1020.8,-7.8,0,80.3
124
+ Washington,2022-02-01,6:00:00 PM,0,1020.5,5,3,42.5
125
+ Washington,2022-02-02,12:00:00 AM,0,1020.2,-0.6,4,66.3
126
+ Washington,2022-02-02,6:00:00 AM,0,1019.8,-0.6,3,78.3
127
+ Washington,2022-02-02,12:00:00 PM,0,1019.2,0,0,92.3
128
+ Washington,2022-02-02,6:00:00 PM,0,1017.8,7.2,8,55.5
129
+ Washington,2022-02-03,12:00:00 AM,0,1014.1,5.6,5,67.2
130
+ Washington,2022-02-03,6:00:00 AM,0,1013.8,5,4,88.7
131
+ Washington,2022-02-03,12:00:00 PM,6,1013.1,5.6,0,92
132
+ Washington,2022-02-03,6:00:00 PM,7,1010.8,8.3,5,92.8
133
+ Washington,2022-02-04,12:00:00 AM,2,1006.4,9.4,4,96.7
134
+ Washington,2022-02-04,6:00:00 AM,1,1001,12.8,9,93
135
+ Washington,2022-02-04,12:00:00 PM,3,1000,10,11,89.2
136
+ Washington,2022-02-04,6:00:00 PM,8,1001.7,3.9,11,88.6
137
+ Washington,2022-02-05,12:00:00 AM,5,1006.1,3.3,10,72.8
138
+ Washington,2022-02-05,6:00:00 AM,0,1008.4,0,8,65.9
139
+ Washington,2022-02-05,12:00:00 PM,0,1012.8,-1.1,9,55.4
140
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+ Washington,2022-11-10,6:00:00 AM,0,1020.2,4.4,4,93.2
1240
+ Washington,2022-11-10,12:00:00 PM,0,1018.8,6.1,0,93.5
1241
+ Washington,2022-11-10,6:00:00 PM,0,1015.1,18.9,0,87.4
1242
+ Washington,2022-11-11,12:00:00 AM,0,1012.1,16.1,4,90
1243
+ Washington,2022-11-11,6:00:00 AM,0,1009.1,16.1,10,72.4
1244
+ Washington,2022-11-11,12:00:00 PM,2,1004.4,16.1,14,52.7
1245
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1246
+ Washington,2022-11-12,12:00:00 AM,0,990.6,22.8,8,58
1247
+ Washington,2022-11-12,6:00:00 AM,4,986.9,20.6,3,57.5
1248
+ Washington,2022-11-12,12:00:00 PM,1,995.7,17.2,4,45.3
1249
+ Washington,2022-11-12,6:00:00 PM,0,995.3,20.6,15,44.8
1250
+ Washington,2022-11-13,12:00:00 AM,0,995.7,17.8,16,54.8
1251
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1252
+ Washington,2022-11-13,12:00:00 PM,0,1004.7,6.7,12,38.8
1253
+ Washington,2022-11-13,6:00:00 PM,0,1007.1,7.8,6,61.3
1254
+ Washington,2022-11-14,12:00:00 AM,0,1011.1,6.1,0,78.3
1255
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1256
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1257
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1258
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1259
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1260
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1261
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1262
+ Washington,2022-11-16,12:00:00 AM,13,1010.1,3.3,0,59.1
1263
+ Washington,2022-11-16,6:00:00 AM,17,1005.4,3.9,13,45.7
1264
+ Washington,2022-11-16,12:00:00 PM,0,1005.7,2.8,13,42.8
1265
+ Washington,2022-11-16,6:00:00 PM,0,1006.1,8.9,5,50.1
1266
+ Washington,2022-11-17,12:00:00 AM,0,1006.7,6.1,10,71.9
1267
+ Washington,2022-11-17,6:00:00 AM,0,1005.7,2.8,21,71.2
1268
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1269
