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Initial deployment: Full-featured EDA Gapminder dashboard

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- Comprehensive Streamlit EDA tool with 800+ lines
- 7 descriptive statistics tabs
- Interactive visualizations and animations
- Pre-loaded Gapminder dataset
- Docker SDK deployment

Files changed (5) hide show
  1. Dockerfile +25 -0
  2. README.md +41 -0
  3. app.py +803 -0
  4. gapminder.tsv +1705 -0
  5. requirements.txt +3 -0
Dockerfile ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ FROM python:3.11-slim
2
+
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+ WORKDIR /app
4
+
5
+ # Install system dependencies
6
+ RUN apt-get update && apt-get install -y \
7
+ build-essential \
8
+ curl \
9
+ && rm -rf /var/lib/apt/lists/*
10
+
11
+ # Copy requirements and install Python dependencies
12
+ COPY requirements.txt .
13
+ RUN pip3 install --no-cache-dir -r requirements.txt
14
+
15
+ # Copy application files
16
+ COPY . .
17
+
18
+ # Expose Streamlit port
19
+ EXPOSE 8501
20
+
21
+ # Health check
22
+ HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1
23
+
24
+ # Run Streamlit
25
+ ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
README.md ADDED
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1
+ ---
2
+ title: EDA Gapminder
3
+ emoji: 📊
4
+ colorFrom: green
5
+ colorTo: blue
6
+ sdk: docker
7
+ app_port: 8501
8
+ pinned: false
9
+ ---
10
+
11
+ # Exploratory Data Analysis Gapminder
12
+
13
+ Interactive exploratory data analysis tool for CSV datasets.
14
+
15
+ ## Features
16
+ - 📋 Data preview with first 10 rows
17
+ - 📊 Automatic summary statistics
18
+ - 🔍 Column information with null counts
19
+ - 📈 Interactive scatter plots
20
+ - 📊 Distribution histograms
21
+ - 🔥 Correlation heatmap
22
+
23
+ ## Tech Stack
24
+ - Streamlit for UI
25
+ - Plotly for interactive visualizations
26
+ - pandas for data processing
27
+
28
+ ## Usage
29
+ 1. Upload any CSV file
30
+ 2. View automatic data preview and statistics
31
+ 3. Select columns for visualization
32
+ 4. Explore scatter plots and distributions
33
+ 5. Analyze correlations between numeric columns
34
+
35
+ ## Supported Datasets
36
+ Works with any CSV file containing:
37
+ - Numeric data (int, float)
38
+ - Categorical data (strings)
39
+ - Mixed data types
40
+
41
+ Best results with datasets that have at least 2 numeric columns.
app.py ADDED
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1
+ import streamlit as st
2
+ import pandas as pd
3
+ import numpy as np
4
+ import plotly.express as px
5
+ import plotly.graph_objects as go
6
+ from plotly.subplots import make_subplots
7
+
8
+ # Set page config
9
+ st.set_page_config(page_title="Gapminder EDA v2", layout="wide")
10
+
11
+ # Custom CSS to set Helvetica for body text while keeping headers in Source Sans Pro
12
+ st.markdown("""
13
+ <style>
14
+ /* Body text, paragraphs, and general content */
15
+ .stMarkdown p, .stMarkdown li, .stMarkdown span {
16
+ font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
17
+ }
18
+
19
+ /* Dataframe text */
20
+ .stDataFrame, .stDataFrame * {
21
+ font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
22
+ }
23
+
24
+ /* Metric values */
25
+ .stMetric {
26
+ font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
27
+ }
28
+
29
+ /* Select boxes and inputs */
30
+ .stSelectbox, .stMultiSelect {
31
+ font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
32
+ }
33
+
34
+ /* Tab labels */
35
+ .stTabs [data-baseweb="tab"] {
36
+ font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
37
+ }
38
+
39
+ /* Info boxes */
40
+ .stAlert {
41
+ font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
42
+ }
43
+
44
+ /* Caption text */
45
+ .stCaption {
46
+ font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
47
+ }
48
+
49
+ /* Keep headers in Source Sans Pro (default Streamlit font) */
50
+ h1, h2, h3, h4, h5, h6 {
51
+ font-family: 'Source Sans Pro', sans-serif !important;
52
+ }
53
+ </style>
54
+ """, unsafe_allow_html=True)
55
+
56
+ # Title
57
+ st.title("🌍 Gapminder Dataset - Exploratory Data Analysis v2")
58
+
59
+ # Load data
60
+ @st.cache_data
61
+ def load_data():
62
+ """Load Gapminder dataset from local file with fallback to URL"""
63
+ # For HuggingFace Spaces, use gapminder.tsv in the same directory
64
+ local_path = 'gapminder.tsv'
65
+
66
+ try:
67
+ # Try loading from local file first
68
+ df = pd.read_csv(local_path, sep='\t')
69
+ except FileNotFoundError:
70
+ # Fallback to data/ directory (for local development)
71
+ try:
72
+ df = pd.read_csv('data/gapminder.tsv', sep='\t')
73
+ except FileNotFoundError:
74
+ # Final fallback to downloading from GitHub
75
+ url = "https://raw.githubusercontent.com/jennybc/gapminder/master/inst/extdata/gapminder.tsv"
76
+ df = pd.read_csv(url, sep='\t')
77
+
78
+ return df
79
+
80
+ df = load_data()
81
+
82
+ # Display basic info
83
+ st.header("Dataset Overview")
84
+ col1, col2, col3 = st.columns(3)
85
+ with col1:
86
+ st.metric("Total Rows", df.shape[0])
87
+ with col2:
88
+ st.metric("Total Columns", df.shape[1])
89
+ with col3:
90
+ st.metric("Countries", df['country'].nunique())
91
+
92
+ st.subheader("Sample Data")
93
+ st.dataframe(df.head(10))
94
+
95
+ # Descriptive Statistics Section
96
+ st.header("📊 Descriptive Statistics")
97
+
98
+ # Select numerical columns
99
+ numerical_cols = ['lifeExp', 'pop', 'gdpPercap']
100
+
101
+ # Create tabs for each metric
102
+ tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs([
103
+ "Mean", "Median", "Mode", "Standard Deviation",
104
+ "Range & IQR", "Coefficient of Variation", "Percentiles"
105
+ ])
106
+
107
+ with tab1:
108
+ st.subheader("Mean (Average)")
109
+ st.write("The mean represents the average value of the data.")
110
+
111
+ mean_data = {
112
+ 'Metric': numerical_cols,
113
+ 'Mean Value': [df[col].mean() for col in numerical_cols]
114
+ }
115
+ mean_df = pd.DataFrame(mean_data)
116
+
117
+ col1, col2 = st.columns([1, 2])
118
+ with col1:
119
+ st.dataframe(mean_df, hide_index=True)
120
+ with col2:
121
+ fig = px.bar(mean_df, x='Metric', y='Mean Value',
122
+ title='Mean Values by Metric',
123
+ color='Metric')
124
+ st.plotly_chart(fig, use_container_width=True)
125
+
126
+ with tab2:
127
+ st.subheader("Median")
128
+ st.write("The median represents the middle value when data is sorted.")
129
+
130
+ median_data = {
131
+ 'Metric': numerical_cols,
132
+ 'Median Value': [df[col].median() for col in numerical_cols]
133
+ }
134
+ median_df = pd.DataFrame(median_data)
135
+
136
+ col1, col2 = st.columns([1, 2])
137
+ with col1:
138
+ st.dataframe(median_df, hide_index=True)
139
+ with col2:
140
+ fig = px.bar(median_df, x='Metric', y='Median Value',
141
+ title='Median Values by Metric',
142
+ color='Metric')
143
+ st.plotly_chart(fig, use_container_width=True)
144
+
145
+ with tab3:
146
+ st.subheader("Mode")
147
+ st.write("The mode represents the most frequently occurring value(s).")