+ Washington,2022-11-17,6:00:00 PM,0,1008.7,6.1,3,55.8
1270
+ Washington,2022-11-18,12:00:00 AM,0,1010.4,3.3,5,68.5
1271
+ Washington,2022-11-18,6:00:00 AM,0,1010.4,-1.7,6,34.8
1272
+ Washington,2022-11-18,12:00:00 PM,0,1012.1,-2.2,7,55.8
1273
+ Washington,2022-11-19,12:00:00 AM,0,1010.4,6.7,0,68.4
1274
+ Washington,2022-11-19,6:00:00 AM,0,1013.1,0.6,10,46.7
1275
+ Washington,2022-11-19,12:00:00 PM,0,1014.5,-2.2,4,24.4
1276
+ Washington,2022-11-19,6:00:00 PM,0,1011.1,6.1,5,27.4
1277
+ Washington,2022-11-20,12:00:00 AM,0,1007.4,0.6,12,45.7
1278
+ Washington,2022-11-20,6:00:00 AM,0,1006.7,-2.8,14,59.6
1279
+ Washington,2022-11-20,12:00:00 PM,0,1011.8,-0.6,8,23.6
1280
+ Washington,2022-11-20,6:00:00 PM,0,1013.8,2.8,0,35.8
1281
+ Washington,2022-11-21,12:00:00 AM,0,1017.1,0.6,3,53.6
1282
+ Washington,2022-11-21,6:00:00 AM,0,1019.5,-3.9,10,74.2
1283
+ Washington,2022-11-21,12:00:00 PM,0,1021.5,-5.6,4,18.9
1284
+ Washington,2022-11-21,6:00:00 PM,0,1017.5,7.8,0,30.9
1285
+ Washington,2022-11-22,12:00:00 AM,0,1014.8,5,0,68.6
1286
+ Washington,2022-11-22,6:00:00 AM,0,1016.5,0,9,77.8
1287
+ Washington,2022-11-22,12:00:00 PM,0,1017.8,-4.4,4,23
1288
+ Washington,2022-11-22,6:00:00 PM,0,1015.1,13.9,3,45.3
1289
+ Washington,2022-11-23,12:00:00 AM,0,1013.4,7.8,0,78.2
1290
+ Washington,2022-11-23,6:00:00 AM,0,1012.1,-1.7,5,84.8
1291
+ Washington,2022-11-23,12:00:00 PM,0,1013.4,-3.9,3,31.6
1292
+ Washington,2022-11-23,6:00:00 PM,0,1013.4,16.1,0,45.8
1293
+ Washington,2022-11-24,12:00:00 AM,0,1014.1,7.8,0,73.6
1294
+ Washington,2022-11-24,6:00:00 AM,0,1016.1,-1.1,8,85.9
1295
+ Washington,2022-11-24,12:00:00 PM,0,1016.8,-2.2,5,55.4
1296
+ Washington,2022-11-24,6:00:00 PM,0,1015.5,15,4,47.6
1297
+ Washington,2022-11-25,12:00:00 AM,0,1013.1,9.4,4,69.7
1298
+ Washington,2022-11-25,6:00:00 AM,0,1009.4,7.2,17,81.9
1299
+ Washington,2022-11-25,12:00:00 PM,2,1004.4,7.2,9,31.6
1300
+ Washington,2022-11-25,6:00:00 PM,1,1001,15.6,0,59.3
1301
+ Washington,2022-11-26,12:00:00 AM,0,1004.7,10,3,55.3
1302
+ Washington,2022-11-26,6:00:00 AM,0,1008.1,3.3,3,70.5
1303
+ Washington,2022-11-26,12:00:00 PM,0,1009.8,-0.6,3,96
1304
+ Washington,2022-11-26,6:00:00 PM,0,1008.1,15,5,96.1
1305
+ Washington,2022-11-27,12:00:00 AM,0,1007.4,5.6,4,92.8
1306
+ Washington,2022-11-27,6:00:00 AM,0,1003.7,7.8,3,69.1
1307
+ Washington,2022-11-27,12:00:00 PM,0,1000.4,7.2,3,52.3
1308
+ Washington,2022-11-27,6:00:00 PM,2,991.6,8.9,0,60.6
1309
+ Washington,2022-11-28,12:00:00 AM,1,987.6,11.7,19,81.5
1310
+ Washington,2022-11-28,6:00:00 AM,0,990,9.4,13,88.6
1311
+ Washington,2022-11-28,12:00:00 PM,0,994.3,12.2,8,53.9
1312
+ Washington,2022-11-28,6:00:00 PM,0,1000.4,11.1,0,67.7
1313
+ Washington,2022-11-29,12:00:00 AM,0,1005.7,8.9,0,73.6
1314
+ Washington,2022-11-29,6:00:00 AM,0,1009.4,1.1,9,92.8
1315
+ Washington,2022-11-29,12:00:00 PM,0,1012.4,2.8,4,89.4