148
+
149
+ mode_results = []
150
+ for col in numerical_cols:
151
+ mode_val = df[col].mode()
152
+ if len(mode_val) > 0:
153
+ mode_results.append({
154
+ 'Metric': col,
155
+ 'Mode Value': mode_val[0],
156
+ 'Frequency': (df[col] == mode_val[0]).sum()
157
+ })
158
+
159
+ mode_df = pd.DataFrame(mode_results)
160
+ st.dataframe(mode_df, hide_index=True)
161
+
162
+ st.info("Note: For continuous data like life expectancy and GDP, mode may not be very meaningful as values rarely repeat exactly.")
163
+
164
+ with tab4:
165
+ st.subheader("Standard Deviation")
166
+ st.write("Standard deviation measures the amount of variation or dispersion in the data.")
167
+
168
+ std_data = {
169
+ 'Metric': numerical_cols,
170
+ 'Standard Deviation': [df[col].std() for col in numerical_cols],
171
+ 'Variance': [df[col].var() for col in numerical_cols]
172
+ }
173
+ std_df = pd.DataFrame(std_data)
174
+
175
+ col1, col2 = st.columns([1, 2])
176
+ with col1:
177
+ st.dataframe(std_df, hide_index=True)
178
+ with col2:
179
+ fig = px.bar(std_df, x='Metric', y='Standard Deviation',
180
+ title='Standard Deviation by Metric',
181
+ color='Metric')
182
+ st.plotly_chart(fig, use_container_width=True)
183
+
184
+ with tab5:
185
+ st.subheader("Range & Interquartile Range (IQR)")
186
+ st.write("Range shows the total spread (Max - Min), while IQR shows the spread of the middle 50% of data.")
187
+
188
+ range_data = {
189
+ 'Metric': numerical_cols,
190
+ 'Min': [df[col].min() for col in numerical_cols],
191
+ 'Q1 (25%)': [df[col].quantile(0.25) for col in numerical_cols],
192
+ 'Q3 (75%)': [df[col].quantile(0.75) for col in numerical_cols],
193
+ 'Max': [df[col].max() for col in numerical_cols],
194
+ 'Range': [df[col].max() - df[col].min() for col in numerical_cols],
195
+ 'IQR': [df[col].quantile(0.75) - df[col].quantile(0.25) for col in numerical_cols]
196
+ }
197
+ range_df = pd.DataFrame(range_data)
198
+
199
+ st.dataframe(range_df, hide_index=True)
200
+
201
+ # Visualizations
202
+ col1, col2 = st.columns(2)
203
+
204
+ with col1:
205
+ fig = px.bar(range_df, x='Metric', y='Range',
206
+ title='Range by Metric',
207
+ color='Metric')
208
+ fig.update_layout(transition={'duration': 500})
209
+ st.plotly_chart(fig, use_container_width=True)
210
+
211
+ with col2:
212
+ fig = px.bar(range_df, x='Metric', y='IQR',
213
+ title='Interquartile Range (IQR) by Metric',
214
+ color='Metric')
215
+ fig.update_layout(transition={'duration': 500})
216
+ st.plotly_chart(fig, use_container_width=True)
217
+
218
+ st.info("💡 IQR is more robust to outliers than Range. A larger IQR indicates more variability in the middle 50% of the data.")
219
+
220
+ with tab6:
221
+ st.subheader("Coefficient of Variation (CV)")
222
+ st.write("CV = (Standard Deviation / Mean) × 100. It allows comparison of variability across different scales.")
223
+
224
+ cv_data = {
225
+ 'Metric': numerical_cols,
226
+ 'Mean': [df[col].mean() for col in numerical_cols],
227
+ 'Std Dev': [df[col].std() for col in numerical_cols],
228
+ 'CV (%)': [(df[col].std() / df[col].mean()) * 100 for col in numerical_cols]
229
+ }
230
+ cv_df = pd.DataFrame(cv_data)
231
+
232
+ col1, col2 = st.columns([1, 2])
233
+
234
+ with col1:
235
+ st.dataframe(cv_df, hide_index=True)
236
+
237
+ st.markdown("**Interpretation:**")
238
+ st.markdown("- CV < 15%: Low variability")
239
+ st.markdown("- CV 15-30%: Moderate variability")
240
+ st.markdown("- CV > 30%: High variability")
241
+
242
+ with col2:
243
+ fig = px.bar(cv_df, x='Metric', y='CV (%)',
244
+ title='Coefficient of Variation by Metric',
245
+ color='Metric')
246
+ fig.update_layout(
247
+ yaxis_title="CV (%)",
248
+ transition={'duration': 500}
249
+ )
250
+
251
+ # Add reference lines for interpretation
252
+ fig.add_hline(y=15, line_dash="dash", line_color="green",
253
+ annotation_text="Low variability threshold")
254
+ fig.add_hline(y=30, line_dash="dash", line_color="orange",
255
+ annotation_text="High variability threshold")
256
+
257
+ st.plotly_chart(fig, use_container_width=True)
258
+
259
+ st.info("💡 Higher CV indicates greater relative variability. This is especially useful when comparing metrics with different units or scales.")
260
+
261
+ with tab7:
262
+ st.subheader("Percentiles Analysis")
263
+ st.write("Percentiles divide the data into 100 equal parts. Key percentiles help understand data distribution.")
264
+
265
+ percentiles = [0, 10, 25, 50, 75, 90, 100]
266
+ percentile_data = {'Percentile': [f'{p}th' if p not in [0, 100] else ('Min' if p == 0 else 'Max')
267
+ for p in percentiles]}
268
+
269
+ for col in numerical_cols:
270
+ percentile_data[col] = [df[col].quantile(p/100) for p in percentiles]
271
+
272
+ percentile_df = pd.DataFrame(percentile_data)
273
+
274
+ st.dataframe(percentile_df, hide_index=True)
275
+
276
+ # Visualizations
277
+ st.subheader("Percentile Visualizations")
278
+
279
+ selected_percentile_metric = st.selectbox(
280
+ "Select metric for percentile visualization",
281
+ options=numerical_cols,
282
+ format_func=lambda x: {
283
+ 'lifeExp': 'Life Expectancy',
284
+ 'pop': 'Population',
285
+ 'gdpPercap': 'GDP per Capita'
286
+ }[x],
287
+ key='percentile_metric'
288
+ )
289
+
290
+ col1, col2 = st.columns(2)
291
+
292
+ with col1:
293
+ # Line chart showing percentile progression
294
+ perc_viz_data = pd.DataFrame({
295
+ 'Percentile': percentiles,
296
+ 'Value': [df[selected_percentile_metric].quantile(p/100) for p in percentiles]
297
+ })
298
+
299
+ fig = px.line(perc_viz_data, x='Percentile', y='Value',
300
+ title=f'Percentile Progression - {selected_percentile_metric}',
301
+ markers=True)
302
+ fig.update_traces(line=dict(width=3), marker=dict(size=10))
303
+ fig.update_layout(transition={'duration': 600})
304
+ st.plotly_chart(fig, use_container_width=True)
305
+
306
+ with col2:
307
+ # Enhanced box plot with percentile annotations
308
+ fig = px.box(df, y=selected_percentile_metric,
309
+ title=f'Box Plot with Percentiles - {selected_percentile_metric}')
310
+
311
+ # Add percentile annotations
312
+ for p in [10, 25, 50, 75, 90]:
313
+ val = df[selected_percentile_metric].quantile(p/100)
314
+ fig.add_hline(y=val, line_dash="dot", line_color="red",
315
+ annotation_text=f"{p}th percentile",
316
+ annotation_position="right")
317
+
318
+ fig.update_layout(transition={'duration': 600})
319
+ st.plotly_chart(fig, use_container_width=True)
320
+
321
+ st.info("💡 The 50th percentile is the median. The difference between 75th and 25th percentiles is the IQR.")