1316
+ Washington,2022-11-29,6:00:00 PM,0,1012.8,10,5,32.5
1317
+ Washington,2022-11-30,12:00:00 AM,0,1012.1,7.8,8,42.3
1318
+ Washington,2022-11-30,6:00:00 AM,0,1008.7,7.2,18,49.5
1319
+ Washington,2022-11-30,12:00:00 PM,4,1004.1,8.3,11,34.7
1320
+ Washington,2022-11-30,6:00:00 PM,3,996.3,12.8,4,40.3
1321
+ Washington,2022-12-01,12:00:00 AM,0,1004.4,8.9,16,71.7
1322
+ Washington,2022-12-01,6:00:00 AM,0,1007.7,3.3,11,84.4
1323
+ Washington,2022-12-01,12:00:00 PM,0,1015.1,1.1,5,31.6
1324
+ Washington,2022-12-01,6:00:00 PM,0,1019.5,6.7,0,41.1
1325
+ Washington,2022-12-02,12:00:00 AM,0,1020.5,3.3,0,47.8
1326
+ Washington,2022-12-02,6:00:00 AM,0,1022.2,-2.8,9,89.2
1327
+ Washington,2022-12-02,12:00:00 PM,0,1022.8,-5.6,6,75.1
1328
+ Washington,2022-12-02,6:00:00 PM,0,1019.5,10,14,45.1
1329
+ Washington,2022-12-03,12:00:00 AM,0,1018.1,6.7,10,54.9
1330
+ Washington,2022-12-03,6:00:00 AM,0,1014.8,9.4,16,69.2
1331
+ Washington,2022-12-03,12:00:00 PM,4,1011.1,10,15,41.2
1332
+ Washington,2022-12-03,6:00:00 PM,4,1006.1,16.1,0,60.8
1333
+ Washington,2022-12-04,12:00:00 AM,0,1009.4,13.9,8,80.8
1334
+ Washington,2022-12-04,6:00:00 AM,0,1012.8,5,8,88.5
1335
+ Washington,2022-12-04,12:00:00 PM,0,1014.1,1.1,4,47.3
1336
+ Washington,2022-12-04,6:00:00 PM,0,1012.4,6.1,0,54.9
1337
+ Washington,2022-12-05,12:00:00 AM,0,1011.4,0.6,0,85
1338
+ Washington,2022-12-05,6:00:00 AM,0,1011.4,-3.9,8,82.1
1339
+ Washington,2022-12-05,12:00:00 PM,0,1012.4,-5.6,6,89.6
1340
+ Washington,2022-12-05,6:00:00 PM,0,1012.1,8.9,0,89.1
1341
+ Washington,2022-12-06,12:00:00 AM,0,1012.1,5,3,92.9
1342
+ Washington,2022-12-06,6:00:00 AM,0,1012.4,-0.6,5,92.3
1343
+ Washington,2022-12-06,12:00:00 PM,0,1012.4,5,8,89.9
1344
+ Washington,2022-12-06,6:00:00 PM,1,1011.4,8.3,4,93
1345
+ Washington,2022-12-07,12:00:00 AM,0,1011.4,8.9,3,96
1346
+ Washington,2022-12-07,6:00:00 AM,0,1010.8,10,3,76.7
1347
+ Washington,2022-12-07,12:00:00 PM,0,1010.1,10.6,0,50.7
1348
+ Washington,2022-12-07,6:00:00 PM,1,1009.8,12.2,3,56.6
1349
+ Washington,2022-12-08,12:00:00 AM,0,1009.8,12.2,5,62.5
1350
+ Washington,2022-12-08,6:00:00 AM,0,1011.8,8.9,10,67.2
1351
+ Washington,2022-12-08,12:00:00 PM,0,1013.8,10,7,44.1
1352
+ Washington,2022-12-08,6:00:00 PM,0,1013.8,13.3,7,54.7
1353
+ Washington,2022-12-09,12:00:00 AM,0,1013.1,11.1,6,78.2
1354
+ Washington,2022-12-09,6:00:00 AM,0,1013.4,7.8,5,75.3
1355
+ Washington,2022-12-09,12:00:00 PM,0,1014.8,3.3,6,50.6
1356
+ Washington,2022-12-09,6:00:00 PM,0,1013.8,9.4,4,61.9
1357
+ Washington,2022-12-10,12:00:00 AM,0,1013.4,5.6,0,75.9
1358
+ Washington,2022-12-10,6:00:00 AM,0,1014.5,-1.1,3,85.7
1359
+ Washington,2022-12-10,12:00:00 PM,0,1015.8,1.7,0,79.5
1360
+ Washington,2022-12-10,6:00:00 PM,0,1015.8,5,5,79.4
1361
+ Washington,2022-12-11,12:00:00 AM,0,1014.8,5,4,79.1
1362
+ Washington,2022-12-11,6:00:00 AM,0,1013.4,5,6,52.5
1363
+ Washington,2022-12-11,12:00:00 PM,0,1012.4,5,4,54.9
1364
+ Washington,2022-12-11,6:00:00 PM,0,1008.4,7.2,5,57.3
1365
+ Washington,2022-12-12,12:00:00 AM,0,1008.1,6.1,11,67.2
1366
+ Washington,2022-12-12,6:00:00 AM,0,1008.1,4.4,12,68.9
1367
+ Washington,2022-12-12,12:00:00 PM,0,1010.4,5,5,46.2
1368
+ Washington,2022-12-12,6:00:00 PM,0,1011.8,5,6,49.3
1369
+ Washington,2022-12-13,12:00:00 AM,0,1013.8,4.4,6,81.2
1370
+ Washington,2022-12-13,6:00:00 AM,0,1014.5,3.3,5,84.4
1371
+ Washington,2022-12-13,12:00:00 PM,0,1015.8,-0.6,3,39.1
1372
+ Washington,2022-12-13,6:00:00 PM,0,1014.8,5,0,47.8
1373
+ Washington,2022-12-14,12:00:00 AM,0,1014.1,1.7,0,61.3
1374
+ Washington,2022-12-14,6:00:00 AM,0,1011.8,-5.6,8,91.6
1375
+ Washington,2022-12-14,12:00:00 PM,0,1012.1,-5.6,6,92.4
1376
+ Washington,2022-12-14,6:00:00 PM,0,1010.8,4.4,3,92.5
1377
+ Washington,2022-12-15,12:00:00 AM,0,1010.4,3.3,4,92.5
1378
+ Washington,2022-12-15,6:00:00 AM,0,1010.1,2.8,7,89.2
1379
+ Washington,2022-12-15,12:00:00 PM,10,1009.8,0.6,11,52
1380
+ Washington,2022-12-15,6:00:00 PM,17,1003,2.2,11,62.2
1381
+ Washington,2022-12-16,12:00:00 AM,18,996.7,3.3,4,75.3
1382
+ Washington,2022-12-16,6:00:00 AM,4,994.7,3.3,18,63.9
1383
+ Washington,2022-12-16,12:00:00 PM,1,996.3,3.3,5,39.7
1384
+ Washington,2022-12-16,6:00:00 PM,0,995.3,10,3,48.3
1385
+ Washington,2022-12-17,12:00:00 AM,0,997.7,4.4,4,59.2
1386
+ Washington,2022-12-17,6:00:00 AM,0,997,1.7,8,58.9
1387
+ Washington,2022-12-17,12:00:00 PM,0,999.4,1.7,5,40.9
1388
+ Washington,2022-12-17,6:00:00 PM,0,998,7.2,4,49.5
1389
+ Washington,2022-12-18,12:00:00 AM,0,1000,5,6,60.7
1390
+ Washington,2022-12-18,6:00:00 AM,0,1001.4,3.3,14,62.9
1391
+ Washington,2022-12-18,12:00:00 PM,0,1003.4,2.2,9,40.9
1392
+ Washington,2022-12-18,6:00:00 PM,0,1004.7,5,4,53.9
1393
+ Washington,2022-12-19,12:00:00 AM,0,1010.8,1.1,3,60.7
1394
+ Washington,2022-12-19,6:00:00 AM,0,1012.4,-1.7,11,71.4
1395
+ Washington,2022-12-19,12:00:00 PM,0,1015.1,-2.2,12,47.8
1396
+ Washington,2022-12-19,6:00:00 PM,0,1015.5,5,9,56
1397
+ Washington,2022-12-20,12:00:00 AM,0,1018.5,1.1,4,80.7
1398
+ Washington,2022-12-20,6:00:00 AM,0,1019.8,-1.7,4,84.3
1399
+ Washington,2022-12-20,12:00:00 PM,0,1020.8,-3.9,4,44.8
1400
+ Washington,2022-12-20,6:00:00 PM,0,1019.8,3.3,0,65.9
1401
+ Washington,2022-12-21,12:00:00 AM,0,1019.8,1.1,0,78.1
1402
+ Washington,2022-12-21,6:00:00 AM,0,1020.2,-5,3,78.8
1403
+ Washington,2022-12-21,12:00:00 PM,0,1020.8,-6.7,3,88.6
1404
+ Washington,2022-12-21,6:00:00 PM,0,1020.5,6.1,0,96.1
1405
+ Washington,2022-12-22,12:00:00 AM,0,1019.2,0,6,92.6
1406
+ Washington,2022-12-22,6:00:00 AM,0,1018.8,-1.7,10,92.7
1407
+ Washington,2022-12-22,12:00:00 PM,0,1014.1,2.2,10,41.2
1408
+ Washington,2022-12-22,6:00:00 PM,11,1006.4,3.9,9,39.8
1409