322
+
323
+ # Comprehensive Statistics Table
324
+ st.header("📈 Complete Statistical Summary")
325
+ summary_stats = df[numerical_cols].describe().T
326
+ summary_stats['mode'] = [df[col].mode()[0] if len(df[col].mode()) > 0 else np.nan for col in numerical_cols]
327
+ summary_stats = summary_stats[['count', 'mean', 'mode', 'std', '50%', 'min', 'max']]
328
+ summary_stats.columns = ['Count', 'Mean', 'Mode', 'Std Dev', 'Median', 'Min', 'Max']
329
+ st.dataframe(summary_stats)
330
+
331
+ # Add Min/Max country details
332
+ st.subheader("🌟 Extreme Values - Countries")
333
+ st.write("Discover which countries hold the minimum and maximum values for each metric")
334
+
335
+ extreme_cols = st.columns(3)
336
+
337
+ for idx, col in enumerate(numerical_cols):
338
+ with extreme_cols[idx]:
339
+ col_name = {
340
+ 'lifeExp': 'Life Expectancy',
341
+ 'pop': 'Population',
342
+ 'gdpPercap': 'GDP per Capita'
343
+ }[col]
344
+
345
+ # Find min and max
346
+ min_val = df[col].min()
347
+ max_val = df[col].max()
348
+
349
+ min_row = df[df[col] == min_val].iloc[0]
350
+ max_row = df[df[col] == max_val].iloc[0]
351
+
352
+ st.markdown(f"**{col_name}**")
353
+
354
+ # Min value
355
+ st.markdown(f"**📉 Minimum:** {min_val:,.2f}")
356
+ st.caption(f"📍 {min_row['country']} ({min_row['continent']}) in {int(min_row['year'])}")
357
+
358
+ # Max value
359
+ st.markdown(f"**📈 Maximum:** {max_val:,.2f}")
360
+ st.caption(f"📍 {max_row['country']} ({max_row['continent']}) in {int(max_row['year'])}")
361
+
362
+ st.markdown("---")
363
+
364
+ # Correlation Analysis
365
+ st.header("🔗 Correlation Analysis")
366
+ st.write("Correlation measures the strength and direction of relationships between variables (-1 to +1).")
367
+
368
+ col1, col2 = st.columns([1, 1])
369
+
370
+ with col1:
371
+ # Calculate correlation matrix
372
+ correlation_matrix = df[numerical_cols].corr()
373
+
374
+ st.subheader("Correlation Matrix")
375
+ st.dataframe(correlation_matrix.round(3))
376
+
377
+ st.markdown("**Interpretation:**")
378
+ st.markdown("- **+1**: Perfect positive correlation")
379
+ st.markdown("- **0**: No correlation")
380
+ st.markdown("- **-1**: Perfect negative correlation")
381
+ st.markdown("- **|r| > 0.7**: Strong correlation")
382
+ st.markdown("- **|r| 0.3-0.7**: Moderate correlation")
383
+ st.markdown("- **|r| < 0.3**: Weak correlation")
384
+
385
+ with col2:
386
+ # Heatmap visualization
387
+ fig = px.imshow(correlation_matrix,
388
+ text_auto='.2f',
389
+ aspect='auto',
390
+ color_continuous_scale='RdYlGn',
391
+ color_continuous_midpoint=0,
392
+ title='Correlation Heatmap',
393
+ labels=dict(color="Correlation"))
394
+
395
+ fig.update_layout(
396
+ height=400,
397
+ xaxis_title="",
398
+ yaxis_title=""
399
+ )
400
+
401
+ st.plotly_chart(fig, use_container_width=True)
402
+
403
+ # Detailed correlation insights
404
+ st.subheader("🔍 Key Correlation Insights")
405
+
406
+ col1, col2, col3 = st.columns(3)
407
+
408
+ # Get correlations
409
+ corr_life_gdp = correlation_matrix.loc['lifeExp', 'gdpPercap']
410
+ corr_life_pop = correlation_matrix.loc['lifeExp', 'pop']
411
+ corr_gdp_pop = correlation_matrix.loc['gdpPercap', 'pop']
412
+
413
+ with col1:
414
+ st.metric(
415
+ "Life Expectancy ↔ GDP per Capita",
416
+ f"{corr_life_gdp:.3f}",
417
+ delta="Strong positive" if abs(corr_life_gdp) > 0.7 else "Moderate" if abs(corr_life_gdp) > 0.3 else "Weak"
418
+ )
419
+ st.caption("Higher GDP tends to correlate with longer life expectancy")
420
+
421
+ with col2:
422
+ st.metric(
423
+ "Life Expectancy ↔ Population",
424
+ f"{corr_life_pop:.3f}",
425
+ delta="Strong" if abs(corr_life_pop) > 0.7 else "Moderate" if abs(corr_life_pop) > 0.3 else "Weak"
426
+ )
427
+ st.caption("Relationship between population size and life expectancy")
428
+
429
+ with col3:
430
+ st.metric(
431
+ "GDP per Capita ↔ Population",
432
+ f"{corr_gdp_pop:.3f}",
433
+ delta="Strong" if abs(corr_gdp_pop) > 0.7 else "Moderate" if abs(corr_gdp_pop) > 0.3 else "Weak"
434
+ )
435
+ st.caption("Relationship between wealth and population size")
436
+
437
+ st.info("💡 Correlation does not imply causation! High correlation indicates variables move together, but one doesn't necessarily cause the other.")
438
+
439
+ # Filter by Year or Country
440
+ st.header("🔎 Filter & Analyze")
441
+ col1, col2 = st.columns(2)
442
+
443
+ with col1:
444
+ selected_year = st.selectbox("Select Year", sorted(df['year'].unique()))
445
+ with col2:
446
+ selected_continent = st.multiselect("Select Continent(s)",
447
+ df['continent'].unique(),
448
+ default=df['continent'].unique())
449
+
450
+ filtered_df = df[(df['year'] == selected_year) & (df['continent'].isin(selected_continent))]
451
+
452
+ if not filtered_df.empty:
453
+ st.subheader(f"Statistics for {selected_year} - {', '.join(selected_continent)}")
454
+
455
+ # Metrics in columns
456
+ col1, col2, col3 = st.columns(3)
457
+
458
+ with col1:
459
+ st.metric("Life Expectancy (Mean)", f"{filtered_df['lifeExp'].mean():.2f}")
460
+ st.metric("Life Expectancy (Median)", f"{filtered_df['lifeExp'].median():.2f}")
461
+ st.metric("Life Expectancy (Std Dev)", f"{filtered_df['lifeExp'].std():.2f}")
462
+
463
+ with col2:
464
+ st.metric("Population (Mean)", f"{filtered_df['pop'].mean():,.0f}")
465
+ st.metric("Population (Median)", f"{filtered_df['pop'].median():,.0f}")
466
+ st.metric("Population (Std Dev)", f"{filtered_df['pop'].std():,.0f}")
467
+
468
+ with col3:
469
+ st.metric("GDP per Capita (Mean)", f"${filtered_df['gdpPercap'].mean():,.2f}")
470
+ st.metric("GDP per Capita (Median)", f"${filtered_df['gdpPercap'].median():.2f}")
471
+ st.metric("GDP per Capita (Std Dev)", f"${filtered_df['gdpPercap'].std():,.2f}")
472
+
473
+ # NEW: Add visualizations for filtered data
474
+ st.subheader("📊 Visual Analysis")
475
+
476
+ # Create tabs for different visualizations
477
+ viz_tab1, viz_tab2, viz_tab3, viz_tab4, viz_tab5 = st.tabs([
478
+ "Comparative Bar Charts",
479
+ "Scatter Analysis",
480
+ "Geographic Distribution",
481
+ "Statistical Distribution",
482
+ "Animated Timeline"
483
+ ])
484
+
485
+ with viz_tab1:
486
+ st.write("Compare Mean, Median, and Standard Deviation across metrics")
487
+
488
+ # Create comparison charts
489
+ col1, col2 = st.columns(2)
490
+
491
+ with col1:
492
+ # Mean vs Median comparison
493
+ comparison_data = pd.DataFrame({
494
+ 'Metric': numerical_cols * 2,
495
+ 'Statistic': ['Mean']*3 + ['Median']*3,
496
+ 'Value': [
497
+ filtered_df['lifeExp'].mean(),
498
+ filtered_df['pop'].mean(),
499
+ filtered_df['gdpPercap'].mean(),
500
+ filtered_df['lifeExp'].median(),
501
+ filtered_df['pop'].median(),
502
+ filtered_df['gdpPercap'].median()
503
+ ]
504
+ })
505
+
506
+ fig = px.bar(comparison_data, x='Metric', y='Value',
507
+ color='Statistic', barmode='group',
508
+ title='Mean vs Median Comparison',
509
+ labels={'Value': 'Value'},
510
+ animation_frame=None)
511
+ fig.update_layout(transition={'duration': 500})
512
+ st.plotly_chart(fig, use_container_width=True)
513
+
514
+ with col2:
515
+ # Standard Deviation
516
+ std_data = pd.DataFrame({
517
+ 'Metric': numerical_cols,
518
+ 'Std Dev': [
519
+ filtered_df['lifeExp'].std(),
520
+ filtered_df['pop'].std(),
521
+ filtered_df['gdpPercap'].std()
522
+ ]
523
+ })
524
+
525
+ fig = px.bar(std_data, x='Metric', y='Std Dev',
526
+ title='Standard Deviation by Metric',
527
+ color='Metric')
528
+ fig.update_layout(transition={'duration': 500})
529
+ st.plotly_chart(fig, use_container_width=True)
530
+
531
+ with viz_tab2:
532
+ st.write("Explore relationships between different metrics")
533
+
534
+ col1, col2 = st.columns(2)
535
+
536
+ with col1:
537
+ # GDP vs Life Expectancy
538
+ fig = px.scatter(filtered_df, x='gdpPercap', y='lifeExp',
539
+ size='pop', color='continent',
540
+ hover_name='country',
541
+ title=f'GDP per Capita vs Life Expectancy ({selected_year})',
542
+ labels={'gdpPercap': 'GDP per Capita',
543
+ 'lifeExp': 'Life Expectancy'},
544
+ log_x=True)
545
+ fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')))
546
+ fig.update_layout(transition={'duration': 800, 'easing': 'cubic-in-out'})
547
+ st.plotly_chart(fig, use_container_width=True)
548
+
549
+ with col2:
550
+ # Population vs Life Expectancy
551
+ fig = px.scatter(filtered_df, x='pop', y='lifeExp',
552
+ size='gdpPercap', color='continent',
553
+ hover_name='country',
554
+ title=f'Population vs Life Expectancy ({selected_year})',
555
+ labels={'pop': 'Population',
556
+ 'lifeExp': 'Life Expectancy'},
557
+ log_x=True)
558
+ fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')))
559
+ fig.update_layout(transition={'duration': 800, 'easing': 'cubic-in-out'})
560
+ st.plotly_chart(fig, use_container_width=True)
561
+
562
+ with viz_tab3:
563
+ st.write("Geographic distribution of metrics across countries")
564
+
565
+ # Bar chart by country
566
+ metric_choice = st.selectbox(
567
+ "Select metric to visualize by country",
568
+ options=['lifeExp', 'pop', 'gdpPercap'],
569
+ format_func=lambda x: {
570
+ 'lifeExp': 'Life Expectancy',
571
+ 'pop': 'Population',
572
+ 'gdpPercap': 'GDP per Capita'
573
+ }[x]
574
+ )
575
+
576
+ # Sort by selected metric
577
+ sorted_df = filtered_df.sort_values(metric_choice, ascending=False)
578
+
579
+ fig = px.bar(sorted_df, x='country', y=metric_choice,
580
+ color='continent',
581
+ title=f'{metric_choice.replace("_", " ").title()} by Country ({selected_year})',
582
+ labels={metric_choice: metric_choice.replace('_', ' ').title()})
583
+ fig.update_layout(
584
+ xaxis_tickangle=-45,
585
+ height=500,
586
+ transition={'duration': 500, 'easing': 'cubic-in-out'}
587
+ )
588
+ st.plotly_chart(fig, use_container_width=True)
589
+
590
+ # Top 10 and Bottom 10
591
+ col1, col2 = st.columns(2)
592
+
593
+ with col1:
594
+ st.write(f"🏆 Top 10 Countries - {metric_choice.replace('_', ' ').title()}")
595
+ top_10 = sorted_df[['country', metric_choice, 'continent']].head(10)
596
+ st.dataframe(top_10, hide_index=True)
597
+
598
+ with col2:
599
+ st.write(f"⚠️ Bottom 10 Countries - {metric_choice.replace('_', ' ').title()}")
600
+ bottom_10 = sorted_df[['country', metric_choice, 'continent']].tail(10)
601
+ st.dataframe(bottom_10, hide_index=True)
602
+
603
+ with viz_tab4:
604
+ st.write("Distribution patterns and box plots for filtered data")
605
+
606
+ # Box plots by continent
607
+ selected_metric_dist = st.selectbox(
608
+ "Select metric for distribution analysis",
609
+ options=['lifeExp', 'pop', 'gdpPercap'],
610
+ format_func=lambda x: {
611
+ 'lifeExp': 'Life Expectancy',
612
+ 'pop': 'Population',
613
+ 'gdpPercap': 'GDP per Capita'
614
+ }[x],
615
+ key='dist_metric'
616
+ )
617
+
618
+ col1, col2 = st.columns(2)
619
+
620
+ with col1:
621
+ # Box plot
622
+ fig = px.box(filtered_df, x='continent', y=selected_metric_dist,
623
+ color='continent',
624
+ title=f'{selected_metric_dist.replace("_", " ").title()} Distribution by Continent',
625
+ labels={selected_metric_dist: selected_metric_dist.replace('_', ' ').title()})
626
+ fig.update_layout(transition={'duration': 600})
627
+ st.plotly_chart(fig, use_container_width=True)
628
+
629
+ with col2:
630
+ # Violin plot
631
+ fig = px.violin(filtered_df, x='continent', y=selected_metric_dist,
632
+ color='continent', box=True,
633
+ title=f'{selected_metric_dist.replace("_", " ").title()} Violin Plot',
634
+ labels={selected_metric_dist: selected_metric_dist.replace('_', ' ').title()})
635
+ fig.update_layout(transition={'duration': 600})
636
+ st.plotly_chart(fig, use_container_width=True)
637
+
638
+ with viz_tab5:
639
+ st.write("🎬 Watch how metrics evolve over time with animated visualizations")
640
+
641
+ # Animation type selector
642
+ anim_type = st.radio(
643
+ "Select Animation Type",
644
+ options=["Scatter Plot Timeline", "Bar Chart Race", "Line Chart Evolution"],
645
+ horizontal=True
646
+ )
647
+
648
+ if anim_type == "Scatter Plot Timeline":
649
+ # Animated scatter plot over all years
650
+ st.subheader("GDP vs Life Expectancy Over Time")
651
+
652
+ # Filter by selected continents
653
+ anim_df = df[df['continent'].isin(selected_continent)]
654
+
655
+ fig = px.scatter(anim_df,
656
+ x='gdpPercap',
657
+ y='lifeExp',
658
+ animation_frame='year',
659
+ animation_group='country',
660
+ size='pop',
661
+ color='continent',
662
+ hover_name='country',
663
+ log_x=True,
664
+ size_max=60,
665
+ range_x=[100, 120000],
666
+ range_y=[20, 90],
667
+ title='GDP per Capita vs Life Expectancy (Animated)',
668
+ labels={'gdpPercap': 'GDP per Capita',
669
+ 'lifeExp': 'Life Expectancy',
670
+ 'pop': 'Population'})
671
+
672
+ fig.update_layout(
673
+ height=600,
674
+ transition={'duration': 800}
675
+ )
676
+
677
+ fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 800
678
+ fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 500
679
+
680
+ st.plotly_chart(fig, use_container_width=True)
681
+ st.info("Click the Play button to watch the animation! Each frame represents a year from 1952 to 2007.")