+ Washington,2022-12-23,12:00:00 AM,21,995.3,10.6,11,45
1410
+ Washington,2022-12-23,6:00:00 AM,22,986.6,5,19,51.7
1411
+ Washington,2022-12-23,12:00:00 PM,1,978.9,6.7,15,38.8
1412
+ Washington,2022-12-23,6:00:00 PM,4,987.6,-4.4,8,51
1413
+ Washington,2022-12-24,12:00:00 AM,0,995,-10,6,79.6
1414
+ Washington,2022-12-24,6:00:00 AM,0,999,-12.2,6,76.3
1415
+ Washington,2022-12-24,12:00:00 PM,0,1002,-13.9,4,40.7
1416
+ Washington,2022-12-24,6:00:00 PM,0,1002.4,-7.8,3,46.2
1417
+ Washington,2022-12-25,12:00:00 AM,0,1003.4,-8.9,0,73.3
1418
+ Washington,2022-12-25,6:00:00 AM,0,1003,-12.2,6,58.7
1419
+ Washington,2022-12-25,12:00:00 PM,0,1004.1,-12.8,6,42.8
1420
+ Washington,2022-12-25,6:00:00 PM,0,1004.1,-1.7,0,52.8
1421
+ Washington,2022-12-26,12:00:00 AM,0,1006.1,-2.2,7,65.8
1422
+ Washington,2022-12-26,6:00:00 AM,0,1009.8,-8.9,5,71.4
1423
+ Washington,2022-12-26,12:00:00 PM,0,1013.4,-7.2,3,33
1424
+ Washington,2022-12-26,6:00:00 PM,0,1013.4,-0.6,0,45.4
1425
+ Washington,2022-12-27,12:00:00 AM,0,1013.1,-2.8,0,74.1
1426
+ Washington,2022-12-27,6:00:00 AM,0,1013.1,-2.8,4,77.5
1427
+ Washington,2022-12-27,12:00:00 PM,0,1014.5,-3.9,5,40.3
1428
+ Washington,2022-12-27,6:00:00 PM,0,1014.1,5.6,0,66.7
1429
+ Washington,2022-12-28,12:00:00 AM,0,1014.8,1.1,0,61.3
1430
+ Washington,2022-12-28,6:00:00 AM,0,1014.1,-5,7,80.8
1431
+ Washington,2022-12-28,12:00:00 PM,0,1014.8,-5.6,3,39.2
1432
+ Washington,2022-12-28,6:00:00 PM,0,1014.8,8.3,4,55
1433
+ Washington,2022-12-29,12:00:00 AM,0,1015.1,1.1,0,81.4
1434
+ Washington,2022-12-29,6:00:00 AM,0,1013.8,2.8,3,84.9
1435
+ Washington,2022-12-29,12:00:00 PM,0,1015.1,-3.9,0,43.8
1436
+ Washington,2022-12-29,6:00:00 PM,0,1014.5,11.7,0,64
1437
+ Washington,2022-12-30,12:00:00 AM,0,1015.1,6.7,0,
1438
+ Washington,2022-12-30,6:00:00 AM,0,1014.8,0,9,
1439
+ Washington,2022-12-30,12:00:00 PM,0,1014.8,-1.7,5,
1440
+ Washington,2022-12-30,6:00:00 PM,0,1012.1,15,,
1441
+ Washington,2022-12-31,12:00:00 AM,0,1011.1,13.9,,
app.py ADDED
@@ -0,0 +1,230 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import gradio as gr
3
+ import pandas as pd
4
+ import matplotlib.pyplot as plt
5
+
6
+ # search weather data that is in csv file
7
+ def dataSearch(month, day, weather_elements, location, precipitation):
8
+ if location=='Seoul':
9
+ df = pd.read_csv('Seoul.csv')
10
+ elif location=='Washington':
11
+ df = pd.read_csv('Washington.csv')
12
+ if precipitation:
13
+ weather_elements.append('precipitation(mm)')
14
+ if day in ['1','2','3','4','5','6','7','8','9']:
15
+ today = '2022-'+month +'-0'+day
16
+ else:
17
+ today = '2022-'+month+'-'+day
18
+
19
+ df1 = df[df.date == today]
20
+ columns = ['location', 'date', 'time'] + weather_elements
21
+ df2 = df1.loc[:, columns]
22
+
23
+ return df2
24
+
25
+ # show weather data in plot using matplotlib
26