682
+
683
+ elif anim_type == "Bar Chart Race":
684
+ # Animated bar chart race
685
+ st.subheader("Top Countries Bar Chart Race")
686
+
687
+ metric_race = st.selectbox(
688
+ "Select metric for bar chart race",
689
+ options=['lifeExp', 'pop', 'gdpPercap'],
690
+ format_func=lambda x: {
691
+ 'lifeExp': 'Life Expectancy',
692
+ 'pop': 'Population',
693
+ 'gdpPercap': 'GDP per Capita'
694
+ }[x],
695
+ key='race_metric'
696
+ )
697
+
698
+ # Get top 15 countries for the latest year
699
+ latest_year_df = df[df['year'] == df['year'].max()]
700
+ top_countries = latest_year_df.nlargest(15, metric_race)['country'].tolist()
701
+
702
+ # Filter data for these countries
703
+ race_df = df[df['country'].isin(top_countries) & df['continent'].isin(selected_continent)]
704
+
705
+ fig = px.bar(race_df,
706
+ x=metric_race,
707
+ y='country',
708
+ color='continent',
709
+ animation_frame='year',
710
+ orientation='h',
711
+ title=f'Top Countries by {metric_race.replace("_", " ").title()} Over Time',
712
+ labels={metric_race: metric_race.replace('_', ' ').title()},
713
+ range_x=[0, race_df[metric_race].max() * 1.1])
714
+
715
+ fig.update_layout(
716
+ height=600,
717
+ yaxis={'categoryorder': 'total ascending'}
718
+ )
719
+
720
+ fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 1000
721
+ fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 500
722
+
723
+ st.plotly_chart(fig, use_container_width=True)
724
+ st.info("Watch the ranking change over time! Countries rise and fall based on their performance.")
725
+
726
+ else: # Line Chart Evolution
727
+ st.subheader("Metric Evolution by Continent")
728
+
729
+ metric_line = st.selectbox(
730
+ "Select metric for time evolution",
731
+ options=['lifeExp', 'pop', 'gdpPercap'],
732
+ format_func=lambda x: {
733
+ 'lifeExp': 'Life Expectancy',
734
+ 'pop': 'Population',
735
+ 'gdpPercap': 'GDP per Capita'
736
+ }[x],
737
+ key='line_metric'
738
+ )
739
+
740
+ # Calculate mean by continent and year
741
+ line_df = df[df['continent'].isin(selected_continent)].groupby(['year', 'continent'])[metric_line].mean().reset_index()
742
+
743
+ fig = px.line(line_df,
744
+ x='year',
745
+ y=metric_line,
746
+ color='continent',
747
+ markers=True,
748
+ title=f'Average {metric_line.replace("_", " ").title()} Evolution by Continent',
749
+ labels={metric_line: f'Average {metric_line.replace("_", " ").title()}',
750
+ 'year': 'Year'})
751
+
752
+ fig.update_traces(
753
+ mode='lines+markers',
754
+ line=dict(width=3),
755
+ marker=dict(size=8)
756
+ )
757
+
758
+ fig.update_layout(
759
+ height=500,
760
+ hovermode='x unified',
761
+ transition={'duration': 600}
762
+ )
763
+
764
+ # Add animation on load
765
+ fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightGray')
766
+ fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGray')
767
+
768
+ st.plotly_chart(fig, use_container_width=True)
769
+ st.info("Hover over the chart to see detailed values for each year and continent.")
770
+
771
+ # Distribution Visualizations
772
+ st.header("📉 Overall Data Distributions")
773
+ selected_metric = st.selectbox("Select metric to visualize", numerical_cols,
774
+ format_func=lambda x: {
775
+ 'lifeExp': 'Life Expectancy',
776
+ 'pop': 'Population',
777
+ 'gdpPercap': 'GDP per Capita'
778
+ }[x])
779
+
780
+ col1, col2 = st.columns(2)
781
+
782
+ with col1:
783
+ fig = px.histogram(df, x=selected_metric, nbins=50,
784
+ title=f'Distribution of {selected_metric}',
785
+ labels={selected_metric: selected_metric.replace('_', ' ').title()})
786
+ fig.add_vline(x=df[selected_metric].mean(), line_dash="dash",
787
+ line_color="red", annotation_text="Mean")
788
+ fig.add_vline(x=df[selected_metric].median(), line_dash="dash",
789
+ line_color="green", annotation_text="Median")
790
+ fig.update_layout(transition={'duration': 700})
791
+ st.plotly_chart(fig, use_container_width=True)
792
+
793
+ with col2:
794
+ fig = px.box(df, y=selected_metric, x='continent',
795
+ title=f'{selected_metric} by Continent',
796
+ labels={selected_metric: selected_metric.replace('_', ' ').title()})
797
+ fig.update_layout(transition={'duration': 700})
798
+ st.plotly_chart(fig, use_container_width=True)
799
+
800
+ # Footer
801
+ st.markdown("---")
802
+ st.markdown("Data source: [Gapminder](https://www.gapminder.org/)")
803
+ st.markdown("**Version 2** - Enhanced with interactive visualizations in Filter & Analyze section")
gapminder.tsv ADDED
@@ -0,0 +1,1705 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ country continent year lifeExp pop gdpPercap
2
+ Afghanistan Asia 1952 28.801 8425333 779.4453145
3
+ Afghanistan Asia 1957 30.332 9240934 820.8530296
4
+ Afghanistan Asia 1962 31.997 10267083 853.10071
5
+ Afghanistan Asia 1967 34.02 11537966 836.1971382
6
+ Afghanistan Asia 1972 36.088 13079460 739.9811058
7
+ Afghanistan Asia 1977 38.438 14880372 786.11336
8
+ Afghanistan Asia 1982 39.854 12881816 978.0114388
9
+ Afghanistan Asia 1987 40.822 13867957 852.3959448
10
+ Afghanistan Asia 1992 41.674 16317921 649.3413952
11
+ Afghanistan Asia 1997 41.763 22227415 635.341351
12
+ Afghanistan Asia 2002 42.129 25268405 726.7340548
13
+ Afghanistan Asia 2007 43.828 31889923 974.5803384
14
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15
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16
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17
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18
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19
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20
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21
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22
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23
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24
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25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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38
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39
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40
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
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101
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102
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103
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104
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105
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106
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
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124
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125
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126
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127
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128
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129
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130
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131
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132
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133
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134
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135
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136
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137
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138
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139
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140
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141
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142
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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175
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176
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177
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178
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179
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180
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181
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182
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183
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184
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185
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186
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187
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188
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189
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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206
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207
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208
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209
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210
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211
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212
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213
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
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235
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236
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237
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238
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239
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240
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241
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242
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243
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244
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245
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246
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247
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248
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249
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250
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251
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252
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253
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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526
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527
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529
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530
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540
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542
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544
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565
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567
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568
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570
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575
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576
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577
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578
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579
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580
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588
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591
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598
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599
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600
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622
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625
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628
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636
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638
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639
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663
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669
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670
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672
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673
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674
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675
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680
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684
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686
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687
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690
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720
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722
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723
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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809
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810
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811
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812
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813
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814
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815
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816
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817
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818
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819
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820
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821
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822
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823
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824
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825
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826
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827
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828
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829
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830
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831
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832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
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850
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851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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898
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899
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900
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901
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902
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903
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904
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905
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906
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907
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908
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909
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910
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911
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912
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913
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914
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915
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916
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917
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918
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919
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920
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921
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922
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923
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
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967
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968
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969
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970
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971
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972
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973
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974
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975
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976
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977
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978
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979
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980
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981
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982
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983
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984
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985
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986
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987
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988
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989
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990
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991
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992
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993
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994
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995
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996
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997
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998
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999
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1000
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1001
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1002
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1003
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1004
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1005
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1006
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1007
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1008
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1009
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1010
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1011
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1012
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1013
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1014
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1015
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1016
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1017
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1018
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1019
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1020
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1021
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1022
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1023
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1024
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1025
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1026
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1027
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1028
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1029
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1030
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1031
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1032
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1033
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1034
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1035
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1036
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1037
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1038
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1039
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1040
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1041
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1042
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1043
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1044
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1045
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1046
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1047
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1048
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1049
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1050
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1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1086
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1087
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1088
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1089
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1090
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1091
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1092
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1093
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1094
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1095
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1096
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1097
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1098
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1099
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1100
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1101
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1102
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1103
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1104
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1105
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1106
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1107
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1108
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1109
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1110
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1111
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1112
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1113
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1114
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1115
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1116
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1117
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1118
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1119
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1120
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1121
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1122
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1123
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1124
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1125
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1126
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1127
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1128
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1129
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1130
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1131
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1132
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1133
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1134
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1135
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1136
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1137
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1138
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1139
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1140
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1141
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1142
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1143
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
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1158
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1159
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1160
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1161
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1162
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1163
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1164
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1165
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1166
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1167
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1168
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1169
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1170
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1171
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1172
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1173
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1174
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1175
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1176
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1177
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1178
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1179
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1180
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1181
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1182
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1183
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1184
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1185
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1186
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1187
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1188
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1189
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1190
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1191
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1192
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1193
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1194
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1195
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1196
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1197
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1198
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1199
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1200
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1201
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1202
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1203
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1204
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1205
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1206
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1207
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1208
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1209
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1210
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1211
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1212
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1213
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1214
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1215
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1216
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1217
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1218