+ def showOutput(month, day, weather_elements, location, precipitation):
27
+ if month=='January':
28
+ month = '01'
29
+ elif month=='February':
30
+ month = '02'
31
+ elif month=='March':
32
+ month = '03'
33
+ elif month=='April':
34
+ month = '04'
35
+ elif month=='May':
36
+ month = '05'
37
+ elif month=='June':
38
+ month = '06'
39
+ elif month=='July':
40
+ month = '07'
41
+ elif month=='August':
42
+ month = '08'
43
+ elif month=='September':
44
+ month = '09'
45
+ elif month=='October':
46
+ month = '10'
47
+ elif month=='November':
48
+ month = '11'
49
+ elif month=='December':
50
+ month = '12'
51
+
52
+ weatherTable = dataSearch(month, day, weather_elements, location, precipitation)
53
+
54
+ if precipitation:
55
+ weather_elements.remove('precipitation(mm)')
56
+
57
+ if day in ['1','2','3','4','5','6','7','8','9']:
58
+ xname = '2022-'+month +'-0'+day
59
+ else:
60
+ xname = '2022-'+month+'-'+day
61
+
62
+ y_value=[0]*len(weather_elements)
63
+
64
+ x_value = weatherTable['time']
65
+
66
+ if 'humidity(%)' in weather_elements:
67
+ humidity_index = weather_elements.index('humidity(%)')
68
+ if weather_elements[humidity_index] != weather_elements[-1]:
69
+ temp = weather_elements[humidity_index]
70
+ weather_elements[humidity_index] = weather_elements[-1]
71
+ weather_elements[-1] = temp
72
+ for i in range(len(weather_elements)):
73
+ y_value[i] = weatherTable[weather_elements[i]]
74
+
75
+
76
+ if len(weather_elements) == 1:
77
+ weatherPlot = plt.figure(figsize=(10,10))
78
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
79
+ plt.xlabel(xname,labelpad=5, fontsize=15)
80
+ plt.ylabel(weather_elements[0], labelpad=15, fontsize=15)
81
+ plt.xticks(size=10, rotation=45)
82
+
83
+ plt.bar(x_value, y_value[-1], color='skyblue', label=weather_elements[0])
84
+ plt.legend(loc = "upper left")
85
+
86
+ elif len(weather_elements) == 2:
87
+ weatherPlot, ax1 = plt.subplots(figsize=(10,10))
88
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
89
+ plt.xticks(size=10, rotation=45)
90
+
91
+ ax1.bar(x_value, y_value[-1], color='skyblue', label=weather_elements[1])
92
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
93
+ ax1.set_ylabel(weather_elements[1], labelpad=15, fontsize=15)
94
+ ax1.legend(loc='upper left')
95
+
96
+ ax1_sub = ax1.twinx()
97
+ ax1_sub.plot(x_value, y_value[0], color='red', marker = "o", label=weather_elements[0])
98
+ ax1_sub.set_ylabel(weather_elements[0], labelpad=25, fontsize=15, rotation=270)
99
+ ax1_sub.legend(loc='upper right')
100
+
101
+ elif len(weather_elements) == 3:
102
+ weatherPlot, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,15), constrained_layout=True)
103
+
104
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label=weather_elements[0])
105
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
106
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