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1219
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1220
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1221
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1222
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1223
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1224
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1225
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1226
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1227
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1228
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1229
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1230
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1231
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1232
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1233
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1234
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1235
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1236
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1237
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1238
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1239
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1240
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1241
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1242
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1243
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1244
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1245
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1246
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1247
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1248
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1249
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1250
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1251
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1252
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1253
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1254
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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
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1263
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1264
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1265
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1266
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1267
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1268
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1269
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1270
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1271
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1272
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1273
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1274
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1275
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1276
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1277
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1278
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1279
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1280
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1281
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1282
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1283
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1284
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1285
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1286
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1287
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1288
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1289
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1290
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1291
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1292
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1293
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1294
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1295
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1296
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1297
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1298
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1299
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1300
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1301
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1302
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1303
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1304
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1305
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1306
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1307
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1308
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1309
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1310
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1311
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1312
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1313
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1314
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1315
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1316
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1317
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1318
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1319
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1320
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1321
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1322
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1323
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1324
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1325
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1326
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1327
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1328
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1329
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1330
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1331
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1332
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1333
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1334
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1335
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1336
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1337
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1338
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1339
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1340
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1341
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1342
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1343
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1344
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1345
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1346
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1347
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1348
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1349
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1350
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1351
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1352
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1353
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1354
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1355
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1356
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1357
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1358
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1359
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1360
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1361
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1362
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1363
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1364
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1365
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1366
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1367
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1368
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1369
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1370
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1371
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1372
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1373
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1374
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1375
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1376
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1377
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1378
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1379
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1380
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1381
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1382
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1383
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1384
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1385
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1386
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1387
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1388
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1389
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1390
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1391
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1392
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1393
+ Slovenia Europe 2007 77.926 2009245 25768.25759
1394
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1395
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1396
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1397
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1398
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1399
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1400
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1401
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1402
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1403
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1404
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1405
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1406
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1407
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1408
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1409
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1410
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1411
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1412
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1413
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1414
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1415
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1416
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1417
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1418
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1419
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1420
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1421
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1422
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1423
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1424
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1425
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1426
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1427
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1428
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1429
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1430
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1431
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1432
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1433
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1434
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1435
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1436
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1437
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1438
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1439
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1440
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1441
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1442
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1443
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1444
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1445
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1446
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1447
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1448
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1449
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1450
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1451
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1452
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1453
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1454
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1455
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1456
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1457
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1458
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1459
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1460
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1461
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1462
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1463