107
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
108
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
109
+
110
+ ax1_sub = ax1.twinx()
111
+ line2 = ax1_sub.plot(x_value, y_value[1], color='blue', marker = "o", label=weather_elements[1])
112
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
113
+
114
+ lines = line1 + line2
115
+ labels = [l.get_label() for l in lines]
116
+ ax1.legend(lines, labels, loc='upper left')
117
+
118
+ ax2.bar(x_value, y_value[-1], color='skyblue', label=weather_elements[-1])
119
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
120
+ ax2.set_ylabel(weather_elements[-1], labelpad=15, fontsize=15)
121
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
122
+ ax2.legend(loc='upper right')
123
+
124
+ elif len(weather_elements) == 4:
125
+ weatherPlot, (ax1, ax2) = plt.subplots(2,1, figsize=(10,15), constrained_layout=True)
126
+
127
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label=weather_elements[0])
128
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
129
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
130
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
131
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
132
+
133
+ ax1_sub = ax1.twinx()
134
+ line2 = ax1_sub.plot(x_value, y_value[1], color='blue', marker = "o", label=weather_elements[1])
135
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
136
+
137
+ lines = line1 + line2
138
+ labels = [l.get_label() for l in lines]
139
+ ax1.legend(lines, labels, loc='upper left')
140
+
141
+ ax2.bar(x_value, y_value[-1], color='skyblue', label=weather_elements[-1])
142
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
143
+ ax2.set_ylabel(weather_elements[-1], labelpad=15, fontsize=15)
144
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
145
+ ax2.legend(loc='upper left')
146
+
147
+ ax2_sub = ax2.twinx()
148
+ ax2_sub.plot(x_value, y_value[2], color='gray', marker = "o", label=weather_elements[2])
149
+ ax2_sub.set_ylabel(weather_elements[2], labelpad=25, fontsize=15, rotation=270)
150
+ ax2_sub.legend(loc='upper right')
151
+
152
+
153
+ else:
154
+ for i in range(len(weather_elements)):
155
+ y_value[i] = weatherTable[weather_elements[i]]
156
+
157
+ if len(weather_elements) == 1:
158
+ weatherPlot = plt.figure(figsize=(10,10))
159
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
160
+ plt.xlabel(xname,labelpad=5, fontsize=15)
161
+ plt.ylabel(weather_elements[0], labelpad=15, fontsize=15)
162
+ plt.xticks(size=10, rotation=45)
163
+ plt.plot(x_value, y_value[0], color='red', marker='o', label=weather_elements[0])
164
+ plt.legend(loc = "upper left")
165
+
166
+ elif len(weather_elements) == 2:
167
+ weatherPlot, ax1 = plt.subplots(figsize=(10,10))
168
+ plt.title("2022 Weather Graph", fontsize=20, fontweight='bold')
169
+ plt.xticks(size=10, rotation=45)
170
+
171
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker='o', label=weather_elements[0])
172
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
173
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
174
+
175
+ ax1_sub = ax1.twinx()
176
+ line2 = ax1_sub.plot(x_value, y_value[1], color='skyblue', marker='o', label=weather_elements[1])
177
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
178
+
179
+ lines = line1 + line2
180
+ labels = [l.get_label() for l in lines]
181
+ ax1.legend(lines, labels, loc='upper left')
182
+
183
+ elif len(weather_elements) == 3:
184
+ weatherPlot, (ax1, ax2) = plt.subplots(2,1, figsize=(10,15), constrained_layout=True)
185
+
186
+ line1 = ax1.plot(x_value, y_value[0], color='red', marker = "o", label=weather_elements[0])
187
+ ax1.set_xlabel(xname, labelpad=5, fontsize=15)
188
+ ax1.set_ylabel(weather_elements[0], labelpad=15, fontsize=15)
189
+ ax1.set_title("2022 Weather Graph", fontsize=20, fontweight='bold')
190
+ ax1.tick_params(axis='x', rotation=45, labelsize=10)
191
+
192
+ ax1_sub = ax1.twinx()
193
+ line2 = ax1_sub.plot(x_value, y_value[1], color='skyblue', marker = "o", label=weather_elements[1])
194
+ ax1_sub.set_ylabel(weather_elements[1], labelpad=25, fontsize=15, rotation=270)
195
+
196
+ lines = line1 + line2
197
+ labels = [l.get_label() for l in lines]
198
+ ax1.legend(lines, labels, loc='upper left')
199
+
200
+ ax2.plot(x_value, y_value[2], color='gray', marker = "o", label=weather_elements[2])
201
+ ax2.set_xlabel(xname, labelpad=5, fontsize=15)
202
+ ax2.set_ylabel(weather_elements[2], labelpad=15, fontsize=15)
203
+ ax2.tick_params(axis='x', rotation=45, labelsize=10)
204
+ ax2.legend(loc='upper left')
205
+
206
+ return [weatherTable, weatherPlot]
207
+
208
+ output1 = gr.Dataframe()
209
+ output2 = gr.Plot()
210
+
211
+ # make gradio interface
212
+ demo = gr.Interface(
213
+ fn=showOutput,
214
+ inputs=[
215
+ gr.Dropdown(["January", "February", "March", "April", "May", "June",
216
+ "July", "August", "September", "October", "November", "December"],
217
+ label="Month", info="Select Months"),
218
+ gr.Dropdown(["1", "2", "3", "4", "5", "6", "7", "8", "9", "10",
219
+ "11", "12", "13", "14", "15", "16", "17", "18", "19", "20",
220
+ "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31"], label="Day", info="Select Day"),
221
+ gr.CheckboxGroup(["temperature(˚C)", "wind(m/s)", "humidity(%)", "air_pressure(hPa)"],
222
+ label="Weather element", info="Choose weather element"),
223
+ gr.Radio(["Washington", "Seoul"],
224
+ label="Location", info="Choose location"),
225
+ gr.Checkbox(label="precipation?")],
226
+ outputs=[output1,output2]
227
+ )
228
+
229
+ if __name__=="__main__":
230
+ demo.launch()
readme_files/gradio.svg ADDED
readme_files/screenshot.gif ADDED