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1464
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1465
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1466
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1467
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1468
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1469
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1470
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1471
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1472
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1473
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1474
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1475
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1476
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1477
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1478
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1479
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1480
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1481
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1482
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1483
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1484
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1485
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1486
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1487
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1488
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1489
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1490
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1491
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1492
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1493
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1494
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1495
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1496
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1497
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1498
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1499
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1500
+ Syria Asia 2002 73.053 17155814 4090.925331
1501
+ Syria Asia 2007 74.143 19314747 4184.548089
1502
+ Taiwan Asia 1952 58.5 8550362 1206.947913
1503
+ Taiwan Asia 1957 62.4 10164215 1507.86129
1504
+ Taiwan Asia 1962 65.2 11918938 1822.879028
1505
+ Taiwan Asia 1967 67.5 13648692 2643.858681
1506
+ Taiwan Asia 1972 69.39 15226039 4062.523897
1507
+ Taiwan Asia 1977 70.59 16785196 5596.519826
1508
+ Taiwan Asia 1982 72.16 18501390 7426.354774
1509
+ Taiwan Asia 1987 73.4 19757799 11054.56175
1510
+ Taiwan Asia 1992 74.26 20686918 15215.6579
1511
+ Taiwan Asia 1997 75.25 21628605 20206.82098
1512
+ Taiwan Asia 2002 76.99 22454239 23235.42329
1513
+ Taiwan Asia 2007 78.4 23174294 28718.27684
1514
+ Tanzania Africa 1952 41.215 8322925 716.6500721
1515
+ Tanzania Africa 1957 42.974 9452826 698.5356073
1516
+ Tanzania Africa 1962 44.246 10863958 722.0038073
1517
+ Tanzania Africa 1967 45.757 12607312 848.2186575
1518
+ Tanzania Africa 1972 47.62 14706593 915.9850592
1519
+ Tanzania Africa 1977 49.919 17129565 962.4922932
1520
+ Tanzania Africa 1982 50.608 19844382 874.2426069
1521
+ Tanzania Africa 1987 51.535 23040630 831.8220794
1522
+ Tanzania Africa 1992 50.44 26605473 825.682454
1523
+ Tanzania Africa 1997 48.466 30686889 789.1862231
1524
+ Tanzania Africa 2002 49.651 34593779 899.0742111
1525
+ Tanzania Africa 2007 52.517 38139640 1107.482182
1526
+ Thailand Asia 1952 50.848 21289402 757.7974177
1527
+ Thailand Asia 1957 53.63 25041917 793.5774148
1528
+ Thailand Asia 1962 56.061 29263397 1002.199172
1529
+ Thailand Asia 1967 58.285 34024249 1295.46066
1530
+ Thailand Asia 1972 60.405 39276153 1524.358936
1531
+ Thailand Asia 1977 62.494 44148285 1961.224635
1532
+ Thailand Asia 1982 64.597 48827160 2393.219781
1533
+ Thailand Asia 1987 66.084 52910342 2982.653773
1534
+ Thailand Asia 1992 67.298 56667095 4616.896545
1535
+ Thailand Asia 1997 67.521 60216677 5852.625497
1536
+ Thailand Asia 2002 68.564 62806748 5913.187529
1537
+ Thailand Asia 2007 70.616 65068149 7458.396327
1538
+ Togo Africa 1952 38.596 1219113 859.8086567
1539
+ Togo Africa 1957 41.208 1357445 925.9083202
1540
+ Togo Africa 1962 43.922 1528098 1067.53481
1541
+ Togo Africa 1967 46.769 1735550 1477.59676
1542
+ Togo Africa 1972 49.759 2056351 1649.660188
1543
+ Togo Africa 1977 52.887 2308582 1532.776998
1544
+ Togo Africa 1982 55.471 2644765 1344.577953
1545
+ Togo Africa 1987 56.941 3154264 1202.201361
1546
+ Togo Africa 1992 58.061 3747553 1034.298904
1547
+ Togo Africa 1997 58.39 4320890 982.2869243
1548
+ Togo Africa 2002 57.561 4977378 886.2205765
1549
+ Togo Africa 2007 58.42 5701579 882.9699438
1550
+ Trinidad and Tobago Americas 1952 59.1 662850 3023.271928
1551
+ Trinidad and Tobago Americas 1957 61.8 764900 4100.3934
1552
+ Trinidad and Tobago Americas 1962 64.9 887498 4997.523971
1553
+ Trinidad and Tobago Americas 1967 65.4 960155 5621.368472
1554
+ Trinidad and Tobago Americas 1972 65.9 975199 6619.551419
1555
+ Trinidad and Tobago Americas 1977 68.3 1039009 7899.554209
1556
+ Trinidad and Tobago Americas 1982 68.832 1116479 9119.528607
1557
+ Trinidad and Tobago Americas 1987 69.582 1191336 7388.597823
1558
+ Trinidad and Tobago Americas 1992 69.862 1183669 7370.990932
1559
+ Trinidad and Tobago Americas 1997 69.465 1138101 8792.573126
1560
+ Trinidad and Tobago Americas 2002 68.976 1101832 11460.60023
1561
+ Trinidad and Tobago Americas 2007 69.819 1056608 18008.50924
1562
+ Tunisia Africa 1952 44.6 3647735 1468.475631
1563
+ Tunisia Africa 1957 47.1 3950849 1395.232468
1564
+ Tunisia Africa 1962 49.579 4286552 1660.30321
1565
+ Tunisia Africa 1967 52.053 4786986 1932.360167
1566
+ Tunisia Africa 1972 55.602 5303507 2753.285994
1567
+ Tunisia Africa 1977 59.837 6005061 3120.876811
1568
+ Tunisia Africa 1982 64.048 6734098 3560.233174
1569
+ Tunisia Africa 1987 66.894 7724976 3810.419296
1570
+ Tunisia Africa 1992 70.001 8523077 4332.720164
1571
+ Tunisia Africa 1997 71.973 9231669 4876.798614
1572
+ Tunisia Africa 2002 73.042 9770575 5722.895655
1573
+ Tunisia Africa 2007 73.923 10276158 7092.923025
1574
+ Turkey Europe 1952 43.585 22235677 1969.10098
1575
+ Turkey Europe 1957 48.079 25670939 2218.754257
1576
+ Turkey Europe 1962 52.098 29788695 2322.869908
1577
+ Turkey Europe 1967 54.336 33411317 2826.356387
1578
+ Turkey Europe 1972 57.005 37492953 3450.69638
1579
+ Turkey Europe 1977 59.507 42404033 4269.122326
1580
+ Turkey Europe 1982 61.036 47328791 4241.356344
1581
+ Turkey Europe 1987 63.108 52881328 5089.043686
1582
+ Turkey Europe 1992 66.146 58179144 5678.348271
1583
+ Turkey Europe 1997 68.835 63047647 6601.429915
1584
+ Turkey Europe 2002 70.845 67308928 6508.085718
1585
+ Turkey Europe 2007 71.777 71158647 8458.276384
1586
+ Uganda Africa 1952 39.978 5824797 734.753484
1587
+ Uganda Africa 1957 42.571 6675501 774.3710692
1588
+ Uganda Africa 1962 45.344 7688797 767.2717398
1589
+ Uganda Africa 1967 48.051 8900294 908.9185217
1590
+ Uganda Africa 1972 51.016 10190285 950.735869
1591
+ Uganda Africa 1977 50.35 11457758 843.7331372
1592
+ Uganda Africa 1982 49.849 12939400 682.2662268
1593
+ Uganda Africa 1987 51.509 15283050 617.7244065
1594
+ Uganda Africa 1992 48.825 18252190 644.1707969
1595
+ Uganda Africa 1997 44.578 21210254 816.559081
1596
+ Uganda Africa 2002 47.813 24739869 927.7210018
1597
+ Uganda Africa 2007 51.542 29170398 1056.380121
1598
+ United Kingdom Europe 1952 69.18 50430000 9979.508487
1599
+ United Kingdom Europe 1957 70.42 51430000 11283.17795
1600
+ United Kingdom Europe 1962 70.76 53292000 12477.17707
1601
+ United Kingdom Europe 1967 71.36 54959000 14142.85089
1602
+ United Kingdom Europe 1972 72.01 56079000 15895.11641
1603
+ United Kingdom Europe 1977 72.76 56179000 17428.74846
1604
+ United Kingdom Europe 1982 74.04 56339704 18232.42452
1605
+ United Kingdom Europe 1987 75.007 56981620 21664.78767
1606
+ United Kingdom Europe 1992 76.42 57866349 22705.09254
1607
+ United Kingdom Europe 1997 77.218 58808266 26074.53136
1608
+ United Kingdom Europe 2002 78.471 59912431 29478.99919
1609
+ United Kingdom Europe 2007 79.425 60776238 33203.26128
1610
+ United States Americas 1952 68.44 157553000 13990.48208
1611
+ United States Americas 1957 69.49 171984000 14847.12712
1612
+ United States Americas 1962 70.21 186538000 16173.14586
1613
+ United States Americas 1967 70.76 198712000 19530.36557
1614
+ United States Americas 1972 71.34 209896000 21806.03594
1615
+ United States Americas 1977 73.38 220239000 24072.63213
1616
+ United States Americas 1982 74.65 232187835 25009.55914
1617
+ United States Americas 1987 75.02 242803533 29884.35041
1618
+ United States Americas 1992 76.09 256894189 32003.93224
1619
+ United States Americas 1997 76.81 272911760 35767.43303
1620
+ United States Americas 2002 77.31 287675526 39097.09955
1621
+ United States Americas 2007 78.242 301139947 42951.65309
1622
+ Uruguay Americas 1952 66.071 2252965 5716.766744
1623
+ Uruguay Americas 1957 67.044 2424959 6150.772969
1624
+ Uruguay Americas 1962 68.253 2598466 5603.357717
1625
+ Uruguay Americas 1967 68.468 2748579 5444.61962
1626
+ Uruguay Americas 1972 68.673 2829526 5703.408898
1627
+ Uruguay Americas 1977 69.481 2873520 6504.339663
1628
+ Uruguay Americas 1982 70.805 2953997 6920.223051
1629
+ Uruguay Americas 1987 71.918 3045153 7452.398969
1630
+ Uruguay Americas 1992 72.752 3149262 8137.004775
1631
+ Uruguay Americas 1997 74.223 3262838 9230.240708
1632
+ Uruguay Americas 2002 75.307 3363085 7727.002004
1633
+ Uruguay Americas 2007 76.384 3447496 10611.46299
1634
+ Venezuela Americas 1952 55.088 5439568 7689.799761
1635
+ Venezuela Americas 1957 57.907 6702668 9802.466526
1636
+ Venezuela Americas 1962 60.77 8143375 8422.974165
1637
+ Venezuela Americas 1967 63.479 9709552 9541.474188
1638
+ Venezuela Americas 1972 65.712 11515649 10505.25966
1639
+ Venezuela Americas 1977 67.456 13503563 13143.95095
1640
+ Venezuela Americas 1982 68.557 15620766 11152.41011
1641
+ Venezuela Americas 1987 70.19 17910182 9883.584648
1642
+ Venezuela Americas 1992 71.15 20265563 10733.92631
1643
+ Venezuela Americas 1997 72.146 22374398 10165.49518
1644
+ Venezuela Americas 2002 72.766 24287670 8605.047831
1645
+ Venezuela Americas 2007 73.747 26084662 11415.80569
1646
+ Vietnam Asia 1952 40.412 26246839 605.0664917
1647
+ Vietnam Asia 1957 42.887 28998543 676.2854478
1648
+ Vietnam Asia 1962 45.363 33796140 772.0491602
1649
+ Vietnam Asia 1967 47.838 39463910 637.1232887
1650
+ Vietnam Asia 1972 50.254 44655014 699.5016441
1651
+ Vietnam Asia 1977 55.764 50533506 713.5371196
1652
+ Vietnam Asia 1982 58.816 56142181 707.2357863
1653
+ Vietnam Asia 1987 62.82 62826491 820.7994449
1654
+ Vietnam Asia 1992 67.662 69940728 989.0231487
1655
+ Vietnam Asia 1997 70.672 76048996 1385.896769
1656
+ Vietnam Asia 2002 73.017 80908147 1764.456677
1657
+ Vietnam Asia 2007 74.249 85262356 2441.576404
1658
+ West Bank and Gaza Asia 1952 43.16 1030585 1515.592329
1659
+ West Bank and Gaza Asia 1957 45.671 1070439 1827.067742
1660
+ West Bank and Gaza Asia 1962 48.127 1133134 2198.956312
1661
+ West Bank and Gaza Asia 1967 51.631 1142636 2649.715007
1662
+ West Bank and Gaza Asia 1972 56.532 1089572 3133.409277
1663
+ West Bank and Gaza Asia 1977 60.765 1261091 3682.831494
1664
+ West Bank and Gaza Asia 1982 64.406 1425876 4336.032082
1665
+ West Bank and Gaza Asia 1987 67.046 1691210 5107.197384
1666
+ West Bank and Gaza Asia 1992 69.718 2104779 6017.654756
1667
+ West Bank and Gaza Asia 1997 71.096 2826046 7110.667619
1668
+ West Bank and Gaza Asia 2002 72.37 3389578 4515.487575
1669
+ West Bank and Gaza Asia 2007 73.422 4018332 3025.349798
1670
+ Yemen, Rep. Asia 1952 32.548 4963829 781.7175761
1671
+ Yemen, Rep. Asia 1957 33.97 5498090 804.8304547
1672
+ Yemen, Rep. Asia 1962 35.18 6120081 825.6232006
1673
+ Yemen, Rep. Asia 1967 36.984 6740785 862.4421463
1674
+ Yemen, Rep. Asia 1972 39.848 7407075 1265.047031
1675
+ Yemen, Rep. Asia 1977 44.175 8403990 1829.765177
1676
+ Yemen, Rep. Asia 1982 49.113 9657618 1977.55701
1677
+ Yemen, Rep. Asia 1987 52.922 11219340 1971.741538
1678
+ Yemen, Rep. Asia 1992 55.599 13367997 1879.496673
1679
+ Yemen, Rep. Asia 1997 58.02 15826497 2117.484526
1680
+ Yemen, Rep. Asia 2002 60.308 18701257 2234.820827
1681
+ Yemen, Rep. Asia 2007 62.698 22211743 2280.769906
1682
+ Zambia Africa 1952 42.038 2672000 1147.388831
1683
+ Zambia Africa 1957 44.077 3016000 1311.956766
1684
+ Zambia Africa 1962 46.023 3421000 1452.725766
1685
+ Zambia Africa 1967 47.768 3900000 1777.077318
1686
+ Zambia Africa 1972 50.107 4506497 1773.498265
1687
+ Zambia Africa 1977 51.386 5216550 1588.688299
1688
+ Zambia Africa 1982 51.821 6100407 1408.678565
1689
+ Zambia Africa 1987 50.821 7272406 1213.315116
1690
+ Zambia Africa 1992 46.1 8381163 1210.884633
1691
+ Zambia Africa 1997 40.238 9417789 1071.353818
1692
+ Zambia Africa 2002 39.193 10595811 1071.613938
1693
+ Zambia Africa 2007 42.384 11746035 1271.211593
1694
+ Zimbabwe Africa 1952 48.451 3080907 406.8841148
1695
+ Zimbabwe Africa 1957 50.469 3646340 518.7642681
1696
+ Zimbabwe Africa 1962 52.358 4277736 527.2721818
1697
+ Zimbabwe Africa 1967 53.995 4995432 569.7950712
1698
+ Zimbabwe Africa 1972 55.635 5861135 799.3621758
1699
+ Zimbabwe Africa 1977 57.674 6642107 685.5876821
1700
+ Zimbabwe Africa 1982 60.363 7636524 788.8550411
1701
+ Zimbabwe Africa 1987 62.351 9216418 706.1573059
1702
+ Zimbabwe Africa 1992 60.377 10704340 693.4207856
1703
+ Zimbabwe Africa 1997 46.809 11404948 792.4499603
1704
+ Zimbabwe Africa 2002 39.989 11926563 672.0386227
1705
+ Zimbabwe Africa 2007 43.487 12311143 469.7092981
requirements.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ streamlit>=1.51.0
2
+ pandas>=2.0.0
3
+ plotly>=5.17.0